# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/cloud/automl_v1beta1/proto/classification.proto
import sys
_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
from google.protobuf.internal import enum_type_wrapper
from google.protobuf import descriptor as _descriptor
from google.protobuf import message as _message
from google.protobuf import reflection as _reflection
from google.protobuf import symbol_database as _symbol_database
# @@protoc_insertion_point(imports)
_sym_db = _symbol_database.Default()
from google.cloud.automl_v1beta1.proto import (
temporal_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_temporal__pb2,
)
from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name="google/cloud/automl_v1beta1/proto/classification.proto",
package="google.cloud.automl.v1beta1",
syntax="proto3",
serialized_options=_b(
"\n\037com.google.cloud.automl.v1beta1B\023ClassificationProtoZAgoogle.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl\312\002\033Google\\Cloud\\AutoMl\\V1beta1\352\002\036Google::Cloud::AutoML::V1beta1"
),
serialized_pb=_b(
'\n6google/cloud/automl_v1beta1/proto/classification.proto\x12\x1bgoogle.cloud.automl.v1beta1\x1a\x30google/cloud/automl_v1beta1/proto/temporal.proto\x1a\x1cgoogle/api/annotations.proto")\n\x18\x43lassificationAnnotation\x12\r\n\x05score\x18\x01 \x01(\x02"\xc7\x01\n\x1dVideoClassificationAnnotation\x12\x0c\n\x04type\x18\x01 \x01(\t\x12X\n\x19\x63lassification_annotation\x18\x02 \x01(\x0b\x32\x35.google.cloud.automl.v1beta1.ClassificationAnnotation\x12>\n\x0ctime_segment\x18\x03 \x01(\x0b\x32(.google.cloud.automl.v1beta1.TimeSegment"\xa9\x07\n\x1f\x43lassificationEvaluationMetrics\x12\x0e\n\x06\x61u_prc\x18\x01 \x01(\x02\x12\x17\n\x0b\x62\x61se_au_prc\x18\x02 \x01(\x02\x42\x02\x18\x01\x12\x0e\n\x06\x61u_roc\x18\x06 \x01(\x02\x12\x10\n\x08log_loss\x18\x07 \x01(\x02\x12u\n\x18\x63onfidence_metrics_entry\x18\x03 \x03(\x0b\x32S.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry\x12\x66\n\x10\x63onfusion_matrix\x18\x04 \x01(\x0b\x32L.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix\x12\x1a\n\x12\x61nnotation_spec_id\x18\x05 \x03(\t\x1a\xfc\x02\n\x16\x43onfidenceMetricsEntry\x12\x1c\n\x14\x63onfidence_threshold\x18\x01 \x01(\x02\x12\x1a\n\x12position_threshold\x18\x0e \x01(\x05\x12\x0e\n\x06recall\x18\x02 \x01(\x02\x12\x11\n\tprecision\x18\x03 \x01(\x02\x12\x1b\n\x13\x66\x61lse_positive_rate\x18\x08 \x01(\x02\x12\x10\n\x08\x66\x31_score\x18\x04 \x01(\x02\x12\x12\n\nrecall_at1\x18\x05 \x01(\x02\x12\x15\n\rprecision_at1\x18\x06 \x01(\x02\x12\x1f\n\x17\x66\x61lse_positive_rate_at1\x18\t \x01(\x02\x12\x14\n\x0c\x66\x31_score_at1\x18\x07 \x01(\x02\x12\x1b\n\x13true_positive_count\x18\n \x01(\x03\x12\x1c\n\x14\x66\x61lse_positive_count\x18\x0b \x01(\x03\x12\x1c\n\x14\x66\x61lse_negative_count\x18\x0c \x01(\x03\x12\x1b\n\x13true_negative_count\x18\r \x01(\x03\x1a\xc0\x01\n\x0f\x43onfusionMatrix\x12\x1a\n\x12\x61nnotation_spec_id\x18\x01 \x03(\t\x12\x14\n\x0c\x64isplay_name\x18\x03 \x03(\t\x12]\n\x03row\x18\x02 \x03(\x0b\x32P.google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row\x1a\x1c\n\x03Row\x12\x15\n\rexample_count\x18\x01 \x03(\x05*Y\n\x12\x43lassificationType\x12#\n\x1f\x43LASSIFICATION_TYPE_UNSPECIFIED\x10\x00\x12\x0e\n\nMULTICLASS\x10\x01\x12\x0e\n\nMULTILABEL\x10\x02\x42\xb8\x01\n\x1f\x63om.google.cloud.automl.v1beta1B\x13\x43lassificationProtoZAgoogle.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl\xca\x02\x1bGoogle\\Cloud\\AutoMl\\V1beta1\xea\x02\x1eGoogle::Cloud::AutoML::V1beta1b\x06proto3'
),
dependencies=[
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_temporal__pb2.DESCRIPTOR,
google_dot_api_dot_annotations__pb2.DESCRIPTOR,
],
)
_CLASSIFICATIONTYPE = _descriptor.EnumDescriptor(
name="ClassificationType",
full_name="google.cloud.automl.v1beta1.ClassificationType",
filename=None,
file=DESCRIPTOR,
values=[
_descriptor.EnumValueDescriptor(
name="CLASSIFICATION_TYPE_UNSPECIFIED",
index=0,
number=0,
serialized_options=None,
type=None,
),
_descriptor.EnumValueDescriptor(
name="MULTICLASS", index=1, number=1, serialized_options=None, type=None
),
_descriptor.EnumValueDescriptor(
name="MULTILABEL", index=2, number=2, serialized_options=None, type=None
),
],
containing_type=None,
serialized_options=None,
serialized_start=1352,
serialized_end=1441,
)
_sym_db.RegisterEnumDescriptor(_CLASSIFICATIONTYPE)
ClassificationType = enum_type_wrapper.EnumTypeWrapper(_CLASSIFICATIONTYPE)
