def output_graph(self, geography="nyc", filename=None):
"""Output a PDF of a plot showing the grades over time in a certain
geography, either 'nyc' for the whole city, or each
borough ('bronx', 'queens', 'brooklyn', 'manhattan', 'staten')."""
print "Outputting figure...%s" % filename
sys.stdout.flush()
# Get counts and percents
counts, pcts = self.get_grade_counts_by_year(self.cut_to_geography(geography))
counts.fillna(value=0, inplace=True)
pcts.fillna(value=0, inplace=True)
# Create axes and figure
fig, axes = plt.subplots(2, 1, sharex=False)
fig.set_size_inches(7, 11)
fig.subplots_adjust(right=.8)
percentage_graph(pcts, axes[0])
bar_graph(counts, axes[1])
if filename is not None:
fig.savefig(filename)
else:
plt.show()
def select_best_model(training_X,training_Y,data_title=None):
##print training_X.shape[0]
##split data into 4 section by every 4th item
list1x = training_X[0::10]
list2x = training_X[1::10]
list3x = training_X[2::10]
list4x = training_X[3::10]
list5x = training_X[4::10]
list6x = training_X[5::10]
list7x = training_X[6::10]
list8x = training_X[7::10]
list9x = training_X[8::10]
list10x = training_X[9::10]
list1y = training_Y[0::10]
list2y = training_Y[1::10]
list3y = training_Y[2::10]
list4y = training_Y[3::10]
list5y = training_Y[4::10]
list6y = training_Y[5::10]
list7y = training_Y[6::10]
list8y = training_Y[7::10]
list9y = training_Y[8::10]
list10y = training_Y[9::10]
train_set_X = numpy.concatenate((list2x,list3x,list4x,list5x,list6x,list7x))
train_set_Y = numpy.concatenate((list2y,list3y,list4y,list5y,list6y,list7y))
validate_set_X = list8x
validate_set_Y = list8y
full_train_set_X = numpy.concatenate((list2x,list3x,list4x,list5x,list6x,list7x,list8x))
full_train_set_Y = numpy.concatenate((list2y,list3y,list4y,list5y,list6y,list7y,list8y))
test_set_X = numpy.concatenate((list1x,list9x,list10x))
test_set_Y = numpy.concatenate((list1y,list9y,list10y))
##evuate and find best alpha for each model
start_time = time.clock()
dict_alpha0 = optimize_alpha("dc_tree",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha0 = int(argmax(dict_alpha0)[0])
print "\talpha: "+str(best_alpha0)
train_time0 = time.clock() - start_time
start_time = time.clock()
y_test0 = scikit_dc_tree(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha0)
predict0 = time.clock() - start_time
print
best_alpha1 = 0.5
y_test1 = 0.5
start_time = time.clock()
dict_alpha2 = {"5":0.5}
best_alpha2 = float(argmax(dict_alpha2)[0])
print "\talpha: "+str(best_alpha2)
train_time2 = time.clock() - start_time
start_time = time.clock()
y_test2 = 0.5
predict2 = time.clock() - start_time
print
##ensemble classifiers:
start_time = time.clock()
dict_alpha3 = optimize_alpha("ran_forest",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha3 = int(argmax(dict_alpha3)[0])
best_beta3 = int(argmax(dict_alpha3)[1])
print "\talpha: "+str(best_alpha3)+" beta: "+str(best_beta3)
train_time3 = time.clock() - start_time
start_time = time.clock()
y_test3 = scikit_ran_forest(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha=best_alpha3,beta=best_beta3)
predict3 = time.clock() - start_time
print
start_time = time.clock()
dict_alpha4 = optimize_alpha("ada_boost",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha4 = int(argmax(dict_alpha4)[0])
print "\talpha: "+str(best_alpha4)
train_time4 = time.clock() - start_time
start_time = time.clock()
y_test4 = scikit_ada_boost(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha4)
predict4 = time.clock() - start_time
print
""" SVM extras too slow
start_time = time.clock()
dict_alpha2 = optimize_alpha("svm",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha2 = float(argmax(dict_alpha2))
print "\t"+str(best_alpha2)
train_time2 = time.clock() - start_time
start_time = time.clock()
y_test2 = scikit_onevsrest(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha0)
predict2 = time.clock() - start_time
print
"""
