From 3fc12f7f44f020157071d28ce0b55136ca6f81a2 Mon Sep 17 00:00:00 2001
From: Anuraj Suman <anuras92@zedat.fu-berlin.de>
Date: Fri, 5 May 2023 11:49:43 +0200
Subject: [PATCH] Adding code and results for PCR implementation
---
PCR/PCR_MTBLS92.xlsx | Bin 0 -> 5084 bytes
PCR/PCR_MTBLS92_Working_Copy.ipynb | 1602 ++++++++++++++++++++++++++++
2 files changed, 1602 insertions(+)
create mode 100644 PCR/PCR_MTBLS92.xlsx
create mode 100644 PCR/PCR_MTBLS92_Working_Copy.ipynb
diff --git a/PCR/PCR_MTBLS92.xlsx b/PCR/PCR_MTBLS92.xlsx
new file mode 100644
index 0000000000000000000000000000000000000000..7d381b81ce44ba7b51fec47ce4adb735baacd936
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diff --git a/PCR/PCR_MTBLS92_Working_Copy.ipynb b/PCR/PCR_MTBLS92_Working_Copy.ipynb
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@@ -0,0 +1,1602 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": false,
+ "id": "UUpisputu93L"
+ },
+ "source": [
+ "<div style=\"text-align: justify; padding:5px; background-color:rgb(252, 253, 255); border: 1px solid lightgrey; padding-left: 1em; padding-right: 1em;\">\n",
+ " <font color='red'>To begin: Click anywhere in this cell and press <kbd>Run</kbd> on the menu bar. This executes the current cell and then highlights the next cell. There are two types of cells. A <i>text cell</i> and a <i>code cell</i>. When you <kbd>Run</kbd> a text cell (<i>we are in a text cell now</i>), you advance to the next cell without executing any code. When you <kbd>Run</kbd> a code cell (<i>identified by <span style=\"font-family: courier; color:black; background-color:white;\">In[ ]:</span> to the left of the cell</i>) you advance to the next cell after executing all the Python code within that cell. Any visual results produced by the code (text/figures) are reported directly below that cell. Press <kbd>Run</kbd> again. Repeat this process until the end of the notebook. <b>NOTE:</b> All the cells in this notebook can be automatically executed sequentially by clicking <kbd>Kernel</kbd><font color='black'>→</font><kbd>Restart and Run All</kbd>. Should anything crash then restart the Jupyter Kernal by clicking <kbd>Kernel</kbd><font color='black'>→</font><kbd>Restart</kbd>, and start again from the top.\n",
+ " \n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "VzZ5ilpru93N"
+ },
+ "source": [
+ "<div style=\"text-align: justify; padding:5px; background-color:rgb(252, 253, 255); border: 1px solid lightgrey; padding-left: 1em; padding-right: 1em;\">\n",
+ "<img src=\"https://github.com/CIMCB/MetabComparisonBinaryML/blob/master/cimcb_logo.png?raw=true\" width=\"180px\" align=\"right\" style=\"padding: 20px\">\n",
+ "\n",
+ "\n",
+ "<h1> PCR_MTBLS92 </h1>\n",
+ "\n",
+ "<br>\n",
+ "<br>\n",
+ "<br>\n",
+ "<p style=\"text-align: justify\"> The study used in this tutorial has been previously published by <a href=\"https://europepmc.org/abstract/MED/24114462\">Hilvo et al. (2013)</a>, and the deconvolved and annotated data file deposited at the Metabolights data repository. The data can be accessed directly via its study ID: <a href=\"https://www.ebi.ac.uk/metabolights/MTBLS92\">MTBLS92</a>. This workflow requires data to be formatted as a Microsoft Excel file, using the Tidy Data framework (i.e. each column is a variable, and row is an observation). As such, the Excel file contains a Data Sheet and Peak Sheet. The Data Sheet contains all the metabolite concentrations and metadata associated with each observation (requiring the inclusion of the columns: Idx, SampleID, and Class). The Peak Sheet contains all the metadata pertaining to each measured metabolite (requiring the inclusion of the columns: Idx, Name, and Label). Please inspect the Excel file <a href=\"https://github.com/CIMCB/MetabComparisonBinaryML/blob/master/dynamic/data/MTBLS92.xlsx?raw=true\">MTBLS92.xlsx</a> used in this workflow before proceeding.</p>\n",
+ "\n",
+ "<p style=\"text-align: justify\">This is a plasma LC-MS dataset consisting of 138 named metabolites. The primary outcome for this paper was before and after neoadjuvant chemotherapy in breast cancer patients. For the purpose of this study, we compare before (Class=1; n=142) and after (Class=0; n=111) neoadjuvant chemotherapy in a binary discriminant analysis.</p>\n",
+ " \n",
+ "<br>\n",
+ "\n",
+ "\n",
+ "</ol> \n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZDJfwXKSu93O"
+ },
+ "source": [
+ "<div style=\"text-align: justify; padding:5px; background-color:rgb(252, 253, 255); border: 1px solid lightgrey; padding-left: 1em; padding-right: 1em;\">\n",
+ " \n",
+ "<h1> PCR Workflow </h1> \n",
+ "<br>\n",
+ "<p style=\"text-align: justify\"> This Jupyter Notebook implements the complete workflow for creating, optimising, and evaluating a principal component regression (PCR) model. PCR is a two-stage algorithm combining principal component analysis (PCA) and multiple linear regression (MLR), where the first N principal component scores act as the independent variables of the MLR, and the binary classification the is the dependent variable. The value of N is chosen by the user. <b> PCA was implemented using <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html\">PCA</a>\n",
+ " and MLR using <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html\">Linear Regression</a> from scikit-learn.</b></p>\n",
+ "<i style=\"text-align: justify\"> Please refer to the 'cimcb' package documentation for further details regarding this specific implementation: <a href=\"https://cimcb.github.io/cimcb\">https://cimcb.github.io/cimcb</a></i>\n",
+ " \n",
+ "<br>\n",
+ "<br>\n",
+ "<b style=\"text-align: justify\"> PCR uses the following Hyperparameter(s):</b>\n",
+ "<ul style=\"list-style-type: square;\">\n",
+ " <li><code>n_components</code>: number of principal components projected into the MLR (default = 2)</li>\n",
+ "</ul>\n",
+ "<i style=\"text-align: justify\">The purpose of each hyperparameter is explained here: <a href=\"https://link.springer.com/content/pdf/10.1007/978-3-642-04898-2_455.pdf\">Jolliffe (2011)</a></i>\n",
+ " \n",
+ "<br>\n",
+ "<br>\n",
+ "\n",
+ "<b style=\"text-align: justify\"> The notebook workflow is broken into the following steps:</b>\n",
+ "\n",
+ "<ol>\n",
+ " <li><b><i>Import Packages</i></b>: First, the Python packages required for this workflow need to be imported (<a href=\"http://www.numpy.org/\"><code>numpy</code></a>, <a href=\"https://pandas.pydata.org/\"><code>pandas</code></a>, and <a href=\"https://cimcb.github.io/cimcb\"><code>cimcb</code></a>).\n",
+ "</li>\n",
+ " <li><b><i>Load Data & Peak Sheet:</i></b> From the Excel spreadsheet, import the Data and Peak spreadsheets and create two respective <a href=\"https://pandas.pydata.org/\">Pandas</a> tables: <code>DataTable</code> and <code>PeakTable</code>.</li>\n",
+ " <li><b><i>Extract X & Y:</i></b> Next, we reduce the data in <code>DataTable</code> to include only those observations needed for the binary comparison and create a new table: <code>DataTable2</code>. We define one column of the data table to be the \"outcome\" variable <code>Outcomes</code>, and convert the class labels in this column to a binary outcome vector <code>Y</code>, where <code>1</code> is the positive outcome, and <code>0</code> the negative outcome (eg. case=1 & control=0). A new variable <code>peaklist</code> is created to hold the names (M1...Mn) of the metabolites to be used in the discriminant analysis. To create an independent dataset to evaluate, <a href=\"https://scikit-learn.org/stable/\">scikit-learn</a> module's <a href=\"https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.train_test_split.html\"><code>train_test_split()</code></a> function is used. The data is split into 2/3rd training (<code>DataTrain</code> and <code>YTrain</code>), and 1/3rd test (<code>DataTest</code> and <code>YTest</code>). The metabolite data corresponding to <code>peaklist</code> is extracted from <code>DataTrain</code> and placed in a matrix <code>XTrain</code>. The <code>XTrain</code> matrix is log-transformed and auto-scaled, with missing values imputed using k-nearest neighbours (k=3). Then the metabolite data corresponding to <code>peaklist</code> is extracted from <code>DataTest</code> and placed in a matrix <code>XTest</code>. The <code>XTest</code> matrix is log-transformed and auto-scaled (using mu and sigma from <code>XTrain</code>), with missing values imputed using k-nearest neighbours (k=3).\n",
