import random
import time
import numpy as np
from sklearn.model_selection import KFold, cross_validate
from experimentation.Sets import Datasets
from stree import Stree
from experimentation.Utils import TextColor
from experimentation.Database import MySQL


def normalize(data: np.array) -> np.array:
    min_data = data.min()
    return (data - min_data) / (data.max() - min_data)


def normalize_rows(data: np.array) -> np.array:
    res = data.copy()
    for col in range(res.shape[1]):
        res[:, col] = normalize(res[:, col])
    return res


def header():
    print("Processing Datasets with stree default.\n")
    print(
        f"{'Dataset':30s} {'No Norm.':9s} {'Normaliz.':9s} "
        f"{'Col.Norm.':9s} {'Context B':9s} {'Best score in crossval':25s}"
    )
    print("=" * 30 + " " + ("=" * 9 + " ") * 4 + "=" * 25)


def process_dataset(X, y, normalize):
    scores = []
    # return random.uniform(0, 1)
    for random_state in random_seeds:
        random.seed(random_state)
        clf_test = Stree(random_state=random_state, normalize=normalize)
        np.random.seed(random_state)
        kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
        res = cross_validate(clf_test, X, y, cv=kfold, return_estimator=True)
        scores.append(res["test_score"])
    return np.mean(scores)


start = time.time()
models_tree = [
    "stree",
    "stree_default",
    "wodt",
    "j48svm",
    "oc1",
    "cart",
    "baseRaF",
]
dbh = MySQL()
database = dbh.get_connection()
random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
dt = Datasets(normalize=False, standardize=False, set_of_files="tanveer")
header()
total = [0, 0, 0, 0]
line = TextColor.LINE1
for data in dt:
    name = data[0]
    X, y = dt.load(name)
    record = dbh.find_best(name, models_tree, "crossval")
    X2 = normalize(X)
    X3 = normalize_rows(X)
    ac1 = process_dataset(X, y, False)
    ac2 = process_dataset(X2, y, False)
    ac3 = process_dataset(X3, y, False)
    ac4 = process_dataset(X, y, True)
    max_value = round(max(ac1, ac2, ac3, ac4), 6)
    line = TextColor.LINE2 if line == TextColor.LINE1 else TextColor.LINE1
    print(line + f"{name:30s} ", end="", flush=True)
    total[np.argmax([ac1, ac2, ac3, ac4])] += 1
    color1 = TextColor.SUCCESS if ac1 == max_value else line
    color2 = TextColor.SUCCESS if ac2 == max_value else line
    color3 = TextColor.SUCCESS if ac3 == max_value else line
    color4 = TextColor.SUCCESS if ac4 == max_value else line
    print(color1 + f"{ac1:9.6f} " + TextColor.ENDC, end="", flush=True)
    print(color2 + f"{ac2:9.6f} " + TextColor.ENDC, end="", flush=True)
    print(color3 + f"{ac3:9.6f} " + TextColor.ENDC, end="", flush=True)
    print(color4 + f"{ac4:9.6f}" + TextColor.ENDC, end="", flush=True)
    best_accuracy = round(record[5], 6)
    best_color = TextColor.UNDERLINE if best_accuracy >= max_value else ""
    print(
        line
        + best_color
        + f"{best_accuracy:9.6f} {record[3]}"
        + TextColor.ENDC
    )
print(f"{'Total':30s} {total[0]:9d} {total[1]:9d} {total[2]:9d} {total[3]:9d}")
stop = time.time()
hours, rem = divmod(stop - start, 3600)
minutes, seconds = divmod(rem, 60)
print(f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s")
dbh.close()