Log-Normal distribution GoF criteria

pysatl_criterion/statistics/__init__.py

@@ -33,6 +33,48 @@
     WeExponentialityGofStatistic,
     WongWongExponentialityGofStatistic,
 )
+from pysatl_criterion.statistics.log_normal import (  # type: ignore[attr-defined]
+    AbstractLogNormalGofStatistic,
+    AndersonDarlingLogNormalGofStatistic,
+    BonettSeierLogNormalGofStatistic,
+    BontempsMeddahi1LogNormalGofStatistic,
+    BontempsMeddahi2LogNormalGofStatistic,
+    CabanaCabana1LogNormalGofStatistic,
+    CabanaCabana2LogNormalGofStatistic,
+    ChenShapiroLogNormalGofStatistic,
+    CoinLogNormalGofStatistic,
+    CramerVonMiseLogNormalGofStatistic,
+    DagostinoLogNormalGofStatistic,
+    DAPLogNormalGofStatistic,
+    DesgagneLafayeLogNormalGofStatistic,
+    DoornikHansenLogNormalGofStatistic,
+    EppsPulleyLogNormalGofStatistic,
+    FilliLogNormalGofStatistic,
+    GlenLeemisBarrLogNormalGofStatistic,
+    GMGLogNormalGofStatistic,
+    Hosking1LogNormalGofStatistic,
+    Hosking2LogNormalGofStatistic,
+    Hosking3LogNormalGofStatistic,
+    Hosking4LogNormalGofStatistic,
+    JBLogNormalGofStatistic,
+    KolmogorovSmirnovLogNormalGofStatistic,
+    KurtosisLogNormalGofStatistic,
+    LillieforsLogNormalGofStatistic,
+    LooneyGulledgeLogNormalGofStatistic,
+    MartinezIglewiczLogNormalGofStatistic,
+    QuesenberryMillerLogNormalGofStatistic,
+    RobustJarqueBeraLogNormalGofStatistic,
+    RyanJoinerLogNormalGofStatistic,
+    SFLogNormalGofStatistic,
+    ShapiroWilkLogNormalGofStatistic,
+    SkewLogNormalGofStatistic,
+    SpiegelhalterLogNormalGofStatistic,
+    SWRGLogNormalGofStatistic,
+    ZhangQLogNormalGofStatistic,
+    ZhangQStarLogNormalGofStatistic,
+    ZhangWuALogNormalGofStatistic,
+    ZhangWuCLogNormalGofStatistic,
+)
 from pysatl_criterion.statistics.models import AbstractStatistic
 from pysatl_criterion.statistics.normal import (
     AbstractNormalityGofStatistic,
@@ -196,4 +238,44 @@
     "ZhangQStarNormalityGofStatistic",
     "ZhangWuANormalityGofStatistic",
     "ZhangWuCNormalityGofStatistic",
+    "AbstractLogNormalGofStatistic",
+    "AndersonDarlingLogNormalGofStatistic",
+    "BonettSeierLogNormalGofStatistic",
+    "BontempsMeddahi1LogNormalGofStatistic",
+    "BontempsMeddahi2LogNormalGofStatistic",
+    "CabanaCabana1LogNormalGofStatistic",
+    "CabanaCabana2LogNormalGofStatistic",
+    "ChenShapiroLogNormalGofStatistic",
+    "CoinLogNormalGofStatistic",
+    "CramerVonMiseLogNormalGofStatistic",
+    "DagostinoLogNormalGofStatistic",
+    "DAPLogNormalGofStatistic",
+    "DesgagneLafayeLogNormalGofStatistic",
+    "DoornikHansenLogNormalGofStatistic",
+    "EppsPulleyLogNormalGofStatistic",
+    "FilliLogNormalGofStatistic",
+    "GlenLeemisBarrLogNormalGofStatistic",
+    "GMGLogNormalGofStatistic",
+    "Hosking1LogNormalGofStatistic",
+    "Hosking2LogNormalGofStatistic",
+    "Hosking3LogNormalGofStatistic",
+    "Hosking4LogNormalGofStatistic",
+    "JBLogNormalGofStatistic",
+    "KolmogorovSmirnovLogNormalGofStatistic",
+    "KurtosisLogNormalGofStatistic",
+    "LillieforsLogNormalGofStatistic",
+    "LooneyGulledgeLogNormalGofStatistic",
+    "MartinezIglewiczLogNormalGofStatistic",
+    "QuesenberryMillerLogNormalGofStatistic",
+    "RobustJarqueBeraLogNormalGofStatistic",
+    "RyanJoinerLogNormalGofStatistic",
+    "SFLogNormalGofStatistic",
+    "ShapiroWilkLogNormalGofStatistic",
+    "SkewLogNormalGofStatistic",
+    "SpiegelhalterLogNormalGofStatistic",
+    "SWRGLogNormalGofStatistic",
+    "ZhangQLogNormalGofStatistic",
+    "ZhangQStarLogNormalGofStatistic",
+    "ZhangWuALogNormalGofStatistic",
+    "ZhangWuCLogNormalGofStatistic",
 ]

pysatl_criterion/statistics/log_normal.py

@@ -0,0 +1,173 @@
+import inspect
+import sys
+
+import numpy as np
+import scipy.stats as scipy_stats
+from typing_extensions import override
+
+from pysatl_criterion.statistics import normal
+from pysatl_criterion.statistics.common import CrammerVonMisesStatistic, KSStatistic
+from pysatl_criterion.statistics.goodness_of_fit import AbstractGoodnessOfFitStatistic
+from pysatl_criterion.statistics.normal import AbstractNormalityGofStatistic
+
+
+class AbstractLogNormalGofStatistic(AbstractGoodnessOfFitStatistic):
+    """
+    Base class for Log-Normal distribution Goodness-of-Fit statistics.
+    """
+
+    def __init__(self, s=1, scale=1):
+        """
+        :param s: shape parameter (sigma)
+        :param scale: scale parameter (exp(mu))
+        """
+        if s <= 0:
+            raise ValueError("Shape parameter s must be positive")
+        if scale <= 0:
+            raise ValueError("Scale parameter must be positive")
+
+        self.s = s
+        self.scale = scale
+
+    @staticmethod
+    @override
+    def code():
+        return f"LOGNORMAL_{AbstractGoodnessOfFitStatistic.code()}"
+
+
+# =================================================================================================
+# EXPLICIT IMPLEMENTATIONS
+# =================================================================================================
+
+
+class KolmogorovSmirnovLogNormalGofStatistic(AbstractLogNormalGofStatistic, KSStatistic):
+    @override
+    def __init__(self, alternative="two-sided", mode="auto", s=1, scale=1):
+        AbstractLogNormalGofStatistic.__init__(self, s=s, scale=scale)
+        KSStatistic.__init__(self, alternative, mode)
+
+    @staticmethod
+    @override
+    def code():
+        return f"KS_{AbstractLogNormalGofStatistic.code()}"
+
+    @override
+    def execute_statistic(self, rvs, **kwargs):
+        rvs = np.sort(rvs)
+        cdf_vals = scipy_stats.lognorm.cdf(rvs, s=self.s, scale=self.scale)
+        return KSStatistic.execute_statistic(self, rvs, cdf_vals)
+
+
+class CramerVonMiseLogNormalGofStatistic(AbstractLogNormalGofStatistic, CrammerVonMisesStatistic):
+    @staticmethod
+    @override
+    def code():
+        return f"CVM_{AbstractLogNormalGofStatistic.code()}"
+
+    @override
+    def execute_statistic(self, rvs, **kwargs):
+        rvs_sorted = np.sort(rvs)
+        cdf_vals = scipy_stats.lognorm.cdf(rvs_sorted, s=self.s, scale=self.scale)
+        return super().execute_statistic(rvs, cdf_vals)
+
+
+class QuesenberryMillerLogNormalGofStatistic(AbstractLogNormalGofStatistic):
+    """
+    Quesenberry and Miller's Q-test for Log-Normal distribution.
