Repository 'sklearn_numeric_clustering'
hg clone https://toolshed.g2.bx.psu.edu/repos/bgruening/sklearn_numeric_clustering

Changeset 33:ad7e0b4291ee (2020-10-01)
Previous changeset 32:1dd433d2c92c (2020-01-22) Next changeset 34:816b65d52c33 (2021-04-13)
Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"
modified:
main_macros.xml
ml_visualization_ex.py
added:
pca.py
test-data/ml_confusion_predicted.tabular
test-data/ml_confusion_true.tabular
test-data/ml_confusion_viz.png
test-data/pca_classical_header_names_output.dat
test-data/pca_classical_output.dat
test-data/pca_incremental_header_names_output.dat
test-data/pca_incremental_output.dat
test-data/pca_input.dat
test-data/pca_input_with_headers.dat
test-data/predicted_header.tabular
test-data/true_header.tabular
b
diff -r 1dd433d2c92c -r ad7e0b4291ee main_macros.xml
--- a/main_macros.xml Wed Jan 22 08:08:34 2020 -0500
+++ b/main_macros.xml Thu Oct 01 20:40:11 2020 +0000
b
@@ -1,5 +1,5 @@
 <macros>
-  <token name="@VERSION@">1.0.8.1</token>
+  <token name="@VERSION@">1.0.8.2</token>
 
   <xml name="python_requirements">
       <requirements>
b
diff -r 1dd433d2c92c -r ad7e0b4291ee ml_visualization_ex.py
--- a/ml_visualization_ex.py Wed Jan 22 08:08:34 2020 -0500
+++ b/ml_visualization_ex.py Thu Oct 01 20:40:11 2020 +0000
[
@@ -13,7 +13,7 @@
 from keras.utils import plot_model
 from sklearn.feature_selection.base import SelectorMixin
 from sklearn.metrics import precision_recall_curve, average_precision_score
-from sklearn.metrics import roc_curve, auc
+from sklearn.metrics import roc_curve, auc, confusion_matrix
 from sklearn.pipeline import Pipeline
 from galaxy_ml.utils import load_model, read_columns, SafeEval
 
@@ -266,12 +266,29 @@
               os.path.join(folder, "output"))
 
 
+def get_dataframe(file_path, plot_selection, header_name, column_name):
+    header = 'infer' if plot_selection[header_name] else None
+    column_option = plot_selection[column_name]["selected_column_selector_option"]
+    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
+        col = plot_selection[column_name]["col1"]
+    else:
+        col = None
+    _, input_df = read_columns(file_path, c=col,
+                                   c_option=column_option,
+                                   return_df=True,
+                                   sep='\t', header=header,
+                                   parse_dates=True)
+    return input_df
+
+
 def main(inputs, infile_estimator=None, infile1=None,
          infile2=None, outfile_result=None,
          outfile_object=None, groups=None,
          ref_seq=None, intervals=None,
          targets=None, fasta_path=None,
-         model_config=None):
+         model_config=None, true_labels=None,
+         predicted_labels=None, plot_color=None,
+         title=None):
     """
     Parameter
     ---------
@@ -311,6 +328,18 @@
 
     model_config : str, default is None
         File path to dataset containing JSON config for neural networks
+
+    true_labels : str, default is None
+        File path to dataset containing true labels
+
+    predicted_labels : str, default is None
+        File path to dataset containing true predicted labels
+
+    plot_color : str, default is None
+        Color of the confusion matrix heatmap
+
+    title : str, default is None
+        Title of the confusion matrix heatmap
     """
     warnings.simplefilter('ignore')
 
@@ -543,6 +572,32 @@
 
         return 0
 
+    elif plot_type == 'classification_confusion_matrix':
+        plot_selection = params["plotting_selection"]
+        input_true = get_dataframe(true_labels, plot_selection, "header_true", "column_selector_options_true")
+        header_predicted = 'infer' if plot_selection["header_predicted"] else None
+        input_predicted = pd.read_csv(predicted_labels, sep='\t', parse_dates=True, header=header_predicted)
+        true_classes = input_true.iloc[:, -1].copy()
+        predicted_classes = input_predicted.iloc[:, -1].copy()
+        axis_labels = list(set(true_classes))
+        c_matrix = confusion_matrix(true_classes, predicted_classes)
+        fig, ax = plt.subplots(figsize=(7, 7))
+        im = plt.imshow(c_matrix, cmap=plot_color)
+        for i in range(len(c_matrix)):
+            for j in range(len(c_matrix)):
+                ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k")
+        ax.set_ylabel('True class labels')
+        ax.set_xlabel('Predicted class labels')
+        ax.set_title(title)
+        ax.set_xticks(axis_labels)
+        ax.set_yticks(axis_labels)
+        fig.colorbar(im, ax=ax)
+        fig.tight_layout()
+        plt.savefig("output.png", dpi=125)
+        os.rename('output.png', 'output')
+
+        return 0
+
     # save pdf file to disk
     # fig.write_image("image.pdf", format='pdf')
     # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
@@ -562,10 +617,17 @@
     aparser.add_argument("-t", "--targets", dest="targets")
     aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
     aparser.add_argument("-c", "--model_config", dest="model_config")
+    aparser.add_argument("-tl", "--true_labels", dest="true_labels")
+    aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels")
+    aparser.add_argument("-pc", "--plot_color", dest="plot_color")
+    aparser.add_argument("-pt", "--title", dest="title")
     args = aparser.parse_args()
 
     main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
          args.outfile_result, outfile_object=args.outfile_object,
          groups=args.groups, ref_seq=args.ref_seq, intervals=args.intervals,
          targets=args.targets, fasta_path=args.fasta_path,
-         model_config=args.model_config)
+         model_config=args.model_config, true_labels=args.true_labels,
+         predicted_labels=args.predicted_labels,
+         plot_color=args.plot_color,
+         title=args.title)
b
diff -r 1dd433d2c92c -r ad7e0b4291ee pca.py
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/pca.py Thu Oct 01 20:40:11 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()
b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/ml_confusion_predicted.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_confusion_predicted.tabular Thu Oct 01 20:40:11 2020 +0000
b
@@ -0,0 +1,34 @@
+0
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+1
+0
+0
+0
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+0
+0
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+0
+0
+0
b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/ml_confusion_true.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/ml_confusion_true.tabular Thu Oct 01 20:40:11 2020 +0000
b
@@ -0,0 +1,34 @@
+0
+0
+0
+0
+0
+0
+0
+0
+0
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+0
+1
+1
+1
+1
+1
+0
+0
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+0
+1
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+1
+1
+1
+1
+0
+0
+0
+0
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+0
b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/ml_confusion_viz.png
b
Binary file test-data/ml_confusion_viz.png has changed
b
diff -r 1dd433d2c92c -r ad7e0b4291ee 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 Thu Oct 01 20:40:11 2020 +0000
b
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/pca_classical_output.dat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_classical_output.dat Thu Oct 01 20:40:11 2020 +0000
b
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee 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 Thu Oct 01 20:40:11 2020 +0000
b
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/pca_incremental_output.dat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_incremental_output.dat Thu Oct 01 20:40:11 2020 +0000
b
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/pca_input.dat
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/pca_input.dat Thu Oct 01 20:40:11 2020 +0000
b
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee 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 Thu Oct 01 20:40:11 2020 +0000
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b
diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/predicted_header.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/predicted_header.tabular Thu Oct 01 20:40:11 2020 +0000
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diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/true_header.tabular
--- /dev/null Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/true_header.tabular Thu Oct 01 20:40:11 2020 +0000
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