comparison utils.py @ 23:75bcb7c19fcf draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 57f4407e278a615f47a377a3328782b1d8e0b54d

22:686d0c3e3b62

import sys
import os
import pandas
import re
import pickle
import warnings
import numpy as np
import xgboost
import scipy
import sklearn
from asteval import Interpreter, make_symbol_table
from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,
                    gaussian_process, kernel_approximation, metrics,
                    model_selection, naive_bayes, neighbors, pipeline, preprocessing,
                    svm, linear_model, tree, discriminant_analysis)

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


class SafePickler(pickle.Unpickler):
    """
    Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
    Usage:
        eg.: SafePickler.load(pickled_file_object)
    """
    def find_class(self, module, name):

        bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue',
                    'def', 'del', 'elif', 'else', 'except', 'exec',
                    'finally', 'for', 'from', 'global', 'if', 'import',
                    'in', 'is', 'lambda', 'not', 'or', 'pass', 'print',

            fullname = module + '.' + name
            if (fullname in good_names)\
                or  (   (   module.startswith('sklearn.')
                            or module.startswith('xgboost.')
                            or module.startswith('skrebate.')
                            or module.startswith('numpy.')
                            or module == 'numpy'
                        )
                        and (name not in bad_names)
                    ):
                # TODO: replace with a whitelist checker
                if fullname not in sk_whitelist['SK_NAMES'] + sk_whitelist['SKR_NAMES'] + sk_whitelist['XGB_NAMES'] + sk_whitelist['NUMPY_NAMES'] + good_names:
                    print("Warning: global %s is not in pickler whitelist yet and will loss support soon. Contact tool author or leave a message at github.com" % fullname)
                mod = sys.modules[module]
                return getattr(mod, name)

        raise pickle.UnpicklingError("global '%s' is forbidden" % fullname)

    y = data.values
    if return_df:
        return y, data
    else:
        return y
    return y


## generate an instance for one of sklearn.feature_selection classes
def feature_selector(inputs):
    selector = inputs["selected_algorithm"]
    selector = getattr(sklearn.feature_selection, selector)
    options = inputs["options"]

    if inputs['selected_algorithm'] == 'SelectFromModel':
        if not options['threshold'] or options['threshold'] == 'None':
            options['threshold'] = None
        if inputs['model_inputter']['input_mode'] == 'prefitted':
            model_file = inputs['model_inputter']['fitted_estimator']
            with open(model_file, 'rb') as model_handler:
                fitted_estimator = load_model(model_handler)
            new_selector = selector(fitted_estimator, prefit=True, **options)
        else:
            estimator_json = inputs['model_inputter']["estimator_selector"]
            estimator = get_estimator(estimator_json)
            new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == 'RFE':
        estimator = get_estimator(inputs["estimator_selector"])
        new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == 'RFECV':
        options['scoring'] = get_scoring(options['scoring'])
        options['n_jobs'] = N_JOBS
        options['cv'] = get_cv(options['cv'].strip())
        estimator = get_estimator(inputs["estimator_selector"])
        new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == "VarianceThreshold":
        new_selector = selector(**options)

    else:
        score_func = inputs["score_func"]
        score_func = getattr(sklearn.feature_selection, score_func)
        new_selector = selector(score_func, **options)

    return new_selector


def get_X_y(params, file1, file2):
    input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
    if input_type == "tabular":
        header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
        column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["selected_column_selector_option"]
        if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
            c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
        else:
            c = None
        X = read_columns(
            file1,
            c=c,

            parse_dates=True
        )
    else:
        X = mmread(file1)

    header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
    column_option = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["selected_column_selector_option2"]
    if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]:
        c = params["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
    else:
        c = None
    y = read_columns(
        file2,
        c=c,

    return X, y


class SafeEval(Interpreter):

    def __init__(self, load_scipy=False, load_numpy=False):

        # File opening and other unneeded functions could be dropped
        unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']

