def parseCMF(fout):
try:
with open(RES_DIR + "output.txt", "r") as CMFResults:
foundMotifs["CMF"] = []
foundMotifsSeqs["CMF"] = DD(lambda: DD(list))
alreadyFound = {}
readmode = 0 # 0=looking for motif,1=reading pos,2=looking for pos
for line in CMFResults:
if readmode == 2:
readmode = 1
continue
if readmode == 1:
if len(line) < 2:
readmode = 0
continue
seqName, mpos = line.split("\t")[:2]
seqName = seqName.strip()
#pdb.set_trace()
foundMotifs["CMF"][-1][1].append(SEQ_LENS[seqName[1:]] - int(mpos))
foundMotifsSeqs["CMF"][motif][seqName[1:]].append(int(mpos))
fout.write("\t"+str(SEQ_LENS[seqName[1:]] - int(mpos)))
readmode = 2
continue
if line[0:7] == "MOTIF:\t" and not line[7:] in alreadyFound:
motif = line[7:-1]
fout.write("\nCMF\t"+line[7:-1])
foundMotifs["CMF"] += [[line[7:-1], []]]
#foundMotifsSeqs["CMF"][motif] = DD(list)
#alreadyFound[line[7:]] = 0
elif "Positive Sites:" in line:
readmode = 1
except IOError:
print "Error opening CMF's results file"
def __init__(self, getIndex=0, viewIndex=-1):
self.getIndex, self.viewIndex = getIndex, viewIndex #TODO: Add sanity checks?
self.key2mon = DD(lambda:DD(set)) # Maps a normal key to keys that monitor it.
self.monkeys = set() # List of monitor keys.
# store and waiters are mutually exclusive, and could be kept in the same place
self.store = DD(list)
self.waiters = DD(list)
self.opCounts = {b'get': 0, b'put': 0, b'view': 0, b'wait': 0}
self.ac, self.rc = 0, 0
async def get_fkeys(company, table_name):
with await fkey_lock:
if company not in fkeys:
src_fkeys = DD(list)
tgt_fkeys = DD(list)
src_fkey = NT('src_fkey',
'src_col, tgt_tbl, tgt_col, alt_src, alt_tgt, test')
tgt_fkey = NT('tgt_fkey',
'src_tbl, src_col, tgt_col, is_child, test')
sql = ("SELECT b.table_name, a.col_name, a.fkey "
f"FROM {company}.db_columns a, {company}.db_tables b "
"WHERE b.row_id = a.table_id "
"AND a.deleted_id = 0 "
"AND a.fkey IS NOT NULL")
async with db_session.get_connection() as db_mem_conn:
conn = db_mem_conn.db
cur = await conn.exec_sql(sql)
async for src_tbl, src_col, fkey in cur:
tgt_tbl, tgt_col, alt_src, alt_tgt, is_child, cursor = loads(
fkey)
if isinstance(tgt_tbl, str): # normal case
test = None
src_fkeys[src_tbl].append(
src_fkey(src_col, tgt_tbl, tgt_col, alt_src,
alt_tgt, test))
tgt_fkeys[tgt_tbl].append(
tgt_fkey(src_tbl, src_col, tgt_col, is_child,
test))
else:
col_name, vals_tables = tgt_tbl
for val, tgt_tbl in vals_tables:
test = (col_name, val)
src_fkeys[src_tbl].append(
src_fkey(src_col, tgt_tbl, tgt_col, alt_src,
alt_tgt, test))
tgt_fkeys[tgt_tbl].append(
tgt_fkey(src_tbl, src_col, tgt_col, is_child,
test))
fkeys[company] = src_fkeys, tgt_fkeys
comp_fkeys = fkeys[company]
src_fkeys = comp_fkeys[0][table_name] # returns [] if not found
tgt_fkeys = comp_fkeys[1][table_name] # returns [] if not found
return src_fkeys, tgt_fkeys
def parseBioProspector(fout):
# read sequence mappings
mappings = {}
mfname = FILTERED_SEQ.split('/')[-1]
with open("bp_files/bp_" + mfname + ".mappings", "r") as mapIn:
for line in mapIn:
line = line.split(">>")
mappings[line[0]] = line[1].strip()
with open(RES_DIR + "bp_output.txt", "r") as bpResults:
foundMotifs["BioProspector"] = []
foundMotifsSeqs["BioProspector"] = {}
for line in bpResults:
if "Motif #" in line:
motif = line[line.index("(")+1:line.index("/")]
fout.write("\nBioProspector\t"+motif)
foundMotifs["BioProspector"] += [[motif, []]]
foundMotifsSeqs["BioProspector"][motif] = DD(list)
elif ">seq" in line:
slen = int(line.split()[2])
