def get(self):
helper = Helper(current_app.logger)
games, jp_result = helper.get_jackpot_matches()
datas = []
for result in games:
pos, sub_type_id, odds, game_id, match_id, start_time,\
parent_match_id, away_team, home_team = result
datas.append({
'pos': pos,
'sub_type_id': sub_type_id,
'odds_keys': odds,
'game_id': game_id,
'match_id': match_id,
'start_time': start_time.strftime("%Y-%m-%d %H:%M"),
'parent_match_id': parent_match_id,
'away_team': away_team,
'home_team': home_team
})
resp = {
"data": datas,
'meta': {
'jackpot_event_id': jp_result[0],
'jackpot_type': jp_result[1],
'bet_amount': float(jp_result[2]),
'jackpot_amount': float(jp_result[3]),
'total_games': jp_result[4],
'name': 'Correct Score Jackpot',
'type': 'correctscore'
}
}
return make_response(json.dumps(resp), 200)
def test_LogisticRegression(self): helper = Helper() # Load up data iris = load_iris() X_train, y_train, X_test, y_test = iris.data[:120], iris.target[:120], iris.data[120:], iris.target[120:] # Bring in the default RandomForestClassifier model model_name = 'lr' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LogisticRegression(random_state=0) # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
def post(self):
data = self.params()
helper = Helper(current_app.logger)
sport_id = data.get('id')
page = data.get('page') or 1
limit = data.get('limit') or 10
categories = helper.get_active_categories(sport_id, page, limit)
datas = []
for cat in categories:
category_id, category_name = cat
cat_data = {
'category_id':
category_id,
'category_name':
category_name.decode('unicode_escape').encode('utf-8')
}
competitions = helper.get_competitions(category_id)
c_datas = []
for c in competitions:
competition_id, competition_name = c
c_datas.append({
'competition_id':
competition_id,
'competition_name':
competition_name.decode('unicode_escape').encode('utf-8')
})
cat_data['competitions'] = c_datas
datas.append(cat_data)
return make_response(json.dumps(datas), 200)
def post(self):
#user_id,device_id,fcm_token,update_token=True/False
data = request.get_json()
current_app.logger.info(
"Scorepesa mobile app device register api args %r" % data)
scorepesa_cfgs = LocalConfigParser.parse_configs("SCOREPESAAPP")
scorepesa_bonus_cfgs = LocalConfigParser.parse_configs("SCOREPESA")
try:
http_code = 200
current_app.logger.info("device registeration data..... %r" % data)
helper = Helper(current_app.logger)
result = helper.scorepesa_app_register_device(data)
current_app.logger.info(
"mobile device registration response... {0}".format(result))
if not result:
http_code = 500
result = 'ERROR'
if result and data.get("update_token") is False:
#award bonus
if (helper.award_bonus_to_profile(data)):
#sent app notification/sms
data[
'msg'] = "Congratulations! you have been awarded a bonus worth KES {0}. Now win big with.".format(
scorepesa_bonus_cfgs['registration_bunus_amount'])
data['sc'] = "101010"
helper.push_notification(data)
helper.send_message(data)
except JWTError as e:
current_app.logger.error(
"Exception on device registration.... %r " % e)
http_code = 400
result = "Invalid request."
