07_label_combinations.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "07_label_combinations.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/CrowdSalat/ann-dynamical-systems/blob/master/07_label_combinations.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pa9Mju9lc4Ll",
        "colab_type": "text"
      },
      "source": [
        "# overview\n",
        "\n",
        "Uses actions and an input state to predict the output state."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ijDD21X-gnli",
        "colab_type": "text"
      },
      "source": [
        "# imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_Z4pV678a1zv",
        "colab_type": "code",
        "outputId": "fb7d72e7-a7e2-4262-bdda-c743b1ca7def",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2p4YwEK_f7kq",
        "colab_type": "code",
        "outputId": "87a2fb21-63ca-4c6d-ad6b-59ee0fb2a5df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import os\n",
        "import sys\n",
        "import datetime\n",
        "import math\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "\n",
        "\n",
        "import tensorflow as tf\n",
        "print(tf.__version__)\n",
        "from tensorflow import keras\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.layers import *\n",
        "from tensorflow.keras import metrics"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RPDma-JqgyaM",
        "colab_type": "code",
        "outputId": "d335e0f0-81e1-4664-8481-dfe7dd1a6b68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "base_folder = './results'\n",
        "notebook_folder = '07_labels_less'\n",
        "results_folder = os.path.join(base_folder, notebook_folder)\n",
        "os.makedirs(results_folder, exist_ok=True)\n",
        "print('results_folder : ', results_folder)\n",
        "\n",
        "saved_model_weight = 'saved_weights'\n",
        "saved_model_weight_path = os.path.join(results_folder, saved_model_weight)\n",
        "os.makedirs(saved_model_weight_path, exist_ok=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "results_folder :  ./results/07_labels_less\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rmj8c18xllpT",
        "colab_type": "text"
      },
      "source": [
        "# mount drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nUEqcpzvltdo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "file = 'dataset_all_01.csv'"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TuGWEMS1loFz",
        "colab_type": "code",
        "outputId": "1247985b-cbbd-432d-a5a7-59393615c26b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "root_dir = '/content/drive/My Drive/xplane_data' \n",
        "os.chdir(root_dir)\n",
        "print ('working dir: ' + os.getcwd())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n",
            "working dir: /content/drive/My Drive/xplane_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EF4tx110HmOj",
        "colab_type": "text"
      },
      "source": [
        "# load data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOM6RVAIduGr",
        "colab_type": "text"
      },
      "source": [
        "## define features (actions & states)\n",
        "actions and states at time t (t-1, t-2 ...) are the input for the network.\n",
        "\n",
        "the states at time t + 1 are the desired output of the network.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YH9bVQ3YDyTh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "col_m_time      = '   missn,_time '\n",
        "col_t_time      = '   _totl,_time '\n",
        "col_yaw_v       = '   ____Q,rad/s '\n",
        "\n",
        "col_yaw_deg     = '   _beta,__deg '\n",
        "col_roll_deg    = '   _roll,__deg '\n",
        "col_roll_v      = '   ____R,rad/s '\n",
        "col_pitch_deg   = '   pitch,__deg '\n",
        "col_pitch_v     = '   ____P,rad/s '\n",
        "col_alt         = '   __alt,ftmsl '\n",
        "col_vv          = '   __VVI,__fpm '\n",
        "col_v           = '   Vtrue,_ktas '\n",
        "col_rpm_prop    = '   rpm_1,_prop '\n",
        "col_throttle     = '   thro1,_part '\n",
        "\n",
        "cols_state = [ \n",
        "    col_yaw_v,\n",
        "    col_yaw_deg,\n",
        "    col_roll_deg,\n",
        "    col_roll_v,\n",
        "    col_pitch_deg,\n",
        "    col_pitch_v,\n",
        "    col_vv,\n",
        "    col_alt,\n",
        "    col_v,\n",
        "\n",
        "]\n",
        "\n",
        "cols_time = [\n",
        "    col_m_time,\n",
        "    col_t_time,\n",
        "]\n",
        "\n",
        "col_elev_servo                  = '   _elev,servo '\n",
        "col_ailrn_servo                  = '   ailrn,servo '\n",
        "col_ruddr_servo                  = '   ruddr,servo '\n",
        "\n",
        "cols_action = [\n",
        "    col_elev_servo,\n",
        "    col_ailrn_servo,\n",
        "    col_ruddr_servo,\n",
        "    col_rpm_prop,\n",
        "    col_throttle,\n",
        "]\n",
        "\n",
        "# all input columns\n",
        "cols_features = []\n",
        "cols_features.extend(cols_state)\n",
        "cols_features.extend(cols_action)\n",
        "\n",
        "# orga column\n",
        "col_dataset_index = 'dataset_index'\n",
        "col_maneuver_index = 'maneuver_index'\n",
        "col_maneuver_name = 'maneuver_name'\n",
        "col_maneuver_datapoint_index = 'maneuver_datapoint_index'\n",
        "col_maneuver_type = 'maneuver_type'\n",
        "cols_org = [col_dataset_index, col_maneuver_index, col_maneuver_name,\n",
        "            col_maneuver_datapoint_index,col_maneuver_type]\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gDSNkVBaDv2V",
        "colab_type": "text"
      },
      "source": [
        "## load data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uByPEHX3fS2G",
        "colab_type": "code",
        "outputId": "45dd10f2-4fcb-4cff-be61-6ecf02dd05fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "def load_xplane_csv(csv_path, columns):\n",
        "    return pd.read_csv(csv_path, delimiter=',', usecols = columns, \n",
        "                       float_precision='high')\n",
        "\n",
        "df_data = load_xplane_csv(file, cols_org + cols_state + cols_action)\n",
        "\n",
        "print(len(cols_org))\n",
        "print(len(cols_state))\n",
        "print(len(cols_action))\n",
        "print(df_data.shape)\n",
        "df_data.head(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "5\n",
            "9\n",
            "5\n",
            "(477893, 19)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>dataset_index</th>\n",
              "      <th>maneuver_index</th>\n",
              "      <th>maneuver_name</th>\n",
              "      <th>maneuver_type</th>\n",
              "      <th>maneuver_datapoint_index</th>\n",
              "      <th>Vtrue,_ktas</th>\n",
              "      <th>__VVI,__fpm</th>\n",
              "      <th>____Q,rad/s</th>\n",
              "      <th>____P,rad/s</th>\n",
              "      <th>____R,rad/s</th>\n",
              "      <th>pitch,__deg</th>\n",
              "      <th>_roll,__deg</th>\n",
              "      <th>_beta,__deg</th>\n",
              "      <th>__alt,ftmsl</th>\n",
              "      <th>thro1,_part</th>\n",
              "      <th>rpm_1,_prop</th>\n",
              "      <th>_elev,servo</th>\n",
              "      <th>ailrn,servo</th>\n",
              "      <th>ruddr,servo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
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              "      <td>665.39697</td>\n",
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              "      <td>0.00008</td>\n",
              "      <td>-0.03584</td>\n",
              "      <td>-0.10129</td>\n",
              "      <td>0.00793</td>\n",
              "      <td>953.06787</td>\n",
              "      <td>0.79608</td>\n",
              "      <td>2443.89453</td>\n",
              "      <td>-0.00489</td>\n",
              "      <td>-0.01818</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>115.85403</td>\n",
              "      <td>650.10065</td>\n",
              "      <td>-0.03868</td>\n",
              "      <td>-0.00234</td>\n",
              "      <td>0.00007</td>\n",
              "      <td>-0.14285</td>\n",
              "      <td>-0.10772</td>\n",
              "      <td>0.00770</td>\n",
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              "      <td>-0.01815</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   dataset_index  maneuver_index  ...     ailrn,servo      ruddr,servo \n",
              "0              0               0  ...         -0.01818              0.0\n",
              "1              0               0  ...         -0.01815              0.0\n",
              "\n",
              "[2 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "47xDI8rfLJTv",
        "colab_type": "text"
      },
      "source": [
        "# define data preperation functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-EBCtY9f7KnC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True, cols_label=None):\n",
        "\tdata_in = data.drop(cols_org, axis=1)\n",
        "\tif cols_label == None:\n",
        "\t\tdata_out = data_in.drop(cols_action, axis=1)\n",
        "\telse:\n",
        "\t\tif type(cols_label) != list:\n",
        "\t\t\tcols_label = [cols_label]\n",
        "\t\tdata_out = data_in[cols_label]\n",
        "\n",
        "\tcols_in = data_in.columns\n",
        "\tcols_out = data_out.columns\n",
        "\tcols, names = list(), list()\n",
        "\t# input sequence (t-n, ... t-1, t)\n",
        "\tfor i in range(n_in, 0, -1):\n",
        "\t\tcols.append(data_in.shift(i))\n",
        "\t\tif i == 1:\n",
        "\t\t\tnames += [('%s(t)' % (col_in)) for col_in in cols_in]\n",
        "\t\telse:\n",
        "\t\t\tnames += [('%s(t-%d)' % (col_in, i-1)) for col_in in cols_in]\n",
        "\n",
        "\t# forecast sequence (t+1, ... t+n)\n",
        "\tfor i in range(0, n_out):\n",
        "\t\tcols.append(data_out.shift(-i))\n",
        "\t\tnames += [('%s(t+%d)' % (col_out, i+1)) for col_out in cols_out]\n",
        "\t# put it all together\n",
        "\tagg = pd.concat(cols, axis=1)\n",
        "\tagg.columns = names\n",
        "\t# drop rows with NaN values\n",
        "\tif dropnan:\n",
        "\t\tagg.dropna(inplace=True)\n",
        "\treturn agg, cols_out"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOzBV1rYVGCn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def split_feature_label(data_labeled, n_input_steps):\n",
        "  data_X = data_labeled.values[:, 0:n_input_steps*len(cols_features)]\n",
        "  data_Y = data_labeled.values[:, n_input_steps*len(cols_features):]\n",
        "  return data_X, data_Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "otFsVggWMbpz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def scale_data(data_X, data_Y):\n",
        "  feature_scaler = MinMaxScaler()\n",
        "  data_X = feature_scaler.fit_transform(data_X)\n",
        "  label_scaler = MinMaxScaler()\n",
        "  data_Y = label_scaler.fit_transform(data_Y)\n",
        "  return feature_scaler, label_scaler, data_X, data_Y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYXja56FWJnk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_datasets(data, n_in=1, n_out=1, cols_label=None):\n",
        "  #train-test-split\n",
        "  test = data.query('dataset_index==4')\n",
        "  train = data.query('dataset_index!=4')\n",
        "  \n",
        "  #add-label\n",
        "  test_labeled, labels = series_to_supervised(test, n_in=n_in, n_out=n_out,\n",
        "                                              dropnan=True, cols_label=cols_label)\n",
        "  train_labeled, labels = series_to_supervised(train, n_in=n_in, n_out=n_out,\n",
        "                                               dropnan=True, cols_label=cols_label)\n",
        "  \n",
        "\n",
        "  #split-feature-label\n",
        "  train_X, train_Y = split_feature_label(train_labeled, n_in)\n",
        "  test_X, test_Y = split_feature_label(test_labeled, n_in)\n",
        "  #scale-feat\n",
        "  feature_scaler, label_scaler, train_X, train_Y = scale_data(train_X, train_Y)\n",
        "  #scale-label\n",
        "  test_X, test_Y = feature_scaler.transform(test_X), label_scaler.transform(test_Y)\n",
        "\n",
        "  return train_X, train_Y, test_X, test_Y, feature_scaler, label_scaler, labels\n",
        "\n",
        "\n",
        "#TODO für RNN muss noch reshape stattfinden"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_kDrS1qH7kc",
        "colab_type": "text"
      },
      "source": [
        "# train models"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjgpogwABFhX",
        "colab_type": "text"
      },
      "source": [
        "## define callbacks functions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "18w_KQkpBFEY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_tensorboard_callback(model_name=''):\n",
        "  logs_base_dir = \"./tensorbaord_logs\"\n",
        "  model_log_dir = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\") + '_' + model_name\n",
        "  target_log_dir = os.path.join(logs_base_dir, model_log_dir)\n",
        "  os.makedirs(target_log_dir, exist_ok=True)\n",
        "  tensorboard_callback = tf.keras.callbacks.TensorBoard(target_log_dir, histogram_freq=1)\n",
        "  return tensorboard_callback\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYl_6s7sBMpy",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def prepare_checkpoint_callback(model_name):\n",
        "  checkpoint_base_dir = './checkpoints'\n",
        "  os.makedirs(checkpoint_base_dir, exist_ok=True)\n",
        "  filepath = os.path.join(checkpoint_base_dir, 'cp_'+ model_name)\n",
        "  cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=filepath,\n",
        "                                                  save_weights_only=True,\n",
        "                                                  verbose=1)\n",
        "  return cp_callback "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3y07Q50xkLvG",
        "colab_type": "text"
      },
      "source": [
        "## define plot_and_save functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "l8uMddMPkSc1",
        "colab_type": "text"
      },
      "source": [
        "### loss historie"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I9UHTxwnkWyp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def save_plots_before_show(diagramm_name):\n",
        "  if not os.path.exists(results_folder):\n",
        "      os.makedirs(diagramm_folder)\n",
        "\n",
        "  plt.savefig(results_folder + '/' + diagramm_name +'.pdf')\n",
        "  plt.savefig(results_folder + '/' + diagramm_name +'.svg')\n",
        "  plt.savefig(results_folder + '/' + diagramm_name +'.png')\n",
        "\n",
        "def pas_history(model_tupels, histories, key='mean_squared_error', suffix=''):\n",
        "  model_names = [model_tupel[0] for model_tupel in model_tupels]\n",
        "  plt.figure(figsize=(16,10))\n",
        "  for i, history in enumerate(histories):\n",
        "    history_epoch= range(0, len(history['loss']) )\n",
        "\n",
        "    val = plt.plot(history_epoch, history['val_'+key],\n",
        "                   '--', label=model_names[i]+' Val')\n",
        "    plt.plot(history_epoch, history[key], color=val[0].get_color(),\n",
        "             label=model_names[i]+' Train')\n",
        "\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.yscale(\"log\")\n",
        "  plt.ylabel(key.replace('_',' ').title())\n",
        "  plt.legend()\n",
        "\n",
        "  plt.xlim([0,max(history_epoch)])\n",
        "  save_plots_before_show('history_loss'+'_'+key+suffix)\n",
        "  plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VJNfegiglg59",
        "colab_type": "text"
      },
      "source": [
        "### metrics (overall and for all single targets )\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "be-UQiKHli16",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def pas_metrics_scaled(model_tupel):\n",
        "  model_name = model_tupel[0]\n",
        "  df = _df_prediction_metrics(model_tupel, scaled=True)\n",
        "  file_path = os.path.join(results_folder, model_name + '_scaled.csv')\n",
        "  df.to_csv(path_or_buf=file_path)\n",
        "  return df\n",
        "  \n",
        "def pas_metrics(model_tupel):\n",
        "  model_name = model_tupel[0]\n",
        "  df = _df_prediction_metrics(model_tupel, scaled=False)\n",
        "  file_path = os.path.join(results_folder, model_name + '.csv')\n",
        "  df.to_csv(path_or_buf=file_path)\n",
        "  return df\n",
        "\n",
        "\n",
        "def _df_prediction_metrics(model_tupel, scaled):\n",
        "  '''\n",
        "  creates describe dataframe of labels and \n",
        "  connect them with mse,rmse and mae per target and overall \n",
        "  '''\n",
        "  \n",
        "  model = model_tupel[1]\n",
        "  train_X, train_Y, test_X, test_Y = model_tupel[2]\n",
        "  cols_label = model_tupel[4]\n",
        "\n",
        "\n",
        "  pred_Y = model.predict(test_X)\n",
        "  \n",
        "  if scaled:\n",
        "    truth_Y = test_Y\n",
        "  else:\n",
        "    label_scaler = model_tupel[3]\n",
        "    pred_Y = label_scaler.inverse_transform(pred_Y)\n",
        "    truth_Y = label_scaler.inverse_transform(test_Y)\n",
        "\n",
        "  df_describe = pd.DataFrame(truth_Y,columns=cols_label).describe()\n",
        "  mses = mean_squared_error(truth_Y, pred_Y, multioutput='raw_values')\n",
        "  rmses = np.array([math.sqrt(mse) for mse in mses])\n",
        "  maes = mean_absolute_error(truth_Y, pred_Y, multioutput='raw_values')\n",
        "  # average over list should do the job as well\n",
        "  mse = mean_squared_error(truth_Y, pred_Y)\n",
        "  rmse = math.sqrt(mse)\n",
        "  mae = mean_absolute_error(truth_Y, pred_Y)\n",
        "\n",
        "\n",
        "  df_mse_per_feature = pd.DataFrame(data=mses.reshape(1,-1),    # values\n",
        "            index=['mses'],     # index\n",
        "            columns=cols_label)  #  column names\n",
        "  df_rmse_per_feature = pd.DataFrame(data=rmses.reshape(1,-1),    # values\n",
        "        index=['rmes'],     # index\n",
        "        columns=cols_label)  #  column names\n",
        "  df_mae_per_feature = pd.DataFrame(data=maes.reshape(1,-1),    # values\n",
        "        index=['maes'],     # index\n",
        "        columns=cols_label)  #  column names\n",
        "  \n",
        "  \n",
        "  df_mse_all = pd.DataFrame(data=mse,    # values\n",
        "            index=['mse'],     # index\n",
        "            columns=cols_label)  #  column names\n",
        "  df_rmse_all = pd.DataFrame(data=rmse,    # values\n",
        "        index=['rme'],     # index\n",
        "        columns=cols_label)  #  column names\n",
        "  df_mae_all = pd.DataFrame(data=mae,    # values\n",
        "        index=['mae'],     # index\n",
        "        columns=cols_label)  #  column names\n",
        "  \n",
        "\n",
        "  metrics = [df_mse_per_feature, df_rmse_per_feature, df_mae_per_feature, \n",
        "             df_mse_all,df_rmse_all,df_mae_all]\n",
        "  return df_describe.append(metrics)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4gaR7IV326E_",
        "colab_type": "text"
      },
      "source": [
        "## define models"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fhc2M4VD2_YP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def create_model(model_name, layers, keras_activation, kernel_initializer, \n",
        "                 loss, metrics,  n_in=1, n_out=1, cols_label=None):\n",
        "  if not layers:\n",
        "    raise Exception('No layers defined. Expected in format [100,100]') \n",
        "\n",
        " #prepare data\n",
        "  train_X, train_Y, test_X, test_Y, feature_scaler, label_scaler, labels = prepare_datasets(df_data, n_in, n_out,\n",
        "                                                                                             cols_label=cols_label)\n",
        "  features_nr = train_X.shape[1]\n",
        "  target_nr = train_Y.shape[1]\n",
        "    \n",
        "  model = keras.Sequential(name=model_name)\n",
        "  \n",
        "  model.add(Dense(layers[0],input_shape=(features_nr,), \n",
        "                          kernel_initializer=kernel_initializer))\n",
        "  model.add(keras_activation())\n",
        "  for i in range(1, len(layers)):\n",
        "    layer_val = layers[i]\n",
        "    model.add(Dense(layer_val, kernel_initializer=kernel_initializer))\n",
        "    model.add(keras_activation())\n",
        "\n",
        "  model.add(Dense(target_nr ))\n",
        "\n",
        "  model.compile(optimizer='adam',\n",
        "                        loss=loss,\n",
        "                        metrics=metrics)\n",
        "\n",
        "  model.summary()\n",
        "  print(model.to_json())\n",
        "  print(train_X.shape, train_Y.shape, test_X.shape, test_Y.shape)\n",
