def train_r_0(self): """ Trains the rule model that outputs rules that predict for class 0. """ # the rules generated predict for label 0. br_0 = BooleanRuleCG(CNF = False) br_0.fit(self.binarized_train_data, self.train_labels) self.r_0 = br_0 return
def train_r_1(self): """ Trains the rule model that outputs rules that predict for class 1. """ # the rules generated predict for label 0, so we hvae to # invert the labels to generate rules that predict # for label 1. br_1 = BooleanRuleCG(CNF = False) inverted_train_labels = [] for label in self.train_labels: if label: inverted_train_labels.append(0) else: inverted_train_labels.append(1) br_1.fit(self.binarized_train_data, np.array(inverted_train_labels)) self.r_1 = br_1 return
def aix360_rules_wrapper(
df_anomalies,
numerical_cols,
categorical_cols,
rule_algorithm="",
simplify_rules=False,
model_params={},
):
"""
Rules obtained using brlg or logrr.
Parameters
----------
df_anomalies : TYPE
DESCRIPTION.
numerical_cols : TYPE
DESCRIPTION.
categorical_cols : TYPE
DESCRIPTION.
rule_algorithm : TYPE, optional
DESCRIPTION. The default is "".
simplify_rules : TYPE, optional
DESCRIPTION. The default is False.
model_params : TYPE, optional
DESCRIPTION. The default is {}.
Raises
------
ValueError
DESCRIPTION.
Returns
-------
df_rules_inliers : TYPE
DESCRIPTION.
df_rules_outliers : TYPE
DESCRIPTION.
"""
# Define variables
feature_cols = numerical_cols + categorical_cols
X = df_anomalies[feature_cols].astype(float)
y = df_anomalies["predictions"].astype(int)
y_inliers = np.array([x if x > 0 else 0
for x in y]) # Defined for inliers levels
y_outliers = np.array([1 if x < 0 else 0
for x in y]) # Defined for outlier levels
# Feature binarize
fb = FeatureBinarizer(negations=True,
returnOrd=True,
colsCateg=categorical_cols,
numThres=90)
X_fb, X_std = fb.fit_transform(X)
# Choose model
if rule_algorithm == "brlg":
# Default params
if "lambda0" not in model_params.keys():
model_params["lambda0"] = 1e-3
if "lambda1" not in model_params.keys():
model_params["lambda1"] = 1e-3
if "CNF" not in model_params.keys():
model_params["CNF"] = False
# Inliers
model_rules = BooleanRuleCG(**model_params)
model_rules.fit(X_fb, y_inliers)
list_rules_inliers = model_rules.explain()["rules"]
# Outliers
model_rules = BooleanRuleCG(**model_params)
model_rules.fit(X_fb, y_outliers)
list_rules_outliers = model_rules.explain()["rules"]
elif rule_algorithm == "logrr":
# Default params
if "lambda0" not in model_params.keys():
model_params["lambda0"] = 0.005
if "lambda1" not in model_params.keys():
model_params["lambda1"] = 0.001
# Obtain rules [Inliers]
model_rules = LogisticRuleRegression(**model_params)
model_rules.fit(X_fb, y_inliers, X_std)
df_rules = model_rules.explain()
try:
# Inliers
df_rules_inliers = df_rules[
(df_rules["coefficient"] > 0)
& (df_rules["rule/numerical feature"] != "(intercept)")]
list_rules_inliers = list(
df_rules_inliers["rule/numerical feature"])
# Outliers
df_rules_outliers = df_rules[
(df_rules["coefficient"] < 0)
& (df_rules["rule/numerical feature"] != "(intercept)")]
list_rules_outliers = list(
df_rules_outliers["rule/numerical feature"])
except KeyError:
# Inliers
df_rules_inliers = df_rules[(df_rules["coefficient"] > 0)
& (df_rules["rule"] != "(intercept)")]
list_rules_inliers = list(df_rules_inliers["rule"])
# Outliers
df_rules_outliers = df_rules[(df_rules["coefficient"] < 0)
& (df_rules["rule"] != "(intercept)")]
list_rules_outliers = list(df_rules_outliers["rule"])
else:
raise ValueError(
"Argument {0} not recognised -- use 'brlg' or 'logrr' instead")
# Turn to DF
list_rules_inliers = [x.replace("AND", "&") for x in list_rules_inliers]
list_rules_outliers = [x.replace("AND", "&") for x in list_rules_outliers]
df_inliers = turn_rules_to_df(list_rules=list_rules_inliers,
list_cols=feature_cols)
df_outliers = turn_rules_to_df(list_rules=list_rules_outliers,
list_cols=feature_cols)
# Get rule size
df_inliers = df_inliers.reset_index(drop=True)
df_inliers["size_rules"] = [len(x.split("&")) for x in list_rules_inliers]
df_outliers = df_outliers.reset_index(drop=True)
df_outliers["size_rules"] = [
len(x.split("&")) for x in list_rules_outliers
]
# Prune rules
if simplify_rules:
if len(df_inliers) > 0:
df_rules_pruned = simplifyRules(
df_inliers.drop(columns=["size_rules"]), categorical_cols)
df_rules_pruned = df_rules_pruned.reset_index().merge(
df_inliers.reset_index()[["index", "size_rules"]], how="left")
df_rules_pruned.index = df_rules_pruned["index"]
df_rules_pruned = df_rules_pruned.drop(columns=["index"],
errors="ignore")
df_rules_inliers = df_rules_pruned.copy()
df_rules_inliers["rule_prediction"] = 1
else:
df_rules_inliers = pd.DataFrame()
if len(df_outliers) > 0:
df_rules_pruned = simplifyRules(
df_outliers.drop(columns=["size_rules"]), categorical_cols)
df_rules_pruned = df_rules_pruned.reset_index().merge(
df_outliers.reset_index()[["index", "size_rules"]], how="left")
df_rules_pruned.index = df_rules_pruned["index"]
df_rules_pruned = df_rules_pruned.drop(columns=["index"],
errors="ignore")
df_rules_outliers = df_rules_pruned.copy()
df_rules_outliers["rule_prediction"] = -1
else:
df_rules_outliers = pd.DataFrame()
else:
df_rules_inliers = df_inliers
df_rules_inliers["rule_prediction"] = 1
df_rules_outliers = df_outliers
df_rules_outliers["rule_prediction"] = -1
return df_rules_inliers, df_rules_outliers