I have tried to import two csv files into
df2. Concatenated them to make
df3. I tried to call the
mutual_info_regression on them but I am getting a value error
ValueError: Found array with 0 sample(s) (shape=(0, 1)) while a minimum of 1 is required. I have checked the dimensions of
discrete_features. They all seem okay.
Since the code works with other
csv files (I have tested), I think the problem is with my
csv files and not the code.
import numpy as np import pandas as pd df1 = pd.read_csv("WT_MDE.csv", index_col=0) df1["Interact"] = 1 df2 = pd.read_csv("M_MDE.csv", index_col=0) df2["Interact"] = 0 data = pd.concat([df1, df2]) X = data.copy() y = X.pop("Interact") discrete_features = X.dtypes == float from sklearn.feature_selection import mutual_info_regression def make_mi_scores(X, y, discrete_features): mi_scores = mutual_info_regression(X, y, discrete_features = discrete_features) mi_scores = pd.Series(mi_scores, name="MI Scores", index=X.columns) mi_scores = mi_scores.sort_values(ascending=False) return mi_scores mi_scores = make_mi_scores(X, y, discrete_features)
I would really appreciate if anyone could help.