mirror of
https://github.com/amazon-science/chronos-forecasting
synced 2026-05-24 01:58:27 +00:00
Merge branch 'main' into chronos2-js
This commit is contained in:
Kashif Rasul
GitHub
commit
35bd38ffc4
30 changed files with 3541 additions and 6568 deletions
2
.gitattributes
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2
.gitattributes
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@ -1 +1 @@
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.ipynb linguist-documentation
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*.ipynb linguist-language=Python
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2
.github/ISSUE_TEMPLATE/bug_report.md
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2
.github/ISSUE_TEMPLATE/bug_report.md
vendored
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@ -30,4 +30,4 @@ CUDA version:
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PyTorch version:
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HuggingFace transformers version:
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HuggingFace accelerate version:
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||||
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Pandas version:
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4
.github/workflows/eval-model.yml
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4
.github/workflows/eval-model.yml
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@ -36,13 +36,13 @@ jobs:
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run: pip install ".[dev]" -f https://download.pytorch.org/whl/cpu/torch_stable.html
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||||
- name: Run Eval Script for Chronos-2
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||||
run: python scripts/evaluation/evaluate.py chronos-2 ci/evaluate/backtest_config.yaml $CHRONOS_2_RESULTS_CSV --model-id=s3://autogluon/chronos-2 --device=cpu --torch-dtype=float32
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run: python scripts/evaluation/evaluate.py chronos-2 ci/evaluate/backtest_config.yaml $CHRONOS_2_RESULTS_CSV --model-id=amazon/chronos-2 --device=cpu --torch-dtype=float32
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||||
- name: Print Chronos-2 CSV
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||||
run: cat $CHRONOS_2_RESULTS_CSV
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||||
- name: Run Eval Script for Chronos-Bolt
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||||
run: python scripts/evaluation/evaluate.py chronos-bolt ci/evaluate/backtest_config.yaml $CHRONOS_BOLT_RESULTS_CSV --model-id=amazon/chronos-bolt-small --device=cpu --torch-dtype=float32
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- name: Print Chronos-Bolt CSV
|
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run: cat $CHRONOS_BOLT_RESULTS_CSV
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|||
7
.gitignore
vendored
7
.gitignore
vendored
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@ -160,4 +160,9 @@ cython_debug/
|
|||
#.idea/
|
||||
|
||||
# macOS stuff
|
||||
.DS_store
|
||||
.DS_store
|
||||
|
||||
chronos-2-finetuned
|
||||
|
||||
# Kiro IDE
|
||||
.kiro
|
||||
|
|
|
|||
33
README.md
33
README.md
|
|
@ -1,7 +1,3 @@
|
|||
<div align="center">
|
||||
<img src="https://raw.githubusercontent.com/amazon-science/chronos-forecasting/main/figures/chronos-logo.png" width="60%">
|
||||
</div>
|
||||
|
||||
<div align="center">
|
||||
|
||||
# Chronos: Pretrained Models for Time Series Forecasting
|
||||
|
|
@ -11,7 +7,7 @@
|
|||
[](https://huggingface.co/datasets/autogluon/chronos_datasets)
|
||||
[](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444)
|
||||
[](https://github.com/autogluon/fev)
|
||||
[](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb)
|
||||
[](notebooks/deploy-chronos-to-amazon-sagemaker.ipynb)
|
||||
[](https://github.com/amazon-science/chronos-forecasting/issues?q=is%3Aissue+label%3AFAQ)
|
||||
[](https://opensource.org/licenses/Apache-2.0)
|
||||
|
||||
|
|
@ -19,8 +15,8 @@
|
|||
|
||||
|
||||
## 🚀 News
|
||||
- **20 Oct 2025**: 🚀 [Chronos-2](https://arxiv.org/abs/2510.15821) released. It offers _zero-shot_ support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 achieves the best performance on fev-bench, GIFT-Eval and Chronos Benchmark II amongst pretrained models. Check out [this notebook](notebooks/chronos-2-quickstart.ipynb) to get started with Chronos-2.
|
||||
- **14 Feb 2025**: 🚀 Chronos-Bolt is now available on Amazon SageMaker JumpStart! Check out the [tutorial notebook](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos endpoints for production use in 3 lines of code.
|
||||
- **30 Dec 2025**: ☁️ Deploy Chronos-2 to AWS with Amazon SageMaker: new guide covers real-time inference (GPU/CPU), serverless endpoints with automatic scaling, and batch transform for large-scale forecasting. See the [deployment tutorial](notebooks/deploy-chronos-to-amazon-sagemaker.ipynb).
|
||||
- **20 Oct 2025**: 🚀 [Chronos-2](https://huggingface.co/amazon/chronos-2) released. It offers _zero-shot_ support for univariate, multivariate, and covariate-informed forecasting tasks. Chronos-2 achieves the best performance on fev-bench, GIFT-Eval and Chronos Benchmark II amongst pretrained models. Check out [this notebook](notebooks/chronos-2-quickstart.ipynb) to get started with Chronos-2.
|
||||
- **12 Dec 2024**: 📊 We released [`fev`](https://github.com/autogluon/fev), a lightweight package for benchmarking time series forecasting models based on the [Hugging Face `datasets`](https://huggingface.co/docs/datasets/en/index) library.
|
||||
- **26 Nov 2024**: ⚡️ Chronos-Bolt models released [on HuggingFace](https://huggingface.co/collections/amazon/chronos-models-65f1791d630a8d57cb718444). Chronos-Bolt models are more accurate (5% lower error), up to 250x faster and 20x more memory efficient than the original Chronos models of the same size!
|
||||
- **13 Mar 2024**: 🚀 Chronos [paper](https://arxiv.org/abs/2403.07815) and inference code released.
|
||||
|
|
@ -39,7 +35,9 @@ This package provides an interface to the Chronos family of **pretrained time se
|
|||
|
||||
| Model ID | Parameters |
|
||||
| ---------------------------------------------------------------------- | ---------- |
|
||||
| [`s3://autogluon/chronos-2`](https://arxiv.org/abs/2510.15821) | 120M |
|
||||
| [`amazon/chronos-2`](https://huggingface.co/amazon/chronos-2) | 120M |
|
||||
| [`autogluon/chronos-2-synth`](https://huggingface.co/autogluon/chronos-2-synth) | 120M |
|
||||
| [`autogluon/chronos-2-small`](https://huggingface.co/autogluon/chronos-2-small) | 28M |
|
||||
| [`amazon/chronos-bolt-tiny`](https://huggingface.co/amazon/chronos-bolt-tiny) | 9M |
|
||||
| [`amazon/chronos-bolt-mini`](https://huggingface.co/amazon/chronos-bolt-mini) | 21M |
|
||||
| [`amazon/chronos-bolt-small`](https://huggingface.co/amazon/chronos-bolt-small) | 48M |
|
||||
|
|
@ -48,7 +46,7 @@ This package provides an interface to the Chronos family of **pretrained time se
|
|||
| [`amazon/chronos-t5-mini`](https://huggingface.co/amazon/chronos-t5-mini) | 20M |
|
||||
| [`amazon/chronos-t5-small`](https://huggingface.co/amazon/chronos-t5-small) | 46M |
|
||||
| [`amazon/chronos-t5-base`](https://huggingface.co/amazon/chronos-t5-base) | 200M |
|
||||
| [`amazon/chronos-t5-large`](https://huggingface.co/amazon/chronos-t5-large) | 710M |
|
||||
| [`amazon/chronos-t5-large`](https://huggingface.co/amazon/chronos-t5-large) | 710M |
|
||||
|
||||
</div>
|
||||
|
||||
|
|
@ -60,6 +58,10 @@ To perform inference with Chronos, the easiest way is to install this package th
|
|||
pip install chronos-forecasting
|
||||
```
|
||||
|
||||
> [!TIP]
|
||||
> For reliable production use, we recommend using Chronos-2 models through [Amazon SageMaker JumpStart](https://aws.amazon.com/sagemaker/ai/jumpstart/). Check out [this tutorial](notebooks/deploy-chronos-to-amazon-sagemaker.ipynb) to learn how to deploy Chronos-2 inference endpoints to AWS with just a few lines of code.
|
||||
|
||||
|
||||
### Forecasting
|
||||
|
||||
A minimal example showing how to perform forecasting using Chronos-2:
|
||||
|
|
@ -68,7 +70,7 @@ A minimal example showing how to perform forecasting using Chronos-2:
|
|||
import pandas as pd # requires: pip install 'pandas[pyarrow]'
|
||||
from chronos import Chronos2Pipeline
|
||||
|
||||
pipeline = Chronos2Pipeline.from_pretrained("s3://autogluon/chronos-2", device_map="cuda")
|
||||
pipeline = Chronos2Pipeline.from_pretrained("amazon/chronos-2", device_map="cuda")
|
||||
|
||||
# Load historical target values and past values of covariates
|
||||
context_df = pd.read_parquet("https://autogluon.s3.amazonaws.com/datasets/timeseries/electricity_price/train.parquet")
|
||||
|
|
@ -115,8 +117,15 @@ plt.legend()
|
|||
## Example Notebooks
|
||||
|
||||
- [Chronos-2 Quick Start](notebooks/chronos-2-quickstart.ipynb)
|
||||
- [Deploy Chronos-Bolt on Amazon SageMaker](notebooks/deploy-chronos-bolt-to-amazon-sagemaker.ipynb)
|
||||
- Deploy Chronos-2 on Amazon SageMaker (coming soon!)
|
||||
|
||||
<a href="https://studiolab.sagemaker.aws/import/github/amazon-science/chronos-forecasting/blob/main/notebooks/chronos-2-quickstart.ipynb">
|
||||
<img src="https://studiolab.sagemaker.aws/studiolab.svg" alt="Open In SageMaker Studio Lab" height="18" align="absmiddle">
|
||||
</a>
|
||||
|
||||
<a href="https://colab.research.google.com/github/amazon-science/chronos-forecasting/blob/main/notebooks/chronos-2-quickstart.ipynb">
|
||||
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab" height="18" align="absmiddle">
|
||||
</a>
|
||||
- [Deploy Chronos-2 on Amazon SageMaker](notebooks/deploy-chronos-to-amazon-sagemaker.ipynb)
|
||||
|
||||
## 📝 Citation
|
||||
|
||||
|
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@ -14,13 +14,12 @@ readme = "README.md"
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|||
license = { file = "LICENSE" }
|
||||
requires-python = ">=3.10"
|
||||
dependencies = [
|
||||
"torch>=2.0,<3",
|
||||
"transformers>=4.49,<5",
|
||||
"torch>=2.2,<3",
|
||||
"transformers>=4.41,<5",
|
||||
"accelerate>=0.34,<2",
|
||||
"numpy>=1.21,<3",
|
||||
"einops>=0.7.0,<1",
|
||||
"scikit-learn>=1.6.0,<2",
|
||||
"boto3",
|
||||
]
|
||||
classifiers = [
|
||||
"Programming Language :: Python :: 3",
|
||||
|
|
@ -40,7 +39,19 @@ packages = ["src/chronos"]
|
|||
path = "src/chronos/__about__.py"
|
||||
|
||||
[project.optional-dependencies]
|
||||
test = ["pytest~=8.0", "numpy>=1.21,<3", "fev>=0.6.1", "pandas>=2.0,<2.4"]
|
||||
extras = [
|
||||
"boto3>=1.10,<2",
|
||||
"peft>=0.13.0,<0.18",
|
||||
"fev>=0.6.1",
|
||||
"pandas[pyarrow]>=2.0,<2.4",
|
||||
]
|
||||
test = [
|
||||
"pytest~=8.0",
|
||||
"boto3>=1.10,<2",
|
||||
"peft>=0.13.0,<1",
|
||||
"fev>=0.6.1",
|
||||
"pandas[pyarrow]>=2.0,<2.4",
|
||||
]
|
||||
typecheck = ["mypy~=1.9"]
|
||||
dev = [
|
||||
"gluonts[pro]~=0.16",
|
||||
|
|
|
|||
|
|
@ -295,7 +295,7 @@ def chronos_2(
|
|||
device: str = "cuda",
|
||||
torch_dtype: str = "float32",
|
||||
batch_size: int = 32,
|
||||
predict_batches_jointly: bool = False,
|
||||
cross_learning: bool = False,
|
||||
):
|
||||
"""Evaluate Chronos-2 models.
|
||||
|
||||
|
|
@ -316,7 +316,7 @@ def chronos_2(
|
|||
batch_size : int, optional, default = 32
|
||||
Batch size for inference. For Chronos-Bolt models, significantly larger
|
||||
batch sizes can be used
|
||||
predict_batches_jointly: bool, optional, default = False
|
||||
cross_learning: bool, optional, default = False
|
||||
If True, cross-learning is enables and model makes joint predictions for all
|
||||
items in the batch
|
||||
"""
|
||||
|
|
@ -335,7 +335,7 @@ def chronos_2(
|
|||
metrics_path=metrics_path,
|
||||
model_id=model_id,
|
||||
batch_size=batch_size,
|
||||
predict_batches_jointly=predict_batches_jointly,
|
||||
cross_learning=cross_learning,
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
__version__ = "2.0.0"
|
||||
__version__ = "2.2.2"
|
||||
|
|
|
|||
|
|
@ -17,8 +17,10 @@ import torch
|
|||
if TYPE_CHECKING:
|
||||
import datasets
|
||||
import fev
|
||||
import pandas as pd
|
||||
from transformers import PreTrainedModel
|
||||
|
||||
|
||||
from .utils import left_pad_and_stack_1D
|
||||
|
||||
|
||||
|
|
@ -53,6 +55,14 @@ class BaseChronosPipeline(metaclass=PipelineRegistry):
|
|||
# for easy access to the inner HF-style model
|
||||
self.inner_model = inner_model
|
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|
||||
@property
|
||||
def model_context_length(self) -> int:
|
||||
raise NotImplementedError()
|
||||
|
||||
@property
|
||||
def model_prediction_length(self) -> int:
|
||||
raise NotImplementedError()
|
||||
|
||||
def _prepare_and_validate_context(self, context: Union[torch.Tensor, List[torch.Tensor]]):
|
||||
if isinstance(context, list):
|
||||
context = left_pad_and_stack_1D(context)
|
||||
|
|
@ -122,6 +132,123 @@ class BaseChronosPipeline(metaclass=PipelineRegistry):
|
|||
"""
|
||||
raise NotImplementedError()
|
||||
|
||||
def predict_df(
|
||||
self,
|
||||
df: "pd.DataFrame",
|
||||
*,
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
target: str = "target",
|
||||
prediction_length: int | None = None,
|
||||
quantile_levels: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
|
||||
validate_inputs: bool = True,
|
||||
freq: str | None = None,
|
||||
**predict_kwargs,
|
||||
) -> "pd.DataFrame":
|
||||
"""
|
||||
Perform forecasting on time series data in a long-format pandas DataFrame.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df
|
||||
Time series data in long format with an id column, a timestamp, and one target column.
|
||||
Any other columns, if present, will be ignored
|
||||
id_column
|
||||
The name of the column which contains the unique time series identifiers, by default "item_id"
|
||||
timestamp_column
|
||||
The name of the column which contains timestamps, by default "timestamp"
|
||||
All time series in the dataframe must have regular timestamps with the same frequency (no gaps)
|
||||
target
|
||||
The name of the column which contains the target variables to be forecasted, by default "target"
|
||||
prediction_length
|
||||
Number of steps to predict for each time series
|
||||
quantile_levels
|
||||
Quantile levels to compute
|
||||
validate_inputs
|
||||
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
|
||||
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
|
||||
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
|
||||
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
|
||||
timestamps are regularly spaced (e.g., with hourly frequency).
|
||||
freq
|
||||
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used when
|
||||
validate_inputs=False. When provided, skips frequency inference from the data.
|
||||
**predict_kwargs
|
||||
Additional arguments passed to predict_quantiles
|
||||
|
||||
Returns
|
||||
-------
|
||||
The forecasts dataframe generated by the model with the following columns
|
||||
- `id_column`: The time series ID
|
||||
- `timestamp_column`: Future timestamps
|
||||
- "target_name": The name of the target column
|
||||
- "predictions": The point predictions generated by the model
|
||||
- One column for predictions at each quantile level in `quantile_levels`
|
||||
"""
|
||||
try:
|
||||
import pandas as pd
|
||||
|
||||
from .df_utils import convert_df_input_to_list_of_dicts_input
|
||||
except ImportError:
|
||||
raise ImportError("pandas is required for predict_df. Please install it with `pip install pandas`.")
|
||||
|
||||
if not isinstance(target, str):
|
||||
raise ValueError(
|
||||
f"Expected `target` to be str, but found {type(target)}. {self.__class__.__name__} only supports univariate forecasting."
