{ "cells": [ { "cell_type": "code", "execution_count": 160, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "from tabulate import tabulate\n", "\n", "from pyfdm import methods\n", "from pyfdm.methods import utils\n", "from pyfdm.TFN import TFN\n", "from pyfdm.graphs import single_tfn_plot, tfn_membership_plot, tfn_criteria_plot, s_norm_tfn_plot\n", "from pyfdm import weights as f_weights\n", "from pyfdm import correlations as corrs\n", "from pyfdm.helpers import rank, generate_fuzzy_matrix\n", "\n", "import warnings\n", "\n", "warnings.filterwarnings('ignore')\n", "np.set_printoptions(suppress=True, precision=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Input data\n", "\n", "To perform the multi-criteria evaluation, the decision matrix needs to be defined. It can be determined based on the real data, or created with the method provided in the library." ] }, { "cell_type": "code", "execution_count": 161, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[[6.86 , 7.668, 8.874],\n", " [6.24 , 7.576, 8.856],\n", " [7.406, 7.491, 8.739],\n", " [6.042, 9.201, 9.594]],\n", "\n", " [[8.548, 9.743, 9.826],\n", " [5.898, 8.25 , 9.78 ],\n", " [5.321, 5.875, 7.233],\n", " [7.737, 7.988, 9.468]],\n", "\n", " [[7.284, 8.336, 9.148],\n", " [7.38 , 8.961, 9.485],\n", " [5.186, 6.063, 7.842],\n", " [5.742, 6.829, 7.196]],\n", "\n", " [[8.019, 8.117, 8.684],\n", " [5.558, 7.964, 8.283],\n", " [5.042, 7.974, 8.843],\n", " [6.167, 6.635, 8.55 ]],\n", "\n", " [[5.821, 5.869, 7.805],\n", " [5.095, 5.492, 7.695],\n", " [7.633, 8.037, 9.974],\n", " [7.281, 7.351, 8.37 ]]])" ] }, "execution_count": 161, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# real data matrix\n", "real_matrix = np.array([\n", " [[5, 7, 9], [5, 7, 9], [7, 9, 9]],\n", " [[1, 3, 5], [3, 5, 7], [3, 5, 7]],\n", " [[1, 1, 3], [1, 3, 5], [1, 3, 5]],\n", " [[7, 9, 9], [7, 9, 9], [7, 9, 9]]\n", "])\n", "\n", "# randomly generated matrix\n", "# 5 alternatives\n", "# 4 criteria\n", "# lower bound = 5\n", "# upper bound = 10\n", "random_matrix = generate_fuzzy_matrix(5, 4, 5, 10)\n", "random_matrix" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Normalization\n", "\n", "Data normalization allows for comparing numbers with each other. It converts the range of values that they fit in range between 0 and 1. Below the usage examples of methods implemented in the library. Types parameter is responsible for the direction of the normalization. One columns' values could be more preferred is the values are lower (`-1`), other one could be more preferred if the values are greater (`1`)." ] }, { "cell_type": "code", "execution_count": 162, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "COCOSO \n", " [[[0.5 0.75 1. ]\n", " [0. 0.25 0.5 ]\n", " [0.75 1. 1. ]]\n", "\n", " [[0. 0.25 0.5 ]\n", " [0.25 0.5 0.75]\n", " [0.25 0.5 0.75]]]\n", "Sum \n", " [[[0.167 0.269 0.5 ]\n", " [0.354 0.181 0.066]\n", " [0.269 0.45 0.643]]\n", "\n", " [[0.033 0.115 0.278]\n", " [0.59 0.254 0.085]\n", " [0.115 0.25 0.5 ]]]\n", "Max \n", " [[[0.714 0.778 1. ]\n", " [0.286 0.222 0. ]\n", " [1. 1. 1. ]]\n", "\n", " [[0.143 0.333 0.556]\n", " [0.571 0.444 0.222]\n", " [0.429 0.556 0.778]]]\n", "Linear \n", " [[[0.556 0.778 1. ]\n", " [0.111 0.143 0.2 ]\n", " [0.778 1. 1. ]]\n", "\n", " [[0.111 0.333 0.556]\n", " [0.143 0.2 0.333]\n", " [0.333 0.556 0.778]]]\n", "Minmax \n", " [[[ 0.5 0.75 1. ]\n", " [-0. 0.25 0.5 ]\n", " [ 0.75 1. 1. ]]\n", "\n", " [[ 0. 0.25 0.5 ]\n", " [ 0.25 0.5 0.75]\n", " [ 0.25 0.5 0.75]]]\n", "Vector \n", " [[[0.246 0.345 0.443]\n", " [0.227 0.318 0.409]\n", " [0.301 0.387 0.387]]\n", "\n", " [[0.049 0.148 0.246]\n", " [0.136 0.227 0.318]\n", " [0.129 0.215 0.301]]]\n", "SAW \n", " [[[0.556 0.778 1. ]\n", " [0.556 0.778 1. ]\n", " [0.778 1. 1. ]]\n", "\n", " [[0.111 0.333 0.556]\n", " [0.333 0.556 0.778]\n", " [0.333 0.556 0.778]]]\n", "SQRT \n", " [[[0.427 0.597 0.768]\n", " [0.394 0.551 0.709]\n", " [0.522 0.671 0.671]]\n", "\n", " [[0.085 0.256 0.427]\n", " [0.236 0.394 0.551]\n", " [0.224 0.373 0.522]]]\n", "WASPAS \n", " [[[0.556 0.778 1. ]\n", " [0.2 0.143 0.111]\n", " [0.778 1. 1. ]]\n", "\n", " [[0.111 0.333 0.556]\n", " [0.333 0.2 0.143]\n", " [0.333 0.556 0.778]]]\n" ] } ], "source": [ "normalizations = {\n", " 'COCOSO': utils.normalizations.cocoso_normalization,\n", " 'Sum': utils.normalizations.sum_normalization,\n", " 'Max': utils.normalizations.max_normalization,\n", " 'Linear': utils.normalizations.linear_normalization,\n", " 'Minmax': utils.normalizations.minmax_normalization,\n", " 'Vector': utils.normalizations.vector_normalization,\n", " 'SAW': utils.normalizations.saw_normalization,\n", " 'SQRT': utils.normalizations.sqrt_normalization,\n", " 'WASPAS': utils.normalizations.waspas_normalization,\n", "}\n", "\n", "types = np.array([1, -1, 1])\n", "\n", "for name, norm in normalizations.items():\n", " nmatrix = norm(real_matrix, types)\n", " print(f'{name} \\n {nmatrix[:2]}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Distance\n", "\n", "Distance is a measure that allows for indicating how far are two Triangular Fuzzy Numbers from each other. Different techniques have been developed to this end. The measures implemented in the library and their usage are presented below." ] }, { "cell_type": "code", "execution_count": 163, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Euclidean: 3.0\n", "Weighted Euclidean: 1.8027756377319946\n", "Hamming: 5\n", "Weighted Hamming: 1.75\n", "Vertex: 1.7320508075688772\n", "Tran-Duckstein: 6.444444444444445\n", "L-R: 15.25\n", "Mahdavi: 1.7795130420052185\n", "Chebyshev: 2\n", "Canberra: 