Source code for gluonts.testutil.dummy_datasets

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# Licensed under the Apache License, Version 2.0 (the "License").
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from random import randint
from typing import List, Tuple

import numpy as np
import pandas as pd

from gluonts.dataset.common import ListDataset
from gluonts.dataset.field_names import FieldName


[docs]def make_dummy_datasets_with_features( num_ts: int = 5, start: str = "2018-01-01", freq: str = "D", min_length: int = 5, max_length: int = 10, prediction_length: int = 3, cardinality: List[int] = [], num_feat_dynamic_real: int = 0, num_past_feat_dynamic_real: int = 0, ) -> Tuple[ListDataset, ListDataset]: data_iter_train = [] data_iter_test = [] for k in range(num_ts): ts_length = randint(min_length, max_length) data_entry_train = { FieldName.START: start, FieldName.TARGET: [0.0] * ts_length, } if len(cardinality) > 0: data_entry_train[FieldName.FEAT_STATIC_CAT] = [ randint(0, c) for c in cardinality ] if num_past_feat_dynamic_real > 0: data_entry_train[FieldName.PAST_FEAT_DYNAMIC_REAL] = [ [float(1 + k)] * ts_length for k in range(num_past_feat_dynamic_real) ] # Since used directly in predict and not in make_evaluate_predictions, # where the test target would be chopped, test and train target have the same lengths data_entry_test = data_entry_train.copy() if num_feat_dynamic_real > 0: data_entry_train[FieldName.FEAT_DYNAMIC_REAL] = [ [float(1 + k)] * ts_length for k in range(num_feat_dynamic_real) ] data_entry_test[FieldName.FEAT_DYNAMIC_REAL] = [ [float(1 + k)] * (ts_length + prediction_length) for k in range(num_feat_dynamic_real) ] data_iter_train.append(data_entry_train) data_iter_test.append(data_entry_test) return ( ListDataset(data_iter=data_iter_train, freq=freq), ListDataset(data_iter=data_iter_test, freq=freq), )
[docs]def get_dataset(): data_entry_list = [ { "target": np.c_[ np.array([0.2, 0.7, 0.2, 0.5, 0.3, 0.3, 0.2, 0.1]), np.array([0, 1, 2, 0, 1, 2, 2, 2]), ].T, "start": pd.Timestamp("2011-01-01 00:00:00", freq="H"), "end": pd.Timestamp("2011-01-01 03:00:00", freq="H"), }, { "target": np.c_[ np.array([0.2, 0.1, 0.2, 0.5, 0.4]), np.array([0, 1, 2, 1, 1]) ].T, "start": pd.Timestamp("2011-01-01 00:00:00", freq="H"), "end": pd.Timestamp("2011-01-01 03:00:00", freq="H"), }, { "target": np.c_[ np.array([0.2, 0.7, 0.2, 0.5, 0.1, 0.2, 0.1]), np.array([0, 1, 2, 0, 1, 0, 2]), ].T, "start": pd.Timestamp("2011-01-01 00:00:00", freq="H"), "end": pd.Timestamp("2011-01-01 03:00:00", freq="H"), }, ] return ListDataset(data_entry_list, freq="H", one_dim_target=False)