Merge remote-tracking branch 'cesium-ml/master'

cesium_app/handlers/prediction.py

@@ -25,10 +25,10 @@ def _get_prediction(self, prediction_id):
         try:
             d = Prediction.get(Prediction.id == prediction_id)
         except Prediction.DoesNotExist:
-            raise AccessError('No such dataset')
+            raise AccessError('No such prediction')
 
         if not d.is_owned_by(self.get_username()):
-            raise AccessError('No such dataset')
+            raise AccessError('No such prediction')
 
         return d
 
@@ -67,6 +67,9 @@ def post(self):
 
         dataset_id = data['datasetID']
         model_id = data['modelID']
+        # If only a subset of specified dataset is to be used, a list of the
+        # corresponding time series file names can be provided
+        ts_names = data.get('ts_names')
 
         dataset = Dataset.get(Dataset.id == data["datasetID"])
         model = Model.get(Model.id == data["modelID"])
@@ -88,7 +91,15 @@ def post(self):
 
         executor = yield self._get_executor()
 
-        all_time_series = executor.map(time_series.load, dataset.uris)
+        # If only a subset of the dataset is to be used, get specified files
+        if ts_names:
+            ts_uris = [f.uri for f in dataset.files if os.path.basename(f.name)
+                       in ts_names or os.path.basename(f.name).split('.npz')[0]
+                       in ts_names]
+        else:
+            ts_uris = dataset.uris
+
+        all_time_series = executor.map(time_series.load, ts_uris)
         all_labels = executor.map(lambda ts: ts.label, all_time_series)
         all_features = executor.map(featurize.featurize_single_ts,
                                     all_time_series,

cesium_app/tests/frontend/test_predict.py

@@ -7,6 +7,9 @@
 from os.path import join as pjoin
 import numpy as np
 import numpy.testing as npt
+from cesium_app.config import cfg
+import json
+import requests
 from cesium_app.tests.fixtures import (create_test_project, create_test_dataset,
                                        create_test_featureset, create_test_model,
                                        create_test_prediction)
@@ -204,3 +207,31 @@ def test_download_prediction_csv_regr(driver):
                  [4, 3.1, 3.1]])
         finally:
             os.remove('/tmp/cesium_prediction_results.csv')
+
+
+def test_predict_specific_ts_name():
+    with create_test_project() as p, create_test_dataset(p) as ds,\
+         create_test_featureset(p) as fs, create_test_model(fs) as m:
+        ts_data = [[1, 2, 3, 4], [32.2, 53.3, 32.3, 32.52], [0.2, 0.3, 0.6, 0.3]]
+        impute_kwargs = {'strategy': 'constant', 'value': None}
+        data = {'datasetID': ds.id,
+                'ts_names': ['217801'],
+                'modelID': m.id}
+        response = requests.post('{}/predictions'.format(cfg['server']['url']),
+                                 data=json.dumps(data)).json()
+        assert response['status'] == 'success'
+
+        n_secs = 0
+        while n_secs < 5:
+            pred_info = requests.get('{}/predictions/{}'.format(
+                cfg['server']['url'], response['data']['id'])).json()
+            if pred_info['status'] == 'success' and pred_info['data']['finished']:
+                assert isinstance(pred_info['data']['results']['217801']
+                                  ['features']['total_time'],
+                                  float)
+                assert 'Mira' in pred_info['data']['results']['217801']['prediction']
+                break
+            n_secs += 1
+            time.sleep(1)
+        else:
+            raise Exception('test_predict_specific_ts_name timed out')

tools/watch_logs.py

@@ -96,7 +96,6 @@ def logs_from_config(supervisor_conf):
 with nostdout():
     from cesium_app.config import cfg
 
-watched.append(cfg['paths']['err_log_path'])
 watched.append('log/error.log')
 watched.append('log/nginx-error.log')