def _init_features(self, feature_names, X):
"""
Init the features the model will begin evolving. If feature names have not been specified they are
generated as x1, x2, ..., xk.
Parameters
----------
feature_names : list of strings
Names of the features.
X : ndarray
training data
"""
features = []
if feature_names is None:
feature_names = ['x' + str(x) for x in range(X.shape[1])]
for i, name in enumerate(feature_names):
features.append(
Feature(X[:, i],
name,
name,
original_variable=self.preserve_originals))
for _ in range(self.range_operators):
features.append(
RangeOperation(self.variable_type_indices, feature_names, X))
return feature_names, features
def _swap_range_operators(self, X):
for f in self.current_features_:
if type(f) == RangeOperation and f.original_variable:
self.current_features_.remove(f)
for _ in range(self.range_operators):
self.current_features_.append(
RangeOperation(self.variable_type_indices,
self.predictor_names_, X))
def build_basis_from_features(infix_features, predictor_names, X, variable_type_indices, operators):
basis = np.zeros((X.shape[0], len(infix_features)))
for j, f in enumerate(infix_features):
if variable_type_indices and f.startswith('RangeOperation'):
range_operation = RangeOperation(variable_type_indices, predictor_names, X, string=f)
basis[:, j] = np.squeeze(range_operation.value)
elif f in predictor_names:
variable_index = predictor_names.index(f)
basis[:, j] = X[:, variable_index]
else:
operation_stack = build_operation_stack(f)
basis[:, j] = get_feature_value(operation_stack, predictor_names, X, variable_type_indices, operators)
return basis
def get_feature_value(stack, feature_names, X, variable_type_indices, operators):
variables_stack = []
while len(stack) > 0:
current = stack.pop()
if variable_type_indices and current.startswith('RangeOperation'):
range_operation = RangeOperation(variable_type_indices, feature_names, X, string=current)
variables_stack.append(np.squeeze(range_operation.value))
elif current in feature_names:
variable_index = feature_names.index(current)
variables_stack.append(X[:, variable_index])
elif operators.contains(current):
operator = operators.get(current)
variables = []
for _ in range(operator.parity):
variables.append(variables_stack.pop())
result = operator.operation(*variables)
variables_stack.append(result)
return variables_stack.pop()