def compare_ensemble(name='g', N=1, T=50, H=4):
from EnsembleCE import TreeEnsembleCEExtractor
from sklearn.ensemble import RandomForestClassifier
from utils import Database
S = Database(filename=name)
X, X_ts, y, y_ts, features, types, categories = S.get_dataset(split=True)
mdl = RandomForestClassifier(n_estimators=T, max_depth=H, random_state=0)
mdl = mdl.fit(X, y)
denied_individual = [
i for i in np.where(mdl.predict(X_ts) == 1)[0] if y_ts[i] == 1
]
K = 6
N = np.min([N, len(denied_individual)])
for i in range(N):
x = X_ts[denied_individual[i]]
print('---------- PCC ----------')
cee = TreeEnsembleCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='PCC')
if (action != None): print(cee.explanation_)
print('---------- TLPS ----------')
cee = TreeEnsembleCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='TLPS')
if (action != None): print(cee.explanation_)
print('---------- MAD ----------')
cee = TreeEnsembleCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='MAD')
if (action != None): print(cee.explanation_)
print('---------- Proposed (alp. = 0.01) ----------')
cee = TreeEnsembleCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x, max_mod=K, interaction=True, alpha=0.01)
if (action != None): print(cee.explanation_)
print(
'-----------------------------------------------------------------'
)
def compare_linear(name='g', N=1):
from LinearCE import LinearCEExtractor
from sklearn.linear_model import LogisticRegression
from utils import Database
S = Database(filename=name)
X, X_ts, y, y_ts, features, types, categories = S.get_dataset(split=True)
mdl = LogisticRegression(C=1.0,
penalty='l2',
solver='liblinear',
random_state=0)
mdl = mdl.fit(X, y)
denied_individual = [
i for i in np.where(mdl.predict(X_ts) == 1)[0] if y_ts[i] == 1
]
N = np.min([N, len(denied_individual)])
K = 6
for i in range(N):
x = X_ts[denied_individual[i]]
print('---------- PCC ----------')
cee = LinearCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='PCC')
if (action != None): print(cee.explanation_)
print('---------- TLPS ----------')
cee = LinearCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='TLPS')
if (action != None): print(cee.explanation_)
print('---------- MAD ----------')
cee = LinearCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x,
max_mod=K,
interaction=False,
alpha=-1,
weight_type='MAD')
if (action != None): print(cee.explanation_)
print('---------- Proposed (alp. = 0.1) ----------')
cee = LinearCEExtractor(mdl,
X=X,
Y=y,
FeatureNames=features,
FeatureTypes=types,
Categories=categories)
action = cee.extract(x, max_mod=K, interaction=True, alpha=0.1)
if (action != None): print(cee.explanation_)
print(
'-----------------------------------------------------------------'
)