|
||||
)
|
||||
|
||||
if prediction_length is None:
|
||||
prediction_length = self.model_prediction_length
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
id_column=id_column,
|
||||
timestamp_column=timestamp_column,
|
||||
target_columns=[target],
|
||||
prediction_length=prediction_length,
|
||||
freq=freq,
|
||||
validate_inputs=validate_inputs,
|
||||
)
|
||||
|
||||
# NOTE: any covariates, if present, are ignored here
|
||||
context = [torch.tensor(item["target"]).squeeze(0) for item in inputs] # squeeze the extra variate dim
|
||||
|
||||
# Generate forecasts
|
||||
quantiles, mean = self.predict_quantiles(
|
||||
inputs=context,
|
||||
prediction_length=prediction_length,
|
||||
quantile_levels=quantile_levels,
|
||||
limit_prediction_length=False,
|
||||
**predict_kwargs,
|
||||
)
|
||||
|
||||
quantiles_np = quantiles.numpy() # [n_series, horizon, num_quantiles]
|
||||
mean_np = mean.numpy() # [n_series, horizon]
|
||||
|
||||
series_ids = list(prediction_timestamps.keys())
|
||||
future_ts = list(prediction_timestamps.values())
|
||||
|
||||
data = {
|
||||
id_column: np.repeat(series_ids, prediction_length),
|
||||
timestamp_column: np.concatenate(future_ts),
|
||||
"target_name": target,
|
||||
"predictions": mean_np.ravel(),
|
||||
}
|
||||
|
||||
quantiles_flat = quantiles_np.reshape(-1, len(quantile_levels))
|
||||
for q_idx, q_level in enumerate(quantile_levels):
|
||||
data[str(q_level)] = quantiles_flat[:, q_idx]
|
||||
|
||||
predictions_df = pd.DataFrame(data)
|
||||
# If validate_inputs=False, the df is used as-is without sorting by item_id, no reordering required
|
||||
if validate_inputs:
|
||||
predictions_df.set_index(id_column, inplace=True)
|
||||
predictions_df = predictions_df.loc[original_order]
|
||||
predictions_df.reset_index(inplace=True)
|
||||
|
||||
return predictions_df
|
||||
|
||||
def predict_fev(
|
||||
self, task: "fev.Task", batch_size: int = 32, **kwargs
|
||||
) -> tuple[list["datasets.DatasetDict"], float]:
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@
|
|||
import logging
|
||||
import os
|
||||
import re
|
||||
import warnings
|
||||
from pathlib import Path
|
||||
|
||||
import boto3
|
||||
|
|
@ -110,6 +111,12 @@ def cache_model_from_s3(
|
|||
|
||||
# Use CloudFront CDN for faster, cached downloads if available
|
||||
if cloudfront_url:
|
||||
warnings.warn(
|
||||
f"Loading {s3_uri} from CloudFront is deprecated and will be removed in a future version. "
|
||||
f'Please specify a HuggingFace model_id instead. For example: Chronos2Pipeline.from_pretrained("amazon/chronos-2")',
|
||||
category=FutureWarning,
|
||||
stacklevel=3,
|
||||
)
|
||||
try:
|
||||
download_model_files_from_cloudfront(
|
||||
cloudfront_url=cloudfront_url,
|
||||
|
|
|
|||
|
|
@ -377,6 +377,14 @@ class ChronosPipeline(BaseChronosPipeline):
|
|||
self.tokenizer = tokenizer
|
||||
self.model = model
|
||||
|
||||
@property
|
||||
def model_context_length(self) -> int:
|
||||
return self.model.config.context_length
|
||||
|
||||
@property
|
||||
def model_prediction_length(self) -> int:
|
||||
return self.model.config.prediction_length
|
||||
|
||||
def _prepare_and_validate_context(self, context: Union[torch.Tensor, List[torch.Tensor]]):
|
||||
if isinstance(context, list):
|
||||
context = left_pad_and_stack_1D(context)
|
||||
|
|
|
|||
|
|
@ -15,7 +15,6 @@ from torch.utils.data import IterableDataset
|
|||
if TYPE_CHECKING:
|
||||
import datasets
|
||||
import fev
|
||||
import pandas as pd
|
||||
|
||||
|
||||
TensorOrArray: TypeAlias = torch.Tensor | np.ndarray
|
||||
|
|
@ -105,9 +104,29 @@ def validate_and_prepare_single_dict_task(
|
|||
f"Found invalid type for `past_covariates` in element at index {idx}. "
|
||||
f'Expected dict with {{"feat_1": tensor_1, "feat_2": tensor_2, ...}}, but found {type(task_past_covariates)}'
|
||||
)
|
||||
|
||||
# gather keys and ensure known-future keys come last to match downstream assumptions
|
||||
task_covariates_keys = sorted(task_past_covariates.keys())
|
||||
|
||||
task_future_covariates = task.get("future_covariates", {})
|
||||
if not isinstance(task_future_covariates, dict):
|
||||
raise ValueError(
|
||||
f"Found invalid type for `future_covariates` in element at index {idx}. "
|
||||
f'Expected dict with {{"feat_1": tensor_1, "feat_2": tensor_2, ...}}, but found {type(task_future_covariates)}'
|
||||
)
|
||||
task_future_covariates_keys = sorted(task_future_covariates.keys())
|
||||
if not set(task_future_covariates_keys).issubset(task_covariates_keys):
|
||||
raise ValueError(
|
||||
f"Expected keys in `future_covariates` to be a subset of `past_covariates` {task_covariates_keys}, "
|
||||
f"but found {task_future_covariates_keys} in element at index {idx}"
|
||||
)
|
||||
|
||||
# create ordered keys: past-only first, then known-future (so known-future are the last rows)
|
||||
task_past_only_keys = [k for k in task_covariates_keys if k not in task_future_covariates_keys] # past_only_keys
|
||||
task_ordered_covariate_keys = task_past_only_keys + task_future_covariates_keys
|
||||
|
||||
task_past_covariates_list: list[torch.Tensor] = []
|
||||
for key in task_covariates_keys:
|
||||
for key in task_ordered_covariate_keys:
|
||||
tensor = task_past_covariates[key]
|
||||
if isinstance(tensor, np.ndarray):
|
||||
# apply encoding to categorical variates
|
||||
|
|
@ -140,21 +159,10 @@ def validate_and_prepare_single_dict_task(
|
|||
if task_past_covariates_list
|
||||
else torch.zeros((0, history_length), device=task_target.device)
|
||||
)
|
||||
# validate future_covariates
|
||||
task_future_covariates = task.get("future_covariates", {})
|
||||
if not isinstance(task_future_covariates, dict):
|
||||
raise ValueError(
|
||||
f"Found invalid type for `future_covariates` in element at index {idx}. "
|
||||
f'Expected dict with {{"feat_1": tensor_1, "feat_2": tensor_2, ...}}, but found {type(task_future_covariates)}'
|
||||
)
|
||||
task_future_covariates_keys = sorted(task_future_covariates.keys())
|
||||
if not set(task_future_covariates_keys).issubset(task_covariates_keys):
|
||||
raise ValueError(
|
||||
f"Expected keys in `future_covariates` to be a subset of `past_covariates` {task_covariates_keys}, "
|
||||
f"but found {task_future_covariates_keys} in element at index {idx}"
|
||||
)
|
||||
|
||||
# validate future_covariates (build rows in the same task_ordered_covariate_keys order)
|
||||
task_future_covariates_list: list[torch.Tensor] = []
|
||||
for key in task_covariates_keys:
|
||||
for key in task_ordered_covariate_keys:
|
||||
# future values of past-only covariates are filled with NaNs
|
||||
tensor = task_future_covariates.get(key, torch.full((prediction_length,), fill_value=torch.nan))
|
||||
if isinstance(tensor, np.ndarray):
|
||||
|
|
@ -186,7 +194,8 @@ def validate_and_prepare_single_dict_task(
|
|||
).to(dtype=torch.float32)
|
||||
task_n_targets = task_target.shape[0]
|
||||
task_n_covariates = task_past_covariates_tensor.shape[0]
|
||||
task_n_future_covariates = len(task_future_covariates_list)
|
||||
# number of known-future covariates
|
||||
task_n_future_covariates = len(task_future_covariates_keys)
|
||||
|
||||
return (
|
||||
task_context_tensor,
|
||||
|
|
@ -265,308 +274,6 @@ def convert_tensor_input_to_list_of_dicts_input(tensor: TensorOrArray) -> list[d
|
|||
return output
|
||||
|
||||
|
||||
def _validate_df_types_and_cast(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
) -> tuple["pd.DataFrame", "pd.DataFrame | None"]:
|
||||
import pandas as pd
|
||||
|
||||
astype_dict = {}
|
||||
future_astype_dict = {}
|
||||
for col in df.columns.drop([id_column, timestamp_column]):
|
||||
col_dtype = df[col].dtype
|
||||
if col in target_columns and not pd.api.types.is_numeric_dtype(df[col]):
|
||||
raise ValueError(f"All target columns must be numeric but got {col=} with dtype={col_dtype}")
|
||||
|
||||
if (
|
||||
pd.api.types.is_object_dtype(df[col])
|
||||
or pd.api.types.is_string_dtype(df[col])
|
||||
or isinstance(col_dtype, pd.CategoricalDtype)
|
||||
):
|
||||
astype_dict[col] = "category"
|
||||
elif pd.api.types.is_numeric_dtype(df[col]) or pd.api.types.is_bool_dtype(df[col]):
|
||||
astype_dict[col] = "float32"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"All columns must contain numeric, object, category, string, or bool dtype but got {col=} with dtype={col_dtype}"
|
||||
)
|
||||
|
||||
if future_df is not None and col in future_df.columns:
|
||||
if future_df[col].dtype != col_dtype:
|
||||
raise ValueError(
|
||||
f"Column {col} in future_df has dtype {future_df[col].dtype} but column in df has dtype {col_dtype}"
|
||||
)
|
||||
future_astype_dict[col] = astype_dict[col]
|
||||
|
||||
df = df.astype(astype_dict, copy=True)
|
||||
if future_df is not None:
|
||||
future_df = future_df.astype(future_astype_dict, copy=True)
|
||||
|
||||
return df, future_df
|
||||
|
||||
|
||||
def validate_df_inputs(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
prediction_length: int,
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
) -> tuple["pd.DataFrame", "pd.DataFrame | None", "pd.Timedelta", list[int], list[int] | None, np.ndarray]:
|
||||
"""
|
||||
Validates and prepares dataframe inputs passed to `Chronos2Pipeline.predict_df`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df
|
||||
Input dataframe containing time series data with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Timestamps for each observation
|
||||
- target_columns: One or more target variables to forecast
|
||||
- Additional columns are treated as covariates
|
||||
future_df
|
||||
Optional dataframe containing future covariate values with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Future timestamps
|
||||
- Subset of covariate columns from df
|
||||
target_columns
|
||||
Names of target columns to forecast
|
||||
prediction_length
|
||||
Number of future time steps to predict
|
||||
id_column
|
||||
Name of column containing time series identifiers
|
||||
timestamp_column
|
||||
Name of column containing timestamps
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- Validated and sorted input dataframe
|
||||
- Validated and sorted future dataframe (if provided)
|
||||
- Inferred frequency of the time series
|
||||
- List of series lengths from input dataframe
|
||||
- List of series lengths from future dataframe (if provided)
|
||||
- Original order of time series IDs
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If validation fails for:
|
||||
- Missing required columns
|
||||
- Invalid data types
|
||||
- Inconsistent frequencies
|
||||
- Insufficient data points
|
||||
- Mismatched series between df and future_df
|
||||
- Invalid future_df lengths
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
required_cols = [id_column, timestamp_column] + target_columns
|
||||
missing_cols = [col for col in required_cols if col not in df.columns]
|
||||
if missing_cols:
|
||||
raise ValueError(f"df does not contain all expected columns. Missing columns: {missing_cols}")
|
||||
|
||||
if future_df is not None:
|
||||
future_required_cols = [id_column, timestamp_column]
|
||||
missing_future_cols = [col for col in future_required_cols if col not in future_df.columns]
|
||||
targets_in_future = [col for col in future_df.columns if col in target_columns]
|
||||
extra_future_cols = [col for col in future_df.columns if col not in df.columns]
|
||||
if missing_future_cols:
|
||||
raise ValueError(
|
||||
f"future_df does not contain all expected columns. Missing columns: {missing_future_cols}"
|
||||
)
|
||||
if targets_in_future:
|
||||
raise ValueError(
|
||||
f"future_df cannot contain target columns. Target columns found in future_df: {targets_in_future}"
|
||||
)
|
||||
if extra_future_cols:
|
||||
raise ValueError(f"future_df cannot contain columns not present in df. Extra columns: {extra_future_cols}")
|
||||
|
||||
df, future_df = _validate_df_types_and_cast(
|
||||
df, future_df, id_column=id_column, timestamp_column=timestamp_column, target_columns=target_columns
|
||||
)
|
||||
|
||||
# Get the original order of time series IDs
|
||||
original_order = df[id_column].unique()
|
||||
|
||||
# Sort and prepare df
|
||||
df[timestamp_column] = pd.to_datetime(df[timestamp_column])
|
||||
df = df.sort_values([id_column, timestamp_column])
|
||||
|
||||
# Get series lengths
|
||||
series_lengths = df[id_column].value_counts(sort=False).to_list()
|
||||
|
||||
def validate_freq(timestamps: pd.Series, series_id: str):
|
||||
freq = pd.infer_freq(timestamps)
|
||||
if not freq:
|
||||
raise ValueError(f"Could not infer frequency for series {series_id}")
|
||||
return freq
|
||||
|
||||
# Validate each series
|
||||
all_freqs = []
|
||||
start_idx = 0
|
||||
for length in series_lengths:
|
||||
if length < 3:
|
||||
series_id = df.iloc[start_idx][id_column]
|
||||
raise ValueError(
|
||||
f"Every time series must have at least 3 data points, found {length=} for series {series_id}"
|
||||
)
|
||||
|
||||
series_data = df.iloc[start_idx : start_idx + length]
|
||||
timestamps = series_data[timestamp_column]
|
||||
series_id = series_data.iloc[0][id_column]
|
||||
all_freqs.append(validate_freq(timestamps, series_id))
|
||||
start_idx += length
|
||||
|
||||
if len(set(all_freqs)) > 1:
|
||||
raise ValueError("All time series must have the same frequency")
|
||||
|
||||
inferred_freq = all_freqs[0]
|
||||
|
||||
# Sort future_df if provided and validate its series lengths
|
||||
future_series_lengths = None
|
||||
if future_df is not None:
|
||||
future_df[timestamp_column] = pd.to_datetime(future_df[timestamp_column])
|
||||
future_df = future_df.sort_values([id_column, timestamp_column])
|
||||
|
||||
# Validate that future_df contains all series from df
|
||||
context_ids = set(df[id_column].unique())
|
||||
future_ids = set(future_df[id_column].unique())
|
||||
if context_ids != future_ids:
|
||||
raise ValueError("future_df must contain the same time series IDs as df")
|
||||
|
||||
future_series_lengths = future_df[id_column].value_counts(sort=False).to_list()
|
||||
|
||||
# Validate future series lengths match prediction_length
|
||||
future_start_idx = 0
|
||||
for future_length in future_series_lengths:
|
||||
future_series_data = future_df.iloc[future_start_idx : future_start_idx + future_length]
|
||||
future_timestamps = future_series_data[timestamp_column]
|
||||
future_series_id = future_series_data.iloc[0][id_column]
|
||||
if future_length != prediction_length:
|
||||
raise ValueError(
|
||||
f"Future covariates all time series must have length {prediction_length}, got {future_length} for series {future_series_id}"
|
||||
)
|
||||
if future_length < 3 or inferred_freq != validate_freq(future_timestamps, future_series_id):
|
||||
raise ValueError(
|
||||
f"Future covariates must have the same frequency as context, found series {future_series_id} with a different frequency"
|
||||
)
|
||||
future_start_idx += future_length
|
||||
|
||||
assert len(series_lengths) == len(future_series_lengths)
|
||||
|
||||
return df, future_df, inferred_freq, series_lengths, future_series_lengths, original_order
|
||||
|
||||
|
||||
def convert_df_input_to_list_of_dicts_input(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
prediction_length: int,
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
) -> tuple[list[dict[str, np.ndarray | dict[str, np.ndarray]]], np.ndarray, dict[str, "pd.DatetimeIndex"]]:
|
||||
"""
|
||||
Convert from dataframe input format to a list of dictionaries input format.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df
|
||||
Input dataframe containing time series data with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Timestamps for each observation
|
||||
- target_columns: One or more target variables to forecast
|
||||
- Additional columns are treated as covariates
|
||||
future_df
|
||||
Optional dataframe containing future covariate values with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Future timestamps
|
||||
- Subset of covariate columns from df
|
||||
target_columns
|
||||
Names of target columns to forecast
|
||||
prediction_length
|
||||
Number of future time steps to predict
|
||||
id_column
|
||||
Name of column containing time series identifiers
|
||||
timestamp_column
|
||||
Name of column containing timestamps
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- List of dictionaries in the format expected by `Chronos2Pipeline.predict`
|
||||
- Original order of time series IDs
|
||||
- Dictionary mapping series IDs to future time index
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
df, future_df, freq, series_lengths, future_series_lengths, original_order = validate_df_inputs(
|
||||
df,
|
||||
future_df=future_df,
|
||||
id_column=id_column,
|
||||
timestamp_column=timestamp_column,
|
||||
target_columns=target_columns,
|
||||
prediction_length=prediction_length,
|
||||
)
|
||||
|
||||
# Convert to list of dicts format
|
||||
inputs: list[dict[str, np.ndarray | dict[str, np.ndarray]]] = []
|
||||
prediction_timestamps: dict[str, pd.DatetimeIndex] = {}
|
||||
start_idx: int = 0
|
||||
future_start_idx: int = 0
|
||||
|
||||
for i, length in enumerate(series_lengths):
|
||||
series_data = df.iloc[start_idx : start_idx + length]
|
||||
# Extract target(s)
|
||||
target_data = series_data[target_columns].to_numpy().T # Shape: (n_targets, history_length)
|
||||
task: dict[str, np.ndarray | dict[str, np.ndarray]] = {"target": target_data}
|
||||
|
||||
# Generate future timestamps
|
||||
series_id = series_data.iloc[0][id_column]
|
||||
last_timestamp = series_data[timestamp_column].iloc[-1]
|
||||
future_ts = pd.date_range(start=last_timestamp, periods=prediction_length + 1, freq=freq)[1:]
|
||||
prediction_timestamps[series_id] = future_ts
|
||||
|
||||
# Handle covariates if present
|
||||
covariate_cols = [
|
||||
col for col in series_data.columns if col not in [id_column, timestamp_column] + target_columns
|
||||
]
|
||||
|
||||
if covariate_cols:
|
||||
past_covariates = {col: series_data[col].to_numpy() for col in covariate_cols}
|
||||
task["past_covariates"] = past_covariates
|
||||
|
||||
# Handle future covariates
|
||||
if future_df is not None:
|
||||
assert future_series_lengths is not None
|
||||
future_length = future_series_lengths[i]
|
||||
