def fix_experiment(file_loc):
full_path = os.path.join(wd, file_loc)
if 'params.experiment' in file_loc:
exp_params = load_obj(full_path)
if 'stimuli_sets' in exp_params:
exp_params.pop('stimuli_sets')
save_obj(exp_params, full_path)
def __init__(self):
self.data = tools.load_from_csv(cfg.CSV_PATH)
self.all_json = tools.load_obj(cfg.SAVING_FILE)
self.mapping = {}
self.stats = defaultdict(tuple)
self.max_freq = 0
self.max_area = 0
self.max_shared_logos = 0
plt.rc('font', size=8)
def get_name2code(wgt_data):
file_path = config.name2code
if os.path.exists(file_path):
name2code = load_obj(file_path)
return name2code
name2code = {}
all_code = wgt_data['ts_code'].drop_duplicates().to_list()
for code in all_code:
stock_name = wgt_data[wgt_data.ts_code == code]['stock_name']
stock_name = stock_name.iloc[0]
name2code[stock_name] = code
save_obj(file_path, name2code)
return name2code
def get_gics2name():
file_path = config.gics2name
if os.path.exists(file_path):
gics2name = load_obj(file_path)
return gics2name
hs300_industry_df = prepare_hs300_industry_data_df()
industry_name = hs300_industry_df['industry_name'].to_list()
gics_code = hs300_industry_df['gics_code'].to_list()
tuple_list = [(i[0], i[1]) for i in zip(gics_code, industry_name)]
tuple_set = set(tuple_list)
gics2name = dict(list(tuple_set))
save_obj(file_path, gics2name)
return gics2name
def get_code2gics():
file_path = config.code2gics
if os.path.exists(file_path):
code2gics_info = load_obj(file_path)
return code2gics_info
hs300_industry_df = prepare_hs300_industry_data_df()
code = hs300_industry_df['ts_code'].to_list()
gics_code = hs300_industry_df['gics_code'].to_list()
tuple_list = [(i[0], i[1]) for i in zip(code, gics_code)]
tuple_set = set(tuple_list)
code2gics = dict(list(tuple_set))
save_obj(file_path, code2gics)
return code2gics
data_file = open("player_stats2.tab","a")
"""data_file.write("wards placed/min\twards killed/min\tkill participation\tdragon kills/min\tbaron kills/min\t")
data_file.write("games played\tminion kills/game\tn.m. kills/game\tturrets killed/game\t")
data_file.write("diffrent champions/game\tchamp mastery 5\tchamp mastery 10\tchamp mastery 20\t")
data_file.write("".join(["champ winrate " + str(i)+ "\t" for i in range(5)]))
data_file.write("tier\n")"""
read_files = 0
pulled_summoners = list()
def write_attribute(file, value):
file.write(str(value) + '\t')
while os.path.isfile('../data/game_data_{0}.zip'.format(read_files)):
game = load_obj('../data/game_data_{0}.zip'.format(read_files))
if not game or (game['matchVersion'][:4] != '6.10' and game['matchVersion'][:3] != '6.9') :
read_files += 1
print(str(read_files) + "/" + str(6000))
continue
for summoner_stats in game['participantStats']:
summoner_id = summoner_stats['summonerId']
if summoner_id not in pulled_summoners and summoner_id in game['participantTier']:
wards_placed=wards_killed=kill_participation=dragon_kills=baron_kills=total_games=0
summoner_match_history = game['participantMatchHistory'][summoner_id]
for g in summoner_match_history:
if g['matchId'] == -1:
continue
# check if we acttualy need to open another file to access this match
break
except Exception, e:
print("Error occured when trying to get match history details for " + str(match_id) + " data: " + str(e))
time.sleep(1)
continue
if num_try >=11:
if match_id in newer_match_ids:
return [{'matchId':-1, 'saved_file':-1}]
return matches_data
#restore program state
while os.path.isfile('game_data_{0}.zip'.format(saved_files)):
with zipfile.ZipFile('game_data_{0}.zip'.format(saved_files), 'r') as myzip:
myzip.extractall()
match = load_obj('game_data_{0}.pkl'.format(saved_files))
pulled_matches[match['matchId']] = saved_files
pulled_matches_count += 1
os.remove('game_data_{0}.pkl'.format(saved_files))
