sklearn_tree.py 文件源码

def train_tree_classifer(features, labels, model_output_path):
    """
    train_tree_classifer will train a DecisionTree and write it out to a pdf file

    features: 2D array of each input feature for each sample
    labels: array of string labels classifying each sample
    model_output_path: path for storing the trained tree model
    """
    # save 20% of data for performance evaluation
    X_train, X_test, y_train, y_test = cross_validation.train_test_split(features, labels, test_size=0.2)

    param = [
        {
            "max_depth": [None, 10, 100, 1000, 10000]
        }
    ]

    dtree = tree.DecisionTreeClassifier(random_state=0)

    # 10-fold cross validation, use 4 thread as each fold and each parameter set can be train in parallel
    clf = grid_search.GridSearchCV(dtree, param,
            cv=10, n_jobs=20, verbose=3)

    clf.fit(X_train, y_train)

    if os.path.exists(model_output_path):
        joblib.dump(clf.best_estimator_, model_output_path)
    else:
        print("Cannot save trained tree model to {0}.".format(model_output_path))

    dot_data = tree.export_graphviz(clf.best_estimator_, out_file=None)
    graph = pydotplus.graph_from_dot_data(dot_data)
    graph.write_pdf('best_tree.pdf')

    print("\nBest parameters set:")
    print(clf.best_params_)

    y_predict=clf.predict(X_test)

    labels=sorted(list(set(labels)))
    print("\nConfusion matrix:")
    print("Labels: {0}\n".format(",".join(labels)))
    print(confusion_matrix(y_test, y_predict, labels=labels))

    print("\nClassification report:")
    print(classification_report(y_test, y_predict))