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solution
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solution
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"# 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"
]
},
{
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"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)"
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second
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"df_test = pd.read_csv('test-A/in.tsv', names=in_columns, sep='\\t')\n",
solution
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"len(df)"
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" <td>...</td>\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",
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" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
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" <td>0</td>\n",
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" <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",
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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",
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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",
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" <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",
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" id_stacji_250190160 id_stacji_250190390 id_stacji_250210130 \\\n",
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" id_stacji_251170090 ... miesiąc_3 miesiąc_4 miesiąc_5 miesiąc_6 \\\n",
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"8755 0 ... 0 0 0 0 \n",
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"8759 0 ... 0 0 0 0 \n",
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" 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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"8755 0 1 0 0 0 0 \n",
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"\n",
"[8760 rows x 73 columns]"
]
},
new
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"execution_count": 302,
solution
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"x"
]
},
{
"cell_type": "code",
new
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"execution_count": 303,
solution
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"id": "ede98181",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" 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",
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],
"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]"
]
},
new
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"execution_count": 303,
solution
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"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",
new
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"execution_count": 304,
solution
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"id": "9a950571",
"metadata": {},
"outputs": [],
"source": [
"# Define model\n",
"model = Sequential()\n",
new
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"model.add(Dense(1024, input_dim=73, activation= \"relu\"))\n",
"model.add(Dense(512, activation= \"relu\"))\n",
"model.add(Dense(256, activation= \"relu\"))\n",
"model.add(Dense(128, activation= \"relu\"))\n",
solution
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"model.add(Dense(64, activation= \"relu\"))\n",
"model.add(Dense(32, activation= \"relu\"))\n",
second
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"model.add(Dense(16, activation= \"relu\"))\n",
solution
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"model.add(Dense(1))\n",
"#model.summary() #Print model Summary"
]
},
{
"cell_type": "code",
new
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"execution_count": 305,
solution
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"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",
new
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"execution_count": 306,
solution
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"id": "c1036c04",
"metadata": {},
"outputs": [],
"source": [
"y = np.asarray(y).astype('float32')"
]
},
{
"cell_type": "code",
new
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"execution_count": 307,
solution
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"id": "cec44474",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(None, 73) <dtype: 'float32'>\n",
"(None, 1) <dtype: 'float32'>\n",
new
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"dense_95 (None, 73) float32\n",
"dense_96 (None, 1024) float32\n",
"dense_97 (None, 512) float32\n",
"dense_98 (None, 256) float32\n",
"dense_99 (None, 128) float32\n",
"dense_100 (None, 64) float32\n",
"dense_101 (None, 32) float32\n",
"dense_102 (None, 16) float32\n"
solution
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]
},
{
"data": {
"text/plain": [
new
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"[None, None, None, None, None, None, None, None]"
solution
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]
},
new
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"execution_count": 307,
solution
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"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",
new
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"execution_count": 308,
solution
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"id": "eb9cb318",
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
new
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"Epoch 1/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 1216.5399 - mean_squared_error: 1216.5399\n",
"Epoch 2/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 794.1711 - mean_squared_error: 794.1711\n",
"Epoch 3/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 580.7461 - mean_squared_error: 580.7461\n",
"Epoch 4/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 484.1317 - mean_squared_error: 484.1317\n",
"Epoch 5/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 441.7448 - mean_squared_error: 441.7448\n",
"Epoch 6/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 392.2047 - mean_squared_error: 392.2047\n",
"Epoch 7/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 361.4105 - mean_squared_error: 361.4105\n",
"Epoch 8/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 312.9633 - mean_squared_error: 312.9633\n",
"Epoch 9/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 275.2529 - mean_squared_error: 275.2529\n",
"Epoch 10/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 246.7625 - mean_squared_error: 246.7625\n",
"Epoch 11/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 195.6685 - mean_squared_error: 195.6685\n",
"Epoch 12/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 168.8491 - mean_squared_error: 168.8491\n",
"Epoch 13/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 150.1201 - mean_squared_error: 150.1201\n",
"Epoch 14/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 122.6171 - mean_squared_error: 122.6171\n",
