index int64 0 731k | package stringlengths 2 98 ⌀ | name stringlengths 1 76 | docstring stringlengths 0 281k ⌀ | code stringlengths 4 1.07M ⌀ | signature stringlengths 2 42.8k ⌀ |
|---|---|---|---|---|---|
4,529 | optbinning.binning.binning_process | _transform | null | def _transform(self, X, metric, metric_special, metric_missing,
show_digits, check_input):
# Check X dtype
if not isinstance(X, (pd.DataFrame, np.ndarray)):
raise TypeError("X must be a pandas.DataFrame or numpy.ndarray.")
n_samples, n_variables = X.shape
mask = self.get_support()... | (self, X, metric, metric_special, metric_missing, show_digits, check_input) |
4,530 | optbinning.binning.binning_process | _transform_disk | null | def _transform_disk(self, input_path, output_path, chunksize, metric,
metric_special, metric_missing, show_digits, **kwargs):
# check input_path and output_path extensions
input_extension = input_path.split(".")[1]
output_extension = output_path.split(".")[1]
if input_extension != "c... | (self, input_path, output_path, chunksize, metric, metric_special, metric_missing, show_digits, **kwargs) |
4,531 | sklearn.base | _validate_data | Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, dataframe} of shape (n_samples, n_features), default='no validation'
The input samples.
If `'no_validation'`, no validation is perfo... | def _validate_data(
self,
X="no_validation",
y="no_validation",
reset=True,
validate_separately=False,
cast_to_ndarray=True,
**check_params,
):
"""Validate input data and set or check the `n_features_in_` attribute.
Parameters
----------
X : {array-like, sparse matrix, datafr... | (self, X='no_validation', y='no_validation', reset=True, validate_separately=False, cast_to_ndarray=True, **check_params) |
4,532 | sklearn.base | _validate_params | Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints` for a description of the
accep... | def _validate_params(self):
"""Validate types and values of constructor parameters
The expected type and values must be defined in the `_parameter_constraints`
class attribute, which is a dictionary `param_name: list of constraints`. See
the docstring of `validate_parameter_constraints` for a descriptio... | (self) |
4,533 | optbinning.binning.binning_process | fit | Fit the binning process. Fit the optimal binning to all variables
according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
.. versionch... | def fit(self, X, y, sample_weight=None, check_input=False):
"""Fit the binning process. Fit the optimal binning to all variables
according to the given training data.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is th... | (self, X, y, sample_weight=None, check_input=False) |
4,534 | optbinning.binning.binning_process | fit_disk | Fit the binning process according to the given training data on
disk.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv or .parquet.
target : str
Target column.
**kwargs : keyword arguments
Ke... | def fit_disk(self, input_path, target, **kwargs):
"""Fit the binning process according to the given training data on
disk.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv or .parquet.
target : str
Target column.
**kwargs : keyword ar... | (self, input_path, target, **kwargs) |
4,535 | optbinning.binning.binning_process | fit_from_dict | Fit the binning process from a dict of OptimalBinning objects
already fitted.
Parameters
----------
dict_optb : dict
Dictionary with OptimalBinning objects for binary, continuous
or multiclass target. All objects must share the same class.
Returns
... | def fit_from_dict(self, dict_optb):
"""Fit the binning process from a dict of OptimalBinning objects
already fitted.
Parameters
----------
dict_optb : dict
Dictionary with OptimalBinning objects for binary, continuous
or multiclass target. All objects must share the same class.
R... | (self, dict_optb) |
4,536 | optbinning.binning.binning_process | fit_transform | Fit the binning process according to the given training data, then
transform it.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
y : array-like of shape (n_samples,)
... | def fit_transform(self, X, y, sample_weight=None, metric=None,
metric_special=0, metric_missing=0, show_digits=2,
check_input=False):
"""Fit the binning process according to the given training data, then
transform it.
Parameters
----------
X : {array-like, sparse ... | (self, X, y, sample_weight=None, metric=None, metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,537 | optbinning.binning.binning_process | fit_transform_disk | Fit the binning process according to the given training data on
disk, then transform it and save to comma-separated values (csv) file.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv.
output_path : str
Any valid... | def fit_transform_disk(self, input_path, output_path, target, chunksize,
metric=None, metric_special=0, metric_missing=0,
show_digits=2, **kwargs):
"""Fit the binning process according to the given training data on
disk, then transform it and save to comma-separated... | (self, input_path, output_path, target, chunksize, metric=None, metric_special=0, metric_missing=0, show_digits=2, **kwargs) |
4,538 | optbinning.binning.binning_process | get_binned_variable | Return optimal binning object for a given variable name.
Parameters
----------
name : string
The variable name.
| def get_binned_variable(self, name):
"""Return optimal binning object for a given variable name.
Parameters
----------
name : string
The variable name.
"""
self._check_is_fitted()
if not isinstance(name, str):
raise TypeError("name must be a string.")
if name in self.vari... | (self, name) |
4,539 | sklearn.utils._metadata_requests | get_metadata_routing | Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
routing informat... | def get_metadata_routing(self):
"""Get metadata routing of this object.
