SVC_submission.ipynb

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    "### SVC-BASED CLASSIFICATION WITH A TABULAR VECTOR BORNE DISEASE DATASET"
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    "#### TEAM MEMBERS:\n",
    "\n",
    "* #### Maryam Jahangir           (11159172)\n",
    "* #### Niels Schwung             (11160885)\n",
    "* #### Nnamdi Ogbonnaya Odii     (11160333)"
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    "#### 1.0 The Problem\n",
    "Classification with a Tabular Vector Borne Disease Dataset is the 2023 edition of Kaggle's Playground Series Competition.\n",
    "The goal of this competition is to develop robust machine learning models that can accurately classify instances of vector-borne diseases based on a tabular dataset containing various features.\n",
    "Participants are expected to preprocess and analyze the dataset, perform feature engineering if necessary, and train machine learning models using various algorithms and techniques. The models should be able to generalize well to unseen data and provide accurate predictions on the test set.\n",
    "\n",
    "Our task is to train the Support Vector Machine classifier(SVC), evaluate its performance using the map@k and then predict on an unseen dataset, likely top_3 vector borne diseases, given some features."
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    "#### 2.0 The Dataset\n",
    "Two datasets were provided for this task: the train dataset, labeled as train.csv and the test.csv, for prediction purpose.\n",
    "#### 2.0.1 Train.csv\n",
    "* The train dataset consists of 707x64 features (symptoms) and 707x1 labels (prognosis). \n",
    "* The features are binary digits (0s and 1s) where 0 represents absence of a particular symptom and 1 represents its presence. Each feature column is depicted by a particular symptom. \n",
    "* The labels are string-values of different kinds of vector borne diseases. This target variable serves as the ground truth for training the classification model.\n",
    "\n",
    "#### 2.0.2 Test.csv\n",
    "* The Test.csv dataset consists of 303x64 features (symptoms).\n",
    "* The test dataset is also a tabular dataset that follows a similar structure to the training dataset.\n",
    "* However, unlike the training dataset, the test dataset does not contain the target variable.\n",
    "* The purpose of the test dataset is to evaluate the performance and generalization ability of the machine learning model developed.\n",
    "* By uploading the predictions for the test dataset to Kaggle, a MAPK score for the test.csv is obtained."
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    "#### 3.0 The Preprocessing Methods\n",
    "#### 3.0.1 Feature Engingeering\n",
    "Feature engineering is the process of transforming raw data into a format that is suitable for machine learning algorithms to extract meaningful patterns and make accurate predictions. It involves creating new features or modifying existing features in the dataset to enhance the performance of a machine learning model. \n",
    "\n",
    "We did the following preprocessing activities on the data based on the solution handed in to Kaggle by R.J. Zhang [1]:\n",
    "* Encoding:We used the OrdinalEncoder class to encode the categorical label column into numerical representation.\n",
    "* Feature combination: This step aims to capture complex relationships or interactions between features that may not be explicitly present in the original dataset. We applied feature combination using logical operators AND,OR and XOR, as well as clustering to increase the number of features to 6116.\n",
    "* Feature Subset selection: This step helps to focus on the most important features, improve model performance, interpretability, and computational efficiency. SelectKBest feature selection method was used to select the top k features based on the specified scoring function chi2. This function is the chi-square feature selection statistical test used to examine the differences between categorical variables from a random sample in order to judge the goodness of fit between expected and observed results.\n",
    "It computes the chi-square statistics for each feature in X and the corresponding target variable y. The feature selector ranks the features based on their chi-square scores and identifies the top k features with the highest scores.\n",
    "\n",
    "$\\chi^{2}=\\sum \\frac{\\left({O}_{i}-E_{i}\\right)^{2}}{E_{i}}$\n",
    "\n",
    "$\\chi^2\t$=\tchi squared, \n",
    "${O}_i\t$=\tobserved value, \n",
    "$E_{i}\t$=\texpected value\n"
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    "#### 3.0.2 Hyperparameter Tuning\n",
    "We applied the surrogate model optimization-based hyperparameter tuning approach based on the Python version of SPOT. This enabled us to tune the default values of the SVC hyperparameters to obtain best values within the defined search space and number of evaluations.\n",
    "\n",
    "#### The SVC Hyperparameters\n",
    "* C: This is the regularization parameter that controls the trade-off between maximizing the margin and minimizing the classification error. A smaller value of C allows for a larger margin but may misclassify more training examples.\n",
    "* Kernel: The kernel function maps the input data into a higher-dimensional space to make it linearly separable. It could be linear, polynomial, RBF or sigmoid.\n",
    "* Degree: It specifies the order of the polynomial used to transform the data.\n",
    "* gamma: Used for other kernels but linear. It controls the influence of each training example. Higher values of gamma will result in a more complex decision boundary, potentially overfitting the training data.\n",
    "* coeff0: It affects the influence of high-degree polynomials compared to low-degree ones.\n",
    "* shrinking: This is a boolean parameter that can speed up training for large datasets but may slightly increase the prediction time.\n",
    "* probability: If set to True, the predict_proba method can be used to obtain class probabilities.\n",
    "* tol: It determines the stopping criterion for the optimization solver.\n",
    "* cache_size: The kernel cache is used to store computed kernel values and can speed up training for larger datasets.\n",
    "* break_ties: This is a boolean parameter that specifies how to handle ties when predicting class labels."
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    "#### 4.0 Results\n",
    "We carried out some evaluations on the provided data. First evaluations were done using the default hyperparameter values\n",
    "while the second evaluations were carried out using tuned parameters. Both evaluations were done using 10-fold cross validation\n",
    "on the train and test subsets as well as on the complete dataset.\n",
    "\n",
    "The following results were obtained:\n",
    "\n",
    "    * Default values:\n",
    "        * map@k           : 0.38\n",
    "        * Train           : 0.36\n",
    "        * Test            : 0.29\n",
    "        * Complete Data   : 0.37\n",
    "        \n",
    "    * Tuned values:\n",
    "        * map@k           : 0.39\n",
    "        * Train           : 0.36\n",
    "        * Test            : 0.31\n",
    "        * Complete Data   : 0.37\n",
    "Also, using the trained classifier, we made prediction on the test.csv and obtained top3 probabilities for each sample.\n",
    "\n",
    "Our result was submitted to Kaggle and otained the following result:\n"
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OwvEudGL2t0scWpXjkyW5j2IyttYqBhQSeB71peKrC/vdVjGnRyb20u7iWUA7Vdtm0FugJwcUPS39dLiWun9b2Ojt721u2kW1uYZ2ibbII5AxQ+hx0NFvfWl3JIlrdQzvEcSLHIGKH0OOlcVpdrKtyLizt53lttPlj+ztpRtI2JAxE7E/NyONuR1555pJYX93Lt02CaN20m4gGzTWs44nITbGC3JOc8kkenen/X5/5f11Fqdx/bli+pW9lbzJcSTs65hdWEZQZIbB4NWo721muZLeG5hkni/1kSyAsn1HUVyEMMN14g0iTRtIuLHyLWaOSWSzaFYmKAKhyBnBzyMj0PNVPD2mzJdaRFMlzFdWe7zlXSzGFOwht05OHDE5yuSTg49E9BdLnX3WvWFssRWeOfzJo4SIXVtnmH5Wbnge9XIL21urcz21zDNCM5kjkDKMdeRxXDDRzB8O9GhbSJZJFuYJLq2SD94wD5bcuMnj1qS+smv57+70vTbhdNP2Xz7c2zQtdbJC0m2NgCfkIHT5sY5p21t5/wCQ2drb3lrd25ntbmGeEZzJHIGXjryOKZDqNlcW5ngvLeWFW2GRJVKhs4xkHrk1yV0jTSave6PpDNZyWkULQ3Fk6C4fedzeUQrNtQ+gz0HSsu50+5vF1URWtzcQzx2S5/s5rdZNs/zAIRkgL1J5x7UWu0g6HoB1TTxDHKb62EUzbI3My7XbOMA55Oe1JpmoRarpsV7ArpHLnAcDIwSO30rAvra1sfEkst/pUlzZS2SQ2wgtDMsbBmLptUHbu3LycA468Vd8IW8tr4NsoLi3lgkSNg0LjDr8x4570ulw62NaG9tbmaWG3uYZZYTiREkDMh9wOlFve2t20i2tzDO0TbZBHIGKH0OOhrg7SyvPsc2naPazMP7PnijmutPNtPbEgbYzLwsmT3X0zk1oywWuoaPdW2g6Nc2V4NOeESPatbhc4/dZYDcTjqMgdc88n9f1/X/BOp1UN/Z3Kytb3cEqwnEpSQNsPvjp+NNOp2AglmN7biKE4kk81dqH0Jzx1FcnPBFfTh9D0q4tI4NMuIpw9m0O7coCRAEDeQQTxkD15qO70ma20jw28VsYLe3hzcoliZzHKYwA7RDknO4E4JBan/X5/wCQHcRyJNGskLrIjDKspyCPUGqGqauNOkt4IrWa8urnd5UEJUEhRkkliAAMjv3rM8NH+zbJITDeul5dytGWsjCsQxnJTJ2KSCRnByelT+JFtWe0N/ZXkkSlit3Y+YZLZsccR/PgjI4yOORSYIswa0Z44ibGeCRrn7PLFOyI0ZxnP3sOOn3SevsauR6hZTXAghu4JJiu4RrKpYj1xnOK5KJNTuxphmS9uLeDWQ0EtzCVm8kRMNzjAIG4kAkAkYzUemaM8GgaDINOaK8j1IvK3kESKpeQEtxnG0jr2xT/AM/8v8w/r8zsxe2rXjWi3MJuVXcYRIN4Hrt64oW9tXvGtEuYWuUG5oRIC6j1K9a5XRIba3FrZX2j3L6rHdPJJcC1YLuLMfO87hSCD0yTzjFZmj6Xcpdadb3guVvbe8Mspj0sjnLFnNwSFKsM9CSc4x2oWoPS56CZY1kWNnUO4JVSeWx1wKimv7S3hkmnuoIoo22u7yAKp9CT0NZHi0tZ6dDrMSF5dLlE5UdXjPyyL/3ySfqBWTHpraY2j3erWkt1F5c8l0I4Gm8u5lKtuKKCSMblzjj8aSA7AXEJ8rE0Z84Zjww+cYzx68U5JElBMbq4Vip2nOCOo+tcPa2s+n3Gmak1jdJYx3906QR27PJBFIhC5jUFgCwJxjjcM1v+FVl+wXkk9vNbGa/nlVJ02ttZ8g4+lP8Ar8g/r8zUS9tZbt7WO5he4jGXhWQF1HqR1FIt/aPNNDFcwyTQjMkSSAsn1HUVx2m289vrEdtp1pMymW4Zje6cUksywY7xOPlcFsDAySD14qroemTpJpcMqXMd1Zq3mqulmMKdjBg05OHDE/w5JODj0l7XH1OytdcsbjRrXU5ZktLe6RWQ3DqnXoOuM1bub21s4llu7mGCNiAHlkCgk9Bk15/Bp17bWuiy3sUsUCaUsGG01roxSZyylBypI284/hxV2wsk0uXT5dWsbu7s1sDFBvsjI0DGQkqY03bcqVA9AuDjpVO1/wCvP/L8Sen9eR1k2sWFvqlvp81zGtzcoXjQsOQMD9c8evPpU63tq941olzC1yg3NCJAXUepXrXOC1ih1jw9cxaXPb2y20sKoYC7W5YoUDbc7RweTwPUVi6Rpd0lzp1vdi4W9t7wyyGPSyOcsWc3BIUqwz0JJzjHaj+vxH0udle65Y2MixNMksxmjiaGN1LoXYKCRnIGTUt/qUWny2aTI7G8uBbptA4YqzZPt8priUswNO0mxbR7o6rbalFJczi0bA/e5aTzcYYN14J689OOn8RW8091ohgieQRairuUUnYvluMn0GSOfel29f8AIHu/T/M1VvbV7xrRLmFrlBuaESAuo9SvWkS9tZbt7WO5he4jGXhWQF1HqR1FcHo+l3SXWnW94Llb23vDLKY9LI5yxZzcEhSrDPQknOMdquaVC9prkMFpY3E0PnzvILvTzHLa7gxLLOPkfccADkkEc8UdAZ2EN7a3M0sNvcwyywnEiJIGZD7gdKhk1exi1iPTJLhFu5IzIsZYZIBA/Pnp9a5nwvE9pqltbW1nK9rFbuplu9ONvNa8qQhk4WTJznaO2cmtW8gEfjiwu3tXeNrSWETJCXCuXQjcQDt4B5OB70+q/ruHRlzSdfsNYgia3mRJZAzC3d18wBWK52g9Mira31o941ol1C1ygy0IkBdR6letcbpmjPb+H9CddOaK8j1PfKwgIkVS8mS3GQMEcntiq9tFdzazpbmxkt5o9RaSeGDTGiSEEOCzTEfPnI5Bwc9KFq/68v8AMHp/Xr/kdymoWUtwLeK7geZlLCNZVLEDgnGc4qHU9T/s5IQlrNdzzv5cUMIGWOCeSxAAAB5JrlLDRnh0DSZBpzJeJq/mO3kkSKhmYFjxnG09Txiuk8Q6rPpenq1lazXFxM/loY4JJViz/G4QE4HoOvT3C6D62LGl6mmqQSusMtvLDKYZoZcbo3GDjgkHgg5B6GphfWpvDaC5hNyF3GHzBvA9dvXFUPDkVtDpZS1N07GRmmlu7d4ZJZDyzEOoPPsMDGO1YFvC9r4mVbOymmEmoPLLHd6eQYN27dKlwPlx6A5ODjjs+tielzsFuYGRHWeMrI21GDjDH0HqeD+VU9O12x1IKsUyRzM0irBI6iQhHKk7c5xlTXK6elzFZ6JpTaffCey1N3nc2ziNVzJht+MEHcOhPviq9tZg6O1hHo91Fq76m00c7WjAKPPLCXzcYACcYzk9Mciha/16f5jen9ev+R3R1KxE/km8txLhj5fmruwv3jjPbBz6VHJqcJt7eazMd3FPMsQeKZNoyeuSecegyfauRGhebZ7ptMLyt4iaVi0GWMXmn5jx93b36Yqa60yeLUpktbKRYP7etZ1EcRC7fKTe4wOmc5PTNKOtvl+n+f4A9L/P9f8AI65L21lu3tY7mF7iMZeFZAXUepHUVG2pWpFytvPDPNbKWkhSVdy4HQ5Py/jiuU0iF7TXoIbSynli8+d5BeaeY5LXduJYTj5HDHAA5JB68VUsbWaCGSysLGeaEWNym6600wz2xK8J5g+WXcePlyT1yaXS/kNb2O2Gp2yW9tJdzQ2r3KgpHJMuSSM7Qc4Y89s0+51Czs/+Pu7gg6f62QL14HX1wfyrjLS2jsY3/tzSbm7+0aZbxQBbNpsbY8NEcA7Du55wOfarWkaHKdYhXWrMXBj0SCB5JY9679z7l3HgnGM1UtG/67/5fiStr/10/wAzsQcjI5FFYPha58jRtL0u6WWO9SwSR45EIKKMLg56HPb2reoasxhRRRSAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigArN1lcxxN2BI/P/wDVWlUN3B9otXjHXqv1oAkjfzIlcdGANcB468Pa6njTQ/GXhayj1O605Ht7iweZYjLE2eVZuARuP6da7HS7jKG3k4dOmfStCjrcOljxa8s/EGh6H448ceIbNdP1TWYEs7OwilErQqQI1yy8E8qePQ9M4rpNc0zxd4d+FmlaF4AsFn1BLdLeabzo4zANvzOu8gFi2ee2c4r0Wij7PL6fh/w7uHW/r+Nv8keffC3T9W0Ozk0vUPBraHEE86S/k1SK7kvJiRkvsGcnk56DGK7+R/LiZz0UE06s/VLjCC3j5d+uPSqbuxJWG6MuI5W7Egfl/wDrrSqG0g+z2qRnr1b61NUjCiiigAooooAKKKKACiuBstUutS1i70N7m4toZNSuA90SwLqpBEET9jjJPoM456aviLxLPoc0qW8mnlLaESG3kkd55Rg5GFHycD7xyD7UdE+4dWjqaK5m71/VJF1G50m2tWtdNX94J2YPK3lhyFxwuAw65yfTrSpr2q37P/ZdvaEWttFNcCdmBkZ037Ex0wMcnPXpRsrgtdjpaK4iPxTLDNJq90GMZ0O3ufsyuQnmPIw4z05IGfStTQfEkuo6q9hcvaTt5JmSaz37VwQCjBu/Iwe/PAp2f9fP/IXn/X9anR0VzPiTxLcaNcSCCXT8Qw+cYJHdppevAVR8g4+8cj8qsJ4hkkm1YJCgSyso7mPOcsXRmwf++RS6XKtrY3qK5SDWtR/t1ri5khXTl0lLx4VU7hncTg565H5Yq1Z61qi3GnPqttbJbamdsIhZi8DFC4V88NkA8jGD2NO39ff/AJE3/r8ToaKxLnxJGdMvJ9MtLy4nt494iks5ot3OOCyDPrgZNUB4teDQbi9ln068dZo4YmtpGVQzkDEinJTGc9yQOgpDOqorhbvxNc3unzW/nwmaG5tGFzYl1R0eYArzyDxyMnIIqw/jhhfu0f2M2sd0bYwZc3DAPsMg429cnb6Dr2ppX/r0/wAwOyorAi1XV768nl061tXsbe5NuySORLLtOHYH7owc4BznHUZrOTxBqOmW+u3Wovbzpb3y29ugygUsEABPOF+bJ4z1pf1+X+Yf1/X3HYUVyNv4ykW3vhOtreTW6RvC9oWWOQu+wId3KkMRk88HPtVizuNXHjSC21VoNv2CWQfZWYRsfMQcq3cevv2p21Dpc6aiss6rIPEdxp3lr5cVktyH5ySWZcfT5ayLTxDrOqzWsOnW9lG82mw3ryTlyqlywKADnt17c9aW/wDXr/kw/r8v8zq6KztM1GXVtCW7hjWC4ZXUo53KkikqRkYyNwP4Vg6RresSaDpETG3uNR1DeUlkDBERRkswHJPQYGOooA6+is3RtRnvftcF9FHHd2U3ky+USUb5QwZc8gEMOD096xtSs3t9atltb68n1a5uhMMTuI4bcMNwaPO3bt+UcZLH16HVB0OrorL8QanPpOl/aLWESMZFRmZWZYlJ5dgoyQPb9KoWniKeabSYi9jdLfTyxNPaSFkIRCwI/unjBUk49aFqGx0dFcjqWs6hcPafYlVZo9be0Vd7KrqI35fHUdyPan3HiXU7NzY3FtaPqC3cEG5WYROku7a3cjBUgjnp70LX+vT/ADB6P+vP/I6uiuSbxFrdtFdy3VvYNHptykFyY2cGUNsO5Afu4Djg5zg9KSTXprGLUntoFTbqpt5J5TJLHEvlqfMYAkgdBgYHI6Uf1+X+Yf1+f+R11Fcxd+JLuDR7C5R7Ai5kZZL6MvNbRAZwx2884xyQAc5NQT+Ixa3kd7cxQzsukyXBe1uWeNyJFAC/wkE45xkdM0f1+Fw/r8bHXUVzcut6rpshj1eC0LzWs08Bty2FaNdxRs9eD94Y6HgUyz8QarvsnvrS2eLULR57dLZm3qyoG2Nng5B6joeOetH9f19wHT0Vz/hrXbjWHkFzLYlhGHaGEus0DE8o6Nz/AMC4+lVp47uX4hlpTC9va2IliQ78rksMgZxuyOuOlD0eobo6miuTsfEWs31hprLBYrc6qm+3XLlYUVcsz926jAGOvWpJfEuoxrHafZrYagNQFjLlm8v5ojIHHfGMcfUZ707O9g6XOoork4/EWtRxvNd29iYbS+WyufLZ9zlmVQ6Z6Ab14Oe/NbWu6i+maeJopbOFmkCB7yQoi59AOWPHCjGfWl0uHWxpUVyMPi29ubGD7PDbPcyal9gLnesbfuy4cA/MO3B9+e9Oj8Ra1HG813b2JhtL5bK58tn3OWZVDpnoBvXg57807X/r0/zQf1+f+TOsqs+nWsmpR38kZa4iQpGzOxCA9cLnAPOM4zjisK68RajHDfalb21u2mafM8UquzedIEOHdT0GDnAOc46jNQ3HibVI11K8it7RrHTbvyZVZm8yRcKSV7Ajd3zn2pLX+v67g9DrKK5nUNe1ZV1a50u2tHtdL3K6zM2+Z1QM23HAAyOuc4PStO/1K4tvDLanbwrLKkKztFzyuAWA98Zx70dLh1sadFc5eeKJEuLgWEEdxGn2eGFixAkmmPAz2UKVY8d6hvfEmpaStxb6jBayXcYgkjeHcI3SSURng8gjPqc5H0p2u7AdTRXO634juNLbVfKgjk+w20EyBiRuLuykH/vmtJ7i+s9CuLm+Fu11DE8m2ENs4BIHPJ+v8qWyuO13Y0KK5mz17VVXTp9UtrVbbUk/dCFm3wv5ZcBs8HIU9MYPr1qax8Qz3Vl4fmeGNTqoPmAZ+T90z8fitD0v5E3Ogori9A1nWLbQdLu9QSGWxnkS3LF2acFn2q5J4IzjjsD17VDp+tfZLiKa+8yYRyanIZGlclUjkHygZweOORxjjFHX7/wH/X9fcd1RWRpV1rc8kUmpWtottcR+YvkSEtAeCFbP3uD1GOR071RvrjV/+E0e20locHT0kb7SzGNT5jDhV6k9M+g/CnbWwdLnS0VyFx40c29lHGLWzup1lMzXTM0cRjfYwG3BbLZx04Gfalk8X3L6XY3EaW1qJpJY57qcO8ETIcDkAHDdQSQMetIDrqK5o6jqs/iLRBFPZi1ubSSWZImMiuQUyVbjP3uD7mrk/iOJtNvJtNtLy5ntojIIZLOaLefQFkGfoMn2o6AtTZorlrfxRO2kyziTTr+czRQQC1kZBvkOAsitkpg8+47CrNxqus6dbCO+tbSW6nnjgtXhdljdnzncDlhtAJPXPbFOwHQUVy174k1LS7W9hvLa1kv7X7O6GNmWOVJZNnfJUgg+vY+1Qarrd7o15PNdQ2815BpbTkxvIIz++AC7ScdD1xn8OKX9fhcDsKK5mbxHfaVLeprNvbv5VkbyH7KzcgNt2Hd3yRz79BU1tqesw67ZWGrQWW27ikkElsW+Qrt+XB6/e69/QU7AdBRWadTkHikaXsXyzZG53987wuPpzWLaeI9X1WSzg0+CyjmuLH7WzTlyq4fbgAcnt/8AX6Uv6/P/ACD+v6+86yiuRn8X3RsNNnSO1sUuoWeS4vN5hR1OPL3LgDJz8xI4HQ0/U/FstvfLaW0mnwyJbpPI1xI7o5bOEQqPb7x9RxQB1dFcS/iow3s+rMsggOjwTLavIQokeVlwfTkgE46CpoPGkkUd6LoWt48MAmiayLKrEsEEZ3Dg5ZefQngYp2Yf1/X3nT3unWuoiJbyMyrE4dU3sFJHTIBw30ORVmuZ1S78R2vh3VJLhbKOWK0eWK4ty2FIByu085A5DdPYVDfeJ7uwazsJJ7CK8a1FxNPcB/LwTgKqjnJwec8Y75pf1/X3B/X9fedZRXMWviS/1lbOLSLe3inltzcTG5LFEAfZtAGCckHB44GcdqueErm4utAMt5u843VwGVn37cTONoPcDoPpQBt0Vyen+Kry6ju5J/sMckNvLMbEs6XEBXoGVvvD1IAA96uNrk8tvpY8tVOoWElw5UnKMEVhj/vqh6K/9df8hpXdjoKK5HQdY1eDStCk1SOB7S+jihVldjMjlMqzk8Nuxz0xnvTbTxJrl3Dp08dtp4j1GWSCIFn3Rsu87m9RhD8o9ufRtWdiU7q52FFcpba3Pd6vpcV7bJ9qS5ureQwyuE3RpnIGcEEY4bOKXSvFF7ex3UkkVpJNFbvN/Z0bOlzEy9EZWHzZ6bgAM+tIZ1VFcVP4jmu/C2pT3L2F15McbGK2llikjJYAo65DLj1yM88VoXfiDUkTUL+0trZ9O02R45Vdm82XYPnZSOBjnAOc47U7BudLRXK33iPVUl1eWwt7N7XSgsj+azB5kMQkIGOAeTyc9uO9SX/iW5t9aS1T7HawlI2ja+Lp9p3dQj/dBHpySe3eiweZ01Fc/wCNjdjwvMLKRIy0kaSFiwJVnUEAqQec4PtmolvtUhaex0i1stmlxIs4kLgSuU3bI/7oxjk569KXS4HS0Vzml+ILzW9X8uxit47FLeC4d5dxkKyqTtAHGeOv6Hs/xF4ibSr22sbeS1hmnjaUzXe4oiqQMYXkkk+o6Gh6aAtToKK5jT/F3n+RJexRw28kE5MikkeZC3zYJxlSvzDjOAabdeI7+HTtPldtNs7i6g85orhnZ+eiLGvzHg8t2Pajb+v67AdTRXCyeKrgSTaxCh2No1vcLbO5KKzSsD+Pv7CuivtaktNTu7YLbhILEXIeeXy13FmXDNyAOOuKbVvx/C/+Qb/h+Nv8zYoriZfFOoXOk61Fbz2b3NrYm5iurZXEePmBA3dSMcEEg56DpVm41uTS7yKfUYlmmi0iS4doXdQxDqAoUnHORyRn8KX9fn/kH9fl/mdbRXNy63qumyGPV4LQvNazTwG3LYVo13FGz14P3hjoeBS6XrmpzXumrqUFqsGqW5mg8hm3RkKGw2eDkHqMY6c9adv6/r0A6OimTRLPC8Um7a6lTtYqcH0I5B9xXK6PpMA8U6whnv2SxeEwI+oTsFzHuOQX+bnsc1N7agdbRXF+GNY1eLSdBbUfIntb/wDcq+92mDbWYMxPBB2njtxyadaeOGub6Bl+yNaXFwIFgQuZ0BbaJCcbcZwSOwPU1VtbAdcIIhctcCNfOZAhfHJUEkD8yakoopAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAGfe2TNJ9otuJByQO/vRb6op+S6GxxxnHH/1q0KhntIbj/WJz/eHBpgSJIkgyjqw9jmh5EjGXdVHucVntoyE/JMwHuM0LoyA/PMxHsMUAOuNUUfJaje54zjj/AOvRZWTLJ9oueZDyAe3vVqC0ht/9WnP948mpqACiiikAUUUUAFFFFABRRRQBkP4asn0+7tGebFzctdeYGAeKUnIZDjggjjr+NQ33hWK+a8zqV9BHfRhLqOIxgS4XbuyUJBx6EDjpWsb21W8FobmEXLLuEJkG8j129cU77TB5Pm+fH5e7bv3jbnOMZ9c8fWgOpyeuaJeSSX0GnWV8Y7uARlre7iWGZtu3MobDLjjOzOQOfSnahot5blUs7O+lLWSW8slldxxrOVGMSK+Co/2k+bBPoK6hb60e8a0S6ha5QZaESAuo9SvWq+laxbatYw3MBMfn7ykchAchWKk4B9R+oo3QbGbYeEraPS4YL8tJJ/ZsVhMEbCYTJyvGQck8+wrR0/S5bKdpp9Uvb5igRRcMgVRnP3UVQT7nJqxNqFlb2/n3F3BFDu2+Y8oVc5xjJOM5BqG51rT7S8tLW4uo0lvM+SCw+bAz+vb1p3bYrWVilqPhmLULi8cX95apfRiO6igKASgDaOWUkHBxwRSXfhW3uncpfXtsJbZbWdYHUCZFzjOVJB5PKkda1fttr9s+yfaYftO3d5PmDfj129cVV1PXLHSoZTPMjSxqGNujr5hBIGdpPTml5DI18P2yXUEyzTARWotHjJUrNGOgbK57k/KRUdj4bis7m2ke+vLqOyBFpDOylYMjbxhQWIXIBYnANa8sscETSzSLHGgyzucBR6k1FFe2s8UUsNzDJHMcROkgIkPoD36Hp6U7sAvbUXtnJbmaaDeMCSB9jqfUH/I9ayx4Wt5I7v7Zd3V1cXRjLXLlFdDGcoV2KANp56fXNXb3V7WwaAStuEtwLclWGI22lvm544H6ip47+0mtluIbqCSBjtWVZAVJzjGenXikBmv4cW4t5Ir/AFO+vC8sUm+RkG3y2DKAqqFHI5OMn16Uq+HViume21K+t7Z5/Pe0idVjLk5PO3eATyQGAOT6mrqatpzrGyX9qwlcxxkTKd7Dqo55PtQmoR7rz7QFt4rVwpleVNpG0HJwfl6/xY9aAKTeHU+2yywahe20E0wnltYXVUd+MnO3cM45AYA/iaWbw3aztfh57gR3zrK8asoEcq7cSIcbg3yqepHHSrzajYrYi9a8txakZE5lXYf+BZxSfbla9t4YlEkc8TSCZZFxgYxxnJznqAR+dHkBSPh5J7O6t9S1C9vxcoEJmdV8sA5BUIqqDnnOM8Cix8PraasNSn1C8vboQGANOUACEg4wqqM5HX3Oe2NCG/tLnzfs91BL5JxLskDeWfQ46fjRBf2lzM0VtdQzSIAzJHIGKg9CQKAKOoaDHf34vFvLu0lMPkSG2dR5sec7TlTjnPIweTzWTF4VuINdi+xXt5Z2lvpkVpHcRPGWfazZDBlI6EHOB7d66gzRK7I0iBlXeylhkL6n24PNQXN/DBZtPG0cx8ppY0Eqr5gAzwSQMdOc45o2/r1/zYb/ANegun2EGmafDZ2oIiiXA3HJPcknuSck/WsxPC0ENjb28F9eRtaStJazApvgBGCg+XDLgnhgT79KsHxDp4uZbVpl+1Q2wuHgDKWCkE4GDgng/oe9TaZrNlq1vFJaTxl3hSZod6mSMMARuAPHWnqAumaZFpcMipLLPJNIZZp5iC8jnAycAAcADAAAAqjD4ca21K5vYNZ1BHupRJKu2Bg2Oi5MRYKBwBnjnuavXer2NjfWtndXCRz3RIiVmAzgZ/8ArfWorDX7C/meFJkinWeSEQyOodyjFSVGckcUuodCxqFkb63EaXdxaOrh1lt3AYEeoIII9iCKzB4UtxCpW+vFu1ujdfbVKeYZCu08bdmNvGNuK13vbWO7S1kuYVuJBlIWkAdh6gdTUWo6lDpkcD3AYrNOkAK44LHAJz2oAyz4RtDZJAL2+DR3hvVn81fM80gjOduMc5xj9OKkXwvbFhJc3V1c3P2qO6e4kKhnaPhVICgBR6ADrWrb3lrd25ntbmGeEZBkjkDKMdeRSW97a3duZ7W5hmhUkGSOQMox15FGwFKfw/a3FvqELyTBdQmWaUhhlWUIBt46fIOue9MPh5E+0NaX97aSz3RujJE68MVCkbSpUrgdCDzzU1z4g0q1003730D2wkEfmRyBgWJxjIPXmpNS1SDTdFn1N8zQQxGU+UQSy4zxzg0bD30Kcfhxbe0SOz1O+tplleZ7hHQtKznLF1KlD/3zx2xUcPhDTIUCN50qfZZLV1kYfvFkfe7HAHzE+mBz0qa18QeZfR2l/p91p0kyM8JuGjZZAoy2CjtggHODjirD6vbebCltJFcB5PLkaOdP3XylskE88DoOcHPSj+v0Ev6/MqR+GYiXa91C9vmNu9tG07JmFGGG27VGScD5jk8VP/YNr5enp5s4FhA0ERV9pKlAhJIGc4HUY5q/9ohKxMJo9sxAjO4YfIyMevHNRJqNlKZhFeW7m3z5wWVT5f8Avc8fjQCKen6CtnqAvZ7+7vp0h8iN7kp8iZBI+VVySQMk5PFWjpsLapJfln82S3FuRkbdoJOenXk1HZ63p19pI1KC7iFpjmV3ChPrnp+NXIpY54llgkWSNxlXQ5DD1BofmBlf8I1bJpen2dtc3MD6cALa5QqZF42nOVKnI6gjFEXhq1jWAvPcSzRXn21pnZd00u0rlsDGMHGAB0FRr4mEkhkh0y9ksVmMJvECFdwbaTt3byoPGcdvTmtZ721iuo7aW5hS4kGUiaQB2HsOpp67h5FF/D9rJa3UBkm23V2t25DDIcMrADjplB+vNTappSamLdvtE1rPbSebDNDt3I20qeGBBBDEcirD3trHdpayXMK3EgykLSAOw9QOppBe2pufs4uYTPz+6Eg3cdeOvGRSAy7fwtawFGe6u55FvvtxklZcvJs2c4UDGOwx7ccVO/h+1ktbqAyTbbq7W7chhkOGVgBx0yg/Xmr1te2t6rNZ3MNwqNtYxSBgp9DjvUf20LfTQSIESGJZTM0i45JzxnIxjqRj8qNv6/rsg/r+vvM648L29xNOPtl3HaXUvm3FkjJ5crcZySu4A45AYA/ialk8OWklhqVo0kwj1GYzSkMMqSFGF44HyjrmtG2u7e9hE1nPFcRE4DxOGU/iKPtMAR3M8e1G2O28YVvQ+h5HFGwHJ+IdFvbqfUobCxvQl/GFZoLuNYJWK7d0ith1xxnZncAM+ldMUez0lY0ga7eKIJ5SFQZMDH8RA/M1I17areLaNcwi5YblhMg3keoXrR9ttftn2T7TD9p27vJ8wb8eu3rijpYOtzD0bwrFbeEU0u83JI7ec7RyZaOTIK7W9UwoB/2asN4Wt7i3vFv7y7vJ7tFRrmRkV0VTuULtUKMNz05PXNWtS1yx0xH86ZHmTZugR1MmGYKDtz0yRS65rNvoWlyXtzhgpCom8KXYnAAJ+v5ZNO/UCi/hK2nhvlvL++uXv4445pZHTdhGLLtAUAdfT9cmtTVIJLnR7yCFd0ksDogzjJKkCmR6pbi3tXvJ7e3kuQPLQzqd5PZT/F+FTve2sc6wSXMKTMQqxtIAxJ5AA/A0mrqwJ2dzE0Pw0bS20yTULu6uJLOALHbzOhSBym1sFVBbAyAWJwDxUll4Ut7K5s5Fvr6WOxZjbQSOmyIMpUrwoJGG4ySRgc9c7fmx+d5W9fM27tmecdM49Kgm1Gyt4RNPeW8URfYHeVQu7OMZJ657U27u4krKxk2XhG2sltYRf301nayCWK1ldCgcHIYkKGOCc4zjPapYvCthFMjlppAhuSUdgVbz2y4PHTjj+ta/nRmURiRPMZd4XcMlfXHpUMmo2UMQklvLdI2GQzSqARnGc59SB+NIZT03Q/7OmjY6nfXMcMflwwzOuyNeP7qgseMZYk0l9oC3mrDUoL+8sroQCDfAUIKZJxtZWB5PX2GO9O13XINCs45ZgryTSrFFG0qpuYnGST0A7ntU1vqaSNKLlY7cRlFDNOjByyhuMHI/EDPUcUb6htoU/wDhGbaK3tFsLq5sprRWRLiJlZ2DHL7t6kNk8nI69MVK+hyfZ7dINY1KGWHdmbzVdpdxydwZSp9sAY6DA4q9Hf2k1obqG6hktwCTMsgKDHX5ulVbnXLOC3tJ4pBcxXdylvG8DB13McA5z0o8gIIPDdraDTfsk9xCdOVlQqynzFYguHyp6kA8YPpitK8theWctuZZofMXHmQOUdfcEdDS3N1b2cBmu544Il6ySuFUfiabLfWkFoLqe5hjtyARM8gCEHpyeKAMr/hFreVLo3t5d3dxciMG5corx+WSybdigAgnPT68cU9vDiT2ssd7qN9dTPIkiXDuoaFk5UoFUKMf7vPfNaU19aW9qLm4uYYoDjEryBVOenJ4omv7S3iWS4uoIo2XcrPIFBHHOT25H5igDLbwvbzW1yl5eXV1NdPE0txIUDkRsGRQFUKACDwB3NSar4bs9YmnluZJ1aa0No3lsBhN4bIyDzkVoG9tReC0NzCLll3CHzBvI9dvXFT0f1+gGXqOiwXck9y8RuJGsntfId9qSKxBwSBkHIHPb0rG0fSNQbxFaXt3Ffxx2lvJHuv7mKRiW24VBGegwSWb5jxXW0ULT+v67gZmoaIl/fRXiXl1Z3EcbQl7ZlG9CQSp3Ke46jBHrTNM8OWmkz28ttJOzW9p9kTzGBym7dk8dc961qKNgMQeGVhs4Lay1TULRIo2jPlujB1ZsnKspXPPBABo/wCEXggWH+y72700x262xa3KEyRr90HercjJwRg8mtuigDHm8M2Nw0puHuJfNtEtGLy5O1GLBt3Xfk5znsKUeHlmt7mHUtRvdQS4i8oid1UIvXICKo3f7RBPHWteigDIj0AG3uob/Ur6/W5gNu3nuoCoQQcBFUZ5+8QTTW8O/wDHvJDql7DdwRGE3SiLfJHnO1gU2nHY7QfzOdmigDHl8OoxtpLfUb63ureIw/alkV5JEJyQ+9WB5GenHb0q1pOkwaNposbV5XiDu+6V8tlmLHn6k1eooAxYfDMS3Uc15f3l+IY3jhjuXUiNXGG5ChmOOMsT/WmWfhSC0lt3bUL+5FrA9vAkzptjRgBjhRngDk5Pua3aKAMLT/CsGnmyU397c29gP9Gt52QpG23bu4UEnBOMkgZ4HTE9r4dtLO306GKSYrp8zzRFmGWLBwd3HT5z0x2rWop3uFjITw3Zx6hHeCSfzI55pwN4A3SrtYcDOMdOc1HH4YjEyy3Wp6hdNFC8MBllUNCr4BIdVDFuByxJrbopAYEvhG3u4rkahqF7eSXEKwedIYwyRht2BtQDkjkkE1JdeF7e6muf9Mu4rW8cSXNnGyiOZuM5JUsMgDIVhn8626KAMyTQLWVNWUvKBqq7ZsEfKPL2fLxxwO+eagv/AA0moK8Umo3yWkyKk1qrqY5Av+8pK577SM/WtqigCrqOnQ6nYNaTs6xsyNlCAflYMOoPcCqV94eS8u554b+8sjdII7lbdkAmABAzuUkHBxlcHH4Vr0UBsZ9hotppt5NcWgdPNiih8vI2osYIUDv0PcmjUdIW/uIbmK6nsruAMqTwbc7WxlSGUgjgHkdRWhRRvqBjX3hi01LSIdPvri7nWGXzfOeXMjnnIJxjBDEEAAYOBipb7Q1u9QF5DfXVlKYfIkNvs/eJkkA7lOOSeRg81qUUAYCeDtOSza2L3DxtYrYnc4zsUkhsgfeyevTgcUreEreYXRvb++u5LmBYGkldMqFbcpUKoAIPtz3zW9R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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(\"Kaggle_Submission_DDMO.JPG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12d04880-c8cf-4dc9-bccd-5e2bd5640506",
   "metadata": {},
   "source": [
    "_Image 1: Results of the Kaggle Submission_\n",
    "\n",
    "#### 5.0 Discussion and Conclusion\n",
    "Above results are the best we got using the outlined steps. By this experiment, we obtained slightly improved results after tuning the hyperparameters using spot tuner. For instance, while we got 38% and 29% for map@K and X_test after 10 Kfold evaluations using default values, we got 39% and 31% for the same evaluations respectively using tuned parameters. Hence, the results for the unseen data improved with the tuning. However, the same evaluations on the complete data yielded the same results.\n",
    "\n",
    "Further investigations are possible to yield even better results. Such investigations could consist of using different feature engineering methods, selection techniques (like RFE) as well as hyperparameter tuners like optuna or random search. \n",
    "                           "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca590df8-6830-4602-b0a4-d6af8e598a85",
   "metadata": {},
   "source": [
    "#### 6.0 References\n",
    "[1]https://www.kaggle.com/competitions/playground-series-s3e13/discussion/407829\n",
    "\n",
    "[2]https://git-ce.rwth-aachen.de/spotseven-lab/ddmo-ait-sommersemester-2023/-/blob/main/Notebooks.d/18_spot_hpt_sklearn_multiclass_classification_svc.ipynb\n",
    "\n",
    "[3]https://sequential-parameter-optimization.github.io/spotPython/01_spot_intro.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8efc3a5-6a25-423f-975c-30089e3b26e8",
   "metadata": {},
   "source": [
    "#### 7.0 Code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a24ceb9-f8b4-4f78-aad9-4ebe627468cc",
   "metadata": {},
   "source": [
    "#### 1.0 Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dcdbf9af-f436-47f2-90b6-53fcb61ce806",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:36:55.315367Z",
     "iopub.status.busy": "2023-07-14T16:36:55.315121Z",
     "iopub.status.idle": "2023-07-14T16:37:01.256722Z",
     "shell.execute_reply": "2023-07-14T16:37:01.256021Z",
     "shell.execute_reply.started": "2023-07-14T16:36:55.315341Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-07-14 18:36:58.948449: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "#!pip install tabulate\n",
    "#tabulate may not be installed by default\n",
    "\n",
    "from tabulate import tabulate\n",
    "import warnings\n",
    "import numpy as np\n",
    "from math import inf\n",
    "import pandas as pd\n",
    "import random\n",
    "\n",
    "from scipy.optimize import differential_evolution\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import OneHotEncoder , MinMaxScaler, StandardScaler\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.pipeline import make_pipeline , Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.model_selection import cross_validate\n",
    "from sklearn.datasets import fetch_openml\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import itertools\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "\n",
    "from spotPython.utils.convert import class_for_name\n",
    "from spotPython.utils.eda import (\n",
    "    get_stars,\n",
    "    gen_design_table)\n",
    "from spotPython.utils.transform import transform_hyper_parameter_values\n",
    "from spotPython.utils.convert import get_Xy_from_df\n",
    "from spotPython.plot.validation import plot_cv_predictions, plot_roc, plot_confusion_matrix\n",
    "from spotPython.utils.init import fun_control_init\n",
    "from spotPython.data.sklearn_hyper_dict import SklearnHyperDict\n",
    "from spotPython.fun.hypersklearn import HyperSklearn\n",
    "from spotPython.utils.metrics import mapk_score\n",
    "from spotPython.spot import spot\n",
    "from spotPython.hyperparameters.values import (\n",
    "    add_core_model_to_fun_control,\n",
    "    assign_values,\n",
    "    convert_keys,\n",
    "    get_bound_values,\n",
    "    get_default_hyperparameters_for_core_model,\n",
    "    get_default_values,\n",
    "    get_default_hyperparameters_as_array,\n",
    "    get_dict_with_levels_and_types,\n",
    "    get_values_from_dict,\n",
    "    get_var_name,\n",
    "    get_var_type,\n",
    "    iterate_dict_values,\n",
    "    modify_hyper_parameter_levels,\n",
    "    modify_hyper_parameter_bounds,\n",
    "    replace_levels_with_positions,\n",
    "    return_conf_list_from_var_dict,\n",
    "    get_one_core_model_from_X,\n",
    "    transform_hyper_parameter_values,\n",
    "    get_dict_with_levels_and_types,\n",
    "    convert_keys,\n",
    "    iterate_dict_values,\n",
    "    get_one_sklearn_model_from_X\n",
    ")\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bfa8e6b-2d67-4a3e-ae18-4091f8ee9928",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-06T17:38:13.827981Z",
     "iopub.status.busy": "2023-07-06T17:38:13.827491Z",
     "iopub.status.idle": "2023-07-06T17:38:13.831591Z",
     "shell.execute_reply": "2023-07-06T17:38:13.830589Z",
     "shell.execute_reply.started": "2023-07-06T17:38:13.827953Z"
    },
    "tags": []
   },
   "source": [
    "#### 2.0 Load the Training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "94c4e3d2-f55a-489b-82b7-9d687dacc0ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:01.262133Z",
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     "shell.execute_reply.started": "2023-07-14T16:37:01.262105Z"
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   },
   "outputs": [],
   "source": [
    "# Set a random seed for reproducibility\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d656ff2a-0626-417c-abb1-af2f5004943d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:01.271541Z",
     "iopub.status.busy": "2023-07-14T16:37:01.269758Z",
     "iopub.status.idle": "2023-07-14T16:37:01.286743Z",
     "shell.execute_reply": "2023-07-14T16:37:01.286062Z",
     "shell.execute_reply.started": "2023-07-14T16:37:01.271518Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "dataset = pd.read_csv('train.csv')\n",
    "    # remove the id column\n",
    "dataset = dataset.drop(columns=['id'])\n",
    "n_samples = dataset.shape[0] # Number of samples/rows in the dataset(707)\n",
    "n_features = dataset.shape[1] - 1 # Number of features/columns in the dataset(64)\n",
    "target_column = \"prognosis\" # The target column name"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8718b373-bece-42cb-8046-77aec145f5fc",
   "metadata": {},
   "source": [
    "#### 3.0 Feature Engingeering\n",
    "#### 3.0.1(a) Encoding the target_coulmn string values to integer values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "iopub.status.idle": "2023-07-14T16:37:01.327522Z",
     "shell.execute_reply": "2023-07-14T16:37:01.326749Z",
     "shell.execute_reply.started": "2023-07-14T16:37:01.291805Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(707, 65)\n"
     ]
    },
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      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = OrdinalEncoder() #Creates an instance of the OrdinalEncoder class.\n",
    "dataset[target_column] = enc.fit_transform(dataset[[target_column]]) #Fits & transforms the target_column to integer values.\n",
    "dataset.columns = [f\"x{i}\" for i in range(1, n_features+1)] + [target_column]#Assigns new column names to the dataset.\n",
    "print(dataset.shape)\n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ffb259e0-e785-4a71-bbc7-86da84992d0e",
   "metadata": {},
   "source": [
    "#### 3.0.1(b) Feature Combination"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "644fe14b-a539-45d5-a7c3-73b9d82d9797",
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       "</table>\n",
       "<p>707 rows × 6112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1  x2  x3  x4  x5  x6  x7  x8  x9  x10  ...  x61_xor_x64  x62_and_x63  \\\n",
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       "706   1   1   0   0   0   0   1   0   0    0  ...            1            0   \n",
       "\n",
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       "0             0            0            0           0            0   \n",
       "1             0            0            0           0            0   \n",
       "2             1            0            1           1            0   \n",
       "3             0            0            0           0            0   \n",
       "4             1            1            0           1            1   \n",
       "..          ...          ...          ...         ...          ...   \n",
       "702           0            0            0           0            0   \n",
       "703           0            0            0           0            0   \n",
       "704           0            0            0           0            0   \n",
       "705           0            0            0           0            0   \n",
       "706           1            1            0           1            1   \n",
       "\n",
       "     x63_and_x64  x63_or_x64  x63_xor_x64  \n",
       "0              0           0            0  \n",
