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changeset 5:f1761288587e draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 35fa73d6e9ba8f0789ddfb743d893d950a68af02
author bgruening
date Tue, 10 Apr 2018 15:18:51 -0400
parents 0431274c367d
children baf68d822e6c
files ensemble.xml main_macros.xml test-data/gbc_model01 test-data/gbc_result01 test-data/gbr_model01 test-data/gbr_prediction_result01.tabular test-data/regression_X.tabular test-data/regression_test_X.tabular test-data/regression_y.tabular
diffstat 9 files changed, 880 insertions(+), 10 deletions(-) [+]
line wrap: on
line diff
--- a/ensemble.xml	Thu Mar 22 13:46:46 2018 -0400
+++ b/ensemble.xml	Tue Apr 10 15:18:51 2018 -0400
@@ -31,6 +31,21 @@
 
 algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
 options = params["selected_tasks"]["selected_algorithms"]["options"]
+if "select_max_features" in options:
+    if options["select_max_features"]["max_features"] == "number_input":
+        options["select_max_features"]["max_features"] = options["select_max_features"]["num_max_features"]
+        options["select_max_features"].pop("num_max_features")
+    options["max_features"] = options["select_max_features"]["max_features"]
+    options.pop("select_max_features")
+if "presort" in options:
+    if options["presort"] == "true":
+        options["presort"] = True
+    if options["presort"] == "false":
+        options["presort"] = False
+if "min_samples_leaf" in options and options["min_samples_leaf"] == 1.0:
+    options["min_samples_leaf"] = 1
+if "min_samples_split" in options and options["min_samples_split"] > 1.0:
+    options["min_samples_split"] = int(options["min_samples_split"])
 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
@@ -52,6 +67,7 @@
         header=header,
         parse_dates=True
 )
+y=y.ravel()
 
 my_class = getattr(sklearn.ensemble, algorithm)
 estimator = my_class(**options)
@@ -60,7 +76,8 @@
 
