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{
"cells": [
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"cell_type": "markdown",
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"# Tutorial 2 - Feature selection, KNN and SVR\n",
"In this second tutorial, we will cover three main topics:\n",
"1. **Feature selection**: In Tutorial 1, we had started to explore our data. In this tutorial, we will explore the data further, and see how we can sekect features to improve the model performance.\n",
"2. **Hyper-parameter tuning**: We will look at some systematic approaches to optimize the hyper-parameters of KNN and SVR.\n",
"3. **Cross-validation**: To choose the hyper-parameters, we will introduce k-fold cross-validation to reduce the randomnes in the train-test-split that we have seen in Tutorial 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
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"# Part 1: Data revisited\n",
"Let's look at the data again: In Tutorial 1, we looked at the statistics and the distribution of the individual variables. This is OK for an initial model, but in order to improve it, we also need to look at the **dependencies** between the variables. There are two types of dependencies that we are interested in:\n",
"- dependencies **between features** --> these dependencies are *__unwanted__*\n",
"- dependencies between **features and target** --> these dependencies are *__wanted__*\n",
"\n",
"Why do we separate between the two? The two dependencies have different effects on the model: If there are a lot of *dependent features*, this means that these do not contribute to improving the model, since they do not provide *new* information. **A well-constructed feature set hence consists of independent or nearly independent features**.\n",
"\n",
"On the other hand, if some features are independent of the target, this means that the data in these features does not contain any useful information for the prediction of the target. This can \"confuse\" a Machine Learning model (some more than others, as we will see), and hence *reduce performance*. **A well-constructed feature set hence shows a significant dependency between the target and each of the features.**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1.1 - Load packages and data\n",
"As in the previous tutorials, we start by loading the packages and data:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# Import the necessary packages to read the data\n",
"import pandas as pd\n",
"import numpy as np # numpy is always useful\n",
"import os # Loads the functions related to the operating system "
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"DATA_PATH = 'public/data' ## ADD CORRECT FILE PATH (default: 'data')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# Read the csv file with the training data - the index, i.e. the identifier of the roofs, is the first column (DF_UID)\n",
"data = pd.read_csv( os.path.join( DATA_PATH, 'training_sample.csv' ), index_col = 0 )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1.2 - Correlation between variables\n",
"The probably most widely used meausure of dependency between variables is their **correlation**. The standard (Pearson's) correlation coefficient between two variables $X$ and $Y$ is computed as:\n",
"\n",
"$$ corr(X,Y) = \\frac{cov(X,Y)}{\\sigma_X \\sigma_Y}$$\n",
"\n",
"where $\\sigma$ denotes the standard deviation. The maximum value of the correlation coefficient is 1, denoting a perfect linear dependency between $X$ and $Y$, and the minimum values is -1, denoting an exact inverse dependency of $X$ and $Y$. A correlation of 0 means that $X$ and $Y$ are *independent* of each other.\n",
"\n",
"**NOTE**: From the above logic it follows that the correlation coefficient between a variable and itself is always 1."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Using `pandas`, we can compute the correlation between each pair of variables. To improve the readability of the resulting table, we will style the table with red colors for positive values, and blue colors for negative values:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
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" background-color: #de614d;\n",
" color: #000000;\n",
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" background-color: #ec7f63;\n",
" color: #000000;\n",
" }#T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col17,#T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col14{\n",
" background-color: #a1c0ff;\n",
" color: #000000;\n",
" }#T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col16,#T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col15{\n",
" background-color: #c83836;\n",
" color: #f1f1f1;\n",
" }</style><table id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6f\" ><thead> <tr> <th class=\"blank level0\" ></th> <th class=\"col_heading level0 col0\" >roof_tilt</th> <th class=\"col_heading level0 col1\" >roof_aspect</th> <th class=\"col_heading level0 col2\" >roof_area</th> <th class=\"col_heading level0 col3\" >roof_x</th> <th class=\"col_heading level0 col4\" >roof_y</th> <th class=\"col_heading level0 col5\" >SIS</th> <th class=\"col_heading level0 col6\" >SISDIR</th> <th class=\"col_heading level0 col7\" >DHI</th> <th class=\"col_heading level0 col8\" >horizon_S</th> <th class=\"col_heading level0 col9\" >horizon_SSW</th> <th class=\"col_heading level0 col10\" >horizon_SWW</th> <th class=\"col_heading level0 col11\" >horizon_W</th> <th class=\"col_heading level0 col12\" >horizon_NWW</th> <th class=\"col_heading level0 col13\" >horizon_SSE</th> <th class=\"col_heading level0 col14\" >horizon_SEE</th> <th class=\"col_heading level0 col15\" >horizon_E</th> <th class=\"col_heading level0 col16\" >horizon_NEE</th> <th class=\"col_heading level0 col17\" >tilted_radiation</th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row0\" class=\"row_heading level0 row0\" >roof_tilt</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col0\" class=\"data row0 col0\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col1\" class=\"data row0 col1\" >0.025</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col2\" class=\"data row0 col2\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col3\" class=\"data row0 col3\" >-0.056</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col4\" class=\"data row0 col4\" >0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col5\" class=\"data row0 col5\" >-0.12</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col6\" class=\"data row0 col6\" >-0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col7\" class=\"data row0 col7\" >0.044</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col8\" class=\"data row0 col8\" >0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col9\" class=\"data row0 col9\" >0.061</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col10\" class=\"data row0 col10\" >-0.0059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col11\" class=\"data row0 col11\" >-0.036</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col12\" class=\"data row0 col12\" >-0.097</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col13\" class=\"data row0 col13\" >0.1</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col14\" class=\"data row0 col14\" >0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col15\" class=\"data row0 col15\" >0.047</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col16\" class=\"data row0 col16\" >0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow0_col17\" class=\"data row0 col17\" >-0.17</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row1\" class=\"row_heading level0 row1\" >roof_aspect</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col0\" class=\"data row1 col0\" >0.025</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col1\" class=\"data row1 col1\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col2\" class=\"data row1 col2\" >0.0052</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col3\" class=\"data row1 col3\" >-0.045</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col4\" class=\"data row1 col4\" >0.016</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col5\" class=\"data row1 col5\" >0.0007</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col6\" class=\"data row1 col6\" >-0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col7\" class=\"data row1 col7\" >0.048</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col8\" class=\"data row1 col8\" >-0.0016</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col9\" class=\"data row1 col9\" >-0.22</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col10\" class=\"data row1 col10\" >-0.29</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col11\" class=\"data row1 col11\" >-0.26</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col12\" class=\"data row1 col12\" >-0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col13\" class=\"data row1 col13\" >0.052</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col14\" class=\"data row1 col14\" >0.31</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col15\" class=\"data row1 col15\" >0.37</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col16\" class=\"data row1 col16\" >0.35</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow1_col17\" class=\"data row1 col17\" >-0.036</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row2\" class=\"row_heading level0 row2\" >roof_area</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col0\" class=\"data row2 col0\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col1\" class=\"data row2 col1\" >0.0052</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col2\" class=\"data row2 col2\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col3\" class=\"data row2 col3\" >-0.065</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col4\" class=\"data row2 col4\" >-0.028</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col5\" class=\"data row2 col5\" >0.011</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col6\" class=\"data row2 col6\" >0.025</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col7\" class=\"data row2 col7\" >-0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col8\" class=\"data row2 col8\" >-0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col9\" class=\"data row2 col9\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col10\" class=\"data row2 col10\" >-0.043</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col11\" class=\"data row2 col11\" >-0.039</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col12\" class=\"data row2 col12\" >-0.034</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col13\" class=\"data row2 col13\" >-0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col14\" class=\"data row2 col14\" >0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col15\" class=\"data row2 col15\" >-0.0064</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col16\" class=\"data row2 col16\" >-0.017</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow2_col17\" class=\"data row2 col17\" >0.028</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row3\" class=\"row_heading level0 row3\" >roof_x</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col0\" class=\"data row3 col0\" >-0.056</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col1\" class=\"data row3 col1\" >-0.045</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col2\" class=\"data row3 col2\" >-0.065</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col3\" class=\"data row3 col3\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col4\" class=\"data row3 col4\" >0.23</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col5\" class=\"data row3 col5\" >0.065</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col6\" class=\"data row3 col6\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col7\" class=\"data row3 col7\" >0.4</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col8\" class=\"data row3 col8\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col9\" class=\"data row3 col9\" >-0.028</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col10\" class=\"data row3 col10\" >0.02</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col11\" class=\"data row3 col11\" >0.0083</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col12\" class=\"data row3 col12\" >0.02</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col13\" class=\"data row3 col13\" >-0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col14\" class=\"data row3 col14\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col15\" class=\"data row3 col15\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col16\" class=\"data row3 col16\" >-0.12</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow3_col17\" class=\"data row3 col17\" >0.0088</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row4\" class=\"row_heading level0 row4\" >roof_y</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col0\" class=\"data row4 col0\" >0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col1\" class=\"data row4 col1\" >0.016</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col2\" class=\"data row4 col2\" >-0.028</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col3\" class=\"data row4 col3\" >0.23</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col4\" class=\"data row4 col4\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col5\" class=\"data row4 col5\" >-0.76</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col6\" class=\"data row4 col6\" >-0.82</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col7\" class=\"data row4 col7\" >0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col8\" class=\"data row4 col8\" >0.066</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col9\" class=\"data row4 col9\" >0.051</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col10\" class=\"data row4 col10\" >0.0053</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col11\" class=\"data row4 col11\" >-0.0051</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col12\" class=\"data row4 col12\" >-0.05</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col13\" class=\"data row4 col13\" >0.038</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col14\" class=\"data row4 col14\" >-0.012</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col15\" class=\"data row4 col15\" >-0.043</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col16\" class=\"data row4 col16\" >-0.022</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow4_col17\" class=\"data row4 col17\" >-0.14</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row5\" class=\"row_heading level0 row5\" >SIS</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col0\" class=\"data row5 col0\" >-0.12</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col1\" class=\"data row5 col1\" >0.0007</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col2\" class=\"data row5 col2\" >0.011</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col3\" class=\"data row5 col3\" >0.065</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col4\" class=\"data row5 col4\" >-0.76</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col5\" class=\"data row5 col5\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col6\" class=\"data row5 col6\" >0.9</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col7\" class=\"data row5 col7\" >0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col8\" class=\"data row5 col8\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col9\" class=\"data row5 col9\" >-0.072</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col10\" class=\"data row5 col10\" >0.005</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col11\" class=\"data row5 col11\" >0.011</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col12\" class=\"data row5 col12\" >0.059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col13\" class=\"data row5 col13\" >-0.089</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col14\" class=\"data row5 col14\" >-0.032</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col15\" class=\"data row5 col15\" >0.0046</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col16\" class=\"data row5 col16\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow5_col17\" class=\"data row5 col17\" >0.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row6\" class=\"row_heading level0 row6\" >SISDIR</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col0\" class=\"data row6 col0\" >-0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col1\" class=\"data row6 col1\" >-0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col2\" class=\"data row6 col2\" >0.025</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col3\" class=\"data row6 col3\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col4\" class=\"data row6 col4\" >-0.82</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col5\" class=\"data row6 col5\" >0.9</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col6\" class=\"data row6 col6\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col7\" class=\"data row6 col7\" >-0.25</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col8\" class=\"data row6 col8\" >-0.087</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col9\" class=\"data row6 col9\" >-0.068</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col10\" class=\"data row6 col10\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col11\" class=\"data row6 col11\" >-0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col12\" class=\"data row6 col12\" >0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col13\" class=\"data row6 col13\" >-0.059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col14\" class=\"data row6 col14\" >-0.015</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col15\" class=\"data row6 col15\" >0.0087</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col16\" class=\"data row6 col16\" >-0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow6_col17\" class=\"data row6 col17\" >0.