def splitData():
for i in range(1,FILES+1):
print '-'*50
print 'Split dataset 1 %d: ' % i
rfile = file(DATA_SET,'r')
reader = csv.reader(rfile)
j = i + 10
if j != 32:
train_file = file('splited_data/%d.csv'%i,'w')
result_file = file('splited_data/%s_%d.csv'%('result',i),'w')
train_writer = csv.writer(train_file)
result_writer = csv.writer(result_file)
for line in reader:
progressBar(reader.line_ num,DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j: train_writer.writerow(line)
if int(line[5]) == j and int(line[2]) == 4: result_writer.writerow([line[0],line[1]])
train_file.close()
result_file.close()
else:
train_file = file('splited_data/for_prediction.csv','w')
train_writer = csv.writer(train_file)
for line in reader:
progressBar(reader.line_num,DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j: train_writer.writerow(line)
train_file.close()
rfile.close()
def prepare_validation(filename):
REPLICATE, DATES, INDIVIDUALS, WEIGHTED = 1, 2, 4, 8
# Load the data, note the unique dates, replicates
data = pd.read_csv(filename, header=None)
dates = data[DATES].unique().tolist()
replicates = data[REPLICATE].unique().tolist()
# Calculate the 561H frequency for each date, replicate
count, frequencies = 0, []
for replicate in replicates:
byReplicate = data[data[REPLICATE] == replicate]
frequency = []
for date in dates:
byDate = byReplicate[byReplicate[DATES] == date]
frequency.append(sum(byDate[WEIGHTED]) / sum(byDate[INDIVIDUALS]))
if len(frequencies) != 0:
frequencies = np.vstack((frequencies, frequency))
else:
frequencies = frequency
# Update the progress bar
count += 1
progressBar(count, len(replicates))
# Return the results
return dates, frequencies
def report(title, dates, districtData):
for district in rwanda.DISTRICTS:
rwanda.plot_summary(title,
dates,
districtData[district],
district=district)
progressBar(district, len(rwanda.DISTRICTS))
def sampling():
cutoffLine('*')
print 'Sampling using EasyEnsemble method'
start_time = time.time()
TRAIN_SET = 'training_set'
if not os.path.exists(TRAIN_SET): os.mkdir(TRAIN_SET)
propotion = 10
negative_size = POSITIVE * propotion
r_file = file(PRE_DIR + '/negative_set.csv', 'r')
reader = csv.reader(r_file)
positive_set = readCSV(PRE_DIR + '/positive_set.csv', int)
negative_set = []
set_count = 0
for line in reader:
progressBar(reader.line_num, NEGATIVE)
line = map(int, line)
if line[-1] == 1: positive_set.append(line)
if line[-1] == 0: negative_set.append(line)
if len(negative_set) == negative_size or reader.line_num == NEGATIVE:
set_count += 1
training_set = positive_set + negative_set
random.shuffle(training_set)
file_name = TRAIN_SET + '/' + '%d.csv'%set_count
writeCSV(training_set, file_name)
negative_set = []
r_file.close()
end_time = time.time()
duration = timekeeper(start_time, end_time)
cutoffLine('*')
print 'It takes %s to sampling' % duration
def splitData():
cutoffLine('*')
print 'Start split data with window %d' % WINDOW
start_time = time.time()
stat_file = file(PRE_DIR + '/stat.csv', 'w')
stat_writer = csv.writer(stat_file)
for i in range(1, FILES + 1):
cutoffLine('-')
print 'Split dataset %d/%d: ' % (i, FILES)
rfile = file(DATA_SET, 'r')
reader = csv.reader(rfile)
j = i + WINDOW
if j != TOTAL_DAY + 1:
if j == TOTAL_DAY:
train_file_name = 'test.csv'
result_file_name = 'result_test.csv'
else:
train_file_name = '%d.csv' % i
result_file_name = '%s_%d.csv' % ('result', i)
train_file = file(PRE_DIR + '/' + train_file_name, 'w')
result_file = file(PRE_DIR + '/' + result_file_name, 'w')
train_writer = csv.writer(train_file)
result_writer = csv.writer(result_file)
train_count = 0
result_count = 0
for line in reader:
progressBar(reader.line_num, DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
if int(line[5]) == j and int(line[2]) == 4:
result_writer.writerow([line[0], line[1]])
result_count += 1
stat_writer.writerow([train_file_name, train_count])
stat_writer.writerow([result_file_name, result_count])
train_file.close()
result_file.close()
else:
forpredict_file_name = 'for_prediction.csv'
train_file = file(PRE_DIR + '/' + forpredict_file_name, 'w')
train_writer = csv.writer(train_file)
train_count = 0
for line in reader:
progressBar(reader.line_num, DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
stat_writer.writerow([forpredict_file_name, train_count])
train_file.close()
rfile.close()
stat_file.close()
end_time = time.time()
duration = timekeeper(start_time, end_time)
cutoffLine('-')
print 'It takes ' + duration + ' to split dataset.'
