Calibration from scratch (Platt Scaling).ipynb

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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import make_classification\n",
    "import numpy as np\n",
    "from sklearn.svm import SVC\n",
    "from tqdm.notebook import tqdm\n",
    "import matplotlib.pyplot as plt\n",
    "import math "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Data set"
   ]
  },
  {
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   "source": [
    "X, y = make_classification(n_samples=5000, n_features=5, n_redundant=2,\n",
    "                           n_classes=2, weights=[0.7], class_sep=0.7, random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
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   "source": [
    "X_train1,X_test,y_train1,y_test=train_test_split(X,y,stratify=y,random_state=1,train_size=0.8) \n",
    "X_train,X_cv,y_train,y_cv=train_test_split(X_train1,y_train1,stratify=y_train1,train_size=0.8,random_state=1)"
   ]
  },
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   "execution_count": 92,
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    "id": "ZXadRJcN39YZ",
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   "outputs": [],
   "source": [
    "clf = SVC(gamma=0.001, C=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
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    {
     "data": {
      "text/plain": [
       "SVC(C=100, gamma=0.001)"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train,y_train)"
   ]
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   "source": [
    "support=clf.support_vectors_\n",
    "aiyi=clf.dual_coef_\n",
    "intercept=clf.intercept_"
   ]
  },
  {
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   "source": [
    "def decision_function(support,aiyi,intercept,xq,sigma): \n",
    "  yy1=[]\n",
    "  for j in tqdm(xq):\n",
    "    yy=0\n",
    "    for i in range(len(support)):\n",
    "      yy+=aiyi[0][i]*np.exp(-0.001*sum((j-support[i])**2))\n",
    "    yy=yy+intercept\n",
    "    yy1.append(yy)\n",
    "  return np.array(yy1).reshape(1,-1)"
   ]
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    {
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      "text/plain": [
       "  0%|          | 0/800 [00:00<?, ?it/s]"
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   "source": [
    "v=decision_function(support,aiyi,intercept,X_cv,0.001)"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "array([-2.76146863,  1.88255985,  1.91945783, -2.20004135,  0.02981274,\n",
       "       -2.71181644, -2.86811546, -2.5712531 , -1.24294605,  2.11880867])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
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   ],
   "source": [
    "v[0][:10] # using decision function above"
   ]
  },
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    {
     "data": {
      "text/plain": [
       "array([-2.76146863,  1.88255985,  1.91945783, -2.20004135,  0.02981274,\n",
       "       -2.71181644, -2.86811546, -2.5712531 , -1.24294605,  2.11880867])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
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    }
   ],
   "source": [
    "clf.decision_function(X_cv)[:10] #sklearn decision function "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "c0bKCboN4Zxu"
   },
   "source": [
    "<h2><font color='Geen'>Implementing Platt Scaling to find P(Y==1|X)</font></h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nMn7OEN94Zxw"
   },
   "source": [
    "Check this <a href='https://drive.google.com/open?id=133odBinMOIVb_rh_GQxxsyMRyW-Zts7a'>PDF</a>\n",
    "<img src='https://i.imgur.com/CAMnVnh.png'>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "execution": {
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    "id": "mZVnvH-PnI7u",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def log_loss(w,b,X,Y):\n",
    "  N=len(X)\n",
    "  sum_log=0\n",
    "  for i in range(N):\n",
    "    sum_log+=Y[i]*np.log10(z)+(1-Y[i]*np.log10(1-Z))\n",
    "  return -1*sum_log/N"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "execution": {
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    "id": "zTHRBaQ6yvnv",
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   "outputs": [],
   "source": [
    "from numpy import zeros_like\n",
    "def initialize_weights(row_vector):\n",
    "    ''' In this function, we will initialize our weights and bias'''\n",
    "    w=zeros_like(row_vector)\n",
    "    b=0\n",
    "    return w,b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "execution": {
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    },
    "id": "pLoRQNTVHB41",
    "tags": []
   },
   "outputs": [],
   "source": [
    "from math import *\n",
    "def sigmoid(z):\n",
    "    ''' In this function, we will return sigmoid of z'''\n",
    "    return 1/(1+np.exp(-z))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "execution": {
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    "id": "AyxSP5H-yv0C",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def logloss(y_true,y_pred):\n",
    "    loss=-1*np.mean(y_true*np.log10(y_pred)+(1-y_true)*np.log10(1-y_pred))\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "execution": {
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    "id": "I8upBXsTHdWY",
    "tags": []
   },
   "outputs": [],
   "source": [
    "def gradient_dw(x,y,w,b,alpha,N):\n",
    "    '''In this function, we will compute the gardient w.r.to w '''\n",
    "    dw=x*(y-sigmoid(np.dot(w,x)+b))-(alpha/N)*w\n",
    "    return dw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "execution": {
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    "id": "SUTyw3SAHdY8",
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   },
   "outputs": [],
   "source": [
    "def gradient_db(x,y,w,b):\n",
    "     '''In this function, we will compute gradient w.r.to b '''\n",
    "     db=y-sigmoid(np.dot(w,x)+b)\n",
    "     return db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-23T08:14:51.026146Z",
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    "id": "FxWfW47NHdbJ",
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   },
