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Untitled6.ipynb
{
"cells": [
{
"metadata": {
"ExecuteTime": {
"start_time": "2021-06-21T10:07:21.957903Z",
"end_time": "2021-06-21T10:07:36.299529Z"
},
"trusted": true
},
"cell_type": "code",
"source": "from tsai.all import *",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2021-06-21T10:07:36.307853Z",
"end_time": "2021-06-21T10:07:36.409093Z"
},
"trusted": true
},
"cell_type": "code",
"source": "data1 = np.concatenate([np.ones((20,1), dtype=int), np.arange(20).reshape(-1,1).repeat(3,1) * np.array([1,10,100])], 1)\ndata2 = np.concatenate([np.ones((15,1), dtype=int)*2, np.arange(15).reshape(-1,1).repeat(3,1) * np.array([1,10,100]) + .5], 1)\ndata = np.concatenate([data1, data2])\ndf = pd.DataFrame(data, columns=['unique_id', 'var1', 'var2', 'target'])\ndf",
"execution_count": 2,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 2,
"data": {
"text/plain": " unique_id var1 var2 target\n0 1.0 0.0 0.0 0.0\n1 1.0 1.0 10.0 100.0\n2 1.0 2.0 20.0 200.0\n3 1.0 3.0 30.0 300.0\n4 1.0 4.0 40.0 400.0\n5 1.0 5.0 50.0 500.0\n6 1.0 6.0 60.0 600.0\n7 1.0 7.0 70.0 700.0\n8 1.0 8.0 80.0 800.0\n9 1.0 9.0 90.0 900.0\n10 1.0 10.0 100.0 1000.0\n11 1.0 11.0 110.0 1100.0\n12 1.0 12.0 120.0 1200.0\n13 1.0 13.0 130.0 1300.0\n14 1.0 14.0 140.0 1400.0\n15 1.0 15.0 150.0 1500.0\n16 1.0 16.0 160.0 1600.0\n17 1.0 17.0 170.0 1700.0\n18 1.0 18.0 180.0 1800.0\n19 1.0 19.0 190.0 1900.0\n20 2.0 0.5 0.5 0.5\n21 2.0 1.5 10.5 100.5\n22 2.0 2.5 20.5 200.5\n23 2.0 3.5 30.5 300.5\n24 2.0 4.5 40.5 400.5\n25 2.0 5.5 50.5 500.5\n26 2.0 6.5 60.5 600.5\n27 2.0 7.5 70.5 700.5\n28 2.0 8.5 80.5 800.5\n29 2.0 9.5 90.5 900.5\n30 2.0 10.5 100.5 1000.5\n31 2.0 11.5 110.5 1100.5\n32 2.0 12.5 120.5 1200.5\n33 2.0 13.5 130.5 1300.5\n34 2.0 14.5 140.5 1400.5",
"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>unique_id</th>\n <th>var1</th>\n <th>var2</th>\n <th>target</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1.0</td>\n <td>1.0</td>\n <td>10.0</td>\n <td>100.0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1.0</td>\n <td>2.0</td>\n <td>20.0</td>\n <td>200.0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1.0</td>\n <td>3.0</td>\n <td>30.0</td>\n <td>300.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1.0</td>\n <td>4.0</td>\n <td>40.0</td>\n <td>400.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>50.0</td>\n <td>500.0</td>\n </tr>\n <tr>\n <th>6</th>\n <td>1.0</td>\n <td>6.0</td>\n <td>60.0</td>\n <td>600.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>1.0</td>\n <td>7.0</td>\n <td>70.0</td>\n <td>700.