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Commit c4778e73 authored by wxchan's avatar wxchan Committed by Guolin Ke
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add more config to sklearn (#160)

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docs/Python-API.md
View file @ c4778e73
......@@ -576,7 +576,7 @@ The methods of each Class is in alphabetical order.
###Common Methods
####__init__(boosting_type="gbdt", num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, silent=True, objective="regression", nthread=-1, min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0)
####__init__(boosting_type="gbdt", num_leaves=31, max_depth=-1, learning_rate=0.1, n_estimators=10, max_bin=255, subsample_for_bin=50000, objective="regression", min_split_gain=0, min_child_weight=5, min_child_samples=10, subsample=1, subsample_freq=1, colsample_bytree=1, reg_alpha=0, reg_lambda=0, scale_pos_weight=1, is_unbalance=False, seed=0, nthread=-1, silent=True, sigmoid=1.0, max_position=20, label_gain=None, drop_rate=0.1, skip_drop=0.5, max_drop=50, uniform_drop=False, xgboost_dart_mode=False)
Implementation of the Scikit-Learn API for LightGBM.
......@@ -595,14 +595,12 @@ The methods of each Class is in alphabetical order.
Number of boosted trees to fit.
max_bin : int
Number of bucketed bin for feature values
silent : boolean
Whether to print messages while running boosting.
subsample_for_bin : int
Number of samples for constructing bins.
objective : str or callable
Specify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below).
default: binary for LGBMClassifier, regression for LGBMRegressor, lambdarank for LGBMRanker
nthread : int
Number of parallel threads
min_split_gain : float
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
......@@ -625,6 +623,18 @@ The methods of each Class is in alphabetical order.
Is unbalance for binary classification
seed : int
Random number seed.
nthread : int
Number of parallel threads
silent : boolean
Whether to print messages while running boosting.
sigmoid : float
Only used in binary classification and lambdarank. Parameter for sigmoid function.
max_position : int
Only used in lambdarank, will optimize NDCG at this position.
label_gain : list of float
Only used in lambdarank, relevant gain for labels.
For example, the gain of label 2 is 3 if using default label gains.
None (default) means use default value of CLI version: {0,1,3,7,15,31,63,...}.
drop_rate : float
Only used when boosting_type='dart'. Probablity to select dropping trees.
skip_drop : float
......
python-package/lightgbm/sklearn.py
View file @ c4778e73
......@@ -135,11 +135,12 @@ class LGBMModel(LGBMModelBase):
def __init__(self, boosting_type="gbdt", num_leaves=31, max_depth=-1,
learning_rate=0.1, n_estimators=10, max_bin=255,
silent=True, objective="regression",
nthread=-1, min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample_for_bin=50000, objective="regression",
min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample=1, subsample_freq=1, colsample_bytree=1,
reg_alpha=0, reg_lambda=0, scale_pos_weight=1,
is_unbalance=False, seed=0,
is_unbalance=False, seed=0, nthread=-1, silent=True,
sigmoid=1.0, max_position=20, label_gain=None,
drop_rate=0.1, skip_drop=0.5, max_drop=50,
uniform_drop=False, xgboost_dart_mode=False):
"""
......@@ -160,14 +161,12 @@ class LGBMModel(LGBMModelBase):
Number of boosted trees to fit.
max_bin : int
Number of bucketed bin for feature values
silent : boolean
Whether to print messages while running boosting.
subsample_for_bin : int
Number of samples for constructing bins.
objective : string or callable
Specify the learning task and the corresponding learning objective or
a custom objective function to be used (see note below).
default: binary for LGBMClassifier, lambdarank for LGBMRanker
nthread : int
Number of parallel threads
min_split_gain : float
Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int
......@@ -190,6 +189,18 @@ class LGBMModel(LGBMModelBase):
Is unbalance for binary classification
seed : int
Random number seed.
nthread : int
Number of parallel threads
silent : boolean
Whether to print messages while running boosting.
sigmoid : float
Only used in binary classification and lambdarank. Parameter for sigmoid function.
max_position : int
Only used in lambdarank, will optimize NDCG at this position.
label_gain : list of float
Only used in lambdarank, relevant gain for labels.
For example, the gain of label 2 is 3 if using default label gains.
