Migrated from GitHub

data/README.md

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+# An evaluation of ML algorithms for syngas fermentation
+
+The purpose of this study is to determine the feasibility of using machine learning to model production rates from syngas fermentation. In this study, we transformed time course metabolite concentration data from ten syngas fermentation conditions to calculate production rates of four products: acetate, ethanol, butyrate, and butanol. The data was used to train and test six machine learning algorithms’ ability to predict the rate of production of the four products. We found support vector machine performed the best on unseen testing data when performance across all conditions were considered. We also found that models with relatively few fitted variables, including elastic net and lasso, had the most consistent performance while models with more fitted variables, including neural networks and random forests, had greater variability in their prediction accuracy. Additionally, feature importance analysis was able to reproduce domain knowledge that the most important gas components for producing butyrate and butanol are carbon monoxide and hydrogen gas respectively.
+
+- [System Requirements](#system-requirements)
+- [Instructions for use](#instructions-for-use)
+- [Summary of notebooks](#summary-of-notebooks)
+- [Reference](#reference)
+- [License](#license)
+
+## System Requirements
+
+The code was written using python 3.7
+
+## Instructions for use
+
+To run the code in this repository use the following commands:
+
+<ol>
+  <li>git clone https://github.com/garrettroell/SyngasMachineLearning.git</li>
+  <li>python3 -m venv venv</li>
+  <li>source venv/bin/activate</li>
+  <li>pip install -r requirements.txt</li>
+</ol>
+
+## Summary of notebooks
+
+- Notebook A: Converts experimental data into smoothed data with rows that contain pairs of reactor conditions and production rates
+- Notebook B: Finds the optimal hyperparameters for the algorithms using a grid search with shuffle/split cross validation
+- Notebook C: Trains the models with optimal hyperparameters from notebook B and evaluates their performance on training and test data
+
+## Reference
+
+[GW Roell, A Sathish, N Wan, Z Wen, YJ Tang, FS Bao. A comparative evaluation of machine learning algorithms in predicting syngas fermentation outcomes using limited experimental data. Biochemical Engineering Journal. 186 (2022)](https://www.sciencedirect.com/science/article/pii/S1369703X22002479?via%3Dihub)
+<br>
+[Non-paywalled pdf](https://garrettroell.com/papers/syngas-machine-learning)
+
+## License
+
+This code is distributed under the 3-Clause BSD license specified in the [license][1] file. It is open source and commercially usable.
+
+---
+
+[1]: license

data/data/experimental_data.csv

@@ -0,0 +1,177 @@
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data/data/gases_importance.csv

@@ -0,0 +1,6 @@
+,CO,CO2,H2,flow rate
+acetate,0.002198403341377692,0.012518083005000976,0.0025766782229310003,0.009944401275548022
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data/data/rate_time_course_data/bayesian.csv

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data/license

@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2007-2021 Garrett Roell, Yinjie Tang, Forrest Bao.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+* Neither the name of the copyright holder nor the names of its
+  contributors may be used to endorse or promote products derived from
+  this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

