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cezary.maszczyk
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Merge branch 'develop'
2 parents 236835a + 90780cd commit 19d97cb

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6 files changed

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-55
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6 files changed

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-55
lines changed

‎rulekit/__init__.py

+2-2
Original file line numberDiff line numberDiff line change
@@ -1,5 +1,5 @@
11
# pylint: disable=missing-module-docstring
22
from .main import RuleKit
33

4-
__VERSION__ = '1.7.5'
5-
__RULEKIT_RELEASE_VERSION__ = '1.7.4'
4+
__VERSION__ = '1.7.6'
5+
__RULEKIT_RELEASE_VERSION__ = '1.7.5'

‎rulekit/classification.py

+16-16
Original file line numberDiff line numberDiff line change
@@ -73,7 +73,7 @@ class RuleClassifier(BaseOperator, BaseClassifier):
7373

7474
def __init__(
7575
self,
76-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
76+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
7777
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
7878
pruning_measure: Union[Measures,
7979
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -88,14 +88,14 @@ def __init__(
8888
max_rule_count: int = DEFAULT_PARAMS_VALUE['max_rule_count'],
8989
approximate_induction: bool = DEFAULT_PARAMS_VALUE['approximate_induction'],
9090
approximate_bins_count: int = DEFAULT_PARAMS_VALUE['approximate_bins_count'],
91-
min_rule_covered: Optional[int] = None,
91+
min_rule_covered: Optional[float] = None,
9292
):
9393
"""
9494
Parameters
9595
----------
96-
minsupp_new : int = 5
97-
positive integer representing minimum number of previously uncovered examples to be
98-
covered by a new rule (positive examples for classification problems); default: 5
96+
minsupp_new : float = 5.0
97+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
98+
to be covered by a new rule (positive examples for classification problems); default: 5,
9999
induction_measure : :class:`rulekit.params.Measures` = :class:`rulekit.params.\
100100
Measures.Correlation`
101101
measure used during induction; default measure is correlation
@@ -137,7 +137,7 @@ def __init__(
137137
data sets, results may change in future;
138138
approximate_bins_count: int = 100
139139
maximum number of bins for an attribute evaluated in the approximate induction.
140-
min_rule_covered : int = None
140+
min_rule_covered : float = None
141141
alias to `minsupp_new`. Parameter is deprecated and will be removed in the next major
142142
version, use `minsupp_new`
143143
@@ -327,7 +327,7 @@ class ExpertRuleClassifier(ExpertKnowledgeOperator, RuleClassifier):
327327

328328
def __init__(
329329
self,
330-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
330+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
331331
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
332332
pruning_measure: Union[Measures,
333333
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -352,14 +352,14 @@ def __init__(
352352
'preferred_conditions_per_rule'],
353353
preferred_attributes_per_rule: int = DEFAULT_PARAMS_VALUE[
354354
'preferred_attributes_per_rule'],
355-
min_rule_covered: Optional[int] = None
355+
min_rule_covered: Optional[float] = None
356356
):
357357
"""
358358
Parameters
359359
----------
360-
minsupp_new : int = 5
361-
positive integer representing minimum number of previously uncovered examples to be
362-
covered by a new rule (positive examples for classification problems); default: 5
360+
minsupp_new : float = 5.0
361+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
362+
to be covered by a new rule (positive examples for classification problems); default: 5,
363363
induction_measure : :class:`rulekit.params.Measures` = \
364364
:class:`rulekit.params.Measures.Correlation`
365365
measure used during induction; default measure is correlation
@@ -421,7 +421,7 @@ def __init__(
421421
maximum number of preferred conditions per rule; default: unlimited,
422422
preferred_attributes_per_rule : int = None
423423
maximum number of preferred attributes per rule; default: unlimited.
424-
min_rule_covered : int = None
424+
min_rule_covered : float = None
425425
alias to `minsupp_new`. Parameter is deprecated and will be removed in the next major
426426
version, use `minsupp_new`
427427
@@ -555,7 +555,7 @@ def __init__(
555555
penalty_strength: float = DEFAULT_PARAMS_VALUE['penalty_strength'],
556556
penalty_saturation: float = DEFAULT_PARAMS_VALUE['penalty_saturation'],
557557

