def fit_increment(self, X, y, num_boost_round=1, params=None): trainDmatrix = DMatrix(X, label=y, nthread=self.n_jobs, missing=self.missing) extra_params = params params = self.get_xgb_params() if extra_params is not None: for k, v in extra_params.items(): params[k] = v if "n_estimators" in params: params.pop("n_estimators") if callable(self.objective): obj = _objective_decorator(self.objective) params["objective"] = "reg:linear" else: obj = None if "_Booster" not in dir(self) or self._Booster is None: self._Booster = train(params=params, dtrain=trainDmatrix, num_boost_round=num_boost_round, obj=obj) else: self._Booster = train(params=params, dtrain=trainDmatrix, num_boost_round=num_boost_round, obj=obj, xgb_model=self._Booster) return self
def my_train(clf, X_train, y_train, n_estimators): xgb_options = clf.get_xgb_params() xgb_options['num_class'] = 3 xgb_options.update({"eval_metric":'mlogloss'}) train_dmatrix = DMatrix(csr_matrix(X_train), label=y_train) train(xgb_options, train_dmatrix, n_estimators)
def fit_increment(self, X, y, num_boost_round=1, params=None): trainDmatrix = DMatrix(X, label=y, nthread=self.n_jobs, missing=self.missing) extra_params = params params = { 'objective': 'reg:squarederror', 'learning_rate': None, 'max_depth': None, 'min_child_weight': None, 'n_jobs': None } print(params, extra_params) if extra_params is not None: for k, v in extra_params.items(): params[k] = v if callable(self.objective): obj = _objective_decorator(self.objective) params["objective"] = "reg:linear" else: obj = None if self._Booster is None: self._Booster = train(params=params, dtrain=trainDmatrix, num_boost_round=num_boost_round, obj=obj) else: self._Booster = train(params=params, dtrain=trainDmatrix, num_boost_round=num_boost_round, obj=obj, xgb_model=self._Booster) return self
def fit(self, X, y, # sample_weight=None, eval_metric=None, early_stopping_rounds=None, verbose=True, nfold=3, seed=1): xgb_options = self.get_xgb_params() self._le = LabelEncoder().fit(y) training_labels = self._le.transform(y) train_dmatrix = DMatrix(X, label=training_labels, missing=self.missing) evaluation_history = cv( xgb_options, train_dmatrix, num_boost_round=self.n_estimators, nfold=nfold, stratified=True, # folds=None, metrics=(eval_metric), # obj=None, # feval=None, # maximize=False, early_stopping_rounds=early_stopping_rounds, # fpreproc=None, # as_pandas=True, verbose_eval=verbose, show_stdv=False, seed=seed ) best_iteration = evaluation_history.index[-1] self._Booster = train( xgb_options, train_dmatrix, num_boost_round=best_iteration, verbose_eval=verbose, ) return self
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable = attribute-defined-outside-init,arguments-differ """ 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 eval_metric : str, callable, optional If a str, should be a built-in evaluation metric to use. See doc/parameter.md. 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 every <early_stopping_rounds> round(s) to continue training. 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: bst.best_score and bst.best_iteration. verbose : bool If `verbose` and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. """ evals_result = {} self.classes_ = list(np.unique(y)) self.n_classes_ = len(self.classes_) if self.n_classes_ > 2: # Switch to using a multiclass objective in the underlying XGB instance self.objective = "multi:softprob" xgb_options = self.get_xgb_params() xgb_options['num_class'] = self.n_classes_ else: xgb_options = self.get_xgb_params() 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}) if eval_set is not None: # TODO: use sample_weight if given? evals = list(DMatrix(x[0], label=x[1]) for x in eval_set) nevals = len(evals) eval_names = ["validation_{}".format(i) for i in range(nevals)] evals = list(zip(evals, eval_names)) else: evals = () self._le = LabelEncoder().fit(y) training_labels = self._le.transform(y) if sample_weight is not None: train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight, missing=self.missing) else: train_dmatrix = DMatrix(X, label=training_labels, missing=self.missing) self._Booster = train(xgb_options, train_dmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, feval=feval, verbose_eval=verbose) 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 return self
