durbin-notebook-lgbm.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optiver Trading At The Close\n",
    "\n",
    "[Competition](https://www.kaggle.com/competitions/optiver-trading-at-the-close)\n",
    "\n",
    "[GitHub Repository](https://github.com/cvaisnor/DNN-Final-Project)\n",
    "\n",
    "[Google Slides Presentation](https://docs.google.com/presentation/d/1Xc5F1_NveFi1il3GqHej2aqVmklR_jZU4kx6ZJhiDEM/edit?usp=sharing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# General Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def load_data(kaggle_environment=False):\n",
    "    '''Loads and returns the data files.'''\n",
    "    if kaggle_environment:\n",
    "        data_path = '/kaggle/input/'\n",
    "    else:\n",
    "        data_path = 'kaggle/input/'\n",
    "\n",
    "    train = pd.read_csv(data_path + 'optiver-trading-at-the-close/train.csv')\n",
    "    test = pd.read_csv(data_path + 'optiver-trading-at-the-close/example_test_files/test.csv')\n",
    "    revealed_targets = pd.read_csv(data_path + 'optiver-trading-at-the-close/example_test_files/revealed_targets.csv')\n",
    "    sample_submission = pd.read_csv(data_path + 'optiver-trading-at-the-close/example_test_files/sample_submission.csv')\n",
    "\n",
    "    return train, test, revealed_targets, sample_submission\n",
    "\n",
    "def submit_dummy_predictions(env, predictions):\n",
    "    '''Submits a dummy prediction to the Optiver API. Use when you have an error during processing\n",
    "    of iter_test and you do not want to re-run the whole notebook to figure out if you fixed it.'''\n",
    "    predictions[\"target\"] = [0 for i in range(predictions.shape[0])]\n",
    "    env.predict(predictions)\n",
    "\n",
    "def plot_predictions(actuals, predicted):\n",
    "    '''Plots actual vs predicted values to get a sense of whether the predictions make sense.'''\n",
    "\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.plot(actuals, label='Actual')\n",
    "    plt.plot(predicted, label='Predicted')\n",
    "    plt.title('Actual vs Predicted Values')\n",
    "    plt.xlabel('Samples')\n",
    "    plt.ylabel('Values')\n",
    "    plt.legend()\n",
    "    plt.show()\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.base import BaseEstimator, TransformerMixin\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import IterativeImputer\n",
    "\n",
    "\n",
    "class LogFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for applying logarithmic transformation to specified columns.'''\n",
    "    def __init__(self, columns):\n",
    "        self.columns = columns\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        for column in self.columns:\n",
    "            X[f'{column}_log'] = np.log1p(X[column].clip(lower=0.00001))\n",
    "        return X\n",
    "\n",
    "class CombineFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer to combine features by multiplication.'''\n",
    "    def __init__(self, combined_features_list):\n",
    "        self.combined_features_list = combined_features_list\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        for combined_features in self.combined_features_list:\n",
    "            combined_feature_name = '_'.join(combined_features)\n",
    "            X[combined_feature_name] = X[combined_features].prod(axis=1)\n",
    "        return X\n",
    "\n",
    "class SubtractFeatures(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"Transformer to subtract one feature from another.\"\"\"\n",
    "\n",
    "    def __init__(self, base_feature, feature_to_subtract):\n",
    "        self.feature_to_subtract = feature_to_subtract\n",
    "        self.base_feature = base_feature\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        new_feature_name = f\"{self.base_feature}_minus_{self.feature_to_subtract}\"\n",
    "        X[new_feature_name] = X[self.base_feature] - X[self.feature_to_subtract]\n",
    "\n",
    "        return X\n",
    "\n",
    "class LagFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for creating lag features for specified columns and shift sizes.'''\n",
    "    def __init__(self, features, shift_sizes, default_value):\n",
    "        self.features = features\n",
    "        self.shift_sizes = shift_sizes\n",
    "        self.default_value = default_value\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        for feature in self.features:\n",
