def get(self):
solr_args = dict(request.args)
if 'max_groups' in solr_args:
del solr_args['min_percent_word']
if 'min_occurrences_word' in solr_args:
del solr_args['min_occurrences_word']
solr_args["rows"] = min(int(solr_args.get("rows", [current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS")])[0]), current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS"))
solr_args['fields'] = ['id']
solr_args['defType'] = 'aqp'
solr_args['tv'] = 'true'
solr_args['tv.tf_idf'] = 'true'
solr_args['tv.tf'] = 'true'
solr_args['tv.positions'] ='false'
solr_args['tf.offsets'] = 'false'
solr_args['tv.fl'] ='abstract,title'
solr_args['fl'] ='id,abstract,title'
solr_args['wt'] = 'json'
headers = {'X-Forwarded-Authorization' : request.headers.get('Authorization')}
response = client().get(current_app.config.get("VIS_SERVICE_TVRH_PATH") , params = solr_args, headers=headers)
if response.status_code == 200:
data = response.json()
else:
return {"Error": "There was a connection error. Please try again later", "Error Info": response.text}, response.status_code
if data:
min_percent_word = request.args.get("min_percent_word", current_app.config.get("VIS_SERVICE_WC_MIN_PERCENT_WORD"))
min_occurrences_word = request.args.get("min_occurrences_word", current_app.config.get("VIS_SERVICE_WC_MIN_OCCURRENCES_WORD"))
word_cloud_json = word_cloud.generate_wordcloud(data, min_percent_word = min_percent_word, min_occurrences_word = min_occurrences_word)
if word_cloud_json:
return word_cloud_json, 200
else:
return {"Error": "Empty word cloud. Try changing your minimum word parameters or expanding your query."}, 200
def post(self):
solr_args = request.json
if not solr_args:
return {'Error' : 'there was a problem with your request', 'Error Info': 'no data provided with request'}, 403
if 'max_groups' in solr_args:
del solr_args['min_percent_word']
if 'min_occurrences_word' in solr_args:
del solr_args['min_occurrences_word']
elif 'query' in request.json:
try:
solr_args = json.loads(request.json["query"][0])
except Exception:
return {'Error' : 'there was a problem with your request', 'Error Info': 'couldn\'t decode query, it should be json-encoded before being sent (so double encoded)'}, 403
solr_args["rows"] = min(int(solr_args.get("rows", [current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS")])[0]), current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS"))
solr_args['fl'] ='abstract,title'
solr_args['wt'] = 'json'
headers = {'X-Forwarded-Authorization' : request.headers.get('Authorization')}
response = client().get(current_app.config.get("VIS_SERVICE_SOLR_PATH") , params = solr_args, headers=headers)
if response.status_code == 200:
data = response.json()
else:
return {"Error": "There was a connection error. Please try again later", "Error Info": response.text}, response.status_code
if data:
records = [unicode(". ".join(d.get('title', '')[:current_app.config.get("VIS_SERVICE_WC_MAX_TITLE_SIZE")]) + ". " + d.get('abstract', '')[:current_app.config.get("VIS_SERVICE_WC_MAX_ABSTRACT_SIZE")]) for d in data["response"]["docs"]]
word_cloud_json = word_cloud.generate_wordcloud(records, n_most_common=current_app.config.get("VIS_SERVICE_WC_MAX_WORDS"), n_threads=current_app.config.get("VIS_SERVICE_WC_THREADS"), accepted_pos=(u'NN', u'NNP', u'NNS', u'NNPS', u'JJ', u'RB', u'VB', u'VBD', u'VBG', u'VBN', u'VBP', u'VBZ'))
if word_cloud_json:
return word_cloud_json, 200
else:
return {"Error": "Empty word cloud. Try changing your minimum word parameters or expanding your query."}, 200
def test_word_cloud_resource(self):
self.maxDiff = None
# function: add_punc_and_remove_redundancies
# uses the text returned from solr to do some cleaning up of the idf info returned by solr,
# reducing counts of token components of slashed or dashed words
# after this point the solr text is ignored, only the tf/idf data is used
tf_idf_dict = {'word':{'tf' :[3], 'tf-idf' : [0.5]}, 'dashed' : {'tf' :[1], 'tf-idf' : [0.5]}, 'slashed' : {'tf' :[1], 'tf-idf' : [0.5]}, 'dashedword' : {'tf' :[1], 'tf-idf' : [0.5]}, 'slashedword' : {'tf' :[1], 'tf-idf' : [0.5]}}
text_list = ['word', 'dashed-word', 'slashed/word']
updated_info_dict = word_cloud.add_punc_and_remove_redundancies(tf_idf_dict, text_list)