"Epoch 15/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 100.8923 - mean_squared_error: 100.8923\n",
"Epoch 16/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 87.8484 - mean_squared_error: 87.8484\n",
"Epoch 17/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 77.6876 - mean_squared_error: 77.6876\n",
"Epoch 18/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 63.2032 - mean_squared_error: 63.2032\n",
"Epoch 19/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 57.2543 - mean_squared_error: 57.2543\n",
"Epoch 20/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 45.0924 - mean_squared_error: 45.0924\n",
"Epoch 21/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 49.1593 - mean_squared_error: 49.1593\n",
"Epoch 22/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 58.2306 - mean_squared_error: 58.2306\n",
"Epoch 23/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 48.0242 - mean_squared_error: 48.0242\n",
"Epoch 24/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 38.6356 - mean_squared_error: 38.6356\n",
"Epoch 25/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 30.9926 - mean_squared_error: 30.9926\n",
"Epoch 26/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 29.7819 - mean_squared_error: 29.7819\n",
"Epoch 27/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 32.5139 - mean_squared_error: 32.5139\n",
"Epoch 28/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 40.1129 - mean_squared_error: 40.1129\n",
"Epoch 29/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 51.6793 - mean_squared_error: 51.6793\n",
"Epoch 30/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 37.1284 - mean_squared_error: 37.1284\n",
"Epoch 31/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 30.2074 - mean_squared_error: 30.2074\n",
"Epoch 32/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 27.1982 - mean_squared_error: 27.1982\n",
"Epoch 33/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 26.5477 - mean_squared_error: 26.5477\n",
"Epoch 34/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 25.7544 - mean_squared_error: 25.7544\n",
"Epoch 35/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 24.1754 - mean_squared_error: 24.1754\n",
"Epoch 36/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 27.5213 - mean_squared_error: 27.5213\n",
"Epoch 37/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 30.3435 - mean_squared_error: 30.3435\n",
"Epoch 38/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 32.7374 - mean_squared_error: 32.7374\n",
"Epoch 39/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 29.2545 - mean_squared_error: 29.2545\n",
"Epoch 40/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 28.4834 - mean_squared_error: 28.4834\n",
"Epoch 41/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 22.9177 - mean_squared_error: 22.9177\n",
"Epoch 42/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 21.6796 - mean_squared_error: 21.6796\n",
"Epoch 43/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 20.2429 - mean_squared_error: 20.2429\n",
"Epoch 44/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 21.2112 - mean_squared_error: 21.2112\n",
"Epoch 45/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 25.0341 - mean_squared_error: 25.0341\n",
"Epoch 46/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 22.3963 - mean_squared_error: 22.3963\n",
"Epoch 47/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 23.1122 - mean_squared_error: 23.1122\n",
"Epoch 48/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 28.0343 - mean_squared_error: 28.0343\n",
"Epoch 49/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 22.2908 - mean_squared_error: 22.2908\n",
"Epoch 50/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 21.7871 - mean_squared_error: 21.7871\n",
"Epoch 51/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 19.8841 - mean_squared_error: 19.8841\n",
"Epoch 52/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 20.5390 - mean_squared_error: 20.5390\n",
"Epoch 53/100\n",
"274/274 [==============================] - 2s 5ms/step - loss: 22.3869 - mean_squared_error: 22.3869\n",
"Epoch 54/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 20.6540 - mean_squared_error: 20.6540\n",
"Epoch 55/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 18.3056 - mean_squared_error: 18.3056\n",
"Epoch 56/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 22.7574 - mean_squared_error: 22.7574\n",
"Epoch 57/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 20.1425 - mean_squared_error: 20.1425\n",
"Epoch 58/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 17.5521 - mean_squared_error: 17.5521\n",
"Epoch 59/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 18.2735 - mean_squared_error: 18.2735\n",
"Epoch 60/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 17.6372 - mean_squared_error: 17.6372\n",
"Epoch 61/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 15.2790 - mean_squared_error: 15.2790\n",
"Epoch 62/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 12.9527 - mean_squared_error: 12.9527\n",
"Epoch 63/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 13.2732 - mean_squared_error: 13.2732\n",
"Epoch 64/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 18.0740 - mean_squared_error: 18.0740\n",
"Epoch 65/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 23.5823 - mean_squared_error: 23.5823\n",
"Epoch 66/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 22.4731 - mean_squared_error: 22.4731\n",
"Epoch 67/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 17.0889 - mean_squared_error: 17.0889\n",
"Epoch 68/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 13.5507 - mean_squared_error: 13.5507\n",
"Epoch 69/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 14.6270 - mean_squared_error: 14.6270\n",
"Epoch 70/100\n",
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"Epoch 71/100\n",
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"Epoch 72/100\n",
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"Epoch 74/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 16.4538 - mean_squared_error: 16.4538\n",
"Epoch 75/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 13.7226 - mean_squared_error: 13.7226\n",
"Epoch 76/100\n",
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"Epoch 77/100\n",
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"Epoch 78/100\n",
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"Epoch 79/100\n",
"274/274 [==============================] - 2s 7ms/step - loss: 14.9138 - mean_squared_error: 14.9138\n",
"Epoch 80/100\n",
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"Epoch 81/100\n",
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"Epoch 82/100\n",
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"Epoch 86/100\n",
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"Epoch 87/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 19.6175 - mean_squared_error: 19.6175\n",
"Epoch 88/100\n",
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"Epoch 89/100\n",