    "            for shift_size in self.shift_sizes:\n",
    "                lag_col = f'{feature}_lag_{shift_size}'\n",
    "                X[lag_col] = X.groupby(['stock_id', 'date_id'])[feature].shift(shift_size)\n",
    "                X[lag_col].fillna(self.default_value, inplace=True)\n",
    "\n",
    "        return X\n",
    "\n",
    "\n",
    "class RollingMeanFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for calculating rolling mean of each feature over each specified window size.'''\n",
    "    def __init__(self, features, window_sizes):\n",
    "        self.features = features\n",
    "        self.window_sizes = window_sizes\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        rolling_mean_columns = []\n",
    "\n",
    "        for feature in self.features:\n",
    "            for window_size in self.window_sizes:\n",
    "                rolling_mean_col = f'{feature}_rolling_mean{window_size}'\n",
    "                rolling_mean_columns.append(rolling_mean_col)\n",
    "\n",
    "                X[rolling_mean_col] = X.groupby(['stock_id', 'date_id'])[feature].rolling(window=window_size).mean().reset_index(level=[0,1], drop=True)\n",
    "\n",
    "        for col in rolling_mean_columns:\n",
    "            X[col].fillna(1.0, inplace=True)\n",
    "\n",
    "        return X\n",
    "\n",
    "\n",
    "class DiffFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for computing the difference of each feature column (i.e., first discrete difference).'''\n",
    "    def __init__(self, features):\n",
    "        self.features = features\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        for feature in self.features:\n",
    "            diff_col = f'{feature}_diff'\n",
    "\n",
    "            X[diff_col] = X.groupby(['stock_id', 'date_id'])[feature].diff()\n",
    "            X[diff_col].fillna(0.0, inplace=True)\n",
    "\n",
    "        return X\n",
    "\n",
    "\n",
    "class ExpandingMeanFeatures(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for calculating the expanding mean of each of the feature columns.'''\n",
    "    def __init__(self, features):\n",
    "        self.features = features\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        expanding_mean_columns = []\n",
    "\n",
    "        for feature in self.features:\n",
    "            expanding_mean_col = f'{feature}_expanding_mean'\n",
    "            expanding_mean_columns.append(expanding_mean_col)\n",
    "\n",
    "            X[expanding_mean_col] = X.groupby(['stock_id', 'date_id'])[feature].expanding().mean().reset_index(level=[0,1], drop=True)\n",
    "\n",
    "        for col in expanding_mean_columns:\n",
    "            X[col].fillna(1.0, inplace=True)\n",
    "\n",
    "        return X\n",
    "\n",
    "class DropColumns(BaseEstimator, TransformerMixin):\n",
    "    '''Drops columns from the data frame.'''\n",
    "    def __init__(self, columns):\n",
    "        self.columns = columns\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        all_columns = X.columns\n",
    "        drop_columns = []\n",
    "        for column in self.columns:\n",
    "            if column in all_columns:\n",
    "                drop_columns.append(column)\n",
    "        return X.drop(columns=drop_columns)\n",
    "\n",
    "class ForwardFillValues(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for forward filling NaN values in the DataFrame, grouped by stock_id.'''\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        for column in X.columns:\n",
    "            # X[column] = X.groupby(['stock_id', 'date_id'])[column].transform(lambda group: group.ffill())\n",
    "            X[column] = X.groupby('stock_id')[column].transform(lambda group: group.ffill())\n",
    "        return X\n",
    "\n",
    "\n",
    "class FillZero(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for replacing NaN values with zero in the DataFrame.'''\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        X = X.fillna(0.0)\n",
    "        return X\n",
    "\n",
    "class FillOne(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for replacing NaN values with one in the DataFrame.'''\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        X = X.fillna(1.0)\n",
    "        return X\n",
    "\n",
    "class DataFrameWrapper(BaseEstimator, TransformerMixin):\n",
