From 0aab4024cffff1f4b3f648ca80a3aed81d6c6ad0 Mon Sep 17 00:00:00 2001 From: Cypher2k2 Date: Thu, 12 Dec 2024 18:52:31 +0100 Subject: [PATCH 1/6] fixing new model implementation errors --- 0.11.0 | 36 ++ 1.10.0 | 23 + cornac/datasets/movielens.py | 2 +- cornac/experiment/experiment.py | 2 +- cornac/models/__init__.py | 1 + cornac/models/globallocalkernel/__init__.py | 1 + .../recom_globallocalkernel.py | 401 ++++++++++++++++++ .../models/globallocalkernel/requirements.txt | 2 + try.py | 73 ++++ tutorials/vbpr_text.ipynb | 340 ++++++++++----- 10 files changed, 783 insertions(+), 98 deletions(-) create mode 100644 0.11.0 create mode 100644 1.10.0 create mode 100644 cornac/models/globallocalkernel/__init__.py create mode 100644 cornac/models/globallocalkernel/recom_globallocalkernel.py create mode 100644 cornac/models/globallocalkernel/requirements.txt create mode 100644 try.py diff --git a/0.11.0 b/0.11.0 new file mode 100644 index 000000000..7d491e8ea --- /dev/null +++ b/0.11.0 @@ -0,0 +1,36 @@ +Collecting torch + Downloading torch-2.5.1-cp312-cp312-win_amd64.whl.metadata (28 kB) +Collecting torchvision + Downloading torchvision-0.20.1-cp312-cp312-win_amd64.whl.metadata (6.2 kB) +Collecting filelock (from torch) + Downloading filelock-3.16.1-py3-none-any.whl.metadata (2.9 kB) +Requirement already satisfied: typing-extensions>=4.8.0 in c:\python312\lib\site-packages\setuptools\_vendor (from torch) (4.12.2) +Collecting networkx (from torch) + Downloading networkx-3.4.2-py3-none-any.whl.metadata (6.3 kB) +Collecting jinja2 (from torch) + Downloading jinja2-3.1.4-py3-none-any.whl.metadata (2.6 kB) +Collecting fsspec (from torch) + Downloading fsspec-2024.10.0-py3-none-any.whl.metadata (11 kB) +Requirement already satisfied: setuptools in c:\python312\lib\site-packages (from torch) (75.3.0) +Collecting sympy==1.13.1 (from torch) + Downloading sympy-1.13.1-py3-none-any.whl.metadata (12 kB) +Requirement already satisfied: mpmath<1.4,>=1.1.0 in c:\python312\lib\site-packages 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MarkupSafe, fsspec, filelock, jinja2, torch diff --git a/cornac/datasets/movielens.py b/cornac/datasets/movielens.py index 99aa92b4d..b2f0ddc8a 100644 --- a/cornac/datasets/movielens.py +++ b/cornac/datasets/movielens.py @@ -77,7 +77,7 @@ def load_feedback(fmt="UIR", variant="100K", reader=None): data: array-like Data in the form of a list of tuples depending on the given data format. """ - + fmt = validate_format(fmt, VALID_DATA_FORMATS) ml = ML_DATASETS.get(variant.upper(), None) diff --git a/cornac/experiment/experiment.py b/cornac/experiment/experiment.py index 139417908..455c4fded 100644 --- a/cornac/experiment/experiment.py +++ b/cornac/experiment/experiment.py @@ -157,7 +157,7 @@ def run(self): if self.val_result is not None: output += "\nVALIDATION:\n...\n{}".format(self.val_result) output += "\nTEST:\n...\n{}".format(self.result) - + print('helllooooooooooooow') print(output) timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S-%f") diff --git a/cornac/models/__init__.py b/cornac/models/__init__.py index 9507dbc50..386a98879 100644 --- a/cornac/models/__init__.py +++ b/cornac/models/__init__.py @@ -84,3 +84,4 @@ from .vbpr import VBPR from .vmf import VMF from .wmf import WMF +from .globallocalkernel import GlobalLocalKernel diff --git a/cornac/models/globallocalkernel/__init__.py b/cornac/models/globallocalkernel/__init__.py new file mode 100644 index 000000000..d960142e7 --- /dev/null +++ b/cornac/models/globallocalkernel/__init__.py @@ -0,0 +1 @@ +from .recom_globallocalkernel import GlobalLocalKernel \ No newline at end of file diff --git a/cornac/models/globallocalkernel/recom_globallocalkernel.py b/cornac/models/globallocalkernel/recom_globallocalkernel.py new file mode 100644 index 000000000..9242403eb --- /dev/null +++ b/cornac/models/globallocalkernel/recom_globallocalkernel.py @@ -0,0 +1,401 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +import numpy as np +import time + +from ..recommender import Recommender + +# =========================== +# Define your model layers +# =========================== + +def local_kernel(u, v): + dist = torch.norm(u - v, p=2, dim=2) + hat = torch.clamp(1. - dist**2, min=0.) + return hat + +class KernelLayer(nn.Module): + def __init__(self, n_in, n_hid, n_dim, lambda_s, lambda_2, activation=nn.Sigmoid()): + super().__init__() + self.W = nn.Parameter(torch.randn(n_in, n_hid)) + self.u = nn.Parameter(torch.randn(n_in, 1, n_dim)) + self.v = nn.Parameter(torch.randn(1, n_hid, n_dim)) + self.b = nn.Parameter(torch.randn(n_hid)) + + self.lambda_s = lambda_s + self.lambda_2 = lambda_2 + + nn.init.xavier_uniform_(self.W, gain=torch.nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self.u, gain=torch.nn.init.calculate_gain("relu")) + nn.init.xavier_uniform_(self.v, gain=torch.nn.init.calculate_gain("relu")) + nn.init.zeros_(self.b) + self.activation = activation + + def forward(self, x): + w_hat = local_kernel(self.u, self.v) + sparse_reg = torch.nn.functional.mse_loss(w_hat, torch.zeros_like(w_hat)) + sparse_reg_term = self.lambda_s * sparse_reg + l2_reg = torch.nn.functional.mse_loss(self.W, torch.zeros_like(self.W)) + l2_reg_term = self.lambda_2 * l2_reg + + W_eff = self.W * w_hat # Local kernelised weight matrix + y = torch.matmul(x, W_eff) + self.b + y = self.activation(y) + + return y, sparse_reg_term + l2_reg_term + +class KernelNet(nn.Module): + def __init__(self, n_u, n_hid, n_dim, n_layers, lambda_s, lambda_2): + super().__init__() + layers = [] + for i in range(n_layers): + if i == 0: + + layers.append(KernelLayer(n_u, n_hid, n_dim, lambda_s, lambda_2)) + else: + + layers.append(KernelLayer(n_hid, n_hid, n_dim, lambda_s, lambda_2)) + # Output layer + layers.append(KernelLayer(n_hid, n_u, n_dim, lambda_s, lambda_2, activation=nn.Identity())) + self.layers = nn.ModuleList(layers) + self.dropout = nn.Dropout(0.33) + + def forward(self, x): + total_reg = None + for i, layer in enumerate(self.layers): + x, reg = layer(x) + if i < len(self.layers)-1: + x = self.dropout(x) + total_reg = reg if total_reg is None else total_reg + reg + return x, total_reg + +class CompleteNet(nn.Module): + def __init__(self, kernel_net, n_u, n_m, n_hid, n_dim, n_layers, lambda_s, lambda_2, gk_size, dot_scale): + super().__init__() + self.gk_size = gk_size + self.dot_scale = dot_scale + self.local_kernel_net = kernel_net + self.conv_kernel = torch.nn.Parameter(torch.randn(n_m, gk_size**2) * 0.1) + nn.init.xavier_uniform_(self.conv_kernel, gain=torch.nn.init.calculate_gain("relu")) + + def forward(self, x, x_local): + gk = self.global_kernel(x_local, self.gk_size, self.dot_scale) + x = self.global_conv(x, gk) + x, global_reg_loss = self.local_kernel_net(x) + return x, global_reg_loss + + def global_kernel(self, input, gk_size, dot_scale): + avg_pooling = torch.mean(input, dim=1) # Item based average pooling (axis=1) + avg_pooling = avg_pooling.view(1, -1) + gk = torch.matmul(avg_pooling, self.conv_kernel) * dot_scale + gk = gk.view(1, 1, gk_size, gk_size) + return gk + + def global_conv(self, input, W): + input = input.unsqueeze(0).unsqueeze(0) + conv2d = nn.LeakyReLU()(F.conv2d(input, W, stride=1, padding=1)) + return conv2d.squeeze(0).squeeze(0) + +class Loss(nn.Module): + def forward(self, pred_p, reg_loss, train_m, train_r): + diff = train_m * (train_r - pred_p) + sqE = torch.nn.functional.mse_loss(diff, torch.zeros_like(diff)) + loss_p = sqE + reg_loss + return loss_p + +# =========================== +# Cornac Recommender Wrapper +# =========================== + +class GlobalLocalKernel(Recommender): + """Global-Local Kernel Recommender. + + Parameters + ---------- + n_hid: int, default: 64 + Size of the hidden dimension. + n_dim: int, default: 10 + Kernel dimension. + n_layers: int, default: 2 + Number of kernel layers (not counting the final output layer). + lambda_s: float, default: 0.001 + Sparsity regularization term. + lambda_2: float, default: 0.001 + L2 regularization term. + gk_size: int, default: 3 + Size of the global kernel. + dot_scale: float, default: 0.1 + Scaling factor for the global kernel. + max_epoch_p: int, default: 100 + Max epochs for pre-training phase. + max_epoch_f: int, default: 100 + Max epochs for fine-tuning phase. + tol_p: float, default: 1e-4 + Tolerance for early stopping in pre-training. + tol_f: float, default: 1e-4 + Tolerance for early stopping in fine-tuning. + patience_p: int, default: 10 + Patience for early stopping in pre-training. + patience_f: int, default: 10 + Patience for early stopping in fine-tuning. + lr: float, default: 0.001 + Learning rate. + verbose: bool, default: False + Whether to print training progress. + device: str, default: 'auto' + 'cpu', 'cuda', or 'auto' to automatically detect GPU. + """ + + def __init__( + self, + n_hid=10, # size of hidden layers + n_dim=2, # inner AE embedding size + n_layers=2, # number of hidden layers + lambda_s=0.006, # regularization of sparsity of the final matrix + lambda_2=20., # regularization of number of parameters + gk_size=3, # width=height of kernel for convolution + dot_scale=1, # dot product weight for global kernel + max_epoch_p=2, # max number of epochs for pretraining + max_epoch_f=2, # max number of epochs for finetuning + tol_p=1e-2, # min threshold for difference between consecutive values of train rmse for pretraining + tol_f=1e-2, # min threshold for difference for finetuning + patience_p=1, # early stopping patience for pretraining + patience_f=1, # early stopping patience for finetuning + lr=0.001, + verbose=False, + name="GlobalLocalKernel", + trainable=True + ): + super().__init__(name=name, trainable=trainable, verbose=verbose) + self.n_hid = n_hid + self.n_dim = n_dim + self.n_layers = n_layers + self.lambda_s = lambda_s + self.lambda_2 = lambda_2 + self.gk_size = gk_size + self.dot_scale = dot_scale + self.max_epoch_p = max_epoch_p + self.max_epoch_f = max_epoch_f + self.tol_p = tol_p + self.tol_f = tol_f + self.patience_p = patience_p + self.patience_f = patience_f + self.lr = lr + self.verbose = verbose + + # Device + if torch.cuda.is_available() and (self.device != 'cpu'): + self.device = torch.device("cuda") + else: + self.device = torch.device("cpu") + + self.verbose = verbose + self.model = None + self.train_r_local = None + + def fit(self, train_set, val_set=None): + # Prepare training data + # train_r = train_set.csr_matrix.toarray().astype(np.float32) + # print('train : ', train_r.shape) # (943, 1656) + + # n_u, n_m = train_r.shape + # train_mask = (train_r > 0).astype(np.float32) + # # Initialize models + # kernel_net = KernelNet(n_u, self.n_hid, self.n_dim, self.n_layers, self.lambda_s, self.lambda_2).double().to(self.device) + # complete_model = CompleteNet(kernel_net, n_u, n_m, self.n_hid, self.n_dim, self.n_layers, self.lambda_s, self.lambda_2, self.gk_size, self.dot_scale).double().to(self.device) + + self.min_rating = 1.0 + self.max_rating = 5.0 + + # Extract user-item-rating tuples + train_users, train_items, train_ratings = train_set.uir_tuple + test_users, test_items, test_ratings = ([], [], []) # For now, if val_set is None + + # Get total numbers of users and items + n_u = train_set.num_users + n_m = train_set.num_items + + # Construct train_r as (n_m, n_u), same as in your notebook + train_r = np.zeros((n_m, n_u), dtype='float32') + train_r[train_items, train_users] = train_ratings + + # Now train_r is shaped (n_m, n_u) and aligned with your model expectations. + train_mask = (train_r > 0).astype(np.float32) + + + self._train_r = train_r + self._train_mask = train_mask + + + + # Update variables accordingly + # After this, n_m = train_r.shape[0], n_u = train_r.shape[1] + n_m, n_u = train_r.shape + + # Now initialize models with n_u and n_m that match the shape of train_r + kernel_net = KernelNet(n_u, self.n_hid, self.n_dim, self.n_layers, self.lambda_s, self.lambda_2).double().to(self.device) + complete_model = CompleteNet(kernel_net, n_u, n_m, self.n_hid, self.n_dim, self.n_layers, self.lambda_s, self.lambda_2, self.gk_size, self.dot_scale).double().to(self.device) + + + # Pre-Training (KernelNet only) + optimizer = torch.optim.AdamW(complete_model.local_kernel_net.parameters(), lr=self.lr) + best_rmse = np.inf + last_rmse = np.inf + counter = 0 + + tic = time.time() + + for epoch in range(self.max_epoch_p): + # def closure(): + # optimizer.zero_grad() + # x = torch.tensor(train_r, dtype=torch.double, device=self.device) + # m = torch.tensor(train_mask, dtype=torch.double, device=self.device) + # complete_model.local_kernel_net.train() + # pred, reg = complete_model.local_kernel_net(x) + # loss = Loss().to(self.device)(pred, reg, m, x) + # loss.backward() + # return loss + + + def closure(): + optimizer.zero_grad() + # Use train_r instead of train_r + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + m = torch.tensor(train_mask, dtype=torch.double, device=self.device) + complete_model.local_kernel_net.train() + pred, reg = complete_model.local_kernel_net(x) + loss = Loss().to(self.device)(pred, reg, m, x) + loss.backward() + return loss + + + optimizer.step(closure) + + complete_model.local_kernel_net.eval() + with torch.no_grad(): + # print('complete model train_r :' , train_r) + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + pred, _ = kernel_net(x) + pre = pred.float().cpu().numpy() + # Compute training RMSE + train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) + + if last_rmse - train_rmse < self.tol_p: + counter += 1 + else: + counter = 0 + last_rmse = train_rmse + + if counter >= self.patience_p: + if self.verbose: + print("Early stopping pre-training at epoch:", epoch+1) + break + + if self.verbose and epoch % 10 == 0: + print(f"Pre-Training Epoch {epoch+1}/{self.max_epoch_p}, Train RMSE: {train_rmse:.4f}") + + # After pre-training + self.train_r_local = np.clip(pre, 1., 5.) + + # Fine-Tuning + optimizer = torch.optim.AdamW(complete_model.parameters(), lr=self.lr) + last_rmse = np.inf + counter = 0 + + for epoch in range(self.max_epoch_f): + def closure(): + optimizer.zero_grad() + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) + m = torch.tensor(train_mask, dtype=torch.double, device=self.device) + complete_model.train() + pred, reg = complete_model(x, x_local) + loss = Loss().to(self.device)(pred, reg, m, x) + loss.backward() + return loss + + optimizer.step(closure) + + complete_model.eval() + with torch.no_grad(): + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) + pred, _ = complete_model(x, x_local) + pre = pred.float().cpu().numpy() + + # Compute training RMSE + train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) + + if last_rmse - train_rmse < self.tol_f: + counter += 1 + else: + counter = 0 + last_rmse = train_rmse + + if counter >= self.patience_f: + if self.verbose: + print("Early stopping fine-tuning at epoch:", epoch+1) + break + + if self.verbose and epoch % 10 == 0: + print(f"Fine-Tuning Epoch {epoch+1}/{self.max_epoch_f}, Train RMSE: {train_rmse:.4f}") + + # Store the trained model + self.model = complete_model + return self + + def score(self, user_id, item_id=None): + """Predict the scores/ratings of a user for an item. + + Parameters + ---------- + user_id: int, required + The index of the user for whom to perform score prediction. + + item_id: int, optional, default: None + The index of the item for which to perform score prediction. + If None, scores for all items will be returned. + + Returns + ------- + res : A scalar or a Numpy array + """ + if self.model is None: + raise RuntimeError("You must train the model before calling score()!") + + # Inference: provide predictions for given user_id, item_id + # We'll assume we've stored training rating matrix in `fit` if needed. + # For simplicity, assume `train_r_local` and `model` are available. + + # Note: For large datasets, keep user and item embeddings precomputed. + with torch.no_grad(): + # We can re-use self.train_r_local as input + # If user_id given, we create a vector with only that user + # We'll do a full prediction for all users/items and slice. + # In a production scenario, you'd probably have a more efficient inference method. + + # self.model expects full matrix input: + # Construct a matrix with shape (num_users, num_items), with zeros if needed + # For scoring, you can either store the training data or create a neutral input + # Here we just re-use training data as input context. + input_mat = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) + # We must pass also the original train_r for global kernel step: + # If we have it stored somewhere, we should keep it. Here we assume we have them: + # Ideally, you might store self.train_r in self.fit for scoring: + # For demonstration, let's assume we stored train_r in self._train_r + x_global = torch.tensor(self._train_r, dtype=torch.double, device=self.device) + # Compute predictions + pred, _ = self.model(x_global, input_mat) + pred = pred.float().cpu().numpy() + + if item_id is None: + # return predictions for all items for this user + return pred[:, user_id] + else: + # return prediction for this single item + return pred[item_id , user_id] + + def rate(self, user_id, item_id): + # Optionally override if needed, or rely on default Recommender.rate() + return super().rate(user_id, item_id) diff --git a/cornac/models/globallocalkernel/requirements.txt b/cornac/models/globallocalkernel/requirements.txt new file mode 100644 index 000000000..13be8e9f2 --- /dev/null +++ b/cornac/models/globallocalkernel/requirements.txt @@ -0,0 +1,2 @@ +torch>=1.10.0 +torchvision>=0.11.0 diff --git a/try.py b/try.py new file mode 100644 index 000000000..eb400084f --- /dev/null +++ b/try.py @@ -0,0 +1,73 @@ +# import cornac +# from cornac.eval_methods import RatioSplit +# from cornac.models import MF, PMF, BPR +# from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP + +# # load the built-in MovieLens 100K and split the data based on ratio +# ml_100k = cornac.datasets.movielens.load_feedback() +# rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) + +# # initialize models, here we are comparing: Biased MF, PMF, and BPR +# mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123) +# pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123) +# bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123) +# models = [mf, pmf, bpr] + +# # define metrics to evaluate the models +# metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()] + +# # put it together in an experiment, voilà! +# cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run() + + + + +import numpy as np +import cornac +from cornac.models import GlobalLocalKernel +from cornac.eval_methods import RatioSplit +from cornac.metrics import MAE, RMSE + +# Load the MovieLens 100K dataset +ml_100k = cornac.datasets.movielens.load_feedback() + +# Split the data +rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) + +# Extract user, item, rating tuples +train_users, train_items, train_ratings = rs.train_set.uir_tuple +test_users, test_items, test_ratings = rs.test_set.uir_tuple + +# Get the total number of users and items +n_u = rs.total_users +n_m = rs.total_items + +# Prepare rating matrices in (n_m, n_u) format +train_r = np.zeros((n_m, n_u), dtype='float32') +test_r = np.zeros((n_m, n_u), dtype='float32') + +# Populate the train and test matrices +train_r[train_items, train_users] = train_ratings +test_r[test_items, test_users] = test_ratings + +train_m = (train_r > 1e-12).astype('float32') +test_m = (test_r > 1e-12).astype('float32') + +print('data matrix loaded') +print('num of users: {}'.format(n_u)) +print('num of movies: {}'.format(n_m)) +print('num of training ratings: {}'.format(len(train_ratings))) +print('num of test ratings: {}'.format(len(test_ratings))) + +# Initialize your model +my_model = GlobalLocalKernel() + +# Provide the model with pre-processed train data +# my_model._train_mat = train_r # Store original train matrix for scoring, if needed by score() +# my_model.train_r_local = train_r # For pre-training phase if needed by the model + +# Define some basic metrics +metrics = [MAE(), RMSE()] + +# Run the experiment +cornac.Experiment(eval_method=rs, models=[my_model], metrics=metrics , user_based=True).run() diff --git a/tutorials/vbpr_text.ipynb b/tutorials/vbpr_text.ipynb index 22ab5dc73..a2a541c89 100644 --- a/tutorials/vbpr_text.ipynb +++ b/tutorials/vbpr_text.ipynb @@ -1,35 +1,10 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.5.2" - }, - "colab": { - "name": "vbpr_text.ipynb", - "provenance": [] - }, - "accelerator": "GPU" - }, "cells": [ { "cell_type": "markdown", "metadata": { - "id": "uxfPR6vG5GP0", - "colab_type": "text" + "colab_type": "text", + "id": "uxfPR6vG5GP0" }, "source": [ "*Copyright (c) Cornac Authors. All rights reserved.*\n", @@ -42,8 +17,8 @@ { "cell_type": "markdown", "metadata": { - "id": "G0R8gyyt5GP4", - "colab_type": "text" + "colab_type": "text", + "id": "G0R8gyyt5GP4" }, "source": [ "\n", @@ -59,8 +34,8 @@ { "cell_type": "markdown", "metadata": { - "id": "TURsVngV5GP5", - "colab_type": "text" + "colab_type": "text", + "id": "TURsVngV5GP5" }, "source": [ "## Overview\n", @@ -72,29 +47,47 @@ }, { "cell_type": "code", + "execution_count": null, "metadata": { - "id": "d7087AnL5Jte", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "d7087AnL5Jte" }, + "outputs": [], "source": [ "# install Cornac and PyTorch (VBPR model implementation uses PyTorch)\n", "!pip3 install cornac torch>=0.4.1" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 1, "metadata": { - "id": "8e1edON-5GP7", - "colab_type": "code", "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, + "colab_type": "code", + "id": "8e1edON-5GP7", "outputId": "f609c795-cadb-45d5-fab1-25a0879853ff" }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\ProgramFiles\\Anaconda\\envs\\cornac\\Lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Cornac version: 2.2.2\n" + ] + } + ], "source": [ "import cornac\n", "from cornac.data import Reader\n", @@ -104,23 +97,13 @@ "from cornac.data.text import BaseTokenizer\n", "\n", "print(\"Cornac version: {}\".format(cornac.__version__))" - ], - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "text": [ - "Cornac version: 1.4.0\n" - ], - "name": "stdout" - } ] }, { "cell_type": "markdown", "metadata": { - "id": "H8OadBhB5GQE", - "colab_type": "text" + "colab_type": "text", + "id": "H8OadBhB5GQE" }, "source": [ "## Prepare data\n", @@ -129,25 +112,97 @@ }, { "cell_type": "code", + "execution_count": 2, "metadata": { - "id": "bP9jY6dl5GQF", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "bP9jY6dl5GQF" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data from https://static.preferred.ai/cornac/datasets/movielens/ml_plot.zip\n", + "will be cached into C:\\Users\\Rachid\\.cornac\\movielens/ml_plot.dat\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "3.60MB [00:05, 680kB/s] \n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unzipping ...\n", + "File cached!\n" + ] + } + ], "source": [ "plots, movie_ids = movielens.load_plot()\n", "\n", "# movies without plots are filtered out by `cornac.data.Reader`\n", "ml_100k = movielens.load_feedback(reader=Reader(item_set=movie_ids))" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('196', '242', 3.0),\n", + " ('186', '302', 3.0),\n", + " ('22', '377', 1.0),\n", + " ('244', '51', 2.0),\n", + " ('166', '346', 1.0)]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } ], - "execution_count": 0, - "outputs": [] + "source": [ + "ml_100k[:5]" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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+ " 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+ " 'thing\\tdo\\tnot\\tseem\\tto\\tchange\\tmuch\\tin\\twabasha\\tcounty\\tmax\\tand\\tjohn\\tbe\\tstill\\tfight\\tafter\\t35\\tyear\\tgrandpa\\tstill\\tdrink\\tsmoke\\tand\\tchase\\twoman\\tand\\tnobody\\tbe\\table\\tto\\tcatch\\tthe\\tfabled\\tcatfish\\thunter\\ta\\tgigantic\\tcatfish\\tthat\\tactually\\tsmile\\tat\\tfisherman\\twho\\ttry\\tto\\tsnare\\tit\\tsix\\tmonth\\tago\\tjohn\\tmarry\\tthe\\tnew\\tgirl\\tin\\ttown\\tariel\\tand\\tpeople\\tbegin\\tto\\tsuspect\\tthat\\tmax\\tmight\\tbe\\tmiss\\tsomething\\tsimilar\\tin\\the\\tlife\\tthe\\tonly\\tjoy\\tmax\\tclaim\\tbe\\tleave\\tin\\the\\tlife\\tbe\\tfishing\\tbut\\tthat\\tmight\\tchange\\twith\\tthe\\tnew\\towner\\tof\\tthe\\tbait\\tshop\\t|',\n", + " 'this\\tstory\\tbase\\ton\\tthe\\tbest\\tselling\\tnovel\\tby\\tterry\\tmcmillan\\tfollow\\tthe\\tlife\\tof\\tfour\\twoman\\tas\\tthey\\ttry\\tto\\tdeal\\twith\\tthey\\tvery\\tlive\\tfriendship\\tbecome\\tthe\\tstrongest\\tbond\\tbetween\\tthese\\twoman\\tas\\tman\\tcareer\\tand\\tfamily\\ttake\\tthey\\tin\\tdifferent\\tdirection\\toften\\tthis\\tmovie\\tspeak\\tabout\\tsome\\tof\\tthe\\tproblem\\tand\\tstruggle\\tthe\\tmodern\\twoman\\tface\\tin\\ttoday\\tworld\\t|',\n", + " 'in\\tthis\\tsequel\\tto\\tfather\\tof\\tthe\\tbride\\tgeorge\\tbanks\\tmust\\taccept\\tthe\\treality\\tof\\twhat\\the\\tdaughter\\tascension\\tfrom\\tdaughter\\tto\\twife\\tand\\tnow\\tto\\tmother\\tmean\\twhen\\tplace\\tinto\\tperspective\\tagainst\\the\\town\\tstage\\tof\\tlife\\tas\\tthe\\tcomfortable\\tfamily\\tunit\\tstart\\tto\\tunravel\\tin\\the\\tmind\\ta\\trapid\\tprogression\\tinto\\tcrisis\\tbe\\tin\\the\\tfuture\\the\\tjourney\\tto\\tregain\\the\\tyouth\\tact\\tas\\ta\\tcatalyst\\tfor\\ta\\tkind\\tof\\trebirth\\tof\\the\\tattitude\\ton\\tlife\\twhen\\the\\tand\\the\\twife\\tnina\\tfind\\thow\\tthey\\tlife\\tbe\\tabout\\tto\\tchange\\tas\\twell\\t|family\\ttrouble\\tcontinue\\tto\\tplague\\tgeorge\\tbanks\\thave\\tsurvive\\the\\tdaughter\\tmarriage\\tin\\tthe\\tfirst\\tfilm\\the\\tmust\\tnow\\tdeal\\twith\\tshe\\tpregnancy\\tto\\tcomplicate\\tmatter\\the\\twife\\tnina\\tbe\\tpregnant\\tas\\twell\\t|']" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "plots[:5]\n" + ] }, { "cell_type": "markdown", "metadata": { - "id": "43wktekd5GQJ", - "colab_type": "text" + "colab_type": "text", + "id": "43wktekd5GQJ" }, "source": [ "## Cross modality\n", @@ -157,24 +212,24 @@ }, { "cell_type": "code", + "execution_count": 8, "metadata": { - "id": "0VHNg6tm5GQK", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "0VHNg6tm5GQK" }, + "outputs": [], "source": [ "item_text_modality = TextModality(corpus=plots, ids=movie_ids, \n", " tokenizer=BaseTokenizer(sep='\\t', stop_words='english'),\n", " max_vocab=5000, max_doc_freq=0.5).build()" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "xGYc5L7e5GQS", - "colab_type": "text" + "colab_type": "text", + "id": "xGYc5L7e5GQS" }, "source": [ "Next step is to create an `ImageModality`, which is use by VBPR, using our text representations. In this case, we take the word-count matrix to substitute for visual features." @@ -182,23 +237,23 @@ }, { "cell_type": "code", + "execution_count": 9, "metadata": { - "id": "gUGJ5AjI5GQU", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "gUGJ5AjI5GQU" }, + "outputs": [], "source": [ "features = item_text_modality.count_matrix.A\n", "item_image_modality = ImageModality(features=features, ids=movie_ids)" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "Rkxw-D_m5GQY", - "colab_type": "text" + "colab_type": "text", + "id": "Rkxw-D_m5GQY" }, "source": [ "In Cornac, every model relies on the modality for which it was designed for (i.e., visual recommendation algorithms always work with `ImageModality`). This ensures consistency with models' original assumptions, and helps us avoid confusions regarding which modality to use when integrating a new recommender model.\n", @@ -210,25 +265,52 @@ }, { "cell_type": "code", + "execution_count": 17, "metadata": { - "id": "cc66mPy35GQZ", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "cc66mPy35GQZ" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "rating_threshold = 1.0\n", + "exclude_unknowns = True\n", + "---\n", + "Training data:\n", + "Number of users = 926\n", + "Number of items = 1162\n", + "Number of ratings = 9480\n", + "Max rating = 5.0\n", + "Min rating = 1.0\n", + "Global mean = 3.5\n", + "---\n", + "Test data:\n", + "Number of users = 926\n", + "Number of items = 1162\n", + "Number of ratings = 82993\n", + "Number of unknown users = 0\n", + "Number of unknown items = 0\n", + "---\n", + "Total users = 926\n", + "Total items = 1162\n" + ] + } + ], "source": [ "ratio_split = RatioSplit(data=ml_100k, test_size=0.9,\n", " item_image=item_image_modality,\n", " exclude_unknowns=True, \n", " verbose=True, seed=123)" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "bKalGw5x5GQe", - "colab_type": "text" + "colab_type": "text", + "id": "bKalGw5x5GQe" }, "source": [ "We are now ready to evaluate performance of VBPR. The [BPR](https://arxiv.org/ftp/arxiv/papers/1205/1205.2618.pdf) model is also included as a baseline to examine the effectiveness of the text auxiliary data." @@ -236,54 +318,95 @@ }, { "cell_type": "code", + "execution_count": 18, "metadata": { - "id": "rczsfDrO5GQg", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "rczsfDrO5GQg" }, + "outputs": [], "source": [ "vbpr = cornac.models.VBPR(k=10, k2=10, n_epochs=20, batch_size=10, learning_rate=0.001,\n", " lambda_w=1.0, lambda_b=0.0, lambda_e=100.0, use_gpu=True, seed=123)\n", "\n", "bpr = cornac.models.BPR(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 19, "metadata": { - "id": "giDcupSL5GQk", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "giDcupSL5GQk" }, + "outputs": [], "source": [ "auc = cornac.metrics.AUC()\n", "rec_50 = cornac.metrics.Recall(k=50)" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "code", + "execution_count": 20, "metadata": { - "id": "yes6tv-15GQo", + "colab": {}, "colab_type": "code", - "colab": {} + "id": "yes6tv-15GQo" }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[BPR] Training started!\n", + "\n", + "[BPR] Evaluation started!\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Ranking: 100%|██████████| 926/926 [00:01<00:00, 605.81it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[VBPR] Training started!\n" + ] + }, + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'torch'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[20], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m cornac\u001b[39m.\u001b[39;49mExperiment(eval_method\u001b[39m=\u001b[39;49mratio_split,\n\u001b[0;32m 2\u001b[0m models\u001b[39m=\u001b[39;49m[bpr, vbpr],\n\u001b[1;32m----> 3\u001b[0m metrics\u001b[39m=\u001b[39;49m[auc, rec_50])\u001b[39m.\u001b[39;49mrun()\n", + "File \u001b[1;32md:\\ProgramFiles\\Anaconda\\envs\\cornac\\Lib\\site-packages\\cornac\\experiment\\experiment.py:142\u001b[0m, in \u001b[0;36mExperiment.run\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 139\u001b[0m model\u001b[39m.\u001b[39mverbose \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose\n\u001b[0;32m 141\u001b[0m \u001b[39mfor\u001b[39;00m model \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mmodels:\n\u001b[1;32m--> 142\u001b[0m test_result, val_result \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49meval_method\u001b[39m.\u001b[39;49mevaluate(\n\u001b[0;32m 143\u001b[0m model\u001b[39m=\u001b[39;49mmodel,\n\u001b[0;32m 144\u001b[0m metrics\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmetrics,\n\u001b[0;32m 145\u001b[0m user_based\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49muser_based,\n\u001b[0;32m 146\u001b[0m show_validation\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mshow_validation,\n\u001b[0;32m 147\u001b[0m )\n\u001b[0;32m 149\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mresult\u001b[39m.\u001b[39mappend(test_result)\n\u001b[0;32m 150\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mval_result \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n", + "File \u001b[1;32md:\\ProgramFiles\\Anaconda\\envs\\cornac\\Lib\\site-packages\\cornac\\eval_methods\\base_method.py:734\u001b[0m, in \u001b[0;36mBaseMethod.evaluate\u001b[1;34m(self, model, metrics, user_based, show_validation)\u001b[0m\n\u001b[0;32m 731\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m[\u001b[39m\u001b[39m{}\u001b[39;00m\u001b[39m] Training started!\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39mformat(model\u001b[39m.\u001b[39mname))\n\u001b[0;32m 733\u001b[0m start \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime()\n\u001b[1;32m--> 734\u001b[0m model\u001b[39m.\u001b[39;49mfit(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mtrain_set, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mval_set)\n\u001b[0;32m 735\u001b[0m train_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m start\n\u001b[0;32m 737\u001b[0m \u001b[39m##############\u001b[39;00m\n\u001b[0;32m 738\u001b[0m \u001b[39m# EVALUATION #\u001b[39;00m\n\u001b[0;32m 739\u001b[0m \u001b[39m##############\u001b[39;00m\n", + "File \u001b[1;32md:\\ProgramFiles\\Anaconda\\envs\\cornac\\Lib\\site-packages\\cornac\\models\\vbpr\\recom_vbpr.py:165\u001b[0m, in \u001b[0;36mVBPR.fit\u001b[1;34m(self, train_set, val_set)\u001b[0m\n\u001b[0;32m 158\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_init(\n\u001b[0;32m 159\u001b[0m n_users\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtotal_users,\n\u001b[0;32m 160\u001b[0m n_items\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtotal_items,\n\u001b[0;32m 161\u001b[0m features\u001b[39m=\u001b[39mtrain_features,\n\u001b[0;32m 162\u001b[0m )\n\u001b[0;32m 164\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mtrainable:\n\u001b[1;32m--> 165\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_fit_torch(train_set, train_features)\n\u001b[0;32m 167\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\n", + "File \u001b[1;32md:\\ProgramFiles\\Anaconda\\envs\\cornac\\Lib\\site-packages\\cornac\\models\\vbpr\\recom_vbpr.py:170\u001b[0m, in \u001b[0;36mVBPR._fit_torch\u001b[1;34m(self, train_set, train_features)\u001b[0m\n\u001b[0;32m 169\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_fit_torch\u001b[39m(\u001b[39mself\u001b[39m, train_set, train_features):\n\u001b[1;32m--> 170\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mtorch\u001b[39;00m\n\u001b[0;32m 172\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_l2_loss\u001b[39m(\u001b[39m*\u001b[39mtensors):\n\u001b[0;32m 173\u001b[0m l2_loss \u001b[39m=\u001b[39m \u001b[39m0\u001b[39m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'torch'" + ] + } + ], "source": [ "cornac.Experiment(eval_method=ratio_split,\n", " models=[bpr, vbpr],\n", " metrics=[auc, rec_50]).run()" - ], - "execution_count": 0, - "outputs": [] + ] }, { "cell_type": "markdown", "metadata": { - "id": "_Nbplrac5GQs", - "colab_type": "text" + "colab_type": "text", + "id": "_Nbplrac5GQs" }, "source": [ "Results after running the experiment:\n", @@ -298,5 +421,30 @@ "" ] } - ] -} \ No newline at end of file + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "vbpr_text.ipynb", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From c44f1fe1b1340d0cc84e1586a0a6e8cbd6a51b35 Mon Sep 17 00:00:00 2001 From: Cypher2k2 Date: Sat, 14 Dec 2024 16:54:30 +0100 Subject: [PATCH 2/6] fix training and add tqdm to training --- cornac/experiment/experiment.py | 2 +- .../recom_globallocalkernel.py | 318 +++--- experiment_log.txt | 102 ++ experiment_log_1.txt | 102 ++ experiment_log_10.txt | 102 ++ experiment_log_11.txt | 102 ++ experiment_log_12.txt | 112 ++ experiment_log_13.txt | 102 ++ experiment_log_14.txt | 102 ++ experiment_log_15.txt | 191 ++++ experiment_log_16.txt | 402 +++++++ experiment_log_17.txt | 427 ++++++++ experiment_log_18.txt | 389 +++++++ experiment_log_19.txt | 211 ++++ experiment_log_2.txt | 102 ++ experiment_log_20.txt | 152 +++ experiment_log_21.txt | 90 ++ experiment_log_22.txt | 175 +++ experiment_log_23.txt | 81 ++ experiment_log_24.txt | 94 ++ experiment_log_25.txt | 94 ++ experiment_log_26.txt | 94 ++ experiment_log_27.txt | 94 ++ experiment_log_28.txt | 94 ++ experiment_log_29.txt | 84 ++ experiment_log_3.txt | 102 ++ experiment_log_30.txt | 81 ++ experiment_log_31.txt | 82 ++ experiment_log_32.txt | 84 ++ experiment_log_33.txt | 80 ++ experiment_log_34.txt | 80 ++ experiment_log_35.txt | 80 ++ experiment_log_36.txt | 113 ++ experiment_log_37.txt | 80 ++ experiment_log_38.txt | 82 ++ experiment_log_39.txt | 83 ++ experiment_log_4.txt | 102 ++ experiment_log_40.txt | 83 ++ experiment_log_41.txt | 111 ++ experiment_log_42.txt | 142 +++ experiment_log_43.txt | 80 ++ experiment_log_44.txt | 111 ++ experiment_log_45.txt | 112 ++ experiment_log_46.txt | 80 ++ experiment_log_47.txt | 80 ++ experiment_log_48.txt | 80 ++ experiment_log_49.txt | 80 ++ experiment_log_5.txt | 102 ++ experiment_log_50.txt | 80 ++ experiment_log_51.txt | 999 ++++++++++++++++++ experiment_log_52.txt | 80 ++ experiment_log_53.txt | 80 ++ experiment_log_54.txt | 80 ++ experiment_log_55.txt | 81 ++ experiment_log_6.txt | 102 ++ experiment_log_7.txt | 102 ++ experiment_log_8.txt | 102 ++ experiment_log_9.txt | 113 ++ try.py | 112 +- 59 files changed, 7670 insertions(+), 189 deletions(-) create mode 100644 experiment_log.txt create mode 100644 experiment_log_1.txt create mode 100644 experiment_log_10.txt create mode 100644 experiment_log_11.txt create mode 100644 experiment_log_12.txt create mode 100644 experiment_log_13.txt create mode 100644 experiment_log_14.txt create mode 100644 experiment_log_15.txt create mode 100644 experiment_log_16.txt create mode 100644 experiment_log_17.txt create mode 100644 experiment_log_18.txt create mode 100644 experiment_log_19.txt create mode 100644 experiment_log_2.txt create mode 100644 experiment_log_20.txt create mode 100644 experiment_log_21.txt create mode 100644 experiment_log_22.txt create mode 100644 experiment_log_23.txt create mode 100644 experiment_log_24.txt create mode 100644 experiment_log_25.txt create mode 100644 experiment_log_26.txt create mode 100644 experiment_log_27.txt create mode 100644 experiment_log_28.txt create mode 100644 experiment_log_29.txt create mode 100644 experiment_log_3.txt create mode 100644 experiment_log_30.txt create mode 100644 experiment_log_31.txt create mode 100644 experiment_log_32.txt create mode 100644 experiment_log_33.txt create mode 100644 experiment_log_34.txt create mode 100644 experiment_log_35.txt create mode 100644 experiment_log_36.txt create mode 100644 experiment_log_37.txt create mode 100644 experiment_log_38.txt create mode 100644 experiment_log_39.txt create mode 100644 experiment_log_4.txt create mode 100644 experiment_log_40.txt create mode 100644 experiment_log_41.txt create mode 100644 experiment_log_42.txt create mode 100644 experiment_log_43.txt create mode 100644 experiment_log_44.txt create mode 100644 experiment_log_45.txt create mode 100644 experiment_log_46.txt create mode 100644 experiment_log_47.txt create mode 100644 experiment_log_48.txt create mode 100644 experiment_log_49.txt create mode 100644 experiment_log_5.txt create mode 100644 experiment_log_50.txt create mode 100644 experiment_log_51.txt create mode 100644 experiment_log_52.txt create mode 100644 experiment_log_53.txt create mode 100644 experiment_log_54.txt create mode 100644 experiment_log_55.txt create mode 100644 experiment_log_6.txt create mode 100644 experiment_log_7.txt create mode 100644 experiment_log_8.txt create mode 100644 experiment_log_9.txt diff --git a/cornac/experiment/experiment.py b/cornac/experiment/experiment.py index 455c4fded..d4d8bfd01 100644 --- a/cornac/experiment/experiment.py +++ b/cornac/experiment/experiment.py @@ -157,7 +157,7 @@ def run(self): if self.val_result is not None: output += "\nVALIDATION:\n...\n{}".format(self.val_result) output += "\nTEST:\n...\n{}".format(self.result) - print('helllooooooooooooow') + # print('helllooooooooooooow') print(output) timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S-%f") diff --git a/cornac/models/globallocalkernel/recom_globallocalkernel.py b/cornac/models/globallocalkernel/recom_globallocalkernel.py index 9242403eb..74251eb57 100644 --- a/cornac/models/globallocalkernel/recom_globallocalkernel.py +++ b/cornac/models/globallocalkernel/recom_globallocalkernel.py @@ -3,9 +3,16 @@ import torch.nn.functional as F import numpy as np import time +import sys +import logging + from ..recommender import Recommender +from tqdm import tqdm + + + # =========================== # Define your model layers # =========================== @@ -26,9 +33,14 @@ def __init__(self, n_in, n_hid, n_dim, lambda_s, lambda_2, activation=nn.Sigmoid self.lambda_s = lambda_s self.lambda_2 = lambda_2 - nn.init.xavier_uniform_(self.W, gain=torch.nn.init.calculate_gain("relu")) - nn.init.xavier_uniform_(self.u, gain=torch.nn.init.calculate_gain("relu")) - nn.init.xavier_uniform_(self.v, gain=torch.nn.init.calculate_gain("relu")) + # nn.init.xavier_uniform_(self.W, gain=torch.nn.init.calculate_gain("sigmoid")) + # nn.init.xavier_uniform_(self.u, gain=torch.nn.init.calculate_gain("sigmoid")) + # nn.init.xavier_uniform_(self.v, gain=torch.nn.init.calculate_gain("sigmoid")) + + nn.init.kaiming_uniform_(self.W,nonlinearity = "sigmoid") + nn.init.kaiming_uniform_(self.u, nonlinearity = "sigmoid") + nn.init.kaiming_uniform_(self.v,nonlinearity = "sigmoid") + nn.init.zeros_(self.b) self.activation = activation @@ -149,20 +161,27 @@ class GlobalLocalKernel(Recommender): def __init__( self, - n_hid=10, # size of hidden layers + n_hid=1, # size of hidden layers n_dim=2, # inner AE embedding size n_layers=2, # number of hidden layers + + + # lambda_s=0.0001, # regularization of sparsity of the final matrix + # lambda_2=0.0001, # regularization of number of parameters + lambda_s=0.006, # regularization of sparsity of the final matrix - lambda_2=20., # regularization of number of parameters + lambda_2=0.001, # regularization of number of parameters + gk_size=3, # width=height of kernel for convolution dot_scale=1, # dot product weight for global kernel - max_epoch_p=2, # max number of epochs for pretraining - max_epoch_f=2, # max number of epochs for finetuning - tol_p=1e-2, # min threshold for difference between consecutive values of train rmse for pretraining - tol_f=1e-2, # min threshold for difference for finetuning - patience_p=1, # early stopping patience for pretraining - patience_f=1, # early stopping patience for finetuning - lr=0.001, + max_epoch_p=10, # max number of epochs for pretraining + max_epoch_f=10, # max number of epochs for finetuning + tol_p=1e-4, # min threshold for difference between consecutive values of train rmse for pretraining + tol_f=1e-5, # min threshold for difference for finetuning + patience_p=10, # early stopping patience for pretraining + patience_f=10, # early stopping patience for finetuning + lr_p=0.01, + lr_f=0.001, verbose=False, name="GlobalLocalKernel", trainable=True @@ -181,7 +200,8 @@ def __init__( self.tol_f = tol_f self.patience_p = patience_p self.patience_f = patience_f - self.lr = lr + self.lr_p = lr_p + self.lr_f = lr_f self.verbose = verbose # Device @@ -206,7 +226,7 @@ def fit(self, train_set, val_set=None): # complete_model = CompleteNet(kernel_net, n_u, n_m, self.n_hid, self.n_dim, self.n_layers, self.lambda_s, self.lambda_2, self.gk_size, self.dot_scale).double().to(self.device) self.min_rating = 1.0 - self.max_rating = 5.0 + self.max_rating = 4.0 # Extract user-item-rating tuples train_users, train_items, train_ratings = train_set.uir_tuple @@ -239,162 +259,186 @@ def fit(self, train_set, val_set=None): # Pre-Training (KernelNet only) - optimizer = torch.optim.AdamW(complete_model.local_kernel_net.parameters(), lr=self.lr) + optimizer = torch.optim.AdamW(complete_model.local_kernel_net.parameters(), lr=self.lr_p) best_rmse = np.inf last_rmse = np.inf counter = 0 tic = time.time() - - for epoch in range(self.max_epoch_p): - # def closure(): - # optimizer.zero_grad() - # x = torch.tensor(train_r, dtype=torch.double, device=self.device) - # m = torch.tensor(train_mask, dtype=torch.double, device=self.device) - # complete_model.local_kernel_net.train() - # pred, reg = complete_model.local_kernel_net(x) - # loss = Loss().to(self.device)(pred, reg, m, x) - # loss.backward() - # return loss - - - def closure(): - optimizer.zero_grad() - # Use train_r instead of train_r - x = torch.tensor(train_r, dtype=torch.double, device=self.device) - m = torch.tensor(train_mask, dtype=torch.double, device=self.device) - complete_model.local_kernel_net.train() - pred, reg = complete_model.local_kernel_net(x) - loss = Loss().to(self.device)(pred, reg, m, x) - loss.backward() - return loss - - - optimizer.step(closure) - - complete_model.local_kernel_net.eval() - with torch.no_grad(): - # print('complete model train_r :' , train_r) - x = torch.tensor(train_r, dtype=torch.double, device=self.device) - pred, _ = kernel_net(x) - pre = pred.float().cpu().numpy() - # Compute training RMSE - train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) - - if last_rmse - train_rmse < self.tol_p: - counter += 1 - else: - counter = 0 - last_rmse = train_rmse - - if counter >= self.patience_p: - if self.verbose: - print("Early stopping pre-training at epoch:", epoch+1) - break - - if self.verbose and epoch % 10 == 0: - print(f"Pre-Training Epoch {epoch+1}/{self.max_epoch_p}, Train RMSE: {train_rmse:.4f}") + + #Pre-training process with tqdm for every group of 10 epochs + for group in range(0, self.max_epoch_p, 10): # Split epochs into groups of 10 + start_epoch = group + end_epoch = min(group + 10, self.max_epoch_p) # Handle the last group + + # Initialize the progress bar for the group + with tqdm(total=end_epoch - start_epoch, desc=f"Epochs {start_epoch + 1}-{end_epoch} (Pre-Training)", leave=True) as pbar: + for epoch in range(start_epoch, end_epoch): + + # Define the closure function + def closure(): + optimizer.zero_grad() + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + m = torch.tensor(train_mask, dtype=torch.double, device=self.device) + complete_model.local_kernel_net.train() + pred, reg = complete_model.local_kernel_net(x) + loss = Loss().to(self.device)(pred, reg, m, x) + loss.backward() + return loss + + optimizer.step(closure) + + complete_model.local_kernel_net.eval() + with torch.no_grad(): + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + pred, _ = kernel_net(x) + + pre = pred.float().cpu().numpy() + + # Compute training RMSE + train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) + + # Update the current progress bar + pbar.set_postfix({"Train RMSE": f"{train_rmse:.4f}"}) + pbar.update(1) + + # Check for early stopping + if last_rmse - train_rmse < self.tol_p: + counter += 1 + else: + counter = 0 + last_rmse = train_rmse + + if counter >= self.patience_p: + tqdm.write(f"Early stopping pre-training at epoch: {epoch + 1}") + break + + # Log at the current epoch + if self.verbose: + logging.info(f"Pre-Training Epoch {epoch + 1}/{self.max_epoch_p}, Train RMSE: {train_rmse:.4f}") # After pre-training self.train_r_local = np.clip(pre, 1., 5.) # Fine-Tuning - optimizer = torch.optim.AdamW(complete_model.parameters(), lr=self.lr) + optimizer = torch.optim.AdamW(complete_model.parameters(), lr=self.lr_f) last_rmse = np.inf counter = 0 - - for epoch in range(self.max_epoch_f): - def closure(): - optimizer.zero_grad() - x = torch.tensor(train_r, dtype=torch.double, device=self.device) - x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) - m = torch.tensor(train_mask, dtype=torch.double, device=self.device) - complete_model.train() - pred, reg = complete_model(x, x_local) - loss = Loss().to(self.device)(pred, reg, m, x) - loss.backward() - return loss - - optimizer.step(closure) - - complete_model.eval() - with torch.no_grad(): - x = torch.tensor(train_r, dtype=torch.double, device=self.device) - x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) - pred, _ = complete_model(x, x_local) - pre = pred.float().cpu().numpy() - - # Compute training RMSE - train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) - - if last_rmse - train_rmse < self.tol_f: - counter += 1 - else: - counter = 0 - last_rmse = train_rmse - - if counter >= self.patience_f: - if self.verbose: - print("Early stopping fine-tuning at epoch:", epoch+1) - break - - if self.verbose and epoch % 10 == 0: - print(f"Fine-Tuning Epoch {epoch+1}/{self.max_epoch_f}, Train RMSE: {train_rmse:.4f}") + + for group in range(0, self.max_epoch_f, 10): # Split epochs into groups of 10 + start_epoch = group + end_epoch = min(group + 10, self.max_epoch_f) # Handle the last group + + + # Initialize the progress bar for the group + with tqdm(total=end_epoch - start_epoch, desc=f"Epochs {start_epoch + 1}-{end_epoch} (Fine-Tuning)", leave=True) as pbar: + for epoch in range(start_epoch, end_epoch): + + # Define the closure function + def closure(): + optimizer.zero_grad() + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) + m = torch.tensor(train_mask, dtype=torch.double, device=self.device) + complete_model.train() + pred, reg = complete_model(x, x_local) + loss = Loss().to(self.device)(pred, reg, m, x) + loss.backward() + return loss + + optimizer.step(closure) + + complete_model.eval() + with torch.no_grad(): + x = torch.tensor(train_r, dtype=torch.double, device=self.device) + x_local = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) + pred, _ = complete_model(x, x_local) + + pre = pred.float().cpu().numpy() + + # Compute training RMSE + train_rmse = np.sqrt(((train_mask * (np.clip(pre, 1., 5.) - train_r))**2).sum() / train_mask.sum()) + + # Update the current progress bar + pbar.set_postfix({"Train RMSE": f"{train_rmse:.4f}"}) + pbar.update(1) + + # Check for early stopping + if last_rmse - train_rmse < self.tol_f: + counter += 1 + else: + counter = 0 + last_rmse = train_rmse + + if counter >= self.patience_f: + tqdm.write(f"Early stopping fine-tuning at epoch: {epoch + 1}") + break + + # Log at the current epoch + if self.verbose: + logging.info(f"Fine-Tuning Epoch {epoch + 1}/{self.max_epoch_f}, Train RMSE: {train_rmse:.4f}") # Store the trained model self.model = complete_model return self - def score(self, user_id, item_id=None): - """Predict the scores/ratings of a user for an item. + def score(self, user_id, item_id=None, batch_size=10): + """Predict the scores/ratings of a user for an item or batch of items. Parameters ---------- - user_id: int, required + user_id: int The index of the user for whom to perform score prediction. - - item_id: int, optional, default: None - The index of the item for which to perform score prediction. + item_id: int or list of int, optional + The index (or indices) of the item(s) for which to perform score prediction. If None, scores for all items will be returned. + batch_size: int, optional, default: 10 + Number of items to process in a batch for tqdm progress bar. Returns ------- - res : A scalar or a Numpy array + res: A scalar, Numpy array, or dictionary + If `item_id` is None, returns an array of scores for all items for the user. + If `item_id` is a single integer, returns a scalar score for that item. + If `item_id` is a list of integers, returns a dictionary of scores. """ if self.model is None: raise RuntimeError("You must train the model before calling score()!") - # Inference: provide predictions for given user_id, item_id - # We'll assume we've stored training rating matrix in `fit` if needed. - # For simplicity, assume `train_r_local` and `model` are available. - - # Note: For large datasets, keep user and item embeddings precomputed. with torch.no_grad(): - # We can re-use self.train_r_local as input - # If user_id given, we create a vector with only that user - # We'll do a full prediction for all users/items and slice. - # In a production scenario, you'd probably have a more efficient inference method. - - # self.model expects full matrix input: - # Construct a matrix with shape (num_users, num_items), with zeros if needed - # For scoring, you can either store the training data or create a neutral input - # Here we just re-use training data as input context. input_mat = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) - # We must pass also the original train_r for global kernel step: - # If we have it stored somewhere, we should keep it. Here we assume we have them: - # Ideally, you might store self.train_r in self.fit for scoring: - # For demonstration, let's assume we stored train_r in self._train_r x_global = torch.tensor(self._train_r, dtype=torch.double, device=self.device) - # Compute predictions - pred, _ = self.model(x_global, input_mat) - pred = pred.float().cpu().numpy() - if item_id is None: - # return predictions for all items for this user - return pred[:, user_id] - else: - # return prediction for this single item - return pred[item_id , user_id] + if item_id is None: + # Predict scores for all items for the specified user + n_items = input_mat.shape[0] + preds = np.zeros((n_items,), dtype=np.float32) + + with tqdm(total=n_items, desc=f"Scoring all items for user {user_id}", leave=True) as pbar: + for i in range(n_items): + pred, _ = self.model(x_global, input_mat) + preds[i] = pred[i, user_id].item() + pbar.update(1) + + return preds + + elif isinstance(item_id, list): + # Predict scores for a list of items + preds = {} + with tqdm(total=len(item_id), desc=f"Scoring items for user {user_id}", leave=True) as pbar: + for i in item_id: + pred, _ = self.model(x_global, input_mat) + preds[i] = pred[i, user_id].item() + pbar.update(1) + + return preds + + else: + # print(f"Debug: item_id is a single value: {item_id}. Scoring for user {user_id}.") + # Predict score for a single item + pred, _ = self.model(x_global, input_mat) + return pred[item_id, user_id].item() + def rate(self, user_id, item_id): # Optionally override if needed, or rely on default Recommender.rate() diff --git a/experiment_log.txt b/experiment_log.txt new file mode 100644 index 000000000..52dd46751 --- /dev/null +++ b/experiment_log.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:30:35.698931 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] + [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] + [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] + ... + [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] + [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] + [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7996 +Pre-Training Epoch 2/11, Train RMSE: 2.7996 +Pre-Training Epoch 3/11, Train RMSE: 2.7980 +Pre-Training Epoch 4/11, Train RMSE: 2.5566 +Pre-Training Epoch 5/11, Train RMSE: 2.1277 +Pre-Training Epoch 6/11, Train RMSE: 1.6573 +Pre-Training Epoch 7/11, Train RMSE: 1.2255 +Pre-Training Epoch 8/11, Train RMSE: 0.9784 +Pre-Training Epoch 9/11, Train RMSE: 0.9623 +Pre-Training Epoch 10/11, Train RMSE: 0.9372 +Pre-Training Epoch 11/11, Train RMSE: 0.8444 +Fine-Tuning Epoch 1/10, Train RMSE: 1.2315 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1229 +Fine-Tuning Epoch 3/10, Train RMSE: 0.9482 +Fine-Tuning Epoch 4/10, Train RMSE: 0.8739 +Fine-Tuning Epoch 5/10, Train RMSE: 0.8417 +Fine-Tuning Epoch 6/10, Train RMSE: 0.8378 +Fine-Tuning Epoch 7/10, Train RMSE: 0.8520 +Fine-Tuning Epoch 8/10, Train RMSE: 0.8780 +Fine-Tuning Epoch 9/10, Train RMSE: 0.9065 +Fine-Tuning Epoch 10/10, Train RMSE: 0.9070 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0235 | 1.0309 | 1.6137 | 0.0870 + + +======================================== diff --git a/experiment_log_1.txt b/experiment_log_1.txt new file mode 100644 index 000000000..45471017e --- /dev/null +++ b/experiment_log_1.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:37:28.221567 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): ReLU() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] + [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] + [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] + ... + [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] + [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] + [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7996 +Pre-Training Epoch 2/11, Train RMSE: 2.7996 +Pre-Training Epoch 3/11, Train RMSE: 2.7996 +Pre-Training Epoch 4/11, Train RMSE: 2.7996 +Pre-Training Epoch 5/11, Train RMSE: 2.7996 +Pre-Training Epoch 6/11, Train RMSE: 2.7996 +Pre-Training Epoch 7/11, Train RMSE: 2.7996 +Pre-Training Epoch 8/11, Train RMSE: 2.7996 +Pre-Training Epoch 9/11, Train RMSE: 2.7996 +Pre-Training Epoch 10/11, Train RMSE: 2.7996 +Pre-Training Epoch 11/11, Train RMSE: 2.7263 +Fine-Tuning Epoch 1/10, Train RMSE: 2.6358 +Fine-Tuning Epoch 2/10, Train RMSE: 2.5462 +Fine-Tuning Epoch 3/10, Train RMSE: 2.4575 +Fine-Tuning Epoch 4/10, Train RMSE: 2.3699 +Fine-Tuning Epoch 5/10, Train RMSE: 2.2835 +Fine-Tuning Epoch 6/10, Train RMSE: 2.1985 +Fine-Tuning Epoch 7/10, Train RMSE: 2.1150 +Fine-Tuning Epoch 8/10, Train RMSE: 2.0332 +Fine-Tuning Epoch 9/10, Train RMSE: 1.9533 +Fine-Tuning Epoch 10/10, Train RMSE: 1.8753 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.6314 | 1.6441 | 1.5506 | 0.0860 + + +======================================== diff --git a/experiment_log_10.txt b/experiment_log_10.txt new file mode 100644 index 000000000..770de83fe --- /dev/null +++ b/experiment_log_10.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:57:20.687730 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.990679 3.8487284 1.8037997 ... 3.490999 4.269434 2.5483623] + [3.023685 3.8958206 1.817734 ... 3.5314374 4.3215194 2.573359 ] + [3.0264924 3.9006164 1.8189137 ... 3.53659 4.3273363 2.575559 ] + ... + [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922] + [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922] + [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7954 +Pre-Training Epoch 4/11, Train RMSE: 2.5679 +Pre-Training Epoch 5/11, Train RMSE: 2.0900 +Pre-Training Epoch 6/11, Train RMSE: 1.5734 +Pre-Training Epoch 7/11, Train RMSE: 1.1784 +Pre-Training Epoch 8/11, Train RMSE: 1.1459 +Pre-Training Epoch 9/11, Train RMSE: 1.2550 +Pre-Training Epoch 10/11, Train RMSE: 1.2081 +Pre-Training Epoch 11/11, Train RMSE: 1.0682 +Fine-Tuning Epoch 1/10, Train RMSE: 1.5611 +Fine-Tuning Epoch 2/10, Train RMSE: 1.2967 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0950 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0737 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0739 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0530 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0426 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0595 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0967 +Fine-Tuning Epoch 10/10, Train RMSE: 1.1278 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9825 | 1.0726 | 2.1376 | 8.5963 + + +======================================== diff --git a/experiment_log_11.txt b/experiment_log_11.txt new file mode 100644 index 000000000..f879c6751 --- /dev/null +++ b/experiment_log_11.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:59:02.433557 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.288805 4.575432 1.982252 ... 3.7468586 4.439199 2.8227978] + [3.37377 4.7019467 2.038978 ... 3.840653 4.5570164 2.8820019] + [3.369288 4.691076 2.035483 ... 3.8347905 4.548342 2.8783784] + ... + [3.2805226 4.5634556 1.9769341 ... 3.737924 4.4280543 2.8172026] + [3.2644706 4.541155 1.9667293 ... 3.7208102 4.4069934 2.8064775] + [3.2644706 4.541155 1.9667293 ... 