diff utils.py @ 24:5552eda109bd draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ab963ec9498bd05d2fb2f24f75adb2fccae7958c

--- a/utils.py	Sun Dec 30 02:00:37 2018 -0500
+++ b/utils.py	Wed May 15 07:39:54 2019 -0400
@@ -1,80 +1,134 @@
+import ast
 import json
+import imblearn
 import numpy as np
-import os
 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 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)
+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 skrebate
-except ModuleNotFoundError:
+    import model_validations
+except ImportError:
+    pass
+
+try:
+    import feature_selectors
+except ImportError:
     pass
 
-
-N_JOBS = int(os.environ.get('GALAXY_SLOTS', 1))
+try:
+    import preprocessors
+except ImportError:
+    pass
 
-try:
-    sk_whitelist
-except NameError:
-    sk_whitelist = None
+# 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):
+class _SafePickler(pickle.Unpickler, object):
     """
-    Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
+    Used to safely deserialize scikit-learn model objects
     Usage:
-        eg.: SafePickler.load(pickled_file_object)
+        eg.: _SafePickler.load(pickled_file_object)
     """
-    def find_class(self, module, name):
+    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)
 
-        # 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)
+        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__')
 
-        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']
+        # 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.')
-                            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)
+                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)
 
@@ -82,10 +136,15 @@
 
 
 def load_model(file):
-    return SafePickler(file).load()
+    """Load pickled object with `_SafePicker`
+    """
+    return _SafePickler(file).load()
 
 
-def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
+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))
@@ -106,10 +165,21 @@
         return y
 
 
-## generate an instance for one of sklearn.feature_selection classes
-def feature_selector(inputs):
+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']
-    selector = getattr(sklearn.feature_selection, selector)
+    if selector != 'DyRFECV':
+        selector = getattr(sklearn.feature_selection, selector)
     options = inputs['options']
 
     if inputs['selected_algorithm'] == 'SelectFromModel':
@@ -128,27 +198,60 @@
         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':
-        estimator = get_estimator(inputs['estimator_selector'])
         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'))
-        # TODO support group cv splitters
-        options['cv'] = splitter
+        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)
 
@@ -161,12 +264,20 @@
 
 
 def get_X_y(params, file1, file2):
-    input_type = params['selected_tasks']['selected_algorithms']['input_options']['selected_input']
+    """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']
+        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(
@@ -175,15 +286,19 @@
             c_option=column_option,
             sep='\t',
             header=header,
-            parse_dates=True
-        )
+            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']
+    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(
@@ -192,15 +307,17 @@
         c_option=column_option,
         sep='\t',
         header=header,
-        parse_dates=True
-    )
+        parse_dates=True)
     y = y.ravel()
+
     return X, y
 
 
 class SafeEval(Interpreter):
-
-    def __init__(self, load_scipy=False, load_numpy=False, load_estimators=False):
+    """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']
@@ -208,7 +325,8 @@
         # Allowed symbol table. Add more if needed.
         new_syms = {
             'np_arange': getattr(np, 'arange'),
-            'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
+            'ensemble_ExtraTreesClassifier':
+                getattr(ensemble, 'ExtraTreesClassifier')
         }
 
         syms = make_symbol_table(use_numpy=False, **new_syms)
@@ -216,80 +334,109 @@
         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)):
+                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']
+            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'),
+                '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')
+                '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)
-
+        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 == 'customer_estimator':
+    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':
-        cls = getattr(xgboost, estimator_cls)
+        klass = getattr(xgboost, estimator_cls)
     else:
         module = getattr(sklearn, estimator_module)
-        cls = getattr(module, estimator_cls)
+        klass = getattr(module, estimator_cls)
 
-    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)
@@ -301,9 +448,13 @@
 
 
 def get_cv(cv_json):
-    """
-    cv_json:
-            e.g.:
+    """ Return CV splitter from Galaxy tool inputs
+
+    Parameters
+    ----------
+    cv_json : dict
+        From Galaxy tool inputs.
+        e.g.:
             {
                 'selected_cv': 'StratifiedKFold',
                 'n_splits': 3,
@@ -315,15 +466,25 @@
     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(',')]
+    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 == '':
@@ -341,7 +502,12 @@
     if test_size and test_size > 1.0:
         cv_json['test_size'] = int(test_size)
 
-    cv_class = getattr(model_selection, cv)
+    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
@@ -349,6 +515,9 @@
 
 # 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)
@@ -360,21 +529,71 @@
 
 
 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)
+        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']]
+            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']:
-                scoring[scorer] = my_scorers[scorer]
-        return 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)