f09e3b44-ef34-4638-a136-6dc1a5893c0a

Committed 26 Oct 2023 11:49PM UTC coverage: 92.368% (-0.8%) from 93.203%

Build # f09e3b44-ef34-4638-a136-6dc1a5893c0a

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Merge branch 'georgia-tech-db:master' into master

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96.81

/evadb/executor/create_function_executor.py

# coding=utf-8
# Copyright 2018-2023 EvaDB
#
# 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.
import hashlib
import os
import pickle
from pathlib import Path
from typing import Dict, List

import pandas as pd

from evadb.catalog.catalog_utils import get_metadata_properties
from evadb.catalog.models.function_catalog import FunctionCatalogEntry
from evadb.catalog.models.function_io_catalog import FunctionIOCatalogEntry
from evadb.catalog.models.function_metadata_catalog import FunctionMetadataCatalogEntry
from evadb.configuration.constants import (
    DEFAULT_TRAIN_REGRESSION_METRIC,
    DEFAULT_TRAIN_TIME_LIMIT,
    EvaDB_INSTALLATION_DIR,
)
from evadb.database import EvaDBDatabase
from evadb.executor.abstract_executor import AbstractExecutor
from evadb.functions.decorators.utils import load_io_from_function_decorators
from evadb.models.storage.batch import Batch
from evadb.plan_nodes.create_function_plan import CreateFunctionPlan
from evadb.third_party.huggingface.create import gen_hf_io_catalog_entries
from evadb.utils.errors import FunctionIODefinitionError
from evadb.utils.generic_utils import (
    load_function_class_from_file,
    string_comparison_case_insensitive,
    try_to_import_ludwig,
    try_to_import_neuralforecast,
    try_to_import_sklearn,
    try_to_import_statsforecast,
    try_to_import_torch,
    try_to_import_ultralytics,
    try_to_import_xgboost,
)
from evadb.utils.logging_manager import logger


class CreateFunctionExecutor(AbstractExecutor):
    def __init__(self, db: EvaDBDatabase, node: CreateFunctionPlan):
        super().__init__(db, node)
        self.function_dir = Path(EvaDB_INSTALLATION_DIR) / "functions"

    def handle_huggingface_function(self):
        """Handle HuggingFace functions

        HuggingFace functions are special functions that are not loaded from a file.
        So we do not need to call the setup method on them like we do for other functions.
        """
        # We need at least one deep learning framework for HuggingFace
        # Torch or Tensorflow
        try_to_import_torch()
        impl_path = f"{self.function_dir}/abstract/hf_abstract_function.py"
        io_list = gen_hf_io_catalog_entries(self.node.name, self.node.metadata)
        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def handle_ludwig_function(self):
        """Handle ludwig functions

        Use Ludwig's auto_train engine to train/tune models.
        """
        try_to_import_ludwig()
        from ludwig.automl import auto_train

        assert (
            len(self.children) == 1
        ), "Create ludwig function expects 1 child, finds {}.".format(
            len(self.children)
        )

        aggregated_batch_list = []
        child = self.children[0]
        for batch in child.exec():
            aggregated_batch_list.append(batch)
        aggregated_batch = Batch.concat(aggregated_batch_list, copy=False)
        aggregated_batch.drop_column_alias()

        arg_map = {arg.key: arg.value for arg in self.node.metadata}
        auto_train_results = auto_train(
            dataset=aggregated_batch.frames,
            target=arg_map["predict"],
            tune_for_memory=arg_map.get("tune_for_memory", False),
            time_limit_s=arg_map.get("time_limit", DEFAULT_TRAIN_TIME_LIMIT),
            output_directory=self.db.config.get_value("storage", "tmp_dir"),
        )
        model_path = os.path.join(
            self.db.config.get_value("storage", "model_dir"), self.node.name
        )
        auto_train_results.best_model.save(model_path)
        self.node.metadata.append(
            FunctionMetadataCatalogEntry("model_path", model_path)
        )

        impl_path = Path(f"{self.function_dir}/ludwig.py").absolute().as_posix()
        io_list = self._resolve_function_io(None)
        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def handle_sklearn_function(self):
        """Handle sklearn functions

