def query_from_elasticsearch(fawkes_config_file=constants.FAWKES_CONFIG_FILE,
query_term="",
format=constants.JSON):
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
if query_term == "":
endpoint = app_config.elastic_config.elastic_search_url + "_" + constants.SEARCH
else:
endpoint = app_config.elastic_config.elastic_search_url + query_term + "/" + "_" + constants.SEARCH
response = requests.get(endpoint)
results = json.loads(response.text)
query_response_file = constants.ELASTICSEARCH_FETCH_DATA_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.query_folder,
app_name=app_config.app.name,
extension=format)
utils.write_query_results(results, query_response_file, format)
return results
def send_email(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Path where the generated email in html format will be stored
email_summary_generated_file_path = constants.EMAIL_SUMMARY_GENERATED_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.emails_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(email_summary_generated_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
template_html = ""
with open(email_summary_generated_file_path, "r") as email_file_handle:
template_html = email_file_handle.read()
for email_id in app_config.email_config.email_list:
send_email_helper(app_config.email_config.sender_email_address,
email_id,
app_config.email_config.email_subject_name,
template_html,
app_config.email_config.sendgrid_api_key)
def get_similar_reviews_for_app(app_config_file, query, num_results):
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Log the current operation which is being performed.
logging.info(logs.QUERY_START, FawkesActions.QUERY_SIMILAR_REVIEWS, "ALL",
app_config.app.name)
# Path where the user reviews were stored after parsing.
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(days=app_config.algorithm_config.algorithm_days_filter))
similar_reviews = get_similar_reviews(reviews, query, num_results)
# Log the current operation which is being performed.
logging.info(logs.QUERY_END, FawkesActions.QUERY_SIMILAR_REVIEWS, "ALL",
app_config.app.name)
# Create the intermediate folders
query_results_file_path = constants.QUERY_RESULTS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.query_folder,
app_name=app_config.app.name,
query_hash=utils.calculate_hash(query))
dir_name = os.path.dirname(query_results_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
utils.dump_json(
[{
"score": score,
"review": review.to_dict(),
} for score, review in similar_reviews],
query_results_file_path,
)
def dump_lifetime_ratings(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
if app_config.elastic_config.lifetime_rating_index != None:
time = datetime.strftime(datetime.now() - timedelta(1),
constants.TIMESTAMP_FORMAT)
playstore_rating = getPlayStoreLifetimeRating(app_config)
appstore_rating = getAppStoreLifetimeRating(app_config)
# Creating template for uploading lifetime rating
playstore_doc = Review(
{},
timestamp=time,
rating=playstore_rating,
app_name=app_config.app.name,
channel_name="playstore-lifetime",
channel_type="playstore-lifetime",
hash_id=utils.calculate_hash(app_config.app.name +
ReviewChannelTypes.ANDROID))
appstore_doc = Review(
{},
timestamp=time,
rating=playstore_rating,
app_name=app_config.app.name,
channel_name="appstore-lifetime",
channel_type="appstore-lifetime",
hash_id=utils.calculate_hash(app_config.app.name +
ReviewChannelTypes.IOS))
# Deleting document to override
elasticsearch.delete_document(
app_config.elastic_config.elastic_search_url,
app_config.elastic_config.lifetime_rating_index, "_doc",
playstore_doc.hash_id)
elasticsearch.delete_document(
app_config.elastic_config.elastic_search_url,
app_config.elastic_config.lifetime_rating_index, "_doc",
appstore_doc.hash_id)
# Uploading again
elasticsearch.create_document(
app_config.elastic_config.elastic_search_url,
app_config.elastic_config.lifetime_rating_index, "_doc",
playstore_doc.hash_id, playstore_doc)
elasticsearch.create_document(
app_config.elastic_config.elastic_search_url,
app_config.elastic_config.lifetime_rating_index, "_doc",
appstore_doc.hash_id, appstore_doc)
def send_reviews_to_slack(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
## Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Log the current operation which is being performed.
