CUSTOM-LinAlg+Regression.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNnjjII8+LPYqQ/M7CItgTj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":166,"metadata":{"id":"k6x0AziMZLT2","executionInfo":{"status":"ok","timestamp":1674292985833,"user_tz":-330,"elapsed":387,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}}},"outputs":[],"source":["import numpy as np\n","import pandas as pd\n","from autograd import jacobian, hessian\n","import sklearn.preprocessing as pp\n","from sklearn.linear_model import LogisticRegression\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import accuracy_score, confusion_matrix"]},{"cell_type":"markdown","source":["### Part 2.1: Getting Familiar with Linear Algebraic functions"],"metadata":{"id":"N8f3eAuIaPNB"}},{"cell_type":"code","source":["# 1: Create x of size 10*10 with random integer numbers\n","x = np.array(np.random.randint(0, 1000, size=100)).reshape(10, 10)\n","x"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wDPqlRCXaIYT","executionInfo":{"status":"ok","timestamp":1674292988274,"user_tz":-330,"elapsed":761,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"60abe0f1-8104-4b31-91da-618e16228c4d"},"execution_count":167,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[985, 405, 528, 776,  64, 446, 344, 182, 346, 110],\n","       [516, 901, 586, 846, 897, 496, 894, 765,  67,  26],\n","       [462, 835,  70, 902, 194, 133, 967, 650, 742, 294],\n","       [930, 711, 229, 741, 330, 994, 206, 936, 594, 562],\n","       [448, 486,  35, 150, 281, 734, 387, 722, 754,  28],\n","       [408, 596, 217, 825, 787, 256,  75, 481, 567,  39],\n","       [479, 765, 737, 794, 630, 290, 685, 546,  47, 813],\n","       [332, 411, 801, 844, 883, 951, 402, 638, 710, 195],\n","       [195,  89, 989, 301, 956, 720, 253, 135, 622, 396],\n","       [ 77,  99, 156, 764, 937, 884, 981, 108, 955, 351]])"]},"metadata":{},"execution_count":167}]},{"cell_type":"code","source":["# Compute the following linear algebric operations on the x \n","# using built in functions supported in Numpy, Scipy etc.\n","# 2: Find inverse of the x and print it\n","invx = np.linalg.inv(x)\n","invx"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"kz_PN0e5chIx","executionInfo":{"status":"ok","timestamp":1674292988274,"user_tz":-330,"elapsed":26,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"ace5d6f8-3c54-4f84-c9ad-a4665314a2a3"},"execution_count":168,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[-5.50222466e-03,  1.04299999e-02,  1.17272194e-02,\n","         1.54707952e-02, -1.73298846e-02, -5.16585281e-03,\n","        -1.77514923e-02, -9.71596372e-03,  1.56322706e-02,\n","        -2.80747212e-03],\n","       [ 1.50090713e-02, 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-9.66987934e-05]])"]},"metadata":{},"execution_count":168}]},{"cell_type":"code","source":["# 3: Calculate dot product of the x with same x in transpose A.AT\n","dotprodx = np.dot(x, x.T)\n","dotprodx"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V1WLHdRvc-k5","executionInfo":{"status":"ok","timestamp":1674292988275,"user_tz":-330,"elapsed":25,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"2be8b8c8-802d-44a2-9f59-820951e59127"},"execution_count":169,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[2501498, 2590501, 2341911, 2872937, 1646834, 1876394, 2397284,\n","        2573519, 1736566, 1971564],\n","       [2590501, 4577420, 3453931, 3625219, 2382139, 2879569, 3803352,\n","        3888878, 2611088, 3178389],\n","       [2341911, 3453931, 3779087, 3317557, 2313901, 2449577, 3079832,\n","        2999296, 1696710, 2948268],\n","       [2872937, 3625219, 3317557, 4656091, 2922803, 2802770, 3379662,\n","        3857808, 2495809, 2999459],\n","       [1646834, 2382139, 2313901, 2922803, 2318695, 1817753, 1838676,\n","        2606284, 1682952, 2302344],\n","       [1876394, 2879569, 2449577, 2802770, 1817753, 2494295, 2408758,\n","        2936109, 1984262, 2398992],\n","       [2397284, 3803352, 3079832, 3379662, 1838676, 2408758, 3899790,\n","        3381619, 2538654, 2742077],\n","       [2573519, 3888878, 2999296, 3857808, 2606284, 2936109, 3381619,\n","        4427945, 3383096, 3713841],\n","       [1736566, 2611088, 1696710, 2495809, 1682952, 1984262, 2538654,\n","        3383096, 3172938, 2936105],\n","       [1971564, 3178389, 2948268, 2999459, 2302344, 2398992, 2742077,\n","        3713841, 2936105, 4292438]])"]},"metadata":{},"execution_count":169}]},{"cell_type":"code","source":["# 4: Decompose the original x using eigen decomposition \n","#    and print the eigen values and eigen vectors\n","eigval, eigvec = np.linalg.eig(x)\n","eigval"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dqlRZvrbdISL","executionInfo":{"status":"ok","timestamp":1674292988275,"user_tz":-330,"elapsed":23,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"d7f7fb7d-cdf6-4f63-eaef-cebf000aba94"},"execution_count":170,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([ 5190.42726996  +0.j        , -1229.74802493  +0.j        ,\n","        -791.07483811  +0.j        ,  -738.87009417  +0.j        ,\n","         928.96969957  +0.j        ,   691.6049118   +0.j        ,\n","         605.26940224+320.13296227j,   605.26940224-320.13296227j,\n","         -16.763823    +0.j        ,   284.9160944   +0.j        ])"]},"metadata":{},"execution_count":170}]},{"cell_type":"code","source":["eigvec"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"unVPT2rNd8qR","executionInfo":{"status":"ok","timestamp":1674292988275,"user_tz":-330,"elapsed":22,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"9924b00f-e3b4-4d2a-c5d5-ceef97c6f05e"},"execution_count":171,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[ 0.25203553+0.j        , -0.01707083+0.j        ,\n","        -0.03662833+0.j        ,  0.07405516+0.j        ,\n","         0.40941736+0.j        ,  0.16735857+0.j        ,\n","         0.11655453-0.26394393j,  0.11655453+0.26394393j,\n","        -0.3077813 +0.j        ,  0.10450618+0.j        ],\n","       [ 0.3743759 +0.j        , -0.17395632+0.j        ,\n","         0.25991996+0.j        , -0.21613845+0.j        ,\n","         0.45451766+0.j        , -0.73851927+0.j        ,\n","         0.2215555 -0.25803085j,  0.2215555 +0.25803085j,\n","         0.69250312+0.j        ,  0.70476301+0.j        ],\n","       [ 0.33486628+0.j        ,  0.48732895+0.j        ,\n","         0.0845219 +0.j        , -0.11038044+0.j        ,\n","        -0.07302582+0.j        ,  0.02369766+0.j        ,\n","         0.36906691-0.10347301j,  0.36906691+0.10347301j,\n","         0.03163525+0.j        , -0.11374763+0.j        ],\n","       [ 0.37106687+0.j        ,  0.23589764+0.j        ,\n","        -0.49714389+0.j        ,  0.43393999+0.j        ,\n","         0.08224553+0.j        ,  0.16421367+0.j        ,\n","        -0.29171813+0.30907022j, -0.29171813-0.30907022j,\n","         0.0027547 +0.j        , -0.31255527+0.j        ],\n","       [ 0.23586651+0.j        ,  0.51125533+0.j        ,\n","        -0.53454708+0.j        ,  0.53890512+0.j        ,\n","        -0.03205206+0.j        , -0.11094   +0.j        ,\n","        -0.15010271-0.10658816j, -0.15010271+0.10658816j,\n","        -0.1906936 +0.j        ,  0.17831595+0.j  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Chauhan","userId":"12250269168455404130"}}},"execution_count":172,"outputs":[]},{"cell_type":"markdown","source":["### Part 2.2: Logistic Regression using newton method"],"metadata":{"id":"8BrYgBNDjnZz"}},{"cell_type":"code","source":["# 1: Download, load the data and print first 5 and last 5 rows\n","df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/00529/diabetes_data_upload.csv')\n","df.head(5)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"vnSg9dYBjv16","executionInfo":{"status":"ok","timestamp":1674292988276,"user_tz":-330,"elapsed":22,"user":{"displayName":"Anand Chauhan","userId":"12250269168455404130"}},"outputId":"a93679a7-f5c1-4409-adec-d06d924427fb"},"execution_count":173,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Age Gender Polyuria Polydipsia sudden weight loss weakness Polyphagia  \\\n","0   40   Male       No        Yes                 No      Yes         No   \n","1   58   Male       No         No                 No      Yes         No   \n","2   41   Male      Yes         No                 No      Yes        Yes   \n","3   45   Male       No         No                Yes      Yes        Yes   \n","4   60   Male      Yes        Yes                Yes      Yes        Yes   \n","\n","  Genital thrush visual blurring Itching Irritability delayed healing  \\\n","0             No              No     Yes           No             Yes   \n","1             No             Yes      No           No              No   \n","2             No              No     Yes           No             Yes   \n","3            Yes              No     Yes           No             Yes   \n","4             No             Yes     Yes          Yes             Yes   \n","\n","  partial paresis muscle stiffness Alopecia Obesity     class  \n","0              No              Yes      Yes     Yes  Positive  \n","1             Yes               No      Yes      No  Positive  \n","2              No              Yes      Yes      No  Positive  \n","3              No               No       No      No  Positive  \n","4             Yes              Yes      Yes     Yes  Positive  "],"text/html":["\n","  <div id=\"df-c53b158d-53bf-4067-ac19-bd0bba917b45\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Age</th>\n","      <th>Gender</th>\n","      <th>Polyuria</th>\n","      <th>Polydipsia</th>\n","      <th>sudden weight loss</th>\n","      <th>weakness</th>\n","      <th>Polyphagia</th>\n","      <th>Genital thrush</th>\n","      <th>visual blurring</th>\n","      <th>Itching</th>\n","      <th>Irritability</th>\n","      <th>delayed healing</th>\n","      <th>partial paresis</th>\n","      <th>muscle stiffness</th>\n","      <th>Alopecia</th>\n","      <th>Obesity</th>\n","      <th>class</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>40</td>\n","      <td>Male</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Positive</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>58</td>\n","      <td>Male</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>No</td>\n","  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<td>Positive</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>60</td>\n","      <td>Male</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>No</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Yes</td>\n","      <td>Positive</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c53b158d-53bf-4067-ac19-bd0bba917b45')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 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\n","518             No             Yes     Yes           No             Yes   \n","519             No              No      No           No              No   \n","\n","    partial paresis muscle stiffness Alopecia Obesity     class  \n","515             Yes               No       No      No  Positive  \n","516             Yes               No       No      No  Positive  \n","517             Yes              Yes       No     Yes  Positive  \n","518              No               No      Yes      No  Negative  \n","519              No               No       No      No  Negative  "],"text/html":["\n","  <div id=\"df-2ff2b41a-1c11-4464-a4bf-8ec9f344f96a\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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