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Commit 0f0004c

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Algorithmica
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import sys
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path = 'I://New Folder//utils'
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sys.path.append(path)
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import common_utils as utils
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import regression_utils as rutils
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from sklearn import metrics, linear_model, svm, model_selection
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scoring = metrics.make_scorer(rutils.rmse, greater_is_better=False)
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#linear pattern in 2d
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X, y = rutils.generate_linear_synthetic_data_regression(n_samples=100, n_features=1,
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n_informative=1,
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noise = 200)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
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rutils.plot_data_2d_regression(X_train, y_train)
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linear_estimator = linear_model.LinearRegression()
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linear_grid = {'normalize': [False]}
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final_linear_model = utils.grid_search_best_model(linear_estimator, linear_grid, X_train, y_train, scoring = scoring)
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print(final_linear_model.coef_)
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print(final_linear_model.intercept_)
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rutils.plot_model_2d_regression(final_linear_model, X_train, y_train)
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rutils.regression_performance(final_linear_model, X_test, y_test)
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lasso_estimator = linear_model.Lasso(max_iter=5000)
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lasso_grid = {'alpha': [0, 0.1, 0.5, 1.0, 10]}
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final_lasso_model = utils.grid_search_best_model(lasso_estimator, lasso_grid, X_train, y_train, scoring = scoring)
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print(final_lasso_model.coef_)
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print(final_lasso_model.intercept_)
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rutils.plot_model_2d_regression(final_lasso_model, X_train, y_train)
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rutils.regression_performance(final_lasso_model, X_test, y_test)
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ridge_estimator = linear_model.Ridge()
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ridge_grid = {'alpha': [0, 0.1, 0.5, 1.0, 10]}
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final_ridge_model = utils.grid_search_best_model(ridge_estimator, ridge_grid, X_train, y_train, scoring = scoring)
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print(final_ridge_model.coef_)
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print(final_ridge_model.intercept_)
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rutils.plot_model_2d_regression(final_ridge_model, X_train, y_train)
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rutils.regression_performance(final_ridge_model, X_test, y_test)
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svm_estimator = svm.LinearSVR()
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svm_grid = {'C':[0.1, 0.3, 0.5, 0.7, 1, 10] }
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final_svm_model = utils.grid_search_best_model(svm_estimator, svm_grid, X_train, y_train, scoring = scoring)
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print(final_svm_model.coef_)
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print(final_svm_model.intercept_)
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rutils.plot_model_2d_regression(final_svm_model, X_train, y_train)
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rutils.regression_performance(final_svm_model, X_test, y_test)
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#linear pattern in 3d
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X, y = rutils.generate_linear_synthetic_data_regression(n_samples=200, n_features=2,
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n_informative=2,
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noise = 10)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
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rutils.plot_data_3d_regression(X_train, y_train)
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linear_estimator = linear_model.LinearRegression()
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linear_grid = {'normalize': [True, False]}
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final_linear_model = utils.grid_search_best_model(linear_estimator, linear_grid, X_train, y_train, scoring=scoring)
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print(final_linear_model.coef_)
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print(final_linear_model.intercept_)
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rutils.plot_model_3d_regression(final_linear_model, X_train, y_train)
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rutils.regression_performance(final_linear_model, X_test, y_test)
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svm_estimator = svm.LinearSVR()
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svm_grid = {'C':[0.1, 0.3, 0.5, 0.7, 1, 10] }
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final_svm_model = utils.grid_search_best_model(svm_estimator, svm_grid, X_train, y_train, scoring = scoring)
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print(final_svm_model.coef_)
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print(final_svm_model.intercept_)
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rutils.plot_model_3d_regression(final_svm_model, X_train, y_train)
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rutils.regression_performance(final_svm_model, X_test, y_test)
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import sys
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path = 'I://New Folder//utils'
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sys.path.append(path)
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import common_utils as utils
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import regression_utils as rutils
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from sklearn import metrics, tree, neighbors, model_selection, ensemble
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scoring = metrics.make_scorer(rutils.rmse, greater_is_better=False)
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#linear pattern in 2d
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X, y = rutils.generate_nonlinear_synthetic_data_regression(n_samples=200, n_features=1)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
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rutils.plot_data_2d_regression(X_train, y_train)
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dt_estimator = tree.DecisionTreeRegressor()
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dt_grid = {'max_depth':list(range(1,9)) }
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final_dt_model = utils.grid_search_best_model(dt_estimator, dt_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_dt_model, X_train, y_train)
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rutils.regression_performance(final_dt_model, X_test, y_test)
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knn_estimator = neighbors.KNeighborsRegressor()
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knn_grid = {'n_neighbors':list(range(1,21)), 'weights':['uniform', 'distance'] }
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final_knn_model = utils.grid_search_best_model(knn_estimator,knn_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_knn_model, X_train, y_train)
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rutils.regression_performance(final_knn_model, X_test, y_test)
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rf_estimator = ensemble.RandomForestRegressor()
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rf_grid = {'n_estimators':list(range(10,200,50)), 'max_depth':list(range(3,6))}
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final_rf_model = utils.grid_search_best_model(rf_estimator, rf_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_rf_model, X_train, y_train)
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rutils.regression_performance(final_rf_model, X_test, y_test)
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et_estimator = ensemble.ExtraTreesRegressor()
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et_grid = {'n_estimators':list(range(10,200,20)), 'max_depth':list(range(3,6))}
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final_et_model = utils.grid_search_best_model(et_estimator, et_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_et_model, X_train, y_train)
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rutils.regression_performance(final_et_model, X_test, y_test)
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gb_estimator = ensemble.GradientBoostingRegressor()
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gb_grid = {'n_estimators':list(range(10,100,40)), 'max_depth':list(range(3,5)), 'learning_rate':[0.1,0.5]}
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final_gb_model = utils.grid_search_best_model(gb_estimator, gb_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_gb_model, X_train, y_train)
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rutils.regression_performance(final_gb_model, X_test, y_test)
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import sys
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path = 'I://New Folder//utils'
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sys.path.append(path)
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import common_utils as utils
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import kernel_utils as kutils
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import regression_utils as rutils
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from sklearn import metrics, model_selection, linear_model, preprocessing, pipeline, kernel_ridge, svm
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scoring = metrics.make_scorer(rutils.rmse, greater_is_better=False)
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#linear pattern in 2d
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X, y = rutils.generate_nonlinear_synthetic_data_regression(n_samples=200, n_features=1)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.1, random_state=1)
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rutils.plot_data_2d_regression(X_train, y_train)
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lr_pipeline = pipeline.Pipeline([
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('features', preprocessing.PolynomialFeatures()),
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('reg', linear_model.LinearRegression())
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])
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lr_pipeline_grid = {'features__degree':[2,3,5,10]}
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pipeline_object = utils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train, scoring = scoring)
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final_linear_model = pipeline_object.named_steps['reg']
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print(final_linear_model.coef_)
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print(final_linear_model.intercept_)
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rutils.plot_model_2d_regression(pipeline_object, X_train, y_train)
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rutils.regression_performance(pipeline_object, X_test, y_test)
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lr_pipeline = pipeline.Pipeline([
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('features', kutils.GaussianFeatures() ),
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('reg', linear_model.LinearRegression())
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])
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lr_pipeline_grid = {'features__n_centres':[15, 20, 30, 36] }
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pipeline_object = utils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train, scoring = scoring)
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final_linear_model = pipeline_object.named_steps['reg']
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print(final_linear_model.coef_)
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print(final_linear_model.intercept_)
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rutils.plot_model_2d_regression(pipeline_object, X_train, y_train)
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rutils.regression_performance(pipeline_object, X_test, y_test)
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kernel_lr = kernel_ridge.KernelRidge(kernel="rbf")
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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]}
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final_kernel_lr_model = utils.grid_search_best_model(kernel_lr, kernel_lr_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_kernel_lr_model, X_train, y_train)
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rutils.regression_performance(final_kernel_lr_model, X_test, y_test)
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kernel_svm = svm.SVR(kernel="rbf")
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kernel_svm_grid = {'C':[0.2, 0.5, 10, 20, 50, 100], 'gamma':[0.01, 0.1, 1, 2, 3, 4, 5, 10]}
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final_kernel_svm_model = utils.grid_search_best_model(kernel_svm, kernel_svm_grid, X_train, y_train, scoring = scoring)
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rutils.plot_model_2d_regression(final_kernel_svm_model, X_train, y_train)
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rutils.regression_performance(final_kernel_svm_model, X_test, y_test)

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