本文例項為大家分享了python sklearn分類演算法模型呼叫的具體程式碼，供大家參考，具體內容如下

實現對'NB','KNN','LR','RF','DT','SVM','SVMCV','GBDT'模型的簡單呼叫。

# coding=gbk
 
import time 
from sklearn import metrics 
import pickle as pickle 
import pandas as pd
 
 
# 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 list(best_parameters.items()): 
    print(para,val) 
  model = SVC(kernel='rbf',C=best_parameters['C'],gamma=best_parameters['gamma'],train_y) 
  return model 
 
def read_data(data_file): 
  data = pd.read_csv(data_file)
  train = data[:int(len(data)*0.9)]
  test = data[int(len(data)*0.9):]
  train_y = train.label
  train_x = train.drop('label',axis=1)
  test_y = test.label
  test_x = test.drop('label',axis=1)
  return train_x,train_y,test_x,test_y
   
if __name__ == '__main__': 
  data_file = "H:\\Research\\data\\trainCG.csv" 
  thresh = 0.5 
  model_save_file = None 
  model_save = {} 
  
  test_classifiers = ['NB','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,test_y = read_data(data_file) 
   
  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 
    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')) 

以上就是本文的全部內容，希望對大家的學習有所幫助，也希望大家多多支援我們。