saved_files += 1
while True:
if len(unpulled_summoners) == 0:
#if we run out of summoners, hopefully older summoners already played new matches
unpulled_summoners = random.sample(pulled_summoners, 15)
pulled_summoners = list()
current_summoner_id = unpulled_summoners.pop(0)
try:
def get_winner(game):
team = game['teams'][0]
if team['teamId'] == BLUE:
blue_team_winner = team['winner']
else:
blue_team_winner = not team['winner']
if blue_team_winner:
return BLUE
else:
return RED
while os.path.isfile('data/game_data_{0}.zip'.format(read_files)):
game = load_obj('data/game_data_{0}.zip'.format(read_files))
if not game or (game['matchVersion'][:4] != '6.10'
and game['matchVersion'][:3] != '6.9'):
print(str(read_files) + "/" + str(6000))
read_files += 1
continue
m_id = game['matchId']
#list of all atributes to be written in file
blue_attributes = list()
red_attributes = list()
#map summoner ids to team_ids and summoner ids to champion ids - commonly needed
summoner_team = dict()
summoner_champion = dict()
summoner_p_id = dict()
def main():
# Setup champion matrices
champs = load_obj('champion_id_to_name.pkl')
champId_to_champName = load_obj('champion_id_to_name.pkl')
champs = [str(champId_to_champName[key]) for key in champId_to_champName]
champs.sort()
champs = [str(x) for x in champs]
champName_to_champID = {v: k for k,v in champId_to_champName.items()}
# Champ ID --> matrix index
champID_to_matrixIndex = {}
for i,c in enumerate(champs):
champID_to_matrixIndex[champName_to_champID[c]] = i;
# Matrix index --> Champ ID
matrixIndex_to_champID = {v: k for k,v in champID_to_matrixIndex.items()}
matrixIndex_to_champName = {}
for champid,index in champID_to_matrixIndex.items():
matrixIndex_to_champName[index] = champId_to_champName[champid]
champName_to_matrixIndex = {v: k for k,v in matrixIndex_to_champName.items()}
#print(champName_to_matrixIndex)
print("Generating Affinity Matrix...")
affinity = [ [0.0 for i in range(len(champs))] for j in range(len(champs)) ]
winnersfile = 'winners_temp.txt' #sys.argv[1]
losersfile = 'losers_temp.txt' #sys.argv[2]
winners = open(winnersfile).readlines()
losers = open(losersfile).readlines()
winners = [set([int(x) for x in line.strip().split(",")]) for line in winners]
losers = [set([int(x) for x in line.strip().split(",")]) for line in losers]
stop = len(winners)
composition = winners + losers # You can use this line or the below line; in theory I think this should work better, but it is up to you. Try both and look at the results.
#composition = [winners[i] | losers[i] for i in range(stop)]
stop = len(composition)
total = 0.0
count = 0.0
for i in range(stop):
if(champName_to_champID['Lucian'] in composition[i]):
total += 1.0
if(champName_to_champID['Ziggs'] in composition[i]):
count += 1.0
for champ in composition[i]:
for other in composition[i]:
affinity[champID_to_matrixIndex[champ]][champID_to_matrixIndex[other]] += 0.5
affinity[champID_to_matrixIndex[other]][champID_to_matrixIndex[champ]] += 0.5
for c1 in range(len(champs)):
for c2 in range(len(champs)):
if(c1 == c2): continue
affinity[c1][c2] = affinity[c1][c2] / (affinity[c2][c2]) #(0.5*affinity[c1][c1]+0.5*affinity[c2][c2])
for c1 in range(len(champs)):
affinity[c1][c1] = 0
averages = [i for i in range(len(champs))]
for i in averages:
averages[i] = sum(affinity[i])/len(affinity[i])
#print(sum(affinity[champName_to_matrixIndex['Urgot']])/len(affinity[champName_to_matrixIndex['Urgot']]))
for i in range(len(affinity)):
affinity[i] = [-x/averages[i] for x in affinity[i]]
#affinity = open('temp.txt').readlines()
#affinity = [line.strip().split() for line in affinity]
#affinity = [[float(x) for x in line] for line in affinity]
affinity = np.array(affinity)
print(affinity)
