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examples/sklearn/'cache'/datasets/17/description.xml create mode 100644 examples/sklearn/'cache'/datasets/17/features.xml create mode 100644 examples/sklearn/'cache'/datasets/17/qualities.xml create mode 100644 examples/sklearn/'cache'/locks/datasets.functions.get_dataset:17 create mode 100644 examples/sklearn/'cache'/locks/datasets.functions.get_dataset:97 create mode 100644 examples/sklearn/'cache'/tasks/97/datasplits.arff create mode 100644 examples/sklearn/'cache'/tasks/97/datasplits.pkl create mode 100644 examples/sklearn/'cache'/tasks/97/task.xml diff --git a/doc/contributing.rst b/doc/contributing.rst index e614c8a25..3951b7445 100644 --- a/doc/contributing.rst +++ b/doc/contributing.rst @@ -16,6 +16,48 @@ Please mark yourself as contributor in a github issue if you start working on something to avoid duplicate work. If you're part of the OpenML organization you can use github's assign feature, otherwise you can just leave a comment. +How to Contribute +~~~~~~~~~~~~~~~~~ + +The preferred workflow for contributing to the OpenML python connector is to fork the [main repository](https://github.com/openml/openml-python) on GitHub, clone, check out the branch `develop`, and develop on a new branch branch. Steps: + +* Fork the [project repository](https://github.com/openml/openml-python) by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/). + +* Clone your fork of the openml-python repo from your GitHub account to your local disk: + +.. code:: bash + + $ git clone git@github.com:YourLogin/openml-python.git + $ cd openml-python + +* Switch to the develop branch: + +.. code:: bash + + $ git checkout develop + +* Create a feature branch to hold your development changes: + +.. code:: bash + + $ git checkout -b feature/my-feature + +Always use a feature branch. It's good practice to never work on the master or develop branch! To make the nature of your pull request easily visible, please prepend the name of the branch with the type of changes you want to merge, such as feature if it contains a new feature, fix for a bugfix, doc for documentation and maint for other maintenance on the package. + +Develop the feature on your feature branch. Add changed files using git add and then git commit files: + +.. code:: bash + + $ git add modified_files + $ git commit + +to record your changes in Git, then push the changes to your GitHub account with: + +$ git push -u origin my-feature +Follow these instructions to create a pull request from your fork. This will send an email to the committers. + +(If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.) + .. _scope: Scope of the package diff --git a/doc/examples/create_upload_tutorial.ipynb b/doc/examples/create_upload_tutorial.ipynb new file mode 100644 index 000000000..73c5c04d4 --- /dev/null +++ b/doc/examples/create_upload_tutorial.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\nDataset upload tutorial\n=======================\n\nA tutorial on how to create and upload a dataset to OpenML.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nimport pandas as pd\nimport sklearn.datasets\nfrom scipy.sparse import coo_matrix\n\nimport openml\nfrom openml.datasets.functions import create_dataset" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For this tutorial we will upload to the test server to not pollute the live\nserver with countless copies of the same dataset.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "openml.config.server = 'https://test.openml.org/api/v1/xml'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we will cover the following cases of the dataset object:\n\n* A numpy array\n* A list\n* A pandas dataframe\n* A sparse matrix\n* A pandas sparse dataframe\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset is a numpy array\n========================\nA numpy array can contain lists in the case of dense data or it can contain\nOrderedDicts in the case of sparse data.\n\nPrepare dataset\n^^^^^^^^^^^^^^^\nLoad an example dataset from scikit-learn which we will upload to OpenML.org\nvia the API.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "diabetes = sklearn.datasets.load_diabetes()\nname = 'Diabetes(scikit-learn)'\nX = diabetes.data\ny = diabetes.target\nattribute_names = diabetes.feature_names\ndescription = diabetes.DESCR" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenML does not distinguish between the attributes and targets on the data\nlevel and stores all data in a single matrix.\n\nThe target feature is indicated as meta-data of the dataset (and tasks on\nthat data).\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = np.concatenate((X, y.reshape((-1, 1))), axis=1)\nattribute_names = list(attribute_names)\nattributes = [\n (attribute_name, 'REAL') for attribute_name in attribute_names\n] + [('class', 'INTEGER')]\ncitation = (\n \"Bradley Efron, Trevor Hastie, Iain Johnstone and \"\n \"Robert Tibshirani (2004) (Least Angle Regression) \"\n \"Annals of Statistics (with discussion), 407-499\"\n)\npaper_url = (\n 'http://web.stanford.edu/~hastie/Papers/'\n 'LARS/LeastAngle_2002.pdf'\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the dataset object\n^^^^^^^^^^^^^^^^^^^^^^^^^\nThe definition of all fields can be found in the XSD files describing the\nexpected format:\n\nhttps://github.com/openml/OpenML/blob/master/openml_OS/views/pages/api_new/v1/xsd/openml.data.upload.xsd\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "diabetes_dataset = create_dataset(\n # The name of the dataset (needs to be unique).\n # Must not be longer than 128 characters and only contain\n # a-z, A-Z, 0-9 and the following special characters: _\\-\\.(),\n name=name,\n # Textual description of the dataset.\n description=description,\n # The person who created the dataset.\n creator=\"Bradley Efron, Trevor Hastie, \"\n \"Iain Johnstone and Robert Tibshirani\",\n # People who contributed to the current version of the dataset.\n contributor=None,\n # The date the data was originally collected, given by the uploader.\n collection_date='09-01-2012',\n # Language in which the data is represented.\n # Starts with 1 upper case letter, rest lower case, e.g. 'English'.\n language='English',\n # License under which the data is/will be distributed.\n licence='BSD (from scikit-learn)',\n # Name of the target. Can also have multiple values (comma-separated).\n default_target_attribute='class',\n # The attribute that represents the row-id column, if present in the\n # dataset.\n row_id_attribute=None,\n # Attributes that should be excluded in modelling, such as identifiers and\n # indexes.\n ignore_attribute=None,\n # How to cite the paper.\n citation=citation,\n # Attributes of the data\n attributes=attributes,\n data=data,\n # A version label which is provided by the user.\n version_label='test',\n original_data_url=(\n 'http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html'\n ),\n paper_url=paper_url,\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "upload_did = diabetes_dataset.publish()\nprint('URL for dataset: %s/data/%d' % (openml.config.server, upload_did))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset is a list\n=================\nA list can contain lists in the case of dense data or it can contain\nOrderedDicts in the case of sparse data.\n\nWeather dataset:\nhttp://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "data = [\n ['sunny', 85, 85, 'FALSE', 'no'],\n ['sunny', 80, 90, 'TRUE', 'no'],\n ['overcast', 83, 86, 'FALSE', 'yes'],\n ['rainy', 70, 96, 'FALSE', 'yes'],\n ['rainy', 68, 80, 'FALSE', 'yes'],\n ['rainy', 65, 70, 'TRUE', 'no'],\n ['overcast', 64, 65, 'TRUE', 'yes'],\n ['sunny', 72, 95, 'FALSE', 'no'],\n ['sunny', 69, 70, 'FALSE', 'yes'],\n ['rainy', 75, 80, 'FALSE', 'yes'],\n ['sunny', 75, 70, 'TRUE', 'yes'],\n ['overcast', 72, 90, 'TRUE', 'yes'],\n ['overcast', 81, 75, 'FALSE', 'yes'],\n ['rainy', 71, 91, 'TRUE', 'no'],\n]\n\nattribute_names = [\n ('outlook', ['sunny', 'overcast', 'rainy']),\n ('temperature', 'REAL'),\n ('humidity', 'REAL'),\n ('windy', ['TRUE', 'FALSE']),\n ('play', ['yes', 'no']),\n]\n\ndescription = (\n 'The weather problem is a tiny dataset that we will use repeatedly'\n ' to illustrate machine learning methods. Entirely fictitious, it '\n 'supposedly concerns the conditions that are suitable for playing '\n 'some unspecified game. In general, instances in a dataset are '\n 'characterized by the values of features, or attributes, that measure '\n 'different aspects of the instance. In this case there are four '\n 'attributes: outlook, temperature, humidity, and windy. '\n 'The outcome is whether to play or not.'\n)\n\ncitation = (\n 'I. H. Witten, E. Frank, M. A. Hall, and ITPro,'\n 'Data mining practical machine learning tools and techniques, '\n 'third edition. Burlington, Mass.: Morgan Kaufmann Publishers, 2011'\n)\n\nweather_dataset = create_dataset(\n name=\"Weather\",\n description=description,\n creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro',\n contributor=None,\n collection_date='01-01-2011',\n language='English',\n licence=None,\n default_target_attribute='play',\n row_id_attribute=None,\n ignore_attribute=None,\n citation=citation,\n attributes=attribute_names,\n data=data,\n version_label='example',\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "upload_did = weather_dataset.publish()\nprint('URL for dataset: %s/data/%d' % (openml.config.server, upload_did))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset is a pandas DataFrame\n=============================\nIt might happen that your dataset is made of heterogeneous data which can be\nusually stored as a Pandas DataFrame. DataFrame offers the adavantages to\nstore the type of data for each column as well as the attribute names.\nTherefore, when providing a Pandas DataFrame, OpenML can infer those\ninformation without the need to specifically provide them when calling the\nfunction :func:`create_dataset`. In this regard, you only need to pass\n``'auto'`` to the ``attributes`` parameter.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "df = pd.DataFrame(data, columns=[col_name for col_name, _ in attribute_names])\n# enforce the categorical column to have a categorical dtype\ndf['outlook'] = df['outlook'].astype('category')\ndf['windy'] = df['windy'].astype('bool')\ndf['play'] = df['play'].astype('category')\nprint(df.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We enforce the column 'outlook', 'windy', and 'play' to be a categorical\ndtype while the column 'rnd_str' is kept as a string column. Then, we can\ncall :func:`create_dataset` by passing the dataframe and fixing the parameter\n``attributes`` to ``'auto'``.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "weather_dataset = create_dataset(\n name=\"Weather\",\n description=description,\n creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro',\n contributor=None,\n collection_date='01-01-2011',\n language='English',\n licence=None,\n default_target_attribute='play',\n row_id_attribute=None,\n ignore_attribute=None,\n citation=citation,\n attributes='auto',\n data=df,\n version_label='example',\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "upload_did = weather_dataset.publish()\nprint('URL for dataset: %s/data/%d' % (openml.config.server, upload_did))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset is a sparse matrix\n==========================\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sparse_data = coo_matrix((\n [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1])\n))\n\ncolumn_names = [\n ('input1', 'REAL'),\n ('input2', 'REAL'),\n ('y', 'REAL'),\n]\n\nxor_dataset = create_dataset(\n name=\"XOR\",\n description='Dataset representing the XOR operation',\n creator=None,\n contributor=None,\n collection_date=None,\n language='English',\n licence=None,\n default_target_attribute='y',\n row_id_attribute=None,\n ignore_attribute=None,\n citation=None,\n attributes=column_names,\n data=sparse_data,\n version_label='example',\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "upload_did = xor_dataset.publish()\nprint('URL for dataset: %s/data/%d' % (openml.config.server, upload_did))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset is a pandas sparse dataframe\n====================================\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sparse_data = coo_matrix((\n [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],\n ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1])\n))\ncolumn_names = ['input1', 'input2', 'y']\ndf = pd.SparseDataFrame(sparse_data, columns=column_names)\nprint(df.info())\n\nxor_dataset = create_dataset(\n name=\"XOR\",\n description='Dataset representing the XOR operation',\n creator=None,\n contributor=None,\n collection_date=None,\n language='English',\n licence=None,\n default_target_attribute='y',\n row_id_attribute=None,\n ignore_attribute=None,\n citation=None,\n attributes='auto',\n data=df,\n version_label='example',\n)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "upload_did = xor_dataset.publish()\nprint('URL for dataset: %s/data/%d' % (openml.config.server, upload_did))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/create_upload_tutorial.py b/doc/examples/create_upload_tutorial.py new file mode 100644 index 000000000..f04875467 --- /dev/null +++ b/doc/examples/create_upload_tutorial.py @@ -0,0 +1,311 @@ +""" +Dataset upload tutorial +======================= + +A tutorial on how to create and upload a dataset to OpenML. +""" +import numpy as np +import pandas as pd +import sklearn.datasets +from scipy.sparse import coo_matrix + +import openml +from openml.datasets.functions import create_dataset + +############################################################################ +# For this tutorial we will upload to the test server to not pollute the live +# server with countless copies of the same dataset. +openml.config.server = 'https://test.openml.org/api/v1/xml' + +############################################################################ +# Below we will cover the following cases of the dataset object: +# +# * A numpy array +# * A list +# * A pandas dataframe +# * A sparse matrix +# * A pandas sparse dataframe + +############################################################################ +# Dataset is a numpy array +# ======================== +# A numpy array can contain lists in the case of dense data or it can contain +# OrderedDicts in the case of sparse data. +# +# Prepare dataset +# ^^^^^^^^^^^^^^^ +# Load an example dataset from scikit-learn which we will upload to OpenML.org +# via the API. + +diabetes = sklearn.datasets.load_diabetes() +name = 'Diabetes(scikit-learn)' +X = diabetes.data +y = diabetes.target +attribute_names = diabetes.feature_names +description = diabetes.DESCR + +############################################################################ +# OpenML does not distinguish between the attributes and targets on the data +# level and stores all data in a single matrix. +# +# The target feature is indicated as meta-data of the dataset (and tasks on +# that data). + +data = np.concatenate((X, y.reshape((-1, 1))), axis=1) +attribute_names = list(attribute_names) +attributes = [ + (attribute_name, 'REAL') for attribute_name in attribute_names +] + [('class', 'INTEGER')] +citation = ( + "Bradley Efron, Trevor Hastie, Iain Johnstone and " + "Robert Tibshirani (2004) (Least Angle Regression) " + "Annals of Statistics (with discussion), 407-499" +) +paper_url = ( + 'http://web.stanford.edu/~hastie/Papers/' + 'LARS/LeastAngle_2002.pdf' +) + +############################################################################ +# Create the dataset object +# ^^^^^^^^^^^^^^^^^^^^^^^^^ +# The definition of all fields can be found in the XSD files describing the +# expected format: +# +# https://github.com/openml/OpenML/blob/master/openml_OS/views/pages/api_new/v1/xsd/openml.data.upload.xsd + +diabetes_dataset = create_dataset( + # The name of the dataset (needs to be unique). + # Must not be longer than 128 characters and only contain + # a-z, A-Z, 0-9 and the following special characters: _\-\.(), + name=name, + # Textual description of the dataset. + description=description, + # The person who created the dataset. + creator="Bradley Efron, Trevor Hastie, " + "Iain Johnstone and Robert Tibshirani", + # People who contributed to the current version of the dataset. + contributor=None, + # The date the data was originally collected, given by the uploader. + collection_date='09-01-2012', + # Language in which the data is represented. + # Starts with 1 upper case letter, rest lower case, e.g. 'English'. + language='English', + # License under which the data is/will be distributed. + licence='BSD (from scikit-learn)', + # Name of the target. Can also have multiple values (comma-separated). + default_target_attribute='class', + # The attribute that represents the row-id column, if present in the + # dataset. + row_id_attribute=None, + # Attributes that should be excluded in modelling, such as identifiers and + # indexes. + ignore_attribute=None, + # How to cite the paper. + citation=citation, + # Attributes of the data + attributes=attributes, + data=data, + # A version label which is provided by the user. + version_label='test', + original_data_url=( + 'http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html' + ), + paper_url=paper_url, +) + +############################################################################ + +upload_did = diabetes_dataset.publish() +print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + +############################################################################ +# Dataset is a list +# ================= +# A list can contain lists in the case of dense data or it can contain +# OrderedDicts in the case of sparse data. +# +# Weather dataset: +# http://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html + +data = [ + ['sunny', 85, 85, 'FALSE', 'no'], + ['sunny', 80, 90, 'TRUE', 'no'], + ['overcast', 83, 86, 'FALSE', 'yes'], + ['rainy', 70, 96, 'FALSE', 'yes'], + ['rainy', 68, 80, 'FALSE', 'yes'], + ['rainy', 65, 70, 'TRUE', 'no'], + ['overcast', 64, 65, 'TRUE', 'yes'], + ['sunny', 72, 95, 'FALSE', 'no'], + ['sunny', 69, 70, 'FALSE', 'yes'], + ['rainy', 75, 80, 'FALSE', 'yes'], + ['sunny', 75, 70, 'TRUE', 'yes'], + ['overcast', 72, 90, 'TRUE', 'yes'], + ['overcast', 81, 75, 'FALSE', 'yes'], + ['rainy', 71, 91, 'TRUE', 'no'], +] + +attribute_names = [ + ('outlook', ['sunny', 'overcast', 'rainy']), + ('temperature', 'REAL'), + ('humidity', 'REAL'), + ('windy', ['TRUE', 'FALSE']), + ('play', ['yes', 'no']), +] + +description = ( + 'The weather problem is a tiny dataset that we will use repeatedly' + ' to illustrate machine learning methods. Entirely fictitious, it ' + 'supposedly concerns the conditions that are suitable for playing ' + 'some unspecified game. In general, instances in a dataset are ' + 'characterized by the values of features, or attributes, that measure ' + 'different aspects of the instance. In this case there are four ' + 'attributes: outlook, temperature, humidity, and windy. ' + 'The outcome is whether to play or not.' +) + +citation = ( + 'I. H. Witten, E. Frank, M. A. Hall, and ITPro,' + 'Data mining practical machine learning tools and techniques, ' + 'third edition. Burlington, Mass.: Morgan Kaufmann Publishers, 2011' +) + +weather_dataset = create_dataset( + name="Weather", + description=description, + creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro', + contributor=None, + collection_date='01-01-2011', + language='English', + licence=None, + default_target_attribute='play', + row_id_attribute=None, + ignore_attribute=None, + citation=citation, + attributes=attribute_names, + data=data, + version_label='example', +) + +############################################################################ + +upload_did = weather_dataset.publish() +print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + +############################################################################ +# Dataset is a pandas DataFrame +# ============================= +# It might happen that your dataset is made of heterogeneous data which can be +# usually stored as a Pandas DataFrame. DataFrame offers the adavantages to +# store the type of data for each column as well as the attribute names. +# Therefore, when providing a Pandas DataFrame, OpenML can infer those +# information without the need to specifically provide them when calling the +# function :func:`create_dataset`. In this regard, you only need to pass +# ``'auto'`` to the ``attributes`` parameter. + +df = pd.DataFrame(data, columns=[col_name for col_name, _ in attribute_names]) +# enforce the categorical column to have a categorical dtype +df['outlook'] = df['outlook'].astype('category') +df['windy'] = df['windy'].astype('bool') +df['play'] = df['play'].astype('category') +print(df.info()) + +############################################################################ +# We enforce the column 'outlook', 'windy', and 'play' to be a categorical +# dtype while the column 'rnd_str' is kept as a string column. Then, we can +# call :func:`create_dataset` by passing the dataframe and fixing the parameter +# ``attributes`` to ``'auto'``. + +weather_dataset = create_dataset( + name="Weather", + description=description, + creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro', + contributor=None, + collection_date='01-01-2011', + language='English', + licence=None, + default_target_attribute='play', + row_id_attribute=None, + ignore_attribute=None, + citation=citation, + attributes='auto', + data=df, + version_label='example', +) + +############################################################################ + +upload_did = weather_dataset.publish() +print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + +############################################################################ +# Dataset is a sparse matrix +# ========================== + +sparse_data = coo_matrix(( + [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]) +)) + +column_names = [ + ('input1', 'REAL'), + ('input2', 'REAL'), + ('y', 'REAL'), +] + +xor_dataset = create_dataset( + name="XOR", + description='Dataset representing the XOR operation', + creator=None, + contributor=None, + collection_date=None, + language='English', + licence=None, + default_target_attribute='y', + row_id_attribute=None, + ignore_attribute=None, + citation=None, + attributes=column_names, + data=sparse_data, + version_label='example', +) + +############################################################################ + +upload_did = xor_dataset.publish() +print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +############################################################################ +# Dataset is a pandas sparse dataframe +# ==================================== + +sparse_data = coo_matrix(( + [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]) +)) +column_names = ['input1', 'input2', 'y'] +df = pd.SparseDataFrame(sparse_data, columns=column_names) +print(df.info()) + +xor_dataset = create_dataset( + name="XOR", + description='Dataset representing the XOR operation', + creator=None, + contributor=None, + collection_date=None, + language='English', + licence=None, + default_target_attribute='y', + row_id_attribute=None, + ignore_attribute=None, + citation=None, + attributes='auto', + data=df, + version_label='example', +) + +############################################################################ + +upload_did = xor_dataset.publish() +print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) diff --git a/doc/examples/create_upload_tutorial.rst b/doc/examples/create_upload_tutorial.rst new file mode 100644 index 000000000..a8fff2e69 --- /dev/null +++ b/doc/examples/create_upload_tutorial.rst @@ -0,0 +1,421 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_create_upload_tutorial.py: + + +Dataset upload tutorial +======================= + +A tutorial on how to create and upload a dataset to OpenML. + + +.. code-block:: default + + import numpy as np + import pandas as pd + import sklearn.datasets + from scipy.sparse import coo_matrix + + import openml + from openml.datasets.functions import create_dataset + + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/create_upload_tutorial.py", line 13, in + from openml.datasets.functions import create_dataset + ImportError: cannot import name 'create_dataset' + + + + +For this tutorial we will upload to the test server to not pollute the live +server with countless copies of the same dataset. + + +.. code-block:: default + + openml.config.server = 'https://test.openml.org/api/v1/xml' + + +Below we will cover the following cases of the dataset object: + +* A numpy array +* A list +* A pandas dataframe +* A sparse matrix +* A pandas sparse dataframe + +Dataset is a numpy array +======================== +A numpy array can contain lists in the case of dense data or it can contain +OrderedDicts in the case of sparse data. + +Prepare dataset +^^^^^^^^^^^^^^^ +Load an example dataset from scikit-learn which we will upload to OpenML.org +via the API. + + +.. code-block:: default + + + diabetes = sklearn.datasets.load_diabetes() + name = 'Diabetes(scikit-learn)' + X = diabetes.data + y = diabetes.target + attribute_names = diabetes.feature_names + description = diabetes.DESCR + + +OpenML does not distinguish between the attributes and targets on the data +level and stores all data in a single matrix. + +The target feature is indicated as meta-data of the dataset (and tasks on +that data). + + +.. code-block:: default + + + data = np.concatenate((X, y.reshape((-1, 1))), axis=1) + attribute_names = list(attribute_names) + attributes = [ + (attribute_name, 'REAL') for attribute_name in attribute_names + ] + [('class', 'INTEGER')] + citation = ( + "Bradley Efron, Trevor Hastie, Iain Johnstone and " + "Robert Tibshirani (2004) (Least Angle Regression) " + "Annals of Statistics (with discussion), 407-499" + ) + paper_url = ( + 'http://web.stanford.edu/~hastie/Papers/' + 'LARS/LeastAngle_2002.pdf' + ) + + +Create the dataset object +^^^^^^^^^^^^^^^^^^^^^^^^^ +The definition of all fields can be found in the XSD files describing the +expected format: + +https://github.com/openml/OpenML/blob/master/openml_OS/views/pages/api_new/v1/xsd/openml.data.upload.xsd + + +.. code-block:: default + + + diabetes_dataset = create_dataset( + # The name of the dataset (needs to be unique). + # Must not be longer than 128 characters and only contain + # a-z, A-Z, 0-9 and the following special characters: _\-\.(), + name=name, + # Textual description of the dataset. + description=description, + # The person who created the dataset. + creator="Bradley Efron, Trevor Hastie, " + "Iain Johnstone and Robert Tibshirani", + # People who contributed to the current version of the dataset. + contributor=None, + # The date the data was originally collected, given by the uploader. + collection_date='09-01-2012', + # Language in which the data is represented. + # Starts with 1 upper case letter, rest lower case, e.g. 'English'. + language='English', + # License under which the data is/will be distributed. + licence='BSD (from scikit-learn)', + # Name of the target. Can also have multiple values (comma-separated). + default_target_attribute='class', + # The attribute that represents the row-id column, if present in the + # dataset. + row_id_attribute=None, + # Attributes that should be excluded in modelling, such as identifiers and + # indexes. + ignore_attribute=None, + # How to cite the paper. + citation=citation, + # Attributes of the data + attributes=attributes, + data=data, + # A version label which is provided by the user. + version_label='test', + original_data_url=( + 'http://www4.stat.ncsu.edu/~boos/var.select/diabetes.html' + ), + paper_url=paper_url, + ) + + + +.. code-block:: default + + + upload_did = diabetes_dataset.publish() + print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +Dataset is a list +================= +A list can contain lists in the case of dense data or it can contain +OrderedDicts in the case of sparse data. + +Weather dataset: +http://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html + + +.. code-block:: default + + + data = [ + ['sunny', 85, 85, 'FALSE', 'no'], + ['sunny', 80, 90, 'TRUE', 'no'], + ['overcast', 83, 86, 'FALSE', 'yes'], + ['rainy', 70, 96, 'FALSE', 'yes'], + ['rainy', 68, 80, 'FALSE', 'yes'], + ['rainy', 65, 70, 'TRUE', 'no'], + ['overcast', 64, 65, 'TRUE', 'yes'], + ['sunny', 72, 95, 'FALSE', 'no'], + ['sunny', 69, 70, 'FALSE', 'yes'], + ['rainy', 75, 80, 'FALSE', 'yes'], + ['sunny', 75, 70, 'TRUE', 'yes'], + ['overcast', 72, 90, 'TRUE', 'yes'], + ['overcast', 81, 75, 'FALSE', 'yes'], + ['rainy', 71, 91, 'TRUE', 'no'], + ] + + attribute_names = [ + ('outlook', ['sunny', 'overcast', 'rainy']), + ('temperature', 'REAL'), + ('humidity', 'REAL'), + ('windy', ['TRUE', 'FALSE']), + ('play', ['yes', 'no']), + ] + + description = ( + 'The weather problem is a tiny dataset that we will use repeatedly' + ' to illustrate machine learning methods. Entirely fictitious, it ' + 'supposedly concerns the conditions that are suitable for playing ' + 'some unspecified game. In general, instances in a dataset are ' + 'characterized by the values of features, or attributes, that measure ' + 'different aspects of the instance. In this case there are four ' + 'attributes: outlook, temperature, humidity, and windy. ' + 'The outcome is whether to play or not.' + ) + + citation = ( + 'I. H. Witten, E. Frank, M. A. Hall, and ITPro,' + 'Data mining practical machine learning tools and techniques, ' + 'third edition. Burlington, Mass.: Morgan Kaufmann Publishers, 2011' + ) + + weather_dataset = create_dataset( + name="Weather", + description=description, + creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro', + contributor=None, + collection_date='01-01-2011', + language='English', + licence=None, + default_target_attribute='play', + row_id_attribute=None, + ignore_attribute=None, + citation=citation, + attributes=attribute_names, + data=data, + version_label='example', + ) + + + +.. code-block:: default + + + upload_did = weather_dataset.publish() + print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +Dataset is a pandas DataFrame +============================= +It might happen that your dataset is made of heterogeneous data which can be +usually stored as a Pandas DataFrame. DataFrame offers the adavantages to +store the type of data for each column as well as the attribute names. +Therefore, when providing a Pandas DataFrame, OpenML can infer those +information without the need to specifically provide them when calling the +function :func:`create_dataset`. In this regard, you only need to pass +``'auto'`` to the ``attributes`` parameter. + + +.. code-block:: default + + + df = pd.DataFrame(data, columns=[col_name for col_name, _ in attribute_names]) + # enforce the categorical column to have a categorical dtype + df['outlook'] = df['outlook'].astype('category') + df['windy'] = df['windy'].astype('bool') + df['play'] = df['play'].astype('category') + print(df.info()) + + +We enforce the column 'outlook', 'windy', and 'play' to be a categorical +dtype while the column 'rnd_str' is kept as a string column. Then, we can +call :func:`create_dataset` by passing the dataframe and fixing the parameter +``attributes`` to ``'auto'``. + + +.. code-block:: default + + + weather_dataset = create_dataset( + name="Weather", + description=description, + creator='I. H. Witten, E. Frank, M. A. Hall, and ITPro', + contributor=None, + collection_date='01-01-2011', + language='English', + licence=None, + default_target_attribute='play', + row_id_attribute=None, + ignore_attribute=None, + citation=citation, + attributes='auto', + data=df, + version_label='example', + ) + + + +.. code-block:: default + + + upload_did = weather_dataset.publish() + print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +Dataset is a sparse matrix +========================== + + +.. code-block:: default + + + sparse_data = coo_matrix(( + [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]) + )) + + column_names = [ + ('input1', 'REAL'), + ('input2', 'REAL'), + ('y', 'REAL'), + ] + + xor_dataset = create_dataset( + name="XOR", + description='Dataset representing the XOR operation', + creator=None, + contributor=None, + collection_date=None, + language='English', + licence=None, + default_target_attribute='y', + row_id_attribute=None, + ignore_attribute=None, + citation=None, + attributes=column_names, + data=sparse_data, + version_label='example', + ) + + + +.. code-block:: default + + + upload_did = xor_dataset.publish() + print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + + +Dataset is a pandas sparse dataframe +==================================== + + +.. code-block:: default + + + sparse_data = coo_matrix(( + [0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], + ([0, 1, 1, 2, 2, 3, 3], [0, 1, 2, 0, 2, 0, 1]) + )) + column_names = ['input1', 'input2', 'y'] + df = pd.SparseDataFrame(sparse_data, columns=column_names) + print(df.info()) + + xor_dataset = create_dataset( + name="XOR", + description='Dataset representing the XOR operation', + creator=None, + contributor=None, + collection_date=None, + language='English', + licence=None, + default_target_attribute='y', + row_id_attribute=None, + ignore_attribute=None, + citation=None, + attributes='auto', + data=df, + version_label='example', + ) + + + +.. code-block:: default + + + upload_did = xor_dataset.publish() + print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.139 seconds) + + +.. _sphx_glr_download_examples_create_upload_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: create_upload_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: create_upload_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/datasets_tutorial.ipynb b/doc/examples/datasets_tutorial.ipynb new file mode 100644 index 000000000..ee0dba57d --- /dev/null +++ b/doc/examples/datasets_tutorial.ipynb @@ -0,0 +1,202 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Datasets\n\n\nHow to list and download datasets.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openml\nimport pandas as pd" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "List datasets\n=============\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "openml_list = openml.datasets.list_datasets() # returns a dict\n\n# Show a nice table with some key data properties\ndatalist = pd.DataFrame.from_dict(openml_list, orient='index')\ndatalist = datalist[[\n 'did', 'name', 'NumberOfInstances',\n 'NumberOfFeatures', 'NumberOfClasses'\n]]\n\nprint(\"First 10 of %s datasets...\" % len(datalist))\ndatalist.head(n=10)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise 1\n**********\n\n* Find datasets with more than 10000 examples.\n* Find a dataset called 'eeg_eye_state'.\n* Find all datasets with more than 50 classes.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "datalist[datalist.NumberOfInstances > 10000\n ].sort_values(['NumberOfInstances']).head(n=20)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "datalist.query('name == \"eeg-eye-state\"')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "datalist.query('NumberOfClasses > 50')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download datasets\n=================\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# This is done based on the dataset ID ('did').\ndataset = openml.datasets.get_dataset(68)\n# NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68\n\n# Print a summary\nprint(\"This is dataset '%s', the target feature is '%s'\" %\n (dataset.name, dataset.default_target_attribute))\nprint(\"URL: %s\" % dataset.url)\nprint(dataset.description[:500])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the actual data.\n\nThe dataset can be returned in 2 possible formats: as a NumPy array, a SciPy\nsparse matrix, or as a Pandas DataFrame (or SparseDataFrame). The format is\ncontrolled with the parameter ``dataset_format`` which can be either 'array'\n(default) or 'dataframe'. Let's first build our dataset from a NumPy array\nand manually create a dataframe.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X, y, attribute_names = dataset.get_data(\n dataset_format='array',\n target=dataset.default_target_attribute,\n return_attribute_names=True,\n)\neeg = pd.DataFrame(X, columns=attribute_names)\neeg['class'] = y\nprint(eeg[:10])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instead of manually creating the dataframe, you can already request a\ndataframe with the correct dtypes.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X, y = dataset.get_data(target=dataset.default_target_attribute,\n dataset_format='dataframe')\nprint(X.head())\nprint(X.info())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sometimes you only need access to a dataset's metadata.\nIn those cases, you can download the dataset without downloading the\ndata file. The dataset object can be used as normal.\nWhenever you use any functionality that requires the data,\nsuch as `get_data`, the data will be downloaded.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dataset = openml.datasets.get_dataset(68, download_data=False)\n# NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise 2\n**********\n* Explore the data visually.