def test_e2e_csv_blockifier_plugin():
client = get_steamship_client()
csv_blockifier_plugin_path = PLUGINS_PATH / "blockifiers" / "csv_blockifier.py"
version_config_template = dict(
text_column=dict(type="string"),
tag_columns=dict(type="string"),
tag_kind=dict(type="string"),
) # TODO (enias): Derive this from Config
instance_config = dict( # Has to match up
text_column="Message",
tag_columns="Category",
tag_kind="Intent",
)
with deploy_plugin(
client,
csv_blockifier_plugin_path,
"blockifier",
version_config_template=version_config_template,
instance_config=instance_config,
) as (plugin, version, instance):
with upload_file(client, "utterances.csv") as file:
assert len(file.refresh().data.blocks) == 0
file.blockify(plugin_instance=instance.handle).wait()
# Check the number of blocks
blocks = file.refresh().data.blocks
assert len(blocks) == 5
for block in blocks:
assert block.tags is not None
assert len(block.tags) > 0
for tag in block.tags:
assert tag.name is not None
assert tag.kind is not None
file.delete()
def test_get_training_parameters():
"""Any trainable plugin needs a Python+Lambda component that can report its trainable params.
This tests that all the plumbing works for that to be returned"""
client = get_steamship_client()
tagger_path = PLUGINS_PATH / "taggers" / "plugin_trainable_tagger.py"
# Now make a trainable tagger to train on those tags
with deploy_plugin(
client,
tagger_path,
"tagger",
training_platform=HostingType.LAMBDA,
) as (tagger, taggerVersion, taggerInstance):
training_request = TrainingParameterPluginInput(plugin_instance=taggerInstance.handle)
res = taggerInstance.get_training_parameters(
training_request
) # TODO (enias): How is this working?
assert res.data is not None
params = res.data
assert params.training_epochs is not None
assert params.training_epochs == TRAINING_PARAMETERS.training_epochs
assert math.isclose(
params.testing_holdout_percent,
TRAINING_PARAMETERS.testing_holdout_percent,
abs_tol=0.0001,
)
assert params.training_params == TRAINING_PARAMETERS.training_params
def test_e2e_corpus_importer():
client = get_steamship_client()
corpus_importer_path = PLUGINS_PATH / "importers" / "plugin_corpus_importer.py"
file_importer_path = PLUGINS_PATH / "importers" / "plugin_file_importer.py"
with temporary_space(client) as space:
with deploy_plugin(client,
file_importer_path,
"fileImporter",
space_id=space.id) as (
_,
_,
fi_instance,
):
with deploy_plugin(client,
corpus_importer_path,
"corpusImporter",
space_id=space.id) as (
plugin,
version,
instance,
):
req = CorpusImportRequest(
type="file",
value="dummy-value",
plugin_instance=instance.handle,
file_importer_plugin_instance=fi_instance.handle,
)
res = client.post(
"plugin/instance/importCorpus",
req,
expect=CorpusImportResponse,
space_id=space.id,
)
res.wait()
# We should now have two files!
files = File.list(client, space_id=space.id).data
assert files.files is not None
assert len(files.files) == 2
for file in files.files:
data = file.raw().data
assert data.decode("utf-8") == TEST_DOC
file.delete()
def test_e2e_blockifier_plugin():
client = get_steamship_client()
blockifier_path = PLUGINS_PATH / "blockifiers" / "blockifier.py"
with deploy_plugin(client, blockifier_path, "blockifier") as (
plugin,
version,
instance,
):
file = File.create(client=client, content="This is a test.").data
assert len(file.refresh().data.blocks) == 0
file.blockify(plugin_instance=instance.handle).wait()
assert len(file.refresh().data.blocks) == 4
file.delete()
def test_e2e_tagger():
client = get_steamship_client()
parser_path = PLUGINS_PATH / "taggers" / "plugin_parser.py"
# TODO (enias): Use Enum for plugin type
with deploy_plugin(client, parser_path,
"tagger") as (plugin, version, instance):
test_doc = "Hi there"
res = instance.tag(doc=test_doc)
res.wait()
