pyvalues¶

class pyvalues.AttainmentScore(*, attained: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, constrained: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0)¶

Bases: BaseModel

A total Score for a value, split into a score for value (partially) attained and value (partially) constrained.

A score represents an effect size, confidence, or something else between (both inclusive) 0 (no effect/confidence/etc.) and 1 (maximum effect/confidence/etc.).

attained: Annotated[float, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=1)])]¶: Score for partially attaining a value (in some way).

constrained: Annotated[float, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=1)])]¶: Score for partially constraining a value (in some way).

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class pyvalues.Document(*, id: str | None = None, language: LanguageAlpha2 = 'en', segments: list[str] | None = None)¶

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

static read_tsv(input_file: str | ~pathlib.Path, segmenter: ~typing.Callable[[str, ~pydantic_extra_types.language_code.LanguageAlpha2], ~typing.Iterable[str]] = <function Document.<lambda>>, document_id: str | None = None, language: ~pydantic_extra_types.language_code.LanguageAlpha2 | str = 'en', delimiter: str = '\t', document_id_field: str | None = None, language_field: str | None = None, text_field: str = 'Text', **kwargs) → Generator[Document, None, None]¶

Reads a tab-separated file (or one with a different delimiter).

By default, each row is treated as its own document unless either (1) the document_id_field parameter is set and specifies a column name of the file, in which case consecutive rows with the same ID are treated as one document; or (2) the document_id parameter is set, in which case the set value is used for rows without ID.

Parameters:

input_file (str | Path) – The tab-separated values file to read.
segmenter (Callable[[str, LanguageAlpha2], Iterable[str]]) – Segmenter used to split the text into segments. Takes a the text to split and the text language as parameters. Default: use complete text as single segment.
document_id (str | None) – Default document ID to use when no ID is found in the row or when document_id_field is not specified.
language (LanguageAlpha2 | str) – Default language (ISO 639-1 / alpha-2) to use when no language is found in the row or when language_field is not specified.
delimiter (str) – Field delimiter used in the file (defaults to tab).
document_id_field (str | None) – Name of the column containing document IDs. When provided, consecutive rows with the same ID are grouped into a single document; Default: None
language_field (str | None) – Name of the column containing language codes. When provided, the value in this column overrides the default language for the current row (and thus the current document); Default: None
text_field (str) – Name of the column containing segment text. Values from this column are collected into the segments attribute of the resulting document after running them through the segmenter; Default: “Text”
kwargs – Additional keyword arguments passed to csv.DictReader.

Returns:

A generator yielding the read documents.

Return type:

Generator[Document, None, None]

static read_txt(input_file: str | ~pathlib.Path, segmenter: ~typing.Callable[[str], ~typing.Iterable[str]] = <function Document.<lambda>>, document_id: str | None = None, language: ~pydantic_extra_types.language_code.LanguageAlpha2 | str = 'en') → Document¶

Reads a text file.

By default, the text file is read as one segment, but a segmenter can be provided to split the text content.

Parameters:

input_file (str | Path) – The text file to read.
segmenter (Callable[[str], Iterable[str]]) – Segmenter used to split the text into segments. Default: use complete text as single segment.
document_id (str | None) – Default document ID to use when no ID is found in the row or when document_id_field is not specified.
language (LanguageAlpha2 | str) – Default language (ISO 639-1 / alpha-2) to use when no language is found in the row or when language_field is not specified.

class pyvalues.OriginalValues(*, self_direction: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, stimulation: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, hedonism: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, achievement: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, power: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, security: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, tradition: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, conformity: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, benevolence: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, universalism: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0)¶

Bases: ValuesWithoutAttainment

Scores for the ten values from Schwartz original system.

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → OriginalValues¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

class pyvalues.OriginalValuesWithAttainment(*, self_direction: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), stimulation: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), hedonism: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), achievement: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), power: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), security: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), tradition: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), conformity: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), benevolence: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), universalism: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0))¶

Bases: ValuesWithAttainment

Scores with attainment for the ten values from Schwartz original system.

attained() → OriginalValues¶

Takes the scores of the values for attained only, dropping for constrained.

Returns:: The attained scores
Return type:: OriginalValues

constrained() → OriginalValues¶

Takes the scores of the values for constrained only, dropping for attained.

Returns:: The constrained scores
Return type:: OriginalValues

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → OriginalValuesWithAttainment¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

without_attainment() → OriginalValues¶

Combines the scores of the values with the same attainment by taking their sum.

E.g., ‘Achievement attained’ and ‘Achievement constrained’ to ‘Achievement’.

Returns:: The combined and scores
Return type:: OriginalValues

class pyvalues.RefinedCoarseValues(*, self_direction: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, stimulation: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, hedonism: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, achievement: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, power: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, face: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, security: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, tradition: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, conformity: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, humility: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, benevolence: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, universalism: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0)¶

Bases: ValuesWithoutAttainment

Scores for the twelve values from Schwartz refined system (19 values) when combining values with same name prefix.

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → RefinedCoarseValues¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

original_values() → OriginalValues¶

Drops Face and Humility scores.

Returns:: The reduced scores
Return type:: OriginalValues

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

class pyvalues.RefinedCoarseValuesWithAttainment(*, self_direction: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), stimulation: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), hedonism: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), achievement: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), power: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), face: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), security: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), tradition: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), conformity: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), humility: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), benevolence: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), universalism: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0))¶

Bases: ValuesWithAttainment

Scores with attainment for the twelve values from Schwartz refined system (19 values) when combining values with same name prefix.

attained() → RefinedCoarseValues¶

Takes the scores of the values for attained only, dropping for constrained.

Returns:: The attained scores
Return type:: RefinedCoarseValues

constrained() → RefinedCoarseValues¶

Takes the scores of the values for constrained only, dropping for attained.

Returns:: The constrained scores
Return type:: RefinedCoarseValues

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → RefinedCoarseValuesWithAttainment¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

original_values() → OriginalValuesWithAttainment¶

Drops Face and Humility scores.

Returns:: The reduced scores
Return type:: OriginalValuesWithAttainment

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

without_attainment() → RefinedCoarseValues¶

Combines the scores of the values with the same attainment by taking their sum.

E.g., ‘Achievement attained’ and ‘Achievement constrained’ to ‘Achievement’.

Returns:: The combined scores
Return type:: RefinedCoarseValues

class pyvalues.RefinedValues(*, self_direction_thought: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, self_direction_action: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, stimulation: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, hedonism: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, achievement: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, power_dominance: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, power_resources: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, face: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, security_personal: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, security_societal: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, tradition: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, conformity_rules: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, conformity_interpersonal: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, humility: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, benevolence_caring: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, benevolence_dependability: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, universalism_concern: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, universalism_nature: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0, universalism_tolerance: Annotated[float, Ge(ge=0), Le(le=1)] = 0.0)¶

Bases: ValuesWithoutAttainment

Scores for the 19 values from Schwartz refined system.

coarse_values(mode: ~typing.Callable[[~typing.Iterable[float]], float] = <built-in function max>) → RefinedCoarseValues¶

Combines the scores of the values with the same prefix.

E.g., ‘Universalism: concern’, ‘Universalism: nature’, and ‘Universalism: tolerance’ to ‘Universalism’.

Parameters:: mode (Callable[[Iterable[float]], float]) – Function to combine the scores (default: max)
Returns:: The combined scores
Return type:: RefinedCoarseValues

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → RefinedValues¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

original_values(mode: ~typing.Callable[[~typing.Iterable[float]], float] = <built-in function max>) → OriginalValues¶

Combines the scores of the values with the same prefix and drops Face and Humility.

E.g., ‘Universalism: concern’, ‘Universalism: nature’, and ‘Universalism: tolerance’ to ‘Universalism’.

Parameters:: mode (Callable[[Iterable[float]], float]) – Function to combine the scores (default: max)
Returns:: The combined and reduced scores
Return type:: OriginalValues

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

class pyvalues.RefinedValuesWithAttainment(*, self_direction_thought: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), self_direction_action: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), stimulation: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), hedonism: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), achievement: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), power_dominance: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), power_resources: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), face: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), security_personal: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), security_societal: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), tradition: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), conformity_rules: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), conformity_interpersonal: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), humility: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), benevolence_caring: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), benevolence_dependability: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), universalism_concern: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), universalism_nature: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0), universalism_tolerance: AttainmentScore = AttainmentScore(attained=0.0, constrained=0.0))¶

Bases: ValuesWithAttainment

Scores with attainment for the 19 values from Schwartz refined system.

attained() → RefinedValues¶

Takes the scores of the values for attained only, dropping for constrained.

Returns:: The attained scores
Return type:: RefinedValues

coarse_values(mode: ~typing.Callable[[~typing.Iterable[float]], float] = <built-in function max>) → RefinedCoarseValuesWithAttainment¶

Combines the scores of the values with the same prefix and attainment.

E.g., ‘Universalism: concern attained’, ‘Universalism: nature attained’, and ‘Universalism: tolerance attained’ to ‘Universalism attained’.

Parameters:: mode (Callable[[Iterable[float]], float]) – Function to combine the scores (default: max)
Returns:: The combined scores
Return type:: RefinedCoarseValuesWithAttainment

constrained() → RefinedValues¶

Takes the scores of the values for constrained only, dropping for attained.

Returns:: The constrained scores
Return type:: RefinedValues

convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

classmethod from_list(scores: list[float], cap_at_one: bool = False) → RefinedValuesWithAttainment¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {'extra': 'forbid', 'serialize_by_alias': True}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

original_values(mode: ~typing.Callable[[~typing.Iterable[float]], float] = <built-in function max>) → OriginalValuesWithAttainment¶

Combines the scores of the values with the same prefix and attainment, and drops Face and Humility.

E.g., ‘Universalism: concern attained’, ‘Universalism: nature attained’, and ‘Universalism: tolerance attained’ to ‘Universalism attained’.

Parameters:: mode (Callable[[Iterable[float]], float]) – Function to combine the scores (default: max)
Returns:: The combined and reduced scores
Return type:: OriginalValuesWithAttainment

to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

without_attainment() → RefinedValues¶

Combines the scores of the values with the same attainment by taking their sum.

E.g., ‘Achievement attained’ and ‘Achievement constrained’ to ‘Achievement’.

Returns:: The combined scores
Return type:: RefinedValues

class pyvalues.Values¶

Bases: ABC, BaseModel

Scores (with or without attainment) for any system of values.

classmethod average(value_scores_list: Iterable[Self]) → Self¶

Creates a new values score object with each score being the average of the respective scores in the input

Parameters:: value_scores_list (Iterable[Self]) – The scores to average
Returns:: The averaged scores
Return type:: Self

classmethod average_documents(documents: Iterable[ValuesAnnotatedDocument[Self]]) → Self¶

Creates a new values score object with each score being the average of the respective average score of each document in the input

Parameters:: documents (Iterable[ValuesAnnotatedDocument[Self]]) – The documents to average
Returns:: The averaged scores
Return type:: Self

binarize(threshold: Annotated[float, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=1)])] | Self = 0.5) → Self¶

Gets the scores as either 1 (if at least at threshold) or 0 (otherwise).

Parameters:: threshold (Score | Self) – The threshold for becoming 1, either a single number for all values or a values score object with each score being the corresponding threshold for that value
Returns:: A new object with scores either 0 or 1
Return type:: Self

abstractmethod convert(target: Type[VALUES2]) → VALUES2¶

Converts the scores to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted scores
Return type:: Values

abstractmethod classmethod from_list(scores: list[float], cap_at_one: bool = False) → Self¶

Creates a value scores object from a list of scores (in the order of names())

Parameters:

scores (list[float]) – The scores in the order of names()
cap_at_one – Whether to cap the scores at 1 - for scores with attainment, scale attained and constrained down to have at most 1 total; Default: False (throw an error instead of capping)

Returns:

The value scores object

Return type:

Self

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

abstractmethod classmethod names() → list[str]¶

Gets the names of the value scores.

If the scores have attainment, one is returned for value (partially) attained (with suffix “ attained”) and another one for value (partially) constrained (” constrained”).

The order is the same as for to_list().

Returns:: The list of names
Return type:: list[str]

classmethod read_tsv(input_file: str | Path, document_id: str | None = None, language: LanguageAlpha2 | str = 'en', delimiter: str = '\t', document_id_field: str | None = None, language_field: str | None = None, segment_field: str | None = None, **kwargs) → Generator[ValuesAnnotatedDocument[Self], None, None]¶

Reads a tab-separated values file (or one with a different delimiter).

By default, each row is treated as its own document unless either (1) the document_id_field parameter is set and specifies a column name of the file, in which case consecutive rows with the same ID are treated as one document; or (2) the document_id parameter is set, in which case the set value is used for rows without ID.

Parameters:

input_file (str | Path) – The tab-separated values file to read.
document_id (str | None) – Default document ID to use when no ID is found in the row or when document_id_field is not specified.
language (LanguageAlpha2 | str) – Default language (ISO 639-1 / alpha-2) to use when no language is found in the row or when language_field is not specified.
delimiter (str) – Field delimiter used in the file (defaults to tab).
document_id_field (str | None) – Name of the column containing document IDs. When provided, consecutive rows with the same ID are grouped into a single document; Default: None
language_field (str | None) – Name of the column containing language codes. When provided, the value in this column overrides the default language for the current row (and thus the current document); Default: None
segment_field (str | None) – Name of the column containing segment text. When provided, values from this column are collected into the segments attribute of the resulting document; Default: None
kwargs – Additional keyword arguments passed to csv.DictReader.

Returns:

A generator yielding ValuesAnnotatedDocument[Self] instances.

Return type:

Generator[ValuesAnnotatedDocument[Self], None, None]

abstractmethod to_list() → list[float]¶

Gets the single scores, in the same order as names().

Returns:: The list of scores
Return type:: list[float]

class pyvalues.ValuesAnnotatedDocument(*, id: str | None = None, language: LanguageAlpha2 = 'en', segments: list[str] | None = None)¶

Bases: Document, Generic[VALUES]

average() → ValuesAnnotatedDocument¶

Creates a new document with each score being the average of this document’s respective scores

Returns:: The averaged scores
Return type:: Self

binarize(threshold: Annotated[float, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=1)])] | VALUES = 0.5) → ValuesAnnotatedDocument¶

Gets the scores as either 1 (if at least at threshold) or 0 (otherwise).

Parameters:: threshold (Score | VALUES) – The threshold for becoming 1, either a single number for all values or a values score object with each score being the corresponding threshold for that value
Returns:: A new document with scores either 0 or 1
Return type:: Self

convert(target: Type[VALUES2]) → ValuesAnnotatedDocument[TypeVar]¶

Create a new document with all scores converted to the target class if possible.

If the scores can not be converted, a ValueError is raised.

Parameters:: target (Type[Values]) – The target class
Returns:: The converted document
Return type:: ValuesAnnotatedDocument[Values]

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class pyvalues.ValuesWithAttainment¶

Bases: Values

Scores with attainment for any system of values.

binarize(threshold: Annotated[float, FieldInfo(annotation=NoneType, required=True, metadata=[Ge(ge=0), Le(le=1)])] | Self = 0.5) → Self¶

Gets the scores as either 1 (if at least at threshold) or 0 (otherwise).

Parameters:: threshold (Score | Self) – The threshold for becoming 1, either a single number for all values or a values score object with each score being the corresponding threshold for that value
Returns:: A new object with scores either 0 or 1
Return type:: Self

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

class pyvalues.ValuesWithoutAttainment¶

Bases: Values

Scores without attainment for any system of values.

model_config: ClassVar[ConfigDict] = {}¶: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

plot(label=None, linecolor='black', fillcolor=None, fillalpha=0.25, **kwargs)¶

Plot theses scores in a radar plot.

Returns the matplotlib module, so one can directly use savefig(file) or show() on the returned value.

import pyvalues
values = pyvalues.OriginalValues.from_list([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
values.plot(label="my values").show()

Parameters:

label (str | None) – The label to use in the legend, if any
linecolor (str) – The color of the line to draw
fillcolor (str | None) – The area fill to draw, if any
fillalpha (float) – The alpha channel of the fill color
kwargs – Arguments to pass on for plotting, especially gridlines (list[float])

static plot_all(value_scores_list: Sequence[ValuesWithoutAttainment], **kwargs)¶

Plot scores in a radar plot.

Returns the matplotlib module, so one can directly use savefig(file) or show() on the returned value.

import pyvalues
values = pyvalues.OriginalValues.from_list([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0])
pyvalues.plot_all([values], labels=["my values"]).show()

Parameters:

value_scores_list (Sequence["ValuesWithoutAttainment"]) – The scores to plot
kwargs – Arguments to pass on for plotting, especially labels (list[str]), linecolors (list[str]), fillcolors (list[str]), fillalphas (list[str]), and gridlines (list[float])

pyvalues¶

pyvalues

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