text: export const pipelines = [
    {
      id: '5da7cc308c5ceceb9f28901c',
      insert_time: '2019-10-17T02:04:32.151000',
      name: 'lstm',
      created_by: null,
      mlpipeline: {
        primitives: [
          'mlprimitives.custom.timeseries_preprocessing.time_segments_average',
          'sklearn.impute.SimpleImputer',
          'sklearn.preprocessing.MinMaxScaler',
          'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences',
          'keras.Sequential.LSTMTimeSeriesRegressor',
          'mlprimitives.custom.timeseries_anomalies.regression_errors',
          'mlprimitives.custom.timeseries_anomalies.find_anomalies',
        ],
        init_params: {
          'mlprimitives.custom.timeseries_preprocessing.time_segments_average#1': {
            time_column: 'timestamp',
            interval: 21600,
          },
          'sklearn.preprocessing.MinMaxScaler#1': {
            feature_range: [-1, 1],
          },
          'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1': {
            target_column: 0,
            window_size: 250,
          },
          'keras.Sequential.LSTMTimeSeriesRegressor': {
            epochs: 35,
            verbose: false,
            layers: [
              {
                class: 'keras.layers.CuDNNLSTM',
                parameters: {
                  input_shape: 'input_shape',
                  units: 'lstm_1_units',
                  return_sequences: true,
                },
              },
              {
                class: 'keras.layers.Dropout',
                parameters: {
                  rate: 'dropout_1_rate',
                },
              },
              {
                class: 'keras.layers.CuDNNLSTM',
                parameters: {
                  units: 'lstm_2_units',
                  return_sequences: false,
                },
              },
              {
                class: 'keras.layers.Dropout',
                parameters: {
                  rate: 'dropout_2_rate',
                },
              },
              {
                class: 'keras.layers.Dense',
                parameters: {
                  units: 'dense_units',
                  activation: 'linear',
                },
              },
            ],
          },
        },
        input_names: {
          'mlprimitives.custom.timeseries_anomalies.find_anomalies#1': {
            index: 'target_index',
          },
        },
        output_names: {
          'keras.Sequential.LSTMTimeSeriesRegressor#1': {
            y: 'y_hat',
          },
        },
        outputs: {
          default: [
            {
              name: 'events',
              variable: 'mlprimitives.custom.timeseries_anomalies.find_anomalies#1.y',
            },
          ],
          visualization: [
            {
              name: 'X_raw',
              variable: 'sklearn.impute.SimpleImputer#1.X',
            },
            {
              name: 'raw_index',
              variable: 'mlprimitives.custom.timeseries_preprocessing.time_segments_average#1.index',
            },
            {
              name: 'X_nm',
              variable: 'sklearn.preprocessing.MinMaxScaler#1.X',
            },
            {
              name: 'y',
              variable: 'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1.y',
            },
            {
              name: 'y_hat',
              variable: 'keras.Sequential.LSTMTimeSeriesRegressor#1.y_hat',
            },
            {
              name: 'es',
              variable: 'mlprimitives.custom.timeseries_anomalies.regression_errors#1.errors',
            },
            {
              name: 'target_index',
              variable: 'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1.target_index',
            },
          ],
        },
      },
    },
    {
      id: '5da7cc308c5ceceb9f28901d',
      insert_time: '2019-10-17T02:04:32.200000',
      name: 'cyclegan',
      created_by: null,
      mlpipeline: {
        primitives: [
          'mlprimitives.custom.timeseries_preprocessing.time_segments_aggregate',
          'sklearn.impute.SimpleImputer',
          'sklearn.preprocessing.MinMaxScaler',
          'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences',
          'mlprimitives.candidates.timeseries.cyclegan.CycleGAN',
          'mlprimitives.candidates.timeseries.cyclegan.score_anomalies',
          'mlprimitives.custom.timeseries_anomalies.find_anomalies',
        ],
        init_params: {
          'mlprimitives.custom.timeseries_preprocessing.time_segments_aggregate#1': {
            time_column: 'timestamp',
            interval: 21600,
            method: 'mean',
          },
          'sklearn.preprocessing.MinMaxScaler#1': {
            feature_range: [-1, 1],
          },
          'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1': {
            target_column: 0,
