docs/examples/tutorial/wrappers/05_model_selection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross Validation\n",
    "As time-series have the inherent structure we could run into problems with traditional shuffled Kfolds cross-validation. hcrystalball implements forward rolling cross-validation making training set consist only of observations that occurred prior to the observations that form the test set.\n",
    "\n",
    "<center><img src=\"../../../_static/FinerTimeSplit.svg\"></center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.model_selection import FinerTimeSplit\n",
    "from sklearn.model_selection import cross_validate\n",
    "from hcrystalball.wrappers import ExponentialSmoothingWrapper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "# plt.style.use('fivethirtyeight')\n",
    "plt.rcParams['figure.figsize'] = [12, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.utils import get_sales_data\n",
    "\n",
    "df = get_sales_data(n_dates=100, \n",
    "                    n_assortments=1,\n",
    "                    n_states=1, \n",
    "                    n_stores=1)\n",
    "X, y = pd.DataFrame(index=df.index), df['Sales']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Native Cross Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.01100016, 0.00534296]),\n",
       " 'score_time': array([0.00643301, 0.00403714]),\n",
       " 'test_score': array([-4822.46061508, -5489.06393347])}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validate(ExponentialSmoothingWrapper(), \n",
    "               X, \n",
    "               y, \n",
    "               cv=FinerTimeSplit(horizon=5, n_splits=2), \n",
    "               scoring='neg_mean_absolute_error')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Grid search and model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Model selection and parameter tuning is the area where hcrystalball really shines. There is ongoing and probably a never-ending discussion about superiority or inferiority of ML techniques over common statistical/econometrical ones. Why not try both? The problem of a simple comparison between the performance of different kind of algorithms such as SARIMAX, Prophet, regularized linear models, and XGBoost lead to hcrystalball. Let's see how to do it!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.compose import TSColumnTransformer\n",
    "from hcrystalball.feature_extraction import SeasonalityTransformer\n",
    "from hcrystalball.wrappers import ProphetWrapper\n",
    "from hcrystalball.wrappers import get_sklearn_wrapper\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "from hcrystalball.wrappers import SarimaxWrapper\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define our pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sklearn_model_pipeline = Pipeline([\n",
    "    ('seasonality', SeasonalityTransformer(freq='D')),\n",
    "    ('model', 'passthrough') # this will be overwritten by param grid\n",
    "]) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define pipeline parameters including different models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = [{'model': [sklearn_model_pipeline],\n",
    "               'model__model':[get_sklearn_wrapper(RandomForestRegressor), \n",
    "                               get_sklearn_wrapper(LinearRegression)]},\n",
    "              {'model': [ProphetWrapper()],\n",
    "               'model__seasonality_mode':['multiplicative', 'additive']},\n",
    "              {'model': [SarimaxWrapper(order=(2,1,1), suppress_warnings=True)]}\n",
    "             ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run native grid search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=sklearn_model_pipeline, \n",
    "                           param_grid=param_grid, \n",
    "                           scoring='neg_mean_absolute_error', \n",
    "                           cv=FinerTimeSplit(horizon=5, n_splits=2),\n",
    "                           refit=False, \n",
    "                           error_score=np.nan) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_model</th>\n",
       "      <th>param_model__model</th>\n",
       "      <th>param_model__seasonality_mode</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.019943</td>\n",
       "      <td>0.002446</td>\n",
       "      <td>0.050979</td>\n",
       "      <td>0.000094</td>\n",
       "      <td>(SeasonalityTransformer(auto=True, freq='D', m...</td>\n",
       "      <td>SklearnWrapper(clip_predictions_lower=None, cl...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'model': (SeasonalityTransformer(auto=True, f...</td>\n",
       "      <td>-1263.234636</td>\n",
       "      <td>-1466.042533</td>\n",
       "      <td>-1364.638585</td>\n",
       "      <td>101.403949</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.022864</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.190258</td>\n",
       "      <td>0.011604</td>\n",
       "      <td>(SeasonalityTransformer(auto=True, freq='D', m...</td>\n",
       "      <td>SklearnWrapper(bootstrap=True, ccp_alpha=0.0, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'model': (SeasonalityTransformer(auto=True, f...</td>\n",
       "      <td>-1985.424000</td>\n",
       "      <td>-1729.036000</td>\n",
       "      <td>-1857.230000</td>\n",
       "      <td>128.194000</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.012166</td>\n",
       "      <td>0.622536</td>\n",
       "      <td>1.637623</td>\n",
       "      <td>0.076543</td>\n",
       "      <td>ProphetWrapper(changepoint_prior_scale=0.05, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>multiplicative</td>\n",
       "      <td>{'model': ProphetWrapper(changepoint_prior_sca...</td>\n",
       "      <td>-2601.205624</td>\n",
       "      <td>-1407.257183</td>\n",
       "      <td>-2004.231403</td>\n",
       "      <td>596.974220</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.497856</td>\n",
       "      <td>0.167629</td>\n",
       "      <td>2.964522</td>\n",
       "      <td>0.931830</td>\n",
       "      <td>ProphetWrapper(changepoint_prior_scale=0.05, c...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>additive</td>\n",
       "      <td>{'model': ProphetWrapper(changepoint_prior_sca...</td>\n",
       "      <td>-2687.583938</td>\n",
       "      <td>-1330.634452</td>\n",
       "      <td>-2009.109195</td>\n",
       "      <td>678.474743</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.286065</td>\n",
       "      <td>0.192093</td>\n",
       "      <td>0.025675</td>\n",
       "      <td>0.001419</td>\n",
       "      <td>SarimaxWrapper(always_search_model=False, auto...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>{'model': SarimaxWrapper(always_search_model=F...</td>\n",
       "      <td>-1121.756502</td>\n",
       "      <td>-14636.329690</td>\n",
       "      <td>-7879.043096</td>\n",
       "      <td>6757.286594</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time  \\\n",
       "1       0.019943      0.002446         0.050979        0.000094   \n",
       "0       0.022864      0.002706         0.190258        0.011604   \n",
       "2       1.012166      0.622536         1.637623        0.076543   \n",
       "3       0.497856      0.167629         2.964522        0.931830   \n",
       "4       1.286065      0.192093         0.025675        0.001419   \n",
       "\n",
       "                                         param_model  \\\n",
       "1  (SeasonalityTransformer(auto=True, freq='D', m...   \n",
       "0  (SeasonalityTransformer(auto=True, freq='D', m...   \n",
       "2  ProphetWrapper(changepoint_prior_scale=0.05, c...   \n",
       "3  ProphetWrapper(changepoint_prior_scale=0.05, c...   \n",
       "4  SarimaxWrapper(always_search_model=False, auto...   \n",
       "\n",
       "                                  param_model__model  \\\n",
       "1  SklearnWrapper(clip_predictions_lower=None, cl...   \n",
       "0  SklearnWrapper(bootstrap=True, ccp_alpha=0.0, ...   \n",
       "2                                                NaN   \n",
       "3                                                NaN   \n",
       "4                                                NaN   \n",
       "\n",
       "  param_model__seasonality_mode  \\\n",
       "1                           NaN   \n",
       "0                           NaN   \n",
       "2                multiplicative   \n",
       "3                      additive   \n",
       "4                           NaN   \n",
       "\n",
       "                                              params  split0_test_score  \\\n",
       "1  {'model': (SeasonalityTransformer(auto=True, f...       -1263.234636   \n",
       "0  {'model': (SeasonalityTransformer(auto=True, f...       -1985.424000   \n",
       "2  {'model': ProphetWrapper(changepoint_prior_sca...       -2601.205624   \n",
       "3  {'model': ProphetWrapper(changepoint_prior_sca...       -2687.583938   \n",
       "4  {'model': SarimaxWrapper(always_search_model=F...       -1121.756502   \n",
       "\n",
       "   split1_test_score  mean_test_score  std_test_score  rank_test_score  \n",
       "1       -1466.042533     -1364.638585      101.403949                1  \n",
       "0       -1729.036000     -1857.230000      128.194000                2  \n",
       "2       -1407.257183     -2004.231403      596.974220                3  \n",
       "3       -1330.634452     -2009.109195      678.474743                4  \n",
       "4      -14636.329690     -7879.043096     6757.286594                5  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(results.cv_results_).sort_values('rank_test_score').head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It seems that the best model is sklearn with RandomForestRegressor, but in time-series, it is often also a good idea to check how the forecasts look like. Unfortunately, this is not possible with sklearn. grid_search is returning just the results, not the predictions of individual models for different splits. hcrystalball thus implements special scoring functions that track all data from the grid_search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Custom scorer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hcrystalball.metrics import make_ts_scorer\n",
