find_best_attributes_surrogate_svm.py

@@ -0,0 +1,287 @@
+# main imports
+import os
+import sys
+import argparse
+import pandas as pd
+import numpy as np
+import logging
+import datetime
+import random
+
+# model imports
+from sklearn.model_selection import train_test_split
+from sklearn.model_selection import GridSearchCV
+from sklearn.linear_model import LogisticRegression
+from sklearn.ensemble import RandomForestClassifier, VotingClassifier
+
+import joblib
+import sklearn.svm as svm
+from sklearn.utils import shuffle
+from sklearn.metrics import roc_auc_score
+from sklearn.model_selection import cross_val_score
+
+# modules and config imports
+sys.path.insert(0, '') # trick to enable import of main folder module
+
+import custom_config as cfg
+import models as mdl
+
+from optimization.ILSPopSurrogate import ILSPopSurrogate
+from macop.solutions.discrete import BinarySolution
+from macop.evaluators.base import Evaluator
+
+from macop.operators.discrete.mutators import SimpleMutation
+from macop.operators.discrete.mutators import SimpleBinaryMutation
+from macop.operators.discrete.crossovers import SimpleCrossover
+from macop.operators.discrete.crossovers import RandomSplitCrossover
+from optimization.operators.SimplePopCrossover import SimplePopCrossover, RandomPopCrossover
+
+from macop.policies.reinforcement import UCBPolicy
+
+from macop.callbacks.classicals import BasicCheckpoint
+from macop.callbacks.policies import UCBCheckpoint
+from optimization.callbacks.MultiPopCheckpoint import MultiPopCheckpoint
+from optimization.callbacks.SurrogateMonoCheckpoint import SurrogateMonoCheckpoint
+#from sklearn.ensemble import RandomForestClassifier
+
+# variables and parameters
+models_list         = cfg.models_names_list
+
+from warnings import simplefilter
+simplefilter("ignore")
+
+# default validator
+def validator(solution):
+
+    # at least 5 attributes and at most 16
+    if list(solution.data).count(1) < 5 and list(solution.data).count(1) > 16:
+        return False
+
+    return True
+
+def loadDataset(filename):
+
+    ########################
+    # 1. Get and prepare data
+    ########################
+    # scene_name; zone_id; image_index_end; label; data
+
+    dataset_train = pd.read_csv(filename + '.train', header=None, sep=";")
+    dataset_test = pd.read_csv(filename + '.test', header=None, sep=";")
+
+    # default first shuffle of data
+    dataset_train = shuffle(dataset_train)
+    dataset_test = shuffle(dataset_test)
+
+    # get dataset with equal number of classes occurences
+    noisy_df_train = dataset_train[dataset_train.iloc[:, 3] == 1]
+    not_noisy_df_train = dataset_train[dataset_train.iloc[:, 3] == 0]
+    #nb_noisy_train = len(noisy_df_train.index)
+
+    noisy_df_test = dataset_test[dataset_test.iloc[:, 3] == 1]
+    not_noisy_df_test = dataset_test[dataset_test.iloc[:, 3] == 0]
+    #nb_noisy_test = len(noisy_df_test.index)
+
+    # use of all data
+    final_df_train = pd.concat([not_noisy_df_train, noisy_df_train])
+    final_df_test = pd.concat([not_noisy_df_test, noisy_df_test])
+
+    # shuffle data another time
+    final_df_train = shuffle(final_df_train)
+    final_df_test = shuffle(final_df_test)
+
+    # use of the whole data set for training
+    x_dataset_train = final_df_train.iloc[:, 4:]
+    x_dataset_test = final_df_test.iloc[:, 4:]
+
+    y_dataset_train = final_df_train.iloc[:, 3]
+    y_dataset_test = final_df_test.iloc[:, 3]
+
+    return x_dataset_train, y_dataset_train, x_dataset_test, y_dataset_test
+
+def _get_best_model(X_train, y_train):
+
+    Cs = [0.001, 0.01, 0.1, 1, 10, 100, 1000]
+    gammas = [0.001, 0.01, 0.1, 5, 10, 100]
+    param_grid = {'kernel':['rbf'], 'C': Cs, 'gamma' : gammas}
+
+    svc = svm.SVC(probability=True, class_weight='balanced')
+    #clf = GridSearchCV(svc, param_grid, cv=5, verbose=1, scoring=my_accuracy_scorer, n_jobs=-1)
+    clf = GridSearchCV(svc, param_grid, cv=5, verbose=0, n_jobs=22)
+
+    clf.fit(X_train, y_train)
+