+ " <li><b><i>Hyperparameter Optimisation:</i></b> Here, we use the helper function <code>cb.cross_val.KFold()</code> to carry out 5-fold cross-validation of a set of PCR models configured with different numbers of principal components (1 to 30). This helper function is generally applicable, and the values being passed to it are: \n",
+ " <ul>\n",
+ " <li>The class of model to be created by the function, <code>cb.model.PCR</code>.</li>\n",
+ " <li>The metabolite matrix, <code>XTknn</code>, and binary outcome vector, <code>Y</code>.</li>\n",
+ " <li>A dictionary, <code>param_dict</code>, describing key:value pairs where the key is a parameter that is passed to the model, and the value is a list of values to be passed to that parameter.</li>\n",
+ " <li>The number of folds in the cross-validation, <code>folds</code>, and the number of Monte Carlo repetitions of the k-fold CV, <code>n_mc</code>.</li></ul>\n",
+ "When <code>cv.run()</code> followed by <code>cv.plot(metric='r2q2')</code> are run the results are displayed as 2 plots of $R^2$ and $Q^2$ statistics: (a) the difference ($R^2 - Q^2$) vs. $Q^2$, and (b) absolute values of both $R^2$ and $Q^2$ against the number of components. These plots are used to aid in selecting the optimal number of components.</li>\n",
+ " <li><b><i>Build Model & Evaluate:</i></b> Here, we use the class <code>cb.model.PCR()</code> to building a PCR model using the optimal hyperparameter values determined in step 4. The model is trained on the training dataset, <code>XTrainKnn</code>, and tested on the independent test dataset, <code>XTestKnn</code>. Next, the trained model's <code>.evaluate()</code> method is used to visualise model performance for both the training and independent test dataset using: a <a href=\"https://www.data-to-viz.com/graph/violin.html\">violin plot</a> showing the distributions of negative and positive responses as violin and box-whisker plots; a <a href=\"https://books.google.com.au/books?id=7WBMrZ9umRYC\">probability density function</a> plot for each response type, and a <a href=\"https://doi.org/10.1007/s11306-012-0482-9\">ROC curve</a> that displays the curve for the training dataset (green) and test dataset (yellow).\n",
+ " <li><b><i>Bootstrap Evaluation:</i></b> Finally, to create an estimate of the robustness and a measure of generalised predictive ability of this model we perform <a href=\"https://link.springer.com/article/10.1007%2FBF00058655\">bootstrap aggregation</a> (Bagging) using the helper function <code>cb.bootstrap.Per()</code> with 100 boostrapped models. This generates a population of 100 model predictions for both the training set (in-bag prediction - IB) and the holdout test set (out-of-bag - OOB) from the full dataset, with the metabolite matrix, <code>XBootKnn</code>, and binary outcome vector, <code>Y</code>. These predictions are visualised with a box-violin and probability density function plot for the aggregate model. The ROC curve displays the curve for the training dataset (green) and test dataset (yellow) from section 5 with 95% confidence intervals (light green band = IB & light yellow band = OOB).\n",
+ " <li><b><i>Export Results:</i></b> Exporting the model evaluation results as an Excel spreadsheet.</li>\n",
+ "</ol> \n",
+ "\n",
+ "</div>"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "toc-hr-collapsed": true,
+ "id": "LDbBpYzZu93P"
+ },
+ "source": [
+ "### 1. Import Packages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YZ92lys8u93P",
+ "outputId": "5a170bec-4200-49f2-e223-b27514d70b2a"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+ "Collecting https://github.com/KevinMMendez/cimcb/archive/master.zip\n",
+ " Downloading https://github.com/KevinMMendez/cimcb/archive/master.zip\n",
+ "\u001b[2K \u001b[32m\\\u001b[0m \u001b[32m486.2 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m \u001b[33m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: bokeh>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (2.4.3)\n",
+ "Requirement already satisfied: keras>=2.2.4 in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (2.12.0)\n",
+ "Requirement already satisfied: numpy>=1.12 in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (1.22.4)\n",
+ "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (1.5.3)\n",
+ "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (1.10.1)\n",
+ "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (1.2.2)\n",
+ "Requirement already satisfied: statsmodels in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (0.13.5)\n",
+ "Collecting theano\n",
+ " Downloading Theano-1.0.5.tar.gz (2.8 MB)\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.8/2.8 MB\u001b[0m \u001b[31m36.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (4.65.0)\n",
+ "Requirement already satisfied: xlrd in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (2.0.1)\n",
+ "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from cimcb==2.1.0) (1.2.0)\n",
+ "Requirement already satisfied: typing-extensions>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (4.5.0)\n",
+ "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (3.1.2)\n",
+ "Requirement already satisfied: packaging>=16.8 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (23.1)\n",
+ "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (6.2)\n",
+ "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (8.4.0)\n",
+ "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.10/dist-packages (from bokeh>=1.0.0->cimcb==2.1.0) (6.0)\n",
+ "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->cimcb==2.1.0) (2.8.2)\n",
+ "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->cimcb==2.1.0) (2022.7.1)\n",
+ "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->cimcb==2.1.0) (3.1.0)\n",
+ "Requirement already satisfied: patsy>=0.5.2 in /usr/local/lib/python3.10/dist-packages (from statsmodels->cimcb==2.1.0) (0.5.3)\n",
+ "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.10/dist-packages (from theano->cimcb==2.1.0) (1.16.0)\n",
+ "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from Jinja2>=2.9->bokeh>=1.0.0->cimcb==2.1.0) (2.1.2)\n",
+ "Building wheels for collected packages: cimcb, theano\n",
+ " Building wheel for cimcb (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for cimcb: filename=cimcb-2.1.0-py3-none-any.whl size=166785 sha256=72f4cb3e7e0b8f4de5aa5942c317bc5c52a8fe8bef7e5f401900c4dba166b231\n",
+ " Stored in directory: /tmp/pip-ephem-wheel-cache-nxpxnfxj/wheels/7b/86/72/e33231802da24f264f585c8e6cc4c045a6fc4a903b09cec5c2\n",
+ " Building wheel for theano (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+ " Created wheel for theano: filename=Theano-1.0.5-py3-none-any.whl size=2668109 sha256=118bf6800c1a804b9bb27294bf3f29b4383d0f52149d41f7ac070174f33df990\n",
+ " Stored in directory: /root/.cache/pip/wheels/d9/e6/7d/2267d21a99e4ab8276f976f293b4ff23f50c9d809f4a216ebb\n",
+ "Successfully built cimcb theano\n",
+ "Installing collected packages: theano, cimcb\n",
+ "Successfully installed cimcb-2.1.0 theano-1.0.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install https://github.com/KevinMMendez/cimcb/archive/master.zip"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "import cimcb as cb\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "\n",
