notebooks/Dimensionality_reduction.ipynb

{
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   "cell_type": "markdown",
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   "source": [
    "## **Dimensionality reduction**\n",
    "\n",
    "In several scenarios, you may be dealing with functional data. That is the data could be sampled from a multi-dimensional space, and in some cases the number of dimensions could be very huge and may contain redundant information. Dimensionality reduction is a process used to reduce the number of features (or dimensions) in a dataset while retaining as much relevant information as possible.\n",
    "\n",
    "Gaussian Processes(GPs) do not scale very well to high-dimensional data. `twinLab` provides the functionality to perform dimensionality reduction on the data, truncate the number of dimensions, and train GPs effectively.\n",
    "\n",
    "In this Jupyter Notebook you will:\n",
    "\n",
    "1. [Load and define a CSV file.](#1.-Load-and-define-a-CSV-file)\n",
    "2. [Define the training parameters for your emulator.](#2.-Define-the-training-parameters-for-your-emulator)\n",
    "3. [Train your emulator using `emulator.train`.](#3.-Train-your-emulator-using-emulator.train)\n",
    "4. [Make your predictions using `emulator.predict`.](#4.-Make-your-predictions-using-emulator.predict)\n",
    "5. [Create a second emulator by adjusting the `output_retained_dimensions`.](#5.-Create-a-second-emulator-by-adjusting-the-output_retained_dimensions)\n",
    "6. [Plot the predictions and analyse the results.](#6.-Plot-the-predictions-and-analyse-the-results)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Start by importing all the necessary packages you need for this tutorial.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# system imports\n",
    "import os\n",
    "from itertools import product\n",
    "\n",
    "# Third party imports\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# twinLab import\n",
    "import twinlab as tl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure you set up your API key and other information to be able to use `twinLab`, either through a `.env` file, using `secrets`, or through `tl.set_user`/`tl.set_api_key`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **1. Load and define a CSV file**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`twinLab` does offers large example dataset as part of our available example datasets, so we'll be using that primarily for this tutorial. However, you might want to experiment using `twinLab` with your own large dataset, so we have included some optional code to demonstrate how you could go about doing this. First, you'll want to read the train and test DataFrames that `twinLab` expects from their respective `.csv` files that you have stored locally. \n",
    "\n",
    "Assuming you have your files saved in your current working directory, let's check what that working directory is to find the filepath to your `.csv` file.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Optional) If using your own dataset, uncomment the line below to find the path for your csv file\n",
    "# path = os.getcwd()\n",
    "\n",
    "# print(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, use `pandas` to read your required test and train CSV files, and load them into `pandas DataFrames`. This might look something like:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Optional) If using your own dataset, uncomment the line below to load data from a csv file\n",
    "# my_data = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, we can explore dimension reduction using an example dataset from `twinLab`. The dataset we're using explores the topic of tritium-desorption in a nuclear reactor, and contains datapoints with 5 input dimensions and 624 output dimensions. This number of output dimensions is huge, and it might be better to train our emulators on truncated outputs for efficient and effective training.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "\n",
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       "3    2.405455e+17  2.382445e+17  2.359435e+17  2.336425e+17  2.313415e+17   \n",
       "4    3.434468e+16  3.408240e+16  3.382012e+16  3.355785e+16  3.329557e+16   \n",
       "..            ...           ...           ...           ...           ...   \n",
       "395  2.099361e+17  2.070631e+17  2.041900e+17  2.013170e+17  1.984440e+17   \n",
       "396  1.136794e+17  1.125222e+17  1.113649e+17  1.102076e+17  1.090504e+17   \n",
       "397  4.119450e+16  4.079302e+16  4.039155e+16  3.999007e+16  3.958860e+16   \n",
       "398  2.415848e+17  2.377526e+17  2.339204e+17  2.300882e+17  2.262560e+17   \n",
       "399  9.499925e+16  9.378554e+16  9.257182e+16  9.135811e+16  9.014439e+16   \n",
       "\n",
       "             y619          y620          y621          y622          y623  \n",
       "0    2.248615e+17  2.234616e+17  2.220618e+17  2.206620e+17  2.192622e+17  \n",
       "1    2.618484e+16  2.596381e+16  2.574279e+16  2.552176e+16  2.530073e+16  \n",
       "2    2.073510e+16  2.072265e+16  2.071021e+16  2.069777e+16  2.068533e+16  \n",
       "3    2.290405e+17  2.267395e+17  2.244385e+17  2.221375e+17  2.198365e+17  \n",
       "4    3.303329e+16  3.277101e+16  3.250873e+16  3.224646e+16  3.198418e+16  \n",
       "..            ...           ...           ...           ...           ...  \n",
       "395  1.955710e+17  1.926980e+17  1.898249e+17  1.869519e+17  1.840789e+17  \n",
       "396  1.078931e+17  1.067358e+17  1.055786e+17  1.044213e+17  1.032640e+17  \n",
       "397  3.918712e+16  3.878565e+16  3.838417e+16  3.798270e+16  3.758122e+16  \n",
       "398  2.224238e+17  2.185916e+17  2.147594e+17  2.109272e+17  2.072690e+17  \n",
       "399  8.893068e+16  8.771697e+16  8.650325e+16  8.528954e+16  8.407582e+16  \n",
       "\n",
       "[400 rows x 629 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load example training dataset and grid dataset\n",
    "df = tl.load_example_dataset(\"tritium-desorption-small\")\n",
    "df_grid = tl.load_example_dataset(\"tritium-desorption-temperature-grid\")\n",
    "\n",
    "# View your dataset\n",
    "display(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output columns contain values that are of a very high order (up to 1e+18). This can cause numerical instability when training the dimensionality reduction model. To avoid this you can divide all the output values by 1e+18, making them closer to an order of 1 and causing less instability when predicting your results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define input and output columns\n",
    "input_columns = [\"E1\", \"E2\", \"E3\", \"n1\", \"n2\"]\n",
    "output_columns = [\n",
    "    f\"y{i}\" for i in range(0, df.shape[1] - len(input_columns))\n",
    "]  # Loops through output column headers\n",
    "\n",
    "# Divide output data by 1e+18\n",
    "df[output_columns] = df[output_columns].div(1e18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define your train and test data.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define test and train size\n",
    "train_len = 350\n",
    "eval_len = len(df) - train_len\n",
    "\n",
    "# Define train and test data\n",
    "df_train = df[:train_len]\n",
    "df_eval = df[train_len : train_len + eval_len]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define your input and output columns. As there are a large number of output columns, you can use a list comprehension to loop through each of the output column headers.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define a `Dataset` object and upload it to the `twinLab` cloud.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataframe is uploading.\n",
      "Summarising dataset\n",
      "Dataset summary created and saved.\n"
     ]
    }
   ],
   "source": [
    "# Define the name of the dataset\n",
    "dataset_id = \"Tritium_Desorption_Data\"\n",
    "\n",
    "# Intialise a Dataset object\n",
    "dataset = tl.Dataset(id=dataset_id)\n",
    "\n",
    "# Upload the dataset\n",
    "dataset.upload(df_train, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **2. Define the training parameters for your emulator**\n",
    "\n",
    "In `twinlab`, dimensionality reduction is implemented in the form of truncated Singular Value Decomposition (tSVD), and is accessible in two ways.\n",
    "\n",
    "It can be performed by specifying the number of dimensions you wish to truncate the data to, using `input_retained_dimensions` for inputs and `output_retained_dimensions` for outputs. This can also be specified through the amount of variance to be explained by the data after truncation through `input_explained_variance` for inputs and `output_explained_variance` for outputs.\n",
    "\n",
    "These parameters are part of the `TrainParams` object, and can then be further passed to the training function `Emulator.train`.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explained Variance\n",
    "\n",
    "In this method, you specify the amount of variance to be explained by the retained dimensions. First, you need to set up the parameters and initialise the emulator.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create TrainParams object with explained variance\n",
