Download

Download this notebook: glm_for_classification.ipynb!

Fit GLM for Classification

The ClassifierGLM models categorical or discrete outcomes such as behavioral choices.

Key differences from standard GLM:

• predict returns predicted class labels

• predict_proba returns (log-)probabilities for each class

• set_classes set class labels. By Default, classes are assumed to be {0, ..., n_classes - 1} however, this assumption can be overridden by calling set_classes passing an array of class labels.

• The observation_model parameter cannot be set at model initialization, since CategoricalObservations is the only compatible observation model.

Generate Synthetic Data

In this example, we simulate categorical choice data to demonstrate the classifier. With real data, you would replace this section by loading your experimental observations.

import jax
import numpy as np
import nemos as nmo

np.random.seed(200)

n_samples, n_features, n_classes = 1000, 5, 3
X = np.random.randn(n_samples, n_features)

# simulate categorical choices using known coefficients
true_coef = 2 * np.random.randn(n_features, n_classes)
true_intercept = np.zeros(n_classes)

model = nmo.glm.ClassifierGLM(n_classes)
model.coef_ = true_coef
model.intercept_ = true_intercept
# set classes
model.set_classes(np.arange(n_classes))
true_choice, _ = model.simulate(jax.random.PRNGKey(124), X)

Fit the Model and Predict Choices

model = nmo.glm.ClassifierGLM(n_classes)

train_samples = 500
model.fit(X[:train_samples], true_choice[:train_samples])

# predict
predicted_choice = model.predict(X)

# get class probabilities
probs = model.predict_proba(X)
print(f"Probability shape: {probs.shape}")  # (n_samples, n_classes)

Probability shape: (1000, 3)

Visualize Results

import matplotlib.pyplot as plt
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix

cm = confusion_matrix(true_choice[train_samples:], predicted_choice[train_samples:])
disp = ConfusionMatrixDisplay(cm)
disp.plot(text_kw=dict(fontsize=15))
plt.title("Confusion Matrix", fontsize=20)
plt.xlabel(disp.ax_.get_xlabel(), fontsize=15)
plt.ylabel(disp.ax_.get_ylabel(), fontsize=15)
plt.show()

Show code cell source

Hide code cell source

# save image for thumbnail
from pathlib import Path
import os

fig = disp.figure_
root = os.environ.get("READTHEDOCS_OUTPUT")
if root:
   path = Path(root) / "html/_static/thumbnails/how_to_guide"
# if local store in ../_build/html/...
else:
   path = Path("../_build/html/_static/thumbnails/how_to_guide")

# make sure the folder exists if run from build
if root or Path("../assets/stylesheets").exists():
   path.mkdir(parents=True, exist_ok=True)

if path.exists():
  fig.savefig(path / "glm_for_classification.svg")