future_data = future_df.iloc[future_start_idx : future_start_idx + future_length]
|
||||
assert future_data[timestamp_column].iloc[0] == future_ts[0], (
|
||||
f"the first timestamp in future_df must be the first forecast timestamp, found mismatch "
|
||||
f"({future_data[timestamp_column].iloc[0]} != {future_ts[0]}) in series {series_id}"
|
||||
)
|
||||
|
||||
if len(future_data) > 0:
|
||||
future_covariates = {
|
||||
col: future_data[col].to_numpy() for col in covariate_cols if col in future_data.columns
|
||||
}
|
||||
if future_covariates:
|
||||
task["future_covariates"] = future_covariates
|
||||
future_start_idx += future_length
|
||||
|
||||
inputs.append(task)
|
||||
start_idx += length
|
||||
|
||||
assert len(inputs) == len(series_lengths)
|
||||
|
||||
return inputs, original_order, prediction_timestamps
|
||||
|
||||
|
||||
def _cast_fev_features(
|
||||
past_data: "datasets.Dataset",
|
||||
future_data: "datasets.Dataset",
|
||||
|
|
@ -770,6 +477,7 @@ class Chronos2Dataset(IterableDataset):
|
|||
task_n_covariates,
|
||||
task_n_future_covariates,
|
||||
) = self.tasks[task_idx]
|
||||
task_past_tensor, task_future_tensor = task_past_tensor.clone(), task_future_tensor.clone()
|
||||
task_n_past_only_covariates = task_n_covariates - task_n_future_covariates
|
||||
|
||||
full_length = task_past_tensor.shape[-1]
|
||||
|
|
@ -795,7 +503,9 @@ class Chronos2Dataset(IterableDataset):
|
|||
# the task_context_tensor by slicing the appropriate indices which we do below
|
||||
if self.mode in [DatasetMode.TRAIN, DatasetMode.VALIDATION]:
|
||||
# the first task_n_targets elements in task_context_tensor are the targets
|
||||
task_future_target = task_past_tensor[:, slice_idx : slice_idx + self.prediction_length]
|
||||
task_future_target = task_past_tensor[:, slice_idx : slice_idx + self.prediction_length].clone()
|
||||
# mask out all rows corresponding to covariates
|
||||
task_future_target[task_n_targets:] = torch.nan
|
||||
|
||||
if task_n_future_covariates > 0:
|
||||
# the last task_n_future_covariates elements in task_context_tensor are the known covariates
|
||||
|
|
|
|||
|
|
@ -547,6 +547,74 @@ class Chronos2Model(PreTrainedModel):
|
|||
|
||||
return loss
|
||||
|
||||
def encode(
|
||||
self,
|
||||
context: torch.Tensor,
|
||||
context_mask: torch.Tensor | None = None,
|
||||
group_ids: torch.Tensor | None = None,
|
||||
future_covariates: torch.Tensor | None = None,
|
||||
future_covariates_mask: torch.Tensor | None = None,
|
||||
num_output_patches: int = 1,
|
||||
future_target: torch.Tensor | None = None,
|
||||
future_target_mask: torch.Tensor | None = None,
|
||||
output_attentions: bool = False,
|
||||
):
|
||||
self._validate_input(
|
||||
context=context,
|
||||
context_mask=context_mask,
|
||||
future_covariates=future_covariates,
|
||||
future_covariates_mask=future_covariates_mask,
|
||||
group_ids=group_ids,
|
||||
num_output_patches=num_output_patches,
|
||||
future_target=future_target,
|
||||
future_target_mask=future_target_mask,
|
||||
)
|
||||
|
||||
batch_size = context.shape[0]
|
||||
patched_context, attention_mask, loc_scale = self._prepare_patched_context(
|
||||
context=context, context_mask=context_mask
|
||||
)
|
||||
num_context_patches = attention_mask.shape[-1]
|
||||
|
||||
# get input embeddings of shape (batch, num_context_patches, d_model)
|
||||
input_embeds: torch.Tensor = self.input_patch_embedding(patched_context)
|
||||
# append [REG] special token embedding, if needed
|
||||
if self.chronos_config.use_reg_token:
|
||||
reg_input_ids = torch.full((batch_size, 1), self.config.reg_token_id, device=input_embeds.device)
|
||||
reg_embeds = self.shared(reg_input_ids)
|
||||
input_embeds = torch.cat([input_embeds, reg_embeds], dim=-2)
|
||||
attention_mask = torch.cat(
|
||||
[attention_mask.to(self.dtype), torch.ones_like(reg_input_ids).to(self.dtype)], dim=-1
|
||||
)
|
||||
|
||||
patched_future, patched_future_covariates_mask = self._prepare_patched_future(
|
||||
future_covariates=future_covariates,
|
||||
future_covariates_mask=future_covariates_mask,
|
||||
loc_scale=loc_scale,
|
||||
num_output_patches=num_output_patches,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
future_attention_mask = torch.ones(batch_size, num_output_patches, dtype=self.dtype, device=self.device)
|
||||
|
||||
# get future embeddings of shape (batch, num_output_patches, d_model)
|
||||
future_embeds: torch.Tensor = self.input_patch_embedding(patched_future)
|
||||
|
||||
# concatenate context and future embeddings and masks
|
||||
input_embeds = torch.cat([input_embeds, future_embeds], dim=-2)
|
||||
attention_mask = torch.cat([attention_mask, future_attention_mask], dim=-1)
|
||||
|
||||
if group_ids is None:
|
||||
# by default, each time series is treated independently, i.e., no mixing across the batch
|
||||
group_ids = torch.arange(batch_size, dtype=torch.long, device=self.device)
|
||||
|
||||
encoder_outputs: Chronos2EncoderOutput = self.encoder(
|
||||
attention_mask=attention_mask,
|
||||
inputs_embeds=input_embeds,
|
||||
group_ids=group_ids,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
return encoder_outputs, loc_scale, patched_future_covariates_mask, num_context_patches
|
||||
|
||||
def forward(
|
||||
self,
|
||||
context: torch.Tensor,
|
||||
|
|
@ -625,63 +693,19 @@ class Chronos2Model(PreTrainedModel):
|
|||
- enc_time_self_attn_weights: Time self attention weights, if output_attentions=True
|
||||
- enc_group_self_attn_weights: Group self attention weights, if output_attentions=True
|
||||
"""
|
||||
|
||||
self._validate_input(
|
||||
batch_size = context.shape[0]
|
||||
encoder_outputs, loc_scale, patched_future_covariates_mask, num_context_patches = self.encode(
|
||||
context=context,
|
||||
context_mask=context_mask,
|
||||
group_ids=group_ids,
|
||||
future_covariates=future_covariates,
|
||||
future_covariates_mask=future_covariates_mask,
|
||||
group_ids=group_ids,
|
||||
num_output_patches=num_output_patches,
|
||||
future_target=future_target,
|
||||
future_target_mask=future_target_mask,
|
||||
)
|
||||
|
||||
batch_size = context.shape[0]
|
||||
patched_context, attention_mask, loc_scale = self._prepare_patched_context(
|
||||
context=context, context_mask=context_mask
|
||||
)
|
||||
num_context_patches = attention_mask.shape[-1]
|
||||
|
||||
# get input embeddings of shape (batch, num_context_patches, d_model)
|
||||
input_embeds: torch.Tensor = self.input_patch_embedding(patched_context)
|
||||
# append [REG] special token embedding, if needed
|
||||
if self.chronos_config.use_reg_token:
|
||||
reg_input_ids = torch.full((batch_size, 1), self.config.reg_token_id, device=input_embeds.device)
|
||||
reg_embeds = self.shared(reg_input_ids)
|
||||
input_embeds = torch.cat([input_embeds, reg_embeds], dim=-2)
|
||||
attention_mask = torch.cat(
|
||||
[attention_mask.to(self.dtype), torch.ones_like(reg_input_ids).to(self.dtype)], dim=-1
|
||||
)
|
||||
|
||||
patched_future, patched_future_covariates_mask = self._prepare_patched_future(
|
||||
future_covariates=future_covariates,
|
||||
future_covariates_mask=future_covariates_mask,
|
||||
loc_scale=loc_scale,
|
||||
num_output_patches=num_output_patches,
|
||||
batch_size=batch_size,
|
||||
)
|
||||
future_attention_mask = torch.ones(batch_size, num_output_patches, dtype=self.dtype, device=self.device)
|
||||
|
||||
# get future embeddings of shape (batch, num_output_patches, d_model)
|
||||
future_embeds: torch.Tensor = self.input_patch_embedding(patched_future)
|
||||
|
||||
# concatenate context and future embeddings and masks
|
||||
input_embeds = torch.cat([input_embeds, future_embeds], dim=-2)
|
||||
attention_mask = torch.cat([attention_mask, future_attention_mask], dim=-1)
|
||||
|
||||
if group_ids is None:
|
||||
# by default, each time series is treated independently, i.e., no mixing across the batch
|
||||
group_ids = torch.arange(batch_size, dtype=torch.long, device=self.device)
|
||||
|
||||
encoder_outputs: Chronos2EncoderOutput = self.encoder(
|
||||
attention_mask=attention_mask,
|
||||
inputs_embeds=input_embeds,
|
||||
group_ids=group_ids,
|
||||
output_attentions=output_attentions,
|
||||
)
|
||||
hidden_states: torch.Tensor = encoder_outputs[0]
|
||||
|
||||
assert hidden_states.shape == (batch_size, num_context_patches + 1 + num_output_patches, self.model_dim)
|
||||
|
||||
# slice the last num_output_patches hidden states to be input into the output_patch_embedding
|
||||
|
|
|
|||
|
|
@ -9,29 +9,29 @@ import time
|
|||
import warnings
|
||||
from copy import deepcopy
|
||||
from pathlib import Path
|
||||
from typing import TYPE_CHECKING, Any, Mapping, Sequence
|
||||
from typing import TYPE_CHECKING, Callable, Literal, Mapping, Sequence
|
||||
|
||||
import numpy as np
|
||||
import torch
|
||||
from einops import rearrange, repeat
|
||||
from torch.utils.data import DataLoader
|
||||
from transformers import AutoConfig
|
||||
from transformers.utils.import_utils import is_peft_available
|
||||
from transformers.utils.peft_utils import find_adapter_config_file
|
||||
|
||||
import chronos.chronos2
|
||||
from chronos.base import BaseChronosPipeline, ForecastType
|
||||
from chronos.chronos2 import Chronos2Model
|
||||
from chronos.chronos2.dataset import (
|
||||
Chronos2Dataset,
|
||||
DatasetMode,
|
||||
TensorOrArray,
|
||||
convert_df_input_to_list_of_dicts_input,
|
||||
)
|
||||
from chronos.chronos2.dataset import Chronos2Dataset, DatasetMode, TensorOrArray
|
||||
from chronos.df_utils import convert_df_input_to_list_of_dicts_input
|
||||
from chronos.utils import interpolate_quantiles, weighted_quantile
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import datasets
|
||||
import fev
|
||||
import pandas as pd
|
||||
from peft import LoraConfig
|
||||
from transformers.trainer_callback import TrainerCallback
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
|
@ -103,13 +103,18 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
| Sequence[TensorOrArray]
|
||||
| Sequence[Mapping[str, TensorOrArray | Mapping[str, TensorOrArray | None]]]
|
||||
| None = None,
|
||||
finetune_mode: Literal["full", "lora"] = "full",
|
||||
lora_config: "LoraConfig | dict | None" = None,
|
||||
context_length: int | None = None,
|
||||
learning_rate: float = 1e-5,
|
||||
learning_rate: float = 1e-6,
|
||||
num_steps: int = 1000,
|
||||
batch_size: int = 256,
|
||||
output_dir: Path | str | None = None,
|
||||
min_past: int | None = None,
|
||||
finetuned_ckpt_name: str = "finetuned-ckpt",
|
||||
callbacks: list["TrainerCallback"] | None = None,
|
||||
remove_printer_callback: bool = False,
|
||||
disable_data_parallel: bool = True,
|
||||
**extra_trainer_kwargs,
|
||||
) -> "Chronos2Pipeline":
|
||||
"""
|
||||
|
|
@ -127,10 +132,16 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
validation_inputs
|
||||
The time series used for validation and model selection. The format of `validation_inputs` is exactly the same as `inputs`, by default None which
|
||||
means that no validation is performed. Note that enabling validation may slow down fine-tuning for large datasets.
|
||||
finetune_mode
|
||||
One of "full" (performs full fine-tuning) or "lora" (performs Low Rank Adaptation (LoRA) fine-tuning), by default "full"
|
||||
lora_config
|
||||
The configuration to use for LoRA fine-tuning when finetune_mode="lora". Can be a `LoraConfig` object or a dict which is used to initialize `LoraConfig`.
|
||||
When unspecified and finetune_mode="lora", a default configuration is used
|
||||
context_length
|
||||
The maximum context length used during fine-tuning, by default set to the model's default context length
|
||||
learning_rate
|
||||
The learning rate for the optimizer, by default 1e-5
|
||||
The learning rate for the optimizer, by default 1e-6
|
||||
When finetune_mode="lora", we recommend using a higher value of the learning rate, such as 1e-5
|
||||
num_steps
|
||||
The number of steps to fine-tune for, by default 1000
|
||||
batch_size
|
||||
|
|
@ -144,6 +155,12 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
are filtered out, by default set equal to prediction_length
|
||||
finetuned_ckpt_name
|
||||
The name of the directory inside `output_dir` in which the final fine-tuned checkpoint will be saved, by default "finetuned-ckpt"
|
||||
callbacks
|
||||
A list of `TrainerCallback`s which will be forwarded to the HuggingFace `Trainer`
|
||||
remove_printer_callback
|
||||
If True, all instances of `PrinterCallback` are removed from callbacks
|
||||
disable_data_parallel
|
||||
If True, ensures that DataParallel is disabled and training happens on a single GPU
|
||||
**extra_trainer_kwargs
|
||||
Extra kwargs are directly forwarded to `TrainingArguments`
|
||||
|
||||
|
|
@ -153,21 +170,59 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
"""
|
||||
|
||||
import torch.cuda
|
||||
from transformers.trainer_callback import PrinterCallback
|
||||
from transformers.training_args import TrainingArguments
|
||||
|
||||
if finetune_mode == "lora":
|
||||
if is_peft_available():
|
||||
from peft import LoraConfig, get_peft_model
|
||||
else:
|
||||
warnings.warn(
|
||||
"`peft` is required for `finetune_mode='lora'`. Please install it with `pip install peft`. Falling back to `finetune_mode='full'`."
|
||||
)
|
||||
finetune_mode = "full"
|
||||
lora_config = None
|
||||
|
||||
from chronos.chronos2.trainer import Chronos2Trainer, EvaluateAndSaveFinalStepCallback
|
||||
|
||||
warnings.warn(
|
||||
"Fine-tuning support is experimental and may be changed in future versions.",
|
||||
category=FutureWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
assert finetune_mode in ["full", "lora"], f"finetune_mode must be one of ['full', 'lora'], got {finetune_mode}"
|
||||
|
||||
if finetune_mode == "full" and lora_config is not None:
|
||||
raise ValueError(
|
||||
"lora_config should not be specified when `finetune_mode='full'`. To enable LoRA, set `finetune_mode='lora'`."
|
||||
)
|
||||
|
||||
# Create a copy of the model to avoid modifying the original
|
||||
config = deepcopy(self.model.config)
|
||||
model = Chronos2Model(config).to(self.model.device) # type: ignore
|
||||
model.load_state_dict(self.model.state_dict())
|
||||
|
||||
if finetune_mode == "lora":
|
||||
if lora_config is None:
|
||||
lora_config = LoraConfig(
|
||||
r=8,
|
||||
lora_alpha=16,
|
||||
target_modules=[
|
||||
"self_attention.q",
|
||||
"self_attention.v",
|
||||
"self_attention.k",
|
||||
"self_attention.o",
|
||||
"output_patch_embedding.output_layer",
|
||||
],
|
||||
)
|
||||
elif isinstance(lora_config, dict):
|
||||
lora_config = LoraConfig(**lora_config)
|
||||
else:
|
||||
assert isinstance(lora_config, LoraConfig), (
|
||||
f"lora_config must be an instance of LoraConfig or a dict, got {type(lora_config)}"
|
||||
)
|
||||
|
||||
model = get_peft_model(model, lora_config)
|
||||
n_trainable_params, n_params = model.get_nb_trainable_parameters()
|
||||
logger.info(
|
||||
f"Using LoRA. Number of trainable parameters: {n_trainable_params}, total parameters: {n_params}."
|
||||
)
|
||||
|
||||
if context_length is None:
|
||||
context_length = self.model_context_length
|
||||
|
||||
|
|
@ -217,7 +272,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
report_to="none",
|
||||
max_steps=num_steps,
|
||||
gradient_accumulation_steps=1,
|
||||
dataloader_num_workers=1,
|
||||
dataloader_num_workers=0,
|
||||
tf32=has_sm80 and not use_cpu,
|
||||
bf16=has_sm80 and not use_cpu,
|
||||
save_only_model=True,
|
||||
|
|
@ -233,7 +288,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
)
|
||||
|
||||
eval_dataset = None
|
||||
callbacks = []
|
||||
callbacks = callbacks or []
|
||||
if validation_inputs is not None:
|
||||
# construct validation dataset
|
||||
eval_dataset = Chronos2Dataset.convert_inputs(
|
||||
|
|
@ -267,6 +322,11 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
|
||||
training_args = TrainingArguments(**training_kwargs)
|
||||
|
||||
if disable_data_parallel and not use_cpu:
|
||||
# This is a hack to disable the default `transformers` behavior of using DataParallel
|
||||
training_args._n_gpu = 1
|
||||
assert training_args.n_gpu == 1 # Ensure that the hack worked
|
||||
|
||||
trainer = Chronos2Trainer(
|
||||
model=model,
|
||||
args=training_args,
|
||||
|
|
@ -274,12 +334,19 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
eval_dataset=eval_dataset,
|
||||
callbacks=callbacks,
|
||||
)
|
||||
|
||||
if remove_printer_callback:
|
||||
trainer.pop_callback(PrinterCallback)
|
||||
|
||||
trainer.train()
|
||||
|
||||
# update max_output_patches, if the model was fine-tuned with longer prediction_length
|
||||
# update context_length and max_output_patches, if the model was fine-tuned with larger values
|
||||
model.chronos_config.context_length = max(model.chronos_config.context_length, context_length)
|
||||
model.chronos_config.max_output_patches = max(
|
||||
model.chronos_config.max_output_patches, math.ceil(prediction_length / self.model_output_patch_size)
|
||||
)
|
||||
# update chronos_config in model's config, so it is saved correctly
|
||||
model.config.chronos_config = model.chronos_config.__dict__
|
||||
|
||||
# Create a new pipeline with the fine-tuned model
|
||||
finetuned_pipeline = Chronos2Pipeline(model=model)
|
||||
|
|
@ -388,7 +455,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
prediction_length: int | None = None,
|
||||
batch_size: int = 256,
|
||||
context_length: int | None = None,
|
||||
predict_batches_jointly: bool = False,
|
||||
cross_learning: bool = False,
|
||||
limit_prediction_length: bool = False,
|
||||
**kwargs,
|
||||
) -> list[torch.Tensor]:
|
||||
|
|
@ -474,7 +541,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
will be lower than this value, by default 256
|
||||
context_length
|
||||
The maximum context length used during for inference, by default set to the model's default context length
|
||||
predict_batches_jointly
|
||||
cross_learning
|
||||
If True, cross-learning is enabled, i.e., all the tasks in `inputs` will be predicted jointly and the model will share information across all inputs, by default False
|
||||
The following must be noted when using cross-learning:
|
||||
- Cross-learning doesn't always improve forecast accuracy and must be tested for individual use cases.