       "1              0           0            0  \n",
       "2              1           1            0  \n",
       "3              0           0            0  \n",
       "4              0           0            0  \n",
       "..           ...         ...          ...  \n",
       "702            0           0            0  \n",
       "703            0           0            0  \n",
       "704            0           0            0  \n",
       "705            0           0            0  \n",
       "706            0           0            0  \n",
       "\n",
       "[707 rows x 6112 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_col = dataset ['prognosis']\n",
    "dataset = dataset.drop(columns=['prognosis'])\n",
    "dataset = dataset.astype(int)#Ensures that all the feature elements are represented as integers.\n",
    "for col1, col2 in itertools.combinations(dataset.columns,2):#Uses the combinations function from the itertools module to generate pairs of column names.\n",
    "    dataset[f'{col1}_and_{col2}'] = dataset[col1] & dataset[col2]#creates a new column in the dataset between the values in col1 and col2\n",
    "    dataset[f'{col1}_or_{col2}'] = dataset[col1] | dataset[col2]#creates another new column in the dataset between the values in col1 and col2\n",
    "    dataset[f'{col1}_xor_{col2}'] = dataset[col1] ^ dataset[col2]#creates yet another new column in the dataset between the values in col1 and col2\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "373971aa-5121-4766-87f5-874df229b7c1",
   "metadata": {},
   "source": [
    "#### 3.0.1(c) Feature Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ec19e412-b9de-4f80-95ed-e2d20b387ada",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:06.386465Z",
     "iopub.status.busy": "2023-07-14T16:37:06.384872Z",
     "iopub.status.idle": "2023-07-14T16:37:06.438412Z",
     "shell.execute_reply": "2023-07-14T16:37:06.437850Z",
     "shell.execute_reply.started": "2023-07-14T16:37:06.386438Z"
    },
    "tags": []
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "     x1  x2  x3  x4  x5  x6  x7  x8  x9  x10  ...  x62_and_x64  x62_or_x64  \\\n",
       "0     1   1   0   1   1   1   1   0   1    1  ...            0           0   \n",
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       "706   1   1   0   0   0   0   1   0   0    0  ...            0           1   \n",
       "\n",
       "     x62_xor_x64  x63_and_x64  x63_or_x64  x63_xor_x64  cols_A  cols_B  \\\n",
       "0              0            0           0            0     0.0     0.0   \n",
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       "4              1            0           0            0     0.0     0.0   \n",
       "..           ...          ...         ...          ...     ...     ...   \n",
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       "\n",
       "     cols_C  cols_D  \n",
       "0       0.0     0.0  \n",
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       "\n",
       "[707 rows x 6116 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This code block creates new columns in the dataset DataFrame by extracting subsets of columns based on specific substrings\n",
    "#('pain', 'inflammation', 'bleed', 'skin'). It calculates the sum of the values in each subset of columns across rows and\n",
    "#assigns the results to the corresponding new columns. This process allows aggregating related information from multiple\n",
    "#columns into single columns, potentially capturing important patterns or information in the dataset.\n",
    "\n",
    "cols_A = dataset.columns[dataset.columns.str.contains('pain')]#creates a new variable cols_A and assigns it the column names from the dataset DataFrame that contain the substring 'pain'\n",
    "cols_B = dataset.columns[dataset.columns.str.contains('inflammation')]\n",
    "cols_C = dataset.columns[dataset.columns.str.contains('bleed')]\n",
    "cols_D = dataset.columns[dataset.columns.str.contains('skin')]\n",
    "dataset['cols_A'] = dataset[cols_A].sum(axis=1)#Calculates the sum of the columns specified by cols_A across each row using the sum method with axis=1. The result is assigned to the new column,cols_A\n",
    "dataset['cols_B'] = dataset[cols_B].sum(axis=1)\n",
    "dataset['cols_C'] = dataset[cols_C].sum(axis=1)\n",
    "dataset['cols_D'] = dataset[cols_D].sum(axis=1)\n",
    "dataset1 = dataset\n",
    "dataset1"
   ]
  },
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      ],
      "text/plain": [
       "     x1  x2  x3  x4  x5  x6  x7  x8  x9  x10  ...  x62_or_x64  x62_xor_x64  \\\n",
       "0     1   1   0   1   1   1   1   0   1    1  ...           0            0   \n",
       "1     0   0   0   0   0   0   1   0   1    0  ...           0            0   \n",
       "2     0   1   1   1   0   1   1   1   1    1  ...           1            0   \n",
       "3     0   0   1   1   1   1   0   1   0    1  ...           0            0   \n",
       "4     0   0   0   0   0   0   0   0   1    0  ...           1            1   \n",
       "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ...         ...          ...   \n",
       "702   0   0   1   1   1   0   0   0   0    0  ...           0            0   \n",
       "703   1   0   1   1   1   1   0   1   1    0  ...           0            0   \n",
       "704   1   0   1   0   1   0   0   1   1    1  ...           0            0   \n",
       "705   1   1   0   0   1   0   1   0   1    0  ...           0            0   \n",
       "706   1   1   0   0   0   0   1   0   0    0  ...           1            1   \n",
       "\n",
       "     x63_and_x64  x63_or_x64  x63_xor_x64  cols_A  cols_B  cols_C  cols_D  \\\n",
       "0              0           0            0     0.0     0.0     0.0     0.0   \n",
       "1              0           0            0     0.0     0.0     0.0     0.0   \n",
       "2              1           1            0     0.0     0.0     0.0     0.0   \n",
       "3              0           0            0     0.0     0.0     0.0     0.0   \n",
       "4              0           0            0     0.0     0.0     0.0     0.0   \n",
       "..           ...         ...          ...     ...     ...     ...     ...   \n",
       "702            0           0            0     0.0     0.0     0.0     0.0   \n",
       "703            0           0            0     0.0     0.0     0.0     0.0   \n",
       "704            0           0            0     0.0     0.0     0.0     0.0   \n",
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       "706            0           0            0     0.0     0.0     0.0     0.0   \n",
       "\n",
       "     prognosis  \n",
       "0          3.0  \n",
       "1          7.0  \n",
       "2          3.0  \n",
       "3         10.0  \n",
       "4          6.0  \n",
       "..         ...  \n",
       "702        5.0  \n",
       "703        4.0  \n",
       "704       10.0  \n",
       "705        5.0  \n",
       "706        7.0  \n",
       "\n",
       "[707 rows x 6117 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1['prognosis'] = y_col# Adds the 'prognosis' column to the modified dataset1 DataFrame.\n",
    "dataset = dataset1\n",
    "dataset\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e3849cc-e706-428e-b1e2-0c8a6eca0f39",
   "metadata": {},
   "source": [
    "#### 3.0.1(d) Feature Subset Selection"
   ]
  },
  {
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       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(k=200, score_func=&lt;function chi2 at 0x1518099ddc60&gt;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=200, score_func=&lt;function chi2 at 0x1518099ddc60&gt;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k=200, score_func=<function chi2 at 0x1518099ddc60>)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = dataset[target_column]\n",
    "X = dataset.drop(columns=[target_column], axis = 1)\n",
    "selector = SelectKBest(score_func=chi2, k=200)\n",
    "selector.fit(X, y)#Fits the feature selector to the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "bf557a45-156a-4198-bc0e-a93a98c7ea88",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.031395Z",
     "iopub.status.busy": "2023-07-14T16:37:07.029683Z",
     "iopub.status.idle": "2023-07-14T16:37:07.039666Z",
     "shell.execute_reply": "2023-07-14T16:37:07.038987Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.031370Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the selected feature indices and names\n",
    "selected_indices = selector.get_support(indices=True)\n",
    "selected_features = X.columns[selected_indices]\n",
    "len(selected_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0426a4f3-aa4e-48c6-a291-cb5ba4908188",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.045979Z",
     "iopub.status.busy": "2023-07-14T16:37:07.044384Z",
     "iopub.status.idle": "2023-07-14T16:37:07.073646Z",
     "shell.execute_reply": "2023-07-14T16:37:07.072910Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.045953Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "<p>707 rows × 201 columns</p>\n",
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      ],
      "text/plain": [
       "     x36  x60  x61  x62  x2_and_x56  x2_and_x58  x3_and_x56  x3_and_x58  \\\n",
       "0      0    0    0    0           0           0           0           0   \n",
       "1      0    0    0    0           0           0           0           0   \n",
       "2      1    1    0    1           1           1           1           1   \n",
       "3      0    0    0    0           0           0           0           0   \n",
       "4      0    1    1    1           0           0           0           0   \n",
       "..   ...  ...  ...  ...         ...         ...         ...         ...   \n",
       "702    1    0    0    0           0           0           0           0   \n",
       "703    1    0    0    0           0           0           0           0   \n",
       "704    1    0    0    0           0           0           0           0   \n",
       "705    0    0    0    0           1           1           0           0   \n",
       "706    0    1    1    1           0           0           0           0   \n",
       "\n",
       "     x3_and_x59  x14_and_x28  ...  x61_or_x62  x61_or_x63  x61_xor_x63  \\\n",
       "0             0            0  ...           0           0            0   \n",
       "1             0            0  ...           0           0            0   \n",
       "2             1            1  ...           1           1            1   \n",
       "3             0            1  ...           0           0            0   \n",
       "4             0            0  ...           1           1            1   \n",
       "..          ...          ...  ...         ...         ...          ...   \n",
       "702           0            1  ...           0           0            0   \n",
       "703           0            0  ...           0           0            0   \n",
       "704           0            0  ...           0           0            0   \n",
       "705           0            0  ...           0           0            0   \n",
       "706           0            0  ...           1           1            1   \n",
       "\n",
       "     x61_or_x64  x61_xor_x64  x62_or_x63  x62_xor_x63  x62_or_x64  \\\n",
       "0             0            0           0            0           0   \n",
       "1             0            0           0            0           0   \n",
       "2             1            1           1            0           1   \n",
       "3             0            0           0            0           0   \n",
       "4             1            1           1            1           1   \n",
       "..          ...          ...         ...          ...         ...   \n",
       "702           0            0           0            0           0   \n",
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       "705           0            0           0            0           0   \n",
       "706           1            1           1            1           1   \n",
       "\n",
       "     x62_xor_x64  prognosis  \n",
       "0              0        3.0  \n",
       "1              0        7.0  \n",
       "2              0        3.0  \n",
       "3              0       10.0  \n",
       "4              1        6.0  \n",
       "..           ...        ...  \n",
       "702            0        5.0  \n",
       "703            0        4.0  \n",
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       "705            0        5.0  \n",
       "706            1        7.0  \n",
       "\n",
       "[707 rows x 201 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset with the selected features\n",
    "selected_data = dataset[selected_features]#Creates a new dataframe containing only the selected features\n",
    "selected_data['prognosis'] = y_col #copies the target variable (prognosis) into the selected_data DataFrame.\n",
    "#selected_data = selected_data.copy()\n",
    "selected_data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a78ccb5-4a7c-4787-a0ae-c978996d5f9c",
   "metadata": {},
   "source": [
    "#### 4.0 Splitting the selected_data into train & test subsets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "df6d754f-220c-440b-9c83-09ef35ed998c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.078013Z",
     "iopub.status.busy": "2023-07-14T16:37:07.076460Z",
     "iopub.status.idle": "2023-07-14T16:37:07.107646Z",
     "shell.execute_reply": "2023-07-14T16:37:07.106886Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.077989Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Stratified splitting helps to maintain the same class distribution as in the original dataset, providing a more reliable evaluation of the model's performance.\n",
    "\n",
    "n_samples = selected_data.shape[0]\n",
    "n_features = selected_data.shape[1] - 1\n",
    "target_column = \"prognosis\"\n",
    "train_df = selected_data\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(train_df.drop(target_column, axis=1), train_df[target_column],\n",
    "                                                    random_state=42,\n",
    "                                                    test_size=0.25,\n",
    "                                                    stratify=train_df[target_column])\n",
    "train = pd.DataFrame(np.hstack((X_train, np.array(y_train).reshape(-1, 1)))) #converts the target variable y_train into a 2D array with a single column, so that it can be concatenated with X_train\n",
    "test = pd.DataFrame(np.hstack((X_test, np.array(y_test).reshape(-1, 1))))\n",
    "train.columns = [f\"x{i}\" for i in range(1, n_features + 1)] + [target_column]\n",
    "test.columns = [f\"x{i}\" for i in range(1, n_features + 1)] + [target_column]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3310a9ea-4eff-4bfa-a8ae-0c28b439c439",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.112067Z",
     "iopub.status.busy": "2023-07-14T16:37:07.110416Z",
     "iopub.status.idle": "2023-07-14T16:37:07.139797Z",
     "shell.execute_reply": "2023-07-14T16:37:07.139001Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.112042Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(530, 201)\n",
      "(177, 201)\n"
     ]
    },
    {
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       "      <th>prognosis</th>\n",
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       "      <th>0</th>\n",
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       "    x1   x2   x3   x4   x5   x6   x7   x8   x9  x10  ...  x192  x193  x194  \\\n",
       "0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   0.0   0.0   0.0   \n",
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       "2  0.0  1.0  1.0  1.0  0.0  0.0  0.0  0.0  0.0  0.0  ...   1.0   1.0   1.0   \n",
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       "\n",
       "   x195  x196  x197  x198  x199  x200  prognosis  \n",
       "0   0.0   0.0   0.0   0.0   0.0   0.0        2.0  \n",
       "1   0.0   0.0   0.0   0.0   0.0   0.0        4.0  \n",
       "2   1.0   0.0   1.0   1.0   1.0   0.0        1.0  \n",
       "3   0.0   0.0   0.0   0.0   0.0   0.0        6.0  \n",
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       "\n",
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      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f7d26f-12be-4696-8454-40b6ac3f38d8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-06T18:35:33.551724Z",
     "iopub.status.busy": "2023-07-06T18:35:33.551465Z",
     "iopub.status.idle": "2023-07-06T18:35:33.555305Z",
     "shell.execute_reply": "2023-07-06T18:35:33.554302Z",
     "shell.execute_reply.started": "2023-07-06T18:35:33.551698Z"
    },
    "tags": []
   },
   "source": [
    "#### 5.0 Initialize fun_control dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66ff5f2f-9c6a-4843-96f8-894da942c175",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.144336Z",
     "iopub.status.busy": "2023-07-14T16:37:07.142654Z",
     "iopub.status.idle": "2023-07-14T16:37:07.148966Z",
     "shell.execute_reply": "2023-07-14T16:37:07.148063Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.144312Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Initialize fun_control dictionary\n",
    "fun_control = fun_control_init(task=\"classification\")\n",
    "\n",
    "# add the dataset to the fun_control\n",
    "fun_control.update({\"data\": train_df, # full dataset,\n",
    "               \"train\": train,\n",
    "               \"test\": test,\n",
    "               \"n_samples\": n_samples,\n",
    "               \"target_column\": target_column})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a15aa1e2-f361-45e9-9369-6b571cd77298",
   "metadata": {},
   "source": [
    "### 6.0 Assigning the SVC algorithm to the Core_Model\n",
    "#### The SVM Classifier\n",
    "A Support Vector Machine (SVM) classifier is a supervised machine learning algorithm used for classification tasks. It is used to separate data points into different classes by creating a hyperplane in a high-dimensional feature space.\n",
    "The main objective of an SVM classifier is to find the optimal hyperplane that maximally separates the data points belonging to different classes. This hyperplane is defined as the decision boundary that maximizes the margin, which is the distance between the hyperplane and the nearest data points of each class.\n",
    "\n",
    "The 'add_core_model_to_fun_control' function is used to update the fun_control dictionary with the specified core model and its associated hyperparameter dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e7d414c0-f579-4312-a6ff-307f550cf6da",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.153752Z",
     "iopub.status.busy": "2023-07-14T16:37:07.152142Z",
     "iopub.status.idle": "2023-07-14T16:37:07.159561Z",
     "shell.execute_reply": "2023-07-14T16:37:07.158952Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.153728Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "core_model=SVC\n",
    "fun_control = add_core_model_to_fun_control(core_model=core_model,\n",
    "                              fun_control=fun_control,\n",
    "                              hyper_dict=SklearnHyperDict,\n",
    "                              filename=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5502c9a4-d9b1-4eaf-9d2a-5e9715fe96cc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.164087Z",
     "iopub.status.busy": "2023-07-14T16:37:07.162433Z",
     "iopub.status.idle": "2023-07-14T16:37:07.170325Z",
     "shell.execute_reply": "2023-07-14T16:37:07.169662Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.164060Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C\n",
      "kernel\n",
      "degree\n",
      "gamma\n",
      "coef0\n",
      "shrinking\n",
      "probability\n",
      "tol\n",
      "cache_size\n",
      "break_ties\n"
     ]
    }
   ],
   "source": [
    "print(*fun_control[\"core_model_hyper_dict\"].keys(), sep=\"\\n\")#Printing the SVC hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5e17fea5-78f7-4f28-a43e-9cb326ad58bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.174633Z",
     "iopub.status.busy": "2023-07-14T16:37:07.173071Z",
     "iopub.status.idle": "2023-07-14T16:37:07.179214Z",
     "shell.execute_reply": "2023-07-14T16:37:07.178132Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.174610Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fun_control = modify_hyper_parameter_levels(fun_control, \"kernel\", [\"rbf\"])\n",
    "fun_control = modify_hyper_parameter_bounds(fun_control, \"probability\", bounds=[1, 1])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca08073a-442d-400d-911f-3d11af2b6cf5",
   "metadata": {},
   "source": [
    "#### Selecting the Metric \n",
    "The mapk_score is a scoring metric commonly used in information retrieval tasks, which calculates the mean average precision at k. It is often used for evaluating ranked predictions, where k represents the number of predicted items to consider.The fun_control dictionary is then updated with these metrics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1f781e3d-0de1-4381-81e9-c71232cbc27c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.184105Z",
     "iopub.status.busy": "2023-07-14T16:37:07.182441Z",
     "iopub.status.idle": "2023-07-14T16:37:07.188569Z",
     "shell.execute_reply": "2023-07-14T16:37:07.187673Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.184078Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "fun_control.update({\n",
    "               \"weights\": -1,#spotPython performs a minimization, therefore, metrics that should be maximized have to be multiplied by -1\n",
    "               \"metric_sklearn\": mapk_score,#\"metric_sklearn\" is used for the sklearn based evaluation\n",
    "               \"predict_proba\": True,#return the probabilities of the specific classes instead of the predicted values\n",
    "               \"metric_params\": {\"k\": 3},\n",
    "               })"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "467954f8-ba79-4d9d-baad-e0f9ccce097c",
   "metadata": {},
   "source": [
    "###  7.0 Evaluations\n",
    "#### 7.0.1 Evaluations on Default Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "cd42d265-438f-44c2-bfb8-73f8617a32e0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.193144Z",
     "iopub.status.busy": "2023-07-14T16:37:07.191470Z",
     "iopub.status.idle": "2023-07-14T16:37:07.200579Z",
     "shell.execute_reply": "2023-07-14T16:37:07.199971Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.193117Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0,\n",
       " 'kernel': 'rbf',\n",
       " 'degree': 3,\n",
       " 'gamma': 'scale',\n",
       " 'coef0': 0.0,\n",
       " 'shrinking': 0,\n",
       " 'probability': 1,\n",
       " 'tol': 0.001,\n",
       " 'cache_size': 200.0,\n",
       " 'break_ties': 0}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Here, we retrieved the default hyperparameters as specified in the fun_control dictionary.\n",
    "\n",
    "values_default = get_default_values(fun_control)\n",
    "values_default = transform_hyper_parameter_values(fun_control=fun_control, hyper_parameter_values=values_default)\n",
    "values_default.update({\"probability\": 1})#Default value for \"probability\" is False,set to True for the metric \"mapk_score\".\n",
    "values_default"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d275f8a6-5010-4a97-b0d1-677eea301a0f",
   "metadata": {},
   "source": [
    "Create a sequential pipeline was created where the data is first preprocessed as defined in the \"prep_model\" and then passed to the SVC for training and prediction. The resulting model_default is an instance of the pipeline that can be fit to data and used for making predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9fab65bb-3ae6-46ad-a014-25cbfb3ca352",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.205128Z",
     "iopub.status.busy": "2023-07-14T16:37:07.203471Z",
     "iopub.status.idle": "2023-07-14T16:37:07.216446Z",
     "shell.execute_reply": "2023-07-14T16:37:07.215798Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.205102Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;nonetype&#x27;, None),\n",
       "                (&#x27;svc&#x27;,\n",
       "                 SVC(break_ties=0, cache_size=200.0, probability=1,\n",
       "                     shrinking=0))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;nonetype&#x27;, None),\n",
       "                (&#x27;svc&#x27;,\n",
       "                 SVC(break_ties=0, cache_size=200.0, probability=1,\n",
       "                     shrinking=0))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">None</label><div class=\"sk-toggleable__content\"><pre>None</pre></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(break_ties=0, cache_size=200.0, probability=1, shrinking=0)</pre></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('nonetype', None),\n",
       "                ('svc',\n",
       "                 SVC(break_ties=0, cache_size=200.0, probability=1,\n",
       "                     shrinking=0))])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_default = make_pipeline(fun_control[\"prep_model\"], fun_control[\"core_model\"](**values_default))\n",
    "model_default"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "deb82f86-c656-46bd-a93c-28a1dec6140a",
   "metadata": {},
   "source": [
    "#### Training the model\n",
    "The model_default fits the pipeline(applying the sequential preprocessing steps) to the training data 'X_train, y_train' and returns the trained SVC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ad218741-1926-4a5b-a1d5-fc63f26244b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.220987Z",
     "iopub.status.busy": "2023-07-14T16:37:07.219350Z",
     "iopub.status.idle": "2023-07-14T16:37:07.593605Z",
     "shell.execute_reply": "2023-07-14T16:37:07.592999Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.220960Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVC(break_ties=0, cache_size=200.0, probability=1, shrinking=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(break_ties=0, cache_size=200.0, probability=1, shrinking=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(break_ties=0, cache_size=200.0, probability=1, shrinking=0)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_default.fit(X_train, y_train)[\"svc\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8ac0adb-5757-4095-a0ca-909e26be90a9",
   "metadata": {},
   "source": [
    "#### Making predictions on the test subset\n",
    "The trained model_default is used to predict the probabilities of the target classes for the test data 'X_test'.The predict_proba method returns the predicted probabilities for each class.\n",
    "The mapk_score function compares the true target labels y_test with the predicted probabilities y_pred and computes the average precision at k (AP@k) for each sample. The MAP@k score is the average of AP@k scores across all samples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b00ac9e8-a506-4cbb-961f-1492a90779c4",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.597868Z",
     "iopub.status.busy": "2023-07-14T16:37:07.596298Z",
     "iopub.status.idle": "2023-07-14T16:37:07.639755Z",
     "shell.execute_reply": "2023-07-14T16:37:07.639205Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.597844Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.3851224105461394"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model_default.predict_proba(X_test)\n",
    "mapk = mapk_score(y_true=y_test, y_pred=y_pred, k=3)\n",
    "mapk"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "04dd0a85-9185-4679-b466-e6f09ae89a37",
   "metadata": {},
   "source": [
    "#### Cross-Validated Evaluations based on k_fold = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28e9adc9-0a4e-470a-be7f-0a77c88895a7",
   "metadata": {},
   "source": [
    "#### (a)Train subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5ca94b13-9c8e-454d-934b-bfb8a51b9457",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:07.643922Z",
     "iopub.status.busy": "2023-07-14T16:37:07.642369Z",
     "iopub.status.idle": "2023-07-14T16:37:10.003486Z",
     "shell.execute_reply": "2023-07-14T16:37:10.002522Z",
     "shell.execute_reply.started": "2023-07-14T16:37:07.643892Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3691823899371069, None)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spotPython.sklearn.traintest import evaluate_cv\n",
    "fun_control.update({\n",
    "     \"eval\": \"train_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_default, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f438fa0c-20eb-4688-bf48-ccd522e38b29",
   "metadata": {},
   "source": [
    "#### (b)Test subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "19333301-7912-45e2-ba77-d923975ce773",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:10.007944Z",
     "iopub.status.busy": "2023-07-14T16:37:10.006356Z",
     "iopub.status.idle": "2023-07-14T16:37:10.439296Z",
     "shell.execute_reply": "2023-07-14T16:37:10.438671Z",
     "shell.execute_reply.started": "2023-07-14T16:37:10.007911Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.2963507625272331, None)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun_control.update({\n",
    "     \"eval\": \"test_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_default, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "98e0b1c6-d7b1-4787-a482-28233b502401",
   "metadata": {},
   "source": [
    "#### (c)Complete data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8e3526f6-7173-4638-bec5-cfe24b07c25e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:10.443480Z",
     "iopub.status.busy": "2023-07-14T16:37:10.441924Z",
     "iopub.status.idle": "2023-07-14T16:37:14.870759Z",
     "shell.execute_reply": "2023-07-14T16:37:14.869997Z",
     "shell.execute_reply.started": "2023-07-14T16:37:10.443456Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3712810194500335, None)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun_control.update({\n",
    "     \"eval\": \"data_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_default, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d511f7d-f7af-43e3-9d1d-0c97afdf33dc",
   "metadata": {},
   "source": [
    "#### Perform 30 runs using the default settings to obtain the mean & standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "25a297dc-8254-4516-9f05-05df2406b608",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:14.875005Z",
     "iopub.status.busy": "2023-07-14T16:37:14.873420Z",
     "iopub.status.idle": "2023-07-14T16:37:14.881203Z",
     "shell.execute_reply": "2023-07-14T16:37:14.880052Z",
     "shell.execute_reply.started": "2023-07-14T16:37:14.874981Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def repeated_eval(n, model):\n",
    "    res_values = []\n",
    "    for i in range(n):\n",
    "        model.fit(X_train, y_train)\n",
    "        y_pred = model.predict_proba(X_test)\n",
    "        res = mapk_score(y_true=y_test, y_pred=y_pred, k=3)\n",
    "        res_values.append(res)\n",
    "    mean_res = np.mean(res_values)\n",
    "    print(f\"mean_res: {mean_res}\")\n",
    "    std_res = np.std(res_values)\n",
    "    print(f\"std_res: {std_res}\")\n",
    "    min_res = np.min(res_values)\n",
    "    print(f\"min_res: {min_res}\")\n",
    "    max_res = np.max(res_values)\n",
    "    print(f\"max_res: {max_res}\")\n",
