Mlflow tracking simple example

mlflow_tracking_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c5073817",
   "metadata": {},
   "source": [
    "### Пример использования mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "225f5600",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ed4d4e14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Указываем доступ в БД\n",
    "sql_string = \"postgresql://unicorn_user:magical_password@0.0.0.0/rainbow_database\"\n",
    "mlflow.set_tracking_uri(sql_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f9cdf6ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Создаем эксперимент\n",
    "expname = \"Wine_dataset\"\n",
    "mlflow.create_experiment(expname, artifact_location=\"s3://mlflow-example-bds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3272c06",
   "metadata": {},
   "source": [
    "###  Wine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9b792c0f",
   "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>name</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>malicAcid</th>\n",
       "      <th>ash</th>\n",
       "      <th>ashalcalinity</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>totalPhenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonFlavanoidPhenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>colorIntensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280_od315</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name  alcohol  malicAcid   ash  ashalcalinity  magnesium  totalPhenols  \\\n",
       "0     1    14.23       1.71  2.43           15.6        127          2.80   \n",
       "1     1    13.20       1.78  2.14           11.2        100          2.65   \n",
       "\n",
       "   flavanoids  nonFlavanoidPhenols  proanthocyanins  colorIntensity   hue  \\\n",
       "0        3.06                 0.28             2.29            5.64  1.04   \n",
       "1        2.76                 0.26             1.28            4.38  1.05   \n",
       "\n",
       "   od280_od315  proline  \n",
       "0         3.92     1065  \n",
       "1         3.40     1050  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('data/Wine.csv',header=None)\n",
    "df.columns = [  'name'\n",
    "                 ,'alcohol'\n",
    "                 ,'malicAcid'\n",
    "                 ,'ash'\n",
    "                 ,'ashalcalinity'\n",
    "                 ,'magnesium'\n",
    "                 ,'totalPhenols'\n",
    "                 ,'flavanoids'\n",
    "                 ,'nonFlavanoidPhenols'\n",
    "                 ,'proanthocyanins'\n",
    "                 ,'colorIntensity'\n",
    "                 ,'hue'\n",
    "                 ,'od280_od315'\n",
    "                 ,'proline'\n",
    "                ]\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "22559279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Выведем таблицу попарной корреляции признаков\n",
    "corr = df[df.columns].corr()\n",
    "swarm_plot = sns.heatmap(corr, cmap=\"YlGnBu\", annot = True)\n",
    "fig = swarm_plot.get_figure()\n",
    "fig.savefig('heatmap.jpeg') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb135389",
   "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>alcohol</th>\n",
       "      <th>malicAcid</th>\n",
       "      <th>ashalcalinity</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>totalPhenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonFlavanoidPhenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>colorIntensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280_od315</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malicAcid  ashalcalinity  magnesium  totalPhenols  flavanoids  \\\n",
       "0    14.23       1.71           15.6        127          2.80        3.06   \n",
       "1    13.20       1.78           11.2        100          2.65        2.76   \n",
       "2    13.16       2.36           18.6        101          2.80        3.24   \n",
       "3    14.37       1.95           16.8        113          3.85        3.49   \n",
       "4    13.24       2.59           21.0        118          2.80        2.69   \n",
       "\n",
       "   nonFlavanoidPhenols  proanthocyanins  colorIntensity   hue  od280_od315  \\\n",
       "0                 0.28             2.29            5.64  1.04         3.92   \n",
       "1                 0.26             1.28            4.38  1.05         3.40   \n",
       "2                 0.30             2.81            5.68  1.03         3.17   \n",
       "3                 0.24             2.18            7.80  0.86         3.45   \n",
       "4                 0.39             1.82            4.32  1.04         2.93   \n",
       "\n",
       "   proline  \n",
       "0     1065  \n",
       "1     1050  \n",
       "2     1185  \n",
       "3     1480  \n",
       "4      735  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= df.drop(['name','ash'], axis=1)\n",
    "\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b8e1b52",
   "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>name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name\n",
       "0     1\n",
       "1     1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y=df.iloc[:,:1]\n",
    "Y.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f49e651",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(124, 12)\n",
      "(54, 12)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=0)\n",
    "\n",
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43262157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Models appended...\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "models = []\n",
    "\n",
    "models.append((\"Logistic Regression:\",LogisticRegression()))\n",
    "models.append((\"Naive Bayes:\",GaussianNB()))\n",
    "models.append((\"K-Nearest Neighbour:\",KNeighborsClassifier(n_neighbors=3)))\n",
    "models.append((\"Decision Tree:\",DecisionTreeClassifier()))\n",
    "models.append((\"Support Vector Machine-linear:\",SVC(kernel=\"linear\")))\n",
    "models.append((\"Support Vector Machine-rbf:\",SVC(kernel=\"rbf\")))\n",
    "models.append((\"Random Forest:\",RandomForestClassifier(n_estimators=7)))\n",
    "models.append((\"MLP:\",MLPClassifier(hidden_layer_sizes=(45,30,15),solver='sgd',learning_rate_init=0.01,max_iter=500)))\n",
    "models.append((\"AdaBoostClassifier:\",AdaBoostClassifier()))\n",
    "models.append((\"GradientBoostingClassifier:\",GradientBoostingClassifier()))\n",
    "\n",
    "print('Models appended...')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "445d54d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression: 95.32051282051283\n",
      "Naive Bayes: 96.85897435897436\n",
      "K-Nearest Neighbour: 71.08974358974358\n",
      "Decision Tree: 92.75641025641026\n",
      "Support Vector Machine-linear: 95.96153846153845\n",
      "Support Vector Machine-rbf: 62.948717948717956\n",
      "Random Forest: 95.1923076923077\n",
      "MLP: 37.82051282051282\n",
      "AdaBoostClassifier: 89.42307692307693\n",
      "GradientBoostingClassifier: 91.98717948717949\n"
     ]
    }
   ],
   "source": [
    "results = []\n",
    "names = []\n",
    "for name,model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=0,shuffle=True)\n",
    "    cv_result = cross_val_score(model,X_train,Y_train.values.ravel(), cv = kfold,scoring = \"accuracy\")\n",
    "    names.append(name)\n",
    "    results.append(cv_result)\n",
    "for i in range(len(names)):\n",
    "    print(names[i],results[i].mean()*100)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fd83eab",
   "metadata": {},
   "source": [
    "### Mlflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c8102f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Library/Frameworks/Python.framework/Versions/3.9/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:763: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "for name,model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=0,shuffle=True)\n",
    "    cv_result = cross_val_score(model,X_train,Y_train.values.ravel(), cv = kfold,scoring = \"accuracy\")\n",
    "    names.append(name)\n",
    "    results.append(cv_result)\n",
    "    with mlflow.start_run(experiment_id=1, run_name=name):\n",
    "        mlflow.log_param('model_name', 'name')\n",
    "        mlflow.log_metric(\"accuracy\",results[i].mean()*100)\n",
    "        mlflow.log_artifact(\"heatmap.jpeg\")"
   ]
  }
 ],
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