scikit-learn中用gridsearchcv给随机森林（RF）自动调参
全文参考 1：http://scikit-learn.org/stable/auto_examples/model_selection/grid_search_digits.html#parameter-estimation-using-grid-search-with-cross-validation
全文参考 2：http://scikit-learn.org/stable/modules/model_evaluation.html#the-scoring-parameter-defining-model-evaluation-rules
全文参考 3：http://scikit-learn.org/stable/modules/generated/sklearn.metrics.roc_auc_score.html#sklearn.metrics.roc_auc_score
全文参考 4：http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py
实验重点：随机森林(RandomForest) + 5折交叉验证(Cross-Validation) + 网格参数寻优(GridSearchCV) + 二分类问题中ROC曲线的绘制。
由于原始数据本身质量很好，且正负样本基本均衡，没有做数据预处理工作。
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import pandas
as pd
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import numpy
as np
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import matplotlib.pyplot
as plt
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from sklearn.metrics
import roc_curve
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from sklearn.metrics
import roc_auc_score
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from sklearn.metrics
import classification_report
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from sklearn.model_selection
import GridSearchCV
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from sklearn.ensemble
import RandomForestClassifier
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#导入数据，来源于：http://mldata.org/repository/tags/data/IDA_Benchmark_Repository/，见上图
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dataset = pd.read_csv(
'image_data.csv', header=
None, encoding=
'utf-8')
-
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dataset_positive = dataset[dataset[
0] ==
1.0]
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dataset_negative = dataset[dataset[
0] ==
-1.0]
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#训练集和测试集按照7:3分割，分割时兼顾正负样本所占比例
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#其中训练集基于5折交叉验证做网格搜索找出最优参数，应用于测试集以评价算法性能
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train_dataset = pd.concat([dataset_positive[
0:
832], dataset_negative[
0:
628]])
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train_recon = train_dataset.sort_index(axis=
0, ascending=
True)
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test_dataset = pd.concat([dataset_positive[
832:
1188], dataset_negative[
628:
898]])
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test_recon = test_dataset.sort_index(axis=
0, ascending=
True)
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y_train = np.array(train_recon[
0])
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X_train = np.array(train_recon.drop([
0], axis=
1))
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y_test = np.array(test_recon[
0])
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X_test = np.array(test_recon.drop([
0], axis=
1))
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# Set the parameters by cross-validation
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parameter_space = {
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"n_estimators": [
10,
15,
20],
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"criterion": [
"gini",
"entropy"],
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"min_samples_leaf": [
2,
4,
6],
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}
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#scores = ['precision', 'recall', 'roc_auc']
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scores = [
'roc_auc']
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for score
in scores:
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print(
"# Tuning hyper-parameters for %s" % score)
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print()
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clf = RandomForestClassifier(random_state=
14)
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grid = GridSearchCV(clf, parameter_space, cv=
5, scoring=
'%s' % score)
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#scoring='%s_macro' % score：precision_macro、recall_macro是用于multiclass/multilabel任务的
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grid.fit(X_train, y_train)
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print(
"Best parameters set found on development set:")
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print()
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print(grid.best_params_)
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print()
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print(
"Grid scores on development set:")
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print()
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means = grid.cv_results_[
'mean_test_score']
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stds = grid.cv_results_[
'std_test_score']
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for mean, std, params
in zip(means, stds, grid.cv_results_[
'params']):
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print(
"%0.3f (+/-%0.03f) for %r"
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% (mean, std *
2, params))
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print()
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print(
"Detailed classification report:")
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print()
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print(
"The model is trained on the full development set.")
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print(
"The scores are computed on the full evaluation set.")
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print()
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bclf = grid.best_estimator_
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bclf.fit(X_train, y_train)
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y_true = y_test
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y_pred = bclf.predict(X_test)
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y_pred_pro = bclf.predict_proba(X_test)
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y_scores = pd.DataFrame(y_pred_pro, columns=bclf.classes_.tolist())[
1].values
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print(classification_report(y_true, y_pred))
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auc_value = roc_auc_score(y_true, y_scores)
输出结果：
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#绘制ROC曲线
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fpr, tpr, thresholds = roc_curve(y_true, y_scores, pos_label=
1.0)
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plt.figure()
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lw =
2
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plt.plot(fpr, tpr, color=
'darkorange', linewidth=lw, label=
'ROC curve (area = %0.4f)' % auc_value)
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plt.plot([
0,
1], [
0,
1], color=
'navy', linewidth=lw, linestyle=
'--')
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plt.xlim([
0.0,
1.0])
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plt.ylim([
0.0,
1.05])
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plt.xlabel(
'False Positive Rate')
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plt.ylabel(
'True Positive Rate')
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plt.title(
'Receiver operating characteristic example')
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plt.legend(loc=
"lower right")
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plt.show()
