comparison utils.py @ 21:ce75dea7d3f0 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 2a058459e6daf0486871f93845f00fdb4a4eaca1

20:e1f65390f076

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
    Usage:
        eg.: SafePickler.load(pickled_file_object)
    """
    @classmethod
    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',

                    '__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 github.com" % fullname)
                mod = sys.modules[module]
                return getattr(mod, name)

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

    @classmethod
    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(',')]

        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)
        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)

        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:

        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):

                                '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)

    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(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)

        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'] ]

21:ce75dea7d3f0

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',

                    '__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_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)


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(',')]

        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)

        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:

        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):

                                '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)

    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(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)

        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']]