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tea_tasting.metrics.base #

Base classes for metrics.

AggrCols #

Bases: NamedTuple

Columns to be aggregated for a metric analysis.

Attributes:

Name Type Description
has_count bool

If True, include the sample size.
mean_cols Sequence[str]

Column names for calculation of sample means.
var_cols Sequence[str]

Column names for calculation of sample variances.
cov_cols Sequence[tuple[str, str]]

Pairs of column names for calculation of sample covariances.

MetricBase #

Bases: ABC, Generic[R], ReprMixin

Base class for metrics.

analyze(data, control, treatment, variant) abstractmethod #

Analyze a metric in an experiment.

Parameters:

Name Type Description Default
data IntoFrame | Table

Experimental data.

 required
control object

Control variant.

 required
treatment object

Treatment variant.

 required
variant str

Variant column name.

 required

Returns:

Type Description
R

Analysis result.
Source code in src/tea_tasting/metrics/base.py 
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@abc.abstractmethod
def analyze(
    self,
    data: narwhals.typing.IntoFrame | ibis.expr.types.Table,
    control: object,
    treatment: object,
    variant: str,
) -> R:
    """Analyze a metric in an experiment.

    Args:
        data: Experimental data.
        control: Control variant.
        treatment: Treatment variant.
        variant: Variant column name.

    Returns:
        Analysis result.
    """

MetricBaseAggregated #

Bases: MetricBase[R], _HasAggrCols

Base class for metrics, which are analyzed using aggregated statistics.

aggr_cols: AggrCols abstractmethod property #

Columns to be aggregated for an analysis.

analyze(data, control, treatment, variant=None) #

Analyze a metric in an experiment.

Parameters:

Name Type Description Default
data IntoFrame | Table | dict[object, Aggregates]

Experimental data.

 required
control object

Control variant.

 required
treatment object

Treatment variant.

 required
variant str | None

Variant column name.

 None

Returns:

Type Description
R

Analysis result.
Source code in src/tea_tasting/metrics/base.py 
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def analyze(
    self,
    data: narwhals.typing.IntoFrame | ibis.expr.types.Table | dict[
        object, tea_tasting.aggr.Aggregates],
    control: object,
    treatment: object,
    variant: str | None = None,
) -> R:
    """Analyze a metric in an experiment.

    Args:
        data: Experimental data.
        control: Control variant.
        treatment: Treatment variant.
        variant: Variant column name.

    Returns:
        Analysis result.
    """
    aggr = aggregate_by_variants(
        data,
        aggr_cols=self.aggr_cols,
        variant=variant,
    )
    return self.analyze_aggregates(
        control=aggr[control],
        treatment=aggr[treatment],
    )

analyze_aggregates(control, treatment) abstractmethod #

Analyze metric in an experiment using aggregated statistics.

Parameters:

Name Type Description Default
control Aggregates

Control data.

 required
treatment Aggregates

Treatment data.

 required

Returns:

Type Description
R

Analysis result.
Source code in src/tea_tasting/metrics/base.py 
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@abc.abstractmethod
def analyze_aggregates(
    self,
    control: tea_tasting.aggr.Aggregates,
    treatment: tea_tasting.aggr.Aggregates,
) -> R:
    """Analyze metric in an experiment using aggregated statistics.

    Args:
        control: Control data.
        treatment: Treatment data.

    Returns:
        Analysis result.
    """

MetricBaseGranular #

Bases: MetricBase[R], _HasCols

Base class for metrics, which are analyzed using granular data.

cols: Sequence[str] abstractmethod property #

Columns to be fetched for an analysis.

analyze(data, control, treatment, variant=None) #

Analyze a metric in an experiment.

Parameters:

Name Type Description Default
data IntoFrame | Table | dict[object, Table]

Experimental data.

 required
control object

Control variant.

 required
treatment object

Treatment variant.

 required
variant str | None

Variant column name.

 None

Returns:

Type Description
R

Analysis result.
Source code in src/tea_tasting/metrics/base.py 
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def analyze(
    self,
    data: (
        narwhals.typing.IntoFrame |
        ibis.expr.types.Table |
        dict[object, pa.Table]
    ),
    control: object,
    treatment: object,
    variant: str | None = None,
) -> R:
    """Analyze a metric in an experiment.