3.7208102 4.4069934 2.8064775]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7954 +Pre-Training Epoch 4/11, Train RMSE: 2.7333 +Pre-Training Epoch 5/11, Train RMSE: 2.3719 +Pre-Training Epoch 6/11, Train RMSE: 1.9474 +Pre-Training Epoch 7/11, Train RMSE: 1.5101 +Pre-Training Epoch 8/11, Train RMSE: 1.1549 +Pre-Training Epoch 9/11, Train RMSE: 1.0785 +Pre-Training Epoch 10/11, Train RMSE: 1.1469 +Pre-Training Epoch 11/11, Train RMSE: 1.1273 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3074 +Fine-Tuning Epoch 2/10, Train RMSE: 1.2829 +Fine-Tuning Epoch 3/10, Train RMSE: 1.1283 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0634 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0511 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0356 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0337 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0641 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0882 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0760 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9387 | 1.0353 | 1.8300 | 8.2491 + + +======================================== diff --git a/experiment_log_12.txt b/experiment_log_12.txt new file mode 100644 index 000000000..c0b34d05e --- /dev/null +++ b/experiment_log_12.txt @@ -0,0 +1,112 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:03:35.945173 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.4343624 3.8545873 2.7392533 ... 3.457223 3.9285028 1.9940946] + [2.4660501 3.8995204 2.7729685 ... 3.4968312 3.9814517 2.0049841] + [2.4496148 3.870842 2.7558439 ... 3.4721377 3.9461834 1.9956937] + ... + [2.4251416 3.8361835 2.7235134 ... 3.446127 3.9152336 1.9902709] + [2.4251416 3.8361835 2.7235134 ... 3.446127 3.9152336 1.9902709] + [2.4389083 3.8435922 2.739875 ... 3.4509299 3.9247258 1.9837745]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7857 +Pre-Training Epoch 4/11, Train RMSE: 2.4588 +Pre-Training Epoch 5/11, Train RMSE: 1.9610 +Pre-Training Epoch 6/11, Train RMSE: 1.4614 +Pre-Training Epoch 7/11, Train RMSE: 1.1359 +Pre-Training Epoch 8/11, Train RMSE: 1.1327 +Pre-Training Epoch 9/11, Train RMSE: 1.1802 +Pre-Training Epoch 10/11, Train RMSE: 1.0934 +Pre-Training Epoch 11/11, Train RMSE: 1.0821 +Pre-Training Epoch 1/11, Train RMSE: 1.2752 +Fine-Tuning Epoch 1/10, Train RMSE: 1.2752 +Pre-Training Epoch 2/11, Train RMSE: 1.1445 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1445 +Pre-Training Epoch 3/11, Train RMSE: 1.0750 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0750 +Pre-Training Epoch 4/11, Train RMSE: 1.0659 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0659 +Pre-Training Epoch 5/11, Train RMSE: 1.1005 +Fine-Tuning Epoch 5/10, Train RMSE: 1.1005 +Pre-Training Epoch 6/11, Train RMSE: 1.0901 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0901 +Pre-Training Epoch 7/11, Train RMSE: 1.0659 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0659 +Pre-Training Epoch 8/11, Train RMSE: 1.0470 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0470 +Pre-Training Epoch 9/11, Train RMSE: 1.0353 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0353 +Pre-Training Epoch 10/11, Train RMSE: 1.0350 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0350 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8653 | 0.9704 | 1.9956 | 8.5123 + + +======================================== diff --git a/experiment_log_13.txt b/experiment_log_13.txt new file mode 100644 index 000000000..7faaabedb --- /dev/null +++ b/experiment_log_13.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:04:28.514215 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.7538408 3.343685 1.9311216 ... 3.7609093 4.285689 2.8714743] + [1.7473786 3.3813841 1.9722533 ... 3.841642 4.3844547 2.9412758] + [1.773718 3.4069777 1.9794767 ... 3.855084 4.396268 2.9370563] + ... + [1.7524282 3.326015 1.9154572 ... 3.7306907 4.2482576 2.8472283] + [1.7492262 3.3228312 1.9150296 ... 3.7304652 4.246798 2.8469868] + [1.76094 3.3494902 1.9330635 ... 3.7632113 4.2881966 2.871563 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7035 +Pre-Training Epoch 4/11, Train RMSE: 2.1628 +Pre-Training Epoch 5/11, Train RMSE: 1.5384 +Pre-Training Epoch 6/11, Train RMSE: 1.1252 +Pre-Training Epoch 7/11, Train RMSE: 1.1852 +Pre-Training Epoch 8/11, Train RMSE: 1.2196 +Pre-Training Epoch 9/11, Train RMSE: 1.0642 +Pre-Training Epoch 10/11, Train RMSE: 1.1462 +Pre-Training Epoch 11/11, Train RMSE: 1.3379 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3656 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1017 +Fine-Tuning Epoch 3/10, Train RMSE: 1.1460 +Fine-Tuning Epoch 4/10, Train RMSE: 1.3414 +Fine-Tuning Epoch 5/10, Train RMSE: 1.3363 +Fine-Tuning Epoch 6/10, Train RMSE: 1.2144 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0916 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0387 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0310 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0233 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8440 | 0.9594 | 1.8963 | 14.6467 + + +======================================== diff --git a/experiment_log_14.txt b/experiment_log_14.txt new file mode 100644 index 000000000..be6f48d76 --- /dev/null +++ b/experiment_log_14.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:05:19.179192 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] + [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] + [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] + ... + [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] + [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] + [4.7619658 3.218526 5. ... 2.2828622 3.1661565 4.0636473]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7996 +Pre-Training Epoch 2/11, Train RMSE: 2.7996 +Pre-Training Epoch 3/11, Train RMSE: 2.7613 +Pre-Training Epoch 4/11, Train RMSE: 2.3618 +Pre-Training Epoch 5/11, Train RMSE: 1.8220 +Pre-Training Epoch 6/11, Train RMSE: 1.3232 +Pre-Training Epoch 7/11, Train RMSE: 1.0369 +Pre-Training Epoch 8/11, Train RMSE: 0.9935 +Pre-Training Epoch 9/11, Train RMSE: 0.9563 +Pre-Training Epoch 10/11, Train RMSE: 0.8588 +Pre-Training Epoch 11/11, Train RMSE: 0.9126 +Fine-Tuning Epoch 1/10, Train RMSE: 0.9313 +Fine-Tuning Epoch 2/10, Train RMSE: 1.0484 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0414 +Fine-Tuning Epoch 4/10, Train RMSE: 0.9479 +Fine-Tuning Epoch 5/10, Train RMSE: 0.8739 +Fine-Tuning Epoch 6/10, Train RMSE: 0.8452 +Fine-Tuning Epoch 7/10, Train RMSE: 0.8545 +Fine-Tuning Epoch 8/10, Train RMSE: 0.8723 +Fine-Tuning Epoch 9/10, Train RMSE: 0.8531 +Fine-Tuning Epoch 10/10, Train RMSE: 0.8400 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0731 | 1.0796 | 1.5165 | 0.0800 + + +======================================== diff --git a/experiment_log_15.txt b/experiment_log_15.txt new file mode 100644 index 000000000..592762861 --- /dev/null +++ b/experiment_log_15.txt @@ -0,0 +1,191 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:05:36.896727 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 100 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] + [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] + [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] + ... + [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] + [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] + [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/100, Train RMSE: 2.7996 +Pre-Training Epoch 2/100, Train RMSE: 2.7996 +Pre-Training Epoch 3/100, Train RMSE: 2.7725 +Pre-Training Epoch 4/100, Train RMSE: 2.4399 +Pre-Training Epoch 5/100, Train RMSE: 1.9927 +Pre-Training Epoch 6/100, Train RMSE: 1.5273 +Pre-Training Epoch 7/100, Train RMSE: 1.1520 +Pre-Training Epoch 8/100, Train RMSE: 1.0083 +Pre-Training Epoch 9/100, Train RMSE: 1.0070 +Pre-Training Epoch 10/100, Train RMSE: 0.9736 +Pre-Training Epoch 11/100, Train RMSE: 0.8776 +Pre-Training Epoch 12/100, Train RMSE: 0.8808 +Pre-Training Epoch 13/100, Train RMSE: 1.0238 +Pre-Training Epoch 14/100, Train RMSE: 1.1063 +Pre-Training Epoch 15/100, Train RMSE: 1.0707 +Pre-Training Epoch 16/100, Train RMSE: 0.9579 +Pre-Training Epoch 17/100, Train RMSE: 0.8636 +Pre-Training Epoch 18/100, Train RMSE: 0.8317 +Pre-Training Epoch 19/100, Train RMSE: 0.8230 +Pre-Training Epoch 20/100, Train RMSE: 0.8141 +Pre-Training Epoch 21/100, Train RMSE: 0.8301 +Pre-Training Epoch 22/100, Train RMSE: 0.8626 +Pre-Training Epoch 23/100, Train RMSE: 0.8663 +Pre-Training Epoch 24/100, Train RMSE: 0.8418 +Pre-Training Epoch 25/100, Train RMSE: 0.8026 +Pre-Training Epoch 26/100, Train RMSE: 0.7860 +Pre-Training Epoch 27/100, Train RMSE: 0.7824 +Pre-Training Epoch 28/100, Train RMSE: 0.7859 +Pre-Training Epoch 29/100, Train RMSE: 0.7999 +Pre-Training Epoch 30/100, Train RMSE: 0.8251 +Pre-Training Epoch 31/100, Train RMSE: 0.8355 +Pre-Training Epoch 32/100, Train RMSE: 0.8193 +Pre-Training Epoch 33/100, Train RMSE: 0.7915 +Pre-Training Epoch 34/100, Train RMSE: 0.7735 +Pre-Training Epoch 35/100, Train RMSE: 0.7687 +Pre-Training Epoch 36/100, Train RMSE: 0.7700 +Pre-Training Epoch 37/100, Train RMSE: 0.7746 +Pre-Training Epoch 38/100, Train RMSE: 0.7828 +Pre-Training Epoch 39/100, Train RMSE: 0.7982 +Pre-Training Epoch 40/100, Train RMSE: 0.8089 +Pre-Training Epoch 41/100, Train RMSE: 0.8077 +Pre-Training Epoch 42/100, Train RMSE: 0.7934 +Pre-Training Epoch 43/100, Train RMSE: 0.7811 +Pre-Training Epoch 44/100, Train RMSE: 0.7793 +Pre-Training Epoch 45/100, Train RMSE: 0.7792 +Pre-Training Epoch 46/100, Train RMSE: 0.7800 +Pre-Training Epoch 47/100, Train RMSE: 0.7793 +Pre-Training Epoch 48/100, Train RMSE: 0.7723 +Pre-Training Epoch 49/100, Train RMSE: 0.7692 +Pre-Training Epoch 50/100, Train RMSE: 0.7663 +Pre-Training Epoch 51/100, Train RMSE: 0.7675 +Pre-Training Epoch 52/100, Train RMSE: 0.7740 +Pre-Training Epoch 53/100, Train RMSE: 0.7764 +Pre-Training Epoch 54/100, Train RMSE: 0.7738 +Pre-Training Epoch 55/100, Train RMSE: 0.7652 +Pre-Training Epoch 56/100, Train RMSE: 0.7642 +Pre-Training Epoch 57/100, Train RMSE: 0.7638 +Pre-Training Epoch 58/100, Train RMSE: 0.7684 +Pre-Training Epoch 59/100, Train RMSE: 0.7717 +Pre-Training Epoch 60/100, Train RMSE: 0.7722 +Pre-Training Epoch 61/100, Train RMSE: 0.7615 +Pre-Training Epoch 62/100, Train RMSE: 0.7605 +Pre-Training Epoch 63/100, Train RMSE: 0.7669 +Pre-Training Epoch 64/100, Train RMSE: 0.7759 +Pre-Training Epoch 65/100, Train RMSE: 0.7781 +Pre-Training Epoch 66/100, Train RMSE: 0.7721 +Pre-Training Epoch 67/100, Train RMSE: 0.7657 +Pre-Training Epoch 68/100, Train RMSE: 0.7621 +Pre-Training Epoch 69/100, Train RMSE: 0.7682 +Pre-Training Epoch 70/100, Train RMSE: 0.7793 +Pre-Training Epoch 71/100, Train RMSE: 0.7786 +Pre-Training Epoch 72/100, Train RMSE: 0.7687 +Pre-Training Epoch 73/100, Train RMSE: 0.7656 +Pre-Training Epoch 74/100, Train RMSE: 0.7606 +Pre-Training Epoch 75/100, Train RMSE: 0.7592 +Pre-Training Epoch 76/100, Train RMSE: 0.7602 +Pre-Training Epoch 77/100, Train RMSE: 0.7615 +Pre-Training Epoch 78/100, Train RMSE: 0.7732 +Pre-Training Epoch 79/100, Train RMSE: 0.7779 +Pre-Training Epoch 80/100, Train RMSE: 0.7658 +Pre-Training Epoch 81/100, Train RMSE: 0.7532 +Pre-Training Epoch 82/100, Train RMSE: 0.7502 +Pre-Training Epoch 83/100, Train RMSE: 0.7540 +Pre-Training Epoch 84/100, Train RMSE: 0.7636 +Pre-Training Epoch 85/100, Train RMSE: 0.7745 +Pre-Training Epoch 86/100, Train RMSE: 0.7705 +Pre-Training Epoch 87/100, Train RMSE: 0.7647 +Pre-Training Epoch 88/100, Train RMSE: 0.7571 +Pre-Training Epoch 89/100, Train RMSE: 0.7532 +Pre-Training Epoch 90/100, Train RMSE: 0.7540 +Pre-Training Epoch 91/100, Train RMSE: 0.7613 +Pre-Training Epoch 92/100, Train RMSE: 0.7635 +Pre-Training Epoch 93/100, Train RMSE: 0.7573 +Pre-Training Epoch 94/100, Train RMSE: 0.7535 +Pre-Training Epoch 95/100, Train RMSE: 0.7502 +Pre-Training Epoch 96/100, Train RMSE: 0.7514 +Pre-Training Epoch 97/100, Train RMSE: 0.7569 +Pre-Training Epoch 98/100, Train RMSE: 0.7624 +Pre-Training Epoch 99/100, Train RMSE: 0.7577 +Pre-Training Epoch 100/100, Train RMSE: 0.7533 +Fine-Tuning Epoch 1/10, Train RMSE: 1.0218 +Fine-Tuning Epoch 2/10, Train RMSE: 0.8217 +Fine-Tuning Epoch 3/10, Train RMSE: 0.7950 +Fine-Tuning Epoch 4/10, Train RMSE: 0.7787 +Fine-Tuning Epoch 5/10, Train RMSE: 0.7598 +Fine-Tuning Epoch 6/10, Train RMSE: 0.7763 +Fine-Tuning Epoch 7/10, Train RMSE: 0.8094 +Fine-Tuning Epoch 8/10, Train RMSE: 0.8292 +Fine-Tuning Epoch 9/10, Train RMSE: 0.8239 +Fine-Tuning Epoch 10/10, Train RMSE: 0.8055 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0599 | 1.0604 | 2.1100 | 0.0793 + + +======================================== diff --git a/experiment_log_16.txt b/experiment_log_16.txt new file mode 100644 index 000000000..16a8e92b3 --- /dev/null +++ b/experiment_log_16.txt @@ -0,0 +1,402 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:05:59.405414 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 1000 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] + [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] + [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] + ... + [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] + [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] + [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/1000, Train RMSE: 2.7996 +Pre-Training Epoch 2/1000, Train RMSE: 2.7996 +Pre-Training Epoch 3/1000, Train RMSE: 2.7966 +Pre-Training Epoch 4/1000, Train RMSE: 2.5794 +Pre-Training Epoch 5/1000, Train RMSE: 2.1492 +Pre-Training Epoch 6/1000, Train RMSE: 1.6813 +Pre-Training Epoch 7/1000, Train RMSE: 1.2783 +Pre-Training Epoch 8/1000, Train RMSE: 1.0321 +Pre-Training Epoch 9/1000, Train RMSE: 0.9429 +Pre-Training Epoch 10/1000, Train RMSE: 0.8746 +Pre-Training Epoch 11/1000, Train RMSE: 0.8428 +Pre-Training Epoch 12/1000, Train RMSE: 0.9878 +Pre-Training Epoch 13/1000, Train RMSE: 1.0866 +Pre-Training Epoch 14/1000, Train RMSE: 1.0247 +Pre-Training Epoch 15/1000, Train RMSE: 0.9088 +Pre-Training Epoch 16/1000, Train RMSE: 0.8343 +Pre-Training Epoch 17/1000, Train RMSE: 0.8287 +Pre-Training Epoch 18/1000, Train RMSE: 0.8250 +Pre-Training Epoch 19/1000, Train RMSE: 0.8244 +Pre-Training Epoch 20/1000, Train RMSE: 0.8766 +Pre-Training Epoch 21/1000, Train RMSE: 0.9425 +Pre-Training Epoch 22/1000, Train RMSE: 0.9565 +Pre-Training Epoch 23/1000, Train RMSE: 0.8998 +Pre-Training Epoch 24/1000, Train RMSE: 0.8409 +Pre-Training Epoch 25/1000, Train RMSE: 0.8162 +Pre-Training Epoch 26/1000, Train RMSE: 0.8054 +Pre-Training Epoch 27/1000, Train RMSE: 0.7968 +Pre-Training Epoch 28/1000, Train RMSE: 0.8093 +Pre-Training Epoch 29/1000, Train RMSE: 0.8460 +Pre-Training Epoch 30/1000, Train RMSE: 0.8526 +Pre-Training Epoch 31/1000, Train RMSE: 0.8159 +Pre-Training Epoch 32/1000, Train RMSE: 0.7773 +Pre-Training Epoch 33/1000, Train RMSE: 0.7680 +Pre-Training Epoch 34/1000, Train RMSE: 0.7680 +Pre-Training Epoch 35/1000, Train RMSE: 0.7701 +Pre-Training Epoch 36/1000, Train RMSE: 0.7874 +Pre-Training Epoch 37/1000, Train RMSE: 0.8167 +Pre-Training Epoch 38/1000, Train RMSE: 0.8188 +Pre-Training Epoch 39/1000, Train RMSE: 0.7929 +Pre-Training Epoch 40/1000, Train RMSE: 0.7676 +Pre-Training Epoch 41/1000, Train RMSE: 0.7612 +Pre-Training Epoch 42/1000, Train RMSE: 0.7601 +Pre-Training Epoch 43/1000, Train RMSE: 0.7638 +Pre-Training Epoch 44/1000, Train RMSE: 0.7845 +Pre-Training Epoch 45/1000, Train RMSE: 0.8071 +Pre-Training Epoch 46/1000, Train RMSE: 0.8127 +Pre-Training Epoch 47/1000, Train RMSE: 0.7883 +Pre-Training Epoch 48/1000, Train RMSE: 0.7632 +Pre-Training Epoch 49/1000, Train RMSE: 0.7577 +Pre-Training Epoch 50/1000, Train RMSE: 0.7589 +Pre-Training Epoch 51/1000, Train RMSE: 0.7690 +Pre-Training Epoch 52/1000, Train RMSE: 0.8011 +Pre-Training Epoch 53/1000, Train RMSE: 0.8279 +Pre-Training Epoch 54/1000, Train RMSE: 0.8179 +Pre-Training Epoch 55/1000, Train RMSE: 0.7892 +Pre-Training Epoch 56/1000, Train RMSE: 0.7701 +Pre-Training Epoch 57/1000, Train RMSE: 0.7628 +Pre-Training Epoch 58/1000, Train RMSE: 0.7640 +Pre-Training Epoch 59/1000, Train RMSE: 0.7752 +Pre-Training Epoch 60/1000, Train RMSE: 0.7981 +Pre-Training Epoch 61/1000, Train RMSE: 0.8031 +Pre-Training Epoch 62/1000, Train RMSE: 0.7804 +Pre-Training Epoch 63/1000, Train RMSE: 0.7629 +Pre-Training Epoch 64/1000, Train RMSE: 0.7553 +Pre-Training Epoch 65/1000, Train RMSE: 0.7545 +Pre-Training Epoch 66/1000, Train RMSE: 0.7603 +Pre-Training Epoch 67/1000, Train RMSE: 0.7763 +Pre-Training Epoch 68/1000, Train RMSE: 0.7895 +Pre-Training Epoch 69/1000, Train RMSE: 0.7847 +Pre-Training Epoch 70/1000, Train RMSE: 0.7723 +Pre-Training Epoch 71/1000, Train RMSE: 0.7591 +Pre-Training Epoch 72/1000, Train RMSE: 0.7586 +Pre-Training Epoch 73/1000, Train RMSE: 0.7584 +Pre-Training Epoch 74/1000, Train RMSE: 0.7690 +Pre-Training Epoch 75/1000, Train RMSE: 0.7866 +Pre-Training Epoch 76/1000, Train RMSE: 0.8023 +Pre-Training Epoch 77/1000, Train RMSE: 0.7887 +Pre-Training Epoch 78/1000, Train RMSE: 0.7612 +Pre-Training Epoch 79/1000, Train RMSE: 0.7504 +Pre-Training Epoch 80/1000, Train RMSE: 0.7483 +Pre-Training Epoch 81/1000, Train RMSE: 0.7490 +Pre-Training Epoch 82/1000, Train RMSE: 0.7566 +Pre-Training Epoch 83/1000, Train RMSE: 0.7712 +Pre-Training Epoch 84/1000, Train RMSE: 0.7762 +Pre-Training Epoch 85/1000, Train RMSE: 0.7684 +Pre-Training Epoch 86/1000, Train RMSE: 0.7628 +Pre-Training Epoch 87/1000, Train RMSE: 0.7599 +Pre-Training Epoch 88/1000, Train RMSE: 0.7591 +Pre-Training Epoch 89/1000, Train RMSE: 0.7626 +Pre-Training Epoch 90/1000, Train RMSE: 0.7657 +Pre-Training Epoch 91/1000, Train RMSE: 0.7667 +Pre-Training Epoch 92/1000, Train RMSE: 0.7665 +Pre-Training Epoch 93/1000, Train RMSE: 0.7619 +Pre-Training Epoch 94/1000, Train RMSE: 0.7614 +Pre-Training Epoch 95/1000, Train RMSE: 0.7628 +Pre-Training Epoch 96/1000, Train RMSE: 0.7653 +Pre-Training Epoch 97/1000, Train RMSE: 0.7644 +Pre-Training Epoch 98/1000, Train RMSE: 0.7634 +Pre-Training Epoch 99/1000, Train RMSE: 0.7591 +Pre-Training Epoch 100/1000, Train RMSE: 0.7555 +Pre-Training Epoch 101/1000, Train RMSE: 0.7533 +Pre-Training Epoch 102/1000, Train RMSE: 0.7518 +Pre-Training Epoch 103/1000, Train RMSE: 0.7531 +Pre-Training Epoch 104/1000, Train RMSE: 0.7534 +Pre-Training Epoch 105/1000, Train RMSE: 0.7539 +Pre-Training Epoch 106/1000, Train RMSE: 0.7540 +Pre-Training Epoch 107/1000, Train RMSE: 0.7526 +Pre-Training Epoch 108/1000, Train RMSE: 0.7545 +Pre-Training Epoch 109/1000, Train RMSE: 0.7563 +Pre-Training Epoch 110/1000, Train RMSE: 0.7585 +Pre-Training Epoch 111/1000, Train RMSE: 0.7554 +Pre-Training Epoch 112/1000, Train RMSE: 0.7527 +Pre-Training Epoch 113/1000, Train RMSE: 0.7500 +Pre-Training Epoch 114/1000, Train RMSE: 0.7494 +Pre-Training Epoch 115/1000, Train RMSE: 0.7494 +Pre-Training Epoch 116/1000, Train RMSE: 0.7513 +Pre-Training Epoch 117/1000, Train RMSE: 0.7531 +Pre-Training Epoch 118/1000, Train RMSE: 0.7540 +Pre-Training Epoch 119/1000, Train RMSE: 0.7549 +Pre-Training Epoch 120/1000, Train RMSE: 0.7543 +Pre-Training Epoch 121/1000, Train RMSE: 0.7570 +Pre-Training Epoch 122/1000, Train RMSE: 0.7566 +Pre-Training Epoch 123/1000, Train RMSE: 0.7560 +Pre-Training Epoch 124/1000, Train RMSE: 0.7548 +Pre-Training Epoch 125/1000, Train RMSE: 0.7520 +Pre-Training Epoch 126/1000, Train RMSE: 0.7502 +Pre-Training Epoch 127/1000, Train RMSE: 0.7485 +Pre-Training Epoch 128/1000, Train RMSE: 0.7498 +Pre-Training Epoch 129/1000, Train RMSE: 0.7535 +Pre-Training Epoch 130/1000, Train RMSE: 0.7530 +Pre-Training Epoch 131/1000, Train RMSE: 0.7504 +Pre-Training Epoch 132/1000, Train RMSE: 0.7467 +Pre-Training Epoch 133/1000, Train RMSE: 0.7440 +Pre-Training Epoch 134/1000, Train RMSE: 0.7417 +Pre-Training Epoch 135/1000, Train RMSE: 0.7413 +Pre-Training Epoch 136/1000, Train RMSE: 0.7425 +Pre-Training Epoch 137/1000, Train RMSE: 0.7448 +Pre-Training Epoch 138/1000, Train RMSE: 0.7475 +Pre-Training Epoch 139/1000, Train RMSE: 0.7486 +Pre-Training Epoch 140/1000, Train RMSE: 0.7483 +Pre-Training Epoch 141/1000, Train RMSE: 0.7468 +Pre-Training Epoch 142/1000, Train RMSE: 0.7451 +Pre-Training Epoch 143/1000, Train RMSE: 0.7445 +Pre-Training Epoch 144/1000, Train RMSE: 0.7438 +Pre-Training Epoch 145/1000, Train RMSE: 0.7439 +Pre-Training Epoch 146/1000, Train RMSE: 0.7449 +Pre-Training Epoch 147/1000, Train RMSE: 0.7467 +Pre-Training Epoch 148/1000, Train RMSE: 0.7470 +Pre-Training Epoch 149/1000, Train RMSE: 0.7462 +Pre-Training Epoch 150/1000, Train RMSE: 0.7446 +Pre-Training Epoch 151/1000, Train RMSE: 0.7441 +Pre-Training Epoch 152/1000, Train RMSE: 0.7442 +Pre-Training Epoch 153/1000, Train RMSE: 0.7444 +Pre-Training Epoch 154/1000, Train RMSE: 0.7438 +Pre-Training Epoch 155/1000, Train RMSE: 0.7441 +Pre-Training Epoch 156/1000, Train RMSE: 0.7439 +Pre-Training Epoch 157/1000, Train RMSE: 0.7436 +Pre-Training Epoch 158/1000, Train RMSE: 0.7425 +Pre-Training Epoch 159/1000, Train RMSE: 0.7417 +Pre-Training Epoch 160/1000, Train RMSE: 0.7415 +Pre-Training Epoch 161/1000, Train RMSE: 0.7417 +Pre-Training Epoch 162/1000, Train RMSE: 0.7419 +Pre-Training Epoch 163/1000, Train RMSE: 0.7429 +Pre-Training Epoch 164/1000, Train RMSE: 0.7436 +Pre-Training Epoch 165/1000, Train RMSE: 0.7435 +Pre-Training Epoch 166/1000, Train RMSE: 0.7432 +Pre-Training Epoch 167/1000, Train RMSE: 0.7430 +Pre-Training Epoch 168/1000, Train RMSE: 0.7419 +Pre-Training Epoch 169/1000, Train RMSE: 0.7408 +Pre-Training Epoch 170/1000, Train RMSE: 0.7402 +Pre-Training Epoch 171/1000, Train RMSE: 0.7403 +Pre-Training Epoch 172/1000, Train RMSE: 0.7415 +Pre-Training Epoch 173/1000, Train RMSE: 0.7430 +Pre-Training Epoch 174/1000, Train RMSE: 0.7433 +Pre-Training Epoch 175/1000, Train RMSE: 0.7430 +Pre-Training Epoch 176/1000, Train RMSE: 0.7426 +Pre-Training Epoch 177/1000, Train RMSE: 0.7419 +Pre-Training Epoch 178/1000, Train RMSE: 0.7410 +Pre-Training Epoch 179/1000, Train RMSE: 0.7406 +Pre-Training Epoch 180/1000, Train RMSE: 0.7409 +Pre-Training Epoch 181/1000, Train RMSE: 0.7411 +Pre-Training Epoch 182/1000, Train RMSE: 0.7412 +Pre-Training Epoch 183/1000, Train RMSE: 0.7410 +Pre-Training Epoch 184/1000, Train RMSE: 0.7410 +Pre-Training Epoch 185/1000, Train RMSE: 0.7412 +Pre-Training Epoch 186/1000, Train RMSE: 0.7409 +Pre-Training Epoch 187/1000, Train RMSE: 0.7411 +Pre-Training Epoch 188/1000, Train RMSE: 0.7415 +Pre-Training Epoch 189/1000, Train RMSE: 0.7409 +Pre-Training Epoch 190/1000, Train RMSE: 0.7405 +Pre-Training Epoch 191/1000, Train RMSE: 0.7399 +Pre-Training Epoch 192/1000, Train RMSE: 0.7395 +Pre-Training Epoch 193/1000, Train RMSE: 0.7393 +Pre-Training Epoch 194/1000, Train RMSE: 0.7395 +Pre-Training Epoch 195/1000, Train RMSE: 0.7397 +Pre-Training Epoch 196/1000, Train RMSE: 0.7400 +Pre-Training Epoch 197/1000, Train RMSE: 0.7402 +Pre-Training Epoch 198/1000, Train RMSE: 0.7404 +Pre-Training Epoch 199/1000, Train RMSE: 0.7406 +Pre-Training Epoch 200/1000, Train RMSE: 0.7409 +Pre-Training Epoch 201/1000, Train RMSE: 0.7411 +Pre-Training Epoch 202/1000, Train RMSE: 0.7410 +Pre-Training Epoch 203/1000, Train RMSE: 0.7407 +Pre-Training Epoch 204/1000, Train RMSE: 0.7405 +Pre-Training Epoch 205/1000, Train RMSE: 0.7401 +Pre-Training Epoch 206/1000, Train RMSE: 0.7403 +Pre-Training Epoch 207/1000, Train RMSE: 0.7398 +Pre-Training Epoch 208/1000, Train RMSE: 0.7391 +Pre-Training Epoch 209/1000, Train RMSE: 0.7386 +Pre-Training Epoch 210/1000, Train RMSE: 0.7385 +Pre-Training Epoch 211/1000, Train RMSE: 0.7385 +Pre-Training Epoch 212/1000, Train RMSE: 0.7386 +Pre-Training Epoch 213/1000, Train RMSE: 0.7388 +Pre-Training Epoch 214/1000, Train RMSE: 0.7389 +Pre-Training Epoch 215/1000, Train RMSE: 0.7394 +Pre-Training Epoch 216/1000, Train RMSE: 0.7397 +Pre-Training Epoch 217/1000, Train RMSE: 0.7398 +Pre-Training Epoch 218/1000, Train RMSE: 0.7397 +Pre-Training Epoch 219/1000, Train RMSE: 0.7395 +Pre-Training Epoch 220/1000, Train RMSE: 0.7393 +Pre-Training Epoch 221/1000, Train RMSE: 0.7391 +Pre-Training Epoch 222/1000, Train RMSE: 0.7390 +Pre-Training Epoch 223/1000, Train RMSE: 0.7389 +Pre-Training Epoch 224/1000, Train RMSE: 0.7389 +Pre-Training Epoch 225/1000, Train RMSE: 0.7391 +Pre-Training Epoch 226/1000, Train RMSE: 0.7395 +Pre-Training Epoch 227/1000, Train RMSE: 0.7395 +Pre-Training Epoch 228/1000, Train RMSE: 0.7395 +Pre-Training Epoch 229/1000, Train RMSE: 0.7394 +Pre-Training Epoch 230/1000, Train RMSE: 0.7391 +Pre-Training Epoch 231/1000, Train RMSE: 0.7388 +Pre-Training Epoch 232/1000, Train RMSE: 0.7387 +Pre-Training Epoch 233/1000, Train RMSE: 0.7389 +Pre-Training Epoch 234/1000, Train RMSE: 0.7391 +Pre-Training Epoch 235/1000, Train RMSE: 0.7394 +Pre-Training Epoch 236/1000, Train RMSE: 0.7395 +Pre-Training Epoch 237/1000, Train RMSE: 0.7393 +Pre-Training Epoch 238/1000, Train RMSE: 0.7392 +Pre-Training Epoch 239/1000, Train RMSE: 0.7390 +Pre-Training Epoch 240/1000, Train RMSE: 0.7389 +Pre-Training Epoch 241/1000, Train RMSE: 0.7388 +Pre-Training Epoch 242/1000, Train RMSE: 0.7389 +Pre-Training Epoch 243/1000, Train RMSE: 0.7390 +Pre-Training Epoch 244/1000, Train RMSE: 0.7387 +Pre-Training Epoch 245/1000, Train RMSE: 0.7381 +Pre-Training Epoch 246/1000, Train RMSE: 0.7375 +Pre-Training Epoch 247/1000, Train RMSE: 0.7374 +Pre-Training Epoch 248/1000, Train RMSE: 0.7374 +Pre-Training Epoch 249/1000, Train RMSE: 0.7376 +Pre-Training Epoch 250/1000, Train RMSE: 0.7376 +Pre-Training Epoch 251/1000, Train RMSE: 0.7375 +Pre-Training Epoch 252/1000, Train RMSE: 0.7373 +Pre-Training Epoch 253/1000, Train RMSE: 0.7372 +Pre-Training Epoch 254/1000, Train RMSE: 0.7373 +Pre-Training Epoch 255/1000, Train RMSE: 0.7374 +Pre-Training Epoch 256/1000, Train RMSE: 0.7376 +Pre-Training Epoch 257/1000, Train RMSE: 0.7377 +Pre-Training Epoch 258/1000, Train RMSE: 0.7378 +Pre-Training Epoch 259/1000, Train RMSE: 0.7380 +Pre-Training Epoch 260/1000, Train RMSE: 0.7383 +Pre-Training Epoch 261/1000, Train RMSE: 0.7386 +Early stopping pre-training at epoch: 262 +Fine-Tuning Epoch 1/100, Train RMSE: 0.7572 +Fine-Tuning Epoch 2/100, Train RMSE: 0.7414 +Fine-Tuning Epoch 3/100, Train RMSE: 0.7461 +Fine-Tuning Epoch 4/100, Train RMSE: 0.7493 +Fine-Tuning Epoch 5/100, Train RMSE: 0.7477 +Fine-Tuning Epoch 6/100, Train RMSE: 0.7445 +Fine-Tuning Epoch 7/100, Train RMSE: 0.7416 +Fine-Tuning Epoch 8/100, Train RMSE: 0.7393 +Fine-Tuning Epoch 9/100, Train RMSE: 0.7382 +Fine-Tuning Epoch 10/100, Train RMSE: 0.7379 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7379 +Fine-Tuning Epoch 12/100, Train RMSE: 0.7381 +Fine-Tuning Epoch 13/100, Train RMSE: 0.7384 +Fine-Tuning Epoch 14/100, Train RMSE: 0.7385 +Fine-Tuning Epoch 15/100, Train RMSE: 0.7383 +Fine-Tuning Epoch 16/100, Train RMSE: 0.7380 +Fine-Tuning Epoch 17/100, Train RMSE: 0.7375 +Fine-Tuning Epoch 18/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 19/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 20/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 22/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 23/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 24/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 25/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 26/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 27/100, Train RMSE: 0.7366 +Fine-Tuning Epoch 28/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 29/100, Train RMSE: 0.7364 +Fine-Tuning Epoch 30/100, Train RMSE: 0.7364 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 32/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 33/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 34/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 35/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 36/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 37/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 38/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 39/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 40/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 41/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 42/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 43/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 44/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 45/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 54/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 55/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 56/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 57/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 58/100, Train RMSE: 0.7358 +Early stopping fine-tuning at epoch: 59 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0178 | 1.0288 | 3.6884 | 0.0810 + + +======================================== diff --git a/experiment_log_17.txt b/experiment_log_17.txt new file mode 100644 index 000000000..04d5021e9 --- /dev/null +++ b/experiment_log_17.txt @@ -0,0 +1,427 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:07:01.134181 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 1000 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] + [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] + [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] + ... + [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] + [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] + [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/1000, Train RMSE: 2.7996 +Pre-Training Epoch 2/1000, Train RMSE: 2.7996 +Pre-Training Epoch 3/1000, Train RMSE: 2.7988 +Pre-Training Epoch 4/1000, Train RMSE: 2.5971 +Pre-Training Epoch 5/1000, Train RMSE: 2.1581 +Pre-Training Epoch 6/1000, Train RMSE: 1.6675 +Pre-Training Epoch 7/1000, Train RMSE: 1.2457 +Pre-Training Epoch 8/1000, Train RMSE: 1.0472 +Pre-Training Epoch 9/1000, Train RMSE: 1.0313 +Pre-Training Epoch 10/1000, Train RMSE: 0.9660 +Pre-Training Epoch 11/1000, Train RMSE: 0.8494 +Pre-Training Epoch 12/1000, Train RMSE: 0.8960 +Pre-Training Epoch 13/1000, Train RMSE: 1.0640 +Pre-Training Epoch 14/1000, Train RMSE: 1.0938 +Pre-Training Epoch 15/1000, Train RMSE: 1.0054 +Pre-Training Epoch 16/1000, Train RMSE: 0.9029 +Pre-Training Epoch 17/1000, Train RMSE: 0.8539 +Pre-Training Epoch 18/1000, Train RMSE: 0.8467 +Pre-Training Epoch 19/1000, Train RMSE: 0.8399 +Pre-Training Epoch 20/1000, Train RMSE: 0.8346 +Pre-Training Epoch 21/1000, Train RMSE: 0.8454 +Pre-Training Epoch 22/1000, Train RMSE: 0.8776 +Pre-Training Epoch 23/1000, Train RMSE: 0.9034 +Pre-Training Epoch 24/1000, Train RMSE: 0.9067 +Pre-Training Epoch 25/1000, Train RMSE: 0.8730 +Pre-Training Epoch 26/1000, Train RMSE: 0.8277 +Pre-Training Epoch 27/1000, Train RMSE: 0.7908 +Pre-Training Epoch 28/1000, Train RMSE: 0.7770 +Pre-Training Epoch 29/1000, Train RMSE: 0.7745 +Pre-Training Epoch 30/1000, Train RMSE: 0.7743 +Pre-Training Epoch 31/1000, Train RMSE: 0.7785 +Pre-Training Epoch 32/1000, Train RMSE: 0.7881 +Pre-Training Epoch 33/1000, Train RMSE: 0.7980 +Pre-Training Epoch 34/1000, Train RMSE: 0.8041 +Pre-Training Epoch 35/1000, Train RMSE: 0.8028 +Pre-Training Epoch 36/1000, Train RMSE: 0.8002 +Pre-Training Epoch 37/1000, Train RMSE: 0.7936 +Pre-Training Epoch 38/1000, Train RMSE: 0.7882 +Pre-Training Epoch 39/1000, Train RMSE: 0.7870 +Pre-Training Epoch 40/1000, Train RMSE: 0.7887 +Pre-Training Epoch 41/1000, Train RMSE: 0.7900 +Pre-Training Epoch 42/1000, Train RMSE: 0.7905 +Pre-Training Epoch 43/1000, Train RMSE: 0.7927 +Pre-Training Epoch 44/1000, Train RMSE: 0.8055 +Pre-Training Epoch 45/1000, Train RMSE: 0.8079 +Pre-Training Epoch 46/1000, Train RMSE: 0.7972 +Pre-Training Epoch 47/1000, Train RMSE: 0.7876 +Pre-Training Epoch 48/1000, Train RMSE: 0.7749 +Pre-Training Epoch 49/1000, Train RMSE: 0.7685 +Pre-Training Epoch 50/1000, Train RMSE: 0.7685 +Pre-Training Epoch 51/1000, Train RMSE: 0.7775 +Pre-Training Epoch 52/1000, Train RMSE: 0.7869 +Pre-Training Epoch 53/1000, Train RMSE: 0.7977 +Pre-Training Epoch 54/1000, Train RMSE: 0.7936 +Pre-Training Epoch 55/1000, Train RMSE: 0.7838 +Pre-Training Epoch 56/1000, Train RMSE: 0.7742 +Pre-Training Epoch 57/1000, Train RMSE: 0.7701 +Pre-Training Epoch 58/1000, Train RMSE: 0.7733 +Pre-Training Epoch 59/1000, Train RMSE: 0.7819 +Pre-Training Epoch 60/1000, Train RMSE: 0.7911 +Pre-Training Epoch 61/1000, Train RMSE: 0.7839 +Pre-Training Epoch 62/1000, Train RMSE: 0.7722 +Pre-Training Epoch 63/1000, Train RMSE: 0.7674 +Pre-Training Epoch 64/1000, Train RMSE: 0.7672 +Pre-Training Epoch 65/1000, Train RMSE: 0.7705 +Pre-Training Epoch 66/1000, Train RMSE: 0.7851 +Pre-Training Epoch 67/1000, Train RMSE: 0.7952 +Pre-Training Epoch 68/1000, Train RMSE: 0.7874 +Pre-Training Epoch 69/1000, Train RMSE: 0.7653 +Pre-Training Epoch 70/1000, Train RMSE: 0.7589 +Pre-Training Epoch 71/1000, Train RMSE: 0.7582 +Pre-Training Epoch 72/1000, Train RMSE: 0.7610 +Pre-Training Epoch 73/1000, Train RMSE: 0.7680 +Pre-Training Epoch 74/1000, Train RMSE: 0.7772 +Pre-Training Epoch 75/1000, Train RMSE: 0.7783 +Pre-Training Epoch 76/1000, Train RMSE: 0.7690 +Pre-Training Epoch 77/1000, Train RMSE: 0.7610 +Pre-Training Epoch 78/1000, Train RMSE: 0.7541 +Pre-Training Epoch 79/1000, Train RMSE: 0.7529 +Pre-Training Epoch 80/1000, Train RMSE: 0.7573 +Pre-Training Epoch 81/1000, Train RMSE: 0.7661 +Pre-Training Epoch 82/1000, Train RMSE: 0.7652 +Pre-Training Epoch 83/1000, Train RMSE: 0.7599 +Pre-Training Epoch 84/1000, Train RMSE: 0.7534 +Pre-Training Epoch 85/1000, Train RMSE: 0.7505 +Pre-Training Epoch 86/1000, Train RMSE: 0.7535 +Pre-Training Epoch 87/1000, Train RMSE: 0.7639 +Pre-Training Epoch 88/1000, Train RMSE: 0.7813 +Pre-Training Epoch 89/1000, Train RMSE: 0.7856 +Pre-Training Epoch 90/1000, Train RMSE: 0.7672 +Pre-Training Epoch 91/1000, Train RMSE: 0.7542 +Pre-Training Epoch 92/1000, Train RMSE: 0.7497 +Pre-Training Epoch 93/1000, Train RMSE: 0.7493 +Pre-Training Epoch 94/1000, Train RMSE: 0.7536 +Pre-Training Epoch 95/1000, Train RMSE: 0.7656 +Pre-Training Epoch 96/1000, Train RMSE: 0.7737 +Pre-Training Epoch 97/1000, Train RMSE: 0.7726 +Pre-Training Epoch 98/1000, Train RMSE: 0.7582 +Pre-Training Epoch 99/1000, Train RMSE: 0.7521 +Pre-Training Epoch 100/1000, Train RMSE: 0.7516 +Pre-Training Epoch 101/1000, Train RMSE: 0.7558 +Pre-Training Epoch 102/1000, Train RMSE: 0.7650 +Pre-Training Epoch 103/1000, Train RMSE: 0.7747 +Pre-Training Epoch 104/1000, Train RMSE: 0.7705 +Pre-Training Epoch 105/1000, Train RMSE: 0.7580 +Pre-Training Epoch 106/1000, Train RMSE: 0.7505 +Pre-Training Epoch 107/1000, Train RMSE: 0.7481 +Pre-Training Epoch 108/1000, Train RMSE: 0.7475 +Pre-Training Epoch 109/1000, Train RMSE: 0.7513 +Pre-Training Epoch 110/1000, Train RMSE: 0.7629 +Pre-Training Epoch 111/1000, Train RMSE: 0.7728 +Pre-Training Epoch 112/1000, Train RMSE: 0.7668 +Pre-Training Epoch 113/1000, Train RMSE: 0.7589 +Pre-Training Epoch 114/1000, Train RMSE: 0.7528 +Pre-Training Epoch 115/1000, Train RMSE: 0.7507 +Pre-Training Epoch 116/1000, Train RMSE: 0.7512 +Pre-Training Epoch 117/1000, Train RMSE: 0.7517 +Pre-Training Epoch 118/1000, Train RMSE: 0.7527 +Pre-Training Epoch 119/1000, Train RMSE: 0.7559 +Pre-Training Epoch 120/1000, Train RMSE: 0.7543 +Pre-Training Epoch 121/1000, Train RMSE: 0.7523 +Pre-Training Epoch 122/1000, Train RMSE: 0.7499 +Pre-Training Epoch 123/1000, Train RMSE: 0.7477 +Pre-Training Epoch 124/1000, Train RMSE: 0.7468 +Pre-Training Epoch 125/1000, Train RMSE: 0.7473 +Pre-Training Epoch 126/1000, Train RMSE: 0.7483 +Pre-Training Epoch 127/1000, Train RMSE: 0.7513 +Pre-Training Epoch 128/1000, Train RMSE: 0.7556 +Pre-Training Epoch 129/1000, Train RMSE: 0.7553 +Pre-Training Epoch 130/1000, Train RMSE: 0.7533 +Pre-Training Epoch 131/1000, Train RMSE: 0.7490 +Pre-Training Epoch 132/1000, Train RMSE: 0.7466 +Pre-Training Epoch 133/1000, Train RMSE: 0.7455 +Pre-Training Epoch 134/1000, Train RMSE: 0.7465 +Pre-Training Epoch 135/1000, Train RMSE: 0.7484 +Pre-Training Epoch 136/1000, Train RMSE: 0.7517 +Pre-Training Epoch 137/1000, Train RMSE: 0.7526 +Pre-Training Epoch 138/1000, Train RMSE: 0.7503 +Pre-Training Epoch 139/1000, Train RMSE: 0.7469 +Pre-Training Epoch 140/1000, Train RMSE: 0.7450 +Pre-Training Epoch 141/1000, Train RMSE: 0.7454 +Pre-Training Epoch 142/1000, Train RMSE: 0.7479 +Pre-Training Epoch 143/1000, Train RMSE: 0.7482 +Pre-Training Epoch 144/1000, Train RMSE: 0.7468 +Pre-Training Epoch 145/1000, Train RMSE: 0.7449 +Pre-Training Epoch 146/1000, Train RMSE: 0.7446 +Pre-Training Epoch 147/1000, Train RMSE: 0.7463 +Pre-Training Epoch 148/1000, Train RMSE: 0.7499 +Pre-Training Epoch 149/1000, Train RMSE: 0.7514 +Pre-Training Epoch 150/1000, Train RMSE: 0.7499 +Pre-Training Epoch 151/1000, Train RMSE: 0.7468 +Pre-Training Epoch 152/1000, Train RMSE: 0.7447 +Pre-Training Epoch 153/1000, Train RMSE: 0.7438 +Pre-Training Epoch 154/1000, Train RMSE: 0.7435 +Pre-Training Epoch 155/1000, Train RMSE: 0.7442 +Pre-Training Epoch 156/1000, Train RMSE: 0.7466 +Pre-Training Epoch 157/1000, Train RMSE: 0.7469 +Pre-Training Epoch 158/1000, Train RMSE: 0.7462 +Pre-Training Epoch 159/1000, Train RMSE: 0.7479 +Pre-Training Epoch 160/1000, Train RMSE: 0.7467 +Pre-Training Epoch 161/1000, Train RMSE: 0.7449 +Pre-Training Epoch 162/1000, Train RMSE: 0.7434 +Pre-Training Epoch 163/1000, Train RMSE: 0.7424 +Pre-Training Epoch 164/1000, Train RMSE: 0.7422 +Pre-Training Epoch 165/1000, Train RMSE: 0.7426 +Pre-Training Epoch 166/1000, Train RMSE: 0.7443 +Pre-Training Epoch 167/1000, Train RMSE: 0.7444 +Pre-Training Epoch 168/1000, Train RMSE: 0.7441 +Pre-Training Epoch 169/1000, Train RMSE: 0.7438 +Pre-Training Epoch 170/1000, Train RMSE: 0.7424 +Pre-Training Epoch 171/1000, Train RMSE: 0.7412 +Pre-Training Epoch 172/1000, Train RMSE: 0.7403 +Pre-Training Epoch 173/1000, Train RMSE: 0.7403 +Pre-Training Epoch 174/1000, Train RMSE: 0.7412 +Pre-Training Epoch 175/1000, Train RMSE: 0.7424 +Pre-Training Epoch 176/1000, Train RMSE: 0.7432 +Pre-Training Epoch 177/1000, Train RMSE: 0.7429 +Pre-Training Epoch 178/1000, Train RMSE: 0.7421 +Pre-Training Epoch 179/1000, Train RMSE: 0.7419 +Pre-Training Epoch 180/1000, Train RMSE: 0.7419 +Pre-Training Epoch 181/1000, Train RMSE: 0.7426 +Pre-Training Epoch 182/1000, Train RMSE: 0.7428 +Pre-Training Epoch 183/1000, Train RMSE: 0.7425 +Pre-Training Epoch 184/1000, Train RMSE: 0.7425 +Pre-Training Epoch 185/1000, Train RMSE: 0.7430 +Pre-Training Epoch 186/1000, Train RMSE: 0.7433 +Pre-Training Epoch 187/1000, Train RMSE: 0.7427 +Pre-Training Epoch 188/1000, Train RMSE: 0.7415 +Pre-Training Epoch 189/1000, Train RMSE: 0.7407 +Pre-Training Epoch 190/1000, Train RMSE: 0.7401 +Pre-Training Epoch 191/1000, Train RMSE: 0.7400 +Pre-Training Epoch 192/1000, Train RMSE: 0.7403 +Pre-Training Epoch 193/1000, Train RMSE: 0.7407 +Pre-Training Epoch 194/1000, Train RMSE: 0.7406 +Pre-Training Epoch 195/1000, Train RMSE: 0.7402 +Pre-Training Epoch 196/1000, Train RMSE: 0.7396 +Pre-Training Epoch 197/1000, Train RMSE: 0.7394 +Pre-Training Epoch 198/1000, Train RMSE: 0.7396 +Pre-Training Epoch 199/1000, Train RMSE: 0.7395 +Pre-Training Epoch 200/1000, Train RMSE: 0.7396 +Pre-Training Epoch 201/1000, Train RMSE: 0.7396 +Pre-Training Epoch 202/1000, Train RMSE: 0.7392 +Pre-Training Epoch 203/1000, Train RMSE: 0.7390 +Pre-Training Epoch 204/1000, Train RMSE: 0.7387 +Pre-Training Epoch 205/1000, Train RMSE: 0.7385 +Pre-Training Epoch 206/1000, Train RMSE: 0.7388 +Pre-Training Epoch 207/1000, Train RMSE: 0.7392 +Pre-Training Epoch 208/1000, Train RMSE: 0.7401 +Pre-Training Epoch 209/1000, Train RMSE: 0.7403 +Pre-Training Epoch 210/1000, Train RMSE: 0.7407 +Pre-Training Epoch 211/1000, Train RMSE: 0.7408 +Pre-Training Epoch 212/1000, Train RMSE: 0.7400 +Pre-Training Epoch 213/1000, Train RMSE: 0.7392 +Pre-Training Epoch 214/1000, Train RMSE: 0.7387 +Pre-Training Epoch 215/1000, Train RMSE: 0.7385 +Pre-Training Epoch 216/1000, Train RMSE: 0.7386 +Pre-Training Epoch 217/1000, Train RMSE: 0.7390 +Pre-Training Epoch 218/1000, Train RMSE: 0.7400 +Pre-Training Epoch 219/1000, Train RMSE: 0.7408 +Pre-Training Epoch 220/1000, Train RMSE: 0.7411 +Pre-Training Epoch 221/1000, Train RMSE: 0.7409 +Pre-Training Epoch 222/1000, Train RMSE: 0.7397 +Pre-Training Epoch 223/1000, Train RMSE: 0.7388 +Pre-Training Epoch 224/1000, Train RMSE: 0.7382 +Pre-Training Epoch 225/1000, Train RMSE: 0.7379 +Pre-Training Epoch 226/1000, Train RMSE: 0.7377 +Pre-Training Epoch 227/1000, Train RMSE: 0.7377 +Pre-Training Epoch 228/1000, Train RMSE: 0.7378 +Pre-Training Epoch 229/1000, Train RMSE: 0.7382 +Pre-Training Epoch 230/1000, Train RMSE: 0.7385 +Pre-Training Epoch 231/1000, Train RMSE: 0.7387 +Pre-Training Epoch 232/1000, Train RMSE: 0.7387 +Pre-Training Epoch 233/1000, Train RMSE: 0.7384 +Pre-Training Epoch 234/1000, Train RMSE: 0.7382 +Pre-Training Epoch 235/1000, Train RMSE: 0.7379 +Pre-Training Epoch 236/1000, Train RMSE: 0.7377 +Pre-Training Epoch 237/1000, Train RMSE: 0.7378 +Pre-Training Epoch 238/1000, Train RMSE: 0.7378 +Pre-Training Epoch 239/1000, Train RMSE: 0.7380 +Pre-Training Epoch 240/1000, Train RMSE: 0.7383 +Pre-Training Epoch 241/1000, Train RMSE: 0.7381 +Pre-Training Epoch 242/1000, Train RMSE: 0.7380 +Pre-Training Epoch 243/1000, Train RMSE: 0.7377 +Pre-Training Epoch 244/1000, Train RMSE: 0.7377 +Pre-Training Epoch 245/1000, Train RMSE: 0.7378 +Pre-Training Epoch 246/1000, Train RMSE: 0.7381 +Pre-Training Epoch 247/1000, Train RMSE: 0.7381 +Pre-Training Epoch 248/1000, Train RMSE: 0.7381 +Pre-Training Epoch 249/1000, Train RMSE: 0.7381 +Pre-Training Epoch 250/1000, Train RMSE: 0.7378 +Pre-Training Epoch 251/1000, Train RMSE: 0.7376 +Pre-Training Epoch 252/1000, Train RMSE: 0.7375 +Pre-Training Epoch 253/1000, Train RMSE: 0.7377 +Pre-Training Epoch 254/1000, Train RMSE: 0.7377 +Pre-Training Epoch 255/1000, Train RMSE: 0.7378 +Pre-Training Epoch 256/1000, Train RMSE: 0.7378 +Pre-Training Epoch 257/1000, Train RMSE: 0.7375 +Pre-Training Epoch 258/1000, Train RMSE: 0.7371 +Pre-Training Epoch 259/1000, Train RMSE: 0.7368 +Pre-Training Epoch 260/1000, Train RMSE: 0.7367 +Pre-Training Epoch 261/1000, Train RMSE: 0.7369 +Pre-Training Epoch 262/1000, Train RMSE: 0.7371 +Pre-Training Epoch 263/1000, Train RMSE: 0.7372 +Pre-Training Epoch 264/1000, Train RMSE: 0.7371 +Pre-Training Epoch 265/1000, Train RMSE: 0.7371 +Pre-Training Epoch 266/1000, Train RMSE: 0.7371 +Pre-Training Epoch 267/1000, Train RMSE: 0.7372 +Pre-Training Epoch 268/1000, Train RMSE: 0.7373 +Early stopping pre-training at epoch: 269 +Fine-Tuning Epoch 1/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 2/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 3/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 4/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 5/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 6/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 7/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 8/100, Train RMSE: 0.7366 +Fine-Tuning Epoch 9/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 10/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 12/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 13/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 14/100, Train RMSE: 0.7366 +Fine-Tuning Epoch 15/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 16/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 17/100, Train RMSE: 0.7364 +Fine-Tuning Epoch 18/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 19/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 20/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 22/100, Train RMSE: 0.7366 +Fine-Tuning Epoch 23/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 24/100, Train RMSE: 0.7364 +Fine-Tuning Epoch 25/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 26/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 27/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 28/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 29/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 30/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 32/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 33/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 34/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 35/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 36/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 37/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 38/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 39/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 40/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 41/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 42/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 43/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 44/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 45/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 54/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 55/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 56/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 57/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 58/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 59/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 60/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 61/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 62/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 63/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 64/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 65/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 66/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 67/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 68/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 69/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 70/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 71/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 72/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 73/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 74/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 75/100, Train RMSE: 0.7358 +Fine-Tuning Epoch 76/100, Train RMSE: 0.7358 +Early stopping fine-tuning at epoch: 77 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0171 | 1.0282 | 3.9581 | 0.1591 + + +======================================== diff --git a/experiment_log_18.txt b/experiment_log_18.txt new file mode 100644 index 000000000..1d6583915 --- /dev/null +++ b/experiment_log_18.txt @@ -0,0 +1,389 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:07:52.184470 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 1000 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] + [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] + [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] + ... + [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] + [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] + [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/1000, Train RMSE: 2.7996 +Pre-Training Epoch 2/1000, Train RMSE: 2.7996 +Pre-Training Epoch 3/1000, Train RMSE: 2.7996 +Pre-Training Epoch 4/1000, Train RMSE: 2.7721 +Pre-Training Epoch 5/1000, Train RMSE: 2.4431 +Pre-Training Epoch 6/1000, Train RMSE: 2.0148 +Pre-Training Epoch 7/1000, Train RMSE: 1.5615 +Pre-Training Epoch 8/1000, Train RMSE: 1.1799 +Pre-Training Epoch 9/1000, Train RMSE: 1.0018 +Pre-Training Epoch 10/1000, Train RMSE: 1.0040 +Pre-Training Epoch 11/1000, Train RMSE: 0.9687 +Pre-Training Epoch 12/1000, Train RMSE: 0.8649 +Pre-Training Epoch 13/1000, Train RMSE: 0.8380 +Pre-Training Epoch 14/1000, Train RMSE: 0.9721 +Pre-Training Epoch 15/1000, Train RMSE: 1.0795 +Pre-Training Epoch 16/1000, Train RMSE: 1.1053 +Pre-Training Epoch 17/1000, Train RMSE: 1.0581 +Pre-Training Epoch 18/1000, Train RMSE: 0.9716 +Pre-Training Epoch 19/1000, Train RMSE: 0.8863 +Pre-Training Epoch 20/1000, Train RMSE: 0.8382 +Pre-Training Epoch 21/1000, Train RMSE: 0.8164 +Pre-Training Epoch 22/1000, Train RMSE: 0.8025 +Pre-Training Epoch 23/1000, Train RMSE: 0.7936 +Pre-Training Epoch 24/1000, Train RMSE: 0.7948 +Pre-Training Epoch 25/1000, Train RMSE: 0.8194 +Pre-Training Epoch 26/1000, Train RMSE: 0.8546 +Pre-Training Epoch 27/1000, Train RMSE: 0.8700 +Pre-Training Epoch 28/1000, Train RMSE: 0.8554 +Pre-Training Epoch 29/1000, Train RMSE: 0.8335 +Pre-Training Epoch 30/1000, Train RMSE: 0.8082 +Pre-Training Epoch 31/1000, Train RMSE: 0.7925 +Pre-Training Epoch 32/1000, Train RMSE: 0.7848 +Pre-Training Epoch 33/1000, Train RMSE: 0.7811 +Pre-Training Epoch 34/1000, Train RMSE: 0.7814 +Pre-Training Epoch 35/1000, Train RMSE: 0.7870 +Pre-Training Epoch 36/1000, Train RMSE: 0.7927 +Pre-Training Epoch 37/1000, Train RMSE: 0.7985 +Pre-Training Epoch 38/1000, Train RMSE: 0.7989 +Pre-Training Epoch 39/1000, Train RMSE: 0.7949 +Pre-Training Epoch 40/1000, Train RMSE: 0.7880 +Pre-Training Epoch 41/1000, Train RMSE: 0.7836 +Pre-Training Epoch 42/1000, Train RMSE: 0.7848 +Pre-Training Epoch 43/1000, Train RMSE: 0.7882 +Pre-Training Epoch 44/1000, Train RMSE: 0.7927 +Pre-Training Epoch 45/1000, Train RMSE: 0.7959 +Pre-Training Epoch 46/1000, Train RMSE: 0.7873 +Pre-Training Epoch 47/1000, Train RMSE: 0.7807 +Pre-Training Epoch 48/1000, Train RMSE: 0.7781 +Pre-Training Epoch 49/1000, Train RMSE: 0.7705 +Pre-Training Epoch 50/1000, Train RMSE: 0.7660 +Pre-Training Epoch 51/1000, Train RMSE: 0.7680 +Pre-Training Epoch 52/1000, Train RMSE: 0.7722 +Pre-Training Epoch 53/1000, Train RMSE: 0.7779 +Pre-Training Epoch 54/1000, Train RMSE: 0.7878 +Pre-Training Epoch 55/1000, Train RMSE: 0.7936 +Pre-Training Epoch 56/1000, Train RMSE: 0.7854 +Pre-Training Epoch 57/1000, Train RMSE: 0.7758 +Pre-Training Epoch 58/1000, Train RMSE: 0.7656 +Pre-Training Epoch 59/1000, Train RMSE: 0.7630 +Pre-Training Epoch 60/1000, Train RMSE: 0.7645 +Pre-Training Epoch 61/1000, Train RMSE: 0.7717 +Pre-Training Epoch 62/1000, Train RMSE: 0.7778 +Pre-Training Epoch 63/1000, Train RMSE: 0.7817 +Pre-Training Epoch 64/1000, Train RMSE: 0.7752 +Pre-Training Epoch 65/1000, Train RMSE: 0.7637 +Pre-Training Epoch 66/1000, Train RMSE: 0.7585 +Pre-Training Epoch 67/1000, Train RMSE: 0.7573 +Pre-Training Epoch 68/1000, Train RMSE: 0.7588 +Pre-Training Epoch 69/1000, Train RMSE: 0.7638 +Pre-Training Epoch 70/1000, Train RMSE: 0.7692 +Pre-Training Epoch 71/1000, Train RMSE: 0.7728 +Pre-Training Epoch 72/1000, Train RMSE: 0.7693 +Pre-Training Epoch 73/1000, Train RMSE: 0.7629 +Pre-Training Epoch 74/1000, Train RMSE: 0.7595 +Pre-Training Epoch 75/1000, Train RMSE: 0.7600 +Pre-Training Epoch 76/1000, Train RMSE: 0.7596 +Pre-Training Epoch 77/1000, Train RMSE: 0.7610 +Pre-Training Epoch 78/1000, Train RMSE: 0.7632 +Pre-Training Epoch 79/1000, Train RMSE: 0.7686 +Pre-Training Epoch 80/1000, Train RMSE: 0.7717 +Pre-Training Epoch 81/1000, Train RMSE: 0.7729 +Pre-Training Epoch 82/1000, Train RMSE: 0.7674 +Pre-Training Epoch 83/1000, Train RMSE: 0.7624 +Pre-Training Epoch 84/1000, Train RMSE: 0.7584 +Pre-Training Epoch 85/1000, Train RMSE: 0.7566 +Pre-Training Epoch 86/1000, Train RMSE: 0.7598 +Pre-Training Epoch 87/1000, Train RMSE: 0.7650 +Pre-Training Epoch 88/1000, Train RMSE: 0.7697 +Pre-Training Epoch 89/1000, Train RMSE: 0.7627 +Pre-Training Epoch 90/1000, Train RMSE: 0.7531 +Pre-Training Epoch 91/1000, Train RMSE: 0.7507 +Pre-Training Epoch 92/1000, Train RMSE: 0.7508 +Pre-Training Epoch 93/1000, Train RMSE: 0.7549 +Pre-Training Epoch 94/1000, Train RMSE: 0.7621 +Pre-Training Epoch 95/1000, Train RMSE: 0.7653 +Pre-Training Epoch 96/1000, Train RMSE: 0.7608 +Pre-Training Epoch 97/1000, Train RMSE: 0.7556 +Pre-Training Epoch 98/1000, Train RMSE: 0.7514 +Pre-Training Epoch 99/1000, Train RMSE: 0.7524 +Pre-Training Epoch 100/1000, Train RMSE: 0.7533 +Pre-Training Epoch 101/1000, Train RMSE: 0.7546 +Pre-Training Epoch 102/1000, Train RMSE: 0.7568 +Pre-Training Epoch 103/1000, Train RMSE: 0.7539 +Pre-Training Epoch 104/1000, Train RMSE: 0.7532 +Pre-Training Epoch 105/1000, Train RMSE: 0.7551 +Pre-Training Epoch 106/1000, Train RMSE: 0.7587 +Pre-Training Epoch 107/1000, Train RMSE: 0.7569 +Pre-Training Epoch 108/1000, Train RMSE: 0.7519 +Pre-Training Epoch 109/1000, Train RMSE: 0.7486 +Pre-Training Epoch 110/1000, Train RMSE: 0.7469 +Pre-Training Epoch 111/1000, Train RMSE: 0.7473 +Pre-Training Epoch 112/1000, Train RMSE: 0.7502 +Pre-Training Epoch 113/1000, Train RMSE: 0.7561 +Pre-Training Epoch 114/1000, Train RMSE: 0.7648 +Pre-Training Epoch 115/1000, Train RMSE: 0.7661 +Pre-Training Epoch 116/1000, Train RMSE: 0.7593 +Pre-Training Epoch 117/1000, Train RMSE: 0.7518 +Pre-Training Epoch 118/1000, Train RMSE: 0.7497 +Pre-Training Epoch 119/1000, Train RMSE: 0.7503 +Pre-Training Epoch 120/1000, Train RMSE: 0.7531 +Pre-Training Epoch 121/1000, Train RMSE: 0.7530 +Pre-Training Epoch 122/1000, Train RMSE: 0.7509 +Pre-Training Epoch 123/1000, Train RMSE: 0.7487 +Pre-Training Epoch 124/1000, Train RMSE: 0.7474 +Pre-Training Epoch 125/1000, Train RMSE: 0.7476 +Pre-Training Epoch 126/1000, Train RMSE: 0.7511 +Pre-Training Epoch 127/1000, Train RMSE: 0.7517 +Pre-Training Epoch 128/1000, Train RMSE: 0.7528 +Pre-Training Epoch 129/1000, Train RMSE: 0.7512 +Pre-Training Epoch 130/1000, Train RMSE: 0.7494 +Pre-Training Epoch 131/1000, Train RMSE: 0.7473 +Pre-Training Epoch 132/1000, Train RMSE: 0.7468 +Pre-Training Epoch 133/1000, Train RMSE: 0.7457 +Pre-Training Epoch 134/1000, Train RMSE: 0.7460 +Pre-Training Epoch 135/1000, Train RMSE: 0.7475 +Pre-Training Epoch 136/1000, Train RMSE: 0.7486 +Pre-Training Epoch 137/1000, Train RMSE: 0.7473 +Pre-Training Epoch 138/1000, Train RMSE: 0.7470 +Pre-Training Epoch 139/1000, Train RMSE: 0.7473 +Pre-Training Epoch 140/1000, Train RMSE: 0.7469 +Pre-Training Epoch 141/1000, Train RMSE: 0.7441 +Pre-Training Epoch 142/1000, Train RMSE: 0.7429 +Pre-Training Epoch 143/1000, Train RMSE: 0.7425 +Pre-Training Epoch 144/1000, Train RMSE: 0.7442 +Pre-Training Epoch 145/1000, Train RMSE: 0.7456 +Pre-Training Epoch 146/1000, Train RMSE: 0.7463 +Pre-Training Epoch 147/1000, Train RMSE: 0.7448 +Pre-Training Epoch 148/1000, Train RMSE: 0.7448 +Pre-Training Epoch 149/1000, Train RMSE: 0.7449 +Pre-Training Epoch 150/1000, Train RMSE: 0.7455 +Pre-Training Epoch 151/1000, Train RMSE: 0.7450 +Pre-Training Epoch 152/1000, Train RMSE: 0.7436 +Pre-Training Epoch 153/1000, Train RMSE: 0.7420 +Pre-Training Epoch 154/1000, Train RMSE: 0.7409 +Pre-Training Epoch 155/1000, Train RMSE: 0.7405 +Pre-Training Epoch 156/1000, Train RMSE: 0.7413 +Pre-Training Epoch 157/1000, Train RMSE: 0.7432 +Pre-Training Epoch 158/1000, Train RMSE: 0.7457 +Pre-Training Epoch 159/1000, Train RMSE: 0.7468 +Pre-Training Epoch 160/1000, Train RMSE: 0.7468 +Pre-Training Epoch 161/1000, Train RMSE: 0.7450 +Pre-Training Epoch 162/1000, Train RMSE: 0.7426 +Pre-Training Epoch 163/1000, Train RMSE: 0.7407 +Pre-Training Epoch 164/1000, Train RMSE: 0.7405 +Pre-Training Epoch 165/1000, Train RMSE: 0.7416 +Pre-Training Epoch 166/1000, Train RMSE: 0.7444 +Pre-Training Epoch 167/1000, Train RMSE: 0.7473 +Pre-Training Epoch 168/1000, Train RMSE: 0.7472 +Pre-Training Epoch 169/1000, Train RMSE: 0.7456 +Pre-Training Epoch 170/1000, Train RMSE: 0.7431 +Pre-Training Epoch 171/1000, Train RMSE: 0.7416 +Pre-Training Epoch 172/1000, Train RMSE: 0.7410 +Pre-Training Epoch 173/1000, Train RMSE: 0.7411 +Pre-Training Epoch 174/1000, Train RMSE: 0.7413 +Pre-Training Epoch 175/1000, Train RMSE: 0.7428 +Pre-Training Epoch 176/1000, Train RMSE: 0.7442 +Pre-Training Epoch 177/1000, Train RMSE: 0.7458 +Pre-Training Epoch 178/1000, Train RMSE: 0.7459 +Pre-Training Epoch 179/1000, Train RMSE: 0.7440 +Pre-Training Epoch 180/1000, Train RMSE: 0.7423 +Pre-Training Epoch 181/1000, Train RMSE: 0.7410 +Pre-Training Epoch 182/1000, Train RMSE: 0.7401 +Pre-Training Epoch 183/1000, Train RMSE: 0.7397 +Pre-Training Epoch 184/1000, Train RMSE: 0.7398 +Pre-Training Epoch 185/1000, Train RMSE: 0.7404 +Pre-Training Epoch 186/1000, Train RMSE: 0.7417 +Pre-Training Epoch 187/1000, Train RMSE: 0.7428 +Pre-Training Epoch 188/1000, Train RMSE: 0.7436 +Pre-Training Epoch 189/1000, Train RMSE: 0.7438 +Pre-Training Epoch 190/1000, Train RMSE: 0.7428 +Pre-Training Epoch 191/1000, Train RMSE: 0.7409 +Pre-Training Epoch 192/1000, Train RMSE: 0.7397 +Pre-Training Epoch 193/1000, Train RMSE: 0.7391 +Pre-Training Epoch 194/1000, Train RMSE: 0.7389 +Pre-Training Epoch 195/1000, Train RMSE: 0.7390 +Pre-Training Epoch 196/1000, Train RMSE: 0.7395 +Pre-Training Epoch 197/1000, Train RMSE: 0.7402 +Pre-Training Epoch 198/1000, Train RMSE: 0.7407 +Pre-Training Epoch 199/1000, Train RMSE: 0.7404 +Pre-Training Epoch 200/1000, Train RMSE: 0.7401 +Pre-Training Epoch 201/1000, Train RMSE: 0.7398 +Pre-Training Epoch 202/1000, Train RMSE: 0.7396 +Pre-Training Epoch 203/1000, Train RMSE: 0.7391 +Pre-Training Epoch 204/1000, Train RMSE: 0.7388 +Pre-Training Epoch 205/1000, Train RMSE: 0.7387 +Pre-Training Epoch 206/1000, Train RMSE: 0.7385 +Pre-Training Epoch 207/1000, Train RMSE: 0.7386 +Pre-Training Epoch 208/1000, Train RMSE: 0.7388 +Pre-Training Epoch 209/1000, Train RMSE: 0.7391 +Pre-Training Epoch 210/1000, Train RMSE: 0.7390 +Pre-Training Epoch 211/1000, Train RMSE: 0.7389 +Pre-Training Epoch 212/1000, Train RMSE: 0.7387 +Pre-Training Epoch 213/1000, Train RMSE: 0.7389 +Pre-Training Epoch 214/1000, Train RMSE: 0.7391 +Pre-Training Epoch 215/1000, Train RMSE: 0.7391 +Pre-Training Epoch 216/1000, Train RMSE: 0.7387 +Pre-Training Epoch 217/1000, Train RMSE: 0.7383 +Pre-Training Epoch 218/1000, Train RMSE: 0.7379 +Pre-Training Epoch 219/1000, Train RMSE: 0.7380 +Pre-Training Epoch 220/1000, Train RMSE: 0.7380 +Pre-Training Epoch 221/1000, Train RMSE: 0.7381 +Pre-Training Epoch 222/1000, Train RMSE: 0.7383 +Pre-Training Epoch 223/1000, Train RMSE: 0.7387 +Pre-Training Epoch 224/1000, Train RMSE: 0.7387 +Pre-Training Epoch 225/1000, Train RMSE: 0.7386 +Pre-Training Epoch 226/1000, Train RMSE: 0.7383 +Pre-Training Epoch 227/1000, Train RMSE: 0.7381 +Pre-Training Epoch 228/1000, Train RMSE: 0.7381 +Pre-Training Epoch 229/1000, Train RMSE: 0.7379 +Pre-Training Epoch 230/1000, Train RMSE: 0.7376 +Pre-Training Epoch 231/1000, Train RMSE: 0.7376 +Pre-Training Epoch 232/1000, Train RMSE: 0.7376 +Pre-Training Epoch 233/1000, Train RMSE: 0.7377 +Pre-Training Epoch 234/1000, Train RMSE: 0.7377 +Pre-Training Epoch 235/1000, Train RMSE: 0.7376 +Pre-Training Epoch 236/1000, Train RMSE: 0.7376 +Pre-Training Epoch 237/1000, Train RMSE: 0.7375 +Pre-Training Epoch 238/1000, Train RMSE: 0.7374 +Pre-Training Epoch 239/1000, Train RMSE: 0.7373 +Pre-Training Epoch 240/1000, Train RMSE: 0.7373 +Pre-Training Epoch 241/1000, Train RMSE: 0.7373 +Pre-Training Epoch 242/1000, Train RMSE: 0.7373 +Pre-Training Epoch 243/1000, Train RMSE: 0.7373 +Pre-Training Epoch 244/1000, Train RMSE: 0.7373 +Pre-Training Epoch 245/1000, Train RMSE: 0.7374 +Pre-Training Epoch 246/1000, Train RMSE: 0.7375 +Pre-Training Epoch 247/1000, Train RMSE: 0.7377 +Early stopping pre-training at epoch: 248 +Fine-Tuning Epoch 1/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 2/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 3/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 4/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 5/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 6/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 7/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 8/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 9/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 10/100, Train RMSE: 0.7369 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7371 +Fine-Tuning Epoch 12/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 13/100, Train RMSE: 0.7370 +Fine-Tuning Epoch 14/100, Train