allow parameter min_sup between 0 and 1

sliceline/slicefinder.py

@@ -2,7 +2,7 @@
 The slicefinder module implements the Slicefinder class.
 """
 import logging
-from typing import Tuple
+from typing import Tuple, Union
 
 import numpy as np
 from scipy import sparse as sp
@@ -52,9 +52,10 @@ class Slicefinder(BaseEstimator, TransformerMixin):
         Maximum lattice level.
         In other words: the maximum number of predicate to define a slice.
 
-    min_sup: int, default=10
+    min_sup: int or float, default=10
         Minimum support threshold.
         Inspired by frequent itemset mining, it ensures statistical significance.
+        If `min_sup` is a float (0 < `min_sup` < 1), it represents the faction of the input dataset (`X`)
 
     verbose: bool, default=True
         Controls the verbosity.
@@ -79,7 +80,7 @@ def __init__(
         alpha: float = 0.6,
         k: int = 1,
         max_l: int = 4,
-        min_sup: int = 10,
+        min_sup: Union[int, float] = 10,
         verbose: bool = True,
     ):
         self.alpha = alpha
@@ -107,7 +108,10 @@ def _check_params(self):
         if self.max_l <= 0:
             raise ValueError(f"Invalid 'max_l' parameter: {self.max_l}")
 
-        if self.min_sup < 0:
+        if (
+            self.min_sup < 0 or
+            (isinstance(self.min_sup, float) and self.min_sup >= 1)
+        ):
             raise ValueError(f"Invalid 'min_sup' parameter: {self.min_sup}")
 
     def _check_top_slices(self):
@@ -138,6 +142,10 @@ def fit(self, X, errors):
         """
         self._check_params()
 
+        # Update min_sup for a fraction of the input dataset size
+        if 0 < self.min_sup < 1:
+            self.min_sup = int(self.min_sup * len(X))
+
         # Check that X and e have correct shape
         X_array, errors = check_X_e(X, errors)
 

tests/conftest.py

@@ -321,6 +321,16 @@ def experiments():
         X_16, errors_16, expected_top_slices_16, alpha=0.01, max_l=3, min_sup=7
     )
 
+    # Experiment 17: Experiment 4 w/ min_sup=0.1
+    expected_top_slices_17 = np.array(
+        [
+            [1.0, 1.0, None, None, None, None],
+            [1.0, None, None, None, None, None],
+            [None, 1.0, None, None, None, None]
+        ]
+    )
+    experiment_17 = Experiment(X_4, errors_4, expected_top_slices_17, min_sup=0.5)
+
     return {
         "experiment_1": experiment_1,
         "experiment_2": experiment_2,
@@ -338,4 +348,5 @@ def experiments():
         "experiment_14": experiment_14,
         "experiment_15": experiment_15,
         "experiment_16": experiment_16,
+        "experiment_17": experiment_17
     }

tests/experiment.py

@@ -36,9 +36,10 @@ class Experiment:
         Maximum lattice level.
         In other words: the maximum number of predicate to define a slice.
 
-    min_sup: int, default=10
+    min_sup: int or float, default=10
         Minimum support threshold.
         Inspired by frequent itemset mining, it ensures statistical significance.
+        If `min_sup` is a float (0 < `min_sup` < 1), it represents the faction of the input dataset (`X`)
 
     verbose: bool, default=True
         Controls the verbosity.

tests/test_slicefinder.py

@@ -303,6 +303,7 @@ def test_search_slices(benchmark, basic_test_data):
         "experiment_14",
         "experiment_15",
         "experiment_16",
+        "experiment_17",
     ],
 )
 def test_experiments(benchmark, experiments, experiment_name):