comparison simple_model_fit.py @ 1:49525d37843a draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit d6333e7294e67be5968a41f404b66699cad4ae53"

0:734c66aa945a

import pandas as pd
import pickle

from galaxy_ml.utils import load_model, read_columns
from sklearn.pipeline import Pipeline


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

        params = json.load(param_handler)

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

    X_train, y_train = _get_X_y(params, infile1, infile2)

    estimator.fit(X_train, y_train)
    

1:49525d37843a

import pandas as pd
import pickle

from galaxy_ml.utils import load_model, read_columns
from sklearn.pipeline import Pipeline


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


# TODO import from galaxy_ml.utils in future versions
def clean_params(estimator, n_jobs=None):
    """clean unwanted hyperparameter settings

    If n_jobs is not None, set it into the estimator, if applicable

    Return
    ------
    Cleaned estimator object
    """
    ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN',
                         'ReduceLROnPlateau', 'CSVLogger', 'None')

    estimator_params = estimator.get_params()

    for name, p in estimator_params.items():
        # all potential unauthorized file write
        if name == 'memory' or name.endswith('__memory') \
                or name.endswith('_path'):
            new_p = {name: None}
            estimator.set_params(**new_p)
        elif n_jobs is not None and (name == 'n_jobs' or
                                     name.endswith('__n_jobs')):
            new_p = {name: n_jobs}
            estimator.set_params(**new_p)
        elif name.endswith('callbacks'):
            for cb in p:
                cb_type = cb['callback_selection']['callback_type']
                if cb_type not in ALLOWED_CALLBACKS:
                    raise ValueError(
                        "Prohibited callback type: %s!" % cb_type)

    return estimator


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

        params = json.load(param_handler)

    # load model
    with open(infile_estimator, 'rb') as est_handler:
        estimator = load_model(est_handler)
    estimator = clean_params(estimator, n_jobs=N_JOBS)

    X_train, y_train = _get_X_y(params, infile1, infile2)

    estimator.fit(X_train, y_train)