Mercurial > repos > bgruening > sklearn_discriminant_classifier
view search_model_validation.py @ 32:d3e99d3df1ca draft
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 756f8be9c3cd437e131e6410cd625c24fe078e8c"
author | bgruening |
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date | Wed, 22 Jan 2020 07:41:13 -0500 |
parents | 64b771b1471a |
children | eeaf989f1024 |
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import argparse import collections import imblearn import joblib import json import numpy as np import os import pandas as pd import pickle import skrebate import sys import warnings from scipy.io import mmread from sklearn import (cluster, decomposition, feature_selection, kernel_approximation, model_selection, preprocessing) from sklearn.exceptions import FitFailedWarning from sklearn.model_selection._validation import _score, cross_validate from sklearn.model_selection import _search, _validation from sklearn.pipeline import Pipeline from galaxy_ml.utils import (SafeEval, get_cv, get_scoring, load_model, read_columns, try_get_attr, get_module, clean_params, get_main_estimator) _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(os.environ.get('GALAXY_SLOTS', 1)) # handle disk cache CACHE_DIR = os.path.join(os.getcwd(), 'cached') del os NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path', 'nthread', 'callbacks') 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 _handle_X_y(estimator, params, infile1, infile2, loaded_df={}, ref_seq=None, intervals=None, targets=None, fasta_path=None): """read inputs Params ------- estimator : estimator object params : dict Galaxy tool parameter inputs infile1 : str File path to dataset containing features infile2 : str File path to dataset containing target values loaded_df : dict Contains loaded DataFrame objects with file path as keys ref_seq : str File path to dataset containing genome sequence file interval : 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 Returns ------- estimator : estimator object after setting new attributes X : numpy array y : numpy array """ estimator_params = estimator.get_params() 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) if df_key in loaded_df: infile1 = loaded_df[df_key] 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 return estimator, X, y def _do_outer_cv(searcher, X, y, outer_cv, scoring, error_score='raise', outfile=None): """Do outer cross-validation for nested CV Parameters ---------- searcher : object SearchCV object X : numpy array Containing features y : numpy array Target values or labels outer_cv : int or CV splitter Control the cv splitting scoring : object Scorer error_score: str, float or numpy float Whether to raise fit error or return an value outfile : str File path to store the restuls """ if error_score == 'raise': rval = cross_validate( searcher, X, y, scoring=scoring, cv=outer_cv, n_jobs=N_JOBS, verbose=0, error_score=error_score) else: warnings.simplefilter('always', FitFailedWarning) with warnings.catch_warnings(record=True) as w: try: rval = cross_validate( searcher, X, y, scoring=scoring, cv=outer_cv, n_jobs=N_JOBS, verbose=0, error_score=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, sep='\t', header=True, index=False) def _do_train_test_split_val(searcher, X, y, params, error_score='raise', primary_scoring=None, groups=None, outfile=None): """ do train test split, searchCV validates on the train and then use the best_estimator_ to evaluate on the test Returns -------- Fitted SearchCV object """ train_test_split = try_get_attr( 'galaxy_ml.model_validations', 'train_test_split') 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) if 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)) scorer_ = searcher.scorer_ if isinstance(scorer_, collections.Mapping): is_multimetric = True else: is_multimetric = False best_estimator_ = getattr(searcher, 'best_estimator_') # TODO Solve deep learning models in pipeline if best_estimator_.__class__.__name__ == 'KerasGBatchClassifier': 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, sep='\t', header=True, index=False) return searcher 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') # store read dataframe object loaded_df = {} with open(inputs, 'r') as param_handler: params = json.load(param_handler) # Override the refit parameter params['search_schemes']['options']['refit'] = True \ if params['save'] != 'nope' else False with open(infile_estimator, 'rb') as estimator_handler: estimator = load_model(estimator_handler) 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) groups = pd.read_csv(groups, sep='\t', header=header, parse_dates=True) loaded_df[df_key] = groups 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 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 params_builder = params['search_schemes']['search_params_builder'] param_grid = _eval_search_params(params_builder) estimator = clean_params(estimator) # save the SearchCV object without fit if params['save'] == 'save_no_fit': searcher = optimizer(estimator, param_grid, **options) print(searcher) with open(outfile_object, 'wb') as output_handler: pickle.dump(searcher, output_handler, pickle.HIGHEST_PROTOCOL) return 0 # read inputs and loads new attributes, like paths estimator, X, y = _handle_X_y(estimator, params, infile1, infile2, loaded_df=loaded_df, ref_seq=ref_seq, intervals=intervals, targets=targets, fasta_path=fasta_path) # cache iraps_core fits could increase search speed significantly memory = joblib.Memory(location=CACHE_DIR, verbose=0) main_est = get_main_estimator(estimator) if main_est.__class__.__name__ == 'IRAPSClassifier': main_est.set_params(memory=memory) searcher = optimizer(estimator, param_grid, **options) split_mode = params['outer_split'].pop('split_mode') if split_mode == 'nested_cv': # 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) outer_cv, _ = get_cv(params['outer_split']['cv_selector']) # nested CV, outer cv using cross_validate if options['error_score'] == 'raise': rval = cross_validate( searcher, X, y, scoring=options['scoring'], cv=outer_cv, n_jobs=N_JOBS, verbose=options['verbose'], return_estimator=(params['save'] == 'save_estimator'), error_score=options['error_score'], return_train_score=True) 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=options['verbose'], return_estimator=(params['save'] == 'save_estimator'), error_score=options['error_score'], return_train_score=True) except ValueError: pass for warning in w: print(repr(warning.message)) fitted_searchers = rval.pop('estimator', []) if fitted_searchers: import os pwd = os.getcwd() save_dir = os.path.join(pwd, 'cv_results_in_folds') try: os.mkdir(save_dir) for idx, obj in enumerate(fitted_searchers): target_name = 'cv_results_' + '_' + 'split%d' % idx target_path = os.path.join(pwd, save_dir, target_name) cv_results_ = getattr(obj, 'cv_results_', None) if not cv_results_: print("%s is not available" % target_name) continue cv_results_ = pd.DataFrame(cv_results_) cv_results_ = cv_results_[sorted(cv_results_.columns)] cv_results_.to_csv(target_path, sep='\t', header=True, index=False) except Exception as e: print(e) finally: del os 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) return 0 # deprecate train test split mode """searcher = _do_train_test_split_val( searcher, X, y, params, primary_scoring=primary_scoring, error_score=options['error_score'], groups=groups, outfile=outfile_result)""" # no outer split else: searcher.set_params(n_jobs=N_JOBS) 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)) 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) memory.clear(warn=False) # output best estimator, and weights if applicable 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 # clean prams best_estimator_ = clean_params(best_estimator_) main_est = get_main_estimator(best_estimator_) 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: print("Best estimator is saved: %s " % repr(best_estimator_)) 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)