        "  return (model_name, model, (train_X, train_Y, test_X, test_Y), label_scaler, labels)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KwMj6E7mBL9m",
        "colab_type": "code",
        "outputId": "3dfbf7af-4149-4e96-8a9f-ca349dead1dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "loss='mean_squared_error',\n",
        "metrics=['mean_squared_error', 'mean_absolute_error']\n",
        "\n",
        "\n",
        "\n",
        "lag1_translation = create_model('lag1_translation', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 1,1, [col_vv, col_v])\n",
        "\n",
        "lag2_translation = create_model('lag2_translation', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 2,1, [col_vv, col_v])\n",
        "\n",
        "lag1_rotation = create_model('lag1_rotation', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 1,1, [col_yaw_v,col_pitch_v, col_roll_v])\n",
        "\n",
        "lag2_rotation = create_model('lag2_rotation', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 2,1, [col_vv, col_v,col_yaw_v,col_pitch_v, col_roll_v])\n",
        "\n",
        "lag1_agg = create_model('lag1_agg', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 1,1, [col_vv, col_v,col_yaw_v,col_pitch_v, col_roll_v])\n",
        "\n",
        "lag2_agg = create_model('lag2_agg', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 2,1, [col_vv, col_v,col_yaw_v,col_pitch_v, col_roll_v])\n",
        "\n",
        "lag_pos = create_model('lag_pos', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 1,1, [col_alt, col_pitch_deg, col_roll_deg, col_yaw_deg])\n",
        "\n",
        "lag2_pos = create_model('lag2_pos', [200], ReLU, 'he_uniform', \n",
        "             loss, metrics, 2,1, [col_alt, col_pitch_deg, col_roll_deg, col_yaw_deg])\n",
        "\n",
        "models = [\n",
        "          lag1_translation, lag2_translation,\n",
        "          lag1_rotation, lag2_rotation,\n",
        "          lag1_agg, lag2_agg,\n",
        "          lag_pos, lag2_pos,\n",
        "          ]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"lag1_translation\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 200)               3000      \n",
            "_________________________________________________________________\n",
            "re_lu (ReLU)                 (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 2)                 402       \n",
            "=================================================================\n",
            "Total params: 3,402\n",
            "Trainable params: 3,402\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag1_translation\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense\", \"trainable\": true, \"batch_input_shape\": [null, 14], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_1\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 2, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330984, 14) (330984, 2) (146907, 14) (146907, 2)\n",
            "Model: \"lag2_translation\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_2 (Dense)              (None, 200)               5800      \n",
            "_________________________________________________________________\n",
            "re_lu_1 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 2)                 402       \n",
            "=================================================================\n",
            "Total params: 6,202\n",
            "Trainable params: 6,202\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag2_translation\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_2\", \"trainable\": true, \"batch_input_shape\": [null, 28], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_1\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_3\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 2, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330983, 28) (330983, 2) (146906, 28) (146906, 2)\n",
            "Model: \"lag1_rotation\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_4 (Dense)              (None, 200)               3000      \n",
            "_________________________________________________________________\n",
            "re_lu_2 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 3)                 603       \n",
            "=================================================================\n",
            "Total params: 3,603\n",
            "Trainable params: 3,603\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag1_rotation\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_4\", \"trainable\": true, \"batch_input_shape\": [null, 14], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_2\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_5\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 3, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330984, 14) (330984, 3) (146907, 14) (146907, 3)\n",
            "Model: \"lag2_rotation\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 200)               5800      \n",
            "_________________________________________________________________\n",
            "re_lu_3 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 5)                 1005      \n",
            "=================================================================\n",
            "Total params: 6,805\n",
            "Trainable params: 6,805\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag2_rotation\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_6\", \"trainable\": true, \"batch_input_shape\": [null, 28], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_3\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_7\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 5, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330983, 28) (330983, 5) (146906, 28) (146906, 5)\n",
            "Model: \"lag1_agg\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_8 (Dense)              (None, 200)               3000      \n",
            "_________________________________________________________________\n",
            "re_lu_4 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 5)                 1005      \n",
            "=================================================================\n",
            "Total params: 4,005\n",
            "Trainable params: 4,005\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag1_agg\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_8\", \"trainable\": true, \"batch_input_shape\": [null, 14], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_4\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_9\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 5, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330984, 14) (330984, 5) (146907, 14) (146907, 5)\n",
            "Model: \"lag2_agg\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_10 (Dense)             (None, 200)               5800      \n",
            "_________________________________________________________________\n",
            "re_lu_5 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 5)                 1005      \n",
            "=================================================================\n",
            "Total params: 6,805\n",
            "Trainable params: 6,805\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag2_agg\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_10\", \"trainable\": true, \"batch_input_shape\": [null, 28], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_5\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_11\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 5, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330983, 28) (330983, 5) (146906, 28) (146906, 5)\n",
            "Model: \"lag_pos\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_12 (Dense)             (None, 200)               3000      \n",
            "_________________________________________________________________\n",
            "re_lu_6 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 4)                 804       \n",
            "=================================================================\n",
            "Total params: 3,804\n",
            "Trainable params: 3,804\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag_pos\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_12\", \"trainable\": true, \"batch_input_shape\": [null, 14], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_6\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_13\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 4, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330984, 14) (330984, 4) (146907, 14) (146907, 4)\n",
            "Model: \"lag2_pos\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_14 (Dense)             (None, 200)               5800      \n",
            "_________________________________________________________________\n",
            "re_lu_7 (ReLU)               (None, 200)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 4)                 804       \n",
            "=================================================================\n",
            "Total params: 6,604\n",
            "Trainable params: 6,604\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"lag2_pos\", \"layers\": [{\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_14\", \"trainable\": true, \"batch_input_shape\": [null, 28], \"dtype\": \"float32\", \"units\": 200, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"VarianceScaling\", \"config\": {\"scale\": 2.0, \"mode\": \"fan_in\", \"distribution\": \"uniform\", \"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"ReLU\", \"config\": {\"name\": \"re_lu_7\", \"trainable\": true, \"dtype\": \"float32\", \"max_value\": null, \"negative_slope\": 0.0, \"threshold\": 0.0}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_15\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 4, \"activation\": \"linear\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "(330983, 28) (330983, 4) (146906, 28) (146906, 4)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HtFN-OP2HlqQ",
        "colab_type": "text"
      },
      "source": [
        "## train, save and load model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DDmtdBRxJKJd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_model(model_tupel, epochs, batch_size):\n",
        "  model_name = model_tupel[0]\n",
        "  keras_model = model_tupel[1]\n",
        "  train_X, train_Y, test_X, test_Y = model_tupel[2]\n",
        "\n",
        "  callbacks = [\n",
        "              #prepare_checkpoint_callback(model_name=model_name),\n",
        "              #prepare_tensorboard_callback(model_name=model_name)\n",
        "              ]\n",
        "\n",
        "  history = keras_model.fit(train_X,\n",
        "                            train_Y,\n",
        "                            epochs=epochs,\n",
        "                            batch_size=batch_size,\n",
        "                            validation_data=(test_X, test_Y),\n",
        "                            verbose=2,\n",
        "                            callbacks=callbacks\n",
        "                            )\n",
        "  return history  \n",
        "\n",
        "def _format_model_path(model_name):\n",
        "  file_name = '{}_end'.format(model_name)\n",
        "  return os.path.join(saved_model_weight_path, file_name)\n",
        "\n",
        "def save_model(model_tupel):\n",
        "  model_name = model_tupel[0]\n",
        "  keras_model = model_tupel[1]\n",
        "  file_path = _format_model_path(model_name)\n",
        "  keras_model.save_weights(file_path)\n",
        "\n",
        "def load_model(model_tupel):\n",
        "  model_name = model_tupel[0]\n",
        "  keras_model = model_tupel[1]\n",
        "  file_path = _format_model_path(model_name)\n",
        "  exact_file_path = file_path + '.index'\n",
        "  if os.path.isfile(exact_file_path):\n",
        "    print('load model {}'.format(model_name))\n",
        "    keras_model.load_weights(file_path)\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "def _format_history_path(model_name):\n",
        "  file_name = '{}_history_dict'.format(model_name)\n",
        "  return os.path.join(results_folder, file_name)\n",
        "  \n",
        "\n",
        "def save_history(model_tupel, history):\n",
        "  file_path = _format_history_path(model_tupel[0])\n",
        "  with open(file_path, 'wb') as file_hist:\n",
        "        pickle.dump(history.history, file_hist)\n",
        "  \n",
        "\n",
        "def load_history(model_tupel):\n",
        "  file_path = _format_history_path(model_tupel[0])\n",
        "  if not os.path.isfile(file_path):\n",
        "    return dict()\n",
        "  with open(file_path, 'rb') as file_hist:\n",
        "    return pickle.load(file_hist)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DxQg934JhE_H",
        "colab_type": "code",
        "outputId": "8799bb42-8e53-460e-b101-797c57d9912b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "histories = []\n",
        "for model in models:\n",
        "  if not load_model(model):\n",
        "    print(model[0])\n",
        "    history = train_model(model, epochs=50, batch_size=64)\n",
        "    save_model(model)\n",
        "    save_history(model, history)\n",
        "    histories.append(history.history)\n",
        "    print('*******************')\n",
        "  else:\n",
        "    history = load_history(model)\n",
        "    histories.append(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lag1_translation\n",
            "Train on 330984 samples, validate on 146907 samples\n",
            "Epoch 1/50\n",
            "330984/330984 - 23s - loss: 9.0087e-04 - mean_squared_error: 9.0087e-04 - mean_absolute_error: 0.0078 - val_loss: 1.2929e-04 - val_mean_squared_error: 1.2929e-04 - val_mean_absolute_error: 0.0068\n",
            "Epoch 2/50\n",
            "330984/330984 - 21s - loss: 2.3862e-05 - mean_squared_error: 2.3862e-05 - mean_absolute_error: 0.0036 - val_loss: 6.5228e-05 - val_mean_squared_error: 6.5228e-05 - val_mean_absolute_error: 0.0048\n",
            "Epoch 3/50\n",
            "330984/330984 - 21s - loss: 1.6372e-05 - mean_squared_error: 1.6372e-05 - mean_absolute_error: 0.0031 - val_loss: 4.9461e-05 - val_mean_squared_error: 4.9461e-05 - val_mean_absolute_error: 0.0043\n",
            "Epoch 4/50\n",
            "330984/330984 - 21s - loss: 1.3503e-05 - mean_squared_error: 1.3503e-05 - mean_absolute_error: 0.0028 - val_loss: 3.7975e-05 - val_mean_squared_error: 3.7975e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 5/50\n",
            "330984/330984 - 21s - loss: 1.1314e-05 - mean_squared_error: 1.1314e-05 - mean_absolute_error: 0.0025 - val_loss: 2.9726e-05 - val_mean_squared_error: 2.9726e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 6/50\n",
            "330984/330984 - 22s - loss: 1.0215e-05 - mean_squared_error: 1.0215e-05 - mean_absolute_error: 0.0024 - val_loss: 2.7923e-05 - val_mean_squared_error: 2.7923e-05 - val_mean_absolute_error: 0.0030\n",
            "Epoch 7/50\n",
            "330984/330984 - 21s - loss: 9.1433e-06 - mean_squared_error: 9.1433e-06 - mean_absolute_error: 0.0023 - val_loss: 2.1586e-05 - val_mean_squared_error: 2.1586e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 8/50\n",
            "330984/330984 - 21s - loss: 8.3673e-06 - mean_squared_error: 8.3673e-06 - mean_absolute_error: 0.0021 - val_loss: 2.5930e-05 - val_mean_squared_error: 2.5930e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 9/50\n",
            "330984/330984 - 21s - loss: 7.7144e-06 - mean_squared_error: 7.7144e-06 - mean_absolute_error: 0.0021 - val_loss: 3.3771e-05 - val_mean_squared_error: 3.3771e-05 - val_mean_absolute_error: 0.0035\n",
            "Epoch 10/50\n",
            "330984/330984 - 21s - loss: 6.8748e-06 - mean_squared_error: 6.8748e-06 - mean_absolute_error: 0.0020 - val_loss: 2.2352e-05 - val_mean_squared_error: 2.2352e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 11/50\n",
            "330984/330984 - 21s - loss: 6.2962e-06 - mean_squared_error: 6.2962e-06 - mean_absolute_error: 0.0019 - val_loss: 1.8657e-05 - val_mean_squared_error: 1.8657e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 12/50\n",
            "330984/330984 - 21s - loss: 5.9152e-06 - mean_squared_error: 5.9152e-06 - mean_absolute_error: 0.0018 - val_loss: 1.7483e-05 - val_mean_squared_error: 1.7483e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 13/50\n",
            "330984/330984 - 21s - loss: 5.6548e-06 - mean_squared_error: 5.6548e-06 - mean_absolute_error: 0.0017 - val_loss: 1.6800e-05 - val_mean_squared_error: 1.6800e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 14/50\n",
            "330984/330984 - 21s - loss: 5.1417e-06 - mean_squared_error: 5.1417e-06 - mean_absolute_error: 0.0017 - val_loss: 2.2142e-05 - val_mean_squared_error: 2.2142e-05 - val_mean_absolute_error: 0.0034\n",
            "Epoch 15/50\n",
            "330984/330984 - 21s - loss: 4.6045e-06 - mean_squared_error: 4.6045e-06 - mean_absolute_error: 0.0016 - val_loss: 1.3369e-05 - val_mean_squared_error: 1.3369e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 16/50\n",
            "330984/330984 - 21s - loss: 4.3968e-06 - mean_squared_error: 4.3968e-06 - mean_absolute_error: 0.0015 - val_loss: 1.2043e-05 - val_mean_squared_error: 1.2043e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 17/50\n",
            "330984/330984 - 21s - loss: 4.2132e-06 - mean_squared_error: 4.2132e-06 - mean_absolute_error: 0.0015 - val_loss: 1.0486e-05 - val_mean_squared_error: 1.0486e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 18/50\n",
            "330984/330984 - 21s - loss: 3.6847e-06 - mean_squared_error: 3.6847e-06 - mean_absolute_error: 0.0014 - val_loss: 1.0390e-05 - val_mean_squared_error: 1.0390e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 19/50\n",
            "330984/330984 - 21s - loss: 3.7037e-06 - mean_squared_error: 3.7037e-06 - mean_absolute_error: 0.0014 - val_loss: 7.8948e-06 - val_mean_squared_error: 7.8948e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 20/50\n",
            "330984/330984 - 21s - loss: 3.4699e-06 - mean_squared_error: 3.4699e-06 - mean_absolute_error: 0.0014 - val_loss: 1.1827e-05 - val_mean_squared_error: 1.1827e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 21/50\n",
            "330984/330984 - 22s - loss: 3.2575e-06 - mean_squared_error: 3.2575e-06 - mean_absolute_error: 0.0013 - val_loss: 7.4244e-06 - val_mean_squared_error: 7.4244e-06 - val_mean_absolute_error: 0.0015\n",
            "Epoch 22/50\n",
            "330984/330984 - 21s - loss: 3.0942e-06 - mean_squared_error: 3.0942e-06 - mean_absolute_error: 0.0013 - val_loss: 9.6068e-06 - val_mean_squared_error: 9.6068e-06 - val_mean_absolute_error: 0.0019\n",
            "Epoch 23/50\n",
            "330984/330984 - 21s - loss: 3.0593e-06 - mean_squared_error: 3.0593e-06 - mean_absolute_error: 0.0012 - val_loss: 8.4765e-06 - val_mean_squared_error: 8.4765e-06 - val_mean_absolute_error: 0.0018\n",
            "Epoch 24/50\n",
            "330984/330984 - 21s - loss: 2.9568e-06 - mean_squared_error: 2.9568e-06 - mean_absolute_error: 0.0012 - val_loss: 6.2308e-06 - val_mean_squared_error: 6.2308e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 25/50\n",
            "330984/330984 - 21s - loss: 2.8030e-06 - mean_squared_error: 2.8030e-06 - mean_absolute_error: 0.0012 - val_loss: 7.0252e-06 - val_mean_squared_error: 7.0252e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 26/50\n",
            "330984/330984 - 21s - loss: 2.7170e-06 - mean_squared_error: 2.7170e-06 - mean_absolute_error: 0.0012 - val_loss: 6.0463e-06 - val_mean_squared_error: 6.0463e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 27/50\n",
            "330984/330984 - 21s - loss: 2.6093e-06 - mean_squared_error: 2.6093e-06 - mean_absolute_error: 0.0012 - val_loss: 5.7665e-06 - val_mean_squared_error: 5.7665e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 28/50\n",
            "330984/330984 - 21s - loss: 2.5462e-06 - mean_squared_error: 2.5462e-06 - mean_absolute_error: 0.0011 - val_loss: 5.3354e-06 - val_mean_squared_error: 5.3354e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 29/50\n",
            "330984/330984 - 20s - loss: 2.4098e-06 - mean_squared_error: 2.4098e-06 - mean_absolute_error: 0.0011 - val_loss: 5.9982e-06 - val_mean_squared_error: 5.9982e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 30/50\n",
            "330984/330984 - 20s - loss: 2.3079e-06 - mean_squared_error: 2.3079e-06 - mean_absolute_error: 0.0011 - val_loss: 4.7362e-06 - val_mean_squared_error: 4.7362e-06 - val_mean_absolute_error: 7.7761e-04\n",
            "Epoch 31/50\n",
            "330984/330984 - 20s - loss: 2.2338e-06 - mean_squared_error: 2.2338e-06 - mean_absolute_error: 0.0011 - val_loss: 5.5744e-06 - val_mean_squared_error: 5.5744e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 32/50\n",
            "330984/330984 - 20s - loss: 2.1600e-06 - mean_squared_error: 2.1600e-06 - mean_absolute_error: 0.0010 - val_loss: 7.3265e-06 - val_mean_squared_error: 7.3265e-06 - val_mean_absolute_error: 0.0015\n",
            "Epoch 33/50\n",
