feat(spark): model quality and drift for current - multiclass classif…

…ication (#74)

* feat: add multiclass model quality calc for current

* feat: add model quality in job

* feat: add drift and refactoring

* fix: remove strange timezone from files

spark/jobs/current_job.py

@@ -9,7 +9,6 @@
 from metrics.statistics import calculate_statistics_current
 from models.current_dataset import CurrentDataset
 from models.reference_dataset import ReferenceDataset
-from utils.reference_regression import ReferenceMetricsRegressionService
 from utils.current_binary import CurrentMetricsService
 from utils.current_multiclass import CurrentMetricsMulticlassService
 from utils.models import JobStatus, ModelOut, ModelType
@@ -57,9 +56,8 @@ def main(
         case ModelType.BINARY:
             metrics_service = CurrentMetricsService(
                 spark_session=spark_session,
-                current=current_dataset.current,
-                reference=reference_dataset.reference,
-                model=model,
+                current=current_dataset,
+                reference=reference_dataset,
             )
             statistics = calculate_statistics_current(current_dataset)
             data_quality = metrics_service.calculate_data_quality()
@@ -80,25 +78,25 @@ def main(
         case ModelType.MULTI_CLASS:
             metrics_service = CurrentMetricsMulticlassService(
                 spark_session=spark_session,
-                current=current_dataset.current,
-                reference=reference_dataset.reference,
-                model=model,
+                current=current_dataset,
+                reference=reference_dataset,
             )
             statistics = calculate_statistics_current(current_dataset)
             data_quality = metrics_service.calculate_data_quality()
+            model_quality = metrics_service.calculate_model_quality()
+            drift = metrics_service.calculate_drift()
             complete_record["STATISTICS"] = statistics.model_dump_json(
                 serialize_as_any=True
             )
             complete_record["DATA_QUALITY"] = data_quality.model_dump_json(
                 serialize_as_any=True
             )
-        case ModelType.REGRESSION:
-            metrics_service = ReferenceMetricsRegressionService(
-                reference=reference_dataset
+            complete_record["MODEL_QUALITY"] = orjson.dumps(model_quality).decode(
+                "utf-8"
             )
+            complete_record["DRIFT"] = orjson.dumps(drift).decode("utf-8")
+        case ModelType.REGRESSION:
             statistics = calculate_statistics_current(current_dataset)
-            data_quality = metrics_service.calculate_data_quality()
-
             complete_record["STATISTICS"] = statistics.model_dump_json(
                 serialize_as_any=True
             )

spark/jobs/metrics/drift_calculator.py

@@ -0,0 +1,79 @@
+from pyspark.sql import SparkSession
+
+from metrics.chi2 import Chi2Test
+from metrics.ks import KolmogorovSmirnovTest
+from models.current_dataset import CurrentDataset
+from models.reference_dataset import ReferenceDataset
+
+
+class DriftCalculator:
+    @staticmethod
+    def calculate_drift(
+        spark_session: SparkSession,
+        reference_dataset: ReferenceDataset,
+        current_dataset: CurrentDataset,
+    ):
+        drift_result = dict()
+        drift_result["feature_metrics"] = []
+
+        categorical_features = [
+            categorical.name
+            for categorical in reference_dataset.model.get_categorical_features()
+        ]
+        chi2 = Chi2Test(
+            spark_session=spark_session,
+            reference_data=reference_dataset.reference,
+            current_data=current_dataset.current,
+        )
+
+        for column in categorical_features:
+            feature_dict_to_append = {
+                "feature_name": column,
+                "drift_calc": {
+                    "type": "CHI2",
+                },
+            }
+            if (
+                reference_dataset.reference_count > 5
+                and current_dataset.current_count > 5
+            ):
+                result_tmp = chi2.test(column, column)
+                feature_dict_to_append["drift_calc"]["value"] = float(
+                    result_tmp["pValue"]
+                )
+                feature_dict_to_append["drift_calc"]["has_drift"] = bool(
+                    result_tmp["pValue"] <= 0.05
+                )
+            else:
+                feature_dict_to_append["drift_calc"]["value"] = None
+                feature_dict_to_append["drift_calc"]["has_drift"] = False
+            drift_result["feature_metrics"].append(feature_dict_to_append)
+
+        numerical_features = [
+            numerical.name
+            for numerical in reference_dataset.model.get_numerical_features()
+        ]
+        ks = KolmogorovSmirnovTest(
+            reference_data=reference_dataset.reference,
+            current_data=current_dataset.current,
+            alpha=0.05,
+            phi=0.004,
+        )
+
+        for column in numerical_features:
+            feature_dict_to_append = {
+                "feature_name": column,
+                "drift_calc": {
+                    "type": "KS",
+                },
+            }
+            result_tmp = ks.test(column, column)
+            feature_dict_to_append["drift_calc"]["value"] = float(
+                result_tmp["ks_statistic"]
+            )
+            feature_dict_to_append["drift_calc"]["has_drift"] = bool(
+                result_tmp["ks_statistic"] > result_tmp["critical_value"]
+            )
+            drift_result["feature_metrics"].append(feature_dict_to_append)
+
+        return drift_result

spark/jobs/models/current_dataset.py

@@ -1,8 +1,10 @@
 from typing import List
 
+from pyspark.ml.feature import StringIndexer
 from pyspark.sql import DataFrame
 from pyspark.sql.types import DoubleType, StructField, StructType
 
+from models.reference_dataset import ReferenceDataset
 from utils.models import ModelOut, ModelType, ColumnDefinition
 from utils.spark import apply_schema_to_dataframe
 
@@ -95,3 +97,47 @@ def get_all_variables(self) -> List[ColumnDefinition]:
             + [self.model.timestamp]
             + self.model.outputs.output
         )
+
+    def get_string_indexed_dataframe(self, reference: ReferenceDataset):
+        """
+        Source: https://stackoverflow.com/questions/65911146/how-to-transform-multiple-categorical-columns-to-integers-maintaining-shared-val
+        Current dataset will be indexed with columns from both reference and current in order to have complete data
+        """
+        predictions_df_current = self.current.select(
+            self.model.outputs.prediction.name
+        ).withColumnRenamed(self.model.outputs.prediction.name, "classes")
+        target_df_current = self.current.select(
+            self.model.target.name
+        ).withColumnRenamed(self.model.target.name, "classes")
+        predictions_df_reference = reference.reference.select(
+            self.model.outputs.prediction.name
+        ).withColumnRenamed(self.model.outputs.prediction.name, "classes")
+        target_df_reference = reference.reference.select(
+            self.model.target.name
+        ).withColumnRenamed(self.model.target.name, "classes")
+        prediction_target_df = (
+            predictions_df_current.union(target_df_current)
+            .union(predictions_df_reference)
+            .union(target_df_reference)
+        )
+        indexer = StringIndexer(
+            inputCol="classes",
+            outputCol="classes_index",
+            stringOrderType="alphabetAsc",
+            handleInvalid="skip",
+        )
+        indexer_model = indexer.fit(prediction_target_df)
+        indexer_prediction = indexer_model.setInputCol(
+            self.model.outputs.prediction.name
+        ).setOutputCol(f"{self.model.outputs.prediction.name}-idx")
+        indexed_prediction_df = indexer_prediction.transform(self.current)
+        indexer_target = indexer_model.setInputCol(self.model.target.name).setOutputCol(
+            f"{self.model.target.name}-idx"
+        )
+        indexed_target_df = indexer_target.transform(indexed_prediction_df)
+
+        index_label_map = {
+            str(float(index)): str(label)
+            for index, label in enumerate(indexer_model.labelsArray[0])
+        }
+        return index_label_map, indexed_target_df

spark/jobs/reference_job.py

@@ -53,9 +53,7 @@ def main(
 
     match model.model_type:
         case ModelType.BINARY:
-            metrics_service = ReferenceMetricsService(
-                reference_dataset.reference, model=model
-            )
+            metrics_service = ReferenceMetricsService(reference=reference_dataset)
             model_quality = metrics_service.calculate_model_quality()
             statistics = calculate_statistics_reference(reference_dataset)
             data_quality = metrics_service.calculate_data_quality()