logging.info(logs.OPERATION, FawkesActions.PUSH_SLACK, "ALL",
app_config.app.name)
# Create the intermediate folders
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(minutes=app_config.slack_config.slack_run_interval))
# Log the number of reviews we got.
logging.info(logs.NUM_REVIEWS, len(reviews), "ALL",
app_config.app.name)
reviews = sorted(
reviews,
key=lambda review: review.derived_insight.sentiment["compound"],
reverse=True)
for review in reviews:
send_review_to_slack(app_config.slack_config.slack_hook_url,
app_config.slack_config.slack_channel, review,
app_config)
def run_bug_feature_categorization(reviews, app_config, num_processes):
if Algorithms.BUG_FEATURE_CATEGORIZATION in app_config.algorithm_config.algorithms_to_run:
# Log the number of reviews we got.
logging.info(
logs.CURRENT_ALGORITHM_START,
Algorithms.BUG_FEATURE_CATEGORIZATION,
"ALL",
app_config.app.name)
# We read from the topic file first
topics = {}
topics = utils.open_json(
app_config.algorithm_config.categorization.bug_feature_keywords_weights_file)
# Adding bug/feature classification
with Pool(num_processes) as process:
reviews = process.map(
partial(
bug_feature_classification,
topics=topics
),
reviews
)
# Log the number of reviews we got.
logging.info(
logs.CURRENT_ALGORITHM_END,
Algorithms.BUG_FEATURE_CATEGORIZATION,
"ALL",
app_config.app.name)
return reviews
def parse_keywords_file(keyword_file_name, enable_remove_stop_words=True):
# Topics is a dict, key = Topic Name. value = list of words and weights.
topics = {}
keywords_list = utils.open_json(keyword_file_name)
for topic_keyword in keywords_list:
topic = {}
line = " ".join(keywords_list[topic_keyword])
# Remove all trailing and beginning write spaces
line = line.lower()
line = line.strip()
# We will replace all the non-alphabet charectors with a space
cleaned_line = re.sub("[^a-zA-Z]+", " ", line)
# Replace multiple spaces with a single space
cleaned_line = re.sub(" +", " ", cleaned_line)
# Split the line according to space to get the words
cleaned_line = cleaned_line.split()
# Remove the stopwords.
if enable_remove_stop_words:
cleaned_line = utils.remove_stop_words(cleaned_line)
# For each word assign a weight
for word in list(set(cleaned_line)):
# Add the word to the topic
topic[lmtzr.lemmatize(word.lower())] = 1
topics[topic_keyword] = topic
return topics
def parse_json(raw_user_reviews_file_path, review_channel, app_config):
""" Parses the JSON files to a Review object """
reviews = utils.open_json(raw_user_reviews_file_path)
parsed_reviews = []
for review in reviews:
# TODO: Conver this to a standard format like jsonpath.
# Extract the message.
message = utils.get_json_key_value(review, review_channel.message_key.split("."))
# Extract the timestamp.
timestamp = utils.get_json_key_value(review, review_channel.timestamp_key.split("."))
# Extract the rating if present.
rating = None
if review_channel.rating_key != None:
rating = utils.get_json_key_value(review, review_channel.rating_key.split("."))
# Add the review object to the parsed reviews
parsed_reviews.append(
Review(
review,
message=message,
timestamp=timestamp,
rating=rating,
app_name=app_config.app.name,
channel_name=review_channel.channel_name,
channel_type=review_channel.channel_type,
review_timezone=review_channel.timezone,
timestamp_format=review_channel.timestamp_format,
)
)
return parsed_reviews
def validate_app_config_schema(self, document):
try:
schema = utils.open_json(constants.APP_CONFIG_SCHEMA_FILE)
jsonschema.validate(document, schema)
except ValidationError as e:
raise ValidationError("App config schema validation failed: " +
str(e.message))
def generate_keyword_weights(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# First look at the category keywords.
