# -*- coding: utf-8 -*-
# Generated by the protocol buffer compiler. DO NOT EDIT!
# source: google/cloud/automl_v1beta1/proto/tables.proto
import sys
_b = sys.version_info[0] < 3 and (lambda x: x) or (lambda x: x.encode("latin1"))
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 (
classification_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_classification__pb2,
)
from google.cloud.automl_v1beta1.proto import (
column_spec_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_column__spec__pb2,
)
from google.cloud.automl_v1beta1.proto import (
data_items_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_data__items__pb2,
)
from google.cloud.automl_v1beta1.proto import (
data_stats_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_data__stats__pb2,
)
from google.cloud.automl_v1beta1.proto import (
ranges_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_ranges__pb2,
)
from google.cloud.automl_v1beta1.proto import (
temporal_pb2 as google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_temporal__pb2,
)
from google.protobuf import struct_pb2 as google_dot_protobuf_dot_struct__pb2
from google.protobuf import timestamp_pb2 as google_dot_protobuf_dot_timestamp__pb2
from google.api import annotations_pb2 as google_dot_api_dot_annotations__pb2
DESCRIPTOR = _descriptor.FileDescriptor(
name="google/cloud/automl_v1beta1/proto/tables.proto",
package="google.cloud.automl.v1beta1",
syntax="proto3",
serialized_options=_b(
"\n\037com.google.cloud.automl.v1beta1P\001ZAgoogle.golang.org/genproto/googleapis/cloud/automl/v1beta1;automl\312\002\033Google\\Cloud\\AutoMl\\V1beta1\352\002\036Google::Cloud::AutoML::V1beta1"
),
serialized_pb=_b(
'\n.google/cloud/automl_v1beta1/proto/tables.proto\x12\x1bgoogle.cloud.automl.v1beta1\x1a\x36google/cloud/automl_v1beta1/proto/classification.proto\x1a\x33google/cloud/automl_v1beta1/proto/column_spec.proto\x1a\x32google/cloud/automl_v1beta1/proto/data_items.proto\x1a\x32google/cloud/automl_v1beta1/proto/data_stats.proto\x1a.google/cloud/automl_v1beta1/proto/ranges.proto\x1a\x30google/cloud/automl_v1beta1/proto/temporal.proto\x1a\x1cgoogle/protobuf/struct.proto\x1a\x1fgoogle/protobuf/timestamp.proto\x1a\x1cgoogle/api/annotations.proto"\xb0\x03\n\x15TablesDatasetMetadata\x12\x1d\n\x15primary_table_spec_id\x18\x01 \x01(\t\x12\x1d\n\x15target_column_spec_id\x18\x02 \x01(\t\x12\x1d\n\x15weight_column_spec_id\x18\x03 \x01(\t\x12\x1d\n\x15ml_use_column_spec_id\x18\x04 \x01(\t\x12t\n\x1atarget_column_correlations\x18\x06 \x03(\x0b\x32P.google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry\x12\x35\n\x11stats_update_time\x18\x07 \x01(\x0b\x32\x1a.google.protobuf.Timestamp\x1an\n\x1dTargetColumnCorrelationsEntry\x12\x0b\n\x03key\x18\x01 \x01(\t\x12<\n\x05value\x18\x02 \x01(\x0b\x32-.google.cloud.automl.v1beta1.CorrelationStats:\x02\x38\x01"\x89\x03\n\x13TablesModelMetadata\x12\x43\n\x12target_column_spec\x18\x02 \x01(\x0b\x32\'.google.cloud.automl.v1beta1.ColumnSpec\x12K\n\x1ainput_feature_column_specs\x18\x03 \x03(\x0b\x32\'.google.cloud.automl.v1beta1.ColumnSpec\x12\x1e\n\x16optimization_objective\x18\x04 \x01(\t\x12T\n\x18tables_model_column_info\x18\x05 \x03(\x0b\x32\x32.google.cloud.automl.v1beta1.TablesModelColumnInfo\x12%\n\x1dtrain_budget_milli_node_hours\x18\x06 \x01(\x03\x12#\n\x1btrain_cost_milli_node_hours\x18\x07 \x01(\x03\x12\x1e\n\x16\x64isable_early_stopping\x18\x0c \x01(\x08"\xe5\x01\n\x10TablesAnnotation\x12\r\n\x05score\x18\x01 \x01(\x02\x12\x45\n\x13prediction_interval\x18\x04 \x01(\x0b\x32(.google.cloud.automl.v1beta1.DoubleRange\x12%\n\x05value\x18\x02 \x01(\x0b\x32\x16.google.protobuf.Value\x12T\n\x18tables_model_column_info\x18\x03 \x03(\x0b\x32\x32.google.cloud.automl.v1beta1.TablesModelColumnInfo"j\n\x15TablesModelColumnInfo\x12\x18\n\x10\x63olumn_spec_name\x18\x01 \x01(\t\x12\x1b\n\x13\x63olumn_display_name\x18\x02 \x01(\t\x12\x1a\n\x12\x66\x65\x61ture_importance\x18\x03 \x01(\x02\x42\xa5\x01\n\x1f\x63om.google.cloud.automl.v1beta1P\x01ZAgoogle.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_classification__pb2.DESCRIPTOR,
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_column__spec__pb2.DESCRIPTOR,
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_data__items__pb2.DESCRIPTOR,
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_data__stats__pb2.DESCRIPTOR,
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_ranges__pb2.DESCRIPTOR,
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_temporal__pb2.DESCRIPTOR,
google_dot_protobuf_dot_struct__pb2.DESCRIPTOR,
