fix typos

Author: holyseven

interpretdl/interpreter/bidirectional_transformer.py

@@ -110,7 +110,6 @@ def interpret(self,
                 R = R + np.matmul(np.matmul(attn, m), R)
         else:
             assert "please specify the attentional perception mode"
-                     
 
         total_gradients = np.zeros((b, h, s, s))
         for alpha in np.linspace(0, 1, steps):
@@ -229,7 +228,7 @@ def text_to_input_fn(raw_text):
         R = np.eye(s, s, dtype=attns[0].dtype)
         R = np.expand_dims(R, 0)
 
-        if ap_mode == 'head':            
+        if ap_mode == 'head':
             for i, attn in enumerate(attns):
                 if i < start_layer:
                     continue
@@ -268,8 +267,8 @@ def text_to_input_fn(raw_text):
         explanation = R[:, 0, :] * grad_head_mean[:, 0, :]  # NLP tasks return explanations for all tokens, including [CLS] and [SEP].
 
         # intermediate results, for possible further usages.
-        self.predcited_label = preds
-        self.predcited_proba = proba
+        self.predicted_label = preds
+        self.predicted_proba = proba
 
         if visual:
             # TODO: visualize if tokenizer is given.

interpretdl/interpreter/gradient_shap.py

@@ -72,11 +72,11 @@ def interpret(self,
 
         self._build_predict_fn(gradient_of='probability')
 
-        _, predcited_labels, predcited_probas = self.predict_fn(data, labels)
-        self.predcited_labels = predcited_labels
-        self.predcited_probas = predcited_probas
+        _, predicted_label, predicted_proba = self.predict_fn(data, labels)
+        self.predicted_label = predicted_label
+        self.predicted_proba = predicted_proba
         if labels is None:
-            labels = predcited_labels
+            labels = predicted_label
 
         def add_noise_to_inputs(data):
             max_axis = tuple(np.arange(1, data.ndim))
@@ -181,8 +181,8 @@ def interpret(self,
             bs = data.shape[0]
 
         gradients, labels, data_out, probas = self.predict_fn(data, labels, None)
-        self.predcited_labels = labels
-        self.predcited_probas = probas
+        self.predicted_label = labels
+        self.predicted_proba = probas
 
         labels = labels.reshape((bs, ))
         total_gradients = np.zeros_like(gradients)

interpretdl/interpreter/integrated_gradients.py

@@ -87,11 +87,11 @@ def interpret(self,
             self.baselines = baselines
 
         # obtain the labels (and initialization).
-        _, predcited_labels, predcited_probas = self.predict_fn(data, labels)
-        self.predcited_labels = predcited_labels
-        self.predcited_probas = predcited_probas
+        _, predicted_label, predicted_proba = self.predict_fn(data, labels)
+        self.predicted_label = predicted_label
+        self.predicted_proba = predicted_proba
         if labels is None:
-            labels = predcited_labels
+            labels = predicted_label
 
         labels = np.array(labels).reshape((bsz, ))
 
@@ -208,8 +208,8 @@ def text_to_input_fn(raw_text):
         ig_gradients = total_gradients * data_out / steps
 
         # intermediate results, for possible further usages.
-        self.predcited_label = label
-        self.predcited_proba = proba
+        self.predicted_label = label
+        self.predicted_proba = proba
 
         if visual:
             # TODO: visualize if tokenizer is given.

interpretdl/interpreter/lime.py

@@ -261,7 +261,7 @@ def predict_fn_for_lime(*inputs):
                                                                          batch_size=batch_size)
 
         # intermediate results, for possible further usages.
-        self.predcited_proba = probability
+        self.predicted_proba = probability
         self.lime_results['probability'] = {c: probability[c] for c in classes_to_interpret.ravel()}
         self.lime_results['r2_scores'] = r2_scores
         self.lime_results['lime_weights'] = lime_weights

interpretdl/interpreter/occlusion.py

@@ -83,8 +83,8 @@ def interpret(self,
             baselines = np.repeat(baselines, len(data), 0)
 
         probas, label, _ = self.predict_fn(data, None)
-        self.predcited_labels = labels
-        self.predcited_probas = probas        
+        self.predicted_label = labels
+        self.predicted_proba = probas
 
         sliding_windows = np.ones(sliding_window_shapes)
 

interpretdl/interpreter/smooth_grad.py

@@ -72,11 +72,11 @@ def interpret(self,
         self._build_predict_fn(gradient_of='probability')
 
         # obtain the labels (and initialization).
-        _, predcited_labels, predcited_probas = self.predict_fn(data, labels)
-        self.predcited_labels = predcited_labels
-        self.predcited_probas = predcited_probas
+        _, predicted_label, predicted_proba = self.predict_fn(data, labels)
+        self.predicted_label = predicted_label
+        self.predicted_proba = predicted_proba
         if labels is None:
-            labels = predcited_labels
+            labels = predicted_label
         labels = np.array(labels).reshape((bsz, ))
 
         # SmoothGrad
@@ -196,8 +196,8 @@ def text_to_input_fn(raw_text):
         sg_gradients = total_gradients / n_samples
 
         # intermediate results, for possible further usages.
-        self.predcited_label = label
-        self.predcited_proba = proba
+        self.predicted_label = label
+        self.predicted_proba = proba
 
         if visual:
             # TODO: visualize if tokenizer is given.

interpretdl/interpreter/smooth_grad_v2.py

@@ -79,11 +79,11 @@ def interpret(self,
         self._build_predict_fn(gradient_of='probability')
 
         # obtain the labels (and initialization).
-        _, predcited_labels, predcited_probas = self.predict_fn(data, labels)
-        self.predcited_labels = predcited_labels
-        self.predcited_probas = predcited_probas
+        _, predicted_label, predicted_proba = self.predict_fn(data, labels)
+        self.predicted_label = predicted_label
+        self.predicted_proba = predicted_proba
         if labels is None:
-            labels = predcited_labels
+            labels = predicted_label
 
         labels = np.array(labels).reshape((1, ))