diff --git a/numalogic/models/forecast/variants/__init__.py b/numalogic/models/forecast/variants/__init__.py index 3d21cbd2..a0db2318 100644 --- a/numalogic/models/forecast/variants/__init__.py +++ b/numalogic/models/forecast/variants/__init__.py @@ -1,3 +1,7 @@ -from numalogic.models.forecast.variants.naive import BaselineForecaster, SeasonalNaiveForecaster +from numalogic.models.forecast.variants.naive import ( + BaselineForecaster, + SeasonalNaiveForecaster, +) +from numalogic.models.forecast.variants.xgboost import XGBoostForecaster -__all__ = ["BaselineForecaster", "SeasonalNaiveForecaster"] +__all__ = ["BaselineForecaster", "SeasonalNaiveForecaster", "XGBoostForecaster"] diff --git a/numalogic/models/forecast/variants/xgboost.py b/numalogic/models/forecast/variants/xgboost.py new file mode 100644 index 00000000..22b2ec30 --- /dev/null +++ b/numalogic/models/forecast/variants/xgboost.py @@ -0,0 +1,150 @@ +import logging + +import numpy as np +import pandas as pd +import torch +from torch.utils.data import DataLoader +from xgboost import XGBRegressor, callback + +from numalogic.tools.data import ForecastDataset +from numalogic.transforms._covariates import CovariatesGenerator + +_LOGGER = logging.getLogger(__name__) + + +def _check_data_format(df) -> bool: + if not isinstance(df, pd.DataFrame): + raise TypeError("df should be of type pd.DataFrame") + if not df.shape[1] > 0: + raise ValueError("df should have more than 0 column") + if not isinstance(df.index, pd.DatetimeIndex): + raise TypeError("df index should be of type pd.DatetimeIndex") + return True + + +class XGBoostForecaster: + """ + A forecaster that uses XGBoost regressor to predict future values. + + Args: + ____ + horizon: number of time steps to predict into the future + seq_len: number of time steps to consider for prediction + l_rate: learning rate for the XGBoost regress + regressor_params: additional parameters for the XGBoost regressor + """ + + __slots__ = ( + "_horizon", + "_seq_len", + "_val_split", + "_model", + "_early_stop_callback", + "_early_stopping", + ) + + def __init__( + self, + horizon: int, + seq_len: int, + early_stopping=True, + val_split: float = 0.1, + **regressor_params, + ): + self._horizon = horizon + self._seq_len = seq_len + self._val_split = 0 + self._early_stopping = early_stopping + if early_stopping: + self._val_split = val_split + early_stop_callback = callback.EarlyStopping( + rounds=20, metric_name="rmse", save_best=True, maximize=False, min_delta=1e-4 + ) + default_params = { + "learning_rate": 0.1, + "n_estimators": 1000, + "booster": "gbtree", + "max_depth": 7, + "min_child_weight": 1, + "gamma": 0.0, + "subsample": 0.9, + "colsample_bytree": 0.8, + "reg_alpha": 0.1, + "nthread": 4, + "seed": 27, + "objective": "reg:squarederror", + "random_state": 42, + } + if early_stopping: + default_params.update({"callbacks": [early_stop_callback]}) + if regressor_params: + default_params.update(default_params) + + self._model = XGBRegressor(**default_params) + + def prepare_data(self, x: np.array): + """ + Prepare data in the format required for forecasting. + + Args: + ---- + x: np.array: input data + seq_len: int: sequence length + horizon: int: forecast horizon + """ + ds = ForecastDataset(x, seq_len=self._seq_len, horizon=self._horizon) + dataloaders = DataLoader(ds, batch_size=1) + + X = np.empty((0, self._seq_len, x.shape[1])) + Y = np.empty((0, self._horizon, 1)) + for x, y in dataloaders: + X = np.concatenate([X, x.numpy()], axis=0) + Y = np.concatenate([Y, y[:, :, 0].unsqueeze(-1).numpy()], axis=0) + X = X.reshape(X.shape[0], -1) + Y = Y.reshape(Y.shape[0], -1) + return X, Y + + def fit(self, df: pd.DataFrame): + _check_data_format(df) + + # Split the data into training and validation sets + train_df = df.iloc[: int(len(df) * (1 - self._val_split)), :] + val_df = df.iloc[int(len(df) * (1 - self._val_split)) :, :] if self._val_split else None + + # Transform and prepare the training data + transformed_train_data = CovariatesGenerator().transform(train_df) + x_train, y_train = self.prepare_data(transformed_train_data) + + # Fit the model with or without validation data + if val_df is not None: + transformed_val_data = CovariatesGenerator().transform(val_df) + x_val, y_val = self.prepare_data(transformed_val_data) + _LOGGER.info("Fitting the model with validation data") + self._model.fit(x_train, y_train, eval_set=[(x_val, y_val)], verbose=True) + else: + _LOGGER.info("Fitting the model without validation data") + self._model.fit(x_train, y_train, verbose=False) + + def predict_horizon(self, df: pd.DataFrame) -> np.ndarray: + _check_data_format(df) + transformed_test_data = CovariatesGenerator().transform(df) + _LOGGER.info("Predicting