mlm_insights.core.transformers package

Subpackages

• mlm_insights.core.transformers.asteval package
  • Submodules
  • mlm_insights.core.transformers.asteval.ast_evaluator module
    • Empty
    • ExceptionHolder
      • ExceptionHolder.get_error()
    • Interpreter
      • Interpreter.dump()
      • Interpreter.eval()
      • Interpreter.node_assign()
      • Interpreter.on_arg()
      • Interpreter.on_assert()
      • Interpreter.on_assign()
      • Interpreter.on_attribute()
      • Interpreter.on_augassign()
      • Interpreter.on_binop()
      • Interpreter.on_boolop()
      • Interpreter.on_break()
      • Interpreter.on_call()
      • Interpreter.on_compare()
      • Interpreter.on_constant()
      • Interpreter.on_continue()
      • Interpreter.on_delete()
      • Interpreter.on_dict()
      • Interpreter.on_ellipsis()
      • Interpreter.on_excepthandler()
      • Interpreter.on_expr()
      • Interpreter.on_expression()
      • Interpreter.on_extslice()
      • Interpreter.on_for()
      • Interpreter.on_functiondef()
      • Interpreter.on_if()
      • Interpreter.on_ifexp()
      • Interpreter.on_index()
      • Interpreter.on_interrupt()
      • Interpreter.on_list()
      • Interpreter.on_listcomp()
      • Interpreter.on_module()
      • Interpreter.on_name()
      • Interpreter.on_nameconstant()
      • Interpreter.on_num()
      • Interpreter.on_pass()
      • Interpreter.on_raise()
      • Interpreter.on_repr()
      • Interpreter.on_return()
      • Interpreter.on_slice()
      • Interpreter.on_str()
      • Interpreter.on_subscript()
      • Interpreter.on_try()
      • Interpreter.on_tuple()
      • Interpreter.on_unaryop()
      • Interpreter.on_while()
      • Interpreter.parse()
      • Interpreter.raise_exception()
      • Interpreter.remove_nodehandler()
      • Interpreter.run()
      • Interpreter.set_nodehandler()
      • Interpreter.unimplemented()
      • Interpreter.user_defined_symbols()
    • NAME_MATCH()
    • NameFinder
      • NameFinder.generic_visit()
    • Procedure
    • get_ast_names()
    • make_symbol_table()
    • op2func()
    • safe_add()
    • safe_lshift()
    • safe_mult()
    • safe_pow()
    • valid_symbol_name()
  • Module contents
• mlm_insights.core.transformers.exceptions package
  • Submodules
  • mlm_insights.core.transformers.exceptions.conditional_feature_expression_validation_exception module
    • ConditionalFeatureExpressionValidationException
  • mlm_insights.core.transformers.exceptions.conditional_feature_transformer_exception module
    • ConditionalFeatureTransformerException
  • mlm_insights.core.transformers.exceptions.expression_evaluation_exception module
    • ExpressionEvaluationException
  • Module contents
• mlm_insights.core.transformers.interfaces package
  • Submodules
  • mlm_insights.core.transformers.interfaces.transformer module
    • Transformer
      • Transformer.create()
      • Transformer.get_output_schema()
      • Transformer.transform()
  • Module contents

Submodules

mlm_insights.core.transformers.ast_based_expression_evaluator module

class mlm_insights.core.transformers.ast_based_expression_evaluator.ASTBasedExpressionEvaluator(execution_mode: ExecutionMode = ExecutionMode.SINGLE_EXPRESSION, symbol_table: Dict[str, Any] = {})

Bases: ExpressionEvaluator

evaluate(expression: Expression) → Any

validate(expression: Expression) → ValidationResult

class mlm_insights.core.transformers.ast_based_expression_evaluator.ExecutionMode(value)

Bases: Enum

An enumeration.

