orcanet_contrib.orca_handler_util
Michael’s orcanet utility stuff.
Module Contents
Functions
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Update the organizer for using the model. |
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Returns one of the sample modifiers used for Orca networks. |
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Returns one of the label modifiers used for Orca networks. |
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Returns one of the dataset modifiers used for predicting with OrcaNet. |
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Convert a dict with 2d np arrays to a 2d struc array, with column |
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Returns one of the learning rate schedules used for Orca networks. |
- orcanet_contrib.orca_handler_util.update_objects(orga, model_file)[source]
Update the organizer for using the model.
Look up and load in the respective sample-, label-, and dataset- modifiers, as well as the custom objects. Will assert that the respective objects have not already been set to a non-default value (nothing is overwritten).
- Parameters
- orgaobject Organizer
Contains all the configurable options in the OrcaNet scripts.
- model_filestr
Path to a toml file which has the infos about which modifiers to use.
- orcanet_contrib.orca_handler_util.orca_sample_modifiers(name)[source]
Returns one of the sample modifiers used for Orca networks.
They will permute columns, and/or add permuted columns to xs.
- The input to the functions is:
- xs_filesdict
Dict that contains the input samples from the file(s). The keys are the names of the inputs in the toml list file. The values are a single batch of data from each corresponding file.
- The output is:
- xs_layerdict
Dict that contains the input samples for a Keras NN. The keys are the names of the input layers of the network. The values are a single batch of data for each input layer.
- Parameters
- nameNone/str
Name of the sample modifier to return.
- Returns
- sample_modifierfunction
The sample modifier function.
- orcanet_contrib.orca_handler_util.orca_label_modifiers(name)[source]
Returns one of the label modifiers used for Orca networks.
CAREFUL: y_values is a structured numpy array! if you use advanced numpy indexing, this may lead to errors. Let’s suppose you want to assign a particular value to one or multiple elements of the y_values array.
E.g. y_values[1][‘bjorkeny’] = 5 This works, since it is basic indexing.
Likewise, y_values[1:3][‘bjorkeny’] = 5 works as well, because basic indexing gives you a view (!).
Advanced indexing though, gives you a copy. So this y_values[[1,2,4]][‘bjorkeny’] = 5 will NOT work! Same with boolean indexing, like
bool_idx = np.array([True,False,False,True,False]) # if len(y_values) = 5 y_values[bool_idx][‘bjorkeny’] = 10 This will NOT work as well!!
Instead, use np.place(y_values[‘bjorkeny’], bool_idx, 10) This works.
- Parameters
- namestr
Name of the label modifier that should be used.
- Returns
- label_modifierfunction
The label modifier function.
- orcanet_contrib.orca_handler_util.orca_dataset_modifiers(name)[source]
Returns one of the dataset modifiers used for predicting with OrcaNet.
- Parameters
- namestr
Name of the dataset modifier that should be used.
- orcanet_contrib.orca_handler_util.dict_to_recarray(data_dict)[source]
Convert a dict with 2d np arrays to a 2d struc array, with column names derived from the dict keys.
- Parameters
- data_dictdict
Keys: name of the output layer. Values: 2d arrays, first dimension matches
- Returns
- recarrayndarray
- orcanet_contrib.orca_handler_util.orca_learning_rates(name, total_file_no)[source]
Returns one of the learning rate schedules used for Orca networks.
- Parameters
- namestr
Name of the schedule.
- total_file_noint
How many files there are to train on.
- Returns
- learning_ratefunction
The learning rate schedule.