comparison ml_visualization_ex.py @ 33:ecd247e1ea9c draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2afb24f3c81d625312186750a714d702363012b5"

32:a84e6ffd3735

from keras.models import model_from_json
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.pipeline import Pipeline
from galaxy_ml.utils import load_model, read_columns, SafeEval


safe_eval = SafeEval()

    plt.savefig(os.path.join(folder, "output.svg"), format="svg")
    os.rename(os.path.join(folder, "output.svg"),
              os.path.join(folder, "output"))


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):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    fasta_path : str, default is None
        File path to dataset containing fasta file

    model_config : str, default is None
        File path to dataset containing JSON config for neural networks
    """
    warnings.simplefilter('ignore')

    with open(inputs, 'r') as param_handler:
        params = json.load(param_handler)

        plot_model(model, to_file="output.png")
        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)

    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-b", "--intervals", dest="intervals")
    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")
    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)

33:ecd247e1ea9c

from keras.models import model_from_json
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, confusion_matrix
from sklearn.pipeline import Pipeline
from galaxy_ml.utils import load_model, read_columns, SafeEval


safe_eval = SafeEval()

    plt.savefig(os.path.join(folder, "output.svg"), format="svg")
    os.rename(os.path.join(folder, "output.svg"),
              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, true_labels=None,
         predicted_labels=None, plot_color=None,
         title=None):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    fasta_path : str, default is None
        File path to dataset containing fasta file

    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')

    with open(inputs, 'r') as param_handler:
        params = json.load(param_handler)

        plot_model(model, to_file="output.png")
        os.rename('output.png', 'output')

        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)

    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-b", "--intervals", dest="intervals")
    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, true_labels=args.true_labels,
         predicted_labels=args.predicted_labels,
         plot_color=args.plot_color,
         title=args.title)