mpos = int(line.split()[-1])
if line.split()[-2] == 'r':
mpos -= len(motif)
foundMotifs["BioProspector"][-1][1].append(slen - mpos)
foundMotifsSeqs["BioProspector"][motif][mappings[line.split()[0][1:]]].append(mpos)
fout.write("\t" + str(slen - mpos))
def getSMData(SMDir, targetNode, start, stop):
#print ("getSMdata %s"%targetNode)
sm = SearchIndex(SMDir+'/%s_sm.px'%targetNode, 40, compTimestamps)
smd = IndexedHostData(SMDir, targetNode)
usr2d = DD(list)
pos = sm.find('%020d'%start)
#print (pos)
for x in range(pos, sm.len):
offset = int(sm[x][20:])
ts, nd = smd.readData (offset, stop)
if ( nd == None): break
#print("nd=" + repr(nd))
for usrdata in nd[3:]: # username, userdata
usr2d[usrdata[0]].append([ts] + list(usrdata[1:7]))
lseries, mseries = [], []
for usrname in sorted(usr2d.keys()):
l, m = [], []
for e in usr2d[usrname]:
ts = e[0]*1000
l.append([ts, e[4]])
m.append([ts, e[6]])
lseries.append({'data': l, 'name': usrname})
mseries.append({'data': m, 'name': usrname})
#[{'name':username, 'data':[[timestamp, value]...]} ...]
return lseries, mseries
def voteRank(sequences, motifs):
poll = {}
for seq in sequences:
poll[seq] = [0.0] * len(sequences[seq])
# perform poll
for tool in motifs:
for motif in motifs[tool]:
for seq in motifs[tool][motif]:
sequence = best(sequences, seq)
for pos in motifs[tool][motif][seq]:
for i in xrange(pos, pos + len(motif)):
try:
# instead of weighting all results the same (1), we
# could bias based on tool or number of results or something like that
poll[sequence][i - 1] += 1
except Exception as e:
print e
print 'It appears a tool has reported finding a motif',\
'outside the bounds of a sequence'
print 'such as finding a motif of length 10 at position',\
'195 in a sequence with length 200'
pdb.set_trace()
# add up votes for each motif
ress = DD(int)
for tool in motifs:
for motif in motifs[tool]:
for seq in motifs[tool][motif]:
for pos in motifs[tool][motif][seq]:
for p in xrange(pos, pos + len(motif)):
ress[motif] += poll[best(sequences, seq)][p-1]
# sort motifs by number of votes
return sorted(map(lambda a: list(a[::-1]), ress.iteritems()))
def parseMEME(fout):
try:
with open(RES_DIR + "/meme/meme.txt", "r") as memeResults:
foundMotifs["MEME"] = []
foundMotifsSeqs["MEME"] = {}
readMode = 0 # 0 is looking for next motif, 1 is looking for pos's, 2 is reading pos's
for line in memeResults:
if readMode == 1:
readMode = 2
continue
if readMode == 2:
if "----------------------" in line:
readMode = 0
continue
seqName, sPos = line.split()[0:2]
seqName = seqName[0:19].strip()
foundMotifs["MEME"][-1][1].append(SEQ_LENS[seqName] - int(sPos))
foundMotifsSeqs["MEME"][motif][seqName].append(int(sPos))
fout.write("\t" + str(SEQ_LENS[seqName] - int(sPos)))
if "Multilevel" in line:
motif = line.strip().replace("Multilevel","").replace(" ","")
fout.write("\nMEME\t"+motif)
foundMotifs["MEME"] += [[motif, []]]
foundMotifsSeqs["MEME"][motif] = DD(list)
elif "Sequence name" in line and "Start" in line:
readMode = 1
except IOError:
print "Error opening MEME's results file"
def parseXXmotif(fout):
try:
foundMotifs['XXmotif'] = []
foundMotifsSeqs['XXmotif'] = {}
reportedMotifs = [None]
lets = 'ACGT'
baseFname = RES_DIR + '.'.join(
FILTERED_SEQ.split('/')[-1].split('.')[:-1])
#baseFname = 'XXmotif/results/hsap_core_promoters_all'
# parse motifs
#pdb.set_trace()
with open(baseFname + '.pwm') as XXmotifResults:
cm = []
for i, line in enumerate(XXmotifResults):
if i % 6 == 0 or i % 6 == 5:
if len(cm) == 4:
motif = ''.join([
lets[p.index(max(p))]
for p in zip(cm[0], cm[1], cm[2], cm[3])
])
foundMotifs['XXmotif'].append([motif, []])
foundMotifsSeqs['XXmotif'][motif] = DD(list)
cm = []
else:
cm.append(map(float, line.split()))
seqs = []
# parse sequence mappings
with open(baseFname + '_sequence.txt') as XXmotifSequences:
for i in xrange(4):
XXmotifSequences.next()
for line in XXmotifSequences:
seqs.append(line.split('\t')[-1].strip())
# parse instance locations
with open(baseFname + '_Pvals.txt') as XXmotifPoss:
for i in xrange(4):
XXmotifPoss.next()
mnum = 0
for line in XXmotifPoss:
if len(line) < 4:
continue
if line[:6] == 'Motif ':
mnum = int(line.split()[1][:-1]) - 1
else:
pos = int(line.split('\t')[4])
seq = seqs[int(line.split('\t')[3]) - 1]
foundMotifs['XXmotif'][mnum][1].append(pos)
foundMotifsSeqs['XXmotif'][foundMotifs['XXmotif'][mnum]
[0]][seq].append(pos)
for motif, poss in foundMotifs['XXmotif']:
fout.write('\nXXmotif\t' + motif)
for pos in poss:
fout.write('\t' + str(pos))
except IOError as e:
print e, e.filename
print "Error opening XXmotif's result file"
def reset_log():
# TODO: make it such that if key is not in dict, it's init with incoming content
# this way, we won't have to know in advance what we want to monitor
return DD(list)
logs = OD()
for name in [
'inner log p(x|z)', 'log p(x|z)', 'log p(x|z) nn', 'commit', 'vq',
'kl', 'bpd', 'elbo'
]:
logs[name] = []
return logs
def parseDECOD(fout):
try:
with open(RES_DIR + "decod_found_motifs.txt", "r") as _decodResults:
foundMotifs["DECOD"] = []
foundMotifsSeqs["DECOD"] = {}
alreadyFound = {}
decodResults = _decodResults.readlines()
lineNum = 0
chars = ["A", "C", "G", "T"]
for motif_num in xrange(int(config["DECOD"]["-nmotif"])):
PWM = {}
# parse PWM
for line in decodResults[lineNum:]:
lineNum+=1
if len(line) > 1 and line[0] in chars:
PWM[line[0]] = line.strip("ACGT []\n").split()
if line[0] == "T":
break
# construct motif from PWM
motif = ""
for i in xrange(len(PWM['A'])):
col = [PWM[x][i] for x in chars]
motif += chars[col.index(max(col))]
foundMotifsSeqs["DECOD"][motif] = DD(list)
# parse instances
if not motif in alreadyFound:
fout.write("\nDECOD\t"+motif)
# find beginning of instances
while decodResults[lineNum][0] != ">":
lineNum+=1
# save instances
positions = []
while decodResults[lineNum][0] == ">":
line = decodResults[lineNum]
lineNum+=1
if "|revcom" in line:
continue
seqName = line[1:line.find("\t")]
seqName = seqName.strip()
pos = int(line.split("\t")[1])
foundMotifsSeqs["DECOD"][motif][seqName].append(pos)
pos = SEQ_LENS[seqName] - pos
positions.append(pos)
fout.write("\t" + str(pos))
foundMotifs["DECOD"] += [[motif, positions]]
alreadyFound[motif] = 0
except IOError:
print "Error opening DECOD's results file"
def Find_Matches(pattern, Suffa, Words):
PS = pattern.split()
#length = len(PS)
length = len(pattern)
index = []
Returner = DD(list)
ceiling_cost = (0.3 * len(pattern))
for i in range(0, length):
for j in range(length, i, -1):
remain = length - j + 1
current = pattern[i:j + 1]
index = FIND_IN_SUFFIX_ARRAY(Suffa, Words, pattern[i:j + 1])
Matches = [Match]
if (len(index) > 1):
for m in index:
if m.segment_id != -1:
pbfor = i
m.leftmin = abs(m.start - pbfor)
if m.leftmin == 0 and i > 0:
m.leftmin = 1
paftr = len(pattern[j + 1:])
m.rightmin = abs(m.remain - paftr)
#m.rightmin = abs(abs(m.remain) - abs(paftr))
if m.rightmin == 0 and remain > 0:
m.rightmin = 1
min_cost = m.leftmin + m.rightmin
m.pstart = i
