IDAO_Lipatov.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IDAO_Lipatov.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "authorship_tag": "ABX9TyMUByuHSA7Y6UuQ7QtM8f2o",
      "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/VGODIE/ML_kaggle_competitions/blob/master/IDAO_Lipatov.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1DPRm-7DLuw",
        "colab_type": "code",
        "outputId": "4b370f61-231a-46eb-fe5b-b4af066519fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "try:\n",
        "  # %tensorflow_version only exists in Colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (8, 6)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TensorFlow 2.x selected.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JilpjlOyGUyG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Rug3yougDo_K",
        "colab_type": "code",
        "outputId": "c18d8a30-bb97-4111-a469-b50f9a261e5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.1.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilRCmnpt8GT-",
        "colab_type": "code",
        "outputId": "cd2a262b-9b94-4a9f-e056-5c48c8ac7aec",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "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&response_type=code&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\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s0gSWlNh7RQ9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = pd.read_csv(\"/content/drive/My Drive/train.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDPLS94r8m8h",
        "colab_type": "code",
        "outputId": "06728df4-48ec-4b51-ad96-4600e7e44c5f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 165
        }
      },
      "source": [
        "data.head(2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>epoch</th>\n",
              "      <th>sat_id</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>Vx</th>\n",
              "      <th>Vy</th>\n",
              "      <th>Vz</th>\n",
              "      <th>x_sim</th>\n",
              "      <th>y_sim</th>\n",
              "      <th>z_sim</th>\n",
              "      <th>Vx_sim</th>\n",
              "      <th>Vy_sim</th>\n",
              "      <th>Vz_sim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>2014-01-01T00:00:00.000</td>\n",
              "      <td>0</td>\n",
              "      <td>-8855.823863</td>\n",
              "      <td>13117.780146</td>\n",
              "      <td>-20728.353233</td>\n",
              "      <td>-0.908303</td>\n",
              "      <td>-3.808436</td>\n",
              "      <td>-2.022083</td>\n",
              "      <td>-8843.131454</td>\n",
              "      <td>13138.221690</td>\n",
              "      <td>-20741.615306</td>\n",
              "      <td>-0.907527</td>\n",
              "      <td>-3.804930</td>\n",
              "      <td>-2.024133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>2014-01-01T00:46:43.000</td>\n",
              "      <td>0</td>\n",
              "      <td>-10567.672384</td>\n",
              "      <td>1619.746066</td>\n",
              "      <td>-24451.813271</td>\n",
              "      <td>-0.302590</td>\n",
              "      <td>-4.272617</td>\n",
              "      <td>-0.612796</td>\n",
              "      <td>-10555.500066</td>\n",
              "      <td>1649.289367</td>\n",
              "      <td>-24473.089556</td>\n",
              "      <td>-0.303704</td>\n",
              "      <td>-4.269816</td>\n",
              "      <td>-0.616468</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   id                    epoch  sat_id  ...    Vx_sim    Vy_sim    Vz_sim\n",
              "0   0  2014-01-01T00:00:00.000       0  ... -0.907527 -3.804930 -2.024133\n",
              "1   1  2014-01-01T00:46:43.000       0  ... -0.303704 -4.269816 -0.616468\n",
              "\n",
              "[2 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hBTbdZ-UogQJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "freq = np.unique(data.sat_id.values, return_counts=True)[1]\n",
        "#split = (freq*0.8).apply_along_axis(lambda x: int(x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78TBdWIYE8Rs",
        "colab_type": "text"
      },
      "source": [
        "# Data visualization"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zRDuXKllE_lQ",
        "colab_type": "code",
        "outputId": "7394e4dd-53bd-4e53-a5ba-9662e42b9d44",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "#plt.subplot(target.x[:freq[0] + 1])\n",
        "plt.plot(target.x[:freq[0] + 1])\n",
        "#plt.plot(target.y[:freq[0] + 1])\n",
        "#plt.plot(target.z[:freq[0] + 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f23c8aacc88>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ODoPrQMied+NajOtdZFlBwTlLXkx33uYLyH0/SD+M9USIw2uq5wGNa2cHufO2WEhdOysO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bMZxvst9wVa/vUakPUV3xZ3ErM0Ld2vBPq2D8jS9hwgXy+8nkEOhJTAU/68x/97rkP7vF03g2EU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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOKy0Xn1E_pc",
        "colab_type": "code",
        "outputId": "197d531a-5140-42ef-9464-1c544fde5616",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "plt.plot(target.y[:freq[0] + 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f23c8a8e1d0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7l6FN/M573pUS5glnxsU48btA8rsDBxpujg8oWJWCQ7R8w3i+s00XhsCbRVC6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\nU6dOnTp16jRSNwo6derUqVOnTgCA/x/PDqbIllG14AAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wl7TXclOFtuY",
        "colab_type": "code",
        "outputId": "ef3ca8dc-d5ff-4235-e971-5452d65afd88",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "plt.plot(target.z[:freq[0] + 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f23c89990b8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0XImAhwboVSCaxHZpc0xe0yvGoPJxEGukn+CzsEHxXDQDUUtQucLI9iBKZ28c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xyjytl5DOYsnlr/wtaTsQ4+ZAXyOjKpI45r89vqoCxsFuRppdxGYkyrfkDNd3rUEsBmldTGw4qO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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nOu3TEawGMoO",
        "colab_type": "code",
        "outputId": "40c64650-3215-46c3-8899-fba9b976d076",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "sns.lineplot(data.epoch.values[:freq[0] + 1] , target.Vx[:freq[0] + 1])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f23c6ce0ef0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Y8jnK3y06r4jGzYumtUrny6WHclbDiYmBQ2IAHzY8IYdiYdQIR9SiECkhmqnd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\nSUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2NfUob11GQQO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target.Vy[:freq[0] + 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7cjeAM3mGLLa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "target.Vz[:freq[0] + 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IFav5WkHxX_X",
