✨ Add Scoring task #301.

.travis.yml

@@ -67,7 +67,7 @@ jobs:
     - stage: Document
       python: "3.6"
       install:
-        - echo -e "machine github.com\n  login ${GITHUB_TOKEN}" > ~/.netrc
+        - echo -e "machine github.com\n login ${GITHUB_TOKEN}" > ~/.netrc
         - pip install mkdocs mkdocs-material pymdown-extensions
       script:
         - cd mkdocs

kashgari/__init__.py

@@ -23,6 +23,7 @@
 custom_objects = keras_bert.get_custom_objects()
 CLASSIFICATION = TaskType.CLASSIFICATION
 LABELING = TaskType.LABELING
+SCORING = TaskType.SCORING
 
 from kashgari.version import __version__
 

kashgari/embeddings/base_embedding.py

@@ -16,7 +16,7 @@
 from tensorflow import keras
 
 import kashgari
-from kashgari.processors import ClassificationProcessor, LabelingProcessor
+from kashgari.processors import ClassificationProcessor, LabelingProcessor, ScoringProcessor
 from kashgari.processors.base_processor import BaseProcessor
 
 L = keras.layers
@@ -74,8 +74,10 @@ def __init__(self,
                 self.processor = ClassificationProcessor()
             elif task == kashgari.LABELING:
                 self.processor = LabelingProcessor()
+            elif task == kashgari.SCORING:
+                self.processor = ScoringProcessor()
             else:
-                raise ValueError()
+                raise ValueError('Need to set the processor param, value: {labeling, classification, scoring}')
         else:
             self.processor = processor
 

kashgari/macros.py

@@ -23,6 +23,7 @@
 class TaskType(object):
     CLASSIFICATION = 'classification'
     LABELING = 'labeling'
+    SCORING = 'scoring'
 
 
 class Config(object):

kashgari/processors/__init__.py

@@ -10,3 +10,4 @@
 
 from kashgari.processors.classification_processor import ClassificationProcessor
 from kashgari.processors.labeling_processor import LabelingProcessor
+from kashgari.processors.scoring_processor import ScoringProcessor

kashgari/processors/scoring_processor.py

@@ -0,0 +1,95 @@
+# encoding: utf-8
+
+# author: BrikerMan
+# contact: eliyar917@gmail.com
+# blog: https://eliyar.biz
+
+# file: scoring_processor.py
+# time: 11:10 上午
+
+from typing import List, Optional
+
+import numpy as np
+
+import kashgari
+from kashgari import utils
+from kashgari.processors.base_processor import BaseProcessor
+
+
+class ScoringProcessor(BaseProcessor):
+    """
+    Corpus Pre Processor class
+    """
+
+    def __init__(self, output_dim=None, **kwargs):
+        super(ScoringProcessor, self).__init__(**kwargs)
+        self.output_dim = output_dim
+
+    def info(self):
+        info = super(ScoringProcessor, self).info()
+        info['task'] = kashgari.SCORING
+        return info
+
+    def _build_label_dict(self,
+                          label_list: List[List[float]]):
+        """
+        Build label2idx dict for sequence labeling task
+
+        Args:
+            label_list: corpus label list
+        """
+        if self.output_dim is None:
+            label_sample = label_list[0]
+            if isinstance(label_sample, (float, int)):
+                self.output_dim = 1
+            elif isinstance(label_sample, list):
+                self.output_dim = len(label_sample)
+            elif isinstance(label_sample, np.ndarray) and len(label_sample.shape) == 1:
+                self.output_dim = label_sample.shape[0]
+            else:
+                raise ValueError('Scoring Label Sample must be a float, float array or 1D numpy array')
+        # np_labels = np.array(label_list)
+        # if np_labels.max() > 1 or np_labels.min() < 0:
+        #     raise ValueError('Scoring Label Sample must be in range[0,1]')
+
+    def process_y_dataset(self,
+                          data: List[List[str]],
+                          max_len: Optional[int] = None,
+                          subset: Optional[List[int]] = None) -> np.ndarray:
+        if subset is not None:
+            target = utils.get_list_subset(data, subset)
+        else:
+            target = data[:]
+        y = np.array(target)
+        return y
+
+    def numerize_token_sequences(self,
+                                 sequences: List[List[str]]):
+
+        result = []
+        for seq in sequences:
+            if self.add_bos_eos:
+                seq = [self.token_bos] + seq + [self.token_eos]
+            unk_index = self.token2idx[self.token_unk]
+            result.append([self.token2idx.get(token, unk_index) for token in seq])
+        return result
+
+    def numerize_label_sequences(self,
+                                 sequences: List[List[str]]) -> List[List[int]]:
+        return sequences
+
+    def reverse_numerize_label_sequences(self,
+                                         sequences,
+                                         lengths=None):
+        return sequences
+
+
+if __name__ == "__main__":
+    from kashgari.corpus import SMP2018ECDTCorpus
+
+    x, y = SMP2018ECDTCorpus.load_data()
+    x = x[:3]
+    y = [0.2, 0.3, 0.2]
+    p = ScoringProcessor()
+    p.analyze_corpus(x, y)
+    print(p.process_y_dataset(y))