            "330984/330984 - 20s - loss: 2.1226e-06 - mean_squared_error: 2.1226e-06 - mean_absolute_error: 0.0010 - val_loss: 6.3942e-06 - val_mean_squared_error: 6.3942e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 34/50\n",
            "330984/330984 - 20s - loss: 2.0080e-06 - mean_squared_error: 2.0080e-06 - mean_absolute_error: 9.8781e-04 - val_loss: 4.9484e-06 - val_mean_squared_error: 4.9484e-06 - val_mean_absolute_error: 9.0852e-04\n",
            "Epoch 35/50\n",
            "330984/330984 - 20s - loss: 1.9628e-06 - mean_squared_error: 1.9628e-06 - mean_absolute_error: 9.7950e-04 - val_loss: 7.1404e-06 - val_mean_squared_error: 7.1404e-06 - val_mean_absolute_error: 0.0017\n",
            "Epoch 36/50\n",
            "330984/330984 - 21s - loss: 1.8586e-06 - mean_squared_error: 1.8586e-06 - mean_absolute_error: 9.7810e-04 - val_loss: 6.6105e-06 - val_mean_squared_error: 6.6105e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 37/50\n",
            "330984/330984 - 20s - loss: 1.8911e-06 - mean_squared_error: 1.8911e-06 - mean_absolute_error: 9.9518e-04 - val_loss: 6.4678e-06 - val_mean_squared_error: 6.4678e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 38/50\n",
            "330984/330984 - 21s - loss: 1.8026e-06 - mean_squared_error: 1.8026e-06 - mean_absolute_error: 9.5415e-04 - val_loss: 5.8555e-06 - val_mean_squared_error: 5.8555e-06 - val_mean_absolute_error: 8.8282e-04\n",
            "Epoch 39/50\n",
            "330984/330984 - 21s - loss: 1.6679e-06 - mean_squared_error: 1.6679e-06 - mean_absolute_error: 9.3590e-04 - val_loss: 8.6583e-06 - val_mean_squared_error: 8.6583e-06 - val_mean_absolute_error: 0.0020\n",
            "Epoch 40/50\n",
            "330984/330984 - 20s - loss: 1.8036e-06 - mean_squared_error: 1.8036e-06 - mean_absolute_error: 9.6206e-04 - val_loss: 5.3153e-06 - val_mean_squared_error: 5.3153e-06 - val_mean_absolute_error: 8.9401e-04\n",
            "Epoch 41/50\n",
            "330984/330984 - 21s - loss: 1.5829e-06 - mean_squared_error: 1.5829e-06 - mean_absolute_error: 8.7774e-04 - val_loss: 9.0299e-06 - val_mean_squared_error: 9.0299e-06 - val_mean_absolute_error: 0.0023\n",
            "Epoch 42/50\n",
            "330984/330984 - 21s - loss: 1.5863e-06 - mean_squared_error: 1.5863e-06 - mean_absolute_error: 9.0767e-04 - val_loss: 8.1063e-06 - val_mean_squared_error: 8.1063e-06 - val_mean_absolute_error: 0.0019\n",
            "Epoch 43/50\n",
            "330984/330984 - 21s - loss: 1.6114e-06 - mean_squared_error: 1.6114e-06 - mean_absolute_error: 8.8852e-04 - val_loss: 6.8612e-06 - val_mean_squared_error: 6.8611e-06 - val_mean_absolute_error: 0.0015\n",
            "Epoch 44/50\n",
            "330984/330984 - 21s - loss: 1.5447e-06 - mean_squared_error: 1.5447e-06 - mean_absolute_error: 8.7498e-04 - val_loss: 6.6421e-06 - val_mean_squared_error: 6.6421e-06 - val_mean_absolute_error: 0.0015\n",
            "Epoch 45/50\n",
            "330984/330984 - 21s - loss: 1.5029e-06 - mean_squared_error: 1.5029e-06 - mean_absolute_error: 8.7497e-04 - val_loss: 5.4468e-06 - val_mean_squared_error: 5.4468e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 46/50\n",
            "330984/330984 - 21s - loss: 1.5097e-06 - mean_squared_error: 1.5097e-06 - mean_absolute_error: 8.4823e-04 - val_loss: 5.5348e-06 - val_mean_squared_error: 5.5348e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 47/50\n",
            "330984/330984 - 21s - loss: 1.4262e-06 - mean_squared_error: 1.4262e-06 - mean_absolute_error: 8.4577e-04 - val_loss: 4.9813e-06 - val_mean_squared_error: 4.9813e-06 - val_mean_absolute_error: 9.1842e-04\n",
            "Epoch 48/50\n",
            "330984/330984 - 21s - loss: 1.4075e-06 - mean_squared_error: 1.4075e-06 - mean_absolute_error: 8.4342e-04 - val_loss: 6.2445e-06 - val_mean_squared_error: 6.2445e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 49/50\n",
            "330984/330984 - 21s - loss: 1.4277e-06 - mean_squared_error: 1.4277e-06 - mean_absolute_error: 8.4504e-04 - val_loss: 3.9695e-06 - val_mean_squared_error: 3.9695e-06 - val_mean_absolute_error: 6.8632e-04\n",
            "Epoch 50/50\n",
            "330984/330984 - 21s - loss: 1.3413e-06 - mean_squared_error: 1.3413e-06 - mean_absolute_error: 7.8555e-04 - val_loss: 4.9912e-06 - val_mean_squared_error: 4.9912e-06 - val_mean_absolute_error: 0.0010\n",
            "*******************\n",
            "lag2_translation\n",
            "Train on 330983 samples, validate on 146906 samples\n",
            "Epoch 1/50\n",
            "330983/330983 - 22s - loss: 0.0037 - mean_squared_error: 0.0037 - mean_absolute_error: 0.0111 - val_loss: 1.4415e-04 - val_mean_squared_error: 1.4415e-04 - val_mean_absolute_error: 0.0073\n",
            "Epoch 2/50\n",
            "330983/330983 - 21s - loss: 4.0258e-05 - mean_squared_error: 4.0258e-05 - mean_absolute_error: 0.0048 - val_loss: 6.1585e-05 - val_mean_squared_error: 6.1585e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 3/50\n",
            "330983/330983 - 21s - loss: 3.1411e-05 - mean_squared_error: 3.1411e-05 - mean_absolute_error: 0.0042 - val_loss: 1.1146e-04 - val_mean_squared_error: 1.1146e-04 - val_mean_absolute_error: 0.0095\n",
            "Epoch 4/50\n",
            "330983/330983 - 21s - loss: 2.5458e-05 - mean_squared_error: 2.5458e-05 - mean_absolute_error: 0.0038 - val_loss: 1.1695e-04 - val_mean_squared_error: 1.1695e-04 - val_mean_absolute_error: 0.0092\n",
            "Epoch 5/50\n",
            "330983/330983 - 21s - loss: 2.1762e-05 - mean_squared_error: 2.1762e-05 - mean_absolute_error: 0.0035 - val_loss: 8.6485e-05 - val_mean_squared_error: 8.6484e-05 - val_mean_absolute_error: 0.0078\n",
            "Epoch 6/50\n",
            "330983/330983 - 21s - loss: 1.8534e-05 - mean_squared_error: 1.8533e-05 - mean_absolute_error: 0.0032 - val_loss: 3.8307e-05 - val_mean_squared_error: 3.8307e-05 - val_mean_absolute_error: 0.0043\n",
            "Epoch 7/50\n",
            "330983/330983 - 21s - loss: 1.6366e-05 - mean_squared_error: 1.6366e-05 - mean_absolute_error: 0.0030 - val_loss: 6.9765e-05 - val_mean_squared_error: 6.9765e-05 - val_mean_absolute_error: 0.0074\n",
            "Epoch 8/50\n",
            "330983/330983 - 21s - loss: 1.4070e-05 - mean_squared_error: 1.4070e-05 - mean_absolute_error: 0.0028 - val_loss: 3.8275e-05 - val_mean_squared_error: 3.8275e-05 - val_mean_absolute_error: 0.0042\n",
            "Epoch 9/50\n",
            "330983/330983 - 21s - loss: 1.2412e-05 - mean_squared_error: 1.2412e-05 - mean_absolute_error: 0.0027 - val_loss: 3.3654e-05 - val_mean_squared_error: 3.3654e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 10/50\n",
            "330983/330983 - 21s - loss: 1.0964e-05 - mean_squared_error: 1.0964e-05 - mean_absolute_error: 0.0025 - val_loss: 2.6242e-05 - val_mean_squared_error: 2.6242e-05 - val_mean_absolute_error: 0.0035\n",
            "Epoch 11/50\n",
            "330983/330983 - 21s - loss: 9.7793e-06 - mean_squared_error: 9.7793e-06 - mean_absolute_error: 0.0023 - val_loss: 2.0555e-05 - val_mean_squared_error: 2.0555e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 12/50\n",
            "330983/330983 - 21s - loss: 8.7193e-06 - mean_squared_error: 8.7193e-06 - mean_absolute_error: 0.0022 - val_loss: 1.6094e-05 - val_mean_squared_error: 1.6094e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 13/50\n",
            "330983/330983 - 21s - loss: 7.7402e-06 - mean_squared_error: 7.7402e-06 - mean_absolute_error: 0.0021 - val_loss: 1.2450e-05 - val_mean_squared_error: 1.2450e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 14/50\n",
            "330983/330983 - 21s - loss: 7.2995e-06 - mean_squared_error: 7.2995e-06 - mean_absolute_error: 0.0019 - val_loss: 1.4080e-05 - val_mean_squared_error: 1.4080e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 15/50\n",
            "330983/330983 - 21s - loss: 6.3354e-06 - mean_squared_error: 6.3353e-06 - mean_absolute_error: 0.0019 - val_loss: 1.6771e-05 - val_mean_squared_error: 1.6771e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 16/50\n",
            "330983/330983 - 22s - loss: 5.6999e-06 - mean_squared_error: 5.6999e-06 - mean_absolute_error: 0.0018 - val_loss: 9.8994e-06 - val_mean_squared_error: 9.8994e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 17/50\n",
            "330983/330983 - 21s - loss: 5.0994e-06 - mean_squared_error: 5.0994e-06 - mean_absolute_error: 0.0017 - val_loss: 1.7113e-05 - val_mean_squared_error: 1.7113e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 18/50\n",
            "330983/330983 - 20s - loss: 4.8401e-06 - mean_squared_error: 4.8401e-06 - mean_absolute_error: 0.0016 - val_loss: 9.9179e-06 - val_mean_squared_error: 9.9179e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 19/50\n",
            "330983/330983 - 21s - loss: 4.0827e-06 - mean_squared_error: 4.0827e-06 - mean_absolute_error: 0.0015 - val_loss: 1.2384e-05 - val_mean_squared_error: 1.2384e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 20/50\n",
            "330983/330983 - 21s - loss: 3.7885e-06 - mean_squared_error: 3.7885e-06 - mean_absolute_error: 0.0014 - val_loss: 1.0130e-05 - val_mean_squared_error: 1.0130e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 21/50\n",
            "330983/330983 - 21s - loss: 3.4698e-06 - mean_squared_error: 3.4698e-06 - mean_absolute_error: 0.0013 - val_loss: 1.2280e-05 - val_mean_squared_error: 1.2280e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 22/50\n",
            "330983/330983 - 21s - loss: 3.2306e-06 - mean_squared_error: 3.2306e-06 - mean_absolute_error: 0.0013 - val_loss: 1.0783e-05 - val_mean_squared_error: 1.0783e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 23/50\n",
            "330983/330983 - 21s - loss: 2.9757e-06 - mean_squared_error: 2.9757e-06 - mean_absolute_error: 0.0013 - val_loss: 1.0536e-05 - val_mean_squared_error: 1.0536e-05 - val_mean_absolute_error: 9.8339e-04\n",
            "Epoch 24/50\n",
            "330983/330983 - 21s - loss: 2.8419e-06 - mean_squared_error: 2.8419e-06 - mean_absolute_error: 0.0013 - val_loss: 9.6659e-06 - val_mean_squared_error: 9.6660e-06 - val_mean_absolute_error: 9.7925e-04\n",
            "Epoch 25/50\n",
            "330983/330983 - 21s - loss: 2.5710e-06 - mean_squared_error: 2.5710e-06 - mean_absolute_error: 0.0011 - val_loss: 1.0084e-05 - val_mean_squared_error: 1.0084e-05 - val_mean_absolute_error: 9.0216e-04\n",
            "Epoch 26/50\n",
            "330983/330983 - 21s - loss: 2.3242e-06 - mean_squared_error: 2.3242e-06 - mean_absolute_error: 0.0011 - val_loss: 1.3307e-05 - val_mean_squared_error: 1.3307e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 27/50\n",
            "330983/330983 - 22s - loss: 2.2762e-06 - mean_squared_error: 2.2762e-06 - mean_absolute_error: 0.0011 - val_loss: 1.0458e-05 - val_mean_squared_error: 1.0458e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 28/50\n",
            "330983/330983 - 21s - loss: 2.0781e-06 - mean_squared_error: 2.0781e-06 - mean_absolute_error: 0.0010 - val_loss: 1.1264e-05 - val_mean_squared_error: 1.1264e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 29/50\n",
            "330983/330983 - 21s - loss: 1.8837e-06 - mean_squared_error: 1.8837e-06 - mean_absolute_error: 9.9711e-04 - val_loss: 1.1546e-05 - val_mean_squared_error: 1.1546e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 30/50\n",
            "330983/330983 - 21s - loss: 1.8435e-06 - mean_squared_error: 1.8435e-06 - mean_absolute_error: 9.7304e-04 - val_loss: 1.1185e-05 - val_mean_squared_error: 1.1185e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 31/50\n",
            "330983/330983 - 22s - loss: 1.7791e-06 - mean_squared_error: 1.7791e-06 - mean_absolute_error: 9.5199e-04 - val_loss: 1.2933e-05 - val_mean_squared_error: 1.2933e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 32/50\n",
            "330983/330983 - 21s - loss: 1.7383e-06 - mean_squared_error: 1.7383e-06 - mean_absolute_error: 9.2188e-04 - val_loss: 1.0972e-05 - val_mean_squared_error: 1.0972e-05 - val_mean_absolute_error: 0.0010\n",
            "Epoch 33/50\n",
            "330983/330983 - 21s - loss: 1.5340e-06 - mean_squared_error: 1.5340e-06 - mean_absolute_error: 8.9487e-04 - val_loss: 1.1689e-05 - val_mean_squared_error: 1.1689e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 34/50\n",
            "330983/330983 - 21s - loss: 1.5115e-06 - mean_squared_error: 1.5115e-06 - mean_absolute_error: 8.7078e-04 - val_loss: 1.3730e-05 - val_mean_squared_error: 1.3730e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 35/50\n",
            "330983/330983 - 21s - loss: 1.4472e-06 - mean_squared_error: 1.4472e-06 - mean_absolute_error: 8.3032e-04 - val_loss: 1.1242e-05 - val_mean_squared_error: 1.1242e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 36/50\n",
            "330983/330983 - 21s - loss: 1.3810e-06 - mean_squared_error: 1.3810e-06 - mean_absolute_error: 8.2696e-04 - val_loss: 1.1673e-05 - val_mean_squared_error: 1.1673e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 37/50\n",
            "330983/330983 - 21s - loss: 1.3432e-06 - mean_squared_error: 1.3432e-06 - mean_absolute_error: 8.3464e-04 - val_loss: 1.1459e-05 - val_mean_squared_error: 1.1459e-05 - val_mean_absolute_error: 9.0819e-04\n",
            "Epoch 38/50\n",
            "330983/330983 - 21s - loss: 1.3175e-06 - mean_squared_error: 1.3175e-06 - mean_absolute_error: 7.9968e-04 - val_loss: 1.2217e-05 - val_mean_squared_error: 1.2217e-05 - val_mean_absolute_error: 0.0010\n",
            "Epoch 39/50\n",
            "330983/330983 - 21s - loss: 1.3014e-06 - mean_squared_error: 1.3014e-06 - mean_absolute_error: 8.0927e-04 - val_loss: 1.2816e-05 - val_mean_squared_error: 1.2816e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 40/50\n",
            "330983/330983 - 21s - loss: 1.2718e-06 - mean_squared_error: 1.2718e-06 - mean_absolute_error: 7.7410e-04 - val_loss: 1.3141e-05 - val_mean_squared_error: 1.3141e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 41/50\n",
            "330983/330983 - 21s - loss: 1.2976e-06 - mean_squared_error: 1.2976e-06 - mean_absolute_error: 7.9883e-04 - val_loss: 1.2554e-05 - val_mean_squared_error: 1.2554e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 42/50\n",
            "330983/330983 - 21s - loss: 1.2353e-06 - mean_squared_error: 1.2353e-06 - mean_absolute_error: 7.7062e-04 - val_loss: 1.3178e-05 - val_mean_squared_error: 1.3178e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 43/50\n",
            "330983/330983 - 20s - loss: 1.2265e-06 - mean_squared_error: 1.2265e-06 - mean_absolute_error: 7.6038e-04 - val_loss: 1.4094e-05 - val_mean_squared_error: 1.4094e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 44/50\n",
            "330983/330983 - 20s - loss: 1.1873e-06 - mean_squared_error: 1.1873e-06 - mean_absolute_error: 7.4706e-04 - val_loss: 1.4482e-05 - val_mean_squared_error: 1.4482e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 45/50\n",
            "330983/330983 - 21s - loss: 1.1948e-06 - mean_squared_error: 1.1948e-06 - mean_absolute_error: 7.5864e-04 - val_loss: 1.4431e-05 - val_mean_squared_error: 1.4431e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 46/50\n",
            "330983/330983 - 22s - loss: 1.1712e-06 - mean_squared_error: 1.1712e-06 - mean_absolute_error: 7.2674e-04 - val_loss: 1.5482e-05 - val_mean_squared_error: 1.5482e-05 - val_mean_absolute_error: 0.0010\n",
            "Epoch 47/50\n",
            "330983/330983 - 20s - loss: 1.1571e-06 - mean_squared_error: 1.1571e-06 - mean_absolute_error: 7.3937e-04 - val_loss: 1.5023e-05 - val_mean_squared_error: 1.5023e-05 - val_mean_absolute_error: 9.3820e-04\n",
            "Epoch 48/50\n",
            "330983/330983 - 20s - loss: 1.1785e-06 - mean_squared_error: 1.1785e-06 - mean_absolute_error: 7.4908e-04 - val_loss: 1.3752e-05 - val_mean_squared_error: 1.3752e-05 - val_mean_absolute_error: 9.1564e-04\n",
            "Epoch 49/50\n",
            "330983/330983 - 21s - loss: 1.0765e-06 - mean_squared_error: 1.0765e-06 - mean_absolute_error: 7.0543e-04 - val_loss: 1.5632e-05 - val_mean_squared_error: 1.5632e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 50/50\n",
            "330983/330983 - 21s - loss: 1.1391e-06 - mean_squared_error: 1.1391e-06 - mean_absolute_error: 7.3217e-04 - val_loss: 1.5469e-05 - val_mean_squared_error: 1.5469e-05 - val_mean_absolute_error: 0.0016\n",
            "*******************\n",
            "lag1_rotation\n",
            "Train on 330984 samples, validate on 146907 samples\n",
            "Epoch 1/50\n",
            "330984/330984 - 21s - loss: 4.9971e-04 - mean_squared_error: 4.9971e-04 - mean_absolute_error: 0.0078 - val_loss: 1.1053e-04 - val_mean_squared_error: 1.1053e-04 - val_mean_absolute_error: 0.0060\n",
            "Epoch 2/50\n",
            "330984/330984 - 21s - loss: 3.0469e-05 - mean_squared_error: 3.0469e-05 - mean_absolute_error: 0.0040 - val_loss: 8.0628e-05 - val_mean_squared_error: 8.0628e-05 - val_mean_absolute_error: 0.0062\n",
            "Epoch 3/50\n",
            "330984/330984 - 21s - loss: 2.3951e-05 - mean_squared_error: 2.3951e-05 - mean_absolute_error: 0.0036 - val_loss: 5.1888e-05 - val_mean_squared_error: 5.1888e-05 - val_mean_absolute_error: 0.0040\n",
            "Epoch 4/50\n",
            "330984/330984 - 21s - loss: 2.1336e-05 - mean_squared_error: 2.1336e-05 - mean_absolute_error: 0.0033 - val_loss: 5.4153e-05 - val_mean_squared_error: 5.4153e-05 - val_mean_absolute_error: 0.0049\n",
            "Epoch 5/50\n",
            "330984/330984 - 21s - loss: 1.9498e-05 - mean_squared_error: 1.9498e-05 - mean_absolute_error: 0.0032 - val_loss: 1.2173e-04 - val_mean_squared_error: 1.2173e-04 - val_mean_absolute_error: 0.0076\n",
            "Epoch 6/50\n",
            "330984/330984 - 21s - loss: 1.8793e-05 - mean_squared_error: 1.8793e-05 - mean_absolute_error: 0.0031 - val_loss: 5.8252e-05 - val_mean_squared_error: 5.8252e-05 - val_mean_absolute_error: 0.0047\n",
            "Epoch 7/50\n",
            "330984/330984 - 21s - loss: 1.7200e-05 - mean_squared_error: 1.7200e-05 - mean_absolute_error: 0.0030 - val_loss: 4.5140e-05 - val_mean_squared_error: 4.5140e-05 - val_mean_absolute_error: 0.0041\n",
            "Epoch 8/50\n",
            "330984/330984 - 20s - loss: 1.6955e-05 - mean_squared_error: 1.6955e-05 - mean_absolute_error: 0.0029 - val_loss: 4.7293e-05 - val_mean_squared_error: 4.7293e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 9/50\n",
            "330984/330984 - 21s - loss: 1.6119e-05 - mean_squared_error: 1.6119e-05 - mean_absolute_error: 0.0028 - val_loss: 4.3443e-05 - val_mean_squared_error: 4.3443e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 10/50\n",
            "330984/330984 - 21s - loss: 1.5205e-05 - mean_squared_error: 1.5205e-05 - mean_absolute_error: 0.0028 - val_loss: 5.8495e-05 - val_mean_squared_error: 5.8495e-05 - val_mean_absolute_error: 0.0048\n",
            "Epoch 11/50\n",
            "330984/330984 - 22s - loss: 1.4690e-05 - mean_squared_error: 1.4690e-05 - mean_absolute_error: 0.0027 - val_loss: 4.3384e-05 - val_mean_squared_error: 4.3384e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 12/50\n",
            "330984/330984 - 21s - loss: 1.3943e-05 - mean_squared_error: 1.3943e-05 - mean_absolute_error: 0.0026 - val_loss: 5.3740e-05 - val_mean_squared_error: 5.3740e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 13/50\n",
            "330984/330984 - 21s - loss: 1.3707e-05 - mean_squared_error: 1.3707e-05 - mean_absolute_error: 0.0026 - val_loss: 5.0686e-05 - val_mean_squared_error: 5.0686e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 14/50\n",
            "330984/330984 - 21s - loss: 1.2939e-05 - mean_squared_error: 1.2939e-05 - mean_absolute_error: 0.0025 - val_loss: 4.7354e-05 - val_mean_squared_error: 4.7354e-05 - val_mean_absolute_error: 0.0033\n",
            "Epoch 15/50\n",
            "330984/330984 - 21s - loss: 1.2722e-05 - mean_squared_error: 1.2722e-05 - mean_absolute_error: 0.0025 - val_loss: 4.9233e-05 - val_mean_squared_error: 4.9233e-05 - val_mean_absolute_error: 0.0040\n",
            "Epoch 16/50\n",
            "330984/330984 - 21s - loss: 1.1996e-05 - mean_squared_error: 1.1996e-05 - mean_absolute_error: 0.0024 - val_loss: 3.8474e-05 - val_mean_squared_error: 3.8474e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 17/50\n",
            "330984/330984 - 20s - loss: 1.1391e-05 - mean_squared_error: 1.1391e-05 - mean_absolute_error: 0.0024 - val_loss: 3.4300e-05 - val_mean_squared_error: 3.4300e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 18/50\n",
            "330984/330984 - 20s - loss: 1.0888e-05 - mean_squared_error: 1.0888e-05 - mean_absolute_error: 0.0023 - val_loss: 3.8956e-05 - val_mean_squared_error: 3.8956e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 19/50\n",
            "330984/330984 - 20s - loss: 1.0667e-05 - mean_squared_error: 1.0667e-05 - mean_absolute_error: 0.0022 - val_loss: 6.1089e-05 - val_mean_squared_error: 6.1089e-05 - val_mean_absolute_error: 0.0059\n",
            "Epoch 20/50\n",
            "330984/330984 - 20s - loss: 1.0132e-05 - mean_squared_error: 1.0132e-05 - mean_absolute_error: 0.0022 - val_loss: 3.2975e-05 - val_mean_squared_error: 3.2975e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 21/50\n",
