Classification_Projects/Logistic_Regression/Heart_Disease_Classification/Heart_Disease_Classification.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1 style=\"text-align: center;\">Heart Disease Prediction | Logistic Regression</h1>\n",
    "<h4 style=\"text-align: center;\">Ghani Haider |  github.com/Ghani-Haider/</h4>\n",
    "\n",
    "The [dataset](https://archive.ics.uci.edu/ml/datasets/Heart+Disease) contains 14 physical attributes based on physical testing of a patient. Blood samples are taken and the patient also conducts a brief exercise test. The \"goal\" field refers to the presence of heart disease in the patient. It is integer (0 for no presence, 1 for presence). In general, to confirm 100% if a patient has heart disease can be quite an invasive process, so creating a model that accurately predicts the likelihood of heart disease, we can help avoid expensive and invasive procedures.\n",
    "\n",
    "The dataset consist of following features/ attributes:\n",
    "* age\n",
    "* sex\n",
    "* chest pain type (4 values)\n",
    "* resting blood pressure\n",
    "* serum cholestoral in mg/dl\n",
    "* fasting blood sugar > 120 mg/dl\n",
    "* resting electrocardiographic results (values 0,1,2)\n",
    "* maximum heart rate achieved\n",
    "* exercise induced angina\n",
    "* oldpeak = ST depression induced by exercise relative to rest\n",
    "* the slope of the peak exercise ST segment\n",
    "* number of major vessels (0-3) colored by flourosopy\n",
    "* thal: 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "* target:0 for no presence of heart disease, 1 for presence of heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/heart.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# target variable values\n",
    "df['target'].unique()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis and Visualization\n",
    "\n",
    "Exploring the dataset to find missing data, creating statistical summary of the numerical features and finding trends and relationship among features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 303 entries, 0 to 302\n",
      "Data columns (total 14 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   age       303 non-null    int64  \n",
      " 1   sex       303 non-null    int64  \n",
      " 2   cp        303 non-null    int64  \n",
      " 3   trestbps  303 non-null    int64  \n",
      " 4   chol      303 non-null    int64  \n",
      " 5   fbs       303 non-null    int64  \n",
      " 6   restecg   303 non-null    int64  \n",
      " 7   thalach   303 non-null    int64  \n",
      " 8   exang     303 non-null    int64  \n",
      " 9   oldpeak   303 non-null    float64\n",
      " 10  slope     303 non-null    int64  \n",
      " 11  ca        303 non-null    int64  \n",
      " 12  thal      303 non-null    int64  \n",
      " 13  target    303 non-null    int64  \n",
      "dtypes: float64(1), int64(13)\n",
      "memory usage: 33.3 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>303.0</td>\n",
       "      <td>54.366337</td>\n",
       "      <td>9.082101</td>\n",
       "      <td>29.0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trestbps</th>\n",
       "      <td>303.0</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>94.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>chol</th>\n",
       "      <td>303.0</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>126.0</td>\n",
       "      <td>211.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>274.5</td>\n",
       "      <td>564.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fbs</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>restecg</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thalach</th>\n",
       "      <td>303.0</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>71.0</td>\n",
       "      <td>133.5</td>\n",
       "      <td>153.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>202.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>exang</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oldpeak</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>slope</th>\n",
       "      <td>303.0</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ca</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>thal</th>\n",
       "      <td>303.0</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <td>303.0</td>\n",
       "      <td>0.544554</td>\n",
       "      <td>0.498835</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count        mean        std    min    25%    50%    75%    max\n",
       "age       303.0   54.366337   9.082101   29.0   47.5   55.0   61.0   77.0\n",
       "sex       303.0    0.683168   0.466011    0.0    0.0    1.0    1.0    1.0\n",
       "cp        303.0    0.966997   1.032052    0.0    0.0    1.0    2.0    3.0\n",
       "trestbps  303.0  131.623762  17.538143   94.0  120.0  130.0  140.0  200.0\n",
       "chol      303.0  246.264026  51.830751  126.0  211.0  240.0  274.5  564.0\n",
       "fbs       303.0    0.148515   0.356198    0.0    0.0    0.0    0.0    1.0\n",
       "restecg   303.0    0.528053   0.525860    0.0    0.0    1.0    1.0    2.0\n",
       "thalach   303.0  149.646865  22.905161   71.0  133.5  153.0  166.0  202.0\n",
       "exang     303.0    0.326733   0.469794    0.0    0.0    0.0    1.0    1.0\n",
       "oldpeak   303.0    1.039604   1.161075    0.0    0.0    0.8    1.6    6.2\n",
       "slope     303.0    1.399340   0.616226    0.0    1.0    1.0    2.0    2.0\n",
       "ca        303.0    0.729373   1.022606    0.0    0.0    0.0    1.0    4.0\n",
       "thal      303.0    2.313531   0.612277    0.0    2.0    2.0    3.0    3.0\n",
       "target    303.0    0.544554   0.498835    0.0    0.0    1.0    1.0    1.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical summary\n",
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# target variable values\n",
    "sns.countplot(x=df['target'])\n",
    "plt.title('Presence/ Absence of Heart Disease')\n",
    "plt.ylabel('Patient count')\n",
    "plt.xlabel('Heart Disease (yes/ no)')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relationships between target and features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1058.88x1000 with 14 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot feature & target relationship\n",
    "sns.pairplot(df[['age','trestbps', 'chol','thalach','target']], corner=True, diag_kind='kde', hue='target')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating a heatmap to display the correlation between all the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# heatmap of feature/target correlation\n",
