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MohdAzamSayeed/Module 8–Dimensionality Reduction Case Study 1.ipynb

Created May 26, 2020 22:43
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Module 8–Dimensionality Reduction Case Study 1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"1.Scikit learn comes with pre-loaded dataset, load the digits dataset from that collection and write a helper function to plot the image using matplotlib.[Hint:Explore datasets module from scikit learn"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.datasets import load_digits"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [],
"source": [
"digits = load_digits()"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'data': array([[ 0., 0., 5., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 10., 0., 0.],\n",
" [ 0., 0., 0., ..., 16., 9., 0.],\n",
" ...,\n",
" [ 0., 0., 1., ..., 6., 0., 0.],\n",
" [ 0., 0., 2., ..., 12., 0., 0.],\n",
" [ 0., 0., 10., ..., 12., 1., 0.]]),\n",
" 'target': array([0, 1, 2, ..., 8, 9, 8]),\n",
" 'target_names': array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
" 'images': array([[[ 0., 0., 5., ..., 1., 0., 0.],\n",
" [ 0., 0., 13., ..., 15., 5., 0.],\n",
" [ 0., 3., 15., ..., 11., 8., 0.],\n",
" ...,\n",
" [ 0., 4., 11., ..., 12., 7., 0.],\n",
" [ 0., 2., 14., ..., 12., 0., 0.],\n",
" [ 0., 0., 6., ..., 0., 0., 0.]],\n",
" \n",
" [[ 0., 0., 0., ..., 5., 0., 0.],\n",
" [ 0., 0., 0., ..., 9., 0., 0.],\n",
" [ 0., 0., 3., ..., 6., 0., 0.],\n",
" ...,\n",
" [ 0., 0., 1., ..., 6., 0., 0.],\n",
" [ 0., 0., 1., ..., 6., 0., 0.],\n",
" [ 0., 0., 0., ..., 10., 0., 0.]],\n",
" \n",
" [[ 0., 0., 0., ..., 12., 0., 0.],\n",
" [ 0., 0., 3., ..., 14., 0., 0.],\n",
" [ 0., 0., 8., ..., 16., 0., 0.],\n",
" ...,\n",
" [ 0., 9., 16., ..., 0., 0., 0.],\n",
" [ 0., 3., 13., ..., 11., 5., 0.],\n",
" [ 0., 0., 0., ..., 16., 9., 0.]],\n",
" \n",
" ...,\n",
" \n",
" [[ 0., 0., 1., ..., 1., 0., 0.],\n",
" [ 0., 0., 13., ..., 2., 1., 0.],\n",
" [ 0., 0., 16., ..., 16., 5., 0.],\n",
" ...,\n",
" [ 0., 0., 16., ..., 15., 0., 0.],\n",
" [ 0., 0., 15., ..., 16., 0., 0.],\n",
" [ 0., 0., 2., ..., 6., 0., 0.]],\n",
" \n",
" [[ 0., 0., 2., ..., 0., 0., 0.],\n",
" [ 0., 0., 14., ..., 15., 1., 0.],\n",
" [ 0., 4., 16., ..., 16., 7., 0.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 16., 2., 0.],\n",
" [ 0., 0., 4., ..., 16., 2., 0.],\n",
" [ 0., 0., 5., ..., 12., 0., 0.]],\n",
" \n",
" [[ 0., 0., 10., ..., 1., 0., 0.],\n",
" [ 0., 2., 16., ..., 1., 0., 0.],\n",
" [ 0., 0., 15., ..., 15., 0., 0.],\n",
" ...,\n",
" [ 0., 4., 16., ..., 16., 6., 0.],\n",
" [ 0., 8., 16., ..., 16., 8., 0.],\n",
" [ 0., 1., 8., ..., 12., 1., 0.]]]),\n",
" 'DESCR': \".. _digits_dataset:\\n\\nOptical recognition of handwritten digits dataset\\n--------------------------------------------------\\n\\n**Data Set Characteristics:**\\n\\n :Number of Instances: 5620\\n :Number of Attributes: 64\\n :Attribute Information: 8x8 image of integer pixels in the range 0..16.\\n :Missing Attribute Values: None\\n :Creator: E. Alpaydin (alpaydin '@' boun.edu.tr)\\n :Date: July; 1998\\n\\nThis is a copy of the test set of the UCI ML hand-written digits datasets\\nhttp://archive.ics.uci.edu/ml/datasets/Optical+Recognition+of+Handwritten+Digits\\n\\nThe data set contains images of hand-written digits: 10 classes where\\neach class refers to a digit.\\n\\nPreprocessing programs made available by NIST were used to extract\\nnormalized bitmaps of handwritten digits from a preprinted form. From a\\ntotal of 43 people, 30 contributed to the training set and different 13\\nto the test set. 32x32 bitmaps are divided into nonoverlapping blocks of\\n4x4 and the number of on pixels are counted in each block. This generates\\nan input matrix of 8x8 where each element is an integer in the range\\n0..16. This reduces dimensionality and gives invariance to small\\ndistortions.