        "colab_type": "code",
        "outputId": "6ba97143-dbbe-4d7a-b1a9-f6bcf8dd26d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.distplot(freq)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3a546b9a90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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18lY6Giv528e7dHMYEZFzoGD32anRSYbGs3Q0VvldSqAk4jH++K0beOnYkM61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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rwpo6sgDttRX",
        "colab_type": "text"
      },
      "source": [
        "# Data preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29_93YYA9f8Y",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del data_real_scaled"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsnYjMfJWiX6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "data_real = data[[\"x\", \"y\", \"z\", \"Vx\", \"Vy\", \"Vz\"]].values\n",
        "scaler = StandardScaler()\n",
        "data_real = scaler.fit_transform(data_real)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bs74hLp3nYoN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "data_sim = data[[\"x_sim\", \"y_sim\", \"z_sim\", \"Vx_sim\", \"Vy_sim\", \"Vz_sim\"]].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z8OzAbrpiKqP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cos_sin = np.concatenate((np.sin(data_sim), np.cos(data_sim)), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rm3ziNv3Hap0",
        "colab_type": "text"
      },
      "source": [
        "Massive preprocessing for time series\n",
        "Skip if u want ot test another model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5EiBnsenOxpj",
        "colab_type": "code",
        "outputId": "504c588a-effa-49d3-9a2c-396b382138dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data_sim.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(649912, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B97_qFR_ZVeL",
        "colab_type": "text"
      },
      "source": [
        "Сделаем кнн предсказания для каждого наблюдения и добавим их как новые 6 регрессоров"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-VIQWu-OOHI9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neighbors import KNeighborsRegressor\n",
        "knn_list = []\n",
        "for i in range(6):\n",
        "    knn = KNeighborsRegressor()\n",
        "    knn.fit(data_sim, data_real[:,i])\n",
        "    knn_list.append(knn)\n",
        "    if i == 0:\n",
        "      ktr_preds = knn.predict(data_sim).reshape(-1, 1)\n",
        "    else:\n",
        "      ktr_preds = np.concatenate((ktr_preds, knn.predict(data_sim).reshape(-1, 1)), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "alzFjN4HQUHk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_sim = np.concatenate((data_sim, ktr_preds), axis=1)\n",
        "del ktr_preds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZCWdQjosix3e",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler_sim = StandardScaler()\n",
        "data_sim = scaler_sim.fit_transform(data_sim)\n",
        "data_sim = np.concatenate((data_sim, cos_sin), axis=1)\n",
        "del cos_sin"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ofqzLmnzCdM9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def cut(data, freq):\n",
        "  start = 0\n",
        "  end = freq[0]\n",
        "  arr = []\n",
        "  for i in range(len(freq)):\n",
        "    arr.append(data[start:end])\n",
        "    start = end\n",
        "      \n",
        "    if i != len(freq) - 1:\n",
        "      end += freq[i+1]\n",
        "  return arr"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hAA_teDT_jWz",
        "colab_type": "code",
        "outputId": "7112974f-1874-4f88-dbce-325031f91acd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "data_real.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(649912, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "25VnBOOWD_jZ",
        "colab_type": "code",
        "outputId": "79686bfc-2f5d-4010-c82e-7ae761ea27b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cut_data_sim = cut(data_sim, freq)\n",
        "cut_data_real = cut(data_real, freq)\n",
        "len(cut_data_real) == len(cut_data_sim)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1CsAxslKZY1K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "window_size = 35\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9a_fg3vUTsD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_window_data(data_sim, window, data_real=None, train=True):\n",
        "    # Get window data and scale\n",
        "    #scaler_sim = MinMaxScaler(feature_range=(0, 1))\n",
        "    #scaler_real = MinMaxScaler(feature_range=(0, 1))\n",
        "\n",
        "    X = []\n",
        "    y = []\n",
        "\n",
        "    for i in range(len(data_sim) - window):\n",
        "        X.append(data_sim[i : i + window])\n",
        "        if train == True:\n",
        "          y.append(data_real[i + window])\n",
        "\n",
        "    X = np.asarray(X)\n",
        "    y = np.asarray(y)\n",
        "    return X, y"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G2GJmmrwKEGV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(len(cut_data_sim)):\n",
        "  regressor, target = get_window_data(cut_data_sim[i], window_size, cut_data_real[i])\n",
        "  if i == 0:\n",
        "    q = regressor\n",
        "    w = target\n",
        "  else:\n",
        "    q = np.concatenate((q, regressor))\n",
        "    w = np.concatenate((w, target))\n",
        "  del regressor\n",
        "  del target"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlc3PKLaUoTM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "del all_x\n",
        "del all_y\n",
        "del cut_data_real"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_dAfOseXf2wq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ma2oXa9nUShc",
        "colab_type": "text"
      },
      "source": [
        "# Model architecture"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4h7PWrIw5Y_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import sklearn\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.linear_model import LinearRegression"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "btg_U4jgIBJO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.random.seed(42)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1IY2cDzhqjP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def smape(satellite_predicted_values, satellite_true_values): \n",
        "    # the division, addition and subtraction are pointwise \n",
        "    return np.mean(np.abs((satellite_predicted_values - satellite_true_values) \n",
        "        / (np.abs(satellite_predicted_values) + np.abs(satellite_true_values))))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MzRtcBt2IUl8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_sim, data_real)\n",
        "X_train_sc ,X_test_sc, y_train_scaled, y_test_scaled = train_test_split(data_sim, data_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jegIq2tDHlyc",
        "colab_type": "text"
      },
      "source": [
        "### Linear regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jXorqszgMAVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def train_linreg(x_train, y_train, x_test, y_test):\n",
        "  for i in range(6):\n",
        "    lr = LinearRegression()\n",
        "    lr.fit(x_train, y_train[:,i])\n",
        "    if i ==0:\n",
        "      lr_preds = lr.predict(x_test).reshape(-1,1)\n",
        "    else:\n",
        "      lr_preds = np.concatenate((lr_preds, lr.predict(x_test).reshape(-1,1)), axis=1)\n",
        "\n",
        "  print(\"smape =\", smape(lr_preds, y_test))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cFqgrqlVM4Y5",
        "colab_type": "text"
      },
      "source": [
        "Сначала без скейлинга таргета"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YNEtJqqbMrJM",
        "colab_type": "code",
        "outputId": "f7d527c0-1904-4abb-cbe0-464658954434",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_linreg(X_train, y_train, X_test, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "smape = 0.04181814786857531\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxCS5APhNGCp",
        "colab_type": "text"
      },
      "source": [
        "Теперь с norme скейлингом"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SbREPBLMLlCV",
        "colab_type": "code",
        "outputId": "f5172892-de67-442c-e6b9-0f3c33d13609",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_linreg(X_train_sc, y_train_scaled, X_test_sc, y_test_scaled, 6)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "smape = 0.22862262065880565\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wWMyLkFyN43x",
        "colab_type": "text"
      },
      "source": [
        "Добавим квадратичные признаки"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5etELh2mN32q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_sim = np.concatenate((data_sim, data_sim**2), axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data_sim, data_real)\n",
        "X_train_sc ,X_test_sc, y_train_scaled, y_test_scaled = train_test_split(data_sim, data_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QFEm2xzvN30R",
        "colab_type": "code",
        "outputId": "c258b796-0dfa-4025-881b-4794167e3346",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "print(\"without scaling\")\n",
        "train_linreg(X_train, y_train, X_test, y_test)\n",
        "print('\\n')\n",
        "print(\"with scaling\")\n",
        "train_linreg(X_train_sc, y_train_scaled, X_test_sc, y_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "without scaling\n",
            "smape = 0.22928967279698123\n",
            "\n",
            "\n",
            "with scaling\n",
            "smape = 0.22861719952727963\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YaELYUJ9PaBG",
        "colab_type": "text"
      },
      "source": [
        "Добавим синус и косинус"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aIJAXkOQQQGS",
        "colab_type": "code",
        "outputId": "46d9f7ba-2b66-4efa-82eb-f8f1a3ae8615",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "data_sim = np.concatenate((data_sim, np.cos(data_sim)), axis=1)\n",
        "data_sim = np.concatenate((data_sim, np.sin(data_sim)), axis=1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(data_sim, data_real)\n",
        "X_train_sc ,X_test_sc, y_train_scaled, y_test_scaled = train_test_split(data_sim, data_scaled)\n",
        "\n",
        "print(\"without scaling\")\n",
        "train_linreg(X_train, y_train, X_test, y_test)\n",
        "print('\\n')\n",
        "print(\"with scaling\")\n",
        "train_linreg(X_train_sc, y_train_scaled, X_test_sc, y_test_scaled)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "without scaling\n",
            "smape = 0.22567926828094492\n",
            "\n",
            "\n",
            "with scaling\n",
            "smape = 0.225971629925241\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GKgyKy4DioY0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_sim = data[[\"x_sim\", \"y_sim\", \"z_sim\", \"Vx_sim\", \"Vy_sim\", \"Vz_sim\"]].values\n",
        "data_sim = np.concatenate((data_sim, data_sim**2), axis=1)\n",
        "data_sim = np.concatenate((data_sim, np.cos(data_sim[:,:6])), axis=1)\n",
        "data_sim = np.concatenate((data_sim, np.sin(data_sim[:,:6])), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0LCAX8tgi5hs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "scaler_sim = StandardScaler()\n",
        "data_sim_scaled = scaler_sim.fit_transform(data_sim)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DFmiJRY_jBac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_sim_scaled, data_real)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yQ9jK3oIjHZy",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AK3fjbB6jHWv",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sNagvw7kGlYJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.layers import LSTM, Dropout, Dense, Bidirectional, GRU\n",
        "from tensorflow.keras import Sequential"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5YP5ygeYTF4",
        "colab_type": "code",
        "outputId": "65b29737-d61d-4d7c-b349-f1d60670492c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#test_split = int(len(q) * 0.8)\n",
        "test_split = (len(w) - freq[-1] - freq[-2])\n",
        "print(test_split)\n",
        "#X_train = q[:test_split]\n",
        "#X_test = q[test_split:]\n",
        "\n",
        "#y_train = w[:test_split]\n",
        "#y_test = w[test_split:]\n",
        "\n",
        "X_train = q[:test_split]\n",
        "X_test = q[test_split:]\n",
        "\n",
        "y_train = w[:test_split]\n",
        "y_test = w[test_split:]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "626467\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OxnpNkVV9ApD",
        "colab_type": "code",
        "outputId": "eb4e651a-eea6-4e7a-a133-9d4eda0e68b0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_split"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "626467"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ptb6Vzcwh-De",
        "colab_type": "code",
        "outputId": "70165416-2e1e-42b1-fb4b-e44ef10cf60c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "model = tf.keras.models.load_model(\"model_one.hdf5\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Sequential models without an `input_shape` passed to the first layer cannot reload their optimizer state. As a result, your model isstarting with a freshly initialized optimizer.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "93X17gBMYRj7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# ------- Building the LSTM model --------- #\n",