    "explained_variance_params = tl.TrainParams(\n",
    "    train_test_ratio=0.80,  # Specifies ratio of train data to test data (0.80 = 80% train data and 20% test data)\n",
    "    estimator=\"gaussian_process_regression\",  # Specifies type of estimation\n",
    "    input_explained_variance=0.95,\n",
    "    output_explained_variance=0.90,\n",
    ")\n",
    "\n",
    "# Initialise emulator\n",
    "emulator_id = \"TritiumDesorptionGP-ExplainedVariance\"  # Specifies type of emulator\n",
    "emulator = tl.Emulator(id=emulator_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, you can train your emulator.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# (Optional) Train the emulator using the explained variance (uncomment code below to use)\n",
    "# emulator.train(\n",
    "#     dataset=dataset,\n",
    "#     inputs=input_columns,\n",
    "#     outputs=output_columns,\n",
    "#     params=params,\n",
    "#     verbose=True,\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Retained Dimensions\n",
    "\n",
    "In this method, you explicitly specify the number of dimensions to retain. One can decompose the inputs, outputs, or both in the same `Emulator`. The parameters below will sets the output dimensions to **2**.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise emulator\n",
    "emulator_id = \"TritiumDesorptionGP1\"  # Specifies type of emulator\n",
    "emulator1 = tl.Emulator(id=emulator_id)\n",
    "\n",
    "# Define the training parameters for your emulator\n",
    "params = tl.TrainParams(\n",
    "    train_test_ratio=0.80,  # Specifies ratio of train data to test data (0.80 = 80% train data and 20% test data)\n",
    "    estimator=\"gaussian_process_regression\",  # Specifies type of estimation\n",
    "    output_retained_dimensions=2,  # Specifies the number of output dimensions you wish to truncate the data to\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **3. Train your emulator using `emulator.train`**\n",
    "\n",
    "Set the parameters (`params`) to `params` defined above. This will train the emulator by truncating the the number of output dimensions to **2** (from 624).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emulator 'TritiumDesorptionGP1' has begun training.\n",
      "0:00:00: Job status: processing\n",
      "0:00:01: Job status: processing\n",
      "0:00:03: Job status: processing\n",
      "0:00:05: Job status: processing\n",
      "0:00:07: Job status: processing\n",
      "0:00:09: Job status: processing\n",
      "0:00:12: Job status: processing\n",
      "0:00:15: Job status: processing\n",
      "0:00:18: Job status: processing\n",
      "0:00:21: Job status: processing\n",
      "0:00:25: Job status: success\n",
      "Training of emulator TritiumDesorptionGP1 is complete!\n"
     ]
    }
   ],
   "source": [
    "emulator1.train(\n",
    "    dataset=dataset,\n",
    "    inputs=input_columns,\n",
    "    outputs=output_columns,\n",
    "    params=params,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **4. Make your predictions using `emulator.predict`**\n",
    "\n",
    "Use test data (`df_eval`) to predict the results.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Job predict process ID: predict-thin-pinto-cent\n",
      "0:00:00: Job status: processing\n",
      "0:00:01: Job status: processing\n",
      "0:00:03: Job status: processing\n",
      "0:00:05: Job status: processing\n",
      "0:00:07: Job status: processing\n",
      "0:00:09: Job status: processing\n",
      "0:00:12: Job status: processing\n",
      "0:00:15: Job status: processing\n",
      "0:00:18: Job status: processing\n",
      "0:00:21: Job status: processing\n",
      "0:00:25: Job status: processing\n",
      "0:00:29: Job status: processing\n",
      "0:00:34: Job status: success\n",
      "Mean predictions:\n",
      "          y0        y1        y2        y3        y4        y5        y6  \\\n",
      "0   0.046534  0.050301  0.054371  0.058755  0.063459  0.068469  0.073785   \n",
      "1   0.038614  0.041832  0.045324  0.049098  0.053160  0.057496  0.062109   \n",
      "2   0.171483  0.185254  0.200114  0.216108  0.233255  0.251502  0.270852   \n",
      "3   0.076626  0.082785  0.089433  0.096588  0.104260  0.112425  0.121084   \n",
      "4  -0.007474 -0.007938 -0.008415 -0.008911 -0.009424 -0.009954 -0.010500   \n",
      "5   0.077476  0.083782  0.090600  0.097950  0.105840  0.114247  0.123171   \n",
      "6   0.081271  0.087792  0.094828  0.102400  0.110517  0.119155  0.128314   \n",
      "7   0.098327  0.106303  0.114923  0.124211  0.134179  0.144797  0.156065   \n",
      "8  -0.033979 -0.036552 -0.039303 -0.042244 -0.045376 -0.048691 -0.052187   \n",
      "9   0.093865  0.101441  0.109623  0.118434  0.127885  0.137947  0.148622   \n",
      "10  0.077167  0.083410  0.090155  0.097420  0.105215  0.113515  0.122323   \n",
      "11  0.126734  0.136904  0.147876  0.159685  0.172344  0.185815  0.200099   \n",
      "12 -0.026577 -0.028590 -0.030743 -0.033044 -0.035495 -0.038089 -0.040825   \n",
      "13  0.132214  0.142832  0.154291  0.166623  0.179845  0.193915  0.208836   \n",
      "14  0.007693  0.008428  0.009242  0.010133  0.011104  0.012151  0.013276   \n",
      "15 -0.019707 -0.021180 -0.022752 -0.024428 -0.026212 -0.028096 -0.030081   \n",
      "16 -0.027241 -0.029255 -0.031400 -0.033684 -0.036111 -0.038673 -0.041369   \n",
      "17  0.058371  0.063149  0.068320  0.073898  0.079889  0.086276  0.093060   \n",
      "18  0.053065  0.057344  0.061965  0.066940  0.072277  0.077958  0.083985   \n",
      "19  0.106888  0.115522  0.124846  0.134889  0.145661  0.157132  0.169301   \n",
      "20  0.032201  0.034885  0.037797  0.040945  0.044333  0.047950  0.051797   \n",
      "21  0.148033  0.159945  0.172803  0.186645  0.201488  0.217287  0.234044   \n",
      "22  0.039275  0.042571  0.046151  0.050023  0.054192  0.058647  0.063387   \n",
      "23  0.076113  0.082245  0.088866  0.095995  0.103640  0.111778  0.120410   \n",
      "24 -0.017672 -0.018900 -0.020193 -0.021559 -0.022998 -0.024507 -0.026084   \n",
      "25  0.167056  0.180445  0.194887  0.210428  0.227085  0.244808  0.263599   \n",
      "26  0.124228  0.134240  0.145050  0.156689  0.169171  0.182459  0.196555   \n",
      "27  0.132702  0.143382  0.154911  0.167323  0.180633  0.194800  0.209826   \n",
      "28  0.062459  0.067477  0.072893  0.078722  0.084972  0.091623  0.098677   \n",
      "29  0.013303  0.014484  0.015777  0.017183  0.018706  0.020340  0.022086   \n",
      "30  0.056077  0.060660  0.065619  0.070967  0.076711  0.082833  0.089334   \n",
      "31  0.018573  0.020108  0.021771  0.023567  0.025498  0.027558  0.029748   \n",
      "32  0.178424  0.192716  0.208133  0.224722  0.242501  0.261417  0.281472   \n",
      "33  0.153352  0.165617  0.178844  0.193074  0.208323  0.224545  0.241741   \n",
      "34  0.095039  0.102699  0.110970  0.119876  0.129427  0.139595  0.150380   \n",
      "35  0.087729  0.094828  0.102497  0.110758  0.119622  0.129061  0.139078   \n",
      "36  0.114621  0.123856  0.133827  0.144563  0.156076  0.168333  0.181334   \n",
      "37  0.006985  0.007631  0.008342  0.009119  0.009962  0.010871  0.011844   \n",
      "38  0.019187  0.020797  0.022544  0.024434  0.026469  0.028643  0.030957   \n",
      "39 -0.020333 -0.021803 -0.023363 -0.025019 -0.026774 -0.028622 -0.030562   \n",
      "40  0.052531  0.056766  0.061338  0.066261  0.071540  0.077161  0.083123   \n",
      "41  0.112477  0.121540  0.131325  0.141861  0.153160  0.165188  0.177947   \n",
      "42  0.117026  0.126432  0.136582  0.147508  0.159223  0.171690  0.184912   \n",
      "43  0.066111  0.071409  0.077124  0.083274  0.089865  0.096879  0.104315   \n",
      "44 -0.020314 -0.021816 -0.023416 -0.025121 -0.026932 -0.028844 -0.030856   \n",
      "45  0.159771  0.172580  0.186398  0.201268  0.217206  0.234166  0.252147   \n",
      "46  0.007614  0.008377  0.009226  0.010160  0.011181  0.012285  0.013475   \n",
      "47  0.063421  0.068573  0.074143  0.080146  0.086589  0.093453  0.100738   \n",
      "48  0.025818  0.027956  0.030273  0.032776  0.035467  0.038339  0.041392   \n",
      "49 -0.010340 -0.011010 -0.011706 -0.012434 -0.013194 -0.013983 -0.014801   \n",
      "\n",
      "          y7        y8        y9  ...      y614      y615      y616      y617  \\\n",
      "0   0.079637  0.085759  0.092370  ...  0.095892  0.094724  0.093569  0.092434   \n",
      "1   0.067208  0.072552  0.078342  ...  0.162486  0.160507  0.158559  0.156645   \n",
      "2   0.292131  0.314376  0.338376  ...  0.254745  0.251639  0.248561  0.245533   \n",
      "3   0.130607  0.140563  0.151306  ...  0.118962  0.117512  0.116075  0.114662   \n",
      "4  -0.011069 -0.011648 -0.012244  ...  0.111485  0.110129  0.108799  0.107495   \n",
      "5   0.133005  0.143294  0.154414  ...  0.190186  0.187869  0.185582  0.183333   \n",
      "6   0.138384  0.148911  0.160268  ...  0.115869  0.114456  0.113056  0.111677   \n",
      "7   0.168475  0.181458  0.195482  ...  0.217532  0.214882  0.212264  0.209690   \n",