utils.dump_json(
parse_keywords_file(
app_config.algorithm_config.category_keywords_file),
app_config.algorithm_config.category_keywords_weights_file,
)
# Then look at the bug-feature keywords
utils.dump_json(
parse_keywords_file(
app_config.algorithm_config.bug_feature_keywords_file, False),
app_config.algorithm_config.bug_feature_keywords_weights_file,
)
def run_algo(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Path where the user reviews were stored after parsing.
parsed_user_reviews_file_path = constants.PARSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.parsed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(parsed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(days=app_config.algorithm_config.algorithm_days_filter))
# Number of process to make
num_processes = min(constants.PROCESS_NUMBER, os.cpu_count())
if constants.CIRCLECI in os.environ:
num_processes = 2
# Adding sentiment
with Pool(num_processes) as process:
reviews = process.map(add_review_sentiment_score, reviews)
if app_config.algorithm_config.categorization_algorithm != None and app_config.algorithm_config.category_keywords_weights_file != None:
# We read from the topic file first
topics = {}
topics = utils.open_json(
app_config.algorithm_config.category_keywords_weights_file)
# Adding text-match categorization
with Pool(num_processes) as process:
reviews = process.map(
partial(text_match_categortization,
app_config=app_config,
topics=topics), reviews)
if app_config.algorithm_config.bug_feature_keywords_weights_file != None:
# We read from the topic file first
topics = {}
topics = utils.open_json(
app_config.algorithm_config.bug_feature_keywords_weights_file)
# Adding bug/feature classification
with Pool(num_processes) as process:
reviews = process.map(
partial(bug_feature_classification, topics=topics),
reviews)
if app_config.algorithm_config.categorization_algorithm == CategorizationAlgorithms.LSTM_CLASSIFICATION:
# Load the TensorFlow model
model = tf.keras.models.load_model(
constants.LSTM_CATEGORY_MODEL_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
))
# Load the article tokenizer file
tokenizer_json = utils.open_json(
constants.LSTM_CATEGORY_ARTICLE_TOKENIZER_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
), )
article_tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(
tokenizer_json)
# Load the label tokenizer file
tokenizer_json = utils.open_json(
constants.LSTM_CATEGORY_LABEL_TOKENIZER_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
), )
label_tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(
tokenizer_json)
cleaned_labels = {}
for review in reviews:
label = review.derived_insight.category
cleaned_label = re.sub(r'\W+', '', label)
cleaned_label = cleaned_label.lower()
cleaned_labels[cleaned_label] = label
# Adding LSTM categorization
reviews = lstm_classification(reviews, model, article_tokenizer,
label_tokenizer, cleaned_labels)
# Create the intermediate folders
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(processed_user_reviews_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
utils.dump_json(
[review.to_dict() for review in reviews],
processed_user_reviews_file_path,
)
def fetch_reviews(fawkes_config_file = constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(
utils.open_json(fawkes_config_file)
)
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(
utils.open_json(
app_config_file
)
)
# Each app has a list of review channels from which the user reviews are fetched.
for review_channel in app_config.review_channels:
if review_channel.is_channel_enabled and review_channel.channel_type != ReviewChannelTypes.BLANK:
# Log the current operation which is being performed.
logging.info(logs.OPERATION, FawkesActions.FETCH, review_channel.channel_name, app_config.app.name)
reviews = []
# Depending on the channel type, we have different "fetchers" to get the data.
if review_channel.channel_type == ReviewChannelTypes.TWITTER:
reviews = tweets.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.SALESFORCE:
reviews = salesforce.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.SPREADSHEET:
reviews = spreadsheet.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.CSV:
reviews = comma_separated_values.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.ANDROID:
reviews = playstore.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.IOS:
reviews = appstore.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.SPLUNK:
reviews = splunk.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.REMOTE_FILE:
reviews = remote.fetch(
review_channel
)
elif review_channel.channel_type == ReviewChannelTypes.VERTICA:
reviews = vertica.fetch(
review_channel
)
else:
continue
# Log the number of reviews we got.
logging.info(logs.NUM_REVIEWS, len(reviews), review_channel.channel_name, app_config.app.name)