+    model = clf.best_estimator_
+
+    return model
+
+def main():
+
+    parser = argparse.ArgumentParser(description="Train and find best filters to use for model")
+
+    parser.add_argument('--data', type=str, help='dataset filename prefix (without .train and .test)', required=True)
+    parser.add_argument('--start_surrogate', type=int, help='number of evalution before starting surrogare model', required=True)
+    parser.add_argument('--train_every', type=int, help='max number of evalution before retraining surrogare model', required=True)
+    parser.add_argument('--length', type=int, help='max data length (need to be specify for evaluator)', required=True)
+    parser.add_argument('--pop', type=int, help='pop size', required=True)
+    parser.add_argument('--order', type=int, help='walsh order function', required=True)
+    parser.add_argument('--ils', type=int, help='number of total iteration for ils algorithm', required=True)
+    parser.add_argument('--ls', type=int, help='number of iteration for Local Search algorithm', required=True)
+    parser.add_argument('--output', type=str, help='output surrogate model name')
+
+    args = parser.parse_args()
+
+    p_data_file = args.data
+    p_length    = args.length
+    p_pop       = args.pop
+    p_order     = args.order
+    p_start     = args.start_surrogate
+    p_retrain   = args.train_every
+    p_ils_iteration = args.ils
+    p_ls_iteration  = args.ls
+    p_output = args.output
+
+    print(p_data_file)
+
+    # load data from file
+    x_train, y_train, x_test, y_test = loadDataset(p_data_file)
+
+    # create `logs` folder if necessary
+    if not os.path.exists(cfg.output_logs_folder):
+        os.makedirs(cfg.output_logs_folder)
+
+    logging.basicConfig(format='%(asctime)s %(message)s', filename='data/logs/{0}.log'.format(p_output), level=logging.DEBUG)
+
+    # init solution (`n` attributes)
+    def init():
+        return BinarySolution.random(p_length, validator)
+
+
+    class ModelEvaluator(Evaluator):
+
+        # define evaluate function here (need of data information)
+        def compute(self, solution):
+            print(f'Solution is composed of {list(solution.data).count(1)} attributes')
+            start = datetime.datetime.now()
+
+            # get indices of filters data to use (filters selection from solution)
+            indices = []
+
+            for index, value in enumerate(solution.data): 
+                if value == 1: 
+                    indices.append(index) 
+
+            # keep only selected filters from solution
+            x_train_filters = self._data['x_train'].iloc[:, indices]
+            y_train_filters = self._data['y_train']
+            x_test_filters = self._data['x_test'].iloc[:, indices]
+            
+            model = _get_best_model(x_train_filters, y_train_filters)
+            # model = RandomForestClassifier(n_estimators=500, class_weight='balanced', bootstrap=True, max_samples=0.75, n_jobs=-1)
+            # model = model.fit(x_train_filters, y_train_filters)
+            
+            y_test_model = model.predict(x_test_filters)
+            test_roc_auc = roc_auc_score(self._data['y_test'], y_test_model)
+
+            end = datetime.datetime.now()
+
+            diff = end - start
+
+            print('----')
+            print("Real evaluation took: {}, score found: {}".format(divmod(diff.days * 86400 + diff.seconds, 60), test_roc_auc))
+
+            return test_roc_auc
+
+
+    # build all output folder and files based on `output` name
+    backup_model_folder = os.path.join(cfg.output_backup_folder, p_output)
+    surrogate_output_model = os.path.join(cfg.output_surrogates_model_folder, p_output)
+    surrogate_output_data = os.path.join(cfg.output_surrogates_data_folder, p_output)
+
+    if not os.path.exists(backup_model_folder):
+        os.makedirs(backup_model_folder)
+
+    if not os.path.exists(cfg.output_surrogates_model_folder):
+        os.makedirs(cfg.output_surrogates_model_folder)
+
