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model.pb.go
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/
model.pb.go
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// Copyright 2021 Google LLC
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
// Code generated by protoc-gen-go. DO NOT EDIT.
// versions:
// protoc-gen-go v1.26.0
// protoc v3.12.2
// source: google/cloud/bigquery/v2/model.proto
package bigquery
import (
context "context"
reflect "reflect"
sync "sync"
_ "google.golang.org/genproto/googleapis/api/annotations"
grpc "google.golang.org/grpc"
codes "google.golang.org/grpc/codes"
status "google.golang.org/grpc/status"
protoreflect "google.golang.org/protobuf/reflect/protoreflect"
protoimpl "google.golang.org/protobuf/runtime/protoimpl"
emptypb "google.golang.org/protobuf/types/known/emptypb"
timestamppb "google.golang.org/protobuf/types/known/timestamppb"
wrapperspb "google.golang.org/protobuf/types/known/wrapperspb"
)
const (
// Verify that this generated code is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(20 - protoimpl.MinVersion)
// Verify that runtime/protoimpl is sufficiently up-to-date.
_ = protoimpl.EnforceVersion(protoimpl.MaxVersion - 20)
)
// Indicates the type of the Model.
type Model_ModelType int32
const (
Model_MODEL_TYPE_UNSPECIFIED Model_ModelType = 0
// Linear regression model.
Model_LINEAR_REGRESSION Model_ModelType = 1
// Logistic regression based classification model.
Model_LOGISTIC_REGRESSION Model_ModelType = 2
// K-means clustering model.
Model_KMEANS Model_ModelType = 3
// Matrix factorization model.
Model_MATRIX_FACTORIZATION Model_ModelType = 4
// DNN classifier model.
Model_DNN_CLASSIFIER Model_ModelType = 5
// An imported TensorFlow model.
Model_TENSORFLOW Model_ModelType = 6
// DNN regressor model.
Model_DNN_REGRESSOR Model_ModelType = 7
// Boosted tree regressor model.
Model_BOOSTED_TREE_REGRESSOR Model_ModelType = 9
// Boosted tree classifier model.
Model_BOOSTED_TREE_CLASSIFIER Model_ModelType = 10
// ARIMA model.
Model_ARIMA Model_ModelType = 11
// [Beta] AutoML Tables regression model.
Model_AUTOML_REGRESSOR Model_ModelType = 12
// [Beta] AutoML Tables classification model.
Model_AUTOML_CLASSIFIER Model_ModelType = 13
// New name for the ARIMA model.
Model_ARIMA_PLUS Model_ModelType = 19
)
// Enum value maps for Model_ModelType.
var (
Model_ModelType_name = map[int32]string{
0: "MODEL_TYPE_UNSPECIFIED",
1: "LINEAR_REGRESSION",
2: "LOGISTIC_REGRESSION",
3: "KMEANS",
4: "MATRIX_FACTORIZATION",
5: "DNN_CLASSIFIER",
6: "TENSORFLOW",
7: "DNN_REGRESSOR",
9: "BOOSTED_TREE_REGRESSOR",
10: "BOOSTED_TREE_CLASSIFIER",
11: "ARIMA",
12: "AUTOML_REGRESSOR",
13: "AUTOML_CLASSIFIER",
19: "ARIMA_PLUS",
}
Model_ModelType_value = map[string]int32{
"MODEL_TYPE_UNSPECIFIED": 0,
"LINEAR_REGRESSION": 1,
"LOGISTIC_REGRESSION": 2,
"KMEANS": 3,
"MATRIX_FACTORIZATION": 4,
"DNN_CLASSIFIER": 5,
"TENSORFLOW": 6,
"DNN_REGRESSOR": 7,
"BOOSTED_TREE_REGRESSOR": 9,
"BOOSTED_TREE_CLASSIFIER": 10,
"ARIMA": 11,
"AUTOML_REGRESSOR": 12,
"AUTOML_CLASSIFIER": 13,
"ARIMA_PLUS": 19,
}
)
func (x Model_ModelType) Enum() *Model_ModelType {
p := new(Model_ModelType)
*p = x
return p
}
func (x Model_ModelType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_ModelType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[0].Descriptor()
}
func (Model_ModelType) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[0]
}
func (x Model_ModelType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_ModelType.Descriptor instead.
func (Model_ModelType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 0}
}
// Loss metric to evaluate model training performance.
type Model_LossType int32
const (
Model_LOSS_TYPE_UNSPECIFIED Model_LossType = 0
// Mean squared loss, used for linear regression.
Model_MEAN_SQUARED_LOSS Model_LossType = 1
// Mean log loss, used for logistic regression.
Model_MEAN_LOG_LOSS Model_LossType = 2
)
// Enum value maps for Model_LossType.
var (
Model_LossType_name = map[int32]string{
0: "LOSS_TYPE_UNSPECIFIED",
1: "MEAN_SQUARED_LOSS",
2: "MEAN_LOG_LOSS",
}
Model_LossType_value = map[string]int32{
"LOSS_TYPE_UNSPECIFIED": 0,
"MEAN_SQUARED_LOSS": 1,
"MEAN_LOG_LOSS": 2,
}
)
func (x Model_LossType) Enum() *Model_LossType {
p := new(Model_LossType)
*p = x
return p
}
func (x Model_LossType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_LossType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[1].Descriptor()
}
func (Model_LossType) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[1]
}
func (x Model_LossType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_LossType.Descriptor instead.
func (Model_LossType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 1}
}
// Distance metric used to compute the distance between two points.
type Model_DistanceType int32
const (
Model_DISTANCE_TYPE_UNSPECIFIED Model_DistanceType = 0
// Eculidean distance.
Model_EUCLIDEAN Model_DistanceType = 1
// Cosine distance.
Model_COSINE Model_DistanceType = 2
)
// Enum value maps for Model_DistanceType.
var (
Model_DistanceType_name = map[int32]string{
0: "DISTANCE_TYPE_UNSPECIFIED",
1: "EUCLIDEAN",
2: "COSINE",
}
Model_DistanceType_value = map[string]int32{
"DISTANCE_TYPE_UNSPECIFIED": 0,
"EUCLIDEAN": 1,
"COSINE": 2,
}
)
func (x Model_DistanceType) Enum() *Model_DistanceType {
p := new(Model_DistanceType)
*p = x
return p
}
func (x Model_DistanceType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_DistanceType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[2].Descriptor()
}
func (Model_DistanceType) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[2]
}
func (x Model_DistanceType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_DistanceType.Descriptor instead.
func (Model_DistanceType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 2}
}
// Indicates the method to split input data into multiple tables.
type Model_DataSplitMethod int32
const (
Model_DATA_SPLIT_METHOD_UNSPECIFIED Model_DataSplitMethod = 0
// Splits data randomly.
Model_RANDOM Model_DataSplitMethod = 1
// Splits data with the user provided tags.
Model_CUSTOM Model_DataSplitMethod = 2
// Splits data sequentially.
Model_SEQUENTIAL Model_DataSplitMethod = 3
// Data split will be skipped.
Model_NO_SPLIT Model_DataSplitMethod = 4
// Splits data automatically: Uses NO_SPLIT if the data size is small.
// Otherwise uses RANDOM.
Model_AUTO_SPLIT Model_DataSplitMethod = 5
)
// Enum value maps for Model_DataSplitMethod.
var (
Model_DataSplitMethod_name = map[int32]string{
0: "DATA_SPLIT_METHOD_UNSPECIFIED",
1: "RANDOM",
2: "CUSTOM",
3: "SEQUENTIAL",
4: "NO_SPLIT",
5: "AUTO_SPLIT",
}
Model_DataSplitMethod_value = map[string]int32{
"DATA_SPLIT_METHOD_UNSPECIFIED": 0,
"RANDOM": 1,
"CUSTOM": 2,
"SEQUENTIAL": 3,
"NO_SPLIT": 4,
"AUTO_SPLIT": 5,
}
)
func (x Model_DataSplitMethod) Enum() *Model_DataSplitMethod {
p := new(Model_DataSplitMethod)
*p = x
return p
}
func (x Model_DataSplitMethod) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_DataSplitMethod) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[3].Descriptor()
}
func (Model_DataSplitMethod) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[3]
}
func (x Model_DataSplitMethod) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_DataSplitMethod.Descriptor instead.
func (Model_DataSplitMethod) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 3}
}
// Type of supported data frequency for time series forecasting models.
type Model_DataFrequency int32
const (
Model_DATA_FREQUENCY_UNSPECIFIED Model_DataFrequency = 0
// Automatically inferred from timestamps.
