Added sample results

Author: ambareeshravi

_shap.py

@@ -33,6 +33,8 @@ def __init__(
     ):
         '''
         Different datasets need different alpha values for blending for better visualization
+        
+        # 0.85 for IR_DISTRACTION
         '''
         self.blend_alpha = blend_alpha
         # set of background examples to take an expectation over

run_pipeline.py

@@ -10,22 +10,22 @@
 create_directory(output_path)
 
 config = {
-    'lime': {
-        'num_features': 128*128,
-        'mask_features': 5,
-        'num_samples': 1000,
-    },
-    'counterfactual': {
-        'threshold_pct': 0.98,
-        'block_size': 32
-    },
+#     'lime': {
+#         'num_features': 128*128,
+#         'mask_features': 5,
+#         'num_samples': 1000,
+#     },
+#     'counterfactual': {
+#         'threshold_pct': 0.98,
+#         'block_size': 32
+#     },
     'shap': {
-        'blend_alpha': 0.85,
+        'blend_alpha': 0.7,
         'background_samples': 100
     },
-    'elrp': {
-        'max_loss_value': 1000
-    },
+#     'elrp': {
+#         'max_loss_value': 1000
+#     },
     'output_path': output_path,
     'test_data_path': join_paths(["TEST_SETS/", train_data.__name__, ""]),
     'train_data': train_data.data, # np.array of normal images
@@ -38,18 +38,18 @@
 rec_model = get_model(model_path)
 loss_model = get_model(model_path, rec=False)
 
-p = Pipeline(rec_model, loss_model, config)
-p.run()
+# p = Pipeline(rec_model, loss_model, config)
+# p.run()
 
-# output_path = config["output_path"]
-# test_path = config['test_data_path']
-# for category_dir in read_directory_contents(test_path):
-#     category_type = os.path.split(category_dir)[-1]
-#     INFO(category_type)
-#     config["output_path"] = join_paths([output_path, category_type, ""])
-#     config['data_name'] = category_type
-#     create_directory(config["output_path"])
-#     config['test_data_path'] = category_dir
+output_path = config["output_path"]
+test_path = config['test_data_path']
+for category_dir in read_directory_contents(test_path):
+    category_type = os.path.split(category_dir)[-1]
+    INFO(category_type)
+    config["output_path"] = join_paths([output_path, category_type, ""])
+    config['data_name'] = category_type
+    create_directory(config["output_path"])
+    config['test_data_path'] = category_dir
 
-#     _pipline = Pipeline(rec_model, loss_model, config)
-#     _pipline.run()
+    _pipline = Pipeline(rec_model, loss_model, config)
+    _pipline.run()