conmo.preprocesses.RULImputation

class conmo.preprocesses.RULImputation(threshold: int)[source]

__init__(threshold: int) → None[source]

apply(in_dir, out_dir) → None[source]

RUL imputation per TIME sample based on SEQUENCE labels, generating labels for each TIME.

load_input(in_dir: str) -> (<class 'pandas.core.frame.DataFrame'>, <class 'pandas.core.frame.DataFrame'>)

Read parquet data and labels files of the chosen dataset before it’s split.

Parameters

in_dir (str) – Input directory where the files are located.

Returns

data (Pandas Dataframe) – Loaded data file.

labels (Pandas Dataframe) – Loaded labels file.

save_output(out_dir: str, data: DataFrame, labels: DataFrame) → None

Save preprocessed dataset to parquet format.

Parameters

out_dir (str) – Output directory where the results will be saved.

data (Pandas Dataframe) – Preprocessed data.

labels (Pandas Dataframe) – Preprocessed labels.

show_start_message() → None

Simple method to print on the terminal the name of the selected splitter.

Methods

`__init__`(threshold)
`apply`(in_dir, out_dir)	RUL imputation per TIME sample based on SEQUENCE labels, generating labels for each TIME.
`load_input`(in_dir)	Read parquet data and labels files of the chosen dataset before it's split.
`save_output`(out_dir, data, labels)	Save preprocessed dataset to parquet format.
`show_start_message`()	Simple method to print on the terminal the name of the selected splitter.