ir

package
v0.4.2 Latest Latest
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 Go to latest Published: Aug 28, 2020 License: Apache-2.0 Imports: 12 Imported by: 0

Documentation

Overview

Package ir is the Intermediate Representation of parsed SQL statements

Index

Constants

View Source 
const (
	Int int = iota
	Float
	String
)

Possible DType values in FieldDesc

View Source 
const (
	// TensorFlow is a kind of `TrainStmt`
	TensorFlow = iota
	// XGBoost is a kind of `TrainStmt`
	XGBoost
	// KMeans is a kind of `TrainStmt`
	KMeans
)

Variables

This section is empty.

Functions

func AttrToPythonValue added in v0.4.1 

func AttrToPythonValue(attr interface{}) string

AttrToPythonValue format the WITH attributes to corresponding Python code.

func DTypeToString added in v0.4.1 

func DTypeToString(dt int) string

DTypeToString returns string value of dtype

func InferFeatureColumns 

func InferFeatureColumns(trainStmt *TrainStmt, db *database.DB) error

InferFeatureColumns fill up featureColumn and columnSpec structs for all fields. if wr is not nil, then write

func LogDerivationResult 

func LogDerivationResult(wr *pipe.Writer, trainStmt *TrainStmt)

LogDerivationResult write messages to wr to log the feature derivation results

func MarshalToJSONString added in v0.4.1 

func MarshalToJSONString(in interface{}) (string, error)

MarshalToJSONString converts any data to JSON string.

Types

type BucketColumn 

type BucketColumn struct {
	SourceColumn *NumericColumn
	Boundaries   []int
}

BucketColumn represents `tf.feature_column.bucketized_column` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/bucketized_column

func (*BucketColumn) ApplyTo 

func (c *BucketColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*BucketColumn) GenPythonCode added in v0.4.1 

func (c *BucketColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*BucketColumn) GetFieldDesc 

func (c *BucketColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

func (*BucketColumn) NumClass 

func (c *BucketColumn) NumClass() int64

NumClass returns class number of BucketColumn

type CategoryColumn 

type CategoryColumn interface {
	FeatureColumn
	NumClass() int64
}

CategoryColumn corresponds to categorical column

type CategoryHashColumn 

type CategoryHashColumn struct {
	FieldDesc  *FieldDesc
	BucketSize int64
}

CategoryHashColumn represents `tf.feature_column.categorical_column_with_hash_bucket` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_hash_bucket

func (*CategoryHashColumn) ApplyTo 

func (c *CategoryHashColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*CategoryHashColumn) GenPythonCode added in v0.4.1 

func (c *CategoryHashColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*CategoryHashColumn) GetFieldDesc 

func (c *CategoryHashColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

func (*CategoryHashColumn) NumClass 

func (c *CategoryHashColumn) NumClass() int64

NumClass returns class number of CategoryHashColumn

type CategoryIDColumn 

type CategoryIDColumn struct {
	FieldDesc  *FieldDesc
	BucketSize int64
}

CategoryIDColumn represents `tf.feature_column.categorical_column_with_identity` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/categorical_column_with_identity

func (*CategoryIDColumn) ApplyTo 

func (c *CategoryIDColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*CategoryIDColumn) GenPythonCode added in v0.4.1 

func (c *CategoryIDColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*CategoryIDColumn) GetFieldDesc 

func (c *CategoryIDColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

func (*CategoryIDColumn) NumClass 

func (c *CategoryIDColumn) NumClass() int64

NumClass returns class number of CategoryIDColumn

type ColumnMap 

type ColumnMap map[string]map[string][]FeatureColumn

ColumnMap is like: target -> key -> []FeatureColumn one column's data can be used by multiple feature columns, e.g. EMBEDDING(c1), CROSS(c1, c2)

type CrossColumn 

type CrossColumn struct {
	Keys           []interface{}
	HashBucketSize int64
}

CrossColumn represents `tf.feature_column.crossed_column` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/crossed_column

func (*CrossColumn) ApplyTo 

func (c *CrossColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*CrossColumn) GenPythonCode added in v0.4.1 

func (c *CrossColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*CrossColumn) GetFieldDesc 

func (c *CrossColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

func (*CrossColumn) NumClass 

func (c *CrossColumn) NumClass() int64

NumClass returns class number of CrossColumn

type EmbeddingColumn 

type EmbeddingColumn struct {
	CategoryColumn
	Dimension   int
	Combiner    string
	Initializer string
	// only used when EMBEDDING(col_name, ...) this will set CategoryColumn = nil
	// will fill the feature column details using feature_derivation
	Name string
}