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "eegs = eeg.sample(n=1000)\n_ = pd.plotting.scatter_matrix(\n eegs.iloc[:100, :4],\n c=eegs[:100]['class'],\n figsize=(10, 10),\n marker='o',\n hist_kwds={'bins': 20},\n alpha=.8,\n cmap='plasma'\n)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/datasets_tutorial.py b/doc/examples/datasets_tutorial.py new file mode 100644 index 000000000..9b4f8be36 --- /dev/null +++ b/doc/examples/datasets_tutorial.py @@ -0,0 +1,103 @@ +""" +======== +Datasets +======== + +How to list and download datasets. +""" + +import openml +import pandas as pd + +############################################################################ +# List datasets +# ============= + +openml_list = openml.datasets.list_datasets() # returns a dict + +# Show a nice table with some key data properties +datalist = pd.DataFrame.from_dict(openml_list, orient='index') +datalist = datalist[[ + 'did', 'name', 'NumberOfInstances', + 'NumberOfFeatures', 'NumberOfClasses' +]] + +print("First 10 of %s datasets..." % len(datalist)) +datalist.head(n=10) + +############################################################################ +# Exercise 1 +# ********** +# +# * Find datasets with more than 10000 examples. +# * Find a dataset called 'eeg_eye_state'. +# * Find all datasets with more than 50 classes. +datalist[datalist.NumberOfInstances > 10000 + ].sort_values(['NumberOfInstances']).head(n=20) +############################################################################ +datalist.query('name == "eeg-eye-state"') +############################################################################ +datalist.query('NumberOfClasses > 50') + +############################################################################ +# Download datasets +# ================= + +# This is done based on the dataset ID ('did'). +dataset = openml.datasets.get_dataset(68) +# NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 + +# Print a summary +print("This is dataset '%s', the target feature is '%s'" % + (dataset.name, dataset.default_target_attribute)) +print("URL: %s" % dataset.url) +print(dataset.description[:500]) + +############################################################################ +# Get the actual data. +# +# The dataset can be returned in 2 possible formats: as a NumPy array, a SciPy +# sparse matrix, or as a Pandas DataFrame (or SparseDataFrame). The format is +# controlled with the parameter ``dataset_format`` which can be either 'array' +# (default) or 'dataframe'. Let's first build our dataset from a NumPy array +# and manually create a dataframe. +X, y, attribute_names = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute, + return_attribute_names=True, +) +eeg = pd.DataFrame(X, columns=attribute_names) +eeg['class'] = y +print(eeg[:10]) + +############################################################################ +# Instead of manually creating the dataframe, you can already request a +# dataframe with the correct dtypes. +X, y = dataset.get_data(target=dataset.default_target_attribute, + dataset_format='dataframe') +print(X.head()) +print(X.info()) + +############################################################################ +# Sometimes you only need access to a dataset's metadata. +# In those cases, you can download the dataset without downloading the +# data file. The dataset object can be used as normal. +# Whenever you use any functionality that requires the data, +# such as `get_data`, the data will be downloaded. +dataset = openml.datasets.get_dataset(68, download_data=False) +# NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 + +############################################################################ +# Exercise 2 +# ********** +# * Explore the data visually. +eegs = eeg.sample(n=1000) +_ = pd.plotting.scatter_matrix( + eegs.iloc[:100, :4], + c=eegs[:100]['class'], + figsize=(10, 10), + marker='o', + hist_kwds={'bins': 20}, + alpha=.8, + cmap='plasma' +) diff --git a/doc/examples/datasets_tutorial.rst b/doc/examples/datasets_tutorial.rst new file mode 100644 index 000000000..e4453ec41 --- /dev/null +++ b/doc/examples/datasets_tutorial.rst @@ -0,0 +1,247 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_datasets_tutorial.py: + + +======== +Datasets +======== + +How to list and download datasets. + + +.. code-block:: default + + + import openml + import pandas as pd + + + + + + + +List datasets +============= + + +.. code-block:: default + + + openml_list = openml.datasets.list_datasets() # returns a dict + + # Show a nice table with some key data properties + datalist = pd.DataFrame.from_dict(openml_list, orient='index') + datalist = datalist[[ + 'did', 'name', 'NumberOfInstances', + 'NumberOfFeatures', 'NumberOfClasses' + ]] + + print("First 10 of %s datasets..." % len(datalist)) + datalist.head(n=10) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + First 10 of 15672 datasets... + + +Exercise 1 +********** + +* Find datasets with more than 10000 examples. +* Find a dataset called 'eeg_eye_state'. +* Find all datasets with more than 50 classes. + + +.. code-block:: default + + datalist[datalist.NumberOfInstances > 10000 + ].sort_values(['NumberOfInstances']).head(n=20) + + + + + + + +.. code-block:: default + + datalist.query('name == "eeg-eye-state"') + + + + + + + +.. code-block:: default + + datalist.query('NumberOfClasses > 50') + + + + + + + +Download datasets +================= + + +.. code-block:: default + + + # This is done based on the dataset ID ('did'). + dataset = openml.datasets.get_dataset(68) + # NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 + + # Print a summary + print("This is dataset '%s', the target feature is '%s'" % + (dataset.name, dataset.default_target_attribute)) + print("URL: %s" % dataset.url) + print(dataset.description[:500]) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + This is dataset 'eeg-eye-state', the target feature is 'Class' + URL: https://test.openml.org/data/v1/download/68/eeg-eye-state.arff + **Author**: Oliver Roesler, it12148'@'lehre.dhbw-stuttgart.de + **Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State), Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany + **Please cite**: + + All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video fr + + +Get the actual data. + +The dataset can be returned in 2 possible formats: as a NumPy array, a SciPy +sparse matrix, or as a Pandas DataFrame (or SparseDataFrame). The format is +controlled with the parameter ``dataset_format`` which can be either 'array' +(default) or 'dataframe'. Let's first build our dataset from a NumPy array +and manually create a dataframe. + + +.. code-block:: default + + X, y, attribute_names = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute, + return_attribute_names=True, + ) + eeg = pd.DataFrame(X, columns=attribute_names) + eeg['class'] = y + print(eeg[:10]) + + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/datasets_tutorial.py", line 67, in + return_attribute_names=True, + TypeError: get_data() got an unexpected keyword argument 'dataset_format' + + + + +Instead of manually creating the dataframe, you can already request a +dataframe with the correct dtypes. + + +.. code-block:: default + + X, y = dataset.get_data(target=dataset.default_target_attribute, + dataset_format='dataframe') + print(X.head()) + print(X.info()) + + +Sometimes you only need access to a dataset's metadata. +In those cases, you can download the dataset without downloading the +data file. The dataset object can be used as normal. +Whenever you use any functionality that requires the data, +such as `get_data`, the data will be downloaded. + + +.. code-block:: default + + dataset = openml.datasets.get_dataset(68, download_data=False) + # NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 + + +Exercise 2 +********** +* Explore the data visually. + + +.. code-block:: default + + eegs = eeg.sample(n=1000) + _ = pd.plotting.scatter_matrix( + eegs.iloc[:100, :4], + c=eegs[:100]['class'], + figsize=(10, 10), + marker='o', + hist_kwds={'bins': 20}, + alpha=.8, + cmap='plasma' + ) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 6.782 seconds) + + +.. _sphx_glr_download_examples_datasets_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: datasets_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: datasets_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/examples_jupyter.zip b/doc/examples/examples_jupyter.zip new file mode 100644 index 0000000000000000000000000000000000000000..68c542fa3f168a385e284ae7649e24a6cc117df8 GIT binary patch literal 51574 zcmeI5OK%)mmftlVKj6Wvj7Hml-Gd7XOj>yQ89=PQ>k>GwN)w>K&e z>wa1c`_-(d`_tL9tnze}K{?D; z*5jq@MK+jC^Rnm<%2_exhGI4vHJ8WPG##eXl<#9}YOfrP(n*~S`FbxM)mdomr_FQa zY&w}utt(HOFY9tv4b%tUJ?q*#<8(S1mD5qa>+)ih7uiZ^?1}w1G#OvMIFxZ(JsXyX z#e6vJ{?2=I(u;e=$C^&Zbh-yjXRp{<_~{kB;``;Ho0e``rFpTzoV(PGnZ1!cbl@+} z=zThL(}T=aSv?y~>qL*9VH9uHg5={#SxsFz$%^r)SL{{g*i9x?UQAuMgqxMET3NSgZ9Kw7Pk7@CcL1SrK8c&#ioJF?%rpQZr^aX z7*d^0-L(%~_9CyRwF_gNX7$w7S@k@t+`)7@sc&p-=)1&|O;T3-8^eujAM^^%ikjF7 z6qST8>SRBg_QQwnwGUQ%#TV=Di2K5K&Gl}t5T^)V_xH*Qma)@Gt13O}tow`8w4%A4 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zYk})79K?B>cjql!zjyIg-u5`ot3aQ(^2a~Cq#k{_nPt^HZ|0}} do feature encoding\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "dataset = openml.datasets.get_dataset(17)\nX, y, categorical = dataset.get_data(\n dataset_format='array',\n target=dataset.default_target_attribute,\n return_categorical_indicator=True,\n)\nprint(\"Categorical features: %s\" % categorical)\nenc = preprocessing.OneHotEncoder(categorical_features=categorical)\nX = enc.fit_transform(X)\nclf.fit(X, y)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Runs: Easily explore models\n^^^^^^^^^^^^^^^^^^^^^^^^^^^\nWe can run (many) scikit-learn algorithms on (many) OpenML tasks.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get a task\ntask = openml.tasks.get_task(403)\n\n# Build any classifier or pipeline\nclf = tree.ExtraTreeClassifier()\n\n# Run the flow\nrun = openml.runs.run_model_on_task(clf, task)\n\n# pprint(vars(run), depth=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Share the run on the OpenML server\n\nSo far the run is only available locally. By calling the publish function,\nthe run is sent to the OpenML server:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "myrun = run.publish()\n# For this tutorial, our configuration publishes to the test server\n# as to not pollute the main server.\nprint(\"Uploaded to http://test.openml.org/r/\" + str(myrun.run_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now also inspect the flow object which was automatically created:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "flow = openml.flows.get_flow(run.flow_id)\npprint(vars(flow), depth=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It also works with pipelines\n############################\n\nWhen you need to handle 'dirty' data, build pipelines to model then automatically.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "task = openml.tasks.get_task(115)\npipe = pipeline.Pipeline(steps=[\n ('Imputer', preprocessing.Imputer(strategy='median')),\n ('OneHotEncoder', preprocessing.OneHotEncoder(sparse=False, handle_unknown='ignore')),\n ('Classifier', ensemble.RandomForestClassifier())\n])\n\nrun = openml.runs.run_model_on_task(pipe, task, avoid_duplicate_runs=False)\nmyrun = run.publish()\nprint(\"Uploaded to http://test.openml.org/r/\" + str(myrun.run_id))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Running flows on tasks offline for later upload\n^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFor those scenarios where there is no access to internet, it is possible to run\na model on a task without uploading results or flows to the server immediately.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# To perform the following line offline, it is required to have been called before\n# such that the task is cached on the local openml cache directory:\ntask = openml.tasks.get_task(6)\n\n# The following lines can then be executed offline:\nrun = openml.runs.run_model_on_task(\n pipe,\n task,\n avoid_duplicate_runs=False,\n upload_flow=False)\n\n# The run may be stored offline, and the flow will be stored along with it:\nrun.to_filesystem(directory='myrun')\n\n# They made later be loaded and uploaded\nrun = openml.runs.OpenMLRun.from_filesystem(directory='myrun')\nrun.publish()\n\n# Publishing the run will automatically upload the related flow if\n# it does not yet exist on the server." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Alternatively, one can also directly run flows.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Get a task\ntask = openml.tasks.get_task(403)\n\n# Build any classifier or pipeline\nclf = tree.ExtraTreeClassifier()\n\n# Obtain the scikit-learn extension interface to convert the classifier\n# into a flow object.\nextension = openml.extensions.get_extension_by_model(clf)\nflow = extension.model_to_flow(clf)\n\nrun = openml.runs.run_flow_on_task(flow, task)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Challenge\n^^^^^^^^^\n\nTry to build the best possible models on several OpenML tasks,\ncompare your results with the rest of the class and learn from\nthem. Some tasks you could try (or browse openml.org):\n\n* EEG eye state: data_id:`1471 `_,\n task_id:`14951 `_\n* Volcanoes on Venus: data_id:`1527 `_,\n task_id:`10103 `_\n* Walking activity: data_id:`1509 `_,\n task_id:`9945 `_, 150k instances.\n* Covertype (Satellite): data_id:`150 `_,\n task_id:`218 `_, 500k instances.\n* Higgs (Physics): data_id:`23512 `_,\n task_id:`52950 `_, 100k instances, missing values.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Easy benchmarking:\nfor task_id in [115, ]: # Add further tasks. Disclaimer: they might take some time\n task = openml.tasks.get_task(task_id)\n data = openml.datasets.get_dataset(task.dataset_id)\n clf = neighbors.KNeighborsClassifier(n_neighbors=5)\n\n run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False)\n myrun = run.publish()\n print(\"kNN on %s: http://test.openml.org/r/%d\" % (data.name, myrun.run_id))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/flows_and_runs_tutorial.py b/doc/examples/flows_and_runs_tutorial.py new file mode 100644 index 000000000..420db5705 --- /dev/null +++ b/doc/examples/flows_and_runs_tutorial.py @@ -0,0 +1,161 @@ +""" +Flows and Runs +============== + +How to train/run a model and how to upload the results. +""" + +import openml +from pprint import pprint +from sklearn import ensemble, neighbors, preprocessing, pipeline, tree + +############################################################################ +# Train machine learning models +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# +# Train a scikit-learn model on the data manually. + +# NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 +dataset = openml.datasets.get_dataset(68) +X, y = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute +) +clf = neighbors.KNeighborsClassifier(n_neighbors=1) +clf.fit(X, y) + +############################################################################ +# You can also ask for meta-data to automatically preprocess the data. +# +# * e.g. categorical features -> do feature encoding +dataset = openml.datasets.get_dataset(17) +X, y, categorical = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute, + return_categorical_indicator=True, +) +print("Categorical features: %s" % categorical) +enc = preprocessing.OneHotEncoder(categorical_features=categorical) +X = enc.fit_transform(X) +clf.fit(X, y) + +############################################################################ +# Runs: Easily explore models +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# We can run (many) scikit-learn algorithms on (many) OpenML tasks. + +# Get a task +task = openml.tasks.get_task(403) + +# Build any classifier or pipeline +clf = tree.ExtraTreeClassifier() + +# Run the flow +run = openml.runs.run_model_on_task(clf, task) + +# pprint(vars(run), depth=2) + +############################################################################ +# Share the run on the OpenML server +# +# So far the run is only available locally. By calling the publish function, +# the run is sent to the OpenML server: + +myrun = run.publish() +# For this tutorial, our configuration publishes to the test server +# as to not pollute the main server. +print("Uploaded to http://test.openml.org/r/" + str(myrun.run_id)) + +############################################################################ +# We can now also inspect the flow object which was automatically created: + +flow = openml.flows.get_flow(run.flow_id) +pprint(vars(flow), depth=1) + +############################################################################ +# It also works with pipelines +# ############################ +# +# When you need to handle 'dirty' data, build pipelines to model then automatically. +task = openml.tasks.get_task(115) +pipe = pipeline.Pipeline(steps=[ + ('Imputer', preprocessing.Imputer(strategy='median')), + ('OneHotEncoder', preprocessing.OneHotEncoder(sparse=False, handle_unknown='ignore')), + ('Classifier', ensemble.RandomForestClassifier()) +]) + +run = openml.runs.run_model_on_task(pipe, task, avoid_duplicate_runs=False) +myrun = run.publish() +print("Uploaded to http://test.openml.org/r/" + str(myrun.run_id)) + +############################################################################### +# Running flows on tasks offline for later upload +# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +# For those scenarios where there is no access to internet, it is possible to run +# a model on a task without uploading results or flows to the server immediately. + +# To perform the following line offline, it is required to have been called before +# such that the task is cached on the local openml cache directory: +task = openml.tasks.get_task(6) + +# The following lines can then be executed offline: +run = openml.runs.run_model_on_task( + pipe, + task, + avoid_duplicate_runs=False, + upload_flow=False) + +# The run may be stored offline, and the flow will be stored along with it: +run.to_filesystem(directory='myrun') + +# They made later be loaded and uploaded +run = openml.runs.OpenMLRun.from_filesystem(directory='myrun') +run.publish() + +# Publishing the run will automatically upload the related flow if +# it does not yet exist on the server. + +############################################################################ +# Alternatively, one can also directly run flows. + +# Get a task +task = openml.tasks.get_task(403) + +# Build any classifier or pipeline +clf = tree.ExtraTreeClassifier() + +# Obtain the scikit-learn extension interface to convert the classifier +# into a flow object. +extension = openml.extensions.get_extension_by_model(clf) +flow = extension.model_to_flow(clf) + +run = openml.runs.run_flow_on_task(flow, task) + +############################################################################ +# Challenge +# ^^^^^^^^^ +# +# Try to build the best possible models on several OpenML tasks, +# compare your results with the rest of the class and learn from +# them. Some tasks you could try (or browse openml.org): +# +# * EEG eye state: data_id:`1471 `_, +# task_id:`14951 `_ +# * Volcanoes on Venus: data_id:`1527 `_, +# task_id:`10103 `_ +# * Walking activity: data_id:`1509 `_, +# task_id:`9945 `_, 150k instances. +# * Covertype (Satellite): data_id:`150 `_, +# task_id:`218 `_, 500k instances. +# * Higgs (Physics): data_id:`23512 `_, +# task_id:`52950 `_, 100k instances, missing values. + +# Easy benchmarking: +for task_id in [115, ]: # Add further tasks. Disclaimer: they might take some time + task = openml.tasks.get_task(task_id) + data = openml.datasets.get_dataset(task.dataset_id) + clf = neighbors.KNeighborsClassifier(n_neighbors=5) + + run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) + myrun = run.publish() + print("kNN on %s: http://test.openml.org/r/%d" % (data.name, myrun.run_id)) diff --git a/doc/examples/flows_and_runs_tutorial.rst b/doc/examples/flows_and_runs_tutorial.rst new file mode 100644 index 000000000..7a5961d5c --- /dev/null +++ b/doc/examples/flows_and_runs_tutorial.rst @@ -0,0 +1,262 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_flows_and_runs_tutorial.py: + + +Flows and Runs +============== + +How to train/run a model and how to upload the results. + + +.. code-block:: default + + + import openml + from pprint import pprint + from sklearn import ensemble, neighbors, preprocessing, pipeline, tree + + + + + + + +Train machine learning models +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Train a scikit-learn model on the data manually. + + +.. code-block:: default + + + # NOTE: Dataset 68 exists on the test server https://test.openml.org/d/68 + dataset = openml.datasets.get_dataset(68) + X, y = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute + ) + clf = neighbors.KNeighborsClassifier(n_neighbors=1) + clf.fit(X, y) + + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/flows_and_runs_tutorial.py", line 22, in + target=dataset.default_target_attribute + TypeError: get_data() got an unexpected keyword argument 'dataset_format' + + + + +You can also ask for meta-data to automatically preprocess the data. + +* e.g. categorical features -> do feature encoding + + +.. code-block:: default + + dataset = openml.datasets.get_dataset(17) + X, y, categorical = dataset.get_data( + dataset_format='array', + target=dataset.default_target_attribute, + return_categorical_indicator=True, + ) + print("Categorical features: %s" % categorical) + enc = preprocessing.OneHotEncoder(categorical_features=categorical) + X = enc.fit_transform(X) + clf.fit(X, y) + + +Runs: Easily explore models +^^^^^^^^^^^^^^^^^^^^^^^^^^^ +We can run (many) scikit-learn algorithms on (many) OpenML tasks. + + +.. code-block:: default + + + # Get a task + task = openml.tasks.get_task(403) + + # Build any classifier or pipeline + clf = tree.ExtraTreeClassifier() + + # Run the flow + run = openml.runs.run_model_on_task(clf, task) + + # pprint(vars(run), depth=2) + + +Share the run on the OpenML server + +So far the run is only available locally. By calling the publish function, +the run is sent to the OpenML server: + + +.. code-block:: default + + + myrun = run.publish() + # For this tutorial, our configuration publishes to the test server + # as to not pollute the main server. + print("Uploaded to http://test.openml.org/r/" + str(myrun.run_id)) + + +We can now also inspect the flow object which was automatically created: + + +.. code-block:: default + + + flow = openml.flows.get_flow(run.flow_id) + pprint(vars(flow), depth=1) + + +It also works with pipelines +############################ + +When you need to handle 'dirty' data, build pipelines to model then automatically. + + +.. code-block:: default + + task = openml.tasks.get_task(115) + pipe = pipeline.Pipeline(steps=[ + ('Imputer', preprocessing.Imputer(strategy='median')), + ('OneHotEncoder', preprocessing.OneHotEncoder(sparse=False, handle_unknown='ignore')), + ('Classifier', ensemble.RandomForestClassifier()) + ]) + + run = openml.runs.run_model_on_task(pipe, task, avoid_duplicate_runs=False) + myrun = run.publish() + print("Uploaded to http://test.openml.org/r/" + str(myrun.run_id)) + + +Running flows on tasks offline for later upload +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +For those scenarios where there is no access to internet, it is possible to run +a model on a task without uploading results or flows to the server immediately. + + +.. code-block:: default + + + # To perform the following line offline, it is required to have been called before + # such that the task is cached on the local openml cache directory: + task = openml.tasks.get_task(6) + + # The following lines can then be executed offline: + run = openml.runs.run_model_on_task( + pipe, + task, + avoid_duplicate_runs=False, + upload_flow=False) + + # The run may be stored offline, and the flow will be stored along with it: + run.to_filesystem(directory='myrun') + + # They made later be loaded and uploaded + run = openml.runs.OpenMLRun.from_filesystem(directory='myrun') + run.publish() + + # Publishing the run will automatically upload the related flow if + # it does not yet exist on the server. + + +Alternatively, one can also directly run flows. + + +.. code-block:: default + + + # Get a task + task = openml.tasks.get_task(403) + + # Build any classifier or pipeline + clf = tree.ExtraTreeClassifier() + + # Obtain the scikit-learn extension interface to convert the classifier + # into a flow object. + extension = openml.extensions.get_extension_by_model(clf) + flow = extension.model_to_flow(clf) + + run = openml.runs.run_flow_on_task(flow, task) + + +Challenge +^^^^^^^^^ + +Try to build the best possible models on several OpenML tasks, +compare your results with the rest of the class and learn from +them. Some tasks you could try (or browse openml.org): + +* EEG eye state: data_id:`1471 `_, + task_id:`14951 `_ +* Volcanoes on Venus: data_id:`1527 `_, + task_id:`10103 `_ +* Walking activity: data_id:`1509 `_, + task_id:`9945 `_, 150k instances. +* Covertype (Satellite): data_id:`150 `_, + task_id:`218 `_, 500k instances. +* Higgs (Physics): data_id:`23512 `_, + task_id:`52950 `_, 100k instances, missing values. + + +.. code-block:: default + + + # Easy benchmarking: + for task_id in [115, ]: # Add further tasks. Disclaimer: they might take some time + task = openml.tasks.get_task(task_id) + data = openml.datasets.get_dataset(task.dataset_id) + clf = neighbors.KNeighborsClassifier(n_neighbors=5) + + run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) + myrun = run.publish() + print("kNN on %s: http://test.openml.org/r/%d" % (data.name, myrun.run_id)) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.044 seconds) + + +.. _sphx_glr_download_examples_flows_and_runs_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: flows_and_runs_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: flows_and_runs_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ 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usage. + + + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_introduction_tutorial_thumb.png + + :ref:`sphx_glr_examples_introduction_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/introduction_tutorial + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_tasks_tutorial_thumb.png + + :ref:`sphx_glr_examples_tasks_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/tasks_tutorial + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_datasets_tutorial_thumb.png + + :ref:`sphx_glr_examples_datasets_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/datasets_tutorial + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_run_setup_tutorial_thumb.png + + :ref:`sphx_glr_examples_run_setup_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/run_setup_tutorial + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_flows_and_runs_tutorial_thumb.png + + :ref:`sphx_glr_examples_flows_and_runs_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/flows_and_runs_tutorial + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/images/thumb/sphx_glr_create_upload_tutorial_thumb.png + + :ref:`sphx_glr_examples_create_upload_tutorial.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/create_upload_tutorial +.. raw:: html + +
+ + + +.. _sphx_glr_examples_sklearn: + +Experiment Examples +=================== + +OpenML experiment examples using a sklearn classifier/pipeline. + + + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /examples/sklearn/images/thumb/sphx_glr_openml_run_example_thumb.png + + :ref:`sphx_glr_examples_sklearn_openml_run_example.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /examples/sklearn/openml_run_example +.. raw:: html + +
+ + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-gallery + + + .. container:: sphx-glr-download + + :download:`Download all examples in Python source code: examples_python.zip ` + + + + .. container:: sphx-glr-download + + :download:`Download all examples in Jupyter notebooks: examples_jupyter.zip ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/introduction_tutorial.ipynb b/doc/examples/introduction_tutorial.ipynb new file mode 100644 index 000000000..f78c2f709 --- /dev/null +++ b/doc/examples/introduction_tutorial.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\nIntroduction\n===================\n\nAn introduction to OpenML, followed up by a simple example.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "OpenML is an online collaboration platform for machine learning which allows\nyou to:\n\n* Find or share interesting, well-documented datasets\n* Define research / modelling goals (tasks)\n* Explore large amounts of machine learning algorithms, with APIs in Java, R, Python\n* Log and share reproducible experiments, models, results\n* Works seamlessly with scikit-learn and other libraries\n* Large scale benchmarking, compare to state of the art\n\nInstallation\n^^^^^^^^^^^^\nInstallation is done via ``pip``:\n\n.. code:: bash\n\n pip install openml\n\nFor further information, please check out the installation guide at\nhttps://openml.github.io/openml-python/master/contributing.html#installation\n\nAuthentication\n^^^^^^^^^^^^^^\n\nThe OpenML server can only be accessed by users who have signed up on the\nOpenML platform. If you don\u2019t have an account yet, sign up now.\nYou will receive an API key, which will authenticate you to the server\nand allow you to download and upload datasets, tasks, runs and flows.\n\n* Create an OpenML account (free) on http://www.openml.org.\n* After logging in, open your account page (avatar on the top right)\n* Open 'Account Settings', then 'API authentication' to find your API key.\n\nThere are two ways to authenticate:\n\n* Create a plain text file **~/.openml/config** with the line\n **'apikey=MYKEY'**, replacing **MYKEY** with your API key. The config\n file must be in the directory ~/.openml/config and exist prior to\n importing the openml module.\n* Run the code below, replacing 'YOURKEY' with your API key.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openml\nfrom sklearn import neighbors\n\n# Uncomment and set your OpenML key. Don't share your key with others.\n# openml.config.apikey = 'YOURKEY'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Caching\n^^^^^^^\nWhen downloading datasets, tasks, runs and flows, they will be cached to\nretrieve them without calling the server later. As with the API key,\nthe cache directory can be either specified through the config file or\nthrough the API:\n\n* Add the line **cachedir = 'MYDIR'** to the config file, replacing\n 'MYDIR' with the path to the cache directory. By default, OpenML\n will use **~/.openml/cache** as the cache directory.\n* Run the code below, replacing 'YOURDIR' with the path to the cache directory.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Uncomment and set your OpenML cache directory\n# import os\n# openml.config.cache_directory = os.path.expanduser('YOURDIR')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Simple Example\n^^^^^^^^^^^^^^\nDownload the OpenML task for the eeg-eye-state.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "task = openml.tasks.get_task(403)\ndata = openml.datasets.get_dataset(task.dataset_id)\nclf = neighbors.KNeighborsClassifier(n_neighbors=5)\nrun = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False)\n# Publish the experiment on OpenML (optional, requires an API key).\n# For this tutorial, our configuration publishes to the test server\n# as to not pollute the main server.\nmyrun = run.publish()\nprint(\"kNN on %s: http://test.openml.org/r/%d\" % (data.name, myrun.run_id))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/introduction_tutorial.py b/doc/examples/introduction_tutorial.py new file mode 100644 index 000000000..63f8880d3 --- /dev/null +++ b/doc/examples/introduction_tutorial.py @@ -0,0 +1,85 @@ +""" +Introduction +=================== + +An introduction to OpenML, followed up by a simple example. +""" +############################################################################ +# OpenML is an online collaboration platform for machine learning which allows +# you to: +# +# * Find or share interesting, well-documented datasets +# * Define research / modelling goals (tasks) +# * Explore large amounts of machine learning algorithms, with APIs in Java, R, Python +# * Log and share reproducible experiments, models, results +# * Works seamlessly with scikit-learn and other libraries +# * Large scale benchmarking, compare to state of the art +# +# Installation +# ^^^^^^^^^^^^ +# Installation is done via ``pip``: +# +# .. code:: bash +# +# pip install openml +# +# For further information, please check out the installation guide at +# https://openml.github.io/openml-python/master/contributing.html#installation +# +# Authentication +# ^^^^^^^^^^^^^^ +# +# The OpenML server can only be accessed by users who have signed up on the +# OpenML platform. If you don’t have an account yet, sign up now. +# You will receive an API key, which will authenticate you to the server +# and allow you to download and upload datasets, tasks, runs and flows. +# +# * Create an OpenML account (free) on http://www.openml.org. +# * After logging in, open your account page (avatar on the top right) +# * Open 'Account Settings', then 'API authentication' to find your API key. +# +# There are two ways to authenticate: +# +# * Create a plain text file **~/.openml/config** with the line +# **'apikey=MYKEY'**, replacing **MYKEY** with your API key. The config +# file must be in the directory ~/.openml/config and exist prior to +# importing the openml module. +# * Run the code below, replacing 'YOURKEY' with your API key. + +############################################################################ +import openml +from sklearn import neighbors + +# Uncomment and set your OpenML key. Don't share your key with others. +# openml.config.apikey = 'YOURKEY' + +############################################################################ +# Caching +# ^^^^^^^ +# When downloading datasets, tasks, runs and flows, they will be cached to +# retrieve them without calling the server later. As with the API key, +# the cache directory can be either specified through the config file or +# through the API: +# +# * Add the line **cachedir = 'MYDIR'** to the config file, replacing +# 'MYDIR' with the path to the cache directory. By default, OpenML +# will use **~/.openml/cache** as the cache directory. +# * Run the code below, replacing 'YOURDIR' with the path to the cache directory. + +# Uncomment and set your OpenML cache directory +# import os +# openml.config.cache_directory = os.path.expanduser('YOURDIR') + +############################################################################ +# Simple Example +# ^^^^^^^^^^^^^^ +# Download the OpenML task for the eeg-eye-state. +task = openml.tasks.get_task(403) +data = openml.datasets.get_dataset(task.dataset_id) +clf = neighbors.KNeighborsClassifier(n_neighbors=5) +run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) +# Publish the experiment on OpenML (optional, requires an API key). +# For this tutorial, our configuration publishes to the test server +# as to not pollute the main server. +myrun = run.publish() +print("kNN on %s: http://test.openml.org/r/%d" % (data.name, myrun.run_id)) diff --git a/doc/examples/introduction_tutorial.rst b/doc/examples/introduction_tutorial.rst new file mode 100644 index 000000000..ea133a714 --- /dev/null +++ b/doc/examples/introduction_tutorial.rst @@ -0,0 +1,165 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_introduction_tutorial.py: + + +Introduction +=================== + +An introduction to OpenML, followed up by a simple example. + +OpenML is an online collaboration platform for machine learning which allows +you to: + +* Find or share interesting, well-documented datasets +* Define research / modelling goals (tasks) +* Explore large amounts of machine learning algorithms, with APIs in Java, R, Python +* Log and share reproducible experiments, models, results +* Works seamlessly with scikit-learn and other libraries +* Large scale benchmarking, compare to state of the art + +Installation +^^^^^^^^^^^^ +Installation is done via ``pip``: + +.. code:: bash + + pip install openml + +For further information, please check out the installation guide at +https://openml.github.io/openml-python/master/contributing.html#installation + +Authentication +^^^^^^^^^^^^^^ + +The OpenML server can only be accessed by users who have signed up on the +OpenML platform. If you don’t have an account yet, sign up now. +You will receive an API key, which will authenticate you to the server +and allow you to download and upload datasets, tasks, runs and flows. + +* Create an OpenML account (free) on http://www.openml.org. +* After logging in, open your account page (avatar on the top right) +* Open 'Account Settings', then 'API authentication' to find your API key. + +There are two ways to authenticate: + +* Create a plain text file **~/.openml/config** with the line + **'apikey=MYKEY'**, replacing **MYKEY** with your API key. The config + file must be in the directory ~/.openml/config and exist prior to + importing the openml module. +* Run the code below, replacing 'YOURKEY' with your API key. + + +.. code-block:: default + + import openml + from sklearn import neighbors + + # Uncomment and set your OpenML key. Don't share your key with others. + # openml.config.apikey = 'YOURKEY' + + + + + + + +Caching +^^^^^^^ +When downloading datasets, tasks, runs and flows, they will be cached to +retrieve them without calling the server later. As with the API key, +the cache directory can be either specified through the config file or +through the API: + +* Add the line **cachedir = 'MYDIR'** to the config file, replacing + 'MYDIR' with the path to the cache directory. By default, OpenML + will use **~/.openml/cache** as the cache directory. +* Run the code below, replacing 'YOURDIR' with the path to the cache directory. + + +.. code-block:: default + + + # Uncomment and set your OpenML cache directory + # import os + # openml.config.cache_directory = os.path.expanduser('YOURDIR') + + + + + + + +Simple Example +^^^^^^^^^^^^^^ +Download the OpenML task for the eeg-eye-state. + + +.. code-block:: default + + task = openml.tasks.get_task(403) + data = openml.datasets.get_dataset(task.dataset_id) + clf = neighbors.KNeighborsClassifier(n_neighbors=5) + run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) + # Publish the experiment on OpenML (optional, requires an API key). + # For this tutorial, our configuration publishes to the test server + # as to not pollute the main server. + myrun = run.publish() + print("kNN on %s: http://test.openml.org/r/%d" % (data.name, myrun.run_id)) + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/introduction_tutorial.py", line 80, in + run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/runs/functions.py", line 36, in run_model_on_task + flow = sklearn_to_flow(model) + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow + raise TypeError(o, type(o)) + TypeError: (, ) + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.531 seconds) + + +.. _sphx_glr_download_examples_introduction_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: introduction_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: introduction_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/run_setup_tutorial.ipynb b/doc/examples/run_setup_tutorial.ipynb new file mode 100644 index 000000000..8cdf2d049 --- /dev/null +++ b/doc/examples/run_setup_tutorial.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Run Setup\n\n\nBy: Jan N. van Rijn\n\nOne of the key features of the openml-python library is that is allows to\nreinstantiate flows with hyperparameter settings that were uploaded before.\nThis tutorial uses the concept of setups. Although setups are not extensively\ndescribed in the OpenML documentation (because most users will not directly\nuse them), they form a important concept within OpenML distinguishing between\nhyperparameter configurations.\nA setup is the combination of a flow with all its hyperparameters set.\n\nA key requirement for reinstantiating a flow is to have the same scikit-learn\nversion as the flow that was uploaded. However, this tutorial will upload the\nflow (that will later be reinstantiated) itself, so it can be ran with any\nscikit-learn version that is supported by this library. In this case, the\nrequirement of the corresponding scikit-learn versions is automatically met.\n\nIn this tutorial we will\n 1) Create a flow and use it to solve a task;\n 2) Download the flow, reinstantiate the model with same hyperparameters,\n and solve the same task again;\n 3) We will verify that the obtained results are exactly the same.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import logging\nimport numpy as np\nimport openml\nimport sklearn.ensemble\nimport sklearn.impute\nimport sklearn.preprocessing\n\n\nroot = logging.getLogger()\nroot.setLevel(logging.INFO)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "1) Create a flow and use it to solve a task\n##############################################################################\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# first, let's download the task that we are interested in\ntask = openml.tasks.get_task(6)\n\n\n# we will create a fairly complex model, with many preprocessing components and\n# many potential hyperparameters. Of course, the model can be as complex and as\n# easy as you want it to be\nmodel_original = sklearn.pipeline.make_pipeline(\n sklearn.impute.SimpleImputer(),\n sklearn.ensemble.RandomForestClassifier()\n)\n\n\n# Let's change some hyperparameters. Of course, in any good application we\n# would tune them using, e.g., Random Search or Bayesian Optimization, but for\n# the purpose of this tutorial we set them to some specific values that might\n# or might not be optimal\nhyperparameters_original = {\n 'simpleimputer__strategy': 'median',\n 'randomforestclassifier__criterion': 'entropy',\n 'randomforestclassifier__max_features': 0.2,\n 'randomforestclassifier__min_samples_leaf': 1,\n 'randomforestclassifier__n_estimators': 16,\n 'randomforestclassifier__random_state': 42,\n}\nmodel_original.set_params(**hyperparameters_original)\n\n# solve the task and upload the result (this implicitly creates the flow)\nrun = openml.runs.run_model_on_task(\n model_original,\n task,\n avoid_duplicate_runs=False)\nrun_original = run.publish() # this implicitly uploads the flow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "2) Download the flow and solve the same task again.