+ "print('All packages successfully loaded')\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "rAXuIT2CvOZQ",
+ "outputId": "49afb395-c48a-4a1c-dbda-5c3e3145a9d1"
+ },
+ "execution_count": 2,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "All packages successfully loaded\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "bDI8ezfwu93R"
+ },
+ "source": [
+ "### 2. Load Data & Peak Sheet"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "tcXC0zf2u93R",
+ "outputId": "2637a69b-5a64-41d8-ced5-50c9a048444c"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Loadings PeakFile: Peak\n",
+ "Loadings DataFile: Data\n",
+ "Data Table & Peak Table is suitable.\n",
+ "TOTAL SAMPLES: 447 TOTAL PEAKS: 138\n",
+ "Done!\n"
+ ]
+ }
+ ],
+ "source": [
+ "file = 'MTBLS92.xlsx' \n",
+ "\n",
+ "DataTable,PeakTable = cb.utils.load_dataXL(file, DataSheet='Data', PeakSheet='Peak') "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "pE_8MoRZu93R"
+ },
+ "source": [
+ "### 3. Extract X & Y"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {
+ "id": "6q-xZ7_Pu93R"
+ },
+ "outputs": [],
+ "source": [
+ "# Extract PeakList\n",
+ "PeakList = PeakTable['Name'] \n",
+ "\n",
+ "# Select Subset of Data\n",
+ "DataTable2 = DataTable[(DataTable.Class == 1) | (DataTable.Class == 0)]\n",
+ "\n",
+ "# Create a Binary Y Vector \n",
+ "Outcomes = DataTable2['Class']\n",
+ "Y = Outcomes.values \n",
+ "\n",
+ "# Split Data into Train (2/3) and Test (1/3)\n",
+ "DataTrain, DataTest, YTrain, YTest = train_test_split(DataTable2, Y, test_size=1/3, stratify=Y, random_state=11)\n",
+ "\n",
+ "# Extract Train Data \n",
+ "XTrain = DataTrain[PeakList] \n",
+ "XTrainLog = np.log(XTrain) \n",
+ "XTrainScale, mu, sigma = cb.utils.scale(XTrainLog, method='auto', return_mu_sigma=True) \n",
+ "XTrainKnn = cb.utils.knnimpute(XTrainScale, k=3) \n",
+ "\n",
+ "# Extract Test Data\n",
+ "XTest = DataTest[PeakList] \n",
+ "XTestLog = np.log(XTest) \n",
+ "XTestScale = cb.utils.scale(XTestLog, method='auto', mu=mu, sigma=sigma) \n",
+ "XTestKnn = cb.utils.knnimpute(XTestScale, k=3) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "xKD_OdaYu93S"
+ },
+ "source": [
+ "### 4. Hyperparameter Optimisation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "scrolled": false,
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 679
+ },
+ "id": "vkHDmCpau93S",
+ "outputId": "c462a140-38bb-4cb3-e45d-a9b21e5cf77d"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Number of cores set too high. It will be set to the max number of cores in the system.\n",
+ "Number of cores set to: 2\n",
+ "Running ...\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "1/2: 100%|██████████| 30/30 [00:12<00:00, 2.45it/s]\n",
+ "2/2: 100%|██████████| 300/300 [00:16<00:00, 17.83it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Time taken: 0.50 minutes with 2 cores\n",
+ "Done!\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "/usr/local/lib/python3.10/dist-packages/cimcb/cross_val/BaseCrossVal.py:559: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
+ " for key, values in full_std.iteritems():\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/cross_val/BaseCrossVal.py:564: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
+ " for key, values in cv_std.iteritems():\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " const force = true;\n",
+ "\n",
+ " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+ " root._bokeh_onload_callbacks = [];\n",
+ " root._bokeh_is_loading = undefined;\n",
+ " }\n",
+ "\n",
+ "const JS_MIME_TYPE = 'application/javascript';\n",
+ " const HTML_MIME_TYPE = 'text/html';\n",
+ " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+ " const CLASS_NAME = 'output_bokeh rendered_html';\n",
+ "\n",
+ " /**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ " function render(props, node) {\n",
+ " const script = document.createElement(\"script\");\n",
+ " node.appendChild(script);\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when an output is cleared or removed\n",
+ " */\n",
+ " function handleClearOutput(event, handle) {\n",
+ " const cell = handle.cell;\n",
+ "\n",
+ " const id = cell.output_area._bokeh_element_id;\n",
+ " const server_id = cell.output_area._bokeh_server_id;\n",
+ " // Clean up Bokeh references\n",
+ " if (id != null && id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ "\n",
+ " if (server_id !== undefined) {\n",
+ " // Clean up Bokeh references\n",
+ " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+ " cell.notebook.kernel.execute(cmd_clean, {\n",
+ " iopub: {\n",
+ " output: function(msg) {\n",
+ " const id = msg.content.text.trim();\n",
+ " if (id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " // Destroy server and session\n",
+ " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+ " cell.notebook.kernel.execute(cmd_destroy);\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when a new output is added\n",
+ " */\n",
+ " function handleAddOutput(event, handle) {\n",
+ " const output_area = handle.output_area;\n",
+ " const output = handle.output;\n",
+ "\n",
+ " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+ " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+ " return\n",
+ " }\n",
+ "\n",
+ " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+ "\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+ " // store reference to embed id on output_area\n",
+ " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+ " }\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+ " const bk_div = document.createElement(\"div\");\n",
+ " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " const script_attrs = bk_div.children[0].attributes;\n",
+ " for (let i = 0; i < script_attrs.length; i++) {\n",
+ " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+ " }\n",
+ " // store reference to server id on output_area\n",
+ " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function register_renderer(events, OutputArea) {\n",
+ "\n",
+ " function append_mime(data, metadata, element) {\n",
+ " // create a DOM node to render to\n",
+ " const toinsert = this.create_output_subarea(\n",
+ " metadata,\n",
+ " CLASS_NAME,\n",
+ " EXEC_MIME_TYPE\n",
+ " );\n",
+ " this.keyboard_manager.register_events(toinsert);\n",
+ " // Render to node\n",
+ " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+ " render(props, toinsert[toinsert.length - 1]);\n",
+ " element.append(toinsert);\n",
+ " return toinsert\n",
+ " }\n",
+ "\n",
+ " /* Handle when an output is cleared or removed */\n",
+ " events.on('clear_output.CodeCell', handleClearOutput);\n",
+ " events.on('delete.Cell', handleClearOutput);\n",
+ "\n",
+ " /* Handle when a new output is added */\n",
+ " events.on('output_added.OutputArea', handleAddOutput);\n",
+ "\n",
+ " /**\n",
+ " * Register the mime type and append_mime function with output_area\n",
+ " */\n",
+ " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+ " /* Is output safe? */\n",
+ " safe: true,\n",
+ " /* Index of renderer in `output_area.display_order` */\n",
+ " index: 0\n",
+ " });\n",
+ " }\n",
+ "\n",
+ " // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+ " if (root.Jupyter !== undefined) {\n",
+ " const events = require('base/js/events');\n",
+ " const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+ "\n",
+ " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+ " register_renderer(events, OutputArea);\n",
+ " }\n",
+ " }\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\n",
+ " }\n",
+ "\n",
+ " const NB_LOAD_WARNING = {'data': {'text/html':\n",
+ " \"<div style='background-color: #fdd'>\\n\"+\n",
+ " \"<p>\\n\"+\n",
+ " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+ " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+ " \"</p>\\n\"+\n",
+ " \"<ul>\\n\"+\n",
+ " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+ " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+ " \"</ul>\\n\"+\n",
+ " \"<code>\\n\"+\n",
+ " \"from bokeh.resources import INLINE\\n\"+\n",
+ " \"output_notebook(resources=INLINE)\\n\"+\n",
+ " \"</code>\\n\"+\n",
+ " \"</div>\"}};\n",
+ "\n",
+ " function display_loaded() {\n",
+ " const el = document.getElementById(null);\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS is loading...\";\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+ " }\n",
+ " } else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(display_loaded, 100)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function run_callbacks() {\n",
+ " try {\n",
+ " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+ " if (callback != null)\n",
+ " callback();\n",
+ " });\n",
+ " } finally {\n",
+ " delete root._bokeh_onload_callbacks\n",
+ " }\n",
+ " console.debug(\"Bokeh: all callbacks have finished\");\n",
+ " }\n",
+ "\n",
+ " function load_libs(css_urls, js_urls, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ " if (root._bokeh_is_loading > 0) {\n",
+ " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+ " return null;\n",
+ " }\n",
+ " if (js_urls == null || js_urls.length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+ "\n",
+ " function on_load() {\n",
+ " root._bokeh_is_loading--;\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+ " run_callbacks()\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function on_error(url) {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < css_urls.length; i++) {\n",
+ " const url = css_urls[i];\n",
+ " const element = document.createElement(\"link\");\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.rel = \"stylesheet\";\n",
+ " element.type = \"text/css\";\n",
+ " element.href = url;\n",
+ " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < js_urls.length; i++) {\n",
+ " const url = js_urls[i];\n",
+ " const element = document.createElement('script');\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.async = false;\n",
+ " element.src = url;\n",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " function inject_raw_css(css) {\n",
+ " const element = document.createElement(\"style\");\n",
+ " element.appendChild(document.createTextNode(css));\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n",
+ " const css_urls = [];\n",
+ "\n",
+ " const inline_js = [ function(Bokeh) {\n",
+ " Bokeh.set_log_level(\"info\");\n",
+ " },\n",
+ "function(Bokeh) {\n",
+ " }\n",
+ " ];\n",
+ "\n",
+ " function run_inline_js() {\n",
+ " if (root.Bokeh !== undefined || force === true) {\n",
+ " for (let i = 0; i < inline_js.length; i++) {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ " }\n",
+ "} else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(run_inline_js, 100);\n",
+ " } else if (!root._bokeh_failed_load) {\n",
+ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+ " root._bokeh_failed_load = true;\n",
+ " } else if (force !== true) {\n",
+ " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
+ " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+ " run_inline_js();\n",
+ " } else {\n",
+ " load_libs(css_urls, js_urls, function() {\n",
+ " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+ " run_inline_js();\n",
+ " });\n",
+ " }\n",
+ "}(window));"
+ ],
+ "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
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+ " <div class=\"bk-root\" id=\"ca8c693a-6bdb-4534-815a-2fd5b99e34e8\" data-root-id=\"1203\"></div>\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
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Q\\u00b2\",\"axis_label_text_font_size\":\"10pt\",\"coordinates\":null,\"formatter\":{\"id\":\"1107\"},\"group\":null,\"major_label_policy\":{\"id\":\"1108\"},\"major_label_text_font_size\":\"8pt\",\"ticker\":{\"id\":\"1072\"}},\"id\":\"1071\",\"type\":\"LinearAxis\"},{\"attributes\":{\"fill_alpha\":{\"value\":0.2},\"fill_color\":{\"value\":\"green\"},\"hatch_alpha\":{\"value\":0.2},\"hatch_color\":{\"value\":\"green\"},\"line_alpha\":{\"value\":0.2},\"line_color\":{\"value\":\"green\"},\"size\":{\"value\":11},\"x\":{\"field\":\"cv\"},\"y\":{\"field\":\"diff\"}},\"id\":\"1051\",\"type\":\"Circle\"},{\"attributes\":{\"line_alpha\":0.2,\"line_color\":\"green\",\"line_width\":3,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1102\",\"type\":\"Line\"},{\"attributes\":{\"fill_alpha\":0.2,\"fill_color\":\"green\",\"hatch_alpha\":0.2,\"hatch_color\":\"green\",\"line_alpha\":0.2,\"line_color\":\"green\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1096\",\"type\":\"Patch\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1042\"},\"glyph\":{\"id\":\"1043\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1045\"},\"nonselection_glyph\":{\"id\":\"1044\"},\"view\":{\"id\":\"1047\"}},\"id\":\"1046\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"axis\":{\"id\":\"1071\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null,\"visible\":false},\"id\":\"1074\",\"type\":\"Grid\"},{\"attributes\":{},\"id\":\"1010\",\"type\":\"LinearScale\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1099\"},\"glyph\":{\"id\":\"1100\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1102\"},\"nonselection_glyph\":{\"id\":\"1101\"},\"view\":{\"id\":\"1104\"}},\"id\":\"1103\",\"type\":\"GlyphRenderer\"},{\"attributes\":{\"end\":0.3291365992062737,\"start\":-0.016333979650060254},\"id\":\"1008\",\"type\":\"Range1d\"},{\"attributes\":{\"factors\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"23\",\"24\",\"25\",\"26\",\"27\",\"28\",\"29\",\"30\"]},\"id\":\"1060\",\"type\":\"FactorRange\"},{\"attributes\":{\"fill_alpha\":0.1,\"fill_color\":\"green\",\"hatch_alpha\":0.1,\"hatch_color\":\"green\",\"line_alpha\":0.1,\"line_color\":\"green\",\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1094\",\"type\":\"Patch\"},{\"attributes\":{},\"id\":\"1027\",\"type\":\"SaveTool\"},{\"attributes\":{},\"id\":\"1168\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"axis\":{\"id\":\"1018\"},\"coordinates\":null,\"dimension\":1,\"group\":null,\"ticker\":null,\"visible\":false},\"id\":\"1021\",\"type\":\"Grid\"},{\"attributes\":{\"tools\":[{\"id\":\"1075\"},{\"id\":\"1076\"},{\"id\":\"1077\"},{\"id\":\"1078\"},{\"id\":\"1079\"},{\"id\":\"1080\"},{\"id\":\"1081\"},{\"id\":\"1160\"},{\"id\":\"1162\"},{\"id\":\"1164\"}]},\"id\":\"1085\",\"type\":\"Toolbar\"},{\"attributes\":{},\"id\":\"1169\",\"type\":\"AllLabels\"},{\"attributes\":{},\"id\":\"1171\",\"type\":\"BasicTickFormatter\"},{\"attributes\":{\"source\":{\"id\":\"1093\"}},\"id\":\"1098\",\"type\":\"CDSView\"},{\"attributes\":{\"source\":{\"id\":\"1002\"}},\"id\":\"1053\",\"type\":\"CDSView\"},{\"attributes\":{\"overlay\":{\"id\":\"1084\"}},\"id\":\"1080\",\"type\":\"LassoSelectTool\"},{\"attributes\":{},\"id\":\"1172\",\"type\":\"AllLabels\"},{\"attributes\":{\"fill_color\":{\"value\":\"green\"},\"line_width\":{\"value\":2}},\"id\":\"1054\",\"type\":\"Circle\"},{\"attributes\":{\"data\":{\"x\":[\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\"20\",\"21\",\"22\",\"23\",\"24\",\"25\",\"26\",\"27\",\"28\",\"29\",\"30\",\"30\",\"29\",\"28\",\"27\",\"26\",\"25\",\"24\",\"23\",\"22\",\"21\",\"20\",\"19\",\"18\",\"17\",\"16\",\"15\",\"14\",\"13\",\"12\",\"11\",\"10\",\"9\",\"8\",\"7\",\"6\",\"5\",\"4\",\"3\",\"2\",\"1\"],\"y\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[60]}},\"selected\":{\"id\":\"1113\"},\"selection_policy\":{\"id\":\"1112\"}},\"id\":\"1093\",\"type\":\"ColumnDataSource\"},{\"attributes\":{\"overlay\":{\"id\":\"1031\"}},\"id\":\"1025\",\"type\":\"BoxZoomTool\"},{\"attributes\":{\"callback\":null,\"renderers\":[{\"id\":\"1052\"}],\"tooltips\":[[\"Components\",\"@values_string\"],[\"R\\u00b2\",\"@full_hover\"],[\"Q\\u00b2\",\"@cv_hover\"],[\"| R\\u00b2-Q\\u00b2 |\",\"@diff_hover\"]]},\"id\":\"1055\",\"type\":\"HoverTool\"},{\"attributes\":{},\"id\":\"1024\",\"type\":\"WheelZoomTool\"},{\"attributes\":{\"line_alpha\":0.1,\"line_color\":\"green\",\"line_width\":3,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1101\",\"type\":\"Line\"},{\"attributes\":{\"coordinates\":null,\"group\":null,\"items\":[{\"id\":\"1120\"},{\"id\":\"1152\"}],\"location\":\"bottom_right\"},\"id\":\"1119\",\"type\":\"Legend\"},{\"attributes\":{\"align\":\"center\",\"coordinates\":null,\"group\":null,\"text\":\"R\\u00b2 & Q\\u00b2 over no. of Components\",\"text_font_size\":\"12pt\"},\"id\":\"1058\",\"type\":\"Title\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1082\",\"type\":\"BoxAnnotation\"},{\"attributes\":{},\"id\":\"1078\",\"type\":\"ResetTool\"},{\"attributes\":{},\"id\":\"1173\",\"type\":\"UnionRenderers\"},{\"attributes\":{},\"id\":\"1012\",\"type\":\"LinearScale\"},{\"attributes\":{\"line_alpha\":0.25,\"line_width\":3,\"x\":{\"field\":\"x\"},\"y\":{\"field\":\"y\"}},\"id\":\"1043\",\"type\":\"Line\"},{\"attributes\":{\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"syncable\":false,\"xs_units\":\"screen\",\"ys_units\":\"screen\"},\"id\":\"1032\",\"type\":\"PolyAnnotation\"},{\"attributes\":{},\"id\":\"1111\",\"type\":\"AllLabels\"},{\"attributes\":{\"coordinates\":null,\"data_source\":{\"id\":\"1002\"},\"glyph\":{\"id\":\"1049\"},\"group\":null,\"hover_glyph\":null,\"muted_glyph\":{\"id\":\"1051\"},\"nonselection_glyph\":{\"id\":\"1050\"},\"selection_glyph\":{\"id\":\"1054\"},\"view\":{\"id\":\"1053\"}},\"id\":\"1052\",\"type\":\"GlyphRenderer\"},{\"attributes\":{},\"id\":\"1079\",\"type\":\"SaveTool\"},{\"attributes\":{\"bottom_units\":\"screen\",\"coordinates\":null,\"fill_alpha\":0.5,\"fill_color\":\"lightgrey\",\"group\":null,\"left_units\":\"screen\",\"level\":\"overlay\",\"line_alpha\":1.0,\"line_color\":\"black\",\"line_dash\":[4,4],\"line_width\":2,\"right_units\":\"screen\",\"syncable\":false,\"top_units\":\"screen\"},\"id\":\"1030\",\"type\":\"BoxAnnotation\"}],\"root_ids\":[\"1203\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
+ " const render_items = [{\"docid\":\"b7b452cb-b35c-4183-be4e-1199d5a88fce\",\"root_ids\":[\"1203\"],\"roots\":{\"1203\":\"ca8c693a-6bdb-4534-815a-2fd5b99e34e8\"}}];\n",
+ " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " let attempts = 0;\n",
+ " const timer = setInterval(function(root) {\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " clearInterval(timer);\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " attempts++;\n",
+ " if (attempts > 100) {\n",
+ " clearInterval(timer);\n",
+ " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+ " }\n",
+ " }\n",
+ " }, 10, root)\n",
+ " }\n",
+ "})(window);"
+ ],
+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "1203"
+ }
+ }
+ }
+ ],
+ "source": [
+ "# Parameter Dictionary\n",
+ "param_dict = {'n_components': [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]} \n",
+ "\n",
+ "# Initialise\n",
+ "cv = cb.cross_val.KFold(model=cb.model.PCR, \n",
+ " X=XTrainKnn, \n",
+ " Y=YTrain, \n",
+ " param_dict=param_dict, \n",
+ " folds=5,\n",
+ " n_mc=10,\n",
+ " n_cores= 32) \n",
+ "\n",
+ "# Run and Plot\n",
+ "cv.run() \n",
+ "cv.plot(metric='r2q2')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "nH0InH-Lu93S"
+ },
+ "source": [
+ "### 5. Build Model & Evaluate"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {
+ "scrolled": false,
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 823
+ },
+ "id": "W2Lryqs2u93S",
+ "outputId": "10f3bbbb-4d24-4bbf-f314-b0a5c10c21ee"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/model/BaseModel.py:367: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][0] = np.round(self.table[i][0], 2)\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/model/BaseModel.py:368: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][1] = np.round(self.table[i][1], 2)\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/model/BaseModel.py:372: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][2] = \"%0.2e\" % self.table[i][2]\n",
+ "BokehDeprecationWarning: 'WidgetBox' is deprecated and will be removed in Bokeh 3.0, use 'bokeh.models.Column' instead\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " const force = true;\n",
+ "\n",
+ " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+ " root._bokeh_onload_callbacks = [];\n",
+ " root._bokeh_is_loading = undefined;\n",
+ " }\n",
+ "\n",
+ "const JS_MIME_TYPE = 'application/javascript';\n",
+ " const HTML_MIME_TYPE = 'text/html';\n",
+ " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+ " const CLASS_NAME = 'output_bokeh rendered_html';\n",
+ "\n",
+ " /**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ " function render(props, node) {\n",
+ " const script = document.createElement(\"script\");\n",
+ " node.appendChild(script);\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when an output is cleared or removed\n",
+ " */\n",
+ " function handleClearOutput(event, handle) {\n",
+ " const cell = handle.cell;\n",
+ "\n",
+ " const id = cell.output_area._bokeh_element_id;\n",
+ " const server_id = cell.output_area._bokeh_server_id;\n",
+ " // Clean up Bokeh references\n",
+ " if (id != null && id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ "\n",
+ " if (server_id !== undefined) {\n",
+ " // Clean up Bokeh references\n",
+ " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+ " cell.notebook.kernel.execute(cmd_clean, {\n",
+ " iopub: {\n",
+ " output: function(msg) {\n",
+ " const id = msg.content.text.trim();\n",
+ " if (id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " // Destroy server and session\n",
+ " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+ " cell.notebook.kernel.execute(cmd_destroy);\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when a new output is added\n",
+ " */\n",
+ " function handleAddOutput(event, handle) {\n",
+ " const output_area = handle.output_area;\n",
+ " const output = handle.output;\n",
+ "\n",
+ " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+ " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+ " return\n",
+ " }\n",
+ "\n",
+ " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+ "\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+ " // store reference to embed id on output_area\n",
+ " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+ " }\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+ " const bk_div = document.createElement(\"div\");\n",
+ " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " const script_attrs = bk_div.children[0].attributes;\n",
+ " for (let i = 0; i < script_attrs.length; i++) {\n",
+ " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+ " }\n",
+ " // store reference to server id on output_area\n",
+ " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function register_renderer(events, OutputArea) {\n",
+ "\n",
+ " function append_mime(data, metadata, element) {\n",
+ " // create a DOM node to render to\n",
+ " const toinsert = this.create_output_subarea(\n",
+ " metadata,\n",
+ " CLASS_NAME,\n",
+ " EXEC_MIME_TYPE\n",
+ " );\n",
+ " this.keyboard_manager.register_events(toinsert);\n",
+ " // Render to node\n",
+ " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+ " render(props, toinsert[toinsert.length - 1]);\n",
+ " element.append(toinsert);\n",
+ " return toinsert\n",
+ " }\n",
+ "\n",
+ " /* Handle when an output is cleared or removed */\n",
+ " events.on('clear_output.CodeCell', handleClearOutput);\n",
+ " events.on('delete.Cell', handleClearOutput);\n",
+ "\n",
+ " /* Handle when a new output is added */\n",
+ " events.on('output_added.OutputArea', handleAddOutput);\n",
+ "\n",
+ " /**\n",
+ " * Register the mime type and append_mime function with output_area\n",
+ " */\n",
+ " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+ " /* Is output safe? */\n",
+ " safe: true,\n",
+ " /* Index of renderer in `output_area.display_order` */\n",
+ " index: 0\n",
+ " });\n",
+ " }\n",
+ "\n",
+ " // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+ " if (root.Jupyter !== undefined) {\n",
+ " const events = require('base/js/events');\n",
+ " const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+ "\n",
+ " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+ " register_renderer(events, OutputArea);\n",
+ " }\n",
+ " }\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\n",
+ " }\n",
+ "\n",
+ " const NB_LOAD_WARNING = {'data': {'text/html':\n",
+ " \"<div style='background-color: #fdd'>\\n\"+\n",
+ " \"<p>\\n\"+\n",
+ " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+ " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+ " \"</p>\\n\"+\n",
+ " \"<ul>\\n\"+\n",
+ " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+ " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+ " \"</ul>\\n\"+\n",
+ " \"<code>\\n\"+\n",
+ " \"from bokeh.resources import INLINE\\n\"+\n",
+ " \"output_notebook(resources=INLINE)\\n\"+\n",
+ " \"</code>\\n\"+\n",
+ " \"</div>\"}};\n",
+ "\n",
+ " function display_loaded() {\n",
+ " const el = document.getElementById(null);\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS is loading...\";\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+ " }\n",
+ " } else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(display_loaded, 100)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function run_callbacks() {\n",
+ " try {\n",
+ " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+ " if (callback != null)\n",
+ " callback();\n",
+ " });\n",
+ " } finally {\n",
+ " delete root._bokeh_onload_callbacks\n",
+ " }\n",
+ " console.debug(\"Bokeh: all callbacks have finished\");\n",
+ " }\n",
+ "\n",
+ " function load_libs(css_urls, js_urls, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ " if (root._bokeh_is_loading > 0) {\n",
+ " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+ " return null;\n",
+ " }\n",
+ " if (js_urls == null || js_urls.length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+ "\n",
+ " function on_load() {\n",
+ " root._bokeh_is_loading--;\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+ " run_callbacks()\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function on_error(url) {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < css_urls.length; i++) {\n",
+ " const url = css_urls[i];\n",
+ " const element = document.createElement(\"link\");\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.rel = \"stylesheet\";\n",
+ " element.type = \"text/css\";\n",
+ " element.href = url;\n",
+ " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < js_urls.length; i++) {\n",
+ " const url = js_urls[i];\n",
+ " const element = document.createElement('script');\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.async = false;\n",
+ " element.src = url;\n",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " function inject_raw_css(css) {\n",
+ " const element = document.createElement(\"style\");\n",
+ " element.appendChild(document.createTextNode(css));\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n",
+ " const css_urls = [];\n",
+ "\n",
+ " const inline_js = [ function(Bokeh) {\n",
+ " Bokeh.set_log_level(\"info\");\n",
+ " },\n",
+ "function(Bokeh) {\n",
+ " }\n",
+ " ];\n",
+ "\n",
+ " function run_inline_js() {\n",
+ " if (root.Bokeh !== undefined || force === true) {\n",
+ " for (let i = 0; i < inline_js.length; i++) {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ " }\n",
+ "} else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(run_inline_js, 100);\n",
+ " } else if (!root._bokeh_failed_load) {\n",
+ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+ " root._bokeh_failed_load = true;\n",
+ " } else if (force !== true) {\n",
+ " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
+ " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+ " run_inline_js();\n",
+ " } else {\n",
+ " load_libs(css_urls, js_urls, function() {\n",
+ " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+ " run_inline_js();\n",
+ " });\n",
+ " }\n",
+ "}(window));"
+ ],
+ "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
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+ "metadata": {}
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IU+awbTNE/doMp8lk32D92gynyWTfYP3aDKfJZN9g/doMp8lk32D92gynyWTfYP3aDKfJZN9g/doMp8lk32D92gynyWTfYP3aDKfJZN9g/doMp8lk32D92gynyWTfYP1Pks24wRd4/U+SzbjBF3j9T5LNuMEXeP1Pks24wRd4/U+SzbjBF3j9T5LNuMEXeP1Pks24wRd4/U+SzbjBF3j9T5LNuMEXeP1Pks24wRd4/KvJZN5gi3z8q8lk3mCLfPyryWTeYIt8/KvJZN5gi3z8q8lk3mCLfPyryWTeYIt8/KvJZN5gi3z8q8lk3mCLfPyryWTeYIt8/KvJZN5gi3z8q8lk3mCLfP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjP28wRT7rBuM/bzBFPusG4z9vMEU+6wbjPwdT5LNuMOU/B1Pks24w5T8HU+SzbjDlPwdT5LNuMOU/B1Pks24w5T8HU+SzbjDlPwdT5LNuMOU/B1Pks24w5T8HU+SzbjDlPwdT5LNuMOU/B1Pks24w5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/8lk3mCKf5T/yWTeYIp/lP/JZN5gin+U/3WCKfNYN5j/dYIp81g3mP91ginzWDeY/3WCKfNYN5j/dYIp81g3mP91ginzWDeY/3WCKfNYN5j/dYIp81g3mP91ginzWDeY/3WCKfNYN5j92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP3aDKfJZN+g/doMp8lk36D92gynyWTfoP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP2GKfNYNpug/YYp81g2m6D9hinzWDaboP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T9Mkc+6wRTpP0yRz7rBFOk/TJHPusEU6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/I591gyny6T8jn3WDKfLpPyOfdYMp8uk/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP/msG0yRz+o/+awbTJHP6j/5rBtMkc/qP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z/QusEU+azrP9C6wRT5rOs/0LrBFPms6z+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+myGfdYIrsP6bIZ91giuw/pshn3WCK7D+Rz7rBFPnsP5HPusEU+ew/kc+6wRT57D+Rz7rBFPnsP5HPusEU+ew/kc+6wRT57D+Rz7rBFPnsP5HPusEU+ew/kc+6wRT57D+Rz7rBFPnsP5HPusEU+ew/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T991g2myGftP33WDabIZ+0/fdYNpshn7T9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe4/U+SzbjBF7j9T5LNuMEXuP1Pks24wRe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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1002]},\"y\":{\"__ndarray__\":\"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Application\",\"version\":\"2.4.3\"}};\n",
+ " const render_items = [{\"docid\":\"1ab309e4-b186-4844-868f-daaeae84c136\",\"root_ids\":[\"1743\"],\"roots\":{\"1743\":\"1dc170b3-ecd9-4ca9-9ac9-c561a1b18726\"}}];\n",
+ " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " let attempts = 0;\n",
+ " const timer = setInterval(function(root) {\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " clearInterval(timer);\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " attempts++;\n",
+ " if (attempts > 100) {\n",
+ " clearInterval(timer);\n",
+ " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+ " }\n",
+ " }\n",
+ " }, 10, root)\n",
+ " }\n",
+ "})(window);"
+ ],
+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "1743"
+ }
+ }
+ }
+ ],
+ "source": [
+ "# Build Mode1\n",
+ "model = cb.model.PCR(n_components=23)\n",
+ "YPredTrain = model.train(XTrainKnn, YTrain)\n",
+ "YPredTest = model.test(XTestKnn)\n",
+ "\n",
+ "# Put YTrain and YPredTrain in a List\n",
+ "EvalTrain = [YTrain, YPredTrain]\n",
+ "\n",
+ "# Put YTest and YPrestTest in a List\n",
+ "EvalTest = [YTest, YPredTest]\n",
+ "\n",
+ "# Evaluate Model (include Test Dataset)\n",
+ "model.evaluate(testset=EvalTest) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Oh6gnMeGu93S"
+ },
+ "source": [
+ "### 6. Bootstrap Evaluation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 909
+ },
+ "id": "css8ThjRu93T",
+ "outputId": "1550f474-379f-4159-c193-bed67f4cb85f"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Number of cores set to: 2\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 100/100 [00:03<00:00, 26.76it/s]\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Time taken: 0.07 minutes with 2 cores\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/bootstrap/BaseBootstrap.py:343: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][0] = np.round(self.table[i][0], 2)\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/bootstrap/BaseBootstrap.py:344: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][1] = np.round(self.table[i][1], 2)\n",
+ "/usr/local/lib/python3.10/dist-packages/cimcb/bootstrap/BaseBootstrap.py:348: SettingWithCopyWarning: \n",
+ "A value is trying to be set on a copy of a slice from a DataFrame\n",
+ "\n",
+ "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+ " self.table[i][2] = \"%0.2e\" % self.table[i][2]\n",
+ "BokehDeprecationWarning: 'WidgetBox' is deprecated and will be removed in Bokeh 3.0, use 'bokeh.models.Column' instead\n"
+ ]
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function now() {\n",
+ " return new Date();\n",
+ " }\n",
+ "\n",
+ " const force = true;\n",
+ "\n",
+ " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n",
+ " root._bokeh_onload_callbacks = [];\n",
+ " root._bokeh_is_loading = undefined;\n",
+ " }\n",
+ "\n",
+ "const JS_MIME_TYPE = 'application/javascript';\n",
+ " const HTML_MIME_TYPE = 'text/html';\n",
+ " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n",
+ " const CLASS_NAME = 'output_bokeh rendered_html';\n",
+ "\n",
+ " /**\n",
+ " * Render data to the DOM node\n",
+ " */\n",
+ " function render(props, node) {\n",
+ " const script = document.createElement(\"script\");\n",
+ " node.appendChild(script);\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when an output is cleared or removed\n",
+ " */\n",
+ " function handleClearOutput(event, handle) {\n",
+ " const cell = handle.cell;\n",
+ "\n",
+ " const id = cell.output_area._bokeh_element_id;\n",
+ " const server_id = cell.output_area._bokeh_server_id;\n",
+ " // Clean up Bokeh references\n",
+ " if (id != null && id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ "\n",
+ " if (server_id !== undefined) {\n",
+ " // Clean up Bokeh references\n",
+ " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n",
+ " cell.notebook.kernel.execute(cmd_clean, {\n",
+ " iopub: {\n",
+ " output: function(msg) {\n",
+ " const id = msg.content.text.trim();\n",
+ " if (id in Bokeh.index) {\n",
+ " Bokeh.index[id].model.document.clear();\n",
+ " delete Bokeh.index[id];\n",
+ " }\n",
+ " }\n",
+ " }\n",
+ " });\n",
+ " // Destroy server and session\n",
+ " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n",
+ " cell.notebook.kernel.execute(cmd_destroy);\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " /**\n",
+ " * Handle when a new output is added\n",
+ " */\n",
+ " function handleAddOutput(event, handle) {\n",
+ " const output_area = handle.output_area;\n",
+ " const output = handle.output;\n",
+ "\n",
+ " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n",
+ " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n",
+ " return\n",
+ " }\n",
+ "\n",
+ " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n",
+ "\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n",
+ " // store reference to embed id on output_area\n",
+ " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n",
+ " }\n",
+ " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n",
+ " const bk_div = document.createElement(\"div\");\n",
+ " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n",
+ " const script_attrs = bk_div.children[0].attributes;\n",
+ " for (let i = 0; i < script_attrs.length; i++) {\n",
+ " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n",
+ " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n",
+ " }\n",
+ " // store reference to server id on output_area\n",
+ " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function register_renderer(events, OutputArea) {\n",
+ "\n",
+ " function append_mime(data, metadata, element) {\n",
+ " // create a DOM node to render to\n",
+ " const toinsert = this.create_output_subarea(\n",
+ " metadata,\n",
+ " CLASS_NAME,\n",
+ " EXEC_MIME_TYPE\n",
+ " );\n",
+ " this.keyboard_manager.register_events(toinsert);\n",
+ " // Render to node\n",
+ " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
+ " render(props, toinsert[toinsert.length - 1]);\n",
+ " element.append(toinsert);\n",
+ " return toinsert\n",
+ " }\n",
+ "\n",
+ " /* Handle when an output is cleared or removed */\n",
+ " events.on('clear_output.CodeCell', handleClearOutput);\n",
+ " events.on('delete.Cell', handleClearOutput);\n",
+ "\n",
+ " /* Handle when a new output is added */\n",
+ " events.on('output_added.OutputArea', handleAddOutput);\n",
+ "\n",
+ " /**\n",
+ " * Register the mime type and append_mime function with output_area\n",
+ " */\n",
+ " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
+ " /* Is output safe? */\n",
+ " safe: true,\n",
+ " /* Index of renderer in `output_area.display_order` */\n",
+ " index: 0\n",
+ " });\n",
+ " }\n",
+ "\n",
+ " // register the mime type if in Jupyter Notebook environment and previously unregistered\n",
+ " if (root.Jupyter !== undefined) {\n",
+ " const events = require('base/js/events');\n",
+ " const OutputArea = require('notebook/js/outputarea').OutputArea;\n",
+ "\n",
+ " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
+ " register_renderer(events, OutputArea);\n",
+ " }\n",
+ " }\n",
+ " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n",
+ " root._bokeh_timeout = Date.now() + 5000;\n",
+ " root._bokeh_failed_load = false;\n",
+ " }\n",
+ "\n",
+ " const NB_LOAD_WARNING = {'data': {'text/html':\n",
+ " \"<div style='background-color: #fdd'>\\n\"+\n",
+ " \"<p>\\n\"+\n",
+ " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
+ " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
+ " \"</p>\\n\"+\n",
+ " \"<ul>\\n\"+\n",
+ " \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
+ " \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
+ " \"</ul>\\n\"+\n",
+ " \"<code>\\n\"+\n",
+ " \"from bokeh.resources import INLINE\\n\"+\n",
+ " \"output_notebook(resources=INLINE)\\n\"+\n",
+ " \"</code>\\n\"+\n",
+ " \"</div>\"}};\n",
+ "\n",
+ " function display_loaded() {\n",
+ " const el = document.getElementById(null);\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS is loading...\";\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " if (el != null) {\n",
+ " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n",
+ " }\n",
+ " } else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(display_loaded, 100)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function run_callbacks() {\n",
+ " try {\n",
+ " root._bokeh_onload_callbacks.forEach(function(callback) {\n",
+ " if (callback != null)\n",
+ " callback();\n",
+ " });\n",
+ " } finally {\n",
+ " delete root._bokeh_onload_callbacks\n",
+ " }\n",
+ " console.debug(\"Bokeh: all callbacks have finished\");\n",
+ " }\n",
+ "\n",
+ " function load_libs(css_urls, js_urls, callback) {\n",
+ " if (css_urls == null) css_urls = [];\n",
+ " if (js_urls == null) js_urls = [];\n",
+ "\n",
+ " root._bokeh_onload_callbacks.push(callback);\n",
+ " if (root._bokeh_is_loading > 0) {\n",
+ " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n",
+ " return null;\n",
+ " }\n",
+ " if (js_urls == null || js_urls.length === 0) {\n",
+ " run_callbacks();\n",
+ " return null;\n",
+ " }\n",
+ " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n",
+ " root._bokeh_is_loading = css_urls.length + js_urls.length;\n",
+ "\n",
+ " function on_load() {\n",
+ " root._bokeh_is_loading--;\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n",
+ " run_callbacks()\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " function on_error(url) {\n",
+ " console.error(\"failed to load \" + url);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < css_urls.length; i++) {\n",
