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 predict_proba(self, data, output_margin=False, ntree_limit=0): test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs) class_probs = self.get_booster().predict(test_dmatrix, output_margin=output_margin, ntree_limit=ntree_limit) 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 predict(self, data, output_margin=False, ntree_limit=0): test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs) class_probs = self.get_booster().predict(test_dmatrix, output_margin=output_margin, ntree_limit=ntree_limit) 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 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_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_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=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 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 three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (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). """ 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: # TODO: use sample_weight if given? evals = list( DMatrix(x[0], label=self._le.transform(x[1]), missing=self.missing, nthread=self.n_jobs) 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._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, # Only the last kwarg in of this call was # changed in this file!!! verbose_eval=verbose, xgb_model=xgb_model) 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 fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None, early_stopping_rounds=None, verbose=True, xgb_model=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 sample_weight : array_like instance weights 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 three additional fields: bst.best_score, bst.best_iteration and bst.best_ntree_limit. (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). """ if sample_weight is not None: trainDmatrix = DMatrix(X, label=y, weight=sample_weight, missing=self.missing, nthread=self.n_jobs) else: trainDmatrix = DMatrix(X, label=y, missing=self.missing, nthread=self.n_jobs) evals_result = {} if eval_set is not None: evals = list( DMatrix(x[0], label=x[1], missing=self.missing, nthread=self.n_jobs) 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() if callable(self.objective): obj = _objective_decorator(self.objective) params["objective"] = "reg:linear" else: obj = None 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, obj=obj, feval=feval, verbose_eval=verbose, xgb_model=xgb_model) 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 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 predict(self, data, output_margin=False, ntree_limit=0): # pylint: disable=missing-docstring,invalid-name test_dmatrix = DMatrix(data, missing=self.missing) return self.booster().predict(test_dmatrix, output_margin=output_margin, ntree_limit=ntree_limit)
async def predict(msg, send, context): line_number, ch, line_content = msg while len(context.doc) <= line_number: context.doc.append('') context.doc[line_number] = line_content doc = '\n'.join(context.doc) context.content = doc if is_parameter_of_def(context.doc, line_number, ch): # don't make prediction if it is defining function parameters await send( 'Prediction', { 'line': line_number, 'ch': ch, 'result': [], 'parameterDefinition': True }) return try: with Timer(f'Prediction ({line_number}, {ch})'): j = jedi.Script(doc, line_number + 1, ch, str(context.path)) completions = j.completions() offset = 0 with Timer(f'Rest ({line_number}, {ch})'): if completions: context.currentCompletions = { completion.name: completion for completion in completions } completion = completions[0] offset = len(completion.complete) - len(completion.name) feature_extractor = context.feature_extractor feature_extractor.extract_online(completions, line_content, line_number, ch, context.doc, j.call_signatures()) # scores = model.predict_proba(feature_extractor.X)[:, 1] * 1000 d_test = DMatrix(feature_extractor.X) scores = model.predict( d_test, output_margin=True, validate_features=False) * 1000 # c.name_with_symbol is not reliable # e.g. def something(path): len(p|) # will return "path=" result = [ PredictionRow(c=c.name, t=c.type, s=int(s), p=c.name_with_symbols[len(c.name):]) for c, s in zip(completions, scores) ] else: result = [] await send('Prediction', { 'line': line_number, 'ch': ch, 'offset': offset, 'result': result, }) context.pos = (line_number, ch) except Exception as e: logger.exception(e) await send('RequestFullSync', None)