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convert_from_path\n", "\n", "\n", - "\n", - "\n", "def pdf_to_images(pdf_path, dpi=900):\n", " \"\"\"\n", " Converts PDFs to images with an option to specify the DPI for higher quality.\n", @@ -198,7 +196,9 @@ "\n", "\n", "titles = [\"A\", \"B\", \"C\"]\n", - "display_images_in_subplot(images, _its, _rc, titles, save_path=\"./fig/Fig1_old_aam_its_rc.pdf\")" + "display_images_in_subplot(\n", + " images, _its, _rc, titles, save_path=\"./fig/Fig1_old_aam_its_rc.pdf\"\n", + ")" ] }, { diff --git a/Docs/Analysis/_2b_aam_analysis.ipynb b/Docs/Analysis/_2b_aam_analysis.ipynb index 34c7fea..6f70138 100644 --- a/Docs/Analysis/_2b_aam_analysis.ipynb +++ b/Docs/Analysis/_2b_aam_analysis.ipynb @@ -323,7 +323,9 @@ "sys.path.append(\"../../\")\n", "from syntemp.SynUtils.utils import load_database\n", "\n", - "data = load_database(\"../../Data/AAM/results_benchmark/golden/golden_aam_reactions.json.gz\")" + "data = load_database(\n", + " \"../../Data/AAM/results_benchmark/golden/golden_aam_reactions.json.gz\"\n", + ")" ] }, { @@ -582,7 +584,9 @@ "sys.path.append(\"../../\")\n", "from syntemp.SynUtils.utils import load_database\n", "\n", - "final_df = pd.DataFrame(load_database(\"../../Data/AAM/results_benchmark/aam_benchmark.json.gz\"))" + "final_df = pd.DataFrame(\n", + " load_database(\"../../Data/AAM/results_benchmark/aam_benchmark.json.gz\")\n", + ")" ] }, { diff --git a/Docs/Analysis/_3_tool_benchmark.ipynb b/Docs/Analysis/_3_tool_benchmark.ipynb index 0985e19..2de3a19 100644 --- a/Docs/Analysis/_3_tool_benchmark.ipynb +++ b/Docs/Analysis/_3_tool_benchmark.ipynb @@ -96,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -141,69 +141,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Ground Truth (%)
RXNMapper93.53
Graphormer95.10
LocalMapper100.00
\n", - "
" - ], - "text/plain": [ - " Ground Truth (%)\n", - "RXNMapper 93.53\n", - "Graphormer 95.10\n", - "LocalMapper 100.00" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "ground_data = pd.DataFrame(\n", " [\n", " {\n", - " \"RXNMapper\": round(\n", - " 100 * df_u1[\"RXNMapper_correct\"].sum() / len(df_u1), 2\n", - " ),\n", + " \"RXNMapper\": round(100 * df_u1[\"RXNMapper_correct\"].sum() / len(df_u1), 2),\n", " \"Graphormer\": round(\n", " 100 * df_u1[\"GraphMapper_correct\"].sum() / len(df_u1), 2\n", " ),\n", @@ -223,12 +168,10 @@ "metadata": {}, "outputs": [], "source": [ - "cgrtool_old = pd.DataFrame(\n", + "cgrtool_u1 = pd.DataFrame(\n", " [\n", " {\n", - " \"RXNMapper\": round(\n", - " 100 * df_u1[\"CGRTool_rxnmapper\"].sum() / len(df_u1), 2\n", - " ),\n", + " \"RXNMapper\": round(100 * df_u1[\"CGRTool_rxnmapper\"].sum() / len(df_u1), 2),\n", " \"Graphormer\": round(\n", " 100 * df_u1[\"CGRTool_graphmapper\"].sum() / len(df_u1), 2\n", " ),\n", @@ -239,8 +182,8 @@ " ]\n", ").T\n", "\n", - "cgrtool_old.rename(columns={0: \"CGRTools 1 (%)\"}, inplace=True)\n", - "cgrtool_old" + "cgrtool_u1.rename(columns={0: \"CGRTools 1 (%)\"}, inplace=True)\n", + "cgrtool_u1" ] }, { @@ -249,24 +192,22 @@ "metadata": {}, "outputs": [], "source": [ - "cgrtool_new = pd.DataFrame(\n", + "cgrtool_u2 = pd.DataFrame(\n", " [\n", " {\n", - " \"RXNMapper\": round(\n", - " 100 * df_new[\"CGRTool_rxnmapper\"].sum() / len(df_new), 2\n", - " ),\n", + " \"RXNMapper\": round(100 * df_u2[\"CGRTool_rxnmapper\"].sum() / len(df_u2), 2),\n", " \"Graphormer\": round(\n", - " 100 * df_new[\"CGRTool_graphmapper\"].sum() / len(df_new), 2\n", + " 100 * df_u2[\"CGRTool_graphmapper\"].sum() / len(df_u2), 2\n", " ),\n", " \"LocalMapper\": round(\n", - " 100 * df_new[\"CGRTool_localmapper\"].sum() / len(df_new), 2\n", + " 100 * df_u2[\"CGRTool_localmapper\"].sum() / len(df_u2), 2\n", " ),\n", " }\n", " ]\n", ").T\n", "\n", - "cgrtool_new.rename(columns={0: \"CGRTools 2 (%)\"}, inplace=True)\n", - "cgrtool_new" + "cgrtool_u2.rename(columns={0: \"CGRTools 2 (%)\"}, inplace=True)\n", + "cgrtool_u2" ] }, { @@ -275,8 +216,8 @@ "metadata": {}, "outputs": [], "source": [ - "cgr_data = pd.concat([ground_data, cgrtool_old, cgrtool_new], axis=1)\n", - "cgr_data" + "cgr_data = pd.concat([ground_data, cgrtool_u1, cgrtool_u2], axis=1)\n", + "cgr_data.rename(index={\"Graphormer\": \"GraphMapper\"}, inplace=True)" ] }, { @@ -287,54 +228,14 @@ "source": [ "from syntemp.SynAAM.aam_validator import AAMValidator\n", "\n", - "df_old = pd.read_csv(\n", - " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_old.csv\", index_col=0\n", - ")\n", - "df_new = pd.read_csv(\n", - " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_new.csv\", index_col=0\n", - ")\n", - "results_old_its = AAMValidator.validate_smiles(\n", - " data=df_old,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"ITS\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=False,\n", - ")\n", - "\n", - "\n", - "results_new_its = AAMValidator.validate_smiles(\n", - " data=df_new,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"ITS\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_old = pd.read_csv(\n", + "df_u1 = pd.read_csv(\n", " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_old.csv\", index_col=0\n", ")\n", - "df_new = pd.read_csv(\n", + "df_u2 = pd.read_csv(\n", " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_new.csv\", index_col=0\n", ")\n", - "results_old = AAMValidator.validate_smiles(\n", - " data=df_old,\n", + "syntemp_u1 = AAMValidator.validate_smiles(\n", + " data=df_u1,\n", " ground_truth_col=\"GroundTruth\",\n", " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", " check_method=\"RC\",\n", @@ -347,8 +248,8 @@ ")\n", "\n", "\n", - "results_new = AAMValidator.validate_smiles(\n", - " data=df_new,\n", + "syntemp_u2 = AAMValidator.validate_smiles(\n", + " data=df_u2,\n", " ground_truth_col=\"GroundTruth\",\n", " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", " check_method=\"RC\",\n", @@ -367,295 +268,19 @@ "metadata": {}, "outputs": [], "source": [ - "import numpy as np\n", + "temp_u1 = pd.DataFrame(syntemp_u1[0])\n", + "temp_u1.rename(columns={\"accuracy\": \"syntemp_u1\"}, inplace=True)\n", + "temp_u1.index = temp_u1[\"mapper\"]\n", "\n", - "np.sum(results_new_its[0][0][\"results\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.sum(results_new[0][0][\"results\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "np.sum(results_old[0][0][\"results\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(results_new[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(results_old[0])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(results_new_its[0][0][\"results\"]) != pd.DataFrame(\n", - " results_new[0][0][\"results\"]\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wrong_index = []\n", - "for key, value in enumerate(results_new[0][0][\"results\"]):\n", - " if value != results_new_its[0][0][\"results\"][key]:\n", - " print(value)\n", - " wrong_index.append(key)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "wrong_index" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results_new[0][0][\"results\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aam_new = pd.DataFrame(results_new[0])[[\"mapper\", \"accuracy\"]]\n", - "aam_new[\"mapper\"][1] = \"Graphormer\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aam_new = pd.DataFrame(results_new[0])[[\"mapper\", \"accuracy\"]]\n", - "aam_new[\"mapper\"][1] = \"Graphormer\"\n", - "aam_new.index = aam_new[\"mapper\"].tolist()\n", - "aam_new.drop([\"mapper\"], axis=1, inplace=True)\n", - "aam_new.rename(columns={\"accuracy\": \"SynTemp 2 (%)\"}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aam_new" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "aam_old = pd.DataFrame(results_old[0])[[\"mapper\", \"accuracy\"]]\n", - "aam_old[\"mapper\"][1] = \"Graphormer\"\n", - "aam_old.index = aam_old[\"mapper\"].tolist()\n", - "aam_old.drop([\"mapper\"], axis=1, inplace=True)\n", - "aam_old.rename(columns={\"accuracy\": \"SynTemp 1 (%)\"}, inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark = pd.concat([cgr_data, aam_old, aam_new], axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "benchmark" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### 1.2.2. EEquaam" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from syntemp.SynChemistry.balance_checker import BalanceReactionCheck\n", + "temp_u2 = pd.DataFrame(syntemp_u2[0])\n", + "temp_u2.rename(columns={\"accuracy\": \"syntemp_u2\"}, inplace=True)\n", + "temp_u2.index = temp_u2[\"mapper\"]\n", "\n", - "df_old = pd.read_csv(\n", - " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_old.csv\", index_col=0\n", - ")\n", - "df_new = pd.read_csv(\n", - " \"../../Data/AAM/cgrtool_benchmark/uspto_3k_cgrtool_new.csv\", index_col=0\n", - ")\n", - "check_balance = BalanceReactionCheck()\n", - "df_new_balance, _ = check_balance.dicts_balance_check(\n", - " df_new.to_dict(\"records\"), \"GroundTruth\"\n", + "benchmark_df = pd.concat(\n", + " [cgr_data, temp_u1[\"syntemp_u1\"], temp_u2[\"syntemp_u2\"]], axis=1\n", ")\n", "\n", - "df_old_balance, _ = check_balance.dicts_balance_check(\n", - " df_old.to_dict(\"records\"), \"GroundTruth\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results_old_aam, _ = AAMValidator.validate_smiles(\n", - " data=df_old_balance,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"RC\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=False,\n", - ")\n", - "\n", - "\n", - "results_new_aam, _ = AAMValidator.validate_smiles(\n", - " data=df_new_balance,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"RC\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=False,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'results_old_aam' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_3_tool_benchmark.ipynb Cell 36\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> 1\u001b[0m pd\u001b[39m.\u001b[39mDataFrame(results_old_aam)\n", - "\u001b[0;31mNameError\u001b[0m: name 'results_old_aam' is not defined" - ] - } - ], - "source": [ - "pd.DataFrame(results_old_aam)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(results_new_aam)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "results_old_eqquaam, _ = AAMValidator.validate_smiles(\n", - " data=df_old_balance,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"ITS\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=True,\n", - ")\n", - "\n", - "\n", - "results_new_eqquaam, _ = AAMValidator.validate_smiles(\n", - " data=df_new_balance,\n", - " ground_truth_col=\"GroundTruth\",\n", - " mapped_cols=[\"RXNMapper\", \"GraphMapper\", \"LocalMapper\"],\n", - " check_method=\"ITS\",\n", - " ignore_aromaticity=False,\n", - " n_jobs=4,\n", - " verbose=0,\n", - " ensemble=False,\n", - " strategies=[[\"rxn_mapper\", \"graphormer\", \"local_mapper\"]],\n", - " ignore_tautomers=True,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pd.DataFrame(results_new_eqquaam)" + "benchmark_df" ] }, { @@ -671,7 +296,7 @@ "metadata": {}, "outputs": [], "source": [ - "data_check = pd.DataFrame(results_new[0])" + "data_check = pd.DataFrame(syntemp_u2[0])" ] }, { @@ -681,12 +306,12 @@ "outputs": [], "source": [ "list_diff_rxn = []\n", - "for key, value in enumerate(df_new[\"RXNMapper_correct\"]):\n", + "for key, value in enumerate(df_u2[\"RXNMapper_correct\"]):\n", " if value != data_check[\"results\"][0][key]:\n", " list_diff_rxn.append(key)\n", "\n", "list_diff_graph = []\n", - "for key, value in enumerate(df_new[\"GraphMapper_correct\"]):\n", + "for key, value in enumerate(df_u2[\"GraphMapper_correct\"]):\n", " if value != data_check[\"results\"][1][key]:\n", " list_diff_graph.append(key)\n", "print(\"Differences in RXNMapper:\", list_diff_rxn)\n", @@ -705,15 +330,15 @@ "i = 192\n", "display(\n", " vis.visualize_reaction(\n", - " df_new.loc[i, \"GroundTruth\"], img_size=(1000, 300), show_atom_map=True\n", + " df_u2.loc[i, \"GroundTruth\"], img_size=(1000, 300), show_atom_map=True\n", " )\n", ")\n", "display(\n", " vis.visualize_reaction(\n", - " df_new.loc[i, \"RXNMapper\"], img_size=(1000, 300), show_atom_map=True\n", + " df_u2.loc[i, \"RXNMapper\"], img_size=(1000, 300), show_atom_map=True\n", " )\n", ")\n", - "print(df_new.loc[i, \"RXNMapper_correct\"])" + "print(df_u2.loc[i, \"RXNMapper_correct\"])" ] }, { @@ -725,162 +350,15 @@ "i = 2157\n", "display(\n", " vis.visualize_reaction(\n", - " df_new.loc[i, \"GroundTruth\"], img_size=(1000, 300), show_atom_map=True\n", + " df_u2.loc[i, \"GroundTruth\"], img_size=(1000, 300), show_atom_map=True\n", " )\n", ")\n", "display(\n", " vis.visualize_reaction(\n", - " df_new.loc[i, \"RXNMapper\"], img_size=(1000, 300), show_atom_map=True\n", + " df_u2.loc[i, \"RXNMapper\"], img_size=(1000, 300), show_atom_map=True\n", " )\n", ")\n", - "print(df_new.loc[i, \"RXNMapper_correct\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_new.loc[i, \"RXNMapper\"]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "reaction_smiles = [\n", - " df_new.loc[192, \"GroundTruth\"],\n", - " df_new.loc[192, \"RXNMapper\"],\n", - " df_new.loc[2157, \"GroundTruth\"],\n", - " df_new.loc[2157, \"RXNMapper\"],\n", - "]\n", - "subtitles = [\"A\", \"B\", \"C\", \"D\"]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1.4. Analyze difference from CGRTool" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "old_rxn = df_old[df_old[\"CGRTool_rxnmapper\"] != data_check[\"results\"][0]]\n", - "old_graph = df_old[df_old[\"CGRTool_graphmapper\"] != data_check[\"results\"][1]]\n", - "\n", - "new_rxn = df_new[df_new[\"CGRTool_rxnmapper\"] != data_check[\"results\"][0]]\n", - "new_local = df_new[df_new[\"CGRTool_localmapper\"] != data_check[\"results\"][2]]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def op_results(bool):\n", - " if bool:\n", - " return False\n", - " else:\n", - " return True" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "data_1 = old_rxn[[\"RXNMapper\", \"CGRTool_rxnmapper\", \"GroundTruth\"]]\n", - "data_1.rename(\n", - " columns={\"RXNMapper\": \"Mapped\", \"CGRTool_rxnmapper\": \"CGRTool\"}, inplace=True\n", - ")\n", - "data_1[\"SynTemp\"] = data_1[\"CGRTool\"].apply(op_results)\n", - "\n", - "\n", - "data_2 = old_graph[[\"GraphMapper\", \"CGRTool_graphmapper\", \"GroundTruth\"]]\n", - "data_2.rename(\n", - " columns={\"GraphMapper\": \"Mapped\", \"CGRTool_graphmapper\": \"CGRTool\"}, inplace=True\n", - ")\n", - "data_2[\"SynTemp\"] = data_2[\"CGRTool\"].apply(op_results)\n", - "\n", - "\n", - "data_3 = new_rxn[[\"RXNMapper\", \"CGRTool_rxnmapper\", \"GroundTruth\"]]\n", - "data_3.rename(\n", - " columns={\"RXNMapper\": \"Mapped\", \"CGRTool_rxnmapper\": \"CGRTool\"}, inplace=True\n", - ")\n", - "data_3[\"SynTemp\"] = data_3[\"CGRTool\"].apply(op_results)\n", - "\n", - "data_4 = new_local[[\"LocalMapper\", \"CGRTool_localmapper\", \"GroundTruth\"]]\n", - "data_4.rename(\n", - " columns={\"LocalMapper\": \"Mapped\", \"CGRTool_localmapper\": \"CGRTool\"}, inplace=True\n", - ")\n", - "data_4[\"SynTemp\"] = data_4[\"CGRTool\"].apply(op_results)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "all_data = pd.concat([data_1, data_2, data_3, data_4], axis=0)\n", - "all_data = all_data.drop_duplicates(subset=[\"Mapped\"])\n", - "all_data.shape" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "test = all_data.to_dict(\"records\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "save_database(test, \"../../Data/AAM/cgrtool_benchmark/cgr_diff.json.gz\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from synrbl.SynVis import save_reactions_to_pdf\n", - "\n", - "save_reactions_to_pdf(\n", - " test,\n", - " old_reaction_col=\"GroundTruth\",\n", - " new_reaction_col=\"Mapped\",\n", - " pdf_filename=\"../../Data/AAM/cgrtool_benchmark/cgr_diff.pdf\",\n", - " compare=True,\n", - " show_atom_numbers=True,\n", - " orientation=\"vertical\",\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "df_old.iloc[2157, :][\"LocalMapper\"]" + "print(df_u2.loc[i, \"RXNMapper_correct\"])" ] }, { diff --git a/Docs/Analysis/_4_templates_analysis.ipynb b/Docs/Analysis/_4_templates_analysis.ipynb index 82c8d06..bd24679 100644 --- a/Docs/Analysis/_4_templates_analysis.ipynb +++ b/Docs/Analysis/_4_templates_analysis.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -52,21 +52,14 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[313, 1577, 9798, 22248]\n", - "[311, 1552, 9699, 22104]\n" - ] - } - ], + "outputs": [], "source": [ "raw = load_from_pickle(\"../../Data/Temp/Benchmark/Raw/templates.pkl.gz\")\n", "complete = load_from_pickle(\"../../Data/Temp/Benchmark/Complete/templates.pkl.gz\")\n", + "\n", + "\n", "def calculate(data):\n", " number = []\n", " for i in range(len(data)):\n", @@ -90,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -110,38 +103,18 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/homes/biertank/tieu/Documents/Project/TACsy/SynEco/SynTemp/Docs/Analysis/_analysis/_plot_analysis.py:56: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " barplot = sns.barplot(\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", + "\n", "plt.rc(\"text\", usetex=True)\n", "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\")\n", "\n", "\n", "from _analysis._plot_analysis import plot_top_rules_with_seaborn\n", + "\n", "fig, ax = plt.subplots(figsize=(16, 10)) # Correctly create a figure and an axes object\n", "\n", "plot_top_rules_with_seaborn(temp_0, top_n=20, ax=ax) # Use the ax object correctly\n", @@ -165,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -174,18 +147,9 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Elementary': 54.34, 'Complicated': 45.66}\n", - "{'Elementary': 86.97, 'Complicated': 13.03}\n" - ] - } - ], + "outputs": [], "source": [ "print(calculate_value_percentage(temp_0, \"Reaction Type\"))\n", "print(calculate_value_percentage(data_cluster, \"Reaction Type\"))" @@ -193,18 +157,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{'Single Cyclic': 48.55, 'Combinatorial Cyclic': 40.84, 'Complex Cyclic': 4.82, 'Acyclic': 5.79}\n", - "{'Single Cyclic': 86.57, 'Combinatorial Cyclic': 11.78, 'Complex Cyclic': 1.25, 'Acyclic': 0.4}\n" - ] - } - ], + "outputs": [], "source": [ "print(calculate_value_percentage(temp_0, \"Topo Type\"))\n", "print(calculate_value_percentage(data_cluster, \"Topo Type\"))" @@ -212,18 +167,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "{1: 54.34, 2: 33.12, 3: 7.07, 4: 3.86, 5: 1.29, 6: 0.32}\n", - "{1: 86.97, 2: 11.83, 3: 0.35, 4: 0.69, 5: 0.16, 6: 0.0}\n" - ] - } - ], + "outputs": [], "source": [ "print(calculate_value_percentage(temp_0, \"Reaction Step\"))\n", "print(calculate_value_percentage(data_cluster, \"Reaction Step\"))" @@ -231,20 +177,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Acyclic {(0,): 100.0}\n", - "Single Cyclic {(4,): 72.19, (6,): 19.21, (5,): 5.96, (7,): 1.99, (8,): 0.66}\n", - "Combinatorial Cyclic {(4, 4): 24.41, (3, 3): 1.57, (4, 4, 4): 0.79, (4, 5, 5): 0.79, (3, 5): 8.66, (4, 4, 4, 7): 0.79, (4, 5): 12.6, (4, 5, 6, 6): 0.79, (4, 4, 5, 6): 3.15, (4, 5, 5, 6, 8): 0.79, (4, 6): 17.32, (4, 4, 5): 6.3, (4, 4, 4, 4, 9): 0.79, (6, 6): 1.57, (4, 5, 7): 0.79, (5, 5): 1.57, (5, 5, 6): 0.79, (6, 6, 6): 0.79, (4, 4, 6, 8): 1.57, (5, 7, 7): 0.79, (5, 5, 5): 0.79, (4, 5, 6): 0.79, (4, 4, 5, 7): 0.79, (4, 6, 8): 0.79, (4, 4, 6, 6, 8): 0.79, (4, 7): 1.57, (4, 6, 7, 9): 0.79, (4, 4, 6): 0.79, (6, 7, 7): 0.79, (5, 6, 8, 9): 0.79, (4, 5, 5, 6): 0.79, (4, 4, 4, 5, 7): 0.79, (6, 7): 0.79, (3, 3, 3): 0.79, (5, 7): 0.79, (3, 4): 0.79}\n", - "Complex Cyclic {(0, 3): 33.33, (0, 4): 46.67, (0, 4, 4): 13.33, (0, 4, 4, 4, 5, 7): 6.67}\n" - ] - } - ], + "outputs": [], "source": [ "acyl = [value for value in temp_0 if value[\"Topo Type\"] == \"Acyclic\"]\n", "single = [value for value in temp_0 if value[\"Topo Type\"] == \"Single Cyclic\"]\n", @@ -258,20 +193,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Acyclic {(0,): 100.0}\n", - "Single Cyclic {(4,): 98.4, (6,): 1.39, (5,): 0.17, (7,): 0.03, (8,): 0.0}\n", - "Combinatorial Cyclic {(4, 4): 73.12, (3, 3): 0.96, (4, 4, 4): 0.32, (4, 5, 5): 0.22, (3, 5): 12.37, (4, 4, 4, 7): 1.16, (4, 5): 1.46, (4, 5, 6, 6): 0.1, (4, 4, 5, 6): 0.67, (4, 5, 5, 6, 8): 0.2, (4, 6): 1.16, (4, 4, 7, 7): 0.91, (4, 4, 5): 0.54, (4, 4, 4, 4, 9): 0.1, (6, 6): 0.32, (4, 5, 7): 0.32, (5, 5): 0.07, (6, 7, 7): 0.05, (5, 5, 6): 0.17, (6, 6, 6): 0.07, (4, 4, 4, 5, 7): 0.12, (4, 4, 6, 8): 0.07, (4, 5, 6, 7): 1.78, (4, 4, 6): 0.64, (4, 5, 6): 0.2, (4, 4, 5, 6, 8): 0.3, (4, 4, 5, 7): 0.64, (5, 7, 7): 0.02, (4, 6, 6, 8): 0.12, (5, 5, 5): 0.02, (4, 6, 7): 0.07, (5, 5, 6, 6): 0.27, (4, 4, 4, 4, 7): 0.12, (4, 6, 8): 0.02, (4, 4, 6, 6, 8): 0.02, (4, 7): 0.12, (5, 6): 0.15, (5, 6, 7): 0.05, (4, 6, 7, 9): 0.07, (4, 4, 6, 8, 8): 0.05, (4, 4, 8, 8): 0.02, (4, 4, 5, 5, 8): 0.2, (4, 4, 5, 7, 8): 0.1, (5, 5, 7): 0.1, (5, 6, 8, 9): 0.02, (4, 4, 8, 8, 8): 0.02, (4, 4, 5, 5, 7): 0.07, (4, 5, 5, 6): 0.02, (6, 7): 0.1, (4, 4, 4, 7, 8): 0.05, (3, 3, 3): 0.05, (5, 7): 0.02, (3, 4): 0.02}\n", - "Complex Cyclic {(0, 3): 76.33, (0, 4): 22.97, (0, 4, 4): 0.46, (0, 4, 4, 4, 5, 7): 0.23}\n" - ] - } - ], + "outputs": [], "source": [ "acyl = [value for value in data_cluster if value[\"Topo Type\"] == \"Acyclic\"]\n", "single = [value for value in data_cluster if value[\"Topo Type\"] == \"Single Cyclic\"]\n", @@ -301,52 +225,42 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key, value in enumerate(temp_0):\n", - " if value['Topo Type'] == 'Acyclic':\n", - " temp_0[key]['Topo Type'] = 'Acyclic Graph'\n", - " elif value['Topo Type'] == 'Complex':\n", - " temp_0[key]['Topo Type'] = 'Hybrid Graph'" + " if value[\"Topo Type\"] == \"Acyclic\":\n", + " temp_0[key][\"Topo Type\"] = \"Acyclic Graph\"\n", + " elif value[\"Topo Type\"] == \"Complex\":\n", + " temp_0[key][\"Topo Type\"] = \"Hybrid Graph\"" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "for key, value in enumerate(data_cluster):\n", - " if value['Topo Type'] == 'Acyclic':\n", - " data_cluster[key]['Topo Type'] = 'Acyclic Graph'\n", - " elif value['Topo Type'] == 'Complex':\n", - " data_cluster[key]['Topo Type'] = 'Hybrid Graph'" + " if value[\"Topo Type\"] == \"Acyclic\":\n", + " data_cluster[key][\"Topo Type\"] = \"Acyclic Graph\"\n", + " elif value[\"Topo Type\"] == \"Complex\":\n", + " data_cluster[key][\"Topo Type\"] = \"Hybrid Graph\"" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# from _analysis._plot_analysis import create_pie_chart\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import seaborn as sns\n", "\n", + "\n", "def create_pie_chart(data, column, ax=None, title=None, color_pallet=\"pastel\"):\n", " \"\"\"\n", " Generates a pie chart for the specified column from a list of dictionaries.\n", @@ -421,7 +335,7 @@ " return ax\n", "\n", "\n", - "fig, axs = plt.subplots(2, 2, figsize=(18, 10)) \n", + "fig, axs = plt.subplots(2, 2, figsize=(18, 10))\n", "create_pie_chart(\n", " temp_0,\n", " \"Reaction Type\",\n", @@ -474,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -483,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -495,7 +409,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -511,12 +425,14 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from typing import *\n", "from matplotlib.axes import Axes\n", + "\n", + "\n", "def plot_rules_distribution(\n", " rules: Dict[str, int],\n", " rule_type: str = \"single\",\n", @@ -614,54 +530,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_2000178/3104729133.py:74: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(ax=ax, x=types_of_rules, y=percentages, palette=color_pallet)\n", - "/tmp/ipykernel_2000178/3104729133.py:81: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "/tmp/ipykernel_2000178/3104729133.py:74: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(ax=ax, x=types_of_rules, y=percentages, palette=color_pallet)\n", - "/tmp/ipykernel_2000178/3104729133.py:81: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "/tmp/ipykernel_2000178/3104729133.py:74: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(ax=ax, x=types_of_rules, y=percentages, palette=color_pallet)\n", - "/tmp/ipykernel_2000178/3104729133.py:81: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n", - "/tmp/ipykernel_2000178/3104729133.py:74: FutureWarning: \n", - "\n", - "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n", - "\n", - " sns.barplot(ax=ax, x=types_of_rules, y=percentages, palette=color_pallet)\n", - "/tmp/ipykernel_2000178/3104729133.py:81: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n", - " ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha=\"right\")\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", @@ -706,7 +577,10 @@ "\n", "plt.tight_layout()\n", "plt.savefig(\n", - " \"../../Docs/Analysis/fig/Fig9_rings_type.pdf\", dpi=600, bbox_inches=\"tight\", pad_inches=0\n", + " \"../../Docs/Analysis/fig/Fig9_rings_type.pdf\",\n", + " dpi=600,\n", + " bbox_inches=\"tight\",\n", + " pad_inches=0,\n", ")\n", "plt.show()" ] @@ -720,7 +594,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -744,20 +618,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "\n", @@ -795,7 +658,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -807,7 +670,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -819,43 +682,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2024-09-16 15:05:34,509 - INFO - Extracting ITS graph with 1 CPUs.\n", - "2024-09-16 15:05:34,530 - INFO - Combine batch data.\n", - "2024-09-16 15:05:34,531 - INFO - Processing equivalent ITS correct\n", - "2024-09-16 15:05:34,532 - INFO - Processing unequivalent ITS correct\n", - "2024-09-16 15:05:34,533 - INFO - Processing ambiguous hydrogen-ITS\n", - "2024-09-16 15:05:34,534 - INFO - Number of correct mappers: 2\n", - "2024-09-16 15:05:34,534 - INFO - Number of incorrect mappers: 0\n", - "2024-09-16 15:05:34,535 - INFO - Number of uncertain hydrogen:0\n", - "2024-09-16 15:05:34,538 - INFO - Hierarchical clustering initialized successfully.\n", - "2024-09-16 15:05:34,540 - INFO - Processing with templates\n", - "2024-09-16 15:05:34,541 - INFO - Parent level\n", - "2024-09-16 15:05:34,544 - INFO - Child level with radius 1\n", - "2024-09-16 15:05:34,544 - INFO - Child level with radius 2\n", - "2024-09-16 15:05:34,545 - INFO - Child level with radius 3\n", - "2024-09-16 15:05:34,550 - INFO - Clustering completed and data extracted.\n", - "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=4)]: Done 2 out of 2 | elapsed: 1.2s finished\n", - "2024-09-16 15:05:35,753 - INFO - Rules extracted for template at radius 0\n", - "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=4)]: Done 2 out of 2 | elapsed: 0.2s finished\n", - "2024-09-16 15:05:35,916 - INFO - Rules extracted for template at radius 1\n", - "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=4)]: Done 2 out of 2 | elapsed: 0.0s finished\n", - "2024-09-16 15:05:35,929 - INFO - Rules extracted for template at radius 2\n", - "[Parallel(n_jobs=4)]: Using backend LokyBackend with 4 concurrent workers.\n", - "[Parallel(n_jobs=4)]: Done 2 out of 2 | elapsed: 0.0s finished\n", - "2024-09-16 15:05:35,940 - INFO - Rules extracted for template at radius 3\n" - ] - } - ], + "outputs": [], "source": [ "its_correct, its_incorrect, all_uncertain_hydrogen = extract_its(\n", " data, mapper_types=[\"rsmi\"], n_jobs=1\n", @@ -871,7 +700,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -886,47 +715,16 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "rule [\n", - "\truleID \"r_{4}\"\n", - "\tlabelType \"string\"\n", - "\tleft [\n", - "\t\tedge [ source 0 target 1 label \"#\" ]\n", - "\t\tedge [ source 2 target 3 label \"-\" ]\n", - "\t\tedge [ source 4 target 5 label \"-\" ]\n", - "\t]\n", - "\tcontext [\n", - "\t\tnode [ id 0 label \"C\" ]\n", - "\t\tnode [ id 1 label \"C\" ]\n", - "\t\tnode [ id 2 label \"H\" ]\n", - "\t\tnode [ id 3 label \"H\" ]\n", - "\t\tnode [ id 4 label \"H\" ]\n", - "\t\tnode [ id 5 label \"H\" ]\n", - "\t]\n", - "\tright [\n", - "\t\tedge [ source 0 target 1 label \"-\" ]\n", - "\t\tedge [ source 0 target 2 label \"-\" ]\n", - "\t\tedge [ source 0 target 4 label \"-\" ]\n", - "\t\tedge [ source 1 target 3 label \"-\" ]\n", - "\t\tedge [ source 1 target 5 label \"-\" ]\n", - "\t]\n", - "]\n" - ] - } - ], + "outputs": [], "source": [ "print(combo[0].getGMLString())" ] }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ diff --git a/Docs/Analysis/_5_rule_application.ipynb b/Docs/Analysis/_5_rule_application.ipynb index cefa1c8..01f9d8c 100644 --- a/Docs/Analysis/_5_rule_application.ipynb +++ b/Docs/Analysis/_5_rule_application.ipynb @@ -26,53 +26,7 @@ "outputs": [], "source": [ "from typing import *\n", - "from _analysis._rule_app_analysis import load_database, coverage_rate\n", - "\n", - "\n", - "def automatic_results(\n", - " test_types: List[str],\n", - " temp_types: List[str],\n", - " predict_types: List[str],\n", - " radii: List[int],\n", - " base_path=\"../../Data/Temp/Benchmark\",\n", - ") -> Dict[str, Dict[str, Tuple[float, float, float]]]:\n", - " \"\"\"\n", - " Automatically computes coverage rates for combinations of test type, template type,\n", - " predict type, and radii. Iterates over the provided parameter lists, loads data,\n", - " and computes statistics.\n", - "\n", - " Parameters:\n", - " - test_types (List[str]): List of test types.\n", - " - temp_types (List[str]): List of template types.\n", - " - predict_types (List[str]): List of prediction types.\n", - " - radii (List[int]): List of radii values.\n", - " - base_path (str): path to data\n", - "\n", - " Returns:\n", - " - Dict[str, Dict[str, Tuple[float, float, float]]]: A dictionary where the key\n", - " is the test type and the value is another dictionary. The inner dictionary's keys are\n", - " combinations of parameters as strings, and its values are tuples with the results from\n", - " `coverage_rate` (average solutions, coverage rate, false positive rate).