Module ruleskit.ruleset

Expand source code
from abc import ABC
import ast
from copy import copy
from typing import List, Union, Tuple, Any, Optional
from collections import Counter
import numpy as np
import itertools
from collections import OrderedDict

from .condition import HyperrectangleCondition

from .rule import Rule, ClassificationRule, RegressionRule
from .activation import Activation
import logging

logger = logging.getLogger(__name__)

class RuleSet(ABC):

"""A set of rules"""

NLINES = 5  # half how many rules to show in str(self)
CHECK_DUPLICATED = False
all_features_indexes = {}

condition_index = ["features_names", "features_indexes", "bmins", "bmaxs"]
rule_index = ["prediction"]
classification_rule_index = rule_index + ["coverage", "criterion"]
regression_rule_index = classification_rule_index + ["std"]

@staticmethod
def check_duplicated_rules(rules, name_or_index: str = "index"):
if name_or_index == "index":
str_rules = [str(r.features_indexes) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
else:
str_rules = [str(r.features_names) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
if len(set(str_rules)) < len(str_rules):
duplicated = {}
for r in str_rules:
if r not in duplicated:
duplicated[r] = 0
duplicated[r] += 1
duplicated = [
f"{r}: {duplicated[r]} (positions {[i for i, x in enumerate(str_rules) if x == r]})\n"
f"   underlying rules : {[str(rules[i]) for i in [i for i, x in enumerate(str_rules) if x == r]]}"
for r in duplicated
if duplicated[r] > 1
]
s = "\n -".join(duplicated)
raise ValueError(f"There are {len(duplicated)} duplicated rules in your ruleset !\n {s}")

def __init__(
self,
rules_list: Union[List[Rule], None] = None,
remember_activation: bool = True,
stack_activation: bool = False,
):
"""

Parameters
----------
rules_list: Union[List[Rule], None]
The list of rules to start with. Can be None, since a RuleSet can be filled after its creation.
remember_activation: bool
The activation of the RuleSet is the logical OR of the activation of all its rules. It is only computed
if remember_activation is True. (default value = True)
stack_activation: bool
If True, the RuleSet will keep in memory 2-D np.ndarray containing the activations of all its rules. This
can take a lot of memory, but can save time if you apply numpy methods on this stacked vector instead of on
each rule separately. (default value = False)
"""
self._rules = []
self.features_names = []
self.features_indexes = []
self._activation = None
self._coverage = None  # in case Activation is not available
self.stacked_activations = None
self.remember_activation = remember_activation
self.stack_activation = stack_activation
self.rule_type = None
if rules_list is not None:
names_available = all([hasattr(r.condition, "features_names") for r in self])
for rule in rules_list:
if not isinstance(rule, Rule) and rule is not None:
raise TypeError(f"Some rules in given iterable were not of type 'Rule' but of type {type(rule)}")
if rule is not None:
self.append(rule, update_activation=False)
if self.remember_activation:
self.compute_self_activation()
if self.stack_activation:
self.compute_stacked_activation()
if names_available:
self.features_names = list(set(itertools.chain(*[rule.features_names for rule in self])))
self.set_features_indexes()
if RuleSet.CHECK_DUPLICATED:
self.check_duplicated_rules(self.rules, name_or_index="name" if len(self.features_names) > 0 else "index")

# noinspection PyProtectedMember,PyTypeChecker
"""Appends a rule or each rules of another RuleSet to self and updates activation vector and stacked activations
if needed. Also updates features_indexes, and features_names if possible."""
if isinstance(other, Rule):
self._rules.append(other)
else:
self._rules += other._rules
self.features_indexes = list(set(self.features_indexes + other.features_indexes))
if hasattr(other, "features_names"):
self.features_names = list(set(self.features_names + other.features_names))
if self.remember_activation:
self._update_activation(other)
if self.stack_activation:
self._update_stacked_activation(other)
return self

"""Returns the RuleSet resulting in appendind a rule or each rules of another RuleSet to self."""
remember_activation = self.remember_activation
stack_activation = self.stack_activation
if isinstance(other, Rule):
rules = self.rules + [other]
else:
remember_activation &= other.remember_activation
stack_activation &= other.stack_activation
rules = list(set(self.rules + other.rules))
return self.__class__(rules, remember_activation=remember_activation, stack_activation=stack_activation)

def __getattr__(self, item):
"""If item is not found in self, try to fetch it from its activation."""
if item == "_activation":
raise AttributeError(f"'RuleSet' object has no attribute '{item}'")

if self._activation is not None and hasattr(self._activation, item):
return getattr(self._activation, item)
raise AttributeError(f"'RuleSet' object has no attribute '{item}'")

def __len__(self):
"""The length of a RuleSet its the number of rules stored in it."""
return len(self.rules)

def __eq__(self, other: "RuleSet"):
return set(self.rules) == set(other.rules)

def __iter__(self):
if hasattr(self, "_rules"):
return self.rules.__iter__()
else:
return [].__iter__()

def __getitem__(self, key):
if isinstance(key, slice):
indices = range(*key.indices(len(self.rules)))
return self.__class__([self.rules[i] for i in indices])
return self.rules.__getitem__(key)

def __str__(self):
if len(self) < 2 * RuleSet.NLINES:
return "\n".join([str(self[i]) for i in range(len(self))])
else:
return "\n".join(
[str(self[i]) for i in range(RuleSet.NLINES)]
+ ["..."]
+ [str(self[i]) for i in range(len(self) - RuleSet.NLINES, len(self))]
)

def __hash__(self) -> hash:
return hash(frozenset(self.to_hash))

# noinspection PyProtectedMember
def __contains__(self, other: Rule) -> bool:
"""A RuleSet contains another Rule if the two rule's conditions and predictions are the same"""
name_or_index = "name" if len(self.features_names) > 0 else "index"

if name_or_index == "index":
str_rules = [str(r.features_indexes) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in self]
str_rule = str(other.features_indexes) + str(other.bmins) + str(other.bmaxs) + str(other.prediction)
else:
str_rules = [str(r.features_names) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in self]
str_rule = str(other.features_names) + str(other.bmins) + str(other.bmaxs) + str(other.prediction)

return str_rule in str_rules

@property
def rules(self) -> List[Rule]:
return self._rules

@rules.setter
def rules(self, rules: Union[List[Rule], None]):
ruleset = RuleSet(rules, remember_activation=self.remember_activation, stack_activation=self.stack_activation)
self._rules = ruleset._rules
self.features_names = ruleset.features_names
self.features_indexes = ruleset.features_indexes
self.stacked_activations = ruleset.stacked_activations
self._activation = ruleset._activation

@property
def to_hash(self) -> Tuple[str]:
if len(self) == 0:
return "rs",
to_hash = ("rs",)
for r in self:
rule_hash = r.to_hash[1:]
to_hash += rule_hash

@property
def activation_available(self) -> bool:
"""Returns True if the RuleSet has an activation vector, and if this Activation's object data is available."""
if self._activation is None:
return False
if self._activation.data_format == "file":
return self._activation.data.is_file()
else:
return self._activation.data is not None

@property
def stacked_activations_available(self) -> bool:
"""Returns True is the RuleSet has its rules' stacked activations."""
if self.stack_activation is None:
return False
return True

@property
def activation(self) -> Union[None, np.ndarray]:
"""Returns the Activation vector's data in a form of a 1-D np.ndarray, or None if not available.

