project/energy_forecast.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot as plt\n",
    "import os\n",
    "from dotenv import load_dotenv\n",
    "load_dotenv() \n",
    "\n",
    "# import pickle\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "# import boto3\n",
    "# s3 = boto3.client('s3')\n",
    "# s3.upload_file('path/to/your/file', 'mlflow-artifacts-evs', 'your/s3/path')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "os.environ[\"AWS_PROFILE\"] = \"mac2aws\" \n",
    "TRACKING_SERVER_HOST = os.environ[\"TRACKING_SERVER_HOST\"]  \n",
    "mlflow.set_tracking_uri(f\"http://{TRACKING_SERVER_HOST}:5000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "notebook_dir = os.getcwd()\n",
    "\n",
    "file_path = os.path.join(notebook_dir, 'data', 'df_filtered.csv')\n",
    "if not os.path.exists(file_path):\n",
    "    print(f\"File not found: {file_path}\")\n",
    "df_filtered = pd.read_csv(file_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tracking URI: 'http://ec2-3-14-112-137.us-east-2.compute.amazonaws.com:5000'\n"
     ]
    }
   ],
   "source": [
    "print(f\"tracking URI: '{mlflow.get_tracking_uri()}'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Experiment: artifact_location='s3://mlflow-artifacts-evs/5', creation_time=1725572707889, experiment_id='5', last_update_time=1725572707889, lifecycle_stage='active', name='energy-prediction-pipeline_updated', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/4', creation_time=1723958095275, experiment_id='4', last_update_time=1723958095275, lifecycle_stage='active', name='energy-prediction-pipeline', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/3', creation_time=1723952998415, experiment_id='3', last_update_time=1723952998415, lifecycle_stage='active', name='my-experiment-3', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/2', creation_time=1723492994445, experiment_id='2', last_update_time=1723492994445, lifecycle_stage='active', name='my-experiment-2', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/1', creation_time=1723478188985, experiment_id='1', last_update_time=1723478188985, lifecycle_stage='active', name='my-experiment-1', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/0', creation_time=1723466290987, experiment_id='0', last_update_time=1723466290987, lifecycle_stage='active', name='Default', tags={}>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.search_experiments() # list_experiments API has been removed, you can use search_experiments instead()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Experiment: artifact_location='s3://mlflow-artifacts-evs/5', creation_time=1725572707889, experiment_id='5', last_update_time=1725572707889, lifecycle_stage='active', name='energy-prediction-pipeline_updated', tags={}>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.set_experiment(\"energy-prediction-pipeline_updated\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered['created_date'] = pd.to_datetime(df_filtered['created_date'])\n",
    "df_filtered['day_of_week'] = df_filtered['created_date'].dt.dayofweek\n",
    "df_filtered['month'] = df_filtered['created_date'].dt.month\n",
    "df_filtered['day'] = df_filtered['created_date'].dt.day\n",
    "df_filtered['year'] = df_filtered['created_date'].dt.year\n",
    "df_filtered['Sta_Loc'] = df_filtered['stationId'].astype(str) + '_' + df_filtered['locationId'].astype(str)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = ['day_of_week', 'created_date', 'chargeTimeHrs', 'distance','stationId', 'locationId']\n",
    "\n",
    "X = df_filtered[features]\n",
    "# X = df_filtered.drop(columns=['kwhTotal'])\n",
    "\n",
    "y = df_filtered['kwhTotal']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train, df_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Convert Timestamps to strings\n",
    "df_train['created_date'] = df_train['created_date'].astype(str)\n",
    "df_test['created_date'] = df_test['created_date'].astype(str)\n",
    "\n",
    "# Save training and test datasets to CSV files\n",
    "train_df = pd.DataFrame(df_train, columns=df_train.columns)\n",
    "train_df['kwhTotal'] = y_train\n",
    "train_df.to_csv(\"./data/train.csv\", index=False)\n",
    "\n",
    "test_df = pd.DataFrame(df_test, columns=df_test.columns)\n",
    "test_df['kwhTotal'] = y_test\n",
    "test_df.to_csv(\"./data/test.csv\", index=False)\n",
    "\n",
    "# Convert DataFrame rows to dictionaries\n",
    "df_train_dicts = df_train.to_dict(orient='records')\n",
    "df_test_dicts = df_test.to_dict(orient='records')\n",
    "\n",
    "# dv = DictVectorizer()\n",
    "\n",
    "# # Fit and transform the training data\n",
    "# X_train = dv.fit_transform(df_train_dicts)\n",
    "\n",
    "# # Transform the test data\n",
    "# X_test = dv.transform(df_test_dicts)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/amberm/.local/share/virtualenvs/project-O1Wr8Khj/lib/python3.10/site-packages/sklearn/metrics/_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 20, 'n_estimators': 150, 'min_samples_leaf': 10, 'random_state': 42} 1.3121425774455544 0.681228112732849\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024/09/05 17:47:17 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.\n",
      "2024/09/05 17:47:20 INFO mlflow.tracking._tracking_service.client: 🏃 View run caring-wasp-487 at: http://ec2-3-14-112-137.us-east-2.compute.amazonaws.com:5000/#/experiments/5/runs/9c137f1ba2434e1a9d3c07bb9b0a0dc4.\n",