\\n\\nFor info on NIST preprocessing routines, see M. D. Garris, J. L. Blue, G.\\nT. Candela, D. L. Dimmick, J. Geist, P. J. Grother, S. A. Janet, and C.\\nL. Wilson, NIST Form-Based Handprint Recognition System, NISTIR 5469,\\n1994.\\n\\n.. topic:: References\\n\\n - C. Kaynak (1995) Methods of Combining Multiple Classifiers and Their\\n Applications to Handwritten Digit Recognition, MSc Thesis, Institute of\\n Graduate Studies in Science and Engineering, Bogazici University.\\n - E. Alpaydin, C. Kaynak (1998) Cascading Classifiers, Kybernetika.\\n - Ken Tang and Ponnuthurai N. Suganthan and Xi Yao and A. Kai Qin.\\n Linear dimensionalityreduction using relevance weighted LDA. School of\\n Electrical and Electronic Engineering Nanyang Technological University.\\n 2005.\\n - Claudio Gentile. A New Approximate Maximal Margin Classification\\n Algorithm. NIPS. 2000.\"}"
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"digits"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"x=digits.images\n",
"y=digits.target"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"def displayImg(img,y):\n",
" plt.imshow(img,cmap='gray')\n",
" print(\"target: \",y)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [],
"source": [
"def plotImages(images_arr):\n",
" fig, axes = plt.subplots(1, 5, figsize=(10,10))\n",
" axes = axes.flatten()\n",
" for img, ax in zip( images_arr, axes):\n",
" ax.imshow(img,cmap='gray')\n",
" ax.axis('off')\n",
" plt.tight_layout()\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"target: 0\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAACstJREFUeJzt3V+IXOUZx/Hfr6vS+g9Da4vshsYVCUihxoSABITGtMQq2osaElCoFNYbRWlBY+9655XYiyKEqBVMlW5UELHaBBUrtNbdJG2NG0u6WLKJNoqRqIWGxKcXO4E0XTtnM+e858zj9wPB/TPs+0zWb87Z2ZnzOiIEIKcvtT0AgOYQOJAYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJndXEF7Wd8ulxS5YsKbre6OhosbWOHj1abK2DBw8WW+vEiRPF1iotItzvNo0EntW6deuKrnf//fcXW2vnzp3F1tq8eXOxtY4cOVJsrS7iFB1IjMCBxAgcSIzAgcQIHEiMwIHECBxIjMCBxCoFbnu97bdt77dd7lkKAAbSN3DbI5J+Kek6SVdI2mT7iqYHAzC4Kkfw1ZL2R8RsRByT9KSkm5odC0AdqgQ+KunAKe/P9T4GoOOqvNhkoVes/M+rxWxPSJoYeCIAtakS+Jykpae8Pybp0Ok3iogtkrZIeV8uCgybKqfob0i63Palts+RtFHSs82OBaAOfY/gEXHc9h2SXpQ0IumRiNjb+GQABlbpgg8R8byk5xueBUDNeCYbkBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4mxs8kilNxpRJLGx8eLrVVyW6YPP/yw2FobNmwotpYkTU5OFl2vH47gQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiVXY2ecT2YdtvlhgIQH2qHMF/JWl9w3MAaEDfwCPiVUnlnjwMoDb8DA4kVturydi6COie2gJn6yKgezhFBxKr8muyJyT9QdJy23O2f9z8WADqUGVvsk0lBgFQP07RgcQIHEiMwIHECBxIjMCBxAgcSIzAgcQIHEhs6LcuWrlyZbG1Sm4lJEmXXXZZsbVmZ2eLrbVjx45ia5X8/0Ni6yIABRE4kBiBA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJBYlYsuLrX9su0Z23tt31ViMACDq/Jc9OOSfhoRu2xfIGna9o6IeKvh2QAMqMreZO9GxK7e2x9LmpE02vRgAAa3qFeT2V4maYWk1xf4HFsXAR1TOXDb50t6StLdEXH09M+zdRHQPZUeRbd9tubj3hYRTzc7EoC6VHkU3ZIeljQTEQ80PxKAulQ5gq+RdKuktbb39P58v+G5ANSgyt5kr0lygVkA1IxnsgGJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQ2NDvTbZkyZJia01PTxdbSyq7X1hJpf8ev8g4ggOJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQGIEDiVW56OKXbf/J9p97Wxf9vMRgAAZX5amq/5a0NiI+6V0++TXbv42IPzY8G4ABVbnoYkj6pPfu2b0/bGwADIGqGx+M2N4j6bCkHRGx4NZFtqdsT9U9JIAzUynwiDgREVdKGpO02va3FrjNlohYFRGr6h4SwJlZ1KPoEfGRpFckrW9kGgC1qvIo+sW2L+q9/RVJ6yTta3owAIOr8ij6JZIesz2i+X8QfhMRzzU7FoA6VHkU/S+a3xMcwJDhmWxAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYgQOJMbWRYuwc+fOYmtlVvJ7duTIkWJrdRFHcCAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgscqB966Nvts212MDhsRijuB3SZppahAA9au6s8mYpOslbW12HAB1qnoEf1DSPZI+a3AWADWrsvHBDZIOR8R0n9uxNxnQMVWO4Gsk3Wj7HUlPSlpr+/HTb8TeZED39A08Iu6LiLGIWCZpo6SXIuKWxicDMDB+Dw4ktqgrukTEK5rfXRTAEOAIDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiQ791UcmtaVauXFlsrdJKbidU8u9xcnKy2FpdxBEcSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcQIHEis0jPZeldU/VjSCUnHuXIqMBwW81TV70TEB41NAqB2nKIDiVUNPCT9zva07YkmBwJQn6qn6Gsi4pDtr0vaYXtfRLx66g164RM/0CGVjuARcaj338OSnpG0eoHbsHUR0DFVNh88z/YFJ9+W9D1JbzY9GIDBVTlF/4akZ2yfvP2vI+KFRqcCUIu+gUfErKRvF5gFQM34NRmQGIEDiRE4kBiBA4kROJAYgQOJETiQGIEDiTki6v+idv1f9HOMj4+XWkpTU1PF1pKk22+/vdhaN998c7G1Sn7PVq3K+9KIiHC/23AEBxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcSqxS47Ytsb7e9z/aM7aubHgzA4KpeF/0Xkl6IiB/aPkfSuQ3OBKAmfQO3faGkayT9SJIi4pikY82OBaAOVU7RxyW9L+lR27ttb+1dHx1Ax1UJ/CxJV0l6KCJWSPpU0ubTb2R7wvaU7bIvuQLwuaoEPidpLiJe772/XfPB/xe2LgK6p2/gEfGepAO2l/c+dK2ktxqdCkAtqj6Kfqekbb1H0Gcl3dbcSADqUinwiNgjiVNvYMjwTDYgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEiNwILGh35uspImJiaLr3XvvvcXWmp6eLrbWhg0biq2VGXuTAV9wBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYn0Dt73c9p5T/hy1fXeJ4QAMpu9FFyPibUlXSpLtEUkHJT3T8FwAarDYU/RrJf09Iv7RxDAA6lX1uugnbZT0xEKfsD0hqeyrMQD8X5WP4L1ND26UNLnQ59m6COiexZyiXydpV0T8s6lhANRrMYFv0uecngPopkqB2z5X0nclPd3sOADqVHVvsn9J+mrDswCoGc9kAxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCCxprYuel/SYl9S+jVJH9Q+TDdkvW/cr/Z8MyIu7nejRgI/E7ansr4SLet94351H6foQGIEDiTWpcC3tD1Ag7LeN+5Xx3XmZ3AA9evSERxAzToRuO31tt+2vd/25rbnqYPtpbZftj1je6/tu9qeqU62R2zvtv1c27PUyfZFtrfb3tf73l3d9kyDaP0UvXet9b9p/ooxc5LekLQpIt5qdbAB2b5E0iURscv2BZKmJf1g2O/XSbZ/ImmVpAsj4oa256mL7cck/T4itvYuNHpuRHzU9lxnqgtH8NWS9kfEbEQck/SkpJtanmlgEfFuROzqvf2xpBlJo+1OVQ/bY5Kul7S17VnqZPtCSddIeliSIuLYMMctdSPwUUkHTnl/TklCOMn2MkkrJL3e7iS1eVDSPZI+a3uQmo1Lel/So70fP7baPq/toQbRhcC9wMfSPLRv+3xJT0m6OyKOtj3PoGzfIOlwREy3PUsDzpJ0laSHImKFpE8lDfVjQl0IfE7S0lPeH5N0qKVZamX7bM3HvS0islyRdo2kG22/o/kfp9bafrzdkWozJ2kuIk6eaW3XfPBDqwuBvyHpctuX9h7U2Cjp2ZZnGphta/5nuZmIeKDteeoSEfdFxFhELNP89+qliLil5bFqERHvSTpge3nvQ9dKGuoHRRe7N1ntIuK47TskvShpRNIjEbG35bHqsEbSrZL+antP72M/i4jnW5wJ/d0paVvvYDMr6baW5xlI678mA9CcLpyiA2gIgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJ/Qcpuo92pLZ1pQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#plt.rcParams()\n",
"displayImg(x[0],y[0])\n"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 720x720 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotImages(x)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1797"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(x)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0., 0., 5., 13., 9., 1., 0., 0., 0., 0., 13., 15., 10.,\n",
" 15., 5., 0., 0., 3., 15., 2., 0., 11., 8., 0., 0., 4.,\n",
" 12., 0., 0., 8., 8., 0., 0., 5., 8., 0., 0., 9., 8.,\n",
" 0., 0., 4., 11., 0., 1., 12., 7., 0., 0., 2., 14., 5.,\n",
" 10., 12., 0., 0., 0., 0., 6., 13., 10., 0., 0., 0.])"
]
},
"execution_count": 149,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a=np.ravel(x[0])\n",
"a"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [],
"source": [
"for i in range(1,len(x)):\n",
" b=np.ravel(x[i])\n",
" a=np.vstack([a,b])\n",
" \n",
"#print(a) "
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1797"
]
},
"execution_count": 151,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(a)"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1797"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(y)"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"z=np.hstack([a,np.array(y).reshape(len(y),1)])"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [],
"source": [
"data=pd.DataFrame(z,dtype=int)"
]
},
{
"cell_type": "code",
"execution_count": 157,
"metadata": {},
"outputs": [],
"source": [
"data=data.rename(columns={64:'digit'})"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"2.Make a train -test split with 20% of the data set aside for testing. Fit a logistic regression model and observe the accuracy."