MULTI_EXPRESSION = 2

SINGLE_EXPRESSION = 1

mlm_insights.core.transformers.conditional_feature_transformer module

class mlm_insights.core.transformers.conditional_feature_transformer.ConditionalFeatureMetadata(feature_metadata: mlm_insights.core.features.feature.FeatureMetadata, expression: mlm_insights.core.transformers.expression_evaluator.Expression)

Bases: object

expression: Expression

feature_metadata: FeatureMetadata

class mlm_insights.core.transformers.conditional_feature_transformer.ConditionalFeatureTransformer(conditional_features: List[ConditionalFeatureMetadata])

Bases: Transformer

Base Class that defines a ConditionalFeatureTransformer.

This evaluates conditional feature expressions and creates new features/columns on the input dataframe.

classmethod create(config: Dict[str, Any]) → ConditionalFeatureTransformer

Factory Method to create a ConditionalFeatureTransformer.

The conditional feature metadata will be available in config.

Returns

ConditionalFeatureTransformer

An Instance of ConditionalFeatureTransformer.

Examples

conditional_feature_metadata_no_column_type = ConditionalFeatureMetadata(
feature_metadata=FeatureMetadata(
    feature_name="feature_1",
    feature_type=FeatureType(
        data_type=DataType.INTEGER,
        variable_type=VariableType.DISCRETE
    )
),
expression=Expression("df['test_column']%2==0", ExpressionType.python))

conditional_feature_metadata_with_column_type = ConditionalFeatureMetadata(
    feature_metadata=FeatureMetadata(
        feature_name="feature_2",
        feature_type=FeatureType(
            data_type=DataType.INTEGER,
            variable_type=VariableType.DISCRETE,
            column_type=ColumnType.TARGET
        )
    ),
    expression=Expression("df['test_column']%2==0", ExpressionType.python))

cf_transformer = ConditionalFeatureTransformer(conditional_features=[conditional_feature_metadata_no_column_type,
                                                                     conditional_feature_metadata_with_column_type])
output_schema = cf_transformer.get_output_schema(input_schema=pyarrow.schema(fields=[]))
assert output_schema is not None
column_type_1 = output_schema.field_by_name("feature_1").metadata.get(b'column_type', None)
assert column_type_1 is not None
assert column_type_1.decode("utf-8") == ColumnType.INPUT.name

column_type_2 = output_schema.field_by_name("feature_2").metadata.get(b'column_type', None)
assert column_type_2 is not None
assert column_type_2.decode("utf-8") == ColumnType.TARGET.name

get_output_schema(input_schema: Schema, **kwargs: Any) → Schema

Generates a new schema after transforming conditional features.

Parameters

input_schema: pa.Schema

Schema of the input data frame

Returns

output_schema: pa.Schema

Feature schema for the transformed dataset after transforming Conditional Features

transform(data_frame: DataFrame, **kwargs: Any) → DataFrame

Evaluates Conditional Feature Expressions and create new Features applying Conditional Transformer and append it to input dataframe.

Returns

pandas DataFrame: DataFrame containing all the features after applying Conditional Transformer

validate() → List[ComponentValidationResult]

Validates Conditional Feature Expressions.

Returns

List:

list of ComponentValidationResult

mlm_insights.core.transformers.expression_evaluator module

class mlm_insights.core.transformers.expression_evaluator.Expression(value: str, type: mlm_insights.core.transformers.expression_evaluator.ExpressionType)

Bases: object

type: ExpressionType

value: str

class mlm_insights.core.transformers.expression_evaluator.ExpressionEvaluator

Bases: ABC

evaluate(expression: Expression) → Any

validate(expression: Expression) → ValidationResult

class mlm_insights.core.transformers.expression_evaluator.ExpressionType(value)

Bases: Enum

An enumeration.

python = 'python'

class mlm_insights.core.transformers.expression_evaluator.ValidationResult(success: bool, expression: str = '', message: str = '')

Bases: object

expression: str = ''

message: str = ''

success: bool

Module contents