Model_AUTO_FREQUENCY Model_DataFrequency = 1
// Yearly data.
Model_YEARLY Model_DataFrequency = 2
// Quarterly data.
Model_QUARTERLY Model_DataFrequency = 3
// Monthly data.
Model_MONTHLY Model_DataFrequency = 4
// Weekly data.
Model_WEEKLY Model_DataFrequency = 5
// Daily data.
Model_DAILY Model_DataFrequency = 6
// Hourly data.
Model_HOURLY Model_DataFrequency = 7
// Per-minute data.
Model_PER_MINUTE Model_DataFrequency = 8
)
// Enum value maps for Model_DataFrequency.
var (
Model_DataFrequency_name = map[int32]string{
0: "DATA_FREQUENCY_UNSPECIFIED",
1: "AUTO_FREQUENCY",
2: "YEARLY",
3: "QUARTERLY",
4: "MONTHLY",
5: "WEEKLY",
6: "DAILY",
7: "HOURLY",
8: "PER_MINUTE",
}
Model_DataFrequency_value = map[string]int32{
"DATA_FREQUENCY_UNSPECIFIED": 0,
"AUTO_FREQUENCY": 1,
"YEARLY": 2,
"QUARTERLY": 3,
"MONTHLY": 4,
"WEEKLY": 5,
"DAILY": 6,
"HOURLY": 7,
"PER_MINUTE": 8,
}
)
func (x Model_DataFrequency) Enum() *Model_DataFrequency {
p := new(Model_DataFrequency)
*p = x
return p
}
func (x Model_DataFrequency) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_DataFrequency) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[4].Descriptor()
}
func (Model_DataFrequency) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[4]
}
func (x Model_DataFrequency) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_DataFrequency.Descriptor instead.
func (Model_DataFrequency) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 4}
}
// Type of supported holiday regions for time series forecasting models.
type Model_HolidayRegion int32
const (
// Holiday region unspecified.
Model_HOLIDAY_REGION_UNSPECIFIED Model_HolidayRegion = 0
// Global.
Model_GLOBAL Model_HolidayRegion = 1
// North America.
Model_NA Model_HolidayRegion = 2
// Japan and Asia Pacific: Korea, Greater China, India, Australia, and New
// Zealand.
Model_JAPAC Model_HolidayRegion = 3
// Europe, the Middle East and Africa.
Model_EMEA Model_HolidayRegion = 4
// Latin America and the Caribbean.
Model_LAC Model_HolidayRegion = 5
// United Arab Emirates
Model_AE Model_HolidayRegion = 6
// Argentina
Model_AR Model_HolidayRegion = 7
// Austria
Model_AT Model_HolidayRegion = 8
// Australia
Model_AU Model_HolidayRegion = 9
// Belgium
Model_BE Model_HolidayRegion = 10
// Brazil
Model_BR Model_HolidayRegion = 11
// Canada
Model_CA Model_HolidayRegion = 12
// Switzerland
Model_CH Model_HolidayRegion = 13
// Chile
Model_CL Model_HolidayRegion = 14
// China
Model_CN Model_HolidayRegion = 15
// Colombia
Model_CO Model_HolidayRegion = 16
// Czechoslovakia
Model_CS Model_HolidayRegion = 17
// Czech Republic
Model_CZ Model_HolidayRegion = 18
// Germany
Model_DE Model_HolidayRegion = 19
// Denmark
Model_DK Model_HolidayRegion = 20
// Algeria
Model_DZ Model_HolidayRegion = 21
// Ecuador
Model_EC Model_HolidayRegion = 22
// Estonia
Model_EE Model_HolidayRegion = 23
// Egypt
Model_EG Model_HolidayRegion = 24