m.pend = j + 1
m.leftmax = max(m.start, pbfor)
m.rightmax = max(m.remain, paftr)
if (min_cost <= ceiling_cost + 1):
#index.remove(m)
Returner[pattern[i:j + 1]].append(m)
break
#if(len(index) > 1):
# print(pattern[i:j+1],len(index)-1,file = filep )#,file = open("test.txt","a"))
#for v in index:
# Returner[pattern[i:j+1]].append(v)
#break
return Returner
def Filter_N_Gram(Matches):
Copy = DD(list)
Last = -1
for all in Matches.keys():
LS = Matches[all]
LASTIN = LS[0].pstart
if LASTIN != Last:
for val in LS:
Copy[all].append(val)
Last = LASTIN
return Copy
def CreateSuffixArrayDD(text):
Suffix = DD(list)
Words = text.split(sep=' ')
Suff_Arr = []
count = 0
for i in Words:
if i != '':
Suffix[i[0]].append(count)
count += 1
SuffA = []
for i in sorted(Suffix):
SuffA.extend(SortAlpha(Suffix[i], Words))
return (SuffA, Words)
def Find_Matches_NO_FILTER(pattern, Suffa, Words):
PS = pattern.split()
#length = len(PS)
length = len(pattern)
index = []
Returner = DD(list)
ceiling_cost = (0.3 * len(pattern))
for i in range(0, length):
for j in range(length, i, -1):
remain = length - j + 1
current = pattern[i:j + 1]
index = FIND_IN_SUFFIX_ARRAY_MY_METHOD(Suffa, Words,
pattern[i:j + 1])
Matches = [Match]
if (len(index) > 1):
for m in index:
Returner[pattern[i:j + 1]].append(m)
return Returner
def conll_evaluate(l0_inputs, alphas, conll_eval_path, all_top_antecedent_scores):
print("Compiling clusters and evaluators for conll suite")
coref_predictions = [{} for _ in alphas]
coref_evaluators = [metrics.CorefEvaluator() for _ in alphas]
subtoken_maps = {}
with open(l0_inputs, "rb") as f:
data_dicts = np.load(f, allow_pickle=True).item().get("data_dicts")
for example_num, data_dict in enumerate(tqdm(data_dicts)):
example = data_dict["example"]
subtoken_maps[example["doc_key"]] = example["subtoken_map"]
top_span_starts = data_dict["top_span_starts"]
top_span_ends = data_dict["top_span_ends"]
top_antecedents = data_dict["top_antecedents"]
for i in range(len(alphas)):
top_antecedent_scores = all_top_antecedent_scores[example["doc_key"]][i]
predicted_antecedents = get_predicted_antecedents(top_antecedents, top_antecedent_scores)
coref_predictions[i][example["doc_key"]] = evaluate_coref(top_span_starts,
top_span_ends, predicted_antecedents, example["clusters"], coref_evaluators[i])
summary_dict = DD(list)
for i in range(len(alphas)):
print("\n*****************************")
print("******* alpha = %f *******" % alphas[i])
summary_dict["alpha"].append(alphas[i])
conll_results = conll.evaluate_conll(conll_eval_path, coref_predictions[i], subtoken_maps, official_stdout=True)
average_f1 = sum(results["f"] for results in conll_results.values()) / len(conll_results)
summary_dict["Average F1 (conll)"].append(average_f1)
print("Average F1 (conll): {:.2f}%".format(average_f1))
p,r,f = coref_evaluators[i].get_prf()
summary_dict["Average F1 (py)"].append(f)
print("Average F1 (py): {:.2f}% on {} docs".format(f * 100, len(subtoken_maps.keys())))
summary_dict["Average precision (py)"].append(p)
print("Average precision (py): {:.2f}%".format(p * 100))
summary_dict["Average recall (py)"].append(r)
print("Average recall (py): {:.2f}%".format(r * 100))
return summary_dict
def parseWeeder(fout):
try:
with open(RES_DIR + get_filename(FILTERED_SEQ) + ".w2", "r") as _weederResults:
foundMotifs["Weeder"] = []
foundMotifsSeqs["Weeder"] = {}
weederResults = _weederResults.readlines()[6:]
for line in weederResults:
if "Matrix" in line:
result = line.split()
