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Author: Algorithmica

2019-october/8.geometrical viz - classification algorithms/feature transformation.py

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+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+from sklearn import preprocessing
+import numpy as np
+
+X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
+X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
+
+cutils.plot_data_2d_classification(X, y)
+
+#guassian basis transformation
+tmp = np.exp(-(X ** 2).sum(1))
+X_3d = np.c_[X, tmp]
+cutils.plot_data_3d_classification(X_3d, y, new_window=True, title="Linearly separable data in 3D with basis change")
+
+#polynomial basis transformation
+poly_features = preprocessing.PolynomialFeatures()
+X_poly1 = poly_features.fit_transform(X)
+
+poly_features = preprocessing.PolynomialFeatures(degree=3)
+X_poly2 = poly_features.fit_transform(X)

2019-october/8.geometrical viz - classification algorithms/non linear models-2.py

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+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+from sklearn import model_selection, linear_model, svm, preprocessing, pipeline
+
+
+#2-d classification pattern
+X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
+X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
+
+cutils.plot_data_2d_classification(X, y)
+
+X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
+cutils.plot_data_2d_classification(X_train, y_train)
+
+#perceptron algorithm
+stages = [
+            ('features', preprocessing.PolynomialFeatures()),
+            ('clf', linear_model.Perceptron(max_iter=1000))
+        ]
+perceptron_pipeline = pipeline.Pipeline(stages)
+perceptron_pipeline_grid  = {'clf__penalty':['l1'], 'clf__alpha':[0, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10]}
+pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X_train, y_train)
+final_estimator = pipeline_object.named_steps['clf']
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
+
+#logistic regression algorithm
+stages = [
+            ('features', preprocessing.PolynomialFeatures()),
+            ('clf', linear_model.LogisticRegression())
+        ]
+lr_pipeline = pipeline.Pipeline(stages)
+lr_pipeline_grid  = {'clf__penalty':['l1'], 'clf__C':[0.01, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10]}
+pipeline_object = cutils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train)
+final_estimator = pipeline_object.named_steps['clf']
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
+
+#linear svm algorithm
+stages = [
+            ('features', preprocessing.PolynomialFeatures()),
+            ('clf', svm.LinearSVC())
+        ]
+svm_pipeline = pipeline.Pipeline(stages)
+svm_pipeline_grid  = {'clf__penalty':['l2'], 'clf__C':[0.01, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10, 50, 100]}
+pipeline_object = cutils.grid_search_best_model(svm_pipeline, svm_pipeline_grid, X_train, y_train)
+final_estimator = pipeline_object.named_steps['clf']
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)

2019-october/8.geometrical viz - classification algorithms/pipelines.py

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+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+from sklearn import preprocessing, linear_model, pipeline
+
+X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
+X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
+
+cutils.plot_data_2d_classification(X, y)
+
+stages = [
+            ('features', preprocessing.PolynomialFeatures()),
+            ('perceptron', linear_model.Perceptron(max_iter=1000))
+        ]
+perceptron_pipeline = pipeline.Pipeline(stages)
+perceptron_pipeline_grid  = {'perceptron__penalty':['l1'], 'perceptron__alpha':[0, 0.1, 0.3, 0.5], 'features__degree':[2,3]}
+pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X, y)
+final_estimator = pipeline_object.named_steps['perceptron']
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(pipeline_object, X, y)