Project import generated by Copybara. (#11)

GitOrigin-RevId: bb45da353e3d3cc7b84b11926c2c294eb1ca6359

Co-authored-by: Snowflake Authors <[email protected]>

Author: snowflake-provisioner

ci/conda_recipe/meta.yaml

@@ -20,14 +20,22 @@ requirements:
   run:
     - python
     - absl-py>=0.15,<2
+    - anyio>=3.5.0,<4
     - fsspec>=2022.11,<=2023.1
     - numpy>=1.23,<1.24
     - pyyaml>=6.0,<7
     - scipy>=1.9,<2
-    - scikit-learn==1.2.1
     - snowflake-connector-python
-    - snowflake-snowpark-python>=1.0.0,<=1.3
+    - snowflake-snowpark-python>=1.3.0,<=2
     - sqlparse>=0.4,<1
+
+    # TODO(snandamuri): Versions of these packages must be exactly same between user's workspace and
+    # snowpark sandbox. Generic definitions like scikit-learn>=1.1.0,<2 wont work because snowflake conda channel
+    # only has a few allowlisted versions of scikit-learn available, so we must force users to use scikit-learn
+    # versions that are available in the snowflake conda channel. Since there is no way to specify allow list of
+    # versions in the requirements file, we are pinning the versions here.
+    - joblib>=1.0.0,<=1.1.1
+    - scikit-learn==1.2.1
     - xgboost==1.7.3
 about:
   home: https://github.com/snowflakedb/snowflake-ml-python

codegen/sklearn_wrapper_generator.py

@@ -369,17 +369,11 @@ class WrapperGeneratorBase:
 
     original_class_name              INFERRED   Class name for the given scikit-learn
                                                 estimator.
-    estimator_class_name             GENERATED  Name for the new estimator class.
-    transformer_class_name           GENERATED  [TODO] Name for the new transformer
-                                                class.
     module_name                      INFERRED   Name of the module that given class is
                                                 is contained in.
     estimator_imports                GENERATED  Imports needed for the estimator / fit()
                                                 call.
     fit_sproc_imports                GENERATED  Imports needed for the fit sproc call.
-    transform_function_name          INFERRED   Name for the transformer function. This
-                                                will be one of "transform" or
-                                                "predict()" depending on the class.
     ------------------------------------------------------------------------------------
     SIGNATURES AND ARGUMENTS
     ------------------------------------------------------------------------------------
@@ -444,9 +438,6 @@ def __init__(self, module_name: str, class_object: Tuple[str, type]) -> None:
 
         # Naming of the class.
         self.original_class_name = ""
-        self.estimator_class_name = ""
-        self.transformer_class_name = ""
-        self.transform_function_name = ""
 
         # The signature and argument passing the __init__ functions.
         self.original_init_signature = inspect.Signature()
@@ -456,33 +447,32 @@ def __init__(self, module_name: str, class_object: Tuple[str, type]) -> None:
         self.sklearn_init_args_dict = ""
         self.estimator_init_member_args = ""
 
+        # Doc strings
         self.original_class_docstring = ""
         self.estimator_class_docstring = ""
         self.transformer_class_docstring = ""
-
-        self.estimator_imports = ""
-        self.estimator_imports_list: List[str] = []
-
         self.original_fit_docstring = ""
         self.fit_docstring = ""
         self.original_transform_docstring = ""
         self.transform_docstring = ""
 
+        # Import strings
+        self.estimator_imports = ""
+        self.estimator_imports_list: List[str] = []
+        self.additional_import_statements = ""
+
+        # Test strings
         self.test_dataset_func = ""
         self.test_estimator_input_args = ""
         self.test_estimator_input_args_list: List[str] = []
         self.test_class_name = ""
         self.test_estimator_imports = ""
         self.test_estimator_imports_list: List[str] = []
 
-        self.additional_import_statements = ""
-
+        # Dependencies
         self.predict_udf_deps = ""
         self.fit_sproc_deps = ""
 
-    # TODO(amauser): Make fit a no-op if there is no internal state
-    # TODO(amauser): handling sparse input and output (LabelBinarizer)
-
     def _format_default_value(self, default_value: Any) -> str:
         if isinstance(default_value, str):
             return f'"{default_value}"'
@@ -561,26 +551,13 @@ def split_long_lines(line: str) -> str:
         self.estimator_class_docstring = class_docstring
 
     def _populate_class_names(self) -> None:
-        # TODO(snandamuri): All the 3 fields have exact same value. Do we really need these
-        # 3 separate fields?
         self.original_class_name = self.class_object[0]
-        self.estimator_class_name = self.original_class_name
-        self.transformer_class_name = self.estimator_class_name
-
         self.test_class_name = f"{self.original_class_name}Test"
 
     def _populate_function_names_and_signatures(self) -> None:
         for member in inspect.getmembers(self.class_object[1]):
             if member[0] == "__init__":
                 self.original_init_signature = inspect.signature(member[1])
-            elif member[0] == "predict" or member[0] == "transform":
-                if self.transform_function_name != "":
-                    print("ERROR: Class has both transform() and predict() methods.")
-                    # TODO(snandamuri): Add support for both transform() and predict() methods in estimators.
-                    # For now, resolve to predict() method when both predict() and transform() are available.
-                    self.transform_function_name = "predict"
-                else:
-                    self.transform_function_name = member[0]
 
         signature_lines = []
         sklearn_init_lines = []
@@ -642,6 +619,7 @@ def _populate_function_names_and_signatures(self) -> None:
         self.estimator_init_member_args = "\n        ".join(init_member_args)
         self.estimator_args_transform_calls = "\n        ".join(arg_transform_calls)
 
+        # TODO(snandamuri): Implement type inference for classifiers.
         self.udf_datatype = "float" if self._from_data_py or self._is_regressor else ""
 
     def _populate_file_paths(self) -> None:
@@ -825,7 +803,7 @@ def generate(self) -> "SklearnWrapperGenerator":
             self.test_estimator_input_args_list.extend(["min_samples_leaf=1", "max_leaf_nodes=100"])
 
         self.fit_sproc_deps = self.predict_udf_deps = (
-            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'scikit-learn=={sklearn.__version__}',"
+            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'scikit-learn=={sklearn.__version__}', "
             "f'xgboost=={xgboost.__version__}', f'joblib=={joblib.__version__}'"
         )
         self._construct_string_from_lists()
@@ -842,7 +820,7 @@ def generate(self) -> "XGBoostWrapperGenerator":
         self.test_estimator_input_args_list.extend(["random_state=0", "subsample=1.0", "colsample_bynode=1.0"])
         self.fit_sproc_imports = "import xgboost"
         self.fit_sproc_deps = self.predict_udf_deps = (
-            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'xgboost=={xgboost.__version__}',"
+            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'xgboost=={xgboost.__version__}', "
             "f'joblib=={joblib.__version__}'"
         )
         self._construct_string_from_lists()
@@ -859,7 +837,7 @@ def generate(self) -> "LightGBMWrapperGenerator":
         self.test_estimator_input_args_list.extend(["random_state=0"])
         self.fit_sproc_imports = "import lightgbm"
         self.fit_sproc_deps = self.predict_udf_deps = (
-            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'lightgbm=={lightgbm.__version__}',"
+            "f'numpy=={np.__version__}', f'pandas=={pd.__version__}', f'lightgbm=={lightgbm.__version__}', "
             "f'joblib=={joblib.__version__}'"
         )
         self._construct_string_from_lists()