    "    '''Wrapper for transforming output of a transformer to a DataFrame with optional specified columns.'''\n",
    "    def __init__(self, transformer, columns=None):\n",
    "        self.transformer = transformer\n",
    "        self.columns = columns\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        self.transformer.fit(X, y)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        result = self.transformer.transform(X)\n",
    "        if isinstance(result, pd.DataFrame):\n",
    "            return result\n",
    "        if self.columns is None:\n",
    "            columns = X.columns\n",
    "        else:\n",
    "            columns = []\n",
    "            for column in X.columns:\n",
    "                if column in self.columns:\n",
    "                    columns.append(column)\n",
    "        return pd.DataFrame(result, columns=columns)\n",
    "\n",
    "class PolynomialFeaturesWrapper(BaseEstimator, TransformerMixin):\n",
    "    '''Transformer for generating polynomial and interaction features with specified degree.'''\n",
    "    def __init__(self, degree=2):\n",
    "        self.degree = degree\n",
    "        self.poly = PolynomialFeatures(degree=self.degree, include_bias=False)\n",
    "        self.new_feature_names = None\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        self.poly.fit(X)\n",
    "        self.new_feature_names = self.poly.get_feature_names_out(X.columns)\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        transformed_array = self.poly.transform(X)\n",
    "        return pd.DataFrame(transformed_array, columns=self.new_feature_names)\n",
    "\n",
    "class MissingValueImputer(BaseEstimator, TransformerMixin):\n",
    "    '''Impute missing values in specified columns of a DataFrame using IterativeImputer.'''\n",
    "    def __init__(self, columns, max_iter=10, random_state=0):\n",
    "        self.columns = columns\n",
    "        self.max_iter = max_iter\n",
    "        self.random_state = random_state\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        columns_to_impute = [col for col in self.columns if col in X.columns]\n",
    "\n",
    "        # Select the columns for imputation\n",
    "        columns_data = X[columns_to_impute]\n",
    "\n",
    "        # Check if there are any missing values and if the columns are numeric\n",
    "        if columns_data.isna().any().any() and columns_data.select_dtypes(include='number').shape[1] == len(columns_to_impute):\n",
    "            imputer = IterativeImputer(max_iter=self.max_iter, random_state=self.random_state)\n",
    "\n",
    "            # Apply imputation\n",
    "            imputed_data = imputer.fit_transform(columns_data)\n",
    "            imputed_df = pd.DataFrame(imputed_data, columns=columns_to_impute, index=X.index)\n",
    "\n",
    "            # Update the original DataFrame\n",
    "            X.update(imputed_df)\n",
    "        return X\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import importlib\n",
    "\n",
    "# import helpers\n",
    "\n",
    "kaggle_environment = False # True if running on Kaggle, don't forget to add the dataset!\n",
    "\n",
    "# from helpers.common import load_data, plot_predictions, submit_dummy_predictions\n",
    "# from helpers.pipeline import LogFeatures, LagFeatures, RollingMeanFeatures, DiffFeatures, ExpandingMeanFeatures, \\\n",
    "# ForwardFillValues, FillZero, DataFrameWrapper, PolynomialFeaturesWrapper, MissingValueImputer, DropColumns, FillOne\n",
    "\n",
    "# if not kaggle_environment:\n",
    "#     # Reload the modules\n",
    "#     importlib.reload(helpers.common)\n",
    "#     importlib.reload(helpers.pipeline)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "original_train, test, revealed_targets, sample_submission = load_data(kaggle_environment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_ratio = 0.8  # 80% for training, 20% for testing\n",
    "split_idx = int(len(original_train) * split_ratio)\n",
    "\n",
    "train = original_train.iloc[:split_idx]\n",
    "test = original_train.iloc[split_idx:]\n",
    "\n",
    "train = train.dropna(subset=['target', 'imbalance_size'])\n",
    "test = test.dropna(subset=['target', 'imbalance_size'])\n",
    "\n",
    "# train = train.dropna()\n",
    "# test = test.dropna()\n",
    "\n",
    "y = train['target'].values\n",
