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python/Supervised Learning/Training and Tuning/detecting_quiz.py

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+# Import, read, and split data
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.svm import SVC
+from sklearn.model_selection import learning_curve
+from sklearn.model_selection import ShuffleSplit
+
+data = pd.read_csv('data.csv')
+X = np.array(data[['x1', 'x2']])
+y = np.array(data['y'])
+
+# Fix random seed
+np.random.seed(55)
+
+# TODO: Uncomment one of the three classifiers, and hit "Test Run"
+# to see the learning curve. Use these to answer the quiz below.
+
+# Logistic Regression
+estimator0 = LogisticRegression(solver='lbfgs')
+
+# Decision Tree
+estimator1 = GradientBoostingClassifier()
+
+# Support Vector Machine
+estimator2 = SVC(kernel='rbf', gamma=1000)
+
+
+def randomize(X, Y):
+    permutation = np.random.permutation(Y.shape[0])
+    X2 = X[permutation, :]
+    Y2 = Y[permutation]
+    return X2, Y2
+
+
+X2, y2 = randomize(X, y)
+
+
+def draw_learning_curves(X, y, estimator, num_trainings):
+
+    cv = ShuffleSplit(n_splits=5, test_size=0.3)
+
+    train_sizes, train_scores, test_scores = learning_curve(
+        estimator, X2, y2, cv=cv, n_jobs=1,
+        train_sizes=np.linspace(.1, 1.0, num_trainings))
+    print(test_scores)
+    train_scores_mean = np.mean(train_scores, axis=1)
+    train_scores_std = np.std(train_scores, axis=1)
+    test_scores_mean = np.mean(test_scores, axis=1)
+    test_scores_std = np.std(test_scores, axis=1)
+    plt.grid()
+
+    plt.title("Learning Curves")
+    plt.xlabel("Training examples")
+    plt.ylabel("Score")
+
+    # plt.plot(train_scores_mean, 'o-', color="g",
+    #          label="Training score")
+    # plt.plot(test_scores_mean, 'o-', color="y",
+    #          label="Cross-validation score")
+    plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
+                     train_scores_mean + train_scores_std, alpha=0.1,
+                     color='r')
+    plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
+                     test_scores_mean + test_scores_std, alpha=0.1,
+                     color='g')
+    plt.plot(train_sizes, train_scores_mean, 'o-', color='r',
+             label='Training Score')
+    plt.plot(train_sizes, test_scores_mean, 'o-', color='g',
+             label='Cross-Validation Score')
+    plt.legend(loc="best")
+    plt.show()
+
+
+draw_learning_curves(X2, y2, estimator2, 10)
+cv = ShuffleSplit(n_splits=5, test_size=0.3)
+
+print(cv)
+for i, j in cv.split(X2):
+    print(i, j)
+