Python习题——2018-06-13作业

Scikit-Learn 练习题

---

Code

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

# Create a classification dataset.
dataset = datasets.make_classification(n_samples=1000, n_features=10)
[X, y] = dataset
acc, f1, auc = 0, 0, 0

# Split the dataset using 10-fold cross validation.
ten_fold = cross_validation.KFold(len(X), n_folds=10, shuffle=True)
for train_index, test_index in ten_fold:
    X_train, y_train = X[train_index], y[train_index]
    X_test, y_test   = X[test_index], y[test_index]

    # Train the algorithms using GaussianNB.
    clf = GaussianNB()
    clf.fit(X_train, y_train)
    pred = clf.predict(X_test)
    # Evaluate the cross-validated performance
    acc += metrics.accuracy_score(y_test, pred)
    f1  += metrics.f1_score(y_test, pred)
    auc += metrics.roc_auc_score(y_test, pred)


print("GaussianNB:")
print("- Accuracy: %f" % (acc/10))
print("- F1-score: %f" % (f1/10))
print("- AUC ROC: %f" % (auc/10))
print()

for value in [1e-02, 1e-01, 1e00, 1e01, 1e02]:
    acc, f1, auc = 0, 0, 0
    for train_index, test_index in ten_fold:
        X_train, y_train = X[train_index], y[train_index]
        X_test, y_test   = X[test_index], y[test_index]

        # Train the algorithms using SVC.
        clf = SVC(C=value, kernel='rbf', gamma=0.1)
        clf.fit(X_train, y_train)
        pred = clf.predict(X_test)
        # Evaluate the cross-validated performance
        acc += metrics.accuracy_score(y_test, pred)
        f1  += metrics.f1_score(y_test, pred)
        auc += metrics.roc_auc_score(y_test, pred)

    print("\nSVC (C = %.0e):" % value)
    print("- Accuracy: %f" % (acc/10))
    print("- F1-score: %f" % (f1/10))
    print("- AUC ROC: %f" % (auc/10))

print()

for value in [10, 100, 1000]:
    acc, f1, auc = 0, 0, 0
    for train_index, test_index in ten_fold:
        X_train, y_train = X[train_index], y[train_index]
        X_test, y_test   = X[test_index], y[test_index]

        # Train the algorithms using RandomForestClassifier.
        clf = RandomForestClassifier(n_estimators=value)
        clf.fit(X_train, y_train)
        pred = clf.predict(X_test)
        # Evaluate the cross-validated performance
        acc += metrics.accuracy_score(y_test, pred)
        f1  += metrics.f1_score(y_test, pred)
        auc += metrics.roc_auc_score(y_test, pred)

    print("\nRandomForestClassifier (n_estimators = %d):" % value)
    print("- Accuracy: %f" % (acc/10))
    print("- F1-score: %f" % (f1/10))
    print("- AUC ROC: %f" % (auc/10))

Output

GaussianNB:
- Accuracy: 0.914000
- F1-score: 0.915247
- AUC ROC: 0.914527


SVC (C = 1e-02):
- Accuracy: 0.865000
- F1-score: 0.868321
- AUC ROC: 0.873646

SVC (C = 1e-01):
- Accuracy: 0.935000
- F1-score: 0.936352
- AUC ROC: 0.935286

SVC (C = 1e+00):
- Accuracy: 0.942000
- F1-score: 0.944010
- AUC ROC: 0.942180

SVC (C = 1e+01):
- Accuracy: 0.932000
- F1-score: 0.933763
- AUC ROC: 0.932221

SVC (C = 1e+02):
- Accuracy: 0.904000
- F1-score: 0.905260
- AUC ROC: 0.904708


RandomForestClassifier (n_estimators = 10):
- Accuracy: 0.961000
- F1-score: 0.961153
- AUC ROC: 0.960277

RandomForestClassifier (n_estimators = 100):
- Accuracy: 0.965000
- F1-score: 0.965509
- AUC ROC: 0.964309

RandomForestClassifier (n_estimators = 1000):
- Accuracy: 0.963000
- F1-score: 0.963489
- AUC ROC: 0.962328

---

Report

• SVC的三种得分均随着参数C的增大呈先上升后下降的趋势，说明参数C的取值对SVC的表现有一定影响。
• 参数n_estimators的改变对RandomForestClassifier的三种得分几乎没有影响，但是从实际运行来看，随着参数n_estimators的增大，训练时间也会增加，综合时间成本来看，选择较小的n_estimators可能更好。
• 综合三种模型的表现来看，RandomForestClassifier的表现最好，但运行时间也最长；而GaussianNB的运行时间最短，但表现最差（相对而言）。