def absllm(p1, p2, words, We):
p1 = p1.split()
p2 = p2.split()
total = 0
for i in p1:
v1 = lookup(We, words, i)
max = 0
for j in p2:
v2 = lookup(We, words, j)
score = -1 * cosine(v1, v2) + 1
if (abs(score) > abs(max)):
max = score
total += max
llm_score = 0.5 * total / len(p1)
total = 0
for i in p2:
v1 = lookup(We, words, i)
max = 0
for j in p1:
v2 = lookup(We, words, j)
score = -1 * cosine(v1, v2) + 1
if (abs(score) > abs(max)):
max = score
total += max
llm_score += 0.5 * total / len(p2)
return llm_score
def train_naive_bayes(freqs, train_x, train_y):
'''
Input:
freqs: dictionary from (word, label) to how often the word appears
train_x: a list of tweets
train_y: a list of labels correponding to the tweets (0,1)
Output:
logprior: the log prior.
loglikelihood: the log likelihood of you Naive bayes equation.
'''
loglikelihood = {}
logprior = 0
# calculate V, the number of unique words in the vocabulary
vocab = set([pair[0] for pair in freqs.keys()])
V = len(vocab)
# calculate N_pos and N_neg
N_pos = N_neg = 0
for pair in freqs.keys():
# if the label is positive (greater than zero)
if pair[1] > 0:
# Increment the number of positive words by the count for this (word, label) pair
N_pos += freqs[pair]
# else, the label is negative
else:
# increment the number of negative words by the count for this (word,label) pair
N_neg += freqs[pair]
# Calculate D, the number of documents
D = len(train_y)
# Calculate D_pos, the number of positive documents
D_pos = np.sum(train_y)
# Calculate D_neg, the number of negative documents
D_neg = D - D_pos
# Calculate logprior
logprior = np.log(D_pos) - np.log(D_neg)
# For each word in the vocabulary...
for word in vocab:
# get the positive and negative frequency of the word
freq_pos = lookup(freqs, word, 1)
freq_neg = lookup(freqs, word, 0)
# calculate the probability that each word is positive, and negative
p_w_pos = (freq_pos + 1) / (N_pos + V)
p_w_neg = (freq_neg + 1) / (N_neg + V)
# calculate the log likelihood of the word
loglikelihood[word] = np.log(p_w_pos / p_w_neg)
return logprior, loglikelihood
def prepare_data(p1,p2,model,params):
if not params.nntype == "charagram":
chars = model.chars
X1 = []; X2 = []
p1 = " "+p1+" "; p2 = " "+p2+" "
for i in p1:
X1.append(utils.lookup(chars,i))
for i in p2:
X2.append(utils.lookup(chars,i))
return X1, X2
else:
return model.hash(p1), model.hash(p2)
def add(p1, p2, words, We):
p1 = p1.split()
p2 = p2.split()
accumulator = np.zeros(lookup(We, words, p1[0]).shape)
for i in p1:
v = lookup(We, words, i)
accumulator = accumulator + v
p1_emb = accumulator / len(p1)
accumulator = np.zeros(lookup(We, words, p2[0]).shape)
for i in p2:
v = lookup(We, words, i)
accumulator = accumulator + v
p2_emb = accumulator / len(p1)
return -1 * cosine(p1_emb, p2_emb) + 1
def parse_pair(k, v):
parser_pair = utils.lookup(k, parsers, aliases=aliases)
if parser_pair is None:
warnings.warn("ignoring field %s:%s" % (k, v))
return None
k, parser = parser_pair
return k, parser(v)
def goto(self, frame, lookup_columns, lookup_values, zh_travel=0):
"""
Finds lookup_values in lookup_columns of frame's position_list; retrieves corresponding X,Y,Z.
Transforms X,Y,Z to hardware X,Y,Z by frame's transform.
Moves to hardware X,Y,Z, taking into account zh_travel.
:param frame: (str) frame that specifies position_list and transform
:param lookup_columns: (str | list) column(s) to search in position_table
:param lookup_values: (val | list) values(s) to find in lookup_columns
:param zh_travel: (float) hardware height at which to travel
"""
trans, position_table = self.frames[frame]
if lookup_columns == 'xyz':
lookup_values = tuple(lookup_values) + (1, )
xh, yh, zh, _ = np.dot(lookup_values, trans)
else:
xyz = tuple(
ut.lookup(position_table, lookup_columns,
lookup_values)[['x', 'y', 'z']].iloc[0])
xyzw = xyz + (1, ) # concatenate for translation
xh, yh, zh, _ = np.dot(xyzw, trans) # get hardware coordinates
if zh_travel:
self.Z.goto(zh_travel)
else:
self.Z.home()
self.XY.goto_xy(xh, yh)
self.Z.goto(zh)
def get_ratio(freqs, word):
'''
Input:
freqs: dictionary containing the words
word: string to lookup
Output: a dictionary with keys 'positive', 'negative', and 'ratio'.
