Cast values to datetime

sdmetrics/timeseries/base.py

@@ -2,6 +2,8 @@
 
 from operator import attrgetter
 
+import pandas as pd
+
 from sdmetrics.base import BaseMetric
 from sdmetrics.utils import get_columns_from_metadata
 
@@ -61,6 +63,14 @@ def _validate_inputs(cls, real_data, synthetic_data, metadata=None, sequence_key
                 if field not in real_data.columns:
                     raise ValueError(f'Field {field} not found in data')
 
+            for column, sdtype in metadata['columns'].items():
+                if sdtype['sdtype'] == 'datetime':
+                    try:
+                        real_data[column] = pd.to_datetime(real_data[column])
+                        synthetic_data[column] = pd.to_datetime(synthetic_data[column])
+                    except ValueError:
+                        raise ValueError(f"Column '{column}' is not a valid datetime")
+
         else:
             dtype_kinds = real_data.dtypes.apply(attrgetter('kind'))
             metadata = {'columns': dtype_kinds.apply(cls._DTYPES_TO_TYPES.get).to_dict()}

sdmetrics/timeseries/detection.py

@@ -74,6 +74,7 @@ def compute(cls, real_data, synthetic_data, metadata=None, sequence_key=None):
             Union[float, tuple[float]]:
                 Metric output.
         """
+        real_data, synthetic_data = real_data.copy(), synthetic_data.copy()
         _, sequence_key = cls._validate_inputs(real_data, synthetic_data, metadata, sequence_key)
 
         ht = HyperTransformer()

tests/integration/timeseries/test_timeseries.py

@@ -58,3 +58,38 @@ def test_compute_lstmdetection_multiple_categorical_columns():
     # Assert
     assert not pd.isna(output)
     assert LSTMDetection.min_value <= output <= LSTMDetection.max_value
+
+
+def test_compute_lstmdetection_mismatching_datetime_columns():
+    """Test LSTMDetection metric with mismatching datetime columns.
+
+    Test it when the real data has a date column and the synthetic data has a string column.
+    """
+    # Setup
+    df1 = pd.DataFrame( {
+        's_key': [1, 2, 3, 4, 5],
+        'visits': pd.to_datetime(['1/1/2019', '1/2/2019', '1/3/2019', '1/4/2019', '1/5/2019'])
+    })
+    df1['visits'] = df1['visits'].dt.date
+    df2 = pd.DataFrame({
+        's_key': [1, 2, 3, 4, 5],
+        'visits': ['1/2/2019', '1/2/2019', '1/3/2019', '1/4/2019', '1/5/2019']
+    })
+    metadata = {
+        'columns': {
+            's_key': {'sdtype': 'numerical'},
+            'visits': {'sdtype': 'datetime'}
+        },
+        'sequence_key': 's_key'
+    }
+
+    # Run
+    score = LSTMDetection.compute(
+        real_data=df1,
+        synthetic_data=df2,
+        sequence_key=['s_key'],
+        metadata=metadata
+    )
+
+    # Assert
+    assert score == 0.6666666666666667