from sklearn.model_selection import train_test_split
from numpy import genfromtxt
from sklearn.metrics import classification_report
from mlxtend.feature_selection import SequentialFeatureSelector as sfs
from sklearn.neighbors import LSHForest
my_data = genfromtxt('datasets/pd_speech.csv', delimiter=',', dtype=str)
X = my_data[2:758, 1:754].astype(float)
y = my_data[2:758, 754].astype(int)
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
lshf = LSHForest(random_state=42)
lshf.fit(X_train)
print(lshf.get_params())
#
#
#
#
# clf = RandomForestClassifier(n_estimators=10)
#
# #clf = SVC(kernel='linear')
#
# #try multiple scoring parameters, like 'accuracy', 'neg_mean_squared_error', None
# sfs1 = sfs(clf,
# k_features=20,
# forward=True,
# floating=True,
# verbose=2,
