def search(table_name):
if os.path.exists(databases_root_folder + table_name):
db = ScaDatabase(databases_root_folder + table_name)
analysis_all = db.select_all(Analysis)
analyses = []
hp = []
for analysis in analysis_all:
final_key_ranks = db.select_final_key_rank_json_from_analysis(KeyRank, analysis.id)
if len(final_key_ranks) > 0:
hyper_parameters = db.select_from_analysis(HyperParameter, analysis.id)
training_hyper_parameters = hyper_parameters.hyper_parameters
training_hyper_parameters[0]['guessing_entropy'] = final_key_ranks[0][0]['key_rank']
hp.append(training_hyper_parameters[0])
exp = hip.Experiment().from_iterable(hp)
exp.display_data(hip.Displays.PARALLEL_PLOT).update({
'hide': ['uid', 'key_rank', 'key'], # Hide some columns
'order': ['guessing_entropy'], # Put column time first on the left
})
exp.validate()
exp.to_html("webapp/templates/hiplot.html")
return render_template("dashboard/search.html", analyses=analyses)
return render_template("dashboard/search.html", analyses=[])
def table():
db_files = []
# r=root, d=directories, f = files
for r, d, f in os.walk(databases_root_folder):
for file in f:
if file.endswith(".sqlite"):
db_files.append(file)
all_tables = []
all_tables_names = []
for db_file in db_files:
if os.path.exists(databases_root_folder + db_file):
db = ScaDatabase(databases_root_folder + db_file)
analysis_all = db.select_all(Analysis)
analyses = []
for analysis in analysis_all:
if not analysis.deleted:
localtimezone = pytz.timezone(os.getenv("TIME_ZONE"))
analysis_datetime = datetime.strptime(str(analysis.datetime), "%Y-%m-%d %H:%M:%S.%f").astimezone(
localtimezone).__format__(
"%b %d, %Y %H:%M:%S")
final_key_ranks = db.select_final_key_rank_json_from_analysis(KeyRank, analysis.id)
final_success_rates = db.select_final_success_rate_from_analysis(SuccessRate, analysis.id)
neural_network = db.select_from_analysis(NeuralNetwork, analysis.id)
analyses.append({
"id": analysis.id,
"datetime": analysis_datetime,
"dataset": analysis.dataset,
"settings": analysis.settings,
"elapsed_time": time.strftime('%H:%M:%S', time.gmtime(analysis.elapsed_time)),
"key_ranks": final_key_ranks,
"success_rates": final_success_rates,
"neural_network_name": "not ready" if neural_network is None else neural_network.model_name
})
all_tables.append(analyses)
all_tables_names.append(db_file)
return render_template("tables.html", all_tables=all_tables, all_tables_names=all_tables_names)