mirror of
https://github.com/amazon-science/chronos-forecasting
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e48f48071f
*Issue #, if available:* *Description of changes:* This PR adds `predict_df` to the base pipeline which enables pandas support for the univariate Chronos and Chronos-Bolt models. By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.
377 lines
13 KiB
Python
377 lines
13 KiB
Python
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
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# SPDX-License-Identifier: Apache-2.0
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from pathlib import Path
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import datasets
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import fev
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import numpy as np
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import pandas as pd
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import pytest
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import torch
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from chronos import BaseChronosPipeline, ChronosBoltPipeline
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from chronos.chronos_bolt import InstanceNorm, Patch
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from test.util import create_df, get_forecast_start_times, validate_tensor
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DUMMY_MODEL_PATH = Path(__file__).parent / "dummy-chronos-bolt-model"
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@pytest.fixture
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def pipeline() -> ChronosBoltPipeline:
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return BaseChronosPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu")
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def test_base_chronos_pipeline_loads_from_huggingface():
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BaseChronosPipeline.from_pretrained("amazon/chronos-bolt-tiny", device_map="cpu")
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@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
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@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
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def test_pipeline_predict(torch_dtype: torch.dtype, input_dtype: torch.dtype):
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pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=torch_dtype)
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context = 10 * torch.rand(size=(4, 16)) + 10
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context = context.to(dtype=input_dtype)
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expected_num_quantiles = len(pipeline.quantiles)
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# input: tensor of shape (batch_size, context_length)
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quantiles = pipeline.predict(context, prediction_length=3)
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validate_tensor(quantiles, (4, expected_num_quantiles, 3), dtype=torch.float32)
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with pytest.raises(ValueError):
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quantiles = pipeline.predict(context, prediction_length=65, limit_prediction_length=True)
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quantiles = pipeline.predict(context, prediction_length=65)
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validate_tensor(quantiles, (4, expected_num_quantiles, 65))
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# input: batch_size-long list of tensors of shape (context_length,)
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quantiles = pipeline.predict(list(context), prediction_length=3)
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validate_tensor(quantiles, (4, expected_num_quantiles, 3), dtype=torch.float32)
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with pytest.raises(ValueError):
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quantiles = pipeline.predict(
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list(context),
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prediction_length=65,
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limit_prediction_length=True,
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)
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quantiles = pipeline.predict(list(context), prediction_length=65)
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validate_tensor(quantiles, (4, expected_num_quantiles, 65), dtype=torch.float32)
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# input: tensor of shape (context_length,)
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quantiles = pipeline.predict(context[0, ...], prediction_length=3)
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validate_tensor(quantiles, (1, expected_num_quantiles, 3), dtype=torch.float32)
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with pytest.raises(ValueError):
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quantiles = pipeline.predict(
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context[0, ...],
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prediction_length=65,
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limit_prediction_length=True,
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)
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quantiles = pipeline.predict(
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context[0, ...],
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prediction_length=65,
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)
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validate_tensor(quantiles, (1, expected_num_quantiles, 65), dtype=torch.float32)
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@pytest.mark.parametrize("torch_dtype", [torch.float32, torch.bfloat16])
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@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
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@pytest.mark.parametrize("prediction_length", [3, 65])
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@pytest.mark.parametrize("quantile_levels", [[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9], [0.1, 0.5, 0.9]])
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def test_pipeline_predict_quantiles(
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torch_dtype: torch.dtype,
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input_dtype: torch.dtype,
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prediction_length: int,
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quantile_levels: list[float],
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):
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pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=torch_dtype)
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context = 10 * torch.rand(size=(4, 16)) + 10
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context = context.to(dtype=input_dtype)
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num_expected_quantiles = len(quantile_levels)
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# input: tensor of shape (batch_size, context_length)
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quantiles, mean = pipeline.predict_quantiles(
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context,
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prediction_length=prediction_length,
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quantile_levels=quantile_levels,
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)
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validate_tensor(quantiles, (4, prediction_length, num_expected_quantiles), dtype=torch.float32)
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validate_tensor(mean, (4, prediction_length), dtype=torch.float32)
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# input: batch_size-long list of tensors of shape (context_length,)
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quantiles, mean = pipeline.predict_quantiles(
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list(context),
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prediction_length=prediction_length,
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quantile_levels=quantile_levels,
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)
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validate_tensor(quantiles, (4, prediction_length, num_expected_quantiles), dtype=torch.float32)
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validate_tensor(mean, (4, prediction_length), dtype=torch.float32)
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# input: tensor of shape (context_length,)
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quantiles, mean = pipeline.predict_quantiles(
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context[0, ...],
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prediction_length=prediction_length,