cutoffLine('*')
def splitData():
cutoffLine('*')
print 'Start split data with window %d' % WINDOW
start_time = time.time()
stat_file = file(PRE_DIR + '/stat.csv','w')
stat_writer = csv.writer(stat_file)
for i in range(1,FILES+1):
cutoffLine('-')
print 'Split dataset %d/%d: ' % (i, FILES)
rfile = file(DATA_SET,'r')
reader = csv.reader(rfile)
j = i + WINDOW
if j != TOTAL_DAY + 1:
if j == TOTAL_DAY:
train_file_name = 'test.csv'
result_file_name = 'result_test.csv'
else:
train_file_name = '%d.csv'%i
result_file_name = '%s_%d.csv'%('result',i)
train_file = file(PRE_DIR + '/' + train_file_name,'w')
result_file = file(PRE_DIR + '/' + result_file_name,'w')
train_writer = csv.writer(train_file)
result_writer = csv.writer(result_file)
train_count = 0
result_count = 0
for line in reader:
progressBar(reader.line_num, DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
if int(line[5]) == j and int(line[2]) == 4:
result_writer.writerow([line[0],line[1]])
result_count += 1
stat_writer.writerow([train_file_name, train_count])
stat_writer.writerow([result_file_name, result_count])
train_file.close()
result_file.close()
else:
forpredict_file_name = 'for_prediction.csv'
train_file = file(PRE_DIR + '/' + forpredict_file_name,'w')
train_writer = csv.writer(train_file)
train_count = 0
for line in reader:
progressBar(reader.line_num,DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
stat_writer.writerow([forpredict_file_name, train_count])
train_file.close()
rfile.close()
stat_file.close()
end_time = time.time()
duration = timekeeper(start_time,end_time)
cutoffLine('-')
print 'It takes ' + duration + ' to split dataset.'
cutoffLine('*')
def process_datasets():
print("Preparing data sets...")