Please check :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
Returns
-------
routing : MetadataRequest
A :class:`~sklearn.utils.metadata_routing.MetadataRequest` encapsulating
routing... | (self) |
4,540 | sklearn.base | get_params |
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 : dict
Parameter n... | def get_params(self, 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 : dict
Parameter nam... | (self, deep=True) |
4,541 | optbinning.binning.binning_process | get_support | Get a mask, or integer index, or names of the variables selected.
Parameters
----------
indices : boolean (default=False)
If True, the return value will be an array of integers, rather
than a boolean mask.
names : boolean (default=False)
If True, the... | def get_support(self, indices=False, names=False):
"""Get a mask, or integer index, or names of the variables selected.
Parameters
----------
indices : boolean (default=False)
If True, the return value will be an array of integers, rather
than a boolean mask.
names : boolean (default... | (self, indices=False, names=False) |
4,542 | optbinning.binning.binning_process | information | Print overview information about the options settings and
statistics.
Parameters
----------
print_level : int (default=1)
Level of details.
| def information(self, print_level=1):
"""Print overview information about the options settings and
statistics.
Parameters
----------
print_level : int (default=1)
Level of details.
"""
self._check_is_fitted()
if not isinstance(print_level, numbers.Integral) or print_level < 0:
... | (self, print_level=1) |
4,543 | optbinning.binning.binning_process | save | Save binning process to pickle file.
Parameters
----------
path : str
Pickle file path.
| def save(self, path):
"""Save binning process to pickle file.
Parameters
----------
path : str
Pickle file path.
"""
if not isinstance(path, str):
raise TypeError("path must be a string.")
with open(path, "wb") as f:
pickle.dump(self, f)
| (self, path) |
4,544 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.binning_process.BinningProcess, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.binning_process.BinningProcess |
4,545 | sklearn.base | set_params | Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each component of a nested object.
... | def set_params(self, **params):
"""Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects
(such as :class:`~sklearn.pipeline.Pipeline`). The latter have
parameters of the form ``<component>__<parameter>`` so that it's
possible to update each componen... | (self, **params) |
4,546 | sklearn.utils._metadata_requests | set_transform_request | Request metadata passed to the ``transform`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each par... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.binning_process.BinningProcess, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', metric: Union[bool, NoneType, str] = '$UNCHANGED$', metric_missing: Union[bool, NoneType, str] = '$UNCHANGED$', metric_special: Union[bool, NoneType, str] = '$UNCHANGED$', show_digits: Union[bool, NoneT... |
4,547 | optbinning.binning.binning_process | summary | Binning process summary with main statistics for all binned
variables.
Parameters
----------
df_summary : pandas.DataFrame
Binning process summary.
| def summary(self):
"""Binning process summary with main statistics for all binned
variables.
Parameters
----------
df_summary : pandas.DataFrame
Binning process summary.
"""
self._check_is_fitted()
if self._is_updated:
self._binning_selection_criteria()
self._is_u... | (self) |
4,548 | optbinning.binning.binning_process | transform | Transform given data to metric using bins from each fitted optimal
binning.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
metric : str or None, (default=None)
... | def transform(self, X, metric=None, metric_special=0, metric_missing=0,
show_digits=2, check_input=False):
"""Transform given data to metric using bins from each fitted optimal
binning.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Train... | (self, X, metric=None, metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,549 | optbinning.binning.binning_process | transform_disk | Transform given data on disk to metric using bins from each fitted
optimal binning. Save to comma-separated values (csv) file.
Parameters
----------
input_path : str
Any valid string path to a file with extension .csv.
output_path : str
Any valid string ... | def transform_disk(self, input_path, output_path, chunksize, metric=None,
metric_special=0, metric_missing=0, show_digits=2,
**kwargs):
"""Transform given data on disk to metric using bins from each fitted
optimal binning. Save to comma-separated values (csv) file.
Para... | (self, input_path, output_path, chunksize, metric=None, metric_special=0, metric_missing=0, show_digits=2, **kwargs) |
4,550 | optbinning.binning.binning_process | update_binned_variable | Update optimal binning object for a given variable.
Parameters
----------
name : string
The variable name.
optb : object
The optimal binning object already fitted.
| def update_binned_variable(self, name, optb):
"""Update optimal binning object for a given variable.
Parameters
----------
name : string
The variable name.
optb : object
The optimal binning object already fitted.
"""
self._check_is_fitted()
if not isinstance(name, str):
... | (self, name, optb) |
4,551 | optbinning.binning.distributed.binning_process_sketch | BinningProcessSketch | Binning process over data streams to compute optimal binning of
variables with respect to a binary target.
Parameters
----------
variable_names : array-like
List of variable names.
max_n_prebins : int (default=20)
The maximum number of bins after pre-binning (prebins).
min_n_b... | class BinningProcessSketch(BaseSketch, BaseEstimator, BaseBinningProcess):
"""Binning process over data streams to compute optimal binning of
variables with respect to a binary target.
