5.3 - janestreet-dimensionality-reduction-evaluation.ipynb

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   "source": [
    "# Jane Street Market Prediction (#5.3)\n",
    "## Dimensionality Reduction Evaluation\n",
    "\n",
    "Notebook Navigation<br>\n",
    "[All](https://www.kaggle.com/wendellavila/janestreet-index/) | [#1](https://www.kaggle.com/wendellavila/janestreet-model-selection/) | [#2.1](https://www.kaggle.com/wendellavila/janestreet-preprocessing-selection) | [#2.2](https://www.kaggle.com/wendellavila/janestreet-data-preprocessing) | [#3](https://www.kaggle.com/wendellavila/janestreet-regularization-selection) | [#4](https://www.kaggle.com/wendellavila/janestreet-hyperparameter-tuning) | [#4.1](https://www.kaggle.com/wendellavila/janestreet-hyperparameter-evaluation) | [#5.1](https://www.kaggle.com/wendellavila/janestreet-pca) | [#5.2](https://www.kaggle.com/wendellavila/janestreet-autoencoder) | [#5.3](https://www.kaggle.com/wendellavila/janestreet-dimensionality-reduction-evaluation) |[#6](https://www.kaggle.com/wendellavila/janestreet-ensemble)"
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   },
   "source": [
    "## Imports"
   ]
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   "source": [
    "#import janestreet\n",
    "import os\n",
    "import glob\n",
    "import IPython\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "pd.set_option('display.max_columns', 150)\n",
    "%matplotlib inline\n",
    "\n",
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "from keras import layers\n",
    "from keras.models import load_model\n",
    "from keras.callbacks import Callback, ModelCheckpoint, EarlyStopping, ReduceLROnPlateau\n",
    "\n",
    "import sklearn\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis as LDA"
   ]
  },
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "## Visualization"
   ]
  },
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   "source": [
    "%config InlineBackend.figure_format = 'svg'\n",
    "def plot_metric(history, metric):\n",
    "    train_metrics = history.history[metric]\n",
    "    val_metrics = history.history['val_'+metric]\n",
    "    epochs = range(1, len(train_metrics) + 1)\n",
    "    plt.plot(epochs, train_metrics, 'b')\n",
    "    plt.plot(epochs, val_metrics, 'g')\n",
    "    if metric == 'AUC':\n",
    "        plt.axhline(y=0.5,ls=\":\",c=\"gray\")\n",
    "        plt.ylim(ymin=0.48)\n",
    "    elif metric == 'MSE':\n",
    "        plt.axhline(y=0.0,ls=\":\",c=\"gray\")\n",
    "        plt.ylim(ymin=-0.05, ymax=0.25)\n",
    "    plt.title('Training and validation '+ metric)\n",
    "    plt.xlabel(\"Epochs\")\n",
    "    plt.ylabel(metric)\n",
    "    plt.legend([\"train_\"+metric, 'val_'+metric])\n",
    "    plt.show()"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Misc"
   ]
  },
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   "source": [
    "def set_seed(seed : int):    \n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    os.environ['PYTHONHASHSEED'] = str(seed)\n",
    "    tf.random.set_seed(seed)"
   ]
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   "source": [
    "# weighted average as per Donate et al.'s formula\n",
    "# https://doi.org/10.1016/j.neucom.2012.02.053\n",
    "def weighted_average(a):\n",
    "    w = []\n",
    "    n = len(a)\n",
    "    for i in range(1, n + 1):\n",
    "        i = 2 if i == 1 else i\n",
    "        w.append(1 / (2**(n + 1 - i)))\n",
    "    return np.average(a, weights = w)"
   ]
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   "source": [
    "class ClearTrainingOutput(tf.keras.callbacks.Callback):\n",
    "      def on_train_end(*args, **kwargs):\n",
    "        IPython.display.clear_output(wait = True)"
   ]
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   "source": [
    "class Hyperparameters:\n",
    "  def __init__(self, topology, dropout_rates, learning_rate, activation, num_input,\n",
    "               num_batch, num_epochs, label_smoothing, use_batchnorm, use_encoder):\n",
    "    self.topology = topology\n",
    "    self.dropout_rates = dropout_rates\n",
    "    self.learning_rate = learning_rate\n",
    "    self.activation = activation\n",
    "    self.num_input = num_input\n",
    "    self.num_batch = num_batch\n",
    "    self.num_epochs = num_epochs\n",
    "    self.label_smoothing = label_smoothing\n",
    "    self.use_batchnorm = use_batchnorm\n",
    "    self.use_encoder = use_encoder"
   ]
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    "tags": []
   },
   "source": [