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>1.0</td>\n <td>8.0</td>\n <td>80.0</td>\n <td>800.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>1.0</td>\n <td>9.0</td>\n <td>90.0</td>\n <td>900.0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>1.0</td>\n <td>10.0</td>\n <td>100.0</td>\n <td>1000.0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>1.0</td>\n <td>11.0</td>\n <td>110.0</td>\n <td>1100.0</td>\n </tr>\n <tr>\n <th>12</th>\n <td>1.0</td>\n <td>12.0</td>\n <td>120.0</td>\n <td>1200.0</td>\n </tr>\n <tr>\n <th>13</th>\n <td>1.0</td>\n <td>13.0</td>\n <td>130.0</td>\n <td>1300.0</td>\n </tr>\n <tr>\n <th>14</th>\n <td>1.0</td>\n <td>14.0</td>\n <td>140.0</td>\n <td>1400.0</td>\n </tr>\n <tr>\n <th>15</th>\n <td>1.0</td>\n <td>15.0</td>\n <td>150.0</td>\n <td>1500.0</td>\n </tr>\n <tr>\n <th>16</th>\n <td>1.0</td>\n <td>16.0</td>\n <td>160.0</td>\n <td>1600.0</td>\n </tr>\n <tr>\n <th>17</th>\n <td>1.0</td>\n <td>17.0</td>\n <td>170.0</td>\n <td>1700.0</td>\n </tr>\n <tr>\n <th>18</th>\n <td>1.0</td>\n <td>18.0</td>\n <td>180.0</td>\n <td>1800.0</td>\n </tr>\n <tr>\n <th>19</th>\n <td>1.0</td>\n <td>19.0</td>\n <td>190.0</td>\n <td>1900.0</td>\n </tr>\n <tr>\n <th>20</th>\n <td>2.0</td>\n <td>0.5</td>\n <td>0.5</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>21</th>\n <td>2.0</td>\n <td>1.5</td>\n <td>10.5</td>\n <td>100.5</td>\n </tr>\n <tr>\n <th>22</th>\n <td>2.0</td>\n <td>2.5</td>\n <td>20.5</td>\n <td>200.5</td>\n </tr>\n <tr>\n <th>23</th>\n <td>2.0</td>\n <td>3.5</td>\n <td>30.5</td>\n <td>300.5</td>\n </tr>\n <tr>\n <th>24</th>\n <td>2.0</td>\n <td>4.5</td>\n <td>40.5</td>\n <td>400.5</td>\n </tr>\n <tr>\n <th>25</th>\n <td>2.0</td>\n <td>5.5</td>\n <td>50.5</td>\n <td>500.5</td>\n </tr>\n <tr>\n <th>26</th>\n <td>2.0</td>\n <td>6.5</td>\n <td>60.5</td>\n <td>600.5</td>\n </tr>\n <tr>\n <th>27</th>\n <td>2.0</td>\n <td>7.5</td>\n <td>70.5</td>\n <td>700.5</td>\n </tr>\n <tr>\n <th>28</th>\n <td>2.0</td>\n <td>8.5</td>\n <td>80.5</td>\n <td>800.5</td>\n </tr>\n <tr>\n <th>29</th>\n <td>2.0</td>\n <td>9.5</td>\n <td>90.5</td>\n <td>900.5</td>\n </tr>\n <tr>\n <th>30</th>\n <td>2.0</td>\n <td>10.5</td>\n <td>100.5</td>\n <td>1000.5</td>\n </tr>\n <tr>\n <th>31</th>\n <td>2.0</td>\n <td>11.5</td>\n <td>110.5</td>\n <td>1100.5</td>\n </tr>\n <tr>\n <th>32</th>\n <td>2.0</td>\n <td>12.5</td>\n <td>120.5</td>\n <td>1200.5</td>\n </tr>\n <tr>\n <th>33</th>\n <td>2.0</td>\n <td>13.5</td>\n <td>130.5</td>\n <td>1300.5</td>\n </tr>\n <tr>\n <th>34</th>\n <td>2.0</td>\n <td>14.5</td>\n <td>140.5</td>\n <td>1400.5</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2021-06-21T10:07:36.416676Z",
