From 17eb8952a630ce638086881ace432a7e3da62f79 Mon Sep 17 00:00:00 2001
From: Andrey <asmokhov@mail.ru>
Date: Tue, 19 Nov 2024 12:20:12 +0300
Subject: [PATCH] lab3

---
 README.md                    |    3 +
 assets/mlflow/research.ipynb | 1493 +++++++++++++++++++++++++++++++++-
 labs/lab3.md                 |  171 ++++
 3 files changed, 1654 insertions(+), 13 deletions(-)
 create mode 100644 labs/lab3.md

diff --git a/README.md b/README.md
index 5c76e67..478c6d3 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,6 @@
 # Интеллектуальные информационные системы
 
+
 ## Лекции
 
 |    Дата    |Лекция                                                              | 
@@ -31,4 +32,6 @@
     
 [Лабораторная работа 2.](./labs/lab2.md)   Проведение исследований по настройке модели.
 
+[Лабораторная работа 3.](./labs/lab3.md)   Создание микросервиса предсказаний моделью ML.
+
 ## [Журнал](https://docs.google.com/spreadsheets/d/10juwyGqOhiD_czxfVziLj10aHYTaCp5oDmhesBJkxYM/edit?gid=1516016995#gid=1516016995)
\ No newline at end of file
diff --git a/assets/mlflow/research.ipynb b/assets/mlflow/research.ipynb
index 4ec120c..e5e4310 100644
--- a/assets/mlflow/research.ipynb
+++ b/assets/mlflow/research.ipynb
@@ -7,7 +7,7 @@
    "outputs": [],
    "source": [
     "import os\n",
-    "import mlflow\n",
+    "\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "import pandas as pd\n",
@@ -26,9 +26,36 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Int64Index: 547701 entries, 313199 to 690900\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column         Non-Null Count   Dtype   \n",
+      "---  ------         --------------   -----   \n",
+      " 0   price          547701 non-null  int64   \n",
+      " 1   date           547701 non-null  object  \n",
+      " 2   time           547701 non-null  object  \n",
+      " 3   geo_lat        547701 non-null  float32 \n",
+      " 4   geo_lon        547701 non-null  float32 \n",
+      " 5   region         547701 non-null  category\n",
+      " 6   building_type  547701 non-null  category\n",
+      " 7   level          547701 non-null  int8    \n",
+      " 8   levels         547701 non-null  int8    \n",
+      " 9   rooms          547701 non-null  int8    \n",
+      " 10  area           547701 non-null  float16 \n",
+      " 11  kitchen_area   547701 non-null  float16 \n",
+      " 12  object_type    547701 non-null  category\n",
+      "dtypes: category(3), float16(2), float32(2), int64(1), int8(3), object(2)\n",
+      "memory usage: 26.1+ MB\n"
+     ]
+    }
+   ],
    "source": [
     "df = pd.read_pickle('data/clean_data.pkl').sample(frac=0.1, random_state = 2) # Уменьшаем размер чтобы модель быстрее обучалась на лекции\n",
     "df.info()"
@@ -46,9 +73,238 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "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>target</th>\n",
+       "      <th>geo_lat</th>\n",
+       "      <th>geo_lon</th>\n",
+       "      <th>region</th>\n",
+       "      <th>building_type</th>\n",
+       "      <th>level</th>\n",
+       "      <th>levels</th>\n",
+       "      <th>rooms</th>\n",
+       "      <th>area</th>\n",
+       "      <th>kitchen_area</th>\n",
+       "      <th>object_type</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>313199</th>\n",
+       "      <td>4999999</td>\n",
+       "      <td>59.958458</td>\n",
+       "      <td>30.215530</td>\n",
+       "      <td>2661</td>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "      <td>13</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36.00000</td>\n",
+       "      <td>7.199219</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2437764</th>\n",
+       "      <td>2150000</td>\n",
+       "      <td>45.072674</td>\n",
+       "      <td>41.936996</td>\n",
+       "      <td>2900</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>52.00000</td>\n",
+       "      <td>15.000000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4949072</th>\n",
+       "      <td>8600000</td>\n",
+       "      <td>59.939358</td>\n",
+       "      <td>30.437069</td>\n",
+       "      <td>2661</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>22</td>\n",
+       "      <td>1</td>\n",
+       "      <td>37.09375</td>\n",
+       "      <td>9.796875</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4109465</th>\n",
+       "      <td>5100000</td>\n",
+       "      <td>59.740479</td>\n",
+       "      <td>30.569540</td>\n",
+       "      <td>2661</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>74.50000</td>\n",
+       "      <td>9.500000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2187702</th>\n",
+       "      <td>3470000</td>\n",
+       "      <td>56.324062</td>\n",
+       "      <td>44.005390</td>\n",
+       "      <td>2871</td>\n",
+       "      <td>2</td>\n",
+       "      <td>11</td>\n",
+       "      <td>26</td>\n",
+       "      <td>2</td>\n",
+       "      <td>54.00000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5188085</th>\n",
+       "      <td>2300000</td>\n",
+       "      <td>57.750603</td>\n",
+       "      <td>40.866467</td>\n",
+       "      <td>4189</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>38.00000</td>\n",
+       "      <td>11.000000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4542014</th>\n",
+       "      <td>6700000</td>\n",
+       "      <td>55.911720</td>\n",
+       "      <td>37.737419</td>\n",
+       "      <td>81</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "      <td>66.37500</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3306731</th>\n",
+       "      <td>3850000</td>\n",