Returns:
--------
Union[None, np.ndarray]
of the form [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, ...]
"""
if self._activation:
return self._activation.raw
return None

@property
def ruleset_coverage(self) -> float:
"""Coverage is the fraction of points equal to 1 in the activation vector"""
if not self.activation_available:
return self._coverage
else:
return self._activation.coverage

# noinspection PyProtectedMember,PyTypeChecker
def _update_activation(self, other: Union[Rule, "RuleSet"]):
"""Updates the activation vector of the RuleSet with the activation vector of a new Rule or RuleSet."""
if other.activation_available:
if self._activation is None:
self._activation = Activation(other.activation, to_file=Rule.LOCAL_ACTIVATION)
else:
self._activation = self._activation | other._activation

# noinspection PyProtectedMember,PyTypeChecker
def _update_stacked_activation(self, other: Union[Rule, "RuleSet"]):
"""Updates the stacked activation vectors of the RuleSet with the activation vector of a new Rule or
the stacked activation vectors of another RuleSet."""
if other.activation_available:
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked activations requied pandas. Please run\npip install pandas")

if self.stacked_activations is None:
if isinstance(other, Rule):
self.stacked_activations = pd.DataFrame(
data=np.array(other.activation).T, columns=[str(other.condition)]
)
else:
self.stacked_activations = other.stacked_activations
else:
if isinstance(other, Rule):
self.stacked_activations[str(other.condition)] = other.activation
else:
self.stacked_activations = pd.concat([self.stacked_activations, other.stacked_activations], axis=1)

def set_features_indexes(self):
if len(RuleSet.all_features_indexes) > 0:
self.features_indexes = [RuleSet.all_features_indexes[f] for f in self.features_names]
for r in self._rules:
# noinspection PyProtectedMember
r._condition._features_indexes = [RuleSet.all_features_indexes[f] for f in r.features_names]
else:
list(set(itertools.chain(*[rule.features_indexes for rule in self])))

# noinspection PyProtectedMember
def fit(self, xs: Optional[Union["pd.DataFrame", np.ndarray]], y: Union[np.ndarray, "pd.Series"]) -> None:
if len(self) == 0:
logger.debug("Ruleset is empty. Nothing to fit.")
return

if RuleSet.STACKED_FIT:
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked fit requies pandas. Please run\npip install pandas")

clean_activation = not self.stack_activation
self.stack_activation = True
self.calc_activation(xs)
self.stack_activation = not clean_activation

if isinstance(y, np.ndarray) and not len(self.stacked_activations.index) == y.shape:
raise IndexError("Stacked activation and y have different number of rows. Use pd.Series for y to"
"reindex stacked activations automatically.")
else:
self.stacked_activations.index = y.index
pred, prediction_vector = self.calc_prediction(y)

if clean_activation:
self.del_stacked_activations()
else:
self.stacked_activations.index = pd.RangeIndex(len(y.index))

prediction_vector.index = self.stack_activations.index
criterion = self.calc_criterion(prediction_vector, y)
to_drop = []

for ir in range(len(self)):
self._rules[ir]._criterion = criterion.iloc[ir] if isinstance(criterion, pd.Series) else criterion
self._rules[ir]._prediction = pred.iloc[ir] if isinstance(pred, pd.Series) else pred

self._rules[ir].check_thresholds("prediction")
if not self._rules[ir].good:
to_drop.append(self._rules[ir])
break
else:
[r.fit(xs=xs, y=y) for r in self]
to_drop = [r for r in self if not r.good]

if len(to_drop) > 0:
rules = [r for r in self.rules if r not in to_drop]
self._rules = []
self._activation.clear()
self.del_stacked_activations()
for r in rules:
self.append(r)

def append(self, rule: Rule, update_activation: bool = True):
"""Appends a new rule to self. The updates of activation vector and the stacked activation vectors can be
blocked by specifying update_activation=False. Otherwise, will use self.remember_activation and
self.stack_activation to determine if the updates should be done or not."""
if not isinstance(rule, Rule):
raise TypeError(f"RuleSet's append method expects a Rule object, got {type(rule)}")
if self.rule_type is None:
self.rule_type = type(rule)
else:
if not isinstance(rule, self.rule_type):
raise TypeError(
f"Ruleset previously had rules of type {self.rule_type}, so can not add rule of type {type(rule)}"
)
remember_activation = self.remember_activation
stack_activation = self.stack_activation
if not update_activation:
self.remember_activation = False
self.stack_activation = False
self.remember_activation = remember_activation
self.stack_activation = stack_activation

def sort(self, criterion: str = None, reverse: bool = False):
"""Sorts the RuleSet.

* If criterion is not speficied:
Will sort the rules according to :
1. The number of features they talk about
2. For a same number of features (sorted in alphabetical order, or index if names are not available,
optionally reversed), the bmins and bmaxs of the rules
* If criterion is specified, it must be an float or interger attribute of rule, condition or activation. Then
sorts according to this criterion, optionally reversed.
"""
if len(self) == 0:
return

if criterion is None or criterion == "":
if not (hasattr(self.condition, "bmins") and hasattr(self.condition, "bmaxs")):
return
# The set of all the features the RuleSet talks about
which = "index"
if len(self.features_names) > 0:
which = "name"
fnames_or_indexes = list(set([str(r.features_names) for r in self]))
else:
fnames_or_indexes = list(set([str(r.features_indexes) for r in self]))
dict_names = {}
lmax = 1
for f in fnames_or_indexes:
l_ = len(ast.literal_eval(f))
if l_ > lmax:
lmax = l_
if l_ not in dict_names:
dict_names[l_] = []
dict_names[l_].append(f)
for l_ in dict_names:
dict_names[l_].sort(reverse=reverse)
fnames_or_indexes = []
for l_ in range(1, lmax + 1):
if l_ in dict_names:
fnames_or_indexes += dict_names[l_]

rules_by_fnames = OrderedDict({f: [] for f in fnames_or_indexes})
for rule in self:
# noinspection PyUnresolvedReferences
if which == "name":
v = str(rule.features_names)
else:
v = str(rule.features_indexes)
rules_by_fnames[v].append(rule)
rules_by_fnames = {
n: sorted(rules_by_fnames[n], key=lambda x: x.condition.bmins + x.condition.bmaxs)
for n in rules_by_fnames
}
self._rules = []
for n in rules_by_fnames:
self._rules += rules_by_fnames[n]
elif hasattr(self, criterion):
self._rules = sorted(self, key=lambda x: getattr(x, criterion), reverse=reverse)
else:
raise ValueError(f"Can not sort RuleSet according to criterion {criterion}")
if self.stack_activation:
self.stacked_activations = self.stacked_activations[[str(r.condition) for r in self]]

def compute_self_activation(self):
"""Computes the activation vector of self from its rules, using time-efficient Activation.multi_logical_or."""
if len(self) == 0:
return
activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self._activation = Activation.multi_logical_or([r._activation for r in self])

def compute_stacked_activation(self):
"""Computes the stacked activation vectors of self from its rules."""
if len(self) == 0:
return
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked activations requies pandas. Please run\npip install pandas")

activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self.stacked_activations = pd.DataFrame(
data=np.array([r.activation for r in self]).T, columns=[str(r.condition) for r in self]
)

def del_activations(self):
"""Deletes the data, but not the relevent attributes, of the activation vector or each rules in self."""
for r in self:
r.del_activation()

def del_activation(self):
"""Deletes the activation vector's data of self, but not the object itself, so any computed attribute remains
available"""
if hasattr(self, "_activation") and self._activation is not None:
self._activation.delete()

def del_stacked_activations(self):
"""Deletes stacked activation vectors of self. Set it to None."""
if hasattr(self, "stacked_activations") and self.stacked_activations is not None:
del self.stacked_activations
self.stacked_activations = None

def evaluate(self, xs: Union["pd.DataFrame", np.ndarray]) -> Activation:
"""Computes and returns the activation vector from an array of features.

