The answer by piRSquared works great but it removes all columns with correlation above the cutoff, which overdoes it compared to how findCorrelation behaves in R. Assuming these are features in a machine learning model, we need to drop columns just enough so that the pairwise correlation coefficients among the columns are less than some cutoff point (perhaps multicollinearity is a problem etc.). Dropping too many would potentially hurt whatever model that is built on this data. As Sergey Bushmanov mentions in a comment, between columns C and H, only one should be dropped.
R's caret::findCorrelation looks at the mean absolute correlation of each variable and removes the variable with the largest mean absolute correlation for each pair of columns. The following function (named findCorrelation) implements the very same logic.
Depending on the size of the correlation matrix, caret::findCorrelation calls one of two functions: the fully vectorized findCorrelation_fast or the loopy findCorrelation_exact (you can call either regardless of dataframe size by using the exact= argument appropriately). The function below does the very same.
The only behavior different from caret::findCorrelation is that it returns a list of column names whereas caret::findCorrelation returns the index of the columns. I believe it's more natural to return column names which we can pass to drop later on.
import numpy as np
import pandas as pd
def findCorrelation(corr, cutoff=0.9, exact=None):
"""
This function is the Python implementation of the R function
`findCorrelation()`.
Relies on numpy and pandas, so must have them pre-installed.
It searches through a correlation matrix and returns a list of column names
to remove to reduce pairwise correlations.
For the documentation of the R function, see
https://www.rdocumentation.org/packages/caret/topics/findCorrelation
and for the source code of `findCorrelation()`, see
https://github.com/topepo/caret/blob/master/pkg/caret/R/findCorrelation.R
-----------------------------------------------------------------------------
Parameters:
-----------
corr: pandas dataframe.
A correlation matrix as a pandas dataframe.
cutoff: float, default: 0.9.
A numeric value for the pairwise absolute correlation cutoff
exact: bool, default: None
A boolean value that determines whether the average correlations be
recomputed at each step
-----------------------------------------------------------------------------
Returns:
--------
list of column names
-----------------------------------------------------------------------------
Example:
--------
R1 = pd.DataFrame({
'x1': [1.0, 0.86, 0.56, 0.32, 0.85],
'x2': [0.86, 1.0, 0.01, 0.74, 0.32],
'x3': [0.56, 0.01, 1.0, 0.65, 0.91],
'x4': [0.32, 0.74, 0.65, 1.0, 0.36],
'x5': [0.85, 0.32, 0.91, 0.36, 1.0]
}, index=['x1', 'x2', 'x3', 'x4', 'x5'])
findCorrelation(R1, cutoff=0.6, exact=False) # ['x4', 'x5', 'x1', 'x3']
findCorrelation(R1, cutoff=0.6, exact=True) # ['x1', 'x5', 'x4']
"""
def _findCorrelation_fast(corr, avg, cutoff):
combsAboveCutoff = corr.where(lambda x: (np.tril(x)==0) & (x > cutoff)).stack().index
rowsToCheck = combsAboveCutoff.get_level_values(0)
colsToCheck = combsAboveCutoff.get_level_values(1)
msk = avg[colsToCheck] > avg[rowsToCheck].values
deletecol = pd.unique(np.r_[colsToCheck[msk], rowsToCheck[~msk]]).tolist()
return deletecol
def _findCorrelation_exact(corr, avg, cutoff):
x = corr.loc[(*[avg.sort_values(ascending=False).index]*2,)]
if (x.dtypes.values[:, None] == ['int64', 'int32', 'int16', 'int8']).any():
x = x.astype(float)
x.values[(*[np.arange(len(x))]*2,)] = np.nan
deletecol = []
for ix, i in enumerate(x.columns[:-1]):
for j in x.columns[ix+1:]:
if x.loc[i, j] > cutoff:
if x[i].mean() > x[j].mean():
deletecol.append(i)
x.loc[i] = x[i] = np.nan
else:
deletecol.append(j)
x.loc[j] = x[j] = np.nan
return deletecol
if not np.allclose(corr, corr.T) or any(corr.columns!=corr.index):
raise ValueError("correlation matrix is not symmetric.")
acorr = corr.abs()
avg = acorr.mean()
if exact or exact is None and corr.shape[1]<100:
return _findCorrelation_exact(acorr, avg, cutoff)
else:
return _findCorrelation_fast(acorr, avg, cutoff)
You can call findCorrelation to find the columns to drop and call drop() on the dataframe to drop those columns (exactly how you would use this function is R).
Using piRSquared's setup, it returns the following output.
corr = df.corr()
hc = findCorrelation(corr, cutoff=0.5)
trimmed_df = df.drop(columns=hc)
![res]()
