opengood.py-ml-plot 1.10.0

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 a 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",
)

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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/