ALL_ALGORITHMS CLASSIFICATION.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "cccd9051",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df= pd.read_excel('output_params_Phase.xlsx')\n",
    "\n",
    "# # FCC=0, FCC+BCC=1, BCC=2\n",
    "\n",
    "data= df.drop('Phase',axis='columns')\n",
    "target= df['Phase']\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "xtrain,xtest,ytrain,ytest= train_test_split(data,target,test_size=0.2,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0b2a9d92",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a381f2ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf_rf =RandomForestClassifier(criterion ='entropy', max_depth= None, min_samples_leaf= 1, n_estimators= 200,random_state=42)\n",
    "clf_dt = DecisionTreeClassifier(criterion= 'gini', max_depth=10,max_features= None,min_samples_leaf= 5,min_samples_split= 2, splitter= 'best', random_state=42)\n",
    "clf_svm =SVC(C= 1, gamma= 'scale', kernel= 'linear')\n",
    "clf_knn = KNeighborsClassifier(algorithm= 'auto',n_neighbors= 4, weights= 'distance')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8c312146",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(n_neighbors=4, weights='distance')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_rf.fit(xtrain, ytrain)\n",
    "clf_dt.fit(xtrain, ytrain)\n",
    "clf_svm.fit(xtrain, ytrain)\n",
    "clf_knn.fit(xtrain, ytrain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1d9d6543",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9823943661971831\n",
      "0.9769170579029735\n",
      "0.9761345852895149\n",
      "0.9753521126760564\n"
     ]
    }
   ],
   "source": [
    "print(clf_rf.score(xtest, ytest))\n",
    "print(clf_dt.score(xtest, ytest))\n",
    "print(clf_svm.score(xtest, ytest))\n",
    "print(clf_knn.score(xtest, ytest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b59a4e0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n",
      "0.9847402914995598\n",
      "0.9755453389416022\n",
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(clf_rf.score(xtrain, ytrain))\n",
    "print(clf_dt.score(xtrain, ytrain))\n",
    "print(clf_svm.score(xtrain, ytrain))\n",
    "print(clf_knn.score(xtrain, ytrain))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "06aeaa17",
   "metadata": {},
   "outputs": [],
   "source": [
    "ypred_rf= clf_rf.predict(xtest)\n",
    "ypred_dt= clf_dt.predict(xtest)\n",
    "ypred_svm= clf_svm.predict(xtest)\n",
    "ypred_knn= clf_knn.predict(xtest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc857c2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98       717\n",
      "           1       0.96      0.98      0.97       710\n",
      "           2       0.99      0.99      0.99      1129\n",
      "\n",
      "    accuracy                           0.98      2556\n",
      "   macro avg       0.98      0.98      0.98      2556\n",
      "weighted avg       0.98      0.98      0.98      2556\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "import scikitplot as skplt\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_rf,title='randomforest confusion matrix',normalize=False,figsize=(8,6))\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "\n",
    "print(classification_report(ytest, ypred_rf))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "90036a8a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.98      0.96      0.97       717\n",
      "           1       0.95      0.96      0.96       710\n",
      "           2       0.99      0.99      0.99      1129\n",
      "\n",
      "    accuracy                           0.98      2556\n",
      "   macro avg       0.97      0.97      0.97      2556\n",
      "weighted avg       0.98      0.98      0.98      2556\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_dt,title='DecisionTree confusion matrix',normalize=False,figsize=(8,6))\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "\n",
    "print(classification_report(ytest, ypred_dt))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "29976bbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.96      0.96       717\n",
      "           1       0.96      0.96      0.96       710\n",
      "           2       1.00      1.00      1.00      1129\n",
      "\n",
      "    accuracy                           0.98      2556\n",
      "   macro avg       0.97      0.97      0.97      2556\n",
      "weighted avg       0.98      0.98      0.98      2556\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_svm,title='SVM confusion matrix',normalize=False,figsize=(8,6))\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "\n",
    "print(classification_report(ytest, ypred_svm))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "bd51c633",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.99      0.97      0.98       717\n",
      "           1       0.95      0.96      0.96       710\n",
      "           2       0.99      0.99      0.99      1129\n",
      "\n",
      "    accuracy                           0.98      2556\n",
      "   macro avg       0.97      0.97      0.97      2556\n",
      "weighted avg       0.98      0.98      0.98      2556\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scikitplot as skplt\n",
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_knn,title='KNN confusion matrix',normalize=False,figsize=(8,6))\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "\n",
