:orphan:

.. _chart_gallery.classification_plot:

=======================================
Machine Learning - Classification Plots
=======================================

.. Necessary Code Elements

.. ipython:: python
    :suppress:

    import vastorbit as vo
    import vastorbit.machine_learning.vast as vml
    import numpy as np

    N = 100 # Number of Records
    k = 10 # step

    # Normal Distributions
    x = np.random.normal(5, 1, round(N / 2))
    y = np.random.normal(3, 1, round(N / 2))
    z = np.random.normal(3, 1, round(N / 2))

    # Creating a VastFrame with two clusters
    data = vo.VastFrame({
    "x": np.concatenate([x, x + k]),
    "y": np.concatenate([y, y + k]),
    "z": np.concatenate([z, z + k]),
    "c": [0 for i in range(round(N / 2))] + [1 for i in range(round(N / 2))]
    })

    # Defining the Models
    model_logit_1d = vml.LogisticRegression()
    model_svc_1d = vml.LinearSVC(max_iter = 1000)
    model_svc_2d = vml.LinearSVC(max_iter = 1000)
    model_svc_3d = vml.LinearSVC(max_iter = 1000)

    # Fitting the models
    model_logit_1d.fit(data, "x", "c")
    model_svc_1d.fit(data, "x", "c")
    model_svc_2d.fit(data, ["x", "y"], "c")
    model_svc_3d.fit(data, ["x", "y", "z"], "c")

    # Displaying the VastFrame
    display(data)


General
-------

In this example, we aim to present several regression plots, including linear regression, tree-based algorithms, and various residual plots. It's important to note that these plots are purely illustrative and are based on generated data. To make the data more realistically representative, we introduce some noise, resulting in an approximately linear relationship.

Let's begin by importing ``vastorbit``.

.. ipython:: python

    import vastorbit as vo

Let's also import ``numpy`` to create a random dataset.

.. ipython:: python

    import numpy as np

Let's generate a dataset using the following data.

.. code-block:: python
        
    N = 100 # Number of Records
    k = 10 # step

    # Normal Distributions
    x = np.random.normal(5, 1, round(N / 2))
    y = np.random.normal(3, 1, round(N / 2))
    z = np.random.normal(3, 1, round(N / 2))

    # Creating a VastFrame with two clusters
    data = vo.VastFrame({
    "x": np.concatenate([x, x + k]),
    "y": np.concatenate([y, y + k]),
    "z": np.concatenate([z, z + k]),
    "c": [0 for i in range(round(N / 2))] + [1 for i in range(round(N / 2))]
    })

Let's proceed by creating both a logistic regression model and a linear SVC model using the complete dataset.

.. code-block:: python
    
    # Importing the VAST ML module
    import vastorbit.machine_learning.vast as vml

    # Defining the Models
    model_logit_1d = vml.LogisticRegression()
    model_svc_1d = vml.LinearSVC(max_iter = 1000)
    model_svc_2d = vml.LinearSVC(max_iter = 1000)
    model_svc_3d = vml.LinearSVC(max_iter = 1000)

    # Fitting the models
    model_logit_1d.fit(data, "x", "c")
    model_svc_1d.fit(data, "x", "c")
    model_svc_2d.fit(data, ["x", "y"], "c")
    model_svc_3d.fit(data, ["x", "y", "z"], "c")

    # Displaying the VastFrame
    display(data)

In the context of data visualization, we have the flexibility to harness multiple plotting libraries to craft a wide range of graphical representations. vastorbit, as a versatile tool, provides support for several graphic libraries, such as Matplotlib and Plotly. Each of these libraries offers unique features and capabilities, allowing us to choose the most suitable one for our specific data visualization needs.

.. image:: ../../docs/source/_static/plotting_libs.png
   :width: 80%
   :align: center

.. note::
    
    To select the desired plotting library, we simply need to use the :py:func:`~vastorbit.set_option` function. vastorbit offers the flexibility to smoothly transition between different plotting libraries. In instances where a particular graphic is not supported by the chosen library or is not supported within the vastorbit framework, the tool will automatically generate a warning and then switch to an alternative library where the graphic can be created.

Please click on the tabs to view the various graphics generated by the different plotting libraries.

.. ipython:: python
    :suppress:

    import vastorbit as vo

.. tab:: Plotly

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "plotly")

    We can switch to using the ``plotly`` module.

    .. code-block:: python
        
        vo.set_option("plotting_lib", "plotly")
    
    .. tab:: 1D

      .. code-block:: python
          
          model_svc_1d.plot()

      .. ipython:: python
          :suppress:
          :okwarning:
        
          fig = model_svc_1d.plot()
          fig.write_html("figures/plotting_plotly_svc_1d_1.html")

      .. raw:: html
          :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_1d_1.html

      **Logit Plot**

      .. code-block:: python
          
          model_logit_1d.plot()

      .. ipython:: python
          :suppress:
          :okwarning:
        
          fig = model_logit_1d.plot()
          fig.write_html("figures/plotting_plotly_logit_1d_2.html")

      .. raw:: html
          :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_logit_1d_2.html

    .. tab:: 2D

      .. code-block:: python
          
          model_svc_2d.plot()

      .. ipython:: python
          :suppress:
          :okwarning:
        
          fig = model_svc_2d.plot()
          fig.write_html("figures/plotting_plotly_svc_2d_1.html")

      .. raw:: html
          :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_2d_1.html

    .. tab:: 3D

      .. code-block:: python
          
          model_svc_3d.plot()

      .. ipython:: python
          :suppress:
          :okwarning:
        
          fig = model_svc_3d.plot()
          fig.write_html("figures/plotting_plotly_svc_3d_1.html")

      .. raw:: html
          :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_3d_1.html
        
.. tab:: Matplotlib

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "matplotlib")

    We can switch to using the ``matplotlib`` module.

