merlin.schema package#

class merlin.schema.Schema(column_schemas=None)[source]#

Bases: object

A collection of column schemas for a dataset.

property column_names#

select(selector) → merlin.schema.schema.Schema[source]#

Select matching columns from this Schema object using a ColumnSelector

Parameters: selector (ColumnSelector) – Selector that describes which columns match
Returns: New object containing only the ColumnSchemas of selected columns
Return type: Schema

apply(selector) → merlin.schema.schema.Schema[source]#

excluding(selector) → merlin.schema.schema.Schema[source]#

Select non-matching columns from this Schema object using a ColumnSelector

Parameters: selector (ColumnSelector) – Selector that describes which columns match
Returns: New object containing only the ColumnSchemas of selected columns
Return type: Schema

apply_inverse(selector) → merlin.schema.schema.Schema[source]#

select_by_tag(tags: Union[str, merlin.schema.tags.Tags, List[Union[str, merlin.schema.tags.Tags]]]) → merlin.schema.schema.Schema[source]#

Select matching columns from this Schema object using a list of tags

Parameters: tags (List[Union[str, Tags]] :) – List of tags that describes which columns match
Returns: New object containing only the ColumnSchemas of selected columns
Return type: Schema

excluding_by_tag(tags) → merlin.schema.schema.Schema[source]#

remove_by_tag(tags) → merlin.schema.schema.Schema[source]#

select_by_name(names: List[str]) → merlin.schema.schema.Schema[source]#

Select matching columns from this Schema object using a list of column names

Parameters: names (List[str] :) – List of column names that describes which columns match
Returns: New object containing only the ColumnSchemas of selected columns
Return type: Schema

excluding_by_name(col_names: List[str])[source]#

Remove columns from this Schema object by name

Parameters: col_names (List[str]) – Names of the column to remove
Returns: New Schema object after the columns are removed
Return type: Schema

remove_col(col_name: str) → merlin.schema.schema.Schema[source]#

Remove a column from this Schema object by name

Parameters: col_name (str) – Name of the column to remove
Returns: This Schema object after the column is removed
Return type: Schema

without(col_names: List[str]) → merlin.schema.schema.Schema[source]#

get(col_name: str, default: Optional[merlin.schema.schema.ColumnSchema] = None) → merlin.schema.schema.ColumnSchema[source]#

Get a ColumnSchema by name

Parameters

col_name (str) – Name of the column to get
default (ColumnSchema :) –

Default value to return if column is not found.
(Default value = None)

Returns

Retrieved column schema (or default value, if not found)

Return type

ColumnSchema

property first: merlin.schema.schema.ColumnSchema#

Returns the first ColumnSchema in the Schema. Useful for cases where you select down to a single column via select_by_name or select_by_tag, and just want the value

Returns: The first column schema present in this Schema object
Return type: ColumnSchema
Raises: ValueError – If this Schema object contains no column schemas

to_pandas() → pandas.core.frame.DataFrame[source]#

Convert this Schema object to a pandas DataFrame

Returns: DataFrame containing the column schemas in this Schema object
Return type: pd.DataFrame

class merlin.schema.ColumnSchema(name: str, tags: typing.Optional[merlin.schema.tags.TagSet] = <factory>, properties: typing.Optional[typing.Dict] = <factory>, dtype: typing.Optional[object] = None, is_list: typing.Optional[bool] = None, is_ragged: typing.Optional[bool] = None)[source]#

Bases: object

A schema containing metadata of a dataframe column.

name: str#

tags: Optional[merlin.schema.tags.TagSet]#

properties: Optional[Dict]#

dtype: Optional[object] = None#

is_list: Optional[bool] = None#

is_ragged: Optional[bool] = None#

property quantity#

Describes the number of elements in each row of this column

Returns: SCALAR when one element per row FIXED_LIST when the same number of elements per row RAGGED_LIST when different numbers of elements per row
Return type: ColumnQuantity

with_name(name: str) → merlin.schema.schema.ColumnSchema[source]#

Create a copy of this ColumnSchema object with a different column name

Parameters: name (str) – New column name
Returns: Copied object with new column name
Return type: ColumnSchema

with_tags(tags: Union[str, merlin.schema.tags.Tags]) → merlin.schema.schema.ColumnSchema[source]#

Create a copy of this ColumnSchema object with different column tags

Parameters: tags (Union[str, Tags]) – New column tags
Returns: Copied object with new column tags
Return type: ColumnSchema

with_properties(properties: dict) → merlin.schema.schema.ColumnSchema[source]#

Create a copy of this ColumnSchema object with different column properties

Parameters: properties (dict) – New column properties
Returns: Copied object with new column properties
Return type: ColumnSchema
Raises: TypeError – If properties are not a dict

with_dtype(dtype, is_list: Optional[bool] = None, is_ragged: Optional[bool] = None) → merlin.schema.schema.ColumnSchema[source]#

Create a copy of this ColumnSchema object with different column dtype

Parameters

dtype (np.dtype) – New column dtype
is_list (bool :) –

Whether rows in this column contain lists.
(Default value = None)
is_ragged (bool :) –

Whether lists in this column have varying lengths.
(Default value = None)

Returns

Copied object with new column dtype

Return type

ColumnSchema

property int_domain: Optional[merlin.schema.schema.Domain]#

property float_domain: Optional[merlin.schema.schema.Domain]#

property value_count: Optional[merlin.schema.schema.Domain]#

class merlin.schema.Tags(value)[source]#

Bases: enum.Enum

Standard tags used in the Merlin ecosystem

CATEGORICAL = 'categorical'#

CONTINUOUS = 'continuous'#

LIST = 'list'#

SEQUENCE = 'sequence'#

TEXT = 'text'#

TOKENIZED = 'tokenized'#

TIME = 'time'#

ID = 'id'#

USER = 'user'#

ITEM = 'item'#

SESSION = 'session'#

CONTEXT = 'context'#

TARGET = 'target'#

REGRESSION = 'regression'#

CLASSIFICATION = 'classification'#

BINARY = 'binary'#

MULTI_CLASS = 'multi_class'#

USER_ID = 'user_id'#

ITEM_ID = 'item_id'#

SESSION_ID = 'session_id'#

TEXT_TOKENIZED = 'text_tokenized'#

BINARY_CLASSIFICATION = 'binary_classification'#

MULTI_CLASS_CLASSIFICATION = 'multi_class_classification'#