            "330984/330984 - 21s - loss: 9.9650e-06 - mean_squared_error: 9.9650e-06 - mean_absolute_error: 0.0022 - val_loss: 3.5244e-05 - val_mean_squared_error: 3.5244e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 22/50\n",
            "330984/330984 - 21s - loss: 9.5064e-06 - mean_squared_error: 9.5063e-06 - mean_absolute_error: 0.0021 - val_loss: 3.7792e-05 - val_mean_squared_error: 3.7792e-05 - val_mean_absolute_error: 0.0034\n",
            "Epoch 23/50\n",
            "330984/330984 - 21s - loss: 9.1702e-06 - mean_squared_error: 9.1702e-06 - mean_absolute_error: 0.0021 - val_loss: 3.7380e-05 - val_mean_squared_error: 3.7380e-05 - val_mean_absolute_error: 0.0030\n",
            "Epoch 24/50\n",
            "330984/330984 - 21s - loss: 8.9707e-06 - mean_squared_error: 8.9707e-06 - mean_absolute_error: 0.0020 - val_loss: 3.2120e-05 - val_mean_squared_error: 3.2120e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 25/50\n",
            "330984/330984 - 21s - loss: 8.6750e-06 - mean_squared_error: 8.6750e-06 - mean_absolute_error: 0.0020 - val_loss: 5.0094e-05 - val_mean_squared_error: 5.0094e-05 - val_mean_absolute_error: 0.0044\n",
            "Epoch 26/50\n",
            "330984/330984 - 22s - loss: 8.3389e-06 - mean_squared_error: 8.3389e-06 - mean_absolute_error: 0.0019 - val_loss: 3.2423e-05 - val_mean_squared_error: 3.2423e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 27/50\n",
            "330984/330984 - 21s - loss: 8.4283e-06 - mean_squared_error: 8.4282e-06 - mean_absolute_error: 0.0019 - val_loss: 2.8786e-05 - val_mean_squared_error: 2.8786e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 28/50\n",
            "330984/330984 - 21s - loss: 7.9415e-06 - mean_squared_error: 7.9415e-06 - mean_absolute_error: 0.0019 - val_loss: 3.2446e-05 - val_mean_squared_error: 3.2446e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 29/50\n",
            "330984/330984 - 21s - loss: 7.7954e-06 - mean_squared_error: 7.7954e-06 - mean_absolute_error: 0.0018 - val_loss: 3.6236e-05 - val_mean_squared_error: 3.6236e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 30/50\n",
            "330984/330984 - 21s - loss: 7.5632e-06 - mean_squared_error: 7.5632e-06 - mean_absolute_error: 0.0018 - val_loss: 2.7838e-05 - val_mean_squared_error: 2.7838e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 31/50\n",
            "330984/330984 - 20s - loss: 7.2685e-06 - mean_squared_error: 7.2685e-06 - mean_absolute_error: 0.0018 - val_loss: 2.8495e-05 - val_mean_squared_error: 2.8495e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 32/50\n",
            "330984/330984 - 21s - loss: 6.9964e-06 - mean_squared_error: 6.9964e-06 - mean_absolute_error: 0.0017 - val_loss: 2.8756e-05 - val_mean_squared_error: 2.8756e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 33/50\n",
            "330984/330984 - 21s - loss: 6.9871e-06 - mean_squared_error: 6.9871e-06 - mean_absolute_error: 0.0017 - val_loss: 2.9157e-05 - val_mean_squared_error: 2.9157e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 34/50\n",
            "330984/330984 - 21s - loss: 6.6365e-06 - mean_squared_error: 6.6365e-06 - mean_absolute_error: 0.0016 - val_loss: 2.6285e-05 - val_mean_squared_error: 2.6285e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 35/50\n",
            "330984/330984 - 21s - loss: 6.4813e-06 - mean_squared_error: 6.4813e-06 - mean_absolute_error: 0.0016 - val_loss: 2.9299e-05 - val_mean_squared_error: 2.9299e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 36/50\n",
            "330984/330984 - 21s - loss: 6.2375e-06 - mean_squared_error: 6.2375e-06 - mean_absolute_error: 0.0016 - val_loss: 2.8523e-05 - val_mean_squared_error: 2.8523e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 37/50\n",
            "330984/330984 - 20s - loss: 6.1179e-06 - mean_squared_error: 6.1179e-06 - mean_absolute_error: 0.0016 - val_loss: 2.3939e-05 - val_mean_squared_error: 2.3939e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 38/50\n",
            "330984/330984 - 21s - loss: 5.9068e-06 - mean_squared_error: 5.9068e-06 - mean_absolute_error: 0.0015 - val_loss: 2.7878e-05 - val_mean_squared_error: 2.7878e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 39/50\n",
            "330984/330984 - 21s - loss: 5.7884e-06 - mean_squared_error: 5.7884e-06 - mean_absolute_error: 0.0015 - val_loss: 2.4949e-05 - val_mean_squared_error: 2.4949e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 40/50\n",
            "330984/330984 - 21s - loss: 5.7255e-06 - mean_squared_error: 5.7255e-06 - mean_absolute_error: 0.0015 - val_loss: 2.9776e-05 - val_mean_squared_error: 2.9776e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 41/50\n",
            "330984/330984 - 22s - loss: 5.5616e-06 - mean_squared_error: 5.5616e-06 - mean_absolute_error: 0.0014 - val_loss: 2.4490e-05 - val_mean_squared_error: 2.4490e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 42/50\n",
            "330984/330984 - 21s - loss: 5.4838e-06 - mean_squared_error: 5.4838e-06 - mean_absolute_error: 0.0014 - val_loss: 2.6326e-05 - val_mean_squared_error: 2.6326e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 43/50\n",
            "330984/330984 - 21s - loss: 5.2867e-06 - mean_squared_error: 5.2867e-06 - mean_absolute_error: 0.0014 - val_loss: 2.1818e-05 - val_mean_squared_error: 2.1818e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 44/50\n",
            "330984/330984 - 21s - loss: 5.2953e-06 - mean_squared_error: 5.2953e-06 - mean_absolute_error: 0.0014 - val_loss: 2.5269e-05 - val_mean_squared_error: 2.5269e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 45/50\n",
            "330984/330984 - 21s - loss: 5.1247e-06 - mean_squared_error: 5.1247e-06 - mean_absolute_error: 0.0013 - val_loss: 2.5549e-05 - val_mean_squared_error: 2.5549e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 46/50\n",
            "330984/330984 - 21s - loss: 5.1440e-06 - mean_squared_error: 5.1440e-06 - mean_absolute_error: 0.0013 - val_loss: 3.2234e-05 - val_mean_squared_error: 3.2234e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 47/50\n",
            "330984/330984 - 21s - loss: 4.9613e-06 - mean_squared_error: 4.9613e-06 - mean_absolute_error: 0.0013 - val_loss: 2.6974e-05 - val_mean_squared_error: 2.6974e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 48/50\n",
            "330984/330984 - 21s - loss: 4.8189e-06 - mean_squared_error: 4.8189e-06 - mean_absolute_error: 0.0013 - val_loss: 2.4766e-05 - val_mean_squared_error: 2.4766e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 49/50\n",
            "330984/330984 - 21s - loss: 4.8817e-06 - mean_squared_error: 4.8817e-06 - mean_absolute_error: 0.0013 - val_loss: 2.6937e-05 - val_mean_squared_error: 2.6937e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 50/50\n",
            "330984/330984 - 21s - loss: 4.7278e-06 - mean_squared_error: 4.7278e-06 - mean_absolute_error: 0.0013 - val_loss: 2.5068e-05 - val_mean_squared_error: 2.5068e-05 - val_mean_absolute_error: 0.0023\n",
            "*******************\n",
            "lag2_rotation\n",
            "Train on 330983 samples, validate on 146906 samples\n",
            "Epoch 1/50\n",
            "330983/330983 - 22s - loss: 8.2200e-04 - mean_squared_error: 8.2200e-04 - mean_absolute_error: 0.0088 - val_loss: 1.3218e-04 - val_mean_squared_error: 1.3218e-04 - val_mean_absolute_error: 0.0075\n",
            "Epoch 2/50\n",
            "330983/330983 - 21s - loss: 3.1517e-05 - mean_squared_error: 3.1517e-05 - mean_absolute_error: 0.0041 - val_loss: 8.7869e-05 - val_mean_squared_error: 8.7869e-05 - val_mean_absolute_error: 0.0060\n",
            "Epoch 3/50\n",
            "330983/330983 - 21s - loss: 2.2785e-05 - mean_squared_error: 2.2785e-05 - mean_absolute_error: 0.0035 - val_loss: 5.9406e-05 - val_mean_squared_error: 5.9406e-05 - val_mean_absolute_error: 0.0045\n",
            "Epoch 4/50\n",
            "330983/330983 - 21s - loss: 1.9065e-05 - mean_squared_error: 1.9065e-05 - mean_absolute_error: 0.0032 - val_loss: 5.6837e-05 - val_mean_squared_error: 5.6837e-05 - val_mean_absolute_error: 0.0045\n",
            "Epoch 5/50\n",
            "330983/330983 - 21s - loss: 1.6879e-05 - mean_squared_error: 1.6879e-05 - mean_absolute_error: 0.0030 - val_loss: 4.8510e-05 - val_mean_squared_error: 4.8510e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 6/50\n",
            "330983/330983 - 22s - loss: 1.5024e-05 - mean_squared_error: 1.5024e-05 - mean_absolute_error: 0.0028 - val_loss: 6.1663e-05 - val_mean_squared_error: 6.1663e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 7/50\n",
            "330983/330983 - 21s - loss: 1.3646e-05 - mean_squared_error: 1.3646e-05 - mean_absolute_error: 0.0027 - val_loss: 4.8562e-05 - val_mean_squared_error: 4.8562e-05 - val_mean_absolute_error: 0.0035\n",
            "Epoch 8/50\n",
            "330983/330983 - 21s - loss: 1.2310e-05 - mean_squared_error: 1.2310e-05 - mean_absolute_error: 0.0025 - val_loss: 4.4079e-05 - val_mean_squared_error: 4.4079e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 9/50\n",
            "330983/330983 - 21s - loss: 1.1486e-05 - mean_squared_error: 1.1486e-05 - mean_absolute_error: 0.0024 - val_loss: 4.3768e-05 - val_mean_squared_error: 4.3768e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 10/50\n",
            "330983/330983 - 21s - loss: 1.0615e-05 - mean_squared_error: 1.0615e-05 - mean_absolute_error: 0.0023 - val_loss: 6.0054e-05 - val_mean_squared_error: 6.0054e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 11/50\n",
            "330983/330983 - 21s - loss: 9.9657e-06 - mean_squared_error: 9.9657e-06 - mean_absolute_error: 0.0022 - val_loss: 4.7284e-05 - val_mean_squared_error: 4.7284e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 12/50\n",
            "330983/330983 - 21s - loss: 9.1868e-06 - mean_squared_error: 9.1868e-06 - mean_absolute_error: 0.0021 - val_loss: 3.4903e-05 - val_mean_squared_error: 3.4904e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 13/50\n",
            "330983/330983 - 21s - loss: 8.6689e-06 - mean_squared_error: 8.6689e-06 - mean_absolute_error: 0.0021 - val_loss: 3.8952e-05 - val_mean_squared_error: 3.8952e-05 - val_mean_absolute_error: 0.0033\n",
            "Epoch 14/50\n",
            "330983/330983 - 21s - loss: 8.3119e-06 - mean_squared_error: 8.3119e-06 - mean_absolute_error: 0.0020 - val_loss: 3.4402e-05 - val_mean_squared_error: 3.4401e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 15/50\n",
            "330983/330983 - 21s - loss: 7.5915e-06 - mean_squared_error: 7.5915e-06 - mean_absolute_error: 0.0019 - val_loss: 3.0021e-05 - val_mean_squared_error: 3.0021e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 16/50\n",
            "330983/330983 - 21s - loss: 7.3918e-06 - mean_squared_error: 7.3918e-06 - mean_absolute_error: 0.0019 - val_loss: 3.1602e-05 - val_mean_squared_error: 3.1602e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 17/50\n",
            "330983/330983 - 21s - loss: 7.0449e-06 - mean_squared_error: 7.0449e-06 - mean_absolute_error: 0.0018 - val_loss: 3.5130e-05 - val_mean_squared_error: 3.5130e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 18/50\n",
            "330983/330983 - 20s - loss: 6.4910e-06 - mean_squared_error: 6.4910e-06 - mean_absolute_error: 0.0017 - val_loss: 3.2158e-05 - val_mean_squared_error: 3.2158e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 19/50\n",
            "330983/330983 - 21s - loss: 6.1958e-06 - mean_squared_error: 6.1958e-06 - mean_absolute_error: 0.0017 - val_loss: 3.3872e-05 - val_mean_squared_error: 3.3872e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 20/50\n",
            "330983/330983 - 21s - loss: 5.9551e-06 - mean_squared_error: 5.9551e-06 - mean_absolute_error: 0.0016 - val_loss: 3.3429e-05 - val_mean_squared_error: 3.3429e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 21/50\n",
            "330983/330983 - 21s - loss: 5.6751e-06 - mean_squared_error: 5.6751e-06 - mean_absolute_error: 0.0016 - val_loss: 3.1426e-05 - val_mean_squared_error: 3.1426e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 22/50\n",
            "330983/330983 - 21s - loss: 5.5087e-06 - mean_squared_error: 5.5087e-06 - mean_absolute_error: 0.0016 - val_loss: 4.5887e-05 - val_mean_squared_error: 4.5887e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 23/50\n",
            "330983/330983 - 21s - loss: 5.2191e-06 - mean_squared_error: 5.2191e-06 - mean_absolute_error: 0.0015 - val_loss: 2.9333e-05 - val_mean_squared_error: 2.9333e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 24/50\n",
            "330983/330983 - 21s - loss: 5.0446e-06 - mean_squared_error: 5.0446e-06 - mean_absolute_error: 0.0015 - val_loss: 2.9521e-05 - val_mean_squared_error: 2.9521e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 25/50\n",
            "330983/330983 - 21s - loss: 4.8817e-06 - mean_squared_error: 4.8817e-06 - mean_absolute_error: 0.0014 - val_loss: 2.6866e-05 - val_mean_squared_error: 2.6866e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 26/50\n",
            "330983/330983 - 21s - loss: 4.7360e-06 - mean_squared_error: 4.7360e-06 - mean_absolute_error: 0.0014 - val_loss: 2.7863e-05 - val_mean_squared_error: 2.7863e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 27/50\n",
            "330983/330983 - 21s - loss: 4.5485e-06 - mean_squared_error: 4.5485e-06 - mean_absolute_error: 0.0014 - val_loss: 2.5759e-05 - val_mean_squared_error: 2.5759e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 28/50\n",
            "330983/330983 - 21s - loss: 4.4457e-06 - mean_squared_error: 4.4457e-06 - mean_absolute_error: 0.0013 - val_loss: 2.9674e-05 - val_mean_squared_error: 2.9674e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 29/50\n",
            "330983/330983 - 21s - loss: 4.2321e-06 - mean_squared_error: 4.2321e-06 - mean_absolute_error: 0.0013 - val_loss: 2.5246e-05 - val_mean_squared_error: 2.5246e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 30/50\n",
            "330983/330983 - 21s - loss: 4.0701e-06 - mean_squared_error: 4.0701e-06 - mean_absolute_error: 0.0013 - val_loss: 2.3915e-05 - val_mean_squared_error: 2.3915e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 31/50\n",
            "330983/330983 - 21s - loss: 3.9995e-06 - mean_squared_error: 3.9995e-06 - mean_absolute_error: 0.0012 - val_loss: 2.8215e-05 - val_mean_squared_error: 2.8214e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 32/50\n",
            "330983/330983 - 21s - loss: 3.8546e-06 - mean_squared_error: 3.8546e-06 - mean_absolute_error: 0.0012 - val_loss: 2.2783e-05 - val_mean_squared_error: 2.2783e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 33/50\n",
            "330983/330983 - 21s - loss: 3.7650e-06 - mean_squared_error: 3.7650e-06 - mean_absolute_error: 0.0012 - val_loss: 3.0073e-05 - val_mean_squared_error: 3.0073e-05 - val_mean_absolute_error: 0.0031\n",
            "Epoch 34/50\n",
            "330983/330983 - 21s - loss: 3.7595e-06 - mean_squared_error: 3.7595e-06 - mean_absolute_error: 0.0012 - val_loss: 2.4627e-05 - val_mean_squared_error: 2.4627e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 35/50\n",
            "330983/330983 - 21s - loss: 3.5970e-06 - mean_squared_error: 3.5970e-06 - mean_absolute_error: 0.0012 - val_loss: 2.2471e-05 - val_mean_squared_error: 2.2471e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 36/50\n",
            "330983/330983 - 21s - loss: 3.5554e-06 - mean_squared_error: 3.5554e-06 - mean_absolute_error: 0.0011 - val_loss: 2.4547e-05 - val_mean_squared_error: 2.4547e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 37/50\n",
            "330983/330983 - 21s - loss: 3.4964e-06 - mean_squared_error: 3.4964e-06 - mean_absolute_error: 0.0011 - val_loss: 2.4872e-05 - val_mean_squared_error: 2.4872e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 38/50\n",
            "330983/330983 - 20s - loss: 3.4664e-06 - mean_squared_error: 3.4664e-06 - mean_absolute_error: 0.0011 - val_loss: 2.4083e-05 - val_mean_squared_error: 2.4083e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 39/50\n",
            "330983/330983 - 21s - loss: 3.3501e-06 - mean_squared_error: 3.3501e-06 - mean_absolute_error: 0.0011 - val_loss: 2.1976e-05 - val_mean_squared_error: 2.1976e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 40/50\n",
            "330983/330983 - 21s - loss: 3.3487e-06 - mean_squared_error: 3.3487e-06 - mean_absolute_error: 0.0011 - val_loss: 2.3408e-05 - val_mean_squared_error: 2.3408e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 41/50\n",
            "330983/330983 - 21s - loss: 3.2741e-06 - mean_squared_error: 3.2741e-06 - mean_absolute_error: 0.0011 - val_loss: 2.5358e-05 - val_mean_squared_error: 2.5358e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 42/50\n",
            "330983/330983 - 21s - loss: 3.2495e-06 - mean_squared_error: 3.2495e-06 - mean_absolute_error: 0.0010 - val_loss: 2.0953e-05 - val_mean_squared_error: 2.0953e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 43/50\n",
            "330983/330983 - 21s - loss: 3.1936e-06 - mean_squared_error: 3.1936e-06 - mean_absolute_error: 0.0010 - val_loss: 2.1883e-05 - val_mean_squared_error: 2.1883e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 44/50\n",
            "330983/330983 - 21s - loss: 3.1754e-06 - mean_squared_error: 3.1754e-06 - mean_absolute_error: 0.0010 - val_loss: 2.6100e-05 - val_mean_squared_error: 2.6100e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 45/50\n",
            "330983/330983 - 21s - loss: 3.1560e-06 - mean_squared_error: 3.1560e-06 - mean_absolute_error: 0.0010 - val_loss: 2.0526e-05 - val_mean_squared_error: 2.0526e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 46/50\n",
            "330983/330983 - 21s - loss: 3.1216e-06 - mean_squared_error: 3.1216e-06 - mean_absolute_error: 0.0010 - val_loss: 1.9752e-05 - val_mean_squared_error: 1.9752e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 47/50\n",
            "330983/330983 - 20s - loss: 3.0728e-06 - mean_squared_error: 3.0728e-06 - mean_absolute_error: 0.0010 - val_loss: 1.9565e-05 - val_mean_squared_error: 1.9565e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 48/50\n",
            "330983/330983 - 21s - loss: 3.0929e-06 - mean_squared_error: 3.0929e-06 - mean_absolute_error: 0.0010 - val_loss: 2.4525e-05 - val_mean_squared_error: 2.4525e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 49/50\n",
            "330983/330983 - 21s - loss: 3.0420e-06 - mean_squared_error: 3.0420e-06 - mean_absolute_error: 9.9324e-04 - val_loss: 1.9593e-05 - val_mean_squared_error: 1.9593e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 50/50\n",
            "330983/330983 - 22s - loss: 2.9624e-06 - mean_squared_error: 2.9624e-06 - mean_absolute_error: 9.8740e-04 - val_loss: 1.6335e-05 - val_mean_squared_error: 1.6335e-05 - val_mean_absolute_error: 0.0015\n",
            "*******************\n",
            "lag1_agg\n",
            "Train on 330984 samples, validate on 146907 samples\n",
            "Epoch 1/50\n",
            "330984/330984 - 22s - loss: 7.9561e-04 - mean_squared_error: 7.9561e-04 - mean_absolute_error: 0.0086 - val_loss: 1.2415e-04 - val_mean_squared_error: 1.2415e-04 - val_mean_absolute_error: 0.0065\n",
            "Epoch 2/50\n",
            "330984/330984 - 21s - loss: 2.1136e-05 - mean_squared_error: 2.1136e-05 - mean_absolute_error: 0.0034 - val_loss: 6.3486e-05 - val_mean_squared_error: 6.3486e-05 - val_mean_absolute_error: 0.0043\n",
            "Epoch 3/50\n",
            "330984/330984 - 21s - loss: 1.5068e-05 - mean_squared_error: 1.5068e-05 - mean_absolute_error: 0.0028 - val_loss: 5.3881e-05 - val_mean_squared_error: 5.3881e-05 - val_mean_absolute_error: 0.0042\n",
            "Epoch 4/50\n",
            "330984/330984 - 21s - loss: 1.3289e-05 - mean_squared_error: 1.3289e-05 - mean_absolute_error: 0.0026 - val_loss: 4.6606e-05 - val_mean_squared_error: 4.6606e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 5/50\n",
            "330984/330984 - 21s - loss: 1.1928e-05 - mean_squared_error: 1.1928e-05 - mean_absolute_error: 0.0025 - val_loss: 4.1608e-05 - val_mean_squared_error: 4.1608e-05 - val_mean_absolute_error: 0.0033\n",
            "Epoch 6/50\n",
            "330984/330984 - 21s - loss: 1.1381e-05 - mean_squared_error: 1.1381e-05 - mean_absolute_error: 0.0024 - val_loss: 4.2444e-05 - val_mean_squared_error: 4.2444e-05 - val_mean_absolute_error: 0.0039\n",
            "Epoch 7/50\n",
            "330984/330984 - 21s - loss: 1.0343e-05 - mean_squared_error: 1.0343e-05 - mean_absolute_error: 0.0023 - val_loss: 3.8546e-05 - val_mean_squared_error: 3.8546e-05 - val_mean_absolute_error: 0.0035\n",
            "Epoch 8/50\n",
            "330984/330984 - 21s - loss: 9.9893e-06 - mean_squared_error: 9.9893e-06 - mean_absolute_error: 0.0022 - val_loss: 3.1509e-05 - val_mean_squared_error: 3.1509e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 9/50\n",
            "330984/330984 - 22s - loss: 9.4380e-06 - mean_squared_error: 9.4380e-06 - mean_absolute_error: 0.0022 - val_loss: 3.6866e-05 - val_mean_squared_error: 3.6866e-05 - val_mean_absolute_error: 0.0034\n",
            "Epoch 10/50\n",
