{ "cells": [ { "cell_type": "markdown", "id": "db3c2e94-9499-4d7d-bf0f-1ab892a61fa1", "metadata": {}, "source": [ "### ASSOCIATION RULES" ] }, { "cell_type": "markdown", "id": "7e642b4f-11cf-4bd5-bc7f-c97da0efc7e2", "metadata": {}, "source": [ "#### FACEPLATE DATASET" ] }, { "cell_type": "code", "execution_count": 25, "id": "c6b19ec8-aa53-4b0f-b2ab-3c3e5c8eacbd", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Red White Blue Orange Green Yellow\n", "Transaction \n", "1 1 1 0 0 1 0\n", "2 0 1 0 1 0 0\n", "3 0 1 1 0 0 0\n", "4 1 1 0 1 0 0\n", "5 1 0 1 0 0 0\n", "6 0 1 1 0 0 0\n", "7 1 0 1 0 0 0\n", "8 1 1 1 0 1 0\n", "9 1 1 1 0 0 0\n", "10 0 0 0 0 0 1" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(r'/Users/patriciaxufre/Documents/SBE - Disciplinas/2957 | ABA/2024-25/Datasets Examples/Faceplate.csv')\n", "df.set_index('Transaction', inplace = True)\n", "df" ] }, { "cell_type": "code", "execution_count": 29, "id": "97b4e61c-c99e-447c-b160-a9d9c4748fd2", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/lib/python3.12/site-packages/mlxtend/frequent_patterns/fpcommon.py:161: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n", " warnings.warn(\n" ] }, { "data": { "text/html": [ "
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antecedentsconsequentssupportconfidenceliftrepresentativityleveragezhangs_metricjaccardcertaintykulczynski
12(Red, White)(Green)0.20.52.5000001.00.121.0000.5000000.3750.750000
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8(Green)(White)0.21.01.4285711.00.060.3750.2857141.0000.642857
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" ], "text/plain": [ " antecedents consequents support confidence lift \\\n", "12 (Red, White) (Green) 0.2 0.5 2.500000 \n", "15 (Green) (Red, White) 0.2 1.0 2.500000 \n", "4 (Green) (Red) 0.2 1.0 1.666667 \n", "14 (White, Green) (Red) 0.2 1.0 1.666667 \n", "7 (Orange) (White) 0.2 1.0 1.428571 \n", "8 (Green) (White) 0.2 1.0 1.428571 \n", "\n", " representativity leverage zhangs_metric jaccard certainty kulczynski \n", "12 1.0 0.12 1.000 0.500000 0.375 0.750000 \n", "15 1.0 0.12 0.750 0.500000 1.000 0.750000 \n", "4 1.0 0.08 0.500 0.333333 1.000 0.666667 \n", "14 1.0 0.08 0.500 0.333333 1.000 0.666667 \n", "7 1.0 0.06 0.375 0.285714 1.000 0.642857 \n", "8 1.0 0.06 0.375 0.285714 1.000 0.642857 " ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Frequent ItemSets\n", "\n", "# Association Rules\n" ] }, { "cell_type": "markdown", "id": "ca7d7ef3-8080-45fe-84cb-2b4e460db23c", "metadata": {}, "source": [ "#### BOOK CLUB DATASET" ] }, { "cell_type": "code", "execution_count": 6, "id": "a8e53481-02ba-450d-9d1f-ad2ecd8458cf", "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pylab as plt\n", "from mlxtend.frequent_patterns import apriori, association_rules" ] }, { "cell_type": "code", "execution_count": 8, "id": "c3eb56d6-3b1c-4044-b70c-1ac81bd56384", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " Seq# ID# Gender M R F FirstPurch ChildBks YouthBks CookBks \\\n", "0 1 25 1 297 14 2 22 0 1 1 \n", "1 2 29 0 128 8 2 10 0 0 0 \n", "2 3 46 1 138 22 7 56 2 1 2 \n", "3 4 47 1 228 2 1 2 0 0 0 \n", "4 5 51 1 257 10 1 10 0 0 0 \n", "\n", " ... ItalCook ItalAtlas ItalArt Florence Related Purchase Mcode \\\n", "0 ... 