+    if not os.path.exists(cfg.output_surrogates_data_folder):
+        os.makedirs(cfg.output_surrogates_data_folder)
+
+    backup_file_path = os.path.join(backup_model_folder, p_output + '.csv')
+    ucb_backup_file_path = os.path.join(backup_model_folder, p_output + '_ucbPolicy.csv')
+    surrogate_performanche_file_path = os.path.join(cfg.output_surrogates_data_folder, p_output + '_performance.csv')
+
+    # prepare optimization algorithm (only use of mutation as only ILS are used here, and local search need only local permutation)
+    operators = [SimpleBinaryMutation(), SimpleMutation(), RandomPopCrossover(), SimplePopCrossover()]
+    policy = UCBPolicy(operators, C=100, exp_rate=0.1)
+
+    # define first line if necessary
+    if not os.path.exists(surrogate_output_data):
+        with open(surrogate_output_data, 'w') as f:
+            f.write('x;y\n')
+
+    # custom ILS for surrogate use
+    algo = ILSPopSurrogate(initalizer=init, 
+                        evaluator=ModelEvaluator(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
+                        operators=operators, 
+                        policy=policy, 
+                        validator=validator,
+                        population_size=p_pop,
+                        surrogate_file_path=surrogate_output_model,
+                        start_train_surrogate=p_start, # start learning and using surrogate after 1000 real evaluation
+                        solutions_file=surrogate_output_data,
+                        walsh_order=p_order,
+                        inter_policy_ls_file=os.path.join(backup_model_folder, p_output + '_ls_ucbPolicy.csv'),
+                        ls_train_surrogate=p_retrain,
+                        maximise=True)
+    
+    algo.addCallback(MultiPopCheckpoint(every=1, filepath=backup_file_path))
+    algo.addCallback(UCBCheckpoint(every=1, filepath=ucb_backup_file_path))
+    algo.addCallback(SurrogateMonoCheckpoint(every=1, filepath=surrogate_performanche_file_path))
+
+    bestSol = algo.run(p_ils_iteration, p_ls_iteration)
+
+    # print best solution found
+    print("Found ", bestSol)
+
+    # save model information into .csv file
+    if not os.path.exists(cfg.results_information_folder):
+        os.makedirs(cfg.results_information_folder)
+
+    filename_path = os.path.join(cfg.results_information_folder, cfg.optimization_attributes_result_filename)
+
+    filters_counter = 0
+
+    # count number of filters
+    for index, item in enumerate(bestSol.data):
+        if index != 0 and index % 2 == 1:
+
+            # if two attributes are used
+            if item == 1 or bestSol.data[index - 1] == 1:
+                filters_counter += 1
+
+
+    line_info = p_output + ';' + p_data_file + ';' + str(bestSol.data) + ';' + str(list(bestSol.data).count(1)) + ';' + str(filters_counter) + ';' + str(bestSol.fitness)
+
+    # check if results are already saved...
+    already_saved = False
+
+    if os.path.exists(filename_path):
+        with open(filename_path, 'r') as f:
+            lines = f.readlines()
+
+            for line in lines:
+                output_name = line.split(';')[0]
+                
+                if p_output == output_name:
+                    already_saved = True
+
+    if not already_saved:
+        with open(filename_path, 'a') as f:
+            f.write(line_info + '\n')
+    
+    print('Result saved into %s' % filename_path)
+
+
+if __name__ == "__main__":
+    main()

prediction/model_prediction_data_rf.py

@@ -198,7 +198,8 @@ def main():
         # prepare input data
         # ToDo check data input
         
-        input_data = np.array([ l.replace('\n', '').split(' ') for l in data[4:] ], 'float32').flatten()
+        input_data = [ l.replace('\n', '').split(' ') for l in data[4:] ]
+        input_data = np.array([x for i, x in enumerate(input_data) if p_solution[i] == 1 ], 'float32').flatten()
         # print(input_data.flatten())
         input_data = np.expand_dims(input_data, axis=0)