motif = result[2]
fout.write("\nweeder\t"+result[2])
foundMotifs["Weeder"] += [[result[2], []]]
foundMotifsSeqs["Weeder"][motif] = DD(list)
elif line[0] == ">":
seqName, mpos = line.split('\t')[0::4]
seqName = seqName.strip()
foundMotifs["Weeder"][-1][1].append(SEQ_LENS[seqName[1:]] - int(mpos))
foundMotifsSeqs["Weeder"][motif][seqName[1:]].append(int(mpos))
fout.write("\t"+str(SEQ_LENS[seqName[1:]] - int(mpos)))
except IOError:
print "Error opening Weeder's results file"
def Find_All_Matches(Suffa,Words,pattern):
length = len(pattern)
Matches = DD(list)
ceiling_cost = (0.3 * len(pattern))
for i in range(0,length):
for j in range(length,i,-1) :
search = pattern[i:j+1]
ind = FIND_IN_SUFFIX_ARRAY(Suffa,Words,search)
start = i
end = j+1
remain = len(pattern[j+1:])
for M in ind:
if M.segment_id == -1:
break
#min_cost = MatchFiltering(start,end,remain,MS)
M.leftmin = abs(M.start - start)
if M.leftmin == 0 and start > 0:
M.leftmin = 1
M.rightmin = abs(M.remain - remain)
if M.rightmin == 0 and remain > 0:
M.rightmin = 1
min_cost = M.leftmin + M.rightmin
if min_cost <= ceiling_cost:
M.leftmax = max(M.start,start)
M.rightmax = max(M.remain,remain)
M.pstart = start
M.pend = end
Matches[(pattern[i:j+1])].append(M)
return Matches
def main():
logging.basicConfig(level=logging.INFO)
parser = argparse.ArgumentParser(
description='''Script to find out what words BERT\'s
attention attends to in layers 0, 9, 10, 11.''')
parser.add_argument('-i', '--src', type=argparse.FileType('r'), metavar='PATH',
help='''File containing multiple lines of input text, if not specified,
uses some predefined text.''')
parser.add_argument('-t', '--top', type=int, default=10, metavar='K',
help='Find top K words that BERT attends to. Default 10.')
parser.add_argument('-b', '--batch_size', type=int, default=20, metavar='B',
help='Specify batch size=B. Default 20.')
parser.add_argument('-g', '--gpu', action='store_true',
help='Option to use GPU.')
parser.add_argument('-a', '--all_layers', action='store_true',
help='Output the attention of each layer')
parser.add_argument('-T', '--out_top', type=int, metavar='N',
help='Output top N words in final output. If -a is set, default value is 100')
parser.add_argument('-m', '--mask', action='store_true',
help='Mask attended words and compare predictions with original words. Not functional yet.')
parser.add_argument('-o', '--out', type=argparse.FileType('w'),
help='File to write results', required=True)
args = parser.parse_args()
top_k = args.top
batch_size = args.batch_size
use_gpu = args.gpu
do_mask = args.mask
all_layers = args.all_layers
out_top_k = args.out_top
print('all_layers', all_layers)
if args.src is not None:
src = args.src
else:
text = 'burma has put five cities on a security alert after religious unrest involving buddhists and moslems in the northern city of mandalay , an informed source said wednesday.'
text1 = 'police arrested five anti-nuclear protesters friday after they sought to disrupt loading of a french antarctic research and supply vessel , a spokesman for the protesters said .'
text2 = 'turkmen president gurbanguly berdymukhammedov will begin a two-day visit to russia , his country \'s main energy partner , on monday for trade talks , the kremlin press office said .'
text3 = 'israel \'s new government barred yasser arafat from flying to the west bank to meet with former prime minister shimon peres on thursday , a move palestinian officials said violated the israel-plo peace accords .'
