我在尝试使用多个sklearn分类器构建一个用于集成的投票分类器。

为了测试，我有一个包含一组列的数据框，这些列代表工具技能（从0到10的数值，表示一个人对该技能的掌握程度），以及一个“适合工作”的列作为类变量。例如：

import pandas as pddf = pd.DataFrame(columns=["Python", "Scikit-learn", "Pandas", "Fit to Job"])total_mock_samples= 100for i in range(total_mock_samples):    df=df.append(mockResults(df.columns, 'Fit to Job', good_values=i > total_mock_samples/2), ignore_index=True)#用模拟数据填充数据框#输出如下：print(np.array(df))#[[1. 3. 6. 1.]# [3. 2. 3. 0.]# [1. 4. 0. 0.]# ...# [7. 8. 8. 1.]# [8. 7. 9. 1.]]

然后我构建了我的集成分类器：

from sklearn.ensemble import RandomForestClassifier, VotingClassifierfrom sklearn.svm import SVCfrom sklearn.naive_bayes import GaussianNBfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn.linear_model import LinearRegressionfrom sklearn.model_selection import cross_val_scoreimport numpy as npX = np.array(df[df.columns[:-1]])y = np.array(df[df.columns[-1]])rfc = RandomForestClassifier(n_estimators=10)svc = SVC(kernel='linear')knn = KNeighborsClassifier(n_neighbors=5)nb = GaussianNB()lr = LinearRegression()ensemble = VotingClassifier(estimators=[("Random forest", rfc), ("KNN",knn), ("Naive Bayes", nb), ("SVC",svc), ("Linear Reg.",lr)])

最后，我尝试用交叉验证来评估它，像这样：

cval_score = cross_val_score(ensemble, X, y, cv=10)

但我得到了以下错误：

TypeError                                 Traceback (most recent call last)<ipython-input-13-f7c01fa872d2> in <module>    182 ensemble = VotingClassifier(estimators=[("Random forest", rfc), ("KNN",knn), ("Naive Bayes", nb), ("SVC",svc), ("Linear Reg.",lr)])    183 --> 184 cval_score = cross_val_score(ensemble, X, y, cv=10)[...]TypeError: Cannot cast array data from dtype('float64') to dtype('int64') according to the rule 'safe'

我查看了其他答案，但它们都涉及到numpy数据转换。错误发生在交叉验证阶段。我尝试应用他们的解决方案但没有成功。

我还尝试在计算得分之前更改数据类型，但没有成功。

也许有人能更敏锐地看出问题在哪里。

编辑01：模拟结果生成函数

def mockResults(columns, result_column_name='Fit', min_value = 0, max_value=10, good_values=False):    mock_res = {}    for column in columns:        mock_res[column] = 0        if column == result_column_name:            if good_values == True:                mock_res[column] = float(1)            else:                mock_res[column] = float(0)        elif good_values == True:            mock_res[column] = float(random.randrange(int(max_value*0.7), max_value))        else:            mock_res[column] = float(random.randrange(min_value, int(max_value*0.5)))    return mock_res

回答：

df = pd.DataFrame(columns=["Python", "Scikit-learn", "Pandas", "Fit to Job"], data=np.random.randint(1, 10,size=(400,4)))    class LinearRegressionInt(LinearRegression):    def predict(self,X):        predictions = self._decision_function(X)        return np.asarray(predictions, dtype=np.int64).ravel()... lr = LinearRegressionInt()...ensemble = VotingClassifier(estimators=[("lr",lr),("Random forest", rfc), ("KNN",knn), ("Naive Bayes", nb), ("SVC",svc)] )cval_score = cross_val_score(ensemble, X, y, cv=10)cval_scorearray([ 0.09090909,  0.11904762,  0.17073171,  0.14634146,  0.17073171,    0.15384615,  0.07692308,  0.15384615,  0.10810811,  0.08108108])

参考：投票分类器的类型错误