Understanding the fbprophet Error (ValueError: lam value too large)

In this blog post, we’ll delve into the details of an error that occurs when using the popular forecasting library fbprophet. Specifically, we’ll explore how to resolve the ValueError: lam value too large issue.

Introduction

Facebook Prophet is a software for forecasting time series data. It uses additive and multiplicative seasonality models with support for daily, weekly, monthly, year-to-date (YTD), and yearly seasonality patterns. However, in recent versions of Prophet, the library has undergone significant changes that can lead to errors when using older packages or methods.

One such error is the ValueError: lam value too large issue, which occurs during model sampling. This problem typically arises when the number of components used in the model exceeds a certain threshold. In this post, we’ll examine the causes and solutions for this error, as well as provide guidance on how to install older versions of Prophet using Conda.

The Error (ValueError: lam value too large)

The ValueError: lam value too large error is raised by Prophet’s sample_posterior_predictive function when it attempts to generate predictive values for the model. This occurs when the number of components in the model exceeds a certain threshold, which can lead to numerical instability.

{< highlight python >}
# File "F:\Anaconda\envs\pyqt_env\lib\site-packages\fbprophet\forecaster.py", line 1435, in predict_uncertainty
    sim_values = self.sample_posterior_predictive(df)
    File "F:\Anaconda\envs\pyqt_env\lib\site-packages\fbprophet\forecaster.py", line 1393, in sample_model
    trend = self.sample_predictive_trend(df, iteration)
    File "F:\Anaconda\envs\pyqt_env\lib\site-packages\fbprophet\forecaster.py", line 1501, in sample_predictive_trend
        n_changes = np.random.poisson(S * (T - 1))
    File "mtrand.pyx", line 3592, in numpy.random.mtrand.RandomState.poisson
    File "_common.pyx", line 865, in numpy.random._common.disc
    File "_common.pyx", line 414, in numpy.random._common.check_constraint
ValueError: lam value too large
</code>

Resolving the Error

To resolve the ValueError: lam value too large issue, you can modify your code to ensure that the number of components used in the model does not exceed the threshold. One way to do this is by using a smaller number of seasonality components.

For example:

# Make dataframe for Facebook Prophet prediction model.
df_prophet = df_korea.rename(columns={
    'date': 'ds',
    'confirmed': 'y'})

df_prophet['ds'] = pd.to_datetime(df_prophet['ds'])

# Define the seasonality parameters
seasonal_periods = [7, 30]  # weekly and monthly

# Create a seasonal decomposition model with limited components
from fbprophet.seasonal import SeasonalDecomposition
model = SeasonalDecomposition(df_prophet['y'], freq=seasonal_periods)
trend = model.trend
seasonal = model.seasonal
residuals = model.residual

# Plot the decomposition
import matplotlib.pyplot as plt

plt.plot(trend, label='Trend')
plt.plot(seasonal, label='Seasonality')
plt.plot(residuals, label='Residuals')
plt.legend()
plt.show()

Installing Older Versions of Prophet using Conda

If you’re unable to resolve the error by modifying your code, you can try installing an older version of Prophet using Conda. However, this is not a straightforward process.

Prophet’s official GitHub repository no longer supports releases for older versions. Instead, you can download historical releases from the PyPI page and install them manually using Conda.

For example:

# Install an older release of Prophet
conda install package.tar.gz

Note that this method may not work as expected due to potential issues with compatibility between different versions of Conda and Python.

Conclusion

The ValueError: lam value too large error is a common issue in recent versions of Facebook Prophet. By understanding the causes and modifications needed to resolve the error, you can avoid this problem and effectively use the library for time series forecasting. Additionally, installing older versions of Prophet using Conda may provide an alternative solution, but it requires careful consideration and experimentation.

Last modified on 2024-01-30