changeset 0:9a9396e5d153 draft

planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
author bgruening
date Fri, 16 Feb 2018 09:16:30 -0500
parents
children be9b169a1e05
files README.rst main_macros.xml svm.xml test-data/RF01704.fasta test-data/accuracy_score.txt test-data/auc.txt test-data/average_precision_score.txt test-data/blobs.txt test-data/brier_score_loss.txt test-data/circles.txt test-data/class.txt test-data/classification_report.txt test-data/cluster_result01.txt test-data/cluster_result02.txt test-data/cluster_result03.txt test-data/cluster_result04.txt test-data/cluster_result05.txt test-data/cluster_result06.txt test-data/cluster_result07.txt test-data/cluster_result08.txt test-data/cluster_result09.txt test-data/cluster_result10.txt test-data/cluster_result11.txt test-data/cluster_result12 test-data/cluster_result12.txt test-data/cluster_result13.txt test-data/cluster_result14.txt test-data/cluster_result15.txt test-data/cluster_result16.txt test-data/cluster_result17.txt test-data/cluster_result18.txt test-data/cluster_result19.txt test-data/cluster_result20.txt test-data/cluster_result21.txt test-data/confusion_matrix.txt test-data/converter_result01.json test-data/converter_result02.json test-data/csc_sparse1.mtx test-data/csc_sparse2.mtx test-data/csc_stack_result01.mtx test-data/csr_sparse1.mtx test-data/csr_sparse2.mtx test-data/csr_stack_result01.mtx test-data/empty_file.txt test-data/f1_score.txt test-data/fbeta_score.txt test-data/friedman1.txt test-data/friedman2.txt test-data/friedman3.txt test-data/gaus.txt test-data/glm_model01 test-data/glm_model02 test-data/glm_model03 test-data/glm_model04 test-data/glm_model05 test-data/glm_model06 test-data/glm_model07 test-data/glm_model08 test-data/glm_result01 test-data/glm_result02 test-data/glm_result03 test-data/glm_result04 test-data/glm_result05 test-data/glm_result06 test-data/glm_result07 test-data/glm_result08 test-data/hamming_loss.txt test-data/hastie.txt test-data/hinge_loss.txt test-data/jaccard_similarity_score.txt test-data/lda_model01 test-data/lda_model02 test-data/lda_prediction_result01.tabular test-data/lda_prediction_result02.tabular test-data/log_loss.txt test-data/matthews_corrcoef.txt test-data/moons.txt test-data/nn_model01.txt test-data/nn_model02.txt test-data/nn_model03.txt test-data/nn_prediction_result01.tabular test-data/nn_prediction_result02.tabular test-data/nn_prediction_result03.tabular test-data/numeric_values.tabular test-data/precision_recall_curve.txt test-data/precision_recall_fscore_support.txt test-data/precision_score.txt test-data/prp_model01 test-data/prp_model02 test-data/prp_model03 test-data/prp_model04 test-data/prp_model05 test-data/prp_model06 test-data/prp_model07 test-data/prp_model08 test-data/prp_model09 test-data/prp_result01 test-data/prp_result02 test-data/prp_result03 test-data/prp_result04 test-data/prp_result05 test-data/prp_result06 test-data/prp_result07 test-data/prp_result08 test-data/prp_result09 test-data/pw_metric01.tabular test-data/pw_metric02.tabular test-data/pw_metric03.tabular test-data/qda_model01 test-data/qda_prediction_result01.tabular test-data/recall_score.txt test-data/regression.txt test-data/regression_test.tabular test-data/regression_train.tabular test-data/rfc_model01 test-data/rfc_result01 test-data/rfc_result02 test-data/rfr_model01 test-data/rfr_result01 test-data/roc_auc_score.txt test-data/roc_curve.txt test-data/scurve.txt test-data/sparse.mtx test-data/sparse_u.txt test-data/svc_model01.txt test-data/svc_model02.txt test-data/svc_model03.txt test-data/svc_prediction_result01.tabular test-data/svc_prediction_result02.tabular test-data/svc_prediction_result03.tabular test-data/swiss_r.txt test-data/test.tabular test-data/test2.tabular test-data/test_set.tabular test-data/train.tabular test-data/train_set.tabular test-data/vectorizer_result01.mtx test-data/vectorizer_result02.mtx test-data/vectorizer_result03.mtx test-data/vectorizer_result04.mtx test-data/y.tabular test-data/zero_one_loss.txt
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/README.rst	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,150 @@
+***************
+Galaxy wrapper for scikit-learn library
+***************
+
+Contents
+========
+- `What is scikit-learn?`_
+	- `Scikit-learn main package groups`_
+	- `Tools offered by this wrapper`_
+
+- `Machine learning workflows`_
+	- `Supervised learning workflows`_
+	- `Unsupervised learning workflows`_
+
+
+____________________________
+
+
+.. _What is scikit-learn?
+
+What is scikit-learn?
+===========================
+
+Scikit-learn is an open-source machine learning library for the Python programming language. It offers various algorithms for performing supervised and unsupervised learning as well as data preprocessing and transformation, model selection and evaluation, and dataset utilities. It is built upon SciPy (Scientific Python) library.
+
+Scikit-learn source code can be accessed at https://github.com/scikit-learn/scikit-learn.
+Detailed installation instructions can be found at http://scikit-learn.org/stable/install.html
+
+
+.. _Scikit-learn main package groups:
+
+======
+Scikit-learn main package groups
+======
+
+Scikit-learn provides the users with several main groups of related operations.
+These are:
+
+- Classification
+    - Identifying to which category an object belongs.
+- Regression
+    - Predicting a continuous-valued attribute associated with an object.
+- Clustering
+    - Automatic grouping of similar objects into sets.
+- Preprocessing
+    - Feature extraction and normalization.
+- Model selection and evaluation
+    - Comparing, validating and choosing parameters and models.
+- Dimensionality reduction
+    - Reducing the number of random variables to consider.
+
+Each group consists of a number of well-known algorithms from the category. For example, one can find hierarchical, spectral, kmeans, and other clustering methods in sklearn.cluster package.
+
+
+.. _Tools offered by this wrapper:
+
+===================
+Available tools in the current wrapper
+===================
+
+The current release of the wrapper offers a subset of the packages from scikit-learn library. You can find:
+
+- A subset of classification metric functions
+- Linear and quadratic discriminant classifiers
+- Random forest and Ada boost classifiers and regressors
+- All the clustering methods
+- All support vector machine classifiers
+- A subset of data preprocessing estimator classes
+- Pairwise metric measurement functions
+
+In addition, several tools for performing matrix operations, generating problem-specific datasets, and encoding text and extracting features have been prepared to help the user with more advanced operations.
+
+.. _Machine learning workflows:
+
+Machine learning workflows
+===============
+
+Machine learning is about processes. No matter what machine learning algorithm we use, we can apply typical workflows and dataflows to produce more robust models and better predictions.
+Here we discuss supervised and unsupervised learning workflows.
+
+.. _Supervised learning workflows:
+
+===================
+Supervised machine learning workflows
+===================
+
+**What is supervised learning?**
+
+In this machine learning task, given sample data which are labeled, the aim is to build a model which can predict the labels for new observations.
+In practice, there are five steps which we can go through to start from raw input data and end up getting reasonable predictions for new samples:
+
+1. Preprocess the data::
+
+    * Change the collected data into the proper format and datatype.
+    * Adjust the data quality by filling the missing values, performing
+    required scaling and normalizations, etc.
+    * Extract features which are the most meaningfull for the learning task.
+    * Split the ready dataset into training and test samples.
+
+2. Choose an algorithm::
+
+    * These factors help one to choose a learning algorithm:
+        - Nature of the data (e.g. linear vs. nonlinear data)
+        - Structure of the predicted output (e.g. binary vs. multilabel classification)
+        - Memory and time usage of the training
+        - Predictive accuracy on new data
+        - Interpretability of the predictions
+
+3. Choose a validation method
+
+	Every machine learning model should be evaluated before being put into practicical use.
