/
predictor.py
162 lines (145 loc) · 6.27 KB
/
predictor.py
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from .. import utils as U
from ..imports import *
from ..predictor import Predictor
from .preprocessor import TextPreprocessor, TransformersPreprocessor, detect_text_format
class TextPredictor(Predictor):
"""
```
predicts text classes
```
"""
def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):
if not isinstance(model, keras.Model):
raise ValueError("model must be of instance keras.Model")
if not isinstance(preproc, TextPreprocessor):
# if type(preproc).__name__ != 'TextPreprocessor':
raise ValueError("preproc must be a TextPreprocessor object")
self.model = model
self.preproc = preproc
self.c = self.preproc.get_classes()
self.batch_size = batch_size
def get_classes(self):
return self.c
def predict(self, texts, return_proba=False):
"""
```
Makes predictions for a list of strings where each string is a document
or text snippet.
If return_proba is True, returns probabilities of each class.
Args:
texts(str|list): For text classification, texts should be either a str or
a list of str.
For sentence pair classification, texts should be either
a tuple of form (str, str) or list of tuples.
A single tuple of the form (str, str) is automatically treated as sentence pair classification, so
please refrain from using tuples for text classification tasks.
return_proba(bool): If True, return probabilities instead of predicted class labels
```
"""
is_array, is_pair = detect_text_format(texts)
if not is_array:
texts = [texts]
classification, multilabel = U.is_classifier(self.model)
# get predictions
if U.is_huggingface(model=self.model):
tseq = self.preproc.preprocess_test(texts, verbose=0)
tseq.batch_size = self.batch_size
tfd = tseq.to_tfdataset(train=False)
preds = self.model.predict(tfd)
if hasattr(
preds, "logits"
): # dep_fix: breaking change - also needed for LongFormer
# if type(preds).__name__ == 'TFSequenceClassifierOutput': # dep_fix: undocumented breaking change in transformers==4.0.0
# REFERENCE: https://discuss.huggingface.co/t/new-model-output-types/195
preds = preds.logits
# dep_fix: transformers in TF 2.2.0 returns a tuple insead of NumPy array for some reason
if isinstance(preds, tuple) and len(preds) == 1:
preds = preds[0]
else:
texts = self.preproc.preprocess(texts)
preds = self.model.predict(texts, batch_size=self.batch_size)
# process predictions
if U.is_huggingface(model=self.model):
# convert logits to probabilities for Hugging Face models
if multilabel and self.c:
preds = keras.activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
elif self.c:
preds = keras.activations.softmax(tf.convert_to_tensor(preds)).numpy()
else:
preds = np.squeeze(preds)
if len(preds.shape) == 0:
preds = np.expand_dims(preds, -1)
result = (
preds
if return_proba or multilabel or not self.c
else [self.c[np.argmax(pred)] for pred in preds]
)
if multilabel and not return_proba:
result = [list(zip(self.c, r)) for r in result]
if not is_array:
return result[0]
else:
return result
def predict_proba(self, texts):
"""
```
Makes predictions for a list of strings where each string is a document
or text snippet.
Returns probabilities of each class.
```
"""
return self.predict(texts, return_proba=True)
def explain(self, doc, truncate_len=512, all_targets=False, n_samples=2500):
"""
Highlights text to explain prediction
Args:
doc (str): text of documnet
truncate_len(int): truncate document to this many words
all_targets(bool): If True, show visualization for
each target.
n_samples(int): number of samples to generate and train on.
Larger values give better results, but will take more time.
Lower this value if explain is taking too long.
"""
is_array, is_pair = detect_text_format(doc)
if is_pair:
warnings.warn(
"currently_unsupported: explain does not currently support sentence pair classification"
)
return
if not self.c:
warnings.warn(
"currently_unsupported: explain does not support text regression"
)
return
try:
import eli5
from eli5.lime import TextExplainer
except:
msg = (
"ktrain requires a forked version of eli5 to support tf.keras. "
+ "Install with: pip install https://github.com/amaiya/eli5-tf/archive/refs/heads/master.zip"
)
warnings.warn(msg)
return
if not isinstance(doc, str):
raise TypeError("text must of type str")
prediction = [self.predict(doc)] if not all_targets else None
if self.preproc.is_nospace_lang():
doc = self.preproc.process_chinese([doc])
doc = doc[0]
doc = " ".join(doc.split()[:truncate_len])
te = TextExplainer(random_state=42, n_samples=n_samples)
_ = te.fit(doc, self.predict_proba)
return te.show_prediction(
target_names=self.preproc.get_classes(), targets=prediction
)
def _save_model(self, fpath):
if isinstance(self.preproc, TransformersPreprocessor):
self.model.save_pretrained(fpath)
# As of 0.26.3, make sure we save tokenizer in predictor folder
tok = self.preproc.get_tokenizer()
tok.save_pretrained(fpath)
else:
super()._save_model(fpath)
return