diff --git a/CO2-SGD.ipynb b/CO2-SGD.ipynb index 62215a33199dccef6b2fbfacf5bd3486f197025c..94022bb24da199b71f002ecae38e087493c8980f 100644 --- a/CO2-SGD.ipynb +++ b/CO2-SGD.ipynb @@ -187,7 +187,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 6, +======= + "execution_count": 2, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:18:46.497035Z", @@ -242,6 +246,7 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 7, "metadata": { "ExecuteTime": { @@ -267,6 +272,25 @@ "execution_count": 8, "metadata": { "ExecuteTime": { +======= + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-02T18:10:17.784555Z", + "start_time": "2020-09-02T18:10:17.670121Z" + } + }, + "outputs": [], + "source": [ + "!pwd" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "ExecuteTime": { +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "end_time": "2020-09-02T18:18:47.040354Z", "start_time": "2020-09-02T18:18:46.498873Z" }, @@ -277,13 +301,21 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "4a0ffc4600db49adbfe35609ec40fcca", +======= + "model_id": "dc177749d36b4a72b187d9d7ca3d1456", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, "text/plain": [ "FigureWidget({\n", +<<<<<<< HEAD " 'data': [{'hovertemplate': '<b>%{text}</b><br><br>OCO-angle: %{x:,.1f}°<br>l(C-O): %{y:,.2f…" +======= + " 'data': [{'hovertemplate': '<b>%{text}</b><br><br>x-axis: %{x:,.1f}<br>y-axis: %{y:,.2f}<br…" +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a ] }, "metadata": {}, @@ -292,7 +324,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "47069df0dc7743f8ac8df92299691731", +======= + "model_id": "104291bb5d3b4c2e8045bf122bcdecb0", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -306,7 +342,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "cae8a873814b4445a5d38c09c9ddef32", +======= + "model_id": "a8a324298c65453c838dd773f28243c2", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -320,7 +360,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "0a134e0e9ced42e3b75dc50dd1a03310", +======= + "model_id": "1f353fd11d8b4d6e9e293016b51ec7ff", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -375,21 +419,34 @@ " x=df['OCO-angle, degree'],\n", " y=df['l(C-O), Å'],\n", " mode='markers',\n", +<<<<<<< HEAD " name='Adsorbed CO<sub>2</sub>',\n", " marker=dict(size=marker_size)\n", "))\n", "\n", "hovertemplate = r\"<b>%{text}</b><br><br>\"+\"OCO-angle: %{x:,.1f}°<br>\"+\"l(C-O): %{y:,.2f} Å<br>\"\n", +======= + " name='Adsorbed CO2',\n", + " marker=dict(size=marker_size)\n", + "))\n", + "\n", + "hovertemplate = r\"<b>%{text}</b><br><br>\"+\"x-axis: %{x:,.1f}<br>\"+\"y-axis: %{y:,.2f}<br>\"\n", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "\n", "fig.update_traces (text=ads_sites,hovertemplate=hovertemplate )\n", "fig.add_trace(\n", " go.Scatter(\n", " x=OCO_gas,\n", " y=lCO_gas,\n", +<<<<<<< HEAD " name=\"charged gas-phase CO<sub>2</sub><br>with charge = 0.9–2.0 <i>e</i>\",\n", " line=dict(color=line_color,width=line_width),\n", " mode='lines+markers',\n", " marker=dict(size=marker_size),\n", +======= + " name=\"charged gas-phase CO2<br>with charge = 0.9-2.0 e\",\n", + " line=dict(color=line_color,width=line_width),\n", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a " hoverinfo='skip',\n", " ))\n", "fig.update_layout ( \n", @@ -482,6 +539,7 @@ " value=\"Click 'Print' to export the plot in the desired format. The resolution of the image can be increased\"\n", " \" by increasing the 'Scale' value.