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1437, 65)\n",
"(360, 65)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"train,test=train_test_split(data,test_size=0.2,random_state=3)\n",
"print(train.shape)\n",
"print(test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [],
"source": [
"train_x=train.iloc[:,:-1]\n",
"train_y=train['digit']\n",
"test_x=test.iloc[:,:-1]\n",
"test_y=test['digit']"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\softwares\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"D:\\softwares\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"logistic=LogisticRegression()\n",
"logistic.fit(train_x,train_y)"
]
},
{
"cell_type": "code",
"execution_count": 164,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9527777777777777"
]
},
"execution_count": 164,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import metrics\n",
"y_pred=logistic.predict(test_x)\n",
"metrics.accuracy_score(y_pred,test_y)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"3.Using scikit learn perform a PCA transformation such that the transformed dataset can explain 95% of the variance in the original dataset. Find out the number of components in the projected subspace.[Hint:Refer to decomposition module of scikit learn]"
]
},
{
"cell_type": "code",
"execution_count": 184,
"metadata": {},
"outputs": [],
"source": [
"dataPCA=data\n",
"xdata=dataPCA.iloc[:,:-1]"
]
},
{
"cell_type": "code",
"execution_count": 185,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0 0.0\n",
"1 0.14890593583962786\n",
"2 0.2850936482368944\n",
"3 0.4030395858761546\n",
"4 0.48713938008666224\n",
"5 0.5449635266639954\n",
"6 0.594132629682615\n",
"7 0.6372919939178193\n",
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"9 0.707435955285958\n",
"10 0.7382263287265188\n",
"11 0.761948655405748\n",
"12 0.7846757792110632\n",
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"24 0.9260717474719197\n",
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"26 0.9389917473659796\n",
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"43 0.9931999526903914\n",
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"45 0.9957719632305019\n",
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"47 0.9978109415065078\n",
"48 0.9985855717776009\n",
"49 0.999142783330584\n",
"50 0.9995471140234804\n",
"51 0.9997570303506731\n",
"52 0.9998395100604511\n",
"53 0.9998920250584977\n",
"54 0.9999425494303961\n",
"55 0.9999755455666685\n",
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"57 0.9999950303903163\n",
"58 0.9999980447117098\n",
"59 0.9999991070197107\n",
"60 0.999999657094298\n",
"61 1.0\n",
"62 1.0\n",
"63 1.0\n"
]
}
],
"source": [
"\n",
"from sklearn.decomposition import PCA\n",
"for i in range(64):\n",
" pca = PCA(n_components=i)\n",
" pca.fit(xdata)\n",
" print (i,pca.explained_variance_ratio_.sum())\n"
]
},
{
"cell_type": "code",
"execution_count": 186,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PCA(copy=True, iterated_power='auto', n_components=29, random_state=None,\n",
" svd_solver='auto', tol=0.0, whiten=False)"
]
},
"execution_count": 186,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pca = PCA(n_components=29)\n",
"pca.fit(xdata)"
]
},
{
"cell_type": "code",
"execution_count": 187,
"metadata": {},
"outputs": [
{
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},
"execution_count": 187,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"existing_2d = pca.transform(xdata)\n",
"existing_df_2d = pd.DataFrame(existing_2d)\n",
"existing_df_2d.index = dataPCA.index\n",
"#existing_df_2d.columns = ['PC1','PC2']\n",
"existing_df_2d.head()"
]
},
{
"cell_type": "code",
"execution_count": 189,
"metadata": {},
"outputs": [],
"source": [
"existing_df_2d['digit']=dataPCA['digit']"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [
{
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"text/plain": [