    "print(classification_report(ytest, ypred_knn))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88714d9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#  NORMALIZED CM\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "6b2749fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_rf,title='randomforest confusion matrix',normalize=True,figsize=(8,6), cmap='Greens')\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('normalized_cm_rf.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9a03605f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_dt,title='DecisionTree confusion matrix',normalize=True,figsize=(8,6),cmap='Greens')\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('normalized_cm_dt.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "691ee61b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_svm,title='SVM confusion matrix',normalize=True,figsize=(8,6),cmap='Greens')\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('normalized_cm_svm.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "839b2607",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "skplt.metrics.plot_confusion_matrix(ytest, ypred_knn,title='KNN confusion matrix',normalize=True,figsize=(8,6),cmap='Greens')\n",
    "\n",
    "plt.xlabel('PREDICTED PHASE')\n",
    "plt.ylabel('''ACTUAL PHASE\n",
    "            (BCC= 2, FCC+BCC= 1, FCC= 0)  ''')\n",
    "plt.savefig('normalized_cm_knn.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21dd932c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c763460e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c27129ff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f1ca9802",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e53ad004",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fbae8345",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b86e899",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edbfd0ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "db659f7d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       importance\n",
      "VEC      0.381708\n",
      "Ni       0.019270\n",
      "δ        0.016453\n",
      "Δsₘᵢₓ    0.005517\n",
      "Fe       0.004695\n",
      "Tm       0.004245\n",
      "ΔHₘᵢₓ    0.003619\n",
      "Δχ       0.003443\n",
      "Al       0.003326\n",
      "Ω        0.002758\n",
      "Co       0.001937\n",
      "Cr       0.001702\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='importance'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.inspection import permutation_importance\n",
    "\n",
    "\n",
    "pi_rf= permutation_importance(clf_rf, xtrain, ytrain,random_state=42)\n",
    "result_rf=pi_rf.importances_mean\n",
    "\n",
    "fi_rf = pd.DataFrame(result_rf, index= xtrain.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi_rf)\n",
    "\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=fi_rf.index,y='importance',data=fi_rf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "e9811dd4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       importance\n",
      "VEC      0.538453\n",
      "Al       0.156901\n",
      "δ        0.042668\n",
      "Fe       0.033474\n",
      "Ni       0.022185\n",
      "Tm       0.005047\n",
      "Co       0.004265\n",
      "Δsₘᵢₓ    0.004069\n",
      "Δχ       0.001624\n",
      "Ω        0.001604\n",
      "ΔHₘᵢₓ    0.001428\n",
      "Cr       0.000254\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='importance'>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "pi_dt= permutation_importance(clf_dt, xtrain, ytrain,random_state=42)\n",
    "result_dt=pi_dt.importances_mean\n",
    "\n",
    "fi_dt = pd.DataFrame(result_dt, index= xtrain.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi_dt)\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=fi_dt.index,y='importance',data=fi_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3182df85",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       importance\n",
      "Tm       0.478783\n",
      "Ni       0.384095\n",
      "Al       0.224533\n",
      "Cr       0.224337\n",
      "Co       0.128142\n",
      "ΔHₘᵢₓ    0.101536\n",
      "Fe       0.070782\n",
      "Ω        0.046757\n",
      "δ        0.022850\n",
      "Δsₘᵢₓ    0.009156\n",
      "Δχ       0.000020\n",
      "VEC     -0.000685\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='importance'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pi_svm= permutation_importance(clf_svm, xtrain, ytrain,random_state=42)\n",
    "result_svm=pi_svm.importances_mean\n",
    "\n",
    "fi_svm = pd.DataFrame(result_svm, index= xtrain.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi_svm)\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=fi_svm.index,y='importance',data=fi_svm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c97e977b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       importance\n",
      "Tm       0.432789\n",
      "Ni       0.318987\n",
      "Cr       0.143930\n",
      "Al       0.086804\n",
      "Co       0.067182\n",
      "Fe       0.062878\n",
      "ΔHₘᵢₓ    0.007669\n",
      "δ        0.003502\n",
      "Ω        0.001996\n",
      "VEC      0.000919\n",
      "Δsₘᵢₓ    0.000763\n",
      "Δχ       0.000000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='importance'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pi_knn= permutation_importance(clf_knn, xtrain, ytrain,random_state=42)\n",
    "result_knn=pi_knn.importances_mean\n",
    "\n",
    "fi_knn = pd.DataFrame(result_knn, index= xtrain.columns, columns= ['importance']).sort_values('importance', ascending=False)\n",
    "\n",
    "print(fi_knn)\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.barplot(x=fi_knn.index,y='importance',data=fi_knn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e38f6dac",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}