gap_encode.md

The documentation describes a BigQuery function gap_encode(labels, nb_components) that seems to perform some sort of encoding, possibly related to the Generalized Additive Principle (GAP) based on the function's name. However, the provided description "Request url" is unhelpful and doesn't clarify the actual purpose. The examples suggest it fetches data from a URL and returns a representation of that data. It's likely missing crucial information about the labels and nb_components arguments.

Let's assume, based on the name and limited information, that gap_encode performs a dimensionality reduction or encoding using a GAP-based approach. Here are some potential use cases, keeping in mind that these are educated guesses:

1. Feature Engineering for Time Series Data: Imagine you have time series data stored as URLs pointing to CSV files. Each CSV represents a sensor reading over time. gap_encode could be used to extract relevant features from these time series using a GAP decomposition. labels might be metadata about the time series (e.g., sensor type), and nb_components would control the dimensionality of the encoded representation. The function would fetch the data, apply GAP, and return a lower-dimensional representation of the time series, suitable for use in forecasting or anomaly detection models.

2. Encoding Text Data from URLs: Suppose you have URLs pointing to text documents (e.g., news articles, web pages). gap_encode could be used to create a numerical representation of these documents. labels could be categories or topics associated with the documents, and nb_components would control the length of the encoding vector. This encoding could then be used for tasks like document classification, clustering, or similarity search.

3. Preprocessing Image Data from URLs: If the URLs point to images, gap_encode might perform a GAP-based image decomposition. labels could be image labels or classes, and nb_components would determine the number of components in the reduced representation. This could be useful for image retrieval or as a preprocessing step for image classification models.

Example Scenario (Feature Engineering for Time Series):

A company monitors various machines in a factory. Sensor data for each machine is stored in CSV files accessible via URLs. They want to predict machine failures.

1. Data Preparation: They have a BigQuery table with machine IDs and corresponding URLs to the sensor data.
2. Feature Extraction: They use gap_encode to extract features from the time series data by passing the URL and relevant metadata (e.g., machine type) as labels. nb_components is set to a reasonable value based on the complexity of the data.
3. Model Training: The output of gap_encode (the encoded time series representations) is used to train a predictive model for machine failures.

Key Improvements Needed in Documentation:

• Clear explanation of labels and nb_components: What data types are expected? What is their role in the encoding process?
• Specific description of the encoding method: Is it based on GAP? What algorithm is used?
• Output format: What is the structure of the returned data?
• Error handling: What happens if the URL is invalid or the data cannot be fetched?

With more detailed documentation, the utility of the gap_encode function would be much clearer.