resp = make_response(json.dumps(result), http_code)
return resp
def test_AdaBoostRegressor(self): helper = Helper() # Load up data X, y = load_boston(return_X_y=True) X_train, y_train, X_test, y_test = X[:405], y[:405], X[405:], y[405:] # Bring in the default RandomForestClassifier model model_name = 'ab:regressor' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = AdaBoostRegressor(random_state=0) # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
def test_init(self):
helper = Helper()
model_name = 'rf:classifier'
model = helper.getDefaultModel(model_name)
self.assertEqual( model, RandomForestClassifier )
args = helper.getDefaultArgs(model_name)
self.assertEqual( args, {'random_state':0} )
model = model(**args)
self.assertEqual( type(model), RandomForestClassifier )
helper.setRandomState(1)
model_name = 'dt:regressor'
model = helper.getDefaultModel(model_name)
self.assertEqual( model, DecisionTreeRegressor )
args = helper.getDefaultArgs(model_name)
self.assertEqual( args, {'random_state':1} )
model = model(**args)
self.assertEqual( type(model), DecisionTreeRegressor )
def post(self):
data = self.get_params()
current_app.logger.info("Reading my bets request => %r" % data)
helper = Helper(current_app.logger)
data = helper.get_mybets(data.get('token'),
data.get('page') or 1,
data.get('limit') or 10)
current_app.logger.info("Reading my bets get_mybets => %r" % data)
datas = []
for d in data:
created, bet_id, total_matches, jackpot_bet_id, total_odd, bet_message, bet_amount, possible_win, status = d
value = {
'created': created,
'bet_id': bet_id,
'total_matches': total_matches,
'jackpot_bet_id': jackpot_bet_id,
'total_odd': float(total_odd),
'bet_message': bet_message,
'possible_win': float(possible_win),
'status': status,
'bet_amount': float(bet_amount)
}
datas.append(value)
current_app.logger.info("Reading my bets get_mybets FINAL String %r" %
datas)
resp = make_response(json.dumps(datas), 200)
return resp
def post(self):
#version_number
data = request.get_json()
current_app.logger.info(
"Scorepesa mobile app change version api args %r" % data)
scorepesa_cfgs = LocalConfigParser.parse_configs("SCOREPESAAPP")
try:
http_code = 200
current_app.logger.info(
"scorepesa app api change version data %r" % data)
helper = Helper(current_app.logger)
message = {"vnum": data.get('version_number')}
result = helper.scorepesa_app_update_version(message)
current_app.logger.info(
"mobile change version response... {0}".format(result))
if not result:
http_code = 500
result = 'ERROR'
except JWTError as e:
current_app.logger.error("Exception on version check ..... %r " %
e)
http_code = 400
result = "Invalid request."
resp = make_response(json.dumps(result), http_code)
return resp
def post(self):
data = self.params()
helper = Helper(current_app.logger)
profile_id = helper.validate_token(data.get('token'))
if not profile_id:
message = {
"status": 401,
"error": {
"status": 401,
"message": "Unauthorized"
}
}
else:
scorepesa = Scorepesa(current_app.logger)
balance, bonus = scorepesa.get_account_balance(
{'profile_id': profile_id})
message = {
"status": 200,
"data": {
"balance": balance,
'bonus': bonus,
'points': 0
}
}
resp = make_response(json.dumps(message), 200)
return resp
def test_LinearDiscriminantAnalysis(self): helper = Helper() # Test with dataset type: sklearn DataBunch # Load up data iris = load_iris() X_train, y_train, X_test, y_test = iris.data[:120], iris.target[:120], iris.data[120:], iris.target[120:] # Bring in the default LinearDiscriminantAnalysis model model_name = 'lda' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LinearDiscriminantAnalysis() # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy ) # Test with dataset type: pandas DataFrame # Load up data iris_df = load_iris(as_frame=True).frame X = iris_df.loc[:, iris_df.columns != 'target'] y = iris_df['target'] X_train, y_train = X.iloc[:120], y.iloc[:120] X_test, y_test = X.iloc[120:], y.iloc[120:] # Bring in the default LinearDiscriminantAnalysis model model_name = 'lda' actual_model = helper.getBuiltModel(model_name) # Explicity create the model we expect to get from getBuiltModel call expected_model = LinearDiscriminantAnalysis() # Make sure the models are the same type before continuing self.assertEqual( type(actual_model), type(expected_model) ) # Train this default model on the iris dataset actual_model.fit(X_train, y_train) # Get default model accuracy on testing set actual_accuracy = actual_model.score(X_test, y_test) # Complete the same process, however we make the model explicitly expected_model.fit(X_train, y_train) expected_accuracy = expected_model.score(X_test, y_test) # Make sure that the accuracy reported from both models is the same self.assertEqual( actual_accuracy, expected_accuracy )
def post(self):
helper = Helper(current_app.logger)
data = self.params()
msisdn = clean_msisdn(data.get('mobile'))
code = data.get('code')
ok = helper.verify_code(msisdn, code)
if ok:
profile_data = helper.get_profile_data(msisdn)
token = helper.create_hash_token(msisdn,
profile_data[0],
