def _predict_as_table(self, prediction, confidence): from Orange.data import Domain, ContinuousVariable means, lows, highs = [], [], [] n_vars = prediction.shape[2] if len(prediction.shape) > 2 else 1 for i, name in zip(range(n_vars), self._table_var_names or range(n_vars)): mean = ContinuousVariable('{} (forecast)'.format(name)) low = ContinuousVariable('{} ({:d}%CI low)'.format(name, confidence)) high = ContinuousVariable('{} ({:d}%CI high)'.format(name, confidence)) low.ci_percent = high.ci_percent = confidence mean.ci_attrs = (low, high) means.append(mean) lows.append(low) highs.append(high) domain = Domain(means + lows + highs) X = np.column_stack(prediction) table = Timeseries.from_numpy(domain, X) table.name = (self._table_name or '') + '({} forecast)'.format(self) return table
def _predict_as_table(self, prediction, confidence): from Orange.data import Domain, ContinuousVariable means, lows, highs = [], [], [] n_vars = prediction.shape[2] if len(prediction.shape) > 2 else 1 for i, name in zip(range(n_vars), self._table_var_names or range(n_vars)): mean = ContinuousVariable('{} (forecast)'.format(name)) low = ContinuousVariable('{} ({:d}%CI low)'.format( name, confidence)) high = ContinuousVariable('{} ({:d}%CI high)'.format( name, confidence)) low.ci_percent = high.ci_percent = confidence mean.ci_attrs = (low, high) means.append(mean) lows.append(low) highs.append(high) domain = Domain(means + lows + highs) X = np.column_stack(prediction) table = Timeseries.from_numpy(domain, X) table.name = (self._table_name or '') + '({} forecast)'.format(self) return table