      "8  -0.055996 -0.059960 -0.064204  ...  0.088730  0.087651  0.086599  0.085568   \n",
      "9   0.160370  0.172655  0.185919  ...  0.173732  0.171615  0.169521  0.167461   \n",
      "10  0.132019  0.142161  0.153113  ...  0.155897  0.153997  0.152120  0.150273   \n",
      "11  0.215804  0.232222  0.249934  ...  0.181296  0.179085  0.176894  0.174737   \n",
      "12 -0.043806 -0.046909 -0.050231  ...  0.068772  0.067936  0.067121  0.066321   \n",
      "13  0.225244  0.242397  0.260904  ...  0.197093  0.194691  0.192309  0.189966   \n",
      "14  0.014540  0.015874  0.017339  ...  0.117164  0.115738  0.114337  0.112962   \n",
      "15 -0.032239 -0.034482 -0.036879  ...  0.069030  0.068191  0.067371  0.066567   \n",
      "16 -0.044293 -0.047330 -0.050571  ...  0.115307  0.113905  0.112534  0.111190   \n",
      "17  0.100541  0.108371  0.116839  ...  0.167936  0.165890  0.163873  0.161890   \n",
      "18  0.090617  0.097552  0.105038  ...  0.094917  0.093760  0.092616  0.091490   \n",
      "19  0.182694  0.196701  0.211825  ...  0.203239  0.200763  0.198313  0.195905   \n",
      "20  0.056050  0.060507  0.065337  ...  0.135807  0.134153  0.132525  0.130925   \n",
      "21  0.252475  0.271747  0.292545  ...  0.241486  0.238543  0.235628  0.232761   \n",
      "22  0.068633  0.074133  0.080097  ...  0.185526  0.183267  0.181044  0.178860   \n",
      "23  0.129908  0.139839  0.150558  ...  0.131122  0.129524  0.127942  0.126387   \n",
      "24 -0.027774 -0.029519 -0.031361  ...  0.145572  0.143801  0.142067  0.140366   \n",
      "25  0.284257  0.305848  0.329139  ...  0.223539  0.220814  0.218109  0.215447   \n",
      "26  0.212063  0.228279  0.245783  ...  0.216402  0.213764  0.211154  0.208587   \n",
      "27  0.226355  0.243637  0.262288  ...  0.218485  0.215822  0.213185  0.210591   \n",
      "28  0.106434  0.114543  0.123294  ...  0.095135  0.093975  0.092826  0.091695   \n",
      "29  0.024032  0.026079  0.028312  ...  0.120678  0.119209  0.117766  0.116348   \n",
      "30  0.096502  0.104005  0.112116  ...  0.154924  0.153037  0.151175  0.149346   \n",
      "31  0.032165  0.034698  0.037439  ...  0.066259  0.065452  0.064657  0.063876   \n",
      "32  0.303518  0.326560  0.351414  ...  0.232813  0.229974  0.227156  0.224383   \n",
      "33  0.260639  0.280389  0.301689  ...  0.183201  0.180967  0.178747  0.176561   \n",
      "34  0.162247  0.174656  0.188050  ...  0.166471  0.164442  0.162434  0.160460   \n",
      "35  0.150104  0.161638  0.174093  ...  0.178408  0.176234  0.174085  0.171972   \n",
      "36  0.195637  0.210593  0.226737  ...  0.197828  0.195417  0.193031  0.190683   \n",
      "37  0.012933  0.014082  0.015339  ...  0.086506  0.085453  0.084419  0.083403   \n",
      "38  0.033517  0.036201  0.039111  ...  0.089659  0.088568  0.087493  0.086438   \n",
      "39 -0.032659 -0.034831 -0.037141  ...  0.115685  0.114278  0.112901  0.111551   \n",
      "40  0.089684  0.096543  0.103948  ...  0.092319  0.091194  0.090081  0.088986   \n",
      "41  0.191984  0.206662  0.222506  ...  0.194592  0.192221  0.189874  0.187565   \n",
      "42  0.199453  0.214655  0.231059  ...  0.180600  0.178398  0.176217  0.174071   \n",
      "43  0.112489  0.121033  0.130250  ...  0.088293  0.087217  0.086148  0.085097   \n",
      "44 -0.033038 -0.035305 -0.037724  ...  0.085324  0.084287  0.083272  0.082278   \n",
      "45  0.271916  0.292579  0.314869  ...  0.217915  0.215258  0.212622  0.210028   \n",
      "46  0.014818  0.016239  0.017804  ...  0.147366  0.145572  0.143811  0.142082   \n",
      "47  0.108764  0.117160  0.126232  ...  0.147445  0.145648  0.143875  0.142131   \n",
      "48  0.044764  0.048296  0.052121  ...  0.095874  0.094706  0.093556  0.092426   \n",
      "49 -0.015663 -0.016546 -0.017465  ...  0.128434  0.126871  0.125340  0.123838   \n",
      "\n",
      "        y618      y619      y620      y621      y622      y623  \n",
      "0   0.091327  0.090257  0.089233  0.088246  0.087300  0.086394  \n",
      "1   0.154805  0.153051  0.151379  0.149752  0.148189  0.146700  \n",
      "2   0.242551  0.239639  0.236841  0.234168  0.231608  0.229142  \n",
      "3   0.113272  0.111917  0.110616  0.109372  0.108180  0.107032  \n",
      "4   0.106261  0.105108  0.104014  0.102935  0.101896  0.100916  \n",
      "5   0.181151  0.179051  0.177042  0.175102  0.173241  0.171460  \n",
      "6   0.110318  0.108989  0.107711  0.106492  0.105324  0.104199  \n",
      "7   0.207186  0.204769  0.202457  0.200227  0.198088  0.196040  \n",
      "8   0.084610  0.083733  0.082907  0.082080  0.081281  0.080533  \n",
      "9   0.165448  0.163495  0.161624  0.159826  0.158103  0.156449  \n",
      "10  0.148473  0.146731  0.145063  0.143458  0.141918  0.140443  \n",
      "11  0.172611  0.170532  0.168534  0.166626  0.164799  0.163039  \n",
      "12  0.065580  0.064900  0.064260  0.063619  0.063000  0.062421  \n",
      "13  0.187660  0.185407  0.183244  0.181176  0.179195  0.177288  \n",
      "14  0.111653  0.110418  0.109244  0.108093  0.106985  0.105936  \n",
      "15  0.065816  0.065124  0.064471  0.063820  0.063191  0.062602  \n",
      "16  0.109931  0.108766  0.107666  0.106572  0.105517  0.104526  \n",
      "17  0.159972  0.158132  0.156375  0.154673  0.153039  0.151479  \n",
      "18  0.090389  0.089319  0.088294  0.087310  0.086367  0.085461  \n",
      "19  0.193552  0.191272  0.189088  0.186988  0.184975  0.183043  \n",
      "20  0.129387  0.127921  0.126524  0.125164  0.123858  0.122614  \n",
      "21  0.229947  0.227207  0.224579  0.222061  0.219648  0.217328  \n",
      "22  0.176763  0.174767  0.172865  0.171013  0.169233  0.167539  \n",
      "23  0.124863  0.123381  0.121961  0.120598  0.119292  0.118038  \n",
      "24  0.138762  0.137267  0.135852  0.134452  0.133103  0.131832  \n",
      "25  0.212816  0.210237  0.207756  0.205392  0.203128  0.200945  \n",
      "26  0.206072  0.203630  0.201287  0.199040  0.196886  0.194818  \n",
      "27  0.208046  0.205569  0.203193  0.200916  0.198734  0.196637  \n",
      "28  0.090583  0.089498  0.088455  0.087459  0.086504  0.085585  \n",
      "29  0.114994  0.113714  0.112497  0.111305  0.110159  0.109072  \n",
      "30  0.147574  0.145874  0.144249  0.142676  0.141167  0.139725  \n",
      "31  0.063123  0.062403  0.061717  0.061050  0.060410  0.059800  \n",
      "32  0.221639  0.218946  0.216355  0.213888  0.211526  0.209247  \n",
      "33  0.174391  0.172253  0.170193  0.168238  0.166367  0.164558  \n",
      "34  0.158526  0.156648  0.154847  0.153119  0.151462  0.149871  \n",
      "35  0.169912  0.167920  0.166012  0.164176  0.162414  0.160726  \n",
      "36  0.188384  0.186149  0.184006  0.181951  0.179981  0.178088  \n",
      "37  0.082435  0.081521  0.080652  0.079801  0.078982  0.078206  \n",
      "38  0.085424  0.084459  0.083540  0.082644  0.081784  0.080965  \n",
      "39  0.110282  0.109103  0.107988  0.106883  0.105817  0.104814  \n",
      "40  0.087913  0.086872  0.085873  0.084915  0.083997  0.083115  \n",
      "41  0.185303  0.183106  0.180998  0.178977  0.177039  0.175178  \n",
      "42  0.171961  0.169903  0.167927  0.166037  0.164226  0.162484  \n",
      "43  0.084058  0.083039  0.082058  0.081125  0.080230  0.079368  \n",
      "44  0.081346  0.080485  0.079671  0.078861  0.078081  0.077347  \n",
      "45  0.207466  0.204956  0.202542  0.200241  0.198038  0.195913  \n",
      "46  0.140437  0.138886  0.137413  0.135968  0.134577  0.133260  \n",
      "47  0.140436  0.138803  0.137240  0.135733  0.134286  0.132902  \n",
      "48  0.091338  0.090298  0.089307  0.088343  0.087418  0.086536  \n",
      "49  0.122418  0.121092  0.119835  0.118594  0.117399  0.116271  \n",
      "\n",
      "[50 rows x 624 columns]\n",
      "Standard deviation predictions:\n",
      "          y0        y1        y2        y3        y4        y5        y6  \\\n",
      "0   0.045027  0.048095  0.051379  0.054887  0.058620  0.062564  0.066720   \n",
      "1   0.044892  0.047947  0.051218  0.054711  0.058428  0.062356  0.066494   \n",
      "2   0.044916  0.047973  0.051246  0.054742  0.058462  0.062393  0.066533   \n",
      "3   0.044894  0.047950  0.051221  0.054714  0.058432  0.062360  0.066498   \n",
      "4   0.044872  0.047925  0.051194  0.054685  0.058400  0.062325  0.066460   \n",
      "5   0.044871  0.047924  0.051193  0.054684  0.058398  0.062324  0.066458   \n",
      "6   0.044890  0.047945  0.051215  0.054708  0.058425  0.062353  0.066490   \n",
      "7   0.044895  0.047951  0.051222  0.054715  0.058433  0.062361  0.066499   \n",
      "8   0.044944  0.048004  0.051280  0.054779  0.058501  0.062436  0.066580   \n",
      "9   0.044891  0.047947  0.051217  0.054710  0.058427  0.062355  0.066492   \n",
      "10  0.044879  0.047933  0.051202  0.054694  0.058409  0.062336  0.066472   \n",
      "11  0.044873  0.047927  0.051195  0.054687  0.058401  0.062327  0.066462   \n",
      "12  0.044906  0.047963  0.051235  0.054730  0.058448  0.062378  0.066517   \n",
      "13  0.044894  0.047950  0.051221  0.054714  0.058432  0.062360  0.066498   \n",
      "14  0.044875  0.047929  0.051198  0.054690  0.058405  0.062331  0.066466   \n",
      "15  0.044892  0.047948  0.051218  0.054712  0.058428  0.062356  0.066494   \n",
      "16  0.044876  0.047931  0.051200  0.054691  0.058407  0.062333  0.066468   \n",
      "17  0.044891  0.047946  0.051217  0.054710  0.058427  0.062354  0.066492   \n",
      "18  0.044915  0.047972  0.051245  0.054741  0.058460  0.062391  0.066532   \n",
      "19  0.044879  0.047933  0.051202  0.054695  0.058410  0.062336  0.066472   \n",
      "20  0.044876  0.047931  0.051200  0.054691  0.058406  0.062332  0.066468   \n",
      "21  0.044921  0.047979  0.051253  0.054749  0.058469  0.062401  0.066542   \n",
      "22  0.044889  0.047944  0.051215  0.054708  0.058424  0.062352  0.066489   \n",
      "23  0.044889  0.047944  0.051214  0.054707  0.058424  0.062352  0.066489   \n",
      "24  0.044902  0.047959  0.051230  0.054725  0.058443  0.062372  0.066511   \n",
      "25  0.044884  0.047939  0.051208  0.054701  0.058417  0.062344  0.066480   \n",
      "26  0.044876  0.047931  0.051200  0.054691  0.058406  0.062332  0.066468   \n",
      "27  0.044887  0.047942  0.051212  0.054705  0.058421  0.062348  0.066485   \n",
      "28  0.044874  0.047928  0.051197  0.054689  0.058403  0.062329  0.066464   \n",
      "29  0.044879  0.047933  0.051202  0.054694  0.058410  0.062336  0.066472   \n",
      "30  0.044899  0.047955  0.051226  0.054720  0.058438  0.062367  0.066505   \n",
      "31  0.045034  0.048103  0.051387  0.054896  0.058629  0.062575  0.066731   \n",
      "32  0.044875  0.047929  0.051198  0.054690  0.058405  0.062331  0.066466   \n",
      "33  0.044899  0.047955  0.051226  0.054720  0.058438  0.062367  0.066505   \n",
      "34  0.044874  0.047928  0.051197  0.054688  0.058403  0.062329  0.066464   \n",
      "35  0.044888  0.047944  0.051214  0.054707  0.058423  0.062351  0.066488   \n",
      "36  0.044892  0.047948  0.051219  0.054712  0.058429  0.062357  0.066495   \n",
      "37  0.044882  0.047937  0.051206  0.054698  0.058414  0.062341  0.066477   \n",
      "38  0.044920  0.047978  0.051251  0.054748  0.058468  0.062399  0.066541   \n",
      "39  0.044873  0.047927  0.051195  0.054687  0.058401  0.062327  0.066462   \n",
      "40  0.044893  0.047949  0.051219  0.054713  0.058430  0.062358  0.066496   \n",
      "41  0.044870  0.047923  0.051192  0.054683  0.058397  0.062322  0.066457   \n",
      "42  0.044887  0.047942  0.051213  0.054705  0.058422  0.062349  0.066486   \n",
      "43  0.044876  0.047930  0.051199  0.054691  0.058406  0.062332  0.066467   \n",
      "44  0.044877  0.047931  0.051200  0.054692  0.058408  0.062334  0.066469   \n",
      "45  0.044875  0.047929  0.051198  0.054690  0.058404  0.062330  0.066466   \n",
      "46  0.044897  0.047953  0.051224  0.054718  0.058435  0.062364  0.066502   \n",
      "47  0.044879  0.047933  0.051203  0.054695  0.058410  0.062336  0.066472   \n",
      "48  0.044939  0.047999  0.051274  0.054772  0.058495  0.062428  0.066572   \n",
      "49  0.044879  0.047933  0.051203  0.054695  0.058410  0.062336  0.066472   \n",
      "\n",
      "          y7        y8        y9  ...      y614      y615      y616      y617  \\\n",
      "0   0.071231  0.075922  0.080930  ...  0.135767  0.134073  0.132395  0.130763   \n",
      "1   0.070985  0.075655  0.080640  ...  0.135742  0.134048  0.132371  0.130738   \n",
      "2   0.071029  0.075702  0.080691  ...  0.135747  0.134053  0.132375  0.130743   \n",
      "3   0.070990  0.075660  0.080646  ...  0.135742  0.134049  0.132371  0.130739   \n",
      "4   0.070949  0.075615  0.080597  ...  0.135737  0.134044  0.132366  0.130734   \n",
      "5   0.070947  0.075613  0.080595  ...  0.135737  0.134044  0.132366  0.130734   \n",
      "6   0.070981  0.075651  0.080636  ...  0.135741  0.134048  0.132370  0.130738   \n",
      "7   0.070991  0.075661  0.080647  ...  0.135743  0.134049  0.132371  0.130739   \n",
      "8   0.071080  0.075757  0.080751  ...  0.135752  0.134058  0.132380  0.130748   \n",
      "9   0.070984  0.075653  0.080639  ...  0.135741  0.134048  0.132370  0.130737   \n",
      "10  0.070961  0.075629  0.080612  ...  0.135739  0.134045  0.132368  0.130735   \n",
      "11  0.070951  0.075618  0.080600  ...  0.135738  0.134044  0.132366  0.130734   \n",
      "12  0.071011  0.075683  0.080671  ...  0.135744  0.134051  0.132373  0.130741   \n",
      "13  0.070990  0.075660  0.080646  ...  0.135742  0.134049  0.132371  0.130739   \n",
      "14  0.070955  0.075622  0.080605  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "15  0.070986  0.075655  0.080641  ...  0.135742  0.134048  0.132371  0.130738   \n",
      "16  0.070958  0.075625  0.080608  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "17  0.070983  0.075653  0.080638  ...  0.135741  0.134048  0.132370  0.130738   \n",
      "18  0.071027  0.075700  0.080689  ...  0.135746  0.134053  0.132375  0.130742   \n",
      "19  0.070962  0.075629  0.080613  ...  0.135739  0.134045  0.132368  0.130735   \n",
      "20  0.070957  0.075625  0.080608  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "21  0.071038  0.075712  0.080703  ...  0.135747  0.134054  0.132376  0.130744   \n",
      "22  0.070980  0.075650  0.080635  ...  0.135741  0.134047  0.132370  0.130737   \n",
      "23  0.070980  0.075649  0.080634  ...  0.135741  0.134047  0.132370  0.130737   \n",
      "24  0.071004  0.075675  0.080663  ...  0.135743  0.134050  0.132372  0.130740   \n",
      "25  0.070971  0.075639  0.080623  ...  0.135740  0.134046  0.132369  0.130736   \n",
      "26  0.070958  0.075625  0.080608  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "27  0.070976  0.075645  0.080630  ...  0.135741  0.134048  0.132370  0.130737   \n",
      "28  0.070954  0.075620  0.080603  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "29  0.070962  0.075629  0.080613  ...  0.135739  0.134045  0.132368  0.130735   \n",
      "30  0.070998  0.075669  0.080655  ...  0.135743  0.134049  0.132371  0.130739   \n",
      "31  0.071243  0.075935  0.080944  ...  0.135771  0.134077  0.132399  0.130766   \n",
      "32  0.070955  0.075622  0.080605  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "33  0.070998  0.075668  0.080655  ...  0.135743  0.134050  0.132372  0.130739   \n",
      "34  0.070953  0.075620  0.080603  ...  0.135738  0.134044  0.132367  0.130734   \n",
      "35  0.070979  0.075648  0.080633  ...  0.135741  0.134047  0.132369  0.130737   \n",
      "36  0.070986  0.075656  0.080642  ...  0.135742  0.134048  0.132371  0.130738   \n",
      "37  0.070967  0.075635  0.080619  ...  0.135739  0.134046  0.132368  0.130736   \n",
      "38  0.071037  0.075711  0.080701  ...  0.135747  0.134053  0.132375  0.130743   \n",
      "39  0.070951  0.075618  0.080600  ...  0.135738  0.134044  0.132366  0.130734   \n",
      "40  0.070988  0.075658  0.080643  ...  0.135742  0.134048  0.132370  0.130738   \n",
      "41  0.070946  0.075612  0.080594  ...  0.135737  0.134044  0.132366  0.130734   \n",
      "42  0.070977  0.075646  0.080631  ...  0.135741  0.134047  0.132369  0.130737   \n",
      "43  0.070957  0.075624  0.080607  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "44  0.070959  0.075626  0.080609  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "45  0.070955  0.075622  0.080605  ...  0.135738  0.134045  0.132367  0.130735   \n",
      "46  0.070995  0.075665  0.080651  ...  0.135743  0.134049  0.132371  0.130739   \n",
      "47  0.070962  0.075630  0.080613  ...  0.135739  0.134046  0.132368  0.130735   \n",
      "48  0.071071  0.075748  0.080741  ...  0.135751  0.134057  0.132379  0.130747   \n",
      "49  0.070962  0.075630  0.080613  ...  0.135739  0.134046  0.132368  0.130736   \n",
      "\n",
      "        y618      y619      y620      y621      y622      y623  \n",
      "0   0.129151  0.127648  0.126210  0.124854  0.123582  0.122356  \n",
      "1   0.129127  0.127625  0.126186  0.124831  0.123560  0.122333  \n",
      "2   0.129132  0.127629  0.126191  0.124835  0.123564  0.122338  \n",
      "3   0.129127  0.127625  0.126187  0.124831  0.123560  0.122334  \n",
      "4   0.129123  0.127620  0.126182  0.124827  0.123556  0.122329  \n",
      "5   0.129123  0.127620  0.126182  0.124827  0.123556  0.122329  \n",
      "6   0.129126  0.127624  0.126185  0.124830  0.123559  0.122333  \n",
      "7   0.129127  0.127625  0.126187  0.124832  0.123560  0.122334  \n",
      "8   0.129136  0.127634  0.126195  0.124840  0.123569  0.122342  \n",
      "9   0.129126  0.127624  0.126185  0.124830  0.123559  0.122333  \n",
      "10  0.129124  0.127622  0.126183  0.124828  0.123557  0.122331  \n",
      "11  0.129123  0.127621  0.126182  0.124827  0.123556  0.122330  \n",
      "12  0.129129  0.127627  0.126188  0.124833  0.123562  0.122336  \n",
      "13  0.129127  0.127625  0.126186  0.124831  0.123560  0.122334  \n",
      "14  0.129123  0.127621  0.126183  0.124828  0.123556  0.122330  \n",
      "15  0.129127  0.127625  0.126186  0.124831  0.123560  0.122333  \n",
      "16  0.129124  0.127621  0.126183  0.124828  0.123557  0.122330  \n",
      "17  0.129126  0.127624  0.126185  0.124830  0.123559  0.122333  \n",
      "18  0.129131  0.127629  0.126190  0.124835  0.123564  0.122337  \n",
      "19  0.129124  0.127622  0.126183  0.124828  0.123557  0.122331  \n",
      "20  0.129124  0.127621  0.126183  0.124828  0.123557  0.122330  \n",
      "21  0.129132  0.127630  0.126191  0.124836  0.123565  0.122338  \n",
      "22  0.129126  0.127624  0.126185  0.124830  0.123559  0.122332  \n",
      "23  0.129126  0.127624  0.126185  0.124830  0.123559  0.122332  \n",
      "24  0.129128  0.127626  0.126187  0.124832  0.123561  0.122335  \n",
      "25  0.129125  0.127623  0.126184  0.124829  0.123558  0.122332  \n",
      "26  0.129124  0.127621  0.126183  0.124828  0.123556  0.122330  \n",
      "27  0.129126  0.127624  0.126185  0.124830  0.123559  0.122333  \n",
      "28  0.129123  0.127621  0.126183  0.124827  0.123556  0.122330  \n",
      "29  0.129124  0.127622  0.126183  0.124828  0.123557  0.122331  \n",
      "30  0.129128  0.127625  0.126187  0.124832  0.123560  0.122334  \n",
      "31  0.129155  0.127652  0.126213  0.124858  0.123586  0.122359  \n",
      "32  0.129123  0.127621  0.126183  0.124828  0.123556  0.122330  \n",
      "33  0.129128  0.127626  0.126187  0.124832  0.123561  0.122334  \n",
      "34  0.129123  0.127621  0.126182  0.124827  0.123556  0.122330  \n",
      "35  0.129126  0.127624  0.126185  0.124830  0.123559  0.122332  \n",
      "36  0.129127  0.127625  0.126186  0.124831  0.123560  0.122333  \n",
      "37  0.129125  0.127622  0.126184  0.124829  0.123557  0.122331  \n",
      "38  0.129131  0.127629  0.126190  0.124835  0.123564  0.122338  \n",
      "39  0.129123  0.127621  0.126182  0.124827  0.123556  0.122330  \n",
      "40  0.129127  0.127625  0.126186  0.124831  0.123560  0.122333  \n",
      "41  0.129122  0.127620  0.126182  0.124827  0.123555  0.122329  \n",
      "42  0.129126  0.127623  0.126185  0.124830  0.123558  0.122332  \n",
      "43  0.129124  0.127621  0.126183  0.124828  0.123557  0.122330  \n",
      "44  0.129124  0.127621  0.126183  0.124828  0.123557  0.122330  \n",
      "45  0.129123  0.127621  0.126183  0.124827  0.123556  0.122330  \n",
      "46  0.129128  0.127625  0.126187  0.124832  0.123560  0.122334  \n",
      "47  0.129124  0.127622  0.126183  0.124828  0.123557  0.122331  \n",
      "48  0.129135  0.127633  0.126194  0.124839  0.123567  0.122341  \n",
      "49  0.129124  0.127622  0.126183  0.124828  0.123557  0.122331  \n",
      "\n",
      "[50 rows x 624 columns]\n"
     ]
    }
   ],
   "source": [
    "predictions = emulator1.predict(df_eval)\n",
    "result_df = pd.concat([predictions[0], predictions[1]], axis=1)\n",
    "df_mean1, df_std1 = (\n",
    "    result_df.iloc[\n",
    "        :, : len(output_columns)\n",
    "    ],  # define first section as the mean predictions\n",
    "    result_df.iloc[\n",
    "        :, len(output_columns) :\n",
    "    ],  # define second section as the standard deviation predictions\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **5. Create a second emulator by adjusting the `output_retained_dimensions`**\n",
    "\n",
    "Now try and train a new emulator with slightly increased number of dimensions, but still significantly less than the original number of output dimensions. Train a new emulator with only **six** dimensions. First, set up the new emulator and parameters required for six dimensions:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise emulator\n",
    "emulator_id = \"TritiumDesorptionGP2\"\n",
    "\n",
    "emulator2 = tl.Emulator(id=emulator_id)\n",
    "\n",
    "# Define the training parameters for your emulator\n",
    "params = tl.TrainParams(\n",
    "    train_test_ratio=0.80,\n",
    "    estimator=\"gaussian_process_regression\",\n",
    "    output_retained_dimensions=6,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then train the emulator and run predict:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Emulator 'TritiumDesorptionGP2' has begun training.\n",
      "0:00:00: Job status: processing\n",
      "0:00:01: Job status: processing\n",
      "0:00:03: Job status: processing\n",
      "0:00:05: Job status: processing\n",
      "0:00:07: Job status: processing\n",
      "0:00:09: Job status: processing\n",
      "0:00:12: Job status: processing\n",
      "0:00:15: Job status: processing\n",
      "0:00:18: Job status: processing\n",
      "0:00:21: Job status: processing\n",
      "0:00:25: Job status: processing\n",
      "0:00:29: Job status: processing\n",
      "0:00:34: Job status: processing\n",
      "0:00:39: Job status: success\n",
      "Training of emulator TritiumDesorptionGP2 is complete!\n",
      "Job predict process ID: predict-succulent-scratch-vase\n",
      "0:00:00: Job status: processing\n",
      "0:00:01: Job status: processing\n",
      "0:00:03: Job status: processing\n",
      "0:00:05: Job status: processing\n",
      "0:00:07: Job status: processing\n",
      "0:00:09: Job status: processing\n",
      "0:00:12: Job status: processing\n",
      "0:00:15: Job status: processing\n",
      "0:00:18: Job status: processing\n",
      "0:00:21: Job status: success\n",
      "Mean predictions:\n",
      "          y0        y1        y2        y3        y4        y5        y6  \\\n",
      "0  -0.005920 -0.006095 -0.006248 -0.006381 -0.006493 -0.006582 -0.006646   \n",
      "1   0.018277  0.020018  0.021929  0.024018  0.026289  0.028737  0.031363   \n",
      "2   0.132067  0.143160  0.155173  0.168147  0.182103  0.197000  0.212839   \n",
      "3   0.116423  0.125492  0.135247  0.145718  0.156916  0.168805  0.181385   \n",
      "4   0.003269  0.003533  0.003830  0.004155  0.004507  0.004887  0.005293   \n",
      "5   0.043504  0.047463  0.051785  0.056484  0.061570  0.067028  0.072862   \n",
      "6   0.035401  0.038647  0.042188  0.046037  0.050199  0.054664  0.059433   \n",
      "7   0.044618  0.048548  0.052832  0.057484  0.062513  0.067905  0.073662   \n",
      "8  -0.007140 -0.007655 -0.008197 -0.008771 -0.009380 -0.010022 -0.010697   \n",
      "9   0.154687  0.166685  0.179590  0.193439  0.208246  0.223965  0.240595   \n",
      "10  0.025290  0.027671  0.030282  0.033131  0.036223  0.039550  0.043116   \n",
      "11  0.132483  0.143126  0.154611  0.166970  0.180218  0.194314  0.209260   \n",
      "12 -0.003846 -0.004149 -0.004465 -0.004802 -0.005161 -0.005542 -0.005945   \n",
      "13  0.206042  0.222055  0.239277  0.257761  0.277525  0.298506  0.320705   \n",
      "14  0.004674  0.005230  0.005854  0.006548  0.007314  0.008149  0.009054   \n",
      "15 -0.007795 -0.008342 -0.008918 -0.009528 -0.010174 -0.010853 -0.011566   \n",
      "16 -0.002816 -0.002972 -0.003124 -0.003275 -0.003428 -0.003581 -0.003735   \n",
      "17  0.021944  0.023926  0.026093  0.028453  0.031011  0.033759  0.036699   \n",
      "18  0.009540  0.010588  0.011749  0.013028  0.014427  0.015945  0.017582   \n",
      "19  0.046771  0.051047  0.055721  0.060804  0.066309  0.072220  0.078540   \n",
      "20  0.013404  0.014784  0.016310  0.017986  0.019815  0.021793  0.023922   \n",
      "21  0.124260  0.134641  0.145878  0.158009  0.171053  0.184971  0.199765   \n",
      "22  0.025846  0.028153  0.030674  0.033417  0.036386  0.039575  0.042985   \n",
      "23  0.162080  0.174341  0.187503  0.201597  0.216634  0.232566  0.249391   \n",
      "24  0.005090  0.005576  0.006119  0.006718  0.007371  0.008077  0.008837   \n",
      "25  0.167254  0.180755  0.195326  0.211013  0.227837  0.245746  0.264741   \n",
      "26  0.062192  0.067742  0.073790  0.080356  0.087453  0.095061  0.103182   \n",
      "27  0.177074  0.191089  0.206190  0.222423  0.239804  0.258279  0.277850   \n",
      "28  0.161293  0.173217  0.185990  0.199642  0.214180  0.229556  0.245769   \n",
      "29  0.007227  0.008024  0.008912  0.009892  0.010968  0.012136  0.013399   \n",
      "30  0.038151  0.041447  0.045030  0.048913  0.053103  0.057588  0.062370   \n",
      "31 -0.004399 -0.004689 -0.004988 -0.005301 -0.005629 -0.005971 -0.006327   \n",
      "32  0.167771  0.181384  0.196083  0.211914  0.228898  0.246984  0.266173   \n",
      "33  0.202218  0.217980  0.234936  0.253137  0.272598  0.293259  0.315120   \n",
      "34  0.174444  0.187868  0.202296  0.217768  0.234298  0.251834  0.270374   \n",
      "35  0.028035  0.030640  0.033493  0.036604  0.039980  0.043611  0.047500   \n",
      "36  0.132809  0.143454  0.154936  0.167293  0.180538  0.194633  0.209577   \n",
      "37  0.002855  0.003053  0.003276  0.003519  0.003780  0.004059  0.004356   \n",
      "38  0.004684  0.005231  0.005842  0.006518  0.007263  0.008075  0.008954   \n",
      "39  0.000413  0.000431  0.000463  0.000502  0.000546  0.000596  0.000650   \n",
      "40  0.006686  0.007534  0.008484  0.009537  0.010698  0.011963  0.013334   \n",
      "41  0.119114  0.128855  0.139381  0.150726  0.162903  0.175876  0.189647   \n",
      "42  0.194491  0.209506  0.225649  0.242965  0.261469  0.281103  0.301866   \n",
      "43  0.133147  0.143094  0.153757  0.165162  0.177316  0.190180  0.203752   \n",
      "44 -0.002612 -0.002777 -0.002943 -0.003114 -0.003290 -0.003472 -0.003658   \n",
      "45  0.132260  0.143206  0.155047  0.167818  0.181539  0.196167  0.211706   \n",
      "46  0.010682  0.011640  0.012697  0.013850  0.015103  0.016450  0.017894   \n",
      "47  0.027810  0.030376  0.033183  0.036240  0.039555  0.043117  0.046930   \n",
      "48  0.008451  0.009194  0.010008  0.010896  0.011857  0.012892  0.013998   \n",
      "49  0.004035  0.004425  0.004862  0.005344  0.005871  0.006442  0.007057   \n",
      "\n",
      "          y7        y8        y9  ...      y614      y615      y616      y617  \\\n",
      "0  -0.006663 -0.006653 -0.006592  ...  0.439213  0.433786  0.428414  0.423182   \n",
      "1   0.034307  0.037413  0.040817  ...  0.105030  0.103534  0.102041  0.100503   \n",
      "2   0.230337  0.248669  0.268520  ...  0.128207  0.126553  0.124887  0.123165   \n",
      "3   0.195169  0.209553  0.225028  ...  0.095837  0.094456  0.093080  0.091688   \n",
      "4   0.005748  0.006227  0.006753  ...  0.173996  0.172009  0.170070  0.168220   \n",
      "5   0.079361  0.086198  0.093651  ...  0.023320  0.023163  0.023006  0.022818   \n",
      "6   0.064741  0.070322  0.076399  ...  0.333850  0.329650  0.325475  0.321367   \n",
      "7   0.080068  0.086804  0.094140  ...  0.198554  0.196204  0.193880  0.191595   \n",
      "8  -0.011428 -0.012188 -0.012998  ...  0.103784  0.102599  0.101451  0.100348   \n",
      "9   0.258813  0.277822  0.298269  ...  0.020203  0.019847  0.019497  0.019118   \n",
      "10  0.047105  0.051310  0.055909  ...  0.230232  0.227516  0.224831  0.222214   \n",
      "11  0.225691  0.242865  0.261391  ...  0.225872  0.223249  0.220661  0.218151   \n",
      "12 -0.006384 -0.006843 -0.007335  ...  0.064282  0.063767  0.063293  0.062892   \n",
      "13  0.345023  0.370398  0.397693  ...  0.023645  0.023228  0.022813  0.022362   \n",
      "14  0.010088  0.011188  0.012410  ...  0.026871  0.026579  0.026299  0.026007   \n",
      "15 -0.012335 -0.013133 -0.013981  ...  0.130826  0.129281  0.127769  0.126309   \n",
      "16 -0.003888 -0.004040 -0.004188  ...  0.086913  0.085906  0.084933  0.083986   \n",
      "17  0.039983  0.043440  0.047217  ...  0.338159  0.333610  0.329085  0.324576   \n",
      "18  0.019432  0.021393  0.023554  ...  0.296022  0.292227  0.288456  0.284739   \n",
      "19  0.085586  0.093002  0.101090  ...  0.113509  0.112457  0.111427  0.110441   \n",
      "20  0.026321  0.028858  0.031648  ...  0.027175  0.026983  0.026802  0.026617   \n",
      "21  0.216099  0.233207  0.251725  ...  0.111600  0.110062  0.108509  0.106885   \n",
      "22  0.046788  0.050791  0.055159  ...  0.025399  0.025108  0.024826  0.024512   \n",
      "23  0.267764  0.286907  0.307446  ...  0.115109  0.114082  0.113116  0.112293   \n",
      "24  0.009694  0.010600  0.011597  ...  0.097116  0.095870  0.094653  0.093437   \n",
      "25  0.285639  0.307490  0.331074  ...  0.078254  0.077329  0.076402  0.075453   \n",
      "26  0.112214  0.121707  0.132040  ...  0.101618  0.100665  0.099724  0.098806   \n",
      "27  0.299334  0.321772  0.345948  ...  0.072824  0.071934  0.071053  0.070160   \n",
      "28  0.263427  0.281800  0.301470  ...  0.172464  0.170700  0.169011  0.167508   \n",
      "29  0.014830  0.016349  0.018027  ...  0.021751  0.021475  0.021206  0.020909   \n",
      "30  0.067676  0.073249  0.079305  ...  0.023769  0.023340  0.022902  0.022401   \n",
      "31 -0.006704 -0.007091 -0.007497  ...  0.439486  0.433950  0.428472  0.423136   \n",
      "32  0.287295  0.309385  0.333237  ...  0.140494  0.138854  0.137221  0.135596   \n",
      "33  0.339069  0.364059  0.390941  ...  0.434907  0.429251  0.423638  0.418120   \n",
      "34  0.290661  0.311818  0.334554  ...  0.078412  0.077480  0.076574  0.075703   \n",
      "35  0.051849  0.056432  0.061444  ...  0.311351  0.307477  0.303636  0.299860   \n",
      "36  0.226008  0.243183  0.261713  ...  0.033792  0.033206  0.032609  0.031944   \n",
      "37  0.004686  0.005033  0.005410  ...  0.231329  0.228724  0.226177  0.223766   \n",
      "38  0.009956  0.011020  0.012201  ...  0.094197  0.092862  0.091530  0.090174   \n",
      "39  0.000714  0.000783  0.000862  ...  0.166731  0.164929  0.163185  0.161547   \n",
      "40  0.014896  0.016557  0.018399  ...  0.294671  0.291085  0.287534  0.284075   \n",
      "41  0.204815  0.220684  0.237828  ...  0.044060  0.043568  0.043076  0.042554   \n",
      "42  0.324592  0.348297  0.373777  ...  0.113834  0.112651  0.111510  0.110452   \n",
      "43  0.218549  0.233952  0.250457  ...  0.362756  0.358244  0.353799  0.349518   \n",
      "44 -0.003852 -0.004048 -0.004250  ...  0.077015  0.076059  0.075128  0.074211   \n",
      "45  0.228841  0.246777  0.266172  ...  0.263671  0.260470  0.257288  0.254147   \n",
      "46  0.019510  0.021213  0.023078  ...  0.080281  0.079532  0.078820  0.078162   \n",
      "47  0.051189  0.055674  0.060573  ...  0.013445  0.013461  0.013482  0.013497   \n",
      "48  0.015236  0.016540  0.017965  ...  0.218160  0.215310  0.212484  0.209695   \n",
      "49  0.007752  0.008488  0.009299  ...  0.143460  0.141577  0.139722  0.137878   \n",
      "\n",
      "        y618      y619      y620      y621      y622      y623  \n",
      "0   0.418065  0.413263  0.408722  0.404356  0.400181  0.396167  \n",
      "1   0.099006  0.097601  0.096255  0.094997  0.093817  0.092654  \n",
      "2   0.121459  0.119749  0.118121  0.116552  0.114999  0.113440  \n",
      "3   0.090292  0.088925  0.087561  0.086329  0.085190  0.084040  \n",
      "4   0.166466  0.164814  0.163250  0.161694  0.160193  0.158809  \n",
      "5   0.022670  0.022478  0.022324  0.022107  0.021838  0.021598  \n",
      "6   0.317332  0.313502  0.309874  0.306384  0.303010  0.299702  \n",
      "7   0.189378  0.187276  0.185304  0.183394  0.181578  0.179901  \n",
      "8   0.099334  0.098367  0.097448  0.096495  0.095540  0.094626  \n",
      "9   0.018728  0.018285  0.017790  0.017381  0.017037  0.016693  \n",
      "10  0.219671  0.217262  0.215004  0.212801  0.210680  0.208689  \n",
      "11  0.215699  0.213290  0.210991  0.208744  0.206555  0.204456  \n",
      "12  0.062574  0.062253  0.061965  0.061586  0.061167  0.060823  \n",
      "13  0.021884  0.021325  0.020703  0.020188  0.019744  0.019287  \n",
      "14  0.025761  0.025523  0.025302  0.025056  0.024804  0.024580  \n",
      "15  0.124926  0.123618  0.122379  0.121139  0.119920  0.118752  \n",
      "16  0.083124  0.082306  0.081524  0.080716  0.079912  0.079152  \n",
      "17  0.320135  0.315988  0.312019  0.308313  0.304845  0.301456  \n",
      "18  0.281085  0.277655  0.274398  0.271302  0.268356  0.265488  \n",
      "19  0.109521  0.108597  0.107779  0.106882  0.105952  0.105144  \n",
      "20  0.026482  0.026328  0.026208  0.026026  0.025808  0.025634  \n",
      "21  0.105274  0.103665  0.102118  0.100648  0.099205  0.097733  \n",
      "22  0.024244  0.023981  0.023735  0.023481  0.023241  0.023049  \n",
      "23  0.111518  0.110689  0.109857  0.109016  0.108222  0.107576  \n",
      "24  0.092302  0.091234  0.090202  0.089189  0.088213  0.087272  \n",
      "25  0.074503  0.073492  0.072507  0.071563  0.070636  0.069727  \n",
      "26  0.097942  0.097059  0.096275  0.095422  0.094532  0.093742  \n",
      "27  0.069278  0.068332  0.067380  0.066478  0.065606  0.064745  \n",
      "28  0.166053  0.164611  0.163179  0.161805  0.160555  0.159497  \n",
      "29  0.020652  0.020401  0.020167  0.019917  0.019662  0.019420  \n",
      "30  0.021912  0.021438  0.020978  0.020565  0.020185  0.019803  \n",
      "31  0.417908  0.413016  0.408359  0.403921  0.399708  0.395637  \n",
      "32  0.133987  0.132350  0.130781  0.129259  0.127766  0.126319  \n",
      "33  0.412647  0.407405  0.402323  0.397569  0.393088  0.388682  \n",
      "34  0.074843  0.073937  0.073003  0.072140  0.071346  0.070595  \n",
      "35  0.296163  0.292686  0.289398  0.286258  0.283277  0.280435  \n",
      "36  0.031269  0.030559  0.029835  0.029193  0.028595  0.027969  \n",
      "37  0.221455  0.219281  0.217235  0.215212  0.213272  0.211498  \n",
      "38  0.088845  0.087606  0.086418  0.085311  0.084274  0.083245  \n",
      "39  0.160022  0.158577  0.157215  0.155818  0.154449  0.153206  \n",
      "40  0.280696  0.277509  0.274506  0.271598  0.268797  0.266108  \n",
      "41  0.042050  0.041475  0.040915  0.040341  0.039736  0.039122  \n",
      "42  0.109423  0.108333  0.107238  0.106178  0.105170  0.104253  \n",
      "43  0.345299  0.341287  0.337397  0.333743  0.330330  0.327065  \n",
      "44  0.073354  0.072548  0.071774  0.071004  0.070256  0.069534  \n",
      "45  0.251061  0.248050  0.245187  0.242407  0.239688  0.237028  \n",
      "46  0.077588  0.077033  0.076525  0.075958  0.075389  0.074926  \n",
      "47  0.013544  0.013557  0.013603  0.013589  0.013544  0.013559  \n",
      "48  0.206959  0.204408  0.201969  0.199680  0.197540  0.195476  \n",
      "49  0.136109  0.134453  0.132859  0.131333  0.129885  0.128476  \n",
      "\n",
      "[50 rows x 624 columns]\n",
      "Standard deviation predictions:\n",
      "          y0        y1        y2        y3        y4        y5        y6  \\\n",
      "0   0.016228  0.016931  0.017698  0.018531  0.019430  0.020395  0.021424   \n",