0.9166666666666666\n" ] } ], "source": [ "distances = {\n", " 'Euclidean': utils.distances.euclidean_distance,\n", " 'Weighted Euclidean': utils.distances.weighted_euclidean_distance,\n", " 'Hamming': utils.distances.hamming_distance,\n", " 'Weighted Hamming': utils.distances.weighted_hamming_distance,\n", " 'Vertex': utils.distances.vertex_distance,\n", " 'Tran-Duckstein': utils.distances.tran_duckstein_distance,\n", " 'L-R': utils.distances.lr_distance,\n", " 'Mahdavi': utils.distances.mahdavi_distance,\n", " 'Chebyshev': utils.distances.chebyshev_distance,\n", " 'Canberra': utils.distances.canberra_distance,\n", "}\n", "\n", "x = np.array([2, 4, 5])\n", "y = np.array([1, 2, 3])\n", "\n", "for name, distance in distances.items():\n", " d = distance(x, y)\n", " print(f'{name}: {d}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Defuzzification\n", "\n", "To create a crisp ranking from the calculations performed in fuzzy environment, the obtained results should be defuzzified. Different techniques can be used to achieve this. The implemented methods and the example of their usage are presented below." ] }, { "cell_type": "code", "execution_count": 164, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean: 0.6166666666666667\n", "Mean Area: 0.6000000000000001\n", "Graded Mean Average: 0.5833333333333334\n", "Weighted Mean: 0.6000000000000001\n", "Bisector: 0.65\n", "Height: 0.55\n", "LOM: 1.1\n", "SOM: 0.2\n" ] } ], "source": [ "defuzzifications = {\n", " 'Mean': utils.defuzzifications.mean_defuzzification,\n", " 'Mean Area': utils.defuzzifications.mean_area_defuzzification,\n", " 'Graded Mean Average': utils.defuzzifications.graded_mean_average_defuzzification,\n", " 'Weighted Mean': utils.defuzzifications.weighted_mean_defuzzification,\n", " 'Bisector': utils.defuzzifications.bisector_defuzzification, \n", " 'Height': utils.defuzzifications.height_defuzzification, \n", " 'LOM': utils.defuzzifications.lom_defuzzification, \n", " 'SOM': utils.defuzzifications.som_defuzzification, \n", "}\n", "\n", "x = np.array([0.2, 0.55, 1.1])\n", "\n", "for name, defuzzy in defuzzifications.items():\n", " d = defuzzy(x)\n", " print(f'{name}: {d}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Weights\n", "\n", "Criteria weights in multi-criteria problems are responsible for the importance of each parameter taken into consideration. The greater value assigned to the given criterion, the more important it will be in the assessment. For the purpose of weights definition, 4 methods from the library can be used. They are based on the statistical approach, which makes it possible to define the weights objectively, relying only on data diversity." ] }, { "cell_type": "code", "execution_count": 165, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Equal \n", " [[0.25 0.25 0.25]\n", " [0.25 0.25 0.25]\n", " [0.25 0.25 0.25]\n", " [0.25 0.25 0.25]]\n", "Shannon Entropy \n", " [[0.296 0.268 0.256]\n", " [0.222 0.254 0.254]\n", " [0.228 0.227 0.243]\n", " [0.254 0.251 0.247]]\n", "STD \n", " [[0.259 0.292 0.204]\n", " [0.212 0.273 0.237]\n", " [0.316 0.218 0.29 ]\n", " [0.212 0.217 0.269]]\n", "Variance \n", " [[0.261 0.335 0.163]\n", " [0.175 0.293 0.221]\n", " [0.388 0.187 0.331]\n", " [0.175 0.185 0.285]]\n" ] } ], "source": [ "weights_methods = {\n", " 'Equal': f_weights.equal_weights,\n", " 'Shannon Entropy' : f_weights.shannon_entropy_weights,\n", " 'STD': f_weights.standard_deviation_weights,\n", " 'Variance': f_weights.variance_weights\n", "}\n", "\n", "for name, method in weights_methods.items():\n", " w = method(random_matrix)\n", " print(f'{name} \\n {w}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluation \n", "\n", "Different techniques from the group of Fuzzy Multi-Criteria Decision Analysis methods based on the Triangular Fuzzy Numbers can be used to assess the alternatives. The library contains 10 methods which can be used for this purpose. The examples of their application are presented below." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Decision matrix\n", "\n", "Decision matrix represents the alternatives taken into consideration in the problem. Rows represent amount of alternatives, when columns describes the amount of criteria in the given problem. In the case presented below, we have 4 alternatives and 3 criteria. Moreover, all elements in the matrix should be represent as the Triangular Fuzzy Number." ] }, { "cell_type": "code", "execution_count": 166, "metadata": {}, "outputs": [], "source": [ "matrix = np.array([\n", " [[3, 4, 5],[4, 5, 6],[8, 9, 9]],\n", " [[6, 7, 8],[4, 5, 6],[1, 2, 3]],\n", " [[5, 6, 7],[2, 3, 4],[3, 4, 5]],\n", " [[5, 6, 8],[2, 3, 4],[2, 3, 4]],\n", " [[7, 8, 9],[7, 8, 9],[5, 6, 7]],\n", "])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Weights\n", "\n", "Weights can be defined objectively, as shown above with the given examples. However, the weights can be also defined directly based on expert knowledge. The library is implemented in a way to handle both crisp and fuzzy weights. Amount of weights should equal the criteria amount. They can be determined as follow." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 1\n", "Crisp weights" ] }, { "cell_type": "code", "execution_count": 167, "metadata": {}, "outputs": [], "source": [ "crisp_weights = np.array([0.4, 0.4, 0.2])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2\n", "Triangular Fuzzy Weights" ] }, { "cell_type": "code", "execution_count": 168, "metadata": {}, "outputs": [], "source": [ "fuzzy_weights = np.array([[5, 7, 9], [7, 9, 9], [3, 5, 7]])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Criteria \n", "\n", "Criteria types are ment to reflect the direction of the values that is preferable in the problem. If the values for given criterion should be as big as possible, it is then a profit type and represent as `1` in the criteria types array. If the values should be as low as possible, it is then cost and should be represent as `-1` in the array. Moreover, the criteria types amount should equal amount of criteria in the decision matrix." ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [], "source": [ "types = np.array([1, -1, 1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy ARAS" ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [], "source": [ "f_aras = methods.fARAS()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuzzy ARAS evaluation results with crisp and fuzzy weights " ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Crisp weights: [0.835 0.856 1.114 1.113 0.893]\n", "Fuzzy weights: [0.879 0.834 1.103 1.097 0.896]\n" ] } ], "source": [ "print(f'Crisp weights: {f_aras(matrix, crisp_weights, types)}')\n", "print(f'Fuzzy weights: {f_aras(matrix, fuzzy_weights, types)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ranking based on the obtained preferences can be calculated directly from the fARAS object." ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4., 5., 1., 2., 3.])" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "f_aras.rank()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or with the `rank` method." ] }, { "cell_type": "code", "execution_count": 173, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4., 5., 1., 2., 3.])" ] }, "execution_count": 173, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rank(f_aras(matrix, fuzzy_weights, types))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also use ARAS method with different normalizations. Default, it is a `sum_normalization`." ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [], "source": [ "aras = {\n", " 'Sum': methods.fARAS(utils.normalizations.sum_normalization),\n", " 'Max': methods.fARAS(utils.normalizations.max_normalization),\n", " 'Linear': methods.fARAS(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fARAS(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fARAS(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fARAS(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For every normalization technique, we can perform assessment to obtain results and check if the type of normalization impacts the outcome." ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in aras.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- ----\n", "Sum 0.88 0.83 1.1 1.1 0.9\n", "Max 0.98 0.87 1.06 1.04 0.89\n", "Linear 0.84 0.75 0.96 0.95 0.86\n", "Minmax 0.93 0.88 1.08 1.07 0.89\n", "Vector 0.64 0.56 0.48 0.47 0.85\n", "SAW 0.63 0.57 0.49 0.48 0.85\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that different preferences are obtained with different normalizations. To check if the alternatives are ranked at the same place despite used normalization method, we can use the method from the library called `rank` which calculates ascending or descending position order based on given array. Since the ARAS method assess better alternatives with higher values, the order should be descending. " ] }, { "cell_type": "code", "execution_count": 177, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- ----\n", "Sum 4 5 1 2 3\n", "Max 3 5 1 2 4\n", "Linear 4 5 1 2 3\n", "Minmax 3 5 1 2 4\n", "Vector 2 3 4 5 1\n", "SAW 2 3 4 5 1\n" ] } ], "source": [ "print(tabulate([[name, *rank(pref, descending=True)] for name, pref in results.items()], \n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be seen, that the ranking of alternatives is different for different normalization techniques. So the user should bear in mind that different methods can have impact the final result obtained within selected evaluation method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy COCOSO" ] }, { "cell_type": "code", "execution_count": 178, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [133.388 30.96 87.659 98.299 96.886]\n", "Ranking: [1. 5. 4. 2. 3.]\n" ] } ], "source": [ "f_cocoso = methods.fCOCOSO()\n", "print(f'Preferences: {f_cocoso(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_cocoso.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Within the COCOSO method we can also use different normalizations, as it was in the previous methods. In addition, we can modify the used normalization and defuzzification techniques. Default, the fCOCOSO method has set the normalization to `cocoso_normalization` and the defuzzification to the `mean_defuzzification`." ] }, { "cell_type": "code", "execution_count": 179, "metadata": {}, "outputs": [], "source": [ "cocoso = {\n", " 'Sum': methods.fCOCOSO(utils.normalizations.sum_normalization),\n", " 'Max': methods.fCOCOSO(utils.normalizations.max_normalization),\n", " 'Linear': methods.fCOCOSO(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fCOCOSO(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fCOCOSO(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fCOCOSO(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "code", "execution_count": 180, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in cocoso.