conmo.preprocesses.SklearnPreprocess

class conmo.preprocesses.SklearnPreprocess(to_data: Union[bool, Iterable[str]], to_labels: Union[bool, Iterable[str]], test_set: bool, preprocess: Union[Binarizer, FunctionTransformer, KBinsDiscretizer, KernelCenterer, LabelBinarizer, LabelEncoder, MultiLabelBinarizer, MaxAbsScaler, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler])

Class used to wrap existing preprocess in the Scikit-Learn library. It also allows this preprocess to be applied to certain columns of the dataset.

__init__(to_data: Union[bool, Iterable[str]], to_labels: Union[bool, Iterable[str]], test_set: bool, preprocess: Union[Binarizer, FunctionTransformer, KBinsDiscretizer, KernelCenterer, LabelBinarizer, LabelEncoder, MultiLabelBinarizer, MaxAbsScaler, MinMaxScaler, Normalizer, OneHotEncoder, OrdinalEncoder, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, StandardScaler]) → None

apply(in_dir: str, out_dir: str) → None

Applies the preprocess to the given dataset.

Parameters

• in_dir (str) – Input directory where the files are located. Usually, this is the output directory of the splitter step.

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

extract_columns(df: DataFrame, columns: Union[bool, Iterable[str]]) → Iterable[str]

Returns a list containig all the column’s name of the data.

Parameters

• df (Pandas Dataframe) – Dataframe containing the data.

• columns (Union[bool, Iterable[str]]) – Bool value if the dataframe has columns or the list of columns.

Returns

columns – List containing the names of the dataframe’s columns.

Return type

Iterable[str]

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.

transform(df: DataFrame, columns: Iterable[str]) → DataFrame

Performs the preprocess over the dataframe with the given columns.

Parameters

• df (Pandas Dataframe) – Dataframe containing the data or the labels of the dataset.

• columns (Iterable[str]) – List of columns that will be used in the preprocess. Also the columns of the final dataframe.

Returns

Dataframe preprocessed.

Return type

Pandas Dataframe

Methods

__init__(to_data, to_labels, test_set, ...)
apply(in_dir, out_dir)	Applies the preprocess to the given dataset.
extract_columns(df, columns)	Returns a list containig all the column's name of the data.
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.
transform(df, columns)	Performs the preprocess over the dataframe with the given columns.