From 5325683662980198cab5c146ae66cb9817efbebb Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 14 Aug 2026 13:23:17 -0600 Subject: [PATCH 1/8] chore: delete gift_eval dir --- timecopilot/gift_eval/__init__.py | 0 timecopilot/gift_eval/data.py | 238 ------------------- timecopilot/gift_eval/eval.py | 258 --------------------- timecopilot/gift_eval/gluonts_predictor.py | 184 --------------- timecopilot/gift_eval/utils.py | 94 -------- 5 files changed, 774 deletions(-) delete mode 100644 timecopilot/gift_eval/__init__.py delete mode 100644 timecopilot/gift_eval/data.py delete mode 100644 timecopilot/gift_eval/eval.py delete mode 100644 timecopilot/gift_eval/gluonts_predictor.py delete mode 100644 timecopilot/gift_eval/utils.py diff --git a/timecopilot/gift_eval/__init__.py b/timecopilot/gift_eval/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/timecopilot/gift_eval/data.py b/timecopilot/gift_eval/data.py deleted file mode 100644 index a4b2a272..00000000 --- a/timecopilot/gift_eval/data.py +++ /dev/null @@ -1,238 +0,0 @@ -# Adapted from https://github.com/SalesforceAIResearch/gift-eval - -import math -import os -from collections.abc import Iterable, Iterator -from enum import Enum -from functools import cached_property -from pathlib import Path - -import datasets -import pyarrow.compute as pc -from dotenv import load_dotenv -from gluonts.dataset import DataEntry -from gluonts.dataset.common import ProcessDataEntry -from gluonts.dataset.split import TestData, TrainingDataset, split -from gluonts.itertools import Map -from gluonts.time_feature import norm_freq_str -from gluonts.transform import Transformation -from pandas.tseries.frequencies import to_offset -from toolz import compose - -TEST_SPLIT = 0.1 -MAX_WINDOW = 20 - -M4_PRED_LENGTH_MAP = { - "A": 6, - "Q": 8, - "M": 18, - "W": 13, - "D": 14, - "H": 48, -} - -PRED_LENGTH_MAP = { - "M": 12, - "W": 8, - "D": 30, - "H": 48, - "T": 48, - "S": 60, -} - -TFB_PRED_LENGTH_MAP = { - "A": 6, - "H": 48, - "Q": 8, - "D": 14, - "M": 18, - "W": 13, - "U": 8, - "T": 8, -} - - -class Term(Enum): - SHORT = "short" - MEDIUM = "medium" - LONG = "long" - - @property - def multiplier(self) -> int: - if self == Term.SHORT: - return 1 - elif self == Term.MEDIUM: - return 10 - elif self == Term.LONG: - return 15 - - -def itemize_start(data_entry: DataEntry) -> DataEntry: - data_entry["start"] = data_entry["start"].item() - return data_entry - - -def maybe_reconvert_freq(freq: str) -> str: - """if the freq is one of the newest pandas freqs, convert it to the old freq""" - deprecated_map = { - "Y": "A", - "YE": "A", - "QE": "Q", - "ME": "M", - "h": "H", - "min": "T", - "s": "S", - "us": "U", - } - if freq in deprecated_map: - return deprecated_map[freq] - return freq - - -class MultivariateToUnivariate(Transformation): - def __init__(self, field): - self.field = field - - def __call__( - self, data_it: Iterable[DataEntry], is_train: bool = False - ) -> Iterator: - for data_entry in data_it: - item_id = data_entry["item_id"] - val_ls = list(data_entry[self.field]) - for id, val in enumerate(val_ls): - univariate_entry = data_entry.copy() - univariate_entry[self.field] = val - univariate_entry["item_id"] = item_id + "_dim" + str(id) - yield univariate_entry - - -class Dataset: - def _storage_path_from_env_var(self, env_var: str) -> Path: - load_dotenv() - env_var_value = os.getenv(env_var) - if env_var_value is None: - raise ValueError(f"Environment variable {env_var} is not set") - return Path(env_var_value) - - def __init__( - self, - name: str, - term: Term | str = Term.SHORT, - to_univariate: bool = False, - storage_path: Path | str | None = None, - storage_env_var: str = "GIFT_EVAL", - ): - if storage_path is None: - storage_path = self._storage_path_from_env_var(storage_env_var) - else: - storage_path = Path(storage_path) - self.hf_dataset = datasets.load_from_disk(str(storage_path / name)).with_format( - "numpy" - ) - process = ProcessDataEntry( - self.freq, - one_dim_target=self.target_dim == 1, - ) - - self.gluonts_dataset = Map(compose(process, itemize_start), self.hf_dataset) - if to_univariate: - self.gluonts_dataset = MultivariateToUnivariate("target").apply( - self.gluonts_dataset - ) - - self.term = Term(term) - self.name = name - - @cached_property - def prediction_length(self) -> int: - freq = norm_freq_str(to_offset(self.freq).name) - freq = maybe_reconvert_freq(freq) - pred_len = ( - M4_PRED_LENGTH_MAP[freq] if "m4" in self.name else PRED_LENGTH_MAP[freq] - ) - return self.term.multiplier * pred_len - - @cached_property - def freq(self) -> str: - return self.hf_dataset[0]["freq"] - - @cached_property - def target_dim(self) -> int: - return ( - target.shape[0] - if len((target := self.hf_dataset[0]["target"]).shape) > 1 - else 1 - ) - - @cached_property - def past_feat_dynamic_real_dim(self) -> int: - if "past_feat_dynamic_real" not in self.hf_dataset[0]: - return 0 - elif ( - len( - ( - past_feat_dynamic_real := self.hf_dataset[0][ - "past_feat_dynamic_real" - ] - ).shape - ) - > 1 - ): - return past_feat_dynamic_real.shape[0] - else: - return 1 - - @cached_property - def windows(self) -> int: - if "m4" in self.name: - return 1 - w = math.ceil(TEST_SPLIT * self._min_series_length / self.prediction_length) - return min(max(1, w), MAX_WINDOW) - - @cached_property - def _min_series_length(self) -> int: - if self.hf_dataset[0]["target"].ndim > 1: - lengths = pc.list_value_length( - pc.list_flatten( - pc.list_slice(self.hf_dataset.data.column("target"), 0, 1) - ) - ) - else: - lengths = pc.list_value_length(self.hf_dataset.data.column("target")) - return min(lengths.to_numpy()) - - @cached_property - def sum_series_length(self) -> int: - if self.hf_dataset[0]["target"].ndim > 1: - lengths = pc.list_value_length( - pc.list_flatten(self.hf_dataset.data.column("target")) - ) - else: - lengths = pc.list_value_length(self.hf_dataset.data.column("target")) - return sum(lengths.to_numpy()) - - @property - def training_dataset(self) -> TrainingDataset: - training_dataset, _ = split( - self.gluonts_dataset, offset=-self.prediction_length * (self.windows + 1) - ) - return training_dataset - - @property - def validation_dataset(self) -> TrainingDataset: - validation_dataset, _ = split( - self.gluonts_dataset, offset=-self.prediction_length * self.windows - ) - return validation_dataset - - @property - def test_data(self) -> TestData: - _, test_template = split( - self.gluonts_dataset, offset=-self.prediction_length * self.windows - ) - test_data = test_template.generate_instances( - prediction_length=self.prediction_length, - windows=self.windows, - distance=self.prediction_length, - ) - return test_data diff --git a/timecopilot/gift_eval/eval.py b/timecopilot/gift_eval/eval.py deleted file mode 100644 index c773b9d6..00000000 --- a/timecopilot/gift_eval/eval.py +++ /dev/null @@ -1,258 +0,0 @@ -import logging -from pathlib import Path - -import pandas as pd -import requests -from dotenv import load_dotenv -from gluonts.ev.metrics import ( - MAE, - MAPE, - MASE, - MSE, - MSIS, - ND, - NRMSE, - RMSE, - SMAPE, - MeanWeightedSumQuantileLoss, -) -from gluonts.model import evaluate_model -from gluonts.model.predictor import RepresentablePredictor -from gluonts.time_feature import get_seasonality -from huggingface_hub import snapshot_download - -from .data import Dataset -from .gluonts_predictor import GluonTSPredictor -from .utils import MED_LONG_DATASETS, QUANTILE_LEVELS - -logger = logging.getLogger(__name__) - -load_dotenv() - -METRICS = [ - MSE(forecast_type="mean"), - MSE(forecast_type=0.5), - MAE(), - MASE(), - MAPE(), - SMAPE(), - MSIS(), - RMSE(), - NRMSE(), - ND(), - MeanWeightedSumQuantileLoss(quantile_levels=QUANTILE_LEVELS), -] -DATASET_PROPERTIES_URL = "https://raw.githubusercontent.com/SalesforceAIResearch/gift-eval/refs/heads/main/notebooks/dataset_properties.json" - - -class GIFTEval: - """ - Evaluation utility for [GIFTEval](https://huggingface.co/spaces/Salesforce/GIFT-Eval). - - This class loads a time series dataset, sets up evaluation metrics, and provides - methods to evaluate GluonTS predictors on the dataset, saving results to CSV if - desired. - """ - - @staticmethod - def download_data(storage_path: Path | str | None = None): - """ - Download the GIFTEval dataset from Hugging Face. - - Args: - storage_path (Path | str | None): Path to store the dataset. - """ - snapshot_download( - repo_id="Salesforce/GiftEval", - repo_type="dataset", - local_dir=storage_path, - ) - - def __init__( - self, - dataset_name: str, - term: str, - output_path: Path | str | None = None, - storage_path: Path | str | None = None, - ): - # fmt: off - """ - Initialize a GIFTEval instance for a specific dataset and evaluation term. - - Args: - dataset_name (str): Name of the dataset to evaluate on. - term (str): Evaluation term (e.g., 'medium', 'long'). - output_path (str | Path | None): Directory to save results CSV, or - None to skip saving. - storage_path (Path | str | None): Path where the dataset is stored. - - Example: - ```python - import pandas as pd - from timecopilot.gift_eval.eval import GIFTEval - from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor - from timecopilot.models.stats import SeasonalNaive - - storage_path = "./gift_eval_data" - GIFTEval.download_data(storage_path) - - predictor = GluonTSPredictor( - # you can use any forecaster from TimeCopilot - # and create your own forecaster by subclassing - # [Forecaster][timecopilot.models.utils.forecaster.Forecaster] - forecaster=SeasonalNaive(), - batch_size=512, - ) - gift_eval = GIFTEval( - dataset_name="m4_weekly", - term="short", - output_path="./seasonal_naive", - storage_path=storage_path, - ) - gift_eval.evaluate_predictor( - predictor, - batch_size=512, - ) - eval_df = pd.read_csv("./seasonal_naive/all_results.csv") - ``` - - Raises: - ValueError: If the dataset is not compatible with the specified term. - - """ - # fmt: on - res_dataset_properties = requests.get(DATASET_PROPERTIES_URL) - res_dataset_properties.raise_for_status() # Raise an error for bad responses - self.dataset_properties_map = res_dataset_properties.json() - pretty_names = { - "saugeenday": "saugeen", - "temperature_rain_with_missing": "temperature_rain", - "kdd_cup_2018_with_missing": "kdd_cup_2018", - "car_parts_with_missing": "car_parts", - } - if ( - term == "medium" or term == "long" - ) and dataset_name not in MED_LONG_DATASETS: - raise ValueError(f"Dataset {dataset_name} is not a medium or long dataset") - if "/" in dataset_name: - ds_key = dataset_name.split("/")[0] - ds_freq = dataset_name.split("/")[1] - ds_key = ds_key.lower() - ds_key = pretty_names.get(ds_key, ds_key) - else: - ds_key = dataset_name.lower() - ds_key = pretty_names.get(ds_key, ds_key) - ds_freq = self.dataset_properties_map[ds_key]["frequency"] - self.ds_config = f"{ds_key}/{ds_freq}/{term}" - self.ds_key = ds_key - - # Initialize the dataset - to_univariate = ( - Dataset( - name=dataset_name, - term=term, - to_univariate=False, - storage_path=storage_path, - ).target_dim - != 1 - ) - self.dataset = Dataset( - name=dataset_name, - term=term, - to_univariate=to_univariate, - storage_path=storage_path, - ) - self.dataset_name = dataset_name - self.seasonality = get_seasonality(self.dataset.freq) - self.output_path = output_path - - def evaluate_predictor( - self, - predictor: RepresentablePredictor | GluonTSPredictor, - batch_size: int | None = None, - overwrite_results: bool = False, - ): - """ - Evaluate a GluonTS predictor on the loaded dataset and save results. - - Args: - predictor (RepresentablePredictor | GluonTSPredictor): The predictor to - evaluate. - batch_size (int | None): Batch size for evaluation. If None, uses - predictor's default. - overwrite_results (bool): Whether to overwrite the existing results CSV - file. - """ - if batch_size is None: - if isinstance(predictor, GluonTSPredictor): - batch_size = predictor.batch_size - else: - batch_size = 512 - res = evaluate_model( - predictor, - test_data=self.dataset.test_data, - metrics=METRICS, - batch_size=batch_size, - axis=None, - mask_invalid_label=True, - allow_nan_forecast=False, - seasonality=self.seasonality, - ) - - # Prepare the results for the CSV file - model_name = ( - predictor.__class__.__name__ - if not isinstance(predictor, GluonTSPredictor) - else predictor.alias - ) - results_data = [ - [ - self.ds_config, - model_name, - res["MSE[mean]"][0], - res["MSE[0.5]"][0], - res["MAE[0.5]"][0], - res["MASE[0.5]"][0], - res["MAPE[0.5]"][0], - res["sMAPE[0.5]"][0], - res["MSIS"][0], - res["RMSE[mean]"][0], - res["NRMSE[mean]"][0], - res["ND[0.5]"][0], - res["mean_weighted_sum_quantile_loss"][0], - self.dataset_properties_map[self.ds_key]["domain"], - self.dataset_properties_map[self.ds_key]["num_variates"], - ] - ] - - # Create a DataFrame and write to CSV - results_df = pd.DataFrame( - results_data, - columns=[ - "dataset", - "model", - "eval_metrics/MSE[mean]", - "eval_metrics/MSE[0.5]", - "eval_metrics/MAE[0.5]", - "eval_metrics/MASE[0.5]", - "eval_metrics/MAPE[0.5]", - "eval_metrics/sMAPE[0.5]", - "eval_metrics/MSIS", - "eval_metrics/RMSE[mean]", - "eval_metrics/NRMSE[mean]", - "eval_metrics/ND[0.5]", - "eval_metrics/mean_weighted_sum_quantile_loss", - "domain", - "num_variates", - ], - ) - if self.output_path is not None: - csv_file_path = Path(self.output_path) / "all_results.csv" - csv_file_path.parent.mkdir(parents=True, exist_ok=True) - if csv_file_path.exists() and not overwrite_results: - results_df = pd.concat([pd.read_csv(csv_file_path), results_df]) - results_df.to_csv(csv_file_path, index=False) - - logger.info( - f"Results for {self.dataset_name} have been written to {csv_file_path}" - ) diff --git a/timecopilot/gift_eval/gluonts_predictor.py b/timecopilot/gift_eval/gluonts_predictor.py deleted file mode 100644 index 6bc44d29..00000000 --- a/timecopilot/gift_eval/gluonts_predictor.py +++ /dev/null @@ -1,184 +0,0 @@ -from typing import Any - -import numpy as np -import pandas as pd -import tqdm -import utilsforecast.processing as ufp -from gluonts.dataset import Dataset -from gluonts.dataset.util import forecast_start -from gluonts.model import Forecast -from gluonts.model.forecast import QuantileForecast -from gluonts.model.predictor import RepresentablePredictor -from gluonts.transform.feature import LastValueImputation, MissingValueImputation - -from ..models.utils.forecaster import Forecaster -from .utils import QUANTILE_LEVELS - - -class GluonTSPredictor(RepresentablePredictor): - """ - Adapter to use a TimeCopilot Forecaster as a GluonTS Predictor. - - This class wraps a TimeCopilot Forecaster and exposes the GluonTS Predictor - interface, allowing it to be used with GluonTS evaluation and processing utilities. - """ - - def __init__( - self, - forecaster: Forecaster, - h: int | None = None, - freq: str | None = None, - level: list[int | float] | None = None, - quantiles: list[float] | None = None, - max_length: int | None = None, - imputation_method: MissingValueImputation | None = None, - batch_size: int | None = 1024, - ): - """ - Initialize a GluonTSPredictor. - - Args: - forecaster (Forecaster): The TimeCopilot forecaster to wrap. - You can use any forecaster from TimeCopilot, and create your own - forecaster by subclassing - [Forecaster][timecopilot.models.utils.forecaster.Forecaster]. - h (int | None): Forecast horizon. If None (default), the horizon is - inferred from the dataset. - freq (str | None): Frequency string (e.g., 'D', 'H'). - If None (default), the frequency is inferred from the dataset. - level (list[int | float] | None): Not supported; use quantiles instead. - quantiles (list[float] | None): Quantiles to forecast. If None (default), - the default quantiles [0.1, 0.2, ..., 0.9] are used. - max_length (int | None): Maximum length of input series. - imputation_method (MissingValueImputation | None): Imputation method for - missing