None (default) means use default value of CLI version: {0,1,3,7,15,31,63,...}.
drop_rate : float
Only used when boosting_type='dart'. Probablity to select dropping trees.
skip_drop : float
......@@ -232,9 +243,8 @@ class LGBMModel(LGBMModelBase):
self.learning_rate = learning_rate
self.n_estimators = n_estimators
self.max_bin = max_bin
self.silent = silent
self.subsample_for_bin = subsample_for_bin
self.objective = objective
self.nthread = nthread
self.min_split_gain = min_split_gain
self.min_child_weight = min_child_weight
self.min_child_samples = min_child_samples
......@@ -246,6 +256,11 @@ class LGBMModel(LGBMModelBase):
self.scale_pos_weight = scale_pos_weight
self.is_unbalance = is_unbalance
self.seed = seed
self.nthread = nthread
self.silent = silent
self.sigmoid = sigmoid
self.max_position = max_position
self.label_gain = label_gain
self.drop_rate = drop_rate
self.skip_drop = skip_drop
self.max_drop = max_drop
......@@ -339,8 +354,9 @@ class LGBMModel(LGBMModelBase):
if hasattr(self, 'eval_at'):
params['ndcg_eval_at'] = self.eval_at
if self.fobj:
# objective = nullptr for unknown objective
params['objective'] = 'None'
params['objective'] = 'None' # objective = nullptr for unknown objective
if 'label_gain' in params and params['label_gain'] is None:
del params['label_gain'] # use default of cli version
if callable(eval_metric):
feval = _eval_function_wrapper(eval_metric)
......@@ -483,23 +499,25 @@ class LGBMClassifier(LGBMModel, LGBMClassifierBase):
def __init__(self, boosting_type="gbdt", num_leaves=31, max_depth=-1,
learning_rate=0.1, n_estimators=10, max_bin=255,
silent=True, objective="binary",
nthread=-1, min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample_for_bin=50000, objective="binary",
min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample=1, subsample_freq=1, colsample_bytree=1,
reg_alpha=0, reg_lambda=0, scale_pos_weight=1,
is_unbalance=False, seed=0,
is_unbalance=False, seed=0, nthread=-1,
silent=True, sigmoid=1.0,
drop_rate=0.1, skip_drop=0.5, max_drop=50,
uniform_drop=False, xgboost_dart_mode=False):
self.classes, self.n_classes = None, None
super(LGBMClassifier, self).__init__(boosting_type=boosting_type, num_leaves=num_leaves,
max_depth=max_depth, learning_rate=learning_rate,
n_estimators=n_estimators, max_bin=max_bin,
silent=silent, objective=objective, nthread=nthread,
subsample_for_bin=subsample_for_bin, objective=objective,
min_split_gain=min_split_gain, min_child_weight=min_child_weight,
min_child_samples=min_child_samples, subsample=subsample,
subsample_freq=subsample_freq, colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight, is_unbalance=is_unbalance, seed=seed,
scale_pos_weight=scale_pos_weight, is_unbalance=is_unbalance,
seed=seed, nthread=nthread, silent=silent, sigmoid=sigmoid,
drop_rate=drop_rate, skip_drop=skip_drop, max_drop=max_drop,
uniform_drop=uniform_drop, xgboost_dart_mode=xgboost_dart_mode)
......@@ -581,22 +599,25 @@ class LGBMRanker(LGBMModel):
def __init__(self, boosting_type="gbdt", num_leaves=31, max_depth=-1,
learning_rate=0.1, n_estimators=10, max_bin=255,
silent=True, objective="lambdarank",
nthread=-1, min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample_for_bin=50000, objective="lambdarank",
min_split_gain=0, min_child_weight=5, min_child_samples=10,
subsample=1, subsample_freq=1, colsample_bytree=1,
reg_alpha=0, reg_lambda=0, scale_pos_weight=1,
is_unbalance=False, seed=0,
is_unbalance=False, seed=0, nthread=-1, silent=True,
sigmoid=1.0, max_position=20, label_gain=None,
drop_rate=0.1, skip_drop=0.5, max_drop=50,
uniform_drop=False, xgboost_dart_mode=False):
super(LGBMRanker, self).__init__(boosting_type=boosting_type, num_leaves=num_leaves,
max_depth=max_depth, learning_rate=learning_rate,
n_estimators=n_estimators, max_bin=max_bin,
silent=silent, objective=objective, nthread=nthread,
subsample_for_bin=subsample_for_bin, objective=objective,
min_split_gain=min_split_gain, min_child_weight=min_child_weight,
min_child_samples=min_child_samples, subsample=subsample,
subsample_freq=subsample_freq, colsample_bytree=colsample_bytree,
reg_alpha=reg_alpha, reg_lambda=reg_lambda,
scale_pos_weight=scale_pos_weight, is_unbalance=is_unbalance, seed=seed,
scale_pos_weight=scale_pos_weight, is_unbalance=is_unbalance,
seed=seed, nthread=nthread, silent=silent,
sigmoid=sigmoid, max_position=max_position, label_gain=label_gain,
drop_rate=drop_rate, skip_drop=skip_drop, max_drop=max_drop,
uniform_drop=uniform_drop, xgboost_dart_mode=xgboost_dart_mode)
......
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