anml.data#

anml.data.prototype#

class anml.data.prototype.DataPrototype(components)[source]#

Bases: object

A set of components for easy validation and accessing the data frame. The class will automatically render the values in the components as the attributes of the class with the keys as the names of the attributes. For an example please check the class anml.data.example.DataExample.

Parameters

components (Dict[str, anml.data.component.Component]) – Components to validate and access data in a data frame.

property components#

Components to validate and access data in a data frame.

attach(df)[source]#

Attach data frame to every component.

Parameters

df (pandas.core.frame.DataFrame) – Given data frame.

clear()[source]#

Clear stored value for each component.

anml.data.component#

class anml.data.component.Component(key, validators=None, default_value=None)[source]#

Bases: object

Component class validates, accesses and stores data from a data frame.

Parameters
  • key (str) – Key to access the column in a data frame.

  • validators (Optional[List[anml.data.validator.Validator]]) – A list of validators to check the corresponding column value in a data frame. Default to None.

  • default_value (Optional[Any]) – A default value used to create the column when the key doesn’t exist in the data frame. If default_value=None and the data frame doesn’t contain the key, a KeyError will be raised. Default to None.

property value#

Stored value from a data frame. This property can only be modified through class functions.

property key#

Key to access the column in a data frame.

property validators#

A list of validators to check the corresponding column value in a data frame.

attach(df)[source]#

Validate, fill and store value from a data frame.

Parameters

df (pandas.core.frame.DataFrame) – Given data frame.

Raises

KeyError – Raised when data frame doesn’t contain the key and there is no default value.

clear()[source]#

Clear stored value.

anml.data.validator#

class anml.data.validator.Validator[source]#

Bases: abc.ABC

Validator class validates the data satisfy the condition. The instance is callable. And if the condition is not met, the call will raise value error.

class anml.data.validator.NoNans[source]#

Bases: anml.data.validator.Validator

Validate there is no ‘nan’s in the array.

class anml.data.validator.Positive[source]#

Bases: anml.data.validator.Validator

Validate there is no non-poisitive value in the array.

class anml.data.validator.Unique[source]#

Bases: anml.data.validator.Validator

Validate all the values in the array are unique.

anml.data.example#

class anml.data.example.DataExample(obs, obs_se)[source]#

Bases: anml.data.prototype.DataPrototype

An example class for simple least-square problem. And for that purpose we only need observations and their standard deviations.

Parameters
  • obs (str) – The observation column name in the data frame.

  • obs_se (str) – The observation standard deviation column name in the data frame.