# After fetching the review for that particular channel, we dump it into a file.
# The file has a particular format.
# {base_folder}/{dir_name}/{app_name}/{channel_name}-raw-feedback.{extension}
raw_user_reviews_file_path = constants.RAW_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.raw_data_folder,
app_name=app_config.app.name,
channel_name=review_channel.channel_name,
extension=review_channel.file_type)
# Create the intermediate folders
dir_name = os.path.dirname(raw_user_reviews_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
if review_channel.file_type == constants.JSON:
utils.dump_json(reviews, raw_user_reviews_file_path)
else:
with open(raw_user_reviews_file_path, "w") as file:
file.write(reviews)
# There are lot of use-cases where we need to execute custom code after the data is fetched.
# This might include data-transformation, cleanup etc.
# This is the right place to do that.
if app_config.custom_code_module_path != None:
custom_code_module = importlib.import_module(app_config.custom_code_module_path, package=None)
reviews = custom_code_module.run_custom_code_post_fetch()
def parse_reviews(fawkes_config_file = constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(
utils.open_json(fawkes_config_file)
)
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(
utils.open_json(
app_config_file
)
)
parsed_reviews = []
# We now read the review details for each channel
for review_channel in app_config.review_channels:
# We parse the channels only if its enabled!
if review_channel.is_channel_enabled and review_channel.channel_type != ReviewChannelTypes.BLANK:
raw_user_reviews_file_path = constants.RAW_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.raw_data_folder,
app_name=app_config.app.name,
channel_name=review_channel.channel_name,
extension=review_channel.file_type
)
if review_channel.file_type == constants.JSON: # Parse JSON
channel_reviews = parse_json(
raw_user_reviews_file_path,
review_channel, app_config
)
elif review_channel.file_type == constants.CSV: # Parse CSV
channel_reviews = parse_csv(
raw_user_reviews_file_path,
review_channel,
app_config
)
elif review_channel.file_type == constants.JSON_LINES:
channel_reviews = parse_json_lines(
raw_user_reviews_file_path,
review_channel,
app_config
)
else: # Unsupported file format
raise (
"Format not supported exception. Check your file-type key in your config."
)
parsed_reviews += channel_reviews
# Executing custom code after parsing.
if app_config.custom_code_module_path != None:
custom_code_module = importlib.import_module(app_config.custom_code_module_path, package=None)
parsed_reviews = custom_code_module.run_custom_code_post_parse(
parsed_reviews)
# After parsing the reviews for that all channels, we dump it into a file.
# The file has a particular format.
# {base_folder}/{dir_name}/{app_name}/parsed-user-feedback.{extension}
parsed_user_reviews_file_path = constants.PARSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.parsed_data_folder,
app_name=app_config.app.name,
)
# Create the intermediate folders
dir_name = os.path.dirname(parsed_user_reviews_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
utils.dump_json(
[parsed_review.to_dict() for parsed_review in parsed_reviews],
parsed_user_reviews_file_path
)
def push_data_to_elasticsearch(
fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Log the current operation which is being performed.
logging.info(logs.OPERATION, FawkesActions.PUSH_ELASTICSEARCH, "ALL",
app_config.app.name)