    "y_test = test['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4190274, 17)\n",
      "(1047486, 17)\n"
     ]
    }
   ],
   "source": [
    "print(train.shape)\n",
    "print(test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237975</th>\n",
       "      <td>195</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>2440722.89</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000317</td>\n",
       "      <td>28280361.74</td>\n",
       "      <td>0.999734</td>\n",
       "      <td>0.999734</td>\n",
       "      <td>1.000317</td>\n",
       "      <td>32257.04</td>\n",
       "      <td>1.000434</td>\n",
       "      <td>319862.40</td>\n",
       "      <td>1.000328</td>\n",
       "      <td>2.310276</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237976</th>\n",
       "      <td>196</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>349510.47</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.000643</td>\n",
       "      <td>9187699.11</td>\n",
       "      <td>1.000129</td>\n",
       "      <td>1.000386</td>\n",
       "      <td>1.000643</td>\n",
       "      <td>205108.40</td>\n",
       "      <td>1.000900</td>\n",
       "      <td>93393.07</td>\n",
       "      <td>1.000819</td>\n",
       "      <td>-8.220077</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237977</th>\n",
       "      <td>197</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>12725436.10</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>0.995789</td>\n",
       "      <td>16790.66</td>\n",
       "      <td>0.995883</td>\n",
       "      <td>180038.32</td>\n",
       "      <td>0.995797</td>\n",
       "      <td>1.169443</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237978</th>\n",
       "      <td>198</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>1000898.84</td>\n",
       "      <td>1</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>94773271.05</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>0.998970</td>\n",
       "      <td>125631.72</td>\n",
       "      <td>0.999210</td>\n",
       "      <td>669893.00</td>\n",
       "      <td>0.999008</td>\n",
       "      <td>-1.540184</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5237979</th>\n",
       "      <td>199</td>\n",
       "      <td>480</td>\n",
       "      <td>540</td>\n",
       "      <td>1884285.71</td>\n",
       "      <td>-1</td>\n",
       "      <td>1.002129</td>\n",
       "      <td>24073677.32</td>\n",
       "      <td>1.000859</td>\n",
       "      <td>1.001494</td>\n",
       "      <td>1.002129</td>\n",
       "      <td>250081.44</td>\n",
       "      <td>1.002447</td>\n",
       "      <td>300167.56</td>\n",
       "      <td>1.002274</td>\n",
       "      <td>-6.530285</td>\n",
       "      <td>26454</td>\n",
       "      <td>480_540_199</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1047486 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         stock_id  date_id  seconds_in_bucket  imbalance_size  \\\n",
       "4190384         4      385                420      9375764.34   \n",
       "4190385         5      385                420       297718.61   \n",
       "4190386         6      385                420       740319.79   \n",
       "4190387         7      385                420            0.00   \n",
       "4190388         8      385                420      1607504.50   \n",
       "...           ...      ...                ...             ...   \n",
       "5237975       195      480                540      2440722.89   \n",
       "5237976       196      480                540       349510.47   \n",
       "5237977       197      480                540            0.00   \n",
       "5237978       198      480                540      1000898.84   \n",
       "5237979       199      480                540      1884285.71   \n",
       "\n",
       "         imbalance_buy_sell_flag  reference_price  matched_size  far_price  \\\n",
       "4190384                        1         1.001523   39592192.74   1.002241   \n",
       "4190385                        1         1.006371    6720703.44   1.017943   \n",
       "4190386                       -1         0.999097   13409310.36   0.980418   \n",
       "4190387                        0         1.001721   80638467.42   1.001721   \n",
       "4190388                       -1         1.000868   54598272.07   0.989186   \n",
       "...                          ...              ...           ...        ...   \n",
       "5237975                       -1         1.000317   28280361.74   0.999734   \n",
       "5237976                       -1         1.000643    9187699.11   1.000129   \n",
       "5237977                        0         0.995789   12725436.10   0.995789   \n",
       "5237978                        1         0.999210   94773271.05   0.999210   \n",
       "5237979                       -1         1.002129   24073677.32   1.000859   \n",
       "\n",
       "         near_price  bid_price   bid_size  ask_price   ask_size       wap  \\\n",
       "4190384    1.002020   1.001413    5801.28   1.001634     181.33  1.001627   \n",
       "4190385    1.007021   1.006111   26154.44   1.006371   79412.40  1.006175   \n",
       "4190386    0.998220   0.998964  223708.10   0.999628    8653.29  0.999603   \n",
       "4190387    1.001721   1.001296   22252.93   1.001799    4142.16  1.001720   \n",
       "4190388    1.000294   1.000755  269769.55   1.001571  143664.26  1.001288   \n",
       "...             ...        ...        ...        ...        ...       ...   \n",
       "5237975    0.999734   1.000317   32257.04   1.000434  319862.40  1.000328   \n",
       "5237976    1.000386   1.000643  205108.40   1.000900   93393.07  1.000819   \n",
       "5237977    0.995789   0.995789   16790.66   0.995883  180038.32  0.995797   \n",
       "5237978    0.999210   0.998970  125631.72   0.999210  669893.00  0.999008   \n",
       "5237979    1.001494   1.002129  250081.44   1.002447  300167.56  1.002274   \n",
       "\n",
       "            target  time_id       row_id  \n",
       "4190384  10.470152    21217    385_420_4  \n",
       "4190385 -16.819836    21217    385_420_5  \n",
       "4190386  -2.580285    21217    385_420_6  \n",
       "4190387  -4.959702    21217    385_420_7  \n",
       "4190388  -2.650022    21217    385_420_8  \n",
       "...            ...      ...          ...  \n",
       "5237975   2.310276    26454  480_540_195  \n",
       "5237976  -8.220077    26454  480_540_196  \n",
       "5237977   1.169443    26454  480_540_197  \n",
       "5237978  -1.540184    26454  480_540_198  \n",
       "5237979  -6.530285    26454  480_540_199  \n",
       "\n",
       "[1047486 rows x 17 columns]"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature Engineering Section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 6.04 s, sys: 1.33 s, total: 7.37 s\n",
      "Wall time: 7.55 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.preprocessing import OneHotEncoder, StandardScaler\n",
    "\n",
    "# drop_columns=['target', 'bid_size', 'ask_size', 'imbalance_size', 'matched_size', 'time_id', 'row_id', 'currently_scored']\n",
    "drop_columns=['target', 'time_id', 'row_id', 'currently_scored']\n",
    "# drop_columns=['seconds_in_bucket', 'target', 'bid_size', 'ask_size', 'imbalance_size', 'matched_size', 'time_id', 'row_id', 'currently_scored']\n",
    "\n",
    "# Main Pipeline\n",
    "pipeline = Pipeline([\n",
    "    # ('logs', LogFeatures(['imbalance_size', 'matched_size', 'bid_size', 'ask_size'])),\n",
    "    ('combine_features', CombineFeatures([\n",
    "        ['imbalance_buy_sell_flag', 'reference_price', 'ask_price', 'bid_price'],\n",
    "        ['imbalance_buy_sell_flag', 'far_price', 'near_price', 'ask_price'],\n",
    "    ])),\n",
    "    ('subtract_features', SubtractFeatures('ask_price', 'bid_price')),\n",
    "    ('wap_lags', LagFeatures(['wap', 'imbalance_buy_sell_flag_reference_price_ask_price_bid_price', 'ask_price_minus_bid_price'], [1, 2], 1.0)),\n",
    "    ('matched_size_lags', LagFeatures(['matched_size'], [1, 2], 0.0)),\n",
    "    ('wap_rolling_means', RollingMeanFeatures(['wap'], [2])),\n",
    "    ('diff_features', DiffFeatures(['wap', 'matched_size', 'ask_price', 'reference_price'])),\n",
    "    # ('diff_features', DiffFeatures(['wap', 'imbalance_buy_sell_flag', 'matched_size', 'ask_price', 'reference_price'])),\n",
    "    ('diff_features_2', DiffFeatures(['wap_diff', 'matched_size_diff', 'ask_price_diff', 'reference_price_diff'])),\n",
    "    # ('diff_features_3', DiffFeatures(['wap_diff_diff', 'matched_size_diff_diff', 'ask_price_diff_diff', 'reference_price_diff_diff'])),\n",
    "    ('wap_expanding_mean', ExpandingMeanFeatures(['wap'])),\n",