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 181, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- -------- ------- ------- ------- --------\n", "Sum 1200.64 397.55 486.86 569.71 1201.88\n", "Max 5.07 2.44 2.06 2.27 5.66\n", "Linear 46.62 14.91 30.75 33.93 40.68\n", "Minmax 133.39 30.96 87.66 98.3 96.89\n", "Vector 21970 1432.57 3352.53 2207.49 12331.6\n", "SAW 65.58 21.9 13.78 17.84 102.23\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "code", "execution_count": 182, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- ----\n", "Sum 2 5 4 3 1\n", "Max 2 3 5 4 1\n", "Linear 1 5 4 3 2\n", "Minmax 1 5 4 2 3\n", "Vector 1 5 3 4 2\n", "SAW 2 3 5 4 1\n" ] } ], "source": [ "print(tabulate([[name, *rank(pref, descending=True)] for name, pref in results.items()], \n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy CODAS" ] }, { "cell_type": "code", "execution_count": 183, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [ 0.3 -27.223 28.96 23.976 -26.013]\n", "Ranking: [3. 5. 1. 2. 4.]\n" ] } ], "source": [ "f_codas = methods.fCODAS()\n", "print(f'Preferences: {f_codas(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_codas.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Within the CODAS method we can also use different normalizations, as it was in the ARAS method. In addition, we can use different distance metrics to calculate the alternatives preference. Default the `distance_1` is the `euclidean_distance` and `distance_2` is the `hamming_distance`. While calling the fuzzy CODAS object, the `tau` parameter can be given, which is set to `0.02` as default. It is treated as the threshold parameter while calculating the relative assessment matrix. CODAS also assessed better alternatives with higher preferences." ] }, { "cell_type": "code", "execution_count": 184, "metadata": {}, "outputs": [], "source": [ "codas = {\n", " 'Pair 1': methods.fCODAS(distance_1=utils.distances.euclidean_distance, distance_2=utils.distances.hamming_distance),\n", " 'Pair 2': methods.fCODAS(distance_1=utils.distances.weighted_euclidean_distance, distance_2=utils.distances.weighted_hamming_distance),\n", " 'Pair 3': methods.fCODAS(distance_1=utils.distances.vertex_distance, distance_2=utils.distances.lr_distance),\n", " 'Pair 4': methods.fCODAS(distance_1=utils.distances.mahdavi_distance, distance_2=utils.distances.lr_distance),\n", " \n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, when we defined the CODAS object with different pairs of distances, we can calculate the results." ] }, { "cell_type": "code", "execution_count": 185, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in codas.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 186, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ----- ------- ------ ------ -------\n", "Pair 1 0.3 -27.22 28.96 23.98 -26.01\n", "Pair 2 0.55 -11.46 12.67 9.81 -11.57\n", "Pair 3 15.6 -161.23 148.14 142.27 -144.78\n", "Pair 4 15.8 -157.83 148.41 141.97 -148.34\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see, that different distance metrics also have impact on the final results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy COPRAS" ] }, { "cell_type": "code", "execution_count": 187, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [0.804 0.736 0.993 1. 0.824]\n", "Ranking: [4. 5. 2. 1. 3.]\n" ] } ], "source": [ "f_copras = methods.fCOPRAS()\n", "print(f'Preferences: {f_copras(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_copras.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As in the case of the ARAS method, in the COPRAS technique, we can also modify the used normalization method. The `saw_normalization` is set as default. Similarly to previous methods, better alternatives are assessed with higher preferences." ] }, { "cell_type": "code", "execution_count": 188, "metadata": {}, "outputs": [], "source": [ "copras = {\n", " 'Sum': methods.fCOPRAS(utils.normalizations.sum_normalization),\n", " 'Max': methods.fCOPRAS(utils.normalizations.max_normalization),\n", " 'Linear': methods.fCOPRAS(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fCOPRAS(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fCOPRAS(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fCOPRAS(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "code", "execution_count": 189, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in copras.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 190, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- ----\n", "Sum 0.7 0.69 0.59 0.61 1\n", "Max 1 0.88 0.92 0.93 0\n", "Linear 0.73 0.67 0.59 0.59 1\n", "Minmax 0.97 0.93 0.94 1 1\n", "Vector 0.81 0.71 1 1 0.79\n", "SAW 0.8 0.74 0.99 1 0.82\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy EDAS" ] }, { "cell_type": "code", "execution_count": 191, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [0.691 0.292 0.698 0.74 0.468]\n", "Ranking: [3. 5. 2. 1. 4.]\n" ] } ], "source": [ "f_edas = methods.fEDAS()\n", "print(f'Preferences: {f_edas(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_edas.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In case of using the fuzzy EDAS method, we can modify the used defuzzification technique. Default, the fEDAS method has set the defuzzification to the `mean_defuzzification`. EDAS also evaluate better alternatives with higher preferences." ] }, { "cell_type": "code", "execution_count": 192, "metadata": {}, "outputs": [], "source": [ "edas = {\n", " 'Mean': methods.fEDAS(defuzzify=utils.defuzzifications.mean_defuzzification),\n", " 'Mean Area': methods.fEDAS(defuzzify=utils.defuzzifications.mean_area_defuzzification),\n", " 'Graded Mean Average': methods.fEDAS(defuzzify=utils.defuzzifications.graded_mean_average_defuzzification),\n", " 'Weighted Mean': methods.fEDAS(defuzzify=utils.defuzzifications.weighted_mean_defuzzification) \n", "}\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After fEDAS object definition, we can calculate the results based on using different defuzzification methods." ] }, { "cell_type": "code", "execution_count": 193, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in edas.