Parameters
----------
variable_names : array-like
List of variable names.
max_n_prebins : int (defaul... | (variable_names, max_n_prebins=20, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, max_pvalue=None, max_pvalue_policy='consecutive', selection_criteria=None, categorical_variables=None, special_codes=None, split_digits=None, binning_fit_params=None, binning_transform_params=None, verbose=False) |
4,553 | optbinning.binning.distributed.binning_process_sketch | __init__ | null | def __init__(self, variable_names, max_n_prebins=20, min_n_bins=None,
max_n_bins=None, min_bin_size=None, max_bin_size=None,
max_pvalue=None, max_pvalue_policy="consecutive",
selection_criteria=None, categorical_variables=None,
special_codes=None, split_digits=None,
... | (self, variable_names, max_n_prebins=20, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, max_pvalue=None, max_pvalue_policy='consecutive', selection_criteria=None, categorical_variables=None, special_codes=None, split_digits=None, binning_fit_params=None, binning_transform_params=None, verbose=F... |
4,559 | optbinning.binning.distributed.base | _check_is_solved | null | def _check_is_solved(self):
if not self._is_solved:
raise NotSolvedError("This {} instance is not solved yet. Call "
"'solve' with appropriate arguments."
.format(self.__class__.__name__))
| (self) |
4,570 | optbinning.binning.distributed.binning_process_sketch | add | Add new data X, y to the binning sketch of each variable.
Parameters
----------
X : pandas.DataFrame, shape (n_samples, n_features)
y : array-like of shape (n_samples,)
Target vector relative to x.
check_input : bool (default=False)
Whether to check inp... | def add(self, X, y, check_input=False):
"""Add new data X, y to the binning sketch of each variable.
Parameters
----------
X : pandas.DataFrame, shape (n_samples, n_features)
y : array-like of shape (n_samples,)
Target vector relative to x.
check_input : bool (default=False)
Whet... | (self, X, y, check_input=False) |
4,571 | optbinning.binning.distributed.binning_process_sketch | get_binned_variable | Return optimal binning sketch object for a given variable name.
Parameters
----------
name : string
The variable name.
| def get_binned_variable(self, name):
"""Return optimal binning sketch object for a given variable name.
Parameters
----------
name : string
The variable name.
"""
self._check_is_solved()
if not isinstance(name, str):
raise TypeError("name must be a string.")
if name in se... | (self, name) |
4,574 | optbinning.binning.distributed.binning_process_sketch | get_support | Get a mask, or integer index, or names of the variables selected.
Parameters
----------
indices : boolean (default=False)
If True, the return value will be an array of integers, rather
than a boolean mask.
names : boolean (default=False)
If True, the... | def get_support(self, indices=False, names=False):
"""Get a mask, or integer index, or names of the variables selected.
Parameters
----------
indices : boolean (default=False)
If True, the return value will be an array of integers, rather
than a boolean mask.
names : boolean (default... | (self, indices=False, names=False) |
4,575 | optbinning.binning.distributed.binning_process_sketch | information | Print overview information about the options settings and
statistics.
Parameters
----------
print_level : int (default=1)
Level of details.
| def information(self, print_level=1):
"""Print overview information about the options settings and
statistics.
Parameters
----------
print_level : int (default=1)
Level of details.
"""
self._check_is_solved()
if not isinstance(print_level, numbers.Integral) or print_level < 0:
... | (self, print_level=1) |
4,576 | optbinning.binning.distributed.binning_process_sketch | merge | Merge current instance with another BinningProcessSketch instance.
Parameters
----------
bpsketch : object
BinningProcessSketch instance.
| def merge(self, bpsketch):
"""Merge current instance with another BinningProcessSketch instance.
Parameters
----------
bpsketch : object
BinningProcessSketch instance.
"""
if not self.mergeable(bpsketch):
raise Exception("bpsketch does not share signature.")
for name in self.... | (self, bpsketch) |
4,577 | optbinning.binning.distributed.binning_process_sketch | mergeable | Check whether two BinningProcessSketch instances can be merged.
Parameters
----------
bpsketch : object
BinningProcessSketch instance.
Returns
-------
mergeable : bool
| def mergeable(self, bpsketch):
"""Check whether two BinningProcessSketch instances can be merged.
Parameters
----------
bpsketch : object
BinningProcessSketch instance.
Returns
-------
mergeable : bool
"""
return self.get_params() == bpsketch.get_params()
| (self, bpsketch) |
4,581 | optbinning.binning.distributed.binning_process_sketch | solve | Solve optimal binning for all variables using added data.
Returns
-------
self : BinningProcessSketch
Current fitted binning process.
| def solve(self):
"""Solve optimal binning for all variables using added data.
Returns
-------
self : BinningProcessSketch
Current fitted binning process.
"""
time_init = time.perf_counter()
# Check if data was added
if not self._n_add:
raise NotDataAddedError(
... | (self) |
4,582 | optbinning.binning.distributed.binning_process_sketch | summary | Binning process summary with main statistics for all binned
variables.
Parameters
----------
df_summary : pandas.DataFrame
Binning process summary.
| def summary(self):
"""Binning process summary with main statistics for all binned
variables.
Parameters
----------
df_summary : pandas.DataFrame
Binning process summary.
"""
self._check_is_solved()
df_summary = pd.DataFrame.from_dict(self._variable_stats).T
df_summary.reset_i... | (self) |
4,583 | optbinning.binning.distributed.binning_process_sketch | transform | Transform given data to metric using bins from each fitted optimal
binning.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples.
metric : str (default="woe")
The... | def transform(self, X, metric="woe", metric_special=0, metric_missing=0,
show_digits=2, check_input=False):
"""Transform given data to metric using bins from each fitted optimal
binning.
