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dev.guide
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------------------------------------------------------------------------------------------
|
|
| __POSITION CLUSTERING DEVELOPMENT GUIDE__
|
|
| [?] accessing dataset pipeline methods and data members
| like plotting data and fetching a sample, is done
| through the pc_model.dataloader_.dataset object
|
|
| ex : (plotting data before clustering)
| pc_model.dataloader_.dataset.plot_data_()
|
| args :
| plotting_method=plot_method
|
| ex : (get a sample from the dataset)
| pc_model.dataloader_.dataset[2]
|
|
| ex : (get the scaled data)
| pc_model.dataloader_.dataset.data
|
|
| ex : (get the unscaled data)
|
| pc_model.dataloader_.dataset.get_raw()
|
|
| ex : (dataset information)
| pc_model.dataloader_.dataset.__repr__() or pc_model.dataloader_.dataset
|
|
| ex : (pre-trained model object)
| pc_mode.vae_model
|
|
| ex : (plot the whole training loss in an entire epoch)
| pc_model.plot_loss()
|
|
| ex : (get latent space of the data)
| pc_model(data=dataloader().dataset.data)
|
|
| ex : (decode the latent space to reconstruct the input point)
| pc_model.decode(latent=latent)
|
|
|
| [?] if cluster_method is hdbscan to access
| sample label and its score do like this :
|
| cluster_sample_label = cluster_[45][0]
| cluster_sample_label_score = cluster_[45][1]
|
|
| [?] cluster_.set() will export a csv of dataset with their labels
|
| [?] cluster_.plot() plot the clustered data with a specified method and clustering algo
|
|
|
------------------------------------------------------------------------------------------
------------------------------------------------------------------------------------------
|
|
| __POSITION CLASSIFICATION DEVELOPMENT GUIDE__
|
|
| ex : (accessing training dataset tensors)
| dataloader()[0].dataset.tensors
| pc_model.training_dataloader_.dataset.tensors
|
|
| ex : (accessing training dataloader pipeline)
| dataloader()[0]
| pc_model.dataloader_
|
|
| ex : (accessing testing dataset tensors)
| dataloader()[1].dataset.tensors
| pc_model.testing_dataloader_.dataset.tensors
|
|
| ex : (accessing testing dataloader pipeline)
| dataloader()[1]
| pc_model.testing_dataloader_
|
|
| ex : (accessing classifier model after training)
| classifier_.model
|
|
| ex : (classifier loss tracker)
| classifier_.loss_tracker
|
|
| ex : (train and test on training and testing data)
| pc_model(data=dataloader())
|
|
| ex : (classify either numpyndarray of input data or a csv path of input data)
| data = input_data # numpyndarray
| data = csv_path_to_input_data
| classifier_(data)
|
|
|
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