Final analyze .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "96eb7b6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "\n",
    "def parse_all_xlsx_files(folder_path):\n",
    "    all_dfs = []\n",
    "\n",
    "    # Walk through the directory and its subdirectories\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            # Check if the file has a .xlsx extension\n",
    "            if file.endswith('.xlsx'):\n",
    "                file_path = os.path.join(root, file)\n",
    "\n",
    "                # Try to read the Excel file into a DataFrame\n",
    "                try:\n",
    "                    df = pd.read_excel(file_path)\n",
    "                    \n",
    "                    # Store both the file name and the DataFrame in a tuple\n",
    "                    result = (file, df)\n",
    "                    all_dfs.append(result)\n",
    "                except Exception as e:\n",
    "                    print(f\"Error reading file {file}: {e}\")\n",
    "\n",
    "    return all_dfs\n",
    "\n",
    "# Specify the folder path containing Excel files\n",
    "folder_path = 'models'\n",
    "\n",
    "# Call the function to parse all Excel files in the folder and its subfolders\n",
    "list_of_dfs = parse_all_xlsx_files(folder_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "062ffe77",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The PostScript backend does not support transparency; partially transparent artists will be rendered opaque.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Define a sequence of markers\n",
    "markers = ['o', '^', 's', 'D', 'v', '<', '>', 'p', '*', 'h']\n",
    "\n",
    "# Create an empty list to store legend handles and labels\n",
    "legend_handles = []\n",
    "\n",
    "# Assuming list_of_dfs is already populated\n",
    "for d in range(len(list_of_dfs)):\n",
    "    index_to_plot = d\n",
    "    \n",
    "    df_to_plot = list_of_dfs[index_to_plot][1]\n",
    "    file_name = list_of_dfs[index_to_plot][0]\n",
    "\n",
    "    # Split the file name by underscore and get the second part\n",
    "    entity = file_name.split('_')[1]\n",
    "\n",
    "    # Split the entity by dot and get the first part\n",
    "    parsed_entity = entity.split('.')[0]\n",
    "\n",
    "    # Set a range for the y-axis to avoid extreme values\n",
    "    y_axis_lower_limit = -0.05  # Set your desired lower limit\n",
    "    y_axis_upper_limit = 1.05   # Set your desired upper limit\n",
    "\n",
    "    # Filter values within the specified range\n",
    "    filtered_df = df_to_plot[(df_to_plot['Overall'] >= y_axis_lower_limit) & (df_to_plot['Overall'] <= y_axis_upper_limit)]\n",
    "\n",
    "    # Set 'Name' column as the index\n",
    "    filtered_df.set_index('Name', inplace=True)\n",
    "\n",
    "    # Get the marker for the current plot\n",
    "    current_marker = markers[d % len(markers)]\n",
    "\n",
    "    # Create a scatter plot using 'Name' as x-axis labels and the current marker\n",
    "    scatter = plt.scatter(filtered_df.index, filtered_df['Overall'], label=f'VM of {parsed_entity}', marker=current_marker)\n",
    "\n",
    "    # Add zebra-like background\n",
    "    for i, index_name in enumerate(filtered_df.index):\n",
    "        color = 'lightgray' if i % 2 == 0 else 'white'\n",
    "        # Adjust the span to cover the entire area between x-axis ticks\n",
    "        plt.axvspan(i - 0.5, i + 0.5, facecolor=color, alpha=0.3, zorder=-1)\n",
    "\n",
    "    # Append the scatter plot handle to the legend_handles list\n",
    "    legend_handles.append(scatter)\n",
    "\n",
    "# Draw horizontal red lines at y=0 and y=1\n",
    "plt.axhline(0, color='red', linestyle='--', linewidth=1, label='y=0')\n",
    "plt.axhline(1, color='red', linestyle='--', linewidth=1, label='y=1')\n",
    "\n",
    "# Set labels and title\n",
    "plt.title('Variance Metric (VM) score depending on the characteristic')\n",
    "plt.xlabel('Name of characteristic')\n",
    "plt.ylabel('Variance Metric score')\n",
    "\n",
    "# Rotate the figure 90 degrees clockwise\n",
    "plt.xticks(rotation=90)\n",
    "\n",
    "# Add legend outside the plot\n",
    "plt.legend(handles=legend_handles, loc='upper left', bbox_to_anchor=(1, 1))\n",
    "\n",
    "# Set a white background\n",
    "plt.gca().set_facecolor('white')\n",
    "\n",
    "# Adjust figure size if needed\n",
    "# Adjust figure size if needed\n",
    "plt.gcf().set_size_inches(12, 8)\n",
    "\n",
    "# Save the figure with an explicit bounding box\n",
    "plt.savefig('VM_all_checked.eps', bbox_inches='tight', format='eps', dpi=300)\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f71a8992",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Thermal conductivity': {'AdaBoost': 274.6851134251215, 'Bagging': 27.16293718711307, 'Decision Tree': 30.2056410971164, 'Elastic Net': 523.07618514004, 'Gradient Boosting': 37.44665552099447, 'KNeighborsRegressor': 77.14452499927133, 'Lasso': 523.07618514004, 'Linear Regression': 9132306659914.848, 'Random Forest': 45.5947209432056, 'SVR': 122.1966307751935, 'XGBoost': 34.4758821048127}, 'Dynamic mechanical properties loss tangent': {'AdaBoost': 1656.223144668726, 'Bagging': 345.6723685207627, 'Decision Tree': 76.94106729604854, 'Elastic Net': 767.8460581358668, 'Gradient Boosting': 308.5012320816782, 'KNeighborsRegressor': 193.7576749525487, 'Lasso': 767.8460581358668, 'Linear Regression': 806893159597039.8, 'Random Forest': 270.7179611626937, 'SVR': 249.6291848729725, 'XGBoost': 181.457457718336}, 'Thermal decomposition temperature': {'AdaBoost': 8.21525739462773, 'Bagging': 3.363775456029029, 'Decision Tree': 2.361809401032059, 'Elastic Net': 5.290218392892728, 'Gradient Boosting': 3.53363776644003, 'KNeighborsRegressor': 6.547767306295016, 'Lasso': 4.286791575095529, 'Linear Regression': 2.899025965819442, 'Random Forest': 2.998655156015384, 'SVR': 7.05260675920399, 'XGBoost': 2.248981550485998}, 'Hansen parameter delta-h: hydrogen bonding': {'AdaBoost': 13.40143540737005, 'Bagging': 21.04789016563712, 'Decision Tree': 5.46983671517387, 'Elastic Net': 63.87364669770663, 'Gradient Boosting': 13.89012551870016, 'KNeighborsRegressor': 68.7065437598318, 'Lasso': 67.96812203954438, 'Linear Regression': 97253357091.77338, 'Random Forest': 12.14592757214029, 'SVR': 59.46306822327995, 'XGBoost': 2.561384113521019}, 'Flexural modulus': {'AdaBoost': 197.7243857601278, 'Bagging': 41.28588214831633, 'Decision Tree': 10.42877304733508, 'Elastic Net': 247.1828440492738, 'Gradient Boosting': 101.1399941700557, 'KNeighborsRegressor': 63.89096220228628, 'Lasso': 190.2530792105547, 'Linear Regression': 5887886608675.989, 'Random Forest': 64.96852907489021, 'SVR': 70.45804057844562, 'XGBoost': 50.38196433560147}, 'Vicat softening temperature': {'AdaBoost': 2.150136148556763, 'Bagging': 6.302709912321447, 'Decision Tree': 3.414413009716031, 'Elastic Net': 2.748703458207527, 'Gradient Boosting': 4.985257677352246, 'KNeighborsRegressor': 2.33567268349477, 'Lasso': 0.4533832201046553, 'Linear Regression': 4477587192274.074, 'Random Forest': 3.472948431102406, 'SVR': 9.941716762804457, 'XGBoost': 7.466998004762599}, 'Dynamic mechanical properties loss modulus': {'AdaBoost': 261.876267388841, 'Bagging': 170.1005529206662, 'Decision Tree': 189.7833976293073, 'Elastic Net': 1760.878678203984, 'Gradient Boosting': 156.6930788604686, 'KNeighborsRegressor': 173.1279166881233, 'Lasso': 1851.307641364152, 'Linear Regression': 525187221496.1042, 'Random Forest': 161.306771139957, 'SVR': 195.0803397679795, 'XGBoost': 155.0257722027938}, 'Pvt relation specific volume': {'AdaBoost': 0.5783138873586869, 'Bagging': 1.070534526092316, 'Decision Tree': 3.37188914866113, 'Elastic Net': 7.954276221618377, 'Gradient Boosting': 4.511945139985013, 'KNeighborsRegressor': 0.03568298152731529, 'Lasso': 7.954276221618377, 'Linear Regression': 6662767150.630674, 'Random Forest': 0.3138472003674478, 'SVR': 0.01452159721987512, 'XGBoost': 5.556312200964848}, 'Tensile modulus': {'AdaBoost': 41402.57003913545, 'Bagging': 4320.629752884975, 'Decision Tree': 2460.028900964297, 'Elastic Net': 5822.695436123671, 'Gradient Boosting': 3790.24065943483, 'KNeighborsRegressor': 2884.093691808806, 'Lasso': 5964.607487719627, 'Linear Regression': 173169741112241.6, 'Random Forest': 6155.533351095736, 'SVR': 2539.232398328227, 'XGBoost': 2424.137177910065}, 'Heat of fusion': {'AdaBoost': 1835.604827726936, 'Bagging': 241.666305301866, 'Decision Tree': 251.5138032323175, 'Elastic Net': 1167.576218477705, 'Gradient Boosting': 499.8445474470643, 'KNeighborsRegressor': 625.5797904063025, 'Lasso': 1167.576218477705, 'Linear Regression': 17170892450696.25, 'Random Forest': 324.124307324365, 'SVR': 5194.580591075843, 'XGBoost': 201.9102031772042}, 'Water vapor transmission': {'AdaBoost': 2230.531553044398, 'Bagging': 1117.372728935089, 'Decision Tree': 506.3534614880335, 'Elastic Net': 35226.18215384087, 'Gradient Boosting': 1036.63089418431, 'KNeighborsRegressor': 4358.232389997309, 'Lasso': 38797.96927698539, 'Linear Regression': 3160501083026551.0, 'Random Forest': 2115.497151532203, 'SVR': 5262.124564858104, 'XGBoost': 819.1172586445722}, 'Lc phase transition temperature': {'AdaBoost': 19.14384501124037, 'Bagging': 3.015693177103638, 'Decision Tree': 3.216178130732814, 'Elastic Net': 9.64866543346488, 'Gradient Boosting': 4.556512337356261, 'KNeighborsRegressor': 3.615870826684667, 'Lasso': 5.397364087475257, 'Linear Regression': 339631765263.9229, 'Random Forest': 3.183815132554515, 'SVR': 9.060757239328074, 'XGBoost': 4.170957192920106}, 'Dynamic flexural properties storage modulus': {'AdaBoost': 390.0003665620152, 'Bagging': 366.9943247316173, 'Decision Tree': 415.3504458330468, 'Elastic Net': 460.1929405293232, 'Gradient Boosting': 269.2642000550306, 'KNeighborsRegressor': 290.7850009564572, 'Lasso': 460.1929405293232, 'Linear Regression': 43886920062590.77, 'Random Forest': 165.286476468975, 'SVR': 192.4522151155107, 'XGBoost': 34.92579092012056}, 'Thermal decomposition weight loss': {'AdaBoost': 356.5031932331567, 'Bagging': 46.7244309883129, 'Decision Tree': 14.64602030149479, 'Elastic Net': 100.5754691745977, 'Gradient Boosting': 81.2500067284484, 'KNeighborsRegressor': 48.51537167549214, 'Lasso': 100.5509274079727, 'Linear Regression': 52.18524460240357, 'Random Forest': 46.01459286672805, 'SVR': 22.27376564867651, 'XGBoost': 46.20903545021719}, 'Melting temperature': {'AdaBoost': 2.38261588746506, 'Bagging': 14.70754684238155, 'Decision Tree': 1.054176901906686, 'Elastic Net': 6.657352523388808, 'Gradient Boosting': 1.939028221773845, 'KNeighborsRegressor': 15.90476087648912, 'Lasso': 5.758950325509423, 'Linear Regression': 8.84804039095924, 'Random Forest': 11.67545369767608, 'SVR': 9.1902457187649, 'XGBoost': 15.06879265828224}, 'Hansen parameter delta p polar': {'AdaBoost': 24.92979850569971, 'Bagging': 2.57297433152712, 'Decision Tree': 3.15575974694852, 'Elastic Net': 25.62885808492436, 'Gradient Boosting': 34.92145624953267, 'KNeighborsRegressor': 6.200950603393386, 'Lasso': 30.28955480886786, 'Linear Regression': 254312241479.5647, 'Random Forest': 7.571992129255231, 'SVR': 0.449479316303103, 'XGBoost': 6.511732211958961}, 'Volume resistivity': {'AdaBoost': 1.123280665042004e+23, 'Bagging': 8.595885599442364e+18, 'Decision Tree': 2.703910012199965e+19, 'Elastic Net': 1.265154367366834e+22, 'Gradient Boosting': 3.232513278019269e+20, 'KNeighborsRegressor': 2.456969291196006e+20, 'Lasso': 4.60637889630562e+23, 'Linear Regression': 2.898716551894427e+32, 'Random Forest': 9.527290138756465e+18, 'SVR': 8421065966934731.0, 'XGBoost': 1.396349968319249e+21}, 'Crystallization kinetics k': {'AdaBoost': 37439030.640729, 'Bagging': 4309384.943848756, 'Decision Tree': 23441.49434772167, 'Elastic Net': 201595769.1072495, 'Gradient Boosting': 8003599.131736105, 'KNeighborsRegressor': 7238838.472993408, 'Lasso': 201595769.1072495, 'Linear Regression': 316883880.4167805, 'Random Forest': 20464657.2456156, 'SVR': 65681350.47132844, 'XGBoost': 5033251.106018843}, 'Heat of fusion mol conversion': {'AdaBoost': 1263078.234191757, 'Bagging': 1712989.084742004, 'Decision Tree': 2619650.231765671, 'Elastic Net': 1463501.83231191, 'Gradient Boosting': 1939579.517269991, 'KNeighborsRegressor': 1658382.20449361, 'Lasso': 1463501.83231191, 'Linear Regression': 2.000879390648438e+17, 'Random Forest': 1820707.480945586, 'SVR': 1080051.568982759, 'XGBoost': 2010714.481853312}, 'Elongation at yield': {'AdaBoost': 149.1498744968876, 'Bagging': 26.65228133271421, 'Decision Tree': 60.97545783836158, 'Elastic Net': 123.3267394225971, 'Gradient Boosting': 116.1946189526727, 'KNeighborsRegressor': 215.1186932909661, 'Lasso': 63.31650006140646, 'Linear Regression': 1577759666580.224, 'Random Forest': 49.40057805122849, 'SVR': 22.5658750887877, 'XGBoost': 14.02502781196942}, 'Dynamic shear properties loss tangent': {'AdaBoost': 2035.361830745289, 'Bagging': 375.0166015580594, 'Decision Tree': 243.0205866866866, 'Elastic Net': 12096.49680485263, 'Gradient Boosting': 949.3371325551252, 'KNeighborsRegressor': 783.7731842867194, 'Lasso': 7651.187507176274, 'Linear Regression': 11749415885901.52, 'Random Forest': 671.0558309944959, 'SVR': 1178.880045907295, 'XGBoost': 3.836444502681821}, 'Dielectric loss factor': {'AdaBoost': 5231.805412756663, 'Bagging': 49250.39194813698, 'Decision Tree': 1933.104085116894, 'Elastic Net': 24404.70087231143, 'Gradient Boosting': 5679.750113624426, 'KNeighborsRegressor': 10181.36725388663, 'Lasso': 23731.84731766365, 'Linear Regression': 1241114214451047.0, 'Random Forest': 17656.95534509452, 'SVR': 708.9404927955225, 'XGBoost': 1804.327081289465}, 'Cohesive energy density': {'AdaBoost': 11.81518944246141, 'Bagging': 1.706350975920912, 'Decision Tree': 4.066440605024116, 'Elastic Net': 3.527472172983951, 'Gradient Boosting': 0.9595394792906236, 'KNeighborsRegressor': 1.925927498985346, 'Lasso': 1.562096387800711, 'Linear Regression': 1355050877149.366, 'Random Forest': 3.133485200402539, 'SVR': 5.690697068701706, 'XGBoost': 4.86713629613599}, 'Glass transition temperature': {'AdaBoost': 54.72793876003998, 'Bagging': 2.394552627991603, 'Decision Tree': 20.50709171121228, 'Elastic Net': 16.31288564076992, 'Gradient Boosting': 5.745794249961991, 'KNeighborsRegressor': 4.210077255865665, 'Lasso': 1.229773319576692, 'Linear Regression': 23.04437300918561, 'Random Forest': 4.192367486416471, 'SVR': 31.42791292803401, 'XGBoost': 11.44716450392205}, 'Dynamic shear properties storage modulus': {'AdaBoost': 49252917.96728885, 'Bagging': 34895.91764480586, 'Decision Tree': 46678.45355312336, 'Elastic Net': 90488873.85841699, 'Gradient Boosting': 7593190.130715353, 'KNeighborsRegressor': 73368.73037399798, 'Lasso': 90488873.85841699, 'Linear Regression': 3695829577051070.0, 'Random Forest': 634076.0753299284, 'SVR': 22226246.22935544, 'XGBoost': 106915.3353899665}, 'Crystallization kinetics n': {'AdaBoost': 3.853923810857565, 'Bagging': 9.693677826093763, 'Decision Tree': 11.0773540113939, 'Elastic Net': 7.154982482254138, 'Gradient Boosting': 9.833167746790552, 'KNeighborsRegressor': 10.28989037295115, 'Lasso': 7.154982482254138, 'Linear Regression': 194673047399.7452, 'Random Forest': 6.430429755332824, 'SVR': 9.092535943263035, 'XGBoost': 10.2773454566326}, 'Heat of crystallization': {'AdaBoost': 215.7741692193406, 'Bagging': 151.5351113556451, 'Decision Tree': 141.507708446062, 'Elastic Net': 182.722227005657, 'Gradient Boosting': 178.2951270581637, 'KNeighborsRegressor': 238.4231213054128, 'Lasso': 176.0906226465425, 'Linear Regression': 1653816082832.409, 'Random 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'Elastic Net': 3.252273175567208e+23, 'Gradient Boosting': 6.140011533231315e+20, 'KNeighborsRegressor': 3.881288189775786e+20, 'Lasso': 1.223015153878934e+26, 'Linear Regression': 3.242367022282779e+30, 'Random Forest': 2.227739554925895e+17, 'SVR': 9.658677157903822e+16, 'XGBoost': 5.066205980926816e+18}, 'Elongation at break': {'AdaBoost': 1545.467325165685, 'Bagging': 377.545384273904, 'Decision Tree': 148.664676532145, 'Elastic Net': 676.6947690006928, 'Gradient Boosting': 462.9597622738003, 'KNeighborsRegressor': 711.6478541330644, 'Lasso': 566.9688603515142, 'Linear Regression': 123964878840650.0, 'Random Forest': 372.7077907550165, 'SVR': 82.93049475553205, 'XGBoost': 264.6828171945861}, 'Dynamic shear properties loss modulus': {'AdaBoost': 163278.0166788099, 'Bagging': 236023.3939961765, 'Decision Tree': 478.8521798231576, 'Elastic Net': 250405.0082787963, 'Gradient Boosting': 261529.7834453654, 'KNeighborsRegressor': 41044.1830044275, 'Lasso': 250405.0082787963, 'Linear 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1866.15334581633, 'Bagging': 8114.770199275363, 'Decision Tree': 21873.57086530264, 'Elastic Net': 4850.131076566109, 'Gradient Boosting': 5270.88791447974, 'KNeighborsRegressor': 5434.343667945439, 'Lasso': 1235.798300167993, 'Linear Regression': 72703429075.1693, 'Random Forest': 7440.163787787725, 'SVR': 387.84097820114, 'XGBoost': 196.1739675335059}, 'Intrinsic viscosity eta': {'AdaBoost': 1305.833625338206, 'Bagging': 233.4166758593761, 'Decision Tree': 433.7640500054238, 'Elastic Net': 546.6992508664911, 'Gradient Boosting': 380.1473253608259, 'KNeighborsRegressor': 225.7861572993453, 'Lasso': 546.6992508664911, 'Linear Regression': 16917160213846.45, 'Random Forest': 296.0527645312153, 'SVR': 167.5380698410087, 'XGBoost': 346.031965234439}, 'Fiber tensile elongation at break': {'AdaBoost': 327.1179593035696, 'Bagging': 178.6087372886169, 'Decision Tree': 106.2864327176656, 'Elastic Net': 267.8471621751906, 'Gradient Boosting': 273.3212571496151, 'KNeighborsRegressor': 396.2833359591075, 'Lasso': 317.7083614515365, 'Linear Regression': 390569049194624.4, 'Random Forest': 160.4129744282747, 'SVR': 66.33475220830024, 'XGBoost': 335.8377745484989}, 'Dielectric loss tangent': {'AdaBoost': 172710.1635466549, 'Bagging': 44953.26352908473, 'Decision Tree': 52.19340010650717, 'Elastic Net': 36224.35117433118, 'Gradient Boosting': 22121.13887344685, 'KNeighborsRegressor': 168655.028940735, 'Lasso': 36224.35117433118, 'Linear Regression': 2.641535386409126e+17, 'Random Forest': 41363.04908379746, 'SVR': 5322.736883263817, 'XGBoost': 3705.704281283099}, 'Isothermal weight loss temperature': {'AdaBoost': 0.2121303738628652, 'Bagging': 0.1349619170049794, 'Decision Tree': 2.892069024441983, 'Elastic Net': 1.768905048040275, 'Gradient Boosting': 1.973040599155559, 'KNeighborsRegressor': 3.304290702835374, 'Lasso': 2.240557961925726, 'Linear Regression': 252188349836.2027, 'Random Forest': 1.939705307007133, 'SVR': 8.54926858053373, 'XGBoost': 2.532247687986391}, 'Solubility parameter': {'AdaBoost': 2.516748264766484, 'Bagging': 