#return 0
# Check which values of preference result in 5 clusters
# I am trying values between -10 and 10, you can choose others
# if you think they could result in 5 clusters.
preferenceList = []
preferenceTest = True
if(preferenceTest):
#for i in range(-500,500):
for i in range(-10,10):
af = AffinityPropagation(affinity='precomputed',preference=i).fit(affinity)
cluster_centers_indices = af.cluster_centers_indices_
if(cluster_centers_indices != None):
n_clusters_ = len(cluster_centers_indices)
if(n_clusters_ != len(champs)): print(n_clusters_,i)
if(n_clusters_== 5): preferenceList.append(i)
# Set a role dictionary by hand to give labels to the clusters
# Hopefully these will never change
roles = {
"Top": ['Irelia', 'Renekton'],
"Mid": ['Ahri', 'Ziggs'],
"Jungle": ['Amumu', 'Nautilus'],
"Adc": ['Draven', 'Caitlyn'],
"Support": ['Taric', 'Braum']}
# Compute Affinity Propagation
# Set preferenceList to the values you found above that result in 5 clusters
if(not preferenceTest): preferenceList = [-3,-4,-5,-6]
clusterLists = [{} for i in range(len(preferenceList))]
for ind,preference in enumerate(preferenceList):
print("Computing Affinity Propagation with preference={0}...".format(preference))
af = AffinityPropagation(affinity='precomputed',preference=preference).fit(affinity)
cluster_centers_indices = af.cluster_centers_indices_
labels = af.labels_
n_clusters_ = len(cluster_centers_indices)
print("\nCenter of {0} clusters:".format(n_clusters_))
print([matrixIndex_to_champName[x] for x in cluster_centers_indices])
clusters = {}
for i in range(n_clusters_):
clusters[i] = []
for i,cnum in enumerate(labels):
clusters[cnum].append(i)
print("\nClusters: {0}".format([len(x) for key,x in clusters.items()]))
for key in clusters:
lane = None
for role,c in roles.items():
if(all([champName_to_matrixIndex[x] in clusters[key] for x in c])):
lane = role
if(lane == None): raise Exception("You need to redefine the roles dictionary. The canonical champions I picked are not correct for this dataset.")
clusterLists[ind][lane] = [matrixIndex_to_champName[x] for x in clusters[key]]
print( lane + ':\t' + ', '.join(clusterLists[ind][lane]))
print('')
# Compare clusters for different values of "preference"
# You can pick to use any of these.
# I use the last one because that one is what the other variables
# (e.g. cluster_centers_indices) have been calculated for.
# You can choose a different one, but you have to manually specify it
# and change some code.
table = [ ['Champion'], ['changed from lane'], ['to lane:'] ]
for cluster in clusterLists[1:]: # Compare each i+1 list to the i=0 list
for key in roles:
for champ in cluster[key]:
if(champ not in clusterLists[0][key]):
fromLane = None
for role in clusterLists[0]:
if(champ in clusterLists[0][role]): fromLane = role
#print("Champion {0}\tchanged from\t{2}\tto {1}".format(champ,key,fromLane))
table[0].append(champ)
table[1].append(fromLane)
table[2].append(key)
table = printTable(table)
print(table)
print('')
# Calculate and save/print the final table of data that you want to see.
if(True):
order = []
for c in cluster_centers_indices:
for role in roles:
if(matrixIndex_to_champName[c] in clusterLists[-1][role]):
order.append(role)
table = [ ['Role Matrix'] ]#, ['Jungle'], ['Top'], ['Support'], ['Adc'], ['Mid'] ]
table.append(['Primary Role'])
for role in order:
table.append([role])
f = open('output.txt','w')
for champID,champName in matrixIndex_to_champName.items():