remember=0)
if ok and token:
message = {
'success': {
'status': 201,
"message": "Verification code has been sent to your phone"
},
'token': token
}
else:
message = {
'error': {
'status':
401,
"message":
"Verification failed. Kindly check code and try again or click resend code to receive new code"
}
}
return make_response(json.dumps(message), 200)
def get(self):
helper = Helper(current_app.logger)
sports = helper.get_active_sports()
datas = []
for sport in sports:
sport_id, sport_name = sport
datas.append({"sport_name": sport_name, 'sport_id': sport_id})
return make_response(json.dumps(datas), 200)
def get(self):
helper = Helper(current_app.logger)
tab = request.args.get('tab', 'Mechi Kali')
limit = request.args.get('limit', 7) or 7
page = request.args.get('page', 1) or 1
match_id = request.args.get('id', None)
games_result = helper.get_matches(tab, page, limit, match_id)
datas = prepare_match_data(games_result)
return make_response(json.dumps(datas), 200)
def post(self):
data = self.params()
helper = Helper(current_app.logger)
limit = data.get('limit', 10) or 10
page = data.get('page', 1) or 1
competition_id = data.get('id', None)
games_result = helper.get_competition_matches(competition_id, page,
limit)
datas = prepare_match_data(games_result)
return make_response(json.dumps(datas), 200)
def process_response(self):
data = self.params()
current_app.logger.info("Login requesrt eceived: %r" % data)
msisdn = clean_msisdn(data.get('msisdn'))
if not msisdn:
return {
"status": 401,
"message": "Invalid username and or password",
'token': ''
}
password = data.get("password")
if not password:
return {
"status": 401,
"message": "Invalid username and or password",
'token': ''
}
helper = Helper(current_app.logger)
profile_data = helper.get_profile_data(msisdn)
if not profile_data:
return {
'status': 401,
"message": "Invalid username and or password",
'token': ''
}
real_pass = profile_data[1]
if not checkpw(password, real_pass):
return {
"status": 401,
"message": "Invalid username and or password",
'token': ''
}
token = helper.create_hash_token(msisdn,
profile_data[0],
remember=data.get('remember'))
if not token:
return {
"status": 401,
"message": "Could not create token",
'token': ''
}
return {
"status": 200,
"user": {
'token': token,
'profile_id': str(profile_data[0]),
'balance': float(profile_data[3]),
'msisdn': msisdn,
'bonus': float(profile_data[4])
}
}
def main(_):
FLAGS = flags.FLAGS
pp.pprint(flags.FLAGS.__flags)
print(tf.__version__)
# load data
data = input_data.read_data_sets("MNIST_data/", one_hot=True)
helper = Helper(FLAGS)
FLAGS = helper.setup_directories()
set_seed(42)
test_data = test_batch(data, 1000, FLAGS)
train(FLAGS, data, test_data)
def post(self):
helper = Helper(current_app.logger)
data = self.params()
current_app.logger.info("Android code request with data => %r" % data)
msisdn = clean_msisdn(data.get('mobile'))
network = get_network_from_msisdn_prefix(msisdn)
helper = Helper(current_app.logger)
code, profile_id = helper.get_existing_code(msisdn)
if code:
profile_data = helper.get_profile_data(msisdn)
token = helper.create_hash_token(msisdn,
profile_data[0],
remember=0)
message = "Namba yako ya uthibitisho wa akaunti yako ya Scorepesa"\
" ni %s. Ingiza namba kwenye tovuti kukamilisha mchakato huu" % (code, )
outbox_id = helper.create_outbox(profile_id, msisdn, network,
message)
send_notification(msisdn, outbox_id, network, message)
message = {
'success': {
'status': 200,
'id': profile_id,
"message": "Verification code has been sent to your phone",
'code': code
},
'token': token
}
else:
message = {
'error': {
'status':
401,
"message":
"Verification code could not be resent. Kindly check your mobile number and try again"
}
}
return make_response(json.dumps(message), 200)
def test_getBuiltModel(self): helper = Helper() model_name = 'rf:classifier' actual = helper.getBuiltModel(model_name) expected = RandomForestClassifier(random_state=0) self.assertEqual( type(actual), type(expected) ) try: model_name = 'jonathan' model = helper.getBuiltModel(model_name) fail(self) except ValueError as ve: self.assertEqual( str(ve), 'No default model found for jonathan')
def test_setRandomState(self): helper = Helper() self.assertEqual( helper.random_state, 0 ) try: helper.setRandomState( 'cheeseburger' ) # This should never run! self.assertEqual(0, 1) except ValueError as ve: self.assertEqual( str(ve), 'Random state must be an integer: cheeseburger' ) helper.setRandomState(101) self.assertEqual( helper.random_state, 101 )
def main(_):
FLAGS = flags.FLAGS
pp.pprint(flags.FLAGS.__flags)
# load data
data = DataSet(dataset=FLAGS.dataset, modes=FLAGS.modes)
helper = Helper(FLAGS)
FLAGS = helper.setup_directories()
opt_methods = load_opt_arrays(FLAGS)
for i, opt in enumerate(FLAGS.opt_methods.split(" ")):
learning_rate = float(FLAGS.learning_rates.split(" ")[i])
opt_methods = train(opt, opt_methods, data, helper, FLAGS, learning_rate)
helper.print_opt_methods(opt_methods)
def get(self):
current_app.logger.info(
"Scorepesa mobile app check version request....")
