Questions about sample code?

Hello, I am new to python and machine learning but need to use the library for a project. I read the website and the sample code but am still confused on how I can retrieve the features that have been (selected?) by each of the Relief algorithms.

Apologies if the site goes over this, but I didn’t see any information on this. I had a couple questions:

1. How do we get back the features selected by each algorithm?
2. The sample code below for the ReliefF algorithm prints a number at the end of running the code, is this number relevant to feature selection?

import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import ReliefF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
                           'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
                           sep='\t', compression='gzip')

features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values

clf = make_pipeline(ReliefF(n_features_to_select=2, n_neighbors=100),
                    RandomForestClassifier(n_estimators=100))

print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795

Thanks for any help, I’ve been trying to figure out this code using the internet for a couple weeks now but have not really gotten anywhere

[ryanurbs]

It was supposed to have been set up to handle strings as well, but I’ll have to take a closer look, not sure when I will be able to get to that. In the meantime I’d suggest encoding your variables as integers to avoid the error. Thanks Ryan

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From: Megan [e-mail] Sent: Wednesday, January 23, 2019 3:40:31 PM To: EpistasisLab/scikit-rebate Cc: Ryan Urbanowicz; Mention Subject: [External] Re: [EpistasisLab/scikit-rebate] Questions about sample code? (#57)

@ryanurbshttps://github.com/ryanurbs I had another question about allowable datatypes. Are strings not supported by this library? I noticed most of the sample data in this repo contains numbers for each feature and no strings. I am currently trying to use data that has strings, and I receive the following error:

TypeError: unsupported operand type(s) for /: ‘str’ and ‘int’

My data looks something like this:

feature1 feature2 feature3 feature4 red on large open blue off small open

My current code:

feature_pairs = pd.DataFrame(feature_value_pairs)

Separate the features, from the label(s) (bug name(s))

features, labels = feature_pairs.drop(‘class’, axis=1).values, feature_pairs[‘class’].values

Make sure to compute the feature importance scores from only your training set

X_train, X_test, y_train, y_test = train_test_split(features, labels)

fs = ReliefF() fs.fit(X_train, y_train) # This is where the TypeError occurs

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[txsing]

Hello, I am new to python and machine learning but need to use the library for a project. I read the website and the sample code but am still confused on how I can retrieve the features that have been (selected?) by each of the Relief algorithms.

Apologies if the site goes over this, but I didn’t see any information on this. I had a couple questions:

1. How do we get back the features selected by each algorithm?
2. The sample code below for the ReliefF algorithm prints a number at the end of running the code, is this number relevant to feature selection?

import pandas as pd
import numpy as np
from sklearn.pipeline import make_pipeline
from skrebate import ReliefF
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score

genetic_data = pd.read_csv('https://github.com/EpistasisLab/scikit-rebate/raw/master/data/'
                           'GAMETES_Epistasis_2-Way_20atts_0.4H_EDM-1_1.tsv.gz',
                           sep='\t', compression='gzip')

features, labels = genetic_data.drop('class', axis=1).values, genetic_data['class'].values

clf = make_pipeline(ReliefF(n_features_to_select=2, n_neighbors=100),
                    RandomForestClassifier(n_estimators=100))

print(np.mean(cross_val_score(clf, features, labels)))
>>> 0.795

Thanks for any help, I’ve been trying to figure out this code using the internet for a couple weeks now but have not really gotten anywhere

I met the same problem, it seems a little bit difficult to find clear instructions on how to get ReliefF object from the pipeline object and to get to know the final selected features. I kept getting 'AttributeError: 'ReliefF' object has no attribute 'feature_importances_' error prompt by calling print(clf['relieff'].feature_importances_)

It will be great if the developer could give a simpler version of the example code showing the intermediate steps without using pipeline.