2.57528041113496, 'Decision Tree': 3.642132590268647, 'Elastic Net': 0.04351092890080507, 'Gradient Boosting': 2.628519729136732, 'KNeighborsRegressor': 2.039180504859419, 'Lasso': 0.2423386080635236, 'Linear Regression': 802339537627.5287, 'Random Forest': 2.871599147232409, 'SVR': 4.243463490611647, 'XGBoost': 3.62351249890492}, 'Deflection temperature under load hdt': {'AdaBoost': 7.841976674373506, 'Bagging': 7.447404637196961, 'Decision Tree': 18.6989489539992, 'Elastic Net': 11.83930233924966, 'Gradient Boosting': 10.95633942158646, 'KNeighborsRegressor': 8.695987184666789, 'Lasso': 10.2257936832562, 'Linear Regression': 9877723991461.459, 'Random Forest': 3.996161169128247, 'SVR': 35.90570008301475, 'XGBoost': 6.674452125110557}, 'Specific heat capacity cp': {'AdaBoost': 46.35126160035153, 'Bagging': 12.52587426144066, 'Decision Tree': 22.93823957545029, 'Elastic Net': 15.66134324495683, 'Gradient Boosting': 18.54782225887455, 'KNeighborsRegressor': 11.7432791693929, 'Lasso': 15.66134324495683, 'Linear Regression': 48278958996484.49, 'Random Forest': 16.24088064292205, 'SVR': 9.592151392159632, 'XGBoost': 22.27169119438196}, 'Fiber tensile stress strength at break': {'AdaBoost': 137.3425070833215, 'Bagging': 29.67744248970118, 'Decision Tree': 25.92161257678384, 'Elastic Net': 674.6897978404547, 'Gradient Boosting': 1.095852055210109, 'KNeighborsRegressor': 593.9461054059971, 'Lasso': 10327.60670662555, 'Linear Regression': 96058731821493.88, 'Random Forest': 21.01134082222301, 'SVR': 7.972855538891658, 'XGBoost': 25.22000983890046}, 'Brittleness temperature': {'AdaBoost': 5.254600828768316, 'Bagging': 17.74868359128163, 'Decision Tree': 25.49465240641712, 'Elastic Net': 11.09350027362063, 'Gradient Boosting': 1.749713573181029, 'KNeighborsRegressor': 5.586887502475743, 'Lasso': 1.200470244424971, 'Linear Regression': 11237557876426.32, 'Random Forest': 21.55963844207718, 'SVR': 6.079352958591552, 'XGBoost': 3.475991250217778}, 'Tensile stress strength at yield': {'AdaBoost': 5892.836387513179, 'Bagging': 2622.424076479717, 'Decision Tree': 9.632560287592817, 'Elastic Net': 7055.017908519818, 'Gradient Boosting': 3535.365080179121, 'KNeighborsRegressor': 2009.205724398608, 'Lasso': 7055.017908519818, 'Linear Regression': 44444542184205.19, 'Random Forest': 1983.739759447883, 'SVR': 10613.35347930304, 'XGBoost': 1294.509205952464}, 'Dynamic mechanical properties storage modulus': {'AdaBoost': 533.2156126667481, 'Bagging': 229.8800248813164, 'Decision Tree': 153.3603484203236, 'Elastic Net': 405.1247191120137, 'Gradient Boosting': 286.3404046419042, 'KNeighborsRegressor': 306.9352961857741, 'Lasso': 405.1247191120137, 'Linear Regression': 2108994979639657.0, 'Random Forest': 302.3682228931419, 'SVR': 149.6070403182464, 'XGBoost': 397.8949963421099}, 'Dynamic flexural properties loss tanget': {'AdaBoost': 12983.06147562641, 'Bagging': 7731.181647526576, 'Decision 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2.335898608112762e+16, 'Random Forest': 191429.0100472788, 'SVR': 2308857.900547889, 'XGBoost': 112008.7065707482}, 'Oxygen index': {'AdaBoost': 18.43296171577722, 'Bagging': 12.24143955866896, 'Decision Tree': 13.19915955416843, 'Elastic Net': 25.02041377131845, 'Gradient Boosting': 23.97967035561662, 'KNeighborsRegressor': 13.62186054511694, 'Lasso': 20.31728673623869, 'Linear Regression': 148402755315.2355, 'Random Forest': 15.04787199400744, 'SVR': 19.50526655015106, 'XGBoost': 15.84167116126332}, 'Pvt relation pressure': {'AdaBoost': 6370.681596761351, 'Bagging': 8695.318305185367, 'Decision Tree': 1562.705824284304, 'Elastic Net': 8094.505919358507, 'Gradient Boosting': 6580.299895439521, 'KNeighborsRegressor': 6504.407243499897, 'Lasso': 5229.58642080249, 'Linear Regression': 4383.009667323964, 'Random Forest': 8251.509909865681, 'SVR': 6750.285081426385, 'XGBoost': 9658.403355743461}, 'Izod impact': {'AdaBoost': 36789.89192806105, 'Bagging': 22860.76054319949, 'Decision Tree': 660.198288008288, 'Elastic Net': 40360.81898045843, 'Gradient Boosting': 8626.78154218316, 'KNeighborsRegressor': 49904.82875116186, 'Lasso': 10204.83199112937, 'Linear Regression': 1340708253070806.0, 'Random Forest': 11336.72349830157, 'SVR': 12526.65562031707, 'XGBoost': 1098.602524585225}, 'Gas permeability coefficient p': {'AdaBoost': 567076.8435286551, 'Bagging': 350499.4174537582, 'Decision Tree': 354923.8207876546, 'Elastic Net': 354923.8207876546, 'Gradient Boosting': 354923.8207876546, 'KNeighborsRegressor': 284229.7052049361, 'Lasso': 354923.8207876546, 'Linear Regression': 701591215252620.5, 'Random Forest': 340020.6379133429, 'SVR': 35686035.18908095, 'XGBoost': 354923.8197531241}, 'Thermal diffusivity': {'AdaBoost': 105.9983267884515, 'Bagging': 4.132031240343655, 'Decision Tree': 21.32740176423991, 'Elastic Net': 2943.247346222378, 'Gradient Boosting': 9.670266834469198, 'KNeighborsRegressor': 4540.326354644502, 'Lasso': 2943.247346222378, 'Linear Regression': 4949.446239887803, 'Random Forest': 676.4775778589191, 'SVR': 34311.01186001458, 'XGBoost': 2943.247314234288}, 'Pvt relation temperature': {'AdaBoost': 683.0887625094804, 'Bagging': 498.2714587378982, 'Decision Tree': 160.809715269742, 'Elastic Net': 648.3598579379043, 'Gradient Boosting': 341.950683465822, 'KNeighborsRegressor': 425.0147264051294, 'Lasso': 251.4635844552195, 'Linear Regression': 292.661595592942, 'Random Forest': 383.1896797237347, 'SVR': 378.1966079083055, 'XGBoost': 418.0800338908207}, 'Radius of gyration': {'AdaBoost': 99.4946094585719, 'Bagging': 7.406826510535399, 'Decision Tree': 25.46371156629336, 'Elastic Net': 70.38012523908277, 'Gradient Boosting': 4.406616928841594, 'KNeighborsRegressor': 40.36446686507695, 'Lasso': 40.89268900475022, 'Linear Regression': 177516709831.3101, 'Random Forest': 26.5062598201221, 'SVR': 22.10147763531094, 'XGBoost': 16.7127050851522}, 'Crystallization kinetics half time of crystallization': {'AdaBoost': 1125.051979058566, 'Bagging': 348.3449735863499, 'Decision Tree': 352.2504544845077, 'Elastic Net': 414.0815138524425, 'Gradient Boosting': 147.3534637145724, 'KNeighborsRegressor': 1393.393367759999, 'Lasso': 861.0545639507324, 'Linear Regression': 593146353230087.9, 'Random Forest': 471.3207714395677, 'SVR': 243.6948232228799, 'XGBoost': 283.0525673135652}, 'Crystallization temperature': {'AdaBoost': 18.4548948595488, 'Bagging': 15.90847645939966, 'Decision Tree': 13.16272244212212, 'Elastic Net': 27.97625574960811, 'Gradient Boosting': 6.296890096758904, 'KNeighborsRegressor': 10.28130778023507, 'Lasso': 18.56029288803932, 'Linear Regression': 15222027828599.82, 'Random Forest': 8.882529185735045, 'SVR': 47.92232338143878, 'XGBoost': 21.26128353062285}, 'Gas solubility coefficient s': {'AdaBoost': 3545.085274491626, 'Bagging': 210249.971861026, 'Decision Tree': 788.2023661031337, 'Elastic Net': 600891.5973497161, 'Gradient Boosting': 77573.9907207359, 'KNeighborsRegressor': 127163.5540899012, 'Lasso': 600891.5973497161, 'Linear Regression': 117080243795390.5, 'Random Forest': 147326.2926350743, 'SVR': 9310025.573569825, 'XGBoost': 11602.70268172322}, 'Second virial coefficient': {'AdaBoost': 744.4441162028425, 'Bagging': 32174.36131179925, 'Decision Tree': 466.639996540205, 'Elastic Net': 146968.7611689301, 'Gradient Boosting': 109481.9182055699, 'KNeighborsRegressor': 304.0428226342707, 'Lasso': 146968.7611689301, 'Linear Regression': 45002865817742.13, 'Random Forest': 40229.82729369316, 'SVR': 80030.6513567453, 'XGBoost': 350.974068334799}, 'Hansen parameter delta-d: dispersion component': {'AdaBoost': 0.2409793068388772, 'Bagging': 2.050870760780942, 'Decision Tree': 1.859194454507607, 'Elastic Net': 12.71752205662354, 'Gradient Boosting': 0.7014629662862223, 'KNeighborsRegressor': 9.654381560204436, 'Lasso': 12.71534035545018, 'Linear Regression': 7979606208.459229, 'Random Forest': 4.767660601434521, 'SVR': 17.07096901617922, 'XGBoost': 3.245906283001052}, 'Softening temperature': {'AdaBoost': 61.32173023057573, 'Bagging': 20.72849777564443, 'Decision Tree': 26.30609296589629, 'Elastic Net': 45.81608269763366, 'Gradient Boosting': 33.31257914529717, 'KNeighborsRegressor': 21.98612103354772, 'Lasso': 33.94917712358138, 'Linear Regression': 544533261811.202, 'Random Forest': 22.4745331187391, 'SVR': 58.59785505238658, 'XGBoost': 24.85926686944195}, 'Dielectric constant ac': {'AdaBoost': 89.34434382246737, 'Bagging': 201.4777281108421, 'Decision Tree': 863.6338966509591, 'Elastic Net': 431.7879270186021, 'Gradient Boosting': 214.8800393047899, 'KNeighborsRegressor': 19.97134296763855, 'Lasso': 131.5573003872662, 'Linear Regression': 1734633654594.444, 'Random Forest': 161.1752662233606, 'SVR': 2.813923816574735, 'XGBoost': 21.51306594743219}, 'Fiber tensile modulus': {'AdaBoost': 106.1414705086918, 'Bagging': 44.93235930946099, 'Decision Tree': 37.08987166821358, 'Elastic Net': 112.6736357943552, 'Gradient Boosting': 40.00712636007616, 'KNeighborsRegressor': 112.6550380851465, 'Lasso': 59.79953515943763, 'Linear Regression': 486918601144.7983, 'Random Forest': 52.72594142201181, 'SVR': 34.77970101471595, 'XGBoost': 69.33888053735728}, 'Crystallization kinetics r': {'AdaBoost': 808.5824495596831, 'Bagging': 66648.4997322446, 'Decision Tree': 220087.737370373, 'Elastic Net': 39807.77747322961, 'Gradient Boosting': 96385.78056928585, 'KNeighborsRegressor': 119457.7638291923, 'Lasso': 142847.1687655617, 'Linear Regression': 820687818998972.4, 'Random Forest': 33924.19726682716, 'SVR': 647.3151333329332, 'XGBoost': 295.3335058563167}, 'Contact angle': {'AdaBoost': 6.290332161900008, 'Bagging': 6.137780065133006, 'Decision Tree': 12.95798055075642, 'Elastic Net': 11.08078958772906, 'Gradient Boosting': 8.32785612387434, 'KNeighborsRegressor': 6.215002041289843, 'Lasso': 10.58323730746112, 'Linear Regression': 66443431889.44066, 'Random Forest': 7.44640864401681, 'SVR': 12.56087940721959, 'XGBoost': 3.388830382946916}, 'Surface tension': {'AdaBoost': 19.59800668887849, 'Bagging': 1.488053741189081, 'Decision Tree': 0.0558445043972139, 'Elastic Net': 11.95248157404891, 'Gradient Boosting': 3.016775071672101, 'KNeighborsRegressor': 5.263454634741375, 'Lasso': 12.34266766686777, 'Linear Regression': 44720310440.90576, 'Random Forest': 0.4447997083556932, 'SVR': 5.326889009694397, 'XGBoost': 1.575068776978512}, 'Specific volume': {'AdaBoost': 7.876295541365476, 'Bagging': 5.075011888856835, 'Decision Tree': 3.752443356243171, 'Elastic Net': 4.816161191408002, 'Gradient Boosting': 3.527994286445203, 'KNeighborsRegressor': 4.119176382672763, 'Lasso': 4.816161191408002, 'Linear Regression': 20151898959.54559, 'Random Forest': 3.808679935072596, 'SVR': 4.307673823097778, 'XGBoost': 2.750312998781549}}\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty dictionary\n",
    "result_dict = {}\n",
    "\n",
    "# Iterate through the list_of_dfs\n",
    "for i in range(len(list_of_dfs)):\n",
    "    # Extract relevant information\n",
    "    entity = list_of_dfs[i][0].split('_')[1].split('.')[0]\n",
    "    names = list_of_dfs[i][1]['Name'].tolist()\n",
    "    r2_values = list_of_dfs[i][1]['MPE'].tolist()\n",
    "\n",
    "    # Create a dictionary for each name\n",
    "    for j in range(len(names)):\n",
    "        name = names[j]\n",
    "        r2_value = r2_values[j]\n",
    "\n",
    "        # If the name is not already a key in result_dict, add it\n",
    "        if name not in result_dict:\n",
    "            result_dict[name] = {}\n",
    "\n",
    "        # Add the entity and its corresponding R2 value to the name's dictionary\n",
    "        result_dict[name][entity] = r2_value\n",
    "\n",
    "# Print or use the dictionary as needed\n",
    "print(result_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3b711723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>MPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Thermal conductivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>27.162937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>76.941067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.248982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.561384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Flexural modulus</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>10.428773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Fiber tensile modulus</td>\n",
       "      <td>SVR</td>\n",
       "      <td>34.779701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Crystallization kinetics r</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>295.333506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>Contact angle</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>3.388830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.055845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.750313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 Charactristic      Regressor         MPE\n",
       "0                         Thermal conductivity        Bagging   27.162937\n",
       "1   Dynamic mechanical properties loss tangent  Decision Tree   76.941067\n",
       "2            Thermal decomposition temperature        XGBoost    2.248982\n",
       "3   Hansen parameter delta-h: hydrogen bonding        XGBoost    2.561384\n",
       "4                             Flexural modulus  Decision Tree   10.428773\n",
       "..                                         ...            ...         ...\n",
       "63                       Fiber tensile modulus            SVR   34.779701\n",
       "64                  Crystallization kinetics r        XGBoost  295.333506\n",
       "65                               Contact angle        XGBoost    3.388830\n",
       "66                             Surface tension  Decision Tree    0.055845\n",
       "67                             Specific volume        XGBoost    2.750313\n",
       "\n",
       "[68 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Create an empty DataFrame to store the results\n",
    "table_data = {'Charactristic': [], 'Regressor': [], 'MPE': []}\n",
    "for high_level_key, entity_dict in result_dict.items():\n",
    "    # Find the lower-level key with the max R2 value\n",
    "    max_entity = min(entity_dict, key=entity_dict.get)\n",
    "    max_r2_value = entity_dict[max_entity]\n",
    "\n",
    "    # Append data to the table_data dictionary\n",
    "    table_data['Charactristic'].append(high_level_key)\n",
    "    table_data['Regressor'].append(max_entity)\n",
    "    table_data['MPE'].append(max_r2_value)\n",
    "\n",
    "# Create a DataFrame from the table_data\n",
    "result_table = pd.DataFrame(table_data)\n",
    "\n",
    "# Display the result_table\n",
    "result_table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "48fdd5da",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>MPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Thermal conductivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>27.162937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>76.941067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.248982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.561384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Flexural modulus</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>10.428773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>Fiber tensile modulus</td>\n",
       "      <td>SVR</td>\n",
       "      <td>34.779701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>64</td>\n",
       "      <td>Crystallization kinetics r</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>295.333506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>Contact angle</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>3.388830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.055845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.750313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>68 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                               Charactristic      Regressor  \\\n",
       "0            0                        Thermal conductivity        Bagging   \n",
       "1            1  Dynamic mechanical properties loss tangent  Decision Tree   \n",
       "2            2           Thermal decomposition temperature        XGBoost   \n",
       "3            3  Hansen parameter delta-h: hydrogen bonding        XGBoost   \n",
       "4            4                            Flexural modulus  Decision Tree   \n",
       "..         ...                                         ...            ...   \n",
       "63          63                       Fiber tensile modulus            SVR   \n",
       "64          64                  Crystallization kinetics r        XGBoost   \n",
       "65          65                               Contact angle        XGBoost   \n",
       "66          66                             Surface tension  Decision Tree   \n",
       "67          67                             Specific volume        XGBoost   \n",
       "\n",
       "           MPE  \n",
       "0    27.162937  \n",
       "1    76.941067  \n",
       "2     2.248982  \n",
       "3     2.561384  \n",
       "4    10.428773  \n",
       "..         ...  \n",
       "63   34.779701  \n",
       "64  295.333506  \n",
       "65    3.388830  \n",
       "66    0.055845  \n",
       "67    2.750313  \n",
       "\n",
       "[68 rows x 4 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_table_MPE = pd.read_csv('result_table_MPE.csv')\n",
    "result_table_MPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4465ce8f",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>MPE</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Thermal conductivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>27.162937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.248982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.561384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Flexural modulus</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>10.428773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.453383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Pvt relation specific volume</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.014522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>3.015693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12</td>\n",
