<filename>ktrain/text/predictor.py
from ..imports import *
from ..predictor import Predictor
from .preprocessor import TextPreprocessor, TransformersPreprocessor, detect_text_format
from .. import utils as U

class TextPredictor(Predictor):
    """
    predicts text classes
    """

    def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):

        if not isinstance(model, Model):
            raise ValueError('model must be of instance 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 type(preds).__name__ == 'TFSequenceClassifierOutput': # dep_fix: undocumented breaking change in transformers==4.0.0
                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 = activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
            elif self.c:
                preds = 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 git+https://github.com/amaiya/eli5@tfkeras_0_10_1'
            warnings.warn(msg)
            return
        if not hasattr(eli5, 'KTRAIN_ELI5_TAG') or eli5.KTRAIN_ELI5_TAG != KTRAIN_ELI5_TAG:
            msg = 'ktrain requires a forked version of eli5 to support tf.keras. It is either missing or not up-to-date. '+\
                  'Uninstall the current version and install/re-install the fork with: pip install git+https://github.com/amaiya/eli5@tfkeras_0_10_1'
            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 analyze_valid(self, val_tup, print_report=True, multilabel=None):
        """
        Makes predictions on validation set and returns the confusion matrix.
        Accepts as input the validation set in the standard form of a tuple of
        two arrays: (X_test, y_test), wehre X_test is a Numpy array of strings
        where each string is a document or text snippet in the validation set.

        Optionally prints a classification report.
        Currently, this method is only supported for binary and multiclass 
        problems, not multilabel classification problems.
        """
        U.data_arg_check(val_data=val_tup, val_required=True, ndarray_only=True)
        if multilabel is None:
            multilabel = U.is_multilabel(val_tup)
        if multilabel:
            warnings.warn('multilabel_confusion_matrix not yet supported')
            return

        y_true = val_tup[1]
        y_true = np.argmax(y_true, axis=1)
        y_pred = self.model.predict(val_tup[0])
        y_pred = np.argmax(y_pred, axis=1)
        
        if print_report:
            print(classification_report(y_true, y_pred, target_names=self.c))
            cm_func = confusion_matrix
        cm =  confusion_matrix(y_true,  y_pred)
        return cm


    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


    def export_model_to_onnx(self, fpath, quantize=False, target_opset=None, verbose=1):
        """
        Export model to onnx
        Args:
          fpath(str): String representing full path to model file where ONNX model will be saved.
                      Example: '/tmp/my_model.onnx'
          quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx: 
                         1) ONNX model  (created directly using keras2onnx
                         2) an optimized ONNX model (created by transformers library)
                         3) a quantized version of optimized ONNX model (created by transformers library)
                         All files will be created in the parent folder of fpath:
                         Example: 
                           If fpath='/tmp/model.onnx', then both /tmp/model-optimized.onnx and
                           /tmp/model-optimized-quantized.onnx will also be created.
          verbose(bool): verbosity
        Returns:
          str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
        """
        try:
            import onnxruntime, onnxruntime_tools, onnx, keras2onnx
        except ImportError:
            raise Exception('This method requires ONNX libraries to be installed: '+\
                            'pip install -q --upgrade onnxruntime==1.5.1 onnxruntime-tools onnx keras2onnx')
        from pathlib import Path
        if type(self.preproc).__name__ == 'BERTPreprocessor':
            raise Exception('currently_unsupported:  BERT models created with text_classifier("bert",...) are not supported (i.e., keras_bert models). ' +\
                            'Only BERT models created with Transformer(...) are supported.')

        if verbose: print('converting to ONNX format ... this may take a few moments...')
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer('Name', return_tensors='tf',
                                   padding='max_length', max_length=maxlen)

            if version.parse(tf.__version__) < version.parse('2.2'):
                raise Exception('export_model_to_tflite requires tensorflow>=2.2')
                #self.model._set_inputs(input_spec, training=False) # for tf < 2.2
            self.model._saved_model_inputs_spec = None # for tf > 2.2
            self.model._set_save_spec(input_dict) # for tf > 2.2
            self.model._get_save_spec()

        onnx_model = keras2onnx.convert_keras(self.model, self.model.name, target_opset=target_opset)
        keras2onnx.save_model(onnx_model, fpath)
        return_fpath = fpath

