我继续研究pipeline。我的目标是仅通过pipeline来执行机器学习的每个步骤。这样会更加灵活，并且更容易将我的pipeline适应其他用例。因此，我所做的步骤如下：
- 步骤1：填充NaN值
- 步骤2：将分类值转换为数字
- 步骤3：分类器
- 步骤4：网格搜索
- 步骤5：添加度量（失败）
这是我的代码：
import pandas as pdfrom sklearn.base import BaseEstimator, TransformerMixinfrom sklearn.feature_selection import SelectKBestfrom sklearn.preprocessing import LabelEncoderfrom sklearn.model_selection import GridSearchCVfrom sklearn.model_selection import train_test_splitfrom sklearn.ensemble import RandomForestClassifierfrom sklearn.pipeline import Pipelinefrom sklearn.metrics import roc_curve, aucimport matplotlib.pyplot as pltfrom sklearn.metrics import confusion_matrixfrom sklearn.metrics import f1_scoreclass FillNa(BaseEstimator, TransformerMixin): def transform(self, x, y=None): non_numerics_columns = x.columns.difference( x._get_numeric_data().columns) for column in x.columns: if column in non_numerics_columns: x.loc[:, column] = x.loc[:, column].fillna( df[column].value_counts().idxmax()) else: x.loc[:, column] = x.loc[:, column].fillna( x.loc[:, column].mean()) return x def fit(self, x, y=None): return selfclass CategoricalToNumerical(BaseEstimator, TransformerMixin): def transform(self, x, y=None): non_numerics_columns = x.columns.difference( x._get_numeric_data().columns) le = LabelEncoder() for column in non_numerics_columns: x.loc[:, column] = x.loc[:, column].fillna( x.loc[:, column].value_counts().idxmax()) le.fit(x.loc[:, column]) x.loc[:, column] = le.transform(x.loc[:, column]).astype(int) return x def fit(self, x, y=None): return selfclass Perf(BaseEstimator, TransformerMixin): def fit(self, clf, x, y, perf="all"): """Only for classifier model. Return AUC, ROC, Confusion Matrix and F1 score from a classifier and df You can put a list of eval instead a string for eval paramater. Example: eval=['all', 'auc', 'roc', 'cm', 'f1'] will return these 4 evals. """ evals = {} y_pred_proba = clf.predict_proba(x)[:, 1] y_pred = clf.predict(x) perf_list = perf.split(',') if ("all" or "roc") in perf.split(','): fpr, tpr, _ = roc_curve(y, y_pred_proba) roc_auc = round(auc(fpr, tpr), 3) plt.style.use('bmh') plt.figure(figsize=(12, 9)) plt.title('ROC Curve') plt.plot(fpr, tpr, 'b', label='AUC = {}'.format(roc_auc)) plt.legend(loc='lower right', borderpad=1, labelspacing=1, prop={"size": 12}, facecolor='white') plt.plot([0, 1], [0, 1], 'r--') plt.xlim([-0.1, 1.]) plt.ylim([-0.1, 1.]) plt.ylabel('True Positive Rate') plt.xlabel('False Positive Rate') plt.show() if "all" in perf_list or "auc" in perf_list: fpr, tpr, _ = roc_curve(y, y_pred_proba) evals['auc'] = auc(fpr, tpr) if "all" in perf_list or "cm" in perf_list: evals['cm'] = confusion_matrix(y, y_pred) if "all" in perf_list or "f1" in perf_list: evals['f1'] = f1_score(y, y_pred) return evalspath = '~/proj/akd-doc/notebooks/data/'df = pd.read_csv(path + 'titanic_tuto.csv', sep=';')y = df.pop('Survival-Status').replace(to_replace=['dead', 'alive'], value=[0., 1.])X = df.copy()X_train, X_test, y_train, y_test = train_test_split( X.copy(), y.copy(), test_size=0.2, random_state=42)percent = 0.50nb_features = round(percent * df.shape[1]) + 1clf = RandomForestClassifier()pipeline = Pipeline([('fillna', FillNa()), ('categorical_to_numerical', CategoricalToNumerical()), ('features_selection', SelectKBest(k=nb_features)), ('random_forest', clf), ('perf', Perf())])params = dict(random_forest__max_depth=list(range(8, 12)), random_forest__n_estimators=list(range(30, 110, 10)))cv = GridSearchCV(pipeline, param_grid=params)cv.fit(X_train, y_train)我知道打印ROC曲线不是最理想的做法，但这目前不是问题所在。
所以，当我执行这段代码时，我得到的是：
TypeError: If no scoring is specified, the estimator passed should have a 'score' method. The estimator Pipeline(steps=[('fillna', FillNa()), ('categorical_to_numerical', CategoricalToNumerical()), ('features_selection', SelectKBest(k=10, score_func=<function f_classif at 0x7f4ed4c3eae8>)), ('random_forest', RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini', max_depth=None,...=1, oob_score=False, random_state=None, verbose=0, warm_start=False)), ('perf', Perf())]) does not.我对所有想法都感兴趣…
回答：
正如错误信息所述，您需要在GridSearchCV中指定scoring参数。
使用
GridSearchCV(pipeline, param_grid=params, scoring = 'accuracy')
编辑（基于评论中的问题）：
如果您需要整个X_train和y_train的roc、auc曲线和f1（而不是GridSearchCV的所有拆分），最好将Perf类保留在pipeline之外。
pipeline = Pipeline([('fillna', FillNa()), ('categorical_to_numerical', CategoricalToNumerical()), ('features_selection', SelectKBest(k=nb_features)), ('random_forest', clf)])#Fit the data in the pipelinepipeline.fit(X_train, y_train)performance_meas = Perf()performance_meas.fit(pipeline, X_train, y_train)