def plot_learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves NeuralNetwork"
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
estimator = self.get_pure_model()
Learning_curve_plotter(estimator, title, self.data.X, self.data.Y, cv=cv)
plt.show()
def learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves NeuralNetwork"
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
estimator = self.get_pure_model()
Learning_curve_plotter(estimator, title, self.data.X, self.data.Y, cv=cv)
bytes_image = io.BytesIO()
plt.savefig(bytes_image, format='png')
bytes_image.seek(0)
return bytes_image
def learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves LinearRegression"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = linear_model.LinearRegression()
Learning_curve_plotter(estimator, title, self.data.X, self.data.Y, cv=cv)
bytes_image = io.BytesIO()
plt.savefig(bytes_image, format='png')
bytes_image.seek(0)
return bytes_image
def plot_learning_curves(self):
"""
Plots the learning curves of the model..
"""
warnings.filterwarnings("ignore")
title = "Learning Curves LinearRegression"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = linear_model.LinearRegression()
Learning_curve_plotter(estimator, title, self.data.X, self.data.Y, cv=cv)
plt.show()
def plot_learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves Lasso"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = linear_model.Lasso(alpha=1.0)
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
plt.show()
def plot_learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves ElasticNet"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = ElasticNetModel(alpha=0.1)
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
plt.show()
def plot_learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves RandomForest"
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
estimator = RandomForestRegressor(max_depth=2, random_state=0)
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
plt.show()
def plot_learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves DecisionTree"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = tree.DecisionTreeRegressor()
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
plt.show()
def learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves ElasticNet"
cv = ShuffleSplit(n_splits=50, test_size=0.2, random_state=0)
estimator = ElasticNetModel(alpha=1.0)
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
bytes_image = io.BytesIO()
plt.savefig(bytes_image, format='png')
bytes_image.seek(0)
return bytes_image
def learning_curves(self):
warnings.filterwarnings("ignore")
title = "Learning Curves RandomForest"
cv = ShuffleSplit(n_splits=5, test_size=0.2, random_state=0)
estimator = RandomForestRegressor(max_depth=2, random_state=0)
Learning_curve_plotter(estimator,
title,
self.data.X,
self.data.Y,
cv=cv)
bytes_image = io.BytesIO()
plt.savefig(bytes_image, format='png')
bytes_image.seek(0)
return bytes_image