        Use Sklearn's regression to train models.
        """
        try_to_import_sklearn()
        from sklearn.linear_model import LinearRegression

        assert (
            len(self.children) == 1
        ), "Create sklearn function expects 1 child, finds {}.".format(
            len(self.children)
        )

        aggregated_batch_list = []
        child = self.children[0]
        for batch in child.exec():
            aggregated_batch_list.append(batch)
        aggregated_batch = Batch.concat(aggregated_batch_list, copy=False)
        aggregated_batch.drop_column_alias()

        arg_map = {arg.key: arg.value for arg in self.node.metadata}
        model = LinearRegression()
        Y = aggregated_batch.frames[arg_map["predict"]]
        aggregated_batch.frames.drop([arg_map["predict"]], axis=1, inplace=True)
        model.fit(X=aggregated_batch.frames, y=Y)
        model_path = os.path.join(
            self.db.config.get_value("storage", "model_dir"), self.node.name
        )
        pickle.dump(model, open(model_path, "wb"))
        self.node.metadata.append(
            FunctionMetadataCatalogEntry("model_path", model_path)
        )
        # Pass the prediction column name to sklearn.py
        self.node.metadata.append(
            FunctionMetadataCatalogEntry("predict_col", arg_map["predict"])
        )

        impl_path = Path(f"{self.function_dir}/sklearn.py").absolute().as_posix()
        io_list = self._resolve_function_io(None)
        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def handle_xgboost_function(self):
        """Handle xgboost functions

        We use the Flaml AutoML model for training xgboost models.
        """
        try_to_import_xgboost()

        assert (
            len(self.children) == 1
        ), "Create sklearn function expects 1 child, finds {}.".format(
            len(self.children)
        )

        aggregated_batch_list = []
        child = self.children[0]
        for batch in child.exec():
            aggregated_batch_list.append(batch)
        aggregated_batch = Batch.concat(aggregated_batch_list, copy=False)
        aggregated_batch.drop_column_alias()

        arg_map = {arg.key: arg.value for arg in self.node.metadata}
        from flaml import AutoML

        model = AutoML()
        settings = {
            "time_budget": arg_map.get("time_limit", DEFAULT_TRAIN_TIME_LIMIT),
            "metric": arg_map.get("metric", DEFAULT_TRAIN_REGRESSION_METRIC),
            "estimator_list": ["xgboost"],
            "task": "regression",
        }
        model.fit(
            dataframe=aggregated_batch.frames, label=arg_map["predict"], **settings
        )
        model_path = os.path.join(
            self.db.config.get_value("storage", "model_dir"), self.node.name
        )
        pickle.dump(model, open(model_path, "wb"))
        self.node.metadata.append(
            FunctionMetadataCatalogEntry("model_path", model_path)
        )
        # Pass the prediction column to xgboost.py.
        self.node.metadata.append(
            FunctionMetadataCatalogEntry("predict_col", arg_map["predict"])
        )

        impl_path = Path(f"{self.function_dir}/xgboost.py").absolute().as_posix()
        io_list = self._resolve_function_io(None)
        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def handle_ultralytics_function(self):
        """Handle Ultralytics functions"""
        try_to_import_ultralytics()

        impl_path = (
            Path(f"{self.function_dir}/yolo_object_detector.py").absolute().as_posix()
        )
        function = self._try_initializing_function(
            impl_path, function_args=get_metadata_properties(self.node)
        )
        io_list = self._resolve_function_io(function)
        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def handle_forecasting_function(self):
        """Handle forecasting functions"""
        os.environ["CUDA_VISIBLE_DEVICES"] = ""
        aggregated_batch_list = []
        child = self.children[0]
        for batch in child.exec():
            aggregated_batch_list.append(batch)
        aggregated_batch = Batch.concat(aggregated_batch_list, copy=False)
        aggregated_batch.drop_column_alias()

        arg_map = {arg.key: arg.value for arg in self.node.metadata}
        if not self.node.impl_path:
            impl_path = Path(f"{self.function_dir}/forecast.py").absolute().as_posix()
        else:
            impl_path = self.node.impl_path.absolute().as_posix()
        library = "statsforecast"
        supported_libraries = ["statsforecast", "neuralforecast"]

        if "horizon" not in arg_map.keys():
            raise ValueError(
                "Horizon must be provided while creating function of type FORECASTING"
            )
        try:
            horizon = int(arg_map["horizon"])
        except Exception as e:
            err_msg = f"{str(e)}. HORIZON must be integral."
            logger.error(err_msg)
            raise FunctionIODefinitionError(err_msg)

        if "library" in arg_map.keys():
            try:
                assert arg_map["library"].lower() in supported_libraries
            except Exception:
                err_msg = (
                    "EvaDB currently supports " + str(supported_libraries) + " only."
                )
                logger.error(err_msg)
                raise FunctionIODefinitionError(err_msg)

            library = arg_map["library"].lower()