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 194, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "------------------- ---- ---- ---- ---- ----\n", "Mean 0.69 0.29 0.7 0.74 0.47\n", "Mean Area 0.71 0.3 0.72 0.65 0.45\n", "Graded Mean Average 0.73 0.31 0.75 0.67 0.42\n", "Weighted Mean 0.71 0.3 0.72 0.65 0.45\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It can be noticed that the results are highly similar while using different methods to defuzzify fuzzy numbers and obtain crisp values." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy MABAC" ] }, { "cell_type": "code", "execution_count": 195, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [-0.061 -0.644 1.82 1.695 -0.549]\n", "Ranking: [3. 5. 1. 2. 4.]\n" ] } ], "source": [ "f_mabac = methods.fMABAC()\n", "print(f'Preferences: {f_mabac(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_mabac.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "While using the fuzzy MABAC method, the normalization and defuzzification methods can be adjusted. Default, normalization is set to `minmax_normalization` and defuzzify to `mean_defuzzification`. MABAC classify better alternatives with higher preferences" ] }, { "cell_type": "code", "execution_count": 196, "metadata": {}, "outputs": [], "source": [ "mabac = {\n", " 'Sum': methods.fMABAC(utils.normalizations.sum_normalization),\n", " 'Max': methods.fMABAC(utils.normalizations.max_normalization),\n", " 'Linear': methods.fMABAC(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fMABAC(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fMABAC(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fMABAC(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "code", "execution_count": 197, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in mabac.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 198, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ----- ----- ----- ----- -----\n", "Sum -0.36 -0.52 0.61 0.6 -0.08\n", "Max 0.35 -0.79 1.57 1.34 -0.77\n", "Linear -0.19 -1.49 1.53 1.31 0.16\n", "Minmax -0.06 -0.64 1.82 1.7 -0.55\n", "Vector 0.43 -0.31 -0.93 -1.06 2.17\n", "SAW 0.79 -0.5 -2.02 -2.25 5.27\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Again we can see, that different techniques used in the assessment have impact on the final result from the fuzzy MCDA method." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy MAIRCA" ] }, { "cell_type": "code", "execution_count": 199, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [3.039 3.193 3.315 3.337 2.683]\n", "Ranking: [4. 3. 2. 1. 5.]\n" ] } ], "source": [ "f_mairca = methods.fMAIRCA()\n", "print(f'Preferences: {f_mairca(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_mairca.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuzzy MAIRCA method allows for adjusting the parameters responsible for the normalization and the distance measures. Default settings covers the `vector_normalization` and the `vertex_distance`. MAIRCA assigns higher preference values to better classified alternatives." ] }, { "cell_type": "code", "execution_count": 200, "metadata": {}, "outputs": [], "source": [ "mairca = {\n", " 'Euclidean': methods.fMAIRCA(distance=utils.distances.euclidean_distance),\n", " 'Weighted Euclidean': methods.fMAIRCA(distance=utils.distances.weighted_euclidean_distance),\n", " 'Hamming': methods.fMAIRCA(distance=utils.distances.hamming_distance),\n", " 'Weighted Hamming': methods.fMAIRCA(distance=utils.distances.weighted_hamming_distance),\n", " 'Vertex': methods.fMAIRCA(distance=utils.distances.vertex_distance),\n", " 'Tran-Duckstein': methods.fMAIRCA(distance=utils.distances.tran_duckstein_distance),\n", " 'L-R': methods.fMAIRCA(distance=utils.distances.lr_distance),\n", " 'Mahdavi': methods.fMAIRCA(distance=utils.distances.mahdavi_distance)\n", "}\n" ] }, { "cell_type": "code", "execution_count": 201, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in mairca.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 202, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "------------------ ----- ----- ----- ----- ----\n", "Euclidean 5.26 5.53 5.74 5.78 4.65\n", "Weighted Euclidean 3.05 3.21 3.34 3.37 2.7\n", "Hamming 8.96 9.41 9.78 9.85 7.91\n", "Weighted Hamming 3.01 3.17 3.3 3.33 2.67\n", "Vertex 3.04 3.19 3.31 3.34 2.68\n", "Tran-Duckstein 5.53 5.75 6.42 6.52 4.08\n", "L-R 13.32 13.9 15.49 15.72 9.8\n", "Mahdavi 3.04 3.2 3.32 3.35 2.69\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy MOORA" ] }, { "cell_type": "code", "execution_count": 203, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [1.403 0.707 1.641 1.573 0.931]\n", "Ranking: [3. 5. 1. 2. 4.]\n" ] } ], "source": [ "f_moora = methods.fMOORA()\n", "print(f'Preferences: {f_moora(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_moora.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Fuzzy MOORA assigns higher preferences to better alternatives. It allows for the modification of the normalization technique, and the default method is set to `vector_normalization`." ] }, { "cell_type": "code", "execution_count": 204, "metadata": {}, "outputs": [], "source": [ "moora = {\n", " 'Sum': methods.fMOORA(utils.normalizations.sum_normalization),\n", " 'Max': methods.fMOORA(utils.normalizations.max_normalization),\n", " 'Linear': methods.fMOORA(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fMOORA(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fMOORA(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fMOORA(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "code", "execution_count": 205, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in moora.