Release 0.20

CHANGELOG.md

@@ -6,6 +6,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 ## [Unreleased]
 
+## 0.20
+
 ### Added
 * Objects can control the keys that will be used in `serialize`/`jsonld`/`as_dict` by specifying a list of keys in `terse_keys`.
 e.g.
@@ -27,6 +29,7 @@ e.g.
 * Plugin and parameter descriptions are now formatted with (showdown)[https://github.com/showdownjs/showdown].
 * The web UI requests extra_parameters from the server. This is useful for pipelines. See #52
 * First batch of semantic tests (using SPARQL)
+* `Plugin.path()` method to get a file path from a relative path (using the senpy data folder)
 
 ### Changed
 * `install_deps` now checks what requirements are already met before installing with pip.

senpy/plugins/__init__.py

@@ -251,11 +251,15 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
                 return alternative
         raise IOError('File does not exist: {}'.format(fname))
 
+    def path(self, fpath):
+        if not os.path.isabs(fpath):
+            fpath = os.path.join(self.data_folder, fpath)
+        return fpath
+
     def open(self, fpath, mode='r'):
         if 'w' in mode:
             # When writing, only use absolute paths or data_folder
-            if not os.path.isabs(fpath):
-                fpath = os.path.join(self.data_folder, fpath)
+            fpath = self.path(fpath)
         else:
             fpath = self.find_file(fpath)
 
@@ -381,7 +385,9 @@ class SentimentPlugin(Analyser, Evaluable, models.SentimentPlugin):
             activity = self.activity(parameters)
         entries = []
         for feat in X:
-            entries.append(models.Entry(nif__isString=feat[0]))
+            if isinstance(feat, list):
+                feat = ' '.join(feat)
+            entries.append(models.Entry(nif__isString=feat))
         labels = []
         for e in self.process_entries(entries, activity):
             sent = e.sentiments[0].polarity

tests/test_plugins.py

@@ -320,24 +320,32 @@ class PluginsTest(TestCase):
         for i in range(50):
             testdata.append(["good", 1])
         for i in range(50):
-            testdata.append(["bad", 0])
+            testdata.append(["bad", -1])
         dataset = pd.DataFrame(testdata, columns=['text', 'polarity'])
 
         class DummyPlugin(plugins.SentimentBox):
             description = 'Plugin to test evaluation'
             version = 0
 
+            classes = ['marl:Positive', 'marl:Negative']
+
             def predict_one(self, features, **kwargs):
-                return 0
+                print(features[0])
+                return [0, 1]
 
         class SmartPlugin(plugins.SentimentBox):
             description = 'Plugin to test evaluation'
             version = 0
 
+            classes = ['marl:Positive', 'marl:Negative']
+
             def predict_one(self, features, **kwargs):
+                print(features[0])
                 if features[0] == 'good':
-                    return 1
-                return 0
+                    print('positive')
+                    return [1, 0]
+                print('negative')
+                return [0, 1]
 
         dpipe = DummyPlugin()
         results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[dpipe], flatten=True)