Projeto 3 cdd.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Projeto 3\n",
    "### Luiza Silveira e Jose Antonio Bechara"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Objetivo: \n",
    "### Determinar se uma pessoa é homem ou mulher dado caracteristicas da voz ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import matplotlib.axes as ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "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>meanfreq</th>\n",
       "      <th>sd</th>\n",
       "      <th>median</th>\n",
       "      <th>Q25</th>\n",
       "      <th>Q75</th>\n",
       "      <th>IQR</th>\n",
       "      <th>skew</th>\n",
       "      <th>kurt</th>\n",
       "      <th>sp.ent</th>\n",
       "      <th>sfm</th>\n",
       "      <th>...</th>\n",
       "      <th>centroid</th>\n",
       "      <th>meanfun</th>\n",
       "      <th>minfun</th>\n",
       "      <th>maxfun</th>\n",
       "      <th>meandom</th>\n",
       "      <th>mindom</th>\n",
       "      <th>maxdom</th>\n",
       "      <th>dfrange</th>\n",
       "      <th>modindx</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.064241</td>\n",
       "      <td>0.032027</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.090193</td>\n",
       "      <td>0.075122</td>\n",
       "      <td>12.863462</td>\n",
       "      <td>274.402906</td>\n",
       "      <td>0.893369</td>\n",
       "      <td>0.491918</td>\n",
       "      <td>...</td>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.084279</td>\n",
       "      <td>0.015702</td>\n",
       "      <td>0.275862</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.067310</td>\n",
       "      <td>0.040229</td>\n",
       "      <td>0.019414</td>\n",
       "      <td>0.092666</td>\n",
       "      <td>0.073252</td>\n",
       "      <td>22.423285</td>\n",
       "      <td>634.613855</td>\n",
       "      <td>0.892193</td>\n",
       "      <td>0.513724</td>\n",
       "      <td>...</td>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.107937</td>\n",
       "      <td>0.015826</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.009014</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.046875</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.083829</td>\n",
       "      <td>0.036718</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.131908</td>\n",
       "      <td>0.123207</td>\n",
       "      <td>30.757155</td>\n",
       "      <td>1024.927705</td>\n",
       "      <td>0.846389</td>\n",
       "      <td>0.478905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.098706</td>\n",
       "      <td>0.015656</td>\n",
       "      <td>0.271186</td>\n",
       "      <td>0.007990</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.046512</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.072111</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>0.096582</td>\n",
       "      <td>0.207955</td>\n",
       "      <td>0.111374</td>\n",
       "      <td>1.232831</td>\n",
       "      <td>4.177296</td>\n",
       "      <td>0.963322</td>\n",
       "      <td>0.727232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.088965</td>\n",
       "      <td>0.017798</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.201497</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.562500</td>\n",
       "      <td>0.554688</td>\n",
       "      <td>0.247119</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.079146</td>\n",
       "      <td>0.124656</td>\n",
       "      <td>0.078720</td>\n",
       "      <td>0.206045</td>\n",
       "      <td>0.127325</td>\n",