    "from sklearn.metrics import mean_absolute_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "scoring = make_ts_scorer(mean_absolute_error, \n",
    "                         greater_is_better=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(estimator=sklearn_model_pipeline, \n",
    "                           param_grid=param_grid, \n",
    "                           scoring=scoring, \n",
    "                           cv=FinerTimeSplit(horizon=5, n_splits=2),\n",
    "                           refit=False, \n",
    "                           error_score=np.nan) \n",
    "results = grid_search.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "results.scorer_.cv_data.loc[:,lambda x: x.columns != 'split'].plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "hcrystalball internally tracks data based on unique model hashes since model string represantations (reprs) are very long for usable columns names in dataframe, but if you are curious i.e. what was the worse model not to use it for further experiment, you can do it with scorers `estimator_ids` attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>split</th>\n",
       "      <th>y_true</th>\n",
       "      <th>feef8e885b65983a6b2a2afa81790f63</th>\n",
       "      <th>f15ffb80abe21be5aa49ca2f4bef1428</th>\n",
       "      <th>443c753f6a53b3387028bafe49a286eb</th>\n",
       "      <th>6f632e060699dce1d328412e4b83c850</th>\n",
       "      <th>12436b23a2222e4b1df46a139cef649f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-22</th>\n",
       "      <td>0</td>\n",
       "      <td>18046.0</td>\n",
       "      <td>21096.67</td>\n",
       "      <td>20676.741135</td>\n",
       "      <td>24428.095232</td>\n",
       "      <td>24065.094696</td>\n",
       "      <td>20108.466066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-23</th>\n",
       "      <td>0</td>\n",
       "      <td>19532.0</td>\n",
       "      <td>20989.04</td>\n",
       "      <td>18015.494274</td>\n",
       "      <td>21852.187052</td>\n",
       "      <td>21566.265711</td>\n",
       "      <td>17871.044437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-24</th>\n",
       "      <td>0</td>\n",
       "      <td>17420.0</td>\n",
       "      <td>22755.20</td>\n",
       "      <td>18084.973453</td>\n",
       "      <td>20794.186776</td>\n",
       "      <td>20548.954160</td>\n",
       "      <td>19143.537103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-25</th>\n",
       "      <td>0</td>\n",
       "      <td>13558.0</td>\n",
       "      <td>13172.93</td>\n",
       "      <td>12739.066714</td>\n",
       "      <td>14475.237140</td>\n",
       "      <td>14713.923592</td>\n",
       "      <td>13445.105480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-26</th>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-685.019580</td>\n",
       "      <td>-12.321919</td>\n",
       "      <td>1099.681531</td>\n",
       "      <td>-48.929259</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           split   y_true  feef8e885b65983a6b2a2afa81790f63  \\\n",
       "2015-07-22     0  18046.0                          21096.67   \n",
       "2015-07-23     0  19532.0                          20989.04   \n",
       "2015-07-24     0  17420.0                          22755.20   \n",
       "2015-07-25     0  13558.0                          13172.93   \n",
       "2015-07-26     0      0.0                              0.00   \n",
       "\n",
       "            f15ffb80abe21be5aa49ca2f4bef1428  \\\n",
       "2015-07-22                      20676.741135   \n",
       "2015-07-23                      18015.494274   \n",
       "2015-07-24                      18084.973453   \n",
       "2015-07-25                      12739.066714   \n",
       "2015-07-26                       -685.019580   \n",
       "\n",
       "            443c753f6a53b3387028bafe49a286eb  \\\n",
       "2015-07-22                      24428.095232   \n",
       "2015-07-23                      21852.187052   \n",
       "2015-07-24                      20794.186776   \n",
       "2015-07-25                      14475.237140   \n",
       "2015-07-26                        -12.321919   \n",
       "\n",
       "            6f632e060699dce1d328412e4b83c850  12436b23a2222e4b1df46a139cef649f  \n",
       "2015-07-22                      24065.094696                      20108.466066  \n",
       "2015-07-23                      21566.265711                      17871.044437  \n",
       "2015-07-24                      20548.954160                      19143.537103  \n",
       "2015-07-25                      14713.923592                      13445.105480  \n",
       "2015-07-26                       1099.681531                        -48.929259  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.scorer_.cv_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get to the model definitions using hash in `results.scorer_.estimator_ids` dict"
   ]
  }
 ],
 "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": "3.7.6"
  }
 },
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 "nbformat_minor": 4
}