add_remove_keys#

Transformations that transform dictionaries using AddRemoveKeys.

Note that several of the transformations in dictionary also support AddRemoveKeys. In particular

Classes#

`TransformValue`	Base class transforming a specified key using an existing transformation.
`FilterValue`	Applies a `Filter` to create a new element from specified value.
`PublicJoinValue`	Applies a `PublicJoin` to create a new element from specified value.
`FlatMapValue`	Applies a `FlatMap` to create a new element from specified value.
`MapValue`	Applies a `Map` to create a new element from specified value.
`DropInfsValue`	Applies a `DropInfs` to create a new element from specified value.
`DropNaNsValue`	Applies a `DropNaNs` to create a new element from specified value.
`DropNullsValue`	Applies a `DropNulls` to create a new element from specified value.
`ReplaceInfsValue`	Applies a `ReplaceInfs` to create a new element from specified value.
`ReplaceNaNsValue`	Applies a `ReplaceNaNs` to create a new element from specified value.
`ReplaceNullsValue`	Applies a `ReplaceNulls` to create a new element from specified value.
`PersistValue`	Applies a `Persist` to create a new element from specified value.
`UnpersistValue`	Applies a `Unpersist` to create a new element from specified value.
`SparkActionValue`	Applies a `SparkAction` to create a new element from specified value.
`RenameValue`	Applies a `Rename` to create a new element from specified value.
`SelectValue`	Applies a `Select` to create a new element from specified value.

class TransformValue(input_domain, transformation, key, new_key)#

Bases: tmlt.core.transformations.base.Transformation

Base class transforming a specified key using an existing transformation.

This class can be subclassed for the purposes of making a claim that a kind of Transformation (like Filter) can be applied to a DataFrame and augment the input dictionary with the output without violating the closeness of neighboring dataframes with AddRemoveKeys.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
transformation (tmlt.core.transformations.base.Transformation) –
key (Any) –
new_key (Any) –

__init__(input_domain, transformation, key, new_key)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
transformation (TransformationTransformation) – The DataFrame to DataFrame transformation to apply. Input and output metric must both be IfGroupedBy(column, SymmetricDifference()) using the same column.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class FilterValue(input_domain, column, key, new_key, filter_expr)#

Bases: TransformValue

Applies a Filter to create a new element from specified value.

See TransformValue, and Filter for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
filter_expr (str) –

__init__(input_domain, column, key, new_key, filter_expr)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
filter_expr (strstr) – A string of SQL expression specifying the filter to apply to the data. The language is the same as the one used by pyspark.sql.DataFrame.filter().

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class PublicJoinValue(input_domain, column, key, new_key, public_df, public_df_domain=None, join_cols=None, join_on_nulls=False)#

Bases: TransformValue

Applies a PublicJoin to create a new element from specified value.

See TransformValue, and PublicJoin for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
public_df (pyspark.sql.DataFrame) –
public_df_domain (Optional[tmlt.core.domains.spark_domains.SparkDataFrameDomain]) –
join_cols (Optional[List[str]]) –
join_on_nulls (bool) –

__init__(input_domain, column, key, new_key, public_df, public_df_domain=None, join_cols=None, join_on_nulls=False)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
public_df (DataFrameDataFrame) – A Spark DataFrame to join with.
public_df_domain (SparkDataFrameDomain | NoneOptional[SparkDataFrameDomain] (default: None)) – Domain of public DataFrame to join with. If this domain indicates that a float column does not allow nans (or infs), all rows in public_df containing a nan (or an inf) in that column will be dropped. If None, domain is inferred from the schema of public_df and any float column will be marked as allowing inf and nan values.
join_cols (List[str] | NoneOptional[List[str]] (default: None)) – Names of columns to join on. If None, a natural join is performed.
join_on_nulls (boolbool (default: False)) – If True, null values on corresponding join columns of the public and private dataframes will be considered to be equal.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class FlatMapValue(input_domain, column, key, new_key, row_transformer, max_num_rows)#

Bases: TransformValue

Applies a FlatMap to create a new element from specified value.

See TransformValue, and FlatMap for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
row_transformer (tmlt.core.transformations.spark_transformations.map.RowToRowsTransformation) –
max_num_rows (int) –

__init__(input_domain, column, key, new_key, row_transformer, max_num_rows)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
row_transformer (RowToRowsTransformationRowToRowsTransformation) – Transformation to apply to each row.
max_num_rows (intint) – The maximum number of rows to allow from row_transformer. If more rows are output, the additional rows are suppressed.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class MapValue(input_domain, column, key, new_key, row_transformer)#

Bases: TransformValue

Applies a Map to create a new element from specified value.

See TransformValue, and Map for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
row_transformer (tmlt.core.transformations.spark_transformations.map.RowToRowTransformation) –

__init__(input_domain, column, key, new_key, row_transformer)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
row_transformer (RowToRowTransformationRowToRowTransformation) – Transformation to apply to each row.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class DropInfsValue(input_domain, column, key, new_key, columns)#

Bases: TransformValue

Applies a DropInfs to create a new element from specified value.

See TransformValue, and DropInfs for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
columns (List[str]) –

__init__(input_domain, column, key, new_key, columns)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
columns (List[str]List[str]) – Columns to drop +inf and -inf from.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class DropNaNsValue(input_domain, column, key, new_key, columns)#

Bases: TransformValue

Applies a DropNaNs to create a new element from specified value.

See TransformValue, and DropNaNs for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
columns (List[str]) –

__init__(input_domain, column, key, new_key, columns)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
columns (List[str]List[str]) – Columns to drop NaNs from.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class DropNullsValue(input_domain, column, key, new_key, columns)#

Bases: TransformValue

Applies a DropNulls to create a new element from specified value.

See TransformValue, and DropNulls for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
columns (List[str]) –

__init__(input_domain, column, key, new_key, columns)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
columns (List[str]List[str]) – Columns to drop nulls from.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class ReplaceInfsValue(input_domain, column, key, new_key, replace_map)#

Bases: TransformValue

Applies a ReplaceInfs to create a new element from specified value.

See TransformValue, and ReplaceInfs for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
replace_map (Dict[str, Tuple[float, float]]) –

__init__(input_domain, column, key, new_key, replace_map)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
replace_map ({str: Tuple[float, float]}Dict[str, Tuple[float, float]]) – Dictionary mapping column names to a tuple. The first value in the tuple will be used to replace -inf in that column, and the second value in the tuple will be used to replace +inf in that column.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class ReplaceNaNsValue(input_domain, column, key, new_key, replace_map)#

Bases: TransformValue

Applies a ReplaceNaNs to create a new element from specified value.

See TransformValue, and ReplaceNaNs for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
replace_map (Dict[str, Any]) –

__init__(input_domain, column, key, new_key, replace_map)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
replace_map ({str: Any}Dict[str, Any]) – Dictionary mapping column names to value to be used for replacing NaNs in that column.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class ReplaceNullsValue(input_domain, column, key, new_key, replace_map)#

Bases: TransformValue

Applies a ReplaceNulls to create a new element from specified value.

See TransformValue, and ReplaceNulls for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
replace_map (Dict[str, Any]) –

__init__(input_domain, column, key, new_key, replace_map)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
replace_map ({str: Any}Dict[str, Any]) – Dictionary mapping column names to value to be used for replacing nulls in that column.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class PersistValue(input_domain, column, key, new_key)#

Bases: TransformValue

Applies a Persist to create a new element from specified value.

See TransformValue, and Persist for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –

__init__(input_domain, column, key, new_key)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class UnpersistValue(input_domain, column, key, new_key)#

Bases: TransformValue

Applies a Unpersist to create a new element from specified value.

See TransformValue, and Unpersist for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –

__init__(input_domain, column, key, new_key)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class SparkActionValue(input_domain, column, key, new_key)#

Bases: TransformValue

Applies a SparkAction to create a new element from specified value.

See TransformValue, and SparkAction for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –

__init__(input_domain, column, key, new_key)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class RenameValue(input_domain, column, key, new_key, rename_mapping)#

Bases: TransformValue

Applies a Rename to create a new element from specified value.

See TransformValue, and Rename for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
rename_mapping (Dict[str, str]) –

__init__(input_domain, column, key, new_key, rename_mapping)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
rename_mapping ({str: str}Dict[str, str]) – Dictionary from existing column names to target column names.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber

__call__(self, data)#

Returns a new dictionary augmented with the transformed DataFrame.

Parameters: data (Dict[Any, pyspark.sql.DataFrame]) –
Return type: Dict[Any, pyspark.sql.DataFrame]

property input_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property input_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

property output_domain(self)#

Return input domain for the measurement.

Return type: tmlt.core.domains.base.Domain

property output_metric(self)#

Distance metric on input domain.

Return type: tmlt.core.metrics.Metric

stability_relation(self, d_in, d_out)#

Returns True only if close inputs produce close outputs.

See the privacy and stability tutorial (add link?) for more information.

Parameters

d_in (Any) – Distance between inputs under input_metric.
d_out (Any) – Distance between outputs under output_metric.

Return type

bool

__or__(self, other: Transformation) → Transformation#
__or__(self, other: tmlt.core.measurements.base.Measurement) → tmlt.core.measurements.base.Measurement: Return this transformation chained with another component.

class SelectValue(input_domain, column, key, new_key, columns)#

Bases: TransformValue

Applies a Select to create a new element from specified value.

See TransformValue, and Select for more information.

Methods#
`transformation()`	Returns the transformation that will be applied to create the new element.
`key()`	Returns the key for the DataFrame to transform.
`new_key()`	Returns the new key for the transformed DataFrame.
`stability_function()`	Returns the smallest d_out satisfied by the transformation.
`__call__()`	Returns a new dictionary augmented with the transformed DataFrame.
`input_domain()`	Return input domain for the measurement.
`input_metric()`	Distance metric on input domain.
`output_domain()`	Return input domain for the measurement.
`output_metric()`	Distance metric on input domain.
`stability_relation()`	Returns True only if close inputs produce close outputs.
`__or__()`	Return this transformation chained with another component.

Parameters

input_domain (tmlt.core.domains.collections.DictDomain) –
column (str) –
key (Any) –
new_key (Any) –
columns (List[str]) –

__init__(input_domain, column, key, new_key, columns)#

Constructor.

Parameters

input_domain (DictDomainDictDomain) – The Domain of the input dictionary of Spark DataFrames.
column (strstr) – The column to use for the input and output AddRemoveKeys metric.
key (AnyAny) – The key for the DataFrame to transform.
new_key (AnyAny) – The key to put the transformed output in. The key must not already be in the input domain.
columns (List[str]List[str]) – A list of existing column names to keep.

property transformation(self)#

Returns the transformation that will be applied to create the new element.

Return type: tmlt.core.transformations.base.Transformation

property key(self)#

Returns the key for the DataFrame to transform.

Return type: Any

property new_key(self)#

Returns the new key for the transformed DataFrame.

Return type: Any

stability_function(self, d_in)#

Returns the smallest d_out satisfied by the transformation.

See the privacy and stability tutorial (add link?) for more information.

Parameters: d_in (tmlt.core.utils.exact_number.ExactNumberInput) – Distance between inputs under input_metric.
Raises: NotImplementedError – If not overridden.
Return type: tmlt.core.utils.exact_number.ExactNumber