ada_grid,
X_train,
y_trans,
scoring=scoring)
plot_feature_importances(ada_selector, X_train, 50)
important_features_ada = get_important_features(ada_selector, X_train)
important_features = set(important_features_lasso) | set(
important_features_rf) | set(important_features_ada)
print(len(important_features))
X_train1 = X_train[list(important_features)]
pca_data = feature_reduction_pca(X_train1, X_train1.shape[1])
tsne_data = feature_reduction_tsne(X_train1, 2)
plot_data_3d_regression(tsne_data, y_train)
scoring = metrics.make_scorer(log_rmse, greater_is_better=False)
gb_estimator = ensemble.GradientBoostingRegressor()
gb_grid = {
'n_estimators': list(range(100, 501, 200)),
'learning_rate': [0.1, 1.0],
'max_depth': [1, 3, 5]
}
gb_model = get_best_model(gb_estimator,
gb_grid,
X_train1,
y_trans,
scoring=scoring)
X_train,
y_train,
scoring=scoring)
print(final_svm_model.coef_)
print(final_svm_model.intercept_)
rutils.plot_model_2d_regression(final_svm_model, X_train, y_train)
rutils.regression_performance(final_svm_model, X_test, y_test)
#linear pattern in 3d
X, y = rutils.generate_linear_synthetic_data_regression(n_samples=200,
n_features=2,
n_informative=2,
noise=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_3d_regression(X_train, y_train)
linear_estimator = linear_model.LinearRegression()
linear_grid = {'normalize': [True, 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_3d_regression(final_linear_model, X_train, y_train)
rutils.regression_performance(final_linear_model, X_test, y_test)
svm_estimator = svm.LinearSVR()
svm_grid = {'C': [0.1, 0.3, 0.5, 0.7, 1, 10]}
lasso_selector.fit(X_train, y_train)
print(lasso_selector.coef_)
utils.plot_feature_importances(lasso_selector, X_train, 40)
X_train1 = utils.select_features(lasso_selector, X_train)
utils.corr_heatmap(X_train1)
lpca = decomposition.PCA(0.95)
lpca.fit(X_train1)
print(np.cumsum(lpca.explained_variance_ratio_))
pca_data = lpca.transform(X_train1)
print(pca_data.shape)
tsne = manifold.TSNE(n_components=2)
tsne_data = tsne.fit_transform(pca_data)
rutils.plot_data_3d_regression(tsne_data, y_train)
scoring = metrics.make_scorer(log_rmse, greater_is_better=False)
sns.distplot(y_train)
y_trans = np.log1p(y_train)
sns.distplot(y_trans)
knn_estimator = neighbors.KNeighborsRegressor()
knn_grid = {'n_neighbors': list(range(5, 15))}
final_model = utils.grid_search_best_model(knn_estimator,
knn_grid,
pca_data,
y_trans,
scoring=scoring)
#build preprocessing pipeline for all features
cat_features = utils.get_non_continuous_features(house_train1)
num_features = utils.get_continuous_features(house_train1)
preprocess_pipeline = compose.ColumnTransformer([
('cat', categorical_pipeline, cat_features),
('num', numerical_pipeline, num_features)
])
viz_pipeline = pipeline.Pipeline([('preprocess', preprocess_pipeline),
('pca',
decomposition.PCA(n_components=0.95)),
('tsne', manifold.TSNE(2))])
tsne_data = viz_pipeline.fit_transform(house_train1)
rutils.plot_data_3d_regression(tsne_data, house_train['SalePrice'])
#build feature selection pipeline
features_pipeline = pipeline.FeatureUnion([
('pca_selector', decomposition.PCA()),
('et_selector',
feature_selection.SelectFromModel(ensemble.ExtraTreesClassifier()))
])
regressor = svm.LinearSVR()
#build complete pipeline with feature selection and ml algorithms
complete_pipeline = pipeline.Pipeline([
('preprocess', preprocess_pipeline),
('zv_filter', feature_selection.VarianceThreshold()),
('features', features_pipeline),
('tregressor',