Add infer_sdtypes and infer_keys parameters to `detect_from_dataf…

…rames` method (#2363)

sdv/metadata/metadata.py

@@ -61,8 +61,13 @@ def load_from_dict(cls, metadata_dict, single_table_name=None):
         instance._set_metadata_dict(metadata_dict, single_table_name)
         return instance
 
+    @staticmethod
+    def _validate_infer_sdtypes(infer_sdtypes):
+        if not isinstance(infer_sdtypes, bool):
+            raise ValueError("'infer_sdtypes' must be a boolean value.")
+
     @classmethod
-    def detect_from_dataframes(cls, data):
+    def detect_from_dataframes(cls, data, infer_sdtypes=True, infer_keys='primary_and_foreign'):
         """Detect the metadata for all tables in a dictionary of dataframes.
 
         This method automatically detects the ``sdtypes`` for the given ``pandas.DataFrames``.
@@ -71,23 +76,50 @@ def detect_from_dataframes(cls, data):
         Args:
             data (dict):
                 Dictionary of table names to dataframes.
+            infer_sdtypes (bool):
+                A boolean describing whether to infer the sdtypes of each column.
+                If True it infers the sdtypes based on the data.
+                If False it does not infer the sdtypes and all columns are marked as unknown.
+                Defaults to True.
+            infer_keys (str):
+                A string describing whether to infer the primary and/or foreign keys. Options are:
+                    - 'primary_and_foreign': Infer the primary keys in each table,
+                       and the foreign keys in other tables that refer to them
+                    - 'primary_only': Infer only the primary keys of each table
+                    - None: Do not infer any keys
+                Defaults to 'primary_and_foreign'.
 
         Returns:
             Metadata:
                 A new metadata object with the sdtypes detected from the data.
         """
         if not data or not all(isinstance(df, pd.DataFrame) for df in data.values()):
             raise ValueError('The provided dictionary must contain only pandas DataFrame objects.')
+        if infer_keys not in ['primary_and_foreign', 'primary_only', None]:
+            raise ValueError(
+                "'infer_keys' must be one of: 'primary_and_foreign', 'primary_only', None."
+            )
+        cls._validate_infer_sdtypes(infer_sdtypes)
 
         metadata = Metadata()
         for table_name, dataframe in data.items():
-            metadata.detect_table_from_dataframe(table_name, dataframe)
+            metadata.detect_table_from_dataframe(
+                table_name, dataframe, infer_sdtypes, None if infer_keys is None else 'primary_only'
+            )
+
+        if infer_keys == 'primary_and_foreign':
+            metadata._detect_relationships(data)
 
-        metadata._detect_relationships(data)
         return metadata
 
     @classmethod
-    def detect_from_dataframe(cls, data, table_name=DEFAULT_SINGLE_TABLE_NAME):
+    def detect_from_dataframe(
+        cls,
+        data,
+        table_name=DEFAULT_SINGLE_TABLE_NAME,
+        infer_sdtypes=True,
+        infer_keys='primary_only',
+    ):
         """Detect the metadata for a DataFrame.
 
         This method automatically detects the ``sdtypes`` for the given ``pandas.DataFrame``.
@@ -96,16 +128,29 @@ def detect_from_dataframe(cls, data, table_name=DEFAULT_SINGLE_TABLE_NAME):
         Args:
             data (pandas.DataFrame):
                 Dictionary of table names to dataframes.
+            infer_sdtypes (bool):
+                A boolean describing whether to infer the sdtypes of each column.
+                If True it infers the sdtypes based on the data.
+                If False it does not infer the sdtypes and all columns are marked as unknown.
+                Defaults to True.
+            infer_keys (str):
+                A string describing whether to infer the primary keys. Options are:
+                    - 'primary_only': Infer only the primary keys of each table
+                    - None: Do not infer any keys
+                Defaults to 'primary_only'.
 
         Returns:
             Metadata:
                 A new metadata object with the sdtypes detected from the data.
         """
         if not isinstance(data, pd.DataFrame):
             raise ValueError('The provided data must be a pandas DataFrame object.')
+        if infer_keys not in ['primary_only', None]:
+            raise ValueError("'infer_keys' must be one of: 'primary_only', None.")
+        cls._validate_infer_sdtypes(infer_sdtypes)
 
         metadata = Metadata()
-        metadata.detect_table_from_dataframe(table_name, data)
+        metadata.detect_table_from_dataframe(table_name, data, infer_sdtypes, infer_keys)
         return metadata
 
     def _set_metadata_dict(self, metadata, single_table_name=None):

sdv/metadata/multi_table.py

@@ -530,7 +530,9 @@ def _detect_relationships(self, data=None):
                         )
                         continue
 
-    def detect_table_from_dataframe(self, table_name, data):
+    def detect_table_from_dataframe(
+        self, table_name, data, infer_sdtypes=True, infer_keys='primary_only'
+    ):
         """Detect the metadata for a table from a dataframe.
 
