ValueError: Falsche Anzahl der Elemente übergeben - Bedeutung und Vorschläge?

Ich erhalte die Fehlermeldung:
ValueError: Wrong number of items passed 3, placement implies 1, und ich bin kämpfen, um herauszufinden, wo und wie ich anfangen kann das problem anzugehen.

Ich nicht wirklich verstehen, die Bedeutung der Fehler; das macht es schwierig für mich zu beheben. Ich habe auch den code-block ausgelöst wird der Fehler in meinem Jupyter Notebook.

Den Daten ist schwer zu befestigen; also ich bin nicht auf der Suche für jedermann zu versuchen und neu erstellen, diese Fehler für mich. Ich bin gerade auf der Suche nach etwas Rückgespräch auf, wie ich konnte-Adresse dieser Fehler Auftritt.

KeyError                                  Traceback (most recent call last)
C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
   1944             try:
-> 1945                 return self._engine.get_loc(key)
   1946             except KeyError:

pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4154)()

pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4018)()

pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12368)()

pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12322)()

KeyError: 'predictedY'

During handling of the above exception, another exception occurred:

KeyError                                  Traceback (most recent call last)
C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in set(self, item, value, check)
   3414         try:
-> 3415             loc = self.items.get_loc(item)
   3416         except KeyError:

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\indexes\base.py in get_loc(self, key, method, tolerance)
   1946             except KeyError:
-> 1947                 return self._engine.get_loc(self._maybe_cast_indexer(key))
   1948 

pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4154)()

pandas\index.pyx in pandas.index.IndexEngine.get_loc (pandas\index.c:4018)()

pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12368)()

pandas\hashtable.pyx in pandas.hashtable.PyObjectHashTable.get_item (pandas\hashtable.c:12322)()

KeyError: 'predictedY'

During handling of the above exception, another exception occurred:

ValueError                                Traceback (most recent call last)
<ipython-input-95-476dc59cd7fa> in <module>()
     26     return gp, results
     27 
---> 28 gp_dailyElectricity, results_dailyElectricity = predictAll(3, 0.04, trainX_dailyElectricity, trainY_dailyElectricity, testX_dailyElectricity, testY_dailyElectricity, testSet_dailyElectricity, 'Daily Electricity')

<ipython-input-95-476dc59cd7fa> in predictAll(theta, nugget, trainX, trainY, testX, testY, testSet, title)
      8 
      9     results = testSet.copy()
---> 10     results['predictedY'] = predictedY
     11     results['sigma'] = sigma
     12 

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py in __setitem__(self, key, value)
   2355         else:
   2356             # set column
-> 2357             self._set_item(key, value)
   2358 
   2359     def _setitem_slice(self, key, value):

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\frame.py in _set_item(self, key, value)
   2422         self._ensure_valid_index(value)
   2423         value = self._sanitize_column(key, value)
-> 2424         NDFrame._set_item(self, key, value)
   2425 
   2426         # check if we are modifying a copy

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\generic.py in _set_item(self, key, value)
   1462 
   1463     def _set_item(self, key, value):
-> 1464         self._data.set(key, value)
   1465         self._clear_item_cache()
   1466 

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in set(self, item, value, check)
   3416         except KeyError:
   3417             # This item wasn't present, just insert at end
-> 3418             self.insert(len(self.items), item, value)
   3419             return
   3420 

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in insert(self, loc, item, value, allow_duplicates)
   3517 
   3518         block = make_block(values=value, ndim=self.ndim,
-> 3519                            placement=slice(loc, loc + 1))
   3520 
   3521         for blkno, count in _fast_count_smallints(self._blknos[loc:]):

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in make_block(values, placement, klass, ndim, dtype, fastpath)
   2516                      placement=placement, dtype=dtype)
   2517 
-> 2518     return klass(values, ndim=ndim, fastpath=fastpath, placement=placement)
   2519 
   2520 # TODO: flexible with index=None and/or items=None

C:\Users\brennn1\AppData\Local\Continuum\Anaconda3\lib\site-packages\pandas\core\internals.py in __init__(self, values, placement, ndim, fastpath)
     88             raise ValueError('Wrong number of items passed %d, placement '
     89                              'implies %d' % (len(self.values),
---> 90                                              len(self.mgr_locs)))
     91 
     92     @property

ValueError: Wrong number of items passed 3, placement implies 1

Mein code ist wie folgt:

def predictAll(theta, nugget, trainX, trainY, testX, testY, testSet, title):

    gp = gaussian_process.GaussianProcess(theta0=theta, nugget =nugget)
    gp.fit(trainX, trainY)

    predictedY, MSE = gp.predict(testX, eval_MSE = True)
    sigma = np.sqrt(MSE)

    results = testSet.copy()
    results['predictedY'] = predictedY
    results['sigma'] = sigma

    print ("Train score R2:", gp.score(trainX, trainY))
    print ("Test score R2:", sklearn.metrics.r2_score(testY, predictedY))

    plt.figure(figsize = (9,8))
    plt.scatter(testY, predictedY)
    plt.plot([min(testY), max(testY)], [min(testY), max(testY)], 'r')
    plt.xlim([min(testY), max(testY)])
    plt.ylim([min(testY), max(testY)])
    plt.title('Predicted vs. observed: ' + title)
    plt.xlabel('Observed')
    plt.ylabel('Predicted')
    plt.show()

    return gp, results

gp_dailyElectricity, results_dailyElectricity = predictAll(3, 0.04, trainX_dailyElectricity, trainY_dailyElectricity, testX_dailyElectricity, testY_dailyElectricity, testSet_dailyElectricity, 'Daily Electricity')

InformationsquelleAutor Gary | 2017-04-04