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precipitation-pl/solution.ipynb
Tomasz Grzybowski 1e2d6f554a second
2022-05-21 19:36:08 +02:00

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"cells": [
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"cell_type": "code",
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"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": 2,
"id": "70e3b6e3",
"metadata": {},
"outputs": [
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"execution_count": 2,
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"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",
"len(df)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "44f404d6",
"metadata": {},
"outputs": [
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"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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],
"source": [
"df_test = pd.read_csv('test-A/in.tsv', names=in_columns, sep='\\t')\n",
"len(df_test)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c760402a",
"metadata": {},
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"source": [
"df = pd.concat([df,df_test])\n",
"len(df)"
]
},
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"execution_count": 5,
"id": "06f39e15",
"metadata": {},
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"source": [
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"x"
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},
{
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"execution_count": 7,
"id": "037f1315",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <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>8756</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>...</td>\n",
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" <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>8757</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",
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" <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>8758</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",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8759</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",
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" <td>0</td>\n",
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" <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>8760 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",
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"3 1 0 0 \n",
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"8759 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",
"8755 0 ... 0 0 0 0 \n",
"8756 0 ... 0 0 0 0 \n",
"8757 0 ... 0 0 0 0 \n",
"8758 0 ... 0 0 0 0 \n",
"8759 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",
"8755 0 1 0 0 0 0 \n",
"8756 0 0 1 0 0 0 \n",
"8757 0 0 0 1 0 0 \n",
"8758 0 0 0 0 1 0 \n",
"8759 0 0 0 0 0 1 \n",
"\n",
"[8760 rows x 73 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "ede98181",
"metadata": {},
"outputs": [
{
"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>rainfall</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>19.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>43.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>72.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>25.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>89.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
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" <tr>\n",
" <th>8755</th>\n",
" <td>114.9</td>\n",
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" <tr>\n",
" <th>8756</th>\n",
" <td>101.2</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>20.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8758</th>\n",
" <td>93.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8759</th>\n",
" <td>46.9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8760 rows × 1 columns</p>\n",
"</div>"
],
"text/plain": [
" rainfall\n",
"0 19.4\n",
"1 43.2\n",
"2 72.2\n",
"3 25.3\n",
"4 89.3\n",
"... ...\n",
"8755 114.9\n",
"8756 101.2\n",
"8757 20.4\n",
"8758 93.2\n",
"8759 46.9\n",
"\n",
"[8760 rows x 1 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = pd.read_csv('train/expected.tsv', sep='\\t', names=['rainfall'])\n",
"#y = np.array(y).reshape(1,-1)\n",
"y"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "9a950571",
"metadata": {},
"outputs": [],
"source": [
"# Define model\n",
"model = Sequential()\n",
"model.add(Dense(16, input_dim=73, activation= \"relu\"))\n",
"model.add(Dense(32, 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": 11,
"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": 12,
"id": "c1036c04",
"metadata": {},
"outputs": [],
"source": [
"y = np.asarray(y).astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "cec44474",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(None, 73) <dtype: 'float32'>\n",
"(None, 1) <dtype: 'float32'>\n",
"dense (None, 73) float32\n",
"dense_1 (None, 16) float32\n",
"dense_2 (None, 32) float32\n",
"dense_3 (None, 64) float32\n",
"dense_4 (None, 32) float32\n",
"dense_5 (None, 16) float32\n"
]
},
{
"data": {
"text/plain": [
"[None, None, None, None, None, None]"
]
},
"execution_count": 13,
"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": 14,
"id": "eb9cb318",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/100\n",
"274/274 [==============================] - 1s 1ms/step - loss: 1904.0205 - mean_squared_error: 1904.0205\n",
"Epoch 2/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 977.0018 - mean_squared_error: 977.0018\n",
"Epoch 3/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 930.0125 - mean_squared_error: 930.0125\n",
"Epoch 4/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 902.6553 - mean_squared_error: 902.6553\n",
"Epoch 5/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 863.2485 - mean_squared_error: 863.2485\n",
"Epoch 6/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 811.9504 - mean_squared_error: 811.9504\n",
"Epoch 7/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 770.9260 - mean_squared_error: 770.9260\n",
"Epoch 8/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 724.6091 - mean_squared_error: 724.6091\n",
"Epoch 9/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 692.6209 - mean_squared_error: 692.6209\n",
"Epoch 10/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 659.7095 - mean_squared_error: 659.7095\n",
"Epoch 11/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 625.7371 - mean_squared_error: 625.7371\n",
"Epoch 12/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 602.4116 - mean_squared_error: 602.4116\n",
