Optimal binning with continuous target

class optbinning.ContinuousOptimalBinning(name='', dtype='numerical', prebinning_method='cart', max_n_prebins=20, min_prebin_size=0.05, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, monotonic_trend='auto', min_mean_diff=0, max_pvalue=None, max_pvalue_policy='consecutive', gamma=0, outlier_detector=None, outlier_params=None, cat_cutoff=None, cat_unknown=None, user_splits=None, user_splits_fixed=None, special_codes=None, split_digits=None, time_limit=100, verbose=False, **prebinning_kwargs)

Bases: optbinning.binning.binning.OptimalBinning

Optimal binning of a numerical or categorical variable with respect to a continuous target.

Parameters

  • name (str, optional (default="")) – The variable name.

  • dtype (str, optional (default="numerical")) – The variable data type. Supported data types are “numerical” for continuous and ordinal variables and “categorical” for categorical and nominal variables.

  • prebinning_method (str, optional (default="cart")) – The pre-binning method. Supported methods are “cart” for a CART decision tree, “quantile” to generate prebins with approximately same frequency and “uniform” to generate prebins with equal width. Method “cart” uses sklearn.tree.DecisionTreeRegressor.

  • max_n_prebins (int (default=20)) – The maximum number of bins after pre-binning (prebins).

  • min_prebin_size (float (default=0.05)) – The fraction of mininum number of records for each prebin.

  • min_n_bins (int or None, optional (default=None)) – The minimum number of bins. If None, then min_n_bins is a value in [0, max_n_prebins].

  • max_n_bins (int or None, optional (default=None)) – The maximum number of bins. If None, then max_n_bins is a value in [0, max_n_prebins].

  • min_bin_size (float or None, optional (default=None)) – The fraction of minimum number of records for each bin. If None, min_bin_size = min_prebin_size.

  • max_bin_size (float or None, optional (default=None)) – The fraction of maximum number of records for each bin. If None, max_bin_size = 1.0.

  • monotonic_trend (str or None, optional (default="auto")) – The mean monotonic trend. Supported trends are “auto”, “auto_heuristic” and “auto_asc_desc” to automatically determine the trend minimize the L1-norm using a machine learning classifier, “ascending”, “descending”, “concave”, “convex”, “peak” and “peak_heuristic” to allow a peak change point, and “valley” and “valley_heuristic” to allow a valley change point. Trends “auto_heuristic”, “peak_heuristic” and “valley_heuristic” use a heuristic to determine the change point, and are significantly faster for large size instances (max_n_prebins> 20). Trend “auto_asc_desc” is used to automatically select the best monotonic trend between “ascending” and “descending”. If None, then the monotonic constraint is disabled.

  • min_mean_diff (float, optional (default=0)) – The minimum mean difference between consecutives bins.

  • max_pvalue (float or None, optional (default=None)) – The maximum p-value among bins. The T-test is used to detect bins not satisfying the p-value constraint.

  • max_pvalue_policy (str, optional (default="consecutive")) – The method to determine bins not satisfying the p-value constraint. Supported methods are “consecutive” to compare consecutive bins and “all” to compare all bins.

  • gamma (float, optional (default=0)) –

    Regularization strength to reduce the number of dominating bins. Larger values specify stronger regularization.

    New in version 0.14.0.

  • outlier_detector (str or None, optional (default=None)) – The outlier detection method. Supported methods are “range” to use the interquartile range based method, “zcore” to use the modified Z-score method or “yquantile” to use the y-axis detector over quantiles.

  • outlier_params (dict or None, optional (default=None)) – Dictionary of parameters to pass to the outlier detection method.

  • cat_cutoff (float or None, optional (default=None)) – Generate bin others with categories in which the fraction of occurrences is below the cat_cutoff value. This option is available when dtype is “categorical”.

  • cat_unknown (float, str or None (default=None)) –

    The assigned value to the unobserved categories in training but occurring during transform.

    If None, the assigned value to an unknown category follows this rule:

    • if transform metric == ‘mean’ then mean target

    • if transform metric == ‘indices’ then -1

    • if transform metric == ‘bins’ then ‘unknown’

    New in version 0.17.1.

  • user_splits (array-like or None, optional (default=None)) – The list of pre-binning split points when dtype is “numerical” or the list of prebins when dtype is “categorical”.

  • user_splits_fixed (array-like or None (default=None)) – The list of pre-binning split points that must be fixed.

