Removed prints and now saving config file in result directory

multiview_platform/mono_multi_view_classifiers/exec_classif.py

@@ -807,7 +807,7 @@ def exec_classif(arguments):
 
             directory = execution.init_log_file(dataset_name, args["Base"]["views"], args["Classification"]["type"],
                                               args["Base"]["log"], args["Base"]["debug"], args["Base"]["label"],
-                                              args["Base"]["res_dir"], args["Base"]["add_noise"], noise_std)
+                                              args["Base"]["res_dir"], args["Base"]["add_noise"], noise_std, args)
             random_state = execution.init_random_state(args["Base"]["random_state"], directory)
             stats_iter_random_states = execution.init_stats_iter_random_states(stats_iter,
                                                                         random_state)

multiview_platform/mono_multi_view_classifiers/multiview_classifiers/weighted_linear_early_fusion.py

@@ -31,7 +31,6 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
     def __init__(self, random_state=None, view_weights=None,
                  monoview_classifier_name="decision_tree",
                  monoview_classifier_config={}):
-        print(type(view_weights), view_weights)
         super(WeightedLinearEarlyFusion, self).__init__(random_state=random_state)
         self.view_weights = view_weights
         self.monoview_classifier_name = monoview_classifier_name
@@ -84,12 +83,10 @@ class WeightedLinearEarlyFusion(BaseMultiviewClassifier, BaseFusionClassifier):
         example_indices, self.view_indices = get_examples_views_indices(dataset,
                                                                         example_indices,
                                                                         view_indices)
-        print(type(self.view_weights))
         if self.view_weights is None:
             self.view_weights = np.ones(len(self.view_indices), dtype=float)
         else:
             self.view_weights = np.array(self.view_weights)
-        print(self.view_weights)
         self.view_weights /= float(np.sum(self.view_weights))
 
         X = self.hdf5_to_monoview(dataset, example_indices)

multiview_platform/mono_multi_view_classifiers/utils/configuration.py

 import yaml
+import os
 
 
 def get_the_args(path_to_config_file="../config_files/config.yml"):
@@ -18,3 +19,11 @@ def get_the_args(path_to_config_file="../config_files/config.yml"):
     with open(path_to_config_file, 'r') as stream:
         yaml_config = yaml.safe_load(stream)
     return yaml_config
+
+
+def save_config(directory, arguments):
+    """
+    Saves the config file in the result directory.
+    """
+    with open(os.path.join(directory, "config_file.yml"), "w") as stream:
+        yaml.dump(arguments, stream)
\ No newline at end of file

multiview_platform/mono_multi_view_classifiers/utils/execution.py

@@ -8,6 +8,7 @@ import numpy as np
 import sklearn
 
 from . import get_multiview_db as DB
+from ..utils.configuration import save_config
 
 
 def parse_the_args(arguments):
@@ -116,7 +117,7 @@ def get_database_function(name, type_var):
 
 
 def init_log_file(name, views, cl_type, log, debug, label,
-                  result_directory, add_noise, noise_std):
+                  result_directory, add_noise, noise_std, args):
     r"""Used to init the directory where the preds will be stored and the log file.
 
     First this function will check if the result directory already exists (only one per minute is allowed).
@@ -172,7 +173,7 @@ def init_log_file(name, views, cl_type, log, debug, label,
                         filemode='w')
     if log:
         logging.getLogger().addHandler(logging.StreamHandler())
-
+    save_config(result_directory, args)
     return result_directory