comparison fitted_model_eval.py @ 10:9b6faa256f15 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ea12f973df4b97a2691d9e4ce6bf6fae59d57717"

9:e3b420d0b71a

    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":
        header = "infer" if params["input_options"]["header1"] else None
        column_option = params["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
        if column_option in [
            "by_index_number",
            "all_but_by_index_number",
            "by_header_name",
            "all_but_by_header_name",

    elif input_type == "sparse":
        X = mmread(open(infile1, "r"))

    # Get target y
    header = "infer" if params["input_options"]["header2"] else None
    column_option = params["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
    if column_option in [
        "by_index_number",
        "all_but_by_index_number",
        "by_header_name",
        "all_but_by_header_name",

        infile2 = loaded_df[df_key]
    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

    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

    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():

10:9b6faa256f15

    input_type = params["input_options"]["selected_input"]
    # tabular input
    if input_type == "tabular":
        header = "infer" if params["input_options"]["header1"] else None
        column_option = params["input_options"]["column_selector_options_1"][
            "selected_column_selector_option"
        ]
        if column_option in [
            "by_index_number",
            "all_but_by_index_number",
            "by_header_name",
            "all_but_by_header_name",

    elif input_type == "sparse":
        X = mmread(open(infile1, "r"))

    # Get target y
    header = "infer" if params["input_options"]["header2"] else None
    column_option = params["input_options"]["column_selector_options_2"][
        "selected_column_selector_option2"
    ]
    if column_option in [
        "by_index_number",
        "all_but_by_index_number",
        "by_header_name",
        "all_but_by_header_name",

        infile2 = loaded_df[df_key]
    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

    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

    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():