Parameters
----------
X : {array-like, sparse matrix} of shape (n_samples, n_features)
Trai... | (self, X, metric='woe', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,584 | optbinning.binning.continuous_binning | ContinuousOptimalBinning | 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
... | class ContinuousOptimalBinning(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 dat... | (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_cutof... |
4,586 | optbinning.binning.continuous_binning | __init__ | null | def __init__(self, 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="consec... | (self, 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... |
4,593 | optbinning.binning.continuous_binning | _compute_prebins | null | def _compute_prebins(self, splits_prebinning, x, y, sw):
n_splits = len(splits_prebinning)
if not n_splits:
return splits_prebinning, np.array([]), np.array([])
if self.dtype == "categorical" and self.user_splits is not None:
indices = np.digitize(x, splits_prebinning, right=True)
n_... | (self, splits_prebinning, x, y, sw) |
4,594 | optbinning.binning.continuous_binning | _fit | null | def _fit(self, x, y, sample_weight, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Optimal binning started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# Pre-processing
if self.verbose:
logger.info("Pre-proce... | (self, x, y, sample_weight, check_input) |
4,595 | optbinning.binning.continuous_binning | _fit_optimizer | null | def _fit_optimizer(self, splits, n_records, sums, ssums, stds):
if self.verbose:
logger.info("Optimizer started.")
time_init = time.perf_counter()
if len(n_records) <= 1:
self._status = "OPTIMAL"
self._splits_optimal = splits
self._solution = np.zeros(len(splits)).astype(bool... | (self, splits, n_records, sums, ssums, stds) |
4,596 | optbinning.binning.binning | _fit_prebinning | null | def _fit_prebinning(self, x, y, y_missing, x_special, y_special, y_others,
class_weight=None, sw_clean=None, sw_missing=None,
sw_special=None, sw_others=None):
min_bin_size = int(np.ceil(self.min_prebin_size * self._n_samples))
prebinning = PreBinning(method=self.prebinni... | (self, x, y, y_missing, x_special, y_special, y_others, class_weight=None, sw_clean=None, sw_missing=None, sw_special=None, sw_others=None) |
4,601 | optbinning.binning.continuous_binning | _prebinning_refinement | null | def _prebinning_refinement(self, splits_prebinning, x, y, y_missing,
x_special, y_special, y_others, sw_clean,
sw_missing, sw_special, sw_others):
# Compute n_records, sum and std for special, missing and others
[self._n_records_special, self._sum_special, s... | (self, splits_prebinning, x, y, y_missing, x_special, y_special, y_others, sw_clean, sw_missing, sw_special, sw_others) |
4,606 | optbinning.binning.continuous_binning | fit | 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_... | def fit(self, 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,)
Targ... | (self, x, y, sample_weight=None, check_input=False) |
4,607 | optbinning.binning.continuous_binning | fit_transform | 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 rela... | def fit_transform(self, 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 ... | (self, x, y, sample_weight=None, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,610 | optbinning.binning.binning | information | Print overview information about the options settings, problem
statistics, and the solution of the computation.
Parameters
----------
print_level : int (default=1)
Level of details.
| def information(self, 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.
"""
self._check_is_fitted()
if not isinstance(print_level... | (self, print_level=1) |
4,611 | optbinning.binning.continuous_binning | read_json |
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
| def read_json(self, path):
"""
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
"""
if path is None:
raise ValueError('Specify the path for the json file.')
self._is_fitted = True
with open... | (self, path) |
4,612 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.continuous_binning.ContinuousOptimalBinning, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', sample_weight: Union[bool, NoneType, str] = '$UNCHANGED$', x: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.continuous_binning.ContinuousOptimalBinning |
4,614 | sklearn.utils._metadata_requests | set_transform_request | Request metadata passed to the ``transform`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each par... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.continuous_binning.ContinuousOptimalBinning, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', metric: Union[bool, NoneType, str] = '$UNCHANGED$', metric_missing: Union[bool, NoneType, str] = '$UNCHANGED$', metric_special: Union[bool, NoneType, str] = '$UNCHANGED$', show_digits: Unio... |
4,615 | optbinning.binning.continuous_binning | to_json |
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.
| def to_json(self, 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.
"""
if path is None:
raise ValueError('Specify the path for the json file.')
table = ... | (self, path) |
4,616 | optbinning.binning.continuous_binning | transform | 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... | def transform(self, 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 i... | (self, x, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,617 | optbinning.binning.multidimensional.continuous_binning_2d | ContinuousOptimalBinning2D | Optimal binning of two numerical variables with respect to a continuous
target.
Parameters
----------
name_x : str, optional (default="")
The name of variable x.
name_y : str, optional (default="")
The name of variable y.
dtype_x : str, optional (default="numerical")
T... | class ContinuousOptimalBinning2D(OptimalBinning2D):
"""Optimal binning of two numerical variables with respect to a continuous
target.
Parameters
----------
name_x : str, optional (default="")
The name of variable x.
name_y : str, optional (default="")
The name of variable y.
