Mercurial > repos > bgruening > svm_classifier
diff svm.xml @ 0:9a9396e5d153 draft
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9
author | bgruening |
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date | Fri, 16 Feb 2018 09:16:30 -0500 |
parents | |
children | 78c664cc1841 |
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--- /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>