logistic-regression-iris Overview

A logistic regression model trained on the Iris dataset. It takes two inputs: 'PetalLengthCm' and 'PetalWidthCm'. It predicts whether the species is 'Iris-setosa'. It is a PyTorch adaptation of the scikit-learn model in Chapter 10 of Aurelien Geron's book 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow'. Code: https://github.com/sambitmukherjee/handson-ml3-pytorch/blob/main/chapter10/logistic_regression_iris.ipynb Model: https://huggingface.co/sadhaklal/logistic-regression-iris

Project visibility

Public

Last active

3/11/2024, 7:51:23 AM

Owner

Sambit Mukherjee

Contributors

1 user

Total runs

Total compute

2 minutes

Export & update data

Use our Public API to export data or update data, such as editing the config of existing runs. Learn more in the docs →

Find the run path

To use the public API, you'll often need the run path which is <entity>/<project>/<run_id>. In the app UI, open a run page, then click on the Overview tab to see the run path.

Update config for an existing run

This example updates one of your configuration settings.

import wandb api = wandb.Api() run = api.run("sadhaklal/logistic-regression-iris/<run_id>") run.config["key"] = updated_value run.update()

Export metrics from a single run to a CSV file

This snippet finds all the metrics saved for a single run and saves them to a CSV file.

import wandb api = wandb.Api() # run is specified by <entity>/<project>/<run_id> run = api.run("sadhaklal/logistic-regression-iris/<run_id>") # save the metrics for the run to a csv file metrics_dataframe = run.history() metrics_dataframe.to_csv("metrics.csv")

Read metrics for a run

Pull down the accuracy and timestamps for logged metric data. In this example, data was logged with wandb.log({"accuracy": acc}) to a run with the run path <entity>/<project>/<run_id>.

import wandb api = wandb.Api() run = api.run("sadhaklal/logistic-regression-iris/<run_id>") if run.state == "finished": for i, row in run.history().iterrows(): print(row["_timestamp"], row["accuracy"])

Get unsampled metric data

When you pull data from history, by default it's sampled to 500 points. Get all the logged data points using run.scan_history(). Here's an example downloading all the loss data points logged in history.

import wandb api = wandb.Api() run = api.run("sadhaklal/logistic-regression-iris/<run_id>") history = run.scan_history() losses = [row["loss"] for row in history]

Download the best model file from a sweep

This snippet downloads the model file with the highest validation accuracy from a sweep with runs that saved model files to model.h5.

import wandb api = wandb.Api() sweep = api.sweep("sadhaklal/logistic-regression-iris/<sweep_id>") runs = sorted(sweep.runs, key=lambda run: run.summary.get("val_acc", 0), reverse=True) val_acc = runs[0].summary.get("val_acc", 0) print(f"Best run {runs[0].name} with {val_acc}% validation accuracy") runs[0].file("model.h5").download(replace=True) print("Best model saved to model-best.h5")