Machine Learning - Regression Plots

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.

import vastorbit as vo

Let’s also import numpy to create a random dataset.

import numpy as np

Let’s generate a dataset using the following data.

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.

# 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.

Note

To select the desired plotting library, we simply need to use the 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.

Plotly

We can switch to using the plotly module.

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

LR

model_lr.plot()

Residual Plot

data.scatter(["y", "noise_lr"])

RF

model_rf.plot()

Residual Plot

data.scatter(["y", "noise_rf"])

Matplotlib

We can switch to using the matplotlib module.

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

LR

model_lr.plot()

Residual Plot

data.scatter(["y", "noise_lr"])

RF

model_rf.plot()

Residual Plot

data.scatter(["y", "noise_rf"])

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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

Plotly

Custom colors

fig = model_lr.plot()
fig.update_traces(marker = dict(color="red"))

Matplolib

Custom colors

model_lr.plot(colors = "red")

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Size

Plotly

Custom Width and Height

model_lr.plot(width = 300, height = 300)

Matplolib

Custom Width and Height

model_lr.plot(width = 6, height = 3)

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Text

Plotly

Custom Title

model_lr.plot().update_layout(title_text = "Custom Title")

Custom Axis Titles

model_lr.plot(yaxis_title = "Custom Y-Axis Title")

Matplolib

Custom Title Text

model_lr.plot().set_title("Custom Title")

Custom Axis Titles

model_lr.plot().set_ylabel("Custom Y Axis")