Machine Learning

# Core sklearn submodules - all classes and functions available via dynamic import
from xorbits.sklearn import cluster        # Clustering algorithms
from xorbits.sklearn import datasets       # Dataset loading utilities  
from xorbits.sklearn import decomposition  # Matrix decomposition algorithms
from xorbits.sklearn import ensemble       # Ensemble methods
from xorbits.sklearn import linear_model   # Linear models
from xorbits.sklearn import metrics        # Model evaluation metrics
from xorbits.sklearn import model_selection # Model selection and validation
from xorbits.sklearn import neighbors      # Nearest neighbors algorithms
from xorbits.sklearn import preprocessing  # Data preprocessing
from xorbits.sklearn import semi_supervised # Semi-supervised learning

class KMeans:
    """
    K-Means clustering algorithm with distributed computing support.
    
    Provides the same API as sklearn.cluster.KMeans but enables
    clustering of large datasets across multiple workers.
    """

# XGBoost classes and functions available via dynamic import
class DMatrix:
    """
    Data matrix for XGBoost with distributed computing support.
    
    Equivalent to xgboost.DMatrix but works with Xorbits
    distributed arrays and DataFrames.
    """

def train(params, dtrain, **kwargs):
    """
    Train XGBoost model with distributed data.
    
    Parameters:
    - params: dict, XGBoost parameters
    - dtrain: DMatrix, training data
    - **kwargs: Additional training parameters
    
    Returns:
    - Trained XGBoost model
    """

def predict(model, dtest, **kwargs):
    """
    Make predictions with XGBoost model.
    
    Parameters:
    - model: Trained XGBoost model
    - dtest: DMatrix, test data
    - **kwargs: Additional prediction parameters
    
    Returns:
    - Predictions array
    """

# LightGBM classes and functions available via dynamic import
class Dataset:
    """
    LightGBM dataset with distributed computing support.
    
    Equivalent to lightgbm.Dataset but works with Xorbits
    distributed arrays and DataFrames.
    """

def train(params, train_set, **kwargs):
    """
    Train LightGBM model with distributed data.
    
    Parameters:
    - params: dict, LightGBM parameters
    - train_set: Dataset, training data
    - **kwargs: Additional training parameters
    
    Returns:
    - Trained LightGBM model
    """

import xorbits
import xorbits.pandas as pd
import xorbits.numpy as np
from xorbits.sklearn.cluster import KMeans
from xorbits.sklearn.model_selection import train_test_split
from xorbits.sklearn.preprocessing import StandardScaler
from xorbits.sklearn.linear_model import LinearRegression
from xorbits.sklearn.metrics import accuracy_score

xorbits.init()

# Load large dataset
data = pd.read_csv('large_dataset.csv')
X = data.drop('target', axis=1)
y = data['target']

# Preprocessing with distributed computing
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# Train-test split
X_train, X_test, y_train, y_test = train_test_split(
    X_scaled, y, test_size=0.2, random_state=42
)

# Clustering
kmeans = KMeans(n_clusters=10, random_state=42)
clusters = kmeans.fit_predict(X_train)

# Linear regression
lr = LinearRegression()
lr.fit(X_train, y_train)
predictions = lr.predict(X_test)

# Evaluate model
accuracy = accuracy_score(y_test, predictions > 0.5)

# Execute computations
results = xorbits.run(clusters, predictions, accuracy)

xorbits.shutdown()

import xorbits
import xorbits.pandas as pd
import xorbits.xgboost as xgb

xorbits.init()

# Load distributed data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# Prepare XGBoost data matrices
X_train = train_data.drop('target', axis=1)
y_train = train_data['target']
X_test = test_data.drop('target', axis=1)
y_test = test_data['target']

dtrain = xgb.DMatrix(X_train, label=y_train)
dtest = xgb.DMatrix(X_test, label=y_test)

# Set XGBoost parameters
params = {
    'objective': 'binary:logistic',
    'max_depth': 6,
    'eta': 0.3,
    'subsample': 0.8,
    'colsample_bytree': 0.8
}

# Train model with distributed computing
model = xgb.train(
    params,
    dtrain,
    num_boost_round=100,
    evals=[(dtest, 'test')],
    early_stopping_rounds=10
)

# Make predictions
predictions = xgb.predict(model, dtest)

# Execute computation
computed_predictions = xorbits.run(predictions)

xorbits.shutdown()

import xorbits
import xorbits.pandas as pd
import xorbits.lightgbm as lgb

xorbits.init()

# Load distributed data
train_data = pd.read_csv('train.csv')
test_data = pd.read_csv('test.csv')

# Prepare LightGBM datasets
X_train = train_data.drop('target', axis=1)
y_train = train_data['target']
X_test = test_data.drop('target', axis=1)
y_test = test_data['target']

train_dataset = lgb.Dataset(X_train, label=y_train)
test_dataset = lgb.Dataset(X_test, label=y_test, reference=train_dataset)

# Set LightGBM parameters
params = {
    'objective': 'binary',
    'metric': 'binary_logloss',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9
}

# Train model with distributed computing
model = lgb.train(
    params,
    train_dataset,
    valid_sets=[test_dataset],
    num_boost_round=100,
    callbacks=[lgb.early_stopping(10)]
)

# Make predictions
predictions = model.predict(X_test)

# Execute computation
computed_predictions = xorbits.run(predictions)

xorbits.shutdown()

import xorbits
import xorbits.pandas as pd
from xorbits.sklearn.pipeline import Pipeline
from xorbits.sklearn.preprocessing import StandardScaler, OneHotEncoder
from xorbits.sklearn.compose import ColumnTransformer
from xorbits.sklearn.ensemble import RandomForestClassifier
from xorbits.sklearn.model_selection import GridSearchCV, cross_val_score

xorbits.init()

# Load large dataset
data = pd.read_csv('large_ml_dataset.csv')
X = data.drop('target', axis=1)
y = data['target']

# Define preprocessing for different column types
numeric_features = X.select_dtypes(include=['int64', 'float64']).columns
categorical_features = X.select_dtypes(include=['object']).columns

preprocessor = ColumnTransformer(
    transformers=[
        ('num', StandardScaler(), numeric_features),
        ('cat', OneHotEncoder(drop='first'), categorical_features)
    ]
)

# Create ML pipeline
pipeline = Pipeline([
    ('preprocessor', preprocessor),
    ('classifier', RandomForestClassifier(random_state=42))
])

# Hyperparameter tuning with distributed computing
param_grid = {
    'classifier__n_estimators': [100, 200],
    'classifier__max_depth': [10, 20, None],
    'classifier__min_samples_split': [2, 5]
}

grid_search = GridSearchCV(
    pipeline, 
    param_grid, 
    cv=5, 
    scoring='accuracy',
    n_jobs=-1
)

# Fit with distributed computing
grid_search.fit(X, y)

# Cross-validation scores
cv_scores = cross_val_score(grid_search.best_estimator_, X, y, cv=5)

# Execute computations
results = xorbits.run(grid_search.best_params_, cv_scores)

xorbits.shutdown()