:orphan:
.. _chart_gallery.lof:
================================
Machine Learning - LOF Plot
================================
.. 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]),
})
# Defining the Models
model_lof_1d = vml.LocalOutlierFactor()
model_lof_2d = vml.LocalOutlierFactor()
model_lof_3d = vml.LocalOutlierFactor()
# Fitting the models
model_lof_1d.fit(data, "x")
model_lof_2d.fit(data, ["x", "y"])
model_lof_3d.fit(data, ["x", "y", "z"])
General
-------
vastorbit's Local Outlier Factor (LOF) functionality is a powerful tool for detecting local anomalies or outliers within your data. LOF operates by evaluating the density of data points in the vicinity of each observation, highlighting instances that exhibit significantly different local behavior compared to their neighbors. This feature helps data analysts uncover irregularities in their datasets that may not be apparent through traditional global outlier detection methods. By providing insights into local data structures, vastorbit's LOF empowers users to make informed decisions and identify critical data points that may require further investigation or action.
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]),
})
Let's proceed by creating Local Outlier Factors (LOFs) for various dimensions, including 1D, 2D, and 3D.
.. code-block:: python
# Importing the VAST ML module
import vastorbit.machine_learning.vast as vml
# Defining the Models
model_lof_1d = vml.LocalOutlierFactor()
model_lof_2d = vml.LocalOutlierFactor()
model_lof_3d = vml.LocalOutlierFactor()
# Fitting the models
model_lof_1d.fit(data, "x")
model_lof_2d.fit(data, ["x", "y"])
model_lof_3d.fit(data, ["x", "y", "z"])
# 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_lof_1d.plot()
.. ipython:: python
:suppress:
fig = model_lof_1d.plot(width = 600)
fig.write_html("figures/plotting_plotly_lof_1d_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_1d_1.html
.. tab:: 2D
.. code-block:: python
model_lof_2d.plot()
.. ipython:: python
:suppress:
fig = model_lof_2d.plot(width = 600)
fig.write_html("figures/plotting_plotly_lof_2d_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_2d_1.html
.. tab:: 3D
.. code-block:: python
model_lof_3d.plot()
.. ipython:: python
:suppress:
fig = model_lof_3d.plot()
fig.write_html("figures/plotting_plotly_lof_3d_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_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_lof_1d_1.png
model_lof_1d.plot()
.. tab:: 2D
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lof_2d_1.png
model_lof_2d.plot()
.. tab:: 3D
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lof_3d_1.png
model_lof_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 `__, `matplotlib `__.
Colors
~~~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom colors**
.. code-block:: python
model_lof_2d.plot(colors = ["red", "blue"])
.. ipython:: python
:suppress:
fig = model_lof_2d.plot(colors = ["red", "blue"], width = 600)
fig.write_html("figures/plotting_plotly_lof_2d_plot_custom_color_1.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_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_lof_2d_plot_custom_color_1.png
model_lof_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_lof_2d.plot(width = 300, height = 300)
.. ipython:: python
:suppress:
fig = model_lof_2d.plot(width = 300, height = 300)
fig.write_html("figures/plotting_plotly_lof_2d_plot_custom_size.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_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_lof_2d_plot_single_custom_size.png
model_lof_2d.plot(width = 6, height = 3)
_____
Text
~~~~
.. tab:: Plotly
.. ipython:: python
:suppress:
vo.set_option("plotting_lib", "plotly")
**Custom Title**
.. code-block:: python
model_lof_2d.plot().update_layout(title_text = "Custom Title")
.. ipython:: python
:suppress:
fig = model_lof_2d.plot().update_layout(title_text = "Custom Title", width = 600)
fig.write_html("figures/plotting_plotly_lof_2d_plot_custom_main_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_2d_plot_custom_main_title.html
**Custom Axis Titles**
.. code-block:: python
model_lof_2d.plot(yaxis_title = "Custom Y-Axis Title")
.. ipython:: python
:suppress:
fig = model_lof_2d.plot(yaxis_title = "Custom Y-Axis Title", width = 600)
fig.write_html("figures/plotting_plotly_lof_2d_plot_custom_y_title.html")
.. raw:: html
:file: /Users/badr.ouali/Documents/VastOrbit-master/docs/figures/plotting_plotly_lof_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_lof_2d_plot_custom_title_label.png
model_lof_2d.plot().set_title("Custom Title")
**Custom Axis Titles**
.. ipython:: python
:okwarning:
@savefig plotting_matplotlib_lof_2d_plot_custom_yaxis_label.png
model_lof_2d.plot().set_ylabel("Custom Y Axis")
.. ipython:: python
:suppress:
from vastorbit._utils._sql._sys import purge_memory
purge_memory()