    .. code-block:: python
        
        vo.set_option("plotting_lib", "matplotlib")

    .. tab:: 1D

      .. ipython:: python
          :okwarning:

          @savefig plotting_matplotlib_svc_1d_1.png
          model_svc_1d.plot()

      **Logit Plot**

      .. ipython:: python
          :okwarning:

          @savefig plotting_matplotlib_logit_1d_2.png
          model_logit_1d.plot()

    .. tab:: 2D

      .. ipython:: python
          :okwarning:

          @savefig plotting_matplotlib_svc_2d_1.png
          model_svc_2d.plot()

    .. tab:: 3D

      .. ipython:: python
          :okwarning:

          @savefig plotting_matplotlib_svc_3d_1.png
          model_svc_3d.plot()

___________________


Chart Customization
-------------------

vastorbit empowers users with a high degree of flexibility when it comes to tailoring the visual aspects of their plots. 
This customization extends to essential elements such as **color schemes**, **text labels**, and **plot sizes**, as well as a wide range of other attributes that can be fine-tuned to align with specific design preferences and analytical requirements. Whether you want to make your visualizations more visually appealing or need to convey specific insights with precision, vastorbit's customization options enable you to craft graphics that suit your exact needs.

.. Important:: Different customization parameters are available for Plotly and Matplotlib. 
    For a comprehensive list of customization features, please consult the documentation of the respective 
    libraries: `plotly <https://plotly.com/python-api-reference/>`__, `matplotlib <https://matplotlib.org/stable/api/matplotlib_configuration_api.html>`__.

Colors
~~~~~~

.. tab:: Plotly

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "plotly")

    **Custom colors**

    .. code-block:: python
        
        model_svc_2d.plot(colors = ["red", "blue"])

    .. ipython:: python
        :suppress:
        :okwarning:

        fig = model_svc_2d.plot(colors = ["red", "blue"])
        fig.write_html("figures/plotting_plotly_svc_2d_plot_custom_color_1.html")

    .. raw:: html
        :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_2d_plot_custom_color_1.html

.. tab:: Matplolib

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "matplotlib")

    **Custom colors**

    .. ipython:: python
        :okwarning:

        @savefig plotting_matplotlib_svc_2d_plot_custom_color_1.png
        model_svc_2d.plot(colors = ["red", "blue"])

____

Size
~~~~

.. tab:: Plotly

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "plotly")

    **Custom Width and Height**

    .. code-block:: python
        
        model_svc_2d.plot(width = 300, height = 300)

    .. ipython:: python
        :suppress:
        :okwarning:

        fig = model_svc_2d.plot(width = 300, height = 300)
        fig.write_html("figures/plotting_plotly_svc_2d_plot_custom_size.html")

    .. raw:: html
        :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_2d_plot_custom_size.html

.. tab:: Matplolib

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "matplotlib")

    **Custom Width and Height**

    .. ipython:: python
        :okwarning:

        @savefig plotting_matplotlib_svc_2d_plot_single_custom_size.png
        model_svc_2d.plot(width = 6, height = 3)

_____


Text
~~~~

.. tab:: Plotly

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "plotly")

    **Custom Title**

    .. code-block:: python
        
        model_svc_2d.plot().update_layout(title_text = "Custom Title")

    .. ipython:: python
        :suppress:
        :okwarning:

        fig = model_svc_2d.plot().update_layout(title_text = "Custom Title")
        fig.write_html("figures/plotting_plotly_svc_2d_plot_custom_main_title.html")

    .. raw:: html
        :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_2d_plot_custom_main_title.html


    **Custom Axis Titles**

    .. code-block:: python
        
        model_svc_2d.plot(yaxis_title = "Custom Y-Axis Title")

    .. ipython:: python
        :suppress:
        :okwarning:

        fig = model_svc_2d.plot(yaxis_title = "Custom Y-Axis Title")
        fig.write_html("figures/plotting_plotly_svc_2d_plot_custom_y_title.html")

    .. raw:: html
        :file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_svc_2d_plot_custom_y_title.html

.. tab:: Matplolib

    .. ipython:: python
        :suppress:

        vo.set_option("plotting_lib", "matplotlib")

    **Custom Title Text**

    .. ipython:: python
        :okwarning:

        @savefig plotting_matplotlib_svc_2d_plot_custom_title_label.png
        model_svc_2d.plot().set_title("Custom Title")

    **Custom Axis Titles**

    .. ipython:: python
        :okwarning:

        @savefig plotting_matplotlib_svc_2d_plot_custom_yaxis_label.png
        model_svc_2d.plot().set_ylabel("Custom Y Axis")

.. ipython:: python
   :suppress:

   from vastorbit._utils._sql._sys import purge_memory
   purge_memory()