feat: added from_str_to_temporal and continues prediction

docs/tutorials/regression.ipynb

@@ -30,10 +30,24 @@
     "pricing.slice_rows(0,15)"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:42.984142700Z",
+     "start_time": "2024-05-24T11:02:42.899167900Z"
+    }
    },
-   "execution_count": null,
-   "outputs": []
+   "execution_count": 1,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "+-----+------+-------+-----+---+----------------+---------------+------------------+--------+\n|  id | year | month | day | … | year_renovated | sqft_lot_15nn | sqft_living_15nn |  price |\n| --- |  --- |   --- | --- |   |            --- |           --- |              --- |    --- |\n| i64 |  i64 |   i64 | i64 |   |            i64 |           i64 |              i64 |    i64 |\n+===========================================================================================+\n|   0 | 2014 |     5 |   2 | … |              0 |          9176 |             2310 | 285000 |\n|   1 | 2014 |     5 |   2 | … |              0 |          8595 |             1750 | 285000 |\n|   2 | 2014 |     5 |   2 | … |              0 |          9000 |             1850 | 440000 |\n|   3 | 2014 |     5 |   2 | … |              0 |          8942 |             1260 | 435000 |\n|   4 | 2014 |     5 |   2 | … |              0 |         10836 |             1450 | 430000 |\n|   … |    … |     … |   … | … |              … |             … |                … |      … |\n|  10 | 2014 |     5 |   2 | … |              0 |         24967 |             4050 | 604000 |\n|  11 | 2014 |     5 |   2 | … |              0 |         16215 |             1450 | 335000 |\n|  12 | 2014 |     5 |   2 | … |              0 |         35100 |             2340 | 437500 |\n|  13 | 2014 |     5 |   2 | … |              0 |         39299 |             2390 | 630000 |\n|  14 | 2014 |     5 |   2 | … |              0 |         48351 |             2820 | 675000 |\n+-----+------+-------+-----+---+----------------+---------------+------------------+--------+",
+      "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 23)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>year</th><th>month</th><th>day</th><th>zipcode</th><th>latitude</th><th>longitude</th><th>sqft_lot</th><th>sqft_living</th><th>sqft_above</th><th>sqft_basement</th><th>floors</th><th>bedrooms</th><th>bathrooms</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>year_built</th><th>year_renovated</th><th>sqft_lot_15nn</th><th>sqft_living_15nn</th><th>price</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td></tr></thead><tbody><tr><td>0</td><td>2014</td><td>5</td><td>2</td><td>98001</td><td>47.3406</td><td>-122.269</td><td>9397</td><td>2200</td><td>2200</td><td>0</td><td>2.0</td><td>4</td><td>2.5</td><td>0</td><td>1</td><td>3</td><td>8</td><td>1987</td><td>0</td><td>9176</td><td>2310</td><td>285000</td></tr><tr><td>1</td><td>2014</td><td>5</td><td>2</td><td>98003</td><td>47.3537</td><td>-122.303</td><td>10834</td><td>2090</td><td>1360</td><td>730</td><td>1.0</td><td>3</td><td>2.5</td><td>0</td><td>1</td><td>4</td><td>8</td><td>1987</td><td>0</td><td>8595</td><td>1750</td><td>285000</td></tr><tr><td>2</td><td>2014</td><td>5</td><td>2</td><td>98006</td><td>47.5443</td><td>-122.177</td><td>8119</td><td>2160</td><td>1080</td><td>1080</td><td>1.0</td><td>4</td><td>2.25</td><td>0</td><td>1</td><td>3</td><td>8</td><td>1966</td><td>0</td><td>9000</td><td>1850</td><td>440000</td></tr><tr><td>3</td><td>2014</td><td>5</td><td>2</td><td>98006</td><td>47.5746</td><