def parseJSON():
data = util.loadJSON(constants.JSON_FILE)
data_index = []
for obj in data:
data_dict = dict()
temp_text = util.removePunctuation(str(data[obj]['text']))
stopped_temp_text = util.removeStopWords(temp_text, constants.STOP_LIST)
temp_length = len(temp_text.split(" "))
data_dict['text'] = temp_text.lower()
data_dict['doc_length'] = temp_length
data_dict['doc_length_stopped'] = len(stopped_temp_text.split(" "))
meta_data = {
"index": {
"_index": constants.INDEX_NAME,
"_type": constants.TYPE_NAME,
"_id": str(obj)
}
}
data_index.append(meta_data)
data_index.append(data_dict)
print "Complete JSON parsed..."
return data_index
def createMetaData(review_data, userListMap):
globalBusinessMap = {}
for review in review_data:
userID = review['user_id']
if userID in userListMap:
reviewID = str(review['review_id'])
businessID = str(review['business_id'])
if businessID not in userListMap[userID]['restaurantMap']:
userListMap[userID]['restaurantMap'][businessID] = []
userListMap[userID]['restaurantMap'][businessID].append(reviewID)
userListMap[userID]['reviewMap'][reviewID] = (review['stars'], review['date'])
if businessID not in globalBusinessMap.keys():
globalBusinessMap[businessID] = [userID]
#print 'business added!'
elif userID not in globalBusinessMap[businessID]:
globalBusinessMap[businessID].append(userID)
#print 'user added!'
business_data = util.loadJSON('../yelp/restaurants.JSON')
for user_id, user in userListMap.items():
# print user
restaurantSet = set(user['restaurantMap'].keys())
# print restaurantSet
calculateAttrPref(user, restaurantSet, business_data)
user_map_file = open('userMap.json', 'w')
json.dump(userListMap, user_map_file, indent=4)
user_map_file.close()
business_map_file = open('globalBusinessMap.json', 'w')
json.dump(globalBusinessMap, business_map_file, indent=4)
business_map_file.close()
def getModel(num_categories, split, get_names=False):
bucket_width = 4.0 / num_categories
buckets = [1 + bucket_width * (i + 1) for i in range(num_categories - 1)]
buckets.append(5)
if get_names:
train_inputs, train_labels, ingredient_list = loadJSON(input_file,
split,
buckets=buckets,
get_names=True)
model = train(train_inputs, train_labels, num_categories)
return model, ingredient_list, buckets, bucket_width
train_inputs, train_labels, test_inputs, test_labels = loadJSON(
input_file, split, buckets=buckets)
model = train(train_inputs, train_labels, num_categories)
return model, test_inputs, test_labels
def downloadData(param: Param, download: bool = True):
'''Download user data (if {download} is True) to json files, merge them into a flat pandas.DataFrame, and write it to disk.'''
logging.info(f"{param.filePath().name.replace('.','|')}")
if download:
subMethod = param.splitMethod(lower=True)
for f in param.filePath(glob='*json'):
f.unlink()
pbarManager = enlighten.get_manager()
with pbarManager.counter(unit='page', leave=False) as pbar:
while param.page <= param.nPages:
fileName = param.filePath(ext=f'.{param.page:04d}.json')
response = getReq(param=param,
pbarManager=pbarManager,
collapse=False)
param.page = int(
response.get(subMethod).get('@attr').get('page'))
param.nPages = int(
response.get(subMethod).get('@attr').get('totalPages'))
pbar.total = param.nPages # [tqdm: update total without resetting time elapsed](https://stackoverflow.com/a/58961015/13019084)
pbar.update()
param.filePath().parent.mkdir(exist_ok=True)
with open(file=fileName, mode='w') as jsonF:
json.dump(obj=response, fp=jsonF)
param.page += 1
time.sleep(param.sleep)
pbarManager.stop()
