{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y7wSVnqQ7xZB"
   },
   "source": [
    "# MLFlow Bike Sharing Use Case\n",
    "\n",
    "---\n",
    "\n",
    "This notebook demonstrates an example of dataset preprocessing, model training and evaluation, model tuning via MLflow tracking, finding best trained model and finally deploying the model using KServe.\n",
    "\n",
    "---\n",
    "- **Dateset:** Bike Sharing Dataset: http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip\n",
    "- **Goal:** predict `rented_bikes` (count per hour) based on weather and time information.\n",
    "\n",
    "\n",
    "**References:**\n",
    "- https://docs.databricks.com/_static/notebooks/gbt-regression.html\n",
    "- https://www.kaggle.com/pratsiuk/mlflow-experiment-automation-top-9\n",
    "- https://mlflow.org/docs/latest/tracking.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set Experiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2026/02/06 07:34:26 INFO mlflow.tracking.fluent: Experiment with name 'bike-sharing-exp-2' does not exist. Creating a new experiment.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token successfully refreshed.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='s3://mlflow.houpcaihpe/21', creation_time=1770363266885, experiment_id='21', last_update_time=1770363266885, lifecycle_stage='active', name='bike-sharing-exp-2', tags={}>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%update_token\n",
    "import mlflow\n",
    "experiment_name = 'bike-sharing-exp-2'\n",
    "\n",
    "mlflow.set_experiment(experiment_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6dR-JRDBngFJ"
   },
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pydotplus\n",
      "  Downloading pydotplus-2.0.2.tar.gz (278 kB)\n",
      "  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting graphviz\n",
      "  Downloading graphviz-0.21-py3-none-any.whl.metadata (12 kB)\n",
      "Requirement already satisfied: seaborn in /opt/conda/lib/python3.11/site-packages (0.13.2)\n",
      "Requirement already satisfied: pyparsing>=2.0.1 in /opt/conda/lib/python3.11/site-packages (from pydotplus) (3.2.3)\n",
      "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /opt/conda/lib/python3.11/site-packages (from seaborn) (1.26.4)\n",
      "Requirement already satisfied: pandas>=1.2 in /opt/conda/lib/python3.11/site-packages (from seaborn) (2.1.4)\n",
      "Requirement already satisfied: matplotlib!=3.6.1,>=3.4 in /opt/conda/lib/python3.11/site-packages (from seaborn) (3.8.4)\n",
      "Requirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.3.1)\n",
      "Requirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (0.12.1)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (4.57.0)\n",
      "Requirement already satisfied: kiwisolver>=1.3.1 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (1.4.7)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (24.2)\n",
      "Requirement already satisfied: pillow>=8 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (11.1.0)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in /opt/conda/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.4->seaborn) (2.9.0.post0)\n",
      "Requirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2025.2)\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.4->seaborn) (1.17.0)\n",
      "Downloading graphviz-0.21-py3-none-any.whl (47 kB)\n",
      "Building wheels for collected packages: pydotplus\n",
      "  Building wheel for pydotplus (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for pydotplus: filename=pydotplus-2.0.2-py3-none-any.whl size=24635 sha256=3208b96ed230957fd7004025959203a2dea8221d1c6c412ed478caadc5b5a044\n",
      "  Stored in directory: /home/harshal.patil-gmail.com/.cache/pip/wheels/bd/ce/e8/ff9d9c699514922f57caa22fbd55b0a32761114b4c4acc9e03\n",
      "Successfully built pydotplus\n",
      "Installing collected packages: pydotplus, graphviz\n",
      "Successfully installed graphviz-0.21 pydotplus-2.0.2\n"
     ]
    }
   ],
   "source": [
    "#!pip3 install --proxy <PROXY> pydotplus graphviz seaborn\n",
    "!pip3 install pydotplus graphviz seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "id": "5C1LuP7Oodd8",
    "outputId": "6a47110e-5994-4d05-e380-58540915e624"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from urllib.parse import urlparse\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import json, datetime\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "from mlflow import log_metric, log_param, log_artifact\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
    "from sklearn.inspection import permutation_importance\n",
    "from mlflow.models.signature import infer_signature\n",
    "from sklearn import tree\n",
    "\n",