td>-122.135</td><td>8800</td><td>1450</td><td>1450</td><td>0</td><td>1.0</td><td>4</td><td>1.0</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1954</td><td>0</td><td>8942</td><td>1260</td><td>435000</td></tr><tr><td>4</td><td>2014</td><td>5</td><td>2</td><td>98006</td><td>47.5725</td><td>-122.133</td><td>10000</td><td>1920</td><td>1070</td><td>850</td><td>1.0</td><td>4</td><td>1.5</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1954</td><td>0</td><td>10836</td><td>1450</td><td>430000</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>10</td><td>2014</td><td>5</td><td>2</td><td>98023</td><td>47.3256</td><td>-122.378</td><td>33151</td><td>3240</td><td>3240</td><td>0</td><td>2.0</td><td>3</td><td>2.5</td><td>0</td><td>3</td><td>3</td><td>10</td><td>1995</td><td>0</td><td>24967</td><td>4050</td><td>604000</td></tr><tr><td>11</td><td>2014</td><td>5</td><td>2</td><td>98024</td><td>47.5643</td><td>-121.897</td><td>16215</td><td>1580</td><td>1580</td><td>0</td><td>1.0</td><td>3</td><td>2.25</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1978</td><td>0</td><td>16215</td><td>1450</td><td>335000</td></tr><tr><td>12</td><td>2014</td><td>5</td><td>2</td><td>98027</td><td>47.4635</td><td>-121.991</td><td>35100</td><td>1970</td><td>1970</td><td>0</td><td>2.0</td><td>3</td><td>2.25</td><td>0</td><td>1</td><td>4</td><td>9</td><td>1977</td><td>0</td><td>35100</td><td>2340</td><td>437500</td></tr><tr><td>13</td><td>2014</td><td>5</td><td>2</td><td>98027</td><td>47.4634</td><td>-121.987</td><td>37277</td><td>2710</td><td>2710</td><td>0</td><td>2.0</td><td>4</td><td>2.75</td><td>0</td><td>1</td><td>3</td><td>9</td><td>2000</td><td>0</td><td>39299</td><td>2390</td><td>630000</td></tr><tr><td>14</td><td>2014</td><td>5</td><td>2</td><td>98029</td><td>47.5794</td><td>-122.025</td><td>67518</td><td>2820</td><td>2820</td><td>0</td><td>2.0</td><td>5</td><td>2.5</td><td>0</td><td>1</td><td>3</td><td>8</td><td>1979</td><td>0</td><td>48351</td><td>2820</td><td>675000</td></tr></tbody></table></div>"
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
   },
   {
    "cell_type": "markdown",
@@ -53,14 +67,20 @@
     "test_table = testing_table.remove_columns([\"price\"]).shuffle_rows()"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:43.020110800Z",
+     "start_time": "2024-05-24T11:02:42.978634800Z"
+    }
    },
-   "execution_count": null,
+   "execution_count": 2,
    "outputs": []
   },
   {
    "cell_type": "markdown",
-   "source": "3. Mark the `price` `Column` as the target variable to be predicted. Include the `id` column only as an extra column, which is completely ignored by the model:",
+   "source": [
+    "3. Mark the `price` `Column` as the target variable to be predicted. Include the `id` column only as an extra column, which is completely ignored by the model:"
+   ],
    "metadata": {
     "collapsed": false
    }
@@ -73,14 +93,20 @@
     "train_tabular_dataset = train_table.to_tabular_dataset(\"price\", extra_names=extra_names)\n"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:43.020110800Z",
+     "start_time": "2024-05-24T11:02:42.987977400Z"
+    }
    },
-   "execution_count": null,
+   "execution_count": 3,
    "outputs": []
   },
   {
    "cell_type": "markdown",
-   "source": "4. Use `Decision Tree` regressor as a model for the regression. Pass the \"train_tabular_dataset\" table to the fit function of the model:\n",
+   "source": [
+    "4. Use `Decision Tree` regressor as a model for the regression. Pass the \"train_tabular_dataset\" table to the fit function of the model:\n"
