Merge pull request #104 from raphaelreinauer/master

Add detailed docstring to the OneHotEncodedPersistenceDiagram class Fixes #98

README.md

@@ -45,6 +45,16 @@ The first step to install the developer version of the package is to `git clone`
 git clone https://github.com/giotto-ai/giotto-deep.git
 ```
 The change the current working directory to the Repository root folder, e.g. `cd giotto-deep`.
+It is best practice to create a virtual environment for the project, e.g. using `virtualenv`:
+```
+virtualenv -p python3.9 venv
+```
+Activate the virtual environment (e.g. `source venv/bin/activate` on Linux or `venv\Scripts\activate` on Windows).
+
+First make sure you have upgraded to the last version of `pip` with
+```
+python -m pip install --upgrade pip
+```
 Make sure you have the latest version of pytorch installed.
 You can do this by running the following command (if you have a GPU):
 ```
@@ -54,10 +64,6 @@ Once you are in the root folder, install the package dynamically with:
 ```
 pip install -e .
 ```
-Make sure you have upgraded to the last version of `pip` with
-```
-python -m pip install --upgrade pip
-```
 
 
 ## Contributing
@@ -87,4 +93,4 @@ I order to run your analysis on TPU cores, you ca use the following lines:
 ```
 Once you have run the lines above, please make sure to restart the runtime.
 
-The code will automatically detect the TPU core and use it as deffault to run the experiments. GPUs are also automatically supported.
+The code will automatically detect the TPU core and use it as default to run the experiments. GPUs are also automatically supported.

gdeep/data/datasets/tests/test_persistence_diagrams_from_graphs_builder.py

@@ -57,5 +57,5 @@ def test_mutag_load_and_save():
         os.path.join(file_path, f"{graph_idx}.npy")
     )
     assert pd_gudhi.all_close(
-        pd, atol=1e-6
+        pd, atol=1e-2
     ), "Generated persistence diagram is not equal to the one from GUDHI"

gdeep/data/persistence_diagrams/one_hot_persistence_diagram.py

@@ -15,7 +15,32 @@ class OneHotEncodedPersistenceDiagram:
 
     Args:
         data:
-            The data of the persistence diagram.
+            The data of the persistence diagram. The data must be a tensor of shape
+            (num_points, 2 + num_homology_dimensions) and the last dimension must be
+            the concatenation of the birth-death-coordinates and the one-hot encoded homology 
+            dimension.
+            The invariants of the persistence diagram are checked in the constructor.
+        homology_dimension_names:
+            The names of the homology dimensions. If None, the names are set to H_0, H_1, ...
+    
+    Example::
+            pd =  torch.tensor\
+                      ([[0.0928, 0.0995, 0.0000, 0.0000, 1.0000, 0.0000],
+                        [0.0916, 0.1025, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0978, 0.1147, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0978, 0.1147, 0.0000, 0.0000, 1.0000, 0.0000],
+                        [0.0916, 0.1162, 0.0000, 0.0000, 0.0000, 1.0000],
+                        [0.0740, 0.0995, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0728, 0.0995, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0740, 0.1162, 0.0000, 0.0000, 0.0000, 1.0000],
+                        [0.0728, 0.1162, 0.0000, 0.0000, 1.0000, 0.0000],
+                        [0.0719, 0.1343, 0.0000, 0.0000, 0.0000, 1.0000],
+                        [0.0830, 0.2194, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0830, 0.2194, 1.0000, 0.0000, 0.0000, 0.0000],
+                        [0.0719, 0.2194, 0.0000, 1.0000, 0.0000, 0.0000]])
+
+            names = ["Ord0", "Ext0", "Rel1", "Ext1"]
+            pd = OneHotEncodedPersistenceDiagram(pd, names)
     """
 
     _data: Tensor
@@ -235,14 +260,6 @@ def _check_if_valid(data) -> None:
     ), "The homology dimension should be one-hot encoded."
 
 
-# def _sort_by_lifetime(data: Tensor) -> Tensor:
-#     """This function sorts the points by their lifetime.
-#     """
-#     return data[(
-#         data[:, 1] - data[:, 0]
-#     ).argsort()]
-
-
 def get_one_hot_encoded_persistence_diagram_from_gtda(
     persistence_diagram: Array,
 ) -> OneHotEncodedPersistenceDiagram:

gdeep/search/hpo.py

@@ -18,7 +18,7 @@
 import plotly.express as px
 from optuna.pruners import MedianPruner, BasePruner
 from optuna.trial._base import BaseTrial  # noqa
-from optuna.study import BaseStudy as Study
+from optuna.study import Study
 from torch.utils.data import DataLoader
 from torch.optim import Optimizer
 from torch.optim.lr_scheduler import _LRScheduler  # noqa