Module ktrain.text.predictor

Classes

class TextPredictor (model, preproc, batch_size=32)

predicts text classes

Expand source code

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, use_tf_dataset=False, verbose=0):
        """
        ```

        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
          use_tf_dataset(bool): If True, wraps dataset in a tf.Dataset when passing input to model.

          verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
        ```
        """

        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)
            if use_tf_dataset:
                tseq.batch_size = self.batch_size
                tfd = tseq.to_tfdataset(train=False)
                preds = self.model.predict(tfd, verbose=verbose)
            else:
                data = tseq.to_array()
                preds = self.model.predict(
                    data, batch_size=self.batch_size, verbose=verbose
                )
            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, verbose=verbose
            )

        # 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, verbose=0):
        """
        ```
        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, verbose=verbose)

    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

Ancestors

• Predictor
• abc.ABC

Methods

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.

Expand source code

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 get_classes(self)

Expand source code

def get_classes(self):
    return self.c

def predict(self, texts, return_proba=False, use_tf_dataset=False, verbose=0)

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
  use_tf_dataset(bool): If True, wraps dataset in a tf.Dataset when passing input to model.

  verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)

Expand source code

def predict(self, texts, return_proba=False, use_tf_dataset=False, verbose=0):
    """
    ```

    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
      use_tf_dataset(bool): If True, wraps dataset in a tf.Dataset when passing input to model.

      verbose(int): verbosity: 0 (silent), 1 (progress bar), 2 (single line)
    ```
    """

    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)
        if use_tf_dataset:
            tseq.batch_size = self.batch_size
            tfd = tseq.to_tfdataset(train=False)
            preds = self.model.predict(tfd, verbose=verbose)
        else:
            data = tseq.to_array()
            preds = self.model.predict(
                data, batch_size=self.batch_size, verbose=verbose
            )
        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, verbose=verbose
        )

    # 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, verbose=0)

Makes predictions for a list of strings where each string is a document
or text snippet.
Returns probabilities of each class.

Expand source code

def predict_proba(self, texts, verbose=0):
    """
    ```
    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, verbose=verbose)

Inherited members

• Predictor:
  • create_onnx_session
  • export_model_to_onnx
  • export_model_to_tflite
  • save