replicates = pd.read_csv(REPLICATES_LIST, header=None)
configurations = replicates[2].unique()
count = 0
progressBar(count, len(configurations))
for configuration in configurations:
data = replicates[replicates[2] == configuration]
filename = os.path.join(DATASET_DIRECTORY,
configuration.replace('yml', 'csv'))
merge_data(data[-50:][3].to_numpy(), filename)
count = count + 1
progressBar(count, len(configurations))
def prepare(filename):
REPLICATE, DATES, DISTRICT, INDIVIDUALS, WEIGHTED = 1, 2, 3, 4, 8
# Load the data, note the unique dates, replicates
data = pd.read_csv(filename, header=None)
dates = data[DATES].unique().tolist()
replicates = data[REPLICATE].unique().tolist()
# Build the dictionary that will be used to store data
districtData = {}
for district in rwanda.DISTRICTS:
districtData[district] = {}
for key in rwanda.REPORT_LAYOUT:
districtData[district][key] = []
# Start by filtering by the replicate
count = 0
for replicate in replicates:
byReplicate = data[data[REPLICATE] == replicate]
# Load the relevent data for each district
for district in rwanda.DISTRICTS:
byDistrict = byReplicate[byReplicate[DISTRICT] == district]
# Load the simple data
for key in rwanda.REPORT_LAYOUT:
if key != 'frequency':
# Append the basic information
index = rwanda.REPORT_LAYOUT[key][rwanda.REPORT_INDEX]
if len(districtData[district][key]) != 0:
districtData[district][key] = np.vstack(
(districtData[district][key], byDistrict[index]))
else:
districtData[district][key] = byDistrict[index]
else:
# Append the 561H frequency data
frequency = byDistrict[WEIGHTED] / byDistrict[INDIVIDUALS]
if len(districtData[district][key]) != 0:
districtData[district][key] = np.vstack(
(districtData[district][key], frequency))
else:
districtData[district][key] = frequency
# Update the progress bar
count += 1
progressBar(count, len(replicates))
# Return the results
return dates, districtData
def main(method, filename, progress=True):
# Load the relevent ASC data
[ascHeader, pfpr] = load_asc("../../GIS/rwa_pfpr2to10.asc")
[_, population] = load_asc("../../GIS/rwa_population.asc")
[_, treatments] = load_asc("../../GIS/rwa_treatment.asc")
beta = []
for row in range(0, ascHeader['nrows']):
beta.append([])
for col in range(0, ascHeader['ncols']):
# Append no data and continue
if pfpr[row][col] == ascHeader['nodata']:
beta[row].append(ascHeader['nodata'])
continue
# If PfPR is zero, then beta is zero
if pfpr[row][col] == 0:
beta[row].append(0)
continue
# Get the population bin, find the beta for the cell
popBin = get_bin(population[row][col], POPULATION_BINS)
target = round(pfpr[row][col] * 100.0, 8)
result = method("data/calibration.csv", popBin,
treatments[row][col], target)
# Check for errors before updating the array
if result is None:
sys.stderr.write("Null value returned for beta, exiting\n")
sys.exit(1)
if result < 0:
sys.stderr.write(
"Projected beta {} is less than zero, exiting\n".format(
result))
# Only exit if the debug flag isn't set
if progress:
sys.exit(1)
beta[row].append(result)
# Note the progress
if progress:
progressBar(row + 1, ascHeader['nrows'])
# Save the calculated beta values
print("Saving {}".format(filename))
write_asc(ascHeader, beta, filename)
def prepare_national(filename):
REPLICATE, DATES, INDIVIDUALS, WEIGHTED = 1, 2, 4, 8
# Load the data, note the unique dates, replicates
data = pd.read_csv(filename, header=None)
dates = data[DATES].unique().tolist()