Example: {'positive': 10, 'negative': 20, 'ratio': 0.5}
'''
pos_neg_ratio = {'positive': 0, 'negative': 0, 'ratio': 0.0}
pos_neg_ratio['positive'] = lookup(freqs, word, 1)
pos_neg_ratio['negative'] = lookup(freqs, word, 0)
# calculate the ratio of positive to negative counts for the word
pos_neg_ratio['ratio'] = (pos_neg_ratio['positive'] + 1) / (pos_neg_ratio['negative'] + 1)
return pos_neg_ratio
def test_targeting(self):
ips = {
"Google":"8.8.8.8" # Le Googs
,"USC":"129.252.37.66" # USC
,"MIT":"18.62.0.96" # MIT
,"Stanford":"171.64.168.31" # Stanford
}
for ip in ips:
self.assertTrue(utils.lookup(ips[ip]) == ip)
def open(self, lookup_cols, lookup_vals):
"""
Finds lookup_vals in lookup_cols of valvemap; retrieves corresponding valve_num.
Opens valve_num.
:param lookup_cols: (str | list) column(s) to search in valvemap
:param lookup_vals: (val | list) value(s) to find in lookup_cols
"""
valve_num = ut.lookup(self.valvemap, lookup_cols, lookup_vals)[['valve']].iloc[0]
self.depressurize(valve_num)
def __init__(self, name, hopcount, description, numeric=0, flags='', protocol_version=0):
self.name=name
self.hopcount=int(hopcount)
self.description=description
self.numeric=int(numeric)
self.flags=flags
self.protocol_version=int(protocol_version)
self.protoctl=lookup()
self.nickchars=[]
self.chanmodes={}
def get_ratio(freqs, word):
'''
Input:
freqs: dictionary containing the words
Output: a dictionary with keys 'positive', 'negative', and 'ratio'.
Example: {'positive': 10, 'negative': 20, 'ratio': 0.5}
'''
pos_neg_ratio = {'positive': 0, 'negative': 0, 'ratio': 0.0}
### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###
# use lookup() to find positive counts for the word (denoted by the integer 1)
pos_neg_ratio['positive'] = lookup(freqs,word,1)
# use lookup() to find negative counts for the word (denoted by integer 0)
pos_neg_ratio['negative'] = lookup(freqs,word,0)
# calculate the ratio of positive to negative counts for the word
pos_neg_ratio['ratio'] = (pos_neg_ratio['positive'] + 1)/(pos_neg_ratio['negative'] + 1)
### END CODE HERE ###
return pos_neg_ratio
def generate_tensor_text(patient_text_list, w2i_lookup, conf_max_len):
patient_list_of_indices = []
max_words = 0
max_notes = 0
for patient_notes in patient_text_list:
# each patient_text is a list of text
list_of_word_idx = []
for note in patient_notes:
# each note is a list of word
indices = list(
map(lambda x: lookup(w2i_lookup, x),
str(note).split()))
if conf_max_len > 0:
indices = indices[:conf_max_len]
list_of_word_idx.append(indices)
max_words = max(len(indices), max_words)
patient_list_of_indices.append(list_of_word_idx)
max_notes = max(len(list_of_word_idx), max_notes)
pad_token = w2i_lookup['<pad>']
if max_notes == 0 or max_words <= 4:
# in case all icu stay in a batch don't have text or all notes are too short to support bigram or trigam conv
max_notes = 1
max_words = 20
# 3. 3d pad, padding token.
# 4. convert to numpy tensor and return
def extra_pad_tokens(cnt):
return [pad_token] * cnt
padded_patient_list_of_indices = []
for pt in patient_list_of_indices:
padded_pt = []
if len(pt) < max_notes:
pt = pt + [[]] * (max_notes - len(pt))
for l in pt:
l = l + extra_pad_tokens(max_words - len(l))
padded_pt.append(l)
padded_patient_list_of_indices.append(padded_pt)
x = np.array(padded_patient_list_of_indices)
try:
assert len(x.shape) == 3
assert x.shape[0] == len(patient_text_list), "x: {}, l: {}".format(
str(x.shape), str(len(patient_text_list)))
return x
except:
print('bad shape of x', x.shape)
def set(self, lookup_cols, lookup_vals, pressure=0.0):
"""
Sets pressure of specified channel.