expected_outcome_info_dict = {'word':{'tf' :[1], 'tf-idf' : [0.5]}, 'dashed-word': {'tf' :[1], 'tf-idf' : [0.5]}, 'slashed/word' : {'tf' :[1], 'tf-idf' : [0.5]}, 'dashed' : {'tf' :[-1], 'tf-idf' : [0.5]}, 'slashed' : {'tf' :[0], 'tf-idf' : [0.5]}}
self.assertEqual(updated_info_dict, expected_outcome_info_dict)
# function: build_dict
# is a parent function to add_punc_and_remove_redundancies that takes an tf idf info and text info
# and returns a token and acronym dictionary. The token dictionary is grouped by stem and includes
# a list of idf for each different word
tf_idf_dict = {
'fakeId': {
'abstract': {
'word': {
'tf': [3],
'tf-idf': [0.5]
},
'dashed': {
'tf': [1],
'tf-idf': [0.5]
},
'slashed': {
'tf': [1],
'tf-idf': [0.5]
},
'dashedword': {
'tf': [1],
'tf-idf': [0.5]
},
'slashedword': {
'tf': [1],
'tf-idf': [0.5]
}
},
'title': {
'research': {
'tf': [1],
'tf-idf': [0.1]
},
'researcher': {
'tf': [1],
'tf-idf': [0.9]
},
'acr::fake': {
'tf': [1],
'tf-idf': [0.5]
}
}
}
}
text_list = [{'id': 'fakeId', 'abstract': 'word dashed-word slashed/word', 'title' : 'research researcher FAKE'}]
expected_outcome_info_dict = ({'dashedword': {'idf': [0.5], 'tokens': {'dashed-word': 1}, 'record_count' : ['fakeId']},
'research': {'idf': [0.9, 0.1], 'tokens': {'research': 1, 'researcher': 1}, 'record_count' : ['fakeId', 'fakeId']},
'slashedword': {'idf': [0.5], 'tokens': {'slashed/word': 1}, 'record_count' : ['fakeId']},
'word': {'idf': [0.5], 'tokens': {'word': 1}, 'record_count' : ['fakeId']}},
{'FAKE': {'idf': [0.5], 'total_occurrences': 1, 'record_count' : ['fakeId']}})
updated_info_dict = word_cloud.build_dict(tf_idf_dict, text_list)
self.assertEqual(updated_info_dict, expected_outcome_info_dict)
#function: combine_and_process_dicts
#uses the expected outcome from the previous function
combined_dict = word_cloud.combine_and_process_dicts(expected_outcome_info_dict[0], expected_outcome_info_dict[1])
expected_combined_dict = {
'dashed-word': {'idf': 0.5, 'total_occurrences' :1, 'record_count' :1 },
'research' : {'idf': 0.5, 'total_occurrences' :2, 'record_count' :1 },
'slashed/word':{'idf': 0.5, 'total_occurrences' :1, 'record_count' :1 },
'word': {'idf': 0.5, 'total_occurrences' :1, 'record_count' :1 },
'FAKE' : {'idf': 0.5, 'total_occurrences' :1, 'record_count' :1 }
}
self.assertEqual(combined_dict, expected_combined_dict)
#testing the main word cloud generation function with large data
processed_data = word_cloud.generate_wordcloud(input_js_word_cloud, min_occurrences_word=2, min_percent_word=3)
self.assertEqual(json.loads(json.dumps(processed_data)), test_js_word_cloud)
processed_data = word_cloud.generate_wordcloud(input_js_word_cloud, min_occurrences_word=5, min_percent_word=3)
self.assertEqual(json.loads(json.dumps(processed_data)), test_json_word_cloud_min_occurrences)
def test_word_cloud_resource(self):
self.maxDiff = None
# function: add_punc_and_remove_redundancies
# uses the text returned from solr to do some cleaning up of the idf info returned by solr,
# reducing counts of token components of slashed or dashed words
# after this point the solr text is ignored, only the tf/idf data is used
tf_idf_dict = {
'word': {
'tf': [3],
'tf-idf': [0.5]
},
'dashed': {
'tf': [1],
'tf-idf': [0.5]
},
'slashed': {
'tf': [1],
'tf-idf': [0.5]
},
'dashedword': {
'tf': [1],
'tf-idf': [0.5]
},
'slashedword': {
'tf': [1],
'tf-idf': [0.5]
}
}
text_list = ['word', 'dashed-word', 'slashed/word']
updated_info_dict = word_cloud.add_punc_and_remove_redundancies(
tf_idf_dict, text_list)
expected_outcome_info_dict = {
'word': {
'tf': [1],
'tf-idf': [0.5]
},
'dashed-word': {
'tf': [1],
'tf-idf': [0.5]
},
'slashed/word': {
'tf': [1],
'tf-idf': [0.5]
},
'dashed': {
'tf': [-1],
'tf-idf': [0.5]
},
'slashed': {
'tf': [0],
'tf-idf': [0.5]
}
}
self.assertEqual(updated_info_dict, expected_outcome_info_dict)
# function: build_dict
# is a parent function to add_punc_and_remove_redundancies that takes an tf idf info and text info
# and returns a token and acronym dictionary. The token dictionary is grouped by stem and includes
# a list of idf for each different word
tf_idf_dict = {
'fakeId': {
'abstract': {
'word': {
'tf': [3],
'tf-idf': [0.5]
