main.py

import pandas as pd
import numpy as np
from utils import to_categorical
from mlp import MultiLayerPerceptron
from layer import Input, Dense
from sklearn.model_selection import train_test_split


def main():
    data = pd.read_csv("blood.csv")
    x_train = data.drop(["a"], axis=1)
    y_train = data["a"]

    x_train = pd.get_dummies(x_train)
    target_map = dict()
    if y_train.dtype == "object":
        target_map = {val: i for (i, val) in enumerate(np.unique(y_train))}
        y_train = y_train.map(target_map)
        y_train = to_categorical(y_train)
    x_train = x_train.values

    x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.2)

    mean_train = x_train.mean(axis=0)
    std_train = np.std(x_train, axis=0)

    x_train = (x_train - mean_train) / std_train
    x_test = (x_test - mean_train) / std_train

    mlp = MultiLayerPerceptron()
    mlp.add(Input(x_train.shape[1]))
    mlp.add(Dense(32, activation="relu"))
    mlp.add(Dense(2, activation="sigmoid"))
    mlp.build()
    mlp.fit(x_train, y_train, epoch=40, lr=0.05, validation_data=(x_test, y_test))
    mlp.draw()


if __name__ == "__main__":
    main()