XGBoost-CPU

import xgboost as xgb

# Core functionality
from xgboost import DMatrix, Booster, train, cv

# Scikit-learn interface
from xgboost import XGBClassifier, XGBRegressor, XGBRanker

# Distributed computing
from xgboost import dask as dxgb  # Dask integration
from xgboost import spark as spark_xgb  # Spark integration

# Utilities
from xgboost import plot_importance, plot_tree
from xgboost import get_config, set_config

import xgboost as xgb
import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Create sample data
X, y = make_classification(n_samples=1000, n_features=20, n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Method 1: Using XGBoost's native API
dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

params = {
    'objective': 'binary:logistic',
    'eval_metric': 'logloss',
    'max_depth': 6,
    'learning_rate': 0.1
}

model = xgb.train(params, dtrain, num_boost_round=100, 
                  evals=[(dtrain, 'train'), (dtest, 'test')])

# Make predictions
y_pred = model.predict(dtest)

# Method 2: Using scikit-learn interface
from xgboost import XGBClassifier

clf = XGBClassifier(objective='binary:logistic', max_depth=6, 
                   learning_rate=0.1, n_estimators=100)
clf.fit(X_train, y_train)
y_pred_sklearn = clf.predict_proba(X_test)[:, 1]

# Visualize feature importance
xgb.plot_importance(model, max_num_features=10)

class DMatrix:
    def __init__(self, data, label=None, *, weight=None, base_margin=None, 
                 missing=None, silent=False, feature_names=None, 
                 feature_types=None, nthread=None, group=None, qid=None, 
                 label_lower_bound=None, label_upper_bound=None, 
                 feature_weights=None, enable_categorical=False):
        """Optimized data matrix for XGBoost training and prediction."""

class Booster:
    def __init__(self, params=None, cache=(), model_file=None):
        """XGBoost model containing training, prediction, and evaluation routines."""
    
    def predict(self, data, *, output_margin=False, pred_leaf=False, 
                pred_contribs=False, approx_contribs=False, 
                pred_interactions=False, validate_features=True, 
                training=False, iteration_range=(0, 0), strict_shape=False):
        """Make predictions using the trained model."""

class QuantileDMatrix:
    def __init__(self, data, label=None, *, ref=None, **kwargs):
        """Memory-efficient DMatrix variant using quantized data."""

def train(params, dtrain, num_boost_round=10, evals=(), obj=None, 
          maximize=None, early_stopping_rounds=None, evals_result=None, 
          verbose_eval=True, xgb_model=None, callbacks=None, custom_metric=None):
    """Train a booster with given parameters."""

def cv(params, dtrain, num_boost_round=10, nfold=3, stratified=False, 
       folds=None, metrics=(), obj=None, maximize=None, 
       early_stopping_rounds=None, fpreproc=None, as_pandas=True, 
       verbose_eval=None, show_stdv=True, seed=0, callbacks=None, 
       shuffle=True, custom_metric=None):
    """Cross-validation with given parameters."""

class XGBClassifier:
    def __init__(self, *, max_depth=6, learning_rate=0.3, n_estimators=100, 
                 objective=None, booster='gbtree', tree_method='auto', 
                 n_jobs=None, gamma=0, min_child_weight=1, max_delta_step=0, 
                 subsample=1, colsample_bytree=1, reg_alpha=0, reg_lambda=1, 
                 scale_pos_weight=1, base_score=None, random_state=None, 
                 missing=np.nan, **kwargs):
        """XGBoost classifier following scikit-learn API."""
    
    def fit(self, X, y, *, sample_weight=None, base_margin=None, 
            eval_set=None, verbose=True, xgb_model=None, 
            sample_weight_eval_set=None, base_margin_eval_set=None, 
            feature_weights=None):
        """Fit the model to training data."""
    
    def predict_proba(self, X, *, validate_features=True, base_margin=None, 
                      iteration_range=None):
        """Predict class probabilities."""

class XGBRegressor:
    """XGBoost regressor following scikit-learn API."""

class XGBRanker:
    """XGBoost ranker for learning-to-rank tasks."""

# Dask integration
from xgboost import dask as dxgb

def dxgb.train(client, params, dtrain, num_boost_round=10, evals=(), 
               obj=None, maximize=None, early_stopping_rounds=None, 
               evals_result=None, verbose_eval=True, xgb_model=None, 
               callbacks=None):
    """Train XGBoost model using Dask."""

class dxgb.DaskXGBClassifier:
    """Dask-distributed XGBoost classifier."""

# Spark integration  
from xgboost import spark as spark_xgb

class spark_xgb.SparkXGBClassifier:
    """PySpark XGBoost classifier."""

def plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, 
                   title='Feature importance', xlabel='F score', 
                   ylabel='Features', fmap='', importance_type='weight', 
                   max_num_features=None, grid=True, show_values=True, 
                   values_format='{v}'):
    """Plot feature importance based on fitted trees."""

def plot_tree(booster, fmap='', num_trees=0, rankdir=None, ax=None, **kwargs):
    """Plot specified tree using matplotlib."""

def set_config(**new_config):
    """Set global XGBoost configuration."""

def get_config():
    """Get current global configuration values."""

from typing import Union, Optional, List, Dict, Any, Tuple, Callable
import numpy as np
import pandas as pd

# Common type aliases used throughout XGBoost
ArrayLike = Union[List, np.ndarray, pd.DataFrame, pd.Series]
PathLike = Union[str, os.PathLike]
Metric = Union[str, List[str], Callable]
Objective = Union[str, Callable]
EvalSet = List[Tuple[DMatrix, str]]
FeatureNames = List[str]
FeatureTypes = List[str]
FloatCompatible = Union[float, np.float32, np.float64]

# Callback types
from xgboost.callback import TrainingCallback
EvalsLog = Dict[str, Dict[str, List[float]]]
CallbackList = Optional[List[TrainingCallback]]

# Data splitting modes  
from enum import IntEnum

class DataSplitMode(IntEnum):
    """Supported data split mode for DMatrix."""
    ROW = 0  # Split by rows (default)
    COL = 1  # Split by columns

# Collective communication operations
class Op(IntEnum):
    """Supported operations for allreduce."""
    MAX = 0
    MIN = 1
    SUM = 2
    BITWISE_AND = 3
    BITWISE_OR = 4
    BITWISE_XOR = 5