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- import numpy as np
- import os
- import logging
-
- logger = logging.getLogger('telemanom')
-
-
- class Channel:
- def __init__(self,n_predictions,l_s):
- # , config, chan_id):
- """
- Load and reshape channel values (predicted and actual).
-
- Args:
- config (obj): Config object containing parameters for processing
- chan_id (str): channel id
-
- Attributes:
- id (str): channel id
- config (obj): see Args
- X_train (arr): training inputs with dimensions
- [timesteps, l_s, input dimensions)
- X_test (arr): test inputs with dimensions
- [timesteps, l_s, input dimensions)
- y_train (arr): actual channel training values with dimensions
- [timesteps, n_predictions, 1)
- y_test (arr): actual channel test values with dimensions
- [timesteps, n_predictions, 1)
- train (arr): train data loaded from .npy file
- test(arr): test data loaded from .npy file
- """
-
- # self.id = chan_id
- # self.config = config
- self.X_train = None
- self.y_train = None
- self.X_test = None
- self.y_test = None
- self.y_hat = None
- self.train = None
- self.test = None
-
- self._n_predictions = n_predictions
- self._l_s = l_s
-
- def shape_train_data(self, arr):
- # , train=True):
- """Shape raw input streams for ingestion into LSTM. config.l_s specifies
- the sequence length of prior timesteps fed into the model at
- each timestep t.
-
- Args:
- arr (np array): array of input streams with
- dimensions [timesteps, 1, input dimensions]
- train (bool): If shaping training data, this indicates
- data can be shuffled
- """
- # print("in shape data")
- # print("arr shape",arr.shape)
- # print("ls",self.config.l_s)
- # print("n_pred",self.config.n_predictions)
- data = []
-
- for i in range(len(arr) - self._l_s - self._n_predictions):
- data.append(arr[i:i + self._l_s + self._n_predictions])
- data = np.array(data)
- # print("data shape",data.shape)
- # assert len(data.shape) == 3
-
- # if train:
- # # np.random.shuffle(data)
- # self.X_train = data[:, :-self.config.n_predictions, :]
- # self.y_train = data[:, -self.config.n_predictions:, :] # telemetry value is at position 0
- # self.y_train = np.reshape(self.y_train,(self.y_train.shape[0],self.y_train.shape[1]*self.y_train.shape[2]))
- # print("X train shape",self.X_train .shape)
- # print("Y train shape",self.y_train .shape)
- # else:
-
- self.X_train = data[:, :-self._n_predictions, :]
- self.y_train = data[:, -self._n_predictions:, :] # telemetry value is at position 0
- self.y_train = np.reshape(self.y_train,(self.y_train.shape[0],self.y_train.shape[1]*self.y_train.shape[2]))
-
-
-
-
- def shape_test_data(self, arr):
- data = []
-
- for i in range(len(arr) - self._l_s - self._n_predictions):
- data.append(arr[i:i + self._l_s + self._n_predictions])
- data = np.array(data)
- # print("data shape",data.shape)
- self.X_test = data[:, :-self._n_predictions, :]
- self.y_test = data[:, -self._n_predictions:, :] # telemetry value is at position 0
- self.y_test = np.reshape(self.y_test,(self.y_test.shape[0],self.y_test.shape[1]*self.y_test.shape[2]))
-
-
- # def load_data(self):
- # """
- # Load train and test data from local.
- # """
- # # try:
- # # self.train = np.load(os.path.join("data", "train", "{}.npy".format(self.id)))
- # # self.test = np.load(os.path.join("data", "test", "{}.npy".format(self.id)))
-
- # # except FileNotFoundError as e:
- # # # logger.critical(e)
- # # # logger.critical("Source data not found, may need to add data to repo: <link>")
- # # print("Source data not found, may need to add data to repo: <link>")
-
- # print("before shape function")
- # print(self.train.shape)
- # self.shape_data(self.train)
- # self.shape_data(self.test, train=False)
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