Merge pull request #33 from DataDome/allow-min_sup-parameter-between-…

…0-and-1

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):