diff search_model_validation.py @ 0:f96efab83b65 draft

"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit ba6a47bdf76bbf4cb276206ac1a8cbf61332fd16"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/search_model_validation.py	Fri Sep 13 12:23:39 2019 -0400
@@ -0,0 +1,581 @@
+import argparse
+import collections
+import imblearn
+import joblib
+import json
+import numpy as np
+import pandas as pd
+import pickle
+import skrebate
+import sklearn
+import sys
+import xgboost
+import warnings
+from imblearn import under_sampling, over_sampling, combine
+from scipy.io import mmread
+from mlxtend import classifier, regressor
+from sklearn.base import clone
+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.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
+
+from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model,
+                             read_columns, try_get_attr, get_module)
+
+
+_fit_and_score = try_get_attr('galaxy_ml.model_validations', '_fit_and_score')
+setattr(_search, '_fit_and_score', _fit_and_score)
+setattr(_validation, '_fit_and_score', _fit_and_score)
+
+N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = './cached'
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+                     'CSVLogger', 'None')
+
+
+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)
+            preprocessings = (
+                preprocessing.StandardScaler(), preprocessing.Binarizer(),
+                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(preprocessings[0:35])
+                elif obj == 'sk_prep_all':      # no KernalCenter()
+                    newlist.extend(preprocessings[0:7])
+                elif obj == 'fs_all':
+                    newlist.extend(preprocessings[7:14])
+                elif obj == 'decomp_all':
+                    newlist.extend(preprocessings[14:25])
+                elif obj == 'k_appr_all':
+                    newlist.extend(preprocessings[25:29])
+                elif obj == 'reb_all':
+                    newlist.extend(preprocessings[30:35])
+                elif obj == 'imb_all':
+                    newlist.extend(preprocessings[35:54])
+                elif type(obj) is int and -1 < obj < len(preprocessings):
+                    newlist.append(preprocessings[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,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=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
+
+    outfile_weights : str, optional
+        File path to save model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    # conflict param checker
+    if params['outer_split']['split_mode'] == 'nested_cv' \
+            and params['save'] != 'nope':
+        raise ValueError("Save best estimator is not possible for nested CV!")
+
+    if not (params['search_schemes']['options']['refit']) \
+            and params['save'] != 'nope':
+        raise ValueError("Save best estimator is not possible when refit "
+                         "is False!")
+
+    params_builder = params['search_schemes']['search_params_builder']
+
+    with open(infile_estimator, 'rb') as estimator_handler:
+        estimator = load_model(estimator_handler)
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
+    input_type = params['input_options']['selected_input']
+    # tabular 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
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
+    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
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
+    y = read_columns(
+            infile2,
+            c=c,
+            c_option=column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True)
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    optimizer = params['search_schemes']['selected_search_scheme']
+    optimizer = getattr(model_selection, optimizer)
+
+    # handle gridsearchcv options
+    options = params['search_schemes']['options']
+
+    if groups:
+        header = 'infer' if (options['cv_selector']['groups_selector']
+                                    ['header_g']) else None
+        column_option = (options['cv_selector']['groups_selector']
+                                ['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 = (options['cv_selector']['groups_selector']
+                        ['column_selector_options_g']['col_g'])
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+        options['cv_selector']['groups_selector'] = groups
+
+    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
+
+    # del loaded_df
+    del loaded_df
+
+    # handle memory
+    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 iraps buried in pipeline
+        for p, v in estimator_params.items():
+            if p.endswith('memory'):
+                # for case of `__irapsclassifier__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)
+                # security reason, we don't want memory being
+                # modified unexpectedly
+                elif v:
+                    new_params = {p, None}
+                    estimator.set_params(**new_params)
+            # For now, 1 CPU is suggested for iprasclassifier
+            elif p.endswith('n_jobs'):
+                new_params = {p: 1}
+                estimator.set_params(**new_params)
+            # for security reason, types of callbacks are limited
+            elif p.endswith('callbacks'):
+                for cb in v:
+                    cb_type = cb['callback_selection']['callback_type']
+                    if cb_type not in ALLOWED_CALLBACKS:
+                        raise ValueError(
