practica tar2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 8  7  5  1  4  8  3  2  2  3]\n",
      " [ 9 10  6  6  2  3  4  1  2  4]\n",
      " [ 6  2  6  1  1 10  5  5  6  8]\n",
      " [ 3  4  9  7  2  2  8 10  9  4]\n",
      " [ 6  3  5  6  8  7  6  9 10  4]\n",
      " [ 8 10  2  3  4  1  9  2  1  7]\n",
      " [ 5  2  3  7  5  5  1  4  6  9]\n",
      " [ 4  8  4 10  1  3 10  3  9  1]\n",
      " [ 4  9  2  8  1  9  3  7  3  8]\n",
      " [ 5  5  5  2  2  4  8  8  8  7]]\n"
     ]
    }
   ],
   "source": [
    "mc = np.random.randint(10, size=(10, 10))\n",
    "mc += 1\n",
    "print(mc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def precison(mc,r):\n",
    "    \n",
    "    m = mc[r,r]\n",
    "    #print('m= ',m)\n",
    "    #print('m= ',mc[r,1])\n",
    "    sumMC = np.sum(mc[r,:])\n",
    "    #print(sumMC)\n",
    "    pre = m/sumMC\n",
    "    return pre"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def recall(mc,c):\n",
    "        \n",
    "    m = mc[c,c]\n",
    "    #print('m= ',m)\n",
    "    #print('m= ',mc[r,1])\n",
    "    sumMC = np.sum(mc[:,c])\n",
    "    #print('rec sum:',sumMC)\n",
    "    rec = m/sumMC\n",
    "    #print('rec: ',rec)\n",
    "    return rec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.15841584158415842\n",
      "0.18691588785046728\n",
      "0.12371134020618556\n",
      "0.12844036697247707\n",
      "0.1702127659574468\n",
      "0.0202020202020202\n",
      "0.01923076923076923\n",
      "0.05769230769230769\n",
      "0.05454545454545454\n",
      "0.12844036697247707\n",
      "[0.15841584 0.18691589 0.12371134 0.12844037 0.17021277 0.02020202\n",
      " 0.01923077 0.05769231 0.05454545 0.12844037]\n"
     ]
    }
   ],
   "source": [
    "f_score = []\n",
    "for i in range(10):\n",
    "    pre = precison(mc,i)\n",
    "    #print('precision: ', pre)\n",
    "    rec = recall(mc,i)\n",
    "    #print('recall: ',rec)\n",
    "    ppr = pre*rec\n",
    "    #print(ppr)\n",
    "    pmr = pre+rec\n",
    "    #print(pmr)\n",
    "    f = 2*ppr/pmr\n",
    "    print(f)\n",
    "    f_score = np.append(f_score,f)\n",
    "    \n",
    "print(f_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.10478071212137641\n"
     ]
    }
   ],
   "source": [
    "avgFscore = (1/10)*np.sum(f_score)\n",
    "print(avgFscore)\n",
    "save_metricas_dl(avgFscore,f_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "def save_metricas_dl(avgF, fscore):   \n",
    "    fscore = np.append(fscore,avgF)\n",
    "    archivo = open('estima_dl.csv', 'w')\n",
    "\n",
    "    [archivo.write(f'{fscore[i]},') for i in range(len(fscore)) ]\n",
    "\n",
    "    \n",
    "    archivo.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10, 400)\n"
     ]
    }
   ],
   "source": [
    "data= np.load('x.npy')\n",
    "print(data.shape)\n",
    "df= pd.DataFrame(data=data)\n",
    "#df\n",
    "\n",
    "data2= np.load('y.npy')\n",
    "#data2.shape\n",
    "df2= pd.DataFrame(data=data2)\n",
    "#df2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>390</th>\n",
       "      <th>391</th>\n",
       "      <th>392</th>\n",
       "      <th>393</th>\n",
       "      <th>394</th>\n",
       "      <th>395</th>\n",
       "      <th>396</th>\n",
       "      <th>397</th>\n",
       "      <th>398</th>\n",
       "      <th>399</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000213</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000364</td>\n",
       "      <td>0.000398</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000313</td>\n",
       "      <td>0.000359</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000307</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.000303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000207</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000353</td>\n",
       "      <td>0.000386</td>\n",
       "      <td>0.000279</td>\n",
       "      <td>0.000303</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000226</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000297</td>\n",
       "      <td>0.000248</td>\n",
       "      <td>0.000153</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000170</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000366</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000214</td>\n",
       "      <td>0.000180</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000235</td>\n",