"Epoch 13/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 577.0346 - mean_squared_error: 577.0346\n",
"Epoch 14/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 552.9323 - mean_squared_error: 552.9323\n",
"Epoch 15/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 529.7372 - mean_squared_error: 529.7372\n",
"Epoch 16/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 515.2844 - mean_squared_error: 515.2844\n",
"Epoch 17/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 501.1700 - mean_squared_error: 501.1700\n",
"Epoch 18/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 489.9219 - mean_squared_error: 489.9219\n",
"Epoch 19/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 484.0696 - mean_squared_error: 484.0696\n",
"Epoch 20/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 470.3400 - mean_squared_error: 470.3400\n",
"Epoch 21/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 459.1194 - mean_squared_error: 459.1194\n",
"Epoch 22/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 455.5881 - mean_squared_error: 455.5881\n",
"Epoch 23/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 446.4247 - mean_squared_error: 446.4247\n",
"Epoch 24/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 440.6260 - mean_squared_error: 440.6260\n",
"Epoch 25/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 434.9443 - mean_squared_error: 434.9443\n",
"Epoch 26/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 429.9223 - mean_squared_error: 429.9223\n",
"Epoch 27/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 424.0781 - mean_squared_error: 424.0781\n",
"Epoch 28/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 420.9750 - mean_squared_error: 420.9750\n",
"Epoch 29/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 416.1357 - mean_squared_error: 416.1357\n",
"Epoch 30/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 409.1339 - mean_squared_error: 409.1339\n",
"Epoch 31/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 404.7644 - mean_squared_error: 404.7644\n",
"Epoch 32/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 403.4354 - mean_squared_error: 403.4354\n",
"Epoch 33/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 398.6223 - mean_squared_error: 398.6223\n",
"Epoch 34/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 391.9509 - mean_squared_error: 391.9509\n",
"Epoch 35/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 391.3186 - mean_squared_error: 391.3186\n",
"Epoch 36/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 388.1175 - mean_squared_error: 388.1175\n",
"Epoch 37/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 385.9730 - mean_squared_error: 385.9730\n",
"Epoch 38/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 382.0468 - mean_squared_error: 382.0468\n",
"Epoch 39/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 376.9197 - mean_squared_error: 376.9197\n",
"Epoch 40/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 378.0434 - mean_squared_error: 378.0434\n",
"Epoch 41/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 372.7451 - mean_squared_error: 372.7451\n",
"Epoch 42/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 368.2292 - mean_squared_error: 368.2292\n",
"Epoch 43/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 369.8233 - mean_squared_error: 369.8233\n",
"Epoch 44/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 365.3695 - mean_squared_error: 365.3695\n",
"Epoch 45/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 363.1947 - mean_squared_error: 363.1947\n",
"Epoch 46/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 358.6509 - mean_squared_error: 358.6509\n",
"Epoch 47/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 363.4928 - mean_squared_error: 363.4928\n",
"Epoch 48/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 359.9735 - mean_squared_error: 359.9735\n",
"Epoch 49/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 353.2738 - mean_squared_error: 353.2738\n",
"Epoch 50/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 350.3524 - mean_squared_error: 350.3524\n",
"Epoch 51/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 349.1338 - mean_squared_error: 349.1338\n",
"Epoch 52/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 351.0474 - mean_squared_error: 351.0474\n",
"Epoch 53/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 341.8802 - mean_squared_error: 341.8802\n",
"Epoch 54/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 341.5201 - mean_squared_error: 341.5201\n",
"Epoch 55/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 339.8927 - mean_squared_error: 339.8927\n",
"Epoch 56/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 337.5977 - mean_squared_error: 337.5977\n",
"Epoch 57/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 341.8250 - mean_squared_error: 341.8250\n",
"Epoch 58/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 334.7910 - mean_squared_error: 334.7910\n",
"Epoch 59/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 333.3398 - mean_squared_error: 333.3398\n",
"Epoch 60/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 330.1293 - mean_squared_error: 330.1293\n",
"Epoch 61/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 331.5085 - mean_squared_error: 331.5085\n",
"Epoch 62/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 327.4076 - mean_squared_error: 327.4076\n",
"Epoch 63/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 328.1978 - mean_squared_error: 328.1978\n",
"Epoch 64/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 322.5495 - mean_squared_error: 322.5495\n",
"Epoch 65/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 324.4060 - mean_squared_error: 324.4060\n",
"Epoch 66/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 319.2129 - mean_squared_error: 319.2129\n",
"Epoch 67/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 320.8315 - mean_squared_error: 320.8315\n",
"Epoch 68/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 315.9987 - mean_squared_error: 315.9987\n",
"Epoch 69/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 314.6494 - mean_squared_error: 314.6494\n",
"Epoch 70/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 310.7572 - mean_squared_error: 310.7572\n",
"Epoch 71/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 310.8293 - mean_squared_error: 310.8293\n",
"Epoch 72/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 310.2863 - mean_squared_error: 310.2863\n",
"Epoch 73/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 309.2907 - mean_squared_error: 309.2907\n",
