Merge pull request #35 from Hestia-Homes/model-test

add fit metrics

modules/ml-pipeline/src/pipeline/src/build_model.py

@@ -9,10 +9,13 @@ import pandas as pd
 from typing import Union
 from pathlib import Path
 from core.Logger import logger
+from core.interface.InterfaceMetrics import MLMetrics
 from core.interface.InterfaceModels import MLModel
 from core.interface.InterfaceDataClient import DataClient
 from core.DataClient import dataclient_factory
 from core.MLModels import model_factory
+from core.MLMetrics import metrics_factory
+
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -25,13 +28,18 @@ build_model_params = yaml.safe_load(open(build_model_path))
 feature_process_path = Path(__file__).parent / "configs" / "feature_processor.yaml"
 feature_process_params = yaml.safe_load(open(feature_process_path))
 
+generate_metrics_path = Path(__file__).parent / "configs" / "generate_metrics.yaml"
+generate_metrics_params = yaml.safe_load(open(generate_metrics_path))
+
 
 def build_model(
     dataclient: DataClient,
     model: MLModel,
+    metrics: MLMetrics,
     target: str,
     model_save_location: str,
     model_hyperparameters: dict,
+    fit_metrics_filepath: str,
     train_filepath: Union[str, None] = None,
     test_filepath: Union[str, None] = None,
     train_data: Union[pd.DataFrame, None] = None,
@@ -60,12 +68,35 @@ def build_model(
         data=train_data, target=target, model_hyperparameters=model_hyperparameters
     )
 
+    logger.info("------------------------------")
+    logger.info("--- Generating predictions ---")
+    logger.info("------------------------------")
+
+    prediction_data = train_data.drop(columns=target)
+
+    predictions = model.predict(data=prediction_data)
+
+    logger.info("------------------------------")
+    logger.info("--- Generating fit metrics ---")
+    logger.info("------------------------------")
+
+    metrics_output = metrics.generate_metrics(
+        target=train_data[target],
+        predictions=pd.Series(predictions),
+    )
+
     logger.info("--------------------")
     logger.info("--- Saving model ---")
     logger.info("--------------------")
 
     model.save_model(path=Path(model_save_location))
 
+    logger.info("--------------------------")
+    logger.info("--- Saving fit metrics ---")
+    logger.info("--------------------------")
+
+    dataclient.save_data(obj=metrics_output, location=fit_metrics_filepath)
+
 
 if __name__ == "__main__":
 
@@ -87,6 +118,12 @@ if __name__ == "__main__":
     model_type = build_model_params["model_type"]
     model = model_factory(model_type)
 
+    logger.info("-------------------------")
+    logger.info(f"--- Initiate Metrics ---")
+    logger.info("-------------------------")
+
+    metrics = metrics_factory(generate_metrics_params["metrics_type"])
+
     logger.info("--------------------------")
     logger.info(f"--- Build Model Stage ---")
     logger.info("--------------------------")
@@ -94,11 +131,13 @@ if __name__ == "__main__":
     build_model(
         dataclient=dataclient,
         model=model,
+        metrics=metrics,
         target=feature_process_params["feature_processor_config"]["target"],
         model_save_location=build_model_params["model_save_filepath"],
         model_hyperparameters=build_model_params[model_type],
         train_filepath=prepare_data_params["output_train_filepath"],
         test_filepath=prepare_data_params["output_test_filepath"],
+        fit_metrics_filepath=build_model_params["fit_metrics_filepath"],
     )
 
     logger.info("-------------------------------")

modules/ml-pipeline/src/pipeline/src/configs/build_model.yaml

@@ -1,5 +1,6 @@
 model_type: SKLearnLinearRegression
 model_save_filepath: ./data/model/model.joblib
+fit_metrics_filepath: ./metrics/fit_metrics.json
 