" 0 1 2 3 4 5 6 \\\n",
"0 -1.259466 21.274883 -9.463055 13.014189 -7.128823 -7.440659 3.252837 \n",
"1 7.957611 -20.768699 4.439506 -14.893664 5.896249 -6.485622 2.126228 \n",
"2 6.991923 -9.955986 2.958558 -12.288302 -18.126023 -4.507663 1.843123 \n",
"3 -15.906105 3.332464 9.824372 -12.275838 6.965169 1.089483 -1.042085 \n",
"4 23.306867 4.269061 -5.675129 -13.851524 -0.358124 -2.857574 -0.720497 \n",
"\n",
" 7 8 9 ... 20 21 22 \\\n",
"0 2.553470 -0.581842 3.625697 ... 2.680803 1.009460 2.009851 \n",
"1 -4.615936 3.587450 -1.074703 ... -1.206028 -4.837322 0.859991 \n",
"2 -16.415200 0.719233 4.255808 ... 4.951693 -2.464678 5.996714 \n",
"3 10.973557 -3.259736 6.498754 ... 0.269417 3.624070 2.537532 \n",
"4 13.041696 -3.835412 13.414164 ... 0.207010 5.740131 -7.612906 \n",
"\n",
" 23 24 25 26 27 28 digit \n",
"0 1.295437 1.179166 2.300536 3.671725 -0.943129 -1.133116 0 \n",
"1 -2.974623 5.287670 0.119164 2.182813 -0.524693 2.321924 1 \n",
"2 0.103986 -3.064066 0.167453 4.223083 2.148807 0.821365 2 \n",
"3 -1.355775 -1.741957 -1.340139 -5.848265 4.675831 -1.108978 3 \n",
"4 3.991372 2.781523 -0.166350 0.483090 0.632364 -3.306584 4 \n",
"\n",
"[5 rows x 30 columns]"
]
},
"execution_count": 190,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"existing_df_2d.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"4.Transform the dataset and fit a logistic regression and observe the accuracy. Compare it with the previous model andcomment on the accuracy.[Hint: Project both the train and test samples to the new subspace]"
]
},
{
"cell_type": "code",
"execution_count": 191,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1437, 30)\n",
"(360, 30)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"train,test=train_test_split(existing_df_2d,test_size=0.2,random_state=3)\n",
"print(train.shape)\n",
"print(test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"train_x=train.iloc[:,:-1]\n",
"train_y=train['digit']\n",
"test_x=test.iloc[:,:-1]\n",
"test_y=test['digit']"
]
},
{
"cell_type": "code",
"execution_count": 193,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"D:\\softwares\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
" FutureWarning)\n",
"D:\\softwares\\Anaconda\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:460: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
" \"this warning.\", FutureWarning)\n"
]
},
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, max_iter=100, multi_class='warn',\n",
" n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
" tol=0.0001, verbose=0, warm_start=False)"
]
},
"execution_count": 193,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"logistic=LogisticRegression()\n",
"logistic.fit(train_x,train_y)"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9527777777777777"
]
},
"execution_count": 194,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn import metrics\n",
"y_pred=logistic.predict(test_x)\n",
"metrics.accuracy_score(y_pred,test_y) #getting same accuracy "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"5.Compute the confusion matrix and count the number of instances that has gone wrong. For each of the wrong sample,plot the digit along with predicted and original label."
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [],
"source": [
"cm=metrics.confusion_matrix(y_pred,test_y)"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x1a3b22540f0>"
]
},
"execution_count": 200,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(cm,annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 207,
"metadata": {},
"outputs": [],
"source": [
"final=pd.DataFrame({'test':test_y,'pred':y_pred})"
]
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<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>test</th>\n",
" <th>pred</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>746</th>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1660</th>\n",