        """
        The following rename is needed for statsforecast/neuralforecast, which requires the column name to be the following:
        - The unique_id (string, int or category) represents an identifier for the series.
        - The ds (datestamp) column should be of a format expected by Pandas, ideally YYYY-MM-DD for a date or YYYY-MM-DD HH:MM:SS for a timestamp.
        - The y (numeric) represents the measurement we wish to forecast.
        For reference: https://nixtla.github.io/statsforecast/docs/getting-started/getting_started_short.html
        """
        aggregated_batch.rename(columns={arg_map["predict"]: "y"})
        if "time" in arg_map.keys():
            aggregated_batch.rename(columns={arg_map["time"]: "ds"})
        if "id" in arg_map.keys():
            aggregated_batch.rename(columns={arg_map["id"]: "unique_id"})

        data = aggregated_batch.frames
        if "unique_id" not in list(data.columns):
            data["unique_id"] = [1 for x in range(len(data))]

        if "ds" not in list(data.columns):
            data["ds"] = [x + 1 for x in range(len(data))]

        """
            Set or infer data frequency
        """

        if "frequency" not in arg_map.keys() or arg_map["frequency"] == "auto":
            arg_map["frequency"] = pd.infer_freq(data["ds"])
        frequency = arg_map["frequency"]
        if frequency is None:
            raise RuntimeError(
                f"Can not infer the frequency for {self.node.name}. Please explicitly set it."
            )

        season_dict = {  # https://pandas.pydata.org/docs/user_guide/timeseries.html#timeseries-offset-aliases
            "H": 24,
            "M": 12,
            "Q": 4,
            "SM": 24,
            "BM": 12,
            "BMS": 12,
            "BQ": 4,
            "BH": 24,
        }

        new_freq = (
            frequency.split("-")[0] if "-" in frequency else frequency
        )  # shortens longer frequencies like Q-DEC
        season_length = season_dict[new_freq] if new_freq in season_dict else 1

        """
            Neuralforecast implementation
        """
        if library == "neuralforecast":
            try_to_import_neuralforecast()
            from neuralforecast import NeuralForecast
            from neuralforecast.auto import AutoNBEATS, AutoNHITS
            from neuralforecast.models import NBEATS, NHITS

            model_dict = {
                "AutoNBEATS": AutoNBEATS,
                "AutoNHITS": AutoNHITS,
                "NBEATS": NBEATS,
                "NHITS": NHITS,
            }

            if "model" not in arg_map.keys():
                arg_map["model"] = "NBEATS"

            if "auto" not in arg_map.keys() or (
                arg_map["auto"].lower()[0] == "t"
                and "auto" not in arg_map["model"].lower()
            ):
                arg_map["model"] = "Auto" + arg_map["model"]

            try:
                model_here = model_dict[arg_map["model"]]
            except Exception:
                err_msg = "Supported models: " + str(model_dict.keys())
                logger.error(err_msg)
                raise FunctionIODefinitionError(err_msg)
            model_args = {}

            if "auto" not in arg_map["model"].lower():
                model_args["input_size"] = 2 * horizon
                model_args["early_stop_patience_steps"] = 20
            else:
                model_args["config"] = {
                    "input_size": 2 * horizon,
                    "early_stop_patience_steps": 20,
                }

            if len(data.columns) >= 4:
                exogenous_columns = [
                    x for x in list(data.columns) if x not in ["ds", "y", "unique_id"]
                ]
                if "auto" not in arg_map["model"].lower():
                    model_args["hist_exog_list"] = exogenous_columns
                else:
                    model_args["config"]["hist_exog_list"] = exogenous_columns

            model_args["h"] = horizon

            model = NeuralForecast(
                [model_here(**model_args)],
                freq=new_freq,
            )

