Attentional Factorization Machine

attentional_factorization_machine.ipynb

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We will predict this column.\n    dataset['target'] = dataset.groupby('user id')['movie id'].shift(-1)\n    # create a column that represents average movie rating given by user till that time (represented by row)\n    dataset['mean_rate'] = dataset.groupby('user id')['rating'].cumsum() / dataset['sample_num']\n    \n    # do a left join with movies dataframe and bring all the genre representations (0/1 binary values for each movie representing its category) here.\n    dataset = dataset.merge(items[['movie id'] + GENRES], on='movie id', how='left')\n    \n    # For each genre column (19) creates another column (total 19 more). This column represents a given user's mean score (float value) for a given genre till that time (represented by row).\n    # Note that we also update the genre columns such that each column now has cumulative sum, i.e. the corresponding number of movies that the user has watched in that genre so far.\n    for genre in GENRES:\n        dataset[f'{genre}_rate'] = dataset[genre]*dataset['rating']\n        dataset[genre] = dataset.groupby('user id')[genre].cumsum()\n        dataset[f'{genre}_rate'] = dataset.groupby('user id')[f'{genre}_rate'].cumsum() / dataset[genre]\n    \n    # Next we normalize the scores for movies in each genre such that we divide it by the number of movies that the user has watched so far.\n    dataset[GENRES] = dataset[GENRES].apply(lambda x: x / dataset['sample_num'])\n    # do a left-join on users data and get more information on users\n    dataset = dataset.merge(users, on='user id', how='left')\n    \n    gender_encoder = LabelEncoder()\n    occupations_encoder = LabelEncoder()\n    dataset['gender'] = gender_encoder.fit_transform(dataset['gender'])\n    dataset['occupation'] = occupations_encoder.fit_transform(dataset['occupation'])\n    \n    dataset.drop('zip code', axis=1, inplace=True)\n    \n    COLD_START_TRESH = 5 # take the rows AFTER each user has watched at least 4 movies\n    # filter using threshold and remove null target rows\n    filtred_data = dataset[(dataset['sample_num'] >= COLD_START_TRESH) &\n                           ~(dataset['target'].isna())].sort_values('timestamp')\n    \n    continuous_cols = ['age', 'mean_rate'] + GENRES + [gen+\"_rate\" for gen in GENRES] # 41\n    categoricals = ['gender', 'occupation'] # label encoded\n    field_dims = []\n    field_dims.append(len(gender_encoder.classes_))\n    field_dims.append(len(occupations_encoder.classes_))\n    df_continuous = filtred_data[continuous_cols]\n    df_categorical = filtred_data[categoricals]\n    \n    TEST_SIZE = 0.2 # size of test set\n    X_train_continuous, X_test_continuous = df_continuous[:int(len(df_continuous)*(1-TEST_SIZE))], df_continuous[int(len(df_continuous)*(1-TEST_SIZE)):]\n    X_train_categorical, X_test_categorical = df_categorical[:int(len(df_categorical)*(1-TEST_SIZE))], df_categorical[int(len(df_categorical)*(1-TEST_SIZE)):]\n\n    filtered_train_data, filtered_test_data = filtred_data[:int(len(filtred_data)*(1-TEST_SIZE))], filtred_data[int(len(filtred_data)*(1-TEST_SIZE)):]\n    y_train, y_test = filtered_train_data['target'], filtered_test_data['target']\n    \n    # target\n    target_train = torch.Tensor(y_train.values).long().to(device)\n    target_test = torch.Tensor(y_test.values).long().to(device)\n    target_test_sparse = y_test.apply(lambda x: idx_to_sparse(x, items['movie id'].nunique() + 1)) # to calculate mean rank over test set during training\n    \n    # tensor with continuous features\n    X_train_continuous_tensor = torch.Tensor(X_train_continuous.fillna(0).values).to(device)\n    X_test_continuous_tensor = torch.Tensor(X_test_continuous.fillna(0).values).to(device)\n    X_train_categorical_tensor = torch.Tensor(X_train_categorical.fillna(0).values).to(device)\n    X_test_categorical_tensor = torch.Tensor(X_test_categorical.fillna(0).values).to(device)\n    \n    return X_train_continuous_tensor, X_test_continuous_tensor, X_train_categorical_tensor, X_test_categorical_tensor,  target_train, target_test, target_test_sparse, field_dims, items['movie id'].nunique() + 1\n\nclass AFM(nn.Module):\n    def __init__(self, continuous_dim, field_dims, n_class, attn_size=16, embed_size=16, pad_idx=0):\n        super().