## consolidate and output final result
dict_choices = {"dc_tree":y_test0,"knn":y_test1,"log_reg":y_test2,"ran_forest":y_test3,"ada_boost":y_test4}
dict_alpha = {"dc_tree":best_alpha0,"knn":best_alpha1,"log_reg":best_alpha2,"ran_forest":best_alpha3,"ada_boost":best_alpha4}
dict_beta = {"ran_forest":best_beta3}
print dict_choices
final_model = argmax(dict_choices)[0]
##prep for bar graphs
dict_errors = dict_choices
for key, value in dict_errors.iteritems():
dict_errors[key] = 1- value
errors = [dict_errors["dc_tree"],dict_errors["log_reg"]]
traintimes = [train_time0,train_time2]
predicttimes = [predict0,predict2]
##plot accuracy
plotting.bar_graph(data_title,"decisiontree","log_reg",[1,2],errors,maxy=max(errors)*1.1,ylabel="errors")
##plot training time
plotting.bar_graph(data_title,"decisiontree","log_reg",[1,2],traintimes,maxy=max(traintimes)*1.1,ylabel="training times(s)")
##plot prediction time
plotting.bar_graph(data_title,"decisiontree","log_reg",[1,2],predicttimes,maxy=max(predicttimes)*1.1,ylabel="prediction times(s)")
#prep for line graphs
alpha_X =[]
alpha_X_ints = []
for i in range(1, 2, 1):
for factor in range(3,10,3):
alpha = 10**(i)*factor;
if (alpha > 1):
alpha_X.append(str(alpha))
alpha_X_ints.append(alpha)
alpha_Y_dc = []
alpha_Y_log_reg = []
dict_error0 = dict_alpha0
for key, value in dict_alpha0.iteritems():
dict_error0[key] = 1.0- value
dict_error2 = dict_alpha2
for key, value in dict_alpha2.iteritems():
dict_error2[key] = 1.0- value
for xvalue in alpha_X:
alpha_Y_dc.append(dict_error0[xvalue])
alpha_Y_log_reg.append(dict_error2[xvalue])
alpha_Y = alpha_Y_dc,alpha_Y_log_reg
#plot line graph
plotting.line_graph_alpha_error(data_title,"dc_tree","log_reg",alpha_X_ints,alpha_Y)
return final_model,dict_alpha[final_model],best_beta3
def select_best_model(training_X,training_Y,data_title=None):
##print training_X.shape[0]
##split data into 4 section by every 4th item
list1x = training_X[0::10]
list2x = training_X[1::10]
list3x = training_X[2::10]
list4x = training_X[3::10]
list5x = training_X[4::10]
list6x = training_X[5::10]
list7x = training_X[6::10]
list8x = training_X[7::10]
list9x = training_X[8::10]
list10x = training_X[9::10]
list1y = training_Y[0::10]
list2y = training_Y[1::10]
list3y = training_Y[2::10]
list4y = training_Y[3::10]
list5y = training_Y[4::10]
list6y = training_Y[5::10]
list7y = training_Y[6::10]
list8y = training_Y[7::10]
list9y = training_Y[8::10]
list10y = training_Y[9::10]
##split = training_X.shape[0] * .90
seed = 1
train_set_X = numpy.concatenate((list2x,list3x,list4x,list5x,list6x,list7x))
train_set_Y = numpy.concatenate((list2y,list3y,list4y,list5y,list6y,list7y))
validate_set_X = list8x
validate_set_Y = list8y
full_train_set_X = numpy.concatenate((list2x,list3x,list4x,list5x,list6x,list7x,list8x))
full_train_set_Y = numpy.concatenate((list2y,list3y,list4y,list5y,list6y,list7y,list8y))
test_set_X = numpy.concatenate((list1x,list9x,list10x))
test_set_Y = numpy.concatenate((list1y,list9y,list10y))
#tables
dict_choices =defaultdict(float)
dict_alpha =defaultdict(float)
dict_beta =defaultdict(float)
traintimes =defaultdict(float)
predicttime = defaultdict(float)
##evuate and find best alpha for each model
start_time = time.clock()
dict_alpha0 = optimize_alpha("dc_tree",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha0 = int(argmax(dict_alpha0)[0])
dict_alpha["dc_tree"] = best_alpha0
print "\talpha: "+str(best_alpha0)
traintimes["dc_tree"] = time.clock() - start_time
start_time = time.clock()
dict_choices["dc_tree"] = scikit_dc_tree(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha0)
predicttime["dc_tree"] = time.clock() - start_time
print
start_time = time.clock()
dict_alpha1 = optimize_alpha("knn",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha1 = int(argmax(dict_alpha1)[0])
dict_alpha["knn"] = best_alpha1
print "\talpha: "+str(best_alpha1)
traintimes["knn"] = time.clock() - start_time
start_time = time.clock()
dict_choices["knn"] = scikit_knn_model(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha1)