      "1   0.014436  0.014951  0.015514  0.016129  0.016796  0.017514  0.018283   \n",
      "2   0.014940  0.015508  0.016130  0.016807  0.017540  0.018328  0.019172   \n",
      "3   0.014484  0.015004  0.015573  0.016194  0.016868  0.017593  0.018370   \n",
      "4   0.013791  0.014236  0.014725  0.015259  0.015840  0.016466  0.017139   \n",
      "5   0.013649  0.014079  0.014550  0.015066  0.015628  0.016234  0.016885   \n",
      "6   0.014010  0.014480  0.014996  0.015559  0.016171  0.016831  0.017539   \n",
      "7   0.014033  0.014506  0.015024  0.015590  0.016206  0.016870  0.017582   \n",
      "8   0.015677  0.016321  0.017024  0.017788  0.018615  0.019502  0.020448   \n",
      "9   0.014254  0.014750  0.015293  0.015885  0.016529  0.017223  0.017966   \n",
      "10  0.013885  0.014341  0.014840  0.015387  0.015981  0.016621  0.017309   \n",
      "11  0.013786  0.014231  0.014719  0.015252  0.015832  0.016458  0.017130   \n",
      "12  0.014316  0.014820  0.015371  0.015973  0.016627  0.017332  0.018087   \n",
      "13  0.014524  0.015048  0.015622  0.016248  0.016927  0.017658  0.018441   \n",
      "14  0.013814  0.014262  0.014753  0.015291  0.015875  0.016505  0.017181   \n",
      "15  0.014213  0.014705  0.015244  0.015832  0.016471  0.017159  0.017897   \n",
      "16  0.013807  0.014255  0.014746  0.015282  0.015866  0.016495  0.017171   \n",
      "17  0.014339  0.014844  0.015397  0.016000  0.016655  0.017360  0.018116   \n",
      "18  0.014524  0.015049  0.015625  0.016253  0.016934  0.017667  0.018453   \n",
      "19  0.013946  0.014408  0.014915  0.015469  0.016071  0.016720  0.017417   \n",
      "20  0.013862  0.014316  0.014813  0.015356  0.015947  0.016584  0.017268   \n",
      "21  0.014865  0.015426  0.016040  0.016709  0.017434  0.018213  0.019047   \n",
      "22  0.014083  0.014560  0.015084  0.015656  0.016277  0.016947  0.017665   \n",
      "23  0.014107  0.014587  0.015114  0.015689  0.016313  0.016986  0.017708   \n",
      "24  0.014250  0.014746  0.015289  0.015883  0.016528  0.017222  0.017967   \n",
      "25  0.014074  0.014550  0.015072  0.015642  0.016262  0.016930  0.017646   \n",
      "26  0.013913  0.014372  0.014875  0.015424  0.016022  0.016666  0.017357   \n",
      "27  0.014117  0.014598  0.015126  0.015701  0.016327  0.017001  0.017724   \n",
      "28  0.013832  0.014282  0.014775  0.015314  0.015901  0.016533  0.017212   \n",
      "29  0.013843  0.014295  0.014790  0.015331  0.015920  0.016555  0.017236   \n",
      "30  0.014336  0.014841  0.015395  0.015999  0.016654  0.017361  0.018117   \n",
      "31  0.016862  0.017628  0.018460  0.019363  0.020337  0.021380  0.022491   \n",
      "32  0.013773  0.014216  0.014703  0.015235  0.015814  0.016438  0.017108   \n",
      "33  0.014478  0.014998  0.015568  0.016189  0.016862  0.017588  0.018365   \n",
      "34  0.013938  0.014399  0.014904  0.015457  0.016057  0.016705  0.017399   \n",
      "35  0.014059  0.014534  0.015055  0.015624  0.016242  0.016908  0.017622   \n",
      "36  0.014390  0.014900  0.015459  0.016068  0.016730  0.017442  0.018205   \n",
      "37  0.014009  0.014478  0.014993  0.015555  0.016165  0.016824  0.017530   \n",
      "38  0.014606  0.015141  0.015726  0.016364  0.017057  0.017802  0.018600   \n",
      "39  0.013761  0.014203  0.014688  0.015218  0.015795  0.016418  0.017086   \n",
      "40  0.014205  0.014695  0.015233  0.015820  0.016458  0.017145  0.017882   \n",
      "41  0.013632  0.014060  0.014530  0.015043  0.015603  0.016206  0.016855   \n",
      "42  0.014440  0.014955  0.015519  0.016134  0.016801  0.017519  0.018289   \n",
      "43  0.013923  0.014383  0.014887  0.015437  0.016036  0.016682  0.017374   \n",
      "44  0.014042  0.014514  0.015032  0.015598  0.016213  0.016875  0.017586   \n",
      "45  0.013730  0.014169  0.014651  0.015178  0.015751  0.016369  0.017033   \n",
      "46  0.014259  0.014756  0.015300  0.015895  0.016540  0.017235  0.017980   \n",
      "47  0.013906  0.014364  0.014866  0.015415  0.016012  0.016656  0.017346   \n",
      "48  0.015155  0.015747  0.016394  0.017098  0.017861  0.018680  0.019556   \n",
      "49  0.014015  0.014484  0.014999  0.015561  0.016173  0.016831  0.017538   \n",
      "\n",
      "          y7        y8        y9  ...      y614      y615      y616      y617  \\\n",
      "0   0.022553  0.023740  0.025018  ...  0.044322  0.043797  0.043264  0.042720   \n",
      "1   0.019129  0.020021  0.020985  ...  0.040315  0.039857  0.039390  0.038911   \n",
      "2   0.020098  0.021074  0.022127  ...  0.041502  0.041024  0.040537  0.040037   \n",
      "3   0.019224  0.020125  0.021098  ...  0.040432  0.039973  0.039504  0.039024   \n",
      "4   0.017880  0.018663  0.019510  ...  0.038659  0.038232  0.037795  0.037345   \n",
      "5   0.017603  0.018362  0.019184  ...  0.038269  0.037849  0.037419  0.036977   \n",
      "6   0.018318  0.019142  0.020033  ...  0.039133  0.038698  0.038252  0.037795   \n",
      "7   0.018367  0.019195  0.020091  ...  0.039126  0.038691  0.038246  0.037788   \n",
      "8   0.021487  0.022580  0.023757  ...  0.043329  0.042820  0.042301  0.041772   \n",
      "9   0.018784  0.019647  0.020580  ...  0.039794  0.039346  0.038888  0.038419   \n",
      "10  0.018066  0.018867  0.019732  ...  0.038863  0.038432  0.037991  0.037538   \n",
      "11  0.017871  0.018653  0.019500  ...  0.038634  0.038207  0.037770  0.037321   \n",
      "12  0.018918  0.019795  0.020743  ...  0.039818  0.039371  0.038913  0.038444   \n",
      "13  0.019302  0.020210  0.021190  ...  0.040469  0.040010  0.039541  0.039060   \n",
      "14  0.017927  0.018715  0.019567  ...  0.038703  0.038275  0.037837  0.037386   \n",
      "15  0.018710  0.019567  0.020494  ...  0.039644  0.039199  0.038744  0.038278   \n",
      "16  0.017916  0.018703  0.019555  ...  0.038659  0.038232  0.037794  0.037345   \n",
      "17  0.018947  0.019825  0.020772  ...  0.040023  0.039572  0.039110  0.038637   \n",
      "18  0.019317  0.020229  0.021214  ...  0.040341  0.039884  0.039417  0.038938   \n",
      "19  0.018184  0.018994  0.019870  ...  0.039038  0.038604  0.038160  0.037704   \n",
      "20  0.018022  0.018818  0.019679  ...  0.038822  0.038392  0.037951  0.037499   \n",
      "21  0.019964  0.020930  0.021973  ...  0.041239  0.040766  0.040284  0.039790   \n",
      "22  0.018456  0.019291  0.020194  ...  0.039334  0.038895  0.038446  0.037985   \n",
      "23  0.018503  0.019343  0.020250  ...  0.039397  0.038957  0.038506  0.038044   \n",
      "24  0.018786  0.019651  0.020586  ...  0.039679  0.039234  0.038778  0.038311   \n",
      "25  0.018434  0.019267  0.020167  ...  0.039357  0.038917  0.038467  0.038006   \n",
      "26  0.018119  0.018923  0.019793  ...  0.038971  0.038538  0.038095  0.037640   \n",
      "27  0.018520  0.019360  0.020268  ...  0.039451  0.039009  0.038558  0.038094   \n",
      "28  0.017960  0.018751  0.019606  ...  0.038765  0.038336  0.037896  0.037445   \n",
      "29  0.017987  0.018781  0.019639  ...  0.038750  0.038321  0.037882  0.037432   \n",
      "30  0.018950  0.019829  0.020779  ...  0.039945  0.039495  0.039035  0.038564   \n",
      "31  0.023709  0.024987  0.026363  ...  0.046029  0.045477  0.044916  0.044346   \n",
      "32  0.017847  0.018628  0.019473  ...  0.038577  0.038152  0.037716  0.037268   \n",
      "33  0.019219  0.020121  0.021094  ...  0.040346  0.039889  0.039421  0.038943   \n",
      "34  0.018164  0.018972  0.019845  ...  0.039059  0.038624  0.038179  0.037723   \n",
      "35  0.018409  0.019240  0.020139  ...  0.039283  0.038844  0.038396  0.037935   \n",
      "36  0.019044  0.019929  0.020886  ...  0.040174  0.039720  0.039255  0.038779   \n",
      "37  0.018308  0.019129  0.020018  ...  0.039185  0.038748  0.038301  0.037842   \n",
      "38  0.019478  0.020403  0.021403  ...  0.040554  0.040094  0.039623  0.039141   \n",
      "39  0.017822  0.018601  0.019443  ...  0.038565  0.038140  0.037704  0.037256   \n",
      "40  0.018693  0.019549  0.020474  ...  0.039641  0.039196  0.038741  0.038275   \n",
      "41  0.017570  0.018326  0.019144  ...  0.038229  0.037810  0.037381  0.036939   \n",
      "42  0.019135  0.020027  0.020990  ...  0.040362  0.039904  0.039436  0.038956   \n",
      "43  0.018137  0.018944  0.019815  ...  0.038991  0.038558  0.038115  0.037659   \n",
      "44  0.018369  0.019195  0.020088  ...  0.039298  0.038859  0.038410  0.037949   \n",
      "45  0.017765  0.018539  0.019376  ...  0.038459  0.038035  0.037602  0.037156   \n",
      "46  0.018801  0.019666  0.020602  ...  0.039759  0.039312  0.038856  0.038387   \n",
      "47  0.018107  0.018911  0.019781  ...  0.038918  0.038486  0.038044  0.037590   \n",
      "48  0.020519  0.021532  0.022626  ...  0.041943  0.041458  0.040964  0.040458   \n",
      "49  0.018316  0.019138  0.020026  ...  0.039237  0.038800  0.038352  0.037892   \n",
      "\n",
      "        y618      y619      y620      y621      y622      y623  \n",
      "0   0.042157  0.041651  0.041164  0.040730  0.040347  0.039979  \n",
      "1   0.038411  0.037965  0.037536  0.037158  0.036828  0.036517  \n",
