Neptune + Prophet integration

Experiment tracking for Prophet-trained models.

What will you get with this integration?

Log, organize, visualize, and compare ML experiments in a single place
Monitor model training live
Version and query production-ready models and associated metadata (e.g., datasets)
Collaborate with the team and across the organization

What will be logged to Neptune?

parameters,
forecast data frames,
residual diagnostic charts,
other metadata

Resources

Example

Before you start

Install and set up Neptune.
Have Prophet installed.

Installation

# On the command line
pip install neptune-prophet

Logging example

# In Python
import pandas as pd
from prophet import Prophet
import neptune
import neptune.integrations.prophet as npt_utils

# Start a run
run = neptune.init_run(project="common/fbprophet-integration", api_token=neptune.ANONYMOUS_API_TOKEN)

# Load dataset and fit model
dataset = pd.read_csv(
    "https://raw.githubusercontent.com/facebook/prophet/main/examples/example_wp_log_peyton_manning.csv"
)
model = Prophet()
model.fit(dataset)

# Log summary metadata (including model, dataset, forecast and charts)
run["prophet_summary"] = npt_utils.create_summary(model=model, df=df, fcst=forecast)

# Stop the run
run.stop()

Support

If you got stuck or simply want to talk to us, here are your options:

Check our FAQ page.
You can submit bug reports, feature requests, or contributions directly to the repository.
Chat! In the Neptune app, click the blue message icon in the bottom-right corner and send a message. A real person will talk to you ASAP (typically very ASAP).
You can just shoot us an email at support@neptune.ai.