def dump_lifetime_ratings():
app_configs = utils.open_json(
constants.APP_CONFIG_FILE.format(
file_name=constants.APP_CONFIG_FILE_NAME))
for app_config_file in app_configs:
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 create_parsed_pkl_from_Json(category, random_seed, sample_size,
category_label_number):
file_path = get_source_data_path(category)
review_set = set()
with open(file_path) as file:
for line in tqdm(file,
total=get_num_lines(file_path),
desc="Parsing / Sampling / Writing PKL" +
ntpath.basename(file_path)):
sample = json.loads(line)
reviewer_id = sample['reviewerID']
asin = sample['asin']
helpful = sample['helpful']
text_raw = sample['reviewText']
overall = sample['overall']
summary = sample['summary']
# if text raw not empty add to set
if (text_raw != '') and (not text_raw is None):
# create Amazon Review object
review = Review(category_label_number, reviewer_id, asin,
helpful, text_raw, overall, summary)
# add review object to set
review_set.add(review)
file.close()
random.seed(random_seed)
sampled_review_set = random.sample(review_set, sample_size)
write_reviews_parsed(category, sampled_review_set)
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
message = utils.get_json_key_value(
review, review_channel.message_key.split("."))
timestamp = utils.get_json_key_value(
review, review_channel.timestamp_key.split("."))
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 push_data_to_elasticsearch():
app_configs = utils.open_json(
constants.APP_CONFIG_FILE.format(file_name=constants.APP_CONFIG_FILE_NAME)
)
for app_config_file in app_configs:
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 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 parse_csv(raw_user_reviews_file_path, review_channel, app_config):
""" Parses the CSV files to a Review object """
with open(raw_user_reviews_file_path, "r") as file_handle:
# Read all the reviews from the CSV file
reviews = csv.reader(file_handle, delimiter=",")
# We expect the first row to contain the column names.
# TODO: We should change this to be taken from the configuration.
# There might be usecases where column names are not present in the data.
# People might want to indicate the message, timestamp keys using integer indices to the columns.
json_keys_list = reviews[0]
parsed_reviews = []
# Removing the first row.
reviews = reviews[1:]
# Iterate through all the reviews
for row in reviews:
review = {}
timestamp = ""
message = ""
rating = None
# There are some csvs for which the last column is empty.
# Hence we need to take the min below
for i in range(min(len(json_keys_list), len(row))):
if json_keys_list[i] == review_channel.timestamp_key:
# Storing the timestamp
timestamp = row[i]
elif json_keys_list[i] == review_channel.message_key:
# Storing the message
message = row[i]
elif json_keys_list[i] == review_channel.rating_key:
rating = row[i]
# Storing the raw review as received from the source.
review[json_keys_list[i]] = row[i]
# 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,
))
return parsed_reviews
file_name=constants.APP_CONFIG_FILE_NAME))
for app_config_file in app_configs:
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)
def run_algo():
app_configs = utils.open_json(
constants.APP_CONFIG_FILE.format(file_name=constants.APP_CONFIG_FILE_NAME)
)
for app_config_file in app_configs:
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 train_lstm_model():
app_configs = utils.open_json(
constants.APP_CONFIG_FILE.format(
file_name=constants.APP_CONFIG_FILE_NAME))
for app_config_file in app_configs:
app_config = AppConfig(utils.open_json(app_config_file))
print("[LOG] going through app config ", 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,
)
if not (app_config.algorithm_config.categorization_algorithm != None
and app_config.algorithm_config.categorization_algorithm
== CategorizationAlgorithms.LSTM_CLASSIFICATION):
continue
# 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 = utils.filter_reviews(reviews, app_config)
articles, labels, cleaned_labels = get_articles_and_labels(reviews)
trained_model, article_tokenizer, label_tokenizer = train(
articles, labels)
trained_lstm_categorization_model_file_path = 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,
)
dir_name = os.path.dirname(trained_lstm_categorization_model_file_path)
pathlib.Path(dir_name).mkdir(parents=True, exist_ok=True)
trained_model.save(trained_lstm_categorization_model_file_path)
# Saving the tokenizers
utils.dump_json(
article_tokenizer.to_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,
),
)
# Saving the tokenizers
utils.dump_json(
label_tokenizer.to_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,
),
)