python scikit learn 模板

原文：
http://blog.youkuaiyun.com/zouxy09/article/details/48903179

代码如下：

#!usr/bin/env python  
# -*- coding: utf-8 -*-

import sys
import os
import time
from sklearn import metrics
import numpy as np
import cPickle as pickle

reload(sys)
sys.setdefaultencoding('utf8')


# Multinomial Naive Bayes Classifier  
def naive_bayes_classifier(train_x, train_y):
    from sklearn.naive_bayes import MultinomialNB
    model = MultinomialNB(alpha=0.01)
    model.fit(train_x, train_y)
    return model


# KNN Classifier  
def knn_classifier(train_x, train_y):
    from sklearn.neighbors import KNeighborsClassifier
    model = KNeighborsClassifier()
    model.fit(train_x, train_y)
    return model


# Logistic Regression Classifier  
def logistic_regression_classifier(train_x, train_y):
    from sklearn.linear_model import LogisticRegression
    model = LogisticRegression(penalty='l2')
    model.fit(train_x, train_y)
    return model


# Random Forest Classifier  
def random_forest_classifier(train_x, train_y):
    from sklearn.ensemble import RandomForestClassifier
    model = RandomForestClassifier(n_estimators=8)
    model.fit(train_x, train_y)
    return model


# Decision Tree Classifier  
def decision_tree_classifier(train_x, train_y):
    from sklearn import tree
    model = tree.DecisionTreeClassifier()
    model.fit(train_x, train_y)
    return model


# GBDT(Gradient Boosting Decision Tree) Classifier  
def gradient_boosting_classifier(train_x, train_y):
    from sklearn.ensemble import GradientBoostingClassifier
    model = GradientBoostingClassifier(n_estimators=200)
    model.fit(train_x, train_y)
    return model


# SVM Classifier  
def svm_classifier(train_x, train_y):
    from sklearn.svm import SVC
    model = SVC(kernel='rbf', probability=True)
    model.fit(train_x, train_y)
    return model


# SVM Classifier using cross validation  
def svm_cross_validation(train_x, train_y):
    from sklearn.grid_search import GridSearchCV
    from sklearn.svm import SVC
    model = SVC(kernel='rbf', probability=True)
    param_grid = {'C': [1e-3, 1e-2, 1e-1, 1, 10, 100, 1000], 'gamma': [0.001, 0.0001]}
    grid_search = GridSearchCV(model, param_grid, n_jobs=1, verbose=1)
    grid_search.fit(train_x, train_y)
    best_parameters = grid_search.best_estimator_.get_params()
    for para, val in best_parameters.items():
        print para, val
    model = SVC(kernel='rbf', C=best_parameters['C'], gamma=best_parameters['gamma'], probability=True)
    model.fit(train_x, train_y)
    return model


def read_data_mnist(data_file):
    import gzip
    f = gzip.open(data_file, "rb")
    train, val, test = pickle.load(f)
    f.close()
    train_x = train[0]
    train_y = train[1]
    test_x = test[0]
    test_y = test[1]
    return train_x, train_y, test_x, test_y


def read_data_conversation(data_file):
    data_x = []
    data_y = []
    with open(data_file) as f:
        for line in f:
            strArray = line.split(" ")
            floatArray = [float(x) for x in strArray]
            data_x.append(floatArray[1:])
            data_y.append(floatArray[0])
    return np.array(data_x), np.array(data_y)


def read_data(train_file, test_file):
    train_x, train_y = read_data_conversation(train_file)
    test_x, test_y = read_data_conversation(test_file)
    return train_x, train_y, test_x, test_y


def evaluate(is_binary_class, predict, predict_pos, test_y):
    if is_binary_class:
        precision = metrics.precision_score(test_y, predict)
        recall = metrics.recall_score(test_y, predict)
        print 'precision: %.3f%%\nrecall: %.3f%%' % (100 * precision, 100 * recall)
    accuracy = metrics.accuracy_score(test_y, predict)
    print 'accuracy: %.3f%%' % (100 * accuracy)
    roc_auc = metrics.roc_auc_score(test_y, predict_pos)
    print 'roc_auc: %.3f' % roc_auc


if __name__ == '__main__':
    data_file = "mnist.pkl.gz"
    thresh = 0.9
    model_save_file = None
    model_save = {}

    test_classifiers = ['NB',
                        # 'KNN',
                        'LR',
                        'RF',
                        'DT',
                        'SVM',
                        'GBDT'
                        ]
    classifiers = {'NB': naive_bayes_classifier,
                   'KNN': knn_classifier,
                   'LR': logistic_regression_classifier,
                   'RF': random_forest_classifier,
                   'DT': decision_tree_classifier,
                   'SVM': svm_classifier,
                   'SVMCV': svm_cross_validation,
                   'GBDT': gradient_boosting_classifier
                   }

    print 'reading training and testing data...'
    train_x, train_y, test_x, test_y = read_data("QAFormatResult-train-format.txt", "QAFormatResult-test-format.txt")
    num_train, num_feat = train_x.shape
    num_test, num_feat = test_x.shape
    is_binary_class = (len(np.unique(train_y)) == 2)
    print '******************** Data Info *********************'
    print '#training data: %d, #testing_data: %d, dimension: %d' % (num_train, num_test, num_feat)
    print 'testing train data... '
    print train_x[0]
    print train_y[0]
    print 'testing test data... '
    print test_x[0]
    print test_y[0]

    ensemble_train_x = None
    ensemble_test_x = None
    voting_predict = None

    for classifier in test_classifiers:
        print '******************* %s ********************' % classifier
        start_time = time.time()
        model = classifiers[classifier](train_x, train_y)
        print 'training took %fs!' % (time.time() - start_time)

        predict_proba = model.predict_proba(test_x)
        predict_pos = predict_proba[:, 1]
        predict = np.array([int(x + 0.5) for x in predict_pos.tolist()])
        # print predict
        # predict = model.predict(test_x)

        if voting_predict is None:
            voting_predict = predict
        else:
            voting_predict = np.vstack((voting_predict, predict))

        if ensemble_test_x is None:
            ensemble_test_x = predict_pos
        else:
            ensemble_test_x = np.vstack((ensemble_test_x, predict_pos))
        train_pos = model.predict_proba(train_x)[:, 1]
        if ensemble_train_x is None:
            ensemble_train_x = train_pos
        else:
            ensemble_train_x = np.vstack((ensemble_train_x, train_pos))
        if model_save_file != None:
            model_save[classifier] = model
        evaluate(is_binary_class, predict, predict_pos, test_y)

    ensemble_train_x = ensemble_train_x.T
    ensemble_test_x = ensemble_test_x.T
    print '******************* ensemble ********************'
    start_time = time.time()
    model = logistic_regression_classifier(ensemble_train_x, train_y)
    print 'training took %fs!' % (time.time() - start_time)
    predict_proba = model.predict_proba(ensemble_test_x)
    predict_pos = predict_proba[:, 1]
    predict = np.array([int(x + 0.5) for x in predict_pos.tolist()])
    # print predict
    evaluate(is_binary_class, predict, predict_pos, test_y)

    voting_predict = voting_predict.T
    print '******************* voting ********************'
    voting_predict = np.sum(voting_predict, axis=1)
    predict = np.array([int(2 * (x - 0.1) / len(test_classifiers)) for x in voting_predict.tolist()])
    # print predict
    evaluate(is_binary_class, predict, predict, test_y)

    if model_save_file != None:
        pickle.dump(model_save, open(model_save_file, 'wb'))