Python Machine Learning Library sciki-earn practice, pythonsciki-earn

Python Machine Learning Library sciki-earn practice, pythonsciki-earn

Use Anaconda's spyder: Create 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'))  

Result:

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 ********************

In this data set, the data distribution cluster is good (if you know about this database, you can see it by looking at its t-SNE ing diagram. Because the task is simple, it has been regarded as a toy dataset in the deep learning field. Therefore, KNN does not work well. GBDT is a very good algorithm. In Big Data competitions such as kaggle, it is often seen on the top of the list.