def __init__(self,block,stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
# run function
self.clf = KernelRidge(**inputs['kwargs'])
self.clf.fit(*inputs['args'])
def __init__(self,block,stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
# run function
from sklearn.kernel_ridge import KernelRidge
self.clf = KernelRidge(**inputs['kwargs'])
self.clf.fit(*inputs['args'])
def LeaveOneOut(block, stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
# run function
from sklearn.model_selection import LeaveOneOut
loo = LeaveOneOut(**inputs['kwargs'])
output_dict = {}
fold_gen = loo.split(*inputs['args'])
output_dict["api"] = loo
output_dict["fold_gen"] = fold_gen
def KFold(block, stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
# run function
from sklearn.model_selection import KFold
kf = KFold(**inputs['kwargs'])
output_dict = {}
fold_gen = kf.split(*inputs['args'])
output_dict["api"] = kf
output_dict["fold_gen"] = fold_gen
return output_dict
def scorer_regression(block, stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
output_dict = {}
# run function
from sklearn.metrics import make_scorer
for i in inputs['args']:
if i == 'mean_squared_error' or i == 'mean_absolute_error' or i == 'accuracy_score':
score_func_1 = i
output_dict['score_' + str(i)] = make_scorer(
score_func=score_func_1, *inputs['args'], **inputs['kwargs'])
else:
sys.exit("Function not incoporated as yet")
return output_dict
def train_test_split(block, stack):
# evaluate function inputs
inputs = evaluate_inputs(block['inputs'], stack)
# run function
from sklearn.model_selection import train_test_split
function_output_ = train_test_split(*inputs['args'], **inputs['kwargs'])
n_out = len(function_output_)
assert n_out == 2 * len(inputs['args'])
# create outputs
# names are in this order: train1, test1, train2, test2, train3, test3
output_dict = {}
for i in range(n_out):
if i % 2 == 0:
output_dict["train%i" % (int(i / 2) + 1)] = function_output_[i]
else:
output_dict["test%i" % (int(i / 2) + 1)] = function_output_[i]
return output_dict