snowflake.ml.modeling.xgboost.XGBClassifier¶

class snowflake.ml.modeling.xgboost.XGBClassifier(*, objective='binary:logistic', use_label_encoder=None, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None, **kwargs)¶

Bases: BaseTransformer

Implementation of the scikit-learn API for XGBoost classification For more details on this class, see xgboost.XGBClassifier

n_estimators: int
Number of boosting rounds.

max_depth: Optional[int]
Maximum tree depth for base learners.

max_leaves :
Maximum number of leaves; 0 indicates no limit.

max_bin :
If using histogram-based algorithm, maximum number of bins per feature

grow_policy :
Tree growing policy. 0: favor splitting at nodes closest to the node, i.e. grow depth-wise. 1: favor splitting at nodes with highest loss change.

learning_rate: Optional[float]
Boosting learning rate (xgb’s “eta”)

verbosity: Optional[int]
The degree of verbosity. Valid values are 0 (silent) - 3 (debug).

objective: typing.Union[str, typing.Callable[[numpy.ndarray, numpy.ndarray], typing.Tuple[numpy.ndarray, numpy.ndarray]], NoneType]
Specify the learning task and the corresponding learning objective or a custom objective function to be used (see note below).

booster: Optional[str]
Specify which booster to use: gbtree, gblinear or dart.

tree_method: Optional[str]
Specify which tree method to use. Default to auto. If this parameter is set to default, XGBoost will choose the most conservative option available. It’s recommended to study this option from the parameters document tree method

n_jobs: Optional[int]
Number of parallel threads used to run xgboost. When used with other Scikit-Learn algorithms like grid search, you may choose which algorithm to parallelize and balance the threads. Creating thread contention will significantly slow down both algorithms.

gamma: Optional[float]
(min_split_loss) Minimum loss reduction required to make a further partition on a leaf node of the tree.

min_child_weight: Optional[float]
Minimum sum of instance weight(hessian) needed in a child.

max_delta_step: Optional[float]
Maximum delta step we allow each tree’s weight estimation to be.

subsample: Optional[float]
Subsample ratio of the training instance.

sampling_method :

Sampling method. Used only by gpu_hist tree method.

uniform: select random training instances uniformly.

gradient_based select random training instances with higher probability when the gradient and hessian are larger. (cf. CatBoost)

colsample_bytree: Optional[float]
Subsample ratio of columns when constructing each tree.

colsample_bylevel: Optional[float]
Subsample ratio of columns for each level.

colsample_bynode: Optional[float]
Subsample ratio of columns for each split.

reg_alpha: Optional[float]
L1 regularization term on weights (xgb’s alpha).

reg_lambda: Optional[float]
L2 regularization term on weights (xgb’s lambda).

scale_pos_weight: Optional[float]
Balancing of positive and negative weights.

base_score: Optional[float]
The initial prediction score of all instances, global bias.

random_state: Optional[Union[numpy.random.RandomState, int]]
Random number seed.

Using gblinear booster with shotgun updater is nondeterministic as it uses Hogwild algorithm.

missing: float, default np.nan
Value in the data which needs to be present as a missing value.

num_parallel_tree: Optional[int]
Used for boosting random forest.

monotone_constraints: Optional[Union[Dict[str, int], str]]
Constraint of variable monotonicity. See tutorial for more information.

interaction_constraints: Optional[Union[str, List[Tuple[str]]]]
Constraints for interaction representing permitted interactions. The constraints must be specified in the form of a nested list, e.g. [[0, 1], [2, 3, 4]], where each inner list is a group of indices of features that are allowed to interact with each other. See tutorial for more information

importance_type: Optional[str]
The feature importance type for the feature_importances_ property:

For tree model, it’s either “gain”, “weight”, “cover”, “total_gain” or “total_cover”.