    "    ('drop_columns', DropColumns(drop_columns)),\n",
    "#     ('missing_value_imputer', MissingValueImputer(['far_price', 'near_price'])),\n",
    "    # ('forward_fill', ForwardFillValues()),\n",
    "    # ('fill_zero', FillZero()),\n",
    "    # ('fill_one', FillOne()),\n",
    "    # ('preprocessor', DataFrameWrapper(preprocessor, columns_to_keep)),\n",
    "    ('drop_grouping_columns', DropColumns(['stock_id', 'date_id'])),\n",
    "    # ('scaler', DataFrameWrapper(StandardScaler())),\n",
    "    # ('poly', PolynomialFeaturesWrapper(2)),\n",
    "])\n",
    "\n",
    "import warnings\n",
    "\n",
    "with warnings.catch_warnings():\n",
    "    warnings.simplefilter(\"ignore\", category=pd.errors.SettingWithCopyWarning)\n",
    "    train_transformed = pipeline.fit_transform(train)\n",
    "    test_transformed = pipeline.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = train_transformed\n",
    "X_test = test_transformed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(X.columns)\n",
    "# len(X_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seconds_in_bucket', 'imbalance_size', 'imbalance_buy_sell_flag',\n",
       "       'reference_price', 'matched_size', 'far_price', 'near_price',\n",
       "       'bid_price', 'bid_size', 'ask_price', 'ask_size', 'wap',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price',\n",
       "       'imbalance_buy_sell_flag_far_price_near_price_ask_price',\n",
       "       'ask_price_minus_bid_price', 'wap_lag_1', 'wap_lag_2',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price_lag_1',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price_lag_2',\n",
       "       'ask_price_minus_bid_price_lag_1', 'ask_price_minus_bid_price_lag_2',\n",
       "       'matched_size_lag_1', 'matched_size_lag_2', 'wap_rolling_mean2',\n",
       "       'wap_diff', 'matched_size_diff', 'ask_price_diff',\n",
       "       'reference_price_diff', 'wap_diff_diff', 'matched_size_diff_diff',\n",
       "       'ask_price_diff_diff', 'reference_price_diff_diff',\n",
       "       'wap_expanding_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['seconds_in_bucket', 'imbalance_size', 'imbalance_buy_sell_flag',\n",
       "       'reference_price', 'matched_size', 'far_price', 'near_price',\n",
       "       'bid_price', 'bid_size', 'ask_price', 'ask_size', 'wap',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price',\n",
       "       'imbalance_buy_sell_flag_far_price_near_price_ask_price',\n",
       "       'ask_price_minus_bid_price', 'wap_lag_1', 'wap_lag_2',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price_lag_1',\n",
       "       'imbalance_buy_sell_flag_reference_price_ask_price_bid_price_lag_2',\n",
       "       'ask_price_minus_bid_price_lag_1', 'ask_price_minus_bid_price_lag_2',\n",
       "       'matched_size_lag_1', 'matched_size_lag_2', 'wap_rolling_mean2',\n",
       "       'wap_diff', 'matched_size_diff', 'ask_price_diff',\n",
       "       'reference_price_diff', 'wap_diff_diff', 'matched_size_diff_diff',\n",
       "       'ask_price_diff_diff', 'reference_price_diff_diff',\n",
       "       'wap_expanding_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "# X['wap_lag_1']\n",
    "# X[seconds_in_bucket]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All feature transformations should be combined into a generate_feature() function. This can then be applied to the test data will in the for loop for the Optiver API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generate_features(cumulative_test_df, current_test, pipeline):\n",
    "    transformed_df = pipeline.transform(cumulative_test_df)\n",
    "    \n",
    "    # Only return rows corresponding to the current test dataframe\n",
    "    return transformed_df.iloc[-len(current_test):]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Slicing\n",
    "\n",
    "Generally, don't change the cell below. It is used to slice the data into training and validation sets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_train = X\n",
    "y_train = y "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.040220 seconds.\n",
      "You can set `force_row_wise=true` to remove the overhead.\n",
      "And if memory is not enough, you can set `force_col_wise=true`.\n",
      "[LightGBM] [Info] Total Bins 7963\n",
      "[LightGBM] [Info] Number of data points in the train set: 4190274, number of used features: 33\n",
      "[LightGBM] [Info] Start training from score -0.069737\n",
      "CPU times: user 1min 23s, sys: 19.2 s, total: 1min 42s\n",
      "Wall time: 36.2 s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-12 {color: black;}#sk-container-id-12 pre{padding: 0;}#sk-container-id-12 div.sk-toggleable {background-color: white;}#sk-container-id-12 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-12 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-12 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-12 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-12 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-12 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-12 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-12 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-12 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-12 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-12 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-12 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-12 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-12 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-12 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-12 div.sk-item {position: relative;z-index: 1;}#sk-container-id-12 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-12 div.sk-item::before, #sk-container-id-12 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-12 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-12 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-12 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-12 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-12 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-12 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-12 div.sk-label-container {text-align: center;}#sk-container-id-12 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-12 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-12\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LGBMRegressor(n_jobs=-1, num_leaves=500, objective=&#x27;regression_l1&#x27;,\n",
       "              random_state=0, reg_alpha=0.2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" checked><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LGBMRegressor</label><div class=\"sk-toggleable__content\"><pre>LGBMRegressor(n_jobs=-1, num_leaves=500, objective=&#x27;regression_l1&#x27;,\n",
       "              random_state=0, reg_alpha=0.2)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LGBMRegressor(n_jobs=-1, num_leaves=500, objective='regression_l1',\n",
       "              random_state=0, reg_alpha=0.2)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "import lightgbm as lgb\n",
    "\n",
    "\n",
    "# lgbm = lgb.LGBMRegressor(n_jobs=-1, reg_lambda=0.2, random_state=0, num_leaves=650, boosting_type='gbdt', reg_alpha=0.2, objective='regression_l1')\n",
    "lgbm = lgb.LGBMRegressor(n_jobs=-1, n_estimators=100, num_leaves=500, random_state=0, objective='regression_l1', reg_alpha=0.2)\n",
    "lgbm.fit(X_train, y_train)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error on the test set: 5.9476\n",
      "Mean Absolute Error on the training set: 6.2164\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import mean_absolute_error\n",
    "\n",
    "predictions = lgbm.predict(X_test)\n",
    "mae = mean_absolute_error(y_test, predictions)\n",
    "print(f\"Mean Absolute Error on the test set: {mae:.4f}\")\n",
    "\n",
    "predictions2 = lgbm.predict(X_train)\n",
    "mae2 = mean_absolute_error(y_train, predictions2)\n",
    "print(f\"Mean Absolute Error on the training set: {mae2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(y_test, predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_predictions(y_train, predictions2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-29.537421010414334 33.67310690561213\n"
     ]
    }
   ],
   "source": [
    "print(predictions.min(), predictions.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importances = lgbm.feature_importances_\n",
    "\n",
    "feature_names = X_train.columns\n",
    "\n",
    "feature_importance_df = pd.DataFrame({\n",
    "    'Feature': feature_names,\n",
    "    'Importance': importances\n",
    "})\n",
    "\n",
    "sorted_features = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
    "\n",