google_dot_protobuf_dot_timestamp__pb2.DESCRIPTOR,
google_dot_api_dot_annotations__pb2.DESCRIPTOR,
],
)
_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY = _descriptor.Descriptor(
name="TargetColumnCorrelationsEntry",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="key",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry.key",
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="value",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry.value",
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,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=_b("8\001"),
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=806,
serialized_end=916,
)
_TABLESDATASETMETADATA = _descriptor.Descriptor(
name="TablesDatasetMetadata",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="primary_table_spec_id",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.primary_table_spec_id",
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="target_column_spec_id",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_spec_id",
index=1,
number=2,
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="weight_column_spec_id",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.weight_column_spec_id",
index=2,
number=3,
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="ml_use_column_spec_id",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.ml_use_column_spec_id",
index=3,
number=4,
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="target_column_correlations",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.target_column_correlations",
index=4,
number=6,
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="stats_update_time",
full_name="google.cloud.automl.v1beta1.TablesDatasetMetadata.stats_update_time",
index=5,
number=7,
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=[_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=484,
serialized_end=916,
)
_TABLESMODELMETADATA = _descriptor.Descriptor(
name="TablesModelMetadata",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="target_column_spec",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.target_column_spec",
index=0,
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="input_feature_column_specs",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.input_feature_column_specs",
index=1,
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="optimization_objective",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.optimization_objective",
index=2,
number=4,
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="tables_model_column_info",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.tables_model_column_info",
index=3,
number=5,
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="train_budget_milli_node_hours",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.train_budget_milli_node_hours",
index=4,
number=6,
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="train_cost_milli_node_hours",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.train_cost_milli_node_hours",
index=5,
number=7,
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="disable_early_stopping",
full_name="google.cloud.automl.v1beta1.TablesModelMetadata.disable_early_stopping",
index=6,
number=12,
type=8,
cpp_type=7,
label=1,
has_default_value=False,
default_value=False,
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=919,
serialized_end=1312,
)
_TABLESANNOTATION = _descriptor.Descriptor(
name="TablesAnnotation",
full_name="google.cloud.automl.v1beta1.TablesAnnotation",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="score",
full_name="google.cloud.automl.v1beta1.TablesAnnotation.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,
),
_descriptor.FieldDescriptor(
name="prediction_interval",
full_name="google.cloud.automl.v1beta1.TablesAnnotation.prediction_interval",
index=1,
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="value",
full_name="google.cloud.automl.v1beta1.TablesAnnotation.value",
index=2,
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="tables_model_column_info",
full_name="google.cloud.automl.v1beta1.TablesAnnotation.tables_model_column_info",
index=3,
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,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1315,
serialized_end=1544,
)
_TABLESMODELCOLUMNINFO = _descriptor.Descriptor(
name="TablesModelColumnInfo",
full_name="google.cloud.automl.v1beta1.TablesModelColumnInfo",
filename=None,
file=DESCRIPTOR,
containing_type=None,
fields=[
_descriptor.FieldDescriptor(