       "      <td>1.101174</td>\n",
       "      <td>4.333713</td>\n",
       "      <td>0.971955</td>\n",
       "      <td>0.783568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.106398</td>\n",
       "      <td>0.016931</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.712812</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>5.484375</td>\n",
       "      <td>5.476562</td>\n",
       "      <td>0.208274</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.132786</td>\n",
       "      <td>0.079557</td>\n",
       "      <td>0.119090</td>\n",
       "      <td>0.067958</td>\n",
       "      <td>0.209592</td>\n",
       "      <td>0.141634</td>\n",
       "      <td>1.932562</td>\n",
       "      <td>8.308895</td>\n",
       "      <td>0.963181</td>\n",
       "      <td>0.738307</td>\n",
       "      <td>...</td>\n",
       "      <td>0.132786</td>\n",
       "      <td>0.110132</td>\n",
       "      <td>0.017112</td>\n",
       "      <td>0.253968</td>\n",
       "      <td>0.298222</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>2.726562</td>\n",
       "      <td>2.718750</td>\n",
       "      <td>0.125160</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.150762</td>\n",
       "      <td>0.074463</td>\n",
       "      <td>0.160106</td>\n",
       "      <td>0.092899</td>\n",
       "      <td>0.205718</td>\n",
       "      <td>0.112819</td>\n",
       "      <td>1.530643</td>\n",
       "      <td>5.987498</td>\n",
       "      <td>0.967573</td>\n",
       "      <td>0.762638</td>\n",
       "      <td>...</td>\n",
       "      <td>0.150762</td>\n",
       "      <td>0.105945</td>\n",
       "      <td>0.026230</td>\n",
       "      <td>0.266667</td>\n",
       "      <td>0.479620</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>5.312500</td>\n",
       "      <td>5.304688</td>\n",
       "      <td>0.123992</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.160514</td>\n",
       "      <td>0.076767</td>\n",
       "      <td>0.144337</td>\n",
       "      <td>0.110532</td>\n",
       "      <td>0.231962</td>\n",
       "      <td>0.121430</td>\n",
       "      <td>1.397156</td>\n",
       "      <td>4.766611</td>\n",
       "      <td>0.959255</td>\n",
       "      <td>0.719858</td>\n",
       "      <td>...</td>\n",
       "      <td>0.160514</td>\n",
       "      <td>0.093052</td>\n",
       "      <td>0.017758</td>\n",
       "      <td>0.144144</td>\n",
       "      <td>0.301339</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>0.539062</td>\n",
       "      <td>0.531250</td>\n",
       "      <td>0.283937</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.142239</td>\n",
       "      <td>0.078018</td>\n",
       "      <td>0.138587</td>\n",
       "      <td>0.088206</td>\n",
       "      <td>0.208587</td>\n",
       "      <td>0.120381</td>\n",
       "      <td>1.099746</td>\n",
       "      <td>4.070284</td>\n",
       "      <td>0.970723</td>\n",
       "      <td>0.770992</td>\n",
       "      <td>...</td>\n",
       "      <td>0.142239</td>\n",
       "      <td>0.096729</td>\n",
       "      <td>0.017957</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.336476</td>\n",
       "      <td>0.007812</td>\n",
       "      <td>2.164062</td>\n",
       "      <td>2.156250</td>\n",
       "      <td>0.148272</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.134329</td>\n",
       "      <td>0.080350</td>\n",
       "      <td>0.121451</td>\n",
       "      <td>0.075580</td>\n",
       "      <td>0.201957</td>\n",
       "      <td>0.126377</td>\n",
       "      <td>1.190368</td>\n",
