def _predict(self, X): if not self._initialized: raise NotFittedError() if HAS_PANDAS: X = extract_pandas_data(X) pred = self._session.run(self._model_predictions, feed_dict={self._inp.name: X}) return pred
def setup_predict_data_feeder(X, batch_size=-1): """Returns an iterable for feeding into predict step. Args: X: numpy, pandas, Dask array or iterable. batch_size: Size of batches to split data into. If negative, returns one batch of full size. Returns: List or iterator of parts of data to predict on. """ if HAS_DASK: X = extract_dask_data(X) if HAS_PANDAS: X = extract_pandas_data(X) if _is_iterable(X): return _batch_data(X, batch_size) if len(X.shape) == 1: X = np.reshape(X, (-1, 1)) if batch_size > 0: n_batches = int(math.ceil(float(len(X)) / batch_size)) return [ X[i * batch_size:(i + 1) * batch_size] for i in xrange(n_batches) ] return [X]
def _data_type_filter(X, y): """Filter data types into acceptable format""" if HAS_PANDAS: X = extract_pandas_data(X) y = extract_pandas_labels(y) if HAS_DASK: X = extract_dask_data(X) y = extract_dask_labels(y) return X, y
def _data_type_filter(X, y): """Filter data types into acceptable format""" if HAS_DASK: X = extract_dask_data(X) y = extract_dask_labels(y) if HAS_PANDAS: X = extract_pandas_data(X) y = extract_pandas_labels(y) return X, y
def _predict(self, X): if not self._initialized: raise NotFittedError() if HAS_PANDAS: X = extract_pandas_data(X) if HAS_DASK: X = extract_dask_data(X) if len(X.shape) == 1: X = np.reshape(X, (-1, 1)) pred = self._session.run(self._model_predictions, feed_dict={self._inp.name: X}) return pred
def _predict(self, X): if not self._initialized: raise NotFittedError() if HAS_PANDAS: X = extract_pandas_data(X) if HAS_DASK: X = extract_dask_data(X) if len(X.shape) == 1: X = np.reshape(X, (-1, 1)) pred = self._session.run(self._model_predictions, feed_dict={ self._inp.name: X }) return pred
def setup_predict_data_feeder(X, batch_size=-1): """Returns an iterable for feeding into predict step. Args: X: numpy, pandas, Dask array or iterable. batch_size: Size of batches to split data into. If negative, returns one batch of full size. Returns: List or iterator of parts of data to predict on. """ if HAS_DASK: X = extract_dask_data(X) if HAS_PANDAS: X = extract_pandas_data(X) if _is_iterable(X): return _batch_data(X, batch_size) if len(X.shape) == 1: X = np.reshape(X, (-1, 1)) return [X]