        # Allowed symbol table. Add more if needed.

                                'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
                                'vonmises', 'wald', 'weibull', 'zipf']
            for f in from_numpy_random:
                syms['np_random_' + f] = getattr(np.random, f)

        for key in unwanted:
            syms.pop(key, None)

        super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False,
                                        no_if=True, no_for=True, no_while=True, no_try=True,
                                        no_functiondef=True, no_ifexp=True, no_listcomp=False,
                                        no_augassign=False, no_assert=True, no_delete=True,
                                        no_raise=True, no_print=True)


def get_search_params(params_builder):
    search_params = {}
    safe_eval = SafeEval(load_scipy=True, load_numpy=True)

    for p in params_builder['param_set']:
        search_p = p['search_param_selector']['search_p']
        if search_p.strip() == '':
            continue
        param_type = p['search_param_selector']['selected_param_type']

        lst = search_p.split(":")
        assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
        literal = lst[1].strip()
        ev = safe_eval(literal)
        if param_type == "final_estimator_p":
            search_params["estimator__" + lst[0].strip()] = ev
        else:
            search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev

    return search_params


def get_estimator(estimator_json):
    estimator_module = estimator_json['selected_module']
    estimator_cls = estimator_json['selected_estimator']

    if estimator_module == "xgboost":
        cls = getattr(xgboost, estimator_cls)
    else:
        module = getattr(sklearn, estimator_module)
        cls = getattr(module, estimator_cls)

    estimator = cls()

    estimator_params = estimator_json['text_params'].strip()
    if estimator_params != "":
        try:
            params = safe_eval('dict(' + estimator_params + ')')
        except ValueError:
            sys.exit("Unsupported parameter input: `%s`" % estimator_params)
        estimator.set_params(**params)

        estimator.set_params(n_jobs=N_JOBS)

    return estimator


def get_cv(literal):
    safe_eval = SafeEval()
    if literal == "":
        return None
    if literal.isdigit():
        return int(literal)
    m = re.match(r'^(?P<method>\w+)\((?P<args>.*)\)$', literal)
    if m:
        my_class = getattr(model_selection, m.group('method'))
        args = safe_eval('dict('+ m.group('args') + ')')
        return my_class(**args)
    sys.exit("Unsupported CV input: %s" % literal)


def get_scoring(scoring_json):
    def balanced_accuracy_score(y_true, y_pred):
        C = metrics.confusion_matrix(y_true, y_pred)
        with np.errstate(divide='ignore', invalid='ignore'):
            per_class = np.diag(C) / C.sum(axis=1)
        if np.any(np.isnan(per_class)):
            warnings.warn('y_pred contains classes not in y_true')
            per_class = per_class[~np.isnan(per_class)]
        score = np.mean(per_class)
        return score

    if scoring_json['primary_scoring'] == "default":
        return None

    my_scorers = metrics.SCORERS
    if 'balanced_accuracy' not in my_scorers:
        my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)

23:75bcb7c19fcf

import json
import numpy as np
import os
import pandas
import pickle
import re
import scipy
import sklearn
import sys
import warnings
import xgboost

from asteval import Interpreter, make_symbol_table
from sklearn import (cluster, compose, decomposition, ensemble, feature_extraction,
                    feature_selection, gaussian_process, kernel_approximation, metrics,
                    model_selection, naive_bayes, neighbors, pipeline, preprocessing,
                    svm, linear_model, tree, discriminant_analysis)

try:
    import skrebate
except ModuleNotFoundError:
    pass


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

try:
    sk_whitelist
except NameError:
    sk_whitelist = None


class SafePickler(pickle.Unpickler):
    """
    Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
    Usage:
        eg.: SafePickler.load(pickled_file_object)
    """
    def find_class(self, module, name):

        # sk_whitelist could be read from tool
        global sk_whitelist
        if not sk_whitelist:
            whitelist_file = os.path.join(os.path.dirname(__file__), 'sk_whitelist.json')
            with open(whitelist_file, 'r') as f:
                sk_whitelist = json.load(f)

        bad_names = ('and', 'as', 'assert', 'break', 'class', 'continue',
                    'def', 'del', 'elif', 'else', 'except', 'exec',
                    'finally', 'for', 'from', 'global', 'if', 'import',
                    'in', 'is', 'lambda', 'not', 'or', 'pass', 'print',

            fullname = module + '.' + name
            if (fullname in good_names)\
                or  (   (   module.startswith('sklearn.')
                            or module.startswith('xgboost.')
                            or module.startswith('skrebate.')
                            or module.startswith('imblearn')
                            or module.startswith('numpy.')
                            or module == 'numpy'
                        )
                        and (name not in bad_names)
                    ):
                # TODO: replace with a whitelist checker
                if fullname not in sk_whitelist['SK_NAMES'] + sk_whitelist['SKR_NAMES'] + sk_whitelist['XGB_NAMES'] + sk_whitelist['NUMPY_NAMES'] + sk_whitelist['IMBLEARN_NAMES'] + good_names:
                    print("Warning: global %s is not in pickler whitelist yet and will loss support soon. Contact tool author or leave a message at github.com" % fullname)
                mod = sys.modules[module]
                return getattr(mod, name)

        raise pickle.UnpicklingError("global '%s' is forbidden" % fullname)

    y = data.values
    if return_df:
        return y, data
    else:
        return y