    "from pydotplus import graph_from_dot_data\n",
    "import graphviz\n",
    "from IPython.display import Image\n",
    "\n",
    "import itertools, os\n",
    "\n",
    "plt.style.use(\"fivethirtyeight\")\n",
    "pd.plotting.register_matplotlib_converters()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "if os.path.exists(\"model_artifacts\"):\n",
    "    os.system(\"rm -rf model_artifacts\")\n",
    "os.mkdir(\"model_artifacts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TwKZC40S-e0R"
   },
   "source": [
    "## Import Data\n",
    "\n",
    "Dataset and explanation:\n",
    "http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "\n",
    "- Input file: `bike-sharing.csv` - contains bike sharing counts aggregated on hourly basis. \n",
    "- Size: 17379 hours / rows\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 276
    },
    "id": "1SZF_ZgD-gez",
    "outputId": "2dad51e5-5194-44af-c06c-5481c81d7639"
   },
   "outputs": [],
   "source": [
    "# Dataset is already available in github repository if not you can download and extract csv files as well.\n",
    "#!wget -e use_proxy=yes -e http_proxy=http://web-proxy.corp.hpecorp.net:8080 -nc \"http://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip\"\n",
    "#!unzip -o \"Bike-Sharing-Dataset.zip\"\n",
    "#!rm -rf \"Bike-Sharing-Dataset.zip\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "mFGzYdKCCNiK",
    "outputId": "8783bf81-d46a-4958-d2dc-59a324868a64"
   },
   "outputs": [
    {
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      ],
      "text/plain": [
       "       instant      dteday  season  yr  mnth  hr  holiday  weekday  \\\n",
       "0            1  2011-01-01       1   0     1   0        0        6   \n",
       "1            2  2011-01-01       1   0     1   1        0        6   \n",
       "2            3  2011-01-01       1   0     1   2        0        6   \n",
       "3            4  2011-01-01       1   0     1   3        0        6   \n",
       "4            5  2011-01-01       1   0     1   4        0        6   \n",
       "...        ...         ...     ...  ..   ...  ..      ...      ...   \n",
       "17374    17375  2012-12-31       1   1    12  19        0        1   \n",
       "17375    17376  2012-12-31       1   1    12  20        0        1   \n",
       "17376    17377  2012-12-31       1   1    12  21        0        1   \n",
       "17377    17378  2012-12-31       1   1    12  22        0        1   \n",
       "17378    17379  2012-12-31       1   1    12  23        0        1   \n",
       "\n",
       "       workingday  weathersit  temp   atemp   hum  windspeed  casual  \\\n",
       "0               0           1  0.24  0.2879  0.81     0.0000       3   \n",
       "1               0           1  0.22  0.2727  0.80     0.0000       8   \n",
       "2               0           1  0.22  0.2727  0.80     0.0000       5   \n",
       "3               0           1  0.24  0.2879  0.75     0.0000       3   \n",
       "4               0           1  0.24  0.2879  0.75     0.0000       0   \n",
       "...           ...         ...   ...     ...   ...        ...     ...   \n",
       "17374           1           2  0.26  0.2576  0.60     0.1642      11   \n",
       "17375           1           2  0.26  0.2576  0.60     0.1642       8   \n",
       "17376           1           1  0.26  0.2576  0.60     0.1642       7   \n",
       "17377           1           1  0.26  0.2727  0.56     0.1343      13   \n",
       "17378           1           1  0.26  0.2727  0.65     0.1343      12   \n",
       "\n",
       "       registered  cnt  \n",
       "0              13   16  \n",
       "1              32   40  \n",
       "2              27   32  \n",
       "3              10   13  \n",
       "4               1    1  \n",
       "...           ...  ...  \n",
       "17374         108  119  \n",
       "17375          81   89  \n",
       "17376          83   90  \n",
       "17377          48   61  \n",
       "17378          37   49  \n",
       "\n",
       "[17379 rows x 17 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load input data into pandas dataframe\n",
    "bike_sharing = pd.read_csv(\"bike-sharing.csv\")\n",
    "bike_sharing        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fQk3RQt2FB8x"
   },
   "source": [
    "## Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "vyS5Ru5aE5Y7",
    "outputId": "5d0b2528-9664-437d-8e3f-61119fdaad5e"
   },
   "outputs": [],
   "source": [
    "# remove unused columns\n",
    "bike_sharing.drop(columns=[\"instant\", \"dteday\", \"registered\", \"casual\"], inplace=True)\n",
    "\n",
    "# use better names\n",
    "bike_sharing.rename(\n",
    "    columns={\n",
    "        \"yr\": \"year\",\n",
    "        \"mnth\": \"month\",\n",
