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DOC: Add docstrings to user-facing classes.

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Skipper Seabold 2015-04-27 11:01:46 -05:00
parent abcc09286c
commit efdbec4d4c
1 changed files with 16 additions and 8 deletions
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24
wrapper/xgboost.py
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@ -803,7 +803,7 @@ class XGBModel(XGBModelBase):
gamma : float gamma : float
Minimum loss reduction required to make a further partition on a leaf node of the tree. Minimum loss reduction required to make a further partition on a leaf node of the tree.
min_child_weight : int min_child_weight : int
Minimum sum of instance weight(hessian) needed in a child. Minimum sum of instance weight(hessian) needed in a child.
max_delta_step : int max_delta_step : int
Maximum delta step we allow each tree's weight estimation to be. Maximum delta step we allow each tree's weight estimation to be.
subsample : float subsample : float
@ -816,8 +816,8 @@ class XGBModel(XGBModelBase):
seed : int seed : int
Random number seed. Random number seed.
""" """
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear", def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="reg:linear",
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
base_score=0.5, seed=0): base_score=0.5, seed=0):
if not SKLEARN_INSTALLED: if not SKLEARN_INSTALLED:
raise Exception('sklearn needs to be installed in order to use this module') raise Exception('sklearn needs to be installed in order to use this module')
@ -826,7 +826,7 @@ class XGBModel(XGBModelBase):
self.n_estimators = n_estimators self.n_estimators = n_estimators
self.silent = silent self.silent = silent
self.objective = objective self.objective = objective
self.nthread = nthread self.nthread = nthread
self.gamma = gamma self.gamma = gamma
self.min_child_weight = min_child_weight self.min_child_weight = min_child_weight
@ -836,7 +836,7 @@ class XGBModel(XGBModelBase):
self.base_score = base_score self.base_score = base_score
self.seed = seed self.seed = seed
self._Booster = Booster() self._Booster = Booster()
def get_xgb_params(self): def get_xgb_params(self):
@ -859,10 +859,14 @@ class XGBModel(XGBModelBase):
class XGBClassifier(XGBModel, XGBClassifier): class XGBClassifier(XGBModel, XGBClassifier):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic", __doc__ = """
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1, Implementation of the scikit-learn API for XGBoost classification
""" + "\n".join(XGBModel.__doc__.split('\n')[2:])
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True, objective="binary:logistic",
nthread=-1, gamma=0, min_child_weight=1, max_delta_step=0, subsample=1, colsample_bytree=1,
base_score=0.5, seed=0): base_score=0.5, seed=0):
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective, super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective,
nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree, nthread, gamma, min_child_weight, max_delta_step, subsample, colsample_bytree,
base_score, seed) base_score, seed)
@ -910,4 +914,8 @@ class XGBClassifier(XGBModel, XGBClassifier):
class XGBRegressor(XGBModel, XGBRegressor): class XGBRegressor(XGBModel, XGBRegressor):
__doc__ = """
Implementation of the scikit-learn API for XGBoost regression
""" + "\n".join(XGBModel.__doc__.split('\n')[2:])
pass pass