pm25_forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting PM2.5 concentrations via rolling forecasting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>hour</th>\n",
       "      <th>season</th>\n",
       "      <th>PM</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>HUMI</th>\n",
       "      <th>PRES</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>...</th>\n",
       "      <th>precipitation_max</th>\n",
       "      <th>Iws_max</th>\n",
       "      <th>PM_max</th>\n",
       "      <th>TEMP_min</th>\n",
       "      <th>DEWP_min</th>\n",
       "      <th>HUMI_min</th>\n",
       "      <th>PRES_min</th>\n",
       "      <th>precipitation_min</th>\n",
       "      <th>Iws_min</th>\n",
       "      <th>PM_min</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1027.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.4</td>\n",
       "      <td>58.56</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>61.69</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>63.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1026.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5</td>\n",
       "      <td>65.71</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>65.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.7</td>\n",
       "      <td>68.84</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-12.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>83.0</td>\n",
       "      <td>-8.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>...</td>\n",
       "      <td>1.2</td>\n",
       "      <td>72.86</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.89</td>\n",
       "      <td>55.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 372 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  month  day  hour  season    PM  DEWP  HUMI    PRES  TEMP  ...  \\\n",
       "0  2010      1    3     0       4  63.0  -7.0  92.0  1027.0  -6.0  ...   \n",
       "1  2010      1    3     1       4  65.0  -8.0  85.0  1026.0  -6.0  ...   \n",
       "2  2010      1    3     2       4  55.0  -8.0  92.0  1026.0  -7.0  ...   \n",
       "3  2010      1    3     3       4  65.0  -8.0  92.0  1025.0  -7.0  ...   \n",
       "4  2010      1    3     4       4  83.0  -8.0  92.0  1024.0  -7.0  ...   \n",
       "\n",
       "   precipitation_max  Iws_max  PM_max  TEMP_min  DEWP_min  HUMI_min  PRES_min  \\\n",
       "0                0.4    58.56   181.0     -14.0     -21.0      25.0    1014.0   \n",
       "1                0.5    61.69   181.0     -14.0     -21.0      25.0    1014.0   \n",
       "2                0.5    65.71   181.0     -14.0     -21.0      25.0    1014.0   \n",
       "3                0.7    68.84   181.0     -12.0     -20.0      25.0    1014.0   \n",
       "4                1.2    72.86   181.0     -10.0     -20.0      25.0    1014.0   \n",
       "\n",
       "   precipitation_min  Iws_min  PM_min  \n",
       "0                0.0     0.89    63.0  \n",
       "1                0.0     0.89    63.0  \n",
       "2                0.0     0.89    55.0  \n",
       "3                0.0     0.89    55.0  \n",
       "4                0.0     0.89    55.0  \n",
       "\n",
       "[5 rows x 372 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from tqdm import tqdm\n",
    "from pm25_hyperparameters import *\n",
    "\n",
    "data=pd.read_csv(\"BeijingPM_dataset.csv\")\n",
    "picker=data[\"date\"]\n",
    "picker_test=picker[(picker>=\"2015-09-01 00:00:00\") & (picker<\"2015-09-03 00:00:00\")]\n",
    "test_idx=list(picker_test.index)\n",
    "data.drop(\"date\",axis=1,inplace=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Parameters and functions used for model training and testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_met=[\"year\",\"month\",\"day\",\"hour\",\"season\"]\n",
    "y_met=list(set(prim_fea)-set([\"PM\"]))\n",
    "\n",
    "datetime_cols=[\"is_holiday\",\"is_special_event\"]\n",
    "\n",
    "x_pm=list(set(data.columns)-set([\"PM\"])-set(datetime_cols))\n",
    "\n",
    "def fill_fea(df,pos,cols):\n",
    "    df.loc[pos,]\n",
    "    for col in cols:\n",
    "        lagtmp=[]\n",
    "        for t in range(1,lag_hours+1):\n",
    "            if pos-t>=test_idx[0]:\n",
    "                x=df.loc[pos-t,col]\n",
    "            else:\n",
    "                x=data.loc[pos-t,col]\n",