Parameters
----------
xs: Union[pd.DataFrame, np.ndarray]
The features on which the check whether the rule is activated or not. Must be a 2-D np.ndarray
or pd.DataFrame.

Returns
-------
Activation
"""
if len(self) == 0:
raise ValueError("Can not use evaluate : The ruleset is empty!")
activations = [rule.evaluate(xs) for rule in self.rules]
return Activation.multi_logical_or(activations)

def calc_activation(self, xs: Union[np.ndarray, "pd.DataFrame"]):
"""Uses input xs features data to compute the activation vector of all rules in self, and updates self's
activation if self.remember_activation is True and stacked activation if self.stack_activation is True"""
if len(self) == 0:
raise ValueError("Can not use calc_activation : The ruleset is empty!")
[rule.calc_activation(xs) for rule in self.rules]

if self.remember_activation:
self._activation = None
self.compute_self_activation()
if self.stack_activation:
self.stacked_activations = None
self.compute_stacked_activation()

def get_features_count(self) -> List[Tuple[Any, int]]:
"""
Get a counter of all different features in the ruleset. If names are not available, will use indexes.

Returns:
--------
count : List[Tuple[Any, int]]
Counter of all different features in the ruleset
"""
# noinspection PyUnresolvedReferences
if len(self) == 0:
return []
if len(self.features_names) > 0:
var_in = list(itertools.chain(*[rule.features_names for rule in self]))
else:
var_in = list(itertools.chain(*[rule.feautres_indexes for rule in self]))
count = Counter(var_in)

count = count.most_common()
return count

try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")

else:
if rules.empty:
self._rules = []
else:
self._rules = [self.series_to_rule(rules.loc[r]) for r in rules.index]

if len(self._rules) > 0:
self.rule_type = type(self._rules)
if self.remember_activation:
self.compute_self_activation()
if self.stack_activation:
self.compute_stacked_activation()
self.features_names = list(set(traverse([rule.features_names for rule in self])))

def save(self, path):

try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to save a RuleSet to a file. Please run\npip install pandas")

if len(self) == 0:
if hasattr(path, "write"):
path.write(pd.DataFrame())
else:
pd.DataFrame().to_csv(path)
return

idx = copy(RuleSet.condition_index)
if self.rule_type == ClassificationRule:
idx += RuleSet.classification_rule_index
elif self.rule_type == RegressionRule:
idx += RuleSet.regression_rule_index
elif self.rule_type == Rule:
idx += RuleSet.rule_index
else:
raise TypeError(f"Impossible to save rule type {self.rule_type}")

dfs = [
self.rule_to_series(
(i, r),
index=idx,
)
for i, r in enumerate(self.rules)
]
if len(dfs) > 0:
df = pd.concat(dfs, axis=1).T
else:
df = pd.DataFrame(columns=idx)
if hasattr(path, "write"):
path.write(df)
else:
df.to_csv(path)

# noinspection PyProtectedMember
@staticmethod
def rule_to_series(irule: Tuple[int, Rule], index: list) -> "pd.Series":
try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")
i = irule
rule = irule
name = f"R_{i}({len(rule)})"
sr = pd.Series(data=[str(getattr(rule, ind)) for ind in index], name=name, index=index, dtype=str)
return sr

@staticmethod
def series_to_rule(srule: "pd.Series") -> Rule:

condition_index = {c: None for c in RuleSet.condition_index}

for ind in srule.index:
if ind == "Unnamed: 0":
continue
if ind not in RuleSet.classification_rule_index + RuleSet.condition_index:
raise IndexError(f"Invalid rule attribute '{ind}'")

if "std" in srule.index:
rule = RegressionRule()
rule_idx = copy(RuleSet.regression_rule_index)
elif "criterion" in srule.index:
rule = ClassificationRule()
rule_idx = copy(RuleSet.classification_rule_index)
else:
rule = Rule()
rule_idx = copy(RuleSet.rule_index)

for rule_ind in srule.index:
str_value = str(srule[rule_ind])
if rule_ind in condition_index:
condition_index[rule_ind] = ast.literal_eval(str_value)
# setattr(condition, rule_ind, ast.literal_eval(str_value))
elif rule_ind in rule_idx:
if rule_ind == "activation":
setattr(rule, f"_{rule_ind}", Activation(str_value))
elif str_value == "nan":
setattr(rule, f"_{rule_ind}", np.nan)
elif rule_ind == "sign":
setattr(rule, f"_{rule_ind}", str_value)
else:
if rule_ind == "prediction":  # Prediction can be a str in case of classification
try:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
except ValueError:
setattr(rule, f"_{rule_ind}", str_value)
else:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
else:
continue

rule._condition = HyperrectangleCondition(**condition_index)
return rule

def traverse(o, tree_types=(list, tuple, RuleSet)):
"""Yields each elementary elements from nested list or tuple

Example
-------
>>> list(traverse([[[1, 2], 3,], [4, 5]]))
[1, 2, 3, 4, 5]
"""
if isinstance(o, tree_types):
for value in o:
for subvalue in traverse(value, tree_types):
yield subvalue
else:
yield o

Functions

def traverse(o, tree_types=(<class 'list'>, <class 'tuple'>, <class 'ruleskit.ruleset.RuleSet'>))

Yields each elementary elements from nested list or tuple

Example

>>> list(traverse([[[1, 2], 3,], [4, 5]]))
[1, 2, 3, 4, 5]
Expand source code
def traverse(o, tree_types=(list, tuple, RuleSet)):
"""Yields each elementary elements from nested list or tuple

Example
-------
>>> list(traverse([[[1, 2], 3,], [4, 5]]))
[1, 2, 3, 4, 5]
"""
if isinstance(o, tree_types):
for value in o:
for subvalue in traverse(value, tree_types):
yield subvalue
else:
yield o

Classes

class RuleSet (rules_list: Optional[List[Rule]] = None, remember_activation: bool = True, stack_activation: bool = False)

A set of rules

Parameters

rules_list : Union[List[Rule], None]
The list of rules to start with. Can be None, since a RuleSet can be filled after its creation.
remember_activation : bool
The activation of the RuleSet is the logical OR of the activation of all its rules. It is only computed if remember_activation is True. (default value = True)
stack_activation : bool
If True, the RuleSet will keep in memory 2-D np.ndarray containing the activations of all its rules. This can take a lot of memory, but can save time if you apply numpy methods on this stacked vector instead of on each rule separately. (default value = False)
Expand source code
class RuleSet(ABC):

"""A set of rules"""