"Epoch 74/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 306.9155 - mean_squared_error: 306.9155\n",
"Epoch 75/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 304.8138 - mean_squared_error: 304.8138\n",
"Epoch 76/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 303.4693 - mean_squared_error: 303.4693\n",
"Epoch 77/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 302.1253 - mean_squared_error: 302.1253\n",
"Epoch 78/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 300.5882 - mean_squared_error: 300.5882\n",
"Epoch 79/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 300.8849 - mean_squared_error: 300.8849\n",
"Epoch 80/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 297.9424 - mean_squared_error: 297.9424\n",
"Epoch 81/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 296.6845 - mean_squared_error: 296.6845\n",
"Epoch 82/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 301.2429 - mean_squared_error: 301.2429\n",
"Epoch 83/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 294.7325 - mean_squared_error: 294.7325\n",
"Epoch 84/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 293.9087 - mean_squared_error: 293.9087\n",
"Epoch 85/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 294.8573 - mean_squared_error: 294.8573\n",
"Epoch 86/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 291.5350 - mean_squared_error: 291.5350\n",
"Epoch 87/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 288.5298 - mean_squared_error: 288.5298\n",
"Epoch 88/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 290.0951 - mean_squared_error: 290.0951\n",
"Epoch 89/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 286.3828 - mean_squared_error: 286.3828\n",
"Epoch 90/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 282.4638 - mean_squared_error: 282.4638\n",
"Epoch 91/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 290.5275 - mean_squared_error: 290.5275\n",
"Epoch 92/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 282.0305 - mean_squared_error: 282.0305\n",
"Epoch 93/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 281.5406 - mean_squared_error: 281.5406\n",
"Epoch 94/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 287.6223 - mean_squared_error: 287.6223\n",
"Epoch 95/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 277.7972 - mean_squared_error: 277.7972\n",
"Epoch 96/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 279.9403 - mean_squared_error: 279.9403\n",
"Epoch 97/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 275.0088 - mean_squared_error: 275.0088\n",
"Epoch 98/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 276.8479 - mean_squared_error: 276.8479\n",
"Epoch 99/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 275.8300 - mean_squared_error: 275.8300\n",
"Epoch 100/100\n",
"274/274 [==============================] - 0s 1ms/step - loss: 274.4589 - mean_squared_error: 274.4589\n"
]
},
{
"data": {
"text/plain": [
"<keras.callbacks.History at 0x2ae269e2610>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.compile(loss= \"mean_squared_error\" , optimizer=\"adam\", metrics=[\"mean_squared_error\"])\n",
"model.fit(x, y, epochs=100)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "b01ccebe",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"17.28555466278129"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import math\n",
"math.sqrt(298.7904)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"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('dev-0/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')"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "a3b6fff0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9480"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test = pd.concat([x_test,df_train])\n",
"len(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "cdf89362",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9480"
]
},
"execution_count": 18,
"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": 19,
"id": "fe00b876",
"metadata": {},
"outputs": [
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},
"execution_count": 19,
"metadata": {},
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],
"source": [
"x_test = pd.get_dummies(x_test,columns = ['id_stacji','rok','miesiąc'])\n",
"x_test"
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},
{
"cell_type": "code",
"execution_count": 20,
"id": "657a7976",
"metadata": {},
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" </tr>\n",
" <tr>\n",
" <th>715</th>\n",
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" </tr>\n",
" <tr>\n",
" <th>716</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>717</th>\n",
" <td>0</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>718</th>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>719</th>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>720 rows × 73 columns</p>\n",
"</div>"
],
"text/plain": [
" id_stacji_249180010 id_stacji_249190560 id_stacji_249200370 \\\n",
"0 0 0 1 \n",
"1 0 0 1 \n",
"2 0 0 1 \n",
"3 0 0 1 \n",
"4 0 0 1 \n",
".. ... ... ... \n",
"715 0 0 0 \n",
"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",
"3 0 0 0 \n",
"4 0 0 0 \n",
".. ... ... ... \n",
"715 0 0 0 \n",
"716 0 0 0 \n",
"717 0 0 0 \n",
"718 0 0 0 \n",
"719 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",
"715 0 0 0 \n",
"716 0 0 0 \n",
"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",
"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",
"715 0 ... 0 0 0 0 \n",
"716 0 ... 0 0 0 0 \n",
"717 0 ... 0 0 0 0 \n",
"718 0 ... 0 0 0 0 \n",
"719 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",
"715 0 1 0 0 0 0 \n",
"716 0 0 1 0 0 0 \n",
"717 0 0 0 1 0 0 \n",
"718 0 0 0 0 1 0 \n",
"719 0 0 0 0 0 1 \n",
"\n",
"[720 rows x 73 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x_test = x_test.iloc[:-8760]\n",
"x_test"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "1163c550",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23/23 [==============================] - 0s 909us/step\n"
]
}
],
"source": [
"pred= model.predict(x_test)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "6c24ee76",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"23/23 [==============================] - 0s 955us/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)"
]
}
],
"metadata": {
"interpreter": {
"hash": "754a2b6bedec8aae7cfc361a819067f3f72b778cb88f366be5c7fdc236f21674"
},
"kernelspec": {
"display_name": "Python 3.9.7 ('base')",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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