+   ],
    "metadata": {
     "collapsed": false
    }
@@ -94,9 +120,13 @@
     "fitted_model = model.fit(train_tabular_dataset)"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:43.734095500Z",
+     "start_time": "2024-05-24T11:02:42.991527800Z"
+    }
    },
-   "execution_count": null,
+   "execution_count": 4,
    "outputs": []
   },
   {
@@ -118,10 +148,24 @@
     "prediction.to_table().slice_rows(start=0, length=15)"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:43.747030600Z",
+     "start_time": "2024-05-24T11:02:43.730090600Z"
+    }
    },
-   "execution_count": null,
-   "outputs": []
+   "execution_count": 5,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "+-------+------+-------+-----+---+----------------+---------------+----------------+---------------+\n|    id | year | month | day | … | year_renovated | sqft_lot_15nn | sqft_living_15 |         price |\n|   --- |  --- |   --- | --- |   |            --- |           --- |             nn |           --- |\n|   i64 |  i64 |   i64 | i64 |   |            i64 |           i64 |            --- |           f64 |\n|       |      |       |     |   |                |               |            i64 |               |\n+==================================================================================================+\n| 20953 | 2015 |     4 |  30 | … |           2013 |          4410 |           1970 |  909625.00000 |\n| 21205 | 2015 |     5 |   5 | … |              0 |          8187 |           2300 |  776695.00000 |\n|  1360 | 2014 |     5 |  23 | … |              0 |          5000 |           1290 |  544614.75000 |\n| 15230 | 2015 |     1 |  21 | … |              0 |        217364 |           2580 |  867816.66667 |\n| 12893 | 2014 |    11 |  21 | … |              0 |          8190 |           1430 |  227493.75000 |\n|     … |    … |     … |   … | … |              … |             … |              … |             … |\n|  8807 | 2014 |     9 |  12 | … |              0 |          6060 |           2680 |  499355.20000 |\n| 13089 | 2014 |    11 |  25 | … |              0 |          5000 |           1760 |  492833.33333 |\n| 12016 | 2014 |    11 |   6 | … |              0 |         13001 |           4750 | 1352450.00000 |\n| 17314 | 2015 |     3 |  10 | … |              0 |          9375 |           1880 |  523916.66667 |\n|  4030 | 2014 |     7 |   1 | … |           2015 |          6885 |           2180 |  622937.50000 |\n+-------+------+-------+-----+---+----------------+---------------+----------------+---------------+",
+      "text/html": "<div><style>\n.dataframe > thead > tr,\n.dataframe > tbody > tr {\n  text-align: right;\n  white-space: pre-wrap;\n}\n</style>\n<small>shape: (15, 23)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>id</th><th>year</th><th>month</th><th>day</th><th>zipcode</th><th>latitude</th><th>longitude</th><th>sqft_lot</th><th>sqft_living</th><th>sqft_above</th><th>sqft_basement</th><th>floors</th><th>bedrooms</th><th>bathrooms</th><th>waterfront</th><th>view</th><th>condition</th><th>grade</th><th>year_built</th><th>year_renovated</th><th>sqft_lot_15nn</th><th>sqft_living_15nn</th><th>price</th></tr><tr><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td><td>i64</td><td>f64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>i64</td><td>f64</td></tr></thead><tbody><tr><td>20953</td><td>2015</td><td>4</td><td>30</td><td>98144</td><td>47.5835</td><td>-122.313</td><td>2665</td><td>2960</td><td>1950</td><td>1