testing in for loop, check type

http_submission/sample_evaluation.py

@@ -14,14 +14,16 @@
 class HTTPScanpathModel(MySimpleScanpathModel):
     def __init__(self, url):
         self.url = url
-        self.log_density_url = url + "/conditional_log_density"
-        self.type_url = url + "/type"
-
-
-
 
+    @property
+    def log_density_url(self):
+        return self.url + "/conditional_log_density"
+
+    @property
+    def type_url(self):
+        return self.url + "/type"
+
     def conditional_log_density(self, stimulus, x_hist, y_hist, t_hist, attributes=None, out=None):
-
         # build request
         pil_image = Image.fromarray(stimulus)
         image_bytes = BytesIO()
@@ -48,44 +50,46 @@ def _convert_attribute(attribute):
 
         return np.array(response.json()['log_density'])
 
-    def type(self):
-        response = requests.get(f"{self.type_url}")
-        return np.array(response.json())
+    def check_type(self):
+        response = requests.get(f"{self.type_url}").json()
+        if not response['type'] == 'ScanpathModel':
+            raise ValueError(f"invalid Model type: {response['type']}. Expected 'ScanpathModel'")
+        if not response['version'] in ['v1.0.0']:
+            raise ValueError(f"invalid Model type: {response['version']}. Expected 'v1.0.0'")
 
 
 if __name__ == "__main__":
     http_model = HTTPScanpathModel("http://localhost:4000")
-    type = http_model.type()
+    http_model.check_type()
 
     # for testing
     model = MySimpleScanpathModel()
 
     # get MIT1003 dataset
     stimuli, fixations = pysaliency.get_mit1003(location='pysaliency_datasets')
-    # fixation_index = 32185
-    fixation_index = 2
-    # density_list = []
-    # version_list = []
-    # for fixation_index in range(1000):
 
-    # get server response for one stimulus
-    server_density = http_model.conditional_log_density(
-        stimulus=stimuli.stimuli[fixations.n[fixation_index]], 
-        x_hist=fixations.x_hist[fixation_index], 
-        y_hist=fixations.y_hist[fixation_index], 
-        t_hist=fixations.t_hist[fixation_index]
-    )
-    model_density = model.conditional_log_density(
-        stimulus=stimuli.stimuli[fixations.n[fixation_index]], 
-        x_hist=fixations.x_hist[fixation_index], 
-        y_hist=fixations.y_hist[fixation_index], 
-        t_hist=fixations.t_hist[fixation_index]
-
-    )
-    # get server type
-    print(type)
+    eval_fixations = fixations[fixations.scanpath_history_length > 0]
+    server_density_list = []
+    model_density_list = []
+    for fixation_index in range(10):
+        # get server response for one stimulus
+        server_density = http_model.conditional_log_density(
+            stimulus=stimuli.stimuli[eval_fixations.n[fixation_index]], 
+            x_hist=eval_fixations.x_hist[fixation_index], 
+            y_hist=eval_fixations.y_hist[fixation_index], 
+            t_hist=eval_fixations.t_hist[fixation_index]
+        )
+        # get model response
+        model_density = model.conditional_log_density(
+            stimulus=stimuli.stimuli[eval_fixations.n[fixation_index]], 
+            x_hist=eval_fixations.x_hist[fixation_index], 
+            y_hist=eval_fixations.y_hist[fixation_index], 
+            t_hist=eval_fixations.t_hist[fixation_index]   
+        )
+
+        server_density_list.append(server_density)
+        model_density_list.append(model_density)
 
     # Testing 
-
-    test = np.testing.assert_allclose(server_density, model_density)
+    test = np.testing.assert_allclose(server_density_list, model_density_list)
     print(test)