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Prophet integration guide#

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Custom dashboard displaying metadata logged with Prophet

Prophet is a popular time-series forecasting library. With the Neptune-Prophet integration, you can keep track of parameters, forecast data frames, residual diagnostic charts, cross validation folds, and other metadata while training models with Prophet.

See in Neptune  Code examples 

Before you start#

  • Sign up at neptune.ai/register.
  • Create a project for storing your metadata.
  • Have Prophet installed.
  • (optional) To log the plots as interactive HTML visualizations, you need to have the Plotly graphing library installed.
    • You can enable logging as interactive HTML by setting the log_interactive parameter to True where applicable.
    • Otherwise, the plots are recorded as static images by default.

Installing the integration#

To use your preinstalled version of Neptune together with the integration:

pip
pip install -U neptune-prophet

To install both Neptune and the integration:

pip
pip install -U "neptune[prophet]"
Passing your Neptune credentials

Once you've registered and created a project, set your Neptune API token and full project name to the NEPTUNE_API_TOKEN and NEPTUNE_PROJECT environment variables, respectively.

export NEPTUNE_API_TOKEN="h0dHBzOi8aHR0cHM.4kl0jvYh3Kb8...6Lc"

To find your API token: In the bottom-left corner of the Neptune app, expand the user menu and select Get my API token.

export NEPTUNE_PROJECT="ml-team/classification"

Your full project name has the form workspace-name/project-name. You can copy it from the project settings: Click the menu in the top-right → Details & privacy.

On Windows, navigate to Settings → Edit the system environment variables, or enter the following in Command Prompt: setx SOME_NEPTUNE_VARIABLE 'some-value'

While it's not recommended especially for the API token, you can also pass your credentials in the code when initializing Neptune.

run = neptune.init_run(
    project="ml-team/classification",  # your full project name here
    api_token="h0dHBzOi8aHR0cHM6Lkc78ghs74kl0jvYh...3Kb8",  # your API token here
)

For more help, see Set Neptune credentials.

If you'd rather follow the guide without any setup, you can run the example in Colab .

Logging options#

In order to track the metadata to Neptune, you need to initialize a Neptune run.

import neptune

run = neptune.init_run()
Haven't registered?

If you haven't registered or set up your credentials, you can log anonymously to the public project:

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

You can then use the run object to track metadata with the functions listed below.

Logging Prophet summary#

create_summary()

You can log all relevant metadata at once with this function, such as forecast plots and residual diagnostics.

Parameters

Parameter         Description
model Fitted Prophet model object.
df (optional) The dataset that was used for making the forecast. If provided, additional plots will be recorded.
fcst (optional) Forecast returned by Prophet. If not provided, it'll be calculated using the df data.
log_charts Additionally save the diagnostic plots. Defaults to True.
log_interactive Save the plots as interactive HTML files. Requires the Plotly library. Defaults to False.

Returns

Dictionary with all the plots.

Example

import pandas as pd
from prophet import Prophet
import neptune
import neptune.integrations.prophet as npt_utils

run = neptune.init_run()

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

run["prophet_summary"] = npt_utils.create_summary(
    model=model,
    df=dataset,
    log_interactive=True, # (1)!
)
  1. This option requires Plotly >=5.18 to be installed.

Getting the model configuration#

To extract the configuration from the Prophet model object, use the get_model_config() function:

run = neptune.init_run()
dataset = ...
model = Prophet()
model.fit(dataset)

run["model_config"] = npt_utils.get_model_config(model)

Getting the serialized model#

To serialize the Prophet model, you can use the get_serialized_model() function:

run = neptune.init_run()
dataset = ...
model = Prophet()
model.fit(dataset)

run["model"] = npt_utils.get_serialized_model(model)

Getting forecast components#

To get the forecast components (such as trend and monthly seasonality) produced by Prophet, use the get_forecast_components() function:

run = neptune.init_run()
dataset = ...
model = Prophet()
model.fit(dataset)
predicted = model.predict(dataset)

run["forecast_components"] = npt_utils.get_forecast_components(model, predicted)

Logging forecast plots#

To save Prophet plots (such as forecast components, changepoints, forecast with prediction interval), use the create_forecast_plots() function:

model.fit(dataset)
predicted = model.predict(dataset)

run["forecast_plots"] = npt_utils.create_forecast_plots(model, predicted)

Logging residual forecast plots#

To save additional diagnostic plots (such as histogram of residuals, ACF plot, QQ-plot for residuals) to Neptune, use the create_residual_diagnostics_plots() function:

model.fit(dataset)
predicted = model.predict(dataset)

run["residual_diagnostics_plot"] = npt_utils.create_residual_diagnostics_plots(
    predicted, dataset.y
)

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