scorepesa_cfgs = LocalConfigParser.parse_configs("SCOREPESAAPP")
try:
http_code = 200
helper = Helper(current_app.logger)
result = helper.scorepesa_app_check_current_version()
current_app.logger.info(
"scorepesa app api version response .....{0}".format(result))
except Exception as e:
current_app.logger.error("Exception on version check ..... %r " %
e)
http_code = 400
result = "Invalid request."
resp = make_response(json.dumps(result), http_code)
return resp
def post(self):
data = self.params()
helper = Helper(current_app.logger)
bet_data, slip_data = helper.get_bet_details(data.get('token'),
data.get('bet_id'),
data.get('page'),
data.get('limit'))
datas = []
created, bet_id, total_matches, jackpot_bet_id, total_odd, bet_message, bet_amount, possible_win, status = bet_data
bet_details = {
'created': created,
'bet_id': bet_id,
'total_matches': total_matches,
'jackpot_bet_id': jackpot_bet_id,
'total_odd': float(total_odd),
'bet_message': bet_message,
'possible_win': float(possible_win),
'status': status,
'bet_amount': float(bet_amount)
}
for slip in slip_data:
current_app.logger.info("Slip %r" % slip)
created, bet_id, sub_type_id, odd_value, bet_amount, possible_win, status, win, game_id, start_time, away_team, home_team, bet_pick, bet_type, winning_outcome = slip
slip_value = {
'created': created,
'bet_id': bet_id,
'sub_type_id': sub_type_id,
'odd_value': float(odd_value),
'bet_amount': float(bet_amount),
'possible_win': float(possible_win),
'status': status,
'win': win,
'game_id': game_id,
'start_time': start_time,
'away_team': away_team,
'home_team': home_team,
'bet_type': bet_type,
'bet_pick': bet_pick,
'winning_outcome': winning_outcome
}
datas.append(slip_value)
result = {'data': datas, 'meta': {'bet_info': bet_details}}
resp = make_response(json.dumps(result), 200)
return resp
def test_setDefaultArgs(self):
helper = Helper()
model_name = 'rf:classifier'
default_arguments = {'max_depth': 4, 'random_state': 0}
helper.setDefaultArgs(model_name, default_arguments)
self.assertEqual( helper.getDefaultArgs(model_name), default_arguments )
try:
model_name = 'jefferey'
default_arguments = {'num_of_kids': 3,
'close_with_kids': False,
'kids_call': False,
'happy_marriage': False}
helper.setDefaultArgs(model_name, default_arguments)
fail(self)
except ValueError as ve:
self.assertEqual( str(ve), 'Default model cannot be found: jefferey')
try:
model_name = 'rf:classifier'
default_arguments = list([5, 10, 15, 20])
helper.setDefaultArgs(model_name, default_arguments)
fail(self)
except ValueError as ve:
self.assertEqual( str(ve), 'Arguments must be in dictionary format, not <class \'list\'> type!')
# While there will be an error thrown if the model cannot be found, there is no
# way to check if the arguments are valid right now, so we will error later if
# the model cannot be built
model_name = 'rf:classifier'
default_arguments = {'whatever_sort_of_args': True}
helper.setDefaultArgs(model_name, default_arguments)
self.assertEqual( helper.getDefaultArgs(model_name), default_arguments )
def process_api_outbox(self, data):
try:
_code = 200
self.log.info("process api outbox request data..... %r" % data)
helper = Helper(current_app.logger)
message = {
"msisdn": data.get("user").get('referrer_msisdn'),
"profile_id": data.get("user").get('profile_id')
}
result = helper.get_outbox_messages(message)
self.log.info(
"mobile api got outbox messages response returning ...")
except JWTError as e:
self.log.error("Exception on fetch outbox messages api %r " % e)
_code = 400
result = "Invalid token provided."
