In [2]:

    

%matplotlib qt4

from models import tools, optimize, models, filters

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm


    

In [4]:

    

data = tools.load_data(limit=20000, offset=1500000)
data = data[filters.open_questions(data)]
print len(data)


    

    




11823

In [7]:

    

grid_search = optimize.GridSearch(data)


    

In [23]:

    

intervals_alpha = np.arange(0.1, 5, 0.2)
intervals_beta = np.arange(0.01, 0.5, 0.02)

result_elo = grid_search.search_elo(intervals_alpha, intervals_beta)


    

    




25/25 25/25

In [56]:

    

intervals_gamma = np.arange(0, 6, 0.12)
intervals_delta = np.arange(-3, 3, 0.12)

result_pfae = grid_search.search_pfae(intervals_gamma, intervals_delta)


    

    




50/50 50/50

In [58]:

    

result_pfae.plot_off(cmap=None)


    

    
Out[58]:





<matplotlib.image.AxesImage at 0x7f58af5e79d0>

In [68]:

    

plt.figure(1)

plt.subplot(121)
result_pfae.plot_rmse(cmap=cm.jet_r, title='RMSE')
plt.xlabel(r'$\gamma$', fontsize=18)
plt.ylabel(r'$\delta$', fontsize=18)

plt.subplot(122)
result_pfae.plot_off(cmap=cm.jet_r, title='predicted - observed')
plt.xlabel(r'$\gamma$', fontsize=18)
plt.ylabel(r'$\delta$', fontsize=18)


    

    
Out[68]:





<matplotlib.text.Text at 0x7f58aebb2f90>

In [11]:

    

def elot_factory(x, y):
    return models.EloResponseTime(zeta=x)


    

In [12]:

    

result = grid_search.search(
    elot_factory,
    xvalues=np.arange(1, 50, 2),
    yvalues=[1],
    xlabel='$\zeta$', ylabel='-'
)


    

    




25/25 1/1

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