       "      <td>4.787310</td>\n",
       "      <td>0.975246</td>\n",
       "      <td>0.804505</td>\n",
       "      <td>...</td>\n",
       "      <td>0.134329</td>\n",
       "      <td>0.105881</td>\n",
       "      <td>0.019300</td>\n",
       "      <td>0.262295</td>\n",
       "      <td>0.340365</td>\n",
       "      <td>0.015625</td>\n",
       "      <td>4.695312</td>\n",
       "      <td>4.679688</td>\n",
       "      <td>0.089920</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   meanfreq        sd    median       Q25       Q75       IQR       skew  \\\n",
       "0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122  12.863462   \n",
       "1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252  22.423285   \n",
       "2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207  30.757155   \n",
       "3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374   1.232831   \n",
       "4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325   1.101174   \n",
       "5  0.132786  0.079557  0.119090  0.067958  0.209592  0.141634   1.932562   \n",
       "6  0.150762  0.074463  0.160106  0.092899  0.205718  0.112819   1.530643   \n",
       "7  0.160514  0.076767  0.144337  0.110532  0.231962  0.121430   1.397156   \n",
       "8  0.142239  0.078018  0.138587  0.088206  0.208587  0.120381   1.099746   \n",
       "9  0.134329  0.080350  0.121451  0.075580  0.201957  0.126377   1.190368   \n",
       "\n",
       "          kurt    sp.ent       sfm  ...    centroid   meanfun    minfun  \\\n",
       "0   274.402906  0.893369  0.491918  ...    0.059781  0.084279  0.015702   \n",
       "1   634.613855  0.892193  0.513724  ...    0.066009  0.107937  0.015826   \n",
       "2  1024.927705  0.846389  0.478905  ...    0.077316  0.098706  0.015656   \n",
       "3     4.177296  0.963322  0.727232  ...    0.151228  0.088965  0.017798   \n",
       "4     4.333713  0.971955  0.783568  ...    0.135120  0.106398  0.016931   \n",
       "5     8.308895  0.963181  0.738307  ...    0.132786  0.110132  0.017112   \n",
       "6     5.987498  0.967573  0.762638  ...    0.150762  0.105945  0.026230   \n",
       "7     4.766611  0.959255  0.719858  ...    0.160514  0.093052  0.017758   \n",
       "8     4.070284  0.970723  0.770992  ...    0.142239  0.096729  0.017957   \n",
       "9     4.787310  0.975246  0.804505  ...    0.134329  0.105881  0.019300   \n",
       "\n",
       "     maxfun   meandom    mindom    maxdom   dfrange   modindx  label  \n",
       "0  0.275862  0.007812  0.007812  0.007812  0.000000  0.000000   male  \n",
       "1  0.250000  0.009014  0.007812  0.054688  0.046875  0.052632   male  \n",
       "2  0.271186  0.007990  0.007812  0.015625  0.007812  0.046512   male  \n",
       "3  0.250000  0.201497  0.007812  0.562500  0.554688  0.247119   male  \n",
       "4  0.266667  0.712812  0.007812  5.484375  5.476562  0.208274   male  \n",
       "5  0.253968  0.298222  0.007812  2.726562  2.718750  0.125160   male  \n",
       "6  0.266667  0.479620  0.007812  5.312500  5.304688  0.123992   male  \n",
       "7  0.144144  0.301339  0.007812  0.539062  0.531250  0.283937   male  \n",
       "8  0.250000  0.336476  0.007812  2.164062  2.156250  0.148272   male  \n",
       "9  0.262295  0.340365  0.015625  4.695312  4.679688  0.089920   male  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('voice1.csv', quotechar=',')\n",
    "df.columns=df.columns.str.replace('\"','')\n",
    "df['meanfreq'] = df['meanfreq'].map(lambda x: x.lstrip('\"'))\n",
    "df['label'] = df['label'].map(lambda x: x.lstrip('\"').rstrip('\"\"'))\n",