    "## Utility Score"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "def utility_score(d, w, r, a):\n",
    "    Pi = np.bincount(d, w * r * a)\n",
    "    t = np.sum(Pi) / np.sqrt(np.sum(Pi ** 2)) * np.sqrt(250 / len(Pi))\n",
    "    u = min(max(t, 0), 6) * np.sum(Pi)\n",
    "    return u"
   ]
  },
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   "cell_type": "markdown",
   "id": "33f08fdf",
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    "tags": []
   },
   "source": [
    "## CV - Purged Group Time Series Split"
   ]
  },
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   "source": [
    "import numpy as np\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection._split import _BaseKFold, indexable, _num_samples\n",
    "from sklearn.utils.validation import _deprecate_positional_args\n",
    "\n",
    "# modified code for group gaps; source\n",
    "# https://github.com/getgaurav2/scikit-learn/blob/d4a3af5cc9da3a76f0266932644b884c99724c57/sklearn/model_selection/_split.py#L2243\n",
    "class PurgedGroupTimeSeriesSplit(_BaseKFold):\n",
    "    \"\"\"Time Series cross-validator variant with non-overlapping groups.\n",
    "    Allows for a gap in groups to avoid potentially leaking info from\n",
    "    train into test if the model has windowed or lag features.\n",
    "    Provides train/test indices to split time series data samples\n",
    "    that are observed at fixed time intervals according to a\n",
    "    third-party provided group.\n",
    "    In each split, test indices must be higher than before, and thus shuffling\n",
    "    in cross validator is inappropriate.\n",
    "    This cross-validation object is a variation of :class:`KFold`.\n",
    "    In the kth split, it returns first k folds as train set and the\n",
    "    (k+1)th fold as test set.\n",
    "    The same group will not appear in two different folds (the number of\n",
    "    distinct groups has to be at least equal to the number of folds).\n",
    "    Note that unlike standard cross-validation methods, successive\n",
    "    training sets are supersets of those that come before them.\n",
    "    Read more in the :ref:`User Guide <cross_validation>`.\n",
    "    Parameters\n",
    "    ----------\n",
    "    n_splits : int, default=5\n",
    "        Number of splits. Must be at least 2.\n",
    "    max_train_group_size : int, default=Inf\n",
    "        Maximum group size for a single training set.\n",
    "    group_gap : int, default=None\n",
    "        Gap between train and test\n",
    "    max_test_group_size : int, default=Inf\n",
    "        We discard this number of groups from the end of each train split\n",
    "    \"\"\"\n",
    "\n",
    "    @_deprecate_positional_args\n",
    "    def __init__(self,\n",
    "                 n_splits=5,\n",
    "                 *,\n",
    "                 max_train_group_size=np.inf,\n",
    "                 max_test_group_size=np.inf,\n",
    "                 group_gap=None,\n",
    "                 verbose=False\n",
    "                 ):\n",
    "        super().__init__(n_splits, shuffle=False, random_state=None)\n",
    "        self.max_train_group_size = max_train_group_size\n",
    "        self.group_gap = group_gap\n",
    "        self.max_test_group_size = max_test_group_size\n",
    "        self.verbose = verbose\n",
    "\n",
    "    def split(self, X, y=None, groups=None):\n",
    "        \"\"\"Generate indices to split data into training and test set.\n",
    "        Parameters\n",
    "        ----------\n",
    "        X : array-like of shape (n_samples, n_features)\n",
    "            Training data, where n_samples is the number of samples\n",
    "            and n_features is the number of features.\n",
    "        y : array-like of shape (n_samples,)\n",
    "            Always ignored, exists for compatibility.\n",
    "        groups : array-like of shape (n_samples,)\n",
    "            Group labels for the samples used while splitting the dataset into\n",
    "            train/test set.\n",
    "        Yields\n",
    "        ------\n",
    "        train : ndarray\n",
    "            The training set indices for that split.\n",
    "        test : ndarray\n",
    "            The testing set indices for that split.\n",
    "        \"\"\"\n",
    "        if groups is None:\n",
    "            raise ValueError(\n",
    "                \"The 'groups' parameter should not be None\")\n",
    "        X, y, groups = indexable(X, y, groups)\n",
    "        n_samples = _num_samples(X)\n",
    "        n_splits = self.n_splits\n",
    "        group_gap = self.group_gap\n",
