comparison keras_train_and_eval.py @ 36:420a4bf99244 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 208a8d348e7c7a182cfbe1b6f17868146428a7e2"

35:eeaf989f1024

import argparse
import joblib
import json
import numpy as np
import os
import pandas as pd
import pickle
import warnings
from itertools import chain
from scipy.io import mmread
from sklearn.pipeline import Pipeline
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection._validation import _score
from sklearn.model_selection import _search, _validation
from sklearn.utils import indexable, safe_indexing

from galaxy_ml.externals.selene_sdk.utils import compute_score
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.keras_galaxy_models import _predict_generator
from galaxy_ml.utils import (
    SafeEval,
    get_scoring,
    load_model,
    read_columns,
    try_get_attr,
    get_module,
    clean_params,
    get_main_estimator,
)


_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
setattr(_search, "_fit_and_score", _fit_and_score)
setattr(_validation, "_fit_and_score", _fit_and_score)

        rval = list(chain.from_iterable((safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays))
    else:
        rval = train_test_split(*new_arrays, **kwargs)

    for pos in nones:
        rval[pos * 2 : 2] = [None, None]

    return rval


def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):

36:420a4bf99244

import argparse
import json
import os
import pickle
import warnings
from itertools import chain

import joblib
import numpy as np
import pandas as pd
from galaxy_ml.externals.selene_sdk.utils import compute_score
from galaxy_ml.keras_galaxy_models import _predict_generator
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    clean_params,
    get_main_estimator,
    get_module,
    get_scoring,
    load_model,
    read_columns,
    SafeEval,
    try_get_attr,
)
from scipy.io import mmread
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score
from sklearn.pipeline import Pipeline
from sklearn.utils import indexable, safe_indexing


_fit_and_score = try_get_attr("galaxy_ml.model_validations", "_fit_and_score")
setattr(_search, "_fit_and_score", _fit_and_score)
setattr(_validation, "_fit_and_score", _fit_and_score)

        rval = list(chain.from_iterable((safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays))
    else:
        rval = train_test_split(*new_arrays, **kwargs)

    for pos in nones:
        rval[pos * 2: 2] = [None, None]

    return rval


def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):