name="column_spec_name",
full_name="google.cloud.automl.v1beta1.TablesModelColumnInfo.column_spec_name",
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="column_display_name",
full_name="google.cloud.automl.v1beta1.TablesModelColumnInfo.column_display_name",
index=1,
number=2,
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="feature_importance",
full_name="google.cloud.automl.v1beta1.TablesModelColumnInfo.feature_importance",
index=2,
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,
),
],
extensions=[],
nested_types=[],
enum_types=[],
serialized_options=None,
is_extendable=False,
syntax="proto3",
extension_ranges=[],
oneofs=[],
serialized_start=1546,
serialized_end=1652,
)
_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY.fields_by_name[
"value"
].message_type = (
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_data__stats__pb2._CORRELATIONSTATS
)
_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY.containing_type = (
_TABLESDATASETMETADATA
)
_TABLESDATASETMETADATA.fields_by_name[
"target_column_correlations"
].message_type = _TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY
_TABLESDATASETMETADATA.fields_by_name[
"stats_update_time"
].message_type = google_dot_protobuf_dot_timestamp__pb2._TIMESTAMP
_TABLESMODELMETADATA.fields_by_name[
"target_column_spec"
].message_type = (
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_column__spec__pb2._COLUMNSPEC
)
_TABLESMODELMETADATA.fields_by_name[
"input_feature_column_specs"
].message_type = (
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_column__spec__pb2._COLUMNSPEC
)
_TABLESMODELMETADATA.fields_by_name[
"tables_model_column_info"
].message_type = _TABLESMODELCOLUMNINFO
_TABLESANNOTATION.fields_by_name[
"prediction_interval"
].message_type = (
google_dot_cloud_dot_automl__v1beta1_dot_proto_dot_ranges__pb2._DOUBLERANGE
)
_TABLESANNOTATION.fields_by_name[
"value"
].message_type = google_dot_protobuf_dot_struct__pb2._VALUE
_TABLESANNOTATION.fields_by_name[
"tables_model_column_info"
].message_type = _TABLESMODELCOLUMNINFO
DESCRIPTOR.message_types_by_name["TablesDatasetMetadata"] = _TABLESDATASETMETADATA
DESCRIPTOR.message_types_by_name["TablesModelMetadata"] = _TABLESMODELMETADATA
DESCRIPTOR.message_types_by_name["TablesAnnotation"] = _TABLESANNOTATION
DESCRIPTOR.message_types_by_name["TablesModelColumnInfo"] = _TABLESMODELCOLUMNINFO
_sym_db.RegisterFileDescriptor(DESCRIPTOR)
TablesDatasetMetadata = _reflection.GeneratedProtocolMessageType(
"TablesDatasetMetadata",
(_message.Message,),
dict(
TargetColumnCorrelationsEntry=_reflection.GeneratedProtocolMessageType(
"TargetColumnCorrelationsEntry",
(_message.Message,),
dict(
DESCRIPTOR=_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY,
__module__="google.cloud.automl_v1beta1.proto.tables_pb2"
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesDatasetMetadata.TargetColumnCorrelationsEntry)
),
),
DESCRIPTOR=_TABLESDATASETMETADATA,
__module__="google.cloud.automl_v1beta1.proto.tables_pb2",
__doc__="""Metadata for a dataset used for AutoML Tables.
Attributes:
primary_table_spec_id:
Output only. The table\_spec\_id of the primary table of this
dataset.
target_column_spec_id:
column\_spec\_id of the primary table's column that should be
used as the training & prediction target. This column must be
non-nullable and have one of following data types (otherwise
model creation will error): - CATEGORY - FLOAT64 If the
type is CATEGORY , only up to 100 unique values may exist in
that column across all rows. NOTE: Updates of this field will
instantly affect any other users concurrently working with the
dataset.
weight_column_spec_id:
column\_spec\_id of the primary table's column that should be
used as the weight column, i.e. the higher the value the more
important the row will be during model training. Required
type: FLOAT64. Allowed values: 0 to 10000, inclusive on both
ends; 0 means the row is ignored for training. If not set all
rows are assumed to have equal weight of 1. NOTE: Updates of
this field will instantly affect any other users concurrently
working with the dataset.
ml_use_column_spec_id:
column\_spec\_id of the primary table column which specifies a
possible ML use of the row, i.e. the column will be used to
split the rows into TRAIN, VALIDATE and TEST sets. Required
type: STRING. This column, if set, must either have all of
``TRAIN``, ``VALIDATE``, ``TEST`` among its values, or only
have ``TEST``, ``UNASSIGNED`` values. In the latter case the
rows with ``UNASSIGNED`` value will be assigned by AutoML.