            "330984/330984 - 21s - loss: 9.0544e-06 - mean_squared_error: 9.0544e-06 - mean_absolute_error: 0.0021 - val_loss: 4.5256e-05 - val_mean_squared_error: 4.5256e-05 - val_mean_absolute_error: 0.0046\n",
            "Epoch 11/50\n",
            "330984/330984 - 21s - loss: 8.8475e-06 - mean_squared_error: 8.8475e-06 - mean_absolute_error: 0.0021 - val_loss: 3.0525e-05 - val_mean_squared_error: 3.0525e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 12/50\n",
            "330984/330984 - 21s - loss: 8.3645e-06 - mean_squared_error: 8.3645e-06 - mean_absolute_error: 0.0020 - val_loss: 3.1143e-05 - val_mean_squared_error: 3.1143e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 13/50\n",
            "330984/330984 - 21s - loss: 8.1827e-06 - mean_squared_error: 8.1826e-06 - mean_absolute_error: 0.0020 - val_loss: 3.2434e-05 - val_mean_squared_error: 3.2434e-05 - val_mean_absolute_error: 0.0033\n",
            "Epoch 14/50\n",
            "330984/330984 - 21s - loss: 7.8879e-06 - mean_squared_error: 7.8879e-06 - mean_absolute_error: 0.0019 - val_loss: 3.9261e-05 - val_mean_squared_error: 3.9261e-05 - val_mean_absolute_error: 0.0040\n",
            "Epoch 15/50\n",
            "330984/330984 - 22s - loss: 7.6460e-06 - mean_squared_error: 7.6460e-06 - mean_absolute_error: 0.0019 - val_loss: 2.8664e-05 - val_mean_squared_error: 2.8664e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 16/50\n",
            "330984/330984 - 21s - loss: 7.2441e-06 - mean_squared_error: 7.2441e-06 - mean_absolute_error: 0.0019 - val_loss: 2.8598e-05 - val_mean_squared_error: 2.8598e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 17/50\n",
            "330984/330984 - 20s - loss: 7.1533e-06 - mean_squared_error: 7.1533e-06 - mean_absolute_error: 0.0018 - val_loss: 2.6911e-05 - val_mean_squared_error: 2.6911e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 18/50\n",
            "330984/330984 - 21s - loss: 6.7506e-06 - mean_squared_error: 6.7506e-06 - mean_absolute_error: 0.0018 - val_loss: 2.8329e-05 - val_mean_squared_error: 2.8329e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 19/50\n",
            "330984/330984 - 21s - loss: 6.5500e-06 - mean_squared_error: 6.5500e-06 - mean_absolute_error: 0.0017 - val_loss: 2.4214e-05 - val_mean_squared_error: 2.4214e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 20/50\n",
            "330984/330984 - 21s - loss: 6.3529e-06 - mean_squared_error: 6.3529e-06 - mean_absolute_error: 0.0017 - val_loss: 2.3835e-05 - val_mean_squared_error: 2.3835e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 21/50\n",
            "330984/330984 - 21s - loss: 6.2144e-06 - mean_squared_error: 6.2144e-06 - mean_absolute_error: 0.0017 - val_loss: 2.2392e-05 - val_mean_squared_error: 2.2392e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 22/50\n",
            "330984/330984 - 21s - loss: 5.9284e-06 - mean_squared_error: 5.9284e-06 - mean_absolute_error: 0.0016 - val_loss: 2.5103e-05 - val_mean_squared_error: 2.5103e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 23/50\n",
            "330984/330984 - 21s - loss: 5.7287e-06 - mean_squared_error: 5.7287e-06 - mean_absolute_error: 0.0016 - val_loss: 2.1724e-05 - val_mean_squared_error: 2.1724e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 24/50\n",
            "330984/330984 - 21s - loss: 5.6192e-06 - mean_squared_error: 5.6192e-06 - mean_absolute_error: 0.0016 - val_loss: 2.0830e-05 - val_mean_squared_error: 2.0830e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 25/50\n",
            "330984/330984 - 20s - loss: 5.4279e-06 - mean_squared_error: 5.4279e-06 - mean_absolute_error: 0.0015 - val_loss: 2.4726e-05 - val_mean_squared_error: 2.4726e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 26/50\n",
            "330984/330984 - 21s - loss: 5.2938e-06 - mean_squared_error: 5.2938e-06 - mean_absolute_error: 0.0015 - val_loss: 2.1274e-05 - val_mean_squared_error: 2.1274e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 27/50\n",
            "330984/330984 - 21s - loss: 5.1096e-06 - mean_squared_error: 5.1096e-06 - mean_absolute_error: 0.0015 - val_loss: 2.2632e-05 - val_mean_squared_error: 2.2632e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 28/50\n",
            "330984/330984 - 21s - loss: 5.0060e-06 - mean_squared_error: 5.0059e-06 - mean_absolute_error: 0.0015 - val_loss: 2.4127e-05 - val_mean_squared_error: 2.4127e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 29/50\n",
            "330984/330984 - 21s - loss: 4.9137e-06 - mean_squared_error: 4.9137e-06 - mean_absolute_error: 0.0014 - val_loss: 1.7964e-05 - val_mean_squared_error: 1.7964e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 30/50\n",
            "330984/330984 - 22s - loss: 4.7892e-06 - mean_squared_error: 4.7892e-06 - mean_absolute_error: 0.0014 - val_loss: 1.8941e-05 - val_mean_squared_error: 1.8941e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 31/50\n",
            "330984/330984 - 21s - loss: 4.6108e-06 - mean_squared_error: 4.6108e-06 - mean_absolute_error: 0.0014 - val_loss: 1.9949e-05 - val_mean_squared_error: 1.9949e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 32/50\n",
            "330984/330984 - 21s - loss: 4.5066e-06 - mean_squared_error: 4.5066e-06 - mean_absolute_error: 0.0014 - val_loss: 1.8244e-05 - val_mean_squared_error: 1.8244e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 33/50\n",
            "330984/330984 - 21s - loss: 4.4314e-06 - mean_squared_error: 4.4314e-06 - mean_absolute_error: 0.0013 - val_loss: 2.1462e-05 - val_mean_squared_error: 2.1462e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 34/50\n",
            "330984/330984 - 21s - loss: 4.2966e-06 - mean_squared_error: 4.2966e-06 - mean_absolute_error: 0.0013 - val_loss: 1.7399e-05 - val_mean_squared_error: 1.7399e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 35/50\n",
            "330984/330984 - 21s - loss: 4.1800e-06 - mean_squared_error: 4.1800e-06 - mean_absolute_error: 0.0013 - val_loss: 1.7815e-05 - val_mean_squared_error: 1.7815e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 36/50\n",
            "330984/330984 - 21s - loss: 4.1408e-06 - mean_squared_error: 4.1408e-06 - mean_absolute_error: 0.0013 - val_loss: 1.7411e-05 - val_mean_squared_error: 1.7411e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 37/50\n",
            "330984/330984 - 21s - loss: 4.0741e-06 - mean_squared_error: 4.0741e-06 - mean_absolute_error: 0.0013 - val_loss: 1.7445e-05 - val_mean_squared_error: 1.7445e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 38/50\n",
            "330984/330984 - 21s - loss: 3.9422e-06 - mean_squared_error: 3.9422e-06 - mean_absolute_error: 0.0012 - val_loss: 1.9045e-05 - val_mean_squared_error: 1.9045e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 39/50\n",
            "330984/330984 - 21s - loss: 3.8490e-06 - mean_squared_error: 3.8490e-06 - mean_absolute_error: 0.0012 - val_loss: 1.9009e-05 - val_mean_squared_error: 1.9009e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 40/50\n",
            "330984/330984 - 21s - loss: 3.7016e-06 - mean_squared_error: 3.7016e-06 - mean_absolute_error: 0.0012 - val_loss: 2.0415e-05 - val_mean_squared_error: 2.0415e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 41/50\n",
            "330984/330984 - 21s - loss: 3.6322e-06 - mean_squared_error: 3.6322e-06 - mean_absolute_error: 0.0012 - val_loss: 1.6951e-05 - val_mean_squared_error: 1.6951e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 42/50\n",
            "330984/330984 - 21s - loss: 3.5404e-06 - mean_squared_error: 3.5404e-06 - mean_absolute_error: 0.0012 - val_loss: 1.8831e-05 - val_mean_squared_error: 1.8831e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 43/50\n",
            "330984/330984 - 21s - loss: 3.5092e-06 - mean_squared_error: 3.5092e-06 - mean_absolute_error: 0.0011 - val_loss: 1.8993e-05 - val_mean_squared_error: 1.8993e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 44/50\n",
            "330984/330984 - 21s - loss: 3.4157e-06 - mean_squared_error: 3.4157e-06 - mean_absolute_error: 0.0011 - val_loss: 1.5763e-05 - val_mean_squared_error: 1.5763e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 45/50\n",
            "330984/330984 - 22s - loss: 3.3556e-06 - mean_squared_error: 3.3556e-06 - mean_absolute_error: 0.0011 - val_loss: 1.4920e-05 - val_mean_squared_error: 1.4920e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 46/50\n",
            "330984/330984 - 21s - loss: 3.2847e-06 - mean_squared_error: 3.2847e-06 - mean_absolute_error: 0.0011 - val_loss: 1.4638e-05 - val_mean_squared_error: 1.4638e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 47/50\n",
            "330984/330984 - 21s - loss: 3.2215e-06 - mean_squared_error: 3.2215e-06 - mean_absolute_error: 0.0010 - val_loss: 1.5227e-05 - val_mean_squared_error: 1.5227e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 48/50\n",
            "330984/330984 - 21s - loss: 3.1793e-06 - mean_squared_error: 3.1793e-06 - mean_absolute_error: 0.0010 - val_loss: 1.5496e-05 - val_mean_squared_error: 1.5496e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 49/50\n",
            "330984/330984 - 21s - loss: 3.2031e-06 - mean_squared_error: 3.2031e-06 - mean_absolute_error: 0.0011 - val_loss: 1.6514e-05 - val_mean_squared_error: 1.6514e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 50/50\n",
            "330984/330984 - 21s - loss: 3.1384e-06 - mean_squared_error: 3.1384e-06 - mean_absolute_error: 0.0010 - val_loss: 1.4610e-05 - val_mean_squared_error: 1.4610e-05 - val_mean_absolute_error: 0.0018\n",
            "*******************\n",
            "lag2_agg\n",
            "Train on 330983 samples, validate on 146906 samples\n",
            "Epoch 1/50\n",
            "330983/330983 - 21s - loss: 0.0022 - mean_squared_error: 0.0022 - mean_absolute_error: 0.0109 - val_loss: 1.8059e-04 - val_mean_squared_error: 1.8059e-04 - val_mean_absolute_error: 0.0090\n",
            "Epoch 2/50\n",
            "330983/330983 - 21s - loss: 2.7211e-05 - mean_squared_error: 2.7211e-05 - mean_absolute_error: 0.0038 - val_loss: 6.1340e-05 - val_mean_squared_error: 6.1340e-05 - val_mean_absolute_error: 0.0041\n",
            "Epoch 3/50\n",
            "330983/330983 - 21s - loss: 1.8627e-05 - mean_squared_error: 1.8627e-05 - mean_absolute_error: 0.0031 - val_loss: 5.3615e-05 - val_mean_squared_error: 5.3615e-05 - val_mean_absolute_error: 0.0043\n",
            "Epoch 4/50\n",
            "330983/330983 - 21s - loss: 1.5794e-05 - mean_squared_error: 1.5794e-05 - mean_absolute_error: 0.0029 - val_loss: 4.9506e-05 - val_mean_squared_error: 4.9506e-05 - val_mean_absolute_error: 0.0044\n",
            "Epoch 5/50\n",
            "330983/330983 - 21s - loss: 1.3875e-05 - mean_squared_error: 1.3875e-05 - mean_absolute_error: 0.0027 - val_loss: 5.0867e-05 - val_mean_squared_error: 5.0867e-05 - val_mean_absolute_error: 0.0049\n",
            "Epoch 6/50\n",
            "330983/330983 - 21s - loss: 1.2518e-05 - mean_squared_error: 1.2518e-05 - mean_absolute_error: 0.0025 - val_loss: 3.4559e-05 - val_mean_squared_error: 3.4559e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 7/50\n",
            "330983/330983 - 21s - loss: 1.1550e-05 - mean_squared_error: 1.1550e-05 - mean_absolute_error: 0.0024 - val_loss: 2.9267e-05 - val_mean_squared_error: 2.9267e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 8/50\n",
            "330983/330983 - 21s - loss: 1.0849e-05 - mean_squared_error: 1.0849e-05 - mean_absolute_error: 0.0023 - val_loss: 2.5950e-05 - val_mean_squared_error: 2.5950e-05 - val_mean_absolute_error: 0.0026\n",
            "Epoch 9/50\n",
            "330983/330983 - 21s - loss: 1.0121e-05 - mean_squared_error: 1.0121e-05 - mean_absolute_error: 0.0023 - val_loss: 3.7441e-05 - val_mean_squared_error: 3.7441e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 10/50\n",
            "330983/330983 - 22s - loss: 9.2456e-06 - mean_squared_error: 9.2457e-06 - mean_absolute_error: 0.0022 - val_loss: 3.4966e-05 - val_mean_squared_error: 3.4966e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 11/50\n",
            "330983/330983 - 21s - loss: 8.8907e-06 - mean_squared_error: 8.8907e-06 - mean_absolute_error: 0.0021 - val_loss: 2.8491e-05 - val_mean_squared_error: 2.8491e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 12/50\n",
            "330983/330983 - 21s - loss: 8.3442e-06 - mean_squared_error: 8.3442e-06 - mean_absolute_error: 0.0020 - val_loss: 2.7749e-05 - val_mean_squared_error: 2.7749e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 13/50\n",
            "330983/330983 - 21s - loss: 7.9046e-06 - mean_squared_error: 7.9046e-06 - mean_absolute_error: 0.0020 - val_loss: 2.1379e-05 - val_mean_squared_error: 2.1379e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 14/50\n",
            "330983/330983 - 21s - loss: 7.2012e-06 - mean_squared_error: 7.2012e-06 - mean_absolute_error: 0.0019 - val_loss: 2.2002e-05 - val_mean_squared_error: 2.2002e-05 - val_mean_absolute_error: 0.0031\n",
            "Epoch 15/50\n",
            "330983/330983 - 21s - loss: 7.0040e-06 - mean_squared_error: 7.0040e-06 - mean_absolute_error: 0.0018 - val_loss: 1.6823e-05 - val_mean_squared_error: 1.6823e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 16/50\n",
            "330983/330983 - 21s - loss: 6.6034e-06 - mean_squared_error: 6.6034e-06 - mean_absolute_error: 0.0018 - val_loss: 2.3695e-05 - val_mean_squared_error: 2.3695e-05 - val_mean_absolute_error: 0.0030\n",
            "Epoch 17/50\n",
            "330983/330983 - 21s - loss: 6.2333e-06 - mean_squared_error: 6.2333e-06 - mean_absolute_error: 0.0017 - val_loss: 1.6101e-05 - val_mean_squared_error: 1.6101e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 18/50\n",
            "330983/330983 - 21s - loss: 6.0065e-06 - mean_squared_error: 6.0065e-06 - mean_absolute_error: 0.0017 - val_loss: 1.4668e-05 - val_mean_squared_error: 1.4668e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 19/50\n",
            "330983/330983 - 21s - loss: 5.6782e-06 - mean_squared_error: 5.6782e-06 - mean_absolute_error: 0.0016 - val_loss: 2.6081e-05 - val_mean_squared_error: 2.6081e-05 - val_mean_absolute_error: 0.0033\n",
            "Epoch 20/50\n",
            "330983/330983 - 21s - loss: 5.3559e-06 - mean_squared_error: 5.3559e-06 - mean_absolute_error: 0.0015 - val_loss: 1.4185e-05 - val_mean_squared_error: 1.4185e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 21/50\n",
            "330983/330983 - 21s - loss: 5.1200e-06 - mean_squared_error: 5.1200e-06 - mean_absolute_error: 0.0015 - val_loss: 1.2751e-05 - val_mean_squared_error: 1.2751e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 22/50\n",
            "330983/330983 - 20s - loss: 4.9751e-06 - mean_squared_error: 4.9751e-06 - mean_absolute_error: 0.0015 - val_loss: 1.3237e-05 - val_mean_squared_error: 1.3237e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 23/50\n",
            "330983/330983 - 21s - loss: 4.7766e-06 - mean_squared_error: 4.7766e-06 - mean_absolute_error: 0.0014 - val_loss: 1.5782e-05 - val_mean_squared_error: 1.5782e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 24/50\n",
            "330983/330983 - 21s - loss: 4.5762e-06 - mean_squared_error: 4.5762e-06 - mean_absolute_error: 0.0014 - val_loss: 1.3210e-05 - val_mean_squared_error: 1.3210e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 25/50\n",
            "330983/330983 - 22s - loss: 4.4686e-06 - mean_squared_error: 4.4686e-06 - mean_absolute_error: 0.0014 - val_loss: 1.3369e-05 - val_mean_squared_error: 1.3369e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 26/50\n",
            "330983/330983 - 21s - loss: 4.3411e-06 - mean_squared_error: 4.3411e-06 - mean_absolute_error: 0.0013 - val_loss: 2.0425e-05 - val_mean_squared_error: 2.0425e-05 - val_mean_absolute_error: 0.0030\n",
            "Epoch 27/50\n",
            "330983/330983 - 20s - loss: 4.1043e-06 - mean_squared_error: 4.1043e-06 - mean_absolute_error: 0.0013 - val_loss: 1.4427e-05 - val_mean_squared_error: 1.4427e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 28/50\n",
            "330983/330983 - 21s - loss: 4.0678e-06 - mean_squared_error: 4.0678e-06 - mean_absolute_error: 0.0013 - val_loss: 1.1567e-05 - val_mean_squared_error: 1.1567e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 29/50\n",
            "330983/330983 - 21s - loss: 3.9581e-06 - mean_squared_error: 3.9581e-06 - mean_absolute_error: 0.0012 - val_loss: 1.1081e-05 - val_mean_squared_error: 1.1081e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 30/50\n",
            "330983/330983 - 21s - loss: 3.8548e-06 - mean_squared_error: 3.8548e-06 - mean_absolute_error: 0.0012 - val_loss: 1.1350e-05 - val_mean_squared_error: 1.1350e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 31/50\n",
            "330983/330983 - 21s - loss: 3.6788e-06 - mean_squared_error: 3.6788e-06 - mean_absolute_error: 0.0012 - val_loss: 1.0585e-05 - val_mean_squared_error: 1.0585e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 32/50\n",
            "330983/330983 - 21s - loss: 3.7253e-06 - mean_squared_error: 3.7253e-06 - mean_absolute_error: 0.0012 - val_loss: 9.6508e-06 - val_mean_squared_error: 9.6508e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 33/50\n",
            "330983/330983 - 21s - loss: 3.6106e-06 - mean_squared_error: 3.6106e-06 - mean_absolute_error: 0.0012 - val_loss: 1.0421e-05 - val_mean_squared_error: 1.0421e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 34/50\n",
            "330983/330983 - 21s - loss: 3.5170e-06 - mean_squared_error: 3.5170e-06 - mean_absolute_error: 0.0011 - val_loss: 9.8156e-06 - val_mean_squared_error: 9.8156e-06 - val_mean_absolute_error: 0.0011\n",
            "Epoch 35/50\n",
            "330983/330983 - 21s - loss: 3.5112e-06 - mean_squared_error: 3.5112e-06 - mean_absolute_error: 0.0011 - val_loss: 1.2797e-05 - val_mean_squared_error: 1.2797e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 36/50\n",
            "330983/330983 - 21s - loss: 3.3590e-06 - mean_squared_error: 3.3590e-06 - mean_absolute_error: 0.0011 - val_loss: 1.0340e-05 - val_mean_squared_error: 1.0340e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 37/50\n",
            "330983/330983 - 21s - loss: 3.3004e-06 - mean_squared_error: 3.3004e-06 - mean_absolute_error: 0.0011 - val_loss: 1.3270e-05 - val_mean_squared_error: 1.3270e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 38/50\n",
            "330983/330983 - 21s - loss: 3.3008e-06 - mean_squared_error: 3.3008e-06 - mean_absolute_error: 0.0011 - val_loss: 9.6416e-06 - val_mean_squared_error: 9.6416e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 39/50\n",
            "330983/330983 - 21s - loss: 3.2534e-06 - mean_squared_error: 3.2534e-06 - mean_absolute_error: 0.0011 - val_loss: 1.1128e-05 - val_mean_squared_error: 1.1128e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 40/50\n",
            "330983/330983 - 22s - loss: 3.2556e-06 - mean_squared_error: 3.2556e-06 - mean_absolute_error: 0.0011 - val_loss: 1.0074e-05 - val_mean_squared_error: 1.0074e-05 - val_mean_absolute_error: 0.0011\n",
            "Epoch 41/50\n",
            "330983/330983 - 21s - loss: 3.1276e-06 - mean_squared_error: 3.1276e-06 - mean_absolute_error: 0.0010 - val_loss: 1.0224e-05 - val_mean_squared_error: 1.0224e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 42/50\n",
            "330983/330983 - 21s - loss: 3.1208e-06 - mean_squared_error: 3.1208e-06 - mean_absolute_error: 0.0010 - val_loss: 1.0589e-05 - val_mean_squared_error: 1.0589e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 43/50\n",
            "330983/330983 - 21s - loss: 3.0988e-06 - mean_squared_error: 3.0988e-06 - mean_absolute_error: 0.0010 - val_loss: 9.6747e-06 - val_mean_squared_error: 9.6747e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 44/50\n",
            "330983/330983 - 21s - loss: 3.0627e-06 - mean_squared_error: 3.0627e-06 - mean_absolute_error: 0.0010 - val_loss: 9.8349e-06 - val_mean_squared_error: 9.8349e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 45/50\n",
            "330983/330983 - 21s - loss: 3.0503e-06 - mean_squared_error: 3.0503e-06 - mean_absolute_error: 0.0010 - val_loss: 8.9997e-06 - val_mean_squared_error: 8.9997e-06 - val_mean_absolute_error: 0.0010\n",
            "Epoch 46/50\n",
            "330983/330983 - 21s - loss: 3.0155e-06 - mean_squared_error: 3.0155e-06 - mean_absolute_error: 9.8977e-04 - val_loss: 9.8578e-06 - val_mean_squared_error: 9.8577e-06 - val_mean_absolute_error: 0.0014\n",
            "Epoch 47/50\n",
            "330983/330983 - 21s - loss: 2.9946e-06 - mean_squared_error: 2.9946e-06 - mean_absolute_error: 9.7849e-04 - val_loss: 1.4567e-05 - val_mean_squared_error: 1.4567e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 48/50\n",
            "330983/330983 - 21s - loss: 2.9617e-06 - mean_squared_error: 2.9617e-06 - mean_absolute_error: 9.7547e-04 - val_loss: 9.8115e-06 - val_mean_squared_error: 9.8115e-06 - val_mean_absolute_error: 0.0013\n",
            "Epoch 49/50\n",
            "330983/330983 - 21s - loss: 3.0125e-06 - mean_squared_error: 3.0125e-06 - mean_absolute_error: 9.9186e-04 - val_loss: 9.6531e-06 - val_mean_squared_error: 9.6531e-06 - val_mean_absolute_error: 0.0012\n",
            "Epoch 50/50\n",