    "            df.loc[pos,\"%s_lag%d\" % (col,t)]=x\n",
    "            lagtmp.append(x)\n",
    "        df.loc[pos,\"%s_mean\" % col]=np.mean(lagtmp)\n",
    "        df.loc[pos,\"%s_min\" % col]=np.min(lagtmp)\n",
    "        df.loc[pos,\"%s_max\" % col]=np.max(lagtmp)\n",
    "        \n",
    "lowbs=list(range(test_idx[0],test_idx[-1],24))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the testing set for PM2.5 prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating testing set for [49608:49656]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "(1/2): 100%|██████████| 24/24 [00:02<00:00,  9.08it/s]\n",
      "(2/2): 100%|██████████| 24/24 [00:02<00:00,  8.75it/s]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.multioutput import MultiOutputRegressor\n",
    "from lightgbm import LGBMRegressor\n",
    "\n",
    "pm_test_file=\"BeijingPM_test.csv\"\n",
    "\n",
    "met_fea={}\n",
    "for col in y_met:\n",
    "    met_fea[col]=[]\n",
    "    for t in range(1,lag_hours+1):\n",
    "        met_fea[col].append(\"%s_lag%d\" % (col,t))\n",
    "    for r in [\"mean\",\"max\",\"min\"]:\n",
    "        met_fea[col].append(\"%s_%s\" % (col,r))\n",
    "\n",
    "if os.path.exists(pm_test_file):\n",
    "    print(\"Reading the testing set from %s\" % pm_test_file)\n",
    "    pm_testset=pd.read_csv(pm_test_file)\n",
    "else:\n",
    "    pm_testset=data.loc[test_idx].copy()\n",
    "    print(\"Creating testing set for [%d:%d]\" % (test_idx[0],test_idx[-1]+1))\n",
    "    pre={}\n",
    "    for c in y_met:\n",
    "        pre[c]=[]\n",
    "    for i,lowb in enumerate(lowbs):\n",
    "        met_train=data[:lowb]\n",
    "        models={}\n",
    "        for col in y_met:\n",
    "            models[col]=LGBMRegressor()\n",
    "            models[col].fit(met_train[x_met+met_fea[col]],met_train[col])\n",
    "        for t in tqdm(range(lowb,lowb+24),desc=\"(%d/%d)\" % (i+1,len(lowbs))):\n",
    "            fill_fea(pm_testset,t,y_met)\n",
    "            for col in y_met:\n",
    "                pre[col].append(*models[col].predict(pm_testset.loc[[t],x_met+met_fea[col]]))\n",
    "                pm_testset.loc[t,col]=pre[col][-1]\n",
    "    pm_testset.to_csv(pm_test_file,index=False)\n",
    "\n",
    "pm_testset.index=test_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predict PM2.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting PM2.5 for [49608:49655]\n",
      "[LightGBM] [Warning] min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20\n",
      "[LightGBM] [Warning] bagging_freq is set=50, subsample_freq=0 will be ignored. Current value: bagging_freq=50\n",
      "[LightGBM] [Warning] feature_fraction is set=1.0, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=1.0\n",
      "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[49608:49632] (1/2): 100%|██████████| 24/24 [00:01<00:00, 22.57it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set=20, min_child_samples=20 will be ignored. Current value: min_data_in_leaf=20\n",
      "[LightGBM] [Warning] bagging_freq is set=50, subsample_freq=0 will be ignored. Current value: bagging_freq=50\n",
      "[LightGBM] [Warning] feature_fraction is set=1.0, colsample_bytree=1.0 will be ignored. Current value: feature_fraction=1.0\n",
      "[LightGBM] [Warning] bagging_fraction is set=1.0, subsample=1.0 will be ignored. Current value: bagging_fraction=1.0\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[49632:49656] (2/2): 100%|██████████| 24/24 [00:01<00:00, 21.01it/s]\n"
     ]
    }
   ],
   "source": [
    "pm_predict_file=\"BeijingPM_prediction.csv\"\n",
    "\n",
    "pm_df_list=[]\n",
    "\n",
    "# Parameters for PM2.5 prediction\n",
    "pm_model_params={\n",
    "    'objective': 'l2',\n",
    "    'num_leaves': 64,\n",
    "    'min_data_in_leaf': 20,\n",
    "    'max_depth': 30,\n",
    "    'learning_rate': 0.1,\n",
    "    'feature_fraction': 1.0,\n",
    "    'bagging_freq': 50,\n",
    "    'bagging_fraction': 1.0,\n",
    "    'verbose': -1\n",
    "}\n",
    "\n",
    "\n",
    "model_names=[\"LightGBM\",\"LightGBM + holidays and special events\"]\n",
    "\n",
    "if os.path.exists(pm_predict_file):\n",
    "    print(\"Reading existing PM2.5 prediction from %s\" % pm_predict_file)\n",
    "    pm_df=pd.read_csv(pm_predict_file)\n",
    "else:\n",