... | (name_x='', name_y='', dtype_x='numerical', dtype_y='numerical', prebinning_method='cart', strategy='grid', solver='cp', max_n_prebins_x=5, max_n_prebins_y=5, min_prebin_size_x=0.05, min_prebin_size_y=0.05, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, monotonic_trend_x=None, monotonic_trend_y... |
4,619 | optbinning.binning.multidimensional.continuous_binning_2d | __init__ | null | def __init__(self, name_x="", name_y="", dtype_x="numerical",
dtype_y="numerical", prebinning_method="cart",
strategy="grid", solver="cp", max_n_prebins_x=5,
max_n_prebins_y=5, min_prebin_size_x=0.05,
min_prebin_size_y=0.05, min_n_bins=None, max_n_bins=None,
... | (self, name_x='', name_y='', dtype_x='numerical', dtype_y='numerical', prebinning_method='cart', strategy='grid', solver='cp', max_n_prebins_x=5, max_n_prebins_y=5, min_prebin_size_x=0.05, min_prebin_size_y=0.05, min_n_bins=None, max_n_bins=None, min_bin_size=None, max_bin_size=None, monotonic_trend_x=None, monotonic_t... |
4,626 | optbinning.binning.binning | _compute_prebins | null | def _compute_prebins(self, splits_prebinning, x, y0, y1, sw):
n_splits = len(splits_prebinning)
if not n_splits:
return splits_prebinning, np.array([]), np.array([])
if self.dtype == "categorical" and self.user_splits is not None:
indices = np.digitize(x, splits_prebinning, right=True)
... | (self, splits_prebinning, x, y0, y1, sw) |
4,627 | optbinning.binning.multidimensional.continuous_binning_2d | _fit | null | def _fit(self, x, y, z, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Optimal binning started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# Pre-processing
if self.verbose:
logger.info("Pre-processing starte... | (self, x, y, z, check_input) |
4,628 | optbinning.binning.multidimensional.continuous_binning_2d | _fit_optimizer | null | def _fit_optimizer(self, splits_x, splits_y, R, S, SS):
if self.verbose:
logger.info("Optimizer started.")
time_init = time.perf_counter()
# Min/max number of bins (bin size)
if self.min_bin_size is not None:
min_bin_size = int(np.ceil(self.min_bin_size * self._n_samples))
else:
... | (self, splits_x, splits_y, R, S, SS) |
4,629 | optbinning.binning.multidimensional.binning_2d | _fit_prebinning | null | def _fit_prebinning(self, dtype, x, z, max_n_prebins, min_prebin_size):
# Pre-binning algorithm
min_bin_size = int(np.ceil(min_prebin_size * self._n_samples))
prebinning = PreBinning(method=self.prebinning_method,
n_bins=max_n_prebins,
min_bin_size=min... | (self, dtype, x, z, max_n_prebins, min_prebin_size) |
4,634 | optbinning.binning.multidimensional.continuous_binning_2d | _prebinning_matrices | null | def _prebinning_matrices(self, splits_x, splits_y, x_clean, y_clean,
z_clean, x_missing, y_missing, z_missing,
x_special, y_special, z_special):
self._n_records_missing = len(z_missing)
self._n_records_special = len(z_special)
self._sum_missing = np.sum(z_mi... | (self, splits_x, splits_y, x_clean, y_clean, z_clean, x_missing, y_missing, z_missing, x_special, y_special, z_special) |
4,635 | optbinning.binning.binning | _prebinning_refinement | null | def _prebinning_refinement(self, splits_prebinning, x, y, y_missing,
x_special, y_special, y_others, sw_clean,
sw_missing, sw_special, sw_others):
y0 = (y == 0)
y1 = ~y0
# Compute n_nonevent and n_event for special, missing and others.
self._n_noneve... | (self, splits_prebinning, x, y, y_missing, x_special, y_special, y_others, sw_clean, sw_missing, sw_special, sw_others) |
4,638 | optbinning.binning.multidimensional.binning_2d | _splits_xy_optimal | null | def _splits_xy_optimal(self, selected_rows, splits_x, splits_y, P):
bins_x = np.concatenate([[-np.inf], splits_x, [np.inf]])
bins_y = np.concatenate([[-np.inf], splits_y, [np.inf]])
bins_str_x = np.array([[bins_x[i], bins_x[i+1]]
for i in range(len(bins_x) - 1)])
bins_str_y = ... | (self, selected_rows, splits_x, splits_y, P) |
4,641 | optbinning.binning.multidimensional.continuous_binning_2d | fit | Fit the optimal binning 2D according to the given training data.
Parameters
----------
x : array-like, shape = (n_samples,)
Training vector x, where n_samples is the number of samples.
y : array-like, shape = (n_samples,)
Training vector y, where n_samples is th... | def fit(self, x, y, z, check_input=False):
"""Fit the optimal binning 2D according to the given training data.
Parameters
----------
x : array-like, shape = (n_samples,)
Training vector x, where n_samples is the number of samples.
y : array-like, shape = (n_samples,)
Training vector ... | (self, x, y, z, check_input=False) |
4,642 | optbinning.binning.multidimensional.continuous_binning_2d | fit_transform | Fit the optimal binning 2D according to the given training data,
then transform it.
Parameters
----------
x : array-like, shape = (n_samples,)
Training vector x, where n_samples is the number of samples.
y : array-like, shape = (n_samples,)
Training vect... | def fit_transform(self, x, y, z, metric="mean", metric_special=0,
metric_missing=0, show_digits=2, check_input=False):
"""Fit the optimal binning 2D according to the given training data,
then transform it.