Note that if a given ml use distribution makes it impossible
to create a "good" model, that call will error describing the
issue. If both this column\_spec\_id and primary table's
time\_column\_spec\_id are not set, then all rows are treated
as ``UNASSIGNED``. NOTE: Updates of this field will instantly
affect any other users concurrently working with the dataset.
target_column_correlations:
Output only. Correlations between [TablesDatasetMetadata.targ
et\_column\_spec\_id][google.cloud.automl.v1beta1.TablesDatase
tMetadata.target\_column\_spec\_id], and other columns of the
[TablesDatasetMetadataprimary\_table][google.cloud.automl.v1be
ta1.TablesDatasetMetadata.primary\_table\_spec\_id]. Only set
if the target column is set. Mapping from other column spec id
to its CorrelationStats with the target column. This field may
be stale, see the stats\_update\_time field for for the
timestamp at which these stats were last updated.
stats_update_time:
Output only. The most recent timestamp when
target\_column\_correlations field and all descendant
ColumnSpec.data\_stats and ColumnSpec.top\_correlated\_columns
fields were last (re-)generated. Any changes that happened to
the dataset afterwards are not reflected in these fields
values. The regeneration happens in the background on a best
effort basis.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesDatasetMetadata)
),
)
_sym_db.RegisterMessage(TablesDatasetMetadata)
_sym_db.RegisterMessage(TablesDatasetMetadata.TargetColumnCorrelationsEntry)
TablesModelMetadata = _reflection.GeneratedProtocolMessageType(
"TablesModelMetadata",
(_message.Message,),
dict(
DESCRIPTOR=_TABLESMODELMETADATA,
__module__="google.cloud.automl_v1beta1.proto.tables_pb2",
__doc__="""Model metadata specific to AutoML Tables.
Attributes:
target_column_spec:
Column spec of the dataset's primary table's column the model
is predicting. Snapshotted when model creation started. Only 3
fields are used: name - May be set on CreateModel, if it's not
then the ColumnSpec corresponding to the current
target\_column\_spec\_id of the dataset the model is trained
from is used. If neither is set, CreateModel will error.
display\_name - Output only. data\_type - Output only.
input_feature_column_specs:
Column specs of the dataset's primary table's columns, on
which the model is trained and which are used as the input for
predictions. The [target\_column][google.cloud.automl.v1beta1
.TablesModelMetadata.target\_column\_spec] as well as,
according to dataset's state upon model creation, [weight\_co
lumn][google.cloud.automl.v1beta1.TablesDatasetMetadata.weight
\_column\_spec\_id], and [ml\_use\_column][google.cloud.autom
l.v1beta1.TablesDatasetMetadata.ml\_use\_column\_spec\_id]
must never be included here. Only 3 fields are used: - name
- May be set on CreateModel, if set only the columns specified
are used, otherwise all primary table's columns (except the
ones listed above) are used for the training and prediction
input. - display\_name - Output only. - data\_type -
Output only.
optimization_objective:
Objective function the model is optimizing towards. The
training process creates a model that maximizes/minimizes the
value of the objective function over the validation set. The
supported optimization objectives depend on the prediction
type. If the field is not set, a default objective function is
used. CLASSIFICATION\_BINARY: "MAXIMIZE\_AU\_ROC" (default) -
Maximize the area under the receiver operating characteristic
(ROC) curve. "MINIMIZE\_LOG\_LOSS" - Minimize log loss.
"MAXIMIZE\_AU\_PRC" - Maximize the area under the precision-
recall curve. "MAXIMIZE\_PRECISION\_AT\_RECALL" - Maximize
precision for a specified recall value.
"MAXIMIZE\_RECALL\_AT\_PRECISION" - Maximize recall for a
specified precision value. CLASSIFICATION\_MULTI\_CLASS :
"MINIMIZE\_LOG\_LOSS" (default) - Minimize log loss.
