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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"2022-01-14-nfm-criteo.ipynb","provenance":[{"file_id":"https://github.com/recohut/nbs/blob/main/raw/P917494%20%7C%20NFM%20on%20Criteo%20DAC%20sample%20dataset%20in%20PyTorch.ipynb","timestamp":1644614102319},{"file_id":"1PcrzoopQcJ6T5CwS38RIyYqoayb0ytc7","timestamp":1641536947688},{"file_id":"1FEZmnoLGIsTsGiK2gi1TsIHLAaWCXF_a","timestamp":1640329037065}],"collapsed_sections":[],"toc_visible":true,"mount_file_id":"1FEZmnoLGIsTsGiK2gi1TsIHLAaWCXF_a","authorship_tag":"ABX9TyM0maX1ftvDR7GCOcKO5FYK"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# NFM on Criteo DAC sample dataset in PyTorch"],"metadata":{"id":"2VZ8fr0qMt5t"}},{"cell_type":"code","source":["import numpy as np\n","import pandas as pd\n","import torch\n","from sklearn.model_selection import train_test_split\n","from sklearn.metrics import log_loss, roc_auc_score\n","from sklearn.preprocessing import LabelEncoder, MinMaxScaler\n","from collections import OrderedDict, namedtuple, defaultdict\n","from torch import nn as nn\n","from torch.utils.data import DataLoader, Dataset, TensorDataset"],"metadata":{"id":"EToD4LnRLgyY"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["!wget -q --show-progress https://github.com/RecoHut-Datasets/criteo/raw/v1/dac_sample.txt"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"L60wRz3KLutF","executionInfo":{"status":"ok","timestamp":1641536229646,"user_tz":-330,"elapsed":1342,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"1a380f02-5cf1-43d2-8a40-974119bbecdc"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\rdac_sample.txt        0%[                    ]       0  --.-KB/s               \rdac_sample.txt      100%[===================>]  23.20M  --.-KB/s    in 0.09s   \n"]}]},{"cell_type":"code","source":["class SparseFeat(namedtuple('SparseFeat', ['name', 'vocabulary_size', 'embedding_dim', 'use_hash',\n","                                           'dtype', 'embedding_name', 'group_name'])):\n","    __slots__ = ()\n","    def __new__(cls, name, vocabulary_size, embedding_dim=4, use_hash=False, dtype=\"int32\", embedding_name=None,\n","                group_name='default_group'):\n","        if embedding_name is None:\n","            embedding_name = name\n","        return super(SparseFeat, cls).__new__(cls, name, vocabulary_size, embedding_dim, use_hash, dtype, embedding_name, group_name)\n","\n","    def __hash__(self):\n","        return self.name.__hash__()"],"metadata":{"id":"9Rs8-3zMLSCa"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DenseFeat(namedtuple('DenseFeat', ['name', 'dimension', 'dtype'])):\n","    __slots__ = ()\n","    def __new__(cls, name, dimension=1, dtype='float32'):\n","        return super(DenseFeat, cls).__new__(cls, name, dimension, dtype)\n","    def __hash__(self):\n","        return self.name.__hash__()"],"metadata":{"id":"EVO8EyF6N8ru"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def activation_layer(act_name, hidden_size=None, dice_dim=2):\n","    if isinstance(act_name, str):\n","        if act_name.lower() == 'sigmoid':\n","            act_layer = nn.Sigmoid()\n","        elif act_name.lower() == 'relu':\n","            act_layer = nn.ReLU(inplace=True)\n","        elif act_name.lower() == 'prelu':\n","            act_layer = nn.PReLU()\n","    return act_layer"],"metadata":{"id":"2Ab6UDMIN9ii"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def get_auc(loader, model):\n","    pred, target = [], []\n","    model.eval()\n","    with torch.no_grad():\n","        for x, y in loader:\n","            x, y = x.to(device).float(), y.to(device).float()\n","            y_hat = model(x)\n","            pred += list(y_hat.cpu().numpy())\n","            target += list(y.cpu().numpy())\n","    auc = roc_auc_score(target, pred)\n","    return auc"],"metadata":{"id":"dBqDa29AN-jX"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class DNN(nn.Module):\n","    def __init__(self, inputs_dim, hidden_units, activation='relu', l2_reg=0, dropout_rate=0, use_bn=False,\n","                 init_std=0.0001, dice_dim=3, seed=1024, device='cpu'):\n","        super(DNN, self).