prototorch_models/examples/knn_iris.py
2022-05-17 12:03:43 +02:00

82 lines
1.9 KiB
Python

"""k-NN example using the Iris dataset from scikit-learn."""
import argparse
import logging
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import KNN, VisGLVQ2D
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
X, y = load_iris(return_X_y=True)
X = X[:, 0:3:2]
X_train, X_test, y_train, y_test = train_test_split(
X,
y,
test_size=0.5,
random_state=42,
)
train_ds = pt.datasets.NumpyDataset(X_train, y_train)
test_ds = pt.datasets.NumpyDataset(X_test, y_test)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=16)
test_loader = DataLoader(test_ds, batch_size=16)
# Hyperparameters
hparams = dict(k=5)
# Initialize the model
model = KNN(hparams, data=train_ds)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
# Callbacks
vis = VisGLVQ2D(
data=(X_train, y_train),
resolution=200,
block=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
max_epochs=1,
callbacks=[
vis,
],
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
# This is only for visualization. k-NN has no training phase.
trainer.fit(model, train_loader)
# Recall
y_pred = model.predict(torch.tensor(X_train))
logging.info(y_pred)
# Test
trainer.test(model, dataloaders=test_loader)