X具有14个功能，但是RandomForestClassifier期望20个功能作为输入

Question

我能问一下，我有这个代码（一个可重复的示例）：

from sklearn.model_selection import GridSearchCV 
from sklearn.model_selection import StratifiedKFold,KFold
from sklearn.feature_selection import SelectKBest 
#from xgboost import XGBClassifier 
from sklearn.feature_selection import mutual_info_classif 
from sklearn.feature_selection import SelectKBest, RFECV 
from sklearn.pipeline import Pipeline 
from sklearn.svm import SVC 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.naive_bayes import GaussianNB 
from sklearn.linear_model import LogisticRegression 
from sklearn.metrics import make_scorer, recall_score, accuracy_score, precision_score 
from sklearn.model_selection import StratifiedKFold 
from sklearn.metrics import make_scorer 
from sklearn.metrics import precision_score,recall_score,f1_score,roc_auc_score 
from sklearn import metrics
from sklearn.datasets import make_classification
from numpy import mean 
from sklearn.model_selection import train_test_split
from numpy import std 
from sklearn.utils import shuffle 
import numpy as np 
from sklearn.metrics import roc_curve
from sklearn.pipeline import Pipeline
import matplotlib.pyplot as plt 
import pickle
#import neptune.new as neptune
import pandas as pd
import shap

full_X_train,full_y_train = make_classification(n_samples =500,n_features = 20, random_state=1, n_informative=10,n_redundant=10)


def run_model_with_grid_search(param_grid={}, output_plt_file = 'plt.png',model_name=RandomForestClassifier(),X_train=full_X_train,y_train=full_y_train,model_id='random_forest_with_hpo_no_fs_geno_class', n_splits=5, output_file='random_forest_with_hpo_no_fs_geno_class.txt'): 


      cv_outer = KFold(n_splits=5,shuffle=True,random_state=1)
      acc_list = list()
      f1_list = list()
      precision_list = list()
      recall_list = list()
      auc_list = list()


      #for ROC curve
      tprs = []
      base_fpr = np.linspace(0, 1, 101)
      plt.figure(figsize=(5, 5))
      plt.axes().set_aspect('equal', 'datalim')
      count = 0

      list_shap_values = list()
      list_test_sets = list()

      for train_ix,test_ix in cv_outer.split(X_train):
              split_x_train, split_x_test = X_train[train_ix,:],X_train[test_ix,:] #add in .iloc               
              split_y_train, split_y_test = y_train[train_ix],y_train[test_ix]  #add in .iloc

              cv_inner = KFold(n_splits=3,shuffle=True,random_state=1)
              model = model_name
              rfecv = RFECV(estimator=model, step=1,scoring='accuracy',cv=cv_inner) #change scoring to scoring='roc_auc'
              pipeline = Pipeline([('feature_sele',rfecv),('clf_cv',model)])
              search = GridSearchCV(pipeline,param_grid=param_grid,scoring='roc_auc',cv=cv_inner,refit=True).fit(split_x_train,split_y_train)
              best_model = search.best_estimator_
              best_model_shap = search.best_estimator_['clf_cv'].fit(split_x_train,split_y_train)
              print(search.best_params_)
              
              
              yhat = best_model.predict(split_x_test)  #changed from best_model and split_x_test

              accuracy = accuracy_score(split_y_test,yhat)
              acc_list.append(accuracy)

              f1_sc = f1_score(split_y_test,yhat)
              f1_list.append(f1_sc)

              precision_sc = precision_score(split_y_test,yhat)
              precision_list.append(precision_sc)

              recall_sc = recall_score(split_y_test,yhat)
              recall_list.append(recall_sc)
              
              fpr, tpr, _ = roc_curve(split_y_test, yhat)
              auc = metrics.auc(fpr,tpr)
              auc_list.append(auc)