    "df['meanfreq'] = df['meanfreq'].astype('float')\n",
    "\n",
    "\n",
    "df.head(10)\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['label'] == 'female'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 386,
   "metadata": {},
   "outputs": [],
   "source": [
    "#X = df[['meanfreq', 'sd', 'median', 'Q25', 'Q75', 'IQR', 'skew', 'kurt']]\n",
    "X = df[[\"meanfreq\",\"sd\",\"median\",\"Q25\",\"Q75\",\"IQR\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "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>meanfreq</th>\n",
       "      <th>sd</th>\n",
       "      <th>median</th>\n",
       "      <th>Q25</th>\n",
       "      <th>Q75</th>\n",
       "      <th>IQR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.059781</td>\n",
       "      <td>0.064241</td>\n",
       "      <td>0.032027</td>\n",
       "      <td>0.015071</td>\n",
       "      <td>0.090193</td>\n",
       "      <td>0.075122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.066009</td>\n",
       "      <td>0.067310</td>\n",
       "      <td>0.040229</td>\n",
       "      <td>0.019414</td>\n",
       "      <td>0.092666</td>\n",
       "      <td>0.073252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.077316</td>\n",
       "      <td>0.083829</td>\n",
       "      <td>0.036718</td>\n",
       "      <td>0.008701</td>\n",
       "      <td>0.131908</td>\n",
       "      <td>0.123207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.151228</td>\n",
       "      <td>0.072111</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>0.096582</td>\n",
       "      <td>0.207955</td>\n",
       "      <td>0.111374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.135120</td>\n",
       "      <td>0.079146</td>\n",
       "      <td>0.124656</td>\n",
       "      <td>0.078720</td>\n",
       "      <td>0.206045</td>\n",
       "      <td>0.127325</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   meanfreq        sd    median       Q25       Q75       IQR\n",
       "0  0.059781  0.064241  0.032027  0.015071  0.090193  0.075122\n",
       "1  0.066009  0.067310  0.040229  0.019414  0.092666  0.073252\n",
       "2  0.077316  0.083829  0.036718  0.008701  0.131908  0.123207\n",
       "3  0.151228  0.072111  0.158011  0.096582  0.207955  0.111374\n",
       "4  0.135120  0.079146  0.124656  0.078720  0.206045  0.127325"
      ]
     },
     "execution_count": 426,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4,random_state = 0)\n",
    "\n",
    "X.head(5)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luiza\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  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": 427,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8809148264984227"
      ]
     },
     "execution_count": 429,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "accuracy_score(y_test, y_pred)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          meanfreq           sd       median          Q25          Q75  \\\n",
      "count  1584.000000  1584.000000  1584.000000  1584.000000  1584.000000   \n",
      "mean      0.170813     0.065110     0.175299     0.115562     0.226346   \n",
      "std       0.026254     0.009455     0.037392     0.031999     0.024050   \n",
      "min       0.039363     0.041747     0.010975     0.000240     0.042946   \n",
      "25%       0.155625     0.058957     0.149952     0.101205     0.211918   \n",
      "50%       0.176343     0.061781     0.180612     0.122315     0.228117   \n",
      "75%       0.190593     0.070915     0.202362     0.136044     0.244819   \n",
