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  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "ddcaf12b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required libraries\n",
    "import pandas as pd\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt\n",
    "import sklearn\n",
    "\n",
    "# Import necessary modules\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from math import sqrt\n",
    "\n",
    "# Keras specific\n",
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "70e3b6e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9360"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "in_columns = ['id_stacji', 'nazwa_stacji', 'typ_zbioru', 'rok', 'miesiąc']\n",
    "\n",
    "df = pd.read_csv('train/in.tsv', names=in_columns, sep='\\t')\n",
    "df2 = pd.read_csv('dev-0/in.tsv', names=in_columns, sep='\\t')\n",
    "df = pd.concat([df, df2])\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "44f404d6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "720"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test = pd.read_csv('test-A/in.tsv', names=in_columns, sep='\\t')\n",
    "len(df_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "c760402a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10080"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([df,df_test])\n",
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "06f39e15",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop(['nazwa_stacji','typ_zbioru'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "91c047f6",
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = pd.get_dummies(df,columns = ['id_stacji','rok','miesiąc'])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "037f1315",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.iloc[:-720]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "e03bae07",
   "metadata": {},
   "outputs": [
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>9360 rows × 73 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_stacji_249180010  id_stacji_249190560  id_stacji_249200370  \\\n",
       "0                      1                    0                    0   \n",
       "1                      1                    0                    0   \n",
       "2                      1                    0                    0   \n",
       "3                      1                    0                    0   \n",
       "4                      1                    0                    0   \n",
       "..                   ...                  ...                  ...   \n",
       "595                    0                    0                    0   \n",
       "596                    0                    0                    0   \n",
       "597                    0                    0                    0   \n",
       "598                    0                    0                    0   \n",
       "599                    0                    0                    0   \n",
       "\n",
       "     id_stacji_249200490  id_stacji_249220150  id_stacji_249220180  \\\n",
       "0                      0                    0                    0   \n",
       "1                      0                    0                    0   \n",
       "2                      0                    0                    0   \n",
       "3                      0                    0                    0   \n",
       "4                      0                    0                    0   \n",
       "..                   ...                  ...                  ...   \n",
       "595                    0                    0                    0   \n",
       "596                    0                    0                    0   \n",
       "597                    0                    0                    0   \n",
       "598                    0                    0                    0   \n",
       "599                    0                    0                    0   \n",
       "\n",
       "     id_stacji_250190160  id_stacji_250190390  id_stacji_250210130  \\\n",
       "0                      0                    0                    0   \n",
       "1                      0                    0                    0   \n",
       "2                      0                    0                    0   \n",
       "3                      0                    0                    0   \n",
       "4                      0                    0                    0   \n",
       "..                   ...                  ...                  ...   \n",
       "595                    0                    0                    0   \n",
       "596                    0                    0                    0   \n",
       "597                    0                    0                    0   \n",
       "598                    0                    0                    0   \n",
       "599                    0                    0                    0   \n",
       "\n",
       "     id_stacji_251170090  ...  miesiąc_3  miesiąc_4  miesiąc_5  miesiąc_6  \\\n",
       "0                      0  ...          0          0          0          0   \n",
       "1                      0  ...          0          0          0          0   \n",
       "2                      0  ...          1          0          0          0   \n",
       "3                      0  ...          0          1          0          0   \n",
       "4                      0  ...          0          0          1          0   \n",
       "..                   ...  ...        ...        ...        ...        ...   \n",
       "595                    0  ...          0          0          0          0   \n",
       "596                    0  ...          0          0          0          0   \n",
       "597                    0  ...          0          0          0          0   \n",
       "598                    0  ...          0          0          0          0   \n",
       "599                    0  ...          0          0          0          0   \n",