    "        max_test_group_size = self.max_test_group_size\n",
    "        max_train_group_size = self.max_train_group_size\n",
    "        n_folds = n_splits + 1\n",
    "        group_dict = {}\n",
    "        u, ind = np.unique(groups, return_index=True)\n",
    "        unique_groups = u[np.argsort(ind)]\n",
    "        n_samples = _num_samples(X)\n",
    "        n_groups = _num_samples(unique_groups)\n",
    "        for idx in np.arange(n_samples):\n",
    "            if (groups[idx] in group_dict):\n",
    "                group_dict[groups[idx]].append(idx)\n",
    "            else:\n",
    "                group_dict[groups[idx]] = [idx]\n",
    "        if n_folds > n_groups:\n",
    "            raise ValueError(\n",
    "                (\"Cannot have number of folds={0} greater than\"\n",
    "                 \" the number of groups={1}\").format(n_folds,\n",
    "                                                     n_groups))\n",
    "\n",
    "        group_test_size = min(n_groups // n_folds, max_test_group_size)\n",
    "        group_test_starts = range(n_groups - n_splits * group_test_size,\n",
    "                                  n_groups, group_test_size)\n",
    "        for group_test_start in group_test_starts:\n",
    "            train_array = []\n",
    "            test_array = []\n",
    "\n",
    "            group_st = max(0, group_test_start - group_gap - max_train_group_size)\n",
    "            for train_group_idx in unique_groups[group_st:(group_test_start - group_gap)]:\n",
    "                train_array_tmp = group_dict[train_group_idx]\n",
    "                \n",
    "                train_array = np.sort(np.unique(\n",
    "                                      np.concatenate((train_array,\n",
    "                                                      train_array_tmp)),\n",
    "                                      axis=None), axis=None)\n",
    "\n",
    "            train_end = train_array.size\n",
    " \n",
    "            for test_group_idx in unique_groups[group_test_start:\n",
    "                                                group_test_start +\n",
    "                                                group_test_size]:\n",
    "                test_array_tmp = group_dict[test_group_idx]\n",
    "                test_array = np.sort(np.unique(\n",
    "                                              np.concatenate((test_array,\n",
    "                                                              test_array_tmp)),\n",
    "                                     axis=None), axis=None)\n",
    "\n",
    "            test_array  = test_array[group_gap:]\n",
    "            \n",
    "            \n",
    "            if self.verbose > 0:\n",
    "                    pass\n",
    "                    \n",
    "            yield [int(i) for i in train_array], [int(i) for i in test_array]"
   ]
  },
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    "tags": []
   },
   "source": [
    "## Building and Training Models"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6af4a3a9",
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    "tags": []
   },
   "source": [
    "### Defining model"
   ]
  },
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   "source": [
    "def create_model(topology, dropout_rates, num_input, num_output, lrate, activation,\n",
    "                 label_smoothing, use_batchnorm, use_encoder):\n",
    "    \n",
    "    inp = layers.Input(shape = (num_input,), name='X_train')\n",
    "    \n",
    "    x = inp\n",
    "    if use_encoder == True:\n",
    "        autoencoder, encoder = create_autoencoder(num_input,num_output,noise=0.05)\n",
    "        encoder.load_weights('../input/janestreet-autoencoder/encoder.h5')\n",
    "        x = encoder(x)\n",
    "    if use_batchnorm == True:\n",
    "        x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(dropout_rates[0])(x)\n",
    "        \n",
    "    for i in range(len(topology)): \n",
    "        x = layers.Dense(topology[i])(x)\n",
    "        if use_batchnorm == True:\n",
    "            x = layers.BatchNormalization()(x)\n",
    "        x = layers.Activation(activation)(x)\n",
    "        x = layers.Dropout(dropout_rates[i+1])(x)\n",
    "    \n",
    "    x = layers.Dense(num_output)(x)\n",
    "    out = layers.Activation('sigmoid')(x)\n",
    "    \n",
    "    model = tf.keras.models.Model(inputs=inp, outputs=out)\n",
    "    \n",
    "    opt = tf.keras.optimizers.Adam(learning_rate = lrate)\n",
    "    model.compile(optimizer = opt,\n",
    "                  loss = tf.keras.losses.BinaryCrossentropy(label_smoothing = label_smoothing),\n",
