def main():
# get and aggregate accessibility from cube using import_acc_results.py file
# TARGETS = [12, 35, 55, 71, 75, 82, 86, 87, 88, 106, 108, 115, 121, 231, 241, 247, 256, 258, 260, 261, 676, 730, 733, 1231, 1548] #first set of Cube runs
TARGETS = [
20,
33,
36,
137,
142,
143,
144,
151,
152,
159,
166,
167,
171,
173,
183,
184,
192,
193,
194,
196,
205,
1676,
1692,
2851,
2914,
] # data within: 12-Dec-2013_12_3909_50_0.55556_25.mat #indices between 0 and 2110. the scenarios for which you want to save the damaged bridge data
# TARGETS = [20]
# y = aggregate_accessibility(TARGETS)
# util.write_list(time.strftime("%Y%m%d")+'_accessTot.txt', y)
y = [
18.2339128119,
18.2338120181,
18.2338952366,
18.2338109314,
18.2270352566,
18.2177845713,
18.1998501612,
18.2177377231,
18.233770681,
18.2261430987,
18.1691203163,
18.1849249099,
18.2141010264,
18.2139231104,
18.23383158091398,
18.2253745585,
18.2155757901,
18.2012935522,
18.2138556128,
18.1758345198,
18.226103683,
18.2338211763,
18.2260523679,
18.2339486092,
18.2215360497,
]
weights = get_scenario_weights("12-Dec-2013_12_3909_50_0.55556_25_weights.csv")
# get general x values. These are the various welfare metrics.
the_filename = "/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges1eps_extensive2.txt"
new_x = freq_svm.build_x(TARGETS, the_filename)
the_filename_full = (
"/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges3eps_extensive.txt"
) # indices in the first column start at 0
x_for_predicting = freq_svm.build_x(range(1, 11728), the_filename_full)
# x_for_predicting = freq_svm.build_x(range(1, 3092), the_filename_full)
the_x = np.vstack((new_x, x_for_predicting))
the_x = preprocessing.scale(the_x)
new_x = the_x[0 : new_x.shape[0], :]
x_for_predicting = the_x[new_x.shape[0] :, :]
print "built baby x"
# pick threshold. Above this y value, the data is called a "match" and below is a "miss". For frequent itemsets, we'll be doing frequent items ONLY among the items predicted as a match so VORSICHT!
target_annual_rate = 0.002 # 1 in 475 years
threshold = freq_svm.identify_threshold(target_annual_rate, y, weights)
print "by my method I find the threshold to be: ", threshold
threshold = (
18.19933616
) # from the Matlab script called cubeAnalysiswDamagedTransit.m for 475 year return period #18.2139 #75th quantile
print "I think the threshold is: ", threshold
# label events above threshold as match and below as miss
match_label = 1
miss_label = 0 # for purposes of acesibility, low is bad so these are the true high loss cases
new_y = freq_svm.label(
y, threshold, match_label, miss_label
) # less than threshold is miss label. So, this puts high loss in accessibility as miss (lower value)
print "new_ y: ", new_y # should be mostly 1's
# ############################
# h = .02 # step size in the mesh
# # we create an instance of SVM and fit out data. We do not scale our
# # data since we want to plot the support vectors
# C = 1.0 # SVM regularization parameter
# svc = svm.SVC(kernel='linear', C=C, class_weight='auto').fit(new_x, new_y)
# rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C, class_weight='auto').fit(new_x, new_y)
# poly_svc = svm.SVC(kernel='poly', degree=3, C=C, class_weight='auto').fit(new_x, new_y)
# lin_svc = svm.LinearSVC(C=C, class_weight='auto').fit(new_x, new_y)
# X = new_x.copy()
# # create a mesh to plot in
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
# np.arange(y_min, y_max, h))
# # title for the plots
# titles = ['SVC with linear kernel',
# 'SVC with RBF kernel',
# 'SVC with polynomial (degree 3) kernel',
# 'LinearSVC (linear kernel)']
# for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# # Plot the decision boundary. For that, we will assign a color to each