        if quantize:
            from transformers.convert_graph_to_onnx import optimize, quantize
            #opt_path = optimize(Path(fpath))

            if U.is_huggingface(model=self.model) and\
               type(self.model).__name__ in ['TFDistilBertForSequenceClassification', 'TFBertForSequenceClassification']:
                try:
                    from onnxruntime_tools import optimizer
                    from onnxruntime_tools.transformers.onnx_model_bert import BertOptimizationOptions
                    # disable embedding layer norm optimization for better model size reduction
                    opt_options = BertOptimizationOptions('bert')
                    opt_options.enable_embed_layer_norm = False
                    opt_model = optimizer.optimize_model(
                        fpath,
                       'bert',  # bert_keras causes error with transformers
                        num_heads=12,
                        hidden_size=768,
                        optimization_options=opt_options)
                    opt_model.save_model_to_file(fpath)
                except:
                    warnings.warn('Could not run BERT-specific optimizations')
                    pass
            quantize_path = quantize(Path(fpath))
            return_fpath = quantize_path.as_posix()
        if verbose: print('done.')
        return return_fpath

<filename>ktrain/text/predictor.py
from ..imports import *
from ..predictor import Predictor
from .preprocessor import TextPreprocessor, TransformersPreprocessor, detect_text_format
from .. import utils as U

class TextPredictor(Predictor):
    """
    predicts text classes
    """

    def __init__(self, model, preproc, batch_size=U.DEFAULT_BS):

        if not isinstance(model, Model):
            raise ValueError('model must be of instance 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 type(preds).__name__ == 'TFSequenceClassifierOutput': # dep_fix: undocumented breaking change in transformers==4.0.0
                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 = activations.sigmoid(tf.convert_to_tensor(preds)).numpy()
            elif self.c:
                preds = 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 git+https://github.com/amaiya/eli5@tfkeras_0_10_1'
            warnings.warn(msg)
            return
        if not hasattr(eli5, 'KTRAIN_ELI5_TAG') or eli5.KTRAIN_ELI5_TAG != KTRAIN_ELI5_TAG:
            msg = 'ktrain requires a forked version of eli5 to support tf.keras. It is either missing or not up-to-date. '+\
                  'Uninstall the current version and install/re-install the fork with: pip install git+https://github.com/amaiya/eli5@tfkeras_0_10_1'
            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 analyze_valid(self, val_tup, print_report=True, multilabel=None):
        """
        Makes predictions on validation set and returns the confusion matrix.
        Accepts as input the validation set in the standard form of a tuple of
        two arrays: (X_test, y_test), wehre X_test is a Numpy array of strings
        where each string is a document or text snippet in the validation set.

        Optionally prints a classification report.
        Currently, this method is only supported for binary and multiclass 
        problems, not multilabel classification problems.
        """
        U.data_arg_check(val_data=val_tup, val_required=True, ndarray_only=True)
        if multilabel is None:
            multilabel = U.is_multilabel(val_tup)
        if multilabel:
            warnings.warn('multilabel_confusion_matrix not yet supported')
            return

        y_true = val_tup[1]
        y_true = np.argmax(y_true, axis=1)
        y_pred = self.model.predict(val_tup[0])
        y_pred = np.argmax(y_pred, axis=1)
        
        if print_report:
            print(classification_report(y_true, y_pred, target_names=self.c))
            cm_func = confusion_matrix
        cm =  confusion_matrix(y_true,  y_pred)
        return cm


