def __init__(self, cluid, twarr, prob, hot, level, geo_list, time_list,
keywords):
self.cluid = cluid
self.twarr = twarr
self.prob = prob
self.hot = hot
self.level = level
self.geo_list = geo_list
self.time_list = time_list
self.keywords = keywords
self.od = Od()
summary = Od(
zip(
['cluster_id', 'prob', 'level', 'hot', 'keywords'],
[cluid, prob, level, hot, keywords],
))
geo_table = [
Od(zip(['quality', 'address', 'country', 'bbox'], geo))
for geo in geo_list
]
time_table = Od(
zip(['most_possible_time', 'earliest_time', 'latest_time'],
time_list))
self.od.update(
zip(
['summary', 'geo_infer', 'time_infer', 'tweet_list'],
[summary, geo_table, time_table, twarr],
))
def chain_to_info(**blockchain):
"""
TODO
:param blockchain:
:return:
"""
temp_chain = cp.deepcopy(
blockchain['blocks']) if 'blocks' in blockchain else []
temp_txs = cp.deepcopy(
blockchain['txs']) if 'txs' in blockchain else []
# Converting the blocks in the chain to ordered dictionaries
blocks_info = [
Od([('previous_hash', block.previous_hash), ('index', block.index),
('proof', block.proof),
('transactions', [
Od([('sender', tx.sender), ('recipient', tx.recipient),
('amount', tx.amount), ('timestamp', tx.timestamp),
('signature', tx.signature)])
for tx in block.transactions
]), ('timestamp', block.timestamp)]) for block in temp_chain
]
# Converting the transactions in the chain to ordered dictionaries
txs_info = [
Od([('sender', tx.sender), ('recipient', tx.recipient),
('amount', tx.amount), ('timestamp', tx.timestamp),
('signature', tx.signature)]) for tx in temp_txs
]
return blocks_info, txs_info
def main():
# print出来的结果最好也输出到文件里
base = '/home/cdong/works/research/clu/data/'
files = [
'20ng_tf.pkl',
'20ng_tfidf.pkl',
'Event_tf.pkl',
'Event_tfidf.pkl',
'Google_tf.pkl',
'Google_tfidf.pkl',
'Reuters_tf.pkl',
'Reuters_tfidf.pkl',
'TREC_tf.pkl',
'TREC_tfidf.pkl',
]
for file in files:
file = base + file
print(file)
scores = list()
for i in range(3):
features, y_true = pickle.load(open(file, 'rb'))
y_pred = GANMM(features)
score = Od((s, get_score(y_true, y_pred, s))
for s in ('acc', 'ari', 'nmi'))
scores.append(score)
print(score)
name2scores = Od()
for score in scores:
for score_name, score_value in score.items():
name2scores.setdefault(score_name, list()).append(score_value)
for score_name, score_values in name2scores.items():
print('mean {}: {}\n'.format(score_name,
np.mean(score_values)))
def vec2dict(idx_vec):
od = Od(zip(names, [None] * len(names)))
for n_idx, v_idx in enumerate(idx_vec):
n, v = names[n_idx], values_list[n_idx][v_idx]
if not callable(v):
od[n] = v
return od
def group_iters():
groups_ = Od()
for p_, o_ in param2iter.items():
k_, v_ = (p_[0], p_[1]), (p_[2], o_)
groups_.setdefault(k_, list())
groups_[k_].__add__(v_)
return groups_
def construct_od(self):
"""
从当前聚类信息构造一个字段按顺序保存的字典，该字典维护一个用于描述聚类信息的树形结构，
各字段含义与数值类型见本文件 line 182-209 的说明
:return: OrderedDict，保存了聚类的结构化信息
"""
summary = Od(
zip(
['cluster_id', 'prob', 'level', 'hot', 'keywords'],
[self.cluid, self.prob, self.level, self.hot, self.keywords],
))
od = Od(
zip(
['summary', 'geo_infer', 'time_infer', 'tweet_list'],
[summary, self.geo_table, self.time_table, self.twarr],
))
return od
def _init_months():
temp = [
'Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct',
'Nov', 'Dec'
]
months = Od()
for i in range(1, 13):
months[i] = temp[i - 1]
return months
def get_scores(pred_target, add_on):
preds, trues = list(), list()
for batch in batches:
c_probs = self.sess.run(pred_target,
feed_dict=self.get_fd_by_batch(batch))
preds.extend(np.argmax(c_probs, axis=1).reshape(-1))
trues.extend(d.topic for d in batch)
od = au.scores(trues, preds, au.eval_scores)
return Od((k + add_on, v) for k, v in od.items())
def group_data_frame_columns(data_frame, columns):
groups = [(Od(), data_frame)]
for col in columns:
for _ in range(len(groups)):
p_od, p_df = groups.pop(0)
for n_od, n_df in group_data_frame(p_df, col):
p_od = p_od.copy()
p_od.update(n_od)
groups.append((p_od, n_df))
return groups
def platformPulizie_old(self, db=None):
if db is None:
db = self.DataBase
# todo evitare di caricare le date passate?
# print(os.getcwdb())
self.datePrenotazioni.clear()
# a, m, g = self.amg(self._dataIn)
for anno in db.keys():
for mese in db[anno].keys():
for giorno in db[anno][mese].keys():
data = QtCore.QDate(anno, mese, giorno)
plat = db[anno][mese][giorno]["checkIn"]["platform"]
pulizie = db[anno][mese][giorno]["checkOut"][
'data partenza']
if 'platforms' not in self.datePrenotazioni:
self.datePrenotazioni['platforms'] = Od()
if plat != '':
if plat not in self.datePrenotazioni['platforms']:
dat = {'date': [data]}
self.datePrenotazioni['platforms'][plat] = Od(dat)
# self.datePrenotazioni['platforms'][plat]['colore'] = QtGui.QColor
else:
print("PLAT: ", plat, "##")
self.datePrenotazioni['platforms'][plat][
'date'].append(data)
# if plat == "Booking":
# if data not in self.dateBooking:
# self.dateBooking.append(data)
# elif plat == "AirB&B":
# if data not in self.dateAirbb:
# self.dateAirbb.append(data)
# elif plat == 'Privati':
# if data not in self.datePrivati:
# self.datePrivati.append(data)
if pulizie != '':
if pulizie not in self.datePulizie:
self.datePulizie.append(pulizie)
return self.datePrenotazioni, self.datePulizie
def print_groups(use_max, sort_group, files):
def group_score(frame, col):
group_top = 5
mean = np.mean(frame[col].values[1:group_top])
return mean
board = (read_board_multi if len(files) >= 60 else read_board_files)(
use_max, files)
board.fillna('_', inplace=True)
for s in set(board.columns).intersection(set(group_by)):
print(s, sorted(Counter(board[s])))
sort_by = 'acc'
board.sort_values(by=sort_by, ascending=False, inplace=True)
ret_dfs = list()
for dn_bv, dn_df in au.group_data_frame_columns(board, columns=[dn_]):
data_name = dn_bv[dn_]
print(data_name)
bv_df_list = au.group_data_frame_columns(dn_df, columns=group_by)
bv_df_sc_list = list()
for bv, df in bv_df_list:
evals = ['nmi', 'ari', 'acc']
# evals += [s + '_nis' for s in evals]
sc = Od((s, group_score(df, s)) for s in evals)
bv_df_sc_list.append((bv, df, sc))
if sort_group:
bv_df_sc_list = sorted(
bv_df_sc_list,
key=lambda item:
(item[-1]['acc'], item[-1]['ari'], item[-1]['nmi']))
ret_df = pd.DataFrame()
for bv, df, sc in bv_df_sc_list:
# if not (bv[l1_] == '1.0' and bv[l2_] == '1.0'
# and bv[l3_] == '0.0'):
# continue
print('<{}>'.format(len(df)), end=' ')
print(' '.join(['{}={:7}'.format(*x) for x in bv.items()]),
end='| ')
print(' '.join(
['{} {:.4f}'.format(s, g) for s, g in sc.items()]))
print(df.iloc[:4, :], '\n')
# print(','.join(df[gid_].tolist()))
i = len(ret_df)
for b, v in bv.items():
ret_df.loc[i, b] = v
for s, c in sc.items():
ret_df.loc[i, s] = round(float(c), 4)
ret_df.loc[i, 'LEN'] = len(df)
ret_df.loc[i, dn_] = data_name
print(data_name, '\n' * 3)
# if comment is not None:
# ret_df.to_csv('{}_{}.csv'.format(comment, data_name))
ret_dfs.append(ret_df)
pd.concat(ret_dfs).to_csv('{}.csv'.format(comment))
def ddd(thing):
stack = traceback.extract_stack()
for s in stack:
filename, lineno, function_name, code = s
if filename == '/var/src/conkyconfpy/conkyconfpy.py':
d = Od({
'lineno': lineno,
'function_name': function_name,
'code': code,
})
pprint(d)
def platformPulizie(self, db=None):
if db is None:
db = self.DataBase
# todo evitare di caricare le date passate?
# print(os.getcwdb())
self.datePrenotazioni.clear()
# a, m, g = self.amg(self._dataIn)
for anno in db.keys():
for mese in db[anno].keys():
for giorno in db[anno][mese].keys():
data = QtCore.QDate(anno, mese, giorno)
checkIn = db[anno][mese][giorno]["checkIn"]
plat = checkIn["platform"]
pulizie = db[anno][mese][giorno]["checkOut"][
'data partenza']
note = checkIn['note']
if 'platforms' not in self.datePrenotazioni:
self.datePrenotazioni['platforms'] = Od()
if plat != '':
if plat not in self.datePrenotazioni['platforms']:
dat = {'date': [data]}
self.datePrenotazioni['platforms'][plat] = Od(dat)
# self.datePrenotazioni['platforms'][plat]['colore'] = QtGui.QColor
else:
# print("not in self.datePrenotazioni (managerprenotazioni) PLAT: ", plat, "##")
self.datePrenotazioni['platforms'][plat][
'date'].append(data)
if pulizie != '':
if pulizie not in self.datePulizie:
print('pulizie', pulizie)
self.datePulizie.append(pulizie)
# if spese > 0:
# print('spese append: ', spese)
# if data not in self.dateSpese:
# self.dateSpese.append(data)
if len(note) > 0:
if data not in self.dateNote:
self.dateNote.append(data)
return self.datePrenotazioni, self.datePulizie, self.dateNote
def print_coherent_topics():
from collections import Counter
from data.make_embeddings import load_pretrain_google_news
word2vec = load_pretrain_google_news()
print('word2vec load over')
for file in res_files:
topic_list, c_alpha_list, w_alpha_list, doc_embed, word_embed, clu_embed, d_name = np.load(
file)
print('c_alpha_list.shape', np.array(c_alpha_list).shape)
cluid_list = np.argmax(c_alpha_list, axis=1)
cluid2counter = Od(
(cluid, Counter()) for cluid in range(len(clu_embed)))
for topic, cluid in zip(topic_list, cluid_list):
cluid2counter[cluid][topic] += 1
print(d_name)
ifd, docarr = name2object[d_name].load_ifd_and_docarr()
w_num = 31
c_num = 100
cw_sim = au.cosine_similarity(clu_embed, word_embed)
cw_sim_sort = np.sort(cw_sim, axis=1)[:, :-w_num:-1]
top_sim_clu = np.argsort(np.mean(cw_sim_sort,
axis=1).reshape(-1))[::-1][:c_num]
for cluid in sorted(list(top_sim_clu)):
print(cluid)
topic_distrib = cluid2counter[cluid].most_common()
if len(topic_distrib) == 0:
continue
topic = topic_distrib[0][0]
print('cluid: {}, guess topic: {}, distribution: {}'.format(
cluid, topic, topic_distrib[:5]))
cw_sim_top = cw_sim[cluid]
top_word_id = np.argsort(cw_sim_top)[:-w_num:-1]
valid_words = [
ifd.id2word(wid) for wid in top_word_id
if ifd.id2word(wid) in word2vec
]
print(' '.join(valid_words))
# cw_sim_top = cw_sim[top_sim_clu]
# top_word_id = np.argsort(cw_sim_top, axis=1)[:, :-w_num:-1]
# print(np.sort(np.mean(cw_sim_sort, axis=1).reshape(-1))[:-c_num:-1])
# print(np.array([cw_sim[wid] for wid, cw_sim in zip(top_word_id, cw_sim_top)]))
#
# ifd = name2class[d_name].load_ifd()
# for idx, wid_list in enumerate(top_word_id):
# valid_words = [ifd.id2word(wid) for wid in wid_list if ifd.id2word(wid) in word2vec]
# print('{}: '.format(idx) + ('{} ' * len(valid_words)).format(*valid_words))
print('\n----\n')
def __init__(self, cluid, twarr, prob, hot, level, geo_list, time_list,
keywords):
self.cluid = cluid