 SKLearnLinearRegression: null
 

modules/ml-pipeline/src/pipeline/src/dvc.lock

@@ -5,8 +5,8 @@ stages:
     deps:
     - path: prepare_data.py
       hash: md5
-      md5: 2cfe9e3012280e0cecdb84da12c974d9
-      size: 5009
+      md5: 7531a931a405650dc4e8b5d8c1fd3c66
+      size: 4959
     params:
       configs/prepare_data.yaml:
         output_test_filepath: ./data/prepared_data/test.parquet
@@ -15,20 +15,20 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
+      md5: e36ed6e937196ab64dcfe9b5b97b6e9f.dir
+      size: 13238511
       nfiles: 2
   build_model:
     cmd: python build_model.py
     deps:
     - path: build_model.py
       hash: md5
-      md5: 46bcc34f20c6851cd987640889eefde6
-      size: 3671
+      md5: c07ce0b8fdaf337ddfb7115684932157
+      size: 5048
     - path: data/prepared_data
       hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
+      md5: e36ed6e937196ab64dcfe9b5b97b6e9f.dir
+      size: 13238511
       nfiles: 2
     params:
       configs/build_model.yaml:
@@ -43,31 +43,36 @@ stages:
         SKLearnLinearRegression:
         SKLearnSVMRegression:
           kernel: linear
+        fit_metrics_filepath: ./metrics/fit_metrics.json
         model_save_filepath: ./data/model/model.joblib
         model_type: SKLearnLinearRegression
     outs:
     - path: data/model/
       hash: md5
-      md5: eb2b910dec66481e75bb6058622f6e55.dir
+      md5: 2ace0835c28543512982b69d383b3c49.dir
       size: 1832
       nfiles: 1
+    - path: metrics/fit_metrics.json
+      hash: md5
+      md5: c8c5a40863e2ced7f5f5a844ba203d80
+      size: 180
   generate_predictions:
     cmd: python generate_predictions.py
     deps:
     - path: data/model
       hash: md5
-      md5: eb2b910dec66481e75bb6058622f6e55.dir
+      md5: 2ace0835c28543512982b69d383b3c49.dir
       size: 1832
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
+      md5: e36ed6e937196ab64dcfe9b5b97b6e9f.dir
+      size: 13238511
       nfiles: 2
     - path: generate_predictions.py
       hash: md5
-      md5: d412c8c9b48b59a29f569633280a6e7f
-      size: 4237
+      md5: ab603e9a526a73f2fe17603e6fe6c0a4
+      size: 4261
     params:
       configs/generate_predictions.yaml:
         input_dataclient_type: local
@@ -78,26 +83,26 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
-      size: 643406
+      md5: e87d96ed77d01ab2f24aeab5aaafe344.dir
+      size: 643838
       nfiles: 1
   generate_metrics:
     cmd: python generate_metrics.py
     deps:
     - path: data/predictions
       hash: md5
-      md5: 85ec3fa0cb387a7775eccd23185f7966.dir
-      size: 643406
+      md5: e87d96ed77d01ab2f24aeab5aaafe344.dir
+      size: 643838
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: ea0a2baf3931e692d6344ba609331089.dir
-      size: 13232732
+      md5: e36ed6e937196ab64dcfe9b5b97b6e9f.dir
+      size: 13238511
       nfiles: 2
     - path: generate_metrics.py
       hash: md5
-      md5: 5577a28107458dc1e6bcaaa098388095
-      size: 4144
+      md5: 78a9b9b25d0a7deaf44277f9afad5f98
+      size: 4139
     params:
       configs/generate_metrics.yaml:
         dataclient_type: local
@@ -108,8 +113,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: d79f798a272e6b50597be4d08ae48fa8
-      size: 180
+      md5: f494881710a057f90f82c0bd3a40a41d
+      size: 183
   startup_cleanup:
     cmd: python startup_cleanup.py
     deps:

modules/ml-pipeline/src/pipeline/src/dvc.yaml

@@ -29,6 +29,7 @@ stages:
     - configs/build_model.yaml:
     outs:
     - data/model/
+    - metrics/fit_metrics.json
     always_changed: true
   generate_predictions:
     cmd: python generate_predictions.py
@@ -54,3 +55,4 @@ stages:
     always_changed: true
 metrics:
   - metrics/metrics.json
+  - metrics/fit_metrics.json

modules/ml-pipeline/src/pipeline/src/metrics/.gitignore

@@ -1 +1,2 @@
+/fit_metrics.json
 /metrics.json