       "\n",
       "     miesiąc_7  miesiąc_8  miesiąc_9  miesiąc_10  miesiąc_11  miesiąc_12  \n",
       "0            0          0          0           0           0           0  \n",
       "1            0          0          0           0           0           0  \n",
       "2            0          0          0           0           0           0  \n",
       "3            0          0          0           0           0           0  \n",
       "4            0          0          0           0           0           0  \n",
       "..         ...        ...        ...         ...         ...         ...  \n",
       "595          0          1          0           0           0           0  \n",
       "596          0          0          1           0           0           0  \n",
       "597          0          0          0           1           0           0  \n",
       "598          0          0          0           0           1           0  \n",
       "599          0          0          0           0           0           1  \n",
       "\n",
       "[9360 rows x 73 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "ede98181",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = pd.read_csv('train/expected.tsv', sep='\\t', names=['rainfall'])\n",
    "y2 = pd.read_csv('dev-0/expected.tsv', sep='\\t', names=['rainfall'])\n",
    "#y = np.array(y).reshape(1,-1)\n",
    "y = pd.concat([y,y2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "9a950571",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define model\n",
    "model = Sequential()\n",
    "model.add(Dense(2048, input_dim=73, activation= \"relu\"))\n",
    "model.add(Dense(1024, activation= \"relu\"))\n",
    "model.add(Dense(512, activation= \"relu\"))\n",
    "model.add(Dense(256, activation= \"relu\"))\n",
    "model.add(Dense(128, activation= \"relu\"))\n",
    "model.add(Dense(64, activation= \"relu\"))\n",
    "model.add(Dense(32, activation= \"relu\"))\n",
    "model.add(Dense(16, activation= \"relu\"))\n",
    "model.add(Dense(1))\n",
    "#model.summary() #Print model Summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "f68e43f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['id_stacji'] = np.asarray(df['id_stacji']).astype('float32')\n",
    "df['rok'] = np.asarray(df['rok']).astype('float32')\n",
    "df['miesiąc'] = np.asarray(df['miesiąc']).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "c1036c04",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.asarray(y).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "cec44474",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(None, 73) <dtype: 'float32'>\n",
      "(None, 1) <dtype: 'float32'>\n",
      "dense_44 (None, 73) float32\n",
      "dense_45 (None, 2048) float32\n",
      "dense_46 (None, 1024) float32\n",
      "dense_47 (None, 512) float32\n",
      "dense_48 (None, 256) float32\n",
      "dense_49 (None, 128) float32\n",
      "dense_50 (None, 64) float32\n",
      "dense_51 (None, 32) float32\n",
      "dense_52 (None, 16) float32\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(i.shape, i.dtype) for i in model.inputs]\n",
    "[print(o.shape, o.dtype) for o in model.outputs]\n",
    "[print(l.name, l.input_shape, l.dtype) for l in model.layers]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "eb9cb318",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 1108.6758 - mean_squared_error: 1108.6758\n",
      "Epoch 2/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 671.0632 - mean_squared_error: 671.0632\n",
      "Epoch 3/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 536.2025 - mean_squared_error: 536.2025\n",
      "Epoch 4/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 457.3617 - mean_squared_error: 457.3617\n",
      "Epoch 5/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 406.1862 - mean_squared_error: 406.1862\n",
      "Epoch 6/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 369.4316 - mean_squared_error: 369.4316\n",
      "Epoch 7/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 312.5139 - mean_squared_error: 312.5139\n",
      "Epoch 8/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 270.2833 - mean_squared_error: 270.2833\n",
      "Epoch 9/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 223.4037 - mean_squared_error: 223.4037\n",
      "Epoch 10/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 179.4202 - mean_squared_error: 179.4202\n",
      "Epoch 11/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 143.8777 - mean_squared_error: 143.8777\n",
      "Epoch 12/80\n",
      "293/293 [==============================] - 5s 18ms/step - loss: 135.4522 - mean_squared_error: 135.4522\n",
      "Epoch 13/80\n",
      "293/293 [==============================] - 5s 18ms/step - loss: 109.2838 - mean_squared_error: 109.2838\n",
      "Epoch 14/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 88.6090 - mean_squared_error: 88.6090\n",
      "Epoch 15/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 69.3139 - mean_squared_error: 69.3139\n",
      "Epoch 16/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 67.1195 - mean_squared_error: 67.1195\n",
      "Epoch 17/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 59.6054 - mean_squared_error: 59.6054\n",
      "Epoch 18/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 50.4958 - mean_squared_error: 50.4958\n",
      "Epoch 19/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 41.2413 - mean_squared_error: 41.2413\n",
      "Epoch 20/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 35.0757 - mean_squared_error: 35.0757\n",
      "Epoch 21/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 43.3807 - mean_squared_error: 43.3807\n",
      "Epoch 22/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 48.1348 - mean_squared_error: 48.1348\n",
      "Epoch 23/80\n",
      "293/293 [==============================] - 5s 18ms/step - loss: 52.9108 - mean_squared_error: 52.9108\n",
      "Epoch 24/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 40.8023 - mean_squared_error: 40.8023\n",