data/notebooks/A_data_processing.ipynb

@@ -0,0 +1,1347 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Notebook A: Data Processsing\n",
+    "This notebook takes experimental data and converts it to a form that is usable for machine learning algorithm training.\n",
+    "This involves smoothing the data using the Savitzky–Golay filter, and adding slope values by comparing consecutive concentration values, and dividing by time elapsed. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Setup Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import math\n",
+    "from scipy.signal import savgol_filter "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Import experimental data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.23</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.57</td>\n",
+       "      <td>0.45</td>\n",
+       "      <td>12.81</td>\n",
+       "      <td>10.89</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.09</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>0.44</td>\n",
+       "      <td>15.59</td>\n",
+       "      <td>16.42</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.90</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>17.11</td>\n",
+       "      <td>17.75</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.58</td>\n",
+       "      <td>0.41</td>\n",
+       "      <td>10.93</td>\n",
+       "      <td>21.59</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.65</td>\n",
+       "      <td>0.39</td>\n",
+       "      <td>15.86</td>\n",
+       "      <td>44.28</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.02</td>\n",
+       "      <td>0.46</td>\n",
+       "      <td>8.13</td>\n",
+       "      <td>46.16</td>\n",
+       "      <td>0.21</td>\n",
+       "      <td>0.60</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2.67</td>\n",
+       "      <td>0.49</td>\n",
+       "      <td>10.79</td>\n",
+       "      <td>46.13</td>\n",
+       "      <td>1.18</td>\n",
+       "      <td>3.41</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4.70</td>\n",
+       "      <td>0.64</td>\n",
+       "      <td>20.31</td>\n",
+       "      <td>34.37</td>\n",
+       "      <td>8.44</td>\n",
+       "      <td>9.16</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.06</td>\n",
+       "      <td>0.67</td>\n",
+       "      <td>28.88</td>\n",
+       "      <td>27.06</td>\n",
+       "      <td>14.09</td>\n",
+       "      <td>8.03</td>\n",
+       "      <td>60</td>\n",
+       "      <td>20</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1      1  0.00           0.01          0.00          0.23   \n",
+       "1            1      1  0.57           0.45         12.81         10.89   \n",
+       "2            1      1  0.67           0.44         15.59         16.42   \n",
+       "3            1      1  0.90           0.41         17.11         17.75   \n",
+       "4            1      1  1.58           0.41         10.93         21.59   \n",
+       "5            1      1  1.65           0.39         15.86         44.28   \n",
+       "6            1      1  2.02           0.46          8.13         46.16   \n",
+       "7            1      1  2.67           0.49         10.79         46.13   \n",
+       "8            1      1  4.70           0.64         20.31         34.37   \n",
+       "9            1      1  6.06           0.67         28.88         27.06   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)  N2  CO   CO2   H2  flow rate (mL/min)  \n",
+       "0          0.00           0.06  60  20  15.0  5.0                  20  \n",
+       "1          0.03           0.09  60  20  15.0  5.0                  20  \n",
+       "2          0.03           0.13  60  20  15.0  5.0                  20  \n",
+       "3          0.03           0.07  60  20  15.0  5.0                  20  \n",
+       "4          0.04           0.06  60  20  15.0  5.0                  20  \n",
+       "5          0.05           0.07  60  20  15.0  5.0                  20  \n",
+       "6          0.21           0.60  60  20  15.0  5.0                  20  \n",
+       "7          1.18           3.41  60  20  15.0  5.0                  20  \n",
+       "8          8.44           9.16  60  20  15.0  5.0                  20  \n",
+       "9         14.09           8.03  60  20  15.0  5.0                  20  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "exp_df = pd.read_csv('../data/experimental_data.csv')\n",
+    "exp_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define function to smooth a single trial of experimental data using a second order Savitzky-Golay filter"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# set up savgol filter to have a window length of 15, polynomial order of 2, \n",
+    "# and to output the smoothed values.\n",
+    "savgol = lambda x: savgol_filter(x, 15, 2, deriv=0)\n",
+    "\n",
+    "def smooth_single_trial(trial_df, delta):\n",
+    "    # get list of time values\n",
+    "    times = list(trial_df.time)\n",
+    "\n",
+    "    # get smoothed time values\n",
+    "    max_time = times[-1]\n",
+    "    new_times = np.arange(0,max_time, delta)  \n",
+    "\n",
+    "    # temporarily set time as the index of the dataframe\n",
+    "    trial_df.set_index('time', inplace=True, drop=True)\n",
+    "\n",
+    "    # set the index of the df to the new times\n",
+    "    trial_df = trial_df.reindex(trial_df.index.union(new_times))\n",
+    "\n",
+    "    # find the linear interpolation for all the new data points\n",
+    "    trial_df = trial_df.interpolate()\n",
+    "\n",
+    "    # remove experimental time points, unless they are an exact multiple of delta\n",
+    "    times_to_remove = set(times) - (set(times) & set(new_times))\n",
+    "    trial_df = trial_df.loc[~trial_df.index.isin(times_to_remove)]\n",
+    "\n",
+    "    # convert linear interpolation to smoothed polynomial fit\n",
+    "    trial_df = trial_df.apply(savgol)\n",
+    "    \n",
+    "    # round negative smoothed values to 0\n",
+    "    trial_df.clip(lower=0, inplace=True)\n",
+    "    \n",
+    "    # Prevent floating point errors later\n",
+    "    trial_df.composition = round(trial_df.composition, 0)\n",
+    "    trial_df.trial = round(trial_df.trial, 0)\n",
+    "\n",
+    "    # remove time's status as index column\n",
+    "    trial_df.insert(2, 'time',trial_df.index)\n",
+    "    trial_df.reset_index(inplace=True, drop=True)\n",
+    "    \n",
+    "    return trial_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply smoothing function to each trial of experimental data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
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+       "\n",
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+       "        text-align: right;\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
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+       "      <td>0.097327</td>\n",
+       "      <td>1.917572</td>\n",
+       "      <td>1.660755</td>\n",
+       "      <td>0.005883</td>\n",
+       "      <td>0.066873</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.172225</td>\n",
+       "      <td>4.989251</td>\n",
+       "      <td>4.313160</td>\n",
+       "      <td>0.010819</td>\n",
+       "      <td>0.074034</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.238122</td>\n",
+       "      <td>7.685937</td>\n",
+       "      <td>6.765444</td>\n",
+       "      <td>0.015301</td>\n",
+       "      <td>0.079814</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.295016</td>\n",
+       "      <td>10.007629</td>\n",
+       "      <td>9.017607</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.084213</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.342908</td>\n",
+       "      <td>11.954328</td>\n",
+       "      <td>11.069648</td>\n",
+       "      <td>0.022898</td>\n",
+       "      <td>0.087231</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.381798</td>\n",
+       "      <td>13.526034</td>\n",
+       "      <td>12.921569</td>\n",
+       "      <td>0.026014</td>\n",
+       "      <td>0.088869</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.411686</td>\n",
+       "      <td>14.722747</td>\n",
+       "      <td>14.573368</td>\n",
+       "      <td>0.028676</td>\n",
+       "      <td>0.089125</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.426219</td>\n",
+       "      <td>15.684459</td>\n",
+       "      <td>16.189576</td>\n",
+       "      <td>0.030462</td>\n",
+       "      <td>0.086824</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432018</td>\n",
+       "      <td>16.001520</td>\n",
+       "      <td>16.742923</td>\n",
+       "      <td>0.031439</td>\n",
+       "      <td>0.082842</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0          1.0    1.0    0       0.013426      0.000000      0.000000   \n",
+       "1          1.0    1.0  0.1       0.097327      1.917572      1.660755   \n",
+       "2          1.0    1.0  0.2       0.172225      4.989251      4.313160   \n",
+       "3          1.0    1.0  0.3       0.238122      7.685937      6.765444   \n",
+       "4          1.0    1.0  0.4       0.295016     10.007629      9.017607   \n",
+       "5          1.0    1.0  0.5       0.342908     11.954328     11.069648   \n",
+       "6          1.0    1.0  0.6       0.381798     13.526034     12.921569   \n",
+       "7          1.0    1.0  0.7       0.411686     14.722747     14.573368   \n",
+       "8          1.0    1.0  0.8       0.426219     15.684459     16.189576   \n",
+       "9          1.0    1.0  0.9       0.432018     16.001520     16.742923   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \n",
+       "0      0.000492       0.058332  60.0  20.0  15.0  5.0                20.0  \n",
+       "1      0.005883       0.066873  60.0  20.0  15.0  5.0                20.0  \n",
+       "2      0.010819       0.074034  60.0  20.0  15.0  5.0                20.0  \n",
+       "3      0.015301       0.079814  60.0  20.0  15.0  5.0                20.0  \n",
+       "4      0.019327       0.084213  60.0  20.0  15.0  5.0                20.0  \n",
+       "5      0.022898       0.087231  60.0  20.0  15.0  5.0                20.0  \n",
+       "6      0.026014       0.088869  60.0  20.0  15.0  5.0                20.0  \n",
+       "7      0.028676       0.089125  60.0  20.0  15.0  5.0                20.0  \n",
+       "8      0.030462       0.086824  60.0  20.0  15.0  5.0                20.0  \n",
+       "9      0.031439       0.082842  60.0  20.0  15.0  5.0                20.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# This parameter tunes the distance between\n",
+    "delta = 0.1\n",
+    "\n",
+    "smoothed_df = pd.DataFrame()\n",
+    "\n",
+    "# Loop through compositions\n",
+    "for composition in set(exp_df.composition):\n",
+    "    \n",
+    "    # For each composition, loop over trials\n",
+    "    composition_df = exp_df[exp_df.composition == composition]\n",
+    "    for trial in set(composition_df.trial):\n",
+    "        # isolate the data for a single trial\n",
+    "        trial_df = exp_df[(exp_df.composition == composition) & (exp_df.trial == trial)]\n",
+    "        \n",
+    "        # get the smooth data for that trial\n",
+    "        smooth_trial_df = smooth_single_trial(trial_df, delta)\n",
+    "        \n",
+    "        # add the trial to smoothed_df\n",
+    "        smoothed_df = pd.concat([smoothed_df, smooth_trial_df])\n",
+    "\n",
+    "smoothed_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Define function to add production rates to a single trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_rates_to_trial(trial_df, delta):\n",
+    "    \n",
+    "    trial_rates_df = trial_df.copy()\n",
+    "\n",
+    "    biomass_rates = []\n",
+    "    ethanol_rates = []\n",
+    "    acetate_rates = []\n",
+    "    butanol_rates = []\n",
+    "    butyrate_rates = []\n",
+    "    \n",
+    "    \n",
+    "    # loop over rows of dataframe\n",
+    "    for index, row in trial_df.iterrows():\n",
+    "        # if it is the first row, don't calculate slope\n",
+    "        if row.time == 0:\n",
+    "            biomass_rates.append(0)\n",
+    "            ethanol_rates.append(0)\n",
+    "            acetate_rates.append(0)\n",
+    "            butanol_rates.append(0)\n",
+    "            butyrate_rates.append(0)\n",
+    "        # find diffence between current and previous value divide by the delta value to get production rate\n",
+    "        else:\n",
+    "            previous_row = trial_df[trial_df.index == index-1]\n",
+    "            biomass_rates.append(float((row['biomass (g/L)'] - previous_row['biomass (g/L)'])/delta))\n",
+    "            ethanol_rates.append(float((row['ethanol (mM)'] - previous_row['ethanol (mM)'])/delta))\n",
+    "            acetate_rates.append(float((row['acetate (mM)'] - previous_row['acetate (mM)'])/delta))\n",
+    "            butanol_rates.append(float((row['butanol (mM)'] - previous_row['butanol (mM)'])/delta))\n",
+    "            butyrate_rates.append(float((row['butyrate (mM)'] - previous_row['butyrate (mM)'])/delta))\n",
+    "\n",
+    "    # add production rates to the data frame\n",
+    "    trial_rates_df['biomass rate'] = biomass_rates\n",
+    "    trial_rates_df['ethanol rate'] = ethanol_rates\n",
+    "    trial_rates_df['acetate rate'] = acetate_rates\n",
+    "    trial_rates_df['butanol rate'] = butanol_rates\n",
+    "    trial_rates_df['butyrate rate'] = butyrate_rates\n",
+    "\n",
+    "    return trial_rates_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Apply function to every trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "      <th>biomass rate</th>\n",
+       "      <th>ethanol rate</th>\n",
+       "      <th>acetate rate</th>\n",
+       "      <th>butanol rate</th>\n",
+       "      <th>butyrate rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.013426</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000492</td>\n",
+       "      <td>0.058332</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.097327</td>\n",
+       "      <td>1.917572</td>\n",
+       "      <td>1.660755</td>\n",
+       "      <td>0.005883</td>\n",
+       "      <td>0.066873</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.839006</td>\n",
+       "      <td>19.175716</td>\n",
+       "      <td>16.607550</td>\n",
+       "      <td>0.053914</td>\n",
+       "      <td>0.085414</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.172225</td>\n",
+       "      <td>4.989251</td>\n",
+       "      <td>4.313160</td>\n",
+       "      <td>0.010819</td>\n",
+       "      <td>0.074034</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.748985</td>\n",
+       "      <td>30.716792</td>\n",
+       "      <td>26.524049</td>\n",
+       "      <td>0.049363</td>\n",
+       "      <td>0.071606</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.238122</td>\n",
