updating naming for model uri and inference url

src/config/common.yaml

@@ -62,8 +62,8 @@ pipeline:
     plot_scores: true
     plot_path_template: "data/plots/results/{year}/{month:02}/{plot_name}.png"
     classification_path: "data/classifications.tsv"
-    url: https://tfhub.dev/google/humpback_whale/1
-    model_url: "http://127.0.0.1:5000/predict"
+    model_uri: https://tfhub.dev/google/humpback_whale/1
+    inference_url: "http://127.0.0.1:5000/predict"
     med_filter_size: 3
 
   postprocess:

src/stages/classify.py

@@ -32,7 +32,7 @@ def __init__(self, config: SimpleNamespace):
 
         self.batch_duration     = config.classify.batch_duration
         self.model_sample_rate  = config.classify.model_sample_rate
-        self.model_url          = config.classify.model_url
+        self.inference_url          = config.classify.inference_url
 
         # plotting parameters
         self.hydrophone_sensitivity = config.classify.hydrophone_sensitivity
@@ -271,7 +271,7 @@ def _postprocess(self, pcoll, grouped_outputs):
 class InferenceClient(beam.DoFn):
     def __init__(self, config: SimpleNamespace):
 
-        self.model_url = config.classify.model_url
+        self.inference_url = config.classify.inference_url
         self.retries = config.classify.inference_retries
 
     def process(self, element):
@@ -292,7 +292,7 @@ def process(self, element):
         wait = 0 
         while wait < 5:
             try:
-                response = requests.post(self.model_url, json=data)
+                response = requests.post(self.inference_url, json=data)
                 response.raise_for_status()
                 break
             except requests.exceptions.ConnectionError as e:
@@ -301,7 +301,7 @@ def process(self, element):
                 wait += 1
                 time.sleep(wait*wait)
 
-        response = requests.post(self.model_url, json=data)
+        response = requests.post(self.inference_url, json=data)
         response.raise_for_status()
 
         predictions = response.json().get("predictions", [])
@@ -424,7 +424,7 @@ def sample_run():
             batch_duration=30, # seconds
             hydrophone_sensitivity=-168.8,
             model_sample_rate=10_000,
-            model_url="http://127.0.0.1:5000/predict",
+            inference_url="http://127.0.0.1:5000/predict",
             plot_scores=True,
             plot_path_template="data/plots/results/{year}/{month:02}/{plot_name}.png",
             med_filter_size=3,

src/stages/postprocess.py

@@ -46,29 +46,27 @@ def process(self, element, search_output):
 
     def _build_classification_df(self, element: Tuple) -> pd.DataFrame:
         # convert element to dataframe
-        classifications_df = pd.DataFrame([element], columns=["audio", "start", "end", "encounter_ids", "classifications"])
+        df = pd.DataFrame([element], columns=["audio", "start", "end", "encounter_ids", "classifications"])
+        df = df[df["classifications"].apply(lambda x: len(x) > 0)]  # rm empty rows
 
         # explode encounter_ids
-        classifications_df = classifications_df.explode("encounter_ids").rename(columns={"encounter_ids": "encounter_id"})
-        classifications_df["encounter_id"] = classifications_df["encounter_id"].astype(str)
+        df = df.explode("encounter_ids").rename(columns={"encounter_ids": "encounter_id"})
+        df["encounter_id"] = df["encounter_id"].astype(str)
 
-        # TODO replace classifications check w/ pooled_score check
-        classifications_df = classifications_df[classifications_df["classifications"].apply(lambda x: len(x) > 0)]
         # pool classifications in postprocessing
-        classifications_df["pooled_score"] = classifications_df["classifications"].apply(self._pool_classifications)
+        df["pooled_score"] = df["classifications"].apply(self._pool_classifications)
 
         # convert start and end to isoformat
-        classifications_df["start"] = classifications_df["start"].apply(lambda x: x.isoformat())
-        classifications_df["end"] = classifications_df["end"].apply(lambda x: x.isoformat())
+        df["start"] = df["start"].apply(lambda x: x.isoformat())
+        df["end"] = df["end"].apply(lambda x: x.isoformat())
 
         # drop audio and classification columns 
-        classifications_df = classifications_df.drop(columns=["audio"])
-        classifications_df = classifications_df.drop(columns=["classifications"])
+        df = df.drop(columns=["audio"])
+        df = df.drop(columns=["classifications"])
 
-
-        logging.info(f"Classifications: \n{classifications_df.head()}")
-        logging.info(f"Classifications shape: {classifications_df.shape}")
-        return classifications_df.reset_index(drop=True)
+        logging.info(f"Classifications: \n{df.head()}")
+        logging.info(f"Classifications shape: {df.shape}")
+        return df.reset_index(drop=True)
 
     def _build_search_output_df(self, search_output: Dict[str, Any]) -> pd.DataFrame:
         # convert search_output to dataframe

tests/test_classify.py

@@ -20,7 +20,7 @@ def example_config():
             batch_duration=30, # seconds
             hydrophone_sensitivity=-168.8,
             model_sample_rate=10_000,
-            model_url="http://127.0.0.1:5000/predict",
+            inference_url="http://127.0.0.1:5000/predict",
             plot_scores=True,
             plot_path_template="data/plots/results/{year}/{month:02}/{plot_name}.png",
             med_filter_size=3,