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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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import plotly
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import plotly.graph_objs as go
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from linear_regression import LinearRegression
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plotly.offline.init_notebook_mode() # 在线显示图标,更多功能
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data = pd.read_csv('../data/world-happiness-report-2017.csv')
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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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# 与单特征模型相比,只是多了一个特征列
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input_param_name_1 = 'Economy..GDP.per.Capita.'
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input_param_name_2 = 'Freedom'
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output_param_name = 'Happiness.Score'
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x_train = train_data[[input_param_name_1, input_param_name_2]].values
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y_train = train_data[[output_param_name]].values
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x_test = test_data[[input_param_name_1, input_param_name_2]].values
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y_test = test_data[[output_param_name]].values
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# Configure the plot with training dataset.
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plot_training_trace = go.Scatter3d(
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x=x_train[:, 0].flatten(),
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y=x_train[:, 1].flatten(),
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z=y_train.flatten(),
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name='Training Set',
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mode='markers',
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marker={
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'size': 10,
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'opacity': 1,
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'line': {
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'color': 'rgb(255, 255, 255)',
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'width': 1
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},
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}
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)
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plot_test_trace = go.Scatter3d(
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x=x_test[:, 0].flatten(),
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y=x_test[:, 1].flatten(),
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z=y_test.flatten(),
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name='Test Set',
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mode='markers',
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marker={
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'size': 10,
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'opacity': 1,
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'line': {
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'color': 'rgb(255, 255, 255)',
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'width': 1
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},
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}
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)
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plot_layout = go.Layout(
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title='Date Sets',
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scene={
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'xaxis': {'title': input_param_name_1},
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'yaxis': {'title': input_param_name_2},
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'zaxis': {'title': output_param_name}
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},
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margin={'l': 0, 'r': 0, 'b': 0, 't': 0}
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)
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plot_data = [plot_training_trace, plot_test_trace]
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plot_figure = go.Figure(data=plot_data, layout=plot_layout)
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plotly.offline.plot(plot_figure)
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num_iterations = 500
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learning_rate = 0.01
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polynomial_degree = 0
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sinusoid_degree = 0
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linear_regression = LinearRegression(x_train, y_train, polynomial_degree, sinusoid_degree)
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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('Iterations')
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plt.ylabel('Cost')
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plt.title('Gradient Descent')
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plt.show()
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predictions_num = 10
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x_min = x_train[:, 0].min()
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x_max = x_train[:, 0].max()
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y_min = x_train[:, 1].min()
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y_max = x_train[:, 1].max()
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x_axis = np.linspace(x_min, x_max, predictions_num)
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y_axis = np.linspace(y_min, y_max, predictions_num)
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x_predictions = np.zeros((predictions_num * predictions_num, 1))
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y_predictions = np.zeros((predictions_num * predictions_num, 1))
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x_y_index = 0
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for x_index, x_value in enumerate(x_axis):
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for y_index, y_value in enumerate(y_axis):
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x_predictions[x_y_index] = x_value
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y_predictions[x_y_index] = y_value
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x_y_index += 1
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z_predictions = linear_regression.predict(np.hstack((x_predictions, y_predictions)))
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plot_predictions_trace = go.Scatter3d(
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x=x_predictions.flatten(),
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y=y_predictions.flatten(),
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z=z_predictions.flatten(),
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name='Prediction Plane',
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mode='markers',
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marker={
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'size': 1,
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},
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opacity=0.8,
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surfaceaxis=2,
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)
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plot_data = [plot_training_trace, plot_test_trace, plot_predictions_trace]
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plot_figure = go.Figure(data=plot_data, layout=plot_layout)
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plotly.offline.plot(plot_figure)
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