Optimal piecewise binning with continuous target

class optbinning.ContinuousOptimalPWBinning(name='', objective='l2', degree=1, continuous=True, 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', n_subsamples=None, max_pvalue=None, max_pvalue_policy='consecutive', outlier_detector=None, outlier_params=None, user_splits=None, user_splits_fixed=None, special_codes=None, split_digits=None, solver='auto', h_epsilon=1.35, quantile=0.5, regularization=None, reg_l1=1.0, reg_l2=1.0, random_state=None, verbose=False)

Bases: optbinning.binning.piecewise.base.BasePWBinning

Optimal Piecewise binning of a numerical variable with respect to a binary target.

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

  • objective (str, optional (default="l2")) – The objective function. Supported objectives are “l2”, “l1”, “huber” and “quantile”. Note that “l1”, “huber” and “quantile” are robust objective functions.

  • degree (int (default=1)) –

    The degree of the polynomials.

    • degree = 0: piecewise constant functions.

    • degree = 1: piecewise linear functions.

    • degree > 1: piecewise polynomial functions.

  • continuous (bool (default=True)) – Whether to fit a continuous or discontinuous piecewise regression.

  • 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.DecistionTreeClassifier.

  • 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 monotonic trend. Supported trends are “auto”, “auto_heuristic” and “auto_asc_desc” to automatically determine the trend maximizing IV 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.

  • n_subsamples (int or None (default=None)) – Number of subsamples to fit the piecewise regression algorithm. If None, all values are considered.

  • max_pvalue (float or None, optional (default=0.05)) – The maximum p-value among bins. The Z-test is used to detect bins not satisfying the p-value constraint. Option supported by solvers “cp” and “mip”.

  • 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.

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

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

  • 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 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.

  • solver (str, optional (default="auto")) – The optimizer to solve the underlying mathematical optimization problem. Supported solvers are “ecos”, “osqp”, “direct”, to choose the direct solver, and “auto”, to choose the most appropriate solver for the problem.

  • h_epsilon (float (default=1.35)) – The parameter h_epsilon used when objective="huber", controls the number of samples that should be classified as outliers.

  • quantile (float (default=0.5)) – The parameter quantile is the q-th quantile to be used when objective="quantile".

  • regularization (str or None (default=None)) – Type of regularization. Supported regularization are “l1” (Lasso) and “l2” (Ridge). If None, no regularization is applied.

  • reg_l1 (float (default=1.0)) – L1 regularization term. Increasing this value will smooth the regression model. Only applicable if regularization="l1".

  • reg_l2 (float (default=1.0)) – L2 regularization term. Increasing this value will smooth the regression model. Only applicable if regularization="l2".

  • random_state (int, RandomState instance or None, (default=None)) – If n_subsamples < n_samples, controls the shuffling applied to the data before applying the split.

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

property binning_table

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

Returns

binning_table

Return type

BinningTable.

fit(x, y, lb=None, ub=None, check_input=False)

Fit the optimal piecewise 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.

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

Returns

self – Fitted optimal piecewise binning.

Return type

BasePWBinning

fit_transform(x, y, metric_special=0, metric_missing=0, lb=None, ub=None, check_input=False)

Fit the optimal piecewise 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.

  • 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.

  • lb (float or None (default=None)) – Avoid values below the lower bound lb.

  • ub (float or None (default=None)) – Avoid values above the upper bound ub.

  • 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.

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

transform(x, metric_special=0, metric_missing=0, lb=None, ub=None, check_input=False)

Transform given data using bins from the fitted optimal piecewise binning.

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

  • 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.

  • lb (float or None (default=None)) – Avoid values below the lower bound lb.

  • ub (float or None (default=None)) – Avoid values above the upper bound ub.

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

Returns

x_new – Transformed array.

Return type

numpy array, shape = (n_samples,)