view search_model_validation.py @ 8:1c4a241bef5c draft

planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit c0a3a186966888e5787335a7628bf0a4382637e7

import argparse
import collections
import imblearn
import json
import numpy as np
import pandas
import pickle
import skrebate
import sklearn
import sys
import xgboost
import warnings
import iraps_classifier
import model_validations
import preprocessors
import feature_selectors
from imblearn import under_sampling, over_sampling, combine
from scipy.io import mmread
from mlxtend import classifier, regressor
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 sklearn.exceptions import FitFailedWarning
from sklearn.externals import joblib
from sklearn.model_selection._validation import _score

from utils import (SafeEval, get_cv, get_scoring, get_X_y,
                   load_model, read_columns)
from model_validations import train_test_split


N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
CACHE_DIR = './cached'
NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps',
                  'nthread', 'verbose')


def _eval_search_params(params_builder):
    search_params = {}

    for p in params_builder['param_set']:
        search_list = p['sp_list'].strip()
        if search_list == '':
            continue

        param_name = p['sp_name']
        if param_name.lower().endswith(NON_SEARCHABLE):
            print("Warning: `%s` is not eligible for search and was "
                  "omitted!" % param_name)
            continue

        if not search_list.startswith(':'):
            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
            ev = safe_eval(search_list)
            search_params[param_name] = ev
        else:
            # Have `:` before search list, asks for estimator evaluatio
            safe_eval_es = SafeEval(load_estimators=True)
            search_list = search_list[1:].strip()
            # TODO maybe add regular express check
            ev = safe_eval_es(search_list)
            preprocessors = (
                preprocessing.StandardScaler(), preprocessing.Binarizer(),
                preprocessing.Imputer(), preprocessing.MaxAbsScaler(),
                preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
                preprocessing.PolynomialFeatures(),
                preprocessing.RobustScaler(), feature_selection.SelectKBest(),
                feature_selection.GenericUnivariateSelect(),
                feature_selection.SelectPercentile(),
                feature_selection.SelectFpr(), feature_selection.SelectFdr(),
                feature_selection.SelectFwe(),
                feature_selection.VarianceThreshold(),
                decomposition.FactorAnalysis(random_state=0),
                decomposition.FastICA(random_state=0),
                decomposition.IncrementalPCA(),
                decomposition.KernelPCA(random_state=0, n_jobs=N_JOBS),
                decomposition.LatentDirichletAllocation(
                    random_state=0, n_jobs=N_JOBS),
                decomposition.MiniBatchDictionaryLearning(
                    random_state=0, n_jobs=N_JOBS),
                decomposition.MiniBatchSparsePCA(
                    random_state=0, n_jobs=N_JOBS),
                decomposition.NMF(random_state=0),
                decomposition.PCA(random_state=0),
                decomposition.SparsePCA(random_state=0, n_jobs=N_JOBS),
                decomposition.TruncatedSVD(random_state=0),
                kernel_approximation.Nystroem(random_state=0),
                kernel_approximation.RBFSampler(random_state=0),
                kernel_approximation.AdditiveChi2Sampler(),
                kernel_approximation.SkewedChi2Sampler(random_state=0),
                cluster.FeatureAgglomeration(),
                skrebate.ReliefF(n_jobs=N_JOBS),
                skrebate.SURF(n_jobs=N_JOBS),
                skrebate.SURFstar(n_jobs=N_JOBS),
                skrebate.MultiSURF(n_jobs=N_JOBS),
                skrebate.MultiSURFstar(n_jobs=N_JOBS),
                imblearn.under_sampling.ClusterCentroids(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.CondensedNearestNeighbour(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.EditedNearestNeighbours(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.RepeatedEditedNearestNeighbours(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.AllKNN(random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.InstanceHardnessThreshold(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.NearMiss(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.NeighbourhoodCleaningRule(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.OneSidedSelection(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.under_sampling.RandomUnderSampler(
                    random_state=0),
                imblearn.under_sampling.TomekLinks(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.over_sampling.ADASYN(random_state=0, n_jobs=N_JOBS),
                imblearn.over_sampling.RandomOverSampler(random_state=0),
                imblearn.over_sampling.SMOTE(random_state=0, n_jobs=N_JOBS),
                imblearn.over_sampling.SVMSMOTE(random_state=0, n_jobs=N_JOBS),
                imblearn.over_sampling.BorderlineSMOTE(
                    random_state=0, n_jobs=N_JOBS),
                imblearn.over_sampling.SMOTENC(
                    categorical_features=[], random_state=0, n_jobs=N_JOBS),
                imblearn.combine.SMOTEENN(random_state=0),
                imblearn.combine.SMOTETomek(random_state=0))
            newlist = []
            for obj in ev:
                if obj is None:
                    newlist.append(None)
                elif obj == 'all_0':
                    newlist.extend(preprocessors[0:36])
                elif obj == 'sk_prep_all':      # no KernalCenter()
                    newlist.extend(preprocessors[0:8])
                elif obj == 'fs_all':
                    newlist.extend(preprocessors[8:15])
                elif obj == 'decomp_all':
                    newlist.extend(preprocessors[15:26])
                elif obj == 'k_appr_all':
                    newlist.extend(preprocessors[26:30])
                elif obj == 'reb_all':
                    newlist.extend(preprocessors[31:36])
                elif obj == 'imb_all':
                    newlist.extend(preprocessors[36:55])
                elif type(obj) is int and -1 < obj < len(preprocessors):
                    newlist.append(preprocessors[obj])
                elif hasattr(obj, 'get_params'):       # user uploaded object
                    if 'n_jobs' in obj.get_params():
                        newlist.append(obj.set_params(n_jobs=N_JOBS))
                    else:
                        newlist.append(obj)
                else:
                    sys.exit("Unsupported estimator type: %r" % (obj))

