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Merge pull request #33 from databricks-industry-solutions/mmf_sa
some updates on the neuralforecast models
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,104 @@ | ||
| import pandas as pd | ||
| import numpy as np | ||
| import torch | ||
| from chronos import ChronosPipeline | ||
| from sktime.performance_metrics.forecasting import | ||
| from typing import Iterator | ||
| from pyspark.sql.functions import collect_list, pandas_udf | ||
| from mmf_sa.models.abstract_model import ForecastingRegressor | ||
|
|
||
|
|
||
| class ChronosForecaster(ForecastingRegressor): | ||
| def __init__(self, params): | ||
| super().__init__(params) | ||
| self.params = params | ||
| self.device = None | ||
| self.model = None | ||
|
|
||
| def prepare_data(self, df: pd.DataFrame, future: bool = False) -> pd.DataFrame: | ||
| context = ( | ||
| df.rename( | ||
| columns={ | ||
| self.params.group_id: "unique_id", | ||
| self.params.date_col: "ds", | ||
| self.params.target: "y", | ||
| } | ||
| ) | ||
| ) | ||
|
|
||
| torch.tensor(_df[self.params.target]) | ||
|
|
||
| return context | ||
|
|
||
| def predict(self, hist_df: pd.DataFrame, val_df: pd.DataFrame = None): | ||
| # context must be either a 1D tensor, a list of 1D tensors, | ||
| # or a left-padded 2D tensor with batch as the first dimension | ||
| # forecast shape: [num_series, num_samples, prediction_length] | ||
| hist_df = self.spark | ||
| context = self.prepare_data(hist_df) | ||
| forecast_df = self.model.predict( | ||
| context=context, | ||
| prediction_length=self.params["prediction_length"], | ||
| num_samples=self.params["num_samples"], | ||
| ) | ||
|
|
||
| forecast_df = forecast_df.reset_index(drop=False).rename( | ||
| columns={ | ||
| "unique_id": self.params.group_id, | ||
| "ds": self.params.date_col, | ||
| target: self.params.target, | ||
| } | ||
| ) | ||
| forecast_df[self.params.target] = forecast_df[self.params.target].clip(0.01) | ||
|
|
||
| return forecast_df, self.model | ||
|
|
||
| def forecast(self, df: pd.DataFrame): | ||
| return self.predict(df) | ||
|
|
||
| def calculate_metrics( | ||
| self, hist_df: pd.DataFrame, val_df: pd.DataFrame, curr_date | ||
| ) -> list: | ||
|
|
||
| print(f"hist_df: {hist_df}") | ||
| print(f"val_df: {val_df}") | ||
| pred_df, model_fitted = self.predict(hist_df, val_df) | ||
|
|
||
| keys = pred_df[self.params["group_id"]].unique() | ||
| metrics = [] | ||
| if self.params["metric"] == "smape": | ||
| metric_name = "smape" | ||
| else: | ||
| raise Exception(f"Metric {self.params['metric']} not supported!") | ||
| for key in keys: | ||
| actual = val_df[val_df[self.params["group_id"]] == key][self.params["target"]] | ||
| forecast = pred_df[pred_df[self.params["group_id"]] == key][self.params["target"]].\ | ||
| iloc[-self.params["prediction_length"]:] | ||
| try: | ||
| if metric_name == "smape": | ||
| metric_value = mean_absolute_percentage_error(actual, forecast, symmetric=True) | ||
| metrics.extend( | ||
| [( | ||
| key, | ||
| curr_date, | ||
| metric_name, | ||
| metric_value, | ||
| actual.to_numpy(), | ||
| forecast.to_numpy(), | ||
| b'', | ||
| )]) | ||
| except: | ||
| pass | ||
| return metrics | ||
|
|
||
|
|
||
| class ChronosT5Large(ChronosForecaster): | ||
| def __init__(self, params): | ||
| super().__init__(params) | ||
| self.params = params | ||
| self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') | ||
| self.model = ChronosPipeline.from_pretrained( | ||
| "amazon/chronos-t5-large", | ||
| device_map=self.device, # use "cuda" for GPU and "cpu" for CPU inference | ||
| torch_dtype=torch.bfloat16, | ||
| ) |
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