    Args:
        data: Experimental data.
        control: Control variant.
        treatment: Treatment variant.
        variant: Variant column name.

    Returns:
        Analysis result.
    """
    dfs = read_granular(
        data,
        cols=self.cols,
        variant=variant,
    )
    return self.analyze_granular(
        control=dfs[control],
        treatment=dfs[treatment],
    )

analyze_granular(control, treatment) abstractmethod #

Analyze metric in an experiment using granular data.

Parameters:

Name Type Description Default
control Table

Control data.

 required
treatment Table

Treatment data.

 required

Returns:

Type Description
R

Analysis result.
Source code in src/tea_tasting/metrics/base.py 
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@abc.abstractmethod
def analyze_granular(
    self,
    control: pa.Table,
    treatment: pa.Table,
) -> R:
    """Analyze metric in an experiment using granular data.

    Args:
        control: Control data.
        treatment: Treatment data.

    Returns:
        Analysis result.
    """

MetricPowerResults #

Bases: UserList[P], DictsReprMixin

Power analysis results.

to_arrow() #

Convert the object to a PyArrow Table.

Source code in src/tea_tasting/utils.py 
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def to_arrow(self) -> pa.Table:
    """Convert the object to a PyArrow Table."""
    return pa.Table.from_pylist(self.to_dicts())

to_dicts() #

"Convert the results to a sequence of dictionaries.

Source code in src/tea_tasting/metrics/base.py 
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def to_dicts(self) -> tuple[dict[str, object], ...]:
    """"Convert the results to a sequence of dictionaries."""
    return tuple((v if isinstance(v, dict) else v._asdict()) for v in self)

to_html(keys=None, formatter=get_and_format_num, *, indent=None) #

Convert the object to HTML.

Default formatting rules:

  • If a name starts with "rel_" or equals to "power" consider it a percentage value. Round percentage values to 2 significant digits, multiply by 100 and add "%".
  • Round other values to 3 significant values.
  • If value is less than 0.001 or is greater than or equal to 10_000_000, format it in exponential presentation.
  • If a name ends with "_ci", consider it a confidence interval. Look up for attributes "{name}_lower" and "{name}_upper", and format the interval as "[{lower_bound}, {upper_bound}]".

Parameters:

Name Type Description Default
keys Sequence[str] | None

Keys to convert. If a key is not defined in the dictionary it's assumed to be None.

 None
formatter Callable[[dict[str, object], str], str]

Custom formatter function. It should accept a dictionary of metric result attributes and an attribute name, and return a formatted attribute value.

 get_and_format_num
indent str | None

Whitespace to insert for each indentation level. If None, do not indent.

 None

Returns:

Type Description
str

A table with results rendered as HTML.
Source code in src/tea_tasting/utils.py 
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def to_html(
    self,
    keys: Sequence[str] | None = None,
    formatter: Callable[[dict[str, object], str], str] = get_and_format_num,
    *,
    indent: str | None = None,
) -> str:
    """Convert the object to HTML.

    Default formatting rules:

    - If a name starts with `"rel_"` or equals to `"power"` consider it
        a percentage value. Round percentage values to 2 significant digits,
        multiply by `100` and add `"%"`.
    - Round other values to 3 significant values.
    - If value is less than `0.001` or is greater than or equal to `10_000_000`,
        format it in exponential presentation.
    - If a name ends with `"_ci"`, consider it a confidence interval.
        Look up for attributes `"{name}_lower"` and `"{name}_upper"`,
        and format the interval as `"[{lower_bound}, {upper_bound}]"`.

    Args:
        keys: Keys to convert. If a key is not defined in the dictionary
            it's assumed to be `None`.
        formatter: Custom formatter function. It should accept a dictionary
            of metric result attributes and an attribute name, and return
            a formatted attribute value.
        indent: Whitespace to insert for each indentation level. If `None`,
            do not indent.

