Datasets
Models
Downloads: 1
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting sklearn\n",
" Downloading sklearn-0.0.tar.gz (1.1 kB)\n",
"Requirement already satisfied: scikit-learn in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from sklearn) (0.22.1)\n",
"Requirement already satisfied: joblib>=0.11 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from scikit-learn->sklearn) (0.14.1)\n",
"Requirement already satisfied: numpy>=1.11.0 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from scikit-learn->sklearn) (1.18.1)\n",
"Requirement already satisfied: scipy>=0.17.0 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from scikit-learn->sklearn) (1.4.1)\n",
"Building wheels for collected packages: sklearn\n",
" Building wheel for sklearn (setup.py): started\n",
" Building wheel for sklearn (setup.py): finished with status 'done'\n",
" Created wheel for sklearn: filename=sklearn-0.0-py2.py3-none-any.whl size=1320 sha256=6a8aae0a46e00ab5e60b797ea7eb1b5e8240b278b1bb3a32685cfb2b46f505bb\n",
" Stored in directory: c:\\users\\joel\\appdata\\local\\pip\\cache\\wheels\\46\\ef\\c3\\157e41f5ee1372d1be90b09f74f82b10e391eaacca8f22d33e\n",
"Successfully built sklearn\n",
"Installing collected packages: sklearn\n",
"Successfully installed sklearn-0.0\n"
]
}
],
"source": [
"!pip install sklearn"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.preprocessing import StandardScaler\n",
"from sklearn.metrics import roc_curve, roc_auc_score\n",
"from sklearn.metrics import confusion_matrix"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('softmax_train.csv')"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"train_data = pd.read_csv('softmax_dataset_new.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ax_normal</th>\n",
" <th>ax_abnormal</th>\n",
" <th>ax_acl</th>\n",
" <th>cor_normal</th>\n",
" <th>cor_abnormal</th>\n",
" <th>cor_acl</th>\n",
" <th>sag_normal</th>\n",
" <th>sag_abnormal</th>\n",
" <th>sag_acl</th>\n",
" <th>label</th>\n",
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" <td>0.028046</td>\n",
" <td>0.973639</td>\n",
" <td>0.267321</td>\n",
" <td>0.067432</td>\n",
" <td>0.940061</td>\n",
" <td>0.331581</td>\n",
" <td>0.030936</td>\n",
" <td>0.969067</td>\n",
" <td>0.152936</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>554</th>\n",
" <td>0.104018</td>\n",
" <td>0.910624</td>\n",
" <td>0.161932</td>\n",
" <td>0.119986</td>\n",
" <td>0.885913</td>\n",
" <td>0.576214</td>\n",
" <td>0.021173</td>\n",
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" <tr>\n",
" <th>555</th>\n",
" <td>0.042273</td>\n",
" <td>0.959196</td>\n",
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" <td>0.077663</td>\n",
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" <tr>\n",
" <th>557</th>\n",
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" <td>0.189404</td>\n",
" <td>0.200638</td>\n",
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" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>558 rows × 10 columns</p>\n",
"</div>"
],
"text/plain": [
" ax_normal ax_abnormal ax_acl cor_normal cor_abnormal cor_acl \\\n",
"0 0.009713 0.989749 0.916373 0.119974 0.883050 0.718895 \n",
"1 0.014191 0.980667 0.925633 0.044364 0.956335 0.789032 \n",
"2 0.007830 0.990380 0.900669 0.078129 0.923744 0.740118 \n",
"3 0.009703 0.987407 0.933480 0.128174 0.873558 0.596139 \n",
"4 0.005827 0.993517 0.925906 0.018329 0.982625 0.607551 \n",
".. ... ... ... ... ... ... \n",
"553 0.028046 0.973639 0.267321 0.067432 0.940061 0.331581 \n",
"554 0.104018 0.910624 0.161932 0.119986 0.885913 0.576214 \n",