+	There are numerous performance metrics to evaluate machine learning models.
+	For supervised learning, usually classification or regression metrics are used.
+
+	A validation method helps to evaluate the performance metrics of a trained model in order
+	to optimize its performance or ultimately switch to a more efficient model.
+	Cross-validation is a known validation method.
+
+4. Fit a model
+
+   Given the learning algorithm, validation method, and performance metric(s)
+   repeat the following steps::
+
+    * Train the model.
+    * Evaluate based on metrics.
+    * Optimize unitl satisfied.
+
+5. Use fitted model for prediction::
+
+	This is a final evaluation in which, the optimized model is used to make predictions
+	on unseen (here test) samples. After this, the model is put into production.
+
+.. _Unsupervised learning workflows:
+
+=======================
+Unsupervised machine learning workflows
+=======================
+
+**What is unsupervised learning?**
+
+Unlike supervised learning and more liklely in real life, here the initial data is not labeled.
+The task is to extract the structure from the data and group the samples based on their similarities.
+Clustering and dimensionality reduction are two famous examples of unsupervised learning tasks.
+
+In this case, the workflow is as follows::
+
+    * Preprocess the data (without splitting to train and test).
+    * Train a model.
+    * Evaluate and tune parameters.
+    * Analyse the model and test on real data.
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/main_macros.xml	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,807 @@
+<macros>
+  <token name="@VERSION@">0.9</token>
+
+  <token name="@COLUMNS_FUNCTION@">
+def columns(f,c):
+  data = pandas.read_csv(f, sep='\t', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)
+  cols = c.split (',')
+  cols = map(int, cols)
+  cols = list(map(lambda x: x - 1, cols))
+  y = data.iloc[:,cols].values
+  return y
+  </token>
+
+  <xml name="python_requirements">
+      <requirements>
+          <requirement type="package" version="2.7">python</requirement>
+          <requirement type="package" version="0.19.1">scikit-learn</requirement>
+          <requirement type="package" version="0.22.0">pandas</requirement>
+          <yield />
+      </requirements>
+  </xml>
+
+  <xml name="macro_stdio">
+    <stdio>
+        <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>
+    </stdio>
+  </xml>
+
+
+  <!--Generic interface-->
+  <xml name="train_loadConditional" token_train="tabular" token_data="tabular" token_model="txt">
+    <conditional name="selected_tasks">
+        <param name="selected_task" type="select" label="Select a Classification Task">
+            <option value="train" selected="true">Train a model</option>
+            <option value="load">Load a model and predict</option>
+        </param>
+        <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."/>
+            <conditional name="prediction_options">
+                <param name="prediction_option" type="select" label="Select the type of prediction">
+                    <option value="predict">Predict class labels</option>
+                    <option value="advanced">Include advanced options</option>
+                </param>
+                <when value="predict">
+                </when>
+                <when value="advanced">
+                </when>
+            </conditional>
+        </when>
+        <when value="train">
+            <param name="infile_train" type="data" format="@TRAIN@" label="Training samples (tabular)"/>
+            <conditional name="selected_algorithms">
+                <yield />
+            </conditional>
+        </when>
+    </conditional>
+  </xml>
+
+  <xml name="sl_Conditional" token_train="tabular" token_data="tabular" token_model="txt">
+    <conditional name="selected_tasks">
+        <param name="selected_task" type="select" label="Select a Classification Task">
+            <option value="train" selected="true">Train a model</option>
+            <option value="load">Load a model and predict</option>
+        </param>
+        <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."/>
+            <conditional name="prediction_options">
+                <param name="prediction_option" type="select" label="Select the type of prediction">
+                    <option value="predict">Predict class labels</option>
+                    <option value="advanced">Include advanced options</option>
+                </param>
+                <when value="predict">
+                </when>
+                <when value="advanced">
+                </when>
+            </conditional>
+        </when>
+        <when value="train">
+            <conditional name="selected_algorithms">
+                <yield />
+            </conditional>
+        </when>
+    </conditional>
+  </xml>
+
+  <xml name="advanced_section">
+    <section name="options" title="Advanced Options" expanded="False">
+      <yield />
+    </section>
+  </xml>
+
+
+  <!--Generalized Linear Models-->
+  <xml name="loss" token_help=" " token_select="false">
+    <param argument="loss" type="select" label="Loss function"  help="@HELP@">
+        <option value="squared_loss" selected="@SELECT@">squared loss</option>
+        <option value="huber">huber</option>
+        <option value="epsilon_insensitive">epsilon insensitive</option>
+        <option value="squared_epsilon_insensitive">squared epsilon insensitive</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="penalty" token_help=" ">
+    <param argument="penalty" type="select" label="Penalty (regularization term)"  help="@HELP@">
+        <option value="l2" selected="true">l2</option>
+        <option value="l1">l1</option>
+        <option value="elasticnet">elastic net</option>
+        <option value="none">none</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="l1_ratio" token_default_value="0.15" token_help=" ">
+    <param argument="l1_ratio" type="float" value="@DEFAULT_VALUE@" label="Elastic Net mixing parameter" help="@HELP@"/>
+  </xml>
+
+  <xml name="epsilon" token_default_value="0.1" token_help="Used if loss is ‘huber’, ‘epsilon_insensitive’, or ‘squared_epsilon_insensitive’. ">
+    <param argument="epsilon" type="float" value="@DEFAULT_VALUE@" label="Epsilon (epsilon-sensitive loss functions only)" help="@HELP@"/>
+  </xml>
+
+  <xml name="learning_rate_s" token_help=" " token_selected1="false" token_selected2="false">
+    <param argument="learning_rate" type="select" optional="true" label="Learning rate schedule"  help="@HELP@">
+        <option value="optimal" selected="@SELECTED1@">optimal</option>
+        <option value="constant">constant</option>
+        <option value="invscaling" selected="@SELECTED2@">inverse scaling</option>
+        <yield/>
+    </param>
+  </xml>
+
+  <xml name="eta0" token_default_value="0.0" token_help="Used with ‘constant’ or ‘invscaling’ schedules. ">
+    <param argument="eta0" type="float" value="@DEFAULT_VALUE@" label="Initial learning rate" help="@HELP@"/>
+  </xml>
+
+  <xml name="power_t" token_default_value="0.5" token_help=" ">
+    <param argument="power_t" type="float" value="@DEFAULT_VALUE@" label="Exponent for inverse scaling learning rate" help="@HELP@"/>
+  </xml>
+
+  <xml name="normalize" token_checked="false" token_help=" ">
+    <param argument="normalize" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Normalize samples before training" help=" "/>
+  </xml>
+
+  <xml name="copy_X" token_checked="true" token_help=" ">
+    <param argument="copy_X" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Use a copy of samples" help="If false, samples would be overwritten. "/>
+  </xml>
+
+  <xml name="ridge_params">
+    <expand macro="normalize"/>
+    <expand macro="alpha" default_value="1.0"/>
+    <expand macro="fit_intercept"/>
+    <expand macro="max_iter" default_value=""/>
+    <expand macro="tol" default_value="0.001" help_text="Precision of the solution. "/>
+    <!--class_weight-->
+    <expand macro="copy_X"/>
+    <param argument="solver" type="select" value="" label="Solver to use in the computational routines" help=" ">
+        <option value="auto" selected="true">auto</option>
+        <option value="svd">svd</option>
+        <option value="cholesky">cholesky</option>
+        <option value="lsqr">lsqr</option>
+        <option value="sparse_cg">sparse_cg</option>
+        <option value="sag">sag</option>
+    </param>
+    <expand macro="random_state"/>
+  </xml>
+
+  <!--Ensemble methods-->
+  <xml name="n_estimators" token_default_value="10" token_help=" ">
+    <param argument="n_estimators" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of trees in the forest" help="@HELP@"/>
+  </xml>
+