      "Epoch 25/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 35.4987 - mean_squared_error: 35.4987\n",
      "Epoch 26/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 35.0609 - mean_squared_error: 35.0609\n",
      "Epoch 27/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 39.9937 - mean_squared_error: 39.9937\n",
      "Epoch 28/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 29.5927 - mean_squared_error: 29.5927\n",
      "Epoch 29/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 33.4916 - mean_squared_error: 33.4916\n",
      "Epoch 30/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 37.4889 - mean_squared_error: 37.4889\n",
      "Epoch 31/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 36.7416 - mean_squared_error: 36.7416\n",
      "Epoch 32/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 34.1706 - mean_squared_error: 34.1706\n",
      "Epoch 33/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 29.5588 - mean_squared_error: 29.5588\n",
      "Epoch 34/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 35.8357 - mean_squared_error: 35.8357\n",
      "Epoch 35/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 33.4907 - mean_squared_error: 33.4907\n",
      "Epoch 36/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 26.6265 - mean_squared_error: 26.6265\n",
      "Epoch 37/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 23.5669 - mean_squared_error: 23.5669\n",
      "Epoch 38/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.1027 - mean_squared_error: 20.1027\n",
      "Epoch 39/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 19.0630 - mean_squared_error: 19.0630\n",
      "Epoch 40/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 22.2653 - mean_squared_error: 22.2653\n",
      "Epoch 41/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 28.3499 - mean_squared_error: 28.3499\n",
      "Epoch 42/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 30.2943 - mean_squared_error: 30.2943\n",
      "Epoch 43/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 30.8464 - mean_squared_error: 30.8464\n",
      "Epoch 44/80\n",
      "293/293 [==============================] - 5s 18ms/step - loss: 25.8581 - mean_squared_error: 25.8581\n",
      "Epoch 45/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 22.0973 - mean_squared_error: 22.0973\n",
      "Epoch 46/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 20.3286 - mean_squared_error: 20.3286\n",
      "Epoch 47/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.7386 - mean_squared_error: 20.7386\n",
      "Epoch 48/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.1520 - mean_squared_error: 20.1520\n",
      "Epoch 49/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 21.0666 - mean_squared_error: 21.0666\n",
      "Epoch 50/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.2202 - mean_squared_error: 20.2202\n",
      "Epoch 51/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.7954 - mean_squared_error: 20.7954\n",
      "Epoch 52/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.0701 - mean_squared_error: 16.0701\n",
      "Epoch 53/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.0172 - mean_squared_error: 16.0172\n",
      "Epoch 54/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.2924 - mean_squared_error: 16.2924\n",
      "Epoch 55/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.6287 - mean_squared_error: 16.6287\n",
      "Epoch 56/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.3168 - mean_squared_error: 16.3168\n",
      "Epoch 57/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 18.8847 - mean_squared_error: 18.8847\n",
      "Epoch 58/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 20.1943 - mean_squared_error: 20.1943\n",
      "Epoch 59/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 22.6101 - mean_squared_error: 22.6101\n",
      "Epoch 60/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 19.8736 - mean_squared_error: 19.8736\n",
      "Epoch 61/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 19.8947 - mean_squared_error: 19.8947\n",
      "Epoch 62/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 19.0540 - mean_squared_error: 19.0540\n",
      "Epoch 63/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 18.1842 - mean_squared_error: 18.1842\n",
      "Epoch 64/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 15.4725 - mean_squared_error: 15.4725\n",
      "Epoch 65/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.2298 - mean_squared_error: 16.2298\n",
      "Epoch 66/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 13.2303 - mean_squared_error: 13.2303\n",
      "Epoch 67/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 14.2212 - mean_squared_error: 14.2212\n",
      "Epoch 68/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 12.7895 - mean_squared_error: 12.7895\n",
      "Epoch 69/80\n",
      "293/293 [==============================] - 5s 18ms/step - loss: 15.7551 - mean_squared_error: 15.7551\n",
      "Epoch 70/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 18.4030 - mean_squared_error: 18.4030\n",
      "Epoch 71/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 16.0214 - mean_squared_error: 16.0214\n",
      "Epoch 72/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 12.3694 - mean_squared_error: 12.3694\n",
      "Epoch 73/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 10.5107 - mean_squared_error: 10.5107\n",
      "Epoch 74/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 11.7746 - mean_squared_error: 11.7746\n",
      "Epoch 75/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 11.6589 - mean_squared_error: 11.6589\n",
      "Epoch 76/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 14.1691 - mean_squared_error: 14.1691\n",
      "Epoch 77/80\n",
      "293/293 [==============================] - 5s 16ms/step - loss: 15.9365 - mean_squared_error: 15.9365\n",