    Returns:
        A table with results rendered as HTML.
    """
    if keys is None:
        keys = self.default_keys
    table = ET.Element(
        "table",
        {"class": "dataframe", "style": "text-align: right;"},
    )
    thead = ET.SubElement(table, "thead")
    thead_tr = ET.SubElement(thead, "tr")
    for key in keys:
        th = ET.SubElement(thead_tr, "th")
        th.text = key
    tbody = ET.SubElement(table, "tbody")
    for data in self.to_dicts():
        tr = ET.SubElement(tbody, "tr")
        for key in keys:
            td = ET.SubElement(tr, "td")
            td.text = formatter(data, key)
    if indent is not None:
        ET.indent(table, space=indent)
    return ET.tostring(table, encoding="unicode", method="html")

to_pandas() #

Convert the object to a Pandas DataFrame.

Source code in src/tea_tasting/utils.py 
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def to_pandas(self) -> PandasDataFrame:
    """Convert the object to a Pandas DataFrame."""
    import pandas as pd
    return pd.DataFrame.from_records(self.to_dicts())

to_polars() #

Convert the object to a Polars DataFrame.

Source code in src/tea_tasting/utils.py 
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def to_polars(self) -> PolarsDataFrame:
    """Convert the object to a Polars DataFrame."""
    import polars as pl
    return pl.from_dicts(self.to_dicts())

to_pretty_dicts(keys=None, formatter=get_and_format_num) #

Convert the object to a list of dictionaries with formatted values.

Default formatting rules:

  • If a name starts with "rel_" or equals to "power" consider it a percentage value. Round percentage values to 2 significant digits, multiply by 100 and add "%".
  • Round other values to 3 significant values.
  • If value is less than 0.001 or is greater than or equal to 10_000_000, format it in exponential presentation.
  • If a name ends with "_ci", consider it a confidence interval. Look up for attributes "{name}_lower" and "{name}_upper", and format the interval as "[{lower_bound}, {upper_bound}]".

Parameters:

Name Type Description Default
keys Sequence[str] | None

Keys to convert. If a key is not defined in the dictionary it's assumed to be None.

 None
formatter Callable[[dict[str, object], str], str]

Custom formatter function. It should accept a dictionary of metric result attributes and an attribute name, and return a formatted attribute value.

 get_and_format_num

Returns:

Type Description
list[dict[str, str]]

List of dictionaries with formatted values.
Source code in src/tea_tasting/utils.py 
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def to_pretty_dicts(
    self,
    keys: Sequence[str] | None = None,
    formatter: Callable[[dict[str, object], str], str] = get_and_format_num,
) -> list[dict[str, str]]:
    """Convert the object to a list of dictionaries with formatted values.

    Default formatting rules:

    - If a name starts with `"rel_"` or equals to `"power"` consider it
        a percentage value. Round percentage values to 2 significant digits,
        multiply by `100` and add `"%"`.
    - Round other values to 3 significant values.
    - If value is less than `0.001` or is greater than or equal to `10_000_000`,
        format it in exponential presentation.
    - If a name ends with `"_ci"`, consider it a confidence interval.
        Look up for attributes `"{name}_lower"` and `"{name}_upper"`,
        and format the interval as `"[{lower_bound}, {upper_bound}]"`.

    Args:
        keys: Keys to convert. If a key is not defined in the dictionary
            it's assumed to be `None`.
        formatter: Custom formatter function. It should accept a dictionary
            of metric result attributes and an attribute name, and return
            a formatted attribute value.

    Returns:
        List of dictionaries with formatted values.
    """
    if keys is None:
        keys = self.default_keys
    return [{key: formatter(data, key) for key in keys} for data in self.to_dicts()]

to_string(keys=None, formatter=get_and_format_num) #

Convert the object to a string.

Default formatting rules:

  • If a name starts with "rel_" or equals to "power" consider it a percentage value. Round percentage values to 2 significant digits, multiply by 100 and add "%".
  • Round other values to 3 significant values.
  • If value is less than 0.001 or is greater than or equal to 10_000_000, format it in exponential presentation.
  • If a name ends with "_ci", consider it a confidence interval. Look up for attributes "{name}_lower" and "{name}_upper", and format the interval as "[{lower_bound}, {upper_bound}]".