    "    median_res = np.median(res_values)\n",
    "    print(f\"median_res: {median_res}\")\n",
    "    return mean_res, std_res, min_res, max_res, median_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7f739277-4771-440f-aefd-6f0af1ec5291",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:14.886055Z",
     "iopub.status.busy": "2023-07-14T16:37:14.884370Z",
     "iopub.status.idle": "2023-07-14T16:37:24.828220Z",
     "shell.execute_reply": "2023-07-14T16:37:24.827448Z",
     "shell.execute_reply.started": "2023-07-14T16:37:14.886029Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_res: 0.38700564971751406\n",
      "std_res: 0.008516323204968\n",
      "min_res: 0.3662900188323917\n",
      "max_res: 0.40583804143126173\n",
      "median_res: 0.38841807909604514\n"
     ]
    }
   ],
   "source": [
    "_ = repeated_eval(30, model_default)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1eeec050-9c9d-4aca-ad1b-b87ce4e061d3",
   "metadata": {},
   "source": [
    "### 7.0.2 Evaluations using SpotPython\n",
    "Using the Spot function to tune the model Hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "11c08578-cff8-4350-ac16-580adb911ed5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:24.832796Z",
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     "iopub.status.idle": "2023-07-14T16:37:24.838218Z",
     "shell.execute_reply": "2023-07-14T16:37:24.837144Z",
     "shell.execute_reply.started": "2023-07-14T16:37:24.832772Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#Get types and variable names as well as lower and upper bounds for the hyperparameters.\n",
    "var_type = get_var_type(fun_control)\n",
    "var_name = get_var_name(fun_control)\n",
    "fun_control.update({\"var_type\": var_type,\n",
    "                    \"var_name\": var_name})\n",
    "\n",
    "lower = get_bound_values(fun_control, \"lower\")\n",
    "upper = get_bound_values(fun_control, \"upper\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "32e0edf7-dcf4-46b6-976e-faca9fafce2e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:24.846578Z",
     "iopub.status.busy": "2023-07-14T16:37:24.844905Z",
     "iopub.status.idle": "2023-07-14T16:37:24.855454Z",
     "shell.execute_reply": "2023-07-14T16:37:24.854543Z",
     "shell.execute_reply.started": "2023-07-14T16:37:24.846554Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| name        | type   | default   |    lower |   upper | transform   |\n",
      "|-------------|--------|-----------|----------|---------|-------------|\n",
      "| C           | float  | 1.0       |   0.1    |   10    | None        |\n",
      "| kernel      | factor | rbf       |   0      |    0    | None        |\n",
      "| degree      | int    | 3         |   3      |    3    | None        |\n",
      "| gamma       | factor | scale     |   0      |    1    | None        |\n",
      "| coef0       | float  | 0.0       |   0      |    0    | None        |\n",
      "| shrinking   | factor | 0         |   0      |    1    | None        |\n",
      "| probability | factor | 0         |   1      |    1    | None        |\n",
      "| tol         | float  | 0.001     |   0.0001 |    0.01 | None        |\n",
      "| cache_size  | float  | 200.0     | 100      |  400    | None        |\n",
      "| break_ties  | factor | 0         |   0      |    1    | None        |\n"
     ]
    }
   ],
   "source": [
    "print(gen_design_table(fun_control))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e580b0e8-c913-4ae8-8261-885b574559c7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:24.860100Z",
     "iopub.status.busy": "2023-07-14T16:37:24.858421Z",
     "iopub.status.idle": "2023-07-14T16:37:24.869029Z",
     "shell.execute_reply": "2023-07-14T16:37:24.868330Z",
     "shell.execute_reply.started": "2023-07-14T16:37:24.860074Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.e+00, 2.e+00, 3.e+00, 0.e+00, 0.e+00, 0.e+00, 0.e+00, 1.e-03,\n",
       "        2.e+02, 0.e+00]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Preparing the Spot Optimizer\n",
    "hyper_dict=SklearnHyperDict().load()#load the predefined hyperparameters and their corresponding values\n",
    "X_start = get_default_hyperparameters_as_array(fun_control, hyper_dict)# retrieves the default hyperparameter values for the specified fun_control configuration and hyper_dict\n",
    "X_start # the returned value is assigned to X_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "1c21a743-60a2-44a3-80bd-ab351b06c008",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:24.873381Z",
     "iopub.status.busy": "2023-07-14T16:37:24.871740Z",
     "iopub.status.idle": "2023-07-14T16:37:24.878407Z",
     "shell.execute_reply": "2023-07-14T16:37:24.877631Z",
     "shell.execute_reply.started": "2023-07-14T16:37:24.873357Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#The interface from the sklearn setting to SPOT is provided by the function fun_sklearn\n",
    "fun = HyperSklearn(seed=123, log_level=50).fun_sklearn #provides functionality for hyperparameter optimization in Sklearn model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1bb0bc-6b6c-4a27-a739-1f8a41d1f379",
   "metadata": {},
   "source": [
    "#### 9.0.2.1 Tuning the hyperparameters using spot_tuner\n",
    "This code starts the hyperparameter tuning process using the SPOT framework, attempting to find the optimal hyperparameters for the given optimization function within the specified time limit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "820da288-9e7e-4608-a21a-6254beaba060",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:37:24.882851Z",
     "iopub.status.busy": "2023-07-14T16:37:24.881295Z",
     "iopub.status.idle": "2023-07-14T16:40:30.307456Z",
     "shell.execute_reply": "2023-07-14T16:40:30.306763Z",
     "shell.execute_reply.started": "2023-07-14T16:37:24.882827Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "spotPython tuning: -0.39097744360902253 [----------] 0.51% \n",
      "spotPython tuning: -0.39097744360902253 [----------] 1.13% \n",
      "spotPython tuning: -0.39097744360902253 [----------] 1.64% \n",
      "spotPython tuning: -0.39097744360902253 [----------] 2.22% \n",
      "spotPython tuning: -0.39097744360902253 [----------] 2.69% \n",
      "spotPython tuning: -0.39348370927318294 [----------] 3.46% \n",
      "spotPython tuning: -0.39348370927318294 [----------] 4.21% \n",
      "spotPython tuning: -0.39348370927318294 [----------] 4.96% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 5.70% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 6.47% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 7.28% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 7.95% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 8.71% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 9.56% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 10.54% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 11.36% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 12.07% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 12.97% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 13.66% \n",
      "spotPython tuning: -0.39348370927318294 [#---------] 14.46% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 15.22% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 15.92% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 16.57% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 17.45% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 18.32% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 19.05% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 19.80% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 20.65% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 21.54% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 22.56% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 23.47% \n",
      "spotPython tuning: -0.39348370927318294 [##--------] 24.40% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 25.31% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 26.24% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 27.30% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 28.35% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 29.31% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 30.20% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 31.24% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 32.10% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 33.01% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 33.80% \n",
      "spotPython tuning: -0.39348370927318294 [###-------] 34.67% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 35.59% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 36.44% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 37.29% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 38.20% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 39.13% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 40.17% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 41.11% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 42.11% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 43.11% \n",
      "spotPython tuning: -0.40100250626566414 [####------] 44.16% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 45.16% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 46.26% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 47.37% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 48.39% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 49.38% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 50.47% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 51.42% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 52.51% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 53.55% \n",
      "spotPython tuning: -0.40100250626566414 [#####-----] 54.60% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 55.62% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 56.73% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 57.90% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 59.13% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 60.32% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 61.33% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 62.51% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 63.74% \n",
      "spotPython tuning: -0.40100250626566414 [######----] 64.97% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 66.34% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 67.64% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 68.95% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 70.28% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 71.65% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 73.00% \n",
      "spotPython tuning: -0.40100250626566414 [#######---] 74.34% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 75.65% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 77.04% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 78.30% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 79.78% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 80.99% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 82.44% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 83.75% \n",
      "spotPython tuning: -0.40100250626566414 [########--] 84.90% \n",
      "spotPython tuning: -0.40100250626566414 [#########-] 86.17% \n",
      "spotPython tuning: -0.40100250626566414 [#########-] 87.53% \n",
      "spotPython tuning: -0.40726817042606517 [#########-] 89.10% \n",
      "spotPython tuning: -0.40726817042606517 [#########-] 90.67% \n",
      "spotPython tuning: -0.40726817042606517 [#########-] 92.32% \n",
      "spotPython tuning: -0.40726817042606517 [#########-] 93.91% \n",
      "spotPython tuning: -0.40726817042606517 [##########] 95.54% \n",
      "spotPython tuning: -0.40726817042606517 [##########] 97.32% \n",
      "spotPython tuning: -0.40726817042606517 [##########] 99.17% \n",
      "spotPython tuning: -0.40726817042606517 [##########] 100.00% Done...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<spotPython.spot.spot.Spot at 0x15175340eda0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MAX_TIME = 3 # Time in minutes. Counter starts, after the initial design is evaluated. So, runtime can be larger.\n",
    "INIT_SIZE = 20 # Initial number of designs to evaluate, before the surrogate is build.\n",
    "spot_tuner = spot.Spot(fun=fun,\n",
    "                   lower = lower,\n",
    "                   upper = upper,\n",
    "                   fun_evals = inf,\n",
    "                   fun_repeats = 1,\n",
    "                   max_time = MAX_TIME,\n",
    "                   noise = False,\n",
    "                   tolerance_x = np.sqrt(np.spacing(1)),\n",
    "                   var_type = var_type,\n",
    "                   var_name = var_name,\n",
    "                   infill_criterion = \"y\",\n",
    "                   n_points = 1,\n",
    "                   seed=123,\n",
    "                   log_level = 50,\n",
    "                   show_models= False,\n",
    "                   show_progress= True,\n",
    "                   fun_control = fun_control,\n",
    "                   design_control={\"init_size\": INIT_SIZE,\n",
    "                                   \"repeats\": 1},\n",
    "                   surrogate_control={\"noise\": True,\n",
    "                                      \"cod_type\": \"norm\",\n",
    "                                      \"min_theta\": -4,\n",
    "                                      \"max_theta\": 3,\n",
    "                                      \"n_theta\": len(var_name),\n",
    "                                      \"model_optimizer\": differential_evolution,\n",
    "                                      \"model_fun_evals\": 10_000,\n",
    "                                      \"log_level\": 50\n",
    "                                      })\n",
    "spot_tuner.run(X_start=X_start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "64d64168-b2a2-4cfb-a5dc-b805b89c21fd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.312060Z",
     "iopub.status.busy": "2023-07-14T16:40:30.310540Z",
     "iopub.status.idle": "2023-07-14T16:40:30.709104Z",
     "shell.execute_reply": "2023-07-14T16:40:30.708447Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.312036Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Show the Progress Result of the hyperparameter tuning\n",
    "\n",
    "spot_tuner.plot_progress(log_y=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e4cca481-d8f5-459f-b4d5-d20df288fc80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.713434Z",
     "iopub.status.busy": "2023-07-14T16:40:30.711865Z",
     "iopub.status.idle": "2023-07-14T16:40:30.721186Z",
     "shell.execute_reply": "2023-07-14T16:40:30.720588Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.713410Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "| name        | type   | default   |   lower |   upper |                 tuned | transform   |   importance | stars   |\n",
      "|-------------|--------|-----------|---------|---------|-----------------------|-------------|--------------|---------|\n",
      "| C           | float  | 1.0       |     0.1 |    10.0 |    1.1865329627153145 | None        |        28.83 | *       |\n",
      "| kernel      | factor | rbf       |     0.0 |     0.0 |                   0.0 | None        |         0.00 |         |\n",
      "| degree      | int    | 3         |     3.0 |     3.0 |                   3.0 | None        |         0.00 |         |\n",
      "| gamma       | factor | scale     |     0.0 |     1.0 |                   0.0 | None        |        30.80 | *       |\n",
      "| coef0       | float  | 0.0       |     0.0 |     0.0 |                   0.0 | None        |         0.00 |         |\n",
