melroycaeiro / pd-denoising-using-optimized-wavelet Goto Github PK

while executing the above file observed the below error.
ValueError: Found input variables with inconsistent numbers of samples: [5, 4000]

is this due to clean file.txt not available or I missed something in my code?

can you please help me with this?

full error mentioned below,
ValueError Traceback (most recent call last)
~\AppData\Local\Temp\ipykernel_7076\3079603772.py in
61
62 # Optimization initialization
---> 63 res = minimize(problem=problem,
64 algorithm=algorithm,
65 termination=stop_criteria,

~\anaconda3\lib\site-packages\pymoo\optimize.py in minimize(problem, algorithm, termination, copy_algorithm, copy_termination, **kwargs)
65
66 # actually execute the algorithm
---> 67 res = algorithm.run()
68
69 # store the deep copied algorithm in the result object

~\anaconda3\lib\site-packages\pymoo\core\algorithm.py in run(self)
139 def run(self):
140 while self.has_next():
--> 141 self.next()
142 return self.result()
143

~\anaconda3\lib\site-packages\pymoo\core\algorithm.py in next(self)
159 # call the advance with them after evaluation
160 if infills is not None:
--> 161 self.evaluator.eval(self.problem, infills, algorithm=self)
162 self.advance(infills=infills)
163

~\anaconda3\lib\site-packages\pymoo\core\evaluator.py in eval(self, problem, pop, skip_already_evaluated, evaluate_values_of, count_evals, **kwargs)
67
68 # do the actual evaluation - call the sub-function to set the corresponding values to the population
---> 69 self._eval(problem, pop[I], evaluate_values_of, **kwargs)
70
71 # update the function evaluation counter

~\anaconda3\lib\site-packages\pymoo\core\evaluator.py in _eval(self, problem, pop, evaluate_values_of, **kwargs)
88
89 # call the problem to evaluate the solutions
---> 90 out = problem.evaluate(X, return_values_of=evaluate_values_of, return_as_dictionary=True, **kwargs)
91
92 # for each of the attributes set it to the problem

~\anaconda3\lib\site-packages\pymoo\core\problem.py in evaluate(self, X, return_values_of, return_as_dictionary, *args, **kwargs)
185
186 # this is where the actual evaluation takes place
--> 187 _out = self.do(X, return_values_of, *args, **kwargs)
188
189 out = {}

~\anaconda3\lib\site-packages\pymoo\core\problem.py in do(self, X, return_values_of, *args, **kwargs)
225 # do the function evaluation
226 if self.elementwise:
--> 227 self._evaluate_elementwise(X, out, *args, **kwargs)
228 else:
229 self._evaluate_vectorized(X, out, *args, **kwargs)

~\anaconda3\lib\site-packages\pymoo\core\problem.py in _evaluate_elementwise(self, X, out, *args, **kwargs)
243
244 # execute the runner
--> 245 elems = self.elementwise_runner(f, X)
246
247 # for each evaluation call

~\anaconda3\lib\site-packages\pymoo\core\problem.py in call(self, f, X)
25
26 def call(self, f, X):
---> 27 return [f(x) for x in X]
28
29

~\anaconda3\lib\site-packages\pymoo\core\problem.py in (.0)
25
26 def call(self, f, X):
---> 27 return [f(x) for x in X]
28
29

~\anaconda3\lib\site-packages\pymoo\core\problem.py in call(self, x)
18 def call(self, x):
19 out = dict()
---> 20 self.problem._evaluate(x, out, *self.args, **self.kwargs)
21 return out
22

~\AppData\Local\Temp\ipykernel_7076\3079603772.py in _evaluate(self, x, out, *args, **kwargs)
32
33 f1 = SNR(clean_signal_upsampled, denoising)
---> 34 f2 = MSE(clean_signal_upsampled, denoising)
35 f3 = CC(clean_signal_upsampled, denoising)
36

~\AppData\Local\Temp\ipykernel_7076\1803469571.py in MSE(clean, noisy)
14 As such, we intend to optimize this closest to 0 [minimization]
15 """
---> 16 return (mean_squared_error(clean, noisy))
17
18 def SNR(clean, noisy):

~\anaconda3\lib\site-packages\sklearn\metrics_regression.py in mean_squared_error(y_true, y_pred, sample_weight, multioutput, squared)
436 0.825...
437 """
--> 438 y_type, y_true, y_pred, multioutput = _check_reg_targets(
439 y_true, y_pred, multioutput
440 )

~\anaconda3\lib\site-packages\sklearn\metrics_regression.py in _check_reg_targets(y_true, y_pred, multioutput, dtype)
92 the dtype argument passed to check_array.
93 """
---> 94 check_consistent_length(y_true, y_pred)
95 y_true = check_array(y_true, ensure_2d=False, dtype=dtype)
96 y_pred = check_array(y_pred, ensure_2d=False, dtype=dtype)

~\anaconda3\lib\site-packages\sklearn\utils\validation.py in check_consistent_length(*arrays)
330 uniques = np.unique(lengths)
331 if len(uniques) > 1:
--> 332 raise ValueError(
333 "Found input variables with inconsistent numbers of samples: %r"
334 % [int(l) for l in lengths]

ValueError: Found input variables with inconsistent numbers of samples: [5, 4000]

作者提供的数据集，缺少几个数据，导致代码无法重现，期待作者的回复！