print(message)
output.write(message)
output.write("\n")
output.flush()
all_features = ['hour', 'day_of_week', 'month', 'bank_holiday', 'race_day', 'winddirection', 'windspeed', 'temperature', 'rain', 'pressure', 'atc', 'lane_length', 'length', 'landuse_area', 'leisure_area', 'buildings_area', 'buildings_number']
topTags = ['TW','TWA', 'TWL', 'WA']
topPreds = ["pred_" + tag for tag in topTags]
locations = [2.0, 3.0, 4.0, 6.0, 8.0]
all_columns = all_features + topPreds
topDatagroups = []
data_groups = generateAllDataGroups()
for tag in topTags:
for datagroup in data_groups:
dgtag, _ = getTagAndFeatures(datagroup)
if dgtag == tag:
topDatagroups.append(datagroup)
break
def evalColumns(columns):
overallY = []
overallPred = []
for location in locations:
location2s = [l for l in locations if l != location]
output.write("\n")
for i in range(0, len(data)):
if isinstance(data[i], list):
for j in range(0, len(data[i])):
if j != 0:
output.write(",")
output.write(str(data[i][j]))
else:
output.write(str(data[i]))
output.write("\n")
output.close()
top16datagroups = []
data_groups = generateAllDataGroups()
for tag in top16tags:
for datagroup in data_groups:
dgtag, _ = getTagAndFeatures(datagroup)
if dgtag == tag:
top16datagroups.append(datagroup)
break
all_tags, all_features = getTagAndFeatures(['T', 'W', 'A', 'R', 'L', 'B'])
for location in locations:
print("Location: " + str(location))
trainX1, trainX2, trainY1, trainY2, testX, testY = splitDataForXValidationSampled(
location, "location", sampleRate, 42, data, all_features, "target")
writeOutData(OUTPUT_DIRECTORY + "z_" + str(int(location)) + "_trainX.csv",