      "| shrinking   | factor | 0         |     0.0 |     1.0 |                   0.0 | None        |        10.93 | *       |\n",
      "| probability | factor | 0         |     1.0 |     1.0 |                   1.0 | None        |         0.00 |         |\n",
      "| tol         | float  | 0.001     |  0.0001 |    0.01 | 0.0012225515213617946 | None        |       100.00 | ***     |\n",
      "| cache_size  | float  | 200.0     |   100.0 |   400.0 |     276.5532104086267 | None        |         0.00 |         |\n",
      "| break_ties  | factor | 0         |     0.0 |     1.0 |                   0.0 | None        |         0.00 |         |\n"
     ]
    }
   ],
   "source": [
    "#Print tuning Result\n",
    "\n",
    "print(gen_design_table(fun_control=fun_control, spot=spot_tuner))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a56ccee9-236e-4445-a693-9488fa525367",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.725593Z",
     "iopub.status.busy": "2023-07-14T16:40:30.723965Z",
     "iopub.status.idle": "2023-07-14T16:40:30.840498Z",
     "shell.execute_reply": "2023-07-14T16:40:30.839952Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.725570Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show variable importance\n",
    "\n",
    "spot_tuner.plot_importance(threshold=0.02)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "154542b7-57a4-4d78-92e2-6cbff31483b4",
   "metadata": {},
   "source": [
    "#### 7.0.2.2 Display the optimal values for the hyperparameters found by spot during tuning process\n",
    "Reshapes the best solution into a 2D array with a single row, as the 'to_all_dim' method expects a 2D input.\n",
    "Transforms the solution from the normalized space used by the tuner to the original hyperparameter space."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ad9a8a52-3a97-4865-982e-7d00ce3f60ef",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.844627Z",
     "iopub.status.busy": "2023-07-14T16:40:30.843074Z",
     "iopub.status.idle": "2023-07-14T16:40:30.862408Z",
     "shell.execute_reply": "2023-07-14T16:40:30.861769Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.844604Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.18653296e+00 0.00000000e+00 3.00000000e+00 0.00000000e+00\n",
      "  0.00000000e+00 0.00000000e+00 1.00000000e+00 1.22255152e-03\n",
      "  2.76553210e+02 0.00000000e+00]]\n"
     ]
    }
   ],
   "source": [
    "X = spot_tuner.to_all_dim(spot_tuner.min_X.reshape(1,-1))\n",
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d5f5d959-2a1a-421c-a3d3-afb13d17dc85",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.866810Z",
     "iopub.status.busy": "2023-07-14T16:40:30.865256Z",
     "iopub.status.idle": "2023-07-14T16:40:30.874361Z",
     "shell.execute_reply": "2023-07-14T16:40:30.873557Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.866787Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'C': 1.1865329627153145,\n",
       "  'kernel': 'rbf',\n",
       "  'degree': 3,\n",
       "  'gamma': 'scale',\n",
       "  'coef0': 0.0,\n",
       "  'shrinking': 0,\n",
       "  'probability': 1,\n",
       "  'tol': 0.0012225515213617946,\n",
       "  'cache_size': 276.5532104086267,\n",
       "  'break_ties': 0}]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The optimal hyperparameter values with names are returned as a dictionary and mapped to their corresponding values from X.\n",
    "\n",
    "v_dict = assign_values(X, fun_control[\"var_name\"])\n",
    "return_conf_list_from_var_dict(var_dict=v_dict, fun_control=fun_control)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "76527a61-ad61-4d4f-82ef-cc1857f4d4fc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.878821Z",
     "iopub.status.busy": "2023-07-14T16:40:30.877259Z",
     "iopub.status.idle": "2023-07-14T16:40:30.887458Z",
     "shell.execute_reply": "2023-07-14T16:40:30.886690Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.878798Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    probability=1, shrinking=0, tol=0.0012225515213617946)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVC</label><div class=\"sk-toggleable__content\"><pre>SVC(C=1.1865329627153145, break_ties=0, cache_size=276.5532104086267,\n",
       "    probability=1, shrinking=0, tol=0.0012225515213617946)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVC(C=1.1865329627153145, break_ties=0, cache_size=276.5532104086267,\n",
       "    probability=1, shrinking=0, tol=0.0012225515213617946)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The function uses the values in X to set the hyperparameters of the model according to the configuration specified in fun_control\n",
    "# This is to obtain the trained model that corresponds to the optimized hyperparameter values.\n",
    "model_spot = get_one_sklearn_model_from_X(X, fun_control)\n",
    "model_spot"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "827517bf-a92f-49bb-a0a6-c24cf4b1b103",
   "metadata": {},
   "source": [
    "### 7.0.2.3 Training the model based on spot parameters\n",
    "First, extract the feature matrix X_train & X_test and the target vector y_test & y_train from the training & testing dataset specified in the fun_control dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9d6586d2-c7d5-4871-96a4-d9e0e10f47a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.892035Z",
     "iopub.status.busy": "2023-07-14T16:40:30.890375Z",
     "iopub.status.idle": "2023-07-14T16:40:30.902110Z",
     "shell.execute_reply": "2023-07-14T16:40:30.901370Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.892009Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((177, 200), (177,))"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fetch the data\n",
    "X_train, y_train = get_Xy_from_df(fun_control[\"train\"], fun_control[\"target_column\"])\n",
    "X_test, y_test = get_Xy_from_df(fun_control[\"test\"], fun_control[\"target_column\"])\n",
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "cea097b3-7334-4b97-a301-454ca3c42e5f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:30.906568Z",
     "iopub.status.busy": "2023-07-14T16:40:30.904899Z",
     "iopub.status.idle": "2023-07-14T16:40:31.213790Z",
     "shell.execute_reply": "2023-07-14T16:40:31.213221Z",
     "shell.execute_reply.started": "2023-07-14T16:40:30.906545Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.391713747645951"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the model with the tuned hyperparameters.\n",
    "model_spot.fit(X_train, y_train)\n",
    "\n",
    "#Predict the probabilities of the target classes for the test data 'X_test'\n",
    "y_pred = model_spot.predict_proba(X_test)\n",
    "\n",
    "#Calculate the mean average precision at k (MAP@k) score for the predictions\n",
    "res = mapk_score(y_true=y_test, y_pred=y_pred, k=3)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "fe631637-f442-42d1-a3a3-3e69f8650879",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:31.218183Z",
     "iopub.status.busy": "2023-07-14T16:40:31.216538Z",
     "iopub.status.idle": "2023-07-14T16:40:40.415507Z",
     "shell.execute_reply": "2023-07-14T16:40:40.414885Z",
     "shell.execute_reply.started": "2023-07-14T16:40:31.218159Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mean_res: 0.38647206528562456\n",
      "std_res: 0.007940495581584673\n",
      "min_res: 0.3644067796610169\n",
      "max_res: 0.39924670433145004\n",
      "median_res: 0.3870056497175141\n"
     ]
    }
   ],
   "source": [
    "#Getting the mean & standard deviation from 30 runs\n",
    "\n",
    "_ = repeated_eval(30, model_spot)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b547bc8-0dfb-4672-8b9a-19b50dc1b5ad",
   "metadata": {},
   "source": [
    "### 8.0 Cross Validated Evaluations Based on Tuned Parameters "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58849de0-2159-4ac9-82c1-77b394e54640",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### (a)Train subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "402bc8d5-2742-4bc6-91d5-7bbc563440aa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:40.419599Z",
     "iopub.status.busy": "2023-07-14T16:40:40.418051Z",
     "iopub.status.idle": "2023-07-14T16:40:42.758320Z",
     "shell.execute_reply": "2023-07-14T16:40:42.757498Z",
     "shell.execute_reply.started": "2023-07-14T16:40:40.419576Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.36383647798742136, None)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from spotPython.sklearn.traintest import evaluate_cv\n",
    "fun_control.update({\n",
    "     \"eval\": \"train_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_spot, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d714d6fb-0786-40a4-9514-a587aa2b2d74",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### (b)Test subset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cb6bb859-32d3-49a7-8a7d-a9f2acac7d58",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:42.762727Z",
     "iopub.status.busy": "2023-07-14T16:40:42.761105Z",
     "iopub.status.idle": "2023-07-14T16:40:43.159930Z",
     "shell.execute_reply": "2023-07-14T16:40:43.159194Z",
     "shell.execute_reply.started": "2023-07-14T16:40:42.762703Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.3156862745098039, None)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun_control.update({\n",
    "     \"eval\": \"test_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_spot, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ff0beff-973c-49eb-8108-a2d5303fd94b",
   "metadata": {},
   "source": [
    "#### (c)complete data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "b05bbaf5-fbaa-46ab-bad3-1bcaaf2fff4f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:43.164245Z",
     "iopub.status.busy": "2023-07-14T16:40:43.162641Z",
     "iopub.status.idle": "2023-07-14T16:40:47.682284Z",
     "shell.execute_reply": "2023-07-14T16:40:47.681728Z",
     "shell.execute_reply.started": "2023-07-14T16:40:43.164222Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.37145204560697515, None)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fun_control.update({\n",
    "     \"eval\": \"data_cv\",\n",
    "     \"k_folds\": 10,\n",
    "})\n",
    "evaluate_cv(model=model_spot, fun_control=fun_control, verbose=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c3268356-ad0e-4907-9969-c72d08562e8c",
   "metadata": {},
   "source": [
    "#### 9.0 Visualizing the Top 3 Predictions on the X_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "34dd99be-d3b9-48db-b090-73e22748499b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:47.686483Z",
     "iopub.status.busy": "2023-07-14T16:40:47.684937Z",
     "iopub.status.idle": "2023-07-14T16:40:47.694389Z",
     "shell.execute_reply": "2023-07-14T16:40:47.693741Z",
     "shell.execute_reply.started": "2023-07-14T16:40:47.686460Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 3 predictions for sample 1: ['Tungiasis', 'Rift_Valley_fever', 'Dengue']\n",
      "Top 3 predictions for sample 2: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "Top 3 predictions for sample 3: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "Top 3 predictions for sample 4: ['Yellow_Fever', 'Zika', 'West_Nile_fever']\n",
      "Top 3 predictions for sample 5: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "Top 3 predictions for sample 6: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "Top 3 predictions for sample 7: ['Yellow_Fever', 'Zika', 'West_Nile_fever']\n",
      "Top 3 predictions for sample 8: ['Yellow_Fever', 'Zika', 'West_Nile_fever']\n",
      "Top 3 predictions for sample 9: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "Top 3 predictions for sample 10: ['Dengue', 'Tungiasis', 'Rift_Valley_fever']\n"
     ]
    }
   ],
   "source": [
    "# Display the predictions for the first 10 samples\n",
    "class_labels = model_spot.classes_\n",
    "for i in range(10):\n",
    "    sample_predictions = y_pred[i]\n",
    "    top3_indices = np.argsort(-sample_predictions)[:3]\n",
    "    predicted_labels = enc.inverse_transform(top3_indices.reshape(-1, 1))\n",
    "    top3_predictions = [predicted_labels[j][0] for j in range(3)]\n",
    "    print(f\"Top 3 predictions for sample {i+1}: {top3_predictions}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b9ba3c3-0996-4a9c-b684-290726494fc2",
   "metadata": {},
   "source": [
    "### 10.0 Detailed Hyperparameter Contour Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cc2813e0-ab51-41a9-bff6-5cea919805ca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:47.698693Z",
     "iopub.status.busy": "2023-07-14T16:40:47.697127Z",
     "iopub.status.idle": "2023-07-14T16:40:53.349203Z",
     "shell.execute_reply": "2023-07-14T16:40:53.348575Z",
     "shell.execute_reply.started": "2023-07-14T16:40:47.698671Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:  28.832317259217646\n",
      "gamma:  30.797472960834703\n",
      "shrinking:  10.92701204783907\n",
      "tol:  100.0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "spot_tuner.plot_important_hyperparameter_contour(threshold=0.025)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a5ad81d-95b1-43b5-a1a6-a5272f0b85f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-07T04:50:33.288022Z",
     "iopub.status.busy": "2023-07-07T04:50:33.287768Z",
     "iopub.status.idle": "2023-07-07T04:50:33.291574Z",
     "shell.execute_reply": "2023-07-07T04:50:33.290588Z",
     "shell.execute_reply.started": "2023-07-07T04:50:33.287996Z"
    },
    "tags": []
   },
   "source": [
    "### 11.0 Top 3 Predictions on the Test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eadeb1f4-942a-431e-835d-d3a9ada945c7",
   "metadata": {},
   "source": [
    "#### Load the Training dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bc30fe8b-7d9b-436b-a934-34184d4ad10c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:53.353477Z",
     "iopub.status.busy": "2023-07-14T16:40:53.351951Z",
     "iopub.status.idle": "2023-07-14T16:40:53.367167Z",
     "shell.execute_reply": "2023-07-14T16:40:53.366455Z",
     "shell.execute_reply.started": "2023-07-14T16:40:53.353452Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = pd.read_csv('test.csv')\n",
    "    # remove the id column\n",
    "test = test.drop(columns=['id'])\n",
    "n_samples = test.shape[0] # Number of samples/rows in the dataset(707)\n",
    "n_features = test.shape[1] # Number of features/columns in the dataset(64)\n",
    "#n_features\n",
    "len(test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "4d994561-f4d4-41f9-9b38-4abbd4f9a985",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:53.371460Z",
     "iopub.status.busy": "2023-07-14T16:40:53.369855Z",
     "iopub.status.idle": "2023-07-14T16:40:53.399617Z",
     "shell.execute_reply": "2023-07-14T16:40:53.398999Z",
     "shell.execute_reply.started": "2023-07-14T16:40:53.371437Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(303, 64)\n"
     ]
    },
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      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.columns = [f\"x{i+1}\" for i in range(n_features)] #Assigns new column names to the dataset.\n",
    "print(test.shape)\n",
    "test.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "22f3b4b2-c1a0-4c1d-b7d5-07487a1723ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:53.403744Z",
     "iopub.status.busy": "2023-07-14T16:40:53.402192Z",
     "iopub.status.idle": "2023-07-14T16:40:58.396161Z",
     "shell.execute_reply": "2023-07-14T16:40:58.395554Z",
     "shell.execute_reply.started": "2023-07-14T16:40:53.403721Z"
    },
    "tags": []
   },
   "outputs": [
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 6112 columns</p>\n",
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      ],
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       "4     0   0   1   0   1   1   0   0   1    1  ...            0            0   \n",