      "max       0.225582     0.096030     0.248840     0.226740     0.268924   \n",
      "\n",
      "               IQR  \n",
      "count  1584.000000  \n",
      "mean      0.110784  \n",
      "std       0.020415  \n",
      "min       0.021841  \n",
      "25%       0.100960  \n",
      "50%       0.109940  \n",
      "75%       0.119331  \n",
      "max       0.196168  \n",
      "          meanfreq           sd       median          Q25          Q75  \\\n",
      "count  1583.000000  1583.000000  1583.000000  1583.000000  1583.000000   \n",
      "mean      0.191021     0.049121     0.195945     0.165414     0.223183   \n",
      "std       0.029958     0.018366     0.032159     0.049716     0.023128   \n",
      "min       0.078847     0.018363     0.035114     0.000229     0.127637   \n",
      "25%       0.177073     0.034975     0.181011     0.157904     0.206273   \n",
      "50%       0.192752     0.041960     0.198272     0.175376     0.223742   \n",
      "75%       0.211982     0.060432     0.216214     0.195248     0.241528   \n",
      "max       0.251124     0.115273     0.261224     0.247347     0.273469   \n",
      "\n",
      "               IQR  \n",
      "count  1583.000000  \n",
      "mean      0.057769  \n",
      "std       0.042857  \n",
      "min       0.014558  \n",
      "25%       0.031101  \n",
      "50%       0.042686  \n",
      "75%       0.061220  \n",
      "max       0.252225  \n"
     ]
    }
   ],
   "source": [
    "male = X[0:1584]\n",
    "female = X[1585:3168]\n",
    "print(male.describe())\n",
    "print(female.describe())\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\luiza\\Anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py:51: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0,0,'Mean(M)'),\n",
       " Text(0,0,'Mean(F)'),\n",
       " Text(0,0,'sd(M)'),\n",
       " Text(0,0,'sd(F)'),\n",
       " Text(0,0,'median(M)'),\n",
       " Text(0,0,'median(F)'),\n",
       " Text(0,0,'Q75(M)'),\n",
       " Text(0,0,'Q75(F)'),\n",
       " Text(0,0,'Q25(M)'),\n",
       " Text(0,0,'Q25(F)'),\n",
       " Text(0,0,'IQR(M)'),\n",
       " Text(0,0,'IQR(F)')]"
      ]
     },
     "execution_count": 431,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dd4ecc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#http://blog.bharatbhole.com/creating-boxplots-with-matplotlib/\n",
    "fig = plt.figure(1, figsize=(9, 6))\n",
    "ax = fig.add_subplot(111)\n",
    "data =  [male[\"meanfreq\"],female[\"meanfreq\"],male[\"sd\"],female[\"sd\"],male[\"median\"],female[\"median\"],male[\"Q75\"],female[\"Q75\"],male[\"Q25\"], female[\"Q25\"], male[\"IQR\"], female[\"IQR\"]]\n",
    "bp = ax.boxplot(data, patch_artist=True)\n",
    "ax.set_xticklabels(['Mean(M)', 'Mean(F)', 'sd(M)', 'sd(F)',\"median(M)\",\"median(F)\",\"Q75(M)\",\"Q75(F)\",\"Q25(M)\",\"Q25(F)\",\"IQR(M)\",\"IQR(F)\"])\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dd91cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def verticalLine(plot, x, y, color, nome):\n",
    "    plot.axvline(x, color=color, linestyle=\"dashed\", linewidth=2)\n",
    "    plot.text(1.01*x, y, nome, color=color)    \n",
    "\n",
    "plotm = male[\"meanfreq\"].plot.hist(density=True,color = \"blue\",alpha = 0.7)\n",
    "plotf = female[\"meanfreq\"].plot.hist(density=True,color = \"red\",alpha = 0.7)\n",
    "\n",
    "\n",
    "\n",
    "verticalLine(plotm, male.meanfreq.mean(), 16, \"y\", \"Média\")\n",
    "verticalLine(plotf, female.meanfreq.mean(), 16, \"g\", \"Média\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5dd91080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 = male.Q25.plot.hist(density=True, color = \"blue\",alpha = 0.7)\n",