\n##############################################################################\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# obtain setup id (note that the setup id is assigned by the OpenML server -\n# therefore it was not yet available in our local copy of the run)\nrun_downloaded = openml.runs.get_run(run_original.run_id)\nsetup_id = run_downloaded.setup_id\n\n# after this, we can easily reinstantiate the model\nmodel_duplicate = openml.setups.initialize_model(setup_id)\n# it will automatically have all the hyperparameters set\n\n# and run the task again\nrun_duplicate = openml.runs.run_model_on_task(\n model_duplicate, task, avoid_duplicate_runs=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "3) We will verify that the obtained results are exactly the same.\n##############################################################################\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# the run has stored all predictions in the field data content\nnp.testing.assert_array_equal(run_original.data_content,\n run_duplicate.data_content)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/run_setup_tutorial.py b/doc/examples/run_setup_tutorial.py new file mode 100644 index 000000000..9a76843cb --- /dev/null +++ b/doc/examples/run_setup_tutorial.py @@ -0,0 +1,102 @@ +""" +========= +Run Setup +========= + +By: Jan N. van Rijn + +One of the key features of the openml-python library is that is allows to +reinstantiate flows with hyperparameter settings that were uploaded before. +This tutorial uses the concept of setups. Although setups are not extensively +described in the OpenML documentation (because most users will not directly +use them), they form a important concept within OpenML distinguishing between +hyperparameter configurations. +A setup is the combination of a flow with all its hyperparameters set. + +A key requirement for reinstantiating a flow is to have the same scikit-learn +version as the flow that was uploaded. However, this tutorial will upload the +flow (that will later be reinstantiated) itself, so it can be ran with any +scikit-learn version that is supported by this library. In this case, the +requirement of the corresponding scikit-learn versions is automatically met. + +In this tutorial we will + 1) Create a flow and use it to solve a task; + 2) Download the flow, reinstantiate the model with same hyperparameters, + and solve the same task again; + 3) We will verify that the obtained results are exactly the same. + +""" +import logging +import numpy as np +import openml +import sklearn.ensemble +import sklearn.impute +import sklearn.preprocessing + + +root = logging.getLogger() +root.setLevel(logging.INFO) + +############################################################################### +# 1) Create a flow and use it to solve a task +############################################################################### + +# first, let's download the task that we are interested in +task = openml.tasks.get_task(6) + + +# we will create a fairly complex model, with many preprocessing components and +# many potential hyperparameters. Of course, the model can be as complex and as +# easy as you want it to be +model_original = sklearn.pipeline.make_pipeline( + sklearn.impute.SimpleImputer(), + sklearn.ensemble.RandomForestClassifier() +) + + +# Let's change some hyperparameters. Of course, in any good application we +# would tune them using, e.g., Random Search or Bayesian Optimization, but for +# the purpose of this tutorial we set them to some specific values that might +# or might not be optimal +hyperparameters_original = { + 'simpleimputer__strategy': 'median', + 'randomforestclassifier__criterion': 'entropy', + 'randomforestclassifier__max_features': 0.2, + 'randomforestclassifier__min_samples_leaf': 1, + 'randomforestclassifier__n_estimators': 16, + 'randomforestclassifier__random_state': 42, +} +model_original.set_params(**hyperparameters_original) + +# solve the task and upload the result (this implicitly creates the flow) +run = openml.runs.run_model_on_task( + model_original, + task, + avoid_duplicate_runs=False) +run_original = run.publish() # this implicitly uploads the flow + +############################################################################### +# 2) Download the flow and solve the same task again. +############################################################################### + +# obtain setup id (note that the setup id is assigned by the OpenML server - +# therefore it was not yet available in our local copy of the run) +run_downloaded = openml.runs.get_run(run_original.run_id) +setup_id = run_downloaded.setup_id + +# after this, we can easily reinstantiate the model +model_duplicate = openml.setups.initialize_model(setup_id) +# it will automatically have all the hyperparameters set + +# and run the task again +run_duplicate = openml.runs.run_model_on_task( + model_duplicate, task, avoid_duplicate_runs=False) + + +############################################################################### +# 3) We will verify that the obtained results are exactly the same. +############################################################################### + +# the run has stored all predictions in the field data content +np.testing.assert_array_equal(run_original.data_content, + run_duplicate.data_content) diff --git a/doc/examples/run_setup_tutorial.rst b/doc/examples/run_setup_tutorial.rst new file mode 100644 index 000000000..ecf674f7c --- /dev/null +++ b/doc/examples/run_setup_tutorial.rst @@ -0,0 +1,174 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_run_setup_tutorial.py: + + +========= +Run Setup +========= + +By: Jan N. van Rijn + +One of the key features of the openml-python library is that is allows to +reinstantiate flows with hyperparameter settings that were uploaded before. +This tutorial uses the concept of setups. Although setups are not extensively +described in the OpenML documentation (because most users will not directly +use them), they form a important concept within OpenML distinguishing between +hyperparameter configurations. +A setup is the combination of a flow with all its hyperparameters set. + +A key requirement for reinstantiating a flow is to have the same scikit-learn +version as the flow that was uploaded. However, this tutorial will upload the +flow (that will later be reinstantiated) itself, so it can be ran with any +scikit-learn version that is supported by this library. In this case, the +requirement of the corresponding scikit-learn versions is automatically met. + +In this tutorial we will + 1) Create a flow and use it to solve a task; + 2) Download the flow, reinstantiate the model with same hyperparameters, + and solve the same task again; + 3) We will verify that the obtained results are exactly the same. + + + +.. code-block:: default + + import logging + import numpy as np + import openml + import sklearn.ensemble + import sklearn.impute + import sklearn.preprocessing + + + root = logging.getLogger() + root.setLevel(logging.INFO) + + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/run_setup_tutorial.py", line 33, in + import sklearn.impute + ModuleNotFoundError: No module named 'sklearn.impute' + + + + +1) Create a flow and use it to solve a task +############################################################################## + + +.. code-block:: default + + + # first, let's download the task that we are interested in + task = openml.tasks.get_task(6) + + + # we will create a fairly complex model, with many preprocessing components and + # many potential hyperparameters. Of course, the model can be as complex and as + # easy as you want it to be + model_original = sklearn.pipeline.make_pipeline( + sklearn.impute.SimpleImputer(), + sklearn.ensemble.RandomForestClassifier() + ) + + + # Let's change some hyperparameters. Of course, in any good application we + # would tune them using, e.g., Random Search or Bayesian Optimization, but for + # the purpose of this tutorial we set them to some specific values that might + # or might not be optimal + hyperparameters_original = { + 'simpleimputer__strategy': 'median', + 'randomforestclassifier__criterion': 'entropy', + 'randomforestclassifier__max_features': 0.2, + 'randomforestclassifier__min_samples_leaf': 1, + 'randomforestclassifier__n_estimators': 16, + 'randomforestclassifier__random_state': 42, + } + model_original.set_params(**hyperparameters_original) + + # solve the task and upload the result (this implicitly creates the flow) + run = openml.runs.run_model_on_task( + model_original, + task, + avoid_duplicate_runs=False) + run_original = run.publish() # this implicitly uploads the flow + + +2) Download the flow and solve the same task again. +############################################################################## + + +.. code-block:: default + + + # obtain setup id (note that the setup id is assigned by the OpenML server - + # therefore it was not yet available in our local copy of the run) + run_downloaded = openml.runs.get_run(run_original.run_id) + setup_id = run_downloaded.setup_id + + # after this, we can easily reinstantiate the model + model_duplicate = openml.setups.initialize_model(setup_id) + # it will automatically have all the hyperparameters set + + # and run the task again + run_duplicate = openml.runs.run_model_on_task( + model_duplicate, task, avoid_duplicate_runs=False) + + + +3) We will verify that the obtained results are exactly the same. +############################################################################## + + +.. code-block:: default + + + # the run has stored all predictions in the field data content + np.testing.assert_array_equal(run_original.data_content, + run_duplicate.data_content) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.152 seconds) + + +.. _sphx_glr_download_examples_run_setup_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: run_setup_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: run_setup_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/sg_execution_times.rst b/doc/examples/sg_execution_times.rst new file mode 100644 index 000000000..c94d7634c --- /dev/null +++ b/doc/examples/sg_execution_times.rst @@ -0,0 +1,15 @@ + +:orphan: + +.. _sphx_glr_examples_sg_execution_times: + +Computation times +================= +**00:12.697** total execution time for **examples** files: + +- **00:06.782**: :ref:`sphx_glr_examples_datasets_tutorial.py` (``datasets_tutorial.py``) +- **00:05.049**: :ref:`sphx_glr_examples_tasks_tutorial.py` 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"execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\nOpenML Run Example\n==================\n\nAn example of an automated machine learning experiment.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openml\nfrom sklearn import tree, preprocessing, pipeline\n\n# Uncomment and set your OpenML key. Don't share your key with others.\n# openml.config.apikey = 'YOURKEY'\n\n# Define a scikit-learn pipeline\nclf = pipeline.Pipeline(\n steps=[\n ('imputer', preprocessing.Imputer()),\n ('estimator', tree.DecisionTreeClassifier())\n ]\n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Download the OpenML task for the german credit card dataset.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "task = openml.tasks.get_task(97)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the scikit-learn model on the task (requires an API key).\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "run = openml.runs.run_model_on_task(clf, task)\n# Publish the experiment on OpenML (optional, requires an API key).\nrun.publish()\n\nprint('URL for run: %s/run/%d' % (openml.config.server, run.run_id))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/sklearn/openml_run_example.py b/doc/examples/sklearn/openml_run_example.py new file mode 100644 index 000000000..ec6dd4d53 --- /dev/null +++ b/doc/examples/sklearn/openml_run_example.py @@ -0,0 +1,29 @@ +""" +OpenML Run Example +================== + +An example of an automated machine learning experiment. +""" +import openml +from sklearn import tree, preprocessing, pipeline + +# Uncomment and set your OpenML key. Don't share your key with others. +# openml.config.apikey = 'YOURKEY' + +# Define a scikit-learn pipeline +clf = pipeline.Pipeline( + steps=[ + ('imputer', preprocessing.Imputer()), + ('estimator', tree.DecisionTreeClassifier()) + ] +) +############################################################################ +# Download the OpenML task for the german credit card dataset. +task = openml.tasks.get_task(97) +############################################################################ +# Run the scikit-learn model on the task (requires an API key). +run = openml.runs.run_model_on_task(clf, task) +# Publish the experiment on OpenML (optional, requires an API key). +run.publish() + +print('URL for run: %s/run/%d' % (openml.config.server, run.run_id)) diff --git a/doc/examples/sklearn/openml_run_example.rst b/doc/examples/sklearn/openml_run_example.rst new file mode 100644 index 000000000..6099db3f1 --- /dev/null +++ b/doc/examples/sklearn/openml_run_example.rst @@ -0,0 +1,111 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_sklearn_openml_run_example.py: + + +OpenML Run Example +================== + +An example of an automated machine learning experiment. + + +.. code-block:: default + + import openml + from sklearn import tree, preprocessing, pipeline + + # Uncomment and set your OpenML key. Don't share your key with others. + # openml.config.apikey = 'YOURKEY' + + # Define a scikit-learn pipeline + clf = pipeline.Pipeline( + steps=[ + ('imputer', preprocessing.Imputer()), + ('estimator', tree.DecisionTreeClassifier()) + ] + ) + + + + + + +Download the OpenML task for the german credit card dataset. + + +.. code-block:: default + + task = openml.tasks.get_task(97) + + + + + + +Run the scikit-learn model on the task (requires an API key). + + +.. code-block:: default + + run = openml.runs.run_model_on_task(clf, task) + # Publish the experiment on OpenML (optional, requires an API key). + run.publish() + + print('URL for run: %s/run/%d' % (openml.config.server, run.run_id)) + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/sklearn/openml_run_example.py", line 25, in + run = openml.runs.run_model_on_task(clf, task) + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/runs/functions.py", line 36, in run_model_on_task + flow = sklearn_to_flow(model) + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow + raise TypeError(o, type(o)) + TypeError: (, ) + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.122 seconds) + + +.. _sphx_glr_download_examples_sklearn_openml_run_example.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: openml_run_example.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: openml_run_example.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/doc/examples/sklearn/sg_execution_times.rst b/doc/examples/sklearn/sg_execution_times.rst new file mode 100644 index 000000000..a83ed0fec --- /dev/null +++ b/doc/examples/sklearn/sg_execution_times.rst @@ -0,0 +1,10 @@ + +:orphan: + +.. _sphx_glr_examples_sklearn_sg_execution_times: + +Computation times +================= +**00:00.122** total execution time for **examples_sklearn** files: + +- **00:00.122**: :ref:`sphx_glr_examples_sklearn_openml_run_example.py` (``openml_run_example.py``) diff --git a/doc/examples/tasks_tutorial.ipynb b/doc/examples/tasks_tutorial.ipynb new file mode 100644 index 000000000..5c9bfae62 --- /dev/null +++ b/doc/examples/tasks_tutorial.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\nTasks\n=====\n\nA tutorial on how to list and download tasks.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import openml\nimport pandas as pd\nfrom pprint import pprint" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Tasks are identified by IDs and can be accessed in two different ways:\n\n1. In a list providing basic information on all tasks available on OpenML.\nThis function will not download the actual tasks, but will instead download\nmeta data that can be used to filter the tasks and retrieve a set of IDs.\nWe can filter this list, for example, we can only list tasks having a\nspecial tag or only tasks for a specific target such as\n*supervised classification*.\n\n2. A single task by its ID. It contains all meta information, the target\nmetric, the splits and an iterator which can be used to access the\nsplits in a useful manner.\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Listing tasks\n^^^^^^^^^^^^^\n\nWe will start by simply listing only *supervised classification* tasks:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = openml.tasks.list_tasks(task_type_id=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**openml.tasks.list_tasks()** returns a dictionary of dictionaries, we convert it into a\n`pandas dataframe `_\nto have better visualization and easier access:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = pd.DataFrame.from_dict(tasks, orient='index')\nprint(tasks.columns)\nprint(\"First 5 of %s tasks:\" % len(tasks))\npprint(tasks.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can filter the list of tasks to only contain datasets with more than\n500 samples, but less than 1000 samples:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filtered_tasks = tasks.query('NumberOfInstances > 500 and NumberOfInstances < 1000')\nprint(list(filtered_tasks.index))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Number of tasks\nprint(len(filtered_tasks))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Then, we can further restrict the tasks to all have the same resampling strategy:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "filtered_tasks = filtered_tasks.query('estimation_procedure == \"10-fold Crossvalidation\"')\nprint(list(filtered_tasks.index))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Number of tasks\nprint(len(filtered_tasks))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Resampling strategies can be found on the\n`OpenML Website `_.\n\nSimilar to listing tasks by task type, we can list tasks by tags:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = openml.tasks.list_tasks(tag='OpenML100')\ntasks = pd.DataFrame.from_dict(tasks, orient='index')\nprint(\"First 5 of %s tasks:\" % len(tasks))\npprint(tasks.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Furthermore, we can list tasks based on the dataset id:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = openml.tasks.list_tasks(data_id=61)\ntasks = pd.DataFrame.from_dict(tasks, orient='index')\nprint(\"First 5 of %s tasks:\" % len(tasks))\npprint(tasks.head())" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In addition, a size limit and an offset can be applied both separately and simultaneously:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = openml.tasks.list_tasks(size=10, offset=50)\ntasks = pd.DataFrame.from_dict(tasks, orient='index')\npprint(tasks)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**OpenML 100**\nis a curated list of 100 tasks to start using OpenML. They are all\nsupervised classification tasks with more than 500 instances and less than 50000\ninstances per task. To make things easier, the tasks do not contain highly\nunbalanced data and sparse data. However, the tasks include missing values and\ncategorical features. You can find out more about the *OpenML 100* on\n`the OpenML benchmarking page `_.\n\nFinally, it is also possible to list all tasks on OpenML with:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks = openml.tasks.list_tasks()\ntasks = pd.DataFrame.from_dict(tasks, orient='index')\nprint(len(tasks))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Exercise\n########\n\nSearch for the tasks on the 'eeg-eye-state' dataset.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "tasks.query('name==\"eeg-eye-state\"')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Downloading tasks\n^^^^^^^^^^^^^^^^^\n\nWe provide two functions to download tasks, one which downloads only a\nsingle task by its ID, and one which takes a list of IDs and downloads\nall of these tasks:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "task_id = 1\ntask = openml.tasks.get_task(task_id)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Properties of the task are stored as member variables:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pprint(vars(task))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And:\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ids = [1, 2, 19, 97, 403]\ntasks = openml.tasks.get_tasks(ids)\npprint(tasks[0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/doc/examples/tasks_tutorial.py b/doc/examples/tasks_tutorial.py new file mode 100644 index 000000000..16f62e3a1 --- /dev/null +++ b/doc/examples/tasks_tutorial.py @@ -0,0 +1,140 @@ +""" +Tasks +===== + +A tutorial on how to list and download tasks. +""" + +import openml +import pandas as pd +from pprint import pprint + +############################################################################ +# +# Tasks are identified by IDs and can be accessed in two different ways: +# +# 1. In a list providing basic information on all tasks available on OpenML. +# This function will not download the actual tasks, but will instead download +# meta data that can be used to filter the tasks and retrieve a set of IDs. +# We can filter this list, for example, we can only list tasks having a +# special tag or only tasks for a specific target such as +# *supervised classification*. +# +# 2. A single task by its ID. It contains all meta information, the target +# metric, the splits and an iterator which can be used to access the +# splits in a useful manner. + +############################################################################ +# Listing tasks +# ^^^^^^^^^^^^^ +# +# We will start by simply listing only *supervised classification* tasks: + +tasks = openml.tasks.list_tasks(task_type_id=1) + +############################################################################ +# **openml.tasks.list_tasks()** returns a dictionary of dictionaries, we convert it into a +# `pandas dataframe `_ +# to have better visualization and easier access: + +tasks = pd.DataFrame.from_dict(tasks, orient='index') +print(tasks.columns) +print("First 5 of %s tasks:" % len(tasks)) +pprint(tasks.head()) + +############################################################################ +# We can filter the list of tasks to only contain datasets with more than +# 500 samples, but less than 1000 samples: + +filtered_tasks = tasks.query('NumberOfInstances > 500 and NumberOfInstances < 1000') +print(list(filtered_tasks.index)) + +############################################################################ + +# Number of tasks +print(len(filtered_tasks)) + +############################################################################ +# Then, we can further restrict the tasks to all have the same resampling strategy: + +filtered_tasks = filtered_tasks.query('estimation_procedure == "10-fold Crossvalidation"') +print(list(filtered_tasks.index)) + +############################################################################ + +# Number of tasks +print(len(filtered_tasks)) + +############################################################################ +# Resampling strategies can be found on the +# `OpenML Website `_. +# +# Similar to listing tasks by task type, we can list tasks by tags: + +tasks = openml.tasks.list_tasks(tag='OpenML100') +tasks = pd.DataFrame.from_dict(tasks, orient='index') +print("First 5 of %s tasks:" % len(tasks)) +pprint(tasks.head()) + +############################################################################ +# Furthermore, we can list tasks based on the dataset id: + +tasks = openml.tasks.list_tasks(data_id=61) +tasks = pd.DataFrame.from_dict(tasks, orient='index') +print("First 5 of %s tasks:" % len(tasks)) +pprint(tasks.head()) + +############################################################################ +# In addition, a size limit and an offset can be applied both separately and simultaneously: + +tasks = openml.tasks.list_tasks(size=10, offset=50) +tasks = pd.DataFrame.from_dict(tasks, orient='index') +pprint(tasks) + +############################################################################ +# +# **OpenML 100** +# is a curated list of 100 tasks to start using OpenML. They are all +# supervised classification tasks with more than 500 instances and less than 50000 +# instances per task. To make things easier, the tasks do not contain highly +# unbalanced data and sparse data. However, the tasks include missing values and +# categorical features. You can find out more about the *OpenML 100* on +# `the OpenML benchmarking page `_. +# +# Finally, it is also possible to list all tasks on OpenML with: + +############################################################################ +tasks = openml.tasks.list_tasks() +tasks = pd.DataFrame.from_dict(tasks, orient='index') +print(len(tasks)) + +############################################################################ +# Exercise +# ######## +# +# Search for the tasks on the 'eeg-eye-state' dataset. + +tasks.query('name=="eeg-eye-state"') + +############################################################################ +# Downloading tasks +# ^^^^^^^^^^^^^^^^^ +# +# We provide two functions to download tasks, one which downloads only a +# single task by its ID, and one which takes a list of IDs and downloads +# all of these tasks: + +task_id = 1 +task = openml.tasks.get_task(task_id) + +############################################################################ +# Properties of the task are stored as member variables: + +pprint(vars(task)) + +############################################################################ +# And: + +ids = [1, 2, 19, 97, 403] +tasks = openml.tasks.get_tasks(ids) +pprint(tasks[0]) diff --git a/doc/examples/tasks_tutorial.rst b/doc/examples/tasks_tutorial.rst new file mode 100644 index 000000000..ae6bf6ab2 --- /dev/null +++ b/doc/examples/tasks_tutorial.rst @@ -0,0 +1,354 @@ +.. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code +.. rst-class:: sphx-glr-example-title + +.. _sphx_glr_examples_tasks_tutorial.py: + + +Tasks +===== + +A tutorial on how to list and download tasks. + + +.. code-block:: default + + + import openml + import pandas as pd + from pprint import pprint + + + + + + + +Tasks are identified by IDs and can be accessed in two different ways: + +1. In a list providing basic information on all tasks available on OpenML. +This function will not download the actual tasks, but will instead download +meta data that can be used to filter the tasks and retrieve a set of IDs. +We can filter this list, for example, we can only list tasks having a +special tag or only tasks for a specific target such as +*supervised classification*. + +2. A single task by its ID. It contains all meta information, the target +metric, the splits and an iterator which can be used to access the +splits in a useful manner. + +Listing tasks +^^^^^^^^^^^^^ + +We will start by simply listing only *supervised classification* tasks: + + +.. code-block:: default + + + tasks = openml.tasks.list_tasks(task_type_id=1) + + + + + + + +**openml.tasks.list_tasks()** returns a dictionary of dictionaries, we convert it into a +`pandas dataframe `_ +to have better visualization and easier access: + + +.. code-block:: default + + + tasks = pd.DataFrame.from_dict(tasks, orient='index') + print(tasks.columns) + print("First 5 of %s tasks:" % len(tasks)) + pprint(tasks.head()) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Index(['tid', 'ttid', 'did', 'name', 'task_type', 'status', + 'estimation_procedure', 'source_data', 'target_feature', + 'MajorityClassSize', 'MaxNominalAttDistinctValues', 'MinorityClassSize', + 'NumberOfClasses', 'NumberOfFeatures', 'NumberOfInstances', + 'NumberOfInstancesWithMissingValues', 'NumberOfMissingValues', + 'NumberOfNumericFeatures', 'NumberOfSymbolicFeatures'], + dtype='object') + First 5 of 932 tasks: + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 ... 6.0 33.0 + 2 2 1 ... 6.0 33.0 + 3 3 1 ... 6.0 33.0 + 4 4 1 ... 6.0 33.0 + 5 5 1 ... 6.0 33.0 + + [5 rows x 19 columns] + + +We can filter the list of tasks to only contain datasets with more than +500 samples, but less than 1000 samples: + + +.. code-block:: default + + + filtered_tasks = tasks.query('NumberOfInstances > 500 and NumberOfInstances < 1000') + print(list(filtered_tasks.index)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + [1, 2, 3, 4, 5, 6, 19, 20, 21, 22, 23, 24, 37, 38, 39, 40, 41, 42, 91, 92, 93, 94, 95, 96, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 181, 182, 183, 184, 185, 186, 193, 194, 195, 196, 197, 198, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 229, 230, 231, 232, 233, 234, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 307, 308, 309, 310, 311, 312, 379, 380, 381, 382, 383, 384, 391, 392, 393, 394, 395, 396, 433, 434, 435, 436, 437, 438, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 565, 566, 567, 568, 569, 570, 595, 596, 597, 598, 599, 600, 607, 608, 609, 610, 611, 612, 1069, 1072, 1075, 1084, 1088, 1090, 1093, 1094, 1099, 1101, 1103, 1104, 1105, 1107, 1109, 1110, 1111, 1120, 1132, 1134, 1141, 1154, 1155, 1163, 1168, 1232, 1234, 1239, 1240, 1242, 1244, 1245, 1248, 1250, 1251, 1252, 1253, 1257, 1258, 1260, 1261, 1263, 1264, 1265, 1268, 1269, 1270, 1273, 1276, 1279, 1280, 1283, 1288, 1289, 1297, 1304, 1307, 1309, 1310, 1314, 1317, 1320, 1321, 1323, 1327, 1329, 1330, 1334, 1337, 1339, 1341, 1342, 1343, 1346, 1347, 1349, 1353, 1354, 1355, 1358, 1359, 1366, 1367, 1369, 1376, 1377, 1378, 1380, 1382, 1383, 1384, 1387, 1396, 1399, 1402, 1405, 1406, 1410, 1412, 1413, 1419, 1423, 1426, 1428, 1430, 1433, 1437, 1438, 1439, 1440, 1442, 1443, 1447, 1448, 1453, 1456, 1459, 1460, 1462, 1466, 1469, 1471, 1473, 1476, 1477, 1478, 1479, 1484, 1485, 1487, 1490, 1494, 1496, 1497, 1498, 1502, 1503, 1507, 1508, 1509, 1512, 1515, 1518, 1519, 1520, 1523, 1524, 1531, 1535, 1537, 1539, 1541, 1543] + + + +.. code-block:: default + + + # Number of tasks + print(len(filtered_tasks)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + 309 + + +Then, we can further restrict the tasks to all have the same resampling strategy: + + +.. code-block:: default + + + filtered_tasks = filtered_tasks.query('estimation_procedure == "10-fold Crossvalidation"') + print(list(filtered_tasks.index)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + [1, 19, 37, 91, 115, 127, 145, 151, 181, 193, 205, 211, 217, 229, 241, 247, 253, 307, 379, 391, 433, 511, 517, 565, 595, 607] + + + +.. code-block:: default + + + # Number of tasks + print(len(filtered_tasks)) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + 26 + + +Resampling strategies can be found on the +`OpenML Website `_. + +Similar to listing tasks by task type, we can list tasks by tags: + + +.. code-block:: default + + + tasks = openml.tasks.list_tasks(tag='OpenML100') + tasks = pd.DataFrame.from_dict(tasks, orient='index') + print("First 5 of %s tasks:" % len(tasks)) + pprint(tasks.head()) + + + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + First 5 of 100 tasks: + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 ... 6.0 33.0 + 7 7 1 ... 0.0 37.0 + 13 13 1 ... 16.0 1.0 + 19 19 1 ... 4.0 1.0 + 25 25 1 ... 216.0 1.0 + + [5 rows x 19 columns] + + +Furthermore, we can list tasks based on the dataset id: + + +.. code-block:: default + + + tasks = openml.tasks.list_tasks(data_id=61) + tasks = pd.DataFrame.from_dict(tasks, orient='index') + print("First 5 of %s tasks:" % len(tasks)) + pprint(tasks.head()) + + + + +.. code-block:: pytb + + Traceback (most recent call last): + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage + out = func() + File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ + exec(self.code, self.globals) + File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/tasks_tutorial.py", line 82, in + tasks = openml.tasks.list_tasks(data_id=61) + TypeError: list_tasks() got an unexpected keyword argument 'data_id' + + + + +In addition, a size limit and an offset can be applied both separately and simultaneously: + + +.. code-block:: default + + + tasks = openml.tasks.list_tasks(size=10, offset=50) + tasks = pd.DataFrame.from_dict(tasks, orient='index') + pprint(tasks) + + +**OpenML 100** +is a curated list of 100 tasks to start using OpenML. They are all +supervised classification tasks with more than 500 instances and less than 50000 +instances per task. To make things easier, the tasks do not contain highly +unbalanced data and sparse data. However, the tasks include missing values and +categorical features. You can find out more about the *OpenML 100* on +`the OpenML benchmarking page `_. + +Finally, it is also possible to list all tasks on OpenML with: + + +.. code-block:: default + + tasks = openml.tasks.list_tasks() + tasks = pd.DataFrame.from_dict(tasks, orient='index') + print(len(tasks)) + + +Exercise +######## + +Search for the tasks on the 'eeg-eye-state' dataset. + + +.. code-block:: default + + + tasks.query('name=="eeg-eye-state"') + + +Downloading tasks +^^^^^^^^^^^^^^^^^ + +We provide two functions to download tasks, one which downloads only a +single task by its ID, and one which takes a list of IDs and downloads +all of these tasks: + + +.. code-block:: default + + + task_id = 1 + task = openml.tasks.get_task(task_id) + + +Properties of the task are stored as member variables: + + +.. code-block:: default + + + pprint(vars(task)) + + +And: + + +.. code-block:: default + + + ids = [1, 2, 19, 97, 403] + tasks = openml.tasks.get_tasks(ids) + pprint(tasks[0]) + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 5.049 seconds) + + +.. _sphx_glr_download_examples_tasks_tutorial.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download + + :download:`Download Python source code: tasks_tutorial.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: tasks_tutorial.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/'cache'/datasets/68/dataset.arff b/examples/'cache'/datasets/68/dataset.arff new file mode 100644 index 000000000..8498a7bf9 --- /dev/null +++ b/examples/'cache'/datasets/68/dataset.arff @@ -0,0 +1,15017 @@ +%% Source: +%% +%% Oliver Roesler, it12148 '@' lehre.dhbw-stuttgart.de , Baden-Wuerttemberg +%% Cooperative State University (DHBW), Stuttgart, Germany +%% +%% Data Set Information: +%% +%% All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. +%% The duration of the measurement was 117 seconds. The eye state was detected via a +%% camera during the EEG measurement and added later manually to the file after +%% analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. +%% All values are in chronological order with the first measured value at the top of +%% the data. +%% +%% Attribute Information: +%% +%% Provide information about each attribute in your data set. + +@relation eeg-eye-state + +@attribute V1 numeric +@attribute V2 numeric +@attribute V3 numeric +@attribute V4 numeric +@attribute V5 numeric +@attribute V6 numeric +@attribute V7 numeric +@attribute V8 numeric +@attribute V9 numeric +@attribute V10 numeric +@attribute V11 numeric +@attribute V12 numeric +@attribute V13 numeric +@attribute V14 numeric +@attribute Class {1,2} + +@data +4329.23,4009.23,4289.23,4148.21,4350.26,4586.15,4096.92,4641.03,4222.05,4238.46,4211.28,4280.51,4635.9,4393.85,1 +4324.62,4004.62,4293.85,4148.72,4342.05,4586.67,4097.44,4638.97,4210.77,4226.67,4207.69,4279.49,4632.82,4384.1,1 +4327.69,4006.67,4295.38,4156.41,4336.92,4583.59,4096.92,4630.26,4207.69,4222.05,4206.67,4282.05,4628.72,4389.23,1 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z7hmU$d}GTuvEidRU;B*rUi{U`oAGtN1!kXzW8@p?6lshm1pjZ}iEyx7fao^lieaSMJCh!?