Playing with the backfilling script until the github action works: Tr…

…ial #5

.gitignore

@@ -9,4 +9,5 @@ keyfile
 /.idea
 /data
 /models
-**/__pycache__
+/.cometml-runs
+**/__pycache__

src/feature_pipeline/data_extraction.py

@@ -13,21 +13,14 @@
 2021 would perform, so I've included code that would allow for that data to be 
 downloaded
 """
-# Utilities
 import os
 import requests
-from zipfile import ZipFile
-
-# Data Manipulation and Access
 import pandas as pd
 
-# Logging
 from loguru import logger
-
-# Dates
+from zipfile import ZipFile
 from datetime import datetime as dt
 
-# Custom Code
 from src.setup.paths import RAW_DATA_DIR, make_fundamental_paths
 
 

src/feature_pipeline/station_indexing.py

@@ -215,7 +215,7 @@ def find_rows_with_known_ids_and_names(self, save: bool = True) -> dict[str, tup
 
             for row in tqdm(
                     iterable=range(self.data.shape[0]),
-                    desc="Looking for any row that has either a missing station ID OR a missing station name."
+                    desc="Looking for rows that have either a missing station ID OR a missing station name."
             ):
                 latitude = self.data.iloc[row, self.latitudes_index]
                 longitude = self.data.iloc[row, self.longitudes_index]

src/inference_pipeline/backfill_feature_store.py

@@ -1,3 +1,4 @@
+import json 
 import pandas as pd
 from pathlib import Path
 from datetime import datetime
@@ -110,15 +111,11 @@ def backfill_predictions(self, target_date: datetime, model_name: str = "lightgb
             geocode=False
         )
 
-        print(engineered_features.shape)
-        breakpoint()
-
         try:
-            engineered_features = engineered_features.drop("trips_next_hour", axis=1)
-            logger.success("Dropped target column")
+            engineered_features = engineered_features.drop(["trips_next_hour", f"{scenario}_hour"], axis=1)
         except Exception as error:
-            logger.error(error)
-        
+            logger.error(error)    
+
         predictions_df: pd.DataFrame = inferrer.get_model_predictions(model=model, features=engineered_features)
 
         predictions_feature_group: FeatureGroup = self.api.get_or_create_feature_group(

src/inference_pipeline/inference.py

@@ -176,11 +176,6 @@ def load_predictions_from_store(
             end_time=to_hour + timedelta(days=1)
         )
 
-        print(
-           f" There are {len(predictions_df[f"{self.scenario}_station_id"])} station ids from the feature view"
-        )
-        breakpoint()
-
         predictions_df[f"{self.scenario}_hour"] = pd.to_datetime(predictions_df[f"{self.scenario}_hour"], utc=True)
 
         return predictions_df.sort_values(

src/training_pipeline/training.py

@@ -92,10 +92,9 @@ def train(self, model_name: str) -> float:
             project_name=config.comet_project_name
         )
 
-        experiment.add_tags(tags=[model_name, self.scenario])
-
         if not self.tune_hyperparameters:
-            experiment.set_name(name=f"{model_name.title()}(not tuned) model for the {self.scenario}s of trips")
+            experiment.set_name(name=f"{model_name.title()}(Untuned) model for the {self.scenario}s of trips")
+
             logger.info("Using the default hyperparameters")
             if model_name == "base":
                 pipeline = make_pipeline(model_fn(scenario=self.scenario))
@@ -104,7 +103,6 @@ def train(self, model_name: str) -> float:
                     pipeline = make_pipeline(model_fn)
                 else:
                     pipeline = make_pipeline(model_fn())
-
         else:
             experiment.set_name(name=f"{model_name.title()}(Tuned) model for the {self.scenario}s of trips")
             logger.info(f"Tuning hyperparameters of the {model_name} model. Have a snack...")
@@ -118,6 +116,7 @@ def train(self, model_name: str) -> float:
             )
 
             logger.success(f"Best model hyperparameters {best_model_hyperparameters}")
+
             pipeline = make_pipeline(
                 model_fn(**best_model_hyperparameters)
             )
@@ -141,16 +140,13 @@ def save_model_locally(self, model_fn: Pipeline, model_name: str):
             model_name (str): the name of the model to be saved
         """
         model_file_name = f"{model_name.title()} ({self.tuned_or_not} for {self.scenario}s).pkl"
+
         with open(LOCAL_SAVE_DIR/model_file_name, mode="wb") as file:
             pickle.dump(obj=model_fn, file=file)
+
         logger.success("Saved model to disk")
 
-    def train_and_register_models(
-            self,
-            model_names: list[str],
-            version: str,
-            status: str
-    ) -> None:
+    def train_and_register_models(self, model_names: list[str], version: str, status: str) -> None:
         """
         Train the named models, identify the best performer (on the test data) and
         register it to the CometML model registry.
@@ -160,16 +156,17 @@ def train_and_register_models(
             version: the version of the registered model on CometML.
             status:  the registered status of the model on CometML.
         """
+        models_and_errors = {}
         assert status.lower() in ["staging", "production"], 'The status must be either "staging" or "production"'
 
-        models_and_errors = {}
         for model_name in model_names:
             test_error = self.train(model_name=model_name)
             models_and_errors[model_name] = test_error
 
         test_errors = models_and_errors.values()
         for model_name in model_names:
             if models_and_errors[model_name] == min(test_errors):
+
                 logger.info(f"The best performing model is {model_name} -> Pushing it to the CometML model registry")
 
                 model = load_local_model(