    "                  metrics = tf.keras.metrics.AUC(name='AUC'))\n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cdb0ce5",
   "metadata": {
    "papermill": {
     "duration": 0.00741,
     "end_time": "2022-07-19T05:23:58.212802",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.205392",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining Autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "aaf6287b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.229441Z",
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     "shell.execute_reply": "2022-07-19T05:23:58.236188Z"
    },
    "papermill": {
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     "end_time": "2022-07-19T05:23:58.238992",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.220338",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_autoencoder(num_input,num_output,noise=0.05):\n",
    "    i = layers.Input(num_input)\n",
    "    encoded = layers.BatchNormalization()(i)\n",
    "    encoded = layers.GaussianNoise(noise)(encoded)\n",
    "    encoded = layers.Dense(64,activation='relu')(encoded)\n",
    "    decoded = layers.Dropout(0.2)(encoded)\n",
    "    decoded = layers.Dense(num_input,name='decoded')(decoded)\n",
    "    x = layers.Dense(32,activation='relu')(decoded)\n",
    "    x = layers.BatchNormalization()(x)\n",
    "    x = layers.Dropout(0.2)(x)\n",
    "    x = layers.Dense(num_output,activation='sigmoid',name='label_output')(x)\n",
    "    \n",
    "    encoder = tf.keras.models.Model(inputs=i,outputs=decoded)\n",
    "    autoencoder = tf.keras.models.Model(inputs=i,outputs=[decoded,x])\n",
    "\n",
    "    autoencoder.compile(optimizer=tf.keras.optimizers.Adam(0.001),\n",
    "                        loss={'decoded':'mse','label_output':'binary_crossentropy'})\n",
    "    return autoencoder, encoder"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d075e49",
   "metadata": {
    "papermill": {
     "duration": 0.007468,
     "end_time": "2022-07-19T05:23:58.254514",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.247046",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "964837f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.271060Z",
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    },
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     "exception": false,
     "start_time": "2022-07-19T05:23:58.262005",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def train_model(name, hp, n_folds):\n",
    "    \n",
    "    print(f'{name}: Training...')\n",
    "    cv = PurgedGroupTimeSeriesSplit(\n",
    "        n_splits=n_folds,\n",
    "        max_train_group_size=150,\n",
    "        group_gap=20,\n",
    "        max_test_group_size=60\n",
    "    )\n",
    "    \n",
    "    for fold, (train_index, test_index) in enumerate(cv.split(train_data, groups=train_data['date'].values)):\n",
    "        \n",
    "        print(f'{name} - Fold {fold+1} of {n_folds}')\n",
    "        train_set = train_data.iloc[train_index]\n",
    "        test_set = train_data.iloc[test_index]\n",
    "        \n",
    "        X_train = train_set.loc[:, features].values\n",
    "        X_test = test_set.loc[:, features].values\n",
    "        \n",
    "        y_train = np.stack([(train_set[col] > 0).astype('int') for col in resp_cols]).T #Multitarget\n",
    "        y_test = np.stack([(test_set[col] > 0).astype('int') for col in resp_cols]).T #Multitarget\n",
    "        model = create_model(hp.topology, hp.dropout_rates, hp.num_input, len(resp_cols), hp.learning_rate,\n",
    "                             hp.activation, hp.label_smoothing, hp.use_batchnorm, hp.use_encoder)\n",
    "        metric = 'AUC'\n",
    "        metric_mode = 'max'\n",
    "        \n",
    "        early_stopping = EarlyStopping(monitor=f'val_{metric}',patience=12,min_delta=0.0001, mode=metric_mode,\n",
    "                                   restore_best_weights=True)\n",
    "        #rlr = ReduceLROnPlateau(monitor=f'val_{metric}', factor=0.1, patience=5, verbose=0, min_delta = 0.0001, mode=metric_mode)\n",
    "        \n",
    "        callbacks = [early_stopping, [ClearTrainingOutput()]]\n",
    "\n",
    "        history = model.fit(X_train, y_train,\n",
    "                            batch_size = hp.num_batch,\n",
    "                            epochs = hp.num_epochs,\n",
    "                            validation_data = (X_test, y_test),\n",
    "                            callbacks=callbacks,\n",
    "                            verbose=2)\n",
    "        \n",
    "        plot_metric(history, metric)\n",