# Path where the user reviews were stored after parsing.
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) - timedelta(
days=app_config.elastic_config.elastic_search_days_filter))
# Log the number of reviews we got.
logging.info(logs.NUM_REVIEWS, len(reviews), "ALL",
app_config.app.name)
# We shuffle the reviews. This is because of how elastic search.
random.shuffle(reviews)
# We first list out all the indices
indices = get_indices(app_config.elastic_config.elastic_search_url)
if app_config.elastic_config.index not in indices:
# Create a new index
create_index(app_config.elastic_config.elastic_search_url,
app_config.elastic_config.index)
# Bulk push the data
i = 0
while i * constants.BULK_UPLOAD_SIZE < len(reviews):
response = bulk_push_to_elastic(
app_config.elastic_config.elastic_search_url,
app_config.elastic_config.index,
reviews[i * constants.BULK_UPLOAD_SIZE:min(
(i + 1) * constants.BULK_UPLOAD_SIZE, len(reviews))])
if response.status_code != 200:
print(
"[Error] push_data_to_elasticsearch :: Got status code : ",
response.status_code)
print("[Error] push_data_to_elasticsearch :: Response is : ",
response.text)
i += 1
def generate_summary(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
"""
@param{string}: fawkes_config_file - config file path
@returns{map<string,list<string>>}: summarized_reviews - summarized reviews per category
Main function to create a summary of reviews
- queries to get reviews
- preprocess reviews based on each category
- cluster similar reviews
- rank and summarize amongst cluster to provide a summarize
"""
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we-
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Path where the user reviews were stored after parsing.
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
reviews = queries.getVocByCategory(reviews)
summarized_reviews = {}
# For each category, generate a summary
for category in reviews:
summarized_category_review = []
# get reviews per category
categorized_review = reviews[category]
# Preprocess reviews
sentences = preprocess_review(categorized_review)
# number of sentences in a category should be atleast greater than
# the number of clusters
if (len(sentences) >
app_config.algorithm_config.summarization.num_clusters -
1):
clustered_sentences = k_means_classification(
sentences,
app_config.algorithm_config.summarization.num_clusters)
for cluster in clustered_sentences.values():
if len(cluster) < constants.minimum_reviews_per_cluster:
continue
text = ". ".join(cluster)
gen_summary = summarize_text(
text,
app_config.algorithm_config.summarization.
summary_length_per_cluster,
)
summarized_category_review.append(gen_summary)
else:
logging.info(logs.INSUFFICIENT_DATA, category)
summarized_reviews[category] = summarized_category_review
query_results_file_path = constants.REVIEW_SUMMARY_RESULTS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.query_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(query_results_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
utils.dump_json([{
"summarized_reviews": summarized_reviews
}], query_results_file_path)
return summarized_reviews
def test_sanity(self):
"""
Test for sanity that parsing and algorithms are working
"""
# First we parse the sample data.
parse.parse_reviews()
parsed_output = utils.open_json(
"data/parsed_data/sample-mint/parsed-user-feedback.json")
expected_parsed_output = [{
"message":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending",
"timestamp": "2020/03/15 22:06:17",
"rating": 5.0,
"user_id": None,
"app_name": "sample-mint",
"channel_name": "appstore",
"channel_type": "ios",
"hash_id": "a5461e62ee4eccbab92900ba01d49d9ed0642dcc",
"derived_insight": {
"sentiment": None,
"category": "uncategorized",
"review_message_encoding": None,
"extra_properties": {}
},
"raw_review": {
"updated":
"2020-03-15 14:13:17",
"rating":
5,
"version":
"7.1.0",
"content":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\u00e2\u20ac\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\u00e2\u20ac\u2122m more aware of my spending"
}
}]
self.assertEqual(parsed_output, expected_parsed_output)
# Before running the algorithms, we generate the keyword weights.
text_match_trainer.generate_keyword_weights()
# We run the algorithms on that data
algo.run_algo()
processed_output = utils.open_json(
"data/processed_data/sample-mint/processed-user-feedback.json")
expected_processed_output = [{
"message":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending",
"timestamp": "2020/03/15 22:06:17",
"rating": 5.0,
"user_id": None,
"app_name": "sample-mint",
"channel_name": "appstore",
"channel_type": "ios",
"hash_id": "a5461e62ee4eccbab92900ba01d49d9ed0642dcc",
"derived_insight": {
"sentiment": {
"neg": 0.0,
"neu": 0.928,
"pos": 0.072,
"compound": 0.4767
},
"category": "Application",
"review_message_encoding": None,
"extra_properties": {
"category_scores": {
"User Experience": 0,
"sign-in/sign-up": 0,
"Notification": 0,
"Application": 1,
"ads": 0
},
"bug_feature": "feature"
}
},
"raw_review": {
"updated":
"2020-03-15 14:13:17",
"rating":
5,
"version":
"7.1.0",
"content":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still can\u00e2\u20ac\u2122t add all of my financial institutions so my budget is kind of skewed. But other that I can say I\u00e2\u20ac\u2122m more aware of my spending"
}
}]
self.assertEqual(processed_output, expected_processed_output)
def generate_email_summary_detailed(
fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Path where the user reviews were stored after parsing.