       "      <td>0.000145</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000315</td>\n",
       "      <td>0.000278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000168</td>\n",
       "      <td>0.000330</td>\n",
       "      <td>0.000361</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>0.000325</td>\n",
       "      <td>0.000213</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000232</td>\n",
       "      <td>0.000143</td>\n",
       "      <td>0.000258</td>\n",
       "      <td>0.000311</td>\n",
       "      <td>0.000275</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000177</td>\n",
       "      <td>0.000348</td>\n",
       "      <td>0.000381</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000344</td>\n",
       "      <td>0.000225</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000223</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000270</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000294</td>\n",
       "      <td>0.000245</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000273</td>\n",
       "      <td>0.000329</td>\n",
       "      <td>0.000290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000208</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.000202</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000355</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.000305</td>\n",
       "      <td>0.000350</td>\n",
       "      <td>0.000229</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000228</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>0.000196</td>\n",
       "      <td>0.000299</td>\n",
       "      <td>0.000250</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000278</td>\n",
       "      <td>0.000334</td>\n",
       "      <td>0.000296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000218</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000372</td>\n",
       "      <td>0.000407</td>\n",
       "      <td>0.000295</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000241</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000239</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000314</td>\n",
       "      <td>0.000262</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000292</td>\n",
       "      <td>0.000351</td>\n",
       "      <td>0.000310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.000217</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000185</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000397</td>\n",
       "      <td>0.000287</td>\n",
       "      <td>0.000312</td>\n",
       "      <td>0.000358</td>\n",
       "      <td>0.000234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000233</td>\n",
       "      <td>0.000195</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>0.000200</td>\n",
       "      <td>0.000306</td>\n",
       "      <td>0.000255</td>\n",
       "      <td>0.000157</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000342</td>\n",
       "      <td>0.000302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000201</td>\n",
       "      <td>0.000192</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>0.000337</td>\n",
       "      <td>0.000369</td>\n",
       "      <td>0.000267</td>\n",
       "      <td>0.000290</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000218</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000181</td>\n",
       "      <td>0.000261</td>\n",
       "      <td>0.000186</td>\n",
       "      <td>0.000284</td>\n",
       "      <td>0.000237</td>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000264</td>\n",
       "      <td>0.000318</td>\n",
       "      <td>0.000281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000215</td>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000209</td>\n",
       "      <td>0.000187</td>\n",
       "      <td>0.000367</td>\n",
       "      <td>0.000402</td>\n",
       "      <td>0.000291</td>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.000363</td>\n",
       "      <td>0.000238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000236</td>\n",
       "      <td>0.000197</td>\n",
       "      <td>0.000285</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.000310</td>\n",
       "      <td>0.000259</td>\n",
       "      <td>0.000160</td>\n",
       "      <td>0.000288</td>\n",
       "      <td>0.000347</td>\n",
