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Merge pull request #220 from white1033/master

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*Fix XGBClassifier super()
This commit is contained in:
Tianqi Chen 2015-04-05 09:05:08 -07:00
commit e626b62daa
1 changed files with 35 additions and 36 deletions
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71
wrapper/xgboost.py
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@ -26,7 +26,6 @@ except ImportError:
SKLEARN_INSTALLED = False SKLEARN_INSTALLED = False
__all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train'] __all__ = ['DMatrix', 'CVPack', 'Booster', 'aggcv', 'cv', 'mknfold', 'train']
if sys.version_info[0] == 3: if sys.version_info[0] == 3:
@ -552,20 +551,20 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
early_stopping_rounds: int early_stopping_rounds: int
Activates early stopping. Validation error needs to decrease at least Activates early stopping. Validation error needs to decrease at least
every <early_stopping_rounds> round(s) to continue training. 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. If there's more than one, will use the last.
Returns the model from the last iteration (not the best one). 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. bst.best_score and bst.best_iteration.
Returns Returns
------- -------
booster : a trained booster model booster : a trained booster model
""" """
evals = list(evals) evals = list(evals)
bst = Booster(params, [dtrain] + [d[0] for d in evals]) bst = Booster(params, [dtrain] + [d[0] for d in evals])
if not early_stopping_rounds: if not early_stopping_rounds:
for i in range(num_boost_round): for i in range(num_boost_round):
bst.update(dtrain, i, obj) bst.update(dtrain, i, obj)
@ -576,15 +575,15 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
else: else:
sys.stderr.write(bst_eval_set.decode() + '\n') sys.stderr.write(bst_eval_set.decode() + '\n')
return bst return bst
else: else:
# early stopping # early stopping
if len(evals) < 1: 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)) 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? # is params a list of tuples? are we using multiple eval metrics?
if type(params) == list: if type(params) == list:
if len(params) != len(dict(params).items()): if len(params) != len(dict(params).items()):
@ -597,29 +596,29 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
maximize_metrics = ('auc', 'map', 'ndcg') maximize_metrics = ('auc', 'map', 'ndcg')
if filter(lambda x: params['eval_metric'].startswith(x), maximize_metrics): if filter(lambda x: params['eval_metric'].startswith(x), maximize_metrics):
maximize_score = True maximize_score = True
if maximize_score: if maximize_score:
best_score = 0.0 best_score = 0.0
else: else:
best_score = float('inf') best_score = float('inf')
best_msg = '' best_msg = ''
best_score_i = 0 best_score_i = 0
for i in range(num_boost_round): for i in range(num_boost_round):
bst.update(dtrain, i, obj) bst.update(dtrain, i, obj)
bst_eval_set = bst.eval_set(evals, i, feval) bst_eval_set = bst.eval_set(evals, i, feval)
if isinstance(bst_eval_set, string_types): if isinstance(bst_eval_set, string_types):
msg = bst_eval_set msg = bst_eval_set
else: else:
msg = bst_eval_set.decode() msg = bst_eval_set.decode()
sys.stderr.write(msg + '\n') sys.stderr.write(msg + '\n')
score = float(msg.rsplit(':', 1)[1]) score = float(msg.rsplit(':', 1)[1])
if (maximize_score and score > best_score) or \ if (maximize_score and score > best_score) or \
(not maximize_score and score < best_score): (not maximize_score and score < best_score):
best_score = score best_score = score
best_score_i = i best_score_i = i
best_msg = msg best_msg = msg
@ -628,10 +627,9 @@ def train(params, dtrain, num_boost_round=10, evals=(), obj=None, feval=None, ea
bst.best_score = best_score bst.best_score = best_score
bst.best_iteration = best_score_i bst.best_iteration = best_score_i
return bst return bst
return bst return bst
class CVPack(object): class CVPack(object):
def __init__(self, dtrain, dtest, param): def __init__(self, dtrain, dtest, param):
@ -770,7 +768,7 @@ class XGBModel(BaseEstimator):
self.n_rounds = n_estimators self.n_rounds = n_estimators
self.objective = objective self.objective = objective
self._Booster = Booster() self._Booster = Booster()
def get_params(self, deep=True): def get_params(self, deep=True):
return {'max_depth': self.max_depth, return {'max_depth': self.max_depth,
'learning_rate': self.eta, 'learning_rate': self.eta,
@ -778,22 +776,24 @@ class XGBModel(BaseEstimator):
'silent': True if self.silent == 1 else False, 'silent': True if self.silent == 1 else False,
'objective': self.objective 'objective': self.objective
} }
def get_xgb_params(self): def get_xgb_params(self):
return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective} return {'eta': self.eta, 'max_depth': self.max_depth, 'silent': self.silent, 'objective': self.objective}
def fit(self, X, y): def fit(self, X, y):
trainDmatrix = DMatrix(X, label=y) trainDmatrix = DMatrix(X, label=y)
self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds) self._Booster = train(self.get_xgb_params(), trainDmatrix, self.n_rounds)
return self return self
def predict(self, X): def predict(self, X):
testDmatrix = DMatrix(X) testDmatrix = DMatrix(X)
return self._Booster.predict(testDmatrix) 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): 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): def fit(self, X, y, sample_weight=None):
y_values = list(np.unique(y)) y_values = list(np.unique(y))
if len(y_values) > 2: if len(y_values) > 2:
@ -803,19 +803,19 @@ class XGBClassifier(XGBModel, ClassifierMixin):
xgb_options['num_class'] = len(y_values) xgb_options['num_class'] = len(y_values)
else: else:
xgb_options = self.get_xgb_params() xgb_options = self.get_xgb_params()
self._le = LabelEncoder().fit(y) self._le = LabelEncoder().fit(y)
training_labels = self._le.transform(y) training_labels = self._le.transform(y)
if sample_weight is not None: if sample_weight is not None:
trainDmatrix = DMatrix(X, label=training_labels, weight=sample_weight) trainDmatrix = DMatrix(X, label=training_labels, weight=sample_weight)
else: else:
trainDmatrix = DMatrix(X, label=training_labels) trainDmatrix = DMatrix(X, label=training_labels)
self._Booster = train(xgb_options, trainDmatrix, self.n_rounds) self._Booster = train(xgb_options, trainDmatrix, self.n_rounds)
return self return self
def predict(self, X): def predict(self, X):
testDmatrix = DMatrix(X) testDmatrix = DMatrix(X)
class_probs = self._Booster.predict(testDmatrix) class_probs = self._Booster.predict(testDmatrix)
@ -825,7 +825,7 @@ class XGBClassifier(XGBModel, ClassifierMixin):
column_indexes = np.repeat(0, X.shape[0]) column_indexes = np.repeat(0, X.shape[0])
column_indexes[class_probs > 0.5] = 1 column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes) return self._le.inverse_transform(column_indexes)
def predict_proba(self, X): def predict_proba(self, X):
testDmatrix = DMatrix(X) testDmatrix = DMatrix(X)
class_probs = self._Booster.predict(testDmatrix) class_probs = self._Booster.predict(testDmatrix)
@ -834,9 +834,8 @@ class XGBClassifier(XGBModel, ClassifierMixin):
else: else:
classone_probs = class_probs classone_probs = class_probs
classzero_probs = 1.0 - classone_probs classzero_probs = 1.0 - classone_probs
return np.vstack((classzero_probs,classone_probs)).transpose() return np.vstack((classzero_probs, classone_probs)).transpose()
class XGBRegressor(XGBModel, RegressorMixin):
class XGBRegressor(XGBModel, RegressorMixin):
pass pass