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predict_today_match.py
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predict_today_match.py
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import pandas as pd
import pickle
import json
predict_list = json.load(open('/tmp/fifa/odds.json', 'r'))
last_match_data = pd.read_csv('/tmp/fifa/last_match.csv')
model = pickle.load(open('/tmp/fifa/model.pkl', 'rb'))
random_seed = -1
def predict(home_team, away_team, no_draw=True):
home_team_data = last_match_data.loc[last_match_data['team']
== home_team].iloc[0]
away_team_data = last_match_data.loc[last_match_data['team']
== away_team].iloc[0]
data = {}
data['rank_diff'] = 1.0 * \
(home_team_data['fifa_rank'] -
away_team_data['fifa_rank'])
data['rank_mean'] = 0.5 * \
(home_team_data['fifa_rank'] +
away_team_data['fifa_rank'])
data['total_fifa_points'] = 1.0 * \
(home_team_data['total_fifa_points'] -
away_team_data['total_fifa_points'])
data['goalkeeper_score_diff'] = home_team_data['goalkeeper_score'] - \
away_team_data['goalkeeper_score']
data['mean_defense_score_diff'] = home_team_data['mean_defense_score'] - \
away_team_data['mean_defense_score']
data['mean_offense_score_diff'] = home_team_data['mean_offense_score'] - \
away_team_data['mean_offense_score']
data['mean_midfield_score_diff'] = home_team_data['mean_midfield_score'] - \
away_team_data['mean_midfield_score']
predict_data = pd.DataFrame([data], index=[0])
# [home team win prob, away team win prob]
predict_proba = model.predict_proba(predict_data)[0].tolist()
# [home team win prob, away team win prob, draw_proba]
predict_proba.append(0)
max_pro = max(predict_proba)
result = {}
# if win_pro < 0.55, we think it is draw
if max_pro < 0.55 and not no_draw:
result['win_team'] = 'draw'
predict_proba[2] = 1
# [home team win prob, away team win prob , draw prob]
predict_proba = [x / sum(predict_proba) for x in predict_proba]
result['draw_proba'] = round(predict_proba[2], 3)
elif predict_proba.index(max_pro) == 1:
result['win_team'] = home_team
else:
result['win_team'] = away_team
result['win_proba'] = round(predict_proba[1], 3)
result['lose_proba'] = round(predict_proba[0], 3)
result['draw_proba'] = round(predict_proba[2], 3)
if random_seed >= 0 and no_draw:
predict_proba = [x / sum(predict_proba) for x in predict_proba]
# np.random.seed(random_seed)
win_team = np.random.choice(
[away_team, home_team], p=predict_proba[:2])
result['win_team'] = win_team
result['home_team'] = home_team
result['away_team'] = away_team
print("\n" + "O" * 70, "\n")
print(f"{home_team} vs {away_team}\n")
print(home_team.ljust(15), "-" * 20, result['win_proba'])
print("".ljust(35), "|")
print("".ljust(35), "|", "-" * 10, result['win_team'])
print("".ljust(35), "|")
print(away_team.ljust(15), "-" * 20, result['lose_proba'])
return result
results = []
for data in predict_list:
home_team = data['home_team']
away_team = data['away_team']
result = predict(home_team, away_team, data['no_draw'])
result.update(data)
results.append(result)
result_df = pd.DataFrame(results)
print(result_df)
result_df.to_csv('/tmp/fifa/today_result.csv', index=False)