EmbeddingColumn represents `tf.feature_column.embedding_column` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/embedding_column

func (*EmbeddingColumn) ApplyTo 

func (c *EmbeddingColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*EmbeddingColumn) GenPythonCode added in v0.4.1 

func (c *EmbeddingColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*EmbeddingColumn) GetFieldDesc 

func (c *EmbeddingColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

type EvaluateStmt 

type EvaluateStmt struct {
	OriginalSQL      string
	Select           string
	Attributes       map[string]interface{}
	ModelName        string
	Label            FeatureColumn
	Into             string
	TmpEvaluateTable string
	TrainStmt        *TrainStmt
}

EvaluateStmt is the intermediate representation for code generation of an evaluation job

func GenerateEvaluateStmt 

func GenerateEvaluateStmt(slct *parser.SQLFlowSelectStmt, connStr string, modelDir string, cwd string, getTrainStmtFromModel bool) (*EvaluateStmt, error)

GenerateEvaluateStmt generates a `EvaluateStmt` from the parsed result `slct`

func (*EvaluateStmt) GetOriginalSQL 

func (stmt *EvaluateStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*EvaluateStmt) IsExtended 

func (stmt *EvaluateStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*EvaluateStmt) SetOriginalSQL 

func (stmt *EvaluateStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type ExplainStmt 

type ExplainStmt struct {
	// OriginalSQL record the original SQL statement used to get current IR result
	// FIXME(typhoonzero): OriginalSQL is a temporary field. Can remove this when all moved to IR
	OriginalSQL string
	// Select specifies the query for fetching the analysis data. For example, "select * from iris.test;".
	Select string
	// Attributes is a map of parsed attribute in the WITH clause. For example, after parsing
	// "select ... explain ... with explain.plot_type = "bar"",
	// the Attributes will be {"explain.plot_type": "bar"}
	Attributes map[string]interface{}
	// Explainer types. For example TreeExplainer.
	Explainer string
	// ModelName is the model to be explained, e.g. TO EXPLAIN model_name
	ModelName string
	// Into stores the model explain result. Note that this field is optional.
	Into string
	// When SQLFLOW_submitter == "pai", tmp tables will be created for predicting task
	// see: pai_submitter.go
	TmpExplainTable string
	// TrainStmt is the TrainStmt used for generating the training job of the corresponding model
	TrainStmt *TrainStmt
}

ExplainStmt is the intermediate representation for code generation of a analysis job

func GenerateExplainStmt 

func GenerateExplainStmt(slct *parser.SQLFlowSelectStmt, connStr, modelDir string, cwd string, getTrainStmtFromModel bool) (*ExplainStmt, error)

GenerateExplainStmt generates a `ExplainStmt` from the parsed result `slct`

func (*ExplainStmt) GetOriginalSQL 

func (stmt *ExplainStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*ExplainStmt) IsExtended 

func (stmt *ExplainStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*ExplainStmt) SetOriginalSQL 

func (stmt *ExplainStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type FeatureColumn 

type FeatureColumn interface {
	GetFieldDesc() []*FieldDesc
	ApplyTo(*FieldDesc) (FeatureColumn, error)
	GenPythonCode() string
}

FeatureColumn corresponds to the COLUMN clause in TO TRAIN.

type FieldDesc 

type FieldDesc struct {
	Name      string `json:"name"`      // the name for a field, e.g. "petal_length"
	DType     int    `json:"dtype"`     // e.g. "float", "int32"
	Delimiter string `json:"delimiter"` // Needs to be "," if the field saves strings like "1,23,42".
	Format    string `json:"format"`    // The data format, "", "csv" or "kv"
	Shape     []int  `json:"shape"`     // [3] if the field saves strings of three numbers like "1,23,42".
	IsSparse  bool   `json:"is_sparse"` // If the field saves a sparse tensor.
	// Vocabulary stores all possible enumerate values if the column type is string,
	// e.g. the column values are: "MALE", "FEMALE", "NULL"
	Vocabulary map[string]string `json:"vocabulary"` // use a map to generate a list without duplication
	// if the column data is used as embedding(category_column()), the `num_buckets` should use the maxID
	// appeared in the sample data. if error still occurs, users should set `num_buckets` manually.
	MaxID int64
}