 #else:
 classifier_object = pickle.load(open("$selected_tasks.infile_model", 'r'))
-data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, 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)
 res = pandas.concat([data, prediction_df], axis=1)
@@ -75,8 +92,10 @@
             <param name="selected_algorithm" type="select" label="Select an ensemble method:">
                 <option value="RandomForestClassifier" selected="true">Random forest classifier</option>
                 <option value="AdaBoostClassifier">Ada boost classifier</option>
+                <option value="GradientBoostingClassifier">Gradient Boosting Classifier</option>
                 <option value="RandomForestRegressor">Random forest regressor</option>
                 <option value="AdaBoostRegressor">Ada boost regressor</option>
+                <option value="GradientBoostingRegressor">Gradient Boosting Regressor</option>
             </param>
             <when value="RandomForestClassifier">
                 <expand macro="sl_mixed_input"/>
@@ -91,6 +110,7 @@
                     <expand macro="max_leaf_nodes"/>
                     <expand macro="bootstrap"/>
                     <expand macro="warm_start" checked="false"/>
+                    <expand macro="n_jobs"/>
                     <expand macro="random_state"/>
                     <expand macro="oob_score"/>
                     <!--class_weight=None-->
@@ -109,20 +129,51 @@
                     <expand macro="random_state"/>
                 </section>
             </when>
+            <when value="GradientBoostingClassifier">
+                <expand macro="sl_mixed_input"/>
+                <section name="options" title="Advanced Options" expanded="False">
+                    <!--base_estimator=None-->
+                    <param argument="loss" type="select" label="Loss function">
+                        <option value="deviance" selected="true">deviance - logistic regression with probabilistic outputs</option>
+                        <option value="exponential">exponential - gradient boosting recovers the AdaBoost algorithm</option>
+                    </param>
+                    <expand macro="learning_rate" default_value='0.1'/>
+                    <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
+                    <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
+                    <expand macro="criterion2">
+                        <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
+                    </expand>
+                    <expand macro="min_samples_split" type="float"/>
+                    <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
+                    <expand macro="min_weight_fraction_leaf"/>
+                    <expand macro="subsample"/>
+                    <expand macro="max_features"/>
+                    <expand macro="max_leaf_nodes"/>
+                    <expand macro="min_impurity_decrease"/>
+                    <expand macro="verbose"/>
+                    <expand macro="warm_start" checked="false"/>
+                    <expand macro="random_state"/>
+                    <expand macro="presort"/>
+                </section>
+            </when>
             <when value="RandomForestRegressor">
                 <expand macro="sl_mixed_input"/>
                 <section name="options" title="Advanced Options" expanded="False">
                     <expand macro="n_estimators"/>
+                    <expand macro="criterion2"/>
                     <expand macro="max_features"/>
                     <expand macro="max_depth"/>
                     <expand macro="min_samples_split"/>
                     <expand macro="min_samples_leaf"/>
                     <expand macro="min_weight_fraction_leaf"/>
                     <expand macro="max_leaf_nodes"/>
+                    <expand macro="min_impurity_decrease"/>
                     <expand macro="bootstrap"/>
+                    <expand macro="oob_score"/>
+                    <expand macro="n_jobs"/>
+                    <expand macro="random_state"/>
+                    <expand macro="verbose"/>
                     <expand macro="warm_start" checked="false"/>
-                    <expand macro="random_state"/>
-                    <expand macro="oob_score"/>
                 </section>
             </when>
             <when value="AdaBoostRegressor">
@@ -139,6 +190,36 @@
                     <expand macro="random_state"/>
                 </section>
             </when>
+            <when value="GradientBoostingRegressor">
+                <expand macro="sl_mixed_input"/>
+                <section name="options" title="Advanced Options" expanded="False">
+                    <param argument="loss" type="select" label="Loss function">
+                        <option value="ls" selected="true">ls - least squares regression</option>
+                        <option value="lad">lad - least absolute deviation</option>
+                        <option value="huber">huber - combination of least squares regression and least absolute deviation</option>
+                        <option value="quantile">quantile - use alpha to specify the quantile</option>
+                    </param>
+                    <expand macro="learning_rate" default_value="0.1"/>
+                    <expand macro="n_estimators" default_value="100" help="The number of boosting stages to perform"/>
+                    <expand macro="max_depth" default_value="3" help="maximum depth of the individual regression estimators"/>
+                    <expand macro="criterion2">
+                        <option value="friedman_mse" selected="true">friedman_mse - mean squared error with improvement score by Friedman</option>
+                    </expand>
+                    <expand macro="min_samples_split" type="float"/>
+                    <expand macro="min_samples_leaf" type="float" label="The minimum number of samples required to be at a leaf node"/>
+                    <expand macro="min_weight_fraction_leaf"/>
+                    <expand macro="subsample"/>
+                    <expand macro="max_features"/>
+                    <expand macro="max_leaf_nodes"/>
+                    <expand macro="min_impurity_decrease"/>
+                    <param argument="alpha" type="float" value="0.9" label="alpha" help="The alpha-quantile of the huber loss function and the quantile loss function" />
+                    <!--base_estimator=None-->
+                    <expand macro="verbose"/>
+                    <expand macro="warm_start" checked="false"/>
+                    <expand macro="random_state"/>
+                    <expand macro="presort"/>
+                </section>
+            </when>
         </expand>
     </inputs>
 
@@ -161,7 +242,6 @@
             <param name="selected_task" value="load"/>
             <output name="outfile_predict" file="rfc_result01" compare="sim_size" delta="500"/>
         </test>
-
         <test>
             <param name="infile1" value="regression_train.tabular" ftype="tabular"/>
             <param name="infile2" value="regression_train.tabular" ftype="tabular"/>
@@ -178,6 +258,42 @@
             <param name="selected_task" value="load"/>
             <output name="outfile_predict" file="rfr_result01" compare="sim_size" delta="500"/>
         </test>
+        <test>
+            <param name="infile1" value="regression_X.tabular" ftype="tabular"/>
+            <param name="infile2" value="regression_y.tabular" ftype="tabular"/>
+            <param name="header1" value="True"/>
+            <param name="col1" value="1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17"/>
+            <param name="header2" value="True"/>
+            <param name="col2" value="1"/>
+            <param name="selected_task" value="train"/>
+            <param name="selected_algorithm" value="GradientBoostingRegressor"/>
+            <param name="max_features" value="number_input"/>
+            <param name="num_max_features" value=""/>
+            <param name="random_state" value="42"/>
+            <output name="outfile_fit" file="gbr_model01" compare="sim_size" delta="500"/>
+        </test>
+        <test>
+            <param name="infile_model" value="gbr_model01" ftype="zip"/>
+            <param name="infile_data" value="regression_test_X.tabular" ftype="tabular"/>
+            <param name="selected_task" value="load"/>
+            <param name="header" value="True"/>
+            <output name="outfile_predict" file="gbr_prediction_result01.tabular" compare="sim_size" delta="500"/>
+        </test>
+        <test>
+            <param name="infile1" value="train.tabular" ftype="tabular"/>
+            <param name="infile2" value="train.tabular" ftype="tabular"/>
+            <param name="col1" value="1,2,3,4"/>
+            <param name="col2" value="5"/>
+            <param name="selected_task" value="train"/>
+            <param name="selected_algorithm" value="GradientBoostingClassifier"/>
+            <output name="outfile_fit" file="gbc_model01" compare="sim_size" delta="500"/>
+        </test>
+        <test>
+            <param name="infile_model" value="gbc_model01" ftype="zip"/>
+            <param name="infile_data" value="test.tabular" ftype="tabular"/>
+            <param name="selected_task" value="load"/>
+            <output name="outfile_predict" file="gbc_result01" compare="sim_size" delta="500"/>
+        </test>
     </tests>
     <help><![CDATA[
 ***What it does***
--- a/main_macros.xml	Thu Mar 22 13:46:46 2018 -0400
+++ b/main_macros.xml	Tue Apr 10 15:18:51 2018 -0400
@@ -66,6 +66,7 @@
         <when value="load">
             <param name="infile_model" type="data" format="@MODEL@" label="Models" help="Select a model file."/>
             <param name="infile_data" type="data" format="@DATA@" label="Data (tabular)" help="Select the dataset you want to classify."/>
+            <param name="header" type="boolean" optional="True" truevalue="booltrue" falsevalue="boolfalse" checked="False" label="Does the dataset contain header:" />
             <conditional name="prediction_options">
                 <param name="prediction_option" type="select" label="Select the type of prediction">
                     <option value="predict">Predict class labels</option>
@@ -174,12 +175,12 @@
     <param argument="max_depth" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum depth of the tree" help="@HELP@"/>
   </xml>
 
-  <xml name="min_samples_split" token_default_value="2" token_help=" ">
-    <param argument="min_samples_split" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Minimum number of samples required to split an internal node" help="@HELP@"/>
+  <xml name="min_samples_split" token_type="integer" token_default_value="2" token_help=" ">
+    <param argument="min_samples_split" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="Minimum number of samples required to split an internal node" help="@HELP@"/>
   </xml>
 
-  <xml name="min_samples_leaf" token_default_value="1" token_help=" ">
-    <param argument="min_samples_leaf" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Minimum number of samples in newly created leaves" help="@HELP@"/>
+  <xml name="min_samples_leaf" token_type="integer" token_default_value="1" token_label="Minimum number of samples in newly created leaves" token_help=" ">
+    <param argument="min_samples_leaf" type="@TYPE@" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP@"/>
   </xml>
 
   <xml name="min_weight_fraction_leaf" token_default_value="0.0" token_help=" ">
@@ -190,6 +191,10 @@
     <param argument="max_leaf_nodes" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Maximum number of leaf nodes in best-first method" help="@HELP@"/>
   </xml>
 
+  <xml name="min_impurity_decrease" token_default_value="0" token_help=" ">
+    <param argument="min_impurity_decrease" type="float" value="@DEFAULT_VALUE@" optional="true" label="The threshold value of impurity for stopping node splitting" help="@HELP@"/>
+  </xml>
+
   <xml name="bootstrap" token_checked="true" token_help=" ">
     <param argument="bootstrap" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="@CHECKED@" label="Use bootstrap samples for building trees." help="@HELP@"/>
   </xml>
@@ -202,18 +207,57 @@
     </param>
   </xml>
 
+  <xml name="criterion2" token_help="">
+    <param argument="criterion" type="select" label="Function to measure the quality of a split" >
+      <option value="mse">mse - mean squared error</option>
+      <option value="mae">mae - mean absolute error</option>
+      <yield/>
+    </param>
+  </xml>
+
   <xml name="oob_score" token_checked="false" token_help=" ">
     <param argument="oob_score" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use out-of-bag samples to estimate the generalization error" help="@HELP@"/>
   </xml>
 
-  <xml name="max_features" token_default_value="auto" token_help="This could be an integer, float, string, or None. For more information please refer to help. ">
-    <param argument="max_features" type="text" optional="true" value="@DEFAULT_VALUE@" label="Number of features for finding the best split" help="@HELP@"/>
+  <xml name="max_features">
+    <conditional name="select_max_features">
+      <param argument="max_features" type="select" label="max_features">
+        <option value="auto" selected="true">auto - max_features=n_features</option>
+        <option value="sqrt">sqrt - max_features=sqrt(n_features)</option>
+        <option value="log2">log2 - max_features=log2(n_features)</option>
+        <option value="number_input">I want to type the number in or input None type</option>
+      </param>
+      <when value="auto">
+      </when>
+      <when value="sqrt">
+      </when>
+      <when value="log2">
+      </when>
+      <when value="number_input">
+        <param name="num_max_features" type="float" value="" optional="true" label="Input max_features number:" help="If int, consider the number of features at each split; If float, then max_features is a percentage and int(max_features * n_features) features are considered at each split."/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="verbose" token_default_value="0" token_help="If 1 then it prints progress and performance once in a while. If greater than 1 then it prints progress and performance for every tree.">
+    <param argument="verbose" type="integer" value="@DEFAULT_VALUE@" optional="true" label="Enable verbose output" help="@HELP@"/>
   </xml>
 
   <xml name="learning_rate" token_default_value="1.0" token_help=" ">
     <param argument="learning_rate" type="float" optional="true" value="@DEFAULT_VALUE@" label="Learning rate" help="@HELP@"/>
   </xml>
 
+  <xml name="subsample" token_help=" ">
+    <param argument="subsample" type="float" value="1.0" optional="true" label="The fraction of samples to be used for fitting the individual base learners" help="@HELP@"/>
+  </xml>
+
+  <xml name="presort">
+    <param argument="presort" type="select" label="Whether to presort the data to speed up the finding of best splits in fitting" >
+      <option value="auto" selected="true">auto</option>
+      <option value="true">true</option>
+      <option value="false">false</option>
+    </param>
+  </xml>
 
   <!--Parameters-->
   <xml name="tol" token_default_value="0.0" token_help_text="Early stopping heuristics based on the relative center changes. Set to default (0.0) to disable this convergence detection.">
@@ -228,6 +272,10 @@
     <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Estimate the intercept" help="If false, the data is assumed to be already centered."/>
   </xml>
 
