#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Time-stamp: "2024-03-05 13:17:04 (ywatanabe)"
# import warnings
import numpy as np
import pandas as pd
import torch
[docs]
def to_sktime_df(X):
"""
Converts a dataset to a format compatible with sktime, encapsulating each sample as a pandas DataFrame.
Arguments:
- X (numpy.ndarray or torch.Tensor or pandas.DataFrame): The input dataset with shape (n_samples, n_chs, seq_len).
It should be a 3D array-like structure containing the time series data.
Return:
- sktime_df (pandas.DataFrame): A DataFrame where each element is a pandas Series representing a univariate time series.
Data Types and Shapes:
- If X is a numpy.ndarray, it should have the shape (n_samples, n_chs, seq_len).
- If X is a torch.Tensor, it should have the shape (n_samples, n_chs, seq_len) and will be converted to a numpy array.
- If X is a pandas.DataFrame, it is assumed to already be in the correct format and will be returned as is.
References:
- sktime: https://github.com/alan-turing-institute/sktime
Examples:
--------
>>> X_np = np.random.rand(64, 160, 1024)
>>> sktime_df = to_sktime_df(X_np)
>>> type(sktime_df)
<class 'pandas.core.frame.DataFrame'>
"""
if isinstance(X, pd.DataFrame):
return X
elif torch.is_tensor(X):
X = X.detach().numpy()
elif not isinstance(X, np.ndarray):
raise ValueError(
"Input X must be a numpy.ndarray, torch.Tensor, or pandas.DataFrame"
)
X = X.astype(np.float64)
def _format_a_sample_for_sktime(x):
"""
Formats a single sample for sktime compatibility.
Arguments:
- x (numpy.ndarray): A 2D array with shape (n_chs, seq_len) representing a single sample.
Return:
- dims (pandas.Series): A Series where each element is a pandas Series representing a univariate time series.
"""
return pd.Series([pd.Series(x[d], name=f"dim_{d}") for d in range(x.shape[0])])
sktime_df = pd.DataFrame(
[_format_a_sample_for_sktime(X[i]) for i in range(X.shape[0])]
)
return sktime_df
# # Obsolete warning for future compatibility
# def to_sktime(*args, **kwargs):
# warnings.warn(
# "to_sktime is deprecated; use to_sktime_df instead.", FutureWarning
# )
# return to_sktime_df(*args, **kwargs)
# import pandas as pd
# import numpy as np
# import torch
# def to_sktime(X):
# """
# X.shape: (n_samples, n_chs, seq_len)
# """
# def _format_a_sample_for_sktime(x):
# """
# x.shape: (n_chs, seq_len)
# """
# dims = pd.Series(
# [pd.Series(x[d], name=f"dim_{d}") for d in range(len(x))],
# index=[f"dim_{i}" for i in np.arange(len(x))],
# )
# return dims
# if torch.is_tensor(X):
# X = X.numpy()
# X = X.astype(np.float64)
# return pd.DataFrame(
# [_format_a_sample_for_sktime(X[i]) for i in range(len(X))]
# )