view @ 4:41d0edb7d1fc draft

planemo upload for repository commit 8cf3d813ec755166ee0bd517b4ecbbd4f84d4df1
author bgruening
date Thu, 23 Aug 2018 16:14:13 -0400
parents 297541cc26d0
children 1c5989b930e3
line wrap: on
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import sys
import os
import pandas
import re
import cPickle as pickle
import warnings
import numpy as np
import xgboost
import scipy
import sklearn
import ast
from asteval import Interpreter, make_symbol_table
from sklearn import (cluster, decomposition, ensemble, feature_extraction, feature_selection,
                    gaussian_process, kernel_approximation, linear_model, 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(object):
    Used to safely deserialize scikit-learn model objects serialized by cPickle.dump
        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',
                    '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('numpy.')
                            or module == 'numpy'
                        and (name not in bad_names)
                    ) :
                # TODO: replace with a whitelist checker
                if fullname not in SK_NAMES + SKR_NAMES + XGB_NAMES + 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" % fullname)
                mod = sys.modules[module]
                return getattr(mod, name)

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

    def load(self, file):
        obj = pickle.Unpickler(file)
        obj.find_global = self.find_class
        return obj.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
        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 = SafePickler.load(model_handler)
            new_selector = selector(fitted_estimator, prefit=True, **options)
            estimator_json = inputs['model_inputter']["estimator_selector"]
            estimator = get_estimator(estimator_json)
            new_selector = selector(estimator, **options)

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

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

        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"]
            c = None
        X = read_columns(
            c = c,
            c_option = column_option,
        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"]
        c = None
    y = read_columns(
        c = c,
        c_option = column_option,
    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.
        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)

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

    estimator = cls()

    estimator_params = estimator_json['text_params'].strip()
    if estimator_params != "":
            params = safe_eval('dict(' + estimator_params + ')')
        except ValueError:
            sys.exit("Unsupported parameter input: `%s`" %estimator_params)
    if 'n_jobs' in estimator.get_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,'method') )
        args = safe_eval( 'dict('+'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)

    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'] ]