From 7103856720b8aba08d020f9b1d2806765922fe35 Mon Sep 17 00:00:00 2001
From: Lauri Himanen <lauri.himanen@gmail.com>
Date: Thu, 20 Jun 2024 15:34:13 +0300
Subject: [PATCH] Update.

---
 .../getting_started/archive.json              |  33 ++-
 .../getting_started/example.py                |   3 +-
 .../getting_started/tutorial.ipynb            | 262 ------------------
 .../schema_packages/bayesian_optimization.py  |  18 +-
 4 files changed, 38 insertions(+), 278 deletions(-)
 delete mode 100644 src/nomad_bayesian_optimization/example_uploads/getting_started/tutorial.ipynb

diff --git a/src/nomad_bayesian_optimization/example_uploads/getting_started/archive.json b/src/nomad_bayesian_optimization/example_uploads/getting_started/archive.json
index e89bd35..05b008d 100644
--- a/src/nomad_bayesian_optimization/example_uploads/getting_started/archive.json
+++ b/src/nomad_bayesian_optimization/example_uploads/getting_started/archive.json
@@ -1,14 +1,14 @@
 {
   "data": {
     "m_def": "nomad_bayesian_optimization.schema_packages.bayesian_optimization.BayesianOptimization",
-    "optimization": "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",
+    "optimization": "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",
     "figures": [
       {
         "label": "Optimization results",
         "figure": {
           "data": [
             {
-              "hovertemplate": "x=%{x}<br>y=%{y}<extra></extra>",
+              "hovertemplate": "Iteration=%{x}<br>refractive_index=%{y}<extra></extra>",
               "legendgroup": "",
               "marker": {
                 "color": "#636efa",
@@ -19,11 +19,29 @@
               "orientation": "v",
               "showlegend": false,
               "x": [
-                NaN
+                1,
+                2,
+                3,
+                4,
+                5,
+                6,
+                7,
+                8,
+                9,
+                10
               ],
               "xaxis": "x",
               "y": [
-                1.802604114762339
+                2.079667473416009,
+                0.8709893157455393,
+                1.555503748783007,
+                2.1257166481210845,
+                1.3503182438353836,
+                1.659098503962656,
+                1.4384127557009232,
+                2.4284166175171773,
+                2.490676597333392,
+                2.621132760598248
               ],
               "yaxis": "y",
               "type": "scatter"
@@ -875,12 +893,7 @@
               ],
               "matches": "y",
               "showticklabels": false
-            },
-            "title": {
-              "text": "Optimization results"
-            },
-            "height": 400,
-            "width": 716
+            }
           }
         }
       }
diff --git a/src/nomad_bayesian_optimization/example_uploads/getting_started/example.py b/src/nomad_bayesian_optimization/example_uploads/getting_started/example.py
index b959073..613cd84 100644
--- a/src/nomad_bayesian_optimization/example_uploads/getting_started/example.py
+++ b/src/nomad_bayesian_optimization/example_uploads/getting_started/example.py
@@ -107,7 +107,7 @@ def get_samples(recommendations):
 
 i = 0
 result = 0
-threshold = 1
+threshold = 0.1
 while abs(refractive_index_target - result) > threshold:
     df = campaign.recommend(batch_size=1)
     print('New recommendation:')
@@ -136,6 +136,7 @@ def get_samples(recommendations):
 
 with open('archive.json', 'w') as fout:
     json.dump(archive.m_to_dict(), fout, indent=2)
+
 # print(bopt.search_space)
 # campaign_dict = campaign.to_dict()
 # recommendation = deserialize_dataframe(campaign_dict['_cached_recommendation'])
diff --git a/src/nomad_bayesian_optimization/example_uploads/getting_started/tutorial.ipynb b/src/nomad_bayesian_optimization/example_uploads/getting_started/tutorial.ipynb
deleted file mode 100644
index ca25e27..0000000
--- a/src/nomad_bayesian_optimization/example_uploads/getting_started/tutorial.ipynb
+++ /dev/null
@@ -1,262 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "\n",
-    "<div style=\"\n",
-    "    background-color: #f7f7f7;\n",
-    "    background-image: url('data:image/svg+xml;base64,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'), url('data:image/svg+xml;base64,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') ;\n",
-    "    background-position: left bottom, right top;\n",
-    "    background-repeat: no-repeat,  no-repeat;\n",
-    "    background-size: auto 60px, auto 160px;\n",
-    "    border-radius: 5px;\n",
-    "    box-shadow: 0px 3px 1px -2px rgba(0, 0, 0, 0.2), 0px 2px 2px 0px rgba(0, 0, 0, 0.14), 0px 1px 5px 0px rgba(0,0,0,.12);\">\n",
-    "\n",
-    "<h1 style=\"\n",
-    "    color: #2a4cdf;\n",
-    "    font-style: normal;\n",
-    "    font-size: 2.25rem;\n",
-    "    line-height: 1.4em;\n",
-    "    font-weight: 600;\n",
-    "    padding: 30px 200px 0px 30px;\"> \n",
-    "        Getting started with Baysian Optimization for automated workflows</h1>\n",
-    "<p style=\"font-size: 1.25em; font-style: italic; padding: 5px 200px 30px 30px;\">\n",
-    "    Lauri Himanen</p>\n",
-    "</div>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "tags": []
-   },
-   "source": [
-    "# Introduction\n",
-    "\n",
-    "This tutorial will help you get started with using Baysian Optimation for automating the sampling of experiments/simulations. The main goal is this: you want to optimize certain parameters in your data (e.g. yield, efficiency, energy), but because there are several input parameters that control the optimization target, it is hard to guide this optimization manually or through analytic reasoning. This is where Baysian Optimization can help.\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Install dependencies"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "!pip install baybe"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "jp-MarkdownHeadingCollapsed": true,
-    "tags": []
-   },
-   "source": [
-    "# Define the search space"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from baybe.parameters import (\n",
-    "    CategoricalParameter,\n",
-    "    NumericalDiscreteParameter,\n",
-    "    SubstanceParameter,\n",
-    ")\n",
-    "\n",
-    "parameters = [\n",
-    "    CategoricalParameter(\n",
-    "        name=\"Granularity\",\n",
-    "        values=[\"coarse\", \"medium\", \"fine\"],\n",
-    "        encoding=\"OHE\",  # one-hot encoding of categories\n",
-    "    ),\n",
-    "    NumericalDiscreteParameter(\n",
-    "        name=\"Pressure[bar]\",\n",
-    "        values=[1, 5, 10],\n",
-    "        tolerance=0.2,  # allows experimental inaccuracies up to 0.2 when reading values\n",
-    "    ),\n",
-    "    SubstanceParameter(\n",
-    "        name=\"Solvent\",\n",
-    "        data={\n",
-    "            \"Solvent A\": \"COC\",\n",
-    "            \"Solvent B\": \"CCC\",  # label-SMILES pairs\n",
-    "            \"Solvent C\": \"O\",\n",
-    "            \"Solvent D\": \"CS(=O)C\",\n",
-    "        },\n",
-    "        encoding=\"MORDRED\",  # chemical encoding via mordred package\n",
-    "    ),\n",
-    "]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Defining the optimization objective\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe.targets import NumericalTarget\n",
-    "from baybe.objectives import SingleTargetObjective\n",
-    "\n",
-    "target = NumericalTarget(\n",
-    "    name=\"Yield\",\n",
-    "    mode=\"MAX\",\n",
-    ")\n",
-    "objective = SingleTargetObjective(target=target)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Define acquisition strategy"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe.recommenders import (\n",
-    "    BotorchRecommender,\n",
-    "    FPSRecommender,\n",
-    "    TwoPhaseMetaRecommender,\n",
-    ")\n",
-    "\n",
-    "recommender = TwoPhaseMetaRecommender(\n",
-    "    initial_recommender=FPSRecommender(),  # farthest point sampling\n",
-    "    recommender=BotorchRecommender(),  # Bayesian model-based optimization\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# The optimization loop"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from baybe import Campaign\n",
-    "\n",
-    "campaign = Campaign(searchspace, objective, recommender)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Get initial recommendations"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "df = campaign.recommend(batch_size=3)\n",
-    "print(df)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def create_entry():\n",
-    "    \"\"\"In this function you can decide how the actual experiment/simulation is performed. There are several alternatives:\n",
-    "\t- Maybe you can control measurement devices directly through API calls\n",
-    "\t- Maybe you wait until someone manually inserts the experiment results into NOMAD\n",
-    "\t- Maybe you run the simulation in this notebook\n",
-    "\t- Maybe you run the simulation using an HPC batch system\n",
-    "\t- etc.\n",
-    "\t\"\"\"\"\n",
-    "\tpass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "plaintext"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "target = 0.1\n",
-    "result = 0\n",
-    "while(result < target):\n",
-    "    df = campaign.recommend(batch_size=1)\n",
-    "\tarchive = create_entry()\n",
-    "\tresult = archive.yield\n",
-    "\tcampaign.add_measurements(archive.yield)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/src/nomad_bayesian_optimization/schema_packages/bayesian_optimization.py b/src/nomad_bayesian_optimization/schema_packages/bayesian_optimization.py
index 0a140e8..8094007 100644
--- a/src/nomad_bayesian_optimization/schema_packages/bayesian_optimization.py
+++ b/src/nomad_bayesian_optimization/schema_packages/bayesian_optimization.py
@@ -81,13 +81,21 @@ def normalize(self, archive, logger):
 
         df = deserialize_dataframe(self.optimization)
         target_name = self.objective.target.name
-        first_line = px.scatter(x=df['FitNr'], y=df[target_name])
-        figure1 = make_subplots(rows=1, cols=2, shared_yaxes=True)
+        first_line = px.scatter(
+            df,
+            x='BatchNr',
+            y=target_name,
+            title='Optimization result',
+            labels={
+                'target_name': 'Refractive index',
+                'BatchNr': 'Iteration',
+            },
+        )
+        figure1 = make_subplots(rows=1, cols=1, shared_yaxes=True)
         figure1.add_trace(first_line.data[0], row=1, col=1)
-        figure1.update_layout(height=400, width=716, title_text='Optimization results')
-        self.figures.append(
+        self.figures = [
             PlotlyFigure(label='Optimization results', figure=figure1.to_plotly_json())
-        )
+        ]
 
 
 m_package.__init_metainfo__()