:param lookup_cols: (str | list) column(s) to search in chanmap
:param lookup_vals: (val | list) value(s) to find in lookup_cols
:param pressure: (float) desired pressure; units specified in config file
"""
channel = ut.lookup(self.chanmap, lookup_cols,
lookup_vals)[['channel']].iloc[0]
channel = int(channel)
mbar = pressure * self.config['conversion_to_mbar']
c_error = self.dll.mfcs_set_auto(self.handle, channel, c_float(mbar))
def report_latest():
if len(current_user.session) <= 0:
flash('You need to chat in order to generate a report')
return redirect(url_for('dashboard'))
score, x, scores_array, text = latest_report()
url1 = freq_words(text)
url2 = depression_dist(scores_array)
url3 = depression_trend(x, scores_array)
verdict = lookup(score)
return render_template('report_latest.html',
score=round(score, 2),
verdict=verdict,
plot1=url1,
plot2=url2,
plot3=url3)
def __init__(self,
name,
hopcount,
description,
numeric=0,
flags='',
protocol_version=0):
self.name = name
self.hopcount = int(hopcount)
self.description = description
self.numeric = int(numeric)
self.flags = flags
self.protocol_version = int(protocol_version)
self.protoctl = lookup()
self.nickchars = []
self.chanmodes = {}
def generate_tensor_text(patient_text_list, w2i_lookup, conf_max_len):
patient_list_of_indices = []
max_indices_listlen = -1
max_senteces_listlen = -1
number_of_docs = []
for patient_text in patient_text_list:
# each patient_text is a list of text
list_of_indices = []
number_of_docs.append(len(patient_text))
for sentence in patient_text:
# each sentence is a list of word
indices = list(map(lambda x: lookup(
w2i_lookup, x), str(sentence).split()))
if conf_max_len > 0:
indices = indices[:conf_max_len]
list_of_indices.append(indices)
max_indices_listlen = max(len(indices), max_indices_listlen)
patient_list_of_indices.append(list_of_indices)
max_senteces_listlen = max(len(list_of_indices), max_senteces_listlen)
pad_token = w2i_lookup['<pad>']
# 3. 3d pad, padding token.
# 4. convert to numpy tensor and return
def extra_pad_tokens(cnt): return [pad_token]*cnt
padded_patient_list_of_indices = []
for pt in patient_list_of_indices:
padded_pt = []
if len(pt) < max_senteces_listlen:
pt = pt + [[]]*(max_senteces_listlen-len(pt))
for l in pt:
l = l + extra_pad_tokens(max_indices_listlen - len(l))
padded_pt.append(l)
padded_patient_list_of_indices.append(padded_pt)
x = np.array(padded_patient_list_of_indices)
l = np.array(number_of_docs)
assert len(x.shape) == 3
assert x.shape[0] == l.shape[0]
assert x.shape[0] == len(patient_text_list), "x: {}, l: {}".format(
str(x.shape), str(len(patient_text_list)))
return x, l
def read(self, lookup_cols, lookup_vals):
"""
Reads current pressure of the channel.
:param lookup_cols: (str | list) column(s) to search in chanmap
:param lookup_vals: (val | list) value(s) to find in lookup_cols
:return: (float) current pressure; units specified in config file
"""
pressure = c_float()
timer = c_ushort()
channel = ut.lookup(self.chanmap, lookup_cols,
lookup_vals)[['channel']].iloc[0]
channel = int(channel)
c_error = self.dll.mfcs_read_chan(self.handle, channel,
pointer(pressure), pointer(timer))
mbar = pressure.value
return mbar / self.config['conversion_to_mbar']
async def on_message(message):
channel = message.channel
if message.content[0:2] == ">>":
spell_query = message.content[2:]
try:
resp = utils.lookup(spell_query)
# discord mesage limit
chunks = utils.discordWrapper(resp, [])
for chunk in chunks:
await channel.send(chunk)
#await channel.send(resp)
except:
await channel.send("Spell not found")
if message.content[0:5] == "!roll":
expression = message.content[5:]
try:
resp = utils.parseRoll(expression.strip())
await channel.send(resp)
except:
await channel.send("Invalid expression")
def goto(self, frame, lookup_columns, lookup_values):
"""
Finds lookup_values in lookup_columns of frame's position_list; retrieves corresponding X,Y
Transforms X,Y to hardware X,Y by frame's transform.
