import warnings
from sklearn.datasets import load_wine
from mfs import MFS
from mfs.Metrics import Metrics
from stree import Stree
import numpy as np
from scipy.io import arff
from sklearn.preprocessing import StandardScaler

mfsc = MFS(discrete=False)
mfsd = MFS(discrete=True)

# Xo, y = load_wine(return_X_y=True)
# X = Xo.copy()
# scaler = StandardScaler()
# for c in range(X.shape[1]):
#     X[:, c] = scaler.fit_transform(X[:, c].reshape(-1, 1)).reshape(-1)


# data = np.genfromtxt("balance-scale.csv")
# X = data[:, -1:]
# y = data[:, -1]

filename = (
    "/Users/rmontanana/Code/stree_datasets/data/tanveer/conn-bench-sonar-min"
    "es-rocks/conn-bench-sonar-mines-rocks.arff"
)
data, meta = arff.loadarff(filename)
train = np.array([data[i] for i in meta])
X = train.T
X = X[:, :-1].astype("float64")
y = data["clase"]


for c in range(X.shape[1]):
    print(f"Mean: {np.mean(X[:,c])} Std: {np.std(X[:,c])}")
m, n = X.shape
print("* Differential entropy in X")
for i in range(n):
    print(i, Metrics.differential_entropy(X[:, i], k=10))
print("* Information Gain")
print("- Discrete features")
print(Metrics.information_gain(X, y))
for i in range(n):
    print(i, Metrics.information_gain(X[:, i], y))
print("- Continuous features")
print(Metrics.information_gain_cont(X, y))
for i in range(n):
    print(i, Metrics.information_gain_cont(X[:, i], y))
# Classification
warnings.filterwarnings("ignore")
print("CFS Discrete")
cfs_d = mfsd.cfs(X, y).get_results()
print(cfs_d)
print("CFS continuous")
cfs_f = mfsc.cfs(X, y).get_results()
print(cfs_f)
print("FCBF Discrete")
print(mfsd.fcbf(X, y, 5e-2).get_results())
print("FCBF continuous")
fcfb_f = mfsc.fcbf(X, y, 5e-2).get_results()
print(fcfb_f, len(fcfb_f), "X.shape=", X.shape)
clf = Stree(random_state=0)
print("completo", clf.fit(X, y).score(X, y))
clf = Stree(random_state=0)
print("cfs discreto", clf.fit(X[:, cfs_d], y).score(X[:, cfs_d], y))
print("cfs continuo", clf.fit(X[:, cfs_f], y).score(X[:, cfs_f], y))
clf = Stree(random_state=0)
# subf = fcfb_f[:6]
subf = fcfb_f
print("fcfb", clf.fit(X[:, subf], y).score(X[:, subf], y))

# for c in range(X.shape[1]):
#     for k in range(X.shape[1]):
#         ac = 0
#         for v in range(X[:, c].shape[0]):
#             if X[v, c] == X[v, k]:
#                 ac += 1
#         print(f"{c} {k} {ac}")