classification

2_classification-overfitting.py

+import pandas as pd
+from sklearn import tree
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+
+print("_______________________________________________________________________________")
+print("            Classification, fitting & overfitting. ")
+print("_______________________________________________________________________________")
+
+print(""" --- Load the data ---""")
+csvFileName = r".\Datasets\A-greater-then-B.csv "
+df = pd.read_csv(csvFileName)
+print(df.head())
+print("data shape: ", df.shape)
+
+print(""" --- Set the features (independednt variables, attributes) and target ---""")
+feature_cols = ['A', 'B', 'C']
+target_var = 'A>B'
+
+X = df[feature_cols].values
+y = df[target_var].values
+print("Features: ", feature_cols, "\nTarget:", target_var)
+
+print(""" --- Train-test split ---""")
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+print("train set X shape: ", X_train.shape, "train set y shape: ", y_train.shape)
+print("test set X shape: ", X_test.shape, "test set y shape: ", y_test.shape)
+
+
+print("decision_tree.tree_.node_count\t acc(train)\t acc(test)")
+for i in range(2, 100):
+
+    decision_tree = tree.DecisionTreeClassifier(max_leaf_nodes=i)
+    decision_tree.fit(X_train, y_train)
+
+    y_pred_test = decision_tree.predict(X_test)
+    y_pred_train = decision_tree.predict(X_train)
+
+    acc_train = metrics.accuracy_score(y_train, y_pred_train)
+    acc_test = metrics.accuracy_score(y_test, y_pred_test)
+
+    print("\t{0:5.2f}\t{1:5.2f}\t{2:5.2f}".format(decision_tree.tree_.node_count, acc_train,acc_test ))
+

2_classification.py

+import pandas as pd
+from sklearn import tree
+from sklearn.model_selection import train_test_split
+from sklearn import metrics
+
+print("_______________________________________________________________________________")
+print("            Classification, fitting, predicting ... printing. ")
+print("_______________________________________________________________________________")
+
+print(""" --- Load the data ---""")
+csvFileName = r".\Datasets\A-greater-then-B.csv "
+df = pd.read_csv(csvFileName)
+print(df.head())
+print("data shape: ", df.shape)
+
+print(""" --- Set the features (independednt variables, attributes) and target ---""")
+feature_cols = ['A', 'B', 'C']
+target_var = 'A>B'
+
+X = df[feature_cols].values
+y = df[target_var].values
+print("Features: ", feature_cols, "\nTarget:", target_var)
+
+print(""" --- Train-test split ---""")
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=42)
+print("train set X shape: ", X_train.shape, "train set y shape: ", y_train.shape)
+print("test set X shape: ", X_test.shape, "test set y shape: ", y_test.shape)
+
+print(""" --- Initialize the learner(s) --- """)
+# documentation: https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
+decision_tree = tree.DecisionTreeClassifier()
+print("Parameters of the decision tree: ", decision_tree.get_params())
+
+print(""" --- Fit --- """)
+decision_tree.fit(X_train, y_train)
+print("Depth of the decision tree: ", decision_tree.tree_.max_depth)
+print("Number of nodes of the decision tree: ", decision_tree.tree_.node_count)
+
+print(""" --- Predict --- """)
+y_pred = decision_tree.predict(X_test)
+print("\n Actual   Predicted")
+#for i in range(len(y_test)):
+for i in range(10):
+    print("{0:6.2f}  {1:8.2f}".format(y_test[i], y_pred[i]))
+print(""" -- Performance ---""")
+print("Accuracy  \t{0:5.2f}".format( metrics.accuracy_score(y_test, y_pred)))
+
+
+""" Visualize the tree """               # https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py
+treeFileName = 'decision_tree.dot'
+tree.export_graphviz(decision_tree, out_file=treeFileName)
+#
+# # install GraphViz
+#$ dot -Tps decision_tree.dot -o decision_tree.ps      (PostScript format)
+# # $ dot -Tpng decision_tree.dot -o decision_tree.png    (PNG format)