7_Model_AutoML.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "GOcGwtPQCWj7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Amit\\Anaconda3\\lib\\site-packages\\tpot\\builtins\\__init__.py:36: UserWarning: Warning: optional dependency `torch` is not available. - skipping import of NN models.\n",
      "  warnings.warn(\"Warning: optional dependency `torch` is not available. - skipping import of NN models.\")\n"
     ]
    }
   ],
   "source": [
    "from tpot import TPOTClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 422
    },
    "id": "CMq_sc5vCWkB",
    "outputId": "66e16e71-1456-415e-d7d3-13bbea0aea44"
   },
   "outputs": [
    {
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       "           mfcc_1      mfcc_2     mfcc_3     mfcc_4     mfcc_5    mfcc_6  \\\n",
       "0     -304.234070   94.326431  -7.126974  33.175732   7.065147 -0.331963   \n",
       "1     -309.019653   79.845779  -5.289888  21.285072 -12.034595 -0.241961   \n",
       "2     -379.454376  115.356438 -11.129870  49.783417  -6.287680  4.333356   \n",
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       "4     -284.207062   81.567101  26.056236  38.064480  14.861828  6.161382   \n",
       "...           ...         ...        ...        ...        ...       ...   \n",
       "17225 -281.850769   63.371513   4.073536  25.501684   4.071187  1.362235   \n",
       "17226 -352.283020  147.483002  31.588148  44.751774  23.768219  1.736998   \n",
       "17227 -283.886566   70.052811 -15.057649  28.327362   1.277114 -6.033658   \n",
       "17228 -329.493103  152.794022 -23.235121  38.832031  -9.015368  4.381469   \n",
       "17229 -360.104309  118.855232   6.282503  27.033180  20.994917  6.234730   \n",
       "\n",
       "          mfcc_7     mfcc_8     mfcc_9   mfcc_10    mfcc_11    mfcc_12  \\\n",
       "0     -10.314158   2.147586  -2.013190  5.324332  -0.093334   2.562227   \n",
       "1      -9.138373   0.444917 -13.440741  2.820725 -14.505177  12.173492   \n",
       "2     -10.013806 -10.836457 -21.100973  7.533222  -7.160056  -7.462425   \n",
       "3     -14.943695 -17.998863 -26.322065 -2.699209 -13.864196  -7.851540   \n",
       "4     -27.253889  -6.253015  -4.060058 -1.371932  -8.508703   1.715142   \n",
       "...          ...        ...        ...       ...        ...        ...   \n",
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       "17229   1.383782   6.114986  -8.297801  3.088336  -2.729115   8.707598   \n",
       "\n",
       "         mfcc_13  chroma_stft  spectral_centroid  spectral_bandwidth  label  \n",
       "0       1.275930     0.429254        2478.257487         2170.345949    1.0  \n",
       "1      -7.108042     0.442904        2804.672224         2265.926588    1.0  \n",
       "2      -5.815040     0.404891        1911.933221         1695.401009    1.0  \n",
       "3     -11.660230     0.411506        2820.892019         2428.570850    0.0  \n",
       "4       5.017691     0.325075        2367.901852         2403.353350    0.0  \n",
       "...          ...          ...                ...                 ...    ...  \n",
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       "17226  -5.202733     0.443625        1103.973553         1732.738344    0.0  \n",
       "17227  -9.053541     0.476382        2886.058571         2332.577729    0.0  \n",
       "17228   1.336209     0.364045        1575.800627         1485.843688    0.0  \n",
       "17229  -5.851133     0.394368        1765.241329         2050.230006    0.0  \n",
       "\n",
       "[17230 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Original_Features.csv')\n",
    "df=df.drop(['rolloff','zero_crossing_rate'],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 322,
     "referenced_widgets": [
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     ]
    },
    "id": "UWg9WAr_7E_i",
    "outputId": "b282143b-7716-456c-a419-f5bdefe45f86"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79e5b534fddb462dbd00ceefe5f71a23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=560.0, style=ProgressStyle(de…"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\r\n",
      "Generation 1 - Current best internal CV score: 0.8231791870137771\n",
      "\n",
      "Generation 2 - Current best internal CV score: 0.8231791870137771\n",
      "\n",
      "Generation 3 - Current best internal CV score: 0.8447150323381052\n",
      "\n",
      "Generation 4 - Current best internal CV score: 0.8605145778768563\n",
      "\n",
      "Generation 5 - Current best internal CV score: 0.8605145778768563\n",
      "\n",
      "Generation 6 - Current best internal CV score: 0.8605145778768563\n",
      "\n",
      "Generation 7 - Current best internal CV score: 0.8605145778768563\n",
      "\n",
      "Best pipeline: ExtraTreesClassifier(input_matrix, bootstrap=False, criterion=gini, max_features=0.7500000000000001, min_samples_leaf=2, min_samples_split=6, n_estimators=100)\n",
