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*Fix XGBClassifier super()

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white1033 2015-04-05 21:15:09 +08:00
parent 93d3f4fe61
commit 402e832ce5
1 changed files with 31 additions and 31 deletions
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62
wrapper/xgboost.py
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@ -552,20 +552,20 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
early_stopping_rounds: int
Activates early stopping. Validation error needs to decrease at least
every <early_stopping_rounds> round(s) to continue training.
Requires at least one item in evals.
Requires at least one item in evals.
If there's more than one, will use the last.
Returns the model from the last iteration (not the best one).
If early stopping occurs, the model will have two additional fields:
If early stopping occurs, the model will have two additional fields:
bst.best_score and bst.best_iteration.
Returns
-------
booster : a trained booster model
"""
evals = list(evals)
bst = Booster(params, [dtrain] + [d[0] for d in evals])
if not early_stopping_rounds:
for i in range(num_boost_round):
bst.update(dtrain, i, obj)
@ -576,15 +576,15 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
else:
sys.stderr.write(bst_eval_set.decode() + '\n')
return bst
else:
# early stopping
if len(evals) < 1:
raise ValueError('For early stopping you need at least on set in evals.')
raise ValueError('For early stopping you need at least on set in evals.')
sys.stderr.write("Will train until {} error hasn't decreased in {} rounds.\n".format(evals[-1][1], early_stopping_rounds))
# is params a list of tuples? are we using multiple eval metrics?
if type(params) == list:
if len(params) != len(dict(params).items()):
@ -597,26 +597,26 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
maximize_metrics = ('auc', 'map', 'ndcg')
if filter(lambda x: params['eval_metric'].startswith(x), maximize_metrics):
maximize_score = True
if maximize_score:
best_score = 0.0
else:
best_score = float('inf')
best_msg = ''
best_msg = ''
best_score_i = 0
for i in range(num_boost_round):
bst.update(dtrain, i, obj)
bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, string_types):
msg = bst_eval_set
else:
msg = bst_eval_set.decode()
sys.stderr.write(msg + '\n')
score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score):
@ -628,10 +628,10 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
bst.best_score = best_score
bst.best_iteration = best_score_i
return bst
return bst
class CVPack(object):
def __init__(self, dtrain, dtest, param):
@ -770,7 +770,7 @@ class XGBModel(BaseEstimator):
self.n_rounds = n_estimators
self.objective = objective
self._Booster = Booster()
def get_params(self, deep=True):
return {'max_depth': self.max_depth,
'learning_rate': self.eta,
@ -780,20 +780,20 @@ class XGBModel(BaseEstimator):
}
def get_xgb_params(self):
return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
def fit(self, X, y):
trainDmatrix = DMatrix(X, label=y)
self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds)
return self
def predict(self, X):
testDmatrix = DMatrix(X)
return self._Booster.predict(testDmatrix)
class XGBClassifier(XGBModel, ClassifierMixin):
class XGBClassifier(XGBModel, ClassifierMixin):
def __init__(self, max_depth=3, learning_rate=0.1, n_estimators=100, silent=True):
super().__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic")
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent, objective="binary:logistic")
def fit(self, X, y, sample_weight=None):
y_values = list(np.unique(y))
if len(y_values) > 2:
@ -803,19 +803,19 @@ class XGBClassifier(XGBModel, ClassifierMixin):
xgb_options['num_class'] = len(y_values)
else:
xgb_options = self.get_xgb_params()
self._le = LabelEncoder().fit(y)
training_labels = self._le.transform(y)
if sample_weight is not None:
trainDmatrix = DMatrix(X, label=training_labels, weight=sample_weight)
else:
trainDmatrix = DMatrix(X, label=training_labels)
self._Booster = train(xgb_options, trainDmatrix, self.n_rounds)
return self
def predict(self, X):
testDmatrix = DMatrix(X)
class_probs = self._Booster.predict(testDmatrix)
@ -825,7 +825,7 @@ class XGBClassifier(XGBModel, ClassifierMixin):
column_indexes = np.repeat(0, X.shape[0])
column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes)
def predict_proba(self, X):
testDmatrix = DMatrix(X)
class_probs = self._Booster.predict(testDmatrix)
@ -836,7 +836,7 @@ class XGBClassifier(XGBModel, ClassifierMixin):
classzero_probs = 1.0 - classone_probs
return np.vstack((classzero_probs,classone_probs)).transpose()
class XGBRegressor(XGBModel, RegressorMixin):
class XGBRegressor(XGBModel, RegressorMixin):
pass