   "outputs": [],
   "source": [
    "def pred(w,b, X):\n",
    "    N = len(X)\n",
    "    predict = []\n",
    "    for i in range(N):\n",
    "        z=np.dot(w,X[i])+b\n",
    "        predict.append(sigmoid(z))\n",
    "    return np.array(predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "execution": {
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    },
    "id": "-pS_IAINHoJi",
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   },
   "outputs": [],
   "source": [
    "def train(X_train,y_train,epochs,alpha,eta0):\n",
    "    train_loss = []\n",
    "    w,b = initialize_weights(X_train[0]) # Initialize the weights\n",
    "    #code to perform SGD\n",
    "    for i in tqdm(range(epochs)):\n",
    "        for k,j in zip(X_train,y_train):\n",
    "            dw=gradient_dw(k,j,w,b,alpha,len(X_train))\n",
    "            db=gradient_db(k,j,w,b)\n",
    "            w=w+eta0*dw\n",
    "            b=b+eta0*db\n",
    "        predicted_train=pred(w,b,X_train)\n",
    "        train_loss.append(logloss(y_train,predicted_train))\n",
    "        predicted_test=pred(w,b,X_test)\n",
    "        print(\"train loss {}\".format(train_loss[-1]))\n",
    "    return w,b,train_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "q6YKD_DBHoN_"
   },
   "source": [
    "### Adding Regularization for Platt's Scaling "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "execution": {
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    "id": "tYskD-i3uk-n",
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   },
   "outputs": [],
   "source": [
    "a=y.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "execution": {
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   },
   "outputs": [],
   "source": [
    "N_negative,N_positive=np.unique(y_train,return_counts=True)[1]  #calculating Np and Nn using train data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
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    "y_positive=(N_positive+1)/(N_positive+2)   #N+ \n",
    "y_negative=(1/(N_negative+2))      #N-"
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    "modified_cv=y_cv\n",
    "modified_cv=modified_cv.astype(float)\n",
    "modified_cv[modified_cv==1]=y_positive\n",
    "modified_cv[modified_cv==0]=y_negative    # changing cv data to modified N+ and N-"
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    "alpha=0.001\n",
    "eta0=0.001\n",
    "epochs=200\n",
    "w,b,train_loss=train(v[0],modified_cv,epochs,alpha,eta0)"
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   "source": [
    "### Weight and bias after training "
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(epochs),train_loss,label='loss')\n",
    "plt.xlabel('epochs')\n",
    "plt.ylabel('loss')\n",
    "plt.title('Epochs VS Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-23T08:14:58.681981Z",
     "iopub.status.busy": "2022-07-23T08:14:58.681648Z",
     "iopub.status.idle": "2022-07-23T08:15:01.998626Z",
     "shell.execute_reply": "2022-07-23T08:15:01.997700Z",
     "shell.execute_reply.started": "2022-07-23T08:14:58.681957Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "37e1b340336043a687357ee3d5ce1740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/1000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v_test=decision_function(support,aiyi,intercept,X_test,0.001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-07-23T08:15:02.060532Z",
     "iopub.status.busy": "2022-07-23T08:15:02.060335Z",
     "iopub.status.idle": "2022-07-23T08:15:02.371066Z",
     "shell.execute_reply": "2022-07-23T08:15:02.370475Z",
     "shell.execute_reply.started": "2022-07-23T08:15:02.060511Z"
    },
    "id": "8HZ1H-dNJ_kU",
    "outputId": "2f08863d-1f39-4d2a-c24f-0e61cde4c814",
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=SVC(C=100, gamma=0.001), cv=3)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "calibrated = CalibratedClassifierCV(clf, method='sigmoid', cv=3)\n",
    "calibrated.fit(X_train1,y_train1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparing my result with sklearn calibration function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-07-23T08:15:02.027189Z",
     "iopub.status.busy": "2022-07-23T08:15:02.026734Z",
     "iopub.status.idle": "2022-07-23T08:15:02.039400Z",
     "shell.execute_reply": "2022-07-23T08:15:02.038676Z",
     "shell.execute_reply.started": "2022-07-23T08:15:02.027164Z"
    },
    "id": "7X90Lji1ORNd",
    "outputId": "770f872d-eea5-408c-ae44-7a5a6cd55d1f",
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibrated probabiliies using platt scaling from scratch : [0.00286 0.02418 0.00311 0.95563 0.00678 0.01567 0.01159 0.82448 0.19021\n",
      " 0.22484]\n"
     ]
    }
   ],
   "source": [
    "print('Calibrated probabiliies using platt scaling from scratch :',np.around(pred(w,b,v_test[0]),5)[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-23T08:15:02.372464Z",
     "iopub.status.busy": "2022-07-23T08:15:02.371908Z",
     "iopub.status.idle": "2022-07-23T08:15:02.449875Z",
     "shell.execute_reply": "2022-07-23T08:15:02.449187Z",
     "shell.execute_reply.started": "2022-07-23T08:15:02.372439Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calibrated probabilies using platt scaling using sklearn : [0.00347698 0.02758417 0.00381682 0.94785131 0.0075604  0.01791978\n",
      " 0.01394319 0.81182303 0.19296722 0.22907582]\n"
     ]
    }
   ],
   "source": [
    "print('Calibrated probabilies using platt scaling using sklearn :',calibrated.predict_proba(X_test).T[1][:10])"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "Copy of 8E&F_LR_SVM.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.4"
  },
  "widgets": {
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     "model_module_version": "1.2.0",
     "model_name": "LayoutModel",
     "state": {
      "_model_module": "@jupyter-widgets/base",
      "_model_module_version": "1.2.0",
      "_model_name": "LayoutModel",
      "_view_count": null,
      "_view_module": "@jupyter-widgets/base",
      "_view_module_version": "1.2.0",
      "_view_name": "LayoutView",
      "align_content": null,
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