REGRESSION: "MINIMIZE\_RMSE" (default) - Minimize root-mean-
squared error (RMSE). "MINIMIZE\_MAE" - Minimize mean-absolute
error (MAE). "MINIMIZE\_RMSLE" - Minimize root-mean-squared
log error (RMSLE).
tables_model_column_info:
Output only. Auxiliary information for each of the
input\_feature\_column\_specs with respect to this particular
model.
train_budget_milli_node_hours:
Required. The train budget of creating this model, expressed
in milli node hours i.e. 1,000 value in this field means 1
node hour. The training cost of the model will not exceed
this budget. The final cost will be attempted to be close to
the budget, though may end up being (even) noticeably smaller
- at the backend's discretion. This especially may happen when
further model training ceases to provide any improvements. If
the budget is set to a value known to be insufficient to train
a model for the given dataset, the training won't be attempted
and will error. The train budget must be between 1,000 and
72,000 milli node hours, inclusive.
train_cost_milli_node_hours:
Output only. The actual training cost of the model, expressed
in milli node hours, i.e. 1,000 value in this field means 1
node hour. Guaranteed to not exceed the train budget.
disable_early_stopping:
Use the entire training budget. This disables the early
stopping feature. By default, the early stopping feature is
enabled, which means that AutoML Tables might stop training
before the entire training budget has been used.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesModelMetadata)
),
)
_sym_db.RegisterMessage(TablesModelMetadata)
TablesAnnotation = _reflection.GeneratedProtocolMessageType(
"TablesAnnotation",
(_message.Message,),
dict(
DESCRIPTOR=_TABLESANNOTATION,
__module__="google.cloud.automl_v1beta1.proto.tables_pb2",
__doc__="""Contains annotation details specific to Tables.
Attributes:
score:
Output only. A confidence estimate between 0.0 and 1.0,
inclusive. A higher value means greater confidence in the
returned value. For [target\_column\_spec][google.cloud.autom
l.v1beta1.TablesModelMetadata.target\_column\_spec] of FLOAT64
data type the score is not populated.
prediction_interval:
Output only. Only populated when [target\_column\_spec][googl
e.cloud.automl.v1beta1.TablesModelMetadata.target\_column\_spe
c] has FLOAT64 data type. An interval in which the exactly
correct target value has 95% chance to be in.
value:
The predicted value of the row's [target\_column][google.clou
d.automl.v1beta1.TablesModelMetadata.target\_column\_spec].
The value depends on the column's DataType: - CATEGORY - the
predicted (with the above confidence ``score``) CATEGORY
value. - FLOAT64 - the predicted (with above
``prediction_interval``) FLOAT64 value.
tables_model_column_info:
Output only. Auxiliary information for each of the model's [i
nput\_feature\_column\_specs][google.cloud.automl.v1beta1.Tabl
esModelMetadata.input\_feature\_column\_specs] with respect to
this particular prediction. If no other fields than [column\_
spec\_name][google.cloud.automl.v1beta1.TablesModelColumnInfo.
column\_spec\_name] and [column\_display\_name][google.cloud.
automl.v1beta1.TablesModelColumnInfo.column\_display\_name]
would be populated, then this whole field is not.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesAnnotation)
),
)
_sym_db.RegisterMessage(TablesAnnotation)
TablesModelColumnInfo = _reflection.GeneratedProtocolMessageType(
"TablesModelColumnInfo",
(_message.Message,),
dict(
DESCRIPTOR=_TABLESMODELCOLUMNINFO,
__module__="google.cloud.automl_v1beta1.proto.tables_pb2",
__doc__="""An information specific to given column and Tables Model, in context of
the Model and the predictions created by it.
Attributes:
column_spec_name:
Output only. The name of the ColumnSpec describing the column.
Not populated when this proto is outputted to BigQuery.
column_display_name:
Output only. The display name of the column (same as the
display\_name of its ColumnSpec).
feature_importance:
Output only. When given as part of a Model (always populated):
Measurement of how much model predictions correctness on the
TEST data depend on values in this column. A value between 0
and 1, higher means higher influence. These values are
normalized - for all input feature columns of a given model
they add to 1. When given back by Predict (populated iff
[feature\_importance
param][google.cloud.automl.v1beta1.PredictRequest.params] is
set) or Batch Predict (populated iff [feature\_importance][goo
gle.cloud.automl.v1beta1.PredictRequest.params] param is set):
Measurement of how impactful for the prediction returned for
the given row the value in this column was. A value between 0
and 1, higher means larger impact. These values are normalized
- for all input feature columns of a single predicted row they
add to 1.
""",
# @@protoc_insertion_point(class_scope:google.cloud.automl.v1beta1.TablesModelColumnInfo)
),
)
_sym_db.RegisterMessage(TablesModelColumnInfo)
DESCRIPTOR._options = None
_TABLESDATASETMETADATA_TARGETCOLUMNCORRELATIONSENTRY._options = None
# @@protoc_insertion_point(module_scope)