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Author: Algorithmica

2019-october/10.kaggle(dont overfit2)/dont overfit1.py

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+import pandas as pd
+import os
+from sklearn import tree, ensemble, model_selection, preprocessing, decomposition, manifold, feature_selection, svm
+import seaborn as sns
+import numpy as np
+
+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+
+dir = 'C:/Users/Algorithmica/Downloads/dont-overfit-ii'
+train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(train.info())
+print(train.columns)
+
+#filter unique value features
+train1 = train.iloc[:,2:] 
+y = train['target'].astype(int)
+
+#filter zero-variance features
+variance = feature_selection.VarianceThreshold()
+train2 = variance.fit_transform(train1)
+
+lpca = decomposition.PCA(n_components=0.95)
+lpca.fit(train2)
+np.cumsum(lpca.explained_variance_ratio_)
+train_pca = lpca.transform(train2)
+
+tsne = manifold.TSNE(n_components=3)
+train_tsne = tsne.fit_transform(train_pca)
+cutils.plot_data_3d_classification(train_tsne, y)
+
+X_train, X_eval, y_train, y_eval = model_selection.train_test_split(train_pca, y, test_size=0.1, random_state=1)
+
+sns.countplot(x='target',data=train)
+
+kernel_svm_estimator = svm.SVC(kernel='rbf')
+kernel_svm_grid = {'gamma':[0.01, 0.1, 1, 2, 5, 10], 'C':[0.001, 0.01, 0.1, 0.5] }
+final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, X_train, y_train)
+
+print(final_estimator.score(X_eval, y_eval))
+
+test = pd.read_csv(os.path.join(dir, 'test.csv'))
+print(test.info())
+print(test.columns)
+
+test1 = test.iloc[:,1:] 
+test2 = variance.transform(test1)
+test_pca = lpca.transform(test2)
+test['target'] = final_estimator.predict(test_pca)
+test.to_csv(os.path.join(dir, 'submission.csv'), columns=['id', 'target'], index=False)

2019-october/10.kaggle(dont overfit2)/dont overfit2.py

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+import pandas as pd
+import os
+from sklearn import tree, ensemble, model_selection, preprocessing, decomposition, manifold, feature_selection, svm
+import seaborn as sns
+import numpy as np
+
+import sys
+sys.path.append("E:/New Folder/utils")
+
+import classification_utils as cutils
+import common_utils as utils
+
+dir = 'C:/Users/Algorithmica/Downloads/dont-overfit-ii'
+train = pd.read_csv(os.path.join(dir, 'train.csv'))
+print(train.info())
+print(train.columns)
+
+#filter unique value features
+train1 = train.iloc[:,2:] 
+y = train['target'].astype(int)
+
+#filter zero-variance features
+variance = feature_selection.VarianceThreshold()
+train2 = variance.fit_transform(train1)
+
+rf_estimator = ensemble.RandomForestClassifier()
+rf_grid  = {'max_depth':list(range(1,9)), 'n_estimators':list(range(1,300,100)) }
+rf_final_estimator = cutils.grid_search_best_model(rf_estimator, rf_grid, train1, y)
+embedded_selector = feature_selection.SelectFromModel(rf_final_estimator, prefit=True, threshold='mean')
+train3 = embedded_selector.transform(train1)
+utils.plot_feature_importances(rf_final_estimator,train1, cutoff=50)
+
+et_estimator = ensemble.ExtraTreesClassifier()
+et_grid  = {'max_depth':list(range(1,9)), 'n_estimators':list(range(1,300,100)) }
+et_final_estimator = cutils.grid_search_best_model(et_estimator, et_grid, train1, y)
+embedded_selector = feature_selection.SelectFromModel(et_final_estimator, prefit=True, threshold='mean')
+train3 = embedded_selector.transform(train1)
+utils.plot_feature_importances(et_final_estimator,train1, cutoff=50)
+
+gb_estimator = ensemble.GradientBoostingClassifier()
+gb_grid  = {'max_depth':[1,2,3], 'n_estimators':list(range(50,300, 100)), 'learning_rate':[0.001, 0.1, 1.0] }
+gb_final_estimator = cutils.grid_search_best_model(gb_estimator, gb_grid, train1, y)
+embedded_selector = feature_selection.SelectFromModel(gb_final_estimator, prefit=True, threshold='mean')
+X_train1 = embedded_selector.transform(train1)
+utils.plot_feature_importances(gb_final_estimator, train1)
+
+kernel_svm_estimator = svm.SVC(kernel='rbf')
+kernel_svm_grid = {'gamma':[0.01, 0.1, 1, 2, 5, 10], 'C':[0.001, 0.01, 0.1, 0.5] }
+final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, train1, y)
+embedded_selector = feature_selection.SelectFromModel(final_estimator, prefit=True, threshold='mean')
+X_train1 = embedded_selector.transform(train1)
+utils.plot_feature_importances(final_estimator, train1)
+
+X_train, X_eval, y_train, y_eval = model_selection.train_test_split(train_pca, y, test_size=0.1, random_state=1)
+
+sns.countplot(x='target',data=train)
+
+kernel_svm_estimator = svm.SVC(kernel='rbf')
+kernel_svm_grid = {'gamma':[0.01, 0.1, 1, 2, 5, 10], 'C':[0.001, 0.01, 0.1, 0.5] }
+final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, X_train, y_train)
+
+print(final_estimator.score(X_eval, y_eval))
+
+test = pd.read_csv(os.path.join(dir, 'test.csv'))
+print(test.info())
+print(test.columns)
+
+test1 = test.iloc[:,1:] 
+test2 = variance.transform(test1)
+test_pca = lpca.transform(test2)
+test['target'] = final_estimator.predict(test_pca)
+test.to_csv(os.path.join(dir, 'submission.csv'), columns=['id', 'target'], index=False)

2019-october/9.classification metrics/classification metrics-hard.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, dummy
+
+#binary classification
+X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=2, weights=[0.4,0.6], class_sep=1.5)
+#X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
+
+
+X_train, X_eval, y_train, y_eval = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
+cutils.plot_data_2d_classification(X_train, y_train)
+
+lr_estimator = linear_model.LogisticRegression()
+lr_grid  = {'penalty':['l1', 'l2'], 'C':[0.01, 0.001, 0.1, 0.3, 0.5, 0.7, 1] }
+final_estimator = cutils.grid_search_best_model(lr_estimator, lr_grid, X_train, y_train, scoring='accuracy')
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
+cutils.performance_metrics_hard_binary_classification(final_estimator, X_eval, y_eval)
+
+#multi class classification
+X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=4, weights=[0.3,0.3,0.2,0.2], class_sep=1.5)
+#X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
+
+
+X_train, X_eval, y_train, y_eval = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
+cutils.plot_data_2d_classification(X_train, y_train)
+
+lr_estimator = linear_model.LogisticRegression()
+lr_grid  = {'penalty':['l1', 'l2'], 'C':[0.01, 0.001, 0.1, 0.3, 0.5, 0.7, 1] }
+final_estimator = cutils.grid_search_best_model(lr_estimator, lr_grid, X_train, y_train, scoring='accuracy')
+print(final_estimator.intercept_)
+print(final_estimator.coef_)
+cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
+cutils.performance_metrics_hard_multiclass_classification(final_estimator, X_eval, y_eval)