\n", - " \"\"\"\n", - " all_results = {}\n", - "\n", - " for test in test_types:\n", - " test_results = {}\n", - " for predict in predict_types:\n", - " predict_results = {}\n", - " for temp in temp_types:\n", - " for rad in radii:\n", - " path = f\"{base_path}/{temp}/Output/{test}/{predict}_{rad}.json.gz\"\n", - " name = f\"{temp}_{rad}\"\n", - " data = load_database(path)\n", - " if data:\n", - " predict_results[name] = coverage_rate(data)\n", - " else:\n", - " predict_results[name] = (0.0, 0.0, 0.0)\n", - " test_results[predict] = predict_results\n", - " all_results[test] = test_results\n", - "\n", - " return all_results" + "from _analysis._rule_app_analysis import load_database, coverage_rate" ] }, { @@ -86,7 +40,6 @@ "temp_types = [\"Raw\", \"Complete\", \"Hier\"]\n", "predict_types = [\"fw\", \"bw\"]\n", "radius = [0, 1, 2, 3]\n", - "# radius = [0, 1]\n", "results = automatic_results(test_types, temp_types, predict_types, radius, base_path)" ] }, @@ -161,7 +114,7 @@ "source": [ "import pandas as pd\n", "\n", - "valid = results[\"Test\"]\n", + "valid = results[\"Valid\"]\n", "valid_fw = valid[\"fw\"]\n", "valid_bw = valid[\"bw\"]\n", "fw = pd.DataFrame(valid_fw).T\n", @@ -201,7 +154,8 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib.pyplot as plt" + "import matplotlib.pyplot as plt\n", + "from typing import *" ] }, { @@ -351,7 +305,7 @@ "fig.tight_layout()\n", "fig.subplots_adjust(hspace=0.15, bottom=0.08)\n", "fig.savefig(\n", - " \"./fig/template_false_rate_compare_test.pdf\",\n", + " \"./fig/Fig10_template_false_rate_compare_test.pdf\",\n", " dpi=600,\n", " bbox_inches=\"tight\",\n", " pad_inches=0,\n", @@ -850,7 +804,12 @@ "outputs": [], "source": [ "def convert_seconds_to_hours(times_dict):\n", - " return {key: [round(value / 3600, 2) for value in values] for key, values in times_dict.items()}\n", + " return {\n", + " key: [round(value / 3600, 2) for value in values]\n", + " for key, values in times_dict.items()\n", + " }\n", + "\n", + "\n", "valid_times_compare = convert_seconds_to_hours(valid_times_compare)\n", "valid_times_compare" ] @@ -885,6 +844,8 @@ "import seaborn as sns\n", "import numpy as np\n", "import pandas as pd\n", + "\n", + "\n", "def plot_processing_times(\n", " times: Dict[str, List[float]], ax: Optional[plt.Axes] = None, title: str = \"A\"\n", ") -> None:\n", @@ -999,140 +960,13 @@ "fig, axs = plt.subplots(1, 2, figsize=(16, 8))\n", "plot_processing_times(valid_times_compare, ax=axs[0], title=\"A. Validation set\")\n", "plot_processing_times(test_times_compare, ax=axs[1], title=\"B. Test set\")\n", - "fig.savefig('../../Docs/Analysis/fig/time_process_benchmark.pdf', bbox_inches='tight', pad_inches=0, dpi=600)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "sys.path.append(\"../../\")\n", - "from _analysis._rule_app_analysis import load_results_from_json\n", - "\n", - "results = load_results_from_json(\"../../Data/Temp/Benchmark/raw_results.json\")\n", - "\n", - "valid = results[\"Valid\"]\n", - "\n", - "valid_fw = valid[\"fw\"]\n", - "valid_bw = valid[\"bw\"]\n", - "fw = pd.DataFrame(valid_fw).T\n", - "bw = pd.DataFrame(valid_bw).T\n", - "fw.rename(\n", - " columns={\n", - " 0: \"average_solution\",\n", - " # 1: r'\\mathcal(C)',\n", - " 1: \"C\",\n", - " 2: \"NR\",\n", - " },\n", - " inplace=True,\n", - ")\n", - "bw.rename(\n", - " columns={\n", - " 0: \"average_solution\",\n", - " # 1: r'\\mathcal(C)',\n", - " 1: \"C\",\n", - " 2: \"NR\",\n", - " },\n", - " inplace=True,\n", - ")\n", - "\n", - "fw[[\"Type\", \"Radii\"]] = fw.index.to_series().str.split(\"_\", expand=True)\n", - "bw[[\"Type\", \"Radii\"]] = bw.index.to_series().str.split(\"_\", expand=True)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "# Assuming you have functions like plot_roc_curves and plot_processing_times already defined\n", - "\n", - "# Set up font settings and LaTeX for plot text\n", - "plt.rc(\"text\", usetex=True)\n", - "plt.rc(\"text.latex\", preamble=r\"\\usepackage{amsmath}\") # Ensure amsmath is loaded\n", - "fontsettings = {\n", - " \"title_size\": 24,\n", - " \"label_size\": 20,\n", - " \"ticks_size\": 20,\n", - " \"annotation_size\": 16,\n", - "}\n", - "\n", - "# Create a 2x2 subplot layout\n", - "fig, axs = plt.subplots(\n", - " 2, 2, figsize=(14, 15)\n", - ") # Adjusted figure size for better layout\n", - "\n", - "# Plot time processing in the first row, spanning both columns\n", - "axs[0, 0].remove() # Remove the original first subplot in the first row\n", - "axs[0, 1].remove() # Remove the second subplot in the first row\n", - "ax_time = fig.add_subplot(2, 2, (1, 2)) # Add a new subplot that spans the first row\n", - "plot_processing_times(valid_times_compare, ax=ax_time, title=r\"A. Time Benchmarking\")\n", - "\n", - "# Plot ROC curves in the second row\n", - "legend_handles_fw = plot_roc_curves(\n", - " fw,\n", - " axs[1, 0],\n", - " selected_types=[\"Complete\", \"Refine\"],\n", - " fontsettings=fontsettings,\n", - " title=r\"B. ROC Curves Validation\",\n", - ")\n", - "legend_handles_bw = plot_roc_curves(\n", - " bw,\n", - " axs[1, 1],\n", - " selected_types=[\"Complete\", \"Refine\"],\n", - " fontsettings=fontsettings,\n", - " title=r\"C. ROC Curves Test\",\n", - ")\n", - "\n", - "# Combine legends from the ROC curves\n", - "fig.legend(\n", - " handles=legend_handles_fw,\n", - " loc=\"lower center\",\n", - " fancybox=True,\n", - " title_fontsize=fontsettings[\"label_size\"],\n", - " fontsize=fontsettings[\"annotation_size\"],\n", - " ncol=3,\n", - " bbox_to_anchor=(0.5, 0.05),\n", - " prop={\"size\": 18},\n", - ")\n", - "\n", - "# Adjust layout for better visual display\n", - "fig.tight_layout()\n", - "fig.subplots_adjust(\n", - " hspace=0.15, wspace=0.2, bottom=0.17\n", - ") # Adjust spacing to accommodate the legend\n", "fig.savefig(\n", - " \"../../Docs/Analysis/fig/time_process_rule.pdf\",\n", - " dpi=600,\n", + " \"../../Docs/Analysis/fig/Fig11_time_process_benchmark.pdf\",\n", " bbox_inches=\"tight\",\n", " pad_inches=0,\n", - ")\n", - "plt.show()" + " dpi=600,\n", + ")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/Docs/Analysis/_6_rule_comp.py b/Docs/Analysis/_6_rule_comp.py index 86e45b7..c49a839 100644 --- a/Docs/Analysis/_6_rule_comp.py +++ b/Docs/Analysis/_6_rule_comp.py @@ -199,7 +199,7 @@ ] """ -os.makedirs('out', exist_ok=True) +os.makedirs("out", exist_ok=True) for rule_var in [p_0, p_2, p_238, p_42, p_99, p_170, p_23, p_58, p_36]: ruleGMLString(rule_var) diff --git a/Docs/Analysis/_analysis/_rule_app_analysis.py b/Docs/Analysis/_analysis/_rule_app_analysis.py index 750bc8b..2be6606 100644 --- a/Docs/Analysis/_analysis/_rule_app_analysis.py +++ b/Docs/Analysis/_analysis/_rule_app_analysis.py @@ -142,7 +142,6 @@ def coverage_rate( return round(average_solutions, 2), round(coverage_rate, 2), round(average_fpr, 2) - def automatic_results( test_types: List[str], temp_types: List[str], diff --git a/Docs/Notebook/Example.ipynb b/Docs/Notebook/Example.ipynb index 7a4f6a5..0f0dd88 100644 --- a/Docs/Notebook/Example.ipynb +++ b/Docs/Notebook/Example.ipynb @@ -93,26 +93,6 @@ "fig" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis_graph = ChemicalGraphVisualizer(seed=42)\n", - "fig = vis_graph.vis_three_graph(its_graph_wrong[0][\"rxn_mapper\"])\n", - "display(fig)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis_graph.vis_three_graph(its_graph_wrong[0][\"local_mapper\"])" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -143,7 +123,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# **3. ITS Hydrogen Adjuster**\n", + "# **3. ITS Completation**\n", "\n", "Make sure ITSG be a cyclic graph" ] @@ -192,29 +172,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## 2.2. Uncertain atom mapping refinement" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from syntemp.SynITS.its_refinement import ITSRefinement\n", - "from syntemp.SynUtils.utils import load_from_pickle" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from syntemp.SynVis.chemical_graph_visualizer import ChemicalGraphVisualizer\n", - "\n", - "vis = ChemicalGraphVisualizer(seed=42)\n", - "vis.vis_three_graph(its_graph_wrong[0][\"local_mapper\"])" + "## 3.2. Ambiguous hydrogen" ] }, { @@ -223,40 +181,11 @@ "metadata": {}, "outputs": [], "source": [ - "vis.vis_three_graph(its_graph_wrong[0][\"rxn_mapper\"])" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "process_graphs = ITSRefinement.process_graphs_in_parallel(\n", - " its_graph_wrong, n_jobs=1, verbose=1\n", - ")\n", - "print(len(process_graphs))\n", + "test = \"[CH:10]=1[CH:11]=[CH:12][C:7](=[CH:8][CH:9]=1)[N:5]([OH:6])[C:3](=[O:4])[O:2][CH3:1].[Cl:16][C:14]([Cl:13])([Cl:15])[C:17]#[N:18]>>[Cl:13][C:14]([Cl:16])([Cl:15])[C:17]([NH:18][C:12]=1[C:7](=[CH:8][CH:9]=[CH:10][CH:11]=1)[NH:5][C:3]([O:2][CH3:1])=[O:4])=[O:6]\"\n", + "from syntemp.SynVis.chemical_reaction_visualizer import ChemicalReactionVisualizer\n", "\n", - "process_graphs = [\n", - " value for key, value in enumerate(process_graphs) if value is not None\n", - "]\n", - "print(len(process_graphs))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis.vis_three_graph(process_graphs[0][\"GraphRules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **4. Graph Modelling Language - MØD_rules** " + "vis = ChemicalReactionVisualizer()\n", + "vis.visualize_reaction(test, show_atom_map=True, img_size=(1000, 300))" ] }, { @@ -265,37 +194,20 @@ "metadata": {}, "outputs": [], "source": [ - "from syntemp.SynRule.rule_writing import RuleWriting\n", + "test_arbitrary = [{\"R-id\": \"C1\", \"mapper\": test}]\n", + "from syntemp.SynITS.its_extraction import ITSExtraction\n", "\n", - "results = RuleWriting.auto_extraction(\n", - " process_graph_data,\n", - " reindex=True,\n", - " save_path=None,\n", - " rule_column=\"GraphRules\",\n", - " n_jobs=1,\n", - " attributes=[\"charge\", \"isomer\"],\n", + "mapper_names = [\"mapper\"]\n", + "correct, incorrect = ITSExtraction.parallel_process_smiles(\n", + " test_arbitrary,\n", + " mapper_names=mapper_names,\n", + " check_method=\"RC\",\n", ")\n", - "print(results[0])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **5. MolToGraph**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from syntemp.SynUtils.utils import load_database, load_from_pickle\n", - "from syntemp.SynChemistry.mol_to_graph import MolToGraph\n", - "from syntemp.SynChemistry.graph_to_mol import GraphToMol\n", - "\n", - "graph_test = its_graph_rules[0][\"GraphRules\"][2]" + "react_graph, product_graph, rule_graph = (\n", + " correct[0][\"ITSGraph\"][0],\n", + " correct[0][\"ITSGraph\"][1],\n", + " correct[0][\"ITSGraph\"][2],\n", + ")" ] }, { @@ -304,19 +216,15 @@ "metadata": {}, "outputs": [], "source": [ - "from rdkit import Chem\n", + "from syntemp.SynITS.its_hadjuster import ITSHAdjuster\n", + "from syntemp.SynITS.its_construction import ITSConstruction\n", "\n", - "converter = MolToGraph()\n", - "smiles = \"[NH2:4][c:5]1[cH:6][cH:7][cH:8][c:9]2[cH:10][n:11][cH:12][cH:13][c:14]12\"\n", - "mol = Chem.MolFromSmiles(smiles)\n", - "display(mol)\n", - "graph = converter.mol_to_graph(mol)\n", + "variations = ITSHAdjuster.add_hydrogen_nodes_multiple(react_graph, product_graph)\n", + "its_list = [ITSConstruction.ITSGraph(i[0], i[1]) for i in variations]\n", "\n", - "# Display some graph details\n", - "print(\"Nodes and their attributes:\")\n", - "print(graph.nodes(data=True))\n", - "print(\"\\nEdges and their attributes:\")\n", - "print(graph.edges(data=True))" + "group_1, group_2 = variations[0] + (its_list[0],), variations[1] + (its_list[1],)\n", + "rules_1 = RuleExtraction.extract_reaction_rules(*group_1, extend=False, n_knn=1)\n", + "rules_2 = RuleExtraction.extract_reaction_rules(*group_2, extend=False, n_knn=1)" ] }, { @@ -327,8 +235,10 @@ "source": [ "from syntemp.SynVis.chemical_graph_visualizer import ChemicalGraphVisualizer\n", "\n", - "vis = ChemicalGraphVisualizer(seed=42)\n", - "vis.graph_vis(graph)" + "vis_graph = ChemicalGraphVisualizer(seed=42)\n", + "vis_graph.vis_three_graph(\n", + " rules_1, left_graph_title=\"L\", right_graph_title=\"R\", k_graph_title=\"K\"\n", + ")" ] }, { @@ -337,25 +247,16 @@ "metadata": {}, "outputs": [], "source": [ - "node_attributes = {\n", - " \"element\": \"element\",\n", - " \"charge\": \"charge\",\n", - " \"atom_atom_map\": \"atom_atom_map\",\n", - "}\n", - "edge_attributes = {\"order\": \"order\"}\n", - "converter = GraphToMol(node_attributes, edge_attributes)\n", - "\n", - "# Convert graph to RDKit Mol\n", - "mol = converter.graph_to_mol(graph)\n", - "display(mol)\n", - "print(Chem.MolToSmiles(mol))" + "vis_graph.vis_three_graph(\n", + " rules_2, left_graph_title=\"L\", right_graph_title=\"R\", k_graph_title=\"K\"\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# **6. Graph Rules alignment**" + "# **4. Rule Clustering**" ] }, { @@ -381,46 +282,17 @@ "metadata": {}, "outputs": [], "source": [ - "node_label_names = [\"element\", \"aromatic\", \"charge\"]\n", + "from syntemp.SynRule.hierarchical_clustering import HierarchicalClustering\n", + "\n", "node_label_names = [\"element\", \"charge\"]\n", - "naive_cluster = RuleCluster(\n", + "hier_cluster = HierarchicalClustering(\n", " node_label_names=node_label_names,\n", - " node_label_default=[\"*\", False, 0],\n", + " node_label_default=[\"*\", 0],\n", " edge_attribute=\"order\",\n", ")\n", "\n", - "its_graph_rules_cluster = naive_cluster.process_rules_clustering(\n", - " process_graph_data, rule_column=\"GraphRules\"\n", - ")\n", - "naive = [\n", - " {\"R-id\": d[\"R-id\"], \"naive_cluster\": d[\"naive_cluster\"]}\n", - " for d in its_graph_rules_cluster\n", - "]\n", - "r_id = [d[\"R-id\"] for d in naive]\n", - "its_graph_rules_cluster[0]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import pandas as pd\n", - "\n", - "pd.DataFrame(naive)[\"naive_cluster\"].value_counts()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from SynTemp.SynUtils.utils import stratified_random_sample\n", - "\n", - "sampled_data = stratified_random_sample(\n", - " its_graph_rules_cluster, property_key=\"naive_cluster\", samples_per_class=1, seed=42\n", + "reaction_dicts, templates, hier_templates = hier_cluster.fit(\n", + " process_graph_data, \"ITSGraph\"\n", ")" ] }, @@ -430,75 +302,15 @@ "metadata": {}, "outputs": [], "source": [ - "vis.vis_three_graph(sampled_data[13][\"GraphRules\"])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# **7. Unbalance reaction**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "import sys\n", - "\n", - "sys.path.append(\"../../\")\n", - "from SynTemp.SynUtils.utils import load_database\n", - "import pandas as pd\n", - "\n", - "unb = load_database(\"../../Data/AAM/natcomm/natcomm_aam_reactions.json.gz\")\n", - "\n", - "mapper_name = [\"graphormer\", \"local_mapper\", \"rxn_mapper\"]\n", - "\n", - "from SynTemp.SynITS.its_extraction import ITSExtraction\n", - "\n", - "correct, incorrect = ITSExtraction.parallel_process_smiles(\n", - " unb, mapper_name, n_jobs=4, threshold=2\n", - ")\n", - "\n", - "len(correct), len(incorrect)" + "for i in range(len(templates)):\n", + " print(f\"Number of templates within radii {i}\", len(templates[i]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "# **8. Arbitrary Hydrogen**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from rdkit import Chem\n", - "\n", - "test = \"[CH:10]=1[CH:11]=[CH:12][C:7](=[CH:8][CH:9]=1)[N:5]([OH:6])[C:3](=[O:4])[O:2][CH3:1].[Cl:16][C:14]([Cl:13])([Cl:15])[C:17]#[N:18]>>[Cl:13][C:14]([Cl:16])([Cl:15])[C:17]([NH:18][C:12]=1[C:7](=[CH:8][CH:9]=[CH:10][CH:11]=1)[NH:5][C:3]([O:2][CH3:1])=[O:4])=[O:6]\"\n", - "from SynTemp.SynVis.chemical_reaction_visualizer import ChemicalReactionVisualizer\n", - "\n", - "vis = ChemicalReactionVisualizer()\n", - "vis.visualize_reaction(test, show_atom_map=True, img_size=(1000, 300))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from SynTemp.SynVis.its_visualizer import ITSVisualizer\n", - "from IPython.display import Image\n", - "\n", - "its_vis = ITSVisualizer(test)\n", - "img_sample = Image(its_vis.draw_product_with_modified_bonds(showAtomMaps=True))\n", - "img_sample" + "# **5. Graph Modelling Language - MØD_rules** " ] }, { @@ -507,68 +319,18 @@ "metadata": {}, "outputs": [], "source": [ - "test_arbitrary = [{\"R-id\": \"C1\", \"mapper\": test}]\n", - "from SynTemp.SynITS.its_extraction import ITSExtraction\n", + "from syntemp.SynRule.rule_writing import RuleWriting\n", "\n", - "mapper_names = [\"mapper\"]\n", - "correct, incorrect = ITSExtraction.parallel_process_smiles(\n", - " test_arbitrary, mapper_names=mapper_names, check_method=\"RC\", threshold=0\n", + "results = RuleWriting.auto_extraction(\n", + " process_graph_data,\n", + " reindex=True,\n", + " save_path=None,\n", + " rule_column=\"GraphRules\",\n", + " n_jobs=1,\n", + " attributes=[\"charge\", \"isomer\"],\n", ")\n", - "react_graph, product_graph, rule_graph = (\n", - " correct[0][\"ITSGraph\"][0],\n", - " correct[0][\"ITSGraph\"][1],\n", - " correct[0][\"ITSGraph\"][2],\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from SynTemp.SynITS.its_hadjuster import ITSHAdjuster\n", - "from SynTemp.SynITS.its_construction import ITSConstruction\n", - "\n", - "variations = ITSHAdjuster.add_hydrogen_nodes_multiple(react_graph, product_graph)\n", - "its_list = [ITSConstruction.ITSGraph(i[0], i[1]) for i in variations]\n", - "\n", - "group_1, group_2 = variations[0] + (its_list[0],), variations[1] + (its_list[1],)\n", - "rules_1 = RuleExtraction.extract_reaction_rules(*group_1, extend=False, n_knn=1)\n", - "rules_2 = RuleExtraction.extract_reaction_rules(*group_2, extend=False, n_knn=1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "from SynTemp.SynVis.chemical_graph_visualizer import ChemicalGraphVisualizer\n", - "\n", - "vis_graph = ChemicalGraphVisualizer(seed=42)\n", - "vis_graph.vis_three_graph(\n", - " rules_1, left_graph_title=\"L\", right_graph_title=\"R\", k_graph_title=\"K\"\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "vis_graph.vis_three_graph(\n", - " rules_2, left_graph_title=\"L\", right_graph_title=\"R\", k_graph_title=\"K\"\n", - ")" + "print(results[0])" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": {