feature-selection-lofo 0.0.1

This is an implementation of LOFO for automatic feature selection.

LOFO

Leave One Feature Out (LOFO) is on of the most powerful techniques for feature selection.

This repository contains the implementation of LOFO in Python and can be used with any model of the followings:

1. Any Scikit-Learn model.
2. Any TensorFlow/Keras model.
3. LightGBM.
4. CatBoost.
5. XGBoost.

Usage

• Clone the repo:

git clone https://github.com/Azzam-Radman/LOFO.git

If you are running the code in a Notebook, use "!" before git:

!git clone https://github.com/Azzam-Radman/LOFO.git

• Import lofo

import os
import sys
directory = os.path.join(os.getcwd(), 'LOFO')
sys.path.append(directory)
import lofo

• For more details on LOFO parameters refer to lofo.py.
• Import the needed libraries for your model, cross-validation, etc

Scikit-Learn Model Example

import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
from sklearn.linear_model import LogisticRegression

• Define the paramters

# shutdown warning messages
warnings.filterwarnings('ignore')

X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]
model = LogisticRegression()
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'X': x_train, 'y': y_train}"
predict_type = 'predict_proba'
return_bad_feats = True
groups = None
is_keras_model = False

• Define the LOFO object and call it

lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params, 
                        predict_type, return_bad_feats, groups, is_keras_model)

clean_X, bad_feats = lofo_object()

clean_X: is the dataset containing the useful features only.

bad_feats: are the harmful or useless features.

LightGBM Model Example

import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import lightgbm as lgbm

• Define the paramters

# shutdown warning messages
warnings.filterwarnings('ignore')

X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]
model= lgbm.LGBMClassifier(
                       objective='binary',
                       metric='auc',
                       subsample=0.7,
                       learning_rate=0.03,
                       n_estimators=100,
                       n_jobs=-1)
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'X': x_train, 'y': y_train, 'eval_set': [(x_valid,y_valid)], 'verbose': 0}"
predict_type = 'predict_proba'
return_bad_feats = True
groups = None
is_keras_model = False

• Define the LOFO object and call it

lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params, 
                        predict_type, return_bad_feats, groups, is_keras_model)
clean_X, bad_feats = lofo_object()

TensorFlow/Keras Model Example

import warnings
import numpy as np
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import StratifiedKFold
import tensorflow as tf
from tensorflow.keras import layers

• Construct the model

def nn_model():
    inputs = layers.Input(shape=X.shape[-1],)
    x = layers.Dense(256, activation='relu')(inputs)
    x = layers.Dense(64, activation='relu')(x)
    output = layers.Dense(1, activation='sigmoid')(x)
    
    model = tf.keras.Model(inputs=inputs, outputs=output)
    model.compile(loss='binary_crossentropy',
              optimizer='adam',)
    
    return model

• Define the paramters

# shutdown warning messages
warnings.filterwarnings('ignore')

X = train_df.iloc[:, :-1]
Y = train_df.iloc[:, -1]

tf.keras.backend.clear_session()
model = nn_model()

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=0)
metric = roc_auc_score
direction = 'max'
fit_params = "{'x': x_train, 'y': y_train, 'validation_data': (x_valid, y_valid), 'epochs': 10, 'batch_size': 256, 'verbose': 0}"
predict_type = 'predict'
return_bad_feats = True
groups = None
is_keras_model = True

• Define the LOFO object and call it

lofo_object = lofo.LOFO(X, Y, model, cv, metric, direction, fit_params, 
                        predict_type, return_bad_feats, groups, is_keras_model)

clean_X, bad_feats = lofo_object()