            search_params[param_name] = newlist

    return search_params


def main(inputs, infile_estimator, infile1, infile2,
         outfile_result, outfile_object=None, groups=None):
    """
    Parameter
    ---------
    inputs : str
        File path to galaxy tool parameter

    infile_estimator : str
        File path to estimator

    infile1 : str
        File path to dataset containing features

    infile2 : str
        File path to dataset containing target values

    outfile_result : str
        File path to save the results, either cv_results or test result

    outfile_object : str, optional
        File path to save searchCV object

    groups : str
        File path to dataset containing groups labels
    """

    warnings.simplefilter('ignore')

    with open(inputs, 'r') as param_handler:
        params = json.load(param_handler)
    if groups:
        (params['search_schemes']['options']['cv_selector']
         ['groups_selector']['infile_g']) = groups

    params_builder = params['search_schemes']['search_params_builder']

    input_type = params['input_options']['selected_input']
    if input_type == 'tabular':
        header = 'infer' if params['input_options']['header1'] else None
        column_option = (params['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['input_options']['column_selector_options_1']['col1']
        else:
            c = None
        X = read_columns(
                infile1,
                c=c,
                c_option=column_option,
                sep='\t',
                header=header,
                parse_dates=True).astype(float)
    else:
        X = mmread(open(infile1, 'r'))

    header = 'infer' if params['input_options']['header2'] else None
    column_option = (params['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['input_options']['column_selector_options_2']['col2']
    else:
        c = None
    y = read_columns(
            infile2,
            c=c,
            c_option=column_option,
            sep='\t',
            header=header,
            parse_dates=True)
    y = y.ravel()

    optimizer = params['search_schemes']['selected_search_scheme']
    optimizer = getattr(model_selection, optimizer)

    options = params['search_schemes']['options']

    splitter, groups = get_cv(options.pop('cv_selector'))
    options['cv'] = splitter
    options['n_jobs'] = N_JOBS
    primary_scoring = options['scoring']['primary_scoring']
    options['scoring'] = get_scoring(options['scoring'])
    if options['error_score']:
        options['error_score'] = 'raise'
    else:
        options['error_score'] = np.NaN
    if options['refit'] and isinstance(options['scoring'], dict):
        options['refit'] = primary_scoring
    if 'pre_dispatch' in options and options['pre_dispatch'] == '':
        options['pre_dispatch'] = None

    with open(infile_estimator, 'rb') as estimator_handler:
        estimator = load_model(estimator_handler)