        "adam = tf.keras.optimizers.Adam(amsgrad=True, clipvalue=1.0)\n",
        "adagrad = tf.keras.optimizers.Adagrad(clipvalue=1.0)\n",
        "\n",
        "model = Sequential()\n",
        "model.add(Bidirectional(GRU(100,return_sequences=True, input_shape=(window_size, X_train.shape[-1])))) # 50neurons, and and window_size = 50 \n",
        "model.add(Dropout(0.2))\n",
        "model.add(Bidirectional(GRU(75)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(6))\n",
        "model.compile(loss=\"mean_squared_error\", optimizer= adam)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kNjzIzspalWq",
        "colab_type": "code",
        "outputId": "68fa6569-9395-46a4-dec9-7a6cb69837d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        }
      },
      "source": [
        "history = model.fit(X_train, y_train, epochs=20, batch_size=256, validation_data=(X_test, y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 626467 samples, validate on 2445 samples\n",
            "Epoch 1/20\n",
            "626467/626467 [==============================] - 59s 94us/sample - loss: 0.0494 - val_loss: 0.0024\n",
            "Epoch 2/20\n",
            "626467/626467 [==============================] - 49s 78us/sample - loss: 0.0261 - val_loss: 0.0014\n",
            "Epoch 3/20\n",
            "626467/626467 [==============================] - 49s 78us/sample - loss: 0.0241 - val_loss: 0.0017\n",
            "Epoch 4/20\n",
            "626467/626467 [==============================] - 49s 78us/sample - loss: 0.0232 - val_loss: 0.0013\n",
            "Epoch 5/20\n",
            "626467/626467 [==============================] - 48s 77us/sample - loss: 0.0227 - val_loss: 0.0013\n",
            "Epoch 6/20\n",
            "626467/626467 [==============================] - 45s 72us/sample - loss: 0.0223 - val_loss: 0.0016\n",
            "Epoch 7/20\n",
            "626467/626467 [==============================] - 47s 76us/sample - loss: 0.0221 - val_loss: 0.0013\n",
            "Epoch 8/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0218 - val_loss: 0.0015\n",
            "Epoch 9/20\n",
            "626467/626467 [==============================] - 47s 76us/sample - loss: 0.0216 - val_loss: 0.0014\n",
            "Epoch 10/20\n",
            "626467/626467 [==============================] - 47s 76us/sample - loss: 0.0213 - val_loss: 0.0014\n",
            "Epoch 11/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0213 - val_loss: 0.0021\n",
            "Epoch 12/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0210 - val_loss: 9.2498e-04\n",
            "Epoch 13/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0209 - val_loss: 0.0016\n",
            "Epoch 14/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0208 - val_loss: 0.0013\n",
            "Epoch 15/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0208 - val_loss: 0.0014\n",
            "Epoch 16/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0206 - val_loss: 0.0013\n",
            "Epoch 17/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0205 - val_loss: 0.0014\n",
            "Epoch 18/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0205 - val_loss: 0.0013\n",
            "Epoch 19/20\n",
            "626467/626467 [==============================] - 48s 76us/sample - loss: 0.0204 - val_loss: 0.0013\n",
            "Epoch 20/20\n",
            "626467/626467 [==============================] - 47s 76us/sample - loss: 0.0203 - val_loss: 0.0016\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tqQsUJQqdnhQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import save_model\n",
        "\n",
        "model.save(\"model_one.hdf5\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXkGIj5G8dnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "preds = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvRBgTsD4RMy",
        "colab_type": "code",
        "outputId": "6ef505ce-8528-4267-e696-3fbd822dc141",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "invpreds = scaler.inverse_transform(preds)\n",
        "invtarget = scaler.inverse_transform(y_test)\n",
        "smape(invpreds, invtarget)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06035810643100558"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_5E5JxnC-jkw",
        "colab_type": "code",
        "outputId": "934d92a7-7240-41af-e1fe-e85516ec44a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "smape(preds, y_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.06108911685877551"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Rft1i49apYA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_history(history):\n",
        "    plt.figure(figsize=(6, 4))\n",
        "    plt.plot(history.history[\"loss\"],  label=\"Train\")\n",
        "    plt.plot(history.history[\"val_loss\"], label=\"Test\")\n",
        "    plt.title(\"Loss over epoch\")\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel('Loss')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uwtGe4N2a_Xn",
        "colab_type": "code",
        "outputId": "cbd688d3-b315-4287-988a-85505281bf81",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "plot_history(history)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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lSihF1NGmixtFpHYpoRRRR3sTm3cfIJ32SociIlJ2SihF1NnWxGDK2bavv9KhiIiUnRJK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\nRESkKP5/2sU4qyve3XsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pZCtkXYFEv48",
        "colab_type": "code",
        "outputId": "1f04b29f-2c90-4074-bc8b-60e3a5e7d680",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds[:,0].shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2445,)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZOS3Vy2b6Vm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from math import sqrt\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "def plot(X_test):\n",
        "    pred = model.predict(X_test)\n",
        "    pred_inverse = scaler_sim.inverse_transform(pred.reshape(-1, 1))\n",
        "    y_test_inverse = scaler_real.inverse_transform(y_test.reshape(-1, 1))\n",
        "    \n",
        "    # Calculate mean_squared_error. Previosly we did MinMax scale, so apply inverse_transform to recover values\n",
        "    rmse = sqrt(mean_squared_error(y_test_inverse, pred_inverse))\n",
        "    print('Test RMSE: %.3f' % rmse)\n",
        "    plt.figure(figsize=(15, 8))\n",
        "    plt.plot(pred_inverse, label='predict')\n",
        "    plt.plot(y_test_inverse, label='actual')\n",
        "    plt.legend()\n",