CLASSIFICATION_TYPE_UNSPECIFIED = 0
MULTICLASS = 1
MULTILABEL = 2
_CLASSIFICATIONANNOTATION = _descriptor.Descriptor(
name="ClassificationAnnotation",
full_name="google.cloud.automl.v1beta1.ClassificationAnnotation",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="score",
full_name="google.cloud.automl.v1beta1.ClassificationAnnotation.score",
index=0,
number=1,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
)
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=167,
serialized_end=208,
)
_VIDEOCLASSIFICATIONANNOTATION = _descriptor.Descriptor(
name="VideoClassificationAnnotation",
full_name="google.cloud.automl.v1beta1.VideoClassificationAnnotation",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="type",
full_name="google.cloud.automl.v1beta1.VideoClassificationAnnotation.type",
index=0,
number=1,
type=9,
cpp_type=9,
label=1,
has_default_value=False,
default_value=_b("").decode("utf-8"),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="classification_annotation",
full_name="google.cloud.automl.v1beta1.VideoClassificationAnnotation.classification_annotation",
index=1,
number=2,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="time_segment",
full_name="google.cloud.automl.v1beta1.VideoClassificationAnnotation.time_segment",
index=2,
number=3,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=211,
serialized_end=410,
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFIDENCEMETRICSENTRY = _descriptor.Descriptor(
name="ConfidenceMetricsEntry",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="confidence_threshold",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.confidence_threshold",
index=0,
number=1,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="position_threshold",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.position_threshold",
index=1,
number=14,
type=5,
cpp_type=1,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="recall",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall",
index=2,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="precision",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision",
index=3,
number=3,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="false_positive_rate",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.false_positive_rate",
index=4,
number=8,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="f1_score",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.f1_score",
index=5,
number=4,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="recall_at1",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.recall_at1",
index=6,
number=5,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="precision_at1",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.precision_at1",
index=7,
number=6,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="false_positive_rate_at1",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.false_positive_rate_at1",
index=8,
number=9,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="f1_score_at1",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.f1_score_at1",
index=9,
number=7,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="true_positive_count",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.true_positive_count",
index=10,
number=10,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="false_positive_count",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.false_positive_count",
index=11,
number=11,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="false_negative_count",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.false_negative_count",
index=12,
number=12,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="true_negative_count",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.true_negative_count",
index=13,
number=13,
type=3,
cpp_type=2,
label=1,
has_default_value=False,
default_value=0,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=775,
serialized_end=1155,
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX_ROW = _descriptor.Descriptor(
name="Row",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="example_count",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row.example_count",
index=0,
number=1,
type=5,
cpp_type=1,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
)
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1322,
serialized_end=1350,
