from xgboost import XGBRegressor
from sklearn.model_selection import KFold
import concurrent.futures
from sklearn.datasets import make_regression
import pandas as pd
import numpy as np
from sklearn.model_selection import RandomizedSearchCV

# STEP 1
# ----------------------------------------------------------------------------
# simulate RandomizedSearchCV

data = make_regression(n_samples=500, n_features=100, n_informative=10, n_targets=1, random_state=5)
X = pd.DataFrame(data[0])
y = pd.Series(data[1])
kf = KFold(n_splits = 3, shuffle = True, random_state = 5)
model = XGBRegressor(n_jobs = -1)
params = {
        'min_child_weight':     [0.1, 1, 5],
        'subsample':            [0.5, 0.7, 1.0],
        'colsample_bytree':     [0.5, 0.7, 1.0],
        'eta':                  [0.005, 0.01, 0.1],
        'n_jobs':               [-1]
        }
random_search = RandomizedSearchCV(
        model,
        param_distributions =   params,
        n_iter =                50,
        n_jobs =                -1,
        refit =                 True, # necessary for random_search.best_estimator_
        cv =                    kf.split(X,y),
        verbose =               1,
        random_state =          5
        )
random_search.fit(X, np.array(y))

# STEP 2.0
# ----------------------------------------------------------------------------
# test if multiprocessing is working in the first place

def just_print():
    print('Just printing')

with concurrent.futures.ProcessPoolExecutor() as executor:
    results_temp = [executor.submit(just_print) for i in range(0,12)]
# ----------------------------------------------------------------------------


# STEP 2.1
# ----------------------------------------------------------------------------
# test on a slightly more complex function

def fit_model():
    # JUST CREATING A DATASET, NOT EVEN FITTING ANY MODEL!!! AND IT FREEZES
    data = make_regression(n_samples=500, n_features=100, n_informative=10, n_targets=1, random_state=5)
    # model = XGBRegressor(n_jobs = -1)
    # model.fit(data[0],data[1])
    print('Fit complete')

with concurrent.futures.ProcessPoolExecutor() as executor:
    results_temp = [executor.submit(fit_model) for i in range(0,12)]
# ----------------------------------------------------------------------------