              plt.plot(fpr, tpr, 'b', alpha=0.15)
              tpr = np.interp(base_fpr, fpr, tpr)
              tpr[0] = 0.0
              tprs.append(tpr)
              count +=1

              print('>acc=%.3f,est=%.3f,cfg=%s'%(accuracy,search.best_score_,search.best_params_))

              explainer = shap.TreeExplainer(best_model_shap)
              shap_values = explainer.shap_values(split_x_test)
              list_shap_values.append(shap_values)
              list_test_sets.append(test_ix) 

      test_set = list_test_sets[0]
      shap_values = np.array(list_shap_values[0])

      for i in range(1,len(list_test_sets)):
          test_set = np.concatenate((test_set,list_test_sets[i]),axis=0)
          shap_values = np.concatenate((shap_values,np.array(list_shap_values[i])),axis=1)


      X_test_df = pd.DataFrame(full_X_train[test_set])
      cols = X_test_df.columns
      shap_sum = np.abs(shap_values[1,:,:]).mean(0)
      

      importance_df = pd.DataFrame({
           'column_name':cols,
           'shap_values':shap_sum
      }) 
           
      importance_df.sort_values('shap_values',ascending=False)

      print('Accuracy: %.3f (%.3f)' % (mean(acc_list),std(acc_list)))
      print('F1: %.3f (%.3f)' % (mean(f1_list),std(f1_list)))
      print('Precision: %.3f (%.3f)' % (mean(precision_list),std(precision_list)))
      print('Recall: %.3f (%.3f)' % (mean(recall_list),std(recall_list)))
      print('AUC: %.3f (%.3f)' % (mean(auc_list),std(auc_list)))


      tprs = np.array(tprs)
      mean_tprs = tprs.mean(axis=0)
      tpr_std = tprs.std(axis=0)

      tprs_upper = np.minimum(mean_tprs + tpr_std, 1)
      tprs_lower = mean_tprs - tpr_std


      plt.plot(base_fpr, mean_tprs, 'b')
      plt.fill_between(base_fpr, tprs_lower, tprs_upper, color='grey', alpha=0.3)
      plt.plot([0, 1], [0, 1],'r--')
      plt.xlim([-0.01, 1.01])
      plt.ylim([-0.01, 1.01])
      plt.ylabel('True Positive Rate')
      plt.xlabel('False Positive Rate')
      plt.title('ROC for stratified 5-fold CV (blue line = mean)')
      plt.savefig(output_plt_file)

      print(importance_df)

      return


param_grid = [{
               'clf_cv__min_samples_leaf':[1,3,5],
[200,500,700,1000,1500,2000]
              }]

run_model_with_grid_search(param_grid=param_grid)
        

我得到错误：

X has 14 features, but RandomForestClassifier is expecting 20 features as input

我可以看到其他人遇到了这个问题（我认为） - 这是因为我确实在构建模型之前确实具有选择，但是然后，我不会将X测试数据转换为相同的维度。

我试图基于查看其他人的工作来实现解决方案，例如

              search = GridSearchCV(pipeline,param_grid=param_grid,scoring='roc_auc',cv=cv_inner,refit=True).fit(split_x_train,split_y_train)
              best_model = search.best_estimator_
              best_model_shap = search.best_estimator_['clf_cv'].fit_transform(split_x_train,split_y_train)
              print(search.best_params_)
              
              transformed_x_test = best_model.transform(split_x_test)
              yhat = best_model.predict(transformed_x_test)  #changed from best_model and split_x_test

，这导致其他错误（例如，在这种情况下，“ gridserchcv对象没有属性fit_transform”） - 因此，我不清楚如何将其他人的解决方案实现到我的我的解决方案。问题。

有人可以向我展示如何使此代码正常工作（无论如何，除了此错误外，都应该工作）。另外，如果有人可以向我展示如何通过功能选择过程选择哪些功能，那将是很棒的，但我想我可以在开始工作后弄清楚这一点。