k-nearest neighbors

A KNNClassifier is a k-nearest neighbors model based on scikit-learn.

We want to classify the Iris dataset using a KNN classifier.

from __future__ import annotations

from numpy import array

from gemseo import configure_logger
from gemseo import create_benchmark_dataset
from gemseo.mlearning import create_classification_model

configure_logger()

<RootLogger root (INFO)>

Load Iris dataset

iris = create_benchmark_dataset("IrisDataset", as_io=True)

Create the classification model

Then, we build the k-NN classification model from the discipline cache and displays this model.

model = create_classification_model("KNNClassifier", data=iris)
model.learn()
model

KNNClassifier(input_names=(), n_neighbors=5, output_names=(), parameters={}, transformer={'inputs': <gemseo.mlearning.transformers.scaler.min_max_scaler.MinMaxScaler object at 0x704937e17c50>})

• based on the scikit-learn library
• built from 150 learning samples

Predict output

Once it is built, we can use it for prediction.

input_value = {
    "sepal_length": array([4.5]),
    "sepal_width": array([3.0]),
    "petal_length": array([1.0]),
    "petal_width": array([0.2]),
}
output_value = model.predict(input_value)
output_value

{'specy': array([0])}