## generate an instance for one of sklearn.feature_selection classes
def feature_selector(inputs):
    selector = inputs['selected_algorithm']
    selector = getattr(sklearn.feature_selection, selector)
    options = inputs['options']

    if inputs['selected_algorithm'] == 'SelectFromModel':
        if not options['threshold'] or options['threshold'] == 'None':
            options['threshold'] = None
        else:
            try:
                options['threshold'] = float(options['threshold'])
            except ValueError:
                pass
        if inputs['model_inputter']['input_mode'] == 'prefitted':
            model_file = inputs['model_inputter']['fitted_estimator']
            with open(model_file, 'rb') as model_handler:
                fitted_estimator = load_model(model_handler)
            new_selector = selector(fitted_estimator, prefit=True, **options)
        else:
            estimator_json = inputs['model_inputter']['estimator_selector']
            estimator = get_estimator(estimator_json)
            new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == 'RFE':
        estimator = get_estimator(inputs['estimator_selector'])
        step = options.get('step', None)
        if step and step >= 1.0:
            options['step'] = int(step)
        new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == 'RFECV':
        options['scoring'] = get_scoring(options['scoring'])
        options['n_jobs'] = N_JOBS
        splitter, groups = get_cv(options.pop('cv_selector'))
        # TODO support group cv splitters
        options['cv'] = splitter
        step = options.get('step', None)
        if step and step >= 1.0:
            options['step'] = int(step)
        estimator = get_estimator(inputs['estimator_selector'])
        new_selector = selector(estimator, **options)

    elif inputs['selected_algorithm'] == 'VarianceThreshold':
        new_selector = selector(**options)

    else:
        score_func = inputs['score_func']
        score_func = getattr(sklearn.feature_selection, score_func)
        new_selector = selector(score_func, **options)

    return new_selector


def get_X_y(params, file1, file2):
    input_type = params['selected_tasks']['selected_algorithms']['input_options']['selected_input']
    if input_type == 'tabular':
        header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header1'] else None
        column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['selected_column_selector_option']
        if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
            c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_1']['col1']
        else:
            c = None
        X = read_columns(
            file1,
            c=c,

            parse_dates=True
        )
    else:
        X = mmread(file1)

    header = 'infer' if params['selected_tasks']['selected_algorithms']['input_options']['header2'] else None
    column_option = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['selected_column_selector_option2']
    if column_option in ['by_index_number', 'all_but_by_index_number', 'by_header_name', 'all_but_by_header_name']:
        c = params['selected_tasks']['selected_algorithms']['input_options']['column_selector_options_2']['col2']
    else:
        c = None
    y = read_columns(
        file2,
        c=c,

    return X, y


class SafeEval(Interpreter):

    def __init__(self, load_scipy=False, load_numpy=False, load_estimators=False):

        # File opening and other unneeded functions could be dropped
        unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']

        # Allowed symbol table. Add more if needed.

                                'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
                                'vonmises', 'wald', 'weibull', 'zipf']
            for f in from_numpy_random:
                syms['np_random_' + f] = getattr(np.random, f)

