import numpy as np
from feat import Feat
from sklearn.model_selection import KFold
df = pd.read_csv('d_example_patients.csv')
df.drop('id', axis=1, inplace=True)
X = df.drop('class', axis=1).values
y = df['class'].values
zfile = 'd_example_patients_long.csv'
kf = KFold(n_splits=3)
kf.get_n_splits(X)
clf = Feat(
max_depth=5,
max_dim=min(50, 2 * X.shape[1]),
verbosity=1,
shuffle=True,
ml='LR',
classification=True,
functions=
"max,+,-,*,/,exp,log,and,or,not,=,<,>,ite,mean,median,min,variance,skew,kurtosis,slope,count",
random_state=42)
scores = []
for train_idx, test_idx in kf.split(X):
clf.fit(X[train_idx], y[train_idx], zfile, train_idx)
scores.append(clf.score(X[test_idx], y[test_idx], zfile, test_idx))
print('scores:', scores)
import pandas as pd
from pmlb import fetch_data
df = pd.read_csv('mnist.csv', sep='\t')
print(df.columns)
X = df.drop('class', axis=1).values
y = df['class'].values
from feat import Feat
ft = Feat(classification=True, verbosity=2)
ft.fit(X[:60000], y[:60000])
print(ft.score(X[60000:], y[60000:]))