predicttime["knn"] = time.clock() - start_time
print
"""
start_time = time.clock()
dict_alpha2 = optimize_alpha("log_reg",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha2 = float(argmax(dict_alpha2)[0])
dict_alpha["log_reg"] = best_alpha2
print "\talpha: "+str(best_alpha2)
traintimes["log_reg"] = time.clock() - start_time
start_time = time.clock()
dict_choices["log_reg"] = scikit_log_reg(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha2)
predicttime["log_reg"] = time.clock() - start_time
print
"""
##ensemble classifiers:
start_time = time.clock()
dict_alpha3 = optimize_alpha("ran_forest",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha3 = int(argmax(dict_alpha3)[0])
best_beta3 = int(argmax(dict_alpha3)[1])
dict_alpha["ran_forest"] = best_alpha3
dict_beta["ran_forest"] = best_beta3
print "\talpha: "+str(best_alpha3)+" beta: "+str(best_beta3)
traintimes["ran_forest"] = time.clock() - start_time
start_time = time.clock()
dict_choices["ran_forest"] = scikit_ran_forest(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha=best_alpha3,beta=best_beta3)
predicttime["ran_forest"] = time.clock() - start_time
print
"""
start_time = time.clock()
dict_alpha4 = optimize_alpha("ada_boost",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha4 = int(argmax(dict_alpha4)[0])
dict_alpha["ada_boost"] = best_alpha4
print "\talpha: "+str(best_alpha4)
traintimes["ada_boost"] = time.clock() - start_time
start_time = time.clock()
dict_choices["ada_boost"] = scikit_ada_boost(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha4)
predicttime["ada_boost"] = time.clock() - start_time
print
"""
""" SVM extras too slow
start_time = time.clock()
dict_alpha2 = optimize_alpha("svm",train_set_X,train_set_Y,validate_set_X,validate_set_Y)
best_alpha2 = float(argmax(dict_alpha2))
print "\t"+str(best_alpha2)
train_time2 = time.clock() - start_time
start_time = time.clock()
y_test2 = scikit_onevsrest(full_train_set_X, full_train_set_Y, test_set_X,test_set_Y,alpha = best_alpha0)
predict2 = time.clock() - start_time
print
"""
## consolidate and output final result
print dict_choices
final_model = argmax(dict_choices)[0]
##prep for bar graphs
dict_errors = dict_choices
for key, value in dict_errors.iteritems():
dict_errors[key] = 1- value
errors = [dict_errors["dc_tree"],dict_errors["knn"],dict_errors["ran_forest"]]
traintimes = [traintimes["dc_tree"],traintimes["knn"],traintimes["ran_forest"]]
predicttimes = [predicttime["dc_tree"],predicttime["knn"],predicttime["ran_forest"]]
##plot accuracy
plotting.bar_graph(data_title,"decisiontree","knn","ran_forest",[1,2,3],errors,maxy=max(errors)*1.1,ylabel="errors")
##plot training time
plotting.bar_graph(data_title,"decisiontree","knn","ran_forest",[1,2,3],traintimes,maxy=max(traintimes)*1.1,ylabel="training times(s)")
##plot prediction time
plotting.bar_graph(data_title,"decisiontree","knn","ran_forest",[1,2,3],predicttimes,maxy=max(predicttimes)*1.1,ylabel="prediction times(s)")
#prep for line graphs
alpha_X =[]
alpha_X_ints = []
for i in range(0, 2, 1):
for factor in range(3,10,3):
alpha = 10**(i)*factor;
if (alpha > 1):
alpha_X.append(str(alpha))
alpha_X_ints.append(alpha)
alpha_Y_dc = []
alpha_Y_knn = []
alpha_Y_ran_forest = []
dict_error0 = dict_alpha0
for key, value in dict_alpha0.iteritems():
dict_error0[key] = 1.0- value
dict_error1 = dict_alpha1
for key, value in dict_alpha1.iteritems():
dict_error1[key] = 1.0- value
dict_error3 = dict_alpha3
for key, value in dict_alpha3.iteritems():
dict_error3[key] = 1.0- value
for xvalue in alpha_X:
alpha_Y_dc.append(dict_error0[xvalue])
alpha_Y_knn.append(dict_error1[xvalue])
alpha_Y_ran_forest.append(dict_error3[str(int(xvalue)*3)+","+str(50)])
alpha_Y = alpha_Y_dc,alpha_Y_knn,alpha_Y_ran_forest
#plot line graph
plotting.line_graph_alpha_error(data_title,"dc_tree","knn","ran_forest",alpha_X_ints,alpha_Y)
return final_model,dict_alpha[final_model],best_beta3