assert res.error is None
assert res.data is not None
assert len(res.data.file.blocks) == 1
assert res.data.file.blocks[0].text == test_doc
# Let's try it on a file. This is the same test we run on the Swift test parser.
# Since the python test parser is implemented to behave the same, we can reuse it!
tag_file(client, instance.handle)
def test_e2e_importer(client: Steamship):
file_importer_path = PLUGINS_PATH / "importers" / "plugin_file_importer.py"
with deploy_plugin(client, file_importer_path, "fileImporter") as (
plugin,
version,
instance,
):
# The test FileImporter should always return a string file with contents TEST_DOC
file = File.create(client=client,
content="This is a test.",
plugin_instance=instance.handle).data
# Now fetch the data from Steamship and assert that it is the SAME as the data the FileImporter creates
data = file.raw().data
assert data.decode("utf-8") == TEST_DOC
file.delete()
def test_e2e_corpus_export(client: Steamship):
version_config_template = dict(
text_column=dict(type="string"),
tag_columns=dict(type="string"),
tag_kind=dict(type="string"),
) # TODO (enias): Derive this from Config
instance_config = dict( # Has to match up
text_column="Message",
tag_columns="Category",
tag_kind="Intent",
)
exporter_plugin_r = PluginInstance.create(
client=client,
handle=EXPORTER_HANDLE,
plugin_handle=EXPORTER_HANDLE,
upsert=True,
)
assert exporter_plugin_r.data is not None
exporter_plugin = exporter_plugin_r.data
assert exporter_plugin.handle is not None
_input = ExportPluginInput(handle="default", type="file")
csv_blockifier_path = PLUGINS_PATH / "blockifiers" / "csv_blockifier.py"
# Make a blockifier which will generate our trainable corpus
with deploy_plugin(
client,
csv_blockifier_path,
"blockifier",
version_config_template=version_config_template,
instance_config=instance_config,
) as (plugin, version, instance):
with upload_file(client, "utterances.csv") as file:
assert len(file.refresh().data.blocks) == 0
# Use the plugin we just registered
file.blockify(plugin_instance=instance.handle).wait()
assert len(file.refresh().data.blocks) == 5
# Now export the corpus
raw_data_r = exporter_plugin.export(_input)
assert raw_data_r is not None
# The results of a corpus exporter are MD5 encoded!
_ = raw_data_r.data
def test_e2e_third_party_trainable_tagger_lambda_training():
client = get_steamship_client()
spaceR = Space.get(client) # TODO (enias): Remove
assert spaceR.data is not None
exporter_plugin_r = PluginInstance.create(
client=client,
handle=EXPORTER_HANDLE,
plugin_handle=EXPORTER_HANDLE,
upsert=True, # Don't care if it already exists
)
assert exporter_plugin_r.data is not None
exporter_plugin = exporter_plugin_r.data
assert exporter_plugin.handle is not None
third_party_trainable_tagger_path = (
PLUGINS_PATH / "taggers" / "plugin_third_party_trainable_tagger.py")
# Note that we're going to do the below training on ZERO data for simplicity.
# The particular test model doesn't actually incorporate any data given to it at training time, so
# it would just slow the test down to create, blockify, and export a training corpus.
with deploy_plugin(
client,
third_party_trainable_tagger_path,
"tagger",
training_platform=HostingType.LAMBDA) as (tagger, tagger_version,
tagger_instance):
# Now train the plugin
training_request = TrainingParameterPluginInput(
plugin_instance=tagger_instance.handle,
export_plugin_input=ExportPluginInput(
plugin_instance=exporter_plugin.handle,
type="file",
query="all"),
)
train_result = tagger_instance.train(training_request)
train_result.wait()
assert train_result.data is not None
output = train_result.data
assert output.training_complete
assert output.training_reference_data is not None
assert output.training_reference_data["num_checkins"] == 3
logging.info("Waiting 15 seconds for instance to deploy.")
import time
time.sleep(15)
# Now we'll attempt to USE this plugin. This plugin's behavior is to simply tag every block with
# the parameters `MockClient.LABELS`
# First we'll create a file
test_doc = "Hi there"
res = tagger_instance.tag(doc=test_doc)
res.wait()
assert res.error is None
assert res.data is not None
assert res.data.file is not None
assert not res.data.file.tags
assert res.data.file.blocks is not None
assert len(res.data.file.blocks) > 0
for block in res.data.file.blocks:
assert block.tags is not None
assert sorted([tag.name
for tag in block.tags]) == sorted(MockClient.LABELS)
def test_e2e_trainable_tagger_lambda_training(client: Steamship):
version_config_template = dict(
text_column=dict(type="string"),
tag_columns=dict(type="string"),
tag_kind=dict(type="string"),
)
instance_config = dict(text_column="Message", tag_columns="Category", tag_kind="Intent")
exporter_plugin_r = PluginInstance.create(
client=client,
handle=EXPORTER_HANDLE,
plugin_handle=EXPORTER_HANDLE,
upsert=True,
)
assert exporter_plugin_r.data is not None
exporter_plugin = exporter_plugin_r.data
assert exporter_plugin.handle is not None
csv_blockifier_path = PLUGINS_PATH / "blockifiers" / "csv_blockifier.py"
trainable_tagger_path = PLUGINS_PATH / "taggers" / "plugin_trainable_tagger.py"
# Make a blockifier which will generate our trainable corpus
with deploy_plugin(
client,
csv_blockifier_path,
"blockifier",
version_config_template=version_config_template,
instance_config=instance_config,
) as (plugin, version, instance):
with upload_file(client, "utterances.csv") as file:
assert len(file.refresh().data.blocks) == 0
# Use the plugin we just registered
file.blockify(plugin_instance=instance.handle).wait()
assert len(file.refresh().data.blocks) == 5
# Now make a trainable tagger to train on those tags
with deploy_plugin(
client, trainable_tagger_path, "tagger", training_platform=HostingType.LAMBDA
) as (tagger, tagger_version, tagger_instance):
# Now train the plugin
training_request = TrainingParameterPluginInput(
plugin_instance=tagger_instance.handle,
export_plugin_input=ExportPluginInput(
plugin_instance=EXPORTER_HANDLE, type="file", query='kind "foo1"'
),
training_params=dict(
keyword_list=KEYWORDS # This is a key defined by the test model we're training
),
)
train_result = tagger_instance.train(training_request)
train_result.wait()
