:orphan:
.. _chart_gallery.tsa:
=====================================
Machine Learning - Time Series Plots
=====================================
.. Necessary Code Elements
.. ipython:: python
:suppress:
:okwarning:
import vastorbit as vo
import vastorbit.datasets as vod
from vastorbit.machine_learning.vast.tsa import AR
import numpy as np
data = vod.load_airline_passengers()
# Defining the Models
model = AR(p = 20)
# Fitting the models
model.fit(data, "date", "passengers")
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 use the airline passengers' dataset for this example.
.. ipython:: python
import vastorbit.datasets as vod
data = vod.load_airline_passengers()
Let's proceed by creating an AR model to fit the time-series data.
.. code-block:: python
# Importing the VAST ML module
from vastorbit.machine_learning.vast.tsa import AR
# Defining the Models
model = AR(p = 20)
# Fitting the models
model.fit(data, "date", "passengers")
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")
.. code-block:: python
model.plot(data, "date", "passengers", npredictions = 11, method="forecast")
.. ipython:: python
:suppress:
:okwarning:
fig = model.plot(data, "date", "passengers", npredictions = 20, start = 140)
fig.write_html("figures/plotting_plotly_tsa_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_tsa_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")
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_tsa_1.png
model.plot(data, "date", "passengers", npredictions = 11, method="forecast")
___________________
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 `__, `matplotlib `__.
Colors
~~~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom colors**
.. code-block:: python
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"])
.. ipython:: python
:suppress:
:okwarning:
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"])
fig.write_html("figures/plotting_plotly_tsa_plot_custom_color_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_tsa_plot_custom_color_1.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom colors**
.. ipython:: python
@savefig plotting_matplotlib_tsa_plot_custom_color_1.png
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"])
____
Size
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Width and Height**
.. code-block:: python
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], width = 450, height = 450)
.. ipython:: python
:suppress:
:okwarning:
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], width = 450, height = 450)
fig.write_html("figures/plotting_plotly_tsa_plot_custom_size.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_tsa_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_tsa_plot_single_custom_size.png
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], width = 6, height = 4)
_____
Text
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Title**
.. code-block:: python
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], title_text ="Custom Title")
.. ipython:: python
:suppress:
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], title_text ="Custom Title", width = 600)
fig.write_html("figures/plotting_plotly_tsa_plot_custom_main_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_tsa_plot_custom_main_title.html
**Custom Axis Titles**
.. code-block:: python
model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], yaxis_title = "Custom Y-Axis Title")
.. ipython:: python
:suppress:
fig = model.plot(data, "date", "passengers", npredictions = 11, method="forecast", colors = ["red", "green"], yaxis_title = "Custom Y-Axis Title", width = 600)
fig.write_html("figures/plotting_plotly_tsa_plot_custom_y_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_tsa_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_tsa_plot_custom_title_label.png
model.plot(data, "date", "passengers", npredictions = 11, method="forecast").set_title("Custom Title")
**Custom Axis Titles**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_tsa_plot_custom_yaxis_label.png
model.plot(data, "date", "passengers", npredictions = 11, method="forecast").set_ylabel("Custom Y Axis")
.. ipython:: python
:suppress:
from vastorbit._utils._sql._sys import purge_memory
purge_memory()