2_experiment/duration-prediction_3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41062d8a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c984c564",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4add538c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b135c2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction import DictVectorizer\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "\n",
    "from sklearn.metrics import mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1464985f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import mlflow\n",
    "\n",
    "\n",
    "mlflow.set_tracking_uri(\"sqlite:///mlflow.db\")\n",
    "mlflow.set_experiment(\"nyc-taxi-experiment\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f9e6479e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_dataframe(filename):\n",
    "    df = pd.read_parquet(filename)\n",
    "\n",
    "    df.lpep_dropoff_datetime = pd.to_datetime(df.lpep_dropoff_datetime)\n",
    "    df.lpep_pickup_datetime = pd.to_datetime(df.lpep_pickup_datetime)\n",
    "\n",
    "    df['duration'] = df.lpep_dropoff_datetime - df.lpep_pickup_datetime\n",
    "    df.duration = df.duration.apply(lambda td: td.total_seconds() / 60)\n",
    "\n",
    "    df = df[(df.duration >= 1) & (df.duration <= 60)]\n",
    "\n",
    "    categorical = ['PULocationID', 'DOLocationID']\n",
    "    df[categorical] = df[categorical].astype(str)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8029eba0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = read_dataframe('./data/green_tripdata_2021-01.parquet')\n",
    "df_val = read_dataframe('./data/green_tripdata_2021-02.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "951d51ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train['PU_DO'] = df_train['PULocationID'] + '_' + df_train['DOLocationID']\n",
    "df_val['PU_DO'] = df_val['PULocationID'] + '_' + df_val['DOLocationID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c5cbfc25",
   "metadata": {},
   "outputs": [],
   "source": [
    "categorical = ['PU_DO'] #'PULocationID', 'DOLocationID']\n",
    "numerical = ['trip_distance']\n",
    "\n",
    "dv = DictVectorizer()\n",
    "\n",
    "train_dicts = df_train[categorical + numerical].to_dict(orient='records')\n",
    "X_train = dv.fit_transform(train_dicts)\n",
    "\n",
    "val_dicts = df_val[categorical + numerical].to_dict(orient='records')\n",
    "X_val = dv.transform(val_dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9fb68b",
   "metadata": {},
   "outputs": [],
   "source": [
    "target = 'duration'\n",
    "y_train = df_train[target].values\n",
    "y_val = df_val[target].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22bf6f8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('models/lin_reg.bin', 'wb') as f_out:\n",
    "    pickle.dump((dv, lr), f_out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4999b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mlflow.start_run():\n",
    "\n",
    "    mlflow.set_tag(\"developer\", \"cristian\")\n",
    "\n",
    "    mlflow.log_param(\"train-data-path\", \"./data/green_tripdata_2021-01.csv\")\n",
    "    mlflow.log_param(\"valid-data-path\", \"./data/green_tripdata_2021-02.csv\")\n",
    "\n",
    "    alpha = 0.1\n",
    "    mlflow.log_param(\"alpha\", alpha)\n",
    "    lr = Lasso(alpha)\n",
    "    lr.fit(X_train, y_train)\n",
    "\n",
    "    y_pred = lr.predict(X_val)\n",
    "    rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "    mlflow.log_metric(\"rmse\", rmse)\n",
    "\n",
    "    mlflow.log_artifact(local_path=\"models/lin_reg.bin\", artifact_path=\"models_pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47b0eb9b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f56e97b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xgboost as xgb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "907dc6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
    "from hyperopt.pyll import scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "091ab328",
   "metadata": {},
   "outputs": [],
   "source": [
    "train = xgb.DMatrix(X_train, label=y_train)\n",
    "valid = xgb.DMatrix(X_val, label=y_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fc4d5283",
   "metadata": {},
   "outputs": [],
   "source": [
    "mlflow.xgboost.autolog(disable=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e8cd729",
   "metadata": {},
   "outputs": [],
   "source": [
    "with mlflow.start_run():\n",
    "    \n",
    "    train = xgb.DMatrix(X_train, label=y_train)\n",
    "    valid = xgb.DMatrix(X_val, label=y_val)\n",
    "\n",
    "    best_params = {\n",
    "        'learning_rate': 0.09585355369315604,\n",
    "        'max_depth': 30,\n",
    "        'min_child_weight': 1.060597050922164,\n",
    "        'objective': 'reg:linear',\n",
    "        'reg_alpha': 0.018060244040060163,\n",
    "        'reg_lambda': 0.011658731377413597,\n",
    "        'seed': 42\n",
    "    }\n",
    "\n",
    "    mlflow.log_params(best_params)\n",
    "\n",
    "    booster = xgb.train(\n",
    "        params=best_params,\n",
    "        dtrain=train,\n",
    "        num_boost_round=1000,\n",
    "        evals=[(valid, 'validation')],\n",
    "        early_stopping_rounds=50\n",
    "    )\n",
    "\n",
    "    y_pred = booster.predict(valid)\n",
    "    rmse = mean_squared_error(y_val, y_pred, squared=False)\n",
    "    mlflow.log_metric(\"rmse\", rmse)\n",
    "\n",
    "    with open(\"models/preprocessor.b\", \"wb\") as f_out:\n",
    "        pickle.dump(dv, f_out)\n",
    "    mlflow.log_artifact(\"models/preprocessor.b\", artifact_path=\"preprocessor\")\n",
    "\n",
    "    mlflow.xgboost.log_model(booster, artifact_path=\"models_mlflow\")"
   ]
  }
 ],
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