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 206, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- -----\n", "Sum 2.36 2.22 2.41 2.54 3.49\n", "Max 6.11 4.88 3.57 3.63 11\n", "Linear 5.18 3.85 1.22 1.23 8.37\n", "Minmax 2.16 1.66 2.03 2.5 8.54\n", "Vector 1.4 0.71 1.64 1.57 0.93\n", "SAW 3.97 2.68 4.74 4.66 3.27\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy OCRA" ] }, { "cell_type": "code", "execution_count": 207, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [12.283 0. 9.317 7.306 3.433]\n", "Ranking: [1. 5. 2. 3. 4.]\n" ] } ], "source": [ "f_ocra = methods.fOCRA()\n", "print(f'Preferences: {f_ocra(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_ocra.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "OCRA has one parameter that can be changed during the evaluation. It is the defuzzification method, which default is set to `mean_defuzzification`. OCRA also assess better alternatives with higher preference values." ] }, { "cell_type": "code", "execution_count": 208, "metadata": {}, "outputs": [], "source": [ "ocra = {\n", " 'Mean': methods.fOCRA(defuzzify=utils.defuzzifications.mean_defuzzification),\n", " 'Mean Area': methods.fOCRA(defuzzify=utils.defuzzifications.mean_area_defuzzification),\n", " 'Graded Mean Average': methods.fOCRA(defuzzify=utils.defuzzifications.graded_mean_average_defuzzification),\n", " 'Weighted Mean': methods.fOCRA(defuzzify=utils.defuzzifications.weighted_mean_defuzzification) \n", "}" ] }, { "cell_type": "code", "execution_count": 209, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in ocra.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 210, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "------------------- ----- ---- ---- ---- ----\n", "Mean 12.28 0 9.32 7.31 3.43\n", "Mean Area 12.28 0 9.3 7.17 3.26\n", "Graded Mean Average 12.27 0 9.28 7.03 3.09\n", "Weighted Mean 12.28 0 9.3 7.17 3.26\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy SPOTIS" ] }, { "cell_type": "code", "execution_count": 211, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [0.504 0.415 0.546]\n", "Ranking: [2. 3. 1.]\n" ] } ], "source": [ "spotis_matrix = np.array([\n", " [[0.6, 0.8, 1.0], [0.6, 0.8, 1.0], [0.4, 0.6, 0.8], [0.2, 0.4, 0.6], [0.8, 1.0, 1.0]], \n", " [[0.4, 0.6, 0.8], [0.6, 0.8, 1.0], [0.4, 0.6, 0.8], [0.8, 1.0, 1.0], [0.2, 0.4, 0.6]], \n", " [[0.8, 1.0, 1.0], [0.4, 0.6, 0.8], [0.6, 0.8, 1.0], [0.0, 0.2, 0.4], [0.0, 0.2, 0.4]] \n", "])\n", "\n", "spotis_weights = np.array([0.364, 0.272, 0.203, 0.093, 0.068])\n", "spotis_types = np.array([-1, 1, 1, 1, 1])\n", "spotis_bounds = np.array([[0.0, 1.0]] * 5)\n", "\n", "f_spotis = methods.fSPOTIS()\n", "print(f'Preferences: {f_spotis(spotis_matrix, spotis_weights, spotis_types, spotis_bounds)}')\n", "print(f'Ranking: {f_spotis.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SPOTIS had normalization function by default set to `None`. However, this can be changed by passing as a normalization parameter one of the techniques included in the library. The SPOTIS method also uses a `bound` parameter which is used to assure resistance to the Rank Reversal (RR) phenomenon during the calculations. The SPOTIS method operates only on criteria weights declared in a crisp form." ] }, { "cell_type": "code", "execution_count": 212, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[3, 9],\n", " [2, 9],\n", " [1, 9]])" ] }, "execution_count": 212, "metadata": {}, "output_type": "execute_result" } ], "source": [ "spotis_bounds = f_spotis.make_bounds(matrix)\n", "spotis_bounds" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the Fuzzy SPOTIS method, function `make_bounds` is implemented to allow users automatically generate the bounds for the examined decision matrix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy TOPSIS" ] }, { "cell_type": "code", "execution_count": 213, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [0.564 0.565 0.552 0.551 0.562]\n", "Ranking: [2. 1. 4. 5. 3.]\n" ] } ], "source": [ "f_topsis = methods.fTOPSIS()\n", "print(f'Preferences: {f_topsis(matrix, fuzzy_weights, types)}')\n", "print(f'Ranking: {f_topsis.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TOPSIS technique allows for adjusting the parameters responsible for the normalization and the distance calculation. Default methods are set to `linear_normalization` and `vertex_distance`. TOPSIS assures, that better alternatives have higher preferences values. " ] }, { "cell_type": "code", "execution_count": 214, "metadata": {}, "outputs": [], "source": [ "topsis = {\n", " 'Sum': methods.fTOPSIS(utils.normalizations.sum_normalization),\n", " 'Max': methods.fTOPSIS(utils.normalizations.max_normalization),\n", " 'Linear': methods.fTOPSIS(utils.normalizations.linear_normalization),\n", " 'Minmax': methods.fTOPSIS(utils.normalizations.minmax_normalization),\n", " 'Vector': methods.fTOPSIS(utils.normalizations.vector_normalization),\n", " 'SAW': methods.fTOPSIS(utils.normalizations.saw_normalization)\n", "}" ] }, { "cell_type": "code", "execution_count": 215, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in topsis.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 216, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "-------- ---- ---- ---- ---- ----\n", "Sum 0.67 0.61 0.61 0.59 0.64\n", "Max 0.57 0.57 0.56 0.56 0.52\n", "Linear 0.56 0.57 0.55 0.55 0.56\n", "Minmax 0.56 0.55 0.55 0.55 0.56\n", "Vector 0.65 0.62 0.68 0.66 0.61\n", "SAW 0.56 0.56 0.57 0.57 0.54\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref, 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy VIKOR" ] }, { "cell_type": "code", "execution_count": 217, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "PREFERENCES\n", "S: [7.696 8.478 5.966 6.382 8.454]\n", "R: [5. 