FieldDesc describes a field used as the input to a feature column.

func (*FieldDesc) GenPythonCode added in v0.4.1 

func (fd *FieldDesc) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.field_desc

type FieldDescMap 

type FieldDescMap map[string]*FieldDesc

FieldDescMap is a mapping from column name to ColumnSpec struct

type IndicatorColumn 

type IndicatorColumn struct {
	CategoryColumn
	// only used when INDICATOR(col_name, ...) this will set CategoryColumn = nil
	// will fill the feature column details using feature_derivation
	Name string
}

IndicatorColumn represents `tf.feature_column.indicator_column` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/indicator_column

func (*IndicatorColumn) ApplyTo 

func (c *IndicatorColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*IndicatorColumn) GenPythonCode added in v0.4.1 

func (c *IndicatorColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*IndicatorColumn) GetFieldDesc 

func (c *IndicatorColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

type NormalStmt 

type NormalStmt string

NormalStmt is a SQL statement without using SQLFlow syntax extension.

func (*NormalStmt) GetOriginalSQL 

func (stmt *NormalStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*NormalStmt) IsExtended 

func (stmt *NormalStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*NormalStmt) SetOriginalSQL 

func (stmt *NormalStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type NumericColumn 

type NumericColumn struct {
	FieldDesc *FieldDesc
}

NumericColumn represents a dense tensor for the model input

FieldDesc indicates the meta information for decoding the field. Please be aware that FieldDesc also contains information for dimension and data type

func (*NumericColumn) ApplyTo 

func (c *NumericColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*NumericColumn) GenPythonCode added in v0.4.1 

func (c *NumericColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*NumericColumn) GetFieldDesc 

func (c *NumericColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

type OptimizeExpr 

type OptimizeExpr struct {
	// Objective expression or constraint expression string tokens prepared for generate target code.
	ExpressionTokens []string
	// constraint group by like: SUM(markets) <= capacity GROUP BY plants, will expand to
	// for p in plants:
	//     sum(m for m in markets) <= capacity
	GroupBy string
}

OptimizeExpr is the intermediate code for generating target solver expressions.

type OptimizeStmt 

type OptimizeStmt struct {
	// OriginalSQL records the original SQL statement used to get current IR result
	OriginalSQL string
	// Select is the select statement before TO MAXIMIZE|MINIMIZE clause.
	Select string
	// Variables is the variable name list to be optimized
	Variables []string
	// ResultValueName is the column name of the result variable
	ResultValueName string
	// VariableType
	VariableType string
	// Attributes is a map of parsed attribute in the WITH clause.
	Attributes map[string]interface{}
	// Objective
	Objective OptimizeExpr
	// Direction, "maximize" or "minimize"
	Direction string
	// Constraints
	Constraints []*OptimizeExpr
	// Solver
	Solver string
	// ResultTable is the table name to store results.
	ResultTable string
}

OptimizeStmt is the intermediate representation of "SELECT TO MAXIMIZE|MINIMIZE" statement.

func GenerateOptimizeStmt 

func GenerateOptimizeStmt(optimizeStmt *parser.SQLFlowSelectStmt) (*OptimizeStmt, error)

GenerateOptimizeStmt generates a `OptimizeStmt` from the parsed result `optimizeStmt`

func (*OptimizeStmt) GetOriginalSQL 

func (stmt *OptimizeStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*OptimizeStmt) IsExtended 

func (stmt *OptimizeStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*OptimizeStmt) SetOriginalSQL 

func (stmt *OptimizeStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type PredictStmt 