|
||||
|
|
@ -494,6 +561,14 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
if prediction_length is None:
|
||||
prediction_length = model_prediction_length
|
||||
|
||||
if kwargs.get("predict_batches_jointly") is not None:
|
||||
warnings.warn(
|
||||
"The `predict_batches_jointly` argument is deprecated and will be removed in a future version. "
|
||||
"Please use `cross_learning=True` to enable the cross-learning mode.",
|
||||
category=FutureWarning,
|
||||
stacklevel=2,
|
||||
)
|
||||
cross_learning = kwargs.pop("predict_batches_jointly")
|
||||
# The maximum number of output patches to generate in a single forward pass before the long-horizon heuristic kicks in. Note: A value larger
|
||||
# than the model's default max_output_patches may lead to degradation in forecast accuracy, defaults to a model-specific value
|
||||
max_output_patches = kwargs.pop("max_output_patches", self.max_output_patches)
|
||||
|
|
@ -502,6 +577,8 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
# effective batch size increases by a factor of `len(unrolled_quantiles)` when making long-horizon predictions,
|
||||
# by default [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
|
||||
unrolled_quantiles = kwargs.pop("unrolled_quantiles", [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
|
||||
# A callback which is called after each batch has been processed
|
||||
after_batch_callback: Callable = kwargs.pop("after_batch", lambda: None)
|
||||
|
||||
if len(kwargs) > 0:
|
||||
raise TypeError(f"Unexpected keyword arguments: {list(kwargs.keys())}.")
|
||||
|
|
@ -542,7 +619,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
mode=DatasetMode.TEST,
|
||||
)
|
||||
test_loader = DataLoader(
|
||||
test_dataset, batch_size=None, num_workers=1, pin_memory=True, shuffle=False, drop_last=False
|
||||
test_dataset, batch_size=None, pin_memory=self.model.device.type == "cuda", shuffle=False, drop_last=False
|
||||
)
|
||||
|
||||
all_predictions: list[torch.Tensor] = []
|
||||
|
|
@ -553,7 +630,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
batch_future_covariates = batch["future_covariates"]
|
||||
batch_target_idx_ranges = batch["target_idx_ranges"]
|
||||
|
||||
if predict_batches_jointly:
|
||||
if cross_learning:
|
||||
batch_group_ids = torch.zeros_like(batch_group_ids)
|
||||
|
||||
batch_prediction = self._predict_batch(
|
||||
|
|
@ -566,6 +643,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
target_idx_ranges=batch_target_idx_ranges,
|
||||
)
|
||||
all_predictions.extend(batch_prediction)
|
||||
after_batch_callback()
|
||||
|
||||
return all_predictions
|
||||
|
||||
|
|
@ -744,6 +822,10 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
prediction_length: int | None = None,
|
||||
quantile_levels: list[float] = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9],
|
||||
batch_size: int = 256,
|
||||
context_length: int | None = None,
|
||||
cross_learning: bool = False,
|
||||
validate_inputs: bool = True,
|
||||
freq: str | None = None,
|
||||
**predict_kwargs,
|
||||
) -> "pd.DataFrame":
|
||||
"""
|
||||
|
|
@ -773,6 +855,24 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
The batch size used for prediction. Note that the batch size here means the number of time series, including target(s) and covariates,
|
||||
which are input into the model. If your data has multiple target and/or covariates, the effective number of time series tasks in a batch
|
||||
will be lower than this value, by default 256
|
||||
context_length
|
||||
The maximum context length used during for inference, by default set to the model's default context length
|
||||
cross_learning
|
||||
If True, cross-learning is enabled, i.e., all the tasks in `inputs` will be predicted jointly and the model will share information across all inputs, by default False
|
||||
The following must be noted when using cross-learning:
|
||||
- Cross-learning doesn't always improve forecast accuracy and must be tested for individual use cases.
|
||||
- Results become dependent on batch size. Very large batch sizes may not provide benefits as they deviate from the maximum group size used during pretraining.
|
||||
For optimal results, consider using a batch size around 100 (as used in the Chronos-2 technical report).
|
||||
- Cross-learning is most helpful when individual time series have limited historical context, as the model can leverage patterns from related series in the batch.
|
||||
validate_inputs
|
||||
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
|
||||
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
|
||||
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
|
||||
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
|
||||
timestamps are regularly spaced (e.g., with hourly frequency).
|
||||
freq
|
||||
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used when
|
||||
validate_inputs=False. When provided, skips frequency inference from the data.
|
||||
**predict_kwargs
|
||||
Additional arguments passed to predict_quantiles
|
||||
|
||||
|
|
@ -803,6 +903,8 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
timestamp_column=timestamp_column,
|
||||
target_columns=target,
|
||||
prediction_length=prediction_length,
|
||||
freq=freq,
|
||||
validate_inputs=validate_inputs,
|
||||
)
|
||||
|
||||
# Generate forecasts
|
||||
|
|
@ -812,33 +914,37 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
quantile_levels=quantile_levels,
|
||||
limit_prediction_length=False,
|
||||
batch_size=batch_size,
|
||||
context_length=context_length,
|
||||
cross_learning=cross_learning,
|
||||
**predict_kwargs,
|
||||
)
|
||||
# since predict_df tasks are homogenous by input design, we can safely stack the list of tensors into a single tensor
|
||||
quantiles_np = torch.stack(quantiles).numpy() # [n_tasks, n_variates, horizon, num_quantiles]
|
||||
mean_np = torch.stack(mean).numpy() # [n_tasks, n_variates, horizon]
|
||||
|
||||
results_dfs = []
|
||||
for i, (series_id, future_ts) in enumerate(prediction_timestamps.items()):
|
||||
q_pred = quantiles_np[i] # (n_variates, prediction_length, len(quantile_levels))
|
||||
point_pred = mean_np[i] # (n_variates, prediction_length)
|
||||
n_tasks = len(prediction_timestamps)
|
||||
n_variates = len(target)
|
||||
|
||||
for target_idx, target_col in enumerate(target):
|
||||
series_forecast_data: dict[str | tuple[str, str], Any] = {
|
||||
id_column: series_id,
|
||||
timestamp_column: future_ts,
|
||||
"target_name": target_col,
|
||||
}
|
||||
series_forecast_data["predictions"] = point_pred[target_idx]
|
||||
for q_idx, q_level in enumerate(quantile_levels):
|
||||
series_forecast_data[str(q_level)] = q_pred[target_idx, :, q_idx]
|
||||
series_ids = list(prediction_timestamps.keys())
|
||||
future_ts = list(prediction_timestamps.values())
|
||||
|
||||
results_dfs.append(pd.DataFrame(series_forecast_data))
|
||||
data = {
|
||||
id_column: np.repeat(series_ids, n_variates * prediction_length),
|
||||
timestamp_column: np.concatenate([np.tile(ts, n_variates) for ts in future_ts]),
|
||||
"target_name": np.tile(np.repeat(target, prediction_length), n_tasks),
|
||||
"predictions": mean_np.ravel(),
|
||||
}
|
||||
|
||||
predictions_df = pd.concat(results_dfs, ignore_index=True)
|
||||
predictions_df.set_index(id_column, inplace=True)
|
||||
predictions_df = predictions_df.loc[original_order]
|
||||
predictions_df.reset_index(inplace=True)
|
||||
quantiles_flat = quantiles_np.reshape(-1, len(quantile_levels))
|
||||
for q_idx, q_level in enumerate(quantile_levels):
|
||||
data[str(q_level)] = quantiles_flat[:, q_idx]
|
||||
|
||||
predictions_df = pd.DataFrame(data)
|
||||
# If validate_inputs=False, the df is used as-is without sorting by item_id, no reordering required
|
||||
if validate_inputs:
|
||||
predictions_df.set_index(id_column, inplace=True)
|
||||
predictions_df = predictions_df.loc[original_order]
|
||||
predictions_df.reset_index(inplace=True)
|
||||
|
||||
return predictions_df
|
||||
|
||||
|
|
@ -973,11 +1079,7 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
finetune_kwargs["prediction_length"] = first_window.horizon
|
||||
finetune_kwargs["batch_size"] = finetune_kwargs.get("batch_size", batch_size)
|
||||
|
||||
try:
|
||||
pipeline = self.fit(inputs=inputs, **finetune_kwargs)
|
||||
except Exception as e:
|
||||
msg = f"Finetuning failed with error: {e}. Continuing with the pretrained model."
|
||||
warnings.warn(msg, category=UserWarning, stacklevel=2)
|
||||
pipeline = self.fit(inputs=inputs, **finetune_kwargs)
|
||||
|
||||
predictions_per_window = []
|
||||
inference_time_s = 0.0
|
||||
|
|
@ -994,6 +1096,86 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
|
||||
return predictions_per_window, inference_time_s
|
||||
|
||||
@torch.no_grad()
|
||||
def embed(
|
||||
self, inputs: TensorOrArray | Sequence[TensorOrArray], batch_size: int = 256, context_length: int | None = None
|
||||
) -> tuple[list[torch.Tensor], list[tuple[torch.Tensor, torch.Tensor]]]:
|
||||
"""
|
||||
Get encoder embeddings for the given time series.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
inputs
|
||||
The time series to get embeddings for, can be one of:
|
||||
- A 3-dimensional `torch.Tensor` or `np.ndarray` of shape (batch, n_variates, history_length). When `n_variates > 1`, information
|
||||
will be shared among the different variates of each time series in the batch.
|
||||
- A list of `torch.Tensor` or `np.ndarray` where each element can either be 1-dimensional of shape (history_length,)
|
||||
or 2-dimensional of shape (n_variates, history_length). The history_lengths may be different across elements; left-padding
|
||||
will be applied, if needed.
|
||||
batch_size
|
||||
The batch size used for generating embeddings. Note that the batch size here means the total number of time series which are input into the model.
|
||||
If your data has multiple variates, the effective number of time series tasks in a batch will be lower than this value, by default 256
|
||||
context_length
|
||||
The maximum context length used during for inference, by default set to the model's default context length
|
||||
|
||||
Returns
|
||||
-------
|
||||
embeddings
|
||||
a list of `torch.Tensor` where each element has shape (n_variates, num_patches + 2, d_model) and the number of elements are equal to the number
|
||||
of target time series (univariate or multivariate) in the `inputs`. The extra +2 is due to embeddings of the [REG] token and a masked output patch token.
|
||||
loc_scale
|
||||
a list of tuples with the mean and standard deviation of each time series.
|
||||
"""
|
||||
if context_length is None:
|
||||
context_length = self.model_context_length
|
||||
|
||||
if context_length > self.model_context_length:
|
||||
warnings.warn(
|
||||
f"The specified context_length {context_length} is greater than the model's default context length {self.model_context_length}. "
|
||||
f"Resetting context_length to {self.model_context_length}."
|
||||
)
|
||||
context_length = self.model_context_length
|
||||
|
||||
test_dataset = Chronos2Dataset.convert_inputs(
|
||||
inputs=inputs,
|
||||
context_length=context_length,
|
||||
prediction_length=0,
|
||||
batch_size=batch_size,
|
||||
output_patch_size=self.model_output_patch_size,
|
||||
mode=DatasetMode.TEST,
|
||||
)
|
||||
test_loader = DataLoader(
|
||||
test_dataset,
|
||||
batch_size=None,
|
||||
num_workers=0,
|
||||
pin_memory=self.model.device.type == "cuda",
|
||||
shuffle=False,
|
||||
drop_last=False,
|
||||
)
|
||||
all_embeds: list[torch.Tensor] = []
|
||||
all_loc_scales: list[tuple[torch.Tensor, torch.Tensor]] = []
|
||||
for batch in test_loader:
|
||||
assert batch["future_target"] is None
|
||||
batch_context = batch["context"]
|
||||
batch_group_ids = batch["group_ids"]
|
||||
batch_target_idx_ranges = batch["target_idx_ranges"]
|
||||
|
||||
encoder_outputs, (locs, scales), *_ = self.model.encode(
|
||||
context=batch_context.to(device=self.model.device, dtype=torch.float32),
|
||||
group_ids=batch_group_ids.to(self.model.device),
|
||||
)
|
||||
batch_embeds = [encoder_outputs[0][start:end].cpu() for (start, end) in batch_target_idx_ranges]
|
||||
batch_loc_scales = list(
|
||||
zip(
|
||||
[locs[start:end].cpu() for (start, end) in batch_target_idx_ranges],
|
||||
[scales[start:end].cpu() for (start, end) in batch_target_idx_ranges],
|
||||
)
|
||||
)
|
||||
all_embeds.extend(batch_embeds)
|
||||
all_loc_scales.extend(batch_loc_scales)
|
||||
|
||||
return all_embeds, all_loc_scales
|
||||
|
||||
@classmethod
|
||||
def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
|
||||
"""
|
||||
|
|
@ -1001,9 +1183,25 @@ class Chronos2Pipeline(BaseChronosPipeline):
|
|||
Supports the same arguments as ``AutoConfig`` and ``AutoModel`` from ``transformers``.
|
||||
"""
|
||||
|
||||
# Check if the model is on S3 and cache it locally first
|
||||
# NOTE: Only base models (not LoRA adapters) are supported via S3
|
||||
if str(pretrained_model_name_or_path).startswith("s3://"):
|
||||
return BaseChronosPipeline.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
|
||||
|
||||
# Check if the hub model_id or local path is a LoRA adapter
|
||||
if find_adapter_config_file(pretrained_model_name_or_path) is not None:
|
||||
if not is_peft_available():
|
||||
raise ImportError(
|
||||
f"The model at {pretrained_model_name_or_path} is a `peft` adaptor, but `peft` is not available. "
|
||||
f"Please install `peft` with `pip install peft` to use this model. "
|
||||
)
|
||||
from peft import AutoPeftModel
|
||||
|
||||
model = AutoPeftModel.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
|
||||
model = model.merge_and_unload()
|
||||
return cls(model=model)
|
||||
|
||||
# Handle the case for the base model
|
||||
config = AutoConfig.from_pretrained(pretrained_model_name_or_path, *args, **kwargs)
|
||||
assert hasattr(config, "chronos_config"), "Not a Chronos config file"
|
||||
|
||||
|
|
|
|||
|
|
@ -3,6 +3,7 @@
|
|||
|
||||
# Authors: Abdul Fatir Ansari <ansarnd@amazon.com>
|
||||
|
||||
import warnings
|
||||
from typing import TYPE_CHECKING, cast
|
||||
|
||||
from torch.utils.data import DataLoader, Dataset
|
||||
|
|
@ -48,11 +49,16 @@ class Chronos2Trainer(Trainer):
|
|||
|
||||
train_dataset = cast("Chronos2Dataset", self.train_dataset)
|
||||
|
||||
assert train_dataset.batch_size == self.args.train_batch_size, (
|
||||
f"The batch_size of the train_dataset ({train_dataset.batch_size}) does not match the batch_size "
|
||||
f"in TrainingArguments ({self.args.train_batch_size}). If you're using a machine with multiple GPUs, "
|
||||
f"ensure that only a single GPU is visible by setting the CUDA_VISIBLE_DEVICES environment variable."
|
||||
)
|
||||
if self.args.train_batch_size > train_dataset.batch_size:
|
||||
warnings.warn(
|
||||
f"The batch_size of the train_dataset ({train_dataset.batch_size}) does not match the batch_size "
|
||||
f"in TrainingArguments ({self.args.train_batch_size}). On machines with multiple GPUs, this may indicate "
|
||||
f"that multiple GPUs are visible and transformers is using DataParallel for training by default. "
|
||||
f"This may lead to unnecessary slowdown and unexpected behavior. We strongly recommend setting the CUDA_VISIBLE_DEVICES "
|
||||
f"environment variable to ensure that only a single GPU is visible.",
|
||||
category=UserWarning,
|
||||
stacklevel=3,
|
||||
)
|
||||
|
||||
dataloader_params = {
|
||||
# Disable automatic batching as we handle batching ourselves
|
||||
|
|
@ -74,11 +80,16 @@ class Chronos2Trainer(Trainer):
|
|||
|
||||
eval_dataset = cast("Chronos2Dataset", self.eval_dataset)
|
||||
|
||||
assert eval_dataset.batch_size == self.args.eval_batch_size, (
|
||||
f"The batch_size of the eval_dataset ({eval_dataset.batch_size}) does not match the batch_size "
|
||||
f"in TrainingArguments ({self.args.eval_batch_size}). If you're using a machine with multiple GPUs, "
|
||||
f"ensure that only a single GPU is visible by setting the CUDA_VISIBLE_DEVICES environment variable."
