--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/keras_train_and_eval.py	Mon Dec 16 05:28:32 2019 -0500
@@ -0,0 +1,491 @@
+import argparse
+import joblib
+import json
+import numpy as np
+import os
+import pandas as pd
+import pickle
+import warnings
+from itertools import chain
+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
+
+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)
+
+
+_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')
+del os
+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 == '':
+            continue
+
+        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)
+            continue
+
+        if not swap_value.startswith(':'):
+            safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+            ev = safe_eval(swap_value)
+        else:
+            # Have `:` before search list, asks for estimator evaluatio
+            safe_eval_es = SafeEval(load_estimators=True)
+            swap_value = swap_value[1:].strip()
+            # TODO maybe add regular express check
+            ev = safe_eval_es(swap_value)
+
+        swap_params[param_name] = ev
+
+    return swap_params
+
+
+def train_test_split_none(*arrays, **kwargs):
+    """extend train_test_split to take None arrays
+    and support split by group names.
+    """
+    nones = []
+    new_arrays = []
+    for idx, arr in enumerate(arrays):
+        if arr is None:
+            nones.append(idx)
+        else:
+            new_arrays.append(arr)
+
+    if kwargs['shuffle'] == 'None':
+        kwargs['shuffle'] = 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(',')]
+        new_arrays = indexable(*new_arrays)
+        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))
+    else:
+        rval = train_test_split(*new_arrays, **kwargs)
+
+    for pos in nones:
+        rval[pos * 2: 2] = [None, None]
+
+    return rval
+
+
+def _evaluate(y_true, pred_probas, scorer, is_multimetric=True):
+    """ output scores based on input scorer
+
+    Parameters
+    ----------
+    y_true : array
+        True label or target values
+    pred_probas : array
+        Prediction values, probability for classification problem
+    scorer : dict
+        dict of `sklearn.metrics.scorer.SCORER`
+    is_multimetric : bool, default is True
+    """
+    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')
+        if not is_multimetric:
+            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)
+                scores[name] = score
+
+    # TODO: multi-class metrics
+    # multi-label
+    else:
+        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)
+            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)
+                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):
+    """
+    Parameter
+    ---------
+    inputs : str
+        File path to galaxy tool parameter
+
+    infile_estimator : str
+        File path to estimator
+
+    infile1 : str
+        File path to dataset containing features
+
+    infile2 : str
+        File path to dataset containing target values
+
+    outfile_result : str
+        File path to save the results, either cv_results or test result
+
+    outfile_object : str, optional
+        File path to save searchCV object
+
+    outfile_weights : str, optional
+        File path to save deep learning model weights
+
+    outfile_y_true : str, optional
+        File path to target values for prediction
+
+    outfile_y_preds : str, optional
+        File path to save deep learning model weights
+
+    groups : str
+        File path to dataset containing groups labels
+
+    ref_seq : str
+        File path to dataset containing genome sequence file
+
+    intervals : str
+        File path to dataset containing interval file
+
+    targets : str
+        File path to dataset compressed target bed file
+
+    fasta_path : str
+        File path to dataset containing fasta file
+    """
+    warnings.simplefilter('ignore')
+
+    with open(inputs, 'r') as param_handler:
+        params = json.load(param_handler)
+
+    #  load estimator
+    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']
+    swap_params = _eval_swap_params(swapping)
+    estimator.set_params(**swap_params)
+
+    estimator_params = estimator.get_params()
+
+    # store read dataframe object
+    loaded_df = {}
+
+    input_type = params['input_options']['selected_input']
+    # tabular input
+    if input_type == 'tabular':
+        header = 'infer' if params['input_options']['header1'] else None
+        column_option = (params['input_options']['column_selector_options_1']
+                         ['selected_column_selector_option'])
+        if column_option in ['by_index_number', 'all_but_by_index_number',
+                             'by_header_name', 'all_but_by_header_name']:
+            c = params['input_options']['column_selector_options_1']['col1']
+        else:
+            c = None
+
+        df_key = infile1 + repr(header)
+        df = pd.read_csv(infile1, sep='\t', header=header,
+                         parse_dates=True)
+        loaded_df[df_key] = df
+
+        X = read_columns(df, c=c, c_option=column_option).astype(float)
+    # sparse input
+    elif input_type == 'sparse':
+        X = mmread(open(infile1, 'r'))
+
+    # fasta_file input
+    elif input_type == 'seq_fasta':
+        pyfaidx = get_module('pyfaidx')
+        sequences = pyfaidx.Fasta(fasta_path)
+        n_seqs = len(sequences.keys())
+        X = np.arange(n_seqs)[:, np.newaxis]
+        for param in estimator_params.keys():
+            if param.endswith('fasta_path'):
+                estimator.set_params(
+                    **{param: fasta_path})
+                break
+        else:
+            raise ValueError(
+                "The selected estimator doesn't support "
+                "fasta file input! Please consider using "
+                "KerasGBatchClassifier with "
+                "FastaDNABatchGenerator/FastaProteinBatchGenerator "
+                "or having GenomeOneHotEncoder/ProteinOneHotEncoder "
+                "in pipeline!")
+
+    elif input_type == 'refseq_and_interval':
+        path_params = {
+            'data_batch_generator__ref_genome_path': ref_seq,
+            'data_batch_generator__intervals_path': intervals,
+            'data_batch_generator__target_path': targets
+        }
+        estimator.set_params(**path_params)
+        n_intervals = sum(1 for line in open(intervals))
+        X = np.arange(n_intervals)[:, np.newaxis]
+
+    # Get target y
+    header = 'infer' if params['input_options']['header2'] else None
+    column_option = (params['input_options']['column_selector_options_2']
+                     ['selected_column_selector_option2'])
+    if column_option in ['by_index_number', 'all_but_by_index_number',
+                         'by_header_name', 'all_but_by_header_name']:
+        c = params['input_options']['column_selector_options_2']['col2']
+    else:
+        c = None
+
+    df_key = infile2 + repr(header)
+    if df_key in loaded_df:
+        infile2 = loaded_df[df_key]
+    else:
+        infile2 = pd.read_csv(infile2, sep='\t',
+                              header=header, parse_dates=True)
+        loaded_df[df_key] = infile2
+
+    y = read_columns(
+            infile2,
+            c=c,
+            c_option=column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True)
+    if len(y.shape) == 2 and y.shape[1] == 1:
+        y = y.ravel()
+    if input_type == 'refseq_and_interval':
+        estimator.set_params(
+            data_batch_generator__features=y.ravel().tolist())
+        y = None
+    # end y
+
+    # load groups
+    if groups:
+        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']
+        else:
+            c = None
+
+        df_key = groups + repr(header)
+        if df_key in loaded_df:
+            groups = loaded_df[df_key]
+
+        groups = read_columns(
+                groups,
+                c=c,
+                c_option=column_option,
+                sep='\t',
+                header=header,
+                parse_dates=True)
+        groups = groups.ravel()
+
+    # del loaded_df
+    del loaded_df
+
+    # cache iraps_core fits could increase search speed significantly
+    memory = joblib.Memory(location=CACHE_DIR, verbose=0)
+    main_est = get_main_estimator(estimator)
+    if main_est.__class__.__name__ == 'IRAPSClassifier':
+        main_est.set_params(memory=memory)
+
+    # handle scorer, convert to scorer dict
+    scoring = params['experiment_schemes']['metrics']['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'])
+
+    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
+        else:
+            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)
+
+    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 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
+            else:
+                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)
+
+    # 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))
+        else:
+            estimator.fit(X_train, y_train,
+                          validation_data=(X_test, y_test))
+    else:
+        estimator.fit(X_train, y_train)
+
+    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)
+        scores = _evaluate(y_true, predictions, scorer, is_multimetric=True)
+
+    else:
+        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)
+    if outfile_y_true:
+        try:
+            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)
+        except Exception as e:
+            print("Error in saving predictions: %s" % e)
+
+    # 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)
+
+    memory.clear(warn=False)
+
+    if outfile_object:
+        main_est = estimator
+        if isinstance(estimator, Pipeline):
+            main_est = estimator.steps[-1][-1]
+
+        if hasattr(main_est, 'model_') \
+                and hasattr(main_est, 'save_weights'):
+            if outfile_weights:
+                main_est.save_weights(outfile_weights)
+            del main_est.model_
+            del main_est.fit_params
+            del main_est.model_class_
+            del main_est.validation_data
+            if getattr(main_est, 'data_generator_', None):
+                del main_est.data_generator_
+
+        with open(outfile_object, 'wb') as output_handler:
+            pickle.dump(estimator, output_handler,
+                        pickle.HIGHEST_PROTOCOL)
+
+
+if __name__ == '__main__':
+    aparser = argparse.ArgumentParser()
+    aparser.add_argument("-i", "--inputs", dest="inputs", required=True)
+    aparser.add_argument("-e", "--estimator", dest="infile_estimator")
+    aparser.add_argument("-X", "--infile1", dest="infile1")
+    aparser.add_argument("-y", "--infile2", dest="infile2")
+    aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
+    aparser.add_argument("-w", "--outfile_weights", dest="outfile_weights")
+    aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
+    aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
+    aparser.add_argument("-g", "--groups", dest="groups")
+    aparser.add_argument("-r", "--ref_seq", dest="ref_seq")
+    aparser.add_argument("-b", "--intervals", dest="intervals")
+    aparser.add_argument("-t", "--targets", dest="targets")
+    aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
+    args = aparser.parse_args()
+
+    main(args.inputs, args.infile_estimator, args.infile1, args.infile2,
+         args.outfile_result, outfile_object=args.outfile_object,
+         outfile_weights=args.outfile_weights,
+         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)