[GSProcessing] Categorical Feature Transformation

docs/source/gs-processing/developer/input-configuration.rst

@@ -426,6 +426,25 @@ arguments.
         by specifying a slide-window size ``s``, where ``s`` can an integer or float. GSProcessing then transforms each
         numeric value ``v`` of the property into a range from ``v - s/2`` through ``v + s/2`` , and assigns the value v
         to every bucket that the range covers.
+
+-  ``categorical``
+
+   -  Transforms values from a fixed list of possible values (categorical features) to a one-hot encoding.
+   The length of the resulting vector will be the number of categories in the data minus one, with a 1 in
+   the index of the single category, and zero everywhere else.
+
+.. note::
+    The maximum number of categories in any categorical feature is 100. If a property has more than 100 categories of value,
+    only the most common 99 of them are placed in distinct categories, and the rest are placed in a special category named OTHER.
+
+-  ``multi-categorical``
+
+   -  Encodes vector-like data from a fixed list of possible values (i.e. multi-label/multi-categorical data) using a multi-hot encoding. The length of the resulting vector will be the number of categories in the data minus one, and each value will have a 1 value for every category that appears, and 0 everwhere else.
+   -  ``kwargs``:
+      - ``separator`` (String, optional): Same as the one in the No-op operation, the separator is used to
+        split multiple input values for CSV files e.g. ``detective|noir``. If it is not provided, then the whole value 
+        will be considered as an array. For Parquet files, if the input type is ArrayType(StringType()), then the 
+        separator is ignored; if it is StringType(), it will apply same logic as in CSV. 
 --------------
 
 Examples

graphstorm-processing/graphstorm_processing/config/categorical_configs.py

@@ -0,0 +1,37 @@
+"""
+Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License").
+You may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+from typing import Mapping
+from .feature_config_base import FeatureConfig
+
+
+class MultiCategoricalFeatureConfig(FeatureConfig):
+    """Feature configuration for multi-column categorical features.
+
+    Supported kwargs
+    ----------------
+    separator: str, optional
+        A separator to use when splitting a delimited string into multiple numerical values
+        as a vector. Only applicable to CSV input. Example: for a separator `'|'` the CSV
+        value `1|2|3` would be transformed to a vector, `[1, 2, 3]`. When `None` the expected
+        input format is an array of string values.
+
+    """
+
+    def __init__(self, config: Mapping):
+        super().__init__(config)
+        self.separator = self._transformation_kwargs.get("separator", None)
+
+        self._sanity_check()

graphstorm-processing/graphstorm_processing/config/config_conversion/gconstruct_converter.py

@@ -125,6 +125,15 @@ def _convert_feature(feats: list[dict]) -> list[dict]:
                             "normalizer": "rank-gauss",
                             "imputer": "none",
                         }
+                elif gconstruct_transform_dict["name"] == "to_categorical":
+                    if "separator" in gconstruct_transform_dict:
+                        gsp_transformation_dict["name"] = "multi-categorical"
+                        gsp_transformation_dict["kwargs"] = {
+                            "separator": gconstruct_transform_dict["separator"]
+                        }
+                    else:
+                        gsp_transformation_dict["name"] = "categorical"
+                        gsp_transformation_dict["kwargs"] = {}
                 # TODO: Add support for other common transformations here
                 else:
                     raise ValueError(

graphstorm-processing/graphstorm_processing/config/config_parser.py

@@ -26,6 +26,7 @@
     MultiNumericalFeatureConfig,
     NumericalFeatureConfig,
 )
+from .categorical_configs import MultiCategoricalFeatureConfig
 from .data_config_base import DataStorageConfig
 
 
@@ -62,6 +63,10 @@ def parse_feat_config(feature_dict: Dict) -> FeatureConfig:
         return MultiNumericalFeatureConfig(feature_dict)
     elif transformation_name == "bucket-numerical":
         return BucketNumericalFeatureConfig(feature_dict)
+    elif transformation_name == "categorical":
+        return FeatureConfig(feature_dict)
+    elif transformation_name == "multi-categorical":
+        return MultiCategoricalFeatureConfig(feature_dict)
     else:
         raise RuntimeError(f"Unknown transformation name: '{transformation_name}'")
 

graphstorm-processing/graphstorm_processing/data_transformations/dist_feature_transformer.py

@@ -24,6 +24,8 @@
     DistNumericalTransformation,
     DistMultiNumericalTransformation,
     DistBucketNumericalTransformation,
+    DistCategoryTransformation,
+    DistMultiCategoryTransformation,
 )
 
 
@@ -51,6 +53,10 @@ def __init__(self, feature_config: FeatureConfig):
             self.transformation = DistMultiNumericalTransformation(**default_kwargs, **args_dict)
         elif feat_type == "bucket-numerical":
             self.transformation = DistBucketNumericalTransformation(**default_kwargs, **args_dict)
+        elif feat_type == "categorical":
+            self.transformation = DistCategoryTransformation(**default_kwargs, **args_dict)
+        elif feat_type == "multi-categorical":
+            self.transformation = DistMultiCategoryTransformation(**default_kwargs, **args_dict)
         else:
             raise NotImplementedError(
                 f"Feature {feat_name} has type: {feat_type} that is not supported"

graphstorm-processing/graphstorm_processing/data_transformations/dist_transformations/dist_category_transformation.py

@@ -19,7 +19,7 @@
 
 from pyspark.sql import DataFrame, functions as F
 from pyspark.sql.functions import when
-from pyspark.sql.types import ArrayType, FloatType
+from pyspark.sql.types import ArrayType, FloatType, StringType
 from pyspark.ml.feature import StringIndexer, OneHotEncoder
 from pyspark.ml.functions import vector_to_array
 from pyspark.ml.linalg import Vectors
@@ -141,12 +141,27 @@ def get_transformation_name() -> str:
         return "DistMultiCategoryTransformation"
 
     def apply(self, input_df: DataFrame) -> DataFrame:
-        # Count category frequency
-        distinct_category_counts = (
-            input_df.select(self.multi_column)
-            .withColumn(
-                SINGLE_CATEGORY_COL, F.explode(F.split(F.col(self.multi_column), self.separator))
+        col_datatype = input_df.schema[self.multi_column].dataType
+        is_array_col = False
+        if col_datatype.typeName() == "array":
+            assert isinstance(col_datatype, ArrayType)
+            if not isinstance(col_datatype.elementType, StringType):
+                raise ValueError(
+                    f"Unsupported array type {col_datatype.elementType} "
+                    f"for column {self.multi_column}, expected StringType"
+                )
+
+            is_array_col = True
+
+        if is_array_col:
+            list_df = input_df.select(self.multi_column).alias(self.multi_column)
+        else:
+            list_df = input_df.select(
+                F.split(F.col(self.multi_column), self.separator).alias(self.multi_column)
             )
+
+        distinct_category_counts = (
+            list_df.withColumn(SINGLE_CATEGORY_COL, F.explode(F.col(self.multi_column)))
             .groupBy(SINGLE_CATEGORY_COL)
             .count()
         )
@@ -206,11 +221,6 @@ def apply(self, input_df: DataFrame) -> DataFrame:
         # The encoding for the missing category is an all-zeroes vector
         category_map[MISSING_CATEGORY] = np.array([0] * len(valid_categories))
 
-        # Split tokens along separator to create List objects
-        token_list_df = input_df.select(
-            F.split(F.col(self.multi_column), self.separator).alias(self.multi_column)
-        )
-
         # Use mapping to convert token list to a multi-hot vector by summing one-hot vectors
         missing_vector = (
             category_map[RARE_CATEGORY]
@@ -241,7 +251,7 @@ def token_list_to_multihot(token_list: Optional[List[str]]) -> Optional[List[flo
             token_list_to_multihot, ArrayType(FloatType(), containsNull=False)
         )
 
-        multihot_df = token_list_df.withColumn(
+        multihot_df = list_df.withColumn(
             self.multi_column, token_list_to_multihot_udf(F.col(self.multi_column))
         )
 

graphstorm-processing/graphstorm_processing/graph_loaders/schema_utils.py

@@ -92,7 +92,12 @@ def determine_spark_feature_type(feature_type: str) -> Type[DataType]:
         In case an unsupported feature_type is provided.
     """
     # TODO: Replace with pattern matching after moving to Python 3.10?
-    if feature_type in ["no-op", "multi-numerical"] or feature_type.startswith("text"):
+    if feature_type in [
+        "no-op",
+        "multi-numerical",
+        "categorical",
+        "multi-categorical",
+    ] or feature_type.startswith("text"):
         return StringType
     if feature_type in ["numerical", "bucket-numerical", "none"]:
         return FloatType

graphstorm-processing/tests/test_converter.py

@@ -227,6 +227,14 @@ def test_convert_gsprocessing(converter: GConstructConfigConverter):
                     "feature_name": "rank_gauss2",
                     "transform": {"name": "rank_gauss", "epsilon": 0.1},
                 },
+                {
+                    "feature_col": ["num_citations"],
+                    "transform": {"name": "to_categorical", "mapping": {"1", "2", "3"}},
+                },
+                {
+                    "feature_col": ["num_citations"],
+                    "transform": {"name": "to_categorical", "separator": ","},
+                },
             ],
             "labels": [
                 {"label_col": "label", "task_type": "classification", "split_pct": [0.8, 0.1, 0.1]}
@@ -299,6 +307,20 @@ def test_convert_gsprocessing(converter: GConstructConfigConverter):
                 "kwargs": {"epsilon": 0.1, "normalizer": "rank-gauss", "imputer": "none"},
             },
         },
+        {
+            "column": "num_citations",
+            "transformation": {
+                "name": "categorical",
+                "kwargs": {},
+            },
+        },
+        {
+            "column": "num_citations",
+            "transformation": {
+                "name": "multi-categorical",
+                "kwargs": {"separator": ","},
+            },
+        },
     ]
     assert nodes_output["labels"] == [
         {

graphstorm-processing/tests/test_dist_category_transformation.py

@@ -16,11 +16,13 @@
 from typing import Tuple, Iterator
 import os
 import pytest
+import pandas as pd
+import tempfile
 
 import mock
 from numpy.testing import assert_array_equal
 from pyspark.sql import SparkSession, DataFrame
-from pyspark.sql.types import StructField, StructType, StringType
+from pyspark.sql.types import StructField, StructType, StringType, ArrayType
 
 from graphstorm_processing.data_transformations.dist_transformations import (
     DistCategoryTransformation,
@@ -174,3 +176,78 @@ def test_multi_category_limited_categories(multi_cat_df_and_separator):
     transformed_values = [row[col_name] for row in transformed_df.collect()]
 
     assert_array_equal(expected_values, transformed_values)
+
+
+def test_csv_input_categorical(spark: SparkSession, check_df_schema):
+    data_path = os.path.join(_ROOT, "resources/multi_num_numerical/multi_num.csv")
+    long_vector_df = spark.read.csv(data_path, sep=",", header=True)
+    dist_categorical_transormation = DistCategoryTransformation(cols=["id"])
+
+    transformed_df = dist_categorical_transormation.apply(long_vector_df)
+    check_df_schema(transformed_df)
+    transformed_rows = transformed_df.collect()
+    expected_rows = [
+        [1, 0, 0, 0, 0],
+        [0, 1, 0, 0, 0],
+        [0, 0, 1, 0, 0],
+        [0, 0, 0, 1, 0],
+        [0, 0, 0, 0, 1],
+    ]
+    for row, expected_row in zip(transformed_rows, expected_rows):
+        assert row["id"] == expected_row
+
+
+def test_csv_input_multi_categorical(spark: SparkSession, check_df_schema):
+    data_path = os.path.join(_ROOT, "resources/multi_num_numerical/multi_num.csv")
+    long_vector_df = spark.read.csv(data_path, sep=",", header=True)
+    dist_categorical_transormation = DistMultiCategoryTransformation(cols=["feat"], separator=";")
+
+    transformed_df = dist_categorical_transormation.apply(long_vector_df)
+    check_df_schema(transformed_df)
+    transformed_rows = transformed_df.collect()
+    expected_rows = []
+    for i in range(5):
+        expected_rows.append([1] * 100)
+    for row, expected_row in zip(transformed_rows, expected_rows):
+        assert row["feat"] == expected_row
+
+
+def test_parquet_input_multi_categorical(spark: SparkSession, check_df_schema):
+    # Define the schema for the DataFrame
+    schema = StructType([StructField("names", ArrayType(StringType()), True)])
+
+    # Sample data with arrays of strings
+    data = [
+        (["Alice", "Alicia"],),
+        (["Bob", "Bobby"],),
+        (["Cathy", "Catherine"],),
+        (["David", "Dave"],),
+    ]
+
+    # Create a DataFrame using the sample data and the defined schema
+    df = spark.createDataFrame(data, schema)
+
+    with tempfile.TemporaryDirectory() as tmpdirname:
+        # Define the path for the Parquet file
+        parquet_path = f"{tmpdirname}/people_name.parquet"
+
+        # Write the DataFrame to a Parquet file
+        df.write.mode("overwrite").parquet(parquet_path)
+
+        # Read the Parquet file into a DataFrame
+        df_parquet = spark.read.parquet(parquet_path)
+
+        # Show the DataFrame loaded from the Parquet file
+        dist_categorical_transormation = DistMultiCategoryTransformation(cols=["names"], separator=None)
+
+        transformed_df = dist_categorical_transormation.apply(df_parquet)
+        check_df_schema(transformed_df)
+        transformed_rows = transformed_df.collect()
+        expected_rows = [
+            [1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 1.0, 1.0, 0.0, 0.0],
+            [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 1.0],
+        ]
+        for row, expected_row in zip(transformed_rows, expected_rows):
+            assert row["names"] == expected_row