【scikit-learn】【RandomForest】【GridSearchCV】二分类应用实例及【ROC】曲线绘制

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。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.metrics import roc_curve
from sklearn.metrics import roc_auc_score
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier

#导入数据，来源于：http://mldata.org/repository/tags/data/IDA_Benchmark_Repository/，见上图
dataset = pd.read_csv('image_data.csv', header=None, encoding='utf-8')

dataset_positive = dataset[dataset[0] == 1.0]
dataset_negative = dataset[dataset[0] == -1.0]
#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
#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
train_dataset = pd.concat([dataset_positive[0:832], dataset_negative[0:628]])
train_recon = train_dataset.sort_index(axis=0, ascending=True)
test_dataset = pd.concat([dataset_positive[832:1188], dataset_negative[628:898]])
test_recon = test_dataset.sort_index(axis=0, ascending=True)

y_train = np.array(train_recon[0])
X_train = np.array(train_recon.drop([0], axis=1))
y_test = np.array(test_recon[0])
X_test = np.array(test_recon.drop([0], axis=1))

# Set the parameters by cross-validation
parameter_space = {
"n_estimators": [10, 15, 20],
"criterion": ["gini", "entropy"],
"min_samples_leaf": [2, 4, 6],
}

#scores = ['precision', 'recall', 'roc_auc']
scores = ['roc_auc']

for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()

clf = RandomForestClassifier(random_state=14)
grid = GridSearchCV(clf, parameter_space, cv=5, scoring='%s' % score)
#scoring='%s_macro' % score：precision_macro、recall_macro是用于multiclass/multilabel任务的
grid.fit(X_train, y_train)

print("Best parameters set found on development set:")
print()
print(grid.best_params_)
print()
print("Grid scores on development set:")
print()
means = grid.cv_results_['mean_test_score']
stds = grid.cv_results_['std_test_score']
for mean, std, params in zip(means, stds, grid.cv_results_['params']):
print("%0.3f (+/-%0.03f) for %r"
% (mean, std * 2, params))
print()
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
bclf = grid.best_estimator_
bclf.fit(X_train, y_train)
y_true = y_test
y_pred = bclf.predict(X_test)
y_pred_pro = bclf.predict_proba(X_test)
y_scores = pd.DataFrame(y_pred_pro, columns=bclf.classes_.tolist())[1].values
print(classification_report(y_true, y_pred))
auc_value = roc_auc_score(y_true, y_scores)

输出结果：

#绘制ROC曲线
fpr, tpr, thresholds = roc_curve(y_true, y_scores, pos_label=1.0)
plt.figure()
lw = 2
plt.plot(fpr, tpr, color='darkorange', linewidth=lw, label='ROC curve (area = %0.4f)' % auc_value)
plt.plot([0, 1], [0, 1], color='navy', linewidth=lw, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver operating characteristic example')
plt.legend(loc="lower right")
plt.show()