Basic usage examples
=============

For full examples on two illustrative cases go to:
  * Example of extracting sine wave prototype and explaining class with existence ora absence of a prototype: `Jupyter Notebook <https://github.com/sbobek/tsproto/blob/main/examples/illustrative-example-frequency.ipynb>`_
  * Example of extracting sine wave as a prototype end explaining class by difference in frequency of a prototype `Jupyter Notebook <https://github.com/sbobek/tsproto/blob/main/examples/illustrative-example.ipynb>`_

The basic usage of the TSProto assuming you have your model trained is straightforward:

.. code-block:: python

    from tsproto.models import *
    from tsproto.utils import *

    #assuming that trainX, trainy and model are given

    pe = PrototypeEncoder(clf, n_clusters=2, min_size=50, method='dtw',
                          descriptors=['existance'],
                          jump=1, pen=1,multiplier=None,n_jobs=-1,
                          verbose=1)

    trainX, shapclass = getshap(model=model, X=trainX, y=trainy,shap_version='deep',
                            bg_size = 1000,  absshap = True)

    #The input needs to be a 3D vector: number of samples, lenght of time-series, number of dimensions (features)
    trainXproto = train.reshape((trainX.shape[0], trainX.shape[1],1))
    shapclassXproto = shapclass.reshape((shapclass.shape[0], shapclass.shape[1],1))

    ohe_train, features, target_ohe,weights = pe.fit_transform(trainXproto,shapclassXproto)

    im  = InterpretableModel()
    acc,prec,rec,f1,interpretable_model = im.fit_or_predict(ohe_train, features,
                            target_ohe,
                            intclf=None, # if intclf is given, the funciton behaves as predict,
                            verbose=0, max_depth=2, min_samples_leaf=0.05,
                            weights=None)


After the Interpretable model has been created it now can be visualised.

.. code-block:: python
    # Visualize model
    from  tsproto.plots import *

    ds_final = ohe_train.copy()
    dot = export_decision_tree_with_embedded_histograms(decision_tree=interpretable_model,
                                                  dataset=ds_final,
                                                  target_name='target',
                                                  feature_names=features,
                                                  filename='synthetic',
                                                  proto_encoder=pe, figsize=(6,3))

    from IPython.display import SVG, Image
    Image('synthetic.png')


.. image:: https://raw.githubusercontent.com/sbobek/tsproto/main/pix/illustrative-example.png
    :alt: Illustrative example