         This method automatically detects the ``sdtypes`` for the given ``pandas.DataFrame``,
@@ -541,10 +543,20 @@ def detect_table_from_dataframe(self, table_name, data):
                 Name of the table to detect.
             data (pandas.DataFrame):
                 ``pandas.DataFrame`` to detect the metadata from.
+            infer_sdtypes (bool):
+                A boolean describing whether to infer the sdtypes of each column.
+                If True it infers the sdtypes based on the data.
+                If False it does not infer the sdtypes and all columns are marked as unknown.
+                Defaults to True.
+            infer_keys (str):
+                A string describing whether to infer the primary keys. Options are:
+                    - 'primary_only': Infer only the primary keys of each table
+                    - None: Do not infer any keys
+                Defaults to 'primary_only'.
         """
         self._validate_table_not_detected(table_name)
         table = SingleTableMetadata()
-        table._detect_columns(data, table_name)
+        table._detect_columns(data, table_name, infer_sdtypes, infer_keys)
         self.tables[table_name] = table
         self._log_detected_table(table)
 

sdv/metadata/single_table.py

@@ -595,53 +595,67 @@ def _detect_primary_key(self, data):
 
         return None
 
-    def _detect_columns(self, data, table_name=None):
+    def _detect_columns(self, data, table_name=None, infer_sdtypes=True, infer_keys='primary_only'):
         """Detect the columns' sdtypes from the data.
 
         Args:
             data (pandas.DataFrame):
                 The data to be analyzed.
             table_name (str):
                 The name of the table to be analyzed. Defaults to ``None``.
+            infer_sdtypes (bool):
+                A boolean describing whether to infer the sdtypes of each column.
+                If True it infers the sdtypes based on the data.
+                If False it does not infer the sdtypes and all columns are marked as unknown.
+                Defaults to True.
+            infer_keys (str):
+                A string describing whether to infer the primary keys. Options are:
+                    - 'primary_only': Infer the primary keys.
+                    - None: Do not infer any keys.
+                Defaults to 'primary_only'.
         """
         old_columns = data.columns
         data.columns = data.columns.astype(str)
         for field in data:
-            try:
-                column_data = data[field]
-                clean_data = column_data.dropna()
-                dtype = clean_data.infer_objects().dtype.kind
-
-                sdtype = self._detect_pii_column(field)
-                if sdtype is None:
-                    if dtype in self._DTYPES_TO_SDTYPES:
-                        sdtype = self._DTYPES_TO_SDTYPES[dtype]
-                    elif dtype in ['i', 'f', 'u']:
-                        sdtype = self._determine_sdtype_for_numbers(column_data)
-
-                    elif dtype == 'O':
-                        sdtype = self._determine_sdtype_for_objects(column_data)
+            if infer_sdtypes:
+                try:
+                    column_data = data[field]
+                    clean_data = column_data.dropna()
+                    dtype = clean_data.infer_objects().dtype.kind
 
+                    sdtype = self._detect_pii_column(field)
                     if sdtype is None:
-                        table_str = f"table '{table_name}' " if table_name else ''
-                        error_message = (
-                            f"Unsupported data type for {table_str}column '{field}' (kind: {dtype}"
-                            "). The valid data types are: 'object', 'int', 'float', 'datetime',"
-                            " 'bool'."
-                        )
-                        raise InvalidMetadataError(error_message)
-
-            except Exception as e:
-                error_type = type(e).__name__
-                if error_type == 'InvalidMetadataError':
-                    raise e
-
-                table_str = f"table '{table_name}' " if table_name else ''
-                error_message = (
-                    f"Unable to detect metadata for {table_str}column '{field}' due to an invalid "
-                    f'data format.\n {error_type}: {e}'
-                )
-                raise InvalidMetadataError(error_message) from e
+                        if dtype in self._DTYPES_TO_SDTYPES:
+                            sdtype = self._DTYPES_TO_SDTYPES[dtype]
+                        elif dtype in ['i', 'f', 'u']:
+                            sdtype = self._determine_sdtype_for_numbers(column_data)
+
+                        elif dtype == 'O':
+                            sdtype = self._determine_sdtype_for_objects(column_data)
+
+                        if sdtype is None:
+                            table_str = f"table '{table_name}' " if table_name else ''
+                            error_message = (
+                                f"Unsupported data type for {table_str}column '{field}' "
+                                f"(kind: {dtype}). The valid data types are: 'object', "
+                                "'int', 'float', 'datetime', 'bool'."
+                            )
+                            raise InvalidMetadataError(error_message)
+
+                except Exception as e:
+                    error_type = type(e).__name__
+                    if error_type == 'InvalidMetadataError':
+                        raise e
+
+                    table_str = f"table '{table_name}' " if table_name else ''
+                    error_message = (
+                        f"Unable to detect metadata for {table_str}column '{field}' due "
+                        f'to an invalid data format.\n {error_type}: {e}'
+                    )
+                    raise InvalidMetadataError(error_message) from e
+
+            else:
+                sdtype = 'unknown'
 
             column_dict = {'sdtype': sdtype}
             sdtype_in_reference = sdtype in self._REFERENCE_TO_SDTYPE.values()
@@ -655,7 +669,8 @@ def _detect_columns(self, data, table_name=None):
 
             self.columns[field] = deepcopy(column_dict)
 
-        self.primary_key = self._detect_primary_key(data)
+        if infer_keys == 'primary_only':
+            self.primary_key = self._detect_primary_key(data)
         self._updated = True
         data.columns = old_columns