| Previous changeset 32:1dd433d2c92c (2020-01-22) Next changeset 34:816b65d52c33 (2021-04-13) |
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Commit message:
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5" |
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modified:
main_macros.xml ml_visualization_ex.py |
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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 |
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| 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 |
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| @@ -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> |
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| 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 |
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| @@ -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) |
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| 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 |
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| @@ -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() |
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| 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 |
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| @@ -0,0 +1,34 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 |
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| 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 |
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| @@ -0,0 +1,34 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +0 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 |
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| diff -r 1dd433d2c92c -r ad7e0b4291ee test-data/ml_confusion_viz.png |
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| Binary file test-data/ml_confusion_viz.png has changed |
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| 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 |
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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 |
| b |
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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 |
| b |
| @@ -0,0 +1,35 @@ +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 |
| b |
| 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 |
| b |
| @@ -0,0 +1,35 @@ +cancer +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +0 +1 +1 +1 +1 +1 +0 +0 +1 +1 +0 +1 +1 +1 +1 +1 +1 +1 +0 +0 +0 +0 +0 +0 |