+
+    References
+    ----------
+    .. [1] Quesenberry, C. P., & Miller Jr, F. L. (1977).
+           Power studies of some tests for uniformity.
+           Journal of Statistical Computation and Simulation, 5, 169-191.
+    """
+
+    @staticmethod
+    @override
+    def code():
+        return f"QUESENBERRY_MILLER_{AbstractLogNormalGofStatistic.code()}"
+
+    @override
+    def execute_statistic(self, rvs, **kwargs):
+        rvs = np.asarray(rvs)
+
+        # Transform data to [0, 1] using the CDF of the Log-Normal distribution
+        cdf_vals = scipy_stats.lognorm.cdf(rvs, s=self.s, scale=self.scale)
+
+        x_sorted = np.sort(cdf_vals)
+
+        # Add boundaries 0 and 1 because we are in the probability space [0, 1]
+        x_with_boundaries = np.concatenate([[0.0], x_sorted, [1.0]])
+
+        spacings = np.diff(x_with_boundaries)
+
+        sum_squares = np.sum(spacings**2)
+
+        sum_consecutive_products = np.sum(spacings[:-1] * spacings[1:])
+
+        q = float(sum_squares) + float(sum_consecutive_products)
+
+        return q
+
+
+# =================================================================================================
+# DYNAMIC GENERATION
+# =================================================================================================
+
+
+def _create_lognormal_class(normal_cls):
+    new_class_name = normal_cls.__name__.replace("Normality", "LogNormal")
+    class_code = normal_cls.code().replace("NORMALITY", "LOGNORMAL")
+
+    class LogNormalClass(AbstractLogNormalGofStatistic):
+        @override
+        def execute_statistic(self, rvs, **kwargs):
+            rvs_arr = np.array(rvs)
+
+            if np.any(rvs_arr <= 0):
+                return float("inf")
+
+            log_rvs = np.log(rvs_arr)
+            standardized_log_rvs = (log_rvs - np.log(self.scale)) / self.s
+
+            return normal_cls().execute_statistic(standardized_log_rvs, **kwargs)
+
+        @staticmethod
+        @override
+        def code():
+            return class_code
+
+    LogNormalClass.__name__ = new_class_name
+    LogNormalClass.__qualname__ = new_class_name
+    return LogNormalClass
+
+
+# List of Normal statistics that we have explicitly implemented for LogNormal above.
+# We should NOT generate dynamic wrappers for these.
+EXPLICITLY_IMPLEMENTED_NORMAL_STATS = [
+    "KolmogorovSmirnovNormalityGofStatistic",
+    "CramerVonMiseNormalityGofStatistic",
+    "QuesenberryMillerNormalityGofStatistic",
+    "BHSNormalityGofStatistic",
+]
+
+current_module = sys.modules[__name__]
+__all__ = [
+    "AbstractLogNormalGofStatistic",
+    "KolmogorovSmirnovLogNormalGofStatistic",
+    "CramerVonMiseLogNormalGofStatistic",
+    "QuesenberryMillerLogNormalGofStatistic",
+]
+
+for name, obj in inspect.getmembers(normal):
+    if (
+        inspect.isclass(obj)
+        and issubclass(obj, AbstractNormalityGofStatistic)
+        and obj is not AbstractNormalityGofStatistic
+        and name not in EXPLICITLY_IMPLEMENTED_NORMAL_STATS
+        and not name.startswith("Abstract")
+        and not name.startswith("Graph")
+    ):
+        ln_class = _create_lognormal_class(obj)
+        setattr(current_module, ln_class.__name__, ln_class)
+        __all__.append(ln_class.__name__)