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

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+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import f1_score, make_scorer
+import random
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.metrics import make_scorer
+from sklearn.model_selection import GridSearchCV
+
+
+# Fixing a random seed
+random.seed(42)
+
+
+def load_pts(csv_name):
+    data = np.asarray(pd.read_csv(csv_name, header=None))
+    X = data[:, 0:2]
+    y = data[:, 2]
+
+    plt.scatter(X[np.argwhere(y == 0).flatten(), 0], X[np.argwhere(
+        y == 0).flatten(), 1], s=50, color='blue', edgecolor='k')
+    plt.scatter(X[np.argwhere(y == 1).flatten(), 0], X[np.argwhere(
+        y == 1).flatten(), 1], s=50, color='red', edgecolor='k')
+
+    plt.xlim(-2.05, 2.05)
+    plt.ylim(-2.05, 2.05)
+    plt.grid(False)
+    plt.tick_params(
+        axis='x',
+        which='both',
+        bottom='off',
+        top='off')
+
+    return X, y
+
+
+X, y = load_pts('data_grid.csv')
+plt.show()
+
+
+# Split the data into training and testing sets
+X_train, X_test, y_train, y_test = train_test_split(X, y,
+                                                    test_size=0.2,
+                                                    random_state=42)
+
+
+# Define the model (with default hyperparameters)
+clf = DecisionTreeClassifier(random_state=42)
+
+
+# Fit the model
+clf.fit(X_train, y_train)
+
+
+# Make predictions
+train_predictions = clf.predict(X_train)
+test_predictions = clf.predict(X_test)
+
+
+# Now let's plot the model, and find the testing f1_score, to see how we did.
+def plot_model(X, y, clf):
+    plt.scatter(X[np.argwhere(y == 0).flatten(), 0], X[np.argwhere(
+        y == 0).flatten(), 1], s=50, color='blue', edgecolor='k')
+    plt.scatter(X[np.argwhere(y == 1).flatten(), 0], X[np.argwhere(
+        y == 1).flatten(), 1], s=50, color='red', edgecolor='k')
+
+    plt.xlim(-2.05, 2.05)
+    plt.ylim(-2.05, 2.05)
+    plt.grid(False)
+    plt.tick_params(
+        axis='x',
+        which='both',
+        bottom='off',
+        top='off')
+
+    r = np.linspace(-2.1, 2.1, 300)
+    s, t = np.meshgrid(r, r)
+    s = np.reshape(s, (np.size(s), 1))
+    t = np.reshape(t, (np.size(t), 1))
+    h = np.concatenate((s, t), 1)
+
+    z = clf.predict(h)
+
+    s = s.reshape((np.size(r), np.size(r)))
+    t = t.reshape((np.size(r), np.size(r)))
+    z = z.reshape((np.size(r), np.size(r)))
+
+    plt.contourf(s, t, z, colors=['blue', 'red'],
+                 alpha=0.2, levels=range(-1, 2))
+    if len(np.unique(z)) > 1:
+        plt.contour(s, t, z, colors='k', linewidths=2)
+    plt.legend(loc="best")
+    plt.show()
+
+
+plot_model(X, y, clf)
+print('The Training F1 Score is', f1_score(train_predictions, y_train))
+print('The Testing F1 Score is', f1_score(test_predictions, y_test))
+
+
+# TODO: Create the parameters list you wish to tune.
+parameters = {'max_depth': [2, 4, 6, 8, 10],
+              'min_samples_leaf': [2, 4, 6, 8, 10],
+              'min_samples_split': [2, 4, 6, 8, 10]}
+
+
+# TODO: Make an fbeta_score scoring object.
+scorer = make_scorer(f1_score)
+
+# TODO: Perform grid search on the classifier using 'scorer' as the scoring
+# method.
+grid_obj = GridSearchCV(clf, parameters, scoring=scorer)
+
+# TODO: Fit the grid search object to the training data and find the optimal
+# parameters.
+grid_fit = grid_obj.fit(X_train, y_train)
+
+# Get the estimator.
+best_clf = grid_fit.best_estimator_
+
+# Fit the new model.
+best_clf.fit(X_train, y_train)
+
+# Make predictions using the new model.
+best_train_predictions = best_clf.predict(X_train)
+best_test_predictions = best_clf.predict(X_test)
+
+# Calculate the f1_score of the new model.
+print('The training F1 Score is', f1_score(best_train_predictions, y_train))
+print('The testing F1 Score is', f1_score(best_test_predictions, y_test))
+
+
+# Let's also explore what parameters ended up being used in the new model.
+print(best_clf)
+
+# Plot the new model.
+plot_model(X, y, best_clf)