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fixes deprecated RandomizedPCA for sklearn #5

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fixes deprecated RandomizedPCA for sklearn #5

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8 changes: 4 additions & 4 deletions polyseq/dim.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import RandomizedPCA
from sklearn.decomposition import PCA
from sklearn.neighbors.kde import KernelDensity
from MulticoreTSNE import MulticoreTSNE
import umap as umap_module
Expand Down Expand Up @@ -40,7 +40,7 @@ def pca(data, k=None, n_shuffles=100, alpha=0.05, n_processes=1, max_pcs=100, pl
if k is not None:
with warnings.catch_warnings():
warnings.simplefilter('ignore')
pca = RandomizedPCA(n_components=k)
pca = PCA(n_components=k, svd_solver='randomized')
proj = pca.fit_transform(zscored)
col_names = ["pc-{}".format(i) for i in range(proj.shape[1])]
return ExpressionMatrix(proj, columns=col_names)._finalize(index=data.index)
Expand All @@ -55,7 +55,7 @@ def bootstrap_pc(seed):

with warnings.catch_warnings():
warnings.simplefilter('ignore')
pca = RandomizedPCA(n_components=1)
pca = PCA(n_components=1, svd_solver='randomized')
pca.fit(b)
return pca.explained_variance_[0]

Expand All @@ -65,7 +65,7 @@ def bootstrap_pc(seed):

with warnings.catch_warnings():
warnings.simplefilter('ignore')
pca = RandomizedPCA(n_components=max_pcs)
pca = PCA(n_components=max_pcs, svd_solver='randomized')
proj = pca.fit_transform(zscored)

inds = np.where(pca.explained_variance_ < cutoff)[0]
Expand Down