+       "      <td>7.685937</td>\n",
+       "      <td>6.765444</td>\n",
+       "      <td>0.015301</td>\n",
+       "      <td>0.079814</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.658964</td>\n",
+       "      <td>26.966859</td>\n",
+       "      <td>24.522838</td>\n",
+       "      <td>0.044813</td>\n",
+       "      <td>0.057799</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.295016</td>\n",
+       "      <td>10.007629</td>\n",
+       "      <td>9.017607</td>\n",
+       "      <td>0.019327</td>\n",
+       "      <td>0.084213</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.568943</td>\n",
+       "      <td>23.216926</td>\n",
+       "      <td>22.521627</td>\n",
+       "      <td>0.040263</td>\n",
+       "      <td>0.043991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.342908</td>\n",
+       "      <td>11.954328</td>\n",
+       "      <td>11.069648</td>\n",
+       "      <td>0.022898</td>\n",
+       "      <td>0.087231</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.478922</td>\n",
+       "      <td>19.466992</td>\n",
+       "      <td>20.520417</td>\n",
+       "      <td>0.035712</td>\n",
+       "      <td>0.030183</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.381798</td>\n",
+       "      <td>13.526034</td>\n",
+       "      <td>12.921569</td>\n",
+       "      <td>0.026014</td>\n",
+       "      <td>0.088869</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.388901</td>\n",
+       "      <td>15.717059</td>\n",
+       "      <td>18.519206</td>\n",
+       "      <td>0.031162</td>\n",
+       "      <td>0.016375</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.411686</td>\n",
+       "      <td>14.722747</td>\n",
+       "      <td>14.573368</td>\n",
+       "      <td>0.028676</td>\n",
+       "      <td>0.089125</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.298880</td>\n",
+       "      <td>11.967126</td>\n",
+       "      <td>16.517995</td>\n",
+       "      <td>0.026612</td>\n",
+       "      <td>0.002567</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.426219</td>\n",
+       "      <td>15.684459</td>\n",
+       "      <td>16.189576</td>\n",
+       "      <td>0.030462</td>\n",
+       "      <td>0.086824</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.145324</td>\n",
+       "      <td>9.617115</td>\n",
+       "      <td>16.162079</td>\n",
+       "      <td>0.017860</td>\n",
+       "      <td>-0.023019</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432018</td>\n",
+       "      <td>16.001520</td>\n",
+       "      <td>16.742923</td>\n",
+       "      <td>0.031439</td>\n",
+       "      <td>0.082842</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.057994</td>\n",
+       "      <td>3.170618</td>\n",
+       "      <td>5.533469</td>\n",
+       "      <td>0.009774</td>\n",
+       "      <td>-0.039819</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0          1.0    1.0    0       0.013426      0.000000      0.000000   \n",
+       "1          1.0    1.0  0.1       0.097327      1.917572      1.660755   \n",
+       "2          1.0    1.0  0.2       0.172225      4.989251      4.313160   \n",
+       "3          1.0    1.0  0.3       0.238122      7.685937      6.765444   \n",
+       "4          1.0    1.0  0.4       0.295016     10.007629      9.017607   \n",
+       "5          1.0    1.0  0.5       0.342908     11.954328     11.069648   \n",
+       "6          1.0    1.0  0.6       0.381798     13.526034     12.921569   \n",
+       "7          1.0    1.0  0.7       0.411686     14.722747     14.573368   \n",
+       "8          1.0    1.0  0.8       0.426219     15.684459     16.189576   \n",
+       "9          1.0    1.0  0.9       0.432018     16.001520     16.742923   \n",
+       "\n",
+       "   butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \\\n",
+       "0      0.000492       0.058332  60.0  20.0  15.0  5.0                20.0   \n",
+       "1      0.005883       0.066873  60.0  20.0  15.0  5.0                20.0   \n",
+       "2      0.010819       0.074034  60.0  20.0  15.0  5.0                20.0   \n",
+       "3      0.015301       0.079814  60.0  20.0  15.0  5.0                20.0   \n",
+       "4      0.019327       0.084213  60.0  20.0  15.0  5.0                20.0   \n",
+       "5      0.022898       0.087231  60.0  20.0  15.0  5.0                20.0   \n",
+       "6      0.026014       0.088869  60.0  20.0  15.0  5.0                20.0   \n",
+       "7      0.028676       0.089125  60.0  20.0  15.0  5.0                20.0   \n",
+       "8      0.030462       0.086824  60.0  20.0  15.0  5.0                20.0   \n",
+       "9      0.031439       0.082842  60.0  20.0  15.0  5.0                20.0   \n",
+       "\n",
+       "   biomass rate  ethanol rate  acetate rate  butanol rate  butyrate rate  \n",
+       "0      0.000000      0.000000      0.000000      0.000000       0.000000  \n",
+       "1      0.839006     19.175716     16.607550      0.053914       0.085414  \n",
+       "2      0.748985     30.716792     26.524049      0.049363       0.071606  \n",
+       "3      0.658964     26.966859     24.522838      0.044813       0.057799  \n",
+       "4      0.568943     23.216926     22.521627      0.040263       0.043991  \n",
+       "5      0.478922     19.466992     20.520417      0.035712       0.030183  \n",
+       "6      0.388901     15.717059     18.519206      0.031162       0.016375  \n",
+       "7      0.298880     11.967126     16.517995      0.026612       0.002567  \n",
+       "8      0.145324      9.617115     16.162079      0.017860      -0.023019  \n",
+       "9      0.057994      3.170618      5.533469      0.009774      -0.039819  "
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "rates_df = pd.DataFrame()\n",
+    "\n",
+    "# Loop through compositions\n",
+    "for composition in set(smoothed_df.composition):\n",
+    "    \n",
+    "    # For each composition, loop over trials\n",
+    "    composition_df = smoothed_df[smoothed_df.composition == composition]\n",
+    "    for trial in set(composition_df.trial):\n",
+    "        \n",
+    "        # isolate the data for a single trial\n",
+    "        trial_df = smoothed_df[(smoothed_df.composition == composition) & (smoothed_df.trial == trial)]\n",
+    "        \n",
+    "        # get the rates data for that trial\n",
+    "        trial_rates_df = add_rates_to_trial(trial_df, delta)\n",
+    "        \n",
+    "        # add the trial to rates_df\n",
+    "        rates_df = pd.concat([rates_df, trial_rates_df])\n",
+    "\n",
+    "rates_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Remove data from the first 24 hrs of fermentation (glucose was present)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "      <th>N2</th>\n",
+       "      <th>CO</th>\n",
+       "      <th>CO2</th>\n",
+       "      <th>H2</th>\n",
+       "      <th>flow rate (mL/min)</th>\n",
+       "      <th>biomass rate</th>\n",
+       "      <th>ethanol rate</th>\n",
+       "      <th>acetate rate</th>\n",
+       "      <th>butanol rate</th>\n",
+       "      <th>butyrate rate</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.024890</td>\n",
+       "      <td>-1.303834</td>\n",
+       "      <td>1.506172</td>\n",
+       "      <td>-0.015756</td>\n",
+       "      <td>-0.126633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.421056</td>\n",
+       "      <td>15.583697</td>\n",
+       "      <td>17.988494</td>\n",
+       "      <td>0.028287</td>\n",
+       "      <td>0.056100</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.084727</td>\n",
+       "      <td>-2.874395</td>\n",
+       "      <td>10.949538</td>\n",
+       "      <td>-0.015765</td>\n",
+       "      <td>-0.140778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.410826</td>\n",
+       "      <td>15.121595</td>\n",
+       "      <td>19.816681</td>\n",
+       "      <td>0.028325</td>\n",
+       "      <td>0.046269</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.102302</td>\n",
+       "      <td>-4.621020</td>\n",
+       "      <td>18.281864</td>\n",
+       "      <td>0.000380</td>\n",
+       "      <td>-0.098317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.403602</td>\n",
+       "      <td>14.500537</td>\n",
+       "      <td>22.179671</td>\n",
+       "      <td>0.031348</td>\n",
+       "      <td>0.045430</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>-0.072244</td>\n",
+       "      <td>-6.210585</td>\n",
+       "      <td>23.629906</td>\n",
+       "      <td>0.030235</td>\n",
+       "      <td>-0.008389</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.403688</td>\n",
+       "      <td>13.935178</td>\n",
+       "      <td>25.274862</td>\n",
+       "      <td>0.028703</td>\n",
+       "      <td>0.031849</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.000866</td>\n",
+       "      <td>-5.653590</td>\n",
+       "      <td>30.951909</td>\n",
+       "      <td>-0.026458</td>\n",
+       "      <td>-0.135805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.406240</td>\n",
+       "      <td>13.302534</td>\n",
+       "      <td>28.983542</td>\n",
+       "      <td>0.030988</td>\n",
+       "      <td>0.037619</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.025520</td>\n",
+       "      <td>-6.326440</td>\n",
+       "      <td>37.086802</td>\n",
+       "      <td>0.022851</td>\n",
+       "      <td>0.057692</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.6</td>\n",
+       "      <td>0.410874</td>\n",
+       "      <td>12.629887</td>\n",
+       "      <td>32.875284</td>\n",
+       "      <td>0.043472</td>\n",
+       "      <td>0.075515</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.046335</td>\n",
+       "      <td>-6.726471</td>\n",
+       "      <td>38.917421</td>\n",
+       "      <td>0.124842</td>\n",
+       "      <td>0.378959</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.7</td>\n",
+       "      <td>0.416723</td>\n",
+       "      <td>12.043783</td>\n",
+       "      <td>36.718888</td>\n",
+       "      <td>0.070169</td>\n",
+       "      <td>0.155912</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.058487</td>\n",
+       "      <td>-5.861041</td>\n",
+       "      <td>38.436037</td>\n",
+       "      <td>0.266975</td>\n",
+       "      <td>0.803975</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.8</td>\n",
+       "      <td>0.424176</td>\n",
+       "      <td>11.479518</td>\n",
+       "      <td>40.301776</td>\n",
+       "      <td>0.114038</td>\n",
+       "      <td>0.287944</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.074531</td>\n",
+       "      <td>-5.642644</td>\n",
+       "      <td>35.828875</td>\n",
+       "      <td>0.438688</td>\n",
+       "      <td>1.320317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.9</td>\n",
+       "      <td>0.432878</td>\n",
+       "      <td>10.959046</td>\n",
+       "      <td>43.408944</td>\n",
+       "      <td>0.176694</td>\n",
+       "      <td>0.475785</td>\n",
+       "      <td>60.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>15.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>0.087020</td>\n",
+       "      <td>-5.204725</td>\n",
+       "      <td>31.071681</td>\n",
+       "      <td>0.626561</td>\n",
+       "      <td>1.878416</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    composition  trial time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "10          1.0    1.0    0       0.429529     15.871137     16.893540   \n",
+       "11          1.0    1.0  0.1       0.421056     15.583697     17.988494   \n",
+       "12          1.0    1.0  0.2       0.410826     15.121595     19.816681   \n",
+       "13          1.0    1.0  0.3       0.403602     14.500537     22.179671   \n",
+       "14          1.0    1.0  0.4       0.403688     13.935178     25.274862   \n",
+       "15          1.0    1.0  0.5       0.406240     13.302534     28.983542   \n",
+       "16          1.0    1.0  0.6       0.410874     12.629887     32.875284   \n",
+       "17          1.0    1.0  0.7       0.416723     12.043783     36.718888   \n",
+       "18          1.0    1.0  0.8       0.424176     11.479518     40.301776   \n",
+       "19          1.0    1.0  0.9       0.432878     10.959046     43.408944   \n",
+       "\n",
+       "    butanol (mM)  butyrate (mM)    N2    CO   CO2   H2  flow rate (mL/min)  \\\n",
+       "10      0.029863       0.070178  60.0  20.0  15.0  5.0                20.0   \n",
+       "11      0.028287       0.056100  60.0  20.0  15.0  5.0                20.0   \n",
+       "12      0.028325       0.046269  60.0  20.0  15.0  5.0                20.0   \n",
+       "13      0.031348       0.045430  60.0  20.0  15.0  5.0                20.0   \n",
+       "14      0.028703       0.031849  60.0  20.0  15.0  5.0                20.0   \n",
+       "15      0.030988       0.037619  60.0  20.0  15.0  5.0                20.0   \n",
+       "16      0.043472       0.075515  60.0  20.0  15.0  5.0                20.0   \n",
+       "17      0.070169       0.155912  60.0  20.0  15.0  5.0                20.0   \n",
+       "18      0.114038       0.287944  60.0  20.0  15.0  5.0                20.0   \n",
+       "19      0.176694       0.475785  60.0  20.0  15.0  5.0                20.0   \n",
+       "\n",
+       "    biomass rate  ethanol rate  acetate rate  butanol rate  butyrate rate  \n",
+       "10     -0.024890     -1.303834      1.506172     -0.015756      -0.126633  \n",
+       "11     -0.084727     -2.874395     10.949538     -0.015765      -0.140778  \n",
+       "12     -0.102302     -4.621020     18.281864      0.000380      -0.098317  \n",
+       "13     -0.072244     -6.210585     23.629906      0.030235      -0.008389  \n",
+       "14      0.000866     -5.653590     30.951909     -0.026458      -0.135805  \n",
+       "15      0.025520     -6.326440     37.086802      0.022851       0.057692  \n",
+       "16      0.046335     -6.726471     38.917421      0.124842       0.378959  \n",
+       "17      0.058487     -5.861041     38.436037      0.266975       0.803975  \n",
+       "18      0.074531     -5.642644     35.828875      0.438688       1.320317  \n",
+       "19      0.087020     -5.204725     31.071681      0.626561       1.878416  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# remove all time points before one day has passed\n",
+    "rates_df = rates_df[rates_df['time'] >= 1]\n",
+    "\n",
+    "# subtract one day from all times\n",
+    "rates_df['time'] = rates_df['time'] - 1\n",
+    "\n",
+    "rates_df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Save rates data as csv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rates_df.to_csv(f'../data/rates_data.csv', index=False)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "biodesign_3.7",
+   "language": "python",
+   "name": "biod_3.7"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

data/notebooks/D_time_course.ipynb

@@ -0,0 +1,2290 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Notebook D: Plotting time course curves"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set up imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import pickle\n",
+    "from itertools import *\n",
+    "import sklearn\n",
+    "import scipy"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load trained models to a dictionary"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# dictionary to hold all trained models\n",
+    "models = {}\n",
+    "\n",
+    "for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "    models[algorithm] = {}\n",
+    "    for output in ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']:\n",
+    "        filename = f'../trained_models/{output}/{algorithm}.pkl'\n",
+    "        models[algorithm][output] = pickle.load(open(filename,'rb'))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Load smoothed data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the rates data: 836 rows by 18 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "rates_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the rates data: {rates_df.shape[0]} rows by {rates_df.shape[1]} columns')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Fit Min Max Scaler to match scaler that was applied to training data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "MinMaxScaler()"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "train_data = rates_df[rates_df.composition.isin([1,2,3,4,5,6,7])]\n",
+    "X = train_data [[\n",
+    "    'biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', \n",
+    "    'butyrate (mM)', 'N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)'\n",
+    "]]\n",
+    "\n",
+    "Scaler = sklearn.preprocessing.MinMaxScaler()\n",
+    "Scaler.fit(X)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get list of unique trials"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "17"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "trials = []\n",
+    "\n",
+    "for index, row in rates_df.iterrows():\n",
+    "\n",
+    "    # add unique trials to list as a tuple\n",
+    "    trial = (row['composition'], row['trial'])\n",
+    "    \n",
+    "    if (trial not in trials):\n",
+    "        trials.append(trial)\n",
+    "\n",
+    "len(trials)\n",
+    "  "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Generate simulated data for each trial"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_fine\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.432014</td>\n",
+       "      <td>15.479542</td>\n",
+       "      <td>16.906172</td>\n",
+       "      <td>0.038598</td>\n",
+       "      <td>0.056240</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434586</td>\n",
+       "      <td>15.085316</td>\n",
+       "      <td>16.943084</td>\n",
+       "      <td>0.049784</td>\n",
+       "      <td>0.042031</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437249</td>\n",
+       "      <td>14.688834</td>\n",
+       "      <td>17.014004</td>\n",
+       "      <td>0.063562</td>\n",
+       "      <td>0.027509</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440008</td>\n",
+       "      <td>14.290437</td>\n",
+       "      <td>17.132495</td>\n",
+       "      <td>0.080086</td>\n",
+       "      <td>0.012624</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.426869</td>\n",
+       "      <td>85.963025</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.168106</td>\n",
+       "      <td>0.414633</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.422695</td>\n",
+       "      <td>87.125709</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.263269</td>\n",
+       "      <td>0.360459</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.418362</td>\n",
+       "      <td>88.245367</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.350965</td>\n",
+       "      <td>0.304709</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.413871</td>\n",
+       "      <td>89.325630</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.430966</td>\n",
+       "      <td>0.247447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.409225</td>\n",
+       "      <td>90.370557</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.503077</td>\n",
+       "      <td>0.188738</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432014     15.479542     16.906172   \n",
+       "2            1.0    1.0   0.2       0.434586     15.085316     16.943084   \n",
+       "3            1.0    1.0   0.3       0.437249     14.688834     17.014004   \n",
+       "4            1.0    1.0   0.4       0.440008     14.290437     17.132495   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.426869     85.963025      0.000000   \n",
+       "832         10.0    2.0   3.6       0.422695     87.125709      0.000000   \n",
+       "833         10.0    2.0   3.7       0.418362     88.245367      0.000000   \n",
+       "834         10.0    2.0   3.8       0.413871     89.325630      0.000000   \n",
+       "835         10.0    2.0   3.9       0.409225     90.370557      0.000000   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.038598       0.056240  \n",
+       "2        0.049784       0.042031  \n",
+       "3        0.063562       0.027509  \n",
+       "4        0.080086       0.012624  \n",
+       "..            ...            ...  \n",
+       "831      5.168106       0.414633  \n",
+       "832      5.263269       0.360459  \n",
+       "833      5.350965       0.304709  \n",
+       "834      5.430966       0.247447  \n",
+       "835      5.503077       0.188738  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "nn_coarse\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.431311</td>\n",
+       "      <td>15.569167</td>\n",
+       "      <td>17.028067</td>\n",
+       "      <td>0.032532</td>\n",
+       "      <td>0.082760</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.433155</td>\n",
+       "      <td>15.268922</td>\n",
+       "      <td>17.166065</td>\n",
+       "      <td>0.037896</td>\n",
+       "      <td>0.097939</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.435060</td>\n",
+       "      <td>14.970760</td>\n",
+       "      <td>17.307119</td>\n",
+       "      <td>0.046235</td>\n",
+       "      <td>0.115877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.437029</td>\n",
+       "      <td>14.675046</td>\n",
+       "      <td>17.450478</td>\n",
+       "      <td>0.057844</td>\n",
+       "      <td>0.136749</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.466077</td>\n",
+       "      <td>67.614329</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.159369</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.464660</td>\n",
+       "      <td>68.306439</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.369557</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.463139</td>\n",
+       "      <td>68.933360</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.580341</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.461517</td>\n",
+       "      <td>69.507298</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>10.791943</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.459796</td>\n",
+       "      <td>70.039844</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>11.004542</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.431311     15.569167     17.028067   \n",
+       "2            1.0    1.0   0.2       0.433155     15.268922     17.166065   \n",
+       "3            1.0    1.0   0.3       0.435060     14.970760     17.307119   \n",
+       "4            1.0    1.0   0.4       0.437029     14.675046     17.450478   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.466077     67.614329      0.000000   \n",
+       "832         10.0    2.0   3.6       0.464660     68.306439      0.000000   \n",
+       "833         10.0    2.0   3.7       0.463139     68.933360      0.000000   \n",
+       "834         10.0    2.0   3.8       0.461517     69.507298      0.000000   \n",
+       "835         10.0    2.0   3.9       0.459796     70.039844      0.000000   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
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+       "3        0.046235       0.115877  \n",
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+       "..            ...            ...  \n",
+       "831     10.159369       0.000000  \n",
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+       "833     10.580341       0.000000  \n",
+       "834     10.791943       0.000000  \n",
+       "835     11.004542       0.000000  \n",
+       "\n",
+       "[836 rows x 8 columns]"
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+       "      <td>56.871514</td>\n",
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+       "      <td>15.395463</td>\n",
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+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.427480     15.832392     18.391188   \n",
+       "2            1.0    1.0   0.2       0.425907     15.811894     19.878014   \n",
+       "3            1.0    1.0   0.3       0.424807     15.810819     21.350286   \n",
+       "4            1.0    1.0   0.4       0.424173     15.830296     22.804078   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
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+       "835         10.0    2.0   3.9       1.145986     67.863833     27.382335   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
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+       "833     17.005200       0.000000  \n",
+       "834     17.830097       0.000000  \n",
+       "835     18.667287       0.000000  \n",
+       "\n",
+       "[836 rows x 8 columns]"
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+       "      <td>52.331725</td>\n",
+       "      <td>29.119306</td>\n",
+       "      <td>10.786392</td>\n",
+       "      <td>3.673986</td>\n",
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+       "      <th>832</th>\n",
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+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
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+       "2            1.0    1.0   0.2       0.421964     15.401434     18.630320   \n",
+       "3            1.0    1.0   0.3       0.420243     15.113986     20.574013   \n",
+       "4            1.0    1.0   0.4       0.420516     14.744349     23.250898   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
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+       "835         10.0    2.0   3.9       0.680120     59.687172     24.192422   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.031916       0.076854  \n",
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+       "834     12.277473       3.330053  \n",
+       "835     12.767437       3.194768  \n",
+       "\n",
+       "[836 rows x 8 columns]"
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+       "      <td>0.389010</td>\n",
+       "      <td>0.762065</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.614279</td>\n",
+       "      <td>50.754610</td>\n",
+       "      <td>39.717259</td>\n",
+       "      <td>7.176111</td>\n",
+       "      <td>2.763627</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.620437</td>\n",
+       "      <td>52.506765</td>\n",
+       "      <td>39.330646</td>\n",
+       "      <td>7.604363</td>\n",
+       "      <td>2.747658</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.626539</td>\n",
+       "      <td>54.296126</td>\n",
+       "      <td>38.891499</td>\n",
+       "      <td>8.048661</td>\n",
+       "      <td>2.720214</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.632580</td>\n",
+       "      <td>56.122346</td>\n",
+       "      <td>38.400341</td>\n",
+       "      <td>8.509246</td>\n",
+       "      <td>2.680408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.638557</td>\n",
+       "      <td>57.985069</td>\n",
+       "      <td>37.857799</td>\n",
+       "      <td>8.986358</td>\n",
+       "      <td>2.627331</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432037     16.013903     18.260049   \n",
+       "2            1.0    1.0   0.2       0.434644     16.182674     19.591642   \n",
+       "3            1.0    1.0   0.3       0.437344     16.377525     20.886915   \n",
+       "4            1.0    1.0   0.4       0.440132     16.598493     22.144498   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.614279     50.754610     39.717259   \n",
+       "832         10.0    2.0   3.6       0.620437     52.506765     39.330646   \n",
+       "833         10.0    2.0   3.7       0.626539     54.296126     38.891499   \n",
+       "834         10.0    2.0   3.8       0.632580     56.122346     38.400341   \n",
+       "835         10.0    2.0   3.9       0.638557     57.985069     37.857799   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.107784       0.234883  \n",
+       "2        0.193432       0.405254  \n",
+       "3        0.287082       0.581064  \n",
+       "4        0.389010       0.762065  \n",
+       "..            ...            ...  \n",
+       "831      7.176111       2.763627  \n",
+       "832      7.604363       2.747658  \n",
+       "833      8.048661       2.720214  \n",
+       "834      8.509246       2.680408  \n",
+       "835      8.986358       2.627331  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "lasso\n"
+     ]
+    },
+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
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+       "      <th>butyrate (mM)</th>\n",
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+       "      <th>0</th>\n",
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+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
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+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.1</td>\n",
+       "      <td>0.432036</td>\n",
+       "      <td>16.016089</td>\n",
+       "      <td>18.596465</td>\n",
+       "      <td>0.107784</td>\n",
+       "      <td>0.235610</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434674</td>\n",
+       "      <td>16.192511</td>\n",
+       "      <td>20.240359</td>\n",
+       "      <td>0.194157</td>\n",
+       "      <td>0.407948</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437434</td>\n",
+       "      <td>16.400294</td>\n",
+       "      <td>21.822592</td>\n",
+       "      <td>0.289261</td>\n",
+       "      <td>0.586894</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440308</td>\n",
+       "      <td>16.639262</td>\n",
+       "      <td>23.340638</td>\n",
+       "      <td>0.393371</td>\n",
+       "      <td>0.772123</td>\n",
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+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.612423</td>\n",
+       "      <td>50.776583</td>\n",
+       "      <td>33.701417</td>\n",
+       "      <td>7.248235</td>\n",
+       "      <td>2.741759</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.617981</td>\n",
+       "      <td>52.438738</td>\n",
+       "      <td>32.695054</td>\n",
+       "      <td>7.664781</td>\n",
+       "      <td>2.701489</td>\n",
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+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.623429</td>\n",
+       "      <td>54.126142</td>\n",
+       "      <td>31.621313</td>\n",
+       "      <td>8.095096</td>\n",
+       "      <td>2.647123</td>\n",
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+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.628766</td>\n",
+       "      <td>55.837890</td>\n",
+       "      <td>30.482708</td>\n",
+       "      <td>8.539261</td>\n",
+       "      <td>2.577708</td>\n",
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+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.633986</td>\n",
+       "      <td>57.573084</td>\n",
+       "      <td>29.281987</td>\n",
+       "      <td>8.997349</td>\n",
+       "      <td>2.492283</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
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+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432036     16.016089     18.596465   \n",
+       "2            1.0    1.0   0.2       0.434674     16.192511     20.240359   \n",
+       "3            1.0    1.0   0.3       0.437434     16.400294     21.822592   \n",
+       "4            1.0    1.0   0.4       0.440308     16.639262     23.340638   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.612423     50.776583     33.701417   \n",
+       "832         10.0    2.0   3.6       0.617981     52.438738     32.695054   \n",
+       "833         10.0    2.0   3.7       0.623429     54.126142     31.621313   \n",
+       "834         10.0    2.0   3.8       0.628766     55.837890     30.482708   \n",
+       "835         10.0    2.0   3.9       0.633986     57.573084     29.281987   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.107784       0.235610  \n",
+       "2        0.194157       0.407948  \n",
+       "3        0.289261       0.586894  \n",
+       "4        0.393371       0.772123  \n",
+       "..            ...            ...  \n",
+       "831      7.248235       2.741759  \n",
+       "832      7.664781       2.701489  \n",
+       "833      8.095096       2.647123  \n",
+       "834      8.539261       2.577708  \n",
+       "835      8.997349       2.492283  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
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+     "text": [
+      "knn\n"
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+    {
+     "data": {
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+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
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+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
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+       "      <th>0</th>\n",
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+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
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+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.427040</td>\n",
+       "      <td>15.740754</td>\n",
+       "      <td>17.044157</td>\n",
+       "      <td>0.028288</td>\n",
+       "      <td>0.057515</td>\n",
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+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <td>0.422349</td>\n",
+       "      <td>15.524351</td>\n",
+       "      <td>17.650487</td>\n",
+       "      <td>0.026712</td>\n",
+       "      <td>0.045338</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.414849</td>\n",
+       "      <td>15.232301</td>\n",
+       "      <td>18.707065</td>\n",
+       "      <td>0.025136</td>\n",
+       "      <td>0.032566</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.406761</td>\n",
+       "      <td>14.861683</td>\n",
+       "      <td>20.092511</td>\n",
+       "      <td>0.024390</td>\n",
+       "      <td>0.021919</td>\n",
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+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>63.136891</td>\n",
+       "      <td>22.700234</td>\n",
+       "      <td>6.889430</td>\n",
+       "      <td>2.307406</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>63.788829</td>\n",
+       "      <td>21.920976</td>\n",
+       "      <td>7.064213</td>\n",
+       "      <td>2.377510</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>64.440693</td>\n",
+       "      <td>21.141714</td>\n",
+       "      <td>7.238995</td>\n",
+       "      <td>2.447619</td>\n",
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+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>65.092487</td>\n",
+       "      <td>20.362448</td>\n",
+       "      <td>7.413776</td>\n",
+       "      <td>2.517733</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.525739</td>\n",
+       "      <td>65.744211</td>\n",
+       "      <td>19.583178</td>\n",
+       "      <td>7.588555</td>\n",
+       "      <td>2.587853</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.427040     15.740754     17.044157   \n",
+       "2            1.0    1.0   0.2       0.422349     15.524351     17.650487   \n",
+       "3            1.0    1.0   0.3       0.414849     15.232301     18.707065   \n",
+       "4            1.0    1.0   0.4       0.406761     14.861683     20.092511   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.525739     63.136891     22.700234   \n",
+       "832         10.0    2.0   3.6       0.525739     63.788829     21.920976   \n",
+       "833         10.0    2.0   3.7       0.525739     64.440693     21.141714   \n",