self.twarr = twarr
self.prob = prob
self.hot = hot
self.level = level
self.geo_list = geo_list
self.time_list = time_list
self.keywords = keywords
self.geo_table = [
Od(zip(['quality', 'address', 'country', 'bbox', 'freq'], g))
for g in self.geo_list
]
self.s_geo_table = [
row for row in self.geo_table
if row['quality'] not in self.large_geo
]
self.time_table = Od(zip(['most_possible_time', 'earliest_time', 'latest_time'], self.time_list)) \
if self.time_list is not None else None
def analyze_refine_mean_and_stderr(result_file, mean_std_file):
using_scores = ['nmi', 'h**o', 'cmplt', 'ari']
arg_tpc_clu_list = iu.load_array(result_file)
rows = list()
for kwargs, topics, clusters in arg_tpc_clu_list:
scores = [au.score(topics, clusters, s) for s in using_scores]
res_dict = Od(zip(using_scores, scores))
row = Od(list(kwargs.items()) + list(res_dict.items()))
rows.append(row)
rows = sorted(rows, key=lambda item: item['nmi'], reverse=True)
df = pd.DataFrame(data=rows)
print(df)
score_array = df[using_scores].values
mean = np.mean(score_array, axis=0)
std = np.std(score_array, axis=0, ddof=1)
table = list(zip(*[using_scores, mean, std]))
lines = [
'{}: {} ± {}'.format(name, round(mean, 4), round(std, 4))
for name, mean, std in table
]
iu.write_lines(mean_std_file, lines)
def print_top_words(self, ifd):
from collections import OrderedDict as Od
cluid2counter = Od(
(twh.cluster.cluid, Counter()) for twh in self.twharr)
for twh in self.twharr:
cluid2counter[twh.cluster.cluid][twh.topic] += 1
print('total cluster number: {}'.format(len(self.cludict)))
clu2distrib = Od()
for cluid in sorted(cluid2counter.keys()):
topic_distrib = cluid2counter[cluid].most_common()
topic = topic_distrib[0][0]
print('cluid: {}, topic: {}, t distrib: {}'.format(
cluid, topic, topic_distrib[:10]))
word_distrib = self.cludict[cluid].tokens.most_common()[:60]
valid_words = [ifd.id2word(wid) for wid, cnt in word_distrib]
print(' '.join(valid_words))
clu2distrib[cluid] = {
'topic_distrib': topic_distrib,
'word_distrib': valid_words
}
return clu2distrib
def read_excel_onesheet(fd, sheet_name, same_line_debug=False):
""":
第一列必须唯一，且为英语，作为第一层键；
第一行必须唯一，且为英语，作为二层键
返回dict
"""
if os.path.exists(fd):
pass
else:
print '@Error:We can not find file :%s' % fd
excel_hash = Od()
workbook = xlrd.open_workbook(fd)
worksheets = workbook.sheet_names()
worksheet = workbook.sheet_by_name(sheet_name)
num_rows = worksheet.nrows
num_cols = worksheet.ncols
for rown in range(1, num_rows):
row_name = worksheet.cell_value(rown, 0)
if same_line_debug:
if row_name not in excel_hash.keys():
excel_hash[row_name] = Od()
key1 = row_name
else:
print 'Same line %s' % row_name
exit()
else:
if rown not in excel_hash.keys():
excel_hash[rown] = Od()
key1 = rown
for coln in range(0, num_cols):
col_name = str(worksheet.cell_value(0, coln))
if col_name not in excel_hash[key1].keys():
pass
else:
print 'The same clown %s' % col_name
excel_hash[key1][col_name] = worksheet.cell_value(rown, coln)
return excel_hash
def __init__(self, info=None, shortcut=0):
# self._dataIn = dataIn
# self._domani = dataIn.addDays(1)
# self._nome = nome
# self._cognome = cognome
# opzione per correggere il percorso durante i tests
self.shortcut = shortcut
