ML之Xgboost：利用Xgboost模型对数据集(比马印第安人糖尿病)进行二分类预测(5年内是否患糖尿病)
目录
输出结果
设计思路
核心代码
输出结果
X_train内容： [[ 3. 102. 44. ... 30.8 0.4 26. ] [ 1. 77. 56. ... 33.3 1.251 24. ] [ 9. 124. 70. ... 35.4 0.282 34. ] ... [ 0. 57. 60. ... 21.7 0.735 67. ] [ 1. 105. 58. ... 24.3 0.187 21. ] [ 8. 179. 72. ... 32.7 0.719 36. ]] y_train内容： [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 0. 0. 1. 1. 1. 1. 1. 0. 1. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 1. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 0. 1. 1. 1. 0. 0. 1. 0. 1. 0. 1. 0. 0. 1. 1. 0. 1. 1. 1. 1. 0. 0. 0. 0. 0. 1. 1. 1. 0. 1. 0. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 1. 1. 1. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 1. 1. 1. 0. 0. 0. 1. 0. 0. 1. 0. 1. 0. 1. 1. 1. 0. 1. 0. 0. 1. 0. 0. 1. 0. 1. 1. 0. 1. 0. 0. 1. 1. 1. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1. 1. 0. 1. 0. 0. 0. 0. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 1. 1. 0. 0. 0. 0. 1. 1. 1. 1. 0. 0. 1. 1. 0. 0. 0. 1. 1. 1. 0. 0. 0. 1. 1. 0. 1. 0. 0. 1. 1. 0. 0. 0. 0. 0. 0. 1. 0. 1. 0. 0. 1. 0. 0. 0. 1. 1. 0. 0. 0. 1. 0. 0. 0. 1. 0. 1. 0. 1. 1. 0. 1. 0. 0. 0. 1. 1. 0. 0. 1.]
设计思路
核心代码
class XGBClassifier Found at: xgboost.sklearn
class XGBClassifier(XGBModel, XGBClassifierBase):
# pylint: disable=missing-docstring,too-many-arguments,invalid-name
__doc__ = "Implementation of the scikit-learn API for XGBoost classification.\n\n" + '\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", booster='gbtree',
n_jobs=1, nthread=None, gamma=0, min_child_weight=1,
max_delta_step=0, subsample=1, colsample_bytree=1, colsample_bylevel=1,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1,
base_score=0.5, random_state=0, seed=None, missing=None, **kwargs):
super(XGBClassifier, self).__init__(max_depth, learning_rate, n_estimators, silent,
objective, booster, n_jobs, nthread, gamma, min_child_weight, max_delta_step, subsample,
colsample_bytree, colsample_bylevel, reg_alpha, reg_lambda, scale_pos_weight,
base_score, random_state, seed, missing, **kwargs)
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=
# pylint: disable = attribute-defined-outside-init,arguments-differ
None):
"""
Fit gradient boosting classifier
Parameters
----------
X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
Weight for each instance
eval_set : list, optional
A list of (X, y) pairs to use as a validation set for
early-stopping
sample_weight_eval_set : list, optional
A list of the form [L_1, L_2, ..., L_n], where each L_i is a list of
instance weights on the i-th validation set.
eval_metric : str, callable, optional
If a str, should be a built-in evaluation metric to use. See
doc/parameter.rst. If callable, a custom evaluation metric. The call
signature is func(y_predicted, y_true) where y_true will be a
DMatrix object such that you may need to call the get_label
method. It must return a str, value pair where the str is a name
for the evaluation and value is the value of the evaluation
function. This objective is always minimized.
early_stopping_rounds : int, optional
Activates early stopping. Validation error needs to decrease at
least everyround(s) to continue training.
Requires at least one item in evals. If there's more than one,
will use the last. If early stopping occurs, the model will have
three additional fields: bst.best_score, bst.best_iteration and
bst.best_ntree_limit (bst.best_ntree_limit is the ntree_limit parameter
default value in predict method if not any other value is specified).
(Use bst.best_ntree_limit to get the correct value if num_parallel_tree
and/or num_class appears in the parameters)
verbose : bool
If `verbose` and an evaluation set is used, writes the evaluation
metric measured on the validation set to stderr.
xgb_model : str
file name of stored xgb model or 'Booster' instance Xgb model to be
loaded before training (allows training continuation).
callbacks : list of callback functions
List of callback functions that are applied at end of each iteration.
It is possible to use predefined callbacks by using :ref:`callback_api`.