+       "834         10.0    2.0   3.8       0.525739     65.092487     20.362448   \n",
+       "835         10.0    2.0   3.9       0.525739     65.744211     19.583178   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.028288       0.057515  \n",
+       "2        0.026712       0.045338  \n",
+       "3        0.025136       0.032566  \n",
+       "4        0.024390       0.021919  \n",
+       "..            ...            ...  \n",
+       "831      6.889430       2.307406  \n",
+       "832      7.064213       2.377510  \n",
+       "833      7.238995       2.447619  \n",
+       "834      7.413776       2.517733  \n",
+       "835      7.588555       2.587853  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bayesian\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>composition</th>\n",
+       "      <th>trial</th>\n",
+       "      <th>time</th>\n",
+       "      <th>biomass (g/L)</th>\n",
+       "      <th>ethanol (mM)</th>\n",
+       "      <th>acetate (mM)</th>\n",
+       "      <th>butanol (mM)</th>\n",
+       "      <th>butyrate (mM)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.429529</td>\n",
+       "      <td>15.871137</td>\n",
+       "      <td>16.893540</td>\n",
+       "      <td>0.029863</td>\n",
+       "      <td>0.070178</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.1</td>\n",
+       "      <td>0.432038</td>\n",
+       "      <td>16.016624</td>\n",
+       "      <td>18.511826</td>\n",
+       "      <td>0.110446</td>\n",
+       "      <td>0.232064</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.434673</td>\n",
+       "      <td>16.192371</td>\n",
+       "      <td>20.062051</td>\n",
+       "      <td>0.199193</td>\n",
+       "      <td>0.400361</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.3</td>\n",
+       "      <td>0.437422</td>\n",
+       "      <td>16.398086</td>\n",
+       "      <td>21.541751</td>\n",
+       "      <td>0.296357</td>\n",
+       "      <td>0.574726</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.4</td>\n",
+       "      <td>0.440278</td>\n",
+       "      <td>16.633409</td>\n",
+       "      <td>22.948622</td>\n",
+       "      <td>0.402182</td>\n",
+       "      <td>0.754789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>831</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>0.600663</td>\n",
+       "      <td>47.392041</td>\n",
+       "      <td>25.945417</td>\n",
+       "      <td>6.488046</td>\n",
+       "      <td>1.974172</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>832</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>0.605871</td>\n",
+       "      <td>48.858047</td>\n",
+       "      <td>25.009363</td>\n",
+       "      <td>6.849406</td>\n",
+       "      <td>1.906514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>833</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>0.610998</td>\n",
+       "      <td>50.346140</td>\n",
+       "      <td>24.033811</td>\n",
+       "      <td>7.222358</td>\n",
+       "      <td>1.826244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>834</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.8</td>\n",
+       "      <td>0.616041</td>\n",
+       "      <td>51.855843</td>\n",
+       "      <td>23.022166</td>\n",
+       "      <td>7.606991</td>\n",
+       "      <td>1.732648</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>835</th>\n",
+       "      <td>10.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>0.621002</td>\n",
+       "      <td>53.386715</td>\n",
+       "      <td>21.978031</td>\n",
+       "      <td>8.003397</td>\n",
+       "      <td>1.625010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>836 rows × 8 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     composition  trial  time  biomass (g/L)  ethanol (mM)  acetate (mM)  \\\n",
+       "0            1.0    1.0   0.0       0.429529     15.871137     16.893540   \n",
+       "1            1.0    1.0   0.1       0.432038     16.016624     18.511826   \n",
+       "2            1.0    1.0   0.2       0.434673     16.192371     20.062051   \n",
+       "3            1.0    1.0   0.3       0.437422     16.398086     21.541751   \n",
+       "4            1.0    1.0   0.4       0.440278     16.633409     22.948622   \n",
+       "..           ...    ...   ...            ...           ...           ...   \n",
+       "831         10.0    2.0   3.5       0.600663     47.392041     25.945417   \n",
+       "832         10.0    2.0   3.6       0.605871     48.858047     25.009363   \n",
+       "833         10.0    2.0   3.7       0.610998     50.346140     24.033811   \n",
+       "834         10.0    2.0   3.8       0.616041     51.855843     23.022166   \n",
+       "835         10.0    2.0   3.9       0.621002     53.386715     21.978031   \n",
+       "\n",
+       "     butanol (mM)  butyrate (mM)  \n",
+       "0        0.029863       0.070178  \n",
+       "1        0.110446       0.232064  \n",
+       "2        0.199193       0.400361  \n",
+       "3        0.296357       0.574726  \n",
+       "4        0.402182       0.754789  \n",
+       "..            ...            ...  \n",
+       "831      6.488046       1.974172  \n",
+       "832      6.849406       1.906514  \n",
+       "833      7.222358       1.826244  \n",
+       "834      7.606991       1.732648  \n",
+       "835      8.003397       1.625010  \n",
+       "\n",
+       "[836 rows x 8 columns]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# algorithm = 'rf'\n",
+    "\n",
+    "time_step = 0.1\n",
+    "\n",
+    "outputs = ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']\n",
+    "concentration_labels = ['biomass (g/L)', 'ethanol (mM)', 'acetate (mM)', 'butanol (mM)', 'butyrate (mM)']\n",
+    "gas_labels = ['N2', 'CO', 'CO2', 'H2', 'flow rate (mL/min)']\n",
+    "\n",
+    "for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "    print(algorithm)\n",
+    "    # save output data as a list of dictionaries. Will be converted to data frame below\n",
+    "    output_data = []\n",
+    "\n",
+    "    for trial in trials:\n",
+    "        # print(trial)\n",
+    "\n",
+    "        comp_num = trial[0]\n",
+    "        trial_num = trial[1]\n",
+    "\n",
+    "        # get trial data\n",
+    "        trial_data = rates_df[(rates_df['composition'] == comp_num)  & (rates_df['trial'] == trial_num)]\n",
+    "\n",
+    "        # get max time\n",
+    "        max_time = round(max(trial_data['time']), 1)\n",
+    "\n",
+    "        # get initial concentrations and gas composition for trial\n",
+    "        initial_values = trial_data[trial_data['time'] == 0]\n",
+    "\n",
+    "        # get the row number of the initial values\n",
+    "        index = initial_values.index[0]\n",
+    "\n",
+    "        # make list of gas values\n",
+    "        gas_values = [initial_values[label][index]  for label in gas_labels]\n",
+    "\n",
+    "        # make list of inital concentration values\n",
+    "        concentration_values = [initial_values[label][index]  for label in concentration_labels]\n",
+    "\n",
+    "        # loop over all time points of each trial\n",
+    "        time = 0\n",
+    "        while (round(time, 1) <= max_time):\n",
+    "            # add current data to output_data list\n",
+    "            output_data.append({\n",
+    "              'composition': comp_num,\n",
+    "              'trial': trial_num,\n",
+    "              'time': time,\n",
+    "              'biomass (g/L)': concentration_values[0],\n",
+    "              'ethanol (mM)': concentration_values[1],\n",
+    "              'acetate (mM)': concentration_values[2],\n",
+    "              'butanol (mM)': concentration_values[3],\n",
+    "              'butyrate (mM)': concentration_values[4],\n",
+    "            })\n",
+    "\n",
+    "            # make array for ml input\n",
+    "            input_values = np.array(list(chain(concentration_values,gas_values))).reshape(1, -1)\n",
+    "\n",
+    "            # Scale values to match how the model was trained\n",
+    "            input_values = Scaler.transform(input_values)\n",
+    "\n",
+    "            # get rates for each output\n",
+    "            rates = [models[algorithm][output].predict(input_values) for output in outputs]\n",
+    "\n",
+    "            # update concentration values -- not allowing for negative concentrations\n",
+    "            concentration_values = [max(concentration + time_step * rate[0], 0) for concentration, rate in zip(concentration_values, rates)]\n",
+    "\n",
+    "            # increment time\n",
+    "            time += time_step\n",
+    "\n",
+    "\n",
+    "    prediction_df = pd.DataFrame(output_data)\n",
+    "\n",
+    "    # save data as csv\n",
+    "    filename = f'../data/rate_time_course_data/{algorithm}.csv'\n",
+    "    prediction_df.to_csv(filename, index=False)\n",
+    "\n",
+    "    display(prediction_df)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Define a function to help calculate r squared and rmse of predicted curves"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# function to take a dataframe with two trials and get average values\n",
+    "def average_of_trials(two_trial_df):\n",
+    "\n",
+    "  # get list of unique times\n",
+    "  times = []\n",
+    "  for _, row in two_trial_df.iterrows():\n",
+    "    if row.time not in times:\n",
+    "      times.append(row.time)\n",
+    "\n",
+    "  # for each time, get the average values of the rows\n",
+    "  average_values = []\n",
+    "  for time in times:\n",
+    "    rows = two_trial_df[two_trial_df['time'] == time]\n",
+    "    average_values.append(rows.mean())\n",
+    "\n",
+    "  return pd.DataFrame(average_values)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Get table of r squared values for each composition"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "r squared data\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nn_fine</th>\n",
+       "      <th>nn_coarse</th>\n",
+       "      <th>svm_rbf</th>\n",
+       "      <th>rf</th>\n",
+       "      <th>en</th>\n",
+       "      <th>lasso</th>\n",
+       "      <th>knn</th>\n",
+       "      <th>bayesian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.851</td>\n",
+       "      <td>0.910</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>0.668</td>\n",
+       "      <td>0.653</td>\n",
+       "      <td>0.839</td>\n",
+       "      <td>0.642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.753</td>\n",
+       "      <td>0.909</td>\n",
+       "      <td>0.922</td>\n",
+       "      <td>0.632</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.941</td>\n",
+       "      <td>0.929</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.617</td>\n",
+       "      <td>0.942</td>\n",
+       "      <td>0.969</td>\n",
+       "      <td>0.847</td>\n",
+       "      <td>0.925</td>\n",
+       "      <td>0.971</td>\n",
+       "      <td>0.946</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.935</td>\n",
+       "      <td>0.950</td>\n",
+       "      <td>0.867</td>\n",
+       "      <td>0.453</td>\n",
+       "      <td>0.841</td>\n",
+       "      <td>0.819</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.835</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.946</td>\n",
+       "      <td>0.992</td>\n",
+       "      <td>0.980</td>\n",
+       "      <td>0.977</td>\n",
+       "      <td>0.956</td>\n",
+       "      <td>0.974</td>\n",
+       "      <td>0.997</td>\n",
+       "      <td>0.981</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.945</td>\n",
+       "      <td>0.875</td>\n",
+       "      <td>0.868</td>\n",
+       "      <td>0.818</td>\n",
+       "      <td>0.587</td>\n",
+       "      <td>0.378</td>\n",
+       "      <td>0.944</td>\n",
+       "      <td>0.343</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.864</td>\n",
+       "      <td>0.820</td>\n",
+       "      <td>0.811</td>\n",
+       "      <td>0.894</td>\n",
+       "      <td>0.763</td>\n",
+       "      <td>0.786</td>\n",
+       "      <td>0.897</td>\n",
+       "      <td>0.809</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.861</td>\n",
+       "      <td>0.789</td>\n",
+       "      <td>0.838</td>\n",
+       "      <td>0.923</td>\n",
+       "      <td>0.892</td>\n",
+       "      <td>0.912</td>\n",
+       "      <td>0.449</td>\n",
+       "      <td>0.904</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.208</td>\n",
+       "      <td>0.085</td>\n",
+       "      <td>0.638</td>\n",
+       "      <td>0.891</td>\n",
+       "      <td>0.716</td>\n",
+       "      <td>0.469</td>\n",
+       "      <td>0.016</td>\n",
+       "      <td>0.134</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.684</td>\n",
+       "      <td>0.556</td>\n",
+       "      <td>0.873</td>\n",
+       "      <td>0.809</td>\n",
+       "      <td>0.869</td>\n",
+       "      <td>0.896</td>\n",
+       "      <td>0.958</td>\n",
+       "      <td>0.927</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    nn_fine  nn_coarse  svm_rbf     rf     en  lasso    knn  bayesian\n",
+       "1     0.851      0.910    0.820  0.942  0.668  0.653  0.839     0.642\n",
+       "2     0.753      0.909    0.922  0.632  0.873  0.925  0.941     0.929\n",
+       "3     0.896      0.617    0.942  0.969  0.847  0.925  0.971     0.946\n",
+       "4     0.935      0.950    0.867  0.453  0.841  0.819  0.897     0.835\n",
+       "5     0.946      0.992    0.980  0.977  0.956  0.974  0.997     0.981\n",
+       "6     0.945      0.875    0.868  0.818  0.587  0.378  0.944     0.343\n",
+       "7     0.864      0.820    0.811  0.894  0.763  0.786  0.897     0.809\n",
+       "8     0.861      0.789    0.838  0.923  0.892  0.912  0.449     0.904\n",
+       "9     0.208      0.085    0.638  0.891  0.716  0.469  0.016     0.134\n",
+       "10    0.684      0.556    0.873  0.809  0.869  0.896  0.958     0.927"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rmse data\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>nn_fine</th>\n",
+       "      <th>nn_coarse</th>\n",
+       "      <th>svm_rbf</th>\n",
+       "      <th>rf</th>\n",
+       "      <th>en</th>\n",
+       "      <th>lasso</th>\n",
+       "      <th>knn</th>\n",
+       "      <th>bayesian</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>6.75</td>\n",
+       "      <td>9.26</td>\n",
+       "      <td>6.31</td>\n",
+       "      <td>4.83</td>\n",
+       "      <td>9.08</td>\n",
+       "      <td>8.87</td>\n",
+       "      <td>8.32</td>\n",
+       "      <td>8.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>15.19</td>\n",
+       "      <td>8.44</td>\n",
+       "      <td>8.11</td>\n",
+       "      <td>16.67</td>\n",
+       "      <td>10.72</td>\n",
+       "      <td>7.66</td>\n",
+       "      <td>6.82</td>\n",
+       "      <td>7.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>10.99</td>\n",
+       "      <td>20.87</td>\n",
+       "      <td>7.94</td>\n",
+       "      <td>6.17</td>\n",
+       "      <td>13.62</td>\n",
+       "      <td>8.33</td>\n",
+       "      <td>5.22</td>\n",
+       "      <td>7.08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4.88</td>\n",
+       "      <td>4.30</td>\n",
+       "      <td>6.49</td>\n",
+       "      <td>17.16</td>\n",
+       "      <td>7.22</td>\n",
+       "      <td>7.45</td>\n",
+       "      <td>5.66</td>\n",
+       "      <td>7.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>23.23</td>\n",
+       "      <td>11.36</td>\n",
+       "      <td>2.61</td>\n",
+       "      <td>6.57</td>\n",
+       "      <td>7.38</td>\n",
+       "      <td>12.72</td>\n",
+       "      <td>1.23</td>\n",
+       "      <td>14.55</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>4.98</td>\n",
+       "      <td>14.96</td>\n",
+       "      <td>7.39</td>\n",
+       "      <td>20.54</td>\n",
+       "      <td>13.81</td>\n",
+       "      <td>17.05</td>\n",
+       "      <td>7.28</td>\n",
+       "      <td>17.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>7.26</td>\n",
+       "      <td>9.30</td>\n",
+       "      <td>8.91</td>\n",
+       "      <td>6.20</td>\n",
+       "      <td>9.70</td>\n",
+       "      <td>9.03</td>\n",
+       "      <td>6.14</td>\n",
+       "      <td>8.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>32.65</td>\n",
+       "      <td>14.72</td>\n",
+       "      <td>8.57</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>7.31</td>\n",
+       "      <td>10.82</td>\n",
+       "      <td>18.75</td>\n",
+       "      <td>12.77</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>26.94</td>\n",
+       "      <td>25.58</td>\n",
+       "      <td>16.15</td>\n",
+       "      <td>11.18</td>\n",
+       "      <td>15.56</td>\n",
+       "      <td>19.78</td>\n",
+       "      <td>24.59</td>\n",
+       "      <td>23.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>14.17</td>\n",
+       "      <td>14.47</td>\n",
+       "      <td>7.00</td>\n",
+       "      <td>8.17</td>\n",
+       "      <td>6.74</td>\n",
+       "      <td>6.02</td>\n",
+       "      <td>4.42</td>\n",
+       "      <td>6.07</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    nn_fine  nn_coarse  svm_rbf     rf     en  lasso    knn  bayesian\n",
+       "1      6.75       9.26     6.31   4.83   9.08   8.87   8.32      8.76\n",
+       "2     15.19       8.44     8.11  16.67  10.72   7.66   6.82      7.04\n",
+       "3     10.99      20.87     7.94   6.17  13.62   8.33   5.22      7.08\n",
+       "4      4.88       4.30     6.49  17.16   7.22   7.45   5.66      7.09\n",
+       "5     23.23      11.36     2.61   6.57   7.38  12.72   1.23     14.55\n",
+       "6      4.98      14.96     7.39  20.54  13.81  17.05   7.28     17.56\n",
+       "7      7.26       9.30     8.91   6.20   9.70   9.03   6.14      8.49\n",
+       "8     32.65      14.72     8.57   7.00   7.31  10.82  18.75     12.77\n",
+       "9     26.94      25.58    16.15  11.18  15.56  19.78  24.59     23.50\n",
+       "10    14.17      14.47     7.00   8.17   6.74   6.02   4.42      6.07"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_squared_data = {}\n",
+    "rmse_data = {}\n",
+    "for comp_num in range(1,11):\n",
+    "  r_squared_comp_data = {}\n",
+    "  rmse_comp_data = {}\n",
+    "  for algorithm in ['nn_fine', 'nn_coarse', 'svm_rbf', 'rf', 'en', 'lasso', 'knn', 'bayesian']:\n",
+    "\n",
+    "    # get the average of experimental concentrations for compositions with multiple trials\n",
+    "    experimental_df = rates_df[rates_df['composition'] == comp_num]\n",
+    "    experimental_df =  average_of_trials(experimental_df)\n",
+    "\n",
+    "    # get the average of predictions for compositions with multiple trials\n",
+    "    filename = f'../data/rate_time_course_data/{algorithm}.csv'\n",
+    "    prediction_df = pd.read_csv(filename)\n",
+    "    prediction_df = prediction_df[prediction_df['composition'] == comp_num]\n",
+    "    prediction_df =  average_of_trials(prediction_df)\n",
+    "\n",
+    "    experimental_concentrations = []\n",
+    "    predicted_concentrations = []\n",
+    "\n",
+    "    for output in ['ethanol (mM)',\t'acetate (mM)',\t'butanol (mM)',\t'butyrate (mM)']:\n",
+    "      experimental_concentrations.extend(list(experimental_df[output]))\n",
+    "      predicted_concentrations.extend(list(prediction_df[output]))\n",
+    "\n",
+    "    _, _, r_value, _, _ = scipy.stats.linregress(experimental_concentrations, predicted_concentrations)\n",
+    "    r_squared_comp_data[algorithm] = round(r_value**2, 3)\n",
+    "\n",
+    "    rmse_value = sklearn.metrics.mean_squared_error(experimental_concentrations, predicted_concentrations, squared=False)\n",
+    "    rmse_comp_data[algorithm] = round(rmse_value, 2)\n",
+    "  \n",
+    "  r_squared_data[comp_num] = r_squared_comp_data\n",
+    "  rmse_data[comp_num] = rmse_comp_data\n",
+    "\n",
+    "r_squared_df = pd.DataFrame(r_squared_data).transpose()\n",
+    "rmse_df = pd.DataFrame(rmse_data).transpose()\n",
+    "\n",
+    "print('r squared data')\n",
+    "display(r_squared_df)\n",
+    "\n",
+    "print('rmse data')\n",
+    "display(rmse_df)\n",
+    "\n",
+    "    # print(f'composition {comp_num} {algorithm} r-squared = {r_value**2}')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "b3f28f6adfd1eea15c9e00a250e9367c9752811d529c8274081719d51bde14e5"
+  },
+  "kernelspec": {
+   "display_name": "Python 3.7.9 ('venv': venv)",
+   "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": "3.7.9"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/notebooks/E_time_as_a_variable.ipynb

@@ -0,0 +1,1070 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Syngas prediction with consideration of time\n",
+    "\n",
+    "Copyright 2021-2022 Forrest Sheng Bao at ISU, and Garrett Troll and Yinjie Tang at WUSTL\n",
+    "\n",
+    "In this approach, we consider building a function \n",
+    "$f(N_2, CO, H_2, CO_2, \\text{flowrate}, \\text{time}) = (\\text{ biomass, acetate, butanol, butyrate, ethanol})$\n",
+    "where the outputs are concentration. \n",
+    "\n",
+    "The dataset has multiple trials (done by different labs under different conditions), \n",
+    "some of which for training while the remaining of which for test. \n",
+    "\n",
+    "Because the raw data is very sparse, we also tried data augmentation (curving) using polynomial fitting, \n",
+    "resulting multiple versions of the models (a model always predicts y given X, i.e., y=f(X)): \n",
+    "\n",
+    "| X (input)   | y (output) | X source | y source | note | \n",
+    "| ----------- | ---------- | -------- | ---------| ---- |\n",
+    "| concentration of gas, flow rate, time | concentration | raw | raw |  |\n",
+    "| concentration of gas, flow rate, time | concentration | curved | curved |  |\n",
+    "| concentration of gas, flow rate, time | concentration | curved | raw |   |\n",
+    "| concentration | rate | raw | raw | impossible  |\n",
+    "| concentration | rate | curved | curved | not needed  | \n",
+    "| concentration | rate | curved | raw |  impossible | \n",
+    "\n",
+    "In the concentration-to-rate cases, the function $f$ becomes: \n",
+    "$f(CO, H_2, CO_2, \\text{flowrate}, \\text{time}) = (\\Delta biomass, \\Delta acetate, \\Delta butanol, \\Delta butyrate, \\Delta ethanol)$\n",
+    "where $\\Delta = {\\partial \\text{ concentration} \\over \\partial \\text{ time}}$. \n",
+    "\n",
+    "\n",
+    "## Organization of code (key functions and variables):\n",
+    "* `complete_loop()`: the main function, training and test trial split is hard-coded (`training_test_composition_splits`) in it. \n",
+    "  * `prepare_data_one_y()`: Return the X and y as in common ML tasks from one Pandas dataframe, given the training and test trial ID (integers). The trial ID is in the dataframe. \n",
+    "  * `cv_fixed_trial_split()`: Run cross-validation to find the best hyperparameters for several types of ML regressors (NNs, SVMs, DTs, RFs, etc.). \n",
+    "    * `gen_NN_uni()`: Enumerate fully-connected layers/MLPs given maximal number of layers and maximal number of neurons at each layer. \n",
+    "      * `gen_NN_fixed_n_layers()`: Given the number of layers and maximal number of neurons per layer, produce various MLPs.\n",
+    "  * `beautiful_plot()`: Visualize the results.\n",
+    "  * `full_cfgs` and `test_cfgs`: two dicts storing hyperparameter grids for various types of ML regressors. \n",
+    "\n",
+    "The call of `complete_loop` is at the end of this notebook where the three versions are presented with different input CSV files and arguments. \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 0. Load modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import os\n",
+    "import sys\n",
+    "import sklearn.model_selection\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "import sklearn, sklearn.preprocessing, sklearn.pipeline, sklearn.model_selection\n",
+    "import sklearn.svm, sklearn.tree, sklearn.ensemble, sklearn.neural_network\n",
+    "import sklearn.linear_model, sklearn.gaussian_process, sklearn.neighbors\n",
+    "import sklearn.multioutput\n",
+    "import numpy\n",
+    "import functools, operator, itertools\n",
+    "import time\n",
+    "import json\n",
+    "import scipy.stats, scipy\n",
+    "# import scikitlearn_plus.neural_network\n",
+    "warnings.filterwarnings('ignore')\n",
+    "import typing \n",
+    "import pickle"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Defining functions needed for experiments\n",
+    "## 1.1 The function to extraction training and test data for one output/regressor from the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Shape of the training data: 836 rows by 18 columns\n",
+      "Shape of the test data: 176 rows by 13 columns\n"
+     ]
+    }
+   ],
+   "source": [
+    "def prepare_data_one_y(train_df, test_df, training_trial_indexes:typing.List[int], test_trial_indexes:typing.List[int], X_columns:typing.List[str], y_column:str, scaler_type:str, columns_to_scale:typing.List[str]):\n",
+    "    \"\"\"Prepare the X and y for one yield prediction, given the yield name, and training and test trial indexes \n",
+    "\n",
+    "    train_df, test_df: DataFrames storing X and y. We allow training data and test data to be of different sources, \n",
+    "                e.g., one curved and the other raw.\n",
+    "    \"\"\"\n",
+    "\n",
+    "    # scale for X and y \n",
+    "    if scaler_type == \"minmax\":    \n",
+    "        Scaler = sklearn.preprocessing.MinMaxScaler() \n",
+    "    elif scaler_type == \"standard\":\n",
+    "        Scaler = sklearn.preprocessing.StandardScaler() \n",
+    "    else: \n",
+    "        print (\"wrong scaler type \")\n",
+    "        exit(0) \n",
+    "\n",
+    "\n",
+    "    Scaler.fit(train_df[columns_to_scale])\n",
+    "    train_df[columns_to_scale] = Scaler.transform(train_df[columns_to_scale])\n",
+    "    test_df[columns_to_scale] = Scaler.transform(test_df[columns_to_scale])\n",
+    "\n",
+    "    # Separate training and testing \n",
+    "    training_data = train_df[train_df['composition'].isin(training_trial_indexes)]\n",
+    "    test_data = test_df[test_df['composition'].isin(test_trial_indexes)]\n",
+    "\n",
+    "    training_X, training_y = training_data[X_columns], training_data[y_column]\n",
+    "    test_X, test_y = test_data[X_columns], test_data[y_column]\n",
+    "\n",
+    "    return training_X, training_y, test_X, test_y, Scaler\n",
+    "\n",
+    "\n",
+    "# Test \n",
+    "\n",
+    "train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "print(f'Shape of the training data: {train_df.shape[0]} rows by {train_df.shape[1]} columns')\n",
+    "\n",
+    "test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "print(f'Shape of the test data: {test_df.shape[0]} rows by {test_df.shape[1]} columns')\n",
+    "\n",
+    "training_trial_indexes = [1,2,3,4,5,6,7]\n",
+    "test_trial_indexes = [8,9,10]\n",
+    "\n",
+    "y_column = 'biomass (g/L)' # 'biomass', 'ethanol', 'acetate', 'butanol', or 'butyrate'\n",
+    "X_columns = ['flow rate (mL/min)', 'H2', 'CO', 'CO2'] \n",
+    "columns_to_scale = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)', 'flow rate (mL/min)', 'H2', 'CO', 'CO2']\n",
+    "training_X, training_y, test_X, test_y, Scaler = prepare_data_one_y(train_df,  test_df, training_trial_indexes, test_trial_indexes, X_columns, y_column, \"minmax\", columns_to_scale)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "flow rate (mL/min)    0.0\n",
+       "H2                    0.2\n",
+       "CO                    0.0\n",
+       "CO2                   0.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "training_X.min()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.2 Two functions to enumerate network structures"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def gen_NN_fixed_n_layers(n_layers, n_neurons, neuron_step):\n",
+    "    \"\"\"Generate NN hidden_layer_sizes of n_layers and up to n_neurons per layer \n",
+    "    \"\"\"\n",
+    "    # print (n_layers)\n",
+    "    if n_layers == 1: \n",
+    "        return [[i] for i in range(neuron_step, n_neurons+1, neuron_step)]\n",
+    "    else:\n",
+    "        pairs =  [  (i,  tail) for tail in gen_NN_fixed_n_layers(n_layers-1, n_neurons+1, neuron_step) for i in range(neuron_step, n_neurons+1, neuron_step) ]\n",
+    "        return [[i]+ t for (i, t) in pairs]\n",
+    "\n",
+    "# print (gen_NN_fixed_n_layers(4, 10, 5))\n",
+    "\n",
+    "def gen_NN_uni(n_layers, n_neurons, layer_step, neuron_step):\n",
+    "    \"\"\"Generate hidden layers of various number of layers and number of neurons \n",
+    "    \"\"\" \n",
+    "    various_NNs = [ gen_NN_fixed_n_layers(i , n_neurons, neuron_step) for i in range(2, n_layers+1, layer_step)]\n",
+    "    return  functools.reduce(operator.add, various_NNs)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.3 The function and hyperparameter grid for grid-searching hyperparameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 102,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def cv_fixed_trial_split(training_X, training_y, test_X, test_y, model_cfgs, refit:str):\n",
+    "    \"\"\"Given training and test sets fixed on trials, do Grid search CV to tune hyperparameter and then test on test sets. \n",
+    "    \"\"\"\n",
+    "    estimator, hyperparam_grid = model_cfgs['estimator'], model_cfgs['param_grid']\n",
+    "    \n",
+    "    search = sklearn.model_selection.GridSearchCV(\n",
+    "            estimator = estimator, \n",
+    "            param_grid = hyperparam_grid, \n",
+    "            # scoring = ['neg_root_mean_squared_error'],\n",
+    "            # refit = 'neg_root_mean_squared_error',\n",
+    "            scoring = ['r2',  'neg_mean_absolute_percentage_error'],\n",
+    "            refit = refit,\n",
+    "            cv = sklearn.model_selection.ShuffleSplit(n_splits=10, test_size=0.2, random_state=0), \n",
+    "            n_jobs=15,\n",
+    "            verbose=0\n",
+    "        )\n",
+    "\n",
+    "    # run grid search\n",
+    "    search.fit(training_X, training_y)\n",
+    "\n",
+    "    # test on test set \n",
+    "    test_score = search.score(test_X, test_y)\n",
+    "    # print (\"test score %0.3f:\" % test.score)\n",
+    "\n",
+    "    test_y_hat = search.predict(test_X)\n",
+    "    \n",
+    "#     cc = scipy.stats.pearsonr(test_y_hat, test_y)[0]\n",
+    "    try:\n",
+    "        _, _, cc, _, _ = scipy.stats.linregress(test_y_hat, test_y)\n",
+    "    except ValueError: # when test_y_hat are all the same \n",
+    "        cc = 0 # no correlation at all \n",
+    "\n",
+    "    return test_score, search.best_score_, search.best_params_, cc, search.cv_results_, search.best_estimator_\n",
+    "\n",
+    "\n",
+    "test_cfgs = {\n",
+    "#     \"nn\":{\n",
+    "#         'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n",
+    "# #            Test grid\n",
+    "#         'param_grid':   {\n",
+    "#             # hidden_layer_sizes made the search space many order of magnitudes larger\n",
+    "#             'activation':         ['tanh', 'logistic', 'relu'], \n",
+    "#             'max_iter':           [400*i for i in range(1, 2)], \n",
+    "#             'learning_rate':      ['adaptive']\n",
+    "#         }\n",
+    "#     },\n",
+    "#     \"svm_rbf\":{\n",
+    "#             'estimator': sklearn.svm.SVR(kernel='rbf'),\n",
+    "# #               Test grid\n",
+    "#             'param_grid':   {\n",
+    "#                 'C':       [10**i for i in range(-1, 1)], \n",
+    "#                 'epsilon': [10**i for i in range(-1, 1)],\n",
+    "#             }\n",
+    "#     },\n",
+    "#     \"rf\":{\n",
+    "#         'estimator': sklearn.ensemble.RandomForestRegressor(),\n",
+    "# #            Test grid\n",
+    "#         'param_grid':   {\n",
+    "#             'n_estimators': [10*i for i in range(1, 2)],\n",
+    "#             'max_depth':     [2*i for i in range(1, 1+1)],\n",
+    "#         }\n",
+    "#     }, \n",
+    "#     \"gauss\":{\n",
+    "#         'estimator': sklearn.gaussian_process.GaussianProcessRegressor(),\n",
+    "# #             Full grid \n",
+    "#         'param_grid':   {\n",
+    "#             'alpha': [10**i for i in range(0, 5)], \n",
+    "#             'normalize_y': [True, False], \n",
+    "#             # 'n_restarts_optimizer': [2**i for i in range(0,5)]\n",
+    "#         }\n",
+    "#     },\n",
+    "#     \"knn\":{\n",
+    "#         'estimator': sklearn.neighbors.KNeighborsRegressor(),\n",
+    "# #             Full grid \n",
+    "#         'param_grid':   {\n",
+    "#             'n_neighbors': [i for i in range(1, 30+1,5)], \n",
+    "#             'weights': ['distance'], \n",
+    "#             'algorithm': ['ball_tree'],    \n",
+    "#         }\n",
+    "#     },\n",
+    "    \"bayesian\":{\n",
+    "        'estimator': sklearn.linear_model.BayesianRidge(),\n",
+    "        'param_grid':   {\n",
+    "            'n_iter':  [300, 500], \n",
+    "            'alpha_1': [10**i for i in range(-1, 1)], \n",
+    "            'alpha_2': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_1': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_2': [10**i for i in range(-1, 1)], \n",
+    "        }\n",
+    "    },\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "full_cfgs = {\n",
+    "    \"nn\":{\n",
+    "        'estimator': sklearn.neural_network.MLPRegressor(shuffle=True),\n",
+    "#             Full grid\n",
+    "        'param_grid':   {\n",
+    "            'hidden_layer_sizes': gen_NN_uni(4, 100, 1, 20),  \n",
+    "            'activation':         ['tanh',  'relu'], \n",
+    "            'max_iter':           [5000], \n",
+    "            'learning_rate':      ['adaptive']\n",
+    "        }                \n",
+    "    },\n",
+    "    \"svm_rbf\":{\n",
+    "            'estimator': sklearn.svm.SVR(kernel='rbf'),\n",
+    "#       Full grid\n",
+    "            'param_grid':   {\n",
+    "                'C':       [10**i for i in range(-5, 5)], \n",
+    "                'epsilon': [10**i for i in range(-5, 5)],\n",
+    "                'gamma':   [10**i for i in range(-5, 5)] # gamma gave me an error\n",
+    "            }\n",
+    "    },\n",
+    "    \"rf\":{\n",
+    "        'estimator': sklearn.ensemble.RandomForestRegressor(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'n_estimators': [10*i for i in range(1, 20)],\n",
+    "            'max_depth':     [2*i for i in range(20)], \n",
+    "            'max_samples': [0.05*i for i in range(1, 10+1)] # max samples gave me an error\n",
+    "        }\n",
+    "    }, \n",
+    "    \"en\":{\n",
+    "        'estimator': sklearn.linear_model.ElasticNet(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)],\n",
+    "            'l1_ratio': [0.1*i for i in range(1, 10+1)] \n",
+    "        }\n",
+    "    },\n",
+    "    \"lasso\":{\n",
+    "        'estimator': sklearn.linear_model.Lasso(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)]\n",
+    "        }\n",
+    "    }, \n",
+    "    \"gauss\":{\n",
+    "        'estimator': sklearn.gaussian_process.GaussianProcessRegressor(),\n",
+    "#   Full grid \n",
+    "        'param_grid':   {\n",
+    "            'alpha': [10**i for i in range(-10, 10)], \n",
+    "            'normalize_y': [True, False], \n",
+    "        }\n",
+    "    },\n",
+    "    \"knn\":{\n",
+    "        'estimator': sklearn.neighbors.KNeighborsRegressor(),\n",
+    "#             Full grid \n",
+    "        'param_grid':   {\n",
+    "            'n_neighbors': [i for i in range(1, 30+1)], \n",
+    "            'weights': ['distance'], \n",
+    "            'algorithm': ['ball_tree', 'kd_tree'], \n",
+    "            'leaf_size': [5*i for i in range(1, 10+1)]                \n",
+    "        }\n",
+    "    },\n",
+    "    \"bayesian\":{\n",
+    "        'estimator': sklearn.linear_model.BayesianRidge(),\n",
+    "        'param_grid':   {\n",
+    "            'n_iter':  [300, 500], \n",
+    "            'alpha_1': [10**i for i in range(-1, 1)], \n",
+    "            'alpha_2': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_1': [10**i for i in range(-1, 1)], \n",
+    "            'lambda_2': [10**i for i in range(-1, 1)], \n",
+    "        }\n",
+    "    },\n",
+    "    \n",
+    "}\n",
+    "\n",
+    "# Test \n",
+    "# for name, cfg in test_cfgs.items():\n",
+    "#     print (name)\n",
+    "#     _ = \\\n",
+    "#     ml_fixed_trial_split(training_X, training_y, test_X, test_y, cfg, 'neg_mean_absolute_percentage_error')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.4 The exhaustive CV loop"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def complete_loop(train_df, test_df, model_cfgs, X_columns, y_columns, result_pickle_file, model_pickle_file, refit:str):\n",
+    "    \"\"\"Complete multi-layer loops for entire ML task \n",
+    "\n",
+    "    train_df, test_df: pandas dataframes loaded from CSV files\n",
+    "\n",
+    "    model_cfgs: dict, keys as str (model type) and values as sklearn grid CV parameter dict \n",
+    "\n",
+    "    y_columns: list of str, \n",
+    "        To predict absolute value, use: ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate']\n",
+    "        To predict rate, use: ['biomass_Δ', 'ethanol_Δ', 'acetate_Δ', 'butanol_Δ', 'butyrate_Δ']\n",
+    "\n",
+    "    X_columns: list of str, \n",
+    "        e.g.,  ['flow rate', 'H2', 'CO', 'CO2'] \n",
+    "\n",
+    "    pickle_file: str\n",
+    "        where to save the results using various regressors\n",
+    "\n",
+    "    refit: str\n",
+    "        the sklearn.metrics string \n",
+    "\n",
+    "    \"\"\"\n",
+    "\n",
+    "    loo = sklearn.model_selection.LeaveOneOut()\n",
+    "    compositions = np.array(range(1, 10+1))\n",
+    "    training_test_composition_splits = [\n",
+    "        ( (1,2,3,4,5,6,7), (8,9,10) ),  # training in compositions 1 to 7 and test in compositions 8 to 10\n",
+    "    ] \\\n",
+    "    # + \\\n",
+    "    # [(compositions[train_indexes], compositions[test_indexes]) \n",
+    "    #   for train_indexes, test_indexes in \n",
+    "    #       loo.split(range(7))\n",
+    "    # ] # leave one composition as test \n",
+    "\n",
+    "    # print (training_test_composition_splits)\n",
+    "\n",
+    "    columns_to_scale = X_columns + y_columns\n",
+    "\n",
+    "    full_results = {}\n",
+    "\n",
+    "    best_models, best_score_for_ys = {}, {}\n",
+    "\n",
+    "    for y_column in y_columns:\n",
+    "        # print (y_column)\n",
+    "        for (training_compositions, test_compositions) in training_test_composition_splits: \n",
+    "            # for scaler_type in ['minmax', 'standard']:\n",
+    "            for scaler_type in ['minmax']:\n",
+    "                training_X, training_y, test_X, test_y, Scaler = \\\n",
+    "                    prepare_data_one_y(train_df, test_df, training_compositions, test_compositions, X_columns, y_column, scaler_type, columns_to_scale)\n",
+    "                for estimator_type, cfg in model_cfgs.items():\n",
+    "                # print (estimator_type)\n",
+    "                    test_score, train_score, best_params, cc, cv_results, best_estimator = \\\n",
+    "                        cv_fixed_trial_split(training_X, training_y, test_X, test_y, cfg, refit)\n",
+    "\n",
+    "                    # save result\n",
+    "                    if type(test_compositions) == numpy.ndarray: \n",
+    "                        test_compositions = tuple(test_compositions.tolist())\n",
+    "\n",
+    "                    r2_per_sk = np.mean(cv_results['mean_test_r2'])\n",
+    "\n",
+    "                    cv_results.update({ \"my_cc\":cc, \"r2_per_sk\":r2_per_sk, \"my_test_score\": test_score, \"my_train_score\": train_score})\n",
+    "                    exp_identifier = (scaler_type, y_column, test_compositions, estimator_type)\n",
+    "                    full_results[ exp_identifier] = cv_results\n",
+    "\n",
+    "                    # now print the result \n",
+    "                    line = [scaler_type, y_column, test_compositions, estimator_type, \"{0:.3f}\".format(cc), \"{0:.3f}\".format(r2_per_sk), best_params]\n",
+    "                    line = map(str, line)\n",
+    "                    print (\"\\t\".join(line))\n",
+    "\n",
+    "                    # Store/update the best estimator for each Y\n",
+    "                    if exp_identifier in best_score_for_ys: \n",
+    "                        best_score_so_far =  best_score_for_ys.get(exp_identifier, 0)\n",
+    "                        if best_score_so_far < test_score: \n",
+    "                            best_score_for_ys[exp_identifier[:-1]] = best_score_so_far\n",
+    "                            best_models[exp_identifier[:-1]] =  (best_estimator, exp_identifier[-1], Scaler)\n",
+    "                    else:  # first time \n",
+    "                        best_score_for_ys[exp_identifier] = test_score\n",
+    "                        best_score_for_ys[exp_identifier[:-1]] = test_score\n",
+    "                        best_models[exp_identifier[:-1]] =  (best_estimator, exp_identifier[-1], Scaler)\n",
+    "    \n",
+    "    # json.dump(full_results, open(\"results.json\", \"w\"), indent=2, )\n",
+    "    pickle.dump(full_results, open(result_pickle_file, \"bw\"))\n",
+    "    pickle.dump(best_models, open(model_pickle_file, \"bw\"))\n",
+    "\n",
+    "    return full_results\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##  1.5 The plot function to visualize results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def beautiful_plot(full_results, Y_names, metric, main_title, savefig_path):\n",
+    "    \"\"\"\n",
+    "\n",
+    "    metric: str, \"my_cc\" or \"r2_per_sk'\n",
+    "    \"\"\"\n",
+    "    \n",
+    "    model_name_order   = [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]\n",
+    "    product_name_order = [\"biomass (g/L)\", \"acetate (mM)\", \"ethanol (mM)\", \"butyrate (mM)\", \"butanol (mM)\"]\n",
+    "    table_column_width = 10 \n",
+    "    \n",
+    "    scaler = 'minmax'\n",
+    "    test_trial = (8,9,10)\n",
+    "    bar_width = 0.3\n",
+    "\n",
+    "    CC = {condition: CV_numbers[metric] for condition, CV_numbers in full_results.items() }\n",
+    "\n",
+    "#     from CC to R2\n",
+    "    if metric == \"my_cc\":\n",
+    "        CC = {key:value**2 for key,value in CC.items()}\n",
+    "\n",
+    "    model_names = [condition[3] for condition in full_results.keys()]\n",
+    "    model_names = list(set(model_names))\n",
+    "#     model_names.sort() \n",
+    "    model_names = [name for name in model_name_order if name in model_names]\n",
+    "\n",
+    "    table = [ \n",
+    "                [ \n",
+    "                    CC[(scaler, y, test_trial, model_name,)]\n",
+    "                    for model_name in model_names\n",
+    "                ]\n",
+    "                  for y in Y_names\n",
+    "            ]\n",
+    "\n",
+    "    Y_max = np.nanmax(np.nanmax(table))\n",
+    "\n",
+    "    \n",
+    "    figure = plt.figure(figsize=(25,4))\n",
+    "    index = np.arange(0, len(model_names))*bar_width + bar_width/2\n",
+    "    for row_number , row in enumerate(table):\n",
+    "        # print (100 + len(Y_names)*10 + row_number + 1, index, row)\n",
+    "        plt.subplot(100 + len(Y_names)*10 + row_number + 1)\n",
+    "        plt.ylim(0, Y_max + 0.1)\n",
+    "        plt.xticks(rotation=45)\n",
+    "        plt.bar(index, row, \n",
+    "            width = bar_width, tick_label=model_names, \n",
+    "            color = plt.cm.gist_ncar(np.linspace(0, 0.5, len(model_names))))\n",
+    "        plt.title(Y_names[row_number])\n",
+    "               \n",
+    "        # break \n",
+    "\n",
+    "    figure.suptitle(main_title )\n",
+    "    plt.savefig(savefig_path)\n",
+    "    \n",
+    "    # print the numbers\n",
+    "    header = [name.split()[0] for name in product_name_order ]\n",
+    "    header += [\"mean +- sd\"]\n",
+    "    header = [i.ljust(table_column_width) for i in header]\n",
+    "    header = [\" \"*table_column_width] + header\n",
+    "    print (\"\".join(header))\n",
+    "    for model_name in model_names:\n",
+    "        print (model_name.ljust(table_column_width), end=\": \")\n",
+    "        R_list = []\n",
+    "        for product in product_name_order: \n",
+    "            print (f'{CC.get((scaler, product, test_trial, model_name,), 0):<{table_column_width}.3f}', end=\"\")\n",
+    "            R_list.append(CC.get((scaler, product, test_trial, model_name,), 0))\n",
+    "        print (f'{numpy.mean(R_list):.3f}+-{numpy.std(R_list):.3f}')\n",
+    "#     numbers = {\n",
+    "#         {\n",
+    "#             product: CC[(scaler, product, test_trial, model_name,)]\n",
+    "#             for product in product_names\n",
+    "#         }\n",
+    "#         for model_name in [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]\n",
+    "#     } # keys as models and value as dict: {product: R2}\n",
+    "\n",
+    "    \n",
+    "# Test \n",
+    "# full_results = pickle.load(open(\"test.pickle\", \"br\"))\n",
+    "# beautiful_plot(full_results, \n",
+    "#     ['biomass', 'ethanol', 'acetate', 'butanol', 'butyrate'],\n",
+    "#     \"my_cc\",\n",
+    "#     \"1. Concentration to concentration\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1.6 Add a function to visulize the predicted and groundtruth time courses on test data "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_time_course(\n",
+    "    test_df, # already scaled Pandas data frame\n",
+    "    X_columns: typing.List[str], # input features\n",
+    "    y_columns: typing.List[str], # output targets, need to control the order \n",
+    "    test_compositions: typing.Tuple[int], # \n",
+    "    best_models, \n",
+    "    metric_short: str, \n",
+    "    source: str,  # source data modality\n",
+    "    target: str,  # target data modality \n",
+    "    plot_format: str\n",
+    "    ):\n",
+    "    \"\"\"\n",
+    "    best_models: dict, \n",
+    "            keys as (scaler_type:str, y_column:str, test_compositions: list[int]), \n",
+    "            values as (scikit-learn.estimator, esitmator_type: str, Scaler: scikit-learn.preprocessing.MinMaxScaler-like)\n",
+    "    \"\"\"\n",
+    "\n",
+    "    columns_to_scale = X_columns + y_columns\n",
+    "\n",
+    "    # for key, value in best_models.items():\n",
+    "    #     (scaler_type, y_column, trials) = key\n",
+    "    #     (estimator, estimator_type) = value\n",
+    "\n",
+    "    figure = plt.figure(figsize=(16,4*3*2))\n",
+    "    subplot_counter = 1\n",
+    "\n",
+    "    for column_number, y_column in enumerate(y_columns):\n",
+    "        key = (scaler_type, y_column, test_compositions)\n",
+    "        (estimator, estimator_type, Scaler) = best_models[key]\n",
+    "\n",
+    "        if scaler_type == \"minmax\":    \n",
+    "            Scaler = sklearn.preprocessing.MinMaxScaler() \n",
+    "        elif scaler_type == \"standard\":\n",
+    "            Scaler = sklearn.preprocessing.StandardScaler() \n",
+    "        else: \n",
+    "            print (\"wrong scaler type \")\n",
+    "            exit(0) \n",
+    "\n",
+    "        test_df[columns_to_scale] = Scaler.fit_transform(test_df[columns_to_scale])\n",