__init__()\n        self.embeddings = nn.Embedding(sum(field_dims), embed_size, padding_idx=pad_idx, device=device)\n        self.attention = nn.Linear(embed_size, attn_size, device=device)\n        self.projection = nn.Linear(attn_size, 1, device=device)\n        self.fc = nn.Linear(embed_size, n_class, device=device)\n        self.linear_layer = nn.Linear(continuous_dim, n_class, device=device)\n    \n    def forward(self, continuous_X, categorical_X):\n        embeds_out = self.embeddings(categorical_X)\n        num_fields = embeds_out.shape[1]\n        row, col = list(), list()\n        for i in range(num_fields - 1):\n            for j in range(i + 1, num_fields):\n                row.append(i), col.append(j)\n        p, q = embeds_out[:, row], embeds_out[:, col]\n        inner_product = p * q\n        attn_scores = nn.functional.relu(self.attention(inner_product))\n        attn_scores = nn.functional.softmax(self.projection(attn_scores), dim=1)\n        attn_output = torch.sum(attn_scores * inner_product, dim=1)\n        output = self.linear_layer(continuous_X) + self.fc(attn_output)\n        return output\n\ndef run_gradient_descent_afm(model,\n    learning_rate=1e-3,\n    weight_decay=0.01,\n    num_epochs=10):\n    loss_fn = nn.CrossEntropyLoss(ignore_index=PAD_IDX) # the model doesn't need to predict padding index\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)\n    \n    iters, train_losses, test_losses, mean_test_ranks = [], [], [], []\n    \n    # training\n    n = 0 # the number of iterations\n    for epoch in range(num_epochs):\n        model.train()\n        y_logits = model(X_train_continuous_tensor, X_train_categorical_tensor.long().to(device))\n        loss_train = loss_fn(y_logits, target_train)\n\n        # Backpropagation\n        optimizer.zero_grad() # a clean up step for PyTorch\n        loss_train.backward() # compute updates for each parameter\n        optimizer.step() # make the updates for each parameter\n\n        # save the current training information\n        if n%100 == 0:\n            pred_train = torch.softmax(y_logits, dim=1).argmax(dim=1)\n            acc = accuracy_fn(y_true=target_train, y_pred=pred_train)\n            \n            model.eval()\n            with torch.inference_mode():\n                test_logits = model(X_test_continuous_tensor, X_test_categorical_tensor.long().to(device))\n                test_pred = torch.softmax(test_logits, dim=1).argmax(dim=1)\n                loss_test = loss_fn(test_logits, target_test)\n                test_acc = accuracy_fn(y_true=target_test,y_pred=test_pred)\n            \n            # calculate mean rank on test set\n            softmax = nn.Softmax(dim=0)\n            preds_wnd = softmax(test_logits.float()).cpu().detach().numpy()\n            ranks_wnd = pd.DataFrame(preds_wnd).apply(lambda x: pd.Series(rankdata(-x)), axis=1)\n            ranks_target_wnd = (ranks_wnd.values * target_test_sparse).sum(axis=1)\n            mean_rank_wnd = ranks_target_wnd.mean()\n            \n            print(f\"Epoch: {epoch} | Loss: {loss_train:.5f}, Acc: {acc:.2f}% | Test Loss: {loss_test:.5f}, Test Acc: {test_acc:.2f}% Test mean rank: {mean_rank_wnd:.0f}\")\n            \n            iters.append(n)\n            train_losses.append(float(loss_train))\n            test_losses.append(float(loss_test))\n            mean_test_ranks.append(mean_rank_wnd)\n            \n            # increment the iteration number\n        n += 1\n    \n    # plotting\n    plt.figure(figsize=(12, 8), dpi=100)\n    plt.title(f\"Training Curve (lr={learning_rate})\")\n    plt.plot(iters, train_losses, label=\"Train Loss\")\n    plt.plot(iters, test_losses, label=\"Test Loss\")\n    \n    plt.xlabel(\"Iterations\")\n    plt.ylabel(\"Loss\")\n    plt.legend(loc='best')\n    plt.show()\n    \n    plt.figure(figsize=(12, 8), dpi=100)\n    plt.plot(iters, mean_test_ranks, label=\"Test Rank\")\n    plt.xlabel(\"Iterations\")\n    plt.ylabel(\"Mean Rank on testset\")\n    plt.legend(loc='best')\n    plt.show()\n    \n    return model, iters, train_losses, test_losses","metadata":{"execution":{"iopub.status.busy":"2022-10-31T06:39:13.195660Z","iopub.execute_input":"2022-10-31T06:39:13.198143Z","iopub.status.idle":"2022-10-31T06:39:13.253055Z","shell.execute_reply.started":"2022-10-31T06:39:13.198085Z","shell.execute_reply":"2022-10-31T06:39:13.251983Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"X_train_continuous_tensor, X_test_continuous_tensor, X_train_categorical_tensor, X_test_categorical_tensor,  target_train, target_test, target_test_sparse, field_dims, n_classes = load_and_process_data_afm()","metadata":{"execution":{"iopub.status.busy":"2022-10-31T06:39:13.257182Z","iopub.execute_input":"2022-10-31T06:39:13.257970Z","iopub.status.idle":"2022-10-31T06:39:25.067861Z","shell.execute_reply.started":"2022-10-31T06:39:13.257923Z","shell.execute_reply":"2022-10-31T06:39:25.066773Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"afm_model = AFM(continuous_dim=X_train_continuous_tensor.shape[1], field_dims=field_dims, n_class=n_classes)","metadata":{"execution":{"iopub.status.busy":"2022-10-31T06:39:25.069186Z","iopub.execute_input":"2022-10-31T06:39:25.069941Z","iopub.status.idle":"2022-10-31T06:39:25.083059Z","shell.execute_reply.started":"2022-10-31T06:39:25.069902Z","shell.execute_reply":"2022-10-31T06:39:25.081987Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"afm_model_trained, iters, train_losses, test_losses = run_gradient_descent_afm(afm_model, num_epochs=1000, weight_decay=0, learning_rate=0.03)","metadata":{"execution":{"iopub.status.busy":"2022-10-31T06:39:25.084514Z","iopub.execute_input":"2022-10-31T06:39:25.085128Z","iopub.status.idle":"2022-10-31T06:41:51.173259Z","shell.execute_reply.started":"2022-10-31T06:39:25.085092Z","shell.execute_reply":"2022-10-31T06:41:51.172346Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Epoch: 0 | Loss: 11.19118, Acc: 0.02% | Test Loss: 9.67586, Test Acc: 0.29% Test mean rank: 867\nEpoch: 100 | Loss: 6.07150, Acc: 1.46% | Test Loss: 7.05537, Test Acc: 0.76% Test mean rank: 473\nEpoch: 200 | Loss: 5.96290, Acc: 1.71% | Test Loss: 7.36982, Test Acc: 0.75% Test mean rank: 476\nEpoch: 300 | Loss: 5.90554, Acc: 1.92% | Test Loss: 7.63639, Test Acc: 0.72% Test mean rank: 534\nEpoch: 400 | Loss: 5.86799, Acc: 2.06% | Test Loss: 7.87037, Test Acc: 0.68% Test mean rank: 601\nEpoch: 500 | Loss: 5.84265, Acc: 2.20% | Test Loss: 8.06430, Test Acc: 0.63% Test mean rank: 650\nEpoch: 600 | Loss: 5.82196, Acc: 2.23% | Test Loss: 8.22861, Test Acc: 0.63% Test mean rank: 678\nEpoch: 700 | Loss: 5.80571, Acc: 2.33% | Test Loss: 8.36979, Test Acc: 0.62% Test mean rank: 697\nEpoch: 800 | Loss: 5.79389, Acc: 2.38% | Test Loss: 8.49420, Test Acc: 0.60% Test mean rank: 711\nEpoch: 900 | Loss: 5.78629, Acc: 2.41% | Test Loss: 8.60304, Test Acc: 0.60% Test mean rank: 717\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x800 with 1 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\n"},"metadata":{"needs_background":"light"}},{"output_type":"display_data","data":{"text/plain":"<Figure 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\n"},"metadata":{"needs_background":"light"}}]}]}