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX = _descriptor.Descriptor(
name="ConfusionMatrix",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="annotation_spec_id",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.annotation_spec_id",
index=0,
number=1,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="display_name",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.display_name",
index=1,
number=3,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="row",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.row",
index=2,
number=2,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX_ROW],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1158,
serialized_end=1350,
)
_CLASSIFICATIONEVALUATIONMETRICS = _descriptor.Descriptor(
name="ClassificationEvaluationMetrics",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="au_prc",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.au_prc",
index=0,
number=1,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="base_au_prc",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.base_au_prc",
index=1,
number=2,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=_b("\030\001"),
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="au_roc",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.au_roc",
index=2,
number=6,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="log_loss",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.log_loss",
index=3,
number=7,
type=2,
cpp_type=6,
label=1,
has_default_value=False,
default_value=float(0),
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="confidence_metrics_entry",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.confidence_metrics_entry",
index=4,
number=3,
type=11,
cpp_type=10,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="confusion_matrix",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.confusion_matrix",
index=5,
number=4,
type=11,
cpp_type=10,
label=1,
has_default_value=False,
default_value=None,
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
_descriptor.FieldDescriptor(
name="annotation_spec_id",
full_name="google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.annotation_spec_id",
index=6,
number=5,
type=9,
cpp_type=9,
label=3,
has_default_value=False,
default_value=[],
message_type=None,
enum_type=None,
containing_type=None,
is_extension=False,
extension_scope=None,
serialized_options=None,
file=DESCRIPTOR,
),
],
extensions=[],
nested_types=[
_CLASSIFICATIONEVALUATIONMETRICS_CONFIDENCEMETRICSENTRY,
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX,
],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=413,
serialized_end=1350,
)
_VIDEOCLASSIFICATIONANNOTATION.fields_by_name[
"classification_annotation"
].message_type = _CLASSIFICATIONANNOTATION
_VIDEOCLASSIFICATIONANNOTATION.fields_by_name[
"time_segment"
].message_type = (
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_temporal__pb2._TIMESEGMENT
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFIDENCEMETRICSENTRY.containing_type = (
_CLASSIFICATIONEVALUATIONMETRICS
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX_ROW.containing_type = (
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX
)
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX.fields_by_name[
"row"
].message_type = _CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX_ROW
_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX.containing_type = (
_CLASSIFICATIONEVALUATIONMETRICS
)
_CLASSIFICATIONEVALUATIONMETRICS.fields_by_name[
"confidence_metrics_entry"
].message_type = _CLASSIFICATIONEVALUATIONMETRICS_CONFIDENCEMETRICSENTRY
_CLASSIFICATIONEVALUATIONMETRICS.fields_by_name[
"confusion_matrix"
].message_type = _CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX
DESCRIPTOR.message_types_by_name["ClassificationAnnotation"] = _CLASSIFICATIONANNOTATION
DESCRIPTOR.message_types_by_name[
"VideoClassificationAnnotation"
] = _VIDEOCLASSIFICATIONANNOTATION
DESCRIPTOR.message_types_by_name[
"ClassificationEvaluationMetrics"
] = _CLASSIFICATIONEVALUATIONMETRICS
DESCRIPTOR.enum_types_by_name["ClassificationType"] = _CLASSIFICATIONTYPE
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
ClassificationAnnotation = _reflection.GeneratedProtocolMessageType(
"ClassificationAnnotation",
(_message.Message,),
dict(
DESCRIPTOR=_CLASSIFICATIONANNOTATION,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Contains annotation details specific to classification.
