# HG changeset patch
# User bgruening
# Date 1570000625 14400
# Node ID 79a60f89166aefa2f470c9ef06487cf117bb9572
# Parent f35dbad6fb7b6e36ae8e8a0f1edb020897213412
"planemo upload for repository https://github.com/bgruening/galaxytools/tree/master/tools/sklearn commit 02087ce2966cf8b4aac9197a41171e7f986c11d1-dirty"
diff -r f35dbad6fb7b -r 79a60f89166a main_macros.xml
--- a/main_macros.xml Fri Sep 13 11:37:22 2019 -0400
+++ b/main_macros.xml Wed Oct 02 03:17:05 2019 -0400
@@ -421,27 +421,46 @@
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diff -r f35dbad6fb7b -r 79a60f89166a ml_visualization_ex.py
--- a/ml_visualization_ex.py Fri Sep 13 11:37:22 2019 -0400
+++ b/ml_visualization_ex.py Wed Oct 02 03:17:05 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 f35dbad6fb7b -r 79a60f89166a stacking_ensembles.py
--- a/stacking_ensembles.py Fri Sep 13 11:37:22 2019 -0400
+++ b/stacking_ensembles.py Wed Oct 02 03:17:05 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')
diff -r f35dbad6fb7b -r 79a60f89166a train_test_eval.xml
--- a/train_test_eval.xml Fri Sep 13 11:37:22 2019 -0400
+++ b/train_test_eval.xml Wed Oct 02 03:17:05 2019 -0400
@@ -76,7 +76,7 @@
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