type PredictStmt struct {
	// OriginalSQL record the original SQL statement used to get current IR result
	// FIXME(typhoonzero): OriginalSQL is a temporary field. Can remove this when all moved to IR
	OriginalSQL string
	// Select specifies the query for fetching the prediction data. For example, "select * from iris.test;".
	Select string
	// ResultTable specifies the table to store the prediction result.
	ResultTable string
	// ResultColumn is the column to store predict result in ResultTable
	ResultColumn string
	// Attributes is a map of parsed attribute in the WITH clause. For example, after parsing
	// "select ... predict ... with predict.batch_size = 32 into ...",
	// the Attributes will be {"predict.batch_size": 32}
	Attributes map[string]interface{}
	// Using is the model specified by USING clause.
	Using string
	// TrainStmt is the TrainStmt used for generating the training job of the corresponding model
	TrainStmt *TrainStmt
	// When SQLFLOW_submitter == "pai", tmp tables will be created for predicting task
	// see: pai_submitter.go
	TmpPredictTable string
}

PredictStmt is the intermediate representation for code generation of a prediction job

Please be aware the PredictStmt IR contains the result table name, so the generated Python program is responsible to create and write the result table.

func GeneratePredictStmt 

func GeneratePredictStmt(slct *parser.SQLFlowSelectStmt, connStr string, modelDir string, cwd string, getTrainStmtFromModel bool) (*PredictStmt, error)

GeneratePredictStmt generates a `PredictStmt` from the parsed result `slct`

func MockPredStmt 

func MockPredStmt(trainStmt *TrainStmt) *PredictStmt

MockPredStmt generates a sample PredictStmt for test.

func (*PredictStmt) GetOriginalSQL 

func (stmt *PredictStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*PredictStmt) IsExtended 

func (stmt *PredictStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*PredictStmt) SetOriginalSQL 

func (stmt *PredictStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type RunStmt 

type RunStmt struct {
	// OriginalSQL is the `SELECT TO RUN` statement.
	OriginalSQL string
	// Select is the select statement before TO RUN clause.
	Select string
	// ImageName is the name of the docker image after TO RUN keyword.
	ImageName string
	// Parameters is the command line parameters for the docker image.
	Parameters []string
	// Into is the output table names (0~N, comma separated) after INTO keyword.
	Into string
}

RunStmt is the intermediate representation of `SELECT TO RUN` statement

func GenerateRunStmt 

func GenerateRunStmt(slct *parser.SQLFlowSelectStmt) (*RunStmt, error)

GenerateRunStmt generate the RunStmt result from the parsed result of `TO RUN` statement.

func (*RunStmt) GetOriginalSQL 

func (stmt *RunStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*RunStmt) IsExtended 

func (stmt *RunStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*RunStmt) SetOriginalSQL 

func (stmt *RunStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type SQLFlowStmt 

type SQLFlowStmt interface {
	SetOriginalSQL(string)
	IsExtended() bool
	GetOriginalSQL() string
}

SQLFlowStmt has multiple implementations: TrainStmt, PredictStmt, ExplainStmt and standard SQL.

type SeqCategoryIDColumn 

type SeqCategoryIDColumn struct {
	FieldDesc  *FieldDesc
	BucketSize int64
}

SeqCategoryIDColumn represents `tf.feature_column.sequence_categorical_column_with_identity` ref: https://www.tensorflow.org/api_docs/python/tf/feature_column/sequence_categorical_column_with_identity

func (*SeqCategoryIDColumn) ApplyTo 

func (c *SeqCategoryIDColumn) ApplyTo(other *FieldDesc) (FeatureColumn, error)

ApplyTo applies the FeatureColumn to a new field

func (*SeqCategoryIDColumn) GenPythonCode added in v0.4.1 

func (c *SeqCategoryIDColumn) GenPythonCode() string

GenPythonCode generate Python code to construct a runtime.feature.column.*

func (*SeqCategoryIDColumn) GetFieldDesc 

func (c *SeqCategoryIDColumn) GetFieldDesc() []*FieldDesc

GetFieldDesc returns FieldDesc member

func (*SeqCategoryIDColumn) NumClass 

func (c *SeqCategoryIDColumn) NumClass() int64

NumClass returns class number of SeqCategoryIDColumn

type ShowTrainStmt 

type ShowTrainStmt struct {
	// OriginalSQL is the SHOW TRAIN stmt itself
	OriginalSQL string
	// The model to show the train sql
	ModelName string
}

ShowTrainStmt get and output the original train sql for ModelName

func GenerateShowTrainStmt 

func GenerateShowTrainStmt(showTrain *parser.SQLFlowSelectStmt) (*ShowTrainStmt, error)

GenerateShowTrainStmt a `ShowTrainStmt` from the parsed result `showTrain`

func (*ShowTrainStmt) GetOriginalSQL 

func (stmt *ShowTrainStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*ShowTrainStmt) IsExtended 

func (stmt *ShowTrainStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*ShowTrainStmt) SetOriginalSQL 

func (stmt *ShowTrainStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

type TrainStmt 

type TrainStmt struct {
	// OriginalSQL record the original SQL statement used to get current IR result
	// FIXME(typhoonzero): OriginalSQL is a temporary field. Can remove this when all moved to IR
	OriginalSQL string
	// Select specifies the query for fetching the training data. For example, "select * from iris.train;".
	Select string
	// ValidationSelect specifies the query for fetching the validation data. For example, "select * from iris.val;".
	ValidationSelect string
	// ModelImage is the name of the model's Docker image, for example `TO TRAIN a_data_scientist/regressors:v0.2/MyDNNRegressor`
	// the name "a_data_scientist/regressors:v0.2" is a Docker image.
	ModelImage string
	// Estimator specifies the estimator type. For example, after parsing "select ... train DNNClassifier WITH ...",
	// the Estimator will be "DNNClassifier".
	Estimator string
	// Attributes is a map of parsed attribute in the WITH Clause. For example, after parsing
	// "select ... train ... with train.epoch = 1000, model.hidden_units = [10, 10]",
	// the Attributes will be {"train.epoch": 1000, "model.hidden_units": [10 10]}.
	Attributes map[string]interface{}
	// Features contain a map of a list of feature columns in the COLUMN clause.
	// For multiple COLUMN clauses like
	//   “`
	//   column ... for deep_feature
	//   column ... for wide_feature
	//   “`
	// They will be parsed as {"deep_feature": {...}, "wide_feature": {...}}
	// For single column clause like "column ...", "feature_columns" will be used as the default map key.
	Features map[string][]FeatureColumn
	// Label specifies the feature column in the LABEL clause.
	Label FeatureColumn
	// PreTrainedModel specifies the model name to be loaded for incremental training.
	PreTrainedModel string
	// Into specifies the table name in the INTO clause.
	Into string
	// When SQLFLOW_submitter == "pai", tmp tables will be created for training task
	// see: pai_submitter.go
	TmpTrainTable    string
	TmpValidateTable string
}

TrainStmt is the intermediate representation for code generation of a training job.

func GenerateTrainStmt 

func GenerateTrainStmt(slct *parser.SQLFlowSelectStmt) (*TrainStmt, error)

GenerateTrainStmt generates a `TrainStmt` without inferring feature columns

func GenerateTrainStmtByModel 

func GenerateTrainStmtByModel(slct *parser.SQLFlowSelectStmt, connStr, cwd, modelDir, model string) (*TrainStmt, error)

GenerateTrainStmtByModel generates a `TrainStmt` from a trained model

func GenerateTrainStmtWithInferredColumns 

func GenerateTrainStmtWithInferredColumns(slct *parser.SQLFlowSelectStmt, connStr string, modelDir string, cwd string, loadPreTrainedModel bool, verifyLabel bool) (*TrainStmt, error)

GenerateTrainStmtWithInferredColumns generates a `TrainStmt` with inferred feature columns

func MockTrainStmt 

func MockTrainStmt(isxgboost bool) *TrainStmt

MockTrainStmt generates a sample TrainStmt for test.

func (*TrainStmt) GetModelKind 

func (stmt *TrainStmt) GetModelKind() int

GetModelKind returns the kind of model in the TrainStmt

func (*TrainStmt) GetOriginalSQL 

func (stmt *TrainStmt) GetOriginalSQL() string

GetOriginalSQL returns the original SQL statement used to get current IR result

func (*TrainStmt) IsExtended 

func (stmt *TrainStmt) IsExtended() bool

IsExtended returns whether a SQLFlowStmt is an extended SQL statement

func (*TrainStmt) SetOriginalSQL 

func (stmt *TrainStmt) SetOriginalSQL(sql string)

SetOriginalSQL sets the original sql string

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