"555 0.042273 0.959196 0.600703 0.089168 0.911604 0.204822 \n",
"556 0.111872 0.865286 0.805120 0.015248 0.984486 0.650905 \n",
"557 0.256723 0.742153 0.189404 0.200638 0.808940 0.399186 \n",
"\n",
" sag_normal sag_abnormal sag_acl label \n",
"0 0.015162 0.985038 0.926093 2 \n",
"1 0.017855 0.981429 0.929446 2 \n",
"2 0.016741 0.984230 0.913814 2 \n",
"3 0.010167 0.989766 0.959643 2 \n",
"4 0.013189 0.986476 0.770761 2 \n",
".. ... ... ... ... \n",
"553 0.030936 0.969067 0.152936 1 \n",
"554 0.021173 0.978584 0.746935 1 \n",
"555 0.036724 0.960655 0.284694 1 \n",
"556 0.077663 0.925254 0.683657 1 \n",
"557 0.085117 0.913553 0.456751 1 \n",
"\n",
"[558 rows x 10 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_data[train_data.columns[1:]]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"X = train_data[train_data.columns[1:10]].values\n",
"Y = train_data['label'].values"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(558, 9)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
" intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
" multi_class='multinomial', n_jobs=None, penalty='l2',\n",
" random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
" warm_start=False)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softReg = LogisticRegression(multi_class = 'multinomial', solver = 'lbfgs')\n",
"softReg.fit(X,Y)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"OneVsRestClassifier(estimator=LogisticRegression(C=1.0, class_weight=None,\n",
" dual=False, fit_intercept=True,\n",
" intercept_scaling=1,\n",
" l1_ratio=None, max_iter=100,\n",
" multi_class='multinomial',\n",
" n_jobs=None, penalty='l2',\n",
" random_state=None,\n",
" solver='lbfgs', tol=0.0001,\n",
" verbose=0, warm_start=False),\n",
" n_jobs=None)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softReg = OneVsRestClassifier(LogisticRegression(multi_class = 'multinomial', solver = 'lbfgs'))\n",
"softReg.fit(X,Y)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([ 1.45617458, 1.88473241, -3.34090699]),\n",
" array([[ 1.31082336, -1.37387397, -0.35588262, 0.38049908, -0.30490278,\n",
" 0.04214195, 1.4007989 , -1.41258259, -0.49417648],\n",
" [-0.58331616, 0.6819172 , -1.71525562, -0.390168 , 0.33535153,\n",
" -1.48612828, -1.118819 , 1.13089791, -2.20248492],\n",
" [-0.7275072 , 0.69195676, 2.07113825, 0.00966892, -0.03044874,\n",
" 1.44398634, -0.2819799 , 0.28168468, 2.69666141]]))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"softReg.intercept_,softReg.coef_"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
" 2 2 2 2 2 2 2 2 1 2 2 2 1 1 2 2 1 1 2 2 2 2 1 1 2 2 1 2 2 1 1 1 2 2 2 2 1\n",
" 1 1 2 2 2 2 2 2 2 1 1 0 1 2 1 1 1 2 2 0 2 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
" 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 1 0 2 0 2 0 1 0 0 1 1 1 0 1 1 1 1 0 0 0\n",
" 1 0 0 0 1 0 2 0 1 2 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
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" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
" 1 1 1 2 1 1 0 1 1 1 1 1 2 1 2 1 1 0 1 1 1 2 1 1 2 2 1 1 1 1 1 1 1 2 1 1 1\n",
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" 2 1 1 1 0 1 1 0 1 0 1 1 1 1 1 0 1 2 2 0 1 1 1 2 1 1 2 1 1 1 0 1 2 2 1 1 1\n",
" 1 2 1]\n"
]
}
],
"source": [
"predicted = softReg.predict(X)\n",
"print(predicted)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(558,)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted.shape"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"import pickle"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"filename = 'softmax_reg_new.sav'\n",