        # """
        #     Statsforecast implementation
        # """
        else:
            if "auto" in arg_map.keys() and arg_map["auto"].lower()[0] != "t":
                raise RuntimeError(
                    "Statsforecast implementation only supports automatic hyperparameter optimization. Please set AUTO to true."
                )
            try_to_import_statsforecast()
            from statsforecast import StatsForecast
            from statsforecast.models import AutoARIMA, AutoCES, AutoETS, AutoTheta

            model_dict = {
                "AutoARIMA": AutoARIMA,
                "AutoCES": AutoCES,
                "AutoETS": AutoETS,
                "AutoTheta": AutoTheta,
            }

            if "model" not in arg_map.keys():
                arg_map["model"] = "ARIMA"

            if "auto" not in arg_map["model"].lower():
                arg_map["model"] = "Auto" + arg_map["model"]

            try:
                model_here = model_dict[arg_map["model"]]
            except Exception:
                err_msg = "Supported models: " + str(model_dict.keys())
                logger.error(err_msg)
                raise FunctionIODefinitionError(err_msg)

            model = StatsForecast(
                [model_here(season_length=season_length)], freq=new_freq
            )

        data["ds"] = pd.to_datetime(data["ds"])

        model_save_dir_name = library + "_" + arg_map["model"] + "_" + new_freq
        if len(data.columns) >= 4 and library == "neuralforecast":
            model_save_dir_name += "_exogenous_" + str(sorted(exogenous_columns))

        model_dir = os.path.join(
            self.db.config.get_value("storage", "model_dir"),
            "tsforecasting",
            model_save_dir_name,
            str(hashlib.sha256(data.to_string().encode()).hexdigest()),
        )
        Path(model_dir).mkdir(parents=True, exist_ok=True)

        model_save_name = "horizon" + str(horizon) + ".pkl"

        model_path = os.path.join(model_dir, model_save_name)

        existing_model_files = sorted(
            os.listdir(model_dir),
            key=lambda x: int(x.split("horizon")[1].split(".pkl")[0]),
        )
        existing_model_files = [
            x
            for x in existing_model_files
            if int(x.split("horizon")[1].split(".pkl")[0]) >= horizon
        ]
        if len(existing_model_files) == 0:
            logger.info("Training, please wait...")
            if library == "neuralforecast":
                model.fit(df=data, val_size=horizon)
            else:
                model.fit(df=data[["ds", "y", "unique_id"]])
            f = open(model_path, "wb")
            pickle.dump(model, f)
            f.close()
        elif not Path(model_path).exists():
            model_path = os.path.join(model_dir, existing_model_files[-1])

        io_list = self._resolve_function_io(None)

        metadata_here = [
            FunctionMetadataCatalogEntry("model_name", arg_map["model"]),
            FunctionMetadataCatalogEntry("model_path", model_path),
            FunctionMetadataCatalogEntry(
                "predict_column_rename", arg_map.get("predict", "y")
            ),
            FunctionMetadataCatalogEntry(
                "time_column_rename", arg_map.get("time", "ds")
            ),
            FunctionMetadataCatalogEntry(
                "id_column_rename", arg_map.get("id", "unique_id")
            ),
            FunctionMetadataCatalogEntry("horizon", horizon),
            FunctionMetadataCatalogEntry("library", library),
        ]

        os.environ.pop("CUDA_VISIBLE_DEVICES", None)

        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            metadata_here,
        )

    def handle_generic_function(self):
        """Handle generic functions

        Generic functions are loaded from a file. We check for inputs passed by the user during CREATE or try to load io from decorators.
        """
        impl_path = self.node.impl_path.absolute().as_posix()
        function = self._try_initializing_function(impl_path)
        io_list = self._resolve_function_io(function)

        return (
            self.node.name,
            impl_path,
            self.node.function_type,
            io_list,
            self.node.metadata,
        )

    def exec(self, *args, **kwargs):
        """Create function executor

        Calls the catalog to insert a function catalog entry.
        """
        assert (
            self.node.if_not_exists and self.node.or_replace
        ) is False, (
            "OR REPLACE and IF NOT EXISTS can not be both set for CREATE FUNCTION."
        )

