opengood.py-ml-plot 1.11.0

Modules containing reusable functions for machine learning visualization plotting

Python Machine Learning (ML) Plot

Modules containing reusable functions for machine learning visualization plotting

Compatibility

• Python 3.13 or later

Setup

Add Dependency

python3 -m pip install opengood.py-ml-plot

Note: See Release version badge above for latest version.

Features

Classification Model Plotting

Display 2-D Classification Plot

Display a 2-D classification model results visualization:

import pandas as pd
from matplotlib.colors import ListedColormap
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

from opengood.py_ml_plot import display_classification_plot

dataset = pd.read_csv("data.csv")
x = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values

x_train, _, y_train, y_test = train_test_split(x, y, test_size=0.2,
                                               random_state=0)

sc = StandardScaler()
x_train = sc.fit_transform(x_train)

classifier = LogisticRegression(random_state=0)
classifier.fit(x_train, y_train)

display_classification_plot(
    x_train,
    y_train,
    sc,
    classifier,
    ListedColormap(("salmon", "dodgerblue")),
    "Logistic Regression (Training Set)",
    "Age",
    "Estimated Salary",
)

Output

Visualization Logic

Visualization implementation logic for function display_classification_plot is as follows:

• ListedColormap class from the Matplotlib library creates an object that generates a colormap visual from a list of colors
• x_set and y_set are assigned non-feature scaled values of the matrix of features and dependent variable from the training set using the StandardScalar object feature_scalar created earlier for feature scaling
  • x_set values are inverted from their feature scaled values in x
  • y_set values are not inverted and taken directly from y
• meshgrid function from the NumPy library returns a tuple of coordinate matrices from coordinate vectors
  • 2 sets of matrices (x1 and x2) are returned with coordinate vectors
  • x1
    • arange function is called with a defined start and stop interval
      • x_set[:, 0] returns all the rows for feature x1
      • start parameter
        • Start of interval
        • x_set[:, 0].min() returns the minimum value for feature x1
        • Value of 10 is subtracted for padding
      • stop parameter
        • End of interval
        • x_set[:, 0].max() returns the maximum value for feature x1
        • Value of 10 is added for padding
      • step parameter
        • Spacing between values
        • Value of 0.25 is added for spacing
  • x2
    • arange function is called with a defined start and stop interval
      • x_set[:, 1] returns all the rows for feature x2
      • start parameter
        • Start of interval
        • x_set[:, 1].min() returns the minimum value for feature x2
        • Value of 1000 is subtracted for padding
        • Value of 1000 is used instead of 10 due to the difference in scaling for feature x2 vs. feature x1
      • stop parameter
        • End of interval
        • x_set[:, 1].max() returns the maximum value for feature x2
        • Value of 1000 is added for padding
      • step parameter
        • Spacing between values
        • Value of 0.25 is added for spacing
• contourf function from the Matplotlib library is used for creating filled contour plots
  • It visualizes 3D data in 2D by drawing filled contours representing constant z-values (heights) on an x-y plane
  • These plots are useful for displaying data like temperature distributions, terrain elevations, or any scalar field where the magnitude varies over 2 dimensions
  • The most basic use case of contourf involves providing a 2D array representing the z-values
  • Matplotlib automatically determines the x and y coordinates based on the array's indices
  • X and Y parameters
    • The coordinates of the values in Z
    • X and Y must both be 2D arrays with the same shape as Z
    • x1 is used for X containing x1 values
    • x2 is used for Y containing x2 values
  • Z parameter
    • The height values over which the contour is drawn
    • ravel function from the NumPy library is used to flatten a multidimensional array into a one-dimensional array
      • x1 and x2 are flatten into a 1D array via the ravel function
      • They are then combined via the array function from the NumPy library into a 2D array
      • The result is then reshaped via the reshape function to match the shape of x1
    • Since the values of the reshaped 2D array are not feature scaled, the values are feature scaled via the transform method on the sc object
  • alpha parameter
    • The alpha blending value, between 0 (transparent) and 1 (opaque)
    • Value of 0.75 is used to make the blending mostly opaque
  • cmap parameter
    • The Colormap object instance or registered colormap name used to map scalar data to colors
    • salmon and dodgerblue are used for a ListedColormap object
      • salmon = 0 or negative classifier
      • dodgerblue = 1 or positive classifier
• xlim function from the Matplotlib library is used to get or set the x-axis limits of the current axes
  • min() and max() for x1 are used for the limits
• ylim function from the Matplotlib library is used to get or set the y-axis limits of the current axes
  • min() and max() for x2 are used for the limits
• The values from y_set are iterated over in a for-in loop
  • unique function from the NumPy library returns sorted, unique elements of an array
    • Values of y_set are made unique and sorted
  • Iterator variable i represents the current row of iteration
  • Iterator variable j represents the classification value for the dependent variable
    • 0 negative classifier
    • 1 positive classifier
• scatter method from the Matplotlib library creates a scatter plot of data points with the shaded contour showing the classification for the dependent variable
  • x-axis uses values from x_set where y_set value = 0 (negative classifier)
  • y-axis uses values from x_set where y_set value = 1 (positive classifier)
  • c parameter
    • The marker colors
    • Uses the ListedColormap with the classification colors for the current row at index i
  • label parameter
    • Sets the label
    • Values
      • 0 negative classifier
      • 1 positive classifier

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Development

Python Virtual Environment

Create Python virtual environment:

cd ~/workspace/opengood-aio/py-ml-plot/.venv
python3 -m venv ~/workspace/opengood-aio/py-ml-plot/.venv
source .venv/bin/activate

Install Packages

python3 -m pip install matplotlib
python3 -m pip install numpy
python3 -m pip install pandas
python3 -m pip install scikit-learn

Create Requirements File

pip freeze > requirements.txt

Run Tests

python -m pytest tests/