Example:
.. code-block:: python
[xgb.callback.reset_learning_rate(custom_rates)]
"""
evals_result = {}
self.classes_ = np.unique(y)
self.n_classes_ = len(self.classes_)
xgb_options = self.get_xgb_params()
if callable(self.objective):
obj = _objective_decorator(self.objective)
# Use default value. Is it really not used ?
xgb_options["objective"] = "binary:logistic"
else:
obj = None
if self.n_classes_ > 2:
# Switch to using a multiclass objective in the underlying XGB instance
xgb_options["objective"] = "multi:softprob"
xgb_options['num_class'] = self.n_classes_
feval = eval_metric if callable(eval_metric) else None
if eval_metric is not None:
if callable(eval_metric):
eval_metric = None
else:
xgb_options.update({"eval_metric":eval_metric})
self._le = XGBLabelEncoder().fit(y)
training_labels = self._le.transform(y)
if eval_set is not None:
if sample_weight_eval_set is None:
sample_weight_eval_set = [None] * len(eval_set)
evals = list(
DMatrix(eval_set[i][0], label=self._le.transform(eval_set[i][1]),
missing=self.missing, weight=sample_weight_eval_set[i],
nthread=self.n_jobs) for
i in range(len(eval_set)))
nevals = len(evals)
eval_names = ["validation_{}".format(i) for i in range(nevals)]
evals = list(zip(evals, eval_names))
else:
evals = ()
self._features_count = X.shape[1]
if sample_weight is not None:
train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
missing=self.missing, nthread=self.n_jobs)
else:
train_dmatrix = DMatrix(X, label=training_labels,
missing=self.missing, nthread=self.n_jobs)
self._Booster = train(xgb_options, train_dmatrix, self.n_estimators,
evals=evals,
early_stopping_rounds=early_stopping_rounds,
evals_result=evals_result, obj=obj, feval=feval,
verbose_eval=verbose, xgb_model=xgb_model,
callbacks=callbacks)
self.objective = xgb_options["objective"]
if evals_result:
for val in evals_result.items():
evals_result_key = list(val[1].keys())[0]
evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
self.evals_result_ = evals_result
if early_stopping_rounds is not None:
self.best_score = self._Booster.best_score
self.best_iteration = self._Booster.best_iteration
self.best_ntree_limit = self._Booster.best_ntree_limit
return self
def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
"""
Predict with `data`.
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
of model object and then call ``predict()``.
.. note:: Using ``predict()`` with DART booster
If the booster object is DART type, ``predict()`` will perform dropouts, i.e. only
some of the trees will be evaluated. This will produce incorrect results if ``data`` is
not the training data. To obtain correct results on test sets, set ``ntree_limit`` to
a nonzero value, e.g.
.. code-block:: python
preds = bst.predict(dtest, ntree_limit=num_round)
Parameters
----------
data : DMatrix
The dmatrix storing the input.
output_margin : bool
Whether to output the raw untransformed margin value.
ntree_limit : int
Limit number of trees in the prediction; defaults to best_ntree_limit if defined
(i.e. it has been trained with early stopping), otherwise 0 (use all trees).
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are identical.
Otherwise, it is assumed that the feature_names are the same.
Returns
-------
prediction : numpy array
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix,
output_margin=output_margin,
ntree_limit=ntree_limit,
validate_features=validate_features)
if output_margin:
# If output_margin is active, simply return the scores
return class_probs
if len(class_probs.shape) > 1:
column_indexes = np.argmax(class_probs, axis=1)
else:
column_indexes = np.repeat(0, class_probs.shape[0])
column_indexes[class_probs > 0.5] = 1
return self._le.inverse_transform(column_indexes)
def predict_proba(self, data, ntree_limit=None, validate_features=True):
"""
Predict the probability of each `data` example being of a given class.
.. note:: This function is not thread safe
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
of model object and then call predict
Parameters
----------
data : DMatrix
The dmatrix storing the input.
ntree_limit : int
Limit number of trees in the prediction; defaults to best_ntree_limit if defined
(i.e. it has been trained with early stopping), otherwise 0 (use all trees).
validate_features : bool
When this is True, validate that the Booster's and data's feature_names are identical.
Otherwise, it is assumed that the feature_names are the same.
Returns
-------
prediction : numpy array
a numpy array with the probability of each data example being of a given class.
"""
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix,
ntree_limit=ntree_limit,
validate_features=validate_features)
if self.objective == "multi:softprob":
return class_probs
else:
classone_probs = class_probs
classzero_probs = 1.0 - classone_probs
return np.vstack((classzero_probs, classone_probs)).transpose()
def evals_result(self):
"""Return the evaluation results.
If **eval_set** is passed to the `fit` function, you can call
``evals_result()`` to get evaluation results for all passed **eval_sets**.
When **eval_metric** is also passed to the `fit` function, the
**evals_result** will contain the **eval_metrics** passed to the `fit` function.
Returns
-------
evals_result : dictionary
Example
-------
.. code-block:: python
param_dist = {'objective':'binary:logistic', 'n_estimators':2}
clf = xgb.XGBClassifier(**param_dist)
clf.fit(X_train, y_train,
eval_set=[(X_train, y_train), (X_test, y_test)],
eval_metric='logloss',
verbose=True)
evals_result = clf.evals_result()
The variable **evals_result** will contain
.. code-block:: python
{'validation_0': {'logloss': ['0.604835', '0.531479']},
'validation_1': {'logloss': ['0.41965', '0.17686']}}
"""
if self.evals_result_:
evals_result = self.evals_result_
else:
raise XGBoostError('No results.')
return evals_result
