def restore_db(dir_name='data', db_params=mongohq.ideagenstest):
files = listdir(dir_name)
col_names = [file.split('.json')[0] for file in files]
db = mongohq.get_db(db_params)
existing_cols = db.collection_names()
for file_name in files:
file_path = path.join(dir_name, file_name)
col = file_name.split('.json')[0]
print "writing to data to collection " + col + \
" in db: " + db_params['dbName']
if col != 'users':
data = decode_json_file(file_path)
if col not in existing_cols:
print "creating collection: " + col
db.create_collection(col)
else:
print "inserting into existing collection"
try:
if data:
db[col].insert(data, continue_on_error=True)
except DuplicateKeyError:
print "Attempted insert of document with duplicate key"
else:
print "success"
else:
print "not writing users to db"
def clear_db(db_params=mongohq.ideagenstest):
db = mongohq.get_db(db_params)
cols = db.collection_names()
clear_cols = [col for col in cols if col not in default_collections]
for col in clear_cols:
# Remove all docs from collection
db[col].remove()
def __init__(self, db_params=mongohq.ideagens):
"""
Constructor to instantiate references to Ideagens instance
and its corresponding database
"""
self.db_params = db_params
self.db = mongohq.get_db(db_params)
def dump_db(dir_name='data', db_params=mongohq.ideagenstest):
# set up the connnection
db = mongohq.get_db(db_params)
allCollections = [
col for col in db.collection_names() if col not in default_collections
]
print "list of collections: "
for col in allCollections:
print "collection name: " + col
docs = db[col].find()
data = [doc for doc in docs]
write_json_to_file(data, dir_name, col)
def get_data_output(dir_path='data', db_params=mongohq.ideagenstest):
if not path.exists(dir_path):
mkdir(dir_path, 0774)
db = mongohq.get_db(db_params)
# just grab the ideas and clusters
clusters = {}
for cluster in db.clusters.find():
clusters[cluster[u'_id']] = cluster[u'name']
ideas = []
for idea in db.ideas.find():
rowDict = {}
rowDict["idea"] = idea[u'content']
if len(idea[u'clusterIDs']) > 0:
clusterID = idea[u'clusterIDs'][0]
rowDict["theme"] = clusters[clusterID]
else:
rowDict["theme"] = "-"
rowDict["starred"] = idea[u'isGamechanger']
if idea[u'isGamechanger']:
rowDict["starred"] = idea[u'isGamechanger']
else:
rowDict["starred"] = "-"
rowDict["userID"] = idea[u'userID']
rowDict["promptID"] = idea[u'promptID']
ideas.append(rowDict)
ideasDF = pd.DataFrame(ideas)
file_path = path.join(dir_path, "ideas.csv")
ideasDF.to_csv(file_path)
users = {}
for user in db.myUsers.find():
users[user[u'_id']] = user[u'name']
# usersDF = pd.DataFrame.from_dict(users)
usersOutFile = open(path.join(dir_path, "users.csv"), 'w')
for userID, userName in users.items():
usersOutFile.write(userID + "," + userName + "\n")
usersOutFile.close()
# file_path = path.join(dir_path, "users.csv")
# usersDF.to_csv(file_path)
ideasByPrompt = []
for prompt in db.prompts.find():
# rowDict = {}
thisPromptID = prompt[u'_id']
promptQuestion = prompt[u'question']
for idea in db.ideas.find({u'promptID': thisPromptID}):
rowDict = {}
rowDict['promptID'] = thisPromptID
rowDict['promptQuestion'] = promptQuestion
rowDict['ideaID'] = idea[u'_id']
rowDict['idea'] = idea[u'content']
rowDict['userID'] = idea[u'userID']
rowDict['userName'] = idea[u'userName']
rowDict['submissionTime'] = idea[u'time']
if len(idea[u'clusterIDs']) > 0:
clusterID = idea[u'clusterIDs'][0]
rowDict["theme"] = clusters[clusterID]
ideasByPrompt.append(rowDict)
ideasByPromptDF = pd.DataFrame(ideasByPrompt)
file_path = path.join(dir_path, "ideasByPrompt.csv")
ideasByPromptDF.to_csv(file_path)
notifications = []
for notification in db.notifications.find():
rowDict = {}
if u'message' in notification:
rowDict["message"] = notification[u'message']
elif u'examples' in notification:
examples = [ex[u'content'] for ex in notification[u'examples']]
examplesMessage = "Sent Examples: %s" % (', '.join(examples))
examplesMessage[:-2]
rowDict["message"] = examplesMessage
elif u'theme' in notification:
themeID = notification[u'theme']
rowDict["message"] = "Sent theme: %s" % clusters[themeID]
elif u'prompt' in notification:
rowDict["message"] = "Sent message: %s" % notification[u'prompt']
else:
break
# get author info
# get time?
notifications.append(rowDict)
notificationsDF = pd.DataFrame(notifications)
# Create file path
file_path = path.join(dir_path, "notifications.csv")
notificationsDF.to_csv(file_path)
def set_db(self, db_params=mongohq.ideagenstest):
"""
Set the db where ideagens data sits
"""
self.db = mongohq.get_db(db_params)
def get_data_output(dir_path='data', db_params=mongohq.ideagenstest):
if not path.exists(dir_path):
mkdir(dir_path, 0774)
db = mongohq.get_db(db_params)
# just grab the ideas and clusters
clusters = {}
for cluster in db.clusters.find():
clusters[cluster[u'_id']] = cluster[u'name']
ideas = []
for idea in db.ideas.find():
rowDict = {}
rowDict["idea"] = idea[u'content']
if len(idea[u'clusterIDs']) > 0:
clusterID = idea[u'clusterIDs'][0]
rowDict["theme"] = clusters[clusterID]
else:
rowDict["theme"] = "-"
rowDict["starred"] = idea[u'isGamechanger']
if idea[u'isGamechanger']:
rowDict["starred"] = idea[u'isGamechanger']
else:
rowDict["starred"] = "-"
rowDict["userID"] = idea[u'userID']
rowDict["promptID"] = idea[u'promptID']
ideas.append(rowDict)
ideasDF = pd.DataFrame(ideas)
file_path = path.join(dir_path, "ideas.csv")
ideasDF.to_csv(file_path)
users = {}
for user in db.myUsers.find():
users[user[u'_id']] = user[u'name']
# usersDF = pd.DataFrame.from_dict(users)
usersOutFile = open(path.join(dir_path, "users.csv"),'w')
for userID, userName in users.items():
usersOutFile.write(userID + "," + userName + "\n")
usersOutFile.close()
# file_path = path.join(dir_path, "users.csv")
# usersDF.to_csv(file_path)
ideasByPrompt = []
for prompt in db.prompts.find():
# rowDict = {}
thisPromptID = prompt[u'_id']
promptQuestion = prompt[u'question']
for idea in db.ideas.find({u'promptID':thisPromptID}):
rowDict = {}
rowDict['promptID'] = thisPromptID
rowDict['promptQuestion'] = promptQuestion
rowDict['ideaID'] = idea[u'_id']
rowDict['idea'] = idea[u'content']
rowDict['likes'] = len(idea[u'votes'])
rowDict['userID'] = idea[u'userID']
rowDict['userName'] = idea[u'userName']
rowDict['submissionTime'] = idea[u'time']
if len(idea[u'clusterIDs']) > 0:
clusterID = idea[u'clusterIDs'][0]
rowDict["theme"] = clusters[clusterID]
ideasByPrompt.append(rowDict)
ideasByPromptDF = pd.DataFrame(ideasByPrompt)
file_path = path.join(dir_path,"ideasByPrompt.csv")
ideasByPromptDF.to_csv(file_path)
notifications = []
for notification in db.notifications.find():
rowDict = {}
if u'message' in notification:
rowDict["message"] = notification[u'message']
elif u'examples' in notification:
examples = [ex[u'content'] for ex in notification[u'examples']]
examplesMessage = "Sent Examples: %s" %(', '.join(examples))
examplesMessage[:-2]
rowDict["message"] = examplesMessage
elif u'theme' in notification:
themeID = notification[u'theme']
rowDict["message"] = "Sent theme: %s" %clusters[themeID]
elif u'prompt' in notification:
rowDict["message"] = "Sent message: %s" %notification[u'prompt']
else:
break
# get author info
# get time?
notifications.append(rowDict)
notificationsDF = pd.DataFrame(notifications)
# Create file path
file_path = path.join(dir_path, "notifications.csv")
notificationsDF.to_csv(file_path)
participants = []
for participant in db.participants.find():
print participant
rowDict = {}
rowDict['participantID'] = participant[u'_id']
rowDict['userName'] = participant[u'userName']
expID = participant[u'experimentID']
exp = db.experiments.find_one({u'_id':expID})
print exp
rowDict['experimentID'] = expID
rowDict['experimentName'] = exp[u'description']
rowDict['promptID'] = exp[u'promptID']
cond = db['exp-conditions'].find_one({u'_id':participant[u'conditionID']})
print cond
rowDict['condition'] = cond[u'description']
participants.append(rowDict)
participantsDF = pd.DataFrame(participants)
file_path = path.join(dir_path, "participants.csv")
participantsDF.to_csv(file_path)
# remember: we expect the following fields: {extID, title, content}
# note also: by using pandas read, we automatically get
# the first half of the unicode sandwich, i.e., all text is in unicode (the equivalent of calling theString.decode('utf-8'))
# source: https://stackoverflow.com/questions/23594878/pandas-dataframe-and-character-encoding-when-reading-excel-file
source_docs = pd.read_csv(options.source)
print source_docs.count()
# break the content into sentences
source_docs['sentences'] = [
sent_tokenize(t.decode('utf-8', 'ignore').encode("ascii", "ignore"))
for t in source_docs['content']
]
# create db connection
db_name = options.db
db = mongohq.get_db(db_params.ALL_DBs[db_name])
db_util = mongohq.Data_Utility('data', db_params.ALL_DBs[db_name])
# TODO: add a better dupe check. here we skip if the title is literally duplicated by another doc already in the db
# won't work for slight inconsistencies due to encoding, etc.
inserted_docs = []
for index, doc in source_docs.iterrows():
# print "Inserting doc", doc['extID'], doc['title']
# docID = create_document(doc)
# if docID is not None:
# docData = doc
# docData['db_id'] = docID
# inserted_docs.append(docData)
