diff --git a/molkrr/ml_chemical_space.bkr b/molkrr/ml_chemical_space.bkr index e3b104a19a0b4ef3c75c376d9d0ac3f56020dd09..22f760334771802d0511607f3e651c8fec03de72 100644 --- a/molkrr/ml_chemical_space.bkr +++ b/molkrr/ml_chemical_space.bkr @@ -60,8 +60,8 @@ "state": {}, "selectedType": "Hidden", "pluginName": "IPython", - "shellId": "FF83B45A05414363B50AF93DBCBCDAF5", - "elapsedTime": 339, + "shellId": "C074E3DBE84B47A7807B5EAD0AE3C347", + "elapsedTime": 409, "height": 81, "hidden": true }, @@ -417,7 +417,7 @@ " {", " var in_tag = document.getElementById(\"e_ctrl\");", " var select = document.getElementById(\"e_desc_s\");", - " var inner_html_select =\"<select id=\\\"e_desc\\\"> <option value=\\\"1\\\">HOMO-LUMO gap LDA</option><option value=\\\"2\\\">HOMO-LUMO gap PBE0</option></select>\"", + " var inner_html_select =\"<select id=\\\"e_desc\\\"> <option value=\\\"1\\\">HOMO-LUMO gap LDA</option><option value=\\\"2\\\">HOMO-LUMO gap PBE0</option><option value=\\\"3\\\">None</option></select>\"", " var inner_html_chkbox = \"<input value=\\\"0\\\"type=\\\"checkbox\\\" id=\\\"e_desc\\\" onclick=\\\"hide_e_sigma(id);\\\"> <label>HOMO-LUMO gap</label>\"", " if (value == 3)", " {", @@ -536,11 +536,11 @@ "result": { "type": "BeakerDisplay", "innertype": "Html", - "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n #hidden-settings-div{\n display:none;\n }\n</style>\n<script>\n function validate(evt, value, id) \n {\n var theEvent = evt || window.event;\n var key = theEvent.keyCode || theEvent.which;\n var learn_set;\n var test_set;\n if ((key < 48 || key > 57) && !(key == 8 || key == 9 || key == 13 || \n key == 37 || key == 39 || key == 46 || \n key == 44))\n {\n theEvent.returnValue = false;\n if (theEvent.preventDefault) \n theEvent.preventDefault();\n }\n if (value.indexOf(',')!=-1 && key == 44)\n {\n theEvent.returnValue = false;\n if (theEvent.preventDefault) \n theEvent.preventDefault();\n }\n if (id == \"test_set_size\")\n {\n learn_set = document.getElementById(\"training_set_size\");\n test_set = document.getElementById(id);\n }\n if (id == \"test_set_size\" \n && (key >= 48 && key <= 57))\n {\n var new_key_value = key - 48;\n var val = parseInt(value.concat(new_key_value.toString()));\n var ls_value = parseInt(learn_set.value);\n }\n };\n function check_e_descriptor(event, value)\n {\n var s_desc = document.getElementById(\"s_desc\");\n if (s_desc.value == \"3\" && value == \"1\")\n {\n window.confirm(\"HOMO-LUMO gap is not a strong descriptor by itself\");\n s_desc.value = \"1\";\n }\n };\n function check_s_descriptor(event, value)\n {\n if (value == \"3\")\n {\n document.getElementById(\"s_sigma\").disabled = true;\n }\n else\n {\n document.getElementById(\"s_sigma\").disabled = false;\n }\n };\n function hide_e_sigma(id)\n {\n if ( document.getElementById(id).checked)\n {\n document.getElementById('e_sigma').disabled = false;\n }\n else\n {\n document.getElementById('e_sigma').disabled = true;\n }\n };\n function showCtrl(id, value)\n {\n var in_tag = document.getElementById(\"e_ctrl\");\n var select = document.getElementById(\"e_desc_s\");\n var inner_html_select =\"<select id=\\\"e_desc\\\"> <option value=\\\"1\\\">HOMO-LUMO gap LDA</option><option value=\\\"2\\\">HOMO-LUMO gap PBE0</option></select>\"\n var inner_html_chkbox = \"<input value=\\\"0\\\"type=\\\"checkbox\\\" id=\\\"e_desc\\\" onclick=\\\"hide_e_sigma(id);\\\"> <label>HOMO-LUMO gap</label>\"\n if (value == 3)\n {\n in_tag.innerHTML = inner_html_select;\n document.getElementById('e_sigma').disabled = false;\n }\n else\n {\n in_tag.innerHTML = inner_html_chkbox;\n document.getElementById('e_sigma').disabled = true;\n }\n }\n</script>\n<div style=\"display: block;\" class=\"ml_data\" id=\"hidden-settings-div\">\n <form id=\"features_select\">\n <div class=\"row\">\n <p class=\"selection_descriptors\"><b>ML algorithm settings: </b></p>\n <div class=\"row\">\n <!--<label class =\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Data base type: </i> <select id=\"db_type\">\n <option value=\"1\">8 CONF</option>\n </select></label>-->\n \n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"Number of molecules used to train the algorithm\"> <i>Training set size: </i> </span>\n <input id=\"training_set_size\" value=\"500\" min=\"1\" onkeypress=\"validate(event, value, id)\">\n <!--<i>Training set size: </i> <select id=\"training_set_size\">\n <option value=\"0\">1k</option>\n <option value=\"1\">2k</option>\n <option value=\"2\">5k</option>\n <option value=\"3\">10k</option>\n <option value=\"4\">15k</option>\n </select>--></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"Number of molecules for which to test predictions\"><i>Control set size: </i></span>\n <input id=\"test_set_size\" value=\"100\" min=\"1\" onkeypress=\"validate(event, value, id)\">\n <!-- <i>Control set size: </i> <select id=\"test_set_size\">\n <option value=\"0\">1%</option>\n <option value=\"1\">2%</option>\n <option value=\"2\">10%</option>\n <option value=\"3\">20%</option>\n <option value=\"4\">50%</option>\n </select> --></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"Target theory for the machine learning. In the case that 'Delta ML' is selected, the prediction will be the gap between PBE0 and LDA energies. On this particular case you can select using 'Homo-Lumo gap' from LDA or PBE0.\">\n <i>Theory level to predict: </i></span><select id=\"theory_type\" onchange=\"showCtrl(id, value);\">\n <!--<option value=\"1\">LDA</option>-->\n <option value=\"2\">PBE0</option>\n <option value=\"3\">Delta ML</option>\n </select></label>\n </div>\n <div class=\"row\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"Method for calculating distances between two descriptors. L1 is the sum of absolute values of the differences between descriptors. L2 is the square root of the sum of the squared differences.\"><i>Norm: </i> </span><select id=\"ml_norm\">\n <option value=\"1\">L1</option>\n <option value=\"2\">L2</option>\n </select></label> \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"The learning mechanism stores information as a matrix K based on the distances between descriptors. If descriptors are very similar, the calculation will be badly conditioned. λ is added to the diagonal to improve the condition number, but a large value of λ will reduce the accuracy of the calculation.\"><i>Regularization λ:</i></span> <input id=\"ganma\" value=\"0.02\" min=\"0.02\" onkeypress=\"validate(event, value, id)\"> </label> \n <label id=\"theory_type\" value=\"2\" display=\"none\"></label> <label id=\"method\" value=\"2\" display=\"none\"></label>\n <!-- <label class =\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Method of prediction: </i> <select id=\"method\">\n <option value=\"1\">Prunig</option>\n <option value=\"2\">Full learning set</option>\n </select></label>--> \n </div>\n </div> <!-- End of row-->\n <div class=\"row\">\n <p class=\"selection_descriptors\"><b>Descriptors:</b></p>\n <div class=\"row\"> \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"The structural descriptor attempts to capture the geometric similarity of two molecules as a single number. The structural descriptor does not describe excitation energies well, but structurally similar molecules are likely to have similar excitation energies.\"><i>Structural descriptor: </i> </span> <select id=\"s_desc\" onchange=\"check_s_descriptor(event, value)\">\n <option value=\"1\">Sorted Coulomb matrix</option>\n <option value=\"2\">Diagonalized Coulomb matrix</option>\n <option value=\"3\">None</option>\n </select></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\" title=\"σ value\"> \n <span title=\"Distances between Coulomb matrices are divided by this number. Distances are on the order of 1000 (in the units of the internal representation); hence reasonable normalizations are on the order of 1000. When using two descriptors at the same time, they should be normalized to similar values to achieve a good combination.\"><i>Structural normalization (σ): </i></span>\n <input id=\"s_sigma\" value=\"1000\" min=\"1\" onkeypress=\"validate(event, value, id)\"> </label>\n </div>\n <div class=\"row\"> \n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"The HOMO-LUMO gap is the energy difference between highest occupied state and lowest unoccupied state in the ground-state calculation. This is a reasonable approximation of the excitation energy, and hence a good descriptor for predicting excitation energies. However it is only a single number and therefore cannot distinguish well between two different molecules that have (close to) the same HOMO-LUMO gap without the help of another descriptor.\"><i>Electronic descriptor: </i></span>\n <i id=\"e_ctrl\"><select id=\"e_desc\"> <option value=\"1\">HOMO-LUMO gap LDA</option><option value=\"2\">HOMO-LUMO gap PBE0</option></select></i></label>\n <!--\n <i>Electronic Descriptor: </i> <select id=\"e_desc\" onchange='check_e_descriptor(event, value)'>\n <option value=\"1\">HOMO-LUMO gap</option>\n <option value=\"2\">Density of State</option>\n <option value=\"3\">None</option>\n </select>-->\n <label class=\"col-xs-4 col-md-4 col-lg-3\" title=\"σ value\"> \n <span title=\"Distances between electronic descriptors are divided by this number. Distances are typically between 1 to 10 eV; hence a reasonable normalization is on the order of 1 to 10.\"> <i>Electronic normalization (σ): </i> </span>\n <input disabled=\"\" id=\"e_sigma\" value=\"1\" min=\"1\" onkeypress=\"validate(event, value, id)\"> </label>\n </div> \n </div> <!-- End of row -->\n <div class=\"row\">\n <p class=\"ml_selection_descriptors\"><b>Feature to predict:</b></p>\n <div class=\"row\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Excitation energy (eV)</i>\n <!--<select id=\"proper\">\n <option value=\"1\">Excitation energy (eV)</option>\n <option value=\"2\">Oscillations Strength</option>\n </select>--></label> \n <!--<input id=\"range\" value=\"1\" display=\"none\"> --> \n <!-- <i>How many values: </i><input id=\"range\" value=\"1\" min=\"1\" max=\"2\" onkeypress='validate(event, value, id)' > -->\n </div>\n </div> <!-- End of row -->\n </form>\n</div>" + "object": "<script>\nvar beaker = bkHelper.getBeakerObject().beakerObj;\n</script>\n<style type=\"text/css\">\n #hidden-settings-div{\n display:none;\n }\n</style>\n<script>\n function validate(evt, value, id) \n {\n var theEvent = evt || window.event;\n var key = theEvent.keyCode || theEvent.which;\n var learn_set;\n var test_set;\n if ((key < 48 || key > 57) && !(key == 8 || key == 9 || key == 13 || \n key == 37 || key == 39 || key == 46 || \n key == 44))\n {\n theEvent.returnValue = false;\n if (theEvent.preventDefault) \n theEvent.preventDefault();\n }\n if (value.indexOf(',')!=-1 && key == 44)\n {\n theEvent.returnValue = false;\n if (theEvent.preventDefault) \n theEvent.preventDefault();\n }\n if (id == \"test_set_size\")\n {\n learn_set = document.getElementById(\"training_set_size\");\n test_set = document.getElementById(id);\n }\n if (id == \"test_set_size\" \n && (key >= 48 && key <= 57))\n {\n var new_key_value = key - 48;\n var val = parseInt(value.concat(new_key_value.toString()));\n var ls_value = parseInt(learn_set.value);\n }\n };\n function check_e_descriptor(event, value)\n {\n var s_desc = document.getElementById(\"s_desc\");\n if (s_desc.value == \"3\" && value == \"1\")\n {\n window.confirm(\"HOMO-LUMO gap is not a strong descriptor by itself\");\n s_desc.value = \"1\";\n }\n };\n function check_s_descriptor(event, value)\n {\n if (value == \"3\")\n {\n document.getElementById(\"s_sigma\").disabled = true;\n }\n else\n {\n document.getElementById(\"s_sigma\").disabled = false;\n }\n };\n function hide_e_sigma(id)\n {\n if ( document.getElementById(id).checked)\n {\n document.getElementById('e_sigma').disabled = false;\n }\n else\n {\n document.getElementById('e_sigma').disabled = true;\n }\n };\n function showCtrl(id, value)\n {\n var in_tag = document.getElementById(\"e_ctrl\");\n var select = document.getElementById(\"e_desc_s\");\n var inner_html_select =\"<select id=\\\"e_desc\\\"> <option value=\\\"1\\\">HOMO-LUMO gap LDA</option><option value=\\\"2\\\">HOMO-LUMO gap PBE0</option><option value=\\\"3\\\">None</option></select>\"\n var inner_html_chkbox = \"<input value=\\\"0\\\"type=\\\"checkbox\\\" id=\\\"e_desc\\\" onclick=\\\"hide_e_sigma(id);\\\"> <label>HOMO-LUMO gap</label>\"\n if (value == 3)\n {\n in_tag.innerHTML = inner_html_select;\n document.getElementById('e_sigma').disabled = false;\n }\n else\n {\n in_tag.innerHTML = inner_html_chkbox;\n document.getElementById('e_sigma').disabled = true;\n }\n }\n</script>\n<div class=\"ml_data\" id=\"hidden-settings-div\" style=\"display: block;\">\n <form id=\"features_select\">\n <div class=\"row\">\n <p class=\"selection_descriptors\"><b>ML algorithm settings: </b></p>\n <div class=\"row\">\n <!