def __init__(self, wps_prob_table):
self.game_list = [x for x in wps_prob_table.columns.values.tolist() if 'sch' in x]
d = {}
avg_dollar_returns_dict = fh.read_dict(fh.dollar_returns_file_path)
# set up index and columns for the dataframe
ind_l = [] ; bet_type_l = [] ; bet_l = [] ; pred_prob_l = [] ; avg_dollar_return_l = [] ; predicted_return_l = [] ; selected_l = [] ; act_ret_l = []
for bet_type, bets in avg_dollar_returns_dict.items():
for bet, avg_dollar_return in bets.items():
ind_l.append(bet_type + '_' + str(bet))
bet_type_l.append(bet_type)
bet_l.append(bet)
pred_prob_l.append(0)
avg_dollar_return_l.append(avg_dollar_return)
predicted_return_l.append(0)
selected_l.append(0)
act_ret_l.append(0)
top_df = pd.DataFrame()
for game_name in self.game_list:
ll = []
for ind in ind_l:
ll.append(game_name + "_" + ind)
#ll.append(game_name)
d[game_name]= ll
df =pd.DataFrame(data = {'bet_type':bet_type_l, 'bet':bet_l,'predicted_prob':pred_prob_l,'avg_dollar_return':avg_dollar_return_l,'predicted_return':predicted_return_l,'selected':selected_l,'actual_return':act_ret_l,'my_return':act_ret_l, 'game_name': ll}, index = ind_l)
top_df = top_df.append(df, ignore_index = True)
#top_df.T
top_df = top_df.set_index('game_name')
#print top_df.head()
#print top_df.shape
self.top_df = top_df
def calculate_quadratic_loss_function():
"""
1 -2pi + sum((pj)^2)
"""
tot_qlf = 0
tot_ilf = 0
def calc_qlf(p_vector, WPSL_vector):
qlf = None
inverse_WPSL_vector = [0 if a== 1 else 1 for a in WPSL_vector]
pi = sum([a*b for a,b in zip(p_vector,WPSL_vector)])
pj = sum([x**2 for x in [a*b for a,b in zip(p_vector,inverse_WPSL_vector)]])
qlf = 1 - 2*pi + pj
return qlf
def calc_ilf(p_vector, WPSL_vector):
ilf = 0
pri = sum([a*b for a,b in zip(p_vector,WPSL_vector)])
#!!!!!!pri # if pri = 0 then make it a small probability such as 0.01 ???
if pri <= 0.05:
pri = 0.05
#print pri
ilf = -math.log(pri,2)
return ilf
p_dict = fh.read_dict(fh.prediction_dict_file_path)
ar_dict = fh.read_dict(fh.prediction_actual_results_dict_file_path)
for game_name, game_data in p_dict.items():
# print
# print game_name
# print game_data
# print ar_dict[game_name]
# print game_name, ar_dict[game_name]
for ind, player_dict in enumerate(game_data):
#print player_dict.values()[0]
p_vect = player_dict.values()[0]
#print ar_dict[game_name][ind].values()[0]
WPSL_vec = ar_dict[game_name][ind].values()[0]
#print calc_qlf(p_vect, WPSL_vec)
tot_qlf += calc_qlf(p_vect, WPSL_vec)
tot_ilf += calc_ilf(p_vect, WPSL_vec)
#print tot_qlf
return tot_qlf, tot_ilf
def calculate_ilf(n):
tot_ilf = 0
count = 0
p_dict = fh.read_dict(fh.prediction_dict_file_path)
ar_dict = fh.read_dict(fh.prediction_actual_results_dict_file_path)
for game_name, game_data in p_dict.items():
count += 1
pri = 0
#print game_name
for k in game_data.keys():
# print game_name
#print k, game_data[k], ar_dict[game_name][k]
pri += (game_data[k] * ar_dict[game_name][k])/float(n)
pri = 0.001
#print pri
ilf = -math.log(pri,2)
#print game_name, ilf
tot_ilf += ilf
return tot_ilf
def get_player_season_records():
"""
If the player season record does not exist for the top level data directory
it is created, otherwise it is just returned from a file location
Returns a dictionary of dictionaries for the player season record in the form:
{eggy:richard [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 3]]} called all_player_season_records
"""
fh.set_up_paths(DATA_DIR)
all_player_season_records = {}
if fh.file_exists(fh.player_season_records_file_path):
all_player_season_records = fh.read_dict(fh.player_season_records_file_path)
else:
all_player_season_records = create_player_season_records()
fh.write_dict(fh.player_season_records_file_path,all_player_season_records)
# print fin_list
return all_player_season_records