|
||||
)
|
||||
if self.args.eval_batch_size > eval_dataset.batch_size:
|
||||
warnings.warn(
|
||||
f"The batch_size of the eval_dataset ({eval_dataset.batch_size}) does not match the batch_size "
|
||||
f"in TrainingArguments ({self.args.eval_batch_size}). On machines with multiple GPUs, this may indicate "
|
||||
f"that multiple GPUs are visible and transformers is using DataParallel for training by default. "
|
||||
f"This may lead to unnecessary slowdown and unexpected behavior. We strongly recommend setting the CUDA_VISIBLE_DEVICES "
|
||||
f"environment variable to ensure that only a single GPU is visible.",
|
||||
category=UserWarning,
|
||||
stacklevel=3,
|
||||
)
|
||||
|
||||
dataloader_params = {
|
||||
# Disable automatic batching as we handle batching ourselves
|
||||
|
|
|
|||
|
|
@ -408,6 +408,14 @@ class ChronosBoltPipeline(BaseChronosPipeline):
|
|||
super().__init__(inner_model=model) # type: ignore
|
||||
self.model = model
|
||||
|
||||
@property
|
||||
def model_context_length(self) -> int:
|
||||
return self.model.chronos_config.context_length
|
||||
|
||||
@property
|
||||
def model_prediction_length(self) -> int:
|
||||
return self.model.chronos_config.prediction_length
|
||||
|
||||
@property
|
||||
def quantiles(self) -> List[float]:
|
||||
return self.model.config.chronos_config["quantiles"]
|
||||
|
|
@ -487,14 +495,12 @@ class ChronosBoltPipeline(BaseChronosPipeline):
|
|||
"""
|
||||
context_tensor = self._prepare_and_validate_context(context=inputs)
|
||||
|
||||
model_context_length: int = self.model.config.chronos_config["context_length"]
|
||||
model_prediction_length: int = self.model.config.chronos_config["prediction_length"]
|
||||
if prediction_length is None:
|
||||
prediction_length = model_prediction_length
|
||||
prediction_length = self.model_prediction_length
|
||||
|
||||
if prediction_length > model_prediction_length:
|
||||
if prediction_length > self.model_prediction_length:
|
||||
msg = (
|
||||
f"We recommend keeping prediction length <= {model_prediction_length}. "
|
||||
f"We recommend keeping prediction length <= {self.model_prediction_length}. "
|
||||
"The quality of longer predictions may degrade since the model is not optimized for it. "
|
||||
)
|
||||
if limit_prediction_length:
|
||||
|
|
@ -507,32 +513,46 @@ class ChronosBoltPipeline(BaseChronosPipeline):
|
|||
|
||||
# We truncate the context here because otherwise batches with very long
|
||||
# context could take up large amounts of GPU memory unnecessarily.
|
||||
if context_tensor.shape[-1] > model_context_length:
|
||||
context_tensor = context_tensor[..., -model_context_length:]
|
||||
if context_tensor.shape[-1] > self.model_context_length:
|
||||
context_tensor = context_tensor[..., -self.model_context_length :]
|
||||
|
||||
# TODO: We unroll the forecast of Chronos Bolt greedily with the full forecast
|
||||
# horizon that the model was trained with (i.e., 64). This results in variance collapsing
|
||||
# every 64 steps.
|
||||
context_tensor = context_tensor.to(
|
||||
device=self.model.device,
|
||||
dtype=torch.float32,
|
||||
)
|
||||
while remaining > 0:
|
||||
with torch.no_grad():
|
||||
prediction = self.model(
|
||||
context=context_tensor,
|
||||
).quantile_preds.to(context_tensor)
|
||||
context_tensor = context_tensor.to(device=self.model.device, dtype=torch.float32)
|
||||
# First block prediction
|
||||
with torch.no_grad():
|
||||
prediction: torch.Tensor = self.model(context=context_tensor).quantile_preds.to(context_tensor)
|
||||
|
||||
predictions.append(prediction)
|
||||
remaining -= prediction.shape[-1]
|
||||
|
||||
if remaining <= 0:
|
||||
break
|
||||
# NOTE: The following heuristic for better prediction intervals with long-horizon forecasts
|
||||
# uses all quantiles generated by the model for the first `model_prediction_length` steps,
|
||||
# concatenating each quantile with the context and generating the next `model_prediction_length` steps.
|
||||
# The `num_quantiles * num_quantiles` "samples" thus generated are then reduced to `num_quantiles`
|
||||
# by computing empirical quantiles. Note that this option scales the batch size by `num_quantiles`
|
||||
# when the `prediction_length` is greater than `model_prediction_length`.
|
||||
|
||||
central_idx = torch.abs(torch.tensor(self.quantiles) - 0.5).argmin()
|
||||
central_prediction = prediction[:, central_idx]
|
||||
if remaining > 0:
|
||||
# Expand the context along quantile axis
|
||||
context_tensor = context_tensor.unsqueeze(1).repeat(1, len(self.quantiles), 1)
|
||||
|
||||
context_tensor = torch.cat([context_tensor, central_prediction], dim=-1)
|
||||
quantile_tensor = torch.tensor(self.quantiles, device=context_tensor.device)
|
||||
while remaining > 0:
|
||||
# Append the prediction to context
|
||||
context_tensor = torch.cat([context_tensor, prediction], dim=-1)[..., -self.model_context_length :]
|
||||
(batch_size, n_quantiles, context_length) = context_tensor.shape
|
||||
|
||||
with torch.no_grad():
|
||||
# Reshape (batch, n_quantiles, context_length) -> (batch * n_quantiles, context_length)
|
||||
prediction = self.model(
|
||||
context=context_tensor.reshape(batch_size * n_quantiles, context_length)
|
||||
).quantile_preds.to(context_tensor)
|
||||
# Reshape predictions from (batch * n_quantiles, n_quantiles, model_prediction_length) to (batch, n_quantiles * n_quantiles, model_prediction_length)
|
||||
prediction = prediction.reshape(batch_size, n_quantiles * n_quantiles, -1)
|
||||
# Reduce `n_quantiles * n_quantiles` to n_quantiles and transpose back to (batch_size, n_quantiles, model_prediction_length)
|
||||
prediction = torch.quantile(prediction, q=quantile_tensor, dim=1).transpose(0, 1)
|
||||
|
||||
predictions.append(prediction)
|
||||
remaining -= prediction.shape[-1]
|
||||
|
||||
return torch.cat(predictions, dim=-1)[..., :prediction_length].to(dtype=torch.float32, device="cpu")
|
||||
|
||||
|
|
|
|||
341
src/chronos/df_utils.py
Normal file
341
src/chronos/df_utils.py
Normal file
|
|
@ -0,0 +1,341 @@
|
|||
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
# Authors: Abdul Fatir Ansari <ansarnd@amazon.com>
|
||||
|
||||
|
||||
import warnings
|
||||
from typing import TYPE_CHECKING
|
||||
|
||||
import numpy as np
|
||||
|
||||
if TYPE_CHECKING:
|
||||
import pandas as pd
|
||||
|
||||
|
||||
def _validate_df_types_and_cast(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
) -> tuple["pd.DataFrame", "pd.DataFrame | None"]:
|
||||
import pandas as pd
|
||||
|
||||
astype_dict = {}
|
||||
future_astype_dict = {}
|
||||
for col in df.columns.drop([id_column, timestamp_column]):
|
||||
col_dtype = df[col].dtype
|
||||
if col in target_columns and not pd.api.types.is_numeric_dtype(df[col]):
|
||||
raise ValueError(f"All target columns must be numeric but got {col=} with dtype={col_dtype}")
|
||||
|
||||
if (
|
||||
pd.api.types.is_object_dtype(df[col])
|
||||
or pd.api.types.is_string_dtype(df[col])
|
||||
or isinstance(col_dtype, pd.CategoricalDtype)
|
||||
):
|
||||
astype_dict[col] = "category"
|
||||
elif pd.api.types.is_numeric_dtype(df[col]) or pd.api.types.is_bool_dtype(df[col]):
|
||||
astype_dict[col] = "float32"
|
||||
else:
|
||||
raise ValueError(
|
||||
f"All columns must contain numeric, object, category, string, or bool dtype but got {col=} with dtype={col_dtype}"
|
||||
)
|
||||
|
||||
if future_df is not None and col in future_df.columns:
|
||||
if future_df[col].dtype != col_dtype:
|
||||
raise ValueError(
|
||||
f"Column {col} in future_df has dtype {future_df[col].dtype} but column in df has dtype {col_dtype}"
|
||||
)
|
||||
future_astype_dict[col] = astype_dict[col]
|
||||
|
||||
df = df.astype(astype_dict, copy=True)
|
||||
if future_df is not None:
|
||||
future_df = future_df.astype(future_astype_dict, copy=True)
|
||||
|
||||
return df, future_df
|
||||
|
||||
|
||||
def validate_df_inputs(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
prediction_length: int,
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
) -> tuple["pd.DataFrame", "pd.DataFrame | None", str, list[int], np.ndarray]:
|
||||
"""
|
||||
Validates and prepares dataframe inputs
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df
|
||||
Input dataframe containing time series data with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Timestamps for each observation
|
||||
- target_columns: One or more target variables to forecast
|
||||
- Additional columns are treated as covariates
|
||||
future_df
|
||||
Optional dataframe containing future covariate values with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Future timestamps
|
||||
- Subset of covariate columns from df
|
||||
target_columns
|
||||
Names of target columns to forecast
|
||||
prediction_length
|
||||
Number of future time steps to predict
|
||||
id_column
|
||||
Name of column containing time series identifiers
|
||||
timestamp_column
|
||||
Name of column containing timestamps
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- Validated and sorted input dataframe
|
||||
- Validated and sorted future dataframe (if provided)
|
||||
- Inferred frequency of the time series
|
||||
- List of series lengths from input dataframe
|
||||
- Original order of time series IDs
|
||||
|
||||
Raises
|
||||
------
|
||||
ValueError
|
||||
If validation fails for:
|
||||
- Missing required columns
|
||||
- Invalid data types
|
||||
- Inconsistent frequencies
|
||||
- Insufficient data points
|
||||
- Mismatched series between df and future_df
|
||||
- Invalid future_df lengths
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
required_cols = [id_column, timestamp_column] + target_columns
|
||||
missing_cols = [col for col in required_cols if col not in df.columns]
|
||||
if missing_cols:
|
||||
raise ValueError(f"df does not contain all expected columns. Missing columns: {missing_cols}")
|
||||
|
||||
if future_df is not None:
|
||||
future_required_cols = [id_column, timestamp_column]
|
||||
missing_future_cols = [col for col in future_required_cols if col not in future_df.columns]
|
||||
targets_in_future = [col for col in future_df.columns if col in target_columns]
|
||||
extra_future_cols = [col for col in future_df.columns if col not in df.columns]
|
||||
if missing_future_cols:
|
||||
raise ValueError(
|
||||
f"future_df does not contain all expected columns. Missing columns: {missing_future_cols}"
|
||||
)
|
||||
if targets_in_future:
|
||||
raise ValueError(
|
||||
f"future_df cannot contain target columns. Target columns found in future_df: {targets_in_future}"
|
||||
)
|
||||
if extra_future_cols:
|
||||
raise ValueError(f"future_df cannot contain columns not present in df. Extra columns: {extra_future_cols}")
|
||||
|
||||
df, future_df = _validate_df_types_and_cast(
|
||||
df, future_df, id_column=id_column, timestamp_column=timestamp_column, target_columns=target_columns
|
||||
)
|
||||
|
||||
# Get the original order of time series IDs
|
||||
original_order = df[id_column].unique()
|
||||
|
||||
# Sort and prepare df
|
||||
df[timestamp_column] = pd.to_datetime(df[timestamp_column])
|
||||
df = df.sort_values([id_column, timestamp_column])
|
||||
|
||||
# Get series lengths
|
||||
series_lengths = df[id_column].value_counts(sort=False).to_list()
|
||||
|
||||
def validate_freq(timestamps: pd.DatetimeIndex, series_id: str):
|
||||
freq = pd.infer_freq(timestamps)
|
||||
if not freq:
|
||||
raise ValueError(f"Could not infer frequency for series {series_id}")
|
||||
return freq
|
||||
|
||||
# Validate each series
|
||||
all_freqs = []
|
||||
start_idx = 0
|
||||
timestamp_index = pd.DatetimeIndex(df[timestamp_column])
|
||||
for length in series_lengths:
|
||||
if length < 3:
|
||||
series_id = df[id_column].iloc[start_idx]
|
||||
raise ValueError(
|
||||
f"Every time series must have at least 3 data points, found {length=} for series {series_id}"
|
||||
)
|
||||
timestamps = timestamp_index[start_idx : start_idx + length]
|
||||
series_id = df[id_column].iloc[start_idx]
|
||||
all_freqs.append(validate_freq(timestamps, series_id))
|
||||
start_idx += length
|
||||
|
||||
if len(set(all_freqs)) > 1:
|
||||
raise ValueError("All time series must have the same frequency")
|
||||
|
||||
inferred_freq = all_freqs[0]
|
||||
|
||||
# Sort future_df if provided and validate its series lengths
|
||||
future_series_lengths = None
|
||||
if future_df is not None:
|
||||
future_df[timestamp_column] = pd.to_datetime(future_df[timestamp_column])
|
||||
future_df = future_df.sort_values([id_column, timestamp_column])
|
||||
|
||||
# Validate that future_df contains all series from df
|
||||
context_ids = set(df[id_column].unique())
|
||||
future_ids = set(future_df[id_column].unique())
|
||||
if context_ids != future_ids:
|
||||
raise ValueError("future_df must contain the same time series IDs as df")
|
||||
|
||||
future_series_lengths = future_df[id_column].value_counts(sort=False)
|
||||
if (future_series_lengths != prediction_length).any():
|
||||
invalid_series = future_series_lengths[future_series_lengths != prediction_length]
|
||||
raise ValueError(
|
||||
f"future_df must contain {prediction_length=} values for each series, "
|
||||
f"but found series with different lengths: {invalid_series.to_dict()}"
|
||||
)
|
||||
|
||||
return df, future_df, inferred_freq, series_lengths, original_order
|
||||
|
||||
|
||||
def convert_df_input_to_list_of_dicts_input(
|
||||
df: "pd.DataFrame",
|
||||
future_df: "pd.DataFrame | None",
|
||||
target_columns: list[str],
|
||||
prediction_length: int,
|
||||
id_column: str = "item_id",
|
||||
timestamp_column: str = "timestamp",
|
||||
validate_inputs: bool = True,
|
||||
freq: str | None = None,
|
||||
) -> tuple[list[dict[str, np.ndarray | dict[str, np.ndarray]]], np.ndarray, dict[str, "pd.DatetimeIndex"]]:
|
||||
"""
|
||||
Convert from dataframe input format to a list of dictionaries input format.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df
|
||||
Input dataframe containing time series data with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Timestamps for each observation
|
||||
- target_columns: One or more target variables to forecast
|
||||
- Additional columns are treated as covariates
|
||||
future_df
|
||||
Optional dataframe containing future covariate values with columns:
|
||||
- id_column: Identifier for each time series
|
||||
- timestamp_column: Future timestamps
|
||||
- Subset of covariate columns from df
|
||||
target_columns
|
||||
Names of target columns to forecast
|
||||
prediction_length
|
||||
Number of future time steps to predict
|
||||
id_column
|
||||
Name of column containing time series identifiers
|
||||
timestamp_column
|
||||
Name of column containing timestamps
|
||||
validate_inputs
|
||||
[ADVANCED] When True (default), validates dataframes before prediction. Setting to False removes the
|
||||
validation overhead, but may silently lead to wrong predictions if data is misformatted. When False, you
|
||||
must ensure: (1) all dataframes are sorted by (id_column, timestamp_column); (2) future_df (if provided)
|
||||
has the same item IDs as df with exactly prediction_length rows of future timestamps per item; (3) all
|
||||
timestamps are regularly spaced (e.g., with hourly frequency).
|
||||
freq
|
||||
Frequency string for timestamp generation (e.g., "h", "D", "W"). Can only be used
|
||||
when validate_inputs=False. When provided, skips frequency inference from the data.
|
||||
|
||||
Returns
|
||||
-------
|
||||
A tuple containing:
|
||||
- Time series converted to list of dictionaries format
|
||||
- Original order of time series IDs
|
||||
- Dictionary mapping series IDs to future time index
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
|
||||
if freq is not None and validate_inputs:
|
||||
raise ValueError(
|
||||
"freq can only be provided when validate_inputs=False. "
|
||||
"When using freq with validate_inputs=False, you must ensure: "
|
||||
"(1) all dataframes are sorted by (id_column, timestamp_column); "
|
||||
"(2) future_df (if provided) has the same item IDs as df with exactly "
|
||||
"prediction_length rows of future timestamps per item; "
|
||||
"(3) all timestamps are regularly spaced."