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row7\" class=\"row_heading level0 row7\" >DHI</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col0\" class=\"data row7 col0\" >0.044</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col1\" class=\"data row7 col1\" >0.048</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col2\" class=\"data row7 col2\" >-0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col3\" class=\"data row7 col3\" >0.4</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col4\" class=\"data row7 col4\" >0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col5\" class=\"data row7 col5\" >0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col6\" class=\"data row7 col6\" >-0.25</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col7\" class=\"data row7 col7\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col8\" class=\"data row7 col8\" >-0.042</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col9\" class=\"data row7 col9\" >-0.008</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col10\" class=\"data row7 col10\" >0.064</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col11\" class=\"data row7 col11\" >0.094</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col12\" class=\"data row7 col12\" >0.083</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col13\" class=\"data row7 col13\" >-0.066</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col14\" class=\"data row7 col14\" >-0.036</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col15\" class=\"data row7 col15\" >-0.0093</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col16\" class=\"data row7 col16\" >-0.027</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow7_col17\" class=\"data row7 col17\" >0.033</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row8\" class=\"row_heading level0 row8\" >horizon_S</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col0\" class=\"data row8 col0\" >0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col1\" class=\"data row8 col1\" >-0.0016</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col2\" class=\"data row8 col2\" >-0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col3\" class=\"data row8 col3\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col4\" class=\"data row8 col4\" >0.066</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col5\" class=\"data row8 col5\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col6\" class=\"data row8 col6\" >-0.087</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col7\" class=\"data row8 col7\" >-0.042</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col8\" class=\"data row8 col8\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col9\" class=\"data row8 col9\" >0.71</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col10\" class=\"data row8 col10\" >0.3</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col11\" class=\"data row8 col11\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col12\" class=\"data row8 col12\" >-0.073</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col13\" class=\"data row8 col13\" >0.9</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col14\" class=\"data row8 col14\" >0.55</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col15\" class=\"data row8 col15\" >0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col16\" class=\"data row8 col16\" >0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow8_col17\" class=\"data row8 col17\" >-0.58</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row9\" class=\"row_heading level0 row9\" >horizon_SSW</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col0\" class=\"data row9 col0\" >0.061</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col1\" class=\"data row9 col1\" >-0.22</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col2\" class=\"data row9 col2\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col3\" class=\"data row9 col3\" >-0.028</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col4\" class=\"data row9 col4\" >0.051</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col5\" class=\"data row9 col5\" >-0.072</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col6\" class=\"data row9 col6\" >-0.068</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col7\" class=\"data row9 col7\" >-0.008</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col8\" class=\"data row9 col8\" >0.71</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col9\" class=\"data row9 col9\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col10\" class=\"data row9 col10\" >0.7</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col11\" class=\"data row9 col11\" >0.53</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col12\" class=\"data row9 col12\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col13\" class=\"data row9 col13\" >0.57</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col14\" class=\"data row9 col14\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col15\" class=\"data row9 col15\" >-0.086</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col16\" class=\"data row9 col16\" >-0.096</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow9_col17\" class=\"data row9 col17\" >-0.48</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row10\" class=\"row_heading level0 row10\" >horizon_SWW</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col0\" class=\"data row10 col0\" >-0.0059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col1\" class=\"data row10 col1\" >-0.29</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col2\" class=\"data row10 col2\" >-0.043</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col3\" class=\"data row10 col3\" >0.02</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col4\" class=\"data row10 col4\" >0.0053</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col5\" class=\"data row10 col5\" >0.005</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col6\" class=\"data row10 col6\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col7\" class=\"data row10 col7\" >0.064</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col8\" class=\"data row10 col8\" >0.3</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col9\" class=\"data row10 col9\" >0.7</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col10\" class=\"data row10 col10\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col11\" class=\"data row10 col11\" >0.87</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col12\" class=\"data row10 col12\" >0.56</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col13\" class=\"data row10 col13\" >0.18</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col14\" class=\"data row10 col14\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col15\" class=\"data row10 col15\" >-0.24</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col16\" class=\"data row10 col16\" >-0.22</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow10_col17\" class=\"data row10 col17\" >-0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row11\" class=\"row_heading level0 row11\" >horizon_W</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col0\" class=\"data row11 col0\" >-0.036</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col1\" class=\"data row11 col1\" >-0.26</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col2\" class=\"data row11 col2\" >-0.039</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col3\" class=\"data row11 col3\" >0.0083</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col4\" class=\"data row11 col4\" >-0.0051</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col5\" class=\"data row11 col5\" >0.011</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col6\" class=\"data row11 col6\" >-0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col7\" class=\"data row11 col7\" >0.094</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col8\" class=\"data row11 col8\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col9\" class=\"data row11 col9\" >0.53</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col10\" class=\"data row11 col10\" >0.87</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col11\" class=\"data row11 col11\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col12\" class=\"data row11 col12\" >0.71</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col13\" class=\"data row11 col13\" >0.073</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col14\" class=\"data row11 col14\" >-0.16</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col15\" class=\"data row11 col15\" >-0.24</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col16\" class=\"data row11 col16\" >-0.21</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow11_col17\" class=\"data row11 col17\" >-0.19</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row12\" class=\"row_heading level0 row12\" >horizon_NWW</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col0\" class=\"data row12 col0\" >-0.097</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col1\" class=\"data row12 col1\" >-0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col2\" class=\"data row12 col2\" >-0.034</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col3\" class=\"data row12 col3\" >0.02</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col4\" class=\"data row12 col4\" >-0.05</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col5\" class=\"data row12 col5\" >0.059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col6\" class=\"data row12 col6\" >0.021</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col7\" class=\"data row12 col7\" >0.083</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col8\" class=\"data row12 col8\" >-0.073</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col9\" class=\"data row12 col9\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col10\" class=\"data row12 col10\" >0.56</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col11\" class=\"data row12 col11\" >0.71</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col12\" class=\"data row12 col12\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col13\" class=\"data row12 col13\" >-0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col14\" class=\"data row12 col14\" >-0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col15\" class=\"data row12 col15\" >-0.15</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col16\" class=\"data row12 col16\" >-0.081</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow12_col17\" class=\"data row12 col17\" >-0.014</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row13\" class=\"row_heading level0 row13\" >horizon_SSE</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col0\" class=\"data row13 col0\" >0.1</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col1\" class=\"data row13 col1\" >0.052</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col2\" class=\"data row13 col2\" >-0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col3\" class=\"data row13 col3\" >-0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col4\" class=\"data row13 col4\" >0.038</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col5\" class=\"data row13 col5\" >-0.089</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col6\" class=\"data row13 col6\" >-0.059</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col7\" class=\"data row13 col7\" >-0.066</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col8\" class=\"data row13 col8\" >0.9</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col9\" class=\"data row13 col9\" >0.57</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col10\" class=\"data row13 col10\" >0.18</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col11\" class=\"data row13 col11\" >0.073</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col12\" class=\"data row13 col12\" >-0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col13\" class=\"data row13 col13\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col14\" class=\"data row13 col14\" >0.7</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col15\" class=\"data row13 col15\" >0.31</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col16\" class=\"data row13 col16\" >0.23</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow13_col17\" class=\"data row13 col17\" >-0.56</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row14\" class=\"row_heading level0 row14\" >horizon_SEE</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col0\" class=\"data row14 col0\" >0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col1\" class=\"data row14 col1\" >0.31</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col2\" class=\"data row14 col2\" >0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col3\" class=\"data row14 col3\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col4\" class=\"data row14 col4\" >-0.012</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col5\" class=\"data row14 col5\" >-0.032</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col6\" class=\"data row14 col6\" >-0.015</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col7\" class=\"data row14 col7\" >-0.036</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col8\" class=\"data row14 col8\" >0.55</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col9\" class=\"data row14 col9\" >0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col10\" class=\"data row14 col10\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col11\" class=\"data row14 col11\" >-0.16</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col12\" class=\"data row14 col12\" >-0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col13\" class=\"data row14 col13\" >0.7</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col14\" class=\"data row14 col14\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col15\" class=\"data row14 col15\" >0.74</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col16\" class=\"data row14 col16\" >0.62</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow14_col17\" class=\"data row14 col17\" >-0.39</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row15\" class=\"row_heading level0 row15\" >horizon_E</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col0\" class=\"data row15 col0\" >0.047</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col1\" class=\"data row15 col1\" >0.37</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col2\" class=\"data row15 col2\" >-0.0064</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col3\" class=\"data row15 col3\" >-0.11</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col4\" class=\"data row15 col4\" >-0.043</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col5\" class=\"data row15 col5\" >0.0046</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col6\" class=\"data row15 col6\" >0.0087</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col7\" class=\"data row15 col7\" >-0.0093</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col8\" class=\"data row15 col8\" >0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col9\" class=\"data row15 col9\" >-0.086</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col10\" class=\"data row15 col10\" >-0.24</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col11\" class=\"data row15 col11\" >-0.24</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col12\" class=\"data row15 col12\" >-0.15</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col13\" class=\"data row15 col13\" >0.31</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col14\" class=\"data row15 col14\" >0.74</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col15\" class=\"data row15 col15\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col16\" class=\"data row15 col16\" >0.89</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow15_col17\" class=\"data row15 col17\" >-0.13</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row16\" class=\"row_heading level0 row16\" >horizon_NEE</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col0\" class=\"data row16 col0\" >0.031</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col1\" class=\"data row16 col1\" >0.35</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col2\" class=\"data row16 col2\" >-0.017</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col3\" class=\"data row16 col3\" >-0.12</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col4\" class=\"data row16 col4\" >-0.022</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col5\" class=\"data row16 col5\" >-0.023</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col6\" class=\"data row16 col6\" >-0.01</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col7\" class=\"data row16 col7\" >-0.027</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col8\" class=\"data row16 col8\" >0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col9\" class=\"data row16 col9\" >-0.096</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col10\" class=\"data row16 col10\" >-0.22</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col11\" class=\"data row16 col11\" >-0.21</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col12\" class=\"data row16 col12\" >-0.081</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col13\" class=\"data row16 col13\" >0.23</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col14\" class=\"data row16 col14\" >0.62</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col15\" class=\"data row16 col15\" >0.89</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col16\" class=\"data row16 col16\" >1.0</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow16_col17\" class=\"data row16 col17\" >-0.089</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6flevel0_row17\" class=\"row_heading level0 row17\" >tilted_radiation</th>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col0\" class=\"data row17 col0\" >-0.17</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col1\" class=\"data row17 col1\" >-0.036</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col2\" class=\"data row17 col2\" >0.028</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col3\" class=\"data row17 col3\" >0.0088</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col4\" class=\"data row17 col4\" >-0.14</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col5\" class=\"data row17 col5\" >0.22</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col6\" class=\"data row17 col6\" >0.2</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col7\" class=\"data row17 col7\" >0.033</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col8\" class=\"data row17 col8\" >-0.58</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col9\" class=\"data row17 col9\" >-0.48</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col10\" class=\"data row17 col10\" >-0.25</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col11\" class=\"data row17 col11\" >-0.19</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col12\" class=\"data row17 col12\" >-0.014</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col13\" class=\"data row17 col13\" >-0.56</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col14\" class=\"data row17 col14\" >-0.39</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col15\" class=\"data row17 col15\" >-0.13</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col16\" class=\"data row17 col16\" >-0.089</td>\n",
" <td id=\"T_6503c6e8_348f_11eb_abb6_8c85902f5b6frow17_col17\" class=\"data row17 col17\" >1.0</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feabf540c50>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Compute the correlation between variables\n",
"correlation_matrix = data.corr()\n",
"correlation_matrix.style.background_gradient(cmap='coolwarm', vmin = -1, vmax = 1).format(\"{:.2}\") # Formatting"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. The corrlation matrix shows a strong negative correlation between the y-coordinate of the roof (roof_y) and SIS/SISDIR. Can you explain why? Why do we see no such correlation for roof_x?\n",
"- b. The correlation matrix also shows two correlated \"blocks\" of the horizon values. What causes these blocks and what does that imply for the model?\n",
"- c. Which are the five variables with the strongest correlation to the target variable? Which are the five variables with the smallest correlation?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The strong correlation is due to the fact that the y-coordinate represents the northern *latitude*, which determines the maximum solar angle and the total daylight hours; this influences the global and direct horizontal radiation - the further north we are, the lower the horizontal radiation. The roof_x is the *longitude*, which impacts the annual radiation to a much lesser extent.\n",
"- b. The two \"blocks\" are formed around the east and west horizon values, i.e. the horizon values for eastern azimuths are strongly positively correlated, and are slightly negatively correlated to the horizon values for western azimuths. The strong positive correlation suggests that we don't need *all* the horizon values, but a subset would suffice.\n",
"- c. The strongest correlation with the target is observed for the horizon values for **S, SSE, SSW, SEE and SWW**. The smallest correlation is seen for **roof_x, horizon_NWW, roof_area, DHI and roof_aspect**.<br>**NOTE**: Just because the 5 horizons have the highest correlation, it does not mean that these are the best features to select!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1.3 - Visualising features vs. targets \n",
"Visualising the relationship between variables and between the variables and the target can help to significantly improve our understanding of the data. In particular, it can help to identify non-linear relationships that are not captured by the correlation coefficient.\n",
"\n",
"Pandas has a built-in function to visualise *all* relationships between features and the target as scatterplot, just like the correlation matrix. It is called as: `pd.plotting.scatter_matrix(data)`\n",
"\n",
"As we have a lot of features and targets, this results in a huge plot that becomes difficult to interpret (but feel free to try it!). Here, we will look only at the relationships between each feature and the target."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To start, we set up the matplotlib interactive plotting:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and define the list of features and the target, to know what to plot against each other:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"target_name = 'tilted_radiation'\n",
"features_list = [ 'roof_tilt', 'roof_aspect', 'roof_area', 'roof_x', 'roof_y', 'SIS',\n",
" 'SISDIR', 'DHI', 'horizon_S', 'horizon_SSW', 'horizon_SWW', 'horizon_W',\n",
" 'horizon_NWW', 'horizon_SSE', 'horizon_SEE', 'horizon_E', 'horizon_NEE',]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we can create scatterplots of all features against the target:"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"};\n",
"\n",
"mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
"};\n",
"\n",
"mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"};\n",
"\n",
"mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch (cursor) {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message('ack', {});\n",
"};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = 'image/png';\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src\n",
" );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
" console.log(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\n",
" }\n",
" }\n",
" };\n",
"};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e) {\n",
" e = window.event;\n",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
" return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys(original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object') {\n",
" obj[key] = original[key];\n",
" }\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function (event, name) {\n",
" var canvas_pos = mpl.findpos(event);\n",
"\n",
" if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"};\n",
"\n",
"mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
" if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
" fig.cell_info[0].output_area.element.one(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable();\n",
" fig.parent_element.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
"};\n",
"\n",
"mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"};\n",
"\n",
"mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
" }\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code') {\n",
" for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel !== null) {\n",
" IPython.notebook.kernel.comm_manager.register_target(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
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XW1+1llnWSvNO3fuLLW1tTJ37ly577775JtvvrE6+sUvfiHPPvusbafXX3+93HnnndbnAwYMEF0hfcABB1jXv+eee+Sjjz6yPtN2t99+e+KDD+gV6urq5IgjjrB+B/vuu6+1Y4AesQJ0apPcL8vTTz8tF154oWhttCYapB911FFSUlIi33//vfUbeOWVV6S4uFg0aI92UKPk1oirI4AAAggggAACCCCAAAIIIIAAAggggAACCARbgAA92PXPyNlrWD5r1izJz8+3VkRHbnOsW1VrIKvHLbfcIjfddFNGztNvgz755JPl/PPPlzPOOEPy8vIaDU+3pD7yyCNl5cqV1mdam2jbuX/22WfSq1cva0W0rozWdkVFRfXX0wcjdLX7ggULrBp/+umnVrjO4V3gL3/5i1x11VXWwySnnXaa3HXXXdZF7AJ0auPd2MsZy5cvtx4Yqa6utn4boaA82jVqamqi7uJAjbyI0xYBBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAe8CBOjezTgjjQK6klbfka3HmDFjrC2+Iw9d2XnIIYeIhlWtW7eWiooK6z3dHMkXePXVV+WUU06xOrriiivkgQceaNTp+PHj5aGHHrL+Xleu6wrpyOP999+vfzDi8ssvl7/97W/JH3yW9fDVV19J7969Zdu2bVZgrg+d6AMletgF6NQmuV+CY489VmbOnClt27a1/n3S/+31oEZexWiPAAIIIIAAAggggAACCCCAAAIIIIAAAggggIA3AQJ0b160TrNA+NbFGrIOGTIk6ogmTpxobe2uR3l5ufz0pz9N88iD0b2Gtfvss4812ZNOOkk0UA8/9N3NnTp1srZ611XRGiLaHfr5ihUrpGPHjrJ27Vrehe7xK6QPMqj/6NGj5cknn5Sbb745ZoBObTwCe2yuOynozgt6aC3++Mc/eryCCDXyTMYJCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgh4FiBA90zGCekUGD58uLz99tvSvHlz2bx5s7XFd7RDVzbre9L10C3cQytv0zn2IPS9ceNGadOmjTVVDXBffvnlBtP+/PPP67djt9tBIHSCfj558mTrj3pet27dgkBoZI5Tp06Vc845x3qvtga37dq1cwzQqY0RetuL3HbbbfWvk1i6dKm1O4AemzZtEn39gdYq9Nuxuwg1Sm6NuDoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAipAgM73IKMENAjUsKlfv36yaNEi27FrKKWBlB5nnXWWaKDIkXyBf/3rX3L66adbHf3ud7+Te+65p0Gn06dPF32Xuh7333+/XHnllbaD0s+vvvpq63M978QTT0z+BLKgB32wRFc6f//99/Loo4/KJZdcYs3KaQU6tUlu8XVHhtdee02Ki4ut0PzZZ5+1fh+ffPJJfcf6kIjuGPDb3/5WWrRo0WhA1Ci5NeLqCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAgioAAE634OMEdi5c6cUFRVZ4422PXjkRDSA2r59u/WObV2RzpFcAX33/NChQ2XevHlWR/q++oEDBzboVN9ZP27cOOvvpk2bJmeeeabtoJ5//nnr4Qc99Dxdkc7hLHDppZdawbnuwPDOO+/Ub33vFKBTG2fbRFpoOL5mzRrr4Z+jjjpKHnzwQdvLHXLIIfLf//5X9t9/f34/iaBzLgIIIIAAAggggAACCCCAAAIIIIAAAggggAACcQgQoMeBxinpEVi/fr3su+++Vue6PfVzzz0XcyClpaWybt060TBq8eLF6Rl0gHq977775H/+53+sGZ922mny4osvNpr9n/70J7nmmmusv3/99dfl+OOPtxXSz0Orzu+9915rVS5HbAENzPU1B3l5ebJw4ULp27dv/QlOATq1Se63S1eeb9myRZo0aSLV1dXSqlUrmThxorVjQ8uWLa1/o/R1E/q910MfgNDXVeTm5tYPjBolt0ZcHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABFSBA53uQMQJfffWVdO7c2RrveeedJ08//XTMsWtbPeeAAw6Qzz77LGPmmYkDnT17thx77LGye/du6yEH3ZZaH2CIPMLfAz1z5kwZOXKk7XTffPNNGTVqlPW5nnfDDTdkIk3KxlxTUyP9+/eX5cuXR90+3ylApzbJLVV+fr7U1tZanegDDvqwg+6OEX7oLg76ioNQiB65SwM1Sm6NuDoCCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAipAgM73IGMEWIHuz1ItXbpUjj76aOu9zrq6VreeHjFiRNTBsoI2eTUMBeT64MiyZcukefPmDTpzCtCpTfJqo1cOvVJC//vcc8+V//3f/43aof6edNcMPXR1+gsvvFDfjholt0ZcHQEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAABFSBA53uQMQK8A91/pfriiy+s9zl/++231qpaXTGr27fbHbxnOzk1/PTTT613a+sq9Jdeekl+9rOfNerIKUCnNsmpTeiq7du3l++//97641NPPSXnn3++bYcdO3aUb775Rjp16iRr166tb0eNklsjro4AAggggAACCCCAAAIIIIAAAggggAACCCCAgAoQoPM9yCiBdu3ayYYNG6ywcNGiRbZj19XQJSUl1udnnXWWTJ06NaPmmQmD1dBcV55//vnnkpOTI08++WTMUFDn9Oqrr8opp5xiTe/++++XK6+80naq+vnVV19tfT59+vT696Fngk2qxzhmzBiZPHmydO/eXe64446o3T///PP1q5lvvPFG6d27t9VO66Gr1alNcqs2ePBgmT9/vtWJ0+sLhg4dKu+//761o4M+OBQ6qFFya8TVEUAAAQQQQAABBBBAAAEEEEAAAQQQQAABBBBQAQJ0vgcZJTB8+HB5++23rcBv8+bNou8VjnbMnTtXhg0bZn100003yS233JJR8/T7YPUhBt2mXbcK1+Pvf/+7jB8/3nHYGrbrO+n10NBXV9TaHaFQWD/X87p16+Z4/aA2uOCCC6xVzfEcuotA165dLWNqE4+gu3MuvPBC6yETPcrLy+WnP/2p7YmhsF3/ndu2bVt9O2rkzppWCCCAAAIIIIAAAggggAACCCCAAAIIIIAAAggkIkCAnoge56Zc4A9/+IPcddddVr+6QnPIkCFRxzBx4kS57rrrrM/0ndxlZWUpH2u2dlhZWSkjR46UhQsXWlNU62uvvdbVdPfs2SO6PbWuXj/44INl+fLltuf16tVLdGvyDh06yFdffWWtcueILmAiQKc2yf12PfHEE3LRRRdZnTz88MMyduxY2w7btm0rP/zwg/Ts2VNWrFhR344aJbdGXB0BBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAARUgQOd7kFEC8+bNqw/N7VYw///27h+kriSKA/AgKCgqFmJrk1SxEQwEbExpY6EIFukkhSLYpUmRRgSx0UIJEmy1EBvRPoJVBAU7E0GDVbSK/0BFlrnwwkp012H3wXuT73Ymx/vmfOda/e6bubu7Cx0dHUU429LSEn78+BFqa2urqs9KXezl5WXxMsLW1laxxPfv34eJiYmk5Y6OjhYBYrziTgGvXr367ffjyxFxG+t4xfq5ubmkz1D8u8C/nYFesjab8jw9MRCP56Df3NwU3z6P30J/6Pr8+XPo6ekp/mt4eDh8+vTpXpm/n/LMx10JECBAgAABAgQIECBAgAABAgQIECBAgEBJQIDuWag6gdI27nH79s3NzV9Ba6mR6enp8O7du+LHDx8+hBgcuv67wPX1dXFedin4Gx8fDzMzM8k33t/fDy9evAi3t7ehq6urmGF9ff2v+1xdXYU44+3t7WKL/rhN/PPnz5M/xy/cF3hKgG425X1q/h5+Ly0thaGhoXsfeHZ2Vjz7u7u7xb/HF4Zevnx5r8aMyjsjdydAgAABAgQIECBAgAABAgQIECBAgAABAgJ0z0DVCezs7ITu7u4Qg9bGxsYQt3V//fp18fPy8nJYWFgoeorbH8cQtqmpqep6rMQFDwwMhNXV1WJpcQv3GJ7/07bqdXV1xQweuuL2+nHr93h1dnYWW8DH87cPDg7C1NRUiDOOV6ybnJysRI6qW9NTAvSSudmUZ7wnJyfFSyPfv38vXg6J27j39/eH5ubmsLe3Vzz78diCeI2MjIT5+Xl/P+UZhbsSIECAAAECBAgQIECAAAECBAgQIECAAIFHBQToHo6qFFhbWwtv3rwJP3/+fHD9MbhdX18Pz549q8r+KnHRqWeQt7e3h8PDwwdbidvsv337NiwuLj7aaty+Or4MUVNTU4kcVbempwboZlPe0cajJfr6+sK3b98e/aB4VvrHjx8fPXrCjMo7I3cnQIAAAQIECBAgQIAAAQIECBAgQIAAgT9bQID+Z8+/qrs/OjoKs7OzRVB+fHwc4jeeY2A+ODgYxsbGQkNDQ1X3V2mL/z8D9FJvGxsbRUj+5cuXcHp6GlpbW4stq+P59r29vZVGUNXreWqAbjblH/PFxUWIZ82vrKyEr1+/hvPz89DW1lbsrBGf/bijxlMufz9PUVJDgAABAgQIECBAgAABAgQIECBAgAABAgTSBAToaV6qCRAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQCBTAQF6poPVFgECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAikCQjQ07xUEyBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgECmAgL0TAerLQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBBIExCgp3mpJkCAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAIFMBQTomQ5WWwQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQJiBAT/NSTYAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQKZCgjQMx2stggQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgTUCAnualmgABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQyFRCgZzpYbREgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBAmoAAPc1LNQECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAhkKiBAz3Sw2iJAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBNAEBepqXagIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBDIVECAnulgtUWAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECaQIC9DQv1QQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECCQqYAAPdPBaosAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIE0gQE6GleqgkQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIEAgUwEBeqaD1RYBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIpAkI0NO8VBMgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIBApgIC9EwHqy0CBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQSBMQoKd5qSZAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgACBTAUE6JkOVlsECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkCYgQE/zUk2AAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECmQoI0DMdrLYIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIE1AgJ7mpZoAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIEMhUQoGc6WG0RIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQJqAAD3NSzUBAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIZCogQM90sNoiQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAgTQBAXqal2oCBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQyFRAgJ7pYLVFgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAmkCAvQ0L9UECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECBAgkKmAAD3TwWqLAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAAAECBNIEBOhpXqoJECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIECBAIFMBAXqmg9UWAQIECBAgQIAAAQIECBAgQIAAAQIECBAgQIAAAQIECKQJCNDTvFQTIECAAAECBAgQIECAAAECBAgQIECAAAECBAgQIECAQKYCAvRMB6stAgQIECBAgAABAgQIECBAgAABAgQIECBAgAABAgQIEEgT+At2s5i3NcGTAQAAAABJRU5ErkJggg==\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Create figure\n",
"fig, axes = plt.subplots(5,4, sharey = True, figsize = (10,10))\n",
"ax = axes.flatten()\n",
"for i, feature in enumerate(features_list):\n",
" data.plot.scatter(x = feature, y = target_name, ax = ax[i], s = 2) # Plot feature against target\n",
" # Format figure:\n",
" ax[i].set_xlabel('')\n",
" ax[i].set_title(feature)\n",
" ax[i].grid(ls = '--')\n",
" \n",
" # For the roof area only, show the x-axis with logarithmic values:\n",
" if feature == 'roof_area': ax[i].set_xscale('log')\n",
"for j in range(i+1, len(ax)): ax[j].axis('off') # deactivate unused sub-axes\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. Based on the scatterplots, which variable shows the strongest relatioship with the target?\n",
"- b. What is the correlation coefficient between this varialbe and the target?\n",
"- c. How do you explain the discrepancy, and what does this mean for the modelling?\n",
"- d. Can you think of a way to transform this variable in order to increase its correlation coefficient?\n",
"- e. For the other 4 features with the smallest correlation to the target from Step 1.2, do you observe a similar discrepancy or are the correlation coefficients confirmed by the plots?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The scatterplots suggests that the tilted radiation depends strongly on the *__roof aspect__*. This does not come as a surprise; north-facing roofs receive less solar radiation than south-facing roofs.\n",
"- b. The correlation coefficient of the roof aspect is **-0.036**, making it one of the *lowest* coefficients in the dataset.\n",
"- c. The relationship of the roof_aspect to the tilted radiation is highly **non-linear** (rather quadratic). Consequently, the measure of *linear correlation* is low. This is an important pitfall of using correlation coefficients to measure the importance of features for the modelling. We should hence always cross-check the scatterplots.\n",
"- d. We could transform the roof aspect by dividing it into two features: *abs(roof_aspect)* and *sign(roof_aspect)*. This would help to *linearize* thia feature, and can significantly improve the performance of the linear regression that we have seen in Tutorial 1. Other estimators like KNN or SVR do not require linear features, but in some cases this kind of **feature transformation** can significantly improve model performance.\n",
"- e. In the other 4 cases (roof_x, horizon_NWW, roof_area, DHI), the low correlation coefficient is confirmed by a near-random scatterplot."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1.4 - Feature transformation\n",
"Many algorithms, such as linear regression or KNN, perform better with features that are have a *linear* relationship with the target. As we saw in Step 1.3, this is not the case for the roof aspect, which however appears to be one of the most important features following the scatterplot. An inspection of the roof aspect actually hints to a very simple way to turn this feature into a linear one, by dividing it into two features without any loss of information:\n",
"- 1. The absolute value (computed using python's `abs()`-function)\n",
"- 2. The sign (computed using numpy's `np.sign()` function)\n",