Parameters
----------
x : array-like, shape = (n_samples,)
Training ... | (self, x, y, z, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,646 | optbinning.binning.binning | read_json |
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
| def read_json(self, path):
"""
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
"""
self._is_fitted = True
with open(path, "r") as read_file:
bin_table_attr = json.load(read_file)
for key i... | (self, path) |
4,647 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.multidimensional.continuous_binning_2d.ContinuousOptimalBinning2D, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', x: Union[bool, NoneType, str] = '$UNCHANGED$', z: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.multidimensional.continuous_binning_2d.ContinuousOp... |
4,650 | optbinning.binning.binning | to_json |
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.
| def to_json(self, 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.
"""
if path is None:
raise ValueError('Specify the path for the json file')
table = s... | (self, path) |
4,651 | optbinning.binning.multidimensional.continuous_binning_2d | transform | Transform given data to mean using bins from the fitted optimal
binning 2D.
Parameters
----------
x : array-like, shape = (n_samples,)
Training vector x, where n_samples is the number of samples.
y : array-like, shape = (n_samples,)
Training vector y, wh... | def transform(self, x, y, 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 2D.
Parameters
----------
x : array-like, shape = (n_samples,)
Training vector x, where n_s... | (self, x, y, metric='mean', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,652 | optbinning.binning.piecewise.continuous_binning | ContinuousOptimalPWBinning | 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 "q... | class ContinuousOptimalPWBinning(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. Sup... | (name='', objective='l2', degree=1, continuous=True, continuous_deriv=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_detec... |
4,654 | optbinning.binning.piecewise.continuous_binning | __init__ | null | def __init__(self, name="", objective="l2", degree=1, continuous=True,
continuous_deriv=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... | (self, name='', objective='l2', degree=1, continuous=True, continuous_deriv=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... |
4,661 | optbinning.binning.piecewise.continuous_binning | _fit | null | def _fit(self, x, y, lb, ub, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Optimal piecewise binning started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params(deep=False), estimator=None,
problem_type=self._pr... | (self, x, y, lb, ub, check_input) |
4,662 | optbinning.binning.piecewise.base | _fit_binning | null | def _fit_binning(self, x, y, prediction, lb, ub):
if self.verbose:
logger.info("Pre-binning: optimal binning started.")
time_prebinning = time.perf_counter()
# Determine optimal split points
monotonic_trend = self.monotonic_trend
if self.monotonic_trend in ("concave", "convex"):
mono... | (self, x, y, prediction, lb, ub) |
4,663 | optbinning.binning.piecewise.base | _fit_preprocessing | null | def _fit_preprocessing(self, x, y, check_input):
return split_data(dtype="numerical", x=x, y=y,
special_codes=self.special_codes,
user_splits=self.user_splits,
check_input=check_input,
outlier_detector=self.outlier_detector,
... | (self, x, y, check_input) |
4,672 | optbinning.binning.piecewise.base | fit | 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... | def fit(self, 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,)
... | (self, x, y, lb=None, ub=None, check_input=False) |
4,673 | optbinning.binning.piecewise.continuous_binning | fit_transform | 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 v... | def fit_transform(self, 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, ... | (self, x, y, metric_special=0, metric_missing=0, lb=None, ub=None, check_input=False) |
4,676 | optbinning.binning.piecewise.base | information | Print overview information about the options settings, problem
statistics, and the solution of the computation.
Parameters
----------
print_level : int (default=1)
Level of details.
| def information(self, 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.
"""
self._check_is_fitted()
if not isinstance(print_level... | (self, print_level=1) |
4,677 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.piecewise.continuous_binning.ContinuousOptimalPWBinning, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', lb: Union[bool, NoneType, str] = '$UNCHANGED$', ub: Union[bool, NoneType, str] = '$UNCHANGED$', x: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.piecewise.co... |
4,679 | sklearn.utils._metadata_requests | set_transform_request | Request metadata passed to the ``transform`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each par... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.piecewise.continuous_binning.ContinuousOptimalPWBinning, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', lb: Union[bool, NoneType, str] = '$UNCHANGED$', metric_missing: Union[bool, NoneType, str] = '$UNCHANGED$', metric_special: Union[bool, NoneType, str] = '$UNCHANGED$', ub: Union... |
4,680 | optbinning.binning.piecewise.continuous_binning | transform | 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... | def transform(self, 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 numb... | (self, x, metric_special=0, metric_missing=0, lb=None, ub=None, check_input=False) |
4,681 | optbinning.binning.mdlp | MDLP |
Minimum Description Length Principle (MDLP) discretization algorithm.
Parameters
----------
min_samples_split : int (default=2)
The minimum number of samples required to split an internal node.
min_samples_leaf : int (default=2)
The minimum number of samples required to be at a le... | class MDLP(BaseEstimator):
"""
Minimum Description Length Principle (MDLP) discretization algorithm.
