Few shot forecasting

src/gluonts/nursery/few_shot_prediction/README.md

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+# Probabilistic Few-Shot Forecasting
+We propose a method for probabilistic few-shot time series forecasting.
+In this setting, the model is trained on a large and diverse collection of source data sets and can subsequently make probabilistic predictions for previously unseen target data sets given a small number of representative examples (the support set), without additional retraining or fine-tuning.
+Performance can be improved by choosing the examples in the support set based on a notion of distance to the target time series during both training and prediction.
+The codebase allows training and evaluating our method on a large collection of publicly available real-world data sets.
+
+### Set up
+
+```bash
+# install pyenv
+git clone https://github.com/pyenv/pyenv.git ~/.pyenv
+cd ~/.pyenv && src/configure && make -C src
+echo 'export PYENV_ROOT="$HOME/.pyenv"' >> ~/.profile
+echo 'export PATH="$PYENV_ROOT/bin:$PATH"' >> ~/.profile
+echo 'eval "$(pyenv init --path)"' >> ~/.profile
+source ~/.profile
+pyenv install 3.8.9
+
+# install poetry
+curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python -
+source $HOME/.poetry/env
+poetry config virtualenvs.in-project true
+
+# create virtual env for your project
+cd to_your_project
+pyenv local 3.8.9
+poetry config virtualenvs.in-project true
+poetry install
+```
+
+### Generate and upload data
+Run `scripts/data.py real` to download and process the GluonTS datasets supported be the few-shot predictor. 
+
+### Train a model
+Run `scripts/train.py` with specific arguments to start a training.
+
+
+

src/gluonts/nursery/few_shot_prediction/pyproject.toml

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+[tool.poetry]
+authors = ["Your Name <[email protected]>"]
+description = ""
+name = "meta-tfs"
+packages = [
+  {include = "*", from = "src"},
+]
+version = "0.1.0"
+
+[tool.poetry.dependencies]
+boto3 = "^1.18.43"
+botocore = "^1.21.43"
+click = "^8.0.1"
+geomloss = "^0.2.4"
+gluonts = {git = "https://github.com/awslabs/gluon-ts.git"}
+pandas = "^1.3.1"
+python = "^3.8,<3.10"
+pytorch-lightning = "^1.4.4"
+sagemaker = "^2.40.0,<2.41.0"
+scikit-learn = "^0.24.2"
+torch = "^1.9.0"
+sagemaker-training = "^3.9.2"
+python-dotenv = "^0.19.0"
+xlrd = "^2.0.1"
+lightkit = "^0.3.5"
+catch22 = "^0.2.0"
+seaborn = "^0.11.2"
+
+[tool.poetry.dev-dependencies]
+black = "^21.7b0"
+isort = "^5.9.3"
+jupyter = "^1.0.0"
+pylint = "^2.10.2"
+
+[tool.poetry.scripts]
+schedule = 'schedule:main'
+
+[build-system]
+build-backend = "poetry.core.masonry.api"
+requires = ["poetry-core>=1.0.0"]
+
+[tool.pylint.messages_control]
+disable = [
+  "arguments-differ",
+  "duplicate-code",
+  "missing-module-docstring",
+  "invalid-name",
+  "no-self-use",
+  "too-few-public-methods",
+  "too-many-ancestors",
+  "too-many-arguments",
+  "too-many-branches",
+  "too-many-locals",
+  "too-many-instance-attributes",
+]
+
+[tool.pylint.typecheck]
+generated-members = [
+  "math.*",
+  "torch.*",
+]
+
+[tool.pyright]
+reportIncompatibleMethodOverride = false
+reportMissingStubTypes = false
+reportUnknownArgumentType = false
+reportUnknownMemberType = false
+reportUnknownVariableType = false
+typeCheckingMode = "strict"
+
+[tool.black]
+line-length = 79
+
+[tool.isort]
+force_alphabetical_sort_within_sections = true
+include_trailing_comma = true
+known_first_party = ["embeddings"]
+line_length = 99
+lines_between_sections = 0
+profile = "black"
+skip_gitignore = true

src/gluonts/nursery/few_shot_prediction/src/meta/__init__.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.