DF = loadJSON(param)
df = flattenDF(param=param, DF=DF, writeToDisk=True)
if param.splitMethod() in ['TopArtists', 'TopAlbums', 'TopTracks']:
writeCSV(param=param, df=df)
def createFoodNetworkEdgeLists(userMapFile, reviewJSONFile, thresholdJaccard, thresholdRating, thresholdAttr, numUsers):
userListMap = pickle.load( open( userMapFile, "rb" ) )
review_data = util.loadJSON(reviewJSONFile)
# 1) add meta-data
for userID in userListMap.keys():
userListMap[userID]['restaurantMap'] = {} # create empty restaurantMap for each user
userListMap[userID]['reviewMap'] = {} # create empty reviewMap for each user
createMetaData(review_data, userListMap)
print 'number of users', len(userListMap)
print userListMap
# 2) calculate scores for each edge
jaccardVals = createEdgeVals(userListMap, True, False, False)
attrVals = createEdgeVals(userListMap, False, True, False)
ratiVals = createEdgeVals(userListMap, False, False, True)
# 3) create food network for each score type
jaccardNtwkFile = 'food_ntwk_random/jacc_edge_list_{}users_{}.txt'.format(numUsers, thresholdJaccard)
ratiNtwkFile = 'food_ntwk_random/rati_edge_list_{}users_{}.txt'.format(numUsers, thresholdAttr)
attrNtwkFile = 'food_ntwk_random/attr_edge_list_{}users_{}.txt'.format(numUsers, thresholdRating)
createFoodNetwork(jaccardVals, userListMap, jaccardNtwkFile, thresholdJaccard)
createFoodNetwork(ratiVals, userListMap, ratiNtwkFile, thresholdRating)
createFoodNetwork(attrVals, userListMap, attrNtwkFile, thresholdAttr)
# 4) check if valid edge list and print info about each network
g = snap.LoadEdgeList(snap.PUNGraph, jaccardNtwkFile, 0, 1)
print 'Jaccard Network: Num Nodes = {}, Num Edges = {}'.format(g.GetNodes(), g.GetEdges())
g = snap.LoadEdgeList(snap.PUNGraph, attrNtwkFile, 0, 1)
print 'Attribute Network: Num Nodes = {}, Num Edges = {}'.format(g.GetNodes(), g.GetEdges())
g = snap.LoadEdgeList(snap.PUNGraph, ratiNtwkFile, 0, 1)
print 'Rating Network: Num Nodes = {}, Num Edges = {}'.format(g.GetNodes(), g.GetEdges())
def get_email(query):
names = util.loadJSON("contacts.json")
toField = None
for x in range(len(names)):
if names[x]["name"] in query:
toField = names[x]["email"]
break
return toField
def createUsersToBizMap():
review_data = util.loadJSON('../yelp/review.json')
restaurantMap = load_data('../yelp/restaurants.json')
UsersToBizMap = {}
# userID: set(bizID1, bizID2, ...) --> maps every user that's been to every restaurant
for review in review_data:
review_userID = review['user_id']
businessID = str(review['business_id'])
if businessID not in restaurantMap:
continue
if businessID not in UsersToBizMap[businessID]:
UsersToBizMap[review_userID] = set()
UsersToBizMap[review_userID].add(businessID)
return UsersToBizMap
def writeEmbeddingFiles():
if ".txt" in input_file:
train_inputs, tokens = loadTxt(input_path_prefix + input_file)
elif ".json" in input_file:
train_inputs, tokens = loadJSON(input_path_prefix + input_file)
embeddings = train(train_inputs)
stem = input_file[:input_file.find('.')]
np.savetxt(result_path_prefix + stem + "_embeddings_%d" % dim, embeddings)
fn = result_path_prefix + stem + "_tokens"
if not os.path.isfile(fn):
f = open(fn, 'w+')
f.write(str(tokens))
f.close()
def writeEmbeddingFiles(dims):
if ".txt" in input_file:
train_inputs, tokens = loadTxt(input_path_prefix + input_file)
elif ".json" in input_file:
train_inputs, tokens = loadJSON(input_path_prefix + input_file)
for hidden_size in dims:
embeddings = trainCBOW(train_inputs, hidden_size=hidden_size)
stem = input_file[:input_file.find('.')]
