Improve random state handling #801
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Squashed commits: [58d1612] Remove seed from config and put it in the "shared" for the ProcessingUnit
ClemDoum
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Codecov Report
@@ Coverage Diff @@
## develop #801 +/- ##
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+ Coverage 88.42% 88.47% +0.04%
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Files 76 76
Lines 4571 4571
Branches 882 882
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+ Hits 4042 4044 +2
+ Misses 397 395 -2
Partials 132 132 |
ClemDoum
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adrienball
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May 16, 2019
| while True: | ||
| noise_length = int(random_state.normal(mean_length, std_length)) | ||
| i += 1 |
| @@ -35,8 +35,9 @@ def test_should_get_slots(self): | |||
| - make me [number_of_cups:snips/number](five) cups of tea | |||
| - please I want [number_of_cups](two) cups of tea""") | |||
| dataset = Dataset.from_yaml_files("en", [dataset_stream]).json | |||
| config = CRFSlotFillerConfig(random_seed=42) | |||
| config = CRFSlotFillerConfig() | |||
There was a problem hiding this comment.
The config is not needed anymore here.
| @@ -101,10 +104,11 @@ def test_should_get_sub_builtin_slots(self): | |||
| - find an activity from [start](6pm) to [end](8pm) | |||
| - Book me a trip from [start](this friday) to [end](next tuesday)""") | |||
| dataset = Dataset.from_yaml_files("en", [dataset_stream]).json | |||
| config = CRFSlotFillerConfig(random_seed=42) | |||
| config = CRFSlotFillerConfig() | |||
| @@ -65,9 +66,11 @@ def test_should_get_builtin_slots(self): | |||
| - Can you tell me the weather [datetime] please ? | |||
| - what is the weather forecast [datetime] in [location](paris)""") | |||
| dataset = Dataset.from_yaml_files("en", [dataset_stream]).json | |||
| config = CRFSlotFillerConfig(random_seed=42) | |||
| config = CRFSlotFillerConfig() | |||
There was a problem hiding this comment.
The config is not needed anymore here.
| @@ -356,9 +360,10 @@ def test_should_get_slots_after_deserialization(self): | |||
| - i want [number_of_cups] cups of tea please | |||
| - can you prepare [number_of_cups] cups of tea ?""") | |||
| dataset = Dataset.from_yaml_files("en", [dataset_stream]).json | |||
| config = CRFSlotFillerConfig(random_seed=42) | |||
| config = CRFSlotFillerConfig() | |||
| classifier_config = LogRegIntentClassifierConfig(random_seed=42) | ||
| slot_filler_config = CRFSlotFillerConfig(random_seed=42) | ||
| classifier_config = LogRegIntentClassifierConfig() | ||
| slot_filler_config = CRFSlotFillerConfig() | ||
| parser_config = ProbabilisticIntentParserConfig( |
| classifier_config = LogRegIntentClassifierConfig(random_seed=42) | ||
| slot_filler_config = CRFSlotFillerConfig(random_seed=42) | ||
| classifier_config = LogRegIntentClassifierConfig() | ||
| slot_filler_config = CRFSlotFillerConfig() | ||
| parser_config = ProbabilisticIntentParserConfig( |
| @@ -162,9 +169,12 @@ def test_should_get_intents(self): | |||
| utterances: | |||
| - yili yulu yele""") | |||
| dataset = Dataset.from_yaml_files("en", [dataset_stream]).json | |||
| classifier_config = LogRegIntentClassifierConfig(random_seed=42) | |||
| classifier_config = LogRegIntentClassifierConfig() | |||
| parser_config = ProbabilisticIntentParserConfig(classifier_config) | |||
| random_seed=seed1), | ||
| slot_filler_config=CRFSlotFillerConfig(random_seed=seed2) | ||
| intent_classifier_config=LogRegIntentClassifierConfig(), | ||
| slot_filler_config=CRFSlotFillerConfig() | ||
| ) |
docs/source/tutorial.rst
Outdated
| different outputs. | ||
|
|
||
| If you want to run training in a reproducible way you can pass a random seed to | ||
| your engine: |
There was a problem hiding this comment.
I prefer using a more impersonal form in the documentation, but that's just a suggestion. That would be something like:
Reproducible training and testing can be achieved by passing a
**random seed** to the engine:
adrienball
reviewed
May 16, 2019
docs/source/tutorial.rst
Outdated
| @@ -174,6 +174,26 @@ the dataset we generated earlier: | |||
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| engine.fit(dataset) | |||
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| Note that by default, the training of the engine is non-deterministic: if you | |||
| train your NLU twice on the same data and test it on the same input, you'll get | |||
| different outputs. | |||
There was a problem hiding this comment.
I would be a bit more optimistic in the formulation:
Note that, by default, the training of the NLU engine is a non-deterministic process:
training and testing multiple times on the same data may produce different outputs.
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adrienball
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May 20, 2019
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Description:
Currenlty
scikit-learnbug the intent classification training was not deterministicDone:
sklearn==0.21which contains a fix which makesSGDClassifiertraining deterministicChecklist: