logit regression and singular Matrix error in Python

user_3122731 0
Viewed 44452

am trying to run logit regression for german credit data (www4.stat.ncsu.edu/~boos/var.select/german.credit.html). To test the code, I have used only numerical variables and tried regressing it with the result using the following code.

import pandas as pd
import statsmodels.api as sm
import pylab as pl
import numpy as np

df = pd.read_csv("germandata.txt",delimiter=' ')
df.columns = ["chk_acc","duration","history","purpose","amount","savings_acc","employ_since","install_rate","pers_status","debtors","residence_since","property","age","other_plans","housing","existing_credit","job","no_people_liab","telephone","foreign_worker","admit"]

#pls note that I am only retaining numeric variables
cols_to_keep = ['admit','duration', 'amount', 'install_rate','residence_since','age','existing_credit','no_people_liab']

# rank of cols_to_keep is 8
print np.linalg.matrix_rank(df[cols_to_keep].values)
data = df[cols_to_keep]

data['intercept'] = 1.0

train_cols = data.columns[1:]

#to check the rank of train_cols, which in this case is 8
print np.linalg.matrix_rank(data[train_cols].values)

#fit logit model
logit = sm.Logit(data['admit'], data[train_cols])
result = logit.fit()

All the 8.0 columns seem independent when I check the data. Inspite of this I am getting Singular Matrix Error. Can you please help?

Thanks

statsmodels python-2.7 regression
2 Answers

I've managed to solve this by removing as well low variance columns:

from sklearn.feature_selection import VarianceThreshold

def variance_threshold_selector(data, threshold=0.5):
    # https://stackoverflow.com/a/39813304/1956309
    selector = VarianceThreshold(threshold)
    selector.fit(data)
    return data[data.columns[selector.get_support(indices=True)]]

# min_variance = .9 * (1 - .9)  # You can play here with different values.
min_variance = 0.0001
low_variance = variance_threshold_selector(df, min_variance) 
print('columns removed:')
df.columns ^ low_variance.columns
df.shape
df.shape
X = low_variance
# (Logit(y_train, X), logit.fit()... etc)

To give a bit more of context: I did one-hot encoding to some categorical data prior to this step, and some of the columns had very few 1's

user_19563090
Related