            "330983/330983 - 21s - loss: 2.9233e-06 - mean_squared_error: 2.9233e-06 - mean_absolute_error: 9.7252e-04 - val_loss: 9.9336e-06 - val_mean_squared_error: 9.9336e-06 - val_mean_absolute_error: 0.0013\n",
            "*******************\n",
            "lag_pos\n",
            "Train on 330984 samples, validate on 146907 samples\n",
            "Epoch 1/50\n",
            "330984/330984 - 21s - loss: 6.7401e-04 - mean_squared_error: 6.7401e-04 - mean_absolute_error: 0.0079 - val_loss: 8.1105e-05 - val_mean_squared_error: 8.1105e-05 - val_mean_absolute_error: 0.0053\n",
            "Epoch 2/50\n",
            "330984/330984 - 21s - loss: 2.0073e-05 - mean_squared_error: 2.0073e-05 - mean_absolute_error: 0.0031 - val_loss: 4.6511e-05 - val_mean_squared_error: 4.6511e-05 - val_mean_absolute_error: 0.0041\n",
            "Epoch 3/50\n",
            "330984/330984 - 21s - loss: 1.4623e-05 - mean_squared_error: 1.4623e-05 - mean_absolute_error: 0.0025 - val_loss: 2.9611e-05 - val_mean_squared_error: 2.9611e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 4/50\n",
            "330984/330984 - 21s - loss: 1.2813e-05 - mean_squared_error: 1.2813e-05 - mean_absolute_error: 0.0023 - val_loss: 2.9109e-05 - val_mean_squared_error: 2.9109e-05 - val_mean_absolute_error: 0.0031\n",
            "Epoch 5/50\n",
            "330984/330984 - 22s - loss: 1.2046e-05 - mean_squared_error: 1.2046e-05 - mean_absolute_error: 0.0022 - val_loss: 2.8885e-05 - val_mean_squared_error: 2.8885e-05 - val_mean_absolute_error: 0.0035\n",
            "Epoch 6/50\n",
            "330984/330984 - 21s - loss: 1.2343e-05 - mean_squared_error: 1.2343e-05 - mean_absolute_error: 0.0021 - val_loss: 2.1558e-05 - val_mean_squared_error: 2.1558e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 7/50\n",
            "330984/330984 - 21s - loss: 1.0851e-05 - mean_squared_error: 1.0851e-05 - mean_absolute_error: 0.0019 - val_loss: 2.0042e-05 - val_mean_squared_error: 2.0042e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 8/50\n",
            "330984/330984 - 20s - loss: 1.0449e-05 - mean_squared_error: 1.0449e-05 - mean_absolute_error: 0.0018 - val_loss: 2.5704e-05 - val_mean_squared_error: 2.5704e-05 - val_mean_absolute_error: 0.0034\n",
            "Epoch 9/50\n",
            "330984/330984 - 21s - loss: 1.0164e-05 - mean_squared_error: 1.0164e-05 - mean_absolute_error: 0.0019 - val_loss: 2.0489e-05 - val_mean_squared_error: 2.0489e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 10/50\n",
            "330984/330984 - 21s - loss: 9.6719e-06 - mean_squared_error: 9.6719e-06 - mean_absolute_error: 0.0018 - val_loss: 2.3092e-05 - val_mean_squared_error: 2.3092e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 11/50\n",
            "330984/330984 - 21s - loss: 9.8679e-06 - mean_squared_error: 9.8679e-06 - mean_absolute_error: 0.0018 - val_loss: 3.4426e-05 - val_mean_squared_error: 3.4426e-05 - val_mean_absolute_error: 0.0036\n",
            "Epoch 12/50\n",
            "330984/330984 - 21s - loss: 9.4927e-06 - mean_squared_error: 9.4927e-06 - mean_absolute_error: 0.0017 - val_loss: 1.8805e-05 - val_mean_squared_error: 1.8805e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 13/50\n",
            "330984/330984 - 21s - loss: 8.9227e-06 - mean_squared_error: 8.9227e-06 - mean_absolute_error: 0.0016 - val_loss: 1.8714e-05 - val_mean_squared_error: 1.8714e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 14/50\n",
            "330984/330984 - 21s - loss: 8.4367e-06 - mean_squared_error: 8.4367e-06 - mean_absolute_error: 0.0017 - val_loss: 3.0682e-05 - val_mean_squared_error: 3.0682e-05 - val_mean_absolute_error: 0.0037\n",
            "Epoch 15/50\n",
            "330984/330984 - 21s - loss: 8.4930e-06 - mean_squared_error: 8.4930e-06 - mean_absolute_error: 0.0016 - val_loss: 2.0870e-05 - val_mean_squared_error: 2.0870e-05 - val_mean_absolute_error: 0.0022\n",
            "Epoch 16/50\n",
            "330984/330984 - 21s - loss: 8.9892e-06 - mean_squared_error: 8.9892e-06 - mean_absolute_error: 0.0015 - val_loss: 2.2927e-05 - val_mean_squared_error: 2.2927e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 17/50\n",
            "330984/330984 - 21s - loss: 8.0125e-06 - mean_squared_error: 8.0125e-06 - mean_absolute_error: 0.0014 - val_loss: 2.3028e-05 - val_mean_squared_error: 2.3028e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 18/50\n",
            "330984/330984 - 21s - loss: 7.6354e-06 - mean_squared_error: 7.6354e-06 - mean_absolute_error: 0.0016 - val_loss: 2.1884e-05 - val_mean_squared_error: 2.1884e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 19/50\n",
            "330984/330984 - 21s - loss: 7.4619e-06 - mean_squared_error: 7.4619e-06 - mean_absolute_error: 0.0015 - val_loss: 2.3707e-05 - val_mean_squared_error: 2.3707e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 20/50\n",
            "330984/330984 - 22s - loss: 7.6969e-06 - mean_squared_error: 7.6969e-06 - mean_absolute_error: 0.0014 - val_loss: 2.4022e-05 - val_mean_squared_error: 2.4022e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 21/50\n",
            "330984/330984 - 21s - loss: 7.1093e-06 - mean_squared_error: 7.1093e-06 - mean_absolute_error: 0.0014 - val_loss: 2.2976e-05 - val_mean_squared_error: 2.2976e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 22/50\n",
            "330984/330984 - 21s - loss: 7.0985e-06 - mean_squared_error: 7.0985e-06 - mean_absolute_error: 0.0013 - val_loss: 2.3119e-05 - val_mean_squared_error: 2.3119e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 23/50\n",
            "330984/330984 - 21s - loss: 6.6146e-06 - mean_squared_error: 6.6146e-06 - mean_absolute_error: 0.0013 - val_loss: 2.3329e-05 - val_mean_squared_error: 2.3329e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 24/50\n",
            "330984/330984 - 21s - loss: 7.0690e-06 - mean_squared_error: 7.0690e-06 - mean_absolute_error: 0.0014 - val_loss: 2.2483e-05 - val_mean_squared_error: 2.2483e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 25/50\n",
            "330984/330984 - 21s - loss: 5.9244e-06 - mean_squared_error: 5.9244e-06 - mean_absolute_error: 0.0012 - val_loss: 2.3576e-05 - val_mean_squared_error: 2.3576e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 26/50\n",
            "330984/330984 - 21s - loss: 6.4212e-06 - mean_squared_error: 6.4212e-06 - mean_absolute_error: 0.0013 - val_loss: 2.3635e-05 - val_mean_squared_error: 2.3635e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 27/50\n",
            "330984/330984 - 21s - loss: 6.0200e-06 - mean_squared_error: 6.0200e-06 - mean_absolute_error: 0.0012 - val_loss: 2.6167e-05 - val_mean_squared_error: 2.6167e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 28/50\n",
            "330984/330984 - 21s - loss: 6.0473e-06 - mean_squared_error: 6.0473e-06 - mean_absolute_error: 0.0012 - val_loss: 2.9504e-05 - val_mean_squared_error: 2.9504e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 29/50\n",
            "330984/330984 - 21s - loss: 5.7254e-06 - mean_squared_error: 5.7254e-06 - mean_absolute_error: 0.0011 - val_loss: 2.6972e-05 - val_mean_squared_error: 2.6972e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 30/50\n",
            "330984/330984 - 21s - loss: 5.5682e-06 - mean_squared_error: 5.5682e-06 - mean_absolute_error: 0.0011 - val_loss: 2.2743e-05 - val_mean_squared_error: 2.2743e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 31/50\n",
            "330984/330984 - 21s - loss: 5.5841e-06 - mean_squared_error: 5.5841e-06 - mean_absolute_error: 0.0011 - val_loss: 2.1953e-05 - val_mean_squared_error: 2.1953e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 32/50\n",
            "330984/330984 - 21s - loss: 5.3512e-06 - mean_squared_error: 5.3512e-06 - mean_absolute_error: 0.0011 - val_loss: 2.0291e-05 - val_mean_squared_error: 2.0291e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 33/50\n",
            "330984/330984 - 21s - loss: 5.4314e-06 - mean_squared_error: 5.4314e-06 - mean_absolute_error: 0.0011 - val_loss: 2.1637e-05 - val_mean_squared_error: 2.1637e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 34/50\n",
            "330984/330984 - 21s - loss: 5.0590e-06 - mean_squared_error: 5.0590e-06 - mean_absolute_error: 0.0010 - val_loss: 2.0182e-05 - val_mean_squared_error: 2.0182e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 35/50\n",
            "330984/330984 - 21s - loss: 5.1103e-06 - mean_squared_error: 5.1103e-06 - mean_absolute_error: 0.0010 - val_loss: 2.0919e-05 - val_mean_squared_error: 2.0919e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 36/50\n",
            "330984/330984 - 21s - loss: 4.9282e-06 - mean_squared_error: 4.9282e-06 - mean_absolute_error: 0.0010 - val_loss: 1.9515e-05 - val_mean_squared_error: 1.9515e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 37/50\n",
            "330984/330984 - 21s - loss: 4.5881e-06 - mean_squared_error: 4.5881e-06 - mean_absolute_error: 9.6385e-04 - val_loss: 1.9528e-05 - val_mean_squared_error: 1.9528e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 38/50\n",
            "330984/330984 - 21s - loss: 4.6892e-06 - mean_squared_error: 4.6892e-06 - mean_absolute_error: 9.8093e-04 - val_loss: 1.9724e-05 - val_mean_squared_error: 1.9724e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 39/50\n",
            "330984/330984 - 21s - loss: 5.0700e-06 - mean_squared_error: 5.0700e-06 - mean_absolute_error: 9.6440e-04 - val_loss: 2.1597e-05 - val_mean_squared_error: 2.1597e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 40/50\n",
            "330984/330984 - 21s - loss: 4.4216e-06 - mean_squared_error: 4.4216e-06 - mean_absolute_error: 9.1530e-04 - val_loss: 2.3099e-05 - val_mean_squared_error: 2.3099e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 41/50\n",
            "330984/330984 - 21s - loss: 4.3698e-06 - mean_squared_error: 4.3698e-06 - mean_absolute_error: 8.7119e-04 - val_loss: 1.9452e-05 - val_mean_squared_error: 1.9452e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 42/50\n",
            "330984/330984 - 21s - loss: 4.1999e-06 - mean_squared_error: 4.1999e-06 - mean_absolute_error: 8.6710e-04 - val_loss: 2.4239e-05 - val_mean_squared_error: 2.4239e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 43/50\n",
            "330984/330984 - 20s - loss: 4.6588e-06 - mean_squared_error: 4.6588e-06 - mean_absolute_error: 8.5811e-04 - val_loss: 2.1636e-05 - val_mean_squared_error: 2.1636e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 44/50\n",
            "330984/330984 - 21s - loss: 4.4578e-06 - mean_squared_error: 4.4578e-06 - mean_absolute_error: 8.4536e-04 - val_loss: 2.1636e-05 - val_mean_squared_error: 2.1636e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 45/50\n",
            "330984/330984 - 21s - loss: 4.4091e-06 - mean_squared_error: 4.4091e-06 - mean_absolute_error: 8.4417e-04 - val_loss: 2.1917e-05 - val_mean_squared_error: 2.1917e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 46/50\n",
            "330984/330984 - 21s - loss: 4.1019e-06 - mean_squared_error: 4.1019e-06 - mean_absolute_error: 7.7824e-04 - val_loss: 2.2759e-05 - val_mean_squared_error: 2.2759e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 47/50\n",
            "330984/330984 - 21s - loss: 4.5175e-06 - mean_squared_error: 4.5175e-06 - mean_absolute_error: 7.9164e-04 - val_loss: 2.2268e-05 - val_mean_squared_error: 2.2268e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 48/50\n",
            "330984/330984 - 21s - loss: 3.9775e-06 - mean_squared_error: 3.9775e-06 - mean_absolute_error: 7.7447e-04 - val_loss: 2.2093e-05 - val_mean_squared_error: 2.2093e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 49/50\n",
            "330984/330984 - 22s - loss: 4.0389e-06 - mean_squared_error: 4.0389e-06 - mean_absolute_error: 7.6205e-04 - val_loss: 2.3535e-05 - val_mean_squared_error: 2.3535e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 50/50\n",
            "330984/330984 - 21s - loss: 4.1491e-06 - mean_squared_error: 4.1491e-06 - mean_absolute_error: 7.3621e-04 - val_loss: 2.1878e-05 - val_mean_squared_error: 2.1878e-05 - val_mean_absolute_error: 0.0012\n",
            "*******************\n",
            "lag2_pos\n",
            "Train on 330983 samples, validate on 146906 samples\n",
            "Epoch 1/50\n",
            "330983/330983 - 21s - loss: 0.0017 - mean_squared_error: 0.0017 - mean_absolute_error: 0.0091 - val_loss: 1.3800e-04 - val_mean_squared_error: 1.3800e-04 - val_mean_absolute_error: 0.0066\n",
            "Epoch 2/50\n",
            "330983/330983 - 21s - loss: 2.9423e-05 - mean_squared_error: 2.9423e-05 - mean_absolute_error: 0.0037 - val_loss: 8.7580e-05 - val_mean_squared_error: 8.7580e-05 - val_mean_absolute_error: 0.0051\n",
            "Epoch 3/50\n",
            "330983/330983 - 21s - loss: 2.2298e-05 - mean_squared_error: 2.2298e-05 - mean_absolute_error: 0.0031 - val_loss: 6.0091e-05 - val_mean_squared_error: 6.0091e-05 - val_mean_absolute_error: 0.0044\n",
            "Epoch 4/50\n",
            "330983/330983 - 21s - loss: 1.9081e-05 - mean_squared_error: 1.9081e-05 - mean_absolute_error: 0.0028 - val_loss: 5.5609e-05 - val_mean_squared_error: 5.5609e-05 - val_mean_absolute_error: 0.0038\n",
            "Epoch 5/50\n",
            "330983/330983 - 21s - loss: 1.7087e-05 - mean_squared_error: 1.7087e-05 - mean_absolute_error: 0.0025 - val_loss: 4.1852e-05 - val_mean_squared_error: 4.1852e-05 - val_mean_absolute_error: 0.0032\n",
            "Epoch 6/50\n",
            "330983/330983 - 21s - loss: 1.5693e-05 - mean_squared_error: 1.5693e-05 - mean_absolute_error: 0.0023 - val_loss: 3.7586e-05 - val_mean_squared_error: 3.7586e-05 - val_mean_absolute_error: 0.0030\n",
            "Epoch 7/50\n",
            "330983/330983 - 21s - loss: 1.3957e-05 - mean_squared_error: 1.3957e-05 - mean_absolute_error: 0.0023 - val_loss: 6.0094e-05 - val_mean_squared_error: 6.0094e-05 - val_mean_absolute_error: 0.0054\n",
            "Epoch 8/50\n",
            "330983/330983 - 21s - loss: 1.4057e-05 - mean_squared_error: 1.4057e-05 - mean_absolute_error: 0.0021 - val_loss: 3.0728e-05 - val_mean_squared_error: 3.0728e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 9/50\n",
            "330983/330983 - 21s - loss: 1.2624e-05 - mean_squared_error: 1.2624e-05 - mean_absolute_error: 0.0021 - val_loss: 2.9635e-05 - val_mean_squared_error: 2.9635e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 10/50\n",
            "330983/330983 - 21s - loss: 1.0527e-05 - mean_squared_error: 1.0527e-05 - mean_absolute_error: 0.0019 - val_loss: 2.9984e-05 - val_mean_squared_error: 2.9984e-05 - val_mean_absolute_error: 0.0025\n",
            "Epoch 11/50\n",
            "330983/330983 - 21s - loss: 1.0443e-05 - mean_squared_error: 1.0443e-05 - mean_absolute_error: 0.0019 - val_loss: 2.9729e-05 - val_mean_squared_error: 2.9729e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 12/50\n",
            "330983/330983 - 21s - loss: 1.0513e-05 - mean_squared_error: 1.0513e-05 - mean_absolute_error: 0.0018 - val_loss: 7.2853e-05 - val_mean_squared_error: 7.2853e-05 - val_mean_absolute_error: 0.0053\n",
            "Epoch 13/50\n",
            "330983/330983 - 21s - loss: 8.9538e-06 - mean_squared_error: 8.9538e-06 - mean_absolute_error: 0.0017 - val_loss: 3.0003e-05 - val_mean_squared_error: 3.0003e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 14/50\n",
            "330983/330983 - 21s - loss: 7.5723e-06 - mean_squared_error: 7.5723e-06 - mean_absolute_error: 0.0016 - val_loss: 2.9065e-05 - val_mean_squared_error: 2.9065e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 15/50\n",
            "330983/330983 - 21s - loss: 8.4170e-06 - mean_squared_error: 8.4170e-06 - mean_absolute_error: 0.0015 - val_loss: 2.6953e-05 - val_mean_squared_error: 2.6953e-05 - val_mean_absolute_error: 0.0024\n",
            "Epoch 16/50\n",
            "330983/330983 - 21s - loss: 7.5503e-06 - mean_squared_error: 7.5503e-06 - mean_absolute_error: 0.0015 - val_loss: 2.7119e-05 - val_mean_squared_error: 2.7119e-05 - val_mean_absolute_error: 0.0023\n",
            "Epoch 17/50\n",
            "330983/330983 - 21s - loss: 7.4570e-06 - mean_squared_error: 7.4570e-06 - mean_absolute_error: 0.0014 - val_loss: 2.3443e-05 - val_mean_squared_error: 2.3443e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 18/50\n",
            "330983/330983 - 21s - loss: 7.0736e-06 - mean_squared_error: 7.0736e-06 - mean_absolute_error: 0.0012 - val_loss: 2.4864e-05 - val_mean_squared_error: 2.4864e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 19/50\n",
            "330983/330983 - 21s - loss: 6.8925e-06 - mean_squared_error: 6.8925e-06 - mean_absolute_error: 0.0013 - val_loss: 2.5141e-05 - val_mean_squared_error: 2.5141e-05 - val_mean_absolute_error: 0.0021\n",
            "Epoch 20/50\n",
            "330983/330983 - 21s - loss: 6.2435e-06 - mean_squared_error: 6.2435e-06 - mean_absolute_error: 0.0012 - val_loss: 2.6907e-05 - val_mean_squared_error: 2.6907e-05 - val_mean_absolute_error: 0.0027\n",
            "Epoch 21/50\n",
            "330983/330983 - 21s - loss: 5.9191e-06 - mean_squared_error: 5.9192e-06 - mean_absolute_error: 0.0012 - val_loss: 2.3149e-05 - val_mean_squared_error: 2.3149e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 22/50\n",
            "330983/330983 - 21s - loss: 5.9781e-06 - mean_squared_error: 5.9781e-06 - mean_absolute_error: 0.0011 - val_loss: 2.3148e-05 - val_mean_squared_error: 2.3148e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 23/50\n",
            "330983/330983 - 20s - loss: 5.5727e-06 - mean_squared_error: 5.5727e-06 - mean_absolute_error: 0.0011 - val_loss: 2.4805e-05 - val_mean_squared_error: 2.4805e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 24/50\n",
            "330983/330983 - 21s - loss: 5.2886e-06 - mean_squared_error: 5.2886e-06 - mean_absolute_error: 0.0011 - val_loss: 2.4454e-05 - val_mean_squared_error: 2.4454e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 25/50\n",
            "330983/330983 - 21s - loss: 5.2331e-06 - mean_squared_error: 5.2331e-06 - mean_absolute_error: 9.8631e-04 - val_loss: 2.7306e-05 - val_mean_squared_error: 2.7306e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 26/50\n",
            "330983/330983 - 21s - loss: 5.0301e-06 - mean_squared_error: 5.0301e-06 - mean_absolute_error: 9.5068e-04 - val_loss: 2.7133e-05 - val_mean_squared_error: 2.7133e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 27/50\n",
            "330983/330983 - 21s - loss: 5.0107e-06 - mean_squared_error: 5.0107e-06 - mean_absolute_error: 9.4090e-04 - val_loss: 2.6223e-05 - val_mean_squared_error: 2.6223e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 28/50\n",
            "330983/330983 - 21s - loss: 4.2223e-06 - mean_squared_error: 4.2223e-06 - mean_absolute_error: 8.7930e-04 - val_loss: 2.5835e-05 - val_mean_squared_error: 2.5835e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 29/50\n",
            "330983/330983 - 22s - loss: 4.7074e-06 - mean_squared_error: 4.7074e-06 - mean_absolute_error: 8.5899e-04 - val_loss: 2.6586e-05 - val_mean_squared_error: 2.6586e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 30/50\n",
            "330983/330983 - 21s - loss: 4.8414e-06 - mean_squared_error: 4.8414e-06 - mean_absolute_error: 8.4828e-04 - val_loss: 2.7103e-05 - val_mean_squared_error: 2.7103e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 31/50\n",
            "330983/330983 - 20s - loss: 4.0237e-06 - mean_squared_error: 4.0237e-06 - mean_absolute_error: 8.1303e-04 - val_loss: 2.6113e-05 - val_mean_squared_error: 2.6113e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 32/50\n",
            "330983/330983 - 20s - loss: 4.5716e-06 - mean_squared_error: 4.5716e-06 - mean_absolute_error: 7.8604e-04 - val_loss: 2.8475e-05 - val_mean_squared_error: 2.8475e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 33/50\n",
            "330983/330983 - 21s - loss: 4.1673e-06 - mean_squared_error: 4.1673e-06 - mean_absolute_error: 7.5295e-04 - val_loss: 2.6721e-05 - val_mean_squared_error: 2.6721e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 34/50\n",
            "330983/330983 - 20s - loss: 3.8857e-06 - mean_squared_error: 3.8857e-06 - mean_absolute_error: 7.4430e-04 - val_loss: 2.5869e-05 - val_mean_squared_error: 2.5869e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 35/50\n",
            "330983/330983 - 21s - loss: 4.2940e-06 - mean_squared_error: 4.2940e-06 - mean_absolute_error: 7.4889e-04 - val_loss: 2.7706e-05 - val_mean_squared_error: 2.7706e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 36/50\n",
            "330983/330983 - 21s - loss: 3.9251e-06 - mean_squared_error: 3.9252e-06 - mean_absolute_error: 7.1794e-04 - val_loss: 2.6603e-05 - val_mean_squared_error: 2.6603e-05 - val_mean_absolute_error: 0.0014\n",
            "Epoch 37/50\n",
            "330983/330983 - 21s - loss: 3.9570e-06 - mean_squared_error: 3.9570e-06 - mean_absolute_error: 7.4302e-04 - val_loss: 2.6823e-05 - val_mean_squared_error: 2.6823e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 38/50\n",
            "330983/330983 - 20s - loss: 3.9638e-06 - mean_squared_error: 3.9638e-06 - mean_absolute_error: 7.6203e-04 - val_loss: 3.1468e-05 - val_mean_squared_error: 3.1468e-05 - val_mean_absolute_error: 0.0020\n",