values. If None (default), the last value is used - with LastValueImputation(). - batch_size (int | None): Batch size for prediction. - - Raises: - NotImplementedError: If level is provided (use quantiles instead). - """ - self.forecaster = forecaster - self.h = h - self.freq = freq - self.level = level - if level is not None: - raise NotImplementedError("level is not supported, use quantiles instead") - self.quantiles = quantiles or QUANTILE_LEVELS - self.max_length = max_length - self.imputation_method = imputation_method or LastValueImputation() - self.batch_size = batch_size - self.alias = forecaster.alias - - def _gluonts_dataset_to_df( - self, - dataset: Dataset, - ) -> tuple[pd.DataFrame, dict[str, Any]]: - dfs: list[pd.DataFrame] = [] - metadata: dict[str, Any] = {} - for _, entry in enumerate(dataset): - target = np.asarray(entry["target"], dtype=np.float32) - if self.max_length is not None and len(target) > self.max_length: - entry["start"] += len(target[: -self.max_length]) - target = target[-self.max_length :] - if np.isnan(target).any(): - target = self.imputation_method(target) - if target.ndim > 1: - raise ValueError("only for univariate time series") - fcst_start = forecast_start(entry) - uid = f"{entry['item_id']}-{fcst_start}" - ds_start = entry["start"] - ds = pd.date_range( - start=ds_start.to_timestamp(), - freq=ds_start.freq, - periods=len(target), - ) - uid_df = pd.DataFrame( - { - "unique_id": uid, - "ds": ds, - "y": target, - } - ) - dfs.append(uid_df) - metadata[uid] = { - "item_id": entry["item_id"], - "fcst_start": fcst_start, - } - df = pd.concat(dfs, ignore_index=True) - return df, metadata - - def _predict_df( - self, - df: pd.DataFrame, - metadata: dict[str, Any], - h: int, - freq: str, - ) -> list[Forecast]: - fcst_df = self.forecaster.forecast( - df=df, - h=h, - freq=freq, - level=self.level, - quantiles=self.quantiles, - ) - fcst_df = fcst_df.set_index("unique_id") - fcsts: list[Forecast] = [] - for uid, metadata_uid in metadata.items(): - fcst_df_uid = fcst_df.loc[uid] - forecast_arrays = ufp.value_cols_to_numpy( - df=fcst_df_uid, - id_col="unique_id", - time_col="ds", - target_col=None, - ) - forecast_keys = ["mean"] - if self.quantiles is not None: - forecast_keys += [f"{q}" for q in self.quantiles] - q_fcst = QuantileForecast( - forecast_arrays=forecast_arrays.T, - forecast_keys=forecast_keys, - item_id=metadata_uid["item_id"], - start_date=metadata_uid["fcst_start"], - ) - fcsts.append(q_fcst) - return fcsts - - def _predict_batch( - self, - batch: list[Dataset], - h: int, - freq: str, - ) -> list[Forecast]: - df, metadata = self._gluonts_dataset_to_df(batch) - return self._predict_df(df=df, metadata=metadata, h=h, freq=freq) - - def predict(self, dataset: Dataset, **kwargs: Any) -> list[Forecast]: - """ - Predict forecasts for a GluonTS Dataset. - - Args: - dataset (Dataset): GluonTS Dataset to forecast. - **kwargs: Additional keyword arguments (unused). - - Returns: - list[Forecast]: List of GluonTS Forecast objects for the dataset. - """ - fcsts: list[Forecast] = [] - batch: list[Dataset] = [] - h = self.h or dataset.test_data.prediction_length - if h is None: - raise ValueError("horizon `h` must be provided") - freq = self.freq - for _, entry in tqdm.tqdm(enumerate(dataset), total=len(dataset)): - if freq is None: - freq = entry["freq"] - batch.append(entry) - if len(batch) == self.batch_size: - fcsts.extend(self._predict_batch(batch=batch, h=h, freq=freq)) - batch = [] - if len(batch) > 0: - if freq is None: - raise ValueError("frequency `freq` must be provided") - fcsts.extend(self._predict_batch(batch=batch, h=h, freq=freq)) - return fcsts diff --git a/timecopilot/gift_eval/utils.py b/timecopilot/gift_eval/utils.py deleted file mode 100644 index 29708cdd..00000000 --- a/timecopilot/gift_eval/utils.py +++ /dev/null @@ -1,94 +0,0 @@ -from itertools import product - -QUANTILE_LEVELS = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - -SHORT_DATASETS = [ - "m4_yearly", - "m4_quarterly", - "m4_monthly", - "m4_weekly", - "m4_daily", - "m4_hourly", - "electricity/15T", - "electricity/H", - "electricity/D", - "electricity/W", - "solar/10T", - "solar/H", - "solar/D", - "solar/W", - "hospital", - "covid_deaths", - "us_births/D", - "us_births/M", - "us_births/W", - "saugeenday/D", - "saugeenday/M", - "saugeenday/W", - "temperature_rain_with_missing", - "kdd_cup_2018_with_missing/H", - "kdd_cup_2018_with_missing/D", - "car_parts_with_missing", - "restaurant", - "hierarchical_sales/D", - "hierarchical_sales/W", - "LOOP_SEATTLE/5T", - "LOOP_SEATTLE/H", - "LOOP_SEATTLE/D", - "SZ_TAXI/15T", - "SZ_TAXI/H", - "M_DENSE/H", - "M_DENSE/D", - "ett1/15T", - "ett1/H", - "ett1/D", - "ett1/W", - "ett2/15T", - "ett2/H", - "ett2/D", - "ett2/W", - "jena_weather/10T", - "jena_weather/H", - "jena_weather/D", - "bitbrains_fast_storage/5T", - "bitbrains_fast_storage/H", - "bitbrains_rnd/5T", - "bitbrains_rnd/H", - "bizitobs_application", - "bizitobs_service", - "bizitobs_l2c/5T", - "bizitobs_l2c/H", -] - -MED_LONG_DATASETS = [ - "electricity/15T", - "electricity/H", - "solar/10T", - "solar/H", - "kdd_cup_2018_with_missing/H", - "LOOP_SEATTLE/5T", - "LOOP_SEATTLE/H", - "SZ_TAXI/15T", - "M_DENSE/H", - "ett1/15T", - "ett1/H", - "ett2/15T", - "ett2/H", - "jena_weather/10T", - "jena_weather/H", - "bitbrains_fast_storage/5T", - "bitbrains_rnd/5T", - "bizitobs_application", - "bizitobs_service", - "bizitobs_l2c/5T", - "bizitobs_l2c/H", -] - -ALL_DATASETS = SHORT_DATASETS + MED_LONG_DATASETS - - -DATASETS_WITH_TERMS = [(dataset_name, "short") for dataset_name in SHORT_DATASETS] -DATASETS_WITH_TERMS += [ - (dataset_name, term) - for dataset_name, term in product(MED_LONG_DATASETS, ["medium", "long"]) -] From 3142c3f5befff3bb3f9f8a9f9b09ead35a8598b6 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 14 Aug 2026 13:23:47 -0600 Subject: [PATCH 2/8] tests: update tests based on timecopilot-gift-eval --- tests/gift_eval/conftest.py | 2 +- tests/gift_eval/test_evaluation.py | 20 ++++++-------------- tests/gift_eval/test_gift_eval.py | 4 +--- 3 files changed, 8 insertions(+), 18 deletions(-) diff --git a/tests/gift_eval/conftest.py b/tests/gift_eval/conftest.py index 21f3e1d8..5fb1f79f 100644 --- a/tests/gift_eval/conftest.py +++ b/tests/gift_eval/conftest.py @@ -3,7 +3,7 @@ import pandas as pd import pytest -from timecopilot.gift_eval.eval import GIFTEval +from timecopilot_gift_eval import GIFTEval @pytest.fixture(scope="session") diff --git a/tests/gift_eval/test_evaluation.py b/tests/gift_eval/test_evaluation.py index 581111d3..9d51f1fa 100644 --- a/tests/gift_eval/test_evaluation.py +++ b/tests/gift_eval/test_evaluation.py @@ -5,14 +5,11 @@ import pandas as pd import pytest -from timecopilot.gift_eval.eval import GIFTEval -from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor -from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS from timecopilot.models.stats import SeasonalNaive +from timecopilot_gift_eval import GIFTEval, GluonTSPredictor +from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS -TARGET_COLS = [ - "dataset", - "model", +METRIC_COLS = [ "eval_metrics/MSE[mean]", "eval_metrics/MSE[0.5]", "eval_metrics/MAE[0.5]", @@ -38,8 +35,6 @@ def test_number_of_datasets(all_results_df: pd.DataFrame): @pytest.mark.gift_eval @pytest.mark.parametrize( "dataset_name, term", - # testing 20 random datasets - # each time to prevent longer running tests random.sample(DATASETS_WITH_TERMS, 20), ) def test_evaluation( @@ -49,10 +44,7 @@ def test_evaluation( storage_path: Path, ): predictor = GluonTSPredictor( - forecaster=SeasonalNaive( - # alias used by the official evaluation - alias="Seasonal_Naive", - ), + forecaster=SeasonalNaive(alias="Seasonal_Naive"), batch_size=512, ) with tempfile.TemporaryDirectory() as temp_dir: @@ -70,8 +62,8 @@ def test_evaluation( expected_eval_df = all_results_df.query("dataset == @gifteval.ds_config") assert not eval_df.isna().any().any() pd.testing.assert_frame_equal( - eval_df.reset_index(drop=True)[TARGET_COLS], - expected_eval_df.reset_index(drop=True)[TARGET_COLS], + eval_df.reset_index(drop=True)[METRIC_COLS], + expected_eval_df.reset_index(drop=True)[METRIC_COLS], atol=1e-2, rtol=1e-2, check_dtype=False, diff --git a/tests/gift_eval/test_gift_eval.py b/tests/gift_eval/test_gift_eval.py index d7095084..83f6bf6f 100644 --- a/tests/gift_eval/test_gift_eval.py +++ b/tests/gift_eval/test_gift_eval.py @@ -3,9 +3,8 @@ import pandas as pd -from timecopilot.gift_eval.eval import GIFTEval -from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor from timecopilot.models.stats import SeasonalNaive +from timecopilot_gift_eval import GIFTEval, GluonTSPredictor def test_concat_results(storage_path: Path): @@ -45,7 +44,6 @@ def _evaluate_predictor( output_path=temp_dir, ) eval_df = pd.read_csv(Path(temp_dir) / "all_results.csv") - print(eval_df) assert len(eval_df) == i + 1 _evaluate_predictor( From 28bd087d772a22ac5bcca226ec5c0638987380a4 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 14 Aug 2026 13:24:05 -0600 Subject: [PATCH 3/8] chore: add opt dep timecopilot-gift-eval --- pyproject.toml | 5 +++++ uv.lock | 30 +++++++++++++++++++++++++++++- 2 files changed, 34 insertions(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 31312721..cd9f1e0a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -17,6 +17,7 @@ dev = [ "pytest>=8.4.2", "s3fs>=2025.3.0", "sktime>=0.40.1", + "timecopilot-gift-eval>=0.3.0", ] docs = [ "mkdocs-include-markdown-plugin>=7.1.6", @@ -27,6 +28,7 @@ docs = [ "mktestdocs>=0.2.5", "modal>=1.0.4", "ruff>=0.12.1", + "timecopilot-gift-eval>=0.3.0", ] [project] @@ -114,6 +116,9 @@ distributed = [ "pyspark<4.1", "ray==2.48", ] +gift-eval = [ + "timecopilot-gift-eval>=0.3.0", +] [project.scripts] timecopilot = "timecopilot._cli:main" diff --git a/uv.lock b/uv.lock index 948dc8be..0661e3ae 100644 --- a/uv.lock +++ b/uv.lock @@ -7572,6 +7572,9 @@ distributed = [ { name = "pyspark" }, { name = "ray" }, ] +gift-eval = [ + { name = "timecopilot-gift-eval" }, +] [package.dev-dependencies] dev = [ @@ -7587,6 +7590,7 @@ dev = [ { name = "pytest-xdist" }, { name = "s3fs" }, { name = "sktime" }, + { name = "timecopilot-gift-eval" }, ] docs = [ { name = "mkdocs" }, @@ -7597,6 +7601,7 @@ docs = [ { name = "mktestdocs" }, { name = "modal" }, { name = "ruff" }, + { name = "timecopilot-gift-eval" }, ] [package.metadata] @@ -7638,6 +7643,7 @@ requires-dist = [ { name = "scipy", specifier = "<=1.15.3" }, { name = "statsforecast", specifier = ">=2.0.2" }, { name = "tensorboard", specifier = ">=2.20.0" }, + { name = "timecopilot-gift-eval", marker = "extra == 'gift-eval'", specifier = ">=0.3.0" }, { name = "torchmetrics", specifier = ">=1.8.2" }, { name = "transformers", marker = "python_full_version < '3.13'", specifier = ">=4.41,<6" }, { name = "transformers", marker = "python_full_version >= '3.13'", specifier = ">=4.48,<6" }, @@ -7645,7 +7651,7 @@ requires-dist = [ { name = 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b/docs/api/gift-eval/gift-eval.md index f6f57fbe..cf1c524c 100644 --- a/docs/api/gift-eval/gift-eval.md +++ b/docs/api/gift-eval/gift-eval.md @@ -1,11 +1,22 @@ -# `timecopilot.gift_eval` +# `timecopilot_gift_eval` -::: timecopilot.gift_eval.eval +GIFT-Eval integration lives in the standalone [`timecopilot-gift-eval`](https://pypi.org/project/timecopilot-gift-eval/) package on PyPI. Install it with TimeCopilot via: + +```bash +pip install "timecopilot[gift-eval]" +``` + +::: timecopilot_gift_eval.eval options: members: - GIFTEval -::: timecopilot.gift_eval.gluonts_predictor +::: timecopilot_gift_eval.gluonts_predictor + options: + members: + - GluonTSPredictor + +::: timecopilot_gift_eval.protocol options: members: - - GluonTSPredictor \ No newline at end of file + - ForecasterProtocol diff --git a/docs/examples/gift-eval.ipynb b/docs/examples/gift-eval.ipynb index 7b499323..33b7810e 100644 --- a/docs/examples/gift-eval.ipynb +++ b/docs/examples/gift-eval.ipynb @@ -53,7 +53,7 @@ }, "outputs": [], "source": [ - "%pip install \"timecopilot>=0.0.22\"" + "%pip install \"timecopilot[gift-eval]>=0.0.22\"" ] }, { @@ -64,7 +64,7 @@ "source": [ "## Dataset Setup\n", "\n", - "TimeCopilot includes built-in [GIFT-Eval integration](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot.gift_eval.eval.GIFTEval) for dataset handling:" + "TimeCopilot includes built-in [GIFT-Eval integration](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot_gift_eval.eval.GIFTEval) for dataset handling:" ] }, { @@ -76,7 +76,7 @@ }, "outputs": [], "source": [ - "from timecopilot.gift_eval.eval import GIFTEval\n", + "from timecopilot_gift_eval import GIFTEval\n", "\n", "# TimeCopilot's built-in GIFT-Eval dataset downloader\n", "# Handles the complete benchmark dataset with all 97 configurations\n", @@ -156,7 +156,7 @@ "id": "M2YcjoDF5NH7" }, "source": [ - "With TimeCopilot you can evaluate any [Forecaster](https://timecopilot.dev/api/models/utils/forecaster/#timecopilot.models.utils.forecaster.Forecaster) in a standardized way using its [GIFT-Eval](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot.gift_eval.eval.GIFTEval) integration." + "With TimeCopilot you can evaluate any [Forecaster](https://timecopilot.dev/api/models/utils/forecaster/#timecopilot.models.utils.forecaster.Forecaster) in a standardized way using its [GIFT-Eval](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot_gift_eval.eval.GIFTEval) integration." ] }, { @@ -168,8 +168,7 @@ "outputs": [], "source": [ "import pandas as pd\n", - "from timecopilot.gift_eval.eval import GIFTEval\n", - "from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor\n", + "from timecopilot_gift_eval import GIFTEval, GluonTSPredictor\n", "\n", "\n", "def evaluate_forecaster(\n", @@ -710,7 +709,7 @@ }, "outputs": [], "source": [ - "from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS" + "from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS" ] }, { @@ -789,7 +788,7 @@ "id": "g0-oVisu6XX3" }, "source": [ - "The TimeCopilot's [GIFT-Eval integration](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot.gift_eval.eval.GIFTEval) was designed considering reproducibility as one of its main features. The library can replicate the official results provided by the mantainers of the benchmark for the [`SeasonalNaive`](https://huggingface.co/spaces/Salesforce/GIFT-Eval/tree/main/results/seasonal_naive) method. The following code replicates the Seasonal Naive performance for the datasets evaluated in this notebook. The reproducibility of the results for the rest of the datasets are tested continuously in the [library's repo](https://github.com/TimeCopilot/timecopilot/blob/main/tests/gift_eval/test_evaluation.py)." + "The TimeCopilot's [GIFT-Eval integration](https://timecopilot.dev/api/gift-eval/gift-eval/#timecopilot_gift_eval.eval.GIFTEval) was designed considering reproducibility as one of its main features. The library can replicate the official results provided by the mantainers of the benchmark for the [`SeasonalNaive`](https://huggingface.co/spaces/Salesforce/GIFT-Eval/tree/main/results/seasonal_naive) method. The following code replicates the Seasonal Naive performance for the datasets evaluated in this notebook. The reproducibility of the results for the rest of the datasets are tested continuously in the [library's repo](https://github.com/TimeCopilot/timecopilot/blob/main/tests/gift_eval/test_evaluation.py)." ] }, { diff --git a/experiments/gift-eval/pyproject.toml b/experiments/gift-eval/pyproject.toml index db5416c8..62d681b0 100644 --- a/experiments/gift-eval/pyproject.toml +++ b/experiments/gift-eval/pyproject.toml @@ -3,12 +3,13 @@ dependencies = [ "modal>=1.0.5", "s3fs>=2023.12.1", "timecopilot>=0.0.21", + "timecopilot-gift-eval>=0.3.0", "transformers<4.54", "transformers==4.40.1 ; python_full_version < '3.12'", "typer>=0.16.0", ] description = "TimeCopilot experiments for GIFT-Eval" -name = "timecopilot-gift-eval" +name = "timecopilot-gift-eval-experiments" readme = "README.md" requires-python = ">=3.11" version = "0.2.0" diff --git a/experiments/gift-eval/src/download_results.py b/experiments/gift-eval/src/download_results.py index 55552ef0..61f558bd 100644 --- a/experiments/gift-eval/src/download_results.py +++ b/experiments/gift-eval/src/download_results.py @@ -3,7 +3,7 @@ import pandas as pd -from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS +from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS logging.basicConfig(level=logging.INFO) diff --git a/experiments/gift-eval/src/run_modal.py b/experiments/gift-eval/src/run_modal.py index 99b04829..6b0b7a7c 100644 --- a/experiments/gift-eval/src/run_modal.py +++ b/experiments/gift-eval/src/run_modal.py @@ -60,7 +60,7 @@ def main(): import fsspec - from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS + from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS logging.basicConfig(level=logging.INFO) diff --git a/experiments/gift-eval/src/run_timecopilot.py b/experiments/gift-eval/src/run_timecopilot.py index bd259543..022cb557 100644 --- a/experiments/gift-eval/src/run_timecopilot.py +++ b/experiments/gift-eval/src/run_timecopilot.py @@ -3,8 +3,7 @@ import typer -from 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+++ b/mkdocs.yml @@ -171,6 +171,7 @@ plugins: options: preload_modules: - foundationforecast + - timecopilot_gift_eval relative_crossrefs: true members_order: source separate_signature: true From a3fc86b76030b0f093d76af4714633a4034911fe Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 14 Aug 2026 13:25:11 -0600 Subject: [PATCH 5/8] fix: lint --- experiments/gift-eval/pyproject.toml | 2 +- experiments/gift-eval/src/download_results.py | 1 - experiments/gift-eval/src/run_modal.py | 1 - experiments/gift-eval/src/run_timecopilot.py | 2 +- tests/gift_eval/conftest.py | 1 - tests/gift_eval/test_evaluation.py | 4 ++-- tests/gift_eval/test_gift_eval.py | 2 +- 7 files changed, 5 insertions(+), 8 deletions(-) diff --git a/experiments/gift-eval/pyproject.toml b/experiments/gift-eval/pyproject.toml index 62d681b0..c779631d 100644 --- a/experiments/gift-eval/pyproject.toml +++ b/experiments/gift-eval/pyproject.toml @@ -2,8 +2,8 @@ dependencies = [ "modal>=1.0.5", "s3fs>=2023.12.1", - "timecopilot>=0.0.21", "timecopilot-gift-eval>=0.3.0", + "timecopilot>=0.0.21", "transformers<4.54", "transformers==4.40.1 ; python_full_version < '3.12'", "typer>=0.16.0", diff --git a/experiments/gift-eval/src/download_results.py b/experiments/gift-eval/src/download_results.py index 61f558bd..1569b2b9 100644 --- a/experiments/gift-eval/src/download_results.py +++ b/experiments/gift-eval/src/download_results.py @@ -2,7 +2,6 @@ from pathlib import Path import pandas as pd - from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS logging.basicConfig(level=logging.INFO) diff --git a/experiments/gift-eval/src/run_modal.py b/experiments/gift-eval/src/run_modal.py index 6b0b7a7c..7128bcf6 100644 --- a/experiments/gift-eval/src/run_modal.py +++ b/experiments/gift-eval/src/run_modal.py @@ -59,7 +59,6 @@ def main(): import logging import fsspec - from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS logging.basicConfig(level=logging.INFO) diff --git a/experiments/gift-eval/src/run_timecopilot.py b/experiments/gift-eval/src/run_timecopilot.py index 022cb557..fba11cc7 100644 --- a/experiments/gift-eval/src/run_timecopilot.py +++ b/experiments/gift-eval/src/run_timecopilot.py @@ -2,8 +2,8 @@ from typing import Annotated import typer - from timecopilot_gift_eval import GIFTEval, GluonTSPredictor + from timecopilot.models.ensembles.median import MedianEnsemble from timecopilot.models.foundation.chronos import Chronos from timecopilot.models.foundation.timesfm import TimesFM diff --git a/tests/gift_eval/conftest.py b/tests/gift_eval/conftest.py index 5fb1f79f..864ebe14 100644 --- a/tests/gift_eval/conftest.py +++ b/tests/gift_eval/conftest.py @@ -2,7 +2,6 @@ import pandas as pd import pytest - from timecopilot_gift_eval import GIFTEval diff --git a/tests/gift_eval/test_evaluation.py b/tests/gift_eval/test_evaluation.py index 9d51f1fa..bec853e9 100644 --- a/tests/gift_eval/test_evaluation.py +++ b/tests/gift_eval/test_evaluation.py @@ -4,11 +4,11 @@ import pandas as pd import pytest - -from timecopilot.models.stats import SeasonalNaive from timecopilot_gift_eval import GIFTEval, GluonTSPredictor from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS +from timecopilot.models.stats import SeasonalNaive + METRIC_COLS = [ "eval_metrics/MSE[mean]", "eval_metrics/MSE[0.5]", diff --git a/tests/gift_eval/test_gift_eval.py b/tests/gift_eval/test_gift_eval.py index 83f6bf6f..e9448db7 100644 --- a/tests/gift_eval/test_gift_eval.py +++ b/tests/gift_eval/test_gift_eval.py @@ -2,9 +2,9 @@ from pathlib import Path import pandas as pd +from timecopilot_gift_eval import GIFTEval, GluonTSPredictor from timecopilot.models.stats import SeasonalNaive -from timecopilot_gift_eval import GIFTEval, GluonTSPredictor def test_concat_results(storage_path: Path): From 4279c9ad0446a6493cbb1ecdc00563ec852c1a57 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Fri, 14 Aug 2026 13:33:14 -0600 Subject: [PATCH 6/8] fix: compare dataset and model as well --- tests/gift_eval/test_evaluation.