        "    plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kDNC2FMlcBOc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plot(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f4sxYKRfoNTx",
        "colab_type": "text"
      },
      "source": [
        "##Gradient boosting "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2oS7RX1voalv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingRegressor"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1qqZKQeWoNS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import xgboost as xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32NS8p4DqRXg",
        "colab_type": "code",
        "outputId": "8d428704-b4bc-41cb-b398-94f7f0c51db6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train.shape[0] == y_train.shape[0]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iMFxozFqcYk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "boost_list = []\n",
        "for i in range(6):\n",
        "  boost = GradientBoostingRegressor(loss = \"huber\", learning_rate=0.2, verbose=1, subsample=0.5)\n",
        "  boost.fit(X_train, y_train[:,i])\n",
        "  if i ==0:\n",
        "    preds_lst = boost.predict(X_test).reshape(-1,1)\n",
        "  else:\n",
        "    preds_lst = np.concatenate((preds_lst, boost.predict(X_test).reshape(-1,1)), axis=1)\n",
        "  boost_list.append(boost)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FhawqvZKV8nA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "bst_list = []\n",
        "for i in range(6):\n",
        "  bst = xgb.XGBRegressor(learning_rate=0.2, subsample=0.7, verbosity=2 )\n",
        "  bst.fit(X_train, y_train[:,i], verbose=True)\n",
        "  if i == 0:\n",
        "    preds_arr = bst.predict(X_test).reshape(-1,1)\n",
        "  else:\n",
        "    preds_arr = np.concatenate((preds_arr, bst.predict(X_test).reshape(-1,1)), axis=1)\n",
        "  bst_list.append(bst)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oTS2rBki6ACt",
        "colab_type": "code",
        "outputId": "d9c00933-2ff1-4e4a-b374-6b3c6c0683a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "np.mean(X_train, axis=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-4.26594396e-04, -9.38527660e-04, -8.89313282e-04,  1.73509790e-04,\n",
              "        7.08442611e-04,  1.07574076e-03, -7.09666220e-04,  2.72612001e-05,\n",
              "        2.54346033e-04, -1.03012709e-03, -1.55658553e-04,  1.40345972e-03,\n",
              "       -6.01692909e-05,  7.30487501e-05, -3.11542280e-04,  6.56181584e-04,\n",
              "       -3.04548882e-04, -1.51600066e-03,  1.76794752e-04, -1.70919038e-04,\n",
              "        3.52958511e-04,  1.07385790e-03, -1.28957759e-03,  1.33199238e-04])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpI0KoEb5rRl",
        "colab_type": "code",
        "outputId": "fb83e8c5-3fcf-456a-e41e-3b174d32f449",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "bst_x = xgb.XGBRegressor(learning_rate=0.2,loss=\"reg:squarederror\", subsample=0.5, verbosity=2, n_estimators=150, reg_lambda=1)\n",
        "bst_x.fit(X_train, y_train[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/xgboost/core.py:614: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption\n",
            "  \"because it will generate extra copies and increase memory consumption\")\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[15:22:41] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[15:22:44] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:44] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:45] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:46] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:47] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:48] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:49] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:50] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:50] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:51] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:52] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:53] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:22:54] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
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            "[15:24:41] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:24:42] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:24:43] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:24:43] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:24:44] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 14 extra nodes, 0 pruned nodes, max_depth=3\n",
            "[15:24:45] INFO: /workspace/src/tree/updater_prune.cc:74: tree pruning end, 1 roots, 12 extra nodes, 0 pruned nodes, max_depth=3\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=1, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.2, loss='reg:squarederror',\n",
              "             max_delta_step=0, max_depth=3, min_child_weight=1, missing=None,\n",
              "             n_estimators=150, n_jobs=1, nthread=None, objective='reg:linear',\n",
              "             random_state=0, reg_alpha=0, reg_lambda=1, scale_pos_weight=1,\n",
              "             seed=None, silent=None, subsample=0.5, verbosity=2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdF-ThrQaanq",
        "colab_type": "code",
        "outputId": "0b28559e-528e-43d7-84a5-dbf9bf6282cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "smape(bst_x.predict(X_train), y_train[:,0])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2013370912695377"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7lQXRjiWtwn1",
        "colab_type": "code",
        "outputId": "31b31a22-6c10-4132-f831-c92355dbd28f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "source": [
        "for i in range(6):\n",
        "  print(smape(boost_list[i].predict(X_train), y_train[:,i]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0.18619006397810367\n",
            "0.19481945754496796\n",
            "0.20291636271497085\n",
            "0.1971085299029239\n",
            "0.2027304741381391\n",
            "0.21990640857460575\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m4vht2gqub0O",
        "colab_type": "code",
        "outputId": "03f2ece1-b040-4354-f1eb-ed0f9c3b52bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "boost_x = GradientBoostingRegressor(learning_rate=0.1, n_estimators=150, max_depth=7, subsample=0.5,  verbose=1, random_state=42)\n",
        "boost_x.fit(X_train, y_train[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "      Iter       Train Loss      OOB Improve   Remaining Time \n",
            "         1   523427967.3135   121188508.6940           18.97m\n",
            "         2   423695462.4951    98499459.0050           19.11m\n",
            "         3   346434403.6949    79337750.3662           18.94m\n",