},
'dashed': {
'tf': [1],
'tf-idf': [0.5]
},
'slashed': {
'tf': [1],
'tf-idf': [0.5]
},
'dashedword': {
'tf': [1],
'tf-idf': [0.5]
},
'slashedword': {
'tf': [1],
'tf-idf': [0.5]
}
},
'title': {
'research': {
'tf': [1],
'tf-idf': [0.1]
},
'researcher': {
'tf': [1],
'tf-idf': [0.9]
},
'acr::fake': {
'tf': [1],
'tf-idf': [0.5]
}
}
}
}
text_list = [{
'id': 'fakeId',
'abstract': 'word dashed-word slashed/word',
'title': 'research researcher FAKE'
}]
expected_outcome_info_dict = ({
'dashedword': {
'idf': [0.5],
'tokens': {
'dashed-word': 1
},
'record_count': ['fakeId']
},
'research': {
'idf': [0.9, 0.1],
'tokens': {
'research': 1,
'researcher': 1
},
'record_count': ['fakeId', 'fakeId']
},
'slashedword': {
'idf': [0.5],
'tokens': {
'slashed/word': 1
},
'record_count': ['fakeId']
},
'word': {
'idf': [0.5],
'tokens': {
'word': 1
},
'record_count': ['fakeId']
}
}, {
'FAKE': {
'idf': [0.5],
'total_occurrences': 1,
'record_count': ['fakeId']
}
})
updated_info_dict = word_cloud.build_dict(tf_idf_dict, text_list)
self.assertEqual(updated_info_dict, expected_outcome_info_dict)
#function: combine_and_process_dicts
#uses the expected outcome from the previous function
combined_dict = word_cloud.combine_and_process_dicts(
expected_outcome_info_dict[0], expected_outcome_info_dict[1])
expected_combined_dict = {
'dashed-word': {
'idf': 0.5,
'total_occurrences': 1,
'record_count': 1
},
'research': {
'idf': 0.5,
'total_occurrences': 2,
'record_count': 1
},
'slashed/word': {
'idf': 0.5,
'total_occurrences': 1,
'record_count': 1
},
'word': {
'idf': 0.5,
'total_occurrences': 1,
'record_count': 1
},
'FAKE': {
'idf': 0.5,
'total_occurrences': 1,
'record_count': 1
}
}
self.assertEqual(combined_dict, expected_combined_dict)
#testing the main word cloud generation function with large data
processed_data = word_cloud.generate_wordcloud(input_js_word_cloud,
min_occurrences_word=2,
min_percent_word=3)
self.assertEqual(json.loads(json.dumps(processed_data)),
test_js_word_cloud)
processed_data = word_cloud.generate_wordcloud(input_js_word_cloud,
min_occurrences_word=5,
min_percent_word=3)
self.assertEqual(json.loads(json.dumps(processed_data)),
test_json_word_cloud_min_occurrences)
def post(self):
solr_args = request.json
if not solr_args:
return {
'Error': 'there was a problem with your request',
'Error Info': 'no data provided with request'
}, 403
if 'max_groups' in solr_args:
del solr_args['min_percent_word']
if 'min_occurrences_word' in solr_args:
del solr_args['min_occurrences_word']
elif 'query' in request.json:
try:
solr_args = json.loads(request.json["query"][0])
except Exception:
return {
'Error':
'there was a problem with your request',
'Error Info':
'couldn\'t decode query, it should be json-encoded before being sent (so double encoded)'
}, 403
solr_args["rows"] = min(
int(
solr_args.get(
"rows",
[current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS")
])[0]),
current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS"))
solr_args['fields'] = ['id']
solr_args['defType'] = 'aqp'
solr_args['tv'] = 'true'
solr_args['tv.tf_idf'] = 'true'
solr_args['tv.tf'] = 'true'
solr_args['tv.positions'] = 'false'
solr_args['tf.offsets'] = 'false'
solr_args['tv.fl'] = 'abstract,title'
solr_args['fl'] = 'id,abstract,title'
solr_args['wt'] = 'json'
headers = {
'X-Forwarded-Authorization': request.headers.get('Authorization')
}
response = client().get(
current_app.config.get("VIS_SERVICE_TVRH_PATH"),
params=solr_args,
headers=headers)
if response.status_code == 200:
data = response.json()
else:
return {
"Error":
"There was a connection error. Please try again later",
"Error Info": response.text
}, response.status_code
if data:
min_percent_word = request.args.get(
"min_percent_word",
current_app.config.get("VIS_SERVICE_WC_MIN_PERCENT_WORD"))
min_occurrences_word = request.args.get(
"min_occurrences_word",
current_app.config.get("VIS_SERVICE_WC_MIN_OCCURRENCES_WORD"))
word_cloud_json = word_cloud.generate_wordcloud(
data,
min_percent_word=min_percent_word,
min_occurrences_word=min_occurrences_word)
if word_cloud_json:
return word_cloud_json, 200
else:
return {
"Error":
"Empty word cloud. Try changing your minimum word parameters or expanding your query."
