Welcome to toad’s documentation!

Installation

via pip

pip install toad

via source code

python setup.py install

Tutorial

A basic tutorial is provided.

Contents

toad package

Submodules

toad.detector module

Command line tools for detecting csv data

Team: ESC

Examples

python detector.py -i xxx.csv -o report.csv

toad.detector.countBlank(series, blanks=[None])

Count number and percentage of blank values in series

Parameters:

• series (Series) – data series
• blanks (list) – list of blank values

Returns:

number of blanks str: the percentage of blank values

Return type:

number

toad.detector.detect(dataframe)

Detect data

Parameters:dataframe (DataFrame) – data that will be detected Returns:report of detecting Return type:DataFrame

toad.detector.getDescribe(series, percentiles=[0.25, 0.5, 0.75])

Get describe of series

Parameters:

• series (Series) – data series
• percentiles – the percentiles to include in the output

Returns:

the describe of data include mean, std, min, max and percentiles

Return type:

Series

toad.detector.getTopValues(series, top=5, reverse=False)

Get top/bottom n values

Parameters:

• series (Series) – data series
• top (number) – number of top/bottom n values
• reverse (bool) – it will return bottom n values if True is given

Returns:

Series of top/bottom n values and percentage. [‘value:percent’, None]

Return type:

Series

toad.detector.isNumeric(series)

Check if the series’s type is numeric

Parameters:series (Series) – data series Returns:bool

toad.merge module

toad.merge.ChiMerge()

Chi-Merge

Parameters:

• feature (array-like) – feature to be merged
• target (array-like) – a array of target classes
• n_bins (int) – n bins will be merged into
• min_samples (number) – min sample in each group, if float, it will be the percentage of samples
• min_threshold (number) – min threshold of chi-square

Returns:

array of split points

Return type:

array

toad.merge.DTMerge()

Merge continue

Parameters:

• feature (array-like) –
• target (array-like) – target will be used to fit decision tree
• nan (number) – value will be used to fill nan
• n_bins (int) – n groups that will be merged into
• min_samples (int) – min number of samples in each leaf nodes

Returns:

array of split points

Return type:

array

toad.merge.KMeansMerge()

Merge by KMeans

Parameters:

• feature (array-like) –
• target (array-like) – target will be used to fit kmeans model
• nan (number) – value will be used to fill nan
• n_bins (int) – n groups that will be merged into
• random_state (int) – random state will be used for kmeans model

Returns:

split points of feature

Return type:

array

toad.merge.QuantileMerge()

Merge by quantile

Parameters:

• feature (array-like) –
• nan (number) – value will be used to fill nan
• n_bins (int) – n groups that will be merged into
• q (array-like) – list of percentage split points

Returns:

split points of feature

Return type:

array

toad.merge.StepMerge()

Merge by step

Parameters:

• feature (array-like) –
• nan (number) – value will be used to fill nan
• n_bins (int) – n groups that will be merged into
• clip_v (number | tuple) – min/max value of clipping
• clip_std (number | tuple) – min/max std of clipping
• clip_q (number | tuple) – min/max quantile of clipping

Returns:

split points of feature

Return type:

array

toad.merge.merge()

merge feature into groups

Parameters:

• feature (array-like) –
• target (array-like) –
• method (str) – ‘dt’, ‘chi’, ‘quantile’, ‘step’, ‘kmeans’ - the strategy to be used to merge feature
• return_splits (bool) – if needs to return splits
• n_bins (int) – n groups that will be merged into

Returns:

a array of merged label with the same size of feature array: list of split points

Return type:

array

toad.metrics module

toad.metrics.AIC(y_pred, y, k, llf=None)

Akaike Information Criterion

Parameters:

• y_pred (array-like) –
• y (array-like) –
• k (int) – number of featuers
• llf (float) – result of log-likelihood function

toad.metrics.AUC(score, target)

AUC Score

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target

Returns:

auc score

Return type:

float

toad.metrics.BIC(y_pred, y, k, llf=None)

Bayesian Information Criterion

Parameters:

• y_pred (array-like) –
• y (array-like) –
• k (int) – number of featuers
• llf (float) – result of log-likelihood function

toad.metrics.F1(score, target, split='best', return_split=False)

calculate f1 value

Parameters:

• score (array-like) –
• target (array-like) –

Returns:

best f1 score float: best spliter

Return type:

float

toad.metrics.KS(score, target)

calculate ks value

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target

Returns:

the max KS value

Return type:

float

toad.metrics.KS_bucket(score, target, bucket=10, method='quantile', **kwargs)

calculate ks value by bucket

Parameters:

• score (array-like) – list of score or probability that the model predict
• target (array-like) – list of real target
• bucket (int) – n groups that will bin into
• method (str) – method to bin score. quantile (default), step

Returns:

DataFrame

toad.metrics.KS_by_col(df, by='feature', score='score', target='target')

toad.metrics.MSE(y_pred, y)

mean of squares due to error

toad.metrics.PSI(test, base, combiner=None, return_frame=False)

calculate PSI

Parameters:

• test (array-like) – data to test PSI
• base (array-like) – base data for calculate PSI
• combiner (Combiner|list|dict) – combiner to combine data
• return_frame (bool) – if need to return frame of proportion

Returns:

float|Series

toad.metrics.SSE(y_pred, y)

sum of squares due to error

toad.plot module

toad.plot.badrate_plot(frame, x=None, target='target', by=None, freq=None, format=None, return_counts=False, return_proportion=False, return_frame=False)

plot for badrate

Parameters:

• frame (DataFrame) –
• x (str) – column in frame that will be used as x axis
• target (str) – target column in frame
• by (str) – column in frame that will be calculated badrate by it
• freq (str) – offset aliases string by pandas http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
• format (str) – format string for time
• return_counts (bool) – if need return counts plot
• return_frame (bool) – if need return frame

Returns:

badrate plot Axes: counts plot Axes: proportion plot Dataframe: grouping detail data

Return type:

Axes

toad.plot.bin_plot(frame, x=None, target='target', iv=True)

plot for bins

toad.plot.corr_plot(frame, figure_size=(20, 15))

plot for correlation

Parameters:frame (DataFrame) – frame to draw plot Returns:Axes

toad.plot.proportion_plot(x=None, keys=None)

plot for proportion

Parameters:

• x (Series|list) – series or list of series data for plot
• keys (str|list) – keys for each data

Returns:

Axes

toad.plot.roc_plot(score, target)

plot for roc

Parameters:

• score (array-like) – predicted score
• target (array-like) – true target

Returns:

Axes

toad.scorecard module

class toad.scorecard.ScoreCard(pdo=60, rate=2, base_odds=35, base_score=750, card=None, combiner={}, transer=None, **kwargs)

Bases: sklearn.base.BaseEstimator

bin_to_score(bins, return_sub=False)

predict score from bins

combine(X)

export(to_frame=False, to_json=None, to_csv=None, decimal=2)

generate a scorecard object

Parameters:

• to_frame (bool) – return DataFrame of card
• to_json (str|IOBase) – io to write json file
• to_csv (filepath|IOBase) – file to write csv

Returns:

dict

fit(X, y)

Parameters:

• X (2D DataFrame) –
• Y (array-like) –

generate_card(card=None)

Parameters:card (dict|str|IOBase) – dict of card or io to read json

generate_map(transer, model)

calculate score map by woe

predict(X, **kwargs)

predict score :param X: X to predict :type X: 2D array-like :param return_sub: if need to return sub score, default False :type return_sub: bool

Returns:predicted score DataFrame: sub score for each feature Return type:array-like

proba_to_score(prob)

covert probability to score

set_card(card)

set card dict

set_combiner(combiner)

set combiner

set_model(model)

set logistic regression model

set_score(map)

set score map by dict

testing_frame(**kwargs)

get testing frame with score

Returns:testing frame with score Return type:DataFrame

woe_to_score(woe, weight=None)

calculate score by woe

toad.selection module

class toad.selection.StatsModel(estimator='ols', criterion='aic', intercept=False)

Bases: object

get_criterion(pre, y, k)

get_estimator(name)

loglikelihood(pre, y, k)

p_value(t, n)

stats(X, y)

t_value(pre, y, X, coef)

toad.selection.drop_corr(frame, target=None, threshold=0.7, by='IV', return_drop=False, exclude=None)

drop columns by correlation

Parameters:

• frame (DataFrame) – dataframe that will be used
• target (str) – target name in dataframe
• threshold (float) – drop features that has the smallest weight in each groups whose correlation is greater than threshold
• by (array-like) – weight of features that will be used to drop the features
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped

Return type:

DataFrame

toad.selection.drop_empty(frame, threshold=0.9, nan=None, return_drop=False, exclude=None)

drop columns by empty

Parameters:

• frame (DataFrame) – dataframe that will be used
• threshold (number) – drop the features whose empty num is greater than threshold. if threshold is float, it will be use as percentage
• nan (any) – values will be look like empty
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped

Return type:

DataFrame

toad.selection.drop_iv(frame, target='target', threshold=0.02, return_drop=False, return_iv=False, exclude=None)

drop columns by IV

Parameters:

• frame (DataFrame) – dataframe that will be used
• target (str) – target name in dataframe
• threshold (float) – drop the features whose IV is less than threshold
• return_drop (bool) – if need to return features’ name who has been dropped
• return_iv (bool) – if need to return features’ IV
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped Series: list of features’ IV

Return type:

DataFrame

toad.selection.drop_var(frame, threshold=0, return_drop=False, exclude=None)

drop columns by variance

Parameters:

• frame (DataFrame) – dataframe that will be used
• threshold (float) – drop features whose variance is less than threshold
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped

Return type:

DataFrame

toad.selection.drop_vif(frame, threshold=3, return_drop=False, exclude=None)

variance inflation factor

Parameters:

• frame (DataFrame) –
• threshold (float) – drop features until all vif is less than threshold
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped

Return type:

DataFrame

toad.selection.select(frame, target='target', empty=0.9, iv=0.02, corr=0.7, return_drop=False, exclude=None)

select features by rate of empty, iv and correlation

Parameters:

• frame (DataFrame) –
• target (str) – target’s name in dataframe
• empty (number) – drop the features which empty num is greater than threshold. if threshold is float, it will be use as percentage
• iv (float) – drop the features whose IV is less than threshold
• corr (float) – drop features that has the smallest IV in each groups which correlation is greater than threshold
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature name that will not be dropped

Returns:

selected dataframe dict: list of dropped feature names in each step

Return type:

DataFrame

toad.selection.stepwise(frame, target='target', estimator='ols', direction='both', criterion='aic', p_enter=0.01, p_remove=0.01, p_value_enter=0.2, intercept=False, max_iter=None, return_drop=False, exclude=None)

stepwise to select features

Parameters:

• frame (DataFrame) – dataframe that will be use to select
• target (str) – target name in frame
• estimator (str) – model to use for stats
• direction (str) – direction of stepwise, support ‘forward’, ‘backward’ and ‘both’, suggest ‘both’
• criterion (str) – criterion to statistic model, support ‘aic’, ‘bic’
• p_enter (float) – threshold that will be used in ‘forward’ and ‘both’ to keep features
• p_remove (float) – threshold that will be used in ‘backward’ to remove features
• intercept (bool) – if have intercept
• p_value_enter (float) – threshold that will be used in ‘both’ to remove features
• max_iter (int) – maximum number of iterate
• return_drop (bool) – if need to return features’ name who has been dropped
• exclude (array-like) – list of feature names that will not be dropped

Returns:

selected dataframe array: list of feature names that has been dropped

Return type:

DataFrame

toad.stats module

toad.stats.IV(feature, target, **kwargs)

get the IV of a feature

Parameters:

• feature (array-like) –
• target (array-like) –
• n_bins (int) – n groups that the feature will bin into
• method (str) – the strategy to be used to merge feature, default is ‘dt’
• () (**kwargs) – other options for merge function

toad.stats.VIF(frame)

calculate vif

Parameters:frame (ndarray|DataFrame) – Returns:Series

toad.stats.WOE(y_prob, n_prob)

get WOE of a group

Parameters:

• y_prob – the probability of grouped y in total y
• n_prob – the probability of grouped n in total n

Returns:

woe value

Return type:

number

toad.stats.badrate(target)

calculate badrate

Parameters:target (array-like) – target array which 1 is bad Returns:float

toad.stats.column_quality(feature, target, name='feature', iv_only=False, **kwargs)

calculate quality of a feature

Parameters:

• feature (array-like) –
• target (array-like) –
• name (str) – feature’s name that will be setted in the returned Series
• iv_only (bool) – if only calculate IV

Returns:

a list of quality with the feature’s name

Return type:

Series

toad.stats.entropy(target)

get infomation entropy of a feature

Parameters:target (array-like) – Returns:information entropy Return type:number

toad.stats.entropy_cond(feature, target)

get conditional entropy of a feature

Parameters:

• feature (array-like) –
• target (array-like) –

Returns:

conditional information entropy. If feature is continuous, it will return the best entropy when the feature bins into two groups

Return type:

number

toad.stats.gini(target)

get gini index of a feature

Parameters:target (array-like) – list of target that will be calculate gini Returns:gini value Return type:number

toad.stats.gini_cond(feature, target)

get conditional gini index of a feature

Parameters:

• feature (array-like) –
• target (array-like) –

Returns:

conditional gini value. If feature is continuous, it will return the best gini value when the feature bins into two groups

Return type:

number

toad.stats.probability(target, mask=None)

get probability of target by mask

toad.stats.quality(dataframe, target='target', iv_only=False, **kwargs)

get quality of features in data

Parameters:

• dataframe (DataFrame) – dataframe that will be calculate quality
• target (str) – the target’s name in dataframe
• iv_only (bool) – if only calculate IV

Returns:

quality of features with the features’ name as row name

Return type:

DataFrame

toad.transform module

class toad.transform.Combiner

Bases: sklearn.base.TransformerMixin

Combiner for merge data

dtypes

get the dtypes which is combiner used

Returns:(str|dict)

export(format=False)

export combine rules for score card

Parameters:

• format (bool) – if True, bins will be replace with string label for values
• to_json (str|IOBase) – io to write json file

Returns:

dict

fit(X, y=None, **kwargs)

fit combiner

Parameters:

• X (DataFrame|array-like) – features to be combined
• y (str|array-like) – target data or name of target in X
• method (str) – the strategy to be used to merge X, same as .merge, default is chi
• n_bins (int) – counts of bins will be combined

Returns:

self

set_rules(map, reset=False)

set rules for combiner

Parameters:

• map (dict|array-like) – map of splits
• reset (bool) – if need to reset combiner

Returns:

self

transform(X, **kwargs)

transform X by combiner

Parameters:

• X (DataFrame|array-like) – features to be transformed
• labels (bool) – if need to use labels for resulting bins, False by default

Returns:

array-like

class toad.transform.GBDTTransformer

Bases: sklearn.base.TransformerMixin

GBDT transformer

fit(X, y, **kwargs)

fit GBDT transformer

Parameters:

• X (DataFrame|array-like) –
• y (str|array-like) –
• select_dtypes (str|numpy.dtypes) – ‘object’, ‘number’ etc. only selected dtypes will be transform,

transform(X)

transform woe

Parameters:

• X (DataFrame|array-like) –
• default (str) – ‘min’(default), ‘max’ - the strategy to be used for unknown group

Returns:

array-like

class toad.transform.WOETransformer

Bases: sklearn.base.TransformerMixin

WOE transformer

export()

fit(X, y, **kwargs)

fit WOE transformer

Parameters:

• X (DataFrame|array-like) –
• y (str|array-like) –
• select_dtypes (str|numpy.dtypes) – ‘object’, ‘number’ etc. only selected dtypes will be transform,

transform(X, **kwargs)

transform woe

Parameters:

• X (DataFrame|array-like) –
• default (str) – ‘min’(default), ‘max’ - the strategy to be used for unknown group

Returns:

array-like

toad.transform.support_exclude(fn)

toad.transform.support_save_to_json(fn)

toad.transform.support_select_dtypes(fn)

toad.utils module

class toad.utils.Parallel

Bases: object

apply(func, args=(), kwargs={})

join()

toad.utils.bin_by_splits(feature, splits)

Bin feature by split points

toad.utils.bin_to_number(reg=None)

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

toad.utils.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.diff_time(base, target, format=None, time='day')

toad.utils.diff_time_frame(base, frame, format=None)

toad.utils.feature_splits(feature, target)

find posibility spilt points

toad.utils.fillna(feature, by=-1)

toad.utils.generate_str(size=6, chars='ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789')

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

get dummies

toad.utils.has_nan(arr)

toad.utils.inter_feature(feature, splits)

toad.utils.is_continuous(series)

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

iterate dataframe by split points

Returns:iterator (df, splitter)

toad.utils.np_count(arr, value, default=None)

toad.utils.np_unique(arr, **kwargs)

toad.utils.read_json(file)

read json file

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

save json file

Parameters:

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

toad.utils.split_target(frame, target)

toad.utils.support_dataframe(require_target=True)

decorator for supporting dataframe

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

toad.utils.unpack_tuple(x)

Module contents

Indices and tables

• Index
• Module Index
• Search Page