558-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
558+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
559559
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
560560
pruning_measure: Union[Measures,
561561
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -585,9 +585,9 @@ def __init__(
585585
(s) - penalty strength; Default is 0.5
586586
penalty_saturation: float
587587
the value of p_new / P at which penalty reward saturates; Default is 0.2.
588-
minsupp_new : int = 5
589-
positive integer representing minimum number of previously uncovered examples to be
590-
covered by a new rule (positive examples for classification problems); default: 5
588+
minsupp_new : float = 5.0
589+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
590+
to be covered by a new rule (positive examples for classification problems); default: 5,
591591
induction_measure : :class:`rulekit.params.Measures` = \
592592
:class:`rulekit.params.Measures.Correlation`
593593
measure used during induction; default measure is correlation

‎rulekit/params.py

+4-4
Original file line numberDiff line numberDiff line change
@@ -59,8 +59,8 @@ class Measures(Enum):
5959

6060

6161
DEFAULT_PARAMS_VALUE = {
62-
'minsupp_new': 5,
63-
'min_rule_covered': 5,
62+
'minsupp_new': 5.0,
63+
'min_rule_covered': 5.0,
6464
'induction_measure': Measures.Correlation,
6565
'pruning_measure': Measures.Correlation,
6666
'voting_measure': Measures.Correlation,
@@ -96,8 +96,8 @@ class Measures(Enum):
9696
class ModelsParams(BaseModel):
9797
"""Model for validating models hyperparameters
9898
"""
99-
min_rule_covered: Optional[int] = None
100-
minsupp_new: Optional[int] = DEFAULT_PARAMS_VALUE['minsupp_new']
99+
min_rule_covered: Optional[float] = None
100+
minsupp_new: Optional[float] = DEFAULT_PARAMS_VALUE['minsupp_new']
101101
induction_measure: Optional[Measures] = DEFAULT_PARAMS_VALUE['induction_measure']
102102
pruning_measure: Optional[Measures] = DEFAULT_PARAMS_VALUE['pruning_measure']
103103
voting_measure: Optional[Measures] = DEFAULT_PARAMS_VALUE['voting_measure']

‎rulekit/regression.py

+15-15
Original file line numberDiff line numberDiff line change
@@ -27,7 +27,7 @@ class RuleRegressor(BaseOperator):
2727

2828
def __init__(
2929
self,
30-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
30+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
3131
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
3232
pruning_measure: Union[Measures,
3333
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -40,14 +40,14 @@ def __init__(
4040
complementary_conditions: bool = DEFAULT_PARAMS_VALUE['complementary_conditions'],
4141
mean_based_regression: bool = DEFAULT_PARAMS_VALUE['mean_based_regression'],
4242
max_rule_count: int = DEFAULT_PARAMS_VALUE['max_rule_count'],
43-
min_rule_covered: Optional[int] = None,
43+
min_rule_covered: Optional[float] = None,
4444
):
4545
"""
4646
Parameters
4747
----------
48-
minsupp_new : int = 5
49-
positive integer representing minimum number of previously uncovered examples to be
50-
covered by a new rule (positive examples for classification problems); default: 5
48+
minsupp_new : float = 5.0
49+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
50+
to be covered by a new rule (positive examples for classification problems); default: 5,
5151
induction_measure : :class:`rulekit.params.Measures` = \
5252
:class:`rulekit.params.Measures.Correlation`
5353
measure used during induction; default measure is correlation
@@ -82,7 +82,7 @@ def __init__(
8282
max_rule_count : int = 0
8383
Maximum number of rules to be generated (for classification data sets it applies
8484
to a single class); 0 indicates no limit.
85-
min_rule_covered : int = None
85+
min_rule_covered : float = None
8686
alias to `minsupp_new`. Parameter is deprecated and will be removed in the next major
8787
version, use `minsupp_new`
8888
@@ -176,7 +176,7 @@ class ExpertRuleRegressor(ExpertKnowledgeOperator, RuleRegressor):
176176

177177
def __init__(
178178
self,
179-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
179+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
180180
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
181181
pruning_measure: Union[Measures,
182182
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -199,14 +199,14 @@ def __init__(
199199
preferred_attributes_per_rule: int = DEFAULT_PARAMS_VALUE[
200200
'preferred_attributes_per_rule'],
201201