self._info = info
# if self._info is not None:
manageErr = False
try:
self.info = self.getInfo
self._dataIn = self.info['data arrivo']
self._dataOut = self.info['data partenza']
self._domani = self._dataIn.addDays(1)
self._nome = self.info['nome']
self._cognome = self.info['cognome']
except AttributeError:
manageErr = True
except TypeError:
manageErr = True
if manageErr:
self._dataIn = QtCore.QDate().currentDate()
self._domani = self._dataIn.addDays(1)
self._dataOut = self._domani
self._nome = None
self._cognome = None
self.occupate = []
# old
# self.DataBase = deepc(self.getDb(self._dataIn))
self.DataBase = deepc(self.getDb())
self.counter = 0
# self.DataBase = Od()
datePren = {'platforms': {}}
self.datePrenotazioni = Od(datePren)
self.dateBooking = []
self.dateAirbb = []
self.datePrivati = []
self.datePulizie = []
self.dateSpese = []
self.dateNote = []
self.platformDict = {
'Booking': self.dateBooking,
'AirB&B': self.dateAirbb,
'Privati': self.datePrivati,
'pulizie': self.datePulizie
}
def analyze_mean_and_stderr(result_file):
arg_tpc_clu_list = fu.load_array(result_file)
rows = list()
for kwargs, topics, clusters in arg_tpc_clu_list:
s2v = Od((s, au.score(topics, clusters, s)) for s in au.eval_scores)
row = Od(list(kwargs.items()) + list(s2v.items()))
rows.append(row)
rows = sorted(rows, key=lambda item: item['nmi'], reverse=True)
df = pd.DataFrame(data=rows)
# print(df)
groups = au.group_data_frame(df, column='n_components')
nmi_list, ari_list, acc_list = list(), list(), list()
for _, df_ in groups:
print(result_file)
print(df_)
nmis = df_['nmi'].values[0:6]
aris = df_['ari'].values[0:6]
accs = df_['acc'].values[0:6]
nmi_list.append(au.mean_std(nmis))
ari_list.append(au.mean_std(aris))
acc_list.append(au.mean_std(accs))
print(au.transpose(nmi_list))
print(au.transpose(ari_list))
print(au.transpose(acc_list))
def read_scores_from_file(file):
e_flag = ''
score_od = Od()
for line in iu.read_lines(file):
if line.startswith('b'):
continue
elif line.startswith('e'):
e_flag = line[line.find('-') + 1:]
elif line.startswith('{') and 'nmi' in line:
for k, v in iu.loads(line).items():
score_od.setdefault(k, list()).append(v)
if len(score_od) == 0:
fname = iu.base_name(file)
print('{} - empty'.format(fname[:fname.find(',')]))
epoch = len(list(score_od.values())[0]) if len(score_od) > 0 else 0
return score_od, epoch, e_flag
def evaluate(self, batches):
def get_scores(pred_target, add_on):
preds, trues = list(), list()
for batch in batches:
c_probs = self.sess.run(pred_target,
feed_dict=self.get_fd_by_batch(batch))
preds.extend(np.argmax(c_probs, axis=1).reshape(-1))
trues.extend(d.topic for d in batch)
od = au.scores(trues, preds, au.eval_scores)
return Od((k + add_on, v) for k, v in od.items())
from collections import OrderedDict as Od
scores = Od()
scores.update(get_scores(self.pc_probs, add_on=''))
if self.use_adv_nis:
scores.update(get_scores(self.pc_probs_nis, add_on='_nis'))
return scores
def _write_cytoscape_files(self,
noa_path,
eda_path,
pp_path,
idx_selection,
label=None):
"""
Compute some node and edge attributes and write these to
files that can be loaded in cytoscape.