        overwrite = False
        # check catalog if it already has this function entry
        if self.catalog().get_function_catalog_entry_by_name(self.node.name):
            if self.node.if_not_exists:
                msg = f"Function {self.node.name} already exists, nothing added."
                yield Batch(pd.DataFrame([msg]))
                return
            elif self.node.or_replace:
                # We use DropObjectExecutor to avoid bookkeeping the code. The drop function should be moved to catalog.
                from evadb.executor.drop_object_executor import DropObjectExecutor

                drop_executor = DropObjectExecutor(self.db, None)
                try:
                    drop_executor._handle_drop_function(self.node.name, if_exists=False)
                except RuntimeError:
                    pass
                else:
                    overwrite = True
            else:
                msg = f"Function {self.node.name} already exists."
                logger.error(msg)
                raise RuntimeError(msg)

        # if it's a type of HuggingFaceModel, override the impl_path
        if string_comparison_case_insensitive(self.node.function_type, "HuggingFace"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_huggingface_function()
        elif string_comparison_case_insensitive(self.node.function_type, "ultralytics"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_ultralytics_function()
        elif string_comparison_case_insensitive(self.node.function_type, "Ludwig"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_ludwig_function()
        elif string_comparison_case_insensitive(self.node.function_type, "Sklearn"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_sklearn_function()
        elif string_comparison_case_insensitive(self.node.function_type, "XGBoost"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_xgboost_function()
        elif string_comparison_case_insensitive(self.node.function_type, "Forecasting"):
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_forecasting_function()
        else:
            (
                name,
                impl_path,
                function_type,
                io_list,
                metadata,
            ) = self.handle_generic_function()

        self.catalog().insert_function_catalog_entry(
            name, impl_path, function_type, io_list, metadata
        )

        if overwrite:
            msg = f"Function {self.node.name} overwritten."
        else:
            msg = f"Function {self.node.name} added to the database."
        yield Batch(pd.DataFrame([msg]))

    def _try_initializing_function(
        self, impl_path: str, function_args: Dict = {}
    ) -> FunctionCatalogEntry:
        """Attempts to initialize function given the implementation file path and arguments.

        Args:
            impl_path (str): The file path of the function implementation file.
            function_args (Dict, optional): Dictionary of arguments to pass to the function. Defaults to {}.

        Returns:
            FunctionCatalogEntry: A FunctionCatalogEntry object that represents the initialized function.

        Raises:
            RuntimeError: If an error occurs while initializing the function.
        """

        # load the function class from the file
        try:
            # loading the function class from the file
            function = load_function_class_from_file(impl_path, self.node.name)
            # initializing the function class calls the setup method internally
            function(**function_args)
        except Exception as e:
            err_msg = f"Error creating function {self.node.name}: {str(e)}"
            # logger.error(err_msg)
            raise RuntimeError(err_msg)

        return function

    def _resolve_function_io(
        self, function: FunctionCatalogEntry
    ) -> List[FunctionIOCatalogEntry]:
        """Private method that resolves the input/output definitions for a given function.
        It first searches for the input/outputs in the CREATE statement. If not found, it resolves them using decorators. If not found there as well, it raises an error.

        Args:
            function (FunctionCatalogEntry): The function for which to resolve input and output definitions.

        Returns:
            A List of FunctionIOCatalogEntry objects that represent the resolved input and
            output definitions for the function.

        Raises:
            RuntimeError: If an error occurs while resolving the function input/output
            definitions.
        """
        io_list = []
        try:
            if self.node.inputs:
                io_list.extend(self.node.inputs)
            else:
                # try to load the inputs from decorators, the inputs from CREATE statement take precedence
                io_list.extend(
                    load_io_from_function_decorators(function, is_input=True)
                )

            if self.node.outputs:
                io_list.extend(self.node.outputs)
            else:
                # try to load the outputs from decorators, the outputs from CREATE statement take precedence
                io_list.extend(
                    load_io_from_function_decorators(function, is_input=False)
                )

        except FunctionIODefinitionError as e:
            err_msg = (
                f"Error creating function, input/output definition incorrect: {str(e)}"
            )
            logger.error(err_msg)
            raise RuntimeError(err_msg)

        return io_list

georgia-tech-db / eva / f09e3b44-ef34-4638-a136-6dc1a5893c0a

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