I am working on Gaussian Process Regression with Python on NIR spectrum data. I can get some results with GPR and would like to optimize parameters for GPR. I am trying to use GridSearchCV to optimize parameters, but I keep getting an error and could not find any examples that people used GridSearchCV for Gaussian Process (from sklearn.gaussian_process). My quick question is if I can use GridSearchCV for GPR. If not, what would you recommend to use to optimize parameters.
This is my error:
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-# Tuning hyper-parameters for precision
Traceback (most recent call last):
File "", line 1, in runfile('C:/Users/hkim.N04485/Desktop/Python/untitled14.py', wdir='C:/Users/hkim.N04485/Desktop/Python')
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 699, in runfile execfile(filename, namespace)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\spyderlib\widgets\externalshell\sitecustomize.py", line 74, in execfile exec(compile(scripttext, filename, 'exec'), glob, loc)
File "C:/Users/hkim.N04485/Desktop/Python/untitled14.py", line 39, in gp.fit(X1, y1_glucose)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 804, in fit return self._fit(X, y, ParameterGrid(self.param_grid))
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\grid_search.py", line 553, in _fit for parameters in parameter_iterable
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 804, in call while self.dispatch_one_batch(iterator):
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 662, in dispatch_one_batch self._dispatch(tasks)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 570, in _dispatch job = ImmediateComputeBatch(batch)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 183, in init self.results = batch()
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\externals\joblib\parallel.py", line 72, in call return [func(*args, **kwargs) for func, args, kwargs in self.items]
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1550, in _fit_and_score test_score = _score(estimator, X_test, y_test, scorer)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\cross_validation.py", line 1606, in _score score = scorer(estimator, X_test, y_test)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\scorer.py", line 90, in call **self._kwargs)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 1203, in precision_score sample_weight=sample_weight)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 956, in precision_recall_fscore_support y_type, y_true, y_pred = _check_targets(y_true, y_pred)
File "C:\Users\hkim.N04485\Anaconda2\lib\site-packages\sklearn\metrics\classification.py", line 82, in _check_targets "".format(type_true, type_pred))
ValueError: Can't handle mix of multiclass and continuous
How do I fix this?
Here is my code.
tuned_parameters = [{'corr':['squared_exponential'], 'theta0': [0.01, 0.2, 0.8, 1.]},
{'corr':['cubic'], 'theta0': [0.01, 0.2, 0.8, 1.]}]
scores = ['precision', 'recall']
xy_line=(0,1200)
for score in scores:
print("# Tuning hyper-parameters for %s" % score)
print()
gp = GridSearchCV(GaussianProcess(normalize=False), tuned_parameters, cv=5,
scoring='%s_weighted' % score)
gp.fit(X1, y1_glucose)
print("Best parameters set found on development set:")
print()
print(gp.best_params_)
print()
print("Grid scores on development set:")
print()
for params, mean_score, scores in gp.grid_scores_:
print("%0.3f (+/-%0.03f) for %r"
% (mean_score, scores.std() * 2, params))
y_true, y_pred = y2_glucose, gp.predict(X2)
# Scatter plot (reference vs predicted )
fig, ax = plt.subplots(figsize=(11,13))
ax.scatter(y2_glucose,y_pred)
ax.plot(xy_line, xy_line, 'r--')
major_ticks = np.arange(-300,2000,100)
minor_ticks = np.arange(0,1201,100)
ax.set_xticks(minor_ticks)
ax.set_yticks(major_ticks)
ax.grid()
plt.title('1')
ax.set_xlabel('Reference')
ax.set_ylabel('Predicted')
