pyqt_data_analysis/libdataanalysis/2-线性回归代码实现/LinearRegression/MultivariateLinearRegressio...

138 lines
3.3 KiB
Python

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly
import plotly.graph_objs as go
plotly.offline.init_notebook_mode()
from linear_regression import LinearRegression
data = pd.read_csv('../data/world-happiness-report-2017.csv')
train_data = data.sample(frac=0.8)
test_data = data.drop(train_data.index)
input_param_name_1 = 'Economy..GDP.per.Capita.'
input_param_name_2 = 'Freedom'
output_param_name = 'Happiness.Score'
x_train = train_data[[input_param_name_1, input_param_name_2]].values
y_train = train_data[[output_param_name]].values
x_test = test_data[[input_param_name_1, input_param_name_2]].values
y_test = test_data[[output_param_name]].values
# Configure the plot with training dataset.
plot_training_trace = go.Scatter3d(
x=x_train[:, 0].flatten(),
y=x_train[:, 1].flatten(),
z=y_train.flatten(),
name='Training Set',
mode='markers',
marker={
'size': 10,
'opacity': 1,
'line': {
'color': 'rgb(255, 255, 255)',
'width': 1
},
}
)
plot_test_trace = go.Scatter3d(
x=x_test[:, 0].flatten(),
y=x_test[:, 1].flatten(),
z=y_test.flatten(),
name='Test Set',
mode='markers',
marker={
'size': 10,
'opacity': 1,
'line': {
'color': 'rgb(255, 255, 255)',
'width': 1
},
}
)
plot_layout = go.Layout(
title='Date Sets',
scene={
'xaxis': {'title': input_param_name_1},
'yaxis': {'title': input_param_name_2},
'zaxis': {'title': output_param_name}
},
margin={'l': 0, 'r': 0, 'b': 0, 't': 0}
)
plot_data = [plot_training_trace, plot_test_trace]
plot_figure = go.Figure(data=plot_data, layout=plot_layout)
plotly.offline.plot(plot_figure)
num_iterations = 500
learning_rate = 0.01
polynomial_degree = 0
sinusoid_degree = 0
linear_regression = LinearRegression(x_train, y_train, polynomial_degree, sinusoid_degree)
(theta, cost_history) = linear_regression.train(
learning_rate,
num_iterations
)
print('开始损失',cost_history[0])
print('结束损失',cost_history[-1])
plt.plot(range(num_iterations), cost_history)
plt.xlabel('Iterations')
plt.ylabel('Cost')
plt.title('Gradient Descent Progress')
plt.show()
predictions_num = 10
x_min = x_train[:, 0].min();
x_max = x_train[:, 0].max();
y_min = x_train[:, 1].min();
y_max = x_train[:, 1].max();
x_axis = np.linspace(x_min, x_max, predictions_num)
y_axis = np.linspace(y_min, y_max, predictions_num)
x_predictions = np.zeros((predictions_num * predictions_num, 1))
y_predictions = np.zeros((predictions_num * predictions_num, 1))
x_y_index = 0
for x_index, x_value in enumerate(x_axis):
for y_index, y_value in enumerate(y_axis):
x_predictions[x_y_index] = x_value
y_predictions[x_y_index] = y_value
x_y_index += 1
z_predictions = linear_regression.predict(np.hstack((x_predictions, y_predictions)))
plot_predictions_trace = go.Scatter3d(
x=x_predictions.flatten(),
y=y_predictions.flatten(),
z=z_predictions.flatten(),
name='Prediction Plane',
mode='markers',
marker={
'size': 1,
},
opacity=0.8,
surfaceaxis=2,
)
plot_data = [plot_training_trace, plot_test_trace, plot_predictions_trace]
plot_figure = go.Figure(data=plot_data, layout=plot_layout)
plotly.offline.plot(plot_figure)