evals = logit.cdf(logit_odds)
ev_value = [
curr_ver,
]
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex12/record_ex12_' + mdl_typ + '_' +
str(THRESHOLD) + '.csv',
index=False,
cols=None)
df = pd.DataFrame(prm_note)
df.to_csv('./../result/ex12/prm_note_' + mdl_typ + '.csv',
index=False,
cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex12_short('nml')
ex12_short('rfn')
ex12_short('chrn')
fig.draw_graph(12)
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [curr_ver,]
ev_value.extend( ex01.evaluate_ex(next_df, evals) )
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version','nm','np','nf','nc','f_value']
df = df.sort_index(ascending=False)
df.to_csv( './../result/ex12/record_ex12_' + mdl_typ + '_' + str(THRESHOLD) + '.csv', index=False, cols=None)
df = pd.DataFrame(prm_note)
df.to_csv( './../result/ex12/prm_note_'+mdl_typ+'.csv', index=False, cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex12_short('nml')
ex12_short('rfn')
ex12_short('chrn')
fig.draw_graph(12)
logit_odds = ev_data.dot(coef)
# print logit_odds
evals = logit.cdf(logit_odds)
# print evals
ev_value = [
curr_ver,
]
# ev_value.extend( ex01.evaluate_ex(next_df, evals, mdl_typ,curr_ver) )
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values.append(ev_value)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex2/record_ex2_' + mdl_typ + '_' + str(THRESHOLD) +
'.csv',
index=False,
cols=None)
if __name__ == '__main__':
# ex2_short('nml', 0.3)
# ex2_short('rfn', 0.3)
ex2_short('nml', 0.5)
ex2_short('rfn', 0.5)
ex2_short('chrn', 0.5)
fig.draw_graph(2)
# create model used evaluatopn_ex
ev_data = next_df[ref_prms]
# normalize
ev_data = ev_data.div(ev_data.sum(1), axis=0)
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [
curr_ver,
]
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values_ref.append(ev_value)
df = pd.DataFrame(ev_values_nml)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df.to_csv('./../result/ex3/record_ex3_nml_' + str(THRESHOLD) + '.csv',
index=False,
cols=None)
df = pd.DataFrame(ev_values_ref)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df.to_csv('./../result/ex3/record_ex3_rfn_' + str(THRESHOLD) + '.csv',
index=False,
cols=None)
if __name__ == '__main__':
ex3(0.5)
fig.draw_graph(3)
# create model used evaluatopn_ex
next_df = mmm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [curr_ver,]
ev_value.extend( ex01.evaluate_ex(next_df, evals) )
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version','nm','np','nf','nc','f_value']
df = df.sort_index(ascending=False)
df.to_csv( './../result/ex11/record_ex11_' + mdl_typ + '_' + str(THRESHOLD) + '.csv', index=False, cols=None)
df = pd.DataFrame(prm_note)
df.to_csv( './../result/ex11/prm_note_'+mdl_typ+'.csv', index=False, cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex11_short('nml')
ex11_short('rfn')
ex11_short('chrn')
fig.draw_graph(11)
# get coefficients
params = result.params.values
coef = pd.Series(params, index=ref_prms)
# create model used evaluatopn_ex
ev_data = next_df[ref_prms]
# normalize
ev_data = ev_data.div(ev_data.sum(1),axis=0)
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [curr_ver,]
ev_value.extend( ex01.evaluate_ex(next_df, evals) )
ev_values_ref.append(ev_value)
df = pd.DataFrame(ev_values_nml)
df.columns = ['version','nm','np','nf','nc','f_value']
df.to_csv( './../result/ex3/record_ex3_nml_' + str(THRESHOLD) +'.csv', index=False, cols=None)
df = pd.DataFrame(ev_values_ref)
df.columns = ['version','nm','np','nf','nc','f_value']
df.to_csv( './../result/ex3/record_ex3_rfn_' + str(THRESHOLD) +'.csv', index=False, cols=None)
if __name__ == '__main__':
ex3(0.5)
fig.draw_graph(3)
evals = logit.cdf(logit_odds)
ev_value = [
curr_ver,
]
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values.append(ev_value)
prm_note.append(curr_ver)
prm_note.append(best_prms)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex11/record_ex11_' + mdl_typ + '_' +
str(THRESHOLD) + '.csv',
index=False,
cols=None)
df = pd.DataFrame(prm_note)
df.to_csv('./../result/ex11/prm_note_' + mdl_typ + '.csv',
index=False,
cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex11_short('nml')
ex11_short('rfn')
ex11_short('chrn')
fig.draw_graph(11)
# create model used evaluatopn_ex
next_df = mm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [
curr_ver,
]
ev_value.extend(ex01.evaluate_ex(next_df, evals))
ev_values.append(ev_value)
df = pd.DataFrame(ev_values)
df.columns = ['version', 'nm', 'np', 'nf', 'nc', 'f_value']
df = df.sort_index(ascending=False)
df.to_csv('./../result/ex4/record_ex4_' + mdl_typ + '_' + str(THRESHOLD) +
'.csv',
index=False,
cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex4_short('nml')
ex4_short('rfn')
ex4_short('chrn')
fig.draw_graph(4)
import ex01 import ex02 import draw_figure as fig ex01.ex1(0.5) fig.draw_graph(1) ex02.ex1(0.5) fig.draw_graph(2) fig.draw_grph(1.2,1,'rfn',2,'rfn')
# get coefficients
params = result.params.values
coef = pd.Series(params, index=best_prms)
# print coef
# create model used evaluatopn_ex
next_df = mm.create_df(next_ver)
# explanatory value
ev_data = next_df[list(best_prms)]
# operate evaluation_ex
logit_odds = ev_data.dot(coef)
evals = logit.cdf(logit_odds)
ev_value = [curr_ver,]
ev_value.extend( ex01.evaluate_ex(next_df, evals) )
ev_values.append(ev_value)
df = pd.DataFrame(ev_values)
df.columns = ['version','nm','np','nf','nc','f_value']
df = df.sort_index(ascending=False)
df.to_csv( './../result/ex4/record_ex4_' + mdl_typ + '_' + str(THRESHOLD) + '.csv', index=False, cols=None)
if __name__ == '__main__':
# ex1(0.5)
ex4_short('nml')
ex4_short('rfn')
ex4_short('chrn')
fig.draw_graph(4)