sgd_grid.py

loss = ['hinge', 'log', 'modified_huber', 'squared_hinge', 'perceptron']
penalty = ['l1', 'l2', 'elasticnet']
alpha = [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000]
learning_rate = ['constant', 'optimal', 'invscaling', 'adaptive']
class_weight = [{1:0.5, 0:0.5}, {1:0.4, 0:0.6}, {1:0.6, 0:0.4}, {1:0.7, 0:0.3}]
eta0 = [1, 10, 100]

param_distributions = dict(loss=loss,
                           penalty=penalty,
                           alpha=alpha,
                           learning_rate=learning_rate,
                           class_weight=class_weight,
                           eta0=eta0)

random = RandomizedSearchCV(estimator=sgd,
                            param_distributions=param_distributions,
                            scoring='roc_auc',
                            verbose=1, n_jobs=-1,
                            n_iter=1000)
random_result = random.fit(X_train, y_train)

print('Best Score: ', random_result.best_score_)
print('Best Params: ', random_result.best_params_)