text: """
  =================================================
  SVM-Anova: SVM with univariate feature selection
  =================================================
  
  This example shows how to perform univariate feature selection before running a
  SVC (support vector classifier) to improve the classification scores. We use
  the iris dataset (4 features) and add 36 non-informative features. We can find
  that our model achieves best performance when we select around 10% of features.
  
  """
  
  import numpy as np
  import matplotlib.pyplot as plt
  from sklearn.datasets import load_iris
  from sklearn.feature_selection import SelectPercentile, chi2
  from sklearn.model_selection import cross_val_score
  from sklearn.pipeline import Pipeline
  from sklearn.preprocessing import StandardScaler
  from sklearn.svm import SVC
  
  
  # #############################################################################
  # Import some data to play with
  X, y = load_iris(return_X_y=True)
  # Add non-informative features
  np.random.seed(0)
  X = np.hstack((X, 2 * np.random.random((X.shape[0], 36))))
  
  # #############################################################################
  # Create a feature-selection transform, a scaler and an instance of SVM that we
  # combine together to have a full-blown estimator
  clf = Pipeline(
      [
          ("anova", SelectPercentile(chi2)),
          ("scaler", StandardScaler()),
          ("svc", SVC(gamma="auto")),
      ]
  )
  
  # #############################################################################
  # Plot the cross-validation score as a function of percentile of features
  score_means = list()
  score_stds = list()
  percentiles = (1, 3, 6, 10, 15, 20, 30, 40, 60, 80, 100)
  
  for percentile in percentiles:
      clf.set_params(anova__percentile=percentile)
      this_scores = cross_val_score(clf, X, y)
      score_means.append(this_scores.mean())
      score_stds.append(this_scores.std())
  
  plt.errorbar(percentiles, score_means, np.array(score_stds))
  plt.title("Performance of the SVM-Anova varying the percentile of features selected")
  plt.xticks(np.linspace(0, 100, 11, endpoint=True))
  plt.xlabel("Percentile")
  plt.ylabel("Accuracy Score")
  plt.axis("tight")
  plt.show()
  