Calculating AUC for LogisticRegression model

Question

Let's take data

import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import metrics

data = load_breast_cancer()
X = data.data
y = data.target

I want to create model using only first principal component and calculate AUC for it.

My work so far

scaler = StandardScaler()
scaler.fit(X_train) 
X_scaled = scaler.transform(X)
pca = PCA(n_components=1)
principalComponents = pca.fit_transform(X)
principalDf = pd.DataFrame(data = principalComponents
             , columns = ['principal component 1'])
clf = LogisticRegression()
clf = clf.fit(principalDf, y)
pred = clf.predict_proba(principalDf)

But while I'm trying to use

fpr, tpr, thresholds = metrics.roc_curve(y, pred, pos_label=2)

Following error occurs :

y should be a 1d array, got an array of shape (569, 2) instead.

I tried to reshape my data

fpr, tpr, thresholds = metrics.roc_curve(y.reshape(1,-1), pred, pos_label=2)

But it didn't solve the issue (it outputs) :

multilabel-indicator format is not supported

Do you have any idea how can I perform AUC on this first principal component?

Answer 1

You may wish to try:

from sklearn.datasets import load_breast_cancer
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn import metrics
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline

X,y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X,y)
scaler = StandardScaler()
pca = PCA(2)
clf = LogisticRegression()
ppl = Pipeline([("scaler",scaler),("pca",pca),("clf",clf)])
ppl.fit(X_train, y_train)
preds = ppl.predict(X_test)

fpr, tpr, thresholds = metrics.roc_curve(y_test, preds, pos_label=1)
metrics.plot_roc_curve(ppl, X_test, y_test)