    "        model.save(f'./{name}-fold{str(fold+1)}-seed{str(SEED)}.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62e1b4cb",
   "metadata": {
    "papermill": {
     "duration": 0.007288,
     "end_time": "2022-07-19T05:23:58.298902",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.291614",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Defining Utility Score Validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "260c17d6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.315581Z",
     "iopub.status.busy": "2022-07-19T05:23:58.314905Z",
     "iopub.status.idle": "2022-07-19T05:23:58.323949Z",
     "shell.execute_reply": "2022-07-19T05:23:58.323102Z"
    },
    "papermill": {
     "duration": 0.019341,
     "end_time": "2022-07-19T05:23:58.325791",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.306450",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def validate_model(name, n_folds, n_seeds):\n",
    "    print(f'{name}: Validating...')\n",
    "    \n",
    "    #validation data\n",
    "    X_val = val_data.loc[:, features].values\n",
    "    y_val = val_data.loc[:, 'action'].values \n",
    "    \n",
    "    scores = []\n",
    "    \n",
    "    for SEED in range(n_seeds):\n",
    "        set_seed(SEED)\n",
    "        #loading pretrained models\n",
    "        models = []\n",
    "        for i in range(n_folds):\n",
    "            models.append(tf.keras.models.load_model(f'./{name}-fold{str(i+1)}-seed{str(SEED)}.h5'))\n",
    "        print(f'SEED {SEED} - All models loaded.')\n",
    "    \n",
    "        curr_val_preds = []\n",
    "        for model in models:\n",
    "            curr_val_pred = model.predict(X_val)\n",
    "            curr_val_pred = np.array([np.mean(preds) for preds in curr_val_pred])\n",
    "            curr_val_preds.append(curr_val_pred)\n",
    "        curr_val_preds_avg = np.asarray([weighted_average(idx) for idx in zip(*curr_val_preds)])\n",
    "        curr_val_preds_avg = ((curr_val_preds_avg > 0.501)*1)\n",
    "            \n",
    "        curr_uscore = utility_score(val_data['date'].values, val_data['weight'].values,\n",
    "                                        val_data['resp'].values, curr_val_preds_avg)\n",
    "        print(f'SEED {SEED} {name} {n_folds}-fold Utility Score: {curr_uscore}\\n')\n",
    "        scores.append(curr_uscore)\n",
    "        \n",
    "    print(f'Average {n_folds}-fold Utility Score: {np.mean(scores)}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc5833e5",
   "metadata": {
    "papermill": {
     "duration": 0.007303,
     "end_time": "2022-07-19T05:23:58.340532",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.333229",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "### Training and validating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "07ffd68b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.357137Z",
     "iopub.status.busy": "2022-07-19T05:23:58.356335Z",
     "iopub.status.idle": "2022-07-19T05:23:58.361139Z",
     "shell.execute_reply": "2022-07-19T05:23:58.360323Z"
    },
    "papermill": {
     "duration": 0.014907,
     "end_time": "2022-07-19T05:23:58.363013",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.348106",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "#defining hyperparameters\n",
    "n_seeds = 5\n",
    "n_folds = 5\n",
    "num_batch = 4096\n",
    "num_epochs = 200\n",
    "num_input = 130#len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b87c4287",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.379502Z",
     "iopub.status.busy": "2022-07-19T05:23:58.379253Z",
     "iopub.status.idle": "2022-07-19T05:23:58.384494Z",
     "shell.execute_reply": "2022-07-19T05:23:58.383647Z"
    },
    "papermill": {
     "duration": 0.015856,
     "end_time": "2022-07-19T05:23:58.386329",
     "exception": false,
     "start_time": "2022-07-19T05:23:58.370473",
     "status": "completed"
    },
    "tags": []
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   "outputs": [],
   "source": [
    "topology = [208, 48, 112, 848, 624]\n",
    "dropout_rates = [0.15543385946124283, 0.4769936942603337, 0.2004355862256995, 0.4212563172737503, 0.46071906728999595, 0.43182424636157835]\n",
    "learning_rate = 0.001\n",
    "activation = 'swish'\n",
    "\n",
    "label_smoothing = 0.47213403813669336\n",
    "use_batchnorm = True\n",
    "use_encoder = False\n",
    "model1_hyperparameters = Hyperparameters(topology, dropout_rates, learning_rate,\n",
    "                                  activation, num_input, num_batch, num_epochs,\n",