"end_time": "2021-06-21T10:07:36.497414Z"
},
"trusted": true
},
"cell_type": "code",
"source": "df = df.loc[np.random.choice(len(df), len(df), False)].reset_index()\ndf.rename(columns={'index': 'time'}, inplace=True)\ndf",
"execution_count": 3,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 3,
"data": {
"text/plain": " time unique_id var1 var2 target\n0 30 2.0 10.5 100.5 1000.5\n1 34 2.0 14.5 140.5 1400.5\n2 32 2.0 12.5 120.5 1200.5\n3 9 1.0 9.0 90.0 900.0\n4 0 1.0 0.0 0.0 0.0\n5 26 2.0 6.5 60.5 600.5\n6 3 1.0 3.0 30.0 300.0\n7 13 1.0 13.0 130.0 1300.0\n8 6 1.0 6.0 60.0 600.0\n9 7 1.0 7.0 70.0 700.0\n10 18 1.0 18.0 180.0 1800.0\n11 28 2.0 8.5 80.5 800.5\n12 16 1.0 16.0 160.0 1600.0\n13 2 1.0 2.0 20.0 200.0\n14 4 1.0 4.0 40.0 400.0\n15 14 1.0 14.0 140.0 1400.0\n16 25 2.0 5.5 50.5 500.5\n17 22 2.0 2.5 20.5 200.5\n18 33 2.0 13.5 130.5 1300.5\n19 20 2.0 0.5 0.5 0.5\n20 5 1.0 5.0 50.0 500.0\n21 29 2.0 9.5 90.5 900.5\n22 17 1.0 17.0 170.0 1700.0\n23 1 1.0 1.0 10.0 100.0\n24 24 2.0 4.5 40.5 400.5\n25 23 2.0 3.5 30.5 300.5\n26 21 2.0 1.5 10.5 100.5\n27 12 1.0 12.0 120.0 1200.0\n28 27 2.0 7.5 70.5 700.5\n29 19 1.0 19.0 190.0 1900.0\n30 31 2.0 11.5 110.5 1100.5\n31 11 1.0 11.0 110.0 1100.0\n32 10 1.0 10.0 100.0 1000.0\n33 8 1.0 8.0 80.0 800.0\n34 15 1.0 15.0 150.0 1500.0",
"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>time</th>\n <th>unique_id</th>\n <th>var1</th>\n <th>var2</th>\n <th>target</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>30</td>\n <td>2.0</td>\n <td>10.5</td>\n <td>100.5</td>\n <td>1000.5</td>\n </tr>\n <tr>\n <th>1</th>\n <td>34</td>\n <td>2.0</td>\n <td>14.5</td>\n <td>140.5</td>\n <td>1400.5</td>\n </tr>\n <tr>\n <th>2</th>\n <td>32</td>\n <td>2.0</td>\n <td>12.5</td>\n <td>120.5</td>\n <td>1200.5</td>\n </tr>\n <tr>\n <th>3</th>\n <td>9</td>\n <td>1.0</td>\n <td>9.0</td>\n <td>90.0</td>\n <td>900.0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n <tr>\n <th>5</th>\n <td>26</td>\n <td>2.0</td>\n <td>6.5</td>\n <td>60.5</td>\n <td>600.5</td>\n </tr>\n <tr>\n <th>6</th>\n <td>3</td>\n <td>1.0</td>\n <td>3.0</td>\n <td>30.0</td>\n <td>300.0</td>\n </tr>\n <tr>\n <th>7</th>\n <td>13</td>\n <td>1.0</td>\n <td>13.0</td>\n <td>130.0</td>\n <td>1300.0</td>\n </tr>\n <tr>\n <th>8</th>\n <td>6</td>\n <td>1.0</td>\n <td>6.0</td>\n <td>60.0</td>\n <td>600.0</td>\n </tr>\n <tr>\n <th>9</th>\n <td>7</td>\n <td>1.0</td>\n <td>7.0</td>\n <td>70.0</td>\n <td>700.0</td>\n </tr>\n <tr>\n <th>10</th>\n <td>18</td>\n <td>1.0</td>\n <td>18.0</td>\n <td>180.0</td>\n <td>1800.0</td>\n </tr>\n <tr>\n <th>11</th>\n <td>28</td>\n <td>2.0</td>\n <td>8.5</td>\n <td>80.5</td>\n <td>800.5</td>\n </tr>\n <tr>\n <th>12</th>\n <td>16</td>\n <td>1.0</td>\n <td>16.0</td>\n <td>160.0</td>\n <td>1600.0</td>\n </tr>\n <tr>\n <th>13</th>\n <td>2</td>\n <td>1.0</td>\n <td>2.0</td>\n <td>20.0</td>\n <td>200.0</td>\n </tr>\n <tr>\n <th>14</th>\n <td>4</td>\n <td>1.0</td>\n <td>4.0</td>\n <td>40.0</td>\n <td>400.0</td>\n </tr>\n <tr>\n <th>15</th>\n <td>14</td>\n <td>1.0</td>\n <td>14.0</td>\n <td>140.0</td>\n <td>1400.0</td>\n </tr>\n <tr>\n <th>16</th>\n <td>25</td>\n <td>2.0</td>\n <td>5.5</td>\n <td>50.5</td>\n <td>500.5</td>\n </tr>\n <tr>\n <th>17</th>\n <td>22</td>\n <td>2.0</td>\n <td>2.5</td>\n <td>20.5</td>\n <td>200.5</td>\n </tr>\n <tr>\n <th>18</th>\n <td>33</td>\n <td>2.0</td>\n <td>13.5</td>\n <td>130.5</td>\n <td>1300.5</td>\n </tr>\n <tr>\n <th>19</th>\n <td>20</td>\n <td>2.0</td>\n <td>0.5</td>\n <td>0.5</td>\n <td>0.5</td>\n </tr>\n <tr>\n <th>20</th>\n <td>5</td>\n <td>1.0</td>\n <td>5.0</td>\n <td>50.0</td>\n <td>500.0</td>\n </tr>\n <tr>\n <th>21</th>\n <td>29</td>\n <td>2.0</td>\n <td>9.5</td>\n <td>90.5</td>\n <td>900.5</td>\n </tr>\n <tr>\n <th>22</th>\n <td>17</td>\n <td>1.0</td>\n <td>17.0</td>\n <td>170.0</td>\n <td>1700.0</td>\n </tr>\n <tr>\n <th>23</th>\n <td>1</td>\n <td>1.0</td>\n <td>1.0</td>\n <td>10.0</td>\n <td>100.0</td>\n </tr>\n <tr>\n <th>24</th>\n <td>24</td>\n <td>2.0</td>\n <td>4.5</td>\n <td>40.5</td>\n <td>400.5</td>\n </tr>\n <tr>\n <th>25</th>\n <td>23</td>\n <td>2.0</td>\n <td>3.5</td>\n <td>30.5</td>\n <td>300.5</td>\n </tr>\n <tr>\n <th>26</th>\n <td>21</td>\n <td>2.0</td>\n <td>1.5</td>\n <td>10.5</td>\n <td>100.5</td>\n </tr>\n <tr>\n <th>27</th>\n <td>12</td>\n <td>1.0</td>\n <td>12.0</td>\n <td>120.0</td>\n <td>1200.0</td>\n </tr>\n <tr>\n <th>28</th>\n <td>27</td>\n <td>2.0</td>\n <td>7.5</td>\n <td>70.5</td>\n <td>700.5</td>\n </tr>\n <tr>\n <th>29</th>\n <td>19</td>\n <td>1.0</td>\n <td>19.0</td>\n <td>190.0</td>\n <td>1900.0</td>\n </tr>\n <tr>\n <th>30</th>\n <td>31</td>\n <td>2.0</td>\n <td>11.5</td>\n <td>110.5</td>\n <td>1100.5</td>\n </tr>\n <tr>\n <th>31</th>\n <td>11</td>\n <td>1.0</td>\n <td>11.0</td>\n <td>110.0</td>\n <td>1100.0</td>\n </tr>\n <tr>\n <th>32</th>\n <td>10</td>\n <td>1.0</td>\n <td>10.0</td>\n <td>100.0</td>\n <td>1000.0</td>\n </tr>\n <tr>\n <th>33</th>\n <td>8</td>\n <td>1.0</td>\n <td>8.0</td>\n <td>80.0</td>\n <td>800.0</td>\n </tr>\n <tr>\n <th>34</th>\n <td>15</td>\n <td>1.0</td>\n <td>15.0</td>\n <td>150.0</td>\n <td>1500.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2021-06-21T10:07:36.505215Z",
"end_time": "2021-06-21T10:07:36.592903Z"