NLINES = 5  # half how many rules to show in str(self)
CHECK_DUPLICATED = False
all_features_indexes = {}

condition_index = ["features_names", "features_indexes", "bmins", "bmaxs"]
rule_index = ["prediction"]
classification_rule_index = rule_index + ["coverage", "criterion"]
regression_rule_index = classification_rule_index + ["std"]

@staticmethod
def check_duplicated_rules(rules, name_or_index: str = "index"):
if name_or_index == "index":
str_rules = [str(r.features_indexes) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
else:
str_rules = [str(r.features_names) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
if len(set(str_rules)) < len(str_rules):
duplicated = {}
for r in str_rules:
if r not in duplicated:
duplicated[r] = 0
duplicated[r] += 1
duplicated = [
f"{r}: {duplicated[r]} (positions {[i for i, x in enumerate(str_rules) if x == r]})\n"
f"   underlying rules : {[str(rules[i]) for i in [i for i, x in enumerate(str_rules) if x == r]]}"
for r in duplicated
if duplicated[r] > 1
]
s = "\n -".join(duplicated)
raise ValueError(f"There are {len(duplicated)} duplicated rules in your ruleset !\n {s}")

def __init__(
self,
rules_list: Union[List[Rule], None] = None,
remember_activation: bool = True,
stack_activation: bool = False,
):
"""

Parameters
----------
rules_list: Union[List[Rule], None]
The list of rules to start with. Can be None, since a RuleSet can be filled after its creation.
remember_activation: bool
The activation of the RuleSet is the logical OR of the activation of all its rules. It is only computed
if remember_activation is True. (default value = True)
stack_activation: bool
If True, the RuleSet will keep in memory 2-D np.ndarray containing the activations of all its rules. This
can take a lot of memory, but can save time if you apply numpy methods on this stacked vector instead of on
each rule separately. (default value = False)
"""
self._rules = []
self.features_names = []
self.features_indexes = []
self._activation = None
self._coverage = None  # in case Activation is not available
self.stacked_activations = None
self.remember_activation = remember_activation
self.stack_activation = stack_activation
self.rule_type = None
if rules_list is not None:
names_available = all([hasattr(r.condition, "features_names") for r in self])
for rule in rules_list:
if not isinstance(rule, Rule) and rule is not None:
raise TypeError(f"Some rules in given iterable were not of type 'Rule' but of type {type(rule)}")
if rule is not None:
self.append(rule, update_activation=False)
if self.remember_activation:
self.compute_self_activation()
if self.stack_activation:
self.compute_stacked_activation()
if names_available:
self.features_names = list(set(itertools.chain(*[rule.features_names for rule in self])))
self.set_features_indexes()
if RuleSet.CHECK_DUPLICATED:
self.check_duplicated_rules(self.rules, name_or_index="name" if len(self.features_names) > 0 else "index")

# noinspection PyProtectedMember,PyTypeChecker
"""Appends a rule or each rules of another RuleSet to self and updates activation vector and stacked activations
if needed. Also updates features_indexes, and features_names if possible."""
if isinstance(other, Rule):
self._rules.append(other)
else:
self._rules += other._rules
self.features_indexes = list(set(self.features_indexes + other.features_indexes))
if hasattr(other, "features_names"):
self.features_names = list(set(self.features_names + other.features_names))
if self.remember_activation:
self._update_activation(other)
if self.stack_activation:
self._update_stacked_activation(other)
return self

"""Returns the RuleSet resulting in appendind a rule or each rules of another RuleSet to self."""
remember_activation = self.remember_activation
stack_activation = self.stack_activation
if isinstance(other, Rule):
rules = self.rules + [other]
else:
remember_activation &= other.remember_activation
stack_activation &= other.stack_activation
rules = list(set(self.rules + other.rules))
return self.__class__(rules, remember_activation=remember_activation, stack_activation=stack_activation)

def __getattr__(self, item):
"""If item is not found in self, try to fetch it from its activation."""
if item == "_activation":
raise AttributeError(f"'RuleSet' object has no attribute '{item}'")

if self._activation is not None and hasattr(self._activation, item):
return getattr(self._activation, item)
raise AttributeError(f"'RuleSet' object has no attribute '{item}'")

def __len__(self):
"""The length of a RuleSet its the number of rules stored in it."""
return len(self.rules)

def __eq__(self, other: "RuleSet"):
return set(self.rules) == set(other.rules)

def __iter__(self):
if hasattr(self, "_rules"):
return self.rules.__iter__()
else:
return [].__iter__()

def __getitem__(self, key):
if isinstance(key, slice):
indices = range(*key.indices(len(self.rules)))
return self.__class__([self.rules[i] for i in indices])
return self.rules.__getitem__(key)

def __str__(self):
if len(self) < 2 * RuleSet.NLINES:
return "\n".join([str(self[i]) for i in range(len(self))])
else:
return "\n".join(
[str(self[i]) for i in range(RuleSet.NLINES)]
+ ["..."]
+ [str(self[i]) for i in range(len(self) - RuleSet.NLINES, len(self))]
)

def __hash__(self) -> hash:
return hash(frozenset(self.to_hash))

# noinspection PyProtectedMember
def __contains__(self, other: Rule) -> bool:
"""A RuleSet contains another Rule if the two rule's conditions and predictions are the same"""
name_or_index = "name" if len(self.features_names) > 0 else "index"

if name_or_index == "index":
str_rules = [str(r.features_indexes) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in self]
str_rule = str(other.features_indexes) + str(other.bmins) + str(other.bmaxs) + str(other.prediction)
else:
str_rules = [str(r.features_names) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in self]
str_rule = str(other.features_names) + str(other.bmins) + str(other.bmaxs) + str(other.prediction)

return str_rule in str_rules

@property
def rules(self) -> List[Rule]:
return self._rules

@rules.setter
def rules(self, rules: Union[List[Rule], None]):
ruleset = RuleSet(rules, remember_activation=self.remember_activation, stack_activation=self.stack_activation)
self._rules = ruleset._rules
self.features_names = ruleset.features_names
self.features_indexes = ruleset.features_indexes
self.stacked_activations = ruleset.stacked_activations
self._activation = ruleset._activation

@property
def to_hash(self) -> Tuple[str]:
if len(self) == 0:
return "rs",
to_hash = ("rs",)
for r in self:
rule_hash = r.to_hash[1:]
to_hash += rule_hash

@property
def activation_available(self) -> bool:
"""Returns True if the RuleSet has an activation vector, and if this Activation's object data is available."""
if self._activation is None:
return False
if self._activation.data_format == "file":
return self._activation.data.is_file()
else:
return self._activation.data is not None

@property
def stacked_activations_available(self) -> bool:
"""Returns True is the RuleSet has its rules' stacked activations."""
if self.stack_activation is None:
return False
return True

@property
def activation(self) -> Union[None, np.ndarray]:
"""Returns the Activation vector's data in a form of a 1-D np.ndarray, or None if not available.