resp = make_response(result, _code)
return resp
def __init__(self, name='', models=[], exp_type=None, metrics=[]):
self.helper = Helper()
self.unnamed_model_count = 0
self.random_state = 0
self._valid_experiment_types = ['classification', 'regression']
self.metrics_manager = MetricsManager()
self.type_of_experiment = None
if exp_type:
if type(exp_type) != str:
raise ValueError(
'Experiment type attribute must be string, not {}'.format(
str(type(exp_type))))
if self.isValidExperimentType(exp_type):
self.type_of_experiment = exp_type
else:
raise ValueError(
'The provided experiment type is not supported: \'{}\'\nOnly these experiment types are supported: {}'
.format(exp_type, self.getValidExperimentTypes()))
if type(name) is str:
if name != '':
self.name = name
else:
self.name = 'unnamed_experiment'
else:
raise ValueError(
'Experiment name attribute must be string, not {}'.format(
str(type(name))))
# initialize the dictionary of models within the experiment
self.models_dict = {}
# Add the models (if any) that the user wants to use
if type(models) is list:
if len(models) > 1:
# Then add all of the model objects to the experiment
for model in models:
self.addModel(model)
elif len(models) == 1:
# Add the only model to the experiment list
self.addModel(models)
elif type(models) is dict:
# Add all of the models with their corresponding names provided from the user
for name in models.keys():
self.addModel(model, name)
elif type(models) is str:
self.addModel(models)
else:
raise ValueError(
'Models must be in list format if more than one is provided. Ex. models=[\'rf\', \'Decision Tree\', RandomForestClassifer()... ]'
)
# initialize the metrics list
self.metrics = []
if type(metrics) is list:
self.metrics = metrics
elif type(metrics) is str:
self.metrics = [metrics]
else:
raise ValueError(
'\'metrics\' argument must be string or list of strings. Ex: [\'accuracy\', \'prec\']. Cannot be {}'
.format(str(type(metrics))))
def __init__(self, parserFunction):
self.util = Helper()
self.parser = parserFunction
pass
assembled = parser(operation, operand1, operand2)
return assembled
def _defaultParser(self, operation, operand1, operand2):
return f"{mc(operation)}{mc(operand1)}{mc(operand2)}"
def _LDMParser(self, operation, operand1, operand2):
instr2 = Helper.strToBinary16(operand2)
return f"{mc(operation)}{mc(operand1)}\n{instr2}"
def _SHParser(self, operation, operand1, operand2):
print(operand1, operand2)
immediateBin = Helper.strToBinary16(operand2, fill=5)
return f"{mc(operation)}{mc(operand1)}{mc(operand1)}{immediateBin}"
def _ORGParser(self, operation, operand1, operand2):
return operation + operand1.rjust(
16, '0') # return it like 'ORG00000000100'
def _SETParser(self, operation, operand1, operand2):
print(
"[Warning]: the operation set is not supported for current processor, it will be converted to move"
)
operation = "MOV"
operand2 = '1'
return self._defaultParser(operation, operand1,
operand2) # return it like 'ORG100'
h = Helper()
def post(self):
data = self.params()
msisdn = data.get('msisdn', 0)
if not msisdn or msisdn == 0:
msisdn = data.get('mobile')
if not msisdn or msisdn == 0:
message = {
"success": {
"status": 400,
"message": "Invalid phone number"
}
}
return make_response(json.dumps(message), 200)
scorepesa = Scorepesa(current_app.logger)
profile_id, new = scorepesa.create_profile({"msisdn": msisdn}, 0)
helper = Helper(current_app.logger)
password_set = False
if new:
code = get_code()
password = None
#self.generate_pass_str(data.get('password'))
message = "Namba yako ya uthibitisho wa akaunti yako ya Scorepesa"\
" ni %s. Ingiza namba kwenye tovuti kukamilisha mchakato huu" % (code, )
network = get_network_from_msisdn_prefix(msisdn)
helper.create_profile_settings(profile_id, code, password)
outbox_id = helper.create_outbox(profile_id, msisdn, network,
message)
send_notification(msisdn, outbox_id, network, message)
elif data.get('password'):
password = self.generate_pass_str(data.get('password'))
password_set = helper.set_password(profile_id, password)
if profile_id > 0 and new:
message = {
"success": {
"status": 201,
"message": "Account created success"
}
}
elif password_set:
message = {
"message": "Account password set successfully",
"status": 201
}
elif profile_id == -1:
message = {
"success": {
"status":
500,
"message":
"Encountered problem trying to create profile, Please try again later"
}
}
else:
message = {
"success": {
"status":
200,
"message":
"Account alreay exists, proceed to login and play with scorepesa.com"
}
}
return make_response(json.dumps(message), 200)