comparison fitted_model_eval.py @ 25:fc99237f3392 draft

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

24:301e07345c93

import argparse
import json
import warnings

import pandas as pd
from galaxy_ml.utils import get_scoring, load_model, read_columns
from scipy.io import mmread
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
from sklearn.pipeline import Pipeline


def _get_X_y(params, infile1, infile2):
    """read from inputs and output X and y

    else:
        infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = infile2

    y = read_columns(
        infile2, c=c, c_option=column_option, sep="\t", header=header, parse_dates=True
    )
    if len(y.shape) == 2 and y.shape[1] == 1:
        y = y.ravel()

    return X, y


def main(
    inputs,
    infile_estimator,
    outfile_eval,
    infile_weights=None,
    infile1=None,
    infile2=None,
):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : strgit
        File path to trained estimator input

    outfile_eval : str
        File path to save the evalulation results, tabular

    infile_weights : str
        File path to weights input

    infile1 : str
        File path to dataset containing features

    infile2 : str

        params = json.load(param_handler)

    X_test, y_test = _get_X_y(params, infile1, infile2)

    # 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 no dataset for weights was provided!"
            )
        main_est.load_weights(infile_weights)

    # handle scorer, convert to scorer dict
    # Check if scoring is specified
    scoring = params["scoring"]
    if scoring is not None:
        # get_scoring() expects secondary_scoring to be a comma separated string (not a list)
        # Check if secondary_scoring is specified
        secondary_scoring = scoring.get("secondary_scoring", None)
        if secondary_scoring is not None:
            # If secondary_scoring is specified, convert the list into comman separated string
            scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"])

    scorer = get_scoring(scoring)
    scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer)

    if hasattr(estimator, "evaluate"):
        scores = estimator.evaluate(
            X_test, y_test=y_test, scorer=scorer, is_multimetric=True
        )
    else:
        scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True)

    # handle output
    for name, score in scores.items():
        scores[name] = [score]
    df = pd.DataFrame(scores)

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("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_eval,
        infile_weights=args.infile_weights,
        infile1=args.infile1,
        infile2=args.infile2,
    )

25:fc99237f3392

import argparse
import json
import warnings

import pandas as pd
from galaxy_ml.model_persist import load_model_from_h5
from galaxy_ml.utils import clean_params, get_scoring, read_columns
from scipy.io import mmread
from sklearn.metrics._scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score


def _get_X_y(params, infile1, infile2):
    """read from inputs and output X and y

    else:
        infile2 = pd.read_csv(infile2, sep="\t", header=header, parse_dates=True)
        loaded_df[df_key] = infile2

    y = read_columns(
        infile2,
        c=c,
        c_option=column_option,
        sep="\t",
        header=header,
        parse_dates=True,
    )
    if len(y.shape) == 2 and y.shape[1] == 1:
        y = y.ravel()

    return X, y


def main(inputs, infile_estimator, outfile_eval, infile1=None, infile2=None):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : strgit
        File path to trained estimator input

    outfile_eval : str
        File path to save the evalulation results, tabular

    infile1 : str
        File path to dataset containing features

    infile2 : str

        params = json.load(param_handler)

    X_test, y_test = _get_X_y(params, infile1, infile2)

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

    # handle scorer, convert to scorer dict
    scoring = params["scoring"]
    scorer = get_scoring(scoring)
    if not isinstance(scorer, (dict, list)):
        scorer = [scoring["primary_scoring"]]
    scorer = _check_multimetric_scoring(estimator, scoring=scorer)

    if hasattr(estimator, "evaluate"):
        scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer)
    else:
        scores = _score(estimator, X_test, y_test, scorer)

    # handle output
    for name, score in scores.items():
        scores[name] = [score]
    df = pd.DataFrame(scores)

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("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval")
    args = aparser.parse_args()

    main(
        args.inputs,
        args.infile_estimator,
        args.outfile_eval,
        infile1=args.infile1,
        infile2=args.infile2,
    )