| Next changeset 1:ef41c78f2167 (2018-02-16) |
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Commit message:
planemo upload for repository https://github.com/bgruening/galaxytools/tools/sklearn commit 0e582cf1f3134c777cce3aa57d71b80ed95e6ba9 |
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README.rst main_macros.xml numeric_clustering.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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| diff -r 000000000000 -r c7b8fab00c0f README.rst --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/README.rst Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -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. |
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| diff -r 000000000000 -r c7b8fab00c0f main_macros.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/main_macros.xml Fri Feb 16 09:17:59 2018 -0500 |
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| b'@@ -0,0 +1,807 @@\n+<macros>\n+ <token name="@VERSION@">0.9</token>\n+\n+ <token name="@COLUMNS_FUNCTION@">\n+def columns(f,c):\n+ data = pandas.read_csv(f, sep=\'\\t\', header=None, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False)\n+ cols = c.split (\',\')\n+ cols = map(int, cols)\n+ cols = list(map(lambda x: x - 1, cols))\n+ y = data.iloc[:,cols].values\n+ return y\n+ </token>\n+\n+ <xml name="python_requirements">\n+ <requirements>\n+ <requirement type="package" version="2.7">python</requirement>\n+ <requirement type="package" version="0.19.1">scikit-learn</requirement>\n+ <requirement type="package" version="0.22.0">pandas</requirement>\n+ <yield />\n+ </requirements>\n+ </xml>\n+\n+ <xml name="macro_stdio">\n+ <stdio>\n+ <exit_code range="1:" level="fatal" description="Error occurred. Please check Tool Standard Error"/>\n+ </stdio>\n+ </xml>\n+\n+\n+ <!--Generic interface-->\n+ <xml name="train_loadConditional" token_train="tabular" token_data="tabular" token_model="txt">\n+ <conditional name="selected_tasks">\n+ <param name="selected_task" type="select" label="Select a Classification Task">\n+ <option value="train" selected="true">Train a model</option>\n+ <option value="load">Load a model and predict</option>\n+ </param>\n+ <when value="load">\n+ <param name="infile_model" type="data" format="@MODEL@" label="Models" help="Select a model file."/>\n+ <param name="infile_data" type="data" format="@DATA@" label="Data (tabular)" help="Select the dataset you want to classify."/>\n+ <conditional name="prediction_options">\n+ <param name="prediction_option" type="select" label="Select the type of prediction">\n+ <option value="predict">Predict class labels</option>\n+ <option value="advanced">Include advanced options</option>\n+ </param>\n+ <when value="predict">\n+ </when>\n+ <when value="advanced">\n+ </when>\n+ </conditional>\n+ </when>\n+ <when value="train">\n+ <param name="infile_train" type="data" format="@TRAIN@" label="Training samples (tabular)"/>\n+ <conditional name="selected_algorithms">\n+ <yield />\n+ </conditional>\n+ </when>\n+ </conditional>\n+ </xml>\n+\n+ <xml name="sl_Conditional" token_train="tabular" token_data="tabular" token_model="txt">\n+ <conditional name="selected_tasks">\n+ <param name="selected_task" type="select" label="Select a Classification Task">\n+ <option value="train" selected="true">Train a model</option>\n+ <option value="load">Load a model and predict</option>\n+ </param>\n+ <when value="load">\n+ <param name="infile_model" type="data" format="@MODEL@" label="Models" help="Select a model file."/>\n+ <param name="infile_data" type="data" format="@DATA@" label="Data (tabular)" help="Select the dataset you want to classify."/>\n+ <conditional name="prediction_options">\n+ <param name="prediction_option" type="select" label="Select the type of prediction">\n+ <option value="predict">Predict class labels</option>\n+ <option value="advanced">Include advanced options</option>\n+ </param>\n+ <when value="predict">\n+ </when>\n+ <when value="advanced">\n+ </when>\n+ </conditional>\n+ </when>\n+ <when value="train">\n+ <conditional name="selected_algorithms">\n+ <yield />\n+ </conditional>\n+ </when>\n+ </conditional>\n+ </xml>\n+\n+ <xml name="advanced_section">\n+ <section name="options" title="Advanced Options" expanded="False">\n+ <yield />\n+ </section>\n+ </xml>\n+\n+\n+ <!--Generalized Linear Models-->\n+ <xml name="lo'..b'ata for performing inplace scaling" help=" "/>\n+ <param argument="with_mean" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"\n+ label="Center the data before scaling" help=" "/>\n+ <param argument="with_std" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"\n+ label="Scale the data to unit variance (or unit standard deviation)" help=" "/>\n+ </section>\n+ </when>\n+ <when value="MaxAbsScaler">\n+ <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>\n+ <section name="options" title="Advanced Options" expanded="False">\n+ <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"\n+ label="Use a copy of data for precomputing scaling" help=" "/>\n+ </section>\n+ </when>\n+ <when value="Normalizer">\n+ <expand macro="multitype_input" format="tabular,txt" help="Tabular and sparse datasets are supporetd."/>\n+ <section name="options" title="Advanced Options" expanded="False">\n+ <param argument="norm" type="select" optional="true" label="The norm to use to normalize non zero samples" help=" ">\n+ <option value="l1" selected="true">l1</option>\n+ <option value="l2">l2</option>\n+ <option value="max">max</option>\n+ </param>\n+ <param argument="copy" type="boolean" optional="true" truevalue="booltrue" falsevalue="boolfalse" checked="true"\n+ label="Use a copy of data for precomputing row normalization" help=" "/>\n+ </section>\n+ </when>\n+ <yield/>\n+ </xml>\n+\n+ <!-- Outputs -->\n+\n+ <xml name="output">\n+ <outputs>\n+ <data format="tabular" name="outfile_predict">\n+ <filter>selected_tasks[\'selected_task\'] == \'load\'</filter>\n+ </data>\n+ <data format="zip" name="outfile_fit">\n+ <filter>selected_tasks[\'selected_task\'] == \'train\'</filter>\n+ </data>\n+ </outputs>\n+ </xml>\n+\n+\n+ <!--Citations-->\n+ <xml name="eden_citation">\n+ <citations>\n+ <citation type="bibtex">\n+ @misc{fabrizio_costa_2015_15094,\n+ author = {Fabrizio Costa and\n+ Bj\xc3\xb6rn Gr\xc3\xbcning and\n+ gigolo},\n+ title = {EDeN: EDeN - Graph Vectorizer},\n+ month = feb,\n+ year = 2015,\n+ doi = {10.5281/zenodo.15094},\n+ url = {http://dx.doi.org/10.5281/zenodo.15094}\n+ }\n+ }\n+ </citation>\n+ </citations>\n+ </xml>\n+\n+ <xml name="sklearn_citation">\n+ <citations>\n+ <citation type="bibtex">\n+ @article{scikit-learn,\n+ title={Scikit-learn: Machine Learning in {P}ython},\n+ author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n+ and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n+ and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n+ Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n+ journal={Journal of Machine Learning Research},\n+ volume={12},\n+ pages={2825--2830},\n+ year={2011}\n+ url = {https://github.com/scikit-learn/scikit-learn}\n+ }\n+ </citation>\n+ </citations>\n+ </xml>\n+\n+ <xml name="scipy_citation">\n+ <citations>\n+ <citation type="bibtex">\n+ @Misc{,\n+ author = {Eric Jones and Travis Oliphant and Pearu Peterson and others},\n+ title = {{SciPy}: Open source scientific tools for {Python}},\n+ year = {2001--},\n+ url = "http://www.scipy.org/",\n+ note = {[Online; accessed 2016-04-09]}\n+ }\n+ </citation>\n+ </citations>\n+ </xml>\n+\n+</macros>\n' |
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| diff -r 000000000000 -r c7b8fab00c0f numeric_clustering.xml --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/numeric_clustering.xml Fri Feb 16 09:17:59 2018 -0500 |
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| b'@@ -0,0 +1,388 @@\n+<tool id="sklearn_numeric_clustering" name="Numeric Clustering" version="@VERSION@">\n+ <description></description>\n+ <macros>\n+ <import>main_macros.xml</import>\n+ </macros>\n+ <expand macro="python_requirements"/>\n+ <expand macro="macro_stdio"/>\n+ <version_command>echo "@VERSION@"</version_command>\n+ <command><![CDATA[\n+ python "$cluster_script" \'$inputs\'\n+]]>\n+ </command>\n+ <configfiles>\n+ <inputs name="inputs"/>\n+ <configfile name="cluster_script">\n+<![CDATA[\n+import sys\n+import json\n+import numpy as np\n+import sklearn.cluster\n+import pandas\n+from sklearn import metrics\n+from scipy.io import mmread\n+\n+input_json_path = sys.argv[1]\n+params = json.load(open(input_json_path, "r"))\n+\n+selected_algorithm = params["input_types"]["algorithm_options"]["selected_algorithm"]\n+\n+my_class = getattr(sklearn.cluster, selected_algorithm)\n+cluster_object = my_class()\n+options = params["input_types"]["algorithm_options"]["options"]\n+\n+cluster_object.set_params(**options)\n+\n+#if $input_types.selected_input_type == "sparse":\n+data_matrix = mmread(open("$infile", \'r\'))\n+#else:\n+data = pandas.read_csv("$infile", sep=\'\\t\', header=0, index_col=None, parse_dates=True, encoding=None, tupleize_cols=False )\n+\n+start_column = $input_types.start_column\n+end_column = $input_types.end_column\n+\n+if end_column and start_column:\n+ if end_column >= start_column:\n+ data_matrix = data.values[:, start_column-1:end_column]\n+ else:\n+ data_matrix = data.values\n+else:\n+ data_matrix = data.values\n+#end if\n+\n+prediction = cluster_object.fit_predict( data_matrix )\n+\n+if len(np.unique(prediction)) > 1:\n+ silhouette_score = metrics.silhouette_score(data_matrix,prediction,metric=\'euclidean\')\n+else:\n+ silhouette_score = -1\n+sys.stdout.write(\'silhouette score:\' + \'\\t\' + str(silhouette_score) + \'\\n\')\n+\n+prediction_df = pandas.DataFrame(prediction)\n+\n+#if $input_types.selected_input_type == "sparse":\n+res = prediction_df\n+#else:\n+res = pandas.concat([data, prediction_df], axis=1)\n+#end if\n+\n+res.to_csv(path_or_buf = "$outfile", sep="\\t", index=False, header=False)\n+]]>\n+ </configfile>\n+ </configfiles>\n+ <inputs>\n+ <conditional name="input_types">\n+ <param name="selected_input_type" type="select" label="Select the format of input data">\n+ <option value="tabular" selected="true">Tabular Format (tabular, txt)</option>\n+ <option value="sparse">Sparse Vector Representation (mtx)</option>\n+ </param>\n+ <when value="sparse">\n+ <param name="infile" type="data" format="txt" label="Sparse vector (scipy.sparse.csr_matrix) file:" help="The following clustering algorithms support sparse matrix operations: \'\'Birch\'\', \'\'DBSCAN\'\', \'\'KMeans\'\', \'\'Mini BatchK Means\'\', and \'\'Spectral Clustering\'\'. If your data is in tabular format, please use other clustering algorithms."/>\n+ <expand macro="clustering_algorithms_options"/>\n+ </when>\n+ <when value="tabular">\n+ <param name="infile" type="data" format="tabular" label="Data file with numeric values"/>\n+ <param name="start_column" type="data_column" data_ref="infile" optional="True" label="Select a subset of data. Start column:" />\n+ <param name="end_column" type="data_column" data_ref="infile" optional="True" label="End column:" />\n+ <!--expand macro="clustering_algorithms_options"-->\n+ <conditional name="algorithm_options">\n+ <param name="selected_algorithm" type="select" label="Clustering Algorithm">\n+ <option value="AgglomerativeClustering">Hierarchical Agglomerative Clustering</option>\n+ <option value="AffinityPropagation">Affinity Propagation</option>\n+ <option value="SpectralClustering">Spectral Clustering</option>\n+ <option value='..b' <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result14.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="MiniBatchKMeans"/>\n+ <param name="selected_input_type" value="tabular"/>\n+ <param name="n_init" value="5"/>\n+ <param name="start_column" value="2" />\n+ <param name="end_column" value="4" />\n+ <param name="batch_size" value="10"/>\n+ <param name="n_clusters" value="4"/>\n+ <param name="random_state" value="100"/>\n+ <param name="reassignment_ratio" value="1.0"/>\n+ <output name="outfile" file="cluster_result15.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="numeric_values.tabular" ftype="tabular"/>\n+ <param name="selected_algorithm" value="KMeans"/>\n+ <param name="selected_input_type" value="tabular"/>\n+ <param name="start_column" value="1" />\n+ <param name="end_column" value="1" />\n+ <param name="n_clusters" value="4" />\n+ <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result16.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="sparse.mtx" ftype="txt"/>\n+ <param name="selected_input_type" value="sparse"/>\n+ <param name="selected_algorithm" value="KMeans"/>\n+ <param name="n_clusters" value="2" />\n+ <param name="init" value="k-means++" />\n+ <param name="random_state" value="100"/>\n+ <output name="outfile" file="cluster_result17.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="sparse.mtx" ftype="txt"/>\n+ <param name="selected_algorithm" value="DBSCAN"/>\n+ <param name="selected_input_type" value="sparse"/>\n+ <param name="algorithm" value="kd_tree"/>\n+ <param name="leaf_size" value="10"/>\n+ <param name="eps" value="1.0"/>\n+ <output name="outfile" file="cluster_result18.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="sparse.mtx" ftype="txt"/>\n+ <param name="selected_algorithm" value="Birch"/>\n+ <param name="selected_input_type" value="sparse"/>\n+ <param name="n_clusters" value="2"/>\n+ <param name="threshold" value="0.008"/>\n+ <output name="outfile" file="cluster_result19.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="sparse.mtx" ftype="txt"/>\n+ <param name="selected_algorithm" value="MiniBatchKMeans"/>\n+ <param name="selected_input_type" value="sparse"/>\n+ <param name="n_init" value="5"/>\n+ <param name="batch_size" value="10"/>\n+ <param name="n_clusters" value="2"/>\n+ <param name="random_state" value="100"/>\n+ <param name="reassignment_ratio" value="1.0"/>\n+ <output name="outfile" file="cluster_result20.txt"/>\n+ </test>\n+ <test>\n+ <param name="infile" value="sparse.mtx" ftype="txt"/>\n+ <param name="selected_algorithm" value="SpectralClustering"/>\n+ <param name="selected_input_type" value="sparse"/>\n+ <param name="assign_labels" value="discretize"/>\n+ <param name="n_clusters" value="2"/>\n+ <param name="random_state" value="100"/>\n+ <param name="degree" value="2"/>\n+ <output name="outfile" file="cluster_result21.txt"/>\n+ </test>\n+ </tests>\n+ <help><![CDATA[\n+**What it does**\n+This tool offers different clustering algorithms which are provided by\n+scikit-learn to find similarities among samples and cluster the samples based on these similarities.\n+ ]]></help>\n+ <expand macro="sklearn_citation"/>\n+</tool>\n' |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/RF01704.fasta --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/RF01704.fasta Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,4 @@ +>CP000097.1/1411351-1411410 +CAACGUUCACCUCACAUUUGUGAGGCGCAGACAACCCAGGCCAAGGAACGGGGACCUGGA +>ACNY01000002.1/278641-278580 +GAUCGUUCACUUCGCAUCGCGCGAAGCGCAGUUCGCCUCAGGCCAUGGAACGGGGACCUGAG |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/accuracy_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/accuracy_score.txt Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,2 @@ +accuracy_score : +0.8461538461538461 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/auc.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/auc.txt Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,2 @@ +auc : +2.5 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/average_precision_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/average_precision_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +average_precision_score : +1.0 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/blobs.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/blobs.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/brier_score_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/brier_score_loss.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +brier_score_loss : +0.5641025641025641 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/circles.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/circles.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/class.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/class.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/classification_report.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/classification_report.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 + |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result01.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result01.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result02.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result02.txt Fri Feb 16 09:17:59 2018 -0500 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result03.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result03.txt Fri Feb 16 09:17:59 2018 -0500 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result04.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result04.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result05.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result05.txt Fri Feb 16 09:17:59 2018 -0500 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result06.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result06.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result07.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result07.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/cluster_result08.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/cluster_result08.txt Fri Feb 16 09:17:59 2018 -0500 |
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"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}\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/converter_result02.json --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/converter_result02.json Fri Feb 16 09:17:59 2018 -0500 |
| [ |
| b'@@ -0,0 +1,4 @@\n+{"directed": false, "graph": {"info": "RNAshapes shape_type=2 energy_range=4 max_num=3", "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": 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"-"}], "multigraph": false}\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csc_sparse1.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csc_sparse1.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csc_sparse2.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csc_sparse2.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csc_stack_result01.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csc_stack_result01.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csr_sparse1.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csr_sparse1.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csr_sparse2.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csr_sparse2.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/csr_stack_result01.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/csr_stack_result01.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/empty_file.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/empty_file.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,48 @@ +0 44 64 -76 4 +0 51 48 -73 0 +0 58 65 -49 0 +0 43 61 -49 1 +0 45 43 -79 0 +0 42 60 -98 0 +0 50 55 -59 2 +0 53 53 -56 2 +0 45 44 -61 0 +0 43 65 -84 4 +0 35 52 -75 1 +0 56 56 -70 0 +1 -61 86 43 0 +1 -67 93 15 0 +1 -59 94 36 0 +1 -50 92 62 0 +1 -78 91 70 1 +1 -35 87 47 0 +1 -56 91 52 0 +1 -61 81 46 0 +1 -83 78 34 0 +1 -50 87 45 0 +1 -67 73 50 1 +1 -50 97 45 0 +1 -61 111 45 0 +2 -109 23 -92 0 +2 -94 20 -96 0 +2 -85 26 -88 0 +2 -90 33 -114 1 +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 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/f1_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/f1_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +f1_score : +0.8461538461538461 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/fbeta_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/fbeta_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +fbeta_score : +0.8461538461538461 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/friedman1.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/friedman1.txt Fri Feb 16 09:17:59 2018 -0500 |
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|
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/friedman2.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/friedman2.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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|
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/friedman3.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/friedman3.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| @@ -0,0 +1,5 @@ +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.5282637592226301 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5180352211818147 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.012682414140451959 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.1869842234155321 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.6599360904302456 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/glm_result05 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result05 Fri Feb 16 09:17:59 2018 -0500 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/glm_result06 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result06 Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/glm_result07 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result07 Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,5 @@ +86.97021227350001 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0.6093152833692663 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.5963828164943974 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 -0.07927429227257943 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 0.2621440442022235 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -1.7330414645145749 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/glm_result08 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/glm_result08 Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.9901407239999998 -1.9523364774 1 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/hamming_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hamming_loss.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +hamming_loss : +0.15384615384615385 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/hastie.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hastie.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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|
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/hinge_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/hinge_loss.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +hinge_loss : +2.7688227126800844 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/jaccard_similarity_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/jaccard_similarity_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +jaccard_similarity_score : +0.8461538461538461 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/lda_model01 |
| b |
| Binary file test-data/lda_model01 has changed |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/lda_model02 |
| b |
| Binary file test-data/lda_model02 has changed |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/lda_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lda_prediction_result01.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/lda_prediction_result02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/lda_prediction_result02.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/log_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/log_loss.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +log_loss : +3.7248735402728403 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/matthews_corrcoef.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/matthews_corrcoef.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +matthews_corrcoef : +1.0 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/moons.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/moons.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_model01.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_model01.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_model02.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_model02.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
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No newline at end of file\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_model03.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_model03.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,91 @@ +ccopy_reg +_reconstructor +p0 +(csklearn.neighbors.nearest_centroid +NearestCentroid +p1 +c__builtin__ +object +p2 +Ntp3 +Rp4 +(dp5 +S'centroids_' +p6 +cnumpy.core.multiarray +_reconstruct +p7 +(cnumpy +ndarray +p8 +(I0 +tp9 +S'b' +p10 +tp11 +Rp12 +(I1 +(I4 +I4 +tp13 +cnumpy +dtype +p14 +(S'f8' +p15 +I0 +I1 +tp16 +Rp17 +(I3 +S'<' +p18 +NNNI-1 +I-1 +I0 +tp19 +bI00 +S'\x00\x00\x00\x00\x00\x00\x00\x00\xab\xaa\xaa\xaa\xaa\x8aG@\x00\x00\x00\x00\x00\xc0K@UUUUUEQ\xc0\x00\x00\x00\x00\x00\x00\x00@\xab\xaa\xaa\xaa\xaajV\xc0\xab\xaa\xaa\xaa\xaa*3@\x00\x00\x00\x00\x00\xa0Y\xc0\x00\x00\x00\x00\x00\x00\xf0?\xc5N\xec\xc4N\xecM\xc0;\xb1\x13;\xb1SV@\x14;\xb1\x13;\xb1F@\x00\x00\x00\x00\x00\x00\x08@\xe9\xa2\x8b.\xba\xe8B@\x8c.\xba\xe8\xa2\x0bX\xc0t\xd1E\x17]t\x19@' +p20 +tp21 +bsS'metric' +p22 +Veuclidean +p23 +sS'classes_' +p24 +g7 +(g8 +(I0 +tp25 +g10 +tp26 +Rp27 +(I1 +(I4 +tp28 +g14 +(S'i8' +p29 +I0 +I1 +tp30 +Rp31 +(I3 +S'<' +p32 +NNNI-1 +I-1 +I0 +tp33 +bI00 +S'\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00' +p34 +tp35 +bsS'_sklearn_version' +p36 +S'0.19.1' +p37 +sS'shrink_threshold' +p38 +Nsb. \ No newline at end of file |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_prediction_result01.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,49 @@ +0 58 56 -67 0 +0 44 64 -76 0 +0 51 48 -73 0 +0 58 65 -49 0 +0 43 61 -49 0 +0 45 43 -79 0 +0 42 60 -98 0 +0 50 55 -59 0 +0 53 53 -56 0 +0 45 44 -61 0 +0 43 65 -84 0 +0 35 52 -75 0 +0 56 56 -70 0 +1 -61 86 43 2 +1 -67 93 15 2 +1 -59 94 36 2 +1 -50 92 62 2 +1 -78 91 70 2 +1 -35 87 47 2 +1 -56 91 52 2 +1 -61 81 46 2 +1 -83 78 34 2 +1 -50 87 45 2 +1 -67 73 50 2 +1 -50 97 45 2 +1 -61 111 45 2 +2 -109 23 -92 1 +2 -94 20 -96 1 +2 -85 26 -88 1 +2 -90 33 -114 1 +2 -63 9 -106 1 +2 -79 9 -93 1 +2 -99 26 -108 1 +2 -81 19 -110 1 +2 -108 21 -108 1 +2 -92 27 -106 1 +2 -88 2 -106 1 +2 -88 15 -103 1 +3 54 -74 4 3 +3 42 -92 31 3 +3 39 -99 -7 3 +3 48 -115 -5 3 +3 39 -96 2 3 +3 31 -109 9 3 +3 33 -96 -8 3 +3 23 -102 4 3 +3 38 -90 21 3 +3 34 -107 1 3 +3 35 -78 18 3 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_prediction_result02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_prediction_result02.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,49 @@ +0 58 56 -67 0 +0 44 64 -76 0 +0 51 48 -73 0 +0 58 65 -49 0 +0 43 61 -49 0 +0 45 43 -79 0 +0 42 60 -98 0 +0 50 55 -59 0 +0 53 53 -56 0 +0 45 44 -61 0 +0 43 65 -84 0 +0 35 52 -75 0 +0 56 56 -70 0 +1 -61 86 43 2 +1 -67 93 15 2 +1 -59 94 36 2 +1 -50 92 62 2 +1 -78 91 70 2 +1 -35 87 47 2 +1 -56 91 52 2 +1 -61 81 46 2 +1 -83 78 34 2 +1 -50 87 45 2 +1 -67 73 50 2 +1 -50 97 45 2 +1 -61 111 45 2 +2 -109 23 -92 1 +2 -94 20 -96 1 +2 -85 26 -88 1 +2 -90 33 -114 1 +2 -63 9 -106 1 +2 -79 9 -93 1 +2 -99 26 -108 1 +2 -81 19 -110 1 +2 -108 21 -108 1 +2 -92 27 -106 1 +2 -88 2 -106 1 +2 -88 15 -103 1 +3 54 -74 4 3 +3 42 -92 31 3 +3 39 -99 -7 3 +3 48 -115 -5 3 +3 39 -96 2 3 +3 31 -109 9 3 +3 33 -96 -8 3 +3 23 -102 4 3 +3 38 -90 21 3 +3 34 -107 1 3 +3 35 -78 18 3 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/nn_prediction_result03.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/nn_prediction_result03.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,49 @@ +0 58 56 -67 0 +0 44 64 -76 0 +0 51 48 -73 0 +0 58 65 -49 0 +0 43 61 -49 0 +0 45 43 -79 0 +0 42 60 -98 0 +0 50 55 -59 0 +0 53 53 -56 0 +0 45 44 -61 0 +0 43 65 -84 0 +0 35 52 -75 0 +0 56 56 -70 0 +1 -61 86 43 2 +1 -67 93 15 2 +1 -59 94 36 2 +1 -50 92 62 2 +1 -78 91 70 2 +1 -35 87 47 2 +1 -56 91 52 2 +1 -61 81 46 2 +1 -83 78 34 2 +1 -50 87 45 2 +1 -67 73 50 2 +1 -50 97 45 2 +1 -61 111 45 2 +2 -109 23 -92 1 +2 -94 20 -96 1 +2 -85 26 -88 1 +2 -90 33 -114 1 +2 -63 9 -106 1 +2 -79 9 -93 1 +2 -99 26 -108 1 +2 -81 19 -110 1 +2 -108 21 -108 1 +2 -92 27 -106 1 +2 -88 2 -106 1 +2 -88 15 -103 1 +3 54 -74 4 3 +3 42 -92 31 3 +3 39 -99 -7 3 +3 48 -115 -5 3 +3 39 -96 2 3 +3 31 -109 9 3 +3 33 -96 -8 3 +3 23 -102 4 3 +3 38 -90 21 3 +3 34 -107 1 3 +3 35 -78 18 3 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/numeric_values.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/numeric_values.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/precision_recall_curve.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_recall_curve.txt Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,2 @@ +precision_recall_curve : +(array([1., 1.]), array([1., 0.]), array([1])) |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/precision_recall_fscore_support.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_recall_fscore_support.txt Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,2 @@ +precision_recall_fscore_support : +(0.8461538461538461, 0.8461538461538461, 0.8461538461538461, None) |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/precision_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/precision_score.txt Fri Feb 16 09:17:59 2018 -0500 |
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| @@ -0,0 +1,2 @@ +precision_score : +0.8461538461538461 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model01 |
| b |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model02 |
| b |
| Binary file test-data/prp_model02 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model03 |
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| Binary file test-data/prp_model03 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model04 |
| b |
| Binary file test-data/prp_model04 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model05 |
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| Binary file test-data/prp_model05 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model06 |
| b |
| Binary file test-data/prp_model06 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model07 |
| b |
| Binary file test-data/prp_model07 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model08 |
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| Binary file test-data/prp_model08 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_model09 |
| b |
| Binary file test-data/prp_model09 has changed |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/prp_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/prp_result01 Fri Feb 16 09:17:59 2018 -0500 |
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| diff -r 000000000000 -r c7b8fab00c0f test-data/qda_model01 |
| b |
| Binary file test-data/qda_model01 has changed |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/qda_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/qda_prediction_result01.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 0 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 0 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/recall_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/recall_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +recall_score : +0.8461538461538461 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/regression.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
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|
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/regression_test.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression_test.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 +91.2021798817 -0.621522971207 1.11914889596 0.390012184498 1.28956938152 +-47.4101632272 -0.638416457964 -0.732777468453 -0.864026104978 -1.06109770116 +61.7128046302 -1.09994800577 -0.739679672932 0.585657963012 1.48906827536 +-206.998295124 0.130238853011 0.70574123041 1.33206565264 -1.33220923738 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/regression_train.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/regression_train.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,10 @@ +143.762620712 -0.330941870584 -1.17964571928 0.47944415578 -0.0486946279099 1.57951239219 +-88.5787166225 1.08055532812 -2.57109184022 -0.92512305494 0.317511276982 -1.202358944 +-82.8452345578 0.272541389247 -0.168636324107 0.923988150154 -0.467750945768 -0.719169535969 +72.4951388149 -0.268686605278 0.991068834926 0.731619322189 1.17038734294 0.323842059244 +11.805182128 1.03604670966 -0.709685560786 -1.54916691211 -0.614757954242 0.24176665894 +-63.9354970901 -0.101485840571 0.984112210822 -2.01704822953 0.282058758309 -0.776448499847 +126.325840796 -0.359998340179 0.353534448839 -1.23256828198 0.563632964937 1.15031170568 +23.0341392692 0.518540465136 1.03188231893 -2.53173026594 -0.0419267228327 0.193734455015 +67.6714937696 -0.115688051547 -0.821437865172 -0.368962397052 -0.526743874023 0.94315222831 +47.3927584881 -0.785096541368 -0.0942409319417 0.224267378731 -1.63317786831 1.26458811586 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/rfc_model01 |
| b |
| Binary file test-data/rfc_model01 has changed |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/rfc_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/rfc_result01 Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +3.68258022948 2.82110345641 -3.990140724 -1.9523364774 0 +0.015942057224 -0.7119585943469999 0.125502976978 -0.972218263337 0 +2.0869076882499997 0.929399321468 -2.1292408448400004 -1.9971402218799998 1 +1.4132105208399999 0.523750660422 -1.4210539291 -1.49298569451 1 +0.7683140439399999 1.38267855169 -0.989045048734 0.649504257894 1 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/rfc_result02 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/rfc_result02 Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/rfr_model01 |
| b |
| Binary file test-data/rfr_model01 has changed |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/rfr_result01 --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/rfr_result01 Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,5 @@ +86.9702122735 1.00532111569 -1.01739601979 -0.613139481654 0.641846874331 0 +91.2021798817 -0.6215229712070001 1.11914889596 0.390012184498 1.28956938152 0.8511213285107001 +-47.4101632272 -0.638416457964 -0.7327774684530001 -0.8640261049779999 -1.06109770116 0.05344095304070007 +61.712804630200004 -1.0999480057700002 -0.739679672932 0.585657963012 1.4890682753600002 1.1892759745694002 +-206.998295124 0.130238853011 0.70574123041 1.3320656526399999 -1.3322092373799999 -0.35023626536690006 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/roc_auc_score.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/roc_auc_score.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +roc_auc_score : +1.0 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/roc_curve.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/roc_curve.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
| @@ -0,0 +1,2 @@ +roc_curve : +(array([0., 1.]), array([1., 1.]), array([1, 0])) |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/scurve.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/scurve.txt Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/sparse.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sparse.mtx Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/sparse_u.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/sparse_u.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| b'@@ -0,0 +1,101 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|
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/svc_model01.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/svc_model01.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
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No newline at end of file\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/svc_model02.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/svc_model02.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
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No newline at end of file\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/svc_model03.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/svc_model03.txt Fri Feb 16 09:17:59 2018 -0500 |
| [ |
| @@ -0,0 +1,163 @@ +ccopy_reg +_reconstructor +p0 +(csklearn.svm.classes +LinearSVC +p1 +c__builtin__ +object +p2 +Ntp3 +Rp4 +(dp5 +S'loss' +p6 +Vsquared_hinge +p7 +sS'C' +p8 +F1.0 +sS'max_iter' +p9 +I1000 +sS'verbose' +p10 +I0 +sS'fit_intercept' +p11 +I01 +sS'random_state' +p12 +I5 +sS'classes_' +p13 +cnumpy.core.multiarray +_reconstruct +p14 +(cnumpy +ndarray +p15 +(I0 +tp16 +S'b' +p17 +tp18 +Rp19 +(I1 +(I4 +tp20 +cnumpy +dtype +p21 +(S'i8' +p22 +I0 +I1 +tp23 +Rp24 +(I3 +S'<' +p25 +NNNI-1 +I-1 +I0 +tp26 +bI00 +S'\x00\x00\x00\x00\x00\x00\x00\x00\x01\x00\x00\x00\x00\x00\x00\x00\x02\x00\x00\x00\x00\x00\x00\x00\x03\x00\x00\x00\x00\x00\x00\x00' +p27 +tp28 +bsS'n_iter_' +p29 +cnumpy.core.multiarray +scalar +p30 +(g21 +(S'i4' +p31 +I0 +I1 +tp32 +Rp33 +(I3 +S'<' +p34 +NNNI-1 +I-1 +I0 +tp35 +bS'\xe8\x03\x00\x00' +p36 +tp37 +Rp38 +sS'intercept_scaling' +p39 +F1.0 +sS'penalty' +p40 +Vl2 +p41 +sS'multi_class' +p42 +Vovr +p43 +sS'coef_' +p44 +g14 +(g15 +(I0 +tp45 +g17 +tp46 +Rp47 +(I1 +(I4 +I4 +tp48 +g21 +(S'f8' +p49 +I0 +I1 +tp50 +Rp51 +(I3 +S'<' +p52 +NNNI-1 +I-1 +I0 +tp53 +bI01 +S'@@\xae\xfaP\xc8\xd4\xbf\xe8\x9b\x9e\x13"\xd6\xdb\xbf\xba\xc8\xd0f\xd1\xb9`\xbf\xca\xa9\x1a\xa8\x93\xda&\xbfv\xa2{\x1d\n\x8e\x95?\x1a\xb3\xc3\x80`\x9fn\xbf\xa5E?\x1dKV\x88\xbf\xb4]v\x97t8f?~R\x8dH\xd4\x9c\x89?+\xe8\xb4\x8b\xe7N\x7f\xbf\xdef\x84\xc2\xaa\xbc\x8e?\xc7\x193\xe1OO\x90\xbf\xc6UW_\x17\xdd\x87\xbf\xed\xa5\x82\x11Xbh?\x9b5w\xe8\x7fR\x9d?\xff\x9fao\xeaB|?' +p54 +tp55 +bsS'_sklearn_version' +p56 +S'0.19.1' +p57 +sS'dual' +p58 +I01 +sS'tol' +p59 +F0.001 +sS'intercept_' +p60 +g14 +(g15 +(I0 +tp61 +g17 +tp62 +Rp63 +(I1 +(I4 +tp64 +g51 +I00 +S'\x9e\x05)W2\xcf\xc3\xbf\x98\xa4\x94\x94\x06\xf1\xd1\xbf\x1b<a=\xfb\x89S\xbf[\x9e>\x89\xb1<4\xbf' +p65 +tp66 +bsS'class_weight' +p67 +Nsb. \ No newline at end of file |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/svc_prediction_result01.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/svc_prediction_result01.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,49 @@ +0 58 56 -67 0 +0 44 64 -76 0 +0 51 48 -73 0 +0 58 65 -49 0 +0 43 61 -49 0 +0 45 43 -79 0 +0 42 60 -98 0 +0 50 55 -59 0 +0 53 53 -56 0 +0 45 44 -61 0 +0 43 65 -84 0 +0 35 52 -75 0 +0 56 56 -70 0 +1 -61 86 43 2 +1 -67 93 15 2 +1 -59 94 36 2 +1 -50 92 62 2 +1 -78 91 70 2 +1 -35 87 47 2 +1 -56 91 52 2 +1 -61 81 46 2 +1 -83 78 34 2 +1 -50 87 45 2 +1 -67 73 50 2 +1 -50 97 45 2 +1 -61 111 45 2 +2 -109 23 -92 1 +2 -94 20 -96 1 +2 -85 26 -88 1 +2 -90 33 -114 1 +2 -63 9 -106 1 +2 -79 9 -93 1 +2 -99 26 -108 1 +2 -81 19 -110 1 +2 -108 21 -108 1 +2 -92 27 -106 1 +2 -88 2 -106 1 +2 -88 15 -103 1 +3 54 -74 4 3 +3 42 -92 31 3 +3 39 -99 -7 3 +3 48 -115 -5 3 +3 39 -96 2 3 +3 31 -109 9 3 +3 33 -96 -8 3 +3 23 -102 4 3 +3 38 -90 21 3 +3 34 -107 1 3 +3 35 -78 18 3 |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/svc_prediction_result02.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/svc_prediction_result02.tabular Fri Feb 16 09:17:59 2018 -0500 |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/vectorizer_result02.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/vectorizer_result02.