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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.update_session_entity_type | def update_session_entity_type(
self,
session_entity_type,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified session entity ty... | python | def update_session_entity_type(
self,
session_entity_type,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Updates the specified session entity ty... | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> # TODO: Initialize ``session_entity_type``:
>>> session_entity_type = {}
>>>
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/session_entity_types_client.py | SessionEntityTypesClient.delete_session_entity_type | def delete_session_entity_type(
self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified session entity type.
Example:
>>> import ... | python | def delete_session_entity_type(
self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified session entity type.
Example:
>>> import ... | [
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Example:
>>> import dialogflow_v2
>>>
>>> client = dialogflow_v2.SessionEntityTypesClient()
>>>
>>> name = client.session_entity_type_path('[PROJECT]', '[SESSION]', '[ENTITY_TYPE]')
>>>
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googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | list_knowledge_bases | def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print... | python | def list_knowledge_bases(project_id):
"""Lists the Knowledge bases belonging to a project.
Args:
project_id: The GCP project linked with the agent."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
project_path = client.project_path(project_id)
print... | [
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googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | create_knowledge_base | def create_knowledge_base(project_id, display_name):
"""Creates a Knowledge base.
Args:
project_id: The GCP project linked with the agent.
display_name: The display name of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
proj... | python | def create_knowledge_base(project_id, display_name):
"""Creates a Knowledge base.
Args:
project_id: The GCP project linked with the agent.
display_name: The display name of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
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googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | get_knowledge_base | def get_knowledge_base(project_id, knowledge_base_id):
"""Gets a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
knowl... | python | def get_knowledge_base(project_id, knowledge_base_id):
"""Gets a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
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googleapis/dialogflow-python-client-v2 | samples/knowledge_base_management.py | delete_knowledge_base | def delete_knowledge_base(project_id, knowledge_base_id):
"""Deletes a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
... | python | def delete_knowledge_base(project_id, knowledge_base_id):
"""Deletes a specific Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.KnowledgeBasesClient()
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_with_texttospeech_response.py | detect_intent_with_texttospeech_response | def detect_intent_with_texttospeech_response(project_id, session_id, texts,
language_code):
"""Returns the result of detect intent with texts as inputs and includes
the response in an audio format.
Using the same `session_id` between requests allows continuation... | python | def detect_intent_with_texttospeech_response(project_id, session_id, texts,
language_code):
"""Returns the result of detect intent with texts as inputs and includes
the response in an audio format.
Using the same `session_id` between requests allows continuation... | [
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_with_model_selection.py | detect_intent_with_model_selection | def detect_intent_with_model_selection(project_id, session_id, audio_file_path,
language_code):
"""Returns the result of detect intent with model selection on an audio file
as input
Using the same `session_id` between requests allows continuation
of the conversaio... | python | def detect_intent_with_model_selection(project_id, session_id, audio_file_path,
language_code):
"""Returns the result of detect intent with model selection on an audio file
as input
Using the same `session_id` between requests allows continuation
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.knowledge_base_path | def knowledge_base_path(cls, project, knowledge_base):
"""Return a fully-qualified knowledge_base string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}',
project=project,
knowledge_base=knowledge_base,
) | python | def knowledge_base_path(cls, project, knowledge_base):
"""Return a fully-qualified knowledge_base string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}',
project=project,
knowledge_base=knowledge_base,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.get_knowledge_base | def get_knowledge_base(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified knowledge base.
... | python | def get_knowledge_base(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified knowledge base.
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> name = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
>>> respon... | [
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/knowledge_bases_client.py | KnowledgeBasesClient.create_knowledge_base | def create_knowledge_base(self,
parent,
knowledge_base,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
... | python | def create_knowledge_base(self,
parent,
knowledge_base,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.KnowledgeBasesClient()
>>>
>>> parent = client.project_path('[PROJECT]')
>>>
>>> # TODO: Initialize ``knowledge_base``:
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/session_entity_types_client.py | SessionEntityTypesClient.environment_session_path | def environment_session_path(cls, project, environment, user, session):
"""Return a fully-qualified environment_session string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}',
project=project,
... | python | def environment_session_path(cls, project, environment, user, session):
"""Return a fully-qualified environment_session string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions/{session}',
project=project,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/session_entity_types_client.py | SessionEntityTypesClient.environment_session_entity_type_path | def environment_session_entity_type_path(cls, project, environment, user,
session, entity_type):
"""Return a fully-qualified environment_session_entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environm... | python | def environment_session_entity_type_path(cls, project, environment, user,
session, entity_type):
"""Return a fully-qualified environment_session_entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environm... | [
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.document_path | def document_path(cls, project, knowledge_base, document):
"""Return a fully-qualified document string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}/documents/{document}',
project=project,
knowledge_base=knowledge... | python | def document_path(cls, project, knowledge_base, document):
"""Return a fully-qualified document string."""
