n_features=1)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.1, random_state=1)
rutils.plot_data_2d_regression(X_train, y_train)
kernel_lr = kernel_ridge.KernelRidge(kernel="rbf")
kernel_lr_grid = {
'alpha': [0.0001, 0.01, 0.05, 0.2, 0.5, 1],
'gamma': [0.01, 0.1, 1, 2, 3, 4, 5, 10]
}
final_kernel_lr_model = utils.grid_search_best_model(kernel_lr,
kernel_lr_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(final_kernel_lr_model, X_train, y_train)
rutils.regression_performance(final_kernel_lr_model, X_test, y_test)
kernel_svm = svm.SVR(kernel="rbf")
kernel_svm_grid = {
'C': [0.2, 0.5, 10, 20, 50, 100],
'gamma': [0.01, 0.1, 1, 2, 3, 4, 5, 10]
}
final_kernel_svm_model = utils.grid_search_best_model(kernel_svm,
kernel_svm_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(final_kernel_svm_model, X_train, y_train)
rutils.regression_performance(final_kernel_svm_model, X_test, y_test)
model.add(Dense(units=20, input_shape=(1,), activation='relu'))
model.add(Dense(units=10, activation='relu'))
model.add(Dense(units=1, activation='linear'))
return model
model = getModel3()
model.compile(optimizer='sgd', loss='mean_squared_error', metrics=[metrics.mean_squared_error])
path="I:/"
early_stopping = EarlyStopping(monitor='val_loss', patience=2, verbose=1, mode='auto')
tensorboard = TensorBoard(log_dir=os.path.join(path, "logs"), histogram_freq=1,
write_images=True)
history = model.fit(x=X_train, y=y_train, verbose=3, epochs=100, batch_size=16, validation_split=0.1,
callbacks=[early_stopping, tensorboard] )
print(model.summary())
print(model.get_weights())
kutils.plot_loss(history)
rutils.plot_data_2d_regression(X_train, y_train)
#serialization to h5
model.save(os.path.join(path, "model.h5"))
#deserialization from h5
loaded_model = models.load_model(os.path.join(path, "model.h5"))
print(loaded_model.summary())
print(loaded_model.get_weights())
y_pred = loaded_model.predict(X_test)
rutils.regression_performance(model, X_test, y_test)
y_train[::10] = 250
rutils.plot_data_2d_regression(X_train, y_train)
# Fit linear model
lr_estimator = linear_model.LinearRegression()
lr_grid = {'normalize': [True, False]}
lr_model = utils.grid_search_best_model(lr_estimator,
lr_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(lr_model,
X_train,
y_train,
title="LinearRegression")
rutils.regression_performance(lr_model, X_test, y_test)
# Robustly fit linear model with Huber Regressor algorithm
hr_estimator = linear_model.HuberRegressor()
hr_grid = {'epsilon': [1.1, 1.2, 1.3, 1.5]}
hr_model = utils.grid_search_best_model(hr_estimator,
hr_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(hr_model,
X_train,
y_train,
title="HuberRegression")
rutils.regression_performance(hr_model, X_test, y_test)
X, y = rutils.generate_nonlinear_synthetic_data_regression(n_samples=200, n_features=1)
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
rutils.plot_data_2d_regression(X_train, y_train)
stages = [
('features', preprocessing.PolynomialFeatures()),
('reg', linear_model.LinearRegression())
]
lr_pipeline = pipeline.Pipeline(stages)
lr_pipeline_grid = {'reg__normalize':[True, False], 'features__degree':[2,3,5,10]}
pipeline_object = utils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train, scoring = scoring)
final_linear_model = pipeline_object.named_steps['reg']
print(final_linear_model.coef_)
print(final_linear_model.intercept_)
rutils.plot_model_2d_regression(pipeline_object, X_train, y_train)
rutils.regression_performance(pipeline_object, X_test, y_test)
stages = [
('features', kutils.GaussianFeatures() ),
('reg', linear_model.LinearRegression())
]
lr_pipeline = pipeline.Pipeline(stages)
lr_pipeline_grid = {'reg__normalize':[True, False], 'features__n_centres':[15, 20, 30, 36] }
pipeline_object = utils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train, scoring = scoring)
final_linear_model = pipeline_object.named_steps['reg']
print(final_linear_model.coef_)
print(final_linear_model.intercept_)
rutils.plot_model_2d_regression(pipeline_object, X_train, y_train)
rutils.regression_performance(pipeline_object, X_test, y_test)
stages = [
noise=200)
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.1, random_state=1)
rutils.plot_data_2d_regression(X_train, y_train)
linear_estimator = linear_model.LinearRegression()
linear_grid = {'normalize': [False]}
final_linear_model = utils.grid_search_best_model(linear_estimator,
linear_grid,
X_train,
y_train,
scoring=scoring)
print(final_linear_model.coef_)
print(final_linear_model.intercept_)
rutils.plot_model_2d_regression(final_linear_model, X_train, y_train)
rutils.regression_performance(final_linear_model, X_test, y_test)
lasso_estimator = linear_model.Lasso(max_iter=5000)
lasso_grid = {'alpha': [0, 0.1, 0.5, 1.0, 10]}
final_lasso_model = utils.grid_search_best_model(lasso_estimator,
lasso_grid,
X_train,
y_train,
scoring=scoring)
print(final_lasso_model.coef_)
print(final_lasso_model.intercept_)
rutils.plot_model_2d_regression(final_lasso_model, X_train, y_train)
rutils.regression_performance(final_lasso_model, X_test, y_test)
ridge_estimator = linear_model.Ridge()
ridge_grid = {'alpha': [0, 0.1, 0.5, 1.0, 10]}
X_train, X_test, y_train, y_test = model_selection.train_test_split(
X, y, test_size=0.1, random_state=1)
rutils.plot_data_2d_regression(X_train, y_train)
rf_estimator = ensemble.RandomForestRegressor()
rf_grid = {
'n_estimators': list(range(10, 200, 20)),
'max_depth': list(range(3, 6))
}
final_rf_model = utils.grid_search_best_model(rf_estimator,
rf_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(final_rf_model, X_train, y_train)
rutils.regression_performance(final_rf_model, X_test, y_test)
et_estimator = ensemble.ExtraTreesRegressor()
et_grid = {
'n_estimators': list(range(10, 200, 20)),
'max_depth': list(range(3, 6))
}
final_et_model = utils.grid_search_best_model(et_estimator,
et_grid,
X_train,
y_train,
scoring=scoring)
rutils.plot_model_2d_regression(final_et_model, X_train, y_train)
rutils.regression_performance(final_et_model, X_test, y_test)
dt_estimator = tree.DecisionTreeRegressor()