--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/utils.py	Fri Aug 17 12:27:15 2018 -0400
@@ -0,0 +1,251 @@
+import sys
+import os
+import pandas
+import re
+import pickle
+import warnings
+import numpy as np
+import xgboost
+import scipy
+import sklearn
+import ast
+from asteval import Interpreter, make_symbol_table
+from sklearn import metrics, model_selection, ensemble, svm, linear_model, naive_bayes, tree, neighbors
+
+N_JOBS = int( os.environ.get('GALAXY_SLOTS', 1) )
+
+def read_columns(f, c=None, c_option='by_index_number', return_df=False, **args):
+    data = pandas.read_csv(f, **args)
+    if c_option == 'by_index_number':
+        cols = list(map(lambda x: x - 1, c))
+        data = data.iloc[:,cols]
+    if c_option == 'all_but_by_index_number':
+        cols = list(map(lambda x: x - 1, c))
+        data.drop(data.columns[cols], axis=1, inplace=True)
+    if c_option == 'by_header_name':
+        cols = [e.strip() for e in c.split(',')]
+        data = data[cols]
+    if c_option == 'all_but_by_header_name':
+        cols = [e.strip() for e in c.split(',')]
+        data.drop(cols, axis=1, inplace=True)
+    y = data.values
+    if return_df:
+        return y, data
+    else:
+        return y
+    return y
+
+
+## generate an instance for one of sklearn.feature_selection classes
+def feature_selector(inputs):
+    selector = inputs["selected_algorithm"]
+    selector = getattr(sklearn.feature_selection, selector)
+    options = inputs["options"]
+
+    if inputs['selected_algorithm'] == 'SelectFromModel':
+        if not options['threshold'] or options['threshold'] == 'None':
+            options['threshold'] = None
+        if inputs['model_inputter']['input_mode'] == 'prefitted':
+            model_file = inputs['model_inputter']['fitted_estimator']
+            with open(model_file, 'rb') as model_handler:
+                fitted_estimator = pickle.load(model_handler)
+            new_selector = selector(fitted_estimator, prefit=True, **options)
+        else:
+            estimator_json = inputs['model_inputter']["estimator_selector"]
+            estimator = get_estimator(estimator_json)
+            new_selector = selector(estimator, **options)
+
+    elif inputs['selected_algorithm'] == 'RFE':
+        estimator=get_estimator(inputs["estimator_selector"])
+        new_selector = selector(estimator, **options)
+
+    elif inputs['selected_algorithm'] == 'RFECV':
+        options['scoring'] = get_scoring(options['scoring'])
+        options['n_jobs'] = N_JOBS
+        options['cv'] = get_cv( options['cv'].strip() )
+        estimator=get_estimator(inputs["estimator_selector"])
+        new_selector = selector(estimator, **options)
+
+    elif inputs['selected_algorithm'] == "VarianceThreshold":
+        new_selector = selector(**options)
+
+    else:
+        score_func = inputs["score_func"]
+        score_func = getattr(sklearn.feature_selection, score_func)
+        new_selector = selector(score_func, **options)
+
+    return new_selector
+ 
+
+def get_X_y(params, file1, file2):
+    input_type = params["selected_tasks"]["selected_algorithms"]["input_options"]["selected_input"]
+    if input_type=="tabular":
+        header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header1"] else None
+        column_option = params["selected_tasks"]["selected_algorithms"]["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["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_1"]["col1"]
+        else:
+            c = None
+        X = read_columns(
+            file1,
+            c = c,
+            c_option = column_option,
+            sep='\t',
+            header=header,
+            parse_dates=True
+        )
+    else:
+        X = mmread(file1)
+
+    header = 'infer' if params["selected_tasks"]["selected_algorithms"]["input_options"]["header2"] else None
+    column_option = params["selected_tasks"]["selected_algorithms"]["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["selected_tasks"]["selected_algorithms"]["input_options"]["column_selector_options_2"]["col2"]
+    else:
+        c = None
+    y = read_columns(
+        file2,
+        c = c,
+        c_option = column_option,
+        sep='\t',
+        header=header,
+        parse_dates=True
+    )
+    y=y.ravel()
+    return X, y
+
+
+class SafeEval(Interpreter):
+
+    def __init__(self, load_scipy=False, load_numpy=False):
+
+        # File opening and other unneeded functions could be dropped
+        unwanted = ['open', 'type', 'dir', 'id', 'str', 'repr']
+
+        # Allowed symbol table. Add more if needed.
