comparison keras_train_and_eval.py @ 39:b4ebe47701e4 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 5eca9041ce0154eded5aec07195502d5eb3cdd4f

38:dc17e2878dc1

import numpy as np
import pandas as pd
from galaxy_ml.keras_galaxy_models import (
    _predict_generator,
    KerasGBatchClassifier,
)
from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    clean_params,

        main_est.set_params(memory=memory)

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

    # handle test (first) split
    test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]

        if hasattr(estimator, "predict_proba"):
            predictions = estimator.predict_proba(X_test)
        else:
            predictions = estimator.predict(X_test)

        y_true = y_test
        sk_scores = _score(estimator, X_test, y_test, scorer)
        scores.update(sk_scores)

    # handle output
    if outfile_y_true:
        try:

39:b4ebe47701e4

import numpy as np
import pandas as pd
from galaxy_ml.keras_galaxy_models import (
    _predict_generator,
    KerasGBatchClassifier,
    KerasGClassifier,
    KerasGRegressor
)
from galaxy_ml.model_persist import dump_model_to_h5, load_model_from_h5
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    clean_params,

        main_est.set_params(memory=memory)

    # handle scorer, convert to scorer dict
    scoring = params["experiment_schemes"]["metrics"]["scoring"]
    scorer = get_scoring(scoring)

    # We get 'None' back from the call to 'get_scoring()' if
    # the primary scoring is 'default'. Replace 'default' with
    # the default scoring for classification/regression (accuracy/r2)
    if scorer is None:
        if isinstance(estimator, KerasGClassifier):
            scorer = ['accuracy']
        if isinstance(estimator, KerasGRegressor):
            scorer = ['r2']

    scorer = _check_multimetric_scoring(estimator, scoring=scorer)

    # handle test (first) split
    test_split_options = params["experiment_schemes"]["test_split"]["split_algos"]

        if hasattr(estimator, "predict_proba"):
            predictions = estimator.predict_proba(X_test)
        else:
            predictions = estimator.predict(X_test)

        # Un-do OHE of the validation labels
        if len(y_test.shape) == 2:
            rounded_test_labels = np.argmax(y_test, axis=1)
            y_true = rounded_test_labels
            sk_scores = _score(estimator, X_test, rounded_test_labels, scorer)
        else:
            y_true = y_test
            sk_scores = _score(estimator, X_test, y_true, scorer)

        scores.update(sk_scores)

    # handle output
    if outfile_y_true:
        try: