.. _Evaluate NeurEco Classification model with the Python API:

Evaluate NeurEco Classification model with the Python API
===========================================================

To evaluate a NeurEco Classification model in Python API, import **NeurEcoTabular** library:

.. code-block:: python

  from NeurEco import NeurEcoTabular as Tabular
	
Initialize a NeurEco object to handle the **Classification** problem:

.. code-block:: python

  model = Tabular.Classifier()
	
:std:ref:`Build NeurEco Classification model with the Python API` or load previously build and saved to *"the/path/to/the/saved/classification/model.ernn"* model:

.. code-block:: python

  model.load("the/path/to/the/saved/classification/model.ernn")

Once **model** contains a Classification model, call method **evaluate** with the parameters set accordingly:	

.. code-block:: python

  model.evaluate(inputs, vec=None)

Evaluates a Tabular model on a set of input data.

:inputs: required, NumPy array: input data array: shape (n, m) where n is the number of samples and m is the number of input features.
:vec: optional, NumPy array: perform evaluation with the model's weights set to values in vec.

:return: NumPy array:  output data array: shape (n, p) where n is the number of samples and p is the number of output features.
		
The evaluated array **outputs** is non one-hot encoded. Each column **j** of this array contains the predicted probabilities for the samples to belong to the class number **j**.

Post-treatment to get the predicted class numbers:

.. code-block:: python

  import numpy as np
  output_labels = np.argmax(outputs, axis=1)