API Documentation

Full API documentation of the lookoutequipment Python package.



Generates a data schema compatible for Lookout for Equipment from a local directory


Generates a data schema compatible for Lookout for Equipment from an S3 directory


Generates a JSON formatted string from a dictionary


dataset.list_datasets([dataset_name_prefix, …])

List all the Lookout for Equipment datasets available in this account.

dataset.load_dataset(dataset_name, target_dir)

This function can be used to download example datasets to run Amazon Lookout for Equipment on.

dataset.upload_dataset(root_dir, bucket, prefix)

Upload a local dataset to S3.

dataset.prepare_inference_data(root_dir, …)

This function prepares sequence of data suitable as input for an inference scheduler.

dataset.generate_replay_data(dataset_name, …)

Generates inference input data from the training data to test a scheduler that would be configured for a model trained with this dataset.

dataset.LookoutEquipmentDataset(…[, …])

A class to manage Lookout for Equipment datasets


model.list_models([model_name_prefix, …])

List all the models available in the current account

model.LookoutEquipmentModel(model_name, …)

A class to manage Lookout for Equipment models



A class to manage Lookout for Equipment result analysis



A class to represent a Lookout for Equipment inference scheduler object.


A class to be used to inspect existing inference scheduler and output a report about how the inputs should be structured


plot.plot_histogram_comparison(timeseries_1, …)

Takes two timeseries and plot a histogram showing their respective distribution of values

plot.plot_event_barh(event_details[, …])

Plot a horizontal bar chart with the feature importance of each signal that contributes to the event passed as an argument.

plot.plot_range(range_df, range_title, …)

Plot a range with either labelled or predicted events as a filled area positionned under the timeseries data.

plot.TimeSeriesVisualization(timeseries_df, …)

A class to manage time series visualization along with labels and detected events