"\n",
"Let's transform the data in this way by computing first the sign and then replacing the roof aspect by it's absolute value:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"data['aspect_sign'] = ## COMPUTE THE SIGN OF roof_aspect\n",
"data['roof_aspect'] = ## COMPUTE THE ABSOLUTE VALUE OF roof_aspect"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"data['aspect_sign'] = np.sign(data['roof_aspect']) ## Compute the sign of the roof_aspect\n",
"data['roof_aspect'] = abs(data['roof_aspect']) ## Compute the absolute value of the roof_aspect"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can check the correlation coefficient of the new features with the tilted radiation by using the `corrwith` function, and will see that it is much improved and that the roof_aspect becomes the *most important feature*:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"roof_aspect -0.648147\n",
"aspect_sign -0.037462\n",
"dtype: float64"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data[['roof_aspect', 'aspect_sign']].corrwith(data['tilted_radiation'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we need to add the new feature `aspect_sign` to the feature list:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"features_list = ## NEW FEATURE LIST"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"features_list = [ 'roof_tilt', 'roof_aspect', 'roof_area', 'roof_x', 'roof_y', 'aspect_sign', 'SIS',\n",
" 'SISDIR', 'DHI', 'horizon_S', 'horizon_SSW', 'horizon_SWW', 'horizon_W',\n",
" 'horizon_NWW', 'horizon_SSE', 'horizon_SEE', 'horizon_E', 'horizon_NEE',]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 1.5 - Mutual information\n",
"Now that we have assessed the correlation between features and target and have seen that there are some strong correlations between the features, how do we choose the best features for modelling? \n",
"\n",
"The **mutual information** criterion can provide some useful inputs to answer this question, as mutual information computes accounts for dependencies between features.\n",
"\n",
"We can compute the mutual information of the features towards the target using the `mutual_info_regression`from `sklearn.feature_selection`:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import mutual_info_regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By splitting the data into target (y) and features (X), we can call the function and display it as a pandas dataframe:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = ## EXTRACT THE TARGET COLUMN\n",
"X = ## EXTRACT THE FEATURES"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"y = data[ target_name ] # Extract the target column\n",
"X = data[ features_list ] # Extract the list of all features "
]
},
{
"cell_type": "code",
"execution_count": 13,
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutual_info</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>roof_aspect</th>\n",
" <td>0.610570</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_S</th>\n",
" <td>0.221999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>roof_tilt</th>\n",
" <td>0.185425</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_SSW</th>\n",
" <td>0.173680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_SSE</th>\n",
" <td>0.164102</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SIS</th>\n",
" <td>0.066219</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_SEE</th>\n",
" <td>0.053011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>aspect_sign</th>\n",
" <td>0.043437</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_SWW</th>\n",
" <td>0.040205</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SISDIR</th>\n",
" <td>0.040086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>roof_y</th>\n",
" <td>0.029993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DHI</th>\n",
" <td>0.021485</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_NWW</th>\n",
" <td>0.016094</td>\n",
" </tr>\n",
" <tr>\n",
" <th>roof_x</th>\n",
" <td>0.014420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>roof_area</th>\n",
" <td>0.012550</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_W</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_E</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>horizon_NEE</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Mutual_info\n",
"roof_aspect 0.610570\n",
"horizon_S 0.221999\n",
"roof_tilt 0.185425\n",
"horizon_SSW 0.173680\n",
"horizon_SSE 0.164102\n",
"SIS 0.066219\n",
"horizon_SEE 0.053011\n",
"aspect_sign 0.043437\n",
"horizon_SWW 0.040205\n",
"SISDIR 0.040086\n",
"roof_y 0.029993\n",
"DHI 0.021485\n",
"horizon_NWW 0.016094\n",
"roof_x 0.014420\n",
"roof_area 0.012550\n",
"horizon_W 0.000000\n",
"horizon_E 0.000000\n",
"horizon_NEE 0.000000"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(pd.DataFrame(mutual_info_regression(X, y), index = X.columns, columns = ['Mutual_info'])\n",
" .sort_values('Mutual_info', ascending = False))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. Based on mutual information, which are the most important features?\n",
"- b. How does this answer change compared to the answers from Step 1.2?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The most important features now are **roof aspect, horizon_S, roof_tilt, horizon_SSW and horizon_SSE**.\n",
"- b. The roof aspect and roof tilt have replaced the horizon values for SEE and SWW amongst the most important features. The roof tilt only becomes important in *combination* with the roof aspect and is actually one of the most important features. There is an intuitive explanation for this: Steep roofs have a very low potential if they face north, but a high potential if facing south, while the potential is high for roofs of *any* aspect."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 2 - Feature preprocessing and k-fold cross-validation\n",
"Before modelling the data, we need to pre-process it. In contrast to the previous tutorials, we will now use **cross-validation** instead of simple validation, and hence no longer need to divide the data into training and validation set. To see how the k-fold cross-validation works. In addition, we need to *scale* the data."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The required packages are:"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.preprocessing import scale\n",
"from sklearn.model_selection import KFold\n",
"\n",
"from lib_file import * # Functions in the file lib_file.py, which should be in the same directory as your notebook"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, let's visualise how the k-fold cross-validation works for our dataset. Throughout this tutorial we will work with 5-fold cross-validation, which is the default in all of scikit's `cross_val`-functions:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"N_FOLDS = 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can declare a 5-fold cross-validation and plot the resulting splits for our data:"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/plain": [
"KFold(n_splits=5, random_state=None, shuffle=False)"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cv = KFold(n_splits = N_FOLDS)\n",
"cv"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": false
},
"outputs": [
{
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" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
" button.classList = 'mpl-widget';\n",
" button.setAttribute('role', 'button');\n",
" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
" this.format_dropdown = fmt_picker;\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"};\n",
"\n",
"mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
"};\n",
"\n",
"mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"};\n",
"\n",
"mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch (cursor) {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message('ack', {});\n",
"};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = 'image/png';\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src\n",
" );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
" console.log(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\n",
" }\n",
" }\n",
" };\n",
"};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e) {\n",
" e = window.event;\n",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
" return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys(original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object') {\n",
" obj[key] = original[key];\n",
" }\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function (event, name) {\n",
" var canvas_pos = mpl.findpos(event);\n",
"\n",
" if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"};\n",
"\n",
"mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
" if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
" fig.cell_info[0].output_area.element.one(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable();\n",
" fig.parent_element.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
"};\n",
"\n",
"mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"};\n",
"\n",
"mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
" }\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code') {\n",
" for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel !== null) {\n",
" IPython.notebook.kernel.comm_manager.register_target(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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\" width=\"700\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (7, 3))\n",
"plot_cv_indices(cv, X, y, ax = plt.gca(), n_splits = N_FOLDS, cmap_cv = 'coolwarm')\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see in the figure, the k-fold cross-validation always selects **consecutive** parts of the 1000 data samples for each of the folds. This can pose a problem if the data is *sorted*, as it may lead to large differences between folds.\n",
"\n",
"To avoid this, let's shuffle the entire dataset randomly:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"data_shuffled = data.sample( frac = 1 ) # Randomly sample from the entire dataset (i.e. reshuffle the data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: This step introduces additional randomness in the modelling, and means that your results will vary every time you run the script."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, we need to extract the features and targets again, from the shuffled dataset:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"y = ## EXTRACT THE TARGET COLUMN FROM data_shuffled\n",
"X = ## EXTRACT THE FEATURES FROM data_shuffled"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"y = data_shuffled[ target_name ] # Extract the target column\n",
"X = data_shuffled[ features_list ] # Extract the list of features (later, we may reduce this list)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, we should standardize the data using the `scale` function, as we have seen in the introduction to KNN and SVR:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"X_scaled = ## STANDARDIZE FEATURES"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"X_scaled = scale(X) ## STANDARDIZE FEATURES"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Later, we will need to know the total number of features, so let's assign this value to the variable `n_features_all`:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"18"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_features_all = X_scaled.shape[1]\n",
"n_features_all"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 3 - KNN and feature selection\n",
"Since KNN is a very quick algorithm that performs well even with the default parameters (as we have seen in the introduction tutorial), we will use it to perform feature selection, since we still have no answe to the question:\n",
"\n",
"_**How many features** should we include in the model?_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For the feature selection using KNN the following functions are needed:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.feature_selection import SelectKBest\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.model_selection import cross_val_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3.1: Feature selection - a first glance\n",
"To perform feature selection, we will use the `mutual_info_regression`-function to define *which* are the best features to select. Let's select, just as a test, half of the features.\n",
"\n",
"We can obtain the 9 best features using the `SelectKBest`-function such as:"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Set up a SelectKBest instance with mutual information as decision criterion to return the 8 best features\n",
"mutual_info = SelectKBest(mutual_info_regression, k = 8 ) # Initialise\n",
"X_reduced = mutual_info.fit_transform(X_scaled, y) # Extract the best features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then, we can declare a `KNeighborsRegressor` and perform a cross-validation for the reduced dataset. This requires to pass the model, the features and the target to `cross_val_score`:"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0.73383192, 0.81595504, 0.76138442, 0.74259474, 0.79144855])"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = KNeighborsRegressor() # Initiate model\n",
"cv_score = cross_val_score(knn, X_reduced, y, cv = N_FOLDS)\n",
"cv_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `cv_score` shows the results for all folds, but often it is more informative to obtain the *mean* and *standard deviation* instead:"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.7690429327469429\n",
"0.030661809355532123\n"
]
}
],
"source": [
"print( cv_score.mean() )\n",
"print( cv_score.std() )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. How does the score compare to the default model with all features in the Introduction tutorial?\n",
"- b. What does this very first glance suggest about the importance of feature engineering (transformation, selection, etc.) compared to the importance of model tuning?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The model is improved from a score of around 0.4 to a score above 0.7 - this is a significant improvement, **just by improving the data used for modelling, not by changing the model itself.**\n",
"- b. Feature engineering is at least as important as the modelling process itself!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3.2: Systematic feature selection\n",
"Now, let's approach the model selection in a more systematic way, but iterating over all possible number of features.\n",
"\n",
"**NOTE**: We will do this step using the default knn model in this tutorial. However, be aware that the optimal number of features can **change** for different models and for different hyper-parameters!"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"n_features = range(1, n_features_all + 1) # Construct a list of all possible number of features\n",
"list(n_features)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We next declare an empty dataframe that contains the k folds as rows, and the different number of features as columns:"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"cv_featureSelect = pd.DataFrame(index = range(N_FOLDS), columns = n_features).fillna(0)\n",
"cv_featureSelect.index.name = 'CV iteration'\n",
"cv_featureSelect.columns.name = 'Number of features'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"By iterating over all possible number of features, we compute the cross-validation scores for the default knn:"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"# Iterate over all possible numbers of features\n",
"for n in n_features: \n",
" # Extract the n best features\n",
" mutual_info = SelectKBest(mutual_info_regression, k = n )\n",
" X_reduced = mutual_info.fit_transform(X_scaled, y)\n",
" \n",
" # Train a knn and score the performance with 5-fold cross-validation\n",
" knn = KNeighborsRegressor() # Initiate model\n",
" cv_featureSelect.loc[:,n] = cross_val_score(knn, X_reduced, y, cv = N_FOLDS) "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is the resulting dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 29,
"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>Number of features</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
" <th>5</th>\n",
" <th>6</th>\n",
" <th>7</th>\n",
" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>11</th>\n",
" <th>12</th>\n",
" <th>13</th>\n",
" <th>14</th>\n",
" <th>15</th>\n",
" <th>16</th>\n",
" <th>17</th>\n",
" <th>18</th>\n",
" </tr>\n",
" <tr>\n",
" <th>CV iteration</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.571</td>\n",
" <td>0.630</td>\n",
" <td>0.745</td>\n",
" <td>0.738</td>\n",
" <td>0.731</td>\n",
" <td>0.739</td>\n",
" <td>0.727</td>\n",
" <td>0.713</td>\n",
" <td>0.732</td>\n",
" <td>0.729</td>\n",
" <td>0.731</td>\n",
" <td>0.712</td>\n",
" <td>0.689</td>\n",
" <td>0.680</td>\n",
" <td>0.682</td>\n",
" <td>0.680</td>\n",
" <td>0.675</td>\n",
" <td>0.667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.505</td>\n",
" <td>0.623</td>\n",
" <td>0.795</td>\n",
" <td>0.846</td>\n",
" <td>0.848</td>\n",
" <td>0.850</td>\n",
" <td>0.838</td>\n",
" <td>0.804</td>\n",
" <td>0.831</td>\n",
" <td>0.796</td>\n",
" <td>0.782</td>\n",
" <td>0.776</td>\n",
" <td>0.747</td>\n",
" <td>0.719</td>\n",
" <td>0.724</td>\n",
" <td>0.691</td>\n",
" <td>0.701</td>\n",
" <td>0.693</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.495</td>\n",
" <td>0.552</td>\n",
" <td>0.720</td>\n",
" <td>0.745</td>\n",
" <td>0.734</td>\n",
" <td>0.749</td>\n",
" <td>0.775</td>\n",
" <td>0.744</td>\n",
" <td>0.683</td>\n",
" <td>0.647</td>\n",
" <td>0.642</td>\n",
" <td>0.639</td>\n",
" <td>0.642</td>\n",
" <td>0.619</td>\n",
" <td>0.631</td>\n",
