sklearn_example.py

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neural_network import MLPClassifier
from sklearn.externals import joblib
import pandas as pd
import numpy as np


def main():
    # example of MLP
    iris = load_iris()
    x = iris.data
    y = iris.target

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=0)
    clf = MLPClassifier(solver="sgd", random_state=0, max_iter=10000)
    clf.fit(x_train, y_train)
    print(clf.score(x_test, y_test))

    # dump model
    joblib.dump(clf, "model.pkl")
    # load model
    clf2 = joblib.load("model.pkl")
    # classify
    print(clf2.score(x_test, y_test))

    data = pd.read_csv("sample.csv")
    data['Sex'] = data['Sex'].map(lambda e: 1.0 if e == "female" else 0.0)
    print(data)

    print(data['Sex'].values)

    features = np.vstack((data['Sex'], data['Age'], data['Pclass'])).transpose()
    print(features)


if __name__ == "__main__":
    main()