    "                                  label_smoothing, use_batchnorm, use_encoder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a482c55",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-07-19T05:23:58.402450Z",
     "iopub.status.busy": "2022-07-19T05:23:58.402206Z",
     "iopub.status.idle": "2022-07-19T05:24:09.290107Z",
     "shell.execute_reply": "2022-07-19T05:24:09.289155Z"
    },
    "papermill": {
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     "exception": false,
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     "status": "completed"
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    "tags": []
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   "outputs": [],
   "source": [
    "train_data = pd.read_pickle('../input/janestreet-data-preprocessing/train-mean-indicator.pkl')\n",
    "val_data = pd.read_pickle('../input/janestreet-data-preprocessing/val-mean-indicator.pkl')\n",
    "features = [col for col in train_data.columns if 'feature' in col]\n",
    "resp_cols = [col for col in train_data.columns if 'resp' in col]\n",
    "model1_hyperparameters.num_input = len(features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "04a7e7c9",
   "metadata": {
    "collapsed": true,
    "execution": {
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     "iopub.status.busy": "2022-07-19T05:24:09.309075Z",
     "iopub.status.idle": "2022-07-19T06:26:15.272444Z",
     "shell.execute_reply": "2022-07-19T06:26:15.271422Z"
    },
    "jupyter": {
     "outputs_hidden": true
    },
    "papermill": {
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     "end_time": "2022-07-19T06:26:15.274891",
     "exception": false,
     "start_time": "2022-07-19T05:24:09.300594",
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   "source": [
    "#training model\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('None', model1_hyperparameters, n_folds)"
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   "execution_count": 17,
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     "text": [
      "None: Validating...\n",
      "SEED 0 - All models loaded.\n",
      "SEED 0 None 5-fold Utility Score: 904.0290768154304\n",
      "\n",
      "SEED 1 - All models loaded.\n",
      "SEED 1 None 5-fold Utility Score: 881.2051694184357\n",
      "\n",
      "SEED 2 - All models loaded.\n",
      "SEED 2 None 5-fold Utility Score: 909.7298711185997\n",
      "\n",
      "SEED 3 - All models loaded.\n",
      "SEED 3 None 5-fold Utility Score: 810.1396118476918\n",
      "\n",
      "SEED 4 - All models loaded.\n",
      "SEED 4 None 5-fold Utility Score: 818.1536109235226\n",
      "\n",
      "Average 5-fold Utility Score: 864.6514680247361\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_model('None', n_folds, n_seeds)"
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   "source": [
    "#training model\n",
    "model1_hyperparameters.use_encoder = True\n",
    "model1_hyperparameters.use_batchnorm = True\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('Autoencoder', model1_hyperparameters, n_folds)"
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  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "db2a896a",
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     "shell.execute_reply": "2022-07-19T07:41:01.610705Z"
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     "end_time": "2022-07-19T07:41:01.614618",
     "exception": false,
     "start_time": "2022-07-19T07:13:04.433095",
     "status": "completed"
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   "source": [
    "#training model\n",
    "model1_hyperparameters.use_encoder = True\n",
    "model1_hyperparameters.use_batchnorm = False\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('Autoencoder_no_batchnorm', model1_hyperparameters, n_folds)"
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     "name": "stdout",
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     "text": [
      "Autoencoder: Validating...\n",
      "SEED 0 - All models loaded.\n",
      "SEED 0 Autoencoder 5-fold Utility Score: 801.688321155876\n",
      "\n",
      "SEED 1 - All models loaded.\n",
      "SEED 1 Autoencoder 5-fold Utility Score: 938.1829261264818\n",
      "\n",
      "SEED 2 - All models loaded.\n",
      "SEED 2 Autoencoder 5-fold Utility Score: 922.1782503426181\n",
      "\n",
      "SEED 3 - All models loaded.\n",
      "SEED 3 Autoencoder 5-fold Utility Score: 958.5091982737832\n",
      "\n",
      "SEED 4 - All models loaded.\n",
      "SEED 4 Autoencoder 5-fold Utility Score: 761.8918051946173\n",
      "\n",
      "Average 5-fold Utility Score: 876.4901002186753\n",
      "\n"
     ]