the horizon") + x_test, y_test = self.prepare_data(transformed_test_data) + return self._model.predict(x_test) + + def predict_last(self, df: pd.DataFrame) -> np.ndarray: + _check_data_format(df) + transformed_test_data = CovariatesGenerator().transform(df) + _LOGGER.info("Predicting the last value") + x_test, y_test = self.prepare_data(transformed_test_data) + return self._model.predict(x_test[-1].reshape(1, -1)) + + def save_artifacts(self, path: str) -> None: + artifact = {"model": self._model} + torch.save(artifact, path) + _LOGGER.info("Model saved at %s", path) + + def load_artifacts(self, path: str) -> None: + artifact = torch.load(path) + self._model = artifact["model"] + _LOGGER.info(f"Model loaded from {path}") diff --git a/numalogic/tools/data.py b/numalogic/tools/data.py index 10f956c8..566bf170 100644 --- a/numalogic/tools/data.py +++ b/numalogic/tools/data.py @@ -194,6 +194,61 @@ def __getitem__(self, idx: Union[int, slice]) -> npt.NDArray[float]: return self._data[(idx * self._stride) : (idx * self._stride) + self._seq_len] +class ForecastDataset(StreamingDataset): + """ + A Dataset for generating sequences for forecasting tasks. + + Args: + ---- + data: A numpy array containing the input data in the shape of (batch, num_features). + seq_len: Length of the sliding window sequences to be generated from the input data + horizon: The forecast horizon + stride: Stride to jump between sequences; defaults to 1 + """ + + def __init__(self, data: npt.NDArray[float], seq_len: int, horizon: int, stride: int = 1): + super().__init__(data, seq_len, stride=stride) + self.horizon = horizon + + def create_seq( + self, input_: npt.NDArray[float] + ) -> Generator[tuple[npt.NDArray[float], npt.NDArray[float]], None, None]: + r"""Yields sequences of specified length from the input data. + + Args: + ---- + input_: A numpy array containing the input data. + + Yields + ------ + A tuple of subarray of size (seq_len, num_features) + and forecast horizon of size (seq_len, num_features) + from the input data. + """ + idx = 0 + while idx < len(self._data) - self._seq_len - self.horizon + 1: + yield ( + input_[idx : idx + self._seq_len], + input_[idx + self._seq_len : idx + self._seq_len + self.horizon], + ) + idx += self._stride + + def __getitem__(self, idx: int) -> npt.NDArray[float]: + r"""Retrieves a sequence from the input data at the specified index.""" + if isinstance(idx, slice): + raise NotImplementedError("Slicing not supported!") + if idx >= len(self): + raise IndexError(f"{idx} out of bound!") + return ( + self._data[idx : idx + self._seq_len], + self._data[idx + self._seq_len : idx + self._seq_len + self.horizon], + ) + + def __len__(self) -> int: + r"""Returns the number of sequences that can be generated from the input data.""" + return (len(self._data) - self._seq_len - self.horizon) // self._stride + 1 + + class StreamingDataLoader(DataLoader): """ A DataLoader for convenience that uses StreamingDataset for handling time series data. diff --git a/numalogic/transforms/_covariates.py b/numalogic/transforms/_covariates.py new file mode 100644 index 00000000..4a0141ed --- /dev/null +++ b/numalogic/transforms/_covariates.py @@ -0,0 +1,60 @@ +import logging +from typing import Union + +import numpy as np +import pandas as pd +from darts import TimeSeries +from darts.utils.timeseries_generation import datetime_attribute_timeseries + +from numalogic.base import StatelessTransformer + +_LOGGER = logging.getLogger(__name__) + + +class CovariatesGenerator(StatelessTransformer): + """A transformer/generator that generates covariates for a timeseries dataset. + + Args: + ---- + timestamp_column_name: The name of the timestamp column + columns_to_preserve: The columns to preserve in the dataset + *covariate_attributes: The tuple of attributes to consider for generating covariates + """ + + def __init__( + self, + timestamp_column_name: str = "timestamp", + columns_to_preserve: Union[str, list[str]] = "value", + *covariate_attributes: tuple[str], + ): + self.covariate_attributes = ( + covariate_attributes if covariate_attributes else ("dayofweek", "month", "dayofyear") + ) + self.timestamp_column_name = timestamp_column_name + self.columns_to_preserve = columns_to_preserve + + def _get_covariates(self, df: pd.DataFrame): + covariates = [] + _LOGGER.info("Generating covariates for %s", self.covariate_attributes) + for attribute in self.covariate_attributes: + day_series = datetime_attribute_timeseries( + TimeSeries.from_dataframe( + df.reset_index(), self.timestamp_column_name, self.columns_to_preserve + ), + attribute=attribute, + one_hot=False, + cyclic=True, + ).values() + covariates.append(day_series) + return