    "plt.figure(figsize=(10, 8))\n",
    "sns.heatmap(data=df.corr(), cmap='coolwarm', annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Classification Model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split and Scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "# feature and target separation\n",
    "X = df.drop('target', axis=1)\n",
    "y = df['target']\n",
    "\n",
    "# train test split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.10, random_state=101)\n",
    "\n",
    "# scaling features\n",
    "scaler = StandardScaler()\n",
    "scaled_X_train = scaler.fit_transform(X_train)\n",
    "scaled_X_test = scaler.transform(X_test)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Model\n",
    "Using Logistic Regression model and Cross-Validation to find a well-performing C value for the hyper-parameter search."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l2)\n",
      "  warnings.warn(\n",
      "/home/ghani/.local/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:1165: UserWarning: l1_ratio parameter is only used when penalty is 'elasticnet'. Got (penalty=l1)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=LogisticRegression(max_iter=3000, multi_class=&#x27;ovr&#x27;,\n",
       "                                          solver=&#x27;saga&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,\n",
       "       5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,\n",
       "       3.59381366e+03, 1.00000000e+04]),\n",
       "                         &#x27;l1_ratio&#x27;: array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;]})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(estimator=LogisticRegression(max_iter=3000, multi_class=&#x27;ovr&#x27;,\n",
       "                                          solver=&#x27;saga&#x27;),\n",
       "             param_grid={&#x27;C&#x27;: array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,\n",
       "       5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,\n",
       "       3.59381366e+03, 1.00000000e+04]),\n",
       "                         &#x27;l1_ratio&#x27;: array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                         &#x27;penalty&#x27;: [&#x27;l1&#x27;, &#x27;l2&#x27;, &#x27;elasticnet&#x27;]})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=3000, multi_class=&#x27;ovr&#x27;, solver=&#x27;saga&#x27;)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(max_iter=3000, multi_class=&#x27;ovr&#x27;, solver=&#x27;saga&#x27;)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=LogisticRegression(max_iter=3000, multi_class='ovr',\n",
       "                                          solver='saga'),\n",
       "             param_grid={'C': array([1.00000000e+00, 2.78255940e+00, 7.74263683e+00, 2.15443469e+01,\n",
       "       5.99484250e+01, 1.66810054e+02, 4.64158883e+02, 1.29154967e+03,\n",
       "       3.59381366e+03, 1.00000000e+04]),\n",
       "                         'l1_ratio': array([0.        , 0.11111111, 0.22222222, 0.33333333, 0.44444444,\n",
       "       0.55555556, 0.66666667, 0.77777778, 0.88888889, 1.        ]),\n",
       "                         'penalty': ['l1', 'l2', 'elasticnet']})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "log_model = LogisticRegression(solver='saga', max_iter=3000, multi_class='ovr')\n",
    "\n",
    "# choosing different hyper-parameter values for cv\n",
    "C = np.logspace(0, 4, 10)\n",
    "penalty = ['l1', 'l2', 'elasticnet']\n",
    "l1_ratio = np.linspace(0, 1, 10)\n",
    "\n",
    "# parameter grid for optimal hyper-parameter value search\n",
    "param_grid = {'C': C,\n",
    "              'penalty': penalty,\n",
    "              'l1_ratio': l1_ratio}\n",
    "\n",
    "# grid search model\n",
    "grid_model = GridSearchCV(log_model,\n",
    "                          param_grid,\n",
    "                          verbose=0)\n",
    "# train\n",
    "grid_model.fit(scaled_X_train, y_train)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model's optimal parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'C': 1.0, 'l1_ratio': 0.0, 'penalty': 'l1'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_params_"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model's coefficients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.04753855, -0.6964517 ,  0.88789632, -0.23919609, -0.18640093,\n",
       "         0.00244364,  0.09594789,  0.42195091, -0.40860864, -0.52448861,\n",
       "         0.35764607, -0.83283423, -0.56445329]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_model.best_estimator_.coef_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**BONUS TASK: We didn't show this in the lecture notebooks, but you have the skills to do this! Create a visualization of the coefficients by using a barplot of their values. Even more bonus points if you can figure out how to sort the plot! If you get stuck on this, feel free to quickly view the solutions notebook for hints, there are many ways to do this, the solutions use a combination of pandas and seaborn.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get model's coefficient with feature names\n",
    "coef_ = pd.Series(data=grid_model.best_estimator_.coef_[0], index=X_train.columns).sort_values()\n",
    "\n",
    "# plot coef values\n",
    "sns.barplot(x=coef_.index, y=coef_)\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('coef_ value')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Performance Evaluation"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluating the model on the test set. Creating the following evaluations results:\n",
    "* Confusion Matrix Array\n",
    "* Confusion Matrix Plot\n",
    "* Classification Report\n",
    "* Precision Recall Curve and the ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix\n",
      "[[12  3]\n",
      " [ 2 14]]\n",
      "\n",
      "Classification Accuracy: 0.8387096774193549\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.86      0.80      0.83        15\n",
      "           1       0.82      0.88      0.85        16\n",
      "\n",
      "    accuracy                           0.84        31\n",
      "   macro avg       0.84      0.84      0.84        31\n",
      "weighted avg       0.84      0.84      0.84        31\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, ConfusionMatrixDisplay, confusion_matrix\n",
