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precipitation-pl/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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solution
2022-05-21 18:47:07 +02:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 5,
"id": "99f56426",
"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": 13,
"id": "10b8d271",
"metadata": {},
"outputs": [],
"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')"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "0cd1b792",
"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>id_stacji</th>\n",
" <th>rok</th>\n",
" <th>miesiąc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>249180010</td>\n",
" <td>1981</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>249180010</td>\n",
" <td>1981</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>249180010</td>\n",
" <td>1981</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>249180010</td>\n",
" <td>1981</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>249180010</td>\n",
" <td>1981</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8755</th>\n",
" <td>252230120</td>\n",
" <td>2010</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8756</th>\n",
" <td>252230120</td>\n",
" <td>2010</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8757</th>\n",
" <td>252230120</td>\n",
" <td>2010</td>\n",
" <td>10</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8758</th>\n",
" <td>252230120</td>\n",
" <td>2010</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8759</th>\n",
" <td>252230120</td>\n",
" <td>2010</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>8760 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" id_stacji rok miesiąc\n",
"0 249180010 1981 1\n",
"1 249180010 1981 2\n",
"2 249180010 1981 3\n",
"3 249180010 1981 4\n",
"4 249180010 1981 5\n",
"... ... ... ...\n",
"8755 252230120 2010 8\n",
"8756 252230120 2010 9\n",
"8757 252230120 2010 10\n",
"8758 252230120 2010 11\n",
"8759 252230120 2010 12\n",
"\n",
"[8760 rows x 3 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.drop(['nazwa_stacji','typ_zbioru'], axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "d4fbb03a",
"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",
" </tr>\n",
" <tr>\n",
" <th>8755</th>\n",
" <td>114.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8756</th>\n",
" <td>101.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8757</th>\n",
" <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": 23,
"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": "fdb79c48",
"metadata": {},
"outputs": [],
"source": [
"# Define model\n",
"model = Sequential()\n",
"model.add(Dense(500, input_dim=4, activation= \"relu\"))\n",
"model.add(Dense(100, activation= \"relu\"))\n",
"model.add(Dense(50, activation= \"relu\"))\n",
"model.add(Dense(1))\n",
"#model.summary() #Print model Summary"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "483a10d4",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "could not convert string to float: 'PSZCZYNA'",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9948/1827701549.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'float32'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;31mValueError\u001b[0m: could not convert string to float: 'PSZCZYNA'"
]
}
],
"source": [
"df['id_stacji'] = np.asarray(df['id_stacji']).astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "9d7f9ad2",
"metadata": {},
"outputs": [],
"source": [
"y = np.asarray(y).astype('float32')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "10e71137",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(None, 4) <dtype: 'float32'>\n",
"(None, 1) <dtype: 'float32'>\n",
"dense (None, 4) float32\n",
"dense_1 (None, 500) float32\n",
"dense_2 (None, 100) float32\n",
"dense_3 (None, 50) float32\n"
]
},
{
"data": {
"text/plain": [
"[None, None, None, None]"
]
},
"execution_count": 25,
"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": 27,
"id": "827c4d74",
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "Failed to convert a NumPy array to a Tensor (Unsupported object type int).",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_9948/1196997991.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcompile\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloss\u001b[0m\u001b[1;33m=\u001b[0m \u001b[1;34m\"mean_squared_error\"\u001b[0m \u001b[1;33m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"adam\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmetrics\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"mean_squared_error\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mmodel\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\keras\\utils\\traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m 65\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pylint: disable=broad-except\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 66\u001b[0m \u001b[0mfiltered_tb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 67\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfiltered_tb\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 68\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 69\u001b[0m \u001b[1;32mdel\u001b[0m \u001b[0mfiltered_tb\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\constant_op.py\u001b[0m in \u001b[0;36mconvert_to_eager_tensor\u001b[1;34m(value, ctx, dtype)\u001b[0m\n\u001b[0;32m 100\u001b[0m \u001b[0mdtype\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdtypes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_datatype_enum\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 101\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 102\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mEagerTensor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mctx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdevice_name\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 103\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 104\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: Failed to convert a NumPy array to a Tensor (Unsupported object type int)."
]
}
],
"source": [
"model.compile(loss= \"mean_squared_error\" , optimizer=\"adam\", metrics=[\"mean_squared_error\"])\n",
"model.fit(df, y, epochs=20)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f53bf8ef",
"metadata": {},
"outputs": [],
"source": [
"pred_train= model.predict(X_train)\n",
"print(np.sqrt(mean_squared_error(y_train,pred_train)))\n",
"\n",
"pred= model.predict(X_test)\n",
"print(np.sqrt(mean_squared_error(y_test,pred))) "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"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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