"""
df = self.interactions_.loc[idx_selection, ]
edges = [
sorted([p1, p2]) for (p1, p2) in zip(df[P1].values, df[P2].values)
]
# Compute some selected node-attributes,
# Write the noa (noda-attribute) file.
accessions = sorted(set([p for tup in edges for p in tup]))
gene_names = [self.gene_names_[a] for a in accessions]
node_in_training = [self.node_in_training_set(a) for a in accessions]
cyto_n_attrs = pd.DataFrame(
Od([('name', accessions), ('node in training', node_in_training),
('gene name', gene_names)]))
cyto_n_attrs.to_csv(noa_path, sep=self.sep, index=False)
# Compute some selected edge-attributes a,
# Write the eda (edge-attribute) file.
columns = ['source', 'target', 'name', 'edge in training', 'max-pr']
cyto_e_attrs = dict()
cyto_e_attrs['source'] = [p1 for p1, _ in edges]
cyto_e_attrs['target'] = [p2 for _, p2 in edges]
cyto_e_attrs['name'] = ['{} pp {}'.format(p1, p2) for p1, p2 in edges]
cyto_e_attrs['edge in training'] = [
self.edge_in_training_set(e, label) for e in edges
]
cyto_e_attrs['max-pr'] = list(df['max-pr'].values)
for label in self.labels:
column = self._label_to_column(label)
cyto_e_attrs[column] = df[column].values
columns.append(column)
cyto_interactions = pd.DataFrame(cyto_e_attrs, columns=columns)
cyto_interactions.to_csv(pp_path, sep=self.sep, index=False)
return self
def read_excel_onesheet2(fd,
sheet_name,
seq_name='测序编号',
same_line_debug=False):
""":
第一列必须唯一，且为英语，作为第一层键；
第一行必须唯一，且为英语，作为二层键
返回dict
"""
if os.path.exists(fd):
pass
else:
print '@Error:We can not find file :%s' % fd
excel_hash = Od()
workbook = xlrd.open_workbook(fd)
worksheets = workbook.sheet_names()
# print worksheets #.decode('utf-8')
worksheet = workbook.sheet_by_name(sheet_name)
num_rows = worksheet.nrows
num_cols = worksheet.ncols
num = 0
for rown in range(0, num_rows):
one_line_info = []
for coln in range(0, num_cols):
col_name = str(worksheet.cell_value(0, coln))
if col_name == seq_name:
seq_col_loc = coln
one_line_info.append(worksheet.cell_value(rown, coln))
seq_name_line = worksheet.cell_value(rown, seq_col_loc)
if seq_name_line != "pass":
num += 1
excel_hash[num] = {}
excel_hash[num]['seq_name'] = seq_name_line
excel_hash[num]['one_line'] = one_line_info
# print excel_hash.keys()
return excel_hash
def scores(y_true, y_pred, using_scores=eval_scores):
return Od((s, score(y_true, y_pred, s)) for s in using_scores)
def merge_blast_rdp_file(blast_fp, rdp_fp, result_fp):
blast_info = Od()
rdp_info = {}
data3 = open(result_fp, 'w')
with open(rdp_fp) as data2:
header2 = [
'kingdom', 'phylum', 'class', 'order', 'family', 'genus2',
'rdp_value'
]
for each_line2 in data2:
if each_line2.strip() == '':
continue
cnt2 = each_line2.strip().split('\t')
seq2 = cnt2[0]
taxon_speies = cnt2[1].split(';')
rdp_value = cnt2[2]
taxon_num = len(taxon_speies)
rdp_kindom = '.'