Parameters
----------
min_samples_split : int (default=2)
The minimum number of samples required to split an internal node.
min_samples_leaf : int (default=2)
The minimum number... | (min_samples_split=2, min_samples_leaf=2, max_candidates=32) |
4,683 | optbinning.binning.mdlp | __init__ | null | def __init__(self, min_samples_split=2, min_samples_leaf=2,
max_candidates=32):
self.min_samples_split = min_samples_split
self.min_samples_leaf = min_samples_leaf
self.max_candidates = max_candidates
# auxiliary
self._splits = []
self._is_fitted = None
| (self, min_samples_split=2, min_samples_leaf=2, max_candidates=32) |
4,689 | optbinning.binning.mdlp | _entropy | null | def _entropy(self, x):
n = len(x)
ns1 = np.sum(x)
ns0 = n - ns1
p = np.array([ns0, ns1]) / n
return -special.xlogy(p, p).sum()
| (self, x) |
4,690 | optbinning.binning.mdlp | _entropy_gain | null | def _entropy_gain(self, y, y1, y2):
n = len(y)
n1 = len(y1)
n2 = n - n1
ent_y = self._entropy(y)
ent_y1 = self._entropy(y1)
ent_y2 = self._entropy(y2)
return ent_y - (n1 * ent_y1 + n2 * ent_y2) / n
| (self, y, y1, y2) |
4,691 | optbinning.binning.mdlp | _find_split | null | def _find_split(self, u_x, x, y):
n_x = len(x)
u_x = np.unique(0.5 * (x[1:] + x[:-1])[(y[1:] - y[:-1]) != 0])
if len(u_x) > self.max_candidates:
percentiles = np.linspace(1, 100, self.max_candidates)
splits = np.percentile(u_x, percentiles)
else:
splits = u_x
max_entropy_gain... | (self, u_x, x, y) |
4,692 | optbinning.binning.mdlp | _fit | null | def _fit(self, x, y):
_check_parameters(**self.get_params())
x = check_array(x, ensure_2d=False, force_all_finite=True)
y = check_array(y, ensure_2d=False, force_all_finite=True)
idx = np.argsort(x)
x = x[idx]
y = y[idx]
self._recurse(x, y, 0)
self._is_fitted = True
return self
| (self, x, y) |
4,697 | optbinning.binning.mdlp | _recurse | null | def _recurse(self, x, y, id):
u_x = np.unique(x)
n_x = len(u_x)
n_y = len(np.bincount(y))
split = self._find_split(u_x, x, y)
if split is not None:
self._splits.append(split)
t = np.searchsorted(x, split, side="right")
if not self._terminate(n_x, n_y, y, y[:t], y[t:]):
... | (self, x, y, id) |
4,700 | optbinning.binning.mdlp | _terminate | null | def _terminate(self, n_x, n_y, y, y1, y2):
splittable = (n_x >= self.min_samples_split) and (n_y >= 2)
n = len(y)
n1 = len(y1)
n2 = n - n1
ent_y = self._entropy(y)
ent_y1 = self._entropy(y1)
ent_y2 = self._entropy(y2)
gain = ent_y - (n1 * ent_y1 + n2 * ent_y2) / n
k = len(np.bincount... | (self, n_x, n_y, y, y1, y2) |
4,703 | optbinning.binning.mdlp | fit | Fit MDLP discretization algorithm.
Parameters
----------
x : array-like, shape = (n_samples)
Data samples, where n_samples is the number of samples.
y : array-like, shape = (n_samples)
Target vector relative to x.
Returns
-------
self : ... | def fit(self, x, y):
"""Fit MDLP discretization algorithm.
Parameters
----------
x : array-like, shape = (n_samples)
Data samples, where n_samples is the number of samples.
y : array-like, shape = (n_samples)
Target vector relative to x.
Returns
-------
self : MDLP
""... | (self, x, y) |
4,706 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.mdlp.MDLP, *, x: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.mdlp.MDLP |
4,708 | optbinning.binning.multiclass_binning | MulticlassOptimalBinning | Optimal binning of a numerical variable with respect to a multiclass or
multilabel target.
**Note that the maximum number of classes is set to 100**. To ease
visualization of the binning table, a set of 100 maximally distinct colors
is generated using the library `glasbey
<https://github.com/taketw... | class MulticlassOptimalBinning(OptimalBinning):
"""Optimal binning of a numerical variable with respect to a multiclass or
multilabel target.
**Note that the maximum number of classes is set to 100**. To ease
visualization of the binning table, a set of 100 maximally distinct colors
is generated us... | (name='', prebinning_method='cart', solver='cp', 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_event_rate_diff=0, max_pvalue=None, max_pvalue_policy='consecutive', outlier_detector=None, outlier_params=None, user_splits=None, ... |
4,710 | optbinning.binning.multiclass_binning | __init__ | null | def __init__(self, name="", prebinning_method="cart", solver="cp",
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_event_rate_diff=0, max_pvalue=None,
max_pvalue_p... | (self, name='', prebinning_method='cart', solver='cp', 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_event_rate_diff=0, max_pvalue=None, max_pvalue_policy='consecutive', outlier_detector=None, outlier_params=None, user_splits=... |
4,717 | optbinning.binning.multiclass_binning | _compute_prebins | null | def _compute_prebins(self, splits_prebinning, x, y):
n_splits = len(splits_prebinning)
if not n_splits:
return splits_prebinning, np.array([]), np.array([])
indices = np.digitize(x, splits_prebinning, right=False)
n_bins = n_splits + 1
n_nonevent = np.empty((n_bins, self._n_classes), dtype=n... | (self, splits_prebinning, x, y) |
4,718 | optbinning.binning.multiclass_binning | _fit | null | def _fit(self, x, y, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Optimal binning started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# Pre-processing
if self.verbose:
logger.info("Pre-processing started."... | (self, x, y, check_input) |
4,719 | optbinning.binning.multiclass_binning | _fit_optimizer | null | def _fit_optimizer(self, splits, n_nonevent, n_event):
if self.verbose:
logger.info("Optimizer started.")