return google.api_core.path_template.expand(
'projects/{project}/knowledgeBases/{knowledge_base}/documents/{document}',
project=project,
knowledge_base=knowledge... | [
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.list_documents | def list_documents(self,
parent,
page_size=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Returns the list of all ... | python | def list_documents(self,
parent,
page_size=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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Example:
>>> import dialogflow_v2beta1
>>>
>>> client = dialogflow_v2beta1.DocumentsClient()
>>>
>>> parent = client.knowledge_base_path('[PROJECT]', '[KNOWLEDGE_BASE]')
>>>
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/documents_client.py | DocumentsClient.delete_document | def delete_document(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified document.
Operation <response... | python | def delete_document(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified document.
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.context_path | def context_path(cls, project, session, context):
"""Return a fully-qualified context string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/sessions/{session}/contexts/{context}',
project=project,
session=session,
context=contex... | python | def context_path(cls, project, session, context):
"""Return a fully-qualified context string."""
return google.api_core.path_template.expand(
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project=project,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.get_context | def get_context(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified context.
Example:
>>> import dialog... | python | def get_context(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified context.
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/contexts_client.py | ContextsClient.update_context | def update_context(self,
context,
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retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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context,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
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googleapis/dialogflow-python-client-v2 | samples/session_entity_type_management.py | create_session_entity_type | def create_session_entity_type(project_id, session_id, entity_values,
entity_type_display_name, entity_override_mode):
"""Create a session entity type with the given display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypes... | python | def create_session_entity_type(project_id, session_id, entity_values,
entity_type_display_name, entity_override_mode):
"""Create a session entity type with the given display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypes... | [
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googleapis/dialogflow-python-client-v2 | samples/session_entity_type_management.py | delete_session_entity_type | def delete_session_entity_type(project_id, session_id,
entity_type_display_name):
"""Delete session entity type with the given entity type display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
session_entit... | python | def delete_session_entity_type(project_id, session_id,
entity_type_display_name):
"""Delete session entity type with the given entity type display name."""
import dialogflow_v2 as dialogflow
session_entity_types_client = dialogflow.SessionEntityTypesClient()
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googleapis/dialogflow-python-client-v2 | samples/entity_type_management.py | create_entity_type | def create_entity_type(project_id, display_name, kind):
"""Create an entity type with the given display name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
parent = entity_types_client.project_agent_path(project_id)
entity_type = dialogflow.types.EntityT... | python | def create_entity_type(project_id, display_name, kind):
"""Create an entity type with the given display name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
parent = entity_types_client.project_agent_path(project_id)
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googleapis/dialogflow-python-client-v2 | samples/entity_type_management.py | delete_entity_type | def delete_entity_type(project_id, entity_type_id):
"""Delete entity type with the given entity type name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
project_id, entity_type_id)
entity_... | python | def delete_entity_type(project_id, entity_type_id):
"""Delete entity type with the given entity type name."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
project_id, entity_type_id)
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_texts.py | detect_intent_texts | def detect_intent_texts(project_id, session_id, texts, language_code):
"""Returns the result of detect intent with texts as inputs.
Using the same `session_id` between requests allows continuation
of the conversation."""
import dialogflow_v2 as dialogflow
session_client = dialogflow.SessionsClient... | python | def detect_intent_texts(project_id, session_id, texts, language_code):
"""Returns the result of detect intent with texts as inputs.
Using the same `session_id` between requests allows continuation
of the conversation."""
import dialogflow_v2 as dialogflow
session_client = dialogflow.SessionsClient... | [
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.entity_type_path | def entity_type_path(cls, project, entity_type):
"""Return a fully-qualified entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/entityTypes/{entity_type}',
project=project,
entity_type=entity_type,
) | python | def entity_type_path(cls, project, entity_type):
"""Return a fully-qualified entity_type string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/entityTypes/{entity_type}',
project=project,
entity_type=entity_type,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.get_entity_type | def get_entity_type(self,
name,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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name,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
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>>>
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>>>
>>> name = client.entity_type_path('[PROJECT]', '[ENTITY_TYPE]')
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.create_entity_type | def create_entity_type(self,
parent,
entity_type,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
... | python | def create_entity_type(self,
parent,
entity_type,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.update_entity_type | def update_entity_type(self,
entity_type,
language_code=None,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
... | python | def update_entity_type(self,
entity_type,
language_code=None,
update_mask=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/entity_types_client.py | EntityTypesClient.batch_delete_entities | def batch_delete_entities(self,
parent,
entity_values,
language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,... | python | def batch_delete_entities(self,
parent,
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language_code=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,... | [
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_knowledge.py | detect_intent_knowledge | def detect_intent_knowledge(project_id, session_id, language_code,
knowledge_base_id, texts):
"""Returns the result of detect intent with querying Knowledge Connector.