    "\n",
    "# predict test set\n",
    "y_pred = grid_model.predict(scaled_X_test)\n",
    "\n",
    "# confusion matrix of test set\n",
    "print(f\"Confusion Matrix\\n{confusion_matrix(y_test, y_pred)}\\n\")\n",
    "\n",
    "# model accuracy\n",
    "print(f\"Classification Accuracy: {accuracy_score(y_test, y_pred)}\\n\")\n",
    "\n",
    "# model classfication report\n",
    "print(classification_report(y_test, y_pred))\n",
    "\n",
    "# plot confusion matrix\n",
    "ConfusionMatrixDisplay.from_predictions(y_test, y_pred)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performance Curves\n",
    "\n",
    "Precision recall curve and the ROC curve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjcAAAHHCAYAAABDUnkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/P9b71AAAACXBIWXMAAA9hAAAPYQGoP6dpAABqRUlEQVR4nO3dd1iTZ9sG8DMBEnZAERmiuPe2DlxVUdTWOqutVqlVW1tHK6/WLY46WlurrVZfR0X7tXW1tb7Vqrj3FquCqCjiABSRvZP7+wOTGgFNMCEknL/j4Ci58zxPrjwFc3GvSyKEECAiIiKyEFJTB0BERERkSExuiIiIyKIwuSEiIiKLwuSGiIiILAqTGyIiIrIoTG6IiIjIojC5ISIiIovC5IaIiIgsCpMbIiIisihMboiIiMiiMLkhopcKCQmBRCLRfFlbW8Pb2xvvv/8+7t+/X+g5Qgj89NNP6NChA1xcXGBvb4+GDRti7ty5SE9PL/K1/vjjD/To0QNubm6QyWTw8vLCwIEDceDAAWO9PSKyMBLWliKilwkJCcHw4cMxd+5cVK1aFVlZWTh16hRCQkLg6+uLK1euwNbWVnO8UqnE4MGDsWXLFrRv3x79+vWDvb09jh49il9++QX16tXDvn37ULFiRc05Qgh88MEHCAkJQdOmTTFgwAB4eHggNjYWf/zxB86fP4/jx4/Dz8/PFLeAiMyJICJ6ifXr1wsA4uzZs1rtkydPFgDE5s2btdoXLFggAIiJEycWuNaOHTuEVCoV3bt312pfvHixACA+++wzoVKpCpy3ceNGcfr0aQO8m+JLS0sz6esTkW44LEVExda+fXsAQFRUlKYtMzMTixcvRq1atbBw4cIC5/Tq1QuBgYHYvXs3Tp06pTln4cKFqFOnDr7++mtIJJIC5w0dOhQtW7Z8YTwqlQrLli1Dw4YNYWtriwoVKqB79+44d+4cACA6OhoSiQQhISEFzpVIJJg9e7bm8ezZsyGRSBAeHo7BgwfD1dUV7dq108R3586dAteYOnUqZDIZnjx5omk7ffo0unfvDoVCAXt7e3Ts2BHHjx9/4fsgolfD5IaIii06OhoA4Orqqmk7duwYnjx5gsGDB8Pa2rrQ84YNGwYA+OuvvzTnJCYmYvDgwbCysip2PCNGjMBnn30GHx8ffPnll5gyZQpsbW01SVRxvP3228jIyMCCBQswatQoDBw4EBKJBFu2bClw7JYtW9CtWzfN/Thw4AA6dOiAlJQUBAcHY8GCBUhKSkLnzp1x5syZYsdERC9W+L88RESFSE5ORkJCArKysnD69GnMmTMHcrkcb775puaY8PBwAEDjxo2LvI76uYiICK3/NmzYsNixHTx4ECEhIRg/fjyWLVumaf/Pf/4D8QpTCxs3boxffvlFq61169bYvHkzJk2apGk7e/Ysbt26pen9EUJg9OjR6NSpE/7++29Nb9RHH32E+vXrY8aMGdi7d2+x4yKiorHnhoh05u/vjwoVKsDHxwcDBgyAg4MDduzYgUqVKmmOSU1NBQA4OTkVeR31cykpKVr/fdE5L/Pbb79BIpEgODi4wHOFDXPpavTo0QXaBg0ahPPnz2sNx23evBlyuRy9e/cGAISFheHGjRsYPHgwHj9+jISEBCQkJCA9PR1dunTBkSNHoFKpih0XERWNyQ0R6WzFihUIDQ3Ftm3b0LNnTyQkJEAul2sdo05Q1ElOYZ5PgJydnV96zstERUXBy8sL5cqVK/Y1ClO1atUCbW+//TakUik2b94MIL+XZuvWrejRo4fmvdy4cQMAEBgYiAoVKmh9rV27FtnZ2UhOTjZorESUj8NSRKSzli1bokWLFgCAPn36oF27dhg8eDAiIyPh6OgIAKhbty4A4J9//kGfPn0Kvc4///wDAKhXrx4AoE6dOgCAy5cvF3mOIRTVg6NUKos8x87OrkCbl5cX2rdvjy1btmDatGk4deoUYmJi8OWXX2qOUffKLF68GE2aNCn02up7RkSGxZ4bIioWKysrLFy4EA8ePMDy5cs17e3atYOLiwt++eWXIpOGjRs3AoBmrk67du3g6uqKX3/99YWJxotUr14dDx48QGJiYpHHqCf6JiUlabUXtvLpZQYNGoRLly4hMjISmzdvhr29PXr16qUVD5DfK+Xv71/ol42Njd6vS0Qvx+SGiIrt9ddfR8uWLbF06VJkZWUBAOzt7TFx4kRERkZi+vTpBc7ZuXMnQkJCEBAQgNatW2vOmTx5MiIiIjB58uRCJwD/3//93wtXGPXv3x9CCMyZM6fAc+rrOTs7w83NDUeOHNF6/ocfftD9TT/zelZWVvj111+xdetWvPnmm3BwcNA837x5c1SvXh1ff/010tLSCpz/6NEjvV+TiHTDYSkieiWTJk3C22+/jZCQEM3k2ylTpuDixYv48ssvcfLkSfTv3x92dnY4duwY/u///g9169bFhg0bClzn6tWr+Oabb3Dw4EHNDsVxcXHYvn07zpw5gxMnThQZR6dOnTB06FB89913uHHjBrp37w6VSoWjR4+iU6dOGDt2LABg5MiRWLRoEUaOHIkWLVrgyJEjuH79ut7v293dHZ06dcKSJUuQmpqKQYMGaT0vlUqxdu1a9OjRA/Xr18fw4cPh7e2N+/fv4+DBg3B2dsb//vc/vV+XiHRgyh0Eicg8FLVDsRBCKJVKUb16dVG9enWRl5en1b5+/XrRtm1b4ezsLGxtbUX9+vXFnDlzXrjT77Zt20S3bt1EuXLlhLW1tfD09BSDBg0Shw4demmceXl5YvHixaJOnTpCJpOJChUqiB49eojz589rjsnIyBAjRowQCoVCODk5iYEDB4qHDx8KACI4OFhzXHBwsAAgHj16VOTrrVmzRgAQTk5OIjMzs9BjLl68KPr16yfKly8v5HK5qFKlihg4cKDYv3//S98PERUPa0sRERGRReGcGyIiIrIoTG6IiIjIojC5ISIiIovC5IaIiIgsCpMbIiIisihMboiIiMiilLlN/FQqFR48eAAnJ6dXqhRMREREJUcIgdTUVHh5eUEqfXHfTJlLbh48eAAfHx9Th0FERETFcPfuXVSqVOmFx5S55MbJyQlA/s1xdnY2cTRERESki5SUFPj4+Gg+x1+kzCU36qEoZ2dnJjdERERmRpcpJZxQTERERBaFyQ0RERFZFCY3REREZFGY3BAREZFFYXJDREREFoXJDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRSTJjdHjhxBr1694OXlBYlEgu3bt7/0nEOHDqFZs2aQy+WoUaMGQkJCjB4nERERmQ+TJjfp6elo3LgxVqxYodPxt2/fxhtvvIFOnTohLCwMn332GUaOHIk9e/YYOVIiIiIyFyYtnNmjRw/06NFD5+NXrVqFqlWr4ptvvgEA1K1bF8eOHcO3336LgIAAY4VJREREOlCqBBLSspGdq0Ll8vYmi8OsqoKfPHkS/v7+Wm0BAQH47LPPijwnOzsb2dnZmscpKSnGCo+IiMiipWbl4kFSFh4kZeJ+UiZikzPxICkL95My8SApE3HJWchTCbSr4Yb/G9nKZHGaVXITFxeHihUrarVVrFgRKSkpyMzMhJ2dXYFzFi5ciDlz5pRUiERERGYpV6lCXHJ+4hKb/G/Ckv+VhQfJmUjNynvpdaykEuSpVCUQcdHMKrkpjqlTpyIoKEjzOCUlBT4+PiaMiIiIqGQJIZCUkaudsDxNZNTJS3xqFoR4+bVc7G3gqbCDt4stvFzs/v1S5D92d5LD2sq0i7HNKrnx8PBAfHy8Vlt8fDycnZ0L7bUBALlcDrlcXhLhERERmURWrhKxyc8MFz0dOnqQnKlJaLJyX96bIrOSwtPFFl6K/ITF28UWni7PfK+wg4O89KcOpT/CZ7Rp0wa7du3SagsNDUWbNm1MFBEREZFxqZ5O0r3/dLhIncCoe1xikzORkJaj07XcHOWaHhdPhR28XGzh/UzvS3kHGaRSiZHfkfGZNLlJS0vDzZs3NY9v376NsLAwlCtXDpUrV8bUqVNx//59bNy4EQAwevRoLF++HJ9//jk++OADHDhwAFu2bMHOnTtN9RaIiIheSVp2HmI1Ccu/PS7PJi+5ypePF9nZWMHraeLybMLi9bQnxkNhC1sbqxJ4R6Zn0uTm3Llz6NSpk+axem5MYGAgQkJCEBsbi5iYGM3zVatWxc6dOzFhwgQsW7YMlSpVwtq1a7kMnIiISqU8pQrxqdnaE3Offq/ufUnRYZKuVAJUdLYtkLCov/d2sYPCzgYSifn3uhiCRAhdpg9ZjpSUFCgUCiQnJ8PZ2dnU4RARkZkSQiA5M/ffOS7J2r0vsUmZiEvJgkqHT1lnW2tNj4tnIb0v7k5y2Jh4kq6p6fP5bVZzboiIiEpKdp4ScZol0VmFrjLKyFG+9Do2VhJ4KuzgqbAtOFzkkt/uZGtTAu+o7GByQ0REZY5KJfA4PaeIZdGZuJ+UhYS07JdfCEB5B5lWsvL8cJGbo9wiJumaEyY3RERkcTJy8gqZ46LeoC4/mcnJe/nSaFsb6b/DRYp/57yoe2A8y9AkXXPC5IaIiMyKUiXwMDVLK2F58NxKo6SM3JdeRyIBKjrZas9xUWhvTOdqz0m65ojJDRERlRpCCKRk5RU5XPQgKQtxKVlQ6jBL11Fu/bSHpfBVRhWdbSGzLtuTdC0VkxsiIioxOXlP6xclZxY+XJSUhbTsly+NtpZK4KHuZSlsuMjFFs6cpFtmMbkhIiKDEEIgMT1Hq0r080UYH6Vl61S/qJyDTDPHxVtrZVH+4wpOclhxki4VgckNERHpJDNHqdXjor2bbv732TpM0pVZS/9NWBR28HR5rgijwg52Mk7SpeJjckNERFCqBB6lZhdIXu4/M1yUmK5b/SJ3J3khu+j+u0FdeQcZJ+mSUTG5ISIqA1KzcjW9K/cLGS6KS85Cng6TdB1kVlqribyfGy6qqJBDbs1eFzItJjdERGYuV/l0ku5zQ0TPDh2l6jBJ10oqgYez7XPzW7SXRjvbWrPXhUo9JjdERKWYEAJPMnK1l0M/0+MSm5SF+NQsnSbputjbPB0msi3Q++KpyK9fZF3G6xeRZWByQ0RkQlm5SsQmPzdcpFWEMRNZuTpM0rWS5m9Gp3huuOiZ5MVBzn/yqWzgTzoRkZGoVAIJadmawouxzyQs6uGixzpO0nVzlGvNb1HXLVL3vpR3kLF+EdFTTG6IiIopLTuv4LLopz0wscn5yUyu8uXjRXY2VvB2LaJqtMIOHqxfRKQXJjdERIXIU6oQn5qtnbA8t9ooJevlk3SlEqCis/b2/97P9b4o7Fi/iMiQmNwQUZkjhEByZm4hw0X/9r7Ep2RBh5XRcLa1LrDt/7O9LxU5SZeoxDG5ISKLk5WrfKZ+UcHhogdJmcjIUb70OjZWEngqihgucslvd2L9IqJSh8kNEZkVlUrgcXqOVsKi7n1RF2FMSMvW6VrlHWRaycrzw0VujnJO0iUyQ0xuiKhUSc/OezpMlIXY56pGP0jO73nJ0aF+ka2N9N/hIkX+cJHW8BEn6RJZLCY3RFRi8pQqPEzN1iQvWhvTPd3bJSkj96XXkUiAik622gmLQntjOld7TtIlKquY3BCRQQghkJKVp5Ww3H9muOhBUhbiUrKg1GGWrpPcWmu46PkijB4KW9hwki4RFYHJDRHpJCcvv37Rv1WiC/a+pOswSddaKoGH4tk5LrYFVhs5c5IuEb0CJjdEBCHyJ+nGJmU9s4OudhHGR2nZOtUvKucgey5h0a4aXcFJDitO0iUiI2JyQ1QGZOYonyYqzwwXPZe8ZOswSVdmLf03YVHYFRw6UtjBTsZJukRkWkxuiMycUiXwKDX736KLTxOWZ3tgnugwSRcA3J3kBYaLnu2BKecg4yRdIir1mNwQlXIpWbkFtv1/dnVRXHIW8nSYpOsgs9JaTeT93HBRRYUccmv2uhCR+WNyQ2RCucr8SbrPz295thBjavbL6xdZSSXwcLZ9bohIe2m0s601e12IqExgckNkJEIIPMnIfaboYiYeJGsPFz1M1W2Srou9TaFzXNS9LxUcWb+IiEiNyQ1RMWXlKp/OcckqdLjoQVImsnJ1mKRrJc3fjE6hnbCoExlPhR0c5PxVJSLSFf/FJCqESiWQkJatXSk6WXu46HF6jk7XcnOUayUszxdhLO8gY/0iIiIDYnJDZVJadt4zw0XaVaPVk3RzlS8fL7KzsYK3a2FzXPITmIrOrF9ERFTSmNyQxclVqhCfkoXY5OdXGP2bxKRkvXySrlQCeDjbwvO5hEVdhNHbxQ4KO9YvIiIqbZjckNnJylUi6lHac8NF/yYu8SlZ0GFlNJxtrQts+//scFFFJ07SJSIyR0xuyKzk5KkQsPQI7jzOeOFxNlYSeCqe30n3394XTxc7OHKSLhGRReK/7mRWztxOxJ3HGZBZSVHbw0mzLNr7ucm6bo5yTtIlIiqjmNyQWQkNjwMA9G3qjS8HNDJxNEREVBpxQgGZDSEE9kU8BAB0rVfRxNEQEVFpVayem5iYGNy5cwcZGRmoUKEC6tevD7lcbujYiLSEx6bgflImbG2kaFvDzdThEBFRKaVzchMdHY2VK1di06ZNuHfvHsQze8bLZDK0b98eH374Ifr37w+plB1CZHj7wvN7bdrXrAA7GfeOISKiwumUhYwfPx6NGzfG7du38cUXXyA8PBzJycnIyclBXFwcdu3ahXbt2mHWrFlo1KgRzp49a+y4qQwKjcifb9O1LoekiIioaDr13Dg4OODWrVsoX758gefc3d3RuXNndO7cGcHBwdi9ezfu3r2L1157zeDBUtkVm5yJK/dTIJEAneu6mzocIiIqxXRKbhYuXKjzBbt3717sYIiKsi88HgDQrLIr3Bw5v4uIiIrGyTFkFkK5SoqIiHRksOQmIiIC1apVM9TliDRSs3JxMioBAODP+TZERPQSBktucnJycOfOHUNdjkjjyPUE5CoFqrk5oIa7o6nDISKiUk7npeBBQUEvfP7Ro0evHAxRYdS7EvtzSIqIiHSgc3KzbNkyNGnSBM7OzoU+n5aWZrCgiNRylSocuMb5NkREpDudk5saNWpgwoQJeO+99wp9PiwsDM2bNzdYYEQAcDY6ESlZeSjnIEOzyq6mDoeIiMyAznNuWrRogfPnzxf5vEQi0dq1mMgQ1LsSd67jDitW+SYiIh3o3HPzzTffIDs7u8jnGzduDJVKZZCgiID8QpnqXYm5SoqIiHSlc3Lj4eFhzDiICrgen4a7iZmQWUvRoRYLZRIRkW64iR+VWupVUu1quMFeVqwC9kREVAYxuaFSi7sSExFRcTC5oVIpPiULl+4mAQC61GGhTCIi0h2TGyqV9kXkF8ps4uMCd2dbE0dDRETmxOTJzYoVK+Dr6wtbW1u0atUKZ86ceeHxS5cuRe3atWFnZwcfHx9MmDABWVlZJRQtlRR1FXAOSRERkb6KldwcOXIE586d02o7d+4cjhw5otd1Nm/ejKCgIAQHB+PChQto3LgxAgIC8PDhw0KP/+WXXzBlyhQEBwcjIiIC69atw+bNmzFt2rTivA0qpdKz83A86jEAJjdERKS/YiU3r7/+OoYNG6bVNnToUHTq1Emv6yxZsgSjRo3C8OHDUa9ePaxatQr29vb48ccfCz3+xIkTaNu2LQYPHgxfX19069YN77777kt7e8i8HL3xCDl5KlQpb4+aLJRJRER6KlZyc/v2bezbt0+rbf/+/bh165bO18jJycH58+fh7+//bzBSKfz9/XHy5MlCz/Hz88P58+c1ycytW7ewa9cu9OzZs8jXyc7ORkpKitYXlW57nw5J+detCImEuxITEZF+irV5SJUqVQq0eXl56XWNhIQEKJVKVKyoPexQsWJFXLt2rdBzBg8ejISEBLRr1w5CCOTl5WH06NEvHJZauHAh5syZo1dsZDp5ShUOslAmERG9ApNPKNbHoUOHsGDBAvzwww+4cOECfv/9d+zcuRPz5s0r8pypU6ciOTlZ83X37t0SjJj0df7OEzzJyIWLvQ1aVGGhTCIi0p9OPTeurq46Dw8kJibqdJybmxusrKwQHx+v1R4fH19kqYeZM2di6NChGDlyJACgYcOGSE9Px4cffojp06dDKi2Yq8nlcsjlcp1iItNTLwHvXNsd1lZmlXsTEVEpoVNys3TpUoO/sEwmQ/PmzbF//3706dMHAKBSqbB//36MHTu20HMyMjIKJDBWVlYAwIrkFkAIgVD1fBsOSRERUTHplNwEBgYa5cWDgoIQGBiIFi1aoGXLlli6dCnS09MxfPhwAMCwYcPg7e2NhQsXAgB69eqFJUuWoGnTpmjVqhVu3ryJmTNnolevXpokh8xX1KM0RD/OgMxKig61Kpg6HCIiMlPFmlAcFRWF9evXIyoqCsuWLYO7uzv+/vtvVK5cGfXr19f5OoMGDcKjR48wa9YsxMXFoUmTJti9e7dmknFMTIxWT82MGTMgkUgwY8YM3L9/HxUqVECvXr0wf/784rwNKmXUq6TaVC8PRzkLZRIRUfFIhJ7jOYcPH0aPHj3Qtm1bHDlyBBEREahWrRoWLVqEc+fOYdu2bcaK1SBSUlKgUCiQnJwMZ2dnU4dDz+j3w3FciEnCF30a4L3WBVfkERFR2aXP57feMzanTJmCL774AqGhoZDJZJr2zp0749SpU/pHSwTgUWo2Lj4tlOlfl/NtiIio+PRObi5fvoy+ffsWaHd3d0dCQoJBgqKy58C1eAgBNKqkgIeChTKJiKj49E5uXFxcEBsbW6D94sWL8Pb2NkhQVPaEPrMrMRER0avQO7l55513MHnyZMTFxUEikUClUuH48eOYOHFigXpTRLrIzFHi6I38Xj/uSkxERK9K7+RmwYIFqFOnDnx8fJCWloZ69eqhQ4cO8PPzw4wZM4wRI1m4ozceITtPBW8XO9TxcDJ1OEREZOb0Xm8rk8mwZs0azJw5E1euXEFaWhqaNm2KmjVrGiM+KgPUuxJ3rcdCmURE9OqKvZlI5cqV4ePjAwD8QKJiU6oE9kfkF8rsxiEpIiIygGIV71m3bh0aNGgAW1tb2NraokGDBli7dq2hY6MyIOzuEzxOz4GzrTVeq1rO1OEQEZEF0LvnZtasWViyZAnGjRuHNm3aAABOnjyJCRMmICYmBnPnzjV4kGS51LsSd6rjDhsWyiQiIgPQO7lZuXIl1qxZg3fffVfT9tZbb6FRo0YYN24ckxvSC5eAExGRoen9p3Jubi5atGhRoL158+bIy8szSFBUNkQ9SsOtR+mwsZKgY20WyiQiIsPQO7kZOnQoVq5cWaB99erVGDJkiEGCorJh39Nem9bVysPZ1sbE0RARkaXQaVgqKChI871EIsHatWuxd+9etG7dGgBw+vRpxMTEcBM/0suzS8CJiIgMRafk5uLFi1qPmzdvDgCIiooCALi5ucHNzQ1Xr141cHhkqR6nZeP8nScAgC6cb0NERAakU3Jz8OBBY8dBZcyBaw+hEkB9L2d4u9iZOhwiIrIgXHtLJsFVUkREZCzF2qH43Llz2LJlC2JiYpCTk6P13O+//26QwMhyZeWyUCYRERmP3j03mzZtgp+fHyIiIvDHH38gNzcXV69exYEDB6BQKIwRI1mY4zcTkJmrhJfCFvW9nE0dDhERWZhiVQX/9ttv8b///Q8ymQzLli3DtWvXMHDgQFSuXNkYMZKFUa+S8mehTCIiMgK9k5uoqCi88cYbAPIrhKenp0MikWDChAlYvXq1wQMky6JSCex7WiiT822IiMgY9E5uXF1dkZqaCgDw9vbGlStXAABJSUnIyMgwbHRkcS7dS8Kj1Gw4yq3Rulp5U4dDREQWSO8JxR06dEBoaCgaNmyIt99+G59++ikOHDiA0NBQdOnSxRgxkgVRr5LqWLsCZNZcrEdERIand3KzfPlyZGVlAQCmT58OGxsbnDhxAv3798eMGTMMHiBZFvV8m25cJUVEREaid3JTrlw5zfdSqRRTpkwxaEBkue48Tsf1+DRYSSV4vZa7qcMhIiILpVNyk5KSovMFnZ25tJcKpx6SalW1HBT2LJRJRETGoVNy4+Li8tIlu0IISCQSKJVKgwRGlked3HDjPiIiMibWlqIS8SQ9B+eeFsrkEnAiIjImnZKbjh07GjsOsnAHIx9CqRKo4+EEn3L2pg6HiIgsGNfiUolQr5LikBQRERkbkxsyuuw8JQ5HPgLA5IaIiIyPyQ0Z3Ymox0jPUaKisxwNvFhclYiIjIvJDRndvqerpPzrVoRUykKZRERkXMVKbvLy8rBv3z7897//1dSZevDgAdLS0gwaHJm//EKZ/1YBJyIiMja9dyi+c+cOunfvjpiYGGRnZ6Nr165wcnLCl19+iezsbKxatcoYcZKZuvIgGfEp2XCQWcGvOgtlEhGR8endc/Ppp5+iRYsWePLkCezs7DTtffv2xf79+w0aHJk/9cZ9HWpVgNzaysTREBFRWaB3z83Ro0dx4sQJyGQyrXZfX1/cv3/fYIGRZeCuxEREVNL07rlRqVSFlli4d+8enJycDBIUWYa7iRm4FpcKK6kEnWqzUCYREZUMvZObbt26YenSpZrHEokEaWlpCA4ORs+ePQ0ZG5k59UTiFlVc4eoge8nRREREhqH3sNQ333yDgIAA1KtXD1lZWRg8eDBu3LgBNzc3/Prrr8aIkcwUh6SIiMgU9E5uKlWqhEuXLmHTpk34559/kJaWhhEjRmDIkCFaE4ypbEvOyMXp24kAmNwQEVHJ0ju5ycrKgq2tLd577z1jxEMW4tD1/EKZNd0dUaW8g6nDISKiMkTvOTfu7u4IDAxEaGgoVCqVMWIiC8AhKSIiMhW9k5sNGzYgIyMDvXv3hre3Nz777DOcO3fOGLGRmcrJU2kKZXJXYiIiKml6Jzd9+/bF1q1bER8fjwULFiA8PBytW7dGrVq1MHfuXGPESGbm9O3HSM3OQwUnOZpUcjF1OEREVMYUu3Cmk5MThg8fjr179+Kff/6Bg4MD5syZY8jYyEyFagplurNQJhERlbhiJzdZWVnYsmUL+vTpg2bNmiExMRGTJk0yZGxkhoQQWlXAiYiISpreq6X27NmDX375Bdu3b4e1tTUGDBiAvXv3okOHDsaIj8zM1QcpeJCcBTsbK7St4WbqcIiIqAzSO7np27cv3nzzTWzcuBE9e/aEjY2NMeIiM6Xelbh9TTfY2rBQJhERlTy9k5v4+HjWkKIicQk4ERGZmk7JTUpKCpydnQHkz6lISUkp8lj1cVT2PEjKxNUHKZBKgM51WCiTiIhMQ6fkxtXVFbGxsXB3d4eLiwskkoIrYIQQkEgkhVYMp7JBPSTVvIoryjvKTRwNERGVVTolNwcOHEC5cuUAAAcPHjRqQGS+QrlKioiISgGdkpuOHTtqvq9atSp8fHwK9N4IIXD37l3DRkdmIyUrF6duPQbA+TZERGRaeu9zU7VqVTx69KhAe2JiIqpWrWqQoMj8HI58hFylQLUKDqhWwdHU4RARURmmd3KjnlvzvLS0NNja2hokKDI/6vk27LUhIiJT03kpeFBQEABAIpFg5syZsLe31zynVCpx+vRpNGnSxOABUumXq1Th4LWHAICunG9DREQmpnPPzcWLF3Hx4kUIIXD58mXN44sXL+LatWto3LgxQkJC9A5gxYoV8PX1ha2tLVq1aoUzZ8688PikpCSMGTMGnp6ekMvlqFWrFnbt2qX365LhnL2diJSsPJR3kKFpZVdTh0NERGWczj036lVSw4cPx7Jlywyyn83mzZsRFBSEVatWoVWrVli6dCkCAgIQGRkJd/eC+6Tk5OSga9eucHd3x7Zt2+Dt7Y07d+7AxcXllWOh4tv7dJVU5zrusGKhTCIiMjGJEEKY6sVbtWqF1157DcuXLwcAqFQq+Pj4YNy4cZgyZUqB41etWoXFixfj2rVrxS77kJKSAoVCgeTkZG44aABCCLT/6iDuPcnE6qHN0a2+h6lDIiIiC6TP57dOPTf9+vVDSEgInJ2d0a9fvxce+/vvv+sUZE5ODs6fP4+pU6dq2qRSKfz9/XHy5MlCz9mxYwfatGmDMWPG4M8//0SFChUwePBgTJ48GVZWhdcxys7ORnZ2tubxi3ZXJv1di0vFvSeZkFtL0a4mC2USEZHp6ZTcKBQKzQophUJhkBdOSEiAUqlExYraE1ArVqyIa9euFXrOrVu3cODAAQwZMgS7du3CzZs38cknnyA3NxfBwcGFnrNw4ULMmTPHIDFTQfvC/y2UaS/Tu1QZERGRwen0abR+/fpCvy9pKpUK7u7uWL16NaysrNC8eXPcv38fixcvLjK5mTp1qmalF5Dfc+Pj