One-vs-Rest.py

import matplotlib.pyplot as plt
import numpy as np

from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression

target = []
data = []

with open('target.txt', 'r') as txt_file:
    txt = txt_file.read()
    target_buff = [int(x) for x in txt.split(" ")]
    for i in target_buff:
        if i == 3:
            target.append(2)
        else:
            target.append(i)

with open('data.txt', 'r') as txt_file:
    txt = txt_file.read()
    array = txt.split("\n")
    for item in array:
        xd = []
        digits = item.split(" ")
        if (len(digits) < 2):
            break
        for i in range(len(digits) - 1):
            xd.append(float(digits[i]))
        data.append(xd)

numpy_data = np.array(data)
numpy_target = np.array(target)

X = numpy_data
y = numpy_target

for multi_class in ("multinomial", "ovr"):
    clf = LogisticRegression(
        solver="sag", max_iter=100, random_state=42, multi_class=multi_class
    ).fit(X, y)

    print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))

    _, ax = plt.subplots()
    DecisionBoundaryDisplay.from_estimator(
        clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
    )
    plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
    plt.axis("tight")

    colors = "bry"
    for i, color in zip(clf.classes_, colors):
        idx = np.where(y == i)
        plt.scatter(
            X[idx, 0], X[idx, 1], c=color, cmap=plt.cm.Paired, edgecolor="black", s=20
        )

    xmin, xmax = plt.xlim()
    ymin, ymax = plt.ylim()
    coef = clf.coef_
    intercept = clf.intercept_
    print(clf.predict([[0.32098765432098764, 47.91666666666667]]))


    def plot_hyperplane(c, color):
        def line(x0):
            return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]

        plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)


    for i, color in zip(clf.classes_, colors):
        plot_hyperplane(i, color)

plt.show()