def postcondition(self, instance, new_instance, use_tensor=True):
new_period_x = self.features['loan_duration'].get_feature_value(
new_instance, use_tensor, space='x')
new_credit_x = self.features['credit_amount'].get_feature_value(
new_instance, use_tensor, space='x')
return cond.op_and(
cond.op_and(cond.op_gt(new_period_x, 0, use_tensor, scale=100.),
cond.op_lt(new_period_x, 120, use_tensor, scale=100.),
use_tensor),
cond.op_and(
cond.op_gt(new_credit_x, 0, use_tensor, scale=100000.),
cond.op_lt(new_credit_x, 100000, use_tensor, scale=100000.),
use_tensor), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_edu = self.features['Education num'].get_feature_value(new_instance, use_tensor,
space='x')
old_edu = self.features['Education num'].get_feature_value(instance, use_tensor,
space='x')
new_age = self.features['Age'].get_feature_value(new_instance, use_tensor, space='x')
old_age = self.features['Age'].get_feature_value(instance, use_tensor, space='x')
return cond.op_and(cond.op_and(cond.op_gt(new_age, old_age, use_tensor, scale=100.),
cond.op_lt(new_age, 120, use_tensor, scale=100.),
use_tensor),
cond.op_and(cond.op_gt(new_edu, old_edu, use_tensor, scale=10.),
cond.op_lt(new_edu, 16.5, use_tensor, scale=10.),
use_tensor),
use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_age = self.features['age_in_years'].get_feature_value(new_instance,
use_tensor,
space='x')
old_age = self.features['age_in_years'].get_feature_value(instance,
use_tensor,
space='x')
return cond.op_and(
cond.op_gt(new_age, old_age, use_tensor, scale=100.),
cond.op_lt(new_age, 120, use_tensor, scale=100.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_rat = self.features['DTIRat'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_rat, 0, use_tensor, scale=100.),
cond.op_lt(new_rat, 100, use_tensor, scale=100.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_unit = self.features['NumUnits'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_unit, 0, use_tensor, scale=10.),
cond.op_lt(new_unit, 5, use_tensor, scale=10.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_credit = self.features['CreditScore'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_credit, 300, use_tensor, scale=1000.),
cond.op_lt(new_credit, 850, use_tensor, scale=1000.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_term = self.features['OrLoanTerm'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_term, 0, use_tensor, scale=100.),
cond.op_lt(new_term, 800, use_tensor, scale=100.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_rate = self.features['OrInterestRate'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_rate, 0, use_tensor, scale=100.),
cond.op_lt(new_rate, 30, use_tensor, scale=100.), use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_gain = self.features['Capital Gain'].get_feature_value(new_instance, use_tensor)
return cond.op_and(cond.op_gt(new_gain, 0, use_tensor, scale=100000.),
cond.op_lt(new_gain, 100000, use_tensor, scale=100000.),
use_tensor)
def precondition(self, instance, use_tensor=True):
loss = self.features['Capital Loss'].get_feature_value(instance, use_tensor, space='x')
return cond.op_lt(loss, 1, use_tensor, scale=10000.)
def postcondition(self, instance, new_instance, use_tensor=True):
new_hours = self.features['Hours/Week'].get_feature_value(new_instance, use_tensor,
space='x')
return cond.op_and(cond.op_gt(new_hours, 0, use_tensor, scale=100.),
cond.op_lt(new_hours, 90, use_tensor, scale=100.),
use_tensor)
def postcondition(self, instance, new_instance, use_tensor=True):
new_loss = self.features['Capital Loss'].get_feature_value(new_instance, use_tensor)
return cond.op_and(cond.op_gt(new_loss, 1, use_tensor, scale=10000.),
cond.op_lt(new_loss, 5000, use_tensor, scale=10000.),
use_tensor)
def precondition(self, instance, use_tensor=True):
gain = self.features['Capital Gain'].get_feature_value(instance, use_tensor, space='x')
return cond.op_lt(gain, 1, use_tensor, scale=10000.)