"""
Preprocessing func for the dataset Italy Air Quality.
"""
# Created by Wenjie Du <wenjay.du@gmail.com>
# License: BSD-3-Clause
from typing import Any, Optional, Sequence, Union
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_italy_air_quality(
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 Italy Air Quality.
Parameters
----------
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 Italy Air Quality.
"""
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("italy_air_quality")
df = data["X"]
df = df.drop(columns=["Date", "Time"])
features = df.columns
df = df.to_numpy()
# split the dataset into train, validation, and test sets
all_n_steps = len(df)
val_test_len = round(all_n_steps * 0.2)
train_set = df[: -2 * val_test_len]
val_set = df[-2 * val_test_len : -val_test_len]
test_set = df[-val_test_len:]
scaler = StandardScaler()
train_set_X = scaler.fit_transform(train_set)
val_set_X = scaler.transform(val_set)
test_set_X = scaler.transform(test_set)
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": len(features),
"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