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| b'@@ -0,0 +1,7346 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 7343\n+1 1450 0.0152274037172392\n+1 1961 0.01275776096436371\n+1 4053 0.01582710983069052\n+1 4357 0.01446327540314412\n+1 4379 0.02292554618019654\n+1 4727 0.01275776096436371\n+1 6201 0.02859906492378073\n+1 7170 0.01341424712825137\n+1 8116 0.02292554618019654\n+1 10078 0.05679044019028674\n+1 10495 0.03256516109420249\n+1 12091 0.05275703276896841\n+1 12155 0.01906604328252049\n+1 13013 0.01517172746603997\n+1 13181 0.0152274037172392\n+1 13234 0.0108550536980675\n+1 13280 0.0152274037172392\n+1 14120 0.01906604328252049\n+1 14527 0.0152274037172392\n+1 15083 0.0152274037172392\n+1 15241 0.01446327540314412\n+1 15283 0.01895709357182213\n+1 16177 0.01275776096436371\n+1 17436 0.0152274037172392\n+1 19449 0.02255274822664651\n+1 20975 0.05803273604586524\n+1 21531 0.0144155416907593\n+1 22178 0.00527570327689684\n+1 22283 0.01674245171469339\n+1 22706 0.02892655080628824\n+1 22715 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0.02245327618246397\n+4 1042547 0.01695356088516536\n+4 1042815 0.01586088310577529\n+4 1043231 0.01425830829592147\n+4 1043781 0.01525873096885777\n+4 1046133 0.01917635673617606\n+4 1046509 0.01843238994100031\n+4 1046561 0.03051746193771554\n+4 1046703 0.01917635673617606\n+4 1047335 0.01525873096885777\n+4 1047547 0.01360986045640215\n+4 1048455 0.02245327618246397\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/vectorizer_result03.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/vectorizer_result03.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| b'@@ -0,0 +1,9697 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 9694\n+1 71 0.01361331290463183\n+1 1450 0.01361331290463183\n+1 1961 0.01140545001599714\n+1 3747 0.01361331290463183\n+1 3795 0.02049545933475693\n+1 4053 0.01414945072069242\n+1 4357 0.01293018149022739\n+1 4379 0.02049545933475693\n+1 4727 0.01140545001599714\n+1 5795 0.01704506020053583\n+1 6201 0.02556759030080374\n+1 7170 0.01199234924928183\n+1 8116 0.02049545933475693\n+1 8523 0.01293018149022739\n+1 8531 0.01704506020053583\n+1 9300 0.02041996935694774\n+1 10078 0.05077070567367346\n+1 10495 0.02911328391873078\n+1 12091 0.0471648357356414\n+1 12155 0.01704506020053583\n+1 13013 0.01356353828493919\n+1 13181 0.01361331290463183\n+1 13234 0.009704427972910257\n+1 13280 0.01361331290463183\n+1 14120 0.01704506020053583\n+1 14527 0.01361331290463183\n+1 14873 0.01293018149022739\n+1 15083 0.01361331290463183\n+1 15241 0.01293018149022739\n+1 15283 0.01694765905913661\n+1 16177 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0.01648847232539554\n+4 1033089 0.01715397885778408\n+4 1033256 0.01364951393205028\n+4 1033699 0.01648847232539554\n+4 1033833 0.01217453668277288\n+4 1034350 0.02729902786410057\n+4 1034513 0.02008530766406564\n+4 1035161 0.01275459788430996\n+4 1035177 0.030331207224535\n+4 1036844 0.02573096828667612\n+4 1037044 0.02434907336554575\n+4 1037226 0.02008530766406564\n+4 1037251 0.01648847232539554\n+4 1037326 0.02573096828667612\n+4 1037360 0.01299725962753113\n+4 1037916 0.02837632374130612\n+4 1038731 0.01364951393205028\n+4 1039281 0.02008530766406564\n+4 1039328 0.01715397885778408\n+4 1039896 0.01715397885778408\n+4 1040201 0.01057434499550492\n+4 1040273 0.02128224280597959\n+4 1040325 0.02008530766406564\n+4 1040381 0.02729902786410057\n+4 1040891 0.01648847232539554\n+4 1040913 0.02128224280597959\n+4 1041230 0.01364951393205028\n+4 1042343 0.02008530766406564\n+4 1042547 0.0151656036122675\n+4 1042627 0.01715397885778408\n+4 1042774 0.01715397885778408\n+4 1042815 0.01418816187065306\n+4 1043231 0.01275459788430996\n+4 1043781 0.01364951393205028\n+4 1044112 0.01364951393205028\n+4 1046133 0.01715397885778408\n+4 1046509 0.01648847232539554\n+4 1046561 0.02729902786410057\n+4 1046703 0.01715397885778408\n+4 1047335 0.01364951393205028\n+4 1047547 0.01217453668277288\n+4 1048455 0.02008530766406564\n' |
| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/vectorizer_result04.mtx --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/vectorizer_result04.mtx Fri Feb 16 09:17:59 2018 -0500 |
| b |
| b'@@ -0,0 +1,9697 @@\n+%%MatrixMarket matrix coordinate real general\n+%\n+4 1048577 9694\n+1 71 0.01361331290463183\n+1 1450 0.01361331290463183\n+1 1961 0.01140545001599714\n+1 3747 0.01361331290463183\n+1 3795 0.02049545933475693\n+1 4053 0.01414945072069242\n+1 4357 0.01293018149022739\n+1 4379 0.02049545933475693\n+1 4727 0.01140545001599714\n+1 5795 0.01704506020053583\n+1 6201 0.02556759030080374\n+1 7170 0.01199234924928183\n+1 8116 0.02049545933475693\n+1 8523 0.01293018149022739\n+1 8531 0.01704506020053583\n+1 9300 0.02041996935694774\n+1 10078 0.05077070567367346\n+1 10495 0.02911328391873078\n+1 12091 0.0471648357356414\n+1 12155 0.01704506020053583\n+1 13013 0.01356353828493919\n+1 13181 0.01361331290463183\n+1 13234 0.009704427972910257\n+1 13280 0.01361331290463183\n+1 14120 0.01704506020053583\n+1 14527 0.01361331290463183\n+1 14873 0.01293018149022739\n+1 15083 0.01361331290463183\n+1 15241 0.01293018149022739\n+1 15283 0.01694765905913661\n+1 16177 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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/y.tabular --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/y.tabular Fri Feb 16 09:17:59 2018 -0500 |
| b |
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| b |
| diff -r 000000000000 -r c7b8fab00c0f test-data/zero_one_loss.txt --- /dev/null Thu Jan 01 00:00:00 1970 +0000 +++ b/test-data/zero_one_loss.txt Fri Feb 16 09:17:59 2018 -0500 |
| b |
| @@ -0,0 +1,2 @@ +zero_one_loss : +0.15384615384615385 |