" <td>4</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>361</th>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1551</th>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>378</th>\n",
" <td>8</td>\n",
" <td>3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>7</td>\n",
" <td>4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1018</th>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>69</th>\n",
" <td>9</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>951</th>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1384</th>\n",
" <td>4</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>9</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1666</th>\n",
" <td>8</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>578</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751</th>\n",
" <td>9</td>\n",
" <td>7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>492</th>\n",
" <td>6</td>\n",
" <td>8</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" test pred\n",
"746 4 7\n",
"1660 4 8\n",
"361 9 7\n",
"1551 6 2\n",
"378 8 3\n",
"27 7 4\n",
"1018 5 9\n",
"69 9 8\n",
"951 5 9\n",
"1384 4 6\n",
"5 5 9\n",
"37 9 5\n",
"1666 8 7\n",
"578 3 2\n",
"87 4 5\n",
"751 9 7\n",
"492 6 8"
]
},
"execution_count": 210,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final[final['test']!=final['pred']]"
]
},
{
"cell_type": "code",
"execution_count": 214,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Int64Index([ 746, 1660, 361, 1551, 378, 27, 1018, 69, 951, 1384, 5,\n",
" 37, 1666, 578, 87, 751, 492],\n",
" dtype='int64')"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"final[final['test']!=final['pred']].index"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"target: 4\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 7\n",
"-----------------\n",
"target: 4\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 8\n",
"-----------------\n",
"target: 9\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 7\n",
"-----------------\n",
"target: 6\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 2\n",
"-----------------\n",
"target: 8\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 3\n",
"-----------------\n",
"target: 7\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 4\n",
"-----------------\n",
"target: 5\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 9\n",
"-----------------\n",
"target: 9\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 8\n",
"-----------------\n",
"target: 5\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 9\n",
"-----------------\n",
"target: 4\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 6\n",
"-----------------\n",
"target: 5\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAACqJJREFUeJzt3duLXeUZx/Hfr6PSegy0tkgmZBQ0IIUkIgEJiIltiVVMLnqRgEKkkCsl0oJor+w/oOlFEULUBkyVNh4QsVpBN1ZorUkcW5OJJQ0TMo02Skk8FDpEn17MDsR0yl47+12Hefx+IDiHTd5no1/Xmj1rr9cRIQA5fa3tAQDUh8CBxAgcSIzAgcQIHEiMwIHECBxIjMCBxAgcSOy8Ov5S2ykvj7vmmmsaXW92draxtaanpxtbC2VEhAc9xnVcqpo18F6v1+h6TUa3efPmxtZCGVUC5xQdSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcQqBW57ne33bB+yfX/dQwEoY2Dgtsck/VLSLZKulbTJ9rV1DwZgdFWO4KskHYqIwxExK+kpSevrHQtACVUCXyzp6Bmfz/S/BqDjqrybbL4L2v/nzSS2t0jaMvJEAIqpEviMpCVnfD4u6djZD4qI7ZK2S3nfTQYsNFVO0d+SdLXtK21fIGmjpOfrHQtACQOP4BFxyvbdkl6WNCbpsYjYX/tkAEZW6Y4uEfGipBdrngVAYVzJBiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBi7GwyhKa391m6dGmj6zXlyJEjja01MTHR2FpNY2cT4CuOwIHECBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIrMrOJo/ZPm773SYGAlBOlSP4ryStq3kOADUYGHhEvC7pXw3MAqAwfgYHEqt02+Qq2LoI6J5igbN1EdA9nKIDiVX5NdmTkv4oaZntGds/rn8sACVU2ZtsUxODACiPU3QgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEit2LfpXwYkTJxpdr8mti06ePNnYWr1er7G1Fi1a1NhaUvP/jQzCERxIjMCBxAgcSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcSq3HRxie3XbE/Z3m97axODARhdlWvRT0n6aUTss32JpL22X4mIAzXPBmBEVfYmez8i9vU//