time_init = time.perf_counter()
if not len(n_nonevent):
self._status = "OPTIMAL"
self._splits_optimal = splits
self._solution = np.zeros(len(splits), dtype=bool)
... | (self, splits, n_nonevent, n_event) |
4,725 | optbinning.binning.multiclass_binning | _prebinning_refinement | null | def _prebinning_refinement(self, splits_prebinning, x, y, y_missing,
x_special, y_special, y_others=None,
sw_clean=None, sw_missing=None, sw_special=None,
sw_others=None):
self._classes = np.unique(y)
self._n_classes = len(self._cl... | (self, splits_prebinning, x, y, y_missing, x_special, y_special, y_others=None, sw_clean=None, sw_missing=None, sw_special=None, sw_others=None) |
4,730 | optbinning.binning.multiclass_binning | fit | 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.
check_i... | def fit(self, x, y, 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 t... | (self, x, y, check_input=False) |
4,731 | optbinning.binning.multiclass_binning | fit_transform | 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 rela... | def fit_transform(self, x, y, metric="mean_woe", 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 ve... | (self, x, y, metric='mean_woe', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,735 | optbinning.binning.multiclass_binning | read_json |
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
| def read_json(self, path):
"""
Read json file containing split points and set them as the new split
points.
Parameters
----------
path: The path of the json file.
"""
if path is None:
raise ValueError('Specify the path for the json file.')
self._is_fitted = True
with open... | (self, path) |
4,736 | sklearn.utils._metadata_requests | set_fit_request | Request metadata passed to the ``fit`` method.
Note that this method is only relevant if
``enable_metadata_routing=True`` (see :func:`sklearn.set_config`).
Please see :ref:`User Guide <metadata_routing>` on how the routing
mechanism works.
The options for each parameter... | def __get__(self, instance, owner):
# we would want to have a method which accepts only the expected args
def func(**kw):
"""Updates the request for provided parameters
This docstring is overwritten below.
See REQUESTER_DOC for expected functionality
"""
if not _routing_e... | (self: optbinning.binning.multiclass_binning.MulticlassOptimalBinning, *, check_input: Union[bool, NoneType, str] = '$UNCHANGED$', x: Union[bool, NoneType, str] = '$UNCHANGED$') -> optbinning.binning.multiclass_binning.MulticlassOptimalBinning |
4,739 | optbinning.binning.multiclass_binning | to_json |
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.
| def to_json(self, 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.
"""
if path is None:
raise ValueError('Specify the path for the json file.')
table = ... | (self, path) |
4,740 | optbinning.binning.multiclass_binning | transform | Transform given data to mean Weight of Evidence (WoE) or weighted
mean WoE 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, optional (defau... | def transform(self, x, metric="mean_woe", metric_special=0,
metric_missing=0, show_digits=2, check_input=False):
"""Transform given data to mean Weight of Evidence (WoE) or weighted
mean WoE using bins from the fitted optimal binning.
Parameters
----------
x : array-like, shape = (n_sa... | (self, x, metric='mean_woe', metric_special=0, metric_missing=0, show_digits=2, check_input=False) |
4,741 | optbinning.binning.binning | OptimalBinning | Optimal binning of a numerical or categorical variable with respect to a
binary target.
Parameters
----------
name : str, optional (default="")
The variable name.
dtype : str, optional (default="numerical")
The variable data type. Supported data types are "numerical" for
co... | class OptimalBinning(BaseOptimalBinning):
"""Optimal binning of a numerical or categorical variable with respect to a
binary target.
Parameters
----------
name : str, optional (default="")
The variable name.
dtype : str, optional (default="numerical")
The variable data type. Su... | (name='', dtype='numerical', prebinning_method='cart', solver='cp', divergence='iv', 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, min_bin_n_nonevent=None, max_bin_n_nonevent=None, min_bin_n_event=None, max_bin_n_event=None, monotonic_trend='auto', min_e... |
4,743 | optbinning.binning.binning | __init__ | null | def __init__(self, name="", dtype="numerical", prebinning_method="cart",
solver="cp", divergence="iv", 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, min_bin_n_nonevent=None,
max_bin_n_nonevent=None, min... | (self, name='', dtype='numerical', prebinning_method='cart', solver='cp', divergence='iv', 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, min_bin_n_nonevent=None, max_bin_n_nonevent=None, min_bin_n_event=None, max_bin_n_event=None, monotonic_trend='auto',... |
4,751 | optbinning.binning.binning | _fit | null | def _fit(self, x, y, sample_weight, check_input):
time_init = time.perf_counter()
if self.verbose:
logger.info("Optimal binning started.")
logger.info("Options: check parameters.")
_check_parameters(**self.get_params())
# Pre-processing
if self.verbose:
logger.info("Pre-proce... | (self, x, y, sample_weight, check_input) |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.