            "Epoch 39/50\n",
            "330983/330983 - 21s - loss: 3.7170e-06 - mean_squared_error: 3.7170e-06 - mean_absolute_error: 6.9239e-04 - val_loss: 2.8520e-05 - val_mean_squared_error: 2.8520e-05 - val_mean_absolute_error: 0.0017\n",
            "Epoch 40/50\n",
            "330983/330983 - 20s - loss: 3.8438e-06 - mean_squared_error: 3.8438e-06 - mean_absolute_error: 7.3962e-04 - val_loss: 3.8435e-05 - val_mean_squared_error: 3.8435e-05 - val_mean_absolute_error: 0.0028\n",
            "Epoch 41/50\n",
            "330983/330983 - 20s - loss: 3.6404e-06 - mean_squared_error: 3.6404e-06 - mean_absolute_error: 6.8138e-04 - val_loss: 2.8496e-05 - val_mean_squared_error: 2.8496e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 42/50\n",
            "330983/330983 - 20s - loss: 3.6259e-06 - mean_squared_error: 3.6259e-06 - mean_absolute_error: 7.0364e-04 - val_loss: 4.4499e-05 - val_mean_squared_error: 4.4499e-05 - val_mean_absolute_error: 0.0029\n",
            "Epoch 43/50\n",
            "330983/330983 - 21s - loss: 3.6753e-06 - mean_squared_error: 3.6753e-06 - mean_absolute_error: 6.7173e-04 - val_loss: 2.9197e-05 - val_mean_squared_error: 2.9197e-05 - val_mean_absolute_error: 0.0016\n",
            "Epoch 44/50\n",
            "330983/330983 - 22s - loss: 3.9150e-06 - mean_squared_error: 3.9150e-06 - mean_absolute_error: 6.9135e-04 - val_loss: 2.9722e-05 - val_mean_squared_error: 2.9722e-05 - val_mean_absolute_error: 0.0015\n",
            "Epoch 45/50\n",
            "330983/330983 - 21s - loss: 3.8016e-06 - mean_squared_error: 3.8015e-06 - mean_absolute_error: 6.7341e-04 - val_loss: 3.2024e-05 - val_mean_squared_error: 3.2024e-05 - val_mean_absolute_error: 0.0019\n",
            "Epoch 46/50\n",
            "330983/330983 - 21s - loss: 3.5487e-06 - mean_squared_error: 3.5487e-06 - mean_absolute_error: 6.4904e-04 - val_loss: 2.8843e-05 - val_mean_squared_error: 2.8843e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 47/50\n",
            "330983/330983 - 21s - loss: 3.5138e-06 - mean_squared_error: 3.5138e-06 - mean_absolute_error: 6.5296e-04 - val_loss: 2.9516e-05 - val_mean_squared_error: 2.9516e-05 - val_mean_absolute_error: 0.0018\n",
            "Epoch 48/50\n",
            "330983/330983 - 21s - loss: 3.7142e-06 - mean_squared_error: 3.7142e-06 - mean_absolute_error: 6.6936e-04 - val_loss: 2.9849e-05 - val_mean_squared_error: 2.9849e-05 - val_mean_absolute_error: 0.0012\n",
            "Epoch 49/50\n",
            "330983/330983 - 21s - loss: 3.3210e-06 - mean_squared_error: 3.3210e-06 - mean_absolute_error: 6.1558e-04 - val_loss: 2.8754e-05 - val_mean_squared_error: 2.8754e-05 - val_mean_absolute_error: 0.0013\n",
            "Epoch 50/50\n",
            "330983/330983 - 21s - loss: 3.4902e-06 - mean_squared_error: 3.4902e-06 - mean_absolute_error: 6.3323e-04 - val_loss: 2.9096e-05 - val_mean_squared_error: 2.9096e-05 - val_mean_absolute_error: 0.0012\n",
            "*******************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XRLpUIJnZ-eq",
        "colab_type": "text"
      },
      "source": [
        "## visualize"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8X1nsxWEO59n",
        "colab_type": "text"
      },
      "source": [
        "### Test & Training Loss"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q7eOZ0lAe1CP",
        "colab_type": "code",
        "outputId": "faba0a6d-19ca-43c2-f255-03a036f38946",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 446
        }
      },
      "source": [
        "for i, history in enumerate(histories):\n",
        "  last_loss = history['loss'][-1]\n",
        "  last_val_loss = history['val_loss'][-1]\n",
        "  model_name = models[i][0]\n",
        "  print('{} model last loss: {}'.format(model_name,last_loss))\n",
        "  print('{} model last val loss: {}'.format(model_name,last_val_loss))\n",
        "  print('*******')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lag1_translation model last loss: 1.3413419357117319e-06\n",
            "lag1_translation model last val loss: 4.991152121534542e-06\n",
            "*******\n",
            "lag2_translation model last loss: 1.1391222259496824e-06\n",
            "lag2_translation model last val loss: 1.5469123146412495e-05\n",
            "*******\n",
            "lag1_rotation model last loss: 4.7277978380831616e-06\n",
            "lag1_rotation model last val loss: 2.5068494103838988e-05\n",
            "*******\n",
            "lag2_rotation model last loss: 2.9624486526147868e-06\n",
            "lag2_rotation model last val loss: 1.6334906939513243e-05\n",
            "*******\n",
            "lag1_agg model last loss: 3.1383545171533765e-06\n",
            "lag1_agg model last val loss: 1.4610474153162987e-05\n",
            "*******\n",
            "lag2_agg model last loss: 2.9233366523845737e-06\n",
            "lag2_agg model last val loss: 9.933588022408451e-06\n",
            "*******\n",
            "lag_pos model last loss: 4.149128493207218e-06\n",
            "lag_pos model last val loss: 2.1878092027459898e-05\n",
            "*******\n",
            "lag2_pos model last loss: 3.490227206289441e-06\n",
            "lag2_pos model last val loss: 2.909575614114968e-05\n",
            "*******\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gbFhTgXGITKV",
        "colab_type": "code",
        "outputId": "99f81f6c-f258-4480-8421-4f59255983f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "pas_history(models, histories=histories)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MGjUKu91O165dAZg9ezbV1dWMHDmSxMREHnnkkSuOP3z4cMrKykhMTGTt2rVN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IxHgZPyx/m/RpI5g872i6ndtuo7JwO/3HT0bRgzRMj9OPqcaGqdpOS7UNSZKYeVseosPP2rf3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\ntiCy8bninDEOnnVZCL8eHGrmQ8Dn4717/4S1vZXLH33mmByADBHcXxE27TB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iUC3VhkPh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\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cqStn/VEIzvN1y4mmoXc39AxB79Dic6JH4VeaTqlSZWK2yOhMgdFsgdGZAmML5yKjM3PB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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GTTgGyMNTZ7V",
        "colab_type": "code",
        "outputId": "3c29fa14-177e-4280-f471-19218cc57cb3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "models_lag2 = []\n",
        "histories_lag2 = []\n",
        "for i, model in enumerate(models):\n",
        "  if 'lag1_' in model[0] or 'lag_' in model[0]:\n",
        "    models_lag2.append(model)\n",
        "    histories_lag2.append(histories[i])\n",
        "pas_history(models_lag2, histories=histories_lag2,suffix='_lag1')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Xs3Pnzjbjz5kzh02bNpGamsrKlSspLS29ono/rePTGi7FP17r6tWr8Xg8HDx4kJCQEOLj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p3/eVnMsp/tiaWy7rvvIus55qbfkFH4is3kYwTrR+HNRhm9u7oTfKvJWuF/Y9aFEDKy7lft7u9P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fopO+GR6JCbT7I2dk7e3p8sjqCv6+/jCPrJzK4rHpg305g06bJ0RFq6d/M0F6gSxwZWSGKdGY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\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jWSqL_rCTaRR",
        "colab_type": "code",
        "outputId": "68e5dfb7-481d-450e-b20e-616befe5979d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "source": [
        "models_lag1 = []\n",
        "histories_lag1 = []\n",
        "for i, model in enumerate(models):\n",
        "  if 'lag2_' in model[0]:\n",
        "    models_lag1.append(model)\n",
        "    histories_lag1.append(histories[i])\n",
        "pas_history(models_lag1, histories=histories_lag1, suffix='_lag2')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vtnvs1q1bmT9/fotjkZGR5OXldXRZchksGXBthg3vWS9hgU4FXBERERER6TCp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\nYII=\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4vT7MLiJPSQ2",
        "colab_type": "text"
      },
      "source": [
        "\n",
        "### MSE per target (scaled)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o03Cq_x5nhRe",
        "colab_type": "code",
        "outputId": "24e504c4-56b6-40c8-8966-0e935e817b47",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for model_tupel in models:\n",
        "  df = pas_metrics_scaled(model_tupel)\n",
        "  print(model_tupel[0])\n",
        "  with pd.option_context('display.max_rows', None, 'display.max_columns', None,\n",
        "                         'display.width', 1000): \n",
        "    print(df)\n",
        "  print('*********************************************************')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lag1_translation\n",
            "          __VVI,__fpm      Vtrue,_ktas \n",
            "count    146907.000000    146907.000000\n",
            "mean          0.737227         0.504412\n",
            "std           0.106115         0.106511\n",
            "min           0.315198        -0.145712\n",
            "25%           0.719309         0.491325\n",
            "50%           0.736735         0.516065\n",
            "75%           0.757760         0.540668\n",
            "max           1.112528         0.863996\n",
            "mses          0.000004         0.000006\n",
            "rmes          0.002051         0.002403\n",
            "maes          0.001308         0.000709\n",
            "mse           0.000005         0.000005\n",
            "rme           0.002234         0.002234\n",
            "mae           0.001008         0.001008\n",
            "*********************************************************\n",
            "lag2_translation\n",
            "          __VVI,__fpm      Vtrue,_ktas \n",
            "count    146906.000000    146906.000000\n",
            "mean          0.737227         0.504411\n",
            "std           0.106115         0.106512\n",
            "min           0.315198        -0.145712\n",
            "25%           0.719309         0.491325\n",
            "50%           0.736735         0.516065\n",
            "75%           0.757761         0.540668\n",
            "max           1.112528         0.863996\n",
            "mses          0.000011         0.000020\n",
            "rmes          0.003259         0.004508\n",
            "maes          0.001391         0.001830\n",
            "mse           0.000015         0.000015\n",
            "rme           0.003933         0.003933\n",
            "mae           0.001611         0.001611\n",
            "*********************************************************\n",
            "lag1_rotation\n",
            "          ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146907.000000    146907.000000    146907.000000\n",
            "mean          0.570341         0.485060         0.485233\n",
            "std           0.096593         0.137143         0.297851\n",
            "min           0.075314        -0.114067         0.001815\n",
            "25%           0.483381         0.478066         0.141444\n",
            "50%           0.612474         0.485824         0.495373\n",
            "75%           0.622681         0.493745         0.817728\n",
            "max           0.958919         1.093909         0.990564\n",
            "mses          0.000051         0.000012         0.000012\n",
            "rmes          0.007148         0.003515         0.003428\n",
            "maes          0.003313         0.002053         0.001430\n",
            "mse           0.000025         0.000025         0.000025\n",
            "rme           0.005007         0.005007         0.005007\n",
            "mae           0.002265         0.002265         0.002265\n",
            "*********************************************************\n",
            "lag2_rotation\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146906.000000    146906.000000    146906.000000    146906.000000    146906.000000\n",
            "mean          0.737227         0.504411         0.567109         0.485060         0.485233\n",
            "std           0.106115         0.106512         0.097320         0.137144         0.297852\n",
            "min           0.315198        -0.145712         0.068356        -0.114067         0.001815\n",
            "25%           0.719309         0.491325         0.479493         0.478066         0.141444\n",
            "50%           0.736735         0.516065         0.609557         0.485824         0.495373\n",
            "75%           0.757761         0.540668         0.619842         0.493745         0.817728\n",
            "max           1.112528         0.863996         0.958610         1.093909         0.990564\n",
            "mses          0.000004         0.000031         0.000029         0.000011         0.000006\n",
            "rmes          0.002104         0.005587         0.005421         0.003259         0.002454\n",
            "maes          0.001175         0.001080         0.002295         0.002090         0.000840\n",
            "mse           0.000016         0.000016         0.000016         0.000016         0.000016\n",
            "rme           0.004042         0.004042         0.004042         0.004042         0.004042\n",
            "mae           0.001496         0.001496         0.001496         0.001496         0.001496\n",
            "*********************************************************\n",
            "lag1_agg\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146907.000000    146907.000000    146907.000000    146907.000000    146907.000000\n",
            "mean          0.737227         0.504412         0.570341         0.485060         0.485233\n",
            "std           0.106115         0.106511         0.096593         0.137143         0.297851\n",
            "min           0.315198        -0.145712         0.075314        -0.114067         0.001815\n",
            "25%           0.719309         0.491325         0.483381         0.478066         0.141444\n",
            "50%           0.736735         0.516065         0.612474         0.485824         0.495373\n",
            "75%           0.757760         0.540668         0.622681         0.493745         0.817728\n",
            "max           1.112528         0.863996         0.958919         1.093909         0.990564\n",
            "mses          0.000013         0.000008         0.000020         0.000016         0.000016\n",
            "rmes          0.003642         0.002782         0.004494         0.004022         0.003960\n",
            "maes          0.001636         0.001313         0.002929         0.001906         0.001392\n",
            "mse           0.000015         0.000015         0.000015         0.000015         0.000015\n",
            "rme           0.003822         0.003822         0.003822         0.003822         0.003822\n",
            "mae           0.001835         0.001835         0.001835         0.001835         0.001835\n",
            "*********************************************************\n",
            "lag2_agg\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146906.000000    146906.000000    146906.000000    146906.000000    146906.000000\n",
            "mean          0.737227         0.504411         0.567109         0.485060         0.485233\n",
            "std           0.106115         0.106512         0.097320         0.137144         0.297852\n",
            "min           0.315198        -0.145712         0.068356        -0.114067         0.001815\n",
            "25%           0.719309         0.491325         0.479493         0.478066         0.141444\n",
            "50%           0.736735         0.516065         0.609557         0.485824         0.495373\n",
            "75%           0.757761         0.540668         0.619842         0.493745         0.817728\n",
            "max           1.112528         0.863996         0.958610         1.093909         0.990564\n",
            "mses          0.000004         0.000011         0.000018         0.000012         0.000005\n",
            "rmes          0.002120         0.003334         0.004188         0.003409         0.002214\n",
            "maes          0.000960         0.001161         0.002027         0.001686         0.000807\n",
            "mse           0.000010         0.000010         0.000010         0.000010         0.000010\n",
            "rme           0.003152         0.003152         0.003152         0.003152         0.003152\n",
            "mae           0.001328         0.001328         0.001328         0.001328         0.001328\n",
            "*********************************************************\n",
            "lag_pos\n",
            "          __alt,ftmsl      pitch,__deg      _roll,__deg      _beta,__deg \n",
            "count    146907.000000    146907.000000    146907.000000    146907.000000\n",
            "mean          0.541116         0.531587         0.464654         0.620080\n",
            "std           0.313282         0.109146         0.243538         0.211064\n",
            "min           0.020174         0.194934         0.019929        -0.224448\n",
            "25%           0.258517         0.498651         0.178618         0.402814\n",
            "50%           0.502333         0.523474         0.475117         0.619407\n",
            "75%           0.743406         0.538931         0.733206         0.857215\n",
            "max           0.980654         1.288604         0.928746         1.008269\n",
            "mses          0.000014         0.000042         0.000022         0.000009\n",
            "rmes          0.003790         0.006495         0.004692         0.002992\n",
            "maes          0.000866         0.001349         0.001370         0.001297\n",
            "mse           0.000022         0.000022         0.000022         0.000022\n",
            "rme           0.004677         0.004677         0.004677         0.004677\n",
            "mae           0.001220         0.001220         0.001220         0.001220\n",
            "*********************************************************\n",
            "lag2_pos\n",
            "          __alt,ftmsl      pitch,__deg      _roll,__deg      _beta,__deg \n",
            "count    146906.000000    146906.000000    146906.000000    146906.000000\n",
            "mean          0.541120         0.531587         0.464654         0.620080\n",
            "std           0.313280         0.109146         0.243539         0.211064\n",
            "min           0.020174         0.194934         0.019929        -0.224448\n",
            "25%           0.258518         0.498650         0.178618         0.402814\n",
            "50%           0.502335         0.523474         0.475117         0.619407\n",
            "75%           0.743406         0.538931         0.733207         0.857215\n",
            "max           0.980654         1.288604         0.928746         1.008269\n",
            "mses          0.000035         0.000051         0.000010         0.000020\n",
            "rmes          0.005958         0.007111         0.003144         0.004521\n",
            "maes          0.000974         0.001454         0.000905         0.001535\n",
            "mse           0.000029         0.000029         0.000029         0.000029\n",
            "rme           0.005394         0.005394         0.005394         0.005394\n",
            "mae           0.001217         0.001217         0.001217         0.001217\n",
            "*********************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bVhouDRRpewh",
        "colab_type": "text"
      },
      "source": [
        "### MSE per target (unscaled)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "crfMWfnKuAXz",
        "colab_type": "code",
        "outputId": "b780bb6f-8cb0-4777-92fc-ef10fb1c2ce9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "models[1][0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'lag2_translation'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTTBfKRgRnTP",
        "colab_type": "code",
        "outputId": "c5ad7408-c156-405c-ec17-d80bb7670cef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "for model_tupel in models:\n",
        "  df = pas_metrics(model_tupel)\n",
        "  print(model_tupel[0])\n",
        "  with pd.option_context('display.max_rows', None, 'display.max_columns', None,\n",
        "                        'display.width', 1000):  \n",
        "    print(df)\n",
        "  print('*********************************************************')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lag1_translation\n",
            "          __VVI,__fpm      Vtrue,_ktas \n",
            "count    146907.000000    146907.000000\n",
            "mean          1.454978       116.933735\n",
            "std         276.265852         8.275017\n",
            "min       -1097.281130        66.424780\n",
            "25%         -45.193610       115.917000\n",
            "50%           0.174230       117.839070\n",
            "75%          54.913025       119.750560\n",
            "max         978.538330       144.870320\n",
            "mses         28.517277         0.034858\n",
            "rmes          5.340157         0.186702\n",
            "maes          3.404447         0.055104\n",
