toad.utils.func module

toad.utils.func.to_ndarray(s, dtype=None)

toad.utils.func.bin_by_splits(feature, splits)

Bin feature by split points

toad.utils.func.feature_splits(feature, target)

find posibility spilt points

toad.utils.func.iter_df(dataframe, feature, target, splits)

iterate dataframe by split points

Returns:iterator (df, splitter)

toad.utils.func.split_target(frame, target)

toad.utils.func.save_json(contents, file, indent=4)

save json file

Parameters:

• contents (dict) – contents to save
• file (str|IOBase) – file to save

toad.utils.func.read_json(file)

read json file

toad.utils.func.clip(series, value=None, std=None, quantile=None)

clip series

Parameters:

• series (array-like) – series need to be clipped
• value (number | tuple) – min/max value of clipping
• std (number | tuple) – min/max std of clipping
• quantile (number | tuple) – min/max quantile of clipping

toad.utils.func.flatten_columns(columns, sep='_')

flatten multiple columns to 1-dim columns joined with ‘_’

toad.utils.func.bin_to_number(reg=None)

Returns:func(string) -> number Return type:function

toad.utils.func.generate_target(size, rate=0.5, weight=None, reverse=False)

generate target for reject inference

Parameters:

• size (int) – size of target
• rate (float) – rate of ‘1’ in target
• weight (array-like) – weight of ‘1’ to generate target
• reverse (bool) – if need reverse weight

Returns:

array

toad.utils.func.get_dummies(dataframe, exclude=None, binary_drop=False, **kwargs)

get dummies