Returns:
--------
Union[None, np.ndarray]
of the form [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, ...]
"""
if self._activation:
return self._activation.raw
return None

@property
def ruleset_coverage(self) -> float:
"""Coverage is the fraction of points equal to 1 in the activation vector"""
if not self.activation_available:
return self._coverage
else:
return self._activation.coverage

# noinspection PyProtectedMember,PyTypeChecker
def _update_activation(self, other: Union[Rule, "RuleSet"]):
"""Updates the activation vector of the RuleSet with the activation vector of a new Rule or RuleSet."""
if other.activation_available:
if self._activation is None:
self._activation = Activation(other.activation, to_file=Rule.LOCAL_ACTIVATION)
else:
self._activation = self._activation | other._activation

# noinspection PyProtectedMember,PyTypeChecker
def _update_stacked_activation(self, other: Union[Rule, "RuleSet"]):
"""Updates the stacked activation vectors of the RuleSet with the activation vector of a new Rule or
the stacked activation vectors of another RuleSet."""
if other.activation_available:
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked activations requied pandas. Please run\npip install pandas")

if self.stacked_activations is None:
if isinstance(other, Rule):
self.stacked_activations = pd.DataFrame(
data=np.array(other.activation).T, columns=[str(other.condition)]
)
else:
self.stacked_activations = other.stacked_activations
else:
if isinstance(other, Rule):
self.stacked_activations[str(other.condition)] = other.activation
else:
self.stacked_activations = pd.concat([self.stacked_activations, other.stacked_activations], axis=1)

def set_features_indexes(self):
if len(RuleSet.all_features_indexes) > 0:
self.features_indexes = [RuleSet.all_features_indexes[f] for f in self.features_names]
for r in self._rules:
# noinspection PyProtectedMember
r._condition._features_indexes = [RuleSet.all_features_indexes[f] for f in r.features_names]
else:
list(set(itertools.chain(*[rule.features_indexes for rule in self])))

# noinspection PyProtectedMember
def fit(self, xs: Optional[Union["pd.DataFrame", np.ndarray]], y: Union[np.ndarray, "pd.Series"]) -> None:
if len(self) == 0:
logger.debug("Ruleset is empty. Nothing to fit.")
return

if RuleSet.STACKED_FIT:
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked fit requies pandas. Please run\npip install pandas")

clean_activation = not self.stack_activation
self.stack_activation = True
self.calc_activation(xs)
self.stack_activation = not clean_activation

if isinstance(y, np.ndarray) and not len(self.stacked_activations.index) == y.shape:
raise IndexError("Stacked activation and y have different number of rows. Use pd.Series for y to"
"reindex stacked activations automatically.")
else:
self.stacked_activations.index = y.index
pred, prediction_vector = self.calc_prediction(y)

if clean_activation:
self.del_stacked_activations()
else:
self.stacked_activations.index = pd.RangeIndex(len(y.index))

prediction_vector.index = self.stack_activations.index
criterion = self.calc_criterion(prediction_vector, y)
to_drop = []

for ir in range(len(self)):
self._rules[ir]._criterion = criterion.iloc[ir] if isinstance(criterion, pd.Series) else criterion
self._rules[ir]._prediction = pred.iloc[ir] if isinstance(pred, pd.Series) else pred

self._rules[ir].check_thresholds("prediction")
if not self._rules[ir].good:
to_drop.append(self._rules[ir])
break
else:
[r.fit(xs=xs, y=y) for r in self]
to_drop = [r for r in self if not r.good]

if len(to_drop) > 0:
rules = [r for r in self.rules if r not in to_drop]
self._rules = []
self._activation.clear()
self.del_stacked_activations()
for r in rules:
self.append(r)

def append(self, rule: Rule, update_activation: bool = True):
"""Appends a new rule to self. The updates of activation vector and the stacked activation vectors can be
blocked by specifying update_activation=False. Otherwise, will use self.remember_activation and
self.stack_activation to determine if the updates should be done or not."""
if not isinstance(rule, Rule):
raise TypeError(f"RuleSet's append method expects a Rule object, got {type(rule)}")
if self.rule_type is None:
self.rule_type = type(rule)
else:
if not isinstance(rule, self.rule_type):
raise TypeError(
f"Ruleset previously had rules of type {self.rule_type}, so can not add rule of type {type(rule)}"
)
remember_activation = self.remember_activation
stack_activation = self.stack_activation
if not update_activation:
self.remember_activation = False
self.stack_activation = False
self.remember_activation = remember_activation
self.stack_activation = stack_activation

def sort(self, criterion: str = None, reverse: bool = False):
"""Sorts the RuleSet.

* If criterion is not speficied:
Will sort the rules according to :
1. The number of features they talk about
2. For a same number of features (sorted in alphabetical order, or index if names are not available,
optionally reversed), the bmins and bmaxs of the rules
* If criterion is specified, it must be an float or interger attribute of rule, condition or activation. Then
sorts according to this criterion, optionally reversed.
"""
if len(self) == 0:
return

if criterion is None or criterion == "":
if not (hasattr(self.condition, "bmins") and hasattr(self.condition, "bmaxs")):
return
# The set of all the features the RuleSet talks about
which = "index"
if len(self.features_names) > 0:
which = "name"
fnames_or_indexes = list(set([str(r.features_names) for r in self]))
else:
fnames_or_indexes = list(set([str(r.features_indexes) for r in self]))
dict_names = {}
lmax = 1
for f in fnames_or_indexes:
l_ = len(ast.literal_eval(f))
if l_ > lmax:
lmax = l_
if l_ not in dict_names:
dict_names[l_] = []
dict_names[l_].append(f)
for l_ in dict_names:
dict_names[l_].sort(reverse=reverse)
fnames_or_indexes = []
for l_ in range(1, lmax + 1):
if l_ in dict_names:
fnames_or_indexes += dict_names[l_]

rules_by_fnames = OrderedDict({f: [] for f in fnames_or_indexes})
for rule in self:
# noinspection PyUnresolvedReferences
if which == "name":
v = str(rule.features_names)
else:
v = str(rule.features_indexes)
rules_by_fnames[v].append(rule)
rules_by_fnames = {
n: sorted(rules_by_fnames[n], key=lambda x: x.condition.bmins + x.condition.bmaxs)
for n in rules_by_fnames
}
self._rules = []
for n in rules_by_fnames:
self._rules += rules_by_fnames[n]
elif hasattr(self, criterion):
self._rules = sorted(self, key=lambda x: getattr(x, criterion), reverse=reverse)
else:
raise ValueError(f"Can not sort RuleSet according to criterion {criterion}")
if self.stack_activation:
self.stacked_activations = self.stacked_activations[[str(r.condition) for r in self]]

def compute_self_activation(self):
"""Computes the activation vector of self from its rules, using time-efficient Activation.multi_logical_or."""
if len(self) == 0:
return
activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self._activation = Activation.multi_logical_or([r._activation for r in self])

def compute_stacked_activation(self):
"""Computes the stacked activation vectors of self from its rules."""
if len(self) == 0:
return
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked activations requies pandas. Please run\npip install pandas")

activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self.stacked_activations = pd.DataFrame(
data=np.array([r.activation for r in self]).T, columns=[str(r.condition) for r in self]
)

def del_activations(self):
"""Deletes the data, but not the relevent attributes, of the activation vector or each rules in self."""
for r in self:
r.del_activation()

def del_activation(self):
"""Deletes the activation vector's data of self, but not the object itself, so any computed attribute remains
available"""
if hasattr(self, "_activation") and self._activation is not None:
self._activation.delete()

def del_stacked_activations(self):
"""Deletes stacked activation vectors of self. Set it to None."""
if hasattr(self, "stacked_activations") and self.stacked_activations is not None:
del self.stacked_activations
self.stacked_activations = None

def evaluate(self, xs: Union["pd.DataFrame", np.ndarray]) -> Activation:
"""Computes and returns the activation vector from an array of features.