Args:
project_id: The GCP project linked with the agent you are going to query.
session_id: Id of the sessi... | python | def detect_intent_knowledge(project_id, session_id, language_code,
knowledge_base_id, texts):
"""Returns the result of detect intent with querying Knowledge Connector.
Args:
project_id: The GCP project linked with the agent you are going to query.
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session_id: Id of the session, using the same `session_id` between requests
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.intent_path | def intent_path(cls, project, intent):
"""Return a fully-qualified intent string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/intents/{intent}',
project=project,
intent=intent,
) | python | def intent_path(cls, project, intent):
"""Return a fully-qualified intent string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/intents/{intent}',
project=project,
intent=intent,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.agent_path | def agent_path(cls, project, agent):
"""Return a fully-qualified agent string."""
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project=project,
agent=agent,
) | python | def agent_path(cls, project, agent):
"""Return a fully-qualified agent string."""
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.get_intent | def get_intent(self,
name,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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name,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.create_intent | def create_intent(self,
parent,
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language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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parent,
intent,
language_code=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.update_intent | def update_intent(self,
intent,
language_code,
update_mask=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
... | python | def update_intent(self,
intent,
language_code,
update_mask=None,
intent_view=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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>>>
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>>>
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>>>
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.delete_intent | def delete_intent(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified intent.
Example:
>>> import... | python | def delete_intent(self,
name,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Deletes the specified intent.
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/intents_client.py | IntentsClient.batch_delete_intents | def batch_delete_intents(self,
parent,
intents,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
... | python | def batch_delete_intents(self,
parent,
intents,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/contexts_client.py | ContextsClient.environment_context_path | def environment_context_path(cls, project, environment, user, session,
context):
"""Return a fully-qualified environment_context string."""
return google.api_core.path_template.expand(
'projects/{project}/agent/environments/{environment}/users/{user}/sessions... | python | def environment_context_path(cls, project, environment, user, session,
context):
"""Return a fully-qualified environment_context string."""
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_with_sentiment_analysis.py | detect_intent_with_sentiment_analysis | def detect_intent_with_sentiment_analysis(project_id, session_id, texts,
language_code):
"""Returns the result of detect intent with texts as inputs and analyzes the
sentiment of the query text.
Using the same `session_id` between requests allows continuation
o... | python | def detect_intent_with_sentiment_analysis(project_id, session_id, texts,
language_code):
"""Returns the result of detect intent with texts as inputs and analyzes the
sentiment of the query text.
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googleapis/dialogflow-python-client-v2 | dialogflow_v2beta1/gapic/sessions_client.py | SessionsClient.detect_intent | def detect_intent(self,
session,
query_input,
query_params=None,
output_audio_config=None,
input_audio=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
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session,
query_input,
query_params=None,
output_audio_config=None,
input_audio=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
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googleapis/dialogflow-python-client-v2 | samples/detect_intent_stream.py | detect_intent_stream | def detect_intent_stream(project_id, session_id, audio_file_path,
language_code):
"""Returns the result of detect intent with streaming audio as input.
Using the same `session_id` between requests allows continuation
of the conversaion."""
import dialogflow_v2 as dialogflow
... | python | def detect_intent_stream(project_id, session_id, audio_file_path,
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"""Returns the result of detect intent with streaming audio as input.
Using the same `session_id` between requests allows continuation
of the conversaion."""
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googleapis/dialogflow-python-client-v2 | samples/intent_management.py | create_intent | def create_intent(project_id, display_name, training_phrases_parts,
message_texts):
"""Create an intent of the given intent type."""
import dialogflow_v2 as dialogflow
intents_client = dialogflow.IntentsClient()
parent = intents_client.project_agent_path(project_id)
training_phras... | python | def create_intent(project_id, display_name, training_phrases_parts,
message_texts):
"""Create an intent of the given intent type."""
import dialogflow_v2 as dialogflow
intents_client = dialogflow.IntentsClient()
parent = intents_client.project_agent_path(project_id)
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googleapis/dialogflow-python-client-v2 | samples/intent_management.py | delete_intent | def delete_intent(project_id, intent_id):
"""Delete intent with the given intent type and intent value."""
import dialogflow_v2 as dialogflow
intents_client = dialogflow.IntentsClient()
intent_path = intents_client.intent_path(project_id, intent_id)
intents_client.delete_intent(intent_path) | python | def delete_intent(project_id, intent_id):
"""Delete intent with the given intent type and intent value."""
import dialogflow_v2 as dialogflow
intents_client = dialogflow.IntentsClient()
intent_path = intents_client.intent_path(project_id, intent_id)
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/agents_client.py | AgentsClient.get_agent | def get_agent(self,
parent,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified agent.