+  <xml name="max_depth" token_default_value="" token_help=" ">
+    <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="Maximum depth of the tree" 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>
+
+  <xml name="min_weight_fraction_leaf" token_default_value="0.0" token_help=" ">
+    <param argument="min_weight_fraction_leaf" type="float" optional="true" value="@DEFAULT_VALUE@" label="Minimum weighted fraction of the input samples required to be at a leaf node" help="@HELP@"/>
+  </xml>
+
+  <xml name="max_leaf_nodes" token_default_value="" token_help=" ">
+    <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="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>
+
+  <xml name="criterion" token_help=" ">
+    <param argument="criterion" type="select" label="Function to measure the quality of a split"  help=" ">
+        <option value="gini" selected="true">Gini impurity</option>
+        <option value="entropy">Information gain</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>
+
+  <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>
+
+
+  <!--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.">
+        <param argument="tol" type="float" optional="true" value="@DEFAULT_VALUE@" label="Tolerance" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_clusters" token_default_value="8">
+    <param argument="n_clusters" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of clusters" help=" "/>
+  </xml>
+
+  <xml name="fit_intercept" token_checked="true">
+    <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_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>
+
+  <xml name="shuffle" token_checked="true" token_help_text=" " token_label="Shuffle data after each iteration">
+    <param argument="shuffle" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="random_state" token_default_value="" token_help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data. A fixed seed allows reproducible results.">
+    <param argument="random_state" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Random seed number" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="warm_start" token_checked="true" token_help_text="When set to True, reuse the solution of the previous call to fit as initialization,otherwise, just erase the previous solution.">
+    <param argument="warm_start" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="@CHECKED@" label="Perform warm start" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term.">
+    <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
+  </xml>
+
+  <!--xml name="class_weight" token_default_value="" token_help_text="">
+    <param argument="class_weight" type="" optional="true" value="@DEFAULT_VALUE@" label="" help="@HELP_TEXT@"/>
+  </xml-->
+
+  <xml name="alpha" token_default_value="0.0001" token_help_text="Constant that multiplies the regularization term if regularization is used. ">
+    <param argument="alpha" type="float" optional="true" value="@DEFAULT_VALUE@" label="Regularization coefficient" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_samples" token_default_value="100" token_help_text="The total number of points equally divided among clusters.">
+    <param argument="n_samples" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of samples" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_features" token_default_value="2" token_help_text="Number of different numerical properties produced for each sample.">
+    <param argument="n_features" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of features" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="noise" token_default_value="0.0" token_help_text="Floating point number. ">
+    <param argument="noise" type="float" optional="true" value="@DEFAULT_VALUE@" label="Standard deviation of the Gaussian noise added to the data" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="C" token_default_value="1.0" token_help_text="Penalty parameter C of the error term. ">
+      <param argument="C" type="float" optional="true" value="@DEFAULT_VALUE@" label="Penalty parameter" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="max_iter" token_default_value="300" token_label="Maximum number of iterations per single run" token_help_text=" ">
+      <param argument="max_iter" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="n_init" token_default_value="10" >
+      <param argument="n_init" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Number of runs with different centroid seeds" help=" "/>
+  </xml>
+
+  <xml name="init">
+      <param argument="init" type="select" label="Centroid initialization method"  help="''k-means++'' selects initial cluster centers that speed up convergence. ''random'' chooses k observations (rows) at random from data as initial centroids.">
+          <option value="k-means++">k-means++</option>
+          <option value="random">random</option>
+      </param>
+  </xml>
+
+  <xml name="gamma" token_default_value="1.0" token_label="Scaling parameter" token_help_text=" ">
+    <param argument="gamma" type="float" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="degree" token_default_value="3" token_label="Degree of the polynomial" token_help_text=" ">
+    <param argument="degree" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="coef0" token_default_value="1" token_label="Zero coefficient" token_help_text=" ">
+    <param argument="coef0" type="integer" optional="true" value="@DEFAULT_VALUE@" label="@LABEL@" help="@HELP_TEXT@"/>
+  </xml>
+
+  <xml name="pos_label" token_default_value="">
+    <param argument="pos_label" type="integer" optional="true" value="@DEFAULT_VALUE@" label="Label of the positive class" help=" "/>
+  </xml>
+
+  <xml name="average">
+    <param argument="average" type="select" optional="true" label="Averaging type" help=" ">
+      <option value="micro">Calculate metrics globally by counting the total true positives, false negatives and false positives. (micro)</option>
+      <option value="samples">Calculate metrics for each instance, and find their average. Only meaningful for multilabel. (samples)</option>
+      <option value="macro">Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account. (macro)</option>
+      <option value="weighted">Calculate metrics for each label, and find their average, weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall. (weighted)</option>
+      <option value="None">None</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="beta">
+    <param argument="beta" type="float" value="1.0" label="The strength of recall versus precision in the F-score" help=" "/>
+  </xml>
+
+
+  <!--Data interface-->
+  <xml name="tabular_input">
+    <param name="infile" type="data" format="tabular" label="Data file with numeric values"/>
+    <param name="start_column" type="data_column" data_ref="infile" optional="True" label="Select a subset of data. Start column:" />
+    <param name="end_column" type="data_column" data_ref="infile" optional="True" label="End column:" />
+  </xml>
+
+  <xml name="sample_cols" token_label1="File containing true class labels:" token_label2="File containing predicted class labels:" token_multiple1="False" token_multiple2="False" token_format1="tabular" token_format2="tabular" token_help1="" token_help2="">
+    <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@"/>
+    <param name="col1" multiple="@MULTIPLE1@" type="data_column" data_ref="infile1" label="Select target column(s):"/>
+    <param name="infile2" type="data" format="@FORMAT2@" label="@LABEL2@" help="@HELP2@"/>
+    <param name="col2" multiple="@MULTIPLE2@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+    <yield/>
+  </xml>
+
+  <xml name="samples_tabular" token_multiple1="False" token_multiple2="False">
+    <param name="infile1" type="data" format="tabular" label="Training samples dataset:"/>
+    <param name="col1" multiple="@MULTIPLE1@" type="data_column" data_ref="infile1" label="Select target column(s):"/>
+    <param name="infile2" type="data" format="tabular" label="Dataset containing class labels:"/>
+    <param name="col2" multiple="@MULTIPLE2@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+    <yield/>
+  </xml>
+
+  <xml name="clf_inputs_extended" token_label1=" " token_label2=" " token_multiple="False">
+    <conditional name="true_columns">
+      <param name="selected_input1" type="select" label="Select the input type of true labels dataset:">
+          <option value="tabular" selected="true">Tabular</option>
+          <option value="sparse">Sparse</option>
+      </param>
+      <when value="tabular">
+        <param name="infile1" type="data" label="@LABEL1@"/>
+        <param name="col1" type="data_column" data_ref="infile1" label="Select the target column:"/>
+      </when>
+      <when value="sparse">
