graph LR
libra_queries["libra.queries"]
libra_queries_predict["libra.queries.predict"]
libra_queries_interpret["libra.queries.interpret"]
libra_queries_tune["libra.queries.tune"]
libra_queries_neural_network_query["libra.queries.neural_network_query"]
libra_queries_get_models["libra.queries.get_models"]
libra_queries_plots["libra.queries.plots"]
libra_queries_model["libra.queries.model"]
libra_queries_clearLog["libra.queries.clearLog"]
libra_queries_logger["libra.queries.logger"]
libra_queries_classification_query_ann["libra.queries.classification_query_ann"]
libra_queries_regression_query_ann["libra.queries.regression_query_ann"]
libra_queries -- "dispatches to" --> libra_queries_predict
libra_queries -- "dispatches to" --> libra_queries_interpret
libra_queries -- "dispatches to" --> libra_queries_tune
libra_queries -- "dispatches to" --> libra_queries_neural_network_query
libra_queries -- "dispatches to" --> libra_queries_get_models
libra_queries -- "dispatches to" --> libra_queries_plots
libra_queries -- "dispatches to" --> libra_queries_model
libra_queries -- "initializes with" --> libra_queries_clearLog
libra_queries -- "initializes with" --> libra_queries_logger
libra_queries_predict -- "triggers" --> libra_queries_interpret
libra_queries_neural_network_query -- "dispatches to" --> libra_queries_classification_query_ann
libra_queries_neural_network_query -- "dispatches to" --> libra_queries_regression_query_ann
libra_queries_get_models -- "logs via" --> libra_queries_logger
The Client API subsystem, primarily encapsulated within libra.queries, serves as the user's main interaction point with the ML library. It embodies the Facade Pattern, simplifying complex underlying ML operations.
The primary user-facing component, providing a high-level abstraction for interacting with the ML library. It acts as the central orchestrator, receiving user queries, managing the overall workflow, and presenting results.
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Handles user requests for making predictions using trained models, serving as a core ML inference capability.
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Provides functionalities to explain model predictions or internal workings, enhancing model transparency.
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Manages the process of optimizing model hyperparameters to improve performance.
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Acts as a specialized facade for neural network-related queries, abstracting specific ANN implementations.
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Provides functionality to retrieve a list of available models within the library, enabling users to discover and select models.
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Offers functionalities for generating visualizations and reports based on model results or data, aiding in analysis and presentation.
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Provides a general interface for interacting with various ML models, likely for loading, saving, or querying model metadata.
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Utility for clearing logs.
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Utility for logging information.
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Specific ANN implementation for classification queries.
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Specific ANN implementation for regression queries.
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