       "      <td>Dynamic flexural properties storage modulus</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>34.925791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>Thermal decomposition weight loss</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>14.646020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>1.054177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.449479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>Elongation at yield</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>14.025028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Dynamic shear properties loss tangent</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>3.836445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.959539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.229773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>Crystallization kinetics n</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>3.853924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Density</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.504447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>32</td>\n",
       "      <td>Flexural stress strength at break</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>30.770828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.134962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>0.043511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>3.996161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>41</td>\n",
       "      <td>Specific heat capacity cp</td>\n",
       "      <td>SVR</td>\n",
       "      <td>9.592151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>1.095852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>Brittleness temperature</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>1.200470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>44</td>\n",
       "      <td>Tensile stress strength at yield</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>9.632560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>Refractive index</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.912143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>12.241440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>Thermal diffusivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>4.132031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>55</td>\n",
       "      <td>Radius of gyration</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>4.406617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>6.296890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>60</td>\n",
       "      <td>Hansen parameter delta-d: dispersion component</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.240979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>Softening temperature</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>20.728498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>Dielectric constant ac</td>\n",
       "      <td>SVR</td>\n",
       "      <td>2.813924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>63</td>\n",
       "      <td>Fiber tensile modulus</td>\n",
       "      <td>SVR</td>\n",
       "      <td>34.779701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>65</td>\n",
       "      <td>Contact angle</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>3.388830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.055845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>2.750313</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                   Charactristic  \\\n",
       "0            0                            Thermal conductivity   \n",
       "2            2               Thermal decomposition temperature   \n",
       "3            3      Hansen parameter delta-h: hydrogen bonding   \n",
       "4            4                                Flexural modulus   \n",
       "5            5                     Vicat softening temperature   \n",
       "7            7                    Pvt relation specific volume   \n",
       "11          11                 Lc phase transition temperature   \n",
       "12          12     Dynamic flexural properties storage modulus   \n",
       "13          13               Thermal decomposition weight loss   \n",
       "14          14                             Melting temperature   \n",
       "15          15                  Hansen parameter delta p polar   \n",
       "19          19                             Elongation at yield   \n",
       "20          20           Dynamic shear properties loss tangent   \n",
       "22          22                         Cohesive energy density   \n",
       "23          23                    Glass transition temperature   \n",
       "25          25                      Crystallization kinetics n   \n",
       "27          27                                         Density   \n",
       "32          32               Flexural stress strength at break   \n",
       "38          38              Isothermal weight loss temperature   \n",
       "39          39                            Solubility parameter   \n",
       "40          40           Deflection temperature under load hdt   \n",
       "41          41                       Specific heat capacity cp   \n",
       "42          42          Fiber tensile stress strength at break   \n",
       "43          43                         Brittleness temperature   \n",
       "44          44                Tensile stress strength at yield   \n",
       "47          47                                Refractive index   \n",
       "49          49                                    Oxygen index   \n",
       "53          53                             Thermal diffusivity   \n",
       "55          55                              Radius of gyration   \n",
       "57          57                     Crystallization temperature   \n",
       "60          60  Hansen parameter delta-d: dispersion component   \n",
       "61          61                           Softening temperature   \n",
       "62          62                          Dielectric constant ac   \n",
       "63          63                           Fiber tensile modulus   \n",
       "65          65                                   Contact angle   \n",
       "66          66                                 Surface tension   \n",
       "67          67                                 Specific volume   \n",
       "\n",
       "            Regressor        MPE  \n",
       "0             Bagging  27.162937  \n",
       "2             XGBoost   2.248982  \n",
       "3             XGBoost   2.561384  \n",
       "4       Decision Tree  10.428773  \n",
       "5               Lasso   0.453383  \n",
       "7                 SVR   0.014522  \n",
       "11            Bagging   3.015693  \n",
       "12            XGBoost  34.925791  \n",
       "13      Decision Tree  14.646020  \n",
       "14      Decision Tree   1.054177  \n",
       "15                SVR   0.449479  \n",
       "19            XGBoost  14.025028  \n",
       "20            XGBoost   3.836445  \n",
       "22  Gradient Boosting   0.959539  \n",
       "23              Lasso   1.229773  \n",
       "25           AdaBoost   3.853924  \n",
       "27                SVR   0.504447  \n",
       "32            XGBoost  30.770828  \n",
       "38            Bagging   0.134962  \n",
       "39        Elastic Net   0.043511  \n",
       "40      Random Forest   3.996161  \n",
       "41                SVR   9.592151  \n",
       "42  Gradient Boosting   1.095852  \n",
       "43              Lasso   1.200470  \n",
       "44      Decision Tree   9.632560  \n",
       "47      Decision Tree   0.912143  \n",
       "49            Bagging  12.241440  \n",
       "53            Bagging   4.132031  \n",
       "55  Gradient Boosting   4.406617  \n",
       "57  Gradient Boosting   6.296890  \n",
       "60           AdaBoost   0.240979  \n",
       "61            Bagging  20.728498  \n",
       "62                SVR   2.813924  \n",
       "63                SVR  34.779701  \n",
       "65            XGBoost   3.388830  \n",
       "66      Decision Tree   0.055845  \n",
       "67            XGBoost   2.750313  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_MPE = result_table_MPE[result_table_MPE['MPE']<50]\n",
    "best_MPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0aff6137",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_1 = pd.read_csv('best_res.csv')\n",
    "#final_1 = final_1.sort_values(by='Max $R^2$', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "bb2b6ca7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>Max $R^2$</th>\n",
       "      <th>NMSE</th>\n",
       "      <th>Data Size_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.12</td>\n",
       "      <td>8092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>Intrinsic viscosity eta</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>Density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>Elongation at break</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.21</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28</td>\n",
       "      <td>Softening temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23</td>\n",
       "      <td>Refractive index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27</td>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.23</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Isothermal weight loss time</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.14</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>Dynamic shear properties storage modulus</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.49</td>\n",
       "      <td>141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>Heat of crystallization</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.37</td>\n",
       "      <td>91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22</td>\n",
       "      <td>Brittleness temperature</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>Thermal diffusivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.06</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>Water vapor transmission</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.10</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>Crystallization kinetics k</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>Pvt relation specific volume</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.22</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25</td>\n",
       "      <td>Pvt relation pressure</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                               Charactristic  \\\n",
       "0           11                Glass transition temperature   \n",
       "1            1           Thermal decomposition temperature   \n",
       "2            7                         Melting temperature   \n",
       "3           17                     Intrinsic viscosity eta   \n",
       "4           30                             Specific volume   \n",
       "5           14                                     Density   \n",
       "6           15                         Elongation at break   \n",
       "7            6             Lc phase transition temperature   \n",
       "8           28                       Softening temperature   \n",
       "9           23                            Refractive index   \n",
       "10          27                 Crystallization temperature   \n",
       "11          29                             Surface tension   \n",
       "12          19                        Solubility parameter   \n",
       "13          10                     Cohesive energy density   \n",
       "14           0  Dynamic mechanical properties loss tangent   \n",
       "15          18          Isothermal weight loss temperature   \n",
       "16          16                 Isothermal weight loss time   \n",
       "17          24                                Oxygen index   \n",
       "18          12    Dynamic shear properties storage modulus   \n",
       "19          13                     Heat of crystallization   \n",
       "20          20       Deflection temperature under load hdt   \n",
       "21          21      Fiber tensile stress strength at break   \n",
       "22           3                 Vicat softening temperature   \n",
       "23          22                     Brittleness temperature   \n",
       "24          26                         Thermal diffusivity   \n",
       "25           5                    Water vapor transmission   \n",
       "26           8              Hansen parameter delta p polar   \n",
       "27           2  Hansen parameter delta-h: hydrogen bonding   \n",
       "28           9                  Crystallization kinetics k   \n",
       "29           4                Pvt relation specific volume   \n",
       "30          25                       Pvt relation pressure   \n",
       "\n",
       "              Regressor  Max $R^2$  NMSE  Data Size_x  \n",
       "0         Random Forest       0.88  0.12         8092  \n",
       "1         Random Forest       0.73  0.27         6325  \n",
       "2         Random Forest       0.71  0.29         3844  \n",
       "3     Gradient Boosting       0.74  0.26         1978  \n",
       "4               XGBoost       0.71  0.29         1739  \n",
       "5               XGBoost       0.56  0.44         1739  \n",
       "6     Gradient Boosting       0.55  0.45         1139  \n",
       "7         Random Forest       0.79  0.21          961  \n",
       "8         Random Forest       0.68  0.32          777  \n",
       "9               XGBoost       0.73  0.27          685  \n",
       "10        Random Forest       0.69  0.31          457  \n",
       "11              Bagging       0.59  0.41          348  \n",
       "12              XGBoost       0.77  0.23          324  \n",
       "13              XGBoost       0.82  0.18          324  \n",
       "14    Gradient Boosting       0.52  0.48          301  \n",
       "15              XGBoost       0.97  0.03          273  \n",
       "16              XGBoost       0.86  0.14          228  \n",
       "17              XGBoost       0.65  0.35          176  \n",
       "18  KNeighborsRegressor       0.51  0.49          141  \n",
       "19        Random Forest       0.65  0.35          124  \n",
       "20        Random Forest       0.61  0.39           99  \n",
       "21        Decision Tree       0.63  0.37           91  \n",
       "22    Gradient Boosting       0.67  0.33           82  \n",
       "23  KNeighborsRegressor       0.67  0.33           81  \n",
       "24              Bagging       0.94  0.06           80  \n",
       "25                  SVR       0.73  0.27           73  \n",
       "26              Bagging       0.90  0.10           59  \n",
       "27             AdaBoost       0.59  0.41           59  \n",
       "28              XGBoost       0.97  0.03           59  \n",
       "29        Decision Tree       0.78  0.22           56  \n",
       "30        Decision Tree       0.96  0.04           53  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "89b850b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>Max $R^2$</th>\n",
       "      <th>NMSE</th>\n",
       "      <th>Data Size_x</th>\n",
       "      <th>MPE_x</th>\n",
       "      <th>MPE_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>11</td>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.12</td>\n",
       "      <td>8092</td>\n",
       "      <td>1.229773</td>\n",
       "      <td>1.229773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>6325</td>\n",
       "      <td>2.248982</td>\n",
       "      <td>2.248982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>3844</td>\n",
       "      <td>1.054177</td>\n",
       "      <td>1.054177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17</td>\n",
       "      <td>Intrinsic viscosity eta</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1978</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>30</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.29</td>\n",
       "      <td>1739</td>\n",
       "      <td>2.750313</td>\n",
       "      <td>2.750313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>14</td>\n",
       "      <td>Density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.44</td>\n",
       "      <td>1739</td>\n",
       "      <td>0.504447</td>\n",
       "      <td>0.504447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15</td>\n",
       "      <td>Elongation at break</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.45</td>\n",
       "      <td>1139</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.21</td>\n",
       "      <td>961</td>\n",
       "      <td>3.015693</td>\n",
       "      <td>3.015693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>28</td>\n",
       "      <td>Softening temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.32</td>\n",
       "      <td>777</td>\n",
       "      <td>20.728498</td>\n",
       "      <td>20.728498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>23</td>\n",
       "      <td>Refractive index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>685</td>\n",
       "      <td>0.912143</td>\n",
       "      <td>0.912143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>27</td>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.31</td>\n",
       "      <td>457</td>\n",
       "      <td>6.296890</td>\n",
       "      <td>6.296890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>348</td>\n",
       "      <td>0.055845</td>\n",
       "      <td>0.055845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>19</td>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.23</td>\n",
       "      <td>324</td>\n",
       "      <td>0.043511</td>\n",
       "      <td>0.043511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10</td>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.18</td>\n",
       "      <td>324</td>\n",
       "      <td>0.959539</td>\n",
       "      <td>0.959539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0</td>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.48</td>\n",
       "      <td>301</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>18</td>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>273</td>\n",
       "      <td>0.134962</td>\n",
       "      <td>0.134962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>Isothermal weight loss time</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.14</td>\n",
       "      <td>228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>24</td>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>176</td>\n",
       "      <td>12.241440</td>\n",
       "      <td>12.241440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12</td>\n",
       "      <td>Dynamic shear properties storage modulus</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.49</td>\n",
       "      <td>141</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13</td>\n",
       "      <td>Heat of crystallization</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.35</td>\n",
