用Anaconda的spyder:新建train_test.py
#!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(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 if __name__ == ‘__main__‘: data_file = "mnist.pkl.gz" thresh = 0.5 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(data_file) 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) 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 = model.predict(test_x) if model_save_file != None: model_save[classifier] = model if is_binary_class: precision = metrics.precision_score(test_y, predict) recall = metrics.recall_score(test_y, predict) print ‘precision: %.2f%%, recall: %.2f%%‘ % (100 * precision, 100 * recall) accuracy = metrics.accuracy_score(test_y, predict) print ‘accuracy: %.2f%%‘ % (100 * accuracy) if model_save_file != None: pickle.dump(model_save, open(model_save_file, ‘wb‘))
结果:
reading training and testing data... ******************** Data Info ********************* #training data: 50000, #testing_data: 10000, dimension: 784 ******************* NB ******************** training took 0.558000s! accuracy: 83.69% ******************* KNN ******************** training took 29.467000s! accuracy: 96.64% ******************* LR ******************** training took 104.605000s! accuracy: 91.98% ******************* RF ******************** training took 4.401000s! accuracy: 93.91% ******************* DT ******************** training took 26.923000s! accuracy: 87.07% ******************* SVM ******************** training took 3831.564000s! accuracy: 94.35% ******************* GBDT ********************
在这个数据集中,由于数据分布的团簇性较好(如果对这个数据库了解的话,看它的t-SNE映射图就可以看出来。由于任务简单,其在deep learning界已被认为是toy dataset),因此KNN的效果不赖。GBDT是个非常不错的算法,在kaggle等大数据比赛中,状元探花榜眼之列经常能见其身影。
时间: 2024-10-09 00:22:03