            "         4   280536356.9454    64455020.4575           18.87m\n",
            "         5   230350913.4948    52053856.5084           18.74m\n",
            "         6   187389265.0762    42027252.3336           18.67m\n",
            "         7   152697676.3763    34169958.3782           18.65m\n",
            "         8   124887149.4419    27616414.5230           18.59m\n",
            "         9   101728365.2532    22477685.1083           18.50m\n",
            "        10    83806757.4308    18195166.2448           18.41m\n",
            "        20    14401710.5680     2250320.9192           17.06m\n",
            "        30     5584667.3545      259211.0789           15.80m\n",
            "        40     3992575.7584       30390.2053           14.52m\n",
            "        50     3751534.4240       19192.2514           13.30m\n",
            "        60     3389087.1316       -1437.6703           12.05m\n",
            "        70     3363501.8077       -2247.6590           10.84m\n",
            "        80     3423666.6036        -925.1233            9.58m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kfY9Wou6xDWD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "smape(boost_x.predict(X_test), y_test[:,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Zj1Py6Q1iEXB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "smape(boost_x.predict(X_train), y_train[:,0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sj6EO22U5daV",
        "colab_type": "code",
        "outputId": "b88bff70-2c07-4142-9cc2-fe5ce55ec455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "sklearn.metrics.mean_squared_error(y_train[:,0], boost_x.predict(X_train))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "27441264.274389137"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x8eAgOZkxyZH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "boost_y = GradientBoostingRegressor(learning_rate=0.2, n_estimators=200, max_depth=5, subsample=0.5,  verbose=1)\n",
        "boost_y.fit(X_train, y_train[:,2])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OMMc0SzDx7sR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "smape(boost_y.predict(X_test), y_test[:,2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hO0E81TMAmXb",
        "colab_type": "text"
      },
      "source": [
        "Проблема градиентного бустинга - для скоростей лосс остается довольно большим - нужно тюнить"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VPFihaSmrr2n",
        "colab_type": "code",
        "outputId": "fbc9f98d-2ca5-42f1-f50e-8542bbf7df3e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "preds_arr.shape == y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5p17aftzrz-A",
        "colab_type": "code",
        "outputId": "be1d770c-319b-4a7e-8f96-2b8d0c7fca9c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "smape(scaler_real.inverse_transform(preds_arr), scaler_real.inverse_transform(y_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.21984405377475447"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GtiCdZp5E3WF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gvzr-gakGDVp",
        "colab_type": "text"
      },
      "source": [
        "## Support vector regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "60jr7A5NGNXs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.kernel_approximation import Nystroem\n",
        "\n",
        "feature_map_nystroem = Nystroem(gamma=.2,random_state=1,n_components=300)\n",
        "data_sim_transformed = feature_map_nystroem.fit_transform(data_sim)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8u3FSQ3yXgq3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OaM4KCDdWGnn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(data_sim_transformed, data_real_scaled)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdnYdyqWV5-Z",
        "colab_type": "code",
        "outputId": "f744e087-97c2-4e35-e755-7cccceddec9b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "source": [
        "from sklearn.svm import LinearSVR\n",
        "\n",
        "svr = LinearSVR(epsilon=1)\n",
        "svr.fit(X_train, y_train[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearSVR(C=1.0, dual=True, epsilon=1, fit_intercept=True,\n",
              "          intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=1000,\n",
              "          random_state=None, tol=0.0001, verbose=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPrPlG9FWuKG",
        "colab_type": "code",
        "outputId": "ff5320fb-099a-4847-e9c1-d775ea2c47de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "smape(svr.predict(X_train), y_train[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9584124191568774"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bSo0JW9WX950",
        "colab_type": "text"
      },
      "source": [
        "## RandomForest"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sQH0MSorYBz7",
        "colab_type": "code",
        "outputId": "9b943a68-d5f4-43e0-bfc7-1d95603a5b54",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 139
        }
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "rf = RandomForestRegressor(max_depth=7,n_estimators=15, oob_score=True, verbose=1)\n",
        "rf.fit(X_train, y_train[:,0])\n",
        "rf_preds = rf.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:  2.5min finished\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/ensemble/_forest.py:815: UserWarning: Some inputs do not have OOB scores. This probably means too few trees were used to compute any reliable oob estimates.\n",
            "  warn(\"Some inputs do not have OOB scores. \"\n",
            "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
            "[Parallel(n_jobs=1)]: Done  15 out of  15 | elapsed:    0.2s finished\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxfCm5Dib3L8",
        "colab_type": "code",
        "outputId": "544233f4-350d-4d79-da4b-74962f8b2958",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "smape(rf_preds, y_test[:,0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.05552649026441422"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DH0ZjYIzE6Km",
        "colab_type": "text"
      },
      "source": [
        "# Downloading test data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l6h11UZ2cyFR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test = pd.read_csv(\"/content/drive/My Drive/test.csv\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C78McSCZFP36",
        "colab_type": "code",
        "outputId": "a4525ae8-0136-4461-e076-703da69d6ff0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        }
      },
      "source": [
        "test.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>sat_id</th>\n",
              "      <th>epoch</th>\n",
              "      <th>x_sim</th>\n",