    "plot2 = female.Q25.plot.hist(density=True,color = \"red\",alpha = 0.7)\n",
    "verticalLine(plot1, male.Q25.mean(), 16, \"y\", \"Média\")\n",
    "\n",
    "verticalLine(plot2, female.Q25.mean(), 16, \"b\", \"Média\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4vQ1mdry7rzez44GNHfxmHVAHUF1drb/dIiIpOWQiMLNewDPu/gWgmH/825oLXAPcFb8+1cXvExGRLjrkNQJ3/xD4wMyO7cwXm9kjwCLgVDNba2bXEiWAMWa2ChgTb4uISEBJrxHsBJrNrJ545BCAu3/zYB9w96sO8tZFycMTEZG0JU0Ef4gXERHpYTpMBGY22N3XuPuDHR0nIiLZdagWwe+BkQBm9oS7fyn9kCSphs/fGDqEsjF7dkPoEEQy61CJwNqsn5xmINJ5Vce9FjqEsrFxY1XoEEQy61Cjhvwg6yIi0kMcqkVwppltIWoZHBWvE2+7u/dNNTrpUE3T9YAmnwMYM6YG0ORzIsXoMBG4e69SBSKdN2vNOECJAKBQiGY8USIQ6bykk86JiEgPpUQgIpJzSgQiIjmnRCAiknNKBCIiOdeZh9dLmRl57KuhQygbGzaMDB2CSGYpEWRY47k3hQ6hbMyZo8dUihRLXUMiIjmnRCAiknNKBBlm8+dh8+eFDqMs1NYatbV26ANF5ABKBCIiOadEICKSc0oEIiI5p0QgIpJzSgQiIjmnRCAiknO6szjDZlbeGzqEsrFgwczQIYhklhJBhtUMeSZ0CGWjubkmdAgimaWuIRGRnFMiyLC6N8dS9+bY0GGUhcrKOior9bxikWKoayjDpjffAKiLCODii6cD6iISKYZaBCIiOadEICKSc0oEIiI5p0QgIpJzSgQiIjmnRCAiknMaPpphfumE0CGUjRkzPHQIIpmlFoGISM4pEYiI5FyQriEzWw1sBT4E9rp7dYg4sq7qL3cD0HjuTYEjCW/y5CoA5sxpDByJSPaEvEZwgbtvCvj7mbfs/U+FDqFsDBq0LHQIIpmlriERkZwLlQgcWGBmjWamWcJERAIK1TX0OXdfZ2YVQL2ZrXT3F9oeECeIGoDBgweHiFFEJBeCtAjcfV38uhF4EhjVzjF17l7t7tUDBw4sdYgiIrlR8haBmR0NHObuW+P1i4HbSx2HtG+C7lETyZ0QXUODgCfNrOX3f+PufwoQR+ZNG6xqa9HUNC10CCKZVfJE4O6vA2eW+nd7orrCfaFDKBv19XpMpUixNHxURCTnlAgyrPG9YTS+Nyx0GGWhoqKRigrdVSxSDM0+mmHVL/4M0CykAFOmRLOUaBZSkc5Ti0BEJOfUIhA5iDSH0s6bl953i3SWWgQiIjmnRCAiknPqGuqJutCnceuS4n/2jlHq7xDJIrUIRERyTi2CDGv4/I2hQygbs2c3hA5BJLOUCDKs6rjXQodQNjZurAodgkhmqWtIRCTnlAgyrKbpemqarg8dRlkYM6aGMWP0sDuRYigRZNisNeOYtWZc6DDKQqEwi0JhVugwRDJJiUBEJOeUCEREck6JQEQk55QIRERyTolARCTndENZho089tXQIZSNDRtGhg5BJLOUCDKs8dybQodQNubM0WMqRYqlRCASQFoPvdEDb6QYSgQiGXXrknaySSkeX61s0+PoYnGG2fx52Hz9pQSorTVqay10GCKZpEQgIpJzSgQiIjmnRCAiknNKBCIiOadRQyJd0O7IHZGMUSKQbhPyH8UPgv2ySPYpEWTYzMp7Q4dQNhYsmBk6hLKwZEk63ztqVDrfK+VBiSCjliyBz/BMtL4hcDBlYOIvo/spJqL7KkQ6SxeLRURyTokgw57cMZYnd4wNHUZZ2HvRm+y96M3QYYhkkrqGMuyurTcAcMVRzwSOJLzdNc0A9H5uSOBIRLJHLQIRkZxTIhARybkgXUNmNg74L6AX8N/ufleIOFqkNeQONOxOeoa2f0fu6ObbRTSrdXglTwRm1gu4DxgDrAWWmtlcd/97qWMphTSTjEgI3X7jYGe+LlTWSOtJQkmUoMwhuoZGAa+6++vuvht4FLg8QBwiIkKYrqETgLfabK8Fzg4Qh4hkTcj/mfdgIRJBe4+R8gMOMqsBauLNbWb2j1SjKj8DgE0dHxJV5dkb0w8mgATlb+OClpX5acQSQufKn2Xtn7L8lL99reW3Lj15L9F46hCJYC3wyTbbJwLr9j/I3euAulIFVW7MrMHdq0PHEYrKr/Kr/KUrf4hrBEuBU8zsJDP7GDAJmBsgDhERIUCLwN33mtkNwDNEw0cfcPeXSx2HiIhEgtxH4O5PA0+H+O0MyW23WEzlzzeVv4TM/YDrtCIikiOaYkJEJOeUCErMzMaZ2T/M7FUzu7md948ws9/G7//VzIbG+4ea2Q4zeyleflHq2LtDgvKfZ2bLzGyvmX15v/euMbNV8XJN6aLuPl0s/4dtzn8mB1gkKP+3zezvZtZkZs+Z2ZA27+Xh/HdU/vTOv7trKdFCdHH8NeBk4GPA34BP73fMN4BfxOuTgN/G60OBFaHLUILyDwUKwEPAl9vs7w+8Hr/2i9f7hS5Tqcofv7ctdBlKUP4LgD7x+nVt/vzn5fy3W/60z79aBKWVZHqNy4EH4/XfAReZde2OkjJyyPK7+2p3bwI+2u+zY4F6d3/X3TcD9cC4UgTdjbpS/p4gSfmfd/cP4s3FRPcZQX7O/8HKnyolgtJqb3qNEw52jLvvBd4HPhG/d5KZLTezP5vZuWkHm4Ik5U/js+Wiq2U40swazGyxmU3s3tBKorPlvxb4Y5GfLUddKT+keP71hLLSSjK9xsGOWQ8Mdvd3zKwK+L2Zne7uW7o7yBQlml4khc+Wi66WYbC7rzOzk4H/NbNmd3+tm2IrhcTlN7PJQDVwfmc/W8a6Un5I8fyrRVBaSabX+NcxZtYbOBZ41913ufs7AO7eSNTX+O+pR9y9Ek0vksJny0WXyuDu6+LX14GFwIjuDK4EEpXfzL4A3AJc5u67OvPZMteV8qd7/kNfQMnTQtQCex04idaLRafvd8z17Hux+LF4fSDQK14/Gfgn0D90mbq7/G2O/TUHXix+g+hCYb94PU/l7wccEa8PAFax34XGcl8S/vkfQfSfnFP225+L899B+VM9/8ErJ28LMB74v/hk3xLvu50o+wMcCTwOvAosAU6O938JeDn+w7MMmBC6LCmV/yyi/zltB94BXm7z2alxvbwKfD10WUpZfuCzQHN8/puBa0OXJaXyPwtsAF6Kl7k5O//tlj/t8687i0VEck7XCEREck6JQEQk55QIRERyTolARCTnlAhERHJOiUBEJOeUCEREck6JQEQk5/4fZf6ZAD6sbLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f57bd4588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot1 = male.IQR.plot.hist(density=True, color = \"blue\",alpha = 0.7)\n",
