cleanup

bnpm/cupy_helpers.py

@@ -1,8 +1,3 @@
-def import_cupy_pkgs():
-    import cuml
-    import cuml.decomposition
-    import cupy
-
 def set_device(device_num=0, verbose=True):
     """
     Set the device to use.
@@ -15,6 +10,7 @@ def set_device(device_num=0, verbose=True):
         verbose (bool):
             Whether to print the device name.
     """
+    import cupy
 
     if cupy.cuda.runtime.getDeviceCount() > 0:
         if device_num is None:

bnpm/decomposition.py

@@ -23,85 +23,6 @@
 ########## PCA ############
 ###########################
 
-def simple_pca(X , n_components=None , mean_sub=True, zscore=False, plot_pref=False , n_PCs_toPlot=2):
-    """
-    Performs PCA on X.
-    RH 2021
-
-    Args:
-        X (np.ndarray):
-            Data to be decomposed.
-            2-D array. Columns are features, rows are samples.
-        n_components (int):
-            Number of components to keep. If None, then
-             n_components = X.shape[1]
-        mean_sub (bool):
-            Whether or not to mean subtract ('center') the
-             columns.
-        zscore (bool):
-            Whether or not to z-score the columns. This is
-             equivalent to doing PCA on the correlation-matrix.
-        plot_pref (bool):
-            Whether or not to plot the first n_PCs_toPlot of the
-             PCA.
-        n_PCs_toPlot (int):
-            Number of PCs to plot.
-
-    Returns:
-        components (np.ndarray):
-            The components of the decomposition.
-            2-D array.
-            Each column is a component vector. Each row is a
-             feature weight.
-        scores (np.ndarray):
-            The scores of the decomposition.
-            2-D array.
-            Each column is a score vector. Each row is a
-             sample weight.
-        EVR (np.ndarray):
-            The explained variance ratio of each component.
-            1-D array.
-            Each element is the explained variance ratio of
-             the corresponding component.
-    """
-    if mean_sub and not zscore:
-        X = X - np.mean(X, axis=0)
-    if zscore:
-        # X = scipy.stats.zscore(X, axis=0)
-        X = X - np.mean(X, axis=0)
-        stds = np.std(X, axis=0)
-        X = X / stds[None,:]
-
-    if n_components is None:
-        n_components = X.shape[1]
-    decomp = sklearn.decomposition.PCA(n_components=n_components)
-    decomp.fit_transform(X)
-    components = decomp.components_
-    scores = decomp.transform(X)
-
-    if plot_pref:
-        fig , axs = plt.subplots(4 , figsize=(7,15))
-        axs[0].plot(np.arange(n_components)+1,
-                    decomp.explained_variance_ratio_)
-        axs[0].set_xscale('log')
-        axs[0].set_xlabel('component #')
-        axs[0].set_ylabel('explained variance ratio')
-
-        axs[1].plot(np.arange(n_components)+1, 
-                    np.cumsum(decomp.explained_variance_ratio_))
-        axs[1].set_xscale('log')
-        axs[1].set_ylabel('cumulative explained variance ratio')
-
-        axs[2].plot(scores[:,:n_PCs_toPlot])
-        axs[2].set_xlabel('sample num')
-        axs[2].set_ylabel('a.u.')
-
-        axs[3].plot(components.T[:,:n_PCs_toPlot])
-        axs[3].set_xlabel('feature num')
-        axs[3].set_ylabel('score')
-
-    return components , scores , decomp.explained_variance_ratio_
-
 
 def svd_flip(
     u: torch.Tensor, 

bnpm/h5_handling.py

@@ -2,6 +2,7 @@
 from pathlib import Path
 
 import h5py
+import numpy as np
 
 def close_all_h5():
     '''