       "      <td>124</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.39</td>\n",
       "      <td>99</td>\n",
       "      <td>3.996161</td>\n",
       "      <td>3.996161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.63</td>\n",
       "      <td>0.37</td>\n",
       "      <td>91</td>\n",
       "      <td>1.095852</td>\n",
       "      <td>1.095852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>82</td>\n",
       "      <td>0.453383</td>\n",
       "      <td>0.453383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>22</td>\n",
       "      <td>Brittleness temperature</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.33</td>\n",
       "      <td>81</td>\n",
       "      <td>1.200470</td>\n",
       "      <td>1.200470</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>26</td>\n",
       "      <td>Thermal diffusivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.06</td>\n",
       "      <td>80</td>\n",
       "      <td>4.132031</td>\n",
       "      <td>4.132031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>5</td>\n",
       "      <td>Water vapor transmission</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.73</td>\n",
       "      <td>0.27</td>\n",
       "      <td>73</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>8</td>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.10</td>\n",
       "      <td>59</td>\n",
       "      <td>0.449479</td>\n",
       "      <td>0.449479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.59</td>\n",
       "      <td>0.41</td>\n",
       "      <td>59</td>\n",
       "      <td>2.561384</td>\n",
       "      <td>2.561384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>9</td>\n",
       "      <td>Crystallization kinetics k</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.03</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>4</td>\n",
       "      <td>Pvt relation specific volume</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.22</td>\n",
       "      <td>56</td>\n",
       "      <td>0.014522</td>\n",
       "      <td>0.014522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>25</td>\n",
       "      <td>Pvt relation pressure</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.04</td>\n",
       "      <td>53</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                               Charactristic  \\\n",
       "0           11                Glass transition temperature   \n",
       "1            1           Thermal decomposition temperature   \n",
       "2            7                         Melting temperature   \n",
       "3           17                     Intrinsic viscosity eta   \n",
       "4           30                             Specific volume   \n",
       "5           14                                     Density   \n",
       "6           15                         Elongation at break   \n",
       "7            6             Lc phase transition temperature   \n",
       "8           28                       Softening temperature   \n",
       "9           23                            Refractive index   \n",
       "10          27                 Crystallization temperature   \n",
       "11          29                             Surface tension   \n",
       "12          19                        Solubility parameter   \n",
       "13          10                     Cohesive energy density   \n",
       "14           0  Dynamic mechanical properties loss tangent   \n",
       "15          18          Isothermal weight loss temperature   \n",
       "16          16                 Isothermal weight loss time   \n",
       "17          24                                Oxygen index   \n",
       "18          12    Dynamic shear properties storage modulus   \n",
       "19          13                     Heat of crystallization   \n",
       "20          20       Deflection temperature under load hdt   \n",
       "21          21      Fiber tensile stress strength at break   \n",
       "22           3                 Vicat softening temperature   \n",
       "23          22                     Brittleness temperature   \n",
       "24          26                         Thermal diffusivity   \n",
       "25           5                    Water vapor transmission   \n",
       "26           8              Hansen parameter delta p polar   \n",
       "27           2  Hansen parameter delta-h: hydrogen bonding   \n",
       "28           9                  Crystallization kinetics k   \n",
       "29           4                Pvt relation specific volume   \n",
       "30          25                       Pvt relation pressure   \n",
       "\n",
       "              Regressor  Max $R^2$  NMSE  Data Size_x      MPE_x      MPE_y  \n",
       "0         Random Forest       0.88  0.12         8092   1.229773   1.229773  \n",
       "1         Random Forest       0.73  0.27         6325   2.248982   2.248982  \n",
       "2         Random Forest       0.71  0.29         3844   1.054177   1.054177  \n",
       "3     Gradient Boosting       0.74  0.26         1978        NaN        NaN  \n",
       "4               XGBoost       0.71  0.29         1739   2.750313   2.750313  \n",
       "5               XGBoost       0.56  0.44         1739   0.504447   0.504447  \n",
       "6     Gradient Boosting       0.55  0.45         1139        NaN        NaN  \n",
       "7         Random Forest       0.79  0.21          961   3.015693   3.015693  \n",
       "8         Random Forest       0.68  0.32          777  20.728498  20.728498  \n",
       "9               XGBoost       0.73  0.27          685   0.912143   0.912143  \n",
       "10        Random Forest       0.69  0.31          457   6.296890   6.296890  \n",
       "11              Bagging       0.59  0.41          348   0.055845   0.055845  \n",
       "12              XGBoost       0.77  0.23          324   0.043511   0.043511  \n",
       "13              XGBoost       0.82  0.18          324   0.959539   0.959539  \n",
       "14    Gradient Boosting       0.52  0.48          301        NaN        NaN  \n",
       "15              XGBoost       0.97  0.03          273   0.134962   0.134962  \n",
       "16              XGBoost       0.86  0.14          228        NaN        NaN  \n",
       "17              XGBoost       0.65  0.35          176  12.241440  12.241440  \n",
       "18  KNeighborsRegressor       0.51  0.49          141        NaN        NaN  \n",
       "19        Random Forest       0.65  0.35          124        NaN        NaN  \n",
       "20        Random Forest       0.61  0.39           99   3.996161   3.996161  \n",
       "21        Decision Tree       0.63  0.37           91   1.095852   1.095852  \n",
       "22    Gradient Boosting       0.67  0.33           82   0.453383   0.453383  \n",
       "23  KNeighborsRegressor       0.67  0.33           81   1.200470   1.200470  \n",
       "24              Bagging       0.94  0.06           80   4.132031   4.132031  \n",
       "25                  SVR       0.73  0.27           73        NaN        NaN  \n",
       "26              Bagging       0.90  0.10           59   0.449479   0.449479  \n",
       "27             AdaBoost       0.59  0.41           59   2.561384   2.561384  \n",
       "28              XGBoost       0.97  0.03           59        NaN        NaN  \n",
       "29        Decision Tree       0.78  0.22           56   0.014522   0.014522  \n",
       "30        Decision Tree       0.96  0.04           53        NaN        NaN  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_1 = pd.merge(final_1, best_MPE[['Charactristic', 'MPE']], on='Charactristic', how='left')\n",
    "final_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "71976e74",
   "metadata": {},
   "outputs": [],
   "source": [
    "#final_1.drop(['NMSE'], axis=1, inplace=True)#to_csv('best_res.csv')\n",
    "\n",
    "#final_1 = pd.read_excel('best_res.xlsx')\n",
    "final_1.to_csv('best_res.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5b3913b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_1['MPE'] = final_1['MPE'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f372a9b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dfdebda",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "196fa470",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79373294",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a7bd092",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e26e7d70",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "38a8f991",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "73831ebc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>Max VM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elongation at yield</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.771429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.677508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.569194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Fiber tensile elongation at break</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.766667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.660377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.923077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.895238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.617647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Pvt relation temperature</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.935484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Contact angle</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.906475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Crystallization kinetics half time of crystall...</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.956522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Heat of crystallization</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.649123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Thermal decomposition weight loss</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.957460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Isothermal weight loss time</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.679245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Pvt relation pressure</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.962963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.779661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.786885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Heat of fusion mol conversion</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.605634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.687500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Elongation at break</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.621053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Crystallization kinetics r</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.718750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.732558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.888591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Izod impact</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.733333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Fiber tensile modulus</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.823529</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Radius of gyration</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.626667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Charactristic            Regressor  \\\n",
       "0                                 Elongation at yield    Gradient Boosting   \n",
       "3                        Glass transition temperature        Random Forest   \n",
       "5                                 Melting temperature        Random Forest   \n",
       "13                 Isothermal weight loss temperature          Elastic Net   \n",
       "14                  Fiber tensile elongation at break  KNeighborsRegressor   \n",
       "17                               Solubility parameter    Gradient Boosting   \n",
       "21                        Vicat softening temperature        Random Forest   \n",
       "26                            Cohesive energy density        Random Forest   \n",
       "28             Fiber tensile stress strength at break                  SVR   \n",
       "29                                       Oxygen index              Bagging   \n",
       "30                           Pvt relation temperature             AdaBoost   \n",
       "31                        Crystallization temperature    Gradient Boosting   \n",
       "33                                      Contact angle        Random Forest   \n",
       "34  Crystallization kinetics half time of crystall...             AdaBoost   \n",
       "35                            Heat of crystallization                Lasso   \n",
       "38                  Thermal decomposition weight loss                  SVR   \n",
       "39                        Isothermal weight loss time                  SVR   \n",
       "41                              Pvt relation pressure             AdaBoost   \n",
       "46                    Lc phase transition temperature              XGBoost   \n",
       "48              Deflection temperature under load hdt              Bagging   \n",
       "49                      Heat of fusion mol conversion              Bagging   \n",
       "54                     Hansen parameter delta p polar              Bagging   \n",
       "55                                Elongation at break        Decision Tree   \n",
       "56                         Crystallization kinetics r  KNeighborsRegressor   \n",
       "58                                    Surface tension              Bagging   \n",
       "59                  Thermal decomposition temperature              XGBoost   \n",
       "60                                        Izod impact  KNeighborsRegressor   \n",
       "61                              Fiber tensile modulus             AdaBoost   \n",
       "64                                 Radius of gyration              Bagging   \n",
       "\n",
       "      Max VM  \n",
       "0   0.771429  \n",
       "3   0.677508  \n",
       "5   0.569194  \n",
       "13  1.000000  \n",
       "14  0.766667  \n",
       "17  0.660377  \n",
       "21  0.923077  \n",
       "26  0.895238  \n",
       "28  0.617647  \n",
       "29  0.687500  \n",
       "30  0.935484  \n",
       "31  0.833333  \n",
       "33  0.906475  \n",
       "34  0.956522  \n",
       "35  0.649123  \n",
       "38  0.957460  \n",
       "39  0.679245  \n",
       "41  0.962963  \n",
       "46  0.779661  \n",
       "48  0.786885  \n",
       "49  0.605634  \n",
       "54  0.687500  \n",
       "55  0.621053  \n",
       "56  0.718750  \n",
       "58  0.732558  \n",
       "59  0.888591  \n",
       "60  0.733333  \n",
       "61  0.823529  \n",
       "64  0.626667  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vm = result_table[result_table['Max VM'] > 0.5]\n",
    "vm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b9242953",
   "metadata": {},
   "outputs": [],
   "source": [
    "bests_r2 = pd.read_excel('max_r2.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9fcc8e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>Max $R^2$</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.524970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.725618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Hansen parameter delta-h: hydrogen bonding</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.588484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.668552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>Pvt relation specific volume</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.777472</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Water vapor transmission</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.728422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.793456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.707474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.900058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>Crystallization kinetics k</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.968771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.818874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.879994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Dynamic shear properties storage modulus</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.510739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Heat of crystallization</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.646512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>Density</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.564882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>30</td>\n",
       "      <td>Elongation at break</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.547713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>34</td>\n",
       "      <td>Isothermal weight loss time</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.859393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>35</td>\n",
       "      <td>Intrinsic viscosity eta</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.738814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>38</td>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.967617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>39</td>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.774766</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>40</td>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.614563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.625683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>43</td>\n",
       "      <td>Brittleness temperature</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.668205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>47</td>\n",
       "      <td>Refractive index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.725368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.650719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>Pvt relation pressure</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.957573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>53</td>\n",
       "      <td>Thermal diffusivity</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.944809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>57</td>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.692198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>61</td>\n",