Example:
>>> import dialogflow_v2
... | python | def get_agent(self,
parent,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Retrieves the specified agent.
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/agents_client.py | AgentsClient.train_agent | def train_agent(self,
parent,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Trains the specified agent.
Operation <response: ``google.protobuf.Em... | python | def train_agent(self,
parent,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/agents_client.py | AgentsClient.export_agent | def export_agent(self,
parent,
agent_uri=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
Exports the specified agent to a ZIP... | python | def export_agent(self,
parent,
agent_uri=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
metadata=None):
"""
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googleapis/dialogflow-python-client-v2 | dialogflow_v2/gapic/agents_client.py | AgentsClient.import_agent | def import_agent(self,
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"""
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parent,
agent_uri=None,
agent_content=None,
retry=google.api_core.gapic_v1.method.DEFAULT,
timeout=google.api_core.gapic_v1.method.DEFAULT,
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googleapis/dialogflow-python-client-v2 | samples/document_management.py | list_documents | def list_documents(project_id, knowledge_base_id):
"""Lists the Documents belonging to a Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.DocumentsClient()... | python | def list_documents(project_id, knowledge_base_id):
"""Lists the Documents belonging to a Knowledge base.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow.DocumentsClient()... | [
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googleapis/dialogflow-python-client-v2 | samples/document_management.py | create_document | def create_document(project_id, knowledge_base_id, display_name, mime_type,
knowledge_type, content_uri):
"""Creates a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
display_name: The display name of th... | python | def create_document(project_id, knowledge_base_id, display_name, mime_type,
knowledge_type, content_uri):
"""Creates a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
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googleapis/dialogflow-python-client-v2 | samples/document_management.py | get_document | def get_document(project_id, knowledge_base_id, document_id):
"""Gets a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
document_id: Id of the Document."""
import dialogflow_v2beta1 as dialogflow
client = dialogflow... | python | def get_document(project_id, knowledge_base_id, document_id):
"""Gets a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
document_id: Id of the Document."""
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client = dialogflow... | [
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googleapis/dialogflow-python-client-v2 | samples/document_management.py | delete_document | def delete_document(project_id, knowledge_base_id, document_id):
"""Deletes a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
document_id: Id of the Document."""
import dialogflow_v2beta1 as dialogflow
client = dial... | python | def delete_document(project_id, knowledge_base_id, document_id):
"""Deletes a Document.
Args:
project_id: The GCP project linked with the agent.
knowledge_base_id: Id of the Knowledge base.
document_id: Id of the Document."""
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googleapis/dialogflow-python-client-v2 | samples/entity_management.py | create_entity | def create_entity(project_id, entity_type_id, entity_value, synonyms):
"""Create an entity of the given entity type."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
# Note: synonyms must be exactly [entity_value] if the
# entity_type's kind is KIND_LIST
... | python | def create_entity(project_id, entity_type_id, entity_value, synonyms):
"""Create an entity of the given entity type."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
# Note: synonyms must be exactly [entity_value] if the
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googleapis/dialogflow-python-client-v2 | samples/entity_management.py | delete_entity | def delete_entity(project_id, entity_type_id, entity_value):
"""Delete entity with the given entity type and entity value."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
project_id, entity_type_... | python | def delete_entity(project_id, entity_type_id, entity_value):
"""Delete entity with the given entity type and entity value."""
import dialogflow_v2 as dialogflow
entity_types_client = dialogflow.EntityTypesClient()
entity_type_path = entity_types_client.entity_type_path(
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lucasb-eyer/pydensecrf | pydensecrf/utils.py | softmax_to_unary | def softmax_to_unary(sm, GT_PROB=1):
"""Deprecated, use `unary_from_softmax` instead."""
warning("pydensecrf.softmax_to_unary is deprecated, use unary_from_softmax instead.")
scale = None if GT_PROB == 1 else GT_PROB
return unary_from_softmax(sm, scale, clip=None) | python | def softmax_to_unary(sm, GT_PROB=1):
"""Deprecated, use `unary_from_softmax` instead."""
warning("pydensecrf.softmax_to_unary is deprecated, use unary_from_softmax instead.")
scale = None if GT_PROB == 1 else GT_PROB
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lucasb-eyer/pydensecrf | pydensecrf/utils.py | create_pairwise_gaussian | def create_pairwise_gaussian(sdims, shape):
"""
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Parameters
----------
sdims: list or tuple
The scaling factors per dimensio... | python | def create_pairwise_gaussian(sdims, shape):
"""
Util function that create pairwise gaussian potentials. This works for all
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lucasb-eyer/pydensecrf | pydensecrf/utils.py | create_pairwise_bilateral | def create_pairwise_bilateral(sdims, schan, img, chdim=-1):
"""
Util function that create pairwise bilateral potentials. This works for
all image dimensions. For the 2D case does the same as
`DenseCRF2D.addPairwiseBilateral`.
