EDA and Feature Engineering in Jupyter Notebook and Modeling in a Flyte Task

In this example, we will implement a simple pipeline that takes hyperparameters, does EDA, feature engineering (step 1: EDA and feature engineering in notebook), and measures the Gradient Boosting model’s performace using mean absolute error (MAE) (step 2: Modeling in a Flyte Task).

First, let’s import the libraries we will use in this example.

import os
import pathlib
from dataclasses import dataclass

import numpy as np
import pandas as pd
from dataclasses_json import dataclass_json
from flytekit import Resources, kwtypes, task, workflow
from flytekitplugins.papermill import NotebookTask
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.model_selection import cross_val_score, train_test_split
from sklearn.preprocessing import RobustScaler

We define a dataclass to store the hyperparameters of the Gradient Boosting Regressor.

@dataclass_json
@dataclass
class Hyperparameters(object):
    n_estimators: int = 150
    max_depth: int = 3
    max_features: str = "sqrt"
    min_samples_split: int = 4
    random_state: int = 2
    nfolds: int = 10

We define a NotebookTask to run the Jupyter notebook. This notebook returns dummified_data and dataset as the outputs.

Note

dummified_data is used in this example, and dataset is used in the upcoming example.

nb = NotebookTask(
    name="eda-feature-eng-nb",
    notebook_path=os.path.join(
        pathlib.Path(__file__).parent.absolute(), "supermarket_regression_1.ipynb"
    ),
    outputs=kwtypes(dummified_data=pd.DataFrame, dataset=str),
    requests=Resources(mem="500Mi"),
)

Next, we define a cross_validate function and a modeling task to compute the MAE score of the data against the Gradient Boosting Regressor.

def cross_validate(model, nfolds, feats, targets):
    score = -1 * (
        cross_val_score(
            model, feats, targets, cv=nfolds, scoring="neg_mean_absolute_error"
        )
    )
    return np.mean(score)


@task
def modeling(
    dataset: pd.DataFrame,
    hyperparams: Hyperparameters,
) -> float:
    y_target = dataset["Product_Supermarket_Sales"].tolist()
    dataset.drop(["Product_Supermarket_Sales"], axis=1, inplace=True)

    X_train, X_test, y_train, _ = train_test_split(dataset, y_target, test_size=0.3)

    scaler = RobustScaler()

    scaler.fit(X_train)

    X_train = scaler.transform(X_train)
    X_test = scaler.transform(X_test)

    gb_model = GradientBoostingRegressor(
        n_estimators=hyperparams.n_estimators,
        max_depth=hyperparams.max_depth,
        max_features=hyperparams.max_features,
        min_samples_split=hyperparams.min_samples_split,
        random_state=hyperparams.random_state,
    )

    return cross_validate(gb_model, hyperparams.nfolds, X_train, y_train)

We define a workflow to run the notebook and the modeling task.

@workflow
def notebook_wf(hyperparams: Hyperparameters = Hyperparameters()) -> float:
    output = nb()
    mae_score = modeling(dataset=output.dummified_data, hyperparams=hyperparams)
    return mae_score

We can now run the notebook and the modeling task locally.

if __name__ == "__main__":
    print(notebook_wf())

Total running time of the script: ( 0 minutes 0.000 seconds)

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