Parameters
----------
xs: Union[pd.DataFrame, np.ndarray]
The features on which the check whether the rule is activated or not. Must be a 2-D np.ndarray
or pd.DataFrame.

Returns
-------
Activation
"""
if len(self) == 0:
raise ValueError("Can not use evaluate : The ruleset is empty!")
activations = [rule.evaluate(xs) for rule in self.rules]
return Activation.multi_logical_or(activations)

def calc_activation(self, xs: Union[np.ndarray, "pd.DataFrame"]):
"""Uses input xs features data to compute the activation vector of all rules in self, and updates self's
activation if self.remember_activation is True and stacked activation if self.stack_activation is True"""
if len(self) == 0:
raise ValueError("Can not use calc_activation : The ruleset is empty!")
[rule.calc_activation(xs) for rule in self.rules]

if self.remember_activation:
self._activation = None
self.compute_self_activation()
if self.stack_activation:
self.stacked_activations = None
self.compute_stacked_activation()

def get_features_count(self) -> List[Tuple[Any, int]]:
"""
Get a counter of all different features in the ruleset. If names are not available, will use indexes.

Returns:
--------
count : List[Tuple[Any, int]]
Counter of all different features in the ruleset
"""
# noinspection PyUnresolvedReferences
if len(self) == 0:
return []
if len(self.features_names) > 0:
var_in = list(itertools.chain(*[rule.features_names for rule in self]))
else:
var_in = list(itertools.chain(*[rule.feautres_indexes for rule in self]))
count = Counter(var_in)

count = count.most_common()
return count

try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")

else:
if rules.empty:
self._rules = []
else:
self._rules = [self.series_to_rule(rules.loc[r]) for r in rules.index]

if len(self._rules) > 0:
self.rule_type = type(self._rules)
if self.remember_activation:
self.compute_self_activation()
if self.stack_activation:
self.compute_stacked_activation()
self.features_names = list(set(traverse([rule.features_names for rule in self])))

def save(self, path):

try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to save a RuleSet to a file. Please run\npip install pandas")

if len(self) == 0:
if hasattr(path, "write"):
path.write(pd.DataFrame())
else:
pd.DataFrame().to_csv(path)
return

idx = copy(RuleSet.condition_index)
if self.rule_type == ClassificationRule:
idx += RuleSet.classification_rule_index
elif self.rule_type == RegressionRule:
idx += RuleSet.regression_rule_index
elif self.rule_type == Rule:
idx += RuleSet.rule_index
else:
raise TypeError(f"Impossible to save rule type {self.rule_type}")

dfs = [
self.rule_to_series(
(i, r),
index=idx,
)
for i, r in enumerate(self.rules)
]
if len(dfs) > 0:
df = pd.concat(dfs, axis=1).T
else:
df = pd.DataFrame(columns=idx)
if hasattr(path, "write"):
path.write(df)
else:
df.to_csv(path)

# noinspection PyProtectedMember
@staticmethod
def rule_to_series(irule: Tuple[int, Rule], index: list) -> "pd.Series":
try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")
i = irule
rule = irule
name = f"R_{i}({len(rule)})"
sr = pd.Series(data=[str(getattr(rule, ind)) for ind in index], name=name, index=index, dtype=str)
return sr

@staticmethod
def series_to_rule(srule: "pd.Series") -> Rule:

condition_index = {c: None for c in RuleSet.condition_index}

for ind in srule.index:
if ind == "Unnamed: 0":
continue
if ind not in RuleSet.classification_rule_index + RuleSet.condition_index:
raise IndexError(f"Invalid rule attribute '{ind}'")

if "std" in srule.index:
rule = RegressionRule()
rule_idx = copy(RuleSet.regression_rule_index)
elif "criterion" in srule.index:
rule = ClassificationRule()
rule_idx = copy(RuleSet.classification_rule_index)
else:
rule = Rule()
rule_idx = copy(RuleSet.rule_index)

for rule_ind in srule.index:
str_value = str(srule[rule_ind])
if rule_ind in condition_index:
condition_index[rule_ind] = ast.literal_eval(str_value)
# setattr(condition, rule_ind, ast.literal_eval(str_value))
elif rule_ind in rule_idx:
if rule_ind == "activation":
setattr(rule, f"_{rule_ind}", Activation(str_value))
elif str_value == "nan":
setattr(rule, f"_{rule_ind}", np.nan)
elif rule_ind == "sign":
setattr(rule, f"_{rule_ind}", str_value)
else:
if rule_ind == "prediction":  # Prediction can be a str in case of classification
try:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
except ValueError:
setattr(rule, f"_{rule_ind}", str_value)
else:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
else:
continue

rule._condition = HyperrectangleCondition(**condition_index)
return rule

• abc.ABC

Class variables

var CHECK_DUPLICATED
var NLINES
var all_features_indexes
var classification_rule_index
var condition_index
var regression_rule_index
var rule_index

Static methods

def check_duplicated_rules(rules, name_or_index: str = 'index')
Expand source code
@staticmethod
def check_duplicated_rules(rules, name_or_index: str = "index"):
if name_or_index == "index":
str_rules = [str(r.features_indexes) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
else:
str_rules = [str(r.features_names) + str(r.bmins) + str(r.bmaxs) + str(r.prediction) for r in rules]
if len(set(str_rules)) < len(str_rules):
duplicated = {}
for r in str_rules:
if r not in duplicated:
duplicated[r] = 0
duplicated[r] += 1
duplicated = [
f"{r}: {duplicated[r]} (positions {[i for i, x in enumerate(str_rules) if x == r]})\n"
f"   underlying rules : {[str(rules[i]) for i in [i for i, x in enumerate(str_rules) if x == r]]}"
for r in duplicated
if duplicated[r] > 1
]
s = "\n -".join(duplicated)
raise ValueError(f"There are {len(duplicated)} duplicated rules in your ruleset !\n {s}")
def rule_to_series(irule: Tuple[int, Rule], index: list) ‑> pd.Series
Expand source code
@staticmethod
def rule_to_series(irule: Tuple[int, Rule], index: list) -> "pd.Series":
try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")
i = irule
rule = irule
name = f"R_{i}({len(rule)})"
sr = pd.Series(data=[str(getattr(rule, ind)) for ind in index], name=name, index=index, dtype=str)
return sr
def series_to_rule(srule: pd.Series) ‑> Rule
Expand source code
@staticmethod
def series_to_rule(srule: "pd.Series") -> Rule:

condition_index = {c: None for c in RuleSet.condition_index}

for ind in srule.index:
if ind == "Unnamed: 0":
continue
if ind not in RuleSet.classification_rule_index + RuleSet.condition_index:
raise IndexError(f"Invalid rule attribute '{ind}'")

if "std" in srule.index:
rule = RegressionRule()
rule_idx = copy(RuleSet.regression_rule_index)
elif "criterion" in srule.index:
rule = ClassificationRule()
rule_idx = copy(RuleSet.classification_rule_index)
else:
rule = Rule()
rule_idx = copy(RuleSet.rule_index)

for rule_ind in srule.index:
str_value = str(srule[rule_ind])
if rule_ind in condition_index:
condition_index[rule_ind] = ast.literal_eval(str_value)
# setattr(condition, rule_ind, ast.literal_eval(str_value))
elif rule_ind in rule_idx:
if rule_ind == "activation":
setattr(rule, f"_{rule_ind}", Activation(str_value))
elif str_value == "nan":
setattr(rule, f"_{rule_ind}", np.nan)
elif rule_ind == "sign":
setattr(rule, f"_{rule_ind}", str_value)
else:
if rule_ind == "prediction":  # Prediction can be a str in case of classification
try:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
except ValueError:
setattr(rule, f"_{rule_ind}", str_value)
else:
setattr(rule, f"_{rule_ind}", ast.literal_eval(str_value))
else:
continue

rule._condition = HyperrectangleCondition(**condition_index)
return rule

Instance variables

var activation : Optional[None]

Returns the Activation vector's data in a form of a 1-D np.ndarray, or None if not available.