"pickle.dump(softReg, open(filename, 'wb'))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"207 260 91\n"
]
}
],
"source": [
"acl = 0\n",
"abnormal = 0\n",
"normal = 0\n",
"for i in predicted:\n",
" if i==2:\n",
" acl+=1\n",
" elif i ==1:\n",
" abnormal +=1\n",
" else:\n",
" normal+=1\n",
"print(acl,abnormal,normal) "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.982078853046595"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"548/558"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"actual = train_data.label.values"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"208 250 100\n"
]
}
],
"source": [
"acl = 0\n",
"abnormal = 0\n",
"normal = 0\n",
"for i in actual:\n",
" if i==2:\n",
" acl+=1\n",
" elif i ==1:\n",
" abnormal +=1\n",
" else:\n",
" normal+=1\n",
"print(acl,abnormal,normal) "
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(1, 9)\n",
"1 1\n"
]
}
],
"source": [
"predict = X[550]\n",
"value = Y[550]\n",
"predict1 = [predict]\n",
"predict1 = np.array(predict1)\n",
"print(predict1.shape)\n",
"predicted = int(softReg.predict(predict1))\n",
"print(predicted,value)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(1, 9)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predict1.shape"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.9767025089605734"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"545/558"
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"frames = [train_data[:100],train_data[200:270],train_data[450:],test_data]\n",
"combined = pd.concat(frames)"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [],
"source": [
"Xc = combined[combined.columns[1:10]].values\n",
"Yc = combined['label'].values"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"test_data = pd.read_csv('softmax_test_new.csv')"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [],
"source": [
"predicted = softReg.predict(Xc)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"X = test_data[test_data.columns[1:10]].values\n",
"Y = test_data['label'].values"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"predicted = softReg.predict(X)"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
" 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 1, 2, 2, 0, 2, 2, 1, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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" 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1,\n",
" 2, 1, 2, 1, 1, 0, 1, 1, 1, 2, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 2,\n",
" 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 2, 1, 1, 1, 0, 1, 1, 1, 1, 2, 1, 1, 1, 1, 2, 1, 1, 1,\n",
" 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 2, 2, 0, 1, 1, 1, 2, 1, 1,\n",
" 2, 1, 1, 1, 0, 1, 2, 2, 1, 1, 1, 1, 2, 1, 1, 0, 2, 2, 2, 1, 1, 1,\n",
" 1, 2, 1, 2, 2, 1, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
" 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
" 1], dtype=int64)"
]
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"predicted"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"128 174 95\n"
]
}
],
"source": [
"acl = 0\n",
"abnormal = 0\n",
"normal = 0\n",
"for i in predicted:\n",
" if i==2:\n",
" acl+=1\n",
" elif i ==1:\n",
" abnormal +=1\n",
" else:\n",
" normal+=1\n",
"print(acl,abnormal,normal) "
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"124 188 85\n"
]
}
],
"source": [
"acl1 = 0\n",
"abnormal1 = 0\n",
"normal1 = 0\n",
"for i in Yc:\n",
" if i==2:\n",
" acl1+=1\n",
" elif i ==1:\n",
" abnormal1 +=1\n",
" else:\n",
" normal1+=1\n",
