Neptune.ai Prophet integration library
Project description
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.
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