}, 200
def post(self):
solr_args = request.json
if not solr_args:
return {
'Error': 'there was a problem with your request',
'Error Info': 'no data provided with request'
}, 403
if 'max_groups' in solr_args:
del solr_args['min_percent_word']
if 'min_occurrences_word' in solr_args:
del solr_args['min_occurrences_word']
elif 'query' in request.json:
try:
solr_args = json.loads(request.json["query"][0])
except Exception:
return {
'Error':
'there was a problem with your request',
'Error Info':
'couldn\'t decode query, it should be json-encoded before being sent (so double encoded)'
}, 403
solr_args["rows"] = min(
int(
solr_args.get(
"rows",
[current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS")
])[0]),
current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS"))
solr_args['fl'] = 'abstract,title'
solr_args['wt'] = 'json'
headers = {
'Authorization':
request.headers.get('X-Forwarded-Authorization',
request.headers.get('Authorization', ''))
}
response = client().get(
current_app.config.get("VIS_SERVICE_SOLR_PATH"),
params=solr_args,
headers=headers)
if response.status_code == 200:
data = response.json()
else:
return {
"Error":
"There was a connection error. Please try again later",
"Error Info": response.text
}, response.status_code
if data:
records = [
unicode(". ".join(
d.get('title', '')[:current_app.config.
get("VIS_SERVICE_WC_MAX_TITLE_SIZE")]) +
". " + d.get('abstract', '')
[:current_app.config.
get("VIS_SERVICE_WC_MAX_ABSTRACT_SIZE")])
for d in data["response"]["docs"]
]
word_cloud_json = word_cloud.generate_wordcloud(
records,
n_most_common=current_app.config.get(
"VIS_SERVICE_WC_MAX_WORDS"),
n_threads=current_app.config.get("VIS_SERVICE_WC_THREADS"),
accepted_pos=(u'NN', u'NNP', u'NNS', u'NNPS', u'JJ', u'RB',
u'VB', u'VBD', u'VBG', u'VBN', u'VBP', u'VBZ'))
if word_cloud_json:
return word_cloud_json, 200
else:
return {
"Error":
"Empty word cloud. Try changing your minimum word parameters or expanding your query."
}, 200
def get(self):
solr_args = dict(request.args)
if 'max_groups' in solr_args:
del solr_args['min_percent_word']
if 'min_occurrences_word' in solr_args:
del solr_args['min_occurrences_word']
solr_args["rows"] = min(
int(
solr_args.get(
"rows",
[current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS")
])[0]),
current_app.config.get("VIS_SERVICE_WC_MAX_RECORDS"))
solr_args['fields'] = ['id']
solr_args['defType'] = 'aqp'
solr_args['tv'] = 'true'
solr_args['tv.tf_idf'] = 'true'
solr_args['tv.tf'] = 'true'
solr_args['tv.positions'] = 'false'
solr_args['tf.offsets'] = 'false'
solr_args['tv.fl'] = 'abstract,title'
solr_args['fl'] = 'id,abstract,title'
solr_args['wt'] = 'json'
headers = {
'X-Forwarded-Authorization': request.headers.get('Authorization')
}
response = client().get(
current_app.config.get("VIS_SERVICE_TVRH_PATH"),
params=solr_args,
headers=headers)
if response.status_code == 200:
data = response.json()
else:
return {
"Error":
"There was a connection error. Please try again later",
"Error Info": response.text
}, response.status_code
if data:
min_percent_word = request.args.get(
"min_percent_word",
current_app.config.get("VIS_SERVICE_WC_MIN_PERCENT_WORD"))
min_occurrences_word = request.args.get(
"min_occurrences_word",
current_app.config.get("VIS_SERVICE_WC_MIN_OCCURRENCES_WORD"))
word_cloud_json = word_cloud.generate_wordcloud(
data,
min_percent_word=min_percent_word,
min_occurrences_word=min_occurrences_word)
if word_cloud_json:
return word_cloud_json, 200
else:
return {
"Error":
"Empty word cloud. Try changing your minimum word parameters or expanding your query."
}, 200