# At this point, the PluginInstance will have written a parameter file to disk. We should be able to
# retrieve it since we know that it is tagged as the `default`.
checkpoint = ModelCheckpoint(
client=client,
handle="default",
plugin_instance_id=tagger_instance.id,
)
checkpoint_path = checkpoint.download_model_bundle()
assert checkpoint_path.exists()
keyword_path = Path(checkpoint_path) / TestTrainableTaggerModel.KEYWORD_LIST_FILE
assert keyword_path.exists()
with open(keyword_path, "r") as f:
params = json.loads(f.read())
assert params == KEYWORDS
logging.info("Waiting 15 seconds for instance to deploy.")
import time
time.sleep(15)
# If we're here, we have verified that the plugin instance has correctly recorded its parameters
# into the pluginData bucket under a path unique to the PluginInstnace/ModelCheckpoint.
# Now we'll attempt to USE this plugin. This plugin's behavior is to simply tag any file with the
# tags that parameter it. Since those tags are (see above) ["product", "coupon"] we should expect
# this tagger to apply those tags to any file provided to it.
# First we'll create a file
test_doc = "Hi there"
res = tagger_instance.tag(doc=test_doc)
res.wait()
assert res.error is None
assert res.data is not None
assert res.data.file is not None
assert res.data.file.tags is not None
assert len(res.data.file.tags) == len(KEYWORDS)
assert sorted([tag.name for tag in res.data.file.tags]) == sorted(KEYWORDS)
def test_e2e_parser():
client = get_steamship_client()
tagger_plugin_path = PLUGINS_PATH / "taggers" / "plugin_configurable_tagger.py"
config_template = {
"tagKind": {
"type": "string"
},
"tagName": {
"type": "string"
},
"numberValue": {
"type": "number"
},
"booleanValue": {
"type": "boolean"
},
}
instance_config1 = {
"tagKind": "testTagKind",
"tagName": "testTagName",
"numberValue": 3,
"booleanValue": True,
}
with deploy_plugin(
client,
tagger_plugin_path,
"tagger",
version_config_template=config_template,
instance_config=instance_config1,
) as (plugin, version, instance):
test_doc = "Hi there"
res = instance.tag(doc=test_doc)
res.wait()
assert res.error is None
assert res.data is not None
assert len(res.data.file.blocks) == 1
assert res.data.file.blocks[0].text == test_doc
# Validate configured content
assert len(res.data.file.tags) == 1
tag = res.data.file.tags[0]
assert tag.name == instance_config1["tagName"]
assert tag.kind == instance_config1["tagKind"]
tag_value = tag.value
assert tag_value["numberValue"] == instance_config1["numberValue"]
assert tag_value["booleanValue"] == instance_config1["booleanValue"]
instance_config2 = {
"tagKind": "testTagKind2",
"tagName": "testTagName2",
"numberValue": 4,
"booleanValue": False,
}
instance2 = PluginInstance.create(
client,
plugin_id=plugin.id,
plugin_version_id=version.id,
config=instance_config2,
)
instance2.wait()
assert instance2.error is None
assert instance2.data is not None
instance2 = instance2.data
res = instance2.tag(doc=test_doc)
res.wait()
assert res.error is None
assert res.data is not None
assert len(res.data.file.blocks) == 1
assert res.data.file.blocks[0].text == test_doc
# Validate configured content
assert len(res.data.file.tags) == 1
tag = res.data.file.tags[0]
assert tag.name == instance_config2["tagName"]
assert tag.kind == instance_config2["tagKind"]
tag_value = tag.value
assert tag_value["numberValue"] == instance_config2["numberValue"]
assert tag_value["booleanValue"] == instance_config2["booleanValue"]
