def test_getUpcomingGames(self):
db.DBConnection.UpcomingGames.remove({})
_match = upcoming_match(away_att=77,
away_def=67,
away_mid=69,
away_odds_n=0.27669516494195245,
away_odds_nn=3.2583333333333333,
away_team_name="Sampdoria",
away_team_rank=11,
away_team_received=2,
away_team_scored=1,
date=today,
draw_odds_n=0.31504895018143103,
draw_odds_nn=2.8616666666666664,
home_att=80,
home_def=71,
home_mid=74,
home_odds_n=0.4082558848766165,
home_odds_nn=2.208333333333333,
home_team_name="Fiorentina",
home_team_rank=18,
home_team_received=1,
home_team_scored=2,
league="Serie",
round=1)
db.insertMatchToUpcomingGames(_match)
dtos = db.getUpcomingGames(league='all')
self.assertEqual(_match, dtos[0])
def getUpcomingGames(self, request, context):
print("request: getUpcomingGames")
matchlist = match_pb2.MatchList()
upcoming = DBController.getUpcomingGames(league=request.league)
for game in upcoming:
_match = matchlist.list.add()
_match.league = game.league
_match.date = game.date.isoformat()
_match.round = game.round
_match.home_team_name = game.home_team_name
_match.away_team_name = game.away_team_name
_match.home_team_rank = game.home_team_rank
_match.away_team_rank = game.away_team_rank
_match.home_team_scored = game.home_team_scored
_match.away_team_scored = game.away_team_scored
_match.home_team_received = game.home_team_received
_match.away_team_received = game.away_team_received
_match.home_att = game.home_att
_match.away_att = game.away_att
_match.home_def = game.home_def
_match.away_def = game.away_def
_match.home_mid = game.home_mid
_match.away_mid = game.away_mid
_match.home_odds_n = game.home_odds_n
_match.draw_odds_n = game.draw_odds_n
_match.away_odds_n = game.away_odds_n
_match.home_odds_nn = game.home_odds_nn
_match.draw_odds_nn = game.draw_odds_nn
_match.away_odds_nn = game.away_odds_nn
return matchlist
def predict(league):
"""
@param league: string that represent the league (can be 'all')
@return: list of predictions DTOs
"""
predictions = []
#region Data
all_data = DBController.getAllData(as_dataframe=True)
upcoming_games = DBController.getUpcomingGames(league, as_dataframe=True)
x, y = data_preprocessor.train_preprocess(all_data)
to_predict = data_preprocessor.prediction_preprocess(upcoming_games)
#endregion
#region ANN
for i in range(0, AVG):
ann = NeuralNet(x.shape[1])
ann.train(x, y, EPOC)
predictions.append(ann.predict(to_predict))
#endregion
#region Calculate avg of predictions
lines = predictions[0].shape[0]
columns = predictions[0].shape[1]
avgPrediction = np.zeros((lines, columns))
for line in range(lines):
for cell in range(columns):
sum = 0
for prediction in predictions:
sum = sum + prediction[line, cell]
avgPrediction[line, cell] = sum / AVG
#endregion
#region Converting avgPrediction to pandas DataFrame
y_pred, indexes = apply_indexes(avgPrediction, upcoming_games)
details = upcoming_games.iloc[indexes]
details = details[[
'league', 'date', 'home_team_name', 'away_team_name', 'home_odds_nn',
'draw_odds_nn', 'away_odds_nn'
]]
final = pd.concat([details, y_pred], axis=1, sort=False)
prediction_dtos = []
for row in final.shape[0]:
prediction_dtos.append(
Prediction(
final.loc[row, 'leauge'], final.loc[row, 'date'],
final.loc[row, 'home_team_name'], final.loc[row,
'away_team_name'],
final.loc[row, 'home_odds_nn'], final.loc[row, 'draw_odds_nn'],
final.loc[row, 'away_odds_nn'], final.loc[row, 'pred_1'],
final.loc[row, 'pred_2'], final.loc[row, 'pred_x'],
calc_exp(predictions=[
final.loc[row, 'pred_1'], final.loc[row, 'pred_x'],
final.loc[row, 'pred_2']
],
odds=[
final.loc[row, 'home_odds_nn'],
final.loc[row, 'draw_odds_nn'],
final.loc[row, 'away_odds_nn']
]), final.loc[row, 'result']))
return prediction_dtos