:orphan:
.. _chart_gallery.regression_plot:
===================================
Machine Learning - Regression Plots
===================================
.. Necessary Code Elements
.. ipython:: python
:suppress:
:okwarning:
import vastorbit as vo
import vastorbit.machine_learning.vast as vml
import numpy as np
N = 100 # Number of Records
x = np.random.normal(5, 1, N) # Normal Distribution
e = np.random.random(N) # Noise
data = vo.VastFrame({
"x": x,
"y": x + e,
})
# Defining the Models
model_lr = vml.LinearRegression()
model_rf = vml.RandomForestRegressor(n_estimators = 4)
# Fitting the models
model_lr.fit(data, "x", "y")
model_rf.fit(data, "x", "y")
# Adding the predictions to the VastFrame
model_lr.predict(data, "x", name = "x_lr", inplace = True)
model_rf.predict(data, "x", name = "x_rf", inplace = True)
# Computing the respective noises
data["noise_lr"] = data["x"] - data["x_lr"]
data["noise_rf"] = data["x"] - data["x_rf"]
# 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
x = np.random.normal(5, 1, N) # Normal Distribution
e = np.random.random(N) # Noise
data = vo.VastFrame({
"x": x,
"y": x + e,
})
Let's proceed by creating both a linear regression model and a random forest regressor model using the complete dataset. Following that, we can calculate the respective noise associated with each model.
.. code-block:: python
# Importing the VAST ML module
import vastorbit.machine_learning.vast as vml
# Defining the Models
model_lr = vml.LinearRegression()
model_rf = vml.RandomForestRegressor(n_estimators = 4)
# Fitting the models
model_lr.fit(data, "x", "y")
model_rf.fit(data, "x", "y")
# Adding the predictions to the VastFrame
model_lr.predict(data, "x", name = "x_lr", inplace = True)
model_rf.predict(data, "x", name = "x_rf", inplace = True)
# Computing the respective noises
data["noise_lr"] = data["x"] - data["x_lr"]
data["noise_rf"] = data["x"] - data["x_rf"]
# 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:: LR
.. code-block:: python
model_lr.plot()
.. ipython:: python
:suppress:
fig = model_lr.plot()
fig.write_html("figures/plotting_plotly_lr_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_1.html
**Residual Plot**
.. code-block:: python
data.scatter(["y", "noise_lr"])
.. ipython:: python
:suppress:
fig = data.scatter(["y", "noise_lr"])
fig.write_html("figures/plotting_plotly_lr_2.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_2.html
.. tab:: RF
.. code-block:: python
model_rf.plot()
.. ipython:: python
:suppress:
fig = model_rf.plot()
fig.write_html("figures/plotting_plotly_rf_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_rf_1.html
**Residual Plot**
.. code-block:: python
data.scatter(["y", "noise_rf"])
.. ipython:: python
:suppress:
fig = data.scatter(["y", "noise_rf"])
fig.write_html("figures/plotting_plotly_rf_2.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_rf_2.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:: LR
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lr_1.png
model_lr.plot()
**Residual Plot**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lr_2.png
data.scatter(["y", "noise_lr"])
.. tab:: RF
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_rf_1.png
model_rf.plot()
**Residual Plot**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_rf_2.png
data.scatter(["y", "noise_rf"])
___________________
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_lr.plot()
fig.update_traces(marker = dict(color="red"))
.. ipython:: python
:suppress:
fig = model_lr.plot()
fig.update_traces(marker = dict(color="red"))
fig.write_html("figures/plotting_plotly_lr_plot_custom_color_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_plot_custom_color_1.html
.. tab:: Matplolib
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "matplotlib")
**Custom colors**
.. ipython:: python
@savefig plotting_matplotlib_lr_plot_custom_color_1.png
model_lr.plot(colors = "red")
____
Size
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Width and Height**
.. code-block:: python
model_lr.plot(width = 300, height = 300)
.. ipython:: python
:suppress:
fig = model_lr.plot(width = 300, height = 300)
fig.write_html("figures/plotting_plotly_lr_plot_custom_size.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_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_lr_plot_single_custom_size.png
model_lr.plot(width = 6, height = 3)
_____
Text
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Title**
.. code-block:: python
model_lr.plot().update_layout(title_text = "Custom Title")
.. ipython:: python
:suppress:
fig = model_lr.plot().update_layout(title_text = "Custom Title")
fig.write_html("figures/plotting_plotly_lr_plot_custom_main_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_plot_custom_main_title.html
**Custom Axis Titles**
.. code-block:: python
model_lr.plot(yaxis_title = "Custom Y-Axis Title")
.. ipython:: python
:suppress:
fig = model_lr.plot(yaxis_title = "Custom Y-Axis Title")
fig.write_html("figures/plotting_plotly_lr_plot_custom_y_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lr_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_lr_plot_custom_title_label.png
model_lr.plot().set_title("Custom Title")
**Custom Axis Titles**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lr_plot_custom_yaxis_label.png
model_lr.plot().set_ylabel("Custom Y Axis")
.. ipython:: python
:suppress:
from vastorbit._utils._sql._sys import purge_memory
purge_memory()