      "0.8845037724898432\n"
     ]
    }
   ],
   "source": [
    "# All Features\n",
    "X=df.iloc[:,:-1]\n",
    "y=df.iloc[:,-1]\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.10, random_state=42)\n",
    "tpot = TPOTClassifier(generations=7, population_size=70, verbosity=2, random_state=42, n_jobs=-1)\n",
    "tpot.fit(X_train, y_train)\n",
    "#tpot.export('tpot_exported_pipeline.py')\n",
    "print(tpot.score(X_test, y_test))\n",
    "tpot.export('tpot_pipeline.py')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "z3AXbeTATvsT"
   },
   "outputs": [],
   "source": [
    "# %load \"tpot_pipeline.py\"\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.ensemble import ExtraTreesClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# NOTE: Make sure that the outcome column is labeled 'target' in the data file\n",
    "tpot_data = df\n",
    "features = tpot_data.iloc[:,:-1]\n",
    "training_features, testing_features, training_target, testing_target = \\\n",
    "            train_test_split(features, df.iloc[:,-1], random_state=42)\n",
    "\n",
    "# Average CV score on the training set was: 0.8605145778768563\n",
    "exported_pipeline = ExtraTreesClassifier(bootstrap=False, criterion=\"gini\", max_features=0.7500000000000001, min_samples_leaf=2, min_samples_split=6, n_estimators=100)\n",
    "# Fix random state in exported estimator\n",
    "if hasattr(exported_pipeline, 'random_state'):\n",
    "    setattr(exported_pipeline, 'random_state', 42)\n",
    "\n",
    "exported_pipeline.fit(training_features, training_target)\n",
    "y_pred = exported_pipeline.predict(testing_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy :  86.76880222841226\n",
      "Report :                precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.87      0.90      0.88      2414\n",
      "         1.0       0.86      0.83      0.85      1894\n",
      "\n",
      "    accuracy                           0.87      4308\n",
      "   macro avg       0.87      0.86      0.87      4308\n",
      "weighted avg       0.87      0.87      0.87      4308\n",
      "\n",
      "Predict    0.0        1.0        \n",
      "Actual\n",
      "0.0        2169       245        \n",
      "\n",
      "1.0        325        1569       \n",
      "\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Overall Statistics : \n",
      "\n",
      "95% CI                                                            (0.85757,0.87781)\n",
      "ACC Macro                                                         0.86769\n",
      "ARI                                                               0.54051\n",
      "AUNP                                                              0.86346\n",
      "AUNU                                                              0.86346\n",
      "Bennett S                                                         0.73538\n",
      "CBA                                                               0.84905\n",
      "CSI                                                               0.73077\n",
      "Chi-Squared                                                       2300.51727\n",
      "Chi-Squared DF                                                    1\n",
      "Conditional Entropy                                               0.5562\n",
      "Cramer V                                                          0.73076\n",
      "Cross Entropy                                                     0.99048\n",
      "F1 Macro                                                          0.86507\n",
      "F1 Micro                                                          0.86769\n",
      "FNR Macro                                                         0.13654\n",
      "FNR Micro                                                         0.13231\n",
      "FPR Macro                                                         0.13654\n",
      "FPR Micro                                                         0.13231\n",
      "Gwet AC1                                                          0.74041\n",
      "Hamming Loss                                                      0.13231\n",
      "Joint Entropy                                                     1.54566\n",
      "KL Divergence                                                     0.00102\n",
      "Kappa                                                             0.73024\n",
      "Kappa 95% CI                                                      (0.70961,0.75087)\n",
      "Kappa No Prevalence                                               0.73538\n",
      "Kappa Standard Error                                              0.01053\n",
      "Kappa Unbiased                                                    0.73014\n",
      "Lambda A                                                          0.69905\n",
