Get dataset

hacking/datasets_from_tsp.py

@@ -0,0 +1,41 @@
+# %%
+import pandas as pd
+
+from tsfm_public.toolkit.time_series_preprocessor import TimeSeriesPreprocessor
+
+
+split_config = {"train": [0, 8640], "valid": [8640, 11520], "test": [11520, 14400]}
+
+
+dataset_path = (
+    "https://raw.githubusercontent.com/zhouhaoyi/ETDataset/main/ETT-small/ETTh1.csv"
+)
+
+timestamp_column = "date"
+
+df = pd.read_csv(
+    dataset_path,
+    parse_dates=[timestamp_column],
+)
+
+tsp = TimeSeriesPreprocessor(
+    id_columns=[],
+    timestamp_column=timestamp_column,
+    target_columns=["HUFL", "HULL", "MUFL", "MULL", "LUFL", "LULL", "OT"],
+    observable_columns=[],
+    control_columns=[],
+    conditional_columns=[],
+    static_categorical_columns=[],
+    scaling=True,
+    scaler_type="standard",
+    encode_categorical=False,
+    prediction_length=10,
+    context_length=96,
+)
+
+train, valid, test = tsp.get_datasets(df, split_config)
+
+# %%
+split_config = {"train": [0, 0.7], "valid": [0.7, 0.9], "test": [0.9, 1]}
+
+train, valid, test = tsp.get_datasets(df, split_config)

tests/toolkit/test_time_series_preprocessor.py

@@ -16,6 +16,7 @@
     create_timestamps,
     extend_time_series,
 )
+from tsfm_public.toolkit.util import FractionLocation
 
 
 def test_standard_scaler(sample_data):
@@ -72,7 +73,7 @@ def test_time_series_preprocessor_encodes(sample_data):
     static_categorical_columns = ["cat", "cat2"]
 
     tsp = TimeSeriesPreprocessor(
-        input_columns=["val", "val2"],
+        target_columns=["val", "val2"],
         static_categorical_columns=static_categorical_columns,
     )
     tsp.train(sample_data)
@@ -156,3 +157,71 @@ def test_create_timestamps():
     # it is an error to provide neither freq or sequence
     with pytest.raises(ValueError):
         ts = create_timestamps(start, periods=periods)
+
+
+def test_get_datasets(ts_data):
+    tsp = TimeSeriesPreprocessor(
+        id_columns=["id"],
+        target_columns=["value1", "value2"],
+        prediction_length=5,
+        context_length=10,
+    )
+
+    train, valid, test = tsp.get_datasets(
+        ts_data,
+        split_config={"train": [0, 1 / 3], "valid": [1 / 3, 2 / 3], "test": [2 / 3, 1]},
+    )
+
+    # 3 time series of length 50
+    assert len(train) == 3 * (int((1 / 3) * 50) - (tsp.context_length + tsp.prediction_length) + 1)
+
+    assert len(valid) == len(test)
+
+    # no id columns, so treat as one big time series
+    tsp = TimeSeriesPreprocessor(
+        id_columns=[],
+        target_columns=["value1", "value2"],
+        prediction_length=5,
+        context_length=10,
+    )
+
+    train, valid, test = tsp.get_datasets(
+        ts_data,
+        split_config={
+            "train": [0, 100],
+            "valid": [100, 125],
+            "test": [125, 150],
+        },
+        fewshot_fraction=0.2,
+        fewshot_location=FractionLocation.LAST.value,
+    )
+
+    # new train length should be 20% of 100, minus the usual for context length and prediction length
+    fewshot_train_size = int(100 * 0.2) - (tsp.context_length + tsp.prediction_length) + 1
+    assert len(train) == fewshot_train_size
+
+    assert len(valid) == len(test)
+
+    # no id columns, so treat as one big time series
+    tsp = TimeSeriesPreprocessor(
+        id_columns=[],
+        target_columns=["value1", "value2"],
+        prediction_length=5,
+        context_length=10,
+    )
+
+    train, valid, test = tsp.get_datasets(
+        ts_data,
+        split_config={
+            "train": [0, 100],
+            "valid": [100, 125],
+            "test": [125, 150],
+        },
+        fewshot_fraction=0.2,
+        fewshot_location=FractionLocation.FIRST.value,
+    )
+
+    # new train length should be 20% of 100, minus the usual for context length and prediction length
+    assert len(train) == fewshot_train_size
+
+    assert len(valid) == len(test)