cat_percents = {}
dists = {}
# We have a specific champion specified by the above for loop
# We will calculate the affinity of that champion to all champions in each
# role. E.g. if champName=='Sivir' then we will look at all the roles (let's
# assume role=='Jungle' for now) and calculate the affinity between 'Siver'
# and all the champions who have been identified as 'Jungle'. Then I average
# all these affinity values together and that is the "affinity" for 'Sivir'
# to the 'Jungle' role. I store each of these average values into a dictionary
# called 'cat_percents' which stands for category percents.
# You can think of the 'affinity' as the distance from one champ to another.
# The smaller the distance, the more likely they are to play the same role.
for role in clusterLists[-1]:
dists[role] = []
for c in clusterLists[-1][role]:
c = champName_to_matrixIndex[c]
dists[role].append((-affinity[c,champID]-affinity[champID,c])/2.0)
avg_dist = sum(dists[role])/len(dists[role])
cat_percents[role] = avg_dist
# Do a bunch of normalization on the outputs of cat_percents to get the numbers to be between 0 and 1.
cat_min = min(cat_percents.values())
for role in cat_percents:
cat_percents[role] = cat_percents[role] - cat_min
cat_max = max(cat_percents.values())
for role in cat_percents:
cat_percents[role] = cat_percents[role]/cat_max
cat_percents[role] = 1 - cat_percents[role]
cat_sum = sum(cat_percents.values())
for role in cat_percents:
cat_percents[role] = cat_percents[role]/cat_sum*100
cat_percents[role] = round(cat_percents[role],2)
table[0].append("".join(champName.split())) # Champion name, remove all whitespace
table[2].append(cat_percents[order[0]]) # Order[0] == e.g Jungle
table[3].append(cat_percents[order[1]]) # Order[1]
table[4].append(cat_percents[order[2]]) # Order[2]
table[5].append(cat_percents[order[3]]) # Order[3]
table[6].append(cat_percents[order[4]]) # Order[4]
lane = None
for role in clusterLists[-1]:
if(champName in clusterLists[-1][role]):
lane = role
table[1].append(lane) # Primary Role
table = printTable(table)
print(table)
f.write(table)
f.close()
print('')
# mysql_bdd = MysqlBdd("localhost", "bde", "Coco", "0386479877")
#
# bde_admins = mysql_bdd.get_bde_admin()
#
# # Convert array of tuple in array of id (int)
# temp=[]
# for admin in bde_admins:
# temp.append(admin[0])
# bde_admins = temp
#
# users = mysql_bdd.get_users()
#
# save_obj(bde_admins, "bde_admins.pkl")
# save_obj(users, "users.pkl")
bde_admins = load_obj("bde_admins.pkl")
users = load_obj("users.pkl")
neo4j_bdd.clear_database()
id_bde = neo4j_bdd.add_union('BDE')
id_cercle = neo4j_bdd.add_union('Cercle')
for user in users:
# Check if firstname and lastname are defined
if user[2] == '':
firstname = user[1].split('.')[0]
try:
lastname = user[1].split('.')[1]
except:
lastname = ''
def main():
load_data_from_pickles = True
if(not load_data_from_pickles): # If false, will skip to just load the matrix from file
modelfiles = sys.argv[1:]
modelfiles = [file for file in modelfiles if '.xyz' in file]
modelfiles = natsort.natsorted(modelfiles)
modelfiles = modelfiles[0:5000]
new_modelfiles = [file for file in modelfiles if(not os.path.isfile(file[:-4]+'_VP.txt'))]
cutoff = 3.6
#print(modelfiles)
print("Calculating VP for all models...")
calculate_and_save_vp(new_modelfiles,cutoff)
print("Finding all transformed VPs...")