    "    print(\"Predicting PM2.5 for [%d:%d]\" % (test_idx[0],test_idx[-1]))\n",
    "    pm_test_control=pd.DataFrame(pm_testset,copy=True)\n",
    "    pm_test_datetime=pd.DataFrame(pm_testset,copy=True)\n",
    "    for i,lowb in enumerate(lowbs):\n",
    "        pm_train=data[:lowb]\n",
    "        pm_control=LGBMRegressor(random_state=101,**pm_model_params)\n",
    "        pm_control.fit(pm_train[x_pm],pm_train[\"PM\"])\n",
    "        pm_datetime=LGBMRegressor(random_state=101,**pm_model_params)\n",
    "        pm_datetime.fit(pm_train[x_pm+datetime_cols],pm_train[\"PM\"])\n",
    "        pm_predict_control=[]\n",
    "        pm_predict_datetime=[]\n",
    "        for k in tqdm(range(lowb,lowb+24),desc=\"[%d:%d] (%d/%d)\" % (lowb,lowb+24,i+1,len(lowbs))):\n",
    "            fill_fea(pm_test_control,k,[\"PM\"])\n",
    "            fill_fea(pm_test_datetime,k,[\"PM\"])\n",
    "            pm_predict_control.append(pm_control.predict(pm_test_control.loc[[k],x_pm])[0])\n",
    "            pm_predict_datetime.append(pm_datetime.predict(pm_test_datetime.loc[[k],x_pm+datetime_cols])[0])\n",
    "            pm_test_control.loc[k,\"PM\"]=pm_predict_control[-1]\n",
    "            pm_test_datetime.loc[k,\"PM\"]=pm_predict_datetime[-1]\n",
    "\n",
    "        pm_df_list.append(pd.DataFrame(\n",
    "            {model_names[0]:pm_predict_control,model_names[1]:pm_predict_datetime}))\n",
    "        pm_df=pd.concat(pm_df_list)\n",
    "        pm_df.to_csv(pm_predict_file,index=False)\n",
    "\n",
    "pm_df.index=test_idx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation\n",
    "\n",
    "After predicting PM2.5 concentration, we now begin analyzing the performance. In our study, we use three evaluation metrics to rank our model (let $y_i$ be the true value and $p_i$ be the predicted values).\n",
    "\n",
    "$$MAE=\\frac1N\\sum_{i=1}^N|y_i-p_i|\\quad RMSE=\\sqrt{\\frac1N\\sum_{i=1}^N(y_i-p_i)^2}\\quad SMAPE=\\frac1N\\sum_{i=1}^N\\frac{|y_i-p_i|}{(|y_i|+|p_i|)/2}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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>MAE</th>\n",
       "      <th>RMSE</th>\n",
       "      <th>SMAPE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>LightGBM</th>\n",
       "      <td>5.319195</td>\n",
       "      <td>7.671777</td>\n",
       "      <td>0.444225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LightGBM + holidays and special events</th>\n",
       "      <td>4.783741</td>\n",
       "      <td>6.535935</td>\n",
       "      <td>0.427308</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             MAE      RMSE     SMAPE\n",
       "LightGBM                                5.319195  7.671777  0.444225\n",
       "LightGBM + holidays and special events  4.783741  6.535935  0.427308"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine true values and predicted values for comparison\n",
    "pm_comp=pd.concat([data.loc[pm_df.index,\"PM\"],pm_df],axis=1)\n",
    "pm_comp.index=picker[test_idx]\n",
    "\n",
    "def get_mae(y,p):\n",
    "    return abs(y-p).mean()\n",
    "\n",
    "def get_rmse(y,p):\n",
    "    return np.sqrt(np.square(y-p).mean())\n",
    "\n",
    "def get_smape(y,p):\n",
    "    return 1/len(y)*np.sum(2*abs(y-p)/(abs(y)+abs(p)+1e-7))\n",
    "\n",
    "eval_metrics={\"MAE\":get_mae,\"RMSE\":get_rmse,\"SMAPE\":get_smape}\n",
    "\n",
    "def pm_metrics(col,m):\n",
    "    return eval_metrics[m](pm_comp[\"PM\"],pm_comp[col])\n",
    "\n",
    "pm_metrics={}\n",
    "\n",
    "for m in eval_metrics.keys():\n",
    "    pm_metrics[m]=[]\n",
    "\n",
    "for col in [\"LightGBM\",\"LightGBM + holidays and special events\"]:\n",
    "    for m in pm_metrics.keys():\n",
    "        pm_metrics[m].append(eval_metrics[m](pm_comp[\"PM\"],pm_comp[col]))\n",
    "\n",
    "edf=pd.DataFrame(pm_metrics,index=[\"LightGBM\",\"LightGBM + holidays and special events\"])\n",
    "edf.to_csv(\"model_scores.csv\")\n",
    "edf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,7))\n",
    "ax=plt.gca()\n",
    "ax.set_title(\"True PM2.5 vs. LightGBM prediction during %s and %s\" % (pm_comp.index[0],pm_comp.index[-1]))\n",
    "ax.set_ylabel(\"PM2.5 concentration ($ug/m^3$)\")\n",
    "pm_comp[[\"PM\",*model_names[:2]]].plot(ax=ax)\n",
    "\n",
    "plt.savefig(\"graphs/lgbm_prediction.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}