Checkpoint, about to change train/test dataset generation

choose_hyperparams.py

@@ -22,8 +22,8 @@ def k_folds_ml(x_train, y_train, model, random_state=0):
     rf_cv = GridSearchCV(estimator=current_classifier(),
                          param_grid=current_grid,
                          cv=5,
-                         verbose=10 # to get updates
-                         ) 
+                         verbose=10  # to get updates
+                         )
     rf_cv.fit(x_train, y_train)
     return rf_cv.best_params_
 

main.py

@@ -33,7 +33,7 @@
 paradigm = 'classification'
 
 # cleaning_dataset()
-# create_train_test_datasets()
+create_train_test_datasets()
 
 # if tune_hyperparameters:
 #     for ml_model in ml_models:

replicating_Dorians_features.py

@@ -68,6 +68,7 @@ def extract_features(dataset):
     all_timings = []
     all_original_polynomials = []
     all_projections = []
+    all_cells = []
     for index, projections in enumerate(dataset[0]):
         all_projections.append(projections)
         original_polynomials = projections[0][0]
@@ -76,6 +77,7 @@ def extract_features(dataset):
         all_original_polynomials.append(original_polynomials)
         all_targets.append(dataset[1][index])
         all_timings.append(dataset[2][index])
+        all_cells.append(dataset[3][index])
         names, instance_features = features_from_set_of_polys(
                                        original_polynomials)
         all_features.append(instance_features)
@@ -85,6 +87,7 @@ def extract_features(dataset):
     my_dataset['targets'] = np.array(all_targets)
     my_dataset['timings'] = np.array(all_timings)
     my_dataset['projections'] = np.array(all_projections)
+    my_dataset['cells'] = np.array(all_cells)
     return my_dataset
 
 

test_train_datasets.py

@@ -34,6 +34,7 @@ def create_train_test_datasets():
     y = dict()  # to keep the labels
     t = dict()  # to keep the timings
     p = dict()  # to keep the projections
+    c = dict()  # to keep the number of cells
     # train and test sets are created
     random_state = 0
     x['train_normal'], x['test_normal'], \