" <td>0.634</td>\n",
" <td>0.626</td>\n",
" <td>0.628</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.514</td>\n",
" <td>0.623</td>\n",
" <td>0.734</td>\n",
" <td>0.796</td>\n",
" <td>0.764</td>\n",
" <td>0.783</td>\n",
" <td>0.773</td>\n",
" <td>0.776</td>\n",
" <td>0.802</td>\n",
" <td>0.773</td>\n",
" <td>0.759</td>\n",
" <td>0.709</td>\n",
" <td>0.696</td>\n",
" <td>0.687</td>\n",
" <td>0.680</td>\n",
" <td>0.675</td>\n",
" <td>0.652</td>\n",
" <td>0.642</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.537</td>\n",
" <td>0.574</td>\n",
" <td>0.754</td>\n",
" <td>0.758</td>\n",
" <td>0.747</td>\n",
" <td>0.764</td>\n",
" <td>0.771</td>\n",
" <td>0.762</td>\n",
" <td>0.759</td>\n",
" <td>0.752</td>\n",
" <td>0.748</td>\n",
" <td>0.751</td>\n",
" <td>0.690</td>\n",
" <td>0.687</td>\n",
" <td>0.695</td>\n",
" <td>0.659</td>\n",
" <td>0.666</td>\n",
" <td>0.669</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Number of features 1 2 3 4 5 6 7 8 \\\n",
"CV iteration \n",
"0 0.571 0.630 0.745 0.738 0.731 0.739 0.727 0.713 \n",
"1 0.505 0.623 0.795 0.846 0.848 0.850 0.838 0.804 \n",
"2 0.495 0.552 0.720 0.745 0.734 0.749 0.775 0.744 \n",
"3 0.514 0.623 0.734 0.796 0.764 0.783 0.773 0.776 \n",
"4 0.537 0.574 0.754 0.758 0.747 0.764 0.771 0.762 \n",
"\n",
"Number of features 9 10 11 12 13 14 15 16 \\\n",
"CV iteration \n",
"0 0.732 0.729 0.731 0.712 0.689 0.680 0.682 0.680 \n",
"1 0.831 0.796 0.782 0.776 0.747 0.719 0.724 0.691 \n",
"2 0.683 0.647 0.642 0.639 0.642 0.619 0.631 0.634 \n",
"3 0.802 0.773 0.759 0.709 0.696 0.687 0.680 0.675 \n",
"4 0.759 0.752 0.748 0.751 0.690 0.687 0.695 0.659 \n",
"\n",
"Number of features 17 18 \n",
"CV iteration \n",
"0 0.675 0.667 \n",
"1 0.701 0.693 \n",
"2 0.626 0.628 \n",
"3 0.652 0.642 \n",
"4 0.666 0.669 "
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Display the data\n",
"cv_featureSelect.round(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: Since the data is randomly shuffled before splitting, the results will deviate between different runs and from the solution!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's compute, for each number of features, the mean and standard deviation, which we can visualise as a pandas dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"# Obtain cross-validation statistics\n",
"cv_stats = pd.DataFrame( [ cv_featureSelect.mean().rename('mean'), # compute mean across the k folds\n",
" cv_featureSelect.std() .rename('std') ] ) # compute std. deviaiton across the k folds"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
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" }</style><table id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6f\" ><thead> <tr> <th class=\"index_name level0\" >Number of features</th> <th class=\"col_heading level0 col0\" >1</th> <th class=\"col_heading level0 col1\" >2</th> <th class=\"col_heading level0 col2\" >3</th> <th class=\"col_heading level0 col3\" >4</th> <th class=\"col_heading level0 col4\" >5</th> <th class=\"col_heading level0 col5\" >6</th> <th class=\"col_heading level0 col6\" >7</th> <th class=\"col_heading level0 col7\" >8</th> <th class=\"col_heading level0 col8\" >9</th> <th class=\"col_heading level0 col9\" >10</th> <th class=\"col_heading level0 col10\" >11</th> <th class=\"col_heading level0 col11\" >12</th> <th class=\"col_heading level0 col12\" >13</th> <th class=\"col_heading level0 col13\" >14</th> <th class=\"col_heading level0 col14\" >15</th> <th class=\"col_heading level0 col15\" >16</th> <th class=\"col_heading level0 col16\" >17</th> <th class=\"col_heading level0 col17\" >18</th> </tr></thead><tbody>\n",
" <tr>\n",
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" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col1\" class=\"data row0 col1\" >0.6</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col2\" class=\"data row0 col2\" >0.749</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col3\" class=\"data row0 col3\" >0.777</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col4\" class=\"data row0 col4\" >0.765</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col5\" class=\"data row0 col5\" >0.777</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col6\" class=\"data row0 col6\" >0.777</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col7\" class=\"data row0 col7\" >0.76</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col8\" class=\"data row0 col8\" >0.761</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col9\" class=\"data row0 col9\" >0.739</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col10\" class=\"data row0 col10\" >0.732</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col11\" class=\"data row0 col11\" >0.717</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col12\" class=\"data row0 col12\" >0.693</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col13\" class=\"data row0 col13\" >0.678</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col14\" class=\"data row0 col14\" >0.682</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col15\" class=\"data row0 col15\" >0.668</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col16\" class=\"data row0 col16\" >0.664</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow0_col17\" class=\"data row0 col17\" >0.66</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6flevel0_row1\" class=\"row_heading level0 row1\" >std</th>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col0\" class=\"data row1 col0\" >0.0302</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col1\" class=\"data row1 col1\" >0.035</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col2\" class=\"data row1 col2\" >0.0285</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col3\" class=\"data row1 col3\" >0.0449</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col4\" class=\"data row1 col4\" >0.0483</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col5\" class=\"data row1 col5\" >0.0439</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col6\" class=\"data row1 col6\" >0.0399</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col7\" class=\"data row1 col7\" >0.0341</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col8\" class=\"data row1 col8\" >0.0581</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col9\" class=\"data row1 col9\" >0.0573</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col10\" class=\"data row1 col10\" >0.0536</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col11\" class=\"data row1 col11\" >0.0517</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col12\" class=\"data row1 col12\" >0.0369</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col13\" class=\"data row1 col13\" >0.0365</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col14\" class=\"data row1 col14\" >0.0337</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col15\" class=\"data row1 col15\" >0.0221</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col16\" class=\"data row1 col16\" >0.0275</td>\n",
" <td id=\"T_81e91056_348f_11eb_abb6_8c85902f5b6frow1_col17\" class=\"data row1 col17\" >0.0252</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feac6a08310>"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Visualise dataframe with formatting\n",
"format_df( cv_stats, cmap='YlGn', decimals = 3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: For simplicity and readability of the code, we have created a function `format_df` in the lib_file.py, which allows to display tables with colored background."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The performance of the KNN can also be visualised as a plot with errorbars:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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" } else if (typeof MozWebSocket !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert(\n",
" 'Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.'\n",
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" }\n",
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" warnings.style.display = 'block';\n",
" warnings.textContent =\n",
" 'This browser does not support binary websocket messages. ' +\n",
" 'Performance may be slow.';\n",
" }\n",
" }\n",
"\n",
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"\n",
" var canvas_div = (this.canvas_div = document.createElement('div'));\n",
" canvas_div.setAttribute(\n",
" 'style',\n",
" 'border: 1px solid #ddd;' +\n",
" 'box-sizing: content-box;' +\n",
" 'clear: both;' +\n",
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" this.root.appendChild(canvas_div);\n",
"\n",
" var canvas = (this.canvas = document.createElement('canvas'));\n",
" canvas.classList.add('mpl-canvas');\n",
" canvas.setAttribute('style', 'box-sizing: content-box;');\n",
"\n",
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"\n",
" var backingStore =\n",
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" this.context.webkitBackingStorePixelRatio ||\n",
" this.context.mozBackingStorePixelRatio ||\n",
" this.context.msBackingStorePixelRatio ||\n",
" this.context.oBackingStorePixelRatio ||\n",
" this.context.backingStorePixelRatio ||\n",
" 1;\n",
"\n",
" this.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
" if (this.ratio !== 1) {\n",
" fig.send_message('set_dpi_ratio', { dpi_ratio: this.ratio });\n",
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"\n",
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"\n",
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" }\n",
"\n",
" rubberband_canvas.addEventListener(\n",
" 'mousedown',\n",
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" 'mouseup',\n",
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" });\n",
"\n",
" canvas_div.appendChild(canvas);\n",
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"\n",
" this.rubberband_context = rubberband_canvas.getContext('2d');\n",
" this.rubberband_context.strokeStyle = '#000000';\n",
"\n",
" this._resize_canvas = function (width, height, forward) {\n",
" if (forward) {\n",
" canvas_div.style.width = width + 'px';\n",
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"};\n",
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"\n",
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" /* Instead of a spacer, we start a new button group. */\n",
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" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'mpl-button-group';\n",
" continue;\n",
" }\n",
"\n",
" var button = (fig.buttons[name] = document.createElement('button'));\n",
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" button.setAttribute('aria-disabled', 'false');\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
"\n",
" var icon_img = document.createElement('img');\n",
" icon_img.src = '_images/' + image + '.png';\n",
" icon_img.srcset = '_images/' + image + '_large.png 2x';\n",
" icon_img.alt = tooltip;\n",
" button.appendChild(icon_img);\n",
"\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" var fmt_picker = document.createElement('select');\n",
" fmt_picker.classList = 'mpl-widget';\n",
" toolbar.appendChild(fmt_picker);\n",
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"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = document.createElement('option');\n",
" option.selected = fmt === mpl.default_extension;\n",
" option.innerHTML = fmt;\n",
" fmt_picker.appendChild(option);\n",
" }\n",
"\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"};\n",
"\n",
"mpl.figure.prototype.request_resize = function (x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', { width: x_pixels, height: y_pixels });\n",
"};\n",
"\n",
"mpl.figure.prototype.send_message = function (type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"};\n",
"\n",
"mpl.figure.prototype.send_draw_message = function () {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({ type: 'draw', figure_id: this.id }));\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_resize = function (fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] !== fig.canvas.width || size[1] !== fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1], msg['forward']);\n",
" fig.send_message('refresh', {});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function (fig, msg) {\n",
" var x0 = msg['x0'] / fig.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / fig.ratio;\n",
" var x1 = msg['x1'] / fig.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / fig.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0,\n",
" 0,\n",
" fig.canvas.width / fig.ratio,\n",
" fig.canvas.height / fig.ratio\n",
" );\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function (fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_cursor = function (fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch (cursor) {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_message = function (fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_draw = function (fig, _msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function (fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_history_buttons = function (fig, msg) {\n",
" for (var key in msg) {\n",
" if (!(key in fig.buttons)) {\n",
" continue;\n",
" }\n",
" fig.buttons[key].disabled = !msg[key];\n",
" fig.buttons[key].setAttribute('aria-disabled', !msg[key]);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_navigate_mode = function (fig, msg) {\n",
" if (msg['mode'] === 'PAN') {\n",
" fig.buttons['Pan'].classList.add('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" } else if (msg['mode'] === 'ZOOM') {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.add('active');\n",
" } else {\n",
" fig.buttons['Pan'].classList.remove('active');\n",
" fig.buttons['Zoom'].classList.remove('active');\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message('ack', {});\n",
"};\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function (fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = 'image/png';\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src\n",
" );\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data\n",
" );\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" } else if (\n",
" typeof evt.data === 'string' &&\n",
" evt.data.slice(0, 21) === 'data:image/png;base64'\n",
" ) {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig['handle_' + msg_type];\n",
" } catch (e) {\n",
" console.log(\n",
" \"No handler for the '\" + msg_type + \"' message type: \",\n",
" msg\n",
" );\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\n",
" }\n",
" }\n",
" };\n",
"};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e) {\n",
" e = window.event;\n",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
" return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys(original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object') {\n",
" obj[key] = original[key];\n",
" }\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function (event, name) {\n",
" var canvas_pos = mpl.findpos(event);\n",
"\n",
" if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"};\n",
"\n",
"mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
" if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
" fig.cell_info[0].output_area.element.one(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable();\n",
" fig.parent_element.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
"};\n",
"\n",
"mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"};\n",
"\n",
"mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
" }\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code') {\n",
" for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel !== null) {\n",
" IPython.notebook.kernel.comm_manager.register_target(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
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"<IPython.core.display.Javascript object>"
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\" width=\"800\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (8,3))\n",
"cv_stats.loc['mean'].plot(yerr = cv_stats.loc['std'], ax = plt.gca())\n",
"plt.ylabel('Cross-validation score ($R^2$)')\n",
"plt.grid()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. What is the optimal number of features? **NOTE**: These results can vary due to the re-shuffling of the data!\n",
"- b. How much of the improvement compared to the default model from the introduction tutorial can be attributed to the feature transformation of the `roof_aspect`? How do you measure that?\n",
"- c. How much of the improvement can be attributed to the feature selection?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The optimal number of features should lie somewhere between 4 - 9 features.\n",
"- b. If we compare the results with all features, we see that the score is increased from around 0.4 to 0.65, simply by transforming the `roof_aspect`.\n",
"- c. If we compare the score for the optimal number of features (around 0.75-0.8) to the score for all features, we see that the feature selection increases the average cv-score by another +0.1 approximately."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 3.3: Hyper-Parameter tuning\n",
"Now that we have chosen the features that we use for modelling, we can tune the hyper-parameters of the KNN model. This is quite simple, since we have only one main parameter (`n_neighbors`). We can hence tune it in a similar way as selecting the optimal number of features, by looping over a range of possible values for `n_neighbors` and constructing a metric for the results."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before tuning, let's extract the feature set for the optimal number of features. As the results vary due to the re-shuffling of the data, we will define it here as **6** for consistency. A set of 6 features should always yield some of the best results, even if not the very best."