__init__()\n","        self.dropout = nn.Dropout(dropout_rate)\n","        self.seed = seed\n","        self.l2_reg = l2_reg\n","        self.use_bn = use_bn\n","        hidden_units = [inputs_dim] + list(hidden_units)\n","        self.linears = nn.ModuleList([\n","            nn.Linear(hidden_units[i], hidden_units[i+1]) for i in range(len(hidden_units)-1)\n","        ])\n","        if use_bn:\n","            self.bn = nn.ModuleList([\n","                nn.BatchNorm1d(hidden_units[i], hidden_units[i+1]) for i in range(len(hidden_units)-1)\n","            ])\n","        self.activation_layer = nn.ModuleList([\n","            activation_layer(activation, hidden_units[i+1], dice_dim) for i in range(len(hidden_units)-1)\n","        ])\n","        for name, tensor in self.linears.named_parameters():\n","            if 'weight' in name:\n","                nn.init.normal_(tensor, mean=0, std=init_std)\n","        self.to(device)\n","    def forward(self, inputs):\n","        deep_input = inputs\n","        for i in range(len(self.linears)):\n","            fc = self.linears[i](deep_input)\n","            if self.use_bn:\n","                fc = self.bn[i](fc)\n","            fc = self.activation_layer[i](fc)\n","            fc = self.dropout(fc)\n","            deep_input = fc\n","        return deep_input"],"metadata":{"id":"-JJ1mTc0N7Fv"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class BiInteractionPooling(nn.Module):\n","    def __init__(self):\n","        super(BiInteractionPooling, self).__init__()\n","    def forward(self, inputs):\n","        concated_embeds_value = inputs\n","        square_of_sum = torch.pow(torch.sum(concated_embeds_value, dim=1, keepdim=True), 2)\n","        sum_of_square = torch.sum(concated_embeds_value * concated_embeds_value, dim=1, keepdim=True)\n","        cross_term = 0.5 * (square_of_sum - sum_of_square)\n","        return cross_term"],"metadata":{"id":"QCh31R87N5_5"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["class NFM(nn.Module):\n","    def __init__(self, feat_sizes, embedding_size, linear_feature_columns, dnn_feature_columns, dnn_hidden_units=(128, 128),\n","                 l2_reg_embedding=1e-5, l2_reg_linear=1e-5, l2_reg_dnn=0, init_std=0.0001, seed=1024, bi_dropout=1,\n","                 dnn_dropout=0, dnn_activation='relu', task='binary', device='cpu', gpus=None):\n","        super(NFM, self).__init__()\n","        self.dense_features_columns = list(\n","            filter(lambda x: isinstance(x, DenseFeat), dnn_feature_columns)) if len(dnn_feature_columns) else []\n","        dense_input_dim = sum(map(lambda x: x.dimension, self.dense_features_columns))\n","\n","        self.sparse_features_columns = list(\n","            filter(lambda x: isinstance(x, SparseFeat), dnn_feature_columns)) if len(dnn_feature_columns) else []\n","\n","        self.feat_sizes = feat_sizes\n","        self.embedding_size = embedding_size\n","        self.embedding_dic = nn.ModuleDict({feat.name:nn.Embedding(self.feat_sizes[feat.name], self.embedding_size, sparse=False)\n","                                            for feat in self.sparse_features_columns})\n","        for tensor in self.embedding_dic.values():\n","            nn.init.normal_(tensor.weight, mean=0, std=init_std)\n","\n","        self.feature_index = defaultdict(int)\n","        start = 0\n","        for feat in self.feat_sizes:\n","            if feat in self.feature_index:\n","                continue\n","            self.feature_index[feat] = start\n","            start += 1\n","\n","        self.dnn = DNN(dense_input_dim+self.embedding_size, dnn_hidden_units, activation=dnn_activation,\n","                       l2_reg=l2_reg_dnn, dropout_rate=dnn_dropout, use_bn=False,\n","                       init_std=init_std, device=device)\n","        self.dnn_linear = nn.Linear(dnn_hidden_units[-1], 1, bias=False).to(device)\n","\n","        dnn_hidden_units = [len(self.feature_index)] + list(dnn_hidden_units) + [1]\n","        self.Linears = nn.ModuleList(\n","            [nn.Linear(dnn_hidden_units[i], dnn_hidden_units[i + 1]) for i in range(len(dnn_hidden_units) - 1)])\n","        self.relu = nn.ReLU()\n","        self.bi_pooling = BiInteractionPooling()\n","        self.bi_dropout = bi_dropout\n","        if self.bi_dropout > 0:\n","            self.dropout  = nn.Dropout(bi_dropout)\n","        self.to(device)\n","\n","    def forward(self, X):\n","        sparse_embedding = [self.embedding_dic[feat.name](X[:, self.feature_index[feat.name]].long()).reshape(X.shape[0], 1, -1)\n","                            for feat in self.sparse_features_columns]\n","        dense_values = [X[:, self.feature_index[feat.name]].reshape(-1, 1) for feat in self.dense_features_columns]\n","       # print('sparse_embedding shape', sparse_embedding[0].shape)\n","        dense_input = torch.cat(dense_values, dim=1)\n","       # print('densn_input shape', dense_input.shape)\n","        fm_input = torch.cat(sparse_embedding, dim=1)\n","       # print('fm_input_shape', fm_input.shape)\n","        bi_out = self.bi_pooling(fm_input)\n","       # print('bi_out shape', bi_out.shape)\n","        if self.bi_dropout:\n","            bi_out = self.dropout(bi_out)\n","\n","        bi_out = torch.flatten(torch.cat([bi_out], dim=-1), start_dim=1)\n","\n","        dnn_input = torch.cat((dense_input, bi_out), dim=1)\n","        dnn_output = self.dnn(dnn_input)\n","        dnn_output = self.dnn_linear(dnn_output)\n","\n","       # print('X shape', X.shape)\n","        for i in range(len(self.Linears)):\n","            fc = self.Linears[i](X)\n","            fc = self.relu(fc)\n","            fc = self.dropout(fc)\n","            X = fc\n","\n","        logit = X + dnn_output\n","        y_pred = torch.sigmoid(logit)\n","        return y_pred"],"metadata":{"id":"D_slY7KGN44C"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["batch_size = 1024\n","lr = 1e-3\n","wd = 1e-5\n","epochs = 10\n","seed = 1024\n","embedding_size = 4\n","\n","sparse_features = ['C' + str(i) for i in range(1, 27)]\n","dense_features = ['I' + str(i) for i in range(1, 14)]\n","col_names = ['label'] + dense_features + sparse_features\n","data = pd.read_csv('dac_sample.txt', names=col_names, sep='\\t')\n","\n","data[sparse_features] = data[sparse_features].fillna('-1',)\n","data[dense_features] = data[dense_features].fillna('0', )\n","target = ['label']\n","\n","feat_sizes = {}\n","feat_sizes_dense = {feat: 1 for feat in dense_features}\n","feat_sizes_sparse = {feat: len(data[feat].unique()) for feat in sparse_features}\n","feat_sizes.update(feat_sizes_dense)\n","feat_sizes.update(feat_sizes_sparse)\n","\n","for feat in sparse_features:\n","    lbe = LabelEncoder()\n","    data[feat] = lbe.fit_transform(data[feat])\n","\n","nms = MinMaxScaler(feature_range=(0, 1))\n","data[dense_features] = nms.fit_transform(data[dense_features])\n","\n","fixlen_feature_columns = [SparseFeat(feat, data[feat].nunique()) for feat in sparse_features] + [DenseFeat(feat, 1,)\n","                                                                                                    