|
||||
)
|
||||
|
||||
if validate_inputs:
|
||||
df, future_df, freq, series_lengths, original_order = validate_df_inputs(
|
||||
df,
|
||||
future_df=future_df,
|
||||
id_column=id_column,
|
||||
timestamp_column=timestamp_column,
|
||||
target_columns=target_columns,
|
||||
prediction_length=prediction_length,
|
||||
)
|
||||
else:
|
||||
# Get the original order of time series IDs
|
||||
original_order = df[id_column].unique()
|
||||
|
||||
# Get series lengths
|
||||
series_lengths = df[id_column].value_counts(sort=False).to_list()
|
||||
|
||||
# If freq is not provided, infer from the first series with >= 3 points
|
||||
if freq is None:
|
||||
timestamp_index = pd.DatetimeIndex(df[timestamp_column])
|
||||
start_idx = 0
|
||||
for length in series_lengths:
|
||||
if length < 3:
|
||||
start_idx += length
|
||||
continue
|
||||
timestamps = timestamp_index[start_idx : start_idx + length]
|
||||
freq = pd.infer_freq(timestamps)
|
||||
break
|
||||
|
||||
assert freq is not None, "validate_inputs is False, but could not infer frequency from the dataframe"
|
||||
|
||||
# Convert to list of dicts format
|
||||
inputs: list[dict[str, np.ndarray | dict[str, np.ndarray]]] = []
|
||||
prediction_timestamps: dict[str, pd.DatetimeIndex] = {}
|
||||
|
||||
indptr = np.concatenate([[0], np.cumsum(series_lengths)]).astype("int64")
|
||||
target_array = df[target_columns].to_numpy().T # Shape: (n_targets, len(df))
|
||||
last_ts = pd.DatetimeIndex(df[timestamp_column].iloc[indptr[1:] - 1]) # Shape: (n_series,)
|
||||
offset = pd.tseries.frequencies.to_offset(freq)
|
||||
with warnings.catch_warnings():
|
||||
# Silence PerformanceWarning for non-vectorized offsets https://github.com/pandas-dev/pandas/blob/95624ca2e99b0/pandas/core/arrays/datetimes.py#L822
|
||||
warnings.simplefilter("ignore", category=pd.errors.PerformanceWarning)
|
||||
# Generate all prediction timestamps at once by stacking offsets into shape (n_series * prediction_length)
|
||||
prediction_timestamps_array = pd.DatetimeIndex(
|
||||
np.dstack([last_ts + step * offset for step in range(1, prediction_length + 1)]).ravel()
|
||||
)
|
||||
|
||||
past_covariates_dict = {
|
||||
col: df[col].to_numpy() for col in df.columns if col not in [id_column, timestamp_column] + target_columns
|
||||
}
|
||||
future_covariates_dict = {}
|
||||
if future_df is not None:
|
||||
for col in future_df.columns.drop([id_column, timestamp_column]):
|
||||
future_covariates_dict[col] = future_df[col].to_numpy()
|
||||
if validate_inputs:
|
||||
if (pd.DatetimeIndex(future_df[timestamp_column]) != pd.DatetimeIndex(prediction_timestamps_array)).any():
|
||||
raise ValueError(
|
||||
"future_df timestamps do not match the expected prediction timestamps. "
|
||||
"You can disable this check by setting `validate_inputs=False`"
|
||||
)
|
||||
|
||||
for i in range(len(series_lengths)):
|
||||
start_idx, end_idx = indptr[i], indptr[i + 1]
|
||||
future_start_idx, future_end_idx = i * prediction_length, (i + 1) * prediction_length
|
||||
|
||||
series_id = df[id_column].iloc[start_idx]
|
||||
prediction_timestamps[series_id] = prediction_timestamps_array[future_start_idx:future_end_idx]
|
||||
task: dict[str, np.ndarray | dict[str, np.ndarray]] = {"target": target_array[:, start_idx:end_idx]}
|
||||
|
||||
if len(past_covariates_dict) > 0:
|
||||
task["past_covariates"] = {col: values[start_idx:end_idx] for col, values in past_covariates_dict.items()}
|
||||
if len(future_covariates_dict) > 0:
|
||||
task["future_covariates"] = {
|
||||
col: values[future_start_idx:future_end_idx] for col, values in future_covariates_dict.items()
|
||||
}
|
||||
inputs.append(task)
|
||||
|
||||
assert len(inputs) == len(series_lengths)
|
||||
|
||||
return inputs, original_order, prediction_timestamps
|
||||
35
test/dummy-chronos2-lora/adapter_config.json
Normal file
35
test/dummy-chronos2-lora/adapter_config.json
Normal file
|
|
@ -0,0 +1,35 @@
|
|||
{
|
||||
"alpha_pattern": {},
|
||||
"auto_mapping": {
|
||||
"base_model_class": "Chronos2Model",
|
||||
"parent_library": "chronos.chronos2.model"
|
||||
},
|
||||
"base_model_name_or_path": "test/dummy-chronos2-model",
|
||||
"bias": "none",
|
||||
"fan_in_fan_out": false,
|
||||
"inference_mode": true,
|
||||
"init_lora_weights": true,
|
||||
"layer_replication": null,
|
||||
"layers_pattern": null,
|
||||
"layers_to_transform": null,
|
||||
"loftq_config": {},
|
||||
"lora_alpha": 16,
|
||||
"lora_dropout": 0.0,
|
||||
"megatron_config": null,
|
||||
"megatron_core": "megatron.core",
|
||||
"modules_to_save": null,
|
||||
"peft_type": "LORA",
|
||||
"r": 8,
|
||||
"rank_pattern": {},
|
||||
"revision": null,
|
||||
"target_modules": [
|
||||
"self_attention.q",
|
||||
"self_attention.k",
|
||||
"self_attention.o",
|
||||
"output_patch_embedding.output_layer",
|
||||
"self_attention.v"
|
||||
],
|
||||
"task_type": null,
|
||||
"use_dora": false,
|
||||
"use_rslora": false
|
||||
}
|
||||
BIN
test/dummy-chronos2-lora/adapter_model.safetensors
Normal file
BIN
test/dummy-chronos2-lora/adapter_model.safetensors
Normal file
Binary file not shown.
|
|
@ -3,6 +3,8 @@
|
|||
|
||||
from pathlib import Path
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
|
|
@ -12,7 +14,14 @@ from chronos import (
|
|||
ChronosPipeline,
|
||||
MeanScaleUniformBins,
|
||||
)
|
||||
from test.util import validate_tensor
|
||||
from test.util import create_df, get_forecast_start_times, validate_tensor
|
||||
|
||||
DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos-model"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def pipeline() -> ChronosPipeline:
|
||||
return BaseChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu")
|
||||
|
||||
|
||||
def test_base_chronos_pipeline_loads_from_huggingface():
|
||||
|
|
@ -167,11 +176,7 @@ def test_tokenizer_random_data(use_eos_token: bool):
|
|||
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
||||
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
||||
def test_pipeline_predict(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
||||
pipeline = ChronosPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=model_dtype,
|
||||
)
|
||||
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
||||
|
|
@ -238,11 +243,7 @@ def test_pipeline_predict_quantiles(
|
|||
prediction_length: int,
|
||||
quantile_levels: list[int],
|
||||
):
|
||||
pipeline = ChronosPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=model_dtype,
|
||||
)
|
||||
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
||||
|
|
@ -284,11 +285,7 @@ def test_pipeline_predict_quantiles(
|
|||
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
||||
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
||||
def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
||||
pipeline = ChronosPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=model_dtype,
|
||||
)
|
||||
pipeline = ChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
||||
d_model = pipeline.model.model.config.d_model
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
|
@ -312,6 +309,88 @@ def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
|||
validate_tensor(scale, shape=(1,), dtype=torch.float32)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"context_setup, expected_rows",
|
||||
[
|
||||
# Targets only
|
||||
({}, 6), # 2 series * 3 predictions
|
||||
# Different context lengths
|
||||
(
|
||||
{"series_ids": ["X", "Y", "Z"], "n_points": [10, 17, 56], "target_cols": ["custom_target"]},
|
||||
9,
|
||||
), # 3 series * 3 predictions
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
||||
def test_predict_df_works_for_valid_inputs(pipeline, context_setup, expected_rows, freq):
|
||||
prediction_length = 3
|
||||
df = create_df(**context_setup, freq=freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq)
|
||||
|
||||
series_ids = context_setup.get("series_ids", ["A", "B"])
|
||||
target_columns = context_setup.get("target_cols", ["target"])
|
||||
n_series = len(series_ids)
|
||||
n_targets = len(target_columns)
|
||||
result = pipeline.predict_df(df, target=target_columns[0], prediction_length=prediction_length)
|
||||
|
||||
assert len(result) == expected_rows
|
||||
assert "item_id" in result.columns and np.all(
|
||||
result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
|
||||
)
|
||||
assert "target_name" in result.columns and np.all(
|
||||
result["target_name"].to_numpy() == np.tile(np.array(target_columns).repeat(prediction_length), n_series)
|
||||
)
|
||||
assert "timestamp" in result.columns and np.all(
|
||||
result.groupby("item_id")["timestamp"].min().to_numpy() == pd.to_datetime(forecast_start_times).to_numpy()
|
||||
)
|
||||
assert "predictions" in result.columns
|
||||
assert all(str(q) in result.columns for q in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
|
||||
|
||||
|
||||
def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
|
||||
df = create_df()
|
||||
# Make timestamps non-uniform for series A
|
||||
df.loc[df["item_id"] == "A", "timestamp"] = [
|
||||
"2023-01-01",
|
||||
"2023-01-02",
|
||||
"2023-01-04",
|
||||
"2023-01-05",
|
||||
"2023-01-06",
|
||||
"2023-01-07",
|
||||
"2023-01-08",
|
||||
"2023-01-09",
|
||||
"2023-01-10",
|
||||
"2023-01-11",
|
||||
]
|
||||
|
||||
with pytest.raises(ValueError, match="not infer frequency"):
|
||||
pipeline.predict_df(df)
|
||||
|
||||
|
||||
def test_predict_df_with_inconsistent_frequencies_raises_error(pipeline):
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"item_id": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"],
|
||||
"timestamp": [
|
||||
"2023-01-01",
|
||||
"2023-01-02",
|
||||
"2023-01-03",
|
||||
"2023-01-04",
|
||||
"2023-01-05",
|
||||
"2023-01-01",
|
||||
"2023-02-01",
|
||||
"2023-03-01",
|
||||
"2023-04-01",
|
||||
"2023-05-01",
|
||||
],
|
||||
"target": [1.0] * 10,
|
||||
}
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="same frequency"):
|
||||
pipeline.predict_df(df)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("n_tokens", [10, 1000, 10000])
|
||||
def test_tokenizer_number_of_buckets(n_tokens):
|
||||
config = ChronosConfig(
|
||||
|
|
|
|||
|
|
@ -13,11 +13,10 @@ import pytest
|
|||
import torch
|
||||
|
||||
from chronos import BaseChronosPipeline, Chronos2Pipeline
|
||||
from chronos.chronos2.dataset import convert_df_input_to_list_of_dicts_input
|
||||
from chronos.chronos2.config import Chronos2CoreConfig
|
||||
from chronos.chronos2.layers import MHA
|
||||
|
||||
from test.util import validate_tensor
|
||||
from chronos.df_utils import convert_df_input_to_list_of_dicts_input
|
||||
from test.util import create_df, create_future_df, get_forecast_start_times, validate_tensor, timeout_callback
|
||||
|
||||
DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos2-model"
|
||||
|
||||
|
|
@ -35,6 +34,14 @@ def test_base_chronos2_pipeline_loads_from_s3():
|
|||
BaseChronosPipeline.from_pretrained("s3://autogluon/chronos-2", device_map="cpu")
|
||||
|
||||
|
||||
def test_base_chronos2_pipeline_loads_from_hf():
|
||||
BaseChronosPipeline.from_pretrained("amazon/chronos-2", device_map="cpu")
|
||||
|
||||
|
||||
def test_chronos2_lora_pipeline_loads_from_disk():
|
||||
Chronos2Pipeline.from_pretrained(Path(__file__).parent / "dummy-chronos2-lora", device_map="cpu")
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"inputs, prediction_length, expected_output_shapes",
|
||||
[
|
||||
|
|
@ -324,7 +331,7 @@ def test_when_input_is_invalid_then_predict_raises_value_error(pipeline, inputs,
|
|||
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
||||
def test_pipeline_predict_can_handle_different_model_and_input_dtypes(dtype: torch.dtype, input_dtype: torch.dtype):
|
||||
pipeline = BaseChronosPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos2-model", device_map="cpu", dtype=dtype
|
||||
Path(__file__).parent / "dummy-chronos2-model", device_map="cpu", torch_dtype=dtype
|
||||
)
|
||||
context = 10 * torch.rand(size=(4, 3, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
|
@ -337,6 +344,35 @@ def test_pipeline_predict_can_handle_different_model_and_input_dtypes(dtype: tor
|
|||
validate_tensor(quantiles_item, (3, expected_num_quantiles, 7), dtype=torch.float32)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"inputs, expected_output_shapes",
|
||||
[
|
||||
# NOTE: d_model for the dummy model is 6
|
||||
# Homogenous univariate task
|
||||
(torch.rand(4, 1, 16), [(1, 3, 6)] * 4),
|
||||
# Homogenous multivariate task
|
||||
(torch.rand(4, 3, 37), [(3, 5, 6)] * 4),
|
||||
# Heterogenous tasks with different history lengths
|
||||
(
|
||||
[torch.rand(100), torch.rand(2, 150), torch.rand(120)],
|
||||
[(1, 12, 6), (2, 12, 6), (1, 12, 6)],
|
||||
),
|
||||
],
|
||||
)
|
||||
def test_when_input_is_valid_then_pipeline_can_embed(pipeline, inputs, expected_output_shapes):
|
||||
embeds, loc_scales = pipeline.embed(inputs)
|
||||
|
||||
assert (
|
||||
isinstance(embeds, list)
|
||||
and len(embeds) == len(expected_output_shapes)
|
||||
and len(loc_scales) == len(expected_output_shapes)
|
||||
)
|
||||
for embed, loc_scale, expected_shape in zip(embeds, loc_scales, expected_output_shapes):
|
||||
validate_tensor(embed, expected_shape, dtype=torch.float32)
|
||||
validate_tensor(loc_scale[0], (expected_shape[0], 1), dtype=torch.float32)
|
||||
validate_tensor(loc_scale[1], (expected_shape[0], 1), dtype=torch.float32)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"task_kwargs",
|
||||
[
|
||||
|
|
@ -384,81 +420,54 @@ def test_pipeline_can_evaluate_on_dummy_fev_task(pipeline, task_kwargs):
|
|||
assert isinstance(eval_summary["test_error"], float)
|
||||
|
||||
|
||||
def create_df(series_ids=["A", "B"], n_points=[10, 10], target_cols=["target"], covariates=None, freq="h"):
|
||||
"""Helper to create test context DataFrames."""
|
||||
series_dfs = []
|
||||
for series_id, length in zip(series_ids, n_points):
|
||||
series_data = {"item_id": series_id, "timestamp": pd.date_range(end="2001-10-01", periods=length, freq=freq)}
|
||||
for target_col in target_cols:
|
||||
series_data[target_col] = np.random.randn(length)
|
||||
if covariates:
|
||||
for cov in covariates:
|
||||
series_data[cov] = np.random.randn(length)
|
||||
series_dfs.append(pd.DataFrame(series_data))
|
||||
return pd.concat(series_dfs, ignore_index=True)
|
||||
|
||||
|
||||
def create_future_df(forecast_start_times: list, series_ids=["A", "B"], n_points=[5, 5], covariates=None, freq="h"):
|
||||
"""Helper to create test future DataFrames."""