+       "      <td>51.704510</td>\n",
+       "      <td>39.273037</td>\n",
+       "      <td>2072</td>\n",
+       "      <td>2</td>\n",
+       "      <td>10</td>\n",
+       "      <td>18</td>\n",
+       "      <td>3</td>\n",
+       "      <td>89.50000</td>\n",
+       "      <td>14.203125</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>520293</th>\n",
+       "      <td>1878885</td>\n",
+       "      <td>54.943577</td>\n",
+       "      <td>82.958862</td>\n",
+       "      <td>9654</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3</td>\n",
+       "      <td>87.75000</td>\n",
+       "      <td>12.921875</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>690900</th>\n",
+       "      <td>4097350</td>\n",
+       "      <td>59.882702</td>\n",
+       "      <td>30.451246</td>\n",
+       "      <td>2661</td>\n",
+       "      <td>2</td>\n",
+       "      <td>6</td>\n",
+       "      <td>23</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36.09375</td>\n",
+       "      <td>16.203125</td>\n",
+       "      <td>11</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>547701 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          target    geo_lat    geo_lon region building_type  level  levels  \\\n",
+       "313199   4999999  59.958458  30.215530   2661             3      8      13   \n",
+       "2437764  2150000  45.072674  41.936996   2900             3      5       5   \n",
+       "4949072  8600000  59.939358  30.437069   2661             2     11      22   \n",
+       "4109465  5100000  59.740479  30.569540   2661             1      2       9   \n",
+       "2187702  3470000  56.324062  44.005390   2871             2     11      26   \n",
+       "...          ...        ...        ...    ...           ...    ...     ...   \n",
+       "5188085  2300000  57.750603  40.866467   4189             3      2       3   \n",
+       "4542014  6700000  55.911720  37.737419     81             3      2       5   \n",
+       "3306731  3850000  51.704510  39.273037   2072             2     10      18   \n",
+       "520293   1878885  54.943577  82.958862   9654             1      1      10   \n",
+       "690900   4097350  59.882702  30.451246   2661             2      6      23   \n",
+       "\n",
+       "         rooms      area  kitchen_area object_type  \n",
+       "313199       1  36.00000      7.199219           1  \n",
+       "2437764      1  52.00000     15.000000           1  \n",
+       "4949072      1  37.09375      9.796875           1  \n",
+       "4109465      3  74.50000      9.500000           1  \n",
+       "2187702      2  54.00000      8.000000          11  \n",
+       "...        ...       ...           ...         ...  \n",
+       "5188085      1  38.00000     11.000000           1  \n",
+       "4542014      2  66.37500      8.000000           1  \n",
+       "3306731      3  89.50000     14.203125           1  \n",
+       "520293       3  87.75000     12.921875          11  \n",
+       "690900       1  36.09375     16.203125          11  \n",
+       "\n",
+       "[547701 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "df"
    ]
@@ -62,11 +318,59 @@
     "X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.25, random_state=2)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "geo_lat            56.327686\n",
+       "geo_lon            43.928062\n",
+       "region           2871.000000\n",
+       "building_type       1.000000\n",
+       "level               8.000000\n",
+       "levels             10.000000\n",
+       "rooms               2.000000\n",
+       "area               56.000000\n",
+       "kitchen_area        8.500000\n",
+       "object_type         1.000000\n",
+       "Name: 879487, dtype: float64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.iloc[0]"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['region', 'building_type', 'object_type']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "cat_features = X_train.select_dtypes(include=['category','object']).columns.to_list()\n",
     "cat_features"
@@ -74,9 +378,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['geo_lat', 'geo_lon', 'level', 'levels', 'rooms', 'area', 'kitchen_area']"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "num_features = X_train.select_dtypes(include=['number']).columns.to_list()\n",
     "num_features"
@@ -91,7 +406,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -109,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,9 +439,1105 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Learning rate set to 0.105957\n",
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+     ]
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+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {color: black;}#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>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+       "                                                  [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;,\n",
+       "                                                   &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;,\n",
+       "                                                   &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
+       "                                                                 unknown_value=99999999),\n",
+       "                                                  [&#x27;region&#x27;, &#x27;building_type&#x27;,\n",
+       "                                                   &#x27;object_type&#x27;])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 &lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;)])</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\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