Moves to hardware X,Y.
:param frame: (str) frame that specifies position_list and transform
:param lookup_columns: (str | list) column(s) to search in position_table
:param lookup_values: (val | list) values(s) to find in lookup_columns
"""
trans, position_table = self.frames[frame]
if lookup_columns == 'xy':
lookup_values = tuple(lookup_values) + (1, )
xh, yh = np.dot(lookup_values, trans)
else:
xy = tuple(
ut.lookup(position_table, lookup_columns,
lookup_values)[['x', 'y']].iloc[0])
xyw = xy + (1, ) # concatenate for translation
xh, yh, _ = np.dot(xyw, trans) # get hardware coordinates
self.XY.goto_xy(xh, yh)
def train_naive_bayes(freqs, train_x, train_y):
'''
Input:
freqs: dictionary from (word, label) to how often the word appears
train_x: a list of tweets
train_y: a list of labels correponding to the tweets (0,1)
Output:
logprior: the log prior. (equation 3 above)
loglikelihood: the log likelihood of you Naive bayes equation. (equation 6 above)
'''
loglikelihood = {}
logprior = 0
### START CODE HERE (REPLACE INSTANCES OF 'None' with your code) ###
# calculate V, the number of unique words in the vocabulary
vocab = set([pair[0] for pair in freqs.keys()])
V = len(vocab)
# calculate N_pos and N_neg
N_pos = N_neg = 0
for pair in freqs.keys():
# if the label is positive (greater than zero)
if pair[1] > 0:
# Increment the number of positive words by the count for this (word, label) pair
N_pos += lookup(freqs,pair[0],1)
# else, the label is negative
else:
# increment the number of negative words by the count for this (word,label) pair
N_neg += lookup(freqs,pair[0],0)
# Calculate D, the number of documents
D = len(train_y)
# Calculate D_pos, the number of positive documents (*hint: use sum(<np_array>))
D_pos = sum(train_y)
# Calculate D_neg, the number of negative documents (*hint: compute using D and D_pos)
D_neg = D - D_pos
# Calculate logprior
logprior = np.log(D_pos) - np.log(D_neg)
# For each word in the vocabulary...
for word in vocab:
# get the positive and negative frequency of the word
freq_pos = lookup(freqs,word,1)
freq_neg = lookup(freqs,word,0)
# calculate the probability that each word is positive, and negative
p_w_pos = (freq_pos + 1)/(N_pos + V)
p_w_neg = (freq_neg + 1)/(N_neg + V)
# calculate the log likelihood of the word
loglikelihood[word] = np.log(p_w_pos / p_w_neg)
### END CODE HERE ###
return logprior, loglikelihood
def __init__( self, evalFn = 'scoreEvaluationFunction', depth = '2' ): self.index = 0 # Pacman is always agent index 0 self.evaluationFunction = utils.lookup(evalFn, globals()) self.depth = int(depth)
def populate_embeddings_characters(self, chars):
phrase = " " + self.phrase.lower() + " "
for i in phrase:
self.embeddings.append(utils.lookup(chars, i))
batch_size=args.batch_size,
num_workers=args.num_workers,
root=root)
logging.info("- Done.")
# Training from scratch
model_fd = getattr(models, model_folder)
# Network-based
if args.MulStu:
model_cfg = getattr(model_fd, 'MultiNet')
model = getattr(model_cfg,
'StuNet')(model=args.model,
num_branches=args.num_branches,
num_classes=num_classes,
input_channel=utils.lookup(args.model),
dropout=args.dropout)
# Branch-based
else:
if "resnet" in args.model:
model_cfg = getattr(model_fd, 'resnet_one')
model = getattr(model_cfg,
args.model)(num_classes=num_classes,
num_branches=args.num_branches,
ind=args.ind,
avg=args.avg,
bpscale=args.bpscale)
elif "vgg" in args.model:
model_cfg = getattr(model_fd, 'vgg_one')
model = getattr(model_cfg,
args.model)(num_classes=num_classes,
model_folder = "model_cifar"
root='/home/chendefang/MC/Data'
elif args.dataset == 'imagenet':
num_classes = 1000
model_folder = "model_imagenet"
root = '/home/meijianping/Test/Data'
# Load data
train_loader, test_loader = data_loader.dataloader(data_name = args.dataset, batch_size = args.batch_size, num_workers = args.num_workers, root=root)
logging.info("- Done.")
