machine learning - Python : Gaussian Process Regression and GridSearchCV -


i working on gaussian process regression python on nir spectrum data. can results gpr , optimize parameters gpr. trying use gridsearchcv optimize parameters, keep getting error , not find examples people used gridsearchcv gaussian process (from sklearn.gaussian_process). quick question if can use gridsearchcv gpr. if not, recommend use optimize parameters.

this error:

--------------------------------------------------- -# tuning hyper-parameters 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 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) 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 , continuous

how fix this?

here 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)   score in scores:     print("# tuning hyper-parameters %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() params, mean_score, scores in gp.grid_scores_:     print("%0.3f (+/-%0.03f) %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')

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