Basic regression

This example demonstrates how to use Octopus to create a machine learning regression model. We will use the famous diabetes dataset for this purpose. Please ensure your dataset is clean, with no missing values (NaN), and that all features are numeric.

### Necessary imports for this example
import os

from octopus.example_data import load_diabetes_data
from octopus.study import OctoRegression

### Load the diabetes dataset
df, features, targets = load_diabetes_data()

print("Dataset info:")
print(f"  Features: {len(features)} - {features}")
print(f"  Samples: {df.shape[0]}")
print(f"  Classes: {len(targets)} - {targets}")
print(f"  Target distribution: {df['target'].value_counts().sort_index().to_dict()}")

### Create and run OctoRegression
study = OctoRegression(
    name="basic_regression",
    path=os.environ.get("STUDIES_PATH", "./studies"),
    target_metric="MAE",
    feature_cols=features,
    target_col="target",
    sample_id_col="index",
)

study.fit(data=df)

print("Workflow completed")