    "top_50_features = sorted_features.head(50)\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.barh(top_50_features['Feature'], top_50_features['Importance'])\n",
    "plt.xlabel('Importance')\n",
    "plt.ylabel('Features')\n",
    "plt.title('Top 50 Feature Importances')\n",
    "plt.gca().invert_yaxis()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lgbm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating Predictions\n",
    "\n",
    "Only for Kaggle:\n",
    "When uploading notebook, make sure to add the Optiver data on the right side of the screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "if kaggle_environment:\n",
    "    import optiver2023\n",
    "    env = optiver2023.make_env()\n",
    "    iter_test = env.iter_test()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Uncomment this cell to try to get unstuck from having to restart and run the whole notebook again\n",
    "# submit_dummy_predictions(env, sample_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if kaggle_environment:\n",
    "#     # To count how many time the \"for loop\" runs.\n",
    "#     counter = 0\n",
    "\n",
    "#     # init 3 empty lists\n",
    "#     # test_ls, revealed_targets_ls, sample_prediction_ls = [], [], []\n",
    "#     cumulative_test_df = pd.DataFrame()\n",
    "\n",
    "#     for (test_in, revealed_targets, sample_prediction) in iter_test:\n",
    "#         # Append the dataframe that API return into the list.\n",
    "#         # test_ls.append(test_in.copy())\n",
    "#         # revealed_targets_ls.append(revealed_targets.copy())\n",
    "#         # sample_prediction_ls.append(sample_prediction.copy())\n",
    "\n",
    "#         recent_rows = cumulative_test_df.tail(2000)\n",
    "#         cumulative_test_df = pd.concat([recent_rows, test_in], axis=0, ignore_index=True)\n",
    "\n",
    "#         # Generate features\n",
    "#         test_transformed = generate_features(cumulative_test_df, test_in, pipeline)\n",
    "\n",
    "#         # Writes our predictions \n",
    "#         sample_prediction[\"target\"] = model.predict(test_transformed)\n",
    "        \n",
    "#         # This line submit our predictions.\n",
    "#         env.predict(sample_prediction)\n",
    "#         counter += 1\n",
    "\n",
    "#     print('\\n', '=' * 50, sep=\"\")\n",
    "#     print(f\"counter: {counter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "if kaggle_environment:\n",
    "    # To count how many time the \"for loop\" runs.\n",
    "    counter = 0\n",
    "\n",
    "    # init 3 empty lists\n",
    "    # test_ls, revealed_targets_ls, sample_prediction_ls = [], [], []\n",
    "    cumulative_test_df = pd.DataFrame()\n",
    "\n",
    "    for (test_in, revealed_targets, sample_prediction) in iter_test:\n",
    "        # Append the dataframe that API return into the list.\n",
    "        # test_ls.append(test_in.copy())\n",
    "        # revealed_targets_ls.append(revealed_targets.copy())\n",
    "        # sample_prediction_ls.append(sample_prediction.copy())\n",
    "\n",
    "        test_in['target'] = 0\n",
    "        current_date_id = test_in.iloc[0]['date_id']\n",
    "        cumulative_test_df = pd.concat([cumulative_test_df, test_in], axis=0, ignore_index=True)\n",
    "#         if len(cumulative_test_df.columns) > 0:\n",
    "        cumulative_test_df = cumulative_test_df[cumulative_test_df['date_id'] == current_date_id]\n",
    "#             cumulative_test_df = pd.concat([current_date_rows, test_in], axis=0, ignore_index=True)\n",
    "        print(f'{counter}: {cumulative_test_df.shape[0]}')\n",
    "\n",
    "        # Generate features\n",
    "        test_transformed = generate_features(cumulative_test_df, test_in, pipeline)\n",
    "\n",
    "        # Writes our predictions \n",
    "        sample_prediction[\"target\"] = model.predict(test_transformed)\n",
    "        \n",
    "        # This line submit our predictions.\n",
    "        env.predict(sample_prediction)\n",
    "        counter += 1\n",
    "\n",
    "    print('\\n', '=' * 50, sep=\"\")\n",
    "    print(f\"counter: {counter}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cumulative_test_df.to_csv('final_cumulative_2k_rows.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81.03471690678968"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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