Update metric

sdmetrics/timeseries/inter_row.py

@@ -0,0 +1,78 @@
+"""InterRowMSAS module."""
+
+import numpy as np
+import pandas as pd
+
+from sdmetrics.goal import Goal
+from sdmetrics.single_column.statistical.kscomplement import KSComplement
+
+
+class InterRowMSAS:
+    """Inter-Row 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 = 'Inter-Row Multi-Sequence Aggregate Similarity'
+    goal = Goal.MAXIMIZE
+    min_value = 0.0
+    max_value = 1.0
+
+    @staticmethod
+    def compute(real_data, synthetic_data, n_rows_diff=1, apply_log=False):
+        """Compute this metric.
+
+        This metric compares the inter-row differences of sequences in the real data
+        vs. the synthetic data.
+
+        It works as follows:
+            - Calculate the difference between row r and row r+x for each row in the real data
+            - Take the average over each sequence to form a distribution D_r
+            - 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.
+            n_rows_diff (int):
+                An integer representing the number of rows to consider when taking the difference.
+            apply_log (bool):
+                Whether to apply a natural log before taking the difference.
+
+        Returns:
+            float:
+                The similarity score between the real and synthetic data distributions.
+        """
+        real_keys, real_values = real_data
+        synthetic_keys, synthetic_values = synthetic_data
+
+        if apply_log:
+            real_values = np.log(real_values)
+            synthetic_values = np.log(synthetic_values)
+
+        def calculate_differences(keys, values):
+            differences = []
+            for key in keys.unique():
+                group_values = values[keys == key].to_numpy()
+                if len(group_values) > n_rows_diff:
+                    diff = group_values[n_rows_diff:] - group_values[:-n_rows_diff]
+                    differences.append(np.mean(diff))
+            return pd.Series(differences)
+
+        real_diff = calculate_differences(real_keys, real_values)
+        synthetic_diff = calculate_differences(synthetic_keys, synthetic_values)
+
+        return KSComplement.compute(real_diff, synthetic_diff)

tests/unit/timeseries/test_inter_row.py

@@ -0,0 +1,74 @@
+import numpy as np
+import pandas as pd
+
+from sdmetrics.timeseries.inter_row import InterRowMSAS
+
+
+class TestInterRowMSAS:
+    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(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        synthetic_values = pd.Series([1, 2, 3, 4, 5, 6])
+
+        # Run
+        score = InterRowMSAS.compute(
+            real_data=(real_keys, real_values), synthetic_data=(synthetic_keys, synthetic_values)
+        )
+
+        # Assert
+        assert score == 1
+
+    def test_compute_different_sequences(self):
+        """Test it for distinct distributions."""
+        # 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(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        synthetic_values = pd.Series([1, 3, 5, 2, 4, 6])
+
+        # Run
+        score = InterRowMSAS.compute(
+            real_data=(real_keys, real_values), synthetic_data=(synthetic_keys, synthetic_values)
+        )
+
+        # Assert
+        assert score == 0
+
+    def test_compute_with_log(self):
+        """Test it with logarithmic transformation."""
+        # Setup
+        real_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        real_values = pd.Series([1, 2, 4, 8, 16, 32])
+        synthetic_keys = pd.Series(['id1', 'id1', 'id1', 'id2', 'id2', 'id2'])
+        synthetic_values = pd.Series([1, 2, 4, 8, 16, 32])
+
+        # Run
+        score = InterRowMSAS.compute(
+            real_data=(real_keys, real_values),
+            synthetic_data=(synthetic_keys, synthetic_values),
+            apply_log=True,
+        )
+
+        # Assert
+        assert score == 1
+
+    def test_compute_different_n_rows_diff(self):
+        """Test it with different n_rows_diff."""
+        # Setup
+        real_keys = pd.Series(['id1'] * 10 + ['id2'] * 10)
+        real_values = pd.Series(list(range(10)) + list(range(10)))
+        synthetic_keys = pd.Series(['id1'] * 10 + ['id2'] * 10)
+        synthetic_values = pd.Series(list(range(10)) + list(range(10)))
+
+        # Run
+        score = InterRowMSAS.compute(
+            real_data=(real_keys, real_values),
+            synthetic_data=(synthetic_keys, synthetic_values),
+            n_rows_diff=3,
+        )
+
+        # Assert
+        assert score == 1