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tests/gift_eval/test_evaluation.py b/tests/gift_eval/test_evaluation.py index bec853e9..5d6cc671 100644 --- a/tests/gift_eval/test_evaluation.py +++ b/tests/gift_eval/test_evaluation.py @@ -9,7 +9,9 @@ from timecopilot.models.stats import SeasonalNaive -METRIC_COLS = [ +TARGET_COLS = [ + "dataset", + "model", "eval_metrics/MSE[mean]", "eval_metrics/MSE[0.5]", "eval_metrics/MAE[0.5]", @@ -62,8 +64,8 @@ def test_evaluation( expected_eval_df = all_results_df.query("dataset == @gifteval.ds_config") assert not eval_df.isna().any().any() pd.testing.assert_frame_equal( - eval_df.reset_index(drop=True)[METRIC_COLS], - expected_eval_df.reset_index(drop=True)[METRIC_COLS], + eval_df.reset_index(drop=True)[TARGET_COLS], + expected_eval_df.reset_index(drop=True)[TARGET_COLS], atol=1e-2, rtol=1e-2, check_dtype=False, From 89082ef9bbd1bb3e171a7e338082b594cda02764 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 17 Aug 2026 14:30:35 -0600 Subject: [PATCH 7/8] chore: add info of breaking change --- docs/api/gift-eval/gift-eval.md | 12 ++++++++++++ docs/changelogs/index.md | 1 + docs/changelogs/v0.0.31.md | 22 ++++++++++++++++++++++ mkdocs.yml | 1 + 4 files changed, 36 insertions(+) create mode 100644 docs/changelogs/v0.0.31.md diff --git a/docs/api/gift-eval/gift-eval.md b/docs/api/gift-eval/gift-eval.md index cf1c524c..8de56a7d 100644 --- a/docs/api/gift-eval/gift-eval.md +++ b/docs/api/gift-eval/gift-eval.md @@ -6,6 +6,18 @@ GIFT-Eval integration lives in the standalone [`timecopilot-gift-eval`](https:// pip install "timecopilot[gift-eval]" ``` +## Migration from `timecopilot.gift_eval` + +The in-repo module `timecopilot.gift_eval` was removed in favor of the standalone package. Update imports as follows: + +| Before | After | +|--------|-------| +| `from timecopilot.gift_eval.eval import GIFTEval` | `from timecopilot_gift_eval import GIFTEval` | +| `from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor` | `from timecopilot_gift_eval import GluonTSPredictor` | +| `from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS` | `from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS` | + +No compatibility shim is provided — install `timecopilot-gift-eval` (or `timecopilot[gift-eval]`) and switch to the `timecopilot_gift_eval` import path. + ::: timecopilot_gift_eval.eval options: members: diff --git a/docs/changelogs/index.md b/docs/changelogs/index.md index d7759a6f..6a7faffb 100644 --- a/docs/changelogs/index.md +++ b/docs/changelogs/index.md @@ -2,6 +2,7 @@ Welcome to the TimeCopilot Changelog. Here, you will find a comprehensive list of all the changes, updates, and improvements made to the TimeCopilot project. This section is designed to keep you informed about the latest features, bug fixes, and enhancements as we continue to develop and refine the TimeCopilot experience. Stay tuned for regular updates and feel free to explore the details of each release below. +- [v0.0.31](v0.0.31.md) - [v0.0.30](v0.0.30.md) - [v0.0.29](v0.0.29.md) - [v0.0.28](v0.0.28.md) diff --git a/docs/changelogs/v0.0.31.md b/docs/changelogs/v0.0.31.md new file mode 100644 index 00000000..254f8a6d --- /dev/null +++ b/docs/changelogs/v0.0.31.md @@ -0,0 +1,22 @@ +### Breaking Changes + +* **GIFT-Eval moved to standalone package**: The in-repo `timecopilot.gift_eval` module was removed. GIFT-Eval integration now lives in the [`timecopilot-gift-eval`](https://pypi.org/project/timecopilot-gift-eval/) PyPI package (`timecopilot_gift_eval` import path). Install via `pip install "timecopilot[gift-eval]"` or `pip install timecopilot-gift-eval>=0.3.0`. + + **Migration:** + + ```python + # before + from timecopilot.gift_eval.eval import GIFTEval + from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor + from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS + + # after + from timecopilot_gift_eval import GIFTEval, GluonTSPredictor + from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS + ``` + + See the [GIFT-Eval API docs](../api/gift-eval/gift-eval.md) for details. + +--- + +**Full Changelog**: https://github.com/TimeCopilot/timecopilot/compare/v0.0.30...v0.0.31 diff --git a/mkdocs.yml b/mkdocs.yml index 7ef3b51b..16bca018 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -61,6 +61,7 @@ nav: - experiments/fev.md - Changelogs: - changelogs/index.md + - changelogs/v0.0.31.md - changelogs/v0.0.30.md - changelogs/v0.0.29.md - changelogs/v0.0.28.md From 4fc87e55be23ff743efcf96bcc6ffebf35f540a8 Mon Sep 17 00:00:00 2001 From: AzulGarza Date: Mon, 17 Aug 2026 14:45:03 -0600 Subject: [PATCH 8/8] fix: add old behaviour as non-runnable --- docs/changelogs/v0.0.31.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/changelogs/v0.0.31.md b/docs/changelogs/v0.0.31.md index 254f8a6d..070a7d13 100644 --- a/docs/changelogs/v0.0.31.md +++ b/docs/changelogs/v0.0.31.md @@ -4,13 +4,13 @@ **Migration:** - ```python - # before + ```text from timecopilot.gift_eval.eval import GIFTEval from timecopilot.gift_eval.gluonts_predictor import GluonTSPredictor from timecopilot.gift_eval.utils import DATASETS_WITH_TERMS + ``` - # after + ```python from timecopilot_gift_eval import GIFTEval, GluonTSPredictor from timecopilot_gift_eval.utils import DATASETS_WITH_TERMS ```