+  <xml name="n_jobs" token_default_value="1" token_label="The number of jobs to run in parallel for both fit and predict">
+    <param argument="n_jobs" type="integer" value="@DEFAULT_VALUE@" optional="true" label="@LABEL@" help="If -1, then the number of jobs is set to the number of cores"/>
+  </xml>
+
   <xml name="n_iter" token_default_value="5" token_help_text="The number of passes over the training data (aka epochs). ">
     <param argument="n_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of iterations" help="@HELP_TEXT@"/>
   </xml>
Binary file test-data/gbc_model01 has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbc_result01	Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,6 @@
+0	1	2	3	0
+3.68258022948	2.82110345641	-3.990140724	-1.9523364774	1
+0.015942057224	-0.711958594347	0.125502976978	-0.972218263337	0
+2.08690768825	0.929399321468	-2.12924084484	-1.99714022188	1
+1.41321052084	0.523750660422	-1.4210539291	-1.49298569451	1
+0.76831404394	1.38267855169	-0.989045048734	0.649504257894	1
Binary file test-data/gbr_model01 has changed
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/gbr_prediction_result01.tabular	Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,88 @@
+year	month	day	temp_2	temp_1	average	forecast_noaa	forecast_acc	forecast_under	friend	week_Fri	week_Mon	week_Sat	week_Sun	week_Thurs	week_Tues	week_Wed	0
+2016	9	29	69	68	66.1	63	71	68	57	0	0	0	0	1	0	0	69.8047715468
+2016	4	27	59	60	60.7	59	65	60	50	0	0	0	0	0	0	1	62.3940847433
+2016	11	28	53	48	48.0	46	48	49	44	0	1	0	0	0	0	0	51.1656331745
+2016	10	12	60	62	61.0	60	63	63	52	0	0	0	0	0	0	1	60.7602326565
+2016	6	19	67	65	70.4	69	73	70	58	0	0	0	1	0	0	0	66.2416657667
+2016	5	7	68	77	63.0	61	65	63	83	0	0	1	0	0	0	0	71.7162060939
+2016	7	25	75	80	77.1	75	82	76	81	0	1	0	0	0	0	0	78.6168727393
+2016	8	15	90	83	76.6	76	79	75	70	0	1	0	0	0	0	0	77.9015583717
+2016	10	28	58	60	55.6	52	56	55	52	1	0	0	0	0	0	0	61.4191796096
+2016	6	5	80	81	68.0	64	70	66	54	0	0	0	1	0	0	0	74.4136969328
+2016	3	19	58	63	54.2	54	59	54	62	0	0	1	0	0	0	0	60.9589968112
+2016	6	7	92	86	68.3	67	69	70	58	0	0	0	0	0	1	0	75.5031094008
+2016	12	10	41	36	45.9	44	48	44	65	0	0	1	0	0	0	0	38.5555100028
+2016	4	23	73	64	59.9	56	63	59	57	0	0	1	0	0	0	0	64.0035135524
+2016	6	24	75	68	71.5	67	73	73	65	1	0	0	0	0	0	0	74.5305649268
+2016	2	9	51	57	49.4	45	52	49	57	0	0	0	0	0	1	0	57.0110982119
+2016	11	10	71	65	52.2	52	54	51	38	0	0	0	0	1	0	0	61.876179905
+2016	3	21	61	55	54.5	52	56	55	52	0	1	0	0	0	0	0	56.0732986026
+2016	2	28	60	57	51.3	48	56	53	66	0	0	0	1	0	0	0	56.9672058242
+2016	6	28	78	85	72.4	72	76	74	67	0	0	0	0	0	1	0	78.4438620045
+2016	10	6	63	66	63.3	62	67	63	55	0	0	0	0	1	0	0	63.9639842609
+2016	2	17	55	56	50.0	45	51	49	46	0	0	0	0	0	0	1	54.149464399
+2016	6	15	66	60	69.7	65	73	71	69	0	0	0	0	0	0	1	66.1043951877
+2016	10	15	60	60	59.9	59	62	59	46	0	0	1	0	0	0	0	61.6791270097
+2016	3	26	54	57	55.2	53	57	55	54	0	0	1	0	0	0	0	60.2367595132
+2016	1	26	51	54	48.3	44	53	50	61	0	0	0	0	0	1	0	52.9547372573
+2016	5	23	59	66	66.1	63	68	68	66	0	1	0	0	0	0	0	64.6813560623
+2016	1	10	48	50	46.5	45	48	48	49	0	0	0	1	0	0	0	45.1415524342
+2016	5	22	66	59	65.9	62	66	65	80	0	0	0	1	0	0	0	59.8874932366
+2016	7	15	75	77	76.0	74	80	78	75	1	0	0	0	0	0	0	82.9044308458
+2016	4	22	81	73	59.7	59	64	60	59	1	0	0	0	0	0	0	74.8537745899
+2016	4	29	61	64	61.2	61	65	61	49	1	0	0	0	0	0	0	65.3872817114
+2016	1	23	52	57	48.0	45	49	50	37	0	0	1	0	0	0	0	51.8565179701
+2016	8	16	83	84	76.5	72	78	78	90	0	0	0	0	0	1	0	83.6982049493
+2016	8	1	76	73	77.4	76	78	79	65	0	1	0	0	0	0	0	72.4140203449
+2016	2	27	61	60	51.2	51	53	53	61	0	0	1	0	0	0	0	60.839700499
+2016	2	12	56	55	49.6	49	52	48	33	1	0	0	0	0	0	0	54.9702164699
+2016	1	31	52	48	48.7	47	52	49	61	0	0	0	1	0	0	0	49.8435633428
+2016	9	5	67	68	73.5	71	75	73	54	0	1	0	0	0	0	0	69.325684558
+2016	12	20	39	46	45.1	45	49	45	62	0	0	0	0	0	1	0	43.4575487159
+2016	5	1	61	68	61.6	60	65	60	75	0	0	0	1	0	0	0	65.0535826144
+2016	3	28	59	51	55.5	55	57	55	47	0	1	0	0	0	0	0	57.5541221212
+2016	4	21	81	81	59.4	55	61	59	55	0	0	0	0	1	0	0	76.9948007001
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_X.tabular	Tue Apr 10 15:18:51 2018 -0400
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_test_X.tabular	Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,88 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/regression_y.tabular	Tue Apr 10 15:18:51 2018 -0400
@@ -0,0 +1,262 @@
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