#! /usr/bin/env python3
#
def cancer_classify_decision ( ):

#*****************************************************************************80
#
## cancer_classify_decision() uses a decision tree to classify cancer data.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    18 July 2023
#
#  Author:
#
#    Andreas Mueller, Sarah Guido.
#    Modifications by John Burkardt.
#
#  Reference:
#
#    Andreas Mueller, Sarah Guido,
#    Introduction to Machine Learning with Python,
#    OReilly, 2017,
#    ISBN: 978-1-449-36941-5
#
  from sklearn.tree import export_graphviz
  import graphviz
  import matplotlib.pyplot as plt
  import mglearn
  import numpy as np
  import pandas as pd
  import platform
  import sklearn

  print ( '' )
  print ( 'cancer_classify_decision():' )
  print ( '  Python version: ' + platform.python_version ( ) )
  print ( '  scikit-learn version: '+ sklearn.__version__ )
#
#  Generate the dataset.
#
  print ( '' )
  print ( '  Retrieve the cancer dataset, (X, y).' )
  from sklearn.datasets import load_breast_cancer
  cancer = load_breast_cancer ( )

  from sklearn.model_selection import train_test_split
  X_train, X_test, y_train, y_test = train_test_split ( \
    cancer.data, cancer.target, stratify = cancer.target, random_state = 42 )
#
#  Compute training and testing accuracy for varying number of neighbors.
#
  from sklearn.tree import DecisionTreeClassifier
#
#  Create a tree with unlimited depth.
#
  tree = DecisionTreeClassifier ( random_state = 0 )
  tree.fit ( X_train, y_train )
  print ( '' )
  print ( '  Create an unpruned decision tree:' )
  print ( '    Accuracy on training set = ', tree.score ( X_train, y_train ) )
  print ( '    Accuracy on test set     = ', tree.score ( X_test, y_test ) )
#
#  Visualize the tree.
#
  from sklearn.tree import export_graphviz
  export_graphviz ( tree, 
    out_file = 'tree.dot', 
    class_names = [ 'malignant', 'benign' ], 
    feature_names = cancer.feature_names,
    impurity = False,
    filled = True )

  with open ( 'tree.dot' ) as f:
    dot_graph = f.read ( )
  s = graphviz.Source ( dot_graph, filename = 'unpruned_tree', format = 'png' )
  s.view ( )
  print ( '  Graphics saved as "unpruned_tree.png"' )
#
#  Create a tree with limited depth.
#
  tree = DecisionTreeClassifier ( max_depth = 4, random_state = 0 )
  tree.fit ( X_train, y_train )
  print ( '' )
  print ( '  Create a pruned decision tree (max depth = 4 ):' )
  print ( '    Accuracy on training set = ', tree.score ( X_train, y_train ) )
  print ( '    Accuracy on test set     = ', tree.score ( X_test, y_test ) )
#
#  Visualize the tree.
#
  export_graphviz ( tree, 
    out_file = 'tree.dot', 
    class_names = [ 'malignant', 'benign' ], 
    feature_names = cancer.feature_names,
    impurity = False,
    filled = True )

  with open ( 'tree.dot' ) as f:
    dot_graph = f.read ( )
  s = graphviz.Source ( dot_graph, filename = 'pruned_tree', format = 'png' )
  s.view ( )
  print ( '  Graphics saved as "pruned_tree.png"' )
#
#  Report feature importance.
#
  print ( '' )
  print ( '  Feature importance:' )
  print ( tree.feature_importances_ )

  plot_feature_importances_cancer ( cancer, tree )
#
#  Terminate.
#
  print ( '' )
  print ( 'cancer_classify_decision():' )
  print ( '  Normal end of execution.' )

  return

def plot_feature_importances_cancer ( cancer, model ):

#*****************************************************************************80
#
## plot_feature_importances_cancer() ...
#
  import matplotlib.pyplot as plt
  import numpy as np

  n_features = cancer.data.shape[1]
  plt.barh ( 
    np.arange ( n_features ), 
    model.feature_importances_, 
    align = 'center' )
  plt.yticks ( 
    np.arange ( n_features ),
    cancer.feature_names )
  plt.xlabel ( 'Feature importance' )
  plt.ylabel ( 'Feature' )
  plt.ylim ( -1, n_features )
  filename = 'feature_importance.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as ' + filename + '"' )
  return

def timestamp ( ):

#*****************************************************************************80
#
## timestamp() prints the date as a timestamp.
#
#  Licensing:
#
#    This code is distributed under the MIT license. 
#
#  Modified:
#
#    21 August 2019
#
#  Author:
#
#    John Burkardt
#
  import time

  t = time.time ( )
  print ( time.ctime ( t ) )

  return

if ( __name__ == '__main__' ):
  timestamp ( )
  cancer_classify_decision ( )
  timestamp ( )