kTSlKTFdQ8GYHRDvZvM9oSklZLenOd7bF0EdEzlwG1fLOlpSfdGxMdnf5+ti4DuqfQquu3zNRf3roh4pt6RAJRS5VV0S3pU0lREPFT/SABKqXIEXy3pTklrbU/2//yw5rkAFFBlb7I3JLmBWQAUxpVsQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiTG3mRDmJ6ebnS95cuXN7bWZZdd1thak5OTja3Vtb3CmsYRHEiMwIHECBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIrMpNF79u+8+23+lvXfTzJgYDMLoql6r+R9LaiPi0f/vkN2z/LiL+VPNsAEZU5aaLIenT/qfn9/+wsQGwAFTd+GDM9qSk45JeiYh5ty6yvcf2ntJDAjg3lQKPiM8jYoWkcUmrbH93nsdsj4jrI+L60kMCODdDvYoeESck9SStq2UaAEVVeRX9ctuL+h9/Q9L3JB2sezAAo6vyKvoVknbaHtPc/xB+ExEv1DsWgBKqvIr+F83tCQ5ggeFKNiAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcSY+uiIWzYsKHR9W666abG1lqxYkVjaz388MONrdW0bdu2tT3Cl3AEBxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcSqxx4/97ob9vmfmzAAjHMEXyrpKm6BgFQXtWdTcYl3SppR73jACip6hF8m6T7JH1R4ywACquy8cFtko5HxN4Bj2NvMqBjqhzBV0u63fa0pKckrbX9xNkPYm8yoHsGBh4RD0TEeERMSNoo6dWIuKP2yQCMjN+DA4kNdUeXiOhpbndRAAsAR3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEmProg7r9Xptj7DgTUxMtD1CqziCA4kROJAYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJVbqSrX9H1U8kfS7pFHdOBRaGYS5VXRMRH9U2CYDiOEUHEqsaeEj6ve29trfUORCAcqqeoq+OiGO2vy3pFdsHI+L1Mx/QD5/4gQ6pdASPiGP9fx6X9KykVfM8hq2LgI6psvngRbYvOf2xpB9IerfuwQCMrsop+nckPWv79ON/HREv1ToVgCIGBh4RhyUtb2AWAIXxazIgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEmProiGsX7++0fVOnjzZ2FoPPvhgY2s16bnnnmt7hFZxBAcSI3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEqsUuO1FtnfbPmh7yvYNdQ8GYHRVL1X9haSXIuJHti+QdGGNMwEoZGDgti+VdKOkzZIUEbOSZusdC0AJVU7Rr5L0oaTHbb9te0f//ugAOq5K4OdJuk7SIxGxUtJnku4/+0G2t9jeY3tP4RkBnKMqgc9ImomIN/uf79Zc8F/C1kVA9wwMPCI+kHTU9rL+l26WdKDWqQAUUfVV9Hsk7eq/gn5Y0l31jQSglEqBR8SkJE69gQWGK9mAxAgcSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcTYm2wIa9asaXS9rVu3NrpeU3bu3NnYWr1er7G1uogjOJAYgQOJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQ2MDAbS+zPXnGn49t39vEcABGM/BS1Yh4T9IKSbI9Jukfkp6teS4ABQx7in6zpL9HxJE6hgFQ1rBvNtko6cn5vmF7i6QtI08EoJjKR/D+pge3S/rtfN9n6yKge4Y5Rb9F0r6I+GddwwAoa5jAN+n/nJ4D6KZKgdu+UNL3JT1T7zgASqq6N9m/JX2z5lkAFMaVbEBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4k5ogo/5faH0oa9i2l35L0UfFhuiHrc+N5tWdpRFw+6EG1BH4ubO/J+k60rM+N59V9nKIDiRE4kFiXAt/e9gA1yvrceF4d15mfwQGU16UjOIDCOhG47XW237N9yPb9bc9Tgu0ltl+zPWV7v+2tbc9Uku0x22/bfqHtWUqyvcj2btsH+//ubmh7plG0forev9f63zR3x5gZSW9J2hQRB1odbES2r5B0RUTss32JpL2SNiz053Wa7Z9Iul7SpRFxW9vzlGJ7p6Q/RMSO/o1GL4yIE23Pda66cARfJelQRByOiFlJT0la3/JMI4uI9yNiX//jTyRNSVrc7lRl2B6XdKukHW3PUpLtSyXdKOlRSYqI2YUct9SNwBdLOnrG5zNKEsJptickrZT0ZruTFLNN0n2Svmh7kMKukvShpMf7P37ssH1R20ONoguBe56vpXlp3/bFkp6WdG9EfNz2PKOyfZuk4xGxt+1ZanCepOskPRIRKyV9JmlBvybUhcBnJC054/NxScdamqUo2+drLu5dEZHljrSrJd1ue1pzP06ttf1EuyMVMyNpJiJOn2nt1lzwC1YXAn9L0tW2r+y/qLFR0vMtzzQy29bcz3JTEfFQ2/OUEhEPRMR4RExo7t/VqxFxR8tjFRERH0g6antZ/0s3S1rQL4oOuzdZcRFxyvbdkl6WNCbpsYjY3/JYJayWdKekv9qe7H/tZxHxYoszYbB7JO3qH2wOS7qr5XlG0vqvyQDUpwun6ABqQuBAYgQOJEbgQGIEDiRG4EBiBA4kRuBAYv8FBlaJQ4e6KB8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 9\n",
"-----------------\n",
"target: 9\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAPgAAAD8CAYAAABaQGkdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAACslJREFUeJzt3d2LXeUZhvH77qi0fg60tkgmZBQkEAqZEQlIQExsS6zizEEPElBIEHKkOLQg2rP+AzI9KEKIOoKp0kYFEasVNFihteZjpzVOLGmwZBptlDJ+BRoSnx7MTknTKXtN9rvW2vPk+kFwPjZ5n024XGvW7L1eR4QA5PS1tgcAUB8CBxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCCxS+r4S22nfHncmjVrGl3v5MmTja117NixxtY6c+ZMY2tlFhHu9RjX8VLVrIF3Op20601NTTW21vz8fGNrZVYlcE7RgcQIHEiMwIHECBxIjMCBxAgcSIzAgcQIHEisUuC2N9l+3/YR2w/XPRSAMnoGbntI0i8k3SFpjaQttpt9zSaAC1LlCL5O0pGIOBoRpyQ9K2mi3rEAlFAl8BWSzn0nwlz3awAGXJV3ky32gvb/eTOJ7e2Stvc9EYBiqgQ+J2nlOZ+PSDp+/oMiYoekHVLed5MBy02VU/R3JN1o+3rbl0naLOnFescCUELPI3hEnLZ9v6RXJQ1JeiIiDtU+GYC+VbqjS0S8LOnlmmcBUBivZAMSI3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgsVq2LmrSxERz71xdu3ZtY2tJ0vDwcGNrbd26tbG1pqenG1vrYscRHEiMwIHECBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIrMrOJk/YPmH73SYGAlBOlSP4jKRNNc8BoAY9A4+INyX9s4FZABTGz+BAYsXeTcbWRcDgKRY4WxcBg4dTdCCxKr8me0bS7yWttj1n+776xwJQQpW9ybY0MQiA8jhFBxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCCxZb910aefftr2CLWZmZlpbK2pqanG1mLrouZwBAcSI3AgMQIHEiNwIDECBxIjcCAxAgcSI3AgMQIHEiNwILEqN11cafsN27O2D9l+sInBAPSvymvRT0v6SUTst32VpH22X4uI92qeDUCfquxN9mFE7O9+/LmkWUkr6h4MQP+W9G4y26OSxiW9vcj32LoIGDCVA7d9paTnJE1FxGfnf5+ti4DBU+kquu1LtRD3roh4vt6RAJRS5Sq6JT0uaTYiHq1/JAClVDmCr5d0r6SNtjvdPz+seS4ABVTZm+wtSW5gFgCF8Uo2IDECBxIjcCAxAgcSI3AgMQIHEiNwIDECBxJzRPn3hTT5ZpPR0dGmltKePXsaW0uSVq1a1eh6Tdm2bVtjazW5v1vTIqLnC9A4ggOJETiQGIEDiRE4kBiBA4kROJAYgQOJETiQGIEDiVW56eLXbf/R9sHu1kU/a2IwAP2rcl/0f0naGBFfdG+f/Jbt30TEH2qeDUCfqtx0MSR90f300u4fNjYAloGqGx8M2e5IOiHptYhYdOsi23tt7y09JIALUynwiDgTEWOSRiSts/3dRR6zIyJujoibSw8J4MIs6Sp6RMxL2iNpUy3TACiqylX0a20Pdz/+hqTvSTpc92AA+lflKvp1kp6yPaSF/yH8KiJeqncsACVUuYr+Jy3sCQ5gmeGVbEBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4ktuy3LmrS8PBwo+uNjY01ttbk5GRja912220p15Kk+fn5xtZi6yLgIkfgQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRWOfDuvdEP2OZ+bMAysZQj+IOSZusaBEB5VXc2GZF0p6Sd9Y4DoKSqR/BpSQ9J+qrGWQAUVmXjg7sknYiIfT0ex95kwICpcgRfL+lu2x9IelbSRttPn/8g9iYDBk/PwCPikYgYiYhRSZslvR4R99Q+GYC+8XtwILEqe5P9R0Ts0cLuogCWAY7gQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiTG1kVoXKfTaWyt6enpxtaSpJmZmcbWYusi4CJH4EBiBA4kRuBAYgQOJEbgQGIEDiRG4EBiBA4kVumWTd07qn4u6Yyk09w5FVgelnJPtg0R8UltkwAojlN0ILGqgYek39reZ3t7nQMBKKfqKfr6iDhu+9uSXrN9OCLePPcB3fCJHxgglY7gEXG8+98Tkl6QtG6Rx7B1ETBgqmw+eIXtq85+LOkHkt6tezAA/atyiv4dSS/YPvv4X0bEK7VOBaCInoFHxFFJaxuYBUBh/JoMSIzAgcQIHEiMwIHECBxIjMCBxAgcSIzAgcTYumgJJiYmGl1vfHy8sbUOHDjQ2FobNmxobK3JycnG1pKk0dHRxtZi6yLgIkfgQGIEDiRG4EBiBA4kRuBAYgQOJEbgQGIEDiRWKXDbw7Z32z5se9b2LXUPBqB/Ve+L/nNJr0TEj2xfJunyGmcCUEjPwG1fLelWSVslKSJOSTpV71gASqhyin6DpI8lPWn7gO2d3fujAxhwVQK/RNJNkh6LiHFJX0p6+PwH2d5ue6/tvYVnBHCBqgQ+J2kuIt7ufr5bC8H/F7YuAgZPz8Aj4iNJx2yv7n7pdknv1ToVgCKqXkV/QNKu7hX0o5K21TcSgFIqBR4RHUmcegPLDK9kAxIjcCAxAgcSI3AgMQIHEiNwIDECBxIjcCAxAgcSY2+yJeh0Om2PUJsm99S65pprGlvr4MGDja0lSWNjY42txd5kwEWOwIHECBxIjMCBxAgcSIzAgcQIHEiMwIHECBxIrGfgtlfb7pzz5zPbU00MB6A/PW+6GBHvSxqTJNtDkv4u6YWa5wJQwFJP0W+X9NeI+FsdwwAoq+p90c/aLOmZxb5he7uk7X1PBKCYykfw7qYHd0v69WLfZ+siYPAs5RT9Dkn7I+IfdQ0DoKylBL5F/+f0HMBgqhS47cslfV/S8/WOA6CkqnuTnZT0zZpnAVAYr2QDEiNwIDECBxIjcCAxAgcSI3AgMQIHEiNwILG6ti76WNJS31L6LUmfFB9mMGR9bjyv9qyKiGt7PaiWwC+E7b1Z34mW9bnxvAYfp+hAYgQOJDZIge9oe4AaZX1uPK8BNzA/gwMob5CO4AAKG4jAbW+y/b7tI7YfbnueEmyvtP2G7Vnbh2w/2PZMJdkesn3A9kttz1KS7WHbu20f7v7b3dL2TP1o/RS9e6/1v2jhjjFzkt6RtCUi3mt1sD7Zvk7SdRGx3/ZVkvZJmlzuz+ss2z+WdLOkqyPirrbnKcX2U5J+FxE7uzcavTwi5tue60INwhF8naQjEXE0Ik5JelbSRMsz9S0iPoyI/d2PP5c0K2lFu1OVYXtE0p2SdrY9S0m2r5Z0q6THJSkiTi3nuKXBCHyFpGPnfD6nJCGcZXtU0rikt9udpJhpSQ9J+qrtQQq7QdLHkp7s/vix0/YVbQ/Vj0EI3It8Lc2lfdtXSnpO0lREfNb2PP2yfZekExGxr+1ZanCJpJskPRYR45K+lLSsrwkNQuBzklae8/mIpOMtzVKU7Uu1EPeuiMhyR9r1ku62/YEWfpzaaPvpdkcqZk7SXEScPdParYXgl61BCPwdSTfavr57UWOzpBdbnqlvtq2Fn+VmI+LRtucpJSIeiYiRiBjVwr/V6xFxT8tjFRERH0k6Znt190u3S1rWF0WXujdZcRFx2vb9kl6VNCTpiYg41PJYJayXdK+kP9vudL/204h4ucWZ0NsDknZ1DzZHJW1reZ6+tP5rMgD1GYRTdAA1IXAgMQIHEiNwIDECBxIjcCAxAgcSI3AgsX8DxvClvo8R9A8AAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 5\n",
"-----------------\n",
"target: 8\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 7\n",
"-----------------\n",
"target: 3\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 2\n",
"-----------------\n",
"target: 4\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 5\n",
"-----------------\n",
"target: 9\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 7\n",
"-----------------\n",
"target: 6\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted: 8\n",
"-----------------\n"
]
}
],
"source": [
"for i in final[final['test']!=final['pred']].index:\n",
" displayImg(x[i],y[i])\n",
" print('Predicted:',final.loc[i,'pred'],end='\\n-----------------\\n')"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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