import data_tools as dt
import numpy as np
import matplotlib.pyplot as plt
import models
glicko = models.DoubleModifiedGlickoModel()
surface = ['any', 'clay', 'hard', 'carpet', 'grass']
values = []
for s in surface:
data = dt.get_main_matches_data(s)
data = glicko.test(data)
values.append(np.average(data["ret"]))
N = 4
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
plt.bar(ind, values[1:], width, color='r')
plt.ylabel('Average return rating of a winner')
plt.xticks(ind + width / 2., surface[1:])
plt.axhline(values[0], color='k', linestyle='dashed', linewidth=2)
plt.ylim([1400, 1600])
plt.show()
def analyse_ranking_model(model, report_name="report", verbose=False):
"""
READ BEFORE USE:
Provides full analysis of model that uses ranking. It will work by first
training on years from 2003 - 2011 and then test on 2012, 2013, 2014 and
2015. One must make sure that train and test functions are implemented
as to allow such steps to be performed.
Model must be derived from TennisModel, so read its comments as well and
implement accordingly.
This function will create ready to use latex version of report. If you
set verbose to True then you will also get results in console.
:param model: Model one wants to analyse.
:param report_name: Name of report. It will have timestamp appended.
:param verbose: If True results will be printed in console as well.
:return: void.
"""
bets_taken = np.zeros((4))
betting_result = np.zeros((4))
correct_predict = np.zeros((4))
if verbose:
print "Starting analysis. Loading data."
data = dt.get_main_matches_data()
if verbose:
print "Training."
df = model.test(data, verbose)
train_error = test_error(df)
train_bias = test_bias(df)
train_sqme = test_sqme(df)
train_range = [datetime.date(2003, 1, 1), datetime.date(2012, 1, 1)]
train_data = dt.filter_data_time_range(data, train_range)
model.train(train_data, verbose)
if verbose:
print "Testing:"
for year in xrange(2012, 2016):
if verbose:
print "Year", year
test_range = [datetime.date(year, 1, 1), datetime.date(year + 1, 1, 1)]
test_data = dt.filter_data_time_range(data, test_range)
df = model.test(test_data, verbose)
r = test_profitability(df)
bets_taken[year - 2012] = r[0]
betting_result[year - 2012] = r[2] / r[1] - 1.0
correct_predict[year - 2012] = test_correct_predictions(df)
predict_mean = np.average(correct_predict, axis=0)
predict_var = np.std(correct_predict, axis=0)
h = predict_var * sp.stats.t.ppf((1 + 0.95) / 2.0, 3)
predict_conf95 = (predict_mean - h, predict_mean + h)
profit_mean, profit_var = weighted_avg_and_std(betting_result, bets_taken)
h = profit_var * sp.stats.t.ppf((1 + 0.95) / 2.0, 3)
profit_conf95 = (profit_mean - h, profit_mean + h)
if verbose:
print "Done. Printing result."
print
print "Model:", model.name
print
print "Parameters:"
print model.params
print
print "Train error:", train_error
print
print "Train bias: ", train_bias
print
print "Train sqme: ", train_sqme
print
print
print "Correct predict"
print correct_predict
print
print "Predict mean:", predict_mean
print
print "Predict std:", predict_var
print
print
print "Bets taken"
print bets_taken
print
print "Betting results"
print betting_result
print
print "ROI mean:", profit_mean
print
print "ROI std:", profit_var
print
print "95% confidence interval:", profit_conf95
path = stg.ROOT_PATH + "/reports/" + report_name + " - " + str(datetime.datetime.now()) + ".txt"
with open(path, "w") as f:
f.write(r"\textbf{Model name:} ")
f.write(model.name)
f.write(r" \\" + " \n")
f.write(r"\textbf{Description:} ")
f.write(r" \\" + " \n")
f.write(r"\textbf{Parameters:}" + r" \\")
f.write("\n")
f.write(
r"\[ "
+ str(model.params).replace(r"'", "").replace(r"_", r"\_").replace("{", "").replace("}", "")
+ r" \] \\"
+ "\n"
)
f.write(r"\textbf{Error:}" + " %.5f" % train_error + r" \\")
f.write("\n")
f.write(r"\textbf{Bias:}" + " %.5f" % train_bias + r" \\")
f.write("\n")
f.write(r"\textbf{Sqme:}" + " %.5f\n" % train_sqme)
f.write(r"\\" + "\n" + r"\\ \\" + "\n")
f.write(r"\textbf{Correct predict percentage:} \\ \\")
f.write(latex_year_table(correct_predict))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{Predict mean:}" + " %.5f" % predict_mean + r" \\")
f.write("\n")
f.write(r"\textbf{Predict std:}" + " %.5f" % predict_var + r" \\")
f.write("\n")
f.write(r"\textbf{Predict 95\% confidence interval:}" + " (%.5f, %.5f)" % predict_conf95)
f.write("\n" + r"\\" + "\n" + r"\\ \\" + "\n")
f.write(r"\textbf{Bets placed:}" + r" \\ \\")
f.write(latex_year_table(bets_taken).replace(".", ""))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{Betting ROI:}" + r" \\ \\")
f.write(latex_year_table(betting_result))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{ROI mean:}" + " %.5f" % profit_mean + r" \\" + "\n")
f.write(r"\textbf{ROI std:}" + " %.5f" % profit_var + r" \\" + "\n")
f.write(r"\textbf{ROI 95\% confidence interval:}" + " (%.5f, %.5f)" % profit_conf95 + r" \\" + "\n\n")
def analyse_ranking_model(model, report_name="report", verbose=False):
'''
READ BEFORE USE:
Provides full analysis of model that uses ranking. It will work by first
training on years from 2003 - 2011 and then test on 2012, 2013, 2014 and
2015. One must make sure that train and test functions are implemented
as to allow such steps to be performed.
