comparison model_prediction.py @ 25:118e230e85ce draft

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

24:b1eda492f063

import json
import warnings

import numpy as np
import pandas as pd
from galaxy_ml.utils import get_module, load_model, read_columns, try_get_attr
from scipy.io import mmread
from sklearn.pipeline import Pipeline

N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))


def main(
    inputs,
    infile_estimator,
    outfile_predict,
    infile_weights=None,
    infile1=None,
    fasta_path=None,
    ref_seq=None,
    vcf_path=None,
):

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

    infile_estimator : strgit
        File path to trained estimator input

    outfile_predict : str
        File path to save the prediction results, tabular

    infile_weights : str
        File path to weights input

    infile1 : str
        File path to dataset containing features

    fasta_path : str

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

    # load model
    with open(infile_estimator, "rb") as est_handler:
        estimator = load_model(est_handler)

    main_est = estimator
    if isinstance(estimator, Pipeline):
        main_est = estimator.steps[-1][-1]
    if hasattr(main_est, "config") and hasattr(main_est, "load_weights"):
        if not infile_weights or infile_weights == "None":
            raise ValueError(
                "The selected model skeleton asks for weights, "
                "but dataset for weights wan not selected!"
            )
        main_est.load_weights(infile_weights)

    # handle data input
    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":

if __name__ == "__main__":
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
    aparser.add_argument("-w", "--infile_weights", dest="infile_weights")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-v", "--vcf_path", dest="vcf_path")

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_predict,
        infile_weights=args.infile_weights,
        infile1=args.infile1,
        fasta_path=args.fasta_path,
        ref_seq=args.ref_seq,
        vcf_path=args.vcf_path,
    )

25:118e230e85ce

import json
import warnings

import numpy as np
import pandas as pd
from galaxy_ml.model_persist import load_model_from_h5
from galaxy_ml.utils import (clean_params, get_module, read_columns,
                             try_get_attr)
from scipy.io import mmread

N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1))


def main(
    inputs,
    infile_estimator,
    outfile_predict,
    infile1=None,
    fasta_path=None,
    ref_seq=None,
    vcf_path=None,
):

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

    infile_estimator : str
        File path to trained estimator input

    outfile_predict : str
        File path to save the prediction results, tabular

    infile1 : str
        File path to dataset containing features

    fasta_path : str

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

    # load model
    estimator = load_model_from_h5(infile_estimator)
    estimator = clean_params(estimator)

    # handle data input
    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":

if __name__ == "__main__":
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-O", "--outfile_predict", dest="outfile_predict")
    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
    aparser.add_argument("-v", "--vcf_path", dest="vcf_path")

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_predict,
        infile1=args.infile1,
        fasta_path=args.fasta_path,
        ref_seq=args.ref_seq,
        vcf_path=args.vcf_path,
    )