            "mse          14.276067        14.276067\n",
            "rme           3.778368         3.778368\n",
            "mae           1.729775         1.729775\n",
            "*********************************************************\n",
            "lag2_translation\n",
            "          __VVI,__fpm      Vtrue,_ktas \n",
            "count    146906.000000    146906.000000\n",
            "mean          1.454796       116.933706\n",
            "std         276.266783         8.275038\n",
            "min       -1097.281130        66.424780\n",
            "25%         -45.194615       115.917000\n",
            "50%           0.174215       117.839070\n",
            "75%          54.915493       119.750535\n",
            "max         978.538330       144.870320\n",
            "mses         71.981059         0.122642\n",
            "rmes          8.484165         0.350202\n",
            "maes          3.622393         0.142196\n",
            "mse          36.051850        36.051850\n",
            "rme           6.004319         6.004319\n",
            "mae           1.882295         1.882295\n",
            "*********************************************************\n",
            "lag1_rotation\n",
            "          ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count     1.469070e+05     1.469070e+05     1.469070e+05\n",
            "mean      7.135510e-03    -1.397062e-04    -1.337600e-03\n",
            "std       7.759326e-03     2.510405e-02     3.282916e-02\n",
            "min      -3.263000e-02    -1.098100e-01    -5.462000e-02\n",
            "25%       1.500000e-04    -1.420000e-03    -3.923000e-02\n",
            "50%       1.052000e-02     0.000000e+00    -2.200000e-04\n",
            "75%       1.134000e-02     1.450000e-03     3.531000e-02\n",
            "max       3.835000e-02     1.113100e-01     5.436000e-02\n",
            "mses      3.297500e-07     4.139637e-07     1.427451e-07\n",
            "rmes      5.742386e-04     6.434001e-04     3.778162e-04\n",
            "maes      2.661048e-04     3.758129e-04     1.576200e-04\n",
            "mse       2.954863e-07     2.954863e-07     2.954863e-07\n",
            "rme       5.435865e-04     5.435865e-04     5.435865e-04\n",
            "mae       2.665126e-04     2.665126e-04     2.665126e-04\n",
            "*********************************************************\n",
            "lag2_rotation\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146906.000000    146906.000000     1.469060e+05     1.469060e+05     1.469060e+05\n",
            "mean          1.454796       116.933706     7.135563e-03    -1.397126e-04    -1.337612e-03\n",
            "std         276.266783         8.275038     7.759325e-03     2.510414e-02     3.282927e-02\n",
            "min       -1097.281130        66.424780    -3.263000e-02    -1.098100e-01    -5.462000e-02\n",
            "25%         -45.194615       115.917000     1.500000e-04    -1.420000e-03    -3.923000e-02\n",
            "50%           0.174215       117.839070     1.052000e-02     0.000000e+00    -2.200000e-04\n",
            "75%          54.915493       119.750535     1.134000e-02     1.450000e-03     3.531000e-02\n",
            "max         978.538330       144.870320     3.835000e-02     1.113100e-01     5.436000e-02\n",
            "mses         30.016412         0.188428     1.868021e-07     3.558573e-07     7.316028e-08\n",
            "rmes          5.478724         0.434083     4.322061e-04     5.965378e-04     2.704816e-04\n",
            "maes          3.058241         0.083908     1.829464e-04     3.825557e-04     9.261410e-05\n",
            "mse           6.040968         6.040968     6.040968e+00     6.040968e+00     6.040968e+00\n",
            "rme           2.457838         2.457838     2.457838e+00     2.457838e+00     2.457838e+00\n",
            "mae           0.628561         0.628561     6.285614e-01     6.285614e-01     6.285614e-01\n",
            "*********************************************************\n",
            "lag1_agg\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146907.000000    146907.000000     1.469070e+05     1.469070e+05     1.469070e+05\n",
            "mean          1.454978       116.933735     7.135510e-03    -1.397062e-04    -1.337600e-03\n",
            "std         276.265852         8.275017     7.759326e-03     2.510405e-02     3.282916e-02\n",
            "min       -1097.281130        66.424780    -3.263000e-02    -1.098100e-01    -5.462000e-02\n",
            "25%         -45.193610       115.917000     1.500000e-04    -1.420000e-03    -3.923000e-02\n",
            "50%           0.174230       117.839070     1.052000e-02     0.000000e+00    -2.200000e-04\n",
            "75%          54.913025       119.750560     1.134000e-02     1.450000e-03     3.531000e-02\n",
            "max         978.538330       144.870320     3.835000e-02     1.113100e-01     5.436000e-02\n",
            "mses         89.899053         0.046700     1.303284e-07     5.420257e-07     1.904734e-07\n",
            "rmes          9.481511         0.216102     3.610103e-04     7.362239e-04     4.364326e-04\n",
            "maes          4.260015         0.101972     2.353089e-04     3.488867e-04     1.534005e-04\n",
            "mse          17.989151        17.989151     1.798915e+01     1.798915e+01     1.798915e+01\n",
            "rme           4.241362         4.241362     4.241362e+00     4.241362e+00     4.241362e+00\n",
            "mae           0.872545         0.872545     8.725449e-01     8.725449e-01     8.725449e-01\n",
            "*********************************************************\n",
            "lag2_agg\n",
            "          __VVI,__fpm      Vtrue,_ktas      ____Q,rad/s      ____P,rad/s      ____R,rad/s \n",
            "count    146906.000000    146906.000000     1.469060e+05     1.469060e+05     1.469060e+05\n",
            "mean          1.454796       116.933706     7.135563e-03    -1.397126e-04    -1.337612e-03\n",
            "std         276.266783         8.275038     7.759325e-03     2.510414e-02     3.282927e-02\n",
            "min       -1097.281130        66.424780    -3.263000e-02    -1.098100e-01    -5.462000e-02\n",
            "25%         -45.194615       115.917000     1.500000e-04    -1.420000e-03    -3.923000e-02\n",
            "50%           0.174215       117.839070     1.052000e-02     0.000000e+00    -2.200000e-04\n",
            "75%          54.915493       119.750535     1.134000e-02     1.450000e-03     3.531000e-02\n",
            "max         978.538330       144.870320     3.835000e-02     1.113100e-01     5.436000e-02\n",
            "mses         30.452538         0.067101     1.114803e-07     3.893512e-07     5.954379e-08\n",
            "rmes          5.518382         0.259038     3.338867e-04     6.239801e-04     2.440160e-04\n",
            "maes          2.499546         0.090233     1.616026e-04     3.085885e-04     8.896821e-05\n",
            "mse           6.103928         6.103928     6.103928e+00     6.103928e+00     6.103928e+00\n",
            "rme           2.470613         2.470613     2.470613e+00     2.470613e+00     2.470613e+00\n",
            "mae           0.518068         0.518068     5.180676e-01     5.180676e-01     5.180676e-01\n",
            "*********************************************************\n",
            "lag_pos\n",
            "          __alt,ftmsl      pitch,__deg      _roll,__deg      _beta,__deg \n",
            "count    146907.000000    146907.000000    146907.000000    146907.000000\n",
            "mean       3118.794644        -0.605152        -0.722674         0.024940\n",
            "std        1339.530659         1.788004        12.122463         0.178670\n",
            "min         891.350710        -6.120130       -22.859510        -0.689970\n",
            "25%        1910.458070        -1.144705       -14.960540        -0.158980\n",
            "50%        2952.964840        -0.738060        -0.201880         0.024370\n",
            "75%        3983.743410        -0.484845        12.644855         0.225680\n",
            "max        4998.167480        11.796170        22.378150         0.353550\n",
            "mses        262.634529         0.011320         0.054539         0.000006\n",
            "rmes         16.206003         0.106394         0.233535         0.002533\n",
            "maes          3.701072         0.022106         0.068180         0.001098\n",
            "mse          65.675098        65.675098        65.675098        65.675098\n",
            "rme           8.104017         8.104017         8.104017         8.104017\n",
            "mae           0.948114         0.948114         0.948114         0.948114\n",
            "*********************************************************\n",
            "lag2_pos\n",
            "          __alt,ftmsl      pitch,__deg      _roll,__deg      _beta,__deg \n",
            "count    146906.000000    146906.000000    146906.000000    146906.000000\n",
            "mean       3118.809754        -0.605149        -0.722679         0.024941\n",
            "std        1339.522697         1.788010        12.122505         0.178670\n",
            "min         891.350710        -6.120130       -22.859510        -0.689970\n",
            "25%        1910.463807        -1.144713       -14.960540        -0.158980\n",
            "50%        2952.976195        -0.738055        -0.201900         0.024370\n",
            "75%        3983.744630        -0.484833        12.644902         0.225680\n",
            "max        4998.167480        11.796170        22.378150         0.353550\n",
            "mses        648.934223         0.013570         0.024495         0.000015\n",
            "rmes         25.474187         0.116490         0.156510         0.003827\n",
            "maes          4.165520         0.023825         0.045044         0.001299\n",
            "mse         162.243076       162.243076       162.243076       162.243076\n",
            "rme          12.737467        12.737467        12.737467        12.737467\n",
            "mae           1.058922         1.058922         1.058922         1.058922\n",
            "*********************************************************\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DTCw_9XlMi9P",
        "colab_type": "text"
      },
      "source": [
        "#latex"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rRfK9vukMkXV",
        "colab_type": "code",
        "outputId": "db4f5037-e675-4bcb-c0b1-eef5a5979209",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "source": [
        "def _df_prediction_metrics_all(model_tupel, scaled):\n",
        "  \n",
        "  model_name =  model_tupel[0]\n",
        "  model = model_tupel[1]\n",
        "  train_X, train_Y, test_X, test_Y = model_tupel[2]\n",
        "  cols_label = model_tupel[4]\n",
        "\n",
        "\n",
        "  pred_Y = model.predict(test_X)\n",
        "  \n",
        "  if scaled:\n",
        "    truth_Y = test_Y\n",
        "  else:\n",
        "    label_scaler = model_tupel[3]\n",
        "    pred_Y = label_scaler.inverse_transform(pred_Y)\n",
        "    truth_Y = label_scaler.inverse_transform(test_Y)\n",
        "\n",
        "  df_describe = pd.DataFrame(truth_Y,columns=cols_label).describe()\n",
        "  mses = mean_squared_error(truth_Y, pred_Y, multioutput='raw_values')\n",
        "  #rmses = np.array([math.sqrt(mse) for mse in mses])\n",
        "  #maes = mean_absolute_error(truth_Y, pred_Y, multioutput='raw_values')\n",
        "  # average over list should do the job as well\n",
        "  mse = mean_squared_error(truth_Y, pred_Y)\n",
        "  rmse = math.sqrt(mse)\n",
        "  mae = mean_absolute_error(truth_Y, pred_Y)\n",
        "\n",
        "\n",
        "  df_mse_per_feature = pd.DataFrame(data=mses.reshape(1,-1),    # values\n",
        "            index=[model_name],     # index\n",
        "            columns=cols_label)  #  column names\n",
        "  \n",
        "  df_mse_per_feature['MSE'] = mse\n",
        "  df_mse_per_feature['RMSE'] = rmse\n",
        "  df_mse_per_feature['MAE'] = mae\n",
        "\n",
        "\n",
        "  #df_rmse_per_feature = pd.DataFrame(data=rmses.reshape(1,-1),    # values\n",
        "   #     index=['rmes'],     # index\n",
        "  #      columns=cols_label)  #  column names\n",
        "  #df_mae_per_feature = pd.DataFrame(data=maes.reshape(1,-1),    # values\n",
        "    #    index=['maes'],     # index\n",
        "   #     columns=cols_label)  #  column names\n",
        "  \n",
        "  return df_mse_per_feature\n",
        "\n",
        "\n",
        "def pas_metrics_all(models):\n",
        "  cols_trans = ['MSE', 'RMSE', 'MAE', col_vv,col_alt, col_v]\n",
        "  cols_rot = [col_roll_deg, col_pitch_deg, col_yaw_deg, col_pitch_v, col_yaw_v, col_roll_v]\n",
        "  rows_trans = []\n",
        "  rows_rot = []\n",
        "\n",
        "  for model_tupel in models:\n",
        "    model_row = _df_prediction_metrics_all(model_tupel, scaled=False)\n",
        "    rows_trans.append(model_row[cols_trans])\n",
        "    rows_rot.append(model_row[cols_rot])\n",
        "\n",
        "  df_trans = pd.concat(rows_trans).round(2)\n",
        "  file_path = os.path.join(results_folder, 'all_mse_trans' + '.csv')\n",
        "  df_trans.to_csv(path_or_buf=file_path)\n",
        "  \n",
        "  df_rot = pd.concat(rows_rot).round(10)\n",
        "  file_path = os.path.join(results_folder, 'all_mse_rot' + '.csv')\n",
        "  df_rot.to_csv(path_or_buf=file_path)\n",
        "  return df_trans, df_rot\n",
        "\n",
        "\n",
        "df_trans, df_rot = pas_metrics_all(models)\n",
        "  "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-25-06b996d9f64b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     66\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 68\u001b[0;31m \u001b[0mdf_trans\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf_rot\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpas_metrics_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodels\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     69\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m<ipython-input-25-06b996d9f64b>\u001b[0m in \u001b[0;36mpas_metrics_all\u001b[0;34m(models)\u001b[0m\n\u001b[1;32m     53\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mmodel_tupel\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodels\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     54\u001b[0m     \u001b[0mmodel_row\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_df_prediction_metrics_all\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_tupel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscaled\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 55\u001b[0;31m     \u001b[0mrows_trans\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_row\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols_trans\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     56\u001b[0m     \u001b[0mrows_rot\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_row\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcols_rot\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     57\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2999\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_iterator\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3000\u001b[0m                 \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3001\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3002\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3003\u001b[0m         \u001b[0;31m# take() does not accept boolean indexers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[0;34m(self, obj, axis, is_setter, raise_missing)\u001b[0m\n\u001b[1;32m   1283\u001b[0m                 \u001b[0;31m# When setting, missing keys are not allowed, even with .loc:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m                 \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"raise_missing\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;32mTrue\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mis_setter\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_listlike_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1286\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_get_listlike_indexer\u001b[0;34m(self, key, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1090\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1091\u001b[0m         self._validate_read_indexer(\n\u001b[0;32m-> 1092\u001b[0;31m             \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_axis_number\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mraise_missing\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1093\u001b[0m         )\n\u001b[1;32m   1094\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mkeyarr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_validate_read_indexer\u001b[0;34m(self, key, indexer, axis, raise_missing)\u001b[0m\n\u001b[1;32m   1183\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"loc\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mraise_missing\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1184\u001b[0m                 \u001b[0mnot_found\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1185\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"{} not in index\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnot_found\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1186\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1187\u001b[0m             \u001b[0;31m# we skip the warning on Categorical/Interval\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: \"['   __alt,ftmsl '] not in index\""
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EJx7-29ZM4yp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_trans"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fJUMzC7HM5jz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_rot"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z40fMP9Ttc9Q",
        "colab_type": "text"
      },
      "source": [
        "#predict one ahead"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vxp9pJ3PmbjQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "STOP"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N-8LmbK2eki9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def make_prediction(model, test_X, seq_len, n_labels):\n",
        "  predictions = np.empty(n_labels).reshape(1,-1)  \n",
        "  test_row = test_X[0,:].reshape(1,-1)\n",
        "  for i in range(seq_len):\n",
        "    pred_Y = model.predict(test_row)\n",
        "    state_offset = pred_Y.shape[1]\n",
        "    test_row = np.concatenate((pred_Y, test_X[i, state_offset:].reshape(1,-1)), axis=None).reshape(1,-1)\n",
        "    predictions = np.concatenate((predictions,pred_Y))\n",
        "  return np.delete(predictions, (0), axis=0) #delete first empty row\n",
        "\n",
        "def get_descaled_dataframe(test_Y, predictions, label_scaler):\n",
        "  truth = test_Y[:len(predictions),:]\n",
        "  truth = label_scaler.inverse_transform(truth)\n",
        "  predictions = label_scaler.inverse_transform(predictions)\n",
        "  df_pred = pd.DataFrame(data=predictions, columns=label) \n",
        "  df_real = pd.DataFrame(data=truth, columns=label) \n",
        "  return df_real, df_pred\n",
        "\n",
        "def print_pred_and_actual(pred, actual):\n",
        "  pred_columns = pred.columns\n",
        "  actual_columns = actual.columns\n",
        "  #pred_column_name = pred_columns[0]\n",
        "  #actual_column_name = actual_columns[0]\n",
        "  for pred_column_name, actual_column_name in zip(pred_columns,actual_columns):\n",
        "    pred_column = pred[pred_column_name]\n",
        "    actual_column = actual[actual_column_name]\n",
        "    #for i in range(len(actual_column)):\n",
        "    true = plt.plot(range(0, len(actual_column)), actual_column, label=pred_column_name+' true')\n",
        "    plt.plot(range(0, len(actual_column)), pred_column,'--',color=true[0].get_color(), label=actual_column_name+' pred')\n",
        "    plt.xlabel('Zeitschritte')\n",
        "    plt.ylabel(pred_column_name)\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WpjSxgRRts-r",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model_tupel = models[0]\n",
        "\n",
        "model = model_tupel[1]\n",
        "train_X, train_Y, test_X, test_Y = model_tupel[2]\n",
        "label_scaler = model_tupel[3]\n",
        "label = model_tupel[4]\n",
        "\n",
        "\n",
        "predictions = make_prediction(model, test_X, 200, len(cols_state))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LbkoGGOytuBI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df_real, df_pred = get_descaled_dataframe(test_Y, predictions, label_scaler)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EM3aCfNBt_gx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print_pred_and_actual(df_pred, df_real)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "js2zRzanuGG4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}