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"n_features = 6"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mutual_info = ## INITIALIZE THE MUTUAL_INFO SELECTOR WITH n_features\n",
"X_tuning = ## EXTRACT THE BEST FEATURES FROM X_scale"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"mutual_info = SelectKBest(mutual_info_regression, k = n_features ) # Initialize \n",
"X_tuning = mutual_info.fit_transform(X_scaled, y) # Extract the best features"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, let's define the values of `n_neighbors` to test. Since the KNN is a very fast algorithm, let's test all possible values between 1 and 50:"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"n_neighbors_list = np.arange(1, 50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Similar to the assessment of the number of features, we can construct a dataframe that will hold the results, and iterate over the `n_neighbors_list` to fill the dataframe with cross-validation scores:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"## DECLARE DATAFRAME FOR RESULTS OF KNN HYPER-PARAMETER SELECTION\n",
"knn_select = ## DECLARE DATAFRAME\n",
"## RENAME INDEX\n",
"## RENAME COLUMNS"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"knn_select = pd.DataFrame(index = range(N_FOLDS), columns = n_neighbors_list).fillna(0)\n",
"knn_select.index.name = 'CV iteration'\n",
"knn_select.columns.name = 'Number of neighbors'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for n_neighbors in n_neighbors_list: ## ITERATE OVER NUMBER OF NEIGHBORS\n",
" \n",
" # Train a knn and score the performance with 5-fold cross-validation\n",
" ## DECLARE MODEL\n",
" knn_select.loc[:,n_neighbors] = ## ASSIGN CROSS-VALIDATION SCORES FOR X_tuning"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"for n_neighbors in n_neighbors_list: # Iterate over all possible numbers of neighbours \n",
" \n",
" # Train a knn and score the performance with 5-fold cross-validation\n",
" knn = KNeighborsRegressor(n_neighbors = n_neighbors) \n",
" knn_select.loc[:,n_neighbors] = cross_val_score(knn, X_tuning, y, cv = N_FOLDS) ## DIY"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For better interpretability, let's immediately compute the mean and the standard deviation for each hyper-parameter:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"knn_stats = ## CONSTRUCT DATAFRAME WITH MEAN AND STANDARD DEVIATION FROM knn_select"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"knn_stats = pd.DataFrame( [ knn_select.mean().rename('mean'), \n",
" knn_select.std() .rename('std') ] )"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
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" <tr style=\"text-align: right;\">\n",
" <th>Number of neighbors</th>\n",
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" <th>2</th>\n",
" <th>3</th>\n",
" <th>4</th>\n",
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" <th>8</th>\n",
" <th>9</th>\n",
" <th>10</th>\n",
" <th>...</th>\n",
" <th>40</th>\n",
" <th>41</th>\n",
" <th>42</th>\n",
" <th>43</th>\n",
" <th>44</th>\n",
" <th>45</th>\n",
" <th>46</th>\n",
" <th>47</th>\n",
" <th>48</th>\n",
" <th>49</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.591</td>\n",
" <td>0.723</td>\n",
" <td>0.746</td>\n",
" <td>0.765</td>\n",
" <td>0.777</td>\n",
" <td>0.780</td>\n",
" <td>0.775</td>\n",
" <td>0.777</td>\n",
" <td>0.783</td>\n",
" <td>0.782</td>\n",
" <td>...</td>\n",
" <td>0.733</td>\n",
" <td>0.730</td>\n",
" <td>0.728</td>\n",
" <td>0.728</td>\n",
" <td>0.727</td>\n",
" <td>0.725</td>\n",
" <td>0.724</td>\n",
" <td>0.724</td>\n",
" <td>0.723</td>\n",
" <td>0.722</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.099</td>\n",
" <td>0.050</td>\n",
" <td>0.047</td>\n",
" <td>0.041</td>\n",
" <td>0.044</td>\n",
" <td>0.043</td>\n",
" <td>0.039</td>\n",
" <td>0.040</td>\n",
" <td>0.039</td>\n",
" <td>0.040</td>\n",
" <td>...</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.032</td>\n",
" <td>0.031</td>\n",
" <td>0.030</td>\n",
" <td>0.030</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 49 columns</p>\n",
"</div>"
],
"text/plain": [
"Number of neighbors 1 2 3 4 5 6 7 8 \\\n",
"mean 0.591 0.723 0.746 0.765 0.777 0.780 0.775 0.777 \n",
"std 0.099 0.050 0.047 0.041 0.044 0.043 0.039 0.040 \n",
"\n",
"Number of neighbors 9 10 ... 40 41 42 43 44 \\\n",
"mean 0.783 0.782 ... 0.733 0.730 0.728 0.728 0.727 \n",
"std 0.039 0.040 ... 0.032 0.032 0.032 0.032 0.032 \n",
"\n",
"Number of neighbors 45 46 47 48 49 \n",
"mean 0.725 0.724 0.724 0.723 0.722 \n",
"std 0.032 0.032 0.031 0.030 0.030 \n",
"\n",
"[2 rows x 49 columns]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn_stats.round(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can again plot the data as a line plot with error bars:"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\n",
" \"Exception inside the 'handler_\" + msg_type + \"' callback:\",\n",
" e,\n",
" e.stack,\n",
" msg\n",
" );\n",
" }\n",
" }\n",
" };\n",
"};\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function (e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e) {\n",
" e = window.event;\n",
" }\n",
" if (e.target) {\n",
" targ = e.target;\n",
" } else if (e.srcElement) {\n",
" targ = e.srcElement;\n",
" }\n",
" if (targ.nodeType === 3) {\n",
" // defeat Safari bug\n",
" targ = targ.parentNode;\n",
" }\n",
"\n",
" // pageX,Y are the mouse positions relative to the document\n",
" var boundingRect = targ.getBoundingClientRect();\n",
" var x = e.pageX - (boundingRect.left + document.body.scrollLeft);\n",
" var y = e.pageY - (boundingRect.top + document.body.scrollTop);\n",
"\n",
" return { x: x, y: y };\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys(original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object') {\n",
" obj[key] = original[key];\n",
" }\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function (event, name) {\n",
" var canvas_pos = mpl.findpos(event);\n",
"\n",
" if (name === 'button_press') {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * this.ratio;\n",
" var y = canvas_pos.y * this.ratio;\n",
"\n",
" this.send_message(name, {\n",
" x: x,\n",
" y: y,\n",
" button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event),\n",
" });\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (_event, _name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"};\n",
"\n",
"mpl.figure.prototype.key_event = function (event, name) {\n",
" // Prevent repeat events\n",
" if (name === 'key_press') {\n",
" if (event.which === this._key) {\n",
" return;\n",
" } else {\n",
" this._key = event.which;\n",
" }\n",
" }\n",
" if (name === 'key_release') {\n",
" this._key = null;\n",
" }\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which !== 17) {\n",
" value += 'ctrl+';\n",
" }\n",
" if (event.altKey && event.which !== 18) {\n",
" value += 'alt+';\n",
" }\n",
" if (event.shiftKey && event.which !== 16) {\n",
" value += 'shift+';\n",
" }\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, { key: value, guiEvent: simpleKeys(event) });\n",
" return false;\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function (name) {\n",
" if (name === 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message('toolbar_button', { name: name });\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function (tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Left button pans, Right button zooms\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\\nx/y fixes axis, CTRL fixes aspect\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
"\n",
"mpl.default_extension = \"png\";/* global mpl */\n",
"\n",
"var comm_websocket_adapter = function (comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function () {\n",
" comm.close();\n",
" };\n",
" ws.send = function (m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function (msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data']);\n",
" });\n",
" return ws;\n",
"};\n",
"\n",
"mpl.mpl_figure_comm = function (comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = document.getElementById(id);\n",
" var ws_proxy = comm_websocket_adapter(comm);\n",
"\n",
" function ondownload(figure, _format) {\n",
" window.open(figure.canvas.toDataURL());\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy, ondownload, element);\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element;\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error('Failed to find cell for figure', id, fig);\n",
" return;\n",
" }\n",
" fig.cell_info[0].output_area.element.one(\n",
" 'cleared',\n",
" { fig: fig },\n",
" fig._remove_fig_handler\n",
" );\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function (fig, msg) {\n",
" var width = fig.canvas.width / fig.ratio;\n",
" fig.cell_info[0].output_area.element.off(\n",
" 'cleared',\n",
" fig._remove_fig_handler\n",
" );\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable();\n",
" fig.parent_element.innerHTML =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
" fig.close_ws(fig, msg);\n",
"};\n",
"\n",
"mpl.figure.prototype.close_ws = function (fig, msg) {\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"};\n",
"\n",
"mpl.figure.prototype.push_to_output = function (_remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width / this.ratio;\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] =\n",
" '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"};\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function () {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message('ack', {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () {\n",
" fig.push_to_output();\n",
" }, 1000);\n",
"};\n",
"\n",
"mpl.figure.prototype._init_toolbar = function () {\n",
" var fig = this;\n",
"\n",
" var toolbar = document.createElement('div');\n",
" toolbar.classList = 'btn-toolbar';\n",
" this.root.appendChild(toolbar);\n",
"\n",
" function on_click_closure(name) {\n",
" return function (_event) {\n",
" return fig.toolbar_button_onclick(name);\n",
" };\n",
" }\n",
"\n",
" function on_mouseover_closure(tooltip) {\n",
" return function (event) {\n",
" if (!event.currentTarget.disabled) {\n",
" return fig.toolbar_button_onmouseover(tooltip);\n",
" }\n",
" };\n",
" }\n",
"\n",
" fig.buttons = {};\n",
" var buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" var button;\n",
" for (var toolbar_ind in mpl.toolbar_items) {\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) {\n",
" /* Instead of a spacer, we start a new button group. */\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
" buttonGroup = document.createElement('div');\n",
" buttonGroup.classList = 'btn-group';\n",
" continue;\n",
" }\n",
"\n",
" button = fig.buttons[name] = document.createElement('button');\n",
" button.classList = 'btn btn-default';\n",
" button.href = '#';\n",
" button.title = name;\n",
" button.innerHTML = '<i class=\"fa ' + image + ' fa-lg\"></i>';\n",
" button.addEventListener('click', on_click_closure(method_name));\n",
" button.addEventListener('mouseover', on_mouseover_closure(tooltip));\n",
" buttonGroup.appendChild(button);\n",
" }\n",
"\n",
" if (buttonGroup.hasChildNodes()) {\n",
" toolbar.appendChild(buttonGroup);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = document.createElement('span');\n",
" status_bar.classList = 'mpl-message pull-right';\n",
" toolbar.appendChild(status_bar);\n",
" this.message = status_bar;\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = document.createElement('div');\n",
" buttongrp.classList = 'btn-group inline pull-right';\n",
" button = document.createElement('button');\n",
" button.classList = 'btn btn-mini btn-primary';\n",
" button.href = '#';\n",
" button.title = 'Stop Interaction';\n",
" button.innerHTML = '<i class=\"fa fa-power-off icon-remove icon-large\"></i>';\n",
" button.addEventListener('click', function (_evt) {\n",
" fig.handle_close(fig, {});\n",
" });\n",
" button.addEventListener(\n",
" 'mouseover',\n",
" on_mouseover_closure('Stop Interaction')\n",
" );\n",
" buttongrp.appendChild(button);\n",
" var titlebar = this.root.querySelector('.ui-dialog-titlebar');\n",
" titlebar.insertBefore(buttongrp, titlebar.firstChild);\n",
"};\n",
"\n",
"mpl.figure.prototype._remove_fig_handler = function (event) {\n",
" var fig = event.data.fig;\n",
" fig.close_ws(fig, {});\n",
"};\n",
"\n",
"mpl.figure.prototype._root_extra_style = function (el) {\n",
" el.style.boxSizing = 'content-box'; // override notebook setting of border-box.\n",
"};\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function (el) {\n",
" // this is important to make the div 'focusable\n",
" el.setAttribute('tabindex', 0);\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" } else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype._key_event_extra = function (event, _name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager) {\n",
" manager = IPython.keyboard_manager;\n",
" }\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which === 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_save = function (fig, _msg) {\n",
" fig.ondownload(fig, null);\n",
"};\n",
"\n",
"mpl.find_output_cell = function (html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i = 0; i < ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code') {\n",
" for (var j = 0; j < cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] === html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"};\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel !== null) {\n",
" IPython.notebook.kernel.comm_manager.register_target(\n",
" 'matplotlib',\n",
" mpl.mpl_figure_comm\n",
" );\n",
"}\n"
],
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\" width=\"1000\">"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize = (10,4))\n",
"knn_stats.loc['mean'].plot(yerr = knn_stats.loc['std'], ax = plt.gca(), label = 'Cross-validation scores')\n",
"plt.ylabel('Cross-validation score ($R^2$)')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since there are many values, we can automatically identify the hyper-parameter with the highest score:"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.7826470393341168"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the highest mean cv_score:\n",
"knn_stats.loc['mean'].max() "
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"9"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the index (i.e. the number of neighbors) of the highest mean cv_score:\n",
"knn_stats.loc['mean'].idxmax()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. Which features have been used to tune the KNN?\n",
"- b. What is the optimal number of features for this problem? What is the best performance that can be achieved?\n",
"- c. The standard deviation is quite high. How can the standard deviation be reduced?\n",
"- d. As you may have noticed, the results vary if the code is re-run. How can we achieve more stable results?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. We used the 6 best features according to the mutual information criterion. These are: **roof_aspect, roof_tilt, SIS and horizon_S, SSE and SSW**.\n",
"- b. The optimal number of neighbours should lie close to the default parameters, between 4-10, and lead to a performance of 0.75-0.8 (with deviations possible)\n",
"- c. The standard deviation can be reduced by increasing the size of the training dataset.\n",
"- d. We can achieve more stable results by performing the tuning process multiple times, and averaging over the multiple runs. We should do this for a final model, by repeating the CV process several times (including the reshuffling of the data). At some point the results will start converging."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Part 4 - SVR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"For tuning of the SVR, we will keep the reduced dataset `X_tuning` with 6 features. For doing a full model tuning, we should also re-consider the optimal number of features, but for now let's assume that the optimal features from KNN are representative for the SVR as well."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVR"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Step 4.1: SVR hyperparameter tuning\n",
"Here, we will look into the tuning of the `C` and `gamma` parameters. Other hyper-parameters, primarily the `kernel` and the `epsilon` parameters, can impact model performance. With the tools that you will see below, you can also tune these other hyper-parameters.\n",
"\n",
"To tune multiple hyper-parameters simultaneously, we will use the `GridSearchCV` function. We could also run multiple nested for-loops to perform the tuning, but the grid search has several advantages:\n",
"\n",
"- It automatically stores all the cross-validation scores and computes the statistics\n",
"- It also logs the fitting and scoring times, which gives insight in the trade-off between computational time and performance\n",
"- It can run *in parallel*, which reduces the required time for testing large amounts of hyper-parameters"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"import time # For logging execution times"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Since we changed the training data compared to the SVR introduction tutorial, let's re-compute the default `gamma`:"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.16666666666666666"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gamma_default = 1 / (n_features * X_tuning.var())\n",
"gamma_default"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From our inital tests in the introduction tutorial, we already have some intuition on the range of values for `C` and `gamma`. So, let's start the tuning by varying around the best values we found in the introduction. For this, we first create a *dictionary* with all parameters that we wish to tune, and their values:"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"hyperparams = {\n",
