diff search_model_validation.py @ 8:1a9d5a8fff12 draft

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

--- a/search_model_validation.py	Sun Dec 30 01:50:39 2018 -0500
+++ b/search_model_validation.py	Tue May 14 18:03:50 2019 -0400
@@ -1,7 +1,8 @@
+import argparse
+import collections
 import imblearn
 import json
 import numpy as np
-import os
 import pandas
 import pickle
 import skrebate
@@ -9,93 +10,124 @@
 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 imblearn.pipeline import Pipeline as imbPipeline
-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 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 utils import get_cv, get_scoring, get_X_y, load_model, read_columns, SafeEval
+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(os.environ.get('GALAXY_SLOTS', 1))
+N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1))
+CACHE_DIR = './cached'
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', 'steps',
+                  'nthread', 'verbose')
 
 
-def get_search_params(params_builder):
+def _eval_search_params(params_builder):
     search_params = {}
-    safe_eval = SafeEval(load_scipy=True, load_numpy=True)
-    safe_eval_es = SafeEval(load_estimators=True)
 
     for p in params_builder['param_set']:
-        search_p = p['search_param_selector']['search_p']
-        if search_p.strip() == '':
+        search_list = p['sp_list'].strip()
+        if search_list == '':
             continue
-        param_type = p['search_param_selector']['selected_param_type']
+
+        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
 
-        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()
-        param_name = lst[0].strip()
-        if param_name:
-            if param_name.lower() == 'n_jobs':
-                sys.exit("Parameter `%s` is invalid for search." %param_name)
-            elif not param_name.endswith('-'):
-                ev = safe_eval(literal)
-                if param_type == 'final_estimator_p':
-                    search_params['estimator__' + param_name] = ev
-                else:
-                    search_params['preprocessing_' + param_type[5:6] + '__' + param_name] = ev
-            else:
-                # only for estimator eval, add `-` to the end of param
-                #TODO maybe add regular express check
-                ev = safe_eval_es(literal)
-                for obj in ev:
-                    if 'n_jobs' in obj.get_params():
-                        obj.set_params( n_jobs=N_JOBS )
-                if param_type == 'final_estimator_p':
-                    search_params['estimator__' + param_name[:-1]] = ev
-                else:
-                    search_params['preprocessing_' + param_type[5:6] + '__' + param_name[:-1]] = ev
-        elif param_type != 'final_estimator_p':
-            #TODO regular express check ?
-            ev = safe_eval_es(literal)
-            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)]
+        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:
@@ -114,87 +146,102 @@
                     newlist.extend(preprocessors[31:36])
                 elif obj == 'imb_all':
                     newlist.extend(preprocessors[36:55])
-                elif  type(obj) is int and -1 < obj < len(preprocessors):
+                elif type(obj) is int and -1 < obj < len(preprocessors):
                     newlist.append(preprocessors[obj])
-                elif hasattr(obj, 'get_params'):       # user object
+                elif hasattr(obj, 'get_params'):       # user uploaded object
                     if 'n_jobs' in obj.get_params():
-                        newlist.append( obj.set_params(n_jobs=N_JOBS) )
+                        newlist.append(obj.set_params(n_jobs=N_JOBS))
                     else:
                         newlist.append(obj)
                 else:
-                    sys.exit("Unsupported preprocessor type: %r" %(obj))
-            search_params['preprocessing_' + param_type[5:6]] = newlist
-        else:
-            sys.exit("Parameter name of the final estimator can't be skipped!")
+                    sys.exit("Unsupported estimator type: %r" % (obj))
+
+            search_params[param_name] = newlist
 
     return search_params
 
 
-if __name__ == '__main__':
+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')
 
-    input_json_path = sys.argv[1]
-    with open(input_json_path, 'r') as param_handler:
+    with open(inputs, 'r') as param_handler:
         params = json.load(param_handler)
-
-    infile_pipeline = sys.argv[2]
-    infile1 = sys.argv[3]
-    infile2 = sys.argv[4]
-    outfile_result = sys.argv[5]
-    if len(sys.argv) > 6:
-        outfile_estimator = sys.argv[6]
-    else:
-        outfile_estimator = None
+    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']:
+        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,
+                c=c,
+                c_option=column_option,
                 sep='\t',
                 header=header,
-                parse_dates=True
-        )
+                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']:
+    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,
+            c=c,
+            c_option=column_option,
             sep='\t',
             header=header,
-            parse_dates=True
-    )
+            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'))
-    if groups is None:
-        options['cv'] = splitter
-    elif groups == '':
-        options['cv'] = list( splitter.split(X, y, groups=None) )
-    else:
-        options['cv'] = list( splitter.split(X, y, groups=groups) )
+    options['cv'] = splitter
     options['n_jobs'] = N_JOBS
     primary_scoring = options['scoring']['primary_scoring']
     options['scoring'] = get_scoring(options['scoring'])
@@ -203,32 +250,117 @@
     else:
         options['error_score'] = np.NaN
     if options['refit'] and isinstance(options['scoring'], dict):
-        options['refit'] = 'primary'
+        options['refit'] = primary_scoring
     if 'pre_dispatch' in options and options['pre_dispatch'] == '':
         options['pre_dispatch'] = None
 
-    with open(infile_pipeline, 'rb') as pipeline_handler:
-        pipeline = load_model(pipeline_handler)
+    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)
 
-    search_params = get_search_params(params_builder)
-    searcher = optimizer(pipeline, search_params, **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)
+        searcher.fit(X, y, groups=groups)
     else:
         warnings.simplefilter('always', FitFailedWarning)
         with warnings.catch_warnings(record=True) as w:
             try:
-                searcher.fit(X, y)
+                searcher.fit(X, y, groups=groups)
             except ValueError:
                 pass
             for warning in w:
                 print(repr(warning.message))
 
-    cv_result = pandas.DataFrame(searcher.cv_results_)
-    cv_result.rename(inplace=True, columns={'mean_test_primary': 'mean_test_'+primary_scoring, 'rank_test_primary': 'rank_test_'+primary_scoring})
-    cv_result.to_csv(path_or_buf=outfile_result, sep='\t', header=True, index=False)
+    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
 
-    if outfile_estimator:
-        with open(outfile_estimator, 'wb') as output_handler:
-            pickle.dump(searcher.best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL)
+        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)