202-
min_rule_covered: Optional[int] = None
202+
min_rule_covered: Optional[float] = None
203203
):
204204
"""
205205
Parameters
206206
----------
207-
minsupp_new : int = 5
208-
positive integer representing minimum number of previously uncovered examples to be
209-
covered by a new rule (positive examples for classification problems); default: 5
207+
minsupp_new : float = 5.0
208+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
209+
to be covered by a new rule (positive examples for classification problems); default: 5,
210210
induction_measure : :class:`rulekit.params.Measures` = \
211211
:class:`rulekit.params.Measures.Correlation`
212212
measure used during induction; default measure is correlation
@@ -364,7 +364,7 @@ def __init__(
364364
penalty_strength: float = DEFAULT_PARAMS_VALUE['penalty_strength'],
365365
penalty_saturation: float = DEFAULT_PARAMS_VALUE['penalty_saturation'],
366366

367-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
367+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
368368
induction_measure: Measures = DEFAULT_PARAMS_VALUE['induction_measure'],
369369
pruning_measure: Union[Measures,
370370
str] = DEFAULT_PARAMS_VALUE['pruning_measure'],
@@ -392,9 +392,9 @@ def __init__(
392392
(s) - penalty strength; Default is 0.5
393393
penalty_saturation: float
394394
the value of p_new / P at which penalty reward saturates; Default is 0.2.
395-
minsupp_new : int = 5
396-
positive integer representing minimum number of previously uncovered examples to be
397-
covered by a new rule (positive examples for classification problems); default: 5
395+
minsupp_new : float = 5.0
396+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
397+
to be covered by a new rule (positive examples for classification problems); default: 5,
398398
induction_measure : :class:`rulekit.params.Measures` = \
399399
:class:`rulekit.params.Measures.Correlation`
400400
measure used during induction; default measure is correlation

‎rulekit/survival.py

+16-17
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,6 @@
99

1010
from ._helpers import (
1111
PredictionResultMapper,
12-
get_rule_generator,
1312
create_example_set
1413
)
1514
from ._operator import BaseOperator, ExpertKnowledgeOperator, Data
@@ -21,13 +20,13 @@ class SurvivalModelsParams(BaseModel):
2120
"""Model for validating survival models hyperparameters
2221
"""
2322
survival_time_attr: Optional[str]
24-
minsupp_new: Optional[int] = DEFAULT_PARAMS_VALUE['minsupp_new']
23+
minsupp_new: Optional[float] = DEFAULT_PARAMS_VALUE['minsupp_new']
2524
max_growing: Optional[float] = DEFAULT_PARAMS_VALUE['max_growing']
2625
enable_pruning: Optional[bool] = DEFAULT_PARAMS_VALUE['enable_pruning']
2726
ignore_missing: Optional[bool] = DEFAULT_PARAMS_VALUE['ignore_missing']
2827
max_uncovered_fraction: Optional[float] = DEFAULT_PARAMS_VALUE['max_uncovered_fraction']
2928
select_best_candidate: Optional[bool] = DEFAULT_PARAMS_VALUE['select_best_candidate']
30-
min_rule_covered: Optional[int] = None
29+
min_rule_covered: Optional[float] = None
3130
complementary_conditions: Optional[bool] = DEFAULT_PARAMS_VALUE['complementary_conditions']
3231

