def getMaximumPrice(datas):
"""
Given the dataset, return the maximum price in the dataset
:param datas: input dataset(in QLearning and Neural Nets, it should have same departure date)
:return: maximum price in the dataset
"""
maximumPrice = util.getPrice(datas[0]["MinimumPrice"]) # in our json data files, MinimumPrice means the price in that day
for data in datas:
price = util.getPrice(data["MinimumPrice"])
maximumPrice = price if price>maximumPrice else maximumPrice
return maximumPrice
def getMaximumPrice(datas):
"""
Given the dataset, return the maximum price in the dataset
:param datas: input dataset(in QLearning and Neural Nets, it should have same departure date)
:return: maximum price in the dataset
"""
maximumPrice = util.getPrice(
datas[0]["MinimumPrice"]
) # in our json data files, MinimumPrice means the price in that day
for data in datas:
price = util.getPrice(data["MinimumPrice"])
maximumPrice = price if price > maximumPrice else maximumPrice
return maximumPrice
def locationNamegetPrice():
global uuid
global categoryDescription
global priceC
global priceA
global priceS
global priceO
resp = {}
global loc_prop
if "Get Fare" in loc_prop:
loc_prop.remove("Get Fare")
ticketed = util.getTicketed(cat_description[categoryDescription], uuid)
if not ticketed:
sug = response.suggesstions(loc_prop[:7])
resp = response.responseSuggestions(
f"Entry to {loc_dict[loc_dict['uuid']==uuid]['name'].iloc[0]} is free",
sug)
else:
if util.getPrice(cat_description[categoryDescription], uuid):
[priceC, priceA, priceS,
priceO] = util.getPrice(cat_description[categoryDescription],
uuid)
if priceC != '':
print(util.getPrice(cat_description[categoryDescription],
uuid))
if priceC.replace(".", "").isdigit():
priceC = float(priceC)
if priceA == '':
priceA = priceC
priceA = float(priceA)
sug = response.suggesstions(["Yes", "No"])
resp = response.responseSuggestions(
f"Would you like to share details about number of travellers with you?",
sug)
else:
sug = response.suggesstions(loc_prop[:7])
resp = response.responseSuggestions(f"{priceC}", sug)
else:
sug = response.suggesstions(loc_prop[:7])
resp = response.responseSuggestions(
f"Admission rates of {loc_dict[loc_dict['uuid']==uuid]['name'].iloc[0]} has recently changed.Details for the same can be checked by their management",
sug)
else:
sug = response.suggesstions(loc_prop[:7])
resp = response.responseSuggestions(
f"Entry to {loc_dict[loc_dict['uuid']==uuid]['name'].iloc[0]} is free",
sug)
print(resp)
return resp
def getOptimalState(datas):
"""
Given the dataset, return the state correspongding to minimum price in the dataset
:param datas: input dataset(in QLearning and Neural Nets, it should have same departure date)
:return: minimum price state in the dataset
"""
optimalState = 0
minimumPrice = util.getPrice(datas[0]["MinimumPrice"]) # in our json data files, MinimumPrice means the price in that day
for data in datas:
price = util.getPrice(data["MinimumPrice"])
state = data["State"]
optimalState = state if price<minimumPrice else optimalState
minimumPrice = price if price<minimumPrice else minimumPrice
return optimalState
def getOptimalState(datas):
"""
Given the dataset, return the state correspongding to minimum price in the dataset
:param datas: input dataset(in QLearning and Neural Nets, it should have same departure date)
:return: minimum price state in the dataset
"""
optimalState = 0
minimumPrice = util.getPrice(
datas[0]["MinimumPrice"]
) # in our json data files, MinimumPrice means the price in that day
for data in datas:
price = util.getPrice(data["MinimumPrice"])
state = data["State"]
optimalState = state if price < minimumPrice else optimalState
minimumPrice = price if price < minimumPrice else minimumPrice
return optimalState
def getMaximumPreviousPrice(departureDate, state, datas):
"""
Get the maximum previous price, corresponding to the departure date and the observed date
:param departureDate: departure date
:param state: observed date