41NSIVu8UC4BJyKiUkbvP7UzMzMhhNAsBb9z5w7++OMP1KtXD926ddP5Om5ubrCyskJ8fLxWe3x8PDw8Cp+34enpCRsbG60hqLp16yIuLg45OTmQyWQFzpHL5ZDLWefIGOKSs/DPvWRIJEDnOkxuiIiodNB7E7/evXtj48aNAPKXZbds2RLffPMNevfujZUrV+p8HZlMhubNm2P//v2aNpVKhf3796NNmzaFntO2bVvcvHkTKpVK03b9+nV4enoWmtiQcak37mvq44IKTkwgiYiodNA7ublw4QLat28PANi2bRs8PDxw584dbNy4Ed99951e1woKCsKaNWuwYcMGRERE4OOPP0Z6ejqGDx8OABg2bJjWhOOPP/4YiYmJ+PTTT3H9+nXs3LkTCxYswJgxY/R9G2QA6uTGn0NSRERUiug9LJWRkQEnJycAwN69e9GvXz9IpVK0bt0ad+7c0etagwYNwqNHjzBr1izExcWhSZMm2L17t2aScUxMDKTSf/MvHx8f7NmzBxMmTECjRo3g7e2NTz/9FJMnT9b3bdArSsvOw4mb+YUyuzG5ISKiUkTv5KZGjRrYvn07+vbtq0k0AODhw4fF2jdm7NixGDt2bKHPHTp0qEBbmzZtcOrUKb1fhwzr6PVHyFGq4FveHtVZKJOIiEoRvYelZs2ahYkTJ8LX1xctW7bUzI/Zu3cvmjZtavAAqXQKDf93lVRhhVSJiIhMRe+emwEDBqBdu3aIjY1F48aNNe1dunRB3759DRoclU55ShUOROYXyvRnoUwiIiplirXrmoeHBzw8PHDv3j0AQKVKldCyZUuDBkal17k7T5CUkQtXexs0r8JCmUREVLroPSylUqkwd+5cKBQKVKlSBVWqVIGLiwvmzZuntUSbLJd6V+JOddxhbaX3jxAREZFR6d1zM336dKxbtw6LFi1C27ZtAQDHjh3D7NmzkZWVhfnz5xs8SCo9hBCaXYm5SoqIiEojvZObDRs2YO3atXjrrbc0bepl2Z988gmTGwt382Ea7jzOgMxaivY1K5g6HCIiogL0HlNITExEnTp1CrTXqVMHiYmJBgmKSq+9T4ek2lYvDwc5C2USEVHpo3dy07hxYyxfvrxA+/Lly7VWT5FlUi8B567ERERUWun9p/dXX32FN954A/v27dPscXPy5EncvXsXu3btMniAVHo8TM1C2N0kAFwCTkREpZfePTcdO3bE9evX0a9fPyQlJSEpKQn9+vVDZGSkpuYUWab9Efl72zSupEBFZ1sTR0NERFQ4vXpuoqOjERoaipycHLzzzjto0KCBseKiUmjfM7sSExERlVY6JzcHDx7Em2++iczMzPwTra3x448/4r333jNacFR6ZOTk4djNBACcb0NERKWbzsNSM2fORNeuXXH//n08fvwYo0aNwueff27M2KgUOXojAdl5KviUs0Ptik6mDoeIiKhIOic3V65cwYIFC+Dp6QlXV1csXrwYDx8+xOPHj40ZH5USmkKZdT1YKJOIiEo1nZOblJQUuLm5aR7b29vDzs4OycnJRgmMSg+lSuDAtaeFMuu5mzgaIiKiF9NrQvGePXugUCg0j1UqFfbv348rV65o2p7duZgsw4WYJ0hMz4HCzgav+ZYzdThEREQvpFdyExgYWKDto48+0nwvkUigVCpfPSoqVTSFMmtXgA0LZRIRUSmnc3LDit9ll2a+TT0PE0dCRET0cvwznF4o6lEabiWkw8ZKgg613F5+AhERkYnplNycOnVK5wtmZGTg6tWrxQ6IShd1r02b6m5wsrUxcTREREQvp1NyM3ToUAQEBGDr1q1IT08v9Jjw8HBMmzYN1atXx/nz5w0aJJmOZlfiulwlRURE5kGnOTfh4eFYuXIlZsyYgcGDB6NWrVrw8vKCra0tnjx5gmvXriEtLQ19+/bF3r170bBhQ2PHTSUgIS0b52OeAOCuxEREZD4kQgihzwnnzp3DsWPHcOfOHWRmZsLNzQ1NmzZFp06dUK5c6V8mnJKSAoVCgeTkZDg7O5s6nFJty7m7+HzbP2jg7Yy/xrEoKhERmY4+n996LQUHgBYtWqBFixbFDo7Mx7O7EhMREZkLrpaiQmXlKnH0xiMA3JWYiIjMC5MbKtSxGwnIylXB28UO9Tw5fEdEROaDyQ0Val9E/pCUf113FsokIiKzwuSGClCpBPZF5BfK5K7ERERkbl4pucnKyjJUHFSKXLybhIS0bDjJrdGyaulfAUdERPQsvZMblUqFefPmwdvbG46Ojrh16xYAYObMmVi3bp3BA6SSpx6Ser2OO2TW7NwjIiLzovcn1xdffIGQkBB89dVXkMlkmvYGDRpg7dq1Bg2OTEO9BNyfuxITEZEZ0ju52bhxI1avXo0hQ4bAyspK0964cWNcu3bNoMFRybudkI6bD9NgLZXg9dpMboiIyPzondzcv38fNWrUKNCuUqmQm5trkKDIdNS1pFpXKw+FHQtlEhGR+dE7ualXrx6OHj1aoH3btm1o2rSpQYIi0wmN4JAUERGZN73LL8yaNQuBgYG4f/8+VCoVfv/9d0RGRmLjxo3466+/jBEjlZDE9Byci04EwEKZRERkvvTuuenduzf+97//Yd++fXBwcMCsWbMQERGB//3vf+jatasxYqQScvDaQ6gEUNfTGZVc7U0dDhERUbHo3XMDAO3bt0doaKihYyET0xTKZK8NERGZMb17bqpVq4bHjx8XaE9KSkK1atUMEhSVvKxcJY48LZTZtS6TGyIiMl96JzfR0dFQKpUF2rOzs3H//n2DBEUl72TUY2TkKOHhbIsG3iyUSURE5kvnYakdO3Zovt+zZw8UCoXmsVKpxP79++Hr62vQ4KjkaFZJ1WOhTCIiMm86Jzd9+vQBAEgkEgQGBmo9Z2NjA19fX3zzzTcGDY5KhkolNPvbsFAmERGZO52TG5VKBQCoWrUqzp49Czc3N6MFRSXr8v1kPEzNhqPcGq2rsVAmERGZN71XS92+fdsYcZAJqVdJdaxVAXJrq5ccTUREVLoVayl4eno6Dh8+jJiYGOTk5Gg9N378eIMERiVn3zPzbYiIiMyd3snNxYsX0bNnT2RkZCA9PR3lypVDQkIC7O3t4e7uzuTGzNxNzMC1uFRYSSXoxEKZRERkAfReCj5hwgT06tULT548gZ2dHU6dOoU7d+6gefPm+Prrr40RIxmRekjqNV9XuNjLTBwNERHRq9M7uQkLC8N//vMfSKVSWFlZITs7Gz4+Pvjqq68wbdo0Y8RIRhTKVVJERGRh9E5ubGxsIJXmn+bu7o6YmBgAgEKhwN27dw0bHRlVckYuzjwtlMldiYmIyFLoPeemadOmOHv2LGrWrImOHTti1qxZSEhIwE8//YQGDRoYI0YykoORD6FUCdSu6ITK5Vkok4iILIPePTcLFiyAp6cnAGD+/PlwdXXFxx9/jEePHuG///2vwQMk41HvSsxCmUREZEn07rlp0aKF5nt3d3fs3r3boAFRycjOU+JwZH6hTH8mN0REZEH07rkpyoULF/Dmm28a6nJkZKduJSItOw/uTnI08la8/AQiIiIzoVdys2fPHkycOBHTpk3DrVu3AADXrl1Dnz598Nprr2lKNFDpp64l1aVuRUilLJRJRESWQ+dhqXXr1mHUqFEoV64cnjx5grVr12LJkiUYN24cBg0ahCtXrqBu3brGjJUMRAih2ZW4G4ekiIjIwujcc7Ns2TJ8+eWXSEhIwJYtW5CQkIAffvgBly9fxqpVq5jYmJGrD1IQm5wFe5kV2lQvb+pwiIiIDErn5CYqKgpvv/02AKBfv36wtrbG4sWLUalSJaMFR8ax9+mQVIeaFWBrw0KZRERkWXRObjIzM2Fvn78XikQigVwu1ywJf1UrVqyAr68vbG1t0apVK5w5c0an8zZt2gSJRII+ffoYJI6yQj3fhqukiIjIEum1FHzt2rVwdHQEAOTl5SEkJARubm5ax+hbOHPz5s0ICgrCqlWr0KpVKyxduhQBAQGIjIyEu3vRhRyjo6MxceJEtG/fXq/XK+vuPclAeGwKpBKgcx0WyiQiIssjEUIIXQ709fWFRPLiVTUSiUSzikpXrVq1wmuvvYbly5cDAFQqFXx8fDBu3DhMmTKl0HOUSiU6dOiADz74AEePHkVSUhK2b9+u0+ulpKRAoVAgOTkZzs7OesVqCTaciEbwjqto6VsOW0a3MXU4REREOtHn81vnnpvo6OhXjauAnJwcnD9/HlOnTtW0SaVS+Pv74+TJk0WeN3fuXLi7u2PEiBE4evSoweOyZP8WyuSQFBERWSa9dyg2pISEBCiVSlSsqP1BW7FiRVy7dq3Qc44dO4Z169YhLCxMp9fIzs5Gdna25nFKSkqx4zV3KVm5OHXrMQDOtyEiIstlsB2KS0JqaiqGDh2KNWvWFJjrU5SFCxdCoVBovnx8fIwcZel1KPIR8lQCNdwdUdXNwdThEBERGYVJe27c3NxgZWWF+Ph4rfb4+Hh4eHgUOD4qKgrR0dHo1auXpk29K7K1tTUiIyNRvXp1rXOmTp2KoKAgzeOUlJQym+BoVknVZa8NERFZLpMmNzKZDM2bN8f+/fs1y7lVKhX279+PsWPHFji+Tp06uHz5slbbjBkzkJqaimXLlhWatMjlcsjlcqPEb05ylSocjHwIgPNtiIjIspk0uQGAoKAgBAYGokWLFmjZsiWWLl2K9PR0DB8+HAAwbNgweHt7Y+HChbC1tUWDBg20zndxcQGAAu2k7cztRKRm5cHNUYYmPi6mDoeIiMhoipXcREVFYf369YiKisKyZcvg7u6Ov//+G5UrV0b9+vX1utagQYPw6NEjzJo1C3FxcWjSpAl2796tmWQcExMDqdSspgaVSupVUl3qVIQVC2USEZEF03mfG7XDhw+jR48eaNu2LY4cOYKIiAhUq1YNixYtwrlz57Bt2zZjxWoQZXGfGyEE2n15EPeTMrFmWAsOSxERkdnR5/Nb7y6RKVOm4IsvvkBoaChkMpmmvXPnzjh16pT+0ZLRRcSm4n5SJmxtpGhXQ7dVZkREROZK7+Tm8uXL6Nu3b4F2d3d3JCQkGCQoMqx9EflDUu1rVoCdjIUyiYjIsumd3Li4uCA2NrZA+8WLF+Ht7W2QoMiwNLsScwk4ERGVAXonN++88w4mT56MuLg4SCQSqFQqHD9+HBMnTsSwYcOMESO9gtjkTFy+nwyJBOhcl4UyiYjI8umd3CxYsAB16tSBj48P0tLSUK9ePXTo0AF+fn6YMWOGMWKkV7AvIn9vm2aVXeHmyP1+iIjI8um9FFwmk2HNmjWYOXMmrly5grS0NDRt2hQ1a9Y0Rnz0ilgok4iIyhq9k5tjx46hXbt2qFy5MipXrmyMmMhAUrNycTIqf5I3Sy4QEVFZofewVOfOnVG1alVMmzYN4eHhxoiJDOTI9QTkKgWquTmghrujqcMhIiIqEXonNw8ePMB//vMfHD58GA0aNECTJk2wePFi3Lt3zxjx0StQLwH355AUERGVIXonN25ubhg7diyOHz+OqKgovP3229iwYQN8fX3RuXNnY8RIxZCrVOHANRbKJCKisueVijZVrVoVU6ZMwaJFi9CwYUMcPnzYUHHRKzoX/QTJmbko5yBDs8qupg6HiIioxBQ7uTl+/Dg++eQTeHp6YvDgwWjQoAF27txpyNjoFahXSXWu485CmUREVKbovVpq6tSp2LRpEx48eICuXbti2bJl6N27N+zt7Y0RHxWDEAKhEXEAuEqKiIjKHr2TmyNHjmDSpEkYOHAg3NxYhLE0uh6fhruJmZBZS9GhFv8fERFR2aJ3cnP8+HFjxEEGpF4l1a6GG+xlev8vJiIiMms6ffLt2LEDPXr0gI2NDXbs2PHCY9966y2DBEbFt5e7EhMRURmmU3LTp08fxMXFwd3dHX369CnyOIlEAqVSaajYqBgepmTh0t0kAECXOiyUSUREZY9OyY1KpSr0eyp91IUym/i4wN3Z1sTREBERlTy9l4Jv3LgR2dnZBdpzcnKwceNGgwRFxaeeb8MhKSIiKqv0Tm6GDx+O5OTkAu2pqakYPny4QYKi4knPzsOxm/mFMpncEBFRWaV3ciOEgERScFO4e/fuQaFQGCQoKp6jNxKQk6dClfL2qMlCmUREVEbpvE64adOmkEgkkEgk6NKlC6yt/z1VqVTi9u3b6N69u1GCJN2odyX2r1ux0ASUiIioLNA5uVGvkgoLC0NAQAAcHf/tGZDJZPD19UX//v0NHiDpRqkSOHCN822IiIh0Tm6Cg4MBAL6+vhg0aBBsbbkSpzQ5f+cJnmTkwsXeBi2qsFAmERGVXXpvXxsYGGiMOOgVqVdJda7tDmurVyr2TkREZNZ0Sm7KlSuH69evw83NDa6uri+cz5GYmGiw4Eg3Qoh/59twSIqIiMo4nZKbb7/9Fk5OTprvOVm1dIl6lIbbCemQWUnRoVYFU4dDRERkUjolN88ORb3//vvGioWKKTQ8f1fiNtXLw1HOQplERFS26T0548KFC7h8+bLm8Z9//ok+ffpg2rRpyMnJMWhwpJvQ8DgAXCVFREQEFCO5+eijj3D9+nUAwK1btzBo0CDY29tj69at+Pzzzw0eIL3Yo9RsXHxaKNO/LpMbIiIivZOb69evo0mTJgCArVu3omPHjvjll18QEhKC3377zdDx0UscuBYPIYBGlRTwUHB5PhERUbHKL6grg+/btw89e/YEAPj4+CAhIcGw0dFLqefbsNeGiIgon97JTYsWLfDFF1/gp59+wuHDh/HGG28AAG7fvo2KFfkBW5Iyc5Q4dvMRAM63ISIiUtM7uVm6dCkuXLiAsWPHYvr06ahRowYAYNu2bfDz8zN4gFS0YzcTkJWrgreLHep4OJk6HCIiolJB73XDjRo10lotpbZ48WJYWVkZJCjSzbOrpLj3EBERUb5ib4py/vx5REREAADq1auHZs2aGSwoejmlSmB/RP58Gw5JERER/Uvv5Obhw4cYNGgQDh8+DBcXFwBAUlISOnXqhE2bNqFCBe6QWxLC7j7B4/QcONtao2XVcqYOh4iIqNTQe87NuHHjkJaWhqtXryIxMRGJiYm4cuUKUlJSMH78eGPESIVQr5LqVMcdNiyUSUREpKF3z83u3buxb98+1K1bV9NWr149rFixAt26dTNocFQ09XwbLgEnIiLSpvef/CqVCjY2NgXabWxsNPvfkHHdepSGqEfpsLGSoGNtDgMSERE9S+/kpnPnzvj000/x4MEDTdv9+/cxYcIEdOnSxaDBUeH2RcQDAFpXKw9n24KJJhERUVmmd3KzfPlypKSkwNfXF9WrV0f16tVRtWpVpKSk4PvvvzdGjPScfeFcJUVERFQUvefc+Pj44MKFC9i/f79mKXjdunXh7+9v8OCooMT0HJy7kwgA6ML5NkRERAXoldxs3rwZO3bsQE5ODrp06YJx48YZKy4qwoFrD6ESQH0vZ3i72Jk6HCIiolJH5+Rm5cqVGDNmDGrWrAk7Ozv8/vvviIqKwuLFi40ZHz2Hq6SIiIheTOc5N8uXL0dwcDAiIyMRFhaGDRs24IcffjBmbPScrFwljlzPr7zO+TZERESF0zm5uXXrFgIDAzWPBw8ejLy8PMTGxholMCroRFQCMnOV8FLYor6Xs6nDISIiKpV0Tm6ys7Ph4ODw74lSKWQyGTIzM40SGBWk3pXYn4UyiYiIiqTXhOKZM2fC3t5e8zgnJwfz58+HQqHQtC1ZssRw0ZGGSiU0+9twvg0REVHRdE5uOnTogMjISK02Pz8/3Lp1S/OYvQnGc+leEh6lZsNRbo3W1cqbOhwiIqJSS+fk5tChQ0YMg15G3WvTsXYFyKxZKJOIiKgo/JQ0E6Hh+clNN66SIiIieiEmN2bgzuN0XI9Pg5VUgtdruZs6HCIiolKNyY0ZUPfatKpaDgp7FsokIiJ6ESY3ZoCrpIiIiHTH5KaUS8rIwdnoJwC4KzEREZEuipXcHD16FO+99x7atGmD+/fvAwB++uknHDt2rFhBrFixAr6+vrC1tUWrVq1w5syZIo9ds2YN2rdvD1dXV7i6usLf3/+Fx5u7g5EPoVQJ1PFwgk85+5efQEREVMbpndz89ttvCAgIgJ2dHS5evIjs7GwAQHJyMhYsWKB3AJs3b0ZQUBCCg4Nx4cIFNG7cGAEBAXj48GGhxx86dAjvvvsuDh48iJMnT8LHxwfdunXTJFmWRj3fhr02REREupEIIYQ+JzRt2hQTJkzAsGHD4OTkhEuXLqFatWq4ePEievTogbi4OL0CaNWqFV577TUsX74cAKBSqeDj44Nx48ZhypQpLz1fqVTC1dUVy5cvx7Bhw156fEpKChQKBZKTk+HsXLrrM2XnKdFsbijSc5TYMbYtGlVyMXVIREREJqHP57fePTeRkZHo0KFDgXaFQoGkpCS9rpWTk4Pz58/D39//34CkUvj7++PkyZM6XSMjIwO5ubkoV66cXq9tDk5GPUZ6jhIVneVo4KV4+QlERESkf3Lj4eGBmzdvFmg/duwYqlWrpte1EhISoFQqUbGi9pBLxYoVde4Bmjx5Mry8vLQSpGdlZ2cjJSVF68tcPLtKSiplaQsiIiJd6J3cjBo1Cp9++ilOnz4NiUSCBw8e4Oeff8bEiRPx8ccfGyPGIi1atAibNm3CH3/8AVtb20KPWbhwIRQKhebLx8enRGMsLiEE9j1TBZyIiIh0o1dVcACYMmUKVCoVunTpgoyMDHTo0AFyuRwTJ07EuHHj9LqWm5sbrKysEB8fr9UeHx8PDw+PF5779ddfY9GiRdi3bx8aNWpU5HFTp05FUFCQ5nFKSopZJDhX7qcgLiULDjIr+FVnoUwiIiJd6d1zI5FIMH36dCQmJuLKlSs4deoUHj16hHnz5un94jKZDM2bN8f+/fs1bSqVCvv370ebNm2KPO+rr77CvHnzsHv3brRo0eKFryGXy+Hs7Kz1ZQ5Cw/OH5TrUqgC5tZWJoyEiIjIfevfcqMlkMtSrV++VAwgKCkJgYCBatGiBli1bYunSpUhPT8fw4cMBAMOGDYO3tzcWLlwIAPjyyy8xa9Ys/PLLL/D19dXMzXF0dISjo+Mrx1NahEbkD0lxCTgREZF+9E5uOnXqBImk6MmtBw4c0Ot6gwYNwqNHjzBr1izExcWhSZMm2L17t2aScUxMDKTSfzuYVq5ciZycHAwYMEDrOsHBwZg9e7Zer11a3U3MQERsCqykEnSqzUKZRERE+tA7uWnSpInW49zcXISFheHKlSsIDAwsVhBjx47F2LFjC33u0KFDWo+jo6OL9RrmZP/TVVItqrjC1UFm4miIiIjMi97Jzbffflto++zZs5GWlvbKAREQGsFdiYmIiIrLYIUz33vvPfz444+GulyZlZyZi9O3EgEwuSEiIioOgyU3J0+eLHKvGdLdociHyFMJ1HR3RJXyDqYOh4iIyOzoPSzVr18/rcdCCMTGxuLcuXOYOXOmwQIrq1gok4iI6NXondwoFNo1jqRSKWrXro25c+eiW7duBgusLMrJU+Fw5CMA3JWYiIiouPRKbpRKJYYPH46GDRvC1dXVWDGVWadvP0Zqdh7cHOVowgrgRERExaLXnBsrKyt069ZN7+rfpJt94epCme4slElERFRMek8obtCgAW7dumWMWMo0IQTn2xARERmA3snNF198gYkTJ+Kvv/5CbGwsUlJStL6oeMJjU/AgOQt2NlZoW8PN1OEQERGZLZ3n3MydOxf/+c9/0LNnTwDAW2+9pVWGQQgBiUQCpVJp+CjLAHWvTfuabrC1YaFMIiKi4tI5uZkzZw5Gjx6NgwcPGjOeMmsfdyUmIiIyCJ2TGyEEAKBjx45GC6asepCUiSv3UyCVAJ3rsFAmERHRq9Brzs2LqoFT8akLZTav4oryjnITR0NERGTe9NrnplatWi9NcBITE18poLJor2YJOIekiIiIXpVeyc2cOXMK7FBMryY1Kxenbj0GwPk2REREhqBXcvPOO+/A3Z1zQgzp8PVHyFUKVKvggGoVHE0dDhERkdnTec4N59sYxz5u3EdERGRQOic36tVSZDi5ShUOXHsIAOjK+TZEREQGofOwlEqlMmYcZdLZ6ESkZOWhvIMMTSuzECkREZEh6F1+gQxHvStx5zrusGKhTCIiIoNgcmMiQgjuSkxERGQETG5MJDI+FXcTMyG3lqJdTRbKJCIiMhQmNyay75lCmfYyvVbkExER0QswuTGRUO5KTEREZBRMbkwgPiULl+4lQyIBujC5ISIiMigmNyagnkjc1McFFZxYKJOIiMiQmNyYgGZIiqukiIiIDI7JTQlLz87DiZv5hTK7MbkhIiIyOCY3JezI9UfIUargW94e1Vkok4iIyOCY3JSw0Gc27mMxUiIiIsNjclOC8p4plMkl4ERERMbB5KYEnb/zBEkZuXC1t0HzKiyUSUREZAxMbkqQepVUpzrusLbirSciIjIGfsKWECGEZr4NV0kREREZD5ObEnLzYRruPM6AzFqK9jUrmDocIiIii8XkpoSoe23aVi8PBzkLZRIRERkLk5sSwl2JiYiISgaTmxLwMDULYXeTAHAJOBERkbExuSkBByIeQgigcSUFKjrbmjocIiIii8bkpgTse2ZXYiIiIjIuJjdGlpGTh6M3EgBwvg0REVFJYHJjZMduJCA7TwWfcnaoXdHJ1OEQERFZPCY3RqZZJVWXhTKJiIhKApMbI1KqhKZQJufbEBERlQwmN0Z0MeYJHqfnQGFng9d8y5k6HCIiojKBW+UakXpX4k61K8CGhTKJygwhBPLy8qBUKk0dCpFZsbGxgZWV1Stfh8mNEann23St52HiSIiopOTk5CA2NhYZGRmmDoXI7EgkElSqVAmOjo6vdB0mN0YS9SgNtx6lw8ZKgg613EwdDhGVAJVKhdu3b8PKygpeXl6QyWRcSECkIyEEHj16hHv37qFmzZqv1IPD5MZI9j3ttWlT3Q1OtjYmjoaISkJOTg5UKhV8fHxgb29v6nCIzE6FChUQHR2N3NzcV0puOBHESDRDUnXdTRwJEZU0qZT/tBIVh6F6OvkbaASP07JxPuYJAO5KTEREVNKY3BjB/mv5hTIbeDvDU2Fn6nCIiIjKFCY3RrBPMyTFVVJEZFkkEgm2b99u9Nc5dOgQJBIJkpKSNG3bt29HjRo1YGVlhc8++wwhISFwcXExWgyRkZHw8PBAamqq0V6jrGndujV+++03o78OkxsDy8pVPlMok/NtiMh8xMXFYdy4cahWrRrkcjl8fHzQq1cv7N+/v8Rj8fPzQ2xsLBQKhabto48+woABA3D37l3MmzcPgwYNwvXr140Ww9SpUzFu3Dg4ORWsC1inTh3I5XLExcUVeM7X1xdLly4t0D579mw0adJEq81U93zr1q2oU6cObG1t0bBhQ+zateul5/z8889o3Lgx7O3t4enpiQ8++ACPHz8u9NhNmzZBIpGgT58+Wu0zZszAlClToFKpDPE2isTkxsCO30xAZq4S3i52qOfpbOpwiIh0Eh0djebNm+PAgQNYvHgxLl++jN27d6NTp04YM2ZMiccjk8ng4eGhmWCalpaGhw8fIiAgAF5eXnBycoKdnR3c3V/tj8jc3NxC22NiYvDXX3/h/fffL/DcsWPHkJmZiQEDBmDDhg3Ffm1T3fMTJ07g3XffxYgRI3Dx4kX06dMHffr0wZUrV4o85/jx4xg2bBhGjBiBq1evYuvWrThz5gxGjRpV6PuaOHEi2rdvX+C5Hj16IDU1FX///bdB39PzmNwY2L+FMt25vwURQQiBjJy8Ev8SQugV5yeffAKJRIIzZ86gf//+qFWrFurXr4+goCCcOnWqyPMmT56MWrVqwd7eHtWqVcPMmTO1EoZLly6hU6dOcHJygrOzM5o3b45z584BAO7cuYNevXrB1dUVDg4OqF+/vqYH4dlhqUOHDml6Tzp37gyJRIJDhw4VOiz1559/olmzZrC1tUW1atUwZ84c5OXlaZ6XSCRYuXIl3nrrLTg4OGD+/PmFvq8tW7agcePG8Pb2LvDcunXrMHjwYAwdOhQ//vijbje4EMW9569q2bJl6N69OyZNmoS6deti3rx5aNasGZYvX17kOSdPnoSvry/Gjx+PqlWrol27dvjoo49w5swZreOUSiWGDBmCOXPmoFq1agWuY2VlhZ49e2LTpk0Gf1/P4j43BqRSCeyLUBfK5HwbIgIyc5WoN2tPib9u+NwA2Mt0+yc+MTERu3fvxvz58+Hg4FDg+RfNa3FyckJISAi8vLxw+fJljBo1Ck5OTvj8888BAEOGDEHTpk2xcuVKWFlZISwsDDY2+Xt/jRkzBjk5OThy5AgcHBwQHh5e6M60fn5+iIyMRO3atfHbb7/Bz88P5cqVQ3R0tNZxR48exbBhw/Ddd9+hffv2iIqKwocffggACA4O1hw3e/ZsLFq0CEuXLoW1deH36OjRo2jRokWB9tTUVGzduhWnT59GnTp1kJycjKNHjxbaS/Eir3LPf/75Z3z00UcvvP7ff/9dZEwnT55EUFCQVltAQMAL51K1adMG06ZNw65du9CjRw88fPgQ27ZtQ8+ePbWOmzt3Ltzd3TFixAgcPXq00Gu1bNkSixYtemH8r6pUJDcrVqzA4sWLERcXh8aNG+P7779Hy5Ytizx+69atmDlzJqKjo1GzZk18+eWXBW6wKYTdS0JCWjac5NZoWZWFMonIPNy8eRNCCNSpU0fvc2fMmKH53tfXFxMnTsSmTZs0yU1MTAwmTZqkuXbNmjU1x8fExKB///5o2LAhABT6lz6QP0SlHn4qV64cPDwK/+Nxzpw5mDJlCgIDAzXXmzdvHj7//HOt5Gbw4MEYPnz4C9/XnTt3Ck1uNm3ahJo1a6J+/foAgHfeeQfr1q3TO7l5lXv+1ltvoVWrVi88prAeJ7W4uDhUrKi9TUnFihULnT+k1rZtW/z8888YNGgQsrKykJeXh169emHFihWaY44dO4Z169YhLCzshbF5eXnh7t27UKlURtsTyuTJzebNmxEUFIRVq1ahVatWWLp0KQICAhAZGVnoWKp6rHDhwoV488