       "      <td>Softening temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.678676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>66</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.593994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>67</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.712654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                               Charactristic  \\\n",
       "1            1  Dynamic mechanical properties loss tangent   \n",
       "2            2           Thermal decomposition temperature   \n",
       "3            3  Hansen parameter delta-h: hydrogen bonding   \n",
       "5            5                 Vicat softening temperature   \n",
       "7            7                Pvt relation specific volume   \n",
       "10          10                    Water vapor transmission   \n",
       "11          11             Lc phase transition temperature   \n",
       "14          14                         Melting temperature   \n",
       "15          15              Hansen parameter delta p polar   \n",
       "17          17                  Crystallization kinetics k   \n",
       "22          22                     Cohesive energy density   \n",
       "23          23                Glass transition temperature   \n",
       "24          24    Dynamic shear properties storage modulus   \n",
       "26          26                     Heat of crystallization   \n",
       "27          27                                     Density   \n",
       "30          30                         Elongation at break   \n",
       "34          34                 Isothermal weight loss time   \n",
       "35          35                     Intrinsic viscosity eta   \n",
       "38          38          Isothermal weight loss temperature   \n",
       "39          39                        Solubility parameter   \n",
       "40          40       Deflection temperature under load hdt   \n",
       "42          42      Fiber tensile stress strength at break   \n",
       "43          43                     Brittleness temperature   \n",
       "47          47                            Refractive index   \n",
       "49          49                                Oxygen index   \n",
       "50          50                       Pvt relation pressure   \n",
       "53          53                         Thermal diffusivity   \n",
       "57          57                 Crystallization temperature   \n",
       "61          61                       Softening temperature   \n",
       "66          66                             Surface tension   \n",
       "67          67                             Specific volume   \n",
       "\n",
       "              Regressor  Max $R^2$  \n",
       "1     Gradient Boosting   0.524970  \n",
       "2         Random Forest   0.725618  \n",
       "3              AdaBoost   0.588484  \n",
       "5     Gradient Boosting   0.668552  \n",
       "7         Decision Tree   0.777472  \n",
       "10                  SVR   0.728422  \n",
       "11        Random Forest   0.793456  \n",
       "14        Random Forest   0.707474  \n",
       "15              Bagging   0.900058  \n",
       "17              XGBoost   0.968771  \n",
       "22              XGBoost   0.818874  \n",
       "23        Random Forest   0.879994  \n",
       "24  KNeighborsRegressor   0.510739  \n",
       "26        Random Forest   0.646512  \n",
       "27              XGBoost   0.564882  \n",
       "30    Gradient Boosting   0.547713  \n",
       "34              XGBoost   0.859393  \n",
       "35    Gradient Boosting   0.738814  \n",
       "38              XGBoost   0.967617  \n",
       "39              XGBoost   0.774766  \n",
       "40        Random Forest   0.614563  \n",
       "42        Decision Tree   0.625683  \n",
       "43  KNeighborsRegressor   0.668205  \n",
       "47              XGBoost   0.725368  \n",
       "49              XGBoost   0.650719  \n",
       "50        Decision Tree   0.957573  \n",
       "53              Bagging   0.944809  \n",
       "57        Random Forest   0.692198  \n",
       "61        Random Forest   0.678676  \n",
       "66              Bagging   0.593994  \n",
       "67              XGBoost   0.712654  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2 = bests_r2[bests_r2['Max $R^2$']>0.5]\n",
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "380a53df",
   "metadata": {},
   "outputs": [],
   "source": [
    "vm_list = list(vm['Charactristic'])\n",
    "r2_list = list(r2['Charactristic'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ddf47ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Находим названия, которые есть в all_cols, но отсутствуют в parts_list\n",
    "missing_names = set(r2_list) - set(vm_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "564563ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Brittleness temperature',\n",
       " 'Crystallization kinetics k',\n",
       " 'Density',\n",
       " 'Dynamic mechanical properties loss tangent',\n",
       " 'Dynamic shear properties storage modulus',\n",
       " 'Hansen parameter delta-h: hydrogen bonding',\n",
       " 'Intrinsic viscosity eta',\n",
       " 'Pvt relation specific volume',\n",
       " 'Refractive index',\n",
       " 'Softening temperature',\n",
       " 'Specific volume',\n",
       " 'Thermal diffusivity',\n",
       " 'Water vapor transmission'}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "74e567d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "nonzero_counts = pd.read_excel('non_zero_var.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "29706ede",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Thermal conductivity</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Dynamic mechanical properties loss tangent</td>\n",
       "      <td>216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Dynamic compressive properties loss tangent</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Compressive stress strength at break</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>Surface tension</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>94</td>\n",
       "      <td>Interfacial tension</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95</th>\n",
       "      <td>95</td>\n",
       "      <td>Specific volume</td>\n",
       "      <td>1063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>96</td>\n",
       "      <td>Tensile creep compliance</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>97</td>\n",
       "      <td>Tensile creep rupture strength</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>98 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Unnamed: 0                                Charactristic  count\n",
       "0            0                         Thermal conductivity     23\n",
       "1            1   Dynamic mechanical properties loss tangent    216\n",
       "2            2  Dynamic compressive properties loss tangent      6\n",
       "3            3            Thermal decomposition temperature   2968\n",
       "4            4         Compressive stress strength at break      8\n",
       "..         ...                                          ...    ...\n",
       "93          93                              Surface tension    176\n",
       "94          94                          Interfacial tension     13\n",
       "95          95                              Specific volume   1063\n",
       "96          96                     Tensile creep compliance      3\n",
       "97          97               Tensile creep rupture strength      2\n",
       "\n",
       "[98 rows x 3 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nonzero_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "85355d36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Charactristic</th>\n",
       "      <th>Regressor</th>\n",
       "      <th>Max VM</th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>count_var</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Elongation at yield</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.771429</td>\n",
       "      <td>29</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Glass transition temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.677508</td>\n",
       "      <td>34</td>\n",
       "      <td>6278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Melting temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.569194</td>\n",
       "      <td>24</td>\n",
       "      <td>2182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Isothermal weight loss temperature</td>\n",
       "      <td>Elastic Net</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Fiber tensile elongation at break</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.766667</td>\n",
       "      <td>54</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Solubility parameter</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.660377</td>\n",
       "      <td>58</td>\n",
       "      <td>218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Vicat softening temperature</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.923077</td>\n",
       "      <td>9</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Cohesive energy density</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.895238</td>\n",
       "      <td>33</td>\n",
       "      <td>219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Fiber tensile stress strength at break</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.617647</td>\n",
       "      <td>62</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Oxygen index</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>70</td>\n",
       "      <td>144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Pvt relation temperature</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.935484</td>\n",
       "      <td>76</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Crystallization temperature</td>\n",
       "      <td>Gradient Boosting</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>79</td>\n",
       "      <td>331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Contact angle</td>\n",
       "      <td>Random Forest</td>\n",
       "      <td>0.906475</td>\n",
       "      <td>92</td>\n",
       "      <td>116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Crystallization kinetics half time of crystall...</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.956522</td>\n",
       "      <td>78</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Heat of crystallization</td>\n",
       "      <td>Lasso</td>\n",
       "      <td>0.649123</td>\n",
       "      <td>38</td>\n",
       "      <td>67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Thermal decomposition weight loss</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.957460</td>\n",
       "      <td>23</td>\n",
       "      <td>3567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Isothermal weight loss time</td>\n",
       "      <td>SVR</td>\n",
       "      <td>0.679245</td>\n",
       "      <td>51</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Pvt relation pressure</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.962963</td>\n",
       "      <td>71</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Lc phase transition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.779661</td>\n",
       "      <td>19</td>\n",
       "      <td>430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Deflection temperature under load hdt</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.786885</td>\n",
       "      <td>59</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Heat of fusion mol conversion</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.605634</td>\n",
       "      <td>28</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Hansen parameter delta p polar</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.687500</td>\n",
       "      <td>25</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Elongation at break</td>\n",
       "      <td>Decision Tree</td>\n",
       "      <td>0.621053</td>\n",
       "      <td>46</td>\n",
       "      <td>854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Crystallization kinetics r</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.718750</td>\n",
       "      <td>91</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Surface tension</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.732558</td>\n",
       "      <td>93</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Thermal decomposition temperature</td>\n",
       "      <td>XGBoost</td>\n",
       "      <td>0.888591</td>\n",
       "      <td>3</td>\n",
       "      <td>2968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Izod impact</td>\n",
       "      <td>KNeighborsRegressor</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>72</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Fiber tensile modulus</td>\n",
       "      <td>AdaBoost</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>89</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Radius of gyration</td>\n",
       "      <td>Bagging</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>77</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        Charactristic            Regressor  \\\n",
       "0                                 Elongation at yield    Gradient Boosting   \n",
       "1                        Glass transition temperature        Random Forest   \n",
       "2                                 Melting temperature        Random Forest   \n",
       "3                  Isothermal weight loss temperature          Elastic Net   \n",
       "4                   Fiber tensile elongation at break  KNeighborsRegressor   \n",
       "5                                Solubility parameter    Gradient Boosting   \n",
       "6                         Vicat softening temperature        Random Forest   \n",
       "7                             Cohesive energy density        Random Forest   \n",
       "8              Fiber tensile stress strength at break                  SVR   \n",
       "9                                        Oxygen index              Bagging   \n",
       "10                           Pvt relation temperature             AdaBoost   \n",
       "11                        Crystallization temperature    Gradient Boosting   \n",
       "12                                      Contact angle        Random Forest   \n",
       "13  Crystallization kinetics half time of crystall...             AdaBoost   \n",
       "14                            Heat of crystallization                Lasso   \n",
       "15                  Thermal decomposition weight loss                  SVR   \n",
       "16                        Isothermal weight loss time                  SVR   \n",
       "17                              Pvt relation pressure             AdaBoost   \n",
       "18                    Lc phase transition temperature              XGBoost   \n",
       "19              Deflection temperature under load hdt              Bagging   \n",
       "20                      Heat of fusion mol conversion              Bagging   \n",
       "21                     Hansen parameter delta p polar              Bagging   \n",
       "22                                Elongation at break        Decision Tree   \n",
       "23                         Crystallization kinetics r  KNeighborsRegressor   \n",
       "24                                    Surface tension              Bagging   \n",
       "25                  Thermal decomposition temperature              XGBoost   \n",
       "26                                        Izod impact  KNeighborsRegressor   \n",
       "27                              Fiber tensile modulus             AdaBoost   \n",
       "28                                 Radius of gyration              Bagging   \n",
       "\n",
       "      Max VM  Unnamed: 0  count_var  \n",
       "0   0.771429          29         49  \n",
       "1   0.677508          34       6278  \n",
       "2   0.569194          24       2182  \n",
       "3   1.000000          56        219  \n",
       "4   0.766667          54         31  \n",
       "5   0.660377          58        218  \n",
       "6   0.923077           9         56  \n",
       "7   0.895238          33        219  \n",
       "8   0.617647          62         57  \n",
       "9   0.687500          70        144  \n",
       "10  0.935484          76         26  \n",
       "11  0.833333          79        331  \n",
       "12  0.906475          92        116  \n",
       "13  0.956522          78         26  \n",
       "14  0.649123          38         67  \n",
       "15  0.957460          23       3567  \n",
       "16  0.679245          51        175  \n",
       "17  0.962963          71         26  \n",
       "18  0.779661          19        430  \n",
       "19  0.786885          59         38  \n",
       "20  0.605634          28        154  \n",
       "21  0.687500          25         43  \n",
       "22  0.621053          46        854  \n",
       "23  0.718750          91         21  \n",
       "24  0.732558          93        176  \n",
       "25  0.888591           3       2968  \n",
       "26  0.733333          72         23  \n",
       "27  0.823529          89         40  \n",
       "28  0.626667          77         45  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final = vm.merge(nonzero_counts.rename(columns={'count': 'count_var'}), on='Charactristic', how='left')\n",
    "final\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5449662e",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = pd.read_excel('best VM.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "017b0e7c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "final.to_csv('best VM.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5612040d",
   "metadata": {},
   "outputs": [],
   "source": [
    "final = final.sort_values(by='Max VM', ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "225f6be5",
   "metadata": {},
   "outputs": [],
   "source": [
    "final['Max VM'] = final['Max VM'].round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "dbf3ac58",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "with open('category_dict.json', 'r') as json_file:\n",
    "    category_dict = json.load(json_file)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "940ce4f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Compression characteristics': ['Compressive modulus',\n",
       "  'Compressive stress strength at break',\n",
       "  'Compressive stress strength at yield',\n",
       "  'Dynamic compressive properties storage modulus',\n",
       "  'Dynamic compressive properties loss tangent',\n",
       "  'Dynamic compressive properties loss modulus'],\n",
