--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/utils.py	Tue May 14 18:12:53 2019 -0400
@@ -0,0 +1,599 @@
+import ast
+import json
+import imblearn
+import numpy as np
+import pandas
+import pickle
+import re
+import scipy
+import sklearn
+import skrebate
+import sys
+import warnings
+import xgboost
+
+from collections import Counter
+from asteval import Interpreter, make_symbol_table
+from imblearn import under_sampling, over_sampling, combine
+from imblearn.pipeline import Pipeline as imbPipeline
+from mlxtend import regressor, classifier
+from scipy.io import mmread
+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 iraps_classifier
+except ImportError:
+    pass
+
+try:
+    import model_validations
+except ImportError:
+    pass
+
+try:
+    import feature_selectors
+except ImportError:
+    pass
+
+try:
+    import preprocessors
+except ImportError:
+    pass
+
+# handle pickle white list file
+WL_FILE = __import__('os').path.join(
+    __import__('os').path.dirname(__file__), 'pk_whitelist.json')
+
+N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+
+
+class _SafePickler(pickle.Unpickler, object):
+    """
+    Used to safely deserialize scikit-learn model objects
+    Usage:
+        eg.: _SafePickler.load(pickled_file_object)
+    """
+    def __init__(self, file):
+        super(_SafePickler, self).__init__(file)
+        # load global white list
+        with open(WL_FILE, 'r') as f:
+            self.pk_whitelist = json.load(f)
+
+        self.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__')
+
+        # unclassified good globals
+        self.good_names = [
+            'copy_reg._reconstructor', '__builtin__.object',
+            '__builtin__.bytearray', 'builtins.object',
+            'builtins.bytearray', 'keras.engine.sequential.Sequential',
+            'keras.engine.sequential.Model']
+
+        # custom module in Galaxy-ML
+        self.custom_modules = [
+            '__main__', 'keras_galaxy_models', 'feature_selectors',
+            'preprocessors', 'iraps_classifier', 'model_validations']
+
+    # override
+    def find_class(self, module, name):
+        # balack list first
+        if name in self.bad_names:
+            raise pickle.UnpicklingError("global '%s.%s' is forbidden"
+                                         % (module, name))
+
+        # custom module in Galaxy-ML
+        if module in self.custom_modules:
+            cutom_module = sys.modules.get(module, None)
+            if cutom_module:
+                return getattr(cutom_module, name)
+            else:
+                raise pickle.UnpicklingError("Module %s' is not imported"
+                                             % module)
+
+        # For objects from outside libraries, it's necessary to verify
+        # both module and name. Currently only a blacklist checker
+        # is working.
+        # TODO: replace with a whitelist checker.
+        good_names = self.good_names
+        pk_whitelist = self.pk_whitelist
+        if re.match(r'^[a-zA-Z_][a-zA-Z0-9_]*$', name):
+            fullname = module + '.' + name
+            if (fullname in good_names)\
+                or (module.startswith(('sklearn.', 'xgboost.', 'skrebate.',
+                                       'imblearn.', 'mlxtend.', 'numpy.'))
+                    or module == 'numpy'):
+                if fullname not in (pk_whitelist['SK_NAMES'] +
+                                    pk_whitelist['SKR_NAMES'] +
+                                    pk_whitelist['XGB_NAMES'] +
+                                    pk_whitelist['NUMPY_NAMES'] +
+                                    pk_whitelist['IMBLEARN_NAMES'] +
+                                    pk_whitelist['MLXTEND_NAMES'] +
+                                    good_names):
+                    # raise pickle.UnpicklingError
+                    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):
+    """Load pickled object with `_SafePicker`
+    """
+    return _SafePickler(file).load()
+
+
+def read_columns(f, c=None, c_option='by_index_number',
+                 return_df=False, **args):
+    """Return array from a tabular dataset by various columns selection
+    """
+    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
+
+
+def feature_selector(inputs, X=None, y=None):
+    """generate an instance of sklearn.feature_selection classes
+
+    Parameters
+    ----------
+    inputs : dict
+        From galaxy tool parameters.
+    X : array
+        Containing training features.
+    y : array or list
+        Target values.
+    """
+    selector = inputs['selected_algorithm']
+    if selector != 'DyRFECV':
+        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)
+            check_feature_importances = try_get_attr(
+                'feature_selectors', 'check_feature_importances')
+            estimator = check_feature_importances(estimator)
+            new_selector = selector(estimator, **options)
+
+    elif inputs['selected_algorithm'] == 'RFE':
+        step = options.get('step', None)
+        if step and step >= 1.0:
+            options['step'] = int(step)
+        estimator = get_estimator(inputs["estimator_selector"])
+        check_feature_importances = try_get_attr(
+            'feature_selectors', 'check_feature_importances')
+        estimator = check_feature_importances(estimator)
+        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'))
+        if groups is None:
+            options['cv'] = splitter
+        else:
+            options['cv'] = list(splitter.split(X, y, groups=groups))
+        step = options.get('step', None)
+        if step and step >= 1.0:
+            options['step'] = int(step)
+        estimator = get_estimator(inputs['estimator_selector'])
+        check_feature_importances = try_get_attr(
+            'feature_selectors', 'check_feature_importances')
+        estimator = check_feature_importances(estimator)
+        new_selector = selector(estimator, **options)
+
+    elif inputs['selected_algorithm'] == 'DyRFECV':
+        options['scoring'] = get_scoring(options['scoring'])
+        options['n_jobs'] = N_JOBS
+        splitter, groups = get_cv(options.pop('cv_selector'))
+        if groups is None:
+            options['cv'] = splitter
+        else:
+            options['cv'] = list(splitter.split(X, y, groups=groups))
+        step = options.get('step')
+        if not step or step == 'None':
+            step = None
+        else:
+            step = ast.literal_eval(step)
+        options['step'] = step
+        estimator = get_estimator(inputs["estimator_selector"])
+        check_feature_importances = try_get_attr(
+            'feature_selectors', 'check_feature_importances')
+        estimator = check_feature_importances(estimator)
+        DyRFECV = try_get_attr('feature_selectors', 'DyRFECV')
+
+        new_selector = DyRFECV(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):
+    """Return machine learning inputs X, y from tabluar inputs
+    """
+    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).astype(float)
+    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):
+    """Customized symbol table for safely literal eval
+    """
+    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'),
+                'imblearn_over_sampling': getattr(imblearn, 'over_sampling'),
+                'imblearn_combine': getattr(imblearn, 'combine')
+            }
+            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):
+    """Return a sklearn or compatible estimator from Galaxy tool inputs
+    """
+    estimator_module = estimator_json['selected_module']
+
+    if estimator_module == 'custom_estimator':
+        c_estimator = estimator_json['c_estimator']
+        with open(c_estimator, 'rb') as model_handler:
+            new_model = load_model(model_handler)
+        return new_model
+
+    if estimator_module == "binarize_target":
+        wrapped_estimator = estimator_json['wrapped_estimator']
+        with open(wrapped_estimator, 'rb') as model_handler:
+            wrapped_estimator = load_model(model_handler)
+        options = {}
+        if estimator_json['z_score'] is not None:
+            options['z_score'] = estimator_json['z_score']
+        if estimator_json['value'] is not None:
+            options['value'] = estimator_json['value']
+        options['less_is_positive'] = estimator_json['less_is_positive']
+        if estimator_json['clf_or_regr'] == 'BinarizeTargetClassifier':
+            klass = try_get_attr('iraps_classifier',
+                                 'BinarizeTargetClassifier')
+        else:
+            klass = try_get_attr('iraps_classifier',
+                                 'BinarizeTargetRegressor')
+        return klass(wrapped_estimator, **options)
+
+    estimator_cls = estimator_json['selected_estimator']
+
+    if estimator_module == 'xgboost':
+        klass = getattr(xgboost, estimator_cls)
+    else:
+        module = getattr(sklearn, estimator_module)
+        klass = getattr(module, estimator_cls)
+
+    estimator = klass()
+
+    estimator_params = estimator_json['text_params'].strip()
+    if estimator_params != '':
+        try:
+            safe_eval = SafeEval()
+            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):
+    """ Return CV splitter from Galaxy tool inputs
+
+    Parameters
+    ----------
+    cv_json : dict
+        From Galaxy tool inputs.
+        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_selector', None)
+    if groups is not None:
+        infile_g = groups['infile_g']
+        header = 'infer' if groups['header_g'] else None
+        column_option = (groups['column_selector_options_g']
+                         ['selected_column_selector_option_g'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = groups['column_selector_options_g']['col_g']
+        else:
+            c = None
+        groups = read_columns(
+                infile_g,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+
+    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)
+
+    if cv == 'OrderedKFold':
+        cv_class = try_get_attr('model_validations', 'OrderedKFold')
+    elif cv == 'RepeatedOrderedKFold':
+        cv_class = try_get_attr('model_validations', 'RepeatedOrderedKFold')
+    else:
+        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):
+    """Compute balanced accuracy score, which is now available in
+        scikit-learn from v0.20.0.
+    """
+    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):
+    """Return single sklearn scorer class
+        or multiple scoers in dictionary
+    """
+    if scoring_json['primary_scoring'] == 'default':
+        return None
+
+    my_scorers = metrics.SCORERS
+    my_scorers['binarize_auc_scorer'] =\
+        try_get_attr('iraps_classifier', 'binarize_auc_scorer')
+    my_scorers['binarize_average_precision_scorer'] =\
+        try_get_attr('iraps_classifier', 'binarize_average_precision_scorer')
+    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']:
+        return_scoring = {}
+        primary_scoring = scoring_json['primary_scoring']
+        return_scoring[primary_scoring] = my_scorers[primary_scoring]
+        for scorer in scoring_json['secondary_scoring'].split(','):
+            if scorer != scoring_json['primary_scoring']:
+                return_scoring[scorer] = my_scorers[scorer]
+        return return_scoring
+
+    return my_scorers[scoring_json['primary_scoring']]
+
+
+def get_search_params(estimator):
+    """Format the output of `estimator.get_params()`
+    """
+    params = estimator.get_params()
+    results = []
+    for k, v in params.items():
+        # params below won't be shown for search in the searchcv tool
+        keywords = ('n_jobs', 'pre_dispatch', 'memory', 'steps',
+                    'nthread', 'verbose')
+        if k.endswith(keywords):
+            results.append(['*', k, k+": "+repr(v)])
+        else:
+            results.append(['@', k, k+": "+repr(v)])
+    results.append(
+        ["", "Note:",
+         "@, params eligible for search in searchcv tool."])
+
+    return results
+
+
+def try_get_attr(module, name):
+    """try to get attribute from a custom module
+
+    Parameters
+    ----------
+    module : str
+        Module name
+    name : str
+        Attribute (class/function) name.
+
+    Returns
+    -------
+    class or function
+    """
+    mod = sys.modules.get(module, None)
+    if mod:
+        return getattr(mod, name)
+    else:
+        raise Exception("No module named %s." % module)