In einer Funktion von rasa_nlu rufen sie GridSearchCV.fit() mit clf.fit () auf. Es gibt einige Warnungen. Ich möchte sie fangen und modifizieren, um zu wissen, was sie auslöst:
Fitting 2 folds for each of 6 candidates, totalling 12 fits
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
/home/mike/Programming/Rasa/myflaskapp/rasaenv/lib/python3.5/site-packages/sklearn/metrics/classification.py:1135: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.
'precision', 'predicted', average, warn_for)
[Parallel(n_jobs=1)]: Done 12 out of 12 | elapsed: 0.1s finished
Hier ist, wie GridSearchCV aufgebaut ist:
cv_splits = self._num_cv_splits(y) #Als ich es ausgedruckt habe gab es mir "2", ich wurde etwas mehr mit den Labels in Verbindung gebracht
GridSearchCV(SVC(C=1,
probability=True,
class_weight='balanced'),
param_grid=tuned_parameters,
n_jobs=num_threads,
cv=cv_splits,
scoring='f1_weighted',
verbose=1)
Wo y sind die labels, die in Zahlen umgewandelt wurden
y: [1 0 2 1 1 1 1 1 1 0 0 0 0 0 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
3 3]
labels: ['greet', 'goodbye', 'inform', 'greet', 'greet', 'greet', 'greet', 'greet', 'greet', 'goodbye', 'goodbye', 'goodbye', 'goodbye', 'goodbye', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'inform', 'laughing', 'laughing']
Idealerweise würde ich gerne greifen, welche davon die Warnungen ausgelöst hat. Ich weiß, dass dieser Link helfen kann. Ich habe es jedoch noch nicht geschafft, die Etiketten erscheinen zu lassen.