rdp_phylum = '.'
rdp_class = '.'
rdp_order = '.'
rdp_family = '.'
rdp_genus = '.'
if taxon_num == 1:
rdp_kindom = taxon_speies[0].replace('k__', '')
elif taxon_num == 2:
rdp_kindom = taxon_speies[0].replace('k__', '')
rdp_phylum = taxon_speies[1].replace('p__', '')
elif taxon_num == 3:
rdp_kindom = taxon_speies[0].replace('k__', '')
rdp_phylum = taxon_speies[1].replace('p__', '')
rdp_class = taxon_speies[2].replace('c__', '')
elif taxon_num == 4:
rdp_kindom = taxon_speies[0].replace('k__', '')
rdp_phylum = taxon_speies[1].replace('p__', '')
rdp_class = taxon_speies[2].replace('c__', '')
rdp_order = taxon_speies[3].replace('o__', '')
elif taxon_num == 5:
rdp_kindom = taxon_speies[0].replace('k__', '')
rdp_phylum = taxon_speies[1].replace('p__', '')
rdp_class = taxon_speies[2].replace('c__', '')
rdp_order = taxon_speies[3].replace('o__', '')
rdp_family = taxon_speies[4].replace('f__', '').replace(
'[', '').replace(']', '')
elif taxon_num >= 6:
rdp_kindom = taxon_speies[0].replace('k__', '')
rdp_phylum = taxon_speies[1].replace('p__', '')
rdp_class = taxon_speies[2].replace('c__', '')
rdp_order = taxon_speies[3].replace('o__', '')
rdp_family = taxon_speies[4].replace('f__', '').replace(
'[', '').replace(']', '')
rdp_genus = taxon_speies[5].replace('g__', '')
if rdp_kindom == '':
rdp_kindom = '.'
if rdp_phylum == '':
rdp_phylum = '.'
if rdp_class == '':
rdp_class = '.'
if rdp_order == '':
rdp_order = '.'
if rdp_family == '':
rdp_family = '.'
if rdp_genus == '':
rdp_genus = '.'
rdp_info[seq2] = [
rdp_kindom, rdp_phylum, rdp_class, rdp_order, rdp_family,
rdp_genus, rdp_value
]
with open(blast_fp) as data1:
for each_line in data1:
if each_line.strip == '':
continue
elif each_line.startswith('#'):
header1 = each_line.strip().split('\t')
header = header1 + header2
data3.write('%s\n' % '\t'.join(header))
continue
cnt = each_line.strip().split('\t')
seq = cnt[0]
blast_info[seq] = cnt[1:]
if seq in rdp_info.keys():
info = cnt + rdp_info[seq]
else:
info = cnt + ['.', '.', '.', '.', '.', '.', '.']
data3.write('%s\n' % '\t'.join(info))
data3.close()
def group_data_frame(data_frame, column):
value_set = sorted(set(data_frame[column]))
return [(Od([(column, v)]), data_frame[data_frame[column] == v])
for v in value_set]
def count_occurence(y1, y2):
y1_to_counter = Od((y, Counter()) for y in set(y1))
for v1, v2 in zip(y1, y2):
y1_to_counter[v1][v2] += 1
return y1_to_counter
def read_l3_influence():
from me.analyze import group_data_frame_columns
idx = 0
df = pd.DataFrame()
log_base = Nodes.select(n1702='./logging_tge/', ngpu='./logging')
for file in iu.list_children(log_base, full_path=True, pattern='.txt$'):
scores_od = Od()
early = False
for line in iu.read_lines(file):
if 'early' in line:
early = True
if 'nmi' not in line:
continue
for s_type, s_value in name2entries(line, inter=' ', intra=':'):
s_value = float(s_value)
if s_type not in scores_od:
scores_od[s_type] = [s_value]
else:
scores_od[s_type].__add__(s_value)
file_name = file[file.rfind('/') + 1:]
if len(scores_od) == 0:
print('{} is empty'.format(file_name))
continue
for k, v in name2entries(file_name):
df.loc[idx, k] = v
epoch = scores_od.pop('e', default=None)
for s_type, s_values in scores_od.items():
top_k = 10
top_value = np.mean(sorted(s_values, reverse=True)[:top_k])
last_value = np.mean(s_values[::-1][:top_k])
# df.loc[idx, s_type] = top_value
df.loc[idx, s_type] = last_value
df.loc[idx,
'epoch'] = str(max(epoch) + 1 if epoch is not None else 0) + (
' e.s.' if early else '')
idx += 1
df = df.sort_values(by='nmi', ascending=False)
influence = Od()
df_list = group_data_frame_columns(df, ['dn', 'l3'])
for bv_list, d in df_list:
print(' '.join(['{}={}'.format(*bv) for bv in bv_list]))
nmi_mean, nmi_std = au.mean_std(d['nmi'].values[0:])
ari_mean, ari_std = au.mean_std(d['ari'].values[0:])
print('nmi:{:.4f}+{:.4f}'.format(nmi_mean, nmi_std))
print('ari:{:.4f}+{:.4f}'.format(ari_mean, ari_std))
print(d.iloc[:10, :])
dn, l3 = dict(bv_list)['dn'], float(dict(bv_list)['l3'])
influence.setdefault(dn, list())
# if l3 <= 1e-4:
# nmi_mean -= 0.01
# ari_mean -= 0.01
influence[dn].__add__(
[round(v, 6) for v in [l3, nmi_mean, ari_mean, nmi_std, ari_std]])
print()
for dn, values in influence.items():
influence[dn] = [[round(v, 6) for v in value]
for value in np.array(values, dtype=float).T]
# arr = np.array(influence[dn][1])
# print(arr)
# print(arr - arr[0])
# print()
with open('influence.json', mode='w') as fp:
iu.json.dump(influence, fp)
def read_iterations():
def append_score_line_to_od(od_, score_line_):
for score_name, score_value in name2entries(score_line_,
inter=' ',
intra=':'):
score_value = float(score_value)
if score_name not in od_:
od_[score_name] = [score_value]
else:
od_[score_name].__add__(score_value)
def append_score_od_to_iter(file_name_, od_):
entries = dict(name2entries(file_name_))
param_as_key = tuple(entries[k] for k in desired_keys)
if param_as_key not in param2iter:
param2iter[param_as_key] = [od_]
else:
param2iter[param_as_key].__add__(od_)
def group_iters():
groups_ = Od()
for p_, o_ in param2iter.items():
k_, v_ = (p_[0], p_[1]), (p_[2], o_)
groups_.setdefault(k_, list())
groups_[k_].__add__(v_)
return groups_
param2iter = Od()
log_base = Nodes.select(n1702='./logging_half_r/', ngpu='./logging_r')
for file in iu.list_children(log_base,
full_path=True,
pattern='gid.+\.txt$'):
file_name = file[file.rfind('/') + 1:]
# if dict(name2entries(file_name))['vs'] in {'3', 3}:
# continue
score_dict = Od()
for line in iu.read_lines(file):
if 'nmi' not in line:
continue
append_score_line_to_od(score_dict, line)
if len(score_dict) == 0:
print('{} is empty'.format(file_name))
continue
append_score_od_to_iter(file_name, score_dict)
# for param in sorted(param2iter.keys(), key=lambda i: i[0]):
params = Nodes.select(n1702=[
('TREC', 0.01, 0.1),
('Google', 0.1, 0.1),
('Event', 0.01, 0.01),
('20ng', 0.1, 0.1),
],
ngpu=[
('Reuters', 0.001, 0.1),
])
params = list([tuple(map(str, param)) for param in params])
array = list()
for param in param2iter.keys():
od_list = param2iter[param]
for score_dict in od_list:
score_dict.pop('e', default=None)
array.extend([od_list])
print(len(array))