\"\n", ")\n", +<<<<<<< HEAD "\n", "\n", "def handle_markersize_change(change):\n", @@ -560,12 +618,95 @@ "widg_print_button.on_click(print_button_clicked)\n", "\n", "\n", +======= + "\n", + "\n", + "def handle_markersize_change(change):\n", + " marker_size = int(change.new)\n", + " with fig.batch_update():\n", + " fig.data[0].marker.size = change.new\n", + "def handle_markercolor_change(change):\n", + " marker_color = change.new\n", + " with fig.batch_update():\n", + " try:\n", + " fig.data[0].update(marker=dict(color=marker_color))\n", + " except:\n", + " pass\n", + "def handle_linewidth_change(change):\n", + " line_width = int(change.new)\n", + " with fig.batch_update():\n", + " fig.data[1].line.width = change.new\n", + "def handle_linecolor_change(change):\n", + " with fig.batch_update():\n", + " try:\n", + " fig.data[1].update(line=dict(color=change.new))\n", + " except:\n", + " pass\n", + "def handle_bgcolor_change(change):\n", + " try:\n", + " fig.update_layout(plot_bgcolor=change.new)\n", + " except:\n", + " pass\n", + "def bgtoggle_button_clicked(button):\n", + " global bg_toggle\n", + " if bg_toggle:\n", + " bg_toggle = False\n", + " fig.update_layout(\n", + " plot_bgcolor='white'\n", + " )\n", + " else:\n", + " bg_toggle = True\n", + " fig.update_layout(\n", + " plot_bgcolor=widg_bgcolor.value\n", + " )\n", + "def handle_fontfamily_change(change):\n", + " fig.update_layout(font=dict(family=change.new))\n", + "def handle_fontsize_change(change):\n", + " fig.update_layout(font=dict(size=int(change.new)))\n", + "def plotappearance_button_clicked(button):\n", + " global widgets_container\n", + " if widgets_container.layout.visibility == 'visible':\n", + " widgets_container.layout.visibility = 'hidden'\n", + " else:\n", + " widgets_container.layout.visibility = 'visible'\n", + "def print_button_clicked( button):\n", + "\n", + " path = \"./data/plots/\"\n", + " try:\n", + " os.mkdir(path)\n", + " except:\n", + " pass\n", + " file_name = widg_plot_name.value + '.' + widg_plot_format.value\n", + " fig.write_image(path + file_name, scale=widg_scale.value)\n", + " widg_print_out.clear_output()\n", + " with widg_print_out:\n", + " local_file = FileLink( path + file_name, result_html_prefix=\"Click here to download: \")\n", + " display(local_file)\n", + " \n", + " \n", + "widg_markersize.observe(handle_markersize_change, names='value')\n", + "widg_markercolor.observe(handle_markercolor_change, names='value')\n", + "widg_linecolor.observe(handle_linecolor_change, names='value')\n", + "widg_linewidth.observe(handle_linewidth_change, names='value')\n", + "widg_bgcolor.observe(handle_bgcolor_change, names='value')\n", + "widg_bgtoggle_button.on_click(bgtoggle_button_clicked)\n", + "widg_fontfamily.observe(handle_fontfamily_change, names='value')\n", + "widg_fontsize.observe(handle_fontsize_change, names='value')\n", + "widg_plotutils_button.on_click(plotappearance_button_clicked)\n", + "widg_print_button.on_click(print_button_clicked)\n", + "\n", + "\n", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "box_print = widgets.VBox([widg_printdescription,widgets.HBox([widg_plot_name, widg_plot_format, widg_scale]),\n", " widgets.HBox([widg_empty,widg_print_button, widg_print_out])])\n", "\n", "\n", "display(fig,box_print,widg_plotutils_button,widgets_container)\n", +<<<<<<< HEAD "# fig.show(config=config)" +======= + "# fig.show(config=config)\n" +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a ] }, { @@ -581,7 +722,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 9, +======= + "execution_count": 4, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:18:47.043733Z", @@ -622,7 +767,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "98734efb6f3e494daed212356f811bd5", +======= + "model_id": "ae6043bda0b54dea9f9a60d79e2fb3c2", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -756,7 +905,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "0a6dd493931a425baa3f1d5c2f2cf3e5", +======= + "model_id": "fddffd3ed2fb4985b7abff785fb5a518", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -902,7 +1055,11 @@ { "data": { "application/vnd.jupyter.widget-view+json": { +<<<<<<< HEAD "model_id": "79430f0056ad4f0599ecb652bf94d9d7", +======= + "model_id": "59e49564fcb94976b752978ba64bd54c", +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "version_major": 2, "version_minor": 0 }, @@ -928,7 +1085,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 14, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:45.846335Z", @@ -1007,11 +1168,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 15, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:45.862613Z", "start_time": "2020-09-02T18:17:45.847822Z" +<<<<<<< HEAD } }, "outputs": [ @@ -1023,8 +1189,11 @@ "\n", "quality function (size*shift*narowness) is 0.18623*0.22797*0.3418 = 0.01451106878958\n" ] +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "print(OCO_subgroup[:-1])\n", "print(\"\\nquality function (size*shift*narowness) is \"+OCO_subgroup[-1]+\" = \"+str(eval(OCO_subgroup[-1])))" @@ -1039,11 +1208,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 16, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:45.880212Z", "start_time": "2020-09-02T18:17:45.864282Z" +<<<<<<< HEAD } }, "outputs": [ @@ -1055,8 +1229,11 @@ "\n", "quality function (size*shift*narowness) is 0.12146*0.40566*0.30822 = 0.015186450510792\n" ] +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "print(lCO_subgroup[:-1])\n", "print(\"\\nquality function (size*shift*narowness) is \"+lCO_subgroup[-1]+\" = \"+str(eval(lCO_subgroup[-1])))" @@ -1064,7 +1241,11 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 17, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:46.029525Z", @@ -1202,11 +1383,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 18, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:46.356747Z", "start_time": "2020-09-02T18:17:46.031001Z" +<<<<<<< HEAD } }, "outputs": [ @@ -3588,8 +3774,11 @@ }, "metadata": {}, "output_type": "display_data" +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "x, y, legen = [], [], []\n", "for i in range(len(gauss_angl[0])):\n", @@ -3638,11 +3827,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 19, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:46.424409Z", "start_time": "2020-09-02T18:17:46.358821Z" +<<<<<<< HEAD } }, "outputs": [ @@ -5942,8 +6136,11 @@ }, "metadata": {}, "output_type": "display_data" +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "x, y, legen = [], [], []\n", "for i in range(len(gauss_dist[0])):\n", @@ -5991,11 +6188,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 20, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:46.488370Z", "start_time": "2020-09-02T18:17:46.425871Z" +<<<<<<< HEAD } }, "outputs": [ @@ -11369,8 +11571,11 @@ }, "metadata": {}, "output_type": "display_data" +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "dCO_mse_CV_minLeaf = [0.03255627329984329, 0.034132247895190954, 0.031672092812331014, 0.03059461226995024, 0.030839488861288206, 0.030377333724731434, 0.029382804378371364, 0.028910821550302973, 0.028364501740508002, 0.02780484946404478, 0.02705321359075288, 0.028075143704071444, 0.02989078608329612, 0.029233949367941697, 0.02846342790168268, 0.02870721288864, 0.02879431163747176, 0.02858119669679526, 0.028673638285909592, 0.028226012372936125, 0.027789710645324477, 0.027609437112001405, 0.02777988051498181, 0.027837583150554263, 0.027803473449669068, 0.02749314486784524, 0.027295443275648642, 0.02726767633959103, 0.027267676339591047, 