# HG changeset patch
# User bgruening
# Date 1601629171 0
# Node ID ce2fd1edbc6e599079a7a095fd85e565c735edb4
# Parent  145208b3579d7c22bbce5c9b1d1c9dd219d5c80d
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"

diff -r 145208b3579d -r ce2fd1edbc6e pca.py
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/pca.py	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,99 @@
+import argparse
+import numpy as np
+from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA
+from galaxy_ml.utils import read_columns
+
+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
+    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':
+        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()
diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_classical_header_names_output.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_classical_header_names_output.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,300 @@
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diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_classical_output.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_classical_output.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,300 @@
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diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_incremental_header_names_output.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_incremental_header_names_output.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,300 @@
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diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_incremental_output.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_incremental_output.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,300 @@
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diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_input.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_input.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,300 @@
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diff -r 145208b3579d -r ce2fd1edbc6e test-data/pca_input_with_headers.dat
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_input_with_headers.dat	Fri Oct 02 08:59:31 2020 +0000
@@ -0,0 +1,301 @@
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+2.307814819672060480e+00	4.104095493932964800e+00	2.489576320635186413e+00	4.496836765063628727e-01	4.578631912903595946e+00	1.329946843468508222e+00	1.683934112573501896e-01	7.002919109156724442e-02	2.471193668340454508e+00	5.351430311502515247e-01
diff -r 145208b3579d -r ce2fd1edbc6e train_test_split.xml
--- a/train_test_split.xml	Wed Mar 11 13:51:33 2020 -0400
+++ b/train_test_split.xml	Fri Oct 02 08:59:31 2020 +0000
@@ -268,7 +268,13 @@
     </tests>
     <help><![CDATA[
 **What it does**
-This tool implements splitter function and classes from `sklearn.model_selection` module to split contents (rows) of a table into two subsets for training and test, respectively . The simple train test split mode not only supports shuffle split and stratified shuffle split natively carried by the `train_test_split` function, but also gets extended to do group shuffle. The cross-validation splitter mode supports more diverse splitting strategies. Each tool run outputs one split, train and test. To get different splitting sets, for example, nested CV, multiple tool runs are needed with different `nth_split`.
+
+This tool implements splitter function and classes from `sklearn.model_selection` module to split contents (rows) of a table into
+two subsets for training and test, respectively . The simple train test split mode not only supports shuffle split and stratified
+shuffle split natively carried by the `train_test_split` function, but also gets extended to do group shuffle.
+The cross-validation splitter mode supports more diverse splitting strategies. Each tool run outputs one split, train and test.
+To get different splitting sets, for example, nested CV, multiple tool runs are needed with different `nth_split`.
+Example: 6-fold CV. Set `n_splits` to 6. Run the tool 6 times with the same parameters, but set `nth_split` according to the number of the run (1-6). 
 
 - Train Test Split mode
     - direct split, no shuffle
@@ -288,4 +294,4 @@
 
     ]]></help>
     <expand macro="sklearn_citation"/>
-</tool>
\ No newline at end of file
+</tool>