Attributes:
score:
Output only. A confidence estimate between 0.0 and 1.0. A
higher value means greater confidence that the annotation is
positive. If a user approves an annotation as negative or
positive, the score value remains unchanged. If a user creates
an annotation, the score is 0 for negative or 1 for positive.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationAnnotation)
),
)
_sym_db.RegisterMessage(ClassificationAnnotation)
VideoClassificationAnnotation = _reflection.GeneratedProtocolMessageType(
"VideoClassificationAnnotation",
(_message.Message,),
dict(
DESCRIPTOR=_VIDEOCLASSIFICATIONANNOTATION,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Contains annotation details specific to video classification.
Attributes:
type:
Output only. Expresses the type of video classification.
Possible values: - ``segment`` - Classification done on a
specified by user time segment of a video. AnnotationSpec
is answered to be present in that time segment, if it is
present in any part of it. The video ML model evaluations
are done only for this type of classification. - ``shot``-
Shot-level classification. AutoML Video Intelligence
determines the boundaries for each camera shot in the entire
segment of the video that user specified in the request
configuration. AutoML Video Intelligence then returns
labels and their confidence scores for each detected shot,
along with the start and end time of the shot. WARNING:
Model evaluation is not done for this classification type,
the quality of it depends on training data, but there are no
metrics provided to describe that quality. - ``1s_interval``
- AutoML Video Intelligence returns labels and their
confidence scores for each second of the entire segment of the
video that user specified in the request configuration.
WARNING: Model evaluation is not done for this
classification type, the quality of it depends on training
data, but there are no metrics provided to describe that
quality.
classification_annotation:
Output only . The classification details of this annotation.
time_segment:
Output only . The time segment of the video to which the
annotation applies.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.VideoClassificationAnnotation)
),
)
_sym_db.RegisterMessage(VideoClassificationAnnotation)
ClassificationEvaluationMetrics = _reflection.GeneratedProtocolMessageType(
"ClassificationEvaluationMetrics",
(_message.Message,),
dict(
ConfidenceMetricsEntry=_reflection.GeneratedProtocolMessageType(
"ConfidenceMetricsEntry",
(_message.Message,),
dict(
DESCRIPTOR=_CLASSIFICATIONEVALUATIONMETRICS_CONFIDENCEMETRICSENTRY,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Metrics for a single confidence threshold.
Attributes:
confidence_threshold:
Output only. Metrics are computed with an assumption that the
model never returns predictions with score lower than this
value.
position_threshold:
Output only. Metrics are computed with an assumption that the
model always returns at most this many predictions (ordered by
their score, descendingly), but they all still need to meet
the confidence\_threshold.
recall:
Output only. Recall (True Positive Rate) for the given
confidence threshold.
precision:
Output only. Precision for the given confidence threshold.
false_positive_rate:
Output only. False Positive Rate for the given confidence
threshold.
f1_score:
Output only. The harmonic mean of recall and precision.
recall_at1:
Output only. The Recall (True Positive Rate) when only
considering the label that has the highest prediction score
and not below the confidence threshold for each example.
precision_at1:
Output only. The precision when only considering the label
that has the highest prediction score and not below the
confidence threshold for each example.
false_positive_rate_at1:
Output only. The False Positive Rate when only considering the
label that has the highest prediction score and not below the
confidence threshold for each example.
f1_score_at1:
Output only. The harmonic mean of [recall\_at1][google.cloud.a
utoml.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetric
sEntry.recall\_at1] and [precision\_at1][google.cloud.automl.v
1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry.
precision\_at1].
true_positive_count:
Output only. The number of model created labels that match a
ground truth label.
false_positive_count:
Output only. The number of model created labels that do not
match a ground truth label.
false_negative_count:
Output only. The number of ground truth labels that are not
matched by a model created label.
true_negative_count:
Output only. The number of labels that were not created by the
model, but if they would, they would not match a ground truth
label.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfidenceMetricsEntry)
),
),
ConfusionMatrix=_reflection.GeneratedProtocolMessageType(
"ConfusionMatrix",
(_message.Message,),
dict(
Row=_reflection.GeneratedProtocolMessageType(
"Row",
(_message.Message,),
dict(
DESCRIPTOR=_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX_ROW,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Output only. A row in the confusion matrix.