3332
extend_using_preferred: Optional[bool] = None
@@ -63,9 +62,9 @@ def __init__( # pylint: disable=super-init-not-called
6362
survival_time_attr : str
6463
name of column containing survival time data (use when data passed to model is padnas
6564
dataframe).
66-
minsupp_new : int = 5
67-
positive integer representing minimum number of previously uncovered examples to be
68-
covered by a new rule (positive examples for classification problems); default: 5
65+
minsupp_new : float = 5.0
66+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
67+
to be covered by a new rule (positive examples for classification problems); default: 5,
6968
max_growing : int = 0.0
7069
non-negative integer representing maximum number of conditions which can be added to
7170
the rule in the growing phase (use this parameter for large datasets if execution time
@@ -88,7 +87,7 @@ def __init__( # pylint: disable=super-init-not-called
8887
max_rule_count : int = 0
8988
Maximum number of rules to be generated (for classification data sets it applies
9089
to a single class); 0 indicates no limit.
91-
min_rule_covered : int = None
90+
min_rule_covered : float = None
9291
alias to `minsupp_new`. Parameter is deprecated and will be removed in the next major
9392
version, use `minsupp_new`
9493
@@ -239,7 +238,7 @@ class ExpertSurvivalRules(ExpertKnowledgeOperator, SurvivalRules):
239238
def __init__( # pylint: disable=super-init-not-called
240239
self,
241240
survival_time_attr: str = None,
242-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
241+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
243242
max_growing: int = DEFAULT_PARAMS_VALUE['max_growing'],
244243
enable_pruning: bool = DEFAULT_PARAMS_VALUE['enable_pruning'],
245244
ignore_missing: bool = DEFAULT_PARAMS_VALUE['ignore_missing'],
@@ -256,14 +255,14 @@ def __init__( # pylint: disable=super-init-not-called
256255
preferred_attributes_per_rule: int = DEFAULT_PARAMS_VALUE[
257256
'preferred_attributes_per_rule'],
258257
max_rule_count: int = DEFAULT_PARAMS_VALUE['max_rule_count'],
259-
min_rule_covered: Optional[int] = None
258+
min_rule_covered: Optional[float] = None
260259
):
261260
"""
262261
Parameters
263262
----------
264-
minsupp_new : int = 5
265-
positive integer representing minimum number of previously uncovered examples to be
266-
covered by a new rule (positive examples for classification problems); default: 5
263+
minsupp_new : float = 5.0
264+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
265+
to be covered by a new rule (positive examples for classification problems); default: 5,
267266
survival_time_attr : str
268267
name of column containing survival time data (use when data passed to model is pandas
269268
dataframe).
@@ -309,7 +308,7 @@ def __init__( # pylint: disable=super-init-not-called
309308
maximum number of preferred conditions per rule; default: unlimited,
310309
preferred_attributes_per_rule : int = None
311310
maximum number of preferred attributes per rule; default: unlimited.
312-
min_rule_covered : int = None
311+
min_rule_covered : float = None
313312
alias to `minsupp_new`. Parameter is deprecated and will be removed in the next major
314313
version, use `minsupp_new`
315314
@@ -420,7 +419,7 @@ def __init__( # pylint: disable=super-init-not-called
420419
penalty_saturation: float = DEFAULT_PARAMS_VALUE['penalty_saturation'],
421420

422421
survival_time_attr: str = None,
423-
minsupp_new: int = DEFAULT_PARAMS_VALUE['minsupp_new'],
422+
minsupp_new: float = DEFAULT_PARAMS_VALUE['minsupp_new'],
424423
max_growing: int = DEFAULT_PARAMS_VALUE['max_growing'],
425424
enable_pruning: bool = DEFAULT_PARAMS_VALUE['enable_pruning'],
426425
ignore_missing: bool = DEFAULT_PARAMS_VALUE['ignore_missing'],
@@ -446,9 +445,9 @@ def __init__( # pylint: disable=super-init-not-called
446445
survival_time_attr : str
447446
name of column containing survival time data (use when data passed to model is pandas
448447
dataframe).
449-
minsupp_new : int = 5
450-
positive integer representing minimum number of previously uncovered examples to be
451-
covered by a new rule (positive examples for classification problems); default: 5
448+
minsupp_new : float = 5.0
449+
a minimum number (or fraction, if value < 1.0) of previously uncovered examples
450+
to be covered by a new rule (positive examples for classification problems); default: 5,
452451
max_growing : int = 0.0
453452
non-negative integer representing maximum number of conditions which can be added to
454453
the rule in the growing phase (use this parameter for large datasets if execution time

‎setup.py

+1-1
Original file line numberDiff line numberDiff line change
@@ -9,7 +9,7 @@
99

1010
setuptools.setup(
1111
name="rulekit",
12-
version='1.7.5',
12+
version='1.7.6',
1313
author="Cezary Maszczyk",
1414
author_email="[email protected]",
1515
description="Comprehensive suite for rule-based learning",

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