{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Illustrative example for weighting methods\n", "\n", "This example explains the usage of the Python 3 library package `pyrepo-mcda` that provides methods for multi-criteria decision analysis using objective weighting methods. This library contains module `weighting_methods` with the following weighting methods:\n", "\n", "1. Equal `equal_weighting`\n", "\n", "2. Entropy `entropy_weighting`\n", "\n", "3. Standard deviation `std_weighting`\n", "\n", "4. CRITIC `critic_weighting`\n", "\n", "5. Gini coefficient-based `gini_weighting`\n", "\n", "6. MEREC `merec_weighting`\n", "\n", "7. Statistical variance `stat_var_weighting`\n", "\n", "8. CILOS `cilos_weighting`\n", "\n", "9. IDOCRIW `idocriw_weighting`\n", "\n", "10. Angle `angle_weighting`\n", "\n", "11. Coefficient of variance `coeff_var_weighting`\n", "\n", "In addition to the weighting methods, the library also provides other methods necessary for multi-criteria decision analysis, which are as follows:\n", "\n", "The VIKOR method for multi-criteria decision analysis `VIKOR` in module `mcda_methods`,\n", "\n", "Normalization techniques:\n", "\n", "1. Linear `linear_normalization`\n", "\n", "2. Minimum-Maximum `minmax_normalization`\n", "\n", "3. Maximum `max_normalization`\n", "\n", "4. Sum `sum_normalization`\n", "\n", "5. Vector `vector_normalization`\n", "\n", "Correlation coefficients:\n", "\n", "1. Spearman rank correlation coefficient rs `spearman`\n", "\n", "2. Weighted Spearman rank correlation coefficient rw `weighted_spearman`\n", "\n", "3. Pearson coefficent `pearson_coeff`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import other necessary Python modules." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import copy\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the necessary modules and methods from package `pyrepo_mcda`." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pyrepo_mcda.mcda_methods import VIKOR\n", "from pyrepo_mcda.mcda_methods import VIKOR_SMAA\n", "from pyrepo_mcda.additions import rank_preferences\n", "from pyrepo_mcda import correlations as corrs\n", "from pyrepo_mcda import normalizations as norm_methods\n", "from pyrepo_mcda import weighting_methods as mcda_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Functions for results visualization." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Functions for visualizations\n", "\n", "def plot_barplot(df_plot, x_name, y_name, title):\n", " \"\"\"\n", " Display stacked column chart of weights for criteria for `x_name == Weighting methods`\n", " and column chart of ranks for alternatives `x_name == Alternatives`\n", "\n", " Parameters\n", " ----------\n", " df_plot : dataframe\n", " dataframe with criteria weights calculated different weighting methods\n", " or with alternaives rankings for different weighting methods\n", " x_name : str\n", " name of x axis, Alternatives or Weighting methods\n", " y_name : str\n", " name of y axis, Ranks or Weight values\n", " title : str\n", " name of chart title, Weighting methods or Criteria\n", "\n", " Examples\n", " ----------\n", " >>> plot_barplot(df_plot, x_name, y_name, title)\n", " \"\"\"\n", " \n", " list_rank = np.arange(1, len(df_plot) + 1, 1)\n", " stacked = True\n", " width = 0.5\n", " if x_name == 'Alternatives':\n", " stacked = False\n", " width = 0.8\n", " elif x_name == 'Alternative':\n", " pass\n", " else:\n", " df_plot = df_plot.T\n", " ax = df_plot.plot(kind='bar', width = width, stacked=stacked, edgecolor = 'black', figsize = (9,4))\n", " ax.set_xlabel(x_name, fontsize = 12)\n", " ax.set_ylabel(y_name, fontsize = 12)\n", "\n", " if x_name == 'Alternatives':\n", " ax.set_yticks(list_rank)\n", "\n", " ax.set_xticklabels(df_plot.index, rotation = 'horizontal')\n", " ax.tick_params(axis = 'both', labelsize = 12)\n", "\n", " plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc='lower left',\n", " ncol=4, mode=\"expand\", borderaxespad=0., edgecolor = 'black', title = title, fontsize = 11)\n", "\n", " ax.grid(True, linestyle = '--')\n", " ax.set_axisbelow(True)\n", " plt.tight_layout()\n", " plt.savefig('results/bar_chart_weights_' + x_name + '.pdf')\n", " plt.savefig('results/bar_chart_weights_' + x_name + '.eps')\n", " plt.show()\n", "\n", "\n", "def draw_heatmap(data, title):\n", " \"\"\"\n", " Display heatmap with correlations of compared rankings generated using different methods\n", "\n", " Parameters\n", " ----------\n", " data : dataframe\n", " dataframe with correlation