kashgari/tasks/base_model.py

@@ -61,6 +61,10 @@ def token2idx(self) -> Dict[str, int]:
     def label2idx(self) -> Dict[str, int]:
         return self.embedding.label2idx
 
+    @property
+    def task(self) -> str:
+        return self.__task__
+
     @property
     def pre_processor(self):
         warnings.warn("The 'pre_processor' property is deprecated, "
@@ -414,12 +418,16 @@ def predict(self,
                 lengths = [len(sen) for sen in x_data]
             tensor = self.embedding.process_x_dataset(x_data)
             pred = self.tf_model.predict(tensor, batch_size=batch_size, **predict_kwargs)
-            res = self.embedding.reverse_numerize_label_sequences(pred.argmax(-1),
+            if self.task == 'scoring':
+                t_pred = pred
+            else:
+                t_pred = pred.argmax(-1)
+            res = self.embedding.reverse_numerize_label_sequences(t_pred,
                                                                   lengths)
             if debug_info:
-                logging.info('input: {}'.format(tensor))
-                logging.info('output: {}'.format(pred))
-                logging.info('output argmax: {}'.format(pred.argmax(-1)))
+                print('input: {}'.format(tensor))
+                print('output: {}'.format(pred))
+                print('output argmax: {}'.format(t_pred))
         return res
 
     def evaluate(self,

kashgari/tasks/scoring/__init__.py

@@ -0,0 +1,14 @@
+# encoding: utf-8
+
+# author: BrikerMan
+# contact: eliyar917@gmail.com
+# blog: https://eliyar.biz
+
+# file: __init__.py
+# time: 11:36 上午
+
+
+from kashgari.tasks.scoring.models import BiLSTM_Model
+
+if __name__ == "__main__":
+    pass

kashgari/tasks/scoring/base_model.py

@@ -0,0 +1,63 @@
+# encoding: utf-8
+
+# author: BrikerMan
+# contact: eliyar917@gmail.com
+# blog: https://eliyar.biz
+
+# file: base_model.py
+# time: 11:36 上午
+
+
+from typing import Dict, Any, Tuple
+
+import random
+import logging
+from seqeval.metrics import classification_report
+from seqeval.metrics.sequence_labeling import get_entities
+
+from kashgari.tasks.base_model import BaseModel
+
+
+class BaseScoringModel(BaseModel):
+    """Base Sequence Labeling Model"""
+
+    __task__ = 'scoring'
+
+    @classmethod
+    def get_default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
+        raise NotImplementedError
+
+    def compile_model(self, **kwargs):
+        if kwargs.get('loss') is None:
+            kwargs['loss'] = 'mse'
+        if kwargs.get('optimizer') is None:
+            kwargs['optimizer'] = 'rmsprop'
+        if kwargs.get('metrics') is None:
+            kwargs['metrics'] = ['mae']
+        super(BaseScoringModel, self).compile_model(**kwargs)
+
+    def evaluate(self,
+                 x_data,
+                 y_data,
+                 batch_size=None,
+                 digits=4,
+                 debug_info=False) -> Tuple[float, float, Dict]:
+        """
+        Build a text report showing the main classification metrics.
+
+        Args:
+            x_data:
+            y_data:
+            batch_size:
+            digits:
+            debug_info:
+
+        Returns:
+
+        """
+        pass
+        return {}
+
+
+if __name__ == "__main__":
+    pass