},
"trusted": true
},
"cell_type": "code",
"source": "X, y = SlidingWindowPanel(window_len=5,\n unique_id_cols=['unique_id'],\n stride=5,\n start=0,\n pad_remainder=False,\n padding_value=np.nan,\n add_padding_feature=True,\n get_x=['var1', 'var2', 'target'],\n get_y='target',\n y_func=None,\n horizon=3,\n seq_first=True,\n sort_by=['time'],\n ascending=True,\n check_leakage=True,\n return_key=False,\n verbose=True)(df)\nX, y",
"execution_count": 4,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": "<IPython.core.display.HTML object>",
"text/html": ""
},
"metadata": {}
},
{
"output_type": "execute_result",
"execution_count": 4,
"data": {
"text/plain": "(array([[[0.000e+00, 1.000e+00, 2.000e+00, 3.000e+00, 4.000e+00],\n [0.000e+00, 1.000e+01, 2.000e+01, 3.000e+01, 4.000e+01],\n [0.000e+00, 1.000e+02, 2.000e+02, 3.000e+02, 4.000e+02]],\n \n [[5.000e+00, 6.000e+00, 7.000e+00, 8.000e+00, 9.000e+00],\n [5.000e+01, 6.000e+01, 7.000e+01, 8.000e+01, 9.000e+01],\n [5.000e+02, 6.000e+02, 7.000e+02, 8.000e+02, 9.000e+02]],\n \n [[1.000e+01, 1.100e+01, 1.200e+01, 1.300e+01, 1.400e+01],\n [1.000e+02, 1.100e+02, 1.200e+02, 1.300e+02, 1.400e+02],\n [1.000e+03, 1.100e+03, 1.200e+03, 1.300e+03, 1.400e+03]],\n \n [[5.000e-01, 1.500e+00, 2.500e+00, 3.500e+00, 4.500e+00],\n [5.000e-01, 1.050e+01, 2.050e+01, 3.050e+01, 4.050e+01],\n [5.000e-01, 1.005e+02, 2.005e+02, 3.005e+02, 4.005e+02]],\n \n [[5.500e+00, 6.500e+00, 7.500e+00, 8.500e+00, 9.500e+00],\n [5.050e+01, 6.050e+01, 7.050e+01, 8.050e+01, 9.050e+01],\n [5.005e+02, 6.005e+02, 7.005e+02, 8.005e+02, 9.005e+02]]]),\n array([[ 500. , 600. , 700. ],\n [1000. , 1100. , 1200. ],\n [1500. , 1600. , 1700. ],\n [ 500.5, 600.5, 700.5],\n [1000.5, 1100.5, 1200.5]]))"
},
"metadata": {}
}
]
},
{
"metadata": {
"ExecuteTime": {
"start_time": "2021-06-21T10:07:36.599813Z",
"end_time": "2021-06-21T10:07:36.651982Z"
},
"trusted": true
},
"cell_type": "code",
"source": "output = [df.groupby(['unique_id']).apply(lambda x: SlidingWindow(window_len=5,\n stride=5,\n start=0,\n pad_remainder=False,\n padding_value=np.nan,\n add_padding_feature=True,\n get_x=['var1', 'var2', 'target'],\n get_y='target',\n y_func=None,\n horizon=3,\n seq_first=True,\n sort_by=['time'],\n ascending=True,\n check_leakage=True,\n copy=True, # it's important to set copy to True when used in this way!!!\n )(x))][0].values\n\nX2 = np.concatenate([oi[0] for oi in output])\ny2 = np.concatenate([oi[1] for oi in output])\nX2, y2",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"execution_count": 5,
"data": {
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