+        new_syms = {
+            'np_arange': getattr(np, 'arange'),
+            'ensemble_ExtraTreesClassifier': getattr(ensemble, 'ExtraTreesClassifier')
+        }
+
+        syms = make_symbol_table(use_numpy=False, **new_syms)
+
+        if load_scipy:
+            scipy_distributions = scipy.stats.distributions.__dict__
+            for key in scipy_distributions.keys():
+                if isinstance(scipy_distributions[key], (scipy.stats.rv_continuous, scipy.stats.rv_discrete)):
+                    syms['scipy_stats_' + key] = scipy_distributions[key]
+
+        if load_numpy:
+            from_numpy_random = ['beta', 'binomial', 'bytes', 'chisquare', 'choice', 'dirichlet', 'division',
+                                'exponential', 'f', 'gamma', 'geometric', 'gumbel', 'hypergeometric',
+                                'laplace', 'logistic', 'lognormal', 'logseries', 'mtrand', 'multinomial',
+                                'multivariate_normal', 'negative_binomial', 'noncentral_chisquare', 'noncentral_f',
+                                'normal', 'pareto', 'permutation', 'poisson', 'power', 'rand', 'randint',
+                                'randn', 'random', 'random_integers', 'random_sample', 'ranf', 'rayleigh',
+                                'sample', 'seed', 'set_state', 'shuffle', 'standard_cauchy', 'standard_exponential',
+                                'standard_gamma', 'standard_normal', 'standard_t', 'triangular', 'uniform',
+                                'vonmises', 'wald', 'weibull', 'zipf' ]
+            for f in from_numpy_random:
+                syms['np_random_' + f] = getattr(np.random, f)
+
+        for key in unwanted:
+            syms.pop(key, None)
+
+        super(SafeEval, self).__init__( symtable=syms, use_numpy=False, minimal=False,
+                                        no_if=True, no_for=True, no_while=True, no_try=True,
+                                        no_functiondef=True, no_ifexp=True, no_listcomp=False,
+                                        no_augassign=False, no_assert=True, no_delete=True,
+                                        no_raise=True, no_print=True)
+
+
+def get_search_params(params_builder):
+    search_params = {}
+    safe_eval = SafeEval(load_scipy=True, load_numpy=True)
+
+    for p in params_builder['param_set']:
+        search_p = p['search_param_selector']['search_p']
+        if search_p.strip() == '':
+            continue
+        param_type = p['search_param_selector']['selected_param_type']
+
+        lst = search_p.split(":")
+        assert (len(lst) == 2), "Error, make sure there is one and only one colon in search parameter input."
+        literal = lst[1].strip()
+        ev = safe_eval(literal)
+        if param_type == "final_estimator_p":
+            search_params["estimator__" + lst[0].strip()] = ev
+        else:
+            search_params["preprocessing_" + param_type[5:6] + "__" + lst[0].strip()] = ev
+
+    return search_params
+
+
+def get_estimator(estimator_json):
+    estimator_module = estimator_json['selected_module']
+    estimator_cls = estimator_json['selected_estimator']
+
+    if estimator_module == "xgboost":
+        cls = getattr(xgboost, estimator_cls)
+    else:
+        module = getattr(sklearn, estimator_module)
+        cls = getattr(module, estimator_cls)
+
+    estimator = cls()
+
+    estimator_params = estimator_json['text_params'].strip()
+    if estimator_params != "":
+        try:
+            params = safe_eval('dict(' + estimator_params + ')')
+        except ValueError:
+            sys.exit("Unsupported parameter input: `%s`" %estimator_params)
+        estimator.set_params(**params)
+    if 'n_jobs' in estimator.get_params():
+        estimator.set_params( n_jobs=N_JOBS )
+
+    return estimator
+
+
+def get_cv(literal):
+    safe_eval = SafeEval()
+    if literal == "":
+        return None
+    if literal.isdigit():
+        return int(literal)
+    m = re.match(r'^(?P<method>\w+)\((?P<args>.*)\)$', literal)
+    if m:
+        my_class = getattr( model_selection, m.group('method') )
+        args = safe_eval( 'dict('+ m.group('args') + ')' )
+        return my_class( **args )
+    sys.exit("Unsupported CV input: %s" %literal)
+
+
+def get_scoring(scoring_json):
+    def balanced_accuracy_score(y_true, y_pred):
+        C = metrics.confusion_matrix(y_true, y_pred)
+        with np.errstate(divide='ignore', invalid='ignore'):
+            per_class = np.diag(C) / C.sum(axis=1)
+        if np.any(np.isnan(per_class)):
+            warnings.warn('y_pred contains classes not in y_true')
+            per_class = per_class[~np.isnan(per_class)]
+        score = np.mean(per_class)
+        return score
+
+    if scoring_json['primary_scoring'] == "default":
+        return None
+
+    my_scorers = metrics.SCORERS
+    if 'balanced_accuracy' not in my_scorers:
+        my_scorers['balanced_accuracy'] = metrics.make_scorer(balanced_accuracy_score)
+
+    if scoring_json['secondary_scoring'] != 'None'\
+            and scoring_json['secondary_scoring'] != scoring_json['primary_scoring']:
+        scoring = {}
+        scoring['primary'] = my_scorers[ scoring_json['primary_scoring'] ]
+        for scorer in scoring_json['secondary_scoring'].split(','):
+            if scorer != scoring_json['primary_scoring']:
+                scoring[scorer] = my_scorers[scorer]
+        return scoring
+
+    return my_scorers[ scoring_json['primary_scoring'] ]
+