Returns:

Union[None, np.ndarray] of the form [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, …]

Expand source code
@property
def activation(self) -> Union[None, np.ndarray]:
"""Returns the Activation vector's data in a form of a 1-D np.ndarray, or None if not available.

Returns:
--------
Union[None, np.ndarray]
of the form [0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, ...]
"""
if self._activation:
return self._activation.raw
return None
var activation_available : bool

Returns True if the RuleSet has an activation vector, and if this Activation's object data is available.

Expand source code
@property
def activation_available(self) -> bool:
"""Returns True if the RuleSet has an activation vector, and if this Activation's object data is available."""
if self._activation is None:
return False
if self._activation.data_format == "file":
return self._activation.data.is_file()
else:
return self._activation.data is not None
var rules : List[Rule]
Expand source code
@property
def rules(self) -> List[Rule]:
return self._rules
var ruleset_coverage : float

Coverage is the fraction of points equal to 1 in the activation vector

Expand source code
@property
def ruleset_coverage(self) -> float:
"""Coverage is the fraction of points equal to 1 in the activation vector"""
if not self.activation_available:
return self._coverage
else:
return self._activation.coverage
var stacked_activations_available : bool

Returns True is the RuleSet has its rules' stacked activations.

Expand source code
@property
def stacked_activations_available(self) -> bool:
"""Returns True is the RuleSet has its rules' stacked activations."""
if self.stack_activation is None:
return False
return True
var to_hash : Tuple[str]
Expand source code
@property
def to_hash(self) -> Tuple[str]:
if len(self) == 0:
return "rs",
to_hash = ("rs",)
for r in self:
rule_hash = r.to_hash[1:]
to_hash += rule_hash

Methods

def append(self, rule: Rule, update_activation: bool = True)

Appends a new rule to self. The updates of activation vector and the stacked activation vectors can be blocked by specifying update_activation=False. Otherwise, will use self.remember_activation and self.stack_activation to determine if the updates should be done or not.

Expand source code
def append(self, rule: Rule, update_activation: bool = True):
"""Appends a new rule to self. The updates of activation vector and the stacked activation vectors can be
blocked by specifying update_activation=False. Otherwise, will use self.remember_activation and
self.stack_activation to determine if the updates should be done or not."""
if not isinstance(rule, Rule):
raise TypeError(f"RuleSet's append method expects a Rule object, got {type(rule)}")
if self.rule_type is None:
self.rule_type = type(rule)
else:
if not isinstance(rule, self.rule_type):
raise TypeError(
f"Ruleset previously had rules of type {self.rule_type}, so can not add rule of type {type(rule)}"
)
remember_activation = self.remember_activation
stack_activation = self.stack_activation
if not update_activation:
self.remember_activation = False
self.stack_activation = False
self.remember_activation = remember_activation
self.stack_activation = stack_activation
def calc_activation(self, xs: Union[ForwardRef('pd.DataFrame'), numpy.ndarray])

Uses input xs features data to compute the activation vector of all rules in self, and updates self's activation if self.remember_activation is True and stacked activation if self.stack_activation is True

Expand source code
def calc_activation(self, xs: Union[np.ndarray, "pd.DataFrame"]):
"""Uses input xs features data to compute the activation vector of all rules in self, and updates self's
activation if self.remember_activation is True and stacked activation if self.stack_activation is True"""
if len(self) == 0:
raise ValueError("Can not use calc_activation : The ruleset is empty!")
[rule.calc_activation(xs) for rule in self.rules]

if self.remember_activation:
self._activation = None
self.compute_self_activation()
if self.stack_activation:
self.stacked_activations = None
self.compute_stacked_activation()
def compute_self_activation(self)

Computes the activation vector of self from its rules, using time-efficient Activation.multi_logical_or.

Expand source code
def compute_self_activation(self):
"""Computes the activation vector of self from its rules, using time-efficient Activation.multi_logical_or."""
if len(self) == 0:
return
activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self._activation = Activation.multi_logical_or([r._activation for r in self])
def compute_stacked_activation(self)

Computes the stacked activation vectors of self from its rules.

Expand source code
def compute_stacked_activation(self):
"""Computes the stacked activation vectors of self from its rules."""
if len(self) == 0:
return
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked activations requies pandas. Please run\npip install pandas")

activations_available = all([r.activation_available for r in self])
if activations_available:
# noinspection PyProtectedMember
self.stacked_activations = pd.DataFrame(
data=np.array([r.activation for r in self]).T, columns=[str(r.condition) for r in self]
)
def del_activation(self)

Deletes the activation vector's data of self, but not the object itself, so any computed attribute remains available

Expand source code
def del_activation(self):
"""Deletes the activation vector's data of self, but not the object itself, so any computed attribute remains
available"""
if hasattr(self, "_activation") and self._activation is not None:
self._activation.delete()
def del_activations(self)

Deletes the data, but not the relevent attributes, of the activation vector or each rules in self.

Expand source code
def del_activations(self):
"""Deletes the data, but not the relevent attributes, of the activation vector or each rules in self."""
for r in self:
r.del_activation()
def del_stacked_activations(self)

Deletes stacked activation vectors of self. Set it to None.

Expand source code
def del_stacked_activations(self):
"""Deletes stacked activation vectors of self. Set it to None."""
if hasattr(self, "stacked_activations") and self.stacked_activations is not None:
del self.stacked_activations
self.stacked_activations = None
def evaluate(self, xs: Union[ForwardRef('pd.DataFrame'), numpy.ndarray]) ‑> Activation

Computes and returns the activation vector from an array of features.

Parameters

xs : Union[pd.DataFrame, np.ndarray]
The features on which the check whether the rule is activated or not. Must be a 2-D np.ndarray or pd.DataFrame.

Returns

Activation

Expand source code
def evaluate(self, xs: Union["pd.DataFrame", np.ndarray]) -> Activation:
"""Computes and returns the activation vector from an array of features.

Parameters
----------
xs: Union[pd.DataFrame, np.ndarray]
The features on which the check whether the rule is activated or not. Must be a 2-D np.ndarray
or pd.DataFrame.

