=================
Transform method
=================

The `transform` method alows user to convert original dataframe into it's numerical representation. Thanks to that, 
we can use our metric with external libraries such as scipy or scikit-learn. More on that in `External Api Compatibility` section.

----------
Transform
----------

It is done by calling the `transform` method on a fitted Gower instance.

.. code-block:: python

   import numpy as np

   from gower_metric import Gower

   data = np.array([[1, 'a'], [2, 'b'], [3, 'a'], [4, 'c']], dtype=object)

   feature_types = {
      0: "ratio_scale_interval",
      1: "categorical_nominal",
   }

   gower = Gower(feature_types=feature_types).fit(data)

   transformed_data = gower.transform(data)

All numerical, ratio scale interval features are intact. Binary features are represented as 0 and 1, depending on the value.
Categorical nominal and ordinal features are encoded using ordinal encoding from scikit-learn.

--------------
Fit transform
--------------

For convenience, Gower also implements `fit_transform` method, which combines `fit` and `transform` in one call.

.. code-block:: python

   import numpy as np

   from gower_metric import Gower

   data = np.array([[1, 'a'], [2, 'b'], [3, 'a'], [4, 'c']], dtype=object)

   feature_types = {
      0: "ratio_scale_interval",
      1: "categorical_nominal",
   }

   gower = Gower(feature_types=feature_types)

   transformed_data = gower.fit_transform(data)

.. warning::

    Under the hood, the `fit` method learns the mapping of categorical ordinal values to their numerical representation.
    Therefore, calling `transform` on the same data before and after re-fitting the instance may result in different numerical representations
    or even NaN values. The same applies to the `fit_transform` method. If your data does not contain any categorical ordinal features,
    this warning may not apply (we have not implemented tests for this scenario).

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