~@V{G3tF+nn*wdZ_4z z^Hon_i}FGI#?GPOj_VV%a({pIi=Mohejj_oUCsC1)$;dbaWeHEi6wn|_NmdyFU`LV zFS>Z|lQ;7t#+^P2`^?84{=$3euke%B>wQ8s7rl9-SFqsLmAlF#mAlTkWZ4ni3G361 zew!n;CN;j1eelf>zV=;jNBr@v{&*S+n*Z~lL^5jOJx literal 0 HcmV?d00001 diff --git a/examples/'cache'/datasets/68/description.xml b/examples/'cache'/datasets/68/description.xml new file mode 100644 index 000000000..753f385b3 --- /dev/null +++ b/examples/'cache'/datasets/68/description.xml @@ -0,0 +1,16 @@ + + 68 + eeg-eye-state + 1 + **Author**: Oliver Roesler, it12148'@'lehre.dhbw-stuttgart.de +**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State), Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany +**Please cite**: + +All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data. + + ARFF + 2015-05-22T16:40:04 + Public https://test.openml.org/data/v1/download/68/eeg-eye-state.arff + 68 Class study_14 public active + 2019-03-16 14:30:51 32086b7bec4daaa9cbe5f19efa63368c + diff --git a/examples/'cache'/datasets/68/features.xml b/examples/'cache'/datasets/68/features.xml new file mode 100644 index 000000000..a22a63a50 --- /dev/null +++ b/examples/'cache'/datasets/68/features.xml @@ -0,0 +1,139 @@ + + + 0 + V1 + numeric + false + false + false + 0 + + + 1 + V2 + numeric + false + false + false + 0 + + + 2 + V3 + numeric + false + false + false + 0 + + + 3 + V4 + numeric + false + false + false + 0 + + + 4 + V5 + numeric + false + false + false + 0 + + + 5 + V6 + numeric + false + false + false + 0 + + + 6 + V7 + numeric + false + false + false + 0 + + + 7 + V8 + numeric + false + false + false + 0 + + + 8 + V9 + numeric + false + false + false + 0 + + + 9 + V10 + numeric + false + false + false + 0 + + + 10 + V11 + numeric + false + false + false + 0 + + + 11 + V12 + numeric + false + false + false + 0 + + + 12 + V13 + numeric + false + false + false + 0 + + + 13 + V14 + numeric + false + false + false + 0 + + + 14 + Class + nominal + 1 + 2 + true + false + false + 0 + + diff --git a/examples/'cache'/datasets/68/qualities.xml b/examples/'cache'/datasets/68/qualities.xml new file mode 100644 index 000000000..1d50cdd74 --- /dev/null +++ b/examples/'cache'/datasets/68/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.9984645169904534 + + + + CfsSubsetEval_DecisionStumpAUC + 0.8154762506306992 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.22930574098798398 + + + + CfsSubsetEval_DecisionStumpKappa + 0.5342696977625635 + + + + CfsSubsetEval_NaiveBayesAUC + 0.8154762506306992 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.22930574098798398 + + + + CfsSubsetEval_NaiveBayesKappa + 0.5342696977625635 + + + + CfsSubsetEval_kNN1NAUC + 0.8154762506306992 + + + + CfsSubsetEval_kNN1NErrRate + 0.22930574098798398 + + + + CfsSubsetEval_kNN1NKappa + 0.5342696977625635 + + + + ClassEntropy + 0.992422373387609 + + + + DecisionStumpAUC + 0.5730896889672722 + + + + DecisionStumpErrRate + 0.41141522029372496 + + + + DecisionStumpKappa + 0.12923993775163797 + + + + Dimensionality + 0.0010013351134846463 + + + + EquivalentNumberOfAtts + + + + + J48.00001.AUC + 0.8271462175860198 + + + + J48.00001.ErrRate + 0.18631508678237652 + + + + J48.00001.Kappa + 0.623155313134783 + + + + J48.0001.AUC + 0.8271462175860198 + + + + J48.0001.ErrRate + 0.18631508678237652 + + + + J48.0001.Kappa + 0.623155313134783 + + + + J48.001.AUC + 0.8271462175860198 + + + + J48.001.ErrRate + 0.18631508678237652 + + + + J48.001.Kappa + 0.623155313134783 + + + + MajorityClassPercentage + 55.12016021361815 + + + + MajorityClassSize + 8257.0 + + + + MaxAttributeEntropy + + + + + MaxKurtosisOfNumericAtts + 14979.178735371073 + + + + MaxMeansOfNumericAtts + 4644.02237917223 + + + + MaxMutualInformation + + + + + MaxNominalAttDistinctValues + 2.0 + + + + MaxSkewnessOfNumericAtts + 122.38777688436316 + + + + MaxStdDevOfNumericAtts + 5891.2850425236575 + + + + MeanAttributeEntropy + + + + + MeanKurtosisOfNumericAtts + 8904.72190163755 + + + + MeanMeansOfNumericAtts + 4316.882028546157 + + + + MeanMutualInformation + + + + + MeanNoiseToSignalRatio + + + + + MeanNominalAttDistinctValues + 2.0 + + + + MeanSkewnessOfNumericAtts + 71.73726620900297 + + + + MeanStdDevOfNumericAtts + 1767.2884824189532 + + + + MinAttributeEntropy + + + + + MinKurtosisOfNumericAtts + 2056.5210594418404 + + + + MinMeansOfNumericAtts + 4009.767693591455 + + + + MinMutualInformation + + + + + MinNominalAttDistinctValues + 2.0 + + + + MinSkewnessOfNumericAtts + -13.615160740498586 + + + + MinStdDevOfNumericAtts + 29.292603201776053 + + + + MinorityClassPercentage + 44.879839786381844 + + + + MinorityClassSize + 6723.0 + + + + NaiveBayesAUC + 0.5441157198060068 + + + + NaiveBayesErrRate + 0.4634846461949266 + + + + NaiveBayesKappa + 0.11130397334752887 + + + + NumberOfBinaryFeatures + 1.0 + + + + NumberOfClasses + 2.0 + + + + NumberOfFeatures + 15.0 + + + + NumberOfInstances + 14980.0 + + + + NumberOfInstancesWithMissingValues + 0.0 + + + + NumberOfMissingValues + 0.0 + + + + NumberOfNumericFeatures + 14.0 + + + + NumberOfSymbolicFeatures + 1.0 + + + + PercentageOfBinaryFeatures + 6.666666666666667 + + + + PercentageOfInstancesWithMissingValues + 0.0 + + + + PercentageOfMissingValues + 0.0 + + + + PercentageOfNumericFeatures + 93.33333333333333 + + + + PercentageOfSymbolicFeatures + 6.666666666666667 + + + + Quartile1AttributeEntropy + + + + + Quartile1KurtosisOfNumericAtts + 2713.5598368066608 + + + + Quartile1MeansOfNumericAtts + 4193.079256508679 + + + + Quartile1MutualInformation + + + + + Quartile1SkewnessOfNumericAtts + 22.475026893669664 + + + + Quartile1StdDevOfNumericAtts + 37.98467230942155 + + + + Quartile2AttributeEntropy + + + + + Quartile2KurtosisOfNumericAtts + 9352.695219763142 + + + + Quartile2MeansOfNumericAtts + 4271.6276034712955 + + + + Quartile2MutualInformation + + + + + Quartile2SkewnessOfNumericAtts + 84.61113200877784 + + + + Quartile2StdDevOfNumericAtts + 627.1558153704191 + + + + Quartile3AttributeEntropy + + + + + Quartile3KurtosisOfNumericAtts + 14971.654606997874 + + + + Quartile3MeansOfNumericAtts + 4466.12820822263 + + + + Quartile3MutualInformation + + + + + Quartile3SkewnessOfNumericAtts + 122.34170603745707 + + + + Quartile3StdDevOfNumericAtts + 3343.823788627256 + + + + REPTreeDepth1AUC + 0.8585707193015194 + + + + REPTreeDepth1ErrRate + 0.19072096128170896 + + + + REPTreeDepth1Kappa + 0.6126693923072585 + + + + REPTreeDepth2AUC + 0.8585707193015194 + + + + REPTreeDepth2ErrRate + 0.19072096128170896 + + + + REPTreeDepth2Kappa + 0.6126693923072585 + + + + REPTreeDepth3AUC + 0.8585707193015194 + + + + REPTreeDepth3ErrRate + 0.19072096128170896 + + + + REPTreeDepth3Kappa + 0.6126693923072585 + + + + RandomTreeDepth1AUC + 0.8092130970110125 + + + + RandomTreeDepth1ErrRate + 0.18845126835781043 + + + + RandomTreeDepth1Kappa + 0.6188451689674707 + + + + RandomTreeDepth2AUC + 0.8092130970110125 + + + + RandomTreeDepth2ErrRate + 0.18845126835781043 + + + + RandomTreeDepth2Kappa + 0.6188451689674707 + + + + RandomTreeDepth3AUC + 0.8092130970110125 + + + + RandomTreeDepth3ErrRate + 0.18845126835781043 + + + + RandomTreeDepth3Kappa + 0.6188451689674707 + + + + StdvNominalAttDistinctValues + 0.0 + + + + kNN1NAUC + 0.8331954978013598 + + + + kNN1NErrRate + 0.16355140186915887 + + + + kNN1NKappa + 0.6684228094245078 + + diff --git a/examples/'cache'/locks/datasets.functions.get_dataset:403 b/examples/'cache'/locks/datasets.functions.get_dataset:403 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/locks/datasets.functions.get_dataset:68 b/examples/'cache'/locks/datasets.functions.get_dataset:68 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/tasks/403/datasplits.arff b/examples/'cache'/tasks/403/datasplits.arff new file mode 100644 index 000000000..731f5ec3a --- /dev/null +++ b/examples/'cache'/tasks/403/datasplits.arff @@ -0,0 +1,149808 @@ +@relation eeg-eye-state_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,12101,0,0 +TRAIN,14753,0,0 +TRAIN,10219,0,0 +TRAIN,2974,0,0 +TRAIN,10814,0,0 +TRAIN,13339,0,0 +TRAIN,3307,0,0 +TRAIN,12700,0,0 +TRAIN,4813,0,0 +TRAIN,10736,0,0 +TRAIN,12926,0,0 +TRAIN,9898,0,0 +TRAIN,1991,0,0 +TRAIN,6546,0,0 +TRAIN,14601,0,0 +TRAIN,9780,0,0 +TRAIN,13304,0,0 +TRAIN,6270,0,0 +TRAIN,9539,0,0 +TRAIN,2089,0,0 +TRAIN,13212,0,0 +TRAIN,4670,0,0 +TRAIN,13216,0,0 +TRAIN,12470,0,0 +TRAIN,13043,0,0 +TRAIN,3166,0,0 +TRAIN,13460,0,0 +TRAIN,3067,0,0 +TRAIN,10137,0,0 +TRAIN,13113,0,0 +TRAIN,10358,0,0 +TRAIN,5997,0,0 +TRAIN,9951,0,0 +TRAIN,1740,0,0 +TRAIN,9076,0,0 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mode 100644 index 000000000..913025b45 --- /dev/null +++ b/examples/'cache'/tasks/403/task.xml @@ -0,0 +1,37 @@ + + 403 + Task 403: eeg-eye-state (Supervised Classification) + 1 + Supervised Classification + + +68 +Class + + + +1 +crossvalidation +https://test.openml.org/api_splits/get/403/Task_403_splits.arff +1 +10 + +true + + + + + + + + + +ARFF + + + + + + + OpenML100 + diff --git a/examples/sklearn/'cache'/datasets/17/dataset.arff b/examples/sklearn/'cache'/datasets/17/dataset.arff new file mode 100644 index 000000000..a3347a36e --- /dev/null +++ b/examples/sklearn/'cache'/datasets/17/dataset.arff @@ -0,0 +1,1301 @@ +% Description of the German credit dataset. +% +% 1. Title: German Credit data +% +% 2. Source Information +% +% Professor Dr. Hans Hofmann +% Institut f"ur Statistik und "Okonometrie +% Universit"at Hamburg +% FB Wirtschaftswissenschaften +% Von-Melle-Park 5 +% 2000 Hamburg 13 +% +% 3. Number of Instances: 1000 +% +% Two datasets are provided. the original dataset, in the form provided +% by Prof. Hofmann, contains categorical/symbolic attributes and +% is in the file "german.data". +% +% For algorithms that need numerical attributes, Strathclyde University +% produced the file "german.data-numeric". This file has been edited +% and several indicator variables added to make it suitable for +% algorithms which cannot cope with categorical variables. Several +% attributes that are ordered categorical (such as attribute 17) have +% been coded as integer. This was the form used by StatLog. +% +% +% 6. Number of Attributes german: 20 (7 numerical, 13 categorical) +% Number of Attributes german.numer: 24 (24 numerical) +% +% +% 7. Attribute description for german +% +% Attribute 1: (qualitative) +% Status of existing checking account +% A11 : ... < 0 DM +% A12 : 0 <= ... < 200 DM +% A13 : ... >= 200 DM / +% salary assignments for at least 1 year +% A14 : no checking account +% +% Attribute 2: (numerical) +% Duration in month +% +% Attribute 3: (qualitative) +% Credit history +% A30 : no credits taken/ +% all credits paid back duly +% A31 : all credits at this bank paid back duly +% A32 : existing credits paid back duly till now +% A33 : delay in paying off in the past +% A34 : critical account/ +% other credits existing (not at this bank) +% +% Attribute 4: (qualitative) +% Purpose +% A40 : car (new) +% A41 : car (used) +% A42 : furniture/equipment +% A43 : radio/television +% A44 : domestic appliances +% A45 : repairs +% A46 : education +% A47 : (vacation - does not exist?) +% A48 : retraining +% A49 : business +% A410 : others +% +% Attribute 5: (numerical) +% Credit amount +% +% Attibute 6: (qualitative) +% Savings account/bonds +% A61 : ... < 100 DM +% A62 : 100 <= ... < 500 DM +% A63 : 500 <= ... < 1000 DM +% A64 : .. >= 1000 DM +% A65 : unknown/ no savings account +% +% Attribute 7: (qualitative) +% Present employment since +% A71 : unemployed +% A72 : ... < 1 year +% A73 : 1 <= ... < 4 years +% A74 : 4 <= ... < 7 years +% A75 : .. >= 7 years +% +% Attribute 8: (numerical) +% Installment rate in percentage of disposable income +% +% Attribute 9: (qualitative) +% Personal status and sex +% A91 : male : divorced/separated +% A92 : female : divorced/separated/married +% A93 : male : single +% A94 : male : married/widowed +% A95 : female : single +% +% Attribute 10: (qualitative) +% Other debtors / guarantors +% A101 : none +% A102 : co-applicant +% A103 : guarantor +% +% Attribute 11: (numerical) +% Present residence since +% +% Attribute 12: (qualitative) +% Property +% A121 : real estate +% A122 : if not A121 : building society savings agreement/ +% life insurance +% A123 : if not A121/A122 : car or other, not in attribute 6 +% A124 : unknown / no property +% +% Attribute 13: (numerical) +% Age in years +% +% Attribute 14: (qualitative) +% Other installment plans +% A141 : bank +% A142 : stores +% A143 : none +% +% Attribute 15: (qualitative) +% Housing +% A151 : rent +% A152 : own +% A153 : for free +% +% Attribute 16: (numerical) +% Number of existing credits at this bank +% +% Attribute 17: (qualitative) +% Job +% A171 : unemployed/ unskilled - non-resident +% A172 : unskilled - resident +% A173 : skilled employee / official +% A174 : management/ self-employed/ +% highly qualified employee/ officer +% +% Attribute 18: (numerical) +% Number of people being liable to provide maintenance for +% +% Attribute 19: (qualitative) +% Telephone +% A191 : none +% A192 : yes, registered under the customers name +% +% Attribute 20: (qualitative) +% foreign worker +% A201 : yes +% A202 : no +% +% +% +% 8. Cost Matrix +% +% This dataset requires use of a cost matrix (see below) +% +% +% 1 2 +% ---------------------------- +% 1 0 1 +% ----------------------- +% 2 5 0 +% +% (1 = Good, 2 = Bad) +% +% the rows represent the actual classification and the columns +% the predicted classification. +% +% It is worse to class a customer as good when they are bad (5), +% than it is to class a customer as bad when they are good (1). +% +% +% +% +% +% Relabeled values in attribute checking_status +% From: A11 To: '<0' +% From: A12 To: '0<=X<200' +% From: A13 To: '>=200' +% From: A14 To: 'no checking' +% +% +% Relabeled values in attribute credit_history +% From: A30 To: 'no credits/all paid' +% From: A31 To: 'all paid' +% From: A32 To: 'existing paid' +% From: A33 To: 'delayed previously' +% From: A34 To: 'critical/other existing credit' +% +% +% Relabeled values in attribute purpose +% From: A40 To: 'new car' +% From: A41 To: 'used car' +% From: A42 To: furniture/equipment +% From: A43 To: radio/tv +% From: A44 To: 'domestic appliance' +% From: A45 To: repairs +% From: A46 To: education +% From: A47 To: vacation +% From: A48 To: retraining +% From: A49 To: business +% From: A410 To: other +% +% +% Relabeled values in attribute savings_status +% From: A61 To: '<100' +% From: A62 To: '100<=X<500' +% From: A63 To: '500<=X<1000' +% From: A64 To: '>=1000' +% From: A65 To: 'no known savings' +% +% +% Relabeled values in attribute employment +% From: A71 To: unemployed +% From: A72 To: '<1' +% From: A73 To: '1<=X<4' +% From: A74 To: '4<=X<7' +% From: A75 To: '>=7' +% +% +% Relabeled values in attribute personal_status +% From: A91 To: 'male div/sep' +% From: A92 To: 'female div/dep/mar' +% From: A93 To: 'male single' +% From: A94 To: 'male mar/wid' +% From: A95 To: 'female single' +% +% +% Relabeled values in attribute other_parties +% From: A101 To: none +% From: A102 To: 'co applicant' +% From: A103 To: guarantor +% +% +% Relabeled values in attribute property_magnitude +% From: A121 To: 'real estate' +% From: A122 To: 'life insurance' +% From: A123 To: car +% From: A124 To: 'no known property' +% +% +% Relabeled values in attribute other_payment_plans +% From: A141 To: bank +% From: A142 To: stores +% From: A143 To: none +% +% +% Relabeled values in attribute housing +% From: A151 To: rent +% From: A152 To: own +% From: A153 To: 'for free' +% +% +% Relabeled values in attribute job +% From: A171 To: 'unemp/unskilled non res' +% From: A172 To: 'unskilled resident' +% From: A173 To: skilled +% From: A174 To: 'high qualif/self emp/mgmt' +% +% +% Relabeled values in attribute own_telephone +% From: A191 To: none +% From: A192 To: yes +% +% +% Relabeled values in attribute foreign_worker +% From: A201 To: yes +% From: A202 To: no +% +% +% Relabeled values in attribute class +% From: 1 To: good +% From: 2 To: bad +% +@relation german_credit +@attribute 'checking_status' { '<0', '0<=X<200', '>=200', 'no checking'} +@attribute 'duration' real +@attribute 'credit_history' { 'no credits/all paid', 'all paid', 'existing paid', 'delayed previously', 'critical/other existing credit'} +@attribute 'purpose' { 'new car', 'used car', furniture/equipment, radio/tv, 'domestic appliance', repairs, education, vacation, retraining, business, other} +@attribute 'credit_amount' real +@attribute 'savings_status' { '<100', '100<=X<500', '500<=X<1000', '>=1000', 'no known savings'} +@attribute 'employment' { unemployed, '<1', '1<=X<4', '4<=X<7', '>=7'} +@attribute 'installment_commitment' real +@attribute 'personal_status' { 'male div/sep', 'female div/dep/mar', 'male single', 'male mar/wid', 'female single'} +@attribute 'other_parties' { none, 'co applicant', guarantor} +@attribute 'residence_since' real +@attribute 'property_magnitude' { 'real estate', 'life insurance', car, 'no known property'} +@attribute 'age' real +@attribute 'other_payment_plans' { bank, stores, none} +@attribute 'housing' { rent, own, 'for free'} +@attribute 'existing_credits' real +@attribute 'job' { 'unemp/unskilled non res', 'unskilled resident', skilled, 'high qualif/self emp/mgmt'} +@attribute 'num_dependents' real +@attribute 'own_telephone' { none, yes} +@attribute 'foreign_worker' { yes, no} +@attribute 'class' { good, bad} +@data +'<0',6,'critical/other existing credit',radio/tv,1169,'no known savings','>=7',4,'male single',none,4,'real estate',67,none,own,2,skilled,1,yes,yes,good +'0<=X<200',48,'existing paid',radio/tv,5951,'<100','1<=X<4',2,'female div/dep/mar',none,2,'real estate',22,none,own,1,skilled,1,none,yes,bad +'no checking',12,'critical/other existing credit',education,2096,'<100','4<=X<7',2,'male single',none,3,'real estate',49,none,own,1,'unskilled resident',2,none,yes,good +'<0',42,'existing paid',furniture/equipment,7882,'<100','4<=X<7',2,'male single',guarantor,4,'life insurance',45,none,'for free',1,skilled,2,none,yes,good +'<0',24,'delayed previously','new car',4870,'<100','1<=X<4',3,'male single',none,4,'no known property',53,none,'for free',2,skilled,2,none,yes,bad +'no checking',36,'existing paid',education,9055,'no known savings','1<=X<4',2,'male single',none,4,'no known property',35,none,'for free',1,'unskilled resident',2,yes,yes,good +'no checking',24,'existing paid',furniture/equipment,2835,'500<=X<1000','>=7',3,'male single',none,4,'life insurance',53,none,own,1,skilled,1,none,yes,good +'0<=X<200',36,'existing paid','used car',6948,'<100','1<=X<4',2,'male single',none,2,car,35,none,rent,1,'high qualif/self emp/mgmt',1,yes,yes,good +'no checking',12,'existing paid',radio/tv,3059,'>=1000','4<=X<7',2,'male div/sep',none,4,'real estate',61,none,own,1,'unskilled resident',1,none,yes,good +'0<=X<200',30,'critical/other existing credit','new car',5234,'<100',unemployed,4,'male mar/wid',none,2,car,28,none,own,2,'high qualif/self emp/mgmt',1,none,yes,bad +'0<=X<200',12,'existing paid','new car',1295,'<100','<1',3,'female div/dep/mar',none,1,car,25,none,rent,1,skilled,1,none,yes,bad +'<0',48,'existing paid',business,4308,'<100','<1',3,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,skilled,1,none,yes,bad +'0<=X<200',12,'existing paid',radio/tv,1567,'<100','1<=X<4',1,'female div/dep/mar',none,1,car,22,none,own,1,skilled,1,yes,yes,good +'<0',24,'critical/other existing credit','new car',1199,'<100','>=7',4,'male single',none,4,car,60,none,own,2,'unskilled resident',1,none,yes,bad +'<0',15,'existing paid','new car',1403,'<100','1<=X<4',2,'female div/dep/mar',none,4,car,28,none,rent,1,skilled,1,none,yes,good +'<0',24,'existing paid',radio/tv,1282,'100<=X<500','1<=X<4',4,'female div/dep/mar',none,2,car,32,none,own,1,'unskilled resident',1,none,yes,bad +'no checking',24,'critical/other existing credit',radio/tv,2424,'no known savings','>=7',4,'male single',none,4,'life insurance',53,none,own,2,skilled,1,none,yes,good +'<0',30,'no credits/all paid',business,8072,'no known savings','<1',2,'male single',none,3,car,25,bank,own,3,skilled,1,none,yes,good +'0<=X<200',24,'existing paid','used car',12579,'<100','>=7',4,'female div/dep/mar',none,2,'no known property',44,none,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,bad +'no checking',24,'existing paid',radio/tv,3430,'500<=X<1000','>=7',3,'male single',none,2,car,31,none,own,1,skilled,2,yes,yes,good +'no checking',9,'critical/other existing credit','new car',2134,'<100','1<=X<4',4,'male single',none,4,car,48,none,own,3,skilled,1,yes,yes,good +'<0',6,'existing paid',radio/tv,2647,'500<=X<1000','1<=X<4',2,'male single',none,3,'real estate',44,none,rent,1,skilled,2,none,yes,good +'<0',10,'critical/other existing credit','new car',2241,'<100','<1',1,'male single',none,3,'real estate',48,none,rent,2,'unskilled resident',2,none,no,good +'0<=X<200',12,'critical/other existing credit','used car',1804,'100<=X<500','<1',3,'male single',none,4,'life insurance',44,none,own,1,skilled,1,none,yes,good +'no checking',10,'critical/other existing credit',furniture/equipment,2069,'no known savings','1<=X<4',2,'male mar/wid',none,1,car,26,none,own,2,skilled,1,none,no,good +'<0',6,'existing paid',furniture/equipment,1374,'<100','1<=X<4',1,'male single',none,2,'real estate',36,bank,own,1,'unskilled resident',1,yes,yes,good +'no checking',6,'no credits/all paid',radio/tv,426,'<100','>=7',4,'male mar/wid',none,4,car,39,none,own,1,'unskilled resident',1,none,yes,good +'>=200',12,'all paid',radio/tv,409,'>=1000','1<=X<4',3,'female div/dep/mar',none,3,'real estate',42,none,rent,2,skilled,1,none,yes,good +'0<=X<200',7,'existing paid',radio/tv,2415,'<100','1<=X<4',3,'male single',guarantor,2,'real estate',34,none,own,1,skilled,1,none,yes,good +'<0',60,'delayed previously',business,6836,'<100','>=7',3,'male single',none,4,'no known property',63,none,own,2,skilled,1,yes,yes,bad +'0<=X<200',18,'existing paid',business,1913,'>=1000','<1',3,'male mar/wid',none,3,'real estate',36,bank,own,1,skilled,1,yes,yes,good +'<0',24,'existing paid',furniture/equipment,4020,'<100','1<=X<4',2,'male 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Title: German Credit data + + 2. Source Information + + Professor Dr. Hans Hofmann + Institut f"ur Statistik und "Okonometrie + Universit"at Hamburg + FB Wirtschaftswissenschaften + Von-Melle-Park 5 + 2000 Hamburg 13 + + 3. Number of Instances: 1000 + + Two datasets are provided. the original dataset, in the form provided + by Prof. Hofmann, contains categorical/symbolic attributes and + is in the file "german.data". + + For algorithms that need numerical attributes, Strathclyde University + produced the file "german.data-numeric". This file has been edited + and several indicator variables added to make it suitable for + algorithms which cannot cope with categorical variables. Several + attributes that are ordered categorical (such as attribute 17) have + been coded as integer. This was the form used by StatLog. + + + 6. Number of Attributes german: 20 (7 numerical, 13 categorical) + Number of Attributes german.numer: 24 (24 numerical) + + + 7. Attribute description for german + + Attribute 1: (qualitative) + Status of existing checking account + A11 : ... < 0 DM + A12 : 0 <= ... < 200 DM + A13 : ... >= 200 DM / + salary assignments for at least 1 year + A14 : no checking account + + Attribute 2: (numerical) + Duration in month + + Attribute 3: (qualitative) + Credit history + A30 : no credits taken/ + all credits paid back duly + A31 : all credits at this bank paid back duly + A32 : existing credits paid back duly till now + A33 : delay in paying off in the past + A34 : critical account/ + other credits existing (not at this bank) + + Attribute 4: (qualitative) + Purpose + A40 : car (new) + A41 : car (used) + A42 : furniture/equipment + A43 : radio/television + A44 : domestic appliances + A45 : repairs + A46 : education + A47 : (vacation - does not exist?) + A48 : retraining + A49 : business + A410 : others + + Attribute 5: (numerical) + Credit amount + + Attibute 6: (qualitative) + Savings account/bonds + A61 : ... < 100 DM + A62 : 100 <= ... < 500 DM + A63 : 500 <= ... < 1000 DM + A64 : .. >= 1000 DM + A65 : unknown/ no savings account + + Attribute 7: (qualitative) + Present employment since + A71 : unemployed + A72 : ... < 1 year + A73 : 1 <= ... < 4 years + A74 : 4 <= ... < 7 years + A75 : .. >= 7 years + + Attribute 8: (numerical) + Installment rate in percentage of disposable income + + Attribute 9: (qualitative) + Personal status and sex + A91 : male : divorced/separated + A92 : female : divorced/separated/married + A93 : male : single + A94 : male : married/widowed + A95 : female : single + + Attribute 10: (qualitative) + Other debtors / guarantors + A101 : none + A102 : co-applicant + A103 : guarantor + + Attribute 11: (numerical) + Present residence since + + Attribute 12: (qualitative) + Property + A121 : real estate + A122 : if not A121 : building society savings agreement/ + life insurance + A123 : if not A121/A122 : car or other, not in attribute 6 + A124 : unknown / no property + + Attribute 13: (numerical) + Age in years + + Attribute 14: (qualitative) + Other installment plans + A141 : bank + A142 : stores + A143 : none + + Attribute 15: (qualitative) + Housing + A151 : rent + A152 : own + A153 : for free + + Attribute 16: (numerical) + Number of existing credits at this bank + + Attribute 17: (qualitative) + Job + A171 : unemployed/ unskilled - non-resident + A172 : unskilled - resident + A173 : skilled employee / official + A174 : management/ self-employed/ + highly qualified employee/ officer + + Attribute 18: (numerical) + Number of people being liable to provide maintenance for + + Attribute 19: (qualitative) + Telephone + A191 : none + A192 : yes, registered under the customers name + + Attribute 20: (qualitative) + foreign worker + A201 : yes + A202 : no + + + + 8. Cost Matrix + + This dataset requires use of a cost matrix (see below) + + + 1 2 + ---------------------------- + 1 0 1 + ----------------------- + 2 5 0 + + (1 = Good, 2 = Bad) + + the rows represent the actual classification and the columns + the predicted classification. + + It is worse to class a customer as good when they are bad (5), + than it is to class a customer as bad when they are good (1). + + + + + + Relabeled values in attribute checking_status + From: A11 To: '<0' + From: A12 To: '0<=X<200' + From: A13 To: '>=200' + From: A14 To: 'no checking' + + + Relabeled values in attribute credit_history + From: A30 To: 'no credits/all paid' + From: A31 To: 'all paid' + From: A32 To: 'existing paid' + From: A33 To: 'delayed previously' + From: A34 To: 'critical/other existing credit' + + + Relabeled values in attribute purpose + From: A40 To: 'new car' + From: A41 To: 'used car' + From: A42 To: furniture/equipment + From: A43 To: radio/tv + From: A44 To: 'domestic appliance' + From: A45 To: repairs + From: A46 To: education + From: A47 To: vacation + From: A48 To: retraining + From: A49 To: business + From: A410 To: other + + + Relabeled values in attribute savings_status + From: A61 To: '<100' + From: A62 To: '100<=X<500' + From: A63 To: '500<=X<1000' + From: A64 To: '>=1000' + From: A65 To: 'no known savings' + + + Relabeled values in attribute employment + From: A71 To: unemployed + From: A72 To: '<1' + From: A73 To: '1<=X<4' + From: A74 To: '4<=X<7' + From: A75 To: '>=7' + + + Relabeled values in attribute personal_status + From: A91 To: 'male div/sep' + From: A92 To: 'female div/dep/mar' + From: A93 To: 'male single' + From: A94 To: 'male mar/wid' + From: A95 To: 'female single' + + + Relabeled values in attribute other_parties + From: A101 To: none + From: A102 To: 'co applicant' + From: A103 To: guarantor + + + Relabeled values in attribute property_magnitude + From: A121 To: 'real estate' + From: A122 To: 'life insurance' + From: A123 To: car + From: A124 To: 'no known property' + + + Relabeled values in attribute other_payment_plans + From: A141 To: bank + From: A142 To: stores + From: A143 To: none + + + Relabeled values in attribute housing + From: A151 To: rent + From: A152 To: own + From: A153 To: 'for free' + + + Relabeled values in attribute job + From: A171 To: 'unemp/unskilled non res' + From: A172 To: 'unskilled resident' + From: A173 To: skilled + From: A174 To: 'high qualif/self emp/mgmt' + + + Relabeled values in attribute own_telephone + From: A191 To: none + From: A192 To: yes + + + Relabeled values in attribute foreign_worker + From: A201 To: yes + From: A202 To: no + + + Relabeled values in attribute class + From: 1 To: good + From: 2 To: bad + ARFF + 2014-04-06T23:21:47 + Public https://test.openml.org/data/v1/download/17/credit-g.arff + 17 class 1 study_14 public active + 2019-03-16 14:27:26 9a475053fed0c26ee95cd4525e50074c + diff --git a/examples/sklearn/'cache'/datasets/17/features.xml b/examples/sklearn/'cache'/datasets/17/features.xml new file mode 100644 index 000000000..da1177434 --- /dev/null +++ b/examples/sklearn/'cache'/datasets/17/features.xml @@ -0,0 +1,249 @@ + + + 0 + checking_status + nominal + <0 + 0<=X<200 + >=200 + no checking + false + false + false + 0 + + + 1 + duration + numeric + false + false + false + 0 + + + 2 + credit_history + nominal + no credits/all paid + all paid + existing paid + delayed previously + critical/other existing credit + false + false + false + 0 + + + 3 + purpose + nominal + new car + used car + furniture/equipment + radio/tv + domestic appliance + repairs + education + vacation + retraining + business + other + false + false + false + 0 + + + 4 + credit_amount + numeric + false + false + false + 0 + + + 5 + savings_status + nominal + <100 + 100<=X<500 + 500<=X<1000 + >=1000 + no known savings + false + false + false + 0 + + + 6 + employment + nominal + unemployed + <1 + 1<=X<4 + 4<=X<7 + >=7 + false + false + false + 0 + + + 7 + installment_commitment + numeric + false + false + false + 0 + + + 8 + personal_status + nominal + male div/sep + female div/dep/mar + male single + male mar/wid + female single + false + false + false + 0 + + + 9 + other_parties + nominal + none + co applicant + guarantor + false + false + false + 0 + + + 10 + residence_since + numeric + false + false + false + 0 + + + 11 + property_magnitude + nominal + real estate + life insurance + car + no known property + false + false + false + 0 + + + 12 + age + numeric + false + false + false + 0 + + + 13 + other_payment_plans + nominal + bank + stores + none + false + false + false + 0 + + + 14 + housing + nominal + rent + own + for free + false + false + false + 0 + + + 15 + existing_credits + numeric + false + false + false + 0 + + + 16 + job + nominal + unemp/unskilled non res + unskilled resident + skilled + high qualif/self emp/mgmt + false + false + false + 0 + + + 17 + num_dependents + numeric + false + false + false + 0 + + + 18 + own_telephone + nominal + none + yes + false + false + false + 0 + + + 19 + foreign_worker + nominal + yes + no + false + false + false + 0 + + + 20 + class + nominal + good + bad + true + false + false + 0 + + diff --git a/examples/sklearn/'cache'/datasets/17/qualities.xml b/examples/sklearn/'cache'/datasets/17/qualities.xml new file mode 100644 index 000000000..a52cf1832 --- /dev/null +++ b/examples/sklearn/'cache'/datasets/17/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.5695695695695696 + + + + CfsSubsetEval_DecisionStumpAUC + 0.7208285714285714 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.273 + + + + CfsSubsetEval_DecisionStumpKappa + 0.3056968463886062 + + + + CfsSubsetEval_NaiveBayesAUC + 0.7208285714285714 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.273 + + + + CfsSubsetEval_NaiveBayesKappa + 0.3056968463886062 + + + + CfsSubsetEval_kNN1NAUC + 0.7208285714285714 + + + + CfsSubsetEval_kNN1NErrRate + 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000000000..e69de29bb diff --git a/examples/sklearn/'cache'/tasks/97/datasplits.arff b/examples/sklearn/'cache'/tasks/97/datasplits.arff new file mode 100644 index 000000000..3473c5486 --- /dev/null +++ b/examples/sklearn/'cache'/tasks/97/datasplits.arff @@ -0,0 +1,10008 @@ +@relation credit-g_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,539,0,0 +TRAIN,577,0,0 +TRAIN,619,0,0 +TRAIN,937,0,0 +TRAIN,75,0,0 +TRAIN,608,0,0 +TRAIN,142,0,0 +TRAIN,641,0,0 +TRAIN,336,0,0 +TRAIN,893,0,0 +TRAIN,60,0,0 +TRAIN,96,0,0 +TRAIN,165,0,0 +TRAIN,709,0,0 +TRAIN,66,0,0 +TRAIN,737,0,0 +TRAIN,260,0,0 +TRAIN,803,0,0 +TRAIN,393,0,0 +TRAIN,348,0,0 +TRAIN,891,0,0 +TRAIN,690,0,0 +TRAIN,841,0,0 +TRAIN,527,0,0 +TRAIN,254,0,0 +TRAIN,676,0,0 +TRAIN,329,0,0 +TRAIN,373,0,0 +TRAIN,20,0,0 +TRAIN,343,0,0 +TRAIN,781,0,0 +TRAIN,550,0,0 +TRAIN,791,0,0 +TRAIN,221,0,0 +TRAIN,185,0,0 +TRAIN,743,0,0 +TRAIN,785,0,0 +TRAIN,340,0,0 +TRAIN,196,0,0 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6 May 2019 14:33:22 -0500 Subject: [PATCH 2/6] Updated contribution doc page --- doc/contributing.rst | 200 ++++++++++++++------ doc/examples/create_upload_tutorial.rst | 2 +- doc/examples/datasets_tutorial.rst | 4 +- doc/examples/flows_and_runs_tutorial.rst | 2 +- doc/examples/introduction_tutorial.rst | 4 +- doc/examples/run_setup_tutorial.rst | 2 +- doc/examples/sg_execution_times.rst | 16 +- doc/examples/sklearn/openml_run_example.rst | 4 +- doc/examples/sklearn/sg_execution_times.rst | 4 +- doc/examples/tasks_tutorial.rst | 26 +-- 10 files changed, 170 insertions(+), 94 deletions(-) diff --git a/doc/contributing.rst b/doc/contributing.rst index 3951b7445..b02c1212b 100644 --- a/doc/contributing.rst +++ b/doc/contributing.rst @@ -8,39 +8,63 @@ Contributing ============ Contribution to the OpenML package is highly appreciated. Currently, -there is a lot of work left on implementing API calls, -testing them and providing examples to allow new users to easily use the -OpenML package. See the :ref:`issues` section for open tasks. +there is a lot of work left on implementing API calls, testing them and providing examples to allow new users to easily use the OpenML package. See the :ref:`issues` section for open tasks. Please mark yourself as contributor in a github issue if you start working on something to avoid duplicate work. If you're part of the OpenML organization you can use github's assign feature, otherwise you can just leave a comment. +.. _tips: + + +New contributor tips +~~~~~~~~~~~~~~~~~~~~ + +A great way to start contributing to openml-python is to pick an item +from the list of `Good First Issues `_ in the issue tracker. Resolving these issues allow you to start +contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more experienced developers as it helps free up their time to concentrate on. + +.. _howto: + + How to Contribute ~~~~~~~~~~~~~~~~~ -The preferred workflow for contributing to the OpenML python connector is to fork the [main repository](https://github.com/openml/openml-python) on GitHub, clone, check out the branch `develop`, and develop on a new branch branch. Steps: +There are many ways to contribute to the development of the OpenML python +connector and OpenML in general. We welcome all kinds of contributions, +especially: + +* Source code which fixes an issue, improves usability or implements a new feature. +* Improvements to the documentation, which can be found in the ``doc`` directory. +* New examples - current examples can be found in the ``examples`` directory. +* Bug reports - if something doesn't work for you or is cumbersome, please open a new issue to let us know about the problem. +* Use the package and spread the word. +* `Cite OpenML `_ if you use it in a scientific publication. +* Visit one of our `hackathons `_. +* Check out how to `contribute to the main OpenML project `_. + +The preferred workflow for contributing to the OpenML python connector is to fork the `main repository `_ on GitHub, clone, check out the branch ``develop``, and develop on a new branch branch. Steps: -* Fork the [project repository](https://github.com/openml/openml-python) by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/). +1. Fork the `project repository `_ by clicking on the 'Fork' button near the top right of the page. This creates a copy of the code under your GitHub user account. For more details on how to fork a repository see `this guide `_. -* Clone your fork of the openml-python repo from your GitHub account to your local disk: +2. Clone your fork of the openml-python repo from your GitHub account to your local disk: .. code:: bash - $ git clone git@github.com:YourLogin/openml-python.git - $ cd openml-python + $ git clone git@github.com:YourLogin/openml-python.git + $ cd openml-python -* Switch to the develop branch: +3. Switch to the develop branch: .. code:: bash - $ git checkout develop + $ git checkout develop -* Create a feature branch to hold your development changes: +4. Create a feature branch to hold your development changes: .. code:: bash - $ git checkout -b feature/my-feature + $ git checkout -b feature/my-feature Always use a feature branch. It's good practice to never work on the master or develop branch! To make the nature of your pull request easily visible, please prepend the name of the branch with the type of changes you want to merge, such as feature if it contains a new feature, fix for a bugfix, doc for documentation and maint for other maintenance on the package. @@ -48,18 +72,99 @@ Develop the feature on your feature branch. Add changed files using git add and .. code:: bash - $ git add modified_files - $ git commit + $ git add modified_files + $ git commit to record your changes in Git, then push the changes to your GitHub account with: -$ git push -u origin my-feature +.. code:: bash + + $ git push -u origin my-feature + Follow these instructions to create a pull request from your fork. This will send an email to the committers. (If any of the above seems like magic to you, please look up the Git documentation on the web, or ask a friend or another contributor for help.) +Our guidelines on code contribution can be found in `this file `_. + +.. _issues: + + +Open issues and potential todos +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +We collect open issues and feature requests in an `issue tracker on github `_. +The issue tracker contains issues marked as *Good first issue*, which shows +issues which are good for beginners. We also maintain a somewhat up-to-date +`roadmap `_ which +contains longer-term goals. + + +.. _pull: + + +Pull Request Checklist +~~~~~~~~~~~~~~~~~~~~~~ + +We recommended that your contribution complies with the +following rules before you submit a pull request: + +* Follow the `pep8 style guide `_. + +With the following exceptions or additions: + +* The max line length is 100 characters instead of 80. +* When creating a multi-line expression with binary operators, break before the operator. +* Add type hints to all function signatures. (note: not all functions have type hints yet, this is work in progress.) +* Use the `str.format `_ over `printf `_ style formatting. + +E.g. use ``"{} {}".format('hello', 'world') not "%s %s" % ('hello', 'world')``. (note: old code may still use `printf`-formatting, this is work in progress.) + +* If your pull request addresses an issue, please use the pull request title to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is created. + +* An incomplete contribution -- where you expect to do more work before receiving a full review -- should be prefixed `[WIP]` (to indicate a work in progress) and changed to `[MRG]` when it matures. WIPs may be useful to: indicate you are working on something to avoid duplicated work, request broad review of functionality or API, or seek collaborators. WIPs often benefit from the inclusion of a `task list `_ in the PR description. + +* All tests pass when running ``pytest``. On Unix-like systems, check with (from the toplevel source folder): + +.. code:: bash + + $ pytest + +For Windows systems, execute the command from an Anaconda Prompt or add ``pytest`` to PATH before executing the command. + +* Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with `non-regression tests `_. These tests verify the correct behavior of the fix or feature. In this manner, further modifications on the code base are granted to be consistent with the desired behavior. For the Bug-fixes case, at the time of the PR, this tests should fail for the code base in develop and pass for the PR code. + +* Add your changes to the changelog in the file doc/progress.rst. + + +You can also check for common programming errors with the following tools: + +* Code with good unittest **coverage** (at least 80%), check with: + +.. code:: bash + + $ pip install pytest pytest-cov + $ pytest --cov=. path/to/tests_for_package + +* No style warnings, check with: + +.. code:: bash + + $ pip install flake8 + $ flake8 --ignore E402,W503 --show-source --max-line-length 100 + +* No mypy (typing) issues, check with: + +.. code:: bash + + $ pip install mypy + $ mypy openml --ignore-missing-imports --follow-imports skip + + .. _scope: + +==================== Scope of the package ==================== @@ -78,46 +183,11 @@ Therefore, the connection to other machine learning libraries such as *pytorch*, *keras* or *tensorflow* should not be done directly inside this package, but in a separate package using the OpenML python connector. -.. _issues: - -Open issues and potential todos -=============================== - -We collect open issues and feature requests in an `issue tracker on github `_. -The issue tracker contains issues marked as *Good first issue*, which shows -issues which are good for beginners. We also maintain a somewhat up-to-date -`roadmap `_ which -contains longer-term goals. - -.. _how_to_contribute: - -How to contribute -================= - -There are many ways to contribute to the development of the OpenML python -connector and OpenML in general. We welcome all kinds of contributions, -especially: - -* Source code which fixes an issue, improves usability or implements a new - feature. -* Improvements to the documentation, which can be found in the ``doc`` - directory. -* New examples - current examples can be found in the ``examples`` directory. -* Bug reports - if something doesn't work for you or is cumbersome, please - open a new issue to let us know about the problem. -* Use the package and spread the word. -* `Cite OpenML `_ if you use it in a scientific - publication. -* Visit one of our `hackathons `_. -* Check out how to `contribute to the main OpenML project `_. - -Contributing code -~~~~~~~~~~~~~~~~~ - -Our guidelines on code contribution can be found in `this file `_. .. _installation: + +============ Installation ============ @@ -129,7 +199,7 @@ The package source code is available from .. code:: bash - git clone https://github.com/openml/openml-python.git + $ git clone https://github.com/openml/openml-python.git Once you cloned the package, change into the new directory. @@ -137,30 +207,33 @@ If you are a regular user, install with .. code:: bash - pip install -e . + $ pip install -e . If you are a contributor, you will also need to install test dependencies .. code:: bash - pip install -e ".[test]" + $ pip install -e ".[test]" + + +.. _testing: Testing -======= +~~~~~~~ From within the directory of the cloned package, execute: .. code:: bash - pytest tests/ + $ pytest tests/ Executing a specific test can be done by specifying the module, test case, and test. To obtain a hierarchical list of all tests, run .. code:: bash - pytest --collect-only + $ pytest --collect-only .. code:: bash @@ -180,25 +253,28 @@ To run a specific module, add the module name, for instance: .. code:: bash - pytest tests/test_datasets/test_dataset.py + $ pytest tests/test_datasets/test_dataset.py To run a specific unit test case, add the test case name, for instance: .. code:: bash - pytest tests/test_datasets/test_dataset.py::OpenMLDatasetTest + $ pytest tests/test_datasets/test_dataset.py::OpenMLDatasetTest To run a specific unit test, add the test name, for instance: .. code:: bash - pytest tests/test_datasets/test_dataset.py::OpenMLDatasetTest::test_get_data + $ pytest tests/test_datasets/test_dataset.py::OpenMLDatasetTest::test_get_data Happy testing! +.. _newmllib: + + Connecting new machine learning libraries -========================================= +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Coming soon - please stay tuned! diff --git a/doc/examples/create_upload_tutorial.rst b/doc/examples/create_upload_tutorial.rst index a8fff2e69..2e59a35b3 100644 --- a/doc/examples/create_upload_tutorial.rst +++ b/doc/examples/create_upload_tutorial.rst @@ -390,7 +390,7 @@ Dataset is a pandas sparse dataframe .