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(days=app_config.email_config.email_time_span))
if len(reviews) == 0:
continue
review_by_category = queries.getVocByCategory(reviews)
top_categories = sorted([(len(review_by_category[key]), key)
for key in review_by_category],
reverse=True)
top_categories = top_categories[:5]
max_sentiment_per_category = {}
for category in top_categories:
max_sentiment_per_category[category[1]] = sorted(
review_by_category[category[1]],
key=functools.cmp_to_key(compare_review_by_category_score))[0]
reviewDivHTML = ""
for category in top_categories:
if category[1] == constants.CATEGORY_NOT_FOUND:
continue
template_data = {
"catetgoryName": category[1],
"upOrDown": "down",
"upDownPercentage": 19,
"reviewText": max_sentiment_per_category[category[1]].message,
"usersTalking": len(review_by_category[category[1]])
}
formatted_html = email_utils.generate_email(
constants.WEEKLY_EMAIL_DETAILED_REVIEW_BLOCK_TEMPLATE,
template_data)
reviewDivHTML += formatted_html
# We get all the data.
template_data = {
"appStoreRating":
"{0:.2f}".format(queries.appStoreRating(reviews)),
"playStoreRating":
"{0:.2f}".format(queries.playStoreRating(reviews)),
"positiveReview":
queries.positiveReview(reviews),
"neutralReview":
queries.neutralReview(reviews),
"negativeReview":
queries.negativeReview(reviews),
"fromDate":
queries.fromDate(reviews),
"toDate":
queries.toDate(reviews),
"appLogo":
app_config.app.logo,
"timeSpanWords":
app_config.email_config.email_time_span_in_words,
"reviewBlock":
reviewDivHTML,
"appStoreNumberOfReview":
queries.appStoreNumberReview(reviews),
"playStoreNumberOfReview":
queries.playStoreNumberReview(reviews),
"appStoreLifetimeRating":
lifetime.getAppStoreLifetimeRating(app_config),
"playStoreLifetimeRating":
lifetime.getPlayStoreLifetimeRating(app_config),
"kibanaDashboardURL":
app_config.elastic_config.kibana_url
}
# We finally send the email
formatted_html = email_utils.generate_email(
app_config.email_config.email_template_file, template_data)
# Path where the generated email in html format will be stored
email_summary_generated_file_path = constants.EMAIL_SUMMARY_GENERATED_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.emails_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(email_summary_generated_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
with open(email_summary_generated_file_path, "w") as email_file_handle:
email_file_handle.write(formatted_html)
def generate_email_summary(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Path where the user reviews were stored after parsing.
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(processed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(days=app_config.email_config.email_time_span))
# We get all the data.
template_data = {
"numberOfReview": queries.numberOfReview(reviews),
"topCategory": queries.topCategory(reviews),
"numFeatureReq": queries.numFeatureReq(reviews),
"numBugsReported": queries.numBugsReported(reviews),
"appStoreRating":
"{0:.2f}".format(queries.appStoreRating(reviews)),
"playStoreRating":
"{0:.2f}".format(queries.playStoreRating(reviews)),
"happyReview1": queries.happyReview1(reviews),
"unhappyReview1": queries.unhappyReview1(reviews),
"positiveReview": queries.positiveReview(reviews),
"neutralReview": queries.neutralReview(reviews),
"negativeReview": queries.negativeReview(reviews),
"topCategoryNumberOfReview":