0 0 0 0 0 5 \n", "1 ... 0 0 0 0 0 4 \n", "2 ... 1 0 0 0 2 4 \n", "3 ... 0 0 0 0 0 5 \n", "4 ... 0 0 0 0 0 5 \n", "\n", " Rcode Fcode Yes_Florence No_Florence \n", "0 4 2 0 1 \n", "1 3 2 0 1 \n", "2 4 3 0 1 \n", "3 1 1 0 1 \n", "4 3 1 0 1 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.read_csv(r'/Users/patriciaxufre/Documents/SBE - Disciplinas/2957 | ABA/2024-25/Datasets Examples/CharlesBookClub.csv')\n", "df.head(5)" ] }, { "cell_type": "code", "execution_count": 12, "id": "c351597f-fca7-44de-bf56-b0d39d3a4371", "metadata": {}, "outputs": [], "source": [ "ignore = ['Seq#', 'ID#', 'Gender', 'M', 'R', 'F', 'FirstPurch', 'Related Purchase', 'Mcode', 'Rcode', 'Fcode', 'Yes_Florence', 'No_Florence']\n" ] }, { "cell_type": "code", "execution_count": 14, "id": "43eafcb8-1579-46e6-af4e-c2328c40643a", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/lib/python3.12/site-packages/mlxtend/frequent_patterns/fpcommon.py:161: DeprecationWarning: DataFrames with non-bool types result in worse computationalperformance and their support might be discontinued in the future.Please use a DataFrame with bool type\n", " warnings.warn(\n" ] } ], "source": [ "# Frequent itemsets and Rules\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "418d33c4-150e-48b3-80db-ab28ec5694bf", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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60(YouthBks, DoItYBks)(ChildBks, CookBks)0.067000.6489102.6814481.00.0420142.1589930.6992660.2407910.5368210.462885
80(RefBks, GeogBks)(ChildBks, CookBks)0.050250.6146792.5399951.00.0304671.9671900.6602760.1837290.4916610.411162
69(YouthBks, GeogBks)(ChildBks, CookBks)0.063250.6052632.5010871.00.0379611.9202670.6702110.2233010.4792390.433313
77(DoItYBks, GeogBks)(ChildBks, CookBks)0.060500.5990102.4752481.00.0360581.8903210.6629590.2141590.4709890.424505
66(ChildBks, CookBks, GeogBks)(YouthBks)0.063250.5776262.4244521.00.0371621.8034950.6597820.2223200.4455210.421552
71(ChildBks, RefBks, CookBks)(DoItYBks)0.061250.5917872.3230131.00.0348831.8256420.6352760.2062290.4522470.416110
49(DoItYBks, GeogBks)(YouthBks)0.054500.5396042.2648641.00.0304371.6545540.6212150.1913960.3956070.384178
62(ChildBks, RefBks, CookBks)(YouthBks)0.055250.5338162.2405731.00.0305911.6340130.6176080.1928450.3880100.382858
58(ChildBks, DoItYBks, CookBks)(YouthBks)0.067000.5244622.2013091.00.0365641.6018690.6256520.2240800.3757290.402840