              "      <th>y_sim</th>\n",
              "      <th>z_sim</th>\n",
              "      <th>Vx_sim</th>\n",
              "      <th>Vy_sim</th>\n",
              "      <th>Vz_sim</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3927</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-02-01T00:01:45.162</td>\n",
              "      <td>-13366.891347</td>\n",
              "      <td>-14236.753503</td>\n",
              "      <td>6386.774555</td>\n",
              "      <td>4.333815</td>\n",
              "      <td>-0.692764</td>\n",
              "      <td>0.810774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3928</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-02-01T00:22:57.007</td>\n",
              "      <td>-7370.434039</td>\n",
              "      <td>-14498.771520</td>\n",
              "      <td>7130.411325</td>\n",
              "      <td>5.077413</td>\n",
              "      <td>0.360609</td>\n",
              "      <td>0.313402</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3929</td>\n",
              "      <td>1</td>\n",
              "      <td>2014-02-01T00:44:08.852</td>\n",
              "      <td>-572.068654</td>\n",
              "      <td>-13065.289498</td>\n",
              "      <td>7033.794876</td>\n",
              "      <td>5.519106</td>\n",
              "      <td>2.012830</td>\n",
              "      <td>-0.539412</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     id  sat_id                    epoch  ...    Vx_sim    Vy_sim    Vz_sim\n",
              "0  3927       1  2014-02-01T00:01:45.162  ...  4.333815 -0.692764  0.810774\n",
              "1  3928       1  2014-02-01T00:22:57.007  ...  5.077413  0.360609  0.313402\n",
              "2  3929       1  2014-02-01T00:44:08.852  ...  5.519106  2.012830 -0.539412\n",
              "\n",
              "[3 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XOrsjj5QFRhs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = test[[\"x_sim\", \"y_sim\", \"z_sim\", \"Vx_sim\", \"Vy_sim\", \"Vz_sim\"]].values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dTmNG3IsFzeu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_freq = np.unique(test.sat_id.values, return_counts=True)[1]\n",
        "test_sat_id = np.unique(test.sat_id.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NNTuOypjLMuY",
        "colab_type": "code",
        "outputId": "b326b3c3-9662-46e4-aae9-f775a0f4ceba",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284071, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UMCru7GEZT0x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(6):\n",
        "    if i == 0:\n",
        "      knn_preds_test = knn_list[i].predict(test_data).reshape(-1, 1)\n",
        "    else:\n",
        "      knn_preds_test = np.concatenate((knn_preds_test, knn_list[i].predict(test_data).reshape(-1, 1)), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VLy3MBbibV7Q",
        "colab_type": "code",
        "outputId": "9764a38c-46cb-4695-a0e8-2d08d963627b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_data = np.concatenate((test_data, knn_preds_test), axis=1)\n",
        "test_data.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284071, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Lgu7awkQfOI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_data = scaler_sim.transform(test_data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1UitqeDqyLA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#test_data = np.concatenate((test_data, test_data**2), axis=1)\n",
        "test_data = np.concatenate((test_data, np.cos(test_data[:,:6])), axis=1)\n",
        "test_data = np.concatenate((test_data, np.sin(test_data[:,:6])), axis=1) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WriC8rUDGXVR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cut_test_data = cut(test_data, test_freq)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNph2M3wPe7U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#forthetest!\n",
        "for i,j in zip(test_sat_id, range(len(test_sat_id))):\n",
        "  cut_test_data[j] = np.concatenate((cut_data_sim[i][-window_size:], cut_test_data[j]))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9UZLgrCU7R1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "all_x_test = []\n",
        "for i in range(len(cut_test_data)):\n",
        "  regressor, _ = get_window_data(cut_test_data[i], window_size, train=False)\n",
        "  all_x_test.append(regressor)\n",
        "\n",
        "del regressor\n",
        "del cut_test_data"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ALYeVBNgg08t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "q_test = all_x_test[0]\n",
        "for i in range(1, len(all_x_test)):\n",
        "  q_test = np.concatenate((q_test, all_x_test[i]))\n",
        "\n",
        "del all_x_test"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbAfjTHdXkTJ",
        "colab_type": "code",
        "outputId": "8a8eeca6-0834-44ae-e083-90a2cf839ce8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        }
      },
      "source": [
        "sns.distplot(test_freq)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f40d01a1160>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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n8N0+uSjpTJa4GaVFizfr3MPbjs65/khf/jT8l39ykPXNlW/67J4bVy5aPSIiMn/qsYdM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\nkQhRsIuIiESIgl1ERCRCFOwiIiIR8v8DW0Aput8gC9AAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m6BaM-LqYFPq",
        "colab_type": "code",
        "outputId": "a4822897-7d54-4e38-82b8-fc845626bb72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "q_test.shape[0] == len(test)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SWG06mXLjPI3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_preds = model.predict(q_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3oZRGP34VSD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#если делать через бустинг\n",
        "for count, booster in enumerate(boost_list):\n",
        "  if count == 0:\n",
        "    test_preds = booster.predict(test_data).reshape(-1, 1)\n",
        "  else:\n",
        "    test_preds = np.concatenate((test_preds, booster.predict(test_data).reshape(-1,1)), axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rNq6e1AQ5YpF",
        "colab_type": "code",
        "outputId": "b90a38fe-1bde-40cb-dfe5-3db639397e37",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "test_preds.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(284071, 6)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vymph6eSxrOA",
        "colab_type": "code",
        "outputId": "3438c28c-d298-4fc4-f1c2-a62b7e7f830b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "np.mean(test_preds, axis=0)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.55495175, 0.57856781, 0.47322224, 0.50427423, 0.40077906,\n",