+    "        # FIXME: why the Scaler loaded from Pickle cannot scale data? \n",
+    "\n",
+    "        for row_number, test_composition in enumerate(test_compositions): \n",
+    "            test_data_segment = test_df[test_df['composition'] == test_composition]\n",
+    "\n",
+    "            test_data_segment = test_data_segment[test_data_segment['trial'] == 1]\n",
+    "\n",
+    "            test_X = test_data_segment[X_columns]\n",
+    "            test_y = test_data_segment[y_column]\n",
+    "            test_y_hat = estimator.predict(test_X)\n",
+    "\n",
+    "            test_time = test_data_segment['time']\n",
+    "            \n",
+    "            plt.subplot(len(Y_columns), len(test_compositions), subplot_counter)\n",
+    "            plt.plot(test_time, test_y, \"k-\")\n",
+    "            plt.plot(test_time, test_y_hat, \"r-\")\n",
+    "\n",
+    "            short_y_column = y_column.split()[0] # drop unit. \n",
+    "            plt.title(F\"{short_y_column}, {estimator_type}, composition: {test_composition}\")\n",
+    "\n",
+    "            subplot_counter += 1\n",
+    "\n",
+    "    plot_name = F\"time_course_{source}_{target}_{metric_short}\"\n",
+    "    figure.suptitle(plot_name)\n",
+    "    plt.savefig(plot_name + F\".{plot_format}\")\n",
+    "\n",
+    "\n",
+    "# test "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Experiments \n",
+    "## 2.1 When Refit goal is MAPE\n",
+    "### 2.1.1. Running the experiments"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Why Jupyter is bad here. \n",
+    "# i want variables train_df and test_df to be different for each experiment \n",
+    "# rather than being globa l\n",
+    "\n",
+    "metric = 'neg_mean_absolute_percentage_error'\n",
+    "metric_short = \"MAPE\"\n",
+    "X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)']\n",
+    "\n",
+    "for i in range(3):\n",
+    "\n",
+    "    # 1. raw concentration => raw concentration\n",
+    "    if i == 0:\n",
+    "        train_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "    elif i==1:\n",
+    "        # 2. curved concentration => curved concentration \n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "\n",
+    "    elif i==2: \n",
+    "        # 3. curved concentration => raw concentration\n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "\n",
+    "    # elif i==3:\n",
+    "    #     # 5. curved concentration => curved rate\n",
+    "    #     train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "    #     test_df = train_df\n",
+    "    #     source, target = \"curved_concentration\", \"curved_rate\"\n",
+    "    #     metric = 'neg_mean_absolute_percentage_error'\n",
+    "    #     metric_short = \"MAPE\"\n",
+    "    #     X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "    #     y_columns = ['biomass rate','ethanol rate','acetate rate','butanol rate','butyrate rate']\n",
+    "\n",
+    "    results_raw_concentration = complete_loop(\n",
+    "        train_df, test_df, \n",
+    "#         test_cfgs,  \n",
+    "        full_cfgs,\n",
+    "        X_columns , y_columns , \n",
+    "        F\"result_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        F\"model_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        metric)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.2 Visualize MAPE-based results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          biomass   acetate   ethanol   butyrate  butanol   mean +- sd\n",
+      "nn        : 0.340     0.074     0.247     0.101     0.110     0.174+-0.102\n",
+      "svm_rbf   : 0.276     0.379     0.224     0.104     0.108     0.218+-0.104\n",
+      "rf        : 0.422     0.004     0.053     0.257     0.108     0.169+-0.153\n",
+      "en        : 0.443     0.005     0.280     0.109     0.000     0.167+-0.171\n",
+      "lasso     : 0.443     0.005     0.280     0.109     0.000     0.167+-0.171\n",
+      "knn       : 0.044     0.494     0.001     0.756     0.040     0.267+-0.304\n",
+      "bayesian  : 0.443     0.004     0.278     0.110     0.107     0.188+-0.155\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1800x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_format = \"pdf\"\n",
+    "visual_metric = \"my_cc\"     # this on test set, using square of CC \n",
+    "# visual_metric = \"r2_per_sk\" # this is on validation set, R2 per Scikit-learn\n",
+    "# visual_metric = \"my_test_score\" # this is on test set, MAPE per scikit-learn\n",
+    "# visual_metric = \"my_train_score\" # this is on training set, MAPE per scikit-learn\n",
+    "\n",
+    "refit_metric = \"MAPE\"\n",
+    "Y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)', 'butyrate (mM)']\n",
+    "for i in range(1,2):\n",
+    "    if i == 0 : \n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"1. Raw concentration to raw concentration, refit w/ MAPE\"\n",
+    "    elif i == 1: \n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "        plot_title = \"2. Curved concentration to curved concentration, refit w/ MAPE\"\n",
+    "    elif i == 2:\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"3. Curved concentration to raw concentration, refit w/ MAPE\"\n",
+    "    \n",
+    "\n",
+    "    result_pickle = F\"result_{source}_to_{target}_{refit_metric}.pickle\"\n",
+    "    plot_save = F\"bar_{visual_metric}.{source}.{target}.{refit_metric}.{plot_format}\"\n",
+    "\n",
+    "    full_results = pickle.load(open(result_pickle, \"br\"))\n",
+    "    beautiful_plot(full_results, Y_columns, visual_metric, plot_title, plot_save)\n",
+    "    \n",
+    "    # print order \n",
+    "#     for model_name in [\"nn\", \"svm_rbf\", \"rf\", \"en\", \"lasso\", \"knn\", \"bayesian\"]:\n",
+    "#     for product in [\"acetate (mM)\", \"ethanol (mM)\", \"butyrate (mM)\", \"butanol (mM)\"]: "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.1.3 Plot time course"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# The plot function might be wrong. To fix. \n",
+    "\n",
+    "\n",
+    "# plot_format = \"pdf\"\n",
+    "# metric_short = \"MAPE\"\n",
+    "# source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "# model_file = F\"model_{source}_to_{target}_{metric_short}.pickle\"\n",
+    "# test_compositions = (8,9,10)\n",
+    "# scaler_type = \"minmax\"\n",
+    "\n",
+    "# best_models = pickle.load(open(model_file, \"br\"))\n",
+    "\n",
+    "# plot_time_course(\n",
+    "#     test_df, \n",
+    "#     X_columns, y_columns, test_compositions, \n",
+    "#     best_models, \n",
+    "#     metric_short, source, target, \n",
+    "#     plot_format\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2.2 When Refit goal is R2\n",
+    "\n",
+    "### 2.2.1 Running the experiments\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "metric = metric_short = \"r2\"\n",
+    "X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)','butyrate (mM)']\n",
+    "\n",
+    "for i in range(1,3):\n",
+    "\n",
+    "    # 1. raw concentration => raw concentration\n",
+    "    if i == 0:\n",
+    "        train_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "    elif i==1:\n",
+    "        # 2. curved concentration => curved concentration \n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df  = train_df\n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "\n",
+    "    elif i==2: \n",
+    "        # 3. curved concentration => raw concentration\n",
+    "        train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "        test_df = pd.read_csv(f'../data/experimental_data.csv')\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "\n",
+    "    # elif i==3:\n",
+    "    #     # 5. curved concentration => curved rate\n",
+    "    #     train_df = pd.read_csv(f'../data/rates_data.csv')\n",
+    "    #     test_df = train_df\n",
+    "    #     source, target = \"curved_concentration\", \"curved_rate\"\n",
+    "    #     metric = 'neg_mean_absolute_percentage_error'\n",
+    "    #     metric_short = \"MAPE\"\n",
+    "    #     X_columns = ['flow rate (mL/min)', 'N2', 'H2', 'CO', 'CO2']\n",
+    "    #     y_columns = ['biomass rate','ethanol rate','acetate rate','butanol rate','butyrate rate']\n",
+    "\n",
+    "    results_raw_concentration = complete_loop(\n",
+    "        train_df, test_df, \n",
+    "#         test_cfgs, \n",
+    "        full_cfgs, \n",
+    "        X_columns , y_columns , \n",
+    "        F\"result_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        F\"model_{source}_to_{target}_{metric_short}.pickle\",\n",
+    "        metric)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.2 Visualize R2-based results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          biomass   acetate   ethanol   butyrate  butanol   mean +- sd\n",
+      "nn        : 0.135     0.396     0.297     0.693     0.111     0.327+-0.211\n",
+      "svm_rbf   : 0.387     0.295     0.305     0.398     0.110     0.299+-0.103\n",
+      "rf        : 0.423     0.021     0.118     0.253     0.111     0.185+-0.140\n",
+      "en        : 0.443     0.004     0.280     0.109     0.092     0.186+-0.157\n",
+      "lasso     : 0.443     0.003     0.280     0.109     0.103     0.188+-0.156\n",
+      "knn       : 0.313     0.493     0.008     0.632     0.005     0.290+-0.253\n",
+      "bayesian  : 0.443     0.002     0.278     0.110     0.109     0.188+-0.155\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1800x288 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_format = \"pdf\"\n",
+    "visual_metric = \"my_cc\"     # this on test set, using square of CC \n",
+    "# visual_metric = \"r2_per_sk\" # this is on validation set, R2 per Scikit-learn\n",
+    "# visual_metric = \"my_test_score\" # this is on test set, R2 per scikit-learn\n",
+    "# visual_metric = \"my_train_score\" # this is on training set, R2 per scikit-learn\n",
+    "Y_columns = ['biomass (g/L)','ethanol (mM)','acetate (mM)','butanol (mM)', 'butyrate (mM)']\n",
+    "\n",
+    "refit_metric = \"r2\"\n",
+    "\n",
+    "for i in range(1,2):\n",
+    "    if i == 0 : \n",
+    "        source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"1. Raw concentration to raw concentration, refit w/ R2\"\n",
+    "    elif i == 1: \n",
+    "        source, target = \"curved_concentration\", \"curved_concentration\"\n",
+    "        plot_title = \"2. Curved concentration to curved concentration, refit w/ R2\"\n",
+    "    elif i == 2:\n",
+    "        source, target = \"curved_concentration\", \"raw_concentration\"\n",
+    "        plot_title = \"3. Curved concentration to raw concentration, refit w/ R2\"\n",
+    "    \n",
+    "\n",
+    "    result_pickle = F\"result_{source}_to_{target}_{refit_metric}.pickle\"\n",
+    "    plot_save = F\"bar_{visual_metric}.{source}.{target}.{refit_metric}.{plot_format}\"\n",
+    "\n",
+    "    full_results = pickle.load(open(result_pickle, \"br\"))\n",
+    "    beautiful_plot(full_results, Y_columns, visual_metric, plot_title, plot_save)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### 2.2.3 Plot time course comparison between our prediction and groundtruth"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The plot function might be wrong. To fix. \n",
+    "\n",
+    "# plot_format = \"pdf\"\n",
+    "# metric_short = \"r2\"\n",
+    "# source, target = \"raw_concentration\", \"raw_concentration\"\n",
+    "# model_file = F\"model_{source}_to_{target}_{metric_short}.pickle\"\n",
+    "# test_compositions = (8,9,10)\n",
+    "# scaler_type = \"minmax\"\n",
+    "\n",
+    "# best_models = pickle.load(open(model_file, \"br\"))\n",
+    "\n",
+    "# plot_time_course(\n",
+    "#     test_df, \n",
+    "#     X_columns, y_columns, test_compositions, \n",
+    "#     best_models, \n",
+    "#     metric_short, source, target, \n",
+    "#     plot_format\n",
+    "# )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys([('minmax', 'biomass (g/L)', (8, 9, 10)), ('minmax', 'ethanol (mM)', (8, 9, 10)), ('minmax', 'acetate (mM)', (8, 9, 10)), ('minmax', 'butanol (mM)', (8, 9, 10)), ('minmax', 'butyrate (mM)', (8, 9, 10))])"
+      ]
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "best_models.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
+  },
+  "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": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

data/requirements.txt

@@ -0,0 +1,15 @@
+cycler==0.10.0
+joblib==1.0.1
+kiwisolver==1.3.2
+matplotlib==3.4.3
+numpy==1.21.2
+pandas==1.3.2
+Pillow==8.3.1
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+scikit-learn==0.24.2
+scipy==1.7.1
+six==1.16.0
+sklearn==0.0
+threadpoolctl==2.2.0

data/trained_models/acetate/bayesian.pkl

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+oid sha256:8138bcbe04331dc3e45a97b6bab3cd32fed6e6daa254631e48f1dd47a747fd04
+size 1889

data/trained_models/acetate/en.pkl

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+oid sha256:c61180cda19aab19e3fc314ea6238420882140e032a2bbc464883f245cd99c8f
+size 727

data/trained_models/acetate/knn.pkl

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+oid sha256:38e10664b03b24437c2dda38ef0211923cb40731d2b15a88850e3a08a1664ecd
+size 145551

data/trained_models/acetate/lasso.pkl

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+oid sha256:c5e2199ac70a4ec59634dfa3358269c52ba3af587175085b421565e1144417bf
+size 722

data/trained_models/acetate/nn_coarse.pkl

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