classify_api module

ClassifyApi

class edgeimpulse_api.api.classify_api.ClassifyApi(
		api_client=None
)

Parameters

api_client=None

Methods

classify_sample

edgeimpulse_api.api.classify_api.classify_sample(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

Classify sample (deprecated)

This API is deprecated, use classifySampleV2 instead (/v1/api/{projectId}/classify/v2/{sampleId}). Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

classify_sample_by_learn_block

edgeimpulse_api.api.classify_api.classify_sample_by_learn_block(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		block_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Block ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

Classify sample by learn block

This API is deprecated, use classifySampleByLearnBlockV2 (/v1/api/{projectId}/classify/anomaly-gmm/v2/{blockId}/{sampleId}) instead. Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
block_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Block ID', extra={})]
**kwargs

Return

edgeimpulse_api.models.classify_sample_response.ClassifySampleResponse

classify_sample_by_learn_block_v2

edgeimpulse_api.api.classify_api.classify_sample_by_learn_block_v2(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		block_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Block ID', extra={})],
		variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

Classify sample by learn block

Classify a complete file against the specified learn block. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you'll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
block_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Block ID', extra={})]
variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

classify_sample_for_variants

edgeimpulse_api.api.classify_api.classify_sample_for_variants(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		variants: typing_extensions.Annotated[pydantic.types.StrictStr, FieldInfo(default=Ellipsis, description='List of keras model variants, given as a JSON string', extra={})],
		include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_sample_for_variants200_response.ClassifySampleForVariants200Response

Classify sample for the given set of variants

Classify a complete file against the current impulse, for all given variants. Depending on the size of your file and whether the sample is resampled, you may get a job ID in the response.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
variants: typing_extensions.Annotated[pydantic.types.StrictStr, FieldInfo(default=Ellipsis, description='List of keras model variants, given as a JSON string', extra={})]
include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_sample_for_variants200_response.ClassifySampleForVariants200Response

classify_sample_v2

edgeimpulse_api.api.classify_api.classify_sample_v2(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None,
		variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

Classify sample

Classify a complete file against the current impulse. This will move the sliding window (dependent on the sliding window length and the sliding window increase parameters in the impulse) over the complete file, and classify for every window that is extracted. Depending on the size of your file, whether your sample is resampled, and whether the result is cached you'll get either the result or a job back. If you receive a job, then wait for the completion of the job, and then call this function again to receive the results. The unoptimized (float32) model is used by default, and classification with an optimized (int8) model can be slower.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
include_debug_info: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to return the debug information from FOMO classification.', extra={})] = None
variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_sample_v2200_response.ClassifySampleV2200Response

get_classify_job_result

edgeimpulse_api.api.classify_api.get_classify_job_result(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		feature_explorer_only: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to get only the classification results relevant to the feature explorer.', extra={})] = None,
		variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_job_response.ClassifyJobResponse

Classify job result

Get classify job result, containing the result for the complete testing dataset.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
feature_explorer_only: typing_extensions.Annotated[Optional[pydantic.types.StrictBool], FieldInfo(default=PydanticUndefined, description='Whether to get only the classification results relevant to the feature explorer.', extra={})] = None
variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_job_response.ClassifyJobResponse

get_classify_job_result_page

edgeimpulse_api.api.classify_api.get_classify_job_result_page(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		limit: typing_extensions.Annotated[Optional[pydantic.types.StrictInt], FieldInfo(default=PydanticUndefined, description='Maximum number of results', extra={})] = None,
		offset: typing_extensions.Annotated[Optional[pydantic.types.StrictInt], FieldInfo(default=PydanticUndefined, description='Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.', extra={})] = None,
		variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None,
		**kwargs
) ‑> edgeimpulse_api.models.classify_job_response_page.ClassifyJobResponsePage

Single page of a classify job result

Get classify job result, containing the predictions for a given page.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
limit: typing_extensions.Annotated[Optional[pydantic.types.StrictInt], FieldInfo(default=PydanticUndefined, description='Maximum number of results', extra={})] = None
offset: typing_extensions.Annotated[Optional[pydantic.types.StrictInt], FieldInfo(default=PydanticUndefined, description='Offset in results, can be used in conjunction with LimitResultsParameter to implement paging.', extra={})] = None
variant: typing_extensions.Annotated[Optional[edgeimpulse_api.models.keras_model_variant_enum.KerasModelVariantEnum], FieldInfo(default=PydanticUndefined, description='Keras model variant', extra={})] = None
**kwargs

Return

edgeimpulse_api.models.classify_job_response_page.ClassifyJobResponsePage

get_classify_metrics_all_variants

edgeimpulse_api.api.classify_api.get_classify_metrics_all_variants(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.metrics_all_variants_response.MetricsAllVariantsResponse

Get metrics for all available model variants

Get metrics, calculated during a classify all job, for all available model variants. This is experimental and may change in the future.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
**kwargs

Return

edgeimpulse_api.models.metrics_all_variants_response.MetricsAllVariantsResponse

get_sample_window_from_cache

edgeimpulse_api.api.classify_api.get_sample_window_from_cache(
		self,
		project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})],
		sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})],
		window_index: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample window index', extra={})],
		**kwargs
) ‑> edgeimpulse_api.models.get_sample_response.GetSampleResponse

Get a window of raw sample features from cache, after a live classification job has completed.

Get raw sample features for a particular window. This is only available after a live classification job has completed and raw features have been cached.

Parameters

self
project_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Project ID', extra={})]
sample_id: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample ID', extra={})]
window_index: typing_extensions.Annotated[pydantic.types.StrictInt, FieldInfo(default=Ellipsis, description='Sample window index', extra={})]
**kwargs

Return

edgeimpulse_api.models.get_sample_response.GetSampleResponse

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