🎨 🔧 Log2, scaling and linting

- Optional log2 transform of continuous data
- Black formatting of training_loop.py

src/move/conf/schema.py

@@ -32,6 +32,7 @@ class InputConfig:
 @dataclass
 class ContinuousInputConfig(InputConfig):
     scale: bool = True
+    log2: bool = False
 
 
 @dataclass

src/move/data/preprocessing.py

@@ -64,7 +64,7 @@ def one_hot_encode_single(mapping: dict[str, int], value: Optional[str]) -> IntA
     return encoded_value
 
 
-def scale(x: np.ndarray) -> tuple[FloatArray, BoolArray]:
+def scale(x: np.ndarray, log2: bool = False) -> tuple[FloatArray, BoolArray]:
     """Center to mean and scale to unit variance. Convert NaN values to 0.
 
     Args:
@@ -74,7 +74,9 @@ def scale(x: np.ndarray) -> tuple[FloatArray, BoolArray]:
         Tuple containing (1) scaled output and (2) a 1D mask marking columns
         (i.e., features) without zero variance
     """
-    logx = np.log2(x + 1)
+    logx = x
+    if log2:
+        logx = np.log2(x + 1)
     mask_1d = ~np.isclose(np.nanstd(logx, axis=0), 0.0)
     scaled_x = standardize(logx[:, mask_1d], axis=0)
     scaled_x[np.isnan(scaled_x)] = 0

src/move/tasks/encode_data.py

@@ -55,18 +55,19 @@ def encode_data(config: DataConfig):
         # before preprocessing:
         fig = plot_value_distributions(values)
         fig_path = str(
-            output_path / "Value_distribution_{}_unprocessed.png".format(dataset_name)
+            output_path / f"Value_distribution_{dataset_name}_unprocessed.png"
         )
         fig.savefig(fig_path)
 
-        # Plotting the value distribution for all continuous datasets:
-        fig = plot_value_distributions(values)
-        fig_path = str(output_path / f"Value_distribution_{dataset_name}.png")
-        fig.savefig(fig_path)
-
         if scale:
-            values, mask_1d = preprocessing.scale(values)
+            logger.debug(f"Scaling dataset: {dataset_name}, log2 transform: {input_config.log2}")
+            values, mask_1d = preprocessing.scale(values, input_config.log2)
             names = names[mask_1d]
             logger.debug(f"Columns with zero variance: {np.sum(~mask_1d)}")
-        io.dump_names(interim_data_path / f"{input_config.name}.txt", names)
-        np.save(interim_data_path / f"{input_config.name}.npy", values)
+            # Plotting the value distribution for all continuous datasets:
+            fig = plot_value_distributions(values)
+            fig_path = str(output_path / f"Value_distribution_{dataset_name}.png")
+            fig.savefig(fig_path)
+
+        io.dump_names(interim_data_path / f"{dataset_name}.txt", names)
+        np.save(interim_data_path / f"{dataset_name}.npy", values)

src/move/training/training_loop.py

@@ -73,7 +73,6 @@ def training_loop(
 
     kld_weight = 0.0
 
-
     for epoch in range(1, num_epochs + 1):
         if epoch in kld_warmup_steps:
             kld_weight += 1 / len(kld_warmup_steps)

tutorial/config/data/random_continuous.yaml

@@ -17,4 +17,8 @@ categorical_inputs: [] # no categorical inputs
 
 continuous_inputs: # a list of continuous datasets
   - name: random.continuous.proteomics
+    log2: true
+    scale: true
   - name: random.continuous.metagenomics
+    log2: true
+    scale: true

tutorial/config/data/random_small.yaml

@@ -18,4 +18,8 @@ categorical_inputs: # a list of categorical datasets
 
 continuous_inputs: # a list of continuous datasets
   - name: random.small.proteomics
+    log2: true #apply log2 before scaling
+    scale: true #scale data (z-score normalize)
   - name: random.small.metagenomics
+    log2: true
+    scale: true