--- a/pca.py	Fri Oct 02 08:51:25 2020 +0000
+++ b/pca.py	Tue Apr 13 19:01:30 2021 +0000
@@ -1,98 +1,185 @@
 import argparse
+
 import numpy as np
-from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA
 from galaxy_ml.utils import read_columns
+from sklearn.decomposition import IncrementalPCA, KernelPCA, PCA
+
 
 def main():
-    parser = argparse.ArgumentParser(description='RDKit screen')
-    parser.add_argument('-i', '--infile',
-                        help="Input file")
-    parser.add_argument('--header', action='store_true', help="Include the header row or skip it")
-    parser.add_argument('-c', '--columns', type=str.lower, default='all', choices=['by_index_number', 'all_but_by_index_number',\
-                        'by_header_name', 'all_but_by_header_name', 'all_columns'],
-                        help="Choose to select all columns, or exclude/include some")
-    parser.add_argument('-ci', '--column_indices', type=str.lower,
-                        help="Choose to select all columns, or exclude/include some")
-    parser.add_argument('-n', '--number', nargs='?', type=int, default=None,\
-                        help="Number of components to keep. If not set, all components are kept")
-    parser.add_argument('--whiten', action='store_true', help="Whiten the components")
-    parser.add_argument('-t', '--pca_type', type=str.lower, default='classical', choices=['classical', 'incremental', 'kernel'],
-                        help="Choose which flavour of PCA to use")
-    parser.add_argument('-s', '--svd_solver', type=str.lower, default='auto', choices=['auto', 'full', 'arpack', 'randomized'],
-                        help="Choose the type of svd solver.")
-    parser.add_argument('-b', '--batch_size', nargs='?', type=int, default=None,\
-                        help="The number of samples to use for each batch")
-    parser.add_argument('-k', '--kernel', type=str.lower, default='linear',\
-                        choices=['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'],
-                        help="Choose the type of kernel.")
-    parser.add_argument('-g', '--gamma', nargs='?', type=float, default=None,
-                        help='Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels')
-    parser.add_argument('-tol', '--tolerance', type=float, default=0.0,
-                        help='Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack')
-    parser.add_argument('-mi', '--max_iter', nargs='?', type=int, default=None,\
-                        help="Maximum number of iterations for arpack")
-    parser.add_argument('-d', '--degree', type=int, default=3,\
-                        help="Degree for poly kernels. Ignored by other kernels")
-    parser.add_argument('-cf', '--coef0', type=float, default=1.0,
-                        help='Independent term in poly and sigmoid kernels')
-    parser.add_argument('-e', '--eigen_solver', type=str.lower, default='auto', choices=['auto', 'dense', 'arpack'],
-                        help="Choose the type of eigen solver.")
-    parser.add_argument('-o', '--outfile',
-                        help="Base name for output file (no extension).")
+    parser = argparse.ArgumentParser(description="RDKit screen")
+    parser.add_argument("-i", "--infile", help="Input file")
+    parser.add_argument(
+        "--header", action="store_true", help="Include the header row or skip it"
+    )
+    parser.add_argument(
+        "-c",
+        "--columns",
+        type=str.lower,
+        default="all",
+        choices=[
+            "by_index_number",
+            "all_but_by_index_number",
+            "by_header_name",
+            "all_but_by_header_name",
+            "all_columns",
+        ],
+        help="Choose to select all columns, or exclude/include some",
+    )
+    parser.add_argument(
+        "-ci",
+        "--column_indices",
+        type=str.lower,
+        help="Choose to select all columns, or exclude/include some",
+    )
+    parser.add_argument(
+        "-n",
+        "--number",
+        nargs="?",
+        type=int,
+        default=None,
+        help="Number of components to keep. If not set, all components are kept",
+    )
+    parser.add_argument("--whiten", action="store_true", help="Whiten the components")
+    parser.add_argument(
+        "-t",
+        "--pca_type",
+        type=str.lower,
+        default="classical",
+        choices=["classical", "incremental", "kernel"],
+        help="Choose which flavour of PCA to use",
+    )
+    parser.add_argument(
+        "-s",
+        "--svd_solver",
+        type=str.lower,
+        default="auto",
+        choices=["auto", "full", "arpack", "randomized"],
+        help="Choose the type of svd solver.",
+    )
+    parser.add_argument(
+        "-b",
+        "--batch_size",
+        nargs="?",
+        type=int,
+        default=None,
+        help="The number of samples to use for each batch",
+    )
+    parser.add_argument(
+        "-k",
+        "--kernel",
+        type=str.lower,
+        default="linear",
+        choices=["linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"],
+        help="Choose the type of kernel.",
+    )
+    parser.add_argument(
+        "-g",
+        "--gamma",
+        nargs="?",
+        type=float,
+        default=None,
+        help="Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels",
+    )
+    parser.add_argument(
+        "-tol",
+        "--tolerance",
+        type=float,
+        default=0.0,
+        help="Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack",
+    )
+    parser.add_argument(
+        "-mi",
+        "--max_iter",
+        nargs="?",
+        type=int,
+        default=None,
+        help="Maximum number of iterations for arpack",
+    )
+    parser.add_argument(
+        "-d",
+        "--degree",
+        type=int,
+        default=3,
+        help="Degree for poly kernels. Ignored by other kernels",
+    )
+    parser.add_argument(
+        "-cf",
+        "--coef0",
+        type=float,
+        default=1.0,
+        help="Independent term in poly and sigmoid kernels",
+    )
+    parser.add_argument(
+        "-e",
+        "--eigen_solver",
+        type=str.lower,
+        default="auto",
+        choices=["auto", "dense", "arpack"],
+        help="Choose the type of eigen solver.",
+    )
+    parser.add_argument(
+        "-o", "--outfile", help="Base name for output file (no extension)."
+    )
     args = parser.parse_args()
 
     usecols = None
-    cols = []
     pca_params = {}
 
-    if args.columns == 'by_index_number' or args.columns == 'all_but_by_index_number':
-        usecols = [int(i) for i in args.column_indices.split(',')]
-    elif args.columns == 'by_header_name' or args.columns == 'all_but_by_header_name':
+    if args.columns == "by_index_number" or args.columns == "all_but_by_index_number":
+        usecols = [int(i) for i in args.column_indices.split(",")]
+    elif args.columns == "by_header_name" or args.columns == "all_but_by_header_name":
         usecols = args.column_indices
 
-    header = 'infer' if args.header else None
+    header = "infer" if args.header else None
 
     pca_input = read_columns(
         f=args.infile,
         c=usecols,
         c_option=args.columns,
-        sep='\t',
+        sep="\t",
         header=header,
         parse_dates=True,
         encoding=None,
-        index_col=None)
+        index_col=None,
+    )
 
-    pca_params.update({'n_components': args.number})
+    pca_params.update({"n_components": args.number})
 
-    if args.pca_type == 'classical':
-        pca_params.update({'svd_solver': args.svd_solver, 'whiten': args.whiten})
-        if args.svd_solver == 'arpack':
-            pca_params.update({'tol': args.tolerance})
+    if args.pca_type == "classical":
+        pca_params.update({"svd_solver": args.svd_solver, "whiten": args.whiten})
+        if args.svd_solver == "arpack":
+            pca_params.update({"tol": args.tolerance})
         pca = PCA()
 
-    elif args.pca_type == 'incremental':
-        pca_params.update({'batch_size': args.batch_size, 'whiten': args.whiten})
+    elif args.pca_type == "incremental":
+        pca_params.update({"batch_size": args.batch_size, "whiten": args.whiten})
         pca = IncrementalPCA()
 
-    elif args.pca_type == 'kernel':
-        pca_params.update({'kernel': args.kernel, 'eigen_solver': args.eigen_solver, 'gamma': args.gamma})
+    elif args.pca_type == "kernel":
+        pca_params.update(
+            {
+                "kernel": args.kernel,
+                "eigen_solver": args.eigen_solver,
+                "gamma": args.gamma,
+            }
+        )
 
-        if args.kernel == 'poly':
-            pca_params.update({'degree': args.degree, 'coef0': args.coef0})
-        elif args.kernel == 'sigmoid':
-            pca_params.update({'coef0': args.coef0})
-        elif args.kernel == 'precomputed':
+        if args.kernel == "poly":
+            pca_params.update({"degree": args.degree, "coef0": args.coef0})
+        elif args.kernel == "sigmoid":
+            pca_params.update({"coef0": args.coef0})
+        elif args.kernel == "precomputed":
             pca_input = np.dot(pca_input, pca_input.T)
 
-        if args.eigen_solver == 'arpack':
-            pca_params.update({'tol': args.tolerance, 'max_iter': args.max_iter})
+        if args.eigen_solver == "arpack":
+            pca_params.update({"tol": args.tolerance, "max_iter": args.max_iter})
 
         pca = KernelPCA()
 
     print(pca_params)
     pca.set_params(**pca_params)
     pca_output = pca.fit_transform(pca_input)
-    np.savetxt(fname=args.outfile, X=pca_output, fmt='%.4f', delimiter='\t')
+    np.savetxt(fname=args.outfile, X=pca_output, fmt="%.4f", delimiter="\t")
 
 
 if __name__ == "__main__":