# # point in the mesh [x_min, m_max]x[y_min, y_max].
# pl.subplot(2, 2, i + 1)
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# # Put the result into a color plot
# Z = Z.reshape(xx.shape)
# pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
# pl.axis('off')
# pl.xlabel('Percentage increase of bridges damaged (normalized)')
# pl.ylabel('Percentage incrase of travel time (normlized')
# # Plot also the training points
# pl.scatter(X[:, 0], X[:, 1], c=new_y, cmap=pl.cm.Paired)
# #plot also the prediction
# y_pred = clf.predict(x_for_predicting)
# pl.scatter(x_for_predicting[:, 0], x_for_predicting[:, 1], c= y_pred, marker='^', cmap = pl.cm.Paired)
# pl.title(titles[i])
# pl.savefig('/Users/mahalia/Dropbox/research/dailyWriting/bridges/classificationComp.png')
# ####################
# #train SVM
svm_object = freq_svm.train(new_x, new_y, "auto") # {0:1, 1:1})
######Done using Cube results. Now just use ITA results....#####
# use trained svm to predict values from large set
# print 'built x'
y_pred = freq_svm.predict(x_for_predicting, svm_object)
# y_pred = []
# for i in range(11727):
# y_pred.append(0)
util.write_list(time.strftime("%Y%m%d") + "_predictedY.txt", y_pred)
# count up annual rates for each bridge in the list when event predicted as match
miss_indices = []
for index, value in enumerate(y_pred):
if value == miss_label: # high loss means low accessibility, which means miss
miss_indices.append(index + 1) # matlab indices starting from 1
print 'we have this many "misses"=="predicted high loss cases": ', len(miss_indices)
item_indices = range(3152) # 1743 highway bridges and 1409 bart structures
with open("20131212_3eps_damagedBridges.pkl", "rb") as f:
list_of_baskets = pkl.load(
f
) # this has list of bridge indices (MATLAB INDICES that start from 1) that are damaged
# for basket in list_of_baskets:
# if '609' in basket:
# print 'found one: ', basket
lnsas, weights = travel_main_simple_simplev3.ground_motions(
3, 0.00001, "input/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt"
)
support_list = get_support(weights, miss_indices, item_indices, list_of_baskets)
# output the sum of weights of scenarios where each bridge was damanged to plot in matlab. First column is counter stsarting at 1. second column is support
util.write_2dlist(time.strftime("%Y%m%d") + "_bridgeIndex_support.txt", support_list)
pdb.set_trace()
def main_tt():
print "chin up"
# get and aggregate travel time
# get general x values. These are the various welfare metrics.
the_filename_full = (
"/Users/mahalia/ita/20131212_bridges_flow_path_tt_vmt_bridges3eps_extensive.txt"
) # indices in the first column start at 0
x_raw = freq_svm.build_x(range(1, 11728), the_filename_full)
the_x = preprocessing.scale([[row[0]] for row in x_raw])
the_y = np.array([row[1] for row in x_raw])
break_point = 9383
new_x = np.array(the_x[0:break_point]) # 80%
x_for_predicting = the_x[break_point:] # 20%
y = np.array([row[1] for row in x_raw[0:break_point, :]]) # should be as big as the training dataset
numeps = 3 # the number of epsilons
tol = (
0.00001
) # the minimum annual rate that you care about in the original event set (the weight now is the original annual rate / number of epsilons per event)
lnsas, full_weights = travel_main_simple_simplev3.ground_motions(
numeps,
tol,
"/Users/mahalia/Documents/matlab/Research/Herbst2011/output_data/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt",
)
weights = full_weights[0:break_point]
print "built baby x"
# pick threshold. Above this y value, the data is called a "match" and below is a "miss". For frequent itemsets, we'll be doing frequent items ONLY among the items predicted as a match so VORSICHT!
target_annual_rate = 0.002 # 1 in 475 years
threshold = freq_svm.identify_threshold(target_annual_rate, y, weights)
print "i thought: ", threshold
threshold = (
346420000
) # 18.19933616 #from the Matlab script called cubeAnalysiswDamagedTransit.m for 475 year return period #18.2139 #75th quantile
print "I think the threshold is: ", threshold
# label events above threshold as match and below as miss
match_label = 1
miss_label = 0 # for purposes of accesibility, low is bad so these are the true high loss cases
new_y = np.array(freq_svm.label(y, threshold, match_label, miss_label))
print "new_ y: ", new_y
# ############################
# h = .02 # step size in the mesh
# # we create an instance of SVM and fit out data. We do not scale our
# # data since we want to plot the support vectors
# C = 1.0 # SVM regularization parameter
# svc = svm.SVC(kernel='linear', C=C, class_weight='auto').fit(new_x, new_y)
# rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C, class_weight='auto').fit(new_x, new_y)
# poly_svc = svm.SVC(kernel='poly', degree=3, C=C, class_weight='auto').fit(new_x, new_y)
# lin_svc = svm.LinearSVC(C=C, class_weight='auto').fit(new_x, new_y)
# X = new_x.copy()
# # create a mesh to plot in
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
# np.arange(y_min, y_max, h))
# # title for the plots
# titles = ['SVC with linear kernel',
# 'SVC with RBF kernel',
# 'SVC with polynomial (degree 3) kernel',
# 'LinearSVC (linear kernel)']
# for i, clf in enumerate((svc, rbf_svc, poly_svc, lin_svc)):
# # Plot the decision boundary. For that, we will assign a color to each
# # point in the mesh [x_min, m_max]x[y_min, y_max].
# pl.subplot(2, 2, i + 1)
# Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# # Put the result into a color plot
# Z = Z.reshape(xx.shape)
# pl.contourf(xx, yy, Z, cmap=pl.cm.Paired)
# pl.axis('off')
# pl.xlabel('Percentage increase of bridges damaged (normalized)')
# pl.ylabel('Percentage incrase of travel time (normlized')
# # Plot also the training points
# pl.scatter(X[:, 0], X[:, 1], c=new_y, cmap=pl.cm.Paired)
# #plot also the prediction
# y_pred = clf.predict(x_for_predicting)
# pl.scatter(x_for_predicting[:, 0], x_for_predicting[:, 1], c= y_pred, marker='^', cmap = pl.cm.Paired)
# pl.title(titles[i])
# pl.savefig('/Users/mahalia/Dropbox/research/dailyWriting/bridges/classificationComp.png')
# ####################
# #train SVM
print new_x.shape
print new_y.shape
svm_object = freq_svm.train(new_x, new_y, "auto") # {0:1, 1:1})
######Done using Cube results. Now just use ITA results....#####
# use trained svm to predict values from large set
# print 'built x'
y_pred = freq_svm.predict(x_for_predicting, svm_object)
# y_pred = []
# for i in range(11727):
# y_pred.append(0)
util.write_list(time.strftime("%Y%m%d") + "_predictedY_tt.txt", y_pred)
y_test_raw = [row[1] for row in x_raw[break_point:, :]]
y_test = freq_svm.label(y_test_raw, threshold, match_label, miss_label)
y_tot_raw = [row[1] for row in x_raw]
y_tot = freq_svm.label(y_tot_raw, threshold, match_label, miss_label)
util.write_list(time.strftime("%Y%m%d") + "_actualY_tt.txt", y_test)
print (classification_report(y_test, y_pred))
print (confusion_matrix(y_test, y_pred, labels=range(2)))
scores = cross_validation.cross_val_score(
svm_object, the_x, freq_svm.label(the_y, threshold, match_label, miss_label), cv=3
)
print ("Accuracy: %0.2f (+/- %0.2f)" % (scores.mean(), scores.std() * 2))
# count up annual rates for each bridge in the list when event predicted as match
miss_indices = []
for index, value in enumerate(y_tot): # cheating and just using the actual data instead of predicted one
if value == miss_label:
miss_indices.append(index + 1) # matlab indices starting from 1
print 'we have this many "misses"=="predicted high loss cases": ', len(miss_indices)
item_indices = range(3152) # 1743 highway bridges and 1409 bart structures
with open("20131212_3eps_damagedBridges.pkl", "rb") as f:
list_of_baskets = pkl.load(
f
) # this has list of bridge indices (MATLAB INDICES that start from 1) that are damaged
lnsas, weights = travel_main_simple_simplev3.ground_motions(
3, 0.00001, "input/SF2_mtc_total_3909scenarios_1743bridgesPlusBART_3eps.txt"
)
support_list = get_support(weights, miss_indices, item_indices, list_of_baskets)
# output the sum of weights of scenarios where each bridge was damanged to plot in matlab. First column is counter stsarting at 1. second column is support
util.write_2dlist(time.strftime("%Y%m%d") + "_bridgeIndex_support_tt.txt", support_list)
pdb.set_trace()