+          <param name="infile1" type="data" format="txt" label="@LABEL1@"/>
+      </when>
+    </conditional>
+    <conditional name="predicted_columns">
+      <param name="selected_input2" type="select" label="Select the input type of predicted labels dataset:">
+          <option value="tabular" selected="true">Tabular</option>
+          <option value="sparse">Sparse</option>
+      </param>
+      <when value="tabular">
+        <param name="infile2" type="data" label="@LABEL2@"/>
+        <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+      </when>
+      <when value="sparse">
+          <param name="infile2" type="data" format="txt" label="@LABEL1@"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="clf_inputs" token_label1="Dataset containing true labels (tabular):" token_label2="Dataset containing predicted values (tabular):" token_multiple1="False" token_multiple="False">
+    <param name="infile1" type="data" format="tabular" label="@LABEL1@"/>
+    <param name="col1" multiple="@MULTIPLE1@" type="data_column" data_ref="infile1" label="Select the target column:"/>
+    <param name="infile2" type="data" format="tabular" label="@LABEL2@"/>
+    <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+  </xml>
+
+  <xml name="multiple_input" token_name="input_files" token_max_num="10" token_format="txt" token_label="Sparse matrix file (.mtx, .txt)" token_help_text="Specify a sparse matrix file in .txt format.">
+    <repeat name="@NAME@" min="1" max="@MAX_NUM@" title="Select input file(s):">
+        <param name="input" type="data" format="@FORMAT@" label="@LABEL@" help="@HELP_TEXT@"/>
+    </repeat>
+  </xml>
+
+  <xml name="sparse_target" token_label1="Select a sparse matrix:" token_label2="Select the tabular containing true labels:" token_multiple="False" token_format1="txt" token_format2="tabular" token_help1="" token_help2="">
+    <param name="infile1" type="data" format="@FORMAT1@" label="@LABEL1@" help="@HELP1@"/>
+    <param name="infile2" type="data" format="@FORMAT2@" label="@LABEL2@" help="@HELP2@"/>
+    <param name="col2" multiple="@MULTIPLE@" type="data_column" data_ref="infile2" label="Select target column(s):"/>
+  </xml>
+
+  <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>
+      </param>
+      <when value="tabular">
+          <expand macro="samples_tabular" multiple1="true"/>
+      </when>
+      <when value="sparse">
+          <expand macro="sparse_target"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="multitype_input" token_format="tabular" token_help="All datasets with tabular format are supporetd.">
+    <param name="infile_transform" type="data" format="@FORMAT@" label="Select a dataset to transform:" help="@HELP@"/>
+  </xml>
+
+
+  <!--Advanced options-->
+  <xml name="nn_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <yield/>
+      <param argument="weights" type="select" label="Weight function" help="Used in prediction.">
+          <option value="uniform" selected="true">Uniform weights. All points in each neighborhood are weighted equally. (Uniform)</option>
+          <option value="distance">Weight points by the inverse of their distance. (Distance)</option>
+      </param>
+      <param argument="algorithm" type="select" label="Neighbor selection algorithm" help=" ">
+          <option value="auto" selected="true">Auto</option>
+          <option value="ball_tree">BallTree</option>
+          <option value="kd_tree">KDTree</option>
+          <option value="brute">Brute-force</option>
+      </param>
+      <param argument="leaf_size" type="integer" value="30" label="Leaf size" help="Used with BallTree and KDTree. Affects the time and memory usage of the constructed tree."/>
+      <!--param name="metric"-->
+      <!--param name="p"-->
+      <!--param name="metric_params"-->
+    </section>
+  </xml>
+
+  <xml name="svc_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <yield/>
+        <param argument="kernel" type="select" optional="true" label="Kernel type" help="Kernel type to be used in the algorithm. If none is given, ‘rbf’ will be used.">
+            <option value="rbf" selected="true">rbf</option>
+            <option value="linear">linear</option>
+            <option value="poly">poly</option>
+            <option value="sigmoid">sigmoid</option>
+            <option value="precomputed">precomputed</option>
+        </param>
+        <param argument="degree" type="integer" optional="true" value="3" label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
+        <!--TODO: param argument="gamma" float, optional (default=’auto’) -->
+        <param argument="coef0" type="float" optional="true" value="0.0" label="Zero coefficient (polynomial and sigmoid kernels only)"
+            help="Independent term in kernel function. dafault: 0.0 "/>
+        <param argument="shrinking" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use the shrinking heuristic" help=" "/>
+        <param argument="probability" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
+            label="Enable probability estimates. " help="This must be enabled prior to calling fit, and will slow down that method."/>
+        <!-- param argument="cache_size"-->
+        <!--expand macro="class_weight"/-->
+        <expand macro="tol" default_value="0.001" help_text="Tolerance for stopping criterion. "/>
+        <expand macro="max_iter" default_value="-1" label="Solver maximum number of iterations" help_text="Hard limit on iterations within solver, or -1 for no limit."/>
+        <!--param argument="decision_function_shape"-->
+        <expand macro="random_state" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data for probability estimation. A fixed seed allows reproducible results."/>
+    </section>
+  </xml>
+
+  <xml name="spectral_clustering_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <expand macro="n_clusters"/>
+        <param argument="eigen_solver" type="select" value="" label="Eigen solver" help="The eigenvalue decomposition strategy to use.">
+            <option value="arpack" selected="true">arpack</option>
+            <option value="lobpcg">lobpcg</option>
+            <option value="amg">amg</option>
+            <!--None-->
+        </param>
+        <expand macro="random_state"/>
+        <expand macro="n_init"/>
+        <param argument="gamma" type="float" optional="true" value="1.0" label="Kernel scaling factor" help="Scaling factor of RBF, polynomial, exponential chi^2 and sigmoid affinity kernel. Ignored for affinity=''nearest_neighbors''."/>
+        <param argument="affinity" type="select" label="Affinity" help="Affinity kernel to use. ">
+            <option value="rbf" selected="true">RBF</option>
+            <option value="precomputed">precomputed</option>
+            <option value="nearest_neighbors">Nearset neighbors</option>
+        </param>
+        <param argument="n_neighbors" type="integer" optional="true" value="10" label="Number of neighbors" help="Number of neighbors to use when constructing the affinity matrix using the nearest neighbors method. Ignored for affinity=''rbf''"/>
+        <!--param argument="eigen_tol"-->
+        <param argument="assign_labels" type="select" label="Assign labels" help="The strategy to use to assign labels in the embedding space.">
+            <option value="kmeans" selected="true">kmeans</option>
+            <option value="discretize">discretize</option>
+        </param>
+        <param argument="degree" type="integer" optional="true" value="3"
+            label="Degree of the polynomial (polynomial kernel only)" help="Ignored by other kernels. dafault : 3 "/>
+        <param argument="coef0" type="integer" optional="true" value="1"
+            label="Zero coefficient (polynomial and sigmoid kernels only)" help="Ignored by other kernels. dafault : 1 "/>
+        <!--param argument="kernel_params"-->
+    </section>
+  </xml>
+
+  <xml name="minibatch_kmeans_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+        <expand macro="n_clusters"/>
+        <expand macro="init"/>
+        <expand macro="n_init" default_value="3"/>
+        <expand macro="max_iter" default_value="100"/>
+        <expand macro="tol" help_text="Early stopping heuristics based on normalized center change. To disable set to 0.0 ."/>
+        <expand macro="random_state"/>
+        <param argument="batch_size" type="integer" optional="true" value="100" label="Batch size" help="Size of the mini batches."/>
+        <!--param argument="compute_labels"-->
+        <param argument="max_no_improvement" type="integer" optional="true" value="10" label="Maximum number of improvement attempts" help="
+        Convergence detection based on inertia (the consecutive number of mini batches that doe not yield an improvement on the smoothed inertia).