np.concatenate(covariates, axis=1) + + def transform(self, input_: pd.DataFrame, **__): + """ + Generate covariates for a timeseries dataset. + + Args: + ---- + data: np.array: input data + """ + covariates = self._get_covariates(input_) + return np.concatenate([input_.to_numpy(), covariates], axis=1) diff --git a/numalogic/udfs/preprocess.py b/numalogic/udfs/preprocess.py index ee187e65..b73bab4f 100644 --- a/numalogic/udfs/preprocess.py +++ b/numalogic/udfs/preprocess.py @@ -219,7 +219,9 @@ def exec(self, keys: list[str], datum: Datum) -> Messages: status=Status.RUNTIME_ERROR, ) msgs = Messages( - get_trainer_message(keys, _stream_conf, payload, **_metric_label_values), + get_trainer_message( + keys=keys, stream_conf=_stream_conf, payload=payload, **_metric_label_values + ), ) if _conf.numalogic_conf.score.adjust: msgs.append(get_static_thresh_message(keys, payload)) diff --git a/numalogic/udfs/tools.py b/numalogic/udfs/tools.py index 18d5260c..c6f3a4ef 100644 --- a/numalogic/udfs/tools.py +++ b/numalogic/udfs/tools.py @@ -9,7 +9,6 @@ from pandas import DataFrame from pynumaflow.mapper import Message - from numalogic.registry import ArtifactManager, ArtifactData from numalogic.tools.exceptions import RedisRegistryError from numalogic.tools.types import KEYS, redis_client_t diff --git a/poetry.lock b/poetry.lock index 64e50674..1b963dee 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,91 +1,117 @@ # This file is automatically @generated by Poetry 1.8.2 and should not be 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100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "numalogic" -version = "0.12.4" +version = "0.12.5" description = "Collection of operational Machine Learning models and tools." authors = ["Numalogic Developers"] packages = [{ include = "numalogic" }] @@ -43,6 +43,8 @@ redis = { extras = ["hiredis"], version = "^5.0", optional = true } boto3 = { version = "^1.24.64", optional = true } pydruid = { version = "^0.6", optional = true } PyMySQL = { version = "^1.1.0", optional = true } +xgboost = "^2.1.0" +u8darts = "^0.30.0" [tool.poetry.extras] mlflow = ["mlflow-skinny"] @@ -109,7 +111,7 @@ exclude = ''' line-length = 100 src = ["numalogic", "tests"] select = ["E", "F", "W", "C901", "NPY", "RUF", "TRY", "G", "PLE", "PLW", "UP", "ICN", "RET", "Q", "PLR", "D"] -ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035"] +ignore = ["TRY003", "TRY301", "RUF100", "D100", "D104", "PLR2004", "D102", "D401", "D107", "D205", "D105", "PLW0603", "PLR0915", "UP035", "G004"] target-version = "py39" show-fixes = true show-source = true diff --git a/tests/models/forecast/test_naive.py b/tests/models/forecast/test_naive.py index fd90e1f2..663a7b2a 100644 --- a/tests/models/forecast/test_naive.py +++ b/tests/models/forecast/test_naive.py @@ -1,7 +1,10 @@ import unittest from numalogic.synthetic.timeseries import SyntheticTSGenerator -from numalogic.models.forecast.variants import BaselineForecaster, SeasonalNaiveForecaster +from numalogic.models.forecast.variants import ( + BaselineForecaster, + SeasonalNaiveForecaster, +) class TestBaselineForecaster(unittest.TestCase): diff --git a/tests/models/forecast/test_xgboost.py b/tests/models/forecast/test_xgboost.py new file mode 100644 index 00000000..f9beedc2 --- /dev/null +++ b/tests/models/forecast/test_xgboost.py @@ -0,0 +1,38 @@ +import os + +import numpy as np + +from numalogic.models.forecast.variants import XGBoostForecaster +from numalogic.synthetic import SyntheticTSGenerator + + +def test_XGBForecaster1(): + ts_generator = SyntheticTSGenerator(seq_len=7200, num_series=1, freq="T") + ts_df = ts_generator.gen_tseries() + ts_df.columns = ["value"] + ts_df.index.name = "timestamp" + forecaster = XGBoostForecaster(horizon=6, seq_len=60) + forecaster.fit(ts_df) + forecaster.save_artifacts("model.pkl") + forecaster_dummy = XGBoostForecaster(horizon=6, seq_len=60) + forecaster_dummy.load_artifacts("model.pkl") + os.remove("model.pkl") + pred_1 = forecaster_dummy.predict_horizon(ts_df) + pred_2 = forecaster.predict_horizon(ts_df) + assert np.array_equal(pred_1, pred_2) + + +def test_XGBForecaster2(): + ts_generator = SyntheticTSGenerator(seq_len=7200, num_series=1, freq="T") + ts_df = ts_generator.gen_tseries() + ts_df.columns = ["value"] + ts_df.index.name = "timestamp" + forecaster = XGBoostForecaster(horizon=6, seq_len=60, early_stopping=False) + forecaster.fit(ts_df) + forecaster.save_artifacts("model.pkl") + forecaster_dummy = XGBoostForecaster(horizon=6, seq_len=60) + forecaster_dummy.load_artifacts("model.pkl") + os.remove("model.pkl") + pred_1 = forecaster_dummy.predict_last(ts_df) + pred_2 = forecaster.predict_last(ts_df) + assert np.array_equal(pred_1, pred_2)