:param datas: datasets
:return: maximum previous price
"""
specificDatas = []
specificDatas = [data for data in datas if data["Date"]==departureDate]
maximumPreviousPrice = util.getPrice(specificDatas[0]["MinimumPrice"])
for data in specificDatas:
if util.getPrice(data["MinimumPrice"]) > maximumPreviousPrice and data["State"]>=state:
maximumPreviousPrice = util.getPrice(data["MinimumPrice"])
return maximumPreviousPrice
def getMaximumPreviousPrice(departureDate, state, datas):
"""
Get the maximum previous price, corresponding to the departure date and the observed date
:param departureDate: departure date
:param state: observed date
:param datas: datasets
:return: maximum previous price
"""
specificDatas = []
specificDatas = [data for data in datas if data["Date"] == departureDate]
maximumPreviousPrice = util.getPrice(specificDatas[0]["MinimumPrice"])
for data in specificDatas:
if util.getPrice(data["MinimumPrice"]
) > maximumPreviousPrice and data["State"] >= state:
maximumPreviousPrice = util.getPrice(data["MinimumPrice"])
return maximumPreviousPrice
def getChosenPrice(state, datas):
"""
Given the state, i.e. the days before departure, and the dataset, return the price
:param state: the days before departure
:param datas: input dataset(in QLearning, it should have same departure date)
:return: the chosen price
"""
for data in datas:
if data["State"] == state:
return util.getPrice(data["MinimumPrice"])
def getChosenPrice(state, datas):
"""
Given the state, i.e. the days before departure, and the dataset, return the price
:param state: the days before departure
:param datas: input dataset(in QLearning, it should have same departure date)
:return: the chosen price
"""
for data in datas:
if data["State"] == state:
return util.getPrice(data["MinimumPrice"])
def getPrice():
total_sqft = float(request.form['total_sqft'])
location = request.form['location']
bhk = int(request.form['bhk'])
bath = int(request.form['bath'])
response = jsonify({
'estimated_price': util.getPrice(location, total_sqft, bhk, bath)
})
response.headers.add('Access-Control-Allow-Origin', '*')
return response
def load_for_classification_for_General(dataset="General",
routes=routes_general):
"""
Load the data for classification
:param dataset: dataset name('Specific' or 'General')
:return: X_train, y_train, X_test, y_test
"""
isOneOptimalState = False
# Construct the input data
dim = routes.__len__() + 4
X_train = np.empty(shape=(0, dim))
y_train = np.empty(shape=(0, 1))
y_train_price = np.empty(shape=(0, 1))
for filePrefix in routes:
print filePrefix
datas = load_data_with_prefix_and_dataset(filePrefix, dataset)
for data in datas:
print "Construct route {}, State {}, departureDate {}...".format(
filePrefix, data["State"], data["Date"])
x_i = []
# feature 1: flight number -> dummy variables
for i in range(len(routes)):
"""
!!!need to change!
"""
if i == routes.index(filePrefix):
x_i.append(1)
else:
x_i.append(0)
# feature 2: departure date interval from "20151109", because the first observed date is 20151109
departureDate = data["Date"]
"""
!!!maybe need to change the first observed date
"""
departureDateGap = util.days_between(departureDate, "20151109")
x_i.append(departureDateGap)
# feature 3: observed days before departure date
state = data["State"]
x_i.append(state)
# feature 4: minimum price before the observed date
minimumPreviousPrice = getMinimumPreviousPrice(
data["Date"], state, datas)
x_i.append(minimumPreviousPrice)
# feature 5: maximum price before the observed date
maximumPreviousPrice = getMaximumPreviousPrice(
data["Date"], state, datas)
x_i.append(maximumPreviousPrice)
# output
y_i = [0]
specificDatas = []
specificDatas = [
data2 for data2 in datas if data2["Date"] == departureDate
]
minPrice = getMinimumPrice(specificDatas)
if util.getPrice(data["MinimumPrice"]) == minPrice:
y_i = [1]
# keep price info
y_price = [util.getPrice(data["MinimumPrice"])]
X_train = np.concatenate((X_train, [x_i]), axis=0)
