predicting reading ids per model adding model ids predicting Using Ten

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
predicting...
reading ids per model (1)...
adding model ids...
predicting (1)...
Using TensorFlow backend.
/home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/numpy/core/_methods.py:36: RuntimeWarning: overflow encountered in reduce
return umr_sum(a, axis, dtype, out, keepdims, initial)
WARNING:tensorflow:From /home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.
WARNING:tensorflow:From /home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
2019-03-04 19:47:01.704973: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: FMA
2019-03-04 19:47:01.728106: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 3511855000 Hz
2019-03-04 19:47:01.729018: I tensorflow/compiler/xla/service/service.cc:150] XLA service 0x771ac00 executing computations on platform Host. Devices:
2019-03-04 19:47:01.729081: I tensorflow/compiler/xla/service/service.cc:158] StreamExecutor device (0): <undefined>, <undefined>
Traceback (most recent call last):
File "predict.py", line 125, in <module>
main()
File "predict.py", line 119, in main
p.predict(pairs=args.pair)
File "predict.py", line 48, in predict
mds.append(self._predict_one_model(m, mdf))
File "predict.py", line 71, in _predict_one_model
pred_csv = ModelExporter({}).predict_by_model(model_path, path_to_predict=test_csv)
File "/home/flint/projects/deeplearninc/omnicell-scripts/auger_ml/model_exporter.py", line 300, in predict_by_model
results = model.predict(X_test)
File "/home/flint/projects/deeplearninc/omnicell-scripts/auger_ml/ensembles/algorithms.py", line 52, in predict
return self._ensemble.predict(x)
File "/home/flint/projects/deeplearninc/omnicell-scripts/auger_ml/ensembles/base.py", line 122, in predict
predicts = self._assemble_predicts(self._predict(x))
File "/home/flint/projects/deeplearninc/omnicell-scripts/auger_ml/ensembles/base.py", line 64, in _predict
res.append(p.predict(x_fold))
File "/home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/xgboost/sklearn.py", line 433, in predict
validate_features=validate_features)
File "/home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/xgboost/core.py", line 1218, in predict
self._validate_features(data)
File "/home/flint/.virtualenvs/omnicell/local/lib/python2.7/site-packages/xgboost/core.py", line 1541, in _validate_features
data.feature_names))
ValueError: feature_names mismatch: [u'date_col', u'item_id', u'rx_name', u'qty_parlvl', u'qty_min', u'qty_onhand', u'qty_total', u'consumption_last_0-6', u'consumption_last_24-30', u'consumption_last_6-12', u'consumption_last_30-36', u'consumption_last_12-18', u'consumption_last_36-42', u'consumption_last_18-24', u'consumption_last_42-48', u'consumption_week_0-6', u'consumption_week_6-12', u'consumption_week_12-18', u'consumption_week_18-24', u'consumption_month_0-6', u'consumption_month_6-12', u'consumption_month_12-18', u'consumption_month_18-24', u'consumption_same_day_0-6', u'consumption_same_day_6-12', u'consumption_same_day_12-18', u'consumption_same_day_18-24', u'consumption_hourly_0h', u'consumption_hourly_1h', u'consumption_hourly_2h', u'consumption_hourly_3h', u'consumption_hourly_4h', u'consumption_hourly_5h', u'consumption_hourly_6h', u'consumption_hourly_7h', u'consumption_hourly_8h', u'consumption_hourly_9h', u'consumption_hourly_10h', u'consumption_hourly_11h', u'consumption_hourly_12h', u'consumption_hourly_13h', u'consumption_hourly_14h', u'consumption_hourly_15h', u'consumption_hourly_16h', u'consumption_hourly_17h', u'consumption_hourly_18h', u'consumption_hourly_19h', u'consumption_hourly_20h', u'consumption_hourly_21h', u'consumption_hourly_22h', u'consumption_hourly_23h', u'item_id_total_count', u'state_0', u'state_1', u'state_2'] [u'date_col', u'item_id', u'rx_name', u'qty_parlvl', u'qty_min', u'qty_onhand', u'qty_total', u'consumption_hourly_0h', u'consumption_hourly_1h', u'consumption_hourly_2h', u'consumption_hourly_3h', u'consumption_hourly_4h', u'consumption_hourly_5h', u'consumption_hourly_6h', u'consumption_hourly_7h', u'consumption_hourly_8h', u'consumption_hourly_9h', u'consumption_hourly_10h', u'consumption_hourly_11h', u'consumption_hourly_12h', u'consumption_hourly_13h', u'consumption_hourly_14h', u'consumption_hourly_15h', u'consumption_hourly_16h', u'consumption_hourly_17h', u'consumption_hourly_18h', u'consumption_hourly_19h', u'consumption_hourly_20h', u'consumption_hourly_21h', u'consumption_hourly_22h', u'consumption_hourly_23h', u'item_id_total_count', u'state_0', u'state_1', u'state_2', u'consumption_last_0-6', u'consumption_last_24-30', u'consumption_last_6-12', u'consumption_last_30-36', u'consumption_last_12-18', u'consumption_last_36-42', u'consumption_last_18-24', u'consumption_last_42-48', u'consumption_week_0-6', u'consumption_week_6-12', u'consumption_week_12-18', u'consumption_week_18-24', u'consumption_month_0-6', u'consumption_month_6-12', u'consumption_month_12-18', u'consumption_month_18-24', u'consumption_same_day_0-6', u'consumption_same_day_6-12', u'consumption_same_day_12-18', u'consumption_same_day_18-24']
Exception TypeError: TypeError("'NoneType' object is not callable",) in <bound method Session.__del__ of <tensorflow.python.client.session.Session object at 0x7f3750794f10>> ignored
makefile:94: ошибка выполнения рецепта для цели «data/prediction_subset.csv.gz»
make: *** [data/prediction_subset.csv.gz] Ошибка 1