0.027534507530945742, 0.0280959414929495, 0.027480781967643038, 0.02731191115591742, 0.02696332989063625, 0.027187774260358847, 0.026901245336512208, 0.027017396161290685, 0.026870887344675756, 0.025736113939583312, 0.02527495831980791, 0.02527495831980791, 0.02527495831980794, 0.025274958319807937, 0.02527495831980792, 0.0250764071134889, 0.02507207982446077, 0.025376084945028035, 0.02548596237954964, 0.025391326447260297, 0.025175095250391456, 0.025175095250391466, 0.025175095250391487, 0.025175095250391497, 0.025175095250391438, 0.02517509525039145, 0.026128007765774234, 0.026128007765774237, 0.02612800776577423, 0.02600847388530928, 0.026008473885309243, 0.025970336693884984, 0.025968909548618392, 0.02600386972794675, 0.026072093212203405, 0.026126753848822536, 0.026492885309641293, 0.02644625768410604, 0.02664999545584807, 0.026649995455848098, 0.02664999545584813, 0.026649995455848112, 0.026649995455848115, 0.02664999545584812, 0.026649995455848133, 0.026649995455848112, 0.0266499954558481, 0.026649995455848098, 0.026649995455848074, 0.026649995455848133, 0.0266499954558481, 0.02664999545584811, 0.026649995455848095, 0.02664999545584813, 0.026649995455848112, 0.026649995455848112, 0.02664999545584807, 0.02664999545584812, 0.026649995455848112, 0.026649995455848095, 0.026649995455848122, 0.026649995455848105, 0.026649995455848133, 0.026649995455848133, 0.02664999545584811, 0.026649995455848112, 0.0266499954558481, 0.026649995455848095, 0.026649995455848122, 0.0266499954558481, 0.02664999545584809, 0.02664999545584809, 0.026649995455848112, 0.026649995455848098, 0.026649995455848074, 0.026649995455848095, 0.026649995455848112, 0.026649995455848084, 0.02664999545584805, 0.026649995455848115, 0.026649995455848112, 0.026649995455848122, 0.026667332293300965, 0.026945483544038307, 0.026945483544038352, 0.026945483544038338, 0.02694548354403829, 0.02694548354403835, 0.026945483544038328, 0.02694548354403835, 0.026945483544038317]\n", "dCO_mae_CV_minLeaf = [0.03214951155332549, 0.032439042803013005, 0.03230212261610794, 0.03228395961747872, 0.030708848900760144, 0.03040094754010197, 0.03065095054367432, 0.030210077547485743, 0.02893116219146731, 0.028616549103466213, 0.029383717157774592, 0.02846681070546699, 0.029698041757168545, 0.029862149549362685, 0.03014940865266288, 0.029193425165171934, 0.028488935276671554, 0.02948129510611096, 0.030938786369608794, 0.030571662012697693, 0.030959683853222696, 0.031057149880574276, 0.03040382726813531, 0.02979914204128638, 0.02960172538466276, 0.029214878218328088, 0.02994054499867722, 0.029364955427983386, 0.029749698055828343, 0.029334263035812783, 0.029148093180041638, 0.029815203441789638, 0.030375617485934724, 0.030189784313954256, 0.030136984823485802, 0.030647681223421642, 0.030158068726490794, 0.03061944818718082, 0.03052910604307758, 0.03010975200814355, 0.030246457282426555, 0.029897226118064148, 0.028169060179867634, 0.028351752368207837, 0.02981219895408143, 0.02904265326209066, 0.027607995526731925, 0.027785543673769737, 0.027461622098104637, 0.028621588777172267, 0.02754921984684937, 0.027523692407173405, 0.027146047497505797, 0.027177777299613137, 0.027194437839247777, 0.0279788634797669, 0.027836803908169182, 0.02792097726906992, 0.027914505828108994, 0.02864553720249341, 0.0286413498822406, 0.028390442167703787, 0.028072293168259273, 0.028096583710378948, 0.027709671793999524, 0.027670359329526252, 0.027586063341627023, 0.02835653570887858, 0.028372914323461337, 0.02836857972809836, 0.02833446850151168, 0.028357606495503906, 0.02858392005708733, 0.028604397187522373, 0.02861640762613472, 0.028598275053443135, 0.02860361872296201, 0.028596877041352234, 