def fit(self, X, y, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, obj=None, learning_rates=None): # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init """ Fit the gradient boosting model Parameters ---------- X : array_like Feature matrix y : array_like Labels eval_set : list, optional A list of (X, y) tuple pairs to use as a validation set for early-stopping eval_metric : str, callable, optional If a str, should be a built-in evaluation metric to use. See doc/parameter.md. 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 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. 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: bst.best_score and bst.best_iteration. verbose : bool If `verbose` and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. """ trainDmatrix = DMatrix(X, label=y, missing=self.missing) evals_result = {} if eval_set is not None: evals = list(DMatrix(x[0], label=x[1]) for x in eval_set) evals = list(zip(evals, ["validation_{}".format(i) for i in range(len(evals))])) else: evals = () params = self.get_xgb_params() feval = eval_metric if callable(eval_metric) else None if eval_metric is not None: if callable(eval_metric): eval_metric = None else: params.update({'eval_metric': eval_metric}) self._Booster = train(params, trainDmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, feval=feval, verbose_eval=verbose, obj=obj, learning_rates=learning_rates) 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 return self
def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True): # pylint: disable = attribute-defined-outside-init,arguments-differ """ 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 eval_metric : str, callable, optional If a str, should be a built-in evaluation metric to use. See doc/parameter.md. 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 every <early_stopping_rounds> round(s) to continue training. 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: bst.best_score and bst.best_iteration. verbose : bool If `verbose` and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. """ evals_result = {} self.classes_ = list(np.unique(y)) self.n_classes_ = len(self.classes_) if self.n_classes_ > 2: # Switch to using a multiclass objective in the underlying XGB instance self.objective = "multi:softprob" xgb_options = self.get_xgb_params() xgb_options['num_class'] = self.n_classes_ else: xgb_options = self.get_xgb_params() 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}) if eval_set is not None: # TODO: use sample_weight if given? evals = list(DMatrix(x[0], label=x[1]) for x in eval_set) nevals = len(evals) eval_names = ["validation_{}".format(i) for i in range(nevals)] evals = list(zip(evals, eval_names)) else: evals = () self._le = LabelEncoder().fit(y) training_labels = self._le.transform(y) if sample_weight is not None: train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight, missing=self.missing) else: train_dmatrix = DMatrix(X, label=training_labels, missing=self.missing) self._Booster = train(xgb_options, train_dmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, feval=feval, verbose_eval=verbose) 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 return self
def fit(self, X, y, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, obj=None, learning_rates=None): # pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init """ Fit the gradient boosting model Parameters ---------- X : array_like Feature matrix y : array_like Labels eval_set : list, optional A list of (X, y) tuple pairs to use as a validation set for early-stopping eval_metric : str, callable, optional If a str, should be a built-in evaluation metric to use. See doc/parameter.md. 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 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. 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: bst.best_score and bst.best_iteration. verbose : bool If `verbose` and an evaluation set is used, writes the evaluation metric measured on the validation set to stderr. """ trainDmatrix = DMatrix(X, label=y, missing=self.missing) evals_result = {} if eval_set is not None: evals = list(DMatrix(x[0], label=x[1]) for x in eval_set) evals = list( zip(evals, ["validation_{}".format(i) for i in range(len(evals))])) else: evals = () params = self.get_xgb_params() feval = eval_metric if callable(eval_metric) else None if eval_metric is not None: if callable(eval_metric): eval_metric = None else: params.update({'eval_metric': eval_metric}) self._Booster = train(params, trainDmatrix, self.n_estimators, evals=evals, early_stopping_rounds=early_stopping_rounds, evals_result=evals_result, feval=feval, verbose_eval=verbose, obj=obj, learning_rates=learning_rates) 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 return self
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=None): # pylint: disable = attribute-defined-outside-init,arguments-differ """ 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 every <early_stopping_rounds> round(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.get_num_boosting_rounds(), 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