tests/toolkit/test_util.py

@@ -0,0 +1,26 @@
+"""Tests for util functions"""
+
+import pytest
+
+from tsfm_public.toolkit.util import get_split_params
+
+
+split_cases = [
+    (0, 1, "select_by_index"),
+    (0, 0.1, "select_by_relative_fraction"),
+    (0.0, 0.1, "select_by_relative_fraction"),
+    (0.0, 200.0, "select_by_index"),
+    (0.0, 200, "select_by_index"),
+    (0.5, 1, "select_by_relative_fraction"),
+    (0.5, 1.0, "select_by_relative_fraction"),
+    (10, 100.0, "select_by_index"),
+]
+
+
+@pytest.mark.parametrize("left_arg,right_arg,expected", split_cases)
+def test_get_split_params(left_arg, right_arg, expected):
+    """Test that get_split_params gives the right split function"""
+
+    split_config, split_function = get_split_params({"train": [left_arg, right_arg], "valid": [0, 1], "test": [0, 1]})
+
+    assert split_function["train"].__name__ == expected

tsfm_public/toolkit/time_series_preprocessor.py

@@ -21,7 +21,13 @@
     PreTrainedFeatureExtractor,
 )
 
-from .util import join_list_without_repeat
+from .dataset import ForecastDFDataset
+from .util import (
+    FractionLocation,
+    get_split_params,
+    join_list_without_repeat,
+    select_by_fixed_fraction,
+)
 
 
 INTERNAL_ID_COLUMN = "__id"
@@ -586,6 +592,91 @@ def scale_func(grp, id_columns):
 
         return df
 
+    def get_datasets(
+        self,
+        dataset: Union[Dataset, pd.DataFrame],
+        split_config: Dict[str, Any],
+        fewshot_fraction: Optional[float] = None,
+        fewshot_location: str = FractionLocation.LAST.value,
+    ) -> Tuple[Any]:
+        """Creates the preprocessed pytorch datasets needed for training and evaluation
+        using the HuggingFace trainer
+
+        Args:
+            dataset (Union[Dataset, pd.DataFrame]): Loaded pandas dataframe
+                split_config (Dict[str, Any]): Dictionary of dictionaries containing
+                split parameters. For example:
+                    {
+                        train: [0, 50],
+                        valid: [50, 70],
+                        test:  [70, 100]
+                    }
+                end value is not inclusive
+            fewshot_fraction (float, optional): When non-null, return this percent of the original training
+                dataset. This is done to support fewshot fine-tuning. The fraction of data chosen is at the
+                end of the training dataset.
+            fewshot_location (str): Determines where the fewshot data is chosen. Valid options are "first" and "last"
+                as described in the enum FewshotLocation. Default is to choose the fewshot data at the end
+                of the training dataset (i.e., "last").
+
+        Returns:
+            Tuple of pytorch datasets, including: train, validation, test.
+        """
+
+        data = self._standardize_dataframe(dataset)
+
+        # get split_params
+        # split_params = get_split_params(config, self.context_length, len(data))
+
+        split_params, split_function = get_split_params(split_config, context_length=self.context_length)
+
+        # specify columns
+        column_specifiers = {
+            "id_columns": self.id_columns,
+            "timestamp_column": self.timestamp_column,
+            "target_columns": self.target_columns,
+            "observable_columns": self.observable_columns,
+            "control_columns": self.control_columns,
+            "conditional_columns": self.conditional_columns,
+            "static_categorical_columns": self.static_categorical_columns,
+        }
+
+        # split data
+        train_data = split_function["train"](data, id_columns=self.id_columns, **split_params["train"])
+        valid_data = split_function["valid"](data, id_columns=self.id_columns, **split_params["valid"])
+        test_data = split_function["test"](data, id_columns=self.id_columns, **split_params["test"])
+
+        # data preprocessing
+        self.train(train_data)
+
+        # handle fewshot operation
+        if fewshot_fraction is not None:
+            if not ((fewshot_fraction <= 1.0) and (fewshot_fraction > 0.0)):
+                raise ValueError(f"Fewshot fraction should be between 0 and 1, received {fewshot_fraction}")
+
+            train_data = select_by_fixed_fraction(
+                train_data,
+                id_columns=self.id_columns,
+                fraction=fewshot_fraction,
+                location=fewshot_location,
+            )
+
+        params = column_specifiers
+        params["context_length"] = self.context_length
+        params["prediction_length"] = self.prediction_length
+
+        # get torch datasets
+        test_dataset = ForecastDFDataset(
+            self.preprocess(test_data),
+            **params,
+        )
+        train_dataset = ForecastDFDataset(self.preprocess(train_data), **params)
+        valid_dataset = ForecastDFDataset(
+            self.preprocess(valid_data),
+            **params,
+        )
+        return train_dataset, valid_dataset, test_dataset
+
 
 def create_timestamps(
     last_timestamp: Union[datetime.datetime, pd.Timestamp],