Analyzer

analora.analyzer

Contain analyzers.

analora.analyzer.AccuracyAnalyzer

Bases: BaseTruePredAnalyzer

Implement the accuracy analyzer.

Parameters:

Name	Type	Description	Default
y_true	str	The key of the ground truth target labels.	required
y_pred	str	The key of the predicted labels.	required
missing_policy	str	The policy on how to handle missing keys. The following options are available: 'ignore', 'warn', and 'raise'. If 'raise', an exception is raised if at least one key is missing. If 'warn', a warning is raised if at least one key is missing and the missing keys are ignored. If 'ignore', the missing keys are ignored and no warning message appears.	'raise'
nan_policy	str	The policy on how to handle NaN values in the input arrays. The following options are available: 'omit', 'propagate', and 'raise'.	'propagate'

Example usage:

>>> from analora.analyzer import AccuracyAnalyzer
>>> analyzer = AccuracyAnalyzer(y_true="target", y_pred="pred")
>>> analyzer
AccuracyAnalyzer(y_true='target', y_pred='pred', missing_policy='raise', nan_policy='propagate')
>>> data = {"pred": [3, 2, 0, 1, 0, 1], "target": [3, 2, 0, 1, 0, 1]}
>>> output = analyzer.analyze(data)
>>> output
Output(
  (content): ContentGenerator()
  (evaluator): AccuracyEvaluator(
      (state): AccuracyState(y_true=(6,), y_pred=(6,), y_true_name='target', y_pred_name='pred', nan_policy='propagate')
    )
)

analora.analyzer.BaseAnalyzer

Bases: ABC, Generic[T]

Define the base class to analyze a DataFrame.

Example usage:

# >>> import polars as pl
# >>> from analora.analyzer import AccuracyAnalyzer
# >>> analyzer = AccuracyAnalyzer(y_true="target", y_pred="pred")
# >>> analyzer
# AccuracyAnalyzer(y_true='target', y_pred='pred', drop_nulls=True, missing_policy='raise', nan_policy='propagate')
# >>> data = pl.DataFrame({"pred": [3, 2, 0, 1, 0, 1], "target": [3, 2, 0, 1, 0, 1]})
# >>> output = analyzer.analyze(data)
# >>> output
# AccuracyOutput(
#   (state): AccuracyState(y_true=(6,), y_pred=(6,), y_true_name='target', y_pred_name='pred', nan_policy='propagate')
# )

analora.analyzer.BaseAnalyzer.analyze abstractmethod

analyze(data: T, lazy: bool = True) -> BaseOutput

Analyze the data.

Parameters:

Name	Type	Description	Default
data	T	The data to analyze.	required
lazy	bool	If True, it forces the computation of the output, otherwise it returns an output object that contains the logic.	True

Returns:

Type	Description
BaseOutput	The generated output.

Example usage:

# >>> import polars as pl
# >>> from analora.analyzer import AccuracyAnalyzer
# >>> analyzer = AccuracyAnalyzer(y_true="target", y_pred="pred")
# >>> data = pl.DataFrame({"pred": [3, 2, 0, 1, 0, 1], "target": [3, 2, 0, 1, 0, 1]})
# >>> output = analyzer.analyze(data)
# >>> output
# AccuracyOutput(
#   (state): AccuracyState(y_true=(6,), y_pred=(6,), y_true_name='target', y_pred_name='pred', nan_policy='propagate')
# )

analora.analyzer.BaseLazyAnalyzer

Bases: BaseAnalyzer[T]

Define a base class to implement a lazy analyzer.

Example usage:

# >>> import polars as pl
# >>> from analora.analyzer import SummaryAnalyzer
# >>> analyzer = SummaryAnalyzer()
# >>> analyzer
# SummaryAnalyzer(columns=None, exclude_columns=(), missing_policy='raise', top=5)
# >>> frame = pl.DataFrame(
# ...     {
# ...         "col1": [0, 1, 1, 0, 0, 1, 0],
# ...         "col2": [0, 1, 0, 1, 0, 1, 0],
# ...         "col3": [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0],
# ...     },
# ...     schema={"col1": pl.Int64, "col2": pl.Int32, "col3": pl.Float64},
# ... )
# >>> output = analyzer.analyze(frame)
# >>> output
# SummaryOutput(
#   (state): DataFrameState(dataframe=(7, 3), nan_policy='propagate', figure_config=MatplotlibFigureConfig(), top=5)
# )

analora.analyzer.BaseTruePredAnalyzer

Bases: BaseLazyAnalyzer[T]

Define a base class to implement a data analyzer that takes two input keys: y_true and y_pred.

Parameters:

Name	Type	Description	Default
y_true	str	The key name of the ground truth target labels.	required
y_pred	str	The key name of the predicted labels.	required
missing_policy	str	The policy on how to handle missing keys. The following options are available: 'ignore', 'warn', and 'raise'. If 'raise', an exception is raised if at least one key is missing. If 'warn', a warning is raised if at least one key is missing and the missing keys are ignored. If 'ignore', the missing keys are ignored and no warning message appears.	required
nan_policy	str	The policy on how to handle NaN values in the input arrays. The following options are available: 'omit', 'propagate', and 'raise'.	required

analora.analyzer.ContentAnalyzer

Bases: BaseLazyAnalyzer[Any]

Implement an analyzer that generates an output with the given custom content.

Parameters:

Name	Type	Description	Default
content	str	The content to use in the HTML code.	required

Example usage:

>>> from analora.analyzer import ContentAnalyzer
>>> analyzer = ContentAnalyzer(content="meow")
>>> analyzer
ContentAnalyzer()
>>> data = {"pred": [3, 2, 0, 1, 0, 1], "target": [3, 2, 0, 1, 0, 1]}
>>> output = analyzer.analyze(data)
>>> output
Output(
  (content): ContentGenerator()
  (evaluator): Evaluator(count=0)
)

analora.analyzer.is_analyzer_config

is_analyzer_config(config: dict) -> bool

Indicate if the input configuration is a configuration for a BaseAnalyzer.

This function only checks if the value of the key _target_ is valid. It does not check the other values. If _target_ indicates a function, the returned type hint is used to check the class.

Parameters:

Name	Type	Description	Default
config	dict	The configuration to check.	required

Returns:

Type	Description
bool	True if the input configuration is a configuration for a BaseAnalyzer object.

Example usage:

# >>> from analora.analyzer import is_analyzer_config
# >>> is_analyzer_config({"_target_": "analora.analyzer.AccuracyAnalyzer"})
# True

analora.analyzer.setup_analyzer

setup_analyzer(
    analyzer: BaseAnalyzer | dict,
) -> BaseAnalyzer

Set up an analyzer.

The analyzer is instantiated from its configuration by using the BaseAnalyzer factory function.

Parameters:

Name	Type	Description	Default
analyzer	BaseAnalyzer | dict	An analyzer or its configuration.	required

Returns:

Type	Description
BaseAnalyzer	An instantiated analyzer.

Example usage:

# >>> from analora.analyzer import setup_analyzer
# >>> analyzer = setup_analyzer(
# ...     {
# ...         "_target_": "analora.analyzer.AccuracyAnalyzer",
# ...         "y_true": "target",
# ...         "y_pred": "pred",
# ...     }
# ... )
# >>> analyzer
# AccuracyAnalyzer(y_true='target', y_pred='pred', drop_nulls=True, missing_policy='raise', nan_policy='propagate')