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.139 seconds) + **Total running time of the script:** ( 0 minutes 0.153 seconds) .. _sphx_glr_download_examples_create_upload_tutorial.py: diff --git a/doc/examples/datasets_tutorial.rst b/doc/examples/datasets_tutorial.rst index e4453ec41..35e4ebe10 100644 --- a/doc/examples/datasets_tutorial.rst +++ b/doc/examples/datasets_tutorial.rst @@ -55,7 +55,7 @@ List datasets .. code-block:: none - First 10 of 15672 datasets... + First 10 of 16821 datasets... Exercise 1 @@ -216,7 +216,7 @@ Exercise 2 .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 6.782 seconds) + **Total running time of the script:** ( 0 minutes 7.223 seconds) .. _sphx_glr_download_examples_datasets_tutorial.py: diff --git a/doc/examples/flows_and_runs_tutorial.rst b/doc/examples/flows_and_runs_tutorial.rst index 7a5961d5c..2d8bb592e 100644 --- a/doc/examples/flows_and_runs_tutorial.rst +++ b/doc/examples/flows_and_runs_tutorial.rst @@ -231,7 +231,7 @@ them. Some tasks you could try (or browse openml.org): .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.044 seconds) + **Total running time of the script:** ( 0 minutes 0.051 seconds) .. _sphx_glr_download_examples_flows_and_runs_tutorial.py: diff --git a/doc/examples/introduction_tutorial.rst b/doc/examples/introduction_tutorial.rst index ea133a714..71d1b77af 100644 --- a/doc/examples/introduction_tutorial.rst +++ b/doc/examples/introduction_tutorial.rst @@ -126,7 +126,7 @@ Download the OpenML task for the eeg-eye-state. flow = sklearn_to_flow(model) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow raise TypeError(o, type(o)) - TypeError: (, ) + TypeError: (, ) @@ -134,7 +134,7 @@ Download the OpenML task for the eeg-eye-state. .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.531 seconds) + **Total running time of the script:** ( 0 minutes 0.659 seconds) .. _sphx_glr_download_examples_introduction_tutorial.py: diff --git a/doc/examples/run_setup_tutorial.rst b/doc/examples/run_setup_tutorial.rst index ecf674f7c..9069a6c42 100644 --- a/doc/examples/run_setup_tutorial.rst +++ b/doc/examples/run_setup_tutorial.rst @@ -143,7 +143,7 @@ In this tutorial we will .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.152 seconds) + **Total running time of the script:** ( 0 minutes 0.172 seconds) .. _sphx_glr_download_examples_run_setup_tutorial.py: diff --git a/doc/examples/sg_execution_times.rst b/doc/examples/sg_execution_times.rst index c94d7634c..e8ad41177 100644 --- a/doc/examples/sg_execution_times.rst +++ b/doc/examples/sg_execution_times.rst @@ -5,11 +5,11 @@ Computation times ================= -**00:12.697** total execution time for **examples** files: - -- **00:06.782**: :ref:`sphx_glr_examples_datasets_tutorial.py` (``datasets_tutorial.py``) -- **00:05.049**: :ref:`sphx_glr_examples_tasks_tutorial.py` (``tasks_tutorial.py``) -- **00:00.531**: :ref:`sphx_glr_examples_introduction_tutorial.py` (``introduction_tutorial.py``) -- **00:00.152**: :ref:`sphx_glr_examples_run_setup_tutorial.py` (``run_setup_tutorial.py``) -- **00:00.139**: :ref:`sphx_glr_examples_create_upload_tutorial.py` (``create_upload_tutorial.py``) -- **00:00.044**: :ref:`sphx_glr_examples_flows_and_runs_tutorial.py` (``flows_and_runs_tutorial.py``) +**00:12.890** total execution time for **examples** files: + +- **00:07.223**: :ref:`sphx_glr_examples_datasets_tutorial.py` (``datasets_tutorial.py``) +- **00:04.633**: :ref:`sphx_glr_examples_tasks_tutorial.py` (``tasks_tutorial.py``) +- **00:00.659**: :ref:`sphx_glr_examples_introduction_tutorial.py` (``introduction_tutorial.py``) +- **00:00.172**: :ref:`sphx_glr_examples_run_setup_tutorial.py` (``run_setup_tutorial.py``) +- **00:00.153**: :ref:`sphx_glr_examples_create_upload_tutorial.py` (``create_upload_tutorial.py``) +- **00:00.051**: :ref:`sphx_glr_examples_flows_and_runs_tutorial.py` (``flows_and_runs_tutorial.py``) diff --git a/doc/examples/sklearn/openml_run_example.rst b/doc/examples/sklearn/openml_run_example.rst index 6099db3f1..8d00fadd9 100644 --- a/doc/examples/sklearn/openml_run_example.rst +++ b/doc/examples/sklearn/openml_run_example.rst @@ -72,7 +72,7 @@ Run the scikit-learn model on the task (requires an API key). flow = sklearn_to_flow(model) File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow raise TypeError(o, type(o)) - TypeError: (, ) + TypeError: (, ) @@ -80,7 +80,7 @@ Run the scikit-learn model on the task (requires an API key). .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.122 seconds) + **Total running time of the script:** ( 0 minutes 0.125 seconds) .. _sphx_glr_download_examples_sklearn_openml_run_example.py: diff --git a/doc/examples/sklearn/sg_execution_times.rst b/doc/examples/sklearn/sg_execution_times.rst index a83ed0fec..f66b2a229 100644 --- a/doc/examples/sklearn/sg_execution_times.rst +++ b/doc/examples/sklearn/sg_execution_times.rst @@ -5,6 +5,6 @@ Computation times ================= -**00:00.122** total execution time for **examples_sklearn** files: +**00:00.125** total execution time for **examples_sklearn** files: -- **00:00.122**: :ref:`sphx_glr_examples_sklearn_openml_run_example.py` (``openml_run_example.py``) +- **00:00.125**: :ref:`sphx_glr_examples_sklearn_openml_run_example.py` (``openml_run_example.py``) diff --git a/doc/examples/tasks_tutorial.rst b/doc/examples/tasks_tutorial.rst index ae6bf6ab2..c15b30692 100644 --- a/doc/examples/tasks_tutorial.rst +++ b/doc/examples/tasks_tutorial.rst @@ -87,12 +87,12 @@ to have better visualization and easier access: 'NumberOfNumericFeatures', 'NumberOfSymbolicFeatures'], dtype='object') First 5 of 932 tasks: - tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures - 1 1 1 ... 6.0 33.0 - 2 2 1 ... 6.0 33.0 - 3 3 1 ... 6.0 33.0 - 4 4 1 ... 6.0 33.0 - 5 5 1 ... 6.0 33.0 + tid ttid did name task_type status estimation_procedure ... NumberOfClasses NumberOfFeatures NumberOfInstances NumberOfInstancesWithMissingValues NumberOfMissingValues NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 1 anneal Supervised Classification active 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + 2 2 1 1 anneal Supervised Classification active 5 times 2-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + 3 3 1 1 anneal Supervised Classification active 10 times 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + 4 4 1 1 anneal Supervised Classification active Leave one out ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + 5 5 1 1 anneal Supervised Classification active 10% Holdout set ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 [5 rows x 19 columns] @@ -207,12 +207,12 @@ Similar to listing tasks by task type, we can list tasks by tags: .. code-block:: none First 5 of 100 tasks: - tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures - 1 1 1 ... 6.0 33.0 - 7 7 1 ... 0.0 37.0 - 13 13 1 ... 16.0 1.0 - 19 19 1 ... 4.0 1.0 - 25 25 1 ... 216.0 1.0 + tid ttid did name task_type status estimation_procedure ... NumberOfClasses NumberOfFeatures NumberOfInstances NumberOfInstancesWithMissingValues NumberOfMissingValues NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 1 anneal Supervised Classification active 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + 7 7 1 2 kr-vs-kp Supervised Classification active 10-fold Crossvalidation ... 2.0 37.0 3196.0 0.0 0.0 0.0 37.0 + 13 13 1 3 letter Supervised Classification active 10-fold Crossvalidation ... 26.0 17.0 20000.0 0.0 0.0 16.0 1.0 + 19 19 1 4 balance-scale Supervised Classification active 10-fold Crossvalidation ... 3.0 5.0 625.0 0.0 0.0 4.0 1.0 + 25 25 1 5 mfeat-factors Supervised Classification active 10-fold Crossvalidation ... 10.0 217.0 2000.0 0.0 0.0 216.0 1.0 [5 rows x 19 columns] @@ -323,7 +323,7 @@ And: .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 5.049 seconds) + **Total running time of the script:** ( 0 minutes 4.633 seconds) .. _sphx_glr_download_examples_tasks_tutorial.py: From 044b17289abec0ecb12eda3802b1bb7dbac3eaae Mon Sep 17 00:00:00 2001 From: Mikhail Mekhedkin Meskhi Date: Sat, 11 May 2019 13:43:04 -0500 Subject: [PATCH 3/6] Created README.md for doc directory --- doc/README.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) create mode 100644 doc/README.md diff --git a/doc/README.md b/doc/README.md new file mode 100644 index 000000000..7cbd1a156 --- /dev/null +++ b/doc/README.md @@ -0,0 +1,20 @@ +# Documentation + +This directory contains all `.rst` files used by `tavis-ci` to generate the documentations. + +### Contributing to the documentation + +Below is a list of currently available pages or sections that need to be updated: + +* Contributing - page: `contributing.rst` +* Front page - page: `index.rst` +* API - Page: papi.rst` +* Progress - page: `progress.rst` +* Usage - page: `usage.rst` + +### Wishlist + +Below is a list of requested documentation pages or sections: + +* Reproducing experiment results - page: `usage.rst` or new page: `experiments.rst` + From 1daeb9fd5d611882be8d2cb95522c6a046dc7c0b Mon Sep 17 00:00:00 2001 From: Mikhail Mekhedkin Meskhi Date: Mon, 8 Jul 2019 13:20:05 -0500 Subject: [PATCH 4/6] Added README for doc --- doc/README.md | 19 +++++++++++++------ 1 file changed, 13 insertions(+), 6 deletions(-) diff --git a/doc/README.md b/doc/README.md index 7cbd1a156..c1e09c1e6 100644 --- a/doc/README.md +++ b/doc/README.md @@ -4,17 +4,24 @@ This directory contains all `.rst` files used by `tavis-ci` to generate the docu ### Contributing to the documentation +For building the documentation, you will need sphinx, sphinx-bootstrap-theme, sphinx-gallery and numpydoc. + +``` +$ pip install sphinx sphinx-bootstrap-theme sphinx-gallery numpydoc +``` + +When dependencies are installed, run + +``` +$ sphinx-build -b html doc YOUR_PREFERRED_OUTPUT_DIRECTORY +``` + Below is a list of currently available pages or sections that need to be updated: * Contributing - page: `contributing.rst` * Front page - page: `index.rst` * API - Page: papi.rst` * Progress - page: `progress.rst` -* Usage - page: `usage.rst` - -### Wishlist - -Below is a list of requested documentation pages or sections: +* Usage - page: `usage.rst` -* Reproducing experiment results - page: `usage.rst` or new page: `experiments.rst` From 483c0d87c43c745738f96c64c808eeb1474e32b3 Mon Sep 17 00:00:00 2001 From: Mikhail Mekhedkin Meskhi Date: Mon, 8 Jul 2019 13:22:10 -0500 Subject: [PATCH 5/6] Added README for doc --- doc/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/doc/README.md b/doc/README.md index c1e09c1e6..3bdc70197 100644 --- a/doc/README.md +++ b/doc/README.md @@ -19,8 +19,8 @@ $ sphinx-build -b html doc YOUR_PREFERRED_OUTPUT_DIRECTORY Below is a list of currently available pages or sections that need to be updated: * Contributing - page: `contributing.rst` -* Front page - page: `index.rst` -* API - Page: papi.rst` +* Front Page - page: `index.rst` +* API - page: `api.rst` * Progress - page: `progress.rst` * Usage - page: `usage.rst` From 3145f3b4efeb8b924388309e6f5ccd5b8a3798f1 Mon Sep 17 00:00:00 2001 From: Mikhail Mekhedkin Meskhi Date: Sat, 13 Jul 2019 14:29:22 -0500 Subject: [PATCH 6/6] Updated Contributing and Start doc pages --- doc/contributing.rst | 47 +- doc/examples/create_upload_tutorial.py.md5 | 1 + doc/examples/create_upload_tutorial.rst | 150 +- doc/examples/datasets_tutorial.rst | 6 +- doc/examples/examples_jupyter.zip | Bin 51574 -> 51574 bytes doc/examples/examples_python.zip | Bin 34270 -> 34270 bytes doc/examples/flows_and_runs_tutorial.rst | 4 +- doc/examples/introduction_tutorial.py.md5 | 1 + doc/examples/introduction_tutorial.rst | 20 +- doc/examples/run_setup_tutorial.py.md5 | 1 + doc/examples/run_setup_tutorial.rst | 28 +- doc/examples/sg_execution_times.rst | 16 +- .../sklearn/openml_run_example.py.md5 | 1 + doc/examples/sklearn/openml_run_example.rst | 20 +- 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100644 examples/'cache'/org/openml/test/tasks/403/datasplits.arff create mode 100644 examples/'cache'/org/openml/test/tasks/403/datasplits.pkl.py3 create mode 100644 examples/'cache'/org/openml/test/tasks/403/task.xml create mode 100644 examples/'cache'/org/openml/test/tasks/6/datasplits.arff create mode 100644 examples/'cache'/org/openml/test/tasks/6/datasplits.pkl.py3 create mode 100644 examples/'cache'/org/openml/test/tasks/6/task.xml create mode 100644 examples/'cache'/org/openml/test/tasks/97/datasplits.arff create mode 100644 examples/'cache'/org/openml/test/tasks/97/datasplits.pkl.py3 create mode 100644 examples/'cache'/org/openml/test/tasks/97/task.xml create mode 100644 examples/sklearn/'cache'/org/openml/test/datasets/17/dataset.arff create mode 100644 examples/sklearn/'cache'/org/openml/test/datasets/17/dataset.pkl.py3 create mode 100644 examples/sklearn/'cache'/org/openml/test/datasets/17/description.xml create mode 100644 examples/sklearn/'cache'/org/openml/test/datasets/17/features.xml create mode 100644 examples/sklearn/'cache'/org/openml/test/datasets/17/qualities.xml create mode 100644 examples/sklearn/'cache'/org/openml/test/flows/12214/flow.xml create mode 100644 examples/sklearn/'cache'/org/openml/test/locks/datasets.functions.get_dataset:17 create mode 100644 examples/sklearn/'cache'/org/openml/test/locks/flows.functions.get_flow:12214 create mode 100644 examples/sklearn/'cache'/org/openml/test/locks/tasks.functions.get_task:97 create mode 100644 examples/sklearn/'cache'/org/openml/test/tasks/97/datasplits.arff create mode 100644 examples/sklearn/'cache'/org/openml/test/tasks/97/datasplits.pkl.py3 create mode 100644 examples/sklearn/'cache'/org/openml/test/tasks/97/task.xml diff --git a/doc/contributing.rst b/doc/contributing.rst index b02c1212b..39d37d5c3 100644 --- a/doc/contributing.rst +++ b/doc/contributing.rst @@ -143,21 +143,21 @@ You can also check for common programming errors with the following tools: .. code:: bash - $ pip install pytest pytest-cov + $ pip3 install pytest pytest-cov $ pytest --cov=. path/to/tests_for_package * No style warnings, check with: .. code:: bash - $ pip install flake8 + $ pip3 install flake8 $ flake8 --ignore E402,W503 --show-source --max-line-length 100 * No mypy (typing) issues, check with: .. code:: bash - $ pip install mypy + $ pip3 install mypy $ mypy openml --ignore-missing-imports --follow-imports skip @@ -180,7 +180,7 @@ libraries written in Python. We aim to keep the package as light-weight as possible and we will try to keep the number of potential installation dependencies as low as possible. Therefore, the connection to other machine learning libraries such as -*pytorch*, *keras* or *tensorflow* should not be done directly inside this +``pytorch``, ``keras`` or ``tensorflow`` should not be done directly inside this package, but in a separate package using the OpenML python connector. @@ -191,8 +191,17 @@ package, but in a separate package using the OpenML python connector. Installation ============ -Installation from github -~~~~~~~~~~~~~~~~~~~~~~~~ +Installing from ``pip3`` +~~~~~~~~~~~~~~~~~~~~~~ + +To install OpenML stable releases, you can use *pip3* as follows: + +.. code:: bash + + $ pip3 install openml + +Installing from Github (``source``) +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The package source code is available from `github `_ and can be obtained with: @@ -207,20 +216,30 @@ If you are a regular user, install with .. code:: bash - $ pip install -e . + $ pip3 install -e . If you are a contributor, you will also need to install test dependencies .. code:: bash - $ pip install -e ".[test]" + $ pip3 install -e ".[test]" +Updating the OpenML Package +~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. _testing: +Due to ongoing development and frequent changes made, updating OpenML can be done via aforementioned options. +.. code:: bash + + $ pip3 install -U openml + +For from source installation, you can simply pull the changes and reinstall. +.. _testing: + +======= Testing -~~~~~~~ +======= From within the directory of the cloned package, execute: @@ -272,9 +291,11 @@ Happy testing! .. _newmllib: - +========================================= Connecting new machine learning libraries -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +========================================= -Coming soon - please stay tuned! +Currently OpenML 0.9.0 support the following libraries: +* ``scikit-learn`` +* ``mlr`` \ No newline at end of file diff --git a/doc/examples/create_upload_tutorial.py.md5 b/doc/examples/create_upload_tutorial.py.md5 new file mode 100644 index 000000000..6cc62e82c --- /dev/null +++ b/doc/examples/create_upload_tutorial.py.md5 @@ -0,0 +1 @@ +9d84e8f389338526d93e1042c88a11b8 \ No newline at end of file diff --git a/doc/examples/create_upload_tutorial.rst b/doc/examples/create_upload_tutorial.rst index 2e59a35b3..397ea898f 100644 --- a/doc/examples/create_upload_tutorial.rst +++ b/doc/examples/create_upload_tutorial.rst @@ -26,17 +26,6 @@ A tutorial on how to create and upload a dataset to OpenML. -.. code-block:: pytb - - Traceback (most recent call last): - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage - out = func() - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ - exec(self.code, self.globals) - File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/create_upload_tutorial.py", line 13, in - from openml.datasets.functions import create_dataset - ImportError: cannot import name 'create_dataset' - @@ -49,6 +38,11 @@ server with countless copies of the same dataset. openml.config.server = 'https://test.openml.org/api/v1/xml' + + + + + Below we will cover the following cases of the dataset object: * A numpy array @@ -79,6 +73,11 @@ via the API. description = diabetes.DESCR + + + + + OpenML does not distinguish between the attributes and targets on the data level and stores all data in a single matrix. @@ -105,6 +104,11 @@ that data). ) + + + + + Create the dataset object ^^^^^^^^^^^^^^^^^^^^^^^^^ The definition of all fields can be found in the XSD files describing the @@ -158,6 +162,11 @@ https://github.com/openml/OpenML/blob/master/openml_OS/views/pages/api_new/v1/xs + + + + + .. code-block:: default @@ -165,6 +174,18 @@ https://github.com/openml/OpenML/blob/master/openml_OS/views/pages/api_new/v1/xs print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + URL for dataset: https://test.openml.org/api/v1/xml/data/2937 + + Dataset is a list ================= A list can contain lists in the case of dense data or it can contain @@ -238,6 +259,11 @@ http://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html + + + + + .. code-block:: default @@ -245,6 +271,18 @@ http://storm.cis.fordham.edu/~gweiss/data-mining/datasets.html print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + URL for dataset: https://test.openml.org/api/v1/xml/data/2938 + + Dataset is a pandas DataFrame ============================= It might happen that your dataset is made of heterogeneous data which can be @@ -267,6 +305,28 @@ function :func:`create_dataset`. In this regard, you only need to pass print(df.info()) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + RangeIndex: 14 entries, 0 to 13 + Data columns (total 5 columns): + outlook 14 non-null category + temperature 14 non-null int64 + humidity 14 non-null int64 + windy 14 non-null bool + play 14 non-null category + dtypes: bool(1), category(2), int64(2) + memory usage: 546.0 bytes + None + + We enforce the column 'outlook', 'windy', and 'play' to be a categorical dtype while the column 'rnd_str' is kept as a string column. Then, we can call :func:`create_dataset` by passing the dataframe and fixing the parameter @@ -295,6 +355,11 @@ call :func:`create_dataset` by passing the dataframe and fixing the parameter + + + + + .. code-block:: default @@ -302,6 +367,18 @@ call :func:`create_dataset` by passing the dataframe and fixing the parameter print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + URL for dataset: https://test.openml.org/api/v1/xml/data/2939 + + Dataset is a sparse matrix ========================== @@ -339,6 +416,11 @@ Dataset is a sparse matrix + + + + + .. code-block:: default @@ -347,6 +429,18 @@ Dataset is a sparse matrix + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + URL for dataset: https://test.openml.org/api/v1/xml/data/2940 + + Dataset is a pandas sparse dataframe ==================================== @@ -381,6 +475,26 @@ Dataset is a pandas sparse dataframe + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + RangeIndex: 4 entries, 0 to 3 + Data columns (total 3 columns): + input1 3 non-null Sparse[float64, nan] + input2 2 non-null Sparse[float64, nan] + y 2 non-null Sparse[float64, nan] + dtypes: Sparse[float64, nan](3) + memory usage: 176.0 bytes + None + + + .. code-block:: default @@ -388,9 +502,21 @@ Dataset is a pandas sparse dataframe print('URL for dataset: %s/data/%d' % (openml.config.server, upload_did)) + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + URL for dataset: https://test.openml.org/api/v1/xml/data/2941 + + + .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.153 seconds) + **Total running time of the script:** ( 0 minutes 5.806 seconds) .. _sphx_glr_download_examples_create_upload_tutorial.py: diff --git a/doc/examples/datasets_tutorial.rst b/doc/examples/datasets_tutorial.rst index 35e4ebe10..572a6d029 100644 --- a/doc/examples/datasets_tutorial.rst +++ b/doc/examples/datasets_tutorial.rst @@ -55,7 +55,7 @@ List datasets .. code-block:: none - First 10 of 16821 datasets... + First 10 of 1568 datasets... Exercise 1 @@ -165,7 +165,7 @@ and manually create a dataframe. exec(self.code, self.globals) File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/datasets_tutorial.py", line 67, in return_attribute_names=True, - TypeError: get_data() got an unexpected keyword argument 'dataset_format' + TypeError: get_data() got an unexpected keyword argument 'return_attribute_names' @@ -216,7 +216,7 @@ Exercise 2 .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 7.223 seconds) + **Total running time of the script:** ( 0 minutes 1.833 seconds) .. _sphx_glr_download_examples_datasets_tutorial.py: diff --git a/doc/examples/examples_jupyter.zip b/doc/examples/examples_jupyter.zip index 68c542fa3f168a385e284ae7649e24a6cc117df8..08d5863f37500e26d0aadad69b2e6d9be1db80ad 100644 GIT binary patch delta 134 zcmex1iTT?kX5IjAW)=|!5O|gQb|de0C1xPK`M6RfgrV%3Dh3wmj!u>UGd>n?6#_F> wwwalO8ISf(`OGJr!L;WICy>_3eJ9+&{Oc#& LK^iA3o(u&5mNY;4 diff --git a/doc/examples/examples_python.zip b/doc/examples/examples_python.zip index 7a428c270bac699d4d26f804a59089381e509754..baf77c817fd3795c4a5f1171364db0ed32ed720b 100644 GIT binary patch delta 134 zcmccD&2+Dui8sKTnMH&F1YYI7-N@@9&J3hCM~I7vfEoGP&-fuC*3Wst3@6Vz2;*>= uwkBBQWBq?-5M%P^W>+w6-C_f#t6Chu^xhVGF#W&98BDvjx`64P)*t{gWIq}J delta 134 zcmccD&2+Dui8sKTnMH&F1b*}_+{o)8&ituw;pPZ&5fNsfz~+4IXZ*}xKqA)9d6{1W z8BU&cykM!rVcMEt#>e{q%pk_(&&{qN-IJ|bY{2}g76*{t$$MMuL0TvOZ*c~TySBQ3 I>7Ldg0M1c8RsaA1 diff --git a/doc/examples/flows_and_runs_tutorial.rst b/doc/examples/flows_and_runs_tutorial.rst index 2d8bb592e..917deb1d3 100644 --- a/doc/examples/flows_and_runs_tutorial.rst +++ b/doc/examples/flows_and_runs_tutorial.rst @@ -56,7 +56,7 @@ Train a scikit-learn model on the data manually. exec(self.code, self.globals) File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/flows_and_runs_tutorial.py", line 22, in target=dataset.default_target_attribute - TypeError: get_data() got an unexpected keyword argument 'dataset_format' + ValueError: too many values to unpack (expected 2) @@ -231,7 +231,7 @@ them. Some tasks you could try (or browse openml.org): .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.051 seconds) + **Total running time of the script:** ( 0 minutes 0.644 seconds) .. _sphx_glr_download_examples_flows_and_runs_tutorial.py: diff --git a/doc/examples/introduction_tutorial.py.md5 b/doc/examples/introduction_tutorial.py.md5 new file mode 100644 index 000000000..4df4950e8 --- /dev/null +++ b/doc/examples/introduction_tutorial.py.md5 @@ -0,0 +1 @@ +e94f169a1a5b93fd519f4eaa6732c8d4 \ No newline at end of file diff --git a/doc/examples/introduction_tutorial.rst b/doc/examples/introduction_tutorial.rst index 71d1b77af..604d496d6 100644 --- a/doc/examples/introduction_tutorial.rst +++ b/doc/examples/introduction_tutorial.rst @@ -113,28 +113,20 @@ Download the OpenML task for the eeg-eye-state. -.. code-block:: pytb - Traceback (most recent call last): - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage - out = func() - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ - exec(self.code, self.globals) - File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/introduction_tutorial.py", line 80, in - run = openml.runs.run_model_on_task(clf, task, avoid_duplicate_runs=False) - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/runs/functions.py", line 36, in run_model_on_task - flow = sklearn_to_flow(model) - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow - raise TypeError(o, type(o)) - TypeError: (, ) +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none + + kNN on eeg-eye-state: http://test.openml.org/r/4052 .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.659 seconds) + **Total running time of the script:** ( 0 minutes 15.633 seconds) .. _sphx_glr_download_examples_introduction_tutorial.py: diff --git a/doc/examples/run_setup_tutorial.py.md5 b/doc/examples/run_setup_tutorial.py.md5 new file mode 100644 index 000000000..dc6f09cff --- /dev/null +++ b/doc/examples/run_setup_tutorial.py.md5 @@ -0,0 +1 @@ +ae0d1621e825a2e3ad60e2e473817e44 \ No newline at end of file diff --git a/doc/examples/run_setup_tutorial.rst b/doc/examples/run_setup_tutorial.rst index 9069a6c42..68e1ec7a1 100644 --- a/doc/examples/run_setup_tutorial.rst +++ b/doc/examples/run_setup_tutorial.rst @@ -51,17 +51,6 @@ In this tutorial we will -.. code-block:: pytb - - Traceback (most recent call last): - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage - out = func() - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ - exec(self.code, self.globals) - File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/run_setup_tutorial.py", line 33, in - import sklearn.impute - ModuleNotFoundError: No module named 'sklearn.impute' - @@ -107,6 +96,11 @@ In this tutorial we will run_original = run.publish() # this implicitly uploads the flow + + + + + 2) Download the flow and solve the same task again. ############################################################################## @@ -129,6 +123,11 @@ In this tutorial we will + + + + + 3) We will verify that the obtained results are exactly the same. ############################################################################## @@ -141,9 +140,14 @@ In this tutorial we will run_duplicate.data_content) + + + + + .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.172 seconds) + **Total running time of the script:** ( 0 minutes 6.747 seconds) .. _sphx_glr_download_examples_run_setup_tutorial.py: diff --git a/doc/examples/sg_execution_times.rst b/doc/examples/sg_execution_times.rst index e8ad41177..63ee1656a 100644 --- a/doc/examples/sg_execution_times.rst +++ b/doc/examples/sg_execution_times.rst @@ -5,11 +5,11 @@ Computation times ================= -**00:12.890** total execution time for **examples** files: - -- **00:07.223**: :ref:`sphx_glr_examples_datasets_tutorial.py` (``datasets_tutorial.py``) -- **00:04.633**: :ref:`sphx_glr_examples_tasks_tutorial.py` (``tasks_tutorial.py``) -- **00:00.659**: :ref:`sphx_glr_examples_introduction_tutorial.py` (``introduction_tutorial.py``) -- **00:00.172**: :ref:`sphx_glr_examples_run_setup_tutorial.py` (``run_setup_tutorial.py``) -- **00:00.153**: :ref:`sphx_glr_examples_create_upload_tutorial.py` (``create_upload_tutorial.py``) -- **00:00.051**: :ref:`sphx_glr_examples_flows_and_runs_tutorial.py` (``flows_and_runs_tutorial.py``) +**00:02.477** total execution time for **examples** files: + +- **00:01.833**: :ref:`sphx_glr_examples_datasets_tutorial.py` (``datasets_tutorial.py``) +- **00:00.644**: :ref:`sphx_glr_examples_flows_and_runs_tutorial.py` (``flows_and_runs_tutorial.py``) +- **00:00.000**: :ref:`sphx_glr_examples_create_upload_tutorial.py` (``create_upload_tutorial.py``) +- **00:00.000**: :ref:`sphx_glr_examples_introduction_tutorial.py` (``introduction_tutorial.py``) +- **00:00.000**: :ref:`sphx_glr_examples_run_setup_tutorial.py` (``run_setup_tutorial.py``) +- **00:00.000**: :ref:`sphx_glr_examples_tasks_tutorial.py` (``tasks_tutorial.py``) diff --git a/doc/examples/sklearn/openml_run_example.py.md5 b/doc/examples/sklearn/openml_run_example.py.md5 new file mode 100644 index 000000000..bb4d486f8 --- /dev/null +++ b/doc/examples/sklearn/openml_run_example.py.md5 @@ -0,0 +1 @@ +48b26834ebede3f341d600bf427d53cc \ No newline at end of file diff --git a/doc/examples/sklearn/openml_run_example.rst b/doc/examples/sklearn/openml_run_example.rst index 8d00fadd9..2672627c2 100644 --- a/doc/examples/sklearn/openml_run_example.rst +++ b/doc/examples/sklearn/openml_run_example.rst @@ -59,28 +59,20 @@ Run the scikit-learn model on the task (requires an API key). -.. code-block:: pytb - Traceback (most recent call last): - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage - out = func() - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ - exec(self.code, self.globals) - File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/sklearn/openml_run_example.py", line 25, in - run = openml.runs.run_model_on_task(clf, task) - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/runs/functions.py", line 36, in run_model_on_task - flow = sklearn_to_flow(model) - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/openml-0.6.0-py3.6.egg/openml/flows/sklearn_converter.py", line 81, in sklearn_to_flow - raise TypeError(o, type(o)) - TypeError: (, ) +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none + + URL for run: https://test.openml.org/api/v1/xml/run/4054 .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 0.125 seconds) + **Total running time of the script:** ( 0 minutes 10.099 seconds) .. _sphx_glr_download_examples_sklearn_openml_run_example.py: diff --git a/doc/examples/sklearn/sg_execution_times.rst b/doc/examples/sklearn/sg_execution_times.rst index f66b2a229..c98a80b4b 100644 --- a/doc/examples/sklearn/sg_execution_times.rst +++ b/doc/examples/sklearn/sg_execution_times.rst @@ -5,6 +5,6 @@ Computation times ================= -**00:00.125** total execution time for **examples_sklearn** files: +**00:10.099** total execution time for **examples_sklearn** files: -- **00:00.125**: :ref:`sphx_glr_examples_sklearn_openml_run_example.py` (``openml_run_example.py``) +- **00:10.099**: :ref:`sphx_glr_examples_sklearn_openml_run_example.py` (``openml_run_example.py``) diff --git a/doc/examples/tasks_tutorial.py.md5 b/doc/examples/tasks_tutorial.py.md5 new file mode 100644 index 000000000..7f7f52b41 --- /dev/null +++ b/doc/examples/tasks_tutorial.py.md5 @@ -0,0 +1 @@ +eff242bd09e05396c76cfad9d0746116 \ No newline at end of file diff --git a/doc/examples/tasks_tutorial.rst b/doc/examples/tasks_tutorial.rst index c15b30692..41223875d 100644 --- a/doc/examples/tasks_tutorial.rst +++ b/doc/examples/tasks_tutorial.rst @@ -86,13 +86,13 @@ to have better visualization and easier access: 'NumberOfInstancesWithMissingValues', 'NumberOfMissingValues', 'NumberOfNumericFeatures', 'NumberOfSymbolicFeatures'], dtype='object') - First 5 of 932 tasks: - tid ttid did name task_type status estimation_procedure ... NumberOfClasses NumberOfFeatures NumberOfInstances NumberOfInstancesWithMissingValues NumberOfMissingValues NumberOfNumericFeatures NumberOfSymbolicFeatures - 1 1 1 1 anneal Supervised Classification active 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 - 2 2 1 1 anneal Supervised Classification active 5 times 2-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 - 3 3 1 1 anneal Supervised Classification active 10 times 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 - 4 4 1 1 anneal Supervised Classification active Leave one out ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 - 5 5 1 1 anneal Supervised Classification active 10% Holdout set ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 + First 5 of 818 tasks: + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 ... 6.0 33.0 + 2 2 1 ... 6.0 33.0 + 3 3 1 ... 6.0 33.0 + 4 4 1 ... 6.0 33.0 + 5 5 1 ... 6.0 33.0 [5 rows x 19 columns] @@ -117,7 +117,7 @@ We can filter the list of tasks to only contain datasets with more than .. code-block:: none - [1, 2, 3, 4, 5, 6, 19, 20, 21, 22, 23, 24, 37, 38, 39, 40, 41, 42, 91, 92, 93, 94, 95, 96, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 181, 182, 183, 184, 185, 186, 193, 194, 195, 196, 197, 198, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 229, 230, 231, 232, 233, 234, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 307, 308, 309, 310, 311, 312, 379, 380, 381, 382, 383, 384, 391, 392, 393, 394, 395, 396, 433, 434, 435, 436, 437, 438, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 565, 566, 567, 568, 569, 570, 595, 596, 597, 598, 599, 600, 607, 608, 609, 610, 611, 612, 1069, 1072, 1075, 1084, 1088, 1090, 1093, 1094, 1099, 1101, 1103, 1104, 1105, 1107, 1109, 1110, 1111, 1120, 1132, 1134, 1141, 1154, 1155, 1163, 1168, 1232, 1234, 1239, 1240, 1242, 1244, 1245, 1248, 1250, 1251, 1252, 1253, 1257, 1258, 1260, 1261, 1263, 1264, 1265, 1268, 1269, 1270, 1273, 1276, 1279, 1280, 1283, 1288, 1289, 1297, 1304, 1307, 1309, 1310, 1314, 1317, 1320, 1321, 1323, 1327, 1329, 1330, 1334, 1337, 1339, 1341, 1342, 1343, 1346, 1347, 1349, 1353, 1354, 1355, 1358, 1359, 1366, 1367, 1369, 1376, 1377, 1378, 1380, 1382, 1383, 1384, 1387, 1396, 1399, 1402, 1405, 1406, 1410, 1412, 1413, 1419, 1423, 1426, 1428, 1430, 1433, 1437, 1438, 1439, 1440, 1442, 1443, 1447, 1448, 1453, 1456, 1459, 1460, 1462, 1466, 1469, 1471, 1473, 1476, 1477, 1478, 1479, 1484, 1485, 1487, 1490, 1494, 1496, 1497, 1498, 1502, 1503, 1507, 1508, 1509, 1512, 1515, 1518, 1519, 1520, 1523, 1524, 1531, 1535, 1537, 1539, 1541, 1543] + [1, 2, 3, 4, 5, 6, 19, 20, 21, 22, 23, 24, 37, 38, 39, 40, 41, 42, 91, 92, 93, 94, 95, 96, 115, 116, 117, 118, 119, 120, 127, 128, 129, 130, 131, 132, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 181, 182, 183, 184, 185, 186, 193, 194, 195, 196, 197, 198, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 229, 230, 231, 232, 233, 234, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 307, 308, 309, 310, 311, 312, 379, 380, 381, 382, 383, 384, 391, 392, 393, 394, 395, 396, 433, 434, 435, 436, 437, 438, 511, 512, 513, 514, 515, 516, 517, 518, 519, 520, 521, 522, 565, 566, 567, 568, 569, 570, 595, 596, 597, 598, 599, 600, 607, 608, 609, 610, 611, 612, 1069, 1072, 1075, 1084, 1088, 1090, 1093, 1094, 1099, 1101, 1103, 1104, 1105, 1107, 1109, 1110, 1111, 1120, 1132, 1134, 1141, 1154, 1155, 1163, 1168, 1206, 1209, 1212, 1221, 1225, 1227, 1230, 1231, 1236, 1238, 1240, 1241, 1242, 1244, 1246, 1247, 1248, 1257, 1267, 1269, 1276, 1289, 1290, 1298, 1303] @@ -137,7 +137,7 @@ We can filter the list of tasks to only contain datasets with more than .. code-block:: none - 309 + 206 Then, we can further restrict the tasks to all have the same resampling strategy: @@ -207,12 +207,12 @@ Similar to listing tasks by task type, we can list tasks by tags: .. code-block:: none First 5 of 100 tasks: - tid ttid did name task_type status estimation_procedure ... NumberOfClasses NumberOfFeatures NumberOfInstances NumberOfInstancesWithMissingValues NumberOfMissingValues NumberOfNumericFeatures NumberOfSymbolicFeatures - 1 1 1 1 anneal Supervised Classification active 10-fold Crossvalidation ... 5.0 39.0 898.0 898.0 22175.0 6.0 33.0 - 7 7 1 2 kr-vs-kp Supervised Classification active 10-fold Crossvalidation ... 2.0 37.0 3196.0 0.0 0.0 0.0 37.0 - 13 13 1 3 letter Supervised Classification active 10-fold Crossvalidation ... 26.0 17.0 20000.0 0.0 0.0 16.0 1.0 - 19 19 1 4 balance-scale Supervised Classification active 10-fold Crossvalidation ... 3.0 5.0 625.0 0.0 0.0 4.0 1.0 - 25 25 1 5 mfeat-factors Supervised Classification active 10-fold Crossvalidation ... 10.0 217.0 2000.0 0.0 0.0 216.0 1.0 + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 1 1 1 ... 6.0 33.0 + 7 7 1 ... 0.0 37.0 + 13 13 1 ... 16.0 1.0 + 19 19 1 ... 4.0 1.0 + 25 25 1 ... 216.0 1.0 [5 rows x 19 columns] @@ -231,18 +231,22 @@ Furthermore, we can list tasks based on the dataset id: -.. code-block:: pytb - Traceback (most recent call last): - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 394, in _memory_usage - out = func() - File "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/sphinx_gallery/gen_rst.py", line 382, in __call__ - exec(self.code, self.globals) - File "/Users/michaelmmeskhi/Documents/GitHub/openml-python/examples/tasks_tutorial.py", line 82, in - tasks = openml.tasks.list_tasks(data_id=61) - TypeError: list_tasks() got an unexpected keyword argument 'data_id' +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + First 5 of 11 tasks: + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 361 361 1 ... 7 1 + 362 362 1 ... 7 1 + 363 363 1 ... 7 1 + 364 364 1 ... 7 1 + 365 365 1 ... 7 1 + [5 rows x 19 columns] In addition, a size limit and an offset can be applied both separately and simultaneously: @@ -256,6 +260,30 @@ In addition, a size limit and an offset can be applied both separately and simul pprint(tasks) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + tid ttid ... NumberOfNumericFeatures NumberOfSymbolicFeatures + 51 51 1 ... 6 1 + 52 52 1 ... 6 1 + 53 53 1 ... 6 1 + 54 54 1 ... 6 1 + 55 55 1 ... 0 241 + 56 56 1 ... 0 241 + 57 57 1 ... 0 241 + 58 58 1 ... 0 241 + 59 59 1 ... 0 241 + 60 60 1 ... 0 241 + + [10 rows x 19 columns] + + **OpenML 100** is a curated list of 100 tasks to start using OpenML. They are all supervised classification tasks with more than 500 instances and less than 50000 @@ -274,6 +302,18 @@ Finally, it is also possible to list all tasks on OpenML with: print(len(tasks)) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + 1305 + + Exercise ######## @@ -286,6 +326,11 @@ Search for the tasks on the 'eeg-eye-state' dataset. tasks.query('name=="eeg-eye-state"') + + + + + Downloading tasks ^^^^^^^^^^^^^^^^^ @@ -301,6 +346,11 @@ all of these tasks: task = openml.tasks.get_task(task_id) + + + + + Properties of the task are stored as member variables: @@ -310,6 +360,33 @@ Properties of the task are stored as member variables: pprint(vars(task)) + + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + {'class_labels': ['1', '2', '3', '4', '5', 'U'], + 'cost_matrix': None, + 'dataset_id': 1, + 'estimation_procedure': {'data_splits_url': 'https://test.openml.org/api_splits/get/1/Task_1_splits.arff', + 'parameters': {'number_folds': '10', + 'number_repeats': '1', + 'percentage': '', + 'stratified_sampling': 'true'}, + 'type': 'crossvalidation'}, + 'estimation_procedure_id': 1, + 'evaluation_measure': None, + 'split': None, + 'target_name': 'class', + 'task_id': 1, + 'task_type': 'Supervised Classification', + 'task_type_id': 1} + + And: @@ -321,9 +398,21 @@ And: pprint(tasks[0]) + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + + + .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 4.633 seconds) + **Total running time of the script:** ( 0 minutes 23.174 seconds) .. _sphx_glr_download_examples_tasks_tutorial.py: diff --git a/doc/index.rst b/doc/index.rst index 8752dbe9b..6fe76987d 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -14,9 +14,9 @@ collaborative machine learning platform `OpenML.org `_. The OpenML Python package allows to use datasets and tasks from OpenML together with scikit-learn and share the results online. -------- -Example -------- +------------- +First Example +------------- .. code:: python @@ -34,17 +34,20 @@ Example ('estimator', tree.DecisionTreeClassifier()) ] ) + # Download the OpenML task for the german credit card dataset with 10-fold # cross-validation. task = openml.tasks.get_task(31) + # Run the scikit-learn model on the task. run = openml.runs.run_model_on_task(clf, task) + # Publish the experiment on OpenML (optional, requires an API key). run.publish() print('View the run online: %s/run/%d' % (openml.config.server, run.run_id)) ---------------------------- -How to get OpenML for python +How to get OpenML for Python ---------------------------- You can install the OpenML package via `pip`: @@ -66,16 +69,25 @@ Content * :ref:`progress` ------------------- -Further information +Further Information ------------------- -* `OpenML documentation `_ -* `OpenML client APIs `_ -* `OpenML developer guide `_ -* `Contact information `_ -* `Citation request `_ -* `OpenML blog `_ -* `OpenML twitter account `_ +For more information on what you can do with OpenML and how to engage in collaborative machine learning, check out the tutorials below. To reach out to us for more informaiton regarding events, citations and updates from the OpenML team, visit our Twitter or Blog. + +Get Started with OpenML +~~~~~~~~~~~~~~~~~~~~~~~ + +* `OpenML Documentation `_ +* `OpenML Client APIs `_ +* `OpenML Developer Guide `_ +* `OpenML Tutorials `_ + +Contact Us +~~~~~~~~~~~~~~~~~ +* `Contact Information `_ +* `Citation Request `_ +* `OpenML Blog `_ +* `OpenML Twitter Account `_ ------------ Contributing diff --git a/doc/usage.rst b/doc/usage.rst index b607c1433..d4b3ad3bf 100644 --- a/doc/usage.rst +++ b/doc/usage.rst @@ -131,6 +131,12 @@ Advanced topics We are working on tutorials for the following topics: * Querying datasets (TODO) -* Creating tasks (TODO) + +-------------- +Creating tasks (TODO) +-------------- + + + * Working offline (TODO) * Analyzing large amounts of results (TODO) diff --git a/examples/'cache'/org/openml/test/datasets/1/dataset.arff b/examples/'cache'/org/openml/test/datasets/1/dataset.arff new file mode 100644 index 000000000..401cb5e72 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/1/dataset.arff @@ -0,0 +1,1064 @@ +% 1. Title of Database: Annealing Data +% +% 2. Source Information: donated by David Sterling and Wray Buntine. +% +% 3. Past Usage: unknown +% +% 4. Relevant Information: +% -- Explanation: I suspect this was left by Ross Quinlan in 1987 at the +% 4th Machine Learning Workshop. I'd have to check with Jeff Schlimmer +% to double check this. +% +% 5. Number of Instances: 798 +% +% 6. Number of Attributes: 38 +% -- 6 continuously-valued +% -- 3 integer-valued +% -- 29 nominal-valued +% +% 7. Attribute Information: +% 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS +% 2. product-type: C, H, G +% 3. steel: -,R,A,U,K,M,S,W,V +% 4. carbon: continuous +% 5. hardness: continuous +% 6. temper_rolling: -,T +% 7. condition: -,S,A,X +% 8. formability: -,1,2,3,4,5 +% 9. strength: continuous +% 10. non-ageing: -,N +% 11. surface-finish: P,M,- +% 12. surface-quality: -,D,E,F,G +% 13. enamelability: -,1,2,3,4,5 +% 14. bc: Y,- +% 15. bf: Y,- +% 16. bt: Y,- +% 17. bw/me: B,M,- +% 18. bl: Y,- +% 19. m: Y,- +% 20. chrom: C,- +% 21. phos: P,- +% 22. cbond: Y,- +% 23. marvi: Y,- +% 24. exptl: Y,- +% 25. ferro: Y,- +% 26. corr: Y,- +% 27. blue/bright/varn/clean: B,R,V,C,- +% 28. lustre: Y,- +% 29. jurofm: Y,- +% 30. s: Y,- +% 31. p: Y,- +% 32. shape: COIL, SHEET +% 33. thick: continuous +% 34. width: continuous +% 35. len: continuous +% 36. oil: -,Y,N +% 37. bore: 0000,0500,0600,0760 +% 38. packing: -,1,2,3 +% classes: 1,2,3,4,5,U +% +% -- The '-' values are actually 'not_applicable' values rather than +% 'missing_values' (and so can be treated as legal discrete +% values rather than as showing the absence of a discrete value). +% +% 8. Missing Attribute Values: Signified with "?" +% Attribute: Number of instances missing its value: +% 1 0 +% 2 0 +% 3 70 +% 4 0 +% 5 0 +% 6 675 +% 7 271 +% 8 283 +% 9 0 +% 10 703 +% 11 790 +% 12 217 +% 13 785 +% 14 797 +% 15 680 +% 16 736 +% 17 609 +% 18 662 +% 19 798 +% 20 775 +% 21 791 +% 22 730 +% 23 798 +% 24 796 +% 25 772 +% 26 798 +% 27 793 +% 28 753 +% 29 798 +% 30 798 +% 31 798 +% 32 0 +% 33 0 +% 34 0 +% 35 0 +% 36 740 +% 37 0 +% 38 789 +% 39 0 +% +% 9. Distribution of Classes +% Class Name: Number of Instances: +% 1 8 +% 2 88 +% 3 608 +% 4 0 +% 5 60 +% U 34 +% --- +% 798 +% +@relation anneal.ORIG +@attribute 'family' { GB , GK , GS , TN , ZA , ZF , ZH , ZM , ZS } +@attribute 'product-type' { C , H , G } +@attribute 'steel' { R , A , U , K , M , S , W , V } +@attribute 'carbon' real +@attribute 'hardness' real +@attribute 'temper_rolling' { T } +@attribute 'condition' { S , A , X } +@attribute 'formability' { 1 , 2 , 3 , 4 , 5 } +@attribute 'strength' real +@attribute 'non-ageing' { N } +@attribute 'surface-finish' { P , M } +@attribute 'surface-quality' { D , E , F , G } +@attribute 'enamelability' { 1 , 2 , 3 , 4 , 5 } +@attribute 'bc' { Y } +@attribute 'bf' { Y } +@attribute 'bt' { Y } +@attribute 'bw%2Fme' { B , M } +@attribute 'bl' { Y } +@attribute 'm' { Y } +@attribute 'chrom' { C } +@attribute 'phos' { P } +@attribute 'cbond' { Y } +@attribute 'marvi' { Y } +@attribute 'exptl' { Y } +@attribute 'ferro' { Y } +@attribute 'corr' { Y } +@attribute 'blue%2Fbright%2Fvarn%2Fclean' { B , R , V , C } +@attribute 'lustre' { Y } +@attribute 'jurofm' { Y } +@attribute 's' { Y } +@attribute 'p' { Y } +@attribute 'shape' { COIL , SHEET } +@attribute 'thick' real +@attribute 'width' real +@attribute 'len' real +@attribute 'oil' { Y , N } +@attribute 'bore' { 0 , 500 , 600 , 760 } +@attribute 'packing' { 1 , 2 , 3 } +@attribute 'class' { 1 , 2 , 3 , 4 , 5 , U } +@data +% +% instances from file: anneal-train.arff +% +?,C,A,8,0,?,S,?,0,?,?,G,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,0.7,610,0,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,Y,?,?,?,COIL,3.2,610,0,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,Y,?,B,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.7,1300,762,?,0,?,3 +?,C,A,0,60,T,?,?,0,?,?,G,?,?,?,?,M,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,2.801,385.1,0,?,0,?,3 +?,C,A,0,60,T,?,?,0,?,?,G,?,?,?,?,B,Y,?,?,?,Y,?,?,?,?,?,?,?,?,?,SHEET,0.801,255,269,?,0,?,3 +?,C,A,0,45,?,S,?,0,?,?,D,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,1.6,610,0,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.699,610,4880,Y,0,?,3 +?,C,A,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,Y,?,?,?,COIL,3.3,152,0,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,Y,?,?,?,COIL,0.699,1320,0,?,0,?,3 +?,C,A,0,0,?,S,3,0,N,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,1,1320,762,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,1.2,610,0,?,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.3,1320,4880,Y,0,?,3 +?,C,R,0,0,?,S,2,0,?,?,E,?,?,?,?,B,Y,?,?,?,Y,?,?,?,?,?,?,?,?,?,SHEET,1.2,610,150,?,0,?,3 +?,C,A,0,45,?,S,?,0,?,?,D,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,1.2,609.9,0,?,0,?,3 +?,C,A,0,0,?,S,2,0,?,?,F,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.6,1220,761,?,0,?,3 +?,C,A,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,4,1320,762,?,0,?,3 +?,C,A,10,0,?,?,?,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,3.201,600,0,?,0,?,U +?,C,A,0,80,T,?,?,0,?,?,G,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.8,610,4170,Y,0,?,U +?,C,A,0,0,?,S,2,0,?,?,E,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,3.2,1320.1,762,?,0,?,3 +?,C,A,0,70,T,?,?,0,?,?,G,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,0.501,1200.1,0,?,0,?,3 +?,C,A,0,0,?,S,2,0,?,?,F,?,?,Y,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,SHEET,0.6,610,761,?,0,?,3 +?,C,K,55,0,?,?,?,0,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,0.8,610,0,?,0,?,3 +?,C,A,0,85,T,?,?,0,?,?,G,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,1.001,50,0,?,0,?,U +?,C,S,70,0,?,?,?,0,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,3.2,609.9,0,?,0,?,3 +?,C,A,3,0,T,?,?,0,?,?,?,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,0.5,610,0,?,0,?,1 +?,C,?,0,50,T,?,?,0,?,?,G,?,?,?,Y,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,COIL,3.2,1320,0,?,0,?,3 +?,C,A,0,60,T,?,?,0,?,?,G,?,?,?,?,B,Y,?,?,?,Y,?,?,?,?,?,?,?,?,?,SHEET,0.8,356,4880,?,0,?,3 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Title of Database: Annealing Data + + 2. Source Information: donated by David Sterling and Wray Buntine. + + 3. Past Usage: unknown + + 4. Relevant Information: + -- Explanation: I suspect this was left by Ross Quinlan in 1987 at the + 4th Machine Learning Workshop. I'd have to check with Jeff Schlimmer + to double check this. + + 5. Number of Instances: 798 + + 6. Number of Attributes: 38 + -- 6 continuously-valued + -- 3 integer-valued + -- 29 nominal-valued + + 7. Attribute Information: + 1. family: --,GB,GK,GS,TN,ZA,ZF,ZH,ZM,ZS + 2. product-type: C, H, G + 3. steel: -,R,A,U,K,M,S,W,V + 4. carbon: continuous + 5. hardness: continuous + 6. temper_rolling: -,T + 7. condition: -,S,A,X + 8. formability: -,1,2,3,4,5 + 9. strength: continuous + 10. non-ageing: -,N + 11. surface-finish: P,M,- + 12. surface-quality: -,D,E,F,G + 13. enamelability: -,1,2,3,4,5 + 14. bc: Y,- + 15. bf: Y,- + 16. bt: Y,- + 17. bw/me: B,M,- + 18. bl: Y,- + 19. m: Y,- + 20. chrom: C,- + 21. phos: P,- + 22. cbond: Y,- + 23. marvi: Y,- + 24. exptl: Y,- + 25. ferro: Y,- + 26. corr: Y,- + 27. blue/bright/varn/clean: B,R,V,C,- + 28. lustre: Y,- + 29. jurofm: Y,- + 30. s: Y,- + 31. p: Y,- + 32. shape: COIL, SHEET + 33. thick: continuous + 34. width: continuous + 35. len: continuous + 36. oil: -,Y,N + 37. bore: 0000,0500,0600,0760 + 38. packing: -,1,2,3 + classes: 1,2,3,4,5,U + + -- The '-' values are actually 'not_applicable' values rather than + 'missing_values' (and so can be treated as legal discrete + values rather than as showing the absence of a discrete value). + + 8. Missing Attribute Values: Signified with "?" + Attribute: Number of instances missing its value: + 1 0 + 2 0 + 3 70 + 4 0 + 5 0 + 6 675 + 7 271 + 8 283 + 9 0 + 10 703 + 11 790 + 12 217 + 13 785 + 14 797 + 15 680 + 16 736 + 17 609 + 18 662 + 19 798 + 20 775 + 21 791 + 22 730 + 23 798 + 24 796 + 25 772 + 26 798 + 27 793 + 28 753 + 29 798 + 30 798 + 31 798 + 32 0 + 33 0 + 34 0 + 35 0 + 36 740 + 37 0 + 38 789 + 39 0 + + 9. Distribution of Classes + Class Name: Number of Instances: + 1 8 + 2 88 + 3 608 + 4 0 + 5 60 + U 34 + --- + 798 + ARFF + 2014-04-06T23:19:24 + Public https://test.openml.org/data/v1/download/1/anneal.arff + 1 class 1 study_14 public active + 2019-07-09 12:41:03 4eaed8b6ec9d8211024b6c089b064761 + diff --git a/examples/'cache'/org/openml/test/datasets/1/features.xml b/examples/'cache'/org/openml/test/datasets/1/features.xml new file mode 100644 index 000000000..8b994ccaa --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/1/features.xml @@ -0,0 +1,433 @@ + + + 0 + family + nominal + GB + GK + GS + TN + ZA + ZF + ZH + ZM + ZS + false + false + false + 772 + + + 1 + product-type + nominal + C + H + G + false + false + false + 0 + + + 2 + steel + nominal + R + A + U + K + M + S + W + V + false + false + false + 86 + + + 3 + carbon + numeric + false + false + false + 0 + + + 4 + hardness + numeric + false + false + false + 0 + + + 5 + temper_rolling + nominal + T + false + false + false + 761 + + + 6 + condition + nominal + S + A + X + false + false + false + 303 + + + 7 + formability + nominal + 1 + 2 + 3 + 4 + 5 + false + false + false + 318 + + + 8 + strength + numeric + false + false + false + 0 + + + 9 + non-ageing + nominal + N + false + false + false + 793 + + + 10 + surface-finish + nominal + P + M + false + false + false + 889 + + + 11 + surface-quality + nominal + D + E + F + G + false + false + false + 244 + + + 12 + enamelability + nominal + 1 + 2 + 3 + 4 + 5 + false + false + false + 882 + + + 13 + bc + nominal + Y + false + false + false + 897 + + + 14 + bf + nominal + Y + false + false + false + 769 + + + 15 + bt + nominal + Y + false + false + false + 824 + + + 16 + bw%2Fme + nominal + B + M + false + false + false + 687 + + + 17 + bl + nominal + Y + false + false + false + 749 + + + 18 + m + nominal + Y + false + false + false + 898 + + + 19 + chrom + nominal + C + false + false + false + 872 + + + 20 + phos + nominal + P + false + false + false + 891 + + + 21 + cbond + nominal + Y + false + false + false + 824 + + + 22 + marvi + nominal + Y + false + false + false + 898 + + + 23 + exptl + nominal + Y + false + false + false + 896 + + + 24 + ferro + nominal + Y + false + false + false + 868 + + + 25 + corr + nominal + Y + false + false + false + 898 + + + 26 + blue%2Fbright%2Fvarn%2Fclean + nominal + B + R + V + C + false + false + false + 892 + + + 27 + lustre + nominal + Y + false + false + false + 847 + + + 28 + jurofm + nominal + Y + false + false + false + 898 + + + 29 + s + nominal + Y + false + false + false + 898 + + + 30 + p + nominal + Y + false + false + false + 898 + + + 31 + shape + nominal + COIL + SHEET + false + false + false + 0 + + + 32 + thick + numeric + false + false + false + 0 + + + 33 + width + numeric + false + false + false + 0 + + + 34 + len + numeric + false + false + false + 0 + + + 35 + oil + nominal + Y + N + false + false + false + 834 + + + 36 + bore + nominal + 0 + 500 + 600 + 760 + false + false + false + 0 + + + 37 + packing + nominal + 1 + 2 + 3 + false + false + false + 889 + + + 38 + class + nominal + 1 + 2 + 3 + 4 + 5 + U + true + false + false + 0 + + diff --git a/examples/'cache'/org/openml/test/datasets/1/qualities.xml b/examples/'cache'/org/openml/test/datasets/1/qualities.xml new file mode 100644 index 000000000..77213b001 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/1/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.6064659977703456 + + + + CfsSubsetEval_DecisionStumpAUC + 0.6612069282390298 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.18151447661469933 + + + + CfsSubsetEval_DecisionStumpKappa + 0.3506408237324379 + + + + CfsSubsetEval_NaiveBayesAUC + 0.8121230445487645 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.38084632516703787 + + + + CfsSubsetEval_NaiveBayesKappa + 0.3414064560678159 + + + + CfsSubsetEval_kNN1NAUC + 0.9535907818069945 + + + + CfsSubsetEval_kNN1NErrRate + 0.07238307349665925 + + + + CfsSubsetEval_kNN1NKappa + 0.8180650751646514 + + + + ClassEntropy + 1.189833856204398 + + + + DecisionStumpAUC + 0.8652735384332186 + + + + DecisionStumpErrRate + 0.22828507795100222 + + + + DecisionStumpKappa + 0.4503332218612649 + + + + Dimensionality + 0.043429844097995544 + + + + EquivalentNumberOfAtts + 26.839183802676523 + + + + J48.00001.AUC + 0.5939933958517685 + + + + J48.00001.ErrRate + 0.19710467706013363 + + + + J48.00001.Kappa + 0.267904435979752 + + + + J48.0001.AUC + 0.5939933958517685 + + + + J48.0001.ErrRate + 0.19710467706013363 + + + + J48.0001.Kappa + 0.267904435979752 + + + + J48.001.AUC + 0.5939933958517685 + + + + J48.001.ErrRate + 0.19710467706013363 + + + + J48.001.Kappa + 0.267904435979752 + + + + MajorityClassPercentage + 76.16926503340757 + + + + MajorityClassSize + 684.0 + + + + MaxAttributeEntropy + 1.8215224482924186 + + + + MaxKurtosisOfNumericAtts + 13.215477213878724 + + + + MaxMeansOfNumericAtts + 1263.0946547884187 + + + + MaxMutualInformation + 0.40908953764451 + + + + MaxNominalAttDistinctValues + 7.0 + + + + MaxSkewnessOfNumericAtts + 3.7616019689156888 + + + + MaxStdDevOfNumericAtts + 1871.3991072665933 + + + + MeanAttributeEntropy + 0.2515351603742048 + + + + MeanKurtosisOfNumericAtts + 4.6480244352098286 + + + + MeanMeansOfNumericAtts + 348.50426818856715 + + + + MeanMutualInformation + 0.044331968697414056 + + + + MeanNoiseToSignalRatio + 4.673900071775454 + + + + MeanNominalAttDistinctValues + 1.6363636363636362 + + + + MeanSkewnessOfNumericAtts + 2.0269825910719437 + + + + MeanStdDevOfNumericAtts + 405.17326983791025 + + + + MinAttributeEntropy + -0.0 + + + + MinKurtosisOfNumericAtts + -0.9723842038435437 + + + + MinMeansOfNumericAtts + 1.1985489977728285 + + + + MinMutualInformation + 0.0 + + + + MinNominalAttDistinctValues + 0.0 + + + + MinSkewnessOfNumericAtts + 0.07299048442083138 + + + + MinStdDevOfNumericAtts + 0.871208280971892 + + + + MinorityClassPercentage + 0.8908685968819599 + + + + MinorityClassSize + 8.0 + + + + NaiveBayesAUC + 0.9315907109421729 + + + + NaiveBayesErrRate + 0.24610244988864144 + + + + NaiveBayesKappa + 0.5569590016631507 + + + + NumberOfBinaryFeatures + 4.0 + + + + NumberOfClasses + 5.0 + + + + NumberOfFeatures + 39.0 + + + + NumberOfInstances + 898.0 + + + + NumberOfInstancesWithMissingValues + 898.0 + + + + NumberOfMissingValues + 22175.0 + + + + NumberOfNumericFeatures + 6.0 + + + + NumberOfSymbolicFeatures + 33.0 + + + + PercentageOfBinaryFeatures + 10.256410256410255 + + + + PercentageOfInstancesWithMissingValues + 100.0 + + + + PercentageOfMissingValues + 63.317343384158534 + + + + PercentageOfNumericFeatures + 15.384615384615385 + + + + PercentageOfSymbolicFeatures + 84.61538461538461 + + + + Quartile1AttributeEntropy + 0.0 + + + + Quartile1KurtosisOfNumericAtts + -0.40305022089010156 + + + + Quartile1MeansOfNumericAtts + 3.025695155902005 + + + + Quartile1MutualInformation + 0.0 + + + + Quartile1SkewnessOfNumericAtts + 0.967384603629726 + + + + Quartile1StdDevOfNumericAtts + 10.505435772171138 + + + + Quartile2AttributeEntropy + 0.0 + + + + Quartile2KurtosisOfNumericAtts + 1.6372437439142264 + + + + Quartile2MeansOfNumericAtts + 21.222160356347437 + + + + Quartile2MutualInformation + 0.0 + + + + Quartile2SkewnessOfNumericAtts + 1.6547313364025702 + + + + Quartile2StdDevOfNumericAtts + 69.85338529046133 + + + + Quartile3AttributeEntropy + 0.2385631077559124 + + + + Quartile3KurtosisOfNumericAtts + 12.741748058445403 + + + + Quartile3MeansOfNumericAtts + 901.2636692650334 + + + + Quartile3MutualInformation + 0.0206465881071925 + + + + Quartile3SkewnessOfNumericAtts + 3.7546438249219056 + + + + Quartile3StdDevOfNumericAtts + 771.8590427889504 + + + + REPTreeDepth1AUC + 0.5692694611232046 + + + + REPTreeDepth1ErrRate + 0.2182628062360802 + + + + REPTreeDepth1Kappa + 0.14164212004759766 + + + + REPTreeDepth2AUC + 0.7133659706134035 + + + + REPTreeDepth2ErrRate + 0.19265033407572382 + + + + REPTreeDepth2Kappa + 0.30086540149138874 + + + + REPTreeDepth3AUC + 0.822151031071005 + + + + REPTreeDepth3ErrRate + 0.1714922048997773 + + + + REPTreeDepth3Kappa + 0.4189509420009749 + + + + RandomTreeDepth1AUC + 0.6369584297524356 + + + + RandomTreeDepth1ErrRate + 0.2383073496659243 + + + + RandomTreeDepth1Kappa + 0.0 + + + + RandomTreeDepth2AUC + 0.6965727371473568 + + + + RandomTreeDepth2ErrRate + 0.24276169265033407 + + + + RandomTreeDepth2Kappa + -0.005216998377390389 + + + + RandomTreeDepth3AUC + 0.7789906449828373 + + + + RandomTreeDepth3ErrRate + 0.22939866369710468 + + + + RandomTreeDepth3Kappa + 0.09436363903399038 + + + + StdvNominalAttDistinctValues + 1.5576059718800395 + + + + kNN1NAUC + 0.8721948540771287 + + + + kNN1NErrRate + 0.06347438752783964 + + + + kNN1NKappa + 0.8261102938928316 + + diff --git a/examples/'cache'/org/openml/test/datasets/17/dataset.arff b/examples/'cache'/org/openml/test/datasets/17/dataset.arff new file mode 100644 index 000000000..a3347a36e --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/17/dataset.arff @@ -0,0 +1,1301 @@ +% Description of the German credit dataset. +% +% 1. Title: German Credit data +% +% 2. Source Information +% +% Professor Dr. Hans Hofmann +% Institut f"ur Statistik und "Okonometrie +% Universit"at Hamburg +% FB Wirtschaftswissenschaften +% Von-Melle-Park 5 +% 2000 Hamburg 13 +% +% 3. Number of Instances: 1000 +% +% Two datasets are provided. the original dataset, in the form provided +% by Prof. Hofmann, contains categorical/symbolic attributes and +% is in the file "german.data". +% +% For algorithms that need numerical attributes, Strathclyde University +% produced the file "german.data-numeric". This file has been edited +% and several indicator variables added to make it suitable for +% algorithms which cannot cope with categorical variables. Several +% attributes that are ordered categorical (such as attribute 17) have +% been coded as integer. This was the form used by StatLog. +% +% +% 6. Number of Attributes german: 20 (7 numerical, 13 categorical) +% Number of Attributes german.numer: 24 (24 numerical) +% +% +% 7. Attribute description for german +% +% Attribute 1: (qualitative) +% Status of existing checking account +% A11 : ... < 0 DM +% A12 : 0 <= ... < 200 DM +% A13 : ... >= 200 DM / +% salary assignments for at least 1 year +% A14 : no checking account +% +% Attribute 2: (numerical) +% Duration in month +% +% Attribute 3: (qualitative) +% Credit history +% A30 : no credits taken/ +% all credits paid back duly +% A31 : all credits at this bank paid back duly +% A32 : existing credits paid back duly till now +% A33 : delay in paying off in the past +% A34 : critical account/ +% other credits existing (not at this bank) +% +% Attribute 4: (qualitative) +% Purpose +% A40 : car (new) +% A41 : car (used) +% A42 : furniture/equipment +% A43 : radio/television +% A44 : domestic appliances +% A45 : repairs +% A46 : education +% A47 : (vacation - does not exist?) +% A48 : retraining +% A49 : business +% A410 : others +% +% Attribute 5: (numerical) +% Credit amount +% +% Attibute 6: (qualitative) +% Savings account/bonds +% A61 : ... < 100 DM +% A62 : 100 <= ... < 500 DM +% A63 : 500 <= ... < 1000 DM +% A64 : .. >= 1000 DM +% A65 : unknown/ no savings account +% +% Attribute 7: (qualitative) +% Present employment since +% A71 : unemployed +% A72 : ... < 1 year +% A73 : 1 <= ... < 4 years +% A74 : 4 <= ... < 7 years +% A75 : .. >= 7 years +% +% Attribute 8: (numerical) +% Installment rate in percentage of disposable income +% +% Attribute 9: (qualitative) +% Personal status and sex +% A91 : male : divorced/separated +% A92 : female : divorced/separated/married +% A93 : male : single +% A94 : male : married/widowed +% A95 : female : single +% +% Attribute 10: (qualitative) +% Other debtors / guarantors +% A101 : none +% A102 : co-applicant +% A103 : guarantor +% +% Attribute 11: (numerical) +% Present residence since +% +% Attribute 12: (qualitative) +% Property +% A121 : real estate +% A122 : if not A121 : building society savings agreement/ +% life insurance +% A123 : if not A121/A122 : car or other, not in attribute 6 +% A124 : unknown / no property +% +% Attribute 13: (numerical) +% Age in years +% +% Attribute 14: (qualitative) +% Other installment plans +% A141 : bank +% A142 : stores +% A143 : none +% +% Attribute 15: (qualitative) +% Housing +% A151 : rent +% A152 : own +% A153 : for free +% +% Attribute 16: (numerical) +% Number of existing credits at this bank +% +% Attribute 17: (qualitative) +% Job +% A171 : unemployed/ unskilled - non-resident +% A172 : unskilled - resident +% A173 : skilled employee / official +% A174 : management/ self-employed/ +% highly qualified employee/ officer +% +% Attribute 18: (numerical) +% Number of people being liable to provide maintenance for +% +% Attribute 19: (qualitative) +% Telephone +% A191 : none +% A192 : yes, registered under the customers name +% +% Attribute 20: (qualitative) +% foreign worker +% A201 : yes +% A202 : no +% +% +% +% 8. Cost Matrix +% +% This dataset requires use of a cost matrix (see below) +% +% +% 1 2 +% ---------------------------- +% 1 0 1 +% ----------------------- +% 2 5 0 +% +% (1 = Good, 2 = Bad) +% +% the rows represent the actual classification and the columns +% the predicted classification. +% +% It is worse to class a customer as good when they are bad (5), +% than it is to class a customer as bad when they are good (1). +% +% +% +% +% +% Relabeled values in attribute checking_status +% From: A11 To: '<0' +% From: A12 To: '0<=X<200' +% From: A13 To: '>=200' +% From: A14 To: 'no checking' +% +% +% Relabeled values in attribute credit_history +% From: A30 To: 'no credits/all paid' +% From: A31 To: 'all paid' +% From: A32 To: 'existing paid' +% From: A33 To: 'delayed previously' +% From: A34 To: 'critical/other existing credit' +% +% +% Relabeled values in attribute purpose +% From: A40 To: 'new car' +% From: A41 To: 'used car' +% From: A42 To: furniture/equipment +% From: A43 To: radio/tv +% From: A44 To: 'domestic appliance' +% From: A45 To: repairs +% From: A46 To: education +% From: A47 To: vacation +% From: A48 To: retraining +% From: A49 To: business +% From: A410 To: other +% +% +% Relabeled values in attribute savings_status +% From: A61 To: '<100' +% From: A62 To: '100<=X<500' +% From: A63 To: '500<=X<1000' +% From: A64 To: '>=1000' +% From: A65 To: 'no known savings' +% +% +% Relabeled values in attribute employment +% From: A71 To: unemployed +% From: A72 To: '<1' +% From: A73 To: '1<=X<4' +% From: A74 To: '4<=X<7' +% From: A75 To: '>=7' +% +% +% Relabeled values in attribute personal_status +% From: A91 To: 'male div/sep' +% From: A92 To: 'female div/dep/mar' +% From: A93 To: 'male single' +% From: A94 To: 'male mar/wid' +% From: A95 To: 'female single' +% +% +% Relabeled values in attribute other_parties +% From: A101 To: none +% From: A102 To: 'co applicant' +% From: A103 To: guarantor +% +% +% Relabeled values in attribute property_magnitude +% From: A121 To: 'real estate' +% From: A122 To: 'life insurance' +% From: A123 To: car +% From: A124 To: 'no known property' +% +% +% Relabeled values in attribute other_payment_plans +% From: A141 To: bank +% From: A142 To: stores +% From: A143 To: none +% +% +% Relabeled values in attribute housing +% From: A151 To: rent +% From: A152 To: own +% From: A153 To: 'for free' +% +% +% Relabeled values in attribute job +% From: A171 To: 'unemp/unskilled non res' +% From: A172 To: 'unskilled resident' +% From: A173 To: skilled +% From: A174 To: 'high qualif/self emp/mgmt' +% +% +% Relabeled values in attribute own_telephone +% From: A191 To: none +% From: A192 To: yes +% +% +% Relabeled values in attribute foreign_worker +% From: A201 To: yes +% From: A202 To: no +% +% +% Relabeled values in attribute class +% From: 1 To: good +% From: 2 To: bad +% +@relation german_credit +@attribute 'checking_status' { '<0', '0<=X<200', '>=200', 'no checking'} +@attribute 'duration' real +@attribute 'credit_history' { 'no credits/all paid', 'all paid', 'existing paid', 'delayed previously', 'critical/other existing credit'} +@attribute 'purpose' { 'new car', 'used car', furniture/equipment, radio/tv, 'domestic appliance', repairs, education, vacation, retraining, business, other} +@attribute 'credit_amount' real +@attribute 'savings_status' { '<100', '100<=X<500', '500<=X<1000', '>=1000', 'no known savings'} +@attribute 'employment' { unemployed, '<1', '1<=X<4', '4<=X<7', '>=7'} +@attribute 'installment_commitment' real +@attribute 'personal_status' { 'male div/sep', 'female div/dep/mar', 'male single', 'male mar/wid', 'female single'} +@attribute 'other_parties' { none, 'co applicant', guarantor} +@attribute 'residence_since' real +@attribute 'property_magnitude' { 'real estate', 'life insurance', car, 'no known property'} +@attribute 'age' real +@attribute 'other_payment_plans' { bank, stores, none} +@attribute 'housing' { rent, own, 'for free'} +@attribute 'existing_credits' real +@attribute 'job' { 'unemp/unskilled non res', 'unskilled resident', skilled, 'high qualif/self emp/mgmt'} +@attribute 'num_dependents' real +@attribute 'own_telephone' { none, yes} +@attribute 'foreign_worker' { yes, no} +@attribute 'class' { good, bad} +@data +'<0',6,'critical/other existing credit',radio/tv,1169,'no known savings','>=7',4,'male single',none,4,'real estate',67,none,own,2,skilled,1,yes,yes,good +'0<=X<200',48,'existing paid',radio/tv,5951,'<100','1<=X<4',2,'female div/dep/mar',none,2,'real estate',22,none,own,1,skilled,1,none,yes,bad +'no checking',12,'critical/other existing credit',education,2096,'<100','4<=X<7',2,'male single',none,3,'real estate',49,none,own,1,'unskilled resident',2,none,yes,good +'<0',42,'existing paid',furniture/equipment,7882,'<100','4<=X<7',2,'male single',guarantor,4,'life insurance',45,none,'for free',1,skilled,2,none,yes,good +'<0',24,'delayed previously','new car',4870,'<100','1<=X<4',3,'male single',none,4,'no known property',53,none,'for 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a/examples/'cache'/org/openml/test/datasets/17/description.xml b/examples/'cache'/org/openml/test/datasets/17/description.xml new file mode 100644 index 000000000..c4bd047e7 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/17/description.xml @@ -0,0 +1,291 @@ + + 17 + credit-g + 1 + **Author**: +**Source**: Unknown - +**Please cite**: + +Description of the German credit dataset. + + 1. Title: German Credit data + + 2. Source Information + + Professor Dr. Hans Hofmann + Institut f"ur Statistik und "Okonometrie + Universit"at Hamburg + FB Wirtschaftswissenschaften + Von-Melle-Park 5 + 2000 Hamburg 13 + + 3. Number of Instances: 1000 + + Two datasets are provided. the original dataset, in the form provided + by Prof. Hofmann, contains categorical/symbolic attributes and + is in the file "german.data". + + For algorithms that need numerical attributes, Strathclyde University + produced the file "german.data-numeric". This file has been edited + and several indicator variables added to make it suitable for + algorithms which cannot cope with categorical variables. Several + attributes that are ordered categorical (such as attribute 17) have + been coded as integer. This was the form used by StatLog. + + + 6. Number of Attributes german: 20 (7 numerical, 13 categorical) + Number of Attributes german.numer: 24 (24 numerical) + + + 7. Attribute description for german + + Attribute 1: (qualitative) + Status of existing checking account + A11 : ... < 0 DM + A12 : 0 <= ... < 200 DM + A13 : ... >= 200 DM / + salary assignments for at least 1 year + A14 : no checking account + + Attribute 2: (numerical) + Duration in month + + Attribute 3: (qualitative) + Credit history + A30 : no credits taken/ + all credits paid back duly + A31 : all credits at this bank paid back duly + A32 : existing credits paid back duly till now + A33 : delay in paying off in the past + A34 : critical account/ + other credits existing (not at this bank) + + Attribute 4: (qualitative) + Purpose + A40 : car (new) + A41 : car (used) + A42 : furniture/equipment + A43 : radio/television + A44 : domestic appliances + A45 : repairs + A46 : education + A47 : (vacation - does not exist?) + A48 : retraining + A49 : business + A410 : others + + Attribute 5: (numerical) + Credit amount + + Attibute 6: (qualitative) + Savings account/bonds + A61 : ... < 100 DM + A62 : 100 <= ... < 500 DM + A63 : 500 <= ... < 1000 DM + A64 : .. >= 1000 DM + A65 : unknown/ no savings account + + Attribute 7: (qualitative) + Present employment since + A71 : unemployed + A72 : ... < 1 year + A73 : 1 <= ... < 4 years + A74 : 4 <= ... < 7 years + A75 : .. >= 7 years + + Attribute 8: (numerical) + Installment rate in percentage of disposable income + + Attribute 9: (qualitative) + Personal status and sex + A91 : male : divorced/separated + A92 : female : divorced/separated/married + A93 : male : single + A94 : male : married/widowed + A95 : female : single + + Attribute 10: (qualitative) + Other debtors / guarantors + A101 : none + A102 : co-applicant + A103 : guarantor + + Attribute 11: (numerical) + Present residence since + + Attribute 12: (qualitative) + Property + A121 : real estate + A122 : if not A121 : building society savings agreement/ + life insurance + A123 : if not A121/A122 : car or other, not in attribute 6 + A124 : unknown / no property + + Attribute 13: (numerical) + Age in years + + Attribute 14: (qualitative) + Other installment plans + A141 : bank + A142 : stores + A143 : none + + Attribute 15: (qualitative) + Housing + A151 : rent + A152 : own + A153 : for free + + Attribute 16: (numerical) + Number of existing credits at this bank + + Attribute 17: (qualitative) + Job + A171 : unemployed/ unskilled - non-resident + A172 : unskilled - resident + A173 : skilled employee / official + A174 : management/ self-employed/ + highly qualified employee/ officer + + Attribute 18: (numerical) + Number of people being liable to provide maintenance for + + Attribute 19: (qualitative) + Telephone + A191 : none + A192 : yes, registered under the customers name + + Attribute 20: (qualitative) + foreign worker + A201 : yes + A202 : no + + + + 8. Cost Matrix + + This dataset requires use of a cost matrix (see below) + + + 1 2 + ---------------------------- + 1 0 1 + ----------------------- + 2 5 0 + + (1 = Good, 2 = Bad) + + the rows represent the actual classification and the columns + the predicted classification. + + It is worse to class a customer as good when they are bad (5), + than it is to class a customer as bad when they are good (1). + + + + + + Relabeled values in attribute checking_status + From: A11 To: '<0' + From: A12 To: '0<=X<200' + From: A13 To: '>=200' + From: A14 To: 'no checking' + + + Relabeled values in attribute credit_history + From: A30 To: 'no credits/all paid' + From: A31 To: 'all paid' + From: A32 To: 'existing paid' + From: A33 To: 'delayed previously' + From: A34 To: 'critical/other existing credit' + + + Relabeled values in attribute purpose + From: A40 To: 'new car' + From: A41 To: 'used car' + From: A42 To: furniture/equipment + From: A43 To: radio/tv + From: A44 To: 'domestic appliance' + From: A45 To: repairs + From: A46 To: education + From: A47 To: vacation + From: A48 To: retraining + From: A49 To: business + From: A410 To: other + + + Relabeled values in attribute savings_status + From: A61 To: '<100' + From: A62 To: '100<=X<500' + From: A63 To: '500<=X<1000' + From: A64 To: '>=1000' + From: A65 To: 'no known savings' + + + Relabeled values in attribute employment + From: A71 To: unemployed + From: A72 To: '<1' + From: A73 To: '1<=X<4' + From: A74 To: '4<=X<7' + From: A75 To: '>=7' + + + Relabeled values in attribute personal_status + From: A91 To: 'male div/sep' + From: A92 To: 'female div/dep/mar' + From: A93 To: 'male single' + From: A94 To: 'male mar/wid' + From: A95 To: 'female single' + + + Relabeled values in attribute other_parties + From: A101 To: none + From: A102 To: 'co applicant' + From: A103 To: guarantor + + + Relabeled values in attribute property_magnitude + From: A121 To: 'real estate' + From: A122 To: 'life insurance' + From: A123 To: car + From: A124 To: 'no known property' + + + Relabeled values in attribute other_payment_plans + From: A141 To: bank + From: A142 To: stores + From: A143 To: none + + + Relabeled values in attribute housing + From: A151 To: rent + From: A152 To: own + From: A153 To: 'for free' + + + Relabeled values in attribute job + From: A171 To: 'unemp/unskilled non res' + From: A172 To: 'unskilled resident' + From: A173 To: skilled + From: A174 To: 'high qualif/self emp/mgmt' + + + Relabeled values in attribute own_telephone + From: A191 To: none + From: A192 To: yes + + + Relabeled values in attribute foreign_worker + From: A201 To: yes + From: A202 To: no + + + Relabeled values in attribute class + From: 1 To: good + From: 2 To: bad + ARFF + 2014-04-06T23:21:47 + Public https://test.openml.org/data/v1/download/17/credit-g.arff + 17 class 1 study_14 public active + 2019-07-09 12:42:24 9a475053fed0c26ee95cd4525e50074c + diff --git a/examples/'cache'/org/openml/test/datasets/17/features.xml b/examples/'cache'/org/openml/test/datasets/17/features.xml new file mode 100644 index 000000000..da1177434 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/17/features.xml @@ -0,0 +1,249 @@ + + + 0 + checking_status + nominal + <0 + 0<=X<200 + >=200 + no checking + false + false + false + 0 + + + 1 + duration + numeric + false + false + false + 0 + + + 2 + credit_history + nominal + no credits/all paid + all paid + existing paid + delayed previously + critical/other existing credit + false + false + false + 0 + + + 3 + purpose + nominal + new car + used car + furniture/equipment + radio/tv + domestic appliance + repairs + education + vacation + retraining + business + other + false + false + false + 0 + + + 4 + credit_amount + numeric + false + false + false + 0 + + + 5 + savings_status + nominal + <100 + 100<=X<500 + 500<=X<1000 + >=1000 + no known savings + false + false + false + 0 + + + 6 + employment + nominal + unemployed + <1 + 1<=X<4 + 4<=X<7 + >=7 + false + false + false + 0 + + + 7 + installment_commitment + numeric + false + false + false + 0 + + + 8 + personal_status + nominal + male div/sep + female div/dep/mar + male single + male mar/wid + female single + false + false + false + 0 + + + 9 + other_parties + nominal + none + co applicant + guarantor + false + false + false + 0 + + + 10 + residence_since + numeric + false + false + false + 0 + + + 11 + property_magnitude + nominal + real estate + life insurance + car + no known property + false + false + false + 0 + + + 12 + age + numeric + false + false + false + 0 + + + 13 + other_payment_plans + nominal + bank + stores + none + false + false + false + 0 + + + 14 + housing + nominal + rent + own + for free + false + false + false + 0 + + + 15 + existing_credits + numeric + false + false + false + 0 + + + 16 + job + nominal + unemp/unskilled non res + unskilled resident + skilled + high qualif/self emp/mgmt + false + false + false + 0 + + + 17 + num_dependents + numeric + false + false + false + 0 + + + 18 + own_telephone + nominal + none + yes + false + false + false + 0 + + + 19 + foreign_worker + nominal + yes + no + false + false + false + 0 + + + 20 + class + nominal + good + bad + true + false + false + 0 + + diff --git a/examples/'cache'/org/openml/test/datasets/17/qualities.xml b/examples/'cache'/org/openml/test/datasets/17/qualities.xml new file mode 100644 index 000000000..a52cf1832 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/17/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.5695695695695696 + + + + CfsSubsetEval_DecisionStumpAUC + 0.7208285714285714 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.273 + + + + CfsSubsetEval_DecisionStumpKappa + 0.3056968463886062 + + + + CfsSubsetEval_NaiveBayesAUC + 0.7208285714285714 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.273 + + + + CfsSubsetEval_NaiveBayesKappa + 0.3056968463886062 + + + + CfsSubsetEval_kNN1NAUC + 0.7208285714285714 + + + + CfsSubsetEval_kNN1NErrRate + 0.273 + + + + CfsSubsetEval_kNN1NKappa + 0.3056968463886062 + + + + ClassEntropy + 0.8812908992306927 + + + + DecisionStumpAUC + 0.6647619047619048 + + + + DecisionStumpErrRate + 0.3 + + + + DecisionStumpKappa + 0.0 + + + + Dimensionality + 0.021 + + + + EquivalentNumberOfAtts + 43.593036016305035 + + + + J48.00001.AUC + 0.6617476190476189 + + + + J48.00001.ErrRate + 0.279 + + + + J48.00001.Kappa + 0.24431202600216673 + + + + J48.0001.AUC + 0.6617476190476189 + + + + J48.0001.ErrRate + 0.279 + + + + J48.0001.Kappa + 0.24431202600216673 + + + + J48.001.AUC + 0.6617476190476189 + + + + J48.001.ErrRate + 0.279 + + + + J48.001.Kappa + 0.24431202600216673 + + + + MajorityClassPercentage + 70.0 + + + + MajorityClassSize + 700.0 + + + + MaxAttributeEntropy + 2.6666777598518516 + + + + MaxKurtosisOfNumericAtts + 4.292590308048501 + + + + MaxMeansOfNumericAtts + 3271.257999999999 + + + + MaxMutualInformation + 0.09473884155264 + + + + MaxNominalAttDistinctValues + 10.0 + + + + MaxSkewnessOfNumericAtts + 1.9496276798326246 + + + + MaxStdDevOfNumericAtts + 2822.7368759604396 + + + + MeanAttributeEntropy + 1.433893225502841 + + + + MeanKurtosisOfNumericAtts + 0.9242775257981102 + + + + MeanMeansOfNumericAtts + 476.58385714285697 + + + + MeanMutualInformation + 0.02021632305905615 + + + + MeanNoiseToSignalRatio + 69.92749860170595 + + + + MeanNominalAttDistinctValues + 4.0 + + + + MeanSkewnessOfNumericAtts + 0.9203791257169068 + + + + MeanStdDevOfNumericAtts + 407.04761882821174 + + + + MinAttributeEntropy + 0.2283640258405646 + + + + MinKurtosisOfNumericAtts + -1.3814485027493755 + + + + MinMeansOfNumericAtts + 1.1549999999999998 + + + + MinMutualInformation + 9.6366001491E-4 + + + + MinNominalAttDistinctValues + 2.0 + + + + MinSkewnessOfNumericAtts + -0.5313481143125632 + + + + MinStdDevOfNumericAtts + 0.3620857717531919 + + + + MinorityClassPercentage + 30.0 + + + + MinorityClassSize + 300.0 + + + + NaiveBayesAUC + 0.786047619047619 + + + + NaiveBayesErrRate + 0.248 + + + + NaiveBayesKappa + 0.3724696356275304 + + + + NumberOfBinaryFeatures + 3.0 + + + + NumberOfClasses + 2.0 + + + + NumberOfFeatures + 21.0 + + + + NumberOfInstances + 1000.0 + + + + NumberOfInstancesWithMissingValues + 0.0 + + + + NumberOfMissingValues + 0.0 + + + + NumberOfNumericFeatures + 7.0 + + + + NumberOfSymbolicFeatures + 14.0 + + + + PercentageOfBinaryFeatures + 14.285714285714285 + + + + PercentageOfInstancesWithMissingValues + 0.0 + + + + PercentageOfMissingValues + 0.0 + + + + PercentageOfNumericFeatures + 33.33333333333333 + + + + PercentageOfSymbolicFeatures + 66.66666666666666 + + + + Quartile1AttributeEntropy + 0.9089779148834296 + + + + Quartile1KurtosisOfNumericAtts + -1.2104731179379757 + + + + Quartile1MeansOfNumericAtts + 1.407 + + + + Quartile1MutualInformation + 0.005310005973835 + + + + Quartile1SkewnessOfNumericAtts + -0.2725698140337198 + + + + Quartile1StdDevOfNumericAtts + 0.5776544682460991 + + + + Quartile2AttributeEntropy + 1.5321036281187235 + + + + Quartile2KurtosisOfNumericAtts + 0.9197813600546327 + + + + Quartile2MeansOfNumericAtts + 2.9730000000000003 + + + + Quartile2MutualInformation + 0.01275318647802 + + + + Quartile2SkewnessOfNumericAtts + 1.09418417155554 + + + + Quartile2StdDevOfNumericAtts + 1.1187146743126737 + + + + Quartile3AttributeEntropy + 1.8749120301493503 + + + + Quartile3KurtosisOfNumericAtts + 1.6492736936699308 + + + + Quartile3MeansOfNumericAtts + 35.54600000000001 + + + + Quartile3MutualInformation + 0.02650410754481 + + + + Quartile3SkewnessOfNumericAtts + 1.909444721297511 + + + + Quartile3StdDevOfNumericAtts + 12.058814452756392 + + + + REPTreeDepth1AUC + 0.7002928571428572 + + + + REPTreeDepth1ErrRate + 0.269 + + + + REPTreeDepth1Kappa + 0.22254335260115596 + + + + REPTreeDepth2AUC + 0.7002928571428572 + + + + REPTreeDepth2ErrRate + 0.269 + + + + REPTreeDepth2Kappa + 0.22254335260115596 + + + + REPTreeDepth3AUC + 0.7002928571428572 + + + + REPTreeDepth3ErrRate + 0.269 + + + + REPTreeDepth3Kappa + 0.22254335260115596 + + + + RandomTreeDepth1AUC + 0.6580238095238096 + + + + RandomTreeDepth1ErrRate + 0.298 + + + + RandomTreeDepth1Kappa + 0.2781007751937982 + + + + RandomTreeDepth2AUC + 0.6580238095238096 + + + + RandomTreeDepth2ErrRate + 0.298 + + + + RandomTreeDepth2Kappa + 0.2781007751937982 + + + + RandomTreeDepth3AUC + 0.6580238095238096 + + + + RandomTreeDepth3ErrRate + 0.298 + + + + RandomTreeDepth3Kappa + 0.2781007751937982 + + + + StdvNominalAttDistinctValues + 2.0380986614602725 + + + + kNN1NAUC + 0.649047619047619 + + + + kNN1NErrRate + 0.286 + + + + kNN1NKappa + 0.30447470817120614 + + diff --git a/examples/'cache'/org/openml/test/datasets/4/dataset.arff b/examples/'cache'/org/openml/test/datasets/4/dataset.arff new file mode 100644 index 000000000..b0c5d965d --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/4/dataset.arff @@ -0,0 +1,694 @@ +%1. Title: Balance Scale Weight & Distance Database +% +%2. Source Information: +% (a) Source: Generated to model psychological experiments reported +% by Siegler, R. S. (1976). Three Aspects of Cognitive +% Development. Cognitive Psychology, 8, 481-520. +% (b) Donor: Tim Hume (hume@ics.uci.edu) +% (c) Date: 22 April 1994 +% +%3. Past Usage: (possibly different formats of this data) +% - Publications +% 1. Klahr, D., & Siegler, R.S. (1978). The Representation of +% Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), +% Advances in Child Development and Behavior, pp. 61-116. New +% York: Academic Press +% 2. Langley,P. (1987). A General Theory of Discrimination +% Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), +% Production System Models of Learning and Development, pp. +% 99-161. Cambridge, MA: MIT Press +% 3. Newell, A. (1990). Unified Theories of Cognition. +% Cambridge, MA: Harvard University Press +% 4. McClelland, J.L. (1988). Parallel Distibuted Processing: +% Implications for Cognition and Development. Technical +% Report AIP-47, Department of Psychology, Carnegie-Mellon +% University +% 5. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling +% Cognitive Development on Balance Scale Phenomena. Machine +% Learning, Vol. 16, pp. 59-88. +% +%4. Relevant Information: +% This data set was generated to model psychological +% experimental results. Each example is classified as having the +% balance scale tip to the right, tip to the left, or be +% balanced. The attributes are the left weight, the left +% distance, the right weight, and the right distance. The +% correct way to find the class is the greater of +% (left-distance * left-weight) and (right-distance * +% right-weight). If they are equal, it is balanced. +% +%5. Number of Instances: 625 (49 balanced, 288 left, 288 right) +% +%6. Number of Attributes: 4 (numeric) + class name = 5 +% +%7. Attribute Information: +% 1. Class Name: 3 (L, B, R) +% 2. Left-Weight: 5 (1, 2, 3, 4, 5) +% 3. Left-Distance: 5 (1, 2, 3, 4, 5) +% 4. Right-Weight: 5 (1, 2, 3, 4, 5) +% 5. Right-Distance: 5 (1, 2, 3, 4, 5) +% +%8. Missing Attribute Values: +% none +% +%9. Class Distribution: +% 1. 46.08 percent are L +% 2. 07.84 percent are B +% 3. 46.08 percent are R +% + +@relation balance-scale +@attribute 'left-weight' real +@attribute 'left-distance' real +@attribute 'right-weight' real +@attribute 'right-distance' real +@attribute 'class' { L, B, R} +@data +1,1,1,1,B +1,1,1,2,R +1,1,1,3,R +1,1,1,4,R +1,1,1,5,R +1,1,2,1,R +1,1,2,2,R +1,1,2,3,R +1,1,2,4,R +1,1,2,5,R +1,1,3,1,R +1,1,3,2,R +1,1,3,3,R +1,1,3,4,R +1,1,3,5,R +1,1,4,1,R +1,1,4,2,R +1,1,4,3,R +1,1,4,4,R +1,1,4,5,R +1,1,5,1,R +1,1,5,2,R +1,1,5,3,R +1,1,5,4,R +1,1,5,5,R +1,2,1,1,L +1,2,1,2,B +1,2,1,3,R +1,2,1,4,R +1,2,1,5,R +1,2,2,1,B +1,2,2,2,R +1,2,2,3,R +1,2,2,4,R +1,2,2,5,R +1,2,3,1,R +1,2,3,2,R +1,2,3,3,R +1,2,3,4,R +1,2,3,5,R +1,2,4,1,R +1,2,4,2,R +1,2,4,3,R +1,2,4,4,R +1,2,4,5,R +1,2,5,1,R +1,2,5,2,R +1,2,5,3,R +1,2,5,4,R +1,2,5,5,R +1,3,1,1,L +1,3,1,2,L +1,3,1,3,B +1,3,1,4,R +1,3,1,5,R +1,3,2,1,L +1,3,2,2,R +1,3,2,3,R +1,3,2,4,R +1,3,2,5,R +1,3,3,1,B +1,3,3,2,R +1,3,3,3,R +1,3,3,4,R 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Title: Balance Scale Weight & Distance Database + +2. Source Information: + (a) Source: Generated to model psychological experiments reported + by Siegler, R. S. (1976). Three Aspects of Cognitive + Development. Cognitive Psychology, 8, 481-520. + (b) Donor: Tim Hume (hume@ics.uci.edu) + (c) Date: 22 April 1994 + +3. Past Usage: (possibly different formats of this data) + - Publications + 1. Klahr, D., & Siegler, R.S. (1978). The Representation of + Children's Knowledge. In H. W. Reese & L. P. Lipsitt (Eds.), + Advances in Child Development and Behavior, pp. 61-116. New + York: Academic Press + 2. Langley,P. (1987). A General Theory of Discrimination + Learning. In D. Klahr, P. Langley, & R. Neches (Eds.), + Production System Models of Learning and Development, pp. + 99-161. Cambridge, MA: MIT Press + 3. Newell, A. (1990). Unified Theories of Cognition. + Cambridge, MA: Harvard University Press + 4. McClelland, J.L. (1988). Parallel Distibuted Processing: + Implications for Cognition and Development. Technical + Report AIP-47, Department of Psychology, Carnegie-Mellon + University + 5. Shultz, T., Mareschal, D., & Schmidt, W. (1994). Modeling + Cognitive Development on Balance Scale Phenomena. Machine + Learning, Vol. 16, pp. 59-88. + +4. Relevant Information: + This data set was generated to model psychological + experimental results. Each example is classified as having the + balance scale tip to the right, tip to the left, or be + balanced. The attributes are the left weight, the left + distance, the right weight, and the right distance. The + correct way to find the class is the greater of + (left-distance * left-weight) and (right-distance * + right-weight). If they are equal, it is balanced. + +5. Number of Instances: 625 (49 balanced, 288 left, 288 right) + +6. Number of Attributes: 4 (numeric) + class name = 5 + +7. Attribute Information: + 1. Class Name: 3 (L, B, R) + 2. Left-Weight: 5 (1, 2, 3, 4, 5) + 3. Left-Distance: 5 (1, 2, 3, 4, 5) + 4. Right-Weight: 5 (1, 2, 3, 4, 5) + 5. Right-Distance: 5 (1, 2, 3, 4, 5) + +8. Missing Attribute Values: + none + +9. Class Distribution: + 1. 46.08 percent are L + 2. 07.84 percent are B + 3. 46.08 percent are R + ARFF + 2014-04-06T23:19:55 + Public https://test.openml.org/data/v1/download/4/balance-scale.arff + 4 class 1 study_14 public active + 2019-07-09 12:42:06 76938608d472f620c170cef9c8c1fa65 + diff --git a/examples/'cache'/org/openml/test/datasets/4/features.xml b/examples/'cache'/org/openml/test/datasets/4/features.xml new file mode 100644 index 000000000..b06b51e4f --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/4/features.xml @@ -0,0 +1,50 @@ + + + 0 + left-weight + numeric + false + false + false + 0 + + + 1 + left-distance + numeric + false + false + false + 0 + + + 2 + right-weight + numeric + false + false + false + 0 + + + 3 + right-distance + numeric + false + false + false + 0 + + + 4 + class + nominal + L + B + R + true + false + false + 0 + + diff --git a/examples/'cache'/org/openml/test/datasets/4/qualities.xml b/examples/'cache'/org/openml/test/datasets/4/qualities.xml new file mode 100644 index 000000000..136e5ab85 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/4/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.6971153846153846 + + + + CfsSubsetEval_DecisionStumpAUC + 0.8406824596109461 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.1984 + + + + CfsSubsetEval_DecisionStumpKappa + 0.6373963655418936 + + + + CfsSubsetEval_NaiveBayesAUC + 0.8406824596109461 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.1984 + + + + CfsSubsetEval_NaiveBayesKappa + 0.6373963655418936 + + + + CfsSubsetEval_kNN1NAUC + 0.8406824596109461 + + + + CfsSubsetEval_kNN1NErrRate + 0.1984 + + + + CfsSubsetEval_kNN1NKappa + 0.6373963655418936 + + + + ClassEntropy + 1.318116246441582 + + + + DecisionStumpAUC + 0.6469194362017804 + + + + DecisionStumpErrRate + 0.376 + + + + DecisionStumpKappa + 0.3026706231454006 + + + + Dimensionality + 0.008 + + + + EquivalentNumberOfAtts + + + + + J48.00001.AUC + 0.8406824596109461 + + + + J48.00001.ErrRate + 0.1984 + + + + J48.00001.Kappa + 0.6373963655418936 + + + + J48.0001.AUC + 0.8406824596109461 + + + + J48.0001.ErrRate + 0.1984 + + + + J48.0001.Kappa + 0.6373963655418936 + + + + J48.001.AUC + 0.8406824596109461 + + + + J48.001.ErrRate + 0.1984 + + + + J48.001.Kappa + 0.6373963655418936 + + + + MajorityClassPercentage + 46.08 + + + + MajorityClassSize + 288.0 + + + + MaxAttributeEntropy + + + + + MaxKurtosisOfNumericAtts + -1.300798439249971 + + + + MaxMeansOfNumericAtts + 3.0 + + + + MaxMutualInformation + + + + + MaxNominalAttDistinctValues + 3.0 + + + + MaxSkewnessOfNumericAtts + 0.0 + + + + MaxStdDevOfNumericAtts + 1.4153462926807447 + + + + MeanAttributeEntropy + + + + + MeanKurtosisOfNumericAtts + -1.3007984392499752 + + + + MeanMeansOfNumericAtts + 3.0 + + + + MeanMutualInformation + + + + + MeanNoiseToSignalRatio + + + + + MeanNominalAttDistinctValues + 3.0 + + + + MeanSkewnessOfNumericAtts + 0.0 + + + + MeanStdDevOfNumericAtts + 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100644 index 000000000..8498a7bf9 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/68/dataset.arff @@ -0,0 +1,15017 @@ +%% Source: +%% +%% Oliver Roesler, it12148 '@' lehre.dhbw-stuttgart.de , Baden-Wuerttemberg +%% Cooperative State University (DHBW), Stuttgart, Germany +%% +%% Data Set Information: +%% +%% All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. +%% The duration of the measurement was 117 seconds. The eye state was detected via a +%% camera during the EEG measurement and added later manually to the file after +%% analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. +%% All values are in chronological order with the first measured value at the top of +%% the data. +%% +%% Attribute Information: +%% +%% Provide information about each attribute in your data set. + +@relation eeg-eye-state + +@attribute V1 numeric +@attribute V2 numeric +@attribute V3 numeric +@attribute V4 numeric +@attribute V5 numeric +@attribute V6 numeric +@attribute V7 numeric +@attribute V8 numeric +@attribute V9 numeric +@attribute V10 numeric +@attribute V11 numeric +@attribute V12 numeric +@attribute V13 numeric +@attribute V14 numeric +@attribute Class {1,2} + +@data +4329.23,4009.23,4289.23,4148.21,4350.26,4586.15,4096.92,4641.03,4222.05,4238.46,4211.28,4280.51,4635.9,4393.85,1 +4324.62,4004.62,4293.85,4148.72,4342.05,4586.67,4097.44,4638.97,4210.77,4226.67,4207.69,4279.49,4632.82,4384.1,1 +4327.69,4006.67,4295.38,4156.41,4336.92,4583.59,4096.92,4630.26,4207.69,4222.05,4206.67,4282.05,4628.72,4389.23,1 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b/examples/'cache'/org/openml/test/datasets/68/description.xml new file mode 100644 index 000000000..c2a2a1fcb --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/68/description.xml @@ -0,0 +1,16 @@ + + 68 + eeg-eye-state + 1 + **Author**: Oliver Roesler, it12148'@'lehre.dhbw-stuttgart.de +**Source**: [UCI](https://archive.ics.uci.edu/ml/datasets/EEG+Eye+State), Baden-Wuerttemberg, Cooperative State University (DHBW), Stuttgart, Germany +**Please cite**: + +All data is from one continuous EEG measurement with the Emotiv EEG Neuroheadset. The duration of the measurement was 117 seconds. The eye state was detected via a camera during the EEG measurement and added later manually to the file after analysing the video frames. '1' indicates the eye-closed and '0' the eye-open state. All values are in chronological order with the first measured value at the top of the data. + + ARFF + 2015-05-22T16:40:04 + Public https://test.openml.org/data/v1/download/68/eeg-eye-state.arff + 68 Class study_14 public active + 2019-07-09 13:34:59 32086b7bec4daaa9cbe5f19efa63368c + diff --git a/examples/'cache'/org/openml/test/datasets/68/features.xml b/examples/'cache'/org/openml/test/datasets/68/features.xml new file mode 100644 index 000000000..a22a63a50 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/68/features.xml @@ -0,0 +1,139 @@ + + + 0 + V1 + numeric + false + false + false + 0 + + + 1 + V2 + numeric + false + false + false + 0 + + + 2 + V3 + numeric + false + false + false + 0 + + + 3 + V4 + numeric + false + false + false + 0 + + + 4 + V5 + numeric + false + false + false + 0 + + + 5 + V6 + numeric + false + false + false + 0 + + + 6 + V7 + numeric + false + false + false + 0 + + + 7 + V8 + numeric + false + false + false + 0 + + + 8 + V9 + numeric + false + false + false + 0 + + + 9 + V10 + numeric + false + false + false + 0 + + + 10 + V11 + numeric + false + false + false + 0 + + + 11 + V12 + numeric + false + false + false + 0 + + + 12 + V13 + numeric + false + false + false + 0 + + + 13 + V14 + numeric + false + false + false + 0 + + + 14 + Class + nominal + 1 + 2 + true + false + false + 0 + + diff --git a/examples/'cache'/org/openml/test/datasets/68/qualities.xml b/examples/'cache'/org/openml/test/datasets/68/qualities.xml new file mode 100644 index 000000000..38a4fc8e0 --- /dev/null +++ b/examples/'cache'/org/openml/test/datasets/68/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.9984645169904534 + + + + CfsSubsetEval_DecisionStumpAUC + 0.815562880843502 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.22923898531375167 + + + + CfsSubsetEval_DecisionStumpKappa + 0.5343988170412974 + + + + CfsSubsetEval_NaiveBayesAUC + 0.815562880843502 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.22923898531375167 + + + + CfsSubsetEval_NaiveBayesKappa + 0.5343988170412974 + + + + CfsSubsetEval_kNN1NAUC + 0.815562880843502 + + + + CfsSubsetEval_kNN1NErrRate + 0.22923898531375167 + + + + CfsSubsetEval_kNN1NKappa + 0.5343988170412974 + + + + ClassEntropy + 0.992422373387609 + + + + DecisionStumpAUC + 0.5730896889672722 + + + + DecisionStumpErrRate + 0.41141522029372496 + + + + DecisionStumpKappa + 0.12923993775163797 + + + + Dimensionality + 0.0010013351134846463 + + + + EquivalentNumberOfAtts + + + + + J48.00001.AUC + 0.8271933877278838 + + + + J48.00001.ErrRate + 0.1863818424566088 + + + + J48.00001.Kappa + 0.6230255040333611 + + + + J48.0001.AUC + 0.8271933877278838 + + + + J48.0001.ErrRate + 0.1863818424566088 + + + + J48.0001.Kappa + 0.6230255040333611 + + + + J48.001.AUC + 0.8271933877278838 + + + + J48.001.ErrRate + 0.1863818424566088 + + + + J48.001.Kappa + 0.6230255040333611 + + + + MajorityClassPercentage + 55.12016021361815 + + + + MajorityClassSize + 8257.0 + + + + MaxAttributeEntropy + + + + + MaxKurtosisOfNumericAtts + 14979.178735371073 + + + + MaxMeansOfNumericAtts + 4644.02237917223 + + + + MaxMutualInformation + + + + + MaxNominalAttDistinctValues + 2.0 + + + + MaxSkewnessOfNumericAtts + 122.38777688436316 + + + + MaxStdDevOfNumericAtts + 5891.2850425236575 + + + + MeanAttributeEntropy + + + + + MeanKurtosisOfNumericAtts + 8904.72190163755 + + + + MeanMeansOfNumericAtts + 4316.882028546157 + + + + MeanMutualInformation + + + + + MeanNoiseToSignalRatio + + + + + MeanNominalAttDistinctValues + 2.0 + + + + MeanSkewnessOfNumericAtts + 71.73726620900297 + + + + MeanStdDevOfNumericAtts + 1767.2884824189532 + + + + MinAttributeEntropy + + + + + MinKurtosisOfNumericAtts + 2056.5210594418404 + + + + MinMeansOfNumericAtts + 4009.767693591455 + + + + MinMutualInformation + + + + + MinNominalAttDistinctValues + 2.0 + + + + MinSkewnessOfNumericAtts + -13.615160740498586 + + + + MinStdDevOfNumericAtts + 29.292603201776053 + + + + MinorityClassPercentage + 44.879839786381844 + + + + MinorityClassSize + 6723.0 + + + + NaiveBayesAUC + 0.5441157198060068 + + + + NaiveBayesErrRate + 0.4634846461949266 + + + + NaiveBayesKappa + 0.11130397334752887 + + + + NumberOfBinaryFeatures + 1.0 + + + + NumberOfClasses + 2.0 + + + + NumberOfFeatures + 15.0 + + + + NumberOfInstances + 14980.0 + + + + NumberOfInstancesWithMissingValues + 0.0 + + + + NumberOfMissingValues + 0.0 + + + + NumberOfNumericFeatures + 14.0 + + + + NumberOfSymbolicFeatures + 1.0 + + + + PercentageOfBinaryFeatures + 6.666666666666667 + + + + PercentageOfInstancesWithMissingValues + 0.0 + + + + PercentageOfMissingValues + 0.0 + + + + PercentageOfNumericFeatures + 93.33333333333333 + + + + PercentageOfSymbolicFeatures + 6.666666666666667 + + + + Quartile1AttributeEntropy + + + + + Quartile1KurtosisOfNumericAtts + 2713.5598368066608 + + + + Quartile1MeansOfNumericAtts + 4193.079256508679 + + + + Quartile1MutualInformation + + + + + Quartile1SkewnessOfNumericAtts + 22.475026893669664 + + + + Quartile1StdDevOfNumericAtts + 37.98467230942155 + + + + Quartile2AttributeEntropy + + + + + Quartile2KurtosisOfNumericAtts + 9352.695219763142 + + + + Quartile2MeansOfNumericAtts + 4271.6276034712955 + + + + Quartile2MutualInformation + + + + + Quartile2SkewnessOfNumericAtts + 84.61113200877784 + + + + Quartile2StdDevOfNumericAtts + 627.1558153704191 + + + + Quartile3AttributeEntropy + + + + + Quartile3KurtosisOfNumericAtts + 14971.654606997874 + + + + Quartile3MeansOfNumericAtts + 4466.12820822263 + + + + Quartile3MutualInformation + + + + + Quartile3SkewnessOfNumericAtts + 122.34170603745707 + + + + Quartile3StdDevOfNumericAtts + 3343.823788627256 + + + + REPTreeDepth1AUC + 0.8585707193015194 + + + + REPTreeDepth1ErrRate + 0.19072096128170896 + + + + REPTreeDepth1Kappa + 0.6126693923072585 + + + + REPTreeDepth2AUC + 0.8585707193015194 + + + + REPTreeDepth2ErrRate + 0.19072096128170896 + + + + REPTreeDepth2Kappa + 0.6126693923072585 + + + + REPTreeDepth3AUC + 0.8585707193015194 + + + + REPTreeDepth3ErrRate + 0.19072096128170896 + + + + REPTreeDepth3Kappa + 0.6126693923072585 + + + + RandomTreeDepth1AUC + 0.8092130970110125 + + + + RandomTreeDepth1ErrRate + 0.18845126835781043 + + + + RandomTreeDepth1Kappa + 0.6188451689674707 + + + + RandomTreeDepth2AUC + 0.8092130970110125 + + + + RandomTreeDepth2ErrRate + 0.18845126835781043 + + + + RandomTreeDepth2Kappa + 0.6188451689674707 + + + + RandomTreeDepth3AUC + 0.8092130970110125 + + + + RandomTreeDepth3ErrRate + 0.18845126835781043 + + + + RandomTreeDepth3Kappa + 0.6188451689674707 + + + + StdvNominalAttDistinctValues + 0.0 + + + + kNN1NAUC + 0.8331954978013598 + + + + kNN1NErrRate + 0.16355140186915887 + + + + kNN1NKappa + 0.6684228094245078 + + diff --git a/examples/'cache'/org/openml/test/flows/12211/flow.xml b/examples/'cache'/org/openml/test/flows/12211/flow.xml new file mode 100644 index 000000000..0a7e4e4b6 --- /dev/null +++ b/examples/'cache'/org/openml/test/flows/12211/flow.xml @@ -0,0 +1,222 @@ + + + 12211 +1159 +sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) +sklearn.pipeline.Pipeline +1 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:25 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + memory + + null + + + + steps + + [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}] + + + + verbose + + false + + + + simpleimputer + + + 12212 +1159 +sklearn.impute._base.SimpleImputer +sklearn.impute._base.SimpleImputer +1 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:25 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + add_indicator + + false + + + + copy + + true + + + + fill_value + + null + + + + missing_values + + NaN + + + + strategy + + "median" + + + + verbose + + 0 + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + + + + randomforestclassifier + + + 12213 +1159 +sklearn.ensemble.forest.RandomForestClassifier +sklearn.ensemble.forest.RandomForestClassifier +5 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:25 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + bootstrap + + true + + + + class_weight + + null + + + + criterion + + "entropy" + + + + max_depth + + null + + + + max_features + + 0.2 + + + + max_leaf_nodes + + null + + + + min_impurity_decrease + + 0.0 + + + + min_impurity_split + + null + + + + min_samples_leaf + + 1 + + + + min_samples_split + + 2 + + + + min_weight_fraction_leaf + + 0.0 + + + + n_estimators + + 16 + + + + n_jobs + + null + + + + oob_score + + false + + + + random_state + + 42 + + + + verbose + + 0 + + + + warm_start + + false + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + diff --git a/examples/'cache'/org/openml/test/flows/6671/flow.xml b/examples/'cache'/org/openml/test/flows/6671/flow.xml new file mode 100644 index 000000000..f1f0d8eb5 --- /dev/null +++ b/examples/'cache'/org/openml/test/flows/6671/flow.xml @@ -0,0 +1,68 @@ + + + 6671 +3229 +sklearn.neighbors.classification.KNeighborsClassifier +sklearn.neighbors.classification.KNeighborsClassifier +2 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-11T20:06:10 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + algorithm + + "auto" + + + + leaf_size + + 30 + + + + metric + + "minkowski" + + + + metric_params + + null + + + + n_jobs + + null + + + + n_neighbors + + 5 + + + + p + + 2 + + + + weights + + "uniform" + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + diff --git a/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:1 b/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:1 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:17 b/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:17 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:4 b/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:4 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:68 b/examples/'cache'/org/openml/test/locks/datasets.functions.get_dataset:68 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/flows.functions.get_flow:12211 b/examples/'cache'/org/openml/test/locks/flows.functions.get_flow:12211 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/flows.functions.get_flow:6671 b/examples/'cache'/org/openml/test/locks/flows.functions.get_flow:6671 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/runs.functions.get_run:4053 b/examples/'cache'/org/openml/test/locks/runs.functions.get_run:4053 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:1 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:1 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:19 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:19 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:2 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:2 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:403 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:403 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:6 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:6 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:97 b/examples/'cache'/org/openml/test/locks/tasks.functions.get_task:97 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/'cache'/org/openml/test/runs/4053/description.xml b/examples/'cache'/org/openml/test/runs/4053/description.xml new file mode 100644 index 000000000..79f3434a5 --- /dev/null +++ b/examples/'cache'/org/openml/test/runs/4053/description.xml @@ -0,0 +1,164 @@ + + 4053 + 1159 + Continuous Integration + 6 + Supervised Classification + 12211 + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1) + 3482 + + + memory + null + 12211 + + + steps + [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}] + 12211 + + + verbose + false + 12211 + + + add_indicator + false + 12212 + + + copy + true + 12212 + + + fill_value + null + 12212 + + + missing_values + NaN + 12212 + + + strategy + "median" + 12212 + + + verbose + 0 + 12212 + + + bootstrap + true + 12213 + + + class_weight + null + 12213 + + + criterion + "entropy" + 12213 + + + max_depth + null + 12213 + + + max_features + 0.2 + 12213 + + + max_leaf_nodes + null + 12213 + + + min_impurity_decrease + 0.0 + 12213 + + + min_impurity_split + null + 12213 + + + min_samples_leaf + 1 + 12213 + + + min_samples_split + 2 + 12213 + + + min_weight_fraction_leaf + 0.0 + 12213 + + + n_estimators + 16 + 12213 + + + n_jobs + null + 12213 + + + oob_score + false + 12213 + + + random_state + 42 + 12213 + + + verbose + 0 + 12213 + + + warm_start + false + 12213 + + openml-python + Sklearn_0.21.2. + + + 1 + anneal + https://www.openml.org/data/download/1666876/phpFsFYVN + + + + + -1 + 11978 + description + https://test.openml.org/data/download/11978/description.xml + + + -1 + 11979 + predictions + https://test.openml.org/data/download/11979/predictions.arff + + + diff --git a/examples/'cache'/org/openml/test/setups/3482/description.xml b/examples/'cache'/org/openml/test/setups/3482/description.xml new file mode 100644 index 000000000..9b4f37324 --- /dev/null +++ b/examples/'cache'/org/openml/test/setups/3482/description.xml @@ -0,0 +1,291 @@ + + 3482 + 12211 + + 83479 + 12211 + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_memory + memory + memory + + null + null + + + 83480 + 12211 + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_steps + steps + steps + + [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}] + [{"oml-python:serialized_object": "component_reference", "value": {"key": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}] + + + 83481 + 12211 + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier) + sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)(1)_verbose + verbose + verbose + + false + false + + + 83482 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_add_indicator + add_indicator + add_indicator + + false + false + + + 83483 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_copy + copy + copy + + true + true + + + 83484 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_fill_value + fill_value + fill_value + + null + null + + + 83485 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_missing_values + missing_values + missing_values + + NaN + NaN + + + 83486 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_strategy + strategy + strategy + + "median" + "median" + + + 83487 + 12212 + sklearn.impute._base.SimpleImputer + sklearn.impute._base.SimpleImputer(1)_verbose + verbose + verbose + + 0 + 0 + + + 83488 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_bootstrap + bootstrap + bootstrap + + true + true + + + 83489 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_class_weight + class_weight + class_weight + + null + null + + + 83490 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_criterion + criterion + criterion + + "entropy" + "entropy" + + + 83491 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_max_depth + max_depth + max_depth + + null + null + + + 83492 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_max_features + max_features + max_features + + 0.2 + 0.2 + + + 83493 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_max_leaf_nodes + max_leaf_nodes + max_leaf_nodes + + null + null + + + 83494 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_min_impurity_decrease + min_impurity_decrease + min_impurity_decrease + + 0.0 + 0.0 + + + 83495 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_min_impurity_split + min_impurity_split + min_impurity_split + + null + null + + + 83496 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_min_samples_leaf + min_samples_leaf + min_samples_leaf + + 1 + 1 + + + 83497 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_min_samples_split + min_samples_split + min_samples_split + + 2 + 2 + + + 83498 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_min_weight_fraction_leaf + min_weight_fraction_leaf + min_weight_fraction_leaf + + 0.0 + 0.0 + + + 83499 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_n_estimators + n_estimators + n_estimators + + 16 + 16 + + + 83500 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_n_jobs + n_jobs + n_jobs + + null + null + + + 83501 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_oob_score + oob_score + oob_score + + false + false + + + 83502 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_random_state + random_state + random_state + + 42 + 42 + + + 83503 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_verbose + verbose + verbose + + 0 + 0 + + + 83504 + 12213 + sklearn.ensemble.forest.RandomForestClassifier + sklearn.ensemble.forest.RandomForestClassifier(5)_warm_start + warm_start + warm_start + + false + false + + + diff --git a/examples/'cache'/org/openml/test/tasks/1/datasplits.arff b/examples/'cache'/org/openml/test/tasks/1/datasplits.arff new file mode 100644 index 000000000..4dce0be17 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/1/datasplits.arff @@ -0,0 +1,8988 @@ +@relation anneal_splits + +@attribute type 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b/examples/'cache'/org/openml/test/tasks/1/task.xml new file mode 100644 index 000000000..58d7534d8 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/1/task.xml @@ -0,0 +1,37 @@ + + 1 + Task 1: anneal (Supervised Classification) + 1 + Supervised Classification + + +1 +class + + + +1 +crossvalidation +https://test.openml.org/api_splits/get/1/Task_1_splits.arff +1 +10 + +true + + + + + + + + + +ARFF + + + + + + + OpenML100 + diff --git a/examples/'cache'/org/openml/test/tasks/19/datasplits.arff b/examples/'cache'/org/openml/test/tasks/19/datasplits.arff new file mode 100644 index 000000000..1c8684c98 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/19/datasplits.arff @@ -0,0 +1,6258 @@ +@relation balance-scale_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,535,0,0 +TRAIN,452,0,0 +TRAIN,227,0,0 +TRAIN,300,0,0 +TRAIN,358,0,0 +TRAIN,592,0,0 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+https://test.openml.org/api_splits/get/19/Task_19_splits.arff +1 +10 + +true + + + + + + + + + +ARFF + + + + + + + OpenML100 + diff --git a/examples/'cache'/org/openml/test/tasks/2/datasplits.arff b/examples/'cache'/org/openml/test/tasks/2/datasplits.arff new file mode 100644 index 000000000..4c53dc9db --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/2/datasplits.arff @@ -0,0 +1,8988 @@ +@relation anneal_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,16,0,0 +TRAIN,897,0,0 +TRAIN,180,0,0 +TRAIN,366,0,0 +TRAIN,497,0,0 +TRAIN,371,0,0 +TRAIN,506,0,0 +TRAIN,189,0,0 +TRAIN,868,0,0 +TRAIN,90,0,0 +TRAIN,527,0,0 +TRAIN,33,0,0 +TRAIN,385,0,0 +TRAIN,157,0,0 +TRAIN,17,0,0 +TRAIN,800,0,0 +TRAIN,896,0,0 +TRAIN,615,0,0 +TRAIN,870,0,0 +TRAIN,836,0,0 +TRAIN,612,0,0 +TRAIN,850,0,0 +TRAIN,68,0,0 +TRAIN,663,0,0 +TRAIN,606,0,0 +TRAIN,790,0,0 +TRAIN,710,0,0 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+https://test.openml.org/api_splits/get/2/Task_2_splits.arff +5 +2 + +true + + + + + + + + + +ARFF + + + + + + + diff --git a/examples/'cache'/org/openml/test/tasks/403/datasplits.arff b/examples/'cache'/org/openml/test/tasks/403/datasplits.arff new file mode 100644 index 000000000..731f5ec3a --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/403/datasplits.arff @@ -0,0 +1,149808 @@ +@relation eeg-eye-state_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,12101,0,0 +TRAIN,14753,0,0 +TRAIN,10219,0,0 +TRAIN,2974,0,0 +TRAIN,10814,0,0 +TRAIN,13339,0,0 +TRAIN,3307,0,0 +TRAIN,12700,0,0 +TRAIN,4813,0,0 +TRAIN,10736,0,0 +TRAIN,12926,0,0 +TRAIN,9898,0,0 +TRAIN,1991,0,0 +TRAIN,6546,0,0 +TRAIN,14601,0,0 +TRAIN,9780,0,0 +TRAIN,13304,0,0 +TRAIN,6270,0,0 +TRAIN,9539,0,0 +TRAIN,2089,0,0 +TRAIN,13212,0,0 +TRAIN,4670,0,0 +TRAIN,13216,0,0 +TRAIN,12470,0,0 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b/examples/'cache'/org/openml/test/tasks/403/task.xml new file mode 100644 index 000000000..913025b45 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/403/task.xml @@ -0,0 +1,37 @@ + + 403 + Task 403: eeg-eye-state (Supervised Classification) + 1 + Supervised Classification + + +68 +Class + + + +1 +crossvalidation +https://test.openml.org/api_splits/get/403/Task_403_splits.arff +1 +10 + +true + + + + + + + + + +ARFF + + + + + + + OpenML100 + diff --git a/examples/'cache'/org/openml/test/tasks/6/datasplits.arff b/examples/'cache'/org/openml/test/tasks/6/datasplits.arff new file mode 100644 index 000000000..734b30665 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/6/datasplits.arff @@ -0,0 +1,906 @@ +@relation anneal_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TEST,412,0,0 +TEST,461,0,0 +TEST,559,0,0 +TEST,876,0,0 +TEST,710,0,0 +TEST,306,0,0 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a/examples/'cache'/org/openml/test/tasks/97/datasplits.arff b/examples/'cache'/org/openml/test/tasks/97/datasplits.arff new file mode 100644 index 000000000..3473c5486 --- /dev/null +++ b/examples/'cache'/org/openml/test/tasks/97/datasplits.arff @@ -0,0 +1,10008 @@ +@relation credit-g_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,539,0,0 +TRAIN,577,0,0 +TRAIN,619,0,0 +TRAIN,937,0,0 +TRAIN,75,0,0 +TRAIN,608,0,0 +TRAIN,142,0,0 +TRAIN,641,0,0 +TRAIN,336,0,0 +TRAIN,893,0,0 +TRAIN,60,0,0 +TRAIN,96,0,0 +TRAIN,165,0,0 +TRAIN,709,0,0 +TRAIN,66,0,0 +TRAIN,737,0,0 +TRAIN,260,0,0 +TRAIN,803,0,0 +TRAIN,393,0,0 +TRAIN,348,0,0 +TRAIN,891,0,0 +TRAIN,690,0,0 +TRAIN,841,0,0 +TRAIN,527,0,0 +TRAIN,254,0,0 +TRAIN,676,0,0 +TRAIN,329,0,0 +TRAIN,373,0,0 +TRAIN,20,0,0 +TRAIN,343,0,0 +TRAIN,781,0,0 +TRAIN,550,0,0 +TRAIN,791,0,0 +TRAIN,221,0,0 +TRAIN,185,0,0 +TRAIN,743,0,0 +TRAIN,785,0,0 +TRAIN,340,0,0 +TRAIN,196,0,0 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b/examples/sklearn/'cache'/org/openml/test/datasets/17/dataset.arff new file mode 100644 index 000000000..a3347a36e --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/datasets/17/dataset.arff @@ -0,0 +1,1301 @@ +% Description of the German credit dataset. +% +% 1. Title: German Credit data +% +% 2. Source Information +% +% Professor Dr. Hans Hofmann +% Institut f"ur Statistik und "Okonometrie +% Universit"at Hamburg +% FB Wirtschaftswissenschaften +% Von-Melle-Park 5 +% 2000 Hamburg 13 +% +% 3. Number of Instances: 1000 +% +% Two datasets are provided. the original dataset, in the form provided +% by Prof. Hofmann, contains categorical/symbolic attributes and +% is in the file "german.data". +% +% For algorithms that need numerical attributes, Strathclyde University +% produced the file "german.data-numeric". This file has been edited +% and several indicator variables added to make it suitable for +% algorithms which cannot cope with categorical variables. Several +% attributes that are ordered categorical (such as attribute 17) have +% been coded as integer. This was the form used by StatLog. +% +% +% 6. Number of Attributes german: 20 (7 numerical, 13 categorical) +% Number of Attributes german.numer: 24 (24 numerical) +% +% +% 7. Attribute description for german +% +% Attribute 1: (qualitative) +% Status of existing checking account +% A11 : ... < 0 DM +% A12 : 0 <= ... < 200 DM +% A13 : ... >= 200 DM / +% salary assignments for at least 1 year +% A14 : no checking account +% +% Attribute 2: (numerical) +% Duration in month +% +% Attribute 3: (qualitative) +% Credit history +% A30 : no credits taken/ +% all credits paid back duly +% A31 : all credits at this bank paid back duly +% A32 : existing credits paid back duly till now +% A33 : delay in paying off in the past +% A34 : critical account/ +% other credits existing (not at this bank) +% +% Attribute 4: (qualitative) +% Purpose +% A40 : car (new) +% A41 : car (used) +% A42 : furniture/equipment +% A43 : radio/television +% A44 : domestic appliances +% A45 : repairs +% A46 : education +% A47 : (vacation - does not exist?) +% A48 : retraining +% A49 : business +% A410 : others +% +% Attribute 5: (numerical) +% Credit amount +% +% Attibute 6: (qualitative) +% Savings account/bonds +% A61 : ... < 100 DM +% A62 : 100 <= ... < 500 DM +% A63 : 500 <= ... < 1000 DM +% A64 : .. >= 1000 DM +% A65 : unknown/ no savings account +% +% Attribute 7: (qualitative) +% Present employment since +% A71 : unemployed +% A72 : ... < 1 year +% A73 : 1 <= ... < 4 years +% A74 : 4 <= ... < 7 years +% A75 : .. >= 7 years +% +% Attribute 8: (numerical) +% Installment rate in percentage of disposable income +% +% Attribute 9: (qualitative) +% Personal status and sex +% A91 : male : divorced/separated +% A92 : female : divorced/separated/married +% A93 : male : single +% A94 : male : married/widowed +% A95 : female : single +% +% Attribute 10: (qualitative) +% Other debtors / guarantors +% A101 : none +% A102 : co-applicant +% A103 : guarantor +% +% Attribute 11: (numerical) +% Present residence since +% +% Attribute 12: (qualitative) +% Property +% A121 : real estate +% A122 : if not A121 : building society savings agreement/ +% life insurance +% A123 : if not A121/A122 : car or other, not in attribute 6 +% A124 : unknown / no property +% +% Attribute 13: (numerical) +% Age in years +% +% Attribute 14: (qualitative) +% Other installment plans +% A141 : bank +% A142 : stores +% A143 : none +% +% Attribute 15: (qualitative) +% Housing +% A151 : rent +% A152 : own +% A153 : for free +% +% Attribute 16: (numerical) +% Number of existing credits at this bank +% +% Attribute 17: (qualitative) +% Job +% A171 : unemployed/ unskilled - non-resident +% A172 : unskilled - resident +% A173 : skilled employee / official +% A174 : management/ self-employed/ +% highly qualified employee/ officer +% +% Attribute 18: (numerical) +% Number of people being liable to provide maintenance for +% +% Attribute 19: (qualitative) +% Telephone +% A191 : none +% A192 : yes, registered under the customers name +% +% Attribute 20: (qualitative) +% foreign worker +% A201 : yes +% A202 : no +% +% +% +% 8. Cost Matrix +% +% This dataset requires use of a cost matrix (see below) +% +% +% 1 2 +% ---------------------------- +% 1 0 1 +% ----------------------- +% 2 5 0 +% +% (1 = Good, 2 = Bad) +% +% the rows represent the actual classification and the columns +% the predicted classification. +% +% It is worse to class a customer as good when they are bad (5), +% than it is to class a customer as bad when they are good (1). +% +% +% +% +% +% Relabeled values in attribute checking_status +% From: A11 To: '<0' +% From: A12 To: '0<=X<200' +% From: A13 To: '>=200' +% From: A14 To: 'no checking' +% +% +% Relabeled values in attribute credit_history +% From: A30 To: 'no credits/all paid' +% From: A31 To: 'all paid' +% From: A32 To: 'existing paid' +% From: A33 To: 'delayed previously' +% From: A34 To: 'critical/other existing credit' +% +% +% Relabeled values in attribute purpose +% From: A40 To: 'new car' +% From: A41 To: 'used car' +% From: A42 To: furniture/equipment +% From: A43 To: radio/tv +% From: A44 To: 'domestic appliance' +% From: A45 To: repairs +% From: A46 To: education +% From: A47 To: vacation +% From: A48 To: retraining +% From: A49 To: business +% From: A410 To: other +% +% +% Relabeled values in attribute savings_status +% From: A61 To: '<100' +% From: A62 To: '100<=X<500' +% From: A63 To: '500<=X<1000' +% From: A64 To: '>=1000' +% From: A65 To: 'no known savings' +% +% +% Relabeled values in attribute employment +% From: A71 To: unemployed +% From: A72 To: '<1' +% From: A73 To: '1<=X<4' +% From: A74 To: '4<=X<7' +% From: A75 To: '>=7' +% +% +% Relabeled values in attribute personal_status +% From: A91 To: 'male div/sep' +% From: A92 To: 'female div/dep/mar' +% From: A93 To: 'male single' +% From: A94 To: 'male mar/wid' +% From: A95 To: 'female single' +% +% +% Relabeled values in attribute other_parties +% From: A101 To: none +% From: A102 To: 'co applicant' +% From: A103 To: guarantor +% +% +% Relabeled values in attribute property_magnitude +% From: A121 To: 'real estate' +% From: A122 To: 'life insurance' +% From: A123 To: car +% From: A124 To: 'no known property' +% +% +% Relabeled values in attribute other_payment_plans +% From: A141 To: bank +% From: A142 To: stores +% From: A143 To: none +% +% +% Relabeled values in attribute housing +% From: A151 To: rent +% From: A152 To: own +% From: A153 To: 'for free' +% +% +% Relabeled values in attribute job +% From: A171 To: 'unemp/unskilled non res' +% From: A172 To: 'unskilled resident' +% From: A173 To: skilled +% From: A174 To: 'high qualif/self emp/mgmt' +% +% +% Relabeled values in attribute own_telephone +% From: A191 To: none +% From: A192 To: yes +% +% +% Relabeled values in attribute foreign_worker +% From: A201 To: yes +% From: A202 To: no +% +% +% Relabeled values in attribute class +% From: 1 To: good +% From: 2 To: bad +% +@relation german_credit +@attribute 'checking_status' { '<0', '0<=X<200', '>=200', 'no checking'} +@attribute 'duration' real +@attribute 'credit_history' { 'no credits/all paid', 'all paid', 'existing paid', 'delayed previously', 'critical/other existing credit'} +@attribute 'purpose' { 'new car', 'used car', furniture/equipment, radio/tv, 'domestic appliance', repairs, education, vacation, retraining, business, other} +@attribute 'credit_amount' real +@attribute 'savings_status' { '<100', '100<=X<500', '500<=X<1000', '>=1000', 'no known savings'} +@attribute 'employment' { unemployed, '<1', '1<=X<4', '4<=X<7', '>=7'} +@attribute 'installment_commitment' real +@attribute 'personal_status' { 'male div/sep', 'female div/dep/mar', 'male single', 'male mar/wid', 'female single'} +@attribute 'other_parties' { none, 'co applicant', guarantor} +@attribute 'residence_since' real +@attribute 'property_magnitude' { 'real estate', 'life insurance', car, 'no known property'} +@attribute 'age' real +@attribute 'other_payment_plans' { bank, stores, none} +@attribute 'housing' { rent, own, 'for free'} +@attribute 'existing_credits' real +@attribute 'job' { 'unemp/unskilled non res', 'unskilled resident', skilled, 'high qualif/self emp/mgmt'} +@attribute 'num_dependents' real +@attribute 'own_telephone' { none, yes} +@attribute 'foreign_worker' { yes, no} +@attribute 'class' { good, bad} +@data +'<0',6,'critical/other existing credit',radio/tv,1169,'no known savings','>=7',4,'male single',none,4,'real estate',67,none,own,2,skilled,1,yes,yes,good +'0<=X<200',48,'existing paid',radio/tv,5951,'<100','1<=X<4',2,'female div/dep/mar',none,2,'real estate',22,none,own,1,skilled,1,none,yes,bad +'no checking',12,'critical/other existing credit',education,2096,'<100','4<=X<7',2,'male single',none,3,'real estate',49,none,own,1,'unskilled resident',2,none,yes,good +'<0',42,'existing paid',furniture/equipment,7882,'<100','4<=X<7',2,'male single',guarantor,4,'life insurance',45,none,'for free',1,skilled,2,none,yes,good +'<0',24,'delayed previously','new car',4870,'<100','1<=X<4',3,'male single',none,4,'no known property',53,none,'for free',2,skilled,2,none,yes,bad +'no checking',36,'existing paid',education,9055,'no known savings','1<=X<4',2,'male single',none,4,'no known property',35,none,'for free',1,'unskilled resident',2,yes,yes,good +'no checking',24,'existing paid',furniture/equipment,2835,'500<=X<1000','>=7',3,'male single',none,4,'life insurance',53,none,own,1,skilled,1,none,yes,good +'0<=X<200',36,'existing paid','used car',6948,'<100','1<=X<4',2,'male single',none,2,car,35,none,rent,1,'high qualif/self emp/mgmt',1,yes,yes,good +'no checking',12,'existing paid',radio/tv,3059,'>=1000','4<=X<7',2,'male div/sep',none,4,'real estate',61,none,own,1,'unskilled resident',1,none,yes,good +'0<=X<200',30,'critical/other existing credit','new car',5234,'<100',unemployed,4,'male mar/wid',none,2,car,28,none,own,2,'high qualif/self emp/mgmt',1,none,yes,bad +'0<=X<200',12,'existing paid','new car',1295,'<100','<1',3,'female div/dep/mar',none,1,car,25,none,rent,1,skilled,1,none,yes,bad +'<0',48,'existing paid',business,4308,'<100','<1',3,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,skilled,1,none,yes,bad +'0<=X<200',12,'existing paid',radio/tv,1567,'<100','1<=X<4',1,'female div/dep/mar',none,1,car,22,none,own,1,skilled,1,yes,yes,good +'<0',24,'critical/other existing credit','new car',1199,'<100','>=7',4,'male single',none,4,car,60,none,own,2,'unskilled resident',1,none,yes,bad +'<0',15,'existing paid','new car',1403,'<100','1<=X<4',2,'female div/dep/mar',none,4,car,28,none,rent,1,skilled,1,none,yes,good +'<0',24,'existing paid',radio/tv,1282,'100<=X<500','1<=X<4',4,'female div/dep/mar',none,2,car,32,none,own,1,'unskilled resident',1,none,yes,bad +'no checking',24,'critical/other existing credit',radio/tv,2424,'no known savings','>=7',4,'male single',none,4,'life insurance',53,none,own,2,skilled,1,none,yes,good +'<0',30,'no credits/all paid',business,8072,'no known savings','<1',2,'male single',none,3,car,25,bank,own,3,skilled,1,none,yes,good +'0<=X<200',24,'existing paid','used car',12579,'<100','>=7',4,'female div/dep/mar',none,2,'no known property',44,none,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,bad +'no checking',24,'existing paid',radio/tv,3430,'500<=X<1000','>=7',3,'male single',none,2,car,31,none,own,1,skilled,2,yes,yes,good +'no checking',9,'critical/other existing credit','new car',2134,'<100','1<=X<4',4,'male single',none,4,car,48,none,own,3,skilled,1,yes,yes,good +'<0',6,'existing paid',radio/tv,2647,'500<=X<1000','1<=X<4',2,'male single',none,3,'real estate',44,none,rent,1,skilled,2,none,yes,good +'<0',10,'critical/other existing credit','new car',2241,'<100','<1',1,'male single',none,3,'real estate',48,none,rent,2,'unskilled resident',2,none,no,good +'0<=X<200',12,'critical/other existing credit','used car',1804,'100<=X<500','<1',3,'male single',none,4,'life insurance',44,none,own,1,skilled,1,none,yes,good +'no checking',10,'critical/other existing credit',furniture/equipment,2069,'no known savings','1<=X<4',2,'male mar/wid',none,1,car,26,none,own,2,skilled,1,none,no,good +'<0',6,'existing paid',furniture/equipment,1374,'<100','1<=X<4',1,'male single',none,2,'real estate',36,bank,own,1,'unskilled resident',1,yes,yes,good +'no checking',6,'no credits/all paid',radio/tv,426,'<100','>=7',4,'male mar/wid',none,4,car,39,none,own,1,'unskilled resident',1,none,yes,good +'>=200',12,'all paid',radio/tv,409,'>=1000','1<=X<4',3,'female div/dep/mar',none,3,'real estate',42,none,rent,2,skilled,1,none,yes,good +'0<=X<200',7,'existing paid',radio/tv,2415,'<100','1<=X<4',3,'male single',guarantor,2,'real estate',34,none,own,1,skilled,1,none,yes,good +'<0',60,'delayed previously',business,6836,'<100','>=7',3,'male single',none,4,'no known property',63,none,own,2,skilled,1,yes,yes,bad +'0<=X<200',18,'existing paid',business,1913,'>=1000','<1',3,'male mar/wid',none,3,'real estate',36,bank,own,1,skilled,1,yes,yes,good +'<0',24,'existing paid',furniture/equipment,4020,'<100','1<=X<4',2,'male single',none,2,car,27,stores,own,1,skilled,1,none,yes,good +'0<=X<200',18,'existing paid','new car',5866,'100<=X<500','1<=X<4',2,'male single',none,2,car,30,none,own,2,skilled,1,yes,yes,good +'no checking',12,'critical/other existing credit',business,1264,'no known savings','>=7',4,'male single',none,4,'no known property',57,none,rent,1,'unskilled resident',1,none,yes,good +'>=200',12,'existing paid',furniture/equipment,1474,'<100','<1',4,'female div/dep/mar',none,1,'life insurance',33,bank,own,1,'high qualif/self emp/mgmt',1,yes,yes,good +'0<=X<200',45,'critical/other existing credit',radio/tv,4746,'<100','<1',4,'male single',none,2,'life insurance',25,none,own,2,'unskilled resident',1,none,yes,bad +'no checking',48,'critical/other existing credit',education,6110,'<100','1<=X<4',1,'male single',none,3,'no known property',31,bank,'for free',1,skilled,1,yes,yes,good +'>=200',18,'existing paid',radio/tv,2100,'<100','1<=X<4',4,'male single','co applicant',2,'real estate',37,stores,own,1,skilled,1,none,yes,bad +'>=200',10,'existing paid','domestic appliance',1225,'<100','1<=X<4',2,'male single',none,2,car,37,none,own,1,skilled,1,yes,yes,good +'0<=X<200',9,'existing paid',radio/tv,458,'<100','1<=X<4',4,'male single',none,3,'real estate',24,none,own,1,skilled,1,none,yes,good +'no checking',30,'existing paid',radio/tv,2333,'500<=X<1000','>=7',4,'male single',none,2,car,30,bank,own,1,'high qualif/self emp/mgmt',1,none,yes,good +'0<=X<200',12,'existing paid',radio/tv,1158,'500<=X<1000','1<=X<4',3,'male div/sep',none,1,car,26,none,own,1,skilled,1,yes,yes,good +'0<=X<200',18,'delayed previously',repairs,6204,'<100','1<=X<4',2,'male single',none,4,'real estate',44,none,own,1,'unskilled resident',2,yes,yes,good +'<0',30,'critical/other existing credit','used car',6187,'100<=X<500','4<=X<7',1,'male mar/wid',none,4,car,24,none,rent,2,skilled,1,none,yes,good +'<0',48,'critical/other existing credit','used car',6143,'<100','>=7',4,'female div/dep/mar',none,4,'no known property',58,stores,'for free',2,'unskilled resident',1,none,yes,bad +'no checking',11,'critical/other existing credit','new car',1393,'<100','<1',4,'female div/dep/mar',none,4,car,35,none,own,2,'high qualif/self emp/mgmt',1,none,yes,good +'no checking',36,'existing paid',radio/tv,2299,'500<=X<1000','>=7',4,'male single',none,4,car,39,none,own,1,skilled,1,none,yes,good +'<0',6,'existing paid','used car',1352,'500<=X<1000',unemployed,1,'female div/dep/mar',none,2,'life insurance',23,none,rent,1,'unemp/unskilled non res',1,yes,yes,good +'no checking',11,'critical/other existing credit','new car',7228,'<100','1<=X<4',1,'male single',none,4,'life insurance',39,none,own,2,'unskilled resident',1,none,yes,good +'no checking',12,'existing paid',radio/tv,2073,'100<=X<500','1<=X<4',4,'female div/dep/mar','co applicant',2,'real estate',28,none,own,1,skilled,1,none,yes,good +'0<=X<200',24,'delayed previously',furniture/equipment,2333,'no known savings','<1',4,'male single',none,2,'life insurance',29,bank,own,1,'unskilled resident',1,none,yes,good +'0<=X<200',27,'delayed previously','used car',5965,'<100','>=7',1,'male single',none,2,car,30,none,own,2,'high qualif/self emp/mgmt',1,yes,yes,good +'no checking',12,'existing paid',radio/tv,1262,'<100','1<=X<4',3,'male single',none,2,car,25,none,own,1,skilled,1,none,yes,good +'no checking',18,'existing paid','used car',3378,'no known savings','1<=X<4',2,'male single',none,1,'life insurance',31,none,own,1,skilled,1,yes,yes,good +'0<=X<200',36,'delayed previously','new car',2225,'<100','>=7',4,'male single',none,4,'no known property',57,bank,'for free',2,skilled,1,yes,yes,bad +'no checking',6,'all paid','new car',783,'no known savings','1<=X<4',1,'male single',guarantor,2,'real estate',26,stores,own,1,'unskilled resident',2,none,yes,good +'0<=X<200',12,'existing paid',radio/tv,6468,'no known savings',unemployed,2,'male single',none,1,'no known property',52,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,bad +'no checking',36,'critical/other existing credit',radio/tv,9566,'<100','1<=X<4',2,'female div/dep/mar',none,2,car,31,stores,own,2,skilled,1,none,yes,good +'>=200',18,'existing paid','new car',1961,'<100','>=7',3,'female div/dep/mar',none,2,car,23,none,own,1,'high qualif/self emp/mgmt',1,none,yes,good +'<0',36,'critical/other existing credit',furniture/equipment,6229,'<100','<1',4,'female div/dep/mar','co applicant',4,'no known property',23,none,rent,2,'unskilled resident',1,yes,yes,bad +'0<=X<200',9,'existing paid',business,1391,'<100','1<=X<4',2,'male mar/wid',none,1,'real estate',27,bank,own,1,skilled,1,yes,yes,good +'0<=X<200',15,'critical/other existing credit',radio/tv,1537,'no known savings','>=7',4,'male single',guarantor,4,'real estate',50,none,own,2,skilled,1,yes,yes,good +'0<=X<200',36,'no credits/all paid',business,1953,'<100','>=7',4,'male single',none,4,'no known property',61,none,'for free',1,'high qualif/self emp/mgmt',1,yes,yes,bad +'0<=X<200',48,'no credits/all paid',business,14421,'<100','1<=X<4',2,'male single',none,2,car,25,none,own,1,skilled,1,yes,yes,bad +'no checking',24,'existing paid',radio/tv,3181,'<100','<1',4,'female div/dep/mar',none,4,'life insurance',26,none,own,1,skilled,1,yes,yes,good +'no checking',27,'existing paid',repairs,5190,'no known savings','>=7',4,'male single',none,4,'life insurance',48,none,own,4,skilled,2,yes,yes,good +'no checking',12,'existing paid',radio/tv,2171,'<100','<1',2,'female div/dep/mar',none,2,car,29,bank,own,1,skilled,1,none,yes,good +'0<=X<200',12,'existing paid','new car',1007,'>=1000','1<=X<4',4,'male mar/wid',none,1,'real estate',22,none,own,1,skilled,1,none,yes,good +'no checking',36,'existing paid',education,1819,'<100','1<=X<4',4,'male single',none,4,'no known property',37,stores,'for free',1,skilled,1,yes,yes,bad +'no checking',36,'existing paid',radio/tv,2394,'no known savings','1<=X<4',4,'female div/dep/mar',none,4,car,25,none,own,1,skilled,1,none,yes,good +'no checking',36,'existing paid','used car',8133,'<100','1<=X<4',1,'female div/dep/mar',none,2,'life insurance',30,bank,own,1,skilled,1,none,yes,good +'no checking',7,'critical/other existing credit',radio/tv,730,'no known savings','>=7',4,'male single',none,2,'life insurance',46,none,rent,2,'unskilled resident',1,yes,yes,good +'<0',8,'critical/other existing credit',other,1164,'<100','>=7',3,'male single',none,4,'no known property',51,bank,'for free',2,'high qualif/self emp/mgmt',2,yes,yes,good +'0<=X<200',42,'critical/other existing credit',business,5954,'<100','4<=X<7',2,'female div/dep/mar',none,1,'real estate',41,bank,own,2,'unskilled resident',1,none,yes,good +'<0',36,'existing paid',education,1977,'no known savings','>=7',4,'male single',none,4,'no known property',40,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,bad +'<0',12,'critical/other existing credit','used car',1526,'<100','>=7',4,'male single',none,4,'no known property',66,none,'for free',2,'high qualif/self emp/mgmt',1,none,yes,good +'<0',42,'existing paid',radio/tv,3965,'<100','<1',4,'male single',none,3,car,34,none,own,1,skilled,1,none,yes,bad +'0<=X<200',11,'delayed previously',radio/tv,4771,'<100','4<=X<7',2,'male single',none,4,'life insurance',51,none,own,1,skilled,1,none,yes,good +'no checking',54,'no credits/all paid','used car',9436,'no known savings','1<=X<4',2,'male single',none,2,'life insurance',39,none,own,1,'unskilled resident',2,none,yes,good +'0<=X<200',30,'existing paid',furniture/equipment,3832,'<100','<1',2,'male mar/wid',none,1,'life insurance',22,none,own,1,skilled,1,none,yes,good +'no checking',24,'existing paid',radio/tv,5943,'no known savings','<1',1,'female div/dep/mar',none,1,car,44,none,own,2,skilled,1,yes,yes,bad +'no checking',15,'existing paid',radio/tv,1213,'500<=X<1000','>=7',4,'male single',none,3,'life insurance',47,stores,own,1,skilled,1,yes,yes,good +'no checking',18,'existing paid',business,1568,'100<=X<500','1<=X<4',3,'female div/dep/mar',none,4,'life insurance',24,none,rent,1,'unskilled resident',1,none,yes,good +'<0',24,'existing paid',other,1755,'<100','>=7',4,'female div/dep/mar',guarantor,4,'real estate',58,none,own,1,'unskilled resident',1,yes,yes,good +'<0',10,'existing paid',radio/tv,2315,'<100','>=7',3,'male single',none,4,'real estate',52,none,own,1,'unskilled resident',1,none,yes,good +'no checking',12,'critical/other existing credit',business,1412,'<100','1<=X<4',4,'female div/dep/mar',guarantor,2,'real estate',29,none,own,2,'high qualif/self emp/mgmt',1,yes,yes,good +'0<=X<200',18,'critical/other existing credit',furniture/equipment,1295,'<100','<1',4,'female div/dep/mar',none,1,'life insurance',27,none,own,2,skilled,1,none,yes,good +'0<=X<200',36,'existing paid',education,12612,'100<=X<500','1<=X<4',1,'male single',none,4,'no known property',47,none,'for free',1,skilled,2,yes,yes,bad +'<0',18,'existing paid','new car',2249,'100<=X<500','4<=X<7',4,'male single',none,3,car,30,none,own,1,'high qualif/self emp/mgmt',2,yes,yes,good +'<0',12,'no credits/all paid',repairs,1108,'<100','4<=X<7',4,'male single',none,3,'real estate',28,none,own,2,skilled,1,none,yes,bad +'no checking',12,'critical/other existing credit',radio/tv,618,'<100','>=7',4,'male single',none,4,'real estate',56,none,own,1,skilled,1,none,yes,good +'<0',12,'critical/other existing credit','used car',1409,'<100','>=7',4,'male single',none,3,'real estate',54,none,own,1,skilled,1,none,yes,good +'no checking',12,'critical/other existing credit',radio/tv,797,'no known savings','>=7',4,'female div/dep/mar',none,3,'life insurance',33,bank,own,1,'unskilled resident',2,none,yes,bad +'>=200',24,'critical/other existing credit',furniture/equipment,3617,'no known savings','>=7',4,'male single','co applicant',4,'no known property',20,none,rent,2,skilled,1,none,yes,good +'0<=X<200',12,'existing paid','new car',1318,'>=1000','>=7',4,'male single',none,4,'real estate',54,none,own,1,skilled,1,yes,yes,good +'0<=X<200',54,'no credits/all paid',business,15945,'<100','<1',3,'male single',none,4,'no known property',58,none,rent,1,skilled,1,yes,yes,bad +'no checking',12,'critical/other existing credit',education,2012,'no known savings','4<=X<7',4,'female div/dep/mar',none,2,car,61,none,own,1,skilled,1,none,yes,good +'0<=X<200',18,'existing paid',business,2622,'100<=X<500','1<=X<4',4,'male single',none,4,car,34,none,own,1,skilled,1,none,yes,good +'0<=X<200',36,'critical/other existing credit',radio/tv,2337,'<100','>=7',4,'male single',none,4,'real estate',36,none,own,1,skilled,1,none,yes,good +'0<=X<200',20,'delayed previously','used car',7057,'no known savings','4<=X<7',3,'male single',none,4,'life insurance',36,bank,rent,2,'high qualif/self emp/mgmt',2,yes,yes,good +'no checking',24,'existing paid','new car',1469,'100<=X<500','>=7',4,'male mar/wid',none,4,'real estate',41,none,rent,1,'unskilled resident',1,none,yes,good +'0<=X<200',36,'existing paid',radio/tv,2323,'<100','4<=X<7',4,'male single',none,4,car,24,none,rent,1,skilled,1,none,yes,good +'no checking',6,'delayed previously',radio/tv,932,'<100','1<=X<4',3,'female div/dep/mar',none,2,'real estate',24,none,own,1,skilled,1,none,yes,good +'0<=X<200',9,'critical/other existing credit',furniture/equipment,1919,'<100','4<=X<7',4,'male single',none,3,car,35,none,rent,1,skilled,1,yes,yes,good +'no checking',12,'existing paid','used car',2445,'no known savings','<1',2,'male mar/wid',none,4,car,26,none,rent,1,skilled,1,yes,yes,good +'0<=X<200',24,'critical/other existing credit',other,11938,'<100','1<=X<4',2,'male single','co applicant',3,car,39,none,own,2,'high qualif/self emp/mgmt',2,yes,yes,bad +'no checking',18,'all paid','new car',6458,'<100','>=7',2,'male single',none,4,'no known property',39,bank,own,2,'high qualif/self emp/mgmt',2,yes,yes,bad +'0<=X<200',12,'existing paid','new car',6078,'<100','4<=X<7',2,'male single',none,2,car,32,none,own,1,skilled,1,none,yes,good +'<0',24,'existing paid',furniture/equipment,7721,'no known savings','<1',1,'female div/dep/mar',none,2,'life insurance',30,none,own,1,skilled,1,yes,no,good +'0<=X<200',14,'existing paid',business,1410,'500<=X<1000','>=7',1,'male mar/wid',none,2,'real estate',35,none,own,1,skilled,1,yes,yes,good +'0<=X<200',6,'delayed previously',business,1449,'100<=X<500','>=7',1,'male div/sep',none,2,car,31,bank,own,2,skilled,2,none,yes,good +'>=200',15,'existing paid',education,392,'<100','<1',4,'female div/dep/mar',none,4,'life insurance',23,none,rent,1,skilled,1,yes,yes,good +'0<=X<200',18,'existing paid','new car',6260,'<100','4<=X<7',3,'male single',none,3,'real estate',28,none,rent,1,'unskilled resident',1,none,yes,good +'no checking',36,'critical/other existing credit','new car',7855,'<100','1<=X<4',4,'female div/dep/mar',none,2,'real estate',25,stores,own,2,skilled,1,yes,yes,bad +'<0',12,'existing paid',radio/tv,1680,'500<=X<1000','>=7',3,'male mar/wid',none,1,'real estate',35,none,own,1,skilled,1,none,yes,good +'no checking',48,'critical/other existing credit',radio/tv,3578,'no known savings','>=7',4,'male single',none,1,'real estate',47,none,own,1,skilled,1,yes,yes,good +'<0',42,'existing paid',radio/tv,7174,'no known savings','4<=X<7',4,'female div/dep/mar',none,3,car,30,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,bad +'<0',10,'critical/other existing credit',furniture/equipment,2132,'no known savings','<1',2,'female div/dep/mar','co applicant',3,'real estate',27,none,rent,2,skilled,1,none,no,good +'<0',33,'critical/other existing credit',furniture/equipment,4281,'500<=X<1000','1<=X<4',1,'female div/dep/mar',none,4,car,23,none,own,2,skilled,1,none,yes,bad +'0<=X<200',12,'critical/other existing credit','new car',2366,'500<=X<1000','4<=X<7',3,'male div/sep',none,3,car,36,none,own,1,'high qualif/self emp/mgmt',1,yes,yes,good +'<0',21,'existing paid',radio/tv,1835,'<100','1<=X<4',3,'female div/dep/mar',none,2,'real estate',25,none,own,2,skilled,1,yes,yes,bad +'no 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a/examples/sklearn/'cache'/org/openml/test/datasets/17/description.xml b/examples/sklearn/'cache'/org/openml/test/datasets/17/description.xml new file mode 100644 index 000000000..c4bd047e7 --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/datasets/17/description.xml @@ -0,0 +1,291 @@ + + 17 + credit-g + 1 + **Author**: +**Source**: Unknown - +**Please cite**: + +Description of the German credit dataset. + + 1. Title: German Credit data + + 2. Source Information + + Professor Dr. Hans Hofmann + Institut f"ur Statistik und "Okonometrie + Universit"at Hamburg + FB Wirtschaftswissenschaften + Von-Melle-Park 5 + 2000 Hamburg 13 + + 3. Number of Instances: 1000 + + Two datasets are provided. the original dataset, in the form provided + by Prof. Hofmann, contains categorical/symbolic attributes and + is in the file "german.data". + + For algorithms that need numerical attributes, Strathclyde University + produced the file "german.data-numeric". This file has been edited + and several indicator variables added to make it suitable for + algorithms which cannot cope with categorical variables. Several + attributes that are ordered categorical (such as attribute 17) have + been coded as integer. This was the form used by StatLog. + + + 6. Number of Attributes german: 20 (7 numerical, 13 categorical) + Number of Attributes german.numer: 24 (24 numerical) + + + 7. Attribute description for german + + Attribute 1: (qualitative) + Status of existing checking account + A11 : ... < 0 DM + A12 : 0 <= ... < 200 DM + A13 : ... >= 200 DM / + salary assignments for at least 1 year + A14 : no checking account + + Attribute 2: (numerical) + Duration in month + + Attribute 3: (qualitative) + Credit history + A30 : no credits taken/ + all credits paid back duly + A31 : all credits at this bank paid back duly + A32 : existing credits paid back duly till now + A33 : delay in paying off in the past + A34 : critical account/ + other credits existing (not at this bank) + + Attribute 4: (qualitative) + Purpose + A40 : car (new) + A41 : car (used) + A42 : furniture/equipment + A43 : radio/television + A44 : domestic appliances + A45 : repairs + A46 : education + A47 : (vacation - does not exist?) + A48 : retraining + A49 : business + A410 : others + + Attribute 5: (numerical) + Credit amount + + Attibute 6: (qualitative) + Savings account/bonds + A61 : ... < 100 DM + A62 : 100 <= ... < 500 DM + A63 : 500 <= ... < 1000 DM + A64 : .. >= 1000 DM + A65 : unknown/ no savings account + + Attribute 7: (qualitative) + Present employment since + A71 : unemployed + A72 : ... < 1 year + A73 : 1 <= ... < 4 years + A74 : 4 <= ... < 7 years + A75 : .. >= 7 years + + Attribute 8: (numerical) + Installment rate in percentage of disposable income + + Attribute 9: (qualitative) + Personal status and sex + A91 : male : divorced/separated + A92 : female : divorced/separated/married + A93 : male : single + A94 : male : married/widowed + A95 : female : single + + Attribute 10: (qualitative) + Other debtors / guarantors + A101 : none + A102 : co-applicant + A103 : guarantor + + Attribute 11: (numerical) + Present residence since + + Attribute 12: (qualitative) + Property + A121 : real estate + A122 : if not A121 : building society savings agreement/ + life insurance + A123 : if not A121/A122 : car or other, not in attribute 6 + A124 : unknown / no property + + Attribute 13: (numerical) + Age in years + + Attribute 14: (qualitative) + Other installment plans + A141 : bank + A142 : stores + A143 : none + + Attribute 15: (qualitative) + Housing + A151 : rent + A152 : own + A153 : for free + + Attribute 16: (numerical) + Number of existing credits at this bank + + Attribute 17: (qualitative) + Job + A171 : unemployed/ unskilled - non-resident + A172 : unskilled - resident + A173 : skilled employee / official + A174 : management/ self-employed/ + highly qualified employee/ officer + + Attribute 18: (numerical) + Number of people being liable to provide maintenance for + + Attribute 19: (qualitative) + Telephone + A191 : none + A192 : yes, registered under the customers name + + Attribute 20: (qualitative) + foreign worker + A201 : yes + A202 : no + + + + 8. Cost Matrix + + This dataset requires use of a cost matrix (see below) + + + 1 2 + ---------------------------- + 1 0 1 + ----------------------- + 2 5 0 + + (1 = Good, 2 = Bad) + + the rows represent the actual classification and the columns + the predicted classification. + + It is worse to class a customer as good when they are bad (5), + than it is to class a customer as bad when they are good (1). + + + + + + Relabeled values in attribute checking_status + From: A11 To: '<0' + From: A12 To: '0<=X<200' + From: A13 To: '>=200' + From: A14 To: 'no checking' + + + Relabeled values in attribute credit_history + From: A30 To: 'no credits/all paid' + From: A31 To: 'all paid' + From: A32 To: 'existing paid' + From: A33 To: 'delayed previously' + From: A34 To: 'critical/other existing credit' + + + Relabeled values in attribute purpose + From: A40 To: 'new car' + From: A41 To: 'used car' + From: A42 To: furniture/equipment + From: A43 To: radio/tv + From: A44 To: 'domestic appliance' + From: A45 To: repairs + From: A46 To: education + From: A47 To: vacation + From: A48 To: retraining + From: A49 To: business + From: A410 To: other + + + Relabeled values in attribute savings_status + From: A61 To: '<100' + From: A62 To: '100<=X<500' + From: A63 To: '500<=X<1000' + From: A64 To: '>=1000' + From: A65 To: 'no known savings' + + + Relabeled values in attribute employment + From: A71 To: unemployed + From: A72 To: '<1' + From: A73 To: '1<=X<4' + From: A74 To: '4<=X<7' + From: A75 To: '>=7' + + + Relabeled values in attribute personal_status + From: A91 To: 'male div/sep' + From: A92 To: 'female div/dep/mar' + From: A93 To: 'male single' + From: A94 To: 'male mar/wid' + From: A95 To: 'female single' + + + Relabeled values in attribute other_parties + From: A101 To: none + From: A102 To: 'co applicant' + From: A103 To: guarantor + + + Relabeled values in attribute property_magnitude + From: A121 To: 'real estate' + From: A122 To: 'life insurance' + From: A123 To: car + From: A124 To: 'no known property' + + + Relabeled values in attribute other_payment_plans + From: A141 To: bank + From: A142 To: stores + From: A143 To: none + + + Relabeled values in attribute housing + From: A151 To: rent + From: A152 To: own + From: A153 To: 'for free' + + + Relabeled values in attribute job + From: A171 To: 'unemp/unskilled non res' + From: A172 To: 'unskilled resident' + From: A173 To: skilled + From: A174 To: 'high qualif/self emp/mgmt' + + + Relabeled values in attribute own_telephone + From: A191 To: none + From: A192 To: yes + + + Relabeled values in attribute foreign_worker + From: A201 To: yes + From: A202 To: no + + + Relabeled values in attribute class + From: 1 To: good + From: 2 To: bad + ARFF + 2014-04-06T23:21:47 + Public https://test.openml.org/data/v1/download/17/credit-g.arff + 17 class 1 study_14 public active + 2019-07-09 12:42:24 9a475053fed0c26ee95cd4525e50074c + diff --git a/examples/sklearn/'cache'/org/openml/test/datasets/17/features.xml b/examples/sklearn/'cache'/org/openml/test/datasets/17/features.xml new file mode 100644 index 000000000..da1177434 --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/datasets/17/features.xml @@ -0,0 +1,249 @@ + + + 0 + checking_status + nominal + <0 + 0<=X<200 + >=200 + no checking + false + false + false + 0 + + + 1 + duration + numeric + false + false + false + 0 + + + 2 + credit_history + nominal + no credits/all paid + all paid + existing paid + delayed previously + critical/other existing credit + false + false + false + 0 + + + 3 + purpose + nominal + new car + used car + furniture/equipment + radio/tv + domestic appliance + repairs + education + vacation + retraining + business + other + false + false + false + 0 + + + 4 + credit_amount + numeric + false + false + false + 0 + + + 5 + savings_status + nominal + <100 + 100<=X<500 + 500<=X<1000 + >=1000 + no known savings + false + false + false + 0 + + + 6 + employment + nominal + unemployed + <1 + 1<=X<4 + 4<=X<7 + >=7 + false + false + false + 0 + + + 7 + installment_commitment + numeric + false + false + false + 0 + + + 8 + personal_status + nominal + male div/sep + female div/dep/mar + male single + male mar/wid + female single + false + false + false + 0 + + + 9 + other_parties + nominal + none + co applicant + guarantor + false + false + false + 0 + + + 10 + residence_since + numeric + false + false + false + 0 + + + 11 + property_magnitude + nominal + real estate + life insurance + car + no known property + false + false + false + 0 + + + 12 + age + numeric + false + false + false + 0 + + + 13 + other_payment_plans + nominal + bank + stores + none + false + false + false + 0 + + + 14 + housing + nominal + rent + own + for free + false + false + false + 0 + + + 15 + existing_credits + numeric + false + false + false + 0 + + + 16 + job + nominal + unemp/unskilled non res + unskilled resident + skilled + high qualif/self emp/mgmt + false + false + false + 0 + + + 17 + num_dependents + numeric + false + false + false + 0 + + + 18 + own_telephone + nominal + none + yes + false + false + false + 0 + + + 19 + foreign_worker + nominal + yes + no + false + false + false + 0 + + + 20 + class + nominal + good + bad + true + false + false + 0 + + diff --git a/examples/sklearn/'cache'/org/openml/test/datasets/17/qualities.xml b/examples/sklearn/'cache'/org/openml/test/datasets/17/qualities.xml new file mode 100644 index 000000000..a52cf1832 --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/datasets/17/qualities.xml @@ -0,0 +1,537 @@ + + + + AutoCorrelation + 0.5695695695695696 + + + + CfsSubsetEval_DecisionStumpAUC + 0.7208285714285714 + + + + CfsSubsetEval_DecisionStumpErrRate + 0.273 + + + + CfsSubsetEval_DecisionStumpKappa + 0.3056968463886062 + + + + CfsSubsetEval_NaiveBayesAUC + 0.7208285714285714 + + + + CfsSubsetEval_NaiveBayesErrRate + 0.273 + + + + CfsSubsetEval_NaiveBayesKappa + 0.3056968463886062 + + + + CfsSubsetEval_kNN1NAUC + 0.7208285714285714 + + + + CfsSubsetEval_kNN1NErrRate + 0.273 + + + + CfsSubsetEval_kNN1NKappa + 0.3056968463886062 + + + + ClassEntropy + 0.8812908992306927 + + + + DecisionStumpAUC + 0.6647619047619048 + + + + DecisionStumpErrRate + 0.3 + + + + DecisionStumpKappa + 0.0 + + + + Dimensionality + 0.021 + + + + EquivalentNumberOfAtts + 43.593036016305035 + + + + J48.00001.AUC + 0.6617476190476189 + + + + J48.00001.ErrRate + 0.279 + + + + J48.00001.Kappa + 0.24431202600216673 + + + + J48.0001.AUC + 0.6617476190476189 + + + + J48.0001.ErrRate + 0.279 + + + + J48.0001.Kappa + 0.24431202600216673 + + + + J48.001.AUC + 0.6617476190476189 + + + + J48.001.ErrRate + 0.279 + + + + J48.001.Kappa + 0.24431202600216673 + + + + MajorityClassPercentage + 70.0 + + + + MajorityClassSize + 700.0 + + + + MaxAttributeEntropy + 2.6666777598518516 + + + + MaxKurtosisOfNumericAtts + 4.292590308048501 + + + + MaxMeansOfNumericAtts + 3271.257999999999 + + + + MaxMutualInformation + 0.09473884155264 + + + + MaxNominalAttDistinctValues + 10.0 + + + + MaxSkewnessOfNumericAtts + 1.9496276798326246 + + + + MaxStdDevOfNumericAtts + 2822.7368759604396 + + + + MeanAttributeEntropy + 1.433893225502841 + + + + MeanKurtosisOfNumericAtts + 0.9242775257981102 + + + + MeanMeansOfNumericAtts + 476.58385714285697 + + + + MeanMutualInformation + 0.02021632305905615 + + + + MeanNoiseToSignalRatio + 69.92749860170595 + + + + MeanNominalAttDistinctValues + 4.0 + + + + MeanSkewnessOfNumericAtts + 0.9203791257169068 + + + + MeanStdDevOfNumericAtts + 407.04761882821174 + + + + MinAttributeEntropy + 0.2283640258405646 + + + + MinKurtosisOfNumericAtts + -1.3814485027493755 + + + + MinMeansOfNumericAtts + 1.1549999999999998 + + + + MinMutualInformation + 9.6366001491E-4 + + + + MinNominalAttDistinctValues + 2.0 + + + + MinSkewnessOfNumericAtts + -0.5313481143125632 + + + + MinStdDevOfNumericAtts + 0.3620857717531919 + + + + MinorityClassPercentage + 30.0 + + + + MinorityClassSize + 300.0 + + + + NaiveBayesAUC + 0.786047619047619 + + + + NaiveBayesErrRate + 0.248 + + + + NaiveBayesKappa + 0.3724696356275304 + + + + NumberOfBinaryFeatures + 3.0 + + + + NumberOfClasses + 2.0 + + + + NumberOfFeatures + 21.0 + + + + NumberOfInstances + 1000.0 + + + + NumberOfInstancesWithMissingValues + 0.0 + + + + NumberOfMissingValues + 0.0 + + + + NumberOfNumericFeatures + 7.0 + + + + NumberOfSymbolicFeatures + 14.0 + + + + PercentageOfBinaryFeatures + 14.285714285714285 + + + + PercentageOfInstancesWithMissingValues + 0.0 + + + + PercentageOfMissingValues + 0.0 + + + + PercentageOfNumericFeatures + 33.33333333333333 + + + + PercentageOfSymbolicFeatures + 66.66666666666666 + + + + Quartile1AttributeEntropy + 0.9089779148834296 + + + + Quartile1KurtosisOfNumericAtts + -1.2104731179379757 + + + + Quartile1MeansOfNumericAtts + 1.407 + + + + Quartile1MutualInformation + 0.005310005973835 + + + + Quartile1SkewnessOfNumericAtts + -0.2725698140337198 + + + + Quartile1StdDevOfNumericAtts + 0.5776544682460991 + + + + Quartile2AttributeEntropy + 1.5321036281187235 + + + + Quartile2KurtosisOfNumericAtts + 0.9197813600546327 + + + + Quartile2MeansOfNumericAtts + 2.9730000000000003 + + + + Quartile2MutualInformation + 0.01275318647802 + + + + Quartile2SkewnessOfNumericAtts + 1.09418417155554 + + + + Quartile2StdDevOfNumericAtts + 1.1187146743126737 + + + + Quartile3AttributeEntropy + 1.8749120301493503 + + + + Quartile3KurtosisOfNumericAtts + 1.6492736936699308 + + + + Quartile3MeansOfNumericAtts + 35.54600000000001 + + + + Quartile3MutualInformation + 0.02650410754481 + + + + Quartile3SkewnessOfNumericAtts + 1.909444721297511 + + + + Quartile3StdDevOfNumericAtts + 12.058814452756392 + + + + REPTreeDepth1AUC + 0.7002928571428572 + + + + REPTreeDepth1ErrRate + 0.269 + + + + REPTreeDepth1Kappa + 0.22254335260115596 + + + + REPTreeDepth2AUC + 0.7002928571428572 + + + + REPTreeDepth2ErrRate + 0.269 + + + + REPTreeDepth2Kappa + 0.22254335260115596 + + + + REPTreeDepth3AUC + 0.7002928571428572 + + + + REPTreeDepth3ErrRate + 0.269 + + + + REPTreeDepth3Kappa + 0.22254335260115596 + + + + RandomTreeDepth1AUC + 0.6580238095238096 + + + + RandomTreeDepth1ErrRate + 0.298 + + + + RandomTreeDepth1Kappa + 0.2781007751937982 + + + + RandomTreeDepth2AUC + 0.6580238095238096 + + + + RandomTreeDepth2ErrRate + 0.298 + + + + RandomTreeDepth2Kappa + 0.2781007751937982 + + + + RandomTreeDepth3AUC + 0.6580238095238096 + + + + RandomTreeDepth3ErrRate + 0.298 + + + + RandomTreeDepth3Kappa + 0.2781007751937982 + + + + StdvNominalAttDistinctValues + 2.0380986614602725 + + + + kNN1NAUC + 0.649047619047619 + + + + kNN1NErrRate + 0.286 + + + + kNN1NKappa + 0.30447470817120614 + + diff --git a/examples/sklearn/'cache'/org/openml/test/flows/12214/flow.xml b/examples/sklearn/'cache'/org/openml/test/flows/12214/flow.xml new file mode 100644 index 000000000..102140975 --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/flows/12214/flow.xml @@ -0,0 +1,192 @@ + + + 12214 +1159 +sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,estimator=sklearn.tree.tree.DecisionTreeClassifier) +sklearn.pipeline.Pipeline +5 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:42 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + memory + + null + + + + steps + + [{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": "estimator"}}] + + + + verbose + + false + + + + imputer + + + 12215 +1159 +sklearn.preprocessing.imputation.Imputer +sklearn.preprocessing.imputation.Imputer +5 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:42 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + axis + + 0 + + + + copy + + true + + + + missing_values + + "NaN" + + + + strategy + + "mean" + + + + verbose + + 0 + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + + + + estimator + + + 12216 +1159 +sklearn.tree.tree.DecisionTreeClassifier +sklearn.tree.tree.DecisionTreeClassifier +5 +openml==0.9.0,sklearn==0.21.2 +Automatically created scikit-learn flow. +2019-07-13T20:56:42 +English +sklearn==0.21.2 +numpy>=1.6.1 +scipy>=0.9 + + class_weight + + null + + + + criterion + + "gini" + + + + max_depth + + null + + + + max_features + + null + + + + max_leaf_nodes + + null + + + + min_impurity_decrease + + 0.0 + + + + min_impurity_split + + null + + + + min_samples_leaf + + 1 + + + + min_samples_split + + 2 + + + + min_weight_fraction_leaf + + 0.0 + + + + presort + + false + + + + random_state + + null + + + + splitter + + "best" + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + + +openml-python +python +scikit-learn +sklearn +sklearn_0.21.2 + diff --git a/examples/sklearn/'cache'/org/openml/test/locks/datasets.functions.get_dataset:17 b/examples/sklearn/'cache'/org/openml/test/locks/datasets.functions.get_dataset:17 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/sklearn/'cache'/org/openml/test/locks/flows.functions.get_flow:12214 b/examples/sklearn/'cache'/org/openml/test/locks/flows.functions.get_flow:12214 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/sklearn/'cache'/org/openml/test/locks/tasks.functions.get_task:97 b/examples/sklearn/'cache'/org/openml/test/locks/tasks.functions.get_task:97 new file mode 100644 index 000000000..e69de29bb diff --git a/examples/sklearn/'cache'/org/openml/test/tasks/97/datasplits.arff b/examples/sklearn/'cache'/org/openml/test/tasks/97/datasplits.arff new file mode 100644 index 000000000..3473c5486 --- /dev/null +++ b/examples/sklearn/'cache'/org/openml/test/tasks/97/datasplits.arff @@ -0,0 +1,10008 @@ +@relation credit-g_splits + +@attribute type {TRAIN,TEST} +@attribute rowid numeric +@attribute repeat numeric +@attribute fold numeric + +@data +TRAIN,539,0,0 +TRAIN,577,0,0 +TRAIN,619,0,0 +TRAIN,937,0,0 +TRAIN,75,0,0 +TRAIN,608,0,0 +TRAIN,142,0,0 +TRAIN,641,0,0 +TRAIN,336,0,0 +TRAIN,893,0,0 +TRAIN,60,0,0 +TRAIN,96,0,0 +TRAIN,165,0,0 +TRAIN,709,0,0 +TRAIN,66,0,0 +TRAIN,737,0,0 +TRAIN,260,0,0 +TRAIN,803,0,0 +TRAIN,393,0,0 +TRAIN,348,0,0 +TRAIN,891,0,0 +TRAIN,690,0,0 +TRAIN,841,0,0 +TRAIN,527,0,0 +TRAIN,254,0,0 +TRAIN,676,0,0 +TRAIN,329,0,0 +TRAIN,373,0,0 +TRAIN,20,0,0 +TRAIN,343,0,0 +TRAIN,781,0,0 +TRAIN,550,0,0 +TRAIN,791,0,0 +TRAIN,221,0,0 +TRAIN,185,0,0 +TRAIN,743,0,0 +TRAIN,785,0,0 +TRAIN,340,0,0 +TRAIN,196,0,0 +TRAIN,876,0,0 +TRAIN,679,0,0 +TRAIN,469,0,0 +TRAIN,371,0,0 +TRAIN,350,0,0 +TRAIN,519,0,0 +TRAIN,442,0,0 +TRAIN,696,0,0 +TRAIN,883,0,0 +TRAIN,330,0,0 +TRAIN,299,0,0 +TRAIN,319,0,0 +TRAIN,575,0,0 +TRAIN,448,0,0 +TRAIN,756,0,0 +TRAIN,703,0,0 +TRAIN,238,0,0 +TRAIN,132,0,0 +TRAIN,79,0,0 +TRAIN,34,0,0 +TRAIN,493,0,0 +TRAIN,78,0,0 +TRAIN,804,0,0 +TRAIN,492,0,0 +TRAIN,536,0,0 +TRAIN,977,0,0 +TRAIN,713,0,0 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