Rl/add types inference

synthpop/synthpop.py

@@ -77,13 +77,48 @@ def post_postprocessing(self,syn_df):
 
         return syn_df
 
+    def _infer_dtypes(self, df):
+        """Automatically infer data types from DataFrame.
+
+        Args:
+            df: pandas DataFrame
+
+        Returns:
+            dict: Mapping of column names to inferred types ('int', 'float', 'datetime', 'category', 'bool')
+        """
+        dtypes = {}
+        for column in df.columns:
+            pd_dtype = str(df[column].dtype)
+
+            if pd_dtype.startswith('int'):
+                dtypes[column] = 'int'
+            elif pd_dtype.startswith('float'):
+                dtypes[column] = 'float'
+            elif pd_dtype.startswith('datetime'):
+                dtypes[column] = 'datetime'
+            elif pd_dtype.startswith('bool'):
+                dtypes[column] = 'bool'
+            else:
+                # For object or string dtypes, check if it should be categorical
+                dtypes[column] = 'category'
+
+        return dtypes
+
     def fit(self, df, dtypes=None):
-        # TODO check df and check/EXTRACT dtypes
-        # - all column names of df are unique
-        # - all columns data of df are consistent
-        # - all dtypes of df are correct ('int', 'float', 'datetime', 'category', 'bool'; no object)
-        # - can map dtypes (if given) correctly to df
-        # should create map col: dtype (self.df_dtypes)
+        """Fit the synthetic data generator.
+
+        Args:
+            df: pandas DataFrame to learn from
+            dtypes: Optional dict mapping column names to types. If not provided, types will be inferred.
+        """
+        # Infer dtypes if not provided
+        if dtypes is None:
+            dtypes = self._infer_dtypes(df)
+
+        # Validate DataFrame
+        if not df.columns.is_unique:
+            raise ValueError("DataFrame column names must be unique")
+
         df,dtypes = self.pre_preprocess(df,dtypes,-8)
 
         self.df_columns = df.columns.tolist()

tests/test_synthpop.py

@@ -4,12 +4,12 @@
 from datasets.adult import df, dtypes
 
 def test_synthpop_default_parameters():
-    """Test Synthpop with default parameters using Adult dataset."""
+    """Test Synthpop with default parameters and automatic type inference."""
     # Initialize Synthpop
     spop = Synthpop()
 
-    # Fit the model
-    spop.fit(df, dtypes)
+    # Fit the model with automatic type inference
+    spop.fit(df)
 
     # Generate synthetic data
     synth_df = spop.generate(len(df))
@@ -20,6 +20,11 @@ def test_synthpop_default_parameters():
     # Verify the synthetic dataframe has the same columns as original
     assert all(synth_df.columns == df.columns)
 
+    # Verify inferred dtypes match expected types
+    assert spop.df_dtypes['age'] == 'int'
+    assert spop.df_dtypes['workclass'] == 'category'
+    assert spop.df_dtypes['education'] == 'category'
+
     # Verify the method attribute contains expected default values
     assert isinstance(spop.method, pd.Series)
     assert 'age' in spop.method.index
@@ -37,6 +42,25 @@ def test_synthpop_default_parameters():
     assert all(spop.predictor_matrix.index == df.columns)
     assert all(spop.predictor_matrix.columns == df.columns)
 
+def test_synthpop_with_manual_dtypes():
+    """Test Synthpop with manually specified dtypes."""
+    # Initialize Synthpop
+    spop = Synthpop()
+
+    # Fit the model with explicit dtypes
+    spop.fit(df, dtypes)
+
+    # Verify the dtypes were set correctly
+    for col, dtype in dtypes.items():
+        assert spop.df_dtypes[col] == dtype
+
+    # Generate synthetic data
+    synth_df = spop.generate(len(df))
+
+    # Verify the synthetic dataframe has the same shape and columns
+    assert synth_df.shape == df.shape
+    assert all(synth_df.columns == df.columns)
+
 def test_synthpop_custom_visit_sequence():
     """Test Synthpop with custom visit sequence using Adult dataset."""
     # Define custom visit sequence
@@ -45,8 +69,8 @@ def test_synthpop_custom_visit_sequence():
     # Initialize Synthpop with custom visit sequence
     spop = Synthpop(visit_sequence=visit_sequence)
 
-    # Fit the model
-    spop.fit(df, dtypes)
+    # Fit the model with automatic type inference
+    spop.fit(df)
 
     # Generate synthetic data
     synth_df = spop.generate(len(df))