Model must be derived from TennisModel, so read its comments as well and
implement accordingly.
This function will create ready to use latex version of report. If you
set verbose to True then you will also get results in console.
:param model: Model one wants to analyse.
:param report_name: Name of report. It will have timestamp appended.
:param verbose: If True results will be printed in console as well.
:return: void.
'''
bets_taken = np.zeros((4))
betting_result = np.zeros((4))
correct_predict = np.zeros((4))
if verbose:
print "Starting analysis. Loading data."
data = dt.get_main_matches_data()
if verbose:
print "Training."
df = model.test(data, verbose)
train_error = test_error(df)
train_bias = test_bias(df)
train_sqme = test_sqme(df)
train_range = [datetime.date(2003, 1, 1), datetime.date(2012, 1, 1)]
train_data = dt.filter_data_time_range(data, train_range)
model.train(train_data, verbose)
if verbose:
print "Testing:"
for year in xrange(2012, 2016):
if verbose:
print "Year", year
test_range = [datetime.date(year, 1, 1), datetime.date(year + 1, 1, 1)]
test_data = dt.filter_data_time_range(data, test_range)
df = model.test(test_data, verbose)
r = test_profitability(df)
bets_taken[year - 2012] = r[0]
betting_result[year - 2012] = r[2] / r[1] - 1.0
correct_predict[year - 2012] = test_correct_predictions(df)
predict_mean = np.average(correct_predict, axis=0)
predict_var = np.std(correct_predict, axis=0)
h = predict_var * sp.stats.t.ppf((1 + 0.95) / 2., 3)
predict_conf95 = (predict_mean - h, predict_mean + h)
profit_mean, profit_var = weighted_avg_and_std(betting_result, bets_taken)
h = profit_var * sp.stats.t.ppf((1 + 0.95) / 2., 3)
profit_conf95 = (profit_mean - h, profit_mean + h)
if verbose:
print "Done. Printing result."
print
print "Model:", model.name
print
print "Parameters:"
print model.params
print
print "Train error:", train_error
print
print "Train bias: ", train_bias
print
print "Train sqme: ", train_sqme
print
print
print "Correct predict"
print correct_predict
print
print "Predict mean:", predict_mean
print
print "Predict std:", predict_var
print
print
print "Bets taken"
print bets_taken
print
print "Betting results"
print betting_result
print
print "ROI mean:", profit_mean
print
print "ROI std:", profit_var
print
print "95% confidence interval:", profit_conf95
path = stg.ROOT_PATH + "/reports/" + report_name + \
" - " + str(datetime.datetime.now()) + ".txt"
with open(path, 'w') as f:
f.write(r"\textbf{Model name:} ")
f.write(model.name)
f.write(r" \\" + " \n")
f.write(r"\textbf{Description:} ")
f.write(r" \\" + " \n")
f.write(r"\textbf{Parameters:}" + r" \\")
f.write("\n")
f.write(r"\[ " + str(model.params).replace(r"'", "").replace(
r"_", r"\_").replace("{", "").replace("}", "") + r" \] \\" + "\n")
f.write(r"\textbf{Error:}" + " %.5f" % train_error + r" \\")
f.write("\n")
f.write(r"\textbf{Bias:}" + " %.5f" % train_bias + r" \\")
f.write("\n")
f.write(r"\textbf{Sqme:}" + " %.5f\n" % train_sqme)
f.write(r"\\" + "\n" + r"\\ \\" + "\n")
f.write(r"\textbf{Correct predict percentage:} \\ \\")
f.write(latex_year_table(correct_predict))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{Predict mean:}" + " %.5f" % predict_mean + r" \\")
f.write("\n")
f.write(r"\textbf{Predict std:}" + " %.5f" % predict_var + r" \\")
f.write("\n")
f.write(r"\textbf{Predict 95\% confidence interval:}" +
" (%.5f, %.5f)" % predict_conf95)
f.write("\n" + r"\\" + "\n" + r"\\ \\" + "\n")
f.write(r"\textbf{Bets placed:}" + r" \\ \\")
f.write(latex_year_table(bets_taken).replace(".", ""))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{Betting ROI:}" + r" \\ \\")
f.write(latex_year_table(betting_result))
f.write(r" \\ \\" + "\n")
f.write(r"\textbf{ROI mean:}" + " %.5f" % profit_mean + r" \\" + "\n")
f.write(r"\textbf{ROI std:}" + " %.5f" % profit_var + r" \\" + "\n")
f.write(r"\textbf{ROI 95\% confidence interval:}" +
" (%.5f, %.5f)" % profit_conf95 + r" \\" + "\n\n")
import data_tools as dt
import numpy as np
import matplotlib.pyplot as plt
import models
glicko = models.DoubleModifiedGlickoModel()
surface = ['any', 'clay', 'hard', 'carpet', 'grass']
values = []
for s in surface:
data = dt.get_main_matches_data(s)
data = glicko.test(data)
values.append(np.average(data["ret"]))
N = 4
ind = np.arange(N) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
plt.bar(ind, values[1:], width, color='r')
plt.ylabel('Average return rating of a winner')
plt.xticks(ind+width/2., surface[1:])
plt.axhline(values[0], color='k', linestyle='dashed', linewidth=2)
plt.ylim([1400, 1600])
plt.show()
import models
import data_tools as dt
model = models.DoubleEloModel()
data = dt.get_main_matches_data("Grass")
models.analyse_ranking_model(model, report_name="double_elo_sur", verbose=True)