010</td><td>2.0</td><td>7</td><td>4.0</td><td>0</td><td>1</td><td>3</td><td>9</td><td>1927</td><td>2013</td><td>4410</td><td>1970</td><td>909625.0</td></tr><tr><td>21205</td><td>2015</td><td>5</td><td>5</td><td>98052</td><td>47.6842</td><td>-122.155</td><td>7800</td><td>2300</td><td>2300</td><td>0</td><td>2.0</td><td>3</td><td>2.5</td><td>0</td><td>3</td><td>3</td><td>9</td><td>1997</td><td>0</td><td>8187</td><td>2300</td><td>776695.0</td></tr><tr><td>1360</td><td>2014</td><td>5</td><td>23</td><td>98115</td><td>47.684</td><td>-122.281</td><td>5000</td><td>1814</td><td>944</td><td>870</td><td>1.0</td><td>4</td><td>1.75</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1951</td><td>0</td><td>5000</td><td>1290</td><td>544614.75</td></tr><tr><td>15230</td><td>2015</td><td>1</td><td>21</td><td>98077</td><td>47.7696</td><td>-122.021</td><td>217800</td><td>3810</td><td>3810</td><td>0</td><td>2.0</td><td>4</td><td>3.0</td><td>0</td><td>1</td><td>3</td><td>9</td><td>2003</td><td>0</td><td>217364</td><td>2580</td><td>867816.666667</td></tr><tr><td>12893</td><td>2014</td><td>11</td><td>21</td><td>98031</td><td>47.4014</td><td>-122.186</td><td>8400</td><td>1070</td><td>1070</td><td>0</td><td>1.0</td><td>2</td><td>2.0</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1980</td><td>0</td><td>8190</td><td>1430</td><td>227493.75</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>8807</td><td>2014</td><td>9</td><td>12</td><td>98045</td><td>47.4759</td><td>-121.735</td><td>5978</td><td>2640</td><td>2640</td><td>0</td><td>2.0</td><td>3</td><td>3.0</td><td>0</td><td>1</td><td>3</td><td>9</td><td>2012</td><td>0</td><td>6060</td><td>2680</td><td>499355.2</td></tr><tr><td>13089</td><td>2014</td><td>11</td><td>25</td><td>98117</td><td>47.6802</td><td>-122.358</td><td>5050</td><td>2090</td><td>1090</td><td>1000</td><td>1.0</td><td>4</td><td>1.75</td><td>0</td><td>1</td><td>4</td><td>7</td><td>1916</td><td>0</td><td>5000</td><td>1760</td><td>492833.333333</td></tr><tr><td>12016</td><td>2014</td><td>11</td><td>6</td><td>98059</td><td>47.5305</td><td>-122.135</td><td>12968</td><td>5550</td><td>5550</td><td>0</td><td>2.0</td><td>4</td><td>4.25</td><td>0</td><td>1</td><td>3</td><td>11</td><td>2005</td><td>0</td><td>13001</td><td>4750</td><td>1.35245e6</td></tr><tr><td>17314</td><td>2015</td><td>3</td><td>10</td><td>98052</td><td>47.631</td><td>-122.098</td><td>9500</td><td>1650</td><td>1650</td><td>0</td><td>1.0</td><td>3</td><td>1.75</td><td>0</td><td>1</td><td>3</td><td>8</td><td>1967</td><td>0</td><td>9375</td><td>1880</td><td>523916.666667</td></tr><tr><td>4030</td><td>2014</td><td>7</td><td>1</td><td>98115</td><td>47.6763</td><td>-122.282</td><td>6885</td><td>2890</td><td>1590</td><td>1300</td><td>1.0</td><td>4</td><td>3.0</td><td>0</td><td>1</td><td>3</td><td>7</td><td>1945</td><td>2015</td><td>6885</td><td>2180</td><td>622937.5</td></tr></tbody></table></div>"
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
   },
   {
    "cell_type": "markdown",
@@ -140,10 +184,23 @@
     "fitted_model.mean_absolute_error(test_tabular_dataset)\n"
    ],
    "metadata": {
-    "collapsed": false
+    "collapsed": false,
+    "ExecuteTime": {
+     "end_time": "2024-05-24T11:02:43.748029100Z",
+     "start_time": "2024-05-24T11:02:43.738085900Z"
+    }
    },
-   "execution_count": null,
-   "outputs": []
+   "execution_count": 6,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "93590.45902700891"
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ]
   }
  ],
  "metadata": {