queries.topCategoryNumberOfReview(reviews),
"fromDate": queries.fromDate(reviews),
"toDate": queries.toDate(reviews),
"appLogo": app_config.app.logo,
"timeSpanWords": app_config.email_config.email_time_span,
"kibanaDashboardURL": app_config.elastic_config.kibana_url
}
# Get the initial HTML from the template file.
formatted_html = email_utils.generate_email(
app_config.email_config.email_template_file, template_data)
# Path where the generated email in html format will be stored
email_summary_generated_file_path = constants.EMAIL_SUMMARY_GENERATED_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.emails_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(email_summary_generated_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
with open(email_summary_generated_file_path, "w") as email_file_handle:
email_file_handle.write(formatted_html)
def run_categorization(reviews, app_config, num_processes):
if Algorithms.CATEGORIZATION in app_config.algorithm_config.algorithms_to_run:
if app_config.algorithm_config.categorization.algorithm == CategorizationAlgorithms.TEXT_MATCH_CLASSIFICATION:
# Log the number of reviews we got.
logging.info(logs.CURRENT_ALGORITHM_START,
CategorizationAlgorithms.TEXT_MATCH_CLASSIFICATION,
"ALL", app_config.app.name)
# We read from the topic file first
topics = {}
topics = utils.open_json(
app_config.algorithm_config.categorization.
category_keywords_weights_file)
# Adding text-match categorization
with Pool(num_processes) as process:
reviews = process.map(
partial(text_match_categortization,
app_config=app_config,
topics=topics), reviews)
# Log the number of reviews we got.
logging.info(logs.CURRENT_ALGORITHM_END,
CategorizationAlgorithms.TEXT_MATCH_CLASSIFICATION,
"ALL", app_config.app.name)
elif app_config.algorithm_config.categorization.algorithm == CategorizationAlgorithms.LSTM_CLASSIFICATION:
# WE import the module only when its required.
import tensorflow as tf
# Log the number of reviews we got.
logging.info(logs.CURRENT_ALGORITHM_START,
CategorizationAlgorithms.LSTM_CLASSIFICATION, "ALL",
app_config.app.name)
# Load the TensorFlow model
model = tf.keras.models.load_model(
constants.LSTM_CATEGORY_MODEL_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
))
# Load the article tokenizer file
tokenizer_json = utils.open_json(
constants.LSTM_CATEGORY_ARTICLE_TOKENIZER_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
), )
article_tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(
tokenizer_json)
# Load the label tokenizer file
tokenizer_json = utils.open_json(
constants.LSTM_CATEGORY_LABEL_TOKENIZER_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.
base_folder,
dir_name=app_config.fawkes_internal_config.data.
models_folder,
app_name=app_config.app.name,
), )
label_tokenizer = tf.keras.preprocessing.text.tokenizer_from_json(
tokenizer_json)
cleaned_labels = {}
for review in reviews:
label = review.derived_insight.category
cleaned_label = re.sub(r'\W+', '', label)
cleaned_label = cleaned_label.lower()
cleaned_labels[cleaned_label] = label
# Adding LSTM categorization
reviews = lstm_classification(reviews, model, article_tokenizer,
label_tokenizer, cleaned_labels)
# Log the number of reviews we got.
logging.info(logs.CURRENT_ALGORITHM_END,
CategorizationAlgorithms.LSTM_CLASSIFICATION, "ALL",
app_config.app.name)
# Log the number of reviews we got.
logging.info(logs.NUM_REVIEWS, len(reviews), "ALL",
app_config.app.name)
return reviews
def test_sanity(self):
"""
Test for sanity that parsing and algorithms are working
"""
