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nlp.py
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nlp.py
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from .imports import *
from .torch_imports import *
from .core import *
from .model import *
from .dataset import *
from .learner import *
from .lm_rnn import *
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from torchtext.datasets import language_modeling
import spacy
re_br = re.compile('<br />')
spacy_en = spacy.load('en')
def sub_br(x): return re_br.sub("\n", x)
def spacy_tok(x): return [tok.text for tok in spacy_en.tokenizer(sub_br(x))]
re_tok = re.compile(f'([{string.punctuation}“”¨«»®´·º½¾¿¡§£₤‘’])')
def tokenize(s): return re_tok.sub(r' \1 ', s).split()
def texts_from_files(src, names):
texts,labels = [],[]
for idx,name in enumerate(names):
path = os.path.join(src, name)
t = [o.strip() for o in open(path, encoding = "ISO-8859-1")]
texts += t
labels += ([idx] * len(t))
return texts,np.array(labels)
def texts_from_folders(src, names):
texts,labels = [],[]
for idx,name in enumerate(names):
path = os.path.join(src, name)
for fname in sorted(os.listdir(path)):
fpath = os.path.join(path, fname)
texts.append(open(fpath).read())
labels.append(idx)
return texts,np.array(labels)
class DotProdNB(nn.Module):
def __init__(self, nf, ny, w_adj=0.4, r_adj=10):
super().__init__()
self.w_adj,self.r_adj = w_adj,r_adj
self.w = nn.Embedding(nf+1, 1, padding_idx=0)
self.w.weight.data.uniform_(-0.1,0.1)
self.r = nn.Embedding(nf+1, ny)
def forward(self, feat_idx, feat_cnt, sz):
w = self.w(feat_idx)
r = self.r(feat_idx)
x = ((w+self.w_adj)*r/self.r_adj).sum(1)
return F.softmax(x)
class SimpleNB(nn.Module):
def __init__(self, nf, ny):
super().__init__()
self.r = nn.Embedding(nf+1, ny, padding_idx=0)
self.b = nn.Parameter(torch.zeros(ny,))
def forward(self, feat_idx, feat_cnt, sz):
r = self.r(feat_idx)
x = r.sum(1)+self.b
return F.softmax(x)
class BOW_Learner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
self.crit = F.l1_loss
def calc_r(y_i, x, y):
p = x[np.argwhere(y==y_i)[:,0]].sum(0)+1
q = x[np.argwhere(y!=y_i)[:,0]].sum(0)+1
return np.log((p/p.sum())/(q/q.sum()))
class BOW_Dataset(Dataset):
def __init__(self, bow, y, max_len):
self.bow,self.max_len = bow,max_len
self.c = int(y.max())+1
self.n,self.vocab_size = bow.shape
self.y = one_hot(y,self.c)
x = self.bow.sign()
self.r = np.stack([calc_r(i, x, y).A1 for i in range(self.c)]).T
def do_pad(self, prepend, a):
return np.array((prepend+a.tolist())[-self.max_len:])
def pad_row(self, row):
prepend = [0] * max(self.max_len - len(row.indices), 0)
return self.do_pad(prepend, row.indices+1), self.do_pad(prepend, row.data)
def __getitem__(self,i):
row = self.bow.getrow(i)
ind,data = self.pad_row(row)
return ind, data, len(row.indices), self.y[i].astype(np.float32)
def __len__(self): return len(self.bow.indptr)-1
class TextClassifierData(ModelData):
@property
def c(self): return self.trn_ds.c
@property
def r(self):
return torch.Tensor(np.concatenate([np.zeros((1,self.c)), self.trn_ds.r]))
def get_model(self, f, **kwargs):
m = to_gpu(f(self.trn_ds.vocab_size, self.c, **kwargs))
m.r.weight.data = to_gpu(self.r)
m.r.weight.requires_grad = False
model = BasicModel(m)
return BOW_Learner(self, model, metrics=[accuracy_thresh(0.5)], opt_fn=optim.Adam)
def dotprod_nb_learner(self, **kwargs): return self.get_model(DotProdNB, **kwargs)
def nb_learner(self, **kwargs): return self.get_model(SimpleNB, **kwargs)
@classmethod
def from_bow(cls, trn_bow, trn_y, val_bow, val_y, sl):
trn_ds = BOW_Dataset(trn_bow, trn_y, sl)
val_ds = BOW_Dataset(val_bow, val_y, sl)
trn_dl = DataLoader(trn_ds, 64, True)
val_dl = DataLoader(val_ds, 64, False)
return cls('.', trn_dl, val_dl)
class LanguageModelLoader():
def __init__(self, ds, bs, bptt):
self.bs,self.bptt = bs,bptt
text = sum([o.text for o in ds], [])
fld = ds.fields['text']
nums = fld.numericalize([text])
self.data = self.batchify(nums)
self.i,self.iter = 0,0
self.n = len(self.data)
def __iter__(self):
self.i,self.iter = 0,0
return self
def __len__(self): return self.n // self.bptt - 1
def __next__(self):
if self.i >= self.n-1 or self.iter>=len(self): raise StopIteration
bptt = self.bptt if np.random.random() < 0.95 else self.bptt / 2.