       " 'Creep characteristics': ['Tensile creep compliance',\n",
       "  'Tensile creep modulus',\n",
       "  'Tensile creep recovery',\n",
       "  'Tensile creep rupture time',\n",
       "  'Tensile creep strain',\n",
       "  'Flexural creep strain',\n",
       "  'Tensile creep rupture strength'],\n",
       " 'Dilute solution property': ['Intrinsic viscosity eta',\n",
       "  'Radius of gyration',\n",
       "  'Second virial coefficient',\n",
       "  'Diffusion coefficient',\n",
       "  'Sedimentation coefficient'],\n",
       " 'Electric property': ['Dielectric constant ac',\n",
       "  'Dielectric loss factor',\n",
       "  'Dielectric loss tangent',\n",
       "  'Electric conductivity',\n",
       "  'Surface resistivity',\n",
       "  'Volume resistivity'],\n",
       " 'Flexural property': ['Dynamic flexural properties storage modulus',\n",
       "  'Dynamic flexural properties loss modulus',\n",
       "  'Dynamic flexural properties loss tanget',\n",
       "  'Flexural modulus',\n",
       "  'Flexural stress strength at break',\n",
       "  'Flexural stress strength at yield'],\n",
       " 'Hardness': ['Shore hardness'],\n",
       " 'Heat characteristics': ['Brittleness temperature',\n",
       "  'Deflection temperature under load hdt',\n",
       "  'Softening temperature',\n",
       "  'Vicat softening temperature'],\n",
       " 'Heat resistance and combustion': ['Oxygen index'],\n",
       " 'Impact strength': ['Charpy impact', 'Izod impact'],\n",
       " 'Optical property': ['Refractive index'],\n",
       " 'Other physical property': ['Bulk modulus',\n",
       "  'Compressibility',\n",
       "  'G value',\n",
       "  'Pvt relation pressure',\n",
       "  'Pvt relation specific volume',\n",
       "  'Pvt relation temperature',\n",
       "  'Radiation resistance'],\n",
       " 'Physical property': ['Density', 'Specific volume'],\n",
       " 'Physicochemical property': ['Cohesive energy density',\n",
       "  'Gas diffusion coefficient d',\n",
       "  'Gas permeability coefficient p',\n",
       "  'Gas solubility coefficient s',\n",
       "  'Hansen parameter delta-d: dispersion component',\n",
       "  'Hansen parameter delta-h: hydrogen bonding',\n",
       "  'Hansen parameter delta p polar',\n",
       "  'Interfacial tension',\n",
       "  'Solubility parameter',\n",
       "  'Surface tension',\n",
       "  'Water absorption',\n",
       "  'Water vapor transmission',\n",
       "  'Contact angle'],\n",
       " 'Rheological property': ['Dynamic viscosity loss tangent'],\n",
       " 'Shear property': ['Dynamic shear properties storage modulus',\n",
       "  'Dynamic shear properties loss modulus',\n",
       "  'Dynamic shear properties loss tangent',\n",
       "  'Shear modulus',\n",
       "  'Shear stress strength at break',\n",
       "  'Shear stress strength at yield'],\n",
       " 'Tensile property': ['Dynamic mechanical properties storage modulus',\n",
       "  'Dynamic mechanical properties loss modulus',\n",
       "  'Dynamic mechanical properties loss tangent',\n",
       "  'Elongation at break',\n",
       "  'Elongation at yield',\n",
       "  'Fiber tensile elongation at break',\n",
       "  'Fiber tensile modulus',\n",
       "  'Fiber tensile stress strength at break',\n",
       "  'Tensile modulus',\n",
       "  'Tensile stress strength at break',\n",
       "  'Tensile stress strength at yield'],\n",
       " 'Thermal property': ['Crystallization kinetics r',\n",
       "  'Crystallization kinetics k',\n",
       "  'Crystallization kinetics n',\n",
       "  'Crystallization kinetics half time of crystallization',\n",
       "  'Crystallization temperature',\n",
       "  'Glass transition temperature',\n",
       "  'Heat of crystallization',\n",
       "  'Heat of fusion',\n",
       "  'Heat of fusion mol conversion',\n",
       "  'Thermal decomposition temperature',\n",
       "  'Thermal decomposition weight loss',\n",
       "  'Isothermal weight loss temperature',\n",
       "  'Isothermal weight loss time',\n",
       "  'Lc phase transition temperature',\n",
       "  'Melting temperature',\n",
       "  'Specific heat capacity cp',\n",
       "  'Specific heat capacity cv',\n",
       "  'Thermal conductivity',\n",
       "  'Thermal diffusivity']}"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "category_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "9e81fbcb",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'list' object has no attribute 'items'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[77], line 35\u001b[0m\n\u001b[0;32m     32\u001b[0m fig\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter(x\u001b[38;5;241m=\u001b[39mx, y\u001b[38;5;241m=\u001b[39my, text\u001b[38;5;241m=\u001b[39m[section], mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, showlegend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n\u001b[0;32m     34\u001b[0m \u001b[38;5;66;03m# Add keys and values\u001b[39;00m\n\u001b[1;32m---> 35\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m j, (key, value) \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[43mkeys\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitems\u001b[49m(), start\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m):\n\u001b[0;32m     36\u001b[0m     fig\u001b[38;5;241m.\u001b[39madd_trace(go\u001b[38;5;241m.\u001b[39mScatter(x\u001b[38;5;241m=\u001b[39mx, y\u001b[38;5;241m=\u001b[39m[y[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m0.1\u001b[39m \u001b[38;5;241m*\u001b[39m j], text\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvalue\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m], mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtext\u001b[39m\u001b[38;5;124m'\u001b[39m, showlegend\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m))\n\u001b[0;32m     38\u001b[0m \u001b[38;5;66;03m# Add arrows from 'Polymers' to each section\u001b[39;00m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'items'"
     ]
    }
   ],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.subplots import make_subplots\n",
    "\n",
    "# Define the sections and their keys in a dictionary\n",
    "sections = {\n",
    "    'Section1': {'key1': 'value1', 'key2': 'value2'},\n",
    "    'Section2': {'key3': 'value3', 'key4': 'value4'},\n",
    "    'Section3': {'key5': 'value5', 'key6': 'value6'},\n",
    "}\n",
    "\n",
    "# Create a Plotly subplot\n",
    "fig = make_subplots()\n",
    "\n",
    "# Add the central node 'Polymers'\n",
    "fig.add_trace(go.Scatter(x=[0], y=[0], text=['Polymers'], mode='text', showlegend=False))\n",
    "\n",
    "# Add nodes for each section\n",
    "for i, (section, keys) in enumerate(sections.items(), start=1):\n",
    "    x = [0.5 * (i % 2) * (-1) ** (i % 2)]  # Alternate sides\n",
    "    y = [-(i // 2) * 0.2]  # Adjust vertical positions\n",
    "\n",
    "    # Add a colorful box around each section\n",
    "    fig.add_shape(\n",
    "        type='rect',\n",
    "        xref='x',\n",
    "        yref='y',\n",
    "        x0=x[0] - 0.2,\n",
    "        x1=x[0] + 0.2,\n",
    "        y0=y[0] - 0.1,\n",
    "        y1=y[0] + 0.1 * (len(keys) + 1),\n",
    "        fillcolor=f'rgba({i * 50}, {i * 30}, {i * 20}, 0.5)',\n",
    "        line=dict(color='black'),\n",
    "    )\n",
    "\n",
    "    # Add section name\n",
    "    fig.add_trace(go.Scatter(x=x, y=y, text=[section], mode='text', showlegend=False))\n",
    "\n",
    "    # Add keys and values\n",
    "    for j, (key, value) in enumerate(keys.items(), start=1):\n",
    "        fig.add_trace(go.Scatter(x=x, y=[y[0] - 0.1 * j], text=[f\"{key}: {value}\"], mode='text', showlegend=False))\n",
    "\n",
    "    # Add arrows from 'Polymers' to each section\n",
    "    fig.add_trace(go.Scatter(\n",
    "        x=[0, x[0]],\n",
    "        y=[0, y[0]],\n",
    "        mode='lines+text',\n",
    "        text=[f\"{key}: {value}\" for key, value in keys.items()],\n",
    "        line=dict(color='black'),\n",
    "        showlegend=False\n",
    "    ))\n",
    "\n",
    "# Update layout for better visualization\n",
    "fig.update_layout(\n",
    "    hovermode='closest',\n",
    "    title='Polymers and Sections',\n",
    "    showlegend=False,\n",
    "    margin=dict(b=0, l=0, r=0, t=30),\n",
    ")\n",
    "\n",
    "# Show the graph\n",
    "fig.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "e8486a62",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid property specified for object of type plotly.graph_objs.Scatter: 'Compression characteristics'\n\nDid you mean \"hovertemplatesrc\"?\n\n    Valid properties:\n        cliponaxis\n            Determines whether or not markers and text nodes are\n            clipped about the subplot axes. To show markers and\n            text nodes above axis lines and tick labels, make sure\n            to set `xaxis.layer` and `yaxis.layer` to *below\n            traces*.\n        connectgaps\n            Determines whether or not gaps (i.e. {nan} or missing\n            values) in the provided data arrays are connected.\n        customdata\n            Assigns extra data each datum. This may be useful when\n            listening to hover, click and selection events. Note\n            that, \"scatter\" traces also appends customdata items in\n            the markers DOM elements\n        customdatasrc\n            Sets the source reference on Chart Studio Cloud for\n            `customdata`.\n        dx\n            Sets the x coordinate step. See `x0` for more info.\n        dy\n            Sets the y coordinate step. See `y0` for more info.\n        error_x\n            :class:`plotly.graph_objects.scatter.ErrorX` instance\n            or dict with compatible properties\n        error_y\n            :class:`plotly.graph_objects.scatter.ErrorY` instance\n            or dict with compatible properties\n        fill\n            Sets the area to fill with a solid color. Defaults to\n            \"none\" unless this trace is stacked, then it gets\n            \"tonexty\" (\"tonextx\") if `orientation` is \"v\" (\"h\") Use\n            with `fillcolor` if not \"none\". \"tozerox\" and \"tozeroy\"\n            fill to x=0 and y=0 respectively. \"tonextx\" and\n            \"tonexty\" fill between the endpoints of this trace and\n            the endpoints of the trace before it, connecting those\n            endpoints with straight lines (to make a stacked area\n            graph); if there is no trace before it, they behave\n            like \"tozerox\" and \"tozeroy\". \"toself\" connects the\n            endpoints of the trace (or each segment of the trace if\n            it has gaps) into a closed shape. \"tonext\" fills the\n            space between two traces if one completely encloses the\n            other (eg consecutive contour lines), and behaves like\n            \"toself\" if there is no trace before it. \"tonext\"\n            should not be used if one trace does not enclose the\n            other. Traces in a `stackgroup` will only fill to (or\n            be filled to) other traces in the same group. With\n            multiple `stackgroup`s or some traces stacked and some\n            not, if fill-linked traces are not already consecutive,\n            the later ones will be pushed down in the drawing\n            order.\n        fillcolor\n            Sets the fill color. Defaults to a half-transparent\n            variant of the line color, marker color, or marker line\n            color, whichever is available.\n        fillpattern\n            Sets the pattern within the marker.\n        groupnorm\n            Only relevant when `stackgroup` is used, and only the\n            first `groupnorm` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Sets the normalization for the sum of\n            this `stackgroup`. With \"fraction\", the value of each\n            trace at each location is divided by the sum of all\n            trace values at that location. \"percent\" is the same\n            but multiplied by 100 to show percentages. If there are\n            multiple subplots, or multiple `stackgroup`s on one\n            subplot, each will be normalized within its own set.\n        hoverinfo\n            Determines which trace information appear on hover. If\n            `none` or `skip` are set, no information is displayed\n            upon hovering. But, if `none` is set, click and hover\n            events are still fired.\n        hoverinfosrc\n            Sets the source reference on Chart Studio Cloud for\n            `hoverinfo`.\n        hoverlabel\n            :class:`plotly.graph_objects.scatter.Hoverlabel`\n            instance or dict with compatible properties\n        hoveron\n            Do the hover effects highlight individual points\n            (markers or line points) or do they highlight filled\n            regions? If the fill is \"toself\" or \"tonext\" and there\n            are no markers or text, then the default is \"fills\",\n            otherwise it is \"points\".\n        hovertemplate\n            Template string used for rendering the information that\n            appear on hover box. Note that this will override\n            `hoverinfo`. Variables are inserted using %{variable},\n            for example \"y: %{y}\" as well as %{xother}, {%_xother},\n            {%_xother_}, {%xother_}. When showing info for several\n            points, \"xother\" will be added to those with different\n            x positions from the first point. An underscore before\n            or after \"(x|y)other\" will add a space on that side,\n            only when this field is shown. Numbers are formatted\n            using d3-format's syntax %{variable:d3-format}, for\n            example \"Price: %{y:$.2f}\".\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n            for details on the formatting syntax. Dates are\n            formatted using d3-time-format's syntax\n            %{variable|d3-time-format}, for example \"Day:\n            %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format for details on the\n            date formatting syntax. The variables available in\n            `hovertemplate` are the ones emitted as event data\n            described at this link\n            https://plotly.com/javascript/plotlyjs-events/#event-\n            data. Additionally, every attributes that can be\n            specified per-point (the ones that are `arrayOk: true`)\n            are available.  Anything contained in tag `<extra>` is\n            displayed in the secondary box, for example\n            \"<extra>{fullData.name}</extra>\". To hide the secondary\n            box completely, use an empty tag `<extra></extra>`.\n        hovertemplatesrc\n            Sets the source reference on Chart Studio Cloud for\n            `hovertemplate`.\n        hovertext\n            Sets hover text elements associated with each (x,y)\n            pair. If a single string, the same string appears over\n            all the data points. If an array of string, the items\n            are mapped in order to the this trace's (x,y)\n            coordinates. To be seen, trace `hoverinfo` must contain\n            a \"text\" flag.\n        hovertextsrc\n            Sets the source reference on Chart Studio Cloud for\n            `hovertext`.\n        ids\n            Assigns id labels to each datum. These ids for object\n            constancy of data points during animation. Should be an\n            array of strings, not numbers or any other type.\n        idssrc\n            Sets the source reference on Chart Studio Cloud for\n            `ids`.\n        legendgroup\n            Sets the legend group for this trace. Traces part of\n            the same legend group hide/show at the same time when\n            toggling legend items.\n        legendgrouptitle\n            :class:`plotly.graph_objects.scatter.Legendgrouptitle`\n            instance or dict with compatible properties\n        legendrank\n            Sets the legend rank for this trace. Items and groups\n            with smaller ranks are presented on top/left side while\n            with `*reversed* `legend.traceorder` they are on\n            bottom/right side. The default legendrank is 1000, so\n            that you can use ranks less than 1000 to place certain\n            items before all unranked items, and ranks greater than\n            1000 to go after all unranked items.\n        line\n            :class:`plotly.graph_objects.scatter.Line` instance or\n            dict with compatible properties\n        marker\n            :class:`plotly.graph_objects.scatter.Marker` instance\n            or dict with compatible properties\n        meta\n            Assigns extra meta information associated with this\n            trace that can be used in various text attributes.\n            Attributes such as trace `name`, graph, axis and\n            colorbar `title.text`, annotation `text`\n            `rangeselector`, `updatemenues` and `sliders` `label`\n            text all support `meta`. To access the trace `meta`\n            values in an attribute in the same trace, simply use\n            `%{meta[i]}` where `i` is the index or key of the\n            `meta` item in question. To access trace `meta` in\n            layout attributes, use `%{data[n[.meta[i]}` where `i`\n            is the index or key of the `meta` and `n` is the trace\n            index.\n        metasrc\n            Sets the source reference on Chart Studio Cloud for\n            `meta`.\n        mode\n            Determines the drawing mode for this scatter trace. If\n            the provided `mode` includes \"text\" then the `text`\n            elements appear at the coordinates. Otherwise, the\n            `text` elements appear on hover. If there are less than\n            20 points and the trace is not stacked then the default\n            is \"lines+markers\". Otherwise, \"lines\".\n        name\n            Sets the trace name. The trace name appear as the\n            legend item and on hover.\n        opacity\n            Sets the opacity of the trace.\n        orientation\n            Only relevant when `stackgroup` is used, and only the\n            first `orientation` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Sets the stacking direction. With \"v\"\n            (\"h\"), the y (x) values of subsequent traces are added.\n            Also affects the default value of `fill`.\n        selected\n            :class:`plotly.graph_objects.scatter.Selected` instance\n            or dict with compatible properties\n        selectedpoints\n            Array containing integer indices of selected points.\n            Has an effect only for traces that support selections.