" 'C' : np.arange(100, 3700, 500), # Get values between 100 and 3700, in intervals of 500\n",
" 'gamma' : np.arange(0.01, 0.42, 0.05).round(2) # Get values between 0.01 and 0.42 and round to 2 decimals\n",
"}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: The `np.arange()` function *excludes* the upper limit from the sampling interval, so we choose this upper limit just above the required value (i.e. 3700 instead of 3600, 0.42 instead of 0.41)\n",
"\n",
"The resulting dictionary's contents look as follows:"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'C': array([ 100, 600, 1100, 1600, 2100, 2600, 3100, 3600]),\n",
" 'gamma': array([0.01, 0.06, 0.11, 0.16, 0.21, 0.26, 0.31, 0.36, 0.41])}"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hyperparams"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: The initial ranges are relatively large. We choose such large ranges as we want to reduce the computational time, which increases quadratically since we tune two hyper-parameters simultaneously. If you have a strong machine and have a LOT of time, you can run many parameters at once, but often it is good to \"test\" for the correct range of hyper-parameters and perfom the tuning in an iterative fashion, closing in on the optimal values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"With the dictionary of hyper-parameters and after declaring an instance of the default SVR model, we can call the `GridSearchCV` function and fit it with the features and the target. To have an idea of how long the full tuning process takes, we also log the time:"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Performed hyperparameter tuning in 66.62s\n"
]
}
],
"source": [
"tt = time.time()\n",
"svr = SVR()\n",
"svr_gridsearch = GridSearchCV(svr, hyperparams)\n",
"svr_gridsearch.fit(X_tuning, y)\n",
"print('Performed hyperparameter tuning in %.2fs' %(time.time()-tt))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**NOTE**: Even in the above case we are testing 72 combinations, which can take some time. If it takes longer than about 5 minutes, consider stopping the notebook and reducing the range of the tested values."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The results are saved in the `cv_results_` attribute of the gridsearch instance. We can visualise them by loading them into a dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\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>mean_fit_time</th>\n",
" <th>std_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>std_score_time</th>\n",
" <th>param_C</th>\n",
" <th>param_gamma</th>\n",
" <th>params</th>\n",
" <th>split0_test_score</th>\n",
" <th>split1_test_score</th>\n",
" <th>split2_test_score</th>\n",
" <th>split3_test_score</th>\n",
" <th>split4_test_score</th>\n",
" <th>mean_test_score</th>\n",
" <th>std_test_score</th>\n",
" <th>rank_test_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.027231</td>\n",
" <td>0.004554</td>\n",
" <td>0.003133</td>\n",
" <td>0.000242</td>\n",
" <td>100</td>\n",
" <td>0.01</td>\n",
" <td>{'C': 100, 'gamma': 0.01}</td>\n",
" <td>0.638526</td>\n",
" <td>0.732151</td>\n",
" <td>0.618883</td>\n",
" <td>0.697720</td>\n",
" <td>0.719345</td>\n",
" <td>0.681325</td>\n",
" <td>0.044785</td>\n",
" <td>72</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.029873</td>\n",
" <td>0.001215</td>\n",
" <td>0.002975</td>\n",
" <td>0.000059</td>\n",
" <td>100</td>\n",
" <td>0.06</td>\n",
" <td>{'C': 100, 'gamma': 0.06}</td>\n",
" <td>0.771526</td>\n",
" <td>0.874403</td>\n",
" <td>0.795422</td>\n",
" <td>0.825412</td>\n",
" <td>0.828222</td>\n",
" <td>0.818997</td>\n",
" <td>0.034657</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.029188</td>\n",
" <td>0.000278</td>\n",
" <td>0.002985</td>\n",
" <td>0.000100</td>\n",
" <td>100</td>\n",
" <td>0.11</td>\n",
" <td>{'C': 100, 'gamma': 0.11}</td>\n",
" <td>0.775787</td>\n",
" <td>0.876926</td>\n",
" <td>0.802721</td>\n",
" <td>0.819661</td>\n",
" <td>0.831078</td>\n",
" <td>0.821235</td>\n",
" <td>0.033494</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n",
"0 0.027231 0.004554 0.003133 0.000242 100 \n",
"1 0.029873 0.001215 0.002975 0.000059 100 \n",
"2 0.029188 0.000278 0.002985 0.000100 100 \n",
"\n",
" param_gamma params split0_test_score \\\n",
"0 0.01 {'C': 100, 'gamma': 0.01} 0.638526 \n",
"1 0.06 {'C': 100, 'gamma': 0.06} 0.771526 \n",
"2 0.11 {'C': 100, 'gamma': 0.11} 0.775787 \n",
"\n",
" split1_test_score split2_test_score split3_test_score split4_test_score \\\n",
"0 0.732151 0.618883 0.697720 0.719345 \n",
"1 0.874403 0.795422 0.825412 0.828222 \n",
"2 0.876926 0.802721 0.819661 0.831078 \n",
"\n",
" mean_test_score std_test_score rank_test_score \n",
"0 0.681325 0.044785 72 \n",
"1 0.818997 0.034657 14 \n",
"2 0.821235 0.033494 12 "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svr_gridsearch_results = pd.DataFrame(svr_gridsearch.cv_results_)\n",
"svr_gridsearch_results.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"To see how the error changes with `C` and `gamma`, we can extract the `mean_test_score`, which shows the average validation score across all k folds. The data is best understandable if we expand it to two dimensions, one for C and one for gamma. For this, we can use panda's `pivot()` function:"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [],
"source": [
"mean_val_score = svr_gridsearch_results.pivot(values = 'mean_test_score', index = 'param_C', columns = 'param_gamma')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Which we can then display as a formatted matrix:"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
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" }</style><table id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6f\" ><thead> <tr> <th class=\"index_name level0\" >param_gamma</th> <th class=\"col_heading level0 col0\" >0.01</th> <th class=\"col_heading level0 col1\" >0.06</th> <th class=\"col_heading level0 col2\" >0.11</th> <th class=\"col_heading level0 col3\" >0.16</th> <th class=\"col_heading level0 col4\" >0.21</th> <th class=\"col_heading level0 col5\" >0.26</th> <th class=\"col_heading level0 col6\" >0.31</th> <th class=\"col_heading level0 col7\" >0.36</th> <th class=\"col_heading level0 col8\" >0.41</th> </tr> <tr> <th class=\"index_name level0\" >param_C</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6flevel0_row0\" class=\"row_heading level0 row0\" >100</th>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col0\" class=\"data row0 col0\" >0.6813</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col1\" class=\"data row0 col1\" >0.819</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col2\" class=\"data row0 col2\" >0.8212</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col3\" class=\"data row0 col3\" >0.8185</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col4\" class=\"data row0 col4\" >0.8132</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow0_col5\" class=\"data row0 col5\" >0.8068</td>\n",
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" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col2\" class=\"data row1 col2\" >0.8301</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col3\" class=\"data row1 col3\" >0.8249</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col4\" class=\"data row1 col4\" >0.8187</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col5\" class=\"data row1 col5\" >0.8103</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col6\" class=\"data row1 col6\" >0.804</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col7\" class=\"data row1 col7\" >0.7992</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow1_col8\" class=\"data row1 col8\" >0.7964</td>\n",
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" <tr>\n",
" <th id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6flevel0_row2\" class=\"row_heading level0 row2\" >1100</th>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col0\" class=\"data row2 col0\" >0.8065</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col1\" class=\"data row2 col1\" >0.8294</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col2\" class=\"data row2 col2\" >0.8266</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col3\" class=\"data row2 col3\" >0.8191</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col4\" class=\"data row2 col4\" >0.8076</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col5\" class=\"data row2 col5\" >0.7998</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col6\" class=\"data row2 col6\" >0.798</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col7\" class=\"data row2 col7\" >0.7974</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow2_col8\" class=\"data row2 col8\" >0.7966</td>\n",
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" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow4_col6\" class=\"data row4 col6\" >0.7951</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow4_col7\" class=\"data row4 col7\" >0.7945</td>\n",
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" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow5_col6\" class=\"data row5 col6\" >0.7933</td>\n",
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" <th id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6flevel0_row6\" class=\"row_heading level0 row6\" >3100</th>\n",
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" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow6_col5\" class=\"data row6 col5\" >0.7871</td>\n",
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" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow6_col7\" class=\"data row6 col7\" >0.7854</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow6_col8\" class=\"data row6 col8\" >0.7776</td>\n",
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" <tr>\n",
" <th id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6flevel0_row7\" class=\"row_heading level0 row7\" >3600</th>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col0\" class=\"data row7 col0\" >0.8183</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col1\" class=\"data row7 col1\" >0.8236</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col2\" class=\"data row7 col2\" >0.8099</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col3\" class=\"data row7 col3\" >0.7821</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col4\" class=\"data row7 col4\" >0.7794</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col5\" class=\"data row7 col5\" >0.7858</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col6\" class=\"data row7 col6\" >0.7872</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col7\" class=\"data row7 col7\" >0.7805</td>\n",
" <td id=\"T_e26308c4_348f_11eb_abb6_8c85902f5b6frow7_col8\" class=\"data row7 col8\" >0.7726</td>\n",
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],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feabecb7950>"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"format_df( mean_val_score, cmap='YlGn', decimals = 4)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can also look at the time for fitting and scoring:"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_fit_time = ## PIVOT THE svr_gridsearch_results TO EXTRACT THE mean_fit_time"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [],
"source": [
"# SOLUTION\n",
"mean_fit_time = svr_gridsearch_results.pivot(values = 'mean_fit_time', index = 'param_C', columns = 'param_gamma')"
]
},
{
"cell_type": "code",
"execution_count": 55,
"metadata": {},
"outputs": [
{
"data": {
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" background-color: #ff1b00;\n",
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" background-color: #ff0c00;\n",
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" color: #f1f1f1;\n",
" }</style><table id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6f\" ><thead> <tr> <th class=\"index_name level0\" >param_gamma</th> <th class=\"col_heading level0 col0\" >0.01</th> <th class=\"col_heading level0 col1\" >0.06</th> <th class=\"col_heading level0 col2\" >0.11</th> <th class=\"col_heading level0 col3\" >0.16</th> <th class=\"col_heading level0 col4\" >0.21</th> <th class=\"col_heading level0 col5\" >0.26</th> <th class=\"col_heading level0 col6\" >0.31</th> <th class=\"col_heading level0 col7\" >0.36</th> <th class=\"col_heading level0 col8\" >0.41</th> </tr> <tr> <th class=\"index_name level0\" >param_C</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row0\" class=\"row_heading level0 row0\" >100</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col0\" class=\"data row0 col0\" >0.0272</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col1\" class=\"data row0 col1\" >0.0299</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col2\" class=\"data row0 col2\" >0.0292</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col3\" class=\"data row0 col3\" >0.0311</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col4\" class=\"data row0 col4\" >0.0313</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col5\" class=\"data row0 col5\" >0.0325</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col6\" class=\"data row0 col6\" >0.0321</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col7\" class=\"data row0 col7\" >0.0336</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow0_col8\" class=\"data row0 col8\" >0.036</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row1\" class=\"row_heading level0 row1\" >600</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col0\" class=\"data row1 col0\" >0.03</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col1\" class=\"data row1 col1\" >0.0374</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col2\" class=\"data row1 col2\" >0.0474</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col3\" class=\"data row1 col3\" >0.0582</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col4\" class=\"data row1 col4\" >0.07</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col5\" class=\"data row1 col5\" >0.0781</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col6\" class=\"data row1 col6\" >0.0874</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col7\" class=\"data row1 col7\" >0.0914</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow1_col8\" class=\"data row1 col8\" >0.108</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row2\" class=\"row_heading level0 row2\" >1100</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col0\" class=\"data row2 col0\" >0.0329</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col1\" class=\"data row2 col1\" >0.0538</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col2\" class=\"data row2 col2\" >0.074</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col3\" class=\"data row2 col3\" >0.0967</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col4\" class=\"data row2 col4\" >0.12</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col5\" class=\"data row2 col5\" >0.133</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col6\" class=\"data row2 col6\" >0.145</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col7\" class=\"data row2 col7\" >0.157</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow2_col8\" class=\"data row2 col8\" >0.184</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row3\" class=\"row_heading level0 row3\" >1600</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col0\" class=\"data row3 col0\" >0.0336</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col1\" class=\"data row3 col1\" >0.0701</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col2\" class=\"data row3 col2\" >0.0926</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col3\" class=\"data row3 col3\" >0.141</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col4\" class=\"data row3 col4\" >0.185</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col5\" class=\"data row3 col5\" >0.198</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col6\" class=\"data row3 col6\" >0.217</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col7\" class=\"data row3 col7\" >0.229</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow3_col8\" class=\"data row3 col8\" >0.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row4\" class=\"row_heading level0 row4\" >2100</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col0\" class=\"data row4 col0\" >0.0357</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col1\" class=\"data row4 col1\" >0.0843</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col2\" class=\"data row4 col2\" >0.126</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col3\" class=\"data row4 col3\" >0.192</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col4\" class=\"data row4 col4\" >0.239</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col5\" class=\"data row4 col5\" >0.25</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col6\" class=\"data row4 col6\" >0.31</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col7\" class=\"data row4 col7\" >0.337</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow4_col8\" class=\"data row4 col8\" >0.309</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row5\" class=\"row_heading level0 row5\" >2600</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col0\" class=\"data row5 col0\" >0.036</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col1\" class=\"data row5 col1\" >0.0898</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col2\" class=\"data row5 col2\" >0.167</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col3\" class=\"data row5 col3\" >0.229</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col4\" class=\"data row5 col4\" >0.279</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col5\" class=\"data row5 col5\" >0.33</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col6\" class=\"data row5 col6\" >0.38</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col7\" class=\"data row5 col7\" >0.444</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow5_col8\" class=\"data row5 col8\" >0.382</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row6\" class=\"row_heading level0 row6\" >3100</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col0\" class=\"data row6 col0\" >0.0367</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col1\" class=\"data row6 col1\" >0.11</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col2\" class=\"data row6 col2\" >0.191</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col3\" class=\"data row6 col3\" >0.278</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col4\" class=\"data row6 col4\" >0.334</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col5\" class=\"data row6 col5\" >0.371</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col6\" class=\"data row6 col6\" >0.437</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col7\" class=\"data row6 col7\" >0.443</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow6_col8\" class=\"data row6 col8\" >0.424</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6flevel0_row7\" class=\"row_heading level0 row7\" >3600</th>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col0\" class=\"data row7 col0\" >0.0396</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col1\" class=\"data row7 col1\" >0.134</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col2\" class=\"data row7 col2\" >0.223</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col3\" class=\"data row7 col3\" >0.33</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col4\" class=\"data row7 col4\" >0.36</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col5\" class=\"data row7 col5\" >0.432</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col6\" class=\"data row7 col6\" >0.437</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col7\" class=\"data row7 col7\" >0.458</td>\n",
