comparison train_test_eval.py @ 11:4a5266c96889 draft

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

10:f5e7df4f7975

import joblib
import numpy as np
import pandas as pd
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (
    get_module,
    get_scoring,
    load_model,
    read_columns,
    SafeEval,
    try_get_attr,
)
from scipy.io import mmread
from sklearn import pipeline
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score
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)

        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()
    if input_type == "refseq_and_interval":
        estimator.set_params(data_batch_generator__features=y.ravel().tolist())
        y = None

        df_key = groups + repr(header)
        if df_key in loaded_df:
            groups = loaded_df[df_key]

        groups = read_columns(groups,
                              c=c,
                              c_option=column_option,
                              sep='\t',
                              header=header,
                              parse_dates=True)
        groups = groups.ravel()

    # del loaded_df
    del loaded_df

        else:
            raise ValueError(
                "Stratified shuffle split is not " "applicable on empty target values!"
            )

    X_train, X_test, y_train, y_test, groups_train, _groups_test = train_test_split_none(
        X, y, groups, **test_split_options
    )

    exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]

    # handle validation (second) split
    if exp_scheme == "train_val_test":

11:4a5266c96889

import joblib
import numpy as np
import pandas as pd
from galaxy_ml.model_validations import train_test_split
from galaxy_ml.utils import (get_module, get_scoring, load_model,
                             read_columns, SafeEval, try_get_attr)
from scipy.io import mmread
from sklearn import pipeline
from sklearn.metrics.scorer import _check_multimetric_scoring
from sklearn.model_selection import _search, _validation
from sklearn.model_selection._validation import _score
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)

        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()
    if input_type == "refseq_and_interval":
        estimator.set_params(data_batch_generator__features=y.ravel().tolist())
        y = None

        df_key = groups + repr(header)
        if df_key in loaded_df:
            groups = loaded_df[df_key]

        groups = read_columns(
            groups,
            c=c,
            c_option=column_option,
            sep="\t",
            header=header,
            parse_dates=True,
        )
        groups = groups.ravel()

    # del loaded_df
    del loaded_df

        else:
            raise ValueError(
                "Stratified shuffle split is not " "applicable on empty target values!"
            )

    (
        X_train,
        X_test,
        y_train,
        y_test,
        groups_train,
        _groups_test,
    ) = train_test_split_none(X, y, groups, **test_split_options)

    exp_scheme = params["experiment_schemes"]["selected_exp_scheme"]

    # handle validation (second) split
    if exp_scheme == "train_val_test":