Update docstring

Author: fealho

sdmetrics/single_column/statistical/sequence_length_similarity.py

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+"""SequenceLengthSimilarity module."""
+
+import pandas as pd
+
+from sdmetrics.goal import Goal
+from sdmetrics.single_column.statistical.kscomplement import KSComplement
+
+
+class SequenceLengthSimilarity:
+    """Sequence Length Similarity 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 = 'Sequence Length Similarity'
+    goal = Goal.MAXIMIZE
+    min_value = 0.0
+    max_value = 1.0
+
+    @staticmethod
+    def compute(real_data: pd.Series, synthetic_data: pd.Series) -> float:
+        """Compute this metric.
+
+        The length of a sequence is determined by the number of times the same sequence key occurs.
+        For example if id_09231 appeared 150 times in the sequence key, then the sequence is of
+        length 150. This metric compares the lengths of all sequence keys in the
+        real data vs. the synthetic data.
+
+        It works as follows:
+            - Calculate the length of each sequence in the real data
+            - Calculate the length of each sequence in the synthetic data
+            - Apply the KSComplement metric to compare the similarities of the distributions
+            - Return this score
+
+        Args:
+            real_data (pd.Series):
+                The values from the real dataset.
+            synthetic_data (pd.Series):
+                The values from the synthetic dataset.
+
+        Returns:
+            float:
+                The score.
+        """
+        return KSComplement.compute(real_data.value_counts(), synthetic_data.value_counts())