Reading · Dr. Juan Camilo Orduz

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In this notebook, we present a simple example to illustrate how to vectorize the ROC curve computation over a Bayesian model. This is helpful when we want to compute the ROC curve using the implementations from scikit-learn . We use the classical moons dataset to generate the data and fit a Gaussian process models similarly as the previous post Scikit-Learn Example in PyMC: Gaussian Process…

In this notebook, we present a simple example to illustrate how to vectorize the ROC curve computation over a Bayesian model. This is helpful when we want to compute the ROC curve using the implementations from scikit-learn. We use the classical moons dataset to generate the data and fit a Gaussian process models similarly as the previous post Scikit-Learn Example in PyMC: Gaussian Process Classifier.

Prepare Notebook

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import preliz as pz
import pymc as pm
import pytensor
import seaborn as sns
import xarray as xr
from sklearn.datasets import make_moons
from sklearn.metrics import roc_auc_score, roc_curve
from sklearn.model_selection import train_test_split

az.style.use("arviz-darkgrid")
plt.rcParams["figure.figsize"] = [10, 6]
plt.rcParams["figure.dpi"] = 100
plt.rcParams["figure.facecolor"] = "white"

%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = "retina"

seed: int = sum(map(ord, "vectorize_roc_curve"))
rng: np.random.Generator = np.random.default_rng(seed=seed)

Generate Synthetic Data

# Generate data
x, y = make_moons(n_samples=150, noise=0.25, random_state=seed)
# Split data into training and test sets
x_train, x_test, y_train, y_test = train_test_split(
    x, y, test_size=0.3, random_state=seed
)
# Get number of samples in training and test sets
n_train = x_train.shape[0]
n_test = x_test.shape[0]
n = n_train + n_test
# Create indices for training and test sets
idx_train = range(n_train)
idx_test = range(n_train, n_train + n_test)
# Get dimension of the domain
domain_dim = x.shape[1]

Let’s start by visualizing the data.

Vectorize ROC Curve for Bayesian Models

Prepare Notebook

Generate Synthetic Data

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