    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
    # cache iraps_core fits could increase search speed significantly
    if estimator.__class__.__name__ == 'IRAPSClassifier':
        estimator.set_params(memory=memory)
    else:
        for p, v in estimator.get_params().items():
            if p.endswith('memory'):
                if len(p) > 8 and p[:-8].endswith('irapsclassifier'):
                    # cache iraps_core fits could increase search
                    # speed significantly
                    new_params = {p: memory}
                    estimator.set_params(**new_params)
                elif v:
                    new_params = {p, None}
                    estimator.set_params(**new_params)
            elif p.endswith('n_jobs'):
                new_params = {p: 1}
                estimator.set_params(**new_params)

    param_grid = _eval_search_params(params_builder)
    searcher = optimizer(estimator, param_grid, **options)

    # do train_test_split
    do_train_test_split = params['train_test_split'].pop('do_split')
    if do_train_test_split == 'yes':
        # make sure refit is choosen
        if not options['refit']:
            raise ValueError("Refit must be `True` for shuffle splitting!")
        split_options = params['train_test_split']

        # splits
        if split_options['shuffle'] == 'stratified':
            split_options['labels'] = y
            X, X_test, y, y_test = train_test_split(X, y, **split_options)
        elif split_options['shuffle'] == 'group':
            if not groups:
                raise ValueError("No group based CV option was "
                                 "choosen for group shuffle!")
            split_options['labels'] = groups
            X, X_test, y, y_test, groups, _ =\
                train_test_split(X, y, **split_options)
        else:
            if split_options['shuffle'] == 'None':
                split_options['shuffle'] = None
            X, X_test, y, y_test =\
                train_test_split(X, y, **split_options)
    # end train_test_split

    if options['error_score'] == 'raise':
        searcher.fit(X, y, groups=groups)
    else:
        warnings.simplefilter('always', FitFailedWarning)
        with warnings.catch_warnings(record=True) as w:
            try:
                searcher.fit(X, y, groups=groups)
            except ValueError:
                pass
            for warning in w:
                print(repr(warning.message))

    if do_train_test_split == 'no':
        # save results
        cv_results = pandas.DataFrame(searcher.cv_results_)
        cv_results = cv_results[sorted(cv_results.columns)]
        cv_results.to_csv(path_or_buf=outfile_result, sep='\t',
                          header=True, index=False)

    # output test result using best_estimator_
    else:
        best_estimator_ = searcher.best_estimator_
        if isinstance(options['scoring'], collections.Mapping):
            is_multimetric = True
        else:
            is_multimetric = False

        test_score = _score(best_estimator_, X_test,
                            y_test, options['scoring'],
                            is_multimetric=is_multimetric)
        if not is_multimetric:
            test_score = {primary_scoring: test_score}
        for key, value in test_score.items():
            test_score[key] = [value]
        result_df = pandas.DataFrame(test_score)
        result_df.to_csv(path_or_buf=outfile_result, sep='\t',
                         header=True, index=False)

    memory.clear(warn=False)

    if outfile_object:
        with open(outfile_object, 'wb') as output_handler:
            pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)


if __name__ == '__main__':
    aparser = argparse.ArgumentParser()
    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
    aparser.add_argument("-X", "--infile1", dest="infile1")
    aparser.add_argument("-y", "--infile2", dest="infile2")
    aparser.add_argument("-r", "--outfile_result", dest="outfile_result")
    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
    aparser.add_argument("-g", "--groups", dest="groups")
    args = aparser.parse_args()

    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
         args.outfile_result, outfile_object=args.outfile_object,
         groups=args.groups)