What is BigFeat?
BigFeat is a scalable and interpretable automated feature engineering framework that optimizes for improving the quality of input features with the aim to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability.
input/output:
Bigfeat takes the original input features and returns a collection of base and engineered features expected to improve the predictive performance.
Installation:
- pip : pip install bigfeat
- local installation: pip install BigFeat/.
Requirement:
- 'pandas',
- 'numpy',
- 'scikit-learn',
- 'lightgbm'
Run BigFeat
To run bigfeat on the test datasets
import pandas as pd
import numpy as np
from numpy.random import RandomState
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
import bigfeat.bigfeat_base as bigfeat
import pandas as pd
import sklearn.preprocessing as preprocessing
def run_tst(df_path,target_ft, random_state):
df = pd.read_csv(df_path)
object_columns = df.select_dtypes(include='object')
if len(object_columns.columns):
df[object_columns.columns] = object_columns.apply(preprocessing.LabelEncoder().fit_transform)
X = df.drop(columns=target_ft)
y = df[target_ft]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=random_state)
return X_train, X_test, y_train, y_test
bf = bigfeat.BigFeat()
datasets=[
("data/shuttle.csv","class"),
("data/blood-transfusion-service-center.csv","Class"),
("data/credit-g.csv","class"),
("data/kc1.csv","defects"),
("data/nomao.csv", "Class"),
("data/eeg_eye_state.csv", "Class"),
("data/gina.csv", "class"),
("data/sonar.csv","Class"),
("data/arcene.csv", "Class"),
("data/madelon.csv", "Class"),
]
# titles = ['Dataset','Original','Random','Smart']
for each in datasets:
X_train, X_test, y_train, y_test = run_tst(each[0], each[1],random_state=0)
res = bf.fit(X_train, y_train, gen_size=5,random_state=0, iterations=5,estimator='avg',feat_imps = True, split_feats = None, check_corr= False, selection = 'fAnova', combine_res = True)
print(each[0])
clf = LogisticRegression(random_state=0).fit(X_train, y_train)
y_pred = clf.predict(X_test)
print("ORG f1 score : {}".format(f1_score(y_test, y_pred)))
clf = LogisticRegression(random_state=0).fit(bf.transform(X_train), y_train)
y_pred_bf = clf.predict(bf.transform(X_test))
print("BF f1 score : {}".format(f1_score(y_test, y_pred_bf)))
#print(res)