src = [text, text1, text2, text3]
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained('bert-base-uncased', output_attentions=True)
model.eval()
if do_mask:
mask_model = BertForMaskedLM.from_predtrained('bert-base-uncased')
mask_model.eval()
mask_token = tokenizer.mask_token
mask_id = tokenizer.convert_tokens_to_ids([mask_token])[0]
ignore_tokens = ['[CLS]', '[SEP]', '.', ',',
'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday',
'said', '#', 'here', '<', 'news', '>', '`',
'has', 'have', 'will', 'the', 'a', 'is', 'was', 'are',
'on', 'as', 'after', 'this', 'in', 'with', 'to']
ignore_ids = tokenizer.convert_tokens_to_ids(ignore_tokens)
if use_gpu:
mask_model = mask_model.cuda()
if use_gpu:
assert torch.cuda.is_available(), 'GPU unavailable!'
model = model.cuda()
print('-- Using GPU --')
device = torch.device('cuda') if use_gpu else torch.device('cpu')
total_words_attended = 0
total_start_time = time.time()
# if batch_size is None:
# count = 0
# layers = [0, 9, 10, 11]
# for line in src:
# if count % 100 == 0:
# print('Processed', count, 'lines')
# count += 1
# line = line.strip()
# tokens_tensor = str_to_idx_tensor(tokenizer, line)
# if use_gpu:
# tokens_tensor = tokens_tensor.cuda()
# str_tokens = tokenizer.convert_ids_to_tokens(tokens_tensor.tolist()[0])
# outputs = model(tokens_tensor)
#
# cum_attn = []
#
# for l in layers:
# layer = outputs[2][l]
# summed = layer.sum(dim=2).sum(dim=1).view(-1)
# summed = (summed / summed.sum(dim=0))
# cum_attn.append(summed)
#
# all_attns = torch.stack(cum_attn).sum(dim=0)
# normalized_attn = (all_attns / all_attns.sum(dim=0)).tolist()
# sorted_by_attn = sorted(list(zip(normalized_attn, str_tokens)), key=lambda p: p[0], reverse=True)
#
# for p in sorted_by_attn[:top_k]:
# attended_word_dict[p[1]] += 1
#
# total_words_attended += len(sorted_by_attn[:top_k])
# if args.src is not None:
# args.src.close()
# batch implementation
if all_layers:
attended_word_tensor = torch.zeros(12, tokenizer.vocab_size,
device=device)
else:
layers = torch.tensor([0], device=device)
attended_word_tensor = torch.zeros(tokenizer.vocab_size, device=device)
batch_iterator = chunked(src, batch_size)
iter = 0
for batch in batch_iterator:
start_time = time.time()
if iter % 10 == 0:
print('Processed', iter, 'batches')
iter += 1
batch_toks, _, attn_mask, _ = batch_to_idx_tensor(tokenizer, batch)
if use_gpu:
batch_toks = batch_toks.cuda()
attn_mask = attn_mask.cuda()
with torch.no_grad():
outputs = model(batch_toks, attention_mask=attn_mask)
attn = torch.stack(outputs[2])
# attn has shape [num_layers (12), batch_size, num_heads, max_src_len, max_src_len]
if all_layers:
summed = attn.sum(dim=3).sum(dim=2) # [num_layers, batch_size, max_src_len]
summed = summed / summed.sum(dim=2, keepdim=True) # normalize
summed.transpose_(0,1)
# summed has shape [bath_size, num_layers, max_src_len]
else:
attn = attn.index_select(0, layers)
summed = attn.sum(dim=3).sum(dim=2).sum(dim=0).view(attn.shape[1], attn.shape[4])
summed = summed / summed.sum(dim=1, keepdim=True) # normalize
# summed has shape [batch_size, max_src_len]
_, topk_idxs = summed.topk(top_k, sorted=True)
# topk_idxs has shape [batch_size, top_k]
# split by each item in batch
split_batch_toks = batch_toks.split(1, dim=0)
split_topk_idxs = topk_idxs.split(1, dim=0)
for idxs, toks in zip(split_topk_idxs, split_batch_toks):
attended_toks = toks.squeeze(0)[idxs.squeeze(0)]
if all_layers:
# record topk attended tokens for each layer
for i in range(summed.shape[1]):
attended_word_tensor[i, attended_toks[i]] += 1
else:
attended_word_tensor[attended_toks] += 1
total_words_attended += topk_idxs.shape[0] * topk_idxs.shape[1]
if args.src is not None:
args.src.close()
if all_layers:
f = args.out
f.write('Total tokens attended: {}\n '.format(total_words_attended))
for i in range(attended_word_tensor.shape[0]):
non_zero_idxs = attended_word_tensor[i].nonzero().view(-1)
counts = attended_word_tensor[i, non_zero_idxs].tolist()
toks = tokenizer.convert_ids_to_tokens(non_zero_idxs.tolist())
attended_word_dict = DD(int)
attended_word_dict.update(zip(toks, counts))
top_attn_count = sorted(attended_word_dict.items(),key=lambda p: p[1],reverse=True)
out_top_k = 300 if not out_top_k else out_top_k
f.write('\nLAYER {}\n'.format(i))
for p in top_attn_count[:out_top_k]:
f.write(p[0] + ' ' + str(p[1]) + '\n')
f.close()
print('Finished, total duration = {:.4}'.format(time.time() - total_start_time))
else:
non_zero_idxs = attended_word_tensor.nonzero().view(-1)
counts = attended_word_tensor[non_zero_idxs].tolist()
toks = tokenizer.convert_ids_to_tokens(non_zero_idxs.tolist())
attended_word_dict = DD(int)
attended_word_dict.update(zip(toks, counts))
# end if of batch implementation
print('Finished, total duration = {:.4}'.format(time.time() - total_start_time))
top_attn_count = sorted(attended_word_dict.items(),key=lambda p: p[1],reverse=True)
f = args.out
f.write('Total tokens attended: {}\n '.format(total_words_attended))
if out_top_k is not None:
top_attn_count = top_attn_count[:out_top_k]
for p in top_attn_count:
f.write(p[0] + ' ' + str(p[1]) + '\n')
f.close()
def run_auto(self):
'''
test direct data feature based transfer accuracy on the new building
'''
rf = RFC(n_estimators=100, criterion='entropy')
rf.fit(self.train_fd, self.train_label)
pred = rf.predict(self.test_fd)
print('direct data feature-based transfer acc on tgt_bldg:',
ACC(pred, self.test_label))
#plot_confusion_matrix(self.test_label, pred)
'''
step1: train base models from bldg1
'''
self.get_base_learners()
'''
step2: TL with name feature on bldg2
'''
label = self.test_label
class_ = np.unique(self.train_label)
for b in self.bl:
print(b.score(self.test_fd, label))
n_class = 32
c = KMeans(init='k-means++', n_clusters=n_class, n_init=10)
c.fit(self.test_fn)
dist = np.sort(c.transform(self.test_fn))
ex_id = DD(list) #example id for each C
for i, j, k in zip(c.labels_, range(len(self.test_fn)), dist):
ex_id[i].append(int(j))
#getting neighors for each ex
nb_c = DD() #nb from clustering results
for exx in ex_id.values():
exx = np.asarray(exx)
for e in exx:
nb_c[e] = exx[exx != e]
nb_f = [DD(), DD(), DD()] #nb from classification results
for b, n in zip(self.bl, nb_f):
preds = b.predict(self.test_fd)
ex_ = DD(list)
for i, j in zip(preds, range(len(self.test_fd))):
ex_[i].append(int(j))
for exx in ex_.values():
exx = np.asarray(exx)
for e in exx:
n[e] = exx[exx != e]
#use base learners' predicitons
acc_ = []
cov_ = []
#for delta in np.linspace(0.1, 0.5, 5):
for delta in np.linspace(self.agreement_threshold,
self.agreement_threshold, 1):
print('running TL with agreement threshold =', delta)
labeled_id = []
confidence = []
output = DD()
preds = np.array([999 for i in range(len(self.test_fd))])
for i in range(len(self.test_fn)):
#get the weight for each bl: by computing sim btw cluster and clf
w = []
v_c = set(nb_c[i])
for n in nb_f:
v_f = set(n[i])
cns = len(v_c & v_f) / float(
len(v_c | v_f)) #original count based weight
#print (len(v_c & v_f) , len(v_c | v_f))
inter = v_c & v_f
union = v_c | v_f
d_i = 0
d_u = 0
for it in inter:
d_i += np.linalg.norm(self.test_fn[i] -
self.test_fn[it])
#print (np.linalg.norm(self.test_fn[i]-self.test_fn[it]))
#input('...')