            window_size: 100,
            target_size: 1,
          },
          'mlprimitives.candidates.timeseries.cyclegan.CycleGAN#1': {
            epochs: 2000,
            layers_encoder: [
              {
                class: 'keras.layers.Bidirectional',
                parameters: {
                  layer: {
                    class: 'keras.layers.CuDNNLSTM',
                    parameters: {
                      units: 100,
                      return_sequences: true,
                    },
                  },
                },
              },
              {
                class: 'keras.layers.Flatten',
                parameters: {},
              },
              {
                class: 'keras.layers.Dense',
                parameters: {
                  units: 20,
                },
              },
              {
                class: 'keras.layers.Reshape',
                parameters: {
                  target_shape: 'encoder_reshape_shape',
                },
              },
            ],
            layers_generator: [
              {
                class: 'keras.layers.Flatten',
                parameters: {},
              },
              {
                class: 'keras.layers.Dense',
                parameters: {
                  units: 50,
                },
              },
              {
                class: 'keras.layers.Reshape',
                parameters: {
                  target_shape: 'generator_reshape_shape',
                },
              },
              {
                class: 'keras.layers.Bidirectional',
                parameters: {
                  layer: {
                    class: 'keras.layers.CuDNNLSTM',
                    parameters: {
                      units: 64,
                      return_sequences: true,
                    },
                  },
                  merge_mode: 'concat',
                },
              },
              {
                class: 'keras.layers.convolutional.UpSampling1D',
                parameters: {
                  size: 2,
                },
              },
              {
                class: 'keras.layers.Bidirectional',
                parameters: {
                  layer: {
                    class: 'keras.layers.CuDNNLSTM',
                    parameters: {
                      units: 64,
                      return_sequences: true,
                    },
                  },
                  merge_mode: 'concat',
                },
              },
              {
                class: 'keras.layers.TimeDistributed',
                parameters: {
                  layer: {
                    class: 'keras.layers.Dense',
                    parameters: {
                      units: 1,
                    },
                  },
                },
              },
              {
                class: 'keras.layers.Activation',
                parameters: {
                  activation: 'tanh',
                },
              },
            ],
          },
        },
        input_names: {
          'mlprimitives.custom.timeseries_anomalies.find_anomalies#1': {
            index: 'index',
          },
          'mlprimitives.candidates.timeseries.cyclegan.score_anomalies#1': {
            y: 'X',
          },
        },
        output_names: {
          'mlprimitives.candidates.timeseries.cyclegan.CycleGAN#1': {
            y: 'y_hat',
          },
          'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1': {
            index: 'X_index',
          },
        },
        outputs: {
          default: [
            {
              name: 'events',
              variable: 'mlprimitives.custom.timeseries_anomalies.find_anomalies#1.y',
            },
          ],
          visualization: [
            {
              name: 'X_raw',
              variable: 'sklearn.impute.SimpleImputer#1.X',
            },
            {
              name: 'raw_index',
              variable: 'mlprimitives.custom.timeseries_preprocessing.time_segments_aggregate#1.index',
            },
            {
              name: 'X_nm',
              variable: 'sklearn.preprocessing.MinMaxScaler#1.X',
            },
            {
              name: 'y',
              variable: 'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1.y',
            },
            {
              name: 'y_hat',
              variable: 'mlprimitives.candidates.timeseries.cyclegan.CycleGAN#1.y_hat',
            },
            {
              name: 'es',
              variable: 'mlprimitives.candidates.timeseries.cyclegan.score_anomalies#1.errors',
            },
            {
              name: 'target_index',
              variable: 'mlprimitives.custom.timeseries_preprocessing.rolling_window_sequences#1.target_index',
            },
          ],
        },
      },
    },
  ];
  