values between compared rankings\n", " title : str\n", " title of chart containing name of used correlation coefficient\n", "\n", " Examples\n", " ----------\n", " >>> draw_heatmap(data, title)\n", " \"\"\"\n", "\n", " plt.figure(figsize = (6, 4))\n", " sns.set(font_scale=1.0)\n", " heatmap = sns.heatmap(data, annot=True, fmt=\".2f\", cmap=\"RdYlBu\",\n", " linewidth=0.5, linecolor='w')\n", " plt.yticks(va=\"center\")\n", " plt.xlabel('Weighting methods')\n", " title = title.replace(\"$\", \"\")\n", " title = title.replace(\"{\", \"\")\n", " title = title.replace(\"}\", \"\")\n", " plt.title('Correlation coefficient: ' + title)\n", " plt.tight_layout()\n", " plt.savefig('results/heatmap_weights.pdf')\n", " plt.savefig('results/heatmap_weights.eps')\n", " plt.show()\n", "\n", " \n", "def draw_heatmap_smaa(data, title):\n", " \"\"\"\n", " Display heatmap with correlations of compared rankings generated using different methods\n", "\n", " Parameters\n", " ----------\n", " data : dataframe\n", " dataframe with correlation values between compared rankings\n", " title : str\n", " title of chart containing name of used correlation coefficient\n", "\n", " Examples\n", " ----------\n", " >>> draw_heatmap(data, title)\n", " \"\"\"\n", "\n", " sns.set(font_scale=1.0)\n", " heatmap = sns.heatmap(data, annot=True, fmt=\".2f\", cmap=\"RdYlBu_r\",\n", " linewidth=0.05, linecolor='w')\n", " plt.yticks(rotation=0)\n", " plt.ylabel('Alternatives')\n", " plt.tick_params(labelbottom=False,labeltop=True)\n", " \n", " plt.title(title)\n", " plt.tight_layout()\n", " plt.savefig('results/heatmap_smaa.pdf')\n", " plt.savefig('results/heatmap_smaa.eps')\n", " plt.show()\n", "\n", "\n", "def plot_boxplot(data):\n", " \"\"\"\n", " Display boxplot showing distribution of criteria weights determined with different methods.\n", "\n", " Parameters\n", " ----------\n", " data : dataframe\n", " dataframe with correlation values between compared rankings\n", "\n", " Examples\n", " ---------\n", " >>> plot_boxplot(data)\n", " \"\"\"\n", " \n", " df_melted = pd.melt(data)\n", " plt.figure(figsize = (7, 4))\n", " ax = sns.boxplot(x = 'variable', y = 'value', data = df_melted, width = 0.6)\n", " ax.grid(True, linestyle = '--')\n", " ax.set_axisbelow(True)\n", " ax.set_xlabel('Criterion', fontsize = 12)\n", " ax.set_ylabel('Different weights distribution', fontsize = 12)\n", " plt.tight_layout()\n", " plt.savefig('results/boxplot_weights.pdf')\n", " plt.savefig('results/boxplot_weights.eps')\n", " plt.show()\n", "\n", "\n", "# Create dictionary class\n", "class Create_dictionary(dict):\n", " \n", " # __init__ function\n", " def __init__(self):\n", " self = dict()\n", " \n", " # Function to add key:value\n", " def add(self, key, value):\n", " self[key] = value" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As an illustrative example, a dataset will be used containing performances of the twelve best-selling electric cars in 2021 according to a ranking available at https://www.caranddriver.com/features/g36278968/best-selling-evs-of-2021/ The dataset is displayed below. $A_1$-$A_{12}$ are the individual alternatives in rows, columns $C_1$-$C_{11}$ denote the criteria, and the Type row contains the criteria type, where 1 indicates a profit criterion (stimulant) and -1 a cost criterion (destimulant). The following are the evaluation criteria for the electric cars evaluated in this research." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameUnitType
Cj
C1Max speedmph1
C2Battery capacitykWh1
C3Electric motorkW1
C4Maximum torqueNm1
C5Horsepowerhp1
C6EPA Fuel Economy CombinedMPGe1
C7EPA Fuel Economy CityMPGe1
C8EPA Fuel Economy HighwayMPGe1
C9EPA rangemiles1
C10Turning Diameter / Radius, curb to curbfeet-1
C11Base priceUSD-1
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" ], "text/plain": [ " Name Unit Type\n", "Cj \n", "C1 Max speed mph 1\n", "C2 Battery capacity kWh 1\n", "C3 Electric motor kW 1\n", "C4 Maximum torque Nm 1\n", "C5 Horsepower hp 1\n", "C6 EPA Fuel Economy Combined MPGe 1\n", "C7 EPA Fuel Economy City MPGe 1\n", "C8 EPA Fuel Economy Highway MPGe 1\n", "C9 EPA range miles 1\n", "C10 Turning Diameter / Radius, curb to curb feet -1\n", "C11 Base price USD -1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "criteria_presentation = pd.read_csv('criteria_electric_cars.csv', index_col = 'Cj')\n", "criteria_presentation" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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NameC1 Max speed [mph]C2 Battery [kWh]C3 Electric motor [kW] FrontC4 Torque [Nm] FrontC5 Mechanical horsepower [hp]C6 EPA Fuel Economy Combined [MPGe]C7 EPA Fuel Economy City [MPGe]C8 EPA Fuel Economy Highway [MPGe]C9 EPA range [miles]C10 Turning Diameter / Radius, curb to curb [feet]C11 Base price [$]