Parameters:

Name Type Description Default
keys Sequence[str] | None

Keys to convert. If a key is not defined in the dictionary it's assumed to be None.

 None
formatter Callable[[dict[str, object], str], str]

Custom formatter function. It should accept a dictionary of metric result attributes and an attribute name, and return a formatted attribute value.

 get_and_format_num

Returns:

Type Description
str

A table with results rendered as string.
Source code in src/tea_tasting/utils.py 
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def to_string(
    self,
    keys: Sequence[str] | None = None,
    formatter: Callable[[dict[str, object], str], str] = get_and_format_num,
) -> str:
    """Convert the object to a string.

    Default formatting rules:

    - If a name starts with `"rel_"` or equals to `"power"` consider it
        a percentage value. Round percentage values to 2 significant digits,
        multiply by `100` and add `"%"`.
    - Round other values to 3 significant values.
    - If value is less than `0.001` or is greater than or equal to `10_000_000`,
        format it in exponential presentation.
    - If a name ends with `"_ci"`, consider it a confidence interval.
        Look up for attributes `"{name}_lower"` and `"{name}_upper"`,
        and format the interval as `"[{lower_bound}, {upper_bound}]"`.

    Args:
        keys: Keys to convert. If a key is not defined in the dictionary
            it's assumed to be `None`.
        formatter: Custom formatter function. It should accept a dictionary
            of metric result attributes and an attribute name, and return
            a formatted attribute value.

    Returns:
        A table with results rendered as string.
    """
    if keys is None:
        keys = self.default_keys
    widths = {key: len(key) for key in keys}

    pretty_dicts = []
    for data in self.to_dicts():
        pretty_dict = {}
        for key in keys:
            val = formatter(data, key)
            widths[key] = max(widths[key], len(val))
            pretty_dict |= {key: val}
        pretty_dicts.append(pretty_dict)

    sep = " "
    rows = [sep.join(key.rjust(widths[key]) for key in keys)]
    rows.extend(
        sep.join(pretty_dict[key].rjust(widths[key]) for key in keys)
        for pretty_dict in pretty_dicts
    )
    return "\n".join(rows)

PowerBase #

Bases: ABC, Generic[S], ReprMixin

Base class for the analysis of power.

solve_power(data, parameter='rel_effect_size') abstractmethod #

Solve for a parameter of the power of a test.

Parameters:

Name Type Description Default
data IntoFrame | Table

Sample data.

 required
parameter Literal['power', 'effect_size', 'rel_effect_size', 'n_obs']

Parameter name.

 'rel_effect_size'

Returns:

Type Description
S

Power analysis result.
Source code in src/tea_tasting/metrics/base.py 
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@abc.abstractmethod
def solve_power(
    self,
    data: narwhals.typing.IntoFrame | ibis.expr.types.Table,
    parameter: Literal[
        "power", "effect_size", "rel_effect_size", "n_obs"] = "rel_effect_size",
) -> S:
    """Solve for a parameter of the power of a test.

    Args:
        data: Sample data.
        parameter: Parameter name.

    Returns:
        Power analysis result.
    """

PowerBaseAggregated #

Bases: PowerBase[S], _HasAggrCols

Base class for the analysis of power using aggregated statistics.

aggr_cols: AggrCols abstractmethod property #

Columns to be aggregated for an analysis.

solve_power(data, parameter='rel_effect_size') #

Solve for a parameter of the power of a test.

Parameters:

Name Type Description Default
data IntoFrame | Table | Aggregates

Sample data.

 required
parameter Literal['power', 'effect_size', 'rel_effect_size', 'n_obs']

Parameter name.

 'rel_effect_size'

Returns:

Type Description
S

Power analysis result.
Source code in src/tea_tasting/metrics/base.py 
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def solve_power(
    self,
    data: (
        narwhals.typing.IntoFrame |
        ibis.expr.types.Table |
        tea_tasting.aggr.Aggregates
    ),
    parameter: Literal[
        "power", "effect_size", "rel_effect_size", "n_obs"] = "rel_effect_size",
) -> S:
    """Solve for a parameter of the power of a test.

    Args:
        data: Sample data.
        parameter: Parameter name.

