# HG changeset patch
# User bgruening
# Date 1570002203 14400
# Node ID 63417d0acc7244a11d5d7bdebf141a5d5978c61d
# Parent  a9474cdda506f0aeeb294b88fd932745721d9a1b
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"

diff -r a9474cdda506 -r 63417d0acc72 generalized_linear.xml
--- a/generalized_linear.xml	Fri Sep 13 12:17:00 2019 -0400
+++ b/generalized_linear.xml	Wed Oct 02 03:43:23 2019 -0400
@@ -45,7 +45,8 @@
 #else:
 with open("$selected_tasks.infile_model", 'rb') as model_handler:
     classifier_object = load_model(model_handler)
-data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
+header = 'infer' if params["selected_tasks"]["header"] else None
+data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=header, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
 prediction = classifier_object.predict(data)
 prediction_df = pandas.DataFrame(prediction, columns=["predicted"])
 res = pandas.concat([data, prediction_df], axis=1)
diff -r a9474cdda506 -r 63417d0acc72 main_macros.xml
--- a/main_macros.xml	Fri Sep 13 12:17:00 2019 -0400
+++ b/main_macros.xml	Wed Oct 02 03:43:23 2019 -0400
@@ -421,27 +421,46 @@
 
   <xml name="sl_mixed_input">
     <conditional name="input_options">
-      <param name="selected_input" type="select" label="Select input type:">
-          <option value="tabular" selected="true">tabular data</option>
-          <option value="sparse">sparse matrix</option>
-          <option value="seq_fasta">sequnences in a fasta file</option>
-          <option value="refseq_and_interval">reference genome and intervals</option>
-      </param>
-      <when value="tabular">
-          <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
-      </when>
-      <when value="sparse">
-          <expand macro="sparse_target"/>
-      </when>
-      <when value="seq_fasta">
-          <expand macro="inputs_seq_fasta"/>
-      </when>
-      <when value="refseq_and_interval">
-          <expand macro="inputs_refseq_and_interval"/>
-      </when>
+        <expand macro="data_input_options"/>
+        <expand macro="data_input_whens"/>
     </conditional>
   </xml>
 
+  <xml name="sl_mixed_input_plus_sequence">
+    <conditional name="input_options">
+        <expand macro="data_input_options">
+            <option value="seq_fasta">sequnences in a fasta file</option>
+            <option value="refseq_and_interval">reference genome and intervals</option>
+        </expand>
+        <expand macro="data_input_whens">
+            <when value="seq_fasta">
+                <expand macro="inputs_seq_fasta"/>
+            </when>
+            <when value="refseq_and_interval">
+                <expand macro="inputs_refseq_and_interval"/>
+            </when>
+        </expand>
+    </conditional>
+  </xml>
+
+  <xml name="data_input_options">
+    <param name="selected_input" type="select" label="Select input type:">
+        <option value="tabular" selected="true">tabular data</option>
+        <option value="sparse">sparse matrix</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="data_input_whens">
+    <when value="tabular">
+        <expand macro="samples_tabular" multiple1="true" multiple2="false"/>
+    </when>
+    <when value="sparse">
+        <expand macro="sparse_target"/>
+    </when>
+    <yield/>
+  </xml>
+
   <xml name="input_tabular_target">
     <param name="infile2" type="data" format="tabular" label="Dataset containing class labels or target values:"/>
     <param name="header2" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false" label="Does the dataset contain header:" />
diff -r a9474cdda506 -r 63417d0acc72 ml_visualization_ex.py
--- a/ml_visualization_ex.py	Fri Sep 13 12:17:00 2019 -0400
+++ b/ml_visualization_ex.py	Wed Oct 02 03:43:23 2019 -0400
@@ -146,7 +146,8 @@
             precision["micro"], recall["micro"], _ = precision_recall_curve(
                 df1.values.ravel(), df2.values.ravel(), pos_label=pos_label)
             ap['micro'] = average_precision_score(
-                df1.values, df2.values, average='micro', pos_label=pos_label or 1)
+                df1.values, df2.values, average='micro',
+                pos_label=pos_label or 1)
 
         data = []
         for key in precision.keys():
@@ -201,7 +202,7 @@
             )
             data.append(trace)
 
-        trace = go.Scatter(x=[0, 1], y=[0, 1], 
+        trace = go.Scatter(x=[0, 1], y=[0, 1],
                            mode='lines', 
                            line=dict(color='black', dash='dash'),
                            showlegend=False)
diff -r a9474cdda506 -r 63417d0acc72 stacking_ensembles.py
--- a/stacking_ensembles.py	Fri Sep 13 12:17:00 2019 -0400
+++ b/stacking_ensembles.py	Wed Oct 02 03:43:23 2019 -0400
@@ -11,7 +11,7 @@
 from sklearn import ensemble
 
 from galaxy_ml.utils import (load_model, get_cv, get_estimator,
-                          get_search_params)
+                             get_search_params)
 
 
 warnings.filterwarnings('ignore')