        if load_estimators:
            estimator_table = {
                'sklearn_svm' : getattr(sklearn, 'svm'),
                'sklearn_tree' : getattr(sklearn, 'tree'),
                'sklearn_ensemble' : getattr(sklearn, 'ensemble'),
                'sklearn_neighbors' : getattr(sklearn, 'neighbors'),
                'sklearn_naive_bayes' : getattr(sklearn, 'naive_bayes'),
                'sklearn_linear_model' : getattr(sklearn, 'linear_model'),
                'sklearn_cluster' : getattr(sklearn, 'cluster'),
                'sklearn_decomposition' : getattr(sklearn, 'decomposition'),
                'sklearn_preprocessing' : getattr(sklearn, 'preprocessing'),
                'sklearn_feature_selection' : getattr(sklearn, 'feature_selection'),
                'sklearn_kernel_approximation' : getattr(sklearn, 'kernel_approximation'),
                'skrebate_ReliefF': getattr(skrebate, 'ReliefF'),
                'skrebate_SURF': getattr(skrebate, 'SURF'),
                'skrebate_SURFstar': getattr(skrebate, 'SURFstar'),
                'skrebate_MultiSURF': getattr(skrebate, 'MultiSURF'),
                'skrebate_MultiSURFstar': getattr(skrebate, 'MultiSURFstar'),
                'skrebate_TuRF': getattr(skrebate, 'TuRF'),
                'xgboost_XGBClassifier' : getattr(xgboost, 'XGBClassifier'),
                'xgboost_XGBRegressor' : getattr(xgboost, 'XGBRegressor')
            }
            syms.update(estimator_table)

        for key in unwanted:
            syms.pop(key, None)

        super(SafeEval, self).__init__(symtable=syms, use_numpy=False, minimal=False,
                                        no_if=True, no_for=True, no_while=True, no_try=True,
                                        no_functiondef=True, no_ifexp=True, no_listcomp=False,
                                        no_augassign=False, no_assert=True, no_delete=True,
                                        no_raise=True, no_print=True)



def get_estimator(estimator_json):

    estimator_module = estimator_json['selected_module']

    if estimator_module == 'customer_estimator':
        c_estimator = estimator_json['c_estimator']
        with open(c_estimator, 'rb') as model_handler:
            new_model = load_model(model_handler)
        return new_model

    estimator_cls = estimator_json['selected_estimator']

    if estimator_module == 'xgboost':
        cls = getattr(xgboost, estimator_cls)
    else:
        module = getattr(sklearn, estimator_module)
        cls = getattr(module, estimator_cls)

    estimator = cls()

    estimator_params = estimator_json['text_params'].strip()
    if estimator_params != '':
        try:
            params = safe_eval('dict(' + estimator_params + ')')
        except ValueError:
            sys.exit("Unsupported parameter input: `%s`" % estimator_params)
        estimator.set_params(**params)

        estimator.set_params(n_jobs=N_JOBS)

    return estimator


def get_cv(cv_json):
    """
    cv_json:
            e.g.:
            {
                'selected_cv': 'StratifiedKFold',
                'n_splits': 3,
                'shuffle': True,
                'random_state': 0
            }
    """
    cv = cv_json.pop('selected_cv')
    if cv == 'default':
        return cv_json['n_splits'], None

    groups = cv_json.pop('groups', None)
    if groups:
        groups = groups.strip()
        if groups != '':
            if groups.startswith('__ob__'):
                groups = groups[6:]
            if groups.endswith('__cb__'):
                groups = groups[:-6]
            groups = [int(x.strip()) for x in groups.split(',')]

    for k, v in cv_json.items():
        if v == '':
            cv_json[k] = None

    test_fold = cv_json.get('test_fold', None)
    if test_fold:
        if test_fold.startswith('__ob__'):
            test_fold = test_fold[6:]
        if test_fold.endswith('__cb__'):
            test_fold = test_fold[:-6]
        cv_json['test_fold'] = [int(x.strip()) for x in test_fold.split(',')]

    test_size = cv_json.get('test_size', None)
    if test_size and test_size > 1.0:
        cv_json['test_size'] = int(test_size)

    cv_class = getattr(model_selection, cv)
    splitter = cv_class(**cv_json)

    return splitter, groups


# needed when sklearn < v0.20
def balanced_accuracy_score(y_true, y_pred):
    C = metrics.confusion_matrix(y_true, y_pred)
    with np.errstate(divide='ignore', invalid='ignore'):
        per_class = np.diag(C) / C.sum(axis=1)
    if np.any(np.isnan(per_class)):
        warnings.warn('y_pred contains classes not in y_true')
        per_class = per_class[~np.isnan(per_class)]
    score = np.mean(per_class)
    return score


def get_scoring(scoring_json):

    if scoring_json['primary_scoring'] == 'default':
        return None

    my_scorers = metrics.SCORERS
    if 'balanced_accuracy' not in my_scorers:
        my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)