Small edits in the docs (#211)

* typo in intro.ipynb * replace Nelder-Mead with SOBO in comments * replace TSEMO with SOBO in comments * expand comments on suggest_experiments * installation: python 3.10 is supported since 0.8.7 * docs fixed typo * followup #138, variable name cannot be a keyword * docs fixed broken link * use prev_res in tsemo docs * remove unused library in example * replace library name in example
sustainable-processes · Oct 19, 2022 · c3deffc · c3deffc
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diff --git a/docs/source/experiments_benchmarks/index.rst b/docs/source/experiments_benchmarks/index.rst
@@ -6,9 +6,9 @@ The :class:`~summit.experiment.Experiment` class provides a generic way of repre
 Here, we present the already implemented benchmarks and show you how to create new onces.
 
 .. _here: ../tutorials/new_benchmarks.ipynb
-.. _tutorial: ../tutorials/tutorial.ipynb
+.. _tutorial: ../tutorials/intro.ipynb
 
 .. toctree::
     implemented_benchmarks
     experiment
-    experimental_emulator
+    experimental_emulator
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
@@ -1,7 +1,7 @@
 Installation
 ============
 
-Note that Summit only works with python 3.8 and 3.9 currently. We are waiting on pytorch to add python 3.10 support.
+Note that Summit only works with python 3.8, 3.9 and 3.10 currently.
 
 The easiest way to install Summit is using pip or a depedency manager that supports pip:
 

diff --git a/docs/source/tutorials/intro.ipynb b/docs/source/tutorials/intro.ipynb
@@ -301,7 +301,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "In the above code, we start by creating the laboratory as we did before. We then setup the random strategy using `Random`and ask it for experiments using `suggest_experiments`. We run those experiments and plot the results. \n",
+    "In the above code, we start by creating the laboratory as we did before. We then setup the random strategy using `Random` which gets used by the runner for getting new experiments to perform using `strategy.suggest_experiments` internally. We run those experiments and plot the results. \n",
     "\n",
     "The above plot is a pareto plot. Each point in the above plot represents the objective values for a different set of reaction conditons. The pareto front shows the conditions that optimize space-time yield at the cost of E-factor and vice versa. "
    ]
@@ -365,7 +365,7 @@
    "source": [
     "exp = SnarBenchmark()\n",
     "\n",
-    "# Since the Snar benchmark has two objectives and Nelder-Mead is single objective, \n",
+    "# Since the Snar benchmark has two objectives and SOBO is single objective, \n",
     "# we need a multi-to-single objective transform\n",
     "transform = MultitoSingleObjective(\n",
     "    exp.domain, expression=\"-sty/1e4+e_factor/100\", maximize=False\n",
@@ -758,7 +758,7 @@
     }
    ],
    "source": [
-    "# Load in previous TSEMO hyperparameters and internal state\n",
+    "# Load in previous SOBO hyperparameters and internal state\n",
     "strategy = SOBO.load(FOLDER / 'snar_sobo_external.json')\n",
     "\n",
     "# Load in experimental results\n",
@@ -831,10 +831,10 @@
     }
    ],
    "source": [
-    "# Get experimental suggestions from TSEMO\n",
+    "# Get experimental suggestions from SOBO\n",
     "second_experiments = strategy.suggest_experiments(1, prev_res=prev_res)\n",
     "\n",
-    "# Save TSEMO state\n",
+    "# Save SOBO state\n",
     "strategy.save(FOLDER / 'snar_sobo_external_2.json')\n",
     "\n",
     "# Save experiments to CSV\n",

diff --git a/docs/source/tutorials/new_benchmarks.ipynb b/docs/source/tutorials/new_benchmarks.ipynb
@@ -190,7 +190,7 @@
     "# Objectives\n",
     "des_5 = \"Yield\"\n",
     "domain += ContinuousVariable(\n",
-    "    name=\"yield\",\n",
+    "    name=\"yld\",\n",
     "    description=des_5,\n",
     "    bounds=[0, 100],\n",
     "    is_objective=True,\n",
@@ -226,7 +226,7 @@
     {
      "data": {
       "text/html": [
-       "<table id='domain' width=100%><tr><td><b>Name</b></td><td><b>Type</b></td><td><b>Description</b></td><td><b>Values</b></td></tr><tr><td>catalyst</td><td>categorical, input</td><td>Catalyst type - different ligands</td><td>8 levels</td></tr><tr><td>t_res</td><td>continuous, input</td><td>Residence time in seconds (s)</td><td>[60,600]</td></tr><tr><td>temperature</td><td>continuous, input</td><td>Reactor temperature in degrees Celsius (ºC)</td><td>[30,110]</td></tr><tr><td>catalyst_loading</td><td>continuous, input</td><td>Catalyst loading in mol%</td><td>[0.5,2.5]</td></tr><tr><td>yield</td><td>continuous, maximize objective</td><td>Yield</td><td>[0,100]</td></tr><tr><td>ton</td><td>continuous, maximize objective</td><td>Turnover number - moles product generated divided by moles catalyst used</td><td>[0,200]</td></tr></table>"
+       "<table id='domain' width=100%><tr><td><b>Name</b></td><td><b>Type</b></td><td><b>Description</b></td><td><b>Values</b></td></tr><tr><td>catalyst</td><td>categorical, input</td><td>Catalyst type - different ligands</td><td>8 levels</td></tr><tr><td>t_res</td><td>continuous, input</td><td>Residence time in seconds (s)</td><td>[60,600]</td></tr><tr><td>temperature</td><td>continuous, input</td><td>Reactor temperature in degrees Celsius (ºC)</td><td>[30,110]</td></tr><tr><td>catalyst_loading</td><td>continuous, input</td><td>Catalyst loading in mol%</td><td>[0.5,2.5]</td></tr><tr><td>yld</td><td>continuous, maximize objective</td><td>Yield</td><td>[0,100]</td></tr><tr><td>ton</td><td>continuous, maximize objective</td><td>Turnover number - moles product generated divided by moles catalyst used</td><td>[0,200]</td></tr></table>"
       ],
       "text/plain": [
        "<summit.domain.Domain at 0x12d073c88>"
@@ -276,7 +276,7 @@
    "source": [
     "## Train the Emulator\n",
     "\n",
-    "Now we only need two lines train the experimental emulator!  We first instantiate `ExperimentalEmulator` passing in the dataset, domain and a name for the model.  Next we train it with two-fold [cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/) and a test set size of 25%.\n",
+    "Now we only need two lines to train the experimental emulator!  We first instantiate `ExperimentalEmulator` passing in the dataset, domain and a name for the model.  Next we train it with two-fold [cross-validation](https://machinelearningmastery.com/k-fold-cross-validation/) and a test set size of 25%.\n",
     "\n",
     "This step will take some time. Change verbose to 1 if you want streaming updates of the training."
    ]
@@ -386,7 +386,7 @@
        "      <th>t_res</th>\n",
        "      <th>temperature</th>\n",
        "      <th>catalyst_loading</th>\n",
-       "      <th>yield</th>\n",
+       "      <th>yld</th>\n",
        "      <th>ton</th>\n",
        "      <th>computation_t</th>\n",
        "      <th>experiment_t</th>\n",
@@ -411,7 +411,7 @@
        "</div>"
       ],
       "text/plain": [
-       "NAME catalyst t_res temperature catalyst_loading      yield        ton  \\\n",
+       "NAME catalyst t_res temperature catalyst_loading      yld        ton  \\\n",
        "TYPE     DATA  DATA        DATA             DATA       DATA       DATA   \n",
        "0       P1-L1    60         100              1.0  30.521872  29.758484   \n",
        "\n",
@@ -491,7 +491,7 @@
        "      <th>t_res</th>\n",
        "      <th>temperature</th>\n",
        "      <th>catalyst_loading</th>\n",
-       "      <th>yield</th>\n",
+       "      <th>yld</th>\n",
        "      <th>ton</th>\n",
        "      <th>computation_t</th>\n",
        "      <th>experiment_t</th>\n",
@@ -516,7 +516,7 @@
        "</div>"
       ],
       "text/plain": [
-       "NAME catalyst t_res temperature catalyst_loading      yield        ton  \\\n",
+       "NAME catalyst t_res temperature catalyst_loading      yld        ton  \\\n",
        "TYPE     DATA  DATA        DATA             DATA       DATA       DATA   \n",
        "0       P1-L1    60         100              1.0  30.521872  29.758484   \n",
        "\n",

diff --git a/summit/strategies/random.py b/summit/strategies/random.py
@@ -132,7 +132,7 @@ class LHS(Strategy):
     Examples
     --------
     >>> from summit.domain import Domain, ContinuousVariable
-    >>> from summit.strategies import Random
+    >>> from summit.strategies import LHS
     >>> import numpy as np
     >>> domain = Domain()
     >>> domain += ContinuousVariable(name='temperature', description='reaction temperature in celsius', bounds=[50, 100])

diff --git a/summit/strategies/sobo.py b/summit/strategies/sobo.py
@@ -52,7 +52,6 @@ class SOBO(Strategy):
     --------
     >>> from summit.domain import *
     >>> from summit.strategies import SOBO
-    >>> import numpy as np
     >>> domain = Domain()
     >>> domain += ContinuousVariable(name='temperature', description='reaction temperature in celsius', bounds=[50, 100])
     >>> domain += CategoricalVariable(name='flowrate_a', description='flow of reactant a in mL/min', levels=[1,2,3,4,5])

diff --git a/summit/strategies/tsemo.py b/summit/strategies/tsemo.py
@@ -72,7 +72,7 @@ class TSEMO(Strategy):
     >>> values = {("temperature", "DATA"): 60,("flowrate_a", "DATA"): 0.5,("flowrate_b", "DATA"): 0.5,("yield_", "DATA"): 50,("de", "DATA"): 90}
     >>> previous_results = DataSet([values], columns=columns)
     >>> strategy = TSEMO(domain)
-    >>> result = strategy.suggest_experiments(5)
+    >>> result = strategy.suggest_experiments(5, prev_res=previous_results)
 
     Notes
     -----