038csvv6BPnz64cOECGjRoYIJ38C/1KqnX67hDZs0RPyIC7GysED43wCSvqyt9h7CetXnzZnz33XeIiopCWloa8vLy4Oz873zDoKAgjBw5Ej/99BP8/f3x9ttvo3r16gCA8ePH4+OPP8bevXvh7++P/v37o1GjRsWO5dKlSzh+/LjWUJNSqURWVhYyMjI0u0YXlrQ8LzMzE7a2tgXaf/zxR7z33nuax++99x46duyI77//vtCJx0V5lXvu5OSk12sZQnh4OD799FPMmjULAQEBiI2NxaRJkzB69GisW7cOqampGDp0KNasWQM3txeXHLKzs4NKpUJ2djbs7IyzXYrJP4GXLFmCUaNGYfjw4ahXrx5WrVoFe3v7IscxizNWWFKenW9DRATkz/Gwl1mX+Jc+c/5q1qwJiUSCa9eu6fXeTp48iSFDhqBnz57466+/cPHiRUyfPh05OTmaY2bPno2rV6/ijTfewIEDB1CvXj388ccfAICRI0fi1q1bGDp0KC5fvowWLVrg+++/1yuGZ6WlpWHOnDkICwvTfF2+fBk3btzQSlQKGwZ6npubG548eaLVFh4ejlOnTuHzzz+HtbU1rK2t0bp1a2RkZGjNIXF2dkZycnKBayYlJWlWfxX3ngP5w1KOjo4v/CpqSAgAPDw8EB8fr9UWHx9fZI8YACxcuBBt27bFpEmT0KhRIwQEBOCHH37Ajz/+iNjYWERFRSE6Ohq9evXS3JuNGzdix44dsLa2RlRUlOZaiYmJcHBwMFpiA5i45yYnJwfnz5/H1KlTNW1SqRT+/v44efJkoefoO1aYnZ2N7OxszeOUlJRXD7wQ0QnpuPEwDdZSCV6vzeSGiMxHuXLlEBAQgBUrVmD8+PEFPvyTkpIKnQNy4sQJVKlSBdOnT9e03blzp8BxtWrVQq1atTBhwgS8++67WL9+Pfr27QsA8PHxwejRozF69GhMnToVa9aswbhx44r1Ppo1a4bIyEjUqFGjWOc/q2nTpggPD9dqW7duHTp06KA1FAMA69evx7p16zQrh2rXro3z588XuOaFCxdQu3ZtAMW/58CrD0u1adMG+/fvx2effaZpCw0NRZs2bYo8JyMjo8D8JHXtJ/Xw2uXLl7WenzFjBlJTU7Fs2TL4+Pho2q9cuYKmTZu+MP5XZdLkJiEhAUqlstCxv6KyWX3HChcuXIg5c+YYJuAXuPskA26OctT2cITCjoUyici8rFixAm3btkXLli0xd+5cNGrUCHl5eQgNDcXKlSsRERFR4JyaNWsiJiYGmzZtwmuvvYadO3dqemWA/KGdSZMmYcCAAahatSru3buHs2fPon///gCAzz77DD169ECtWrXw5MkTHDx4EHXr1i32e5g1axbefPNNVK5cGQMGDIBUKsWlS5dw5coVfPHFF3pdKyAgACNHjoRSqYSVlRVyc3Px008/Ye7cuQWmQIwcORJLlizB1atXUb9+fUyYMAHt27fH/Pnz0a9fPyiVSvz66684efIkfvjhB815xbnnwKsPS3366afo2LEjvvnmG7zxxhvYtGkTzp07h9WrV2uOmTp1Ku7fv4+NGzcCAHr16oVRo0Zh5cqVmmGpzz77DC1btoSXlxcAFLgv6uTs+fajR4+iW7duxY5fJ8KE7t+/LwCIEydOaLVPmjRJtGzZstBzbGxsxC+//KLVtmLFCuHu7l7o8VlZWSI5OVnzdffuXQFAJCcnG+ZNPEOpVImE1CyDX5eIzENmZqYIDw8XmZmZpg6lWB48eCDGjBkjqlSpImQymfD29hZvvfWWOHjwoOYYAOKPP/7QPJ40aZIoX768cHR0FIMGDRLffvutUCgUQgghsrOzxTvvvCN8fHyETCYTXl5eYuzYsZr7M3bsWFG9enUhl8tFhQoVxNChQ0VCQoIQQoiDBw8KAOLJkydCCCGePHkiAGjFsn79es1rqe3evVv4+fkJOzs74ezsLFq2bClWr15dZPxFyc3NFV5eXmL37t1CCCG2bdsmpFKpiIuLK/T4unXrigkTJmge79mzR7Rt21a4urqK8uXLi9dff10cPny4wHm63HNj2LJli6hVq5aQyWSifv36YufOnVrPBwYGio4dO2q1fffdd6JevXrCzs5OeHp6iiFDhoh79+4V+RqBgYGid+/eWm337t0TNjY24u7du4We86LfoeTkZJ0/vyVCvMKspleUk5MDe3t7bNu2TWsXw8DAQCQlJeHPP/8scE7lypURFBSk1Z0WHByM7du349KlSy99zZSUFCgUCiQnJ2tNeiMielVZWVm4ffs2qlatWuhkVDIvK1aswI4dO7BnT8kv5bdUkydPxpMnT7R6iZ71ot8hfT6/TTqhWCaToXnz5lrbTKtUKuzfv7/IsT/1WOGzXjZWSEREpK+PPvoIHTp0YG0pA3J3d8e8efOM/jomXwoeFBSEwMBAtGjRAi1btsTSpUuRnp6u2YNg2LBh8Pb2xsKFCwHoNlZIRET0qqytrbUmS9Or+89//lMir2Py5GbQoEF49OgRZs2ahbi4ODRp0gS7d+/WTBqOiYnR2uTHz88Pv/zyC2bMmIFp06ahZs2a2L59u8n3uCEiIqLSwaRzbkyBc26IyFg454bo1VjEnBsiIktUxv5mJDIYQ/3uMLkhIjIQdUHIjIwME0dCZJ7Uu1urNwgsLpPPuSEishRWVlZwcXHBw4cPAQD29vZ6lUEgKstUKhUePXoEe3v7Iqu164rJDRGRAanr86gTHCLSnVQqReXKlV/5jwImN0REBiSRSODp6Ql3d3fk5uaaOhwisyKTybRWSBcXkxsiIiOwsrJ65XkDRFQ8nFBMREREFoXJDREREVkUJjdERERkUcrcnBv1BkEpKSkmjoSIiIh0pf7c1mWjvzKX3Kiru/r4+Jg4EiIiItJXamoqFArFC48pc7WlVCoVHjx4ACcnJ4NvrpWSkgIfHx/cvXuXdauMiPe5ZPA+lwze55LDe10yjHWfhRBITU2Fl5fXS5eLl7meG6lUikqVKhn1NZydnfmLUwJ4n0sG73PJ4H0uObzXJcMY9/llPTZqnFBMREREFoXJDREREVkUJjcGJJfLERwcDLlcbupQLBrvc8ngfS4ZvM8lh/e6ZJSG+1zmJhQTERGRZWPPDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhcmNnlasWAFfX1/Y2tqiVatWOHPmzAuP37p1K+rUqQNbW1s0bNgQu3btKqFIzZs+93nNmjVo3749XF1d4erqCn9//5f+f6F8+v48q23atAkSiQR9+vQxboAWQt/7nJSUhDFjxsDT0xNyuRy1atXivx060Pc+L126FLVr14adnR18fHwwYcIEZGVllVC05unIkSPo1asXvLy8IJFIsH379peec+jQITRr1gxyuRw1atRASEiI0eOEIJ1t2rRJyGQy8eOPP4qrV6+KUaNGCRcXFxEfH1/o8cePHxdWVlbiq6++EuHh4WLGjBnCxsZGXL58uYQjNy/63ufBgweLFStWiIsXL4qIiAjx/vvvC4VCIe7du1fCkZsXfe+z2u3bt4W3t7do37696N27d8kEa8b0vc/Z2dmiRYsWomfPnuLYsWPi9u3b4tChQyIsLKyEIzcv+t7nn3/+WcjlcvHzzz+L27dviz179ghPT08xYcKEEo7cvOzatUtMnz5d/P777wKA+OOPP154/K1bt4S9vb0ICgoS4eHh4vvvvxdWVlZi9+7dRo2TyY0eWrZsKcaMGaN5rFQqhZeXl1i4cGGhxw8cOFC88cYbWm2tWrUSH330kVHjNHf63ufn5eXlCScnJ7FhwwZjhWgRinOf8/LyhJ+fn1i7dq0IDAxkcqMDfe/zypUrRbVq1UROTk5JhWgR9L3PY8aMEZ07d9ZqCwoKEm3btjVqnJZEl+Tm888/F/Xr19dqGzRokAgICDBiZEJwWEpHOTk5OH/+PPz9/TVtUqkU/v7+OHnyZKHnnDx5Uut4AAgICCjyeCrefX5eRkYGcnNzUa5cOWOFafaKe5/nzp0Ld3d3jBgxoiTCNHvFuc87duxAmzZtMGbMGFSsWBENGjTAggULoFQqSypss1Oc++zn54fz589rhq5u3bqFXbt2oWfPniUSc1lhqs/BMlc4s7gSEhKgVCpRsWJFrfaKFSvi2rVrhZ4TFxdX6PFxcXFGi9PcFec+P2/y5Mnw8vIq8AtF/yrOfT527BjWrVuHsLCwEojQMhTnPt+6dQsHDhzAkCFDsGvXLty8eROffPIJcnNzERwcXBJhm53i3OfBgwcjISEB7dq1gxACeXl5GD16NKZNm1YSIZcZRX0OpqSkIDMzE3Z2dkZ5XfbckEVZtGgRNm3ahD/++AO2tramDsdipKamYujQoVizZg3c3NxMHY5FU6lUcHd3x+rVq9G8eXMMGjQI06dPx6pVq0wdmkU5dOgQFixYgB9++AEXLlzA77//jp07d2LevHmmDo0MgD03OnJzc4OVlRXi4+O12uPj4+Hh4VHoOR4eHnodT8W7z2pff/01Fi1ahH379qFRo0bGDNPs6Xufo6KiEB0djV69emnaVCoVAMDa2hqRkZGoXr26cYM2Q8X5efb09ISNjQ2srKw0bXXr1kVcXBxycnIgk8mMGrM5Ks59njlzJoYOHYqRI0cCABo2bIj09HR8+OGHmD59OqRS/u1vCEV9Djo7Oxut1wZgz43OZDIZmjdvjv3792vaVCoV9u/fjzZt2hR6Tps2bbSOB4DQ0NAij6fi3WcA+OqrrzBv3jzs3r0bLVq0KIlQzZq+97lOnTq4fPkywsLCNF9vvfUWOnXqhLCwMPj4+JRk+GajOD/Pbdu2xc2bNzXJIwBcv34dnp6eTGyKUJz7nJGRUSCBUSeUgiUXDcZkn4NGna5sYTZt2iTkcrkICQkR4eHh4sMPPxQuLi4iLi5OCCHE0KFDxZQpUzTHHz9+XFhbW4uvv/5aREREiODgYC4F14G+93nRokVCJpOJbdu2idjYWM1Xamqqqd6CWdD3Pj+Pq6V0o+99jomJEU5OTmLs2LEiMjJS/PXXX8Ld3V188cUXpnoLZkHf+xwcHCycnJzEr7/+Km7duiX27t0rqlevLgYOHGiqt2AWUlNTxcWLF8XFixcFALFkyRJx8eJFcefOHSGEEFOmTBFDhw7VHK9eCj5p0iQREREhVqxYwaXgpdH3338vKleuLGQymWjZsqU4deqU5rmOHTuKwMBAreO3bNkiatWqJWQymahfv77YuXNnCUdsnvS5z1WqVBEACnwFBweXfOBmRt+f52cxudGdvvf5xIkTolWrVkIul4tq1aqJ+fPni7y8vBKO2vzoc59zc3PF7NmzRfXq1YWtra3w8fERn3zyiXjy5EnJB25GDh48WOi/t+p7GxgYKDp27FjgnCZNmgiZTCaqVasm1q9fb/Q4JUKw/42IiIgsB+fcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRWFyQ0RERBaFyQ0RERFZFCY3REREZFGY3BAVIiQkBC4uLqYOo9gkEgm2b9/+wmPef/999OnTp0TiKW1mzpyJDz/8sERe69ChQ5BIJEhKSnrhcb6+vli6dKlRY9H3NQz1e6DLz6O+wsPDUalSJaSnpxv0umQZmNyQxXr//fchkUgKfN28edPUoSEkJEQTj1QqRaVKlTB8+HA8fPjQINePjY1Fjx49AADR0dGQSCQICwvTOmbZsmUICQkxyOsVZfbs2Zr3aWVlBR8fH3z44YdITEzU6zqGTMTi4uKwbNkyTJ8+Xev66jhlMhlq1KiBuXPnIi8v75Vfz8/PD7GxsVAoFACKThjOnj1bYgmXOZg/fz78/Pxgb29f6P2qV68eWrdujSVLlpR8cFTqMbkhi9a9e3fExsZqfVWtWtXUYQEAnJ2dERsbi3v37mHNmjX4+++/MXToUINc28PDA3K5/IXHKBSKEumdql+/PmJjYxETE4P169dj9+7d+Pjjj43+ukVZu3Yt/Pz8UKVKFa129c/KjRs38J///AezZ8/G4sWLX/n1ZDIZPDw8IJFIXnhchQoVYG9v/8qvZylycnLw9ttvv/BnZfjw4Vi5cqVBklCyLExuyKLJ5XJ4eHhofVlZWWHJkiVo2LAhHBwc4OPjg08++QRpaWlFXufSpUvo1KkTnJyc4OzsjObNm+PcuXOa548dO4b27dvDzs4OPj4+GD9+/Eu7yyUSCTw8PODl5YUePXpg/Pjx2LdvHzIzM6FSqTB37lxUqlQJcrkcTZo0we7duzXn5uTkYOzYsfD09IStrS2qVKmChQsXal1bPQygTuaaNm0KiUSC119/HYB2b8jq1avh5eUFlUqlFWPv3r3xwQcfaB7/+eefaNasGWxtbVGtWjXMmTPnpR8s1tbW8PDwgLe3N/z9/fH2228jNDRU87xSqcSIESNQtWpV2NnZoXbt2li2bJnm+dmzZ2PDhg34888/Nb0rhw4dAgDcvXsXAwcOhIuLC8qVK4fevXsjOjr6hfFs2rQJvXr1KtCu/lmpUqUKPv74Y/j7+2PHjh0AgCdPnmDYsGFwdXWFvb09evTogRs3bmjOvXPnDnr16gVXV1c4ODigfv362LVrFwDtYalDhw5h+PDhSE5O1ryX2bNnA9AeMho8eDAGDRqkFV9ubi7c3NywceNGAIBKpcLChQs1961x48bYtm3bC9/783T9Pdi+fTtq1qwJW1tbBAQE4O7du1rPF+fn4mXmzJmDCRMmoGHDhkUe07VrVyQmJuLw4cOv9FpkeZjcUJkklUrx3Xff4erVq9iwYQMOHDiAzz//vMjjhwwZgkqVKuHs2bM4f/48pkyZAhsbGwBAVFQUunfvjv79++Off/7B5s2bcezYMYwdO1avmOzs7KBSqZCXl4dly5bhm2++wddff41//vkHAQEBeOuttzQfqN999x127NiBLVu2IDIyEj///DN8fX0Lve6ZM2cAAPv27UNsbCx+//33Ase8/fbbePz4MQ4ePKhpS0xMxO7duzFkyBAAwNGjRzFs2DB8+umnCA8Px3//+1+EhIRg/vz5Or/H6Oho7NmzBzKZTNOmUqlQqVIlbN26FeHh4Zg1axamTZuGLVu2AAAmTpyIgQMHavXC+fn5ITc3FwEBAXBycsLRo0dx/PhxODo6onv37sjJySn09RMTExEeHo4WLVq8NFY7OzvNdd5//32cO3cOO3bswMmTJyGEQM+ePZGbmwsAGDNmDLKzs3HkyBFcvnwZX375JRwdHQtc08/PD0uXLtX02sXGxmLixIkFjhsyZAj+97//aSUae/bsQUZGBvr27QsAWLhwITZu3IhVq1bh6tWrmDBhAt577z29Puh1+T3IyMjA/PnzsXHjRhw/fhxJSUl45513NM8X5+fi9ddfx/vvv69znEWRyWRo0qQJjh49+srXIgtj9LrjRCYSGBgorKyshIODg+ZrwIABhR67detWUb58ec3j9evXC4VCoXns5OQkQkJCCj13xIgR4sMPP9RqO3r0qJBKpSIzM7PQc56//vXr10WtWrVEixYthBBCeHl5ifnz52ud89prr4lPPvlECCHEuHHjROfOnYVKpSr0+gDEH3/8IYQQ4vbt2wKAuHjxotYxgYGBonfv3prHvXv3Fh988IHm8X//+1/h5eUllEqlEEKILl26iAULFmhd46effhKenp6FxiCEEMHBwUIqlQoHBwdha2srAAgAYsmSJUWeI4QQY8aMEf379y8yVvVr165dW+seZGdnCzs7O7Fnz55Cr3vx4kUBQMTExGi1P3t9lUolQkNDhVwuFxMnThTXr18XAMTx48c1xyckJAg7OzuxZcsWIYQQDRs2FLNnzy70NQ8ePCgAiCdPngghCv6/V6tSpYr49ttvhRBC5ObmCjc3N7Fx40bN8++++64YNGiQEEKIrKwsYW9vL06cOKF1jREjRoh333230Dief43CFPZ7AECcOnVK0xYRESEAiNOnTwshdPu5ePbnUQghhg4dKqZMmVJkHM8q6n6p9e3bV7z//vs6XYvKDmtTJVVEJaFTp05YuXKl5rGDgwOA/F6MhQsX4tq1a0hJSUFeXh6ysrKQkZFR6LyHoKAgjBw5Ej/99JNmaKV69eoA8oes/vnnH/z888+a44UQUKlUuH37NurWrVtobMnJyXB0dIRKpUJWVhbatWuHtWvXIiUlBQ8ePEDbtm21jm/bti0uXboEIL8noWvXrqhduza6d++ON998E926dXulezVkyBCMGjUKP/zwA+RyOX7++We88847kEqlmvd5/Phxrb/IlUrlC+8bANSuXRs7duxAVlYW/u///g9hYWEYN26c1jErVqzAjz/+iJiYGGRmZiInJwdNmjR5YbyXLl3CzZs34eTkpNWelZWFqKioQs/JzMwEANja2hZ47q+//oKjoyNyc3OhUqkwePBgzJ49G/v374e1tTVatWqlObZ8+fKoXbs2IiIiAADjx4/Hxx9/jL1798Lf3x/9+/dHo0aNXhj/i1hbW2PgwIH4+eefMXToUKSnp+PPP//Epk2bAAA3b95ERkYGunbtqnVeTk4OmjZtqvPr6PJ7YG1tjddee01zTp06deDi4oKIiAi0bNmyWD8X6qE1Q7Czs0NGRobBrkeWgckNWTQHBwfUqFFDqy06OhpvvvkmPv74Y8yfPx/lypXDsWPHMGLECOTk5BT6j/Hs2bMxePBg7Ny5E3///TeCg4OxadMm9O3bF2lpafjoo48wfvz4AudVrly5yNicnJxw4cIFSKVSeHp6ws7ODgCQkpLy0vfVrFkz3L59G3///Tf27duHgQMHwt/fX+85F8/q1asXhBDYuXMnXnvtNRw9ehTffvut5vm0tDTMmTMH/fr1K3BuYcmCmnr1EQAsWrQIb7zxBubMmYN58+YByJ8DM3HiRHzzzTdo06YNnJycsHjxYpw+ffqF8aalpaF58+ZaSaVahQoVCj3Hzc0NQP4cmuePUSfCMpkMXl5esLbW/Z/HkSNHIiAgADt37sTevXuxcOFCfPPNNwWSOH0MGTIEHTt2xMOHDxEaGgo7Ozt0794dADTDVTt37oS3t7fWeS+bSK5WnN+DwhT358JQEhMTNX9oEKkxuaEy5/z581CpVPjmm280vRLq+R0vUqtWLdSqVQsTJkzAu+++i/Xr16Nv375o1qwZwsPDCyRRLyOVSgs9x9nZGV5eXjh+/Dg6duyoaT9+/DhatmypddygQYMwaNAgDBgwAN27d0diYiLKlSundT31/BalUvnCeGxtbdGvXz/8/PPPuHnzJmrXro1mzZppnm/WrBkiIyP1fp/PmzFjBjp37oyPP/5Y8z79/PzwySefaI55vudFJpMViL9Zs2bYvHkz3N3d4ezsrNNrV69eHc7OzggPD0etWrW0nissEQaAunXrIi8vD6dPn4afnx8A4PHjx4iMjES9evU0x/n4+GD06NEYPXo0pk6dijVr1hSa3BT2Xgrj5+cHHx8fbN68GX///TfefvttzTyvevXqQS6XIyYmRutnRB+6/h7k5eXh3Llzmp+9yMhIJCUlaXokDfVzUVxXrlzBgAEDTPLaVHpxQjGVOTVq1EBubi6+//573Lp1Cz/99BNWrVpV5PGZmZkYO3YsDh06hDt37uD48eM4e/as5h/3yZMn48SJExg7dizCwsJw48YN/Pnnn3pPKH7WpEmT8OWXX2Lz5s2IjIzElClTEBYWhk8//RRA/iqXX3/9FdeuXcP169exdetWeHh4FLq0293dHXZ2dti9ezfi4+ORnJxc5OsOGTIEO3fuxI8//qiZSKw2a9YsbNy4EXPmzMHVq1cRERGBTZs2YcaMGXq9tzZt2qBRo0ZYsGABAKBmzZo4d+4c9uzZg+vXr2PmzJk4e/as1jm+vr74559/EBkZiYSEBOTm5mLIkCFwc3ND7969cfToUdy+fRuHDh3C+PHjce/evUJfWyqVwt/fH8eOHdM53po1a6J3794YNWoUjh07hkuXLuG9996Dt7c3evfuDQD47LPPsGfPHty+fRsXLlzAwYMHixyO9PX1RVpaGvbv34+EhIQXDqkMHjwYq1atQmhoqNb/DycnJ0ycOBETJkzAhg0bEBUVhQsXLuD777/Hhg0bdHpfuv4e2NjYYNy4cTh9+jTOnz+P999/H61bt9YkO8X5uRg2bBimTp36wvhiYmIQFhaGmJgYKJVKhIWFISwsTGuSdXR0NO7fvw9/f3+d3jOVIaae9ENkLIVNQlVbsmSJ8PT0FHZ2diIgIEBs3LixyEmf2dnZ4p133hE+Pj5CJpMJLy8vMXbsWK3JwmfOnBFdu3YVjo6OwsHBQTRq1KjAhOBnvWySpFKpFLNnzxbe3t7CxsZGNG7cWPz999+a51evXi2aNGkiHBwchLOzs+jSpYu4cOGC5nk8N4FzzZo1wsfHR0ilUtGxY8ci749SqRSenp4CgIiKiioQ1+7du4Wfn5+ws7MTzs7OomXLlmL16tVFvo/g4GDRuHHjAu2//vqrkMvlIiYmRmRlZYn3339fKBQK4eLiIj7++GMxZcoUrfMePnyoub8AxMGDB4UQQsTGxophw4YJNzc3IZfLRbVq1cSoUaNEcnJykTHt2rVLeHt7ayZKF3UvnpWYmCiGDh0qFAqF5mfm+vXrmufHjh0rqlevLuRyuahQoYIYOnSoSEhIEEIUnFAshBCjR48W5cuXFwBEcHCwEKLwyb7h4eECgKhSpUqByeMqlUosXbpU1K5dW9jY2IgKFSqIgIAAcfjw4SLfx/OvoevvwW+//SaqVasm5HK58Pf3F3fu3NG67st+Lp7/eezYsaMIDAwsMk4h8v+f4OkE9Ge/1P/vhRBiwYIFIiAg4IXXobJJIoQQpkiqiIhMQQiBVq1aaYYXyTzl5OSgZs2a+OWXXwpMvifisBQRlSkSiQSrV6/mrrZmLiYmBtOmTWNiQ4Vizw0RERFZFPbcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRWFyQ0RERBaFyQ0RERFZFCY3REREZFGY3BAREZFF+X8YcV/Y7GAyPwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import PrecisionRecallDisplay, RocCurveDisplay\n",
    "\n",
    "# precision recall curve\n",
    "PrecisionRecallDisplay.from_predictions(y_test, y_pred)\n",
    "plt.title('precision recall curve')\n",
    "\n",
    "# ROC curve\n",
    "RocCurveDisplay.from_predictions(y_test, y_pred)\n",
    "plt.title('ROC curve')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final prediction of a patient with the following features\n",
    "\n",
    "    age          48.0\n",
    "    sex           0.0\n",
    "    cp            2.0\n",
    "    trestbps    130.0\n",
    "    chol        275.0\n",
    "    fbs           0.0\n",
    "    restecg       1.0\n",
    "    thalach     139.0\n",
    "    exang         0.0\n",
    "    oldpeak       0.2\n",
    "    slope         2.0\n",
    "    ca            0.0\n",
    "    thal          2.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "patient = [[ 54. ,   1. ,   0. , 122. , 286. ,   0. ,   0. , 116. ,   1. ,\n",
    "          3.2,   1. ,   2. ,   2. ]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Likelihood of the patient having the heart disease: [0]\n",
      "Expected Probability Per Class: [[1.00000000e+00 1.33406135e-18]]\n"
     ]
    }
   ],
   "source": [
    "# prediction on the patient's features\n",
    "print(f\"Likelihood of the patient having the heart disease: {grid_model.predict(patient)}\")\n",
    "\n",
    "# Expected Probability Per Class (Basically model should be extremely sure its in the 0 class)\n",
    "print(f\"Expected Probability Per Class: {grid_model.predict_proba(patient)}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*** The End ***"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.10.6 (main, Mar 10 2023, 10:55:28) [GCC 11.3.0]"
  },
  "vscode": {
   "interpreter": {
    "hash": "97cc609b13305c559618ec78a438abc56230b9381f827f22d070313b9a1f3777"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}