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

#*****************************************************************************80
#
## moon_classify_forest() uses a random forest to classify the moon data.
#
#  Licensing:
#
#    This code is distributed under the MIT license.
#
#  Modified:
#
#    19 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.datasets import make_moons
  from sklearn.ensemble import RandomForestClassifier
  from sklearn.model_selection import train_test_split
  import matplotlib.pyplot as plt
  import mglearn
  import numpy as np
  import platform
  import sklearn

  print ( '' )
  print ( 'moon_classify_forest():' )
  print ( '  Python version: ' + platform.python_version ( ) )
  print ( '  scikit-learn version: '+ sklearn.__version__ )
  print ( '  Generate 100 samples of the artificial moon dataset.' )
  print ( '  Classify the data using a random forest.' )
  print ( '' )

  X, y = make_moons ( 
    n_samples = 100,
    noise = 0.25,
    random_state = 3 )

  X_train, X_test, y_train, y_test = train_test_split ( \
    X, y, stratify = y, random_state = 42 )

  forest = RandomForestClassifier ( n_estimators = 5, random_state = 2 )
  forest.fit ( X_train, y_train )

  plt.clf ( )
  fig, axes = plt.subplots ( 2, 3, figsize = ( 20, 10 ) )

  for i, ( ax, tree ) in enumerate ( zip ( axes.ravel(), forest.estimators_ ) ):
    ax.set_title ( "Tree {}".format(i) )
    mglearn.plots.plot_tree_partition ( X_train, y_train, tree, ax = ax )

  mglearn.plots.plot_2d_separator ( 
    forest,
    X_train,
    fill = True,
    ax = axes [ -1, -1 ],
    alpha = 0.4 )

  axes[-1,-1].set_title ( "Random Forest" )
  mglearn.discrete_scatter ( X_train[:,0], X_train[:,1], y_train )

  filename = 'moon_classify_forest.png'
  plt.savefig ( filename )
  print ( '  Graphics saved as "' + filename + '"' )
#
#  Terminate.
#
  print ( '' )
  print ( 'moon_classify_forest():' )
  print ( '  Normal end of execution.' )

  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 ( )
  moon_classify_forest ( )
  timestamp ( )