      "2024/09/05 17:47:20 INFO mlflow.tracking._tracking_service.client: 🧪 View experiment at: http://ec2-3-14-112-137.us-east-2.compute.amazonaws.com:5000/#/experiments/5.\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Start the MLflow run\n",
    "with mlflow.start_run() as run:\n",
    "    mlflow.log_param(\"model_type\", \"RandomForest\")\n",
    "\n",
    "    # Log the model parameters\n",
    "    params = dict(max_depth=20, n_estimators=150, min_samples_leaf=10, random_state=42)\n",
    "    mlflow.log_params(params)\n",
    "    \n",
    "    pipeline = make_pipeline(\n",
    "        DictVectorizer(),\n",
    "        RandomForestRegressor(**params, n_jobs=-1)\n",
    "    )\n",
    "\n",
    "    pipeline.fit(df_train_dicts, y_train)\n",
    "    y_pred = pipeline.predict(df_test_dicts)\n",
    "\n",
    "    # Calculate and log metrics\n",
    "    rmse = mean_squared_error(y_pred, y_test, squared=False)\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(params, rmse, r2)\n",
    "\n",
    "    mlflow.log_metric('rmse', rmse)\n",
    "    mlflow.log_metric(\"r2\", r2)\n",
    "    \n",
    "    # Log the training and test datasets\n",
    "    mlflow.log_artifact(\"./data/train.csv\", artifact_path=\"datasets/training\")\n",
    "    mlflow.log_artifact(\"./data/test.csv\", artifact_path=\"datasets/testing\")\n",
    "    # Log the model\n",
    "    mlflow.sklearn.log_model(pipeline, artifact_path=\"models\")\n",
    "\n",
    "    # with open ('./models/xgboost_energy_model_dv.bin', 'wb') as f_out:\n",
    "    #     pickle.dump((dv, model), f_out)\n",
    "    # mlflow.log_artifact('xgboost_energy_model_dv.bin')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<Experiment: artifact_location='s3://mlflow-artifacts-evs/5', creation_time=1725572707889, experiment_id='5', last_update_time=1725572707889, lifecycle_stage='active', name='energy-prediction-pipeline_updated', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/4', creation_time=1723958095275, experiment_id='4', last_update_time=1723958095275, lifecycle_stage='active', name='energy-prediction-pipeline', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/3', creation_time=1723952998415, experiment_id='3', last_update_time=1723952998415, lifecycle_stage='active', name='my-experiment-3', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/2', creation_time=1723492994445, experiment_id='2', last_update_time=1723492994445, lifecycle_stage='active', name='my-experiment-2', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/1', creation_time=1723478188985, experiment_id='1', last_update_time=1723478188985, lifecycle_stage='active', name='my-experiment-1', tags={}>,\n",
       " <Experiment: artifact_location='s3://mlflow-artifacts-evs/0', creation_time=1723466290987, experiment_id='0', last_update_time=1723466290987, lifecycle_stage='active', name='Default', tags={}>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlflow.search_experiments()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interacting with the model registry\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from mlflow.tracking import MlflowClient\n",
    "\n",
    "\n",
    "# Initialize MLflow client\n",
    "client = MlflowClient(f\"http://{TRACKING_SERVER_HOST}:5000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<RegisteredModel: aliases={}, creation_timestamp=1723799381714, description='', last_updated_timestamp=1723799381714, latest_versions=[], name='2kjaF0EfT4lHTkS4fGTgPoCs8ZF', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1724724060792, description='', last_updated_timestamp=1724724060997, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1724724060997, current_stage='None', description='', last_updated_timestamp=1724724060997, name='energy-consumption-prediction-pipeline', run_id='ccb1e1f2811a426396410a62e6c81da5', run_link='', source='s3://mlflow-artifacts-evs/4/ccb1e1f2811a426396410a62e6c81da5/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='1'>], name='energy-consumption-prediction-pipeline', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1723500706238, description='', last_updated_timestamp=1724265006216, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1724265006216, current_stage='None', description='', last_updated_timestamp=1724265006216, name='Energy forecast', run_id='b2d92448b8cb4546bc0dd3cbf639f6be', run_link='', source='s3://mlflow-artifacts-evs/4/b2d92448b8cb4546bc0dd3cbf639f6be/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='17'>], name='Energy forecast', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1723501687908, description='', last_updated_timestamp=1723501688082, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1723501688082, current_stage='None', description='', last_updated_timestamp=1723501688082, name='Energy forecast new', run_id='850c7864fff443a9b354024e78f5f9df', run_link='', source='s3://mlflow-artifacts-evs/2/850c7864fff443a9b354024e78f5f9df/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='1'>], name='Energy forecast new', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1724724062332, description='', last_updated_timestamp=1724724062551, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1724724062551, current_stage='None', description='', last_updated_timestamp=1724724062551, name='energy-prediction-pipeline', run_id='ccb1e1f2811a426396410a62e6c81da5', run_link='', source='s3://mlflow-artifacts-evs/4/ccb1e1f2811a426396410a62e6c81da5/artifacts/models', status='READY', status_message='', tags={}, user_id='', version='1'>], name='energy-prediction-pipeline', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1723501606404, description='', last_updated_timestamp=1723501606572, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1723501606572, current_stage='None', description='This is the forecasting model to predict the energy usage.', last_updated_timestamp=1723501606572, name='Energy usage forecast ', run_id='7cbadd3268cc443c8c45a51f909e9742', run_link='', source='s3://mlflow-artifacts-evs/2/7cbadd3268cc443c8c45a51f909e9742/artifacts/models', status='READY', status_message='', tags={'Energy_Forecast': 'Randomforest'}, user_id='', version='1'>], name='Energy usage forecast ', tags={}>,\n",
       " <RegisteredModel: aliases={}, creation_timestamp=1723501688196, description='', last_updated_timestamp=1723501688378, latest_versions=[<ModelVersion: aliases=[], creation_timestamp=1723501688378, current_stage='None', description='This is the forecasting model to predict the energy usage.', last_updated_timestamp=1723501688378, name='Energy usage forecast v1', run_id='850c7864fff443a9b354024e78f5f9df', run_link='', source='s3://mlflow-artifacts-evs/2/850c7864fff443a9b354024e78f5f9df/artifacts/models', status='READY', status_message='', tags={'Energy_Forecast': 'Randomforest'}, user_id='', version='1'>], name='Energy usage forecast v1', tags={}>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client.search_registered_models()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regester an empty model with no version associated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Registered model 'Energy forecast' already exists. Creating a new version of this model...\n",
      "2024/09/05 17:47:21 INFO mlflow.store.model_registry.abstract_store: Waiting up to 300 seconds for model version to finish creation. Model name: Energy forecast, version 18\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model registered with version: 18\n",
      "Name: Energy forecast\n",
      "Version: 18\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Created version '18' of model 'Energy forecast'.\n"
     ]
    }
   ],
   "source": [
    "registered_name = 'Energy forecast'\n",
    "model_uri = f\"runs:/{run.info.run_id}/models\"\n",
    "\n",
    "# Register an empty registered model with no version associated\n",
    "try:\n",
    "    result = mlflow.register_model(\n",
    "        model_uri=model_uri,\n",
    "        name=registered_name\n",
    "    )\n",
    "    print(f\"Model registered with version: {result.version}\")\n",
    "except Exception as e:\n",
    "    print(f\"An error occurred: {e}\")\n",
    "print(f\"Name: {result.name}\")\n",
    "print(f\"Version: {result.version}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Regester a new version of the model (when incremental development)\n",
    "With the version information, such as `tags` and `description`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Define model details\n",
    "# name = 'Energy usage forecast'\n",
    "# model_uri = f\"runs:/{run.info.run_id}/models\"\n",
    "\n",
    "# tags = {\"Energy_Forecast\": \"Randomforest\"}\n",
    "# desc = \"This is the forecasting model to predict the energy usage.\"\n",
    "\n",
    "# from mlflow.store.artifact.runs_artifact_repo import RunsArtifactRepository\n",
    "\n",
    "# try:\n",
    "#     # Register model name in the model registry\n",
    "#     client.create_registered_model(name)\n",
    "\n",
    "#     # Create a new version of the model under the registered model name\n",
    "#     model_src = RunsArtifactRepository.get_underlying_uri(model_uri)\n",
    "#     client.create_model_version(name = name, source = model_src, run_id = run.info.run_id, tags=tags, description = desc)\n",
    "\n",
    "#     print(f\"New registered model '{name}' created.\")\n",
    "# except mlflow.exceptions.MlflowException as e:\n",
    "#     if \"RESOURCE_ALREADY_EXISTS\" in str(e):\n",
    "#          print(f\"Model '{name}' already exists\")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort the actual and predicted values for better line plotting\n",
    "sorted_indices = np.argsort(y_test.values)\n",
    "sorted_y_test = y_test.values[sorted_indices]\n",
    "sorted_y_pred = y_pred[sorted_indices]\n",
    "\n",
    "plt.figure(figsize=(10, 5))\n",
    "\n",
    "# Plot actual values\n",
    "plt.plot(sorted_y_test, label='Actual kwhTotal', color='blue', linestyle='-', marker='o')\n",
    "\n",
    "# Plot predicted values\n",
    "plt.plot(sorted_y_pred, label='Predicted kwhTotal', color='red', linestyle='-', marker='x')\n",
    "\n",
    "plt.xlabel('Sample Index')\n",
    "plt.ylabel('kwhTotal')\n",
    "plt.title('Actual vs Predicted kwhTotal')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# importances = model.feature_importances_\n",
    "# features = dv.get_feature_names_out()\n",
    "\n",
    "# plt.figure(figsize=(10, 5))\n",
    "# sns.barplot(x=features , y=importances)\n",
    "# plt.title('Feature Importances')\n",
    "# plt.show()"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}