    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


    def export_model_to_onnx(self, fpath, quantize=False, target_opset=None, verbose=1):
        """
        Export model to onnx
        Args:
          fpath(str): String representing full path to model file where ONNX model will be saved.
                      Example: '/tmp/my_model.onnx'
          quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx: 
                         1) ONNX model  (created directly using keras2onnx
                         2) an optimized ONNX model (created by transformers library)
                         3) a quantized version of optimized ONNX model (created by transformers library)
                         All files will be created in the parent folder of fpath:
                         Example: 
                           If fpath='/tmp/model.onnx', then both /tmp/model-optimized.onnx and
                           /tmp/model-optimized-quantized.onnx will also be created.
          verbose(bool): verbosity
        Returns:
          str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
        """
        try:
            import onnxruntime, onnxruntime_tools, onnx, keras2onnx
        except ImportError:
            raise Exception('This method requires ONNX libraries to be installed: '+\
                            'pip install -q --upgrade onnxruntime==1.5.1 onnxruntime-tools onnx keras2onnx')
        from pathlib import Path
        if type(self.preproc).__name__ == 'BERTPreprocessor':
            raise Exception('currently_unsupported:  BERT models created with text_classifier("bert",...) are not supported (i.e., keras_bert models). ' +\
                            'Only BERT models created with Transformer(...) are supported.')

        if verbose: print('converting to ONNX format ... this may take a few moments...')
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer('Name', return_tensors='tf',
                                   padding='max_length', max_length=maxlen)

            if version.parse(tf.__version__) < version.parse('2.2'):
                raise Exception('export_model_to_tflite requires tensorflow>=2.2')
                #self.model._set_inputs(input_spec, training=False) # for tf < 2.2
            self.model._saved_model_inputs_spec = None # for tf > 2.2
            self.model._set_save_spec(input_dict) # for tf > 2.2
            self.model._get_save_spec()

        onnx_model = keras2onnx.convert_keras(self.model, self.model.name, target_opset=target_opset)
        keras2onnx.save_model(onnx_model, fpath)
        return_fpath = fpath

        if quantize:
            from transformers.convert_graph_to_onnx import optimize, quantize
            #opt_path = optimize(Path(fpath))

            if U.is_huggingface(model=self.model) and\
               type(self.model).__name__ in ['TFDistilBertForSequenceClassification', 'TFBertForSequenceClassification']:
                try:
                    from onnxruntime_tools import optimizer
                    from onnxruntime_tools.transformers.onnx_model_bert import BertOptimizationOptions
                    # disable embedding layer norm optimization for better model size reduction
                    opt_options = BertOptimizationOptions('bert')
                    opt_options.enable_embed_layer_norm = False
                    opt_model = optimizer.optimize_model(
                        fpath,
                       'bert',  # bert_keras causes error with transformers
                        num_heads=12,
                        hidden_size=768,
                        optimization_options=opt_options)
                    opt_model.save_model_to_file(fpath)
                except:
                    warnings.warn('Could not run BERT-specific optimizations')
                    pass
            quantize_path = quantize(Path(fpath))
            return_fpath = quantize_path.as_posix()
        if verbose: print('done.')
        return return_fpath

predicts text classes #if type(preproc).__name__ != 'TextPreprocessor': 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 # get predictions # dep_fix: undocumented breaking change in transformers==4.0.0 # dep_fix: transformers in TF 2.2.0 returns a tuple insead of NumPy array for some reason # process predictions # convert logits to probabilities for Hugging Face models Makes predictions for a list of strings where each string is a document         or text snippet.         Returns probabilities of each class. 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. Makes predictions on validation set and returns the confusion matrix.         Accepts as input the validation set in the standard form of a tuple of         two arrays: (X_test, y_test), wehre X_test is a Numpy array of strings         where each string is a document or text snippet in the validation set.          Optionally prints a classification report.         Currently, this method is only supported for binary and multiclass          problems, not multilabel classification problems. # As of 0.26.3, make sure we save tokenizer in predictor folder Export model to onnx         Args:           fpath(str): String representing full path to model file where ONNX model will be saved.                       Example: '/tmp/my_model.onnx'           quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx:                           1) ONNX model  (created directly using keras2onnx                          2) an optimized ONNX model (created by transformers library)                          3) a quantized version of optimized ONNX model (created by transformers library)                          All files will be created in the parent folder of fpath:                          Example:                             If fpath='/tmp/model.onnx', then both /tmp/model-optimized.onnx and                            /tmp/model-optimized-quantized.onnx will also be created.           verbose(bool): verbosity         Returns:           str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath #self.model._set_inputs(input_spec, training=False) # for tf < 2.2 # for tf > 2.2 # for tf > 2.2 #opt_path = optimize(Path(fpath)) # disable embedding layer norm optimization for better model size reduction # bert_keras causes error with transformers