RandomizedSearchCV — Smarter Search for Large Spaces

import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import RandomizedSearchCV, GridSearchCV, train_test_split, cross_val_score
from sklearn.datasets import load_breast_cancer
from scipy.stats import loguniform, randint, uniform
import time

cancer = load_breast_cancer()
X, y = cancer.data, cancer.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

pipe = Pipeline([("scaler", StandardScaler()), ("model", GradientBoostingClassifier(random_state=42))])

# RANDOM SEARCH with DISTRIBUTIONS (samples from continuous ranges)
param_distributions = {
    "model__n_estimators":    randint(50, 500),         # integers from 50 to 500
    "model__learning_rate":   loguniform(0.005, 0.5),   # 0.005 to 0.5 on log scale (better for lr)
    "model__max_depth":       randint(2, 8),
    "model__subsample":       uniform(0.6, 0.4),         # 0.6 to 1.0 uniform
    "model__min_samples_leaf":randint(1, 20),
    "model__max_features":    uniform(0.4, 0.6),         # 0.4 to 1.0
}

# HOW MANY WOULD GRID SEARCH TAKE? With 5 values each on 6 params: 5^6 = 15,625 fits!
# RandomizedSearch: 50 trials x 5-fold = 250 fits

t0 = time.time()
rand_search = RandomizedSearchCV(
    pipe, param_distributions,
    n_iter=50,               # number of random combinations to try
    cv=5,
    scoring="roc_auc",
    n_jobs=-1,
    random_state=42,
    refit=True,
    verbose=0,
)
rand_search.fit(X_train, y_train)
t_rand = time.time() - t0

print(f"RandomizedSearchCV (50 iter): {t_rand:.1f}s | Best AUC: {rand_search.best_score_:.4f}")
print(f"Best params: {rand_search.best_params_}")

# COMPARE: SMALL GRID SEARCH on same budget
param_grid_small = {
    "model__n_estimators":  [100, 200, 300],
    "model__learning_rate": [0.01, 0.05, 0.1, 0.2],
    "model__max_depth":     [3, 4, 5],
    # 3*4*3 = 36 combos, close to 50 random
}
t0 = time.time()
grid = GridSearchCV(pipe, param_grid_small, cv=5, scoring="roc_auc", n_jobs=-1)
grid.fit(X_train, y_train)
t_grid = time.time() - t0

print(f"\nGridSearchCV (36 combos):    {t_grid:.1f}s | Best AUC: {grid.best_score_:.4f}")

# TEST SET EVALUATION
for name, search in [("RandomizedSearch", rand_search), ("GridSearch", grid)]:
    test_auc = cross_val_score(search.best_estimator_, X_test, y_test, cv=None, scoring="roc_auc")
    print(f"{name} test AUC: {search.best_estimator_.score(X_test, y_test):.4f}")

# TIPS FOR RANDOMIZED SEARCH
tips = {
    "n_iter":          "Start with 50-100. Increase if time allows. Diminishing returns after 200.",
    "loguniform":      "Always use for learning_rate, C, alpha, gamma -- they span orders of magnitude",
    "randint":         "For integers: n_estimators, max_depth, min_samples_leaf",
    "uniform":         "For bounded floats: subsample (0.6-1.0), colsample (0.5-1.0)",
    "random_state":    "Fix for reproducibility. Different seeds give slightly different best params.",
    "refit":           "Set True to get best_estimator_ retrained on full training data",
}
print("\nRandomizedSearchCV tips:")
for param, tip in tips.items():
    print(f"  {param:16s}: {tip}")