       "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ...          ...          ...   \n",
       "298   0   1   0   0   0   0   0   0   1    0  ...            0            0   \n",
       "299   1   0   1   0   1   1   0   1   1    1  ...            0            0   \n",
       "300   1   0   0   1   1   0   1   1   1    1  ...            0            0   \n",
       "301   1   0   1   1   1   0   1   0   0    0  ...            0            0   \n",
       "302   1   0   0   0   0   0   1   0   1    0  ...            0            0   \n",
       "\n",
       "     x62_or_x63  x62_xor_x63  x62_and_x64  x62_or_x64  x62_xor_x64  \\\n",
       "0             0            0            0           0            0   \n",
       "1             0            0            0           0            0   \n",
       "2             0            0            0           0            0   \n",
       "3             0            0            0           0            0   \n",
       "4             0            0            0           0            0   \n",
       "..          ...          ...          ...         ...          ...   \n",
       "298           0            0            0           0            0   \n",
       "299           0            0            0           0            0   \n",
       "300           0            0            0           0            0   \n",
       "301           0            0            0           0            0   \n",
       "302           0            0            0           0            0   \n",
       "\n",
       "     x63_and_x64  x63_or_x64  x63_xor_x64  \n",
       "0              0           0            0  \n",
       "1              0           0            0  \n",
       "2              0           0            0  \n",
       "3              0           0            0  \n",
       "4              0           0            0  \n",
       "..           ...         ...          ...  \n",
       "298            0           0            0  \n",
       "299            0           0            0  \n",
       "300            0           0            0  \n",
       "301            0           0            0  \n",
       "302            0           0            0  \n",
       "\n",
       "[303 rows x 6112 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = test.astype(int)#Ensures that all the feature elements are represented as integers.\n",
    "for col1, col2 in itertools.combinations(test.columns,2):#Uses the combinations function from the itertools module to generate pairs of column names.\n",
    "    test[f'{col1}_and_{col2}'] = test[col1] & test[col2]#creates a new column in the dataset between the values in col1 and col2\n",
    "    test[f'{col1}_or_{col2}'] = test[col1] | test[col2]#creates another new column in the dataset between the values in col1 and col2\n",
    "    test[f'{col1}_xor_{col2}'] = test[col1] ^ test[col2]#creates yet another new column in the dataset between the values in col1 and col2\n",
    "\n",
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "676bed77-dfa6-41ac-b4f3-3fc225967a8b",
   "metadata": {},
   "source": [
    "#### Feature Aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1ba1c00-806f-401d-8ef5-9f60696a3c9c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:58.400434Z",
     "iopub.status.busy": "2023-07-14T16:40:58.398880Z",
     "iopub.status.idle": "2023-07-14T16:40:58.451169Z",
     "shell.execute_reply": "2023-07-14T16:40:58.450380Z",
     "shell.execute_reply.started": "2023-07-14T16:40:58.400410Z"
    },
    "tags": []
   },
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>1</td>\n",
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       "<p>303 rows × 6116 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     x1  x2  x3  x4  x5  x6  x7  x8  x9  x10  ...  x62_and_x64  x62_or_x64  \\\n",
       "0     0   0   0   0   0   0   0   0   0    1  ...            0           0   \n",
       "1     1   1   0   1   0   1   1   1   1    1  ...            0           0   \n",
       "2     1   1   0   1   1   1   1   0   1    0  ...            0           0   \n",
       "3     0   1   0   0   0   1   1   1   0    0  ...            0           0   \n",
       "4     0   0   1   0   1   1   0   0   1    1  ...            0           0   \n",
       "..   ..  ..  ..  ..  ..  ..  ..  ..  ..  ...  ...          ...         ...   \n",
       "298   0   1   0   0   0   0   0   0   1    0  ...            0           0   \n",
       "299   1   0   1   0   1   1   0   1   1    1  ...            0           0   \n",
       "300   1   0   0   1   1   0   1   1   1    1  ...            0           0   \n",
       "301   1   0   1   1   1   0   1   0   0    0  ...            0           0   \n",
       "302   1   0   0   0   0   0   1   0   1    0  ...            0           0   \n",
       "\n",
       "     x62_xor_x64  x63_and_x64  x63_or_x64  x63_xor_x64  cols_A  cols_B  \\\n",
       "0              0            0           0            0     0.0     0.0   \n",
       "1              0            0           0            0     0.0     0.0   \n",
       "2              0            0           0            0     0.0     0.0   \n",
       "3              0            0           0            0     0.0     0.0   \n",
       "4              0            0           0            0     0.0     0.0   \n",
       "..           ...          ...         ...          ...     ...     ...   \n",
       "298            0            0           0            0     0.0     0.0   \n",
       "299            0            0           0            0     0.0     0.0   \n",
       "300            0            0           0            0     0.0     0.0   \n",
       "301            0            0           0            0     0.0     0.0   \n",
       "302            0            0           0            0     0.0     0.0   \n",
       "\n",
       "     cols_C  cols_D  \n",
       "0       0.0     0.0  \n",
       "1       0.0     0.0  \n",
       "2       0.0     0.0  \n",
       "3       0.0     0.0  \n",
       "4       0.0     0.0  \n",
       "..      ...     ...  \n",
       "298     0.0     0.0  \n",
       "299     0.0     0.0  \n",
       "300     0.0     0.0  \n",
       "301     0.0     0.0  \n",
       "302     0.0     0.0  \n",
       "\n",
       "[303 rows x 6116 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_A = test.columns[test.columns.str.contains('pain')]#creates a new variable cols_A and assigns it the column names from the dataset DataFrame that contain the substring 'pain'\n",
    "cols_B = test.columns[test.columns.str.contains('inflammation')]\n",
    "cols_C = test.columns[test.columns.str.contains('bleed')]\n",
    "cols_D = test.columns[test.columns.str.contains('skin')]\n",
    "test['cols_A'] = test[cols_A].sum(axis=1)#Calculates the sum of the columns specified by cols_A across each row using the sum method with axis=1. The result is assigned to the new column,cols_A\n",
    "test['cols_B'] = test[cols_B].sum(axis=1)\n",
    "test['cols_C'] = test[cols_C].sum(axis=1)\n",
    "test['cols_D'] = test[cols_D].sum(axis=1)\n",
    "test1 = test\n",
    "test1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba3d49be-41aa-4321-9cbd-065eb9c71714",
   "metadata": {},
   "source": [
    "#### Feature Subset Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "34ebe8f9-c96b-4666-a0e1-a0a65288be0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:58.455500Z",
     "iopub.status.busy": "2023-07-14T16:40:58.453851Z",
     "iopub.status.idle": "2023-07-14T16:40:58.463292Z",
     "shell.execute_reply": "2023-07-14T16:40:58.462658Z",
     "shell.execute_reply.started": "2023-07-14T16:40:58.455476Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the selected feature indices and names\n",
    "selected_indices = selector.get_support(indices=True)\n",
    "selected_features = test1.columns[selected_indices]\n",
    "len(selected_features)"
   ]
  },
  {
   "cell_type": "code",
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       "     x36  x60  x61  x62  x2_and_x56  x2_and_x58  x3_and_x56  x3_and_x58  \\\n",
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       "\n",
       "     x3_and_x59  x14_and_x28  ...  x61_and_x62  x61_or_x62  x61_or_x63  \\\n",
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       "302           0            0  ...            0           0           0   \n",
       "\n",
       "     x61_xor_x63  x61_or_x64  x61_xor_x64  x62_or_x63  x62_xor_x63  \\\n",
       "0              0           0            0           0            0   \n",
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       "\n",
       "[303 rows x 200 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataset with the selected features\n",
    "selected_data = test1[selected_features]#Creates a new dataframe containing only the selected features\n",
    "#selected_data['prognosis'] = y_col #copies the target variable (prognosis) into the selected_data DataFrame.\n",
    "#selected_data = selected_data.copy()\n",
    "selected_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "b6f9cd98-98ac-4aad-8f72-578df1e8155f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T16:40:58.497802Z",
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     "shell.execute_reply.started": "2023-07-14T16:40:58.497779Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "y_pred1 = model_default.predict_proba(selected_data)\n",
    "#y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "17098eed-770a-4611-b228-d9f97f5353d0",
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     "shell.execute_reply.started": "2023-07-14T16:40:58.563522Z"
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "707: ['Rift_Valley_fever', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "708: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "709: ['Rift_Valley_fever', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "710: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "711: ['Yellow_Fever', 'West_Nile_fever', 'Zika']\n",
      "712: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "713: ['Plague', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "714: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "715: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "716: ['West_Nile_fever', 'Plague', 'Zika']\n",
      "717: ['West_Nile_fever', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "718: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "719: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "720: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "721: ['West_Nile_fever', 'Plague', 'Japanese_encephalitis']\n",
      "722: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "723: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "724: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "725: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "726: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "727: ['West_Nile_fever', 'Japanese_encephalitis', 'Malaria']\n",
      "728: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "729: ['West_Nile_fever', 'Japanese_encephalitis', 'Lyme_disease']\n",
      "730: ['Lyme_disease', 'Plague', 'Malaria']\n",
      "731: ['Rift_Valley_fever', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "732: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "733: ['West_Nile_fever', 'Chikungunya', 'Japanese_encephalitis']\n",
      "734: ['Japanese_encephalitis', 'West_Nile_fever', 'Zika']\n",
      "735: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "736: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "737: ['West_Nile_fever', 'Zika', 'Japanese_encephalitis']\n",
      "738: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "739: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "740: ['Yellow_Fever', 'Zika', 'Japanese_encephalitis']\n",
      "741: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "742: ['West_Nile_fever', 'Lyme_disease', 'Plague']\n",
      "743: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "744: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "745: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "746: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "747: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "748: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "749: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "750: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "751: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "752: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "753: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "754: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "755: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "756: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "757: ['Dengue', 'Rift_Valley_fever', 'Tungiasis']\n",
      "758: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "759: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "760: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "761: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "762: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "763: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "764: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "765: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "766: ['Dengue', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "767: ['Zika', 'West_Nile_fever', 'Plague']\n",
      "768: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "769: ['West_Nile_fever', 'Japanese_encephalitis', 'Yellow_Fever']\n",
      "770: ['Chikungunya', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "771: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "772: ['Plague', 'Japanese_encephalitis', 'Malaria']\n",
      "773: ['West_Nile_fever', 'Japanese_encephalitis', 'Plague']\n",
      "774: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "775: ['West_Nile_fever', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "776: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "777: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "778: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "779: ['Lyme_disease', 'Malaria', 'Plague']\n",
      "780: ['Rift_Valley_fever', 'Dengue', 'West_Nile_fever']\n",
      "781: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "782: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "783: ['West_Nile_fever', 'Japanese_encephalitis', 'Plague']\n",
      "784: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "785: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "786: ['Malaria', 'Plague', 'Lyme_disease']\n",
      "787: ['Zika', 'West_Nile_fever', 'Plague']\n",
      "788: ['Zika', 'Yellow_Fever', 'West_Nile_fever']\n",
      "789: ['Yellow_Fever', 'Zika', 'West_Nile_fever']\n",
      "790: ['West_Nile_fever', 'Japanese_encephalitis', 'Malaria']\n",
      "791: ['West_Nile_fever', 'Japanese_encephalitis', 'Dengue']\n",
      "792: ['Japanese_encephalitis', 'Malaria', 'West_Nile_fever']\n",
      "793: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "794: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "795: ['Tungiasis', 'Rift_Valley_fever', 'Dengue']\n",
      "796: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "797: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "798: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "799: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "800: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "801: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "802: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "803: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "804: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "805: ['Japanese_encephalitis', 'West_Nile_fever', 'Zika']\n",