RMSE: 0.7368 +Fine-Tuning Epoch 15/100, Train RMSE: 0.7367 +Fine-Tuning Epoch 16/100, Train RMSE: 0.7365 +Fine-Tuning Epoch 17/100, Train RMSE: 0.7364 +Fine-Tuning Epoch 18/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 19/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 20/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 22/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 23/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 24/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 25/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 26/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 27/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 28/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 29/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 30/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7363 +Fine-Tuning Epoch 32/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 33/100, Train RMSE: 0.7362 +Fine-Tuning Epoch 34/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 35/100, Train RMSE: 0.7361 +Fine-Tuning Epoch 36/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 37/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 38/100, Train RMSE: 0.7360 +Fine-Tuning Epoch 39/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 40/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 41/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 42/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 43/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 44/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 45/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 54/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 55/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 56/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 57/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 58/100, Train RMSE: 0.7359 +Fine-Tuning Epoch 59/100, Train RMSE: 0.7359 +Early stopping fine-tuning at epoch: 60 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0186 | 1.0297 | 4.1892 | 0.0900 + + +======================================== diff --git a/experiment_log_19.txt b/experiment_log_19.txt new file mode 100644 index 000000000..43d867c72 --- /dev/null +++ b/experiment_log_19.txt @@ -0,0 +1,211 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:09:29.955636 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] + [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] + [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] + ... + [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] + [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] + [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7996 +Pre-Training Epoch 2/30, Train RMSE: 2.7996 +Pre-Training Epoch 3/30, Train RMSE: 2.7966 +Pre-Training Epoch 4/30, Train RMSE: 2.6030 +Pre-Training Epoch 5/30, Train RMSE: 2.1884 +Pre-Training Epoch 6/30, Train RMSE: 1.7387 +Pre-Training Epoch 7/30, Train RMSE: 1.3146 +Pre-Training Epoch 8/30, Train RMSE: 1.0291 +Pre-Training Epoch 9/30, Train RMSE: 0.9596 +Pre-Training Epoch 10/30, Train RMSE: 0.9218 +Pre-Training Epoch 11/30, Train RMSE: 0.8358 +Pre-Training Epoch 12/30, Train RMSE: 0.8988 +Pre-Training Epoch 13/30, Train RMSE: 1.0516 +Pre-Training Epoch 14/30, Train RMSE: 1.0739 +Pre-Training Epoch 15/30, Train RMSE: 0.9671 +Pre-Training Epoch 16/30, Train RMSE: 0.8610 +Pre-Training Epoch 17/30, Train RMSE: 0.8380 +Pre-Training Epoch 18/30, Train RMSE: 0.8302 +Pre-Training Epoch 19/30, Train RMSE: 0.8229 +Pre-Training Epoch 20/30, Train RMSE: 0.8402 +Pre-Training Epoch 21/30, Train RMSE: 0.8836 +Pre-Training Epoch 22/30, Train RMSE: 0.9046 +Pre-Training Epoch 23/30, Train RMSE: 0.8725 +Pre-Training Epoch 24/30, Train RMSE: 0.8251 +Pre-Training Epoch 25/30, Train RMSE: 0.7996 +Pre-Training Epoch 26/30, Train RMSE: 0.7898 +Pre-Training Epoch 27/30, Train RMSE: 0.7862 +Pre-Training Epoch 28/30, Train RMSE: 0.7892 +Pre-Training Epoch 29/30, Train RMSE: 0.7988 +Pre-Training Epoch 30/30, Train RMSE: 0.8104 +Fine-Tuning Epoch 1/100, Train RMSE: 0.7923 +Fine-Tuning Epoch 2/100, Train RMSE: 0.7908 +Fine-Tuning Epoch 3/100, Train RMSE: 0.7895 +Fine-Tuning Epoch 4/100, Train RMSE: 0.7883 +Fine-Tuning Epoch 5/100, Train RMSE: 0.7840 +Fine-Tuning Epoch 6/100, Train RMSE: 0.7809 +Fine-Tuning Epoch 7/100, Train RMSE: 0.7775 +Fine-Tuning Epoch 8/100, Train RMSE: 0.7731 +Fine-Tuning Epoch 9/100, Train RMSE: 0.7704 +Fine-Tuning Epoch 10/100, Train RMSE: 0.7681 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7686 +Fine-Tuning Epoch 12/100, Train RMSE: 0.7688 +Fine-Tuning Epoch 13/100, Train RMSE: 0.7694 +Fine-Tuning Epoch 14/100, Train RMSE: 0.7713 +Fine-Tuning Epoch 15/100, Train RMSE: 0.7728 +Fine-Tuning Epoch 16/100, Train RMSE: 0.7711 +Fine-Tuning Epoch 17/100, Train RMSE: 0.7694 +Fine-Tuning Epoch 18/100, Train RMSE: 0.7686 +Fine-Tuning Epoch 19/100, Train RMSE: 0.7668 +Fine-Tuning Epoch 20/100, Train RMSE: 0.7659 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7662 +Fine-Tuning Epoch 22/100, Train RMSE: 0.7663 +Fine-Tuning Epoch 23/100, Train RMSE: 0.7675 +Fine-Tuning Epoch 24/100, Train RMSE: 0.7695 +Fine-Tuning Epoch 25/100, Train RMSE: 0.7704 +Fine-Tuning Epoch 26/100, Train RMSE: 0.7688 +Fine-Tuning Epoch 27/100, Train RMSE: 0.7681 +Fine-Tuning Epoch 28/100, Train RMSE: 0.7678 +Fine-Tuning Epoch 29/100, Train RMSE: 0.7659 +Fine-Tuning Epoch 30/100, Train RMSE: 0.7643 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7632 +Fine-Tuning Epoch 32/100, Train RMSE: 0.7637 +Fine-Tuning Epoch 33/100, Train RMSE: 0.7642 +Fine-Tuning Epoch 34/100, Train RMSE: 0.7664 +Fine-Tuning Epoch 35/100, Train RMSE: 0.7684 +Fine-Tuning Epoch 36/100, Train RMSE: 0.7681 +Fine-Tuning Epoch 37/100, Train RMSE: 0.7660 +Fine-Tuning Epoch 38/100, Train RMSE: 0.7648 +Fine-Tuning Epoch 39/100, Train RMSE: 0.7651 +Fine-Tuning Epoch 40/100, Train RMSE: 0.7644 +Fine-Tuning Epoch 41/100, Train RMSE: 0.7643 +Fine-Tuning Epoch 42/100, Train RMSE: 0.7642 +Fine-Tuning Epoch 43/100, Train RMSE: 0.7624 +Fine-Tuning Epoch 44/100, Train RMSE: 0.7620 +Fine-Tuning Epoch 45/100, Train RMSE: 0.7621 +Fine-Tuning Epoch 46/100, Train RMSE: 0.7640 +Fine-Tuning Epoch 47/100, Train RMSE: 0.7646 +Fine-Tuning Epoch 48/100, Train RMSE: 0.7643 +Fine-Tuning Epoch 49/100, Train RMSE: 0.7648 +Fine-Tuning Epoch 50/100, Train RMSE: 0.7640 +Fine-Tuning Epoch 51/100, Train RMSE: 0.7626 +Fine-Tuning Epoch 52/100, Train RMSE: 0.7606 +Fine-Tuning Epoch 53/100, Train RMSE: 0.7594 +Fine-Tuning Epoch 54/100, Train RMSE: 0.7587 +Fine-Tuning Epoch 55/100, Train RMSE: 0.7589 +Fine-Tuning Epoch 56/100, Train RMSE: 0.7602 +Fine-Tuning Epoch 57/100, Train RMSE: 0.7635 +Fine-Tuning Epoch 58/100, Train RMSE: 0.7683 +Fine-Tuning Epoch 59/100, Train RMSE: 0.7733 +Fine-Tuning Epoch 60/100, Train RMSE: 0.7743 +Fine-Tuning Epoch 61/100, Train RMSE: 0.7730 +Fine-Tuning Epoch 62/100, Train RMSE: 0.7687 +Fine-Tuning Epoch 63/100, Train RMSE: 0.7635 +Fine-Tuning Epoch 64/100, Train RMSE: 0.7575 +Fine-Tuning Epoch 65/100, Train RMSE: 0.7535 +Fine-Tuning Epoch 66/100, Train RMSE: 0.7515 +Fine-Tuning Epoch 67/100, Train RMSE: 0.7517 +Fine-Tuning Epoch 68/100, Train RMSE: 0.7523 +Fine-Tuning Epoch 69/100, Train RMSE: 0.7532 +Fine-Tuning Epoch 70/100, Train RMSE: 0.7555 +Fine-Tuning Epoch 71/100, Train RMSE: 0.7568 +Fine-Tuning Epoch 72/100, Train RMSE: 0.7575 +Fine-Tuning Epoch 73/100, Train RMSE: 0.7563 +Fine-Tuning Epoch 74/100, Train RMSE: 0.7567 +Fine-Tuning Epoch 75/100, Train RMSE: 0.7559 +Fine-Tuning Epoch 76/100, Train RMSE: 0.7573 +Fine-Tuning Epoch 77/100, Train RMSE: 0.7589 +Fine-Tuning Epoch 78/100, Train RMSE: 0.7597 +Fine-Tuning Epoch 79/100, Train RMSE: 0.7589 +Fine-Tuning Epoch 80/100, Train RMSE: 0.7601 +Fine-Tuning Epoch 81/100, Train RMSE: 0.7597 +Fine-Tuning Epoch 82/100, Train RMSE: 0.7568 +Fine-Tuning Epoch 83/100, Train RMSE: 0.7549 +Fine-Tuning Epoch 84/100, Train RMSE: 0.7534 +Fine-Tuning Epoch 85/100, Train RMSE: 0.7528 +Fine-Tuning Epoch 86/100, Train RMSE: 0.7511 +Fine-Tuning Epoch 87/100, Train RMSE: 0.7510 +Fine-Tuning Epoch 88/100, Train RMSE: 0.7519 +Fine-Tuning Epoch 89/100, Train RMSE: 0.7542 +Fine-Tuning Epoch 90/100, Train RMSE: 0.7546 +Fine-Tuning Epoch 91/100, Train RMSE: 0.7550 +Fine-Tuning Epoch 92/100, Train RMSE: 0.7556 +Fine-Tuning Epoch 93/100, Train RMSE: 0.7564 +Fine-Tuning Epoch 94/100, Train RMSE: 0.7585 +Fine-Tuning Epoch 95/100, Train RMSE: 0.7583 +Fine-Tuning Epoch 96/100, Train RMSE: 0.7566 +Fine-Tuning Epoch 97/100, Train RMSE: 0.7539 +Fine-Tuning Epoch 98/100, Train RMSE: 0.7531 +Fine-Tuning Epoch 99/100, Train RMSE: 0.7532 +Fine-Tuning Epoch 100/100, Train RMSE: 0.7538 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0563 | 1.0648 | 2.4660 | 0.0840 + + +======================================== diff --git a/experiment_log_2.txt b/experiment_log_2.txt new file mode 100644 index 000000000..e0318d002 --- /dev/null +++ b/experiment_log_2.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:39:26.679902 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] + [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] + [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] + ... + [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] + [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] + [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7996 +Pre-Training Epoch 2/11, Train RMSE: 2.7996 +Pre-Training Epoch 3/11, Train RMSE: 2.7945 +Pre-Training Epoch 4/11, Train RMSE: 2.5375 +Pre-Training Epoch 5/11, Train RMSE: 2.1313 +Pre-Training Epoch 6/11, Train RMSE: 1.6893 +Pre-Training Epoch 7/11, Train RMSE: 1.2792 +Pre-Training Epoch 8/11, Train RMSE: 1.0196 +Pre-Training Epoch 9/11, Train RMSE: 0.9528 +Pre-Training Epoch 10/11, Train RMSE: 0.9507 +Pre-Training Epoch 11/11, Train RMSE: 0.9094 +Fine-Tuning Epoch 1/10, Train RMSE: 1.0658 +Fine-Tuning Epoch 2/10, Train RMSE: 1.0405 +Fine-Tuning Epoch 3/10, Train RMSE: 0.9448 +Fine-Tuning Epoch 4/10, Train RMSE: 0.9024 +Fine-Tuning Epoch 5/10, Train RMSE: 0.8677 +Fine-Tuning Epoch 6/10, Train RMSE: 0.8433 +Fine-Tuning Epoch 7/10, Train RMSE: 0.8474 +Fine-Tuning Epoch 8/10, Train RMSE: 0.8850 +Fine-Tuning Epoch 9/10, Train RMSE: 0.9034 +Fine-Tuning Epoch 10/10, Train RMSE: 0.8806 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0806 | 1.0899 | 1.6180 | 0.0820 + + +======================================== diff --git a/experiment_log_20.txt b/experiment_log_20.txt new file mode 100644 index 000000000..dbcbb7a9c --- /dev/null +++ b/experiment_log_20.txt @@ -0,0 +1,152 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:13:10.596422 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] + [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] + [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] + ... + [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] + [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] + [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7996 +Pre-Training Epoch 2/30, Train RMSE: 2.7996 +Pre-Training Epoch 3/30, Train RMSE: 2.7995 +Pre-Training Epoch 4/30, Train RMSE: 2.6134 +Pre-Training Epoch 5/30, Train RMSE: 2.2041 +Pre-Training Epoch 6/30, Train RMSE: 1.7501 +Pre-Training Epoch 7/30, Train RMSE: 1.3298 +Pre-Training Epoch 8/30, Train RMSE: 1.0646 +Pre-Training Epoch 9/30, Train RMSE: 0.9996 +Pre-Training Epoch 10/30, Train RMSE: 0.9842 +Pre-Training Epoch 11/30, Train RMSE: 0.8989 +Pre-Training Epoch 12/30, Train RMSE: 0.8207 +Pre-Training Epoch 13/30, Train RMSE: 0.8701 +Pre-Training Epoch 14/30, Train RMSE: 0.9979 +Pre-Training Epoch 15/30, Train RMSE: 1.0676 +Pre-Training Epoch 16/30, Train RMSE: 1.0539 +Pre-Training Epoch 17/30, Train RMSE: 0.9845 +Pre-Training Epoch 18/30, Train RMSE: 0.9100 +Pre-Training Epoch 19/30, Train RMSE: 0.8543 +Pre-Training Epoch 20/30, Train RMSE: 0.8262 +Pre-Training Epoch 21/30, Train RMSE: 0.8108 +Pre-Training Epoch 22/30, Train RMSE: 0.8003 +Pre-Training Epoch 23/30, Train RMSE: 0.7944 +Pre-Training Epoch 24/30, Train RMSE: 0.8037 +Pre-Training Epoch 25/30, Train RMSE: 0.8380 +Pre-Training Epoch 26/30, Train RMSE: 0.8651 +Pre-Training Epoch 27/30, Train RMSE: 0.8807 +Pre-Training Epoch 28/30, Train RMSE: 0.8691 +Pre-Training Epoch 29/30, Train RMSE: 0.8409 +Pre-Training Epoch 30/30, Train RMSE: 0.8123 +Fine-Tuning Epoch 1/100, Train RMSE: 0.8021 +Fine-Tuning Epoch 2/100, Train RMSE: 0.8015 +Fine-Tuning Epoch 3/100, Train RMSE: 0.7963 +Fine-Tuning Epoch 4/100, Train RMSE: 0.7951 +Fine-Tuning Epoch 5/100, Train RMSE: 0.7947 +Fine-Tuning Epoch 6/100, Train RMSE: 0.7890 +Fine-Tuning Epoch 7/100, Train RMSE: 0.7833 +Fine-Tuning Epoch 8/100, Train RMSE: 0.7787 +Fine-Tuning Epoch 9/100, Train RMSE: 0.7752 +Fine-Tuning Epoch 10/100, Train RMSE: 0.7720 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7703 +Fine-Tuning Epoch 12/100, Train RMSE: 0.7707 +Fine-Tuning Epoch 13/100, Train RMSE: 0.7725 +Fine-Tuning Epoch 14/100, Train RMSE: 0.7736 +Fine-Tuning Epoch 15/100, Train RMSE: 0.7765 +Fine-Tuning Epoch 16/100, Train RMSE: 0.7798 +Fine-Tuning Epoch 17/100, Train RMSE: 0.7835 +Fine-Tuning Epoch 18/100, Train RMSE: 0.7873 +Fine-Tuning Epoch 19/100, Train RMSE: 0.7886 +Fine-Tuning Epoch 20/100, Train RMSE: 0.7889 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7888 +Fine-Tuning Epoch 22/100, Train RMSE: 0.7871 +Fine-Tuning Epoch 23/100, Train RMSE: 0.7828 +Fine-Tuning Epoch 24/100, Train RMSE: 0.7783 +Fine-Tuning Epoch 25/100, Train RMSE: 0.7742 +Fine-Tuning Epoch 26/100, Train RMSE: 0.7700 +Fine-Tuning Epoch 27/100, Train RMSE: 0.7665 +Fine-Tuning Epoch 28/100, Train RMSE: 0.7642 +Fine-Tuning Epoch 29/100, Train RMSE: 0.7627 +Fine-Tuning Epoch 30/100, Train RMSE: 0.7618 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7609 +Fine-Tuning Epoch 32/100, Train RMSE: 0.7612 +Fine-Tuning Epoch 33/100, Train RMSE: 0.7625 +Fine-Tuning Epoch 34/100, Train RMSE: 0.7647 +Fine-Tuning Epoch 35/100, Train RMSE: 0.7672 +Fine-Tuning Epoch 36/100, Train RMSE: 0.7707 +Fine-Tuning Epoch 37/100, Train RMSE: 0.7753 +Fine-Tuning Epoch 38/100, Train RMSE: 0.7801 +Fine-Tuning Epoch 39/100, Train RMSE: 0.7826 +Fine-Tuning Epoch 40/100, Train RMSE: 0.7856 +Early stopping fine-tuning at epoch: 41 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0615 | 1.0671 | 2.9344 | 0.0836 + + +======================================== diff --git a/experiment_log_21.txt b/experiment_log_21.txt new file mode 100644 index 000000000..371c3d912 --- /dev/null +++ b/experiment_log_21.txt @@ -0,0 +1,90 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:14:10.064404 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] + [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] + [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] + ... + [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] + [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] + [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7996 +Pre-Training Epoch 11/30, Train RMSE: 1.0861 +Pre-Training Epoch 21/30, Train RMSE: 0.8988 +Fine-Tuning Epoch 1/100, Train RMSE: 0.7946 +Fine-Tuning Epoch 11/100, Train RMSE: 0.7697 +Fine-Tuning Epoch 21/100, Train RMSE: 0.7693 +Fine-Tuning Epoch 31/100, Train RMSE: 0.7706 +Fine-Tuning Epoch 41/100, Train RMSE: 0.7570 +Early stopping fine-tuning at epoch: 50 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0635 | 1.0699 | 2.0601 | 0.0864 + + +======================================== diff --git a/experiment_log_22.txt b/experiment_log_22.txt new file mode 100644 index 000000000..2fd1d3367 --- /dev/null +++ b/experiment_log_22.txt @@ -0,0 +1,175 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:24:41.241576 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] + [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] + [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] + ... + [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] + [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] + [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ]] +_train_r: [[4. 0. 4. ... 0. 0. 0.] + [0. 3. 4. ... 0. 0. 0.] + [5. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 1. ... 0. 0. 0.] + [0. 1. 1. ... 0. 0. 0.] + [1. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7809 +Pre-Training Epoch 11/30, Train RMSE: 1.0940 +Pre-Training Epoch 21/30, Train RMSE: 1.1482 +Fine-Tuning Epoch 1/100, Train RMSE: 1.0471 +Fine-Tuning Epoch 11/100, Train RMSE: 1.0460 +Fine-Tuning Epoch 21/100, Train RMSE: 1.0492 +Fine-Tuning Epoch 31/100, Train RMSE: 1.0420 +Fine-Tuning Epoch 41/100, Train RMSE: 1.0406 +Fine-Tuning Epoch 51/100, Train RMSE: 1.0377 +Fine-Tuning Epoch 61/100, Train RMSE: 1.0358 +Fine-Tuning Epoch 71/100, Train RMSE: 1.0377 +Fine-Tuning Epoch 81/100, Train RMSE: 1.0382 +Fine-Tuning Epoch 91/100, Train RMSE: 1.0377 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8520 | 0.9959 | 16.8043 | 520.8015 + + +======================================== + +======================================== +Experiment conducted on: 2024-12-14 15:38:08.721041 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.3665428 3.4314446 3.5327659 ... 3.2308724 4.0627537 3.251854 ] + [3.3674333 3.4354982 3.5315332 ... 3.230357 4.062786 3.2491977] + [3.364625 3.4343467 3.5296586 ... 3.2268498 4.060521 3.2477775] + ... + [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ] + [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ] + [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ]] +_train_r: [[4. 0. 0. ... 0. 0. 0.] + [0. 5. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + --------- +GlobalLocalKernel | 0.8460 | 1.0098 | 66.2905 | 1081.2129 + + +======================================== diff --git a/experiment_log_23.txt b/experiment_log_23.txt new file mode 100644 index 000000000..b9dc1309f --- /dev/null +++ b/experiment_log_23.txt @@ -0,0 +1,81 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:31:00.342731 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] + [2.700551 3.489955 2.2833154 ... 2.8310664 4.3504834 2.5976305] + [2.7023132 3.4924197 2.2842486 ... 2.8319042 4.3540497 2.5981433] + ... + [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] + [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] + [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8753 | 0.9768 | 19.8530 | 25.2438 + + +======================================== diff --git a/experiment_log_24.txt b/experiment_log_24.txt new file mode 100644 index 000000000..f3af30a16 --- /dev/null +++ b/experiment_log_24.txt @@ -0,0 +1,94 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:39:46.860598 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] + [2.3860507 3.667328 1.9504007 ... 3.1993334 4.6926336 2.6700587] + [2.3848817 3.6678581 1.9507756 ... 3.2012804 4.690115 2.6707287] + ... + [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] + [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] + [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.2273 +Pre-Training Epoch 21/30, Train RMSE: 1.1315 +Fine-Training Epoch 1/100, Train RMSE: 1.0286 +Fine-Training Epoch 11/100, Train RMSE: 1.0196 +Fine-Training Epoch 21/100, Train RMSE: 1.0108 +Fine-Training Epoch 31/100, Train RMSE: 1.0072 +Fine-Training Epoch 41/100, Train RMSE: 1.0058 +Fine-Training Epoch 51/100, Train RMSE: 1.0071 +Fine-Training Epoch 61/100, Train RMSE: 1.0081 +Fine-Training Epoch 71/100, Train RMSE: 1.0089 +Fine-Training Epoch 81/100, Train RMSE: 1.0066 +Fine-Training Epoch 91/100, Train RMSE: 1.0039 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8709 | 0.9735 | 6.0744 | 11.2648 + + +======================================== diff --git a/experiment_log_25.txt b/experiment_log_25.txt new file mode 100644 index 000000000..eb99eab87 --- /dev/null +++ b/experiment_log_25.txt @@ -0,0 +1,94 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:44:29.946562 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] + [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] + [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] + ... + [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] + [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] + [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.2238 +Pre-Training Epoch 21/30, Train RMSE: 1.0398 +Fine-Training Epoch 1/100, Train RMSE: 1.0274 +Fine-Training Epoch 11/100, Train RMSE: 1.0160 +Fine-Training Epoch 21/100, Train RMSE: 1.0126 +Fine-Training Epoch 31/100, Train RMSE: 1.0097 +Fine-Training Epoch 41/100, Train RMSE: 1.0054 +Fine-Training Epoch 51/100, Train RMSE: 1.0062 +Fine-Training Epoch 61/100, Train RMSE: 1.0062 +Fine-Training Epoch 71/100, Train RMSE: 1.0070 +Fine-Training Epoch 81/100, Train RMSE: 1.0057 +Fine-Training Epoch 91/100, Train RMSE: 1.0046 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8607 | 0.9651 | 6.2921 | 8.3071 + + +======================================== diff --git a/experiment_log_26.txt b/experiment_log_26.txt new file mode 100644 index 000000000..f3e0c55fa --- /dev/null +++ b/experiment_log_26.txt @@ -0,0 +1,94 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:49:39.347017 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] + [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] + [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] + ... + [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] + [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] + [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.0765 +Pre-Training Epoch 21/30, Train RMSE: 1.0214 +Fine-Training Epoch 1/100, Train RMSE: 1.0147 +Fine-Training Epoch 11/100, Train RMSE: 1.0079 +Fine-Training Epoch 21/100, Train RMSE: 1.0211 +Fine-Training Epoch 31/100, Train RMSE: 1.0066 +Fine-Training Epoch 41/100, Train RMSE: 1.0174 +Fine-Training Epoch 51/100, Train RMSE: 1.0083 +Fine-Training Epoch 61/100, Train RMSE: 1.0095 +Fine-Training Epoch 71/100, Train RMSE: 1.0093 +Fine-Training Epoch 81/100, Train RMSE: 1.0054 +Fine-Training Epoch 91/100, Train RMSE: 1.0063 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8713 | 0.9730 | 6.3756 | 8.6612 + + +======================================== diff --git a/experiment_log_27.txt b/experiment_log_27.txt new file mode 100644 index 000000000..ba0cf58b9 --- /dev/null +++ b/experiment_log_27.txt @@ -0,0 +1,94 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:50:04.046901 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] + [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] + [2.665841 3.3590596 2.287095 ... 3.9549856 3.8763995 2.669011 ] + ... + [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] + [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] + [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.1403 +Pre-Training Epoch 21/30, Train RMSE: 1.0269 +Fine-Training Epoch 1/100, Train RMSE: 1.0249 +Fine-Training Epoch 11/100, Train RMSE: 1.0190 +Fine-Training Epoch 21/100, Train RMSE: 1.0154 +Fine-Training Epoch 31/100, Train RMSE: 1.0174 +Fine-Training Epoch 41/100, Train RMSE: 1.0155 +Fine-Training Epoch 51/100, Train RMSE: 1.0134 +Fine-Training Epoch 61/100, Train RMSE: 1.0124 +Fine-Training Epoch 71/100, Train RMSE: 1.0171 +Fine-Training Epoch 81/100, Train RMSE: 1.0053 +Fine-Training Epoch 91/100, Train RMSE: 1.0067 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8769 | 0.9787 | 6.2931 | 8.2637 + + +======================================== diff --git a/experiment_log_28.txt b/experiment_log_28.txt new file mode 100644 index 000000000..6c6c22629 --- /dev/null +++ b/experiment_log_28.txt @@ -0,0 +1,94 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:51:02.416105 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] + [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] + [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] + ... + [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] + [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] + [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.1079 +Pre-Training Epoch 21/30, Train RMSE: 1.0317 +Fine-Training Epoch 1/100, Train RMSE: 1.0160 +Fine-Training Epoch 11/100, Train RMSE: 1.0108 +Fine-Training Epoch 21/100, Train RMSE: 1.0191 +Fine-Training Epoch 31/100, Train RMSE: 1.0129 +Fine-Training Epoch 41/100, Train RMSE: 1.0069 +Fine-Training Epoch 51/100, Train RMSE: 1.0138 +Fine-Training Epoch 61/100, Train RMSE: 1.0103 +Fine-Training Epoch 71/100, Train RMSE: 1.0058 +Fine-Training Epoch 81/100, Train RMSE: 1.0056 +Fine-Training Epoch 91/100, Train RMSE: 1.0103 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8767 | 0.9777 | 6.6966 | 9.3149 + + +======================================== diff --git a/experiment_log_29.txt b/experiment_log_29.txt new file mode 100644 index 000000000..bf13d4d66 --- /dev/null +++ b/experiment_log_29.txt @@ -0,0 +1,84 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:52:25.877167 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] + [2.5118506 3.2813966 2.1731827 ... 3.6590734 5. 2.7531 ] + [2.5122426 3.2817035 2.1733239 ... 3.6596854 5. 2.7531 ] + ... + [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] + [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] + [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/30, Train RMSE: 2.7954 +Pre-Training Epoch 11/30, Train RMSE: 1.1884 +Pre-Training Epoch 21/30, Train RMSE: 1.1436 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8667 | 0.9689 | 6.6524 | 8.4069 + + +======================================== diff --git a/experiment_log_3.txt b/experiment_log_3.txt new file mode 100644 index 000000000..5a2b60078 --- /dev/null +++ b/experiment_log_3.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:42:30.735089 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.6109083 3.0878942 2.579492 ... 3.8386712 4.7205243 3.134479 ] + [1.6088798 3.0834882 2.5752678 ... 3.832683 4.7128325 3.1321354] + [1.6130054 3.0879006 2.5810363 ... 3.831977 4.72029 3.1362262] + ... + [1.589895 3.0196843 2.5164669 ... 3.7473495 4.598756 3.0532973] + [1.596155 3.0352452 2.5281205 ... 3.7673159 4.6247973 3.0705748] + [1.589895 3.0196843 2.5164669 ... 3.7473495 4.598756 3.0532973]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7883 +Pre-Training Epoch 4/11, Train RMSE: 2.4445 +Pre-Training Epoch 5/11, Train RMSE: 1.9055 +Pre-Training Epoch 6/11, Train RMSE: 1.3869 +Pre-Training Epoch 7/11, Train RMSE: 1.1063 +Pre-Training Epoch 8/11, Train RMSE: 1.1256 +Pre-Training Epoch 9/11, Train RMSE: 1.1458 +Pre-Training Epoch 10/11, Train RMSE: 1.0537 +Pre-Training Epoch 11/11, Train RMSE: 1.1164 +Fine-Tuning Epoch 1/10, Train RMSE: 1.1590 +Fine-Tuning Epoch 2/10, Train RMSE: 1.2036 +Fine-Tuning Epoch 3/10, Train RMSE: 1.1784 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0788 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0521 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0435 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0376 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0487 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0614 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0608 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9437 | 1.0372 | 7.3951 | 20.8728 + + +======================================== diff --git a/experiment_log_30.txt b/experiment_log_30.txt new file mode 100644 index 000000000..de733b24a --- /dev/null +++ b/experiment_log_30.txt @@ -0,0 +1,81 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:53:45.184100 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] + [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] + [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] + ... + [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] + [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] + [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8703 | 0.9731 | 7.8693 | 10.0462 + + +======================================== diff --git a/experiment_log_31.txt b/experiment_log_31.txt new file mode 100644 index 000000000..dc1ceea40 --- /dev/null +++ b/experiment_log_31.txt @@ -0,0 +1,82 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:55:03.820486 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] + [2.6108704 3.590081 2.0323942 ... 4.13674 3.8902812 2.811247 ] + [2.6108627 3.5901144 2.0323942 ... 4.1367493 3.8903277 2.811247 ] + ... + [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] + [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] + [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 93 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8770 | 0.9788 | 6.3003 | 8.2548 + + +======================================== diff --git a/experiment_log_32.txt b/experiment_log_32.txt new file mode 100644 index 000000000..fed93b704 --- /dev/null +++ b/experiment_log_32.txt @@ -0,0 +1,84 @@ + +======================================== +Experiment conducted on: 2024-12-14 15:55:59.957210 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] + [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] + [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] + ... + [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] + [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] + [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 60 +Early stopping fine-tuning at epoch: 61 +Early stopping fine-tuning at epoch: 71 +Early stopping fine-tuning at epoch: 81 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8810 | 0.9808 | 5.3260 | 8.2245 + + +======================================== diff --git a/experiment_log_33.txt b/experiment_log_33.txt new file mode 100644 index 000000000..85825da57 --- /dev/null +++ b/experiment_log_33.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:05:02.280431 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] + [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] + [2.8019316 3.1359203 1.8802388 ... 3.1990905 4.6265297 2.6383972] + ... + [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] + [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] + [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8746 | 0.9754 | 19.2886 | 20.3352 + + +======================================== diff --git a/experiment_log_34.txt b/experiment_log_34.txt new file mode 100644 index 000000000..1a551bda0 --- /dev/null +++ b/experiment_log_34.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:06:32.139048 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] + [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] + [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] + ... + [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] + [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] + [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0351 | 1.0457 | 2.7236 | 0.0970 + + +======================================== diff --git a/experiment_log_35.txt b/experiment_log_35.txt new file mode 100644 index 000000000..b1ff06085 --- /dev/null +++ b/experiment_log_35.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:06:41.816932 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] + [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] + [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] + ... + [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] + [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] + [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0356 | 1.0401 | 3.3111 | 0.0910 + + +======================================== diff --git a/experiment_log_36.txt b/experiment_log_36.txt new file mode 100644 index 000000000..8ce666392 --- /dev/null +++ b/experiment_log_36.txt @@ -0,0 +1,113 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:07:07.902145 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] + [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] + [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] + ... + [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] + [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] + [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 37 +Early stopping fine-tuning at epoch: 60 +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0527 | 1.0599 | 3.2921 | 0.0980 + + +======================================== diff --git a/experiment_log_37.txt b/experiment_log_37.txt new file mode 100644 index 000000000..7c1641c80 --- /dev/null +++ b/experiment_log_37.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:07:30.418523 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] + [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] + [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] + ... + [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] + [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] + [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0302 | 1.0387 | 3.2679 | 0.1030 + + +======================================== diff --git a/experiment_log_38.txt b/experiment_log_38.txt new file mode 100644 index 000000000..bed583b29 --- /dev/null +++ b/experiment_log_38.txt @@ -0,0 +1,82 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:08:13.554989 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] + [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] + [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] + ... + [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] + [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] + [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 75 +Early stopping fine-tuning at epoch: 97 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0698 | 1.0743 | 3.2584 | 0.0979 + + +======================================== diff --git a/experiment_log_39.txt b/experiment_log_39.txt new file mode 100644 index 000000000..b9cd0b40e --- /dev/null +++ b/experiment_log_39.txt @@ -0,0 +1,83 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:09:18.745774 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] + [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] + [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] + ... + [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] + [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] + [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 36 +Early stopping fine-tuning at epoch: 80 +Early stopping fine-tuning at epoch: 81 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0507 | 1.0606 | 3.9770 | 0.1830 + + +======================================== diff --git a/experiment_log_4.txt b/experiment_log_4.txt new file mode 100644 index 000000000..178994ba4 --- /dev/null +++ b/experiment_log_4.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:46:52.531515 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] + [2.7604942 3.9703507 1.6771085 ... 3.9640617 3.7593672 2.145248 ] + [2.7532134 3.959725 1.6722806 ... 3.9549756 3.751583 2.136241 ] + ... + [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] + [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] + [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7953 +Pre-Training Epoch 4/11, Train RMSE: 2.4713 +Pre-Training Epoch 5/11, Train RMSE: 1.9837 +Pre-Training Epoch 6/11, Train RMSE: 1.4881 +Pre-Training Epoch 7/11, Train RMSE: 1.1384 +Pre-Training Epoch 8/11, Train RMSE: 1.1046 +Pre-Training Epoch 9/11, Train RMSE: 1.1870 +Pre-Training Epoch 10/11, Train RMSE: 1.1467 +Pre-Training Epoch 11/11, Train RMSE: 1.0403 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3540 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1498 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0475 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0420 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0339 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0546 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0932 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0993 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0679 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0383 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9178 | 1.0125 | 6.8028 | 23.1687 + + +======================================== diff --git a/experiment_log_40.txt b/experiment_log_40.txt new file mode 100644 index 000000000..f40140363 --- /dev/null +++ b/experiment_log_40.txt @@ -0,0 +1,83 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:09:46.041475 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] + [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] + [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] + ... + [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] + [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] + [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 34 +Early stopping fine-tuning at epoch: 41 +Early stopping fine-tuning at epoch: 51 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0515 | 1.0587 | 3.2913 | 0.0980 + + +======================================== diff --git a/experiment_log_41.txt b/experiment_log_41.txt new file mode 100644 index 000000000..5bb04f3be --- /dev/null +++ b/experiment_log_41.txt @@ -0,0 +1,111 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:10:02.648400 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] + [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] + [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] + ... + [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] + [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] + [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here +i am here + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0607 | 1.0689 | 3.4881 | 0.0950 + + +======================================== diff --git a/experiment_log_42.txt b/experiment_log_42.txt new file mode 100644 index 000000000..910fbf370 --- /dev/null +++ b/experiment_log_42.txt @@ -0,0 +1,142 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:13:14.098611 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] + [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] + [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] + ... + [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] + [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] + [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Score method called for user_id=32, item_id=210 +Predicting score for user 32 and item 210 +Score method called for user_id=81, item_id=20 +Predicting score for user 81 and item 20 +Score method called for user_id=175, item_id=128 +Predicting score for user 175 and item 128 +Score method called for user_id=83, item_id=78 +Predicting score for user 83 and item 78 +Score method called for user_id=154, item_id=120 +Predicting score for user 154 and item 120 +Score method called for user_id=130, item_id=48 +Predicting score for user 130 and item 48 +Score method called for user_id=62, item_id=24 +Predicting score for user 62 and item 24 +Score method called for user_id=81, item_id=5 +Predicting score for user 81 and item 5 +Score method called for user_id=110, item_id=266 +Predicting score for user 110 and item 266 +Score method called for user_id=157, item_id=235 +Predicting score for user 157 and item 235 +Score method called for user_id=128, item_id=25 +Predicting score for user 128 and item 25 +Score method called for user_id=151, item_id=233 +Predicting score for user 151 and item 233 +Score method called for user_id=75, item_id=14 +Predicting score for user 75 and item 14 +Score method called for user_id=78, item_id=209 +Predicting score for user 78 and item 209 +Score method called for user_id=15, item_id=4 +Predicting score for user 15 and item 4 +Score method called for user_id=45, item_id=27 +Predicting score for user 45 and item 27 +Score method called for user_id=35, item_id=181 +Predicting score for user 35 and item 181 +Score method called for user_id=68, item_id=51 +Predicting score for user 68 and item 51 +Score method called for user_id=58, item_id=85 +Predicting score for user 58 and item 85 +Score method called for user_id=95, item_id=263 +Predicting score for user 95 and item 263 +Score method called for user_id=149, item_id=102 +Predicting score for user 149 and item 102 +Score method called for user_id=138, item_id=156 +Predicting score for user 138 and item 156 +Score method called for user_id=146, item_id=53 +Predicting score for user 146 and item 53 +Score method called for user_id=21, item_id=34 +Predicting score for user 21 and item 34 +Score method called for user_id=135, item_id=147 +Predicting score for user 135 and item 147 +Score method called for user_id=70, item_id=185 +Predicting score for user 70 and item 185 +Score method called for user_id=36, item_id=99 +Predicting score for user 36 and item 99 +Score method called for user_id=105, item_id=296 +Predicting score for user 105 and item 296 +Score method called for user_id=70, item_id=183 +Predicting score for user 70 and item 183 +Score method called for user_id=169, item_id=289 +Predicting score for user 169 and item 289 +Score method called for user_id=26, item_id=94 +Predicting score for user 26 and item 94 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0328 | 1.0391 | 3.3769 | 0.0951 + + +======================================== diff --git a/experiment_log_43.txt b/experiment_log_43.txt new file mode 100644 index 000000000..82d75da29 --- /dev/null +++ b/experiment_log_43.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:19:04.297312 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] + [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] + [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] + ... + [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] + [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] + [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0308 | 1.0396 | 3.4106 | 0.1130 + + +======================================== diff --git a/experiment_log_44.txt b/experiment_log_44.txt new file mode 100644 index 000000000..d97b5ff0e --- /dev/null +++ b/experiment_log_44.txt @@ -0,0 +1,111 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:20:40.472758 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] + [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] + [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] + ... + [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] + [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] + [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Debug: item_id is a single value: 210. Scoring for user 32. +Debug: item_id is a single value: 20. Scoring for user 81. +Debug: item_id is a single value: 128. Scoring for user 175. +Debug: item_id is a single value: 78. Scoring for user 83. +Debug: item_id is a single value: 120. Scoring for user 154. +Debug: item_id is a single value: 48. Scoring for user 130. +Debug: item_id is a single value: 24. Scoring for user 62. +Debug: item_id is a single value: 5. Scoring for user 81. +Debug: item_id is a single value: 266. Scoring for user 110. +Debug: item_id is a single value: 235. Scoring for user 157. +Debug: item_id is a single value: 25. Scoring for user 128. +Debug: item_id is a single value: 233. Scoring for user 151. +Debug: item_id is a single value: 14. Scoring for user 75. +Debug: item_id is a single value: 209. Scoring for user 78. +Debug: item_id is a single value: 4. Scoring for user 15. +Debug: item_id is a single value: 27. Scoring for user 45. +Debug: item_id is a single value: 181. Scoring for user 35. +Debug: item_id is a single value: 51. Scoring for user 68. +Debug: item_id is a single value: 85. Scoring for user 58. +Debug: item_id is a single value: 263. Scoring for user 95. +Debug: item_id is a single value: 102. Scoring for user 149. +Debug: item_id is a single value: 156. Scoring for user 138. +Debug: item_id is a single value: 53. Scoring for user 146. +Debug: item_id is a single value: 34. Scoring for user 21. +Debug: item_id is a single value: 147. Scoring for user 135. +Debug: item_id is a single value: 185. Scoring for user 70. +Debug: item_id is a single value: 99. Scoring for user 36. +Debug: item_id is a single value: 296. Scoring for user 105. +Debug: item_id is a single value: 183. Scoring for user 70. +Debug: item_id is a single value: 289. Scoring for user 169. +Debug: item_id is a single value: 94. Scoring for user 26. + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0535 | 1.0602 | 3.5773 | 0.1190 + + +======================================== diff --git a/experiment_log_45.txt b/experiment_log_45.txt new file mode 100644 index 000000000..aee582ba4 --- /dev/null +++ b/experiment_log_45.txt @@ -0,0 +1,112 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:33:45.280769 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] + [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] + [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] + ... + [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] + [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] + [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 81 +Debug: item_id is a single value: 210. Scoring for user 32. +Debug: item_id is a single value: 20. Scoring for user 81. +Debug: item_id is a single value: 128. Scoring for user 175. +Debug: item_id is a single value: 78. Scoring for user 83. +Debug: item_id is a single value: 120. Scoring for user 154. +Debug: item_id is a single value: 48. Scoring for user 130. +Debug: item_id is a single value: 24. Scoring for user 62. +Debug: item_id is a single value: 5. Scoring for user 81. +Debug: item_id is a single value: 266. Scoring for user 110. +Debug: item_id is a single value: 235. Scoring for user 157. +Debug: item_id is a single value: 25. Scoring for user 128. +Debug: item_id is a single value: 233. Scoring for user 151. +Debug: item_id is a single value: 14. Scoring for user 75. +Debug: item_id is a single value: 209. Scoring for user 78. +Debug: item_id is a single value: 4. Scoring for user 15. +Debug: item_id is a single value: 27. Scoring for user 45. +Debug: item_id is a single value: 181. Scoring for user 35. +Debug: item_id is a single value: 51. Scoring for user 68. +Debug: item_id is a single value: 85. Scoring for user 58. +Debug: item_id is a single value: 263. Scoring for user 95. +Debug: item_id is a single value: 102. Scoring for user 149. +Debug: item_id is a single value: 156. Scoring for user 138. +Debug: item_id is a single value: 53. Scoring for user 146. +Debug: item_id is a single value: 34. Scoring for user 21. +Debug: item_id is a single value: 147. Scoring for user 135. +Debug: item_id is a single value: 185. Scoring for user 70. +Debug: item_id is a single value: 99. Scoring for user 36. +Debug: item_id is a single value: 296. Scoring for user 105. +Debug: item_id is a single value: 183. Scoring for user 70. +Debug: item_id is a single value: 289. Scoring for user 169. +Debug: item_id is a single value: 94. Scoring for user 26. + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0712 | 1.0826 | 3.4043 | 0.1060 + + +======================================== diff --git a/experiment_log_46.txt b/experiment_log_46.txt new file mode 100644 index 000000000..cf9189d25 --- /dev/null +++ b/experiment_log_46.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:34:28.415480 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] + [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] + [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] + ... + [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] + [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] + [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0639 | 1.0698 | 3.5987 | 0.1021 + + +======================================== diff --git a/experiment_log_47.txt b/experiment_log_47.txt new file mode 100644 index 000000000..a0fbc29e3 --- /dev/null +++ b/experiment_log_47.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:37:36.695322 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] + [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] + [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] + ... + [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] + [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] + [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ]] +_train_r: [[5. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 5. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 1. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 1.0537 | 1.0639 | 3.8695 | 0.0930 + + +======================================== diff --git a/experiment_log_48.txt b/experiment_log_48.txt new file mode 100644 index 000000000..ed5617d7f --- /dev/null +++ b/experiment_log_48.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:38:23.408433 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] + [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] + [2.4369004 3.2065606 2.2161467 ... 2.9055655 4.1110654 3.2190366] + ... + [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] + [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] + [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8782 | 0.9792 | 9.2949 | 9.4951 + + +======================================== diff --git a/experiment_log_49.txt b/experiment_log_49.txt new file mode 100644 index 000000000..cf58b1e48 --- /dev/null +++ b/experiment_log_49.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:40:12.851044 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] + [2.648593 3.3552032 2.586387 ... 3.8189757 4.972159 2.889211 ] + [2.6484973 3.3619058 2.586387 ... 3.8188543 4.97607 2.8915353] + ... + [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] + [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] + [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8723 | 0.9745 | 7.5357 | 8.1437 + + +======================================== diff --git a/experiment_log_5.txt b/experiment_log_5.txt new file mode 100644 index 000000000..c64fa1092 --- /dev/null +++ b/experiment_log_5.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:50:07.104995 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] + [1.4919857 2.639934 2.024684 ... 4.0510273 4.8342953 3.5593603] + [1.5025975 2.6664388 2.0519202 ... 4.117863 4.8968983 3.6059248] + ... + [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] + [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] + [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.5473 +Pre-Training Epoch 4/11, Train RMSE: 1.9857 +Pre-Training Epoch 5/11, Train RMSE: 1.4399 +Pre-Training Epoch 6/11, Train RMSE: 1.1069 +Pre-Training Epoch 7/11, Train RMSE: 1.1268 +Pre-Training Epoch 8/11, Train RMSE: 1.1435 +Pre-Training Epoch 9/11, Train RMSE: 1.0451 +Pre-Training Epoch 10/11, Train RMSE: 1.1978 +Pre-Training Epoch 11/11, Train RMSE: 1.3483 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3990 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1572 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0871 +Fine-Tuning Epoch 4/10, Train RMSE: 1.3297 +Fine-Tuning Epoch 5/10, Train RMSE: 1.3649 +Fine-Tuning Epoch 6/10, Train RMSE: 1.2305 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0855 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0254 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0303 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0398 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9017 | 1.0138 | 6.8767 | 26.4232 + + +======================================== diff --git a/experiment_log_50.txt b/experiment_log_50.txt new file mode 100644 index 000000000..e6e4d3674 --- /dev/null +++ b/experiment_log_50.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:41:16.688943 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] + [2.6458108 3.380637 2.1877875 ... 3.1579633 3.5752847 2.5653589] + [2.6459107 3.3808835 2.1877875 ... 