+                            "Prohibited callback type: %s!" % cb_type)
+
+    param_grid = _eval_search_params(params_builder)
+    searcher = optimizer(estimator, param_grid, **options)
+
+    # do nested split
+    split_mode = params['outer_split'].pop('split_mode')
+    # nested CV, outer cv using cross_validate
+    if split_mode == 'nested_cv':
+        outer_cv, _ = get_cv(params['outer_split']['cv_selector'])
+
+        if options['error_score'] == 'raise':
+            rval = cross_validate(
+                searcher, X, y, scoring=options['scoring'],
+                cv=outer_cv, n_jobs=N_JOBS, verbose=0,
+                error_score=options['error_score'])
+        else:
+            warnings.simplefilter('always', FitFailedWarning)
+            with warnings.catch_warnings(record=True) as w:
+                try:
+                    rval = cross_validate(
+                        searcher, X, y,
+                        scoring=options['scoring'],
+                        cv=outer_cv, n_jobs=N_JOBS,
+                        verbose=0,
+                        error_score=options['error_score'])
+                except ValueError:
+                    pass
+                for warning in w:
+                    print(repr(warning.message))
+
+        keys = list(rval.keys())
+        for k in keys:
+            if k.startswith('test'):
+                rval['mean_' + k] = np.mean(rval[k])
+                rval['std_' + k] = np.std(rval[k])
+            if k.endswith('time'):
+                rval.pop(k)
+        rval = pd.DataFrame(rval)
+        rval = rval[sorted(rval.columns)]
+        rval.to_csv(path_or_buf=outfile_result, sep='\t',
+                    header=True, index=False)
+    else:
+        if split_mode == 'train_test_split':
+            train_test_split = try_get_attr(
+                'galaxy_ml.model_validations', 'train_test_split')
+            # make sure refit is choosen
+            # this could be True for sklearn models, but not the case for
+            # deep learning models
+            if not options['refit'] and \
+                    not all(hasattr(estimator, attr)
+                            for attr in ('config', 'model_type')):
+                warnings.warn("Refit is change to `True` for nested "
+                              "validation!")
+                setattr(searcher, 'refit', True)
+            split_options = params['outer_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 groups is None:
+                    raise ValueError("No group based CV option was "
+                                     "choosen for group shuffle!")
+                split_options['labels'] = groups
+                if y is None:
+                    X, X_test, groups, _ =\
+                        train_test_split(X, groups, **split_options)
+                else:
+                    X, X_test, y, y_test, groups, _ =\
+                        train_test_split(X, y, groups, **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
+
+        # shared by both train_test_split and non-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))
+
+        # no outer split
+        if split_mode == 'no':
+            # save results
+            cv_results = pd.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)
+
+        # train_test_split, output test result using best_estimator_
+        # or rebuild the trained estimator using weights if applicable.
+        else:
+            scorer_ = searcher.scorer_
+            if isinstance(scorer_, collections.Mapping):
+                is_multimetric = True
+            else:
+                is_multimetric = False
+
+            best_estimator_ = getattr(searcher, 'best_estimator_', None)
+            if not best_estimator_:
+                raise ValueError("GridSearchCV object has no "
+                                 "`best_estimator_` when `refit`=False!")
+
+            if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier' \
+                    and hasattr(estimator.data_batch_generator, 'target_path'):
+                test_score = best_estimator_.evaluate(
+                    X_test, scorer=scorer_, is_multimetric=is_multimetric)
+            else:
+                test_score = _score(best_estimator_, X_test,
+                                    y_test, scorer_,
+                                    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 = pd.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:
+        best_estimator_ = getattr(searcher, 'best_estimator_', None)
+        if not best_estimator_:
+            warnings.warn("GridSearchCV object has no attribute "
+                          "'best_estimator_', because either it's "
+                          "nested gridsearch or `refit` is False!")
+            return
+
+        main_est = best_estimator_
+        if isinstance(best_estimator_, pipeline.Pipeline):
+            main_est = best_estimator_.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            del main_est.model_
+            del main_est.fit_params
+            del main_est.model_class_
+            del main_est.validation_data
+            if getattr(main_est, 'data_generator_', None):
+                del main_est.data_generator_
+
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(best_estimator_, 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("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
+         args.outfile_result, outfile_object=args.outfile_object,
+         outfile_weights=args.outfile_weights, groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)