Add C param in constructor and creditcard dataset

main.py

@@ -5,8 +5,42 @@ random_state = 1
 X, y = make_classification(n_samples=1500, n_features=3, n_informative=3,
                            n_redundant=0, n_repeated=0, n_classes=2, n_clusters_per_class=2,
                            class_sep=1.5, flip_y=0, weights=[0.5, 0.5], random_state=random_state)
-model = Stree(random_state=random_state)
-model.fit(X, y)
-print(model)
-model.save_sub_datasets()
-print(f"Prediciting [{y[0]}] we have {model.predict(X[0, :].reshape(-1, X.shape[1]))}")
+
+def load_creditcard(n_examples=0):
+    import pandas as pd
+    import numpy as np
+    import random
+    df = pd.read_csv('data/creditcard.csv')
+    print("Fraud: {0:.3f}% {1}".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))
+    print("Valid: {0:.3f}% {1}".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))
+    y = np.expand_dims(df.Class.values, axis=1)
+    X = df.drop(['Class', 'Time', 'Amount'], axis=1).values
+    #Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)
+    #return Xtrain, Xtest, ytrain, ytest
+    if n_examples > 0:
+        # Take first n_examples samples
+        X = X[:n_examples, :]
+        y = y[:n_examples, :]
+    else:
+        # Take all the positive samples with a number of random negatives
+        if n_examples < 0:
+            Xt = X[(y == 1).ravel()]
+            yt = y[(y == 1).ravel()]
+            indices = random.sample(range(X.shape[0]), -1 * n_examples)
+            X = np.append(Xt, X[indices], axis=0)
+            y = np.append(yt, y[indices], axis=0)
+    print("X.shape", X.shape, " y.shape", y.shape)
+    print("Fraud: {0:.3f}% {1}".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))
+    print("Valid: {0:.3f}% {1}".format(len(y[y == 0])*100/X.shape[0], len(y[y == 0])))
+    return X, y
+#X, y = load_creditcard(-5000)
+#X, y = load_creditcard(0)
+
+clf = Stree(C=.01, max_iter=100, random_state=random_state)
+clf.fit(X, y)
+print(clf)
+clf.show_tree()
+clf.save_sub_datasets()
+print(f"Predicting {y[0]} we have {clf.predict(X[0, :].reshape(-1, X.shape[1]))}")
+print(f"Classifier's accuracy: {clf.score(X, y, print_out=False):.4f}")
+clf.show_tree(only_leaves=True)