Parameters
----------
sdims: list or tuple
The scaling fa... | python | def create_pairwise_bilateral(sdims, schan, img, chdim=-1):
"""
Util function that create pairwise bilateral potentials. This works for
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deanmalmgren/textract | textract/parsers/odt_parser.py | Parser.to_string | def to_string(self):
""" Converts the document to a string. """
buff = u""
for child in self.content.iter():
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buff += self.text_to_string(child) + "\n"
# remove last newline char
if buff:
... | python | def to_string(self):
""" Converts the document to a string. """
buff = u""
for child in self.content.iter():
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buff += self.text_to_string(child) + "\n"
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deanmalmgren/textract | textract/parsers/odt_parser.py | Parser.qn | def qn(self, namespace):
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return '{{{}}}{}'.format(nsmap[spl[0]], spl[1]) | python | def qn(self, namespace):
"""Connect tag prefix to longer namespace"""
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deanmalmgren/textract | textract/cli.py | get_parser | def get_parser():
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parser = argparse.ArgumentParser(
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"""Initialize the parser for the command line interface and bind the
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# initialize the parser
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deanmalmgren/textract | textract/cli.py | _get_available_encodings | def _get_available_encodings():
"""Get a list of the available encodings to make it easy to
tab-complete the command line interface.
Inspiration from http://stackoverflow.com/a/3824405/564709
"""
available_encodings = set(encodings.aliases.aliases.values())
paths = [os.path.dirname(encodings.__... | python | def _get_available_encodings():
"""Get a list of the available encodings to make it easy to
tab-complete the command line interface.
Inspiration from http://stackoverflow.com/a/3824405/564709
"""
available_encodings = set(encodings.aliases.aliases.values())
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deanmalmgren/textract | textract/parsers/pdf_parser.py | Parser.extract_pdftotext | def extract_pdftotext(self, filename, **kwargs):
"""Extract text from pdfs using the pdftotext command line utility."""
if 'layout' in kwargs:
args = ['pdftotext', '-layout', filename, '-']
else:
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stdout, _ = self.run(args)
... | python | def extract_pdftotext(self, filename, **kwargs):
"""Extract text from pdfs using the pdftotext command line utility."""
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deanmalmgren/textract | textract/parsers/pdf_parser.py | Parser.extract_pdfminer | def extract_pdfminer(self, filename, **kwargs):
"""Extract text from pdfs using pdfminer."""
stdout, _ = self.run(['pdf2txt.py', filename])
return stdout | python | def extract_pdfminer(self, filename, **kwargs):
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stdout, _ = self.run(['pdf2txt.py', filename])
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deanmalmgren/textract | textract/parsers/audio.py | Parser.convert_to_wav | def convert_to_wav(self, filename):
"""
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Note: for testing, use -
http://www.text2speech.org/,
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temp_filename = '{0}.wav'.format(self.temp_filename())
self.run(['... | python | def convert_to_wav(self, filename):
"""
Uses sox cmdline tool, to convert audio file to .wav
Note: for testing, use -
http://www.text2speech.org/,
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deanmalmgren/textract | textract/parsers/html_parser.py | Parser._visible | def _visible(self, element):
"""Used to filter text elements that have invisible text on the page.
"""
if element.name in self._disallowed_names:
return False
elif re.match(u'<!--.*-->', six.text_type(element.extract())):
return False
return True | python | def _visible(self, element):
"""Used to filter text elements that have invisible text on the page.
"""
if element.name in self._disallowed_names:
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deanmalmgren/textract | textract/parsers/html_parser.py | Parser._find_any_text | def _find_any_text(self, tag):
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text = text.strip()
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"""Looks for any possible text within given tag.
"""
text = ''
if tag is not None:
text = six.text_type(tag)
text = re.sub(r'(<[^>]+>)', '', text)
text = re.sub(r'\s', ' ', text)
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deanmalmgren/textract | textract/parsers/html_parser.py | Parser._join_inlines | def _join_inlines(self, soup):
"""Unwraps inline elements defined in self._inline_tags.
"""
elements = soup.find_all(True)
for elem in elements:
if self._inline(elem):
elem.unwrap()
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"""Unwraps inline elements defined in self._inline_tags.
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deanmalmgren/textract | textract/parsers/utils.py | ShellParser.temp_filename | def temp_filename(self):
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deanmalmgren/textract | textract/parsers/__init__.py | process | def process(filename, encoding=DEFAULT_ENCODING, extension=None, **kwargs):
"""This is the core function used for extracting text. It routes the
``filename`` to the appropriate parser and returns the extracted
text as a byte-string encoded with ``encoding``.
"""
# make sure the filename exists
... | python | def process(filename, encoding=DEFAULT_ENCODING, extension=None, **kwargs):
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deanmalmgren/textract | textract/parsers/__init__.py | _get_available_extensions | def _get_available_extensions():
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"""
extensions = []
# from filenames
parsers_dir = os.path.join(os.path.dirname(__file__))
glob_filename = os.path.join(parsers_dir, "*" + _FILENAME_SUFFIX ... | python | def _get_available_extensions():
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extensions = []
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parsers_dir = os.path.join(os.path.dirname(__file__))
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deanmalmgren/textract | setup.py | parse_requirements | def parse_requirements(requirements_filename):
"""read in the dependencies from the requirements files
"""
dependencies, dependency_links = [], []
requirements_dir = os.path.dirname(requirements_filename)
with open(requirements_filename, 'r') as stream:
for line in stream:
line =... | python | def parse_requirements(requirements_filename):
"""read in the dependencies from the requirements files
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dependencies, dependency_links = [], []
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lyst/lightfm | lightfm/data.py | Dataset.build_interactions | def build_interactions(self, data):
"""
Build an interaction matrix.
Two matrices will be returned: a (num_users, num_items)
COO matrix with interactions, and a (num_users, num_items)
matrix with the corresponding interaction weights.
Parameters
----------
... | python | def build_interactions(self, data):
"""
Build an interaction matrix.
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COO matrix with interactions, and a (num_users, num_items)
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lyst/lightfm | lightfm/data.py | Dataset.mapping | def mapping(self):
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(user id map, user feature map, item id map, item id map): tuple of dictionaries
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return (
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Return the constructed mappings.
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lyst/lightfm | examples/movielens/data.py | _get_movielens_path | def _get_movielens_path():
"""
Get path to the movielens dataset file.
"""
return os.path.join(os.path.dirname(os.path.abspath(__file__)),
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Get path to the movielens dataset file.
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lyst/lightfm | examples/movielens/data.py | _download_movielens | def _download_movielens(dest_path):
"""
Download the dataset.
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url = 'http://files.grouplens.org/datasets/movielens/ml-100k.zip'
req = requests.get(url, stream=True)
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for chunk in req.iter_content():
fd.write(chunk) | python | def _download_movielens(dest_path):
"""
Download the dataset.
"""
url = 'http://files.grouplens.org/datasets/movielens/ml-100k.zip'
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lyst/lightfm | examples/movielens/data.py | _get_movie_raw_metadata | def _get_movie_raw_metadata():
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Get raw lines of the genre file.
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lyst/lightfm | lightfm/lightfm.py | LightFM._initialize | def _initialize(self, no_components, no_item_features, no_user_features):
"""
Initialise internal latent representations.
"""
# Initialise item features.
self.item_embeddings = (
(self.random_state.rand(no_item_features, no_components) - 0.5)
/ no_compone... | python | def _initialize(self, no_components, no_item_features, no_user_features):
"""
Initialise internal latent representations.
"""
# Initialise item features.
self.item_embeddings = (
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lyst/lightfm | lightfm/lightfm.py | LightFM._run_epoch | def _run_epoch(
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"""
Run an individual epoch.
"""
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# The CSR conversion needs to happen be... | python | def _run_epoch(
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Run an individual epoch.
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lyst/lightfm | lightfm/lightfm.py | LightFM.predict | def predict(
self, user_ids, item_ids, item_features=None, user_features=None, num_threads=1
):
"""
Compute the recommendation score for user-item pairs.
For details on how to use feature matrices, see the documentation
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Argumen... | python | def predict(
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Compute the recommendation score for user-item pairs.
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lyst/lightfm | lightfm/lightfm.py | LightFM.get_item_representations | def get_item_representations(self, features=None):
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Get the latent representations for items given model and features.
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"""
Get the latent representations for items given model and features.
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features: np.float32 csr_matrix of shape [n_items, n_item_features], optional
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Get the latent representations for users given model and features.
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Get the latent representations for users given model and features.
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quantopian/empyrical | empyrical/stats.py | _adjust_returns | def _adjust_returns(returns, adjustment_factor):
"""
Returns the returns series adjusted by adjustment_factor. Optimizes for the
case of adjustment_factor being 0 by returning returns itself, not a copy!
Parameters
----------
returns : pd.Series or np.ndarray
adjustment_factor : pd.Series o... | python | def _adjust_returns(returns, adjustment_factor):
"""
Returns the returns series adjusted by adjustment_factor. Optimizes for the
case of adjustment_factor being 0 by returning returns itself, not a copy!
Parameters
----------
returns : pd.Series or np.ndarray
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quantopian/empyrical | empyrical/stats.py | annualization_factor | def annualization_factor(period, annualization):
"""
Return annualization factor from period entered or if a custom
value is passed in.
Parameters
----------
period : str, optional
Defines the periodicity of the 'returns' data for purposes of
annualizing. Value ignored if `annua... | python | def annualization_factor(period, annualization):
"""
Return annualization factor from period entered or if a custom
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period : str, optional
Defines the periodicity of the 'returns' data for purposes of
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quantopian/empyrical | empyrical/stats.py | simple_returns | def simple_returns(prices):
"""
Compute simple returns from a timeseries of prices.
Parameters
----------
prices : pd.Series, pd.DataFrame or np.ndarray
Prices of assets in wide-format, with assets as columns,
and indexed by datetimes.
Returns
-------
returns : array-li... | python | def simple_returns(prices):
"""
Compute simple returns from a timeseries of prices.
Parameters
----------
prices : pd.Series, pd.DataFrame or np.ndarray
Prices of assets in wide-format, with assets as columns,
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quantopian/empyrical | empyrical/stats.py | cum_returns | def cum_returns(returns, starting_value=0, out=None):
"""
Compute cumulative returns from simple returns.
Parameters
----------
returns : pd.Series, np.ndarray, or pd.DataFrame
Returns of the strategy as a percentage, noncumulative.
- Time series with decimal returns.
- Ex... | python | def cum_returns(returns, starting_value=0, out=None):
"""
Compute cumulative returns from simple returns.
Parameters
----------
returns : pd.Series, np.ndarray, or pd.DataFrame
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quantopian/empyrical | empyrical/stats.py | cum_returns_final | def cum_returns_final(returns, starting_value=0):
"""
Compute total returns from simple returns.
Parameters
----------
returns : pd.DataFrame, pd.Series, or np.ndarray
Noncumulative simple returns of one or more timeseries.
starting_value : float, optional
The starting returns.
... | python | def cum_returns_final(returns, starting_value=0):
"""
Compute total returns from simple returns.
Parameters
----------
returns : pd.DataFrame, pd.Series, or np.ndarray
Noncumulative simple returns of one or more timeseries.
starting_value : float, optional
The starting returns.
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quantopian/empyrical | empyrical/stats.py | aggregate_returns | def aggregate_returns(returns, convert_to):
"""
Aggregates returns by week, month, or year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
convert_to : str
Can be 'wee... | python | def aggregate_returns(returns, convert_to):
"""
Aggregates returns by week, month, or year.
Parameters
----------
returns : pd.Series
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
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quantopian/empyrical | empyrical/stats.py | max_drawdown | def max_drawdown(returns, out=None):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
out : array-like, optiona... | python | def max_drawdown(returns, out=None):
"""
Determines the maximum drawdown of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
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quantopian/empyrical | empyrical/stats.py | annual_return | def annual_return(returns, period=DAILY, annualization=None):
"""
Determines the mean annual growth rate of returns. This is equivilent
to the compound annual growth rate.
Parameters
----------
returns : pd.Series or np.ndarray
Periodic returns of the strategy, noncumulative.
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"""
Determines the mean annual growth rate of returns. This is equivilent
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returns : pd.Series or np.ndarray
Periodic returns of the strategy, noncumulative.
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quantopian/empyrical | empyrical/stats.py | annual_volatility | def annual_volatility(returns,
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Determines the annual volatility of a strategy.
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returns : pd.Series or np.ndarray
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quantopian/empyrical | empyrical/stats.py | calmar_ratio | def calmar_ratio(returns, period=DAILY, annualization=None):
"""
Determines the Calmar ratio, or drawdown ratio, of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum... | python | def calmar_ratio(returns, period=DAILY, annualization=None):
"""
Determines the Calmar ratio, or drawdown ratio, of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum... | [
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returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
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quantopian/empyrical | empyrical/stats.py | omega_ratio | def omega_ratio(returns, risk_free=0.0, required_return=0.0,
annualization=APPROX_BDAYS_PER_YEAR):
"""Determines the Omega ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in... | python | def omega_ratio(returns, risk_free=0.0, required_return=0.0,
annualization=APPROX_BDAYS_PER_YEAR):
"""Determines the Omega ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in... | [
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Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
risk_free : int, float
Constant risk-free return throughout the period
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quantopian/empyrical | empyrical/stats.py | sharpe_ratio | def sharpe_ratio(returns,
risk_free=0,
period=DAILY,
annualization=None,
out=None):
"""
Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncu... | python | def sharpe_ratio(returns,
risk_free=0,
period=DAILY,
annualization=None,
out=None):
"""
Determines the Sharpe ratio of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncu... | [
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returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
risk_free : int, float
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quantopian/empyrical | empyrical/stats.py | sortino_ratio | def sortino_ratio(returns,
required_return=0,
period=DAILY,
annualization=None,
out=None,
_downside_risk=None):
"""
Determines the Sortino ratio of a strategy.
Parameters
----------
returns : pd.Series or np.n... | python | def sortino_ratio(returns,
required_return=0,
period=DAILY,
annualization=None,
out=None,
_downside_risk=None):
"""
Determines the Sortino ratio of a strategy.
Parameters
----------
returns : pd.Series or np.n... | [
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Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
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quantopian/empyrical | empyrical/stats.py | downside_risk | def downside_risk(returns,
required_return=0,
period=DAILY,
annualization=None,
out=None):
"""
Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or np.ndarray or pd.DataFrame
... | python | def downside_risk(returns,
required_return=0,
period=DAILY,
annualization=None,
out=None):
"""
Determines the downside deviation below a threshold
Parameters
----------
returns : pd.Series or np.ndarray or pd.DataFrame
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returns : pd.Series or np.ndarray or pd.DataFrame
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
required_return: float / series
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quantopian/empyrical | empyrical/stats.py | excess_sharpe | def excess_sharpe(returns, factor_returns, out=None):
"""
Determines the Excess Sharpe of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_retu... | python | def excess_sharpe(returns, factor_returns, out=None):
"""
Determines the Excess Sharpe of a strategy.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_retu... | [
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returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_returns: float / series
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quantopian/empyrical | empyrical/stats.py | _to_pandas | def _to_pandas(ob):
"""Convert an array-like to a pandas object.
Parameters
----------
ob : array-like
The object to convert.
Returns
-------
pandas_structure : pd.Series or pd.DataFrame
The correct structure based on the dimensionality of the data.
"""
if isinstanc... | python | def _to_pandas(ob):
"""Convert an array-like to a pandas object.
Parameters
----------
ob : array-like
The object to convert.
Returns
-------
pandas_structure : pd.Series or pd.DataFrame
The correct structure based on the dimensionality of the data.
"""
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pandas_structure : pd.Series or pd.DataFrame
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quantopian/empyrical | empyrical/stats.py | _aligned_series | def _aligned_series(*many_series):
"""
Return a new list of series containing the data in the input series, but
with their indices aligned. NaNs will be filled in for missing values.
Parameters
----------
*many_series
The series to align.
Returns
-------
aligned_series : it... | python | def _aligned_series(*many_series):
"""
Return a new list of series containing the data in the input series, but
with their indices aligned. NaNs will be filled in for missing values.
Parameters
----------
*many_series
The series to align.
Returns
-------
aligned_series : it... | [
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The series to align.
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aligned_series : iterable[array-like]
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quantopian/empyrical | empyrical/stats.py | roll_alpha_beta | def roll_alpha_beta(returns, factor_returns, window=10, **kwargs):
"""
Computes alpha and beta over a rolling window.
Parameters
----------
lhs : array-like
The first array to pass to the rolling alpha-beta.
rhs : array-like
The second array to pass to the rolling alpha-beta.
... | python | def roll_alpha_beta(returns, factor_returns, window=10, **kwargs):
"""
Computes alpha and beta over a rolling window.
Parameters
----------
lhs : array-like
The first array to pass to the rolling alpha-beta.
rhs : array-like
The second array to pass to the rolling alpha-beta.
... | [
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The first array to pass to the rolling alpha-beta.
rhs : array-like
The second array to pass to the rolling alpha-beta.
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quantopian/empyrical | empyrical/stats.py | stability_of_timeseries | def stability_of_timeseries(returns):
"""Determines R-squared of a linear fit to the cumulative
log returns. Computes an ordinary least squares linear fit,
and returns R-squared.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
... | python | def stability_of_timeseries(returns):
"""Determines R-squared of a linear fit to the cumulative
log returns. Computes an ordinary least squares linear fit,
and returns R-squared.
Parameters
----------
returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
... | [
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returns : pd.Series or np.ndarray
Daily returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical... | [
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quantopian/empyrical | empyrical/stats.py | capture | def capture(returns, factor_returns, period=DAILY):
"""
Compute capture ratio.
Parameters
----------
returns : pd.Series or np.ndarray
Returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_returns : pd.Series or np.ndarray... | python | def capture(returns, factor_returns, period=DAILY):
"""
Compute capture ratio.
Parameters
----------
returns : pd.Series or np.ndarray
Returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_returns : pd.Series or np.ndarray... | [
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Parameters
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returns : pd.Series or np.ndarray
Returns of the strategy, noncumulative.
- See full explanation in :func:`~empyrical.stats.cum_returns`.
factor_returns : pd.Series or np.ndarray
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