\n            Note that an empty array means an empty selection where\n            the `unselected` are turned on for all points, whereas,\n            any other non-array values means no selection all where\n            the `selected` and `unselected` styles have no effect.\n        showlegend\n            Determines whether or not an item corresponding to this\n            trace is shown in the legend.\n        stackgaps\n            Only relevant when `stackgroup` is used, and only the\n            first `stackgaps` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Determines how we handle locations at\n            which other traces in this group have data but this one\n            does not. With *infer zero* we insert a zero at these\n            locations. With \"interpolate\" we linearly interpolate\n            between existing values, and extrapolate a constant\n            beyond the existing values.\n        stackgroup\n            Set several scatter traces (on the same subplot) to the\n            same stackgroup in order to add their y values (or\n            their x values if `orientation` is \"h\"). If blank or\n            omitted this trace will not be stacked. Stacking also\n            turns `fill` on by default, using \"tonexty\" (\"tonextx\")\n            if `orientation` is \"h\" (\"v\") and sets the default\n            `mode` to \"lines\" irrespective of point count. You can\n            only stack on a numeric (linear or log) axis. Traces in\n            a `stackgroup` will only fill to (or be filled to)\n            other traces in the same group. With multiple\n            `stackgroup`s or some traces stacked and some not, if\n            fill-linked traces are not already consecutive, the\n            later ones will be pushed down in the drawing order.\n        stream\n            :class:`plotly.graph_objects.scatter.Stream` instance\n            or dict with compatible properties\n        text\n            Sets text elements associated with each (x,y) pair. If\n            a single string, the same string appears over all the\n            data points. If an array of string, the items are\n            mapped in order to the this trace's (x,y) coordinates.\n            If trace `hoverinfo` contains a \"text\" flag and\n            \"hovertext\" is not set, these elements will be seen in\n            the hover labels.\n        textfont\n            Sets the text font.\n        textposition\n            Sets the positions of the `text` elements with respects\n            to the (x,y) coordinates.\n        textpositionsrc\n            Sets the source reference on Chart Studio Cloud for\n            `textposition`.\n        textsrc\n            Sets the source reference on Chart Studio Cloud for\n            `text`.\n        texttemplate\n            Template string used for rendering the information text\n            that appear on points. Note that this will override\n            `textinfo`. Variables are inserted using %{variable},\n            for example \"y: %{y}\". Numbers are formatted using\n            d3-format's syntax %{variable:d3-format}, for example\n            \"Price: %{y:$.2f}\".\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n            for details on the formatting syntax. Dates are\n            formatted using d3-time-format's syntax\n            %{variable|d3-time-format}, for example \"Day:\n            %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format for details on the\n            date formatting syntax. Every attributes that can be\n            specified per-point (the ones that are `arrayOk: true`)\n            are available.\n        texttemplatesrc\n            Sets the source reference on Chart Studio Cloud for\n            `texttemplate`.\n        uid\n            Assign an id to this trace, Use this to provide object\n            constancy between traces during animations and\n            transitions.\n        uirevision\n            Controls persistence of some user-driven changes to the\n            trace: `constraintrange` in `parcoords` traces, as well\n            as some `editable: true` modifications such as `name`\n            and `colorbar.title`. Defaults to `layout.uirevision`.\n            Note that other user-driven trace attribute changes are\n            controlled by `layout` attributes: `trace.visible` is\n            controlled by `layout.legend.uirevision`,\n            `selectedpoints` is controlled by\n            `layout.selectionrevision`, and `colorbar.(x|y)`\n            (accessible with `config: {editable: true}`) is\n            controlled by `layout.editrevision`. Trace changes are\n            tracked by `uid`, which only falls back on trace index\n            if no `uid` is provided. So if your app can add/remove\n            traces before the end of the `data` array, such that\n            the same trace has a different index, you can still\n            preserve user-driven changes if you give each trace a\n            `uid` that stays with it as it moves.\n        unselected\n            :class:`plotly.graph_objects.scatter.Unselected`\n            instance or dict with compatible properties\n        visible\n            Determines whether or not this trace is visible. If\n            \"legendonly\", the trace is not drawn, but can appear as\n            a legend item (provided that the legend itself is\n            visible).\n        x\n            Sets the x coordinates.\n        x0\n            Alternate to `x`. Builds a linear space of x\n            coordinates. Use with `dx` where `x0` is the starting\n            coordinate and `dx` the step.\n        xaxis\n            Sets a reference between this trace's x coordinates and\n            a 2D cartesian x axis. If \"x\" (the default value), the\n            x coordinates refer to `layout.xaxis`. If \"x2\", the x\n            coordinates refer to `layout.xaxis2`, and so on.\n        xcalendar\n            Sets the calendar system to use with `x` date data.\n        xhoverformat\n            Sets the hover text formatting rulefor `x`  using d3\n            formatting mini-languages which are very similar to\n            those in Python. For numbers, see:\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n            And for dates see: https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format. We add two items to\n            d3's date formatter: \"%h\" for half of the year as a\n            decimal number as well as \"%{n}f\" for fractional\n            seconds with n digits. For example, *2016-10-13\n            09:15:23.456* with tickformat \"%H~%M~%S.%2f\" would\n            display *09~15~23.46*By default the values are\n            formatted using `xaxis.hoverformat`.\n        xperiod\n            Only relevant when the axis `type` is \"date\". Sets the\n            period positioning in milliseconds or \"M<n>\" on the x\n            axis. Special values in the form of \"M<n>\" could be\n            used to declare the number of months. In this case `n`\n            must be a positive integer.\n        xperiod0\n            Only relevant when the axis `type` is \"date\". Sets the\n            base for period positioning in milliseconds or date\n            string on the x0 axis. When `x0period` is round number\n            of weeks, the `x0period0` by default would be on a\n            Sunday i.e. 2000-01-02, otherwise it would be at\n            2000-01-01.\n        xperiodalignment\n            Only relevant when the axis `type` is \"date\". Sets the\n            alignment of data points on the x axis.\n        xsrc\n            Sets the source reference on Chart Studio Cloud for\n            `x`.\n        y\n            Sets the y coordinates.\n        y0\n            Alternate to `y`. Builds a linear space of y\n            coordinates. Use with `dy` where `y0` is the starting\n            coordinate and `dy` the step.\n        yaxis\n            Sets a reference between this trace's y coordinates and\n            a 2D cartesian y axis. If \"y\" (the default value), the\n            y coordinates refer to `layout.yaxis`. If \"y2\", the y\n            coordinates refer to `layout.yaxis2`, and so on.\n        ycalendar\n            Sets the calendar system to use with `y` date data.\n        yhoverformat\n            Sets the hover text formatting rulefor `y`  using d3\n            formatting mini-languages which are very similar to\n            those in Python. For numbers, see:\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n            And for dates see: https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format. We add two items to\n            d3's date formatter: \"%h\" for half of the year as a\n            decimal number as well as \"%{n}f\" for fractional\n            seconds with n digits. For example, *2016-10-13\n            09:15:23.456* with tickformat \"%H~%M~%S.%2f\" would\n            display *09~15~23.46*By default the values are\n            formatted using `yaxis.hoverformat`.\n        yperiod\n            Only relevant when the axis `type` is \"date\". Sets the\n            period positioning in milliseconds or \"M<n>\" on the y\n            axis. Special values in the form of \"M<n>\" could be\n            used to declare the number of months. In this case `n`\n            must be a positive integer.\n        yperiod0\n            Only relevant when the axis `type` is \"date\". Sets the\n            base for period positioning in milliseconds or date\n            string on the y0 axis. When `y0period` is round number\n            of weeks, the `y0period0` by default would be on a\n            Sunday i.e. 2000-01-02, otherwise it would be at\n            2000-01-01.\n        yperiodalignment\n            Only relevant when the axis `type` is \"date\". Sets the\n            alignment of data points on the y axis.\n        ysrc\n            Sets the source reference on Chart Studio Cloud for\n            `y`.\n        \nDid you mean \"hovertemplatesrc\"?\n\nBad property path:\nCompression characteristics\n^^^^^^^^^^^^^^^^^^^^^^^^^^^",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[71], line 9\u001b[0m\n\u001b[0;32m      6\u001b[0m     graph_json \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(file)\n\u001b[0;32m      8\u001b[0m \u001b[38;5;66;03m# Create Plotly graph from JSON\u001b[39;00m\n\u001b[1;32m----> 9\u001b[0m fig \u001b[38;5;241m=\u001b[39m \u001b[43mgo\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mFigure\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgraph_json\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     11\u001b[0m \u001b[38;5;66;03m# Show the graph\u001b[39;00m\n\u001b[0;32m     12\u001b[0m fig\u001b[38;5;241m.\u001b[39mshow()\n",
      "File \u001b[1;32mD:\\Users\\ivan\\anaconda3\\lib\\site-packages\\plotly\\graph_objs\\_figure.py:594\u001b[0m, in \u001b[0;36mFigure.__init__\u001b[1;34m(self, data, layout, frames, skip_invalid, **kwargs)\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\n\u001b[0;32m      6\u001b[0m     \u001b[38;5;28mself\u001b[39m, data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, layout\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, frames\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, skip_invalid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m      7\u001b[0m ):\n\u001b[0;32m      8\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m      9\u001b[0m \u001b[38;5;124;03m    Create a new :class:Figure instance\u001b[39;00m\n\u001b[0;32m     10\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    592\u001b[0m \u001b[38;5;124;03m        is invalid AND skip_invalid is False\u001b[39;00m\n\u001b[0;32m    593\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[1;32m--> 594\u001b[0m     \u001b[38;5;28msuper\u001b[39m(Figure, \u001b[38;5;28mself\u001b[39m)\u001b[38;5;241m.\u001b[39m\u001b[38;5;21m__init__\u001b[39m(data, layout, frames, skip_invalid, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32mD:\\Users\\ivan\\anaconda3\\lib\\site-packages\\plotly\\basedatatypes.py:514\u001b[0m, in \u001b[0;36mBaseFigure.__init__\u001b[1;34m(self, data, layout_plotly, frames, skip_invalid, **kwargs)\u001b[0m\n\u001b[0;32m    511\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_validator \u001b[38;5;241m=\u001b[39m DataValidator(set_uid\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_set_trace_uid)\n\u001b[0;32m    513\u001b[0m \u001b[38;5;66;03m# ### Import traces ###\u001b[39;00m\n\u001b[1;32m--> 514\u001b[0m data \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_data_validator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mvalidate_coerce\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    515\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mskip_invalid\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mskip_invalid\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_validate\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate\u001b[49m\n\u001b[0;32m    516\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    518\u001b[0m \u001b[38;5;66;03m# ### Save tuple of trace objects ###\u001b[39;00m\n\u001b[0;32m    519\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_data_objs \u001b[38;5;241m=\u001b[39m data\n",
      "File \u001b[1;32mD:\\Users\\ivan\\anaconda3\\lib\\site-packages\\_plotly_utils\\basevalidators.py:2668\u001b[0m, in \u001b[0;36mBaseDataValidator.validate_coerce\u001b[1;34m(self, v, skip_invalid, _validate)\u001b[0m\n\u001b[0;32m   2666\u001b[0m             invalid_els\u001b[38;5;241m.\u001b[39mappend(v_el)\n\u001b[0;32m   2667\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m-> 2668\u001b[0m         trace \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_trace_class(trace_type)(\n\u001b[0;32m   2669\u001b[0m             skip_invalid\u001b[38;5;241m=\u001b[39mskip_invalid, _validate\u001b[38;5;241m=\u001b[39m_validate, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mv_copy\n\u001b[0;32m   2670\u001b[0m         )\n\u001b[0;32m   2671\u001b[0m         res\u001b[38;5;241m.\u001b[39mappend(trace)\n\u001b[0;32m   2672\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
      "File \u001b[1;32mD:\\Users\\ivan\\anaconda3\\lib\\site-packages\\plotly\\graph_objs\\_scatter.py:3370\u001b[0m, in \u001b[0;36mScatter.__init__\u001b[1;34m(self, arg, cliponaxis, connectgaps, customdata, customdatasrc, dx, dy, error_x, error_y, fill, fillcolor, fillpattern, groupnorm, hoverinfo, hoverinfosrc, hoverlabel, hoveron, hovertemplate, hovertemplatesrc, hovertext, hovertextsrc, ids, idssrc, legendgroup, legendgrouptitle, legendrank, line, marker, meta, metasrc, mode, name, opacity, orientation, selected, selectedpoints, showlegend, stackgaps, stackgroup, stream, text, textfont, textposition, textpositionsrc, textsrc, texttemplate, texttemplatesrc, uid, uirevision, unselected, visible, x, x0, xaxis, xcalendar, xhoverformat, xperiod, xperiod0, xperiodalignment, xsrc, y, y0, yaxis, ycalendar, yhoverformat, yperiod, yperiod0, yperiodalignment, ysrc, **kwargs)\u001b[0m\n\u001b[0;32m   3366\u001b[0m arg\u001b[38;5;241m.\u001b[39mpop(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtype\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[0;32m   3368\u001b[0m \u001b[38;5;66;03m# Process unknown kwargs\u001b[39;00m\n\u001b[0;32m   3369\u001b[0m \u001b[38;5;66;03m# ----------------------\u001b[39;00m\n\u001b[1;32m-> 3370\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_process_kwargs(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mdict\u001b[39m(arg, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs))\n\u001b[0;32m   3372\u001b[0m \u001b[38;5;66;03m# Reset skip_invalid\u001b[39;00m\n\u001b[0;32m   3373\u001b[0m \u001b[38;5;66;03m# ------------------\u001b[39;00m\n\u001b[0;32m   3374\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_skip_invalid \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
      "File \u001b[1;32mD:\\Users\\ivan\\anaconda3\\lib\\site-packages\\plotly\\basedatatypes.py:4377\u001b[0m, in \u001b[0;36mBasePlotlyType._process_kwargs\u001b[1;34m(self, **kwargs)\u001b[0m\n\u001b[0;32m   4375\u001b[0m     \u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;241m=\u001b[39m v\n\u001b[0;32m   4376\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_skip_invalid:\n\u001b[1;32m-> 4377\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m err\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid property specified for object of type plotly.graph_objs.Scatter: 'Compression characteristics'\n\nDid you mean \"hovertemplatesrc\"?\n\n    Valid properties:\n        cliponaxis\n            Determines whether or not markers and text nodes are\n            clipped about the subplot axes. To show markers and\n            text nodes above axis lines and tick labels, make sure\n            to set `xaxis.layer` and `yaxis.layer` to *below\n            traces*.\n        connectgaps\n            Determines whether or not gaps (i.e. {nan} or missing\n            values) in the provided data arrays are connected.\n        customdata\n            Assigns extra data each datum. This may be useful when\n            listening to hover, click and selection events. Note\n            that, \"scatter\" traces also appends customdata items in\n            the markers DOM elements\n        customdatasrc\n            Sets the source reference on Chart Studio Cloud for\n            `customdata`.\n        dx\n            Sets the x coordinate step. See `x0` for more info.\n        dy\n            Sets the y coordinate step. See `y0` for more info.\n        error_x\n            :class:`plotly.graph_objects.scatter.ErrorX` instance\n            or dict with compatible properties\n        error_y\n            :class:`plotly.graph_objects.scatter.ErrorY` instance\n            or dict with compatible properties\n        fill\n            Sets the area to fill with a solid color. Defaults to\n            \"none\" unless this trace is stacked, then it gets\n            \"tonexty\" (\"tonextx\") if `orientation` is \"v\" (\"h\") Use\n            with `fillcolor` if not \"none\". \"tozerox\" and \"tozeroy\"\n            fill to x=0 and y=0 respectively. \"tonextx\" and\n            \"tonexty\" fill between the endpoints of this trace and\n            the endpoints of the trace before it, connecting those\n            endpoints with straight lines (to make a stacked area\n            graph); if there is no trace before it, they behave\n            like \"tozerox\" and \"tozeroy\". \"toself\" connects the\n            endpoints of the trace (or each segment of the trace if\n            it has gaps) into a closed shape. \"tonext\" fills the\n            space between two traces if one completely encloses the\n            other (eg consecutive contour lines), and behaves like\n            \"toself\" if there is no trace before it. \"tonext\"\n            should not be used if one trace does not enclose the\n            other. Traces in a `stackgroup` will only fill to (or\n            be filled to) other traces in the same group. With\n            multiple `stackgroup`s or some traces stacked and some\n            not, if fill-linked traces are not already consecutive,\n            the later ones will be pushed down in the drawing\n            order.