Ai
A1Tesla Model Y155.374.0340673456.011111510624439.865440
A2Tesla Model 3162.279.5247639283.011311810726338.860440
A3Ford Mustang Mach-E112.568.0198430266.0981059123038.156575
A4Chevrolet Bolt EV and EUV90.166.0150360201.212013110925934.832495
A5Volkswagen ID.499.477.0150310201.2971029026036.445635
A6Nissan Leaf89.540.0110320147.51111239922634.828425
A7Audi e-tron and e-tron Sportback124.395.0125247187.778787722240.084595
A8Porsche Taycan155.379.2160300214.679798022738.4105150
A9Tesla Model S162.2100.0205420502.912012411540240.396440
A10Hyundai Kona Electric96.339.2100395134.112013210825834.835245
A11Tesla Model X162.2100.0205420502.9981039337140.8127940
A12Hyundai Ioniq Electric102.538.3101295136.113314512117034.834250
TypeNaN1.01.0111.01111-1.0-1
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" ], "text/plain": [ " Name C1 Max speed [mph] C2 Battery [kWh] \\\n", "Ai \n", "A1 Tesla Model Y 155.3 74.0 \n", "A2 Tesla Model 3 162.2 79.5 \n", "A3 Ford Mustang Mach-E 112.5 68.0 \n", "A4 Chevrolet Bolt EV and EUV 90.1 66.0 \n", "A5 Volkswagen ID.4 99.4 77.0 \n", "A6 Nissan Leaf 89.5 40.0 \n", "A7 Audi e-tron and e-tron Sportback 124.3 95.0 \n", "A8 Porsche Taycan 155.3 79.2 \n", "A9 Tesla Model S 162.2 100.0 \n", "A10 Hyundai Kona Electric 96.3 39.2 \n", "A11 Tesla Model X 162.2 100.0 \n", "A12 Hyundai Ioniq Electric 102.5 38.3 \n", "Type NaN 1.0 1.0 \n", "\n", " C3 Electric motor [kW] Front C4 Torque [Nm] Front \\\n", "Ai \n", "A1 340 673 \n", "A2 247 639 \n", "A3 198 430 \n", "A4 150 360 \n", "A5 150 310 \n", "A6 110 320 \n", "A7 125 247 \n", "A8 160 300 \n", "A9 205 420 \n", "A10 100 395 \n", "A11 205 420 \n", "A12 101 295 \n", "Type 1 1 \n", "\n", " C5 Mechanical horsepower [hp] C6 EPA Fuel Economy Combined [MPGe] \\\n", "Ai \n", "A1 456.0 111 \n", "A2 283.0 113 \n", "A3 266.0 98 \n", "A4 201.2 120 \n", "A5 201.2 97 \n", "A6 147.5 111 \n", "A7 187.7 78 \n", "A8 214.6 79 \n", "A9 502.9 120 \n", "A10 134.1 120 \n", "A11 502.9 98 \n", "A12 136.1 133 \n", "Type 1.0 1 \n", "\n", " C7 EPA Fuel Economy City [MPGe] C8 EPA Fuel Economy Highway [MPGe] \\\n", "Ai \n", "A1 115 106 \n", "A2 118 107 \n", "A3 105 91 \n", "A4 131 109 \n", "A5 102 90 \n", "A6 123 99 \n", "A7 78 77 \n", "A8 79 80 \n", "A9 124 115 \n", "A10 132 108 \n", "A11 103 93 \n", "A12 145 121 \n", "Type 1 1 \n", "\n", " C9 EPA range [miles] \\\n", "Ai \n", "A1 244 \n", "A2 263 \n", "A3 230 \n", "A4 259 \n", "A5 260 \n", "A6 226 \n", "A7 222 \n", "A8 227 \n", "A9 402 \n", "A10 258 \n", "A11 371 \n", "A12 170 \n", "Type 1 \n", "\n", " C10 Turning Diameter / Radius, curb to curb [feet] C11 Base price [$] \n", "Ai \n", "A1 39.8 65440 \n", "A2 38.8 60440 \n", "A3 38.1 56575 \n", "A4 34.8 32495 \n", "A5 36.4 45635 \n", "A6 34.8 28425 \n", "A7 40.0 84595 \n", "A8 38.4 105150 \n", "A9 40.3 96440 \n", "A10 34.8 35245 \n", "A11 40.8 127940 \n", "A12 34.8 34250 \n", "Type -1.0 -1 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_presentation = pd.read_csv('electric_cars_2021.csv', index_col = 'Ai')\n", "data_presentation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Load a decision matrix containing only the performance values of the alternatives against the criteria and the criteria type in the last row, as shown below. Transform the decision matrix and criteria type from dataframe to NumPy array." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Decision matrix\n" ] }, { "data": { "text/html": [ "
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C1C2C3C4C5C6C7C8C9C10C11
Ai
A1155.374.0340673456.011111510624439.865440
A2162.279.5247639283.011311810726338.860440
A3112.568.0198430266.0981059123038.156575
A490.166.0150360201.212013110925934.832495
A599.477.0150310201.2971029026036.445635
A689.540.0110320147.51111239922634.828425
A7124.395.0125247187.778787722240.084595
A8155.379.2160300214.679798022738.4105150
A9162.2100.0205420502.912012411540240.396440
A1096.339.2100395134.112013210825834.835245
A11162.2100.0205420502.9981039337140.8127940
A12102.538.3101295136.113314512117034.834250
\n", "