|
||||
series_dfs = []
|
||||
for series_id, length, start in zip(series_ids, n_points, forecast_start_times):
|
||||
series_data = {"item_id": series_id, "timestamp": pd.date_range(start=start, periods=length, freq=freq)}
|
||||
if covariates:
|
||||
for cov in covariates:
|
||||
series_data[cov] = np.random.randn(length)
|
||||
series_dfs.append(pd.DataFrame(series_data))
|
||||
return pd.concat(series_dfs, ignore_index=True)
|
||||
|
||||
|
||||
def get_forecast_start_times(df, freq="h"):
|
||||
context_end_times = df.groupby("item_id")["timestamp"].max()
|
||||
forecast_start_times = [pd.date_range(end_time, periods=2, freq=freq)[-1] for end_time in context_end_times]
|
||||
|
||||
return forecast_start_times
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"context_setup, future_setup, expected_rows",
|
||||
"context_setup, future_setup",
|
||||
[
|
||||
# Targets only
|
||||
({}, None, 6), # 2 series * 3 predictions
|
||||
({}, None),
|
||||
# Multiple targets with different context lengths
|
||||
(
|
||||
{"target_cols": ["sales", "revenue", "profit"], "n_points": [10, 17]},
|
||||
None,
|
||||
18,
|
||||
), # 2 series * 3 targets * 3 predictions
|
||||
({"target_cols": ["sales", "revenue", "profit"], "n_points": [10, 17]}, None),
|
||||
# With past covariates
|
||||
({"covariates": ["cov1"]}, None, 6),
|
||||
({"covariates": ["cov1"]}, None),
|
||||
# With future covariates
|
||||
({"covariates": ["cov1"]}, {"covariates": ["cov1"], "n_points": [3, 3]}, 6),
|
||||
({"covariates": ["cov1"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates
|
||||
({"covariates": ["cov1", "cov2"]}, {"covariates": ["cov1"], "n_points": [3, 3]}, 6),
|
||||
({"covariates": ["cov1", "cov2"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates and different series order
|
||||
(
|
||||
{"series_ids": ["B", "C", "A", "Z"], "n_points": [10, 20, 100, 256], "covariates": ["cov1", "cov2"]},
|
||||
{
|
||||
"series_ids": ["B", "C", "A", "Z"],
|
||||
"covariates": ["cov1"],
|
||||
"n_points": [3, 3, 3, 3],
|
||||
},
|
||||
12,
|
||||
{"series_ids": ["B", "C", "A", "Z"], "covariates": ["cov1"]},
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
||||
def test_predict_df_works_for_valid_inputs(pipeline, context_setup, future_setup, expected_rows, freq):
|
||||
prediction_length = 3
|
||||
@pytest.mark.parametrize("prediction_length", [1, 4])
|
||||
@pytest.mark.parametrize("validate_inputs", [True, False])
|
||||
def test_predict_df_works_for_valid_inputs(
|
||||
pipeline, context_setup, future_setup, freq, validate_inputs, prediction_length
|
||||
):
|
||||
df = create_df(**context_setup, freq=freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq)
|
||||
future_df = create_future_df(forecast_start_times, **future_setup, freq=freq) if future_setup else None
|
||||
if future_setup:
|
||||
series_ids = future_setup.get("series_ids", ["A", "B"])
|
||||
future_setup_with_n_points = {**future_setup, "n_points": [prediction_length] * len(series_ids)}
|
||||
future_df = create_future_df(forecast_start_times, **future_setup_with_n_points, freq=freq)
|
||||
else:
|
||||
future_df = None
|
||||
|
||||
series_ids = context_setup.get("series_ids", ["A", "B"])
|
||||
target_columns = context_setup.get("target_cols", ["target"])
|
||||
n_series = len(series_ids)
|
||||
n_targets = len(target_columns)
|
||||
result = pipeline.predict_df(df, future_df=future_df, target=target_columns, prediction_length=prediction_length)
|
||||
result = pipeline.predict_df(
|
||||
df,
|
||||
future_df=future_df,
|
||||
target=target_columns,
|
||||
prediction_length=prediction_length,
|
||||
validate_inputs=validate_inputs,
|
||||
)
|
||||
|
||||
expected_rows = n_series * n_targets * prediction_length
|
||||
assert len(result) == expected_rows
|
||||
assert "item_id" in result.columns and np.all(
|
||||
result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
|
||||
|
|
@ -513,9 +522,10 @@ def test_predict_df_future_df_validation_errors(pipeline, future_data, error_mat
|
|||
pipeline.predict_df(df, future_df=future_df)
|
||||
|
||||
|
||||
def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
|
||||
@pytest.mark.parametrize("validate_inputs", [True, False])
|
||||
def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline, validate_inputs):
|
||||
df = create_df()
|
||||
# Make timestamps non-uniform for series A
|
||||
# Make timestamps non-uniform for series A (first series)
|
||||
df.loc[df["item_id"] == "A", "timestamp"] = [
|
||||
"2023-01-01",
|
||||
"2023-01-02",
|
||||
|
|
@ -529,8 +539,8 @@ def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
|
|||
"2023-01-11",
|
||||
]
|
||||
|
||||
with pytest.raises(ValueError, match="not infer frequency"):
|
||||
pipeline.predict_df(df)
|
||||
with pytest.raises((ValueError, AssertionError), match="not infer frequency"):
|
||||
pipeline.predict_df(df, validate_inputs=validate_inputs)
|
||||
|
||||
|
||||
def test_predict_df_with_inconsistent_frequencies_raises_error(pipeline):
|
||||
|
|
@ -567,26 +577,80 @@ def test_predict_df_with_future_df_missing_series_raises_error(pipeline):
|
|||
pipeline.predict_df(df, future_df=future_df)
|
||||
|
||||
|
||||
def test_predict_df_with_future_df_with_different_lengths_raises_error(pipeline):
|
||||
df = create_df(series_ids=["A", "B"], covariates=["cov1"])
|
||||
future_df = create_future_df(
|
||||
get_forecast_start_times(df), series_ids=["A", "B"], n_points=[3, 7], covariates=["cov1"]
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="all time series must have length"):
|
||||
pipeline.predict_df(df, future_df=future_df, prediction_length=3)
|
||||
|
||||
|
||||
def test_predict_df_with_future_df_with_different_freq_raises_error(pipeline):
|
||||
df = create_df(series_ids=["A", "B"], covariates=["cov1"], freq="h")
|
||||
future_df = create_future_df(
|
||||
get_forecast_start_times(df), series_ids=["A", "B"], n_points=[3, 3], covariates=["cov1"], freq="D"
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="must have the same frequency as context"):
|
||||
with pytest.raises(ValueError, match="future_df timestamps do not match"):
|
||||
pipeline.predict_df(df, future_df=future_df, prediction_length=3)
|
||||
|
||||
|
||||
def test_predict_df_with_future_df_with_different_lengths_raises_error(pipeline):
|
||||
df = create_df(series_ids=["A", "B"], covariates=["cov1"])
|
||||
future_df = create_future_df(
|
||||
get_forecast_start_times(df), series_ids=["A", "B"], n_points=[3, 7], covariates=["cov1"]
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="future_df must contain prediction"):
|
||||
pipeline.predict_df(df, future_df=future_df, prediction_length=3)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"context_setup, future_setup",
|
||||
[
|
||||
# Targets only
|
||||
({}, None),
|
||||
# Multiple targets with different context lengths
|
||||
({"target_cols": ["sales", "revenue", "profit"], "n_points": [10, 17]}, None),
|
||||
# With past covariates
|
||||
({"covariates": ["cov1"]}, None),
|
||||
# With future covariates
|
||||
({"covariates": ["cov1"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates
|
||||
({"covariates": ["cov1", "cov2"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates and different series order
|
||||
(
|
||||
{"series_ids": ["B", "C", "A", "Z"], "n_points": [10, 20, 100, 256], "covariates": ["cov1", "cov2"]},
|
||||
{"series_ids": ["B", "C", "A", "Z"], "covariates": ["cov1"]},
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("prediction_length", [1, 4])
|
||||
def test_predict_df_outputs_different_results_with_cross_learning_enabled(
|
||||
pipeline, context_setup, future_setup, prediction_length
|
||||
):
|
||||
freq = "h"
|
||||
df = create_df(**context_setup, freq=freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq)
|
||||
if future_setup:
|
||||
series_ids = future_setup.get("series_ids", ["A", "B"])
|
||||
future_setup_with_n_points = {**future_setup, "n_points": [prediction_length] * len(series_ids)}
|
||||
future_df = create_future_df(forecast_start_times, **future_setup_with_n_points, freq=freq)
|
||||
else:
|
||||
future_df = None
|
||||
|
||||
series_ids = context_setup.get("series_ids", ["A", "B"])
|
||||
target_columns = context_setup.get("target_cols", ["target"])
|
||||
result_with_cross_learning = pipeline.predict_df(
|
||||
df,
|
||||
future_df=future_df,
|
||||
target=target_columns,
|
||||
prediction_length=prediction_length,
|
||||
cross_learning=True,
|
||||
)
|
||||
result_without_cross_learning = pipeline.predict_df(
|
||||
df,
|
||||
future_df=future_df,
|
||||
target=target_columns,
|
||||
prediction_length=prediction_length,
|
||||
cross_learning=False,
|
||||
)
|
||||
|
||||
assert not np.array_equal(result_with_cross_learning["predictions"], result_without_cross_learning["predictions"])
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"inputs, prediction_length, expected_output_shapes",
|
||||
[
|
||||
|
|
@ -672,12 +736,20 @@ def test_predict_df_with_future_df_with_different_freq_raises_error(pipeline):
|
|||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("finetune_mode", ["full", "lora"])
|
||||
def test_when_input_is_valid_then_pipeline_can_be_finetuned(
|
||||
pipeline, inputs, prediction_length, expected_output_shapes
|
||||
pipeline, inputs, prediction_length, expected_output_shapes, finetune_mode
|
||||
):
|
||||
# Get outputs before fine-tuning
|
||||
orig_outputs_before = pipeline.predict(inputs, prediction_length=prediction_length)
|
||||
ft_pipeline = pipeline.fit(inputs, prediction_length=prediction_length, num_steps=5, min_past=1, batch_size=32)
|
||||
ft_pipeline = pipeline.fit(
|
||||
inputs,
|
||||
prediction_length=prediction_length,
|
||||
num_steps=5,
|
||||
min_past=1,
|
||||
batch_size=32,
|
||||
finetune_mode=finetune_mode,
|
||||
)
|
||||
# Get outputs from fine-tuned pipeline
|
||||
ft_outputs = ft_pipeline.predict(inputs, prediction_length=prediction_length)
|
||||
# Get outputs from original pipeline after fine-tuning
|
||||
|
|
@ -853,40 +925,36 @@ def test_when_input_time_series_are_too_short_then_finetuning_raises_error(pipel
|
|||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"context_setup, future_setup, expected_rows",
|
||||
"context_setup, future_setup",
|
||||
[
|
||||
# Targets only
|
||||
({}, None, 6), # 2 series * 3 predictions
|
||||
({}, None),
|
||||
# Multiple targets with different context lengths
|
||||
(
|
||||
{"target_cols": ["sales", "revenue", "profit"], "n_points": [10, 17]},
|
||||
None,
|
||||
18,
|
||||
), # 2 series * 3 targets * 3 predictions
|
||||
({"target_cols": ["sales", "revenue", "profit"], "n_points": [10, 17]}, None),
|
||||
# With past covariates
|
||||
({"covariates": ["cov1"]}, None, 6),
|
||||
({"covariates": ["cov1"]}, None),
|
||||
# With future covariates
|
||||
({"covariates": ["cov1"]}, {"covariates": ["cov1"], "n_points": [3, 3]}, 6),
|
||||
({"covariates": ["cov1"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates
|
||||
({"covariates": ["cov1", "cov2"]}, {"covariates": ["cov1"], "n_points": [3, 3]}, 6),
|
||||
({"covariates": ["cov1", "cov2"]}, {"covariates": ["cov1"]}),
|
||||
# With past-only and future covariates and different series order
|
||||
(
|
||||
{"series_ids": ["B", "C", "A", "Z"], "n_points": [10, 20, 100, 256], "covariates": ["cov1", "cov2"]},
|
||||
{
|
||||
"series_ids": ["B", "C", "A", "Z"],
|
||||
"covariates": ["cov1"],
|
||||
"n_points": [3, 3, 3, 3],
|
||||
},
|
||||
12,
|
||||
{"series_ids": ["B", "C", "A", "Z"], "covariates": ["cov1"]},
|
||||
),
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("freq", ["h", "D", "ME"])
|
||||
def test_two_step_finetuning_with_df_input_works(pipeline, context_setup, future_setup, expected_rows, freq):
|
||||
def test_two_step_finetuning_with_df_input_works(pipeline, context_setup, future_setup, freq):
|
||||
prediction_length = 3
|
||||
df = create_df(**context_setup, freq=freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq)
|
||||
future_df = create_future_df(forecast_start_times, **future_setup, freq=freq) if future_setup else None
|
||||
if future_setup:
|
||||
series_ids = future_setup.get("series_ids", ["A", "B"])
|
||||
future_setup_with_n_points = {**future_setup, "n_points": [prediction_length] * len(series_ids)}
|
||||
future_df = create_future_df(forecast_start_times, **future_setup_with_n_points, freq=freq)
|
||||
else:
|
||||
future_df = None
|
||||
|
||||
series_ids = context_setup.get("series_ids", ["A", "B"])
|
||||
target_columns = context_setup.get("target_cols", ["target"])
|
||||
|
|
@ -919,6 +987,7 @@ def test_two_step_finetuning_with_df_input_works(pipeline, context_setup, future
|
|||
)
|
||||
|
||||
# Check predictions from the fine-tuned model are valid
|
||||
expected_rows = n_series * n_targets * prediction_length
|
||||
assert len(result) == expected_rows
|
||||
assert "item_id" in result.columns and np.all(
|
||||
result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
|
||||
|
|
@ -939,6 +1008,17 @@ def test_two_step_finetuning_with_df_input_works(pipeline, context_setup, future
|
|||
assert not np.allclose(orig_result_before["predictions"].to_numpy(), result["predictions"].to_numpy())
|
||||
|
||||
|
||||
def test_when_predict_df_called_with_timeout_callback_then_timeout_error_is_raised(pipeline):
|
||||
num_series = 1000
|
||||
large_df = create_df(series_ids=[j for j in range(num_series)], n_points=[2048] * num_series)
|
||||
with pytest.raises(TimeoutError, match="time limit exceeded"):
|
||||
pipeline.predict_df(
|
||||
large_df,
|
||||
prediction_length=48,
|
||||
after_batch=timeout_callback(0.1),
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("attn_implementation", ["eager", "sdpa"])
|
||||
def test_pipeline_works_with_different_attention_implementations(attn_implementation):
|
||||
"""Test that the pipeline works with different attention implementations."""
|
||||
|
|
@ -1015,13 +1095,13 @@ def test_eager_and_sdpa_produce_identical_outputs(pipeline):
|
|||
# Reload pipeline with SDPA
|
||||
model_path = Path(__file__).parent / "dummy-chronos2-model"
|
||||
pipeline_sdpa = BaseChronosPipeline.from_pretrained(
|
||||
model_path, device_map="cpu", attn_implementation="sdpa", dtype=torch.float32
|
||||
model_path, device_map="cpu", attn_implementation="sdpa", torch_dtype=torch.float32
|
||||
)
|
||||
|
||||
# Note: the original pipeline fixture uses default attn_implementation which should be sdpa
|
||||
# Force eager for comparison
|
||||
pipeline_eager = BaseChronosPipeline.from_pretrained(
|
||||
model_path, device_map="cpu", attn_implementation="eager", dtype=torch.float32
|
||||
model_path, device_map="cpu", attn_implementation="eager", torch_dtype=torch.float32
|
||||
)
|
||||
|
||||
# Test 1: Simple univariate input
|
||||
|
|
|
|||
|
|
@ -5,12 +5,21 @@ from pathlib import Path
|
|||
|
||||
import datasets
|
||||
import fev
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
import torch
|
||||
|
||||
from chronos import BaseChronosPipeline, ChronosBoltPipeline
|
||||
from chronos.chronos_bolt import InstanceNorm, Patch
|
||||
from test.util import validate_tensor
|
||||
from test.util import create_df, get_forecast_start_times, validate_tensor
|
||||
|
||||
DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos-bolt-model"
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def pipeline() -> ChronosBoltPipeline:
|
||||
return BaseChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu")
|
||||
|
||||
|
||||
def test_base_chronos_pipeline_loads_from_huggingface():
|
||||
|
|
@ -20,11 +29,7 @@ def test_base_chronos_pipeline_loads_from_huggingface():
|
|||
@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
|
||||
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
||||
def test_pipeline_predict(torch_dtype: torch.dtype, input_dtype: torch.dtype):
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-bolt-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=torch_dtype,
|
||||
)
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=torch_dtype)
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
expected_num_quantiles = len(pipeline.quantiles)
|
||||
|
|
@ -84,11 +89,7 @@ def test_pipeline_predict_quantiles(
|
|||
prediction_length: int,
|
||||
quantile_levels: list[float],
|
||||
):
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-bolt-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=torch_dtype,
|
||||
)
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=torch_dtype)
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
||||
|
|
@ -127,11 +128,7 @@ def test_pipeline_predict_quantiles(
|
|||
@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
|
||||
@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
|
||||
def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(
|
||||
Path(__file__).parent / "dummy-chronos-bolt-model",
|
||||
device_map="cpu",
|
||||
torch_dtype=model_dtype,
|
||||
)
|
||||
pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
|
||||
d_model = pipeline.model.config.d_model
|
||||
context = 10 * torch.rand(size=(4, 16)) + 10
|
||||
context = context.to(dtype=input_dtype)
|
||||
|
|
@ -160,6 +157,88 @@ def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
|
|||
validate_tensor(loc_scale[1], shape=(1,), dtype=torch.float32)
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"context_setup, expected_rows",
|
||||
[
|
||||
# Targets only
|
||||
({}, 6), # 2 series * 3 predictions
|
||||
# Different context lengths
|
||||
(
|
||||
{"series_ids": ["X", "Y", "Z"], "n_points": [10, 17, 56], "target_cols": ["custom_target"]},
|
||||
9,
|
||||
), # 3 series * 3 predictions
|
||||
],
|
||||
)
|
||||
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
||||
def test_predict_df_works_for_valid_inputs(pipeline, context_setup, expected_rows, freq):
|
||||
prediction_length = 3
|
||||
df = create_df(**context_setup, freq=freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq)
|
||||
|
||||
series_ids = context_setup.get("series_ids", ["A", "B"])
|
||||
target_columns = context_setup.get("target_cols", ["target"])
|
||||
n_series = len(series_ids)
|
||||
n_targets = len(target_columns)
|
||||
result = pipeline.predict_df(df, target=target_columns[0], prediction_length=prediction_length)
|
||||
|
||||
assert len(result) == expected_rows
|
||||
assert "item_id" in result.columns and np.all(
|
||||
result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
|
||||
)
|
||||
assert "target_name" in result.columns and np.all(
|
||||
result["target_name"].to_numpy() == np.tile(np.array(target_columns).repeat(prediction_length), n_series)
|
||||
)
|
||||
assert "timestamp" in result.columns and np.all(
|
||||
result.groupby("item_id")["timestamp"].min().to_numpy() == pd.to_datetime(forecast_start_times).to_numpy()
|
||||
)
|
||||
assert "predictions" in result.columns
|
||||
assert all(str(q) in result.columns for q in [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
|
||||
|
||||
|
||||
def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
|
||||
df = create_df()
|
||||
# Make timestamps non-uniform for series A
|
||||
df.loc[df["item_id"] == "A", "timestamp"] = [
|
||||
"2023-01-01",
|
||||
"2023-01-02",
|
||||
"2023-01-04",
|
||||
"2023-01-05",
|
||||
"2023-01-06",
|
||||
"2023-01-07",
|
||||
"2023-01-08",
|
||||
"2023-01-09",
|
||||
"2023-01-10",
|
||||
"2023-01-11",
|
||||
]
|
||||
|
||||
with pytest.raises(ValueError, match="not infer frequency"):
|
||||
pipeline.predict_df(df)
|
||||
|
||||
|
||||
def test_predict_df_with_inconsistent_frequencies_raises_error(pipeline):
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"item_id": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"],
|
||||
"timestamp": [
|
||||
"2023-01-01",
|
||||
"2023-01-02",
|
||||
"2023-01-03",
|
||||
"2023-01-04",
|
||||
"2023-01-05",
|
||||
"2023-01-01",
|
||||
"2023-02-01",
|
||||
"2023-03-01",
|
||||
"2023-04-01",
|
||||
"2023-05-01",
|
||||
],
|
||||
"target": [1.0] * 10,
|
||||
}
|
||||
)
|
||||
|
||||
with pytest.raises(ValueError, match="same frequency"):
|
||||
pipeline.predict_df(df)
|
||||
|
||||
|
||||
# The following tests have been taken from
|
||||
# https://github.com/autogluon/autogluon/blob/f57beb26cb769c6e0d484a6af2b89eab8aee73a8/timeseries/tests/unittests/models/chronos/pipeline/test_chronos_bolt.py
|
||||
# Author: Caner Turkmen <atturkm@amazon.com>
|
||||
|
|
|
|||
462
test/test_df_utils.py
Normal file
462
test/test_df_utils.py
Normal file
|
|
@ -0,0 +1,462 @@
|
|||
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
|
||||
# SPDX-License-Identifier: Apache-2.0
|
||||
|
||||
from unittest.mock import patch
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import pytest
|
||||
|
||||
from chronos.df_utils import (
|
||||
convert_df_input_to_list_of_dicts_input,
|
||||
validate_df_inputs,
|
||||
)
|
||||
from test.util import create_df, create_future_df, get_forecast_start_times
|
||||
|
||||
# Tests for validate_df_inputs function
|
||||
|
||||
|
||||
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
||||
def test_validate_df_inputs_returns_correct_metadata_for_valid_inputs(freq):
|
||||
"""Test that function returns validated dataframes, frequency, series lengths, and original order."""
|
||||
# Create test data with 2 series
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 15], target_cols=["target"], freq=freq)
|
||||
|
||||
# Call validate_df_inputs
|
||||
validated_df, validated_future_df, inferred_freq, series_lengths, original_order = validate_df_inputs(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
id_column="item_id",
|
||||
timestamp_column="timestamp",
|
||||
)
|
||||
|
||||
# Verify key return values
|
||||
assert validated_future_df is None
|
||||
assert inferred_freq is not None
|
||||
assert series_lengths == [10, 15]
|
||||
assert list(original_order) == ["A", "B"]
|
||||
# Verify dataframe is sorted
|
||||
assert validated_df["item_id"].iloc[0] == "A"
|
||||
assert validated_df["item_id"].iloc[10] == "B"
|
||||
|
||||
|
||||
def test_validate_df_inputs_casts_mixed_dtypes_correctly():
|
||||
"""Test that numeric columns are cast to float32 and categorical/string/object columns are cast to category."""
