Commit 9758a297 authored by J. Fernando Sánchez's avatar J. Fernando Sánchez
Browse files

Release 0.20

parent 8a516d92
...@@ -6,6 +6,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ...@@ -6,6 +6,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
## [Unreleased] ## [Unreleased]
## 0.20
### Added ### Added
* Objects can control the keys that will be used in `serialize`/`jsonld`/`as_dict` by specifying a list of keys in `terse_keys`. * 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. e.g.
...@@ -27,6 +29,7 @@ e.g. ...@@ -27,6 +29,7 @@ e.g.
* Plugin and parameter descriptions are now formatted with (showdown)[https://github.com/showdownjs/showdown]. * 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 * The web UI requests extra_parameters from the server. This is useful for pipelines. See #52
* First batch of semantic tests (using SPARQL) * 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 ### Changed
* `install_deps` now checks what requirements are already met before installing with pip. * `install_deps` now checks what requirements are already met before installing with pip.
......
...@@ -251,11 +251,15 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)): ...@@ -251,11 +251,15 @@ class Plugin(with_metaclass(PluginMeta, models.Plugin)):
return alternative return alternative
raise IOError('File does not exist: {}'.format(fname)) 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'): def open(self, fpath, mode='r'):
if 'w' in mode: if 'w' in mode:
# When writing, only use absolute paths or data_folder # When writing, only use absolute paths or data_folder
if not os.path.isabs(fpath): fpath = self.path(fpath)
fpath = os.path.join(self.data_folder, fpath)
else: else:
fpath = self.find_file(fpath) fpath = self.find_file(fpath)
...@@ -381,7 +385,9 @@ class SentimentPlugin(Analyser, Evaluable, models.SentimentPlugin): ...@@ -381,7 +385,9 @@ class SentimentPlugin(Analyser, Evaluable, models.SentimentPlugin):
activity = self.activity(parameters) activity = self.activity(parameters)
entries = [] entries = []
for feat in X: 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 = [] labels = []
for e in self.process_entries(entries, activity): for e in self.process_entries(entries, activity):
sent = e.sentiments[0].polarity sent = e.sentiments[0].polarity
......
...@@ -320,24 +320,32 @@ class PluginsTest(TestCase): ...@@ -320,24 +320,32 @@ class PluginsTest(TestCase):
for i in range(50): for i in range(50):
testdata.append(["good", 1]) testdata.append(["good", 1])
for i in range(50): for i in range(50):
testdata.append(["bad", 0]) testdata.append(["bad", -1])
dataset = pd.DataFrame(testdata, columns=['text', 'polarity']) dataset = pd.DataFrame(testdata, columns=['text', 'polarity'])
class DummyPlugin(plugins.SentimentBox): class DummyPlugin(plugins.SentimentBox):
description = 'Plugin to test evaluation' description = 'Plugin to test evaluation'
version = 0 version = 0
classes = ['marl:Positive', 'marl:Negative']
def predict_one(self, features, **kwargs): def predict_one(self, features, **kwargs):
return 0 print(features[0])
return [0, 1]
class SmartPlugin(plugins.SentimentBox): class SmartPlugin(plugins.SentimentBox):
description = 'Plugin to test evaluation' description = 'Plugin to test evaluation'
version = 0 version = 0
classes = ['marl:Positive', 'marl:Negative']
def predict_one(self, features, **kwargs): def predict_one(self, features, **kwargs):
print(features[0])
if features[0] == 'good': if features[0] == 'good':
return 1 print('positive')
return 0 return [1, 0]
print('negative')
return [0, 1]
dpipe = DummyPlugin() dpipe = DummyPlugin()
results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[dpipe], flatten=True) results = plugins.evaluate(datasets={'testdata': dataset}, plugins=[dpipe], flatten=True)
......
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