New datasets

genrl/utils/__init__.py

@@ -3,6 +3,8 @@
     CensusDataBandit,
     CovertypeDataBandit,
     DataBasedBandit,
+    FinancialDataBandit,
+    JesterDataBandit,
     MagicDataBandit,
     MushroomDataBandit,
     StatlogDataBandit,

genrl/utils/data_bandits/__init__.py

@@ -2,6 +2,8 @@
 from genrl.utils.data_bandits.base import DataBasedBandit
 from genrl.utils.data_bandits.census_bandit import CensusDataBandit
 from genrl.utils.data_bandits.covertype_bandit import CovertypeDataBandit
+from genrl.utils.data_bandits.financial_bandit import FinancialDataBandit
+from genrl.utils.data_bandits.jester_bandit import JesterDataBandit
 from genrl.utils.data_bandits.magic_bandit import MagicDataBandit
 from genrl.utils.data_bandits.mushroom_bandit import MushroomDataBandit
 from genrl.utils.data_bandits.statlog_bandit import StatlogDataBandit

genrl/utils/data_bandits/financial_bandit.py

@@ -0,0 +1,112 @@
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import pandas as pd
+import torch
+
+from genrl.utils.data_bandits.base import DataBasedBandit
+from genrl.utils.data_bandits.utils import download_data
+
+URL = "https://storage.googleapis.com/bandits_datasets/raw_stock_contexts"
+
+
+class FinancialDataBandit(DataBasedBandit):
+    """A contextual bandit based on Financial Stock data.
+
+    Source:
+        https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits
+
+    Args:
+        path (str, optional): Path to the data. Defaults to "./data/Financial/".
+        download (bool, optional): Whether to download the data. Defaults to False.
+        force_download (bool, optional): Whether to force download even if file exists.
+            Defaults to False.
+        url (Union[str, None], optional): URL to download data from. Defaults to None
+            which implies use of source URL.
+        device (str): Device to use for tensor operations.
+            "cpu" for cpu or "cuda" for cuda. Defaults to "cpu".
+
+    Attributes:
+        n_actions (int): Number of actions available.
+        context_dim (int): The length of context vector.
+        len (int): The number of examples (context, reward pairs) in the dataset.
+        device (torch.device): Device to use for tensor operations.
+
+    Raises:
+        FileNotFoundError: If file is not found at specified path.
+    """
+
+    def __init__(self, **kwargs):
+        super(FinancialDataBandit, self).__init__(kwargs.get("device", "cpu"))
+
+        self.n_actions = 8
+
+        path = kwargs.get("path", "./data/Financial/")
+        download = kwargs.get("download", None)
+        force_download = kwargs.get("force_download", None)
+        url = kwargs.get("url", URL)
+
+        if download:
+            fpath = download_data(path, url, force_download)
+            self.df = pd.read_csv(
+                fpath, header=None, skiprows=[0], sep=" ", dtype=np.float32
+            ).dropna()
+        else:
+            fpath = Path(path).joinpath("raw_stock_contexts")
+            self.df = pd.read_csv(
+                fpath, header=None, skiprows=[0], sep=" ", dtype=np.float32
+            ).dropna()
+
+        self.context_dim = self.df.shape[1]
+        self.len = len(self.df)
+
+        self._generate_rewards()
+
+    def _generate_rewards(self):
+        # Vector with additive noise levels for each action
+        noise_stds = [0.01 * (i + 1) for i in range(self.n_actions)]
+        betas = np.random.uniform(-1, 1, (self.context_dim, self.n_actions))
+        betas /= np.linalg.norm(betas, axis=0)
+
+        mean_rewards = np.dot(self.df, betas)
+        noise = np.random.normal(scale=noise_stds, size=mean_rewards.shape)
+
+        self.rewards = mean_rewards + noise
+        self.max_rewards = np.max(self.rewards, axis=1)
+
+    def reset(self) -> torch.Tensor:
+        """Reset bandit by shuffling indices and get new context.
+
+        Returns:
+            torch.Tensor: Current context selected by bandit.
+        """
+        self._reset()
+        self.df = self.df.sample(frac=1).reset_index(drop=True)
+        self._generate_rewards()
+        return self._get_context()
+
+    def _compute_reward(self, action: int) -> Tuple[int, int]:
+        """Compute the reward for a given action.
+
+        Args:
+            action (int): The action to compute reward for.
+
+        Returns:
+            Tuple[int, int]: Computed reward.
+        """
+        r = self.rewards[self.idx, action]
+        max_r = self.max_rewards[self.idx]
+        return r, max_r
+
+    def _get_context(self) -> torch.Tensor:
+        """Get the vector for current selected context.
+
+        Returns:
+            torch.Tensor: Current context vector.
+        """
+        return torch.tensor(
+            self.df.iloc[self.idx],
+            device=self.device,
+            dtype=torch.float,
+        )