|
||||
# Create dataframe with mixed column types
|
||||
df = pd.DataFrame(
|
||||
{
|
||||
"item_id": ["A"] * 10,
|
||||
"timestamp": pd.date_range(end="2001-10-01", periods=10, freq="h"),
|
||||
"target": np.random.randn(10), # numeric
|
||||
"numeric_cov": np.random.randint(0, 10, 10), # integer numeric
|
||||
"string_cov": ["cat1"] * 5 + ["cat2"] * 5, # string
|
||||
"bool_cov": [True, False] * 5, # boolean
|
||||
}
|
||||
)
|
||||
|
||||
# Call validate_df_inputs
|
||||
validated_df, _, _, _, _ = validate_df_inputs(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify dtypes after validation
|
||||
assert validated_df["target"].dtype == np.float32
|
||||
assert validated_df["numeric_cov"].dtype == np.float32
|
||||
assert validated_df["string_cov"].dtype.name == "category"
|
||||
assert validated_df["bool_cov"].dtype == np.float32 # booleans are cast to float32
|
||||
|
||||
|
||||
def test_validate_df_inputs_raises_error_when_series_has_insufficient_data():
|
||||
"""Test that ValueError is raised for series with < 3 data points."""
|
||||
# Create dataframe with one series having only 2 points
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 2], target_cols=["target"], freq="h")
|
||||
|
||||
# Verify error is raised with series ID in message
|
||||
with pytest.raises(ValueError, match=r"Every time series must have at least 3 data points.*series B"):
|
||||
validate_df_inputs(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
|
||||
def test_validate_df_inputs_raises_error_when_future_df_has_mismatched_series_ids():
|
||||
"""Test that ValueError is raised when future_df has different series IDs than df."""
|
||||
# Create df with series A and B
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 15], target_cols=["target"], freq="h")
|
||||
|
||||
# Create future_df with only series A
|
||||
forecast_start_times = get_forecast_start_times(df, freq="h")
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=[forecast_start_times[0]], series_ids=["A"], n_points=[5], covariates=None, freq="h"
|
||||
)
|
||||
|
||||
# Verify appropriate error is raised
|
||||
with pytest.raises(ValueError, match=r"future_df must contain the same time series IDs as df"):
|
||||
validate_df_inputs(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
|
||||
def test_validate_df_inputs_raises_error_when_future_df_has_incorrect_lengths():
|
||||
"""Test that ValueError is raised when future_df lengths don't match prediction_length."""
|
||||
# Create df with series A and B with a covariate
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 13], target_cols=["target"], covariates=["cov1"], freq="h")
|
||||
|
||||
# Create future_df with varying lengths per series (3 and 7 instead of 5)
|
||||
forecast_start_times = get_forecast_start_times(df, freq="h")
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=forecast_start_times,
|
||||
series_ids=["A", "B"],
|
||||
n_points=[3, 7], # incorrect lengths
|
||||
covariates=["cov1"],
|
||||
freq="h",
|
||||
)
|
||||
|
||||
# Verify error message indicates which series have incorrect lengths
|
||||
with pytest.raises(
|
||||
ValueError, match=r"future_df must contain prediction_length=5 values for each series.*different lengths"
|
||||
):
|
||||
validate_df_inputs(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
|
||||
# Tests for convert_df_input_to_list_of_dicts_input function
|
||||
|
||||
|
||||
def test_convert_df_with_single_target_preserves_values():
|
||||
"""Test conversion with single target column."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], freq="h")
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify output list has correct length (one per series)
|
||||
assert len(inputs) == 2
|
||||
|
||||
# Verify target arrays have correct shape and values match input
|
||||
assert inputs[0]["target"].shape == (1, 10) # (n_targets=1, n_timesteps=10)
|
||||
assert inputs[1]["target"].shape == (1, 12) # (n_targets=1, n_timesteps=12)
|
||||
|
||||
# Verify values are preserved
|
||||
df_sorted = df.sort_values(["item_id", "timestamp"])
|
||||
np.testing.assert_array_almost_equal(
|
||||
inputs[0]["target"][0], df_sorted[df_sorted["item_id"] == "A"]["target"].values
|
||||
)
|
||||
np.testing.assert_array_almost_equal(
|
||||
inputs[1]["target"][0], df_sorted[df_sorted["item_id"] == "B"]["target"].values
|
||||
)
|
||||
|
||||
|
||||
def test_convert_df_with_multiple_targets_preserves_values_and_shape():
|
||||
"""Test conversion with multiple target columns."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 14], target_cols=["target1", "target2"], freq="h")
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target1", "target2"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify target arrays have shape (n_targets, n_timesteps)
|
||||
assert inputs[0]["target"].shape == (2, 10)
|
||||
assert inputs[1]["target"].shape == (2, 14)
|
||||
|
||||
# Verify all target values are preserved for both series
|
||||
df_sorted = df.sort_values(["item_id", "timestamp"])
|
||||
for i, series_id in enumerate(["A", "B"]):
|
||||
series_data = df_sorted[df_sorted["item_id"] == series_id]
|
||||
np.testing.assert_array_almost_equal(inputs[i]["target"][0], series_data["target1"].values)
|
||||
np.testing.assert_array_almost_equal(inputs[i]["target"][1], series_data["target2"].values)
|
||||
|
||||
|
||||
def test_convert_df_with_past_covariates_includes_them_in_output():
|
||||
"""Test conversion with past covariates only."""
|
||||
df = create_df(
|
||||
series_ids=["A", "B"], n_points=[10, 16], target_cols=["target"], covariates=["cov1", "cov2"], freq="h"
|
||||
)
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify output includes past_covariates dictionary
|
||||
assert "past_covariates" in inputs[0]
|
||||
assert "cov1" in inputs[0]["past_covariates"]
|
||||
assert "cov2" in inputs[0]["past_covariates"]
|
||||
|
||||
# Verify covariate values match input for both series
|
||||
assert inputs[0]["past_covariates"]["cov1"].shape == (10,)
|
||||
assert inputs[0]["past_covariates"]["cov2"].shape == (10,)
|
||||
assert inputs[1]["past_covariates"]["cov1"].shape == (16,)
|
||||
assert inputs[1]["past_covariates"]["cov2"].shape == (16,)
|
||||
|
||||
# Verify no future_covariates key in output
|
||||
assert "future_covariates" not in inputs[0]
|
||||
|
||||
|
||||
def test_convert_df_with_past_and_future_covariates_includes_both():
|
||||
"""Test conversion with both past and future covariates."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 18], target_cols=["target"], covariates=["cov1"], freq="h")
|
||||
|
||||
forecast_start_times = get_forecast_start_times(df, freq="h")
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=forecast_start_times,
|
||||
series_ids=["A", "B"],
|
||||
n_points=[5, 5],
|
||||
covariates=["cov1"],
|
||||
freq="h",
|
||||
)
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify output includes both past_covariates and future_covariates dictionaries for both series
|
||||
assert "past_covariates" in inputs[0]
|
||||
assert "future_covariates" in inputs[0]
|
||||
assert "past_covariates" in inputs[1]
|
||||
assert "future_covariates" in inputs[1]
|
||||
|
||||
# Verify all covariate values are preserved with correct shapes
|
||||
assert inputs[0]["past_covariates"]["cov1"].shape == (10,)
|
||||
assert inputs[0]["future_covariates"]["cov1"].shape == (5,)
|
||||
assert inputs[1]["past_covariates"]["cov1"].shape == (18,)
|
||||
assert inputs[1]["future_covariates"]["cov1"].shape == (5,)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
|
||||
def test_convert_df_generates_prediction_timestamps_with_correct_frequency(freq):
|
||||
"""Test that prediction timestamps follow the inferred frequency."""
|
||||
# Use multiple series with irregular lengths
|
||||
df = create_df(series_ids=["A", "B", "C"], n_points=[10, 15, 12], target_cols=["target"], freq=freq)
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
)
|
||||
|
||||
# Verify timestamps for all series
|
||||
for series_id in ["A", "B", "C"]:
|
||||
# Verify timestamps start after last context timestamp
|
||||
last_context_time = df[df["item_id"] == series_id]["timestamp"].max()
|
||||
first_pred_time = prediction_timestamps[series_id][0]
|
||||
assert first_pred_time > last_context_time
|
||||
|
||||
# Verify timestamps are evenly spaced according to frequency
|
||||
pred_times = prediction_timestamps[series_id]
|
||||
assert len(pred_times) == 5
|
||||
inferred_freq = pd.infer_freq(pred_times)
|
||||
assert inferred_freq is not None
|
||||
|
||||
|
||||
def test_convert_df_skips_validation_when_disabled():
|
||||
"""Test that validate_inputs=False skips validation."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], freq="h")
|
||||
|
||||
# Mock validate_df_inputs to verify it's not called when validation is disabled
|
||||
with patch("chronos.df_utils.validate_df_inputs") as mock_validate:
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
validate_inputs=False,
|
||||
)
|
||||
|
||||
# Verify validate_df_inputs was not called
|
||||
mock_validate.assert_not_called()
|
||||
|
||||
# Verify conversion still works
|
||||
assert len(inputs) == 2
|
||||
|
||||
|
||||
def test_convert_df_preserves_all_values_with_random_inputs():
|
||||
"""Generate random dataframe and verify all values are preserved exactly."""
|
||||
# Generate random parameters
|
||||
n_series = np.random.randint(2, 5)
|
||||
n_targets = np.random.randint(1, 4)
|
||||
n_past_only_covariates = np.random.randint(1, 3)
|
||||
n_future_covariates = np.random.randint(1, 3)
|
||||
prediction_length = 5
|
||||
|
||||
series_ids = [f"series_{i}" for i in range(n_series)]
|
||||
n_points = [np.random.randint(10, 20) for _ in range(n_series)]
|
||||
target_cols = [f"target_{i}" for i in range(n_targets)]
|
||||
past_only_covariates = [f"past_cov_{i}" for i in range(n_past_only_covariates)]
|
||||
future_covariates = [f"future_cov_{i}" for i in range(n_future_covariates)]
|
||||
all_covariates = past_only_covariates + future_covariates
|
||||
|
||||
# Create dataframe with all covariates
|
||||
df = create_df(
|
||||
series_ids=series_ids, n_points=n_points, target_cols=target_cols, covariates=all_covariates, freq="h"
|
||||
)
|
||||
|
||||
# Create future_df with only future covariates (not past-only ones)
|
||||
forecast_start_times = get_forecast_start_times(df, freq="h")
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=forecast_start_times,
|
||||
series_ids=series_ids,
|
||||
n_points=[prediction_length] * n_series,
|
||||
covariates=future_covariates,
|
||||
freq="h",
|
||||
)
|
||||
|
||||
# Convert to list-of-dicts format
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=target_cols,
|
||||
prediction_length=prediction_length,
|
||||
)
|
||||
|
||||
# Verify all target values are preserved exactly
|
||||
df_sorted = df.sort_values(["item_id", "timestamp"])
|
||||
for i, series_id in enumerate(series_ids):
|
||||
series_data = df_sorted[df_sorted["item_id"] == series_id]
|
||||
assert inputs[i]["target"].shape == (n_targets, n_points[i])
|
||||
|
||||
for j, target_col in enumerate(target_cols):
|
||||
np.testing.assert_array_almost_equal(inputs[i]["target"][j], series_data[target_col].values)
|
||||
|
||||
# Verify all past covariate values are preserved (both past-only and future covariates)
|
||||
for i, series_id in enumerate(series_ids):
|
||||
series_data = df_sorted[df_sorted["item_id"] == series_id]
|
||||
assert "past_covariates" in inputs[i]
|
||||
for cov in all_covariates:
|
||||
np.testing.assert_array_almost_equal(inputs[i]["past_covariates"][cov], series_data[cov].values)
|
||||
|
||||
# Verify only future covariates are in future_covariates (not past-only ones)
|
||||
future_df_sorted = future_df.sort_values(["item_id", "timestamp"])
|
||||
for i, series_id in enumerate(series_ids):
|
||||
series_future_data = future_df_sorted[future_df_sorted["item_id"] == series_id]
|
||||
assert "future_covariates" in inputs[i]
|
||||
# Only future covariates should be present
|
||||
assert set(inputs[i]["future_covariates"].keys()) == set(future_covariates)
|
||||
for cov in future_covariates:
|
||||
np.testing.assert_array_almost_equal(inputs[i]["future_covariates"][cov], series_future_data[cov].values)
|
||||
|
||||
# Verify output structure is correct
|
||||
assert len(inputs) == n_series
|
||||
assert list(original_order) == series_ids
|
||||
assert len(prediction_timestamps) == n_series
|
||||
|
||||
|
||||
def test_convert_df_with_freq_and_validate_inputs_raises_error():
|
||||
"""Test that providing freq with validate_inputs=True raises ValueError."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], freq="h")
|
||||
|
||||
with pytest.raises(ValueError, match="freq can only be provided when validate_inputs=False"):
|
||||
convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=None,
|
||||
target_columns=["target"],
|
||||
prediction_length=5,
|
||||
freq="h",
|
||||
validate_inputs=True,
|
||||
)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("use_future_df", [True, False])
|
||||
def test_convert_df_with_freq_and_validate_inputs_false(use_future_df):
|
||||
"""Test that freq works with validate_inputs=False."""
|
||||
df = create_df(series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], covariates=["cov1"], freq="h")
|
||||
prediction_length = 5
|
||||
|
||||
future_df = None
|
||||
if use_future_df:
|
||||
forecast_start_times = get_forecast_start_times(df, freq="h")
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=forecast_start_times,
|
||||
series_ids=["A", "B"],
|
||||
n_points=[prediction_length, prediction_length],
|
||||
covariates=["cov1"],
|
||||
freq="h",
|
||||
)
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=["target"],
|
||||
prediction_length=prediction_length,
|
||||
freq="h",
|
||||
validate_inputs=False,
|
||||
)
|
||||
|
||||
assert len(inputs) == 2
|
||||
assert len(prediction_timestamps) == 2
|
||||
for series_id in ["A", "B"]:
|
||||
assert len(prediction_timestamps[series_id]) == prediction_length
|
||||
|
||||
|
||||
@pytest.mark.parametrize("use_future_df", [True, False])
|
||||
def test_convert_df_with_mismatched_freq_uses_user_provided_freq(use_future_df):
|
||||
"""Test that user-provided freq overrides data frequency when validate_inputs=False."""
|
||||
# Create data with hourly frequency
|
||||
data_freq = "h"
|
||||
df = create_df(
|
||||
series_ids=["A", "B"], n_points=[10, 12], target_cols=["target"], covariates=["cov1"], freq=data_freq
|
||||
)
|
||||
prediction_length = 5
|
||||
|
||||
# User provides daily frequency (different from data)
|
||||
user_freq = "D"
|
||||
|
||||
future_df = None
|
||||
if use_future_df:
|
||||
# Create future_df with hourly frequency (matching data, not user freq)
|
||||
forecast_start_times = get_forecast_start_times(df, freq=data_freq)
|
||||
future_df = create_future_df(
|
||||
forecast_start_times=forecast_start_times,
|
||||
series_ids=["A", "B"],
|
||||
n_points=[prediction_length, prediction_length],
|
||||
covariates=["cov1"],
|
||||
freq=data_freq,
|
||||
)
|
||||
|
||||
inputs, original_order, prediction_timestamps = convert_df_input_to_list_of_dicts_input(
|
||||
df=df,
|
||||
future_df=future_df,
|
||||
target_columns=["target"],
|
||||
prediction_length=prediction_length,
|
||||
freq=user_freq,
|
||||
validate_inputs=False,
|
||||
)
|
||||
|
||||
# Prediction should work
|
||||
assert len(inputs) == 2
|
||||
assert len(prediction_timestamps) == 2
|
||||
|
||||
# Forecast timestamps should use user-provided freq (daily), not data freq (hourly)
|
||||
for series_id in ["A", "B"]:
|
||||
pred_ts = prediction_timestamps[series_id]
|
||||
assert len(pred_ts) == prediction_length
|
||||
# Verify the frequency matches user-provided freq
|
||||
inferred_freq = pd.infer_freq(pred_ts)
|
||||
assert inferred_freq == user_freq
|
||||
49
test/util.py
49
test/util.py
|
|
@ -1,5 +1,8 @@
|
|||
from typing import Optional, Tuple
|
||||
import time
|
||||
from typing import Callable, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
import torch
|
||||
|
||||
|
||||
|
|
@ -9,3 +12,47 @@ def validate_tensor(a: torch.Tensor, shape: Tuple[int, ...], dtype: Optional[tor
|
|||
|
||||
if dtype is not None:
|
||||
assert a.dtype == dtype
|
||||
|
||||
|
||||
def create_df(series_ids=["A", "B"], n_points=[10, 10], target_cols=["target"], covariates=None, freq="h"):
|
||||
"""Helper to create test context DataFrames."""
|
||||
series_dfs = []
|
||||
for series_id, length in zip(series_ids, n_points):
|
||||
series_data = {"item_id": series_id, "timestamp": pd.date_range(end="2001-10-01", periods=length, freq=freq)}
|
||||
for target_col in target_cols:
|
||||
series_data[target_col] = np.random.randn(length)
|
||||
if covariates:
|
||||
for cov in covariates:
|
||||
series_data[cov] = np.random.randn(length)
|
||||
series_dfs.append(pd.DataFrame(series_data))
|
||||
return pd.concat(series_dfs, ignore_index=True)
|
||||
|
||||
|
||||
def create_future_df(forecast_start_times: list, series_ids=["A", "B"], n_points=[5, 5], covariates=None, freq="h"):
|
||||
"""Helper to create test future DataFrames."""
|
||||
series_dfs = []
|
||||
for series_id, length, start in zip(series_ids, n_points, forecast_start_times):
|
||||
series_data = {"item_id": series_id, "timestamp": pd.date_range(start=start, periods=length, freq=freq)}
|
||||
if covariates:
|
||||
for cov in covariates:
|
||||
series_data[cov] = np.random.randn(length)
|
||||
series_dfs.append(pd.DataFrame(series_data))
|
||||
return pd.concat(series_dfs, ignore_index=True)
|
||||
|
||||
|
||||
def get_forecast_start_times(df, freq="h"):
|
||||
context_end_times = df.groupby("item_id")["timestamp"].max()
|
||||
forecast_start_times = [pd.date_range(end_time, periods=2, freq=freq)[-1] for end_time in context_end_times]
|
||||
|
||||
return forecast_start_times
|
||||
|
||||
|
||||
def timeout_callback(seconds: float | None) -> Callable:
|
||||
"""Return a callback object that raises an exception if time limit is exceeded."""
|
||||
start_time = time.monotonic()
|
||||
|
||||
def callback() -> None:
|
||||
if seconds is not None and time.monotonic() - start_time > seconds:
|
||||
raise TimeoutError("time limit exceeded")
|
||||
|
||||
return callback
|
||||
|
|
|
|||
Loading…
Reference in a new issue