From 6a63d266f82e6b35ef9c79ba11ad2c10f34f34ea Mon Sep 17 00:00:00 2001 From: Stefano Zamboni <39366866+SteZamboni@users.noreply.github.com> Date: Mon, 8 Jul 2024 14:54:23 +0200 Subject: [PATCH] feat: added model quality metrics for current dataset for regression (#87) * feat: added model quality metrics for current dataset for regression * feat: fixed reference metrics with int values for prediction-target * feat: changed var to variance in regression model quality * feat: removed cast on ground-truth since it's there already --- spark/jobs/current_job.py | 2 + .../model_quality_regression_calculator.py | 20 +- spark/jobs/models/regression_model_quality.py | 4 +- spark/jobs/utils/current_regression.py | 87 + spark/tests/regression_current_test.py | 76 +- spark/tests/regression_reference_test.py | 77 +- .../regression_reference_test_FE.csv | 3343 +++++++++++++++++ 7 files changed, 3599 insertions(+), 10 deletions(-) create mode 100644 spark/tests/resources/reference/regression/regression_reference_test_FE.csv diff --git a/spark/jobs/current_job.py b/spark/jobs/current_job.py index 8ce668a1..95eb74c8 100644 --- a/spark/jobs/current_job.py +++ b/spark/jobs/current_job.py @@ -105,12 +105,14 @@ def main( ) statistics = calculate_statistics_current(current_dataset) data_quality = metrics_service.calculate_data_quality(is_current=True) + model_quality = metrics_service.calculate_model_quality() complete_record["STATISTICS"] = statistics.model_dump_json( serialize_as_any=True ) complete_record["DATA_QUALITY"] = data_quality.model_dump_json( serialize_as_any=True ) + complete_record["MODEL_QUALITY"] = model_quality schema = StructType( [ diff --git a/spark/jobs/metrics/model_quality_regression_calculator.py b/spark/jobs/metrics/model_quality_regression_calculator.py index a49f6ac1..6d1672d3 100644 --- a/spark/jobs/metrics/model_quality_regression_calculator.py +++ b/spark/jobs/metrics/model_quality_regression_calculator.py @@ -9,13 +9,17 @@ class ModelQualityRegressionCalculator: @staticmethod - def __eval_model_quality_metric( + def eval_model_quality_metric( model: ModelOut, dataframe: DataFrame, dataframe_count: int, metric_name: RegressionMetricType, ) -> float: try: + dataframe = dataframe.withColumn( + model.outputs.prediction.name, + F.col(model.outputs.prediction.name).cast("float"), + ) match metric_name: case RegressionMetricType.ADJ_R2: # Source: https://medium.com/analytics-vidhya/adjusted-r-squared-formula-explanation-1ce033e25699 @@ -25,7 +29,7 @@ def __eval_model_quality_metric( p: float = len(model.features) n: float = dataframe_count r2: float = ( - ModelQualityRegressionCalculator.__eval_model_quality_metric( + ModelQualityRegressionCalculator.eval_model_quality_metric( model, dataframe, dataframe_count, RegressionMetricType.R2 ) ) @@ -44,19 +48,25 @@ def __eval_model_quality_metric( ), ) return _dataframe.agg({"mape": "avg"}).collect()[0][0] * 100 + case RegressionMetricType.VAR: + return RegressionEvaluator( + metricName="var", + labelCol=model.target.name, + predictionCol=model.outputs.prediction.name, + ).evaluate(dataframe) case ( RegressionMetricType.MAE | RegressionMetricType.MSE | RegressionMetricType.RMSE | RegressionMetricType.R2 - | RegressionMetricType.VAR ): return RegressionEvaluator( metricName=metric_name.value, labelCol=model.target.name, predictionCol=model.outputs.prediction.name, ).evaluate(dataframe) - except Exception: + except Exception as e: + print(e) return float("nan") @staticmethod @@ -65,7 +75,7 @@ def __calc_mq_metrics( ) -> ModelQualityRegression: return ModelQualityRegression( **{ - metric_name.value: ModelQualityRegressionCalculator.