Feature distribution changes - migrated tests to Pytest, use of abstract base classes (#277)

* added use of ABC, refactored tests

* wip

* fixed base method in Distrubution

Author: ronanstokes-db

CHANGELOG.md

@@ -8,6 +8,8 @@ All notable changes to the Databricks Labs Data Generator will be documented in
 ### Changed
 * Modified data generator to allow specification of constraints to the data generation process
 * Updated documentation for generating text data.
+* Modified data distribiutions to use abstract base classes
+* migrated data distribution tests to use `pytest`
 
 ### Added
 * Added classes for constraints on the data generation via new package `dbldatagen.constraints`

dbldatagen/distributions/data_distribution.py

@@ -20,12 +20,15 @@
 and no further scaling is needed.
 """
 import copy
-import pyspark.sql.functions as F
+from abc import ABC, abstractmethod
+
 import numpy as np
+import pyspark.sql.functions as F
 
 
-class DataDistribution(object):
+class DataDistribution(ABC):
     """ Base class for all distributions"""
+
     def __init__(self):
         self._rounding = False
         self._randomSeed = None
@@ -37,8 +40,8 @@ def get_np_random_generator(random_seed):
         :param random_seed: Numeric random seed to use. If < 0, then no random
         :return:
         """
-        assert random_seed is None or type(random_seed) in [ np.int32, np.int64, int],\
-               f"`randomSeed` must be int or int-like not {type(random_seed)}"
+        assert random_seed is None or type(random_seed) in [np.int32, np.int64, int], \
+            f"`randomSeed` must be int or int-like not {type(random_seed)}"
         from numpy.random import default_rng
         if random_seed not in (-1, -1.0):
             rng = default_rng(random_seed)
@@ -47,17 +50,17 @@ def get_np_random_generator(random_seed):
 
         return rng
 
+    @abstractmethod
     def generateNormalizedDistributionSample(self):
         """ Generate sample of data for distribution
 
         :return: random samples from distribution scaled to values between 0 and 1
+
+        Note implementors should provide implementation for this,
+
+        Return value is expected to be a Pyspark SQL column expression such as F.expr("rand()")
         """
-        if self.randomSeed == -1 or self.randomSeed is None:
-            newDef = F.expr("rand()")
-        else:
-            assert type(self.randomSeed) in [int, float],  "random seed should be numeric"
-            newDef = F.expr(f"rand({self.randomSeed})")
-        return newDef
+        pass
 
     def withRounding(self, rounding):
         """ Create copy of object and set the rounding attribute