      "Epoch 78/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 15.4616 - mean_squared_error: 15.4616\n",
      "Epoch 79/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 13.2958 - mean_squared_error: 13.2958\n",
      "Epoch 80/80\n",
      "293/293 [==============================] - 5s 17ms/step - loss: 11.3826 - mean_squared_error: 11.3826\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x1b61bab69d0>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(loss= \"mean_squared_error\" , optimizer=\"adam\", metrics=[\"mean_squared_error\"])\n",
    "model.fit(x, y, epochs=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "bad4d35a",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_test = pd.read_csv('test-A/in.tsv', sep='\\t', names=in_columns)\n",
    "#y_test = pd.read_csv('test-A/expected.tsv', sep='\\t',names=['rainfall'])\n",
    "#x_test = x_test.drop(['nazwa_stacji', 'typ_zbioru'],axis=1)\n",
    "#df_train = pd.read_csv('train/in.tsv', names=in_columns, sep='\\t')\n",
    "df_train = pd.read_csv('train/in.tsv', names=in_columns, sep='\\t')\n",
    "df2_train = pd.read_csv('dev-0/in.tsv', names=in_columns, sep='\\t')\n",
    "df_train = pd.concat([df_train, df2_train])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "a3b6fff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10080"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = pd.concat([x_test,df_train])\n",
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "cdf89362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10080"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = x_test.drop(['nazwa_stacji', 'typ_zbioru'],axis=1)\n",
    "len(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "fe00b876",
   "metadata": {},
   "outputs": [
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       "     id_stacji_249180010  id_stacji_249190560  id_stacji_249200370  \\\n",
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       "\n",
       "     id_stacji_251170090  ...  miesiąc_3  miesiąc_4  miesiąc_5  miesiąc_6  \\\n",
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       "[10080 rows x 73 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = pd.get_dummies(x_test,columns = ['id_stacji','rok','miesiąc'])\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "657a7976",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "     id_stacji_249180010  id_stacji_249190560  id_stacji_249200370  \\\n",
       "0                      0                    0                    1   \n",
       "1                      0                    0                    1   \n",
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       "..                   ...                  ...                  ...   \n",
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       "716                    0                    0                    0   \n",
       "717                    0                    0                    0   \n",
       "718                    0                    0                    0   \n",
       "719                    0                    0                    0   \n",
       "\n",
       "     id_stacji_249200490  id_stacji_249220150  id_stacji_249220180  \\\n",
       "0                      0                    0                    0   \n",
       "1                      0                    0                    0   \n",
       "2                      0                    0                    0   \n",
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       "..                   ...                  ...                  ...   \n",
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       "718                    0                    0                    0   \n",
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       "\n",
       "     id_stacji_250190160  id_stacji_250190390  id_stacji_250210130  \\\n",
       "0                      0                    0                    0   \n",
       "1                      0                    0                    0   \n",
       "2                      0                    0                    0   \n",
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       "4                      0                    0                    0   \n",
       "..                   ...                  ...                  ...   \n",
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       "717                    0                    0                    0   \n",
       "718                    0                    0                    0   \n",
       "719                    0                    0                    0   \n",
       "\n",
       "     id_stacji_251170090  ...  miesiąc_3  miesiąc_4  miesiąc_5  miesiąc_6  \\\n",
       "0                      0  ...          0          0          0          0   \n",
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       "..                   ...  ...        ...        ...        ...        ...   \n",
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       "\n",
       "     miesiąc_7  miesiąc_8  miesiąc_9  miesiąc_10  miesiąc_11  miesiąc_12  \n",
       "0            0          0          0           0           0           0  \n",
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       "..         ...        ...        ...         ...         ...         ...  \n",
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       "\n",
       "[720 rows x 73 columns]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test = x_test.iloc[:-9360]\n",
    "x_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "1163c550",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "pred= model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "6c24ee76",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23/23 [==============================] - 0s 4ms/step\n"
     ]
    }
   ],
   "source": [
    "pred= model.predict(x_test)\n",
    "out = pd.DataFrame(pred)\n",
    "out.to_csv('test-A/out.tsv',sep='\\t',header=False, index=False)"
   ]
  }
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