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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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from linear_regression import LinearRegression
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data = pd.read_csv('../data/world-happiness-report-2017.csv') # 导入数据
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# 得到训练和测试数据，以8：2切分
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train_data = data.sample(frac=0.8)
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test_data = data.drop(train_data.index)
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input_param_name = 'Economy..GDP.per.Capita.' # 特征features
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output_param_name = 'Happiness.Score' # 标签label
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x_train = train_data[[input_param_name]].values # 构建数据
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y_train = train_data[[output_param_name]].values
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x_test = test_data[[input_param_name]].values
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y_test = test_data[[output_param_name]].values
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# 可视化展示 run, 可以看到训练数据和预测数据的分布
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plt.scatter(x_train, y_train, label='Train data')
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plt.scatter(x_test, y_test, label='Test data')
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plt.xlabel(input_param_name)
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plt.ylabel(output_param_name)
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plt.title('Happy')
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plt.legend()
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plt.show()
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# 训练线性回归模型
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num_iterations = 500 # 迭代次数
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learning_rate = 0.01 # 学习率
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linear_regression = LinearRegression(x_train, y_train) # 初始化模型
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(theta, cost_history) = linear_regression.train(learning_rate, num_iterations)
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print('开始时的损失：', cost_history[0])
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print('训练后的损失：', cost_history[-1])
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plt.plot(range(num_iterations), cost_history)
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plt.xlabel('Iteration')
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plt.ylabel('Cost')
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plt.title('GD')
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plt.show()
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# 测试线性回归模型
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predictions_num = 100 # 预测100个
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# 拿最大和最小值画一条线
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x_predictions = np.linspace(x_train.min(), x_train.max(), predictions_num).reshape(predictions_num, 1)
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y_predictions = linear_regression.predict(x_predictions)
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plt.scatter(x_train, y_train, label='Train data')
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plt.scatter(x_test, y_test, label='Test data')
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plt.plot(x_predictions, y_predictions, 'r', label='Prediction')
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plt.xlabel(input_param_name)
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plt.ylabel(output_param_name)
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plt.title('Happy')
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plt.legend()
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plt.show()
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