Merge branch 'develop'

CHANGELOG.md

@@ -7,6 +7,19 @@ Most recent releases are shown at the top. Each release shows:
 - **Fixed**: Bug fixes that don't change documented behaviour
 
 
+## 0.15.2 (2020-05-15)
+
+### New:
+- N/A
+
+### Changed
+- Added `n_samples` argument to `TextPredictor.explain` to address slowness of `explain` on Google Colab
+- Lock to version 0.21.3 of `scikit-learn` to ensure old-style explanations are generated from `TextPredictor.explain`
+
+### Fixed:
+- added missing `import pickle` to ensure saved topic models can be loaded
+
+
 ## 0.15.1 (2020-05-14)
 
 ### New:

ktrain/text/__init__.py

@@ -9,6 +9,7 @@
 from . import shallownlp
 from .qa import SimpleQA
 from . import textutils
+import pickle
 
 __all__ = [
            'text_classifier', 'text_regression_model',

ktrain/text/predictor.py

@@ -82,14 +82,17 @@ def predict_proba(self, texts):
         return self.predict(texts, return_proba=True)
 
 
-    def explain(self, doc, truncate_len=512, all_targets=False):
+    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: 
@@ -114,7 +117,7 @@ def explain(self, doc, truncate_len=512, all_targets=False):
             doc = self.preproc.process_chinese([doc])
             doc = doc[0]
         doc = ' '.join(doc.split()[:truncate_len])
-        te = TextExplainer(random_state=42)
+        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)
 

ktrain/version.py

@@ -1,2 +1,2 @@
 __all__ = ['__version__']
-__version__ = '0.15.1'
+__version__ = '0.15.2'

setup.py

@@ -23,7 +23,7 @@
   keywords = ['tensorflow', 'keras', 'deep learning', 'machine learning'],
   install_requires=[
           'tensorflow==2.1.0',
-          'scikit-learn >= 0.21.3',
+          'scikit-learn==0.21.3', # affects format of predictor.explain
           'matplotlib >= 3.0.0',
           'pandas >= 1.0.1',
           'fastprogress >= 0.1.21',