第十五周，sklearn

1. Create a classification dataset (n samples ! 1000, n features ! 10)
2. Split the dataset using 10-fold cross validation
3. Train the algorithms
   GaussianNB
   SVC (possible C values [1e-02, 1e-01, 1e00, 1e01, 1e02], RBF kernel)
   RandomForestClassifier (possible n estimators values [10, 100, 1000])
4. Evaluate the cross-validated performance
   Accuracy
   F1-score
   AUC ROC
5. Write a short report summarizing the methodology and the results

只要按照ppt上的教程写代码即可，通过datasets.make_classification生成数据集，通过cross_validation.KFold将数据集划分为训练集和测试集，通过metrics.accuracy_score、metrics.f1_score、metrics.roc_auc_score获得结果。

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)