      "2   0.039518  0.039053  0.038606  0.038211  0.037865  0.037536  \n",
      "3   0.038523  0.038075  0.037645  0.037265  0.036935  0.036622  \n",
      "4   0.036874  0.036456  0.036053  0.035700  0.035395  0.035110  \n",
      "5   0.036512  0.036100  0.035704  0.035357  0.035058  0.034779  \n",
      "6   0.037316  0.036890  0.036480  0.036120  0.035809  0.035516  \n",
      "7   0.037310  0.036884  0.036474  0.036114  0.035803  0.035511  \n",
      "8   0.041223  0.040730  0.040256  0.039836  0.039464  0.039109  \n",
      "9   0.037928  0.037491  0.037070  0.036700  0.036379  0.036076  \n",
      "10  0.037063  0.036641  0.036235  0.035879  0.035572  0.035283  \n",
      "11  0.036850  0.036432  0.036030  0.035677  0.035373  0.035088  \n",
      "12  0.037954  0.037517  0.037097  0.036727  0.036406  0.036103  \n",
      "13  0.038558  0.038110  0.037679  0.037300  0.036969  0.036656  \n",
      "14  0.036914  0.036495  0.036092  0.035738  0.035433  0.035147  \n",
      "15  0.037791  0.037356  0.036939  0.036571  0.036252  0.035952  \n",
      "16  0.036874  0.036456  0.036053  0.035700  0.035396  0.035110  \n",
      "17  0.038142  0.037701  0.037277  0.036904  0.036579  0.036273  \n",
      "18  0.038439  0.037994  0.037565  0.037187  0.036859  0.036548  \n",
      "19  0.037226  0.036801  0.036392  0.036033  0.035723  0.035431  \n",
      "20  0.037025  0.036604  0.036198  0.035843  0.035536  0.035248  \n",
      "21  0.039275  0.038816  0.038373  0.037982  0.037641  0.037316  \n",
      "22  0.037502  0.037072  0.036659  0.036296  0.035982  0.035686  \n",
      "23  0.037560  0.037130  0.036716  0.036352  0.036037  0.035740  \n",
      "24  0.037823  0.037388  0.036970  0.036602  0.036282  0.035981  \n",
      "25  0.037523  0.037093  0.036679  0.036316  0.036001  0.035705  \n",
      "26  0.037163  0.036739  0.036332  0.035974  0.035664  0.035374  \n",
      "27  0.037609  0.037178  0.036763  0.036398  0.036081  0.035784  \n",
      "28  0.036972  0.036551  0.036147  0.035792  0.035486  0.035199  \n",
      "29  0.036959  0.036539  0.036135  0.035781  0.035475  0.035188  \n",
      "30  0.038071  0.037632  0.037210  0.036838  0.036515  0.036210  \n",
      "31  0.043758  0.043227  0.042717  0.042261  0.041856  0.041465  \n",
      "32  0.036798  0.036381  0.035980  0.035628  0.035325  0.035041  \n",
      "33  0.038443  0.037997  0.037569  0.037191  0.036862  0.036550  \n",
      "34  0.037244  0.036819  0.036409  0.036050  0.035739  0.035447  \n",
      "35  0.037453  0.037024  0.036612  0.036249  0.035935  0.035640  \n",
      "36  0.038282  0.037838  0.037412  0.037036  0.036709  0.036400  \n",
      "37  0.037362  0.036935  0.036524  0.036163  0.035851  0.035557  \n",
      "38  0.038638  0.038189  0.037758  0.037377  0.037045  0.036730  \n",
      "39  0.036787  0.036370  0.035969  0.035617  0.035314  0.035030  \n",
      "40  0.037787  0.037352  0.036934  0.036567  0.036248  0.035947  \n",
      "41  0.036475  0.036064  0.035668  0.035322  0.035024  0.034746  \n",
      "42  0.038456  0.038009  0.037579  0.037201  0.036871  0.036558  \n",
      "43  0.037182  0.036758  0.036350  0.035992  0.035683  0.035392  \n",
      "44  0.037467  0.037038  0.036626  0.036263  0.035949  0.035654  \n",
      "45  0.036688  0.036273  0.035874  0.035524  0.035223  0.034941  \n",
      "46  0.037898  0.037461  0.037042  0.036672  0.036352  0.036050  \n",
      "47  0.037115  0.036692  0.036285  0.035928  0.035620  0.035330  \n",
      "48  0.039932  0.039461  0.039008  0.038607  0.038255  0.037920  \n",
      "49  0.037410  0.036982  0.036570  0.036208  0.035895  0.035600  \n",
      "\n",
      "[50 rows x 624 columns]\n"
     ]
    }
   ],
   "source": [
    "# Train the emulator using the train method\n",
    "emulator2.train(\n",
    "    dataset=dataset,\n",
    "    inputs=input_columns,\n",
    "    outputs=output_columns,\n",
    "    params=params,\n",
    "    verbose=True,\n",
    ")\n",
    "\n",
    "# Use test data to predict the results\n",
    "predictions2 = emulator2.predict(df_eval)\n",
    "result_df2 = pd.concat([predictions2[0], predictions2[1]], axis=1)\n",
    "df_mean2, df_std2 = (\n",
    "    result_df2.iloc[:, : len(output_columns)],\n",
    "    result_df2.iloc[:, len(output_columns) :],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **6. Plot the predictions and analyse the results**\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To plot the predictions of the two trained emulators with different output dimensions--alongside the true values from the test data--you can define a function `plot_predictions`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_predictions(df_mean1, df_mean2, df_std1, df_std2, df_grid, output_columns):\n",
    "\n",
    "    # Parameters for plot\n",
    "    error_inflation_factor = 1.0  # Factor to multiply error by for plotting\n",
    "    y_fac = 18  # Factor to divide y by for plotting [log10]\n",
    "    alpha = 0.5  # Plot opacity\n",
    "\n",
    "    # Define data\n",
    "    df_mean = [df_mean1, df_mean2]\n",
    "    df_std = [df_std1, df_std2]\n",
    "\n",
    "    # Define labels and colours for test, emulator 1 and emulator 2 data\n",
    "    label = [\"Test Data\", \"Emulator 1\", \"Emulator 2\"]\n",
    "    color = [\"black\", \"red\", \"green\"]\n",
    "\n",
    "    # Plot results\n",
    "    grid = df_grid.iloc[:, 0]\n",
    "    fig, axs = plt.subplots(1, 5, figsize=(30, 5))\n",
    "\n",
    "    # Model predictions\n",
    "    for example in range(5):\n",
    "        # Plot test data\n",
    "        eval = df_eval[output_columns].iloc[example] / 10**y_fac\n",
    "        axs[example].plot(\n",
    "            grid,\n",
    "            eval,\n",
    "            color=color[0],\n",
    "            alpha=alpha,\n",
    "            label=label[0] if example == 0 else None,\n",
    "        )\n",
    "        for i, emul in enumerate([1, 2]):\n",
    "            # Plot uncertainty bands\n",
    "            for j, emul2 in enumerate([1, 2]):\n",
    "                mean = df_mean[j][output_columns].iloc[example] / 10**y_fac\n",
    "                err = (\n",
    "                    error_inflation_factor\n",
    "                    * df_std[j][output_columns].iloc[example]\n",
    "                    / 10**y_fac\n",
    "                )\n",
    "                axs[example].fill_between(\n",
    "                    grid,\n",
    "                    mean - emul * err,\n",
    "                    mean + emul * err,\n",
    "                    color=color[emul2],\n",
    "                    alpha=alpha / (emul),\n",
    "                    lw=0.0,\n",
    "                    label=label[emul] if (i == 0) and (example == 0) else None,\n",
    "                )\n",
    "            # Plot mean\n",
    "            mean = df_mean[i][output_columns].iloc[example] / 10**y_fac\n",
    "            axs[example].plot(\n",
    "                grid,\n",
    "                mean,\n",
    "                color=color[emul],\n",
    "                label=label[emul] if (example == 0) else None,\n",
    "                alpha=alpha,\n",
    "            )\n",
    "        axs[example].set_xlabel(r\"Temperature [K]\")\n",
    "        axs[example].set_ylabel(\n",
    "            rf\"Desorption rate [$10^{{{y_fac}}}$ $m^{{{-2}}}$ $s^{{{-1}}}$]\"\n",
    "        )\n",
    "        axs[example].legend(label)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now you can execute this plotting function and visualise the two emulators, with two different dimension reductions performed on them:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(df_mean1, df_mean2, df_std1, df_std2, df_grid, output_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model 1\n",
    "\n",
    "It can be observed that the first emulator, trained with very low output dimensions, only 2 of 624, fails to approximate the data properly. This can be attributed to the massive truncation of the output dimensions.\n",
    "\n",
    "#### Model 2\n",
    "\n",
    "The second emulator was trained on 6 output dimensions. This is definitely more than the first emulator but is still significantly lower than the original number of dimensions. With this emulator, you seem to have struck the right balance, as you can see even by how the emulator fits to the test data more accurately.\n",
    "\n",
    "As you can see, your predictions are very accurate! You reduced the number of output dimensions from 624 to 6 and still managed to train a very good surrogate of the underlying functional data.\n",
    "\n",
    "This shows how you can achieve accurate emulators whilst significantly reducing your computation time. The ease with which dimensionality reduction can be implemented in `twinLab`, by simply specifying parameters such as `input_retained_dimensions` and `output_retained_dimensions` or `input_explained_variance` and `output_explained_variance` for outputs, demonstrates the efficiency and power of this tool. By using `twinLab`, you can leverage advanced techniques to optimize your engineering workflows, leading to better performance and faster results.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}