import xgboost as xgb

import numpy as np

import pandas as pd

from sklearn.model_selection import train_test_split

if __name__ == '__main__':

    iris_feature_E = "sepal lenght", "sepal width", "petal length", "petal width"

    iris_feature = "the length of sepal", "the width of sepal", "the length of petal", "the width of petal"

    iris_class = "Iris-setosa", "Iris-versicolor", "Iris-virginica"

    data = pd.read_csv("iris.data", header=None)

    iris_types = data[4].unique()

    for i, type in enumerate(iris_types):

        data.set_value(data[4] == type, 4, i)

    x, y = np.split(data.values, (4,), axis=1)

    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.7, random_state=1)

    data_train = xgb.DMatrix(x_train, label=y_train)

    data_test = xgb.DMatrix(x_test, label=y_test)

    watchlist = [(data_test, 'eval'), (data_train, 'train')]

    param = {'max_depth':3, 'eta':1, 'silent':1, 'objective':'multi:softmax', 'num_class':3}

    bst = xgb.train(param, data_train, num_boost_round=10, evals=watchlist)

    y_hat = bst.predict(data_test)

    result = y_test.reshape(1, -1) == y_hat

    print('the accuracy:\t', float(np.sum(result)) / len(y_hat))