       "      <td>0.000306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 400 columns</p>\n",
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      ],
      "text/plain": [
       "        0         1         2         3         4         5         6    \\\n",
       "0  0.000213  0.000217  0.000207  0.000185  0.000364  0.000398  0.000288   \n",
       "1  0.000207  0.000211  0.000200  0.000180  0.000353  0.000386  0.000279   \n",
       "2  0.000196  0.000200  0.000190  0.000170  0.000334  0.000366  0.000265   \n",
       "3  0.000193  0.000197  0.000187  0.000168  0.000330  0.000361  0.000261   \n",
       "4  0.000204  0.000208  0.000198  0.000177  0.000348  0.000381  0.000276   \n",
       "5  0.000208  0.000212  0.000202  0.000181  0.000355  0.000388  0.000281   \n",
       "6  0.000218  0.000222  0.000211  0.000190  0.000372  0.000407  0.000295   \n",
       "7  0.000212  0.000217  0.000206  0.000185  0.000363  0.000397  0.000287   \n",
       "8  0.000198  0.000201  0.000192  0.000172  0.000337  0.000369  0.000267   \n",
       "9  0.000215  0.000219  0.000209  0.000187  0.000367  0.000402  0.000291   \n",
       "\n",
       "        7         8         9    ...       390       391       392       393  \\\n",
       "0  0.000313  0.000359  0.000235  ...  0.000233  0.000196  0.000282  0.000201   \n",
       "1  0.000303  0.000348  0.000228  ...  0.000226  0.000190  0.000273  0.000195   \n",
       "2  0.000287  0.000330  0.000216  ...  0.000214  0.000180  0.000259  0.000185   \n",
       "3  0.000283  0.000325  0.000213  ...  0.000211  0.000177  0.000255  0.000182   \n",
       "4  0.000299  0.000344  0.000225  ...  0.000223  0.000187  0.000270  0.000193   \n",
       "5  0.000305  0.000350  0.000229  ...  0.000228  0.000191  0.000275  0.000196   \n",
       "6  0.000320  0.000367  0.000241  ...  0.000239  0.000200  0.000288  0.000206   \n",
       "7  0.000312  0.000358  0.000234  ...  0.000233  0.000195  0.000281  0.000200   \n",
       "8  0.000290  0.000333  0.000218  ...  0.000216  0.000181  0.000261  0.000186   \n",
       "9  0.000316  0.000363  0.000238  ...  0.000236  0.000197  0.000285  0.000203   \n",
       "\n",
       "        394       395       396       397       398       399  \n",
       "0  0.000307  0.000256  0.000158  0.000285  0.000343  0.000303  \n",
       "1  0.000297  0.000248  0.000153  0.000276  0.000333  0.000294  \n",
       "2  0.000282  0.000235  0.000145  0.000262  0.000315  0.000278  \n",
       "3  0.000278  0.000232  0.000143  0.000258  0.000311  0.000275  \n",
       "4  0.000294  0.000245  0.000151  0.000273  0.000329  0.000290  \n",
       "5  0.000299  0.000250  0.000154  0.000278  0.000334  0.000296  \n",
       "6  0.000314  0.000262  0.000162  0.000292  0.000351  0.000310  \n",
       "7  0.000306  0.000255  0.000157  0.000284  0.000342  0.000302  \n",
       "8  0.000284  0.000237  0.000146  0.000264  0.000318  0.000281  \n",
       "9  0.000310  0.000259  0.000160  0.000288  0.000347  0.000306  \n",
       "\n",
       "[10 rows x 400 columns]"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "def norm(df):\n",
    "    a = 0.01\n",
    "    b = 0.99\n",
    "    df_norm = pd.DataFrame()\n",
    "    \n",
    "    for col in df:\n",
    "        min_ = df[col].min()\n",
    "        max_ = df[col].max()\n",
    "\n",
    "        df_norm[col] = (df[col] - min_) * (b - a) / (max_ - min_) + a\n",
    "    \n",
    "    x = df_norm\n",
    "    x = x.T\n",
    "    return (x.to_numpy())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.80132565, 0.54342589, 0.11483054, 0.01      , 0.44346538,\n",
       "        0.58799275, 0.99      , 0.77132565, 0.18843668, 0.88419318]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sumatoria = []\n",
    "for i in range(10):\n",
    "    sumatoria = np.append(sumatoria, np.sum(data[i][:]))\n",
    "    \n",
    "f = pd.DataFrame(data = sumatoria)\n",
    "f\n",
    "x=norm(f)\n",
    "\n",
    "\n",
    "sumatoria2 = []\n",
    "for i in range(10):\n",
    "    sumatoria2 = np.append(sumatoria2, np.sum(data2[i][:]))\n",
    "\n",
    "f2 = pd.DataFrame(data = sumatoria2)\n",
    "y=norm(f2)\n",
    "y\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.80132565, 0.54342589, 0.11483054, 0.01      , 0.44346538,\n",
       "        0.58799275, 0.99      , 0.77132565, 0.18843668, 0.88419318]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  }
 ],
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