0.028615346523852572, 0.028613135767712322, 0.028594930328396148, 0.028599956116724064, 0.028597337154142376, 0.028617946154559436, 0.02858469905811349, 0.028599460655711156, 0.028620439456022856, 0.028599903032026086, 0.028610889464527597, 0.028599956116724046, 0.02861600087491042, 0.02860473333612409, 0.02859050548781154, 0.028621288189943892, 0.02860627249242605, 0.02861025268508569, 0.028614002404473902, 0.028596293041380418, 0.028605617918832277, 0.02862834231676134, 0.028615877079911964, 0.02765905420205519, 0.027650270331277264, 0.02767056051164016, 0.027696610383233674, 0.02767880771928468, 0.027708521175786118, 0.027673029445733858, 0.027656712109293758, 0.027651240354049374, 0.02770874034484382, 0.027952859391807094, 0.027544277935055102, 0.02754427793505511, 0.02754427793505511, 0.027544277935055105, 0.0275442779350551, 0.0275442779350551, 0.02754427793505512, 0.027544277935055105]\n", @@ -11434,11 +11639,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 22, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:56.234809Z", "start_time": "2020-09-02T18:17:55.370137Z" +<<<<<<< HEAD }, "scrolled": true }, @@ -11506,20 +11716,12 @@ "text": [ "Decision tree for l(C-O), with MAE cost function, fitting accuracy (RMSE) = 0.020 Å (stand. dev. = 0.028):\n" ] +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 720x720 with 1 Axes>" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "scrolled": true + }, + "outputs": [], "source": [ "df = pd.read_csv(\"./data/co2/train.csv\")\n", "Y_dCO = df['dCO']\n", @@ -11562,11 +11764,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 23, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:56.444204Z", "start_time": "2020-09-02T18:17:56.334492Z" +<<<<<<< HEAD } }, "outputs": [ @@ -13866,8 +14073,11 @@ }, "metadata": {}, "output_type": "display_data" +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "large_lco_mae, large_lco_mse, small_oco = [], [], []\n", "large_lco_mae_E, large_lco_mse_E, small_oco_E = [], [], []\n", @@ -13931,11 +14141,16 @@ }, { "cell_type": "code", +<<<<<<< HEAD "execution_count": 24, +======= + "execution_count": null, +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "metadata": { "ExecuteTime": { "end_time": "2020-09-02T18:17:57.508252Z", "start_time": "2020-09-02T18:17:57.451710Z" +<<<<<<< HEAD } }, "outputs": [ @@ -17652,8 +17867,11 @@ }, "metadata": {}, "output_type": "display_data" +======= +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a } - ], + }, + "outputs": [], "source": [ "fig = make_subplots(rows=1, cols=1)\n", "fig.add_trace(go.Scatter(x=gauss_large_lco_mae[0],y=gauss_large_lco_mae[1],name=\"larger l(C-O) leaf, MAE\"), row=1, col=1)\n", @@ -17729,8 +17947,20 @@ ] }, { +<<<<<<< HEAD "cell_type": "markdown", "metadata": {}, +======= + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-09-02T18:18:00.591750Z", + "start_time": "2020-09-02T18:18:00.558628Z" + } + }, + "outputs": [], +>>>>>>> 36df82724439369851212bbcaf13d8ffd796ac6a "source": [ "Here we present in details how the cross-validation for TR was done. The aim of cross-validation procedure is to obtain the most optimal set of hyperparameters in terms of prediction accuracy. We use here leave-one-out scheme: N samplings of size (N-1) are generated for the data set, for each of the samplings the TR analysis is performed and the accuracy of the prediction is determined for the left-out sample using found descriptors. As mentioned above, two TR hyperparameters are considered - minimal size of a leaf and maximal depth.\n", "\n", @@ -17964,7 +18194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.9" + "version": "3.7.3" } }, "nbformat": 4,