    "        \"hr\": \"hour_of_day\",\n",
    "        \"holiday\": \"is_holiday\",\n",
    "        \"workingday\": \"is_workingday\",\n",
    "        \"weathersit\": \"weather_situation\",\n",
    "        \"temp\": \"temperature\",\n",
    "        \"atemp\": \"feels_like_temperature\",\n",
    "        \"hum\": \"humidity\",\n",
    "        \"cnt\": \"rented_bikes\",\n",
    "    },\n",
    "    inplace=True,\n",
    ")\n",
    "\n",
    "# show samples\n",
    "\n",
    "\n",
    "cols = bike_sharing.select_dtypes(exclude=['float64']).columns\n",
    "\n",
    "for i in ['season', 'year', 'month', 'hour_of_day', 'is_holiday', 'weekday',\n",
    "       'is_workingday', 'weather_situation', 'rented_bikes']:\n",
    "    bike_sharing[i] = bike_sharing[i].astype('float64')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "40MGTHbNFKTP"
   },
   "source": [
    "### Data Visualization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "id": "bNZOegwGHzUR",
    "outputId": "45a00d75-c019-4c39-96c4-08e3995fb381"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Total rented bikes by hour of day'}, xlabel='hour_of_day'>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hour_of_day_agg = bike_sharing.groupby([\"hour_of_day\"])[\"rented_bikes\"].sum()\n",
    "\n",
    "hour_of_day_agg.plot(\n",
    "    kind=\"line\", \n",
    "    title=\"Total rented bikes by hour of day\",\n",
    "    xticks=hour_of_day_agg.index,\n",
    "    figsize=(15, 10),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZMg_JKoUKq9j"
   },
   "source": [
    "## Prepare training and test data sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 52
    },
    "id": "ZwtDgaZ9Ktie",
    "outputId": "4971f3d6-5e99-4583-acb6-4ef73892a633"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training samples: 145980\n",
      "Test samples: 62568\n"
     ]
    }
   ],
   "source": [
    "# Split the dataset randomly into 70% for training and 30% for testing.\n",
    "X = bike_sharing.drop(\"rented_bikes\", axis=1)\n",
    "y = bike_sharing.rented_bikes\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7, test_size=0.3, random_state=42)\n",
    "\n",
    "print(f\"Training samples: {X_train.size}\")\n",
    "print(f\"Test samples: {X_test.size}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "HN0w6zFJSb87"
   },
   "source": [
    "## Evaluation Metrics\n",
    "\n",
    "Create evaluation methods to be used in training stage (next step)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eC1wzz_T_tSA"
   },
   "source": [
    "## Root Mean Square Error (RMSE)\n",
    "\n",
    "References: \n",
    "- https://medium.com/@xaviergeerinck/artificial-intelligence-how-to-measure-performance-accuracy-precision-recall-f1-roc-rmse-611d10e4caac\n",
    "- https://www.kaggle.com/residentmario/model-fit-metrics#Root-mean-squared-error-(RMSE)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "MhPcLCteQy6j"
   },
   "outputs": [],
   "source": [
    "def rmse(y, y_pred):\n",
    "    return np.sqrt(mean_squared_error(y, y_pred))\n",
    "\n",
    "\n",
    "def rmse_score(y, y_pred):\n",
    "    score = rmse(y, y_pred)\n",
    "    print(\"RMSE score: {:.4f}\".format(score))\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fZ3nr3D_AE85"
   },
   "source": [
    "## Cross-Validation RMSLE score\n",
    "\n",
    "cross-validation combines (averages) measures of fitness in prediction to derive a more accurate estimate of model prediction performance.\n",
    "\n",
    "Background: \n",
    "- https://en.wikipedia.org/wiki/Cross-validation_(statistics)\n",
    "- https://www.kaggle.com/carlolepelaars/understanding-the-metric-rmsle\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "9H9CZAP2ASe6"
   },
   "outputs": [],
   "source": [
    "def rmsle_cv(model, X_train, y_train):\n",
    "    kf = KFold(n_splits=3, shuffle=True, random_state=42).get_n_splits(X_train.values)\n",
    "    # Evaluate a score by cross-validation\n",
    "    rmse = np.sqrt(-cross_val_score(model, X_train.values, y_train, scoring=\"neg_mean_squared_error\", cv=kf))\n",
    "    return rmse\n",
    "\n",
    "\n",
    "def rmse_cv_score(model, X_train, y_train):\n",
    "    score = rmsle_cv(model, X_train, y_train)\n",
    "    print(\"Cross-Validation RMSE score: {:.4f} (std = {:.4f})\".format(score.mean(), score.std()))\n",
    "    return score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ad0mABWEarsA"
   },
   "source": [
    "## Feature Importance\n",
    "\n",
    "Background: https://medium.com/bigdatarepublic/feature-importance-whats-in-a-name-79532e59eea3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "OZ7kzjbOWae8"
   },
   "outputs": [],
   "source": [
    "def model_feature_importance(model):\n",
    "    feature_importance = pd.DataFrame(\n",
