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Update notebook.ipynb

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pull/384/head
Anirban Mukherjee 4 years ago
parent 7e1bb8495c
commit f2f16e37e3
1 changed files with 45 additions and 75 deletions
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7-TimeSeries/3-SVR/solution/notebook.ipynb
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@ -18,7 +18,7 @@
},
{
"cell_type": "code",
"execution_count": 57,
"execution_count": 1,
"metadata": {
"id": "M687KNlQFp0-"
},
@ -30,20 +30,11 @@
"import numpy as np\n",
"import pandas as pd\n",
"import datetime as dt\n",
"import math\n",
"\n",
"from common.utils import load_data, mape"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"id": "FvR0MjVlLf_S"
},
"outputs": [],
"source": [
"from sklearn.svm import SVR\n",
"from sklearn.preprocessing import MinMaxScaler\n",
"from sklearn.svm import SVR"
"from common.utils import load_data, mape"
]
},
{
@ -66,7 +57,7 @@
},
{
"cell_type": "code",
"execution_count": 136,
"execution_count": 3,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -121,26 +112,6 @@
" <th>2012-01-01 04:00:00</th>\n",
" <td>2403.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 05:00:00</th>\n",
" <td>2453.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 06:00:00</th>\n",
" <td>2560.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 07:00:00</th>\n",
" <td>2719.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 08:00:00</th>\n",
" <td>2916.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2012-01-01 09:00:00</th>\n",
" <td>3105.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
@ -151,22 +122,17 @@
"2012-01-01 01:00:00 2558.0\n",
"2012-01-01 02:00:00 2444.0\n",
"2012-01-01 03:00:00 2402.0\n",
"2012-01-01 04:00:00 2403.0\n",
"2012-01-01 05:00:00 2453.0\n",
"2012-01-01 06:00:00 2560.0\n",
"2012-01-01 07:00:00 2719.0\n",
"2012-01-01 08:00:00 2916.0\n",
"2012-01-01 09:00:00 3105.0"
"2012-01-01 04:00:00 2403.0"
]
},
"execution_count": 136,
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"energy = load_data('./data')[['load']]\n",
"energy.head(10)"
"energy.head(5)"
]
},
{
@ -180,7 +146,7 @@
},
{
"cell_type": "code",
"execution_count": 137,
"execution_count": 5,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -221,7 +187,7 @@
},
{
"cell_type": "code",
"execution_count": 138,
"execution_count": 6,
"metadata": {
"id": "ysvsNyONGt0Q"
},
@ -233,7 +199,7 @@
},
{
"cell_type": "code",
"execution_count": 139,
"execution_count": 7,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -283,7 +249,7 @@
},
{
"cell_type": "code",
"execution_count": 140,
"execution_count": 8,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -318,7 +284,7 @@
},
{
"cell_type": "code",
"execution_count": 141,
"execution_count": 9,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -386,7 +352,7 @@
"2014-11-01 04:00:00 0.059087"
]
},
"execution_count": 141,
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
@ -399,7 +365,7 @@
},
{
"cell_type": "code",
"execution_count": 142,
"execution_count": 10,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -467,7 +433,7 @@
"2014-12-30 04:00:00 0.302596"
]
},
"execution_count": 142,
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
@ -497,7 +463,7 @@
},
{
"cell_type": "code",
"execution_count": 143,
"execution_count": 12,
"metadata": {
"id": "Rpju-Sc2HFm0"
},
@ -511,7 +477,7 @@
},
{
"cell_type": "code",
"execution_count": 144,
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
@ -522,7 +488,7 @@
},
{
"cell_type": "code",
"execution_count": 145,
"execution_count": 14,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -537,7 +503,7 @@
"(1412, 5)"
]
},
"execution_count": 145,
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
@ -551,7 +517,7 @@
},
{
"cell_type": "code",
"execution_count": 146,
"execution_count": 16,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -566,7 +532,7 @@
"(44, 5)"
]
},
"execution_count": 146,
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
@ -580,7 +546,7 @@
},
{
"cell_type": "code",
"execution_count": 147,
"execution_count": 17,
"metadata": {
"id": "2u0R2sIsLuq5"
},
@ -595,8 +561,6 @@
}
],
"source": [
"# Selecting inputs and outputs from training and testing data\n",
"\n",
"x_train, y_train = train_data_timesteps[:,:timesteps-1],train_data_timesteps[:,[timesteps-1]]\n",
"x_test, y_test = test_data_timesteps[:,:timesteps-1],test_data_timesteps[:,[timesteps-1]]\n",
"\n",
@ -615,18 +579,20 @@
},
{
"cell_type": "code",
"execution_count": 148,
"execution_count": 18,
"metadata": {
"id": "EhA403BEPEiD"
},
"outputs": [],
"source": [
"# Create model using RBF kernel\n",
"\n",
"model = SVR(kernel='rbf',gamma=0.5, C=10, epsilon = 0.05)"
]
},
{
"cell_type": "code",
"execution_count": 149,
"execution_count": 19,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -642,12 +608,14 @@
" kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
]
},
"execution_count": 149,
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Fit model on training data\n",
"\n",
"model.fit(x_train, y_train[:,0])"
]
},
@ -662,7 +630,7 @@
},
{
"cell_type": "code",
"execution_count": 150,
"execution_count": 20,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -681,6 +649,7 @@
],
"source": [
"# Making predictions\n",
"\n",
"y_train_pred = model.predict(x_train).reshape(-1,1)\n",
"y_test_pred = model.predict(x_test).reshape(-1,1)\n",
"\n",
@ -698,7 +667,7 @@
},
{
"cell_type": "code",
"execution_count": 151,
"execution_count": 21,
"metadata": {},
"outputs": [
{
@ -710,8 +679,8 @@
}
],
"source": [
"\n",
"# Scaling the predictions\n",
"\n",
"y_train_pred = scaler.inverse_transform(y_train_pred)\n",
"y_test_pred = scaler.inverse_transform(y_test_pred)\n",
"\n",
@ -720,7 +689,7 @@
},
{
"cell_type": "code",
"execution_count": 152,
"execution_count": 22,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -739,6 +708,7 @@
],
"source": [
"# Scaling the original values\n",
"\n",
"y_train = scaler.inverse_transform(y_train)\n",
"y_test = scaler.inverse_transform(y_test)\n",
"\n",
@ -747,7 +717,7 @@
},
{
"cell_type": "code",
"execution_count": 153,
"execution_count": 23,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -775,7 +745,7 @@
},
{
"cell_type": "code",
"execution_count": 176,
"execution_count": 24,
"metadata": {},
"outputs": [
{
@ -803,7 +773,7 @@
},
{
"cell_type": "code",
"execution_count": 167,
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -826,7 +796,7 @@
},
{
"cell_type": "code",
"execution_count": 168,
"execution_count": 26,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -860,7 +830,7 @@
},
{
"cell_type": "code",
"execution_count": 170,
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -892,7 +862,7 @@
},
{
"cell_type": "code",
"execution_count": 171,
"execution_count": 25,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
@ -929,7 +899,7 @@
},
{
"cell_type": "code",
"execution_count": 172,
"execution_count": 26,
"metadata": {
"id": "ESSAdQgwexIi"
},
@ -945,7 +915,7 @@
},
{
"cell_type": "code",
"execution_count": 182,
"execution_count": 27,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
@ -979,7 +949,7 @@
},
{
"cell_type": "code",
"execution_count": 174,
"execution_count": 28,
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"

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