logistic_regression.py

from sklearn.linear_model import LogisticRegression
import seaborn as sns
import matplotlib.pyplot as plt
from data_processing import process_data

from time import perf_counter

print("Preparing data...")
X_train, X_test, y_train, y_test, categories_mapping = process_data(
    "star_classification.csv"
)

print("Training model...")
model = LogisticRegression(max_iter=1500)

training_start_time = perf_counter()

model.fit(X_train, y_train)

training_end_time = perf_counter()

print(f"Model took {training_end_time - training_start_time:.2f} seconds to train.")

y_pred = model.predict(X_test)

test_accuracy = model.score(X_test, y_test)
print(f"Test Accuracy: {test_accuracy * 100}%")

X_test["Class"] = y_test


def plot_data(x_ax, y_ax):
    plt.scatter(X_test[x_ax], X_test[y_ax], c=y_test, edgecolor="k")
    plt.scatter(X_test[x_ax], y_pred, c="red", marker="x")
    plt.xlabel(x_ax)
    plt.ylabel(y_ax)

    plt.show()


plot_data("u", "Class")
plot_data("g", "Class")
plot_data("r", "Class")
plot_data("i", "Class")
plot_data("z", "Class")
plot_data("redshift", "Class")

sns.pairplot(X_test, hue="Class")