      "806: ['Yellow_Fever', 'Zika', 'Japanese_encephalitis']\n",
      "807: ['West_Nile_fever', 'Japanese_encephalitis', 'Malaria']\n",
      "808: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "809: ['Malaria', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "810: ['Rift_Valley_fever', 'Dengue', 'Japanese_encephalitis']\n",
      "811: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "812: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "813: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "814: ['Rift_Valley_fever', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "815: ['Yellow_Fever', 'West_Nile_fever', 'Zika']\n",
      "816: ['Lyme_disease', 'Malaria', 'Japanese_encephalitis']\n",
      "817: ['Zika', 'Yellow_Fever', 'Plague']\n",
      "818: ['Tungiasis', 'Rift_Valley_fever', 'Dengue']\n",
      "819: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "820: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "821: ['Zika', 'Yellow_Fever', 'West_Nile_fever']\n",
      "822: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "823: ['Zika', 'Plague', 'West_Nile_fever']\n",
      "824: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "825: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "826: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "827: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "828: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "829: ['Japanese_encephalitis', 'West_Nile_fever', 'Plague']\n",
      "830: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "831: ['Rift_Valley_fever', 'Dengue', 'West_Nile_fever']\n",
      "832: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "833: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "834: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "835: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "836: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "837: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "838: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "839: ['West_Nile_fever', 'Zika', 'Plague']\n",
      "840: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "841: ['West_Nile_fever', 'Yellow_Fever', 'Zika']\n",
      "842: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "843: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "844: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "845: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "846: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "847: ['Plague', 'Malaria', 'Japanese_encephalitis']\n",
      "848: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "849: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "850: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "851: ['Yellow_Fever', 'Zika', 'West_Nile_fever']\n",
      "852: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "853: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "854: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "855: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "856: ['Zika', 'Yellow_Fever', 'West_Nile_fever']\n",
      "857: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "858: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "859: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "860: ['West_Nile_fever', 'Japanese_encephalitis', 'Yellow_Fever']\n",
      "861: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "862: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "863: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "864: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "865: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "866: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "867: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "868: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "869: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "870: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "871: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "872: ['Plague', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "873: ['Plague', 'Malaria', 'Lyme_disease']\n",
      "874: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "875: ['Dengue', 'West_Nile_fever', 'Rift_Valley_fever']\n",
      "876: ['Yellow_Fever', 'Zika', 'Japanese_encephalitis']\n",
      "877: ['Zika', 'Yellow_Fever', 'West_Nile_fever']\n",
      "878: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "879: ['Yellow_Fever', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "880: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "881: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "882: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "883: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "884: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "885: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "886: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "887: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "888: ['Rift_Valley_fever', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "889: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "890: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "891: ['Dengue', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "892: ['Lyme_disease', 'West_Nile_fever', 'Malaria']\n",
      "893: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "894: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "895: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "896: ['Zika', 'Plague', 'West_Nile_fever']\n",
      "897: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "898: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "899: ['West_Nile_fever', 'Japanese_encephalitis', 'Dengue']\n",
      "900: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "901: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "902: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "903: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "904: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "905: ['Lyme_disease', 'Plague', 'Zika']\n",
      "906: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "907: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "908: ['Dengue', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "909: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "910: ['Yellow_Fever', 'Zika', 'Japanese_encephalitis']\n",
      "911: ['West_Nile_fever', 'Japanese_encephalitis', 'Plague']\n",
      "912: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "913: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "914: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "915: ['Lyme_disease', 'Plague', 'Japanese_encephalitis']\n",
      "916: ['Tungiasis', 'Rift_Valley_fever', 'Dengue']\n",
      "917: ['Plague', 'Malaria', 'Japanese_encephalitis']\n",
      "918: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "919: ['West_Nile_fever', 'Japanese_encephalitis', 'Yellow_Fever']\n",
      "920: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "921: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "922: ['Dengue', 'Rift_Valley_fever', 'Chikungunya']\n",
      "923: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "924: ['Rift_Valley_fever', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "925: ['Rift_Valley_fever', 'Dengue', 'Japanese_encephalitis']\n",
      "926: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "927: ['Zika', 'Lyme_disease', 'West_Nile_fever']\n",
      "928: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "929: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "930: ['West_Nile_fever', 'Japanese_encephalitis', 'Yellow_Fever']\n",
      "931: ['Dengue', 'Tungiasis', 'Rift_Valley_fever']\n",
      "932: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "933: ['West_Nile_fever', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "934: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "935: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "936: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "937: ['Plague', 'Malaria', 'Lyme_disease']\n",
      "938: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "939: ['Plague', 'Lyme_disease', 'West_Nile_fever']\n",
      "940: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "941: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "942: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "943: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "944: ['West_Nile_fever', 'Japanese_encephalitis', 'Yellow_Fever']\n",
      "945: ['Dengue', 'Rift_Valley_fever', 'Chikungunya']\n",
      "946: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "947: ['West_Nile_fever', 'Japanese_encephalitis', 'Plague']\n",
      "948: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "949: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "950: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "951: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "952: ['Dengue', 'West_Nile_fever', 'Rift_Valley_fever']\n",
      "953: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "954: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "955: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "956: ['West_Nile_fever', 'Rift_Valley_fever', 'Zika']\n",
      "957: ['Japanese_encephalitis', 'West_Nile_fever', 'Rift_Valley_fever']\n",
      "958: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "959: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "960: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "961: ['Dengue', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "962: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "963: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "964: ['Yellow_Fever', 'West_Nile_fever', 'Zika']\n",
      "965: ['West_Nile_fever', 'Japanese_encephalitis', 'Malaria']\n",
      "966: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "967: ['Lyme_disease', 'West_Nile_fever', 'Japanese_encephalitis']\n",
      "968: ['Lyme_disease', 'West_Nile_fever', 'Plague']\n",
      "969: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "970: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "971: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "972: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "973: ['Dengue', 'West_Nile_fever', 'Rift_Valley_fever']\n",
      "974: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "975: ['West_Nile_fever', 'Japanese_encephalitis', 'Lyme_disease']\n",
      "976: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "977: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "978: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "979: ['Plague', 'Japanese_encephalitis', 'Malaria']\n",
      "980: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "981: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "982: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "983: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "984: ['Plague', 'Lyme_disease', 'Malaria']\n",
      "985: ['Malaria', 'Japanese_encephalitis', 'West_Nile_fever']\n",
      "986: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "987: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "988: ['Chikungunya', 'Dengue', 'Rift_Valley_fever']\n",
      "989: ['Chikungunya', 'Dengue', 'West_Nile_fever']\n",
      "990: ['West_Nile_fever', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "991: ['West_Nile_fever', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "992: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "993: ['Yellow_Fever', 'Zika', 'Japanese_encephalitis']\n",
      "994: ['Plague', 'Malaria', 'Japanese_encephalitis']\n",
      "995: ['Tungiasis', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "996: ['Dengue', 'Rift_Valley_fever', 'West_Nile_fever']\n",
      "997: ['Tungiasis', 'Dengue', 'Rift_Valley_fever']\n",
      "998: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "999: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n",
      "1000: ['Chikungunya', 'Japanese_encephalitis', 'Rift_Valley_fever']\n",
      "1001: ['Plague', 'West_Nile_fever', 'Zika']\n",
      "1002: ['Zika', 'Yellow_Fever', 'Japanese_encephalitis']\n",
      "1003: ['Japanese_encephalitis', 'West_Nile_fever', 'Yellow_Fever']\n",
      "1004: ['West_Nile_fever', 'Japanese_encephalitis', 'Plague']\n",
      "1005: ['Zika', 'Plague', 'West_Nile_fever']\n",
      "1006: ['Lyme_disease', 'Malaria', 'Plague']\n",
      "1007: ['Lyme_disease', 'Plague', 'West_Nile_fever']\n",
      "1008: ['West_Nile_fever', 'Rift_Valley_fever', 'Japanese_encephalitis']\n",
      "1009: ['West_Nile_fever', 'Japanese_encephalitis', 'Zika']\n"
     ]
    }
   ],
   "source": [
    "class_labels = model_default.classes_\n",
    "for i in range(707, 1010):\n",
    "    # Assuming y_pred contains the predictions for all data samples\n",
    "    sample_predictions = y_pred1[i-707]  # Adjust the index to match the data sample\n",
    "    top3_indices = np.argsort(-sample_predictions)[:3]\n",
    "    predicted_labels = enc.inverse_transform(top3_indices.reshape(-1, 1))\n",
    "    top3_predictions = [predicted_labels[j][0] for j in range(3)]\n",
    "    print(f\"{i}: {top3_predictions}\")\n",
    "    #print(f\"Indices: {top3_indices}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "56f1a9cb-737b-4739-9e1e-3999c87f731a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T18:13:41.629743Z",
     "iopub.status.busy": "2023-07-14T18:13:41.629445Z",
     "iopub.status.idle": "2023-07-14T18:13:41.656967Z",
     "shell.execute_reply": "2023-07-14T18:13:41.656280Z",
     "shell.execute_reply.started": "2023-07-14T18:13:41.629717Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a list to store the top 3 predictions\n",
    "predictions = []\n",
    "\n",
    "for i in range(707, 1010):\n",
    "    # Assuming y_pred contains the predictions for all data samples\n",
    "    sample_predictions = y_pred1[i-707]  # Adjust the index to match the data sample\n",
    "    top3_indices = np.argsort(-sample_predictions)[:3]\n",
    "    predicted_labels = enc.inverse_transform(top3_indices.reshape(-1, 1))\n",
    "    top3_predictions = [predicted_labels[j][0] for j in range(3)]\n",
    "    predictions.append(top3_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "f04d5932-b9dd-42a7-9292-4626a41afaca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T18:13:44.393008Z",
     "iopub.status.busy": "2023-07-14T18:13:44.392736Z",
     "iopub.status.idle": "2023-07-14T18:13:44.396949Z",
     "shell.execute_reply": "2023-07-14T18:13:44.396099Z",
     "shell.execute_reply.started": "2023-07-14T18:13:44.392984Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create a DataFrame with index and top 3 predictions\n",
    "submission_df = pd.DataFrame({\n",
    "    'id': range(707, 1010),\n",
    "    'Prognosis': predictions\n",
    "})\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "7e5f682f-1bdf-456a-9e1b-4d5d6fa5574f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T18:13:56.177230Z",
     "iopub.status.busy": "2023-07-14T18:13:56.176956Z",
     "iopub.status.idle": "2023-07-14T18:13:56.185024Z",
     "shell.execute_reply": "2023-07-14T18:13:56.184049Z",
     "shell.execute_reply.started": "2023-07-14T18:13:56.177203Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "# Save the DataFrame to submission.csv with only the 'id' and 'Prognosis' columns\n",
    "submission_df.to_csv('submission.csv', columns=['id', 'Prognosis'], index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "890e755c-7ac4-4144-a613-290f5f9eb558",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-07-14T18:14:00.133017Z",
     "iopub.status.busy": "2023-07-14T18:14:00.132703Z",
     "iopub.status.idle": "2023-07-14T18:14:00.139674Z",
     "shell.execute_reply": "2023-07-14T18:14:00.138551Z",
     "shell.execute_reply.started": "2023-07-14T18:14:00.132989Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "ty = pd.read_csv('submission.csv')"
   ]
  }
 ],
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