find_best_attributes_surrogate.py 9.2 KB

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  1. # main imports
  2. import os
  3. import sys
  4. import argparse
  5. import pandas as pd
  6. import numpy as np
  7. import logging
  8. import datetime
  9. import random
  10. # model imports
  11. from sklearn.model_selection import train_test_split
  12. from sklearn.model_selection import GridSearchCV
  13. from sklearn.linear_model import LogisticRegression
  14. from sklearn.ensemble import RandomForestClassifier, VotingClassifier
  15. import joblib
  16. import sklearn.svm as svm
  17. from sklearn.utils import shuffle
  18. from sklearn.metrics import roc_auc_score
  19. from sklearn.model_selection import cross_val_score
  20. # modules and config imports
  21. sys.path.insert(0, '') # trick to enable import of main folder module
  22. import custom_config as cfg
  23. import models as mdl
  24. from optimization.ILSSurrogate import ILSSurrogate
  25. from macop.solutions.discrete import BinarySolution
  26. from macop.evaluators.base import Evaluator
  27. from macop.operators.discrete.mutators import SimpleMutation
  28. from macop.operators.discrete.mutators import SimpleBinaryMutation
  29. from macop.operators.discrete.crossovers import SimpleCrossover
  30. from macop.operators.discrete.crossovers import RandomSplitCrossover
  31. from macop.policies.reinforcement import UCBPolicy
  32. from macop.callbacks.classicals import BasicCheckpoint
  33. from macop.callbacks.policies import UCBCheckpoint
  34. #from sklearn.ensemble import RandomForestClassifier
  35. # variables and parameters
  36. models_list = cfg.models_names_list
  37. # default validator
  38. def validator(solution):
  39. # at least 5 attributes
  40. if list(solution.data).count(1) < 5:
  41. return False
  42. return True
  43. def loadDataset(filename):
  44. ########################
  45. # 1. Get and prepare data
  46. ########################
  47. # scene_name; zone_id; image_index_end; label; data
  48. dataset_train = pd.read_csv(filename + '.train', header=None, sep=";")
  49. dataset_test = pd.read_csv(filename + '.test', header=None, sep=";")
  50. # default first shuffle of data
  51. dataset_train = shuffle(dataset_train)
  52. dataset_test = shuffle(dataset_test)
  53. # get dataset with equal number of classes occurences
  54. noisy_df_train = dataset_train[dataset_train.iloc[:, 3] == 1]
  55. not_noisy_df_train = dataset_train[dataset_train.iloc[:, 3] == 0]
  56. #nb_noisy_train = len(noisy_df_train.index)
  57. noisy_df_test = dataset_test[dataset_test.iloc[:, 3] == 1]
  58. not_noisy_df_test = dataset_test[dataset_test.iloc[:, 3] == 0]
  59. #nb_noisy_test = len(noisy_df_test.index)
  60. # use of all data
  61. final_df_train = pd.concat([not_noisy_df_train, noisy_df_train])
  62. final_df_test = pd.concat([not_noisy_df_test, noisy_df_test])
  63. # shuffle data another time
  64. final_df_train = shuffle(final_df_train)
  65. final_df_test = shuffle(final_df_test)
  66. # use of the whole data set for training
  67. x_dataset_train = final_df_train.iloc[:, 4:]
  68. x_dataset_test = final_df_test.iloc[:, 4:]
  69. y_dataset_train = final_df_train.iloc[:, 3]
  70. y_dataset_test = final_df_test.iloc[:, 3]
  71. return x_dataset_train, y_dataset_train, x_dataset_test, y_dataset_test
  72. def _get_best_model(X_train, y_train):
  73. Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
  74. gammas = [0.001, 0.01, 0.1, 5, 10, 100]
  75. param_grid = {'kernel':['rbf'], 'C': Cs, 'gamma' : gammas}
  76. svc = svm.SVC(probability=True, class_weight='balanced')
  77. #clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, scoring=my_accuracy_scorer, n_jobs=-1)
  78. clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, n_jobs=-1)
  79. clf.fit(X_train, y_train)
  80. model = clf.best_estimator_
  81. return model
  82. def main():
  83. parser = argparse.ArgumentParser(description="Train and find best filters to use for model")
  84. parser.add_argument('--data', type=str, help='dataset filename prefix (without .train and .test)', required=True)
  85. parser.add_argument('--start_surrogate', type=int, help='number of evalution before starting surrogare model', default=100)
  86. parser.add_argument('--length', type=int, help='max data length (need to be specify for evaluator)', required=True)
  87. parser.add_argument('--ils', type=int, help='number of total iteration for ils algorithm', required=True)
  88. parser.add_argument('--ls', type=int, help='number of iteration for Local Search algorithm', required=True)
  89. parser.add_argument('--output', type=str, help='output surrogate model name')
  90. args = parser.parse_args()
  91. p_data_file = args.data
  92. p_length = args.length
  93. p_start = args.start_surrogate
  94. p_ils_iteration = args.ils
  95. p_ls_iteration = args.ls
  96. p_output = args.output
  97. print(p_data_file)
  98. # load data from file
  99. x_train, y_train, x_test, y_test = loadDataset(p_data_file)
  100. # create `logs` folder if necessary
  101. if not os.path.exists(cfg.output_logs_folder):
  102. os.makedirs(cfg.output_logs_folder)
  103. logging.basicConfig(format='%(asctime)s %(message)s', filename='data/logs/{0}.log'.format(p_output), level=logging.DEBUG)
  104. # init solution (`n` attributes)
  105. def init():
  106. return BinarySolution.random(p_length, validator)
  107. class SurrogateEvaluator(Evaluator):
  108. # define evaluate function here (need of data information)
  109. def compute(solution):
  110. start = datetime.datetime.now()
  111. # get indices of filters data to use (filters selection from solution)
  112. indices = []
  113. for index, value in enumerate(solution.data):
  114. if value == 1:
  115. indices.append(index)
  116. # keep only selected filters from solution
  117. x_train_filters = self.data['x_train'].iloc[:, indices]
  118. y_train_filters = self.data['y_train']
  119. x_test_filters = self.data['x_test'].iloc[:, indices]
  120. model = _get_best_model(x_train_filters, y_train_filters)
  121. #model = RandomForestClassifier(n_estimators=10)
  122. #model = model.fit(x_train_filters, y_train_filters)
  123. y_test_model = model.predict(x_test_filters)
  124. test_roc_auc = roc_auc_score(self.data['y_test'], y_test_model)
  125. end = datetime.datetime.now()
  126. diff = end - start
  127. print("Real evaluation took: {}, score found: {}".format(divmod(diff.days * 86400 + diff.seconds, 60), test_roc_auc))
  128. return test_roc_auc
  129. # build all output folder and files based on `output` name
  130. backup_model_folder = os.path.join(cfg.output_backup_folder, p_output)
  131. surrogate_output_model = os.path.join(cfg.output_surrogates_model_folder, p_output)
  132. surrogate_output_data = os.path.join(cfg.output_surrogates_data_folder, p_output)
  133. if not os.path.exists(backup_model_folder):
  134. os.makedirs(backup_model_folder)
  135. if not os.path.exists(cfg.output_surrogates_model_folder):
  136. os.makedirs(cfg.output_surrogates_model_folder)
  137. if not os.path.exists(cfg.output_surrogates_data_folder):
  138. os.makedirs(cfg.output_surrogates_data_folder)
  139. backup_file_path = os.path.join(backup_model_folder, p_output + '.csv')
  140. ucb_backup_file_path = os.path.join(backup_model_folder, p_output + '_ucbPolicy.csv')
  141. # prepare optimization algorithm (only use of mutation as only ILS are used here, and local search need only local permutation)
  142. operators = [SimpleBinaryMutation(), SimpleMutation(), SimpleCrossover(), RandomSplitCrossover()]
  143. policy = UCBPolicy(operators)
  144. # define first line if necessary
  145. if not os.path.exists(surrogate_output_data):
  146. with open(surrogate_output_data, 'w') as f:
  147. f.write('x;y\n')
  148. # custom ILS for surrogate use
  149. algo = ILSSurrogate(initalizer=init,
  150. evaluator=SurrogateEvaluator(data={'x_train': x_train, 'y_train': y_train, 'x_test': x_test, 'y_test': y_test}), # same evaluator by default, as we will use the surrogate function
  151. operators=operators,
  152. policy=policy,
  153. validator=validator,
  154. surrogate_file_path=surrogate_output_model,
  155. start_train_surrogate=p_start, # start learning and using surrogate after 1000 real evaluation
  156. solutions_file=surrogate_output_data,
  157. ls_train_surrogate=5,
  158. maximise=True)
  159. algo.addCallback(BasicCheckpoint(every=1, filepath=backup_file_path))
  160. algo.addCallback(UCBCheckpoint(every=1, filepath=ucb_backup_file_path))
  161. bestSol = algo.run(p_ils_iteration, p_ls_iteration)
  162. # print best solution found
  163. print("Found ", bestSol)
  164. # save model information into .csv file
  165. if not os.path.exists(cfg.results_information_folder):
  166. os.makedirs(cfg.results_information_folder)
  167. filename_path = os.path.join(cfg.results_information_folder, cfg.optimization_attributes_result_filename)
  168. filters_counter = 0
  169. # count number of filters
  170. for index, item in enumerate(bestSol.data):
  171. if index != 0 and index % 2 == 1:
  172. # if two attributes are used
  173. if item == 1 or bestSol.data[index - 1] == 1:
  174. filters_counter += 1
  175. line_info = p_data_file + ';' + str(p_ils_iteration) + ';' + str(p_ls_iteration) + ';' + str(bestSol.data) + ';' + str(list(bestSol.data).count(1)) + ';' + str(filters_counter) + ';' + str(bestSol.fitness())
  176. with open(filename_path, 'a') as f:
  177. f.write(line_info + '\n')
  178. print('Result saved into %s' % filename_path)
  179. if __name__ == "__main__":
  180. main()