genrl/utils/data_bandits/jester_bandit.py

@@ -0,0 +1,101 @@
+from pathlib import Path
+from typing import Tuple
+
+import numpy as np
+import torch
+
+from genrl.utils.data_bandits.base import DataBasedBandit
+from genrl.utils.data_bandits.utils import download_data
+
+URL = (
+    "https://storage.googleapis.com/bandits_datasets/jester_data_40jokes_19181users.npy"
+)
+
+
+class JesterDataBandit(DataBasedBandit):
+    """A contextual bandit based on the Jester Dataset of Goldberg et al., 2001.
+
+    Source:
+        https://github.com/tensorflow/models/tree/archive/research/deep_contextual_bandits
+
+    Args:
+        path (str, optional): Path to the data. Defaults to "./data/Jester/".
+        download (bool, optional): Whether to download the data. Defaults to False.
+        force_download (bool, optional): Whether to force download even if file exists.
+            Defaults to False.
+        url (Union[str, None], optional): URL to download data from. Defaults to None
+            which implies use of source URL.
+        device (str): Device to use for tensor operations.
+            "cpu" for cpu or "cuda" for cuda. Defaults to "cpu".
+
+    Attributes:
+        n_actions (int): Number of actions available.
+        context_dim (int): The length of context vector.
+        len (int): The number of examples (context, reward pairs) in the dataset.
+        device (torch.device): Device to use for tensor operations.
+
+    Raises:
+        FileNotFoundError: If file is not found at specified path.
+    """
+
+    def __init__(self, **kwargs):
+        super(JesterDataBandit, self).__init__(kwargs.get("device", "cpu"))
+
+        self.n_actions = 8
+
+        path = kwargs.get("path", "./data/Jester/")
+        download = kwargs.get("download", None)
+        force_download = kwargs.get("force_download", None)
+        url = kwargs.get("url", URL)
+
+        if download:
+            fpath = download_data(path, url, force_download)
+        else:
+            fpath = Path(path).joinpath("jester_data_40jokes_19181users.npy")
+        self.df = np.load(fpath)
+
+        self.context_dim = self.df.shape[1] - self.n_actions
+        self.len = len(self.df)
+
+        self.rewards = self.df[:, self.context_dim :]
+        self.max_rewards = np.max(self.rewards, axis=1)
+
+    def reset(self) -> torch.Tensor:
+        """Reset bandit by shuffling indices and get new context.
+
+        Returns:
+            torch.Tensor: Current context selected by bandit.
+        """
+        self._reset()
+        self.df = self.df[:, np.random.permutation(self.df.shape[1])]
+        np.random.shuffle(self.df)
+
+        self.rewards = self.df[:, self.context_dim :]
+        self.max_rewards = np.max(self.rewards, axis=1)
+
+        return self._get_context()
+
+    def _compute_reward(self, action: int) -> Tuple[int, int]:
+        """Compute the reward for a given action.
+
+        Args:
+            action (int): The action to compute reward for.
+
+        Returns:
+            Tuple[int, int]: Computed reward.
+        """
+        r = self.rewards[self.idx, action]
+        max_r = self.max_rewards[self.idx]
+        return r, max_r
+
+    def _get_context(self) -> torch.Tensor:
+        """Get the vector for current selected context.
+
+        Returns:
+            torch.Tensor: Current context vector.
+        """
+        return torch.tensor(
+            self.df[self.idx, : self.context_dim],
+            device=self.device,
+            dtype=torch.float,
+        )