--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pca.py	Sat May 01 01:24:32 2021 +0000
@@ -0,0 +1,186 @@
+import argparse
+
+import numpy as np
+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)."
+    )
+    args = parser.parse_args()
+
+    usecols = None
+    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":
+        usecols = args.column_indices
+
+    header = "infer" if args.header else None
+
+    pca_input = read_columns(
+        f=args.infile,
+        c=usecols,
+        c_option=args.columns,
+        sep="\t",
+        header=header,
+        parse_dates=True,
+        encoding=None,
+        index_col=None,
+    )
+
+    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})
+        pca = PCA()
+
+    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,
+            }
+        )
+
+        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})
+
+        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")
+
+
+if __name__ == "__main__":
+    main()