    Returns:
        Power analysis result.
    """
    if not isinstance(data, tea_tasting.aggr.Aggregates):
        data = tea_tasting.aggr.read_aggregates(
            data=data,
            group_col=None,
            **self.aggr_cols._asdict(),
        )
    return self.solve_power_from_aggregates(data=data, parameter=parameter)

solve_power_from_aggregates(data, parameter='rel_effect_size') abstractmethod #

Solve for a parameter of the power of a test.

Parameters:

Name Type Description Default
data Aggregates

Sample data.

 required
parameter Literal['power', 'effect_size', 'rel_effect_size', 'n_obs']

Parameter name.

 'rel_effect_size'

Returns:

Type Description
S

Power analysis result.
Source code in src/tea_tasting/metrics/base.py 
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@abc.abstractmethod
def solve_power_from_aggregates(
    self,
    data: tea_tasting.aggr.Aggregates,
    parameter: Literal[
        "power", "effect_size", "rel_effect_size", "n_obs"] = "rel_effect_size",
) -> S:
    """Solve for a parameter of the power of a test.

    Args:
        data: Sample data.
        parameter: Parameter name.

    Returns:
        Power analysis result.
    """

aggregate_by_variants(data, aggr_cols, variant=None) #

Aggregate experimental data by variants.

Parameters:

Name Type Description Default
data IntoFrame | Table | dict[object, Aggregates]

Experimental data.

 required
aggr_cols AggrCols

Columns to be aggregated.

 required
variant str | None

Variant column name.

 None

Returns:

Type Description
dict[object, Aggregates]

Experimental data as a dictionary of Aggregates.
Source code in src/tea_tasting/metrics/base.py 
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def aggregate_by_variants(
    data: (
        narwhals.typing.IntoFrame |
        ibis.expr.types.Table |
        dict[object, tea_tasting.aggr.Aggregates]
    ),
    aggr_cols: AggrCols,
    variant: str | None = None,
) ->  dict[object, tea_tasting.aggr.Aggregates]:
    """Aggregate experimental data by variants.

    Args:
        data: Experimental data.
        aggr_cols: Columns to be aggregated.
        variant: Variant column name.

    Returns:
        Experimental data as a dictionary of Aggregates.
    """
    if isinstance(data, dict) and all(
        isinstance(v, tea_tasting.aggr.Aggregates) for v in data.values()  # type: ignore
    ):
        return data

    if variant is None:
        raise ValueError("The variant parameter is required but was not provided.")

    return tea_tasting.aggr.read_aggregates(
        data=data,  # type: ignore
        group_col=variant,
        **aggr_cols._asdict(),
    )

read_granular(data, cols, variant=None) #

Read granular experimental data.

Parameters:

Name Type Description Default
data IntoFrame | Table | dict[object, Table]

Experimental data.

 required
cols Sequence[str]

Columns to read.

 required
variant str | None

Variant column name.

 None

Returns:

Type Description
dict[object, Table]

Experimental data as a dictionary of PyArrow Tables.
Source code in src/tea_tasting/metrics/base.py 
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def read_granular(
    data: narwhals.typing.IntoFrame | ibis.expr.types.Table | dict[object, pa.Table],
    cols: Sequence[str],
    variant: str | None = None,
) -> dict[object, pa.Table]:
    """Read granular experimental data.

    Args:
        data: Experimental data.
        cols: Columns to read.
        variant: Variant column name.

    Returns:
        Experimental data as a dictionary of PyArrow Tables.
    """
    if isinstance(data, dict) and all(
        isinstance(v, pa.Table) for v in data.values()
    ):
        return data

    if variant is None:
        raise ValueError("The variant parameter is required but was not provided.")

    if isinstance(data, ibis.expr.types.Table):
        table = data.select(*cols, variant).to_pyarrow()
    else:
        data = nw.from_native(data)
        if not isinstance(data, nw.LazyFrame):
            data = data.lazy()
        table = data.select(*cols, variant).collect().to_arrow()

    variant_col = table[variant]
    table = table.select(cols)
    return {
        var: table.filter(pc.equal(variant_col, pa.scalar(var)))  # type: ignore
        for var in variant_col.unique().to_pylist()
    }