4.406 3.344 3.781 6.214]\n", "Q: [0.156 0.141 0.003 0.043 0.255]\n", "RANKINGS\n", "S: [3. 5. 1. 2. 4.]\n", "R: [4. 3. 1. 2. 5.]\n", "Q: [4. 3. 1. 2. 5.]\n" ] } ], "source": [ "f_vikor = methods.fVIKOR()\n", "res = f_vikor(matrix, fuzzy_weights, types)\n", "print('PREFERENCES')\n", "print(f'S: {res[0]}')\n", "print(f'R: {res[1]}')\n", "print(f'Q: {res[2]}')\n", "\n", "rank_vikor = f_vikor.rank()\n", "print('RANKINGS')\n", "print(f'S: {rank_vikor[0]}')\n", "print(f'R: {rank_vikor[1]}')\n", "print(f'Q: {rank_vikor[2]}')\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "VIKOR method is characterized by returning three assessment vectors (S, R, Q). The difference between them lays in the way how they are calculated in the final phase of the evaluation. The VIKOR method performance can be adjusted with the defuzzification method, and the default settings for this parameter is `mean_area_defuzzification`. Moreover, while calling the fVIKOR object, the `v` parameter can be given, which translates how the weight of the strategy will behave. It is set to `0.5` as default. VIKOR ranking can be calculated by sorting the preferences in the ascending order, so in the `rank` method, the parameter should be sey as `descending=False`." ] }, { "cell_type": "code", "execution_count": 218, "metadata": {}, "outputs": [], "source": [ "vikor = {\n", " 'Mean': methods.fVIKOR(defuzzify=utils.defuzzifications.mean_defuzzification),\n", " 'Mean Area': methods.fVIKOR(defuzzify=utils.defuzzifications.mean_area_defuzzification),\n", " 'Graded Mean Average': methods.fVIKOR(defuzzify=utils.defuzzifications.graded_mean_average_defuzzification),\n", " 'Weighted Mean': methods.fVIKOR(defuzzify=utils.defuzzifications.weighted_mean_defuzzification) \n", "}" ] }, { "cell_type": "code", "execution_count": 219, "metadata": {}, "outputs": [], "source": [ "results = {}\n", "for name, function in vikor.items():\n", " results[name] = function(matrix, fuzzy_weights, types)" ] }, { "cell_type": "code", "execution_count": 220, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Method A1 A2 A3 A4 A5\n", "------------------- ---- ---- ---- ---- ----\n", "Mean 7.85 8.6 6.13 6.48 8.5\n", "Mean Area 7.7 8.48 5.97 6.38 8.45\n", "Graded Mean Average 7.54 8.36 5.8 6.28 8.4\n", "Weighted Mean 7.7 8.48 5.97 6.38 8.45\n" ] } ], "source": [ "print(tabulate([[name, *np.round(pref[0], 2)] for name, pref in results.items()],\n", " headers=['Method'] + [f'A{i+1}' for i in range(10)]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy WASPAS" ] }, { "cell_type": "code", "execution_count": 221, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [0.514 0.487 0.631 0.614 0.545]\n", "Ranking: [4. 5. 1. 2. 3.]\n" ] } ], "source": [ "f_waspas = methods.fWASPAS()\n", "print(f'Preferences: {f_waspas(matrix, crisp_weights, types)}')\n", "print(f'Ranking: {f_waspas.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the WASPAS methods use `waspas_normalization` and `mean_defuzzification` methods in calculations. This could be changed by passing other methods from `normalizations` and `defuzzifications` modules." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy WPM" ] }, { "cell_type": "code", "execution_count": 222, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [5.096 4.729 4.185 4.041 7.552]\n", "Ranking: [2. 3. 4. 5. 1.]\n" ] } ], "source": [ "f_wpm = methods.fWPM()\n", "print(f'Preferences: {f_wpm(matrix, crisp_weights)}')\n", "print(f'Ranking: {f_wpm.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the WPM methods use `mean_defuzzification` method in calculations to calculate crisp values from Triangular Fuzzy Numbers. The data normalization is set to `None`, but it could be changed to standardize the input data. Moreover, the WPM method does not need the criteria types in the calculation process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fuzzy WSM" ] }, { "cell_type": "code", "execution_count": 223, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Preferences: [5.333 5.2 4.4 4.333 7.6 ]\n", "Ranking: [2. 3. 4. 5. 1.]\n" ] } ], "source": [ "f_wsm = methods.fWSM()\n", "print(f'Preferences: {f_wsm(matrix, crisp_weights)}')\n", "print(f'Ranking: {f_wsm.rank()}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly to the WPM method, WSM use `mean_defuzzification` technique and normalization is also set to `None`. Also, the WSM method does not use the criteria types in the calculation process." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation\n", "\n", "Correlation coefficients can be used to indicate the results similarity. They are based on preference or ranking comparison obtained from the multi-criteria assessment. In the library there are available 4 different measures and the example of their usage is presented below. The `pearson_coef` and `spearman_coef` are ment to be used to compare the preference values, while `weighted_spearman_coef` and `ws_rank_similarity_coef` can be used to compare rankings." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 1\n", "Similar preferences" ] }, { "cell_type": "code", "execution_count": 224, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spearman: 0.9588593677597358\n", "Pearson: 0.9588593677597358\n" ] } ], "source": [ "x = np.array([0.69, 0.53, 0.76, 0.81, 0.8])\n", "y = np.array([0.66, 0.54, 0.71, 0.84, 0.77])\n", "\n", "print(f'Spearman: {corrs.spearman_coef(x, y)}')\n", "print(f'Pearson: {corrs.pearson_coef(x, y)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2\n", "Different preferences" ] }, { "cell_type": "code", "execution_count": 225, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Spearman: 0.225511293634533\n", "Pearson: 0.225511293634533\n" ] } ], "source": [ "x = np.array([0.75, 0.39, 0.86, 0.51, 0.63])\n", "y = np.array([0.66, 0.54, 0.71, 0.84, 0.77])\n", "\n", "print(f'Spearman: {corrs.spearman_coef(x, y)}')\n", "print(f'Pearson: {corrs.pearson_coef(x, y)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 3\n", "Similar rankings" ] }, { "cell_type": "code", "execution_count": 226, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weighted Spearman: 0.85\n", "WS rank similarity: 0.7916666666666667\n" ] } ], "source": [ "x = np.array([1, 2, 3, 4, 5])\n", "y = np.array([2, 1, 3, 4, 5])\n", "\n", "print(f'Weighted Spearman: {corrs.weighted_spearman_coef(x, y)}')\n", "print(f'WS rank similarity: {corrs.ws_rank_similarity_coef(x, y)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 4\n", "Different rankings" ] }, { "cell_type": "code", "execution_count": 227, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Weighted Spearman: 0.050000000000000044\n", "WS rank similarity: 0.46354166666666663\n" ] } ], "source": [ "x = np.array([1, 2, 3, 4, 5])\n", "y = np.array([4, 2, 1, 5, 3])\n", "\n", "print(f'Weighted Spearman: {corrs.weighted_spearman_coef(x, y)}')\n", "print(f'WS rank similarity: {corrs.ws_rank_similarity_coef(x, y)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Triangular Fuzzy Numbers" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pyFDM package offers a possibility to use basic mathematical operations for Triangular Fuzzy Numbers, thus making the calculations under incomplete knowledge." ] }, { "cell_type": "code", "execution_count": 228, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Addition result: (3, 5, 7)\n", "Subtraction result: (-3, -1, 1)\n", "Multiplication result: (2, 6, 12)\n", "Division result: (0.25, 0.6666666666666666, 1.5)\n" ] } ], "source": [ "tfn1 = TFN(1, 2, 3)\n", "tfn2 = TFN(2, 3, 4)\n", "\n", "result_addition = tfn1 + tfn2\n", "print(f'Addition result: {result_addition}')\n", "\n", "result_subtraction = tfn1 - tfn2\n", "print(f'Subtraction result: {result_subtraction}')\n", "\n", "result_multiplication = tfn1 * tfn2\n", "print(f'Multiplication result: {result_multiplication}')\n", "\n", "result_division = tfn1 / tfn2\n", "print(f'Division result: {result_division}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculating the membership degree for given TFN is also implemented. The calculations could be done for array of values or for single value. " ] }, { "cell_type": "code", "execution_count": 229, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Single value membership degree: 0.5\n", "Array of values membership degree: [0.5 0.7 0.9 0.9 0.7 0.5]\n" ] } ], "source": [ "tfn = TFN(1, 2, 3)\n", "\n", "# single value\n", "x_value = 2.5\n", "membership_value = tfn.membership_function(x_value)\n", "print(f'Single value membership degree: {membership_value}')\n", "\n", "# array of values\n", "x_array = np.linspace(1.5, 2.5, 6)\n", "membership_array = tfn.membership_function(x_array)\n", "print(f'Array of values membership degree: {membership_array}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comparing the TFN values is possible, with indicating the inclusion of TFNs." ] }, { "cell_type": "code", "execution_count": 230, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TFNs equality: True\n", "TFNs inclusion: True\n" ] } ], "source": [ "# equality check\n", "tfn1 = TFN(1, 2, 3)\n", "tfn2 = TFN(1, 2, 3)\n", "\n", "print(f'TFNs equality: {tfn1 == tfn2}')\n", "\n", "# inclusion check\n", "tfn1 = TFN(1, 2, 3)\n", "tfn2 = TFN(0, 1, 4)\n", "\n", "print(f'TFNs inclusion: {tfn1.is_included_in(tfn2)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "S-norm and T-norm operators are implemented. They allow to analyze the `OR` and `AND` fuzzy operators on Triangular Fuzzy Numbers." ] }, { "cell_type": "code", "execution_count": 231, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "S-norm operation: (2, 3, 4)\n", "T-norm operation: (1, 2, 3)\n" ] } ], "source": [ "# Fuzzy OR operation (S-norm)\n", "tfn1 = TFN(1, 2, 3)\n", "tfn2 = TFN(2, 3, 4)\n", "\n", "result_s_norm = tfn1.s_norm(tfn2)\n", "print(f'S-norm operation: {result_s_norm}')\n", "\n", "# Fuzzy AND operation (T-norm)\n", "tfn1 = TFN(1, 2, 3)\n", "tfn2 = TFN(2, 3, 4)\n", "\n", "result_t_norm = tfn1.t_norm(tfn2)\n", "print(f'T-norm operation: {result_t_norm}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## TFN visualization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The visualization of Triangular Fuzzy Numbers is handled in the pyFDM version `1.1.0`. " ] }, { "cell_type": "code", "execution_count": 232, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Example visualization of single TFN\n", "single_tfn_plot(2, 4, 6)" ] }, { "cell_type": "code", "execution_count": 233, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Example visualization of TFN membership degree\n", "tfn_membership_plot(2, 4, 6, 3)" ] }, { "cell_type": "code", "execution_count": 234, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Example visualization of TFN criteria plot\n", "tfn_criteria_plot([[1, 4, 7], [1, 6, 7], [1, 2, 7]])" ] }, { "cell_type": "code", "execution_count": 235, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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