  • special_codes (array-like, dict or None, optional (default=None)) – List of special codes. Use special codes to specify the data values that must be treated separately.

  • split_digits (int or None, optional (default=None)) – The significant digits of the split points. If split_digits is set to 0, the split points are integers. If None, then all significant digits in the split points are considered.

  • time_limit (int (default=100)) – The maximum time in seconds to run the optimization solver.

  • verbose (bool (default=False)) – Enable verbose output.

  • **prebinning_kwargs (keyword arguments) –

    The pre-binning keywrord arguments.

    New in version 0.6.1.

Notes

The parameter values max_n_prebins and min_prebin_size control complexity and memory usage. The default values generally produce quality results, however, some improvement can be achieved by increasing max_n_prebins and/or decreasing min_prebin_size.

The pre-binning refinement phase guarantee that no prebin has zero number of records by merging those pure prebins. Pure bins produce infinity mean.

property binning_table

Return an instantiated binning table. Please refer to Binning table: continuous target.

Returns

binning_table

Return type

ContinuousBinningTable.

fit(x, y, sample_weight=None, check_input=False)

Fit the optimal binning according to the given training data.

Parameters

  • x (array-like, shape = (n_samples,)) – Training vector, where n_samples is the number of samples.

  • y (array-like, shape = (n_samples,)) – Target vector relative to x.

  • sample_weight (array-like of shape (n_samples,) (default=None)) – Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Only applied if prebinning_method="cart".

  • check_input (bool (default=False)) – Whether to check input arrays.

Returns

self – Fitted optimal binning.

Return type

ContinuousOptimalBinning

fit_transform(x, y, sample_weight=None, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False)

Fit the optimal binning according to the given training data, then transform it.

Parameters

  • x (array-like, shape = (n_samples,)) – Training vector, where n_samples is the number of samples.

  • y (array-like, shape = (n_samples,)) – Target vector relative to x.

  • sample_weight (array-like of shape (n_samples,) (default=None)) – Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight. Only applied if prebinning_method="cart".

  • metric (str (default="mean"):) – The metric used to transform the input vector. Supported metrics are “mean” to choose the mean, “indices” to assign the corresponding indices of the bins and “bins” to assign the corresponding bin interval.

  • metric_special (float or str (default=0)) – The metric value to transform special codes in the input vector. Supported metrics are “empirical” to use the empirical mean, and any numerical value.

  • metric_missing (float or str (default=0)) – The metric value to transform missing values in the input vector. Supported metrics are “empirical” to use the empirical mean, and any numerical value.

  • show_digits (int, optional (default=2)) – The number of significant digits of the bin column. Applies when metric="bins".

  • check_input (bool (default=False)) – Whether to check input arrays.

Returns

x_new – Transformed array.

Return type

numpy array, shape = (n_samples,)

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

information(print_level=1)

Print overview information about the options settings, problem statistics, and the solution of the computation.

Parameters

print_level (int (default=1)) – Level of details.

read_json(path)

Read json file containing split points and set them as the new split points.

Parameters

path (The path of the json file.) –

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

property splits

List of optimal split points when dtype is set to “numerical” or list of optimal bins when dtype is set to “categorical”.

Returns

splits

Return type

numpy.ndarray

property status

The status of the underlying optimization solver.

Returns

status

Return type

str

to_json(path)

Save optimal bins and/or splits points and transformation depending on the target type.

Parameters

path (The path where the json is going to be saved.) –

transform(x, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False)

Transform given data to mean using bins from the fitted optimal binning.

Parameters

  • x (array-like, shape = (n_samples,)) – Training vector, where n_samples is the number of samples.

  • metric (str (default="mean"):) – The metric used to transform the input vector. Supported metrics are “mean” to choose the mean, “indices” to assign the corresponding indices of the bins and “bins” to assign the corresponding bin interval.

  • metric_special (float or str (default=0)) – The metric value to transform special codes in the input vector. Supported metrics are “empirical” to use the empirical mean, and any numerical value.

  • metric_missing (float or str (default=0)) – The metric value to transform missing values in the input vector. Supported metrics are “empirical” to use the empirical mean, and any numerical value.

  • show_digits (int, optional (default=2)) – The number of significant digits of the bin column. Applies when metric="bins".

  • check_input (bool (default=False)) – Whether to check input arrays.

Returns

x_new – Transformed array.

Return type

numpy array, shape = (n_samples,)

Notes

Transformation of data including categories not present during training return zero mean.