annotate utils.py @ 23:75bcb7c19fcf draft

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

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',
                    'raise', 'return', 'try', 'system', 'while', 'with',
                    'True', 'False', 'None', 'eval', 'execfile', '__import__',
                    '__package__', '__subclasses__', '__bases__', '__globals__',
                    '__code__', '__closure__', '__func__', '__self__', '__module__',
                    '__dict__', '__class__', '__call__', '__get__',
                    '__getattribute__', '__subclasshook__', '__new__',
                    '__init__', 'func_globals', 'func_code', 'func_closure',
                    'im_class', 'im_func', 'im_self', 'gi_code', 'gi_frame',
                    '__asteval__', 'f_locals', '__mro__')
        good_names = ['copy_reg._reconstructor', '__builtin__.object']

        if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):
            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)


def load_model(file):
    return SafePickler(file).load()


def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
    data = pandas.read_csv(f, **args)
    if c_option == 'by_index_number':
        cols = list(map(lambda x: x - 1, c))
        data = data.iloc[:, cols]
    if c_option == 'all_but_by_index_number':
        cols = list(map(lambda x: x - 1, c))
        data.drop(data.columns[cols], axis=1, inplace=True)
    if c_option == 'by_header_name':
        cols = [e.strip() for e in c.split(',')]
        data = data[cols]
    if c_option == 'all_but_by_header_name':
        cols = [e.strip() for e in c.split(',')]
        data.drop(cols, axis=1, inplace=True)
    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,
            c_option=column_option,
            sep='\t',
            header=header,
            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,
        c_option=column_option,
        sep='\t',
        header=header,
        parse_dates=True
    )
    y = y.ravel()
    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.
        new_syms = {
            'np_arange': getattr(np, 'arange'),
            'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
        }

        syms = make_symbol_table(use_numpy=False, **new_syms)

        if load_scipy:
            scipy_distributions = scipy.stats.distributions.__dict__
            for k, v in scipy_distributions.items():
                if isinstance(v, (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
                    syms['scipy_stats_' + k] = v

        if load_numpy:
            from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
                                'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
                                'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
                                'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
                                'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
                                'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
                                'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
                                '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)
    if 'n_jobs' in estimator.get_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)

    if scoring_json['secondary_scoring'] != 'None'\
            and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
        scoring = {}
        scoring['primary'] = my_scorers[scoring_json['primary_scoring']]
        for scorer in scoring_json['secondary_scoring'].split(','):
            if scorer != scoring_json['primary_scoring']:
                scoring[scorer] = my_scorers[scorer]
        return scoring

    return my_scorers[scoring_json['primary_scoring']]