+        To disable, set max_no_improvement to None. "/>
+        <param argument="init_size" type="integer" optional="true" value="" label="Number of random initialization samples" help="Number of samples to randomly sample for speeding up the initialization . ( default: 3 * batch_size )"/>
+        <param argument="reassignment_ratio" type="float" optional="true" value="0.01" label="Re-assignment ratio" help="Controls the fraction of the maximum number of counts for a center to be reassigned. Higher values yield better clustering results."/>
+    </section>
+  </xml>
+
+  <xml name="kmeans_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <expand macro="n_clusters"/>
+      <expand macro="init"/>
+      <expand macro="n_init"/>
+      <expand macro="max_iter"/>
+      <expand macro="tol" default_value="0.0001" help_text="Relative tolerance with regards to inertia to declare convergence."/>
+      <!--param argument="precompute_distances"/-->
+      <expand macro="random_state"/>
+      <param argument="copy_x" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true" label="Use a copy of data for precomputing distances" help="Mofifying the original data introduces small numerical differences caused by subtracting and then adding the data mean."/>
+    </section>
+  </xml>
+
+  <xml name="birch_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <param argument="threshold" type="float" optional="true" value="0.5" label="Subcluster radius threshold" help="The radius of the subcluster obtained by merging a new sample; the closest subcluster should be less than the threshold to avoid a new subcluster."/>
+      <param argument="branching_factor" type="integer" optional="true" value="50" label="Maximum number of subclusters per branch" help="Maximum number of CF subclusters in each node."/>
+      <expand macro="n_clusters" default_value="3"/>
+      <!--param argument="compute_labels"/-->
+    </section>
+  </xml>
+
+  <xml name="dbscan_advanced_options">
+    <section name="options" title="Advanced Options" expanded="False">
+      <param argument="eps" type="float" optional="true" value="0.5" label="Maximum neighborhood distance" help="The maximum distance between two samples for them to be considered as in the same neighborhood."/>
+      <param argument="min_samples" type="integer" optional="true" value="5" label="Minimal core point density" help="The number of samples (or total weight) in a neighborhood for a point (including the point itself) to be considered as a core point."/>
+      <param argument="metric" type="text" optional="true" value="euclidean" label="Metric" help="The metric to use when calculating distance between instances in a feature array."/>
+      <param argument="algorithm" type="select" label="Pointwise distance computation algorithm" help="The algorithm to be used by the NearestNeighbors module to compute pointwise distances and find nearest neighbors.">
+          <option value="auto" selected="true">auto</option>
+          <option value="ball_tree">ball_tree</option>
+          <option value="kd_tree">kd_tree</option>
+          <option value="brute">brute</option>
+      </param>
+      <param argument="leaf_size" type="integer" optional="true" value="30" label="Leaf size" help="Leaf size passed to BallTree or cKDTree. Memory and time efficieny factor in tree constrution and querying."/>
+    </section>
+  </xml>
+
+  <xml name="clustering_algorithms_options">
+    <conditional name="algorithm_options">
+      <param name="selected_algorithm" type="select" label="Clustering Algorithm">
+          <option value="KMeans" selected="true">KMeans</option>
+          <option value="SpectralClustering">Spectral Clustering</option>
+          <option value="MiniBatchKMeans">Mini Batch KMeans</option>
+          <option value="DBSCAN">DBSCAN</option>
+          <option value="Birch">Birch</option>
+      </param>
+      <when value="KMeans">
+          <expand macro="kmeans_advanced_options"/>
+      </when>
+      <when value="DBSCAN">
+          <expand macro="dbscan_advanced_options"/>
+      </when>
+      <when value="Birch">
+          <expand macro="birch_advanced_options"/>
+      </when>
+      <when value="SpectralClustering">
+          <expand macro="spectral_clustering_advanced_options"/>
+      </when>
+      <when value="MiniBatchKMeans">
+          <expand macro="minibatch_kmeans_advanced_options"/>
+      </when>
+    </conditional>
+  </xml>
+
+  <xml name="distance_metrics">
+    <param argument="metric" type="select" label="Distance metric" help=" ">
+      <option value="euclidean" selected="true">euclidean</option>
+      <option value="cityblock">cityblock</option>
+      <option value="cosine">cosine</option>
+      <option value="l1">l1</option>
+      <option value="l2">l2</option>
+      <option value="manhattan">manhattan</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="distance_nonsparse_metrics">
+    <option value="braycurtis">braycurtis</option>
+    <option value="canberra">canberra</option>
+    <option value="chebyshev">chebyshev</option>
+    <option value="correlation">correlation</option>
+    <option value="dice">dice</option>
+    <option value="hamming">hamming</option>
+    <option value="jaccard">jaccard</option>
+    <option value="kulsinski">kulsinski</option>
+    <option value="mahalanobis">mahalanobis</option>
+    <option value="matching">matching</option>
+    <option value="minkowski">minkowski</option>
+    <option value="rogerstanimoto">rogerstanimoto</option>
+    <option value="russellrao">russellrao</option>
+    <option value="seuclidean">seuclidean</option>
+    <option value="sokalmichener">sokalmichener</option>
+    <option value="sokalsneath">sokalsneath</option>
+    <option value="sqeuclidean">sqeuclidean</option>
+    <option value="yule">yule</option>
+  </xml>
+
+  <xml name="pairwise_kernel_metrics">
+    <param argument="metric" type="select" label="Pirwise Kernel metric" help=" ">
+      <option value="rbf" selected="true">rbf</option>
+      <option value="sigmoid">sigmoid</option>
+      <option value="polynomial">polynomial</option>
+      <option value="linear" selected="true">linear</option>
+      <option value="chi2">chi2</option>
+      <option value="additive_chi2">additive_chi2</option>
+    </param>
+  </xml>
+
+  <xml name="sparse_pairwise_metric_functions">
+    <param name="selected_metric_function" type="select" label="Select the pairwise metric you want to compute:">
+      <option value="euclidean_distances" selected="true">Euclidean distance matrix</option>
+      <option value="pairwise_distances">Distance matrix</option>
+      <option value="pairwise_distances_argmin">Minimum distances between one point and a set of points</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="pairwise_metric_functions">
+    <option value="additive_chi2_kernel" >Additive chi-squared kernel</option>
+    <option value="chi2_kernel">Exponential chi-squared kernel</option>
+    <option value="linear_kernel">Linear kernel</option>
+    <option value="manhattan_distances">L1 distances</option>
+    <option value="pairwise_kernels">Kernel</option>
+    <option value="polynomial_kernel">Polynomial kernel</option>
+    <option value="rbf_kernel">Gaussian (rbf) kernel</option>
+    <option value="laplacian_kernel">Laplacian kernel</option>
+  </xml>
+
+  <xml name="sparse_pairwise_condition">
+    <when value="pairwise_distances">
+      <section name="options" title="Advanced Options" expanded="False">
+          <expand macro="distance_metrics">
+              <yield/>
+          </expand>
+      </section>
+    </when>
+    <when value="euclidean_distances">
+      <section name="options" title="Advanced Options" expanded="False">
+          <param argument="squared" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="false"
+            label="Return squared Euclidean distances" help=" "/>
+      </section>
+    </when>
+  </xml>
+
+  <xml name="argmin_distance_condition">
+    <when value="pairwise_distances_argmin">
+      <section name="options" title="Advanced Options" expanded="False">
+          <param argument="axis" type="integer" optional="true" value="1" label="Axis" help="Axis along which the argmin and distances are to be computed."/>
+          <expand macro="distance_metrics">
+              <yield/>
+          </expand>
+          <param argument="batch_size" type="integer" optional="true" value="500" label="Batch size" help="Number of rows to be processed in each batch run."/>
+      </section>
+    </when>
+  </xml>
+
+  <xml name="sparse_preprocessors">
+    <param name="selected_pre_processor" type="select" label="Select a preprocessor:">
+      <option value="StandardScaler" selected="true">Standard Scaler (Standardizes features by removing the mean and scaling to unit variance)</option>
+      <option value="Binarizer">Binarizer (Binarizes data)</option>
+      <option value="Imputer">Imputer (Completes missing values)</option>
+      <option value="MaxAbsScaler">Max Abs Scaler (Scales features by their maximum absolute value)</option>
+      <option value="Normalizer">Normalizer (Normalizes samples individually to unit norm)</option>
+      <yield/>
+    </param>
+  </xml>
+
+  <xml name="sparse_preprocessor_options">
+    <when value="Binarizer">
+        <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>
+        <section name="options" title="Advanced Options" expanded="False">
+            <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+                label="Use a copy of data for precomputing binarization" help=" "/>
+            <param argument="threshold" type="float" optional="true" value="0.0"
+                label="Threshold"
+                help="Feature values below or equal to this are replaced by 0, above it by 1. Threshold may not be less than 0 for operations on sparse matrices. "/>
+        </section>
+    </when>
+    <when value="Imputer">
+      <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>
+      <section name="options" title="Advanced Options" expanded="False">
+          <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for precomputing imputation" help=" "/>
+          <param argument="strategy" type="select" optional="true" label="Imputation strategy" help=" ">
+              <option value="mean" selected="true">Replace missing values using the mean along the axis</option>
+              <option value="median">Replace missing values using the median along the axis</option>
+              <option value="most_frequent">Replace missing using the most frequent value along the axis</option>
+          </param>
+          <param argument="missing_values" type="text" optional="true" value="NaN"
+                label="Placeholder for missing values" help="For missing values encoded as numpy.nan, use the string value “NaN”"/>
+          <param argument="axis" type="boolean" optional="true" truevalue="1" falsevalue="0"
+                label="Impute along axis = 1" help="If fasle, axis = 0 is selected for imputation. "/>
+          <!--param argument="axis" type="select" optional="true" label="The axis along which to impute" help=" ">
