comparison keras_train_and_eval.py @ 21:9fda0b82f419 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 80417bf0158a9b596e485dd66408f738f405145a

20:5a125d1ac9b9

    inputs,
    infile_estimator,
    infile1,
    infile2,
    outfile_result,
    outfile_object=None,
    outfile_y_true=None,
    outfile_y_preds=None,
    groups=None,
    ref_seq=None,

        File path to dataset containing target values.

    outfile_result : str
        File path to save the results, either cv_results or test result.

    outfile_object : str, optional
        File path to save searchCV object.

    outfile_y_true : str, optional
        File path to target values for prediction.

    # swap hyperparameter
    swapping = params["experiment_schemes"]["hyperparams_swapping"]
    swap_params = _eval_swap_params(swapping)
    estimator.set_params(**swap_params)

    estimator_params = estimator.get_params()

    # store read dataframe object
    loaded_df = {}

    input_type = params["input_options"]["selected_input"]
    # tabular input

        ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)

    # train and eval
    if hasattr(estimator, "config") and hasattr(estimator, "model_type"):
        if exp_scheme == "train_val_test":
            estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
        else:
            estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
    else:
        estimator.fit(X_train, y_train)

    if isinstance(estimator, KerasGBatchClassifier):
        scores = {}
        steps = estimator.prediction_steps
        batch_size = estimator.batch_size
        data_generator = estimator.data_generator_

    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
    aparser.add_argument("-g", "--groups", dest="groups")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")

        args.inputs,
        args.infile_estimator,
        args.infile1,
        args.infile2,
        args.outfile_result,
        outfile_object=args.outfile_object,
        outfile_y_true=args.outfile_y_true,
        outfile_y_preds=args.outfile_y_preds,
        groups=args.groups,
        ref_seq=args.ref_seq,

21:9fda0b82f419

    inputs,
    infile_estimator,
    infile1,
    infile2,
    outfile_result,
    outfile_history=None,
    outfile_object=None,
    outfile_y_true=None,
    outfile_y_preds=None,
    groups=None,
    ref_seq=None,

        File path to dataset containing target values.

    outfile_result : str
        File path to save the results, either cv_results or test result.

    outfile_history : str, optional
        File path to save the training history.

    outfile_object : str, optional
        File path to save searchCV object.

    outfile_y_true : str, optional
        File path to target values for prediction.

    # swap hyperparameter
    swapping = params["experiment_schemes"]["hyperparams_swapping"]
    swap_params = _eval_swap_params(swapping)
    estimator.set_params(**swap_params)
    estimator_params = estimator.get_params()
    # store read dataframe object
    loaded_df = {}

    input_type = params["input_options"]["selected_input"]
    # tabular input

        ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options)

    # train and eval
    if hasattr(estimator, "config") and hasattr(estimator, "model_type"):
        if exp_scheme == "train_val_test":
            history = estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
        else:
            history = estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
    else:
        history = estimator.fit(X_train, y_train)
    if "callbacks" in estimator_params:
        for cb in estimator_params["callbacks"]:
            if cb["callback_selection"]["callback_type"] == "CSVLogger":
                hist_df = pd.DataFrame(history.history)
                hist_df["epoch"] = np.arange(1, estimator_params["epochs"] + 1)
                epo_col = hist_df.pop('epoch')
                hist_df.insert(0, 'epoch', epo_col)
                hist_df.to_csv(path_or_buf=outfile_history, sep="\t", header=True, index=False)
                break
    if isinstance(estimator, KerasGBatchClassifier):
        scores = {}
        steps = estimator.prediction_steps
        batch_size = estimator.batch_size
        data_generator = estimator.data_generator_

    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
    aparser.add_argument("-hi", "--outfile_history", dest="outfile_history")
    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
    aparser.add_argument("-g", "--groups", dest="groups")
    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")

        args.inputs,
        args.infile_estimator,
        args.infile1,
        args.infile2,
        args.outfile_result,
        outfile_history=args.outfile_history,
        outfile_object=args.outfile_object,
        outfile_y_true=args.outfile_y_true,
        outfile_y_preds=args.outfile_y_preds,
        groups=args.groups,
        ref_seq=args.ref_seq,