def get(self, league, home_team, away_team):
sp_model_file_name = ''
print('get : {}/{}/{}'.format(league, home_team, away_team))
if league in ['England', 'Spain', 'Italy', 'Germany']:
sp_model_file_name = 'modelForProd/' + league.lower(
) + '_sp_model.joblib'
else:
return "league : {} is not supported".format(league), 404
print('load {}'.format(sp_model_file_name))
model = load(sp_model_file_name)
get_db_connector = mongo_API()
get_data_manger = FileDataManger(get_db_connector)
get_redwoodParser = RedWoodParser()
get_data_predict_org = Data_Predict_Organizer()
sp_model = SoccerPredictModel(model, get_redwoodParser,
get_data_manger, league,
get_data_predict_org)
mapped_home_team = mapper.map(home_team)
print(mapped_home_team)
mapped_away_team = mapper.map(away_team)
print(mapped_away_team)
res = sp_model.predict(mapped_home_team, mapped_away_team, 6)
print(res)
return res, 200
def test_get_collection_without_key_val(self):
mongoApi = mongo_API()
res_Cursor = mongoApi.get_collection(db_name=self.db_name, collection=self.db_collection)
res = [json for json in res_Cursor]
res_Cursor.close()
for r in res:
del r['_id']
for index in range(len(self.res_list)):
assert res[index] == self.res_list[index]
def test_basic_model_english_random_forest(self):
db_connector = mongo_API()
data_manger = DataManger(db_connector)
redwoodParser = RedWoodParser()
model = RandomForestClassifier()
data_predict_org = Data_Predict_Organizer()
sp_model = SoccerPredictModel(model, redwoodParser, data_manger,
'England', data_predict_org)
sp_model.train(7, 0.1)
test(sp_model, 10, self.english_csv_file)
def test_basic_model_italy(self):
db_connector = mongo_API()
data_manger = DataManger(db_connector)
redwoodParser = RedWoodParser()
model = XGBClassifier(max_depth=3, booster='gblinear')
data_predict_org = Data_Predict_Organizer()
sp_model_italy = SoccerPredictModel(model, redwoodParser, data_manger,
'Italy', data_predict_org)
sp_model_italy.train(7, 0.1)
test(sp_model_italy, 7, self.italy_csv_file)
def test_basic_model_english_KNN(self):
db_connector = mongo_API()
data_manger = DataManger(db_connector)
redwoodParser = RedWoodParser()
model = KNeighborsClassifier(n_neighbors=3, )
data_predict_org = Data_Predict_Organizer()
sp_model = SoccerPredictModel(model, redwoodParser, data_manger,
'England', data_predict_org)
sp_model.train(7, 0.1)
test(sp_model, 6, self.english_csv_file)
class MyTestCase(unittest.TestCase):
mongoApi = mongo_API()
print(mongoApi.get_ip_and_port())
db_name='testDB'
db_collection='testCollection'
res_list = []
special_key_val = {'HomeTeamName': ["home team test name"],
'AwayTeamName': ["away team test name"],
'homeTeamSecondHalfTestGoals': 3,
'AwayTeamFirstHalfSubTest': 7,
'RoundIdTest': 12}
def test_insert_Json_File(self):
self.res_list = self.mongoApi.insert_json_file('/Users/meornbru/PycharmProjects/SoccerProject/DataParser/testJson.json',
db_name=self.db_name,
collection=self.db_collection)
assert len(self.res_list) == 5
def test_get_collection_without_key_val(self):
mongoApi = mongo_API()
res_Cursor = mongoApi.get_collection(db_name=self.db_name, collection=self.db_collection)
res = [json for json in res_Cursor]
res_Cursor.close()
for r in res:
del r['_id']
for index in range(len(self.res_list)):
assert res[index] == self.res_list[index]
def test_get_collection_with_key_val(self):
mongoApi = mongo_API()
res_Cursor = mongoApi.get_collection(db_name=self.db_name, collection=self.db_collection, filter=self.special_key_val)
res = [json for json in res_Cursor]
res_Cursor.close()
for r in res:
del r['_id']
for index in range(len(self.res_list)):
assert res[index] == self.res_list[index]
for k, v in self.special_key_val.items():
res_Cursor = mongoApi.get_collection(self.db_name, self.db_collection, {k: v})
res = [json for json in res_Cursor]
res_Cursor.close()
assert res[0][k] == v
def test_basic_model_english_xg(self):
db_connector = mongo_API()
data_manger = DataManger(db_connector)
redwoodParser = RedWoodParser()
ignore_list = [
'Date', 'TournamentName', 'SeasonName', 'RoundId', 'HomeTeamName',
'AwayTeamName'
]
redwoodParser.set_ignore_list(ignore_list)
model = XGBClassifier(max_depth=5, booster='gblinear')
data_predict_org = Data_Predict_Organizer()
sp_model = SoccerPredictModel(model, redwoodParser, data_manger,
'England', data_predict_org)
sp_model.train(7, 0.1)
test(sp_model, 6, self.english_csv_file)
def test_get_collection_with_key_val(self):
mongoApi = mongo_API()
res_Cursor = mongoApi.get_collection(db_name=self.db_name, collection=self.db_collection, filter=self.special_key_val)
res = [json for json in res_Cursor]
res_Cursor.close()
for r in res:
del r['_id']
for index in range(len(self.res_list)):
assert res[index] == self.res_list[index]
for k, v in self.special_key_val.items():
res_Cursor = mongoApi.get_collection(self.db_name, self.db_collection, {k: v})
res = [json for json in res_Cursor]
res_Cursor.close()
assert res[0][k] == v
from model.dataManger import FileDataManger
from model.soccer_predict_model import SoccerPredictModel
from model.Data_Predict_Organizer import Data_Predict_Organizer
from model.teams_mapper import TeamMapper as mapper
# from xgboost import XGBClassifier
from joblib import load
app = Flask(__name__)
api = Api(app)
API = CORS(app, supports_credentials=True)
get_db_connector = mongo_API()
data_manger = FileDataManger(get_db_connector)
redwoodParser = RedWoodParser()
data_predict_org = Data_Predict_Organizer()
class ModelServer(Resource):
def get(self, league, home_team, away_team):
sp_model_file_name = ''
print('get : {}/{}/{}'.format(league, home_team, away_team))
if league in ['England', 'Spain', 'Italy', 'Germany']:
sp_model_file_name = 'modelForProd/' + league.lower(
) + '_sp_model.joblib'
else:
def test_grid_search_english_model_xgboost(self):
# general things:
db_connector = mongo_API()
data_manger = DataManger(db_connector)
redwoodParser = RedWoodParser()
data_predict_org = Data_Predict_Organizer()
# model params
max_depths = range(1, 22, 1)
learning_rates = [x / 20 for x in range(1, 21)]
n_estimators = range(50, 400, 50)
objectives = [
"binary:logistic", "reg:linear", "reg:logistic", "binary:logistic",
"binary:logitraw", "count:poisson", "multi:softmax",
"multi:softprob", "rank:pairwise"
]
boosters = ['gbtree', 'gblinear']
gammas = [x / 20 for x in range(0, 105, 5)]
min_child_weights = [x / 2 for x in range(0, 21)]
max_delta_steps = range(0, 10, 1)
subsamples = [x / 20 for x in range(0, 21)]
colsample_bytrees = [x / 20 for x in range(0, 21)]
#reg_alpha = 0
#reg_lambda = 1
base_score = [x / 20 for x in range(1, 21)]
num_of_games = range(0, 15)
test_results = []
for md in max_depths:
for lr in learning_rates:
for ne in n_estimators:
for obj in objectives:
for booster in boosters:
for g in gammas:
for min_child_w in min_child_weights:
for max_delta_step in max_delta_steps:
for sub_sample in subsamples:
for cb in colsample_bytrees:
for bs in base_score:
model = XGBClassifier(
max_depth=md,
learning_rate=lr,
n_estimators=ne,
objective=obj,
booster=booster,
gamma=g,
min_child_weight=
min_child_w,
max_delta_step=
max_delta_step,
subsample=sub_sample,
colsample_bytree=cb,
base_score=bs)
sp_model = SoccerPredictModel(
model, redwoodParser,
data_manger, 'England',
data_predict_org)
sp_model.train(7, 0.1)
for num in num_of_games:
res = test(
sp_model, num,
self.
english_csv_file)
test_results.append({
'max_depth':
md,
'learning_rate':
lr,
'n_estimators':
ne,
'objective':
obj,
'booster':
booster,
'gamma':
g,
'min_child_weight':
min_child_w,
'max_delta_step':
max_delta_step,
'subsample':
sub_sample,
'colsample_bytree':
cb,
'base_score':
bs,
'res':
res
})
print(test_results)
with open(self.english_xg_result) as res_file:
# save fields names
for k, v in test_results[0].items():
res_file.write(k + ',')
for result in test_results:
for k, v in result.items():
res_file.write(v + ',')