src/gluonts/nursery/few_shot_prediction/src/meta/callbacks/__init__.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from .plot import (
+    ForecastPlotLoggerCallback,
+    ForecastSupportSetAttentionPlotLoggerCallback,
+    LossPlotLoggerCallback,
+    CheatLossPlotLoggerCallback,
+    MacroCRPSPlotCallback,
+)
+from .count import ParameterCountCallback
+from .save import InitialSaveCallback
+from .metric import QuantileMetricLoggerCallback
+
+__all__ = [
+    "ForecastPlotLoggerCallback",
+    "ParameterCountCallback",
+    "InitialSaveCallback",
+    "ForecastSupportSetAttentionPlotLoggerCallback",
+    "LossPlotLoggerCallback",
+    "CheatLossPlotLoggerCallback",
+    "MacroCRPSPlotCallback",
+    "QuantileMetricLoggerCallback",
+]

src/gluonts/nursery/few_shot_prediction/src/meta/callbacks/common.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from pytorch_lightning.loggers import CSVLogger
+from pytorch_lightning import Trainer
+import numpy as np
+from pathlib import Path
+
+
+def get_save_dir_from_csvlogger(logger: CSVLogger) -> Path:
+    return Path(logger.save_dir) / logger.name / f"version_{logger.version}"
+
+
+def get_loss_steps(loss_name: str, trainer: Trainer):
+    loss = trainer.logger.experiment.metrics
+    ep = trainer.current_epoch
+    out = [l[loss_name] for l in loss if loss_name in l]
+    start = 1 if "val" in loss_name else 0
+    return out, np.linspace(start, ep, len(out))

src/gluonts/nursery/few_shot_prediction/src/meta/callbacks/count.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from pytorch_lightning import Callback, Trainer, LightningModule
+
+
+class ParameterCountCallback(Callback):  # type: ignore
+    """
+    This callback allows counting model parameters during training.
+    The output is printed to the console and can be retrieved from the log files.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def on_pretrain_routine_start(
+        self, trainer: Trainer, pl_module: LightningModule
+    ) -> None:
+        # compute number of model params
+        model_total_params = sum(
+            p.numel() for p in pl_module.model.parameters()
+        )
+        model_total_trainable_params = sum(
+            p.numel() for p in pl_module.model.parameters() if p.requires_grad
+        )
+
+        # log
+        print("\n" + f"model_total_params: {model_total_params},")
+        print(
+            "\n"
+            + f"model_total_trainable_params: {model_total_trainable_params},"
+        )

src/gluonts/nursery/few_shot_prediction/src/meta/callbacks/metric.py

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+# Copyright 2018 Amazon.com, Inc. or its affiliates. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License").
+# You may not use this file except in compliance with the License.
+# A copy of the License is located at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# or in the "license" file accompanying this file. This file is distributed
+# on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
+# express or implied. See the License for the specific language governing
+# permissions and limitations under the License.
+
+from typing import Optional, Union, List
+from pytorch_lightning import Callback, Trainer
+import torch
+from gluonts.time_feature import get_seasonality
+
+from meta.models.module import MetaLightningModule
+from meta.common.torch import tensor_to_np
+from meta.metrics.numpy import compute_metrics
+
+
+class QuantileMetricLoggerCallback(Callback):
+    """
+    A callback that computes additional metrics on a numpy representation of the dataset every n epochs.
+    The computed values are logged to the output file of the pytorch lightning logger.
+
+    Args:
+        quantiles: The quantiles that are predicted.
+        split: Specifies the split the batch comes from (i.e. from training or validation split)
+        every_n_epochs: Specifies how often the plots are generated.
+            Setting this to a large value can save time since plotting can be time consuming
+            (especially when small datasets are used).
+    """
+
+    def __init__(
+        self,
+        quantiles: List[str],
+        split: Optional[Union["train", "val"]] = None,
+        every_n_epochs: int = 1,
+    ):
+        super().__init__()
+        self.quantiles = quantiles
+        self.split = split
+        self.every_n_epochs = every_n_epochs
+
+    def on_validation_epoch_end(
+        self, trainer: Trainer, pl_module: MetaLightningModule
+    ) -> None:
+        if trainer.current_epoch % self.every_n_epochs:
+            return
+        dm_super = trainer.lightning_module.trainer.datamodule
+        dm_val = dm_super.data_modules_val[0]
+        dl = dm_val.val_dataloader()
+        split = dm_val.splits.val()
+        pred = []
+        for batch in dl:
+            batch = batch.to(pl_module.device)
+            pred.append(
+                pl_module.model(
+                    supps=batch.support_set, query=batch.query_past
+                )
+            )
+
+        # redo standardization for evaluation
+        pred = split.data().rescale_dataset(torch.cat(pred, dim=0).cpu())
+        pred = tensor_to_np(pred)
+
+        # use only the length that should be included for evaluation
+        pred = pred[:, : dm_val.prediction_length, ...]
+        m = compute_metrics(
+            pred,
+            split.evaluation(),
+            quantiles=self.quantiles,
+            seasonality=get_seasonality(dm_val.meta.freq),
+        )
+        self.log("metrics", m)