              "       0.51967696])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ae3-_CgsjSfE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "invscaled = scaler_real.inverse_transform(test_preds)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABgAeaFBleUL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub = pd.DataFrame(test_preds, columns=['x', 'y', 'z', 'Vx', 'Vy', 'Vz'])\n",
        "sub[\"id\"] = test.id"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "grjyoXY5jnGd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "cols = sub.columns.to_list()\n",
        "cols = cols[-1:] + cols[:-1]\n",
        "sub = sub[cols]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VwGj0VBQl2Op",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "sub.to_csv(\"subb.csv\", index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6wASmGjSlZqo",
        "colab_type": "code",
        "outputId": "13c8b0f7-818b-4be3-b94b-fb8016775724",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "pd.read_csv(\"subb.csv\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>x</th>\n",
              "      <th>y</th>\n",
              "      <th>z</th>\n",
              "      <th>Vx</th>\n",
              "      <th>Vy</th>\n",
              "      <th>Vz</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3927</td>\n",
              "      <td>-2109.892642</td>\n",
              "      <td>292.449431</td>\n",
              "      <td>-729.061327</td>\n",
              "      <td>2.219705</td>\n",
              "      <td>-0.107961</td>\n",
              "      <td>0.540094</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3928</td>\n",
              "      <td>-2157.807278</td>\n",
              "      <td>-570.694448</td>\n",
              "      <td>-232.188841</td>\n",
              "      <td>2.037428</td>\n",
              "      <td>0.272408</td>\n",
              "      <td>0.200921</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
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              "      <td>-1861.921036</td>\n",
              "      <td>-1042.985420</td>\n",
              "      <td>-376.103936</td>\n",
              "      <td>2.151781</td>\n",
              "      <td>1.225171</td>\n",
              "      <td>-0.252781</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3930</td>\n",
              "      <td>485.644846</td>\n",
              "      <td>-150.717719</td>\n",
              "      <td>460.781725</td>\n",
              "      <td>2.630969</td>\n",
              "      <td>1.782660</td>\n",
              "      <td>-0.621711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3931</td>\n",
              "      <td>-763.287466</td>\n",
              "      <td>-197.494688</td>\n",
              "      <td>1601.738092</td>\n",
              "      <td>1.362915</td>\n",
              "      <td>2.521799</td>\n",
              "      <td>-1.205368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>284066</th>\n",
              "      <td>1231060</td>\n",
              "      <td>-2287.311390</td>\n",
              "      <td>-107.052880</td>\n",
              "      <td>12.597190</td>\n",
              "      <td>-0.107066</td>\n",
              "      <td>-0.106313</td>\n",
              "      <td>0.029697</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284067</th>\n",
              "      <td>1231061</td>\n",
              "      <td>-2346.433044</td>\n",
              "      <td>-363.317769</td>\n",
              "      <td>12.597190</td>\n",
              "      <td>-0.190861</td>\n",
              "      <td>-0.009286</td>\n",
              "      <td>0.032700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284068</th>\n",
              "      <td>1231062</td>\n",
              "      <td>-2309.875712</td>\n",
              "      <td>-580.143413</td>\n",
              "      <td>-41.190737</td>\n",
              "      <td>-0.190861</td>\n",
              "      <td>0.357273</td>\n",
              "      <td>0.032700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284069</th>\n",
              "      <td>1231063</td>\n",
              "      <td>-2316.973991</td>\n",
              "      <td>-580.143413</td>\n",
              "      <td>-41.190737</td>\n",
              "      <td>0.101222</td>\n",
              "      <td>0.755977</td>\n",
              "      <td>-0.028559</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>284070</th>\n",
              "      <td>1231064</td>\n",
              "      <td>-2212.131038</td>\n",
              "      <td>-838.218097</td>\n",
              "      <td>-56.186299</td>\n",
              "      <td>0.553434</td>\n",
              "      <td>0.887475</td>\n",
              "      <td>-0.028559</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>284071 rows × 7 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "             id            x            y  ...        Vx        Vy        Vz\n",
              "0          3927 -2109.892642   292.449431  ...  2.219705 -0.107961  0.540094\n",
              "1          3928 -2157.807278  -570.694448  ...  2.037428  0.272408  0.200921\n",
              "2          3929 -1861.921036 -1042.985420  ...  2.151781  1.225171 -0.252781\n",
              "3          3930   485.644846  -150.717719  ...  2.630969  1.782660 -0.621711\n",
              "4          3931  -763.287466  -197.494688  ...  1.362915  2.521799 -1.205368\n",
              "...         ...          ...          ...  ...       ...       ...       ...\n",
              "284066  1231060 -2287.311390  -107.052880  ... -0.107066 -0.106313  0.029697\n",
              "284067  1231061 -2346.433044  -363.317769  ... -0.190861 -0.009286  0.032700\n",
              "284068  1231062 -2309.875712  -580.143413  ... -0.190861  0.357273  0.032700\n",
              "284069  1231063 -2316.973991  -580.143413  ...  0.101222  0.755977 -0.028559\n",
              "284070  1231064 -2212.131038  -838.218097  ...  0.553434  0.887475 -0.028559\n",
              "\n",
              "[284071 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mX3oWEo8mTRn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aOhfoqOsHiPU",
        "colab_type": "text"
      },
      "source": [
        "# Идеи"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HclaJ3h5HnTg",
        "colab_type": "text"
      },
      "source": [
        "1. Добавить предсказания кнн как признаки\n",
        "2. Построить отдельные модели для каждой из таргетных переменных\n",
        "3. Попробовать другой лосс\n",
        "4. Добавить метрику смэйп в обучение в исходных масштабах\n",
        "5. Продумать разбиение на траин-тест"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B9vUerK6coZO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}