Returns
-------
Activation
"""
if len(self) == 0:
raise ValueError("Can not use evaluate : The ruleset is empty!")
activations = [rule.evaluate(xs) for rule in self.rules]
return Activation.multi_logical_or(activations)
def fit(self, xs: Union[ForwardRef('pd.DataFrame'), numpy.ndarray, ForwardRef(None)], y: Union[numpy.ndarray, ForwardRef('pd.Series')])
Expand source code
def fit(self, xs: Optional[Union["pd.DataFrame", np.ndarray]], y: Union[np.ndarray, "pd.Series"]) -> None:
if len(self) == 0:
logger.debug("Ruleset is empty. Nothing to fit.")
return

if RuleSet.STACKED_FIT:
try:
import pandas as pd
except ImportError:
raise ImportError("RuleSet's stacked fit requies pandas. Please run\npip install pandas")

clean_activation = not self.stack_activation
self.stack_activation = True
self.calc_activation(xs)
self.stack_activation = not clean_activation

if isinstance(y, np.ndarray) and not len(self.stacked_activations.index) == y.shape:
raise IndexError("Stacked activation and y have different number of rows. Use pd.Series for y to"
"reindex stacked activations automatically.")
else:
self.stacked_activations.index = y.index
pred, prediction_vector = self.calc_prediction(y)

if clean_activation:
self.del_stacked_activations()
else:
self.stacked_activations.index = pd.RangeIndex(len(y.index))

prediction_vector.index = self.stack_activations.index
criterion = self.calc_criterion(prediction_vector, y)
to_drop = []

for ir in range(len(self)):
self._rules[ir]._criterion = criterion.iloc[ir] if isinstance(criterion, pd.Series) else criterion
self._rules[ir]._prediction = pred.iloc[ir] if isinstance(pred, pd.Series) else pred

self._rules[ir].check_thresholds("prediction")
if not self._rules[ir].good:
to_drop.append(self._rules[ir])
break
else:
[r.fit(xs=xs, y=y) for r in self]
to_drop = [r for r in self if not r.good]

if len(to_drop) > 0:
rules = [r for r in self.rules if r not in to_drop]
self._rules = []
self._activation.clear()
self.del_stacked_activations()
for r in rules:
self.append(r)
def get_features_count(self) ‑> List[Tuple[Any, int]]

Get a counter of all different features in the ruleset. If names are not available, will use indexes.

Returns:

count : List[Tuple[Any, int]] Counter of all different features in the ruleset

Expand source code
def get_features_count(self) -> List[Tuple[Any, int]]:
"""
Get a counter of all different features in the ruleset. If names are not available, will use indexes.

Returns:
--------
count : List[Tuple[Any, int]]
Counter of all different features in the ruleset
"""
# noinspection PyUnresolvedReferences
if len(self) == 0:
return []
if len(self.features_names) > 0:
var_in = list(itertools.chain(*[rule.features_names for rule in self]))
else:
var_in = list(itertools.chain(*[rule.feautres_indexes for rule in self]))
count = Counter(var_in)

count = count.most_common()
return count
Expand source code
try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to read a RuleSet from a file. Please run\npip install pandas")

else:
if rules.empty:
self._rules = []
else:
self._rules = [self.series_to_rule(rules.loc[r]) for r in rules.index]

if len(self._rules) > 0:
self.rule_type = type(self._rules)
if self.remember_activation:
self.compute_self_activation()
if self.stack_activation:
self.compute_stacked_activation()
self.features_names = list(set(traverse([rule.features_names for rule in self])))
def save(self, path)
Expand source code
def save(self, path):

try:
import pandas as pd
except ImportError:
raise ImportError("'pandas' is needed to save a RuleSet to a file. Please run\npip install pandas")

if len(self) == 0:
if hasattr(path, "write"):
path.write(pd.DataFrame())
else:
pd.DataFrame().to_csv(path)
return

idx = copy(RuleSet.condition_index)
if self.rule_type == ClassificationRule:
idx += RuleSet.classification_rule_index
elif self.rule_type == RegressionRule:
idx += RuleSet.regression_rule_index
elif self.rule_type == Rule:
idx += RuleSet.rule_index
else:
raise TypeError(f"Impossible to save rule type {self.rule_type}")

dfs = [
self.rule_to_series(
(i, r),
index=idx,
)
for i, r in enumerate(self.rules)
]
if len(dfs) > 0:
df = pd.concat(dfs, axis=1).T
else:
df = pd.DataFrame(columns=idx)
if hasattr(path, "write"):
path.write(df)
else:
df.to_csv(path)
def set_features_indexes(self)
Expand source code
def set_features_indexes(self):
if len(RuleSet.all_features_indexes) > 0:
self.features_indexes = [RuleSet.all_features_indexes[f] for f in self.features_names]
for r in self._rules:
# noinspection PyProtectedMember
r._condition._features_indexes = [RuleSet.all_features_indexes[f] for f in r.features_names]
else:
list(set(itertools.chain(*[rule.features_indexes for rule in self])))
def sort(self, criterion: str = None, reverse: bool = False)

Sorts the RuleSet.

• If criterion is not speficied: Will sort the rules according to : 1. The number of features they talk about 2. For a same number of features (sorted in alphabetical order, or index if names are not available, optionally reversed), the bmins and bmaxs of the rules
• If criterion is specified, it must be an float or interger attribute of rule, condition or activation. Then sorts according to this criterion, optionally reversed.
Expand source code
def sort(self, criterion: str = None, reverse: bool = False):
"""Sorts the RuleSet.

* If criterion is not speficied:
Will sort the rules according to :
1. The number of features they talk about
2. For a same number of features (sorted in alphabetical order, or index if names are not available,
optionally reversed), the bmins and bmaxs of the rules
* If criterion is specified, it must be an float or interger attribute of rule, condition or activation. Then
sorts according to this criterion, optionally reversed.
"""
if len(self) == 0:
return

if criterion is None or criterion == "":
if not (hasattr(self.condition, "bmins") and hasattr(self.condition, "bmaxs")):
return
# The set of all the features the RuleSet talks about
which = "index"
if len(self.features_names) > 0:
which = "name"
fnames_or_indexes = list(set([str(r.features_names) for r in self]))
else:
fnames_or_indexes = list(set([str(r.features_indexes) for r in self]))
dict_names = {}
lmax = 1
for f in fnames_or_indexes:
l_ = len(ast.literal_eval(f))
if l_ > lmax:
lmax = l_
if l_ not in dict_names:
dict_names[l_] = []
dict_names[l_].append(f)
for l_ in dict_names:
dict_names[l_].sort(reverse=reverse)
fnames_or_indexes = []
for l_ in range(1, lmax + 1):
if l_ in dict_names:
fnames_or_indexes += dict_names[l_]

rules_by_fnames = OrderedDict({f: [] for f in fnames_or_indexes})
for rule in self:
# noinspection PyUnresolvedReferences
if which == "name":
v = str(rule.features_names)
else:
v = str(rule.features_indexes)
rules_by_fnames[v].append(rule)
rules_by_fnames = {
n: sorted(rules_by_fnames[n], key=lambda x: x.condition.bmins + x.condition.bmaxs)
for n in rules_by_fnames
}
self._rules = []
for n in rules_by_fnames:
self._rules += rules_by_fnames[n]
elif hasattr(self, criterion):
self._rules = sorted(self, key=lambda x: getattr(x, criterion), reverse=reverse)
else:
raise ValueError(f"Can not sort RuleSet according to criterion {criterion}")
if self.stack_activation:
self.stacked_activations = self.stacked_activations[[str(r.condition) for r in self]]