" ], "text/plain": [ " C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11\n", "Ai \n", "A1 155.3 74.0 340 673 456.0 111 115 106 244 39.8 65440\n", "A2 162.2 79.5 247 639 283.0 113 118 107 263 38.8 60440\n", "A3 112.5 68.0 198 430 266.0 98 105 91 230 38.1 56575\n", "A4 90.1 66.0 150 360 201.2 120 131 109 259 34.8 32495\n", "A5 99.4 77.0 150 310 201.2 97 102 90 260 36.4 45635\n", "A6 89.5 40.0 110 320 147.5 111 123 99 226 34.8 28425\n", "A7 124.3 95.0 125 247 187.7 78 78 77 222 40.0 84595\n", "A8 155.3 79.2 160 300 214.6 79 79 80 227 38.4 105150\n", "A9 162.2 100.0 205 420 502.9 120 124 115 402 40.3 96440\n", "A10 96.3 39.2 100 395 134.1 120 132 108 258 34.8 35245\n", "A11 162.2 100.0 205 420 502.9 98 103 93 371 40.8 127940\n", "A12 102.5 38.3 101 295 136.1 133 145 121 170 34.8 34250" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load data from CSV\n", "filename = 'dataset_cars.csv'\n", "data = pd.read_csv(filename, index_col = 'Ai')\n", "# Load decision matrix from CSV\n", "df_data = data.iloc[:len(data) - 1, :]\n", "# Criteria types are in the last row of CSV\n", "types = data.iloc[len(data) - 1, :].to_numpy()\n", "\n", "# Convert decision matrix from dataframe to numpy ndarray type for faster calculations.\n", "matrix = df_data.to_numpy()\n", "\n", "# Symbols for alternatives Ai\n", "list_alt_names = [r'$A_{' + str(i) + '}$' for i in range(1, df_data.shape[0] + 1)]\n", "# Symbols for columns Cj\n", "cols = [r'$C_{' + str(j) + '}$' for j in range(1, data.shape[1] + 1)]\n", "print('Decision matrix')\n", "df_data" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Criteria types\n" ] }, { "data": { "text/plain": [ "array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., -1., -1.])" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print('Criteria types')\n", "types" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Objective weighting methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculate the weights with the selected weighing method. In this case, the Entropy weighting method (entropy_weighting) is selected." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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$C_{1}$$C_{2}$$C_{3}$$C_{4}$$C_{5}$$C_{6}$$C_{7}$$C_{8}$$C_{9}$$C_{10}$$C_{11}$
Weights0.0577410.0998430.1426730.0964880.2360870.0245440.0324320.0181260.0539580.0038630.234244
\n", "
" ], "text/plain": [ " $C_{1}$ $C_{2}$ $C_{3}$ $C_{4}$ $C_{5}$ $C_{6}$ $C_{7}$ \\\n", "Weights 0.057741 0.099843 0.142673 0.096488 0.236087 0.024544 0.032432 \n", "\n", " $C_{8}$ $C_{9}$ $C_{10}$ $C_{11}$ \n", "Weights 0.018126 0.053958 0.003863 0.234244 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "weights = mcda_weights.entropy_weighting(matrix)\n", "df_weights = pd.DataFrame(weights.reshape(1, -1), index = ['Weights'], columns = cols)\n", "df_weights" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use the VIKOR method to determine the value of the preference function (pref) and the ranking of alternatives (rank). The VIKOR method ranks alternatives ascendingly according to preference function values, so the reverse parameter in the `rank_preferences` method is set to False." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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PrefRank
$A_{1}$0.0000001
$A_{2}$0.3251542
$A_{3}$0.5310504
$A_{4}$0.6822585
$A_{5}$0.7341627
$A_{6}$0.92209110
$A_{7}$0.8848289
$A_{8}$0.8217738
$A_{9}$0.3326003
$A_{10}$0.94046011
$A_{11}$0.6964346
$A_{12}$0.95483212
\n", "
" ], "text/plain": [ " Pref Rank\n", "$A_{1}$ 0.000000 1\n", "$A_{2}$ 0.325154 2\n", "$A_{3}$ 0.531050 4\n", "$A_{4}$ 0.682258 5\n", "$A_{5}$ 0.734162 7\n", "$A_{6}$ 0.922091 10\n", "$A_{7}$ 0.884828 9\n", "$A_{8}$ 0.821773 8\n", "$A_{9}$ 0.332600 3\n", "$A_{10}$ 0.940460 11\n", "$A_{11}$ 0.696434 6\n", "$A_{12}$ 0.954832 12" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create the VIKOR method object\n", "vikor = VIKOR(normalization_method=norm_methods.minmax_normalization)\n", "\n", "# Calculate alternatives preference function values with VIKOR method\n", "pref = vikor(matrix, weights, types)\n", "\n", "# rank alternatives according to preference values\n", "rank = rank_preferences(pref, reverse = False)\n", "df_results = pd.DataFrame(index = list_alt_names)\n", "df_results['Pref'] = pref\n", "df_results['Rank'] = rank\n", "df_results" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The second part of the manual contains codes for benchmarking against several different criteria weighting methods. List the weighting methods you wish to explore." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "# Create a list with weighting methods that you want to explore\n", "weighting_methods_set = [\n", " mcda_weights.equal_weighting,\n", " mcda_weights.entropy_weighting,\n", " #mcda_weights.std_weighting,\n", " mcda_weights.critic_weighting,\n", " mcda_weights.gini_weighting,\n", " mcda_weights.merec_weighting,\n", " mcda_weights.stat_var_weighting,\n", " #mcda_weights.cilos_weighting,\n", " mcda_weights.idocriw_weighting,\n", " mcda_weights.angle_weighting,\n", " mcda_weights.coeff_var_weighting\n", "]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Below is a loop with code to collect results for each weighting technique. Then display the results, namely weights, preference function values and rankings." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "df_weights = pd.DataFrame(index = cols)\n", "df_preferences = pd.DataFrame(index = list_alt_names)\n", "df_rankings = pd.DataFrame(index = list_alt_names)\n", "\n", "# Create dataframes for weights, preference function values and rankings determined using different weighting methods\n", "df_weights = pd.DataFrame(index = cols)\n", "df_preferences = pd.DataFrame(index = list_alt_names)\n", "df_rankings = pd.DataFrame(index = list_alt_names)\n", "\n", "# Create the VIKOR method object\n", "vikor = VIKOR()\n", "for weight_type in weighting_methods_set:\n", " \n", " if weight_type.__name__ in [\"cilos_weighting\", \"idocriw_weighting\", \"angle_weighting\", \"merec_weighting\"]:\n", " weights = weight_type(matrix, types)\n", " else:\n", " weights = weight_type(matrix)\n", " \n", " df_weights[weight_type.__name__[:-10].upper().replace('_', ' ')] = weights\n", " pref = vikor(matrix, weights, types)\n", " rank = rank_preferences(pref, reverse = False)\n", " df_preferences[weight_type.__name__[:-10].upper().replace('_', ' ')] = pref\n", " df_rankings[weight_type.__name__[:-10].upper().replace('_', ' ')] = rank" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EQUALENTROPYCRITICGINIMERECSTAT VARIDOCRIWANGLECOEFF VAR
$C_{1}$0.0909090.0577410.0939600.0808820.0673630.1438550.0893620.0817320.079378
$C_{2}$0.0909090.0998430.0992770.1038000.1251950.1039760.0764050.1030020.101129
$C_{3}$0.0909090.1426730.0661320.1282020.1034890.0673080.0942710.1297020.129595
$C_{4}$0.0909090.0964880.0758740.1032000.0930500.0766650.0795720.1083790.106746
$C_{5}$0.0909090.2360870.0711950.1635130.1245810.1128800.1542350.1623540.166788
$C_{6}$0.0909090.0245440.1128650.0523080.0648860.0743610.0718760.0531450.051074
$C_{7}$0.0909090.0324320.1206020.0603880.0771070.0739250.0768220.0607390.058510
$C_{8}$0.0909090.0181260.1035360.0461880.0537080.0761500.0694180.0460610.044183
$C_{9}$0.0909090.0539580.0655140.0730990.0871090.0605650.0397020.0816910.079337
$C_{10}$0.0909090.0038630.0984320.0211510.0185660.1260250.0170620.0217110.020518
$C_{11}$0.0909090.2342440.0926120.1672700.1849470.0842890.2312760.1514840.162742
\n", "
" ], "text/plain": [ " EQUAL ENTROPY CRITIC GINI MEREC STAT VAR \\\n", "$C_{1}$ 0.090909 0.057741 0.093960 0.080882 0.067363 0.143855 \n", "$C_{2}$ 0.090909 0.099843 0.099277 0.103800 0.125195 0.103976 \n", "$C_{3}$ 0.090909 0.142673 0.066132 0.128202 0.103489 0.067308 \n", "$C_{4}$ 0.090909 0.096488 0.075874 0.103200 0.093050 0.076665 \n", "$C_{5}$ 0.090909 0.236087 0.071195 0.163513 0.124581 0.112880 \n", "$C_{6}$ 0.090909 0.024544 0.112865 0.052308 0.064886 0.074361 \n", "$C_{7}$ 0.090909 0.032432 0.120602 0.060388 0.077107 0.073925 \n", "$C_{8}$ 0.090909 0.018126 0.103536 0.046188 0.053708 0.076150 \n", "$C_{9}$ 0.090909 0.053958 0.065514 0.073099 0.087109 0.060565 \n", "$C_{10}$ 0.090909 0.003863 0.098432 0.021151 0.018566 0.126025 \n", "$C_{11}$ 0.090909 0.234244 0.092612 0.167270 0.184947 0.084289 \n", "\n", " IDOCRIW ANGLE COEFF VAR \n", "$C_{1}$ 0.089362 0.081732 0.079378 \n", "$C_{2}$ 0.076405 0.103002 0.101129 \n", "$C_{3}$ 0.094271 0.129702 0.129595 \n", "$C_{4}$ 0.079572 0.108379 0.106746 \n", "$C_{5}$ 0.154235 0.162354 0.166788 \n", "$C_{6}$ 0.071876 0.053145 0.051074 \n", "$C_{7}$ 0.076822 0.060739 0.058510 \n", "$C_{8}$ 0.069418 0.046061 0.044183 \n", "$C_{9}$ 0.039702 0.081691 0.079337 \n", "$C_{10}$ 0.017062 0.021711 0.020518 \n", "$C_{11}$ 0.231276 0.151484 0.162742 " ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_weights" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EQUALENTROPYCRITICGINIMERECSTAT VARIDOCRIWANGLECOEFF VAR
$A_{1}$0.2769460.0000000.1933240.0000000.0000000.2104770.0000000.0000000.000000
$A_{2}$0.0611140.3251540.0538630.2677840.0966020.0627290.1000570.2901310.285029
$A_{3}$0.4104270.5310500.3519730.5192850.3538530.4421860.3328130.5444290.535374
