mean-relative-humidity-Copy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "adjacent-popularity",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import TimeSeriesSplit\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.model_selection import cross_val_score\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "universal-tracker",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('mean-relative-humidity-Japanweather1.csv') #load the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "exact-drama",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date                            datetime64[ns]\n",
       "total_precipitation                    float64\n",
       "mean_wind_speed                        float64\n",
       "mean_air_temperature                   float64\n",
       "percentage_possible_sunshine           float64\n",
       "total_sunshine_duration                float64\n",
       "solar_radiation                        float64\n",
       "mean_relative_humidity                 float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to explicitly convert the date column to type DATETIME\n",
    "data['date'] = pd.to_datetime(data['date'], dayfirst=True)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ignored-brief",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.set_index('date') #set the index of the dataset as the date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "critical-geometry",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1597: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self.obj[key] = value\n",
      "c:\\users\\user\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\pandas\\core\\indexing.py:1676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self._setitem_single_column(ilocs[0], value, pi)\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_relative_humidity</th>\n",
       "      <th>last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>77.0</td>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>71.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>76.0</td>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>79.0</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>82.0</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>72.4</td>\n",
       "      <td>75.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>73.0</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>74.6</td>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>75.1</td>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>73.7</td>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>142 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            mean_relative_humidity  last_month\n",
       "date                                          \n",
       "2010-02-01                    77.0        67.0\n",
       "2010-03-01                    71.0        77.0\n",
       "2010-04-01                    76.0        71.0\n",
       "2010-05-01                    79.0        76.0\n",
       "2010-06-01                    82.0        79.0\n",
       "...                            ...         ...\n",
       "2022-07-01                    72.4        75.2\n",
       "2022-08-01                    73.0        72.4\n",
       "2022-09-01                    74.6        73.0\n",
       "2022-10-01                    75.1        74.6\n",
       "2022-11-01                    73.7        75.1\n",
       "\n",
       "[142 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_mean_relative_humidity = data[['mean_relative_humidity']] # creating new dataframe from mean_relative_humidity column\n",
    "data_mean_relative_humidity.loc[:,'last_month'] = data_mean_relative_humidity.loc[:,'mean_relative_humidity'].shift() # inserting new column with last month's mean_relative_humidity values\n",
    "data_mean_relative_humidity = data_mean_relative_humidity.dropna() # dropping NAs\n",
    "data_mean_relative_humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "neural-isaac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def rmse(actual, predict):\n",
    "    predict = np.array(predict)\n",
    "    actual = np.array(actual)\n",
    "    distance = predict - actual\n",
    "    square_distance = distance ** 2\n",
    "    mean_square_distance = square_distance.mean()\n",
    "    score = np.sqrt(mean_square_distance)\n",
    "    return score\n",
    "rmse_score = make_scorer(rmse, greater_is_better = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "beneficial-field",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = data_mean_relative_humidity.drop(['mean_relative_humidity'], axis = 1)\n",
    "y_train = data_mean_relative_humidity.loc[:'2022', 'mean_relative_humidity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "typical-holocaust",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>last_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-02-01</th>\n",
       "      <td>67.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-01</th>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>71.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-05-01</th>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-06-01</th>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2022-07-01</th>\n",
       "      <td>75.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-08-01</th>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-09-01</th>\n",
       "      <td>73.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-10-01</th>\n",
       "      <td>74.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2022-11-01</th>\n",
       "      <td>75.1</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "            last_month\n",
       "date                  \n",
       "2010-02-01        67.0\n",
       "2010-03-01        77.0\n",
       "2010-04-01        71.0\n",
       "2010-05-01        76.0\n",
       "2010-06-01        79.0\n",
       "...                ...\n",
       "2022-07-01        75.2\n",
       "2022-08-01        72.4\n",
       "2022-09-01        73.0\n",
       "2022-10-01        74.6\n",
       "2022-11-01        75.1\n",
       "\n",
       "[142 rows x 1 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "alpine-glass",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date\n",
       "2010-02-01    77.0\n",
       "2010-03-01    71.0\n",
       "2010-04-01    76.0\n",
       "2010-05-01    79.0\n",
       "2010-06-01    82.0\n",
       "              ... \n",
       "2022-07-01    72.4\n",
       "2022-08-01    73.0\n",
       "2022-09-01    74.6\n",
       "2022-10-01    75.1\n",
       "2022-11-01    73.7\n",
       "Name: mean_relative_humidity, Length: 142, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "recognized-walnut",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[75.94610709]\n"
     ]
    }
   ],
   "source": [
    "test_data = pd.read_csv('predicted-mean-relative-humidity.csv')\n",
    "test_data = test_data.set_index('date')\n",
    "X_test = test_data.drop(['mean_relative_humidity'], axis = 1)\n",
    "model = RandomForestRegressor()\n",
    "param_search = { \n",
    "    'n_estimators': [20, 50, 100],\n",
    "    'max_features': ['auto', 'sqrt', 'log2'],\n",
    "    'max_depth' : [i for i in range(5,15)]\n",
    "}\n",
    "tscv = TimeSeriesSplit(n_splits=10)\n",
    "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_search, scoring = rmse_score)\n",
    "gsearch.fit(X_train, y_train)\n",
    "best_model = gsearch.best_estimator_\n",
    "y_pred = best_model.predict(X_test)\n",
    "print(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "material-fitting",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>last_month</th>\n",
       "      <th>mean_relative_humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Jan-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Feb-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Mar-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>75.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>May-22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date  last_month  mean_relative_humidity\n",
       "0  Jan-22         NaN                    72.8\n",
       "1  Feb-22         NaN                    74.5\n",
       "2  Mar-22         NaN                    75.2\n",
       "3  Apr-22         NaN                    73.1\n",
       "4  May-22         NaN                    74.2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_data = pd.read_csv('predicted-mean-relative-humidity.csv')\n",
    "predicted_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "contained-compression",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure() # create an empty figure\n",
    "plt.bar(predicted_data['date'], predicted_data['mean_relative_humidity']) #set the dates as the x-axis and the predicted-mean-relative-humidity as the y-axis\n",
    "plt.ylabel(\"Mean relative humidity\") # set the label for the y-axis\n",
    "plt.xlabel(\"Dates\") # set the label for the x-axis\n",
    "fig.set_size_inches(20, 5) #set the size of the figure\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "gross-breeding",
   "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}