"print(acl1,abnormal1,normal1) "
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"377"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"124+188+65"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"fpr = {}\n",
"tpr = {}\n",
"thresh = {}"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
"for i in range(3):\n",
" fpr[i], tpr[i], thresh[i] = roc_curve(Y,predicted,pos_label = i)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Bad key \"text.kerning_factor\" on line 4 in\n",
"C:\\Users\\JOEL\\Anaconda3\\envs\\comp\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
"You probably need to get an updated matplotlibrc file from\n",
"https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
"or from the matplotlib source distribution\n"
]
}
],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(fpr[0], tpr[0], linestyle='--',color='orange', label='Class 0 vs Rest')\n",
"plt.plot(fpr[1], tpr[1], linestyle='--',color='green', label='Class 1 vs Rest')\n",
"plt.plot(fpr[2], tpr[2], linestyle='--',color='blue', label='ACL vs Rest')\n",
"plt.title('Multiclass ROC curve')\n",
"plt.xlabel('False Positive Rate')\n",
"plt.ylabel('True Positive rate')\n",
"plt.legend(loc='best')\n",
"plt.savefig('ACL class ROC',dpi=300);"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [],
"source": [
"cf = confusion_matrix(Yc,predicted)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8673835125448028"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(81+217+186)/558"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting seaborn\n",
" Downloading seaborn-0.11.1-py3-none-any.whl (285 kB)\n",
"Requirement already satisfied: numpy>=1.15 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from seaborn) (1.18.1)\n",
"Requirement already satisfied: matplotlib>=2.2 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from seaborn) (3.2.0)\n",
"Requirement already satisfied: scipy>=1.0 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from seaborn) (1.4.1)\n",
"Requirement already satisfied: pandas>=0.23 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from seaborn) (1.0.3)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from matplotlib>=2.2->seaborn) (1.1.0)\n",
"Requirement already satisfied: cycler>=0.10 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from matplotlib>=2.2->seaborn) (0.10.0)\n",
"Requirement already satisfied: python-dateutil>=2.1 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.7.5)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from matplotlib>=2.2->seaborn) (2.4.6)\n",
"Requirement already satisfied: pytz>=2017.2 in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from pandas>=0.23->seaborn) (2019.3)\n",
"Requirement already satisfied: setuptools in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (45.2.0.post20200210)\n",
"Requirement already satisfied: six in c:\\users\\joel\\anaconda3\\envs\\comp\\lib\\site-packages (from cycler>=0.10->matplotlib>=2.2->seaborn) (1.14.0)\n",
"Installing collected packages: seaborn\n",
"Successfully installed seaborn-0.11.1\n"
]
}
],
"source": [
"!pip install seaborn"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"Bad key \"text.kerning_factor\" on line 4 in\n",
"C:\\Users\\JOEL\\Anaconda3\\envs\\comp\\lib\\site-packages\\matplotlib\\mpl-data\\stylelib\\_classic_test_patch.mplstyle.\n",
"You probably need to get an updated matplotlibrc file from\n",
"https://github.com/matplotlib/matplotlib/blob/v3.1.3/matplotlibrc.template\n",
"or from the matplotlib source distribution\n"
]
}
],
"source": [
