I am using scipy.stats.chi2_contingency method to get chi square statistics. We need to pass frequency table i.e. contingency table as parameter. But I have a feature vector and want to automatically generate the frequency table. Do we have any such function available? I am doing it like this currently:
def contigency_matrix_categorical(data_series,target_series,target_val,indicator_val):
observed_freq={}
for targets in target_val:
observed_freq[targets]={}
for indicators in indicator_val:
observed_freq[targets][indicators['val']]=data_series[((target_series==targets)&(data_series==indicators['val']))].count()
f_obs=[]
var1=0
var2=0
for i in observed_freq:
var1=var1+1
var2=0
for j in observed_freq[i]:
f_obs.append(observed_freq[i][j]+5)
var2=var2+1
arr=np.array(f_obs).reshape(var1,var2)
c,p,dof,expected=chi2_contingency(arr)
return {'score':c,'pval':p,'dof':dof}
Where data series and target series are the columns values and the other two are the name of the indicator. Can anyone help? thanks
You can use pandas.crosstab
to generate a contingency table from a DataFrame. From the documentation:
Compute a simple cross-tabulation of two (or more) factors. By default computes a frequency table of the factors unless an array of values and an aggregation function are passed.
Below is an usage example:
import numpy as np
import pandas as pd
from scipy.stats import chi2_contingency
# Some fake data.
n = 5 # Number of samples.
d = 3 # Dimensionality.
c = 2 # Number of categories.
data = np.random.randint(c, size=(n, d))
data = pd.DataFrame(data, columns=['CAT1', 'CAT2', 'CAT3'])
# Contingency table.
contingency = pd.crosstab(data['CAT1'], data['CAT2'])
# Chi-square test of independence.
c, p, dof, expected = chi2_contingency(contingency)
The following data
table
generates the following contingency
table
Then, scipy.stats.chi2_contingency(contingency)
returns (0.052, 0.819, 1, array([[1.6, 0.4],[2.4, 0.6]]))
.