I want to use the class `FunctionSampler`

from `imblearn`

to create my own custom class for resampling my dataset.

I have a one-dimensional feature Series containing paths for each subject and a label Series containing the labels for each subject. Both come from a `pd.DataFrame`

. I know that I have to reshape the feature array first since it is one-dimensional.

When I use the class `RandomUnderSampler`

everything works fine, however if I pass both the features and labels first to the `fit_resample`

method of `FunctionSampler`

which then creates an instance of `RandomUnderSampler`

and then calls `fit_resample`

on this class, I get the following error:

ValueError: could not convert string to float: ‘path_1’

Here’s a minimal example producing the error:

import pandas as pd
from imblearn.under_sampling import RandomUnderSampler
from imblearn import FunctionSampler
# create one dimensional feature and label arrays X and y
# X has to be converted to numpy array and then reshaped.
X = pd.Series(['path_1','path_2','path_3'])
X = X.values.reshape(-1,1)
y = pd.Series([1,0,0])

# FIRST METHOD (works)

rus = RandomUnderSampler()
X_res, y_res = rus.fit_resample(X,y)

# SECOND METHOD (doesn’t work)

def resample(X, y):
return RandomUnderSampler().fit_resample(X, y)
sampler = FunctionSampler(func=resample)
X_res, y_res = sampler.fit_resample(X, y)

Does anyone know what goes wrong here? It seems as the `fit_resample`

method of `FunctionSampler`

is not equal to the `fit_resample`

method of `RandomUnderSampler`

…

## Answer

Your implementation of `FunctionSampler`

is correct. The problem is with your dataset.

`RandomUnderSampler`

seems to work for text data as well. There is no checking using `check_X_y`

.

But `FunctionSampler()`

has this check, see here

from sklearn.utils import check_X_y
X = pd.Series(['path_1','path_2','path_2'])
X = X.values.reshape(-1,1)
y = pd.Series([1,0,0])
check_X_y(X, y)

This will throw an error

ValueError: could not convert string to float: ‘path_1’

The following example would work!

X = pd.Series(['1','2','2'])
X = X.values.reshape(-1,1)
y = pd.Series([1,0,0])
def resample(X, y):
return RandomUnderSampler().fit_resample(X, y)
sampler = FunctionSampler(func=resample)
X_res, y_res = sampler.fit_resample(X, y)
X_res, y_res
# (array([[2.],
# [1.]]), array([0, 1], dtype=int64))

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