np.savetxt(result_path_prefix + stem + "_CBOWembeddings_%d_%depochs" %(hidden_size, max_epochs), embeddings)
fn = result_path_prefix + stem + "_tokens"
if not os.path.isfile(fn):
f = open(fn, 'w+')
f.write(str(tokens))
f.close()
return embeddings, tokens
def write_edges_file(user_data, user_map, edgesFile, edgesWithScoreFile):
# UsersToBizMap = createUsersToBizMap()
for userID in user_map.keys():
user_map[userID]['restaurantMap'] = {}
user_map[userID]['reviewMap'] = {}
createMetaData(util.loadJSON('../yelp/review.json'), user_map)
friend_list = []
file = open(edgesFile, "w")
file2 = open(edgesWithScoreFile, "w")
scoreMap = {}
count = 0
for i in xrange(len(user_data)):
friend_list.append(user_data[i]['friends'])
# print friend_list[i]
for j in xrange(len(friend_list[i])):
if friend_list[i][j] in user_map.keys():
pair = (user_data[i]['user_id'], friend_list[i][j])
pair2 = (friend_list[i][j], user_data[i]['user_id'])
if pair in scoreMap:
score = scoreMap[pair]
elif pair2 in scoreMap:
score = scoreMap[pair2]
else:
score = getRatingSimScore(friend_list[i][j], user_data[i]['user_id'], user_map) # if user should be in the network
if score > 0:
src_tmp = user_map[user_data[i]['user_id']] # not quite sure what data type this return value is, but it's converted to string
dst_tmp = user_map[friend_list[i][j]] # not quite sure what data type this return value is, but it's converted to string
src = src_tmp['node_id'] # extract node_id for src of edge
dst = dst_tmp['node_id']
# src = str(src_tmp)[12:-1] # extract node_id for src of edge
# dst = str(dst_tmp)[12:-1] # extract node_id for dst of edge
line = "{0} {1}\n".format(src, dst)
lineWithScore = "{0} {1} {2}\n".format(src, dst, score)
file.write(line)
file2.write(lineWithScore)
count += 1
print 'num edges', count
file.close()
file2.close()
def process():
global state
global ins_stack
global com_stack
global out_stack
result = ""
query = request.args.get("query").lower()
if state == 0:
command = None
funcs = util.loadJSON("cmd.json")
for f in funcs:
if funcs[f]["trigger"] in query:
command = funcs[f]
break
if command == None:
if query == "":
return "Hello."
elif "reset" in query:
util.writeJSON("cmd.json", (util.loadJSON("backup.json")))
micro.micro_states = {}
else:
return "I couldn't understand."
else:
ins_stack.append(command["id"])
if (command["internal"] == True):
state = 1
else:
x = 0
t_i_s = [ins_stack.pop()]
t_c_s = []
while x < len(t_i_s):
while internal(t_i_s[x]) == False:
tuple = breakdown(t_i_s, x, funcs)
t_i_s = tuple[0] + t_i_s
t_c_s = tuple[1] + t_c_s
x += 1
ins_stack = t_i_s
com_stack = t_c_s
state = 2
if state == 1:
feedback = call(ins_stack[0], query, out_stack)
if (feedback[1] == True):
ins_stack.pop(0)
state = 0
out_stack.append(feedback[0])
result = feedback[0]
elif state == 2:
feedback = call(ins_stack[0], com_stack[0], out_stack)
while (feedback[1] == True and len(ins_stack) > 0):
ins_stack.pop(0)
com_stack.pop(0)
out_stack.append(feedback[0])
if len(ins_stack) > 0:
feedback = call(ins_stack[0], com_stack[0], out_stack)
result = feedback[0]
if (len(ins_stack) == 0 and feedback[1] == True):
state = 0
return result
def teach_command(command_string, out_stack):
print("DEBUG" + command_string)
frame = inspect.currentframe()
state_key = str(inspect.getframeinfo(frame).function)
DEFAULT_STATE = (None, [])
if state_key not in micro_states:
m1 = re.search("(.+)called (.+)", command_string)
if m1 is None:
micro_states[state_key] = DEFAULT_STATE
return "What should I call this?", False
else:
command_name = m1.group(2)
micro_states[state_key] = (command_name, [])
cmds = util.loadJSON("cmd.json")
conflict = False
for x in cmds:
x_trigger = cmds[x]["trigger"]
if x_trigger in command_name or command_name in x_trigger:
conflict = True
if conflict:
del micro_states[state_key]
return "Invalid command name.", True
return "How do I do this?", False
else:
if "cancel" in command_string:
del micro_states[state_key]
return "Teaching cancelled", True
current_state = micro_states[state_key]
if current_state[0] is None:
command_name = command_string
cmds = util.loadJSON("cmd.json")
conflict = False
for x in cmds:
x_trigger = cmds[x]["trigger"]
if x_trigger in command_name or command_name in x_trigger:
conflict = True
if conflict:
del micro_states[state_key]
return "Invalid command name.", True
micro_states[state_key] = (command_name, [])
return "How do I do this?", False
else:
if "finish" in command_string:
if len(micro_states[state_key][1]) > 0:
seq_id = [i[0] for i in micro_states[state_key][1]]
seq_str = [i[1] for i in micro_states[state_key][1]]
util.saveFunction(micro_states[state_key][0], seq_id,
seq_str)
del micro_states[state_key]
return "New command saved", True
else:
del micro_states[state_key]
return "Incorrect number of steps", True
else:
command_step = command_string
cmds = util.loadJSON("cmd.json")
step_id = None
step_str = command_string
for x in cmds:
x_trigger = cmds[x]["trigger"]
if x_trigger in command_step:
if x_trigger == "teach":
continue
x_id = cmds[x]["id"]
step_id = x_id
break
if step_id is None:
return "Sorry. I didn't get that", False
else:
micro_states[state_key][1].append((step_id, step_str))
return "OK. What next?", False
import cv2
import numpy as np
import util as u
#undistort het camerabeeld met de opgeslagen matrix in:
distortionData = u.loadJSON(
r"C:\Users\reuli\Documents\Bachelor Assignment\Robot project\python\calibration\calibration"
)
#undistort het camerabeeld gray
def undistort(gray):
dst = cv2.undistort(gray, np.float32(distortionData["cameraMatrix"]),
np.float32(distortionData["distCoeffs"]), None,
np.float32(distortionData["newCameraMatrix"]))
x, y, w, h = distortionData["validPixROI"]
dst = dst[y:y + h, x:x + w]
return dst
import cv2
import numpy as np
import util as u
transformData = u.loadJSON(
r"C:\Users\reuli\Documents\Bachelor Assignment\Robot project\python\calibration\transformationMatrix"
)
#perspective transforms image
def transform(gray):
warped = cv2.warpPerspective(
gray, np.float32(transformData["transform_matrix"]),
(transformData["maxWidth"], transformData["maxHeight"]))
return warped
from __future__ import division
from elasticsearch import Elasticsearch
import util, es_utility, math
import constants, search
QUERY = es_utility.readQueryFile("query_desc.51-100.short.txt")
qno = [85, 59, 56, 71, 64, 62, 93, 99, 58, 77, 54, 87, 94, 100, 89, 61, 95, 68, 57, 97, 98, 60, 80, 63, 91]
QUERY_MAP = zip([str(q) for q in qno], QUERY)
expanded_terms = util.loadJSON('prf_data.json')
def saveResults(score_fuction):
filename = score_fuction + "_prf_results.txt"
with open(filename, 'wb') as f:
for n, q in QUERY_MAP:
stemmed_terms = es_utility.getRootQuery(" ".join(q))
new_terms = [t for t in expanded_terms[n] if t not in stemmed_terms]
root = stemmed_terms + new_terms
if score_fuction == "okapi":
body = search.OKAPI_BODY(root)
elif score_fuction == "tfidf":
body = search.TFIDF_BODY(root)
elif score_fuction == "bm25":
body = search.BM25_BODY(root)
elif score_fuction == "lm":
body = search.LM_BODY(root)
elif score_fuction == 'jm':
body = search.JM_BODY(root)
else:
if __name__ == '__main__':
def doRegression():
param_list = train(data_train, labels_train)
best_epoch, best_loss = test_bunch(data_test, labels_test, param_list)
print "Best epoch:", best_epoch
best_params = param_list[best_epoch]
test(data_dev, labels_dev, best_params)
test(data_train, labels_train, best_params, fn="train_preds")
def doClassification():
param_list = trainBuckets(data_train, labels_train)
testBuckets(data_test, labels_test, param_list)
# See util.py for explanation of loading process.
# [0.6, 0.8] means 0.6 train, 0.2 dev, 0.2 test.
all_data = list(loadJSON(input_file, [0.6, 0.8]))
# Optional; spreads data out to approximate a uniform distribution.
# mus/sigmas are needed if wanting to renormalize (see fn above).
mus, sigmas = undoLabelNormalization(all_data)
data_train, labels_train, data_dev, labels_dev, data_test, labels_test = all_data
print "Train size:", data_train.shape[0]
print "Test size:", data_test.shape[0]
doRegression()
# doClassification()
import util, es_utility, glob
from elasticsearch import Elasticsearch
## For reading the documents
ADVANCED_PRE_PROCESSING = False
STREAM = False
## global values
JSON_FILE = "AP_DATA_FULL.json"
GLOBAL_JSON_FILE = "crawler.json"
ES_HOST = {"host": "localhost", "port": 9210}
##INDEX_NAME = 'ap_streamed_none'
INDEX_NAME = 'backup_vs'
TYPE_NAME = 'document'
ANALYZER_NAME = 'my_english'
##QREL_FILENAME = 'qrels.adhoc.51-100.AP89.txt'
QREL_FILENAME = 'QREL.txt'
STOP_LIST = util.getStopList("stoplist.txt")
DOC_LIST = util.getDocList('doclist.txt')
INDEX_CONSTANTS = util.loadJSON(GLOBAL_JSON_FILE)
CORPUS = glob.glob("ap89_collection/ap*")
RESULT_SIZE = 10
ES_CLIENT = Elasticsearch(hosts=[ES_HOST], timeout=180)
##print ES_CLIENT.count(index=INDEX_NAME)['count']