y_train = np.concatenate((y_train, [y_i]), axis=0)
y_train_price = np.concatenate((y_train_price, [y_price]), axis=0)
# end of for datas
# end of for routes
"""
remove duplicate rows
"""
tmp = np.concatenate((X_train, y_train, y_train_price), axis=1)
new_array = [tuple(row) for row in tmp]
tmp = np.unique(new_array)
# # get the result
# X_train = tmp[:, 0:16]
# y_train = tmp[:, 16]
# y_train_price = tmp[:, 17]
# save the result
np.save('inputGeneralRaw/X_train', X_train)
np.save('inputGeneralRaw/y_train', y_train)
np.save('inputGeneralRaw/y_train_price', y_train_price)
np.save('inputGeneralRaw/tmp', tmp)
return X_train, y_train, y_train_price
def load_for_classification_for_Specific(dataset="Specific",
routes=routes_specific):
"""
Load the data for classification
:param dataset: dataset name('Specific' or 'General')
:return: X_train, y_train, X_test, y_test
"""
isOneOptimalState = False
# Construct the input data
dim = routes.__len__() + 4
X_train = np.empty(shape=(0, dim))
y_train = np.empty(shape=(0, 1))
y_train_price = np.empty(shape=(0, 1))
X_test = np.empty(shape=(0, dim))
y_test = np.empty(shape=(0, 1))
y_test_price = np.empty(shape=(0, 1))
for filePrefix in routes:
datas = load_data_with_prefix_and_dataset(filePrefix, dataset)
for data in datas:
print "Construct route {}, State {}, departureDate {}...".format(
filePrefix, data["State"], data["Date"])
x_i = []
# feature 1: flight number -> dummy variables
for i in range(len(routes)):
"""
!!!need to change!
"""
if i == routes.index(filePrefix):
x_i.append(1)
else:
x_i.append(0)
# feature 2: departure date interval from "20151109", because the first observed date is 20151109
departureDate = data["Date"]
"""
!!!maybe need to change the first observed date
"""
departureDateGap = util.days_between(departureDate, "20151109")
x_i.append(departureDateGap)
# feature 3: observed days before departure date
state = data["State"]
x_i.append(state)
# feature 4: minimum price before the observed date
minimumPreviousPrice = getMinimumPreviousPrice(
data["Date"], state, datas)
x_i.append(minimumPreviousPrice)
# feature 5: maximum price before the observed date
maximumPreviousPrice = getMaximumPreviousPrice(
data["Date"], state, datas)
x_i.append(maximumPreviousPrice)
# output
y_i = [0]
specificDatas = []
specificDatas = [
data2 for data2 in datas if data2["Date"] == departureDate
]
# if isOneOptimalState:
# # Method 1: only 1 entry is buy
# optimalState = getOptimalState(specificDatas)
# if data["State"] == optimalState:
# y_i = [1]
# else:
# # Method 2: multiple entries can be buy
# minPrice = getMinimumPrice(specificDatas)
# if util.getPrice(data["MinimumPrice"]) == minPrice:
# y_i = [1]
#Method 2: multiple entries can be buy
minPrice = getMinimumPrice(specificDatas)
if util.getPrice(data["MinimumPrice"]) == minPrice:
y_i = [1]
# keep price info
y_price = [util.getPrice(data["MinimumPrice"])]
if int(departureDate) < 20160229 and int(
departureDate
) >= 20151129: # choose date between "20151129-20160229(20160115)" as training data
X_train = np.concatenate((X_train, [x_i]), axis=0)
y_train = np.concatenate((y_train, [y_i]), axis=0)
y_train_price = np.concatenate((y_train_price, [y_price]),
axis=0)
elif int(departureDate) < 20160508 and int(
departureDate
) >= 20160229: # choose date before "20160508(20160220)" as test data
X_test = np.concatenate((X_test, [x_i]), axis=0)
y_test = np.concatenate((y_test, [y_i]), axis=0)
y_test_price = np.concatenate((y_test_price, [y_price]),
axis=0)
else:
pass
# X_train = np.concatenate((X_train, [x_i]), axis=0)
# y_train = np.concatenate((y_train, [y_i]), axis=0)
# y_train_price = np.concatenate((y_train_price, [y_price]), axis=0)
# end of for datas
# end of for routes
"""
remove duplicate rows for train
"""
tmp_train = np.concatenate((X_train, y_train, y_train_price), axis=1)
new_array = [tuple(row) for row in tmp_train]
tmp_train = np.unique(new_array)
# get the result
X_train = tmp_train[:, 0:12]
y_train = tmp_train[:, 12]
y_train_price = tmp_train[:, 13]
"""
remove duplicate rows for test
"""
tmp_test = np.concatenate((X_test, y_test, y_test_price), axis=1)
new_array = [tuple(row) for row in tmp_test]
tmp_test = np.unique(new_array)
# get the result
X_test = tmp_test[:, 0:12]
y_test = tmp_test[:, 12]
y_test_price = tmp_test[:, 13]
# save the result
np.save('inputSpecificRaw/X_train', X_train)
np.save('inputSpecificRaw/y_train', y_train)
np.save('inputSpecificRaw/y_train_price', y_train_price)
np.save('inputSpecificRaw/X_test', X_test)
np.save('inputSpecificRaw/y_test', y_test)
np.save('inputSpecificRaw/y_test_price', y_test_price)
return X_train, y_train, X_test, y_test
def load_for_classification_for_General(dataset="General", routes=routes_general):
"""
Load the data for classification
:param dataset: dataset name('Specific' or 'General')
:return: X_train, y_train, X_test, y_test
"""
isOneOptimalState = False
# Construct the input data
dim = routes.__len__() + 4
X_train = np.empty(shape=(0, dim))
y_train = np.empty(shape=(0,1))
y_train_price = np.empty(shape=(0,1))
for filePrefix in routes:
print filePrefix
datas = load_data_with_prefix_and_dataset(filePrefix, dataset)
for data in datas:
print "Construct route {}, State {}, departureDate {}...".format(filePrefix, data["State"], data["Date"])
x_i = []
# feature 1: flight number -> dummy variables
for i in range(len(routes)):
"""
!!!need to change!
"""
if i == routes.index(filePrefix):
x_i.append(1)
else:
x_i.append(0)
# feature 2: departure date interval from "20151109", because the first observed date is 20151109
departureDate = data["Date"]
"""
!!!maybe need to change the first observed date
"""
departureDateGap = util.days_between(departureDate, "20151109")
x_i.append(departureDateGap)
# feature 3: observed days before departure date
state = data["State"]
x_i.append(state)
# feature 4: minimum price before the observed date
minimumPreviousPrice = getMinimumPreviousPrice(data["Date"], state, datas)
x_i.append(minimumPreviousPrice)
# feature 5: maximum price before the observed date
maximumPreviousPrice = getMaximumPreviousPrice(data["Date"], state, datas)
x_i.append(maximumPreviousPrice)
# output
y_i = [0]
specificDatas = []
specificDatas = [data2 for data2 in datas if data2["Date"]==departureDate]
minPrice = getMinimumPrice(specificDatas)
if util.getPrice(data["MinimumPrice"]) == minPrice:
y_i = [1]
# keep price info
y_price = [util.getPrice(data["MinimumPrice"])]
X_train = np.concatenate((X_train, [x_i]), axis=0)
y_train = np.concatenate((y_train, [y_i]), axis=0)
y_train_price = np.concatenate((y_train_price, [y_price]), axis=0)
# end of for datas
# end of for routes
"""
remove duplicate rows
"""
tmp = np.concatenate((X_train, y_train, y_train_price), axis=1)
new_array = [tuple(row) for row in tmp]
tmp = np.unique(new_array)
# # get the result
# X_train = tmp[:, 0:16]
# y_train = tmp[:, 16]
# y_train_price = tmp[:, 17]
# save the result
np.save('inputGeneralRaw/X_train', X_train)
np.save('inputGeneralRaw/y_train', y_train)
np.save('inputGeneralRaw/y_train_price', y_train_price)
np.save('inputGeneralRaw/tmp', tmp)
return X_train, y_train, y_train_price
def load_for_classification_for_Specific(dataset="Specific", routes=routes_specific):
"""
Load the data for classification
:param dataset: dataset name('Specific' or 'General')
:return: X_train, y_train, X_test, y_test
"""
isOneOptimalState = False
# Construct the input data
dim = routes.__len__() + 4
X_train = np.empty(shape=(0, dim))
y_train = np.empty(shape=(0,1))
y_train_price = np.empty(shape=(0,1))
X_test = np.empty(shape=(0,dim))
y_test = np.empty(shape=(0,1))
y_test_price = np.empty(shape=(0,1))
for filePrefix in routes:
datas = load_data_with_prefix_and_dataset(filePrefix, dataset)
for data in datas:
print "Construct route {}, State {}, departureDate {}...".format(filePrefix, data["State"], data["Date"])
x_i = []
# feature 1: flight number -> dummy variables
for i in range(len(routes)):
"""
!!!need to change!
