Mercurial > repos > bgruening > sklearn_searchcv

changeset 2:907bb0418c9f draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit b1e5fa3170484d2cc3396f2abe99bb8cfcfa9c65
author bgruening
date Tue, 07 Aug 2018 05:45:28 -0400
parents f727f5ff7d60
children f9fea8323bcb
files main_macros.xml search_model_validation.xml
diffstat 2 files changed, 80 insertions(+), 48 deletions(-) [+]
line wrap: on
line diff
--- a/main_macros.xml	Sat Aug 04 17:34:42 2018 -0400
+++ b/main_macros.xml	Tue Aug 07 05:45:28 2018 -0400
@@ -100,53 +100,55 @@
   return X, y
   </token>
 
+  <token name="@SAFE_EVAL_FUNCTION@">
+def safe_eval(literal):
+
+  FROM_SCIPY_STATS = [  'bernoulli', 'binom', 'boltzmann', 'dlaplace', 'geom', 'hypergeom',
+                        'logser', 'nbinom', 'planck', 'poisson', 'randint', 'skellam', 'zipf' ]
+
+  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' ]
+
+  # 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)
+
+  for method in FROM_SCIPY_STATS:
+    syms['scipy_stats_' + method] = getattr(scipy.stats, method)
+
+  for func in FROM_NUMPY_RANDOM:
+    syms['np_random_' + func] = getattr(np.random, func)
+
+  for key in UNWANTED:
+    syms.pop(key, None)
+
+  aeval = Interpreter(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)
+
+  return aeval(literal)
+  </token>
+
   <token name="@GET_SEARCH_PARAMS_FUNCTION@">
 def get_search_params(params_builder):
   search_params = {}
 
-  def safe_eval(literal):
-
-    FROM_SCIPY_STATS = [  'bernoulli', 'binom', 'boltzmann', 'dlaplace', 'geom', 'hypergeom',
-                          'logser', 'nbinom', 'planck', 'poisson', 'randint', 'skellam', 'zipf' ]
-
-    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' ]
-
-    # 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)
-
-    for method in FROM_SCIPY_STATS:
-      syms['scipy_stats_' + method] = getattr(scipy.stats, method)
-
-    for func in FROM_NUMPY_RANDOM:
-      syms['np_random_' + func] = getattr(np.random, func)
-
-    for key in UNWANTED:
-      syms.pop(key, None)
-
-    aeval = Interpreter(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)
-
-    return aeval(literal)
-
   for p in params_builder['param_set']:
     search_p = p['search_param_selector']['search_p']
     if search_p.strip() == '':
@@ -189,6 +191,20 @@
   return estimator
   </token>
 
+  <token name="@GET_CV_FUNCTION@">
+def get_cv(literal):
+  if literal == "":
+    return None
+  if re.match(r'^\d+$', literal):
+    return int(literal)
+  m = re.match(r'^(?P&lt;method&gt;\w+)\((?P&lt;args&gt;.*)\)$', 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)
+  </token>
+
   <xml name="python_requirements">
       <requirements>
           <requirement type="package" version="2.7">python</requirement>
@@ -1143,7 +1159,7 @@
   </xml>
 
   <xml name="model_validation_common_options">
-    <param argument="cv" type="integer" value="" optional="true" label="cv" help="The number of folds in a (Stratified)KFold" />
+    <param argument="cv" type="text" value="" size="50" optional="true" label="cv" help="Optional. Integer or evalable splitter object, e.g., StratifiedKFold(n_splits=3, shuffle=True, random_state=10). Leave blank for default." />
     <expand macro="n_jobs"/>
     <expand macro="verbose"/>
     <yield/>
--- a/search_model_validation.xml	Sat Aug 04 17:34:42 2018 -0400
+++ b/search_model_validation.xml	Tue Aug 07 05:45:28 2018 -0400
@@ -20,6 +20,7 @@
 import sys
 import json
 import pandas
+import re
 import pickle
 import numpy as np
 import xgboost
@@ -30,7 +31,9 @@
 
 @COLUMNS_FUNCTION@
 @GET_ESTIMATOR_FUNCTION@
+@SAFE_EVAL_FUNCTION@
 @GET_SEARCH_PARAMS_FUNCTION@
+@GET_CV_FUNCTION@
 
 input_json_path = sys.argv[1]
 with open(input_json_path, "r") as param_handler:
@@ -42,9 +45,6 @@
 infile_pipeline = "$search_schemes.infile_pipeline"
 outfile_result = "$outfile_result"
 outfile_estimator = "$outfile_estimator"
-#if $search_schemes.selected_search_scheme == "RandomizedSearchCV":
-np.random.seed($search_schemes.random_seed)
-#end if
 
 params_builder = params['search_schemes']['search_params_builder']
 
@@ -87,6 +87,7 @@
 optimizers = getattr(model_selection, optimizers)
 
 options = params["search_schemes"]["options"]
+options['cv'] = get_cv( options['cv'].strip() )
 if 'scoring' in options and options['scoring'] == '':
     options['scoring'] = None
 if 'pre_dispatch' in options and options['pre_dispatch'] == '':
@@ -123,7 +124,6 @@
                 </section>
             </when>
             <when value="RandomizedSearchCV">
-                <param name="random_seed" type="integer" value="65535" min="0" max="65535" label="Set up random seed:"/>
                 <expand macro="search_cv_estimator"/>
                 <section name="options" title="Advanced Options for SearchCV" expanded="false">
                     <expand macro="search_cv_options"/>
@@ -347,6 +347,22 @@
                 </assert_contents>
             </output>
         </test>
+        <test>
+            <param name="selected_search_scheme" value="GridSearchCV"/>
+            <param name="infile_pipeline" value="pipeline01"/>
+            <conditional name="search_param_selector">
+                <param name="search_p" value="C: [1, 10, 100, 1000]"/>
+                <param name="selected_param_type" value="final_estimator_p"/>
+            </conditional>
+            <param name='cv' value="StratifiedKFold(n_splits=3, shuffle=True, random_state=10)"/>
+            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
+            <param name="header1" value="true" />
+            <param name="selected_column_selector_option" value="all_columns"/>
+            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
+            <param name="header2" value="true" />
+            <param name="selected_column_selector_option2" value="all_columns"/>
+            <output name="outfile_estimator" file="searchCV01" compare="sim_size" delta="1"/>
+        </test>
     </tests>
     <help>
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