model_indexes = []
for model in modelfiles:
indexes = open(model[:-4]+'_VP.txt').readlines()
indexes = [eval(ind.strip()) for ind in indexes]
model_indexes.append(copy.copy(indexes))
d_vpIndex_to_matIndex, d_matIndex_to_vpIndex, matrix = analyze(model_indexes)
save_obj(d_vpIndex_to_matIndex, 'd_vpIndex_to_matIndex.pkl')
save_obj(d_matIndex_to_vpIndex, 'd_matIndex_to_vpIndex.pkl')
save_obj(matrix, 'matrix.pkl')
f = open('output.txt','w')
for row in matrix:
row = ','.join([str(x) for x in row]) + '\n'
f.write(row)
f.close()
# Post analysis
if(load_data_from_pickles):
d_vpIndex_to_matIndex = load_obj('d_vpIndex_to_matIndex.pkl')
d_matIndex_to_vpIndex = load_obj('d_matIndex_to_vpIndex.pkl')
matrix = load_obj('matrix.pkl')
normalized_matrix = copy.copy(matrix)
for i,row in enumerate(normalized_matrix):
normalized_matrix[i][i] = 0
row_total = [ 1.0*sum(row) for row in normalized_matrix ]
#print(row_total)
normalized_matrix = [ [x/row_total[i] for x in row] for i,row in enumerate(normalized_matrix) ]
if(True):
count = 0
to_print = [[] for row in normalized_matrix]
for i,row in enumerate(normalized_matrix):
for j,x in enumerate(row):
#if(i==j): continue
if(x > .01):
line = "{0}:\t{1} -> {2}".format(round(100.0*x,3), d_matIndex_to_vpIndex[i], d_matIndex_to_vpIndex[j])
to_print[i].append(tuple([row_total[i],line]))
count += x*row_total[i]/100.0
to_print = natsort.natsorted(to_print)
to_print = [natsort.natsorted(row) for row in to_print]
for row in to_print:
for x,line in row:
print(line + '\t' + str(100.0*x))
print('')
print("Total transformations: {0}".format(count))
# Find shortest paths to (0, 0, 12, 0) and (0, 6, 0, 8)
ico_index = (0, 0, 12, 0, 0, 0, 0, 0)
bcc_index = (0, 6, 0, 8, 0, 0, 0, 0)
graph = nx.Graph()
for i,row in enumerate(normalized_matrix):
for j,x in enumerate(row):
if(i==j): continue
if(x > 0.00):
#graph.add_edge( d_matIndex_to_vpIndex[i], d_matIndex_to_vpIndex[j] )
#graph[d_matIndex_to_vpIndex[i]][d_matIndex_to_vpIndex[j]]['weight'] = x
graph.add_edge( d_matIndex_to_vpIndex[j], d_matIndex_to_vpIndex[i] )
graph[d_matIndex_to_vpIndex[j]][d_matIndex_to_vpIndex[i]]['weight'] = 1-x
#test = []
bcc_dist = {}
ico_dist = {}
for ind in d_vpIndex_to_matIndex.keys():
try:
path = nx.shortest_path(graph, source=ind, target=ico_index, weight='weight')
dist = 1.0
for i in range(len(path)-1):
dist = dist * (1-graph[path[i]][path[i+1]]['weight'])
ico_dist[ind] = dist
except nx.exception.NetworkXNoPath:
ico_dist[ind] = 0.0
#test.append(tuple([ dist*100,ind, len(path) ]))
try:
path = nx.shortest_path(graph, source=ind, target=bcc_index, weight='weight')
dist = 1.0
for i in range(len(path)-1):
dist = dist * (1-graph[path[i]][path[i+1]]['weight'])
bcc_dist[ind] = dist
except nx.exception.NetworkXNoPath:
bcc_dist[ind] = 0.0
#test.sort()
#for t in test:
# print(t)
test = []
for key in ico_dist:
#print(key, ico_dist[key], bcc_dist[key], ico_dist[key]/bcc_dist[key], sum(matrix[d_vpIndex_to_matIndex[key]))
test.append([key, ico_dist[key], bcc_dist[key], ico_dist[key]/bcc_dist[key], sum(matrix[d_vpIndex_to_matIndex[key]])])
test.sort(key=operator.itemgetter(3))
test.reverse()
for t in test:
t = [str(x) for x in t]
t = '$'.join(t)
print(t)
def compute_factor(data_df, date_list, industry_list, config):
size_factor_weight = config.size_factor_weight