// Spain
Model_ES Model_HolidayRegion = 25
// Finland
Model_FI Model_HolidayRegion = 26
// France
Model_FR Model_HolidayRegion = 27
// Great Britain (United Kingdom)
Model_GB Model_HolidayRegion = 28
// Greece
Model_GR Model_HolidayRegion = 29
// Hong Kong
Model_HK Model_HolidayRegion = 30
// Hungary
Model_HU Model_HolidayRegion = 31
// Indonesia
Model_ID Model_HolidayRegion = 32
// Ireland
Model_IE Model_HolidayRegion = 33
// Israel
Model_IL Model_HolidayRegion = 34
// India
Model_IN Model_HolidayRegion = 35
// Iran
Model_IR Model_HolidayRegion = 36
// Italy
Model_IT Model_HolidayRegion = 37
// Japan
Model_JP Model_HolidayRegion = 38
// Korea (South)
Model_KR Model_HolidayRegion = 39
// Latvia
Model_LV Model_HolidayRegion = 40
// Morocco
Model_MA Model_HolidayRegion = 41
// Mexico
Model_MX Model_HolidayRegion = 42
// Malaysia
Model_MY Model_HolidayRegion = 43
// Nigeria
Model_NG Model_HolidayRegion = 44
// Netherlands
Model_NL Model_HolidayRegion = 45
// Norway
Model_NO Model_HolidayRegion = 46
// New Zealand
Model_NZ Model_HolidayRegion = 47
// Peru
Model_PE Model_HolidayRegion = 48
// Philippines
Model_PH Model_HolidayRegion = 49
// Pakistan
Model_PK Model_HolidayRegion = 50
// Poland
Model_PL Model_HolidayRegion = 51
// Portugal
Model_PT Model_HolidayRegion = 52
// Romania
Model_RO Model_HolidayRegion = 53
// Serbia
Model_RS Model_HolidayRegion = 54
// Russian Federation
Model_RU Model_HolidayRegion = 55
// Saudi Arabia
Model_SA Model_HolidayRegion = 56
// Sweden
Model_SE Model_HolidayRegion = 57
// Singapore
Model_SG Model_HolidayRegion = 58
// Slovenia
Model_SI Model_HolidayRegion = 59
// Slovakia
Model_SK Model_HolidayRegion = 60
// Thailand
Model_TH Model_HolidayRegion = 61
// Turkey
Model_TR Model_HolidayRegion = 62
// Taiwan
Model_TW Model_HolidayRegion = 63
// Ukraine
Model_UA Model_HolidayRegion = 64
// United States
Model_US Model_HolidayRegion = 65
// Venezuela
Model_VE Model_HolidayRegion = 66
// Viet Nam
Model_VN Model_HolidayRegion = 67
// South Africa
Model_ZA Model_HolidayRegion = 68
)
// Enum value maps for Model_HolidayRegion.
var (
Model_HolidayRegion_name = map[int32]string{
0: "HOLIDAY_REGION_UNSPECIFIED",
1: "GLOBAL",
2: "NA",
3: "JAPAC",
4: "EMEA",
5: "LAC",
6: "AE",
7: "AR",
8: "AT",
9: "AU",
10: "BE",
11: "BR",
12: "CA",
13: "CH",
14: "CL",
15: "CN",
16: "CO",
17: "CS",
18: "CZ",
19: "DE",
20: "DK",
21: "DZ",
22: "EC",
23: "EE",
24: "EG",
25: "ES",
26: "FI",
27: "FR",
28: "GB",
29: "GR",
30: "HK",
31: "HU",
32: "ID",
33: "IE",
34: "IL",
35: "IN",
36: "IR",
37: "IT",
38: "JP",
39: "KR",
40: "LV",
41: "MA",
42: "MX",
43: "MY",
44: "NG",
45: "NL",
46: "NO",
47: "NZ",
48: "PE",
49: "PH",
50: "PK",
51: "PL",
52: "PT",
53: "RO",
54: "RS",
55: "RU",
56: "SA",
57: "SE",
58: "SG",
59: "SI",
60: "SK",
61: "TH",
62: "TR",
63: "TW",
64: "UA",
65: "US",
66: "VE",
67: "VN",
68: "ZA",
}
Model_HolidayRegion_value = map[string]int32{