replicates = data[REPLICATE].unique().tolist()
# Build the dictionary for the results
results = {}
for key in rwanda.REPORT_LAYOUT:
results[key] = []
# Start by filtering by the replicate
count = 0
for replicate in replicates:
byReplicate = data[data[REPLICATE] == replicate]
# Prepare the data structure for this replicate
values = {}
for key in rwanda.REPORT_LAYOUT:
values[key] = []
# Next, filter by date so we can properly aggregate
for date in dates:
byDate = byReplicate[byReplicate[DATES] == date]
for key in rwanda.REPORT_LAYOUT:
if key != 'frequency':
index = rwanda.REPORT_LAYOUT[key][rwanda.REPORT_INDEX]
values[key].append(sum(byDate[index]))
else:
values[key].append(
sum(byDate[WEIGHTED]) / sum(byDate[INDIVIDUALS]))
# Append this replicate to our results
for key in rwanda.REPORT_LAYOUT:
if len(results[key]) != 0:
results[key] = np.vstack((results[key], values[key]))
else:
results[key] = values[key]
# Update the progress bar
count += 1
progressBar(count, len(replicates))
# Return the results
return dates, results
def splitData():
stat_file = file('splited_data/stat.csv','w')
stat_writer = csv.writer(stat_file)
for i in range(1,FILES+1):
cutoffLine('-')
print 'Split dataset %d: ' % i
rfile = file(DATA_SET,'r')
reader = csv.reader(rfile)
j = i + 10
if j != TOTAL_DAY + 1:
if j == TOTAL_DAY:
train_file_name = 'test.csv'
result_file_name = 'result_test.csv'
else:
train_file_name = '%d.csv'%i
result_file_name = '%s_%d.csv'%('result',i)
train_file = file(PRE_DIR + '/' + train_file_name,'w')
result_file = file(PRE_DIR + '/' + result_file_name,'w')
train_writer = csv.writer(train_file)
result_writer = csv.writer(result_file)
train_count = 0
result_count = 0
for line in reader:
progressBar(reader.line_num, DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
if int(line[5]) == j and int(line[2]) == 4:
result_writer.writerow([line[0],line[1]])
result_count += 1
stat_writer.writerow([train_file_name, train_count])
stat_writer.writerow([result_file_name, result_count])
train_file.close()
result_file.close()
else:
forpredict_file_name = 'for_prediction.csv'
train_file = file(PRE_DIR + '/' + forpredict_file_name,'w')
train_writer = csv.writer(train_file)
train_count = 0
for line in reader:
progressBar(reader.line_num,DATASET_SIZE)
if int(line[5]) >= i and int(line[5]) < j:
train_writer.writerow(line)
train_count += 1
stat_writer.writerow([forpredict_file_name, train_count])
train_file.close()
rfile.close()
def process_annual_data(studyId):
# Percentage of public market treatments, drawn from configuration
# 3: Status Quo
# 4: Rapid Private Market Elimination
# 5: Ten Year Private Market Elimination
# 6: Status Quo
# 7: Rapid Private Market Elimination
publicmarket = {
3: {2025: 0.832, 2030: 0.832, 2035: 0.832},
4: {2025: 1.0, 2030: 1.0, 2035: 1.0},
5: {2025: 0.916, 2030: 1.0, 2035: 1.0},
6: {2025: 0.832, 2030: 0.832, 2035: 0.832},
7: {2025: 1.0, 2030: 1.0, 2035: 1.0}
}
# Range of dates for the year
ranges = {
2025: (6575, 6606, 6634, 6665, 6695, 6726, 6756, 6787, 6818, 6848, 6879, 6909),
2030: (8401, 8432, 8460, 8491, 8521, 8552, 8582, 8613, 8644, 8674, 8705, 8735),
2035: (10227, 10258, 10286, 10317, 10347, 10378, 10408, 10439, 10470, 10500, 10531, 10561)
}
# Exit if a study was not provided
if studyId not in publicmarket.keys():
print("Invalid study provided, {}".format(studyId))
exit(1)
# Let the user know what we are doing
print("Processing annual data...")
count = 0
progressBar(count, len(ranges.keys()))
for key in ranges.keys():
# Query and save the data
data = get_annual_data(studyId, ranges[key], publicmarket[studyId][key])
with open("out/{}-{}-annual-data.csv".format(key, studyId), "wb") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(["filename", "replicateid", "population", "cases", "reportedcases", "clinicalper1000", "pfpr2to10"])
for row in data:
writer.writerow(list(row))
# Note the process
count = count + 1
progressBar(count, len(ranges.keys()))
def extract_feature(file_name, line_count, start_date, result_name = ''):
r_name = PRE_DIR + '/' + file_name
w_name = PRE_DIR + '/set_' + file_name
r_file = file(r_name, 'r')
w_file = file(w_name, 'w')
reader = csv.reader(r_file)
writer = csv.writer(w_file)
UI_feature = {}
for line in reader:
progressBar(reader.line_num, line_count)
UI = (int(line[0]),int(line[1]))
if UI not in UI_feature: UI_feature[UI] = [0]*40
## 0 ～ 9 前十天浏览量统计
## 10 ～ 19 前十天收藏量统计
## 20 ～ 29 前十天加入购物车统计
## 30 ～ 39 前十天购买量统计
index = int(line[5]) - start_date
if int(line[2]) == 1 : UI_feature[UI][index] += 1
if int(line[2]) == 2 : UI_feature[UI][10+index] += 1
if int(line[2]) == 3 : UI_feature[UI][20+index] += 1
if int(line[2]) == 4 : UI_feature[UI][30+index] += 1
r_file.close()
## 打标签
result_set = set()
if result_name:
r_name = PRE_DIR + '/' + result_name
r_file = file(r_name, 'r')
reader = csv.reader(r_file)
for line in reader: result_set.add((int(line[0]), int(line[1])))
r_file.close()
if result_set:
for UI in UI_feature:
if UI in result_set:
writer.writerow(list(UI) + UI_feature[UI] + [1])
else:
writer.writerow(list(UI) + UI_feature[UI] + [0])
else:
for UI in UI_feature: writer.writerow(list(UI) + UI_feature[UI])
w_file.close()
def process_summaries(replicates, burnIn, modelStartDate):
# Update the user
print("Processing {} replicate summaries...".format(len(replicates)))
# Note the progress
total = 0
progressBar(total, len(replicates) + 1)
# Iterate through all of the rows
for replicate in replicates:
# Only download complete summaries
if replicate[COMPLETE] == False: continue
# Check to see if the work has already been done
filename = GENOTYPE_TEMPLATE.format(replicate[LABEL], replicate[REPLICATEID])
if not os.path.exists(filename):
save_genotype_summary(replicate[LABEL], replicate[REPLICATEID], burnIn)
filename = TREATMENT_TEMPLATE.format(replicate[LABEL], replicate[REPLICATEID])
if not os.path.exists(filename):
save_treatment_summary(replicate[LABEL], replicate[REPLICATEID], burnIn)
filename = FREQUENCIES_TEMPLATE.format(replicate[LABEL], replicate[REPLICATEID])
if not os.path.exists(filename):
save_genotype_frequencies(replicate[LABEL], replicate[REPLICATEID], burnIn, modelStartDate)
# Note the progress
total = total + 1
progressBar(total, len(replicates) + 1)
# Note that we are done
progressBar(len(replicates) + 1, len(replicates) + 1)
def sampling(proportion):
cutoffLine('*')
start_time = time.time()
print 'sampling with propotion %d...'%proportion
negative_needed = POSITIVE * proportion
sample_times = 10
mod = NEGATIVE / sample_times
negative_eachtime = negative_needed / sample_times
training_set = readCSV(PRE_DIR + '/positive_set.csv', int)
## sampling negative example
rfile = file(PRE_DIR + '/' + 'negative_set.csv', 'r')
reader = csv.reader(rfile)
negative_tmp = []
for line in reader:
progressBar(reader.line_num, NEGATIVE)
negative_tmp.append(map(int, line))
if reader.line_num % mod == 0:
random.shuffle(negative_tmp)
training_set = training_set + negative_tmp[0:negative_eachtime]
negative_tmp = []
rfile.close()
wfile = file('data/training_set_%d.csv'%proportion, 'w')
writer = csv.writer(wfile)
random.shuffle(training_set)
writer.writerows(training_set)
wfile.close()
cutoffLine('-')
print "Real proportion: %f" %((len(training_set)-POSITIVE) / float(POSITIVE))
cutoffLine('*')
end_time = time.time()
duration = timekeeper(start_time, end_time)
print 'It takes %s to sampling with proportion %d'%(duration, proportion)
def process_frequencies(replicates, subset):
# If there are not replicates, then return
if len(replicates) == 0: return
# Update the user and note common data
print("Processing {} replicate frequencies...".format(len(replicates)))
nrows = replicates[0][1]
ncols = replicates[0][2]
# Note the progress
total = 0
progressBar(total, len(replicates) + 1)
# Iterate through all of the rows
currentRate = None
for replicate in replicates:
# Reset the replicate count on a new row
if currentRate != replicate[0]:
if currentRate is not None:
save_frequencies(data, currentRate)
del data
currentRate = replicate[0]
data = {}
# Run a short query to see if we have anything to work with
for row in get_580y_frequency_subset(replicate[REPLICATEID], subset):
days = row[0]
if days not in data: data[days] = [[[0, 0, 0] for _ in range(nrows)] for _ in range(ncols)]
c = row[1]
r = row[2]
# Array formatted as: 0 - infectedindividuals (query index 3)
# 1 - weightedoccurrences (query index 5)
# 3 - count
data[days][r][c][0] += row[3]
data[days][r][c][1] += row[5]
data[days][r][c][2] += 1
# Note the progress
total = total + 1
progressBar(total, len(replicates) + 1)
# Save the last data set
save_frequencies(data, currentRate)
progressBar(total + 1, len(replicates) + 1)
def process_replicates():
# Process the replicates to make sure we have all of the data we need locally
print("Querying for replicates list...")
replicates = get_replicates()
save_csv(REPLICATES_LIST, replicates)
print("Processing replicates...")
count = 0
progressBar(count, len(replicates))
for row in replicates:
# Check to see if we already have the data
filename = os.path.join(REPLICATE_DIRECTORY, "{}.csv".format(row[3]))
if os.path.exists(filename): continue
# Query and store the data
replicate = get_replicate(row[3])
save_csv(filename, replicate)
# Update the progress bar
count = count + 1
progressBar(count, len(replicates))
if count != len(replicates): progressBar(len(replicates), len(replicates))
def extract_feature(window, actday, file_name, line_count, start_date, result_name = ''):
r_name = PRE_DIR + '/' + file_name
w_name = PRE_DIR + '/set_' + file_name
r_file = file(r_name, 'r')
w_file = file(w_name, 'w')
reader = csv.reader(r_file)
writer = csv.writer(w_file)
## 统计同类商品排名，为了避免使用未来信息
ci_rank = {}
for c in ci_sale:
## 统计(actday-window)之前商品销量
ci_rank[c] = {}
for item in ci_sale[c]:
ci_rank[c][item] = sum(ci_sale[c][item][0:actday-window])
## 销量排名；销量好的排名在后，方便处理没有销量的商品(设为0)
rank_list = sorted(ci_rank[c].iteritems(), key = lambda x: x[1])
for index, item in enumerate(rank_list):
item = list(item)
item[1] = index + 1
rank_list[index] = item
ci_rank[c] = dict(rank_list)
UI_feature = {}
for line in reader:
progressBar(reader.line_num, line_count)
UI = (int(line[0]),int(line[1]),int(line[4]))
if UI not in UI_feature: UI_feature[UI] = [0]*(window*4)
## 4种行为统计
index = int(line[5]) - start_date
if int(line[2]) == 1 : UI_feature[UI][index] += 1
if int(line[2]) == 2 : UI_feature[UI][window+index] += 1
if int(line[2]) == 3 : UI_feature[UI][2*window+index] += 1
if int(line[2]) == 4 : UI_feature[UI][3*window+index] += 1
r_file.close()
## 商品同类排名
for UI in UI_feature:
if ci_rank[UI[2]].has_key(UI[1]): UI_feature[UI].append(ci_rank[UI[2]][UI[1]])
else: UI_feature[UI].append(0)
## 打标签
result_set = set()
if result_name:
r_name = PRE_DIR + '/' + result_name
r_file = file(r_name, 'r')
reader = csv.reader(r_file)
for line in reader: result_set.add((int(line[0]), int(line[1])))
r_file.close()
if result_set:
for UI in UI_feature:
if (UI[0],UI[1]) in result_set:
writer.writerow(list(UI) + UI_feature[UI] + [1])
else:
writer.writerow(list(UI) + UI_feature[UI] + [0])
else:
for UI in UI_feature: writer.writerow(list(UI) + UI_feature[UI])
w_file.close()