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cross_validation_adaboost_regressor.py
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import numpy as np
import pandas as pd
import datetime
import matplotlib.pyplot as plt
from sklearn.cross_validation import train_test_split
import sklearn.preprocessing as preprocessing
from sklearn import linear_model
from sklearn.metrics import mean_squared_error
from sklearn import ensemble
import read_dataset
def split_and_build_class(X, y):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)
print X_train.shape
print X_test.shape
# Normalize the input data.
imp = preprocessing.Imputer(missing_values='NaN', strategy='mean', axis=0)
fixed_X_train = X_train[:, 1:]
imp.fit(fixed_X_train)
fixed_X_train = imp.transform(fixed_X_train)
preprocessing.normalize(fixed_X_train, copy=False)
X_train[:, 1:] = fixed_X_train
fixed_X_test = X_test[:, 1:]
imp.fit(fixed_X_test)
fixed_X_test = imp.transform(fixed_X_test)
preprocessing.normalize(fixed_X_test, copy=False)
X_test[:, 1:] = fixed_X_test
train_data = read_dataset.microData()
train_data.get_data(X_train)
y_train = train_data.set_output(y_train)
test_data = read_dataset.microData()
test_data.get_data(X_test)
y_test = test_data.set_output(y_test)
return [X_train, X_test, y_train, y_test, train_data, test_data]
def run_regression(X, y):
clf = ensemble.AdaBoostRegressor(n_estimators=1000)
clf.fit(X, y)
return clf
def main():
# Read the dataset.
dataset_path = './dataset/'
dataset_file_path = './dataset_file_path.csv'
df_path = pd.read_csv(dataset_file_path)
all_file_param = read_dataset.read_all_dataset(df_path)
file_amount = len(all_file_param)
plot_files = ['Training set Microclimate (2 hour intervals)']
yield_file = 'Target Variable Water Yield'
# plot_files = ['Training set Microclimate (2 hour intervals)',
# 'Training set Microclimate (5 minute intervals)']
# Set up features for micro files.
micro_features = read_dataset.set_features(dataset_path, plot_files)
# Read yield file for micro training file.
yield_df = None
for k in xrange(file_amount):
file = all_file_param[k]
if file.data_name == yield_file:
yield_path = dataset_path + file.file_path
yield_df = pd.read_csv(yield_path)
break
# Traverse all the dataset.
for k in xrange(file_amount):
file = all_file_param[k]
if file.data_name not in plot_files:
continue
print '==========' + file.data_name + '=========='
path = dataset_path + file.file_path
df = pd.read_csv(path)
# Split the micro training file into training dataset and test dataset.
X_train, X_test, y_train, y_test, train_data, test_data = split_and_build_class(df.values, yield_df.values)
# [train_data, test_data] = split_and_build_class(df.values, yield_df.values)
# Run Ridge Regression.
clf = run_regression(X_train[:, 1:], y_train)
y_hat_test = clf.predict(X_test[:, 1:])
cmap = plt.get_cmap('jet_r')
plt.figure(figsize=(10, 10))
interval = file.interval
intervel_minute = read_dataset.get_interval_minute(interval)
test_size = y_hat_test.shape[0]
plt.plot([i for i in xrange(test_size)], y_hat_test)
plt.plot([i for i in xrange(test_size)], y_test)
plt.legend(['Prediction', 'Real'])
plt.suptitle('Cross validation + Adaboost Regressor')
plt.savefig('Cross validation + Adaboost Regressor.png', bbox_inches='tight')
loss = np.sqrt(mean_squared_error(y_test, y_hat_test))
print 'Cross validation + Adaboost Regressor loss =', loss
main()