Attributes:
example_count:
Output only. Value of the specific cell in the confusion
matrix. The number of values each row has (i.e. the length of
the row) is equal to the length of the ``annotation_spec_id``
field or, if that one is not populated, length of the [display
\_name][google.cloud.automl.v1beta1.ClassificationEvaluationMe
trics.ConfusionMatrix.display\_name] field.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix.Row)
),
),
DESCRIPTOR=_CLASSIFICATIONEVALUATIONMETRICS_CONFUSIONMATRIX,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Confusion matrix of the model running the classification.
Attributes:
annotation_spec_id:
Output only. IDs of the annotation specs used in the confusion
matrix. For Tables CLASSIFICATION [prediction\_type][google.c
loud.automl.v1beta1.TablesModelMetadata.prediction\_type] only
list of [annotation\_spec\_display\_name-s][] is populated.
display_name:
Output only. Display name of the annotation specs used in the
confusion matrix, as they were at the moment of the
evaluation. For Tables CLASSIFICATION [prediction\_type-s][go
ogle.cloud.automl.v1beta1.TablesModelMetadata.prediction\_type
], distinct values of the target column at the moment of the
model evaluation are populated here.
row:
Output only. Rows in the confusion matrix. The number of rows
is equal to the size of ``annotation_spec_id``.
``row[i].value[j]`` is the number of examples that have ground
truth of the ``annotation_spec_id[i]`` and are predicted as
``annotation_spec_id[j]`` by the model being evaluated.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics.ConfusionMatrix)
),
),
DESCRIPTOR=_CLASSIFICATIONEVALUATIONMETRICS,
__module__="google.cloud.automl_v1beta1.proto.classification_pb2",
__doc__="""Model evaluation metrics for classification problems. Note: For Video
Classification this metrics only describe quality of the Video
Classification predictions of "segment\_classification" type.
Attributes:
au_prc:
Output only. The Area Under Precision-Recall Curve metric.
Micro-averaged for the overall evaluation.
base_au_prc:
Output only. The Area Under Precision-Recall Curve metric
based on priors. Micro-averaged for the overall evaluation.
Deprecated.
au_roc:
Output only. The Area Under Receiver Operating Characteristic
curve metric. Micro-averaged for the overall evaluation.
log_loss:
Output only. The Log Loss metric.
confidence_metrics_entry:
Output only. Metrics for each confidence\_threshold in
0.00,0.05,0.10,...,0.95,0.96,0.97,0.98,0.99 and
position\_threshold = INT32\_MAX\_VALUE. ROC and precision-
recall curves, and other aggregated metrics are derived from
them. The confidence metrics entries may also be supplied for
additional values of position\_threshold, but from these no
aggregated metrics are computed.
confusion_matrix:
Output only. Confusion matrix of the evaluation. Only set for
MULTICLASS classification problems where number of labels is
no more than 10. Only set for model level evaluation, not for
evaluation per label.
annotation_spec_id:
Output only. The annotation spec ids used for this evaluation.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.ClassificationEvaluationMetrics)
),
)
_sym_db.RegisterMessage(ClassificationEvaluationMetrics)
_sym_db.RegisterMessage(ClassificationEvaluationMetrics.ConfidenceMetricsEntry)
_sym_db.RegisterMessage(ClassificationEvaluationMetrics.ConfusionMatrix)
_sym_db.RegisterMessage(ClassificationEvaluationMetrics.ConfusionMatrix.Row)
DESCRIPTOR._options = None
_CLASSIFICATIONEVALUATIONMETRICS.fields_by_name["base_au_prc"]._options = None
# @@protoc_insertion_point(module_scope)