    "        model.feature_importances_,\n",
    "        index=X_train.columns,\n",
    "        columns=[\"Importance\"],\n",
    "    )\n",
    "\n",
    "    # sort by importance\n",
    "    feature_importance.sort_values(by=\"Importance\", ascending=False, inplace=True)\n",
    "\n",
    "    # plot\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.barplot(\n",
    "        data=feature_importance.reset_index(),\n",
    "        y=\"index\",\n",
    "        x=\"Importance\",\n",
    "    ).set_title(\"Feature Importance\")\n",
    "    # save image\n",
    "    plt.savefig(\"model_artifacts/feature_importance.png\", bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rYfCxPo8w-Gn"
   },
   "source": [
    "## Permutation Importance\n",
    "\n",
    "Background: https://www.kaggle.com/dansbecker/permutation-importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "b_vzVVbGcS6M"
   },
   "outputs": [],
   "source": [
    "def model_permutation_importance(model):\n",
    "    p_importance = permutation_importance(model, X_test, y_test, random_state=42, n_jobs=-1)\n",
    "\n",
    "    # sort by importance\n",
    "    sorted_idx = p_importance.importances_mean.argsort()[::-1]\n",
    "    p_importance = pd.DataFrame(\n",
    "        data=p_importance.importances[sorted_idx].T,\n",
    "        columns=X_train.columns[sorted_idx]\n",
    "    )\n",
    "\n",
    "    # plot\n",
    "    plt.figure(figsize=(12, 8))\n",
    "    sns.barplot(\n",
    "        data=p_importance,\n",
    "        orient=\"h\"\n",
    "    ).set_title(\"Permutation Importance\")\n",
    "\n",
    "    # save image\n",
    "    plt.savefig(\"model_artifacts/permutation_importance.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VtQGsSNU8hWc"
   },
   "source": [
    "## Decision Tree Visualization\n",
    "\n",
    "Reference: https://towardsdatascience.com/visualizing-decision-trees-with-python-scikit-learn-graphviz-matplotlib-1c50b4aa68dc \n",
    "\n",
    "\n",
    "TODO: plot all trees"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "rxKIpaE-g-b1"
   },
   "outputs": [],
   "source": [
    "def model_tree_visualization(model):\n",
    "    # generate visualization\n",
    "    tree_dot_data = tree.export_graphviz(\n",
    "        decision_tree=model.estimators_[0, 0],  # Get the first tree,\n",
    "        label=\"all\",\n",
    "        feature_names=X_train.columns,\n",
    "        filled=True,\n",
    "        rounded=True,\n",
    "        proportion=True,\n",
    "        impurity=False,\n",
    "        precision=1,\n",
    "    )\n",
    "\n",
    "    # save image\n",
    "    graph_from_dot_data(tree_dot_data).write_png(\"model_artifacts/Decision_Tree_Visualization.png\")\n",
    "\n",
    "    # show tree\n",
    "    return graphviz.Source(tree_dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "warpAv8RFSOI"
   },
   "source": [
    "# MLflow Tracking\n",
    "\n",
    "Reference: https://www.mlflow.org/docs/latest/cli.html#mlflow-ui\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1kU8w1TNGG4Y"
   },
   "source": [
    "## MLflow Logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "kyQRcKslAwv-"
   },
   "outputs": [],
   "source": [
    "# Track params and metrics\n",
    "def log_mlflow_run(model, signature):\n",
    "    # Auto-logging for scikit-learn estimators\n",
    "    # mlflow.sklearn.autolog()\n",
    "\n",
    "    # log estimator_name name\n",
    "    name = model.__class__.__name__\n",
    "    mlflow.set_tag(\"estimator_name\", name)\n",
    "\n",
    "    # log input features\n",
    "    mlflow.set_tag(\"features\", str(X_train.columns.values.tolist()))\n",
    "\n",
    "    # Log tracked parameters only\n",
    "    mlflow.log_params({key: model.get_params()[key] for key in parameters})\n",
    "\n",
    "    mlflow.log_metrics({\n",
    "        'RMSE_CV': score_cv.mean(),\n",
    "        'RMSE': score,\n",
    "    })\n",
    "\n",
    "    # log training loss\n",
    "    for s in model.train_score_:\n",
    "        mlflow.log_metric(\"Train Loss\", s)\n",
    "\n",
    "    # Save model to artifacts\n",
    "    mlflow.sklearn.log_model(model, \"model\")#, signature=signature)\n",
    "\n",
    "    # log charts\n",
    "    mlflow.log_artifacts(\"model_artifacts\")\n",
    "\n",
    "    # misc\n",
    "    # Log all model parameters\n",
    "    # mlflow.log_params(model.get_params())\n",
    "    mlflow.log_param(\"Training size\", X_test.size) \n",
    "    mlflow.log_param(\"Test size\", y_test.size)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "7xRa6vYWMed1"
   },
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mDAdPTeDTjr1"
   },
   "source": [
    "## Model Type & Method\n",
    "\n",
    "For this example,\n",
    "- Approache: Decision tree (Supervised learning)\n",
    "- Tree type: Regression tree\n",
    "- Technique/ensemble method: Gradient boosting\n",
    "\n",
    "**All put together we get:** [GBRT (Gradient Boosted Regression Tree)](https://orbi.uliege.be/bitstream/2268/163521/1/slides.pdf)\n",
    "\n",
    "Background:\n",
    "- Choosing a model: https://scikit-learn.org/stable/tutorial/machine_learning_map\n",
    "- Machine Learning Models Explained\n",
    ": https://docs.paperspace.com/machine-learning/wiki/machine-learning-models-explained\n",
    "- Gradient Boosted Regression Trees: https://orbi.uliege.be/bitstream/2268/163521/1/slides.pdf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "OSbcPvkBThXV"
   },
   "outputs": [],
   "source": [
    "# GBRT (Gradient Boosted Regression Tree) scikit-learn implementation \n",
    "model_class = GradientBoostingRegressor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "l7BYFTSRzLk2"
   },
   "source": [
    "## Model Hyper-parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "1Mu88JOkMiJF"
   },
   "outputs": [],
   "source": [
    "parameters = {\n",
    "    \"learning_rate\": [0.1, 0.05, 0.01],\n",
    "    \"max_depth\": [4, 5, 6],\n",
    "    # \"verbose\": True,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LnUDX2p2j9p_"
   },
   "source": [
    "## Tuning the hyper-parameters: Grid search\n",
    "\n",
    "- Simple but inefficient\n",
    "- more advanced tuning techniques: https://research.fb.com/efficient-tuning-of-online-systems-using-bayesian-optimization/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "CybsVlgCw6n9"
   },
   "outputs": [],
   "source": [
    "# generate parameters combinations\n",
    "params_keys = parameters.keys()\n",
    "params_values = [\n",
    "    parameters[key] if isinstance(parameters[key], list) else [parameters[key]]\n",
    "    for key in params_keys\n",
    "]\n",
    "runs_parameters = [\n",
    "    dict(zip(params_keys, combination)) for combination in itertools.product(*params_values)\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "u23-Tpn_0X7d"
   },
   "source": [
    "## Training runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 639
    },
    "id": "Le6sa7jjg37v",
    "outputId": "7e8c3e45-e75a-45ce-8157-38b097d623cc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Run 0: {'learning_rate': 0.1, 'max_depth': 4}\n",
      "RMSE score: 52.0021\n",
      "Cross-Validation RMSE score: 56.5272 (std = 0.1499)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:36:39 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 0 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/31d7d964507a4efc9cc43985a166357d\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 1: {'learning_rate': 0.1, 'max_depth': 5}\n",
      "RMSE score: 44.7004\n",
      "Cross-Validation RMSE score: 48.1984 (std = 0.1797)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:36:50 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 1 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/5840fcd0062844ae9a53f63e30ba7d74\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 2: {'learning_rate': 0.1, 'max_depth': 6}\n",
      "RMSE score: 41.8550\n",
      "Cross-Validation RMSE score: 44.9626 (std = 0.4529)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:01 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 2 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/32c1762f1c824c3b8e28ab5e0a821302\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 3: {'learning_rate': 0.05, 'max_depth': 4}\n",
      "RMSE score: 63.1478\n",
      "Cross-Validation RMSE score: 67.8214 (std = 1.7227)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:10 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 3 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/28e158c1966a4de19b2019a4c6099ba6\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 4: {'learning_rate': 0.05, 'max_depth': 5}\n",
      "RMSE score: 53.0496\n",
      "Cross-Validation RMSE score: 55.9878 (std = 0.8644)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:20 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 4 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/8d7da4e8d3b248abb2bf824a0eef5dbd\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 5: {'learning_rate': 0.05, 'max_depth': 6}\n",
      "RMSE score: 46.2884\n",
      "Cross-Validation RMSE score: 49.8078 (std = 0.5423)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:30 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 5 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/49a68eb0271846cd95a494f6973793a7\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 6: {'learning_rate': 0.01, 'max_depth': 4}\n",
      "RMSE score: 120.1656\n",
      "Cross-Validation RMSE score: 123.9446 (std = 1.0540)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:39 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 6 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/51d1fc94a7e8448aaf0f583ca02f1416\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 7: {'learning_rate': 0.01, 'max_depth': 5}\n",
      "RMSE score: 112.4921\n",
      "Cross-Validation RMSE score: 116.0879 (std = 0.9958)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:48 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 7 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/1dbc859fed9d4008a1e4a4d62835de7f\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n",
      "Run 8: {'learning_rate': 0.01, 'max_depth': 6}\n",
      "RMSE score: 106.2635\n",
      "Cross-Validation RMSE score: 109.3515 (std = 1.2056)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[31m2026/02/06 07:37:59 WARNING mlflow.models.model: Model logged without a signature and input example. Please set `input_example` parameter when logging the model to auto infer the model signature.\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🏃 View run Run 8 at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21/runs/1f0911c530e7492ebd048064297615ce\n",
      "🧪 View experiment at: http://mlflow.mlflow.svc.cluster.local:5000/#/experiments/21\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# training loop\n",
    "for i, run_parameters in enumerate(runs_parameters):\n",
    "    print(f\"Run {i}: {run_parameters}\")\n",
    "\n",
    "    # mlflow: stop active runs if any\n",
    "    if mlflow.active_run():\n",
    "        mlflow.end_run()\n",
    "    # mlflow:track run\n",
    "    mlflow.start_run(run_name=f\"Run {i}\")\n",
    "\n",
    "    # create model instance\n",
    "    model = model_class(**run_parameters)\n",
    "\n",
    "    # train\n",
    "    model.fit(X_train, y_train)\n",
    "\n",
    "    # get evaluations scores\n",
    "    score = rmse_score(y_test, model.predict(X_test))\n",
    "    score_cv = rmse_cv_score(model, X_train, y_train)\n",
    "    \n",
    "    # generate charts\n",
    "    model_feature_importance(model)\n",
    "    plt.close()\n",
    "    model_permutation_importance(model)\n",
    "    plt.close()\n",
    "    # model_tree_visualization(model)\n",
    "\n",
    "    # get model signature\n",
    "    signature = infer_signature(model_input=X_train, model_output=model.predict(X_train))\n",
    "\n",
    "    # mlflow: log metrics\n",
    "    log_mlflow_run(model, signature)\n",
    "\n",
    "    # mlflow: end tracking\n",
    "    mlflow.end_run()\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tOHX6U3ABTSE"
   },
   "source": [
    "## Best Model Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "I5jKy850zKtS"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b00d2ae7f18345098d904679c974c630",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading artifacts:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "best_run_df = mlflow.search_runs(order_by=['metrics.RMSE_CV ASC'], max_results=1)\n",
    "if len(best_run_df.index) == 0:\n",
    "    raise Exception(f\"Found no runs for experiment '{experiment_name}'\")\n",
    "\n",
    "best_run = mlflow.get_run(best_run_df.at[0, 'run_id'])\n",
    "best_model_uri = f\"{best_run.info.artifact_uri}/model\"\n",
    "with open('best-model-uri.txt','w+') as f:\n",
    "    f.write(best_model_uri)\n",
    "best_model = mlflow.sklearn.load_model(best_model_uri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 104
    },
    "id": "wHVM74A--4-C",
    "outputId": "b28470ff-aa99-4f19-c124-d4b9c3a88233"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best run info:\n",
      "Run id: 32c1762f1c824c3b8e28ab5e0a821302\n",
      "Run parameters: {'learning_rate': '0.1', 'max_depth': '6', 'Training size': '62568', 'Test size': '5214'}\n",
      "Run score: RMSE_CV = 44.9626\n",
      "\n",
      "\n",
      "Run model URI: s3://mlflow.houpcaihpe/21/32c1762f1c824c3b8e28ab5e0a821302/artifacts/model\n"
     ]
    }
   ],
   "source": [
    "# print best run info\n",
    "print(\"Best run info:\")\n",
    "print(f\"Run id: {best_run.info.run_id}\")\n",
    "print(f\"Run parameters: {best_run.data.params}\")\n",
    "print(\"Run score: RMSE_CV = {:.4f}\\n\\n\".format(best_run.data.metrics['RMSE_CV']))\n",
    "print(f\"Run model URI: {best_model_uri}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "id": "WmjSO3vhCP7u",
    "outputId": "52e2c4ac-4aeb-44b8-d65d-7d2de8122fb8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_feature_importance(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 496
    },
    "id": "LQRJKFuJCSBZ",
    "outputId": "3a60cd14-f402-4304-ee8c-7f3647be4721"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model_permutation_importance(best_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 815
    },
    "id": "fR2F0ex7CS4I",
    "outputId": "ff25041b-c91e-4372-ab49-514e350bc709"
   },
   "outputs": [],
   "source": [
    "# model_tree_visualization(best_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UDhu91aa8vuw"
   },
   "source": [
    "## Test the Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 407
    },
    "id": "EiQwrb7TK40n",
    "outputId": "709d749f-bc0d-4b68-c2c4-8c1a0197eca6"
   },
   "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>season</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>hour_of_day</th>\n",
       "      <th>is_holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>is_workingday</th>\n",
       "      <th>weather_situation</th>\n",
       "      <th>temperature</th>\n",
       "      <th>feels_like_temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>rented_bikes</th>\n",
       "      <th>predicted_rented_bikes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12830</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>425.0</td>\n",
       "      <td>397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8688</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>88.0</td>\n",
       "      <td>99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7091</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.3030</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.2836</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12230</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.7121</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.3582</td>\n",
       "      <td>526.0</td>\n",
       "      <td>564</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>431</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.26</td>\n",
       "      <td>0.2273</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.3881</td>\n",
       "      <td>13.0</td>\n",
       "      <td>10</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12749</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.82</td>\n",
       "      <td>0.7727</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.1343</td>\n",
       "      <td>167.0</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11476</th>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.3939</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>214.0</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12847</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.6970</td>\n",
       "      <td>0.33</td>\n",
       "      <td>0.2239</td>\n",
       "      <td>556.0</td>\n",
       "      <td>555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16721</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.5000</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>312.0</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9511</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.1818</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>72.0</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5214 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       season  year  month  hour_of_day  is_holiday  weekday  is_workingday  \\\n",
       "12830     3.0   1.0    6.0         19.0         0.0      6.0            0.0   \n",
       "8688      1.0   1.0    1.0         20.0         1.0      1.0            0.0   \n",
       "7091      4.0   0.0   10.0          2.0         0.0      5.0            1.0   \n",
       "12230     2.0   1.0    5.0         19.0         0.0      2.0            1.0   \n",
       "431       1.0   0.0    1.0          0.0         0.0      4.0            1.0   \n",
       "...       ...   ...    ...          ...         ...      ...            ...   \n",
       "12749     2.0   1.0    6.0         10.0         0.0      3.0            1.0   \n",
       "11476     2.0   1.0    4.0          9.0         0.0      6.0            0.0   \n",
       "12847     3.0   1.0    6.0         12.0         0.0      0.0            0.0   \n",
       "16721     4.0   1.0   12.0         12.0         0.0      2.0            1.0   \n",
       "9511      1.0   1.0    2.0          6.0         0.0      1.0            1.0   \n",
       "\n",
       "       weather_situation  temperature  feels_like_temperature  humidity  \\\n",
       "12830                1.0         0.80                  0.6970      0.27   \n",
       "8688                 1.0         0.24                  0.2273      0.41   \n",
       "7091                 1.0         0.32                  0.3030      0.66   \n",
       "12230                1.0         0.78                  0.7121      0.52   \n",
       "431                  1.0         0.26                  0.2273      0.56   \n",
       "...                  ...          ...                     ...       ...   \n",
       "12749                1.0         0.82                  0.7727      0.52   \n",
       "11476                2.0         0.38                  0.3939      0.37   \n",
       "12847                1.0         0.80                  0.6970      0.33   \n",
       "16721                1.0         0.52                  0.5000      0.68   \n",
       "9511                 1.0         0.16                  0.1818      0.86   \n",
       "\n",
       "       windspeed  rented_bikes  predicted_rented_bikes  \n",
       "12830     0.1940         425.0                     397  \n",
       "8688      0.2239          88.0                      99  \n",
       "7091      0.2836           4.0                      13  \n",
       "12230     0.3582         526.0                     564  \n",
       "431       0.3881          13.0                      10  \n",
       "...          ...           ...                     ...  \n",
       "12749     0.1343         167.0                     182  \n",
       "11476     0.0000         214.0                     241  \n",
       "12847     0.2239         556.0                     555  \n",
       "16721     0.1940         312.0                     297  \n",
       "9511      0.1045          72.0                      74  \n",
       "\n",
       "[5214 rows x 14 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_predictions = X_test.copy()\n",
    "# real output (rented_bikes) from test dataset\n",
    "test_predictions[\"rented_bikes\"] = y_test\n",
    "\n",
    "# add \"predicted_rented_bikes\" from test dataset\n",
    "test_predictions[\"predicted_rented_bikes\"] = best_model.predict(X_test).astype(int)\n",
    "\n",
    "# show results\n",
    "test_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 911
    },
    "id": "SwfQEr_NGlDa",
    "outputId": "e153d67b-b13f-4b13-bbe9-542eed7442a8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: title={'center': 'Rented bikes vs predicted rented bikes'}, xlabel='rented_bikes', ylabel='predicted_rented_bikes'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot truth vs prediction values\n",
    "test_predictions.plot(\n",
    "    kind=\"scatter\",\n",
    "    x=\"rented_bikes\",\n",
    "    y=\"predicted_rented_bikes\",\n",
    "    title=\"Rented bikes vs predicted rented bikes\",\n",
    "    figsize=(15, 15),\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Role Based Access Control\n",
    "\n",
    "By default, users recieve `MANAGE` permissions if they create an object, `NO_PERMISSIONS` otherwise. A full breakdown of all roles and their access is described [here](https://mlflow.org/docs/latest/auth/index.html#permissions)\n",
    "\n",
    "To share experiments/models, MLFlow provides an `AuthServiceCLient` implementing CRUD functionality for `experiment_permission` and `model_permission` objects. `AuthServiceClient` is documented [here](https://mlflow.org/docs/latest/auth/python-api.html#mlflow.server.auth.client.AuthServiceClient)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (2594632561.py, line 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[27], line 3\u001b[0;36m\u001b[0m\n\u001b[0;31m    user = #\" USERNAME\"\u001b[0m\n\u001b[0m           ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from mlflow.server.auth.client import AuthServiceClient\n",
    "\n",
    "user = #\" USERNAME\"\n",
    "permission = #\"READ\", \"EDIT\", \"MANAGE\", \"NO_PERMISSIONS\"\n",
    "exp_id = mlflow.get_experiment_by_name(experiment_name).experiment_id\n",
    "\n",
    "client = AuthServiceClient(mlflow.get_tracking_uri())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Creating Permission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "permission = \"READ\"\n",
    "exp_permission = client.create_experiment_permission(exp_id, user, permission)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Modifying Permission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "permission = \"EDIT\"\n",
    "\n",
    "exp_permission = client.update_experiment_permission(exp_id, user, permission)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "permission = \"NO_PERMISSIONS\"\n",
    "\n",
    "exp_permission = client.update_experiment_permission(exp_id, user, permission)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Delete Permissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "exp_permission = client.delete_experiment_permission(exp_id, user)"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "authorship_tag": "ABX9TyMUyEIXKPIvKiU5I2T//pwx",
   "collapsed_sections": [],
   "include_colab_link": true,
   "name": "MLflow-example-notebook.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.10"
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 },
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}