"HOLIDAY_REGION_UNSPECIFIED": 0,
"GLOBAL": 1,
"NA": 2,
"JAPAC": 3,
"EMEA": 4,
"LAC": 5,
"AE": 6,
"AR": 7,
"AT": 8,
"AU": 9,
"BE": 10,
"BR": 11,
"CA": 12,
"CH": 13,
"CL": 14,
"CN": 15,
"CO": 16,
"CS": 17,
"CZ": 18,
"DE": 19,
"DK": 20,
"DZ": 21,
"EC": 22,
"EE": 23,
"EG": 24,
"ES": 25,
"FI": 26,
"FR": 27,
"GB": 28,
"GR": 29,
"HK": 30,
"HU": 31,
"ID": 32,
"IE": 33,
"IL": 34,
"IN": 35,
"IR": 36,
"IT": 37,
"JP": 38,
"KR": 39,
"LV": 40,
"MA": 41,
"MX": 42,
"MY": 43,
"NG": 44,
"NL": 45,
"NO": 46,
"NZ": 47,
"PE": 48,
"PH": 49,
"PK": 50,
"PL": 51,
"PT": 52,
"RO": 53,
"RS": 54,
"RU": 55,
"SA": 56,
"SE": 57,
"SG": 58,
"SI": 59,
"SK": 60,
"TH": 61,
"TR": 62,
"TW": 63,
"UA": 64,
"US": 65,
"VE": 66,
"VN": 67,
"ZA": 68,
}
)
func (x Model_HolidayRegion) Enum() *Model_HolidayRegion {
p := new(Model_HolidayRegion)
*p = x
return p
}
func (x Model_HolidayRegion) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_HolidayRegion) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[5].Descriptor()
}
func (Model_HolidayRegion) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[5]
}
func (x Model_HolidayRegion) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_HolidayRegion.Descriptor instead.
func (Model_HolidayRegion) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 5}
}
// Indicates the learning rate optimization strategy to use.
type Model_LearnRateStrategy int32
const (
Model_LEARN_RATE_STRATEGY_UNSPECIFIED Model_LearnRateStrategy = 0
// Use line search to determine learning rate.
Model_LINE_SEARCH Model_LearnRateStrategy = 1
// Use a constant learning rate.
Model_CONSTANT Model_LearnRateStrategy = 2
)
// Enum value maps for Model_LearnRateStrategy.
var (
Model_LearnRateStrategy_name = map[int32]string{
0: "LEARN_RATE_STRATEGY_UNSPECIFIED",
1: "LINE_SEARCH",
2: "CONSTANT",
}
Model_LearnRateStrategy_value = map[string]int32{
"LEARN_RATE_STRATEGY_UNSPECIFIED": 0,
"LINE_SEARCH": 1,
"CONSTANT": 2,
}
)
func (x Model_LearnRateStrategy) Enum() *Model_LearnRateStrategy {
p := new(Model_LearnRateStrategy)
*p = x
return p
}
func (x Model_LearnRateStrategy) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_LearnRateStrategy) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[6].Descriptor()
}
func (Model_LearnRateStrategy) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[6]
}
func (x Model_LearnRateStrategy) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_LearnRateStrategy.Descriptor instead.
func (Model_LearnRateStrategy) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 6}
}
// Indicates the optimization strategy used for training.
type Model_OptimizationStrategy int32
const (
Model_OPTIMIZATION_STRATEGY_UNSPECIFIED Model_OptimizationStrategy = 0
// Uses an iterative batch gradient descent algorithm.
Model_BATCH_GRADIENT_DESCENT Model_OptimizationStrategy = 1
// Uses a normal equation to solve linear regression problem.