__eval_model_quality_metric( + metric_name.value: ModelQualityRegressionCalculator.eval_model_quality_metric( model, dataframe, dataframe_count, diff --git a/spark/jobs/models/regression_model_quality.py b/spark/jobs/models/regression_model_quality.py index cea625a0..26b5d804 100644 --- a/spark/jobs/models/regression_model_quality.py +++ b/spark/jobs/models/regression_model_quality.py @@ -10,7 +10,7 @@ class RegressionMetricType(str, Enum): RMSE = "rmse" R2 = "r2" ADJ_R2 = "adj_r2" - VAR = "var" + VAR = "variance" class ModelQualityRegression(BaseModel): @@ -20,4 +20,4 @@ class ModelQualityRegression(BaseModel): rmse: float r2: float adj_r2: float - var: float + variance: float diff --git a/spark/jobs/utils/current_regression.py b/spark/jobs/utils/current_regression.py index 8ced6f23..821116b8 100644 --- a/spark/jobs/utils/current_regression.py +++ b/spark/jobs/utils/current_regression.py @@ -1,6 +1,7 @@ from typing import List, Optional from pyspark.sql import SparkSession +import pyspark.sql.functions as F from metrics.data_quality_calculator import DataQualityCalculator from models.current_dataset import CurrentDataset @@ -11,6 +12,10 @@ RegressionDataQuality, ) from models.reference_dataset import ReferenceDataset +from models.regression_model_quality import ModelQualityRegression, RegressionMetricType +from metrics.model_quality_regression_calculator import ModelQualityRegressionCalculator +from .misc import create_time_format +from .models import Granularity class CurrentMetricsRegressionService: @@ -24,6 +29,88 @@ def __init__( self.current = current self.reference = reference + def calculate_model_quality(self) -> ModelQualityRegression: + metrics = dict() + metrics["global_metrics"] = ModelQualityRegressionCalculator.numerical_metrics( + model=self.current.model, + dataframe=self.current.current, + dataframe_count=self.current.current_count, + ).model_dump(serialize_as_any=True) + metrics["grouped_metrics"] = ( + self.calculate_regression_model_quality_group_by_timestamp() + ) + return metrics + + def calculate_regression_model_quality_group_by_timestamp(self): + if self.current.model.granularity == Granularity.WEEK: + dataset_with_group = self.current.current.select( + [ + self.current.model.outputs.prediction.name, + self.current.model.target.name, + F.date_format( + F.to_timestamp( + F.date_sub( + F.next_day( + F.date_format( + self.current.model.timestamp.name, + create_time_format( + self.current.model.granularity + ), + ), + "sunday", + ), + 7, + ) + ), + "yyyy-MM-dd HH:mm:ss", + ).alias("time_group"), + ] + ) + else: + dataset_with_group = self.current.current.select( + [ + self.current.model.outputs.prediction.name, + self.current.model.target.name, + F.date_format( + F.to_timestamp( + F.date_format( + self.current.model.timestamp.name, + create_time_format(self.current.model.granularity), + ) + ), + "yyyy-MM-dd HH:mm:ss", + ).alias("time_group"), + ] + ) + + list_of_time_group = ( + dataset_with_group.select("time_group") + .distinct() + .orderBy(F.col("time_group").asc()) + .rdd.flatMap(lambda x: x) + .collect() + ) + array_of_groups = [ + dataset_with_group.where(F.col("time_group") == x) + for x in list_of_time_group + ] + + return { + metric_name.value: [ + { + "timestamp": group, + "value": ModelQualityRegressionCalculator.eval_model_quality_metric( + self.current.model, + group_dataset, + group_dataset.count(), + metric_name, + ), + } + for group, group_dataset in zip(list_of_time_group, array_of_groups) + ] + for metric_name in RegressionMetricType + } + def calculate_data_quality_numerical(self) -> List[NumericalFeatureMetrics]: return DataQualityCalculator.calculate_combined_data_quality_numerical( model=self.current.model, diff --git a/spark/tests/regression_current_test.py b/spark/tests/regression_current_test.py index e5e50115..e6ed52e5 100644 --- a/spark/tests/regression_current_test.py +++ b/spark/tests/regression_current_test.py @@ -41,7 +41,7 @@ def model(): ) target = ColumnDefinition(name="ground_truth", type=SupportedTypes.int) timestamp = ColumnDefinition(name="dteday", type=SupportedTypes.datetime) - granularity = Granularity.HOUR + granularity = Granularity.MONTH features = [ ColumnDefinition(name="season", type=SupportedTypes.int), ColumnDefinition(name="yr", type=SupportedTypes.int), @@ -504,3 +504,77 @@ def expected_data_quality(): }, ], } + + +def test_model_quality(spark_fixture, current_dataset, reference_dataset): + metrics_service = CurrentMetricsRegressionService( + spark_session=spark_fixture, + current=current_dataset, + reference=reference_dataset, + ) + + model_quality = metrics_service.calculate_model_quality() + + assert not deepdiff.DeepDiff( + model_quality, + { + "global_metrics": { + "mae": 71.82559791564941, + "mape": 64.05699022707124, + "mse": 17820.506660010054, + "rmse": 133.49347047706138, + "r2": 0.8210737408739541, + "adj_r2": 0.7987079584831984, + "variance": 118288.02759401732, + }, + "grouped_metrics": { + "mae": [ + {"timestamp": "2011-01-01 00:00:00", "value": 35.67896665375808}, + {"timestamp": "2011-02-01 00:00:00", "value": 89.13965238373855}, + {"timestamp": "2011-03-01 00:00:00", "value": 91.54030847549438}, + {"timestamp": "2011-04-01 00:00:00", "value": 63.352996826171875}, + ], + "mape": [ + {"timestamp": "2011-01-01 00:00:00", "value": 106.34668638669385}, + {"timestamp": "2011-02-01 00:00:00", "value": 50.266650033642435}, + {"timestamp": "2011-03-01 00:00:00", "value": 53.63275529139244}, + {"timestamp": "2011-04-01 00:00:00", "value": 14.766409719281478}, + ], + "mse": [ + {"timestamp": "2011-01-01 00:00:00", "value": 2848.1117152678507}, + {"timestamp": "2011-02-01 00:00:00", "value": 21631.812814960613}, + {"timestamp": "2011-03-01 00:00:00", "value": 31460.34954782362}, + {"timestamp": "2011-04-01 00:00:00", "value": 6540.166909402423}, + ], + "rmse": [ + {"timestamp": "2011-01-01 00:00:00", "value": 53.36770292290882}, + {"timestamp": "2011-02-01 00:00:00", "value": 147.07757414018158}, + {"timestamp": "2011-03-01 00:00:00", "value": 177.37065582509305}, + {"timestamp": "2011-04-01 00:00:00", "value": 80.87129842782556}, + ], + "r2": [ + {"timestamp": "2011-01-01 00:00:00", "value": 0.17834457710460982}, + {"timestamp": "2011-02-01 00:00:00", "value": 0.3895389519246505}, + {"timestamp": "2011-03-01 00:00:00", "value": 0.7043715304337479}, + {"timestamp": "2011-04-01 00:00:00", "value": 0.9678020649997567}, + ], + "adj_r2": [ + {"timestamp": "2011-01-01 00:00:00", "value": -0.3533148141806426}, + { + "timestamp": "2011-02-01 00:00:00", + "value": -0.005465255653516854, + }, + {"timestamp": "2011-03-01 00:00:00", "value": 0.5417758721723092}, + {"timestamp": "2011-04-01 00:00:00", "value": 1.1448907075010948}, + ], + "variance": [ + {"timestamp": "2011-01-01 00:00:00", "value": 4720.867246089001}, + {"timestamp": "2011-02-01 00:00:00", "value": 70942.48575413873}, + {"timestamp": "2011-03-01 00:00:00", "value": 150522.0080596708}, + {"timestamp": "2011-04-01 00:00:00", "value": 163422.9263027128}, + ], + }, + }, + ignore_order=True, + ignore_type_subclasses=True, + ) diff --git a/spark/tests/regression_reference_test.py b/spark/tests/regression_reference_test.py index 086e23e2..c0f44bea 100644 --- a/spark/tests/regression_reference_test.py +++ b/spark/tests/regression_reference_test.py @@ -33,6 +33,14 @@ def reference_bike_nulls(spark_fixture, test_data_dir): ) +@pytest.fixture() +def reference_test_fe(spark_fixture, test_data_dir): + yield