# First we parse the sample data.
parse.parse_reviews()
parsed_output = utils.open_json(
"data/parsed_data/sample-mint/parsed-user-feedback.json")
expected_parsed_output = [{
"message":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending",
"timestamp": "2020/03/15 14:13:17",
"rating": 5,
"app_name": "sample-mint",
"channel_name": "appstore",
"channel_type": "ios",
"hash_id": "de848685d11742dbea77e1e5ad7b892088ada9c9",
"derived_insight": {
"sentiment": None,
"category": "uncategorized",
"extra_properties": {}
}
}]
self.assertEqual(parsed_output, expected_parsed_output)
# Before running the algorithms, we generate the keyword weights.
text_match_trainer.generate_keyword_weights()
# We run the algorithms on that data
algo.run_algo()
processed_output = utils.open_json(
"data/processed_data/sample-mint/processed-user-feedback.json")
expected_processed_output = [{
"message":
"I just heard about this budgeting app. So I gave it a try. I am impressed thus far. However I still cant add all of my financial institutions so my budget is kind of skewed. But other that I can say Im more aware of my spending",
"timestamp": "2020/03/15 14:13:17",
"rating": 5,
"app_name": "sample-mint",
"channel_name": "appstore",
"channel_type": "ios",
"hash_id": "6dde3aa82726c0a9e3777623854d839184767571",
"derived_insight": {
"sentiment": {
"neg": 0.0,
"neu": 0.928,
"pos": 0.072,
"compound": 0.4767
},
"category": "Application",
"extra_properties": {
"category_scores": {
"User Experience": 0,
"sign-in/sign-up": 0,
"Notification": 0,
"Application": 1,
"ads": 0
},
"bug_feature": "feature"
}
}
}]
self.assertEqual(processed_output, expected_processed_output)
def run_algo(fawkes_config_file=constants.FAWKES_CONFIG_FILE):
# Read the app-config.json file.
fawkes_config = FawkesConfig(utils.open_json(fawkes_config_file))
# For every app registered in app-config.json we
for app_config_file in fawkes_config.apps:
# Creating an AppConfig object
app_config = AppConfig(utils.open_json(app_config_file))
# Log the current operation which is being performed.
logging.info(logs.OPERATION, FawkesActions.RUN_ALGO, "ALL",
app_config.app.name)
# Path where the user reviews were stored after parsing.
parsed_user_reviews_file_path = constants.PARSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.parsed_data_folder,
app_name=app_config.app.name,
)
# Loading the reviews
reviews = utils.open_json(parsed_user_reviews_file_path)
# Converting the json object to Review object
reviews = [Review.from_review_json(review) for review in reviews]
# Filtering out reviews which are not applicable.
reviews = filter_utils.filter_reviews_by_time(
filter_utils.filter_reviews_by_channel(
reviews,
filter_utils.filter_disabled_review_channels(app_config),
),
datetime.now(timezone.utc) -
timedelta(days=app_config.algorithm_config.algorithm_days_filter))
# Log the number of reviews we got.
logging.info(logs.NUM_REVIEWS, len(reviews), "ALL",
app_config.app.name)
# Number of process to make
num_processes = min(constants.PROCESS_NUMBER, os.cpu_count())
if constants.CIRCLECI in os.environ:
num_processes = 2
# Running sentiment analysis
reviews = run_sentiment_analysis(reviews, app_config, num_processes)
# Running categorization
reviews = run_categorization(reviews, app_config, num_processes)
# Running bug/feature categorizatio
reviews = run_bug_feature_categorization(reviews, app_config,
num_processes)
# Running the message encoding
reviews = run_review_text_encoding(reviews, app_config, num_processes)
# Create the intermediate folders
processed_user_reviews_file_path = constants.PROCESSED_USER_REVIEWS_FILE_PATH.format(
base_folder=app_config.fawkes_internal_config.data.base_folder,
dir_name=app_config.fawkes_internal_config.data.
processed_data_folder,
app_name=app_config.app.name,
)
dir_name = os.path.dirname(processed_user_reviews_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
utils.dump_json(
[review.to_dict() for review in reviews],
processed_user_reviews_file_path,
)