Model_NORMAL_EQUATION Model_OptimizationStrategy = 2
)
// Enum value maps for Model_OptimizationStrategy.
var (
Model_OptimizationStrategy_name = map[int32]string{
0: "OPTIMIZATION_STRATEGY_UNSPECIFIED",
1: "BATCH_GRADIENT_DESCENT",
2: "NORMAL_EQUATION",
}
Model_OptimizationStrategy_value = map[string]int32{
"OPTIMIZATION_STRATEGY_UNSPECIFIED": 0,
"BATCH_GRADIENT_DESCENT": 1,
"NORMAL_EQUATION": 2,
}
)
func (x Model_OptimizationStrategy) Enum() *Model_OptimizationStrategy {
p := new(Model_OptimizationStrategy)
*p = x
return p
}
func (x Model_OptimizationStrategy) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_OptimizationStrategy) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[7].Descriptor()
}
func (Model_OptimizationStrategy) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[7]
}
func (x Model_OptimizationStrategy) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_OptimizationStrategy.Descriptor instead.
func (Model_OptimizationStrategy) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 7}
}
// Indicates the training algorithm to use for matrix factorization models.
type Model_FeedbackType int32
const (
Model_FEEDBACK_TYPE_UNSPECIFIED Model_FeedbackType = 0
// Use weighted-als for implicit feedback problems.
Model_IMPLICIT Model_FeedbackType = 1
// Use nonweighted-als for explicit feedback problems.
Model_EXPLICIT Model_FeedbackType = 2
)
// Enum value maps for Model_FeedbackType.
var (
Model_FeedbackType_name = map[int32]string{
0: "FEEDBACK_TYPE_UNSPECIFIED",
1: "IMPLICIT",
2: "EXPLICIT",
}
Model_FeedbackType_value = map[string]int32{
"FEEDBACK_TYPE_UNSPECIFIED": 0,
"IMPLICIT": 1,
"EXPLICIT": 2,
}
)
func (x Model_FeedbackType) Enum() *Model_FeedbackType {
p := new(Model_FeedbackType)
*p = x
return p
}
func (x Model_FeedbackType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_FeedbackType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[8].Descriptor()
}
func (Model_FeedbackType) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[8]
}
func (x Model_FeedbackType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_FeedbackType.Descriptor instead.
func (Model_FeedbackType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 8}
}
type Model_SeasonalPeriod_SeasonalPeriodType int32
const (
Model_SeasonalPeriod_SEASONAL_PERIOD_TYPE_UNSPECIFIED Model_SeasonalPeriod_SeasonalPeriodType = 0
// No seasonality
Model_SeasonalPeriod_NO_SEASONALITY Model_SeasonalPeriod_SeasonalPeriodType = 1
// Daily period, 24 hours.
Model_SeasonalPeriod_DAILY Model_SeasonalPeriod_SeasonalPeriodType = 2
// Weekly period, 7 days.
Model_SeasonalPeriod_WEEKLY Model_SeasonalPeriod_SeasonalPeriodType = 3
// Monthly period, 30 days or irregular.
Model_SeasonalPeriod_MONTHLY Model_SeasonalPeriod_SeasonalPeriodType = 4
// Quarterly period, 90 days or irregular.
Model_SeasonalPeriod_QUARTERLY Model_SeasonalPeriod_SeasonalPeriodType = 5
// Yearly period, 365 days or irregular.
Model_SeasonalPeriod_YEARLY Model_SeasonalPeriod_SeasonalPeriodType = 6
)
// Enum value maps for Model_SeasonalPeriod_SeasonalPeriodType.
var (
Model_SeasonalPeriod_SeasonalPeriodType_name = map[int32]string{
0: "SEASONAL_PERIOD_TYPE_UNSPECIFIED",
1: "NO_SEASONALITY",
2: "DAILY",
3: "WEEKLY",
4: "MONTHLY",
5: "QUARTERLY",
6: "YEARLY",
}
Model_SeasonalPeriod_SeasonalPeriodType_value = map[string]int32{
"SEASONAL_PERIOD_TYPE_UNSPECIFIED": 0,
"NO_SEASONALITY": 1,
"DAILY": 2,
"WEEKLY": 3,
"MONTHLY": 4,
"QUARTERLY": 5,
"YEARLY": 6,
}
)
func (x Model_SeasonalPeriod_SeasonalPeriodType) Enum() *Model_SeasonalPeriod_SeasonalPeriodType {
p := new(Model_SeasonalPeriod_SeasonalPeriodType)
*p = x
return p
}
func (x Model_SeasonalPeriod_SeasonalPeriodType) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_SeasonalPeriod_SeasonalPeriodType) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[9].Descriptor()
}
func (Model_SeasonalPeriod_SeasonalPeriodType) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[9]
}
func (x Model_SeasonalPeriod_SeasonalPeriodType) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_SeasonalPeriod_SeasonalPeriodType.Descriptor instead.
func (Model_SeasonalPeriod_SeasonalPeriodType) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 0, 0}
}
// Indicates the method used to initialize the centroids for KMeans
// clustering algorithm.
type Model_KmeansEnums_KmeansInitializationMethod int32
const (
// Unspecified initialization method.