+       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+       "                                                  [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;,\n",
+       "                                                   &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;,\n",
+       "                                                   &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
+       "                                                 (&#x27;cat&#x27;,\n",
+       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
+       "                                                                 unknown_value=99999999),\n",
+       "                                                  [&#x27;region&#x27;, &#x27;building_type&#x27;,\n",
+       "                                                   &#x27;object_type&#x27;])])),\n",
+       "                (&#x27;model&#x27;,\n",
+       "                 &lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;)])</pre></div></div></div><div class=\"sk-serial\"><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-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocessor: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;, StandardScaler(),\n",
+       "                                 [&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;, &#x27;level&#x27;, &#x27;levels&#x27;,\n",
+       "                                  &#x27;rooms&#x27;, &#x27;area&#x27;, &#x27;kitchen_area&#x27;]),\n",
+       "                                (&#x27;cat&#x27;,\n",
+       "                                 OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
+       "                                                unknown_value=99999999),\n",
+       "                                 [&#x27;region&#x27;, &#x27;building_type&#x27;, &#x27;object_type&#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-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;geo_lat&#x27;, &#x27;geo_lon&#x27;, &#x27;level&#x27;, &#x27;levels&#x27;, &#x27;rooms&#x27;, &#x27;area&#x27;, &#x27;kitchen_area&#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-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div><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-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;region&#x27;, &#x27;building_type&#x27;, &#x27;object_type&#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-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OrdinalEncoder</label><div class=\"sk-toggleable__content\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=99999999)</pre></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">CatBoostRegressor</label><div class=\"sk-toggleable__content\"><pre>&lt;catboost.core.CatBoostRegressor object at 0x7be8319969b0&gt;</pre></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "Pipeline(steps=[('preprocessor',\n",
+       "                 ColumnTransformer(transformers=[('num', StandardScaler(),\n",
+       "                                                  ['geo_lat', 'geo_lon',\n",
+       "                                                   'level', 'levels', 'rooms',\n",
+       "                                                   'area', 'kitchen_area']),\n",
+       "                                                 ('cat',\n",
+       "                                                  OrdinalEncoder(handle_unknown='use_encoded_value',\n",
+       "                                                                 unknown_value=99999999),\n",
+       "                                                  ['region', 'building_type',\n",
+       "                                                   'object_type'])])),\n",
+       "                ('model',\n",
+       "                 <catboost.core.CatBoostRegressor object at 0x7be8319969b0>)])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "\n",
     "pipeline = Pipeline(steps=[('preprocessor', preprocessor), \n",
@@ -137,9 +1548,65 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
+   "source": [
+    "        model_params = {\n",
+    "            \"geo_lat\":56.327686,\n",
+    "            \"geo_lon\":43.928062,\n",
+    "            \"region\":2871.000000,\n",
+    "            \"building_type\":1.000000,\n",
+    "            \"level\":8.000000,\n",
+    "            \"levels\":10.000000,\n",
+    "            \"rooms\":2.000000,\n",
+    "            \"area\":56.000000,\n",
+    "            \"kitchen_area\":8.500000,\n",
+    "            \"object_type\":1.000000,\n",
+    "            \"floor_level\":0,\n",
+    "            }\n",
+    "        df_pred = pd.DataFrame(model_params, index=[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3414175.89042869])"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "pipeline.predict(df_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'mae': 1447931.3425270966,\n",
+       " 'mape': 1.6294525363466488e+18,\n",
+       " 'mse': 281898017343454.56}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "predictions = pipeline.predict(X_test) \n",
     "\n",
@@ -3299,7 +4766,7 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": ".venv_labs_proj",
+   "display_name": "Python 3 (ipykernel)",
    "language": "python",
    "name": "python3"
   },
diff --git a/labs/lab3.md b/labs/lab3.md
new file mode 100644
index 0000000..ad47465
--- /dev/null
+++ b/labs/lab3.md
@@ -0,0 +1,171 @@
+# LR3 Создание сервиса предсказаний
+
+## Цель
+Создать микросервис предсказаний моделью ML, создать его docker-образ и запустить сервис в контейнере.
+## Задание 
+
+1. Активировать виртуальное окружение. Установить библиотеки `fastapi`, `uvicorn`, не забыв обновить `requirements.txt`.
+2. Запустить mlflow, убедиться, что доступны экспериметы, прогоны и модели, сделанные в ЛР2.
+3. Создать директорию `./services`, в ней еще одну директорию `ml_service`, и в ней основной файл `main.py`.
+```
+my_proj
+ |_____ .venv_my_proj
+ |_____ .git
+ |_____ data
+ |       |___ ...
+ |
+ |_____ eda
+ |       |___ ...
+ |
+ |_____ research
+ |       |___ ...
+ |
+ |_____ services
+ |       |___ ml_service
+ |              |___main.py
+ |              
+ |_____ .gitignore
+ |_____ README.md
+ |_____ requirements.txt
+```
+
+4. В созданном файле создать экземпляр `FastAPI-приложения`, сделать обработку GET-запросов к корню приложения `/`, выдавая в ответ на запрос словарь `{"Hello": "World"}`
+> В ходе работы с файлами `.py` будут создаваться директории `__pycache__`, в которых хранятся скомпилированные байт-код модули `.pyc`. Эти папки коммитить не нужно. Добавьте правило в `.gitignore`.
+5. Запустить сервер `uvicorn` и убедиться, что сервис обрабатывает запросы к корню, пройдя на страницу `http://localhost:8000/docs` и выполнив тестовый запрос.
+6. Создать endpoint `/api/prediction` выдачи предсказания для объекта. Endpoint будет обрабатывает POST-запросы, принимая в качестве URL-параметра идентификатор объекта `item_id`, а в теле запроса (body) все признаки объекта, необходимые для подачи на вход модели и выдачи предсказания. Возвращать будет словарь из двух объектов - `item_id` - тот же идентификатор объекта, и `predict` - предсказанное значение.
+7. Запустить сервер `uvicorn` и убедиться, что сервис обрабатывает запросы к  `/api/prediction`, пройдя на страницу `http://localhost:8000/docs` и выполнив тестовый запрос к этому endpoint-у. Если сервис работает правильно, то вы должны увидеть ответ такой структуры:
+```
+Response body
+{
+  "item_id": 123,
+  "price": 0.7637315626006276
+}
+```
+
+8. В директории `./services` создать директорию `models`, в которой будет храниться обученная модель, а также скрипт по ее получению из mlflow. Создать скрипт `get_model.py` 
+```
+my_proj
+ |_____ .venv_my_proj
+ |_____ .git
+ |_____ data
+ |       |___ ...
+ |
+ |_____ eda
+ |       |___ ...
+ |
+ |_____ research
+ |       |___ ...
+ |
+ |_____ services
+ |       |___ ml_service
+ |       |      |___main.py
+ |       |___models
+ |              |___get_model.py
+ |              |___model.pkl    # Появится после выполнения следующего пункта 
+ |              
+ |_____ .gitignore
+ |_____ README.md
+ |_____ requirements.txt
+```
+
+
+9. Сформировать скрипт `get_model.py`, который должен подключаться к mlflow, выгружать модель по её run_id и сохранять ее в файл `model.pkl`.
+
+10. В GUI MLFlow скопировать run_id production-модели, указать его в скрипте. Запустить скрипт и убедиться, что в директории `./services/models` появился файл `model.pkl`.
+
+11. В директории `ml_service` создать файл `api_handler.py` и в нем описать класс-обработчик запросов к API `FastAPIHandler`. 
+
+Класс должен иметь метод `__init__` в котором загружается модель из файла `/model.pkl`, и метод `predict`, возвращающий предсказание.
+
+12. Доработать основной модуль сервиса и класс-обработчик таким образом, чтобы сервис предсказывал значения для любого произвольного объекта, параметры которого передаются в теле запроса. Убедиться в корректности работы. Остановить сервис.
+
+13. В папке `ml_service` создать файл `requirements.txt`, в который записать те зависимости, которые необходимы для работы сервиса. 
+> Данный файл - не то же самое, что `requirements.txt` в корневой директории проекта. Данный файл будет использоваться для сборки образа, поэтому должен содержать только необходимые зависимости и их версии. Виртуальное окружение на хостовой машине по-прежнему нужно устанавливать из файла в корневой директории.
+
+> Проще всего заполнить этот `requirements.txt`, пройдясь по файлам в папке и выписав все импорты. Абсолютно точно на этом этапе в зависимости нужно добавить `fastapi`, `uvicorn`, `pandas`, `pickle4`.
+```
+my_proj
+ |_____ .venv_my_proj
+ ...
+ |
+ |_____ services
+ |       |___ ml_service
+ |       |      |___main.py
+ |       |      |___api_handler.py
+ |       |      |___Dockerfile
+ |       |      |___requirements.txt  # новый файл, созданный на этом шаге
+ |       |___models
+ |              |___get_model.py
+ |              |___model.pkl   
+ |              
+ |_____ .gitignore
+ |_____ README.md
+ |_____ requirements.txt   # старый файл, созданный в ЛР1
+```
+
+
+14.  В папке `ml_service` cформировать `Dockerfile`
+Образ должен собираться Из базового образа `python:3.11-slim` по следующим шагам: 
+* Скопировать содержимое текущей директории в директорию  `/ml_service` в контейнере
+* Сделать директорию `/models` доступной снаружи контейнера 
+* Сделать `/ml_service` рабочей директорией
+* Выполнить установку всех зависимостей из файла `requirements.txt`
+* Указать, что порт 8000 должен быть доступен снаружи контейнера
+* Финальная команда - запуск сервера. Необходимо явно указать адрес хоста `"0.0.0.0"` и порта `"8000"`, на которых будет запускаться сервер.
+
+Состав директорий проекта на данном шаге должен выглядеть следуюшим образом:
+```
+my_proj
+ |_____ .venv_my_proj
+ |_____ .git
+ |_____ data
+ |       |___ ...
+ |
+ |_____ eda
+ |       |___ ...
+ |
+ |_____ research
+ |       |___ ...
+ |
+ |_____ services
+ |       |___ ml_service
+ |       |      |___main.py
+ |       |      |___api_handler.py
+ |       |      |___Dockerfile
+ |       |      |___requirements.txt
+ |       |___models
+ |              |___get_model.py
+ |              |___model.pkl   
+ |              
+ |_____ .gitignore
+ |_____ README.md
+ |_____ requirements.txt
+```
+
+15. Собрать образ, дать ему понятное название и указав что это первая версия образа. 
+
+Команду по сборке образа записать в конец `Dockerfile`, добавив значок комментария `#`.
+> В рамках это ЛР мы будем создавать первую версию нашего сервиса. Даже если вы несколько раз пересобираете образ, что-то меняя или добавляя в него, в этой ЛР все равно указывайте первую версию.
+
+> Версия указывается через двоеточие после названия образа. Например, для указания 3й версии образа: `my_super_image:3`
+
+16. Запустить контейнер из образа. Команду по запуску контейнера записать в конец `Dockerfile`, добавив значок комментария `#`.
+
+> При запуске не забыть указать, какой хостовый порт должен быть связанным с портом 8000 внутри контейнера.
+
+> Указать, что хостовая директория ../models должна быть связана с одноименной директорией в контейнере. Поскольку мы собираем образ из директории `ml_service`, а интересующая директория с моделью `models` находится на уровень выше, то путь до нее можно указать так: `$(pwd)/../models`. Здесь `$(pwd)` (print working directory) - утилита командной строки, которая выводит полный путь до текущей директории. Через двоеточие от хостовой директории нужно указать соответствующий volume внутри контейнера. Полный агрумент будет выглядеть так: `$(pwd)/../models:/models`
+
+17. Убедиться, что контейнер работает, отправив запрос в сервис. Добиться работоспособности сервиса.
+
+18. Актуализировать файл README: 
+* добавить описание разработанного сервиса: краткое описание файлов в папке ml_service, models.
+* Указать команды для создания образа и запуска контейнера. 
+* Указать, как можно проверить работоспособность сервиса, включив пример тела запроса.
+
+
+
+
+
+
+
+