# HG changeset patch # User bgruening # Date 1618338818 0 # Node ID c3bafda50176bd192cbfebe5c2e3962a607365fb # Parent c12485d058aa8d1ccf7ebb102eea6a96dc106e8c "planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit e2a5eade6d0e5ddf3a47630381a0ad90d80e8a04" diff -r c12485d058aa -r c3bafda50176 fitted_model_eval.py --- a/fitted_model_eval.py Thu Oct 01 21:08:39 2020 +0000 +++ b/fitted_model_eval.py Tue Apr 13 18:33:38 2021 +0000 @@ -11,7 +11,7 @@ def _get_X_y(params, infile1, infile2): - """ read from inputs and output X and y + """read from inputs and output X and y Parameters ---------- @@ -26,35 +26,40 @@ # store read dataframe object loaded_df = {} - input_type = params['input_options']['selected_input'] + 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'] + 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) + 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')) + elif input_type == "sparse": + X = mmread(open(infile1, "r")) # 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'] + 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 @@ -62,26 +67,24 @@ if df_key in loaded_df: infile2 = loaded_df[df_key] else: - infile2 = pd.read_csv(infile2, sep='\t', - header=header, parse_dates=True) + 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) + 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() return X, y -def main(inputs, infile_estimator, outfile_eval, - infile_weights=None, infile1=None, - infile2=None): +def main( + inputs, + infile_estimator, + outfile_eval, + infile_weights=None, + infile1=None, + infile2=None, +): """ Parameter --------- @@ -103,49 +106,55 @@ infile2 : str File path to dataset containing target values """ - warnings.filterwarnings('ignore') + warnings.filterwarnings("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) X_test, y_test = _get_X_y(params, infile1, infile2) # load model - with open(infile_estimator, 'rb') as est_handler: + with open(infile_estimator, "rb") as est_handler: estimator = load_model(est_handler) main_est = estimator if isinstance(estimator, Pipeline): main_est = estimator.steps[-1][-1] - if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): - if not infile_weights or infile_weights == 'None': - raise ValueError("The selected model skeleton asks for weights, " - "but no dataset for weights was provided!") + if hasattr(main_est, "config") and hasattr(main_est, "load_weights"): + if not infile_weights or infile_weights == "None": + raise ValueError( + "The selected model skeleton asks for weights, " "but no dataset for weights was provided!" + ) main_est.load_weights(infile_weights) # handle scorer, convert to scorer dict - scoring = params['scoring'] + # Check if scoring is specified + scoring = params["scoring"] + if scoring is not None: + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = scoring.get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"]) + scorer = get_scoring(scoring) scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) - if hasattr(estimator, 'evaluate'): - scores = estimator.evaluate(X_test, y_test=y_test, - scorer=scorer, - is_multimetric=True) + if hasattr(estimator, "evaluate"): + scores = estimator.evaluate(X_test, y_test=y_test, scorer=scorer, is_multimetric=True) else: - scores = _score(estimator, X_test, y_test, scorer, - is_multimetric=True) + scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) # handle output for name, score in scores.items(): scores[name] = [score] df = pd.DataFrame(scores) df = df[sorted(df.columns)] - df.to_csv(path_or_buf=outfile_eval, sep='\t', - header=True, index=False) + df.to_csv(path_or_buf=outfile_eval, sep="\t", header=True, index=False) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") @@ -155,6 +164,11 @@ aparser.add_argument("-O", "--outfile_eval", dest="outfile_eval") args = aparser.parse_args() - main(args.inputs, args.infile_estimator, args.outfile_eval, - infile_weights=args.infile_weights, infile1=args.infile1, - infile2=args.infile2) + main( + args.inputs, + args.infile_estimator, + args.outfile_eval, + infile_weights=args.infile_weights, + infile1=args.infile1, + infile2=args.infile2, + ) diff -r c12485d058aa -r c3bafda50176 keras_deep_learning.py --- a/keras_deep_learning.py Thu Oct 01 21:08:39 2020 +0000 +++ b/keras_deep_learning.py Tue Apr 13 18:33:38 2021 +0000 @@ -177,11 +177,11 @@ # merge layers if 'merging_layers' in options: idxs = literal_eval(options.pop('merging_layers')) - merging_layers = [all_layers[i-1] for i in idxs] + merging_layers = [all_layers[i - 1] for i in idxs] new_layer = klass(**options)(merging_layers) # non-input layers elif inbound_nodes is not None: - new_layer = klass(**options)(all_layers[inbound_nodes-1]) + new_layer = klass(**options)(all_layers[inbound_nodes - 1]) # input layers else: new_layer = klass(**options) @@ -189,10 +189,10 @@ all_layers.append(new_layer) input_indexes = _handle_shape(config['input_layers']) - input_layers = [all_layers[i-1] for i in input_indexes] + input_layers = [all_layers[i - 1] for i in input_indexes] output_indexes = _handle_shape(config['output_layers']) - output_layers = [all_layers[i-1] for i in output_indexes] + output_layers = [all_layers[i - 1] for i in output_indexes] return Model(inputs=input_layers, outputs=output_layers) @@ -300,8 +300,7 @@ options.update((inputs['mode_selection']['compile_params'] ['optimizer_selection']['optimizer_options'])) - train_metrics = (inputs['mode_selection']['compile_params'] - ['metrics']).split(',') + train_metrics = inputs['mode_selection']['compile_params']['metrics'] if train_metrics[-1] == 'none': train_metrics = train_metrics[:-1] options['metrics'] = train_metrics diff -r c12485d058aa -r c3bafda50176 keras_train_and_eval.py --- a/keras_train_and_eval.py Thu Oct 01 21:08:39 2020 +0000 +++ b/keras_train_and_eval.py Tue Apr 13 18:33:38 2021 +0000 @@ -10,7 +10,6 @@ from scipy.io import mmread from sklearn.pipeline import Pipeline from sklearn.metrics.scorer import _check_multimetric_scoring -from sklearn import model_selection from sklearn.model_selection._validation import _score from sklearn.model_selection import _search, _validation from sklearn.utils import indexable, safe_indexing @@ -18,39 +17,49 @@ from galaxy_ml.externals.selene_sdk.utils import compute_score from galaxy_ml.model_validations import train_test_split from galaxy_ml.keras_galaxy_models import _predict_generator -from galaxy_ml.utils import (SafeEval, get_scoring, load_model, - read_columns, try_get_attr, get_module, - clean_params, get_main_estimator) +from galaxy_ml.utils import ( + SafeEval, + 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) +_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)) -CACHE_DIR = os.path.join(os.getcwd(), 'cached') +N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1)) +CACHE_DIR = os.path.join(os.getcwd(), "cached") del os -NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path', - 'nthread', 'callbacks') -ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau', - 'CSVLogger', 'None') +NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks") +ALLOWED_CALLBACKS = ( + "EarlyStopping", + "TerminateOnNaN", + "ReduceLROnPlateau", + "CSVLogger", + "None", +) def _eval_swap_params(params_builder): swap_params = {} - for p in params_builder['param_set']: - swap_value = p['sp_value'].strip() - if swap_value == '': + for p in params_builder["param_set"]: + swap_value = p["sp_value"].strip() + if swap_value == "": continue - param_name = p['sp_name'] + param_name = p["sp_name"] if param_name.lower().endswith(NON_SEARCHABLE): - warnings.warn("Warning: `%s` is not eligible for search and was " - "omitted!" % param_name) + warnings.warn("Warning: `%s` is not eligible for search and was " "omitted!" % param_name) continue - if not swap_value.startswith(':'): + if not swap_value.startswith(":"): safe_eval = SafeEval(load_scipy=True, load_numpy=True) ev = safe_eval(swap_value) else: @@ -77,34 +86,31 @@ else: new_arrays.append(arr) - if kwargs['shuffle'] == 'None': - kwargs['shuffle'] = None + if kwargs["shuffle"] == "None": + kwargs["shuffle"] = None - group_names = kwargs.pop('group_names', None) + group_names = kwargs.pop("group_names", None) if group_names is not None and group_names.strip(): - group_names = [name.strip() for name in - group_names.split(',')] + group_names = [name.strip() for name in group_names.split(",")] new_arrays = indexable(*new_arrays) - groups = kwargs['labels'] + groups = kwargs["labels"] n_samples = new_arrays[0].shape[0] index_arr = np.arange(n_samples) test = index_arr[np.isin(groups, group_names)] train = index_arr[~np.isin(groups, group_names)] - rval = list(chain.from_iterable( - (safe_indexing(a, train), - safe_indexing(a, test)) for a in new_arrays)) + rval = list(chain.from_iterable((safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays)) else: rval = train_test_split(*new_arrays, **kwargs) for pos in nones: - rval[pos * 2: 2] = [None, None] + rval[pos * 2 : 2] = [None, None] return rval def _evaluate(y_true, pred_probas, scorer, is_multimetric=True): - """ output scores based on input scorer + """output scores based on input scorer Parameters ---------- @@ -118,52 +124,55 @@ """ if y_true.ndim == 1 or y_true.shape[-1] == 1: pred_probas = pred_probas.ravel() - pred_labels = (pred_probas > 0.5).astype('int32') - targets = y_true.ravel().astype('int32') + pred_labels = (pred_probas > 0.5).astype("int32") + targets = y_true.ravel().astype("int32") if not is_multimetric: - preds = pred_labels if scorer.__class__.__name__ == \ - '_PredictScorer' else pred_probas + preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas score = scorer._score_func(targets, preds, **scorer._kwargs) return score else: scores = {} for name, one_scorer in scorer.items(): - preds = pred_labels if one_scorer.__class__.__name__\ - == '_PredictScorer' else pred_probas - score = one_scorer._score_func(targets, preds, - **one_scorer._kwargs) + preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas + score = one_scorer._score_func(targets, preds, **one_scorer._kwargs) scores[name] = score # TODO: multi-class metrics # multi-label else: - pred_labels = (pred_probas > 0.5).astype('int32') - targets = y_true.astype('int32') + pred_labels = (pred_probas > 0.5).astype("int32") + targets = y_true.astype("int32") if not is_multimetric: - preds = pred_labels if scorer.__class__.__name__ == \ - '_PredictScorer' else pred_probas - score, _ = compute_score(preds, targets, - scorer._score_func) + preds = pred_labels if scorer.__class__.__name__ == "_PredictScorer" else pred_probas + score, _ = compute_score(preds, targets, scorer._score_func) return score else: scores = {} for name, one_scorer in scorer.items(): - preds = pred_labels if one_scorer.__class__.__name__\ - == '_PredictScorer' else pred_probas - score, _ = compute_score(preds, targets, - one_scorer._score_func) + preds = pred_labels if one_scorer.__class__.__name__ == "_PredictScorer" else pred_probas + score, _ = compute_score(preds, targets, one_scorer._score_func) scores[name] = score return scores -def main(inputs, infile_estimator, infile1, infile2, - outfile_result, outfile_object=None, - outfile_weights=None, outfile_y_true=None, - outfile_y_preds=None, groups=None, - ref_seq=None, intervals=None, targets=None, - fasta_path=None): +def main( + inputs, + infile_estimator, + infile1, + infile2, + outfile_result, + outfile_object=None, + outfile_weights=None, + outfile_y_true=None, + outfile_y_preds=None, + groups=None, + ref_seq=None, + intervals=None, + targets=None, + fasta_path=None, +): """ Parameter --------- @@ -209,19 +218,19 @@ fasta_path : str File path to dataset containing fasta file """ - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) # load estimator - with open(infile_estimator, 'rb') as estimator_handler: + with open(infile_estimator, "rb") as estimator_handler: estimator = load_model(estimator_handler) estimator = clean_params(estimator) # swap hyperparameter - swapping = params['experiment_schemes']['hyperparams_swapping'] + swapping = params["experiment_schemes"]["hyperparams_swapping"] swap_params = _eval_swap_params(swapping) estimator.set_params(**swap_params) @@ -230,38 +239,39 @@ # store read dataframe object loaded_df = {} - input_type = params['input_options']['selected_input'] + 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'] + 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) + 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')) + elif input_type == "sparse": + X = mmread(open(infile1, "r")) # fasta_file input - elif input_type == 'seq_fasta': - pyfaidx = get_module('pyfaidx') + 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}) + if param.endswith("fasta_path"): + estimator.set_params(**{param: fasta_path}) break else: raise ValueError( @@ -270,25 +280,29 @@ "KerasGBatchClassifier with " "FastaDNABatchGenerator/FastaProteinBatchGenerator " "or having GenomeOneHotEncoder/ProteinOneHotEncoder " - "in pipeline!") + "in pipeline!" + ) - elif input_type == 'refseq_and_interval': + 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 + "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'] + 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 @@ -296,37 +310,35 @@ if df_key in loaded_df: infile2 = loaded_df[df_key] else: - infile2 = pd.read_csv(infile2, sep='\t', - header=header, parse_dates=True) + 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) + 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()) + if input_type == "refseq_and_interval": + estimator.set_params(data_batch_generator__features=y.ravel().tolist()) y = None # end y # load groups if groups: - groups_selector = (params['experiment_schemes']['test_split'] - ['split_algos']).pop('groups_selector') + groups_selector = (params["experiment_schemes"]["test_split"]["split_algos"]).pop("groups_selector") - header = 'infer' if groups_selector['header_g'] else None - column_option = \ - (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 = groups_selector['column_selector_options_g']['col_g'] + header = "infer" if groups_selector["header_g"] else None + column_option = 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 = groups_selector["column_selector_options_g"]["col_g"] else: c = None @@ -334,13 +346,12 @@ 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 = read_columns(groups, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) groups = groups.ravel() # del loaded_df @@ -349,86 +360,99 @@ # 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': + if main_est.__class__.__name__ == "IRAPSClassifier": main_est.set_params(memory=memory) # handle scorer, convert to scorer dict scoring = params['experiment_schemes']['metrics']['scoring'] + if scoring is not None: + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = scoring.get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"]) + scorer = get_scoring(scoring) scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) # handle test (first) split - test_split_options = (params['experiment_schemes'] - ['test_split']['split_algos']) + test_split_options = params["experiment_schemes"]["test_split"]["split_algos"] - if test_split_options['shuffle'] == 'group': - test_split_options['labels'] = groups - if test_split_options['shuffle'] == 'stratified': + if test_split_options["shuffle"] == "group": + test_split_options["labels"] = groups + if test_split_options["shuffle"] == "stratified": if y is not None: - test_split_options['labels'] = y + test_split_options["labels"] = y else: - raise ValueError("Stratified shuffle split is not " - "applicable on empty target values!") + raise ValueError("Stratified shuffle split is not " "applicable on empty target values!") - X_train, X_test, y_train, y_test, groups_train, groups_test = \ - train_test_split_none(X, y, groups, **test_split_options) + ( + X_train, + X_test, + y_train, + y_test, + groups_train, + _groups_test, + ) = train_test_split_none(X, y, groups, **test_split_options) - exp_scheme = params['experiment_schemes']['selected_exp_scheme'] + exp_scheme = params["experiment_schemes"]["selected_exp_scheme"] # handle validation (second) split - if exp_scheme == 'train_val_test': - val_split_options = (params['experiment_schemes'] - ['val_split']['split_algos']) + if exp_scheme == "train_val_test": + val_split_options = params["experiment_schemes"]["val_split"]["split_algos"] - if val_split_options['shuffle'] == 'group': - val_split_options['labels'] = groups_train - if val_split_options['shuffle'] == 'stratified': + if val_split_options["shuffle"] == "group": + val_split_options["labels"] = groups_train + if val_split_options["shuffle"] == "stratified": if y_train is not None: - val_split_options['labels'] = y_train + val_split_options["labels"] = y_train else: - raise ValueError("Stratified shuffle split is not " - "applicable on empty target values!") + raise ValueError("Stratified shuffle split is not " "applicable on empty target values!") - X_train, X_val, y_train, y_val, groups_train, groups_val = \ - train_test_split_none(X_train, y_train, groups_train, - **val_split_options) + ( + X_train, + X_val, + y_train, + y_val, + groups_train, + _groups_val, + ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options) # train and eval - if hasattr(estimator, 'validation_data'): - if exp_scheme == 'train_val_test': - estimator.fit(X_train, y_train, - validation_data=(X_val, y_val)) + if hasattr(estimator, "validation_data"): + if exp_scheme == "train_val_test": + estimator.fit(X_train, y_train, validation_data=(X_val, y_val)) else: - estimator.fit(X_train, y_train, - validation_data=(X_test, y_test)) + estimator.fit(X_train, y_train, validation_data=(X_test, y_test)) else: estimator.fit(X_train, y_train) - if hasattr(estimator, 'evaluate'): + if hasattr(estimator, "evaluate"): steps = estimator.prediction_steps batch_size = estimator.batch_size - generator = estimator.data_generator_.flow(X_test, y=y_test, - batch_size=batch_size) - predictions, y_true = _predict_generator(estimator.model_, generator, - steps=steps) + generator = estimator.data_generator_.flow(X_test, y=y_test, batch_size=batch_size) + predictions, y_true = _predict_generator(estimator.model_, generator, steps=steps) scores = _evaluate(y_true, predictions, scorer, is_multimetric=True) else: - if hasattr(estimator, 'predict_proba'): + if hasattr(estimator, "predict_proba"): predictions = estimator.predict_proba(X_test) else: predictions = estimator.predict(X_test) y_true = y_test - scores = _score(estimator, X_test, y_test, scorer, - is_multimetric=True) + scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) if outfile_y_true: try: - pd.DataFrame(y_true).to_csv(outfile_y_true, sep='\t', - index=False) + pd.DataFrame(y_true).to_csv(outfile_y_true, sep="\t", index=False) pd.DataFrame(predictions).astype(np.float32).to_csv( - outfile_y_preds, sep='\t', index=False, - float_format='%g', chunksize=10000) + outfile_y_preds, + sep="\t", + index=False, + float_format="%g", + chunksize=10000, + ) except Exception as e: print("Error in saving predictions: %s" % e) @@ -437,8 +461,7 @@ scores[name] = [score] df = pd.DataFrame(scores) df = df[sorted(df.columns)] - df.to_csv(path_or_buf=outfile_result, sep='\t', - header=True, index=False) + df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False) memory.clear(warn=False) @@ -447,23 +470,22 @@ if isinstance(estimator, Pipeline): main_est = estimator.steps[-1][-1] - if hasattr(main_est, 'model_') \ - and hasattr(main_est, 'save_weights'): + 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): + if getattr(main_est, "validation_data", None): + 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(estimator, output_handler, - pickle.HIGHEST_PROTOCOL) + with open(outfile_object, "wb") as output_handler: + pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--estimator", dest="infile_estimator") @@ -481,11 +503,19 @@ 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, - outfile_y_true=args.outfile_y_true, - outfile_y_preds=args.outfile_y_preds, - groups=args.groups, - ref_seq=args.ref_seq, intervals=args.intervals, - targets=args.targets, fasta_path=args.fasta_path) + main( + args.inputs, + args.infile_estimator, + args.infile1, + args.infile2, + args.outfile_result, + outfile_object=args.outfile_object, + outfile_weights=args.outfile_weights, + outfile_y_true=args.outfile_y_true, + outfile_y_preds=args.outfile_y_preds, + groups=args.groups, + ref_seq=args.ref_seq, + intervals=args.intervals, + targets=args.targets, + fasta_path=args.fasta_path, + ) diff -r c12485d058aa -r c3bafda50176 lightgbm.xml --- a/lightgbm.xml Thu Oct 01 21:08:39 2020 +0000 +++ b/lightgbm.xml Tue Apr 13 18:33:38 2021 +0000 @@ -1,21 +1,21 @@ - + - train and apply LightGBM models main_macros.xml - lightgbm + lightgbm - + echo "@VERSION@" - + -Classification - - + +
- - - - - - - - - - - - - + + + + + + + + + + + + +
- +
@@ -111,85 +111,87 @@ - - - - - - - - - - - - + + + + + + + + + + + +
- - selected_tasks['selected_task'] == 'load' - - - selected_tasks['selected_task'] == 'train' - + + selected_tasks['selected_task'] == 'load' + + + selected_tasks['selected_task'] == 'train' + - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + @@ -265,7 +267,7 @@ The tool predicts the class labels for new samples and adds them as the last column to the prediction dataset. The new dataset (i.e. tabular input plus an additional column containing predicted values) is then returned as a tabular file. The prediction output format should look like the training dataset. - ]]> + ]]> @incollection{NIPS2017_6907, diff -r c12485d058aa -r c3bafda50176 main_macros.xml --- a/main_macros.xml Thu Oct 01 21:08:39 2020 +0000 +++ b/main_macros.xml Tue Apr 13 18:33:38 2021 +0000 @@ -1,1952 +1,1940 @@ - 1.0.8.2 + 1.0.8.3 - - - python - Galaxy-ML - - - + + + Galaxy-ML + + + - - - - - + + + + + - + - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + - -
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- - - -
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+ - - - - - - - - - - - - - - + + + + + + + + + + + + + + - - - - + + + + - + - - - - selected_tasks['selected_task'] == 'load' - - - selected_tasks['selected_task'] == 'train' - - - + + + + selected_tasks['selected_task'] == 'load' + + + selected_tasks['selected_task'] == 'train' + + + - - - - 10.5281/zenodo.15094 - - + + + + 10.5281/zenodo.15094 + + - - - - @article{scikit-learn, - title={Scikit-learn: Machine Learning in {P}ython}, - author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. + + + + @article{scikit-learn, title={Scikit-learn: Machine Learning in {P}ython}, author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and - Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, - journal={Journal of Machine Learning Research}, - volume={12}, - pages={2825--2830}, - year={2011} + Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.}, journal={Journal of Machine Learning Research}, volume={12}, pages={2825--2830}, year={2011} } - - - - + + + + - - - + + + @Misc{, author = {Eric Jones and Travis Oliphant and Pearu Peterson and others}, title = {{SciPy}: Open source scientific tools for {Python}}, @@ -1954,12 +1942,12 @@ url = "http://www.scipy.org/", note = {[Online; accessed 2016-04-09]} } - - - + + + - - + + @article{DBLP:journals/corr/abs-1711-08477, author = {Ryan J. Urbanowicz and Randal S. Olson and @@ -1977,11 +1965,11 @@ biburl = {https://dblp.org/rec/bib/journals/corr/abs-1711-08477}, bibsource = {dblp computer science bibliography, https://dblp.org} } - - + + - - + + @inproceedings{Chen:2016:XST:2939672.2939785, author = {Chen, Tianqi and Guestrin, Carlos}, title = {{XGBoost}: A Scalable Tree Boosting System}, @@ -1999,11 +1987,11 @@ address = {New York, NY, USA}, keywords = {large-scale machine learning}, } - - + + - - + + @article{JMLR:v18:16-365, author = {Guillaume Lema{{\^i}}tre and Fernando Nogueira and Christos K. Aridas}, title = {Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning}, @@ -2014,22 +2002,14 @@ pages = {1-5}, url = {http://jmlr.org/papers/v18/16-365.html} } - - + + - - - @article{chen2019selene, - title={Selene: a PyTorch-based deep learning library for sequence data}, - author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G}, - journal={Nature methods}, - volume={16}, - number={4}, - pages={315}, - year={2019}, - publisher={Nature Publishing Group} + + + @article{chen2019selene, title={Selene: a PyTorch-based deep learning library for sequence data}, author={Chen, Kathleen M and Cofer, Evan M and Zhou, Jian and Troyanskaya, Olga G}, journal={Nature methods}, volume={16}, number={4}, pages={315}, year={2019}, publisher={Nature Publishing Group} } - - + + diff -r c12485d058aa -r c3bafda50176 ml_visualization_ex.py --- a/ml_visualization_ex.py Thu Oct 01 21:08:39 2020 +0000 +++ b/ml_visualization_ex.py Tue Apr 13 18:33:38 2021 +0000 @@ -22,16 +22,16 @@ # plotly default colors default_colors = [ - '#1f77b4', # muted blue - '#ff7f0e', # safety orange - '#2ca02c', # cooked asparagus green - '#d62728', # brick red - '#9467bd', # muted purple - '#8c564b', # chestnut brown - '#e377c2', # raspberry yogurt pink - '#7f7f7f', # middle gray - '#bcbd22', # curry yellow-green - '#17becf' # blue-teal + "#1f77b4", # muted blue + "#ff7f0e", # safety orange + "#2ca02c", # cooked asparagus green + "#d62728", # brick red + "#9467bd", # muted purple + "#8c564b", # chestnut brown + "#e377c2", # raspberry yogurt pink + "#7f7f7f", # middle gray + "#bcbd22", # curry yellow-green + "#17becf", # blue-teal ] @@ -52,46 +52,31 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - precision, recall, _ = precision_recall_curve( - y_true, y_score, pos_label=pos_label) - ap = average_precision_score( - y_true, y_score, pos_label=pos_label or 1) + precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label) + ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) trace = go.Scatter( x=recall, y=precision, - mode='lines', - marker=dict( - color=default_colors[idx % len(default_colors)] - ), - name='%s (area = %.3f)' % (idx, ap) + mode="lines", + marker=dict(color=default_colors[idx % len(default_colors)]), + name="%s (area = %.3f)" % (idx, ap), ) data.append(trace) layout = go.Layout( - xaxis=dict( - title='Recall', - linecolor='lightslategray', - linewidth=1 - ), - yaxis=dict( - title='Precision', - linecolor='lightslategray', - linewidth=1 - ), + xaxis=dict(title="Recall", linecolor="lightslategray", linewidth=1), + yaxis=dict(title="Precision", linecolor="lightslategray", linewidth=1), title=dict( - text=title or 'Precision-Recall Curve', + text=title or "Precision-Recall Curve", x=0.5, y=0.92, - xanchor='center', - yanchor='top' + xanchor="center", + yanchor="top", ), - font=dict( - family="sans-serif", - size=11 - ), + font=dict(family="sans-serif", size=11), # control backgroud colors - plot_bgcolor='rgba(255,255,255,0)' + plot_bgcolor="rgba(255,255,255,0)", ) """ legend=dict( @@ -112,45 +97,47 @@ plotly.offline.plot(fig, filename="output.html", auto_open=False) # to be discovered by `from_work_dir` - os.rename('output.html', 'output') + os.rename("output.html", "output") def visualize_pr_curve_matplotlib(df1, df2, pos_label, title=None): - """visualize pr-curve using matplotlib and output svg image - """ + """visualize pr-curve using matplotlib and output svg image""" backend = matplotlib.get_backend() if "inline" not in backend: matplotlib.use("SVG") - plt.style.use('seaborn-colorblind') + plt.style.use("seaborn-colorblind") plt.figure() for idx in range(df1.shape[1]): y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - precision, recall, _ = precision_recall_curve( - y_true, y_score, pos_label=pos_label) - ap = average_precision_score( - y_true, y_score, pos_label=pos_label or 1) + precision, recall, _ = precision_recall_curve(y_true, y_score, pos_label=pos_label) + ap = average_precision_score(y_true, y_score, pos_label=pos_label or 1) - plt.step(recall, precision, 'r-', color="black", alpha=0.3, - lw=1, where="post", label='%s (area = %.3f)' % (idx, ap)) + plt.step( + recall, + precision, + "r-", + color="black", + alpha=0.3, + lw=1, + where="post", + label="%s (area = %.3f)" % (idx, ap), + ) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) - plt.xlabel('Recall') - plt.ylabel('Precision') - title = title or 'Precision-Recall Curve' + plt.xlabel("Recall") + plt.ylabel("Precision") + title = title or "Precision-Recall Curve" plt.title(title) folder = os.getcwd() plt.savefig(os.path.join(folder, "output.svg"), format="svg") - os.rename(os.path.join(folder, "output.svg"), - os.path.join(folder, "output")) + os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output")) -def visualize_roc_curve_plotly(df1, df2, pos_label, - drop_intermediate=True, - title=None): +def visualize_roc_curve_plotly(df1, df2, pos_label, drop_intermediate=True, title=None): """output roc-curve in html using plotly df1 : pandas.DataFrame @@ -169,45 +156,31 @@ y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, - drop_intermediate=drop_intermediate) + fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate) roc_auc = auc(fpr, tpr) trace = go.Scatter( x=fpr, y=tpr, - mode='lines', - marker=dict( - color=default_colors[idx % len(default_colors)] - ), - name='%s (area = %.3f)' % (idx, roc_auc) + mode="lines", + marker=dict(color=default_colors[idx % len(default_colors)]), + name="%s (area = %.3f)" % (idx, roc_auc), ) data.append(trace) layout = go.Layout( - xaxis=dict( - title='False Positive Rate', - linecolor='lightslategray', - linewidth=1 - ), - yaxis=dict( - title='True Positive Rate', - linecolor='lightslategray', - linewidth=1 - ), + xaxis=dict(title="False Positive Rate", linecolor="lightslategray", linewidth=1), + yaxis=dict(title="True Positive Rate", linecolor="lightslategray", linewidth=1), title=dict( - text=title or 'Receiver Operating Characteristic (ROC) Curve', + text=title or "Receiver Operating Characteristic (ROC) Curve", x=0.5, y=0.92, - xanchor='center', - yanchor='top' + xanchor="center", + yanchor="top", ), - font=dict( - family="sans-serif", - size=11 - ), + font=dict(family="sans-serif", size=11), # control backgroud colors - plot_bgcolor='rgba(255,255,255,0)' + plot_bgcolor="rgba(255,255,255,0)", ) """ # legend=dict( @@ -229,66 +202,84 @@ plotly.offline.plot(fig, filename="output.html", auto_open=False) # to be discovered by `from_work_dir` - os.rename('output.html', 'output') + os.rename("output.html", "output") -def visualize_roc_curve_matplotlib(df1, df2, pos_label, - drop_intermediate=True, - title=None): - """visualize roc-curve using matplotlib and output svg image - """ +def visualize_roc_curve_matplotlib(df1, df2, pos_label, drop_intermediate=True, title=None): + """visualize roc-curve using matplotlib and output svg image""" backend = matplotlib.get_backend() if "inline" not in backend: matplotlib.use("SVG") - plt.style.use('seaborn-colorblind') + plt.style.use("seaborn-colorblind") plt.figure() for idx in range(df1.shape[1]): y_true = df1.iloc[:, idx].values y_score = df2.iloc[:, idx].values - fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, - drop_intermediate=drop_intermediate) + fpr, tpr, _ = roc_curve(y_true, y_score, pos_label=pos_label, drop_intermediate=drop_intermediate) roc_auc = auc(fpr, tpr) - plt.step(fpr, tpr, 'r-', color="black", alpha=0.3, lw=1, - where="post", label='%s (area = %.3f)' % (idx, roc_auc)) + plt.step( + fpr, + tpr, + "r-", + color="black", + alpha=0.3, + lw=1, + where="post", + label="%s (area = %.3f)" % (idx, roc_auc), + ) plt.xlim([0.0, 1.0]) plt.ylim([0.0, 1.05]) - plt.xlabel('False Positive Rate') - plt.ylabel('True Positive Rate') - title = title or 'Receiver Operating Characteristic (ROC) Curve' + plt.xlabel("False Positive Rate") + plt.ylabel("True Positive Rate") + title = title or "Receiver Operating Characteristic (ROC) Curve" plt.title(title) folder = os.getcwd() plt.savefig(os.path.join(folder, "output.svg"), format="svg") - os.rename(os.path.join(folder, "output.svg"), - os.path.join(folder, "output")) + os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output")) def get_dataframe(file_path, plot_selection, header_name, column_name): - header = 'infer' if plot_selection[header_name] else None + header = "infer" if plot_selection[header_name] else None column_option = plot_selection[column_name]["selected_column_selector_option"] - if column_option in ["by_index_number", "all_but_by_index_number", "by_header_name", "all_but_by_header_name"]: + if column_option in [ + "by_index_number", + "all_but_by_index_number", + "by_header_name", + "all_but_by_header_name", + ]: col = plot_selection[column_name]["col1"] else: col = None _, input_df = read_columns(file_path, c=col, - c_option=column_option, - return_df=True, - sep='\t', header=header, - parse_dates=True) + c_option=column_option, + return_df=True, + sep='\t', header=header, + parse_dates=True) return input_df -def main(inputs, infile_estimator=None, infile1=None, - infile2=None, outfile_result=None, - outfile_object=None, groups=None, - ref_seq=None, intervals=None, - targets=None, fasta_path=None, - model_config=None, true_labels=None, - predicted_labels=None, plot_color=None, - title=None): +def main( + inputs, + infile_estimator=None, + infile1=None, + infile2=None, + outfile_result=None, + outfile_object=None, + groups=None, + ref_seq=None, + intervals=None, + targets=None, + fasta_path=None, + model_config=None, + true_labels=None, + predicted_labels=None, + plot_color=None, + title=None, +): """ Parameter --------- @@ -341,34 +332,39 @@ title : str, default is None Title of the confusion matrix heatmap """ - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) - title = params['plotting_selection']['title'].strip() - plot_type = params['plotting_selection']['plot_type'] - plot_format = params['plotting_selection']['plot_format'] + title = params["plotting_selection"]["title"].strip() + plot_type = params["plotting_selection"]["plot_type"] + plot_format = params["plotting_selection"]["plot_format"] - if plot_type == 'feature_importances': - with open(infile_estimator, 'rb') as estimator_handler: + if plot_type == "feature_importances": + with open(infile_estimator, "rb") as estimator_handler: estimator = load_model(estimator_handler) - column_option = (params['plotting_selection'] - ['column_selector_options'] - ['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['plotting_selection'] - ['column_selector_options']['col1']) + column_option = params["plotting_selection"]["column_selector_options"]["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["plotting_selection"]["column_selector_options"]["col1"] else: c = None - _, input_df = read_columns(infile1, c=c, - c_option=column_option, - return_df=True, - sep='\t', header='infer', - parse_dates=True) + _, input_df = read_columns( + infile1, + c=c, + c_option=column_option, + return_df=True, + sep="\t", + header="infer", + parse_dates=True, + ) feature_names = input_df.columns.values @@ -379,16 +375,14 @@ feature_names = feature_names[mask] estimator = estimator.steps[-1][-1] - if hasattr(estimator, 'coef_'): + if hasattr(estimator, "coef_"): coefs = estimator.coef_ else: - coefs = getattr(estimator, 'feature_importances_', None) + coefs = getattr(estimator, "feature_importances_", None) if coefs is None: - raise RuntimeError('The classifier does not expose ' - '"coef_" or "feature_importances_" ' - 'attributes') + raise RuntimeError("The classifier does not expose " '"coef_" or "feature_importances_" ' "attributes") - threshold = params['plotting_selection']['threshold'] + threshold = params["plotting_selection"]["threshold"] if threshold is not None: mask = (coefs > threshold) | (coefs < -threshold) coefs = coefs[mask] @@ -397,80 +391,74 @@ # sort indices = np.argsort(coefs)[::-1] - trace = go.Bar(x=feature_names[indices], - y=coefs[indices]) + trace = go.Bar(x=feature_names[indices], y=coefs[indices]) layout = go.Layout(title=title or "Feature Importances") fig = go.Figure(data=[trace], layout=layout) - plotly.offline.plot(fig, filename="output.html", - auto_open=False) + plotly.offline.plot(fig, filename="output.html", auto_open=False) # to be discovered by `from_work_dir` - os.rename('output.html', 'output') + os.rename("output.html", "output") return 0 - elif plot_type in ('pr_curve', 'roc_curve'): - df1 = pd.read_csv(infile1, sep='\t', header='infer') - df2 = pd.read_csv(infile2, sep='\t', header='infer').astype(np.float32) + elif plot_type in ("pr_curve", "roc_curve"): + df1 = pd.read_csv(infile1, sep="\t", header="infer") + df2 = pd.read_csv(infile2, sep="\t", header="infer").astype(np.float32) - minimum = params['plotting_selection']['report_minimum_n_positives'] + minimum = params["plotting_selection"]["report_minimum_n_positives"] # filter out columns whose n_positives is beblow the threhold if minimum: mask = df1.sum(axis=0) >= minimum df1 = df1.loc[:, mask] df2 = df2.loc[:, mask] - pos_label = params['plotting_selection']['pos_label'].strip() \ - or None + pos_label = params["plotting_selection"]["pos_label"].strip() or None - if plot_type == 'pr_curve': - if plot_format == 'plotly_html': + if plot_type == "pr_curve": + if plot_format == "plotly_html": visualize_pr_curve_plotly(df1, df2, pos_label, title=title) else: visualize_pr_curve_matplotlib(df1, df2, pos_label, title) - else: # 'roc_curve' - drop_intermediate = (params['plotting_selection'] - ['drop_intermediate']) - if plot_format == 'plotly_html': - visualize_roc_curve_plotly(df1, df2, pos_label, - drop_intermediate=drop_intermediate, - title=title) + else: # 'roc_curve' + drop_intermediate = params["plotting_selection"]["drop_intermediate"] + if plot_format == "plotly_html": + visualize_roc_curve_plotly( + df1, + df2, + pos_label, + drop_intermediate=drop_intermediate, + title=title, + ) else: visualize_roc_curve_matplotlib( - df1, df2, pos_label, + df1, + df2, + pos_label, drop_intermediate=drop_intermediate, - title=title) + title=title, + ) return 0 - elif plot_type == 'rfecv_gridscores': - input_df = pd.read_csv(infile1, sep='\t', header='infer') + elif plot_type == "rfecv_gridscores": + input_df = pd.read_csv(infile1, sep="\t", header="infer") scores = input_df.iloc[:, 0] - steps = params['plotting_selection']['steps'].strip() + steps = params["plotting_selection"]["steps"].strip() steps = safe_eval(steps) data = go.Scatter( x=list(range(len(scores))), y=scores, text=[str(_) for _ in steps] if steps else None, - mode='lines' + mode="lines", ) layout = go.Layout( xaxis=dict(title="Number of features selected"), yaxis=dict(title="Cross validation score"), - title=dict( - text=title or None, - x=0.5, - y=0.92, - xanchor='center', - yanchor='top' - ), - font=dict( - family="sans-serif", - size=11 - ), + title=dict(text=title or None, x=0.5, y=0.92, xanchor="center", yanchor="top"), + font=dict(family="sans-serif", size=11), # control backgroud colors - plot_bgcolor='rgba(255,255,255,0)' + plot_bgcolor="rgba(255,255,255,0)", ) """ # legend=dict( @@ -489,55 +477,43 @@ """ fig = go.Figure(data=[data], layout=layout) - plotly.offline.plot(fig, filename="output.html", - auto_open=False) + plotly.offline.plot(fig, filename="output.html", auto_open=False) # to be discovered by `from_work_dir` - os.rename('output.html', 'output') + os.rename("output.html", "output") return 0 - elif plot_type == 'learning_curve': - input_df = pd.read_csv(infile1, sep='\t', header='infer') - plot_std_err = params['plotting_selection']['plot_std_err'] + elif plot_type == "learning_curve": + input_df = pd.read_csv(infile1, sep="\t", header="infer") + plot_std_err = params["plotting_selection"]["plot_std_err"] data1 = go.Scatter( - x=input_df['train_sizes_abs'], - y=input_df['mean_train_scores'], - error_y=dict( - array=input_df['std_train_scores'] - ) if plot_std_err else None, - mode='lines', + x=input_df["train_sizes_abs"], + y=input_df["mean_train_scores"], + error_y=dict(array=input_df["std_train_scores"]) if plot_std_err else None, + mode="lines", name="Train Scores", ) data2 = go.Scatter( - x=input_df['train_sizes_abs'], - y=input_df['mean_test_scores'], - error_y=dict( - array=input_df['std_test_scores'] - ) if plot_std_err else None, - mode='lines', + x=input_df["train_sizes_abs"], + y=input_df["mean_test_scores"], + error_y=dict(array=input_df["std_test_scores"]) if plot_std_err else None, + mode="lines", name="Test Scores", ) layout = dict( - xaxis=dict( - title='No. of samples' - ), - yaxis=dict( - title='Performance Score' - ), + xaxis=dict(title="No. of samples"), + yaxis=dict(title="Performance Score"), # modify these configurations to customize image title=dict( - text=title or 'Learning Curve', + text=title or "Learning Curve", x=0.5, y=0.92, - xanchor='center', - yanchor='top' + xanchor="center", + yanchor="top", ), - font=dict( - family="sans-serif", - size=11 - ), + font=dict(family="sans-serif", size=11), # control backgroud colors - plot_bgcolor='rgba(255,255,255,0)' + plot_bgcolor="rgba(255,255,255,0)", ) """ # legend=dict( @@ -556,27 +532,26 @@ """ fig = go.Figure(data=[data1, data2], layout=layout) - plotly.offline.plot(fig, filename="output.html", - auto_open=False) + plotly.offline.plot(fig, filename="output.html", auto_open=False) # to be discovered by `from_work_dir` - os.rename('output.html', 'output') + os.rename("output.html", "output") return 0 - elif plot_type == 'keras_plot_model': - with open(model_config, 'r') as f: + elif plot_type == "keras_plot_model": + with open(model_config, "r") as f: model_str = f.read() model = model_from_json(model_str) plot_model(model, to_file="output.png") - os.rename('output.png', 'output') + os.rename("output.png", "output") return 0 - elif plot_type == 'classification_confusion_matrix': + elif plot_type == "classification_confusion_matrix": plot_selection = params["plotting_selection"] input_true = get_dataframe(true_labels, plot_selection, "header_true", "column_selector_options_true") - header_predicted = 'infer' if plot_selection["header_predicted"] else None - input_predicted = pd.read_csv(predicted_labels, sep='\t', parse_dates=True, header=header_predicted) + header_predicted = "infer" if plot_selection["header_predicted"] else None + input_predicted = pd.read_csv(predicted_labels, sep="\t", parse_dates=True, header=header_predicted) true_classes = input_true.iloc[:, -1].copy() predicted_classes = input_predicted.iloc[:, -1].copy() axis_labels = list(set(true_classes)) @@ -586,15 +561,15 @@ for i in range(len(c_matrix)): for j in range(len(c_matrix)): ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k") - ax.set_ylabel('True class labels') - ax.set_xlabel('Predicted class labels') + ax.set_ylabel("True class labels") + ax.set_xlabel("Predicted class labels") ax.set_title(title) ax.set_xticks(axis_labels) ax.set_yticks(axis_labels) fig.colorbar(im, ax=ax) fig.tight_layout() plt.savefig("output.png", dpi=125) - os.rename('output.png', 'output') + os.rename("output.png", "output") return 0 @@ -603,7 +578,7 @@ # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--estimator", dest="infile_estimator") @@ -623,11 +598,21 @@ aparser.add_argument("-pt", "--title", dest="title") 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, ref_seq=args.ref_seq, intervals=args.intervals, - targets=args.targets, fasta_path=args.fasta_path, - model_config=args.model_config, true_labels=args.true_labels, - predicted_labels=args.predicted_labels, - plot_color=args.plot_color, - title=args.title) + main( + args.inputs, + args.infile_estimator, + args.infile1, + args.infile2, + args.outfile_result, + outfile_object=args.outfile_object, + groups=args.groups, + ref_seq=args.ref_seq, + intervals=args.intervals, + targets=args.targets, + fasta_path=args.fasta_path, + model_config=args.model_config, + true_labels=args.true_labels, + predicted_labels=args.predicted_labels, + plot_color=args.plot_color, + title=args.title, + ) diff -r c12485d058aa -r c3bafda50176 model_prediction.py --- a/model_prediction.py Thu Oct 01 21:08:39 2020 +0000 +++ b/model_prediction.py Tue Apr 13 18:33:38 2021 +0000 @@ -1,23 +1,29 @@ import argparse import json +import warnings + import numpy as np import pandas as pd -import warnings - from scipy.io import mmread from sklearn.pipeline import Pipeline -from galaxy_ml.utils import (load_model, read_columns, - get_module, try_get_attr) +from galaxy_ml.utils import (get_module, load_model, + read_columns, try_get_attr) + + +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) -N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) - - -def main(inputs, infile_estimator, outfile_predict, - infile_weights=None, infile1=None, - fasta_path=None, ref_seq=None, - vcf_path=None): +def main( + inputs, + infile_estimator, + outfile_predict, + infile_weights=None, + infile1=None, + fasta_path=None, + ref_seq=None, + vcf_path=None, +): """ Parameter --------- @@ -45,96 +51,94 @@ vcf_path : str File path to dataset containing variants info. """ - warnings.filterwarnings('ignore') + warnings.filterwarnings("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) # load model - with open(infile_estimator, 'rb') as est_handler: + with open(infile_estimator, "rb") as est_handler: estimator = load_model(est_handler) main_est = estimator if isinstance(estimator, Pipeline): main_est = estimator.steps[-1][-1] - if hasattr(main_est, 'config') and hasattr(main_est, 'load_weights'): - if not infile_weights or infile_weights == 'None': - raise ValueError("The selected model skeleton asks for weights, " - "but dataset for weights wan not selected!") + if hasattr(main_est, "config") and hasattr(main_est, "load_weights"): + if not infile_weights or infile_weights == "None": + raise ValueError( + "The selected model skeleton asks for weights, " "but dataset for weights wan not selected!" + ) main_est.load_weights(infile_weights) # handle data input - input_type = params['input_options']['selected_input'] + 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'] + 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 = pd.read_csv(infile1, sep='\t', header=header, parse_dates=True) + df = pd.read_csv(infile1, sep="\t", header=header, parse_dates=True) X = read_columns(df, c=c, c_option=column_option).astype(float) - if params['method'] == 'predict': + if params["method"] == "predict": preds = estimator.predict(X) else: preds = estimator.predict_proba(X) # sparse input - elif input_type == 'sparse': - X = mmread(open(infile1, 'r')) - if params['method'] == 'predict': + elif input_type == "sparse": + X = mmread(open(infile1, "r")) + if params["method"] == "predict": preds = estimator.predict(X) else: preds = estimator.predict_proba(X) # fasta input - elif input_type == 'seq_fasta': - if not hasattr(estimator, 'data_batch_generator'): + elif input_type == "seq_fasta": + if not hasattr(estimator, "data_batch_generator"): raise ValueError( "To do prediction on sequences in fasta input, " "the estimator must be a `KerasGBatchClassifier`" - "equipped with data_batch_generator!") - pyfaidx = get_module('pyfaidx') + "equipped with data_batch_generator!" + ) + pyfaidx = get_module("pyfaidx") sequences = pyfaidx.Fasta(fasta_path) n_seqs = len(sequences.keys()) X = np.arange(n_seqs)[:, np.newaxis] seq_length = estimator.data_batch_generator.seq_length - batch_size = getattr(estimator, 'batch_size', 32) + batch_size = getattr(estimator, "batch_size", 32) steps = (n_seqs + batch_size - 1) // batch_size - seq_type = params['input_options']['seq_type'] - klass = try_get_attr( - 'galaxy_ml.preprocessors', seq_type) + seq_type = params["input_options"]["seq_type"] + klass = try_get_attr("galaxy_ml.preprocessors", seq_type) - pred_data_generator = klass( - fasta_path, seq_length=seq_length) + pred_data_generator = klass(fasta_path, seq_length=seq_length) - if params['method'] == 'predict': - preds = estimator.predict( - X, data_generator=pred_data_generator, steps=steps) + if params["method"] == "predict": + preds = estimator.predict(X, data_generator=pred_data_generator, steps=steps) else: - preds = estimator.predict_proba( - X, data_generator=pred_data_generator, steps=steps) + preds = estimator.predict_proba(X, data_generator=pred_data_generator, steps=steps) # vcf input - elif input_type == 'variant_effect': - klass = try_get_attr('galaxy_ml.preprocessors', - 'GenomicVariantBatchGenerator') + elif input_type == "variant_effect": + klass = try_get_attr("galaxy_ml.preprocessors", "GenomicVariantBatchGenerator") - options = params['input_options'] - options.pop('selected_input') - if options['blacklist_regions'] == 'none': - options['blacklist_regions'] = None + options = params["input_options"] + options.pop("selected_input") + if options["blacklist_regions"] == "none": + options["blacklist_regions"] = None - pred_data_generator = klass( - ref_genome_path=ref_seq, vcf_path=vcf_path, **options) + pred_data_generator = klass(ref_genome_path=ref_seq, vcf_path=vcf_path, **options) pred_data_generator.set_processing_attrs() @@ -143,9 +147,8 @@ # predict 1600 sample at once then write to file gen_flow = pred_data_generator.flow(batch_size=1600) - file_writer = open(outfile_predict, 'w') - header_row = '\t'.join(['chrom', 'pos', 'name', 'ref', - 'alt', 'strand']) + file_writer = open(outfile_predict, "w") + header_row = "\t".join(["chrom", "pos", "name", "ref", "alt", "strand"]) file_writer.write(header_row) header_done = False @@ -155,23 +158,24 @@ try: while steps_done < len(gen_flow): index_array = next(gen_flow.index_generator) - batch_X = gen_flow._get_batches_of_transformed_samples( - index_array) + batch_X = gen_flow._get_batches_of_transformed_samples(index_array) - if params['method'] == 'predict': + if params["method"] == "predict": batch_preds = estimator.predict( batch_X, # The presence of `pred_data_generator` below is to # override model carrying data_generator if there # is any. - data_generator=pred_data_generator) + data_generator=pred_data_generator, + ) else: batch_preds = estimator.predict_proba( batch_X, # The presence of `pred_data_generator` below is to # override model carrying data_generator if there # is any. - data_generator=pred_data_generator) + data_generator=pred_data_generator, + ) if batch_preds.ndim == 1: batch_preds = batch_preds[:, np.newaxis] @@ -181,12 +185,12 @@ if not header_done: heads = np.arange(batch_preds.shape[-1]).astype(str) - heads_str = '\t'.join(heads) + heads_str = "\t".join(heads) file_writer.write("\t%s\n" % heads_str) header_done = True for row in batch_out: - row_str = '\t'.join(row) + row_str = "\t".join(row) file_writer.write("%s\n" % row_str) steps_done += 1 @@ -200,14 +204,14 @@ # output if len(preds.shape) == 1: - rval = pd.DataFrame(preds, columns=['Predicted']) + rval = pd.DataFrame(preds, columns=["Predicted"]) else: rval = pd.DataFrame(preds) - rval.to_csv(outfile_predict, sep='\t', header=True, index=False) + rval.to_csv(outfile_predict, sep="\t", header=True, index=False) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--infile_estimator", dest="infile_estimator") @@ -219,7 +223,13 @@ aparser.add_argument("-v", "--vcf_path", dest="vcf_path") args = aparser.parse_args() - main(args.inputs, args.infile_estimator, args.outfile_predict, - infile_weights=args.infile_weights, infile1=args.infile1, - fasta_path=args.fasta_path, ref_seq=args.ref_seq, - vcf_path=args.vcf_path) + main( + args.inputs, + args.infile_estimator, + args.outfile_predict, + infile_weights=args.infile_weights, + infile1=args.infile1, + fasta_path=args.fasta_path, + ref_seq=args.ref_seq, + vcf_path=args.vcf_path, + ) diff -r c12485d058aa -r c3bafda50176 pca.py --- a/pca.py Thu Oct 01 21:08:39 2020 +0000 +++ b/pca.py Tue Apr 13 18:33:38 2021 +0000 @@ -1,98 +1,185 @@ import argparse + import numpy as np -from sklearn.decomposition import PCA, IncrementalPCA, KernelPCA from galaxy_ml.utils import read_columns +from sklearn.decomposition import IncrementalPCA, KernelPCA, PCA + def main(): - parser = argparse.ArgumentParser(description='RDKit screen') - parser.add_argument('-i', '--infile', - help="Input file") - parser.add_argument('--header', action='store_true', help="Include the header row or skip it") - parser.add_argument('-c', '--columns', type=str.lower, default='all', choices=['by_index_number', 'all_but_by_index_number',\ - 'by_header_name', 'all_but_by_header_name', 'all_columns'], - help="Choose to select all columns, or exclude/include some") - parser.add_argument('-ci', '--column_indices', type=str.lower, - help="Choose to select all columns, or exclude/include some") - parser.add_argument('-n', '--number', nargs='?', type=int, default=None,\ - help="Number of components to keep. If not set, all components are kept") - parser.add_argument('--whiten', action='store_true', help="Whiten the components") - parser.add_argument('-t', '--pca_type', type=str.lower, default='classical', choices=['classical', 'incremental', 'kernel'], - help="Choose which flavour of PCA to use") - parser.add_argument('-s', '--svd_solver', type=str.lower, default='auto', choices=['auto', 'full', 'arpack', 'randomized'], - help="Choose the type of svd solver.") - parser.add_argument('-b', '--batch_size', nargs='?', type=int, default=None,\ - help="The number of samples to use for each batch") - parser.add_argument('-k', '--kernel', type=str.lower, default='linear',\ - choices=['linear', 'poly', 'rbf', 'sigmoid', 'cosine', 'precomputed'], - help="Choose the type of kernel.") - parser.add_argument('-g', '--gamma', nargs='?', type=float, default=None, - help='Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels') - parser.add_argument('-tol', '--tolerance', type=float, default=0.0, - help='Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack') - parser.add_argument('-mi', '--max_iter', nargs='?', type=int, default=None,\ - help="Maximum number of iterations for arpack") - parser.add_argument('-d', '--degree', type=int, default=3,\ - help="Degree for poly kernels. Ignored by other kernels") - parser.add_argument('-cf', '--coef0', type=float, default=1.0, - help='Independent term in poly and sigmoid kernels') - parser.add_argument('-e', '--eigen_solver', type=str.lower, default='auto', choices=['auto', 'dense', 'arpack'], - help="Choose the type of eigen solver.") - parser.add_argument('-o', '--outfile', - help="Base name for output file (no extension).") + parser = argparse.ArgumentParser(description="RDKit screen") + parser.add_argument("-i", "--infile", help="Input file") + parser.add_argument( + "--header", action="store_true", help="Include the header row or skip it" + ) + parser.add_argument( + "-c", + "--columns", + type=str.lower, + default="all", + choices=[ + "by_index_number", + "all_but_by_index_number", + "by_header_name", + "all_but_by_header_name", + "all_columns", + ], + help="Choose to select all columns, or exclude/include some", + ) + parser.add_argument( + "-ci", + "--column_indices", + type=str.lower, + help="Choose to select all columns, or exclude/include some", + ) + parser.add_argument( + "-n", + "--number", + nargs="?", + type=int, + default=None, + help="Number of components to keep. If not set, all components are kept", + ) + parser.add_argument("--whiten", action="store_true", help="Whiten the components") + parser.add_argument( + "-t", + "--pca_type", + type=str.lower, + default="classical", + choices=["classical", "incremental", "kernel"], + help="Choose which flavour of PCA to use", + ) + parser.add_argument( + "-s", + "--svd_solver", + type=str.lower, + default="auto", + choices=["auto", "full", "arpack", "randomized"], + help="Choose the type of svd solver.", + ) + parser.add_argument( + "-b", + "--batch_size", + nargs="?", + type=int, + default=None, + help="The number of samples to use for each batch", + ) + parser.add_argument( + "-k", + "--kernel", + type=str.lower, + default="linear", + choices=["linear", "poly", "rbf", "sigmoid", "cosine", "precomputed"], + help="Choose the type of kernel.", + ) + parser.add_argument( + "-g", + "--gamma", + nargs="?", + type=float, + default=None, + help="Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other kernels", + ) + parser.add_argument( + "-tol", + "--tolerance", + type=float, + default=0.0, + help="Convergence tolerance for arpack. If 0, optimal value will be chosen by arpack", + ) + parser.add_argument( + "-mi", + "--max_iter", + nargs="?", + type=int, + default=None, + help="Maximum number of iterations for arpack", + ) + parser.add_argument( + "-d", + "--degree", + type=int, + default=3, + help="Degree for poly kernels. Ignored by other kernels", + ) + parser.add_argument( + "-cf", + "--coef0", + type=float, + default=1.0, + help="Independent term in poly and sigmoid kernels", + ) + parser.add_argument( + "-e", + "--eigen_solver", + type=str.lower, + default="auto", + choices=["auto", "dense", "arpack"], + help="Choose the type of eigen solver.", + ) + parser.add_argument( + "-o", "--outfile", help="Base name for output file (no extension)." + ) args = parser.parse_args() usecols = None - cols = [] pca_params = {} - if args.columns == 'by_index_number' or args.columns == 'all_but_by_index_number': - usecols = [int(i) for i in args.column_indices.split(',')] - elif args.columns == 'by_header_name' or args.columns == 'all_but_by_header_name': + if args.columns == "by_index_number" or args.columns == "all_but_by_index_number": + usecols = [int(i) for i in args.column_indices.split(",")] + elif args.columns == "by_header_name" or args.columns == "all_but_by_header_name": usecols = args.column_indices - header = 'infer' if args.header else None + header = "infer" if args.header else None pca_input = read_columns( f=args.infile, c=usecols, c_option=args.columns, - sep='\t', + sep="\t", header=header, parse_dates=True, encoding=None, - index_col=None) + index_col=None, + ) - pca_params.update({'n_components': args.number}) + pca_params.update({"n_components": args.number}) - if args.pca_type == 'classical': - pca_params.update({'svd_solver': args.svd_solver, 'whiten': args.whiten}) - if args.svd_solver == 'arpack': - pca_params.update({'tol': args.tolerance}) + if args.pca_type == "classical": + pca_params.update({"svd_solver": args.svd_solver, "whiten": args.whiten}) + if args.svd_solver == "arpack": + pca_params.update({"tol": args.tolerance}) pca = PCA() - elif args.pca_type == 'incremental': - pca_params.update({'batch_size': args.batch_size, 'whiten': args.whiten}) + elif args.pca_type == "incremental": + pca_params.update({"batch_size": args.batch_size, "whiten": args.whiten}) pca = IncrementalPCA() - elif args.pca_type == 'kernel': - pca_params.update({'kernel': args.kernel, 'eigen_solver': args.eigen_solver, 'gamma': args.gamma}) + elif args.pca_type == "kernel": + pca_params.update( + { + "kernel": args.kernel, + "eigen_solver": args.eigen_solver, + "gamma": args.gamma, + } + ) - if args.kernel == 'poly': - pca_params.update({'degree': args.degree, 'coef0': args.coef0}) - elif args.kernel == 'sigmoid': - pca_params.update({'coef0': args.coef0}) - elif args.kernel == 'precomputed': + if args.kernel == "poly": + pca_params.update({"degree": args.degree, "coef0": args.coef0}) + elif args.kernel == "sigmoid": + pca_params.update({"coef0": args.coef0}) + elif args.kernel == "precomputed": pca_input = np.dot(pca_input, pca_input.T) - if args.eigen_solver == 'arpack': - pca_params.update({'tol': args.tolerance, 'max_iter': args.max_iter}) + if args.eigen_solver == "arpack": + pca_params.update({"tol": args.tolerance, "max_iter": args.max_iter}) pca = KernelPCA() print(pca_params) pca.set_params(**pca_params) pca_output = pca.fit_transform(pca_input) - np.savetxt(fname=args.outfile, X=pca_output, fmt='%.4f', delimiter='\t') + np.savetxt(fname=args.outfile, X=pca_output, fmt="%.4f", delimiter="\t") if __name__ == "__main__": diff -r c12485d058aa -r c3bafda50176 search_model_validation.py --- a/search_model_validation.py Thu Oct 01 21:08:39 2020 +0000 +++ b/search_model_validation.py Tue Apr 13 18:33:38 2021 +0000 @@ -11,45 +11,57 @@ import sys import warnings from scipy.io import mmread -from sklearn import (cluster, decomposition, feature_selection, - kernel_approximation, model_selection, preprocessing) +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) +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) +_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)) +N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1)) # handle disk cache -CACHE_DIR = os.path.join(os.getcwd(), 'cached') +CACHE_DIR = os.path.join(os.getcwd(), "cached") del os -NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path', - 'nthread', 'callbacks') +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 == '': + for p in params_builder["param_set"]: + search_list = p["sp_list"].strip() + if search_list == "": continue - param_name = p['sp_name'] + 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) + print("Warning: `%s` is not eligible for search and was " "omitted!" % param_name) continue - if not search_list.startswith(':'): + if not search_list.startswith(":"): safe_eval = SafeEval(load_scipy=True, load_numpy=True) ev = safe_eval(search_list) search_params[param_name] = ev @@ -60,26 +72,27 @@ # TODO maybe add regular express check ev = safe_eval_es(search_list) preprocessings = ( - preprocessing.StandardScaler(), preprocessing.Binarizer(), + preprocessing.StandardScaler(), + preprocessing.Binarizer(), preprocessing.MaxAbsScaler(), - preprocessing.Normalizer(), preprocessing.MinMaxScaler(), + preprocessing.Normalizer(), + preprocessing.MinMaxScaler(), preprocessing.PolynomialFeatures(), - preprocessing.RobustScaler(), feature_selection.SelectKBest(), + preprocessing.RobustScaler(), + feature_selection.SelectKBest(), feature_selection.GenericUnivariateSelect(), feature_selection.SelectPercentile(), - feature_selection.SelectFpr(), feature_selection.SelectFdr(), + 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.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), @@ -94,59 +107,48 @@ 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.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.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.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)) + imblearn.combine.SMOTETomek(random_state=0), + ) newlist = [] for obj in ev: if obj is None: newlist.append(None) - elif obj == 'all_0': + elif obj == "all_0": newlist.extend(preprocessings[0:35]) - elif obj == 'sk_prep_all': # no KernalCenter() + elif obj == "sk_prep_all": # no KernalCenter() newlist.extend(preprocessings[0:7]) - elif obj == 'fs_all': + elif obj == "fs_all": newlist.extend(preprocessings[7:14]) - elif obj == 'decomp_all': + elif obj == "decomp_all": newlist.extend(preprocessings[14:25]) - elif obj == 'k_appr_all': + elif obj == "k_appr_all": newlist.extend(preprocessings[25:29]) - elif obj == 'reb_all': + elif obj == "reb_all": newlist.extend(preprocessings[30:35]) - elif obj == 'imb_all': + 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(): + 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) @@ -158,9 +160,17 @@ return search_params -def _handle_X_y(estimator, params, infile1, infile2, loaded_df={}, - ref_seq=None, intervals=None, targets=None, - fasta_path=None): +def _handle_X_y( + estimator, + params, + infile1, + infile2, + loaded_df={}, + ref_seq=None, + intervals=None, + targets=None, + fasta_path=None, +): """read inputs Params @@ -192,15 +202,18 @@ """ estimator_params = estimator.get_params() - input_type = params['input_options']['selected_input'] + 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'] + 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 @@ -209,25 +222,23 @@ if df_key in loaded_df: infile1 = loaded_df[df_key] - df = pd.read_csv(infile1, sep='\t', header=header, - parse_dates=True) + 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')) + elif input_type == "sparse": + X = mmread(open(infile1, "r")) # fasta_file input - elif input_type == 'seq_fasta': - pyfaidx = get_module('pyfaidx') + 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}) + if param.endswith("fasta_path"): + estimator.set_params(**{param: fasta_path}) break else: raise ValueError( @@ -236,25 +247,29 @@ "KerasGBatchClassifier with " "FastaDNABatchGenerator/FastaProteinBatchGenerator " "or having GenomeOneHotEncoder/ProteinOneHotEncoder " - "in pipeline!") + "in pipeline!" + ) - elif input_type == 'refseq_and_interval': + 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 + "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'] + 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 @@ -262,30 +277,21 @@ if df_key in loaded_df: infile2 = loaded_df[df_key] else: - infile2 = pd.read_csv(infile2, sep='\t', - header=header, parse_dates=True) + 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) + 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()) + 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): +def _do_outer_cv(searcher, X, y, outer_cv, scoring, error_score="raise", outfile=None): """Do outer cross-validation for nested CV Parameters @@ -305,21 +311,31 @@ outfile : str File path to store the restuls """ - if error_score == 'raise': + 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) + searcher, + X, + y, + scoring=scoring, + cv=outer_cv, + n_jobs=N_JOBS, + verbose=0, + error_score=error_score, + ) else: - warnings.simplefilter('always', FitFailedWarning) + warnings.simplefilter("always", FitFailedWarning) with warnings.catch_warnings(record=True) as w: try: rval = cross_validate( - searcher, X, y, + searcher, + X, + y, scoring=scoring, - cv=outer_cv, n_jobs=N_JOBS, + cv=outer_cv, + n_jobs=N_JOBS, verbose=0, - error_score=error_score) + error_score=error_score, + ) except ValueError: pass for warning in w: @@ -327,55 +343,57 @@ 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'): + 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) + 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 +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'] + 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 + 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': + 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 + 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) + 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) + 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 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': + if error_score == "raise": searcher.fit(X, y, groups=groups) else: - warnings.simplefilter('always', FitFailedWarning) + warnings.simplefilter("always", FitFailedWarning) with warnings.catch_warnings(record=True) as w: try: searcher.fit(X, y, groups=groups) @@ -390,33 +408,38 @@ else: is_multimetric = False - best_estimator_ = getattr(searcher, 'best_estimator_') + 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) + 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) + 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) + 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): +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 --------- @@ -456,154 +479,174 @@ fasta_path : str File path to dataset containing fasta file """ - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") # store read dataframe object loaded_df = {} - with open(inputs, 'r') as param_handler: + 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 + params["search_schemes"]["options"]["refit"] = True if params["save"] != "nope" else False - with open(infile_estimator, 'rb') as estimator_handler: + with open(infile_estimator, "rb") as estimator_handler: estimator = load_model(estimator_handler) - optimizer = params['search_schemes']['selected_search_scheme'] + optimizer = params["search_schemes"]["selected_search_scheme"] optimizer = getattr(model_selection, optimizer) # handle gridsearchcv options - options = params['search_schemes']['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']) + 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) + 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, + c=c, + c_option=column_option, + sep="\t", + header=header, + parse_dates=True, + ) groups = groups.ravel() - options['cv_selector']['groups_selector'] = groups + 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' + splitter, groups = get_cv(options.pop("cv_selector")) + options["cv"] = splitter + primary_scoring = options["scoring"]["primary_scoring"] + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = options["scoring"].get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + options["scoring"]["secondary_scoring"] = ",".join(options["scoring"]["secondary_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 + 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'] + 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': + 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) + 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) + 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': + 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') + split_mode = params["outer_split"].pop("split_mode") - if split_mode == 'nested_cv': + 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')): + 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) + setattr(searcher, "refit", True) - outer_cv, _ = get_cv(params['outer_split']['cv_selector']) + outer_cv, _ = get_cv(params["outer_split"]["cv_selector"]) # nested CV, outer cv using cross_validate - if options['error_score'] == 'raise': + 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) + 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) + 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) + 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', []) + fitted_searchers = rval.pop("estimator", []) if fitted_searchers: import os + pwd = os.getcwd() - save_dir = os.path.join(pwd, 'cv_results_in_folds') + 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_name = "cv_results_" + "_" + "split%d" % idx target_path = os.path.join(pwd, save_dir, target_name) - cv_results_ = getattr(obj, 'cv_results_', None) + 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) + cv_results_.to_csv(target_path, sep="\t", header=True, index=False) except Exception as e: print(e) finally: @@ -611,18 +654,14 @@ 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'): + 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 - + rval.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False) # deprecate train test split mode """searcher = _do_train_test_split_val( searcher, X, y, params, @@ -630,14 +669,15 @@ error_score=options['error_score'], groups=groups, outfile=outfile_result)""" + return 0 # no outer split else: searcher.set_params(n_jobs=N_JOBS) - if options['error_score'] == 'raise': + if options["error_score"] == "raise": searcher.fit(X, y, groups=groups) else: - warnings.simplefilter('always', FitFailedWarning) + warnings.simplefilter("always", FitFailedWarning) with warnings.catch_warnings(record=True) as w: try: searcher.fit(X, y, groups=groups) @@ -648,18 +688,19 @@ 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) + 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) + 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!") + warnings.warn( + "GridSearchCV object has no attribute " + "'best_estimator_', because either it's " + "nested gridsearch or `refit` is False!" + ) return # clean prams @@ -667,24 +708,22 @@ main_est = get_main_estimator(best_estimator_) - if hasattr(main_est, 'model_') \ - and hasattr(main_est, 'save_weights'): + 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): + if getattr(main_est, "data_generator_", None): del main_est.data_generator_ - with open(outfile_object, 'wb') as output_handler: + 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) + pickle.dump(best_estimator_, output_handler, pickle.HIGHEST_PROTOCOL) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--estimator", dest="infile_estimator") @@ -700,8 +739,17 @@ 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) + 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, + ) diff -r c12485d058aa -r c3bafda50176 simple_model_fit.py --- a/simple_model_fit.py Thu Oct 01 21:08:39 2020 +0000 +++ b/simple_model_fit.py Tue Apr 13 18:33:38 2021 +0000 @@ -4,10 +4,11 @@ import pickle from galaxy_ml.utils import load_model, read_columns +from scipy.io import mmread from sklearn.pipeline import Pipeline -N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) # TODO import from galaxy_ml.utils in future versions @@ -20,33 +21,35 @@ ------ Cleaned estimator object """ - ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', - 'ReduceLROnPlateau', 'CSVLogger', 'None') + ALLOWED_CALLBACKS = ( + "EarlyStopping", + "TerminateOnNaN", + "ReduceLROnPlateau", + "CSVLogger", + "None", + ) estimator_params = estimator.get_params() for name, p in estimator_params.items(): # all potential unauthorized file write - if name == 'memory' or name.endswith('__memory') \ - or name.endswith('_path'): + if name == "memory" or name.endswith("__memory") or name.endswith("_path"): new_p = {name: None} estimator.set_params(**new_p) - elif n_jobs is not None and (name == 'n_jobs' or - name.endswith('__n_jobs')): + elif n_jobs is not None and (name == 'n_jobs' or name.endswith('__n_jobs')): new_p = {name: n_jobs} estimator.set_params(**new_p) - elif name.endswith('callbacks'): + elif name.endswith("callbacks"): for cb in p: - cb_type = cb['callback_selection']['callback_type'] + cb_type = cb["callback_selection"]["callback_type"] if cb_type not in ALLOWED_CALLBACKS: - raise ValueError( - "Prohibited callback type: %s!" % cb_type) + raise ValueError("Prohibited callback type: %s!" % cb_type) return estimator def _get_X_y(params, infile1, infile2): - """ read from inputs and output X and y + """read from inputs and output X and y Parameters ---------- @@ -61,35 +64,40 @@ # store read dataframe object loaded_df = {} - input_type = params['input_options']['selected_input'] + 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'] + 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) + 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')) + elif input_type == "sparse": + X = mmread(open(infile1, "r")) # 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'] + 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 @@ -97,26 +105,23 @@ if df_key in loaded_df: infile2 = loaded_df[df_key] else: - infile2 = pd.read_csv(infile2, sep='\t', - header=header, parse_dates=True) + 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) + 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() return X, y -def main(inputs, infile_estimator, infile1, infile2, out_object, - out_weights=None): - """ main +def main(inputs, infile_estimator, infile1, infile2, out_object, out_weights=None): + """main Parameters ---------- @@ -139,38 +144,37 @@ File path for output of weights """ - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) # load model - with open(infile_estimator, 'rb') as est_handler: + with open(infile_estimator, "rb") as est_handler: estimator = load_model(est_handler) estimator = clean_params(estimator, n_jobs=N_JOBS) X_train, y_train = _get_X_y(params, infile1, infile2) estimator.fit(X_train, y_train) - + main_est = estimator if isinstance(main_est, Pipeline): main_est = main_est.steps[-1][-1] - if hasattr(main_est, 'model_') \ - and hasattr(main_est, 'save_weights'): + if hasattr(main_est, "model_") and hasattr(main_est, "save_weights"): if out_weights: main_est.save_weights(out_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): + if getattr(main_est, "validation_data", None): + del main_est.validation_data + if getattr(main_est, "data_generator_", None): del main_est.data_generator_ - with open(out_object, 'wb') as output_handler: - pickle.dump(estimator, output_handler, - pickle.HIGHEST_PROTOCOL) + with open(out_object, "wb") as output_handler: + pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-X", "--infile_estimator", dest="infile_estimator") @@ -180,5 +184,11 @@ aparser.add_argument("-t", "--out_weights", dest="out_weights") args = aparser.parse_args() - main(args.inputs, args.infile_estimator, args.infile1, - args.infile2, args.out_object, args.out_weights) + main( + args.inputs, + args.infile_estimator, + args.infile1, + args.infile2, + args.out_object, + args.out_weights, + ) diff -r c12485d058aa -r c3bafda50176 stacking_ensembles.py --- a/stacking_ensembles.py Thu Oct 01 21:08:39 2020 +0000 +++ b/stacking_ensembles.py Tue Apr 13 18:33:38 2021 +0000 @@ -5,22 +5,17 @@ import mlxtend.classifier import pandas as pd import pickle -import sklearn import sys import warnings -from sklearn import ensemble - -from galaxy_ml.utils import (load_model, get_cv, get_estimator, - get_search_params) +from galaxy_ml.utils import load_model, get_cv, get_estimator, get_search_params -warnings.filterwarnings('ignore') +warnings.filterwarnings("ignore") -N_JOBS = int(__import__('os').environ.get('GALAXY_SLOTS', 1)) +N_JOBS = int(__import__("os").environ.get("GALAXY_SLOTS", 1)) -def main(inputs_path, output_obj, base_paths=None, meta_path=None, - outfile_params=None): +def main(inputs_path, output_obj, base_paths=None, meta_path=None, outfile_params=None): """ Parameter --------- @@ -39,87 +34,79 @@ outfile_params : str File path for params output """ - with open(inputs_path, 'r') as param_handler: + with open(inputs_path, "r") as param_handler: params = json.load(param_handler) - estimator_type = params['algo_selection']['estimator_type'] + estimator_type = params["algo_selection"]["estimator_type"] # get base estimators base_estimators = [] - for idx, base_file in enumerate(base_paths.split(',')): - if base_file and base_file != 'None': - with open(base_file, 'rb') as handler: + for idx, base_file in enumerate(base_paths.split(",")): + if base_file and base_file != "None": + with open(base_file, "rb") as handler: model = load_model(handler) else: - estimator_json = (params['base_est_builder'][idx] - ['estimator_selector']) + estimator_json = params["base_est_builder"][idx]["estimator_selector"] model = get_estimator(estimator_json) - if estimator_type.startswith('sklearn'): + if estimator_type.startswith("sklearn"): named = model.__class__.__name__.lower() - named = 'base_%d_%s' % (idx, named) + named = "base_%d_%s" % (idx, named) base_estimators.append((named, model)) else: base_estimators.append(model) # get meta estimator, if applicable - if estimator_type.startswith('mlxtend'): + if estimator_type.startswith("mlxtend"): if meta_path: - with open(meta_path, 'rb') as f: + with open(meta_path, "rb") as f: meta_estimator = load_model(f) else: - estimator_json = (params['algo_selection'] - ['meta_estimator']['estimator_selector']) + estimator_json = params["algo_selection"]["meta_estimator"]["estimator_selector"] meta_estimator = get_estimator(estimator_json) - options = params['algo_selection']['options'] + options = params["algo_selection"]["options"] - cv_selector = options.pop('cv_selector', None) + cv_selector = options.pop("cv_selector", None) if cv_selector: - splitter, groups = get_cv(cv_selector) - options['cv'] = splitter + splitter, _groups = get_cv(cv_selector) + options["cv"] = splitter # set n_jobs - options['n_jobs'] = N_JOBS + options["n_jobs"] = N_JOBS - weights = options.pop('weights', None) + weights = options.pop("weights", None) if weights: weights = ast.literal_eval(weights) if weights: - options['weights'] = weights + options["weights"] = weights - mod_and_name = estimator_type.split('_') + mod_and_name = estimator_type.split("_") mod = sys.modules[mod_and_name[0]] klass = getattr(mod, mod_and_name[1]) - if estimator_type.startswith('sklearn'): - options['n_jobs'] = N_JOBS + if estimator_type.startswith("sklearn"): + options["n_jobs"] = N_JOBS ensemble_estimator = klass(base_estimators, **options) elif mod == mlxtend.classifier: - ensemble_estimator = klass( - classifiers=base_estimators, - meta_classifier=meta_estimator, - **options) + ensemble_estimator = klass(classifiers=base_estimators, meta_classifier=meta_estimator, **options) else: - ensemble_estimator = klass( - regressors=base_estimators, - meta_regressor=meta_estimator, - **options) + ensemble_estimator = klass(regressors=base_estimators, meta_regressor=meta_estimator, **options) print(ensemble_estimator) for base_est in base_estimators: print(base_est) - with open(output_obj, 'wb') as out_handler: + with open(output_obj, "wb") as out_handler: pickle.dump(ensemble_estimator, out_handler, pickle.HIGHEST_PROTOCOL) - if params['get_params'] and outfile_params: + if params["get_params"] and outfile_params: results = get_search_params(ensemble_estimator) - df = pd.DataFrame(results, columns=['', 'Parameter', 'Value']) - df.to_csv(outfile_params, sep='\t', index=False) + df = pd.DataFrame(results, columns=["", "Parameter", "Value"]) + df.to_csv(outfile_params, sep="\t", index=False) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-b", "--bases", dest="bases") aparser.add_argument("-m", "--meta", dest="meta") @@ -128,5 +115,10 @@ aparser.add_argument("-p", "--outfile_params", dest="outfile_params") args = aparser.parse_args() - main(args.inputs, args.outfile, base_paths=args.bases, - meta_path=args.meta, outfile_params=args.outfile_params) + main( + args.inputs, + args.outfile, + base_paths=args.bases, + meta_path=args.meta, + outfile_params=args.outfile_params, + ) diff -r c12485d058aa -r c3bafda50176 test-data/keras_batch_params01.tabular --- a/test-data/keras_batch_params01.tabular Thu Oct 01 21:08:39 2020 +0000 +++ b/test-data/keras_batch_params01.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -27,7 +27,7 @@ @ schedule_decay schedule_decay: None @ seed seed: None @ steps_per_epoch steps_per_epoch: None -@ validation_data validation_data: None +@ validation_fraction validation_fraction: 0.1 @ validation_steps validation_steps: None @ verbose verbose: 0 * data_batch_generator__fasta_path data_batch_generator__fasta_path: 'to_be_determined' diff -r c12485d058aa -r c3bafda50176 test-data/keras_batch_params04.tabular --- a/test-data/keras_batch_params04.tabular Thu Oct 01 21:08:39 2020 +0000 +++ b/test-data/keras_batch_params04.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -26,7 +26,7 @@ @ schedule_decay schedule_decay: None @ seed seed: None @ steps_per_epoch steps_per_epoch: None -@ validation_data validation_data: None +@ validation_fraction validation_fraction: 0.1 @ validation_steps validation_steps: None @ verbose verbose: 0 * layers_0_Dense__class_name layers_0_Dense__class_name: 'Dense' diff -r c12485d058aa -r c3bafda50176 test-data/keras_model01 Binary file test-data/keras_model01 has changed diff -r c12485d058aa -r c3bafda50176 test-data/keras_model02 Binary file test-data/keras_model02 has changed diff -r c12485d058aa -r c3bafda50176 test-data/keras_model04 Binary file test-data/keras_model04 has changed diff -r c12485d058aa -r c3bafda50176 test-data/keras_params04.tabular --- a/test-data/keras_params04.tabular Thu Oct 01 21:08:39 2020 +0000 +++ b/test-data/keras_params04.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -22,7 +22,7 @@ @ schedule_decay schedule_decay: None @ seed seed: 42 @ steps_per_epoch steps_per_epoch: None -@ validation_data validation_data: None +@ validation_fraction validation_fraction: 0.1 @ validation_steps validation_steps: None @ verbose verbose: 0 * layers_0_Dense__class_name layers_0_Dense__class_name: 'Dense' diff -r c12485d058aa -r c3bafda50176 test-data/ohe_in_w_header.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ohe_in_w_header.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -0,0 +1,9 @@ +Label +0 +1 +2 +3 +3 +2 +1 +0 diff -r c12485d058aa -r c3bafda50176 test-data/ohe_in_wo_header.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ohe_in_wo_header.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -0,0 +1,8 @@ +0 +1 +2 +3 +3 +2 +1 +0 diff -r c12485d058aa -r c3bafda50176 test-data/ohe_out_4.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ohe_out_4.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -0,0 +1,8 @@ +1 0 0 0 +0 1 0 0 +0 0 1 0 +0 0 0 1 +0 0 0 1 +0 0 1 0 +0 1 0 0 +1 0 0 0 diff -r c12485d058aa -r c3bafda50176 test-data/ohe_out_5.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/ohe_out_5.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -0,0 +1,8 @@ +1 0 0 0 0 +0 1 0 0 0 +0 0 1 0 0 +0 0 0 1 0 +0 0 0 1 0 +0 0 1 0 0 +0 1 0 0 0 +1 0 0 0 0 diff -r c12485d058aa -r c3bafda50176 test-data/pipeline_params05.tabular --- a/test-data/pipeline_params05.tabular Thu Oct 01 21:08:39 2020 +0000 +++ b/test-data/pipeline_params05.tabular Tue Apr 13 18:33:38 2021 +0000 @@ -13,6 +13,6 @@ * n_jobs n_jobs: 1 @ oob_score oob_score: False @ random_state random_state: 42 -* verbose verbose: 0 +@ verbose verbose: 0 @ warm_start warm_start: False Note: @, params eligible for search in searchcv tool. diff -r c12485d058aa -r c3bafda50176 test-data/pipeline_params18 --- a/test-data/pipeline_params18 Thu Oct 01 21:08:39 2020 +0000 +++ b/test-data/pipeline_params18 Tue Apr 13 18:33:38 2021 +0000 @@ -47,7 +47,7 @@ output_distribution='uniform', random_state=10, subsample=100000))" -* verbose verbose: False +@ verbose verbose: False @ powertransformer__copy powertransformer__copy: True @ powertransformer__method powertransformer__method: 'yeo-johnson' @ powertransformer__standardize powertransformer__standardize: True @@ -75,7 +75,7 @@ * transformedtargetregressor__regressor__n_jobs transformedtargetregressor__regressor__n_jobs: 1 @ transformedtargetregressor__regressor__oob_score transformedtargetregressor__regressor__oob_score: False @ transformedtargetregressor__regressor__random_state transformedtargetregressor__regressor__random_state: 10 -* transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0 +@ transformedtargetregressor__regressor__verbose transformedtargetregressor__regressor__verbose: 0 @ transformedtargetregressor__regressor__warm_start transformedtargetregressor__regressor__warm_start: False @ transformedtargetregressor__transformer "transformedtargetregressor__transformer: QuantileTransformer(copy=True, ignore_implicit_zeros=False, n_quantiles=1000, output_distribution='uniform', random_state=10, diff -r c12485d058aa -r c3bafda50176 test-data/train_test_eval_model01 Binary file test-data/train_test_eval_model01 has changed diff -r c12485d058aa -r c3bafda50176 test-data/train_test_eval_weights01.h5 Binary file test-data/train_test_eval_weights01.h5 has changed diff -r c12485d058aa -r c3bafda50176 test-data/train_test_eval_weights02.h5 Binary file test-data/train_test_eval_weights02.h5 has changed diff -r c12485d058aa -r c3bafda50176 to_categorical.py --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/to_categorical.py Tue Apr 13 18:33:38 2021 +0000 @@ -0,0 +1,50 @@ +import argparse +import json +import warnings + +import numpy as np +import pandas as pd +from keras.utils import to_categorical + + +def main(inputs, infile, outfile, num_classes=None): + """ + Parameter + --------- + input : str + File path to galaxy tool parameter + + infile : str + File paths of input vector + + outfile : str + File path to output matrix + + num_classes : str + Total number of classes. If None, this would be inferred as the (largest number in y) + 1 + + """ + warnings.simplefilter("ignore") + + with open(inputs, "r") as param_handler: + params = json.load(param_handler) + + input_header = params["header0"] + header = "infer" if input_header else None + + input_vector = pd.read_csv(infile, sep="\t", header=header) + + output_matrix = to_categorical(input_vector, num_classes=num_classes) + + np.savetxt(outfile, output_matrix, fmt="%d", delimiter="\t") + + +if __name__ == "__main__": + aparser = argparse.ArgumentParser() + aparser.add_argument("-i", "--inputs", dest="inputs", required=True) + aparser.add_argument("-y", "--infile", dest="infile") + aparser.add_argument("-n", "--num_classes", dest="num_classes", type=int, default=None) + aparser.add_argument("-o", "--outfile", dest="outfile") + args = aparser.parse_args() + + main(args.inputs, args.infile, args.outfile, args.num_classes) diff -r c12485d058aa -r c3bafda50176 train_test_eval.py --- a/train_test_eval.py Thu Oct 01 21:08:39 2020 +0000 +++ b/train_test_eval.py Tue Apr 13 18:33:38 2021 +0000 @@ -1,59 +1,66 @@ import argparse -import joblib import json -import numpy as np import os -import pandas as pd import pickle import warnings + from itertools import chain + +import joblib +import numpy as np +import pandas as pd +from galaxy_ml.model_validations import train_test_split +from galaxy_ml.utils import ( + get_module, + get_scoring, + load_model, + read_columns, + SafeEval, + try_get_attr, +) from scipy.io import mmread -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 import pipeline from sklearn.metrics.scorer import _check_multimetric_scoring -from sklearn.model_selection._validation import _score, cross_validate +from sklearn.model_selection._validation import _score from sklearn.model_selection import _search, _validation +from sklearn.model_selection._validation import _score from sklearn.utils import indexable, safe_indexing -from galaxy_ml.model_validations import train_test_split -from galaxy_ml.utils import (SafeEval, 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) -_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)) -CACHE_DIR = os.path.join(os.getcwd(), 'cached') +N_JOBS = int(os.environ.get("GALAXY_SLOTS", 1)) +CACHE_DIR = os.path.join(os.getcwd(), "cached") del os -NON_SEARCHABLE = ('n_jobs', 'pre_dispatch', 'memory', '_path', - 'nthread', 'callbacks') -ALLOWED_CALLBACKS = ('EarlyStopping', 'TerminateOnNaN', 'ReduceLROnPlateau', - 'CSVLogger', 'None') +NON_SEARCHABLE = ("n_jobs", "pre_dispatch", "memory", "_path", "nthread", "callbacks") +ALLOWED_CALLBACKS = ( + "EarlyStopping", + "TerminateOnNaN", + "ReduceLROnPlateau", + "CSVLogger", + "None", +) def _eval_swap_params(params_builder): swap_params = {} - for p in params_builder['param_set']: - swap_value = p['sp_value'].strip() - if swap_value == '': + for p in params_builder["param_set"]: + swap_value = p["sp_value"].strip() + if swap_value == "": continue - param_name = p['sp_name'] + param_name = p["sp_name"] if param_name.lower().endswith(NON_SEARCHABLE): - warnings.warn("Warning: `%s` is not eligible for search and was " - "omitted!" % param_name) + warnings.warn( + "Warning: `%s` is not eligible for search and was " + "omitted!" % param_name + ) continue - if not swap_value.startswith(':'): + if not swap_value.startswith(":"): safe_eval = SafeEval(load_scipy=True, load_numpy=True) ev = safe_eval(swap_value) else: @@ -80,23 +87,24 @@ else: new_arrays.append(arr) - if kwargs['shuffle'] == 'None': - kwargs['shuffle'] = None + if kwargs["shuffle"] == "None": + kwargs["shuffle"] = None - group_names = kwargs.pop('group_names', None) + group_names = kwargs.pop("group_names", None) if group_names is not None and group_names.strip(): - group_names = [name.strip() for name in - group_names.split(',')] + group_names = [name.strip() for name in group_names.split(",")] new_arrays = indexable(*new_arrays) - groups = kwargs['labels'] + groups = kwargs["labels"] n_samples = new_arrays[0].shape[0] index_arr = np.arange(n_samples) test = index_arr[np.isin(groups, group_names)] train = index_arr[~np.isin(groups, group_names)] - rval = list(chain.from_iterable( - (safe_indexing(a, train), - safe_indexing(a, test)) for a in new_arrays)) + rval = list( + chain.from_iterable( + (safe_indexing(a, train), safe_indexing(a, test)) for a in new_arrays + ) + ) else: rval = train_test_split(*new_arrays, **kwargs) @@ -106,11 +114,20 @@ return rval -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): +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 --------- @@ -150,17 +167,17 @@ fasta_path : str File path to dataset containing fasta file """ - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) # load estimator - with open(infile_estimator, 'rb') as estimator_handler: + with open(infile_estimator, "rb") as estimator_handler: estimator = load_model(estimator_handler) # swap hyperparameter - swapping = params['experiment_schemes']['hyperparams_swapping'] + swapping = params["experiment_schemes"]["hyperparams_swapping"] swap_params = _eval_swap_params(swapping) estimator.set_params(**swap_params) @@ -169,38 +186,41 @@ # store read dataframe object loaded_df = {} - input_type = params['input_options']['selected_input'] + 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'] + 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) + 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')) + elif input_type == "sparse": + X = mmread(open(infile1, "r")) # fasta_file input - elif input_type == 'seq_fasta': - pyfaidx = get_module('pyfaidx') + 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}) + if param.endswith("fasta_path"): + estimator.set_params(**{param: fasta_path}) break else: raise ValueError( @@ -209,25 +229,31 @@ "KerasGBatchClassifier with " "FastaDNABatchGenerator/FastaProteinBatchGenerator " "or having GenomeOneHotEncoder/ProteinOneHotEncoder " - "in pipeline!") + "in pipeline!" + ) - elif input_type == 'refseq_and_interval': + 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 + "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'] + 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 @@ -235,37 +261,39 @@ if df_key in loaded_df: infile2 = loaded_df[df_key] else: - infile2 = pd.read_csv(infile2, sep='\t', - header=header, parse_dates=True) + 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) + 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()) + if input_type == "refseq_and_interval": + estimator.set_params(data_batch_generator__features=y.ravel().tolist()) y = None # end y # load groups if groups: - groups_selector = (params['experiment_schemes']['test_split'] - ['split_algos']).pop('groups_selector') + groups_selector = ( + params["experiment_schemes"]["test_split"]["split_algos"] + ).pop("groups_selector") - header = 'infer' if groups_selector['header_g'] else None - column_option = \ - (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 = groups_selector['column_selector_options_g']['col_g'] + header = "infer" if groups_selector["header_g"] else None + column_option = 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 = groups_selector["column_selector_options_g"]["col_g"] else: c = None @@ -273,13 +301,12 @@ 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 = read_columns(groups, + c=c, + c_option=column_option, + sep='\t', + header=header, + parse_dates=True) groups = groups.ravel() # del loaded_df @@ -288,15 +315,15 @@ # handle memory memory = joblib.Memory(location=CACHE_DIR, verbose=0) # cache iraps_core fits could increase search speed significantly - if estimator.__class__.__name__ == 'IRAPSClassifier': + if estimator.__class__.__name__ == "IRAPSClassifier": estimator.set_params(memory=memory) else: # For iraps buried in pipeline new_params = {} for p, v in estimator_params.items(): - if p.endswith('memory'): + if p.endswith("memory"): # for case of `__irapsclassifier__memory` - if len(p) > 8 and p[:-8].endswith('irapsclassifier'): + if len(p) > 8 and p[:-8].endswith("irapsclassifier"): # cache iraps_core fits could increase search # speed significantly new_params[p] = memory @@ -305,88 +332,98 @@ elif v: new_params[p] = None # handle n_jobs - elif p.endswith('n_jobs'): + elif p.endswith("n_jobs"): # For now, 1 CPU is suggested for iprasclassifier - if len(p) > 8 and p[:-8].endswith('irapsclassifier'): + if len(p) > 8 and p[:-8].endswith("irapsclassifier"): new_params[p] = 1 else: new_params[p] = N_JOBS # for security reason, types of callback are limited - elif p.endswith('callbacks'): + elif p.endswith("callbacks"): for cb in v: - cb_type = cb['callback_selection']['callback_type'] + cb_type = cb["callback_selection"]["callback_type"] if cb_type not in ALLOWED_CALLBACKS: - raise ValueError( - "Prohibited callback type: %s!" % cb_type) + raise ValueError("Prohibited callback type: %s!" % cb_type) estimator.set_params(**new_params) # handle scorer, convert to scorer dict - scoring = params['experiment_schemes']['metrics']['scoring'] + # Check if scoring is specified + scoring = params["experiment_schemes"]["metrics"].get("scoring", None) + if scoring is not None: + # get_scoring() expects secondary_scoring to be a comma separated string (not a list) + # Check if secondary_scoring is specified + secondary_scoring = scoring.get("secondary_scoring", None) + if secondary_scoring is not None: + # If secondary_scoring is specified, convert the list into comman separated string + scoring["secondary_scoring"] = ",".join(scoring["secondary_scoring"]) scorer = get_scoring(scoring) scorer, _ = _check_multimetric_scoring(estimator, scoring=scorer) # handle test (first) split - test_split_options = (params['experiment_schemes'] - ['test_split']['split_algos']) + test_split_options = params["experiment_schemes"]["test_split"]["split_algos"] - if test_split_options['shuffle'] == 'group': - test_split_options['labels'] = groups - if test_split_options['shuffle'] == 'stratified': + if test_split_options["shuffle"] == "group": + test_split_options["labels"] = groups + if test_split_options["shuffle"] == "stratified": if y is not None: - test_split_options['labels'] = y + test_split_options["labels"] = y else: - raise ValueError("Stratified shuffle split is not " - "applicable on empty target values!") + raise ValueError( + "Stratified shuffle split is not " "applicable on empty target values!" + ) - X_train, X_test, y_train, y_test, groups_train, groups_test = \ - train_test_split_none(X, y, groups, **test_split_options) + X_train, X_test, y_train, y_test, groups_train, _groups_test = train_test_split_none( + X, y, groups, **test_split_options + ) - exp_scheme = params['experiment_schemes']['selected_exp_scheme'] + exp_scheme = params["experiment_schemes"]["selected_exp_scheme"] # handle validation (second) split - if exp_scheme == 'train_val_test': - val_split_options = (params['experiment_schemes'] - ['val_split']['split_algos']) + if exp_scheme == "train_val_test": + val_split_options = params["experiment_schemes"]["val_split"]["split_algos"] - if val_split_options['shuffle'] == 'group': - val_split_options['labels'] = groups_train - if val_split_options['shuffle'] == 'stratified': + if val_split_options["shuffle"] == "group": + val_split_options["labels"] = groups_train + if val_split_options["shuffle"] == "stratified": if y_train is not None: - val_split_options['labels'] = y_train + val_split_options["labels"] = y_train else: - raise ValueError("Stratified shuffle split is not " - "applicable on empty target values!") + raise ValueError( + "Stratified shuffle split is not " + "applicable on empty target values!" + ) - X_train, X_val, y_train, y_val, groups_train, groups_val = \ - train_test_split_none(X_train, y_train, groups_train, - **val_split_options) + ( + X_train, + X_val, + y_train, + y_val, + groups_train, + _groups_val, + ) = train_test_split_none(X_train, y_train, groups_train, **val_split_options) # train and eval - if hasattr(estimator, 'validation_data'): - if exp_scheme == 'train_val_test': - estimator.fit(X_train, y_train, - validation_data=(X_val, y_val)) + if hasattr(estimator, "validation_data"): + if exp_scheme == "train_val_test": + estimator.fit(X_train, y_train, validation_data=(X_val, y_val)) else: - estimator.fit(X_train, y_train, - validation_data=(X_test, y_test)) + estimator.fit(X_train, y_train, validation_data=(X_test, y_test)) else: estimator.fit(X_train, y_train) - if hasattr(estimator, 'evaluate'): - scores = estimator.evaluate(X_test, y_test=y_test, - scorer=scorer, - is_multimetric=True) + if hasattr(estimator, "evaluate"): + scores = estimator.evaluate( + X_test, y_test=y_test, scorer=scorer, is_multimetric=True + ) else: - scores = _score(estimator, X_test, y_test, scorer, - is_multimetric=True) + scores = _score(estimator, X_test, y_test, scorer, is_multimetric=True) # handle output for name, score in scores.items(): scores[name] = [score] df = pd.DataFrame(scores) df = df[sorted(df.columns)] - df.to_csv(path_or_buf=outfile_result, sep='\t', - header=True, index=False) + df.to_csv(path_or_buf=outfile_result, sep="\t", header=True, index=False) memory.clear(warn=False) @@ -395,23 +432,25 @@ if isinstance(estimator, pipeline.Pipeline): main_est = estimator.steps[-1][-1] - if hasattr(main_est, 'model_') \ - and hasattr(main_est, 'save_weights'): + 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): + if getattr(main_est, "model_", None): + del main_est.model_ + if getattr(main_est, "fit_params", None): + del main_est.fit_params + if getattr(main_est, "model_class_", None): + del main_est.model_class_ + if getattr(main_est, "validation_data", None): + 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(estimator, output_handler, - pickle.HIGHEST_PROTOCOL) + with open(outfile_object, "wb") as output_handler: + pickle.dump(estimator, output_handler, pickle.HIGHEST_PROTOCOL) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-e", "--estimator", dest="infile_estimator") @@ -427,8 +466,17 @@ 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) + 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, + ) diff -r c12485d058aa -r c3bafda50176 train_test_split.py --- a/train_test_split.py Thu Oct 01 21:08:39 2020 +0000 +++ b/train_test_split.py Tue Apr 13 18:33:38 2021 +0000 @@ -7,9 +7,8 @@ from galaxy_ml.utils import get_cv, read_columns -def _get_single_cv_split(params, array, infile_labels=None, - infile_groups=None): - """ output (train, test) subset from a cv splitter +def _get_single_cv_split(params, array, infile_labels=None, infile_groups=None): + """output (train, test) subset from a cv splitter Parameters ---------- @@ -25,45 +24,50 @@ y = None groups = None - nth_split = params['mode_selection']['nth_split'] + nth_split = params["mode_selection"]["nth_split"] # read groups if infile_groups: - header = 'infer' if (params['mode_selection']['cv_selector'] - ['groups_selector']['header_g']) else None - column_option = (params['mode_selection']['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 = (params['mode_selection']['cv_selector']['groups_selector'] - ['column_selector_options_g']['col_g']) + header = "infer" if (params["mode_selection"]["cv_selector"]["groups_selector"]["header_g"]) else None + column_option = params["mode_selection"]["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 = params["mode_selection"]["cv_selector"]["groups_selector"]["column_selector_options_g"]["col_g"] else: c = None - groups = read_columns(infile_groups, c=c, c_option=column_option, - sep='\t', header=header, parse_dates=True) + groups = read_columns( + infile_groups, + c=c, + c_option=column_option, + sep="\t", + header=header, + parse_dates=True, + ) groups = groups.ravel() - params['mode_selection']['cv_selector']['groups_selector'] = groups + params["mode_selection"]["cv_selector"]["groups_selector"] = groups # read labels if infile_labels: - target_input = (params['mode_selection'] - ['cv_selector'].pop('target_input')) - header = 'infer' if target_input['header1'] else None - col_index = target_input['col'][0] - 1 - df = pd.read_csv(infile_labels, sep='\t', header=header, - parse_dates=True) + target_input = params["mode_selection"]["cv_selector"].pop("target_input") + header = "infer" if target_input["header1"] else None + col_index = target_input["col"][0] - 1 + df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) y = df.iloc[:, col_index].values # construct the cv splitter object - splitter, groups = get_cv(params['mode_selection']['cv_selector']) + splitter, groups = get_cv(params["mode_selection"]["cv_selector"]) total_n_splits = splitter.get_n_splits(array.values, y=y, groups=groups) if nth_split > total_n_splits: - raise ValueError("Total number of splits is {}, but got `nth_split` " - "= {}".format(total_n_splits, nth_split)) + raise ValueError("Total number of splits is {}, but got `nth_split` " "= {}".format(total_n_splits, nth_split)) i = 1 for train_index, test_index in splitter.split(array.values, y=y, groups=groups): @@ -79,8 +83,14 @@ return train, test -def main(inputs, infile_array, outfile_train, outfile_test, - infile_labels=None, infile_groups=None): +def main( + inputs, + infile_array, + outfile_train, + outfile_test, + infile_labels=None, + infile_groups=None, +): """ Parameter --------- @@ -102,45 +112,41 @@ outfile_test : str File path to dataset containing test split """ - warnings.simplefilter('ignore') + warnings.simplefilter("ignore") - with open(inputs, 'r') as param_handler: + with open(inputs, "r") as param_handler: params = json.load(param_handler) - input_header = params['header0'] - header = 'infer' if input_header else None - array = pd.read_csv(infile_array, sep='\t', header=header, - parse_dates=True) + input_header = params["header0"] + header = "infer" if input_header else None + array = pd.read_csv(infile_array, sep="\t", header=header, parse_dates=True) # train test split - if params['mode_selection']['selected_mode'] == 'train_test_split': - options = params['mode_selection']['options'] - shuffle_selection = options.pop('shuffle_selection') - options['shuffle'] = shuffle_selection['shuffle'] + if params["mode_selection"]["selected_mode"] == "train_test_split": + options = params["mode_selection"]["options"] + shuffle_selection = options.pop("shuffle_selection") + options["shuffle"] = shuffle_selection["shuffle"] if infile_labels: - header = 'infer' if shuffle_selection['header1'] else None - col_index = shuffle_selection['col'][0] - 1 - df = pd.read_csv(infile_labels, sep='\t', header=header, - parse_dates=True) + header = "infer" if shuffle_selection["header1"] else None + col_index = shuffle_selection["col"][0] - 1 + df = pd.read_csv(infile_labels, sep="\t", header=header, parse_dates=True) labels = df.iloc[:, col_index].values - options['labels'] = labels + options["labels"] = labels train, test = train_test_split(array, **options) # cv splitter else: - train, test = _get_single_cv_split(params, array, - infile_labels=infile_labels, - infile_groups=infile_groups) + train, test = _get_single_cv_split(params, array, infile_labels=infile_labels, infile_groups=infile_groups) print("Input shape: %s" % repr(array.shape)) print("Train shape: %s" % repr(train.shape)) print("Test shape: %s" % repr(test.shape)) - train.to_csv(outfile_train, sep='\t', header=input_header, index=False) - test.to_csv(outfile_test, sep='\t', header=input_header, index=False) + train.to_csv(outfile_train, sep="\t", header=input_header, index=False) + test.to_csv(outfile_test, sep="\t", header=input_header, index=False) -if __name__ == '__main__': +if __name__ == "__main__": aparser = argparse.ArgumentParser() aparser.add_argument("-i", "--inputs", dest="inputs", required=True) aparser.add_argument("-X", "--infile_array", dest="infile_array") @@ -150,5 +156,11 @@ aparser.add_argument("-t", "--outfile_test", dest="outfile_test") args = aparser.parse_args() - main(args.inputs, args.infile_array, args.outfile_train, - args.outfile_test, args.infile_labels, args.infile_groups) + main( + args.inputs, + args.infile_array, + args.outfile_train, + args.outfile_test, + args.infile_labels, + args.infile_groups, + )