astronomaly.frontend package¶
Submodules¶
astronomaly.frontend.interface module¶
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class
astronomaly.frontend.interface.Controller(pipeline_file)¶ Bases:
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clean_up()¶
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delete_labels()¶ Allows the user to delete all the labels they’ve applied and start again
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get_active_learning_columns()¶ Checks if active learning has been run and returns appropriate columns to use in plotting
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get_coordinates(idx)¶ If available, will return the coordinates of the requested object in object format, ready to pass on to another website like simbad
Parameters: idx (str) – Index of the object Returns: Coordinates Return type: dict
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get_data_type()¶
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get_display_data(idx)¶ Simply calls the underlying Dataset’s function to return display data.
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get_features(idx)¶ Returns the features of instance given by index idx.
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get_max_id()¶
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get_metadata(idx, exclude_keywords=[], include_keywords=[])¶ Returns the metadata for an instance in a format ready for display.
Parameters: Returns: Display-ready metadata
Return type:
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get_original_id_from_index(ind)¶ The frontend iterates through an ordered list that can change depending on the algorithm selected. This function returns the actual index of an instance (which might be ‘obj2487’ or simply ‘1’) when given an array index.
Parameters: ind (int) – The position in an array Returns: The actual object id Return type: str
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get_visualisation_data(color_by_column='')¶ Returns the data for the visualisation plot in the correct json format.
Parameters: color_by_column (str, optional) – If given, the points on the plot will be coloured by this column so for instance, more anomalous objects are brighter. Current options are: ‘score’ (raw ML anomaly score), ‘trained_score’ (score after active learning) and ‘user_predicted_score’ (the regressed values of the human applied labels) Returns: Formatting visualisation plot data Return type: dict
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randomise_ml_scores()¶ Returns the anomaly scores in a random order
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run_active_learning()¶ Runs the selected active learning algorithm.
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run_pipeline()¶ Runs (or reruns) the pipeline. Reimports the pipeline script so changes are reflected.
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set_human_label(idx, label)¶ Sets the human-assigned score to an instance. Creates the column “human_label” if necessary in the anomaly_scores dataframe.
Parameters:
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set_pipeline_script(pipeline_file)¶ Allows the changing of the input pipeline file.
Parameters: pipeline_file (str) – New pipeline file
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sort_ml_scores(column_to_sort_by='score')¶ Returns the anomaly scores sorted by a particular column.
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