import sys
import time
import warnings

from experimentation.Sets import Datasets
from stree import Stree
from mdlp import MDLP
from mfs import MFS


def header(filter_name):
    print("Score files")
    initial = f"{'Dataset':30s} T. Disc T.Selec "
    sec_line = "=" * 30 + " ======= ======= "
    for item in ["Normal", "Discret.", filter_name.upper()]:
        initial += f"{item:10s} "
        sec_line += "=" * 10 + " "
    initial += "Reduction Features selected"
    sec_line += "========= " + "=" * 30
    print(initial)
    print(sec_line)


warnings.filterwarnings("ignore")
if len(sys.argv) > 1:
    filter_name = sys.argv[1]
else:
    filter_name = "cfs"
if filter_name not in ["cfs", "fcbf"]:
    print("First parameter has to be one of: {cfs, fcbf}")
    exit(1)
datasets = Datasets(False, False, "tanveer")
header(filter_name)
better = worse = equal = 0
for dataset in datasets:
    # mdlp = MDLP(random_state=1)
    X, y = datasets.load(dataset[0])
    mfs = MFS(discrete=False)
    now_disc = time.time()
    # X_disc = mdlp.fit_transform(X, y)
    X_disc = X
    time_disc = time.time() - now_disc
    now_selec = time.time()
    if filter_name == "cfs":
        features_selected = mfs.cfs(X_disc, y).get_results()
    else:
        features_selected = mfs.fcbf(X_disc, y, 1e-7).get_results()
    time_selec = time.time() - now_selec
    output = ""
    odte_score = stree_score = 0.0
    now = time.time()
    clf = Stree(random_state=1, multiclass_strategy="ovo")
    score_norm = clf.fit(X, y).score(X, y)
    clf = Stree(random_state=1, multiclass_strategy="ovo")
    score_disc = clf.fit(X_disc, y).score(X_disc, y)
    if len(features_selected) > 0:
        X_feat = X_disc[:, features_selected]
        clf = Stree(random_state=1, multiclass_strategy="ovo")
        score_fs = clf.fit(X_feat, y).score(X_feat, y)
    else:
        score_fs = 0.0
    output = f"{dataset[0]:30s} {time_disc:7.3f} {time_selec:7.3f} "
    output += f"{score_norm:.8f} "
    output += f"{score_disc:.8f} {score_fs:.8f} "
    output += f"{X.shape[1]:3} - {len(features_selected):3} "
    output += f"{features_selected}"
    print(output)