"""
Preprocessing func for the dataset ETT (Electricity Transformer Temperature).
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from typing import Any, Optional, Sequence, Union
import pandas as pd
import tsdb
from sklearn.preprocessing import StandardScaler
from ..utils.logging import logger, print_final_dataset_info
from ..utils.missingness import create_missingness
from ..utils.sliding import sliding_window
from ..utils.task_type import convert_processed_dataset_by_task_type
[docs]
def preprocess_ett(
subset: str,
rate: float,
n_steps: int,
pattern: str = "point",
random_state: Optional[int] = None,
task_type: str = "imputation",
n_pred_steps: int = 1,
forecast_feature_indices: Optional[Union[int, Sequence[int]]] = None,
**kwargs: Any,
) -> dict:
"""Load and preprocess the dataset ETT.
Parameters
----------
subset:
The name of the subset dataset to be loaded.
Must be one of ['ETTm1', 'ETTm2', 'ETTh1', 'ETTh2'].
rate:
The missing rate.
n_steps:
The number of time steps to in the generated data samples.
Also the window size of the sliding window.
pattern:
The missing pattern to apply to the dataset.
Must be one of ['point', 'subseq', 'block'].
random_state:
Controls the randomness of missingness generation.
Pass an int for reproducible missingness masks across runs.
task_type:
Task type for postprocessing. Supported values are
['imputation', 'forecasting', 'classification', 'clustering', 'anomaly_detection'].
n_pred_steps:
Forecasting horizon. Effective only when task_type is 'forecasting'.
forecast_feature_indices:
Target feature indices for forecasting labels. If None, all features are used.
Returns
-------
processed_dataset :
A dictionary containing the processed ETT.
"""
all_subset_names = ["ETTm1", "ETTm2", "ETTh1", "ETTh2"]
assert subset in all_subset_names, f"subset_name should be one of {all_subset_names}, but got {subset}"
assert 0 <= rate < 1, f"rate must be in [0, 1), but got {rate}"
assert n_steps > 0, f"sample_n_steps must be larger than 0, but got {n_steps}"
data = tsdb.load("electricity_transformer_temperature") # load all 4 sub datasets
df = data[subset]
feature_names = df.columns.tolist()
df["datetime"] = pd.to_datetime(df.index)
unique_months = df["datetime"].dt.to_period("M").unique()
selected_as_train = unique_months[:14] # use the first 14 months as train set
logger.info(f"months selected as train set are {selected_as_train}")
selected_as_val = unique_months[14:19] # select the following 5 months as val set
logger.info(f"months selected as val set are {selected_as_val}")
selected_as_test = unique_months[19:] # select the left 5 months as test set
logger.info(f"months selected as test set are {selected_as_test}")
test_set = df[df["datetime"].dt.to_period("M").isin(selected_as_test)]
val_set = df[df["datetime"].dt.to_period("M").isin(selected_as_val)]
train_set = df[df["datetime"].dt.to_period("M").isin(selected_as_train)]
scaler = StandardScaler()
train_set_X = scaler.fit_transform(train_set.loc[:, feature_names])
val_set_X = scaler.transform(val_set.loc[:, feature_names])
test_set_X = scaler.transform(test_set.loc[:, feature_names])
train_X = sliding_window(train_set_X, n_steps)
val_X = sliding_window(val_set_X, n_steps)
test_X = sliding_window(test_set_X, n_steps)
# assemble the final processed data into a dictionary
processed_dataset = {
# general info
"n_steps": n_steps,
"n_features": train_X.shape[-1],
"scaler": scaler,
# train set
"train_X": train_X,
# val set
"val_X": val_X,
# test set
"test_X": test_X,
}
processed_dataset = convert_processed_dataset_by_task_type(
processed_dataset,
task_type=task_type,
n_pred_steps=n_pred_steps,
forecast_feature_indices=forecast_feature_indices,
)
if rate > 0:
if random_state is not None and "random_state" not in kwargs:
kwargs["random_state"] = random_state
# hold out ground truth in the original data for evaluation
train_X_ori = processed_dataset["train_X"]
val_X_ori = processed_dataset["val_X"]
test_X_ori = processed_dataset["test_X"]
# mask values in the train set to keep the same with below validation and test sets
train_X = create_missingness(processed_dataset["train_X"], rate, pattern, **kwargs)
# mask values in the validation set as ground truth
val_X = create_missingness(processed_dataset["val_X"], rate, pattern, **kwargs)
# mask values in the test set as ground truth
test_X = create_missingness(processed_dataset["test_X"], rate, pattern, **kwargs)
processed_dataset["train_X"] = train_X
processed_dataset["train_X_ori"] = train_X_ori
processed_dataset["val_X"] = val_X
processed_dataset["val_X_ori"] = val_X_ori
processed_dataset["test_X"] = test_X
processed_dataset["test_X_ori"] = test_X_ori
else:
logger.warning("rate is 0, no missing values are artificially added.")
print_final_dataset_info(processed_dataset)
return processed_dataset