Python学习笔记——sklearn

练习题目：

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

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


gau_clf = GaussianNB()
gau_clf.fit(X_train, y_train)
gau_pred = gau_clf.predict(X_test)
gau_accuracy_score = metrics.accuracy_score(y_test, gau_pred)
gau_f1_score = metrics.f1_score(y_test, gau_pred)
gau_roc_auc_score = metrics.roc_auc_score(y_test, gau_pred)
print("GaussianNb : ")
print("accuracy score : ", gau_accuracy_score)
print("f1_score : ", gau_f1_score)
print("roc_auc_score : ", gau_roc_auc_score)
print("\n")

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)
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("SVC : ")
print("accuracy_score : ", SVC_accuracy_score)
print("f1_score : ", SVC_f1_score)
print("roc_auc_score : ", SVC_roc_auc_score)
print("\n")

rf_clf = RandomForestClassifier(n_estimators=6)
rf_clf.fit(X_train, y_train)
rf_pred = rf_clf.predict(X_test)
rf_accuracy_score = metrics.accuracy_score(y_test, rf_pred)
rf_f1_score = metrics.f1_score(y_test, rf_pred)
rf_roc_auc_score = metrics.roc_auc_score(y_test, rf_pred)
print("Random Forest : ")
print("accuracy_score : ", rf_accuracy_score)
print("f1_score : ", rf_f1_score)
print("oc_auc_score : ", rf_roc_auc_score)
print("\n")

结果

GaussianNb :
accuracy score :  0.75
f1_score :  0.7422680412371133
roc_auc_score :  0.75

SVC :
accuracy_score :  0.79
f1_score :  0.796116504854369
roc_auc_score :  0.79

Random Forest :
accuracy_score :  0.85
f1_score :  0.8421052631578948
oc_auc_score :  0.8500000000000001

2018/6/19