      "Lambda B                                                          0.68578\n",
      "Mutual Information                                                0.42575\n",
      "NIR                                                               0.56035\n",
      "Overall ACC                                                       0.86769\n",
      "Overall CEN                                                       0.51464\n",
      "Overall J                                                         (1.52542,0.76271)\n",
      "Overall MCC                                                       0.73076\n",
      "Overall MCEN                                                      0.40646\n",
      "Overall RACC                                                      0.50953\n",
      "Overall RACCU                                                     0.5097\n",
      "P-Value                                                           None\n",
      "PPV Macro                                                         0.86731\n",
      "PPV Micro                                                         0.86769\n",
      "Pearson C                                                         0.59001\n",
      "Phi-Squared                                                       0.53401\n",
      "RCI                                                               0.43029\n",
      "RR                                                                2154.0\n",
      "Reference Entropy                                                 0.98946\n",
      "Response Entropy                                                  0.98195\n",
      "SOA1(Landis & Koch)                                               Substantial\n",
      "SOA2(Fleiss)                                                      Intermediate to Good\n",
      "SOA3(Altman)                                                      Good\n",
      "SOA4(Cicchetti)                                                   Good\n",
      "SOA5(Cramer)                                                      Strong\n",
      "SOA6(Matthews)                                                    Strong\n",
      "Scott PI                                                          0.73014\n",
      "Standard Error                                                    0.00516\n",
      "TNR Macro                                                         0.86346\n",
      "TNR Micro                                                         0.86769\n",
      "TPR Macro                                                         0.86346\n",
      "TPR Micro                                                         0.86769\n",
      "Zero-one Loss                                                     570\n",
      "\n",
      "Class Statistics :\n",
      "\n",
      "Classes                                                           0.0           1.0           \n",
      "ACC(Accuracy)                                                     0.86769       0.86769       \n",
      "AGF(Adjusted F-score)                                             0.87481       0.85515       \n",
      "AGM(Adjusted geometric mean)                                      0.85226       0.87559       \n",
      "AM(Difference between automatic and manual classification)        80            -80           \n",
      "AUC(Area under the ROC curve)                                     0.86346       0.86346       \n",
      "AUCI(AUC value interpretation)                                    Very Good     Very Good     \n",
      "AUPR(Area under the PR curve)                                     0.8841        0.84667       \n",
      "BCD(Bray-Curtis dissimilarity)                                    0.00929       0.00929       \n",
      "BM(Informedness or bookmaker informedness)                        0.72691       0.72691       \n",
      "CEN(Confusion entropy)                                            0.47521       0.56683       \n",
      "DOR(Diagnostic odds ratio)                                        42.73986      42.73986      \n",
      "DP(Discriminant power)                                            0.89913       0.89913       \n",
      "DPI(Discriminant power interpretation)                            Poor          Poor          \n",
      "ERR(Error rate)                                                   0.13231       0.13231       \n",
      "F0.5(F0.5 score)                                                  0.8753        0.85738       \n",
      "F1(F1 score - harmonic mean of precision and sensitivity)         0.88386       0.84628       \n",
      "F2(F2 score)                                                      0.89259       0.83546       \n",
      "FDR(False discovery rate)                                         0.13031       0.13506       \n",
      "FN(False negative/miss/type 2 error)                              245           325           \n",
      "FNR(Miss rate or false negative rate)                             0.10149       0.17159       \n",
