examples/elastic_net_wine_model.ipynb

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   "source": [
    "!pip install sklearn mlflow boto3"
   ]
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  {
   "cell_type": "code",
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   "source": [
    "# The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality\n",
    "# P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.\n",
    "# Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.\n",
    "\n",
    "import os\n",
    "import warnings\n",
    "import sys\n",
    "\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import ElasticNet\n",
    "from urllib.parse import urlparse\n",
    "import mlflow\n",
    "import mlflow.sklearn\n",
    "\n",
    "import logging\n",
    "\n",
    "logging.basicConfig(level=logging.WARN)\n",
    "logger = logging.getLogger(__name__)\n",
    "\n",
    "\n",
    "def eval_metrics(actual, pred):\n",
    "    rmse = np.sqrt(mean_squared_error(actual, pred))\n",
    "    mae = mean_absolute_error(actual, pred)\n",
    "    r2 = r2_score(actual, pred)\n",
    "    return rmse, mae, r2\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    warnings.filterwarnings(\"ignore\")\n",
    "    np.random.seed(40)\n",
    "\n",
    "    # Read the wine-quality csv file from the URL\n",
    "    csv_url = \"http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv\"\n",
    "    try:\n",
    "        data = pd.read_csv(csv_url, sep=\";\")\n",
    "    except Exception as e:\n",
    "        logger.exception(\n",
    "            \"Unable to download training & test CSV, check your internet connection. Error: %s\",\n",
    "            e,\n",
    "        )\n",
    "\n",
    "    # Split the data into training and test sets. (0.75, 0.25) split.\n",
    "    train, test = train_test_split(data)\n",
    "\n",
    "    # The predicted column is \"quality\" which is a scalar from [3, 9]\n",
    "    train_x = train.drop([\"quality\"], axis=1)\n",
    "    test_x = test.drop([\"quality\"], axis=1)\n",
    "    train_y = train[[\"quality\"]]\n",
    "    test_y = test[[\"quality\"]]\n",
    "\n",
    "    alpha = 0.5\n",
    "    l1_ratio = 0.5\n",
    "\n",
    "    # create bucket\n",
    "    object_storage = boto3.client(\n",
    "        \"s3\",\n",
    "        endpoint_url=os.getenv(\"MLFLOW_S3_ENDPOINT_URL\"),\n",
    "        config=boto3.session.Config(signature_version=\"s3v4\"),\n",
    "    )\n",
    "    default_bucket_name = \"mlflow\"\n",
    "\n",
    "    buckets_response = object_storage.list_buckets()\n",
    "    result = [\n",
    "        bucket\n",
    "        for bucket in buckets_response[\"Buckets\"]\n",
    "        if bucket[\"Name\"] == default_bucket_name\n",
    "    ]\n",
    "    if not result:\n",
    "        object_storage.create_bucket(Bucket=default_bucket_name)\n",
    "\n",
    "    with mlflow.start_run():\n",
    "        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)\n",
    "        lr.fit(train_x, train_y)\n",
    "\n",
    "        predicted_qualities = lr.predict(test_x)\n",
    "\n",
    "        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)\n",
    "\n",
    "        print(\"Elasticnet model (alpha=%f, l1_ratio=%f):\" % (alpha, l1_ratio))\n",
    "        print(\"  RMSE: %s\" % rmse)\n",
    "        print(\"  MAE: %s\" % mae)\n",
    "        print(\"  R2: %s\" % r2)\n",
    "\n",
    "        mlflow.log_param(\"alpha\", alpha)\n",
    "        mlflow.log_param(\"l1_ratio\", l1_ratio)\n",
    "        mlflow.log_metric(\"rmse\", rmse)\n",
    "        mlflow.log_metric(\"r2\", r2)\n",
    "        mlflow.log_metric(\"mae\", mae)\n",
    "\n",
    "        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme\n",
    "\n",
    "        mlflow.sklearn.log_model(\n",
    "            lr, \"model\", registered_model_name=\"ElasticnetWineModel\"\n",
    "        )\n"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "d1bc48b3-bb03-4cdc-b431-307a74e50bce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# print list of files in the default bucket `mlflow`\n",
    "response = object_storage.list_objects_v2(Bucket=default_bucket_name)\n",
    "files = response.get(\"Contents\")\n",
    "for file in files:\n",
    "    print(f\"file_name: {file['Key']}, size: {file['Size']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e60c8435-e6f4-4e0f-aabb-6dcd9d002ebe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# download file to notebook server\n",
    "# file would show up in the file browser on the left after running this command\n",
    "object_storage.download_file(default_bucket_name,'<minio file path>', '<notebook server file path>')"
   ]
  }
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