--<label class =\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Data base type: </i> <select id=\"db_type\">\n <option value=\"1\">8 CONF</option>\n </select></label>-->\n \n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"Number of molecules used to train the algorithm\"> <i>Training set size: </i> </span>\n <input id=\"training_set_size\" value=\"500\" min=\"1\" onkeypress=\"validate(event, value, id)\">\n <!--<i>Training set size: </i> <select id=\"training_set_size\">\n <option value=\"0\">1k</option>\n <option value=\"1\">2k</option>\n <option value=\"2\">5k</option>\n <option value=\"3\">10k</option>\n <option value=\"4\">15k</option>\n </select>--></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"Number of molecules for which to test predictions\"><i>Control set size: </i></span>\n <input id=\"test_set_size\" value=\"100\" min=\"1\" onkeypress=\"validate(event, value, id)\">\n <!-- <i>Control set size: </i> <select id=\"test_set_size\">\n <option value=\"0\">1%</option>\n <option value=\"1\">2%</option>\n <option value=\"2\">10%</option>\n <option value=\"3\">20%</option>\n <option value=\"4\">50%</option>\n </select> --></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"Target theory for the machine learning. In the case that 'Delta ML' is selected, the prediction will be the gap between PBE0 and LDA energies. On this particular case you can select using 'Homo-Lumo gap' from LDA or PBE0.\">\n <i>Theory level to predict: </i></span><select id=\"theory_type\" onchange=\"showCtrl(id, value);\">\n <!--<option value=\"1\">LDA</option>-->\n <option value=\"2\">PBE0</option>\n <option value=\"3\">Delta ML</option>\n </select></label>\n </div>\n <div class=\"row\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"Method for calculating distances between two descriptors. L1 is the sum of absolute values of the differences between descriptors. L2 is the square root of the sum of the squared differences.\"><i>Norm: </i> </span><select id=\"ml_norm\">\n <option value=\"1\">L1</option>\n <option value=\"2\">L2</option>\n </select></label> \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"The learning mechanism stores information as a matrix K based on the distances between descriptors. If descriptors are very similar, the calculation will be badly conditioned. λ is added to the diagonal to improve the condition number, but a large value of λ will reduce the accuracy of the calculation.\"><i>Regularization λ:</i></span> <input id=\"ganma\" value=\"0.02\" min=\"0.02\" onkeypress=\"validate(event, value, id)\"> </label> \n <label id=\"theory_type\" value=\"2\" display=\"none\"></label> <label id=\"method\" value=\"2\" display=\"none\"></label>\n <!-- <label class =\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Method of prediction: </i> <select id=\"method\">\n <option value=\"1\">Prunig</option>\n <option value=\"2\">Full learning set</option>\n </select></label>--> \n </div>\n </div> <!-- End of row-->\n <div class=\"row\">\n <p class=\"selection_descriptors\"><b>Descriptors:</b></p>\n <div class=\"row\"> \n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <span title=\"The structural descriptor attempts to capture the geometric similarity of two molecules as a single number. The structural descriptor does not describe excitation energies well, but structurally similar molecules are likely to have similar excitation energies.