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quantile_levels=quantile_levels,
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)
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validate_tensor(quantiles, (1, prediction_length, num_expected_quantiles), dtype=torch.float32)
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validate_tensor(mean, (1, prediction_length), dtype=torch.float32)
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@pytest.mark.parametrize("model_dtype", [torch.float32, torch.bfloat16])
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@pytest.mark.parametrize("input_dtype", [torch.float32, torch.bfloat16, torch.int64])
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def test_pipeline_embed(model_dtype: torch.dtype, input_dtype: torch.dtype):
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pipeline = ChronosBoltPipeline.from_pretrained(DUMMY_MODEL_PATH, device_map="cpu", torch_dtype=model_dtype)
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d_model = pipeline.model.config.d_model
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context = 10 * torch.rand(size=(4, 16)) + 10
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context = context.to(dtype=input_dtype)
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# the patch size of dummy model is 16, so only 1 patch is created
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expected_embed_length = 1 + (1 if pipeline.model.config.chronos_config["use_reg_token"] else 0)
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# input: tensor of shape (batch_size, context_length)
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embedding, loc_scale = pipeline.embed(context)
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validate_tensor(embedding, shape=(4, expected_embed_length, d_model), dtype=model_dtype)
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validate_tensor(loc_scale[0], shape=(4,), dtype=torch.float32)
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validate_tensor(loc_scale[1], shape=(4,), dtype=torch.float32)
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# input: batch_size-long list of tensors of shape (context_length,)
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embedding, loc_scale = pipeline.embed(list(context))
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validate_tensor(embedding, shape=(4, expected_embed_length, d_model), dtype=model_dtype)
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validate_tensor(loc_scale[0], shape=(4,), dtype=torch.float32)
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validate_tensor(loc_scale[1], shape=(4,), dtype=torch.float32)
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# input: tensor of shape (context_length,)
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embedding, loc_scale = pipeline.embed(context[0, ...])
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validate_tensor(embedding, shape=(1, expected_embed_length, d_model), dtype=model_dtype)
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validate_tensor(loc_scale[0], shape=(1,), dtype=torch.float32)
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validate_tensor(loc_scale[1], shape=(1,), dtype=torch.float32)
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@pytest.mark.parametrize(
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"context_setup, expected_rows",
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[
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# Targets only
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({}, 6), # 2 series * 3 predictions
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# Different context lengths
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(
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{"series_ids": ["X", "Y", "Z"], "n_points": [10, 17, 56], "target_cols": ["custom_target"]},
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9,
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), # 3 series * 3 predictions
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],
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)
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@pytest.mark.parametrize("freq", ["s", "min", "30min", "h", "D", "W", "ME", "QE", "YE"])
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def test_predict_df_works_for_valid_inputs(pipeline, context_setup, expected_rows, freq):
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prediction_length = 3
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df = create_df(**context_setup, freq=freq)
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forecast_start_times = get_forecast_start_times(df, freq)
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series_ids = context_setup.get("series_ids", ["A", "B"])
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target_columns = context_setup.get("target_cols", ["target"])
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n_series = len(series_ids)
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n_targets = len(target_columns)
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result = pipeline.predict_df(df, target=target_columns[0], prediction_length=prediction_length)
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assert len(result) == expected_rows
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assert "item_id" in result.columns and np.all(
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result["item_id"].to_numpy() == np.array(series_ids).repeat(n_targets * prediction_length)
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)
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assert "target_name" in result.columns and np.all(
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result["target_name"].to_numpy() == np.tile(np.array(target_columns).repeat(prediction_length), n_series)
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)
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assert "timestamp" in result.columns and np.all(
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result.groupby("item_id")["timestamp"].min().to_numpy() == pd.to_datetime(forecast_start_times).to_numpy()
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)
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assert "predictions" in result.columns
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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])
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def test_predict_df_with_non_uniform_timestamps_raises_error(pipeline):
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df = create_df()
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# Make timestamps non-uniform for series A
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df.loc[df["item_id"] == "A", "timestamp"] = [
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"2023-01-01",
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"2023-01-02",
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"2023-01-04",
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"2023-01-05",
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"2023-01-06",
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"2023-01-07",
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"2023-01-08",
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"2023-01-09",
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"2023-01-10",
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"2023-01-11",
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]
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with pytest.raises(ValueError, match="not infer frequency"):
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pipeline.predict_df(df)
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def test_predict_df_with_inconsistent_frequencies_raises_error(pipeline):
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df = pd.DataFrame(
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{
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"item_id": ["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"],
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"timestamp": [
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"2023-01-01",
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"2023-01-02",
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"2023-01-03",
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"2023-01-04",
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"2023-01-05",
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"2023-01-01",
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"2023-02-01",
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"2023-03-01",
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"2023-04-01",
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"2023-05-01",
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],
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"target": [1.0] * 10,
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}
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)
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with pytest.raises(ValueError, match="same frequency"):
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pipeline.predict_df(df)
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# The following tests have been taken from
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# https://github.com/autogluon/autogluon/blob/f57beb26cb769c6e0d484a6af2b89eab8aee73a8/timeseries/tests/unittests/models/chronos/pipeline/test_chronos_bolt.py
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# Author: Caner Turkmen <atturkm@amazon.com>
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def test_given_even_data_patch_operator_output_is_correct():
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batch_size = 17
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patch_len = 16
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patch = Patch(patch_len, patch_len)
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batch = torch.stack([torch.arange(512)] * batch_size) + torch.arange(batch_size)[:, None]
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output = patch(batch)
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assert output.shape == (batch_size, 512 // patch_len, patch_len)
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assert torch.allclose(
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output[:, 0],
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torch.stack([torch.arange(patch_len)] * batch_size) + torch.arange(batch_size)[:, None],
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atol=1e-5,
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)
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assert torch.allclose(
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output[:, 1],
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torch.stack([torch.arange(patch_len, 2 * patch_len)] * batch_size) + torch.arange(batch_size)[:, None],
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atol=1e-5,
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)
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assert not torch.isnan(output).any()
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def test_given_even_data_and_strides_patch_operator_output_is_correct():
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batch_size = 17
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patch_len, patch_stride = 16, 8
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patch = Patch(patch_len, patch_stride)
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offset = torch.arange(batch_size)[:, None]
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batch = torch.stack([torch.arange(512)] * batch_size) + offset
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output = patch(batch)
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assert torch.allclose(
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output[:, 1],
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torch.stack([torch.arange(patch_stride, patch_stride + patch_len)] * batch_size) + offset,
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atol=1e-5,
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)
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assert not torch.isnan(output).any()
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def test_given_uneven_data_patch_operator_pads_and_output_is_correct():
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batch_size = 17
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patch_len = 16
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patch = Patch(patch_len, patch_len)
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batch = (torch.stack([torch.arange(512 - patch_len + 1)] * batch_size) + torch.arange(batch_size)[:, None]).float()
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output = patch(batch)
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assert output.shape == (batch_size, 512 // patch_len, patch_len)
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# check the first portion is padded
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assert torch.isnan(output[:, 0, :-1]).all()
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# check nowhere else is nan
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assert not torch.isnan(output[:, 1:]).any()
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def test_when_instancenorm_applied_then_standardization_correct():
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inorm = InstanceNorm()
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input_ = torch.tensor(
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[
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[1, 2, 3, 4, 5],
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[2, 3, 4, 5, 6],
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]
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).float()
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normalized, (loc, scale) = inorm(input_)
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assert normalized.shape == input_.shape
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assert torch.allclose(normalized[0], normalized[1])
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assert torch.allclose(loc.squeeze(), torch.tensor([3.0, 4.0]))
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assert torch.allclose(scale.squeeze(), torch.tensor(1.41421))
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def test_when_instancenorm_applied_and_reversed_then_nans_preserved():
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inorm = InstanceNorm()
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input_ = torch.tensor(
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[
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[1, torch.nan, 3, 4, 5],
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[2, 3, 4, 5, torch.nan],
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]
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).float()
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normalized, (loc, scale) = inorm(input_)
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assert torch.allclose(normalized.isnan(), input_.isnan())
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output = inorm.inverse(normalized, (loc, scale))
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assert torch.allclose(output, input_, equal_nan=True)
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def test_when_instancenorm_applied_and_reversed_then_output_correct():
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inorm = InstanceNorm()
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input_ = torch.tensor(
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[
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[1, 2, 3, 4, 5],
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[2, 3, 4, 5, 1000],
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]
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).float()
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normalized, loc_scale = inorm(input_)
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output = inorm.inverse(normalized, loc_scale)
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assert torch.allclose(output, input_)
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@pytest.mark.parametrize("task_kwargs", [{}, {"eval_metric": "WQL", "quantile_levels": [0.1, 0.2]}])
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def test_pipeline_can_evaluate_on_dummy_fev_task(task_kwargs):
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pipeline = BaseChronosPipeline.from_pretrained(
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Path(__file__).parent / "dummy-chronos-bolt-model", device_map="cpu"
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)
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task = fev.Task(
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dataset_path="autogluon/chronos_datasets",
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dataset_config="monash_m1_yearly",
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horizon=8,
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**task_kwargs,
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)
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predictions_per_window, inference_time_s = pipeline.predict_fev(task)
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assert isinstance(inference_time_s, float)
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assert isinstance(predictions_per_window, list) and all(
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isinstance(pred, datasets.DatasetDict) for pred in predictions_per_window
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)
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eval_summary = task.evaluation_summary(predictions_per_window, model_name="chronos-bolt")
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assert isinstance(eval_summary["test_error"], float)
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