comparison ml_visualization_ex.py @ 16:dd157beef3e4 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 9981e25b00de29ed881b2229a173a8c812ded9bb

15:5d9bc5782f0e

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
from galaxy_ml.utils import load_model, read_columns, SafeEval
from keras.models import model_from_json
from keras.utils import plot_model
from sklearn.feature_selection.base import SelectorMixin
from sklearn.metrics import (auc, average_precision_score, confusion_matrix,
                             precision_recall_curve, roc_curve)
from sklearn.pipeline import Pipeline

safe_eval = SafeEval()

# plotly default colors
default_colors = [

    folder = os.getcwd()
    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,

    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

    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)

    title = params["plotting_selection"]["title"].strip()
    plot_type = params["plotting_selection"]["plot_type"]
    plot_format = params["plotting_selection"]["plot_format"]

    if plot_type == "feature_importances":
        with open(infile_estimator, "rb") as estimator_handler:
            estimator = load_model(estimator_handler)

        column_option = params["plotting_selection"]["column_selector_options"][
            "selected_column_selector_option"
        ]
        if column_option in [

        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,

        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,
    )

16:dd157beef3e4

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import plotly
import plotly.graph_objs as go
from galaxy_ml.model_persist import load_model_from_h5
from galaxy_ml.utils import read_columns, SafeEval
from sklearn.feature_selection._base import SelectorMixin
from sklearn.metrics import (
    auc,
    average_precision_score,
    precision_recall_curve,
    roc_curve,
)
from sklearn.pipeline import Pipeline
from tensorflow.keras.models import model_from_json
from tensorflow.keras.utils import plot_model

safe_eval = SafeEval()

# plotly default colors
default_colors = [

    folder = os.getcwd()
    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,

    ref_seq=None,
    intervals=None,
    targets=None,
    fasta_path=None,
    model_config=None,
):
    """
    Parameter
    ---------
    inputs : str

    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)

    title = params["plotting_selection"]["title"].strip()
    plot_type = params["plotting_selection"]["plot_type"]
    plot_format = params["plotting_selection"]["plot_format"]

    if plot_type == "feature_importances":
        estimator = load_model_from_h5(infile_estimator)

        column_option = params["plotting_selection"]["column_selector_options"][
            "selected_column_selector_option"
        ]
        if column_option in [

        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,

        ref_seq=args.ref_seq,
        intervals=args.intervals,
        targets=args.targets,
        fasta_path=args.fasta_path,
        model_config=args.model_config,
    )