for u in union:
d_u += np.linalg.norm(self.test_fn[i] -
self.test_fn[u])
if len(inter) != 0:
sim = 1 - (d_i / d_u) / cns
#sim = (d_i/d_u)/cns
if i in output:
output[i].extend(
['%s/%s' % (len(inter), len(union)), 1 - sim])
else:
output[i] = [
'%s/%s' % (len(inter), len(union)), 1 - sim
]
w.append(sim)
output[i].append(np.mean(w))
if np.mean(w) >= delta:
confidence.append(np.mean(w))
w[:] = [float(j) / sum(w) for j in w]
pred_pr = np.zeros(len(class_))
for wi, b in zip(w, self.bl):
pr = b.predict_proba(self.test_fd[i].reshape(1, -1))
pred_pr = pred_pr + wi * pr
preds[i] = class_[np.argmax(pred_pr)]
labeled_id.append(i)
acc_.append(ACC(preds[preds != 999], label[preds != 999]))
cov_.append(1.0 * len(preds[preds != 999]) / len(label))
print('acc =', acc_, ';')
print('cov =', cov_, ';')
return preds[preds != 999], labeled_id, confidence
def voteRefine(sequences, motifs):
#get probabilities
lets = "ACGT"
probability = DD(int)
for seq in sequences:
for let in sequences[seq]:
probability[let] += 1
s = sum(probability.values())
for let in lets:
probability[let] = probability[let] / float(s)
#conductPoll
poll = {}
maxV = 0
maxL = 0
for seq in sequences:
poll[seq] = [0.0] * len(sequences[seq])
if len(sequences[seq]) > maxL:
maxL = len(sequences[seq])
for tool in motifs:
for motif in motifs[tool]:
for seq in motifs[tool][motif]:
sequence = best(sequences, seq)
for pos in motifs[tool][motif][seq]:
for i in xrange(pos, pos + len(motif)):
try:
# instead of weighting all results the same (1), we
# could bias based on tool or number of results or something like that
#poll[sequence][i - 1] += 1
if tool == "CMF":
poll[sequence][i - 1] += 1
if tool == "Weeder":
poll[sequence][i - 1] += 1
if tool == "MEME":
poll[sequence][i - 1] += 1
if tool == "DECOD":
poll[sequence][i - 1] += 1
if tool == "BioProspector":
poll[sequence][i - 1] += 1
if tool == "XXmotif":
poll[sequence][i - 1] += 1
except Exception as e:
print e
print 'It appears a tool has reported finding a motif',\
'outside the bounds of a sequence'
print 'such as finding a motif of length 10 at position',\
'195 in a sequence with length 200'
pdb.set_trace()
if poll[sequence][i - 1] > maxV:
maxV = poll[sequence][i - 1]
#inspectPoll
ress = []
THRESH = 3.7
maxInsts = 0
MLEN = MOTIF_LEN
for seq in poll:
for i in xrange(len(poll[seq]) - MLEN):
if sum(poll[seq][i:i + MLEN]) >= MLEN * THRESH:
curr = sequences[seq][i:i + MLEN]
bestPWM = None
bestMatching = 0
for PWM in ress:
matching = compMotifPWM(curr, PWM)
if matching > bestMatching and matching > MLEN / 2:
bestMatching = matching
bestPWM = PWM
if bestPWM == None:
bestPWM = [[0, 0, 0, 0] for x in xrange(MLEN)]
ress.append(bestPWM)
for c, col in zip(curr, bestPWM):
col[ALPH[c]] += 1
insts = sum(bestPWM[0])
if insts > maxInsts:
maxInsts = insts
votedRess = DD(int)
for PWM in ress:
l = len(PWM)
cons = PWMconsensus(PWM)
for seq in sequences:
for spos in xrange(0, len(sequences[seq]) - l):
# .75% thresh
if compMotifPWM(sequences[seq][spos:spos + l], PWM) >= .75 * l:
for pos in xrange(spos, spos + l):
votedRess[cons] += poll[seq][pos]
return sorted(votedRess.iteritems(), key=lambda a: a[::-1])
def __init__(self):
self._errors = DD(list)
self._workbook_fp = None
self._days_ago = 0