sciunit.scores package¶
Submodules¶
sciunit.scores.base module¶
Base class for SciUnit scores.
-
class
sciunit.scores.base.
ErrorScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A score returned when an error occurs during testing.
-
__module__
= 'sciunit.scores.base'¶
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_describe
() → str[source]¶ Get the description of this score.
- Returns:
- str: The description of this score.
-
norm_score
¶ Get the norm score, which is 0.0 for ErrorScore instance.
- Returns:
- float: The norm score.
-
summary
¶ Summarize the performance of a model on a test.
- Returns:
- str: A textual summary of the score.
-
-
class
sciunit.scores.base.
Score
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.base.SciUnit
Abstract base class for scores.
-
__hash__
= None¶
-
__init__
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Abstract base class for scores.
- Args:
- score (Union[‘Score’, float, int, Quantity], bool): A raw value to wrap in a Score class. related_data (dict, optional): Artifacts to store with the score.
-
__module__
= 'sciunit.scores.base'¶
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_allowed_types
= None¶ List of allowed types for the score argument
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_allowed_types_message
= 'Score of type %s is not an instance of one of the allowed types: %s'¶ Error message when score argument is not one of these types
-
_best
= None¶ The best possible score of this type
-
_check_score
(score: sciunit.scores.base.Score) → None[source]¶ A method for each Score subclass to impose additional constraints on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
-
_describe
() → str[source]¶ Get the description of this score.
- Returns:
- str: The description of this score.
-
_description
= ''¶ A description of this score, i.e. how to interpret it. Provided in the score definition
-
_raw
= None¶ A raw number arising in a test’s compute_score, used to determine this score. Can be set for reporting a raw value determined in Test.compute_score before any transformation, e.g. by a Converter
-
_worst
= None¶ The best possible score of this type
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check_score
(score: sciunit.scores.base.Score) → None[source]¶ Check the score with imposed additional constraints in the subclass on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
- Raises:
- InvalidScoreError: Exception raised if score is not a instance of sciunit score.
-
color
(value: Union[float, Score] = None) → tuple[source]¶ Turn the score intp an RGB color tuple of three 8-bit integers.
- Args:
- value (Union[float,, optional): The score that will be turned to an RGB color. Defaults to None.
- Returns:
- tuple: A tuple of three 8-bit integers that represents an RGB color.
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classmethod
compute
(observation: dict, prediction: dict)[source]¶ Compute whether the observation equals the prediction.
- Args:
- observation (dict): The observation from the real world. prediction (dict): The prediction generated by a model.
- Returns:
- NotImplementedError: Not implemented error.
-
describe
(quiet: bool = False) → Optional[str][source]¶ Get the description of this score instance.
- Args:
- quiet (bool, optional): If True, then log the description, return the description otherwise.
- Defaults to False.
- Returns:
- Union[str, None]: If not quiet, then return the description of this score instance.
- Otherwise, None.
-
describe_from_docstring
() → str[source]¶ Get the description of this score from the docstring.
- Returns:
- str: The description of this score.
-
description
= ''¶ A description of this score, i.e. how to interpret it. For the user to set in bind_score
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classmethod
extract_mean_or_value
(obs_or_pred: dict, key: str = None) → float[source]¶ Extracts the mean, value, or user-provided key from an observation or prediction dictionary.
- Args:
- obs_or_pred (dict): [description] key (str, optional): [description]. Defaults to None.
- Raises:
- KeyError: Key not found.
- Returns:
- float: The mean of the values of preditions or observations.
-
classmethod
extract_means_or_values
(observation: dict, prediction: dict, key: str = None) → Tuple[dict, dict][source]¶ Extracts the mean, value, or user-provided key from the observation and prediction dictionaries.
- Args:
- observation (dict): The observation from the real world. prediction (dict): The prediction generated by a model. key (str, optional): [description]. Defaults to None.
- Returns:
- Tuple[dict, dict]: A tuple that contains the mean of values of observations and the mean of
- values of predictions.
-
get_raw
() → float[source]¶ Get the raw score. If there is not raw score, then get score.
- Returns:
- float: The raw score.
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log10_norm_score
¶ The logarithm base 10 of the norm_score. This is useful for guaranteeing convexity in an error surface.
- Returns:
- np.ndarray: The logarithm base 10 of the norm_score.
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log2_norm_score
¶ The logarithm base 2 of the norm_score. This is useful for guaranteeing convexity in an error surface.
- Returns:
- np.ndarray: The logarithm base 2 of the norm_score.
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log_norm_score
¶ The natural logarithm of the norm_score. This is useful for guaranteeing convexity in an error surface.
- Returns:
- np.ndarray: The natural logarithm of the norm_score.
-
model
= None¶ The model judged. Set automatically by Test.judge.
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norm_score
¶ A floating point version of the score used for sorting. If normalized = True, this must be in the range 0.0 to 1.0, where larger is better (used for sorting and coloring tables).
- Returns:
- Score: The [0-1] normalized score.
-
raw
¶ The raw score in string type.
- Returns:
- str: The raw score.
Data specific to the result of a test run on a model.
-
score
= None¶ The score itself.
-
score_type
¶ The type of the score.
- Returns:
- str: the name of the score class.
-
summary
¶ Summarize the performance of a model on a test.
- Returns:
- str: The summary of this score.
-
test
= None¶ The test taken. Set automatically by Test.judge.
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sciunit.scores.collections module¶
SciUnit score collections, such as arrays and matrices.
These collections allow scores to be organized and visualized by model, test, or both.
-
class
sciunit.scores.collections.
ScoreArray
(tests_or_models, scores=None, weights=None)[source]¶ Bases:
pandas.core.series.Series
,sciunit.base.SciUnit
,sciunit.base.TestWeighted
Represents an array of scores derived from a test suite.
Extends the pandas Series such that items are either models subject to a test or tests taken by a model. Also displays and compute score summaries in sciunit-specific ways.
Can use like this, assuming n tests and m models:
>>> sm[test]
>>> sm[test] (score_1, ..., score_m) >>> sm[model] (score_1, ..., score_n)
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__getattr__
(name)[source]¶ After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.
-
__init__
(tests_or_models, scores=None, weights=None)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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__module__
= 'sciunit.scores.collections'¶
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check_tests_and_models
(tests_or_models: Union[sciunit.tests.Test, sciunit.models.base.Model]) → Union[sciunit.tests.Test, sciunit.models.base.Model][source]¶
-
direct_attrs
= ['score', 'norm_scores', 'related_data']¶
-
get_by_name
(name: str) → Union[sciunit.models.base.Model, sciunit.tests.Test][source]¶ Get a test or a model by name.
- Args:
- name (str): The name of the model or test.
- Raises:
- KeyError: No model or test with name name.
- Returns:
- Union[Model, Test]: The model or test found.
-
mean
() → float[source]¶ Compute a total score for each model over all the tests.
Uses the norm_score attribute, since otherwise direct comparison across different kinds of scores would not be possible.
- Returns:
- float: The computed total score for each model over all the tests.
-
norm_scores
¶ Return the norm_score for each test.
- Returns:
- float: The norm_score for each test.
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stature
(test_or_model: Union[sciunit.models.base.Model, sciunit.tests.Test]) → int[source]¶ Compute the relative rank of a model on a test.
Rank is against other models that were asked to take the test.
- Args:
- test_or_model (Union[Model, Test]): A sciunit model or test instance.
- Returns:
- int: The rank of the model or test instance.
-
-
class
sciunit.scores.collections.
ScoreMatrix
(tests, models, scores=None, weights=None, transpose=False)[source]¶ Bases:
pandas.core.frame.DataFrame
,sciunit.base.SciUnit
,sciunit.base.TestWeighted
Represents a matrix of scores derived from a test suite. Extends the pandas DataFrame such that tests are columns and models are the index. Also displays and compute score summaries in sciunit-specific ways.
Can use like this, assuming n tests and m models:
>>> sm[test]
>>> sm[test] (score_1, ..., score_m) >>> sm[model] (score_1, ..., score_n)
-
T
¶ Get transpose of this ScoreMatrix.
- Returns:
- ScoreMatrix: The transpose of this ScoreMatrix.
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__getattr__
(name)[source]¶ After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.
-
__init__
(tests, models, scores=None, weights=None, transpose=False)[source]¶ Constructor of ScoreMatrix class
- Args:
- tests (List[Test]): Test instances that will be in the ScoreMatrix models (List[Model]): Model instances that will be in the ScoreMatrix scores (List[Score], optional): Score instances that will be in the ScoreMatrix. Defaults to None. weights ([type], optional): [description]. Defaults to None. transpose (bool, optional): [description]. Defaults to False.
-
__module__
= 'sciunit.scores.collections'¶
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annotate
(df: pandas.core.frame.DataFrame, html: str, show_mean: bool, colorize: bool) → Tuple[str, int][source]¶ [summary]
- Args:
- df (DataFrame): [description] html (str): [description] show_mean (bool): [description] colorize (bool): [description]
- Returns:
- Tuple[str, int]: [description]
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annotate_body
(soup: bs4.BeautifulSoup, df: pandas.core.frame.DataFrame, show_mean: bool) → None[source]¶ [summary]
- Args:
- soup (BeautifulSoup): [description] df (DataFrame): [description] show_mean (bool): [description]
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annotate_body_cell
(cell, df: pandas.core.frame.DataFrame, show_mean: bool, i: int, j: int) → None[source]¶ [summary]
- Args:
- cell ([type]): [description] df (DataFrame): [description] show_mean (bool): [description] i (int): [description] j (int): [description]
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annotate_header_cell
(cell, df: pandas.core.frame.DataFrame, show_mean: bool, i: int, j: int) → None[source]¶ [summary]
- Args:
- cell ([type]): [description] df (DataFrame): [description] show_mean (bool): [description] i (int): [description] j (int): [description]
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annotate_headers
(soup: bs4.BeautifulSoup, df: pandas.core.frame.DataFrame, show_mean: bool) → None[source]¶ [summary]
- Args:
- soup ([type]): [description] df (DataFrame): [description] show_mean (bool): [description]
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annotate_mean
(cell, df: pandas.core.frame.DataFrame, i: int) → float[source]¶ [summary]
- Args:
- cell ([type]): [description] df (DataFrame): [description] i (int): [description]
- Returns:
- float: [description]
-
check_tests_models_scores
(tests: Union[sciunit.tests.Test, List[sciunit.tests.Test]], models: Union[sciunit.models.base.Model, List[sciunit.models.base.Model]], scores: Union[sciunit.scores.base.Score, List[sciunit.scores.base.Score]]) → Tuple[List[sciunit.tests.Test], List[sciunit.models.base.Model], List[sciunit.scores.base.Score]][source]¶ Check if tests, models, and scores are lists and covert them to lists if they are not.
- Args:
- tests (List[Test]): A sciunit test instance or a list of the test instances. models (List[Model]): A sciunit model instance or a list of the model instances. scores (List[Score]): A sciunit score instance or a list of the score instances.
- Returns:
- Tuple[List[Test], List[Model], List[Score]]: Tuple of lists of tests, models, and scores instances.
-
direct_attrs
= ['score', 'norm_scores', 'related_data']¶
-
get_by_name
(name: str) → Union[sciunit.models.base.Model, sciunit.tests.Test][source]¶ Get a model or a test from the model or test list by name.
- Args:
- name (str): The name of the test or model.
- Raises:
- KeyError: No model or test found by name.
- Returns:
- Union[Model, Test]: The model or test found.
-
get_group
(x: tuple) → Union[sciunit.models.base.Model, sciunit.tests.Test, sciunit.scores.base.Score][source]¶ [summary]
- Args:
- x (tuple): (test, model) or (model, test).
- Raises:
- TypeError: Expected (test, model) or (model, test).
- Returns:
- Union[Model, Test]: (test, model) or (model, test).
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get_model
(model: sciunit.models.base.Model) → sciunit.scores.collections.ScoreArray[source]¶ Generate a ScoreArray instance with all tests and the model.
- Args:
- model (Model): The model that will be included in the ScoreArray instance.
- Returns:
- ScoreArray: The generated ScoreArray instance.
-
get_test
(test: sciunit.tests.Test) → sciunit.scores.collections.ScoreArray[source]¶ Generate a ScoreArray instance with all models and the test.
- Args:
- test (Test): The test that will be included in the ScoreArray instance.
- Returns:
- ScoreArray: The generated ScoreArray instance.
-
norm_scores
¶ Get a DataFrame instance that contains norm scores as a matrix.
- Returns:
- DataFrame: The DataFrame instance that contains norm scores as a matrix.
-
show_mean
= False¶
-
sortable
= False¶
-
stature
(test: sciunit.tests.Test, model: sciunit.models.base.Model) → int[source]¶ Computes the relative rank of a model on a test compared to other models that were asked to take the test.
- Args:
- test (Test): A sciunit test instance. model (Model): A sciunit model instance.
- Returns:
- int: The relative rank of a model on a test
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to_html
(show_mean: bool = None, sortable: bool = None, colorize: bool = True, *args, **kwargs) → str[source]¶ Extend Pandas built in to_html method for rendering a DataFrame and use it to render a ScoreMatrix.
- Args:
- show_mean (bool, optional): Whether to show the mean value. Defaults to None. sortable (bool, optional): [description]. Defaults to None. colorize (bool, optional): Whether to colorize the table. Defaults to True.
- Returns:
- str: [description]
-
sciunit.scores.collections_m2m module¶
Score collections for direct comparison of models against other models.
-
class
sciunit.scores.collections_m2m.
ScoreArrayM2M
(test: sciunit.tests.Test, models: List[sciunit.models.base.Model], scores: List[sciunit.scores.Score])[source]¶ Bases:
pandas.core.series.Series
Represents an array of scores derived from TestM2M. Extends the pandas Series such that items are either models subject to a test or the test itself.
- Attributes:
- index ([type]): [description]
-
__getattr__
(name: str) → Any[source]¶ After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.
-
__init__
(test: sciunit.tests.Test, models: List[sciunit.models.base.Model], scores: List[sciunit.scores.Score])[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
__module__
= 'sciunit.scores.collections_m2m'¶
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get_by_name
(name: str) → str[source]¶ Get item (can be a model, observation, or test) in index by name.
- Args:
- name (str): name of the item.
- Raises:
- KeyError: Item not found.
- Returns:
- Any: Item found.
-
norm_scores
¶ A series of norm scores.
- Returns:
- Series: A series of norm scores.
-
class
sciunit.scores.collections_m2m.
ScoreMatrixM2M
(test: sciunit.tests.Test, models: List[sciunit.models.base.Model], scores: List[sciunit.scores.Score])[source]¶ Bases:
pandas.core.frame.DataFrame
Represents a matrix of scores derived from TestM2M. Extends the pandas DataFrame such that models/observation are both columns and the index.
-
__getattr__
(name: str) → Any[source]¶ After regular attribute access, try looking up the name This allows simpler access to columns for interactive use.
-
__getitem__
(item: Union[Tuple[sciunit.tests.Test, sciunit.models.base.Model], str, Tuple[list, tuple]]) → Any[source]¶
-
__init__
(test: sciunit.tests.Test, models: List[sciunit.models.base.Model], scores: List[sciunit.scores.Score])[source]¶ Initialize self. See help(type(self)) for accurate signature.
-
__module__
= 'sciunit.scores.collections_m2m'¶
-
get_by_name
(name: str) → Union[sciunit.models.base.Model, sciunit.tests.Test][source]¶ Get the model or test from the models or tests by name.
- Args:
- name (str): The name of the model or test.
- Raises:
- KeyError: Raise an exception if there is not a model or test named name.
- Returns:
- Union[Model, Test]: The test or model found.
-
get_group
(x: list) → Any[source]¶ [summary]
- Args:
- x (list): [description]
- Raises:
- TypeError: [description]
- Returns:
- Any: [description]
-
norm_scores
¶ Get a pandas DataFrame instance that contains norm scores.
- Returns:
- DataFrame: A pandas DataFrame instance that contains norm scores.
-
sciunit.scores.complete module¶
Score types for tests that completed successfully.
These include various representations of goodness-of-fit.
-
class
sciunit.scores.complete.
BooleanScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A boolean score, which must be True or False.
-
__module__
= 'sciunit.scores.complete'¶
-
_allowed_types
= (<class 'bool'>,)¶
-
_best
= True¶
-
_description
= 'True if the observation and prediction were sufficiently similar; False otherwise'¶
-
_worst
= False¶
-
classmethod
compute
(observation: dict, prediction: dict) → sciunit.scores.complete.BooleanScore[source]¶ Compute whether the observation equals the prediction.
- Returns:
- BooleanScore: Boolean score of the observation equals the prediction.
-
norm_score
¶ Return 1.0 for a True score and 0.0 for False score.
- Returns:
- float: 1.0 for a True score and 0.0 for False score.
-
-
class
sciunit.scores.complete.
CohenDScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.complete.ZScore
A Cohen’s D score.
A float indicating difference between two means normalized by the pooled standard deviation.
-
__module__
= 'sciunit.scores.complete'¶
-
_best
= 0.0¶
-
_description
= "The Cohen's D between the prediction and the observation"¶
-
_worst
= inf¶
-
-
class
sciunit.scores.complete.
CorrelationScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A correlation score. A float in the range [-1.0, 1.0] representing the correlation coefficient.
-
__module__
= 'sciunit.scores.complete'¶
-
_best
= 1.0¶
-
_check_score
(score)[source]¶ A method for each Score subclass to impose additional constraints on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
-
_description
= 'A correlation of -1.0 shows a perfect negative correlation,while a correlation of 1.0 shows a perfect positive correlation.A correlation of 0.0 shows no linear relationship between the movement of the two variables'¶
-
_worst
= -1.0¶
-
-
class
sciunit.scores.complete.
FloatScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A float score.
A float with any value.
-
__module__
= 'sciunit.scores.complete'¶
-
_allowed_types
= (<class 'float'>, <class 'quantities.quantity.Quantity'>)¶
-
_best
= 0.0¶
-
_check_score
(score)[source]¶ A method for each Score subclass to impose additional constraints on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
-
_description
= 'There is no canonical mapping between this score type and a measure of agreement between the observation and the prediction'¶
-
_worst
= 0.0¶
-
classmethod
compute_ssd
(observation: dict, prediction: dict) → sciunit.scores.base.Score[source]¶ Compute sum-squared diff between observation and prediction.
- Args:
- observation (dict): The observation to be used for computing the sum-squared diff. prediction (dict): The prediction to be used for computing the sum-squared diff.
- Returns:
- Score: The sum-squared diff between observation and prediction.
-
-
class
sciunit.scores.complete.
PercentScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A percent score.
A float in the range [0, 100.0] where higher is better.
-
__module__
= 'sciunit.scores.complete'¶
-
_best
= 100.0¶
-
_check_score
(score)[source]¶ A method for each Score subclass to impose additional constraints on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
-
_description
= '100.0 is considered perfect agreement between the observation and the prediction. 0.0 is the worst possible agreement'¶
-
_worst
= 0.0¶
-
norm_score
¶ Return 1.0 for a percent score of 100, and 0.0 for 0.
- Returns:
- float: 1.0 if the percent score is 100, else 0.0.
-
-
class
sciunit.scores.complete.
RandomScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.complete.FloatScore
A random score in [0,1].
This has no scientific value and should only be used for debugging purposes. For example, one might assign a random score under some error condition to move forward with an application that requires a numeric score, and use the presence of a RandomScore in the output as an indication of an internal error.
-
__module__
= 'sciunit.scores.complete'¶
-
_allowed_types
= (<class 'float'>,)¶
-
_description
= 'There is a random number in [0,1] and has no relation to the prediction or the observation'¶
-
-
class
sciunit.scores.complete.
RatioScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A ratio of two numbers.
Usually the prediction divided by the observation.
-
__module__
= 'sciunit.scores.complete'¶
-
_allowed_types
= (<class 'float'>,)¶
-
_best
= 1.0¶
-
_check_score
(score)[source]¶ A method for each Score subclass to impose additional constraints on the score, e.g. the range of the allowed score.
- Args:
- score (Score): A sciunit score instance.
-
_description
= 'The ratio between the prediction and the observation'¶
-
_worst
= inf¶
-
classmethod
compute
(observation: dict, prediction: dict, key=None) → sciunit.scores.complete.RatioScore[source]¶ Compute a ratio from an observation and a prediction.
- Returns:
- RatioScore: A RatioScore of ratio from an observation and a prediction.
-
norm_score
¶ Return 1.0 for a ratio of 1, falling to 0.0 for extremely small or large values.
- Returns:
- float: The value of the norm score.
-
-
class
sciunit.scores.complete.
ZScore
(score: Union[Score, float, int, quantities.quantity.Quantity], related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A Z score.
A float indicating standardized difference from a reference mean.
-
__module__
= 'sciunit.scores.complete'¶
-
_allowed_types
= (<class 'float'>,)¶
-
_best
= 0.0¶
-
_description
= 'The difference between the means of the observation and prediction divided by the standard deviation of the observation'¶
-
_worst
= inf¶
-
classmethod
compute
(observation: dict, prediction: dict) → sciunit.scores.complete.ZScore[source]¶ Compute a z-score from an observation and a prediction.
- Returns:
- ZScore: The computed Z-Score.
-
norm_score
¶ Return the normalized score.
Equals 1.0 for a z-score of 0, falling to 0.0 for extremely positive or negative values.
-
sciunit.scores.incomplete module¶
Score types for tests that did not complete successfully.
These include details about the various possible reasons that a particular combination of model and test could not be completed.
-
class
sciunit.scores.incomplete.
InsufficientDataScore
(score: sciunit.scores.base.Score, related_data: dict = None)[source]¶ Bases:
sciunit.scores.incomplete.NoneScore
A score returned when the model or test data is insufficient to score the test.
-
__module__
= 'sciunit.scores.incomplete'¶
-
description
= 'Insufficient Data'¶
-
-
class
sciunit.scores.incomplete.
NAScore
(score: sciunit.scores.base.Score, related_data: dict = None)[source]¶ Bases:
sciunit.scores.incomplete.NoneScore
A N/A (not applicable) score.
Indicates that the model doesn’t have the capabilities that the test requires.
-
__module__
= 'sciunit.scores.incomplete'¶
-
description
= 'N/A'¶
-
-
class
sciunit.scores.incomplete.
NoneScore
(score: sciunit.scores.base.Score, related_data: dict = None)[source]¶ Bases:
sciunit.scores.base.Score
A None score.
Usually indicates that the model has not been checked to see if it has the capabilities required by the test.
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__init__
(score: sciunit.scores.base.Score, related_data: dict = None)[source]¶ Abstract base class for scores.
- Args:
- score (Union[‘Score’, float, int, Quantity], bool): A raw value to wrap in a Score class. related_data (dict, optional): Artifacts to store with the score.
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__module__
= 'sciunit.scores.incomplete'¶
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norm_score
¶ Return None as the norm score of this NoneScore instance.
- Returns:
- None: The norm score, which is None.
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class
sciunit.scores.incomplete.
TBDScore
(score: sciunit.scores.base.Score, related_data: dict = None)[source]¶ Bases:
sciunit.scores.incomplete.NoneScore
A TBD (to be determined) score. Indicates that the model has capabilities required by the test but has not yet taken it.
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__module__
= 'sciunit.scores.incomplete'¶
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description
= 'None'¶
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Module contents¶
Contains classes for different representations of test scores.
It also contains score collections such as arrays and matrices.