# Training from scratch
model_fd = getattr(models, model_folder)
if args.MulStu:
model_cfg = getattr(model_fd, 'MultiNet')
model = getattr(model_cfg, 'StuNet')(model = args.model, num_branches = args.num_branches, num_classes = num_classes, input_channel=utils.lookup(args.model), dropout = args.dropout)
elif args.type == 'DML':
model_cfg = getattr(model_fd, 'DML')
model = getattr(model_cfg, 'MutualNet')(model = args.model, num_branches = args.num_branches, num_classes = num_classes)
else:
if "resnet" in args.model:
model_cfg = getattr(model_fd, 'resnet_GL')
model = getattr(model_cfg, args.model)(num_classes = num_classes, num_branches = args.num_branches, input_channel=utils.lookup(args.model))
elif "vgg" in args.model:
model_cfg = getattr(model_fd, 'vgg_GL')
model = getattr(model_cfg, args.model)(num_classes = num_classes, num_branches = args.num_branches)
elif "densenet" in args.model:
model_cfg = getattr(model_fd, 'densenet_GL')
model = getattr(model_cfg, args.model)(num_classes = num_classes, num_branches = args.num_branches)
cwd = os.getcwd()
startTime = int(time.time())
DEBUG = True
stats = {"normal": 0, "invisible": 0, "servers": 0, "opers": 0, "channels": 0}
def shutdown(code):
global exitCode
exitCode = code
protoctl = lookup(
#"NOQUIT",
"NICKv2",
"VL",
"SJ3",
#"NS",
"NICKIP",
"CLK")
pro_nickchars = []
#see http://www.unrealircd.com/files/docs/technical/serverprotocol.html for details on meaning of flags
flags = ["h"]
#if command line config file location is used, add flag C
if (DEBUG): flags.append("D")
#if we're on windows, append flag W
#if we log to syslog, append flag Y
#if we support ipv6, append flag 6
#if we have ssl support, add flag e
#if we implement ziplinks, add flag Z
"normal": 0,
"invisible": 0,
"servers": 0,
"opers": 0,
"channels": 0
}
def shutdown(code):
global exitCode
exitCode=code
protoctl = lookup(
#"NOQUIT",
"NICKv2",
"VL",
"SJ3",
#"NS",
"NICKIP",
"CLK"
)
pro_nickchars = []
#see http://www.unrealircd.com/files/docs/technical/serverprotocol.html for details on meaning of flags
flags = ["h"]
#if command line config file location is used, add flag C
if(DEBUG): flags.append("D")
#if we're on windows, append flag W
#if we log to syslog, append flag Y
#if we support ipv6, append flag 6
#if we have ssl support, add flag e
#if we implement ziplinks, add flag Z
from dataloaders import MultiModal_Dataset, custom_collate_fn
import functools
import json
from tqdm import tqdm
from sklearn import metrics
from utils import BootStrap, BootStrapDecomp, BootStrapLos, BootStrapIhm, BootStrapPheno, BootStrapLtm
import matplotlib.pyplot as plt
from matplotlib.lines import Line2D
#-----------------------Data locations ---------------------------------------------$
conf = utils.get_config()
args = utils.get_args()
vectors, w2i_lookup = utils.get_embedding_dict(conf)
#Note that some more paths are in conf
if conf.padding_type == 'Zero':
vectors[utils.lookup(w2i_lookup, '<pad>')] = 0
train_val_ts_root_dir = '/home/luca/mutiltasking-for-mimic3/data/expanded_multitask/train'
test_ts_root_dir = '/home/luca/mutiltasking-for-mimic3/data/expanded_multitask/test'
train_val_text_root_dir = '/home/luca/mutiltasking-for-mimic3/data/root/train_text_ds/'
test_text_root_dir = '/home/luca/mutiltasking-for-mimic3/data/root/test_text_ds/'
train_val_tab_root_dir = '/home/luca/MultiModal-EHR/data/root/train/'
test_tab_root_dir = '/home/luca/MultiModal-EHR/data/root/test/'
train_listfile = '4k_train_listfile.csv'
val_listfile = '4k_val_listfile.csv'
test_listfile ='test_listfile.csv'
train_val_starttime_path = conf.starttime_path_train_val
test_starttime_path = conf.starttime_path_test
#======================================Hyperparameters======================================#
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
#If we don't care about a task, set it's weight to 0
def __init__( self, evalFn = 'scoreEvaluationFunction', depth = '2' ): self.index = 0 # Pacman is always agent index 0 self.evaluationFunction = utils.lookup(evalFn, globals()) self.depth = int(depth)