DropCorrelatedFeatures¶
API Reference¶
-
class
feature_engine.selection.
DropCorrelatedFeatures
(variables=None, method='pearson', threshold=0.8, missing_values='ignore')[source]¶ DropCorrelatedFeatures() finds and removes correlated features. Correlation is calculated with
pandas.corr()
.Features are removed on first found first removed basis, without any further insight.
DropCorrelatedFeatures() works only with numerical variables. Categorical variables will need to be encoded to numerical or will be excluded from the analysis.
- Parameters
- variableslist, default=None
The list of variables to evaluate. If None, the transformer will evaluate all numerical variables in the dataset.
- methodstring, default=’pearson’
Can take ‘pearson’, ‘spearman’ or’kendall’. It refers to the correlation method to be used to identify the correlated features.
pearson : standard correlation coefficient
kendall : Kendall Tau correlation coefficient
spearman : Spearman rank correlation
- thresholdfloat, default=0.8
The correlation threshold above which a feature will be deemed correlated with another one and removed from the dataset.
- missing_valuesstr, default=ignore
Takes values ‘raise’ and ‘ignore’. Whether the missing values should be raised as error or ignored when determining correlation.
Attributes
features_to_drop_:
Set with the correlated features that will be dropped.
correlated_feature_sets_:
Groups of correlated features. Each list is a group of correlated features.
See also
pandas.corr
feature_engine.selection.SmartCorrelationSelection
Methods
fit:
Find correlated features.
transform:
Remove correlated features.
fit_transform:
Fit to the data. Then transform it.
Example¶
The DropCorrelatedFeatures() finds and removes correlated variables from a dataframe. The user can pass a list of variables to examine, or alternatively the selector will examine all numerical variables in the data set.
import pandas as pd
from sklearn.datasets import make_classification
from feature_engine.selection import DropCorrelatedFeatures
# make dataframe with some correlated variables
def make_data():
X, y = make_classification(n_samples=1000,
n_features=12,
n_redundant=4,
n_clusters_per_class=1,
weights=[0.50],
class_sep=2,
random_state=1)
# trasform arrays into pandas df and series
colnames = ['var_'+str(i) for i in range(12)]
X = pd.DataFrame(X, columns =colnames)
return X
X = make_data()
tr = DropCorrelatedFeatures(variables=None, method='pearson', threshold=0.8)
Xt = tr.fit_transform(X)
tr.correlated_feature_sets_
[{'var_0', 'var_8'}, {'var_4', 'var_6', 'var_7', 'var_9'}]
tr.correlated_features_
{'var_6', 'var_7', 'var_8', 'var_9'}
print(print(Xt.head()))
var_0 var_1 var_2 var_3 var_4 var_5 var_10 \
0 1.471061 -2.376400 -0.247208 1.210290 -3.247521 0.091527 2.070526
1 1.819196 1.969326 -0.126894 0.034598 -2.910112 -0.186802 1.184820
2 1.625024 1.499174 0.334123 -2.233844 -3.399345 -0.313881 -0.066448
3 1.939212 0.075341 1.627132 0.943132 -4.783124 -0.468041 0.713558
4 1.579307 0.372213 0.338141 0.951526 -3.199285 0.729005 0.398790
var_11
0 -1.989335
1 -1.309524
2 -0.852703
3 0.484649
4 -0.186530