For linear model, only “weight” is defined and it’s the normalized coefficients without bias.

gpu_id: Optional[int]
Device ordinal.

validate_parameters: Optional[bool]
Give warnings for unknown parameter.

predictor: Optional[str]
Force XGBoost to use specific predictor, available choices are [cpu_predictor, gpu_predictor].

enable_categorical: bool

Experimental support for categorical data. When enabled, cudf/pandas.DataFrame should be used to specify categorical data type. Also, JSON/UBJSON serialization format is required.

feature_types: FeatureTypes

Used for specifying feature types without constructing a dataframe. See DMatrix for details.

max_cat_to_onehot: Optional[int]

A threshold for deciding whether XGBoost should use one-hot encoding based split for categorical data. When number of categories is lesser than the threshold then one-hot encoding is chosen, otherwise the categories will be partitioned into children nodes. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

max_cat_threshold: Optional[int]

Maximum number of categories considered for each split. Used only by partition-based splits for preventing over-fitting. Also, enable_categorical needs to be set to have categorical feature support. See Categorical Data and cat-param for details.

eval_metric: Optional[Union[str, List[str], Callable]]

Metric used for monitoring the training result and early stopping. It can be a string or list of strings as names of predefined metric in XGBoost (See doc/parameter.rst), one of the metrics in sklearn.metrics, or any other user defined metric that looks like sklearn.metrics.

If custom objective is also provided, then custom metric should implement the corresponding reverse link function.

Unlike the scoring parameter commonly used in scikit-learn, when a callable object is provided, it’s assumed to be a cost function and by default XGBoost will minimize the result during early stopping.

For advanced usage on Early stopping like directly choosing to maximize instead of minimize, see xgboost.callback.EarlyStopping.

See Custom Objective and Evaluation Metric for more.

This parameter replaces eval_metric in fit() method. The old one receives un-transformed prediction regardless of whether custom objective is being used.

from sklearn.datasets import load_diabetes from sklearn.metrics import mean_absolute_error X, y = load_diabetes(return_X_y=True) reg = xgb.XGBRegressor(

tree_method=”hist”, eval_metric=mean_absolute_error,

) reg.fit(X, y, eval_set=[(X, y)])

early_stopping_rounds: Optional[int]

Activates early stopping. Validation metric needs to improve at least once in every early_stopping_rounds round(s) to continue training. Requires at least one item in eval_set in fit().

The method returns the model from the last iteration (not the best one). If there’s more than one item in eval_set, the last entry will be used for early stopping. If there’s more than one metric in eval_metric, the last metric will be used for early stopping.

If early stopping occurs, the model will have three additional fields: best_score, best_iteration and best_ntree_limit.

This parameter replaces early_stopping_rounds in fit() method.

callbacks: Optional[List[TrainingCallback]]
List of callback functions that are applied at end of each iteration. It is possible to use predefined callbacks by using Callback API.

States in callback are not preserved during training, which means callback objects can not be reused for multiple training sessions without reinitialization or deepcopy.

for params in parameters_grid:
# be sure to (re)initialize the callbacks before each run callbacks = [xgb.callback.LearningRateScheduler(custom_rates)] xgboost.train(params, Xy, callbacks=callbacks)

kwargs: dict, optional
Keyword arguments for XGBoost Booster object. Full documentation of parameters can be found here. Attempting to set a parameter via the constructor args and **kwargs dict simultaneously will result in a TypeError.

**kwargs is unsupported by scikit-learn. We do not guarantee that parameters passed via this argument will interact properly with scikit-learn.

A custom objective function can be provided for the objective parameter. In this case, it should have the signature objective(y_true, y_pred) -> grad, hess:

y_true: array_like of shape [n_samples]
The target values

y_pred: array_like of shape [n_samples]
The predicted values

grad: array_like of shape [n_samples]
The value of the gradient for each sample point.

hess: array_like of shape [n_samples]
The value of the second derivative for each sample point

input_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols and sample-weight_col parameters are considered input columns.
label_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
output_cols: Optional[Union[str, List[str]]]: A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
sample_weight_col: Optional[str]: A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
drop_input_cols: Optional[bool], default=False: If set, the response of predict(), transform() methods will not contain input columns.

Methods

`fit`(dataset)	Fit gradient boosting classifier For more details on this function, see xgboost.XGBClassifier.fit
`predict`(dataset)	Predict with X For more details on this function, see xgboost.XGBClassifier.predict
`predict_proba`(dataset[, output_cols_prefix])	Predict the probability of each X example being of a given class For more details on this function, see xgboost.XGBClassifier.predict_proba
`score`(dataset)	Return the mean accuracy on the given test data and labels For more details on this function, see xgboost.XGBClassifier.score
`set_input_cols`(input_cols)	Input columns setter.
`to_xgboost`()	Get xgboost.XGBClassifier object.

Attributes

model_signatures

Returns model signature of current class.