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"Epoch 90/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 12.2800 - mean_squared_error: 12.2800\n",
"Epoch 91/100\n",
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"Epoch 92/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 9.4647 - mean_squared_error: 9.4647\n",
"Epoch 93/100\n",
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"Epoch 94/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 10.2449 - mean_squared_error: 10.2449\n",
"Epoch 95/100\n",
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"Epoch 96/100\n",
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"Epoch 97/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 11.6955 - mean_squared_error: 11.6955\n",
"Epoch 98/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 11.2546 - mean_squared_error: 11.2546\n",
"Epoch 99/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 10.2126 - mean_squared_error: 10.2126\n",
"Epoch 100/100\n",
"274/274 [==============================] - 2s 6ms/step - loss: 8.5690 - mean_squared_error: 8.5690\n"
solution
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]
},
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"data": {
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new
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"<keras.callbacks.History at 0x20e6929d2e0>"
solution
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]
},
new
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"execution_count": 308,
solution
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"metadata": {},
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}
],
"source": [
"model.compile(loss= \"mean_squared_error\" , optimizer=\"adam\", metrics=[\"mean_squared_error\"])\n",
new
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"model.fit(x, y, epochs=100)"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 309,
solution
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"id": "bad4d35a",
"metadata": {},
"outputs": [],
"source": [
second
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"x_test = pd.read_csv('test-A/in.tsv', sep='\\t', names=in_columns)\n",
new
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"#y_test = pd.read_csv('test-A/expected.tsv', sep='\\t',names=['rainfall'])\n",
second
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"#x_test = x_test.drop(['nazwa_stacji', 'typ_zbioru'],axis=1)\n",
solution
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"df_train = pd.read_csv('train/in.tsv', names=in_columns, sep='\\t')"
]
},
{
"cell_type": "code",
new
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"execution_count": 310,
solution
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"id": "a3b6fff0",
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{
"data": {
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"metadata": {},
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"source": [
second
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"x_test = pd.concat([x_test,df_train])\n",
"len(x_test)"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 311,
solution
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"id": "cdf89362",
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solution
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"metadata": {},
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"source": [
second
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"x_test = x_test.drop(['nazwa_stacji', 'typ_zbioru'],axis=1)\n",
"len(x_test)"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 312,
solution
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second
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" id_stacji_251170090 ... miesiąc_3 miesiąc_4 miesiąc_5 miesiąc_6 \\\n",
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second
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"[9480 rows x 73 columns]"
solution
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]
},
new
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"execution_count": 312,
solution
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"metadata": {},
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}
],
"source": [
second
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"x_test = pd.get_dummies(x_test,columns = ['id_stacji','rok','miesiąc'])\n",
"x_test"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 313,
solution
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"id": "657a7976",
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second
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solution
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second
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second
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"<p>720 rows × 73 columns</p>\n",
solution
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solution
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solution
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"\n",
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second
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solution
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"\n",
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solution
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second
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"[720 rows x 73 columns]"
solution
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]
},
new
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"execution_count": 313,
solution
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],
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second
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"x_test = x_test.iloc[:-8760]\n",
"x_test"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 314,
solution
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"id": "1163c550",
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"text": [
new
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"23/23 [==============================] - 0s 2ms/step\n"
solution
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]
}
],
"source": [
second
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"pred= model.predict(x_test)"
solution
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]
},
{
"cell_type": "code",
new
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"execution_count": 315,
solution
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new
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"23/23 [==============================] - 0s 2ms/step\n"
solution
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]
}
],
"source": [
second
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"pred= model.predict(x_test)\n",
"out = pd.DataFrame(pred)\n",
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solution
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]
}
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Reference in New Issue Copy Permalink