seq_len = max(5, int(np.random.normal(bptt, 5)))
res = self.get_batch(self.i, seq_len)
self.i += seq_len
self.iter += 1
return res
def batchify(self, data):
nb = data.size(0) // self.bs
data = data[:nb*self.bs]
data = data.view(self.bs, -1).t().contiguous()
return to_gpu(data)
def get_batch(self, i, seq_len):
source = self.data
seq_len = min(seq_len, len(source) - 1 - i)
return source[i:i+seq_len], source[i+1:i+1+seq_len].view(-1)
class RNN_Learner(Learner):
def __init__(self, data, models, **kwargs):
super().__init__(data, models, **kwargs)
self.crit = F.cross_entropy
def save_encoder(self, name): save_model(self.model[0], self.get_model_path(name))
def load_encoder(self, name): load_model(self.model[0], self.get_model_path(name))
class ConcatTextDataset(torchtext.data.Dataset):
def __init__(self, path, text_field, newline_eos=True, **kwargs):
fields = [('text', text_field)]
text = []
if os.path.isdir(path): paths=glob(f'{path}/*.*')
else: paths=[path]
for p in paths:
for line in open(p): text += text_field.preprocess(line)
if newline_eos: text.append('<eos>')
examples = [torchtext.data.Example.fromlist([text], fields)]
super().__init__(examples, fields, **kwargs)
class LanguageModelData():
def __init__(self, path, field, train, validation, test=None, bs=64, bptt=70, **kwargs):
self.path,self.bs = path,bs
self.trn_ds,self.val_ds,self.test_ds = ConcatTextDataset.splits(
path, text_field=field, train=train, validation=validation, test=test)
field.build_vocab(self.trn_ds, **kwargs)
self.pad_idx = field.vocab.stoi[field.pad_token]
self.nt = len(field.vocab)
self.trn_dl,self.val_dl,self.test_dl = [LanguageModelLoader(ds, bs, bptt) for ds in
(self.trn_ds,self.val_ds,self.test_ds)]
def get_model(self, opt_fn, emb_sz, n_hid, n_layers, **kwargs):
m = get_language_model(self.bs, self.nt, emb_sz, n_hid, n_layers, self.pad_idx, **kwargs)
model = SingleModel(to_gpu(m))
return RNN_Learner(self, model, opt_fn=opt_fn)
class TextDataLoader():
def __init__(self, src, x_fld, y_fld):
self.src,self.x_fld,self.y_fld = src,x_fld,y_fld
def __len__(self): return len(self.src)-1
def __iter__(self):
it = iter(self.src)
for i in range(len(self)):
b = next(it)
yield getattr(b, self.x_fld), getattr(b, self.y_fld)
class TextModel(BasicModel):
def get_layer_groups(self):
return [self.model[0].encoder, self.model[0].rnns, self.model[1]]
class TextData(ModelData):
def create_td(self, it): return TextDataLoader(it, self.text_fld, self.label_fld)
@classmethod
def from_splits(cls, path, splits, bs, text_name='text', label_name='label'):
text_fld = splits[0].fields[text_name]
label_fld = splits[0].fields[label_name]
label_fld.build_vocab(splits[0])
trn_iter,val_iter = torchtext.data.BucketIterator.splits(splits, batch_size=bs)
trn_dl = TextDataLoader(trn_iter, text_name, label_name)
val_dl = TextDataLoader(val_iter, text_name, label_name)
obj = cls.from_dls(path, trn_dl, val_dl)
obj.bs = bs
obj.pad_idx = text_fld.vocab.stoi[text_fld.pad_token]
obj.nt = len(text_fld.vocab)
obj.c = len(label_fld.vocab)
return obj
def get_model(self, opt_fn, max_sl, bptt, emb_sz, n_hid, n_layers, **kwargs):
m = get_rnn_classifer(max_sl, bptt, self.bs, self.c, self.nt, emb_sz=emb_sz, n_hid=n_hid, n_layers=n_layers,
pad_token=self.pad_idx, **kwargs)
model = TextModel(to_gpu(m))
return RNN_Learner(self, model, opt_fn=opt_fn)