3.1579633 3.5752847 2.5653589] + ... + [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] + [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] + [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8683 | 0.9706 | 7.2455 | 7.7017 + + +======================================== diff --git a/experiment_log_51.txt b/experiment_log_51.txt new file mode 100644 index 000000000..444630040 --- /dev/null +++ b/experiment_log_51.txt @@ -0,0 +1,999 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:42:28.780909 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] + [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] + [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] + ... + [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] + [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] + [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Evaluating user: 163, item: 586, predicted: 3.5113921734681055 +Evaluating user: 0, item: 292, predicted: 2.734770436107598 +Evaluating user: 216, item: 149, predicted: 3.6797601754321314 +Evaluating user: 332, item: 267, predicted: 4.0 +Evaluating user: 295, item: 387, predicted: 2.8394499985036448 +Evaluating user: 73, item: 75, predicted: 3.8573390302045087 +Evaluating user: 316, item: 325, predicted: 3.099025894745082 +Evaluating user: 217, item: 120, predicted: 3.905947738891853 +Evaluating user: 172, item: 330, predicted: 3.7424992882435157 +Evaluating user: 197, item: 458, predicted: 3.0930699310342837 +Evaluating user: 58, item: 193, predicted: 3.69180550429593 +Evaluating user: 92, item: 335, predicted: 3.559073293326748 +Evaluating user: 231, item: 678, predicted: 3.6667805627820957 +Evaluating user: 157, item: 534, predicted: 3.537742933934916 +Evaluating user: 300, item: 106, predicted: 3.2646298075084443 +Evaluating user: 58, item: 938, predicted: 3.69180550429593 +Evaluating user: 77, item: 480, predicted: 2.9199805905495 +Evaluating user: 80, item: 108, predicted: 3.7155653427793984 +Evaluating user: 32, item: 200, predicted: 3.4066725302048 +Evaluating user: 36, item: 224, predicted: 3.460662564244048 +Evaluating user: 51, item: 786, predicted: 3.674316184015763 +Evaluating user: 51, item: 122, predicted: 3.674316184015763 +Evaluating user: 184, item: 745, predicted: 2.8062516091208507 +Evaluating user: 53, item: 101, predicted: 3.1260344951076156 +Evaluating user: 192, item: 322, predicted: 3.8070937172974855 +Evaluating user: 3, item: 517, predicted: 3.7110027566608332 +Evaluating user: 174, item: 572, predicted: 3.9381978923492715 +Evaluating user: 58, item: 482, predicted: 3.69180550429593 +Evaluating user: 152, item: 568, predicted: 3.6981160858188953 +Evaluating user: 79, item: 149, predicted: 2.942217854340261 +Evaluating user: 6, item: 453, predicted: 3.201540374180235 +Evaluating user: 102, item: 635, predicted: 3.177248470947968 +Evaluating user: 147, item: 103, predicted: 4.0 +Evaluating user: 154, item: 865, predicted: 3.9251733893292546 +Evaluating user: 91, item: 386, predicted: 3.3228832822818015 +Evaluating user: 206, item: 437, predicted: 4.0 +Evaluating user: 68, item: 322, predicted: 1.5682077401379722 +Evaluating user: 153, item: 653, predicted: 2.545446004248653 +Evaluating user: 136, item: 558, predicted: 3.1843331274877817 +Evaluating user: 158, item: 437, predicted: 4.0 +Evaluating user: 155, item: 135, predicted: 3.0897378614606397 +Evaluating user: 75, item: 219, predicted: 3.103056831113985 +Evaluating user: 139, item: 73, predicted: 4.0 +Evaluating user: 105, item: 38, predicted: 4.0 +Evaluating user: 11, item: 359, predicted: 4.0 +Evaluating user: 59, item: 170, predicted: 3.3237759115696086 +Evaluating user: 8, item: 392, predicted: 3.6597206732404843 +Evaluating user: 94, item: 96, predicted: 3.204886329269301 +Evaluating user: 8, item: 576, predicted: 3.6597206732404843 +Evaluating user: 222, item: 835, predicted: 3.8982422020285328 +Evaluating user: 41, item: 157, predicted: 3.397777301643506 +Evaluating user: 75, item: 892, predicted: 3.103056831113985 +Evaluating user: 282, item: 254, predicted: 3.564453504640864 +Evaluating user: 131, item: 677, predicted: 3.745733974534817 +Evaluating user: 88, item: 20, predicted: 3.268883991102366 +Evaluating user: 246, item: 788, predicted: 3.4684126369617094 +Evaluating user: 6, item: 352, predicted: 3.201540374180235 +Evaluating user: 293, item: 370, predicted: 4.0 +Evaluating user: 52, item: 166, predicted: 3.449963387639435 +Evaluating user: 87, item: 92, predicted: 3.4224920797908034 +Evaluating user: 107, item: 496, predicted: 4.0 +Evaluating user: 79, item: 239, predicted: 2.942217854340261 +Evaluating user: 114, item: 328, predicted: 3.4122120789616113 +Evaluating user: 46, item: 803, predicted: 3.277755855715274 +Evaluating user: 87, item: 629, predicted: 3.4224920797908034 +Evaluating user: 120, item: 689, predicted: 3.0003930660134595 +Evaluating user: 66, item: 114, predicted: 3.22805337333706 +Evaluating user: 156, item: 703, predicted: 4.0 +Evaluating user: 34, item: 811, predicted: 3.4865196769640567 +Evaluating user: 216, item: 69, predicted: 3.6797601754321314 +Evaluating user: 195, item: 75, predicted: 4.0 +Evaluating user: 83, item: 14, predicted: 3.046583752628486 +Evaluating user: 287, item: 573, predicted: 4.0 +Evaluating user: 105, item: 395, predicted: 4.0 +Evaluating user: 70, item: 399, predicted: 3.16817852056873 +Evaluating user: 0, item: 227, predicted: 2.734770436107598 +Evaluating user: 100, item: 342, predicted: 3.1589717525850505 +Evaluating user: 80, item: 302, predicted: 3.7155653427793984 +Evaluating user: 43, item: 200, predicted: 3.258812818890413 +Evaluating user: 41, item: 456, predicted: 3.397777301643506 +Evaluating user: 27, item: 483, predicted: 3.0452065709348664 +Evaluating user: 3, item: 834, predicted: 3.7110027566608332 +Evaluating user: 187, item: 135, predicted: 3.258594559438348 +Evaluating user: 24, item: 181, predicted: 3.1783635804868453 +Evaluating user: 125, item: 370, predicted: 3.761831276821815 +Evaluating user: 139, item: 211, predicted: 4.0 +Evaluating user: 64, item: 8, predicted: 3.202547478725553 +Evaluating user: 194, item: 105, predicted: 2.8425910264255885 +Evaluating user: 231, item: 312, predicted: 3.6667805627820957 +Evaluating user: 197, item: 965, predicted: 3.0930699310342837 +Evaluating user: 153, item: 731, predicted: 2.545446004248653 +Evaluating user: 54, item: 960, predicted: 3.557432591116923 +Evaluating user: 164, item: 839, predicted: 3.494502219932684 +Evaluating user: 22, item: 98, predicted: 3.796056342470468 +Evaluating user: 99, item: 524, predicted: 3.3120876147675595 +Evaluating user: 30, item: 319, predicted: 3.866272241038406 +Evaluating user: 323, item: 832, predicted: 3.4890009789863967 +Evaluating user: 26, item: 181, predicted: 3.2282415361513763 +Evaluating user: 86, item: 995, predicted: 3.2330500503420048 +Evaluating user: 180, item: 211, predicted: 3.48873440226905 +Evaluating user: 64, item: 721, predicted: 3.202547478725553 +Evaluating user: 8, item: 649, predicted: 3.6597206732404843 +Evaluating user: 181, item: 132, predicted: 3.1358150526443636 +Evaluating user: 172, item: 478, predicted: 3.7424992882435157 +Evaluating user: 22, item: 79, predicted: 3.796056342470468 +Evaluating user: 47, item: 208, predicted: 2.6370263485688588 +Evaluating user: 138, item: 689, predicted: 3.6119977386297535 +Evaluating user: 17, item: 208, predicted: 4.0 +Evaluating user: 139, item: 234, predicted: 4.0 +Evaluating user: 29, item: 966, predicted: 3.7781592966869013 +Evaluating user: 68, item: 842, predicted: 1.5682077401379722 +Evaluating user: 115, item: 31, predicted: 3.6137364550990245 +Evaluating user: 23, item: 238, predicted: 3.103225105551166 +Evaluating user: 199, item: 154, predicted: 4.0 +Evaluating user: 76, item: 115, predicted: 4.0 +Evaluating user: 66, item: 177, predicted: 3.22805337333706 +Evaluating user: 197, item: 159, predicted: 3.0930699310342837 +Evaluating user: 46, item: 195, predicted: 3.277755855715274 +Evaluating user: 160, item: 443, predicted: 2.887138985414394 +Evaluating user: 29, item: 304, predicted: 3.7781592966869013 +Evaluating user: 93, item: 764, predicted: 3.9891568569989357 +Evaluating user: 206, item: 511, predicted: 4.0 +Evaluating user: 153, item: 122, predicted: 2.545446004248653 +Evaluating user: 61, item: 10, predicted: 2.3337088155756582 +Evaluating user: 92, item: 960, predicted: 3.559073293326748 +Evaluating user: 37, item: 70, predicted: 4.0 +Evaluating user: 165, item: 504, predicted: 3.620702831791548 +Evaluating user: 40, item: 23, predicted: 3.6226568169745494 +Evaluating user: 236, item: 580, predicted: 2.7048138774607398 +Evaluating user: 159, item: 73, predicted: 3.9978437662848423 +Evaluating user: 105, item: 297, predicted: 4.0 +Evaluating user: 1, item: 71, predicted: 3.5921467575298585 +Evaluating user: 43, item: 510, predicted: 3.258812818890413 +Evaluating user: 231, item: 2, predicted: 3.6667805627820957 +Evaluating user: 4, item: 796, predicted: 3.768511205275512 +Evaluating user: 278, item: 505, predicted: 3.509220343044845 +Evaluating user: 7, item: 201, predicted: 4.0 +Evaluating user: 27, item: 410, predicted: 3.0452065709348664 +Evaluating user: 16, item: 208, predicted: 3.3142726592872442 +Evaluating user: 139, item: 726, predicted: 4.0 +Evaluating user: 20, item: 11, predicted: 3.870033225798768 +Evaluating user: 187, item: 452, predicted: 3.258594559438348 +Evaluating user: 27, item: 561, predicted: 3.0452065709348664 +Evaluating user: 153, item: 106, predicted: 2.545446004248653 +Evaluating user: 179, item: 537, predicted: 3.3684190043870683 +Evaluating user: 34, item: 807, predicted: 3.4865196769640567 +Evaluating user: 111, item: 487, predicted: 3.529305238189742 +Evaluating user: 161, item: 891, predicted: 3.53804643196736 +Evaluating user: 273, item: 451, predicted: 4.0 +Evaluating user: 161, item: 321, predicted: 3.53804643196736 +Evaluating user: 149, item: 755, predicted: 2.8386717967257202 +Evaluating user: 8, item: 50, predicted: 3.6597206732404843 +Evaluating user: 39, item: 154, predicted: 4.0 +Evaluating user: 276, item: 20, predicted: 3.043588626174305 +Evaluating user: 249, item: 97, predicted: 3.57548640802173 +Evaluating user: 121, item: 251, predicted: 2.959321252635845 +Evaluating user: 84, item: 700, predicted: 3.5967375130272137 +Evaluating user: 281, item: 568, predicted: 4.0 +Evaluating user: 36, item: 506, predicted: 3.460662564244048 +Evaluating user: 171, item: 193, predicted: 3.5537895965250352 +Evaluating user: 15, item: 576, predicted: 2.677068471129866 +Evaluating user: 15, item: 677, predicted: 2.677068471129866 +Evaluating user: 42, item: 817, predicted: 3.6623453377851063 +Evaluating user: 32, item: 219, predicted: 3.4066725302048 +Evaluating user: 282, item: 168, predicted: 3.564453504640864 +Evaluating user: 83, item: 200, predicted: 3.046583752628486 +Evaluating user: 1, item: 83, predicted: 3.5921467575298585 +Evaluating user: 57, item: 813, predicted: 3.631112245165661 +Evaluating user: 109, item: 53, predicted: 3.5828469527895868 +Evaluating user: 123, item: 401, predicted: 4.0 +Evaluating user: 193, item: 367, predicted: 3.9343050252304037 +Evaluating user: 11, item: 667, predicted: 4.0 +Evaluating user: 11, item: 270, predicted: 4.0 +Evaluating user: 100, item: 400, predicted: 3.1589717525850505 +Evaluating user: 85, item: 278, predicted: 3.734290231272179 +Evaluating user: 77, item: 952, predicted: 2.9199805905495 +Evaluating user: 124, item: 305, predicted: 2.9139364224064908 +Evaluating user: 34, item: 988, predicted: 3.4865196769640567 +Evaluating user: 120, item: 161, predicted: 3.0003930660134595 +Evaluating user: 296, item: 558, predicted: 4.0 +Evaluating user: 86, item: 118, predicted: 3.2330500503420048 +Evaluating user: 271, item: 187, predicted: 4.0 +Evaluating user: 184, item: 208, predicted: 2.8062516091208507 +Evaluating user: 79, item: 128, predicted: 2.942217854340261 +Evaluating user: 113, item: 278, predicted: 3.363926080883002 +Evaluating user: 312, item: 2, predicted: 3.222518423609301 +Evaluating user: 68, item: 46, predicted: 1.5682077401379722 +Evaluating user: 302, item: 3, predicted: 2.4282340052533433 +Evaluating user: 25, item: 186, predicted: 3.5732685808614724 +Evaluating user: 20, item: 74, predicted: 3.870033225798768 +Evaluating user: 182, item: 278, predicted: 2.7551743925444994 +Evaluating user: 15, item: 224, predicted: 2.677068471129866 +Evaluating user: 126, item: 45, predicted: 3.6728906294557255 +Evaluating user: 100, item: 204, predicted: 3.1589717525850505 +Evaluating user: 214, item: 251, predicted: 4.0 +Evaluating user: 191, item: 575, predicted: 3.32174777948537 +Evaluating user: 115, item: 990, predicted: 3.6137364550990245 +Evaluating user: 149, item: 46, predicted: 2.8386717967257202 +Evaluating user: 125, item: 79, predicted: 3.761831276821815 +Evaluating user: 138, item: 126, predicted: 3.6119977386297535 +Evaluating user: 213, item: 52, predicted: 3.242631255566721 +Evaluating user: 15, item: 128, predicted: 2.677068471129866 +Evaluating user: 62, item: 11, predicted: 3.694835086445088 +Evaluating user: 83, item: 428, predicted: 3.046583752628486 +Evaluating user: 30, item: 21, predicted: 3.866272241038406 +Evaluating user: 24, item: 65, predicted: 3.1783635804868453 +Evaluating user: 187, item: 322, predicted: 3.258594559438348 +Evaluating user: 208, item: 69, predicted: 3.3190518786307877 +Evaluating user: 327, item: 20, predicted: 3.3520500776469615 +Evaluating user: 77, item: 113, predicted: 2.9199805905495 +Evaluating user: 53, item: 591, predicted: 3.1260344951076156 +Evaluating user: 6, item: 234, predicted: 3.201540374180235 +Evaluating user: 29, item: 931, predicted: 3.7781592966869013 +Evaluating user: 267, item: 663, predicted: 4.0 +Evaluating user: 125, item: 620, predicted: 3.761831276821815 +Evaluating user: 296, item: 452, predicted: 4.0 +Evaluating user: 83, item: 537, predicted: 3.046583752628486 +Evaluating user: 250, item: 496, predicted: 3.365176917503812 +Evaluating user: 15, item: 180, predicted: 2.677068471129866 +Evaluating user: 163, item: 142, predicted: 3.5113921734681055 +Evaluating user: 105, item: 435, predicted: 4.0 +Evaluating user: 165, item: 91, predicted: 3.620702831791548 +Evaluating user: 302, item: 399, predicted: 2.4282340052533433 +Evaluating user: 116, item: 465, predicted: 3.579993717900999 +Evaluating user: 323, item: 963, predicted: 3.4890009789863967 +Evaluating user: 34, item: 114, predicted: 3.4865196769640567 +Evaluating user: 174, item: 519, predicted: 3.9381978923492715 +Evaluating user: 194, item: 545, predicted: 2.8425910264255885 +Evaluating user: 277, item: 599, predicted: 3.191308703467244 +Evaluating user: 271, item: 636, predicted: 4.0 +Evaluating user: 316, item: 146, predicted: 3.099025894745082 +Evaluating user: 82, item: 592, predicted: 4.0 +Evaluating user: 41, item: 431, predicted: 3.397777301643506 +Evaluating user: 42, item: 19, predicted: 3.6623453377851063 +Evaluating user: 229, item: 532, predicted: 2.6482975494993264 +Evaluating user: 76, item: 653, predicted: 4.0 +Evaluating user: 222, item: 978, predicted: 3.8982422020285328 +Evaluating user: 223, item: 890, predicted: 4.0 +Evaluating user: 80, item: 238, predicted: 3.7155653427793984 +Evaluating user: 197, item: 81, predicted: 3.0930699310342837 +Evaluating user: 84, item: 470, predicted: 3.5967375130272137 +Evaluating user: 0, item: 88, predicted: 2.734770436107598 +Evaluating user: 37, item: 226, predicted: 4.0 +Evaluating user: 65, item: 440, predicted: 4.0 +Evaluating user: 8, item: 8, predicted: 3.6597206732404843 +Evaluating user: 101, item: 305, predicted: 3.276372380348297 +Evaluating user: 253, item: 328, predicted: 2.9553383401310103 +Evaluating user: 115, item: 579, predicted: 3.6137364550990245 +Evaluating user: 28, item: 172, predicted: 4.0 +Evaluating user: 11, item: 296, predicted: 4.0 +Evaluating user: 174, item: 275, predicted: 3.9381978923492715 +Evaluating user: 145, item: 717, predicted: 4.0 +Evaluating user: 212, item: 323, predicted: 3.7439136034394465 +Evaluating user: 5, item: 969, predicted: 2.799210054666971 +Evaluating user: 187, item: 448, predicted: 3.258594559438348 +Evaluating user: 226, item: 48, predicted: 2.920628293432053 +Evaluating user: 102, item: 897, predicted: 3.177248470947968 +Evaluating user: 83, item: 259, predicted: 3.046583752628486 +Evaluating user: 126, item: 181, predicted: 3.6728906294557255 +Evaluating user: 324, item: 98, predicted: 3.7267738173588167 +Evaluating user: 35, item: 746, predicted: 3.5455786589764733 +Evaluating user: 65, item: 318, predicted: 4.0 +Evaluating user: 155, item: 21, predicted: 3.0897378614606397 +Evaluating user: 101, item: 773, predicted: 3.276372380348297 +Evaluating user: 8, item: 155, predicted: 3.6597206732404843 +Evaluating user: 236, item: 344, predicted: 2.7048138774607398 +Evaluating user: 124, item: 10, predicted: 2.9139364224064908 +Evaluating user: 90, item: 471, predicted: 2.6208033399664283 +Evaluating user: 100, item: 673, predicted: 3.1589717525850505 +Evaluating user: 74, item: 185, predicted: 3.850148908102371 +Evaluating user: 283, item: 99, predicted: 3.7936047407527855 +Evaluating user: 30, item: 503, predicted: 3.866272241038406 +Evaluating user: 151, item: 264, predicted: 3.3537029933801845 +Evaluating user: 76, item: 28, predicted: 4.0 +Evaluating user: 68, item: 825, predicted: 1.5682077401379722 +Evaluating user: 37, item: 334, predicted: 4.0 +Evaluating user: 65, item: 630, predicted: 4.0 +Evaluating user: 239, item: 226, predicted: 3.562671129833055 +Evaluating user: 37, item: 245, predicted: 4.0 +Evaluating user: 191, item: 437, predicted: 3.32174777948537 +Evaluating user: 103, item: 3, predicted: 3.7376057615125893 +Evaluating user: 27, item: 358, predicted: 3.0452065709348664 +Evaluating user: 78, item: 448, predicted: 3.507489658917522 +Evaluating user: 15, item: 171, predicted: 2.677068471129866 +Evaluating user: 43, item: 459, predicted: 3.258812818890413 +Evaluating user: 273, item: 180, predicted: 4.0 +Evaluating user: 64, item: 387, predicted: 3.202547478725553 +Evaluating user: 127, item: 703, predicted: 2.282856560388726 +Evaluating user: 155, item: 199, predicted: 3.0897378614606397 +Evaluating user: 46, item: 242, predicted: 3.277755855715274 +Evaluating user: 82, item: 729, predicted: 4.0 +Evaluating user: 144, item: 623, predicted: 2.3453813892347712 +Evaluating user: 65, item: 435, predicted: 4.0 +Evaluating user: 231, item: 426, predicted: 3.6667805627820957 +Evaluating user: 105, item: 341, predicted: 4.0 +Evaluating user: 100, item: 437, predicted: 3.1589717525850505 +Evaluating user: 101, item: 830, predicted: 3.276372380348297 +Evaluating user: 266, item: 193, predicted: 2.7481549403698615 +Evaluating user: 194, item: 796, predicted: 2.8425910264255885 +Evaluating user: 167, item: 862, predicted: 3.56005702511892 +Evaluating user: 89, item: 251, predicted: 3.162531742858552 +Evaluating user: 45, item: 27, predicted: 3.421716550196023 +Evaluating user: 27, item: 439, predicted: 3.0452065709348664 +Evaluating user: 106, item: 470, predicted: 2.8632698743667886 +Evaluating user: 191, item: 289, predicted: 3.32174777948537 +Evaluating user: 270, item: 159, predicted: 3.815880658560767 +Evaluating user: 316, item: 489, predicted: 3.099025894745082 +Evaluating user: 125, item: 296, predicted: 3.761831276821815 +Evaluating user: 257, item: 178, predicted: 2.569536997766901 +Evaluating user: 221, item: 16, predicted: 3.111955165178042 +Evaluating user: 94, item: 215, predicted: 3.204886329269301 +Evaluating user: 103, item: 171, predicted: 3.7376057615125893 +Evaluating user: 193, item: 1, predicted: 3.9343050252304037 +Evaluating user: 161, item: 208, predicted: 3.53804643196736 +Evaluating user: 58, item: 131, predicted: 3.69180550429593 +Evaluating user: 97, item: 84, predicted: 3.9880103262613322 +Evaluating user: 304, item: 610, predicted: 2.9127125203052233 +Evaluating user: 39, item: 985, predicted: 4.0 +Evaluating user: 16, item: 537, predicted: 3.3142726592872442 +Evaluating user: 58, item: 91, predicted: 3.69180550429593 +Evaluating user: 221, item: 578, predicted: 3.111955165178042 +Evaluating user: 41, item: 4, predicted: 3.397777301643506 +Evaluating user: 84, item: 689, predicted: 3.5967375130272137 +Evaluating user: 81, item: 161, predicted: 3.837020551056988 +Evaluating user: 317, item: 403, predicted: 1.9971576512570797 +Evaluating user: 105, item: 122, predicted: 4.0 +Evaluating user: 167, item: 300, predicted: 3.56005702511892 +Evaluating user: 91, item: 579, predicted: 3.3228832822818015 +Evaluating user: 8, item: 606, predicted: 3.6597206732404843 +Evaluating user: 194, item: 676, predicted: 2.8425910264255885 +Evaluating user: 234, item: 359, predicted: 2.7806680648778594 +Evaluating user: 8, item: 272, predicted: 3.6597206732404843 +Evaluating user: 85, item: 468, predicted: 3.734290231272179 +Evaluating user: 262, item: 40, predicted: 3.6730438217905075 +Evaluating user: 76, item: 435, predicted: 4.0 +Evaluating user: 67, item: 354, predicted: 3.5841153561372403 +Evaluating user: 47, item: 182, predicted: 2.6370263485688588 +Evaluating user: 269, item: 252, predicted: 4.0 +Evaluating user: 152, item: 468, predicted: 3.6981160858188953 +Evaluating user: 147, item: 892, predicted: 4.0 +Evaluating user: 35, item: 237, predicted: 3.5455786589764733 +Evaluating user: 64, item: 105, predicted: 3.202547478725553 +Evaluating user: 9, item: 8, predicted: 3.6156462106949405 +Evaluating user: 327, item: 687, predicted: 3.3520500776469615 +Evaluating user: 8, item: 86, predicted: 3.6597206732404843 +Evaluating user: 90, item: 832, predicted: 2.6208033399664283 +Evaluating user: 1, item: 181, predicted: 3.5921467575298585 +Evaluating user: 41, item: 21, predicted: 3.397777301643506 +Evaluating user: 191, item: 187, predicted: 3.32174777948537 +Evaluating user: 217, item: 920, predicted: 3.905947738891853 +Evaluating user: 35, item: 236, predicted: 3.5455786589764733 +Evaluating user: 53, item: 319, predicted: 3.1260344951076156 +Evaluating user: 30, item: 350, predicted: 3.866272241038406 +Evaluating user: 73, item: 113, predicted: 3.8573390302045087 +Evaluating user: 8, item: 6, predicted: 3.6597206732404843 +Evaluating user: 46, item: 46, predicted: 3.277755855715274 +Evaluating user: 40, item: 319, predicted: 3.6226568169745494 +Evaluating user: 269, item: 61, predicted: 4.0 +Evaluating user: 24, item: 92, predicted: 3.1783635804868453 +Evaluating user: 216, item: 420, predicted: 3.6797601754321314 +Evaluating user: 142, item: 448, predicted: 3.434401628884774 +Evaluating user: 120, item: 210, predicted: 3.0003930660134595 +Evaluating user: 37, item: 788, predicted: 4.0 +Evaluating user: 29, item: 111, predicted: 3.7781592966869013 +Evaluating user: 46, item: 21, predicted: 3.277755855715274 +Evaluating user: 64, item: 148, predicted: 3.202547478725553 +Evaluating user: 131, item: 211, predicted: 3.745733974534817 +Evaluating user: 92, item: 676, predicted: 3.559073293326748 +Evaluating user: 6, item: 593, predicted: 3.201540374180235 +Evaluating user: 225, item: 296, predicted: 3.9467115737525225 +Evaluating user: 114, item: 479, predicted: 3.4122120789616113 +Evaluating user: 181, item: 387, predicted: 3.1358150526443636 +Evaluating user: 97, item: 350, predicted: 3.9880103262613322 +Evaluating user: 40, item: 292, predicted: 3.6226568169745494 +Evaluating user: 126, item: 67, predicted: 3.6728906294557255 +Evaluating user: 234, item: 323, predicted: 2.7806680648778594 +Evaluating user: 302, item: 355, predicted: 2.4282340052533433 +Evaluating user: 293, item: 129, predicted: 4.0 +Evaluating user: 155, item: 121, predicted: 3.0897378614606397 +Evaluating user: 97, item: 479, predicted: 3.9880103262613322 +Evaluating user: 204, item: 488, predicted: 4.0 +Evaluating user: 6, item: 307, predicted: 3.201540374180235 +Evaluating user: 20, item: 311, predicted: 3.870033225798768 +Evaluating user: 261, item: 77, predicted: 3.0086335343036836 +Evaluating user: 11, item: 729, predicted: 4.0 +Evaluating user: 69, item: 132, predicted: 3.229238415003662 +Evaluating user: 6, item: 948, predicted: 3.201540374180235 +Evaluating user: 92, item: 144, predicted: 3.559073293326748 +Evaluating user: 306, item: 154, predicted: 3.6375006971459865 +Evaluating user: 42, item: 130, predicted: 3.6623453377851063 +Evaluating user: 126, item: 332, predicted: 3.6728906294557255 +Evaluating user: 131, item: 290, predicted: 3.745733974534817 +Evaluating user: 34, item: 450, predicted: 3.4865196769640567 +Evaluating user: 41, item: 126, predicted: 3.397777301643506 +Evaluating user: 78, item: 378, predicted: 3.507489658917522 +Evaluating user: 116, item: 328, predicted: 3.579993717900999 +Evaluating user: 17, item: 748, predicted: 4.0 +Evaluating user: 127, item: 470, predicted: 2.282856560388726 +Evaluating user: 103, item: 449, predicted: 3.7376057615125893 +Evaluating user: 261, item: 66, predicted: 3.0086335343036836 +Evaluating user: 298, item: 689, predicted: 3.130616313639387 +Evaluating user: 121, item: 933, predicted: 2.959321252635845 +Evaluating user: 218, item: 1, predicted: 2.837156981933278 +Evaluating user: 157, item: 924, predicted: 3.537742933934916 +Evaluating user: 65, item: 598, predicted: 4.0 +Evaluating user: 184, item: 387, predicted: 2.8062516091208507 +Evaluating user: 42, item: 252, predicted: 3.6623453377851063 +Evaluating user: 58, item: 746, predicted: 3.69180550429593 +Evaluating user: 30, item: 432, predicted: 3.866272241038406 +Evaluating user: 24, item: 515, predicted: 3.1783635804868453 +Evaluating user: 27, item: 97, predicted: 3.0452065709348664 +Evaluating user: 47, item: 527, predicted: 2.6370263485688588 +Evaluating user: 24, item: 771, predicted: 3.1783635804868453 +Evaluating user: 136, item: 479, predicted: 3.1843331274877817 +Evaluating user: 75, item: 924, predicted: 3.103056831113985 +Evaluating user: 12, item: 306, predicted: 2.664914638376698 +Evaluating user: 13, item: 578, predicted: 3.026749739953794 +Evaluating user: 216, item: 224, predicted: 3.6797601754321314 +Evaluating user: 21, item: 304, predicted: 3.2390720152222396 +Evaluating user: 165, item: 234, predicted: 3.620702831791548 +Evaluating user: 19, item: 236, predicted: 3.7492266327587394 +Evaluating user: 30, item: 381, predicted: 3.866272241038406 +Evaluating user: 9, item: 428, predicted: 3.6156462106949405 +Evaluating user: 191, item: 442, predicted: 3.32174777948537 +Evaluating user: 13, item: 852, predicted: 3.026749739953794 +Evaluating user: 122, item: 264, predicted: 3.8025599268544426 +Evaluating user: 51, item: 98, predicted: 3.674316184015763 +Evaluating user: 46, item: 867, predicted: 3.277755855715274 +Evaluating user: 131, item: 106, predicted: 3.745733974534817 +Evaluating user: 222, item: 69, predicted: 3.8982422020285328 +Evaluating user: 13, item: 237, predicted: 3.026749739953794 +Evaluating user: 256, item: 61, predicted: 4.0 +Evaluating user: 11, item: 913, predicted: 4.0 +Evaluating user: 7, item: 960, predicted: 4.0 +Evaluating user: 293, item: 100, predicted: 4.0 +Evaluating user: 87, item: 318, predicted: 3.4224920797908034 +Evaluating user: 58, item: 44, predicted: 3.69180550429593 +Evaluating user: 242, item: 248, predicted: 4.0 +Evaluating user: 83, item: 293, predicted: 3.046583752628486 +Evaluating user: 275, item: 193, predicted: 4.0 +Evaluating user: 122, item: 522, predicted: 3.8025599268544426 +Evaluating user: 211, item: 21, predicted: 3.757882313503026 +Evaluating user: 280, item: 31, predicted: 3.59147811735182 +Evaluating user: 14, item: 181, predicted: 4.0 +Evaluating user: 73, item: 189, predicted: 3.8573390302045087 +Evaluating user: 51, item: 331, predicted: 3.674316184015763 +Evaluating user: 47, item: 123, predicted: 2.6370263485688588 +Evaluating user: 319, item: 24, predicted: 3.5705134643156864 +Evaluating user: 22, item: 266, predicted: 3.796056342470468 +Evaluating user: 171, item: 393, predicted: 3.5537895965250352 +Evaluating user: 75, item: 328, predicted: 3.103056831113985 +Evaluating user: 323, item: 687, predicted: 3.4890009789863967 +Evaluating user: 196, item: 821, predicted: 3.841976529354005 +Evaluating user: 27, item: 761, predicted: 3.0452065709348664 +Evaluating user: 59, item: 6, predicted: 3.3237759115696086 +Evaluating user: 100, item: 292, predicted: 3.1589717525850505 +Evaluating user: 40, item: 55, predicted: 3.6226568169745494 +Evaluating user: 8, item: 276, predicted: 3.6597206732404843 +Evaluating user: 230, item: 296, predicted: 3.687678112955184 +Evaluating user: 270, item: 142, predicted: 3.815880658560767 +Evaluating user: 208, item: 118, predicted: 3.3190518786307877 +Evaluating user: 24, item: 235, predicted: 3.1783635804868453 +Evaluating user: 83, item: 120, predicted: 3.046583752628486 +Evaluating user: 126, item: 575, predicted: 3.6728906294557255 +Evaluating user: 51, item: 275, predicted: 3.674316184015763 +Evaluating user: 261, item: 195, predicted: 3.0086335343036836 +Evaluating user: 64, item: 237, predicted: 3.202547478725553 +Evaluating user: 17, item: 248, predicted: 4.0 +Evaluating user: 206, item: 71, predicted: 4.0 +Evaluating user: 105, item: 478, predicted: 4.0 +Evaluating user: 145, item: 329, predicted: 4.0 +Evaluating user: 50, item: 729, predicted: 3.2002652030078984 +Evaluating user: 207, item: 211, predicted: 2.8360856694213443 +Evaluating user: 34, item: 20, predicted: 3.4865196769640567 +Evaluating user: 34, item: 410, predicted: 3.4865196769640567 +Evaluating user: 82, item: 713, predicted: 4.0 +Evaluating user: 46, item: 166, predicted: 3.277755855715274 +Evaluating user: 45, item: 183, predicted: 3.421716550196023 +Evaluating user: 29, item: 393, predicted: 3.7781592966869013 +Evaluating user: 45, item: 595, predicted: 3.421716550196023 +Evaluating user: 118, item: 693, predicted: 4.0 +Evaluating user: 43, item: 468, predicted: 3.258812818890413 +Evaluating user: 153, item: 848, predicted: 2.545446004248653 +Evaluating user: 145, item: 248, predicted: 4.0 +Evaluating user: 13, item: 142, predicted: 3.026749739953794 +Evaluating user: 219, item: 438, predicted: 3.3801308861014707 +Evaluating user: 92, item: 88, predicted: 3.559073293326748 +Evaluating user: 94, item: 357, predicted: 3.204886329269301 +Evaluating user: 29, item: 52, predicted: 3.7781592966869013 +Evaluating user: 78, item: 234, predicted: 3.507489658917522 +Evaluating user: 51, item: 83, predicted: 3.674316184015763 +Evaluating user: 199, item: 98, predicted: 4.0 +Evaluating user: 196, item: 97, predicted: 3.841976529354005 +Evaluating user: 113, item: 102, predicted: 3.363926080883002 +Evaluating user: 86, item: 126, predicted: 3.2330500503420048 +Evaluating user: 52, item: 248, predicted: 3.449963387639435 +Evaluating user: 198, item: 392, predicted: 3.9179541097285027 +Evaluating user: 77, item: 653, predicted: 2.9199805905495 +Evaluating user: 103, item: 272, predicted: 3.7376057615125893 +Evaluating user: 90, item: 295, predicted: 2.6208033399664283 +Evaluating user: 6, item: 108, predicted: 3.201540374180235 +Evaluating user: 192, item: 210, predicted: 3.8070937172974855 +Evaluating user: 126, item: 764, predicted: 3.6728906294557255 +Evaluating user: 116, item: 225, predicted: 3.579993717900999 +Evaluating user: 193, item: 456, predicted: 3.9343050252304037 +Evaluating user: 101, item: 136, predicted: 3.276372380348297 +Evaluating user: 14, item: 801, predicted: 4.0 +Evaluating user: 52, item: 329, predicted: 3.449963387639435 +Evaluating user: 221, item: 568, predicted: 3.111955165178042 +Evaluating user: 174, item: 862, predicted: 3.9381978923492715 +Evaluating user: 15, item: 426, predicted: 2.677068471129866 +Evaluating user: 52, item: 156, predicted: 3.449963387639435 +Evaluating user: 58, item: 511, predicted: 3.69180550429593 +Evaluating user: 87, item: 749, predicted: 3.4224920797908034 +Evaluating user: 180, item: 425, predicted: 3.48873440226905 +Evaluating user: 51, item: 16, predicted: 3.674316184015763 +Evaluating user: 102, item: 868, predicted: 3.177248470947968 +Evaluating user: 64, item: 161, predicted: 3.202547478725553 +Evaluating user: 305, item: 501, predicted: 3.968071441335683 +Evaluating user: 161, item: 350, predicted: 3.53804643196736 +Evaluating user: 8, item: 231, predicted: 3.6597206732404843 +Evaluating user: 191, item: 252, predicted: 3.32174777948537 +Evaluating user: 154, item: 731, predicted: 3.9251733893292546 +Evaluating user: 149, item: 26, predicted: 2.8386717967257202 +Evaluating user: 180, item: 535, predicted: 3.48873440226905 +Evaluating user: 273, item: 127, predicted: 4.0 +Evaluating user: 194, item: 265, predicted: 2.8425910264255885 +Evaluating user: 26, item: 566, predicted: 3.2282415361513763 +Evaluating user: 1, item: 695, predicted: 3.5921467575298585 +Evaluating user: 144, item: 352, predicted: 2.3453813892347712 +Evaluating user: 300, item: 156, predicted: 3.2646298075084443 +Evaluating user: 111, item: 401, predicted: 3.529305238189742 +Evaluating user: 165, item: 361, predicted: 3.620702831791548 +Evaluating user: 182, item: 496, predicted: 2.7551743925444994 +Evaluating user: 77, item: 391, predicted: 2.9199805905495 +Evaluating user: 100, item: 533, predicted: 3.1589717525850505 +Evaluating user: 116, item: 446, predicted: 3.579993717900999 +Evaluating user: 40, item: 43, predicted: 3.6226568169745494 +Evaluating user: 87, item: 891, predicted: 3.4224920797908034 +Evaluating user: 21, item: 891, predicted: 3.2390720152222396 +Evaluating user: 172, item: 324, predicted: 3.7424992882435157 +Evaluating user: 191, item: 825, predicted: 3.32174777948537 +Evaluating user: 152, item: 232, predicted: 3.6981160858188953 +Evaluating user: 11, item: 527, predicted: 4.0 +Evaluating user: 292, item: 306, predicted: 3.604057187893226 +Evaluating user: 6, item: 418, predicted: 3.201540374180235 +Evaluating user: 306, item: 689, predicted: 3.6375006971459865 +Evaluating user: 27, item: 381, predicted: 3.0452065709348664 +Evaluating user: 15, item: 352, predicted: 2.677068471129866 +Evaluating user: 80, item: 2, predicted: 3.7155653427793984 +Evaluating user: 61, item: 214, predicted: 2.3337088155756582 +Evaluating user: 151, item: 934, predicted: 3.3537029933801845 +Evaluating user: 46, item: 55, predicted: 3.277755855715274 +Evaluating user: 212, item: 183, predicted: 3.7439136034394465 +Evaluating user: 89, item: 279, predicted: 3.162531742858552 +Evaluating user: 77, item: 318, predicted: 2.9199805905495 +Evaluating user: 176, item: 521, predicted: 3.5946708118458477 +Evaluating user: 46, item: 150, predicted: 3.277755855715274 +Evaluating user: 256, item: 464, predicted: 4.0 +Evaluating user: 148, item: 102, predicted: 3.77329810868265 +Evaluating user: 163, item: 230, predicted: 3.5113921734681055 +Evaluating user: 227, item: 714, predicted: 2.7954260752153313 +Evaluating user: 165, item: 312, predicted: 3.620702831791548 +Evaluating user: 198, item: 533, predicted: 3.9179541097285027 +Evaluating user: 120, item: 387, predicted: 3.0003930660134595 +Evaluating user: 30, item: 61, predicted: 3.866272241038406 +Evaluating user: 101, item: 823, predicted: 3.276372380348297 +Evaluating user: 262, item: 110, predicted: 3.6730438217905075 +Evaluating user: 4, item: 45, predicted: 3.768511205275512 +Evaluating user: 83, item: 328, predicted: 3.046583752628486 +Evaluating user: 59, item: 456, predicted: 3.3237759115696086 +Evaluating user: 13, item: 26, predicted: 3.026749739953794 +Evaluating user: 1, item: 686, predicted: 3.5921467575298585 +Evaluating user: 201, item: 223, predicted: 2.999154250328697 +Evaluating user: 87, item: 38, predicted: 3.4224920797908034 +Evaluating user: 126, item: 368, predicted: 3.6728906294557255 +Evaluating user: 102, item: 441, predicted: 3.177248470947968 +Evaluating user: 51, item: 100, predicted: 3.674316184015763 +Evaluating user: 91, item: 585, predicted: 3.3228832822818015 +Evaluating user: 14, item: 35, predicted: 4.0 +Evaluating user: 14, item: 326, predicted: 4.0 +Evaluating user: 83, item: 281, predicted: 3.046583752628486 +Evaluating user: 127, item: 884, predicted: 2.282856560388726 +Evaluating user: 113, item: 186, predicted: 3.363926080883002 +Evaluating user: 30, item: 119, predicted: 3.866272241038406 +Evaluating user: 147, item: 979, predicted: 4.0 +Evaluating user: 94, item: 385, predicted: 3.204886329269301 +Evaluating user: 16, item: 144, predicted: 3.3142726592872442 +Evaluating user: 116, item: 994, predicted: 3.579993717900999 +Evaluating user: 157, item: 583, predicted: 3.537742933934916 +Evaluating user: 84, item: 161, predicted: 3.5967375130272137 +Evaluating user: 51, item: 703, predicted: 3.674316184015763 +Evaluating user: 69, item: 509, predicted: 3.229238415003662 +Evaluating user: 34, item: 251, predicted: 3.4865196769640567 +Evaluating user: 92, item: 328, predicted: 3.559073293326748 +Evaluating user: 77, item: 89, predicted: 2.9199805905495 +Evaluating user: 114, item: 119, predicted: 3.4122120789616113 +Evaluating user: 8, item: 249, predicted: 3.6597206732404843 +Evaluating user: 77, item: 777, predicted: 2.9199805905495 +Evaluating user: 54, item: 711, predicted: 3.557432591116923 +Evaluating user: 194, item: 103, predicted: 2.8425910264255885 +Evaluating user: 191, item: 137, predicted: 3.32174777948537 +Evaluating user: 27, item: 507, predicted: 3.0452065709348664 +Evaluating user: 113, item: 96, predicted: 3.363926080883002 +Evaluating user: 138, item: 15, predicted: 3.6119977386297535 +Evaluating user: 100, item: 438, predicted: 3.1589717525850505 +Evaluating user: 305, item: 76, predicted: 3.968071441335683 +Evaluating user: 100, item: 269, predicted: 3.1589717525850505 +Evaluating user: 121, item: 506, predicted: 2.959321252635845 +Evaluating user: 106, item: 206, predicted: 2.8632698743667886 +Evaluating user: 114, item: 606, predicted: 3.4122120789616113 +Evaluating user: 77, item: 57, predicted: 2.9199805905495 +Evaluating user: 151, item: 339, predicted: 3.3537029933801845 +Evaluating user: 152, item: 181, predicted: 3.6981160858188953 +Evaluating user: 13, item: 376, predicted: 3.026749739953794 +Evaluating user: 203, item: 541, predicted: 2.8907814945499912 +Evaluating user: 250, item: 470, predicted: 3.365176917503812 +Evaluating user: 104, item: 230, predicted: 3.7505707238004615 +Evaluating user: 169, item: 574, predicted: 3.5973082024533136 +Evaluating user: 30, item: 329, predicted: 3.866272241038406 +Evaluating user: 144, item: 653, predicted: 2.3453813892347712 +Evaluating user: 36, item: 849, predicted: 3.460662564244048 +Evaluating user: 165, item: 734, predicted: 3.620702831791548 +Evaluating user: 33, item: 370, predicted: 3.2658014734099217 +Evaluating user: 97, item: 492, predicted: 3.9880103262613322 +Evaluating user: 151, item: 4, predicted: 3.3537029933801845 +Evaluating user: 162, item: 270, predicted: 2.367676495966542 +Evaluating user: 81, item: 473, predicted: 3.837020551056988 +Evaluating user: 97, item: 525, predicted: 3.9880103262613322 +Evaluating user: 68, item: 565, predicted: 1.5682077401379722 +Evaluating user: 6, item: 403, predicted: 3.201540374180235 +Evaluating user: 156, item: 577, predicted: 4.0 +Evaluating user: 276, item: 534, predicted: 3.043588626174305 +Evaluating user: 197, item: 2, predicted: 3.0930699310342837 +Evaluating user: 50, item: 312, predicted: 3.2002652030078984 +Evaluating user: 11, item: 809, predicted: 4.0 +Evaluating user: 163, item: 378, predicted: 3.5113921734681055 +Evaluating user: 7, item: 593, predicted: 4.0 +Evaluating user: 121, item: 227, predicted: 2.959321252635845 +Evaluating user: 279, item: 251, predicted: 2.598834328060508 +Evaluating user: 33, item: 672, predicted: 3.2658014734099217 +Evaluating user: 301, item: 55, predicted: 3.1598995309591844 +Evaluating user: 194, item: 1, predicted: 2.8425910264255885 +Evaluating user: 208, item: 641, predicted: 3.3190518786307877 +Evaluating user: 251, item: 496, predicted: 2.8715919340172507 +Evaluating user: 106, item: 439, predicted: 2.8632698743667886 +Evaluating user: 78, item: 69, predicted: 3.507489658917522 +Evaluating user: 6, item: 608, predicted: 3.201540374180235 +Evaluating user: 61, item: 330, predicted: 2.3337088155756582 +Evaluating user: 33, item: 681, predicted: 3.2658014734099217 +Evaluating user: 23, item: 8, predicted: 3.103225105551166 +Evaluating user: 194, item: 600, predicted: 2.8425910264255885 +Evaluating user: 144, item: 3, predicted: 2.3453813892347712 +Evaluating user: 73, item: 580, predicted: 3.8573390302045087 +Evaluating user: 65, item: 28, predicted: 4.0 +Evaluating user: 94, item: 259, predicted: 3.204886329269301 +Evaluating user: 144, item: 324, predicted: 2.3453813892347712 +Evaluating user: 15, item: 20, predicted: 2.677068471129866 +Evaluating user: 163, item: 707, predicted: 3.5113921734681055 +Evaluating user: 111, item: 598, predicted: 3.529305238189742 +Evaluating user: 34, item: 740, predicted: 3.4865196769640567 +Evaluating user: 19, item: 609, predicted: 3.7492266327587394 +Evaluating user: 191, item: 562, predicted: 3.32174777948537 +Evaluating user: 326, item: 251, predicted: 3.5140770201757823 +Evaluating user: 74, item: 186, predicted: 3.850148908102371 +Evaluating user: 90, item: 307, predicted: 2.6208033399664283 +Evaluating user: 111, item: 610, predicted: 3.529305238189742 +Evaluating user: 144, item: 496, predicted: 2.3453813892347712 +Evaluating user: 14, item: 376, predicted: 4.0 +Evaluating user: 194, item: 254, predicted: 2.8425910264255885 +Evaluating user: 53, item: 349, predicted: 3.1260344951076156 +Evaluating user: 1, item: 370, predicted: 3.5921467575298585 +Evaluating user: 27, item: 418, predicted: 3.0452065709348664 +Evaluating user: 69, item: 672, predicted: 3.229238415003662 +Evaluating user: 54, item: 10, predicted: 3.557432591116923 +Evaluating user: 221, item: 52, predicted: 3.111955165178042 +Evaluating user: 42, item: 859, predicted: 3.6623453377851063 +Evaluating user: 41, item: 112, predicted: 3.397777301643506 +Evaluating user: 15, item: 623, predicted: 2.677068471129866 +Evaluating user: 120, item: 833, predicted: 3.0003930660134595 +Evaluating user: 58, item: 9, predicted: 3.69180550429593 +Evaluating user: 77, item: 399, predicted: 2.9199805905495 +Evaluating user: 274, item: 392, predicted: 3.517732966209685 +Evaluating user: 268, item: 259, predicted: 2.782795582582197 +Evaluating user: 58, item: 635, predicted: 3.69180550429593 +Evaluating user: 3, item: 218, predicted: 3.7110027566608332 +Evaluating user: 156, item: 483, predicted: 4.0 +Evaluating user: 293, item: 328, predicted: 4.0 +Evaluating user: 170, item: 52, predicted: 3.8016816171003276 +Evaluating user: 127, item: 484, predicted: 2.282856560388726 +Evaluating user: 33, item: 177, predicted: 3.2658014734099217 +Evaluating user: 56, item: 483, predicted: 3.6473008741018886 +Evaluating user: 164, item: 98, predicted: 3.494502219932684 +Evaluating user: 111, item: 300, predicted: 3.529305238189742 +Evaluating user: 83, item: 713, predicted: 3.046583752628486 +Evaluating user: 324, item: 69, predicted: 3.7267738173588167 +Evaluating user: 293, item: 729, predicted: 4.0 +Evaluating user: 172, item: 437, predicted: 3.7424992882435157 +Evaluating user: 185, item: 346, predicted: 3.815111299518758 +Evaluating user: 68, item: 461, predicted: 1.5682077401379722 +Evaluating user: 165, item: 186, predicted: 3.620702831791548 +Evaluating user: 58, item: 273, predicted: 3.69180550429593 +Evaluating user: 100, item: 101, predicted: 3.1589717525850505 +Evaluating user: 105, item: 852, predicted: 4.0 +Evaluating user: 106, item: 259, predicted: 2.8632698743667886 +Evaluating user: 54, item: 321, predicted: 3.557432591116923 +Evaluating user: 213, item: 660, predicted: 3.242631255566721 +Evaluating user: 61, item: 272, predicted: 2.3337088155756582 +Evaluating user: 49, item: 590, predicted: 4.0 +Evaluating user: 194, item: 896, predicted: 2.8425910264255885 +Evaluating user: 22, item: 590, predicted: 3.796056342470468 +Evaluating user: 204, item: 13, predicted: 4.0 +Evaluating user: 100, item: 704, predicted: 3.1589717525850505 +Evaluating user: 2, item: 21, predicted: 2.199927657942208 +Evaluating user: 188, item: 76, predicted: 3.9348976928212727 +Evaluating user: 43, item: 572, predicted: 3.258812818890413 +Evaluating user: 76, item: 597, predicted: 4.0 +Evaluating user: 83, item: 785, predicted: 3.046583752628486 +Evaluating user: 139, item: 562, predicted: 4.0 +Evaluating user: 37, item: 456, predicted: 4.0 +Evaluating user: 153, item: 69, predicted: 2.545446004248653 +Evaluating user: 281, item: 443, predicted: 4.0 +Evaluating user: 126, item: 102, predicted: 3.6728906294557255 +Evaluating user: 267, item: 549, predicted: 4.0 +Evaluating user: 36, item: 9, predicted: 3.460662564244048 +Evaluating user: 20, item: 69, predicted: 3.870033225798768 +Evaluating user: 267, item: 180, predicted: 4.0 +Evaluating user: 154, item: 75, predicted: 3.9251733893292546 +Evaluating user: 68, item: 456, predicted: 1.5682077401379722 +Evaluating user: 83, item: 136, predicted: 3.046583752628486 +Evaluating user: 34, item: 445, predicted: 3.4865196769640567 +Evaluating user: 74, item: 251, predicted: 3.850148908102371 +Evaluating user: 15, item: 139, predicted: 2.677068471129866 +Evaluating user: 116, item: 685, predicted: 3.579993717900999 +Evaluating user: 182, item: 472, predicted: 2.7551743925444994 +Evaluating user: 54, item: 458, predicted: 3.557432591116923 +Evaluating user: 165, item: 139, predicted: 3.620702831791548 +Evaluating user: 84, item: 541, predicted: 3.5967375130272137 +Evaluating user: 14, item: 667, predicted: 4.0 +Evaluating user: 270, item: 376, predicted: 3.815880658560767 +Evaluating user: 36, item: 65, predicted: 3.460662564244048 +Evaluating user: 89, item: 98, predicted: 3.162531742858552 +Evaluating user: 95, item: 341, predicted: 3.6536573003892197 +Evaluating user: 47, item: 160, predicted: 2.6370263485688588 +Evaluating user: 163, item: 798, predicted: 3.5113921734681055 +Evaluating user: 34, item: 904, predicted: 3.4865196769640567 +Evaluating user: 185, item: 251, predicted: 3.815111299518758 +Evaluating user: 226, item: 108, predicted: 2.920628293432053 +Evaluating user: 163, item: 525, predicted: 3.5113921734681055 +Evaluating user: 296, item: 76, predicted: 4.0 +Evaluating user: 84, item: 10, predicted: 3.5967375130272137 +Evaluating user: 123, item: 497, predicted: 4.0 +Evaluating user: 65, item: 252, predicted: 4.0 +Evaluating user: 7, item: 428, predicted: 4.0 +Evaluating user: 160, item: 168, predicted: 2.887138985414394 +Evaluating user: 113, item: 30, predicted: 3.363926080883002 +Evaluating user: 196, item: 875, predicted: 3.841976529354005 +Evaluating user: 51, item: 653, predicted: 3.674316184015763 +Evaluating user: 272, item: 180, predicted: 3.1259065000413004 +Evaluating user: 120, item: 100, predicted: 3.0003930660134595 +Evaluating user: 70, item: 814, predicted: 3.16817852056873 +Evaluating user: 168, item: 230, predicted: 3.4992703869640467 +Evaluating user: 68, item: 865, predicted: 1.5682077401379722 +Evaluating user: 75, item: 451, predicted: 3.103056831113985 +Evaluating user: 315, item: 399, predicted: 4.0 +Evaluating user: 23, item: 2, predicted: 3.103225105551166 +Evaluating user: 75, item: 884, predicted: 3.103056831113985 +Evaluating user: 236, item: 978, predicted: 2.7048138774607398 +Evaluating user: 51, item: 208, predicted: 3.674316184015763 +Evaluating user: 291, item: 259, predicted: 3.607150693961662 +Evaluating user: 269, item: 91, predicted: 4.0 +Evaluating user: 116, item: 482, predicted: 3.579993717900999 +Evaluating user: 80, item: 578, predicted: 3.7155653427793984 +Evaluating user: 211, item: 185, predicted: 3.757882313503026 +Evaluating user: 180, item: 785, predicted: 3.48873440226905 +Evaluating user: 155, item: 54, predicted: 3.0897378614606397 +Evaluating user: 317, item: 718, predicted: 1.9971576512570797 +Evaluating user: 3, item: 572, predicted: 3.7110027566608332 +Evaluating user: 11, item: 886, predicted: 4.0 +Evaluating user: 305, item: 189, predicted: 3.968071441335683 +Evaluating user: 235, item: 11, predicted: 4.0 +Evaluating user: 15, item: 318, predicted: 2.677068471129866 +Evaluating user: 28, item: 154, predicted: 4.0 +Evaluating user: 50, item: 110, predicted: 3.2002652030078984 +Evaluating user: 127, item: 868, predicted: 2.282856560388726 +Evaluating user: 83, item: 177, predicted: 3.046583752628486 +Evaluating user: 103, item: 155, predicted: 3.7376057615125893 +Evaluating user: 130, item: 119, predicted: 3.7679260827724548 +Evaluating user: 58, item: 526, predicted: 3.69180550429593 +Evaluating user: 5, item: 252, predicted: 2.799210054666971 +Evaluating user: 97, item: 154, predicted: 3.9880103262613322 +Evaluating user: 174, item: 252, predicted: 3.9381978923492715 +Evaluating user: 318, item: 131, predicted: 3.6790861884597748 +Evaluating user: 46, item: 427, predicted: 3.277755855715274 +Evaluating user: 296, item: 700, predicted: 4.0 +Evaluating user: 323, item: 403, predicted: 3.4890009789863967 +Evaluating user: 221, item: 55, predicted: 3.111955165178042 +Evaluating user: 172, item: 382, predicted: 3.7424992882435157 +Evaluating user: 56, item: 227, predicted: 3.6473008741018886 +Evaluating user: 152, item: 21, predicted: 3.6981160858188953 +Evaluating user: 111, item: 350, predicted: 3.529305238189742 +Evaluating user: 41, item: 470, predicted: 3.397777301643506 +Evaluating user: 45, item: 838, predicted: 3.421716550196023 +Evaluating user: 77, item: 323, predicted: 2.9199805905495 +Evaluating user: 215, item: 930, predicted: 3.606390771414933 +Evaluating user: 76, item: 565, predicted: 4.0 +Evaluating user: 47, item: 656, predicted: 2.6370263485688588 +Evaluating user: 194, item: 721, predicted: 2.8425910264255885 +Evaluating user: 69, item: 202, predicted: 3.229238415003662 +Evaluating user: 153, item: 663, predicted: 2.545446004248653 +Evaluating user: 144, item: 170, predicted: 2.3453813892347712 +Evaluating user: 317, item: 359, predicted: 1.9971576512570797 +Evaluating user: 43, item: 433, predicted: 3.258812818890413 +Evaluating user: 216, item: 119, predicted: 3.6797601754321314 +Evaluating user: 49, item: 181, predicted: 4.0 +Evaluating user: 92, item: 74, predicted: 3.559073293326748 +Evaluating user: 145, item: 796, predicted: 4.0 +Evaluating user: 190, item: 252, predicted: 3.4036554819828844 +Evaluating user: 138, item: 32, predicted: 3.6119977386297535 +Evaluating user: 293, item: 130, predicted: 4.0 +Evaluating user: 6, item: 295, predicted: 3.201540374180235 +Evaluating user: 300, item: 16, predicted: 3.2646298075084443 +Evaluating user: 151, item: 430, predicted: 3.3537029933801845 +Evaluating user: 105, item: 689, predicted: 4.0 +Evaluating user: 279, item: 443, predicted: 2.598834328060508 +Evaluating user: 118, item: 947, predicted: 4.0 +Evaluating user: 313, item: 32, predicted: 2.8708782488494493 +Evaluating user: 20, item: 888, predicted: 3.870033225798768 +Evaluating user: 21, item: 524, predicted: 3.2390720152222396 +Evaluating user: 216, item: 179, predicted: 3.6797601754321314 +Evaluating user: 11, item: 728, predicted: 4.0 +Evaluating user: 120, item: 328, predicted: 3.0003930660134595 +Evaluating user: 15, item: 674, predicted: 2.677068471129866 +Evaluating user: 180, item: 686, predicted: 3.48873440226905 +Evaluating user: 186, item: 566, predicted: 3.802491625382669 +Evaluating user: 153, item: 740, predicted: 2.545446004248653 +Evaluating user: 77, item: 324, predicted: 2.9199805905495 +Evaluating user: 8, item: 779, predicted: 3.6597206732404843 +Evaluating user: 29, item: 723, predicted: 3.7781592966869013 +Evaluating user: 228, item: 223, predicted: 2.451727278342317 +Evaluating user: 177, item: 868, predicted: 2.912989505250503 +Evaluating user: 100, item: 884, predicted: 3.1589717525850505 +Evaluating user: 82, item: 443, predicted: 4.0 +Evaluating user: 211, item: 35, predicted: 3.757882313503026 +Evaluating user: 13, item: 772, predicted: 3.026749739953794 +Evaluating user: 42, item: 300, predicted: 3.6623453377851063 +Evaluating user: 51, item: 366, predicted: 3.674316184015763 +Evaluating user: 156, item: 23, predicted: 4.0 +Evaluating user: 76, item: 251, predicted: 4.0 +Evaluating user: 184, item: 465, predicted: 2.8062516091208507 +Evaluating user: 43, item: 604, predicted: 3.258812818890413 +Evaluating user: 14, item: 575, predicted: 4.0 +Evaluating user: 165, item: 102, predicted: 3.620702831791548 +Evaluating user: 157, item: 784, predicted: 3.537742933934916 +Evaluating user: 328, item: 46, predicted: 2.841632058126514 +Evaluating user: 24, item: 106, predicted: 3.1783635804868453 +Evaluating user: 197, item: 843, predicted: 3.0930699310342837 +Evaluating user: 11, item: 522, predicted: 4.0 +Evaluating user: 163, item: 65, predicted: 3.5113921734681055 +Evaluating user: 105, item: 181, predicted: 4.0 +Evaluating user: 182, item: 436, predicted: 2.7551743925444994 +Evaluating user: 57, item: 433, predicted: 3.631112245165661 +Evaluating user: 86, item: 92, predicted: 3.2330500503420048 +Evaluating user: 155, item: 251, predicted: 3.0897378614606397 +Evaluating user: 283, item: 687, predicted: 3.7936047407527855 +Evaluating user: 64, item: 642, predicted: 3.202547478725553 +Evaluating user: 11, item: 329, predicted: 4.0 +Evaluating user: 100, item: 211, predicted: 3.1589717525850505 +Evaluating user: 198, item: 621, predicted: 3.9179541097285027 +Evaluating user: 53, item: 353, predicted: 3.1260344951076156 +Evaluating user: 142, item: 165, predicted: 3.434401628884774 +Evaluating user: 53, item: 38, predicted: 3.1260344951076156 +Evaluating user: 116, item: 258, predicted: 3.579993717900999 +Evaluating user: 255, item: 578, predicted: 2.84659202977648 +Evaluating user: 100, item: 179, predicted: 3.1589717525850505 +Evaluating user: 61, item: 672, predicted: 2.3337088155756582 +Evaluating user: 235, item: 39, predicted: 4.0 +Evaluating user: 77, item: 358, predicted: 2.9199805905495 +Evaluating user: 115, item: 42, predicted: 3.6137364550990245 +Evaluating user: 111, item: 667, predicted: 3.529305238189742 +Evaluating user: 78, item: 185, predicted: 3.507489658917522 +Evaluating user: 64, item: 53, predicted: 3.202547478725553 +Evaluating user: 19, item: 186, predicted: 3.7492266327587394 +Evaluating user: 34, item: 5, predicted: 3.4865196769640567 +Evaluating user: 171, item: 830, predicted: 3.5537895965250352 +Evaluating user: 102, item: 192, predicted: 3.177248470947968 +Evaluating user: 161, item: 79, predicted: 3.53804643196736 +Evaluating user: 187, item: 689, predicted: 3.258594559438348 +Evaluating user: 168, item: 118, predicted: 3.4992703869640467 +Evaluating user: 64, item: 612, predicted: 3.202547478725553 +Evaluating user: 194, item: 61, predicted: 2.8425910264255885 +Evaluating user: 6, item: 91, predicted: 3.201540374180235 +Evaluating user: 103, item: 275, predicted: 3.7376057615125893 +Evaluating user: 199, item: 391, predicted: 4.0 +Evaluating user: 187, item: 335, predicted: 3.258594559438348 +Evaluating user: 200, item: 420, predicted: 3.3401568081615203 +Evaluating user: 287, item: 556, predicted: 4.0 +Evaluating user: 284, item: 234, predicted: 4.0 +Evaluating user: 102, item: 429, predicted: 3.177248470947968 +Evaluating user: 54, item: 969, predicted: 3.557432591116923 +Evaluating user: 144, item: 186, predicted: 2.3453813892347712 +Evaluating user: 80, item: 73, predicted: 3.7155653427793984 +Evaluating user: 236, item: 3, predicted: 2.7048138774607398 +Evaluating user: 226, item: 270, predicted: 2.920628293432053 +Evaluating user: 41, item: 378, predicted: 3.397777301643506 +Evaluating user: 76, item: 748, predicted: 4.0 +Evaluating user: 30, item: 928, predicted: 3.866272241038406 +Evaluating user: 53, item: 106, predicted: 3.1260344951076156 +Evaluating user: 13, item: 83, predicted: 3.026749739953794 +Evaluating user: 97, item: 112, predicted: 3.9880103262613322 +Evaluating user: 71, item: 300, predicted: 4.0 +Evaluating user: 29, item: 211, predicted: 3.7781592966869013 +Evaluating user: 120, item: 700, predicted: 3.0003930660134595 +Evaluating user: 136, item: 34, predicted: 3.1843331274877817 +Evaluating user: 43, item: 368, predicted: 3.258812818890413 +Evaluating user: 293, item: 276, predicted: 4.0 +Evaluating user: 221, item: 625, predicted: 3.111955165178042 +Evaluating user: 47, item: 53, predicted: 2.6370263485688588 +Evaluating user: 26, item: 483, predicted: 3.2282415361513763 + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8688 | 0.9697 | 7.1136 | 15.4212 + + +======================================== diff --git a/experiment_log_52.txt b/experiment_log_52.txt new file mode 100644 index 000000000..12f10d0cf --- /dev/null +++ b/experiment_log_52.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:46:22.871136 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] + [2.352788 3.4589586 2.0124576 ... 3.3445687 4.366965 2.3530226] + [2.3527882 3.4589636 2.01246 ... 3.3445687 4.366975 2.3530226] + ... + [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] + [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] + [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8716 | 0.9734 | 8.6582 | 8.4601 + + +======================================== diff --git a/experiment_log_53.txt b/experiment_log_53.txt new file mode 100644 index 000000000..9b72becd8 --- /dev/null +++ b/experiment_log_53.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:47:55.043444 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] + [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] + [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] + ... + [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] + [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] + [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8694 | 0.9713 | 8.7112 | 8.2424 + + +======================================== diff --git a/experiment_log_54.txt b/experiment_log_54.txt new file mode 100644 index 000000000..7ee4f16bb --- /dev/null +++ b/experiment_log_54.txt @@ -0,0 +1,80 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:51:46.421403 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.6885347 3.5069544 2.2635775 ... 2.9335885 4.0031514 2.642272 ] + [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] + [2.692095 3.5101788 2.2650366 ... 2.9335885 4.0056243 2.6447263] + ... + [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] + [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] + [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8772 | 0.9770 | 10.5761 | 11.4029 + + +======================================== diff --git a/experiment_log_55.txt b/experiment_log_55.txt new file mode 100644 index 000000000..3b56f8d9c --- /dev/null +++ b/experiment_log_55.txt @@ -0,0 +1,81 @@ + +======================================== +Experiment conducted on: 2024-12-14 16:52:07.592445 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] + [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] + [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] + ... + [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] + [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] + [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8720 | 0.9737 | 7.3035 | 7.6919 + + +======================================== diff --git a/experiment_log_6.txt b/experiment_log_6.txt new file mode 100644 index 000000000..456bb78cf --- /dev/null +++ b/experiment_log_6.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:51:40.161990 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ] + [3.1601486 4.2336493 1.5548409 ... 3.5341663 4.450225 2.6307678] + [3.1750364 4.279773 1.5742695 ... 3.5885444 4.4989467 2.6467028] + ... + [3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ] + [3.1874812 4.282619 1.5728453 ... 3.577863 4.498159 2.6519115] + [3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7954 +Pre-Training Epoch 4/11, Train RMSE: 2.5856 +Pre-Training Epoch 5/11, Train RMSE: 2.1329 +Pre-Training Epoch 6/11, Train RMSE: 1.6514 +Pre-Training Epoch 7/11, Train RMSE: 1.2474 +Pre-Training Epoch 8/11, Train RMSE: 1.0973 +Pre-Training Epoch 9/11, Train RMSE: 1.1644 +Pre-Training Epoch 10/11, Train RMSE: 1.1596 +Pre-Training Epoch 11/11, Train RMSE: 1.0655 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3416 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1783 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0613 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0382 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0316 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0320 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0603 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0933 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0918 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0662 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9508 | 1.0486 | 8.9631 | 20.1651 + + +======================================== diff --git a/experiment_log_7.txt b/experiment_log_7.txt new file mode 100644 index 000000000..73bf19ce6 --- /dev/null +++ b/experiment_log_7.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:54:59.762037 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[2.7173223 3.4101276 2.3123941 ... 3.9221065 4.5609326 2.7274861] + [2.7639 3.4607534 2.3487926 ... 3.9913561 4.6364484 2.7701225] + [2.7225556 3.4184558 2.3182871 ... 3.9310615 4.5703793 2.7325737] + ... + [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474] + [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474] + [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7954 +Pre-Training Epoch 4/11, Train RMSE: 2.6981 +Pre-Training Epoch 5/11, Train RMSE: 2.3428 +Pre-Training Epoch 6/11, Train RMSE: 1.8658 +Pre-Training Epoch 7/11, Train RMSE: 1.4236 +Pre-Training Epoch 8/11, Train RMSE: 1.1457 +Pre-Training Epoch 9/11, Train RMSE: 1.0830 +Pre-Training Epoch 10/11, Train RMSE: 1.0543 +Pre-Training Epoch 11/11, Train RMSE: 1.0848 +Fine-Tuning Epoch 1/10, Train RMSE: 1.1714 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1615 +Fine-Tuning Epoch 3/10, Train RMSE: 1.0814 +Fine-Tuning Epoch 4/10, Train RMSE: 1.0636 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0850 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0683 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0435 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0323 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0484 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0437 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9325 | 1.0244 | 2.2179 | 8.4871 + + +======================================== diff --git a/experiment_log_8.txt b/experiment_log_8.txt new file mode 100644 index 000000000..988730fc6 --- /dev/null +++ b/experiment_log_8.txt @@ -0,0 +1,102 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:55:57.837021 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] + [1.7242785 2.8223462 2.2117891 ... 4.4059434 4.641674 2.7076774] + [1.723621 2.8460186 2.2296033 ... 4.462454 4.6887846 2.743203 ] + ... + [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] + [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] + [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +Pre-Training Epoch 3/11, Train RMSE: 2.7316 +Pre-Training Epoch 4/11, Train RMSE: 2.2343 +Pre-Training Epoch 5/11, Train RMSE: 1.6676 +Pre-Training Epoch 6/11, Train RMSE: 1.2048 +Pre-Training Epoch 7/11, Train RMSE: 1.1020 +Pre-Training Epoch 8/11, Train RMSE: 1.2023 +Pre-Training Epoch 9/11, Train RMSE: 1.1318 +Pre-Training Epoch 10/11, Train RMSE: 1.0433 +Pre-Training Epoch 11/11, Train RMSE: 1.2329 +Fine-Tuning Epoch 1/10, Train RMSE: 1.3594 +Fine-Tuning Epoch 2/10, Train RMSE: 1.0937 +Fine-Tuning Epoch 3/10, Train RMSE: 1.1745 +Fine-Tuning Epoch 4/10, Train RMSE: 1.3269 +Fine-Tuning Epoch 5/10, Train RMSE: 1.2636 +Fine-Tuning Epoch 6/10, Train RMSE: 1.1358 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0532 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0284 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0206 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0165 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.8802 | 0.9862 | 2.0033 | 9.6254 + + +======================================== diff --git a/experiment_log_9.txt b/experiment_log_9.txt new file mode 100644 index 000000000..f55eea9cf --- /dev/null +++ b/experiment_log_9.txt @@ -0,0 +1,113 @@ + +======================================== +Experiment conducted on: 2024-12-14 14:56:31.285239 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: True +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: None +uid_map: None +iid_map: None +max_rating: 5.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 11 +max_epoch_f: 10 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.1 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[1.7862409 3.7904427 2.2713401 ... 3.9308016 4.1415873 2.3545623] + [1.8098971 3.8735347 2.3031673 ... 3.9851453 4.2259607 2.4135017] + [1.8194615 3.9009283 2.3160815 ... 4.028461 4.2595353 2.4159725] + ... + [1.7831967 3.7818234 2.2670496 ... 3.9160302 4.1308055 2.3544216] + [1.7924736 3.8139198 2.2797391 ... 3.9409547 4.1644974 2.374955 ] + [1.7831967 3.7818234 2.2670496 ... 3.9160302 4.1308055 2.3544216]] +_train_r: [[3. 0. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + [0. 4. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +hi +Pre-Training Epoch 1/11, Train RMSE: 2.7954 +hi +Pre-Training Epoch 2/11, Train RMSE: 2.7954 +hi +Pre-Training Epoch 3/11, Train RMSE: 2.7623 +hi +Pre-Training Epoch 4/11, Train RMSE: 2.3646 +hi +Pre-Training Epoch 5/11, Train RMSE: 1.8258 +hi +Pre-Training Epoch 6/11, Train RMSE: 1.3236 +hi +Pre-Training Epoch 7/11, Train RMSE: 1.0857 +hi +Pre-Training Epoch 8/11, Train RMSE: 1.1549 +hi +Pre-Training Epoch 9/11, Train RMSE: 1.1521 +hi +Pre-Training Epoch 10/11, Train RMSE: 1.0596 +hi +Pre-Training Epoch 11/11, Train RMSE: 1.1653 +Fine-Tuning Epoch 1/10, Train RMSE: 1.2124 +Fine-Tuning Epoch 2/10, Train RMSE: 1.1338 +Fine-Tuning Epoch 3/10, Train RMSE: 1.2263 +Fine-Tuning Epoch 4/10, Train RMSE: 1.1828 +Fine-Tuning Epoch 5/10, Train RMSE: 1.0981 +Fine-Tuning Epoch 6/10, Train RMSE: 1.0623 +Fine-Tuning Epoch 7/10, Train RMSE: 1.0499 +Fine-Tuning Epoch 8/10, Train RMSE: 1.0291 +Fine-Tuning Epoch 9/10, Train RMSE: 1.0305 +Fine-Tuning Epoch 10/10, Train RMSE: 1.0553 +helllooooooooooooow + +TEST: +... + | MAE | RMSE | Train (s) | Test (s) +----------------- + ------ + ------ + --------- + -------- +GlobalLocalKernel | 0.9324 | 1.0286 | 2.0803 | 9.6522 + + +======================================== diff --git a/try.py b/try.py index eb400084f..f436a2426 100644 --- a/try.py +++ b/try.py @@ -1,73 +1,83 @@ -# import cornac -# from cornac.eval_methods import RatioSplit -# from cornac.models import MF, PMF, BPR -# from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP - -# # load the built-in MovieLens 100K and split the data based on ratio -# ml_100k = cornac.datasets.movielens.load_feedback() -# rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) - -# # initialize models, here we are comparing: Biased MF, PMF, and BPR -# mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123) -# pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123) -# bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123) -# models = [mf, pmf, bpr] - -# # define metrics to evaluate the models -# metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()] - -# # put it together in an experiment, voilà! -# cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run() - - - - +import os +from datetime import datetime import numpy as np import cornac from cornac.models import GlobalLocalKernel from cornac.eval_methods import RatioSplit from cornac.metrics import MAE, RMSE +# Function to find the next available log file name +def get_next_log_file(base_name="experiment_log", ext=".txt"): + counter = 1 + while os.path.exists(f"{base_name}_{counter}{ext}"): + counter += 1 + return f"{base_name}_{counter}{ext}" + +# Function to log results +def log_results(log_file, test_results, model_instance): + with open(log_file, "a") as f: + f.write("\n" + "=" * 40 + "\n") + f.write(f"Experiment conducted on: {datetime.now()}\n") + f.write("\nHyperparameters:\n") + for attr, value in vars(model_instance).items(): + f.write(f"{attr}: {value}\n") + f.write("\nTest Results:\n") + f.write(test_results) + f.write("\n" + "=" * 40 + "\n") + # Load the MovieLens 100K dataset ml_100k = cornac.datasets.movielens.load_feedback() +# Take only a subset of the data, e.g., first 5000 interactions for quicker tests +ml_100k = ml_100k[:5000] + # Split the data rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) -# Extract user, item, rating tuples -train_users, train_items, train_ratings = rs.train_set.uir_tuple -test_users, test_items, test_ratings = rs.test_set.uir_tuple - -# Get the total number of users and items +# Get the total number of users and items in the subset n_u = rs.total_users n_m = rs.total_items -# Prepare rating matrices in (n_m, n_u) format -train_r = np.zeros((n_m, n_u), dtype='float32') -test_r = np.zeros((n_m, n_u), dtype='float32') +print('Data matrix loaded') +print('Number of users: {}'.format(n_u)) +print('Number of movies: {}'.format(n_m)) +print('Number of training ratings: {}'.format(len(rs.train_set.uir_tuple[2]))) +print('Number of test ratings: {}'.format(len(rs.test_set.uir_tuple[2]))) -# Populate the train and test matrices -train_r[train_items, train_users] = train_ratings -test_r[test_items, test_users] = test_ratings +# Initialize your model +my_model = GlobalLocalKernel( + # Example hyperparameters + n_hid=10, + n_dim=2, + max_epoch_p=30, + max_epoch_f=100, + lr_p=0.1, + lr_f=0.01, + verbose=True +) -train_m = (train_r > 1e-12).astype('float32') -test_m = (test_r > 1e-12).astype('float32') +# Define some basic metrics +metrics = [MAE(), RMSE()] -print('data matrix loaded') -print('num of users: {}'.format(n_u)) -print('num of movies: {}'.format(n_m)) -print('num of training ratings: {}'.format(len(train_ratings))) -print('num of test ratings: {}'.format(len(test_ratings))) +# Redirect Cornac output to capture experiment results +from io import StringIO +import sys -# Initialize your model -my_model = GlobalLocalKernel() +# Get the next available log file name +log_file = get_next_log_file() +sys.stdout = StringIO() # Redirect stdout to capture results -# Provide the model with pre-processed train data -# my_model._train_mat = train_r # Store original train matrix for scoring, if needed by score() -# my_model.train_r_local = train_r # For pre-training phase if needed by the model +# Run the experiment on the smaller subset +cornac.Experiment(eval_method=rs, models=[my_model], metrics=metrics, user_based=True).run() -# Define some basic metrics -metrics = [MAE(), RMSE()] +# Retrieve experiment results +experiment_results = sys.stdout.getvalue() +sys.stdout = sys.__stdout__ # Restore stdout to original state + +# Print the results to the console +print(experiment_results) + +# Log results to file +log_results(log_file, experiment_results, my_model) -# Run the experiment -cornac.Experiment(eval_method=rs, models=[my_model], metrics=metrics , user_based=True).run() +print(f"Experiment results and hyperparameters saved to {log_file}") From dfe70d1119c3e486bb1a1e54392b4b5015ff2e0e Mon Sep 17 00:00:00 2001 From: Cypher2k2 Date: Sat, 14 Dec 2024 17:00:48 +0100 Subject: [PATCH 3/6] delete log_files --- experiment_log.txt | 102 ----- experiment_log_1.txt | 102 ----- experiment_log_10.txt | 102 ----- experiment_log_11.txt | 102 ----- experiment_log_12.txt | 112 ----- experiment_log_13.txt | 102 ----- experiment_log_14.txt | 102 ----- experiment_log_15.txt | 191 -------- experiment_log_16.txt | 402 ----------------- experiment_log_17.txt | 427 ------------------ experiment_log_18.txt | 389 ---------------- experiment_log_19.txt | 211 --------- experiment_log_2.txt | 102 ----- experiment_log_20.txt | 152 ------- experiment_log_21.txt | 90 ---- experiment_log_22.txt | 175 -------- experiment_log_23.txt | 81 ---- experiment_log_24.txt | 94 ---- experiment_log_25.txt | 94 ---- experiment_log_26.txt | 94 ---- experiment_log_27.txt | 94 ---- experiment_log_28.txt | 94 ---- experiment_log_29.txt | 84 ---- experiment_log_3.txt | 102 ----- experiment_log_30.txt | 81 ---- experiment_log_31.txt | 82 ---- experiment_log_32.txt | 84 ---- experiment_log_33.txt | 80 ---- experiment_log_34.txt | 80 ---- experiment_log_35.txt | 80 ---- experiment_log_36.txt | 113 ----- experiment_log_37.txt | 80 ---- experiment_log_38.txt | 82 ---- experiment_log_39.txt | 83 ---- experiment_log_4.txt | 102 ----- experiment_log_40.txt | 83 ---- experiment_log_41.txt | 111 ----- experiment_log_42.txt | 142 ------ experiment_log_43.txt | 80 ---- experiment_log_44.txt | 111 ----- experiment_log_45.txt | 112 ----- experiment_log_46.txt | 80 ---- experiment_log_47.txt | 80 ---- experiment_log_48.txt | 80 ---- experiment_log_49.txt | 80 ---- experiment_log_5.txt | 102 ----- experiment_log_50.txt | 80 ---- experiment_log_51.txt | 999 ------------------------------------------ experiment_log_52.txt | 80 ---- experiment_log_53.txt | 80 ---- experiment_log_54.txt | 80 ---- experiment_log_55.txt | 81 ---- experiment_log_6.txt | 102 ----- experiment_log_7.txt | 102 ----- experiment_log_8.txt | 102 ----- experiment_log_9.txt | 113 ----- 56 files changed, 7427 deletions(-) delete mode 100644 experiment_log.txt delete mode 100644 experiment_log_1.txt delete mode 100644 experiment_log_10.txt delete mode 100644 experiment_log_11.txt delete mode 100644 experiment_log_12.txt delete mode 100644 experiment_log_13.txt delete mode 100644 experiment_log_14.txt delete mode 100644 experiment_log_15.txt delete mode 100644 experiment_log_16.txt delete mode 100644 experiment_log_17.txt delete mode 100644 experiment_log_18.txt delete mode 100644 experiment_log_19.txt delete mode 100644 experiment_log_2.txt delete mode 100644 experiment_log_20.txt delete mode 100644 experiment_log_21.txt delete mode 100644 experiment_log_22.txt delete mode 100644 experiment_log_23.txt delete mode 100644 experiment_log_24.txt delete mode 100644 experiment_log_25.txt delete mode 100644 experiment_log_26.txt delete mode 100644 experiment_log_27.txt delete mode 100644 experiment_log_28.txt delete mode 100644 experiment_log_29.txt delete mode 100644 experiment_log_3.txt delete mode 100644 experiment_log_30.txt delete mode 100644 experiment_log_31.txt delete mode 100644 experiment_log_32.txt delete mode 100644 experiment_log_33.txt delete mode 100644 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experiment_log_6.txt delete mode 100644 experiment_log_7.txt delete mode 100644 experiment_log_8.txt delete mode 100644 experiment_log_9.txt diff --git a/experiment_log.txt b/experiment_log.txt deleted file mode 100644 index 52dd46751..000000000 --- a/experiment_log.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:30:35.698931 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] - [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] - [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] - ... - [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] - [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ] - [5. 3.7179518 4.8609014 ... 2.5424886 3.3154464 5. ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7996 -Pre-Training Epoch 2/11, Train RMSE: 2.7996 -Pre-Training Epoch 3/11, Train RMSE: 2.7980 -Pre-Training Epoch 4/11, Train RMSE: 2.5566 -Pre-Training Epoch 5/11, Train RMSE: 2.1277 -Pre-Training Epoch 6/11, Train RMSE: 1.6573 -Pre-Training Epoch 7/11, Train RMSE: 1.2255 -Pre-Training Epoch 8/11, Train RMSE: 0.9784 -Pre-Training Epoch 9/11, Train RMSE: 0.9623 -Pre-Training Epoch 10/11, Train RMSE: 0.9372 -Pre-Training Epoch 11/11, Train RMSE: 0.8444 -Fine-Tuning Epoch 1/10, Train RMSE: 1.2315 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1229 -Fine-Tuning Epoch 3/10, Train RMSE: 0.9482 -Fine-Tuning Epoch 4/10, Train RMSE: 0.8739 -Fine-Tuning Epoch 5/10, Train RMSE: 0.8417 -Fine-Tuning Epoch 6/10, Train RMSE: 0.8378 -Fine-Tuning Epoch 7/10, Train RMSE: 0.8520 -Fine-Tuning Epoch 8/10, Train RMSE: 0.8780 -Fine-Tuning Epoch 9/10, Train RMSE: 0.9065 -Fine-Tuning Epoch 10/10, Train RMSE: 0.9070 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0235 | 1.0309 | 1.6137 | 0.0870 - - -======================================== diff --git a/experiment_log_1.txt b/experiment_log_1.txt deleted file mode 100644 index 45471017e..000000000 --- a/experiment_log_1.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:37:28.221567 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): ReLU() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] - [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] - [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] - ... - [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] - [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307] - [1.0844609 1.0795795 1.0844307 ... 1.06154 1.0787796 1.0844307]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7996 -Pre-Training Epoch 2/11, Train RMSE: 2.7996 -Pre-Training Epoch 3/11, Train RMSE: 2.7996 -Pre-Training Epoch 4/11, Train RMSE: 2.7996 -Pre-Training Epoch 5/11, Train RMSE: 2.7996 -Pre-Training Epoch 6/11, Train RMSE: 2.7996 -Pre-Training Epoch 7/11, Train RMSE: 2.7996 -Pre-Training Epoch 8/11, Train RMSE: 2.7996 -Pre-Training Epoch 9/11, Train RMSE: 2.7996 -Pre-Training Epoch 10/11, Train RMSE: 2.7996 -Pre-Training Epoch 11/11, Train RMSE: 2.7263 -Fine-Tuning Epoch 1/10, Train RMSE: 2.6358 -Fine-Tuning Epoch 2/10, Train RMSE: 2.5462 -Fine-Tuning Epoch 3/10, Train RMSE: 2.4575 -Fine-Tuning Epoch 4/10, Train RMSE: 2.3699 -Fine-Tuning Epoch 5/10, Train RMSE: 2.2835 -Fine-Tuning Epoch 6/10, Train RMSE: 2.1985 -Fine-Tuning Epoch 7/10, Train RMSE: 2.1150 -Fine-Tuning Epoch 8/10, Train RMSE: 2.0332 -Fine-Tuning Epoch 9/10, Train RMSE: 1.9533 -Fine-Tuning Epoch 10/10, Train RMSE: 1.8753 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.6314 | 1.6441 | 1.5506 | 0.0860 - - -======================================== diff --git a/experiment_log_10.txt b/experiment_log_10.txt deleted file mode 100644 index 770de83fe..000000000 --- a/experiment_log_10.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:57:20.687730 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.990679 3.8487284 1.8037997 ... 3.490999 4.269434 2.5483623] - [3.023685 3.8958206 1.817734 ... 3.5314374 4.3215194 2.573359 ] - [3.0264924 3.9006164 1.8189137 ... 3.53659 4.3273363 2.575559 ] - ... - [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922] - [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922] - [2.9221745 3.7512634 1.7752833 ... 3.4061668 4.161244 2.4968922]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7954 -Pre-Training Epoch 4/11, Train RMSE: 2.5679 -Pre-Training Epoch 5/11, Train RMSE: 2.0900 -Pre-Training Epoch 6/11, Train RMSE: 1.5734 -Pre-Training Epoch 7/11, Train RMSE: 1.1784 -Pre-Training Epoch 8/11, Train RMSE: 1.1459 -Pre-Training Epoch 9/11, Train RMSE: 1.2550 -Pre-Training Epoch 10/11, Train RMSE: 1.2081 -Pre-Training Epoch 11/11, Train RMSE: 1.0682 -Fine-Tuning Epoch 1/10, Train RMSE: 1.5611 -Fine-Tuning Epoch 2/10, Train RMSE: 1.2967 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0950 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0737 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0739 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0530 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0426 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0595 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0967 -Fine-Tuning Epoch 10/10, Train RMSE: 1.1278 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9825 | 1.0726 | 2.1376 | 8.5963 - - -======================================== diff --git a/experiment_log_11.txt b/experiment_log_11.txt deleted file mode 100644 index f879c6751..000000000 --- a/experiment_log_11.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:59:02.433557 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[3.288805 4.575432 1.982252 ... 3.7468586 4.439199 2.8227978] - [3.37377 4.7019467 2.038978 ... 3.840653 4.5570164 2.8820019] - [3.369288 4.691076 2.035483 ... 3.8347905 4.548342 2.8783784] - ... - [3.2805226 4.5634556 1.9769341 ... 3.737924 4.4280543 2.8172026] - [3.2644706 4.541155 1.9667293 ... 3.7208102 4.4069934 2.8064775] - [3.2644706 4.541155 1.9667293 ... 3.7208102 4.4069934 2.8064775]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7954 -Pre-Training Epoch 4/11, Train RMSE: 2.7333 -Pre-Training Epoch 5/11, Train RMSE: 2.3719 -Pre-Training Epoch 6/11, Train RMSE: 1.9474 -Pre-Training Epoch 7/11, Train RMSE: 1.5101 -Pre-Training Epoch 8/11, Train RMSE: 1.1549 -Pre-Training Epoch 9/11, Train RMSE: 1.0785 -Pre-Training Epoch 10/11, Train RMSE: 1.1469 -Pre-Training Epoch 11/11, Train RMSE: 1.1273 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3074 -Fine-Tuning Epoch 2/10, Train RMSE: 1.2829 -Fine-Tuning Epoch 3/10, Train RMSE: 1.1283 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0634 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0511 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0356 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0337 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0641 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0882 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0760 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9387 | 1.0353 | 1.8300 | 8.2491 - - -======================================== diff --git a/experiment_log_12.txt b/experiment_log_12.txt deleted file mode 100644 index c0b34d05e..000000000 --- a/experiment_log_12.txt +++ /dev/null @@ -1,112 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:03:35.945173 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.4343624 3.8545873 2.7392533 ... 3.457223 3.9285028 1.9940946] - [2.4660501 3.8995204 2.7729685 ... 3.4968312 3.9814517 2.0049841] - [2.4496148 3.870842 2.7558439 ... 3.4721377 3.9461834 1.9956937] - ... - [2.4251416 3.8361835 2.7235134 ... 3.446127 3.9152336 1.9902709] - [2.4251416 3.8361835 2.7235134 ... 3.446127 3.9152336 1.9902709] - [2.4389083 3.8435922 2.739875 ... 3.4509299 3.9247258 1.9837745]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7857 -Pre-Training Epoch 4/11, Train RMSE: 2.4588 -Pre-Training Epoch 5/11, Train RMSE: 1.9610 -Pre-Training Epoch 6/11, Train RMSE: 1.4614 -Pre-Training Epoch 7/11, Train RMSE: 1.1359 -Pre-Training Epoch 8/11, Train RMSE: 1.1327 -Pre-Training Epoch 9/11, Train RMSE: 1.1802 -Pre-Training Epoch 10/11, Train RMSE: 1.0934 -Pre-Training Epoch 11/11, Train RMSE: 1.0821 -Pre-Training Epoch 1/11, Train RMSE: 1.2752 -Fine-Tuning Epoch 1/10, Train RMSE: 1.2752 -Pre-Training Epoch 2/11, Train RMSE: 1.1445 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1445 -Pre-Training Epoch 3/11, Train RMSE: 1.0750 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0750 -Pre-Training Epoch 4/11, Train RMSE: 1.0659 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0659 -Pre-Training Epoch 5/11, Train RMSE: 1.1005 -Fine-Tuning Epoch 5/10, Train RMSE: 1.1005 -Pre-Training Epoch 6/11, Train RMSE: 1.0901 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0901 -Pre-Training Epoch 7/11, Train RMSE: 1.0659 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0659 -Pre-Training Epoch 8/11, Train RMSE: 1.0470 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0470 -Pre-Training Epoch 9/11, Train RMSE: 1.0353 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0353 -Pre-Training Epoch 10/11, Train RMSE: 1.0350 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0350 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8653 | 0.9704 | 1.9956 | 8.5123 - - -======================================== diff --git a/experiment_log_13.txt b/experiment_log_13.txt deleted file mode 100644 index 7faaabedb..000000000 --- a/experiment_log_13.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:04:28.514215 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.7538408 3.343685 1.9311216 ... 3.7609093 4.285689 2.8714743] - [1.7473786 3.3813841 1.9722533 ... 3.841642 4.3844547 2.9412758] - [1.773718 3.4069777 1.9794767 ... 3.855084 4.396268 2.9370563] - ... - [1.7524282 3.326015 1.9154572 ... 3.7306907 4.2482576 2.8472283] - [1.7492262 3.3228312 1.9150296 ... 3.7304652 4.246798 2.8469868] - [1.76094 3.3494902 1.9330635 ... 3.7632113 4.2881966 2.871563 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7035 -Pre-Training Epoch 4/11, Train RMSE: 2.1628 -Pre-Training Epoch 5/11, Train RMSE: 1.5384 -Pre-Training Epoch 6/11, Train RMSE: 1.1252 -Pre-Training Epoch 7/11, Train RMSE: 1.1852 -Pre-Training Epoch 8/11, Train RMSE: 1.2196 -Pre-Training Epoch 9/11, Train RMSE: 1.0642 -Pre-Training Epoch 10/11, Train RMSE: 1.1462 -Pre-Training Epoch 11/11, Train RMSE: 1.3379 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3656 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1017 -Fine-Tuning Epoch 3/10, Train RMSE: 1.1460 -Fine-Tuning Epoch 4/10, Train RMSE: 1.3414 -Fine-Tuning Epoch 5/10, Train RMSE: 1.3363 -Fine-Tuning Epoch 6/10, Train RMSE: 1.2144 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0916 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0387 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0310 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0233 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8440 | 0.9594 | 1.8963 | 14.6467 - - -======================================== diff --git a/experiment_log_14.txt b/experiment_log_14.txt deleted file mode 100644 index be6f48d76..000000000 --- a/experiment_log_14.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:05:19.179192 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] - [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] - [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] - ... - [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] - [4.803648 3.2438526 5. ... 2.3001363 3.1935823 4.1010284] - [4.7619658 3.218526 5. ... 2.2828622 3.1661565 4.0636473]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7996 -Pre-Training Epoch 2/11, Train RMSE: 2.7996 -Pre-Training Epoch 3/11, Train RMSE: 2.7613 -Pre-Training Epoch 4/11, Train RMSE: 2.3618 -Pre-Training Epoch 5/11, Train RMSE: 1.8220 -Pre-Training Epoch 6/11, Train RMSE: 1.3232 -Pre-Training Epoch 7/11, Train RMSE: 1.0369 -Pre-Training Epoch 8/11, Train RMSE: 0.9935 -Pre-Training Epoch 9/11, Train RMSE: 0.9563 -Pre-Training Epoch 10/11, Train RMSE: 0.8588 -Pre-Training Epoch 11/11, Train RMSE: 0.9126 -Fine-Tuning Epoch 1/10, Train RMSE: 0.9313 -Fine-Tuning Epoch 2/10, Train RMSE: 1.0484 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0414 -Fine-Tuning Epoch 4/10, Train RMSE: 0.9479 -Fine-Tuning Epoch 5/10, Train RMSE: 0.8739 -Fine-Tuning Epoch 6/10, Train RMSE: 0.8452 -Fine-Tuning Epoch 7/10, Train RMSE: 0.8545 -Fine-Tuning Epoch 8/10, Train RMSE: 0.8723 -Fine-Tuning Epoch 9/10, Train RMSE: 0.8531 -Fine-Tuning Epoch 10/10, Train RMSE: 0.8400 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0731 | 1.0796 | 1.5165 | 0.0800 - - -======================================== diff --git a/experiment_log_15.txt b/experiment_log_15.txt deleted file mode 100644 index 592762861..000000000 --- a/experiment_log_15.txt +++ /dev/null @@ -1,191 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:05:36.896727 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 100 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] - [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] - [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] - ... - [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] - [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ] - [4.7096515 3.4408255 4.86138 ... 1.997175 3.1949785 5. ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/100, Train RMSE: 2.7996 -Pre-Training Epoch 2/100, Train RMSE: 2.7996 -Pre-Training Epoch 3/100, Train RMSE: 2.7725 -Pre-Training Epoch 4/100, Train RMSE: 2.4399 -Pre-Training Epoch 5/100, Train RMSE: 1.9927 -Pre-Training Epoch 6/100, Train RMSE: 1.5273 -Pre-Training Epoch 7/100, Train RMSE: 1.1520 -Pre-Training Epoch 8/100, Train RMSE: 1.0083 -Pre-Training Epoch 9/100, Train RMSE: 1.0070 -Pre-Training Epoch 10/100, Train RMSE: 0.9736 -Pre-Training Epoch 11/100, Train RMSE: 0.8776 -Pre-Training Epoch 12/100, Train RMSE: 0.8808 -Pre-Training Epoch 13/100, Train RMSE: 1.0238 -Pre-Training Epoch 14/100, Train RMSE: 1.1063 -Pre-Training Epoch 15/100, Train RMSE: 1.0707 -Pre-Training Epoch 16/100, Train RMSE: 0.9579 -Pre-Training Epoch 17/100, Train RMSE: 0.8636 -Pre-Training Epoch 18/100, Train RMSE: 0.8317 -Pre-Training Epoch 19/100, Train RMSE: 0.8230 -Pre-Training Epoch 20/100, Train RMSE: 0.8141 -Pre-Training Epoch 21/100, Train RMSE: 0.8301 -Pre-Training Epoch 22/100, Train RMSE: 0.8626 -Pre-Training Epoch 23/100, Train RMSE: 0.8663 -Pre-Training Epoch 24/100, Train RMSE: 0.8418 -Pre-Training Epoch 25/100, Train RMSE: 0.8026 -Pre-Training Epoch 26/100, Train RMSE: 0.7860 -Pre-Training Epoch 27/100, Train RMSE: 0.7824 -Pre-Training Epoch 28/100, Train RMSE: 0.7859 -Pre-Training Epoch 29/100, Train RMSE: 0.7999 -Pre-Training Epoch 30/100, Train RMSE: 0.8251 -Pre-Training Epoch 31/100, Train RMSE: 0.8355 -Pre-Training Epoch 32/100, Train RMSE: 0.8193 -Pre-Training Epoch 33/100, Train RMSE: 0.7915 -Pre-Training Epoch 34/100, Train RMSE: 0.7735 -Pre-Training Epoch 35/100, Train RMSE: 0.7687 -Pre-Training Epoch 36/100, Train RMSE: 0.7700 -Pre-Training Epoch 37/100, Train RMSE: 0.7746 -Pre-Training Epoch 38/100, Train RMSE: 0.7828 -Pre-Training Epoch 39/100, Train RMSE: 0.7982 -Pre-Training Epoch 40/100, Train RMSE: 0.8089 -Pre-Training Epoch 41/100, Train RMSE: 0.8077 -Pre-Training Epoch 42/100, Train RMSE: 0.7934 -Pre-Training Epoch 43/100, Train RMSE: 0.7811 -Pre-Training Epoch 44/100, Train RMSE: 0.7793 -Pre-Training Epoch 45/100, Train RMSE: 0.7792 -Pre-Training Epoch 46/100, Train RMSE: 0.7800 -Pre-Training Epoch 47/100, Train RMSE: 0.7793 -Pre-Training Epoch 48/100, Train RMSE: 0.7723 -Pre-Training Epoch 49/100, Train RMSE: 0.7692 -Pre-Training Epoch 50/100, Train RMSE: 0.7663 -Pre-Training Epoch 51/100, Train RMSE: 0.7675 -Pre-Training Epoch 52/100, Train RMSE: 0.7740 -Pre-Training Epoch 53/100, Train RMSE: 0.7764 -Pre-Training Epoch 54/100, Train RMSE: 0.7738 -Pre-Training Epoch 55/100, Train RMSE: 0.7652 -Pre-Training Epoch 56/100, Train RMSE: 0.7642 -Pre-Training Epoch 57/100, Train RMSE: 0.7638 -Pre-Training Epoch 58/100, Train RMSE: 0.7684 -Pre-Training Epoch 59/100, Train RMSE: 0.7717 -Pre-Training Epoch 60/100, Train RMSE: 0.7722 -Pre-Training Epoch 61/100, Train RMSE: 0.7615 -Pre-Training Epoch 62/100, Train RMSE: 0.7605 -Pre-Training Epoch 63/100, Train RMSE: 0.7669 -Pre-Training Epoch 64/100, Train RMSE: 0.7759 -Pre-Training Epoch 65/100, Train RMSE: 0.7781 -Pre-Training Epoch 66/100, Train RMSE: 0.7721 -Pre-Training Epoch 67/100, Train RMSE: 0.7657 -Pre-Training Epoch 68/100, Train RMSE: 0.7621 -Pre-Training Epoch 69/100, Train RMSE: 0.7682 -Pre-Training Epoch 70/100, Train RMSE: 0.7793 -Pre-Training Epoch 71/100, Train RMSE: 0.7786 -Pre-Training Epoch 72/100, Train RMSE: 0.7687 -Pre-Training Epoch 73/100, Train RMSE: 0.7656 -Pre-Training Epoch 74/100, Train RMSE: 0.7606 -Pre-Training Epoch 75/100, Train RMSE: 0.7592 -Pre-Training Epoch 76/100, Train RMSE: 0.7602 -Pre-Training Epoch 77/100, Train RMSE: 0.7615 -Pre-Training Epoch 78/100, Train RMSE: 0.7732 -Pre-Training Epoch 79/100, Train RMSE: 0.7779 -Pre-Training Epoch 80/100, Train RMSE: 0.7658 -Pre-Training Epoch 81/100, Train RMSE: 0.7532 -Pre-Training Epoch 82/100, Train RMSE: 0.7502 -Pre-Training Epoch 83/100, Train RMSE: 0.7540 -Pre-Training Epoch 84/100, Train RMSE: 0.7636 -Pre-Training Epoch 85/100, Train RMSE: 0.7745 -Pre-Training Epoch 86/100, Train RMSE: 0.7705 -Pre-Training Epoch 87/100, Train RMSE: 0.7647 -Pre-Training Epoch 88/100, Train RMSE: 0.7571 -Pre-Training Epoch 89/100, Train RMSE: 0.7532 -Pre-Training Epoch 90/100, Train RMSE: 0.7540 -Pre-Training Epoch 91/100, Train RMSE: 0.7613 -Pre-Training Epoch 92/100, Train RMSE: 0.7635 -Pre-Training Epoch 93/100, Train RMSE: 0.7573 -Pre-Training Epoch 94/100, Train RMSE: 0.7535 -Pre-Training Epoch 95/100, Train RMSE: 0.7502 -Pre-Training Epoch 96/100, Train RMSE: 0.7514 -Pre-Training Epoch 97/100, Train RMSE: 0.7569 -Pre-Training Epoch 98/100, Train RMSE: 0.7624 -Pre-Training Epoch 99/100, Train RMSE: 0.7577 -Pre-Training Epoch 100/100, Train RMSE: 0.7533 -Fine-Tuning Epoch 1/10, Train RMSE: 1.0218 -Fine-Tuning Epoch 2/10, Train RMSE: 0.8217 -Fine-Tuning Epoch 3/10, Train RMSE: 0.7950 -Fine-Tuning Epoch 4/10, Train RMSE: 0.7787 -Fine-Tuning Epoch 5/10, Train RMSE: 0.7598 -Fine-Tuning Epoch 6/10, Train RMSE: 0.7763 -Fine-Tuning Epoch 7/10, Train RMSE: 0.8094 -Fine-Tuning Epoch 8/10, Train RMSE: 0.8292 -Fine-Tuning Epoch 9/10, Train RMSE: 0.8239 -Fine-Tuning Epoch 10/10, Train RMSE: 0.8055 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0599 | 1.0604 | 2.1100 | 0.0793 - - -======================================== diff --git a/experiment_log_16.txt b/experiment_log_16.txt deleted file mode 100644 index 16a8e92b3..000000000 --- a/experiment_log_16.txt +++ /dev/null @@ -1,402 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:05:59.405414 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 1000 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] - [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] - [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] - ... - [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] - [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ] - [4.8021793 3.597239 4.918361 ... 1.994898 3.4459996 4.925063 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/1000, Train RMSE: 2.7996 -Pre-Training Epoch 2/1000, Train RMSE: 2.7996 -Pre-Training Epoch 3/1000, Train RMSE: 2.7966 -Pre-Training Epoch 4/1000, Train RMSE: 2.5794 -Pre-Training Epoch 5/1000, Train RMSE: 2.1492 -Pre-Training Epoch 6/1000, Train RMSE: 1.6813 -Pre-Training Epoch 7/1000, Train RMSE: 1.2783 -Pre-Training Epoch 8/1000, Train RMSE: 1.0321 -Pre-Training Epoch 9/1000, Train RMSE: 0.9429 -Pre-Training Epoch 10/1000, Train RMSE: 0.8746 -Pre-Training Epoch 11/1000, Train RMSE: 0.8428 -Pre-Training Epoch 12/1000, Train RMSE: 0.9878 -Pre-Training Epoch 13/1000, Train RMSE: 1.0866 -Pre-Training Epoch 14/1000, Train RMSE: 1.0247 -Pre-Training Epoch 15/1000, Train RMSE: 0.9088 -Pre-Training Epoch 16/1000, Train RMSE: 0.8343 -Pre-Training Epoch 17/1000, Train RMSE: 0.8287 -Pre-Training Epoch 18/1000, Train RMSE: 0.8250 -Pre-Training Epoch 19/1000, Train RMSE: 0.8244 -Pre-Training Epoch 20/1000, Train RMSE: 0.8766 -Pre-Training Epoch 21/1000, Train RMSE: 0.9425 -Pre-Training Epoch 22/1000, Train RMSE: 0.9565 -Pre-Training Epoch 23/1000, Train RMSE: 0.8998 -Pre-Training Epoch 24/1000, Train RMSE: 0.8409 -Pre-Training Epoch 25/1000, Train RMSE: 0.8162 -Pre-Training Epoch 26/1000, Train RMSE: 0.8054 -Pre-Training Epoch 27/1000, Train RMSE: 0.7968 -Pre-Training Epoch 28/1000, Train RMSE: 0.8093 -Pre-Training Epoch 29/1000, Train RMSE: 0.8460 -Pre-Training Epoch 30/1000, Train RMSE: 0.8526 -Pre-Training Epoch 31/1000, Train RMSE: 0.8159 -Pre-Training Epoch 32/1000, Train RMSE: 0.7773 -Pre-Training Epoch 33/1000, Train RMSE: 0.7680 -Pre-Training Epoch 34/1000, Train RMSE: 0.7680 -Pre-Training Epoch 35/1000, Train RMSE: 0.7701 -Pre-Training Epoch 36/1000, Train RMSE: 0.7874 -Pre-Training Epoch 37/1000, Train RMSE: 0.8167 -Pre-Training Epoch 38/1000, Train RMSE: 0.8188 -Pre-Training Epoch 39/1000, Train RMSE: 0.7929 -Pre-Training Epoch 40/1000, Train RMSE: 0.7676 -Pre-Training Epoch 41/1000, Train RMSE: 0.7612 -Pre-Training Epoch 42/1000, Train RMSE: 0.7601 -Pre-Training Epoch 43/1000, Train RMSE: 0.7638 -Pre-Training Epoch 44/1000, Train RMSE: 0.7845 -Pre-Training Epoch 45/1000, Train RMSE: 0.8071 -Pre-Training Epoch 46/1000, Train RMSE: 0.8127 -Pre-Training Epoch 47/1000, Train RMSE: 0.7883 -Pre-Training Epoch 48/1000, Train RMSE: 0.7632 -Pre-Training Epoch 49/1000, Train RMSE: 0.7577 -Pre-Training Epoch 50/1000, Train RMSE: 0.7589 -Pre-Training Epoch 51/1000, Train RMSE: 0.7690 -Pre-Training Epoch 52/1000, Train RMSE: 0.8011 -Pre-Training Epoch 53/1000, Train RMSE: 0.8279 -Pre-Training Epoch 54/1000, Train RMSE: 0.8179 -Pre-Training Epoch 55/1000, Train RMSE: 0.7892 -Pre-Training Epoch 56/1000, Train RMSE: 0.7701 -Pre-Training Epoch 57/1000, Train RMSE: 0.7628 -Pre-Training Epoch 58/1000, Train RMSE: 0.7640 -Pre-Training Epoch 59/1000, Train RMSE: 0.7752 -Pre-Training Epoch 60/1000, Train RMSE: 0.7981 -Pre-Training Epoch 61/1000, Train RMSE: 0.8031 -Pre-Training Epoch 62/1000, Train RMSE: 0.7804 -Pre-Training Epoch 63/1000, Train RMSE: 0.7629 -Pre-Training Epoch 64/1000, Train RMSE: 0.7553 -Pre-Training Epoch 65/1000, Train RMSE: 0.7545 -Pre-Training Epoch 66/1000, Train RMSE: 0.7603 -Pre-Training Epoch 67/1000, Train RMSE: 0.7763 -Pre-Training Epoch 68/1000, Train RMSE: 0.7895 -Pre-Training Epoch 69/1000, Train RMSE: 0.7847 -Pre-Training Epoch 70/1000, Train RMSE: 0.7723 -Pre-Training Epoch 71/1000, Train RMSE: 0.7591 -Pre-Training Epoch 72/1000, Train RMSE: 0.7586 -Pre-Training Epoch 73/1000, Train RMSE: 0.7584 -Pre-Training Epoch 74/1000, Train RMSE: 0.7690 -Pre-Training Epoch 75/1000, Train RMSE: 0.7866 -Pre-Training Epoch 76/1000, Train RMSE: 0.8023 -Pre-Training Epoch 77/1000, Train RMSE: 0.7887 -Pre-Training Epoch 78/1000, Train RMSE: 0.7612 -Pre-Training Epoch 79/1000, Train RMSE: 0.7504 -Pre-Training Epoch 80/1000, Train RMSE: 0.7483 -Pre-Training Epoch 81/1000, Train RMSE: 0.7490 -Pre-Training Epoch 82/1000, Train RMSE: 0.7566 -Pre-Training Epoch 83/1000, Train RMSE: 0.7712 -Pre-Training Epoch 84/1000, Train RMSE: 0.7762 -Pre-Training Epoch 85/1000, Train RMSE: 0.7684 -Pre-Training Epoch 86/1000, Train RMSE: 0.7628 -Pre-Training Epoch 87/1000, Train RMSE: 0.7599 -Pre-Training Epoch 88/1000, Train RMSE: 0.7591 -Pre-Training Epoch 89/1000, Train RMSE: 0.7626 -Pre-Training Epoch 90/1000, Train RMSE: 0.7657 -Pre-Training Epoch 91/1000, Train RMSE: 0.7667 -Pre-Training Epoch 92/1000, Train RMSE: 0.7665 -Pre-Training Epoch 93/1000, Train RMSE: 0.7619 -Pre-Training Epoch 94/1000, Train RMSE: 0.7614 -Pre-Training Epoch 95/1000, Train RMSE: 0.7628 -Pre-Training Epoch 96/1000, Train RMSE: 0.7653 -Pre-Training Epoch 97/1000, Train RMSE: 0.7644 -Pre-Training Epoch 98/1000, Train RMSE: 0.7634 -Pre-Training Epoch 99/1000, Train RMSE: 0.7591 -Pre-Training Epoch 100/1000, Train RMSE: 0.7555 -Pre-Training Epoch 101/1000, Train RMSE: 0.7533 -Pre-Training Epoch 102/1000, Train RMSE: 0.7518 -Pre-Training Epoch 103/1000, Train RMSE: 0.7531 -Pre-Training Epoch 104/1000, Train RMSE: 0.7534 -Pre-Training Epoch 105/1000, Train RMSE: 0.7539 -Pre-Training Epoch 106/1000, Train RMSE: 0.7540 -Pre-Training Epoch 107/1000, Train RMSE: 0.7526 -Pre-Training Epoch 108/1000, Train RMSE: 0.7545 -Pre-Training Epoch 109/1000, Train RMSE: 0.7563 -Pre-Training Epoch 110/1000, Train RMSE: 0.7585 -Pre-Training Epoch 111/1000, Train RMSE: 0.7554 -Pre-Training Epoch 112/1000, Train RMSE: 0.7527 -Pre-Training Epoch 113/1000, Train RMSE: 0.7500 -Pre-Training Epoch 114/1000, Train RMSE: 0.7494 -Pre-Training Epoch 115/1000, Train RMSE: 0.7494 -Pre-Training Epoch 116/1000, Train RMSE: 0.7513 -Pre-Training Epoch 117/1000, Train RMSE: 0.7531 -Pre-Training Epoch 118/1000, Train RMSE: 0.7540 -Pre-Training Epoch 119/1000, Train RMSE: 0.7549 -Pre-Training Epoch 120/1000, Train RMSE: 0.7543 -Pre-Training Epoch 121/1000, Train RMSE: 0.7570 -Pre-Training Epoch 122/1000, Train RMSE: 0.7566 -Pre-Training Epoch 123/1000, Train RMSE: 0.7560 -Pre-Training Epoch 124/1000, Train RMSE: 0.7548 -Pre-Training Epoch 125/1000, Train RMSE: 0.7520 -Pre-Training Epoch 126/1000, Train RMSE: 0.7502 -Pre-Training Epoch 127/1000, Train RMSE: 0.7485 -Pre-Training Epoch 128/1000, Train RMSE: 0.7498 -Pre-Training Epoch 129/1000, Train RMSE: 0.7535 -Pre-Training Epoch 130/1000, Train RMSE: 0.7530 -Pre-Training Epoch 131/1000, Train RMSE: 0.7504 -Pre-Training Epoch 132/1000, Train RMSE: 0.7467 -Pre-Training Epoch 133/1000, Train RMSE: 0.7440 -Pre-Training Epoch 134/1000, Train RMSE: 0.7417 -Pre-Training Epoch 135/1000, Train RMSE: 0.7413 -Pre-Training Epoch 136/1000, Train RMSE: 0.7425 -Pre-Training Epoch 137/1000, Train RMSE: 0.7448 -Pre-Training Epoch 138/1000, Train RMSE: 0.7475 -Pre-Training Epoch 139/1000, Train RMSE: 0.7486 -Pre-Training Epoch 140/1000, Train RMSE: 0.7483 -Pre-Training Epoch 141/1000, Train RMSE: 0.7468 -Pre-Training Epoch 142/1000, Train RMSE: 0.7451 -Pre-Training Epoch 143/1000, Train RMSE: 0.7445 -Pre-Training Epoch 144/1000, Train RMSE: 0.7438 -Pre-Training Epoch 145/1000, Train RMSE: 0.7439 -Pre-Training Epoch 146/1000, Train RMSE: 0.7449 -Pre-Training Epoch 147/1000, Train RMSE: 0.7467 -Pre-Training Epoch 148/1000, Train RMSE: 0.7470 -Pre-Training Epoch 149/1000, Train RMSE: 0.7462 -Pre-Training Epoch 150/1000, Train RMSE: 0.7446 -Pre-Training Epoch 151/1000, Train RMSE: 0.7441 -Pre-Training Epoch 152/1000, Train RMSE: 0.7442 -Pre-Training Epoch 153/1000, Train RMSE: 0.7444 -Pre-Training Epoch 154/1000, Train RMSE: 0.7438 -Pre-Training Epoch 155/1000, Train RMSE: 0.7441 -Pre-Training Epoch 156/1000, Train RMSE: 0.7439 -Pre-Training Epoch 157/1000, Train RMSE: 0.7436 -Pre-Training Epoch 158/1000, Train RMSE: 0.7425 -Pre-Training Epoch 159/1000, Train RMSE: 0.7417 -Pre-Training Epoch 160/1000, Train RMSE: 0.7415 -Pre-Training Epoch 161/1000, Train RMSE: 0.7417 -Pre-Training Epoch 162/1000, Train RMSE: 0.7419 -Pre-Training Epoch 163/1000, Train RMSE: 0.7429 -Pre-Training Epoch 164/1000, Train RMSE: 0.7436 -Pre-Training Epoch 165/1000, Train RMSE: 0.7435 -Pre-Training Epoch 166/1000, Train RMSE: 0.7432 -Pre-Training Epoch 167/1000, Train RMSE: 0.7430 -Pre-Training Epoch 168/1000, Train RMSE: 0.7419 -Pre-Training Epoch 169/1000, Train RMSE: 0.7408 -Pre-Training Epoch 170/1000, Train RMSE: 0.7402 -Pre-Training Epoch 171/1000, Train RMSE: 0.7403 -Pre-Training Epoch 172/1000, Train RMSE: 0.7415 -Pre-Training Epoch 173/1000, Train RMSE: 0.7430 -Pre-Training Epoch 174/1000, Train RMSE: 0.7433 -Pre-Training Epoch 175/1000, Train RMSE: 0.7430 -Pre-Training Epoch 176/1000, Train RMSE: 0.7426 -Pre-Training Epoch 177/1000, Train RMSE: 0.7419 -Pre-Training Epoch 178/1000, Train RMSE: 0.7410 -Pre-Training Epoch 179/1000, Train RMSE: 0.7406 -Pre-Training Epoch 180/1000, Train RMSE: 0.7409 -Pre-Training Epoch 181/1000, Train RMSE: 0.7411 -Pre-Training Epoch 182/1000, Train RMSE: 0.7412 -Pre-Training Epoch 183/1000, Train RMSE: 0.7410 -Pre-Training Epoch 184/1000, Train RMSE: 0.7410 -Pre-Training Epoch 185/1000, Train RMSE: 0.7412 -Pre-Training Epoch 186/1000, Train RMSE: 0.7409 -Pre-Training Epoch 187/1000, Train RMSE: 0.7411 -Pre-Training Epoch 188/1000, Train RMSE: 0.7415 -Pre-Training Epoch 189/1000, Train RMSE: 0.7409 -Pre-Training Epoch 190/1000, Train RMSE: 0.7405 -Pre-Training Epoch 191/1000, Train RMSE: 0.7399 -Pre-Training Epoch 192/1000, Train RMSE: 0.7395 -Pre-Training Epoch 193/1000, Train RMSE: 0.7393 -Pre-Training Epoch 194/1000, Train RMSE: 0.7395 -Pre-Training Epoch 195/1000, Train RMSE: 0.7397 -Pre-Training Epoch 196/1000, Train RMSE: 0.7400 -Pre-Training Epoch 197/1000, Train RMSE: 0.7402 -Pre-Training Epoch 198/1000, Train RMSE: 0.7404 -Pre-Training Epoch 199/1000, Train RMSE: 0.7406 -Pre-Training Epoch 200/1000, Train RMSE: 0.7409 -Pre-Training Epoch 201/1000, Train RMSE: 0.7411 -Pre-Training Epoch 202/1000, Train RMSE: 0.7410 -Pre-Training Epoch 203/1000, Train RMSE: 0.7407 -Pre-Training Epoch 204/1000, Train RMSE: 0.7405 -Pre-Training Epoch 205/1000, Train RMSE: 0.7401 -Pre-Training Epoch 206/1000, Train RMSE: 0.7403 -Pre-Training Epoch 207/1000, Train RMSE: 0.7398 -Pre-Training Epoch 208/1000, Train RMSE: 0.7391 -Pre-Training Epoch 209/1000, Train RMSE: 0.7386 -Pre-Training Epoch 210/1000, Train RMSE: 0.7385 -Pre-Training Epoch 211/1000, Train RMSE: 0.7385 -Pre-Training Epoch 212/1000, Train RMSE: 0.7386 -Pre-Training Epoch 213/1000, Train RMSE: 0.7388 -Pre-Training Epoch 214/1000, Train RMSE: 0.7389 -Pre-Training Epoch 215/1000, Train RMSE: 0.7394 -Pre-Training Epoch 216/1000, Train RMSE: 0.7397 -Pre-Training Epoch 217/1000, Train RMSE: 0.7398 -Pre-Training Epoch 218/1000, Train RMSE: 0.7397 -Pre-Training Epoch 219/1000, Train RMSE: 0.7395 -Pre-Training Epoch 220/1000, Train RMSE: 0.7393 -Pre-Training Epoch 221/1000, Train RMSE: 0.7391 -Pre-Training Epoch 222/1000, Train RMSE: 0.7390 -Pre-Training Epoch 223/1000, Train RMSE: 0.7389 -Pre-Training Epoch 224/1000, Train RMSE: 0.7389 -Pre-Training Epoch 225/1000, Train RMSE: 0.7391 -Pre-Training Epoch 226/1000, Train RMSE: 0.7395 -Pre-Training Epoch 227/1000, Train RMSE: 0.7395 -Pre-Training Epoch 228/1000, Train RMSE: 0.7395 -Pre-Training Epoch 229/1000, Train RMSE: 0.7394 -Pre-Training Epoch 230/1000, Train RMSE: 0.7391 -Pre-Training Epoch 231/1000, Train RMSE: 0.7388 -Pre-Training Epoch 232/1000, Train RMSE: 0.7387 -Pre-Training Epoch 233/1000, Train RMSE: 0.7389 -Pre-Training Epoch 234/1000, Train RMSE: 0.7391 -Pre-Training Epoch 235/1000, Train RMSE: 0.7394 -Pre-Training Epoch 236/1000, Train RMSE: 0.7395 -Pre-Training Epoch 237/1000, Train RMSE: 0.7393 -Pre-Training Epoch 238/1000, Train RMSE: 0.7392 -Pre-Training Epoch 239/1000, Train RMSE: 0.7390 -Pre-Training Epoch 240/1000, Train RMSE: 0.7389 -Pre-Training Epoch 241/1000, Train RMSE: 0.7388 -Pre-Training Epoch 242/1000, Train RMSE: 0.7389 -Pre-Training Epoch 243/1000, Train RMSE: 0.7390 -Pre-Training Epoch 244/1000, Train RMSE: 0.7387 -Pre-Training Epoch 245/1000, Train RMSE: 0.7381 -Pre-Training Epoch 246/1000, Train RMSE: 0.7375 -Pre-Training Epoch 247/1000, Train RMSE: 0.7374 -Pre-Training Epoch 248/1000, Train RMSE: 0.7374 -Pre-Training Epoch 249/1000, Train RMSE: 0.7376 -Pre-Training Epoch 250/1000, Train RMSE: 0.7376 -Pre-Training Epoch 251/1000, Train RMSE: 0.7375 -Pre-Training Epoch 252/1000, Train RMSE: 0.7373 -Pre-Training Epoch 253/1000, Train RMSE: 0.7372 -Pre-Training Epoch 254/1000, Train RMSE: 0.7373 -Pre-Training Epoch 255/1000, Train RMSE: 0.7374 -Pre-Training Epoch 256/1000, Train RMSE: 0.7376 -Pre-Training Epoch 257/1000, Train RMSE: 0.7377 -Pre-Training Epoch 258/1000, Train RMSE: 0.7378 -Pre-Training Epoch 259/1000, Train RMSE: 0.7380 -Pre-Training Epoch 260/1000, Train RMSE: 0.7383 -Pre-Training Epoch 261/1000, Train RMSE: 0.7386 -Early stopping pre-training at epoch: 262 -Fine-Tuning Epoch 1/100, Train RMSE: 0.7572 -Fine-Tuning Epoch 2/100, Train RMSE: 0.7414 -Fine-Tuning Epoch 3/100, Train RMSE: 0.7461 -Fine-Tuning Epoch 4/100, Train RMSE: 0.7493 -Fine-Tuning Epoch 5/100, Train RMSE: 0.7477 -Fine-Tuning Epoch 6/100, Train RMSE: 0.7445 -Fine-Tuning Epoch 7/100, Train RMSE: 0.7416 -Fine-Tuning Epoch 8/100, Train RMSE: 0.7393 -Fine-Tuning Epoch 9/100, Train RMSE: 0.7382 -Fine-Tuning Epoch 10/100, Train RMSE: 0.7379 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7379 -Fine-Tuning Epoch 12/100, Train RMSE: 0.7381 -Fine-Tuning Epoch 13/100, Train RMSE: 0.7384 -Fine-Tuning Epoch 14/100, Train RMSE: 0.7385 -Fine-Tuning Epoch 15/100, Train RMSE: 0.7383 -Fine-Tuning Epoch 16/100, Train RMSE: 0.7380 -Fine-Tuning Epoch 17/100, Train RMSE: 0.7375 -Fine-Tuning Epoch 18/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 19/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 20/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 22/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 23/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 24/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 25/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 26/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 27/100, Train RMSE: 0.7366 -Fine-Tuning Epoch 28/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 29/100, Train RMSE: 0.7364 -Fine-Tuning Epoch 30/100, Train RMSE: 0.7364 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 32/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 33/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 34/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 35/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 36/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 37/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 38/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 39/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 40/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 41/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 42/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 43/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 44/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 45/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 54/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 55/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 56/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 57/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 58/100, Train RMSE: 0.7358 -Early stopping fine-tuning at epoch: 59 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0178 | 1.0288 | 3.6884 | 0.0810 - - -======================================== diff --git a/experiment_log_17.txt b/experiment_log_17.txt deleted file mode 100644 index 04d5021e9..000000000 --- a/experiment_log_17.txt +++ /dev/null @@ -1,427 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:07:01.134181 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 1000 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] - [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] - [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] - ... - [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] - [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ] - [4.9492555 3.5544999 4.9286814 ... 1.9946717 3.4936435 4.872684 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/1000, Train RMSE: 2.7996 -Pre-Training Epoch 2/1000, Train RMSE: 2.7996 -Pre-Training Epoch 3/1000, Train RMSE: 2.7988 -Pre-Training Epoch 4/1000, Train RMSE: 2.5971 -Pre-Training Epoch 5/1000, Train RMSE: 2.1581 -Pre-Training Epoch 6/1000, Train RMSE: 1.6675 -Pre-Training Epoch 7/1000, Train RMSE: 1.2457 -Pre-Training Epoch 8/1000, Train RMSE: 1.0472 -Pre-Training Epoch 9/1000, Train RMSE: 1.0313 -Pre-Training Epoch 10/1000, Train RMSE: 0.9660 -Pre-Training Epoch 11/1000, Train RMSE: 0.8494 -Pre-Training Epoch 12/1000, Train RMSE: 0.8960 -Pre-Training Epoch 13/1000, Train RMSE: 1.0640 -Pre-Training Epoch 14/1000, Train RMSE: 1.0938 -Pre-Training Epoch 15/1000, Train RMSE: 1.0054 -Pre-Training Epoch 16/1000, Train RMSE: 0.9029 -Pre-Training Epoch 17/1000, Train RMSE: 0.8539 -Pre-Training Epoch 18/1000, Train RMSE: 0.8467 -Pre-Training Epoch 19/1000, Train RMSE: 0.8399 -Pre-Training Epoch 20/1000, Train RMSE: 0.8346 -Pre-Training Epoch 21/1000, Train RMSE: 0.8454 -Pre-Training Epoch 22/1000, Train RMSE: 0.8776 -Pre-Training Epoch 23/1000, Train RMSE: 0.9034 -Pre-Training Epoch 24/1000, Train RMSE: 0.9067 -Pre-Training Epoch 25/1000, Train RMSE: 0.8730 -Pre-Training Epoch 26/1000, Train RMSE: 0.8277 -Pre-Training Epoch 27/1000, Train RMSE: 0.7908 -Pre-Training Epoch 28/1000, Train RMSE: 0.7770 -Pre-Training Epoch 29/1000, Train RMSE: 0.7745 -Pre-Training Epoch 30/1000, Train RMSE: 0.7743 -Pre-Training Epoch 31/1000, Train RMSE: 0.7785 -Pre-Training Epoch 32/1000, Train RMSE: 0.7881 -Pre-Training Epoch 33/1000, Train RMSE: 0.7980 -Pre-Training Epoch 34/1000, Train RMSE: 0.8041 -Pre-Training Epoch 35/1000, Train RMSE: 0.8028 -Pre-Training Epoch 36/1000, Train RMSE: 0.8002 -Pre-Training Epoch 37/1000, Train RMSE: 0.7936 -Pre-Training Epoch 38/1000, Train RMSE: 0.7882 -Pre-Training Epoch 39/1000, Train RMSE: 0.7870 -Pre-Training Epoch 40/1000, Train RMSE: 0.7887 -Pre-Training Epoch 41/1000, Train RMSE: 0.7900 -Pre-Training Epoch 42/1000, Train RMSE: 0.7905 -Pre-Training Epoch 43/1000, Train RMSE: 0.7927 -Pre-Training Epoch 44/1000, Train RMSE: 0.8055 -Pre-Training Epoch 45/1000, Train RMSE: 0.8079 -Pre-Training Epoch 46/1000, Train RMSE: 0.7972 -Pre-Training Epoch 47/1000, Train RMSE: 0.7876 -Pre-Training Epoch 48/1000, Train RMSE: 0.7749 -Pre-Training Epoch 49/1000, Train RMSE: 0.7685 -Pre-Training Epoch 50/1000, Train RMSE: 0.7685 -Pre-Training Epoch 51/1000, Train RMSE: 0.7775 -Pre-Training Epoch 52/1000, Train RMSE: 0.7869 -Pre-Training Epoch 53/1000, Train RMSE: 0.7977 -Pre-Training Epoch 54/1000, Train RMSE: 0.7936 -Pre-Training Epoch 55/1000, Train RMSE: 0.7838 -Pre-Training Epoch 56/1000, Train RMSE: 0.7742 -Pre-Training Epoch 57/1000, Train RMSE: 0.7701 -Pre-Training Epoch 58/1000, Train RMSE: 0.7733 -Pre-Training Epoch 59/1000, Train RMSE: 0.7819 -Pre-Training Epoch 60/1000, Train RMSE: 0.7911 -Pre-Training Epoch 61/1000, Train RMSE: 0.7839 -Pre-Training Epoch 62/1000, Train RMSE: 0.7722 -Pre-Training Epoch 63/1000, Train RMSE: 0.7674 -Pre-Training Epoch 64/1000, Train RMSE: 0.7672 -Pre-Training Epoch 65/1000, Train RMSE: 0.7705 -Pre-Training Epoch 66/1000, Train RMSE: 0.7851 -Pre-Training Epoch 67/1000, Train RMSE: 0.7952 -Pre-Training Epoch 68/1000, Train RMSE: 0.7874 -Pre-Training Epoch 69/1000, Train RMSE: 0.7653 -Pre-Training Epoch 70/1000, Train RMSE: 0.7589 -Pre-Training Epoch 71/1000, Train RMSE: 0.7582 -Pre-Training Epoch 72/1000, Train RMSE: 0.7610 -Pre-Training Epoch 73/1000, Train RMSE: 0.7680 -Pre-Training Epoch 74/1000, Train RMSE: 0.7772 -Pre-Training Epoch 75/1000, Train RMSE: 0.7783 -Pre-Training Epoch 76/1000, Train RMSE: 0.7690 -Pre-Training Epoch 77/1000, Train RMSE: 0.7610 -Pre-Training Epoch 78/1000, Train RMSE: 0.7541 -Pre-Training Epoch 79/1000, Train RMSE: 0.7529 -Pre-Training Epoch 80/1000, Train RMSE: 0.7573 -Pre-Training Epoch 81/1000, Train RMSE: 0.7661 -Pre-Training Epoch 82/1000, Train RMSE: 0.7652 -Pre-Training Epoch 83/1000, Train RMSE: 0.7599 -Pre-Training Epoch 84/1000, Train RMSE: 0.7534 -Pre-Training Epoch 85/1000, Train RMSE: 0.7505 -Pre-Training Epoch 86/1000, Train RMSE: 0.7535 -Pre-Training Epoch 87/1000, Train RMSE: 0.7639 -Pre-Training Epoch 88/1000, Train RMSE: 0.7813 -Pre-Training Epoch 89/1000, Train RMSE: 0.7856 -Pre-Training Epoch 90/1000, Train RMSE: 0.7672 -Pre-Training Epoch 91/1000, Train RMSE: 0.7542 -Pre-Training Epoch 92/1000, Train RMSE: 0.7497 -Pre-Training Epoch 93/1000, Train RMSE: 0.7493 -Pre-Training Epoch 94/1000, Train RMSE: 0.7536 -Pre-Training Epoch 95/1000, Train RMSE: 0.7656 -Pre-Training Epoch 96/1000, Train RMSE: 0.7737 -Pre-Training Epoch 97/1000, Train RMSE: 0.7726 -Pre-Training Epoch 98/1000, Train RMSE: 0.7582 -Pre-Training Epoch 99/1000, Train RMSE: 0.7521 -Pre-Training Epoch 100/1000, Train RMSE: 0.7516 -Pre-Training Epoch 101/1000, Train RMSE: 0.7558 -Pre-Training Epoch 102/1000, Train RMSE: 0.7650 -Pre-Training Epoch 103/1000, Train RMSE: 0.7747 -Pre-Training Epoch 104/1000, Train RMSE: 0.7705 -Pre-Training Epoch 105/1000, Train RMSE: 0.7580 -Pre-Training Epoch 106/1000, Train RMSE: 0.7505 -Pre-Training Epoch 107/1000, Train RMSE: 0.7481 -Pre-Training Epoch 108/1000, Train RMSE: 0.7475 -Pre-Training Epoch 109/1000, Train RMSE: 0.7513 -Pre-Training Epoch 110/1000, Train RMSE: 0.7629 -Pre-Training Epoch 111/1000, Train RMSE: 0.7728 -Pre-Training Epoch 112/1000, Train RMSE: 0.7668 -Pre-Training Epoch 113/1000, Train RMSE: 0.7589 -Pre-Training Epoch 114/1000, Train RMSE: 0.7528 -Pre-Training Epoch 115/1000, Train RMSE: 0.7507 -Pre-Training Epoch 116/1000, Train RMSE: 0.7512 -Pre-Training Epoch 117/1000, Train RMSE: 0.7517 -Pre-Training Epoch 118/1000, Train RMSE: 0.7527 -Pre-Training Epoch 119/1000, Train RMSE: 0.7559 -Pre-Training Epoch 120/1000, Train RMSE: 0.7543 -Pre-Training Epoch 121/1000, Train RMSE: 0.7523 -Pre-Training Epoch 122/1000, Train RMSE: 0.7499 -Pre-Training Epoch 123/1000, Train RMSE: 0.7477 -Pre-Training Epoch 124/1000, Train RMSE: 0.7468 -Pre-Training Epoch 125/1000, Train RMSE: 0.7473 -Pre-Training Epoch 126/1000, Train RMSE: 0.7483 -Pre-Training Epoch 127/1000, Train RMSE: 0.7513 -Pre-Training Epoch 128/1000, Train RMSE: 0.7556 -Pre-Training Epoch 129/1000, Train RMSE: 0.7553 -Pre-Training Epoch 130/1000, Train RMSE: 0.7533 -Pre-Training Epoch 131/1000, Train RMSE: 0.7490 -Pre-Training Epoch 132/1000, Train RMSE: 0.7466 -Pre-Training Epoch 133/1000, Train RMSE: 0.7455 -Pre-Training Epoch 134/1000, Train RMSE: 0.7465 -Pre-Training Epoch 135/1000, Train RMSE: 0.7484 -Pre-Training Epoch 136/1000, Train RMSE: 0.7517 -Pre-Training Epoch 137/1000, Train RMSE: 0.7526 -Pre-Training Epoch 138/1000, Train RMSE: 0.7503 -Pre-Training Epoch 139/1000, Train RMSE: 0.7469 -Pre-Training Epoch 140/1000, Train RMSE: 0.7450 -Pre-Training Epoch 141/1000, Train RMSE: 0.7454 -Pre-Training Epoch 142/1000, Train RMSE: 0.7479 -Pre-Training Epoch 143/1000, Train RMSE: 0.7482 -Pre-Training Epoch 144/1000, Train RMSE: 0.7468 -Pre-Training Epoch 145/1000, Train RMSE: 0.7449 -Pre-Training Epoch 146/1000, Train RMSE: 0.7446 -Pre-Training Epoch 147/1000, Train RMSE: 0.7463 -Pre-Training Epoch 148/1000, Train RMSE: 0.7499 -Pre-Training Epoch 149/1000, Train RMSE: 0.7514 -Pre-Training Epoch 150/1000, Train RMSE: 0.7499 -Pre-Training Epoch 151/1000, Train RMSE: 0.7468 -Pre-Training Epoch 152/1000, Train RMSE: 0.7447 -Pre-Training Epoch 153/1000, Train RMSE: 0.7438 -Pre-Training Epoch 154/1000, Train RMSE: 0.7435 -Pre-Training Epoch 155/1000, Train RMSE: 0.7442 -Pre-Training Epoch 156/1000, Train RMSE: 0.7466 -Pre-Training Epoch 157/1000, Train RMSE: 0.7469 -Pre-Training Epoch 158/1000, Train RMSE: 0.7462 -Pre-Training Epoch 159/1000, Train RMSE: 0.7479 -Pre-Training Epoch 160/1000, Train RMSE: 0.7467 -Pre-Training Epoch 161/1000, Train RMSE: 0.7449 -Pre-Training Epoch 162/1000, Train RMSE: 0.7434 -Pre-Training Epoch 163/1000, Train RMSE: 0.7424 -Pre-Training Epoch 164/1000, Train RMSE: 0.7422 -Pre-Training Epoch 165/1000, Train RMSE: 0.7426 -Pre-Training Epoch 166/1000, Train RMSE: 0.7443 -Pre-Training Epoch 167/1000, Train RMSE: 0.7444 -Pre-Training Epoch 168/1000, Train RMSE: 0.7441 -Pre-Training Epoch 169/1000, Train RMSE: 0.7438 -Pre-Training Epoch 170/1000, Train RMSE: 0.7424 -Pre-Training Epoch 171/1000, Train RMSE: 0.7412 -Pre-Training Epoch 172/1000, Train RMSE: 0.7403 -Pre-Training Epoch 173/1000, Train RMSE: 0.7403 -Pre-Training Epoch 174/1000, Train RMSE: 0.7412 -Pre-Training Epoch 175/1000, Train RMSE: 0.7424 -Pre-Training Epoch 176/1000, Train RMSE: 0.7432 -Pre-Training Epoch 177/1000, Train RMSE: 0.7429 -Pre-Training Epoch 178/1000, Train RMSE: 0.7421 -Pre-Training Epoch 179/1000, Train RMSE: 0.7419 -Pre-Training Epoch 180/1000, Train RMSE: 0.7419 -Pre-Training Epoch 181/1000, Train RMSE: 0.7426 -Pre-Training Epoch 182/1000, Train RMSE: 0.7428 -Pre-Training Epoch 183/1000, Train RMSE: 0.7425 -Pre-Training Epoch 184/1000, Train RMSE: 0.7425 -Pre-Training Epoch 185/1000, Train RMSE: 0.7430 -Pre-Training Epoch 186/1000, Train RMSE: 0.7433 -Pre-Training Epoch 187/1000, Train RMSE: 0.7427 -Pre-Training Epoch 188/1000, Train RMSE: 0.7415 -Pre-Training Epoch 189/1000, Train RMSE: 0.7407 -Pre-Training Epoch 190/1000, Train RMSE: 0.7401 -Pre-Training Epoch 191/1000, Train RMSE: 0.7400 -Pre-Training Epoch 192/1000, Train RMSE: 0.7403 -Pre-Training Epoch 193/1000, Train RMSE: 0.7407 -Pre-Training Epoch 194/1000, Train RMSE: 0.7406 -Pre-Training Epoch 195/1000, Train RMSE: 0.7402 -Pre-Training Epoch 196/1000, Train RMSE: 0.7396 -Pre-Training Epoch 197/1000, Train RMSE: 0.7394 -Pre-Training Epoch 198/1000, Train RMSE: 0.7396 -Pre-Training Epoch 199/1000, Train RMSE: 0.7395 -Pre-Training Epoch 200/1000, Train RMSE: 0.7396 -Pre-Training Epoch 201/1000, Train RMSE: 0.7396 -Pre-Training Epoch 202/1000, Train RMSE: 0.7392 -Pre-Training Epoch 203/1000, Train RMSE: 0.7390 -Pre-Training Epoch 204/1000, Train RMSE: 0.7387 -Pre-Training Epoch 205/1000, Train RMSE: 0.7385 -Pre-Training Epoch 206/1000, Train RMSE: 0.7388 -Pre-Training Epoch 207/1000, Train RMSE: 0.7392 -Pre-Training Epoch 208/1000, Train RMSE: 0.7401 -Pre-Training Epoch 209/1000, Train RMSE: 0.7403 -Pre-Training Epoch 210/1000, Train RMSE: 0.7407 -Pre-Training Epoch 211/1000, Train RMSE: 0.7408 -Pre-Training Epoch 212/1000, Train RMSE: 0.7400 -Pre-Training Epoch 213/1000, Train RMSE: 0.7392 -Pre-Training Epoch 214/1000, Train RMSE: 0.7387 -Pre-Training Epoch 215/1000, Train RMSE: 0.7385 -Pre-Training Epoch 216/1000, Train RMSE: 0.7386 -Pre-Training Epoch 217/1000, Train RMSE: 0.7390 -Pre-Training Epoch 218/1000, Train RMSE: 0.7400 -Pre-Training Epoch 219/1000, Train RMSE: 0.7408 -Pre-Training Epoch 220/1000, Train RMSE: 0.7411 -Pre-Training Epoch 221/1000, Train RMSE: 0.7409 -Pre-Training Epoch 222/1000, Train RMSE: 0.7397 -Pre-Training Epoch 223/1000, Train RMSE: 0.7388 -Pre-Training Epoch 224/1000, Train RMSE: 0.7382 -Pre-Training Epoch 225/1000, Train RMSE: 0.7379 -Pre-Training Epoch 226/1000, Train RMSE: 0.7377 -Pre-Training Epoch 227/1000, Train RMSE: 0.7377 -Pre-Training Epoch 228/1000, Train RMSE: 0.7378 -Pre-Training Epoch 229/1000, Train RMSE: 0.7382 -Pre-Training Epoch 230/1000, Train RMSE: 0.7385 -Pre-Training Epoch 231/1000, Train RMSE: 0.7387 -Pre-Training Epoch 232/1000, Train RMSE: 0.7387 -Pre-Training Epoch 233/1000, Train RMSE: 0.7384 -Pre-Training Epoch 234/1000, Train RMSE: 0.7382 -Pre-Training Epoch 235/1000, Train RMSE: 0.7379 -Pre-Training Epoch 236/1000, Train RMSE: 0.7377 -Pre-Training Epoch 237/1000, Train RMSE: 0.7378 -Pre-Training Epoch 238/1000, Train RMSE: 0.7378 -Pre-Training Epoch 239/1000, Train RMSE: 0.7380 -Pre-Training Epoch 240/1000, Train RMSE: 0.7383 -Pre-Training Epoch 241/1000, Train RMSE: 0.7381 -Pre-Training Epoch 242/1000, Train RMSE: 0.7380 -Pre-Training Epoch 243/1000, Train RMSE: 0.7377 -Pre-Training Epoch 244/1000, Train RMSE: 0.7377 -Pre-Training Epoch 245/1000, Train RMSE: 0.7378 -Pre-Training Epoch 246/1000, Train RMSE: 0.7381 -Pre-Training Epoch 247/1000, Train RMSE: 0.7381 -Pre-Training Epoch 248/1000, Train RMSE: 0.7381 -Pre-Training Epoch 249/1000, Train RMSE: 0.7381 -Pre-Training Epoch 250/1000, Train RMSE: 0.7378 -Pre-Training Epoch 251/1000, Train RMSE: 0.7376 -Pre-Training Epoch 252/1000, Train RMSE: 0.7375 -Pre-Training Epoch 253/1000, Train RMSE: 0.7377 -Pre-Training Epoch 254/1000, Train RMSE: 0.7377 -Pre-Training Epoch 255/1000, Train RMSE: 0.7378 -Pre-Training Epoch 256/1000, Train RMSE: 0.7378 -Pre-Training Epoch 257/1000, Train RMSE: 0.7375 -Pre-Training Epoch 258/1000, Train RMSE: 0.7371 -Pre-Training Epoch 259/1000, Train RMSE: 0.7368 -Pre-Training Epoch 260/1000, Train RMSE: 0.7367 -Pre-Training Epoch 261/1000, Train RMSE: 0.7369 -Pre-Training Epoch 262/1000, Train RMSE: 0.7371 -Pre-Training Epoch 263/1000, Train RMSE: 0.7372 -Pre-Training Epoch 264/1000, Train RMSE: 0.7371 -Pre-Training Epoch 265/1000, Train RMSE: 0.7371 -Pre-Training Epoch 266/1000, Train RMSE: 0.7371 -Pre-Training Epoch 267/1000, Train RMSE: 0.7372 -Pre-Training Epoch 268/1000, Train RMSE: 0.7373 -Early stopping pre-training at epoch: 269 -Fine-Tuning Epoch 1/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 2/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 3/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 4/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 5/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 6/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 7/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 8/100, Train RMSE: 0.7366 -Fine-Tuning Epoch 9/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 10/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 12/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 13/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 14/100, Train RMSE: 0.7366 -Fine-Tuning Epoch 15/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 16/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 17/100, Train RMSE: 0.7364 -Fine-Tuning Epoch 18/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 19/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 20/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 22/100, Train RMSE: 0.7366 -Fine-Tuning Epoch 23/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 24/100, Train RMSE: 0.7364 -Fine-Tuning Epoch 25/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 26/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 27/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 28/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 29/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 30/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 32/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 33/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 34/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 35/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 36/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 37/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 38/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 39/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 40/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 41/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 42/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 43/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 44/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 45/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 54/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 55/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 56/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 57/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 58/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 59/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 60/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 61/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 62/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 63/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 64/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 65/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 66/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 67/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 68/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 69/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 70/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 71/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 72/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 73/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 74/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 75/100, Train RMSE: 0.7358 -Fine-Tuning Epoch 76/100, Train RMSE: 0.7358 -Early stopping fine-tuning at epoch: 77 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0171 | 1.0282 | 3.9581 | 0.1591 - - -======================================== diff --git a/experiment_log_18.txt b/experiment_log_18.txt deleted file mode 100644 index 1d6583915..000000000 --- a/experiment_log_18.txt +++ /dev/null @@ -1,389 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:07:52.184470 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 1000 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] - [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] - [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] - ... - [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] - [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ] - [4.903876 3.5658333 4.912453 ... 1.9949112 3.4786963 4.928287 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/1000, Train RMSE: 2.7996 -Pre-Training Epoch 2/1000, Train RMSE: 2.7996 -Pre-Training Epoch 3/1000, Train RMSE: 2.7996 -Pre-Training Epoch 4/1000, Train RMSE: 2.7721 -Pre-Training Epoch 5/1000, Train RMSE: 2.4431 -Pre-Training Epoch 6/1000, Train RMSE: 2.0148 -Pre-Training Epoch 7/1000, Train RMSE: 1.5615 -Pre-Training Epoch 8/1000, Train RMSE: 1.1799 -Pre-Training Epoch 9/1000, Train RMSE: 1.0018 -Pre-Training Epoch 10/1000, Train RMSE: 1.0040 -Pre-Training Epoch 11/1000, Train RMSE: 0.9687 -Pre-Training Epoch 12/1000, Train RMSE: 0.8649 -Pre-Training Epoch 13/1000, Train RMSE: 0.8380 -Pre-Training Epoch 14/1000, Train RMSE: 0.9721 -Pre-Training Epoch 15/1000, Train RMSE: 1.0795 -Pre-Training Epoch 16/1000, Train RMSE: 1.1053 -Pre-Training Epoch 17/1000, Train RMSE: 1.0581 -Pre-Training Epoch 18/1000, Train RMSE: 0.9716 -Pre-Training Epoch 19/1000, Train RMSE: 0.8863 -Pre-Training Epoch 20/1000, Train RMSE: 0.8382 -Pre-Training Epoch 21/1000, Train RMSE: 0.8164 -Pre-Training Epoch 22/1000, Train RMSE: 0.8025 -Pre-Training Epoch 23/1000, Train RMSE: 0.7936 -Pre-Training Epoch 24/1000, Train RMSE: 0.7948 -Pre-Training Epoch 25/1000, Train RMSE: 0.8194 -Pre-Training Epoch 26/1000, Train RMSE: 0.8546 -Pre-Training Epoch 27/1000, Train RMSE: 0.8700 -Pre-Training Epoch 28/1000, Train RMSE: 0.8554 -Pre-Training Epoch 29/1000, Train RMSE: 0.8335 -Pre-Training Epoch 30/1000, Train RMSE: 0.8082 -Pre-Training Epoch 31/1000, Train RMSE: 0.7925 -Pre-Training Epoch 32/1000, Train RMSE: 0.7848 -Pre-Training Epoch 33/1000, Train RMSE: 0.7811 -Pre-Training Epoch 34/1000, Train RMSE: 0.7814 -Pre-Training Epoch 35/1000, Train RMSE: 0.7870 -Pre-Training Epoch 36/1000, Train RMSE: 0.7927 -Pre-Training Epoch 37/1000, Train RMSE: 0.7985 -Pre-Training Epoch 38/1000, Train RMSE: 0.7989 -Pre-Training Epoch 39/1000, Train RMSE: 0.7949 -Pre-Training Epoch 40/1000, Train RMSE: 0.7880 -Pre-Training Epoch 41/1000, Train RMSE: 0.7836 -Pre-Training Epoch 42/1000, Train RMSE: 0.7848 -Pre-Training Epoch 43/1000, Train RMSE: 0.7882 -Pre-Training Epoch 44/1000, Train RMSE: 0.7927 -Pre-Training Epoch 45/1000, Train RMSE: 0.7959 -Pre-Training Epoch 46/1000, Train RMSE: 0.7873 -Pre-Training Epoch 47/1000, Train RMSE: 0.7807 -Pre-Training Epoch 48/1000, Train RMSE: 0.7781 -Pre-Training Epoch 49/1000, Train RMSE: 0.7705 -Pre-Training Epoch 50/1000, Train RMSE: 0.7660 -Pre-Training Epoch 51/1000, Train RMSE: 0.7680 -Pre-Training Epoch 52/1000, Train RMSE: 0.7722 -Pre-Training Epoch 53/1000, Train RMSE: 0.7779 -Pre-Training Epoch 54/1000, Train RMSE: 0.7878 -Pre-Training Epoch 55/1000, Train RMSE: 0.7936 -Pre-Training Epoch 56/1000, Train RMSE: 0.7854 -Pre-Training Epoch 57/1000, Train RMSE: 0.7758 -Pre-Training Epoch 58/1000, Train RMSE: 0.7656 -Pre-Training Epoch 59/1000, Train RMSE: 0.7630 -Pre-Training Epoch 60/1000, Train RMSE: 0.7645 -Pre-Training Epoch 61/1000, Train RMSE: 0.7717 -Pre-Training Epoch 62/1000, Train RMSE: 0.7778 -Pre-Training Epoch 63/1000, Train RMSE: 0.7817 -Pre-Training Epoch 64/1000, Train RMSE: 0.7752 -Pre-Training Epoch 65/1000, Train RMSE: 0.7637 -Pre-Training Epoch 66/1000, Train RMSE: 0.7585 -Pre-Training Epoch 67/1000, Train RMSE: 0.7573 -Pre-Training Epoch 68/1000, Train RMSE: 0.7588 -Pre-Training Epoch 69/1000, Train RMSE: 0.7638 -Pre-Training Epoch 70/1000, Train RMSE: 0.7692 -Pre-Training Epoch 71/1000, Train RMSE: 0.7728 -Pre-Training Epoch 72/1000, Train RMSE: 0.7693 -Pre-Training Epoch 73/1000, Train RMSE: 0.7629 -Pre-Training Epoch 74/1000, Train RMSE: 0.7595 -Pre-Training Epoch 75/1000, Train RMSE: 0.7600 -Pre-Training Epoch 76/1000, Train RMSE: 0.7596 -Pre-Training Epoch 77/1000, Train RMSE: 0.7610 -Pre-Training Epoch 78/1000, Train RMSE: 0.7632 -Pre-Training Epoch 79/1000, Train RMSE: 0.7686 -Pre-Training Epoch 80/1000, Train RMSE: 0.7717 -Pre-Training Epoch 81/1000, Train RMSE: 0.7729 -Pre-Training Epoch 82/1000, Train RMSE: 0.7674 -Pre-Training Epoch 83/1000, Train RMSE: 0.7624 -Pre-Training Epoch 84/1000, Train RMSE: 0.7584 -Pre-Training Epoch 85/1000, Train RMSE: 0.7566 -Pre-Training Epoch 86/1000, Train RMSE: 0.7598 -Pre-Training Epoch 87/1000, Train RMSE: 0.7650 -Pre-Training Epoch 88/1000, Train RMSE: 0.7697 -Pre-Training Epoch 89/1000, Train RMSE: 0.7627 -Pre-Training Epoch 90/1000, Train RMSE: 0.7531 -Pre-Training Epoch 91/1000, Train RMSE: 0.7507 -Pre-Training Epoch 92/1000, Train RMSE: 0.7508 -Pre-Training Epoch 93/1000, Train RMSE: 0.7549 -Pre-Training Epoch 94/1000, Train RMSE: 0.7621 -Pre-Training Epoch 95/1000, Train RMSE: 0.7653 -Pre-Training Epoch 96/1000, Train RMSE: 0.7608 -Pre-Training Epoch 97/1000, Train RMSE: 0.7556 -Pre-Training Epoch 98/1000, Train RMSE: 0.7514 -Pre-Training Epoch 99/1000, Train RMSE: 0.7524 -Pre-Training Epoch 100/1000, Train RMSE: 0.7533 -Pre-Training Epoch 101/1000, Train RMSE: 0.7546 -Pre-Training Epoch 102/1000, Train RMSE: 0.7568 -Pre-Training Epoch 103/1000, Train RMSE: 0.7539 -Pre-Training Epoch 104/1000, Train RMSE: 0.7532 -Pre-Training Epoch 105/1000, Train RMSE: 0.7551 -Pre-Training Epoch 106/1000, Train RMSE: 0.7587 -Pre-Training Epoch 107/1000, Train RMSE: 0.7569 -Pre-Training Epoch 108/1000, Train RMSE: 0.7519 -Pre-Training Epoch 109/1000, Train RMSE: 0.7486 -Pre-Training Epoch 110/1000, Train RMSE: 0.7469 -Pre-Training Epoch 111/1000, Train RMSE: 0.7473 -Pre-Training Epoch 112/1000, Train RMSE: 0.7502 -Pre-Training Epoch 113/1000, Train RMSE: 0.7561 -Pre-Training Epoch 114/1000, Train RMSE: 0.7648 -Pre-Training Epoch 115/1000, Train RMSE: 0.7661 -Pre-Training Epoch 116/1000, Train RMSE: 0.7593 -Pre-Training Epoch 117/1000, Train RMSE: 0.7518 -Pre-Training Epoch 118/1000, Train RMSE: 0.7497 -Pre-Training Epoch 119/1000, Train RMSE: 0.7503 -Pre-Training Epoch 120/1000, Train RMSE: 0.7531 -Pre-Training Epoch 121/1000, Train RMSE: 0.7530 -Pre-Training Epoch 122/1000, Train RMSE: 0.7509 -Pre-Training Epoch 123/1000, Train RMSE: 0.7487 -Pre-Training Epoch 124/1000, Train RMSE: 0.7474 -Pre-Training Epoch 125/1000, Train RMSE: 0.7476 -Pre-Training Epoch 126/1000, Train RMSE: 0.7511 -Pre-Training Epoch 127/1000, Train RMSE: 0.7517 -Pre-Training Epoch 128/1000, Train RMSE: 0.7528 -Pre-Training Epoch 129/1000, Train RMSE: 0.7512 -Pre-Training Epoch 130/1000, Train RMSE: 0.7494 -Pre-Training Epoch 131/1000, Train RMSE: 0.7473 -Pre-Training Epoch 132/1000, Train RMSE: 0.7468 -Pre-Training Epoch 133/1000, Train RMSE: 0.7457 -Pre-Training Epoch 134/1000, Train RMSE: 0.7460 -Pre-Training Epoch 135/1000, Train RMSE: 0.7475 -Pre-Training Epoch 136/1000, Train RMSE: 0.7486 -Pre-Training Epoch 137/1000, Train RMSE: 0.7473 -Pre-Training Epoch 138/1000, Train RMSE: 0.7470 -Pre-Training Epoch 139/1000, Train RMSE: 0.7473 -Pre-Training Epoch 140/1000, Train RMSE: 0.7469 -Pre-Training Epoch 141/1000, Train RMSE: 0.7441 -Pre-Training Epoch 142/1000, Train RMSE: 0.7429 -Pre-Training Epoch 143/1000, Train RMSE: 0.7425 -Pre-Training Epoch 144/1000, Train RMSE: 0.7442 -Pre-Training Epoch 145/1000, Train RMSE: 0.7456 -Pre-Training Epoch 146/1000, Train RMSE: 0.7463 -Pre-Training Epoch 147/1000, Train RMSE: 0.7448 -Pre-Training Epoch 148/1000, Train RMSE: 0.7448 -Pre-Training Epoch 149/1000, Train RMSE: 0.7449 -Pre-Training Epoch 150/1000, Train RMSE: 0.7455 -Pre-Training Epoch 151/1000, Train RMSE: 0.7450 -Pre-Training Epoch 152/1000, Train RMSE: 0.7436 -Pre-Training Epoch 153/1000, Train RMSE: 0.7420 -Pre-Training Epoch 154/1000, Train RMSE: 0.7409 -Pre-Training Epoch 155/1000, Train RMSE: 0.7405 -Pre-Training Epoch 156/1000, Train RMSE: 0.7413 -Pre-Training Epoch 157/1000, Train RMSE: 0.7432 -Pre-Training Epoch 158/1000, Train RMSE: 0.7457 -Pre-Training Epoch 159/1000, Train RMSE: 0.7468 -Pre-Training Epoch 160/1000, Train RMSE: 0.7468 -Pre-Training Epoch 161/1000, Train RMSE: 0.7450 -Pre-Training Epoch 162/1000, Train RMSE: 0.7426 -Pre-Training Epoch 163/1000, Train RMSE: 0.7407 -Pre-Training Epoch 164/1000, Train RMSE: 0.7405 -Pre-Training Epoch 165/1000, Train RMSE: 0.7416 -Pre-Training Epoch 166/1000, Train RMSE: 0.7444 -Pre-Training Epoch 167/1000, Train RMSE: 0.7473 -Pre-Training Epoch 168/1000, Train RMSE: 0.7472 -Pre-Training Epoch 169/1000, Train RMSE: 0.7456 -Pre-Training Epoch 170/1000, Train RMSE: 0.7431 -Pre-Training Epoch 171/1000, Train RMSE: 0.7416 -Pre-Training Epoch 172/1000, Train RMSE: 0.7410 -Pre-Training Epoch 173/1000, Train RMSE: 0.7411 -Pre-Training Epoch 174/1000, Train RMSE: 0.7413 -Pre-Training Epoch 175/1000, Train RMSE: 0.7428 -Pre-Training Epoch 176/1000, Train RMSE: 0.7442 -Pre-Training Epoch 177/1000, Train RMSE: 0.7458 -Pre-Training Epoch 178/1000, Train RMSE: 0.7459 -Pre-Training Epoch 179/1000, Train RMSE: 0.7440 -Pre-Training Epoch 180/1000, Train RMSE: 0.7423 -Pre-Training Epoch 181/1000, Train RMSE: 0.7410 -Pre-Training Epoch 182/1000, Train RMSE: 0.7401 -Pre-Training Epoch 183/1000, Train RMSE: 0.7397 -Pre-Training Epoch 184/1000, Train RMSE: 0.7398 -Pre-Training Epoch 185/1000, Train RMSE: 0.7404 -Pre-Training Epoch 186/1000, Train RMSE: 0.7417 -Pre-Training Epoch 187/1000, Train RMSE: 0.7428 -Pre-Training Epoch 188/1000, Train RMSE: 0.7436 -Pre-Training Epoch 189/1000, Train RMSE: 0.7438 -Pre-Training Epoch 190/1000, Train RMSE: 0.7428 -Pre-Training Epoch 191/1000, Train RMSE: 0.7409 -Pre-Training Epoch 192/1000, Train RMSE: 0.7397 -Pre-Training Epoch 193/1000, Train RMSE: 0.7391 -Pre-Training Epoch 194/1000, Train RMSE: 0.7389 -Pre-Training Epoch 195/1000, Train RMSE: 0.7390 -Pre-Training Epoch 196/1000, Train RMSE: 0.7395 -Pre-Training Epoch 197/1000, Train RMSE: 0.7402 -Pre-Training Epoch 198/1000, Train RMSE: 0.7407 -Pre-Training Epoch 199/1000, Train RMSE: 0.7404 -Pre-Training Epoch 200/1000, Train RMSE: 0.7401 -Pre-Training Epoch 201/1000, Train RMSE: 0.7398 -Pre-Training Epoch 202/1000, Train RMSE: 0.7396 -Pre-Training Epoch 203/1000, Train RMSE: 0.7391 -Pre-Training Epoch 204/1000, Train RMSE: 0.7388 -Pre-Training Epoch 205/1000, Train RMSE: 0.7387 -Pre-Training Epoch 206/1000, Train RMSE: 0.7385 -Pre-Training Epoch 207/1000, Train RMSE: 0.7386 -Pre-Training Epoch 208/1000, Train RMSE: 0.7388 -Pre-Training Epoch 209/1000, Train RMSE: 0.7391 -Pre-Training Epoch 210/1000, Train RMSE: 0.7390 -Pre-Training Epoch 211/1000, Train RMSE: 0.7389 -Pre-Training Epoch 212/1000, Train RMSE: 0.7387 -Pre-Training Epoch 213/1000, Train RMSE: 0.7389 -Pre-Training Epoch 214/1000, Train RMSE: 0.7391 -Pre-Training Epoch 215/1000, Train RMSE: 0.7391 -Pre-Training Epoch 216/1000, Train RMSE: 0.7387 -Pre-Training Epoch 217/1000, Train RMSE: 0.7383 -Pre-Training Epoch 218/1000, Train RMSE: 0.7379 -Pre-Training Epoch 219/1000, Train RMSE: 0.7380 -Pre-Training Epoch 220/1000, Train RMSE: 0.7380 -Pre-Training Epoch 221/1000, Train RMSE: 0.7381 -Pre-Training Epoch 222/1000, Train RMSE: 0.7383 -Pre-Training Epoch 223/1000, Train RMSE: 0.7387 -Pre-Training Epoch 224/1000, Train RMSE: 0.7387 -Pre-Training Epoch 225/1000, Train RMSE: 0.7386 -Pre-Training Epoch 226/1000, Train RMSE: 0.7383 -Pre-Training Epoch 227/1000, Train RMSE: 0.7381 -Pre-Training Epoch 228/1000, Train RMSE: 0.7381 -Pre-Training Epoch 229/1000, Train RMSE: 0.7379 -Pre-Training Epoch 230/1000, Train RMSE: 0.7376 -Pre-Training Epoch 231/1000, Train RMSE: 0.7376 -Pre-Training Epoch 232/1000, Train RMSE: 0.7376 -Pre-Training Epoch 233/1000, Train RMSE: 0.7377 -Pre-Training Epoch 234/1000, Train RMSE: 0.7377 -Pre-Training Epoch 235/1000, Train RMSE: 0.7376 -Pre-Training Epoch 236/1000, Train RMSE: 0.7376 -Pre-Training Epoch 237/1000, Train RMSE: 0.7375 -Pre-Training Epoch 238/1000, Train RMSE: 0.7374 -Pre-Training Epoch 239/1000, Train RMSE: 0.7373 -Pre-Training Epoch 240/1000, Train RMSE: 0.7373 -Pre-Training Epoch 241/1000, Train RMSE: 0.7373 -Pre-Training Epoch 242/1000, Train RMSE: 0.7373 -Pre-Training Epoch 243/1000, Train RMSE: 0.7373 -Pre-Training Epoch 244/1000, Train RMSE: 0.7373 -Pre-Training Epoch 245/1000, Train RMSE: 0.7374 -Pre-Training Epoch 246/1000, Train RMSE: 0.7375 -Pre-Training Epoch 247/1000, Train RMSE: 0.7377 -Early stopping pre-training at epoch: 248 -Fine-Tuning Epoch 1/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 2/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 3/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 4/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 5/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 6/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 7/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 8/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 9/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 10/100, Train RMSE: 0.7369 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7371 -Fine-Tuning Epoch 12/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 13/100, Train RMSE: 0.7370 -Fine-Tuning Epoch 14/100, Train RMSE: 0.7368 -Fine-Tuning Epoch 15/100, Train RMSE: 0.7367 -Fine-Tuning Epoch 16/100, Train RMSE: 0.7365 -Fine-Tuning Epoch 17/100, Train RMSE: 0.7364 -Fine-Tuning Epoch 18/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 19/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 20/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 22/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 23/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 24/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 25/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 26/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 27/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 28/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 29/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 30/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7363 -Fine-Tuning Epoch 32/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 33/100, Train RMSE: 0.7362 -Fine-Tuning Epoch 34/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 35/100, Train RMSE: 0.7361 -Fine-Tuning Epoch 36/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 37/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 38/100, Train RMSE: 0.7360 -Fine-Tuning Epoch 39/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 40/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 41/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 42/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 43/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 44/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 45/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 46/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 47/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 48/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 49/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 50/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 51/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 52/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 53/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 54/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 55/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 56/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 57/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 58/100, Train RMSE: 0.7359 -Fine-Tuning Epoch 59/100, Train RMSE: 0.7359 -Early stopping fine-tuning at epoch: 60 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0186 | 1.0297 | 4.1892 | 0.0900 - - -======================================== diff --git a/experiment_log_19.txt b/experiment_log_19.txt deleted file mode 100644 index 43d867c72..000000000 --- a/experiment_log_19.txt +++ /dev/null @@ -1,211 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:09:29.955636 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] - [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] - [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] - ... - [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] - [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925] - [4.636513 3.426408 4.624447 ... 2.0322764 3.3357723 4.4006925]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7996 -Pre-Training Epoch 2/30, Train RMSE: 2.7996 -Pre-Training Epoch 3/30, Train RMSE: 2.7966 -Pre-Training Epoch 4/30, Train RMSE: 2.6030 -Pre-Training Epoch 5/30, Train RMSE: 2.1884 -Pre-Training Epoch 6/30, Train RMSE: 1.7387 -Pre-Training Epoch 7/30, Train RMSE: 1.3146 -Pre-Training Epoch 8/30, Train RMSE: 1.0291 -Pre-Training Epoch 9/30, Train RMSE: 0.9596 -Pre-Training Epoch 10/30, Train RMSE: 0.9218 -Pre-Training Epoch 11/30, Train RMSE: 0.8358 -Pre-Training Epoch 12/30, Train RMSE: 0.8988 -Pre-Training Epoch 13/30, Train RMSE: 1.0516 -Pre-Training Epoch 14/30, Train RMSE: 1.0739 -Pre-Training Epoch 15/30, Train RMSE: 0.9671 -Pre-Training Epoch 16/30, Train RMSE: 0.8610 -Pre-Training Epoch 17/30, Train RMSE: 0.8380 -Pre-Training Epoch 18/30, Train RMSE: 0.8302 -Pre-Training Epoch 19/30, Train RMSE: 0.8229 -Pre-Training Epoch 20/30, Train RMSE: 0.8402 -Pre-Training Epoch 21/30, Train RMSE: 0.8836 -Pre-Training Epoch 22/30, Train RMSE: 0.9046 -Pre-Training Epoch 23/30, Train RMSE: 0.8725 -Pre-Training Epoch 24/30, Train RMSE: 0.8251 -Pre-Training Epoch 25/30, Train RMSE: 0.7996 -Pre-Training Epoch 26/30, Train RMSE: 0.7898 -Pre-Training Epoch 27/30, Train RMSE: 0.7862 -Pre-Training Epoch 28/30, Train RMSE: 0.7892 -Pre-Training Epoch 29/30, Train RMSE: 0.7988 -Pre-Training Epoch 30/30, Train RMSE: 0.8104 -Fine-Tuning Epoch 1/100, Train RMSE: 0.7923 -Fine-Tuning Epoch 2/100, Train RMSE: 0.7908 -Fine-Tuning Epoch 3/100, Train RMSE: 0.7895 -Fine-Tuning Epoch 4/100, Train RMSE: 0.7883 -Fine-Tuning Epoch 5/100, Train RMSE: 0.7840 -Fine-Tuning Epoch 6/100, Train RMSE: 0.7809 -Fine-Tuning Epoch 7/100, Train RMSE: 0.7775 -Fine-Tuning Epoch 8/100, Train RMSE: 0.7731 -Fine-Tuning Epoch 9/100, Train RMSE: 0.7704 -Fine-Tuning Epoch 10/100, Train RMSE: 0.7681 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7686 -Fine-Tuning Epoch 12/100, Train RMSE: 0.7688 -Fine-Tuning Epoch 13/100, Train RMSE: 0.7694 -Fine-Tuning Epoch 14/100, Train RMSE: 0.7713 -Fine-Tuning Epoch 15/100, Train RMSE: 0.7728 -Fine-Tuning Epoch 16/100, Train RMSE: 0.7711 -Fine-Tuning Epoch 17/100, Train RMSE: 0.7694 -Fine-Tuning Epoch 18/100, Train RMSE: 0.7686 -Fine-Tuning Epoch 19/100, Train RMSE: 0.7668 -Fine-Tuning Epoch 20/100, Train RMSE: 0.7659 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7662 -Fine-Tuning Epoch 22/100, Train RMSE: 0.7663 -Fine-Tuning Epoch 23/100, Train RMSE: 0.7675 -Fine-Tuning Epoch 24/100, Train RMSE: 0.7695 -Fine-Tuning Epoch 25/100, Train RMSE: 0.7704 -Fine-Tuning Epoch 26/100, Train RMSE: 0.7688 -Fine-Tuning Epoch 27/100, Train RMSE: 0.7681 -Fine-Tuning Epoch 28/100, Train RMSE: 0.7678 -Fine-Tuning Epoch 29/100, Train RMSE: 0.7659 -Fine-Tuning Epoch 30/100, Train RMSE: 0.7643 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7632 -Fine-Tuning Epoch 32/100, Train RMSE: 0.7637 -Fine-Tuning Epoch 33/100, Train RMSE: 0.7642 -Fine-Tuning Epoch 34/100, Train RMSE: 0.7664 -Fine-Tuning Epoch 35/100, Train RMSE: 0.7684 -Fine-Tuning Epoch 36/100, Train RMSE: 0.7681 -Fine-Tuning Epoch 37/100, Train RMSE: 0.7660 -Fine-Tuning Epoch 38/100, Train RMSE: 0.7648 -Fine-Tuning Epoch 39/100, Train RMSE: 0.7651 -Fine-Tuning Epoch 40/100, Train RMSE: 0.7644 -Fine-Tuning Epoch 41/100, Train RMSE: 0.7643 -Fine-Tuning Epoch 42/100, Train RMSE: 0.7642 -Fine-Tuning Epoch 43/100, Train RMSE: 0.7624 -Fine-Tuning Epoch 44/100, Train RMSE: 0.7620 -Fine-Tuning Epoch 45/100, Train RMSE: 0.7621 -Fine-Tuning Epoch 46/100, Train RMSE: 0.7640 -Fine-Tuning Epoch 47/100, Train RMSE: 0.7646 -Fine-Tuning Epoch 48/100, Train RMSE: 0.7643 -Fine-Tuning Epoch 49/100, Train RMSE: 0.7648 -Fine-Tuning Epoch 50/100, Train RMSE: 0.7640 -Fine-Tuning Epoch 51/100, Train RMSE: 0.7626 -Fine-Tuning Epoch 52/100, Train RMSE: 0.7606 -Fine-Tuning Epoch 53/100, Train RMSE: 0.7594 -Fine-Tuning Epoch 54/100, Train RMSE: 0.7587 -Fine-Tuning Epoch 55/100, Train RMSE: 0.7589 -Fine-Tuning Epoch 56/100, Train RMSE: 0.7602 -Fine-Tuning Epoch 57/100, Train RMSE: 0.7635 -Fine-Tuning Epoch 58/100, Train RMSE: 0.7683 -Fine-Tuning Epoch 59/100, Train RMSE: 0.7733 -Fine-Tuning Epoch 60/100, Train RMSE: 0.7743 -Fine-Tuning Epoch 61/100, Train RMSE: 0.7730 -Fine-Tuning Epoch 62/100, Train RMSE: 0.7687 -Fine-Tuning Epoch 63/100, Train RMSE: 0.7635 -Fine-Tuning Epoch 64/100, Train RMSE: 0.7575 -Fine-Tuning Epoch 65/100, Train RMSE: 0.7535 -Fine-Tuning Epoch 66/100, Train RMSE: 0.7515 -Fine-Tuning Epoch 67/100, Train RMSE: 0.7517 -Fine-Tuning Epoch 68/100, Train RMSE: 0.7523 -Fine-Tuning Epoch 69/100, Train RMSE: 0.7532 -Fine-Tuning Epoch 70/100, Train RMSE: 0.7555 -Fine-Tuning Epoch 71/100, Train RMSE: 0.7568 -Fine-Tuning Epoch 72/100, Train RMSE: 0.7575 -Fine-Tuning Epoch 73/100, Train RMSE: 0.7563 -Fine-Tuning Epoch 74/100, Train RMSE: 0.7567 -Fine-Tuning Epoch 75/100, Train RMSE: 0.7559 -Fine-Tuning Epoch 76/100, Train RMSE: 0.7573 -Fine-Tuning Epoch 77/100, Train RMSE: 0.7589 -Fine-Tuning Epoch 78/100, Train RMSE: 0.7597 -Fine-Tuning Epoch 79/100, Train RMSE: 0.7589 -Fine-Tuning Epoch 80/100, Train RMSE: 0.7601 -Fine-Tuning Epoch 81/100, Train RMSE: 0.7597 -Fine-Tuning Epoch 82/100, Train RMSE: 0.7568 -Fine-Tuning Epoch 83/100, Train RMSE: 0.7549 -Fine-Tuning Epoch 84/100, Train RMSE: 0.7534 -Fine-Tuning Epoch 85/100, Train RMSE: 0.7528 -Fine-Tuning Epoch 86/100, Train RMSE: 0.7511 -Fine-Tuning Epoch 87/100, Train RMSE: 0.7510 -Fine-Tuning Epoch 88/100, Train RMSE: 0.7519 -Fine-Tuning Epoch 89/100, Train RMSE: 0.7542 -Fine-Tuning Epoch 90/100, Train RMSE: 0.7546 -Fine-Tuning Epoch 91/100, Train RMSE: 0.7550 -Fine-Tuning Epoch 92/100, Train RMSE: 0.7556 -Fine-Tuning Epoch 93/100, Train RMSE: 0.7564 -Fine-Tuning Epoch 94/100, Train RMSE: 0.7585 -Fine-Tuning Epoch 95/100, Train RMSE: 0.7583 -Fine-Tuning Epoch 96/100, Train RMSE: 0.7566 -Fine-Tuning Epoch 97/100, Train RMSE: 0.7539 -Fine-Tuning Epoch 98/100, Train RMSE: 0.7531 -Fine-Tuning Epoch 99/100, Train RMSE: 0.7532 -Fine-Tuning Epoch 100/100, Train RMSE: 0.7538 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0563 | 1.0648 | 2.4660 | 0.0840 - - -======================================== diff --git a/experiment_log_2.txt b/experiment_log_2.txt deleted file mode 100644 index e0318d002..000000000 --- a/experiment_log_2.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:39:26.679902 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] - [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] - [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] - ... - [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] - [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347] - [4.7480116 4.422406 4.266268 ... 1.6119636 3.7070372 4.1667347]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7996 -Pre-Training Epoch 2/11, Train RMSE: 2.7996 -Pre-Training Epoch 3/11, Train RMSE: 2.7945 -Pre-Training Epoch 4/11, Train RMSE: 2.5375 -Pre-Training Epoch 5/11, Train RMSE: 2.1313 -Pre-Training Epoch 6/11, Train RMSE: 1.6893 -Pre-Training Epoch 7/11, Train RMSE: 1.2792 -Pre-Training Epoch 8/11, Train RMSE: 1.0196 -Pre-Training Epoch 9/11, Train RMSE: 0.9528 -Pre-Training Epoch 10/11, Train RMSE: 0.9507 -Pre-Training Epoch 11/11, Train RMSE: 0.9094 -Fine-Tuning Epoch 1/10, Train RMSE: 1.0658 -Fine-Tuning Epoch 2/10, Train RMSE: 1.0405 -Fine-Tuning Epoch 3/10, Train RMSE: 0.9448 -Fine-Tuning Epoch 4/10, Train RMSE: 0.9024 -Fine-Tuning Epoch 5/10, Train RMSE: 0.8677 -Fine-Tuning Epoch 6/10, Train RMSE: 0.8433 -Fine-Tuning Epoch 7/10, Train RMSE: 0.8474 -Fine-Tuning Epoch 8/10, Train RMSE: 0.8850 -Fine-Tuning Epoch 9/10, Train RMSE: 0.9034 -Fine-Tuning Epoch 10/10, Train RMSE: 0.8806 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0806 | 1.0899 | 1.6180 | 0.0820 - - -======================================== diff --git a/experiment_log_20.txt b/experiment_log_20.txt deleted file mode 100644 index dbcbb7a9c..000000000 --- a/experiment_log_20.txt +++ /dev/null @@ -1,152 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:13:10.596422 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] - [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] - [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] - ... - [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] - [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ] - [4.2188277 3.322004 3.6511939 ... 1.7437497 3.1620636 4.629881 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7996 -Pre-Training Epoch 2/30, Train RMSE: 2.7996 -Pre-Training Epoch 3/30, Train RMSE: 2.7995 -Pre-Training Epoch 4/30, Train RMSE: 2.6134 -Pre-Training Epoch 5/30, Train RMSE: 2.2041 -Pre-Training Epoch 6/30, Train RMSE: 1.7501 -Pre-Training Epoch 7/30, Train RMSE: 1.3298 -Pre-Training Epoch 8/30, Train RMSE: 1.0646 -Pre-Training Epoch 9/30, Train RMSE: 0.9996 -Pre-Training Epoch 10/30, Train RMSE: 0.9842 -Pre-Training Epoch 11/30, Train RMSE: 0.8989 -Pre-Training Epoch 12/30, Train RMSE: 0.8207 -Pre-Training Epoch 13/30, Train RMSE: 0.8701 -Pre-Training Epoch 14/30, Train RMSE: 0.9979 -Pre-Training Epoch 15/30, Train RMSE: 1.0676 -Pre-Training Epoch 16/30, Train RMSE: 1.0539 -Pre-Training Epoch 17/30, Train RMSE: 0.9845 -Pre-Training Epoch 18/30, Train RMSE: 0.9100 -Pre-Training Epoch 19/30, Train RMSE: 0.8543 -Pre-Training Epoch 20/30, Train RMSE: 0.8262 -Pre-Training Epoch 21/30, Train RMSE: 0.8108 -Pre-Training Epoch 22/30, Train RMSE: 0.8003 -Pre-Training Epoch 23/30, Train RMSE: 0.7944 -Pre-Training Epoch 24/30, Train RMSE: 0.8037 -Pre-Training Epoch 25/30, Train RMSE: 0.8380 -Pre-Training Epoch 26/30, Train RMSE: 0.8651 -Pre-Training Epoch 27/30, Train RMSE: 0.8807 -Pre-Training Epoch 28/30, Train RMSE: 0.8691 -Pre-Training Epoch 29/30, Train RMSE: 0.8409 -Pre-Training Epoch 30/30, Train RMSE: 0.8123 -Fine-Tuning Epoch 1/100, Train RMSE: 0.8021 -Fine-Tuning Epoch 2/100, Train RMSE: 0.8015 -Fine-Tuning Epoch 3/100, Train RMSE: 0.7963 -Fine-Tuning Epoch 4/100, Train RMSE: 0.7951 -Fine-Tuning Epoch 5/100, Train RMSE: 0.7947 -Fine-Tuning Epoch 6/100, Train RMSE: 0.7890 -Fine-Tuning Epoch 7/100, Train RMSE: 0.7833 -Fine-Tuning Epoch 8/100, Train RMSE: 0.7787 -Fine-Tuning Epoch 9/100, Train RMSE: 0.7752 -Fine-Tuning Epoch 10/100, Train RMSE: 0.7720 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7703 -Fine-Tuning Epoch 12/100, Train RMSE: 0.7707 -Fine-Tuning Epoch 13/100, Train RMSE: 0.7725 -Fine-Tuning Epoch 14/100, Train RMSE: 0.7736 -Fine-Tuning Epoch 15/100, Train RMSE: 0.7765 -Fine-Tuning Epoch 16/100, Train RMSE: 0.7798 -Fine-Tuning Epoch 17/100, Train RMSE: 0.7835 -Fine-Tuning Epoch 18/100, Train RMSE: 0.7873 -Fine-Tuning Epoch 19/100, Train RMSE: 0.7886 -Fine-Tuning Epoch 20/100, Train RMSE: 0.7889 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7888 -Fine-Tuning Epoch 22/100, Train RMSE: 0.7871 -Fine-Tuning Epoch 23/100, Train RMSE: 0.7828 -Fine-Tuning Epoch 24/100, Train RMSE: 0.7783 -Fine-Tuning Epoch 25/100, Train RMSE: 0.7742 -Fine-Tuning Epoch 26/100, Train RMSE: 0.7700 -Fine-Tuning Epoch 27/100, Train RMSE: 0.7665 -Fine-Tuning Epoch 28/100, Train RMSE: 0.7642 -Fine-Tuning Epoch 29/100, Train RMSE: 0.7627 -Fine-Tuning Epoch 30/100, Train RMSE: 0.7618 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7609 -Fine-Tuning Epoch 32/100, Train RMSE: 0.7612 -Fine-Tuning Epoch 33/100, Train RMSE: 0.7625 -Fine-Tuning Epoch 34/100, Train RMSE: 0.7647 -Fine-Tuning Epoch 35/100, Train RMSE: 0.7672 -Fine-Tuning Epoch 36/100, Train RMSE: 0.7707 -Fine-Tuning Epoch 37/100, Train RMSE: 0.7753 -Fine-Tuning Epoch 38/100, Train RMSE: 0.7801 -Fine-Tuning Epoch 39/100, Train RMSE: 0.7826 -Fine-Tuning Epoch 40/100, Train RMSE: 0.7856 -Early stopping fine-tuning at epoch: 41 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0615 | 1.0671 | 2.9344 | 0.0836 - - -======================================== diff --git a/experiment_log_21.txt b/experiment_log_21.txt deleted file mode 100644 index 371c3d912..000000000 --- a/experiment_log_21.txt +++ /dev/null @@ -1,90 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:14:10.064404 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] - [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] - [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] - ... - [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] - [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ] - [4.810285 3.3194206 4.887747 ... 2.03676 3.2246556 4.778924 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7996 -Pre-Training Epoch 11/30, Train RMSE: 1.0861 -Pre-Training Epoch 21/30, Train RMSE: 0.8988 -Fine-Tuning Epoch 1/100, Train RMSE: 0.7946 -Fine-Tuning Epoch 11/100, Train RMSE: 0.7697 -Fine-Tuning Epoch 21/100, Train RMSE: 0.7693 -Fine-Tuning Epoch 31/100, Train RMSE: 0.7706 -Fine-Tuning Epoch 41/100, Train RMSE: 0.7570 -Early stopping fine-tuning at epoch: 50 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0635 | 1.0699 | 2.0601 | 0.0864 - - -======================================== diff --git a/experiment_log_22.txt b/experiment_log_22.txt deleted file mode 100644 index 2fd1d3367..000000000 --- a/experiment_log_22.txt +++ /dev/null @@ -1,175 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:24:41.241576 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] - [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] - [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] - ... - [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] - [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ] - [3.3914974 3.3986533 3.3564575 ... 4.990137 2.910799 4.033721 ]] -_train_r: [[4. 0. 4. ... 0. 0. 0.] - [0. 3. 4. ... 0. 0. 0.] - [5. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 1. ... 0. 0. 0.] - [0. 1. 1. ... 0. 0. 0.] - [1. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7809 -Pre-Training Epoch 11/30, Train RMSE: 1.0940 -Pre-Training Epoch 21/30, Train RMSE: 1.1482 -Fine-Tuning Epoch 1/100, Train RMSE: 1.0471 -Fine-Tuning Epoch 11/100, Train RMSE: 1.0460 -Fine-Tuning Epoch 21/100, Train RMSE: 1.0492 -Fine-Tuning Epoch 31/100, Train RMSE: 1.0420 -Fine-Tuning Epoch 41/100, Train RMSE: 1.0406 -Fine-Tuning Epoch 51/100, Train RMSE: 1.0377 -Fine-Tuning Epoch 61/100, Train RMSE: 1.0358 -Fine-Tuning Epoch 71/100, Train RMSE: 1.0377 -Fine-Tuning Epoch 81/100, Train RMSE: 1.0382 -Fine-Tuning Epoch 91/100, Train RMSE: 1.0377 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8520 | 0.9959 | 16.8043 | 520.8015 - - -======================================== - -======================================== -Experiment conducted on: 2024-12-14 15:38:08.721041 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[3.3665428 3.4314446 3.5327659 ... 3.2308724 4.0627537 3.251854 ] - [3.3674333 3.4354982 3.5315332 ... 3.230357 4.062786 3.2491977] - [3.364625 3.4343467 3.5296586 ... 3.2268498 4.060521 3.2477775] - ... - [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ] - [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ] - [3.368015 3.4378867 3.5306954 ... 3.2301254 4.0626473 3.247624 ]] -_train_r: [[4. 0. 0. ... 0. 0. 0.] - [0. 5. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + --------- -GlobalLocalKernel | 0.8460 | 1.0098 | 66.2905 | 1081.2129 - - -======================================== diff --git a/experiment_log_23.txt b/experiment_log_23.txt deleted file mode 100644 index b9dc1309f..000000000 --- a/experiment_log_23.txt +++ /dev/null @@ -1,81 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:31:00.342731 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] - [2.700551 3.489955 2.2833154 ... 2.8310664 4.3504834 2.5976305] - [2.7023132 3.4924197 2.2842486 ... 2.8319042 4.3540497 2.5981433] - ... - [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] - [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305] - [2.700551 3.4899547 2.2831495 ... 2.8310256 4.3504834 2.5976305]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8753 | 0.9768 | 19.8530 | 25.2438 - - -======================================== diff --git a/experiment_log_24.txt b/experiment_log_24.txt deleted file mode 100644 index f3af30a16..000000000 --- a/experiment_log_24.txt +++ /dev/null @@ -1,94 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:39:46.860598 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] - [2.3860507 3.667328 1.9504007 ... 3.1993334 4.6926336 2.6700587] - [2.3848817 3.6678581 1.9507756 ... 3.2012804 4.690115 2.6707287] - ... - [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] - [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195] - [2.3841808 3.6681569 1.9509947 ... 3.2024183 4.688615 2.6711195]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.2273 -Pre-Training Epoch 21/30, Train RMSE: 1.1315 -Fine-Training Epoch 1/100, Train RMSE: 1.0286 -Fine-Training Epoch 11/100, Train RMSE: 1.0196 -Fine-Training Epoch 21/100, Train RMSE: 1.0108 -Fine-Training Epoch 31/100, Train RMSE: 1.0072 -Fine-Training Epoch 41/100, Train RMSE: 1.0058 -Fine-Training Epoch 51/100, Train RMSE: 1.0071 -Fine-Training Epoch 61/100, Train RMSE: 1.0081 -Fine-Training Epoch 71/100, Train RMSE: 1.0089 -Fine-Training Epoch 81/100, Train RMSE: 1.0066 -Fine-Training Epoch 91/100, Train RMSE: 1.0039 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8709 | 0.9735 | 6.0744 | 11.2648 - - -======================================== diff --git a/experiment_log_25.txt b/experiment_log_25.txt deleted file mode 100644 index eb99eab87..000000000 --- a/experiment_log_25.txt +++ /dev/null @@ -1,94 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:44:29.946562 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] - [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] - [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] - ... - [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] - [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726] - [2.740123 3.741238 2.3513756 ... 2.9395514 4.369216 2.5178726]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.2238 -Pre-Training Epoch 21/30, Train RMSE: 1.0398 -Fine-Training Epoch 1/100, Train RMSE: 1.0274 -Fine-Training Epoch 11/100, Train RMSE: 1.0160 -Fine-Training Epoch 21/100, Train RMSE: 1.0126 -Fine-Training Epoch 31/100, Train RMSE: 1.0097 -Fine-Training Epoch 41/100, Train RMSE: 1.0054 -Fine-Training Epoch 51/100, Train RMSE: 1.0062 -Fine-Training Epoch 61/100, Train RMSE: 1.0062 -Fine-Training Epoch 71/100, Train RMSE: 1.0070 -Fine-Training Epoch 81/100, Train RMSE: 1.0057 -Fine-Training Epoch 91/100, Train RMSE: 1.0046 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8607 | 0.9651 | 6.2921 | 8.3071 - - -======================================== diff --git a/experiment_log_26.txt b/experiment_log_26.txt deleted file mode 100644 index f3e0c55fa..000000000 --- a/experiment_log_26.txt +++ /dev/null @@ -1,94 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:49:39.347017 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] - [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] - [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] - ... - [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] - [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615] - [2.6920643 3.5714657 2.263641 ... 3.7150562 5. 2.9347615]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.0765 -Pre-Training Epoch 21/30, Train RMSE: 1.0214 -Fine-Training Epoch 1/100, Train RMSE: 1.0147 -Fine-Training Epoch 11/100, Train RMSE: 1.0079 -Fine-Training Epoch 21/100, Train RMSE: 1.0211 -Fine-Training Epoch 31/100, Train RMSE: 1.0066 -Fine-Training Epoch 41/100, Train RMSE: 1.0174 -Fine-Training Epoch 51/100, Train RMSE: 1.0083 -Fine-Training Epoch 61/100, Train RMSE: 1.0095 -Fine-Training Epoch 71/100, Train RMSE: 1.0093 -Fine-Training Epoch 81/100, Train RMSE: 1.0054 -Fine-Training Epoch 91/100, Train RMSE: 1.0063 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8713 | 0.9730 | 6.3756 | 8.6612 - - -======================================== diff --git a/experiment_log_27.txt b/experiment_log_27.txt deleted file mode 100644 index ba0cf58b9..000000000 --- a/experiment_log_27.txt +++ /dev/null @@ -1,94 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:50:04.046901 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] - [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] - [2.665841 3.3590596 2.287095 ... 3.9549856 3.8763995 2.669011 ] - ... - [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] - [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662] - [2.6642957 3.357563 2.2869613 ... 3.9533765 3.8751554 2.6688662]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.1403 -Pre-Training Epoch 21/30, Train RMSE: 1.0269 -Fine-Training Epoch 1/100, Train RMSE: 1.0249 -Fine-Training Epoch 11/100, Train RMSE: 1.0190 -Fine-Training Epoch 21/100, Train RMSE: 1.0154 -Fine-Training Epoch 31/100, Train RMSE: 1.0174 -Fine-Training Epoch 41/100, Train RMSE: 1.0155 -Fine-Training Epoch 51/100, Train RMSE: 1.0134 -Fine-Training Epoch 61/100, Train RMSE: 1.0124 -Fine-Training Epoch 71/100, Train RMSE: 1.0171 -Fine-Training Epoch 81/100, Train RMSE: 1.0053 -Fine-Training Epoch 91/100, Train RMSE: 1.0067 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8769 | 0.9787 | 6.2931 | 8.2637 - - -======================================== diff --git a/experiment_log_28.txt b/experiment_log_28.txt deleted file mode 100644 index 6c6c22629..000000000 --- a/experiment_log_28.txt +++ /dev/null @@ -1,94 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:51:02.416105 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] - [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] - [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] - ... - [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] - [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ] - [2.8998227 3.906498 2.4419298 ... 3.4656742 3.8759713 3.049443 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.1079 -Pre-Training Epoch 21/30, Train RMSE: 1.0317 -Fine-Training Epoch 1/100, Train RMSE: 1.0160 -Fine-Training Epoch 11/100, Train RMSE: 1.0108 -Fine-Training Epoch 21/100, Train RMSE: 1.0191 -Fine-Training Epoch 31/100, Train RMSE: 1.0129 -Fine-Training Epoch 41/100, Train RMSE: 1.0069 -Fine-Training Epoch 51/100, Train RMSE: 1.0138 -Fine-Training Epoch 61/100, Train RMSE: 1.0103 -Fine-Training Epoch 71/100, Train RMSE: 1.0058 -Fine-Training Epoch 81/100, Train RMSE: 1.0056 -Fine-Training Epoch 91/100, Train RMSE: 1.0103 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8767 | 0.9777 | 6.6966 | 9.3149 - - -======================================== diff --git a/experiment_log_29.txt b/experiment_log_29.txt deleted file mode 100644 index bf13d4d66..000000000 --- a/experiment_log_29.txt +++ /dev/null @@ -1,84 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:52:25.877167 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] - [2.5118506 3.2813966 2.1731827 ... 3.6590734 5. 2.7531 ] - [2.5122426 3.2817035 2.1733239 ... 3.6596854 5. 2.7531 ] - ... - [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] - [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ] - [2.511853 3.2813983 2.1731837 ... 3.6590772 5. 2.7531 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/30, Train RMSE: 2.7954 -Pre-Training Epoch 11/30, Train RMSE: 1.1884 -Pre-Training Epoch 21/30, Train RMSE: 1.1436 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8667 | 0.9689 | 6.6524 | 8.4069 - - -======================================== diff --git a/experiment_log_3.txt b/experiment_log_3.txt deleted file mode 100644 index 5a2b60078..000000000 --- a/experiment_log_3.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:42:30.735089 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.6109083 3.0878942 2.579492 ... 3.8386712 4.7205243 3.134479 ] - [1.6088798 3.0834882 2.5752678 ... 3.832683 4.7128325 3.1321354] - [1.6130054 3.0879006 2.5810363 ... 3.831977 4.72029 3.1362262] - ... - [1.589895 3.0196843 2.5164669 ... 3.7473495 4.598756 3.0532973] - [1.596155 3.0352452 2.5281205 ... 3.7673159 4.6247973 3.0705748] - [1.589895 3.0196843 2.5164669 ... 3.7473495 4.598756 3.0532973]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7883 -Pre-Training Epoch 4/11, Train RMSE: 2.4445 -Pre-Training Epoch 5/11, Train RMSE: 1.9055 -Pre-Training Epoch 6/11, Train RMSE: 1.3869 -Pre-Training Epoch 7/11, Train RMSE: 1.1063 -Pre-Training Epoch 8/11, Train RMSE: 1.1256 -Pre-Training Epoch 9/11, Train RMSE: 1.1458 -Pre-Training Epoch 10/11, Train RMSE: 1.0537 -Pre-Training Epoch 11/11, Train RMSE: 1.1164 -Fine-Tuning Epoch 1/10, Train RMSE: 1.1590 -Fine-Tuning Epoch 2/10, Train RMSE: 1.2036 -Fine-Tuning Epoch 3/10, Train RMSE: 1.1784 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0788 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0521 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0435 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0376 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0487 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0614 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0608 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9437 | 1.0372 | 7.3951 | 20.8728 - - -======================================== diff --git a/experiment_log_30.txt b/experiment_log_30.txt deleted file mode 100644 index de733b24a..000000000 --- a/experiment_log_30.txt +++ /dev/null @@ -1,81 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:53:45.184100 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] - [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] - [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] - ... - [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] - [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342] - [2.7489126 3.3718867 2.3049712 ... 3.5197139 4.5637465 2.7195342]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8703 | 0.9731 | 7.8693 | 10.0462 - - -======================================== diff --git a/experiment_log_31.txt b/experiment_log_31.txt deleted file mode 100644 index dc1ceea40..000000000 --- a/experiment_log_31.txt +++ /dev/null @@ -1,82 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:55:03.820486 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] - [2.6108704 3.590081 2.0323942 ... 4.13674 3.8902812 2.811247 ] - [2.6108627 3.5901144 2.0323942 ... 4.1367493 3.8903277 2.811247 ] - ... - [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] - [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ] - [2.6109383 3.5897782 2.0323942 ... 4.1366587 3.8898602 2.811247 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 93 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8770 | 0.9788 | 6.3003 | 8.2548 - - -======================================== diff --git a/experiment_log_32.txt b/experiment_log_32.txt deleted file mode 100644 index fed93b704..000000000 --- a/experiment_log_32.txt +++ /dev/null @@ -1,84 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 15:55:59.957210 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] - [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] - [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] - ... - [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] - [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793] - [2.8113995 3.8364682 2.3046346 ... 3.9185 4.873051 2.5080793]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 60 -Early stopping fine-tuning at epoch: 61 -Early stopping fine-tuning at epoch: 71 -Early stopping fine-tuning at epoch: 81 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8810 | 0.9808 | 5.3260 | 8.2245 - - -======================================== diff --git a/experiment_log_33.txt b/experiment_log_33.txt deleted file mode 100644 index 85825da57..000000000 --- a/experiment_log_33.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:05:02.280431 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] - [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] - [2.8019316 3.1359203 1.8802388 ... 3.1990905 4.6265297 2.6383972] - ... - [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] - [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543] - [2.8019316 3.1359627 1.8802388 ... 3.19992 4.6273966 2.6390543]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8746 | 0.9754 | 19.2886 | 20.3352 - - -======================================== diff --git a/experiment_log_34.txt b/experiment_log_34.txt deleted file mode 100644 index 1a551bda0..000000000 --- a/experiment_log_34.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:06:32.139048 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] - [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] - [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] - ... - [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] - [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ] - [3.990643 3.3860412 4.376368 ... 1.8664002 3.0656142 4.234053 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0351 | 1.0457 | 2.7236 | 0.0970 - - -======================================== diff --git a/experiment_log_35.txt b/experiment_log_35.txt deleted file mode 100644 index b1ff06085..000000000 --- a/experiment_log_35.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:06:41.816932 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] - [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] - [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] - ... - [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] - [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445] - [4.6293516 3.2934983 4.7830424 ... 2.2117078 3.6310005 4.7143445]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0356 | 1.0401 | 3.3111 | 0.0910 - - -======================================== diff --git a/experiment_log_36.txt b/experiment_log_36.txt deleted file mode 100644 index 8ce666392..000000000 --- a/experiment_log_36.txt +++ /dev/null @@ -1,113 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:07:07.902145 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] - [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] - [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] - ... - [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] - [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ] - [4.9186144 3.6451223 4.7036924 ... 2.0048983 3.5006468 4.900523 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 37 -Early stopping fine-tuning at epoch: 60 -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0527 | 1.0599 | 3.2921 | 0.0980 - - -======================================== diff --git a/experiment_log_37.txt b/experiment_log_37.txt deleted file mode 100644 index 7c1641c80..000000000 --- a/experiment_log_37.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:07:30.418523 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] - [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] - [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] - ... - [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] - [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683] - [4.5214896 3.1931663 4.530992 ... 2.030869 3.2673206 3.9483683]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0302 | 1.0387 | 3.2679 | 0.1030 - - -======================================== diff --git a/experiment_log_38.txt b/experiment_log_38.txt deleted file mode 100644 index bed583b29..000000000 --- a/experiment_log_38.txt +++ /dev/null @@ -1,82 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:08:13.554989 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] - [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] - [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] - ... - [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] - [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ] - [4.9607587 3.5494778 4.367852 ... 1.9751956 3.3918364 4.870947 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 75 -Early stopping fine-tuning at epoch: 97 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0698 | 1.0743 | 3.2584 | 0.0979 - - -======================================== diff --git a/experiment_log_39.txt b/experiment_log_39.txt deleted file mode 100644 index b9cd0b40e..000000000 --- a/experiment_log_39.txt +++ /dev/null @@ -1,83 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:09:18.745774 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] - [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] - [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] - ... - [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] - [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ] - [4.9222875 3.5619962 4.766569 ... 1.9553328 3.1915967 5. ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 36 -Early stopping fine-tuning at epoch: 80 -Early stopping fine-tuning at epoch: 81 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0507 | 1.0606 | 3.9770 | 0.1830 - - -======================================== diff --git a/experiment_log_4.txt b/experiment_log_4.txt deleted file mode 100644 index 178994ba4..000000000 --- a/experiment_log_4.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:46:52.531515 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] - [2.7604942 3.9703507 1.6771085 ... 3.9640617 3.7593672 2.145248 ] - [2.7532134 3.959725 1.6722806 ... 3.9549756 3.751583 2.136241 ] - ... - [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] - [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101] - [2.749503 3.9545617 1.669784 ... 3.9504209 3.7478228 2.1314101]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7953 -Pre-Training Epoch 4/11, Train RMSE: 2.4713 -Pre-Training Epoch 5/11, Train RMSE: 1.9837 -Pre-Training Epoch 6/11, Train RMSE: 1.4881 -Pre-Training Epoch 7/11, Train RMSE: 1.1384 -Pre-Training Epoch 8/11, Train RMSE: 1.1046 -Pre-Training Epoch 9/11, Train RMSE: 1.1870 -Pre-Training Epoch 10/11, Train RMSE: 1.1467 -Pre-Training Epoch 11/11, Train RMSE: 1.0403 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3540 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1498 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0475 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0420 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0339 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0546 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0932 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0993 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0679 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0383 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9178 | 1.0125 | 6.8028 | 23.1687 - - -======================================== diff --git a/experiment_log_40.txt b/experiment_log_40.txt deleted file mode 100644 index f40140363..000000000 --- a/experiment_log_40.txt +++ /dev/null @@ -1,83 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:09:46.041475 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] - [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] - [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] - ... - [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] - [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ] - [4.579897 3.6092765 4.8573136 ... 1.6330812 3.2701654 4.214141 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 34 -Early stopping fine-tuning at epoch: 41 -Early stopping fine-tuning at epoch: 51 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0515 | 1.0587 | 3.2913 | 0.0980 - - -======================================== diff --git a/experiment_log_41.txt b/experiment_log_41.txt deleted file mode 100644 index 5bb04f3be..000000000 --- a/experiment_log_41.txt +++ /dev/null @@ -1,111 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:10:02.648400 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] - [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] - [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] - ... - [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] - [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193] - [4.0817637 3.7138143 4.331583 ... 2.1820967 3.4220922 4.7256193]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here -i am here - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0607 | 1.0689 | 3.4881 | 0.0950 - - -======================================== diff --git a/experiment_log_42.txt b/experiment_log_42.txt deleted file mode 100644 index 910fbf370..000000000 --- a/experiment_log_42.txt +++ /dev/null @@ -1,142 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:13:14.098611 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] - [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] - [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] - ... - [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] - [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ] - [4.8594646 3.5144248 4.8247657 ... 2.0829008 3.073163 4.326948 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Score method called for user_id=32, item_id=210 -Predicting score for user 32 and item 210 -Score method called for user_id=81, item_id=20 -Predicting score for user 81 and item 20 -Score method called for user_id=175, item_id=128 -Predicting score for user 175 and item 128 -Score method called for user_id=83, item_id=78 -Predicting score for user 83 and item 78 -Score method called for user_id=154, item_id=120 -Predicting score for user 154 and item 120 -Score method called for user_id=130, item_id=48 -Predicting score for user 130 and item 48 -Score method called for user_id=62, item_id=24 -Predicting score for user 62 and item 24 -Score method called for user_id=81, item_id=5 -Predicting score for user 81 and item 5 -Score method called for user_id=110, item_id=266 -Predicting score for user 110 and item 266 -Score method called for user_id=157, item_id=235 -Predicting score for user 157 and item 235 -Score method called for user_id=128, item_id=25 -Predicting score for user 128 and item 25 -Score method called for user_id=151, item_id=233 -Predicting score for user 151 and item 233 -Score method called for user_id=75, item_id=14 -Predicting score for user 75 and item 14 -Score method called for user_id=78, item_id=209 -Predicting score for user 78 and item 209 -Score method called for user_id=15, item_id=4 -Predicting score for user 15 and item 4 -Score method called for user_id=45, item_id=27 -Predicting score for user 45 and item 27 -Score method called for user_id=35, item_id=181 -Predicting score for user 35 and item 181 -Score method called for user_id=68, item_id=51 -Predicting score for user 68 and item 51 -Score method called for user_id=58, item_id=85 -Predicting score for user 58 and item 85 -Score method called for user_id=95, item_id=263 -Predicting score for user 95 and item 263 -Score method called for user_id=149, item_id=102 -Predicting score for user 149 and item 102 -Score method called for user_id=138, item_id=156 -Predicting score for user 138 and item 156 -Score method called for user_id=146, item_id=53 -Predicting score for user 146 and item 53 -Score method called for user_id=21, item_id=34 -Predicting score for user 21 and item 34 -Score method called for user_id=135, item_id=147 -Predicting score for user 135 and item 147 -Score method called for user_id=70, item_id=185 -Predicting score for user 70 and item 185 -Score method called for user_id=36, item_id=99 -Predicting score for user 36 and item 99 -Score method called for user_id=105, item_id=296 -Predicting score for user 105 and item 296 -Score method called for user_id=70, item_id=183 -Predicting score for user 70 and item 183 -Score method called for user_id=169, item_id=289 -Predicting score for user 169 and item 289 -Score method called for user_id=26, item_id=94 -Predicting score for user 26 and item 94 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0328 | 1.0391 | 3.3769 | 0.0951 - - -======================================== diff --git a/experiment_log_43.txt b/experiment_log_43.txt deleted file mode 100644 index 82d75da29..000000000 --- a/experiment_log_43.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:19:04.297312 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] - [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] - [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] - ... - [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] - [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413] - [4.724048 2.9955359 4.9899797 ... 1.931849 3.085559 4.0223413]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0308 | 1.0396 | 3.4106 | 0.1130 - - -======================================== diff --git a/experiment_log_44.txt b/experiment_log_44.txt deleted file mode 100644 index d97b5ff0e..000000000 --- a/experiment_log_44.txt +++ /dev/null @@ -1,111 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:20:40.472758 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] - [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] - [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] - ... - [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] - [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ] - [4.1559496 3.3125288 4.0339484 ... 2.0578027 3.2426875 4.514226 ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Debug: item_id is a single value: 210. Scoring for user 32. -Debug: item_id is a single value: 20. Scoring for user 81. -Debug: item_id is a single value: 128. Scoring for user 175. -Debug: item_id is a single value: 78. Scoring for user 83. -Debug: item_id is a single value: 120. Scoring for user 154. -Debug: item_id is a single value: 48. Scoring for user 130. -Debug: item_id is a single value: 24. Scoring for user 62. -Debug: item_id is a single value: 5. Scoring for user 81. -Debug: item_id is a single value: 266. Scoring for user 110. -Debug: item_id is a single value: 235. Scoring for user 157. -Debug: item_id is a single value: 25. Scoring for user 128. -Debug: item_id is a single value: 233. Scoring for user 151. -Debug: item_id is a single value: 14. Scoring for user 75. -Debug: item_id is a single value: 209. Scoring for user 78. -Debug: item_id is a single value: 4. Scoring for user 15. -Debug: item_id is a single value: 27. Scoring for user 45. -Debug: item_id is a single value: 181. Scoring for user 35. -Debug: item_id is a single value: 51. Scoring for user 68. -Debug: item_id is a single value: 85. Scoring for user 58. -Debug: item_id is a single value: 263. Scoring for user 95. -Debug: item_id is a single value: 102. Scoring for user 149. -Debug: item_id is a single value: 156. Scoring for user 138. -Debug: item_id is a single value: 53. Scoring for user 146. -Debug: item_id is a single value: 34. Scoring for user 21. -Debug: item_id is a single value: 147. Scoring for user 135. -Debug: item_id is a single value: 185. Scoring for user 70. -Debug: item_id is a single value: 99. Scoring for user 36. -Debug: item_id is a single value: 296. Scoring for user 105. -Debug: item_id is a single value: 183. Scoring for user 70. -Debug: item_id is a single value: 289. Scoring for user 169. -Debug: item_id is a single value: 94. Scoring for user 26. - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0535 | 1.0602 | 3.5773 | 0.1190 - - -======================================== diff --git a/experiment_log_45.txt b/experiment_log_45.txt deleted file mode 100644 index aee582ba4..000000000 --- a/experiment_log_45.txt +++ /dev/null @@ -1,112 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:33:45.280769 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] - [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] - [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] - ... - [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] - [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ] - [5. 3.6668642 4.3602905 ... 1.7480708 3.1195893 5. ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Early stopping fine-tuning at epoch: 81 -Debug: item_id is a single value: 210. Scoring for user 32. -Debug: item_id is a single value: 20. Scoring for user 81. -Debug: item_id is a single value: 128. Scoring for user 175. -Debug: item_id is a single value: 78. Scoring for user 83. -Debug: item_id is a single value: 120. Scoring for user 154. -Debug: item_id is a single value: 48. Scoring for user 130. -Debug: item_id is a single value: 24. Scoring for user 62. -Debug: item_id is a single value: 5. Scoring for user 81. -Debug: item_id is a single value: 266. Scoring for user 110. -Debug: item_id is a single value: 235. Scoring for user 157. -Debug: item_id is a single value: 25. Scoring for user 128. -Debug: item_id is a single value: 233. Scoring for user 151. -Debug: item_id is a single value: 14. Scoring for user 75. -Debug: item_id is a single value: 209. Scoring for user 78. -Debug: item_id is a single value: 4. Scoring for user 15. -Debug: item_id is a single value: 27. Scoring for user 45. -Debug: item_id is a single value: 181. Scoring for user 35. -Debug: item_id is a single value: 51. Scoring for user 68. -Debug: item_id is a single value: 85. Scoring for user 58. -Debug: item_id is a single value: 263. Scoring for user 95. -Debug: item_id is a single value: 102. Scoring for user 149. -Debug: item_id is a single value: 156. Scoring for user 138. -Debug: item_id is a single value: 53. Scoring for user 146. -Debug: item_id is a single value: 34. Scoring for user 21. -Debug: item_id is a single value: 147. Scoring for user 135. -Debug: item_id is a single value: 185. Scoring for user 70. -Debug: item_id is a single value: 99. Scoring for user 36. -Debug: item_id is a single value: 296. Scoring for user 105. -Debug: item_id is a single value: 183. Scoring for user 70. -Debug: item_id is a single value: 289. Scoring for user 169. -Debug: item_id is a single value: 94. Scoring for user 26. - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0712 | 1.0826 | 3.4043 | 0.1060 - - -======================================== diff --git a/experiment_log_46.txt b/experiment_log_46.txt deleted file mode 100644 index cf9189d25..000000000 --- a/experiment_log_46.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:34:28.415480 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] - [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] - [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] - ... - [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] - [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155] - [4.7848053 3.3055613 4.3236523 ... 1.9865739 3.2393208 4.2158155]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0639 | 1.0698 | 3.5987 | 0.1021 - - -======================================== diff --git a/experiment_log_47.txt b/experiment_log_47.txt deleted file mode 100644 index a0fbc29e3..000000000 --- a/experiment_log_47.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:37:36.695322 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] - [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] - [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] - ... - [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] - [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ] - [5. 3.6733 4.801543 ... 1.9911524 2.8645885 5. ]] -_train_r: [[5. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 0. 5. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 5. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 0. 1. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 1. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 1.0537 | 1.0639 | 3.8695 | 0.0930 - - -======================================== diff --git a/experiment_log_48.txt b/experiment_log_48.txt deleted file mode 100644 index ed5617d7f..000000000 --- a/experiment_log_48.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:38:23.408433 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] - [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] - [2.4369004 3.2065606 2.2161467 ... 2.9055655 4.1110654 3.2190366] - ... - [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] - [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716] - [2.4394855 3.2099545 2.220104 ... 2.9055655 4.1110654 3.2211716]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8782 | 0.9792 | 9.2949 | 9.4951 - - -======================================== diff --git a/experiment_log_49.txt b/experiment_log_49.txt deleted file mode 100644 index cf58b1e48..000000000 --- a/experiment_log_49.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:40:12.851044 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] - [2.648593 3.3552032 2.586387 ... 3.8189757 4.972159 2.889211 ] - [2.6484973 3.3619058 2.586387 ... 3.8188543 4.97607 2.8915353] - ... - [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] - [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ] - [2.6484973 3.354765 2.586387 ... 3.8188543 4.9710116 2.889211 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8723 | 0.9745 | 7.5357 | 8.1437 - - -======================================== diff --git a/experiment_log_5.txt b/experiment_log_5.txt deleted file mode 100644 index c64fa1092..000000000 --- a/experiment_log_5.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:50:07.104995 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] - [1.4919857 2.639934 2.024684 ... 4.0510273 4.8342953 3.5593603] - [1.5025975 2.6664388 2.0519202 ... 4.117863 4.8968983 3.6059248] - ... - [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] - [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ] - [1.4919057 2.640158 2.0250354 ... 4.053179 4.8363385 3.560497 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.5473 -Pre-Training Epoch 4/11, Train RMSE: 1.9857 -Pre-Training Epoch 5/11, Train RMSE: 1.4399 -Pre-Training Epoch 6/11, Train RMSE: 1.1069 -Pre-Training Epoch 7/11, Train RMSE: 1.1268 -Pre-Training Epoch 8/11, Train RMSE: 1.1435 -Pre-Training Epoch 9/11, Train RMSE: 1.0451 -Pre-Training Epoch 10/11, Train RMSE: 1.1978 -Pre-Training Epoch 11/11, Train RMSE: 1.3483 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3990 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1572 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0871 -Fine-Tuning Epoch 4/10, Train RMSE: 1.3297 -Fine-Tuning Epoch 5/10, Train RMSE: 1.3649 -Fine-Tuning Epoch 6/10, Train RMSE: 1.2305 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0855 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0254 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0303 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0398 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9017 | 1.0138 | 6.8767 | 26.4232 - - -======================================== diff --git a/experiment_log_50.txt b/experiment_log_50.txt deleted file mode 100644 index e6e4d3674..000000000 --- a/experiment_log_50.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:41:16.688943 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] - [2.6458108 3.380637 2.1877875 ... 3.1579633 3.5752847 2.5653589] - [2.6459107 3.3808835 2.1877875 ... 3.1579633 3.5752847 2.5653589] - ... - [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] - [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589] - [2.6442227 3.37672 2.1877875 ... 3.1579633 3.5752847 2.5653589]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8683 | 0.9706 | 7.2455 | 7.7017 - - -======================================== diff --git a/experiment_log_51.txt b/experiment_log_51.txt deleted file mode 100644 index 444630040..000000000 --- a/experiment_log_51.txt +++ /dev/null @@ -1,999 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:42:28.780909 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] - [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] - [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] - ... - [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] - [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243] - [2.9020112 3.608201 2.147703 ... 3.5283904 4.192969 3.1362243]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Evaluating user: 163, item: 586, predicted: 3.5113921734681055 -Evaluating user: 0, item: 292, predicted: 2.734770436107598 -Evaluating user: 216, item: 149, predicted: 3.6797601754321314 -Evaluating user: 332, item: 267, predicted: 4.0 -Evaluating user: 295, item: 387, predicted: 2.8394499985036448 -Evaluating user: 73, item: 75, predicted: 3.8573390302045087 -Evaluating user: 316, item: 325, predicted: 3.099025894745082 -Evaluating user: 217, item: 120, predicted: 3.905947738891853 -Evaluating user: 172, item: 330, predicted: 3.7424992882435157 -Evaluating user: 197, item: 458, predicted: 3.0930699310342837 -Evaluating user: 58, item: 193, predicted: 3.69180550429593 -Evaluating user: 92, item: 335, predicted: 3.559073293326748 -Evaluating user: 231, item: 678, predicted: 3.6667805627820957 -Evaluating user: 157, item: 534, predicted: 3.537742933934916 -Evaluating user: 300, item: 106, predicted: 3.2646298075084443 -Evaluating user: 58, item: 938, predicted: 3.69180550429593 -Evaluating user: 77, item: 480, predicted: 2.9199805905495 -Evaluating user: 80, item: 108, predicted: 3.7155653427793984 -Evaluating user: 32, item: 200, predicted: 3.4066725302048 -Evaluating user: 36, item: 224, predicted: 3.460662564244048 -Evaluating user: 51, item: 786, predicted: 3.674316184015763 -Evaluating user: 51, item: 122, predicted: 3.674316184015763 -Evaluating user: 184, item: 745, predicted: 2.8062516091208507 -Evaluating user: 53, item: 101, predicted: 3.1260344951076156 -Evaluating user: 192, item: 322, predicted: 3.8070937172974855 -Evaluating user: 3, item: 517, predicted: 3.7110027566608332 -Evaluating user: 174, item: 572, predicted: 3.9381978923492715 -Evaluating user: 58, item: 482, predicted: 3.69180550429593 -Evaluating user: 152, item: 568, predicted: 3.6981160858188953 -Evaluating user: 79, item: 149, predicted: 2.942217854340261 -Evaluating user: 6, item: 453, predicted: 3.201540374180235 -Evaluating user: 102, item: 635, predicted: 3.177248470947968 -Evaluating user: 147, item: 103, predicted: 4.0 -Evaluating user: 154, item: 865, predicted: 3.9251733893292546 -Evaluating user: 91, item: 386, predicted: 3.3228832822818015 -Evaluating user: 206, item: 437, predicted: 4.0 -Evaluating user: 68, item: 322, predicted: 1.5682077401379722 -Evaluating user: 153, item: 653, predicted: 2.545446004248653 -Evaluating user: 136, item: 558, predicted: 3.1843331274877817 -Evaluating user: 158, item: 437, predicted: 4.0 -Evaluating user: 155, item: 135, predicted: 3.0897378614606397 -Evaluating user: 75, item: 219, predicted: 3.103056831113985 -Evaluating user: 139, item: 73, predicted: 4.0 -Evaluating user: 105, item: 38, predicted: 4.0 -Evaluating user: 11, item: 359, predicted: 4.0 -Evaluating user: 59, item: 170, predicted: 3.3237759115696086 -Evaluating user: 8, item: 392, predicted: 3.6597206732404843 -Evaluating user: 94, item: 96, predicted: 3.204886329269301 -Evaluating user: 8, item: 576, predicted: 3.6597206732404843 -Evaluating user: 222, item: 835, predicted: 3.8982422020285328 -Evaluating user: 41, item: 157, predicted: 3.397777301643506 -Evaluating user: 75, item: 892, predicted: 3.103056831113985 -Evaluating user: 282, item: 254, predicted: 3.564453504640864 -Evaluating user: 131, item: 677, predicted: 3.745733974534817 -Evaluating user: 88, item: 20, predicted: 3.268883991102366 -Evaluating user: 246, item: 788, predicted: 3.4684126369617094 -Evaluating user: 6, item: 352, predicted: 3.201540374180235 -Evaluating user: 293, item: 370, predicted: 4.0 -Evaluating user: 52, item: 166, predicted: 3.449963387639435 -Evaluating user: 87, item: 92, predicted: 3.4224920797908034 -Evaluating user: 107, item: 496, predicted: 4.0 -Evaluating user: 79, item: 239, predicted: 2.942217854340261 -Evaluating user: 114, item: 328, predicted: 3.4122120789616113 -Evaluating user: 46, item: 803, predicted: 3.277755855715274 -Evaluating user: 87, item: 629, predicted: 3.4224920797908034 -Evaluating user: 120, item: 689, predicted: 3.0003930660134595 -Evaluating user: 66, item: 114, predicted: 3.22805337333706 -Evaluating user: 156, item: 703, predicted: 4.0 -Evaluating user: 34, item: 811, predicted: 3.4865196769640567 -Evaluating user: 216, item: 69, predicted: 3.6797601754321314 -Evaluating user: 195, item: 75, predicted: 4.0 -Evaluating user: 83, item: 14, predicted: 3.046583752628486 -Evaluating user: 287, item: 573, predicted: 4.0 -Evaluating user: 105, item: 395, predicted: 4.0 -Evaluating user: 70, item: 399, predicted: 3.16817852056873 -Evaluating user: 0, item: 227, predicted: 2.734770436107598 -Evaluating user: 100, item: 342, predicted: 3.1589717525850505 -Evaluating user: 80, item: 302, predicted: 3.7155653427793984 -Evaluating user: 43, item: 200, predicted: 3.258812818890413 -Evaluating user: 41, item: 456, predicted: 3.397777301643506 -Evaluating user: 27, item: 483, predicted: 3.0452065709348664 -Evaluating user: 3, item: 834, predicted: 3.7110027566608332 -Evaluating user: 187, item: 135, predicted: 3.258594559438348 -Evaluating user: 24, item: 181, predicted: 3.1783635804868453 -Evaluating user: 125, item: 370, predicted: 3.761831276821815 -Evaluating user: 139, item: 211, predicted: 4.0 -Evaluating user: 64, item: 8, predicted: 3.202547478725553 -Evaluating user: 194, item: 105, predicted: 2.8425910264255885 -Evaluating user: 231, item: 312, predicted: 3.6667805627820957 -Evaluating user: 197, item: 965, predicted: 3.0930699310342837 -Evaluating user: 153, item: 731, predicted: 2.545446004248653 -Evaluating user: 54, item: 960, predicted: 3.557432591116923 -Evaluating user: 164, item: 839, predicted: 3.494502219932684 -Evaluating user: 22, item: 98, predicted: 3.796056342470468 -Evaluating user: 99, item: 524, predicted: 3.3120876147675595 -Evaluating user: 30, item: 319, predicted: 3.866272241038406 -Evaluating user: 323, item: 832, predicted: 3.4890009789863967 -Evaluating user: 26, item: 181, predicted: 3.2282415361513763 -Evaluating user: 86, item: 995, predicted: 3.2330500503420048 -Evaluating user: 180, item: 211, predicted: 3.48873440226905 -Evaluating user: 64, item: 721, predicted: 3.202547478725553 -Evaluating user: 8, item: 649, predicted: 3.6597206732404843 -Evaluating user: 181, item: 132, predicted: 3.1358150526443636 -Evaluating user: 172, item: 478, predicted: 3.7424992882435157 -Evaluating user: 22, item: 79, predicted: 3.796056342470468 -Evaluating user: 47, item: 208, predicted: 2.6370263485688588 -Evaluating user: 138, item: 689, predicted: 3.6119977386297535 -Evaluating user: 17, item: 208, predicted: 4.0 -Evaluating user: 139, item: 234, predicted: 4.0 -Evaluating user: 29, item: 966, predicted: 3.7781592966869013 -Evaluating user: 68, item: 842, predicted: 1.5682077401379722 -Evaluating user: 115, item: 31, predicted: 3.6137364550990245 -Evaluating user: 23, item: 238, predicted: 3.103225105551166 -Evaluating user: 199, item: 154, predicted: 4.0 -Evaluating user: 76, item: 115, predicted: 4.0 -Evaluating user: 66, item: 177, predicted: 3.22805337333706 -Evaluating user: 197, item: 159, predicted: 3.0930699310342837 -Evaluating user: 46, item: 195, predicted: 3.277755855715274 -Evaluating user: 160, item: 443, predicted: 2.887138985414394 -Evaluating user: 29, item: 304, predicted: 3.7781592966869013 -Evaluating user: 93, item: 764, predicted: 3.9891568569989357 -Evaluating user: 206, item: 511, predicted: 4.0 -Evaluating user: 153, item: 122, predicted: 2.545446004248653 -Evaluating user: 61, item: 10, predicted: 2.3337088155756582 -Evaluating user: 92, item: 960, predicted: 3.559073293326748 -Evaluating user: 37, item: 70, predicted: 4.0 -Evaluating user: 165, item: 504, predicted: 3.620702831791548 -Evaluating user: 40, item: 23, predicted: 3.6226568169745494 -Evaluating user: 236, item: 580, predicted: 2.7048138774607398 -Evaluating user: 159, item: 73, predicted: 3.9978437662848423 -Evaluating user: 105, item: 297, predicted: 4.0 -Evaluating user: 1, item: 71, predicted: 3.5921467575298585 -Evaluating user: 43, item: 510, predicted: 3.258812818890413 -Evaluating user: 231, item: 2, predicted: 3.6667805627820957 -Evaluating user: 4, item: 796, predicted: 3.768511205275512 -Evaluating user: 278, item: 505, predicted: 3.509220343044845 -Evaluating user: 7, item: 201, predicted: 4.0 -Evaluating user: 27, item: 410, predicted: 3.0452065709348664 -Evaluating user: 16, item: 208, predicted: 3.3142726592872442 -Evaluating user: 139, item: 726, predicted: 4.0 -Evaluating user: 20, item: 11, predicted: 3.870033225798768 -Evaluating user: 187, item: 452, predicted: 3.258594559438348 -Evaluating user: 27, item: 561, predicted: 3.0452065709348664 -Evaluating user: 153, item: 106, predicted: 2.545446004248653 -Evaluating user: 179, item: 537, predicted: 3.3684190043870683 -Evaluating user: 34, item: 807, predicted: 3.4865196769640567 -Evaluating user: 111, item: 487, predicted: 3.529305238189742 -Evaluating user: 161, item: 891, predicted: 3.53804643196736 -Evaluating user: 273, item: 451, predicted: 4.0 -Evaluating user: 161, item: 321, predicted: 3.53804643196736 -Evaluating user: 149, item: 755, predicted: 2.8386717967257202 -Evaluating user: 8, item: 50, predicted: 3.6597206732404843 -Evaluating user: 39, item: 154, predicted: 4.0 -Evaluating user: 276, item: 20, predicted: 3.043588626174305 -Evaluating user: 249, item: 97, predicted: 3.57548640802173 -Evaluating user: 121, item: 251, predicted: 2.959321252635845 -Evaluating user: 84, item: 700, predicted: 3.5967375130272137 -Evaluating user: 281, item: 568, predicted: 4.0 -Evaluating user: 36, item: 506, predicted: 3.460662564244048 -Evaluating user: 171, item: 193, predicted: 3.5537895965250352 -Evaluating user: 15, item: 576, predicted: 2.677068471129866 -Evaluating user: 15, item: 677, predicted: 2.677068471129866 -Evaluating user: 42, item: 817, predicted: 3.6623453377851063 -Evaluating user: 32, item: 219, predicted: 3.4066725302048 -Evaluating user: 282, item: 168, predicted: 3.564453504640864 -Evaluating user: 83, item: 200, predicted: 3.046583752628486 -Evaluating user: 1, item: 83, predicted: 3.5921467575298585 -Evaluating user: 57, item: 813, predicted: 3.631112245165661 -Evaluating user: 109, item: 53, predicted: 3.5828469527895868 -Evaluating user: 123, item: 401, predicted: 4.0 -Evaluating user: 193, item: 367, predicted: 3.9343050252304037 -Evaluating user: 11, item: 667, predicted: 4.0 -Evaluating user: 11, item: 270, predicted: 4.0 -Evaluating user: 100, item: 400, predicted: 3.1589717525850505 -Evaluating user: 85, item: 278, predicted: 3.734290231272179 -Evaluating user: 77, item: 952, predicted: 2.9199805905495 -Evaluating user: 124, item: 305, predicted: 2.9139364224064908 -Evaluating user: 34, item: 988, predicted: 3.4865196769640567 -Evaluating user: 120, item: 161, predicted: 3.0003930660134595 -Evaluating user: 296, item: 558, predicted: 4.0 -Evaluating user: 86, item: 118, predicted: 3.2330500503420048 -Evaluating user: 271, item: 187, predicted: 4.0 -Evaluating user: 184, item: 208, predicted: 2.8062516091208507 -Evaluating user: 79, item: 128, predicted: 2.942217854340261 -Evaluating user: 113, item: 278, predicted: 3.363926080883002 -Evaluating user: 312, item: 2, predicted: 3.222518423609301 -Evaluating user: 68, item: 46, predicted: 1.5682077401379722 -Evaluating user: 302, item: 3, predicted: 2.4282340052533433 -Evaluating user: 25, item: 186, predicted: 3.5732685808614724 -Evaluating user: 20, item: 74, predicted: 3.870033225798768 -Evaluating user: 182, item: 278, predicted: 2.7551743925444994 -Evaluating user: 15, item: 224, predicted: 2.677068471129866 -Evaluating user: 126, item: 45, predicted: 3.6728906294557255 -Evaluating user: 100, item: 204, predicted: 3.1589717525850505 -Evaluating user: 214, item: 251, predicted: 4.0 -Evaluating user: 191, item: 575, predicted: 3.32174777948537 -Evaluating user: 115, item: 990, predicted: 3.6137364550990245 -Evaluating user: 149, item: 46, predicted: 2.8386717967257202 -Evaluating user: 125, item: 79, predicted: 3.761831276821815 -Evaluating user: 138, item: 126, predicted: 3.6119977386297535 -Evaluating user: 213, item: 52, predicted: 3.242631255566721 -Evaluating user: 15, item: 128, predicted: 2.677068471129866 -Evaluating user: 62, item: 11, predicted: 3.694835086445088 -Evaluating user: 83, item: 428, predicted: 3.046583752628486 -Evaluating user: 30, item: 21, predicted: 3.866272241038406 -Evaluating user: 24, item: 65, predicted: 3.1783635804868453 -Evaluating user: 187, item: 322, predicted: 3.258594559438348 -Evaluating user: 208, item: 69, predicted: 3.3190518786307877 -Evaluating user: 327, item: 20, predicted: 3.3520500776469615 -Evaluating user: 77, item: 113, predicted: 2.9199805905495 -Evaluating user: 53, item: 591, predicted: 3.1260344951076156 -Evaluating user: 6, item: 234, predicted: 3.201540374180235 -Evaluating user: 29, item: 931, predicted: 3.7781592966869013 -Evaluating user: 267, item: 663, predicted: 4.0 -Evaluating user: 125, item: 620, predicted: 3.761831276821815 -Evaluating user: 296, item: 452, predicted: 4.0 -Evaluating user: 83, item: 537, predicted: 3.046583752628486 -Evaluating user: 250, item: 496, predicted: 3.365176917503812 -Evaluating user: 15, item: 180, predicted: 2.677068471129866 -Evaluating user: 163, item: 142, predicted: 3.5113921734681055 -Evaluating user: 105, item: 435, predicted: 4.0 -Evaluating user: 165, item: 91, predicted: 3.620702831791548 -Evaluating user: 302, item: 399, predicted: 2.4282340052533433 -Evaluating user: 116, item: 465, predicted: 3.579993717900999 -Evaluating user: 323, item: 963, predicted: 3.4890009789863967 -Evaluating user: 34, item: 114, predicted: 3.4865196769640567 -Evaluating user: 174, item: 519, predicted: 3.9381978923492715 -Evaluating user: 194, item: 545, predicted: 2.8425910264255885 -Evaluating user: 277, item: 599, predicted: 3.191308703467244 -Evaluating user: 271, item: 636, predicted: 4.0 -Evaluating user: 316, item: 146, predicted: 3.099025894745082 -Evaluating user: 82, item: 592, predicted: 4.0 -Evaluating user: 41, item: 431, predicted: 3.397777301643506 -Evaluating user: 42, item: 19, predicted: 3.6623453377851063 -Evaluating user: 229, item: 532, predicted: 2.6482975494993264 -Evaluating user: 76, item: 653, predicted: 4.0 -Evaluating user: 222, item: 978, predicted: 3.8982422020285328 -Evaluating user: 223, item: 890, predicted: 4.0 -Evaluating user: 80, item: 238, predicted: 3.7155653427793984 -Evaluating user: 197, item: 81, predicted: 3.0930699310342837 -Evaluating user: 84, item: 470, predicted: 3.5967375130272137 -Evaluating user: 0, item: 88, predicted: 2.734770436107598 -Evaluating user: 37, item: 226, predicted: 4.0 -Evaluating user: 65, item: 440, predicted: 4.0 -Evaluating user: 8, item: 8, predicted: 3.6597206732404843 -Evaluating user: 101, item: 305, predicted: 3.276372380348297 -Evaluating user: 253, item: 328, predicted: 2.9553383401310103 -Evaluating user: 115, item: 579, predicted: 3.6137364550990245 -Evaluating user: 28, item: 172, predicted: 4.0 -Evaluating user: 11, item: 296, predicted: 4.0 -Evaluating user: 174, item: 275, predicted: 3.9381978923492715 -Evaluating user: 145, item: 717, predicted: 4.0 -Evaluating user: 212, item: 323, predicted: 3.7439136034394465 -Evaluating user: 5, item: 969, predicted: 2.799210054666971 -Evaluating user: 187, item: 448, predicted: 3.258594559438348 -Evaluating user: 226, item: 48, predicted: 2.920628293432053 -Evaluating user: 102, item: 897, predicted: 3.177248470947968 -Evaluating user: 83, item: 259, predicted: 3.046583752628486 -Evaluating user: 126, item: 181, predicted: 3.6728906294557255 -Evaluating user: 324, item: 98, predicted: 3.7267738173588167 -Evaluating user: 35, item: 746, predicted: 3.5455786589764733 -Evaluating user: 65, item: 318, predicted: 4.0 -Evaluating user: 155, item: 21, predicted: 3.0897378614606397 -Evaluating user: 101, item: 773, predicted: 3.276372380348297 -Evaluating user: 8, item: 155, predicted: 3.6597206732404843 -Evaluating user: 236, item: 344, predicted: 2.7048138774607398 -Evaluating user: 124, item: 10, predicted: 2.9139364224064908 -Evaluating user: 90, item: 471, predicted: 2.6208033399664283 -Evaluating user: 100, item: 673, predicted: 3.1589717525850505 -Evaluating user: 74, item: 185, predicted: 3.850148908102371 -Evaluating user: 283, item: 99, predicted: 3.7936047407527855 -Evaluating user: 30, item: 503, predicted: 3.866272241038406 -Evaluating user: 151, item: 264, predicted: 3.3537029933801845 -Evaluating user: 76, item: 28, predicted: 4.0 -Evaluating user: 68, item: 825, predicted: 1.5682077401379722 -Evaluating user: 37, item: 334, predicted: 4.0 -Evaluating user: 65, item: 630, predicted: 4.0 -Evaluating user: 239, item: 226, predicted: 3.562671129833055 -Evaluating user: 37, item: 245, predicted: 4.0 -Evaluating user: 191, item: 437, predicted: 3.32174777948537 -Evaluating user: 103, item: 3, predicted: 3.7376057615125893 -Evaluating user: 27, item: 358, predicted: 3.0452065709348664 -Evaluating user: 78, item: 448, predicted: 3.507489658917522 -Evaluating user: 15, item: 171, predicted: 2.677068471129866 -Evaluating user: 43, item: 459, predicted: 3.258812818890413 -Evaluating user: 273, item: 180, predicted: 4.0 -Evaluating user: 64, item: 387, predicted: 3.202547478725553 -Evaluating user: 127, item: 703, predicted: 2.282856560388726 -Evaluating user: 155, item: 199, predicted: 3.0897378614606397 -Evaluating user: 46, item: 242, predicted: 3.277755855715274 -Evaluating user: 82, item: 729, predicted: 4.0 -Evaluating user: 144, item: 623, predicted: 2.3453813892347712 -Evaluating user: 65, item: 435, predicted: 4.0 -Evaluating user: 231, item: 426, predicted: 3.6667805627820957 -Evaluating user: 105, item: 341, predicted: 4.0 -Evaluating user: 100, item: 437, predicted: 3.1589717525850505 -Evaluating user: 101, item: 830, predicted: 3.276372380348297 -Evaluating user: 266, item: 193, predicted: 2.7481549403698615 -Evaluating user: 194, item: 796, predicted: 2.8425910264255885 -Evaluating user: 167, item: 862, predicted: 3.56005702511892 -Evaluating user: 89, item: 251, predicted: 3.162531742858552 -Evaluating user: 45, item: 27, predicted: 3.421716550196023 -Evaluating user: 27, item: 439, predicted: 3.0452065709348664 -Evaluating user: 106, item: 470, predicted: 2.8632698743667886 -Evaluating user: 191, item: 289, predicted: 3.32174777948537 -Evaluating user: 270, item: 159, predicted: 3.815880658560767 -Evaluating user: 316, item: 489, predicted: 3.099025894745082 -Evaluating user: 125, item: 296, predicted: 3.761831276821815 -Evaluating user: 257, item: 178, predicted: 2.569536997766901 -Evaluating user: 221, item: 16, predicted: 3.111955165178042 -Evaluating user: 94, item: 215, predicted: 3.204886329269301 -Evaluating user: 103, item: 171, predicted: 3.7376057615125893 -Evaluating user: 193, item: 1, predicted: 3.9343050252304037 -Evaluating user: 161, item: 208, predicted: 3.53804643196736 -Evaluating user: 58, item: 131, predicted: 3.69180550429593 -Evaluating user: 97, item: 84, predicted: 3.9880103262613322 -Evaluating user: 304, item: 610, predicted: 2.9127125203052233 -Evaluating user: 39, item: 985, predicted: 4.0 -Evaluating user: 16, item: 537, predicted: 3.3142726592872442 -Evaluating user: 58, item: 91, predicted: 3.69180550429593 -Evaluating user: 221, item: 578, predicted: 3.111955165178042 -Evaluating user: 41, item: 4, predicted: 3.397777301643506 -Evaluating user: 84, item: 689, predicted: 3.5967375130272137 -Evaluating user: 81, item: 161, predicted: 3.837020551056988 -Evaluating user: 317, item: 403, predicted: 1.9971576512570797 -Evaluating user: 105, item: 122, predicted: 4.0 -Evaluating user: 167, item: 300, predicted: 3.56005702511892 -Evaluating user: 91, item: 579, predicted: 3.3228832822818015 -Evaluating user: 8, item: 606, predicted: 3.6597206732404843 -Evaluating user: 194, item: 676, predicted: 2.8425910264255885 -Evaluating user: 234, item: 359, predicted: 2.7806680648778594 -Evaluating user: 8, item: 272, predicted: 3.6597206732404843 -Evaluating user: 85, item: 468, predicted: 3.734290231272179 -Evaluating user: 262, item: 40, predicted: 3.6730438217905075 -Evaluating user: 76, item: 435, predicted: 4.0 -Evaluating user: 67, item: 354, predicted: 3.5841153561372403 -Evaluating user: 47, item: 182, predicted: 2.6370263485688588 -Evaluating user: 269, item: 252, predicted: 4.0 -Evaluating user: 152, item: 468, predicted: 3.6981160858188953 -Evaluating user: 147, item: 892, predicted: 4.0 -Evaluating user: 35, item: 237, predicted: 3.5455786589764733 -Evaluating user: 64, item: 105, predicted: 3.202547478725553 -Evaluating user: 9, item: 8, predicted: 3.6156462106949405 -Evaluating user: 327, item: 687, predicted: 3.3520500776469615 -Evaluating user: 8, item: 86, predicted: 3.6597206732404843 -Evaluating user: 90, item: 832, predicted: 2.6208033399664283 -Evaluating user: 1, item: 181, predicted: 3.5921467575298585 -Evaluating user: 41, item: 21, predicted: 3.397777301643506 -Evaluating user: 191, item: 187, predicted: 3.32174777948537 -Evaluating user: 217, item: 920, predicted: 3.905947738891853 -Evaluating user: 35, item: 236, predicted: 3.5455786589764733 -Evaluating user: 53, item: 319, predicted: 3.1260344951076156 -Evaluating user: 30, item: 350, predicted: 3.866272241038406 -Evaluating user: 73, item: 113, predicted: 3.8573390302045087 -Evaluating user: 8, item: 6, predicted: 3.6597206732404843 -Evaluating user: 46, item: 46, predicted: 3.277755855715274 -Evaluating user: 40, item: 319, predicted: 3.6226568169745494 -Evaluating user: 269, item: 61, predicted: 4.0 -Evaluating user: 24, item: 92, predicted: 3.1783635804868453 -Evaluating user: 216, item: 420, predicted: 3.6797601754321314 -Evaluating user: 142, item: 448, predicted: 3.434401628884774 -Evaluating user: 120, item: 210, predicted: 3.0003930660134595 -Evaluating user: 37, item: 788, predicted: 4.0 -Evaluating user: 29, item: 111, predicted: 3.7781592966869013 -Evaluating user: 46, item: 21, predicted: 3.277755855715274 -Evaluating user: 64, item: 148, predicted: 3.202547478725553 -Evaluating user: 131, item: 211, predicted: 3.745733974534817 -Evaluating user: 92, item: 676, predicted: 3.559073293326748 -Evaluating user: 6, item: 593, predicted: 3.201540374180235 -Evaluating user: 225, item: 296, predicted: 3.9467115737525225 -Evaluating user: 114, item: 479, predicted: 3.4122120789616113 -Evaluating user: 181, item: 387, predicted: 3.1358150526443636 -Evaluating user: 97, item: 350, predicted: 3.9880103262613322 -Evaluating user: 40, item: 292, predicted: 3.6226568169745494 -Evaluating user: 126, item: 67, predicted: 3.6728906294557255 -Evaluating user: 234, item: 323, predicted: 2.7806680648778594 -Evaluating user: 302, item: 355, predicted: 2.4282340052533433 -Evaluating user: 293, item: 129, predicted: 4.0 -Evaluating user: 155, item: 121, predicted: 3.0897378614606397 -Evaluating user: 97, item: 479, predicted: 3.9880103262613322 -Evaluating user: 204, item: 488, predicted: 4.0 -Evaluating user: 6, item: 307, predicted: 3.201540374180235 -Evaluating user: 20, item: 311, predicted: 3.870033225798768 -Evaluating user: 261, item: 77, predicted: 3.0086335343036836 -Evaluating user: 11, item: 729, predicted: 4.0 -Evaluating user: 69, item: 132, predicted: 3.229238415003662 -Evaluating user: 6, item: 948, predicted: 3.201540374180235 -Evaluating user: 92, item: 144, predicted: 3.559073293326748 -Evaluating user: 306, item: 154, predicted: 3.6375006971459865 -Evaluating user: 42, item: 130, predicted: 3.6623453377851063 -Evaluating user: 126, item: 332, predicted: 3.6728906294557255 -Evaluating user: 131, item: 290, predicted: 3.745733974534817 -Evaluating user: 34, item: 450, predicted: 3.4865196769640567 -Evaluating user: 41, item: 126, predicted: 3.397777301643506 -Evaluating user: 78, item: 378, predicted: 3.507489658917522 -Evaluating user: 116, item: 328, predicted: 3.579993717900999 -Evaluating user: 17, item: 748, predicted: 4.0 -Evaluating user: 127, item: 470, predicted: 2.282856560388726 -Evaluating user: 103, item: 449, predicted: 3.7376057615125893 -Evaluating user: 261, item: 66, predicted: 3.0086335343036836 -Evaluating user: 298, item: 689, predicted: 3.130616313639387 -Evaluating user: 121, item: 933, predicted: 2.959321252635845 -Evaluating user: 218, item: 1, predicted: 2.837156981933278 -Evaluating user: 157, item: 924, predicted: 3.537742933934916 -Evaluating user: 65, item: 598, predicted: 4.0 -Evaluating user: 184, item: 387, predicted: 2.8062516091208507 -Evaluating user: 42, item: 252, predicted: 3.6623453377851063 -Evaluating user: 58, item: 746, predicted: 3.69180550429593 -Evaluating user: 30, item: 432, predicted: 3.866272241038406 -Evaluating user: 24, item: 515, predicted: 3.1783635804868453 -Evaluating user: 27, item: 97, predicted: 3.0452065709348664 -Evaluating user: 47, item: 527, predicted: 2.6370263485688588 -Evaluating user: 24, item: 771, predicted: 3.1783635804868453 -Evaluating user: 136, item: 479, predicted: 3.1843331274877817 -Evaluating user: 75, item: 924, predicted: 3.103056831113985 -Evaluating user: 12, item: 306, predicted: 2.664914638376698 -Evaluating user: 13, item: 578, predicted: 3.026749739953794 -Evaluating user: 216, item: 224, predicted: 3.6797601754321314 -Evaluating user: 21, item: 304, predicted: 3.2390720152222396 -Evaluating user: 165, item: 234, predicted: 3.620702831791548 -Evaluating user: 19, item: 236, predicted: 3.7492266327587394 -Evaluating user: 30, item: 381, predicted: 3.866272241038406 -Evaluating user: 9, item: 428, predicted: 3.6156462106949405 -Evaluating user: 191, item: 442, predicted: 3.32174777948537 -Evaluating user: 13, item: 852, predicted: 3.026749739953794 -Evaluating user: 122, item: 264, predicted: 3.8025599268544426 -Evaluating user: 51, item: 98, predicted: 3.674316184015763 -Evaluating user: 46, item: 867, predicted: 3.277755855715274 -Evaluating user: 131, item: 106, predicted: 3.745733974534817 -Evaluating user: 222, item: 69, predicted: 3.8982422020285328 -Evaluating user: 13, item: 237, predicted: 3.026749739953794 -Evaluating user: 256, item: 61, predicted: 4.0 -Evaluating user: 11, item: 913, predicted: 4.0 -Evaluating user: 7, item: 960, predicted: 4.0 -Evaluating user: 293, item: 100, predicted: 4.0 -Evaluating user: 87, item: 318, predicted: 3.4224920797908034 -Evaluating user: 58, item: 44, predicted: 3.69180550429593 -Evaluating user: 242, item: 248, predicted: 4.0 -Evaluating user: 83, item: 293, predicted: 3.046583752628486 -Evaluating user: 275, item: 193, predicted: 4.0 -Evaluating user: 122, item: 522, predicted: 3.8025599268544426 -Evaluating user: 211, item: 21, predicted: 3.757882313503026 -Evaluating user: 280, item: 31, predicted: 3.59147811735182 -Evaluating user: 14, item: 181, predicted: 4.0 -Evaluating user: 73, item: 189, predicted: 3.8573390302045087 -Evaluating user: 51, item: 331, predicted: 3.674316184015763 -Evaluating user: 47, item: 123, predicted: 2.6370263485688588 -Evaluating user: 319, item: 24, predicted: 3.5705134643156864 -Evaluating user: 22, item: 266, predicted: 3.796056342470468 -Evaluating user: 171, item: 393, predicted: 3.5537895965250352 -Evaluating user: 75, item: 328, predicted: 3.103056831113985 -Evaluating user: 323, item: 687, predicted: 3.4890009789863967 -Evaluating user: 196, item: 821, predicted: 3.841976529354005 -Evaluating user: 27, item: 761, predicted: 3.0452065709348664 -Evaluating user: 59, item: 6, predicted: 3.3237759115696086 -Evaluating user: 100, item: 292, predicted: 3.1589717525850505 -Evaluating user: 40, item: 55, predicted: 3.6226568169745494 -Evaluating user: 8, item: 276, predicted: 3.6597206732404843 -Evaluating user: 230, item: 296, predicted: 3.687678112955184 -Evaluating user: 270, item: 142, predicted: 3.815880658560767 -Evaluating user: 208, item: 118, predicted: 3.3190518786307877 -Evaluating user: 24, item: 235, predicted: 3.1783635804868453 -Evaluating user: 83, item: 120, predicted: 3.046583752628486 -Evaluating user: 126, item: 575, predicted: 3.6728906294557255 -Evaluating user: 51, item: 275, predicted: 3.674316184015763 -Evaluating user: 261, item: 195, predicted: 3.0086335343036836 -Evaluating user: 64, item: 237, predicted: 3.202547478725553 -Evaluating user: 17, item: 248, predicted: 4.0 -Evaluating user: 206, item: 71, predicted: 4.0 -Evaluating user: 105, item: 478, predicted: 4.0 -Evaluating user: 145, item: 329, predicted: 4.0 -Evaluating user: 50, item: 729, predicted: 3.2002652030078984 -Evaluating user: 207, item: 211, predicted: 2.8360856694213443 -Evaluating user: 34, item: 20, predicted: 3.4865196769640567 -Evaluating user: 34, item: 410, predicted: 3.4865196769640567 -Evaluating user: 82, item: 713, predicted: 4.0 -Evaluating user: 46, item: 166, predicted: 3.277755855715274 -Evaluating user: 45, item: 183, predicted: 3.421716550196023 -Evaluating user: 29, item: 393, predicted: 3.7781592966869013 -Evaluating user: 45, item: 595, predicted: 3.421716550196023 -Evaluating user: 118, item: 693, predicted: 4.0 -Evaluating user: 43, item: 468, predicted: 3.258812818890413 -Evaluating user: 153, item: 848, predicted: 2.545446004248653 -Evaluating user: 145, item: 248, predicted: 4.0 -Evaluating user: 13, item: 142, predicted: 3.026749739953794 -Evaluating user: 219, item: 438, predicted: 3.3801308861014707 -Evaluating user: 92, item: 88, predicted: 3.559073293326748 -Evaluating user: 94, item: 357, predicted: 3.204886329269301 -Evaluating user: 29, item: 52, predicted: 3.7781592966869013 -Evaluating user: 78, item: 234, predicted: 3.507489658917522 -Evaluating user: 51, item: 83, predicted: 3.674316184015763 -Evaluating user: 199, item: 98, predicted: 4.0 -Evaluating user: 196, item: 97, predicted: 3.841976529354005 -Evaluating user: 113, item: 102, predicted: 3.363926080883002 -Evaluating user: 86, item: 126, predicted: 3.2330500503420048 -Evaluating user: 52, item: 248, predicted: 3.449963387639435 -Evaluating user: 198, item: 392, predicted: 3.9179541097285027 -Evaluating user: 77, item: 653, predicted: 2.9199805905495 -Evaluating user: 103, item: 272, predicted: 3.7376057615125893 -Evaluating user: 90, item: 295, predicted: 2.6208033399664283 -Evaluating user: 6, item: 108, predicted: 3.201540374180235 -Evaluating user: 192, item: 210, predicted: 3.8070937172974855 -Evaluating user: 126, item: 764, predicted: 3.6728906294557255 -Evaluating user: 116, item: 225, predicted: 3.579993717900999 -Evaluating user: 193, item: 456, predicted: 3.9343050252304037 -Evaluating user: 101, item: 136, predicted: 3.276372380348297 -Evaluating user: 14, item: 801, predicted: 4.0 -Evaluating user: 52, item: 329, predicted: 3.449963387639435 -Evaluating user: 221, item: 568, predicted: 3.111955165178042 -Evaluating user: 174, item: 862, predicted: 3.9381978923492715 -Evaluating user: 15, item: 426, predicted: 2.677068471129866 -Evaluating user: 52, item: 156, predicted: 3.449963387639435 -Evaluating user: 58, item: 511, predicted: 3.69180550429593 -Evaluating user: 87, item: 749, predicted: 3.4224920797908034 -Evaluating user: 180, item: 425, predicted: 3.48873440226905 -Evaluating user: 51, item: 16, predicted: 3.674316184015763 -Evaluating user: 102, item: 868, predicted: 3.177248470947968 -Evaluating user: 64, item: 161, predicted: 3.202547478725553 -Evaluating user: 305, item: 501, predicted: 3.968071441335683 -Evaluating user: 161, item: 350, predicted: 3.53804643196736 -Evaluating user: 8, item: 231, predicted: 3.6597206732404843 -Evaluating user: 191, item: 252, predicted: 3.32174777948537 -Evaluating user: 154, item: 731, predicted: 3.9251733893292546 -Evaluating user: 149, item: 26, predicted: 2.8386717967257202 -Evaluating user: 180, item: 535, predicted: 3.48873440226905 -Evaluating user: 273, item: 127, predicted: 4.0 -Evaluating user: 194, item: 265, predicted: 2.8425910264255885 -Evaluating user: 26, item: 566, predicted: 3.2282415361513763 -Evaluating user: 1, item: 695, predicted: 3.5921467575298585 -Evaluating user: 144, item: 352, predicted: 2.3453813892347712 -Evaluating user: 300, item: 156, predicted: 3.2646298075084443 -Evaluating user: 111, item: 401, predicted: 3.529305238189742 -Evaluating user: 165, item: 361, predicted: 3.620702831791548 -Evaluating user: 182, item: 496, predicted: 2.7551743925444994 -Evaluating user: 77, item: 391, predicted: 2.9199805905495 -Evaluating user: 100, item: 533, predicted: 3.1589717525850505 -Evaluating user: 116, item: 446, predicted: 3.579993717900999 -Evaluating user: 40, item: 43, predicted: 3.6226568169745494 -Evaluating user: 87, item: 891, predicted: 3.4224920797908034 -Evaluating user: 21, item: 891, predicted: 3.2390720152222396 -Evaluating user: 172, item: 324, predicted: 3.7424992882435157 -Evaluating user: 191, item: 825, predicted: 3.32174777948537 -Evaluating user: 152, item: 232, predicted: 3.6981160858188953 -Evaluating user: 11, item: 527, predicted: 4.0 -Evaluating user: 292, item: 306, predicted: 3.604057187893226 -Evaluating user: 6, item: 418, predicted: 3.201540374180235 -Evaluating user: 306, item: 689, predicted: 3.6375006971459865 -Evaluating user: 27, item: 381, predicted: 3.0452065709348664 -Evaluating user: 15, item: 352, predicted: 2.677068471129866 -Evaluating user: 80, item: 2, predicted: 3.7155653427793984 -Evaluating user: 61, item: 214, predicted: 2.3337088155756582 -Evaluating user: 151, item: 934, predicted: 3.3537029933801845 -Evaluating user: 46, item: 55, predicted: 3.277755855715274 -Evaluating user: 212, item: 183, predicted: 3.7439136034394465 -Evaluating user: 89, item: 279, predicted: 3.162531742858552 -Evaluating user: 77, item: 318, predicted: 2.9199805905495 -Evaluating user: 176, item: 521, predicted: 3.5946708118458477 -Evaluating user: 46, item: 150, predicted: 3.277755855715274 -Evaluating user: 256, item: 464, predicted: 4.0 -Evaluating user: 148, item: 102, predicted: 3.77329810868265 -Evaluating user: 163, item: 230, predicted: 3.5113921734681055 -Evaluating user: 227, item: 714, predicted: 2.7954260752153313 -Evaluating user: 165, item: 312, predicted: 3.620702831791548 -Evaluating user: 198, item: 533, predicted: 3.9179541097285027 -Evaluating user: 120, item: 387, predicted: 3.0003930660134595 -Evaluating user: 30, item: 61, predicted: 3.866272241038406 -Evaluating user: 101, item: 823, predicted: 3.276372380348297 -Evaluating user: 262, item: 110, predicted: 3.6730438217905075 -Evaluating user: 4, item: 45, predicted: 3.768511205275512 -Evaluating user: 83, item: 328, predicted: 3.046583752628486 -Evaluating user: 59, item: 456, predicted: 3.3237759115696086 -Evaluating user: 13, item: 26, predicted: 3.026749739953794 -Evaluating user: 1, item: 686, predicted: 3.5921467575298585 -Evaluating user: 201, item: 223, predicted: 2.999154250328697 -Evaluating user: 87, item: 38, predicted: 3.4224920797908034 -Evaluating user: 126, item: 368, predicted: 3.6728906294557255 -Evaluating user: 102, item: 441, predicted: 3.177248470947968 -Evaluating user: 51, item: 100, predicted: 3.674316184015763 -Evaluating user: 91, item: 585, predicted: 3.3228832822818015 -Evaluating user: 14, item: 35, predicted: 4.0 -Evaluating user: 14, item: 326, predicted: 4.0 -Evaluating user: 83, item: 281, predicted: 3.046583752628486 -Evaluating user: 127, item: 884, predicted: 2.282856560388726 -Evaluating user: 113, item: 186, predicted: 3.363926080883002 -Evaluating user: 30, item: 119, predicted: 3.866272241038406 -Evaluating user: 147, item: 979, predicted: 4.0 -Evaluating user: 94, item: 385, predicted: 3.204886329269301 -Evaluating user: 16, item: 144, predicted: 3.3142726592872442 -Evaluating user: 116, item: 994, predicted: 3.579993717900999 -Evaluating user: 157, item: 583, predicted: 3.537742933934916 -Evaluating user: 84, item: 161, predicted: 3.5967375130272137 -Evaluating user: 51, item: 703, predicted: 3.674316184015763 -Evaluating user: 69, item: 509, predicted: 3.229238415003662 -Evaluating user: 34, item: 251, predicted: 3.4865196769640567 -Evaluating user: 92, item: 328, predicted: 3.559073293326748 -Evaluating user: 77, item: 89, predicted: 2.9199805905495 -Evaluating user: 114, item: 119, predicted: 3.4122120789616113 -Evaluating user: 8, item: 249, predicted: 3.6597206732404843 -Evaluating user: 77, item: 777, predicted: 2.9199805905495 -Evaluating user: 54, item: 711, predicted: 3.557432591116923 -Evaluating user: 194, item: 103, predicted: 2.8425910264255885 -Evaluating user: 191, item: 137, predicted: 3.32174777948537 -Evaluating user: 27, item: 507, predicted: 3.0452065709348664 -Evaluating user: 113, item: 96, predicted: 3.363926080883002 -Evaluating user: 138, item: 15, predicted: 3.6119977386297535 -Evaluating user: 100, item: 438, predicted: 3.1589717525850505 -Evaluating user: 305, item: 76, predicted: 3.968071441335683 -Evaluating user: 100, item: 269, predicted: 3.1589717525850505 -Evaluating user: 121, item: 506, predicted: 2.959321252635845 -Evaluating user: 106, item: 206, predicted: 2.8632698743667886 -Evaluating user: 114, item: 606, predicted: 3.4122120789616113 -Evaluating user: 77, item: 57, predicted: 2.9199805905495 -Evaluating user: 151, item: 339, predicted: 3.3537029933801845 -Evaluating user: 152, item: 181, predicted: 3.6981160858188953 -Evaluating user: 13, item: 376, predicted: 3.026749739953794 -Evaluating user: 203, item: 541, predicted: 2.8907814945499912 -Evaluating user: 250, item: 470, predicted: 3.365176917503812 -Evaluating user: 104, item: 230, predicted: 3.7505707238004615 -Evaluating user: 169, item: 574, predicted: 3.5973082024533136 -Evaluating user: 30, item: 329, predicted: 3.866272241038406 -Evaluating user: 144, item: 653, predicted: 2.3453813892347712 -Evaluating user: 36, item: 849, predicted: 3.460662564244048 -Evaluating user: 165, item: 734, predicted: 3.620702831791548 -Evaluating user: 33, item: 370, predicted: 3.2658014734099217 -Evaluating user: 97, item: 492, predicted: 3.9880103262613322 -Evaluating user: 151, item: 4, predicted: 3.3537029933801845 -Evaluating user: 162, item: 270, predicted: 2.367676495966542 -Evaluating user: 81, item: 473, predicted: 3.837020551056988 -Evaluating user: 97, item: 525, predicted: 3.9880103262613322 -Evaluating user: 68, item: 565, predicted: 1.5682077401379722 -Evaluating user: 6, item: 403, predicted: 3.201540374180235 -Evaluating user: 156, item: 577, predicted: 4.0 -Evaluating user: 276, item: 534, predicted: 3.043588626174305 -Evaluating user: 197, item: 2, predicted: 3.0930699310342837 -Evaluating user: 50, item: 312, predicted: 3.2002652030078984 -Evaluating user: 11, item: 809, predicted: 4.0 -Evaluating user: 163, item: 378, predicted: 3.5113921734681055 -Evaluating user: 7, item: 593, predicted: 4.0 -Evaluating user: 121, item: 227, predicted: 2.959321252635845 -Evaluating user: 279, item: 251, predicted: 2.598834328060508 -Evaluating user: 33, item: 672, predicted: 3.2658014734099217 -Evaluating user: 301, item: 55, predicted: 3.1598995309591844 -Evaluating user: 194, item: 1, predicted: 2.8425910264255885 -Evaluating user: 208, item: 641, predicted: 3.3190518786307877 -Evaluating user: 251, item: 496, predicted: 2.8715919340172507 -Evaluating user: 106, item: 439, predicted: 2.8632698743667886 -Evaluating user: 78, item: 69, predicted: 3.507489658917522 -Evaluating user: 6, item: 608, predicted: 3.201540374180235 -Evaluating user: 61, item: 330, predicted: 2.3337088155756582 -Evaluating user: 33, item: 681, predicted: 3.2658014734099217 -Evaluating user: 23, item: 8, predicted: 3.103225105551166 -Evaluating user: 194, item: 600, predicted: 2.8425910264255885 -Evaluating user: 144, item: 3, predicted: 2.3453813892347712 -Evaluating user: 73, item: 580, predicted: 3.8573390302045087 -Evaluating user: 65, item: 28, predicted: 4.0 -Evaluating user: 94, item: 259, predicted: 3.204886329269301 -Evaluating user: 144, item: 324, predicted: 2.3453813892347712 -Evaluating user: 15, item: 20, predicted: 2.677068471129866 -Evaluating user: 163, item: 707, predicted: 3.5113921734681055 -Evaluating user: 111, item: 598, predicted: 3.529305238189742 -Evaluating user: 34, item: 740, predicted: 3.4865196769640567 -Evaluating user: 19, item: 609, predicted: 3.7492266327587394 -Evaluating user: 191, item: 562, predicted: 3.32174777948537 -Evaluating user: 326, item: 251, predicted: 3.5140770201757823 -Evaluating user: 74, item: 186, predicted: 3.850148908102371 -Evaluating user: 90, item: 307, predicted: 2.6208033399664283 -Evaluating user: 111, item: 610, predicted: 3.529305238189742 -Evaluating user: 144, item: 496, predicted: 2.3453813892347712 -Evaluating user: 14, item: 376, predicted: 4.0 -Evaluating user: 194, item: 254, predicted: 2.8425910264255885 -Evaluating user: 53, item: 349, predicted: 3.1260344951076156 -Evaluating user: 1, item: 370, predicted: 3.5921467575298585 -Evaluating user: 27, item: 418, predicted: 3.0452065709348664 -Evaluating user: 69, item: 672, predicted: 3.229238415003662 -Evaluating user: 54, item: 10, predicted: 3.557432591116923 -Evaluating user: 221, item: 52, predicted: 3.111955165178042 -Evaluating user: 42, item: 859, predicted: 3.6623453377851063 -Evaluating user: 41, item: 112, predicted: 3.397777301643506 -Evaluating user: 15, item: 623, predicted: 2.677068471129866 -Evaluating user: 120, item: 833, predicted: 3.0003930660134595 -Evaluating user: 58, item: 9, predicted: 3.69180550429593 -Evaluating user: 77, item: 399, predicted: 2.9199805905495 -Evaluating user: 274, item: 392, predicted: 3.517732966209685 -Evaluating user: 268, item: 259, predicted: 2.782795582582197 -Evaluating user: 58, item: 635, predicted: 3.69180550429593 -Evaluating user: 3, item: 218, predicted: 3.7110027566608332 -Evaluating user: 156, item: 483, predicted: 4.0 -Evaluating user: 293, item: 328, predicted: 4.0 -Evaluating user: 170, item: 52, predicted: 3.8016816171003276 -Evaluating user: 127, item: 484, predicted: 2.282856560388726 -Evaluating user: 33, item: 177, predicted: 3.2658014734099217 -Evaluating user: 56, item: 483, predicted: 3.6473008741018886 -Evaluating user: 164, item: 98, predicted: 3.494502219932684 -Evaluating user: 111, item: 300, predicted: 3.529305238189742 -Evaluating user: 83, item: 713, predicted: 3.046583752628486 -Evaluating user: 324, item: 69, predicted: 3.7267738173588167 -Evaluating user: 293, item: 729, predicted: 4.0 -Evaluating user: 172, item: 437, predicted: 3.7424992882435157 -Evaluating user: 185, item: 346, predicted: 3.815111299518758 -Evaluating user: 68, item: 461, predicted: 1.5682077401379722 -Evaluating user: 165, item: 186, predicted: 3.620702831791548 -Evaluating user: 58, item: 273, predicted: 3.69180550429593 -Evaluating user: 100, item: 101, predicted: 3.1589717525850505 -Evaluating user: 105, item: 852, predicted: 4.0 -Evaluating user: 106, item: 259, predicted: 2.8632698743667886 -Evaluating user: 54, item: 321, predicted: 3.557432591116923 -Evaluating user: 213, item: 660, predicted: 3.242631255566721 -Evaluating user: 61, item: 272, predicted: 2.3337088155756582 -Evaluating user: 49, item: 590, predicted: 4.0 -Evaluating user: 194, item: 896, predicted: 2.8425910264255885 -Evaluating user: 22, item: 590, predicted: 3.796056342470468 -Evaluating user: 204, item: 13, predicted: 4.0 -Evaluating user: 100, item: 704, predicted: 3.1589717525850505 -Evaluating user: 2, item: 21, predicted: 2.199927657942208 -Evaluating user: 188, item: 76, predicted: 3.9348976928212727 -Evaluating user: 43, item: 572, predicted: 3.258812818890413 -Evaluating user: 76, item: 597, predicted: 4.0 -Evaluating user: 83, item: 785, predicted: 3.046583752628486 -Evaluating user: 139, item: 562, predicted: 4.0 -Evaluating user: 37, item: 456, predicted: 4.0 -Evaluating user: 153, item: 69, predicted: 2.545446004248653 -Evaluating user: 281, item: 443, predicted: 4.0 -Evaluating user: 126, item: 102, predicted: 3.6728906294557255 -Evaluating user: 267, item: 549, predicted: 4.0 -Evaluating user: 36, item: 9, predicted: 3.460662564244048 -Evaluating user: 20, item: 69, predicted: 3.870033225798768 -Evaluating user: 267, item: 180, predicted: 4.0 -Evaluating user: 154, item: 75, predicted: 3.9251733893292546 -Evaluating user: 68, item: 456, predicted: 1.5682077401379722 -Evaluating user: 83, item: 136, predicted: 3.046583752628486 -Evaluating user: 34, item: 445, predicted: 3.4865196769640567 -Evaluating user: 74, item: 251, predicted: 3.850148908102371 -Evaluating user: 15, item: 139, predicted: 2.677068471129866 -Evaluating user: 116, item: 685, predicted: 3.579993717900999 -Evaluating user: 182, item: 472, predicted: 2.7551743925444994 -Evaluating user: 54, item: 458, predicted: 3.557432591116923 -Evaluating user: 165, item: 139, predicted: 3.620702831791548 -Evaluating user: 84, item: 541, predicted: 3.5967375130272137 -Evaluating user: 14, item: 667, predicted: 4.0 -Evaluating user: 270, item: 376, predicted: 3.815880658560767 -Evaluating user: 36, item: 65, predicted: 3.460662564244048 -Evaluating user: 89, item: 98, predicted: 3.162531742858552 -Evaluating user: 95, item: 341, predicted: 3.6536573003892197 -Evaluating user: 47, item: 160, predicted: 2.6370263485688588 -Evaluating user: 163, item: 798, predicted: 3.5113921734681055 -Evaluating user: 34, item: 904, predicted: 3.4865196769640567 -Evaluating user: 185, item: 251, predicted: 3.815111299518758 -Evaluating user: 226, item: 108, predicted: 2.920628293432053 -Evaluating user: 163, item: 525, predicted: 3.5113921734681055 -Evaluating user: 296, item: 76, predicted: 4.0 -Evaluating user: 84, item: 10, predicted: 3.5967375130272137 -Evaluating user: 123, item: 497, predicted: 4.0 -Evaluating user: 65, item: 252, predicted: 4.0 -Evaluating user: 7, item: 428, predicted: 4.0 -Evaluating user: 160, item: 168, predicted: 2.887138985414394 -Evaluating user: 113, item: 30, predicted: 3.363926080883002 -Evaluating user: 196, item: 875, predicted: 3.841976529354005 -Evaluating user: 51, item: 653, predicted: 3.674316184015763 -Evaluating user: 272, item: 180, predicted: 3.1259065000413004 -Evaluating user: 120, item: 100, predicted: 3.0003930660134595 -Evaluating user: 70, item: 814, predicted: 3.16817852056873 -Evaluating user: 168, item: 230, predicted: 3.4992703869640467 -Evaluating user: 68, item: 865, predicted: 1.5682077401379722 -Evaluating user: 75, item: 451, predicted: 3.103056831113985 -Evaluating user: 315, item: 399, predicted: 4.0 -Evaluating user: 23, item: 2, predicted: 3.103225105551166 -Evaluating user: 75, item: 884, predicted: 3.103056831113985 -Evaluating user: 236, item: 978, predicted: 2.7048138774607398 -Evaluating user: 51, item: 208, predicted: 3.674316184015763 -Evaluating user: 291, item: 259, predicted: 3.607150693961662 -Evaluating user: 269, item: 91, predicted: 4.0 -Evaluating user: 116, item: 482, predicted: 3.579993717900999 -Evaluating user: 80, item: 578, predicted: 3.7155653427793984 -Evaluating user: 211, item: 185, predicted: 3.757882313503026 -Evaluating user: 180, item: 785, predicted: 3.48873440226905 -Evaluating user: 155, item: 54, predicted: 3.0897378614606397 -Evaluating user: 317, item: 718, predicted: 1.9971576512570797 -Evaluating user: 3, item: 572, predicted: 3.7110027566608332 -Evaluating user: 11, item: 886, predicted: 4.0 -Evaluating user: 305, item: 189, predicted: 3.968071441335683 -Evaluating user: 235, item: 11, predicted: 4.0 -Evaluating user: 15, item: 318, predicted: 2.677068471129866 -Evaluating user: 28, item: 154, predicted: 4.0 -Evaluating user: 50, item: 110, predicted: 3.2002652030078984 -Evaluating user: 127, item: 868, predicted: 2.282856560388726 -Evaluating user: 83, item: 177, predicted: 3.046583752628486 -Evaluating user: 103, item: 155, predicted: 3.7376057615125893 -Evaluating user: 130, item: 119, predicted: 3.7679260827724548 -Evaluating user: 58, item: 526, predicted: 3.69180550429593 -Evaluating user: 5, item: 252, predicted: 2.799210054666971 -Evaluating user: 97, item: 154, predicted: 3.9880103262613322 -Evaluating user: 174, item: 252, predicted: 3.9381978923492715 -Evaluating user: 318, item: 131, predicted: 3.6790861884597748 -Evaluating user: 46, item: 427, predicted: 3.277755855715274 -Evaluating user: 296, item: 700, predicted: 4.0 -Evaluating user: 323, item: 403, predicted: 3.4890009789863967 -Evaluating user: 221, item: 55, predicted: 3.111955165178042 -Evaluating user: 172, item: 382, predicted: 3.7424992882435157 -Evaluating user: 56, item: 227, predicted: 3.6473008741018886 -Evaluating user: 152, item: 21, predicted: 3.6981160858188953 -Evaluating user: 111, item: 350, predicted: 3.529305238189742 -Evaluating user: 41, item: 470, predicted: 3.397777301643506 -Evaluating user: 45, item: 838, predicted: 3.421716550196023 -Evaluating user: 77, item: 323, predicted: 2.9199805905495 -Evaluating user: 215, item: 930, predicted: 3.606390771414933 -Evaluating user: 76, item: 565, predicted: 4.0 -Evaluating user: 47, item: 656, predicted: 2.6370263485688588 -Evaluating user: 194, item: 721, predicted: 2.8425910264255885 -Evaluating user: 69, item: 202, predicted: 3.229238415003662 -Evaluating user: 153, item: 663, predicted: 2.545446004248653 -Evaluating user: 144, item: 170, predicted: 2.3453813892347712 -Evaluating user: 317, item: 359, predicted: 1.9971576512570797 -Evaluating user: 43, item: 433, predicted: 3.258812818890413 -Evaluating user: 216, item: 119, predicted: 3.6797601754321314 -Evaluating user: 49, item: 181, predicted: 4.0 -Evaluating user: 92, item: 74, predicted: 3.559073293326748 -Evaluating user: 145, item: 796, predicted: 4.0 -Evaluating user: 190, item: 252, predicted: 3.4036554819828844 -Evaluating user: 138, item: 32, predicted: 3.6119977386297535 -Evaluating user: 293, item: 130, predicted: 4.0 -Evaluating user: 6, item: 295, predicted: 3.201540374180235 -Evaluating user: 300, item: 16, predicted: 3.2646298075084443 -Evaluating user: 151, item: 430, predicted: 3.3537029933801845 -Evaluating user: 105, item: 689, predicted: 4.0 -Evaluating user: 279, item: 443, predicted: 2.598834328060508 -Evaluating user: 118, item: 947, predicted: 4.0 -Evaluating user: 313, item: 32, predicted: 2.8708782488494493 -Evaluating user: 20, item: 888, predicted: 3.870033225798768 -Evaluating user: 21, item: 524, predicted: 3.2390720152222396 -Evaluating user: 216, item: 179, predicted: 3.6797601754321314 -Evaluating user: 11, item: 728, predicted: 4.0 -Evaluating user: 120, item: 328, predicted: 3.0003930660134595 -Evaluating user: 15, item: 674, predicted: 2.677068471129866 -Evaluating user: 180, item: 686, predicted: 3.48873440226905 -Evaluating user: 186, item: 566, predicted: 3.802491625382669 -Evaluating user: 153, item: 740, predicted: 2.545446004248653 -Evaluating user: 77, item: 324, predicted: 2.9199805905495 -Evaluating user: 8, item: 779, predicted: 3.6597206732404843 -Evaluating user: 29, item: 723, predicted: 3.7781592966869013 -Evaluating user: 228, item: 223, predicted: 2.451727278342317 -Evaluating user: 177, item: 868, predicted: 2.912989505250503 -Evaluating user: 100, item: 884, predicted: 3.1589717525850505 -Evaluating user: 82, item: 443, predicted: 4.0 -Evaluating user: 211, item: 35, predicted: 3.757882313503026 -Evaluating user: 13, item: 772, predicted: 3.026749739953794 -Evaluating user: 42, item: 300, predicted: 3.6623453377851063 -Evaluating user: 51, item: 366, predicted: 3.674316184015763 -Evaluating user: 156, item: 23, predicted: 4.0 -Evaluating user: 76, item: 251, predicted: 4.0 -Evaluating user: 184, item: 465, predicted: 2.8062516091208507 -Evaluating user: 43, item: 604, predicted: 3.258812818890413 -Evaluating user: 14, item: 575, predicted: 4.0 -Evaluating user: 165, item: 102, predicted: 3.620702831791548 -Evaluating user: 157, item: 784, predicted: 3.537742933934916 -Evaluating user: 328, item: 46, predicted: 2.841632058126514 -Evaluating user: 24, item: 106, predicted: 3.1783635804868453 -Evaluating user: 197, item: 843, predicted: 3.0930699310342837 -Evaluating user: 11, item: 522, predicted: 4.0 -Evaluating user: 163, item: 65, predicted: 3.5113921734681055 -Evaluating user: 105, item: 181, predicted: 4.0 -Evaluating user: 182, item: 436, predicted: 2.7551743925444994 -Evaluating user: 57, item: 433, predicted: 3.631112245165661 -Evaluating user: 86, item: 92, predicted: 3.2330500503420048 -Evaluating user: 155, item: 251, predicted: 3.0897378614606397 -Evaluating user: 283, item: 687, predicted: 3.7936047407527855 -Evaluating user: 64, item: 642, predicted: 3.202547478725553 -Evaluating user: 11, item: 329, predicted: 4.0 -Evaluating user: 100, item: 211, predicted: 3.1589717525850505 -Evaluating user: 198, item: 621, predicted: 3.9179541097285027 -Evaluating user: 53, item: 353, predicted: 3.1260344951076156 -Evaluating user: 142, item: 165, predicted: 3.434401628884774 -Evaluating user: 53, item: 38, predicted: 3.1260344951076156 -Evaluating user: 116, item: 258, predicted: 3.579993717900999 -Evaluating user: 255, item: 578, predicted: 2.84659202977648 -Evaluating user: 100, item: 179, predicted: 3.1589717525850505 -Evaluating user: 61, item: 672, predicted: 2.3337088155756582 -Evaluating user: 235, item: 39, predicted: 4.0 -Evaluating user: 77, item: 358, predicted: 2.9199805905495 -Evaluating user: 115, item: 42, predicted: 3.6137364550990245 -Evaluating user: 111, item: 667, predicted: 3.529305238189742 -Evaluating user: 78, item: 185, predicted: 3.507489658917522 -Evaluating user: 64, item: 53, predicted: 3.202547478725553 -Evaluating user: 19, item: 186, predicted: 3.7492266327587394 -Evaluating user: 34, item: 5, predicted: 3.4865196769640567 -Evaluating user: 171, item: 830, predicted: 3.5537895965250352 -Evaluating user: 102, item: 192, predicted: 3.177248470947968 -Evaluating user: 161, item: 79, predicted: 3.53804643196736 -Evaluating user: 187, item: 689, predicted: 3.258594559438348 -Evaluating user: 168, item: 118, predicted: 3.4992703869640467 -Evaluating user: 64, item: 612, predicted: 3.202547478725553 -Evaluating user: 194, item: 61, predicted: 2.8425910264255885 -Evaluating user: 6, item: 91, predicted: 3.201540374180235 -Evaluating user: 103, item: 275, predicted: 3.7376057615125893 -Evaluating user: 199, item: 391, predicted: 4.0 -Evaluating user: 187, item: 335, predicted: 3.258594559438348 -Evaluating user: 200, item: 420, predicted: 3.3401568081615203 -Evaluating user: 287, item: 556, predicted: 4.0 -Evaluating user: 284, item: 234, predicted: 4.0 -Evaluating user: 102, item: 429, predicted: 3.177248470947968 -Evaluating user: 54, item: 969, predicted: 3.557432591116923 -Evaluating user: 144, item: 186, predicted: 2.3453813892347712 -Evaluating user: 80, item: 73, predicted: 3.7155653427793984 -Evaluating user: 236, item: 3, predicted: 2.7048138774607398 -Evaluating user: 226, item: 270, predicted: 2.920628293432053 -Evaluating user: 41, item: 378, predicted: 3.397777301643506 -Evaluating user: 76, item: 748, predicted: 4.0 -Evaluating user: 30, item: 928, predicted: 3.866272241038406 -Evaluating user: 53, item: 106, predicted: 3.1260344951076156 -Evaluating user: 13, item: 83, predicted: 3.026749739953794 -Evaluating user: 97, item: 112, predicted: 3.9880103262613322 -Evaluating user: 71, item: 300, predicted: 4.0 -Evaluating user: 29, item: 211, predicted: 3.7781592966869013 -Evaluating user: 120, item: 700, predicted: 3.0003930660134595 -Evaluating user: 136, item: 34, predicted: 3.1843331274877817 -Evaluating user: 43, item: 368, predicted: 3.258812818890413 -Evaluating user: 293, item: 276, predicted: 4.0 -Evaluating user: 221, item: 625, predicted: 3.111955165178042 -Evaluating user: 47, item: 53, predicted: 2.6370263485688588 -Evaluating user: 26, item: 483, predicted: 3.2282415361513763 - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8688 | 0.9697 | 7.1136 | 15.4212 - - -======================================== diff --git a/experiment_log_52.txt b/experiment_log_52.txt deleted file mode 100644 index 12f10d0cf..000000000 --- a/experiment_log_52.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:46:22.871136 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] - [2.352788 3.4589586 2.0124576 ... 3.3445687 4.366965 2.3530226] - [2.3527882 3.4589636 2.01246 ... 3.3445687 4.366975 2.3530226] - ... - [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] - [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226] - [2.352788 3.4589517 2.0124543 ... 3.3445687 4.3669515 2.3530226]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8716 | 0.9734 | 8.6582 | 8.4601 - - -======================================== diff --git a/experiment_log_53.txt b/experiment_log_53.txt deleted file mode 100644 index 9b72becd8..000000000 --- a/experiment_log_53.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:47:55.043444 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] - [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] - [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] - ... - [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] - [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175] - [2.5164232 3.8007922 2.2638452 ... 3.4509828 4.612975 3.1409175]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8694 | 0.9713 | 8.7112 | 8.2424 - - -======================================== diff --git a/experiment_log_54.txt b/experiment_log_54.txt deleted file mode 100644 index 7ee4f16bb..000000000 --- a/experiment_log_54.txt +++ /dev/null @@ -1,80 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:51:46.421403 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.6885347 3.5069544 2.2635775 ... 2.9335885 4.0031514 2.642272 ] - [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] - [2.692095 3.5101788 2.2650366 ... 2.9335885 4.0056243 2.6447263] - ... - [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] - [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717] - [2.6772354 3.4833765 2.2565286 ... 2.9335885 3.9800777 2.6193717]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8772 | 0.9770 | 10.5761 | 11.4029 - - -======================================== diff --git a/experiment_log_55.txt b/experiment_log_55.txt deleted file mode 100644 index 3b56f8d9c..000000000 --- a/experiment_log_55.txt +++ /dev/null @@ -1,81 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 16:52:07.592445 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 4.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 30 -max_epoch_f: 100 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.01 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] - [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] - [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] - ... - [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] - [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355] - [2.5720422 3.753601 1.8830814 ... 3.6633632 4.9061112 2.5653355]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8720 | 0.9737 | 7.3035 | 7.6919 - - -======================================== diff --git a/experiment_log_6.txt b/experiment_log_6.txt deleted file mode 100644 index 456bb78cf..000000000 --- a/experiment_log_6.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:51:40.161990 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ] - [3.1601486 4.2336493 1.5548409 ... 3.5341663 4.450225 2.6307678] - [3.1750364 4.279773 1.5742695 ... 3.5885444 4.4989467 2.6467028] - ... - [3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ] - [3.1874812 4.282619 1.5728453 ... 3.577863 4.498159 2.6519115] - [3.1541655 4.240152 1.5601997 ... 3.5530827 4.4602547 2.629718 ]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7954 -Pre-Training Epoch 4/11, Train RMSE: 2.5856 -Pre-Training Epoch 5/11, Train RMSE: 2.1329 -Pre-Training Epoch 6/11, Train RMSE: 1.6514 -Pre-Training Epoch 7/11, Train RMSE: 1.2474 -Pre-Training Epoch 8/11, Train RMSE: 1.0973 -Pre-Training Epoch 9/11, Train RMSE: 1.1644 -Pre-Training Epoch 10/11, Train RMSE: 1.1596 -Pre-Training Epoch 11/11, Train RMSE: 1.0655 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3416 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1783 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0613 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0382 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0316 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0320 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0603 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0933 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0918 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0662 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9508 | 1.0486 | 8.9631 | 20.1651 - - -======================================== diff --git a/experiment_log_7.txt b/experiment_log_7.txt deleted file mode 100644 index 73bf19ce6..000000000 --- a/experiment_log_7.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:54:59.762037 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[2.7173223 3.4101276 2.3123941 ... 3.9221065 4.5609326 2.7274861] - [2.7639 3.4607534 2.3487926 ... 3.9913561 4.6364484 2.7701225] - [2.7225556 3.4184558 2.3182871 ... 3.9310615 4.5703793 2.7325737] - ... - [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474] - [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474] - [2.7056482 3.3912408 2.299206 ... 3.9020813 4.5391645 2.7161474]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7954 -Pre-Training Epoch 4/11, Train RMSE: 2.6981 -Pre-Training Epoch 5/11, Train RMSE: 2.3428 -Pre-Training Epoch 6/11, Train RMSE: 1.8658 -Pre-Training Epoch 7/11, Train RMSE: 1.4236 -Pre-Training Epoch 8/11, Train RMSE: 1.1457 -Pre-Training Epoch 9/11, Train RMSE: 1.0830 -Pre-Training Epoch 10/11, Train RMSE: 1.0543 -Pre-Training Epoch 11/11, Train RMSE: 1.0848 -Fine-Tuning Epoch 1/10, Train RMSE: 1.1714 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1615 -Fine-Tuning Epoch 3/10, Train RMSE: 1.0814 -Fine-Tuning Epoch 4/10, Train RMSE: 1.0636 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0850 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0683 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0435 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0323 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0484 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0437 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9325 | 1.0244 | 2.2179 | 8.4871 - - -======================================== diff --git a/experiment_log_8.txt b/experiment_log_8.txt deleted file mode 100644 index 988730fc6..000000000 --- a/experiment_log_8.txt +++ /dev/null @@ -1,102 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:55:57.837021 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] - [1.7242785 2.8223462 2.2117891 ... 4.4059434 4.641674 2.7076774] - [1.723621 2.8460186 2.2296033 ... 4.462454 4.6887846 2.743203 ] - ... - [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] - [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842] - [1.7203797 2.8145874 2.2060485 ... 4.3920593 4.6278296 2.7033842]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -Pre-Training Epoch 3/11, Train RMSE: 2.7316 -Pre-Training Epoch 4/11, Train RMSE: 2.2343 -Pre-Training Epoch 5/11, Train RMSE: 1.6676 -Pre-Training Epoch 6/11, Train RMSE: 1.2048 -Pre-Training Epoch 7/11, Train RMSE: 1.1020 -Pre-Training Epoch 8/11, Train RMSE: 1.2023 -Pre-Training Epoch 9/11, Train RMSE: 1.1318 -Pre-Training Epoch 10/11, Train RMSE: 1.0433 -Pre-Training Epoch 11/11, Train RMSE: 1.2329 -Fine-Tuning Epoch 1/10, Train RMSE: 1.3594 -Fine-Tuning Epoch 2/10, Train RMSE: 1.0937 -Fine-Tuning Epoch 3/10, Train RMSE: 1.1745 -Fine-Tuning Epoch 4/10, Train RMSE: 1.3269 -Fine-Tuning Epoch 5/10, Train RMSE: 1.2636 -Fine-Tuning Epoch 6/10, Train RMSE: 1.1358 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0532 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0284 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0206 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0165 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.8802 | 0.9862 | 2.0033 | 9.6254 - - -======================================== diff --git a/experiment_log_9.txt b/experiment_log_9.txt deleted file mode 100644 index f55eea9cf..000000000 --- a/experiment_log_9.txt +++ /dev/null @@ -1,113 +0,0 @@ - -======================================== -Experiment conducted on: 2024-12-14 14:56:31.285239 - -Hyperparameters: -name: GlobalLocalKernel -trainable: True -verbose: True -is_fitted: False -ignored_attrs: ['train_set', 'val_set', 'test_set'] -num_users: None -num_items: None -uid_map: None -iid_map: None -max_rating: 5.0 -min_rating: 1.0 -global_mean: None -_Recommender__user_ids: None -_Recommender__item_ids: None -n_hid: 10 -n_dim: 2 -n_layers: 2 -lambda_s: 0.006 -lambda_2: 0.001 -gk_size: 3 -dot_scale: 1 -max_epoch_p: 11 -max_epoch_f: 10 -tol_p: 0.0001 -tol_f: 1e-05 -patience_p: 10 -patience_f: 10 -lr_p: 0.1 -lr_f: 0.1 -device: cpu -model: CompleteNet( - (local_kernel_net): KernelNet( - (layers): ModuleList( - (0-1): 2 x KernelLayer( - (activation): Sigmoid() - ) - (2): KernelLayer( - (activation): Identity() - ) - ) - (dropout): Dropout(p=0.33, inplace=False) - ) -) -train_r_local: [[1.7862409 3.7904427 2.2713401 ... 3.9308016 4.1415873 2.3545623] - [1.8098971 3.8735347 2.3031673 ... 3.9851453 4.2259607 2.4135017] - [1.8194615 3.9009283 2.3160815 ... 4.028461 4.2595353 2.4159725] - ... - [1.7831967 3.7818234 2.2670496 ... 3.9160302 4.1308055 2.3544216] - [1.7924736 3.8139198 2.2797391 ... 3.9409547 4.1644974 2.374955 ] - [1.7831967 3.7818234 2.2670496 ... 3.9160302 4.1308055 2.3544216]] -_train_r: [[3. 0. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - [0. 4. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] -_train_mask: [[1. 0. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - [0. 1. 0. ... 0. 0. 0.] - ... - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.] - [0. 0. 0. ... 0. 0. 0.]] - -Test Results: -hi -Pre-Training Epoch 1/11, Train RMSE: 2.7954 -hi -Pre-Training Epoch 2/11, Train RMSE: 2.7954 -hi -Pre-Training Epoch 3/11, Train RMSE: 2.7623 -hi -Pre-Training Epoch 4/11, Train RMSE: 2.3646 -hi -Pre-Training Epoch 5/11, Train RMSE: 1.8258 -hi -Pre-Training Epoch 6/11, Train RMSE: 1.3236 -hi -Pre-Training Epoch 7/11, Train RMSE: 1.0857 -hi -Pre-Training Epoch 8/11, Train RMSE: 1.1549 -hi -Pre-Training Epoch 9/11, Train RMSE: 1.1521 -hi -Pre-Training Epoch 10/11, Train RMSE: 1.0596 -hi -Pre-Training Epoch 11/11, Train RMSE: 1.1653 -Fine-Tuning Epoch 1/10, Train RMSE: 1.2124 -Fine-Tuning Epoch 2/10, Train RMSE: 1.1338 -Fine-Tuning Epoch 3/10, Train RMSE: 1.2263 -Fine-Tuning Epoch 4/10, Train RMSE: 1.1828 -Fine-Tuning Epoch 5/10, Train RMSE: 1.0981 -Fine-Tuning Epoch 6/10, Train RMSE: 1.0623 -Fine-Tuning Epoch 7/10, Train RMSE: 1.0499 -Fine-Tuning Epoch 8/10, Train RMSE: 1.0291 -Fine-Tuning Epoch 9/10, Train RMSE: 1.0305 -Fine-Tuning Epoch 10/10, Train RMSE: 1.0553 -helllooooooooooooow - -TEST: -... - | MAE | RMSE | Train (s) | Test (s) ------------------ + ------ + ------ + --------- + -------- -GlobalLocalKernel | 0.9324 | 1.0286 | 2.0803 | 9.6522 - - -======================================== From d99c8d8620c124ce711ce7c8764fb07cb6bc2765 Mon Sep 17 00:00:00 2001 From: Cypher2k2 Date: Sat, 14 Dec 2024 21:07:12 +0100 Subject: [PATCH 4/6] fix metrics bug --- cornac/metrics/rating.py | 2 +- .../recom_globallocalkernel.py | 122 ++++++++++++------ cornac/models/recommender.py | 22 +++- .../src/QR/CompleteOrthogonalDecomposition.h | 1 - experiment_log_4.txt | 81 ++++++++++++ others.py | 27 ++++ try.py | 8 +- 7 files changed, 215 insertions(+), 48 deletions(-) create mode 100644 experiment_log_4.txt create mode 100644 others.py diff --git a/cornac/metrics/rating.py b/cornac/metrics/rating.py index de68bc053..4ff428aa7 100644 --- a/cornac/metrics/rating.py +++ b/cornac/metrics/rating.py @@ -110,7 +110,7 @@ def compute(self, gt_ratings, pd_ratings, weights=None, **kwargs): ------- mse: A scalar. Mean Squared Error. - + """ mse = np.average((gt_ratings - pd_ratings) ** 2, axis=0, weights=weights) return mse diff --git a/cornac/models/globallocalkernel/recom_globallocalkernel.py b/cornac/models/globallocalkernel/recom_globallocalkernel.py index 74251eb57..3702d0830 100644 --- a/cornac/models/globallocalkernel/recom_globallocalkernel.py +++ b/cornac/models/globallocalkernel/recom_globallocalkernel.py @@ -12,7 +12,8 @@ from tqdm import tqdm - +# Configure logging +# logging.basicConfig(level=logging.INFO, format="%(message)s") # =========================== # Define your model layers # =========================== @@ -333,7 +334,6 @@ def closure(): # Initialize the progress bar for the group with tqdm(total=end_epoch - start_epoch, desc=f"Epochs {start_epoch + 1}-{end_epoch} (Fine-Tuning)", leave=True) as pbar: for epoch in range(start_epoch, end_epoch): - # Define the closure function def closure(): optimizer.zero_grad() @@ -382,64 +382,110 @@ def closure(): self.model = complete_model return self - def score(self, user_id, item_id=None, batch_size=10): + + def score(self, user_idx, item_idx=None): """Predict the scores/ratings of a user for an item or batch of items. Parameters ---------- - user_id: int + user_idx: int, required The index of the user for whom to perform score prediction. - item_id: int or list of int, optional + item_idx: int, optional The index (or indices) of the item(s) for which to perform score prediction. If None, scores for all items will be returned. - batch_size: int, optional, default: 10 - Number of items to process in a batch for tqdm progress bar. Returns ------- - res: A scalar, Numpy array, or dictionary - If `item_id` is None, returns an array of scores for all items for the user. - If `item_id` is a single integer, returns a scalar score for that item. - If `item_id` is a list of integers, returns a dictionary of scores. + res: A scalar or Numpy array + A scalar for a specific item, or a Numpy array of scores for all items. """ if self.model is None: raise RuntimeError("You must train the model before calling score()!") with torch.no_grad(): + # Perform model predictions (full prediction matrix) input_mat = torch.tensor(self.train_r_local, dtype=torch.double, device=self.device) x_global = torch.tensor(self._train_r, dtype=torch.double, device=self.device) + pred, _ = self.model(x_global, input_mat) + pred = pred.cpu().numpy() # Convert to NumPy array - if item_id is None: - # Predict scores for all items for the specified user - n_items = input_mat.shape[0] - preds = np.zeros((n_items,), dtype=np.float32) + if item_idx is None: + # Return scores for all items for the specified user + return pred[:, user_idx] # NumPy array of scores - with tqdm(total=n_items, desc=f"Scoring all items for user {user_id}", leave=True) as pbar: - for i in range(n_items): - pred, _ = self.model(x_global, input_mat) - preds[i] = pred[i, user_id].item() - pbar.update(1) + elif isinstance(item_idx, list): + # Return scores for a list of items + return np.array([pred[i, user_idx] for i in item_idx]) # NumPy array - return preds + else: + # Return score for a single item (scalar) + return pred[item_idx, user_idx] - elif isinstance(item_id, list): - # Predict scores for a list of items - preds = {} - with tqdm(total=len(item_id), desc=f"Scoring items for user {user_id}", leave=True) as pbar: - for i in item_id: - pred, _ = self.model(x_global, input_mat) - preds[i] = pred[i, user_id].item() - pbar.update(1) - return preds - else: - # print(f"Debug: item_id is a single value: {item_id}. Scoring for user {user_id}.") - # Predict score for a single item - pred, _ = self.model(x_global, input_mat) - return pred[item_id, user_id].item() - def rate(self, user_id, item_id): - # Optionally override if needed, or rely on default Recommender.rate() - return super().rate(user_id, item_id) + # def get_vector_measure(self): + # from cornac.utils import MEASURE_DOT + # return MEASURE_DOT + + + # def get_user_vectors(self): + # # Assuming self.U stores the user embeddings + # return self.U.cpu().detach().numpy() + + + # def get_item_vectors(self): + # # Assuming self.V stores the item embeddings + # return self.V.cpu().detach().numpy() + + + # def rank(self, user_idx, item_indices=None, k=None): + # """ + # Rank items for a given user based on predicted scores. + + # Parameters + # ---------- + # user_idx : int + # The index of the user for whom to rank items. + + # item_indices : array-like, optional, default: None + # Indices of items to be ranked. If None, rank all items. + + # k : int, optional, default: None + # Number of top items to return. If None, return all ranked items. + + # Returns + # ------- + # item_rank : np.ndarray + # Indices of items ranked in descending order of predicted scores. + + # item_scores : np.ndarray + # Predicted scores for the ranked items. + # """ + # with torch.no_grad(): + # # Get user embeddings (row from self.U) + # user_embedding = self.U[user_idx].cpu().numpy() + + # # Compute scores for all items or a subset + # if item_indices is None: + # item_embeddings = self.V.cpu().numpy() # All item embeddings + # else: + # item_embeddings = self.V[item_indices].cpu().numpy() # Subset of items + + # # Compute scores (dot product or similarity) + # scores = np.dot(item_embeddings, user_embedding) + + # # Get the ranked indices + # ranked_indices = np.argsort(-scores) # Descending order + # if k is not None: + # ranked_indices = ranked_indices[:k] + + # # Get the corresponding scores for ranked items + # ranked_scores = scores[ranked_indices] + + # # Map back to original item indices if item_indices is provided + # if item_indices is not None: + # ranked_indices = np.array(item_indices)[ranked_indices] + + # return ranked_indices, ranked_scores diff --git a/cornac/models/recommender.py b/cornac/models/recommender.py index c7080a4b2..f9b2ab688 100644 --- a/cornac/models/recommender.py +++ b/cornac/models/recommender.py @@ -21,7 +21,7 @@ from datetime import datetime from glob import glob import json - +import logging import numpy as np from ..exception import ScoreException @@ -328,6 +328,7 @@ def fit(self, train_set, val_set=None): if val_set is not None: val_set.reset() + # get some useful information for prediction self.num_users = train_set.num_users self.num_items = train_set.num_items @@ -496,20 +497,33 @@ def rank(self, user_idx, item_indices=None, k=-1, **kwargs): `item_scores` contains scores of items corresponding to index in `item_indices` input. """ - # obtain item scores from the model + + # logging.info(f"Calling `score` for user {user_idx} with `item_indices={item_indices}`") try: known_item_scores = self.score(user_idx, **kwargs) + except ScoreException: known_item_scores = np.ones(self.total_items) * self.default_score() - # check if the returned scores also cover unknown items - # if not, all unknown items will be given the MIN score + # logging.info(f"`known_item_scores` returned by `score`: {type(known_item_scores)}") + + if self.num_items is None: + self.num_items = len(known_item_scores) # Fallback if undefined + # logging.warning("`self.num_items` was None. Setting it to the length of `known_item_scores`.") + + + # Check if the returned scores cover unknown items if len(known_item_scores) == self.total_items: + # logging.info("num_items : " + str(self.num_items)) + all_item_scores = known_item_scores else: all_item_scores = np.ones(self.total_items) * np.min(known_item_scores) + # logging.info("num_items : " + str(self.num_items)) all_item_scores[: self.num_items] = known_item_scores + # logging.info(f"`all_item_scores`: {all_item_scores}") + # rank items based on their scores item_indices = ( np.arange(self.num_items) diff --git a/cornac/utils/external/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h b/cornac/utils/external/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h index 34c637b70..09ea650b9 100644 --- a/cornac/utils/external/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h +++ b/cornac/utils/external/eigen/Eigen/src/QR/CompleteOrthogonalDecomposition.h @@ -161,7 +161,6 @@ class CompleteOrthogonalDecomposition { applyZAdjointOnTheLeftInPlace(Z); return Z.adjoint(); } - /** \returns a reference to the matrix where the complete orthogonal * decomposition is stored */ diff --git a/experiment_log_4.txt b/experiment_log_4.txt new file mode 100644 index 000000000..116758bdf --- /dev/null +++ b/experiment_log_4.txt @@ -0,0 +1,81 @@ + +======================================== +Experiment conducted on: 2024-12-14 21:04:28.481690 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: False +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: 1656 +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 30 +max_epoch_f: 100 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cpu +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[3.4896953 3.300755 4.0347724 ... 3.2298357 4.5382867 3.3844583] + [3.683822 3.484483 4.2810183 ... 3.384981 4.898886 3.6059442] + [3.5933316 3.3985095 4.1668615 ... 3.3124022 4.7337956 3.5070415] + ... + [3.546636 3.3489509 4.1108484 ... 3.2751315 4.650442 3.4729054] + [3.5501003 3.3515632 4.1113515 ... 3.276139 4.656549 3.4755447] + [3.5533752 3.3547118 4.115988 ... 3.2788885 4.6626425 3.479602 ]] +_train_r: [[4. 0. 0. ... 0. 0. 0.] + [0. 5. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +helllooooooooooooow + +TEST: +... + | MAE | RMSE | AUC | MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s) +----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + -------- +GlobalLocalKernel | 0.8233 | 0.9861 | 0.6304 | 0.0210 | 0.0238 | 0.0214 | 0.0144 | 20.5121 | 841.4456 + + +======================================== diff --git a/others.py b/others.py new file mode 100644 index 000000000..d10210436 --- /dev/null +++ b/others.py @@ -0,0 +1,27 @@ + +import os +from datetime import datetime +import numpy as np +import cornac +from cornac.models import MF, PMF, BPR,GlobalLocalKernel + + +from cornac.eval_methods import RatioSplit +from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP + +ml_100k = cornac.datasets.movielens.load_feedback() +ml_100k = ml_100k[:500] + +rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) + +# initialize models, here we are comparing: Biased MF, PMF, and BPR +mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123) +pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123) +bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123) +models = [bpr] + +# define metrics to evaluate the models +metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()] + +# put it together in an experiment, voilà! +cornac.Experiment(eval_method=rs, models=models, metrics=metrics, user_based=True).run() \ No newline at end of file diff --git a/try.py b/try.py index f436a2426..d6fcb1a5d 100644 --- a/try.py +++ b/try.py @@ -4,7 +4,7 @@ import cornac from cornac.models import GlobalLocalKernel from cornac.eval_methods import RatioSplit -from cornac.metrics import MAE, RMSE +from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP # Function to find the next available log file name def get_next_log_file(base_name="experiment_log", ext=".txt"): @@ -29,7 +29,7 @@ def log_results(log_file, test_results, model_instance): ml_100k = cornac.datasets.movielens.load_feedback() # Take only a subset of the data, e.g., first 5000 interactions for quicker tests -ml_100k = ml_100k[:5000] +# ml_100k = ml_100k[:500] # Split the data rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123) @@ -53,11 +53,11 @@ def log_results(log_file, test_results, model_instance): max_epoch_f=100, lr_p=0.1, lr_f=0.01, - verbose=True + verbose=False ) # Define some basic metrics -metrics = [MAE(), RMSE()] +metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()] # Redirect Cornac output to capture experiment results from io import StringIO From 268faef65659de3d55cb063679a54fc411c07bc3 Mon Sep 17 00:00:00 2001 From: mehdiz5 Date: Sat, 14 Dec 2024 23:25:18 +0100 Subject: [PATCH 5/6] fixed device and added models --- .../recom_globallocalkernel.py | 2 +- experiment_log_1.txt | 86 +++++++++++++++++++ experiment_log_2.txt | 83 ++++++++++++++++++ try.py | 11 ++- 4 files changed, 178 insertions(+), 4 deletions(-) create mode 100644 experiment_log_1.txt create mode 100644 experiment_log_2.txt diff --git a/cornac/models/globallocalkernel/recom_globallocalkernel.py b/cornac/models/globallocalkernel/recom_globallocalkernel.py index 3702d0830..cbc8b4849 100644 --- a/cornac/models/globallocalkernel/recom_globallocalkernel.py +++ b/cornac/models/globallocalkernel/recom_globallocalkernel.py @@ -206,7 +206,7 @@ def __init__( self.verbose = verbose # Device - if torch.cuda.is_available() and (self.device != 'cpu'): + if torch.cuda.is_available(): self.device = torch.device("cuda") else: self.device = torch.device("cpu") diff --git a/experiment_log_1.txt b/experiment_log_1.txt new file mode 100644 index 000000000..2528dfdb2 --- /dev/null +++ b/experiment_log_1.txt @@ -0,0 +1,86 @@ + +======================================== +Experiment conducted on: 2024-12-14 22:16:07.572951 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: False +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: 1656 +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 500 +max_epoch_f: 500 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cuda +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.18834 3.834389 4.190889 ... 3.719673 4.9786563 4.025386 ] + [4.1866603 3.833935 4.1901083 ... 3.7193959 4.9786563 4.0248137] + [3.643859 3.643947 3.9684842 ... 3.4783204 4.9786563 3.7472897] + ... + [3.0270095 3.4925659 3.7013142 ... 3.32962 4.9786563 3.496717 ] + [3.030718 3.493905 3.7029653 ... 3.3311675 4.9786563 3.4985132] + [3.030718 3.493905 3.7029653 ... 3.3311675 4.9786563 3.4985132]] +_train_r: [[4. 0. 0. ... 0. 0. 0.] + [0. 5. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 380 +Early stopping fine-tuning at epoch: 381 +Early stopping fine-tuning at epoch: 391 +Early stopping fine-tuning at epoch: 401 +Early stopping fine-tuning at epoch: 411 +Early stopping fine-tuning at epoch: 432 + +TEST: +... + | MAE | RMSE | AUC | MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s) +----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + -------- +GlobalLocalKernel | 0.8034 | 0.9727 | 0.5107 | 0.0189 | 0.0217 | 0.0192 | 0.0133 | 28.9218 | 190.2286 + + +======================================== diff --git a/experiment_log_2.txt b/experiment_log_2.txt new file mode 100644 index 000000000..58d6e2824 --- /dev/null +++ b/experiment_log_2.txt @@ -0,0 +1,83 @@ + +======================================== +Experiment conducted on: 2024-12-14 22:22:56.926776 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: False +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: 1656 +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 500 +max_epoch_f: 500 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cuda +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.074561 3.7919703 4.1602736 ... 3.5502026 4.9746857 3.943836 ] + [4.0745606 3.79197 4.1602736 ... 3.5502021 4.9746857 3.9438357] + [3.7859447 3.7108083 4.024741 ... 3.356383 4.9746857 3.8038127] + ... + [3.1270785 3.4874184 3.683483 ... 3.1386068 4.9746857 3.4953837] + [3.1272159 3.4874575 3.6835485 ... 3.1387024 4.9746857 3.4954498] + [3.1272159 3.4874575 3.6835485 ... 3.1387024 4.9746857 3.4954498]] +_train_r: [[4. 0. 0. ... 0. 0. 0.] + [0. 5. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: + +TEST: +... + | MAE | RMSE | AUC | MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s) +----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + -------- +GlobalLocalKernel | 0.8051 | 0.9771 | 0.5428 | 0.0232 | 0.0261 | 0.0254 | 0.0169 | 30.8466 | 191.1777 +MF | 0.7430 | 0.8998 | 0.7445 | 0.0548 | 0.0761 | 0.0675 | 0.0463 | 0.0290 | 0.7707 +PMF | 0.7534 | 0.9138 | 0.7744 | 0.0671 | 0.0969 | 0.0813 | 0.0639 | 1.4337 | 1.1157 +BPR | 2.0143 | 2.2267 | 0.8695 | 0.1042 | 0.1500 | 0.1110 | 0.1195 | 1.2896 | 0.7294 + + +======================================== diff --git a/try.py b/try.py index d6fcb1a5d..7fa0cbd58 100644 --- a/try.py +++ b/try.py @@ -5,6 +5,7 @@ from cornac.models import GlobalLocalKernel from cornac.eval_methods import RatioSplit from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP +from cornac.models import MF, PMF, BPR # Function to find the next available log file name def get_next_log_file(base_name="experiment_log", ext=".txt"): @@ -49,13 +50,17 @@ def log_results(log_file, test_results, model_instance): # Example hyperparameters n_hid=10, n_dim=2, - max_epoch_p=30, - max_epoch_f=100, + max_epoch_p=500, + max_epoch_f=500, lr_p=0.1, lr_f=0.01, verbose=False ) +mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123) +pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123) +bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123) + # Define some basic metrics metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()] @@ -68,7 +73,7 @@ def log_results(log_file, test_results, model_instance): sys.stdout = StringIO() # Redirect stdout to capture results # Run the experiment on the smaller subset -cornac.Experiment(eval_method=rs, models=[my_model], metrics=metrics, user_based=True).run() +cornac.Experiment(eval_method=rs, models=[my_model, mf, pmf, bpr], metrics=metrics, user_based=True).run() # Retrieve experiment results experiment_results = sys.stdout.getvalue() From c13366ce0a9a3ca079f2ff6d42128c627e78392f Mon Sep 17 00:00:00 2001 From: mehdiz5 Date: Sun, 15 Dec 2024 16:18:53 +0100 Subject: [PATCH 6/6] added a notebook --- experiment_log_3.txt | 92 +++++++++ project_notebook.ipynb | 421 +++++++++++++++++++++++++++++++++++++++++ 2 files changed, 513 insertions(+) create mode 100644 experiment_log_3.txt create mode 100644 project_notebook.ipynb diff --git a/experiment_log_3.txt b/experiment_log_3.txt new file mode 100644 index 000000000..0eebadbe5 --- /dev/null +++ b/experiment_log_3.txt @@ -0,0 +1,92 @@ + +======================================== +Experiment conducted on: 2024-12-15 14:35:04.892244 + +Hyperparameters: +name: GlobalLocalKernel +trainable: True +verbose: False +is_fitted: False +ignored_attrs: ['train_set', 'val_set', 'test_set'] +num_users: None +num_items: 1656 +uid_map: None +iid_map: None +max_rating: 4.0 +min_rating: 1.0 +global_mean: None +_Recommender__user_ids: None +_Recommender__item_ids: None +n_hid: 10 +n_dim: 2 +n_layers: 2 +lambda_s: 0.006 +lambda_2: 0.001 +gk_size: 3 +dot_scale: 1 +max_epoch_p: 500 +max_epoch_f: 1000 +tol_p: 0.0001 +tol_f: 1e-05 +patience_p: 10 +patience_f: 10 +lr_p: 0.1 +lr_f: 0.01 +device: cuda +model: CompleteNet( + (local_kernel_net): KernelNet( + (layers): ModuleList( + (0-1): 2 x KernelLayer( + (activation): Sigmoid() + ) + (2): KernelLayer( + (activation): Identity() + ) + ) + (dropout): Dropout(p=0.33, inplace=False) + ) +) +train_r_local: [[4.13143 3.8251498 4.2022805 ... 3.46096 4.9777036 3.8819532] + [4.1326137 3.825405 4.202767 ... 3.4611 4.9777055 3.8823788] + [3.8330228 3.6725302 4.1033254 ... 3.3236573 4.9774 3.8047073] + ... + [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011] + [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011] + [3.1168575 3.5055826 3.8102179 ... 3.2242472 4.9762387 3.5493011]] +_train_r: [[4. 0. 0. ... 0. 0. 0.] + [0. 5. 0. ... 0. 0. 0.] + [0. 0. 5. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] +_train_mask: [[1. 0. 0. ... 0. 0. 0.] + [0. 1. 0. ... 0. 0. 0.] + [0. 0. 1. ... 0. 0. 0.] + ... + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.] + [0. 0. 0. ... 0. 0. 0.]] + +Test Results: +Early stopping fine-tuning at epoch: 237 +Early stopping fine-tuning at epoch: 241 +Early stopping fine-tuning at epoch: 251 +Early stopping fine-tuning at epoch: 261 +Early stopping fine-tuning at epoch: 271 +Early stopping fine-tuning at epoch: 281 +Early stopping fine-tuning at epoch: 420 +Early stopping fine-tuning at epoch: 421 +Early stopping fine-tuning at epoch: 900 + +TEST: +... + | MAE | RMSE | AUC | MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s) +----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + -------- +GlobalLocalKernel | 0.8029 | 0.9731 | 0.4371 | 0.0158 | 0.0168 | 0.0137 | 0.0110 | 55.5029 | 232.3715 +MF | 0.7430 | 0.8998 | 0.7445 | 0.0548 | 0.0761 | 0.0675 | 0.0463 | 0.0302 | 0.8123 +PMF | 0.7534 | 0.9138 | 0.7744 | 0.0671 | 0.0969 | 0.0813 | 0.0639 | 1.4669 | 1.2117 +BPR | 2.0143 | 2.2267 | 0.8695 | 0.1042 | 0.1500 | 0.1110 | 0.1195 | 1.3102 | 0.7666 + + +======================================== diff --git a/project_notebook.ipynb b/project_notebook.ipynb new file mode 100644 index 000000000..d8ec55550 --- /dev/null +++ b/project_notebook.ipynb @@ -0,0 +1,421 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Recommendation Systems Project\n", + "\n", + "By : ADJAL Mehdi Zakaria - BENBETKA Rachid - YAMANI Mohammed Kamel - Rami Boukaroura\n", + "\n", + "You will find the report in the following link : " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## How to run this notebook :" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/mehdiz/miniconda3/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "import os\n", + "from datetime import datetime\n", + "import numpy as np\n", + "import cornac\n", + "from cornac.models import GlobalLocalKernel\n", + "from cornac.eval_methods import RatioSplit\n", + "from cornac.metrics import MAE, RMSE, Precision, Recall, NDCG, AUC, MAP\n", + "from cornac.models import MF, PMF, BPR" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to find the next available log file name\n", + "def get_next_log_file(base_name=\"experiment_log\", ext=\".txt\"):\n", + " counter = 1\n", + " while os.path.exists(f\"{base_name}_{counter}{ext}\"):\n", + " counter += 1\n", + " return f\"{base_name}_{counter}{ext}\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Function to log results\n", + "def log_results(log_file, test_results, model_instance):\n", + " with open(log_file, \"a\") as f:\n", + " f.write(\"\\n\" + \"=\" * 40 + \"\\n\")\n", + " f.write(f\"Experiment conducted on: {datetime.now()}\\n\")\n", + " f.write(\"\\nHyperparameters:\\n\")\n", + " for attr, value in vars(model_instance).items():\n", + " f.write(f\"{attr}: {value}\\n\")\n", + " f.write(\"\\nTest Results:\\n\")\n", + " f.write(test_results)\n", + " f.write(\"\\n\" + \"=\" * 40 + \"\\n\")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Load the MovieLens 100K dataset\n", + "ml_100k = cornac.datasets.movielens.load_feedback()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# Split the data\n", + "rs = RatioSplit(data=ml_100k, test_size=0.2, rating_threshold=4.0, seed=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data matrix loaded\n", + "Number of users: 943\n", + "Number of movies: 1656\n", + "Number of training ratings: 80000\n", + "Number of test ratings: 19971\n" + ] + } + ], + "source": [ + "# Get the total number of users and items in the subset\n", + "n_u = rs.total_users\n", + "n_m = rs.total_items\n", + "\n", + "print('Data matrix loaded')\n", + "print('Number of users: {}'.format(n_u))\n", + "print('Number of movies: {}'.format(n_m))\n", + "print('Number of training ratings: {}'.format(len(rs.train_set.uir_tuple[2])))\n", + "print('Number of test ratings: {}'.format(len(rs.test_set.uir_tuple[2])))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize your model\n", + "my_model = GlobalLocalKernel(\n", + " # Example hyperparameters\n", + " n_hid=10, \n", + " n_dim=2, \n", + " max_epoch_p=500, \n", + " max_epoch_f=1000,\n", + " lr_p=0.1,\n", + " lr_f=0.01, \n", + " verbose=False\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Models to compare with\n", + "mf = MF(k=10, max_iter=25, learning_rate=0.01, lambda_reg=0.02, use_bias=True, seed=123)\n", + "pmf = PMF(k=10, max_iter=100, learning_rate=0.001, lambda_reg=0.001, seed=123)\n", + "bpr = BPR(k=10, max_iter=200, learning_rate=0.001, lambda_reg=0.01, seed=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "# Define some basic metrics\n", + "metrics = [MAE(), RMSE(), Precision(k=10), Recall(k=10), NDCG(k=10), AUC(), MAP()]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Early stopping fine-tuning at epoch: 237\n", + "Early stopping fine-tuning at epoch: 241\n", + "Early stopping fine-tuning at epoch: 251\n", + "Early stopping fine-tuning at epoch: 261\n", + "Early stopping fine-tuning at epoch: 271\n", + "Early stopping fine-tuning at epoch: 281\n", + "Early stopping fine-tuning at epoch: 420\n", + "Early stopping fine-tuning at epoch: 421\n", + "Early stopping fine-tuning at epoch: 900\n", + "\n", + "TEST:\n", + "...\n", + " | MAE | RMSE | AUC | MAP | NDCG@10 | Precision@10 | Recall@10 | Train (s) | Test (s)\n", + "----------------- + ------ + ------ + ------ + ------ + ------- + ------------ + --------- + --------- + --------\n", + "GlobalLocalKernel | 0.8029 | 0.9731 | 0.4371 | 0.0158 | 0.0168 | 0.0137 | 0.0110 | 55.5029 | 232.3715\n", + "MF | 0.7430 | 0.8998 | 0.7445 | 0.0548 | 0.0761 | 0.0675 | 0.0463 | 0.0302 | 0.8123\n", + "PMF | 0.7534 | 0.9138 | 0.7744 | 0.0671 | 0.0969 | 0.0813 | 0.0639 | 1.4669 | 1.2117\n", + "BPR | 2.0143 | 2.2267 | 0.8695 | 0.1042 | 0.1500 | 0.1110 | 0.1195 | 1.3102 | 0.7666\n", + "\n", + "\n", + "Experiment results and hyperparameters saved to experiment_log_3.txt\n" + ] + } + ], + "source": [ + "# Redirect Cornac output to capture experiment results\n", + "from io import StringIO\n", + "import sys\n", + "\n", + "# Get the next available log file name\n", + "log_file = get_next_log_file()\n", + "temp = sys.stdout # Store original stdout object for later\n", + "sys.stdout = StringIO() # Redirect stdout to capture results" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Epochs 1-10 (Pre-Training): 100%|██████████| 10/10 [00:01<00:00, 7.78it/s, Train RMSE=1.0664]\n", + "Epochs 11-20 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.31it/s, Train RMSE=1.1418]\n", + "Epochs 21-30 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 31.31it/s, Train RMSE=1.0929]\n", + "Epochs 31-40 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.31it/s, Train RMSE=1.0234]\n", + "Epochs 41-50 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.04it/s, Train RMSE=1.0055]\n", + "Epochs 51-60 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.87it/s, Train RMSE=0.9866]\n", + "Epochs 61-70 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.84it/s, Train RMSE=0.9950]\n", + "Epochs 71-80 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.34it/s, Train RMSE=0.9840]\n", + "Epochs 81-90 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.68it/s, Train RMSE=0.9846]\n", + "Epochs 91-100 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.19it/s, Train RMSE=0.9751]\n", + "Epochs 101-110 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.04it/s, Train RMSE=0.9751]\n", + "Epochs 111-120 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.87it/s, Train RMSE=0.9689]\n", + "Epochs 121-130 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.90it/s, Train RMSE=0.9731]\n", + "Epochs 131-140 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.47it/s, Train RMSE=0.9749]\n", + "Epochs 141-150 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.76it/s, Train RMSE=0.9686]\n", + "Epochs 151-160 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.36it/s, Train RMSE=0.9663]\n", + "Epochs 161-170 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.76it/s, Train RMSE=0.9661]\n", + "Epochs 171-180 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.29it/s, Train RMSE=0.9676]\n", + "Epochs 181-190 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.21it/s, Train RMSE=0.9617]\n", + "Epochs 191-200 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.10it/s, Train RMSE=0.9677]\n", + "Epochs 201-210 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 27.40it/s, Train RMSE=0.9643]\n", + "Epochs 211-220 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 28.24it/s, Train RMSE=0.9621]\n", + "Epochs 221-230 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.32it/s, Train RMSE=0.9631]\n", + "Epochs 231-240 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 31.95it/s, Train RMSE=0.9622]\n", + "Epochs 241-250 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.13it/s, Train RMSE=0.9638]\n", + "Epochs 251-260 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 30.76it/s, Train RMSE=0.9614]\n", + "Epochs 261-270 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.07it/s, Train RMSE=0.9603]\n", + "Epochs 271-280 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.32it/s, Train RMSE=0.9632]\n", + "Epochs 281-290 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 32.24it/s, Train RMSE=0.9629]\n", + "Epochs 291-300 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.41it/s, Train RMSE=0.9623]\n", + "Epochs 301-310 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.49it/s, Train RMSE=0.9590]\n", + "Epochs 311-320 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.98it/s, Train RMSE=0.9647]\n", + "Epochs 321-330 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.35it/s, Train RMSE=0.9590]\n", + "Epochs 331-340 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.30it/s, Train RMSE=0.9618]\n", + "Epochs 341-350 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.56it/s, Train RMSE=0.9651]\n", + "Epochs 351-360 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.91it/s, Train RMSE=0.9625]\n", + "Epochs 361-370 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 37.15it/s, Train RMSE=0.9614]\n", + "Epochs 371-380 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.79it/s, Train RMSE=0.9625]\n", + "Epochs 381-390 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.42it/s, Train RMSE=0.9602]\n", + "Epochs 391-400 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.12it/s, Train RMSE=0.9674]\n", + "Epochs 401-410 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.22it/s, Train RMSE=0.9601]\n", + "Epochs 411-420 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.75it/s, Train RMSE=0.9617]\n", + "Epochs 421-430 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 33.84it/s, Train RMSE=0.9644]\n", + "Epochs 431-440 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.14it/s, Train RMSE=0.9650]\n", + "Epochs 441-450 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 35.13it/s, Train RMSE=0.9620]\n", + "Epochs 451-460 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.43it/s, Train RMSE=0.9619]\n", + "Epochs 461-470 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.61it/s, Train RMSE=0.9631]\n", + "Epochs 471-480 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.99it/s, Train RMSE=0.9624]\n", + "Epochs 481-490 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 34.93it/s, Train RMSE=0.9621]\n", + "Epochs 491-500 (Pre-Training): 100%|██████████| 10/10 [00:00<00:00, 36.42it/s, Train RMSE=0.9585]\n", + "Epochs 1-10 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 10.38it/s, Train RMSE=1.0211]\n", + "Epochs 11-20 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.77it/s, Train RMSE=1.0215]\n", + "Epochs 21-30 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=1.0187]\n", + "Epochs 31-40 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.56it/s, Train RMSE=1.0114]\n", + "Epochs 41-50 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.77it/s, Train RMSE=1.0070]\n", + "Epochs 51-60 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.97it/s, Train RMSE=1.0043]\n", + "Epochs 61-70 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.94it/s, Train RMSE=1.0020]\n", + "Epochs 71-80 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.79it/s, Train RMSE=0.9985]\n", + "Epochs 81-90 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9941]\n", + "Epochs 91-100 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9916]\n", + "Epochs 101-110 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.97it/s, Train RMSE=0.9878]\n", + "Epochs 111-120 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.22it/s, Train RMSE=0.9862]\n", + "Epochs 121-130 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.73it/s, Train RMSE=0.9843]\n", + "Epochs 131-140 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.51it/s, Train RMSE=0.9835]\n", + "Epochs 141-150 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.59it/s, Train RMSE=0.9822]\n", + "Epochs 151-160 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.25it/s, Train RMSE=0.9800]\n", + "Epochs 161-170 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.35it/s, Train RMSE=0.9798]\n", + "Epochs 171-180 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.83it/s, Train RMSE=0.9802]\n", + "Epochs 181-190 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.53it/s, Train RMSE=0.9809]\n", + "Epochs 191-200 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.09it/s, Train RMSE=0.9786]\n", + "Epochs 201-210 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.20it/s, Train RMSE=0.9794]\n", + "Epochs 211-220 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9795]\n", + "Epochs 221-230 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.84it/s, Train RMSE=0.9793]\n", + "Epochs 231-240 (Fine-Tuning): 70%|███████ | 7/10 [00:00<00:00, 25.55it/s, Train RMSE=0.9889]\n", + "Epochs 241-250 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 24.93it/s, Train RMSE=0.9904]\n", + "Epochs 251-260 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 24.58it/s, Train RMSE=0.9919]\n", + "Epochs 261-270 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 25.29it/s, Train RMSE=0.9932]\n", + "Epochs 271-280 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 25.56it/s, Train RMSE=0.9939]\n", + "Epochs 281-290 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 24.34it/s, Train RMSE=0.9945]\n", + "Epochs 291-300 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.30it/s, Train RMSE=0.9963]\n", + "Epochs 301-310 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.97it/s, Train RMSE=0.9944]\n", + "Epochs 311-320 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9927]\n", + "Epochs 321-330 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.74it/s, Train RMSE=0.9924]\n", + "Epochs 331-340 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.59it/s, Train RMSE=0.9921]\n", + "Epochs 341-350 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.34it/s, Train RMSE=0.9898]\n", + "Epochs 351-360 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.34it/s, Train RMSE=0.9880]\n", + "Epochs 361-370 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.82it/s, Train RMSE=0.9884]\n", + "Epochs 371-380 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.31it/s, Train RMSE=0.9881]\n", + "Epochs 381-390 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.86it/s, Train RMSE=0.9871]\n", + "Epochs 391-400 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.93it/s, Train RMSE=0.9845]\n", + "Epochs 401-410 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9825]\n", + "Epochs 411-420 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9951]\n", + "Epochs 421-430 (Fine-Tuning): 10%|█ | 1/10 [00:00<00:00, 26.12it/s, Train RMSE=0.9951]\n", + "Epochs 431-440 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.69it/s, Train RMSE=0.9976]\n", + "Epochs 441-450 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.06it/s, Train RMSE=0.9955]\n", + "Epochs 451-460 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.23it/s, Train RMSE=0.9955]\n", + "Epochs 461-470 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9952]\n", + "Epochs 471-480 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.03it/s, Train RMSE=0.9927]\n", + "Epochs 481-490 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.06it/s, Train RMSE=0.9907]\n", + "Epochs 491-500 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9886]\n", + "Epochs 501-510 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=0.9852]\n", + "Epochs 511-520 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.31it/s, Train RMSE=0.9826]\n", + "Epochs 521-530 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.07it/s, Train RMSE=0.9818]\n", + "Epochs 531-540 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.90it/s, Train RMSE=0.9806]\n", + "Epochs 541-550 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.27it/s, Train RMSE=0.9787]\n", + "Epochs 551-560 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.95it/s, Train RMSE=0.9783]\n", + "Epochs 561-570 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.24it/s, Train RMSE=0.9788]\n", + "Epochs 571-580 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.35it/s, Train RMSE=0.9777]\n", + "Epochs 581-590 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.06it/s, Train RMSE=0.9759]\n", + "Epochs 591-600 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.36it/s, Train RMSE=0.9762]\n", + "Epochs 601-610 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.81it/s, Train RMSE=0.9772]\n", + "Epochs 611-620 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9769]\n", + "Epochs 621-630 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.09it/s, Train RMSE=0.9775]\n", + "Epochs 631-640 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.63it/s, Train RMSE=0.9789]\n", + "Epochs 641-650 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.54it/s, Train RMSE=0.9786]\n", + "Epochs 651-660 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.02it/s, Train RMSE=0.9775]\n", + "Epochs 661-670 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.20it/s, Train RMSE=0.9778]\n", + "Epochs 671-680 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.15it/s, Train RMSE=0.9762]\n", + "Epochs 681-690 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.07it/s, Train RMSE=0.9761]\n", + "Epochs 691-700 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.43it/s, Train RMSE=0.9769]\n", + "Epochs 701-710 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.48it/s, Train RMSE=0.9753]\n", + "Epochs 711-720 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.60it/s, Train RMSE=0.9756]\n", + "Epochs 721-730 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.90it/s, Train RMSE=0.9748]\n", + "Epochs 731-740 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.89it/s, Train RMSE=0.9752]\n", + "Epochs 741-750 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.83it/s, Train RMSE=0.9761]\n", + "Epochs 751-760 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9745]\n", + "Epochs 761-770 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.75it/s, Train RMSE=0.9765]\n", + "Epochs 771-780 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.28it/s, Train RMSE=0.9768]\n", + "Epochs 781-790 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.24it/s, Train RMSE=0.9758]\n", + "Epochs 791-800 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.44it/s, Train RMSE=0.9742]\n", + "Epochs 801-810 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.71it/s, Train RMSE=0.9744]\n", + "Epochs 811-820 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.86it/s, Train RMSE=0.9743]\n", + "Epochs 821-830 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.00it/s, Train RMSE=0.9756]\n", + "Epochs 831-840 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.98it/s, Train RMSE=0.9761]\n", + "Epochs 841-850 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.81it/s, Train RMSE=0.9756]\n", + "Epochs 851-860 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.08it/s, Train RMSE=0.9757]\n", + "Epochs 861-870 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.10it/s, Train RMSE=0.9746]\n", + "Epochs 871-880 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.47it/s, Train RMSE=0.9723]\n", + "Epochs 881-890 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.26it/s, Train RMSE=0.9714]\n", + "Epochs 891-900 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.00it/s, Train RMSE=0.9745]\n", + "Epochs 901-910 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.13it/s, Train RMSE=0.9725]\n", + "Epochs 911-920 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.75it/s, Train RMSE=0.9722]\n", + "Epochs 921-930 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.83it/s, Train RMSE=0.9725]\n", + "Epochs 931-940 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.21it/s, Train RMSE=0.9710]\n", + "Epochs 941-950 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.10it/s, Train RMSE=0.9738]\n", + "Epochs 951-960 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.54it/s, Train RMSE=0.9738]\n", + "Epochs 961-970 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 23.58it/s, Train RMSE=0.9715]\n", + "Epochs 971-980 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 24.55it/s, Train RMSE=0.9731]\n", + "Epochs 981-990 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 25.77it/s, Train RMSE=0.9722]\n", + "Epochs 991-1000 (Fine-Tuning): 100%|██████████| 10/10 [00:00<00:00, 26.22it/s, Train RMSE=0.9720]\n" + ] + } + ], + "source": [ + "# Run the experiment on the smaller subset\n", + "cornac.Experiment(eval_method=rs, models=[my_model, mf, pmf, bpr], metrics=metrics, user_based=True).run()\n", + "\n", + "# Retrieve experiment results\n", + "experiment_results = sys.stdout.getvalue()\n", + "sys.stdout = temp # Restore stdout to original state" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# Print the results to the console\n", + "print(experiment_results)\n", + "\n", + "# Log results to file\n", + "log_results(log_file, experiment_results, my_model)\n", + "\n", + "print(f\"Experiment results and hyperparameters saved to {log_file}\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "base", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}