+ " const url = css_urls[i];\n",
+ " const element = document.createElement(\"link\");\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.rel = \"stylesheet\";\n",
+ " element.type = \"text/css\";\n",
+ " element.href = url;\n",
+ " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " for (let i = 0; i < js_urls.length; i++) {\n",
+ " const url = js_urls[i];\n",
+ " const element = document.createElement('script');\n",
+ " element.onload = on_load;\n",
+ " element.onerror = on_error.bind(null, url);\n",
+ " element.async = false;\n",
+ " element.src = url;\n",
+ " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n",
+ " document.head.appendChild(element);\n",
+ " }\n",
+ " };\n",
+ "\n",
+ " function inject_raw_css(css) {\n",
+ " const element = document.createElement(\"style\");\n",
+ " element.appendChild(document.createTextNode(css));\n",
+ " document.body.appendChild(element);\n",
+ " }\n",
+ "\n",
+ " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n",
+ " const css_urls = [];\n",
+ "\n",
+ " const inline_js = [ function(Bokeh) {\n",
+ " Bokeh.set_log_level(\"info\");\n",
+ " },\n",
+ "function(Bokeh) {\n",
+ " }\n",
+ " ];\n",
+ "\n",
+ " function run_inline_js() {\n",
+ " if (root.Bokeh !== undefined || force === true) {\n",
+ " for (let i = 0; i < inline_js.length; i++) {\n",
+ " inline_js[i].call(root, root.Bokeh);\n",
+ " }\n",
+ "} else if (Date.now() < root._bokeh_timeout) {\n",
+ " setTimeout(run_inline_js, 100);\n",
+ " } else if (!root._bokeh_failed_load) {\n",
+ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
+ " root._bokeh_failed_load = true;\n",
+ " } else if (force !== true) {\n",
+ " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n",
+ " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
+ " }\n",
+ " }\n",
+ "\n",
+ " if (root._bokeh_is_loading === 0) {\n",
+ " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
+ " run_inline_js();\n",
+ " } else {\n",
+ " load_libs(css_urls, js_urls, function() {\n",
+ " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n",
+ " run_inline_js();\n",
+ " });\n",
+ " }\n",
+ "}(window));"
+ ],
+ "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"<div style='background-color: #fdd'>\\n\"+\n \"<p>\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"</p>\\n\"+\n \"<ul>\\n\"+\n \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n \"<li>use INLINE resources instead, as so:</li>\\n\"+\n \"</ul>\\n\"+\n \"<code>\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"</code>\\n\"+\n \"</div>\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-2.4.3.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-2.4.3.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));"
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "text/html": [
+ "\n",
+ " <div class=\"bk-root\" id=\"8d8da8b8-05cb-4d3c-8390-6cdffab2cb08\" data-root-id=\"2549\"></div>\n"
+ ]
+ },
+ "metadata": {}
+ },
+ {
+ "output_type": "display_data",
+ "data": {
+ "application/javascript": [
+ "(function(root) {\n",
+ " function embed_document(root) {\n",
+ " const docs_json = 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Jk/wJelSxR8mT/Al6VLFHyZP8CXpUsUfJk/wJelSxR8mT/Al6VLFHyZP8CXpUsUfJk/wJelSxR8mT/Al6VLFHyZPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/AHGh5iQQlz8AcaHmJBCXPwBxoeYkEJc/8E2FHUDflD/wTYUdQN+UP/BNhR1A35Q/8E2FHUDflD/wTYUdQN+UP/BNhR1A35Q/8E2FHUDflD/wTYUdQN+UP/BNhR1A35Q/8E2FHUDflD8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/MCeBuFBzkj8wJ4G4UHOSPzAngbhQc5I/IARl72tCkD8gBGXva0KQPyAEZe9rQpA/IARl72tCkD8gBGXva0KQPyAEZe9rQpA/IARl72tCkD/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/wLrBFPmsiz/AusEU+ayLP8C6wRT5rIs/oHSJgi9Lhz+gdImCL0uHP6B0iYIvS4c/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AJ4G4UHOCP0AngbhQc4I/QCeBuFBzgj9AwpFMDiN8P0DCkUwOI3w/QMKRTA4jfD9AwpFMDiN8P0DCkUwOI3w/QMKRTA4jfD9AwpFMDiN8P0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyP0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyP0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyP0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyP0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyP0AngbhQc3I/QCeBuFBzcj9AJ4G4UHNyPwAAAAAAAAAA\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1002]},\"lowci\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1002]},\"spec\":{\"__ndarray__\":\"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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1002]},\"uppci\":{\"__ndarray__\":\"AAAAAAAAAADPBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D/PBsDk/2zYP88GwOT/bNg/zwbA5P9s2D8wyHzl883YPzDIfOXzzdg/dSVqV6J22T8hN3IjN3LbPyE3ciM3cts/xVJF8aMA3D/FUkXxowDcP8VSRfGjANw/xVJF8aMA3D/FUkXxowDcP8VSRfGjANw/xVJF8aMA3D/FUkXxowDcP8VSRfGjANw/xVJF8aMA3D/FUkX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\",\"dtype\":\"float64\",\"order\":\"little\",\"shape\":[1002]},\"y\":{\"__ndarray__\":\"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+ " const render_items = [{\"docid\":\"db83121a-7a81-499d-91ed-2fc4a0ba1c56\",\"root_ids\":[\"2549\"],\"roots\":{\"2549\":\"8d8da8b8-05cb-4d3c-8390-6cdffab2cb08\"}}];\n",
+ " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
+ " }\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " let attempts = 0;\n",
+ " const timer = setInterval(function(root) {\n",
+ " if (root.Bokeh !== undefined) {\n",
+ " clearInterval(timer);\n",
+ " embed_document(root);\n",
+ " } else {\n",
+ " attempts++;\n",
+ " if (attempts > 100) {\n",
+ " clearInterval(timer);\n",
+ " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
+ " }\n",
+ " }\n",
+ " }, 10, root)\n",
+ " }\n",
+ "})(window);"
+ ],
+ "application/vnd.bokehjs_exec.v0+json": ""
+ },
+ "metadata": {
+ "application/vnd.bokehjs_exec.v0+json": {
+ "id": "2549"
+ }
+ }
+ }
+ ],
+ "source": [
+ "# Extract X Data\n",
+ "XBoot = DataTable2[PeakList]\n",
+ "XBootLog = np.log(XBoot)\n",
+ "XBootScale = cb.utils.scale(XBootLog, method='auto')\n",
+ "XBootKnn = cb.utils.knnimpute(XBootScale, k=3)\n",
+ "YPredBoot = model.train(XBootKnn, Y)\n",
+ "\n",
+ "# Build Boostrap Models\n",
+ "bootmodel = cb.bootstrap.Per(model, bootnum=100) \n",
+ "bootmodel.run()\n",
+ "\n",
+ "# Boostrap Evaluate Model (include Test Dataset)\n",
+ "bootmodel.evaluate(trainset=EvalTrain, testset=EvalTest) "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "zwQy35puu93T"
+ },
+ "source": [
+ "### 7. Export Results"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "oVVZuI6eu93T",
+ "outputId": "8ad7b89f-d218-4511-9923-cc556eeca579"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Done! Saved results as PCR_MTBLS92.xlsx\n"
+ ]
+ }
+ ],
+ "source": [
+ "file = 'PCR_MTBLS92.xlsx'\n",
+ "bootmodel.save_results(file)\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "features_data = model.model.coef_\n",
+ "ids = np.argsort(features_data, kind = 'mergesort')[::-1][:3] # extractibg the index of top 3 metabolites\n",
+ "metabolites_data = PeakTable.iloc[ids] \n",
+ "print(metabolites_data)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "D5zvN7mXTz2B",
+ "outputId": "c237a735-aab5-468e-810c-28b0912a0c8f"
+ },
+ "execution_count": 14,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ " Idx Name Label\n",
+ "71 71 M71 SM(d18:1/16:1)\n",
+ "20 20 M20 PC(32:5)\n",
+ "55 55 M55 PC(40:5e)\n"
+ ]
+ }
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.7.3"
+ },
+ "toc": {
+ "base_numbering": 1,
+ "nav_menu": {},
+ "number_sections": false,
+ "sideBar": false,
+ "skip_h1_title": false,
+ "title_cell": "Table of Contents",
+ "title_sidebar": "Contents",
+ "toc_cell": false,
+ "toc_position": {},
+ "toc_section_display": false,
+ "toc_window_display": false
+ },
+ "toc-autonumbering": false,
+ "toc-showmarkdowntxt": false,
+ "colab": {
+ "provenance": []
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
\ No newline at end of file
--
GitLab