      "FOR(False omission rate)                                          0.13506       0.13031       \n",
      "FP(False positive/type 1 error/false alarm)                       325           245           \n",
      "FPR(Fall-out or false positive rate)                              0.17159       0.10149       \n",
      "G(G-measure geometric mean of precision and sensitivity)          0.88398       0.84648       \n",
      "GI(Gini index)                                                    0.72691       0.72691       \n",
      "GM(G-mean geometric mean of specificity and sensitivity)          0.86275       0.86275       \n",
      "IBA(Index of balanced accuracy)                                   0.79651       0.69215       \n",
      "ICSI(Individual classification success index)                     0.7682        0.69334       \n",
      "IS(Information score)                                             0.63416       0.97625       \n",
      "J(Jaccard index)                                                  0.79189       0.73352       \n",
      "LS(Lift score)                                                    1.55204       1.96735       \n",
      "MCC(Matthews correlation coefficient)                             0.73076       0.73076       \n",
      "MCCI(Matthews correlation coefficient interpretation)             Strong        Strong        \n",
      "MCEN(Modified confusion entropy)                                  0.67642       0.7711        \n",
      "MK(Markedness)                                                    0.73463       0.73463       \n",
      "N(Condition negative)                                             1894          2414          \n",
      "NLR(Negative likelihood ratio)                                    0.12251       0.19098       \n",
      "NLRI(Negative likelihood ratio interpretation)                    Fair          Fair          \n",
      "NPV(Negative predictive value)                                    0.86494       0.86969       \n",
      "OC(Overlap coefficient)                                           0.89851       0.86494       \n",
      "OOC(Otsuka-Ochiai coefficient)                                    0.88398       0.84648       \n",
      "OP(Optimized precision)                                           0.82709       0.82709       \n",
      "P(Condition positive or support)                                  2414          1894          \n",
      "PLR(Positive likelihood ratio)                                    5.23623       8.16233       \n",
      "PLRI(Positive likelihood ratio interpretation)                    Fair          Fair          \n",
      "POP(Population)                                                   4308          4308          \n",
      "PPV(Precision or positive predictive value)                       0.86969       0.86494       \n",
      "PRE(Prevalence)                                                   0.56035       0.43965       \n",
      "Q(Yule Q - coefficient of colligation)                            0.95428       0.95428       \n",
      "QI(Yule Q interpretation)                                         Strong        Strong        \n",
      "RACC(Random accuracy)                                             0.3244        0.18513       \n",
      "RACCU(Random accuracy unbiased)                                   0.32449       0.18521       \n",
      "TN(True negative/correct rejection)                               1569          2169          \n",
      "TNR(Specificity or true negative rate)                            0.82841       0.89851       \n",
      "TON(Test outcome negative)                                        1814          2494          \n",
      "TOP(Test outcome positive)                                        2494          1814          \n",
      "TP(True positive/hit)                                             2169          1569          \n",
      "TPR(Sensitivity, recall, hit rate, or true positive rate)         0.89851       0.82841       \n",
      "Y(Youden index)                                                   0.72691       0.72691       \n",
      "dInd(Distance index)                                              0.19936       0.19936       \n",
      "sInd(Similarity index)                                            0.85903       0.85903       \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report \n",
    "from pycm import ConfusionMatrix\n",
    "# Function to calculate accuracy \n",
    "def cal_accuracy(y_test, y_pred): \n",
    "    print (\"Accuracy : \", accuracy_score(y_test,y_pred)*100) \n",
    "    print(\"Report : \", classification_report(y_test, y_pred)) \n",
    "    print(ConfusionMatrix(actual_vector=list(y_test),predict_vector=list(y_pred)))\n",
    "\n",
    "cal_accuracy(testing_target, y_pred)"
   ]
  }
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