Exemple #1
0
 def fit(self, X, y):
     """Fit."""
     
     X = X[(self.mdlNr*5 % self.subsample_data)::self.subsample_data]
     y = y[(self.mdlNr*5 % self.subsample_data)::self.subsample_data]
     
     if self.applyPreds:
         if self.jump is not None:
             X = delay_preds(X, delay=self.delay/self.subsample_data, skip=self.skip/self.subsample_data, jump=self.jump/self.subsample_data)
         else:
             X = delay_preds(X, delay=self.delay/self.subsample_data, skip=self.skip/self.subsample_data)
     self.clf = []
     
     widgets = ['Training : ', Percentage(), ' ', Bar(marker=RotatingMarker()),
        ' ', ETA(), ' ']
     pbar = ProgressBar(widgets=widgets, maxval=6)
     pbar.start()
     
     # training separate models for each event
     for col in range(6):
         self.clf.append(xgb.XGBClassifier(n_estimators=self.n_estimators,
                                           max_depth=self.max_depth,
                                           subsample=self.subsample,
                                           nthread=self.nthread))
         self.clf[col].fit(X, y[:, col])
         pbar.update(col)
Exemple #2
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 def predict_proba(self, X):
     """Predict probability."""
     if self.applyPreds:
         p = np.zeros((X.shape[0],6))
         for part in range(self.partsTest):
             start = part*X.shape[0]//self.partsTest-self.delay*(part>0)
             stop = (part+1)*X.shape[0]//self.partsTest
             X_delayed = delay_preds(X[slice(start,stop)], delay=self.delay, skip=self.skip, jump=self.jump)[self.delay*(part>0):]
             start += self.delay*(part>0)
             p[slice(start,stop)] += self._predict_proba(X_delayed)
             X_delayed = None
         return p
     else:
         return self._predict_proba(X)