+              <option value="0" selected="true">Impute along columns</option>
+              <option value="1">Impute along rows</option>
+          </param-->
+      </section>
+    </when>
+    <when value="StandardScaler">
+      <expand macro="multitype_input"/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for performing inplace scaling" help=" "/>
+        <param argument="with_mean" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Center the data before scaling" help=" "/>
+        <param argument="with_std" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Scale the data to unit variance (or unit standard deviation)" help=" "/>
+      </section>
+    </when>
+    <when value="MaxAbsScaler">
+      <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for precomputing scaling" help=" "/>
+      </section>
+    </when>
+    <when value="Normalizer">
+      <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>
+      <section name="options" title="Advanced Options" expanded="False">
+        <param argument="norm" type="select" optional="true" label="The norm to use to normalize non zero samples" help=" ">
+          <option value="l1" selected="true">l1</option>
+          <option value="l2">l2</option>
+          <option value="max">max</option>
+        </param>
+        <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"
+            label="Use a copy of data for precomputing row normalization" help=" "/>
+      </section>
+    </when>
+    <yield/>
+  </xml>
+
+  <!-- Outputs -->
+
+  <xml name="output">
+    <outputs>
+      <data format="tabular" name="outfile_predict">
+          <filter>selected_tasks['selected_task'] == 'load'</filter>
+      </data>
+      <data format="zip" name="outfile_fit">
+          <filter>selected_tasks['selected_task'] == 'train'</filter>
+      </data>
+    </outputs>
+  </xml>
+
+
+  <!--Citations-->
+  <xml name="eden_citation">
+    <citations>
+        <citation type="bibtex">
+            @misc{fabrizio_costa_2015_15094,
+              author       = {Fabrizio Costa and
+                              Björn Grüning and
+                              gigolo},
+              title        = {EDeN: EDeN - Graph Vectorizer},
+              month        = feb,
+              year         = 2015,
+              doi          = {10.5281/zenodo.15094},
+              url          = {http://dx.doi.org/10.5281/zenodo.15094}
+                    }
+                        }
+        </citation>
+    </citations>
+  </xml>
+
+  <xml name="sklearn_citation">
+    <citations>
+        <citation type="bibtex">
+            @article{scikit-learn,
+             title={Scikit-learn: Machine Learning in {P}ython},
+             author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
+                     and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
+                     and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
+                     Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
+             journal={Journal of Machine Learning Research},
+             volume={12},
+             pages={2825--2830},
+             year={2011}
+             url = {https://github.com/scikit-learn/scikit-learn}
+            }
+        </citation>
+    </citations>
+  </xml>
+
+  <xml name="scipy_citation">
+    <citations>
+        <citation type="bibtex">
+          @Misc{,
+          author =    {Eric Jones and Travis Oliphant and Pearu Peterson and others},
+          title =     {{SciPy}: Open source scientific tools for {Python}},
+          year =      {2001--},
+          url = "http://www.scipy.org/",
+          note = {[Online; accessed 2016-04-09]}
+        }
+        </citation>
+    </citations>
+  </xml>
+
+</macros>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/svm.xml	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,176 @@
+<tool id="svm_classifier" name="Support vector machines (SVMs)" version="@VERSION@">
+    <description>for classification</description>
+    <macros>
+        <import>main_macros.xml</import>
+        <!-- macro name="class_weight" argument="class_weight"-->
+    </macros>
+    <expand macro="python_requirements"/>
+    <expand macro="macro_stdio"/>
+    <version_command>echo "@VERSION@"</version_command>
+    <command><![CDATA[
+    python "$svc_script" '$inputs'
+]]>
+    </command>
+    <configfiles>
+        <inputs name="inputs"/>
+        <configfile name="svc_script">
+<![CDATA[
+import sys
+import json
+import numpy as np
+import sklearn.svm
+import pandas
+import pickle
+
+input_json_path = sys.argv[1]
+params = json.load(open(input_json_path, "r"))
+
+#if $selected_tasks.selected_task == "load":
+
+classifier_object = pickle.load(open("$infile_model", 'rb'))
+
+data = pandas.read_csv("$selected_tasks.infile_data", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
+prediction = classifier_object.predict(data)
+prediction_df = pandas.DataFrame(prediction)
+res = pandas.concat([data, prediction_df], axis=1)
+res.to_csv(path_or_buf = "$outfile_predict", sep="\t", index=False)
+
+#else:
+
+data_train = pandas.read_csv("$selected_tasks.infile_train", sep='\t', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )
+
+data = data_train.ix[:,0:len(data_train.columns)-1]
+labels = np.array(data_train[data_train.columns[len(data_train.columns)-1]])
+
+options = params["selected_tasks"]["selected_algorithms"]["options"]
+selected_algorithm = params["selected_tasks"]["selected_algorithms"]["selected_algorithm"]
+
+if not(selected_algorithm=="LinearSVC"):
+    if options["kernel"]:
+        options["kernel"] = str(options["kernel"])
+
+my_class = getattr(sklearn.svm, selected_algorithm)
+classifier_object = my_class(**options)
+classifier_object.fit(data,labels)
+
+pickle.dump(classifier_object,open("$outfile_fit", 'w+'))
+
+#end if
+
+]]>
+        </configfile>
+    </configfiles>
+    <inputs>
+        <expand macro="train_loadConditional" model="zip">
+            <param name="selected_algorithm" type="select" label="Classifier type">
+                <option value="SVC">C-Support Vector Classification</option>
+                <option value="NuSVC">Nu-Support Vector Classification</option>
+                <option value="LinearSVC">Linear Support Vector Classification</option>
+            </param>
+            <when value="SVC">
+                <expand macro="svc_advanced_options">
+                    <expand macro="C"/>
+                </expand>
+            </when>
+            <when value="NuSVC">
+                <expand macro="svc_advanced_options">
+                    <param argument="nu" type="float" optional="true" value="0.5" label="Nu control parameter" help="Controls the number of support vectors. Should be in the interval (0, 1]. "/>
+                </expand>
+            </when>
+            <when value="LinearSVC">
+                <section name="options" title="Advanced Options" expanded="False">
+                    <expand macro="C"/>
+                    <expand macro="tol" default_value="0.001" help_text="Tolerance for stopping criterion. "/>
+                    <expand macro="random_state" help_text="Integer number. The seed of the pseudo random number generator to use when shuffling the data for probability estimation. A fixed seed allows reproducible results."/>
+                    <!--expand macro="class_weight"/-->
+                    <param argument="max_iter" type="integer" optional="true" value="1000" label="Maximum number of iterations" help="The maximum number of iterations to be run."/>
+                    <param argument="loss" type="select" label="Loss function" help="Specifies the loss function. ''squared_hinge'' is the square of the hinge loss.">
+                        <option value="squared_hinge" selected="true">Squared hinge</option>
+                        <option value="hinge">Hinge</option>
+                    </param>
+                    <param argument="penalty" type="select" label="Penalization norm" help=" ">
+                        <option value="l1" >l1</option>
+                        <option value="l2" selected="true">l2</option>
+                    </param>
+                    <param argument="dual" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Use the shrinking heuristic" help="Select the algorithm to either solve the dual or primal optimization problem. Prefer dual=False when n_samples > n_features."/>
+                    <param argument="multi_class" type="select" label="Multi-class strategy" help="Determines the multi-class strategy if y contains more than two classes.">
+                        <option value="ovr" selected="true">ovr</option>
+                        <option value="crammer_singer" >crammer_singer</option>
+                    </param>
+                    <param argument="fit_intercept" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolflase" checked="true" label="Calculate the intercept for this model" help="If set to false, data is expected to be already centered."/>
+                    <param argument="intercept_scaling" type="float" optional="true" value="1" label="Add synthetic feature to the instance vector" help=" "/>
+                </section>
+            </when>
+        </expand>
+    </inputs>
+
+    <expand macro="output"/>
+
+    <tests>
+        <test>
+            <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="train"/>
+            <param name="selected_algorithm" value="SVC"/>
+            <param name="random_state" value="5"/>
+            <output name="outfile_fit" file="svc_model01.txt"/>
+        </test>
+        <test>
+            <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="train"/>
+            <param name="selected_algorithm" value="NuSVC"/>
+            <param name="random_state" value="5"/>
+            <output name="outfile_fit" file="svc_model02.txt"/>
+        </test>
+        <test>
+            <param name="infile_train" value="train_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="train"/>
+            <param name="selected_algorithm" value="LinearSVC"/>
+            <param name="random_state" value="5"/>
+            <output name="outfile_fit" file="svc_model03.txt"/>
+        </test>
+        <test>
+            <param name="infile_model" value="svc_model01.txt" ftype="txt"/>
+            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="load"/>
+            <output name="outfile_predict" file="svc_prediction_result01.tabular"/>
+        </test>
+        <test>
+            <param name="infile_model" value="svc_model02.txt" ftype="txt"/>
+            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="load"/>
+            <output name="outfile_predict" file="svc_prediction_result02.tabular"/>
+        </test>
+        <test>
+            <param name="infile_model" value="svc_model03.txt" ftype="txt"/>
+            <param name="infile_data" value="test_set.tabular" ftype="tabular"/>
+            <param name="selected_task" value="load"/>
+            <output name="outfile_predict" file="svc_prediction_result03.tabular"/>
+        </test>
+    </tests>
+    <help><![CDATA[
+**What it does**
+This module implements the Support Vector Machine (SVM) classification algorithms.
+Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
+
+**The advantages of support vector machines are:**
+
+        1- Effective in high dimensional spaces.
+
+        2- Still effective in cases where number of dimensions is greater than the number of samples.
+
+        3- Uses a subset of training points in the decision function (called support vectors), so it is also memory efficient.
+
+        4- Versatile: different Kernel functions can be specified for the decision function. Common kernels are provided, but it is also possible to specify custom kernels.
+
+**The disadvantages of support vector machines include:**
+
+        1- If the number of features is much greater than the number of samples, the method is likely to give poor performances.
+
+        2- SVMs do not directly provide probability estimates, these are calculated using an expensive five-fold cross-validation
+
+For more information check http://scikit-learn.org/stable/modules/neighbors.html
+
+    ]]>
+    </help>
+    <expand macro="sklearn_citation"/>
+</tool>
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/RF01704.fasta	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+>CP000097.1/1411351-1411410
+CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA
+>ACNY01000002.1/278641-278580
+GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/accuracy_score.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+accuracy_score : 
+0.8461538461538461
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/auc.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+auc : 
+2.5
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/average_precision_score.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+average_precision_score : 
+1.0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/blobs.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,101 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/brier_score_loss.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+brier_score_loss : 
+0.5641025641025641
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/circles.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,101 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/class.txt	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/classification_report.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,9 @@
+classification_report : 
+             precision    recall  f1-score   support
+
+          0       1.00      1.00      1.00        14
+          1       1.00      0.62      0.77        16
+          2       0.60      1.00      0.75         9
+
+avg / total       0.91      0.85      0.85        39
+
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result01.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result02.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result03.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result04.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result05.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result06.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result07.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result08.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result09.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result10.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result11.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result12	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result12.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
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+3	38	-90	21	3
+3	34	-107	1	3
+3	35	-78	18	3
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result13.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
+0	44	64	-76	4
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+3	38	-90	21	1
+3	34	-107	1	1
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result14.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
+0	44	64	-76	2
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+3	34	-107	1	0
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result15.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
+0	44	64	-76	1
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+3	38	-90	21	0
+3	34	-107	1	0
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result16.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
+0	44	64	-76	0
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result17.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+0
+1
+0
+0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result18.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+-1
+-1
+-1
+-1
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result19.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+0
+1
+0
+0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result20.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+0
+1
+0
+0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/cluster_result21.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+0
+1
+0
+0
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/confusion_matrix.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,4 @@
+confusion_matrix : 
+[[14  0  0]
+ [ 0 10  6]
+ [ 0  0  9]]
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result01.json	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+{"directed": false, "graph": {"info": "RNAfold", "id": "CP000097.1/1411351-1411410", "structure": "....((((.((((((....)))))).)..)))...(((((((..(...)..)).))))).", "sequence": "CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA"}, "nodes": [{"position": 0, "id": 0, "label": "C"}, {"position": 1, "id": 1, "label": "A"}, {"position": 2, "id": 2, "label": "A"}, {"position": 3, "id": 3, "label": "C"}, {"position": 4, "id": 4, "label": "G"}, {"position": 5, "id": 5, "label": "U"}, {"position": 6, "id": 6, "label": "U"}, {"position": 7, "id": 7, "label": "C"}, {"position": 8, "id": 8, "label": "A"}, {"position": 9, "id": 9, "label": "C"}, {"position": 10, "id": 10, "label": "C"}, {"position": 11, "id": 11, "label": "U"}, {"position": 12, "id": 12, "label": "C"}, {"position": 13, "id": 13, "label": "A"}, {"position": 14, "id": 14, "label": "C"}, {"position": 15, "id": 15, "label": "A"}, {"position": 16, "id": 16, "label": "U"}, {"position": 17, "id": 17, "label": "U"}, {"position": 18, "id": 18, "label": "U"}, {"position": 19, "id": 19, "label": "G"}, {"position": 20, "id": 20, "label": "U"}, {"position": 21, "id": 21, "label": "G"}, {"position": 22, "id": 22, "label": "A"}, {"position": 23, "id": 23, "label": "G"}, {"position": 24, "id": 24, "label": "G"}, {"position": 25, "id": 25, "label": "C"}, {"position": 26, "id": 26, "label": "G"}, {"position": 27, "id": 27, "label": "C"}, {"position": 28, "id": 28, "label": "A"}, {"position": 29, "id": 29, "label": "G"}, {"position": 30, "id": 30, "label": "A"}, {"position": 31, "id": 31, "label": "C"}, {"position": 32, "id": 32, "label": "A"}, {"position": 33, "id": 33, "label": "A"}, {"position": 34, "id": 34, "label": "C"}, {"position": 35, "id": 35, "label": "C"}, {"position": 36, "id": 36, "label": "C"}, {"position": 37, "id": 37, "label": "A"}, {"position": 38, "id": 38, "label": "G"}, {"position": 39, "id": 39, "label": "G"}, {"position": 40, "id": 40, "label": "C"}, {"position": 41, "id": 41, "label": "C"}, {"position": 42, "id": 42, "label": "A"}, {"position": 43, "id": 43, "label": "A"}, {"position": 44, "id": 44, "label": "G"}, {"position": 45, "id": 45, "label": "G"}, {"position": 46, "id": 46, "label": "A"}, {"position": 47, "id": 47, "label": "A"}, {"position": 48, "id": 48, "label": "C"}, {"position": 49, "id": 49, "label": "G"}, {"position": 50, "id": 50, "label": "G"}, {"position": 51, "id": 51, "label": "G"}, {"position": 52, "id": 52, "label": "G"}, {"position": 53, "id": 53, "label": "A"}, {"position": 54, "id": 54, "label": "C"}, {"position": 55, "id": 55, "label": "C"}, {"position": 56, "id": 56, "label": "U"}, {"position": 57, "id": 57, "label": "G"}, {"position": 58, "id": 58, "label": "G"}, {"position": 59, "id": 59, "label": "A"}], "links": [{"source": 0, "type": "backbone", "target": 1, "len": 1, "label": "-"}, {"source": 1, "type": "backbone", "target": 2, "len": 1, "label": "-"}, {"source": 2, "type": "backbone", "target": 3, "len": 1, "label": "-"}, {"source": 3, "type": "backbone", "target": 4, "len": 1, "label": "-"}, {"source": 4, "type": "backbone", "target": 5, "len": 1, "label": "-"}, {"source": 4, "type": "basepair", "target": 31, "len": 1, "label": "="}, {"source": 5, "type": "basepair", "target": 30, "len": 1, "label": "="}, {"source": 5, "type": "backbone", "target": 6, "len": 1, "label": "-"}, {"source": 6, "type": "basepair", "target": 29, "len": 1, "label": "="}, {"source": 6, "type": "backbone", "target": 7, "len": 1, "label": "-"}, {"source": 7, "type": "backbone", "target": 8, "len": 1, "label": "-"}, {"source": 7, "type": "basepair", "target": 26, "len": 1, "label": "="}, {"source": 8, "type": "backbone", "target": 9, "len": 1, "label": "-"}, {"source": 9, "type": "basepair", "target": 24, "len": 1, "label": "="}, {"source": 9, "type": "backbone", "target": 10, "len": 1, "label": "-"}, {"source": 10, "type": "backbone", "target": 11, "len": 1, "label": "-"}, {"source": 10, "type": "basepair", "target": 23, "len": 1, "label": "="}, {"source": 11, "type": "backbone", "target": 12, "len": 1, "label": "-"}, {"source": 11, "type": "basepair", "target": 22, "len": 1, "label": "="}, {"source": 12, "type": "backbone", "target": 13, "len": 1, "label": "-"}, {"source": 12, "type": "basepair", "target": 21, "len": 1, "label": "="}, {"source": 13, "type": "basepair", "target": 20, "len": 1, "label": "="}, {"source": 13, "type": "backbone", "target": 14, "len": 1, "label": "-"}, {"source": 14, "type": "basepair", "target": 19, "len": 1, "label": "="}, {"source": 14, "type": "backbone", "target": 15, "len": 1, "label": "-"}, {"source": 15, "type": "backbone", "target": 16, "len": 1, "label": "-"}, {"source": 16, "type": "backbone", "target": 17, "len": 1, "label": "-"}, {"source": 17, "type": "backbone", "target": 18, "len": 1, "label": "-"}, {"source": 18, "type": "backbone", "target": 19, "len": 1, "label": "-"}, {"source": 19, "type": "backbone", "target": 20, "len": 1, "label": "-"}, {"source": 20, "type": 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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/converter_result02.json	Fri Feb 16 09:16:30 2018 -0500
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"basepair", "target": 54, "len": 1, "label": "="}, {"source": 43, "type": "backbone", "target": 44, "len": 1, "label": "-"}, {"source": 43, "type": "basepair", "target": 53, "len": 1, "label": "="}, {"source": 44, "type": "backbone", "target": 45, "len": 1, "label": "-"}, {"source": 45, "type": "backbone", "target": 46, "len": 1, "label": "-"}, {"source": 46, "type": "backbone", "target": 47, "len": 1, "label": "-"}, {"source": 47, "type": "backbone", "target": 48, "len": 1, "label": "-"}, {"source": 48, "type": "backbone", "target": 49, "len": 1, "label": "-"}, {"source": 49, "type": "backbone", "target": 50, "len": 1, "label": "-"}, {"source": 50, "type": "backbone", "target": 51, "len": 1, "label": "-"}, {"source": 51, "type": "backbone", "target": 52, "len": 1, "label": "-"}, {"source": 52, "type": "backbone", "target": 53, "len": 1, "label": "-"}, {"source": 53, "type": "backbone", "target": 54, "len": 1, "label": "-"}, {"source": 54, "type": "backbone", "target": 55, "len": 1, "label": "-"}, {"source": 55, "type": "backbone", "target": 56, "len": 1, "label": "-"}, {"source": 56, "type": "backbone", "target": 57, "len": 1, "label": "-"}, {"source": 57, "type": "backbone", "target": 58, "len": 1, "label": "-"}, {"source": 58, "type": "backbone", "target": 59, "len": 1, "label": "-"}, {"source": 59, "type": "backbone", "target": 60, "len": 1, "label": "-"}, {"source": 60, "type": "backbone", "target": 61, "len": 1, "label": "-"}], "multigraph": false}
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse1.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
+3 3 6
+1 1 1
+3 1 2
+3 2 3
+1 3 4
+2 3 5
+3 3 6
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_sparse2.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 6
+1 1 1.5
+3 1 -2
+3 2 0.3
+1 3 41
+2 3 0.1235
+3 3 6
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csc_stack_result01.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 6 12
+1 1 1.000000000000000e+00
+3 1 2.000000000000000e+00
+3 2 3.000000000000000e+00
+1 3 4.000000000000000e+00
+2 3 5.000000000000000e+00
+3 3 6.000000000000000e+00
+1 4 1.500000000000000e+00
+3 4 -2.000000000000000e+00
+3 5 3.000000000000000e-01
+1 6 4.100000000000000e+01
+2 6 1.235000000000000e-01
+3 6 6.000000000000000e+00
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse1.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate integer general
+%
+3 3 6
+1 1 1
+1 3 2
+2 3 3
+3 1 4
+3 2 5
+3 3 6
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_sparse2.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,9 @@
+%%MatrixMarket matrix coordinate real general
+%
+3 3 6
+1 1 1
+1 3 -0.2
+2 3 11
+3 1 0.04
+3 2 -5
+3 3 2.6
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/csr_stack_result01.mtx	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,15 @@
+%%MatrixMarket matrix coordinate real general
+%
+6 3 12
+1 1 1.000000000000000e+00
+1 3 2.000000000000000e+00
+2 3 3.000000000000000e+00
+3 1 4.000000000000000e+00
+3 2 5.000000000000000e+00
+3 3 6.000000000000000e+00
+4 1 1.000000000000000e+00
+4 3 -2.000000000000000e-01
+5 3 1.100000000000000e+01
+6 1 4.000000000000000e-02
+6 2 -5.000000000000000e+00
+6 3 2.600000000000000e+00
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/empty_file.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,48 @@
+0	44	64	-76	4
+0	51	48	-73	0
+0	58	65	-49	0
+0	43	61	-49	1
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+1	-35	87	47	0
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+2	-109	23	-92	0
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+2	-85	26	-88	0
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+2	-63	9	-106	0
+2	-79	9	-93	1
+2	-99	26	-108	3
+2	-81	19	-110	0
+2	-108	21	-108	0
+2	-92	27	-106	3
+2	-88	2	-106	1
+2	-88	15	-103	0
+3	54	-74	4	0
+3	42	-92	31	1
+3	39	-99	-7	1
+3	48	-115	-5	1
+3	39	-96	2	1
+3	31	-109	9	1
+3	33	-96	-8	1
+3	23	-102	4	0
+3	38	-90	21	1
+3	34	-107	1	1
+3	35	-78	18	1
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/f1_score.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+f1_score : 
+0.8461538461538461
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/fbeta_score.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,2 @@
+fbeta_score : 
+0.8461538461538461
--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman1.txt	Fri Feb 16 09:16:30 2018 -0500
@@ -0,0 +1,101 @@
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman2.txt	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/friedman3.txt	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result03	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/glm_result07	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/test-data/hamming_loss.txt	Fri Feb 16 09:16:30 2018 -0500
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--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
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