value_factor_weight = config.value_factor_weight
quality_factor_weight = config.quality_factor_weight
momenta_factor_weight = config.momenta_factor_weight
rsi_factor_weight = config.rsi_factor_weight
volatility_factor_weight = config.volatility_factor_weight
gics2name = load_obj(config.gics2name)
raw_factor = pd.DataFrame()
raw_factor_path = config.raw_factor_path
for i in industry_list:
print('current industry is : ', gics2name[i], '\n')
for j in tqdm(date_list):
raw_file1 = data_df[data_df.trade_date == j]
raw_file1 = raw_file1[raw_file1.gics_code == i]
if raw_file1.empty:
continue
else:
# size
raw_file1["size_factor"] = compute(raw_file1['Log_mkt_Cap'],
nagtive=True)
# Volatility（交易量-为了验证波动率）
raw_file1["volatility_factor"] = compute(
raw_file1['Volatility'], nagtive=True)
# Idiosyncratic volatility
# raw_file1['idioVolatility_Factor'] = compute(raw_file1['idio_vol'], nagtive=True)
# RSI 过去n：14天内多少天下降，多少天上升
raw_file1['RSI_factor'] = compute(raw_file1['RSI'],
nagtive=True)
# Momentum
raw_file1["momentum_factor"] = compute(
raw_file1['last_1mon_pricechange'], nagtive=True)
# Quality
sd_roa = stand(raw_file1['Rev_Over_mktCap'], 0.05) # 资产回报
roa_cdf = ECDF(sd_roa)
sd_acc = stand(raw_file1["q_opincome"], 0.05) # accural ？现金流
acc_cdf = ECDF(sd_acc)
sd_nocfod = stand(raw_file1['NOCF_Over_Debt'], 0.05)
nocfod_cdf = ECDF(sd_nocfod)
raw_file1["quality_factor"] = 0.25 * roa_cdf(
sd_roa) + 0.25 * acc_cdf(sd_acc) + 0.5 * nocfod_cdf(
sd_nocfod)
# Value
sd_cashval = stand(raw_file1['Cash_Over_MktCap'],
0.05) # 现金除以市值
sd_sd_cashval = std_winsor(sd_cashval)
cashval_cdf = ECDF(sd_sd_cashval)
sd_roa = stand(raw_file1['Rev_Over_mktCap'], 0.05) # 收益除以市值
sd_sd_roa = std_winsor(sd_roa)
roa_cdf = ECDF(sd_sd_roa)
sd_bp = stand(raw_file1['pb'], 0.05) # 市净率
sd_sd_bp = std_winsor(sd_bp)
bp_cdf = ECDF(sd_sd_bp)
raw_file1["value_factor"] = 1 / 3 * cashval_cdf(sd_sd_cashval) \
+ 1 / 3 * roa_cdf(sd_sd_roa) \
+ 1 / 3 * bp_cdf(sd_sd_bp)
raw_file1["overall_factor"] = raw_file1["size_factor"] * size_factor_weight \
+ raw_file1["volatility_factor"] * volatility_factor_weight \
+ raw_file1["RSI_factor"] * rsi_factor_weight \
+ raw_file1["momentum_factor"] * momenta_factor_weight \
+ raw_file1["quality_factor"] * quality_factor_weight \
+ raw_file1["value_factor"] * value_factor_weight
if raw_factor.empty:
raw_factor = raw_file1
else:
raw_factor = pd.concat([raw_factor, raw_file1], axis=0)
raw_factor.to_csv(raw_factor_path, mode='w', header=True)
+ str(match_id) + " data: " + str(e))
time.sleep(1)
continue
if num_try >= 11:
if match_id in newer_match_ids:
return [{'matchId': -1, 'saved_file': -1}]
return matches_data
#restore program state
while os.path.isfile('game_data_{0}.zip'.format(saved_files)):
with zipfile.ZipFile('game_data_{0}.zip'.format(saved_files),
'r') as myzip:
myzip.extractall()
match = load_obj('game_data_{0}.pkl'.format(saved_files))
pulled_matches[match['matchId']] = saved_files
pulled_matches_count += 1
os.remove('game_data_{0}.pkl'.format(saved_files))
saved_files += 1
while True:
if len(unpulled_summoners) == 0:
#if we run out of summoners, hopefully older summoners already played new matches
unpulled_summoners = random.sample(pulled_summoners, 15)
pulled_summoners = list()
current_summoner_id = unpulled_summoners.pop(0)
try: