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install pytorch-lightning"],"metadata":{"id":"SL07406rkYAI","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669641212094,"user_tz":300,"elapsed":24502,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}},"outputId":"96caf592-9598-4453-993c-a623f0741634"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting pytorch-lightning\n","  Downloading pytorch_lightning-1.8.3.post1-py3-none-any.whl (798 kB)\n","\u001b[K     |████████████████████████████████| 798 kB 14.9 MB/s \n","\u001b[?25hRequirement already satisfied: fsspec[http]>2021.06.0 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning) (2022.11.0)\n","Requirement already satisfied: numpy>=1.17.2 in /usr/local/lib/python3.7/dist-packages (from pytorch-lightning) (1.21.6)\n","Requirement already satisfied: tqdm>=4.57.0 in 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|████████████████████████████████| 125 kB 53.5 MB/s \n","\u001b[?25hCollecting fire\n","  Downloading fire-0.4.0.tar.gz (87 kB)\n","\u001b[K     |████████████████████████████████| 87 kB 2.2 MB/s \n","\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from fsspec[http]>2021.06.0->pytorch-lightning) (2.23.0)\n","Requirement already satisfied: aiohttp!=4.0.0a0,!=4.0.0a1 in /usr/local/lib/python3.7/dist-packages (from fsspec[http]>2021.06.0->pytorch-lightning) (3.8.3)\n","Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>2021.06.0->pytorch-lightning) (22.1.0)\n","Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.7/dist-packages (from aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>2021.06.0->pytorch-lightning) (4.0.2)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.7/dist-packages (from 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(6.0.2)\n","Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging>=17.0->pytorch-lightning) (3.0.9)\n","Requirement already satisfied: protobuf<=3.20.1,>=3.8.0 in /usr/local/lib/python3.7/dist-packages (from tensorboardX>=2.2->pytorch-lightning) (3.19.6)\n","Requirement already satisfied: idna>=2.0 in /usr/local/lib/python3.7/dist-packages (from yarl<2.0,>=1.0->aiohttp!=4.0.0a0,!=4.0.0a1->fsspec[http]>2021.06.0->pytorch-lightning) (2.10)\n","Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from fire->lightning-utilities==0.3.*->pytorch-lightning) (1.15.0)\n","Requirement already satisfied: termcolor in /usr/local/lib/python3.7/dist-packages (from fire->lightning-utilities==0.3.*->pytorch-lightning) (2.1.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]>2021.06.0->pytorch-lightning) (2022.9.24)\n","Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]>2021.06.0->pytorch-lightning) (1.24.3)\n","Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->fsspec[http]>2021.06.0->pytorch-lightning) (3.0.4)\n","Building wheels for collected packages: fire\n","  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for fire: filename=fire-0.4.0-py2.py3-none-any.whl size=115943 sha256=a0985ae83ea6ef010b283f072e74febacea3b3e4aa2f63fa00f2fb720b38d8fd\n","  Stored in directory: /root/.cache/pip/wheels/8a/67/fb/2e8a12fa16661b9d5af1f654bd199366799740a85c64981226\n","Successfully built fire\n","Installing collected packages: fire, torchmetrics, tensorboardX, lightning-utilities, pytorch-lightning\n","Successfully installed fire-0.4.0 lightning-utilities-0.3.0 pytorch-lightning-1.8.3.post1 tensorboardX-2.5.1 torchmetrics-0.10.3\n"]}]},{"cell_type":"code","execution_count":2,"metadata":{"id":"JOkr56AXMO91","executionInfo":{"status":"ok","timestamp":1669641215707,"user_tz":300,"elapsed":3619,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"outputs":[],"source":["import pandas as pd\n","import numpy as np\n","import tqdm\n","\n","import torch\n","import torch.nn as nn\n","from torch.utils.data import Dataset, DataLoader\n","import pytorch_lightning as pl\n","from google.colab import drive\n","\n","np.random.seed(123)"]},{"cell_type":"code","source":["drive.mount(\"/content/gdrive\")"],"metadata":{"id":"zj44_t38mUlo","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1669641584616,"user_tz":300,"elapsed":20078,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}},"outputId":"dd4f3d5f-6008-4dce-b670-cf4f5f99dd17"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["ratings = pd.read_csv('/content/gdrive/MyDrive/rating.csv', parse_dates=['timestamp'])"],"metadata":{"id":"3xa15kkFM9LM","executionInfo":{"status":"ok","timestamp":1669641638415,"user_tz":300,"elapsed":50020,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["#choosing %30 of users randomly by getting ID\n","rand_userIds = np.random.choice(ratings['userId'].unique(), \n","                                size=int(len(ratings['userId'].unique())*0.3), \n","                                replace=False)\n","#getting ratings of choosen users\n","ratings = ratings.loc[ratings['userId'].isin(rand_userIds)]\n","print('There are {} rows of data from {} users'.format(len(ratings), len(rand_userIds)))\n","# adding a new column to data (rank_latest), giving us the order of the user rate based on the time\n","ratings['rank_latest'] = ratings.groupby(['userId'])['timestamp'] \\\n","                                .rank(method='first', ascending=False)\n","#split tran and test, take the last rating as test rest is training\n","train_ratings = ratings[ratings['rank_latest'] != 1]\n","test_ratings = ratings[ratings['rank_latest'] == 1]\n","# drop columns that we no longer need\n","train_ratings = train_ratings[['userId', 'movieId', 'rating']]\n","test_ratings = test_ratings[['userId', 'movieId', 'rating']]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hZm1CIdhlqAt","outputId":"e345208f-335f-4048-e38d-0a81dbb6b294","executionInfo":{"status":"ok","timestamp":1669641639326,"user_tz":300,"elapsed":927,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["There are 6027314 rows of data from 41547 users\n"]}]},{"cell_type":"code","source":["all_movieIds = ratings['movieId'].unique()\n","users, items, labels = [], [], []\n","# This is the set of items that each user has interaction with\n","user_item_set = set(zip(train_ratings['userId'], train_ratings['movieId']))\n","# 4:1 ratio of negative to positive samples\n","num_negatives = 5\n","for (u, i) in user_item_set:\n","    users.append(u)\n","    items.append(i)\n","    labels.append(1) # items that the user has interacted with are positive\n","    for _ in range(num_negatives):\n","        # randomly select an item\n","        negative_item = np.random.choice(all_movieIds) \n","        # check that the user has not interacted with this item\n","        while (u, negative_item) in user_item_set:\n","            negative_item = np.random.choice(all_movieIds)\n","        users.append(u)\n","        items.append(negative_item)\n","        labels.append(0) # items not interacted with are negative"],"metadata":{"id":"3r_u5L3nN-L0","executionInfo":{"status":"ok","timestamp":1669642116413,"user_tz":300,"elapsed":477089,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["class MovieLensTrainDataset(Dataset):\n","    \"\"\"MovieLens PyTorch Dataset for Training\n","    \n","    Args:\n","        ratings (pd.DataFrame): Dataframe containing the movie ratings\n","        all_movieIds (list): List containing all movieIds\n","    \n","    \"\"\"\n","\n","    def __init__(self, ratings, all_movieIds):\n","        self.users, self.items, self.labels = self.get_dataset(ratings, all_movieIds)\n","\n","    def __len__(self):\n","        return len(self.users)\n","  \n","    def __getitem__(self, idx):\n","        return self.users[idx], self.items[idx], self.labels[idx]\n","\n","    def get_dataset(self, ratings, all_movieIds):\n","        users, items, labels = [], [], []\n","        user_item_set = set(zip(ratings['userId'], ratings['movieId']))\n","\n","        num_negatives = 5\n","        for u, i in user_item_set:\n","            users.append(u)\n","            items.append(i)\n","            labels.append(1)\n","            for _ in range(num_negatives):\n","                negative_item = np.random.choice(all_movieIds)\n","                while (u, negative_item) in user_item_set:\n","                    negative_item = np.random.choice(all_movieIds)\n","                users.append(u)\n","                items.append(negative_item)\n","                labels.append(0)\n","\n","        return torch.tensor(users), torch.tensor(items), torch.tensor(labels)"],"metadata":{"id":"INRphfshNLYl","executionInfo":{"status":"ok","timestamp":1669642116414,"user_tz":300,"elapsed":24,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["class NCF2(pl.LightningModule):\n","    \"\"\" Neural Collaborative Filtering (NCF)\n","    \n","        Args:\n","            num_users (int): Number of unique users\n","            num_items (int): Number of unique items\n","            ratings (pd.DataFrame): Dataframe containing the movie ratings for training\n","            all_movieIds (list): List containing all movieIds (train + test)\n","    \"\"\"\n","    \n","    def __init__(self, num_neuron, num_neuron2, num_users, num_items, ratings, all_movieIds):\n","        super().__init__()\n","        self.user_embedding = nn.Embedding(num_embeddings=num_users, embedding_dim=8)\n","        self.item_embedding = nn.Embedding(num_embeddings=num_items, embedding_dim=8)\n","        self.fc1 = nn.Linear(in_features=16, out_features=num_neuron)\n","        self.fc2 = nn.Linear(in_features=num_neuron, out_features=num_neuron2)\n","        self.output = nn.Linear(in_features=num_neuron2, out_features=1)\n","        self.ratings = ratings\n","        self.all_movieIds = all_movieIds\n","        \n","    def forward(self, user_input, item_input):\n","        \n","        # Pass through embedding layers\n","        user_embedded = self.user_embedding(user_input)\n","        item_embedded = self.item_embedding(item_input)\n","\n","        # Concat the two embedding layers\n","        vector = torch.cat([user_embedded, item_embedded], dim=-1)\n","\n","        # Pass through dense layer\n","        vector = nn.ReLU()(self.fc1(vector))\n","        vector = nn.ReLU()(self.fc2(vector))\n","\n","        # Output layer\n","        pred = nn.Sigmoid()(self.output(vector))\n","\n","        return pred\n","    \n","    def training_step(self, batch, batch_idx):\n","        user_input, item_input, labels = batch\n","        predicted_labels = self(user_input, item_input)\n","        loss = nn.BCELoss()(predicted_labels, labels.view(-1, 1).float())\n","        return loss\n","\n","    def configure_optimizers(self):\n","        return torch.optim.Adam(self.parameters())\n","\n","    def train_dataloader(self):\n","        return DataLoader(MovieLensTrainDataset(self.ratings, self.all_movieIds),\n","                          batch_size=512, num_workers=4)"],"metadata":{"id":"Ul0DoZteNgE5","executionInfo":{"status":"ok","timestamp":1669642116414,"user_tz":300,"elapsed":10,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["class NCF3(pl.LightningModule):\n","    \"\"\" Neural Collaborative Filtering (NCF)\n","    \n","        Args:\n","            num_users (int): Number of unique users\n","            num_items (int): Number of unique items\n","            ratings (pd.DataFrame): Dataframe containing the movie ratings for training\n","            all_movieIds (list): List containing all movieIds (train + test)\n","    \"\"\"\n","    \n","    def __init__(self, num_neuron, num_neuron2,num_neuron3, num_users, num_items, ratings, all_movieIds):\n","        super().__init__()\n","        self.user_embedding = nn.Embedding(num_embeddings=num_users, embedding_dim=8)\n","        self.item_embedding = nn.Embedding(num_embeddings=num_items, embedding_dim=8)\n","        self.fc1 = nn.Linear(in_features=16, out_features=num_neuron)\n","        self.fc2 = nn.Linear(in_features=num_neuron, out_features=num_neuron2)\n","        self.fc3 = nn.Linear(in_features=num_neuron2, out_features=num_neuron3)\n","        self.output = nn.Linear(in_features=num_neuron3, out_features=1)\n","        self.ratings = ratings\n","        self.all_movieIds = all_movieIds\n","        \n","    def forward(self, user_input, item_input):\n","        \n","        # Pass through embedding layers\n","        user_embedded = self.user_embedding(user_input)\n","        item_embedded = self.item_embedding(item_input)\n","\n","        # Concat the two embedding layers\n","        vector = torch.cat([user_embedded, item_embedded], dim=-1)\n","\n","        # Pass through dense layer\n","        vector = nn.ReLU()(self.fc1(vector))\n","        vector = nn.ReLU()(self.fc2(vector))\n","        vector = nn.ReLU()(self.fc3(vector))\n","\n","        # Output layer\n","        pred = nn.Sigmoid()(self.output(vector))\n","\n","        return pred\n","    \n","    def training_step(self, batch, batch_idx):\n","        user_input, item_input, labels = batch\n","        predicted_labels = self(user_input, item_input)\n","        loss = nn.BCELoss()(predicted_labels, labels.view(-1, 1).float())\n","        return loss\n","\n","    def configure_optimizers(self):\n","        return torch.optim.Adam(self.parameters())\n","\n","    def train_dataloader(self):\n","        return DataLoader(MovieLensTrainDataset(self.ratings, self.all_movieIds),\n","                          batch_size=512, num_workers=4)"],"metadata":{"id":"OucUKbDlBnum","executionInfo":{"status":"ok","timestamp":1669642116414,"user_tz":300,"elapsed":10,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["class NCF4(pl.LightningModule):\n","    \"\"\" Neural Collaborative Filtering (NCF)\n","    \n","        Args:\n","            num_users (int): Number of unique users\n","            num_items (int): Number of unique items\n","            ratings (pd.DataFrame): Dataframe containing the movie ratings for training\n","            all_movieIds (list): List containing all movieIds (train + test)\n","    \"\"\"\n","    \n","    def __init__(self, num_neuron, num_neuron2,num_neuron3,num_neuron4, num_users, num_items, ratings, all_movieIds):\n","        super().__init__()\n","        self.user_embedding = nn.Embedding(num_embeddings=num_users, embedding_dim=8)\n","        self.item_embedding = nn.Embedding(num_embeddings=num_items, embedding_dim=8)\n","        self.fc1 = nn.Linear(in_features=16, out_features=num_neuron)\n","        self.fc2 = nn.Linear(in_features=num_neuron, out_features=num_neuron2)\n","        self.fc3 = nn.Linear(in_features=num_neuron2, out_features=num_neuron3)\n","        self.fc4 = nn.Linear(in_features=num_neuron3, out_features=num_neuron4)\n","        self.output = nn.Linear(in_features=num_neuron4, out_features=1)\n","        self.ratings = ratings\n","        self.all_movieIds = all_movieIds\n","        \n","    def forward(self, user_input, item_input):\n","        \n","        # Pass through embedding layers\n","        user_embedded = self.user_embedding(user_input)\n","        item_embedded = self.item_embedding(item_input)\n","\n","        # Concat the two embedding layers\n","        vector = torch.cat([user_embedded, item_embedded], dim=-1)\n","\n","        # Pass through dense layer\n","        vector = nn.ReLU()(self.fc1(vector))\n","        vector = nn.ReLU()(self.fc2(vector))\n","        vector = nn.ReLU()(self.fc3(vector))\n","        vector = nn.ReLU()(self.fc4(vector))\n","\n","        # Output layer\n","        pred = nn.Sigmoid()(self.output(vector))\n","\n","        return pred\n","    \n","    def training_step(self, batch, batch_idx):\n","        user_input, item_input, labels = batch\n","        predicted_labels = self(user_input, item_input)\n","        loss = nn.BCELoss()(predicted_labels, labels.view(-1, 1).float())\n","        return loss\n","\n","    def configure_optimizers(self):\n","        return torch.optim.Adam(self.parameters())\n","\n","    def train_dataloader(self):\n","        return DataLoader(MovieLensTrainDataset(self.ratings, self.all_movieIds),\n","                          batch_size=512, num_workers=4)"],"metadata":{"id":"zTrbdKYrCCkZ","executionInfo":{"status":"ok","timestamp":1669642116415,"user_tz":300,"elapsed":11,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}}},"execution_count":12,"outputs":[]},{"cell_type":"code","source":["num_users = ratings['userId'].max()+1\n","num_items = ratings['movieId'].max()+1\n","all_movieIds = ratings['movieId'].unique()\n","\n","model = NCF2(64,32, num_users, num_items, train_ratings, all_movieIds)\n","trainer = pl.Trainer(max_epochs=5,gpus=1, logger=False, enable_checkpointing=False)\n","trainer.fit(model)\n","\n","# User-item pairs for testing\n","test_user_item_set = set(zip(test_ratings['userId'], test_ratings['movieId']))\n","# Dict of all items that are interacted with by each user\n","user_interacted_items = ratings.groupby('userId')['movieId'].apply(list).to_dict()\n","hits = []\n","for (u,i) in test_user_item_set:\n","    interacted_items = user_interacted_items[u]\n","    not_interacted_items = set(all_movieIds) - set(interacted_items)\n","    selected_not_interacted = list(np.random.choice(list(not_interacted_items), 99))\n","    test_items = selected_not_interacted + [i]\n","    \n","    predicted_labels = np.squeeze(model(torch.tensor([u]*100), \n","                                        torch.tensor(test_items)).detach().numpy())\n","    \n","    top10_items = [test_items[i] for i in np.argsort(predicted_labels)[::-1][0:10].tolist()]\n","    \n","    if i in top10_items:\n","        hits.append(1)\n","    else:\n","        hits.append(0)\n","        \n","print(\"The Hit Ratio @ 10 is {:.4f}\".format(np.average(hits)))"],"metadata":{"id":"EtviHUbxNR88","colab":{"base_uri":"https://localhost:8080/","height":498,"referenced_widgets":["03b17df1969c455586a21fcb2f69cad8","97f5205e337b456c85d369dbfaeedd1e","6c815d89f8704af69326bd63b9e8231b","b4e9d6492d8649a599108a38ebedafa7","cc966ce7d1a84254ba9fbea9717f36de","e1de613ad388484989d5777a97cb9d41","b8bac72695cc4ce6af5c65cfd7025642","c357784d8f064059977c8e0743ac73e1","bafbe731d3ad4255ae037cd13be76f04","e04b6d8adee2441a8127bb2b2bc5283c","44a8fe35635a42899ec6f181fce01027"]},"executionInfo":{"status":"ok","timestamp":1669651169374,"user_tz":300,"elapsed":9052969,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}},"outputId":"7948cf8e-f3cd-4df7-c942-b4fa6a35b1b0"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/pytorch_lightning/trainer/connectors/accelerator_connector.py:447: LightningDeprecationWarning: Setting `Trainer(gpus=1)` is deprecated in v1.7 and will be removed in v2.0. Please use `Trainer(accelerator='gpu', devices=1)` instead.\n","  f\"Setting `Trainer(gpus={gpus!r})` is deprecated in v1.7 and will be removed\"\n","INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n","INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n","INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n","INFO:pytorch_lightning.callbacks.model_summary:\n","  | Name           | Type      | Params\n","---------------------------------------------\n","0 | user_embedding | Embedding | 1.1 M \n","1 | item_embedding | Embedding | 1.1 M \n","2 | fc1            | Linear    | 1.1 K \n","3 | fc2            | Linear    | 2.1 K \n","4 | output         | Linear    | 33    \n","---------------------------------------------\n","2.2 M     Trainable params\n","0         Non-trainable params\n","2.2 M     Total params\n","8.645     Total estimated model params size (MB)\n","/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:566: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n","  cpuset_checked))\n"]},{"output_type":"display_data","data":{"text/plain":["Training: 0it [00:00, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"03b17df1969c455586a21fcb2f69cad8"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"]},{"output_type":"stream","name":"stdout","text":["The Hit Ratio @ 10 is 0.8606\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"iPeEGiylBlBp"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model = NCF3(64,32,64, num_users, num_items, train_ratings, all_movieIds)\n","trainer = pl.Trainer(max_epochs=5,gpus=1, logger=False, enable_checkpointing=False)\n","trainer.fit(model)\n","\n","# User-item pairs for testing\n","test_user_item_set = set(zip(test_ratings['userId'], test_ratings['movieId']))\n","# Dict of all items that are interacted with by each user\n","user_interacted_items = ratings.groupby('userId')['movieId'].apply(list).to_dict()\n","\n","hits = []\n","for (u,i) in test_user_item_set:\n","    interacted_items = user_interacted_items[u]\n","    not_interacted_items = set(all_movieIds) - set(interacted_items)\n","    selected_not_interacted = list(np.random.choice(list(not_interacted_items), 99))\n","    test_items = selected_not_interacted + [i]\n","    predicted_labels = np.squeeze(model(torch.tensor([u]*100), \n","                                        torch.tensor(test_items)).detach().numpy())\n","    top10_items = [test_items[i] for i in np.argsort(predicted_labels)[::-1][0:10].tolist()]\n","\n","    if i in top10_items:\n","        hits.append(1)\n","    else:\n","        hits.append(0)\n","        \n","print(\"The Hit Ratio @ 10 is {:.4f}\".format(np.average(hits)))"],"metadata":{"id":"A_HYC9eQAEh_","colab":{"base_uri":"https://localhost:8080/","height":424,"referenced_widgets":["f807a0ff4998464ea0cc22c5be570f93","69f94a1f1b224728abf0cec13bf7d04e","fe150de1314149269aeaf8025d8852f6","8d4fbce6545c44adaab4536600812a03","6e9560b7701a40f9878606b0201080f5","a9e09624415d472c85214bd1cf1c384e","e4c26c5e7c36412fb51eac983ede20fd","27267e6802f1492e943b81a9962477a4","6639ae7002df419db22d596b8b579cc6","04b7c5658e1e4602a4aa2b17661f993a","ddb083fdfa584e20b79beee05419cd23"]},"executionInfo":{"status":"ok","timestamp":1669659351402,"user_tz":300,"elapsed":8182046,"user":{"displayName":"Osman Bulut","userId":"11961451946429092619"}},"outputId":"9d1cf370-6c02-41fd-995d-9d94879529c1"},"execution_count":14,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n","INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n","INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n","INFO:pytorch_lightning.callbacks.model_summary:\n","  | Name           | Type      | Params\n","---------------------------------------------\n","0 | user_embedding | Embedding | 1.1 M \n","1 | item_embedding | Embedding | 1.1 M \n","2 | fc1            | Linear    | 1.1 K \n","3 | fc2            | Linear    | 2.1 K \n","4 | fc3            | Linear    | 2.1 K \n","5 | output         | Linear    | 65    \n","---------------------------------------------\n","2.2 M     Trainable params\n","0         Non-trainable params\n","2.2 M     Total params\n","8.653     Total estimated model params size (MB)\n"]},{"output_type":"display_data","data":{"text/plain":["Training: 0it [00:00, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f807a0ff4998464ea0cc22c5be570f93"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:`Trainer.fit` stopped: `max_epochs=5` reached.\n"]},{"output_type":"stream","name":"stdout","text":["The Hit Ratio @ 10 is 0.8585\n"]}]},{"cell_type":"code","source":["model = NCF4(64,32,64,32, num_users, num_items, train_ratings, all_movieIds)\n","trainer = pl.Trainer(max_epochs=5,gpus=1, logger=False, enable_checkpointing=False)\n","trainer.fit(model)\n","\n","# User-item pairs for testing\n","test_user_item_set = set(zip(test_ratings['userId'], test_ratings['movieId']))\n","# Dict of all items that are interacted with by each user\n","user_interacted_items = ratings.groupby('userId')['movieId'].apply(list).to_dict()\n","\n","hits = []\n","for (u,i) in test_user_item_set:\n","    interacted_items = user_interacted_items[u]\n","    not_interacted_items = set(all_movieIds) - set(interacted_items)\n","    selected_not_interacted = list(np.random.choice(list(not_interacted_items), 99))\n","    test_items = selected_not_interacted + [i]\n","    predicted_labels = np.squeeze(model(torch.tensor([u]*100), \n","                                        torch.tensor(test_items)).detach().numpy())\n","    top10_items = [test_items[i] for i in np.argsort(predicted_labels)[::-1][0:10].tolist()]\n","\n","    if i in top10_items:\n","        hits.append(1)\n","    else:\n","        hits.append(0)\n","        \n","print(\"The Hit Ratio @ 10 is {:.4f}\".format(np.average(hits)))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":406,"referenced_widgets":["e4972194e45f4441bba8b89e40f1eb05","0c480094544d4557b55110f03ef53484","6a05044558ba4012b320239eb3bc1e30","b1daa4c15f134919b41b7bed418fcd4f","48bb32d4826d4508ac43006346880b67","01191fb01a744b7a92a818a4a7fb0ae5","d28affeecbd941cd936dad18cc831850","45b143918b29485bb65481a64a7d6ac0","f21b1bb1f13249aebb28d9c242bb6182","250bccfe6e354102968cc5809edfc452","a6116604b6a840c1b28c796e2526f2db"]},"id":"YOaV73azslcM","outputId":"1205e04a-7fe9-4f55-cf38-5c405db7c543"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["INFO:pytorch_lightning.utilities.rank_zero:GPU available: True (cuda), used: True\n","INFO:pytorch_lightning.utilities.rank_zero:TPU available: False, using: 0 TPU cores\n","INFO:pytorch_lightning.utilities.rank_zero:IPU available: False, using: 0 IPUs\n","INFO:pytorch_lightning.utilities.rank_zero:HPU available: False, using: 0 HPUs\n","INFO:pytorch_lightning.accelerators.cuda:LOCAL_RANK: 0 - CUDA_VISIBLE_DEVICES: [0]\n","INFO:pytorch_lightning.callbacks.model_summary:\n","  | Name           | Type      | Params\n","---------------------------------------------\n","0 | user_embedding | Embedding | 1.1 M \n","1 | item_embedding | Embedding | 1.1 M \n","2 | fc1            | Linear    | 1.1 K \n","3 | fc2            | Linear    | 2.1 K \n","4 | fc3            | Linear    | 2.1 K \n","5 | fc4            | Linear    | 2.1 K \n","6 | output         | Linear    | 33    \n","---------------------------------------------\n","2.2 M     Trainable params\n","0         Non-trainable params\n","2.2 M     Total params\n","8.662     Total estimated model params size (MB)\n"]},{"output_type":"display_data","data":{"text/plain":["Training: 0it [00:00, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e4972194e45f4441bba8b89e40f1eb05"}},"metadata":{}}]}]}