def show_evaluation_hist_from_path(league_name):
# requested params : [thr, field]
args = request.json
model_dir = args['model_dir']
model_name = args['model_name']
response = check_predict_paths(model_dir, model_name)
if not response['check']:
return make_response(response['msg'], 400)
paths = response['paths']
config, model = load_configs_from_paths(paths)
league_params, data_params = config['league'], config['data']
params = {**args, **league_params, **data_params}
params['save_dir'] = STATIC_DIR
outcome, msg = check_league(league_name)
if (outcome == False):
response = make_response(msg, 404)
else:
# model = models[league_name]
feat_eng = config['feat_eng']
testset, pred, true = real_case_inference(model, params, feat_eng)
pred_df, outcome, fig = evaluate_results(true,
pred,
params,
plot=False)
response = show_plot(fig)
return response
def show_evaluation_hist(league_name):
# requested params : [thr, field]
args = request.json
config = configs[league_name]['league']
params = {**args, **config}
params['save_dir'] = STATIC_DIR
outcome, msg = check_league(league_name)
if (outcome == False):
response = make_response(msg, 404)
else:
model = models[league_name]
feat_eng = configs[league_name]['feat_eng']
testset, pred, true = real_case_inference(model, params, feat_eng)
pred_df, outcome, fig = evaluate_results(true,
pred,
params,
plot=False)
response = show_plot(fig)
return response
def simulate(test_data, pred, true, params):
# Required Params : [field, save_dir,
# thr, thr_list,
# n_matches, money_bet,
# combo_list, plot]
field = params['field']
thr = params['thr']
plot = params['plot'] if 'plot' in list(params.keys()) else True
testset = test_data[field]
testset = testset[testset['f-opponent'].isnull() == False]
pred_df, _, _ = evaluate_results(true, pred, params, plot=plot)
data_result = postprocessing_test_data(testset, pred_df)
_, thr_outcomes, _ = thr_analysis(true, pred, params)
thr_outcome = thr_outcomes[str(thr)]
summary, sim_result, _ = simulation(data_result,
params,
thr_outcome,
plot=plot)
show_summary(summary)
def strategy_stats(testset, pred, true, params):
field = params['field']
thr_list = params['thr_list']
filter_bet_list = params['filter_bet_list']
result_df = pd.DataFrame()
ckp_df = pd.DataFrame()
for thr in tqdm(thr_list, desc='Thr \t'):
for filter_bet in filter_bet_list:
_, _, thr_outcome = thr_analysis(true, pred, params)
if (thr_outcome[str(thr)]['tpr'] != 'nan'):
params['thr'] = thr
params['filter_bet'] = filter_bet
pred_df, outcome, _ = evaluate_results(true,
pred,
params,
plot=False)
sim_data = postprocessing_test_data(testset, pred_df)
sim_result, _ = simulation(sim_data, params, plot=False)
if (sim_result is not None):
summary = summarize_sim_result(sim_result, params)
if (summary is None):
continue
else:
continue
summary_df = summary_dataframe(summary)
summary_df.insert(0, 'match', sim_result['match'].to_list())
summary_df.insert(0, 'Match N.',
sim_result['n_match'].to_list())
summary_df.insert(0, 'Bet N.',
np.arange(1,
len(sim_result) + 1))
result_df = result_df.append(summary_df)
ckp_df = ckp_df.append(checkpoint_view_df(summary_df))
result_df = result_df.reset_index(drop=True)
save_dir = params['save_dir']
if (save_dir is not None):
filepath = f'{save_dir}simulation_analysis_{field}.csv'
result_df.to_csv(filepath, sep=';', decimal=',')
filepath = f'{save_dir}simulation_ckp_analysis_{field}.csv'
ckp_df.to_csv(filepath, sep=';', decimal=',')
return result_df, ckp_df
def strategy_analysis():
"""
Requested Params: dict {'league_name': OPTIONAL 1
'model_dir': OPTIONAL 2
'model_name': OPTIONAL 2,
'thr_list': OPTIONAL,
'filter_bet_list': OPTIONAL,
'money_bet': OPTIONAL,
'combo_list':OPTIONAL,
'n_matches':OPTIONAL
Returns:
"""
params = request.json
response, model, config = load_model_and_config(params, models, configs)
if not response['check']:
return make_response(response['msg'], 400)
league_params = config['league']
data_params = config['data']
feat_eng = config['feat_eng']
params = {**params, **league_params, **data_params}
# params['save_dir'] = STATIC_DIR
for field in [HOME, AWAY]:
params['field'] = field
testset, pred, true = real_case_inference(model, params, feat_eng)
eval_params = check_evaluation_params(params)
pred_df, _, _ = evaluate_results(true, pred, eval_params, plot=False)
simulation_params = check_simulation_params(params)
result_df, ckp_df = strategy_stats(testset, pred, true,
simulation_params)
response = make_response(f'Strategy stats saved at {params["save_dir"]}',
200)
return response
def show_simulation(league_name):
# requested params : [thr, thr_list, field, filter_bet, money_bet, n_matches, combo]
params = request.json
response, model, config = load_model_and_config(params, models, configs)
if not response['check']:
return make_response(response['msg'], 400)
league_params = config['league']
data_params = config['data']
feat_eng = config['feat_eng']
params = {**params, **league_params, **data_params}
# params['save_dir'] = STATIC_DIR
testset, pred, true = real_case_inference(model, params, feat_eng)
eval_params = check_evaluation_params(params)
pred_df, _, _ = evaluate_results(true, pred, eval_params, plot=False)
_, _, thr_dict = thr_analysis(true, pred, params)
thr_outcome = thr_dict[str(params['thr'])]
data_result = postprocessing_test_data(testset, pred_df)
simulation_params = check_simulation_params(params)
sim_result, fig = simulation(data_result, simulation_params, plot=False)
fig.savefig(f'{STATIC_DIR}simulation.png')
# summary_str = show_summary(summary)
# save_str_file(summary_str, f'{STATIC_DIR}summary', mode='w')
# save_str_file(summary_str, f'{data_params["save_dir"]}summary', mode='w')
response = show_plot(fig)
params['field'] = HOME
testset, pred, true = real_case_inference(model, params, feat_eng)
result_home_df = strategy_stats(testset, pred, true, simulation_params)
params['field'] = AWAY
testset, pred, true = real_case_inference(model, params, feat_eng)
result_away_df = strategy_stats(testset, pred, true, simulation_params)
return response