第十五周：scikt-learn

题目叙述

代码

  按照PPT上的教程进行代码的编写即可，代码如下：

from sklearn import metrics
from sklearn import datasets
from sklearn import cross_validation
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier

# Datasets  
dataset = datasets.make_classification(n_samples=1000, n_features=10)

# Cross-validation  
kf = cross_validation.KFold(1000, n_folds=10, shuffle=True)
for train_index, test_index in kf:
    X_train, y_train = dataset[0][train_index], dataset[1][train_index]
    X_test, y_test = dataset[0][test_index], dataset[1][test_index]

# GaussianNB
GaussianNB_clf = GaussianNB()
GaussianNB_clf.fit(X_train, y_train)
GaussianNB_pred = GaussianNB_clf.predict(X_test)

# SVM  
SVC_clf = SVC(C=1e-01, kernel='rbf', gamma=0.1)
SVC_clf.fit(X_train, y_train)
SVC_pred = SVC_clf.predict(X_test)

# Random Forest  
Random_Forest_clf = RandomForestClassifier(n_estimators=6)
Random_Forest_clf.fit(X_train, y_train)
Random_Forest_pred = Random_Forest_clf.predict(X_test)

# Evaluate the cross-validated performance
# GaussianNB
GaussianNB_accuracy_score = metrics.accuracy_score(y_test, GaussianNB_pred)
GaussianNB_f1_score = metrics.f1_score(y_test, GaussianNB_pred)
GaussianNB_roc_auc_score = metrics.roc_auc_score(y_test, GaussianNB_pred)
print("  GaussianNB_accuracy_score: ", GaussianNB_accuracy_score)
print("  GaussianNB_f1_score: ", GaussianNB_f1_score)
print("  GaussianNB_roc_auc_score: ", GaussianNB_roc_auc_score)

# SVC
SVC_accuracy_score = metrics.accuracy_score(y_test, SVC_pred)
SVC_f1_score = metrics.f1_score(y_test, SVC_pred)
SVC_roc_auc_score = metrics.roc_auc_score(y_test, SVC_pred)
print("\n  SVC_accuracy_score: ", SVC_accuracy_score)
print("  SVC_f1_score: ", SVC_f1_score)
print("  SVC_roc_auc_score: ", SVC_roc_auc_score)

# Random_Forest
Random_Forest_accuracy_score = metrics.accuracy_score(y_test, Random_Forest_pred)
Random_Forest_f1_score = metrics.f1_score(y_test, Random_Forest_pred)
Random_Forest_roc_auc_score = metrics.roc_auc_score(y_test, Random_Forest_pred)
print("\n  Random_Forest_accuracy_score: ", Random_Forest_accuracy_score)
print("  Random_Forest_f1_score: ", Random_Forest_f1_score)
print("  Random_Forest_roc_auc_score: ", Random_Forest_roc_auc_score)

结果

GaussianNB_accuracy_score: 0.97
GaussianNB_f1_score: 0.9690721649484536
GaussianNB_roc_auc_score: 0.9716981132075472
SVC_accuracy_score: 0.99
SVC_f1_score: 0.9894736842105264
SVC_roc_auc_score: 0.9905660377358491
Random_Forest_accuracy_score: 0.97
Random_Forest_f1_score: 0.968421052631579
Random_Forest_roc_auc_score: 0.9704937775993576