New VAE Solver from pyod

solvers/vae.py

@@ -0,0 +1,77 @@
+from benchopt import BaseSolver, safe_import_context
+
+with safe_import_context() as import_ctx:
+    from pyod.models.vae import VAE
+    import numpy as np
+    import torch
+
+
+class Solver(BaseSolver):
+    name = "VAE"
+
+    install_cmd = "conda"
+    requirements = ["pyod", "tqdm", "pip:torch"]
+
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    sampling_strategy = "run_once"
+
+    parameters = {
+        "contamination": [0.005, 0.05, 0.1, 0.2],
+        "n_epochs": [50],
+        "window": [False],
+        "window_size": [256],
+        "horizon": [0],
+        "stride": [1],
+        "batch_size": [128],
+        "preprocessing": [True, False],
+        "latent_dim": [2, 5, 10],
+        "batch_norm": [True, False],
+        "dropout_rate": [0.1, 0.2, 0.5],
+    }
+
+    def set_objective(self, X_train, y_test, X_test):
+        self.X_train = X_train
+        self.X_test, self.y_test = X_test, y_test
+        self.clf = VAE(contamination=self.contamination,
+                       preprocessing=self.preprocessing,
+                       batch_size=self.batch_size,
+                       epoch_num=self.n_epochs,
+                       device=self.device,
+                       latent_dim=self.latent_dim,
+                       batch_norm=self.batch_norm,
+                       dropout_rate=self.dropout_rate,
+                       )
+
+        if self.window:
+            self.Xw_train = np.lib.stride_tricks.sliding_window_view(
+                    X_train,
+                    window_shape=self.window_size+self.horizon,
+                    axis=0
+                ).transpose(0, 2, 1)
+
+            if self.X_test is not None:
+                self.Xw_test = np.lib.stride_tricks.sliding_window_view(
+                    X_test,
+                    window_shape=self.window_size+self.horizon,
+                    axis=0
+                ).transpose(0, 2, 1)
+
+            if self.y_test is not None:
+                self.yw_test = np.lib.stride_tricks.sliding_window_view(
+                    self.y_test, window_shape=self.window_size, axis=0
+                )[::self.stride]
+
+                self.yw_test = torch.tensor(
+                    self.yw_test, dtype=torch.float32
+                )
+        else:
+            self.Xw_train = X_train
+            self.Xw_test = X_test
+
+    def run(self, _):
+        self.clf.fit(self.Xw_train)
+        self.y_pred = self.clf.predict(self.Xw_test)
+
+    def get_result(self):
+        return dict(y_hat=self.y_pred)