$A_{4}$0.6654450.6822580.3844200.6296190.3761150.7059290.3532780.6561960.650874
$A_{5}$0.6189930.7341620.4491210.7130590.4854360.6807680.4733330.7378800.731601
$A_{6}$0.8192580.9220910.5583230.8799330.6198880.8568150.5495590.9057040.901084
$A_{7}$1.0000000.8848281.0000000.8690110.6622080.7106090.6576400.8887080.885934
$A_{8}$0.9095590.8217730.9207430.7868660.8093770.4353390.7981930.7971430.796411
$A_{9}$0.3750000.3326000.2237870.2895560.2554990.2632610.3015150.2568220.278596
$A_{10}$0.7459230.9404600.4902340.8680500.5807550.6770000.5285580.8901870.889102
$A_{11}$0.6706930.6964340.4934010.6767740.6829020.5067720.7322540.6132630.652189
$A_{12}$0.7261780.9548320.4536660.8699300.5855750.5448420.4950330.8966130.895689
\n", "
" ], "text/plain": [ " EQUAL ENTROPY CRITIC GINI MEREC STAT VAR \\\n", "$A_{1}$ 0.276946 0.000000 0.193324 0.000000 0.000000 0.210477 \n", "$A_{2}$ 0.061114 0.325154 0.053863 0.267784 0.096602 0.062729 \n", "$A_{3}$ 0.410427 0.531050 0.351973 0.519285 0.353853 0.442186 \n", "$A_{4}$ 0.665445 0.682258 0.384420 0.629619 0.376115 0.705929 \n", "$A_{5}$ 0.618993 0.734162 0.449121 0.713059 0.485436 0.680768 \n", "$A_{6}$ 0.819258 0.922091 0.558323 0.879933 0.619888 0.856815 \n", "$A_{7}$ 1.000000 0.884828 1.000000 0.869011 0.662208 0.710609 \n", "$A_{8}$ 0.909559 0.821773 0.920743 0.786866 0.809377 0.435339 \n", "$A_{9}$ 0.375000 0.332600 0.223787 0.289556 0.255499 0.263261 \n", "$A_{10}$ 0.745923 0.940460 0.490234 0.868050 0.580755 0.677000 \n", "$A_{11}$ 0.670693 0.696434 0.493401 0.676774 0.682902 0.506772 \n", "$A_{12}$ 0.726178 0.954832 0.453666 0.869930 0.585575 0.544842 \n", "\n", " IDOCRIW ANGLE COEFF VAR \n", "$A_{1}$ 0.000000 0.000000 0.000000 \n", "$A_{2}$ 0.100057 0.290131 0.285029 \n", "$A_{3}$ 0.332813 0.544429 0.535374 \n", "$A_{4}$ 0.353278 0.656196 0.650874 \n", "$A_{5}$ 0.473333 0.737880 0.731601 \n", "$A_{6}$ 0.549559 0.905704 0.901084 \n", "$A_{7}$ 0.657640 0.888708 0.885934 \n", "$A_{8}$ 0.798193 0.797143 0.796411 \n", "$A_{9}$ 0.301515 0.256822 0.278596 \n", "$A_{10}$ 0.528558 0.890187 0.889102 \n", "$A_{11}$ 0.732254 0.613263 0.652189 \n", "$A_{12}$ 0.495033 0.896613 0.895689 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_preferences" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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EQUALENTROPYCRITICGINIMERECSTAT VARIDOCRIWANGLECOEFF VAR
$A_{1}$212112111
$A_{2}$121221233
$A_{3}$444445444
$A_{4}$6555510565
$A_{5}$576769677
$A_{6}$1010101291291212
$A_{7}$129121010111099
$A_{8}$1181181241288
$A_{9}$333333322
$A_{10}$911897881010
$A_{11}$76961161156
$A_{12}$8127118771111
\n", "
" ], "text/plain": [ " EQUAL ENTROPY CRITIC GINI MEREC STAT VAR IDOCRIW ANGLE \\\n", "$A_{1}$ 2 1 2 1 1 2 1 1 \n", "$A_{2}$ 1 2 1 2 2 1 2 3 \n", "$A_{3}$ 4 4 4 4 4 5 4 4 \n", "$A_{4}$ 6 5 5 5 5 10 5 6 \n", "$A_{5}$ 5 7 6 7 6 9 6 7 \n", "$A_{6}$ 10 10 10 12 9 12 9 12 \n", "$A_{7}$ 12 9 12 10 10 11 10 9 \n", "$A_{8}$ 11 8 11 8 12 4 12 8 \n", "$A_{9}$ 3 3 3 3 3 3 3 2 \n", "$A_{10}$ 9 11 8 9 7 8 8 10 \n", "$A_{11}$ 7 6 9 6 11 6 11 5 \n", "$A_{12}$ 8 12 7 11 8 7 7 11 \n", "\n", " COEFF VAR \n", "$A_{1}$ 1 \n", "$A_{2}$ 3 \n", "$A_{3}$ 4 \n", "$A_{4}$ 5 \n", "$A_{5}$ 7 \n", "$A_{6}$ 12 \n", "$A_{7}$ 9 \n", "$A_{8}$ 8 \n", "$A_{9}$ 2 \n", "$A_{10}$ 10 \n", "$A_{11}$ 6 \n", "$A_{12}$ 11 " ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_rankings" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visualize the results as column graphs of weights, rankings, and correlations." ] }, { "cell_type": "code", "execution_count": 15, "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_barplot(df_weights, 'Weighting methods', 'Weight value', 'Criteria')" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_boxplot(df_weights.T)" ] }, { "cell_type": "code", "execution_count": 17, "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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plot_barplot(df_rankings, 'Alternatives', 'Rank', 'Weighting methods')" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "results = copy.deepcopy(df_rankings)\n", "method_types = list(results.columns)\n", "dict_new_heatmap_rw = Create_dictionary()\n", "\n", "for el in method_types:\n", " dict_new_heatmap_rw.add(el, [])\n", "\n", "# heatmaps for correlations coefficients\n", "for i, j in [(i, j) for i in method_types[::-1] for j in method_types]:\n", " dict_new_heatmap_rw[j].append(corrs.weighted_spearman(results[i], results[j]))\n", "\n", "df_new_heatmap_rw = pd.DataFrame(dict_new_heatmap_rw, index = method_types[::-1])\n", "df_new_heatmap_rw.columns = method_types\n", "\n", "# correlation matrix with rw coefficient\n", "draw_heatmap(df_new_heatmap_rw, r'$r_w$')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Stochastic Multicriteria Acceptability Analysis Method (SMAA)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "cols_ai = [str(el) for el in range(1, matrix.shape[0] + 1)]" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "# criteria number\n", "n = matrix.shape[1]\n", "# number of SMAA iterations\n", "iterations = 10000" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "# create the VIKOR_SMAA method object\n", "vikor_smaa = VIKOR_SMAA()\n", "# generate multiple weight vectors in matrix\n", "weight_vectors = vikor_smaa._generate_weights(n, iterations)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "# Calculate the rank acceptability index, central weight vector and final ranking\n", "rank_acceptability_index, central_weight_vector, rank_scores = vikor_smaa(matrix, weight_vectors, types)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "acc_in_df = pd.DataFrame(rank_acceptability_index, index = list_alt_names, columns = cols_ai)\n", "acc_in_df.to_csv('results_smaa/ai.csv')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rank acceptability indexes\n", "This is dataframe with rank acceptability indexes for each alternative in relation to ranks. Rank acceptability index shows the share of different scores placing an alternative in a given rank." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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$A_{1}$0.23610.24580.18790.13540.05070.05460.02270.04800.01510.00370.00000.0000
$A_{2}$0.22080.35550.21940.11650.04550.03450.00780.00000.00000.00000.00000.0000
$A_{3}$0.00010.01110.02290.07250.28700.14850.14670.13660.16560.00900.00000.0000
$A_{4}$0.11360.06700.07170.13560.17190.23040.07780.03750.03050.03220.03180.0000
$A_{5}$0.00030.01230.01290.02170.07800.09990.25420.14270.12560.23220.02020.0000
$A_{6}$0.00000.00070.00700.05110.02510.03690.13530.11460.15940.16550.12770.1767
$A_{7}$0.00000.00000.00110.00120.00620.02980.03060.03270.08220.07390.09240.6499
$A_{8}$0.00000.00110.00250.00500.06260.03920.05690.14420.07430.12100.45440.0388
$A_{9}$0.38020.10250.28880.03890.02710.02820.02390.01770.06800.02470.00000.0000
$A_{10}$0.01060.04250.07030.06840.08600.16900.09870.07150.14190.14030.09110.0097
$A_{11}$0.00000.10830.07790.29670.06680.04900.06060.09440.02430.07940.10100.0416
$A_{12}$0.03830.05320.03760.05700.09310.08000.08480.16010.11310.11810.08140.0833
\n", "
" ], "text/plain": [ " 1 2 3 4 5 6 7 8 \\\n", "$A_{1}$ 0.2361 0.2458 0.1879 0.1354 0.0507 0.0546 0.0227 0.0480 \n", "$A_{2}$ 0.2208 0.3555 0.2194 0.1165 0.0455 0.0345 0.0078 0.0000 \n", "$A_{3}$ 0.0001 0.0111 0.0229 0.0725 0.2870 0.1485 0.1467 0.1366 \n", "$A_{4}$ 0.1136 0.0670 0.0717 0.1356 0.1719 0.2304 0.0778 0.0375 \n", "$A_{5}$ 0.0003 0.0123 0.0129 0.0217 0.0780 0.0999 0.2542 0.1427 \n", "$A_{6}$ 0.0000 0.0007 0.0070 0.0511 0.0251 0.0369 0.1353 0.1146 \n", "$A_{7}$ 0.0000 0.0000 0.0011 0.0012 0.0062 0.0298 0.0306 0.0327 \n", "$A_{8}$ 0.0000 0.0011 0.0025 0.0050 0.0626 0.0392 0.0569 0.1442 \n", "$A_{9}$ 0.3802 0.1025 0.2888 0.0389 0.0271 0.0282 0.0239 0.0177 \n", "$A_{10}$ 0.0106 0.0425 0.0703 0.0684 0.0860 0.1690 0.0987 0.0715 \n", "$A_{11}$ 0.0000 0.1083 0.0779 0.2967 0.0668 0.0490 0.0606 0.0944 \n", "$A_{12}$ 0.0383 0.0532 0.0376 0.0570 0.0931 0.0800 0.0848 0.1601 \n", "\n", " 9 10 11 12 \n", "$A_{1}$ 0.0151 0.0037 0.0000 0.0000 \n", "$A_{2}$ 0.0000 0.0000 0.0000 0.0000 \n", "$A_{3}$ 0.1656 0.0090 0.0000 0.0000 \n", "$A_{4}$ 0.0305 0.0322 0.0318 0.0000 \n", "$A_{5}$ 0.1256 0.2322 0.0202 0.0000 \n", "$A_{6}$ 0.1594 0.1655 0.1277 0.1767 \n", "$A_{7}$ 0.0822 0.0739 0.0924 0.6499 \n", "$A_{8}$ 0.0743 0.1210 0.4544 0.0388 \n", "$A_{9}$ 0.0680 0.0247 0.0000 0.0000 \n", "$A_{10}$ 0.1419 0.1403 0.0911 0.0097 \n", "$A_{11}$ 0.0243 0.0794 0.1010 0.0416 \n", "$A_{12}$ 0.1131 0.1181 0.0814 0.0833 " ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "acc_in_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rank acceptability indexes displayed in the form of stacked bar chart." ] }, { "cell_type": "code", "execution_count": 25, "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": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "matplotlib.rcdefaults()\n", "plot_barplot(acc_in_df, 'Alternative', 'Rank acceptability index', 'Rank')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Rank acceptability indexes displayed in the form of heatmap" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "draw_heatmap_smaa(acc_in_df, 'Rank acceptability indexes')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Central weight vector\n", "The central weight vector describes the preferences of a typical decision-maker, supporting this alternative with the assumed preference model. It allows the decision-maker to see what criteria preferences result in the best evaluation of given alternatives. Rows containing only zeroes mean that a given alternative never becomes a leader." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "central_weights_df = pd.DataFrame(central_weight_vector, index = list_alt_names, columns = cols)\n", "central_weights_df.to_csv('results_smaa/cw.csv')" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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$C_{1}$$C_{2}$$C_{3}$$C_{4}$$C_{5}$$C_{6}$$C_{7}$$C_{8}$$C_{9}$$C_{10}$$C_{11}$
$A_{1}$0.0800440.0659130.1669050.1263210.1222060.0774380.0715960.0802310.0566390.0543950.098312
$A_{2}$0.1171950.0897240.0762830.1280390.0562620.0806810.0789590.0811810.0714320.1104240.109820
$A_{3}$0.0033360.0232750.0307710.1027290.2830010.0431350.0024380.0364640.0075050.3118590.155486
$A_{4}$0.0447210.0846700.0655010.0583500.0662410.0892370.0905140.0784410.0801310.2149070.127288
$A_{5}$0.0540540.2770080.0684190.0206850.0408100.0359740.0486250.0257370.0960840.2552330.077371
$A_{6}$0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
$A_{7}$0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
$A_{8}$0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
$A_{9}$0.0961000.1163610.0667500.0619250.1050060.1059050.1006440.1034420.1308030.0544160.058647
$A_{10}$0.0524570.0321780.0398030.1504010.0383530.0997330.1119320.0819250.0686720.2306460.093900
$A_{11}$0.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.0000000.000000
$A_{12}$0.0756940.0373250.0442510.0465340.0462890.1436030.1614650.1381310.0393320.1502180.117158
\n", "
" ], "text/plain": [ " $C_{1}$ $C_{2}$ $C_{3}$ $C_{4}$ $C_{5}$ $C_{6}$ \\\n", "$A_{1}$ 0.080044 0.065913 0.166905 0.126321 0.122206 0.077438 \n", "$A_{2}$ 0.117195 0.089724 0.076283 0.128039 0.056262 0.080681 \n", "$A_{3}$ 0.003336 0.023275 0.030771 0.102729 0.283001 0.043135 \n", "$A_{4}$ 0.044721 0.084670 0.065501 0.058350 0.066241 0.089237 \n", "$A_{5}$ 0.054054 0.277008 0.068419 0.020685 0.040810 0.035974 \n", "$A_{6}$ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{7}$ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{8}$ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{9}$ 0.096100 0.116361 0.066750 0.061925 0.105006 0.105905 \n", "$A_{10}$ 0.052457 0.032178 0.039803 0.150401 0.038353 0.099733 \n", "$A_{11}$ 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{12}$ 0.075694 0.037325 0.044251 0.046534 0.046289 0.143603 \n", "\n", " $C_{7}$ $C_{8}$ $C_{9}$ $C_{10}$ $C_{11}$ \n", "$A_{1}$ 0.071596 0.080231 0.056639 0.054395 0.098312 \n", "$A_{2}$ 0.078959 0.081181 0.071432 0.110424 0.109820 \n", "$A_{3}$ 0.002438 0.036464 0.007505 0.311859 0.155486 \n", "$A_{4}$ 0.090514 0.078441 0.080131 0.214907 0.127288 \n", "$A_{5}$ 0.048625 0.025737 0.096084 0.255233 0.077371 \n", "$A_{6}$ 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{7}$ 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{8}$ 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{9}$ 0.100644 0.103442 0.130803 0.054416 0.058647 \n", "$A_{10}$ 0.111932 0.081925 0.068672 0.230646 0.093900 \n", "$A_{11}$ 0.000000 0.000000 0.000000 0.000000 0.000000 \n", "$A_{12}$ 0.161465 0.138131 0.039332 0.150218 0.117158 " ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "central_weights_df" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Rank scores" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [], "source": [ "rank_scores_df = pd.DataFrame(rank_scores, index = list_alt_names, columns = ['Rank'])\n", "rank_scores_df.to_csv('results_smaa/fr.csv')" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Rank
$A_{1}$3
$A_{2}$1
$A_{3}$6
$A_{4}$4
$A_{5}$9
$A_{6}$10
$A_{7}$12
$A_{8}$11
$A_{9}$2
$A_{10}$7
$A_{11}$5
$A_{12}$8
\n", "
" ], "text/plain": [ " Rank\n", "$A_{1}$ 3\n", "$A_{2}$ 1\n", "$A_{3}$ 6\n", "$A_{4}$ 4\n", "$A_{5}$ 9\n", "$A_{6}$ 10\n", "$A_{7}$ 12\n", "$A_{8}$ 11\n", "$A_{9}$ 2\n", "$A_{10}$ 7\n", "$A_{11}$ 5\n", "$A_{12}$ 8" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rank_scores_df" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.5" } }, "nbformat": 4, "nbformat_minor": 4 }