Model_KmeansEnums_KMEANS_INITIALIZATION_METHOD_UNSPECIFIED Model_KmeansEnums_KmeansInitializationMethod = 0
// Initializes the centroids randomly.
Model_KmeansEnums_RANDOM Model_KmeansEnums_KmeansInitializationMethod = 1
// Initializes the centroids using data specified in
// kmeans_initialization_column.
Model_KmeansEnums_CUSTOM Model_KmeansEnums_KmeansInitializationMethod = 2
// Initializes with kmeans++.
Model_KmeansEnums_KMEANS_PLUS_PLUS Model_KmeansEnums_KmeansInitializationMethod = 3
)
// Enum value maps for Model_KmeansEnums_KmeansInitializationMethod.
var (
Model_KmeansEnums_KmeansInitializationMethod_name = map[int32]string{
0: "KMEANS_INITIALIZATION_METHOD_UNSPECIFIED",
1: "RANDOM",
2: "CUSTOM",
3: "KMEANS_PLUS_PLUS",
}
Model_KmeansEnums_KmeansInitializationMethod_value = map[string]int32{
"KMEANS_INITIALIZATION_METHOD_UNSPECIFIED": 0,
"RANDOM": 1,
"CUSTOM": 2,
"KMEANS_PLUS_PLUS": 3,
}
)
func (x Model_KmeansEnums_KmeansInitializationMethod) Enum() *Model_KmeansEnums_KmeansInitializationMethod {
p := new(Model_KmeansEnums_KmeansInitializationMethod)
*p = x
return p
}
func (x Model_KmeansEnums_KmeansInitializationMethod) String() string {
return protoimpl.X.EnumStringOf(x.Descriptor(), protoreflect.EnumNumber(x))
}
func (Model_KmeansEnums_KmeansInitializationMethod) Descriptor() protoreflect.EnumDescriptor {
return file_google_cloud_bigquery_v2_model_proto_enumTypes[10].Descriptor()
}
func (Model_KmeansEnums_KmeansInitializationMethod) Type() protoreflect.EnumType {
return &file_google_cloud_bigquery_v2_model_proto_enumTypes[10]
}
func (x Model_KmeansEnums_KmeansInitializationMethod) Number() protoreflect.EnumNumber {
return protoreflect.EnumNumber(x)
}
// Deprecated: Use Model_KmeansEnums_KmeansInitializationMethod.Descriptor instead.
func (Model_KmeansEnums_KmeansInitializationMethod) EnumDescriptor() ([]byte, []int) {
return file_google_cloud_bigquery_v2_model_proto_rawDescGZIP(), []int{0, 1, 0}
}
type Model struct {
state protoimpl.MessageState
sizeCache protoimpl.SizeCache
unknownFields protoimpl.UnknownFields
// Output only. A hash of this resource.
Etag string `protobuf:"bytes,1,opt,name=etag,proto3" json:"etag,omitempty"`
// Required. Unique identifier for this model.
ModelReference *ModelReference `protobuf:"bytes,2,opt,name=model_reference,json=modelReference,proto3" json:"model_reference,omitempty"`
// Output only. The time when this model was created, in millisecs since the epoch.
CreationTime int64 `protobuf:"varint,5,opt,name=creation_time,json=creationTime,proto3" json:"creation_time,omitempty"`
// Output only. The time when this model was last modified, in millisecs since the epoch.
LastModifiedTime int64 `protobuf:"varint,6,opt,name=last_modified_time,json=lastModifiedTime,proto3" json:"last_modified_time,omitempty"`
// Optional. A user-friendly description of this model.