kashgari/tasks/scoring/models.py

@@ -0,0 +1,57 @@
+# encoding: utf-8
+
+# author: BrikerMan
+# contact: eliyar917@gmail.com
+# blog: https://eliyar.biz
+
+# file: models.py
+# time: 11:38 上午
+
+
+import logging
+from typing import Dict, Any
+
+from tensorflow import keras
+
+from kashgari.tasks.scoring.base_model import BaseScoringModel
+from kashgari.layers import L
+
+
+class BiLSTM_Model(BaseScoringModel):
+
+    @classmethod
+    def get_default_hyper_parameters(cls) -> Dict[str, Dict[str, Any]]:
+        return {
+            'layer_bi_lstm': {
+                'units': 128,
+                'return_sequences': False
+            },
+            'layer_dense': {
+                'activation': 'linear'
+            }
+        }
+
+    def build_model_arc(self):
+        output_dim = self.processor.output_dim
+        config = self.hyper_parameters
+        embed_model = self.embedding.embed_model
+
+        layer_bi_lstm = L.Bidirectional(L.LSTM(**config['layer_bi_lstm']))
+        layer_dense = L.Dense(output_dim, **config['layer_dense'])
+
+        tensor = layer_bi_lstm(embed_model.output)
+        output_tensor = layer_dense(tensor)
+
+        self.tf_model = keras.Model(embed_model.inputs, output_tensor)
+
+
+if __name__ == "__main__":
+    from kashgari.corpus import SMP2018ECDTCorpus
+    import numpy as np
+
+    x, y = SMP2018ECDTCorpus.load_data('valid')
+    y = np.random.random((len(x), 4))
+    model = BiLSTM_Model()
+    model.fit(x, y)
+    print(model.predict(x[:10]))
+

tests/scoring/test_bi_lstm_model.py

@@ -0,0 +1,47 @@
+# encoding: utf-8
+
+# author: BrikerMan
+# contact: eliyar917@gmail.com
+# blog: https://eliyar.biz
+
+# file: blstm_model.py
+# time: 12:17 下午
+import os
+import tempfile
+import time
+import unittest
+import kashgari
+import numpy as np
+
+from tests.corpus import NERCorpus
+from kashgari.tasks.scoring import BiLSTM_Model
+
+
+class TestBiLSTM_Model(unittest.TestCase):
+    @classmethod
+    def setUpClass(cls):
+        cls.model_class = BiLSTM_Model
+
+    def test_basic_use_build(self):
+        x, _ = NERCorpus.load_corpus()
+        y = np.random.random((len(x), 4))
+        model = self.model_class()
+        model.fit(x, y)
+        res = model.predict(x[:20])
+        model_path = os.path.join(tempfile.gettempdir(), str(time.time()))
+        model.save(model_path)
+
+        pd_model_path = os.path.join(tempfile.gettempdir(), str(time.time()))
+        kashgari.utils.convert_to_saved_model(model,
+                                              pd_model_path)
+
+        new_model = kashgari.utils.load_model(model_path)
+        new_res = new_model.predict(x[:20])
+        assert np.array_equal(new_res, res)
+
+        new_model.compile_model()
+        model.fit(x, y, x, y, epochs=1)
+
+
+if __name__ == "__main__":
+    pass