" <td id=\"T_e26fc974_348f_11eb_abb6_8c85902f5b6frow7_col8\" class=\"data row7 col8\" >0.48</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feaafe9d110>"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"format_df( mean_fit_time, cmap='autumn_r', decimals = 3)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"mean_score_time = ## PIVOT THE svr_gridsearch_results TO EXTRACT THE mean_score_time\n",
"## DISPLAY FORMATTED MATRIX"
]
},
{
"cell_type": "code",
"execution_count": 56,
"metadata": {},
"outputs": [
{
"data": {
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" }</style><table id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6f\" ><thead> <tr> <th class=\"index_name level0\" >param_gamma</th> <th class=\"col_heading level0 col0\" >0.01</th> <th class=\"col_heading level0 col1\" >0.06</th> <th class=\"col_heading level0 col2\" >0.11</th> <th class=\"col_heading level0 col3\" >0.16</th> <th class=\"col_heading level0 col4\" >0.21</th> <th class=\"col_heading level0 col5\" >0.26</th> <th class=\"col_heading level0 col6\" >0.31</th> <th class=\"col_heading level0 col7\" >0.36</th> <th class=\"col_heading level0 col8\" >0.41</th> </tr> <tr> <th class=\"index_name level0\" >param_C</th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> <th class=\"blank\" ></th> </tr></thead><tbody>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row0\" class=\"row_heading level0 row0\" >100</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col0\" class=\"data row0 col0\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col1\" class=\"data row0 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col2\" class=\"data row0 col2\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col3\" class=\"data row0 col3\" >0.0029</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col4\" class=\"data row0 col4\" >0.0029</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col5\" class=\"data row0 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col6\" class=\"data row0 col6\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col7\" class=\"data row0 col7\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow0_col8\" class=\"data row0 col8\" >0.0031</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row1\" class=\"row_heading level0 row1\" >600</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col0\" class=\"data row1 col0\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col1\" class=\"data row1 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col2\" class=\"data row1 col2\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col3\" class=\"data row1 col3\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col4\" class=\"data row1 col4\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col5\" class=\"data row1 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col6\" class=\"data row1 col6\" >0.0029</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col7\" class=\"data row1 col7\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow1_col8\" class=\"data row1 col8\" >0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row2\" class=\"row_heading level0 row2\" >1100</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col0\" class=\"data row2 col0\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col1\" class=\"data row2 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col2\" class=\"data row2 col2\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col3\" class=\"data row2 col3\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col4\" class=\"data row2 col4\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col5\" class=\"data row2 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col6\" class=\"data row2 col6\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col7\" class=\"data row2 col7\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow2_col8\" class=\"data row2 col8\" >0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row3\" class=\"row_heading level0 row3\" >1600</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col0\" class=\"data row3 col0\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col1\" class=\"data row3 col1\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col2\" class=\"data row3 col2\" >0.0033</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col3\" class=\"data row3 col3\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col4\" class=\"data row3 col4\" >0.0032</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col5\" class=\"data row3 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col6\" class=\"data row3 col6\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col7\" class=\"data row3 col7\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow3_col8\" class=\"data row3 col8\" >0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row4\" class=\"row_heading level0 row4\" >2100</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col0\" class=\"data row4 col0\" >0.0034</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col1\" class=\"data row4 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col2\" class=\"data row4 col2\" >0.0032</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col3\" class=\"data row4 col3\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col4\" class=\"data row4 col4\" >0.0035</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col5\" class=\"data row4 col5\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col6\" class=\"data row4 col6\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col7\" class=\"data row4 col7\" >0.0033</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow4_col8\" class=\"data row4 col8\" >0.003</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row5\" class=\"row_heading level0 row5\" >2600</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col0\" class=\"data row5 col0\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col1\" class=\"data row5 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col2\" class=\"data row5 col2\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col3\" class=\"data row5 col3\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col4\" class=\"data row5 col4\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col5\" class=\"data row5 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col6\" class=\"data row5 col6\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col7\" class=\"data row5 col7\" >0.0033</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow5_col8\" class=\"data row5 col8\" >0.0032</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row6\" class=\"row_heading level0 row6\" >3100</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col0\" class=\"data row6 col0\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col1\" class=\"data row6 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col2\" class=\"data row6 col2\" >0.0034</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col3\" class=\"data row6 col3\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col4\" class=\"data row6 col4\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col5\" class=\"data row6 col5\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col6\" class=\"data row6 col6\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col7\" class=\"data row6 col7\" >0.0038</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow6_col8\" class=\"data row6 col8\" >0.0031</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6flevel0_row7\" class=\"row_heading level0 row7\" >3600</th>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col0\" class=\"data row7 col0\" >0.0033</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col1\" class=\"data row7 col1\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col2\" class=\"data row7 col2\" >0.0031</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col3\" class=\"data row7 col3\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col4\" class=\"data row7 col4\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col5\" class=\"data row7 col5\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col6\" class=\"data row7 col6\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col7\" class=\"data row7 col7\" >0.003</td>\n",
" <td id=\"T_e27552cc_348f_11eb_abb6_8c85902f5b6frow7_col8\" class=\"data row7 col8\" >0.0032</td>\n",
" </tr>\n",
" </tbody></table>"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7feabecb7310>"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# SOLUTION\n",
"mean_score_time = svr_gridsearch_results.pivot(values = 'mean_score_time', index = 'param_C', columns = 'param_gamma')\n",
"format_df( mean_score_time, cmap='autumn_r', decimals = 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**_Which are the best hyper-parameters?_**\n",
"The tables provide a good idea of what the best scores are, but we can also obtain this information directly from the dataframe, by sorting it by its `rank_test_score`:"
]
},
{
"cell_type": "code",
"execution_count": 57,
"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",
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" }\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>mean_fit_time</th>\n",
" <th>std_fit_time</th>\n",
" <th>mean_score_time</th>\n",
" <th>std_score_time</th>\n",
" <th>param_C</th>\n",
" <th>param_gamma</th>\n",
" <th>params</th>\n",
" <th>split0_test_score</th>\n",
" <th>split1_test_score</th>\n",
" <th>split2_test_score</th>\n",
" <th>split3_test_score</th>\n",
" <th>split4_test_score</th>\n",
" <th>mean_test_score</th>\n",
" <th>std_test_score</th>\n",
" <th>rank_test_score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.047428</td>\n",
" <td>0.001559</td>\n",
" <td>0.002960</td>\n",
" <td>0.000052</td>\n",
" <td>600</td>\n",
" <td>0.11</td>\n",
" <td>{'C': 600, 'gamma': 0.11}</td>\n",
" <td>0.787993</td>\n",
" <td>0.892144</td>\n",
" <td>0.810955</td>\n",
" <td>0.826710</td>\n",
" <td>0.832839</td>\n",
" <td>0.830128</td>\n",
" <td>0.034664</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.053812</td>\n",
" <td>0.009137</td>\n",
" <td>0.002984</td>\n",
" <td>0.000085</td>\n",
" <td>1100</td>\n",
" <td>0.06</td>\n",
" <td>{'C': 1100, 'gamma': 0.06}</td>\n",
" <td>0.786131</td>\n",
" <td>0.887928</td>\n",
" <td>0.809598</td>\n",
" <td>0.829855</td>\n",
" <td>0.833255</td>\n",
" <td>0.829353</td>\n",
" <td>0.033778</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.070125</td>\n",
" <td>0.005995</td>\n",
" <td>0.003056</td>\n",
" <td>0.000126</td>\n",
" <td>1600</td>\n",
" <td>0.06</td>\n",
" <td>{'C': 1600, 'gamma': 0.06}</td>\n",
" <td>0.786797</td>\n",
" <td>0.891707</td>\n",
" <td>0.808824</td>\n",
" <td>0.826056</td>\n",
" <td>0.832385</td>\n",
" <td>0.829154</td>\n",
" <td>0.035044</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.037440</td>\n",
" <td>0.000372</td>\n",
" <td>0.002958</td>\n",
" <td>0.000050</td>\n",
" <td>600</td>\n",
" <td>0.06</td>\n",
" <td>{'C': 600, 'gamma': 0.06}</td>\n",
" <td>0.787126</td>\n",
" <td>0.882130</td>\n",
" <td>0.807484</td>\n",
" <td>0.831297</td>\n",
" <td>0.833862</td>\n",
" <td>0.828380</td>\n",
" <td>0.031831</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>0.084313</td>\n",
" <td>0.003682</td>\n",
" <td>0.002982</td>\n",
" <td>0.000057</td>\n",
" <td>2100</td>\n",
" <td>0.06</td>\n",
" <td>{'C': 2100, 'gamma': 0.06}</td>\n",
" <td>0.787753</td>\n",
" <td>0.891664</td>\n",
" <td>0.807029</td>\n",
" <td>0.821490</td>\n",
" <td>0.830495</td>\n",
" <td>0.827686</td>\n",
" <td>0.035102</td>\n",
" <td>5</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n",
"11 0.047428 0.001559 0.002960 0.000052 600 \n",
"19 0.053812 0.009137 0.002984 0.000085 1100 \n",
"28 0.070125 0.005995 0.003056 0.000126 1600 \n",
"10 0.037440 0.000372 0.002958 0.000050 600 \n",
"37 0.084313 0.003682 0.002982 0.000057 2100 \n",
"\n",
" param_gamma params split0_test_score \\\n",
"11 0.11 {'C': 600, 'gamma': 0.11} 0.787993 \n",
"19 0.06 {'C': 1100, 'gamma': 0.06} 0.786131 \n",
"28 0.06 {'C': 1600, 'gamma': 0.06} 0.786797 \n",
"10 0.06 {'C': 600, 'gamma': 0.06} 0.787126 \n",
"37 0.06 {'C': 2100, 'gamma': 0.06} 0.787753 \n",
"\n",
" split1_test_score split2_test_score split3_test_score \\\n",
"11 0.892144 0.810955 0.826710 \n",
"19 0.887928 0.809598 0.829855 \n",
"28 0.891707 0.808824 0.826056 \n",
"10 0.882130 0.807484 0.831297 \n",
"37 0.891664 0.807029 0.821490 \n",
"\n",
" split4_test_score mean_test_score std_test_score rank_test_score \n",
"11 0.832839 0.830128 0.034664 1 \n",
"19 0.833255 0.829353 0.033778 2 \n",
"28 0.832385 0.829154 0.035044 3 \n",
"10 0.833862 0.828380 0.031831 4 \n",
"37 0.830495 0.827686 0.035102 5 "
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svr_gridsearch_results.sort_values('rank_test_score').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Additioanlly, the best parameters and the best score are saved as separate attributes of the grid search instance, which can be used to train the optimal model. They are retrieved as:"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"{'C': 600, 'gamma': 0.11}"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svr_gridsearch.best_params_"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8301282035646309"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"svr_gridsearch.best_score_"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## *Questions*\n",
"- a. How does the best performance of the SVR compare to the performance of the tuned KNN algorithm?\n",
"- b. In which range do you find the best hyper-parameters?\n",
"- c. Which process (fitting or scoring) is more time-intensive for the SVR?\n",
"- d. How do the fitting times compare to the performance? Do you notice a pattern? What does this suggest about the over/underfitting of the model?\n",
"- [OPTIONAL] e. Your turn! Refine the hyper-parameters (considering also further parameters) in the grid search to improve performance. What is the best score that you can achieve? What are the hyper-parameters to achieve this score? "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### *SOLUTION*\n",
"- a. The performance of the tuned SVR should lie slightly above the performance of the tuned KNN, around 0.8-0.85.\n",
"- b. The best hyper-parameters are found for gamma around 0.06-0.11 (slightly below the default value) and for C around 600-1600. Around these we see a \"channel\" of near-optimal solutions in the colored matrix, which implies some trade-off between a high `C` and a high `gamma`.\n",
"- c. The fitting is significantly more time-intensive. This is the case for most ML algorithms (except KNN, which has a very simple fitting procedure and performs all the \"work\" during prediction).\n",
"- d. The fitting times increase with `C` and `gamma`. The scores decrease in the direction of the lowest fitting times (low C and low gamma), and towards the highest fitting times (high C and high gamma). Bad performance with low fitting times suggest **underfitting**, as the model is very simple. High training times and low performance, on the other hand, suggest **overfitting**, since too much time is spent fitting exactly to the data.\n",
"- e. This exercise is mostly for your practice and interest. You should be able to improve the performance by another small margin."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
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"nbformat": 4,
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