Quickstart: Tabular Regression with the Python API
----------------------------------------------------

This tutorial uses :std:ref:`Energy Consumption in quickstart` provided with NeurEco installation.

.. note::

  The GUI functionality **Export NeurEco to Python** , see :std:ref:`Export Tabular Regression from the GUI to the Python API`, facilitates the initial transition from the usage of NeurEco with the GUI to its usage with the Python API.


To work with the Tabular NeurEco models in Python, import **NeurEcoTabular** library:

.. code-block:: python

    from NeurEco import NeurEcoTabular as Tabular
	
Import numpy to handle the data sets:

.. code-block:: python

    import numpy as np 

Load the data sets (see :std:ref:`Data preparation for NeurEco Regression python API` and :std:ref:`Energy Consumption in quickstart`):

.. code-block:: python

    x_train = np.genfromtxt("./x_train.csv", delimiter=";", skip_header=True)
    y_train = np.genfromtxt("./y_train.csv", delimiter=";", skip_header=True)
    y_train = np.reshape(y_train, (-1, 1))
    x_test = np.genfromtxt("x_test.csv", delimiter=";", skip_header=True)
    y_test = np.genfromtxt("y_test.csv", delimiter=";", skip_header=True)
    y_test = np.reshape(y_test, (-1, 1))

To initialize a NeurEco object to handle the **Regression** problem:

.. code-block:: python

    regression_model = Tabular.Regressor()

To build the model, call method **build** with the parameters set for the problem under consideration (see :std:ref:`Build NeurEco Regression model with the Python API`):

.. code-block:: python
    
    regression_model.build(input_data=x_train, output_data=y_train,
                    # the rest of these parameters are optional
                    write_model_to="./EnergyConsumptionModel/EnergyConsumption.ednn",
                    checkpoint_address="./EnergyConsumptionModel/EnergyConsumption.checkpoint",
                    valid_percentage=33.33,
                    inputs_shifting="min_centered",
                    inputs_scaling="max_centered")
					
.. note:: 
    For detailed documentation on **build**, see :std:ref:`Build NeurEco Regression model with the Python API`

To evaluate the NeurEco Model on the testing data, call **evaluate** method: 

.. code-block:: python

    neureco_test_outputs = regression_model.evaluate(x_test)
	
.. note::
    For detailed documentation on **evaluate**, see :std:ref:`Evaluate NeurEco Regression model with the Python API`

To export the model to the chosen format, run one of the following commands:

.. code-block:: python

    regression_model.export_c("./EnergyConsumptionModel/EnergyConsumption.h", precision="double")
    regression_model.export_onnx("./EnergyConsumptionModel/EnergyConsumption.onnx", precision="float16")
    regression_model.export_fmu("./EnergyConsumptionModel/EnergyConsumption.fmu")
    regression_model.export_vba("./EnergyConsumptionModel/EnergyConsumption.bas", precision="float")

Export to these formats requires *embed* license.

.. note::
    For detailed documentation on **export**, see :std:ref:`Export NeurEco Regression model with the Python API`

When the model is not needed any more, delete it from the memory:

.. code-block:: python

    regression_model.delete()

.. note::
    For detailed documentation on Tabular Regression with the python API, see :std:ref:`Tabular Regression with the Python API`.