--- a/search_model_validation.py	Tue Jul 09 19:30:35 2019 -0400
+++ b/search_model_validation.py	Fri Aug 09 07:19:24 2019 -0400
@@ -1,22 +1,20 @@
 import argparse
 import collections
 import imblearn
+import joblib
 import json
 import numpy as np
-import pandas
+import pandas as pd
 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.base import clone
 from sklearn import (cluster, compose, decomposition, ensemble,
                      feature_extraction, feature_selection,
                      gaussian_process, kernel_approximation, metrics,
@@ -24,18 +22,23 @@
                      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 sklearn.model_selection._validation import _score, cross_validate
+from sklearn.model_selection import _search, _validation
 
-from utils import (SafeEval, get_cv, get_scoring, get_X_y,
-                   load_model, read_columns)
-from model_validations import train_test_split
+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', 'steps',
-                  'nthread', 'verbose')
+NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path',
+                  'nthread', 'callbacks')
+ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau',
+                     'CSVLogger', 'None')
 
 
 def _eval_search_params(params_builder):
@@ -62,9 +65,9 @@
             search_list = search_list[1:].strip()
             # TODO maybe add regular express check
             ev = safe_eval_es(search_list)
-            preprocessors = (
+            preprocessings = (
                 preprocessing.StandardScaler(), preprocessing.Binarizer(),
-                preprocessing.Imputer(), preprocessing.MaxAbsScaler(),
+                preprocessing.MaxAbsScaler(),
                 preprocessing.Normalizer(), preprocessing.MinMaxScaler(),
                 preprocessing.PolynomialFeatures(),
                 preprocessing.RobustScaler(), feature_selection.SelectKBest(),
@@ -133,21 +136,21 @@
                 if obj is None:
                     newlist.append(None)
                 elif obj == 'all_0':
-                    newlist.extend(preprocessors[0:36])
+                    newlist.extend(preprocessings[0:35])
                 elif obj == 'sk_prep_all':      # no KernalCenter()
-                    newlist.extend(preprocessors[0:8])
+                    newlist.extend(preprocessings[0:7])
                 elif obj == 'fs_all':
-                    newlist.extend(preprocessors[8:15])
+                    newlist.extend(preprocessings[7:14])
                 elif obj == 'decomp_all':
-                    newlist.extend(preprocessors[15:26])
+                    newlist.extend(preprocessings[14:25])
                 elif obj == 'k_appr_all':
-                    newlist.extend(preprocessors[26:30])
+                    newlist.extend(preprocessings[25:29])
                 elif obj == 'reb_all':
-                    newlist.extend(preprocessors[31:36])
+                    newlist.extend(preprocessings[30:35])
                 elif obj == 'imb_all':
-                    newlist.extend(preprocessors[36:55])
-                elif type(obj) is int and -1 < obj < len(preprocessors):
-                    newlist.append(preprocessors[obj])
+                    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))
@@ -162,7 +165,10 @@
 
 
 def main(inputs, infile_estimator, infile1, infile2,
-         outfile_result, outfile_object=None, groups=None):
+         outfile_result, outfile_object=None,
+         outfile_weights=None, groups=None,
+         ref_seq=None, intervals=None, targets=None,
+         fasta_path=None):
     """
     Parameter
     ---------
@@ -184,21 +190,40 @@
     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)
-    if groups:
-        (params['search_schemes']['options']['cv_selector']
-         ['groups_selector']['infile_g']) = groups
 
     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']
@@ -208,16 +233,48 @@
             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:
+
+        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'])
@@ -226,6 +283,15 @@
         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,
@@ -233,13 +299,47 @@
             sep='\t',
             header=header,
             parse_dates=True)
-    y = y.ravel()
+    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
@@ -254,100 +354,199 @@
     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)
+    # 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 p, v in estimator.get_params().items():
+        # 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 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']
+    # 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'])
 
-        # 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)
+        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:
-            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
+            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))
 
-    if options['error_score'] == 'raise':
-        searcher.fit(X, y, groups=groups)
+        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:
-        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 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']
 
-    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)
+            # 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
 
-    # output test result using best_estimator_
-    else:
-        best_estimator_ = searcher.best_estimator_
-        if isinstance(options['scoring'], collections.Mapping):
-            is_multimetric = True
+        # shared by both train_test_split and non-split
+        if options['error_score'] == 'raise':
+            searcher.fit(X, y, groups=groups)
         else:
-            is_multimetric = False
+            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)
 
-        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)
+        # 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_
+                del main_est.data_batch_generator
+
         with open(outfile_object, 'wb') as output_handler:
-            pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL)
+            pickle.dump(best_estimator_, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
 
 
 if __name__ == '__main__':
@@ -356,11 +555,18 @@
     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_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,
-         groups=args.groups)
+         outfile_weights=args.outfile_weights, groups=args.groups,
+         ref_seq=args.ref_seq, intervals=args.intervals,
+         targets=args.targets, fasta_path=args.fasta_path)