57(ChildBks, YouthBks, CookBks)(DoItYBks)0.067000.5583332.1916911.00.0364301.6873580.6178770.2177090.4073580.410668
33(ChildBks, RefBks)(DoItYBks)0.071000.5536062.1731351.00.0383281.6694900.6192550.2275640.4010150.416155
75(ChildBks, CookBks, GeogBks)(DoItYBks)0.060500.5525112.1688381.00.0326051.6654060.6051920.1991770.3995460.395000
20(ChildBks, GeogBks)(YouthBks)0.075500.5162392.1667971.00.0406561.5746420.6307340.2443370.3649350.416567
46(CookBks, GeogBks)(YouthBks)0.080250.5136002.1557191.00.0430231.5660980.6353990.2553700.3614700.425216
61(ChildBks, RefBks, YouthBks)(CookBks)0.055250.8911292.1447151.00.0294895.3687410.5690170.1308470.8137370.512051
16(ChildBks, YouthBks)(DoItYBks)0.080250.5440682.1356931.00.0426741.6345630.6237750.2492240.3882160.429541
51(RefBks, CookBks)(DoItYBks)0.074500.5330952.0926191.00.0388991.5961480.6069520.2328130.3734920.412769
28(RefBks)(ChildBks, CookBks)0.103500.5054952.0888201.00.0539501.5328440.6554680.3015290.3476180.466590
72(RefBks, DoItYBks, CookBks)(ChildBks)0.061250.8221482.0866691.00.0318973.4073210.5626880.1503990.7065140.488802
15(YouthBks)(ChildBks, CookBks)0.120000.5036732.0812921.00.0623441.5272180.6820210.3331020.3452150.499770
70(ChildBks, RefBks, DoItYBks)(CookBks)0.061250.8626762.0762361.00.0317494.2563590.5579750.1440330.7650570.505044
23(ChildBks, CookBks)(DoItYBks)0.127750.5278932.0721981.00.0661011.5785600.6826130.3462060.3665110.514682
25(DoItYBks)(ChildBks, CookBks)0.127750.5014722.0721981.00.0661011.5204760.6942920.3462060.3423110.514682
\n", "
" ], "text/plain": [ " antecedents consequents support confidence \\\n", "64 (RefBks, YouthBks) (ChildBks, CookBks) 0.05525 0.680000 \n", "73 (RefBks, DoItYBks) (ChildBks, CookBks) 0.06125 0.662162 \n", "60 (YouthBks, DoItYBks) (ChildBks, CookBks) 0.06700 0.648910 \n", "80 (RefBks, GeogBks) (ChildBks, CookBks) 0.05025 0.614679 \n", "69 (YouthBks, GeogBks) (ChildBks, CookBks) 0.06325 0.605263 \n", "77 (DoItYBks, GeogBks) (ChildBks, CookBks) 0.06050 0.599010 \n", "66 (ChildBks, CookBks, GeogBks) (YouthBks) 0.06325 0.577626 \n", "71 (ChildBks, RefBks, CookBks) (DoItYBks) 0.06125 0.591787 \n", "49 (DoItYBks, GeogBks) (YouthBks) 0.05450 0.539604 \n", "62 (ChildBks, RefBks, CookBks) (YouthBks) 0.05525 0.533816 \n", "58 (ChildBks, DoItYBks, CookBks) (YouthBks) 0.06700 0.524462 \n", "57 (ChildBks, YouthBks, CookBks) (DoItYBks) 0.06700 0.558333 \n", "33 (ChildBks, RefBks) (DoItYBks) 0.07100 0.553606 \n", "75 (ChildBks, CookBks, GeogBks) (DoItYBks) 0.06050 0.552511 \n", "20 (ChildBks, GeogBks) (YouthBks) 0.07550 0.516239 \n", "46 (CookBks, GeogBks) (YouthBks) 0.08025 0.513600 \n", "61 (ChildBks, RefBks, YouthBks) (CookBks) 0.05525 0.891129 \n", "16 (ChildBks, YouthBks) (DoItYBks) 0.08025 0.544068 \n", "51 (RefBks, CookBks) (DoItYBks) 0.07450 0.533095 \n", "28 (RefBks) (ChildBks, CookBks) 0.10350 0.505495 \n", "72 (RefBks, DoItYBks, CookBks) (ChildBks) 