spark_fixture.read.csv( + f"{test_data_dir}/reference/regression/regression_reference_test_FE.csv", + header=True, + ) + + @pytest.fixture() def expected_data_quality_json(): yield { @@ -484,6 +492,50 @@ def reference_dataset_nulls(spark_fixture, reference_bike_nulls): ) +@pytest.fixture() +def reference_dataset_test_fe(spark_fixture, reference_test_fe): + output = OutputType( + prediction=ColumnDefinition(name="prediction", type=SupportedTypes.int), + prediction_proba=None, + output=[ColumnDefinition(name="prediction", type=SupportedTypes.int)], + ) + target = ColumnDefinition(name="ground_truth", type=SupportedTypes.int) + timestamp = ColumnDefinition(name="timestamp", type=SupportedTypes.datetime) + granularity = Granularity.MONTH + features = [ + ColumnDefinition(name="Sex", type=SupportedTypes.string), + ColumnDefinition(name="Length", type=SupportedTypes.float), + ColumnDefinition(name="Diameter", type=SupportedTypes.float), + ColumnDefinition(name="Height", type=SupportedTypes.float), + ColumnDefinition(name="Whole_weight", type=SupportedTypes.float), + ColumnDefinition(name="Shucked_weight", type=SupportedTypes.float), + ColumnDefinition(name="Viscera_weight", type=SupportedTypes.float), + ColumnDefinition(name="Shell_weight", type=SupportedTypes.float), + ColumnDefinition(name="pred_id", type=SupportedTypes.string), + ] + model = ModelOut( + uuid=uuid.uuid4(), + name="regression model", + description="description", + model_type=ModelType.REGRESSION, + data_type=DataType.TABULAR, + timestamp=timestamp, + granularity=granularity, + outputs=output, + target=target, + features=features, + frameworks="framework", + algorithm="algorithm", + created_at=str(datetime.datetime.now()), + updated_at=str(datetime.datetime.now()), + ) + + yield ReferenceDataset( + raw_dataframe=reference_test_fe, + model=model, + ) + + def test_model_quality_metrics(reference_dataset): assert reference_dataset.reference_count == 731 @@ -498,7 +550,7 @@ def test_model_quality_metrics(reference_dataset): "rmse": 202.2319475218869, "r2": 0.9131323648676931, "adj_r2": 0.9118033746222753, - "var": 393448.3132709007, + "variance": 393448.3132709007, } ) @@ -565,7 +617,28 @@ def test_model_quality_metrics_nulls(reference_dataset_nulls): "rmse": 202.40446933755922, "r2": 0.9130200184348737, "adj_r2": 0.9116855975182538, - "var": 393588.541292358, + "variance": 393588.541292358, + } + ) + + assert model_quality_metrics.model_dump() == expected + + +def test_model_quality_metrics_test_int(reference_dataset_test_fe): + regression_service = ReferenceMetricsRegressionService( + reference=reference_dataset_test_fe + ) + model_quality_metrics = regression_service.calculate_model_quality() + + expected = my_approx( + { + "mae": 1.9473369239976062, + "mape": 32.80513741749641, + "mse": 27.478755236385403, + "rmse": 5.242018240752831, + "r2": 0.5846200997960207, + "adj_r2": 0.5834981252756619, + "variance": 66.40816713170544, } ) diff --git a/spark/tests/resources/reference/regression/regression_reference_test_FE.csv b/spark/tests/resources/reference/regression/regression_reference_test_FE.csv new file mode 100644 index 00000000..9f0fe4a1 --- /dev/null +++ b/spark/tests/resources/reference/regression/regression_reference_test_FE.csv @@ -0,0 +1,3343 @@ +Sex,Length,Diameter,Height,Whole_weight,Shucked_weight,Viscera_weight,Shell_weight,ground_truth,prediction,pred_id,timestamp +M,0.66,0.525,0.2,1.489,0.6065,0.3795,0.421,11,11,e73e8874-9f00-4d78-ab43-9b6840172e38,2024-01-10 08:00:00 +F,0.64,0.505,0.165,1.2235,0.5215,0.2695,0.36,2,2,721e6420-6086-4444-b1be-222404bd1194,2024-01-10 08:00:02 +M,0.65,0.515,0.18,1.463,0.658,0.3135,0.4115,2,2,4b6a472e-a124-40fe-832d-71422eb47671,2024-01-10 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