\"><i>Structural descriptor: </i> </span> <select id=\"s_desc\" onchange=\"check_s_descriptor(event, value)\">\n <option value=\"1\">Sorted Coulomb matrix</option>\n <option value=\"2\">Diagonalized Coulomb matrix</option>\n <option value=\"3\">None</option>\n </select></label>\n <label class=\"col-xs-4 col-md-4 col-lg-3\" title=\"σ value\"> \n <span title=\"Distances between Coulomb matrices are divided by this number. Distances are on the order of 1000 (in the units of the internal representation); hence reasonable normalizations are on the order of 1000. When using two descriptors at the same time, they should be normalized to similar values to achieve a good combination.\"><i>Structural normalization (σ): </i></span>\n <input id=\"s_sigma\" value=\"1000\" min=\"1\" onkeypress=\"validate(event, value, id)\"> </label>\n </div>\n <div class=\"row\"> \n <label class=\"col-xs-4 col-md-4 col-lg-3\">\n <span title=\"The HOMO-LUMO gap is the energy difference between highest occupied state and lowest unoccupied state in the ground-state calculation. This is a reasonable approximation of the excitation energy, and hence a good descriptor for predicting excitation energies. However it is only a single number and therefore cannot distinguish well between two different molecules that have (close to) the same HOMO-LUMO gap without the help of another descriptor.\"><i>Electronic descriptor: </i></span>\n <i id=\"e_ctrl\"><input value=\"0\" id=\"e_desc\" onclick=\"hide_e_sigma(id);\" type=\"checkbox\"> <label>HOMO-LUMO gap</label></i></label>\n <!--\n <i>Electronic Descriptor: </i> <select id=\"e_desc\" onchange='check_e_descriptor(event, value)'>\n <option value=\"1\">HOMO-LUMO gap</option>\n <option value=\"2\">Density of State</option>\n <option value=\"3\">None</option>\n </select>-->\n <label class=\"col-xs-4 col-md-4 col-lg-3\" title=\"σ value\"> \n <span title=\"Distances between electronic descriptors are divided by this number. Distances are typically between 1 to 10 eV; hence a reasonable normalization is on the order of 1 to 10.\"> <i>Electronic normalization (σ): </i> </span>\n <input id=\"e_sigma\" value=\"1\" min=\"1\" onkeypress=\"validate(event, value, id)\" disabled=\"\"> </label>\n </div> \n </div> <!-- End of row -->\n <div class=\"row\">\n <p class=\"ml_selection_descriptors\"><b>Feature to predict:</b></p>\n <div class=\"row\">\n <label class=\"col-xs-4 col-md-4 col-lg-3\"> \n <i>Excitation energy (eV)</i>\n <!--<select id=\"proper\">\n <option value=\"1\">Excitation energy (eV)</option>\n <option value=\"2\">Oscillations Strength</option>\n </select>--></label> \n <!--<input id=\"range\" value=\"1\" display=\"none\"> --> \n <!-- <i>How many values: </i><input id=\"range\" value=\"1\" min=\"1\" max=\"2\" onkeypress='validate(event, value, id)' > -->\n </div>\n </div> <!-- End of row -->\n </form>\n</div>" }, "selectedType": "BeakerDisplay", "elapsedTime": 0, - "height": 272 + "height": 281 }, "evaluatorReader": true, "tags": "settings", @@ -556,7 +556,7 @@ "import ml_chemical_space as ml", "reload(ml)", "import ml_chemical_space as ml", - "#print beaker.param", + "# print beaker.param", "errs, ref_values, prediction , outfile_path = ml.beaker_entry(beaker.param)", "# print errs, ref_values, prediction , outfile_path", "beaker.errs = np.reshape(errs,(len(errs)))", @@ -578,8 +578,8 @@ "state": {}, "selectedType": "Results", "pluginName": "IPython", - "shellId": "FF83B45A05414363B50AF93DBCBCDAF5", - "elapsedTime": 4890, + "shellId": "C074E3DBE84B47A7807B5EAD0AE3C347", + "elapsedTime": 5069, "height": 33, "dataresult": [ "java.lang.NullPointerException: Cannot invoke method getAt() on null object<br/>", @@ -688,8 +688,8 @@ "state": {}, "selectedType": "Plot", "pluginName": "Groovy", - "shellId": "80f35708-5a70-424e-b4a7-d4ea9797e7b3", - "elapsedTime": 1551, + "shellId": "8ff09230-a17c-4b8e-8457-606ae940b56a", + "elapsedTime": 1797, "height": 502 }, "evaluatorReader": true, @@ -734,7 +734,7 @@ ], [ "e_desc", - 1 + 2 ], [ "e_sigma", @@ -764,106 +764,106 @@ ] }, "errs": [ - 0.06417580351249708, - 0.047772012779343065, - 0.2636282766313478, - 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