Add StatisticMSAS

sdmetrics/timeseries/__init__.py

@@ -5,7 +5,7 @@
 from sdmetrics.timeseries.detection import LSTMDetection, TimeSeriesDetectionMetric
 from sdmetrics.timeseries.efficacy import TimeSeriesEfficacyMetric
 from sdmetrics.timeseries.efficacy.classification import LSTMClassifierEfficacy
-
+from sdmetrics.timeseries.statistic_msas import StatisticMSAS
 __all__ = [
     'base',
     'detection',
@@ -16,4 +16,5 @@
     'LSTMDetection',
     'TimeSeriesEfficacyMetric',
     'LSTMClassifierEfficacy',
+    'StatisticMSAS',
 ]

sdmetrics/timeseries/statistic_msas.py

@@ -0,0 +1,91 @@
+"""StatisticMSAS module."""
+
+import numpy as np
+import pandas as pd
+
+from sdmetrics.goal import Goal
+from sdmetrics.single_column.statistical.kscomplement import KSComplement
+
+
+class StatisticMSAS:
+    """Statistic Multi-Sequence Aggregate Similarity (MSAS) metric.
+
+    Attributes:
+        name (str):
+            Name to use when reports about this metric are printed.
+        goal (sdmetrics.goal.Goal):
+            The goal of this metric.
+        min_value (Union[float, tuple[float]]):
+            Minimum value or values that this metric can take.
+        max_value (Union[float, tuple[float]]):
+            Maximum value or values that this metric can take.
+    """
+
+    name = 'Statistic Multi-Sequence Aggregate Similarity'
+    goal = Goal.MAXIMIZE
+    min_value = 0.0
+    max_value = 1.0
+
+    @staticmethod
+    def compute(real_data, synthetic_data, statistic='mean'):
+        """Compute this metric.
+
+        This metric compares the distribution of a given statistic across sequences
+        in the real data vs. the synthetic data.
+
+        It works as follows:
+            - Calculate the specified statistic for each sequence in the real data
+            - Form a distribution D_r from these statistics
+            - Do the same for the synthetic data to form a new distribution D_s
+            - Apply the KSComplement metric to compare the similarities of (D_r, D_s)
+            - Return this score
+
+        Args:
+            real_data (tuple[pd.Series, pd.Series]):
+                A tuple of 2 pandas.Series objects. The first represents the sequence key
+                of the real data and the second represents a continuous column of data.
+            synthetic_data (tuple[pd.Series, pd.Series]):
+                A tuple of 2 pandas.Series objects. The first represents the sequence key
+                of the synthetic data and the second represents a continuous column of data.
+            statistic (str):
+                A string representing the statistic function to use when computing MSAS.
+
+                Available options are:
+                    - 'mean': The arithmetic mean of the sequence
+                    - 'median': The median value of the sequence
+                    - 'std': The standard deviation of the sequence
+                    - 'min': The minimum value in the sequence
+                    - 'max': The maximum value in the sequence
+
+        Returns:
+            float:
+                The similarity score between the real and synthetic data distributions.
+        """
+        statistic_functions = {
+            'mean': np.mean,
+            'median': np.median,
+            'std': np.std,
+            'min': np.min,
+            'max': np.max,
+        }
+        if statistic not in statistic_functions:
+            raise ValueError(
+                f'Invalid statistic: {statistic}.'
+                f" Choose from [{', '.join(statistic_functions.keys())}]."
+            )
+
+        real_keys, real_values = real_data
+        synthetic_keys, synthetic_values = synthetic_data
+        stat_func = statistic_functions[statistic]
+
+        def calculate_statistics(keys, values):
+            statistics = []
+            for key in keys.unique():
+                group_values = values[keys == key].to_numpy()
+                statistics.append(stat_func(group_values))
+            return pd.Series(statistics)
+
+        real_stats = calculate_statistics(real_keys, real_values)
+        synthetic_stats = calculate_statistics(synthetic_keys, synthetic_values)
+
+        return KSComplement.compute(real_stats, synthetic_stats)

tests/unit/timeseries/test_statistic_msas.py

@@ -0,0 +1,96 @@
+import re
+import pandas as pd
+import pytest
+
+from sdmetrics.timeseries import StatisticMSAS
+
+
+class TestStatisticMSAS:
+    def test_compute_identical_sequences(self):
+        """Test it returns 1 when real and synthetic data are identical."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        real_values = pd.Series([1, 2, 3, 4, 5, 6])
+        synthetic_keys = pd.Series(['id3', 'id3', 'id3', 'id4', 'id4', 'id4'])
+        synthetic_values = pd.Series([1, 2, 3, 4, 5, 6])
+
+        # Run and Assert
+        for statistic in ['mean', 'median', 'std', 'min', 'max']:
+            score = StatisticMSAS.compute(
+                real_data=(real_keys, real_values),
+                synthetic_data=(synthetic_keys, synthetic_values),
+                statistic=statistic,
+            )
+            assert score == 1
+
+    def test_compute_different_sequences(self):
+        """Test it for distinct distributions."""
+        # Setup
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        real_values = pd.Series([1, 2, 3, 4, 5, 6])
+        synthetic_keys = pd.Series(['id3', 'id3', 'id3', 'id4', 'id4', 'id4'])
+        synthetic_values = pd.Series([10, 20, 30, 40, 50, 60])
+
+        # Run and Assert
+        for statistic in ['mean', 'median', 'std', 'min', 'max']:
+            score = StatisticMSAS.compute(
+                real_data=(real_keys, real_values),
+                synthetic_data=(synthetic_keys, synthetic_values),
+                statistic=statistic,
+            )
+            assert score == 0
+
+    def test_compute_with_single_sequence(self):
+        """Test it with a single sequence."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1'])
+        real_values = pd.Series([1, 2, 3])
+        synthetic_keys = pd.Series(['id2', 'id2', 'id2'])
+        synthetic_values = pd.Series([1, 2, 3])
+
+        # Run
+        score = StatisticMSAS.compute(
+            real_data=(real_keys, real_values),
+            synthetic_data=(synthetic_keys, synthetic_values),
+            statistic='mean',
+        )
+
+        # Assert
+        assert score == 1
+
+    def test_compute_with_different_sequence_lengths(self):
+        """Test it with different sequence lengths."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2'])
+        real_values = pd.Series([1, 2, 3, 4, 5])
+        synthetic_keys = pd.Series(['id2', 'id2', 'id3', 'id4', 'id5'])
+        synthetic_values = pd.Series([1, 2, 3, 4, 5])
+
+        # Run
+        score = StatisticMSAS.compute(
+            real_data=(real_keys, real_values),
+            synthetic_data=(synthetic_keys, synthetic_values),
+            statistic='mean',
+        )
+
+        # Assert
+        assert score == .75
+
+    def test_compute_with_invalid_statistic(self):
+        """Test it raises ValueError for invalid statistic."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1'])
+        real_values = pd.Series([1, 2, 3])
+        synthetic_keys = pd.Series(['id2', 'id2', 'id2'])
+        synthetic_values = pd.Series([1, 2, 3])
+
+        # Run and Assert
+        err_msg = re.escape(
+            "Invalid statistic: invalid. Choose from [mean, median, std, min, max].")
+        with pytest.raises(ValueError, match=err_msg):
+            StatisticMSAS.compute(
+                real_data=(real_keys, real_values),
+                synthetic_data=(synthetic_keys, synthetic_values),
+                statistic='invalid',
+            )