title: K value for SelectKBest() using chi2
body: <p>I'm using a slightly modified code from here:<a href="https://medium.com/@juniormiranda_23768/ensemble-methods-tuning-a-xgboost-model-with-scikit-learn-54ff669f988a" rel="nofollow noreferrer">Ensemble Methods: Tuning a XGBoost model with Scikit-Learn
  </a></p>
  <p>When I execute it, I keep getting this error:</p>
  <pre><code>ValueError: k should be &gt;=0, &lt;= n_features = 4; got 10. Use k='all' to return all features.
  </code></pre>
  <p>I have four features and a target. I've tried values 1-4 for k in the code below in the in the Pipeline parameters in the SeleckKBest() function, but the same error persists.</p>
  <p>Here is my reproducible code:</p>
  <pre><code>import pandas as pd
  df = pd.DataFrame({'Number1': [11, 12, 13, 14, 15, 16, 17, 18, 19, 20],
                  'Color1': ['Red', 'Blue', 'Green', 'Yellow', 'Orange', 'Red', 
                  'Blue', 'Green', 'Yellow', 'Orange'],
                  'Number2': [221, 222, 223, 224, 225, 226, 227, 228, 229,230],
                  'Trait1': ['Jogger', 'Sedentary', 'Tennis_Player', 'Graveyard', 'Shift_Worker', 'Jogger', 'Fulltime_Mom', 'Tennis_Player', 'Couch_Potato', 'Jogger', 'Graveyard_Shift_Worder'],
                  'Target': ['yes', 'no', 'yes', 'no', 'yes', 'no', 'yes', 'no', 'yes', 'no']})
  
  col = pd.Categorical(df['Target'])
  df['Target'] = col.codes
  
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.preprocessing import MinMaxScaler
  class PreprocessTransformer(BaseEstimator, TransformerMixin):
          def __init__(self, cat_features, num_features):
          self.cat_features = cat_features
          self.num_features = num_features
      def fit(self, X, y=None):
          return self
      def transform(self, X, y=None):
          df = X.copy()
          # Convert columns to categorical
          for name in self.cat_features:
              col = pd.Categorical(df[name])
              df[name] = col.codes
          # Normalize numerical features
          scaler = MinMaxScaler()
          df[self.num_features] = scaler.fit_transform(df[self.num_features])
          return df
  
  from sklearn.model_selection import train_test_split
  # Split the dataset into training and testing
  X_train, X_test, y_train, y_test = train_test_split(
     df.drop('Target', axis=1),
     df['Target'],
     test_size=0.2,
     random_state=42,
    shuffle=True,
    stratify=df['Target']
  )               
  
  from sklearn.pipeline import Pipeline
  from sklearn.feature_selection import SelectKBest, chi2
  import xgboost as xgb
  # Get columns list for categorical and numerical
  categorical_features = df.select_dtypes('object').columns.tolist()
  numerical_features = df.select_dtypes('int64').columns.tolist()
  # Create a pipeline
  pipe = Pipeline([
    ('preproc', PreprocessTransformer(categorical_features, numerical_features)),
    ('fs', SelectKBest(k=0)),
    ('clf', xgb.XGBClassifier(objective='binary:logistic'))
  ])
  
  from sklearn.model_selection import KFold, GridSearchCV
  from sklearn.metrics import accuracy_score, make_scorer
  # Define our search space for grid search
  search_space = [
    {
      'clf__n_estimators': [50, 100, 150, 200],
      'clf__learning_rate': [0.01, 0.1, 0.2, 0.3],
      'clf__max_depth': range(3, 10),
      'clf__colsample_bytree': [i/10.0 for i in range(1, 3)],
      'clf__gamma': [i/10.0 for i in range(3)],
      'fs__score_func': [chi2],
      'fs__k': [10],
    }
  ]
  # Define cross validation
  kfold = KFold(n_splits=8, random_state=42)
  # AUC and accuracy as score
  scoring = {'AUC':'roc_auc', 'Accuracy':make_scorer(accuracy_score)}
  # Define grid search
  grid = GridSearchCV(
    pipe,
    param_grid=search_space,
    cv=kfold,
    scoring=scoring,
    refit='AUC',
    verbose=1,
    n_jobs=-1
  )
  # Fit grid search
  model = grid.fit(X_train, y_train)
  </code></pre>
  <p>Error:</p>
  <pre><code>---------------------------------------------------------------------------
  ValueError                                Traceback (most recent call last)
  &lt;ipython-input-84-5c30ee0bb39f&gt; in &lt;module&gt;
       28 )
       29 # Fit grid search
  ---&gt; 30 model = grid.fit(X_train, y_train)
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/model_selection/_search.py in fit(self, X, y, groups, **fit_params)
      737             refit_start_time = time.time()
      738             if y is not None:
  --&gt; 739                 self.best_estimator_.fit(X, y, **fit_params)
      740             else:
      741                 self.best_estimator_.fit(X, **fit_params)
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
      348             This estimator
      349         &quot;&quot;&quot;
  --&gt; 350         Xt, fit_params = self._fit(X, y, **fit_params)
      351         with _print_elapsed_time('Pipeline',
      352                                  self._log_message(len(self.steps) - 1)):
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/pipeline.py in _fit(self, X, y, **fit_params)
      313                 message_clsname='Pipeline',
      314                 message=self._log_message(step_idx),
  --&gt; 315                 **fit_params_steps[name])
      316             # Replace the transformer of the step with the fitted
      317             # transformer. This is necessary when loading the transformer
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/joblib/memory.py in __call__(self, *args, **kwargs)
      353 
      354     def __call__(self, *args, **kwargs):
  --&gt; 355         return self.func(*args, **kwargs)
      356 
      357     def call_and_shelve(self, *args, **kwargs):
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/pipeline.py in _fit_transform_one(transformer, X, y, weight, message_clsname, message, **fit_params)
      726     with _print_elapsed_time(message_clsname, message):
      727         if hasattr(transformer, 'fit_transform'):
  --&gt; 728             res = transformer.fit_transform(X, y, **fit_params)
      729         else:
      730             res = transformer.fit(X, y, **fit_params).transform(X)
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/base.py in fit_transform(self, X, y, **fit_params)
      572         else:
      573             # fit method of arity 2 (supervised transformation)
  --&gt; 574             return self.fit(X, y, **fit_params).transform(X)
      575 
      576 
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/feature_selection/_univariate_selection.py in fit(self, X, y)
      346                             % (self.score_func, type(self.score_func)))
      347 
  --&gt; 348         self._check_params(X, y)
      349         score_func_ret = self.score_func(X, y)
      350         if isinstance(score_func_ret, (list, tuple)):
  
  ~/anaconda3/envs/python3/lib/python3.6/site-packages/sklearn/feature_selection/_univariate_selection.py in _check_params(self, X, y)
      512             raise ValueError(&quot;k should be &gt;=0, &lt;= n_features = %d; got %r. &quot;
      513                              &quot;Use k='all' to return all features.&quot;
  --&gt; 514                              % (X.shape[1], self.k))
      515 
      516     def _get_support_mask(self):
  
  ValueError: k should be &gt;=0, &lt;= n_features = 4; got 10. Use k='all' to return all features.
  </code></pre>