This is a simple notebook demo to illustrate typically how OptimalFlow's autoFS module work

In [ ]:
# Demo - Classification

import pandas as pd
from optimalflow.autoFS import dynaFS_clf

# Tatanic Cleaned dataset

tr_features = pd.read_csv('./data/classification/train_features.csv')
tr_labels = pd.read_csv('./data/classification/train_labels.csv')

# Set input_form_file = False, when label values are array. Select 'True' from Pandas dataframe.

clf_fs_demo = dynaFS_clf( fs_num =5,random_state=13,cv = 5,input_from_file = True)

# You can find details of each selector's choice in autoFS_logxxxxx.log file in the ./test folder

clf_fs_demo.fit(tr_features,tr_labels)
In [ ]:
# Demo - Regression

import pandas as pd
from optimalflow.autoFS import dynaFS_reg

# Boston Housing Cleaned dataset

tr_features = pd.read_csv('./data/regression/train_features.csv')
tr_labels = pd.read_csv('./data/regression/train_labels.csv')

# Set input_form_file = False, when label values are array. Select 'True' from Pandas dataframe.

reg_fs_demo = dynaFS_reg( fs_num = 5,random_state = 13,cv = 5,input_from_file = True)

# You can find details of each selector's choice in autoFS_logxxxxx.log file in the ./test folder

reg_fs_demo.fit(tr_features,tr_labels)