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   "source": [
    "validate_model('Autoencoder', n_folds, n_seeds)"
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   "outputs": [
    {
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     "output_type": "stream",
     "text": [
      "Autoencoder_no_batchnorm: Validating...\n",
      "SEED 0 - All models loaded.\n",
      "SEED 0 Autoencoder_no_batchnorm 5-fold Utility Score: 881.9961479629467\n",
      "\n",
      "SEED 1 - All models loaded.\n",
      "SEED 1 Autoencoder_no_batchnorm 5-fold Utility Score: 728.9051001990846\n",
      "\n",
      "SEED 2 - All models loaded.\n",
      "SEED 2 Autoencoder_no_batchnorm 5-fold Utility Score: 937.2173152925988\n",
      "\n",
      "SEED 3 - All models loaded.\n",
      "SEED 3 Autoencoder_no_batchnorm 5-fold Utility Score: 616.0589280022359\n",
      "\n",
      "SEED 4 - All models loaded.\n",
      "SEED 4 Autoencoder_no_batchnorm 5-fold Utility Score: 759.7573555001203\n",
      "\n",
      "Average 5-fold Utility Score: 784.7869693913973\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_model('Autoencoder_no_batchnorm', n_folds, n_seeds)"
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  {
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   "execution_count": 22,
   "id": "be9293a4",
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    "train_data = pd.read_pickle('../input/janestreet-pca/train-pca.pkl')\n",
    "val_data = pd.read_pickle('../input/janestreet-pca/val-pca.pkl')\n",
    "features = [col for col in train_data.columns if 'feature' in col]\n",
    "resp_cols = [col for col in train_data.columns if 'resp' in col]\n",
    "model1_hyperparameters.num_input = len(features)"
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   "source": [
    "#training model\n",
    "model1_hyperparameters.use_encoder = False\n",
    "model1_hyperparameters.use_batchnorm = True\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('PCA', model1_hyperparameters, n_folds)"
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4e84f7dc",
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     "iopub.status.idle": "2022-07-19T09:17:55.911209Z",
     "shell.execute_reply": "2022-07-19T09:17:55.910216Z"
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     "end_time": "2022-07-19T09:17:55.914198",
     "exception": false,
     "start_time": "2022-07-19T08:49:25.442300",
     "status": "completed"
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    "#training model\n",
    "model1_hyperparameters.use_encoder = False\n",
    "model1_hyperparameters.use_batchnorm = False\n",
    "for SEED in range(n_seeds):\n",
    "    set_seed(SEED)\n",
    "    train_model('PCA_no_batchnorm', model1_hyperparameters, n_folds)"
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     "name": "stdout",
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     "text": [
      "PCA: Validating...\n",
      "SEED 0 - All models loaded.\n",
      "SEED 0 PCA 5-fold Utility Score: 844.4323962452003\n",
      "\n",
      "SEED 1 - All models loaded.\n",
      "SEED 1 PCA 5-fold Utility Score: 753.1823420606447\n",
      "\n",
      "SEED 2 - All models loaded.\n",
      "SEED 2 PCA 5-fold Utility Score: 769.4060889197967\n",
      "\n",
      "SEED 3 - All models loaded.\n",
      "SEED 3 PCA 5-fold Utility Score: 804.0592001437822\n",
      "\n",
      "SEED 4 - All models loaded.\n",
      "SEED 4 PCA 5-fold Utility Score: 790.0540729874717\n",
      "\n",
      "Average 5-fold Utility Score: 792.2268200713792\n",
      "\n"
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   "source": [
    "validate_model('PCA', n_folds, n_seeds)"
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     "text": [
      "PCA_no_batchnorm: Validating...\n",
      "SEED 0 - All models loaded.\n",
      "SEED 0 PCA_no_batchnorm 5-fold Utility Score: 854.0695083017567\n",
      "\n",
      "SEED 1 - All models loaded.\n",
      "SEED 1 PCA_no_batchnorm 5-fold Utility Score: 715.8710553255415\n",
      "\n",
      "SEED 2 - All models loaded.\n",
      "SEED 2 PCA_no_batchnorm 5-fold Utility Score: 780.8677145736277\n",
      "\n",
      "SEED 3 - All models loaded.\n",
      "SEED 3 PCA_no_batchnorm 5-fold Utility Score: 735.2373497353791\n",
      "\n",
      "SEED 4 - All models loaded.\n",
      "SEED 4 PCA_no_batchnorm 5-fold Utility Score: 785.0523699208568\n",
      "\n",
      "Average 5-fold Utility Score: 774.2195995714325\n",
      "\n"
     ]
    }
   ],
   "source": [
    "validate_model('PCA_no_batchnorm', n_folds, n_seeds)"
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