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x204f59bdc88>"
]
},
"execution_count": 71,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.heatmap(cf,annot = True)"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'd' is not defined",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-89-f54efd20f051>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m sns.heatmap(df/np.sum(df), annot=True, \n\u001b[1;32m----> 2\u001b[1;33m fmt=d, cmap='Blues')\n\u001b[0m",
"\u001b[1;31mNameError\u001b[0m: name 'd' is not defined"
]
}
],
"source": [
"sns.heatmap(df/np.sum(df), annot=True, \n",
" fmt='.2%', cmap='Blues')"
]
},
{
"cell_type": "code",
"execution_count": 74,
"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",
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"\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>Normal</th>\n",
" <th>Abnormal</th>\n",
" <th>ACL</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>65</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9</td>\n",
" <td>162</td>\n",
" <td>17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>11</td>\n",
" <td>111</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Normal Abnormal ACL\n",
"0 65 0 0\n",
"1 9 162 17\n",
"2 2 11 111"
]
},
"execution_count": 74,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x204f7251d48>"
]
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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SFnGjY3AawEope9GzIJRSyiI6BKGUUhZxow6wBrBSyl60B6yUUhZxn/i9DZ6IkZiYSP8+D/PS88/eNO/smb8YGjyA/n0e5vFHurN544bkedMmT6RX1/b07t6JrZs3AnDx4gWeeeIx+vYKYv3a1cl1X/7XUCIiwm9q366e69uSXfNGsXPuq0wdM4CCBRx/x98c0oW9i15n9/z/8Gyf5jctd1+Vcqyb+iK75o1i+w+v0LNt3eR5d91Zkg3TXmLf4teZPnYg3l6eAAzu3Zydc19l4ReDk8ua3l+J91/skQdbmj/M/+F7Bj3anSf6dGf+7Ok3zTfG8OXHY3m8Zyee7PswRw79ljzvp2WL6dezM/16duanZY4HN8THxzPyhWcY9Gh3Fs+bnVz3kzFvcfTwwdzfoFzm6SFZnqxm+wCeM2s6FSpWSnPelG8n0Oqh9kydNZ+3x37IR2PeAeDPP47x80/LmTFvCZ98OYGPxr5LYmIiq1Yup0PnIEKmzGTmtMkAbFy/lqrValC6tH+a72E3d5YuyrN9mtOs7wfU7/Uenh4e9GpXj8e7NiawTDFqd3+HOg+/y9yVu25a9u/Yqwx6bRr1eo4maOjXfPDSwxQtUgiA0cOC+GLGWmoFvc3FmCsM6N4EgAHdm9DgkTHsORTKQ02rAzDyqQ6MCVmRdxttoT9/P8ryxfP5avJMJk6fy9aNGwg9eSJVne1bNhJ66gTT5i5l+Cuv89kH7wIQHRXF9Enf8OWkGXw1eSbTJ31DTHQ0O7duonLVGkz8fj7LFs8H4Pejh0lKSqJy1ep5vo2uJiJZnqxm6wAODzvL5l820KXbw2nOFxEuX74EwKWYS5Ryhugv69bSpl1HChQowJ3lAgkMLM9v+/fh5eVFXFwcV+Pj8RAPEhIS+GHmdPr2y/TJI7bi5elJoYLeeHp6UMinAGciogju9QDvhazA8VAAiLh46abljp0M5/eTEQCciYgi4mIMpUoUAaB5gyos+Hk3ADN+3EaXFrWTl/P28qSwjzdXExJ5tHNDftp4gMiYK7m9mfnCyeN/Ur3mffj4FMLTy4v76tZn4/rVqeps2rCWth27ICLUuLc2ly7FcP5cBDu3baJuwyb4FS2Kr58fdRs2YcfWjXg6f48TExOT2/huwpcMCM7Wwxzyrew8Fdlqtg7gcR+NZciwF/HwSHszBz09hJ+WLyWofSteen4ww19+FYCI8DD8A8ok1/MPKENERBht23di25ZNDB/6NIOeHsKCubPp0LkrPoUK5cn25Ad/RUQxbtpqjqx4hz9XjSb60hVWbz1ExcDS9Gxbj40zXmbRl4O5+x+lM2ynfs27KODlxR+nzlGy2B1ExVwhMTEJgNNhF7nTvygA46atZv20FylVvAhb/vcHj3VuxIS5GzJq2lYqVLqHvf/7laioSGJjr7Bt8y9EhIWlqnMuIpzS/td/X0v7B3AuIpxzEeH4p1Fev2ETLl44x9BBffnnYwPYvGEtVarVSO6AuDsPkSxPmRGRf4nIARHZLyKzRMRHRCqKyDYROSoiP4hIgZyua44PwonIQGPMdzldPrdt2rCO4iVKUK1GTX7duT3NOqt+WkbHLt149PEB7NvzP95+bSTfz12c3ItLSUQo4uvLx5+PByA6OorpUycx5qNxjHnndWKio+nz2ABq1b4/V7fLasV8C9G5RS2qd36DyJi/mfnBIHp3bEDBAl7ExV/lgb4fENSqNhPe6EubQePSbKNMKT8mvduPp16fjjEmza+C1z6CWct2MGvZDgBeDe7A17PX0a5ZTfp2bkjo2YuM+GRhmp+XXdxVsRK9Hx/Iy88FU6hwYe6uXBVP51h4snR+X9PeL4Knlxej3n4fgISEq4wY9gzvfvgFX4/7kPCwM7Tt0IWm/9cyF7Ymb7iqZysi5YDngRrGmCsiMgfojePBm58aY2aLyDfAIGB8Tt7jVnrAb6U3Q0SCRWSniOycOnniLbxFzu3ds5uN69fRo9NDvP7KS+zauY03R41IVWfpogW0fqgdALVq3098fDyRkRfxDyhDeNjZ5HrhYWcpVSp17+C7kPEMGBTMqpXLqVa9JqPeeJcJX6UdOHbSqlE1jv91nnMXL5GQkMSiNXtoXLsip8MusvDn/wGweM0e7q1cLs3lfe/wYcHng3nrq6Vs33ccgHMXL1HUtxCeno5fx3IBxTkTEZVqubKli1Kv5l0sXbePkU+247ERk4m7mkDLhlVzb2PziY5dezBh2hzGfTMFXz8/ygX+I9X8Uv4BRIRf/32NCA+jZKnSlPYPIPyG8lKlU38zWTz/B9p27MqBfXvw9vbmtXc/5Psp1vw/6youHgP2AgqJiBdQGDgDtALmOedPBbrldF0zDGAR2ZvOtA8ISG85Y0yIMaa+MaZ+/yeeyum63ZLBz/2LxSvXsGDZKt4e8xH16jfizdHvp6oTUKYsO7dvBeD4H78THxdH8eIleKB5S37+aTnx8fH8dTqU0FMnqXFvreTlTp08wbmICOrUa0BsbKzjgxQhLi4+T7fRCqfOXqBhrYoU8vEGoGXDqhz+M4wf1+2lRcMqADxYrzLHTt58Voi3lyc/fPwUM5duSx7vvWbDziP0aFMHgL5dGrF03d5U819/thNvf70UAJ+CBTAGkpIMhQt5u3wb85uLF84DEHb2DBvXraZV246p5jd9sAX/Xf4jxhh+27+HO4r4UrJUaeo3asaubZuJiY4mJjqaXds2U79Rs+TlYqKj2bpxA207diUuLhbxcIRSfFxcnm6fq3mKZHlK2Vl0TsHX2jHGnAY+wvHo+TNAFLALiDTGJDirhQJp9zayILMhiACgHXDxhnIBNuf0Ta00cfwXVKtRkwebt+K54f9m7Dtv8MOMaYgIo94ajYhQ6e57aPVQex7t2RUvT09eHPkfPD2vf+2b8NVnPD1kGAAPte/IyOHPM3fW9zw5eKhVm5Vnduw/wcKfd7Nl5ggSEpPYcyiUSfM3UaigN9+915/n+rbi8pU4Br89E4C6Nf7Bkz0f4Nm3Z/Jw27o8UPceShS7g8e6NgYg+PXp7D1ymlGfLWb62IG88Wxn9hw+xZRFW5Lfs3bVQAD2HA4FYOqizeyc+yqhZy8yeoL9z4Z485XhREdF4eXlxfMvvYqvnx8/LpgDQJcej9Co6YNs2/wLj/fshI+PD//+j+NsHr+iRXnsiad59gnHk9UfH/QMfkWLJrc7ffI3PDYwGBGhQaOmLJ43myf7PkyX7r3yfiNdKDtnlxljQoCQtOaJSHEgCKgIRAJzgQ5pNZPtlbz2HhmNn4nIJOA7Y8zGNObNNMY8mtkbnL+cYN8Bunwi8IEXrF4F2zu65mOrV+G2EFi84C2P4A5fcijLmfNJ12rpvp+I9ALaG2MGOV/3A5oAvYAyxpgEEWkCvGmMaZeTdc1wCMIYMyit8HXOyzR8lVIqr7lwDPgk0FhECoujcmvgN2At0NNZpz+wOKfrauvT0JRStx8PyfqUEWPMNhwH234F9uHIyxBgBDBcRI4BJYFJOV1XvReEUspWXHmBhTHmDeCNG4r/ABq6on0NYKWUrXjlh0vcskgDWCllK26UvxrASil70cfSK6WURdwofzWAlVL2kg9u85tlGsBKKVvJDzdazyoNYKWUrbhR/moAK6XsRdzoqXAawEopW9EesFJKWUQDWCmlLJIfHraZVRrASilb8XSjW4xpACulbEWvhFNKKYvoGLBSSlnEjTrAGsBKKXvx0POAlVLKGu7UA3aj44VKKZU5Lw/J8pQZESkmIvNE5JCIHBSRJiJSQkRWichR57/Fc7quGsBKKVsRyfqUBZ8BK40x1YDawEFgJLDaGFMZWO18nSMawEopW/EQyfKUERHxA/4P50M3jTHxxphIIAiY6qw2FeiW43XN6YJKKZUfubAHXAmIAL4Tkd0i8q2I3AEEGGPOADj/9c/pumoAK6VsxSMbk4gEi8jOFFNwiqa8gLrAeGNMHeAytzDckBY9C0IpZSvZuRLOGBMChKQzOxQINcZsc76ehyOAw0SkrDHmjIiUBcJzvK45XVAppfIjV40BG2POAqdEpKqzqDXwG7AE6O8s6w8szum6ag9YKWUrLj4N+DlghogUAP4ABuLouM4RkUHASaBXThvXAFZK2YorL8QwxvwPqJ/GrNauaF8DWCllK3o/YKWUsog7HdjSAFZK2YreDziFQt6euf0Wt72TG8ZZvQq2137cL1avwm1h12stb7kNHYJQSimL6BCEUkpZRHvASillEfeJXw1gpZTNeGoPWCmlrOFG+asBrJSyF3GjQQgNYKWUrWgPWCmlLKJPRVZKKYtoD1gppSyilyIrpZRFsvC0+XxDA1gpZSt6FoRSSlnEjUYgNICVUvbiTj1gd7pxkFJKZcpDsj5lhYh4ishuEVnqfF1RRLaJyFER+cH5vLicrWtOF1RKqfzIVU9FTmEYcDDF6/eBT40xlYGLwKAcr2tOF1RKqfxIsjFl2pZIINAJ+Nb5WoBWwDxnlalAt5yuq44BK6VsxcXnAY8DXgZ8na9LApHGmATn61CgXE4b1x6wUspWstMDFpFgEdmZYgpObkekMxBujNl1Q/M3MjldV+0BK6XsJRsdYGNMCBCSzuxmQFcR6Qj4AH44esTFRMTL2QsOBP7K6apqD1gpZSuuOghnjHnFGBNojKkA9AbWGGP6AmuBns5q/YHFOV7XnC6olFL5kSsPwqVjBDBcRI7hGBOelNOGdAhCKWUvuXAdhjFmHbDO+fMfQENXtKsBrJSyFXe6Ek4DWCllK3ovCKWUsogb5a8GsFLKXsSNusAawEopW3Gj/NUAVkrZixvlrwawUspm3CiBNYCVUraip6HlA2++9iobNqyjRImSzFv4Y5p1du7YxofvjyEhIYFixYoxacr3AGza+Asfvj+apMQkuvXoyRNPOu7P8eqIlzh29AgPNm/Bc8OGAxDyzddUrlKVlq1a582G5TM/zJjKj4vnIwiV7qnMq2+MpmDBgsnzz575izFv/4fIixfx9SvK6++MxT+gDAArli5i6qQJAPQf9DQdOncjPj6ekS8OJSIsjO69etOjVx8A3h/9Bt0f7k2VatXzfiMt8GijQLrVuRNjDMfCL/PWkkPULu/HC23uwctTOHQmhrd/PEyiufk+MNtHteBY+CUAzkbHMfyHfQDcWcyHMT1q4ufjxaGzMby26CAJSYZ/NihHj7p3cjYqjhfn7CMhyXB/+aK0rFaaT1cdy9PtdgV3GgO27aXIXYK689X4ienOj4mO5r1332bcF18zf9FSPvz4MwASExMZO/ptvvx6IvMXL2XlimX8/vsxjhw+DMCcBUvY/esuYmJiiIgI58D+vbdt+EaEhzHvhxlMmjaH6XMWk5SUxOr/Lk9V58txH9K+U1emzl7IwKeeYcKX4wCIjopk8sTxhEyZRcjU2UyeOJ7o6Ci2b9lI1Wo1mTp7IUsWzgXg6JFDmCRz24Rvad8C9G4QyOPf7uSfE3bg6SG0v9efN7tW55UFB/jnhB2ciYqjc+0yaS4fl5DIoxN38ujEncnhC/B867uZse0U3b/eRnRsAt3qlAWgW5076T1hB4fDYmhydwkAnnywAt/+cjzXtzU3iGR9slqmASwi1USktYgUuaG8fe6t1q2rV78BRYsWTXf+iuVLad36IcqWvROAEiVLArB/317K/+MfBJYvj7d3Adp16Mi6tavx8vYiLi6OpKQkrl69iqenB+O/+oLBQ57Pk+3JrxITE4mLiyUhIYG42FhKlfZPNf/4n79Tr0FjAOrWb8QvG9YAsG3LJho0bIJf0WL4+RWlQcMmbNu8EU8vb+LjYklMTEhu49vxX/DkM0PzbqPyAU8PoaCXB54i+Hh5cOVqElcTkzh54QoAW/+4QKtqpbPVZoMKxVj9WwQAS/ecpUXV68t7eQo+Xp4kJBk63RfApmPniYlNSK+pfE2y8Z/VMgxgEXkex51+ngP2i0hQitnv5eaK5bYTJ44THR3NkwMf59FHevDjkkUAhIeHEVCmbHK9gIAyRISFUanS3ZQpW5Y+j/TgoXbtOXXyJMYYqlWvYdUmWK60fwC9HxvAw53b0K19C+4oUoSGjZulqnNP5aqsW7MKgA1rf+bvy5eJiowkIiI8eSgCwFibzwQAAAtrSURBVD8ggIiIcBo0asL58+cI7t+HR/s9wcb1a6haveZNwW5nETHxfL/1FMuGNeGnfzXlUlwCq34Lx8vDg+plHfcFb1O9NGWKFkxz+QJeHkwfVI8pA+vSomopAIoV8iYmNiF5yCI8Jo7Svo5HmU3fcpIpT9Sj+B3e7DkVRef7yjJ35+k82NLc4U494MzGgJ8C6hljLolIBWCeiFQwxnyGWx1rvFliQgIHDx5gwsTviI2Lo/9jvbnvvtpp31rZ+Un9e8SryUXDhj7DqNff4tuQbzhy+BCNmzSlR89H8mjt84fo6Cg2rl/DnCX/xdfXl9dGDOen5T/SrmOX5DpDX/g3n3wwmhU/LqJ23fqU9g/A08sT0hi7FBG8vLx4c/SHACQkXGX40GDGfvIlX3zyPmFnz9C+U1ceaN4qz7bRCr4+XjSvUoouX2zlUmwC7/esSYdaAbyy4AAvtr0Hb08Ptv5xgYSktO8D3umzLZy7FE+5Yj588/j9HAu/xKW4xHTfb/m+MJbvCwPgqf+rwOztoTS9pwSd7itDWFQcn646lvM7jlvAnYIpsyEIT2PMJQBjzHGgBdBBRD4hg+1MeZf5yd+md69ja/kHlKFpswcoVLgwxYsXp269+hw5fBj/gADCzp5JrhcWdpbS/ql7X2vXrKZGzXu5cuUKx44d4YOPx7H0xyVcuXIlrzfDUju3b6XsnYEUL14CLy9v/q9lG/bt3Z2qTqnS/rz34Wd8N3M+wc86hmuKFPGltH8A4WFnk+uFh4VRqlTqr9QL5s6mQ+cgDuzdg5e3N2+N+Tj5oJ2dNapYnNORV4j8+yoJSYY1hyKoHViUfaejeXLqbvpP3sXuk5GcupD279u5S/EAnI6MZdeJSKqW8SXy76v4+njh6exM+PsWJCImPtVypYoUoGZZX9YfOcegByrwyvwDXE1MomHF4rm7wa6WB/ejdJXMAvisiNx/7YUzjDsDpYBa6S1kjAkxxtQ3xtS/dgZBftOiVWt2/7qLhIQErly5wv59e6lYqRI1763FyRMnOB0aytWr8fy0YjktWlzvcV29epVZM6bRb8AgYmNjk8eRjEki4epVqzbHEgFlynJg/x5iY69gjGHXjq1UqHB3qjqRkRdJSkoCYPp339Kpa3cAGjVpxo5tm4mOjiI6Oood2zbTqMn14Yvo6Cg2/7Ke9p2CiI2NxcPDAxEhPj51aNjR2ag4agUWxcfL8b9nwwrF+fPcZYoX9gbA21Po3/Qu5u+6eZjA18cLb0/H72SxQt7UDizKHxGXAdh5PJLWNRx/5DrXLsP6wxGplh3coiLj1/8JgI+3B8ZAkgEfb8/c2dBckgtPRc41mQ1B9ANSjcQ7H8PRT0TydVdk5MvD2bVjB5GRF2nXujnPDHmOhATHpvR6pDeVKt1N02YP8sjDQXh4eNC9R0/uqVwFgBGvvsazzwwiKTGJoO4Pc/c9lZPbnTN7Jp27dqNQoUJUqVIVg6FX9y488GBzfP38LNlWq9S89z5atm7LE3174enpSZWq1enaoxfffvMF1arX5IHmrdi9czsTvhoHItxfpz7DR/wHAL+ixeg/6Bme6vdPAAY8ORi/osWS254ycTz9Bz2NiNCwSTMWzJ1Fv97d6Nbjn5Zsa17a/1c0qw+GM+Op+iQkGQ6fvcSCX//i2RaVeLBKSUSEeTtPs+N4JADVy/rSs96dvLP0MBVLFWZUp6okGfAQmLL5JH+e+xuAz1f/zns9avJsi4ocPnuJRf+7/k2vahnHMfbDZx2nry3afYYfnmlIWFQsIRv+zOM9cGusj9WsE5PGWJwr/R2fy2+guJzB+J5yjfbjfrF6FW4Lu15recv5eSTs7yxnTpWAwpbmtW0vxFBK3Z7yw+llWWXbCzGUUrcnV52GJiLlRWStiBwUkQMiMsxZXkJEVonIUee/OT5KqQGslLIVF54EkQC8aIypDjQGhohIDWAksNoYUxlY7XydIxrASilbEZEsTxkxxpwxxvzq/DkGOAiUA4KAqc5qU4FuOV1XHQNWStlKbpxd5rwQrQ6wDQgwxpwBR0iLSI4v09QesFLKVrIzBJHyojHndNOFC8774MwHXjDGRLtyXbUHrJSyl2z0gI0xIUC6l+uKiDeO8J1hjFngLA4TkbLO3m9ZIDynq6o9YKWUrbjqbmjiGCSeBBw0xnySYtYSoL/z5/44bliWI9oDVkrZigvHgJsBjwP7ROR/zrJXgbHAHBEZBJwEeuX0DTSAlVK24uGiADbGbCT9AQ2XPIVBA1gpZTPucyWcBrBSylbywU3OskwDWCllK26UvxrASil70R6wUkpZJLNLjPMTDWCllK24T/xqACulbMaNOsAawEope3GnG7JrACul7MV98lcDWCllL26UvxrASil7yQ+Pm88qDWCllK24Uf7q7SiVUsoq2gNWStmKO/WANYCVUraip6EppZRFtAeslFIW0QBWSimLuNMQhJ4FoZSyFZGsT5m3Je1F5LCIHBORka5eVw1gpZStSDamDNsR8QS+AjoANYA+IlLDleuqAayUshdXJTA0BI4ZY/4wxsQDs4EgV66qjgErpWzFhZcilwNOpXgdCjRyVeOQBwFcuIA7HZN0EJFgY0yI1euRVYULuN/fUXfbx7tea2n1KmSbu+1jV/HxyvpROBEJBoJTFIWk2GdptWNuZd1upEMQaQvOvIq6RbqPc5/u40wYY0KMMfVTTCn/YIUC5VO8DgT+cuX7awArpVTadgCVRaSiiBQAegNLXPkG7vfdVSml8oAxJkFEhgI/AZ7AZGPMAVe+hwZw2m67cTML6D7OfbqPb5ExZjmwPLfaF2NcOqaslFIqi3QMWCmlLGK7ABYRIyIfp3j9koi8mcfrMEVEeuble+YWEenu3KfVnK9biMhSq9crLXba71l14+fjLKsiIsudl88eFJE5IhKQnz+725XtAhiIA3qISKmcLCwiOi6eWh9gI44jwLlG93uOpfp8RMQHWAaMN8bcY4ypDowHSlu3iio9dvylT8Bx8OFfwKiUM0TkLmAyjl/GCGCgMeakiEwBLgB1gF9FJAaoCJQFqgDDgcY4rgk/DXQxxlwVkdeBLkAhYDPwtLHRoLqIFAGaAS1xnH7zpnOWn4gsBKoCG4BnjTFJInIJ+AzoDFwBgowxYbrfc0c6n8+jwBZjzI/X6hlj1jrrt8j7tVQZsWMPGBw30OgrIkVvKP8SmGaMuQ+YAXyeYl4VoI0x5kXn67uBTjiu/f4eWGuMqYUjWDpda88Y08AYcy+OMOicK1tjnW7ASmPMEeCCiNR1ljcEXgRq4dhPPZzldwBbjTG1cQTzU85y3e+5I63P515gl7WrpbLKlgFsjIkGpgHP3zCrCTDT+fN04IEU8+YaYxJTvF5hjLkK7MNxDuBKZ/k+oILz55Yisk1E9gGtgJou24j8oQ+OG5Dg/LeP8+ftzhuUJAKzuL4f44FrY4y7uL6fdL/njvQ+H+Um7DgEcc044FfguwzqpPzaevmGeXEAzq/WV1N8xU0CvJxjbV8D9Y0xp5wH+nxcsub5gIiUxBFu94qIwRGGBsc5kTd+3b/2OuV+SiT93y/d77cog8/nLaC5leumss6WPWAAY8wFYA4wKEXxZq4fTOqL4+BFTl37n/6ccyzObkffe+IYNrjLGFPBGFMe+BNH77Wh8/JMD+CfZL4fdb+7XnqfzxGgqYhcG665dlPxWlatqEqfbQPY6WMg5dkQzwMDRWQv8DgwLKcNG2MigYk4vhovwnHduJ30ARbeUDYf50EeYCywH8f/9DfWu5Hud9fL6PPpDDwnIkdF5DdgABDurNNaREJTTE3ybI3VTfRKOKWUsojde8BKKZVvaQArpZRFNICVUsoiGsBKKWURDWCllLKIBrBSSllEA1gppSyiAayUUhb5f6bX/gH36aEWAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"#group_names = [‘Normal’,’False Pos’,’False Neg’,’True Pos’]\n",
"#group_counts = [65,0,0,9,162,17,2,11,111]\n",
"group_percentages = [1,0,0,0.048,0.862,0.09,0.016,0.089,0.895]\n",
"\n",
"#labels = [f”{v2}\\n{v3}” for v2, v3 in\n",
" #zip(group_counts,group_percentages)]\n",
"labels = np.asarray(group_percentages).reshape(3,3)\n",
"sns.heatmap(df, annot=labels,fmt = \".2%\" ,cmap='Blues')"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"ename": "ValueError",
"evalue": "'cf' is not one of the example datasets.",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-37-99adbaf43159>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload_dataset\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"cf\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[0mflights\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mflights\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpivot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Normal\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"Abnormal\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"ACL\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0max\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mheatmap\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mflights\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;32m~\\Anaconda3\\envs\\comp\\lib\\site-packages\\seaborn\\utils.py\u001b[0m in \u001b[0;36mload_dataset\u001b[1;34m(name, cache, data_home, **kws)\u001b[0m\n\u001b[0;32m 484\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mexists\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 485\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mget_dataset_names\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 486\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"'{name}' is not one of the example datasets.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 487\u001b[0m \u001b[0murlretrieve\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfull_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 488\u001b[0m \u001b[0mfull_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache_path\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mValueError\u001b[0m: 'cf' is not one of the example datasets."
]
}
],
"source": [
"flights = sns.load_dataset(\"\")\n",
"flights = flights.pivot(\"Normal\", \"Abnormal\", \"ACL\")\n",
"ax = sns.heatmap(flights)"
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"df = df.pivot(\"Normal\",\"Abnormal\",\"ACL\")\n",
"#sns.heatmap(df)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [],
"source": [
"df = pd.DataFrame(cf, columns = ['Normal','Abnormal','ACL'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
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