for feat in dense_features]\n","dnn_feature_columns = fixlen_feature_columns\n","linear_feature_columns = fixlen_feature_columns\n","\n","train, test = train_test_split(data, test_size=0.2, random_state=2022)\n","feature_names = sparse_features + dense_features\n","# train_model_input = {name: train[name] for name in feature_names}\n","# test_model_input = {name: test[name] for name in feature_names}\n","\n","device = 'cpu'\n","model = NFM(feat_sizes, embedding_size, linear_feature_columns, dnn_feature_columns).to(device)\n","\n","train_label = pd.DataFrame(train['label'])\n","train = train.drop(columns=['label'])\n","train_tensor_data = TensorDataset(torch.from_numpy(np.array(train)), torch.from_numpy(np.array(train_label)))\n","train_loader = DataLoader(train_tensor_data, shuffle=True, batch_size=batch_size)\n","\n","test_label = pd.DataFrame(test['label'])\n","test = test.drop(columns=['label'])\n","test_tensor_data = TensorDataset(torch.from_numpy(np.array(test)), torch.from_numpy(np.array(test_label)))\n","test_loader = DataLoader(test_tensor_data,  batch_size=batch_size)\n","\n","loss_func = nn.BCELoss(reduction='mean')\n","optimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=wd)\n","\n","for epoch in range(epochs):\n","    total_loss_epoch = 0.0\n","    total_tmp = 0\n","    model.train()\n","    for index, (x, y) in enumerate(train_loader):\n","        x, y = x.to(device).float(), y.to(device).float()\n","        y_hat = model(x)\n","\n","        optimizer.zero_grad()\n","        loss = loss_func(y_hat, y)\n","        loss.backward()\n","        optimizer.step()\n","        total_loss_epoch += loss.item()\n","        total_tmp += 1\n","    auc = get_auc(test_loader, model)\n","    print('epoch/epoches: {}/{}, train loss: {:.3f}, test auc: {:.3f}'.format(epoch, epochs,\n","                                                                                total_loss_epoch / total_tmp, auc))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ZH69aNy6LmvA","executionInfo":{"status":"ok","timestamp":1641536912733,"user_tz":-330,"elapsed":38318,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"0ba95cc5-745e-41c5-e3c3-88ad3a7e3223"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["epoch/epoches: 0/10, train loss: 0.567, test auc: 0.483\n","epoch/epoches: 1/10, train loss: 0.524, test auc: 0.674\n","epoch/epoches: 2/10, train loss: 0.506, test auc: 0.677\n","epoch/epoches: 3/10, train loss: 0.502, test auc: 0.682\n","epoch/epoches: 4/10, train loss: 0.498, test auc: 0.684\n","epoch/epoches: 5/10, train loss: 0.497, test auc: 0.686\n","epoch/epoches: 6/10, train loss: 0.496, test auc: 0.688\n","epoch/epoches: 7/10, train loss: 0.495, test auc: 0.689\n","epoch/epoches: 8/10, train loss: 0.495, test auc: 0.690\n","epoch/epoches: 9/10, train loss: 0.494, test auc: 0.691\n"]}]},{"cell_type":"code","source":["!pip install -q watermark\n","%reload_ext watermark\n","%watermark -a \"Sparsh A.\" -m -iv -u -t -d"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tmhWlpwiL0bb","executionInfo":{"status":"ok","timestamp":1641536460223,"user_tz":-330,"elapsed":3599,"user":{"displayName":"Sparsh Agarwal","photoUrl":"https://lh3.googleusercontent.com/a/default-user=s64","userId":"13037694610922482904"}},"outputId":"fac5bee6-aa05-4846-ac1d-c13dd1f05222"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Author: Sparsh A.\n","\n","Last updated: 2022-01-07 06:21:01\n","\n","Compiler    : GCC 7.5.0\n","OS          : Linux\n","Release     : 5.4.144+\n","Machine     : x86_64\n","Processor   : x86_64\n","CPU cores   : 2\n","Architecture: 64bit\n","\n","IPython: 5.5.0\n","torch  : 1.10.0+cu111\n","pandas : 1.1.5\n","numpy  : 1.19.5\n","\n"]}]}]}