    "plot2 = female.IQR.plot.hist(density=True, color = \"red\",alpha = 0.7)\n",
    "\n",
    "verticalLine(plot1, male.IQR.mean(), 16, \"y\", \"Média\")\n",
    "\n",
    "\n",
    "verticalLine(plot2, female.IQR.mean(), 16, \"b\", \"Média\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.0000000e+00]\n",
      " [ 0.0000000e+00]\n",
      " [-3.0517578e-05]\n",
      " ...\n",
      " [-1.7089844e-03]\n",
      " [-9.7656250e-04]\n",
      " [-3.0517578e-04]]\n"
     ]
    }
   ],
   "source": [
    "import pyaudio\n",
    "import os\n",
    "import struct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.fftpack import fft\n",
    "import time\n",
    "from tkinter import TclError\n",
    "import sounddevice as sd\n",
    "import pandas as pd\n",
    "\n",
    "fs = 48000\n",
    "\n",
    "duration = 2  # seconds\n",
    "myrecording = sd.rec(int(duration * fs), samplerate=fs, channels=1)\n",
    "\n",
    "sd.default.samplerate = fs\n",
    "sd.default.channels = 1\n",
    "\n",
    "myrecording = sd.rec(int(duration * fs))\n",
    "\n",
    "\n",
    "sd.wait()\n",
    "\n",
    "print(myrecording)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f59596f28>]"
      ]
     },
     "execution_count": 255,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5952fdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sd.play(myrecording, samplerate=None, mapping=None, blocking=False, loop=False)\n",
    "plt.plot(myrecording)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x22f5d8fca20>]"
      ]
     },
     "execution_count": 256,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f5752e8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = np.fft.fft(myrecording, axis=0)\n",
    "X = np.abs(X)\n",
    "\n",
    "fmax = 280\n",
    "nmax = int(len(X) * fmax / fs)\n",
    "\n",
    "X = X[:nmax]\n",
    "X = X / sum(X)\n",
    "f = np.linspace(0, fmax, nmax)\n",
    "plt.plot(f, X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "560\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x22f57568c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cX = np.cumsum(X)\n",
    "plt.plot(f, cX)\n",
    "plt.xlabel(\"Frequência\")\n",
    "plt.ylabel(\"Energia\")\n",
    "\n",
    "print(len(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.19735241502683362 0.2499463327370304 0.05259391771019678\n"
     ]
    }
   ],
   "source": [
    "i25 = 0\n",
    "i75 = 0\n",
    "for i in range(1, len(cX)):\n",
    "    if cX[i] > 0.25 and cX[i-1] <= 0.25:\n",
    "        i25 = i\n",
    "    if cX[i] > 0.75 and cX[i-1] <= 0.75:\n",
    "        i75 = i\n",
    "\n",
    "Q25 = f[i25]/1000        \n",
    "Q75 = f[i75]/1000\n",
    "IQ = Q75 - Q25\n",
    "print(Q25, Q75, IQ)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.13999999999999996, 0.08104589910054591, 0.14)"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfr = pd.DataFrame(f)\n",
    "dfa = pd.DataFrame(cX)\n",
    "\n",
    "mean = dfr.describe()[0].values[1]/1000\n",
    "std = dfr.describe()[0].values[2]/1000\n",
    "median = dfr.describe()[0].values[5]/1000\n",
    "\n",
    "\n",
    "Dataframe = dfr.join(dfa, how='inner',lsuffix='_')\n",
    "Dataframe.rename(columns={'0_':\"Frequencia\",\"0\": \"Energia\"}, inplace=True)\n",
    "Dataframe.head()\n",
    "\n",
    "print(mean,std,median)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ True]\n"
     ]
    }
   ],
   "source": [
    "result = model.predict([[mean, std , median , Q25, Q75 , IQ]])\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}