"""
if i == routes.index(filePrefix):
x_i.append(1)
else:
x_i.append(0)
# feature 2: departure date interval from "20151109", because the first observed date is 20151109
departureDate = data["Date"]
"""
!!!maybe need to change the first observed date
"""
departureDateGap = util.days_between(departureDate, "20151109")
x_i.append(departureDateGap)
# feature 3: observed days before departure date
state = data["State"]
x_i.append(state)
# feature 4: minimum price before the observed date
minimumPreviousPrice = getMinimumPreviousPrice(data["Date"], state, datas)
x_i.append(minimumPreviousPrice)
# feature 5: maximum price before the observed date
maximumPreviousPrice = getMaximumPreviousPrice(data["Date"], state, datas)
x_i.append(maximumPreviousPrice)
# output
y_i = [0]
specificDatas = []
specificDatas = [data2 for data2 in datas if data2["Date"]==departureDate]
# if isOneOptimalState:
# # Method 1: only 1 entry is buy
# optimalState = getOptimalState(specificDatas)
# if data["State"] == optimalState:
# y_i = [1]
# else:
# # Method 2: multiple entries can be buy
# minPrice = getMinimumPrice(specificDatas)
# if util.getPrice(data["MinimumPrice"]) == minPrice:
# y_i = [1]
#Method 2: multiple entries can be buy
minPrice = getMinimumPrice(specificDatas)
if util.getPrice(data["MinimumPrice"]) == minPrice:
y_i = [1]
# keep price info
y_price = [util.getPrice(data["MinimumPrice"])]
if int(departureDate) < 20160229 and int(departureDate) >= 20151129: # choose date between "20151129-20160229(20160115)" as training data
X_train = np.concatenate((X_train, [x_i]), axis=0)
y_train = np.concatenate((y_train, [y_i]), axis=0)
y_train_price = np.concatenate((y_train_price, [y_price]), axis=0)
elif int(departureDate) < 20160508 and int(departureDate) >= 20160229: # choose date before "20160508(20160220)" as test data
X_test = np.concatenate((X_test, [x_i]), axis=0)
y_test = np.concatenate((y_test, [y_i]), axis=0)
y_test_price = np.concatenate((y_test_price, [y_price]), axis=0)
else:
pass
# X_train = np.concatenate((X_train, [x_i]), axis=0)
# y_train = np.concatenate((y_train, [y_i]), axis=0)
# y_train_price = np.concatenate((y_train_price, [y_price]), axis=0)
# end of for datas
# end of for routes
"""
remove duplicate rows for train
"""
tmp_train = np.concatenate((X_train, y_train, y_train_price), axis=1)
new_array = [tuple(row) for row in tmp_train]
tmp_train = np.unique(new_array)
# get the result
X_train = tmp_train[:, 0:12]
y_train = tmp_train[:, 12]
y_train_price = tmp_train[:, 13]
"""
remove duplicate rows for test
"""
tmp_test = np.concatenate((X_test, y_test, y_test_price), axis=1)
new_array = [tuple(row) for row in tmp_test]
tmp_test = np.unique(new_array)
# get the result
X_test = tmp_test[:, 0:12]
y_test = tmp_test[:, 12]
y_test_price = tmp_test[:, 13]
# save the result
np.save('inputSpecificRaw/X_train', X_train)
np.save('inputSpecificRaw/y_train', y_train)
np.save('inputSpecificRaw/y_train_price', y_train_price)
np.save('inputSpecificRaw/X_test', X_test)
np.save('inputSpecificRaw/y_test', y_test)
np.save('inputSpecificRaw/y_test_price', y_test_price)
return X_train, y_train, X_test, y_test