\n        fillcolor\n            Sets the fill color. Defaults to a half-transparent\n            variant of the line color, marker color, or marker line\n            color, whichever is available.\n        fillpattern\n            Sets the pattern within the marker.\n        groupnorm\n            Only relevant when `stackgroup` is used, and only the\n            first `groupnorm` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Sets the normalization for the sum of\n            this `stackgroup`. With \"fraction\", the value of each\n            trace at each location is divided by the sum of all\n            trace values at that location. \"percent\" is the same\n            but multiplied by 100 to show percentages. If there are\n            multiple subplots, or multiple `stackgroup`s on one\n            subplot, each will be normalized within its own set.\n        hoverinfo\n            Determines which trace information appear on hover. If\n            `none` or `skip` are set, no information is displayed\n            upon hovering. But, if `none` is set, click and hover\n            events are still fired.\n        hoverinfosrc\n            Sets the source reference on Chart Studio Cloud for\n            `hoverinfo`.\n        hoverlabel\n            :class:`plotly.graph_objects.scatter.Hoverlabel`\n            instance or dict with compatible properties\n        hoveron\n            Do the hover effects highlight individual points\n            (markers or line points) or do they highlight filled\n            regions? If the fill is \"toself\" or \"tonext\" and there\n            are no markers or text, then the default is \"fills\",\n            otherwise it is \"points\".\n        hovertemplate\n            Template string used for rendering the information that\n            appear on hover box. Note that this will override\n            `hoverinfo`. Variables are inserted using %{variable},\n            for example \"y: %{y}\" as well as %{xother}, {%_xother},\n            {%_xother_}, {%xother_}. When showing info for several\n            points, \"xother\" will be added to those with different\n            x positions from the first point. An underscore before\n            or after \"(x|y)other\" will add a space on that side,\n            only when this field is shown. Numbers are formatted\n            using d3-format's syntax %{variable:d3-format}, for\n            example \"Price: %{y:$.2f}\".\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n            for details on the formatting syntax. Dates are\n            formatted using d3-time-format's syntax\n            %{variable|d3-time-format}, for example \"Day:\n            %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format for details on the\n            date formatting syntax. The variables available in\n            `hovertemplate` are the ones emitted as event data\n            described at this link\n            https://plotly.com/javascript/plotlyjs-events/#event-\n            data. Additionally, every attributes that can be\n            specified per-point (the ones that are `arrayOk: true`)\n            are available.  Anything contained in tag `<extra>` is\n            displayed in the secondary box, for example\n            \"<extra>{fullData.name}</extra>\". To hide the secondary\n            box completely, use an empty tag `<extra></extra>`.\n        hovertemplatesrc\n            Sets the source reference on Chart Studio Cloud for\n            `hovertemplate`.\n        hovertext\n            Sets hover text elements associated with each (x,y)\n            pair. If a single string, the same string appears over\n            all the data points. If an array of string, the items\n            are mapped in order to the this trace's (x,y)\n            coordinates. To be seen, trace `hoverinfo` must contain\n            a \"text\" flag.\n        hovertextsrc\n            Sets the source reference on Chart Studio Cloud for\n            `hovertext`.\n        ids\n            Assigns id labels to each datum. These ids for object\n            constancy of data points during animation. Should be an\n            array of strings, not numbers or any other type.\n        idssrc\n            Sets the source reference on Chart Studio Cloud for\n            `ids`.\n        legendgroup\n            Sets the legend group for this trace. Traces part of\n            the same legend group hide/show at the same time when\n            toggling legend items.\n        legendgrouptitle\n            :class:`plotly.graph_objects.scatter.Legendgrouptitle`\n            instance or dict with compatible properties\n        legendrank\n            Sets the legend rank for this trace. Items and groups\n            with smaller ranks are presented on top/left side while\n            with `*reversed* `legend.traceorder` they are on\n            bottom/right side. The default legendrank is 1000, so\n            that you can use ranks less than 1000 to place certain\n            items before all unranked items, and ranks greater than\n            1000 to go after all unranked items.\n        line\n            :class:`plotly.graph_objects.scatter.Line` instance or\n            dict with compatible properties\n        marker\n            :class:`plotly.graph_objects.scatter.Marker` instance\n            or dict with compatible properties\n        meta\n            Assigns extra meta information associated with this\n            trace that can be used in various text attributes.\n            Attributes such as trace `name`, graph, axis and\n            colorbar `title.text`, annotation `text`\n            `rangeselector`, `updatemenues` and `sliders` `label`\n            text all support `meta`. To access the trace `meta`\n            values in an attribute in the same trace, simply use\n            `%{meta[i]}` where `i` is the index or key of the\n            `meta` item in question. To access trace `meta` in\n            layout attributes, use `%{data[n[.meta[i]}` where `i`\n            is the index or key of the `meta` and `n` is the trace\n            index.\n        metasrc\n            Sets the source reference on Chart Studio Cloud for\n            `meta`.\n        mode\n            Determines the drawing mode for this scatter trace. If\n            the provided `mode` includes \"text\" then the `text`\n            elements appear at the coordinates. Otherwise, the\n            `text` elements appear on hover. If there are less than\n            20 points and the trace is not stacked then the default\n            is \"lines+markers\". Otherwise, \"lines\".\n        name\n            Sets the trace name. The trace name appear as the\n            legend item and on hover.\n        opacity\n            Sets the opacity of the trace.\n        orientation\n            Only relevant when `stackgroup` is used, and only the\n            first `orientation` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Sets the stacking direction. With \"v\"\n            (\"h\"), the y (x) values of subsequent traces are added.\n            Also affects the default value of `fill`.\n        selected\n            :class:`plotly.graph_objects.scatter.Selected` instance\n            or dict with compatible properties\n        selectedpoints\n            Array containing integer indices of selected points.\n            Has an effect only for traces that support selections.\n            Note that an empty array means an empty selection where\n            the `unselected` are turned on for all points, whereas,\n            any other non-array values means no selection all where\n            the `selected` and `unselected` styles have no effect.\n        showlegend\n            Determines whether or not an item corresponding to this\n            trace is shown in the legend.\n        stackgaps\n            Only relevant when `stackgroup` is used, and only the\n            first `stackgaps` found in the `stackgroup` will be\n            used - including if `visible` is \"legendonly\" but not\n            if it is `false`. Determines how we handle locations at\n            which other traces in this group have data but this one\n            does not. With *infer zero* we insert a zero at these\n            locations. With \"interpolate\" we linearly interpolate\n            between existing values, and extrapolate a constant\n            beyond the existing values.\n        stackgroup\n            Set several scatter traces (on the same subplot) to the\n            same stackgroup in order to add their y values (or\n            their x values if `orientation` is \"h\"). If blank or\n            omitted this trace will not be stacked. Stacking also\n            turns `fill` on by default, using \"tonexty\" (\"tonextx\")\n            if `orientation` is \"h\" (\"v\") and sets the default\n            `mode` to \"lines\" irrespective of point count. You can\n            only stack on a numeric (linear or log) axis. Traces in\n            a `stackgroup` will only fill to (or be filled to)\n            other traces in the same group. With multiple\n            `stackgroup`s or some traces stacked and some not, if\n            fill-linked traces are not already consecutive, the\n            later ones will be pushed down in the drawing order.\n        stream\n            :class:`plotly.graph_objects.scatter.Stream` instance\n            or dict with compatible properties\n        text\n            Sets text elements associated with each (x,y) pair. If\n            a single string, the same string appears over all the\n            data points. If an array of string, the items are\n            mapped in order to the this trace's (x,y) coordinates.\n            If trace `hoverinfo` contains a \"text\" flag and\n            \"hovertext\" is not set, these elements will be seen in\n            the hover labels.\n        textfont\n            Sets the text font.\n        textposition\n            Sets the positions of the `text` elements with respects\n            to the (x,y) coordinates.\n        textpositionsrc\n            Sets the source reference on Chart Studio Cloud for\n            `textposition`.\n        textsrc\n            Sets the source reference on Chart Studio Cloud for\n            `text`.\n        texttemplate\n            Template string used for rendering the information text\n            that appear on points. Note that this will override\n            `textinfo`. Variables are inserted using %{variable},\n            for example \"y: %{y}\". Numbers are formatted using\n            d3-format's syntax %{variable:d3-format}, for example\n            \"Price: %{y:$.2f}\".\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format\n            for details on the formatting syntax. Dates are\n            formatted using d3-time-format's syntax\n            %{variable|d3-time-format}, for example \"Day:\n            %{2019-01-01|%A}\". https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format for details on the\n            date formatting syntax. Every attributes that can be\n            specified per-point (the ones that are `arrayOk: true`)\n            are available.\n        texttemplatesrc\n            Sets the source reference on Chart Studio Cloud for\n            `texttemplate`.\n        uid\n            Assign an id to this trace, Use this to provide object\n            constancy between traces during animations and\n            transitions.\n        uirevision\n            Controls persistence of some user-driven changes to the\n            trace: `constraintrange` in `parcoords` traces, as well\n            as some `editable: true` modifications such as `name`\n            and `colorbar.title`. Defaults to `layout.uirevision`.\n            Note that other user-driven trace attribute changes are\n            controlled by `layout` attributes: `trace.visible` is\n            controlled by `layout.legend.uirevision`,\n            `selectedpoints` is controlled by\n            `layout.selectionrevision`, and `colorbar.(x|y)`\n            (accessible with `config: {editable: true}`) is\n            controlled by `layout.editrevision`. Trace changes are\n            tracked by `uid`, which only falls back on trace index\n            if no `uid` is provided. So if your app can add/remove\n            traces before the end of the `data` array, such that\n            the same trace has a different index, you can still\n            preserve user-driven changes if you give each trace a\n            `uid` that stays with it as it moves.\n        unselected\n            :class:`plotly.graph_objects.scatter.Unselected`\n            instance or dict with compatible properties\n        visible\n            Determines whether or not this trace is visible. If\n            \"legendonly\", the trace is not drawn, but can appear as\n            a legend item (provided that the legend itself is\n            visible).\n        x\n            Sets the x coordinates.\n        x0\n            Alternate to `x`. Builds a linear space of x\n            coordinates. Use with `dx` where `x0` is the starting\n            coordinate and `dx` the step.\n        xaxis\n            Sets a reference between this trace's x coordinates and\n            a 2D cartesian x axis. If \"x\" (the default value), the\n            x coordinates refer to `layout.xaxis`. If \"x2\", the x\n            coordinates refer to `layout.xaxis2`, and so on.\n        xcalendar\n            Sets the calendar system to use with `x` date data.\n        xhoverformat\n            Sets the hover text formatting rulefor `x`  using d3\n            formatting mini-languages which are very similar to\n            those in Python. For numbers, see:\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n            And for dates see: https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format. We add two items to\n            d3's date formatter: \"%h\" for half of the year as a\n            decimal number as well as \"%{n}f\" for fractional\n            seconds with n digits. For example, *2016-10-13\n            09:15:23.456* with tickformat \"%H~%M~%S.%2f\" would\n            display *09~15~23.46*By default the values are\n            formatted using `xaxis.hoverformat`.\n        xperiod\n            Only relevant when the axis `type` is \"date\". Sets the\n            period positioning in milliseconds or \"M<n>\" on the x\n            axis. Special values in the form of \"M<n>\" could be\n            used to declare the number of months. In this case `n`\n            must be a positive integer.\n        xperiod0\n            Only relevant when the axis `type` is \"date\". Sets the\n            base for period positioning in milliseconds or date\n            string on the x0 axis. When `x0period` is round number\n            of weeks, the `x0period0` by default would be on a\n            Sunday i.e. 2000-01-02, otherwise it would be at\n            2000-01-01.\n        xperiodalignment\n            Only relevant when the axis `type` is \"date\". Sets the\n            alignment of data points on the x axis.\n        xsrc\n            Sets the source reference on Chart Studio Cloud for\n            `x`.\n        y\n            Sets the y coordinates.\n        y0\n            Alternate to `y`. Builds a linear space of y\n            coordinates. Use with `dy` where `y0` is the starting\n            coordinate and `dy` the step.\n        yaxis\n            Sets a reference between this trace's y coordinates and\n            a 2D cartesian y axis. If \"y\" (the default value), the\n            y coordinates refer to `layout.yaxis`. If \"y2\", the y\n            coordinates refer to `layout.yaxis2`, and so on.\n        ycalendar\n            Sets the calendar system to use with `y` date data.\n        yhoverformat\n            Sets the hover text formatting rulefor `y`  using d3\n            formatting mini-languages which are very similar to\n            those in Python. For numbers, see:\n            https://github.com/d3/d3-format/tree/v1.4.5#d3-format.\n            And for dates see: https://github.com/d3/d3-time-\n            format/tree/v2.2.3#locale_format. We add two items to\n            d3's date formatter: \"%h\" for half of the year as a\n            decimal number as well as \"%{n}f\" for fractional\n            seconds with n digits. For example, *2016-10-13\n            09:15:23.456* with tickformat \"%H~%M~%S.%2f\" would\n            display *09~15~23.46*By default the values are\n            formatted using `yaxis.hoverformat`.\n        yperiod\n            Only relevant when the axis `type` is \"date\". Sets the\n            period positioning in milliseconds or \"M<n>\" on the y\n            axis. Special values in the form of \"M<n>\" could be\n            used to declare the number of months. In this case `n`\n            must be a positive integer.\n        yperiod0\n            Only relevant when the axis `type` is \"date\". Sets the\n            base for period positioning in milliseconds or date\n            string on the y0 axis. When `y0period` is round number\n            of weeks, the `y0period0` by default would be on a\n            Sunday i.e. 2000-01-02, otherwise it would be at\n            2000-01-01.\n        yperiodalignment\n            Only relevant when the axis `type` is \"date\". Sets the\n            alignment of data points on the y axis.\n        ysrc\n            Sets the source reference on Chart Studio Cloud for\n            `y`.\n        \nDid you mean \"hovertemplatesrc\"?\n\nBad property path:\nCompression characteristics\n^^^^^^^^^^^^^^^^^^^^^^^^^^^"
     ]
    }
   ],
   "source": [
    "import json\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "# Read JSON file\n",
    "with open('Category_dict.json', 'r') as file:\n",
    "    graph_json = json.load(file)\n",
    "\n",
    "# Create Plotly graph from JSON\n",
    "fig = go.Figure(graph_json)\n",
    "\n",
    "# Show the graph\n",
    "fig.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "82506674",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35d3e91",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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