%matplotlib inlineimport matplotlib.pyplot as pltimport numpy as npimport pandas as pd
def read_dataset(fname):# 指定第一列作为行索引data = pd.read_csv(fname, index_col=0) #列索引为csv文件第一行# 丢弃无用的数据data.drop(['Name', 'Ticket', 'Cabin'], axis=1, inplace=True)#inplace=False，默认该删除操作不改变原数据，而是返回一个执行删除操作后的新dataframe；#inplace=True，则会直接在原数据上进行删除操作，删除后就回不来了。# 处理性别数据data['Sex'] = (data['Sex'] == 'male').astype('int')# 处理登船港口数据labels = data['Embarked'].unique().tolist() #去重 /starter_____/article/details/79184196data['Embarked'] = data['Embarked'].apply(lambda n: labels.index(n))# 处理缺失数据data = data.fillna(0) #fillna()会填充nan数据，返回填充后的结果。如果希望在原DataFrame中修改，则把inplace设置为Truereturn datatrain = read_dataset('datasets/titanic/train.csv')
train.head(10)
.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }
from sklearn.model_selection import train_test_split #分成训练集和交叉验证集y = train['Survived'].values #提取标记，以ndarray形式X = train.drop(['Survived'], axis=1)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)print('train dataset: {0}; test dataset: {1}'.format(X_train.shape, X_test.shape))
train dataset: (712, 7); test dataset: (179, 7)
from sklearn.tree import DecisionTreeClassifierclf = DecisionTreeClassifier()clf.fit(X_train, y_train)train_score = clf.score(X_train, y_train)test_score = clf.score(X_test, y_test)print('train score: {0}; test score: {1}'.format(train_score, test_score))
train score: 0.9859550561797753; test score: 0.8044692737430168
# 参数选择 max_depthdef cv_score(d):clf = DecisionTreeClassifier(max_depth=d)clf.fit(X_train, y_train)tr_score = clf.score(X_train, y_train)cv_score = clf.score(X_test, y_test)return (tr_score, cv_score)depths = range(2, 15)scores = [cv_score(d) for d in depths]# scorestr_scores = [s[0] for s in scores]cv_scores = [s[1] for s in scores]best_score_index = np.argmax(cv_scores)#返回第一次出现的最大值的索引/u013713117/article/details/53965572best_score = cv_scores[best_score_index] best_param = depths[best_score_index]print('best param: {0}; best score: {1}'.format(best_param, best_score))plt.figure(figsize=(10, 6), dpi=144)plt.grid()plt.xlabel('max depth of decision tree')plt.ylabel('score')plt.plot(depths, cv_scores, '.g-', label='cross-validation score')plt.plot(depths, tr_scores, '.r--', label='training score')plt.legend()
best param: 6; best score: 0.8268156424581006
# 训练模型，并计算评分def cv_score(val):clf = DecisionTreeClassifier(criterion='gini', min_impurity_split=val)clf.fit(X_train, y_train)tr_score = clf.score(X_train, y_train)cv_score = clf.score(X_test, y_test)return (tr_score, cv_score)# 指定参数范围，分别训练模型，并计算评分values = np.linspace(0, 0.5, 50)scores = [cv_score(v) for v in values]tr_scores = [s[0] for s in scores]cv_scores = [s[1] for s in scores]# 找出评分最高的模型参数best_score_index = np.argmax(cv_scores)best_score = cv_scores[best_score_index]best_param = values[best_score_index]print('best param: {0}; best score: {1}'.format(best_param, best_score))# 画出模型参数与模型评分的关系plt.figure(figsize=(10, 6), dpi=144)plt.grid()plt.xlabel('threshold of entropy')plt.ylabel('score')plt.plot(values, cv_scores, '.g-', label='cross-validation score')plt.plot(values, tr_scores, '.r--', label='training score')plt.legend()
best param: 0.12244897959183673; best score: 0.8379888268156425
def plot_curve(train_sizes, cv_results, xlabel):train_scores_mean = cv_results['mean_train_score']train_scores_std = cv_results['std_train_score']test_scores_mean = cv_results['mean_test_score']test_scores_std = cv_results['std_test_score']plt.figure(figsize=(10, 6), dpi=144)plt.title('parameters turning')plt.grid()plt.xlabel(xlabel)plt.ylabel('score')plt.fill_between(train_sizes, train_scores_mean - train_scores_std,train_scores_mean + train_scores_std, alpha=0.1, color="r")plt.fill_between(train_sizes, test_scores_mean - test_scores_std,test_scores_mean + test_scores_std, alpha=0.1, color="g")plt.plot(train_sizes, train_scores_mean, '.--', color="r",label="Training score")plt.plot(train_sizes, test_scores_mean, '.-', color="g",label="Cross-validation score")plt.legend(loc="best")
from sklearn.model_selection import GridSearchCVthresholds = np.linspace(0, 0.5, 50)# Set the parameters by cross-validationparam_grid = {'min_impurity_split': thresholds}clf = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)clf.fit(X, y)print("best param: {0}\nbest score: {1}".format(clf.best_params_, clf.best_score_))plot_curve(thresholds, clf.cv_results_, xlabel='gini thresholds')
from sklearn.model_selection import GridSearchCVentropy_thresholds = np.linspace(0, 1, 50)gini_thresholds = np.linspace(0, 0.5, 50)# Set the parameters by cross-validationparam_grid = [{'criterion': ['entropy'], 'min_impurity_split': entropy_thresholds},{'criterion': ['gini'], 'min_impurity_split': gini_thresholds},{'max_depth': range(2, 10)},{'min_samples_split': range(2, 30, 2)}]clf = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)clf.fit(X, y)print("best param: {0}\nbest score: {1}".format(clf.best_params_, clf.best_score_))