0.06125 0.822148 \n", "15 (YouthBks) (ChildBks, CookBks) 0.12000 0.503673 \n", "70 (ChildBks, RefBks, DoItYBks) (CookBks) 0.06125 0.862676 \n", "23 (ChildBks, CookBks) (DoItYBks) 0.12775 0.527893 \n", "25 (DoItYBks) (ChildBks, CookBks) 0.12775 0.501472 \n", "\n", " lift representativity leverage conviction zhangs_metric jaccard \\\n", "64 2.809917 1.0 0.035588 2.368750 0.701080 0.206157 \n", "73 2.736207 1.0 0.038865 2.243680 0.699207 0.224154 \n", "60 2.681448 1.0 0.042014 2.158993 0.699266 0.240791 \n", "80 2.539995 1.0 0.030467 1.967190 0.660276 0.183729 \n", "69 2.501087 1.0 0.037961 1.920267 0.670211 0.223301 \n", "77 2.475248 1.0 0.036058 1.890321 0.662959 0.214159 \n", "66 2.424452 1.0 0.037162 1.803495 0.659782 0.222320 \n", "71 2.323013 1.0 0.034883 1.825642 0.635276 0.206229 \n", "49 2.264864 1.0 0.030437 1.654554 0.621215 0.191396 \n", "62 2.240573 1.0 0.030591 1.634013 0.617608 0.192845 \n", "58 2.201309 1.0 0.036564 1.601869 0.625652 0.224080 \n", "57 2.191691 1.0 0.036430 1.687358 0.617877 0.217709 \n", "33 2.173135 1.0 0.038328 1.669490 0.619255 0.227564 \n", "75 2.168838 1.0 0.032605 1.665406 0.605192 0.199177 \n", "20 2.166797 1.0 0.040656 1.574642 0.630734 0.244337 \n", "46 2.155719 1.0 0.043023 1.566098 0.635399 0.255370 \n", "61 2.144715 1.0 0.029489 5.368741 0.569017 0.130847 \n", "16 2.135693 1.0 0.042674 1.634563 0.623775 0.249224 \n", "51 2.092619 1.0 0.038899 1.596148 0.606952 0.232813 \n", "28 2.088820 1.0 0.053950 1.532844 0.655468 0.301529 \n", "72 2.086669 1.0 0.031897 3.407321 0.562688 0.150399 \n", "15 2.081292 1.0 0.062344 1.527218 0.682021 0.333102 \n", "70 2.076236 1.0 0.031749 4.256359 0.557975 0.144033 \n", "23 2.072198 1.0 0.066101 1.578560 0.682613 0.346206 \n", "25 2.072198 1.0 0.066101 1.520476 0.694292 0.346206 \n", "\n", " certainty kulczynski \n", "64 0.577836 0.454153 \n", "73 0.554304 0.457631 \n", "60 0.536821 0.462885 \n", "80 0.491661 0.411162 \n", "69 0.479239 0.433313 \n", "77 0.470989 0.424505 \n", "66 0.445521 0.421552 \n", "71 0.452247 0.416110 \n", "49 0.395607 0.384178 \n", "62 0.388010 0.382858 \n", "58 0.375729 0.402840 \n", "57 0.407358 0.410668 \n", "33 0.401015 0.416155 \n", "75 0.399546 0.395000 \n", "20 0.364935 0.416567 \n", "46 0.361470 0.425216 \n", "61 0.813737 0.512051 \n", "16 0.388216 0.429541 \n", "51 0.373492 0.412769 \n", "28 0.347618 0.466590 \n", "72 0.706514 0.488802 \n", "15 0.345215 0.499770 \n", "70 0.765057 0.505044 \n", "23 0.366511 0.514682 \n", "25 0.342311 0.514682 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Display 25 rules with highest lift\n" ] }, { "cell_type": "code", "execution_count": null, "id": "7a15b6fc-b439-4765-a76f-f920579d4e9f", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:base] *", "language": "python", "name": "conda-base-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }