diff --git a/.gitignore b/.gitignore index 84015422..e75b6c06 100644 --- a/.gitignore +++ b/.gitignore @@ -126,6 +126,7 @@ dmypy.json # Test output test_output/ +examples/generated_results/ # Test datasts test_datasets/ diff --git a/README.md b/README.md index c2075f7e..bca59c97 100644 --- a/README.md +++ b/README.md @@ -4,29 +4,29 @@ [![docs-main](https://img.shields.io/readthedocs/tsml-eval/latest?logo=readthedocs&label=docs%20%28latest%29)](https://tsml-eval.readthedocs.io/en/latest/) [![codecov](https://img.shields.io/codecov/c/github/time-series-machine-learning/tsml-eval?label=codecov&logo=codecov)](https://codecov.io/gh/time-series-machine-learning/tsml-eval) [![pypi](https://img.shields.io/pypi/v/tsml-eval?logo=pypi&color=blue)](https://pypi.org/project/tsml-eval/) +[![!conda](https://img.shields.io/conda/vn/conda-forge/tsml-eval?logo=anaconda&color=blue)](https://anaconda.org/conda-forge/tsml-eval) [![python-versions](https://img.shields.io/pypi/pyversions/tsml-eval?logo=python)](https://www.python.org/) [![black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![license](https://img.shields.io/badge/license-BSD%203--Clause-green?logo=style)](https://github.com/time-series-machine-learning/tsml-eval/blob/main/LICENSE) # tsml-eval -tsml-eval contains benchmarking and evaluation tools for time series machine learning +`tsml-eval` contains benchmarking and evaluation tools for time series machine learning algorithms. -The current release of tsml-eval is v0.1.1. +The current release of `tsml-eval` is v0.1.1. -Installation ------------- +## Installation -tsml-eval is available on PyPI and can be installed via pip: +`tsml-eval` is available on PyPI and can be installed via pip: - pip install tsml-eval +```console +pip install tsml-eval +``` -More information available on out documentation webpage: -https://tsml-eval.readthedocs.io/en/stable/ +More information available on our [documentation](https://tsml-eval.readthedocs.io/en/stable/installation.html). -Acknowledgements ----------------- +## Acknowledgements This work is supported by the UK Engineering and Physical Sciences Research Council -(EPSRC) EP/W030756/1 +(EPSRC) EP/W030756/2 diff --git a/docs/_static/css/custom.css b/docs/_static/css/custom.css new file mode 100644 index 00000000..e8e85150 --- /dev/null +++ b/docs/_static/css/custom.css @@ -0,0 +1,16 @@ +/* card images */ + +.tsml-card-image-m { + width: 100%; + height: 150px; + object-fit: contain; + object-position: center; +} + +.tsml-card-image, +.tsml-card-image-l { + width: 100%; + height: 200px; + object-fit: contain; + object-position: center; +} diff --git a/docs/api.md b/docs/api.md index 1cf656c8..9010dc72 100644 --- a/docs/api.md +++ b/docs/api.md @@ -13,8 +13,29 @@ Functions for evaluating the performance of a model. :toctree: auto_generated/ :template: function.rst - evaluation.metrics.clustering_accuracy - evaluation.metrics.davies_bouldin_score_from_file + evaluation.evaluate_classifiers + evaluation.evaluate_classifiers_from_file + evaluation.evaluate_classifiers_by_problem + evaluation.evaluate_clusterers + evaluation.evaluate_clusterers_from_file + evaluation.evaluate_clusterers_by_problem + evaluation.evaluate_regressors + evaluation.evaluate_regressors_from_file + evaluation.evaluate_regressors_by_problem + evaluation.evaluate_forecasters + evaluation.evaluate_forecasters_from_file + evaluation.evaluate_forecasters_by_problem + evaluation.storage.ClassifierResults + evaluation.storage.ClustererResults + evaluation.storage.ForecasterResults + evaluation.storage.RegressorResults + evaluation.storage.load_classifier_results + evaluation.storage.load_clusterer_results + evaluation.storage.load_forecaster_results + evaluation.storage.load_regressor_results + evaluation.efficiency_benchmark.compare_estimators + evaluation.efficiency_benchmark.benchmark_estimator + evaluation.metrics.clustering_accuracy_score ``` ## Experiments: [tsml_eval.experiments](https://github.com/time-series-machine-learning/tsml-eval/tree/main/tsml_eval/experiments) @@ -45,15 +66,25 @@ Public utility functions used elsewhere in the package. :toctree: auto_generated/ :template: function.rst - utils.functions.str_in_nested_list + utils.arguments.parse_args utils.experiments.resample_data utils.experiments.stratified_resample_data utils.experiments.write_classification_results utils.experiments.write_regression_results utils.experiments.write_clustering_results utils.experiments.write_results_to_tsml_format - utils.experiments.validate_results_file utils.experiments.fix_broken_second_line utils.experiments.compare_result_file_resample utils.experiments.assign_gpu + utils.experiments.timing_benchmark + utils.functions.str_in_nested_list + utils.functions.pair_list_to_dict + utils.functions.time_to_milliseconds + utils.functions.rank_array + utils.memory_recorder.record_max_memory + utils.validation.is_sklearn_estimator + utils.validation.is_sklearn_classifier + utils.validation.is_sklearn_regressor + utils.validation.is_sklearn_clusterer + utils.validation.validate_results_file ``` diff --git a/docs/conf.py b/docs/conf.py index 6104c33d..e1bf7809 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -48,11 +48,14 @@ "sphinx.ext.autosectionlabel", "sphinx.ext.intersphinx", "sphinx.ext.linkcode", + "sphinxext.opengraph", "numpydoc", "nbsphinx", "sphinx_design", "sphinx_issues", "sphinx_copybutton", + "sphinx_remove_toctrees", + "versionwarning.extension", "myst_parser", ] @@ -92,9 +95,15 @@ # Link to GitHub repo for github_issues extension issues_github_path = "time-series-machine-learning/tsml-eval" -# sphinx-copybutton configuration +# sphinx-copybutton + copybutton_exclude = ".linenos, .gp, .go" +# sphinx-remove-toctrees configuration +# see https://github.com/pradyunsg/furo/pull/674 + +remove_from_toctrees = ["auto_generated/*"] + # nbsphinx nbsphinx_execute = "never" @@ -171,3 +180,4 @@ def find_source(): html_theme = "furo" html_static_path = ["_static"] +html_css_files = ["css/custom.css"] diff --git a/examples/images/wip.png b/docs/images/wip.png similarity index 100% rename from examples/images/wip.png rename to docs/images/wip.png diff --git a/docs/index.md b/docs/index.md index 07d2eeef..bfb7aef0 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,7 +6,7 @@ hide-toc: true
-``tsml-eval`` is a package containing tools for running experiments with time series +`tsml-eval` is a package containing tools for running experiments with time series machine learning algorithms and evaluating the results. The package currently supports the classification, regression and clustering algorithms. @@ -22,7 +22,7 @@ Installation ^^^ -Instructions to install ``tsml-eval``. +Instructions to install `tsml-eval`. +++ @@ -43,7 +43,7 @@ User Guide ^^^ -Guides for users for running experiments using ``tsml-eval``. +Guides for users for running experiments or evaluation using `tsml-eval`. +++ @@ -57,23 +57,24 @@ User Guide ::: + :::{grid-item-card} :text-align: center -Publications +API ^^^ -Supplementary material for publications using ``tsml-eval``. +``tsml-eval`` API documentation. +++ -```{button-ref} publications +```{button-ref} api :color: primary :click-parent: :expand: -Publications +API ``` ::: @@ -81,31 +82,32 @@ Publications :::{grid-item-card} :text-align: center -API +Publications ^^^ -``tsml-eval`` API documentation. +Supplementary material for publications using that use `tsml-eval`. +++ -```{button-ref} api +```{button-ref} publications :color: primary :click-parent: :expand: -API +Publications ``` ::: :::: + ```{toctree} :hidden: installation.md user_guide.md -publications.md api.md +publications.md ``` diff --git a/docs/installation.md b/docs/installation.md index 2a8d32aa..9271cf77 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -1,72 +1,110 @@ # Installation +The following contains information on installing `tsml-eval` for users and developers +with write access. Those who wish to contribute to `tsml-eval` without write access +will need to create a fork, see the [aeon](https://www.aeon-toolkit.org/en/stable/developer_guide/dev_installation.html) +and [sklearn](https://scikit-learn.org/stable/developers/contributing.html#how-to-contribute) +documentation on contributing and developer installation for guidance. + +We recommend setting up a fresh virtual environment or the conda equivalent before +installing `tsml-eval`. See the [aeon guide](https://www.aeon-toolkit.org/en/stable/installation.html#using-a-pip-venv) +for setup information. + ## Install from PyPi -The easiest way to install ``tsml-eval`` is using ``pip``: +The easiest way to install `tsml-eval` is using `pip`: -```{code-block} console +```console pip install tsml-eval ``` Some estimators require additional dependencies. You can install these individually as -required, or install all of them using the ``all_extras`` extra dependency set: +required, or install all of them using the `all_extras` extra dependency set: -```{code-block} console +```console pip install tsml-eval[all_extras] ``` +```{note} + If this results in a "no matches found" error, it may be due to how your shell + handles special characters. Try surrounding the dependency portion with quotes i.e. + + pip install tsml-eval"[all_extras]" +``` + All extra dependency sets can be found in the [pyproject.toml](https://github.com/time-series-machine-learning/tsml-eval/blob/main/pyproject.toml) -file ``[project.optional-dependencies]`` options. +file `[project.optional-dependencies]` options. To install a specific [release](https://github.com/time-series-machine-learning/tsml-eval/releases) version, specify the version number when installing: -```{code-block} console +```console pip install tsml-eval==0.1.0 ``` -```{code-block} console +```console pip install tsml-eval[all_extras]==0.1.0 ``` +## Install from conda-forge + +`tsml-eval` is also available on [conda-forge](https://anaconda.org/conda-forge/tsml-eval). + +```console +conda create -n tsml-env -c conda-forge tsml-eval +conda activate tsml-env +``` + +Currently for conda installations, optional dependencies must be installed separately. + ## Install fixed dependency versions for a publication -``tsml-eval`` [publications](publications.md) contain a ``requirements.txt`` file that -lists the versions of all dependencies used to generate results at the time of the -release. +`tsml-eval` [publications](publications.md) contain a `static_publication_reqs.txt` +file that lists the versions of all dependencies used to generate results at the time +of the release. To install the dependencies using this file, run: -```{code-block} console -pip install -r requirements.txt +```console +pip install -r static_publication_reqs.txt ``` -## Install latest in-development version from GitHub +## Install the latest in-development version from GitHub -The latest development version of ``tsml-eval`` can be installed directly from GitHub -using ``pip``: +The latest development version of `tsml-eval` can be installed directly from GitHub +using `pip`: -```{code-block} console +```console pip install git+https://github.com/time-series-machine-learning/tsml-eval.git@main ``` -## Install for developers +The latest development version of dependencies can be installed this way, i.e. for +`aeon` + +```console +pip install git+https://github.com/aeon-toolkit/aeon.git@main +``` + +If you have a different version of `tsml-eval` or a dependency installed, you must +uninstall it first before installing the development version. + +## Install for developers with write access -To install ``tsml-eval`` for development, first clone the GitHub repository: +To install `tsml-eval` for development, first clone the GitHub repository: -```{code-block} console +```console git clone https://github.com/time-series-machine-learning/tsml-eval.git ``` Then install the package in editable mode with developer dependencies: -```{code-block} console +```console pip install --editable .[dev] ``` We recommend setting up pre-commit hooks to automatically format code and check for common issues before committing: -```{code-block} console +```console pre-commit install ``` diff --git a/docs/publications.md b/docs/publications.md index c1e75edb..516abf31 100644 --- a/docs/publications.md +++ b/docs/publications.md @@ -5,10 +5,19 @@ the package. ## 2023 +### Classification + +[Bake off redux: a review and experimental evaluation of recent time series classification algorithms](./publications/2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb) + +[Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression](./publications/2023/rist_pipeline/rist_pipeline.ipynb) + +### Clustering + [A Review and Evaluation of Elastic Distance Functions for Time Series Clustering](./publications/2023/distance_based_clustering/distance_based_clustering.ipynb) -[Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](./publications/2023/tser_archive_expansion/tser_archive_expansion.ipynb) +### Regression -[Bake off redux: a review and experimental evaluation of recent time series classification algorithms](./publications/2023/tsc_bakeoff/tsc_bakeoff_2023.ipynb) +[Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression](./publications/2023/tser_archive_expansion/tser_archive_expansion.ipynb) [Extracting Features from Random Subseries: A Hybrid Pipeline for Time Series Classification and Extrinsic Regression](./publications/2023/rist_pipeline/rist_pipeline.ipynb) + diff --git a/docs/user_guide.md b/docs/user_guide.md index 7f1c39d6..08ba25e8 100644 --- a/docs/user_guide.md +++ b/docs/user_guide.md @@ -4,14 +4,15 @@ :gutter: 3 :::{grid-item-card} -:img-top: examples/images/wip.png +:img-top: examples/images/data.png +:class-img-top: tsml-card-image :text-align: center tsml-eval Data Format ^^^ -Placeholder. +An overview of the datatypes used for `tsml-eval` experiments. +++ @@ -26,111 +27,139 @@ Datasets ::: :::{grid-item-card} -:img-top: examples/images/TSC.png +:img-top: examples/images/cd_diagram.png +:class-img-top: tsml-card-image :text-align: center -Time Series Classification +tsml-eval Results Evaluation ^^^ -Placeholder. +Use the `tsml-eval` package to evaluate your results. +++ -```{button-ref} /examples/classification_experiments.ipynb +```{button-ref} /examples/evaluation.ipynb :color: primary :click-parent: :expand: -TSC +Evaluation ``` ::: :::{grid-item-card} -:img-top: examples/images/TSR.png +:img-top: examples/images/results.png +:class-img-top: tsml-card-image :text-align: center -Time Series Extrinsic Regression +tsml-eval Results Format ^^^ -Placeholder. +An overview of the results format output by `tsml-eval` experiments. +++ -```{button-ref} /examples/regression_experiments.ipynb +```{button-ref} /examples/results_format.ipynb :color: primary :click-parent: :expand: -TSER +Results ``` ::: :::{grid-item-card} -:img-top: examples/images/TSCL.png +:img-top: examples/images/TSC.png +:class-img-top: tsml-card-image :text-align: center -Time Series Clustering +Time Series Classification ^^^ -Placeholder. +TODO Placeholder. +++ -```{button-ref} /examples/clustering_experiments.ipynb +```{button-ref} /examples/classification_experiments.ipynb :color: primary :click-parent: :expand: -TSCL +TSC ``` ::: :::{grid-item-card} -:img-top: examples/images/wip.png +:img-top: examples/images/TSR.png +:class-img-top: tsml-card-image :text-align: center -tsml-eval Results Evaluation +Time Series Extrinsic Regression ^^^ -Placeholder. +TODO Placeholder. +++ -```{button-ref} /examples/evaluation.ipynb +```{button-ref} /examples/regression_experiments.ipynb :color: primary :click-parent: :expand: -Evaluation +TSER ``` ::: :::{grid-item-card} -:img-top: examples/images/wip.png +:img-top: examples/images/TSCL.png +:class-img-top: tsml-card-image :text-align: center -tsml-eval Results Format +Time Series Clustering ^^^ -Placeholder. +TODO Placeholder. +++ -```{button-ref} /examples/results_format.ipynb +```{button-ref} /examples/clustering_experiments.ipynb :color: primary :click-parent: :expand: -Results +TSCL +``` + +::: + +:::{grid-item-card} +:img-top: examples/images/forecasting.png +:class-img-top: tsml-card-image +:text-align: center + +Time Series Forecasting + +^^^ + +TODO Placeholder. + ++++ + +```{button-ref} /examples/forecasting_experiments.ipynb +:color: primary +:click-parent: +:expand: + +Forecasting ``` ::: @@ -141,9 +170,10 @@ Results :hidden: ./examples/datasets.ipynb +./examples/evaluation.ipynb +./examples/results_format.ipynb ./examples/classification_experiments.ipynb ./examples/regression_experiments.ipynb ./examples/clustering_experiments.ipynb -./examples/evaluation.ipynb -./examples/results_format.ipynb +./examples/forecasting_experiments.ipynb ``` diff --git a/examples/_wip/diagrams.ipynb b/examples/_wip/diagrams.ipynb deleted file mode 100644 index 4b0d7612..00000000 --- a/examples/_wip/diagrams.ipynb +++ /dev/null @@ -1,270 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "

Diagrams

\n", - "\n", - "To produce diagrams you need result data in either the 'summary df format' or the\n", - "MetricResult format. To get the data in this format see evaluation_metric_results.ipynb." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [], - "source": [ - "import platform\n", - "\n", - "from sktime_estimator_evaluation.evaluation import (\n", - " evaluate_metric_results,\n", - " metric_result_to_summary,\n", - ")\n", - "\n", - "\n", - "def custom_classification(path: str):\n", - " # Check os to determine split value\n", - " if \"Windows\" in platform.platform():\n", - " split_subdir = path.split(\"\\\\\")\n", - " else:\n", - " split_subdir = path.split(\"/\")\n", - " metric_name = \"ACC\"\n", - " file_name_split = split_subdir[-1].split(\"_\")\n", - " estimator_name = file_name_split[0]\n", - " split = file_name_split[0].split(\"FOLDS\")[0].lower()\n", - " return estimator_name, metric_name, split\n", - "\n", - "\n", - "classification_results = evaluate_metric_results(\"../results/\", custom_classification)\n", - "\n", - "\n", - "# Both of the above formats are valid. More detail on both can be found in the\n", - "# evaluation_metric_result.ipynb notebook." - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'tuple' object has no attribute 'iloc'", - "output_type": "error", - "traceback": [ - "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m", - "\u001B[1;31mAttributeError\u001B[0m Traceback (most recent call last)", - "Cell \u001B[1;32mIn [2], line 4\u001B[0m\n\u001B[0;32m 1\u001B[0m \u001B[38;5;66;03m# Critical difference diagrams\u001B[39;00m\n\u001B[0;32m 2\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01msktime_estimator_evaluation\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mevaluation\u001B[39;00m\u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mdiagrams\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m critical_difference_diagram\n\u001B[1;32m----> 4\u001B[0m figures \u001B[38;5;241m=\u001B[39m \u001B[43mcritical_difference_diagram\u001B[49m\u001B[43m(\u001B[49m\u001B[43mclassification_results\u001B[49m\u001B[43m)\u001B[49m\n", - "File \u001B[1;32m~\\Projects\\sktime-estimator-evaluation\\sktime_estimator_evaluation\\evaluation\\diagrams\\_critical_difference_diagram.py:79\u001B[0m, in \u001B[0;36mcritical_difference_diagram\u001B[1;34m(metric_results, output_path, title, alpha, color, space_between_labels, fontsize, title_fontsize, figure_width, figure_height)\u001B[0m\n\u001B[0;32m 22\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mcritical_difference_diagram\u001B[39m(\n\u001B[0;32m 23\u001B[0m metric_results: Union[pd\u001B[38;5;241m.\u001B[39mDataFrame, List[MetricResults]],\n\u001B[0;32m 24\u001B[0m output_path\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 32\u001B[0m figure_height: \u001B[38;5;28mfloat\u001B[39m \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mNone\u001B[39;00m,\n\u001B[0;32m 33\u001B[0m ) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m Union[plt\u001B[38;5;241m.\u001B[39mFigure, List[plt\u001B[38;5;241m.\u001B[39mFigure]]:\n\u001B[0;32m 34\u001B[0m \u001B[38;5;124;03m\"\"\"Create a critical difference diagram.\u001B[39;00m\n\u001B[0;32m 35\u001B[0m \n\u001B[0;32m 36\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 77\u001B[0m \u001B[38;5;124;03m figures is return else plt.Figure is returned.\u001B[39;00m\n\u001B[0;32m 78\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 79\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[43mmetric_result_to_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43mmetric_results\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 80\u001B[0m num_metrics \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(df\u001B[38;5;241m.\u001B[39mcolumns) \u001B[38;5;241m-\u001B[39m \u001B[38;5;241m2\u001B[39m\n\u001B[0;32m 81\u001B[0m figures \u001B[38;5;241m=\u001B[39m []\n", - "File \u001B[1;32m~\\Projects\\sktime-estimator-evaluation\\sktime_estimator_evaluation\\evaluation\\diagrams\\_utils.py:38\u001B[0m, in \u001B[0;36mmetric_result_to_df\u001B[1;34m(result)\u001B[0m\n\u001B[0;32m 35\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(result, pd\u001B[38;5;241m.\u001B[39mDataFrame):\n\u001B[0;32m 36\u001B[0m df \u001B[38;5;241m=\u001B[39m metric_result_to_summary(result)\n\u001B[1;32m---> 38\u001B[0m df \u001B[38;5;241m=\u001B[39m \u001B[43m_check_df\u001B[49m\u001B[43m(\u001B[49m\u001B[43mdf\u001B[49m\u001B[43m)\u001B[49m\n\u001B[0;32m 39\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m df\n", - "File \u001B[1;32m~\\Projects\\sktime-estimator-evaluation\\sktime_estimator_evaluation\\evaluation\\diagrams\\_utils.py:55\u001B[0m, in \u001B[0;36m_check_df\u001B[1;34m(df)\u001B[0m\n\u001B[0;32m 42\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_check_df\u001B[39m(df: pd\u001B[38;5;241m.\u001B[39mDataFrame) \u001B[38;5;241m-\u001B[39m\u001B[38;5;241m>\u001B[39m pd\u001B[38;5;241m.\u001B[39mDataFrame:\n\u001B[0;32m 43\u001B[0m \u001B[38;5;124;03m\"\"\"Check if data frame is valid.\u001B[39;00m\n\u001B[0;32m 44\u001B[0m \n\u001B[0;32m 45\u001B[0m \u001B[38;5;124;03m Parameters\u001B[39;00m\n\u001B[1;32m (...)\u001B[0m\n\u001B[0;32m 53\u001B[0m \u001B[38;5;124;03m Validated dataframe\u001B[39;00m\n\u001B[0;32m 54\u001B[0m \u001B[38;5;124;03m \"\"\"\u001B[39;00m\n\u001B[1;32m---> 55\u001B[0m datasets \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m((\u001B[43mdf\u001B[49m\u001B[38;5;241;43m.\u001B[39;49m\u001B[43miloc\u001B[49m[:, \u001B[38;5;241m1\u001B[39m])\u001B[38;5;241m.\u001B[39munique())\n\u001B[0;32m 56\u001B[0m estimators \u001B[38;5;241m=\u001B[39m (df\u001B[38;5;241m.\u001B[39miloc[:, \u001B[38;5;241m0\u001B[39m])\u001B[38;5;241m.\u001B[39munique()\n\u001B[0;32m 57\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28misinstance\u001B[39m(df, pd\u001B[38;5;241m.\u001B[39mDataFrame):\n", - "\u001B[1;31mAttributeError\u001B[0m: 'tuple' object has no attribute 'iloc'" - ] - } - ], - "source": [ - "# Critical difference diagrams\n", - "from sktime_estimator_evaluation.evaluation.diagrams import critical_difference_diagram\n", - "\n", - "figures = critical_difference_diagram(classification_results)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "data": { - "text/plain": "
", - "image/png": 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\n" 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lSbVf8/py3F5vw3oQ77zwJ7offhgHH31Uh+t9/cmn+b93/8a3fv5fHd6HUu21duUqfn7OVCLhCF6flxsWPtNqCDdu6/F5+dNf/kLxiV9f6dl4/DXRCmT5RKehtSLZ8O2INa8vZ+3KVQ3P33nhz3z5j3+2ax927E+0VHFsO6X7U4VhzevLiYQjiG1jhSOseX15Um2dcISVr77WsC0+6yG+Di/AnDlz8jp8QXvArYovoLNt/QYeuOQ77N25C8eymPmL2zly5Ij92v/pV7/m/Zf/iq8owHce/jXdDzuUHZs3s+C671O37gsAZvzXbXQ9qCdLH1uAy+2m9nfPMuP223jvT4v5xxu1/PGue7h6wSMA/M8Nc9i5uQ5fUREX/+oueh55BI9891q8fj+frf6AvsOHNizGE7dt/Xp+cV4NG//vU044cwLTbrsZgA/+uoyFt99FpL6eqkMP4dL77iFQWsL1/YcybPLZfLjsFc74f1dR0rVLi+2Uas3RJ43E6/NihcHj83L0SSNbbdtr5DA8Pi9OOHpi7bPPPmuYg3vrrbdSX1/fsA5vqu88ka00gNuw4vfP0e+0UUy8fjaObVO/Z2+L7YrKgvx0+VLe+O3veGrOLcx++n948ge3MO7KWRxZPZy6z9fxi/Nq+K83X+PUi2fiLylhwjVXAjBwwjgGnD6WoWefBcCdZ09l5i/voMfhh/HJqndYcP0cvr/o9wBs/fIrfvjSH3G53fvV8PnqD7n1lb/g9fuYM/QkRs+6BF9RgD/edQ83PPc7/CXFvHDPfbz064c4+8brACjt1pX/fOUv7Kyr475vXdpqO6Va0nfYEG5Y+EyLY8CNx4YrTxjI4cOG8sfFi1n4myd57LHHePjhhxtmO1iWheM4uFyuTpuDmw3SGsDGmPHArwA38IiI3N5sex/gCaBLrM0PROTFdNbUXocOGsD8a67DtiwGnzmePsf3a7Hd8PMmR/87ZTJP3XQrAB+98mqTNRr27tzFvl27E37evl27WbtyFb++aFbDa1a4vuHxkHMmthi+AMd88ySKQ2UAHHTUkdR9vo4923fw5T8+5mfjJwFgR8IcPvTrX5Jhk6Ovf/LWOwnbKdWavsOG7Dfu22S81+vlqoVPc/OYCXQ76WDeee11LMtqsoi6iOByuRgzZgy33npr0r3fTC5DmQppC2BjjBu4HxgLrAPeMsYsEpGPGjX7EfA7EXnAGHMs8CJwSLpq6oijTqzmBy88x/uLX+aR787m9KtmceL0afu1a7xcXvyhOA43/+V5vIFA0p8njkNxqIzbXnu5xe3+4uJW3+vxfb0esMvtjo7rinDcqG9yxaMPtLy/ktj+2minVHs0Hu+1gZJV79Pt9Oj/3BvPcvB4PIgItm3j8/mahG9b4ZpLq561Jp0n4YYBa0XkXyISBn4LnN2sjQBlscch4Ms01tMhmz/7nFBVJd/89oWc8q0ZfPq31S22W/m/f2j4b7zneNypo3h53vyGNvE1HwKlpdFLj2MaPy8qC1LRpw9vLYyeBBQRPlv9YYfrP2zoYP755ko2/Ov/AKjfvYf1az/pcDulknH0SSPx+LyY2M0szzxtdMO2xms1LF26lGXLlu23bkPzk3ItrQfRfO2GBQsWdOpKZqmQziGIg4HPGz1fBwxv1uZWYLEx5hqgBBjT0o6MMbOAWQBde3Xo3ncdtuaNWv58769xe734S4q5/MH/brHd7m3bufnE0/D4fVzxSLQXecEdP+F/briJm088Dce2OLJ6BN+++04Gjh/L/d++nHdffIkL7/gpw889m8dnX8/LDz3KVU88zHcevp8F//F9/njXPdhWhGHnnkOf44/rUP1lFRVc+utf8eBlV2LVR+9dde4Pv0+Pvod3qJ1Syeg5ZBCznn2KsrdXM3H0mP16ps3n4Tbfnszt4Bv3pN1uN4899hiWZeVUbzhta0EYY84DxovIZbHn3wKGi8jVjdpcF6vhF8aYauBRoJ+IOK3tV9eCUCq77XZs9jg2M0LdOdjbsdtkJTu8EB+m+Oyzz3j44YexbTvla0ckIxvXgvgC6N3oea/Ya41dCowHEJFaY0wAqAA2prEupVSa7HVsdjk2NaGqDocvNL3pZqITbPGedG1tbcOt5XNpFkU6A/gt4AhjzKFEg3c6MKNZm8+A0cDjxphjgACwKY01KaUOQKLLjvc5Djscm2lllfTxJn/iuTXtuVw42cDONmkLYBGxjDFXAy8RnWI2X0Q+NMbcBqwSkUXAfwAPG2O+R/SE3EWSa+tjKlUgEl12vM9x2OpYTA1WcKivKCP1Zcv6Du2R1nnAsTm9LzZ77ZZGjz8CTkxnDUqp1Gh62XH0ed9hQ6iXaPhODpbT19/6NEm1P10LQimVlPhlxy63u+Gy47A4bLEjTCot52i/XrbeXnopslIqKc0vO/7G0MFssiwmBss5LpD68M31q9ySoQGslEpa/LLjiAib7DATSss5PlCa8D2JgrS1bflwlVsyNICVUu1iibDRDjOupCuDitoO32RuA+92u7nkkksa7k6RzIUY+UDHgJVSSbNF2GiFOa24C0MCwTbbJ7rVe/NtDz30UMNlx/Gr3NyxS5lzZV5ve2kAK6WSYouwwY7wzZIujCgqa7IAVWsSBWl8W3w/ItKktxtfLyJfhx9AhyCUUklwRFhvhzmxKMTIJMMXEl8gEd+2YMGCJus4NL5DMdDQa87HEE7bWhDpomtBKNW5HBHWWxGGFwU5raRL0uHbHo1PxkE0dMvLy5k9e3ZOnIjLxrUglFI5bO3KVfz99eVUVg9h8smjODXJ8D2Q6WOrV69uCF1jDI7j5PVtijSAlVL7WbtyFXeeMxUrdtnxhS8vwTWy9fu9xXVk+ljj9zQOXZfLhdvtxhiTtyfi9CScUmo/a15fjhUOR+9oEY7w2iuvJPW+RLMeknmP4zi43W7cbjd+v5/77rsvr0/EaQ9YKdWEiFA5Yihenw87HGlX77PxIunJvq/5e+655x7q6ury+gq4OD0Jp5RqILGpZn19RXT/4GNef+XVdgdhR8aAc/2y446ehNMAVkoB0fDdaEc4xBtgclkFPqMjlMnqaADrv7BSChFhkx2ht9ev4duJ9F9ZKcVmO0JPj59zg5Uthm9tbW3O3XE4F+hJOKUK3CYrQqXHx3llFQRcLYdvIaxMlgnaA1aqgG22InRze5haVkmRy91im45MLVPJ0QBWqkDVWRFCbjfnh6ooaSV8IfGCOsnQ4YvW6RCEUgVoix2hxOXm/LIqShOELxzYHYd1+CIxDWClCsw2O0LAuKgJVVHmTi4COnrH4cbDF/v27WPBggUawI1oACtVQLbbFu5Y+IaSDN8DMWrUKDweD7ZtIyLMnz+fmTNnAuT0hRepomPAShWI7bYFBmaUVdHV7e2Uz6yurubiiy9uWEXNtm0WLFjA6NGjufnmmxvugFGoNICVKgA7bAsBZpR1p9zTOeEbN3PmTAKBQMNJPEBnVcToEIRSeW6XYxNBuDDUncpODN/G6zs0PokH8MQTT7RrwZ58pQGsVB7b7djscxwuCFXR3ePrtM9tafbDnDlzGrZ3dFZFvtEAVipP7XFs9jg2M0Ld6en1d+pnt3Vb+Y7Oqsg3OgasVB7a69jsdGzOD1VxcIrCtz0XVBTKbeUPlPaAlcoz+xyH7Y7NtGAlfbyBlOyzvRdUHMjFG4VEA1ipPLLPcdjmWJwXrOAwf1HK9tvWkEJLdJihbToEoVSeqBeHrY7FOcFy+vqLU7pvHVJID+0BK5UHwuKwxY4wqbSCo/0lTbal4nY/7RlSyPXbC3UmDWClclxEHDZbFhOD5RwX2D98U7UYTjJDCrr4TvvoEIRSOSwSu5XQhNJuHB8o3W97Z6/lm+rPy/elLLUHrFSOskTYaIcZW9KVQUX7hy907DbxHTVv3jwWLlyIK3ZXjQP9vELoTWsAK5WDbBE2WmFOK+nC0ECw1Xapng5WW1vLggULgOgaD/H9zZs3j+985zsN7c455xxuvPHGA/q8jsy8yDUawErlGFuEDXaYk4tDjCgqa1hprDUtjd125ERZbW0tp556KvX19QDMnz+/IRSfffbZJm337NlzwGHZmb33TNEAViqHOLHwHVkU4qTiUJvh25KO/mkf75HGRSKRhgCeMmUKixcvbtg2ZcqUdtfVXCFczKEBrFSOcERYb4UZXlTGKR0MX+j4n/bxHmm8B+z1eht6pbNmzQLg2WefZcqUKQ3PD1S+X8yhAaxUDnBEWG+HOaEoyKklXXB1MHyh43/aV1dXs3Tp0hbHgCEawqkK3kJhRCTTNbRL74H95cdLX8JzAD+ASuUSEeErO8LAQAmnl3TDnYKffb1YIrWMMW+LyJD2vk97wEplMYn1fPv5Uhe+kP9/2ucKvRBDqSwlIqy3IhzlK+GMYHnKwldlDw1gpbKQiLDBjtDXH2BisJsOueUpDWClsoyIsNGOcKg3wNnBCrxGf03zlX5nlcoi8fDt7fUzuawCn4ZvXtPvrlJZZLMdoafHx7nBSg3fAqDfYaWyxCYrQqXHx9SySgIu/dUsBPpdVioLbLYidHN7mFpWSZHLnelyVCfRAFYqw+qsCCG3m/NDVZTkQfjm+xq+qaQXYiiVQVvsCCUuN+eXVVGaJ+Gb72v4ppL2gJXKkG12hIBxUROqosydH32hzr4DR65LawAbY8YbY/5hjFlrjPlBK22mGWM+MsZ8aIx5Mp31KJUtttkW7lj4hvIkfEHvntxeafvOG2PcwP3AWGAd8JYxZpGIfNSozRHAHOBEEdlqjKlKVz1KZYvttoUxMKOsiq5ub6bLSalCWMM3ldL5v95hwFoR+ReAMea3wNnAR43aXA7cLyJbAURkYxrrUSrjdtgWAlxQVkW5p2n45ssKZbrQT/LSGcAHA583er4OGN6szZEAxpg3ADdwq4j8ufmOjDGzgFkAXXsdnJZilUq3XY5NBOHCUHcqPb4m2/TkVWHK9Ek4D3AEMAqoAR42xnRp3khE5onIEBEZUlLerXMrVCoFdjs2+xyHGWVVdG8WvqAnrwpVOgP4C6B3o+e9Yq81tg5YJCIREfk/4GOigaxU3tjj2OxxbGpCVfT0+lts0xknr3R+bvZJ5xDEW8ARxphDiQbvdGBGszYLifZ8HzPGVBAdkvhXGmtSqlPtdWx2OQ7TQ1Uc3Er4QvpPXukQR3ZKWwCLiGWMuRp4iej47nwR+dAYcxuwSkQWxbaNM8Z8BNjADSJSl66alOpM+xyH7Y7NtGAlfbyBNtun8+RVR2/EqdIrrRMQReRF4MVmr93S6LEA18W+lMob+xyHrbbF1LIKDvMXZbqcDt+IU6VXqwFsjAkAQRHZ1Oz1SmCniOxLd3FK5aJ6cdjqWEwuK6evvzjT5QA6PzdbJeoB3wv8GfjfZq+fBIwDrkxXUUrlqrA4bLEjTCqt4Gh/SabLaULn52afRLMgThCR5uGLiDwHnJK+kpTKTRFx2GxFOLO0nOMC2RW+KjslCuBEfztlev6wUlklIsImO8L40m70D5RmuhyVIxIF6UZjzLDmLxpjhgKbWmivVEGyRNhkhxlT0pXBRcFMl6NySKIx4BuA3xljHgfejr02BJhJdE6vUgXPFmGjFebUki4MDWj4qvZptQcsIiuJLqhjgItiXwYYLiJvdkZxSmUzW4QNdpiTi0OMKCrDGJPpklSOSTQNrRKoEJEfN3v9WGOMNJ+eplQhcWLhO7IoxEnFIQ1f1SGJxoD/G6ho4fVy4FfpKUep7OeIsN4KMzxQxikavuoAJArgviLyavMXReQ1oH/6SlIqezkirLfDnFAU5NSSLrg0fNUBSBTAic4o5Ncy/kolQURYb0cYGChlTElXDV91wBIF8FpjzBnNXzTGTEBXLFMFRmI9336+Yk4v6YZbw1elQKJpaLOBF4wx02g6Da0aOCvNdSmVNUSEDXaEo3wlnBEsb3f45suthlTqtRrAIvJPY8zxRNfw7Rd7+RXgO7oQjyoU8fA93BdgYrAbng6Er67Dq1qTcDlKEakHHos/N8aUAOcZY2pE5Mx0F6dUJokIG+0Ih3gDnB2swGvafwW+rsOrEmnzJ8oY4zPGTDbGPAN8BYwGHkx7ZUplUDx8e3v9TA5W4OtA+ELn3GpI5a5EF2KMI3q7oHHAUmABMFRELu6k2pTKmM2ORU+Pj3ODlfhdHV97StfhVYkkGoL4M/AacFLshpkYY/QCDJX3NlkRKj1eppZVEjiA8I3TdXhVaxIF8GCii+68bIz5F/Bbovd2UypvbbYidHN7mFpWSZFLf9xVeiVajOc9EfmBiBwO/BgYCHiNMX8yxszqrAKV6ix1VoQyt5vzQ1WUZHH45tPt5fPpWDoiqZtyishyYLkx5lpgDNGe8bx0FqZUZ9piRyhxuZleVkVpK+GbDfN582laWz4dS0e1667IIuIAi2NfSuWFbbZFwLioCVVR5m75VyJbwiKfprXl07F0lN5aSBW0bbaF2xhqQlWEWglfaDksMiGfprXl07F0VLt6wErlk+22hTEwo6yKru7E60vFwyLeA85UWOTTtLZ8OpaOMiLS8gZjioGIiERiz48CzgA+beluyZ2l98D+8uOlL7X7klClGtthWzjABaEqKj2+pN6TDWPAKjsZY94WkSHtfV9b84AvBf5pjOkL1AK/Ac4yxgwVkTkdK1WpzNrl2EQQLgh1Tzp8QefzqtRLNAbcVUT+GXv8beApEbkGmICuhqZy1G7HZp/jMKOsih7tCF+l0iFRADcemzgN+AuAiIQBJ51FKZUOexybPY5NTaiKnl5/pstRKuEQxPvGmLuAL4C+xKaeGWO6dEJdSqXUXsdml+MwPVTFwRq+Kksk6gFfDmwGDgHGicie2OvHAneluS6lUmaf47DdsTkvWEEfbyDT5SjVINGC7HuB2+PPjTFeoguzr41dGadU1tvnOGx1LKYGKzjMX5TpcpRqotUesDHmQWPMcbHHIeBvRJekfNcYU9NJ9SnVYfUSDd/JwXL6+oszXY5S+0k0BHGyiHwYe3wx8LGIHA+cANyY9sqUOgBhcaizI0wqLedof0nK9lvoi8eo1Ep0Ei7c6PFY4BkAEVlv9CIIlcUi4rDZinBWsJzjAqkN32xYD0Llj0Q94G3GmLOMMYOAE4lemIExxgPoYJrKShERNtkRxpd2o3+gNKX7zpb1IFT+SNQD/g5wL9ATmC0i62OvjwZeSHdhSrWXJcJGK8zY0q4MLgqmfP/Zsh6Eyh+JZkF8DIxv4fWXgJfSWZRS7WXHwve0ki4MDaQ+fEEXj1Gpl3A1NGPMBOAHwHGxlz4E7hCRF9NdmFLJskXYYIc5uTjEiKIy0nmOQteDUKmU6K7IlxMdhrgRWBV7eQhwuzGml4joHTFUxjkirLfDnFgU4qTiUFrDV6lUS9QD/h7ROyJvafTaX2O94tfRWxKpDHNEWG+FGV4U5BQNX5WDEs2CMM3CFwARqUtjPUolJd7zPaEoyGklXXFp+KoclCiAdxhjBjR/MfbazvSVpFRiIsIGO8IAfyljNHxVDks0BPEfwCJjzGPA27HXhhBdG/jCdBemVEsk1vM91lfC+NJuuDV8VQ5rtQcsIq8Dw2NtLop9uYARsW1Kdap4z/coXwlnBss1fFXOSzgNLXbxxS3x58aYCkDHgFWnExE22hEO9wWYGOym9wRUeSHRamgjjDHLjDH/a4wZZIz5APgA2GCM2e8CDaXSJR6+3/AGODtYgdckOnWhVO5I1AO+D7gJCAF/BSaIyApjzNHAU8TWhlAqneLh28vrZ3KwAp+Gr8ojiX6aPSKyWESeAdaLyAoAEVnTOaUpBZsdi54eH1OClfhdGr4qvyT6iW584829zbYJSqXZZitChdvL1LJKAhq+Kg8lGoIYYIzZARigKPaY2HO9sZZKqzorQhe3h2lllRS53JkuR6m0SLQamv7Uq4yosyME3W6mh6oo0fBVWaS2tjalq+ElnIZ2oGKzJX4FuIFHROT2VtpNAX4PDBWRVS21UYVhix2hxLiZXlZFqYavyiLpuCNK2gbWjDFu4H5gAtFb2dcYY45toV0QuBZ4M121qNywzbYIGBc1oSrK3GntGyjVbum4I0o6z2wMI3oL+3+JSBj4LXB2C+1+AtwB7EtjLSrLbbMt3MYwPVRFSMNXZaH4HVHcbnfK7oiSzp/0g4HPGz1fR/TS5gbGmMFAbxF5wRhzQ2s7MsbMAmYBdO11cBpKVZm0w7YwBmaUVdHN7c10OUq1KB13RMlYV8MY4wJ+SXSNiYRii7/PA+g9sL9OgcsjOx0LB7igrIpyj4avym6pviNKOocgvgB6N3reK/ZaXBDoBywzxvwbGEF09bUhaaxJZZFdjk1YhOmhKio9vkyXo1SnS2cAvwUcYYw51BjjA6YDi+IbRWS7iFSIyCEicgiwApiksyAKw27HZp/jUFNWRQ8NX1Wg0hbAImIBVxO9g/Lfgd+JyIfGmNuMMZPS9bkq++1xbPY4NtNDVRzk9We6HKUyJq1jwLG7J7/Y7LVbWmk7Kp21qOyw17HZ5ThMD1XRS8NXFTi9wF51mn2Oww7H5rxgBX28ejW7UhrAqlPUOw5bHYspwQoO8xdluhylsoIGsEq7sDhscSwmB8vp6y/OdDlKZQ0NYJVWYXHYbEeYWFrO0f6STJejVFbRAFZpExGHzVaEs0rL6RfQ8FWqOQ1glRYRETbZEcaXdqN/oDTT5SiVlTSAVcpZImy0wowp6crgomCmy1Eqa2kAq5SyRdhghTm1pAtDAxq+SiWiAaxSxhZhgx3m5OIQ1UVlGGMyXZJSWU0DWKWEI8J6O0x1URknF4c0fJVKggawOmCOCOutMMMDQb5Z3EXDV6kkaQCrAxLv+Q4uCnJaSVdcGr5KJU0DWHWYiLDBjtDfX8pYDV+l2k0DWHWIxHq+x/qKmVDaDbeGr1LtpgGs2i3e8z3KV8KZwXINX6U6SANYtYuIsNGOcLgvwMRgNzwavkp1mAawSlo8fL/hDXB2sAKv0R8fpQ6E/gappEhsbYdeXj+TgxX4NHyVOmD6W6SSstmx6OHxcW6wAr9Lf2yUSgX9TVJt2mxFqHB5mFpWSZHLnelylMobGsAqoTorQhe3h2mhKg1fpVJMA1i1qs6OEHS7mR6qokTDV6mU0wBWLdpiRyg2bqaXVVGq4atUWmgAq/1ssy0CxsWMUBVlbk+my1Eqb2kAqya22RZuY5geqiKk4atUWmkAqwY7bAtjoKasim5ub6bLUSrvaQArAHY6Fg7CjLIqKjwavkp1Bg1gxS7HJizC9FB3Kj2+TJejVMHQAC5wux2bfY5DTVkVPTR8lepUGsAFbI9js8exmR6q4iCvP9PlKFVwNIAL1F7HZpfjcH6oil4avkplhAZwAdrnOOxwbM4LVtDHG8h0OUoVLA3gAlPvOGx1LKYEKzjMX5TpcpQqaBrABSQsDnVOhMnBcvr6izNdjlIFTwO4QITFYbMdYVJpBUf7SzJdjlIKDeCCEBGHzVaEM0vL6RfQ8FUqW2gA57lI7FZCp5d2Y0CgNNPlqBxXWtr0Z+jxxx/n6quvbni+YMEC+vXrx/HHH8+gQYO46667ALjhhhs4+uij6d+/P5MnT2bbtm2dWXbW0gDOY5YIG60wY0q6MjjLwre2tpa5c+dSW1ub6VJUivzpT3/innvuYfHixaxevZoVK1YQCoUAGDt2LB988AHvv/8+Rx55JHPnzs1wtdlBl7vKU7YIG6wwp5Z0YWggiMmi28fX1tYyevRowuEwPp+PJUuWUF1dnemy1AGaO3cud911FwcddBAAfr+fyy+/HIBx48Y1tBsxYgS///3vM1JjttEAzkO2CBvsMCcXh6guKsuq8AVYtmwZ4XAY27YJh8MsW7ZMAzhH7N27l4EDBzY837JlC5MmTQLggw8+4IQTTmhzH/Pnz+f8889PV4k5RQM4zzgirLfDVBeVcXJxKOvCF2DUqFH4fL6GHvCoUaMyXZJKUlFREe+9917D88cff5xVq1Yl/f6f/exneDweLrjggjRUl3s0gPOII8J6K8ywoiCjirtkZfgCVFdXs2TJEpYtW8aoUaO095snjjvuON5++21OO+20Frc//vjjPP/88yxZsiRrfzY7m56EyxPxnu+goiCjS7riyvIf8OrqaubMmaPhm0fmzJnDDTfcwPr16wEIh8M88sgjAPz5z3/mzjvvZNGiRRQX60VAcdoDzgMiwno7Qn9/KeNyIHxVfjrjjDPYsGEDY8aMQUQwxnDJJZcAcPXVV1NfX8/YsWOB6Im4Bx98MJPlZgUjIpmuoV16D+wvP176Eh4NGSAWvlaYY/0lnBUsx63/Lkp1OmPM2yIypL3v0yGIHCYibLAjHOUv5kwNX6VyjgZwjhIRNtoRDvMFmBgs178IlMpBGsA5KB6+3/AGOCdYgdfot1GpXKS/uTlokx2hl9fP5GAFPg1f1YKRI0cm3faQQw5h8+bNSbdftmwZy5cvb3i+cOFCPvroo3bV11zzNSUKhf725phNdoQeHh/nBivwu/Tbp1rWOCBTLRUBbFlWSmuybTul++ss+hucQzZbESpcHqaWVVLkcme6HJXF4quWffXVV5xyyikMHDiQfv368dprr7XY/s477+T4449n2LBhrF27FoBNmzYxZcoUhg4dytChQ3njjTf497//zYMPPsjdd9/NwIEDeeWVV1i0aBE33HADAwcO5JNPPuGTTz5h/PjxnHDCCZx88smsWbMGgIsuuogrrriC4cOHc+ONN+5Xw5dffsn48eM54ogjmmxfvHgx1dXVDB48mKlTp7Jr1y4g2nP//ve/z+DBg3nmmWdabZfNdB5wjqizInRxe5gWqtLwVUl78sknOf300/nhD3+Ibdvs2bOnxXahUIjVq1ezYMECZs+ezfPPP8+1117L9773PU466SQ+++wzTj/9dP7+979zxRVXUFpayvXXXw/ApEmTOOusszjvvPMAGD16NA8++CBHHHEEb775Jt/97nf561//CsC6detYvnw5bvf+P8Pvvfce7777Ln6/n6OOOoprrrmGoqIifvrTn/Lyyy9TUlLCHXfcwS9/+UtuueUWAMrLy3nnnXfYvHkz5557bqvtslVaA9gYMx74FeAGHhGR25ttvw64DLCATcAlIvJpOmvKRXV2hKDbzfRQFSUavqodhg4dyiWXXEIkEuGcc85pspBOYzU1NQ3//d73vgfAyy+/3GRoYceOHW32Knft2sXy5cuZOnVqw2v19fUNj6dOndpi+EI0uOPLVx577LF8+umnbNu2jY8++ogTTzwRiF5d1/jqyfiiPitWrEjYLlulLYCNMW7gfmAssA54yxizSEQaDxa9CwwRkT3GmCuBOwFdJqmRrXaEYuPm/LIqSjV8VTudcsopvPrqq7zwwgtcdNFFXHfddcycOXO/do3XZog/dhyHFStWEAgkf+dsx3Ho0qVLkwV7Gispaf2OLH6/v+Gx2+3GsixEhLFjx/LUU08l3F9b7bJVOseAhwFrReRfIhIGfguc3biBiCwVkfjfRCuAXmmsJ+dssy18xkVNqIqQW0eLVPt9+umndO/encsvv5zLLruMd955p8V2Tz/9dMN/4z3HcePG8d///d8NbeKhGgwG2blzZ8PrjZ+XlZVx6KGH8swzzwDRYPzb3/7W4fpHjBjBG2+80TAuvXv3bj7++OMOt8s26Qzgg4HPGz1fF3utNZcCf2ppgzFmljFmlTFm1e66LSksMXttty3cxlATqqKLhq/qoGXLljFgwAAGDRrE008/zbXXXttiu61bt9K/f39+9atfcffddwNw7733smrVKvr378+xxx7bsHbDxIkTee655xg4cCCvvfYa06dP5+c//zmDBg3ik08+4Te/+Q2PPvooAwYM4LjjjuMPf/hDh+uvrKzk8ccfp6amhv79+1NdXd1wUq8j7bJN2taCMMacB4wXkctiz78FDBeR/Sb7GWMuBK4Gviki9c23N1YIa0HssC3EwAVl3anweDNdjlKqDR1dCyKdXasvgN6NnveKvdaEMWYM8EOSCN9CsNOxcBBmaPgqlffSOQTxFnCEMeZQY4wPmA4satzAGDMIeAiYJCIb01hLTtjl2IRFmB7qTpXHl+lylFJplrYAFhGL6LDCS8Dfgd+JyIfGmNuMMZNizX4OlALPGGPeM8YsamV3eW+3Y7PPcagpq6KHhq9SBSGtZ3dE5EXgxWav3dLo8Zh0fn6u2OPY7HFsakLdOcjrb/sNSqm8oJciZ9hex2aX4zAtVEUvDV+lCooGcAbtcxx2ODbnBSv4hjf5ye5KqfygAZwh9Y7DVsfi3GAFh/mLMl2OUioDNIAzICwOW5wIk4PlHOHXO8QqVag0gDtZWBw22xEmllZwtL/16+KVUvlPA7gTRcRhsxXhzNJy+gU0fJUqdBrAncQSYZMd4fTSbgwIlGa6HKVUFtAA7gSWCBusMGNKujJYw1cpFaMBnGZ2LHxPLenC0ECwybqrSqnCpgGcRrYIG+wwJxeHqC4q0/BVSjWhAZwmTix8RxSVcXJxSMNXKbUfDeA0cERYb4UZGggyqriLhq9SqkUawCnmiLDeDjOoKMjokq64NHyVUq3QAE4hEWGDHaG/v5RxGr5KqTZoAKeIxIYdjvEVM6G0G24NX6VUGzSAUyDe8z3KX8xZwXINX6VUUjSAD5CIsNGOcJgvwMRgeV7fLFQplVoawAcgHr59vAHOCVbgNfrPqZRKnibGAdhkR+jl9XNusAKfhq9Sqp00NTpokx2hh8fHucEK/C79Z1RKtZ8mRwdstiJUuDxMLaukyOXOdDlKqRylAdxOdVaELm4P00JVGr5KqQOiAdwOdXaEUpeb6aEqSjR8lVIHSAM4SVvtCMUmGr6lGr5KqRTQAE7CNtvCZ1zUhKoIuT2ZLkcplSc0gNuw3bZwG0NNqIouGr5KqRTSAE5gh22BgZqyKrq5vZkuRymVZzSAW7HTsbARasqqqPBo+CqlUk8DuAW7HJuwCDWh7lR5fJkuRymVpzSAm9nt2OxzHGrKquih4auUSiMN4Eb2ODa7HZvzQ1Uc5PVnuhylVJ7TAI7Z69jsjIVvbw1fpVQn0AAG9jkO2x2bqcFKvuENZLocpVSBKPgArncctjoW5wYrOMxflOlylFIFpKADOCwOdU6Ec0rLOdJfnOlylFIFpmADOCwOm6wIE0vLOSZQkulylFIFqCADOCIOm60IZwXLOT5QmulylFIFquAC2BJhkx3h9NJuDNDwVUplUEEFsCXCRivM6JKuDNbwVUplWMEEsC3CBivMN0u6MCwQxOjt45VSGVYQAWyLsMEOc3JxiJFFZRq+SqmskPcB7MTCd0RRGScXhzR8lVJZI68D2BFhvRVmaCDIqOIuGr5KqayStwHsiLDeDjOoKMjokq64NHyVUlkmLwNYRNhgR+jvL2Gchq9SKkvlXQBLbNjhGF8xE0rLcWv4KqWyVF4FsIiw3o5wpL+Ys4Iavkqp7JY3ASwibLQjHO4LMClYjkfDVymV5fIigOPh28cb4JxgBV6TF4ellMpzeZFUm+wIB3v9nBuswKfhq5TKETmfVpvsCN09PqYEK/C7cv5wlFIFJKcTa7MVocLlYWpZJUUud6bLUUqpdklrABtjxhtj/mGMWWuM+UEL2/3GmKdj2980xhyS7L7rrAhd3B6mhqoo1vBVSuWgtAWwMcYN3A9MAI4FaowxxzZrdimwVUT6AncDdySz7y12hFKXm/PLKinV8FVK5ah09oCHAWtF5F8iEgZ+C5zdrM3ZwBOxx78HRps2FmwwQKnLzfRQFUG3J9U1K6VUp0lngh0MfN7o+TpgeGttRMQyxmwHyoHNjRsZY2YBs2JPIld3P+SjqyzLSlPd2aSCZv8WeaxQjrVQjhMK61iP6sibcqILKSLzgHkAxphVTiQyJMMldQpjzCoR0WPNI4VynFB4x9qR96VzCOILoHej571ir7XYxhjjAUJAXRprUkqprJHOAH4LOMIYc6gxxgdMBxY1a7MI+Hbs8XnAX0VE0liTUkpljbQNQcTGdK8GXgLcwHwR+dAYcxuwSkQWAY8C/2OMWQtsIRrSbZmXrpqzkB5r/imU4wQ91jYZ7XAqpVRm5PSVcEoplcs0gJVSKkOyNoDTeRlzNkniOK8zxnxkjHnfGLPEGPONTNSZCm0da6N2U4wxYozJ2SlMyRyrMWZa7Hv7oTHmyc6uMVWS+BnuY4xZaox5N/ZzfEYm6jxQxpj5xpiNxpgPWtlujDH3xv4d3jfGDG5zpyKSdV9ET9p9AhwG+IC/Acc2a/Nd4MHY4+nA05muO03HeSpQHHt8ZS4eZ7LHGmsXBF4FVgBDMl13Gr+vRwDvAl1jz6syXXcaj3UecGXs8bHAvzNddweP9RRgMPBBK9vPAP5E9ILdEcCbbe0zW3vAabmMOQu1eZwislRE9sSeriA6nzoXJfM9BfgJ0TVB9nVmcSmWzLFeDtwvIlsBRGRjJ9eYKskcqwBlscch4MtOrC9lRORVorO1WnM2sECiVgBdjDE9E+0zWwO4pcuYD26tjYhYQPwy5lySzHE2dinR/8PmojaPNfYnW28ReaEzC0uDZL6vRwJHGmPeMMasMMaM77TqUiuZY70VuNAYsw54Ebimc0rrdO39fc6NS5EVGGMuBIYA38x0LelgjHEBvwQuynApncVDdBhiFNG/al41xhwvItsyWVSa1ACPi8gvjDHVROf+9xMRJ9OFZVq29oAL5TLmZI4TY8wY4IfAJBGp76TaUq2tYw0C/YBlxph/Ex1DW5SjJ+KS+b6uAxaJSERE/g/4mGgg55pkjvVS4HcAIlILBIgu1JNvkvp9bixbA7hQLmNu8ziNMYOAh4iGb66OE0Ibxyoi20WkQkQOEZFDiI53TxKRDi1ykmHJ/PwuJNr7xRhTQXRI4l+dWGOqJHOsnwGjAYwxxxAN4E2dWmXnWATMjM2GGAFsF5GvEr4j02cWE5xxPINor+AT4Iex124j+ksJ0W/iM8BaYCVwWKZrTtNxvgxsAN6LfS3KdM3pOtZmbZeRo7Mgkvy+GqJDLh8Bq4Hpma45jcd6LPAG0RkS7wHjMl1zB4/zKeArIEL0L5hLgSuAKxp9T++P/TusTubnVy9FVkqpDMnWIQillMp7GsBKKZUhGsBKKZUhGsBKKZUhGsBKKZUhGsAqJxhjdjV7fpEx5r5Gz2caYz4wxqyOrbp1fez1nxtj1sRWp3rOGNMlwWfMNsbsM8aEmr0+wRizKrZy2bvGmF+09blKJUMDWOU8Y8wEYDbR+aXHE72Kbnts81+AfiLSn+hc1TkJdlVD9MKCcxvtux9wH3ChiBxL9HLwtUl8rlJt0gBW+WAOcL2IfAkgIvUi8nDs8WKJLtYECVaTM8YcDpQCPyIaxHE3Aj8TkTWx/dki8kBbn6tUMnQxHpUriowx7zV63o2vL3ntB7ydxD4uAZ5uZdt0okspvgYcZYzpLiIbYvv+RSvvSfZzlWqR9oBVrtgrIgPjX8At7XmzMeaHgAX8ppUmNcBvJbpC17PA1AMpVqlkaACrfPAhcEJrG40xFwFnARdIC9feG2OOJ7oS2V9iK7FN5+thiET7Tvi5SrVFA1jlg7nAz40xPQCMMT5jzGWxx+OJjuNOkq/vLNJcDXCrxFZiE5GDgINi99/7OXCTMebI2P5cxpgr2vpcpZKhY8Aq54nIi8aY7sDLsdtSCTA/tvk+wE+0dwuwQkSuaLaL6URX9GrsOaIrlN1hjJkNPGWMKY7t+/kkPlepNulqaEoplSE6BKGUUhmiAayUUhmiAayUUhmiAayUUhmiAayUUhmiAayUUhmiAayUUhny/wGZdXLiDE0iqwAAAABJRU5ErkJggg==\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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1PB57+qmdDhV88pgjKakoB1KrpzW+s4oP39/Eu3/9G9+ZMhUALxFnn3EfT1ccf0bq/JvPvdBlO5He2Hf8WD732/t4c9FSZp9wUpvwhdQNELfffjuzZ8/G87yW3Y2t3ThxPs6AgAwGsHMuCNwOTAZWAc855+ab2eutmn0NeMDM7nDOHQg8CuyZqZp6Y/8javjvR37HK08s4O7Pz+GEL8ziiBlnb9Ou9XJ5zQ/N9/n6nx4mXFTU4/cz36ekorzNgj2tRUtKOv3aUCTa8jgQDOJ7HphxUO0xXPqzOzp+vdL063XTTqQ3NnlJ9hw3hqsnncTQULjDNrNmzeLggw/eZj+3UCjEhRdemLczICCzsyDGAyvN7C0ziwP3Aae1a2NAefpxBfBuBuvplfX/eoeK6qEc8+nzOfpT5/L2yys6bLf8t79v+W9zz/GgY2tZcNc9LW3+teJVAIrKyti6eXPL+dbHxeUxqvbYg+ceSn0IaGb8a8Vrva5/73GH8vdly1nz1j8AaNryIatXvtnrdiLdaZ71sGLZcpIYdRXVnYZvs5qaGq666ipmzZrVssDOk08+mdczICCzQxC7Au+0Ol4FHNauzbXAE865LwKlwCQ64JybBcwCGLzbrn1eaFfeWLSEP976Y4LhMNHSEj575486bLdl4/t8/YjjCEUjXHp3qhd53vXf4hdXfpWvH3Ecvpdkv5rD+fQPbmDUlMnc/unP8uKjj3P+9d/msGmnce+cK1jwk5/xhZ//lEt+ejvz/usr/OGmW/CSCcZPO509Dj6oV/WXV1Vx8Y9/yJ2f+RzJptSk9mlXf4Wd9t2nV+2ksDXfwXbAkRPabBff+vnWsx7qPj2Tmgsv4i3o8YyGgbKbRV/I2FoQzrmzgClm9pn08aeAw8xsdqs2l6dr+L5zrgb4GTDCzPzOXldrQYhkR+twDUfCXPnQg9uE8MM338pvv3sD5nlAamguHA7jnCOZTObtjIbergWRySGIfwO7tzreLX2utYuBBwDMbAlQBFRlsCYR6aXWU8qS8QRvPLt4mzZ7HXE4oXTgQmoILZFIZGWt3YEgkwH8HDDcObeXcy4CzADmt2vzL2AigHPuk6QCeF0GaxKRXmqeUhYIBglFwhxw5IQ2z2/1farGjOLXjz/GJZdcQiQSIRgMEg6HWx7n84yG3sjocpTOuZOAW0hNMbvHzL7jnLsOaDCz+emZDz8Fykh9IPdlM3uiq9fUEIRI9nQ2BrzV99ngJ5keq2LfaGpmTes72aDnY8ADUW+HILQesIjskCbzec9LckaskgOihXnnZC6OAYtInoubz0tLl7Lmjv9hwwtt9zNcsmQJc+fO7XBPt66eKyS6FVlEeqT98EPCfF5Ysoyfn3U+iXicu+Ze3zLDoatdhrO1A3EuUg9YRLrVPAXtt9+9gRtPn84by55jnZcg3PAyifQMh6amJq699tqWsd/OZj5kawfiXKQAFpFutZ+C9sIzzzC5dDDnTj6BSCTSsqDOggULmDhxIpWVlZ3OfGjegVizIjQEISI90GZVs3CYsyZOZmxRDJfeTfjaa69lwYIF+L5PPB6nsbGx012Gs7UDcS7SLAgR6ZG/LXuO5595lmkTJzGrdlKbBagKfVxXuyKLSMb4ZpSOGclVRx7FMSUVbcIXUr3aW265hd/85jeMGjWqZVy3kEK4NxTAItIl34zVyQSHF5d3GL6Q6gE3by30xBNPtOztVmg94e2lD+FEpEMrlzfwh5tvZfnSJYwpLuPY0kEdhi98PLOheWcL3/dpamoq6BkOPaEesIhsY+XyBm44fTrJ9Mpn5y9YSGDChE7bN89s2Lp1a8tuFr7vU1lZ2V8lD0jqAYvINt54djHJeBzzPLx4gmeeeqrL9s0zGyZPntzSSw4EAjQ2Nuquty6oBywibZgZQw8fRzgSwYsnejxXt6amhmuvvZZnnnmmZTZEZWVlQc+O6I4CWERamBlrvAQTjzyScxcs4Nmnnt5mrm7rVc7ah2n7Ob4d3fWmAP6YAlhEgFT4rvUS7BUuYmqsksiEIzhqwhFt2vRkvm/7LYUikUhL+0K+660jCmARwcxY5yXYPRzljPIqIq7jj4e2t0eru966pgAWKRBdbai53kuwcyjKtNjQTsMXPp7tsD092kLaZHN7KYBFCkBXG2quSyYYGopwVnkVRYGuJ0apR9u3FMAiBaDtamap433Hj2V9MsGQYIjp5UMpDgR79Frq0fYdzQMWKQAdbajZmExQEQxyTkU1pd2Er+byZoZ6wCIFYN/xY7nyoQdbxoCHjDmEEhfknPJqynoQvprLmxkKYJECse/4sew7fiwbvQQRF6CuopryYPcRoLm8maMhCJEC8r6XJJgO34oehC9oB4tMUg9YpEC87yXBwbnl1QwOhnv8dZr5kDkKYJECsMlLYsB55cOoDPU8fJtp5kNmaAhCJM9t9j0SGHUV1QxNh29vZzVoNkTfUg9YJI9t8T3eWPYc5c+v4K2JkxhWU9PrWQ2aDdH31AMWyVMf+h5/Wbacn515Ljdc+00mTpzYspJZ+1kNPdHbr5POKYBF8tBHvscHvkfp86+SaBeavZ3VoNkQfU9DECJ5Zqvv877vcXZsKGsmTuJH353bZvGc3s5q0GyIvuea928aKHYfNdK+8eTjhDrZHFCkkG31fTb6Sc6KVbFvtAToegF16RvOuefNbGz3LdtSD1gkTzSZzwY/yRmxypbwBU0hy2UaAxbJA3Hzec9LMLWskgOipdkuR3pIASwywCXMZ30yySlllRxUpPAdSBTAIgNYIr2V0IllQzi4qCzb5ch2UgCLDFBJM9Z6cSaVDmZ0scJ3IFIAiwxAnhlrk3GOKxnEuKJYtsuRXlIAiwwwnhlrvDhHlVRweHE5TlMyBywFsMgA4qfDd0JxBUeWVCh8BzgFsMgA4ZuxOhnnsKJyjlb45gUFsMgA4Jux2oszpjjGsaWDCCh884ICWCTHmRmrvQSjisqYVDqYZUuXak3ePKFbkUVymKV7viMipZxQOoTlS5dqTd48oh6wSI4yM1YnE+wfKeWkWCVB57Qmb55RAIvkIDNjjZdg32gRp8aGtKz+pzV584uGIERyjJmx1kuwV7iI02JVhN3H/SStyZtfFMAiOaQ5fHcPRzmjvIqI2/YfqVpeMn9oCEIkh6z3EuwcijAtNrTD8JX8ou+wSI5oWLKUZbfeyR6vraQooF/NQqAhCJEc0LBkKXedWYcfT/DoTbdoelmB0P9mRbKsMZng3SXL8OMJTS8rMApgkSx6z0tQGgjy+eNP0vSyAqQhCJEs2eglKHIB6iqqqThyF00vK0AZDWDn3BTgh0AQuNvMvtdBm7OBawEDXjazczNZk0gu2OglCTaHbzD1a6jpZYUnYwHsnAsCtwOTgVXAc865+Wb2eqs2w4GrgCPMbINzrjpT9Yjkive9JM7BueXVDA6Gs12OZFEmx4DHAyvN7C0ziwP3Aae1a/NZ4HYz2wBgZmszWI9I1m3ykhip8K0MKXwLXSYDeFfgnVbHq9LnWtsP2M85t8g5tzQ9ZLEN59ws51yDc65hS+N7GSpXJLM2+x4JjLqKaoaGItkuR3JAtmdBhIDhQC1QB/zUOTeofSMzu8vMxprZ2NLKIf1boUgf2OJ7bPV9zi2vZpjCV9IyGcD/BnZvdbxb+lxrq4D5ZpYws38AfyMVyCJ540Pf40Pfo66imp3D0WyXIzkkkwH8HDDcObeXcy4CzADmt2vzEKneL865KlJDEm9lsCaRfvWR7/HasudYf+fP+VfDC9kuR3JMxgLYzJLAbOBx4C/AA2b2mnPuOufc1HSzx4FG59zrwJPAlWbWmKmaRPrTVt/nlWXLuefMc/n+td9k4sSJ2kZI2sjoPGAzexR4tN25a1o9NuDy9B+RvLHV99ngJSlteIVEux0sNNdXmmX7QziRvNNkPhv8JGeUV3LmpON1i7F0Srcii/ShuPm85yWYWlbFAdFS0A4W0gUFsEgfSZjP+mSCU2KVHFRU2nJetxhLZzQEIdIHEmas8xJMKRvCyKKybJcjA0SnAeycu9w5d3EH5y92zs3JaFUiA0jSjHVenEmlgzm0OJbtcmQA6aoHfB4wr4PzvwAuykw5IgOLZ8baZJxjSwYxrkjhK9unqwAOmVmi/cn0wjoucyWJDAyeGWu8OEeVVHB4cTnO6ddCtk9XARxwzg1rf7KjcyKFxk+H74TiCo4sqVD4Sq90FcA3Ao84545xzsXSf2qBh4Gb+qM4kVzkm7E6GeewonKOVvjKDuh0GpqZzXPOrQOuA0aQ2rHiNeAaM3usn+oTySm+Gau9OGOKYxxbOoiAwld2QJfzgNNBq7AVAcyM5UuXsnXZCxx9/IkEJkzIdkkywHU1De1G59wlHZy/xDm3zd5uIvksFb5L+J8zz+V/vz2X4ydN0sI6ssO6GgM+Drirg/M/BU7JTDkiucfMWOMl+GDpC3jxRJuFdUR2RFcBHE2vVtaGmfloGpoUiObw3SdSxCUnnKiFdaRPdTUG/JFzbriZ/b31yfROxh9ltiyR7DMz1noJ9gwXcVqsisiEai2sI32qqwC+BnjMOfdt4Pn0ubGktpGfk+G6RLKqOXx3D0c5I1ZFxKX+saiFdaQvdTUN7THn3OnAlcAX06dfA840sxX9UJtI1qz3k+wcijAtNpRoQGtWSWZ0Nw3tVeDTrc8553Z3zl1pZjdmtDKRLFmXTDA0FGZ6+VCKWoXvkiVLNPwgfapH6wE754YC00ltHb8L8LtMFiWSLeuTCYYEQ0wvH0pxINhyfsmSJUycOJF4PE4kEmHhwoUKYdlhXc0DjjnnPu2cexxYDuwD7GVm+5jZFf1WoUg/aUwmKA8GOaeimtJW4QtQX19PvN3ebiI7qqse8FpSwfs14FkzM+fcGf1Tlkj/es9LUBoIMqO8mrJ24QtQW1tLJBJp6QFrCpr0ha4C+CpgBvBj4NfOufv7pySR/rXRS1LkAtRVVFMe7PhXokZ7u0kGuA7utWjbwLm9SQVxHTAc+AbwOzP7W+bL29buo0baN558nJAWQZE+sNFLEnSO8yuqGRwMZ7scGaCcc8+b2djt/bpu59eY2Vtm9l0zO5jUPOBy4NFe1CiSU973kjgH55YrfCU7tmuCo5m9amZXm9m+mSpIpD9s8pIYqfCtDCl8JTs0w1wKzmbfI4Exo6KaoaFItsuRAqYAloKyxffY6vucW17NTgpfyTIFsBSMD32PD32Puopqdg5Hs12OSO8C2DmnXTJkQPnI99js+5xTUc2uCl/JEZ3OA3bOHdrZU8CojFQjkgFbfZ/3fY+zY0PZI1yU7XJEWnR1I8ZzwFN0vPj6oIxUI9LHtvo+G/wk02NV7B0tznY5Im10FcB/AS5pvyA7gHPuncyVJNI3miwVvmfEKtk3WpLtckS20dUY8LVdPP/FTs6L5IS4+TR6CaaWVXJAtDTb5Yh0qKsF2f+vi+ceykg1In0gYT7rkwlOiVVyUJHCV3JXV8tRXu6cu7iD8xc75+ZktCqRXkqYsc5LMKVsCCOLyrJdjkiXuhqCOA+Y18H5XwAXZaYckd5LmrE2GWdS6WAOLY5luxyRbnUVwCEzS7Q/aWZxtC295BgvHb7HlQ5iXJHCVwaGrgI44Jwb1v5kR+dEsskzY40X56iSCg4vLsdpqVIZILoK4BuBR5xzx6S3J4o552qBh4Gb+qM4ke74Zqz24kworuDIkgqFrwwoXc2CmOecWwdcB4xIn34VuMbMdCuyZJ1vxupknMOKYxyt8JUBqLtt6R8DFLaSc5p7vmOKYxxXOpiAwlcGoK7WgvgR0Ol+RWb2/zJSkUg3zIw1XoJDomVMUvjKANZVD7ih1eNvktoLTiSrLN3zPTBSypSyIQQVvjKAdTUG/PPmx865Oa2PRbKhuee7f6SUk2OVCl8Z8Hq6HnDXWyeLZJiZsdZLsE+kiFNjQ7QrtuQF7YghOa85fD8RLuK0WBVhpx9byQ9dfQj3AR/3fEucc5uanwLMzMozXZxIc/juFo5yRqyKiMJX8khXY8C6n1Oybr2fZOdQhDNjQ4kGFL6SX/QTLTlrfTJBVTDM9PKhFCl8JQ/pp1pyUmMywaBgiLPLh1IcCGa7HJGMUABLzmn0EsSCQWZUVFOq8JU8ltEAds5Ncc791Tm30jn33120O9M5Z865sZmsR3Lfe16CUhdkRnk1ZQpfyXMZC2DnXBC4HTgROBCoc84d2EG7GPAlYFmmapGBYaOXpMgFqKuopjzY5TIlInkhkz3g8cBKM3srvYj7fcBpHbT7FnA9sDWDtUiO2+glCTrHjIpqKhS+UiAyGcC7Aq23r1+VPtfCOXcosLuZPdLVCznnZjnnGpxzDVsa3+v7SiWrNnlJnINzy6sZEgxnuxyRfpO1D+GccwHgZuC/umtrZneZ2VgzG1taOSTzxUm/+cBP4pMK38qQwlcKSyYD+N/A7q2Od0ufaxYjtdB7vXPun8DhwHx9EFc4NvsecTNmVFQzNBTJdjki/S6TAfwcMNw5t5dzLgLMAOY3P2lm75tZlZntaWZ7AkuBqWbW0PHLST7Z4nts9X3qyqvZSeErBSpjAWxmSWA28DjwF+ABM3vNOXedc25qpt5Xct+HvseHvseMimp2CUezXY5I1mT042YzexR4tN25azppW5vJWiQ3fOR7bPZ9ZlRUs5vCVwqc7oSTfrPV99nke5wVq2KPcFG2yxHJOgWw9Ism32eDn+TMWBV7R4tZsmQJc+fOZcmSJdkuTSRrNONdMi5uPu/5Sc6IVbJvtIQlS5YwceJE4vE4kUiEhQsXUlNTk+0yRfqdesCSUXHzWe8lOLWskgOipQDU19cTj8fxPI94PE59fX12ixTJEgWwZEzCfNYnE5xSVsmIotKW87W1tUQiEYLBIJFIhNra2uwVKZJFGoKQjEiYsc5LMKVsCCOLyto8V1NTw8KFC6mvr6e2tlbDD1KwFMDS55JmrE3GmVw2mEOLO97ZqqamRsErBU9DENKnPDPWJOMcWzqIcUXaVlCkKwpg6TOeGWu8OEeVVFBTXI5zLtslieQ0BbD0Cd+M1V6cmuJyjiqpUPiK9IACWHaYb8bqZJzDimIcUzJI4SvSQwpg2SHNPd9Di2McVzqYgMJXpMcUwNJrZsYaL8HIaBmTFb4i200BLL1i6Z7vgZESTiwbQlDhK7LdFMCy3Zp7vvtHSjk5VqnwFeklBbBsFzNjrZdgn0gRp8aGEFL4ivSaAlh6rDl8PxEu4rRYFWGnHx+RHaHfIOkRS6/tsFs4yhmxKiIKX5Edpt8i6ZH1fpKdQhGmxaqIBvRjI9IX9Jsk3VqfTFAVCDG9fCjFgWC2yxHJGwpg6VJjMsGgYIizK6oVviJ9TAEsnWr0EsSCQWZUVFOq8BXpcwpg6dB7XoISF2RGeTVlCl+RjFAAyzY2ekmKXIBzK6opD2rNfpFMUQBLGxu9JEHnmFFRTYXCVySjFMDSYpOXxDmoK69mSDCc7XJE8p4CWAD4wE/iY5xbXk1VSOEr0h8UwMJm3yNuxoyKYQwNRbJdjkjBUAAXuC2+x1bfp668mp0UviL9SgFcwD70PT70PWZUVLNLOJrtckQKjgK4QH3ke2z2fc6pqGY3ha9IViiAC9BW32eT73FWrIo9wkXZLkekYCmAC0yT77PBT3JmrIq9o8XZLkekoCmAC0jcfBr9BGfEKtk3WpLtckQKngK4QMTNZ72XYGpZFQdES7NdjoigAC4ICfNZn0xwclklI4oUviK5QgGc5xLprYROKBvCIUVl2S5HBriysrY/Q/feey+zZ89uOZ43bx4jRozg4IMPZvTo0dx0000AXHnllRxwwAGMHDmSM844g40bN/Zn2TlLAZzHkmasTcaZVDqYQxW+kmGPPfYYt9xyC0888QQrVqxg6dKlVFRUADB58mReffVVXnnlFfbbbz/mzp2b5WpzgwI4T3lmrEnGObZ0EOOKYjhtHy8ZNnfuXG666SZ22WUXAKLRKJ/97GcBOP744wmFUqvrHX744axatSprdeYSrTeYhzwz1nhxjiqpoKa4XOErfeajjz5i1KhRLcfvvfceU6dOBeDVV19lzJgx3b7GPffcwznnnJOpEgcUBXCe8c1Y7cWpKS7nqJIKha/0qeLiYl566aWW43vvvZeGhoYef/13vvMdQqEQ5513XgaqG3g0BJFHfDNWJ+OML4pRWzJI4Sv96qCDDuL555/v9Pl7772Xhx9+mF/+8pf62UxTAOeJ5p7v6OIYE0sHE9APuPSzq666iiuvvJLVq1cDEI/HufvuuwH44x//yA033MD8+fMpKdFNQM00BJEHzIzVXoKR0TKOV/hKlpx00kmsWbOGSZMmYWY457jooosAmD17Nk1NTUyePBlIfRB35513ZrPcnODMLNs1bJfdR420bzz5OCGFDJAO32ScA6OlnBKrJKi/F5F+55x73szGbu/XaQhiADMz1ngJ9o+WcLLCV2TAUQAPUGbGWi/B3pEiTo1V6l8EIgOQAngAag7fT4SLOD1WRdjp2ygyEOk3dwBa5yXYLRzljFgVEYWvdGDChAk9brvnnnuyfv36Hrevr69n8eLFLccPPfQQr7/++nbV1177NSUKhX57B5h1XoKdQhGmxaqIBvTtk461Dsi+1hcBnEwm+7Qmz/P69PX6i36DB5D1yQRVgRDTy4dSHAhmuxzJYc2rlv3nP//h6KOPZtSoUYwYMYJnnnmmw/Y33HADBx98MOPHj2flypUArFu3jjPPPJNx48Yxbtw4Fi1axD//+U/uvPNOfvCDHzBq1Cieeuop5s+fz5VXXsmoUaN48803efPNN5kyZQpjxozhqKOO4o033gDgggsu4NJLL+Wwww7jy1/+8jY1vPvuu0yZMoXhw4e3ef6JJ56gpqaGQw89lOnTp7N582Yg1XP/yle+wqGHHsqDDz7YabtcpnnAA0RjMsGgYIizK6oVvtJjv/rVrzjhhBO4+uqr8TyPDz/8sMN2FRUVrFixgnnz5jFnzhwefvhhvvSlL3HZZZdx5JFH8q9//YsTTjiBv/zlL1x66aWUlZVxxRVXADB16lROOeUUzjrrLAAmTpzInXfeyfDhw1m2bBmf//zn+fOf/wzAqlWrWLx4McHgtj/DL730Ei+++CLRaJT999+fL37xixQXF/Ptb3+bBQsWUFpayvXXX8/NN9/MNddcA0BlZSUvvPAC69evZ9q0aZ22y1UZDWDn3BTgh0AQuNvMvtfu+cuBzwBJYB1wkZm9ncmaBqJGL0EsGGRGRTWlCl/ZDuPGjeOiiy4ikUhw+umnt1lIp7W6urqW/1522WUALFiwoM3QwqZNm7rtVW7evJnFixczffr0lnNNTU0tj6dPn95h+EIquJuXrzzwwAN5++232bhxI6+//jpHHHEEkLq7rqampuVrmhf1Wbp0aZftclXGAtg5FwRuByYDq4DnnHPzzaz1YNGLwFgz+9A59zngBkDLJLWywUtQ4oKcU15NmcJXttPRRx/N008/zSOPPMIFF1zA5ZdfzsyZM7dp13pthubHvu+zdOlSiop6vnO27/sMGjSozYI9rZWWdr4jSzQabXkcDAZJJpOYGZMnT+bXv/51l6/XXbtclckx4PHASjN7y8ziwH3Aaa0bmNmTZtb8b6KlwG4ZrGfA2eglibgAdRXVVAQ1WiTb7+2332bYsGF89rOf5TOf+QwvvPBCh+3uv//+lv829xyPP/54fvSjH7W0aQ7VWCzGBx980HK+9XF5eTl77bUXDz74IJAKxpdffrnX9R9++OEsWrSoZVx6y5Yt/O1vf+t1u1yTyQDeFXin1fGq9LnOXAw81tETzrlZzrkG51zDlsb3+rDE3PW+lyToHHUV1QxS+Eov1dfXc8ghhzB69Gjuv/9+vvSlL3XYbsOGDYwcOZIf/vCH/OAHPwDg1ltvpaGhgZEjR3LggQe2rN1w6qmn8rvf/Y5Ro0bxzDPPMGPGDG688UZGjx7Nm2++yS9/+Ut+9rOfccghh3DQQQfx+9//vtf1Dx06lHvvvZe6ujpGjhxJTU1Ny4d6vWmXazK2FoRz7ixgipl9Jn38KeAwM9tmsp9z7nxgNnCMmTW1f761QlgLYpOXxBycVz6MqlA42+WISDd6uxZEJrtW/wZ2b3W8W/pcG865ScDV9CB8C8EHfhIf41yFr0jey+QQxHPAcOfcXs65CDADmN+6gXNuNPATYKqZrc1gLQPCZt8jbsaMimFUhyLZLkdEMixjAWxmSVLDCo8DfwEeMLPXnHPXOeemppvdCJQBDzrnXnLOze/k5fLeFt9jq+9TV17NTgpfkYKQ0U93zOxR4NF2565p9XhSJt9/oPjQ9/jQ96irGMYu4Wj3XyAieUG3ImfZR77HZt/n7IpqdlP4ihQUBXAWbfV9NvkeZ8Wq+ES455PdRSQ/KICzpMn32eAnmRarYu9ocbbLEZEsUABnQdx83vMTnBGrZHhUO8SKFCoFcD+Lm896L8GpZVUcEO38vngRyX8K4H6UMJ/1yQQnl1UyokjhK1LoFMD9JGnGOi/BCWVDOKSoLNvliEgOUAD3g6QZa5JxJpUO5lCFr4ikKYAzzEuH77GlgxhXFGuz7qqIFDYFcAZ5Zqzx4hxVUkFNcbnCV0TaUABniJ8O38OLyzmqpELhKyLbUABngG/G6mSccUUxaksGKXxFpEMK4D7mm7HaizO6OMbE0sEEFL4i0gkFcB8yM9Z4CUZGyzhe4Ssi3VAA9xFLDzt8MlLCiWVDCCp8RaQbCuA+0Nzz3T9awimxSoWviPSIAngHmRlrvQR7R4o4NVaZ15uFikjfUgDvgObw3SNcxOmxKsJOf50i0nNKjB2wzkuwWzjKtFgVEYWviGwnpUYvrfMS7BSKMC1WRTSgv0YR2X5Kjl5Yn0xQFQgxvXwoxYFgtssRkQFKAbydGpMJBgVDnF1RrfAVkR2iAN4OjV6CskCQGRXVlCp8RWQHKYB7aIOXoMSlwrdM4SsifUAB3AMbvSQRF6CuopqKYCjb5YhInlAAd+N9L0nQOeoqqhmk8BWRPqQA7sImLwkO6sqrGRIMZ7scEckzCuBOfOAn8TDqyqupCil8RaTvKYA7sNn3iJtRVzGM6lAk2+WISJ5SALezxffY6vvUlVezk8JXRDJIAdzKh77HFt/jnIpqdglHs12OiOQ5BXDaR77HB+nw3V3hKyL9QAEMbPV93vc9pseG8olwUbbLEZECUfAB3OT7bPCTTItVsXe0ONvliEgBKegAjptPo5/g9LJK9ouWZLscESkwBRvAcfNZl0xwalklnywqzXY5IlKACjKAE+azPpnglFglBxeVZbscESlQBRfASTPWeQlOKBvCIQpfEcmiggrgpBlrk3Emlg7mUIWviGRZwQSwZ8aaZJxjSgcxviiG0/bxIpJlBRHAnhlrvDhHlVQwobhc4SsiOSHvA9hPh+/hxeUcVVKh8BWRnJHXAeybsToZZ1xRjNqSQQpfEckpeRvAvhmrvTiji2NMLB1MQOErIjkmLwPYzFjjJRgZLeV4ha+I5Ki8C2BLDzt8MlLCiWWVBBW+IpKj8iqAzYzVXoL9oiWcElP4ikhuy5sANjPWegn2iRQxNVZJSOErIjkuLwK4OXz3CBdxeqyKsMuLyxKRPJcXSbXOS7BrOMq0WBURha+IDBADPq3WeQmGhSKcGasiGhjwlyMiBWRAJ9b6ZIKqQIjp5UMpDgSzXY6IyHbJaAA756Y45/7qnFvpnPvvDp6POufuTz+/zDm3Z09fuzGZYFAwxPSKakoUviIyAGUsgJ1zQeB24ETgQKDOOXdgu2YXAxvMbF/gB8D1PXnt97wEZYEg55QPpUzhKyIDVCZ7wOOBlWb2lpnFgfuA09q1OQ34efrx/wETXTcLNjigLBBkRkU1sWCor2sWEek3mUywXYF3Wh2vAg7rrI2ZJZ1z7wOVwPrWjZxzs4BZ6YPE7GF7vv6FZDKZobpzSRXt/i7yWKFca6FcJxTWte7fmy8aEF1IM7sLuAvAOdfgJxJjs1xSv3DONZiZrjWPFMp1QuFda2++LpNDEP8Gdm91vFv6XIdtnHMhoAJozGBNIiI5I5MB/Bww3Dm3l3MuAswA5rdrMx/4dPrxWcCfzcwyWJOISM7I2BBEekx3NvA4EATuMbPXnHPXAQ1mNh/4GfAL59xK4D1SId2duzJVcw7SteafQrlO0LV2y6nDKSKSHQP6TjgRkYFMASwikiU5G8CZvI05l/TgOi93zr3unHvFObfQOfeJbNTZF7q71lbtznTOmXNuwE5h6sm1OufOTn9vX3PO/aq/a+wrPfgZ3sM596Rz7sX0z/FJ2ahzRznn7nHOrXXOvdrJ8845d2v67+EV59yh3b6omeXcH1If2r0J7A1EgJeBA9u1+TxwZ/rxDOD+bNedoes8FihJP/7cQLzOnl5rul0MeBpYCozNdt0Z/L4OB14EBqePq7Nddwav9S7gc+nHBwL/zHbdvbzWo4FDgVc7ef4k4DFSN+weDizr7jVztQeckduYc1C312lmT5rZh+nDpaTmUw9EPfmeAnyL1JogW/uzuD7Wk2v9LHC7mW0AMLO1/VxjX+nJtRpQnn5cAbzbj/X1GTN7mtRsrc6cBsyzlKXAIOfczl29Zq4GcEe3Me/aWRszSwLNtzEPJD25ztYuJvV/2IGo22tN/5NtdzN7pD8Ly4CefF/3A/Zzzi1yzi11zk3pt+r6Vk+u9VrgfOfcKuBR4Iv9U1q/297f54FxK7KAc+58YCxwTLZryQTnXAC4Gbggy6X0lxCpYYhaUv+qedo5d7CZbcxmURlSB9xrZt93ztWQmvs/wsz8bBeWbbnaAy6U25h7cp045yYBVwNTzaypn2rra91dawwYAdQ75/5Jagxt/gD9IK4n39dVwHwzS5jZP4C/kQrkgaYn13ox8ACAmS0Bikgt1JNvevT73FquBnCh3Mbc7XU650YDPyEVvgN1nBC6uVYze9/MqsxsTzPbk9R491Qz69UiJ1nWk5/fh0j1fnHOVZEaknirH2vsKz251n8BEwGcc58kFcDr+rXK/jEfmJmeDXE48L6Z/afLr8j2J4tdfOJ4EqlewZvA1elz15H6pYTUN/FBYCWwHNg72zVn6DoXAGuAl9J/5me75kxda7u29QzQWRA9/L46UkMurwMrgBnZrjmD13ogsIjUDImXgOOzXXMvr/PXwH+ABKl/wVwMXApc2up7env672FFT35+dSuyiEiW5OoQhIhI3lMAi4hkiQJYRCRLFMAiIlmiABYRyRIFsAwIzrnN7Y4vcM7d1up4pnPuVefcivSqW1ekz9/onHsjvTrV75xzg7p4jznOua3OuYp25090zjWkVy570Tn3/e7eV6QnFMAy4DnnTgTmkJpfejCpu+jeTz/9J2CEmY0kNVf1qi5eqo7UjQXTWr32COA24HwzO5DU7eAre/C+It1SAEs+uAq4wszeBTCzJjP7afrxE5ZarAm6WE3OObcPUAZ8jVQQN/sy8B0zeyP9ep6Z3dHd+4r0hBbjkYGi2Dn3UqvjIXx8y+sI4PkevMZFwP2dPDeD1FKKzwD7O+eGmdma9Gt/v5Ov6en7inRIPWAZKD4ys1HNf4BrtueLnXNXA0ngl500qQPus9QKXb8Bpu9IsSI9oQCWfPAaMKazJ51zFwCnAOdZB/feO+cOJrUS2Z/SK7HN4ONhiK5eu8v3FemOAljywVzgRufcTgDOuYhz7jPpx1NIjeNOtY93FmmvDrjW0iuxmdkuwC7p/fduBL7qnNsv/XoB59yl3b2vSE9oDFgGPDN71Dk3DFiQ3pbKgHvST98GREn1bgGWmtml7V5iBqkVvVr7HakVyq53zs0Bfu2cK0m/9sM9eF+Rbmk1NBGRLNEQhIhIliiARUSyRAEsIpIlCmARkSxRAIuIZIkCWEQkSxTAIiJZ8v8BYCbFR1yUmq0AAAAASUVORK5CYII=\n" - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Scatter diagrams\n", - "from sktime_estimator_evaluation.evaluation.diagrams import scatter_diagram\n", - "\n", - "# By default the scatter diagram if not specified will be a scatter plot of EVERY,\n", - "# combination of estimator and metric. To only show a subset of the estimators and\n", - "# metrics we can specify which to compare and from\n", - "figures = scatter_diagram(classification_results, compare_estimators_to=[\"HC2\"])" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/_wip/evaluation_metric_results.ipynb b/examples/_wip/evaluation_metric_results.ipynb deleted file mode 100644 index b47a0390..00000000 --- a/examples/_wip/evaluation_metric_results.ipynb +++ /dev/null @@ -1,1718 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "

Evaluation of metrics results

\n", - "\n", - "To evaluate the results there are two states results can be in:\n", - "1) 'Raw' - the results are not yet processed.\n", - "2) 'Metric results' - the raw have been turned into metric results over n folds.\n", - "\n", - "This notebook looks at 'Metric results', see evaluation_raw_results.ipynb for metric results\n", - "\n", - "

Metric results

\n", - "Metric results are those where the raw results have been processed and csv of the\n", - "following format have been produced:\n", - "\n", - "

Example file

\n", - "see notebooks/example_data/ACC.csv" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " 0 1 2 \\\n", - "0 folds 0 1 \n", - "1 ACSF1 0.12 0.14 \n", - "2 Adiac 0.020460358056265986 0.05115089514066496 \n", - "3 ArrowHead 0.08 0.29714285714285715 \n", - "4 Beef 0.23333333333333334 0.26666666666666666 \n", - ".. ... ... ... \n", - "104 Wafer 0.373134328358209 0.373134328358209 \n", - "105 Wine 0.5185185185185185 0.5185185185185185 \n", - "106 Worms 0.22077922077922077 0.1038961038961039 \n", - "107 WormsTwoClass 0.5714285714285714 0.42857142857142855 \n", - "108 Yoga 0.536 0.536 \n", - "\n", - " 3 4 5 \\\n", - "0 2 3 4 \n", - "1 0.05 0.02 0.13 \n", - "2 0.03836317135549872 0.030690537084398978 0.04859335038363171 \n", - "3 0.18857142857142858 0.37142857142857144 0.29714285714285715 \n", - "4 0.2 0.26666666666666666 0.23333333333333334 \n", - ".. ... ... ... \n", - "104 0.6268656716417911 0.6268656716417911 0.6268656716417911 \n", - "105 0.48148148148148145 0.5185185185185185 0.48148148148148145 \n", - "106 0.37662337662337664 0.2987012987012987 0.14285714285714285 \n", - "107 0.5194805194805194 0.5714285714285714 0.5714285714285714 \n", - "108 0.536 0.464 0.464 \n", - "\n", - " 6 7 8 \\\n", - "0 5 6 7 \n", - "1 0.06 0.14 0.06 \n", - "2 0.04603580562659847 0.015345268542199489 0.010230179028132993 \n", - "3 0.11428571428571428 0.37142857142857144 0.44 \n", - "4 0.23333333333333334 0.16666666666666666 0.2 \n", - ".. ... ... ... \n", - "104 0.6268656716417911 0.373134328358209 0.6268656716417911 \n", - "105 0.48148148148148145 0.48148148148148145 0.5185185185185185 \n", - "106 0.3116883116883117 0.15584415584415584 0.18181818181818182 \n", - "107 0.5714285714285714 0.42857142857142855 0.4805194805194805 \n", - "108 0.536 0.464 0.464 \n", - "\n", - " 9 ... 21 22 \\\n", - "0 8 ... 20 21 \n", - "1 0.0 ... 0.11 0.01 \n", - "2 0.030690537084398978 ... 0.03580562659846547 0.07161125319693094 \n", - "3 0.5428571428571428 ... 0.14857142857142858 0.37142857142857144 \n", - "4 0.2 ... 0.16666666666666666 0.3333333333333333 \n", - ".. ... ... ... ... \n", - "104 0.6268656716417911 ... 0.373134328358209 0.6268656716417911 \n", - "105 0.5185185185185185 ... 0.5185185185185185 0.48148148148148145 \n", - "106 0.1038961038961039 ... 0.18181818181818182 0.2597402597402597 \n", - "107 0.5714285714285714 ... 0.5714285714285714 0.42857142857142855 \n", - "108 0.464 ... 0.464 0.536 \n", - "\n", - " 23 24 25 \\\n", - "0 22 23 24 \n", - "1 0.15 0.21 0.11 \n", - "2 0.04092071611253197 0.015345268542199489 0.02557544757033248 \n", - "3 0.5428571428571428 0.42857142857142855 0.36 \n", - "4 0.1 0.26666666666666666 0.16666666666666666 \n", - ".. ... ... ... \n", - "104 0.373134328358209 0.6268656716417911 0.6268656716417911 \n", - "105 0.48148148148148145 0.48148148148148145 0.48148148148148145 \n", - "106 0.14285714285714285 0.15584415584415584 0.11688311688311688 \n", - "107 0.4805194805194805 0.5714285714285714 0.42857142857142855 \n", - "108 0.536 0.464 0.464 \n", - "\n", - " 26 27 28 \\\n", - "0 25 26 27 \n", - "1 0.01 0.05 0.03 \n", - "2 0.005115089514066497 0.07161125319693094 0.015345268542199489 \n", - "3 0.44 0.42857142857142855 0.18857142857142858 \n", - "4 0.23333333333333334 0.23333333333333334 0.4 \n", - ".. ... ... ... \n", - "104 0.6268656716417911 0.6268656716417911 0.373134328358209 \n", - "105 0.5185185185185185 0.5185185185185185 0.5185185185185185 \n", - "106 0.09090909090909091 0.07792207792207792 0.2857142857142857 \n", - "107 0.42857142857142855 0.5714285714285714 0.42857142857142855 \n", - "108 0.464 0.464 0.536 \n", - "\n", - " 29 30 \n", - "0 28 29 \n", - "1 0.08 0.12 \n", - "2 0.0025575447570332483 0.015345268542199489 \n", - "3 0.5428571428571428 0.14857142857142858 \n", - "4 0.2 0.2 \n", - ".. ... ... \n", - "104 0.373134328358209 0.373134328358209 \n", - "105 0.48148148148148145 0.48148148148148145 \n", - "106 0.11688311688311688 0.37662337662337664 \n", - "107 0.5714285714285714 0.5714285714285714 \n", - "108 0.536 0.536 \n", - "\n", - "[109 rows x 31 columns]\n" - ] - } - ], - "source": [ - "import csv\n", - "\n", - "import pandas as pd\n", - "\n", - "with open(\"./example_data/ACC.csv\", newline=\"\\n\") as csvfile:\n", - " results = list(csv.reader(csvfile, delimiter=\",\"))\n", - "\n", - "print(pd.DataFrame(results))\n", - "\n", - "# Each result csv will be in the following format:\n", - "# | folds | 0 | 1 | 2 | 3 | ... |\n", - "# ------------------------------------------\n", - "# | dataset1 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset2 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset3 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset4 | 0.0 | 0.0 | 0.0 | 0.0 | ... |" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

Metric results from 'raw' results

" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 2, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n" - ] - } - ], - "source": [ - "# The results being processed from 'raw' using this packages functionality will return\n", - "# a List[Dict]. See evaluation_raw_results.ipynb for more information.\n", - "from sktime_estimator_evaluation.evaluation import (\n", - " CLUSTER_METRIC_CALLABLES,\n", - " evaluate_raw_results,\n", - ")\n", - "\n", - "returned_metric_results = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=CLUSTER_METRIC_CALLABLES, # List of metrics to evaluate the results with\n", - ")\n", - "\n", - "# This format can be used now across this package" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

Metric results from output directory from 'raw' results" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n", - "[{'metric_name': 'ACC', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.12000 0.140000 0.050000 0.020000 0.130000 0.060000 \n", - "1 Adiac 0.02046 0.051151 0.038363 0.030691 0.048593 0.046036 \n", - "2 ArrowHead 0.08000 0.297143 0.188571 0.371429 0.297143 0.114286 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.140000 0.06000 0.000000 ... 0.110000 0.010000 0.150000 0.210000 \n", - "1 0.015345 0.01023 0.030691 ... 0.035806 0.071611 0.040921 0.015345 \n", - "2 0.371429 0.44000 0.542857 ... 0.148571 0.371429 0.542857 0.428571 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.110000 0.010000 0.050000 0.030000 0.080000 0.120000 \n", - "1 0.025575 0.005115 0.071611 0.015345 0.002558 0.015345 \n", - "2 0.360000 0.440000 0.428571 0.188571 0.542857 0.148571 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.140000\n", - "1 Adiac 0.010230\n", - "2 ArrowHead 0.165714}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.120000 0.120000 0.030000 0.010000 0.150000 0.050000 \n", - "1 Adiac 0.023077 0.051282 0.041026 0.064103 0.051282 0.069231 \n", - "2 ArrowHead 0.111111 0.305556 0.194444 0.583333 0.305556 0.111111 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.130000 0.090000 0.010000 ... 0.100000 0.020000 0.140000 0.190000 \n", - "1 0.023077 0.007692 0.038462 ... 0.033333 0.048718 0.035897 0.010256 \n", - "2 0.611111 0.222222 0.500000 ... 0.055556 0.583333 0.500000 0.305556 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.100000 0.000000 0.030000 0.040000 0.080000 0.110000 \n", - "1 0.012821 0.012821 0.076923 0.041026 0.002564 0.012821 \n", - "2 0.416667 0.222222 0.305556 0.194444 0.500000 0.055556 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.150000\n", - "1 Adiac 0.010256\n", - "2 ArrowHead 0.055556}]}, {'metric_name': 'AMI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.214079 0.288954 0.237809 0.274199 0.279979 0.263023 \n", - "1 Adiac 0.471058 0.453065 0.467764 0.480356 0.475694 0.475837 \n", - "2 ArrowHead 0.254069 0.237431 0.237431 0.237431 0.237431 0.237431 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.300975 0.226974 0.242685 ... 0.276402 0.268325 0.325990 0.257313 \n", - "1 0.487096 0.485954 0.477043 ... 0.480591 0.480616 0.483421 0.465939 \n", - "2 0.254069 0.237431 0.253051 ... 0.253051 0.237431 0.253051 0.253051 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.242685 0.250823 0.246535 0.208308 0.249694 0.225123 \n", - "1 0.487166 0.455596 0.483850 0.476257 0.462893 0.472984 \n", - "2 0.253051 0.237431 0.253051 0.237431 0.253051 0.253051 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.236444\n", - "1 Adiac 0.427568\n", - "2 ArrowHead 0.271135}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.206919 0.232176 0.212781 0.236256 0.201078 0.260339 \n", - "1 Adiac 0.473163 0.451791 0.456030 0.462343 0.459712 0.481747 \n", - "2 ArrowHead 0.356706 0.310465 0.310465 0.310465 0.310465 0.310465 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.285760 0.203044 0.201035 ... 0.216458 0.272010 0.290224 0.173395 \n", - "1 0.471836 0.460533 0.473205 ... 0.465371 0.479494 0.458594 0.462141 \n", - "2 0.356706 0.310465 0.379039 ... 0.379039 0.310465 0.379039 0.379039 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.195093 0.261315 0.266990 0.194284 0.243975 0.262864 \n", - "1 0.467627 0.463873 0.471248 0.456199 0.466153 0.469195 \n", - "2 0.379039 0.310465 0.379039 0.310465 0.379039 0.379039 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.282358\n", - "1 Adiac 0.443758\n", - "2 ArrowHead 0.425406}]}, {'metric_name': 'ARI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.077818 0.120479 0.083939 0.108747 0.100801 0.092901 \n", - "1 Adiac 0.242567 0.241518 0.234532 0.245776 0.240909 0.231806 \n", - "2 ArrowHead 0.223652 0.210042 0.210042 0.210042 0.210042 0.210042 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.142323 0.076336 0.089286 ... 0.088567 0.092217 0.177391 0.094593 \n", - "1 0.246708 0.238063 0.244637 ... 0.247728 0.242667 0.242089 0.241932 \n", - "2 0.223652 0.210042 0.202640 ... 0.202640 0.210042 0.202640 0.202640 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.089286 0.095934 0.092141 0.077342 0.096088 0.082272 \n", - "1 0.254664 0.234327 0.241734 0.252957 0.231873 0.237344 \n", - "2 0.202640 0.210042 0.202640 0.210042 0.202640 0.202640 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.098812\n", - "1 Adiac 0.244806\n", - "2 ArrowHead 0.178624}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.067797 0.095097 0.069916 0.088971 0.068807 0.088011 \n", - "1 Adiac 0.256911 0.241520 0.230389 0.229492 0.232972 0.250583 \n", - "2 ArrowHead 0.296397 0.268152 0.268152 0.268152 0.268152 0.268152 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.121140 0.070555 0.068132 ... 0.066916 0.100169 0.145657 0.061137 \n", - "1 0.233986 0.226609 0.249681 ... 0.245634 0.260837 0.222651 0.235053 \n", - "2 0.296397 0.268152 0.339373 ... 0.339373 0.268152 0.339373 0.339373 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.063966 0.097069 0.104645 0.066421 0.097240 0.099443 \n", - "1 0.241389 0.247196 0.242465 0.235922 0.238531 0.244446 \n", - "2 0.339373 0.268152 0.339373 0.268152 0.339373 0.339373 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.147611\n", - "1 Adiac 0.267304\n", - "2 ArrowHead 0.389330}]}, {'metric_name': 'MI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.592653 0.771585 0.608612 0.790119 0.690434 0.676812 \n", - "1 Adiac 2.070310 2.052517 1.990445 2.045943 2.049756 2.034213 \n", - "2 ArrowHead 0.276436 0.266076 0.266076 0.266076 0.266076 0.266076 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.847280 0.649651 0.632268 ... 0.676450 0.737556 0.853133 0.643258 \n", - "1 2.070648 2.021032 2.060166 ... 2.070221 2.056935 2.029840 2.042673 \n", - "2 0.276436 0.266076 0.269105 ... 0.269105 0.266076 0.269105 0.269105 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.632268 0.706993 0.702689 0.572714 0.673127 0.660013 \n", - "1 2.112989 1.996796 2.058433 2.093766 2.001794 2.003374 \n", - "2 0.269105 0.266076 0.269105 0.266076 0.269105 0.269105 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.673076\n", - "1 Adiac 2.068850\n", - "2 ArrowHead 0.246564}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.581683 0.745228 0.613351 0.766732 0.643694 0.725874 \n", - "1 Adiac 2.140556 2.092508 2.017357 2.067090 2.061991 2.088282 \n", - "2 ArrowHead 0.409368 0.382575 0.382575 0.382575 0.382575 0.382575 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.816781 0.656898 0.578515 ... 0.646106 0.756890 0.823552 0.550499 \n", - "1 2.071613 2.029147 2.094286 ... 2.082910 2.116375 2.016578 2.093393 \n", - "2 0.409368 0.382575 0.419933 ... 0.419933 0.382575 0.419933 0.419933 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.622235 0.749632 0.776622 0.513441 0.673109 0.721277 \n", - "1 2.102251 2.066399 2.061452 2.093137 2.069985 2.060039 \n", - "2 0.419933 0.382575 0.419933 0.382575 0.419933 0.419933 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.830968\n", - "1 Adiac 2.144433\n", - "2 ArrowHead 0.432217}]}, {'metric_name': 'NMI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.333007 0.404885 0.344922 0.408026 0.384718 0.375358 \n", - "1 Adiac 0.628071 0.618677 0.615479 0.629077 0.627153 0.626149 \n", - "2 ArrowHead 0.262317 0.245612 0.245612 0.245612 0.245612 0.245612 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.428591 0.357128 0.354040 ... 0.383898 0.393764 0.433080 0.359035 \n", - "1 0.635744 0.627062 0.629200 ... 0.632737 0.630483 0.628130 0.622366 \n", - "2 0.262317 0.245612 0.261522 ... 0.261522 0.245612 0.261522 0.261522 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.354040 0.381611 0.375767 0.321791 0.363646 0.355686 \n", - "1 0.640616 0.611387 0.631903 0.633883 0.615259 0.619644 \n", - "2 0.261522 0.245612 0.261522 0.245612 0.261522 0.261522 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.358955\n", - "1 Adiac 0.611580\n", - "2 ArrowHead 0.282123}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.330913 0.380841 0.343795 0.393522 0.347209 0.395064 \n", - "1 Adiac 0.639909 0.624802 0.616107 0.626487 0.624032 0.636800 \n", - "2 ArrowHead 0.396350 0.348603 0.348603 0.348603 0.348603 0.348603 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.418215 0.354356 0.326197 ... 0.356594 0.404516 0.416782 0.308678 \n", - "1 0.630697 0.619717 0.633343 ... 0.629543 0.638609 0.618021 0.629852 \n", - "2 0.396350 0.348603 0.420034 ... 0.420034 0.348603 0.420034 0.420034 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.339017 0.401565 0.409193 0.300076 0.369179 0.390258 \n", - "1 0.633168 0.625489 0.628235 0.627692 0.627521 0.627215 \n", - "2 0.420034 0.348603 0.420034 0.348603 0.420034 0.420034 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.421671\n", - "1 Adiac 0.628401\n", - "2 ArrowHead 0.463024}]}, {'metric_name': 'RI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.620000 0.703434 0.617980 0.695354 0.641212 0.631313 \n", - "1 Adiac 0.939157 0.941203 0.932651 0.933622 0.936061 0.931209 \n", - "2 ArrowHead 0.641314 0.645583 0.645583 0.645583 0.645583 0.645583 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.738586 0.620000 0.629091 ... 0.604242 0.659394 0.765455 0.646667 \n", - "1 0.933058 0.931589 0.936770 ... 0.935327 0.934986 0.930933 0.937504 \n", - "2 0.641314 0.645583 0.621346 ... 0.621346 0.645583 0.621346 0.621346 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.629091 0.652121 0.661616 0.626667 0.670707 0.650505 \n", - "1 0.939248 0.936468 0.934501 0.939471 0.934317 0.932245 \n", - "2 0.621346 0.645583 0.621346 0.645583 0.621346 0.621346 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.682424\n", - "1 Adiac 0.947642\n", - "2 ArrowHead 0.566174}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.590909 0.696970 0.597374 0.673535 0.627677 0.625859 \n", - "1 Adiac 0.942496 0.942575 0.934124 0.934625 0.936761 0.934401 \n", - "2 ArrowHead 0.671429 0.684127 0.684127 0.684127 0.684127 0.684127 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.713535 0.621616 0.600606 ... 0.601010 0.657172 0.743838 0.598384 \n", - "1 0.933571 0.933347 0.938804 ... 0.937249 0.940795 0.930130 0.937802 \n", - "2 0.671429 0.684127 0.682540 ... 0.682540 0.684127 0.682540 0.682540 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.616768 0.648081 0.666869 0.581818 0.642626 0.652727 \n", - "1 0.937881 0.939345 0.935574 0.938752 0.936972 0.936102 \n", - "2 0.682540 0.684127 0.682540 0.684127 0.682540 0.682540 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.728081\n", - "1 Adiac 0.951065\n", - "2 ArrowHead 0.696825}]}]\n" - ] - } - ], - "source": [ - "# We can choose to output the metric results using the above function to a directory\n", - "# See evaluation_raw_results.ipynb for more information.\n", - "from sktime_estimator_evaluation.evaluation import evaluate_metric_results\n", - "\n", - "evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=CLUSTER_METRIC_CALLABLES, # List of metrics to evaluate the results with\n", - " output_dir=\"./example_data/output/clustering_results\", # Path to the output directory\n", - ")\n", - "\n", - "# This will output a file we can read in using the following functionality\n", - "metric_results_from_file = evaluate_metric_results(\n", - " path=\"./example_data/output/clustering_results/\"\n", - ")\n", - "print(metric_results_from_file)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

Metric result from a unformatted directory

" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'metric_name': 'ACC', 'test_estimator_results': [{'estimator_name': 'Arsenal', 'result': folds: 0 1 2 3 4 \\\n", - "0 ACSF1 0.890000 0.780000 0.790000 0.840000 0.800000 \n", - "1 Adiac 0.777494 0.772379 0.749361 0.746803 0.795396 \n", - "2 ArrowHead 0.817143 0.862857 0.845714 0.897143 0.857143 \n", - "3 Beef 0.833333 0.800000 0.733333 0.666667 0.900000 \n", - "4 BeetleFly 0.900000 0.850000 0.800000 0.950000 0.850000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.833333 0.944444 0.944444 0.907407 0.870370 \n", - "108 WordSynonyms 0.742947 0.769592 0.755486 0.747649 0.763323 \n", - "109 Worms 0.727273 0.662338 0.740260 0.753247 0.688312 \n", - "110 WormsTwoClass 0.779221 0.805195 0.831169 0.753247 0.792208 \n", - "111 Yoga 0.905333 0.916333 0.905667 0.921333 0.897667 \n", - "\n", - " 5 6 7 8 ... 20 21 \\\n", - "0 0.870000 0.770000 0.840000 0.760000 ... 0.800000 0.750000 \n", - "1 0.774936 0.762148 0.767263 0.762148 ... 0.769821 0.764706 \n", - "2 0.834286 0.897143 0.857143 0.874286 ... 0.925714 0.862857 \n", - "3 0.633333 0.833333 0.700000 0.833333 ... 0.700000 0.800000 \n", - "4 0.900000 1.000000 0.850000 0.800000 ... 0.850000 1.000000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 1.000000 0.907407 0.981481 0.888889 ... 0.925926 0.870370 \n", - "108 0.774295 0.749216 0.757053 0.750784 ... 0.758621 0.772727 \n", - "109 0.701299 0.688312 0.844156 0.662338 ... 0.753247 0.727273 \n", - "110 0.714286 0.753247 0.805195 0.766234 ... 0.766234 0.740260 \n", - "111 0.917000 0.914333 0.914667 0.910333 ... 0.909667 0.920333 \n", - "\n", - " 22 23 24 25 26 27 28 \\\n", - "0 0.780000 0.860000 0.770000 0.780000 0.810000 0.720000 0.790000 \n", - "1 0.795396 0.792839 0.754476 0.792839 0.762148 0.777494 0.810742 \n", - "2 0.805714 0.880000 0.908571 0.891429 0.908571 0.845714 0.897143 \n", - "3 0.833333 0.766667 0.766667 0.600000 0.800000 0.900000 0.700000 \n", - "4 0.850000 0.950000 0.850000 1.000000 0.850000 0.800000 0.850000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.907407 0.925926 0.981481 0.925926 0.981481 0.759259 0.925926 \n", - "108 0.731975 0.766458 0.752351 0.763323 0.750784 0.755486 0.757053 \n", - "109 0.662338 0.740260 0.675325 0.688312 0.649351 0.649351 0.818182 \n", - "110 0.831169 0.818182 0.831169 0.753247 0.805195 0.714286 0.792208 \n", - "111 0.912667 0.902000 0.919000 0.910333 0.908667 0.918000 0.910000 \n", - "\n", - " 29 \n", - "0 0.840000 \n", - "1 0.774936 \n", - "2 0.857143 \n", - "3 0.700000 \n", - "4 1.000000 \n", - ".. ... \n", - "107 0.925926 \n", - "108 0.755486 \n", - "109 0.792208 \n", - "110 0.779221 \n", - "111 0.908667 \n", - "\n", - "[112 rows x 31 columns]}, {'estimator_name': 'BOSS', 'result': folds: 0 1 2 3 4 \\\n", - "0 ACSF1 0.870000 0.730000 0.750000 0.760000 0.750000 \n", - "1 Adiac 0.757033 0.764706 0.736573 0.749361 0.749361 \n", - "2 ArrowHead 0.828571 0.794286 0.868571 0.908571 0.857143 \n", - "3 Beef 0.833333 0.566667 0.600000 0.600000 0.566667 \n", - "4 BeetleFly 0.900000 0.900000 0.850000 0.950000 1.000000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.759259 0.888889 0.925926 0.888889 0.870370 \n", - "108 WordSynonyms 0.637931 0.673981 0.639498 0.666144 0.670846 \n", - "109 Worms 0.558442 0.675325 0.779221 0.779221 0.766234 \n", - "110 WormsTwoClass 0.831169 0.805195 0.831169 0.818182 0.857143 \n", - "111 Yoga 0.918667 0.922333 0.914333 0.919667 0.872333 \n", - "\n", - " 5 6 7 8 ... 20 21 \\\n", - "0 0.780000 0.700000 0.830000 0.770000 ... 0.810000 0.690000 \n", - "1 0.751918 0.746803 0.767263 0.741688 ... 0.757033 0.726343 \n", - "2 0.874286 0.880000 0.868571 0.851429 ... 0.920000 0.851429 \n", - "3 0.533333 0.733333 0.600000 0.566667 ... 0.600000 0.633333 \n", - "4 1.000000 1.000000 1.000000 0.950000 ... 0.850000 0.950000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.981481 0.888889 1.000000 0.833333 ... 0.925926 0.814815 \n", - "108 0.641066 0.652038 0.641066 0.655172 ... 0.678683 0.678683 \n", - "109 0.714286 0.805195 0.792208 0.727273 ... 0.779221 0.636364 \n", - "110 0.727273 0.753247 0.857143 0.818182 ... 0.844156 0.740260 \n", - "111 0.913333 0.927333 0.903667 0.907667 ... 0.920333 0.910000 \n", - "\n", - " 22 23 24 25 26 27 28 \\\n", - "0 0.670000 0.830000 0.820000 0.800000 0.740000 0.810000 0.760000 \n", - "1 0.772379 0.749361 0.734015 0.744246 0.710997 0.739130 0.764706 \n", - "2 0.822857 0.891429 0.914286 0.857143 0.931429 0.880000 0.874286 \n", - "3 0.500000 0.666667 0.566667 0.466667 0.733333 0.533333 0.500000 \n", - "4 1.000000 0.900000 0.950000 1.000000 0.950000 1.000000 0.850000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.944444 0.925926 0.944444 0.944444 0.944444 0.777778 0.888889 \n", - "108 0.656740 0.658307 0.663009 0.655172 0.634796 0.647335 0.681818 \n", - "109 0.701299 0.753247 0.662338 0.688312 0.623377 0.675325 0.818182 \n", - "110 0.766234 0.883117 0.831169 0.740260 0.753247 0.857143 0.909091 \n", - "111 0.913333 0.908000 0.913333 0.896667 0.909333 0.912000 0.897667 \n", - "\n", - " 29 \n", - "0 0.770000 \n", - "1 0.759591 \n", - "2 0.902857 \n", - "3 0.566667 \n", - "4 1.000000 \n", - ".. ... \n", - "107 0.740741 \n", - "108 0.680251 \n", - "109 0.740260 \n", - "110 0.818182 \n", - "111 0.885667 \n", - "\n", - "[112 rows x 31 columns]}, {'estimator_name': 'Catch22', 'result': folds: 0 1 2 3 4 \\\n", - "0 ACSF1 0.850000 0.800000 0.730000 0.800000 0.760000 \n", - "1 Adiac 0.703325 0.680307 0.721228 0.672634 0.695652 \n", - "2 ArrowHead 0.714286 0.691429 0.708571 0.702857 0.760000 \n", - "3 Beef 0.533333 0.466667 0.500000 0.566667 0.500000 \n", - "4 BeetleFly 0.650000 0.850000 0.700000 0.800000 0.800000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.407407 0.722222 0.796296 0.722222 0.722222 \n", - "108 WordSynonyms 0.542320 0.553292 0.540752 0.510972 0.548589 \n", - "109 Worms 0.714286 0.701299 0.649351 0.818182 0.714286 \n", - "110 WormsTwoClass 0.805195 0.792208 0.805195 0.831169 0.753247 \n", - "111 Yoga 0.781667 0.813667 0.781333 0.809000 0.803333 \n", - "\n", - " 5 6 7 8 ... 20 21 \\\n", - "0 0.860000 0.770000 0.770000 0.750000 ... 0.780000 0.690000 \n", - "1 0.690537 0.672634 0.670077 0.677749 ... 0.700767 0.677749 \n", - "2 0.771429 0.805714 0.748571 0.714286 ... 0.805714 0.782857 \n", - "3 0.500000 0.500000 0.500000 0.533333 ... 0.400000 0.433333 \n", - "4 0.850000 0.950000 0.900000 0.850000 ... 0.950000 0.800000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.722222 0.814815 0.685185 0.814815 ... 0.722222 0.574074 \n", - "108 0.556426 0.547022 0.528213 0.562696 ... 0.539185 0.576803 \n", - "109 0.753247 0.740260 0.844156 0.727273 ... 0.753247 0.649351 \n", - "110 0.753247 0.766234 0.883117 0.805195 ... 0.766234 0.766234 \n", - "111 0.815333 0.808333 0.815333 0.794333 ... 0.784333 0.803667 \n", - "\n", - " 22 23 24 25 26 27 28 \\\n", - "0 0.750000 0.780000 0.710000 0.770000 0.800000 0.720000 0.690000 \n", - "1 0.685422 0.644501 0.703325 0.675192 0.680307 0.680307 0.723785 \n", - "2 0.691429 0.754286 0.782857 0.771429 0.731429 0.737143 0.788571 \n", - "3 0.466667 0.500000 0.500000 0.400000 0.466667 0.500000 0.466667 \n", - "4 0.900000 0.900000 0.850000 0.850000 0.850000 0.850000 0.800000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.703704 0.740741 0.814815 0.685185 0.777778 0.518519 0.629630 \n", - "108 0.551724 0.529781 0.534483 0.556426 0.543887 0.537618 0.529781 \n", - "109 0.714286 0.818182 0.662338 0.701299 0.701299 0.623377 0.766234 \n", - "110 0.766234 0.818182 0.818182 0.805195 0.792208 0.766234 0.805195 \n", - "111 0.824000 0.811333 0.807000 0.796333 0.807000 0.800667 0.798667 \n", - "\n", - " 29 \n", - "0 0.820000 \n", - "1 0.695652 \n", - "2 0.697143 \n", - "3 0.466667 \n", - "4 0.800000 \n", - ".. ... \n", - "107 0.592593 \n", - "108 0.532915 \n", - "109 0.753247 \n", - "110 0.792208 \n", - "111 0.814000 \n", - "\n", - "[112 rows x 31 columns]}, {'estimator_name': 'cBOSS', 'result': folds: 0 1 2 3 4 \\\n", - "0 ACSF1 0.820000 0.770000 0.730000 0.760000 0.730000 \n", - "1 Adiac 0.764706 0.762148 0.731458 0.749361 0.749361 \n", - "2 ArrowHead 0.800000 0.857143 0.874286 0.891429 0.914286 \n", - "3 Beef 0.800000 0.633333 0.600000 0.533333 0.666667 \n", - "4 BeetleFly 1.000000 0.950000 0.850000 1.000000 1.000000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.740741 0.888889 0.925926 0.888889 0.833333 \n", - "108 WordSynonyms 0.656740 0.683386 0.645768 0.650470 0.688088 \n", - "109 Worms 0.558442 0.675325 0.610390 0.701299 0.688312 \n", - "110 WormsTwoClass 0.805195 0.818182 0.779221 0.766234 0.792208 \n", - "111 Yoga 0.912333 0.924333 0.913667 0.915333 0.877667 \n", - "\n", - " 5 6 7 8 ... 20 21 \\\n", - "0 0.770000 0.680000 0.800000 0.760000 ... 0.810000 0.740000 \n", - "1 0.749361 0.731458 0.759591 0.723785 ... 0.751918 0.759591 \n", - "2 0.897143 0.891429 0.874286 0.880000 ... 0.920000 0.851429 \n", - "3 0.533333 0.533333 0.600000 0.600000 ... 0.533333 0.633333 \n", - "4 1.000000 1.000000 1.000000 1.000000 ... 0.950000 1.000000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.944444 0.888889 0.981481 0.851852 ... 0.944444 0.814815 \n", - "108 0.647335 0.658307 0.673981 0.675549 ... 0.669279 0.700627 \n", - "109 0.740260 0.688312 0.688312 0.688312 ... 0.727273 0.675325 \n", - "110 0.779221 0.792208 0.844156 0.831169 ... 0.818182 0.740260 \n", - "111 0.908667 0.932000 0.911667 0.909000 ... 0.911000 0.908333 \n", - "\n", - " 22 23 24 25 26 27 28 \\\n", - "0 0.690000 0.770000 0.610000 0.800000 0.680000 0.770000 0.770000 \n", - "1 0.741688 0.751918 0.744246 0.741688 0.731458 0.749361 0.764706 \n", - "2 0.851429 0.857143 0.920000 0.902857 0.920000 0.891429 0.914286 \n", - "3 0.633333 0.533333 0.500000 0.300000 0.600000 0.466667 0.666667 \n", - "4 1.000000 0.950000 0.950000 1.000000 0.950000 0.950000 0.900000 \n", - ".. ... ... ... ... ... ... ... \n", - "107 0.907407 0.796296 0.944444 0.888889 0.944444 0.796296 0.851852 \n", - "108 0.667712 0.670846 0.661442 0.663009 0.652038 0.659875 0.683386 \n", - "109 0.584416 0.701299 0.636364 0.688312 0.623377 0.636364 0.649351 \n", - "110 0.727273 0.844156 0.870130 0.766234 0.740260 0.792208 0.844156 \n", - "111 0.909000 0.909667 0.915667 0.898333 0.906000 0.915667 0.919000 \n", - "\n", - " 29 \n", - "0 0.730000 \n", - "1 0.757033 \n", - "2 0.880000 \n", - "3 0.566667 \n", - "4 0.950000 \n", - ".. ... \n", - "107 0.851852 \n", - "108 0.677116 \n", - "109 0.701299 \n", - "110 0.779221 \n", - "111 0.913000 \n", - "\n", - "[112 rows x 31 columns]}, {'estimator_name': 'CIF', 'result': folds: 0 1 2 3 4 \\\n", - "0 ACSF1 0.830000 0.760000 0.730000 0.780000 0.760000 \n", - "1 Adiac 0.787724 0.780051 0.757033 0.759591 0.790281 \n", - "2 ArrowHead 0.748571 0.788571 0.845714 0.845714 0.851429 \n", - "3 Beef 0.866667 0.700000 0.700000 0.733333 0.933333 \n", - "4 BeetleFly 0.900000 0.850000 0.750000 0.850000 0.850000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.833333 0.851852 0.888889 0.870370 0.833333 \n", - "108 WordSynonyms 0.692790 0.691223 0.677116 0.669279 0.717868 \n", - "109 Worms 0.701299 0.675325 0.688312 0.740260 0.701299 \n", - "110 WormsTwoClass 0.805195 0.844156 0.818182 0.831169 0.818182 \n", - 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"0 ACSF1 0.920000 0.780000 0.760000 0.830000 0.860000 \n", - "1 Adiac 0.780051 0.759591 0.726343 0.726343 0.762148 \n", - "2 ArrowHead 0.885714 0.897143 0.914286 0.908571 0.931429 \n", - "3 Beef 0.833333 0.700000 0.766667 0.633333 0.733333 \n", - "4 BeetleFly 1.000000 1.000000 0.900000 0.950000 0.950000 \n", - ".. ... ... ... ... ... ... \n", - "107 Wine 0.666667 0.907407 0.925926 0.888889 0.833333 \n", - "108 WordSynonyms 0.763323 0.772727 0.739812 0.736677 0.768025 \n", - "109 Worms 0.727273 0.714286 0.662338 0.753247 0.740260 \n", - "110 WormsTwoClass 0.766234 0.831169 0.792208 0.857143 0.844156 \n", - "111 Yoga 0.926000 0.931333 0.927667 0.932000 0.916667 \n", - "\n", - " 5 6 7 8 ... 20 21 \\\n", - "0 0.840000 0.710000 0.800000 0.720000 ... 0.820000 0.750000 \n", - "1 0.736573 0.749361 0.751918 0.731458 ... 0.782609 0.749361 \n", - "2 0.914286 0.880000 0.902857 0.920000 ... 0.942857 0.902857 \n", - "3 0.600000 0.700000 0.733333 0.833333 ... 0.666667 0.800000 \n", - "4 0.900000 1.000000 0.950000 0.950000 ... 0.950000 0.950000 \n", - 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] - } - ], - "source": [ - "# If you have a directory of metric csvs which is not in the format outputted from\n", - "# this package, you need to define how the name of the estimator, metric name and\n", - "# the split are derived from the file. This is done using a function which takes\n", - "# a path parameters which is the path to the csv. It then must return three values:\n", - "# estimator_name, metric_name and split.\n", - "import platform\n", - "\n", - "\n", - "def custom_classification(path: str):\n", - " # Check os to determine split value\n", - " if \"Windows\" in platform.platform():\n", - " split_subdir = path.split(\"\\\\\")\n", - " else:\n", - " split_subdir = path.split(\"/\")\n", - " metric_name = \"ACC\"\n", - " file_name_split = split_subdir[-1].split(\"_\")\n", - " estimator_name = file_name_split[0]\n", - " split = file_name_split[0].split(\"FOLDS\")[0].lower()\n", - " return estimator_name, metric_name, split\n", - "\n", - "\n", - "classification_results = evaluate_metric_results(\"../results/\", custom_classification)\n", - "\n", - "print(classification_results)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

Summary data

" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n", - " estimator dataset RI AMI NMI ARI \\\n", - "0 kmeans-dtw ACSF1 0.660040 0.258515 0.376401 0.100716 \n", - "1 kmeans-dtw Adiac 0.934916 0.475209 0.626178 0.240566 \n", - "2 kmeans-dtw ArrowHead 0.634654 0.245863 0.254177 0.208196 \n", - "3 kmeans-euclidean ACSF1 0.682424 0.236444 0.358955 0.098812 \n", - "4 kmeans-euclidean Adiac 0.947642 0.427568 0.611580 0.244806 \n", - "5 kmeans-euclidean ArrowHead 0.566174 0.271135 0.282123 0.178624 \n", - "\n", - " MI ACC \n", - "0 0.696623 0.089667 \n", - "1 2.042572 0.027451 \n", - "2 0.268425 0.320190 \n", - "3 0.673076 0.140000 \n", - "4 2.068850 0.010230 \n", - "5 0.246564 0.165714 \n", - " estimator dataset ACC AMI ARI MI \\\n", - "0 kmeans-dtw ACSF1 0.089667 0.258515 0.100716 0.696623 \n", - "1 kmeans-dtw Adiac 0.027451 0.475209 0.240566 2.042572 \n", - "2 kmeans-dtw ArrowHead 0.320190 0.245863 0.208196 0.268425 \n", - "3 kmeans-euclidean ACSF1 0.140000 0.236444 0.098812 0.673076 \n", - "4 kmeans-euclidean Adiac 0.010230 0.427568 0.244806 2.068850 \n", - "5 kmeans-euclidean ArrowHead 0.165714 0.271135 0.178624 0.246564 \n", - "\n", - " NMI RI \n", - "0 0.376401 0.660040 \n", - "1 0.626178 0.934916 \n", - "2 0.254177 0.634654 \n", - "3 0.358955 0.682424 \n", - "4 0.611580 0.947642 \n", - "5 0.282123 0.566174 \n" - ] - } - ], - "source": [ - "# The above gives lots of dictionaries in a list, which while for storage is fine,\n", - "# is not very easy to process through functions. We can summaries all the above into\n", - "# one dataframe by aggregating all the folds and creating one big dataframe.\n", - "# This dataframe will be of the format:\n", - "# ----------------------------------\n", - "# | estimator | dataset | metric1 | metric2 |\n", - "# | cls1 | data1 | 1.2 | 1.2 |\n", - "# | cls2 | data2 | 3.4 | 1.4 |\n", - "# | cls1 | data2 | 1.4 | 1.3 |\n", - "# | cls2 | data1 | 1.3 | 1.2 |\n", - "# ----------------------------------\n", - "# How to produce this is shown.\n", - "from sktime_estimator_evaluation.evaluation import metric_result_to_summary\n", - "\n", - "# We can read in results or we can the result evaluate function return\n", - "metrics_from_raw_results = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=CLUSTER_METRIC_CALLABLES, # List of metrics to evaluate the results with\n", - ")\n", - "\n", - "metric_results_from_file = evaluate_metric_results(\n", - " path=\"./example_data/output/clustering_results/\"\n", - ")\n", - "\n", - "df_from_raw_results = metric_result_to_summary(metrics_from_raw_results)\n", - "print(df_from_raw_results)\n", - "\n", - "df_from_file = metric_result_to_summary(metric_results_from_file)\n", - "print(df_from_file)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/_wip/evaluation_raw_results.ipynb b/examples/_wip/evaluation_raw_results.ipynb deleted file mode 100644 index fa12e0a2..00000000 --- a/examples/_wip/evaluation_raw_results.ipynb +++ /dev/null @@ -1,413 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "source": [ - "

Evaluation of raw results

\n", - "\n", - "To evaluate the results there are two states results can be in:\n", - "1) 'Raw' - the results are not yet processed.\n", - "2) 'Metric results' - the raw have been turned into metric results over n folds.\n", - "\n", - "This notebook looks at 'raw', see evaluation_metric_results.ipynb for metric results\n", - "\n", - "

Raw results

\n", - "Raw result are those yet to be processed. A result for an estimator in the 'raw' format\n", - "is a csv that takes the following format:\n", - "\n", - "File name: \\Resample\\.csv e.g. testResample0.csv\n", - "First line: \\,\\,\\,\\,\\,\\\n", - "Second line: \\\n", - "Third line: \\\n", - "Fourth line and onwards: \\,\\,\\,,,...,\\\n", - "\n", - "

Example file

\n", - "See notebooks/example_data/testResample0.csv" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 1, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "First line: ['ACSF1', 'kmeans-ddtw', 'test', '0', 'MILLISECONDS', 'Generated by clustering_experiments on 2022-05-01 16:30:44.347516']\n", - "\n", - "Second line: [\"{'average_params': {'averaging_distance_metric': 'ddtw'}\", \" 'averaging_method': 'mean'\", \" 'distance_params': {'window': 1.0\", \" 'epsilon': 0.05\", \" 'g': 0.05\", \" 'c': 1}\", \" 'init_algorithm': 'random'\", \" 'max_iter': 300\", \" 'metric': 'ddtw'\", \" 'n_clusters': 10\", \" 'n_init': 10\", \" 'random_state': 1\", \" 'tol': 1e-06\", \" 'verbose': False}\"]\n", - "\n", - "Third line: ['0', '19307', '19283', '-1', '-1', '10', '10']\n", - "\n", - "Results: 0 1 2 3 4 5 6 7 8 9 10 11 12\n", - "0 9 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", - "1 9 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", - "2 9 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", - "3 9 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", - "4 9 9 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0\n", - ".. .. .. .. ... ... ... ... ... ... ... ... ... ...\n", - "95 1 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - "96 1 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - "97 1 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - "98 1 4 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0\n", - "99 1 0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", - "\n", - "[100 rows x 13 columns]\n" - ] - } - ], - "source": [ - "import csv\n", - "\n", - "import pandas as pd\n", - "\n", - "line_one = None\n", - "line_two = None\n", - "line_three = None\n", - "results = []\n", - "with open(\"./example_data/testResample0.csv\", newline=\"\\n\") as csvfile:\n", - " reader = csv.reader(csvfile, delimiter=\",\")\n", - " line_one = next(reader)\n", - " line_two = next(reader)\n", - " line_three = next(reader)\n", - " for row in reader:\n", - " results.append(row)\n", - "\n", - "print(f\"First line: {line_one}\\n\")\n", - "print(f\"Second line: {line_two}\\n\")\n", - "print(f\"Third line: {line_three}\\n\")\n", - "print(f\"Results: {pd.DataFrame(results)}\")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "

Evaluating raw results

" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 6, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: kmeans_dba\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-msm\n" - ] - } - ], - "source": [ - "from sktime_estimator_evaluation.evaluation import (\n", - " CLUSTER_METRIC_CALLABLES,\n", - " evaluate_raw_results,\n", - ")\n", - "\n", - "results = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=CLUSTER_METRIC_CALLABLES, # List of metrics to evaluate the results with\n", - ")\n", - "# The return value will take the format of:\n", - "# List[Dict]\n", - "# A list of metric results. Each metric will take the form:\n", - "# {\n", - "# 'metric_name': str,\n", - "# 'test_estimator_results': [\n", - "# {\n", - "# 'estimator_name': str,\n", - "# 'result': pd.DataFrame\n", - "# },\n", - "# {\n", - "# 'estimator_name': str,\n", - "# 'result': pd.DataFrame\n", - "# }\n", - "# ],\n", - "# 'train_estimator_results': [\n", - "# {\n", - "# 'estimator_name': str,\n", - "# 'result': pd.DataFrame\n", - "# },\n", - "# {\n", - "# 'estimator_name': str,\n", - "# 'result': pd.DataFrame\n", - "# }\n", - "# ],\n", - "# }\n", - "# Each result dataframe will be in the following format:\n", - "# | folds | 0 | 1 | 2 | 3 | ... |\n", - "# ------------------------------------------\n", - "# | dataset1 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset2 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset3 | 0.0 | 0.0 | 0.0 | 0.0 | ... |\n", - "# | dataset4 | 0.0 | 0.0 | 0.0 | 0.0 | ... |" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 3, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n" - ] - } - ], - "source": [ - "# If we want to write the results to a file with can specify an output path.\n", - "results_output = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=CLUSTER_METRIC_CALLABLES, # List of metrics to evaluate the results with\n", - " output_dir=\"./example_data/output/clustering_results\", # Path to the output directory\n", - ")" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 4, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n" - ] - } - ], - "source": [ - "# If we want to use a custom metric we can specify it in the metrics list.\n", - "results_str_metric = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\", # The name of the experiment\n", - " path=\"./example_data/clustering_results\", # Path to the raw files\n", - " metrics=[\"ACC\", \"RI\", \"AMI\"], # List of metrics to evaluate the results with\n", - ")\n", - "\n", - "# The full list of available clustering metrics is:\n", - "# RI (rand index), AMI (adjusted mutual information), ACC (accuracy), NMI (normalized mutual information), ARI (adjusted rand index), MI (mutual information)\n", - "valid_cluster_metrics = [\"RI\", \"AMI\", \"ACC\", \"NMI\", \"ARI\", \"MI\"]\n", - "# The full list of available classification metrics is:\n", - "# ACC (accuracy), F1 (f1 score), Precision (precision score), Recall (recall score), Jacard (jaccard score), ROC_AUC (roc auc score), Brier (brier score), Log_Loss (log loss score) Balanced_Accuracy (balanced accuracy score), Top_k_Accuracy (top k accuracy score), Average_Precision (average precision score)\n", - "valid_classification_metrics = [\n", - " \"ACC\",\n", - " \"F1\",\n", - " \"Precision\",\n", - " \"Recall\",\n", - " \"Jacard\",\n", - " \"ROC_AUC\",\n", - " \"Brier\",\n", - " \"Log_Loss\",\n", - " \"Balanced_Accuracy\",\n", - " \"Top_k_Accuracy\",\n", - " \"Average_Precision\",\n", - "]" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": 5, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "evaluating estimator: clustering_results\n", - "----> evaluating experiment: kmeans-dtw\n", - "----> evaluating experiment: kmeans-euclidean\n", - "[{'metric_name': 'custom_NMI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.471553 0.511391 0.496259 0.503164 0.536588 0.519174 \n", - "1 Adiac 0.687087 0.671689 0.689086 0.699429 0.693124 0.697099 \n", - "2 ArrowHead 0.271769 0.247246 0.247246 0.247246 0.247246 0.247246 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.513126 0.486407 0.498182 ... 0.553775 0.510917 0.521075 0.502281 \n", - "1 0.705582 0.705026 0.693871 ... 0.698392 0.698325 0.703904 0.684360 \n", - "2 0.271769 0.247246 0.278141 ... 0.278141 0.247246 0.278141 0.278141 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.498182 0.504014 0.488849 0.455637 0.480972 0.468552 \n", - "1 0.700289 0.676287 0.701198 0.691760 0.683737 0.693983 \n", - "2 0.278141 0.247246 0.278141 0.247246 0.278141 0.278141 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.464958\n", - "1 Adiac 0.649323\n", - "2 ArrowHead 0.375035}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.479524 0.462586 0.484659 0.480955 0.458070 0.529013 \n", - "1 Adiac 0.688464 0.671293 0.679770 0.684926 0.681067 0.701316 \n", - "2 ArrowHead 0.423304 0.348971 0.348971 0.348971 0.348971 0.348971 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.509389 0.467551 0.464877 ... 0.489037 0.525760 0.499311 0.435441 \n", - "1 0.693248 0.683764 0.690632 ... 0.686026 0.694562 0.684769 0.682722 \n", - "2 0.423304 0.348971 0.466122 ... 0.466122 0.348971 0.466122 0.466122 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.454772 0.523863 0.520076 0.458638 0.500850 0.517481 \n", - "1 0.687143 0.682815 0.691376 0.677762 0.686258 0.689476 \n", - "2 0.466122 0.348971 0.466122 0.348971 0.466122 0.466122 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.507081\n", - "1 Adiac 0.661040\n", - "2 ArrowHead 0.562550}]}, {'metric_name': 'custom_dict_NMI', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.471553 0.511391 0.496259 0.503164 0.536588 0.519174 \n", - "1 Adiac 0.687087 0.671689 0.689086 0.699429 0.693124 0.697099 \n", - "2 ArrowHead 0.271769 0.247246 0.247246 0.247246 0.247246 0.247246 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.513126 0.486407 0.498182 ... 0.553775 0.510917 0.521075 0.502281 \n", - "1 0.705582 0.705026 0.693871 ... 0.698392 0.698325 0.703904 0.684360 \n", - "2 0.271769 0.247246 0.278141 ... 0.278141 0.247246 0.278141 0.278141 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.498182 0.504014 0.488849 0.455637 0.480972 0.468552 \n", - "1 0.700289 0.676287 0.701198 0.691760 0.683737 0.693983 \n", - "2 0.278141 0.247246 0.278141 0.247246 0.278141 0.278141 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.464958\n", - "1 Adiac 0.649323\n", - "2 ArrowHead 0.375035}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.479524 0.462586 0.484659 0.480955 0.458070 0.529013 \n", - "1 Adiac 0.688464 0.671293 0.679770 0.684926 0.681067 0.701316 \n", - "2 ArrowHead 0.423304 0.348971 0.348971 0.348971 0.348971 0.348971 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.509389 0.467551 0.464877 ... 0.489037 0.525760 0.499311 0.435441 \n", - "1 0.693248 0.683764 0.690632 ... 0.686026 0.694562 0.684769 0.682722 \n", - "2 0.423304 0.348971 0.466122 ... 0.466122 0.348971 0.466122 0.466122 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.454772 0.523863 0.520076 0.458638 0.500850 0.517481 \n", - "1 0.687143 0.682815 0.691376 0.677762 0.686258 0.689476 \n", - "2 0.466122 0.348971 0.466122 0.348971 0.466122 0.466122 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.507081\n", - "1 Adiac 0.661040\n", - "2 ArrowHead 0.562550}]}, {'metric_name': 'ACC', 'test_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.12000 0.140000 0.050000 0.020000 0.130000 0.060000 \n", - "1 Adiac 0.02046 0.051151 0.038363 0.030691 0.048593 0.046036 \n", - "2 ArrowHead 0.08000 0.297143 0.188571 0.371429 0.297143 0.114286 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.140000 0.06000 0.000000 ... 0.110000 0.010000 0.150000 0.210000 \n", - "1 0.015345 0.01023 0.030691 ... 0.035806 0.071611 0.040921 0.015345 \n", - "2 0.371429 0.44000 0.542857 ... 0.148571 0.371429 0.542857 0.428571 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.110000 0.010000 0.050000 0.030000 0.080000 0.120000 \n", - "1 0.025575 0.005115 0.071611 0.015345 0.002558 0.015345 \n", - "2 0.360000 0.440000 0.428571 0.188571 0.542857 0.148571 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.140000\n", - "1 Adiac 0.010230\n", - "2 ArrowHead 0.165714}], 'train_estimator_results': [{'estimator_name': 'kmeans-dtw', 'result': folds 0 1 2 3 4 5 \\\n", - "0 ACSF1 0.120000 0.120000 0.030000 0.010000 0.150000 0.050000 \n", - "1 Adiac 0.023077 0.051282 0.041026 0.064103 0.051282 0.069231 \n", - "2 ArrowHead 0.111111 0.305556 0.194444 0.583333 0.305556 0.111111 \n", - "\n", - " 6 7 8 ... 20 21 22 23 \\\n", - "0 0.130000 0.090000 0.010000 ... 0.100000 0.020000 0.140000 0.190000 \n", - "1 0.023077 0.007692 0.038462 ... 0.033333 0.048718 0.035897 0.010256 \n", - "2 0.611111 0.222222 0.500000 ... 0.055556 0.583333 0.500000 0.305556 \n", - "\n", - " 24 25 26 27 28 29 \n", - "0 0.100000 0.000000 0.030000 0.040000 0.080000 0.110000 \n", - "1 0.012821 0.012821 0.076923 0.041026 0.002564 0.012821 \n", - "2 0.416667 0.222222 0.305556 0.194444 0.500000 0.055556 \n", - "\n", - "[3 rows x 31 columns]}, {'estimator_name': 'kmeans-euclidean', 'result': folds 0\n", - "0 ACSF1 0.150000\n", - "1 Adiac 0.010256\n", - "2 ArrowHead 0.055556}]}]\n" - ] - } - ], - "source": [ - "# Custom metric or custom parameters to existing metrics\n", - "# A custom metric function can only have two parameters: true labels and predicted labels.\n", - "from sklearn.metrics import normalized_mutual_info_score\n", - "from sktime_estimator_evaluation.evaluation import MetricCallable\n", - "\n", - "\n", - "# In this example we want to use normalized mutual info score but pass additional\n", - "# parameters so you wrap it in a function like so:\n", - "def example_custom_NMI(true_labels, predicted_labels):\n", - " return normalized_mutual_info_score(\n", - " true_labels, predicted_labels, average_method=\"min\"\n", - " )\n", - "\n", - "\n", - "# We then construct a custom MetricCallable object OR dict.\n", - "custom_metric = MetricCallable(name=\"custom_NMI\", callable=example_custom_NMI)\n", - "dict_custom_metric = {\"name\": \"custom_dict_NMI\", \"callable\": example_custom_NMI}\n", - "# Both are valid\n", - "\n", - "result_custom_metric = evaluate_raw_results(\n", - " experiment_name=\"example_experiment_name\",\n", - " path=\"./example_data/clustering_results\",\n", - " metrics=[\n", - " custom_metric,\n", - " dict_custom_metric,\n", - " \"ACC\",\n", - " ], # We can still use other metrics\n", - ")\n", - "\n", - "print(result_custom_metric)" - ], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.6" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/datasets.ipynb b/examples/datasets.ipynb index bbe898a7..e7774fd8 100644 --- a/examples/datasets.ipynb +++ b/examples/datasets.ipynb @@ -5,9 +5,28 @@ "source": [ "# tsml-eval Data Format\n", "\n", - "Placeholder\n", + "`tsml-eval` primarily uses `numpy` arrays as the datatype of choice when running experiments. The type of `numpy` array used however, will depend on the dataset characteristics (i.e. whether it is equal or unequal length) and the learning task.\n", "\n", - "\"wip\" [​](./images/wip.png)" + "Classification, clustering and regression use collections of time series. Forecasting using single time series.\n", + "\n", + "\"tsml-eval [​](./images/data.png)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "## Time Series Collections\n", + "\n", + "There are two types of collection datatypes used in `tsml-eval`:\n", + "- A 3D `numpy` array of shape `(n_samples, n_channels, n_timestamps)` for equal length time series.\n", + "- A list of 2D `numpy` arrays of shape `(n_channels, n_timestamps)` for unequal length time series.\n", + "\n", + "These are both design to accommodate multivariate time series, where `n_channels` is the number of variables in the time series. For univariate time series, `n_channels` is 1.\n", + "\n", + "Below is an example for these formats." ], "metadata": { "collapsed": false @@ -15,14 +34,218 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-11-23T13:35:14.006533100Z", + "start_time": "2023-11-23T13:35:13.189993500Z" + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (40, 1, 24)\n", + "Type: \n", + "[[[ 573. 375. 301. 212. 55. 34. 25. 33. 113. 143. 303.\n", + " 615. 1226. 1281. 1221. 1081. 866. 1096. 1039. 975. 746. 581.\n", + " 409. 182.]]\n", + "\n", + " [[ 394. 264. 140. 144. 104. 28. 28. 25. 70. 153. 401.\n", + " 649. 1216. 1399. 1249. 1240. 1109. 1137. 1290. 1137. 791. 638.\n", + " 597. 316.]]\n", + "\n", + " [[ 603. 348. 176. 177. 47. 30. 40. 42. 101. 180. 401.\n", + " 777. 1344. 1573. 1408. 1243. 1141. 1178. 1256. 1114. 814. 635.\n", + " 304. 168.]]\n", + "\n", + " [[ 428. 309. 199. 117. 82. 43. 24. 64. 152. 183. 408.\n", + " 797. 1288. 1491. 1523. 1460. 1365. 1520. 1700. 1797. 1596. 1139.\n", + " 910. 640.]]\n", + "\n", + " [[ 372. 310. 203. 133. 65. 39. 27. 36. 107. 139. 329.\n", + " 651. 990. 1027. 1041. 971. 1104. 844. 1023. 1019. 862. 643.\n", + " 591. 452.]]]\n" + ] + } + ], "source": [ - "print(\"Placeholder\")" + "from tsml.datasets import load_minimal_chinatown\n", + "\n", + "X, y = load_minimal_chinatown()\n", + "\n", + "print(\"Shape:\", X.shape)\n", + "print(\"Type:\", type(X))\n", + "print(X[:5])" ] + }, + { + "cell_type": "markdown", + "source": [ + "The labels for each time series are stored in a 1D `numpy` array." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (40,)\n", + "Type: \n", + "[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2. 1. 1. 1. 1.\n", + " 1. 1. 1. 1. 1. 1. 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]\n" + ] + } + ], + "source": [ + "print(\"Shape:\", y.shape)\n", + "print(\"Type:\", type(y))\n", + "print(y)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T13:35:14.014022200Z", + "start_time": "2023-11-23T13:35:14.006533100Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "`numpy` arrays do not support unequal length time series, so a list of 2D `numpy` arrays is used instead." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Size: 40\n", + "Type: \n", + "Case 1 shape: (12, 20)\n", + "Case 2 shape: (12, 26)\n", + "Case 1 type: \n" + ] + } + ], + "source": [ + "from tsml.datasets import load_minimal_japanese_vowels\n", + "\n", + "X, _ = load_minimal_japanese_vowels()\n", + "\n", + "print(\"Size:\", len(X))\n", + "print(\"Type:\", type(X))\n", + "print(\"Case 1 shape:\", X[0].shape)\n", + "print(\"Case 2 shape:\", X[1].shape)\n", + "print(\"Case 1 type:\", type(X[0]))" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T13:35:14.075887100Z", + "start_time": "2023-11-23T13:35:14.014022200Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Collection datatypes can be loaded from files in the `aeon` .ts format using the `tsml` loader below." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "(20, 1, 24)" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tsml.datasets import load_from_ts_file\n", + "\n", + "X, y = load_from_ts_file(\n", + " \"../tsml_eval/datasets/MinimalChinatown/MinimalChinatown_TRAIN.ts\"\n", + ")\n", + "X.shape" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T13:35:14.075887100Z", + "start_time": "2023-11-23T13:35:14.044942400Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Single Time Series\n", + "\n", + "Functionality for single series tasks in `tsml-eval` is currently limited. Using current functions, the best datatype to use is a 1D `numpy` array." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape: (24,)\n", + "Type: \n", + "[266. 145.9 183.1 119.3 180.3 168.5 231.8 224.5 192.8 122.9 336.5 185.9\n", + " 194.3 149.5 210.1 273.3 191.4 287. 226. 303.6 289.9 421.6 264.5 342.3]\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "X = pd.read_csv(\n", + " \"../tsml_eval/datasets/ShampooSales/ShampooSales_TRAIN.csv\",\n", + " index_col=0,\n", + ").squeeze(\"columns\")\n", + "X = X.astype(float).to_numpy()\n", + "\n", + "print(\"Shape:\", X.shape)\n", + "print(\"Type:\", type(X))\n", + "print(X)" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T13:35:17.874531400Z", + "start_time": "2023-11-23T13:35:14.069876600Z" + } + } } ], "metadata": { diff --git a/examples/evaluation.ipynb b/examples/evaluation.ipynb index dc9a75b2..8ccce5ad 100644 --- a/examples/evaluation.ipynb +++ b/examples/evaluation.ipynb @@ -5,9 +5,15 @@ "source": [ "# tsml-eval Results Evaluation\n", "\n", - "Placeholder\n", + "This notebook covers the evaluation module of `tsml-eval`, the primary focus of which is to aid the comparison of multiple machine learning algorithms with each other. While in this example we use the classification learning task, there are functions for clustering, regression and forecasting also available.\n", "\n", - "\"wip\" [​](./images/wip.png)" + "\"tsml-eval [​](./images/cd_diagram.png)\n", + "\n", + "## Comparing Multiple Estimators on Multiple Datasets\n", + "\n", + "In this example, we show a simple example for using the evaluation module to compare multiple classifiers on multiple datasets. For mor information on configuring the functions, see the function documentation.\n", + "\n", + "There are alternate methods of inputting results available. `evaluate_classifiers` takes a list of `ClassifierResults` objects, and `evaluate_classifiers_from_file` takes a list of filepaths to the result files." ], "metadata": { "collapsed": false @@ -15,14 +21,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "collapsed": true + "collapsed": true, + "ExecuteTime": { + "end_time": "2023-11-23T12:07:47.823755600Z", + "start_time": "2023-11-23T12:07:37.158656100Z" + } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "All results present, continuing evaluation.\n", + "\n", + "Estimators (3): ['1NN-DTW', 'ROCKET', 'TSF']\n", + "\n", + "Datasets (3): ['Chinatown', 'ItalyPowerDemand', 'Trace']\n", + "\n", + "Resamples (1): [0]\n" + ] + } + ], "source": [ - "print(\"Placeholder\")" + "import pickle\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from tsml_eval.evaluation import evaluate_classifiers_by_problem\n", + "\n", + "classifiers = [\"ROCKET\", \"TSF\", \"1NN-DTW\"]\n", + "datasets = [\"Chinatown\", \"ItalyPowerDemand\", \"Trace\"]\n", + "\n", + "evaluate_classifiers_by_problem(\n", + " \"../tsml_eval/testing/_test_result_files/classification/\",\n", + " classifiers,\n", + " datasets,\n", + " \"./generated_results/\",\n", + " resamples=1,\n", + " eval_name=\"ExampleEval\",\n", + ")" ] + }, + { + "cell_type": "markdown", + "source": [ + "The function will write various .csv files and figures to the chosen directory, displaying and summarising the results of the evaluation." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "testAccuracy,ROCKET,TSF,1NN-DTW\n", + "testAccuracyMean,0.9831551668286362,0.979591836734694,0.9747327502429544\n", + "testAccuracyAvgRank,1.3333333333333333,2.0,2.6666666666666665\n" + ] + } + ], + "source": [ + "with open(\"./generated_results/ExampleEval/ExampleEval_summary.csv\", \"rb\") as f:\n", + " for _ in range(3):\n", + " print(f.readline().decode(\"utf-8\").strip())" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T12:07:47.831718200Z", + "start_time": "2023-11-23T12:07:47.825735300Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "
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zLC9VqpQaNmyobdu2KSIiQn5+fhlqwsPDLZ9HRERozJgxGWr27t2rGzduyNHRUa+++qrpfc5taWlpGjJkiAICAlSzZs0s637++Wdt3bpV3bp1008//aRTp07pgw8+UGpqqsaPH2+pmT9/voYNG6ZRo0Zp9+7d+vDDD+Xo6KhevXopKSlJt27d0tSpUzVp0iRNmzZNGzZs0FtvvaXw8PBMf9AtWbJE1apVyxevFQDklJeXly5fvqz79+9rwoQJ6tevn9XyQ4cOyd/fX3fu3FGRIkW0atUqVa9e3armv//7v/Xpp58qOTlZr7zyisLCwuTo6GhV06VLF61evVopKSlq06aNFi9enOf79iQe/d16/fp17du3T2vWrNGaNWs0dOhQzZo1K8M6hmFo7Nixmjp1qh48eCAPDw8FBwfLyclJ8fHxWrFihVasWKFWrVppxYoVKlq0aKbP/fPPP6tDhw7at2+fJKlatWpq3ry5nJyclJCQoLCwMK1bt06jR49WXFycypcv/9j9MQxDH374ob744gsVKlRI33//vVq0aJHtfmcmfVmvXr0yLEtMTNTGjRuzXO7j4/PYPh9t2JRFixYZH3/8sfHNN98Y8fHxRo8ePQxJxsSJE7NcJzAw0JBkFCpUyJBkDB8+PNO6vn37GpKM8ePHWz0eHh5uSDJcXFwMOzs7w9XV1UhKSsqw/vnz5w1JRg52y+jVq5chyejVq1emy/fv32+0bNnSkGTY29sba9aseew2y5cvb0gyli5dmmXNuHHjDElGq1atMl3++uuvG/b29katWrUMFxcX4+7duxlqPvvsM0OS8dprrz22p2ehf//+Rvny5Y3z589nW1elShXD29vbuH//vuWxmTNnGu7u7pavCxYsaPj7+1utN2jQIKNRo0aGYRhGQkKCIcno0qWLVU2bNm2Mzp07Z3jO27dvG25ubsaMGTNM71c6Dme+eHjPXzzPwyHsn3/+2Th48KCxcOFCo0SJEsby5cutlt+9e9c4efKkERcXZ4wYMcIoWbKkceTIEaua33//3Thx4oQRGRlptGnTxvjLX/5ipKSkWNVcvHjRiI+PN1avXm1Ur17dGDBgwFPt49PK6ndramqqERISYskBu3btyrDukCFDDEmGs7Oz8c9//tNIS0uzWh4dHW1UqlTJkGQ0bNgw09+5v/zyi1G6dGlDkuHv728cOHAgQ82NGzeMadOmGUWLFjX27dtneXz8+PGGJCMwMDBD7926dTMkGcWLFzd27tz5xPttRnqeyklO+iPTh7D79eun6dOnq2vXrvLx8VGBAk++iUGDBqlAgQKaO3eufv31V7NPLQ8PD3Xo0EE3b97M9pB5Xqhdu7Z++uknderUSQ8ePFCvXr1y5Uq04OBgSdK2bdusDt1KDy8yiY6OVt26ddWmTRulpKQoNjY2wzbSRynTt2VLISEhWrt2rcLDw+Xl5ZVtbdmyZVW1alXZ29tbHqtWrZoSExN17949S80f/2KuVq2a5QrrkiVLysHBIduaR4WGhur27dvq2bNnjvYPAPKLihUrytfXV++++66GDh2a4fQvR0dHVa5cWfXq1dOUKVNUu3btDEcQ3dzcVKVKFb322msKDQ3VsWPHtGrVKqsad3d3+fj4qG3btvryyy81f/58Xbx4Ma93zzQHBwdNnz7dMmr4448/Wi0PCwvT7NmzJUnffvut+vTpk+GUt0aNGik8PFzFixdXbGysJk6cmOF5unfvrqSkJDVo0EBbt25VrVq1MtS4urrq008/1Z49e1SmTJls+05JSdGbb76pb775RmXLllVUVFSWpxrkJ8/0KuyaNWuqR48eSklJsRyiNOsf//iHHBwctGDBAp05cyaXO8yenZ2d5s2bJxcXF127ds3qXLyc8vf3l7Ozs27evKk9e/ZYLYuNjVVKSoqCgoIsh2IjIiKsau7fv6/t27dLsm2ANAxDISEhWrVqlbZu3aqKFSs+dp2AgACdOnVKaWlplsdOnDihsmXLWg6hBAQEZJgO6MSJE5bDAY6Ojqpfv362NY9asmSJ2rZtq1KlSpneRwDIr9LS0nT37t2nqjEMQ4ZhZFuT/vP6cc9lK87OzqpSpYqkh6e8PWry5MmSpDZt2qhdu3ZZbsPb21tjx46VJM2dO9fqPMbIyEht27ZNkrRgwQLLbB9ZqVy5ssqWLZvl8hs3bqhly5Zau3atKlWqpB07dmR76ld+8syn8fn73/8uJycnLV261Gq+qSdVpUoVvfvuu7p3716m5wPmtZdeekktW7aU9PCvmafl5ORk+Uvj0fMdpf8fFgMDA/Xqq6/KwcEhQ82ePXt08+ZNOTs7q1GjRk/dT04NHDhQX3/9tZYvXy5XV1clJiYqMTFRKSkplpqePXtq5MiRlq8HDBigq1evavDgwTpx4oTWrVunyZMna+DAgZaaoUOHKiYmRpMnT9apU6e0fPlyLVy40Krmk08+0cqVK7Vo0SKdOnVKX3zxhX788Ud98MEHVj2eOnVKUVFRGc4TAgBbunXrlvbv36/9+/dLks6cOaP9+/dbjqKMHDnS6qjJvHnz9OOPP+rkyZM6efKklixZohkzZqh79+6WmpEjRyoqKkpnz57VoUOHNHLkSEVERKhbt26SHp7DN2XKFO3Zs0fnzp3Tzp071bFjR7m4uKhVq1aSpJ9++klLly7V4cOHdfbsWa1bt079+/dXQECAKlSo8GxenBxIPzr46MjftWvXFBUVJUlPdASqR48elm09OnCzevVqSZKvr6/q1q37VH1evnxZQUFBioqKUu3atbV9+/YnGnzJL555gCxXrpwGDhyoBw8eaNSoUTnaxrhx41S4cGGtWLHC8h/uWapXr54kWc2n9TTSRw4zC5D29vZq0qSJChcuLD8/P0VHR1v95Zf+jf3qq6/KyckpV/rJifnz5+v69esKCgpS2bJlLR+PXkF27tw5q8Me3t7e2rhxo3bv3q1atWrpww8/1ODBgzVixAhLTf369bVq1SqtWLFCNWvW1MSJEzV79mzLD0FJevPNN7VgwQJ99tln8vX11eLFi/Xvf/87w8VN//znP+Xl5aXmzZvn4SsBAObExcWpbt26lkAybNgw1a1b13Kx6MWLF61OyUlLS9PIkSNVp04d+fn5ad68eZo2bZr+/ve/W2qSkpLUs2dPvfLKK2ratKl2796tjRs3qlmzZpIejtRt27ZNrVq1UuXKldWpUye5urpq586dKl26tCTJxcVFixYtUuPGjVWtWjUNHTpUbdu2faKrnG0lPj5eP//8sySpbdu2lsf37dtnGT1t0KDBY7dTsmRJS5h7dJaU9M/r16//VH0mJSWpSZMm2rdvnxo3bqzIyEi5u7s/1TafNdNXYeeG0aNHa8mSJVq1apViYmJMj5y5u7tr6NChmjRpkkaMGKENGzbkUaeZK1mypKSHs/TnhvQAuX37dt2/f18ODg66e/euoqOjVadOHbm5uUl6OBIZExOjmJgYyyHt/HL+o2EYj6354+F36eEh/JiYmGzXa926tVq3bp1tzTvvvKN33nkn25rJkydbDmEAQH4RFBSU7c/Q9GlZ0g0aNEiDBg3KdptLlizJdrmHh0eGu379UXBwsHbu3JltTX5x/fp1xcbGavDgwXrw4IHGjBljNbPJ5cuXLZ8/7pzER+vOnDljtW765+khO6fi4+MlSYUKFdJ3331n+T3/JPr06aM+ffpkufzatWs5nunGDJsEyBIlSmj48OEaNWqUhg8frsjISNPb+OSTT7RgwQJt3LhR4eHhzzRApf8Vk5P5JjPToEEDFSpUSMnJydq9e7f8/f0t5z8+Og1NYGCgpk2bpoiICAUGBur+/fvasWOHJNsHSAAAnqXMgpS9vb2+/vprq6NUOfUkAyM5VblyZd2+fVu//vqr2rVrp40bNz5x6HvcND5/nIYpr9gkQErSkCFD9MUXXygqKkpr16597AjTHxUtWlRjxozRkCFDNHz48EyvTk43derUTM+3nDFjhmU00YwrV65Iyn6SbDMcHR0VEBCgsLAwhYeHy9/f3zJa9+ikngEBAbK3t1dERITGjx+vuLg43bp1S4ULF36iIfns+Pn5KTEx8am28aLIj1cfAsgbFy9efOyMEsgZd3f3LG+i8SQeDVKXL1/Wtm3bdPPmTQ0YMEBVqlSx+r346O/6S5cuqVy5co/dflJSkiRZXXRZqlQpHTt2zLIspzw9PbV48WIFBwdr165datasmTZt2mR1m96s9OvXT717936q588NNguQLi4uGj9+vN5//32NGjXKctKuGQMGDNDs2bO1e/duhYaGZnnZ+4YNGzId5ZwwYUKOAuTevXslPTyJNrcEBwdbAuSoUaMUERGhAgUKqEmTJpaaokWLqm7duoqOjtadO3csh6/T7xX9NBITE5WQkPBU23jRuLq62roFAHkk/f93WloaPxvzqT8GqevXr+vNN99UeHi4/vM//1NHjx5VoUKFJEl169aVnZ2dDMNQbGzsYwPk5cuXLTO9pF/3kP75tm3btHv37qfuv3LlyoqIiFBwcLDi4uL0xhtvKCwsLNcGp/KazQKkJPXt21ezZs3SoUOH9NVXX5le39HRURMnTlSPHj00evRobdq0KdO6zM69y6krV65YZnHPzYsx0g9B79y5Uzdv3lRMTIxq166dYUg7MDBQcXFxiomJsexXbhy+ft5O3rU1V1fXTOcHA/DnMHHiRI0dOzbDreiQe3L7946bm5tWrlwpHx8f/fLLL5o1a5ZltpYSJUqoSZMmioqK0v/8z/+oY8eO2W4rPZO4urpaHQls166dZs+erUOHDmnfvn1PfSV2pUqVFBkZqeDgYO3du9cSIl966aWn2u6zYNMAaW9vr8mTJ+vtt9/WuHHj9Nprr5neRrdu3TRjxgwdOHAgV+ZlzE76XIcpKSkqUaJErt5L2c/PT0WKFNGtW7f0+eefW+Z//KPAwEDNnDlTmzZtytXzH5/mMAIA/Nl06NBBHTp0sHUbMKlUqVIaM2aMhg0bphkzZigkJMQyEDNq1CjLaXOrV6/Oci7I8+fPW25WEhISYnU7w6CgIAUEBGjHjh0aMGCAIiMjs50B5fTp0ypUqFC2c0FWrFjRMhK5b98+NW3aVJs3b87REdJn6ZlP4/NHb731lho2bKhz587p+++/N72+nZ2dpkyZIkmWGebzwsGDB9WqVSutXLnScpJubh7CdHBwsByunjlzpiRlGiCbNGmiAgUK6Msvv1RycrJcXV2thtcBAHiRffDBBypXrpyuX79u+X0qSS1atLBcvd6lSxctW7Ysw4UysbGxCg4O1rVr1+Tn55fpTU++/vprlSxZUrGxsXr99dd16NChDDXJycmaNWuW6tWrl2FC88xUqFBBkZGRevnll3XgwAG9/vrrVld/50emRyD37t1rNUHz6dOnJUlffvml1dxQq1atyjZxP2ratGkKCgrS7du3zbYjSfrrX/+qoKCgXDlUvX37dss5Fffv39fvv/+uw4cP65dffpH08C+FJUuW5MlVz8HBwVq/fr2uXr2a4fzHdMWKFVOtWrUs8182adJEDg42HUgGACDfcHJy0oQJE/TOO+9ozpw5Gjp0qOW8wjlz5qhQoUKaPn26+vTpozFjxqh+/fpycnJSfHy8Dh48KOlh2Fy5cmWmo4sVKlRQdHS03n77be3cuVO1atVS9erV5ePjI0dHRyUkJGjXrl26e/euypQp88TnNJYrV84yEnno0CEFBwdr69atGaYMWrx4cbZ5p3nz5uratesTvlo5Zzp53LhxI9Mrni9cuKALFy5YvjZzm6PAwEC1atXqsXNSZWfatGlq2LBhjtdPd/r0aUsodnJykpubm7y9vdW8eXO1bt1arVq1yrPA9mgorVWrVpZXYwUGBloCJNP3AABgrWfPnpoxY4aOHj2q6dOnW45U2tnZaerUqerRo4fmz5+vLVu2aPPmzUpNTVXp0qXVqVMn9ejRQ//xH/+R7fYrV66sffv2KTQ0VP/+978VGxur9evXKy0tTaVKlVKzZs3Url07de3a1XIhz5Pw9va2nBN55MgRBQUFaevWrVbni+7YscNyCltmihUr9kwCpJ2RlxMdAcCfgJeXlxISEuTp6Wn1hzIAvKhsfg4kAAAAni8ESAAAAJhCgAQAAIApBEgAAACYQoAEAMCGpkyZovr168vV1VWlS5dW+/btdfz48ceu991338nHx0fOzs7y9fXNMJOJYRgaN26cypYtKxcXF73xxhs6efKkVc3Vq1fVrVs3FS1aVMWKFVPfvn1169Ytq5qDBw+qSZMmcnZ2lre3tz777LOn32k89wiQAADYUGRkpAYOHKiYmBiFhYUpNTVVzZs3V3Jycpbr7Ny5U126dFHfvn21b98+tW/fXu3bt9fhw4ctNZ999pnmzp2rBQsWKDY2VoULF1aLFi10584dS023bt105MgRhYWFae3atYqKitJ7771nWX7jxg01b95c5cuX1549ezR9+nRNmDBBCxcuzJsXA88NpvEBgMdgGh88S5cvX1bp0qUVGRmZ5S1+O3XqpOTkZKsbeDRq1Eh16tTRggULZBiGPDw89NFHH+njjz+WJF2/fl1lypTRsmXL1LlzZ8XHx6t69eravXu3/Pz8JEkbNmxQq1atdOHCBXl4eGj+/PkaPXq0EhMT5ejoKEkaMWKEfvjhBx07diyPXwnkZ4xAAgCQj1y/fl2Ssr2DSXR0tN544w2rx1q0aKHo6GhJ0pkzZ5SYmGhV4+bmpoYNG1pqoqOjVaxYMUt4lKQ33nhDBQoUsNwwJDo6Wq+99polPKY/z/Hjx3Xt2rWn3FNz7OzsTH/88ZbA4eHh6ty5s8qXLy9nZ2e5urqqYsWKCg4O1ujRoxUTE5Oj5/3hhx+ezYuQj3APPAAA8om0tDQNGTJEAQEBqlmzZpZ1iYmJKlOmjNVjZcqUUWJiomV5+mPZ1fzxNnkODg4qUaKEVU3FihUzbCN9WVZ3TMsLvXr1yvBYYmKiNm7cmOVyHx8fy+effvqppk+fLkl6+eWX1axZM7m6uurixYvau3evIiIidPz4cYWGhmb6/C1atLC6I8yjypUrZ3p/nncESAAA8omBAwfq8OHD2r59u61byXeWLVuW4bGIiAhLgMxsebp169Zp+vTpcnBw0FdffaXOnTtbLU9NTVVYWJjOnDmT5TZGjBiRYUTzRUaABAAgHwgJCbFcyOLl5ZVtrbu7uy5dumT12KVLlywjZOn/Xrp0SWXLlrWqqVOnjqUmKSnJahv379/X1atXrbaT2fM8+hzPg2+//VaS1LFjxwzhUZIKFiyoVq1aPeu2nmucAwkAgA0ZhqGQkBCtWrVKW7duzXDIODP+/v7asmWL1WNhYWHy9/eXJFWsWFHu7u5WNTdu3FBsbKylxt/fX7///rv27Nljqdm6davS0tLUsGFDS01UVJRSU1OtnueVV155poevn1Z66P3jIXvkHAESAAAbGjhwoL7++mstX75crq6uSkxMVGJiolJSUiw1PXv21MiRIy1fDx48WBs2bNDMmTN17NgxTZgwQXFxcQoJCZH08MKPIUOGaNKkSVqzZo0OHTqknj17ysPDQ+3bt5ckVatWTS1bttS7776rXbt2aceOHQoJCVHnzp3l4eEhSeratascHR3Vt29fHTlyRCtXrtScOXM0bNiwZ/cC5YL0cxRDQ0OVkJBg427+JAwAQLY8PT0NSYanp6etW8GfkKRMP5YuXWqpCQwMNHr16mW13v/+7/8aVatWNRwdHY0aNWoY69ats1qelpZmjB071ihTpozh5ORkNG3a1Dh+/LhVzW+//WZ06dLFKFKkiFG0aFGjT58+xs2bN61qDhw4YDRu3NhwcnIyPD09jalTp+bq/j+N8PBwy+uVnV27dhkODg6GJMPFxcXo0KGDMXv2bCMqKspITk7Odt307YeHh+di588/5oEEgMdgHkggf4qIiFBwcLCkh6cCZGft2rUaMGBAhv/DBQsWVFBQkD755BM1a9Ysw3p2dnbZbrdXr17ZXsDzZ8VFNAAA4E+vdevWatGihTZu3KjNmzdr9+7d2r9/v27fvq2wsDCFhYVp3Lhx+tvf/pbp+llN49O4ceO8bj1fYgQSAB4jfQSyQIECVle0Asg5d3d3xcXFPdU2zIxAZubu3buKiIjQmDFjLL3ExsaqQYMGlpr0Ecjw8HCm8XkEI5AA8Biurq6SHk7yzAn4wJ+Hk5OTWrRooYCAAPn4+CghIUGrV6+2CpDIHAESAB5j4sSJGjt2rG7evGnrVoA/jfw0j2SRIkXk7++v0NBQXblyxdbtPBcIkADwGB06dFCHDh1s3QaAHDIM47EXw5w7d06SHjuJOx4iQAIAgD+1vn37ysPDQ71791blypWtlqWkpGjatGnatWuXHBwc+GPxCREgAQDAn9rVq1e1dOlS/eMf/9DLL7+sGjVqyNXVVUlJSdqzZ4+uXbsme3t7zZ07V9WqVbN1u88FAiQAAPhTmzdvntq3b68tW7bo0KFDio2N1dWrV+Xi4qKKFSuqW7du6t+/v2rUqGHrVp8bTOMDAAAAU7gXNgAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABMIUACAADAFAIkAAAATCFAAgAAwBQCJAAAAEwhQAIAAMAUAiQAAABM+T8tv/w7FjemagAAAABJRU5ErkJggg==" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open(\n", + " \"./generated_results/ExampleEval/Accuracy/figures/\"\n", + " \"Accuracy_critical_difference.pickle\",\n", + " \"rb\",\n", + ") as f:\n", + " fig = pickle.load(f)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T12:07:48.061105500Z", + "start_time": "2023-11-23T12:07:47.831718200Z" + } + } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "
", + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "with open(\n", + " \"./generated_results/ExampleEval/Accuracy/figures/scatters/\"\n", + " \"ROCKET/Accuracy_scatter_ROCKET_TSF.pickle\",\n", + " \"rb\",\n", + ") as f:\n", + " fig = pickle.load(f)\n", + "plt.show()" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-23T12:07:48.147873400Z", + "start_time": "2023-11-23T12:07:48.062102900Z" + } + } } ], "metadata": { diff --git a/examples/example_data/ACC.csv b/examples/example_data/ACC.csv deleted file mode 100644 index 7259749b..00000000 --- a/examples/example_data/ACC.csv +++ /dev/null @@ -1,109 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.12,0.14,0.05,0.02,0.13,0.06,0.14,0.06,0.0,0.17,0.13,0.08,0.15,0.03,0.09,0.14,0.03,0.1,0.11,0.06,0.11,0.01,0.15,0.21,0.11,0.01,0.05,0.03,0.08,0.12 -Adiac,0.020460358056265986,0.05115089514066496,0.03836317135549872,0.030690537084398978,0.04859335038363171,0.04603580562659847,0.015345268542199489,0.010230179028132993,0.030690537084398978,0.01278772378516624,0.0076726342710997444,0.03324808184143223,0.015345268542199489,0.020460358056265986,0.015345268542199489,0.0076726342710997444,0.015345268542199489,0.017902813299232736,0.05370843989769821,0.03324808184143223,0.03580562659846547,0.07161125319693094,0.04092071611253197,0.015345268542199489,0.02557544757033248,0.005115089514066497,0.07161125319693094,0.015345268542199489,0.0025575447570332483,0.015345268542199489 -ArrowHead,0.08,0.29714285714285715,0.18857142857142858,0.37142857142857144,0.29714285714285715,0.11428571428571428,0.37142857142857144,0.44,0.5428571428571428,0.36,0.29714285714285715,0.3942857142857143,0.09714285714285714,0.4228571428571429,0.29714285714285715,0.29714285714285715,0.42857142857142855,0.14857142857142858,0.37142857142857144,0.18857142857142858,0.14857142857142858,0.37142857142857144,0.5428571428571428,0.42857142857142855,0.36,0.44,0.42857142857142855,0.18857142857142858,0.5428571428571428,0.14857142857142858 -Beef,0.23333333333333334,0.26666666666666666,0.2,0.26666666666666666,0.23333333333333334,0.23333333333333334,0.16666666666666666,0.2,0.2,0.3333333333333333,0.26666666666666666,0.26666666666666666,0.26666666666666666,0.16666666666666666,0.3,0.06666666666666667,0.3,0.1,0.2,0.16666666666666666,0.16666666666666666,0.3333333333333333,0.1,0.26666666666666666,0.16666666666666666,0.23333333333333334,0.23333333333333334,0.4,0.2,0.2 -BeetleFly,0.5,0.5,0.55,0.45,0.55,0.45,0.45,0.5,0.5,0.45,0.55,0.5,0.45,0.5,0.55,0.45,0.45,0.5,0.45,0.55,0.5,0.55,0.55,0.55,0.5,0.55,0.5,0.55,0.45,0.5 -BirdChicken,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5 -BME,0.5066666666666667,0.36,0.3333333333333333,0.3333333333333333,0.64,0.37333333333333335,0.16,0.16,0.37333333333333335,0.64,0.64,0.64,0.64,0.16,0.3333333333333333,0.16,0.64,0.2,0.29333333333333333,0.16,0.37333333333333335,0.16,0.29333333333333333,0.64,0.16,0.64,0.37333333333333335,0.37333333333333335,0.2,0.3333333333333333 -Car,0.18333333333333332,0.25,0.21666666666666667,0.16666666666666666,0.31666666666666665,0.2,0.1,0.25,0.4,0.2833333333333333,0.21666666666666667,0.3333333333333333,0.16666666666666666,0.25,0.3333333333333333,0.23333333333333334,0.23333333333333334,0.36666666666666664,0.16666666666666666,0.36666666666666664,0.31666666666666665,0.15,0.21666666666666667,0.21666666666666667,0.3333333333333333,0.15,0.3333333333333333,0.21666666666666667,0.06666666666666667,0.31666666666666665 -CBF,0.37555555555555553,0.37555555555555553,0.37555555555555553,0.17777777777777778,0.4266666666666667,0.03,0.37555555555555553,0.03,0.03,0.17777777777777778,0.4266666666666667,0.5433333333333333,0.4266666666666667,0.03,0.4266666666666667,0.17777777777777778,0.5433333333333333,0.4266666666666667,0.03,0.44666666666666666,0.5433333333333333,0.4266666666666667,0.17777777777777778,0.4266666666666667,0.17777777777777778,0.17777777777777778,0.4266666666666667,0.5433333333333333,0.44666666666666666,0.17777777777777778 -Chinatown,0.6880466472303207,0.3119533527696793,0.3119533527696793,0.35276967930029157,0.6880466472303207,0.6880466472303207,0.3119533527696793,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.3119533527696793,0.6880466472303207,0.3119533527696793,0.3119533527696793,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.3119533527696793,0.6472303206997084,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.6472303206997084,0.6880466472303207,0.6880466472303207,0.6880466472303207,0.3119533527696793,0.6880466472303207 -ChlorineConcentration,0.31197916666666664,0.31197916666666664,0.32421875,0.37057291666666664,0.37057291666666664,0.37057291666666664,0.31197916666666664,0.31197916666666664,0.31197916666666664,0.31197916666666664,0.37057291666666664,0.37994791666666666,0.30520833333333336,0.30807291666666664,0.30807291666666664,0.37057291666666664,0.30807291666666664,0.32421875,0.30520833333333336,0.30520833333333336,0.37994791666666666,0.31197916666666664,0.31197916666666664,0.37994791666666666,0.32421875,0.30520833333333336,0.3109375,0.37057291666666664,0.30807291666666664,0.37994791666666666 -CinCECGTorso,0.18840579710144928,0.24057971014492754,0.2210144927536232,0.27898550724637683,0.26014492753623186,0.21594202898550724,0.3094202898550725,0.2210144927536232,0.2826086956521739,0.21231884057971015,0.23695652173913043,0.29927536231884055,0.1927536231884058,0.23695652173913043,0.263768115942029,0.23768115942028986,0.20434782608695654,0.24782608695652175,0.22028985507246376,0.29927536231884055,0.24347826086956523,0.23695652173913043,0.26956521739130435,0.263768115942029,0.21594202898550724,0.19492753623188405,0.21594202898550724,0.21594202898550724,0.26521739130434785,0.24565217391304348 -Coffee,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.9642857142857143,0.9642857142857143,0.9642857142857143,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.9642857142857143,0.03571428571428571,0.9642857142857143,0.9642857142857143,0.9642857142857143,0.9642857142857143,0.03571428571428571,0.9642857142857143,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.03571428571428571,0.9642857142857143,0.03571428571428571,0.03571428571428571,0.9642857142857143,0.9642857142857143,0.03571428571428571 -Computers,0.42,0.432,0.568,0.432,0.58,0.568,0.42,0.568,0.58,0.42,0.432,0.432,0.432,0.58,0.58,0.568,0.568,0.432,0.568,0.576,0.568,0.58,0.58,0.568,0.58,0.572,0.58,0.432,0.568,0.568 -CricketX,0.05128205128205128,0.1358974358974359,0.09743589743589744,0.1,0.12307692307692308,0.08717948717948718,0.07179487179487179,0.06666666666666667,0.1358974358974359,0.05384615384615385,0.09230769230769231,0.06153846153846154,0.06153846153846154,0.08974358974358974,0.06923076923076923,0.09230769230769231,0.11282051282051282,0.09487179487179487,0.08461538461538462,0.05641025641025641,0.07692307692307693,0.09743589743589744,0.07435897435897436,0.11025641025641025,0.11538461538461539,0.04871794871794872,0.035897435897435895,0.07179487179487179,0.07435897435897436,0.07692307692307693 -CricketY,0.05384615384615385,0.05641025641025641,0.03333333333333333,0.11794871794871795,0.1076923076923077,0.10512820512820513,0.1564102564102564,0.06666666666666667,0.12564102564102564,0.1358974358974359,0.046153846153846156,0.08205128205128205,0.09487179487179487,0.05384615384615385,0.07179487179487179,0.11282051282051282,0.06666666666666667,0.08717948717948718,0.1282051282051282,0.05128205128205128,0.046153846153846156,0.08974358974358974,0.12307692307692308,0.07179487179487179,0.03333333333333333,0.16153846153846155,0.14871794871794872,0.09230769230769231,0.08205128205128205,0.14358974358974358 -CricketZ,0.13846153846153847,0.03076923076923077,0.08205128205128205,0.09230769230769231,0.07435897435897436,0.06153846153846154,0.09743589743589744,0.13333333333333333,0.05128205128205128,0.09487179487179487,0.05641025641025641,0.09743589743589744,0.09487179487179487,0.035897435897435895,0.0641025641025641,0.11538461538461539,0.13333333333333333,0.11538461538461539,0.10512820512820513,0.11794871794871795,0.05897435897435897,0.03333333333333333,0.017948717948717947,0.07179487179487179,0.09743589743589744,0.03333333333333333,0.12307692307692308,0.08974358974358974,0.13333333333333333,0.05897435897435897 -Crop,0.027678571428571427,0.03184523809523809,0.035833333333333335,0.06636904761904762,0.08202380952380953,0.04083333333333333,0.05095238095238095,0.02214285714285714,0.07452380952380952,0.03166666666666667,0.03238095238095238,0.04196428571428571,0.05654761904761905,0.03625,0.029107142857142856,0.040535714285714286,0.04267857142857143,0.07577380952380952,0.027678571428571427,0.01375,0.022916666666666665,0.021904761904761906,0.022202380952380953,0.03267857142857143,0.015416666666666667,0.07422619047619047,0.06488095238095239,0.01988095238095238,0.013392857142857142,0.030476190476190476 -DiatomSizeReduction,0.5882352941176471,0.24509803921568626,0.016339869281045753,0.30392156862745096,0.24509803921568626,0.1503267973856209,0.369281045751634,0.24509803921568626,0.24509803921568626,0.38562091503267976,0.06862745098039216,0.08496732026143791,0.24509803921568626,0.0032679738562091504,0.3660130718954248,0.24509803921568626,0.0032679738562091504,0.30392156862745096,0.08496732026143791,0.08169934640522876,0.016339869281045753,0.32679738562091504,0.08169934640522876,0.08496732026143791,0.0032679738562091504,0.5882352941176471,0.24509803921568626,0.24509803921568626,0.5294117647058824,0.24509803921568626 -DistalPhalanxOutlineAgeGroup,0.2517985611510791,0.2517985611510791,0.04316546762589928,0.35251798561151076,0.35251798561151076,0.2446043165467626,0.2517985611510791,0.28776978417266186,0.35251798561151076,0.35251798561151076,0.2517985611510791,0.2517985611510791,0.3597122302158273,0.35251798561151076,0.2517985611510791,0.04316546762589928,0.3597122302158273,0.28776978417266186,0.28776978417266186,0.04316546762589928,0.04316546762589928,0.7050359712230215,0.7050359712230215,0.2517985611510791,0.28776978417266186,0.35251798561151076,0.7050359712230215,0.04316546762589928,0.7122302158273381,0.28776978417266186 -DistalPhalanxOutlineCorrect,0.5905797101449275,0.5905797101449275,0.5905797101449275,0.40942028985507245,0.5905797101449275,0.40942028985507245,0.40942028985507245,0.40942028985507245,0.5905797101449275,0.5905797101449275,0.5905797101449275,0.5905797101449275,0.40942028985507245,0.40942028985507245,0.40942028985507245,0.40942028985507245,0.5905797101449275,0.5905797101449275,0.5905797101449275,0.5905797101449275,0.40942028985507245,0.5905797101449275,0.5905797101449275,0.40942028985507245,0.40942028985507245,0.40942028985507245,0.5905797101449275,0.40942028985507245,0.40942028985507245,0.40942028985507245 -DistalPhalanxTW,0.0,0.07913669064748201,0.08633093525179857,0.1079136690647482,0.1366906474820144,0.12949640287769784,0.1079136690647482,0.09352517985611511,0.1079136690647482,0.15827338129496402,0.1366906474820144,0.35251798561151076,0.014388489208633094,0.05755395683453238,0.39568345323741005,0.1223021582733813,0.07194244604316546,0.09352517985611511,0.11510791366906475,0.45323741007194246,0.07913669064748201,0.05755395683453238,0.2733812949640288,0.2733812949640288,0.1079136690647482,0.04316546762589928,0.050359712230215826,0.06474820143884892,0.1366906474820144,0.007194244604316547 -Earthquakes,0.7482014388489209,0.2517985611510791,0.7482014388489209,0.7482014388489209,0.2517985611510791,0.7482014388489209,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.7482014388489209,0.7482014388489209,0.7482014388489209,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.2517985611510791,0.7482014388489209,0.7482014388489209,0.7482014388489209,0.7482014388489209,0.7482014388489209,0.2517985611510791,0.2517985611510791,0.7482014388489209 -ECG200,0.62,0.62,0.62,0.38,0.38,0.62,0.62,0.38,0.38,0.38,0.62,0.38,0.62,0.62,0.62,0.38,0.38,0.38,0.62,0.62,0.38,0.62,0.62,0.62,0.38,0.38,0.62,0.38,0.62,0.62 -ECG5000,0.012444444444444444,0.6546666666666666,0.04488888888888889,0.24533333333333332,0.162,0.4508888888888889,0.035111111111111114,0.14355555555555555,0.46266666666666667,0.25466666666666665,0.025777777777777778,0.22666666666666666,0.37933333333333336,0.44622222222222224,0.03133333333333333,0.15,0.4613333333333333,0.07355555555555555,0.041555555555555554,0.13111111111111112,0.014222222222222223,0.43666666666666665,0.05422222222222222,0.021555555555555557,0.378,0.21622222222222223,0.013111111111111112,0.05555555555555555,0.024,0.228 -ECGFiveDays,0.6376306620209059,0.6376306620209059,0.6376306620209059,0.40301974448315914,0.3623693379790941,0.3623693379790941,0.6376306620209059,0.3623693379790941,0.6376306620209059,0.6376306620209059,0.6376306620209059,0.6376306620209059,0.3623693379790941,0.6376306620209059,0.3623693379790941,0.3623693379790941,0.3623693379790941,0.6376306620209059,0.6376306620209059,0.6376306620209059,0.3623693379790941,0.3623693379790941,0.3623693379790941,0.3623693379790941,0.3623693379790941,0.3623693379790941,0.6376306620209059,0.3623693379790941,0.6376306620209059,0.6376306620209059 -ElectricDevices,0.09531837634548049,0.1548437297367397,0.03566333808844508,0.06497211775385812,0.1779276358448969,0.13616910906497212,0.1211256646349371,0.2045130333290105,0.06821423939826222,0.16106860329399558,0.19180391648294645,0.3504085073271949,0.09752301906367528,0.1779276358448969,0.2727272727272727,0.09972766178187005,0.25586824017637144,0.09674490986901829,0.18609778238879524,0.042147581377253274,0.09116846064064324,0.09674490986901829,0.14641421346128908,0.08831539359356763,0.09985734664764621,0.061081571780573204,0.18324471534171963,0.10984308131241084,0.0675658150693814,0.18039164829464402 -EOGHorizontalSignal,0.04419889502762431,0.04696132596685083,0.07734806629834254,0.1132596685082873,0.04143646408839779,0.10497237569060773,0.06629834254143646,0.1464088397790055,0.1878453038674033,0.03038674033149171,0.14917127071823205,0.04143646408839779,0.06629834254143646,0.11049723756906077,0.08287292817679558,0.06353591160220995,0.10497237569060773,0.058011049723756904,0.0856353591160221,0.08011049723756906,0.06906077348066299,0.08839779005524862,0.10220994475138122,0.03314917127071823,0.1408839779005525,0.07458563535911603,0.1850828729281768,0.11049723756906077,0.10220994475138122,0.06353591160220995 -EOGVerticalSignal,0.11602209944751381,0.06077348066298342,0.10497237569060773,0.1408839779005525,0.10773480662983426,0.09668508287292818,0.08011049723756906,0.019337016574585635,0.13535911602209943,0.058011049723756904,0.1464088397790055,0.19060773480662985,0.08011049723756906,0.13812154696132597,0.04696132596685083,0.07734806629834254,0.09944751381215469,0.10497237569060773,0.03038674033149171,0.0856353591160221,0.08011049723756906,0.03038674033149171,0.06629834254143646,0.10497237569060773,0.0718232044198895,0.1298342541436464,0.06077348066298342,0.058011049723756904,0.022099447513812154,0.12154696132596685 -EthanolLevel,0.25,0.222,0.244,0.25,0.242,0.274,0.27,0.256,0.236,0.256,0.262,0.272,0.242,0.246,0.236,0.246,0.248,0.238,0.232,0.278,0.232,0.24,0.252,0.23,0.258,0.248,0.242,0.278,0.282,0.26 -FaceAll,0.0970414201183432,0.016568047337278107,0.02603550295857988,0.05562130177514793,0.16627218934911242,0.07514792899408285,0.023076923076923078,0.11597633136094675,0.026627218934911243,0.09822485207100591,0.23846153846153847,0.058579881656804736,0.007692307692307693,0.06804733727810651,0.06449704142011835,0.06804733727810651,0.04497041420118343,0.01893491124260355,0.06331360946745562,0.022485207100591716,0.12781065088757396,0.059763313609467454,0.0893491124260355,0.08520710059171598,0.02485207100591716,0.049112426035502955,0.0485207100591716,0.07218934911242604,0.01242603550295858,0.18698224852071005 -FaceFour,0.19318181818181818,0.38636363636363635,0.4318181818181818,0.29545454545454547,0.375,0.2727272727272727,0.3522727272727273,0.10227272727272728,0.20454545454545456,0.2159090909090909,0.4318181818181818,0.011363636363636364,0.09090909090909091,0.5909090909090909,0.10227272727272728,0.03409090909090909,0.4659090909090909,0.10227272727272728,0.4659090909090909,0.125,0.4090909090909091,0.45454545454545453,0.10227272727272728,0.045454545454545456,0.10227272727272728,0.4090909090909091,0.29545454545454547,0.20454545454545456,0.29545454545454547,0.2159090909090909 -FacesUCR,0.05219512195121951,0.07170731707317073,0.012682926829268294,0.11463414634146342,0.04780487804878049,0.037073170731707315,0.07268292682926829,0.037073170731707315,0.10341463414634146,0.07658536585365854,0.09170731707317073,0.17317073170731706,0.09073170731707317,0.027804878048780488,0.09268292682926829,0.05024390243902439,0.07853658536585366,0.10536585365853658,0.04975609756097561,0.02829268292682927,0.055121951219512196,0.08292682926829269,0.09609756097560976,0.11853658536585365,0.12146341463414634,0.003902439024390244,0.06146341463414634,0.04682926829268293,0.06634146341463415,0.19560975609756098 -Fish,0.10857142857142857,0.14857142857142858,0.16,0.25142857142857145,0.17142857142857143,0.11428571428571428,0.22857142857142856,0.05714285714285714,0.07428571428571429,0.12,0.05714285714285714,0.32571428571428573,0.10857142857142857,0.21714285714285714,0.14857142857142858,0.14857142857142858,0.06857142857142857,0.05714285714285714,0.07428571428571429,0.045714285714285714,0.09714285714285714,0.12571428571428572,0.09142857142857143,0.22285714285714286,0.11428571428571428,0.14857142857142858,0.05714285714285714,0.14285714285714285,0.13714285714285715,0.09714285714285714 -FordA,0.48409090909090907,0.48409090909090907,0.5159090909090909,0.48409090909090907,0.48409090909090907,0.48409090909090907,0.48409090909090907,0.5159090909090909,0.48409090909090907,0.5159090909090909,0.5159090909090909,0.5159090909090909,0.5159090909090909,0.48409090909090907,0.48409090909090907,0.48409090909090907,0.5159090909090909,0.5159090909090909,0.5159090909090909,0.48409090909090907,0.48409090909090907,0.5159090909090909,0.5159090909090909,0.48409090909090907,0.5159090909090909,0.5159090909090909,0.5159090909090909,0.5159090909090909,0.48409090909090907,0.48409090909090907 -FordB,0.5049382716049383,0.49506172839506174,0.49506172839506174,0.49506172839506174,0.5049382716049383,0.5049382716049383,0.49506172839506174,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.49506172839506174,0.49506172839506174,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.49506172839506174,0.49506172839506174,0.49506172839506174,0.49506172839506174,0.49506172839506174,0.49506172839506174,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383,0.5049382716049383 -FreezerRegularTrain,0.6915789473684211,0.30842105263157893,0.30842105263157893,0.6915789473684211,0.30842105263157893,0.7456140350877193,0.30842105263157893,0.7456140350877193,0.7456140350877193,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.6915789473684211,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.6915789473684211,0.6915789473684211,0.30842105263157893,0.6915789473684211,0.30842105263157893,0.6915789473684211,0.30842105263157893,0.30842105263157893,0.30842105263157893,0.6915789473684211,0.6915789473684211 -FreezerSmallTrain,0.2856140350877193,0.2856140350877193,0.2856140350877193,0.2856140350877193,0.7143859649122807,0.2856140350877193,0.7143859649122807,0.2856140350877193,0.2856140350877193,0.2856140350877193,0.7143859649122807,0.7143859649122807,0.2856140350877193,0.2856140350877193,0.7143859649122807,0.2856140350877193,0.7143859649122807,0.7143859649122807,0.7143859649122807,0.7143859649122807,0.7143859649122807,0.2856140350877193,0.7143859649122807,0.2856140350877193,0.7143859649122807,0.2856140350877193,0.2856140350877193,0.7143859649122807,0.7143859649122807,0.2856140350877193 -GunPoint,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.4866666666666667,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.4866666666666667,0.5133333333333333,0.5133333333333333,0.5133333333333333,0.5133333333333333 -GunPointAgeSpan,0.3924050632911392,0.3924050632911392,0.6075949367088608,0.6075949367088608,0.3924050632911392,0.3924050632911392,0.3924050632911392,0.6075949367088608,0.3924050632911392,0.3924050632911392,0.3924050632911392,0.3924050632911392,0.6075949367088608,0.6075949367088608,0.6075949367088608,0.3924050632911392,0.3924050632911392,0.6075949367088608,0.3924050632911392,0.6075949367088608,0.6075949367088608,0.6075949367088608,0.6075949367088608,0.6075949367088608,0.3924050632911392,0.6075949367088608,0.6075949367088608,0.3924050632911392,0.3924050632911392,0.3924050632911392 -GunPointMaleVersusFemale,0.16455696202531644,0.16455696202531644,0.16455696202531644,0.8354430379746836,0.8354430379746836,0.16455696202531644,0.16455696202531644,0.8354430379746836,0.16455696202531644,0.16455696202531644,0.16455696202531644,0.16455696202531644,0.16455696202531644,0.16455696202531644,0.8354430379746836,0.8354430379746836,0.16455696202531644,0.8354430379746836,0.8354430379746836,0.8354430379746836,0.8354430379746836,0.16455696202531644,0.8354430379746836,0.8354430379746836,0.8354430379746836,0.8354430379746836,0.8354430379746836,0.16455696202531644,0.16455696202531644,0.8354430379746836 -GunPointOldVersusYoung,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.5904761904761905,0.4095238095238095,0.5904761904761905 -Ham,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.580952380952381,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.5904761904761905,0.4095238095238095,0.4095238095238095,0.4095238095238095,0.5904761904761905,0.4095238095238095 -HandOutlines,0.6432432432432432,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.6432432432432432,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.3567567567567568,0.6432432432432432,0.6432432432432432,0.3567567567567568 -Haptics,0.19155844155844157,0.23376623376623376,0.21753246753246752,0.12012987012987013,0.17532467532467533,0.2077922077922078,0.16233766233766234,0.20454545454545456,0.21428571428571427,0.21753246753246752,0.15584415584415584,0.2305194805194805,0.237012987012987,0.1525974025974026,0.2662337662337662,0.22077922077922077,0.17207792207792208,0.2435064935064935,0.1525974025974026,0.19805194805194806,0.2305194805194805,0.18831168831168832,0.2305194805194805,0.275974025974026,0.21428571428571427,0.21103896103896103,0.275974025974026,0.17532467532467533,0.22727272727272727,0.22727272727272727 -Herring,0.5,0.5,0.5,0.46875,0.5,0.5,0.46875,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.53125,0.5,0.5,0.5,0.5,0.5 -HouseTwenty,0.6302521008403361,0.3697478991596639,0.3697478991596639,0.3697478991596639,0.3949579831932773,0.3697478991596639,0.3949579831932773,0.6302521008403361,0.5462184873949579,0.6302521008403361,0.6302521008403361,0.6302521008403361,0.6302521008403361,0.3697478991596639,0.6302521008403361,0.6302521008403361,0.3949579831932773,0.6302521008403361,0.42016806722689076,0.6302521008403361,0.5462184873949579,0.3697478991596639,0.5042016806722689,0.5462184873949579,0.6050420168067226,0.3697478991596639,0.6302521008403361,0.453781512605042,0.6302521008403361,0.42016806722689076 -InlineSkate,0.11090909090909092,0.14545454545454545,0.16545454545454547,0.13454545454545455,0.1709090909090909,0.14363636363636365,0.18,0.13454545454545455,0.14,0.15818181818181817,0.10545454545454545,0.13818181818181818,0.14363636363636365,0.1290909090909091,0.14727272727272728,0.14,0.11818181818181818,0.15272727272727274,0.13818181818181818,0.15636363636363637,0.09636363636363636,0.18,0.19272727272727272,0.10909090909090909,0.09090909090909091,0.15818181818181817,0.14363636363636365,0.14,0.09272727272727273,0.12363636363636364 -InsectEPGRegularTrain,0.5783132530120482,0.18875502008032127,0.6024096385542169,0.570281124497992,0.15261044176706828,0.24096385542168675,0.24497991967871485,0.5742971887550201,0.606425702811245,0.24497991967871485,0.23293172690763053,0.24497991967871485,0.15261044176706828,0.24497991967871485,0.24497991967871485,0.24096385542168675,0.570281124497992,0.6024096385542169,0.24497991967871485,0.1927710843373494,0.570281124497992,0.24497991967871485,0.26907630522088355,0.20883534136546184,0.23694779116465864,0.6024096385542169,0.24497991967871485,0.5742971887550201,0.14859437751004015,0.15261044176706828 -InsectEPGSmallTrain,0.5783132530120482,0.18473895582329317,0.17670682730923695,0.14457831325301204,0.14457831325301204,0.14457831325301204,0.5783132530120482,0.17670682730923695,0.17670682730923695,0.20080321285140562,0.17670682730923695,0.27710843373493976,0.14457831325301204,0.14457831325301204,0.5783132530120482,0.20481927710843373,0.6184738955823293,0.5421686746987951,0.5783132530120482,0.27710843373493976,0.17670682730923695,0.14457831325301204,0.5421686746987951,0.26907630522088355,0.5823293172690763,0.5582329317269076,0.14457831325301204,0.14859437751004015,0.5783132530120482,0.5783132530120482 -InsectWingbeatSound,0.07272727272727272,0.09090909090909091,0.1398989898989899,0.09494949494949495,0.04898989898989899,0.11161616161616161,0.11818181818181818,0.12424242424242424,0.08838383838383838,0.15555555555555556,0.07222222222222222,0.09343434343434344,0.14393939393939395,0.04292929292929293,0.10151515151515152,0.08838383838383838,0.14292929292929293,0.0893939393939394,0.18535353535353535,0.026767676767676767,0.05505050505050505,0.12828282828282828,0.08636363636363636,0.051515151515151514,0.05555555555555555,0.1095959595959596,0.1873737373737374,0.16616161616161615,0.10353535353535354,0.09191919191919191 -ItalyPowerDemand,0.49854227405247814,0.4965986394557823,0.4839650145772595,0.4849368318756074,0.49854227405247814,0.5160349854227405,0.49854227405247814,0.4839650145772595,0.5014577259475219,0.5150631681243926,0.4849368318756074,0.4839650145772595,0.4839650145772595,0.4839650145772595,0.5160349854227405,0.4849368318756074,0.4839650145772595,0.5014577259475219,0.5160349854227405,0.5014577259475219,0.5160349854227405,0.4965986394557823,0.5150631681243926,0.49854227405247814,0.5034013605442177,0.49854227405247814,0.4965986394557823,0.5150631681243926,0.49854227405247814,0.49854227405247814 -LargeKitchenAppliances,0.3546666666666667,0.2986666666666667,0.2986666666666667,0.38666666666666666,0.34933333333333333,0.2853333333333333,0.2853333333333333,0.2853333333333333,0.2853333333333333,0.34933333333333333,0.34933333333333333,0.3546666666666667,0.34933333333333333,0.42933333333333334,0.296,0.2986666666666667,0.29333333333333333,0.288,0.296,0.2853333333333333,0.2853333333333333,0.2986666666666667,0.336,0.2986666666666667,0.2986666666666667,0.3546666666666667,0.416,0.2853333333333333,0.2853333333333333,0.34933333333333333 -Lightning2,0.36065573770491804,0.639344262295082,0.36065573770491804,0.36065573770491804,0.3770491803278688,0.639344262295082,0.36065573770491804,0.36065573770491804,0.36065573770491804,0.3770491803278688,0.36065573770491804,0.639344262295082,0.6229508196721312,0.639344262295082,0.639344262295082,0.36065573770491804,0.39344262295081966,0.639344262295082,0.639344262295082,0.36065573770491804,0.36065573770491804,0.639344262295082,0.36065573770491804,0.639344262295082,0.639344262295082,0.36065573770491804,0.639344262295082,0.639344262295082,0.36065573770491804,0.639344262295082 -Lightning7,0.2465753424657534,0.0273972602739726,0.3150684931506849,0.0821917808219178,0.0821917808219178,0.1643835616438356,0.1506849315068493,0.2465753424657534,0.0821917808219178,0.1643835616438356,0.1095890410958904,0.0136986301369863,0.0136986301369863,0.1232876712328767,0.1917808219178082,0.0958904109589041,0.1506849315068493,0.1917808219178082,0.136986301369863,0.136986301369863,0.0821917808219178,0.0410958904109589,0.0958904109589041,0.0958904109589041,0.0410958904109589,0.2191780821917808,0.2465753424657534,0.1643835616438356,0.1095890410958904,0.0547945205479452 -Mallat,0.25884861407249465,0.11940298507462686,0.1253731343283582,0.1488272921108742,0.016631130063965886,0.006396588486140725,0.25245202558635393,0.018763326226012792,0.2490405117270789,0.06780383795309168,0.1488272921108742,0.0,0.17270788912579957,0.016631130063965886,0.12452025586353944,0.029424307036247335,0.1044776119402985,0.007249466950959489,0.13347547974413645,0.0,0.00042643923240938164,0.23113006396588487,0.12366737739872068,0.12452025586353944,0.12494669509594883,0.11897654584221748,0.3722814498933902,0.14754797441364606,0.2093816631130064,0.14072494669509594 -Meat,0.3333333333333333,0.5666666666666667,0.3333333333333333,0.03333333333333333,0.26666666666666666,0.5666666666666667,0.3333333333333333,0.26666666666666666,0.3333333333333333,0.5666666666666667,0.26666666666666666,0.7,0.3333333333333333,0.3333333333333333,0.3333333333333333,0.5666666666666667,0.7,0.1,0.1,0.7,0.26666666666666666,0.03333333333333333,0.03333333333333333,0.3333333333333333,0.03333333333333333,0.3333333333333333,0.7,0.3333333333333333,0.1,0.3333333333333333 -MedicalImages,0.08289473684210526,0.034210526315789476,0.06578947368421052,0.05263157894736842,0.075,0.07763157894736843,0.07631578947368421,0.02236842105263158,0.14473684210526316,0.06447368421052632,0.2026315789473684,0.07105263157894737,0.0,0.046052631578947366,0.2131578947368421,0.05131578947368421,0.15394736842105264,0.09868421052631579,0.24473684210526317,0.09078947368421053,0.038157894736842106,0.14342105263157895,0.030263157894736843,0.0881578947368421,0.125,0.12236842105263158,0.18947368421052632,0.08026315789473684,0.10263157894736842,0.23157894736842105 -MiddlePhalanxOutlineAgeGroup,0.5064935064935064,0.37662337662337664,0.5064935064935064,0.3961038961038961,0.37662337662337664,0.37662337662337664,0.37662337662337664,0.33766233766233766,0.33766233766233766,0.33766233766233766,0.5064935064935064,0.3961038961038961,0.15584415584415584,0.33766233766233766,0.37662337662337664,0.5064935064935064,0.15584415584415584,0.5064935064935064,0.15584415584415584,0.22727272727272727,0.3961038961038961,0.5064935064935064,0.37662337662337664,0.22727272727272727,0.37662337662337664,0.37662337662337664,0.22727272727272727,0.33766233766233766,0.3961038961038961,0.33766233766233766 -MiddlePhalanxOutlineCorrect,0.584192439862543,0.584192439862543,0.41580756013745707,0.41580756013745707,0.584192439862543,0.41580756013745707,0.41580756013745707,0.41580756013745707,0.584192439862543,0.584192439862543,0.584192439862543,0.584192439862543,0.41580756013745707,0.41580756013745707,0.41580756013745707,0.41580756013745707,0.584192439862543,0.584192439862543,0.584192439862543,0.41580756013745707,0.41580756013745707,0.584192439862543,0.584192439862543,0.41580756013745707,0.41580756013745707,0.584192439862543,0.584192439862543,0.41580756013745707,0.41580756013745707,0.41580756013745707 -MiddlePhalanxTW,0.09090909090909091,0.032467532467532464,0.1038961038961039,0.13636363636363635,0.12337662337662338,0.2987012987012987,0.16883116883116883,0.05844155844155844,0.15584415584415584,0.12337662337662338,0.13636363636363635,0.22727272727272727,0.14935064935064934,0.13636363636363635,0.22727272727272727,0.12987012987012986,0.05194805194805195,0.3181818181818182,0.08441558441558442,0.1038961038961039,0.22727272727272727,0.05194805194805195,0.18181818181818182,0.15584415584415584,0.11038961038961038,0.03896103896103896,0.2987012987012987,0.05844155844155844,0.09090909090909091,0.11038961038961038 -MixedShapesRegularTrain,0.2820618556701031,0.45608247422680415,0.18762886597938144,0.4004123711340206,0.28577319587628863,0.05360824742268041,0.2515463917525773,0.12948453608247423,0.25278350515463915,0.015257731958762887,0.2329896907216495,0.05443298969072165,0.023505154639175258,0.09525773195876289,0.13030927835051545,0.052371134020618555,0.20288659793814434,0.21443298969072164,0.07051546391752578,0.12948453608247423,0.2292783505154639,0.15422680412371134,0.31876288659793817,0.19505154639175257,0.17443298969072166,0.18268041237113403,0.06474226804123712,0.32329896907216493,0.3843298969072165,0.30061855670103094 -MixedShapesSmallTrain,0.0334020618556701,0.23711340206185566,0.5929896907216495,0.22144329896907217,0.2820618556701031,0.16824742268041237,0.23587628865979382,0.015257731958762887,0.31422680412371135,0.2931958762886598,0.12536082474226803,0.22268041237113403,0.20082474226804123,0.18597938144329898,0.2721649484536082,0.16123711340206184,0.3971134020618557,0.08329896907216496,0.12577319587628866,0.12,0.08536082474226804,0.05484536082474227,0.47010309278350515,0.19463917525773197,0.02515463917525773,0.30762886597938144,0.014845360824742268,0.31051546391752577,0.2552577319587629,0.3154639175257732 -MoteStrain,0.1853035143769968,0.1853035143769968,0.1853035143769968,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.8146964856230032,0.8146964856230032,0.17891373801916932,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.1853035143769968,0.1853035143769968,0.8146964856230032,0.8146964856230032,0.1853035143769968,0.8146964856230032,0.1853035143769968,0.1853035143769968,0.8146964856230032,0.8146964856230032,0.8146964856230032 -OliveOil,0.36666666666666664,0.03333333333333333,0.06666666666666667,0.26666666666666666,0.13333333333333333,0.13333333333333333,0.5333333333333333,0.2,0.4666666666666667,0.5666666666666667,0.06666666666666667,0.7333333333333333,0.03333333333333333,0.1,0.06666666666666667,0.03333333333333333,0.6333333333333333,0.1,0.13333333333333333,0.1,0.26666666666666666,0.06666666666666667,0.1,0.36666666666666664,0.5,0.26666666666666666,0.1,0.1,0.03333333333333333,0.03333333333333333 -OSULeaf,0.16115702479338842,0.16115702479338842,0.1322314049586777,0.1652892561983471,0.16115702479338842,0.2768595041322314,0.11983471074380166,0.11983471074380166,0.19008264462809918,0.2231404958677686,0.1859504132231405,0.19421487603305784,0.1652892561983471,0.06611570247933884,0.15289256198347106,0.09917355371900827,0.09917355371900827,0.1487603305785124,0.09917355371900827,0.24380165289256198,0.1652892561983471,0.23140495867768596,0.13636363636363635,0.1859504132231405,0.1859504132231405,0.11983471074380166,0.1652892561983471,0.23140495867768596,0.1322314049586777,0.23140495867768596 -PhalangesOutlinesCorrect,0.6118881118881119,0.6118881118881119,0.6118881118881119,0.3881118881118881,0.6118881118881119,0.6118881118881119,0.3881118881118881,0.3881118881118881,0.6118881118881119,0.3881118881118881,0.6118881118881119,0.6118881118881119,0.3881118881118881,0.3881118881118881,0.6118881118881119,0.3881118881118881,0.3881118881118881,0.3881118881118881,0.6118881118881119,0.6118881118881119,0.3881118881118881,0.6118881118881119,0.6118881118881119,0.3881118881118881,0.3881118881118881,0.3881118881118881,0.6118881118881119,0.3881118881118881,0.3881118881118881,0.3881118881118881 -Phoneme,0.011603375527426161,0.011603375527426161,0.0015822784810126582,0.035864978902953586,0.005274261603375527,0.007911392405063292,0.022151898734177215,0.03217299578059072,0.014767932489451477,0.004219409282700422,0.014240506329113924,0.011603375527426161,0.01529535864978903,0.04061181434599156,0.03375527426160337,0.010021097046413503,0.0058016877637130804,0.017932489451476793,0.05379746835443038,0.015822784810126583,0.013185654008438819,0.11234177215189874,0.03059071729957806,0.00949367088607595,0.0026371308016877636,0.013713080168776372,0.03217299578059072,0.0026371308016877636,0.012130801687763712,0.06118143459915612 -PigAirwayPressure,0.014423076923076924,0.014423076923076924,0.02403846153846154,0.014423076923076924,0.02403846153846154,0.019230769230769232,0.019230769230769232,0.014423076923076924,0.019230769230769232,0.028846153846153848,0.028846153846153848,0.02403846153846154,0.028846153846153848,0.014423076923076924,0.02403846153846154,0.004807692307692308,0.019230769230769232,0.009615384615384616,0.019230769230769232,0.014423076923076924,0.019230769230769232,0.009615384615384616,0.014423076923076924,0.019230769230769232,0.009615384615384616,0.019230769230769232,0.02403846153846154,0.014423076923076924,0.028846153846153848,0.014423076923076924 -PigArtPressure,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232 -PigCVP,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.02403846153846154,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.028846153846153848,0.019230769230769232,0.02403846153846154,0.02403846153846154,0.019230769230769232,0.019230769230769232,0.02403846153846154,0.014423076923076924,0.02403846153846154,0.019230769230769232,0.019230769230769232,0.019230769230769232,0.02403846153846154,0.028846153846153848,0.014423076923076924,0.019230769230769232,0.028846153846153848,0.019230769230769232,0.028846153846153848,0.019230769230769232,0.019230769230769232,0.014423076923076924 -Plane,0.11428571428571428,0.3523809523809524,0.0,0.11428571428571428,0.0,0.1523809523809524,0.0,0.09523809523809523,0.13333333333333333,0.0,0.0,0.0,0.1523809523809524,0.2571428571428571,0.13333333333333333,0.1619047619047619,0.2,0.24761904761904763,0.11428571428571428,0.22857142857142856,0.13333333333333333,0.0,0.11428571428571428,0.4380952380952381,0.23809523809523808,0.0,0.24761904761904763,0.22857142857142856,0.09523809523809523,0.13333333333333333 -PowerCons,0.3888888888888889,0.6111111111111112,0.6111111111111112,0.6111111111111112,0.3888888888888889,0.6222222222222222,0.6111111111111112,0.6111111111111112,0.3888888888888889,0.6111111111111112,0.6111111111111112,0.6111111111111112,0.6111111111111112,0.6111111111111112,0.6222222222222222,0.3888888888888889,0.3888888888888889,0.37777777777777777,0.6111111111111112,0.6111111111111112,0.6111111111111112,0.6222222222222222,0.3888888888888889,0.3888888888888889,0.3888888888888889,0.3888888888888889,0.3888888888888889,0.3888888888888889,0.6111111111111112,0.3888888888888889 -ProximalPhalanxOutlineAgeGroup,0.775609756097561,0.6146341463414634,0.6146341463414634,0.775609756097561,0.775609756097561,0.18048780487804877,0.04390243902439024,0.18048780487804877,0.775609756097561,0.13170731707317074,0.18048780487804877,0.18048780487804877,0.6146341463414634,0.04390243902439024,0.13170731707317074,0.18048780487804877,0.25365853658536586,0.04390243902439024,0.25365853658536586,0.04390243902439024,0.6146341463414634,0.13170731707317074,0.775609756097561,0.13170731707317074,0.04390243902439024,0.775609756097561,0.6146341463414634,0.04390243902439024,0.775609756097561,0.6146341463414634 -ProximalPhalanxOutlineCorrect,0.6426116838487973,0.6426116838487973,0.6426116838487973,0.35738831615120276,0.6426116838487973,0.35738831615120276,0.35738831615120276,0.35738831615120276,0.6426116838487973,0.6426116838487973,0.6426116838487973,0.6426116838487973,0.35738831615120276,0.35738831615120276,0.35738831615120276,0.35738831615120276,0.6426116838487973,0.35738831615120276,0.6426116838487973,0.6426116838487973,0.35738831615120276,0.35738831615120276,0.6426116838487973,0.35738831615120276,0.35738831615120276,0.6426116838487973,0.6426116838487973,0.35738831615120276,0.35738831615120276,0.35738831615120276 -ProximalPhalanxTW,0.16097560975609757,0.13658536585365855,0.3951219512195122,0.1951219512195122,0.06829268292682927,0.2,0.05853658536585366,0.09268292682926829,0.024390243902439025,0.43902439024390244,0.02926829268292683,0.25853658536585367,0.33658536585365856,0.05853658536585366,0.17560975609756097,0.2780487804878049,0.43414634146341463,0.16097560975609757,0.16585365853658537,0.18536585365853658,0.1073170731707317,0.14146341463414633,0.2975609756097561,0.14634146341463414,0.16585365853658537,0.17073170731707318,0.4097560975609756,0.48292682926829267,0.40487804878048783,0.3951219512195122 -RefrigerationDevices,0.336,0.336,0.336,0.336,0.336,0.336,0.336,0.336,0.336,0.328,0.336,0.336,0.3253333333333333,0.3333333333333333,0.328,0.336,0.328,0.3333333333333333,0.336,0.336,0.336,0.336,0.336,0.328,0.328,0.336,0.328,0.336,0.336,0.336 -Rock,0.36,0.24,0.34,0.42,0.06,0.16,0.08,0.26,0.2,0.28,0.26,0.14,0.42,0.26,0.14,0.24,0.24,0.06,0.24,0.3,0.08,0.3,0.24,0.16,0.14,0.42,0.28,0.16,0.3,0.06 -ScreenType,0.33066666666666666,0.32266666666666666,0.31466666666666665,0.336,0.3253333333333333,0.3546666666666667,0.336,0.336,0.31466666666666665,0.3413333333333333,0.33866666666666667,0.3413333333333333,0.32266666666666666,0.32266666666666666,0.31466666666666665,0.36,0.3413333333333333,0.33866666666666667,0.31466666666666665,0.3413333333333333,0.33066666666666666,0.31466666666666665,0.31466666666666665,0.31466666666666665,0.32266666666666666,0.344,0.32266666666666666,0.36,0.33066666666666666,0.328 -SemgHandGenderCh2,0.32166666666666666,0.6783333333333333,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.6783333333333333,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.6783333333333333,0.32166666666666666,0.32166666666666666,0.6783333333333333,0.6783333333333333,0.32166666666666666 -SemgHandMovementCh2,0.18666666666666668,0.15777777777777777,0.13555555555555557,0.15777777777777777,0.2,0.10222222222222223,0.2688888888888889,0.15333333333333332,0.18,0.18444444444444444,0.1111111111111111,0.14,0.20222222222222222,0.18222222222222223,0.06444444444444444,0.24222222222222223,0.23777777777777778,0.12,0.17777777777777778,0.08666666666666667,0.09111111111111111,0.13333333333333333,0.14222222222222222,0.2,0.1711111111111111,0.16444444444444445,0.18,0.14666666666666667,0.19777777777777777,0.14444444444444443 -SemgHandSubjectCh2,0.36,0.12666666666666668,0.1511111111111111,0.35777777777777775,0.38,0.08888888888888889,0.13333333333333333,0.13333333333333333,0.14222222222222222,0.09777777777777778,0.14444444444444443,0.18,0.09777777777777778,0.18666666666666668,0.12666666666666668,0.23777777777777778,0.2644444444444444,0.21777777777777776,0.18222222222222223,0.18,0.1711111111111111,0.26222222222222225,0.19777777777777777,0.15555555555555556,0.1,0.12222222222222222,0.3333333333333333,0.18444444444444444,0.12444444444444444,0.13555555555555557 -ShapeletSim,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5 -ShapesAll,0.016666666666666666,0.015,0.025,0.021666666666666667,0.015,0.0016666666666666668,0.021666666666666667,0.02666666666666667,0.0033333333333333335,0.02,0.0,0.011666666666666667,0.023333333333333334,0.018333333333333333,0.016666666666666666,0.016666666666666666,0.006666666666666667,0.021666666666666667,0.025,0.01,0.011666666666666667,0.02666666666666667,0.013333333333333334,0.02,0.016666666666666666,0.023333333333333334,0.02,0.0033333333333333335,0.015,0.006666666666666667 -SmallKitchenAppliances,0.28,0.28,0.25066666666666665,0.288,0.34933333333333333,0.472,0.3253333333333333,0.344,0.25066666666666665,0.344,0.248,0.25066666666666665,0.25066666666666665,0.28,0.456,0.248,0.27466666666666667,0.26666666666666666,0.28,0.24266666666666667,0.3253333333333333,0.25333333333333335,0.2826666666666667,0.14666666666666667,0.256,0.288,0.24266666666666667,0.2853333333333333,0.4693333333333333,0.24266666666666667 -SmoothSubspace,0.31333333333333335,0.4066666666666667,0.54,0.18666666666666668,0.31333333333333335,0.4266666666666667,0.32666666666666666,0.4,0.5533333333333333,0.56,0.31333333333333335,0.42,0.32666666666666666,0.12,0.17333333333333334,0.32666666666666666,0.4266666666666667,0.12666666666666668,0.4266666666666667,0.17333333333333334,0.56,0.17333333333333334,0.56,0.12666666666666668,0.17333333333333334,0.4266666666666667,0.54,0.17333333333333334,0.4066666666666667,0.4266666666666667 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53911026..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/test/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.12,0.14,0.05,0.02,0.13,0.06,0.14,0.06,0.0,0.17,0.13,0.08,0.15,0.03,0.09,0.14,0.03,0.1,0.11,0.06,0.11,0.01,0.15,0.21,0.11,0.01,0.05,0.03,0.08,0.12 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.07781789009225833,0.120479302832244,0.08393887347513983,0.10874704491725769,0.1008008837337752,0.0929013600831413,0.1423234514340805,0.07633587786259542,0.08928571428571429,0.09500385802469136,0.14592475571938446,0.09378185524974515,0.0995517810804435,0.13991769547325103,0.12521778227392794,0.10072473803259013,0.08393887347513983,0.08042328042328042,0.0936978197605076,0.08484123423558147,0.08856744068011674,0.0922173274596182,0.17739130434782607,0.09459268671466893,0.08928571428571429,0.0959335624284078,0.0921409214092141,0.07734204793028322,0.0960879209518048,0.08227237654320987 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5926529642994685,0.7715853608851412,0.6086115156777591,0.7901186008030234,0.6904339195002238,0.6768115712334513,0.8472801845101963,0.6496508198622186,0.6322679889332319,0.710522448498042,0.828747346620253,0.6731087581952901,0.6569530717528345,0.878481913371731,0.7653105507224535,0.6786955169581338,0.6086115156777591,0.5908943318860835,0.6495795330873267,0.7401748064364134,0.6764497035611365,0.7375561210300207,0.8531333714504699,0.643257653738027,0.6322679889332318,0.7069933141066532,0.7026886338951102,0.5727136836833853,0.6731269425541451,0.6600131151235207 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.33300748257565616,0.40488487916861254,0.34492160541744654,0.40802566066494284,0.3847183119365649,0.37535829511155233,0.42859071954456074,0.35712796179793704,0.35403954936188003,0.38290635564821457,0.4272420354769992,0.37147031548198495,0.3640494220232289,0.44046920440676895,0.3989793657770454,0.37667546235319577,0.34492160541744654,0.330005847648902,0.35791629584250656,0.3843994517529842,0.383898168773946,0.3937644516569933,0.4330799095477822,0.35903504168917894,0.35403954936188,0.3816107230980221,0.37576731692604687,0.32179123055215564,0.3636461223382438,0.3556864630050731 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-ArrowHead,0.26231676968887824,0.24561248836918714,0.24561248836918714,0.2456124883691871,0.24561248836918714,0.24561248836918714,0.26231676968887824,0.24561248836918714,0.2615223647284861,0.2615223647284861,0.24561248836918714,0.2623167696888783,0.26152236472848617,0.26152236472848617,0.24561248836918714,0.24561248836918714,0.2615223647284861,0.2615223647284861,0.2456124883691871,0.24561248836918714,0.2615223647284861,0.2456124883691871,0.2615223647284861,0.2615223647284861,0.2615223647284861,0.24561248836918714,0.2615223647284861,0.24561248836918714,0.2615223647284861,0.2615223647284861 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/test/RI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/test/RI.csv deleted file mode 100644 index d0e88da6..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/test/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.62,0.7034343434343434,0.617979797979798,0.6953535353535354,0.6412121212121212,0.6313131313131313,0.7385858585858586,0.62,0.6290909090909091,0.6553535353535354,0.731919191919192,0.6408080808080808,0.6494949494949495,0.738989898989899,0.7141414141414142,0.6422222222222222,0.617979797979798,0.6329292929292929,0.6511111111111111,0.6628282828282829,0.6042424242424242,0.6593939393939394,0.7654545454545455,0.6466666666666666,0.6290909090909091,0.6521212121212121,0.6616161616161617,0.6266666666666667,0.6707070707070707,0.6505050505050505 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-ArrowHead,0.6413136288998358,0.6455829228243022,0.6455829228243022,0.6455829228243022,0.6455829228243022,0.6455829228243022,0.6413136288998358,0.6455829228243022,0.6213464696223316,0.6213464696223316,0.6455829228243022,0.6413136288998358,0.6213464696223316,0.6213464696223316,0.6455829228243022,0.6455829228243022,0.6213464696223316,0.6213464696223316,0.6455829228243022,0.6455829228243022,0.6213464696223316,0.6455829228243022,0.6213464696223316,0.6213464696223316,0.6213464696223316,0.6455829228243022,0.6213464696223316,0.6455829228243022,0.6213464696223316,0.6213464696223316 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv deleted file mode 100644 index 94e26ed7..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.12,0.12,0.03,0.01,0.15,0.05,0.13,0.09,0.01,0.21,0.13,0.11,0.16,0.04,0.07,0.14,0.05,0.11,0.12,0.03,0.1,0.02,0.14,0.19,0.1,0.0,0.03,0.04,0.08,0.11 -Adiac,0.023076923076923078,0.05128205128205128,0.041025641025641026,0.0641025641025641,0.05128205128205128,0.06923076923076923,0.023076923076923078,0.007692307692307693,0.038461538461538464,0.04358974358974359,0.03076923076923077,0.046153846153846156,0.023076923076923078,0.03076923076923077,0.01282051282051282,0.01282051282051282,0.005128205128205128,0.04358974358974359,0.0641025641025641,0.028205128205128206,0.03333333333333333,0.04871794871794872,0.035897435897435895,0.010256410256410256,0.01282051282051282,0.01282051282051282,0.07692307692307693,0.041025641025641026,0.002564102564102564,0.01282051282051282 -ArrowHead,0.1111111111111111,0.3055555555555556,0.19444444444444445,0.5833333333333334,0.3055555555555556,0.1111111111111111,0.6111111111111112,0.2222222222222222,0.5,0.4166666666666667,0.3055555555555556,0.3888888888888889,0.08333333333333333,0.6388888888888888,0.3055555555555556,0.3055555555555556,0.3055555555555556,0.05555555555555555,0.5833333333333334,0.19444444444444445,0.05555555555555555,0.5833333333333334,0.5,0.3055555555555556,0.4166666666666667,0.2222222222222222,0.3055555555555556,0.19444444444444445,0.5,0.05555555555555555 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv deleted file mode 100644 index f37387a0..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.20691925027222885,0.23217592288356217,0.21278141887249172,0.23625636443118056,0.20107845337016053,0.2603389285777688,0.28576038159665673,0.203043645515266,0.20103509569764005,0.28595274054867376,0.2639512541216537,0.24426331939364831,0.2933976291447917,0.24306936590215114,0.2652990076530626,0.22985530223460693,0.22247666794104942,0.26461282566260547,0.21434544854493634,0.265182309720997,0.21645841386901718,0.2720096861609839,0.2902238049394578,0.17339499442379358,0.19509294761491383,0.2613152835032875,0.2669902138272996,0.19428400854105066,0.2439754768915589,0.2628639511885786 -Adiac,0.4731627007760426,0.4517905388796525,0.45602996815889923,0.46234338783046774,0.4597124907158545,0.4817473937072815,0.47183554265346006,0.46053333787708944,0.4732046647629044,0.4698815152599506,0.4697138089348227,0.46860128844936905,0.4697821964350244,0.46513804799118286,0.47367784483586195,0.4739362273088783,0.4835038194331702,0.49123488803469467,0.46919796363974037,0.45799240384569884,0.4653709874769347,0.4794943907046721,0.4585943757580786,0.46214135105019716,0.4676269932014791,0.46387337439713444,0.47124760288407097,0.45619866667280595,0.4661533585314872,0.46919479917809787 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.06779661016949153,0.09509707140506746,0.06991642272283738,0.08897088970889709,0.0688071287492536,0.08801146029187931,0.12114041018706333,0.07055532999503766,0.06813215066203883,0.10124697561883492,0.11333659013190034,0.09335795104861999,0.1101638723122099,0.10036942313157148,0.11464468001570476,0.07975597721257793,0.06833674164962508,0.11038426127828657,0.07772795216741404,0.0886985486557221,0.06691577545848902,0.10016871535309713,0.14565723386010046,0.061136871028679374,0.06396626743820931,0.09706907925737442,0.10464484920282344,0.06642066420664207,0.09723961957782418,0.09944277753964852 -Adiac,0.25691065356969756,0.2415196527338185,0.23038935198119556,0.2294923221432964,0.23297203850249118,0.25058280517569786,0.23398606756640683,0.22660944910154399,0.249681325597552,0.22638586473404368,0.25102819478710703,0.23967207409882693,0.24806832699468262,0.24658631133366643,0.2482432623215195,0.24032094405258272,0.23476353856083976,0.2616073280535126,0.2506441390216758,0.23223794567061024,0.24563449083615568,0.26083735722258716,0.22265067184982695,0.23505274840478235,0.2413889182842948,0.24719595968027777,0.24246459056491296,0.23592235046856216,0.2385313132994514,0.24444612884539052 -ArrowHead,0.2963970088375255,0.2681517284858674,0.2681517284858674,0.2681517284858674,0.2681517284858674,0.2681517284858674,0.2963970088375255,0.2681517284858674,0.3393733484333711,0.3393733484333711,0.2681517284858674,0.2963970088375255,0.3393733484333711,0.3393733484333711,0.2681517284858674,0.2681517284858674,0.3393733484333711,0.3393733484333711,0.2681517284858674,0.2681517284858674,0.3393733484333711,0.2681517284858674,0.3393733484333711,0.3393733484333711,0.3393733484333711,0.2681517284858674,0.3393733484333711,0.2681517284858674,0.3393733484333711,0.3393733484333711 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/MI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/MI.csv deleted file mode 100644 index d434d138..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5816828262709711,0.7452279012294254,0.6133508662422154,0.7667324325021744,0.6436944254069112,0.7258736333457059,0.8167808533911066,0.6568979968965699,0.5785145581802431,0.7696025676499504,0.7426528411482051,0.6988008205757846,0.7386984070413385,0.786610754449099,0.773449491870008,0.6432409696211783,0.6355064442071394,0.7344210612745282,0.6277277221860058,0.7773831822082242,0.6461063847193762,0.7568903049835516,0.8235522097011508,0.550498630590474,0.6222351369486332,0.7496322790580169,0.7766223466494901,0.5134407266500243,0.67310875819529,0.7212774517407045 -Adiac,2.1405555897867434,2.092507666695445,2.0173566219485437,2.067089761708848,2.0619906818170315,2.088282017315562,2.0716131663359225,2.02914740277096,2.0942863647840193,2.034991721881597,2.1183323577114415,2.047898465239542,2.094371373269451,2.059649561973167,2.102186661689019,2.060499461257006,2.0702486044877926,2.1216421216511128,2.1102449301481023,2.071706659195871,2.0829104508530394,2.1163748579917727,2.0165783104834363,2.093392569622898,2.1022513688743607,2.066399407415986,2.061452257251795,2.0931372024746686,2.0699853442770064,2.0600392923427204 -ArrowHead,0.4093678634802662,0.3825752073444366,0.3825752073444366,0.38257520734443656,0.3825752073444366,0.38257520734443656,0.4093678634802662,0.3825752073444366,0.4199330820971311,0.41993308209713115,0.3825752073444366,0.4093678634802662,0.4199330820971311,0.4199330820971312,0.3825752073444366,0.3825752073444366,0.4199330820971311,0.41993308209713115,0.38257520734443656,0.3825752073444366,0.41993308209713115,0.38257520734443656,0.4199330820971311,0.4199330820971311,0.41993308209713115,0.3825752073444366,0.4199330820971311,0.3825752073444366,0.4199330820971311,0.41993308209713115 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv deleted file mode 100644 index 926d2873..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.3309126460907339,0.3808411181560581,0.34379549333097364,0.39352183110977407,0.3472093556275676,0.395064153683701,0.4182147116690464,0.35435595442236206,0.32619657108497996,0.41509392359621117,0.38877658576132895,0.37685766669116716,0.4046851955716228,0.39543787505158484,0.39789313582612323,0.3557790166609051,0.3545724151891805,0.3949831416323497,0.34627285461911034,0.4100458648761678,0.35659390344460024,0.40451645388601115,0.41678150365492184,0.3086777302754939,0.33901691602864725,0.40156483397702336,0.4091932748996733,0.3000756362154244,0.3691789150808401,0.3902583503370382 -Adiac,0.6399087449885241,0.62480235716162,0.6161074221934953,0.6264868945800979,0.624032412033949,0.6367999624612521,0.6306966322243839,0.6197169307877682,0.6333430708044213,0.6247017565102522,0.6352414153676611,0.6253924365337187,0.6322790736475994,0.625244179486084,0.6349689507348433,0.6291970292654702,0.6350482516325954,0.6453152429417185,0.6340474275274932,0.6248875715061745,0.6295434511741521,0.6386088892888904,0.6180213972816665,0.6298516275942879,0.6331678859528097,0.6254889490441329,0.6282349483554516,0.627691570362922,0.6275205097734339,0.62721513489477 -ArrowHead,0.3963497045467665,0.34860264614456316,0.34860264614456316,0.3486026461445631,0.34860264614456316,0.3486026461445631,0.3963497045467665,0.34860264614456316,0.4200336461550096,0.42003364615500965,0.34860264614456316,0.3963497045467665,0.4200336461550096,0.4200336461550097,0.34860264614456316,0.34860264614456316,0.4200336461550096,0.42003364615500965,0.3486026461445631,0.34860264614456316,0.42003364615500965,0.3486026461445631,0.4200336461550096,0.4200336461550096,0.42003364615500965,0.34860264614456316,0.4200336461550096,0.34860264614456316,0.4200336461550096,0.42003364615500965 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/RI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/RI.csv deleted file mode 100644 index 938eabd4..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-dtw/train/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5909090909090909,0.696969696969697,0.5973737373737373,0.6735353535353535,0.6276767676767677,0.6258585858585859,0.7135353535353536,0.6216161616161616,0.6006060606060606,0.6452525252525253,0.7,0.6482828282828282,0.654949494949495,0.7092929292929293,0.7101010101010101,0.6232323232323232,0.5983838383838384,0.6628282828282829,0.6260606060606061,0.6482828282828282,0.601010101010101,0.6571717171717172,0.7438383838383839,0.5983838383838384,0.6167676767676767,0.648080808080808,0.6668686868686868,0.5818181818181818,0.6426262626262627,0.6527272727272727 -Adiac,0.9424955507217718,0.9425746490013842,0.9341243161294576,0.9346252719003362,0.9367609254498714,0.934401160108101,0.9335706281721706,0.9333465163799354,0.9388042976731923,0.9304462461274801,0.9415068222266165,0.9345198075275196,0.9388042976731923,0.9379869487838639,0.9384615384615385,0.9346516379935403,0.9309603849449608,0.9355876343022873,0.940821303803309,0.9375255421527915,0.937248698174148,0.9407949377101048,0.9301298530090304,0.937802386131435,0.9378814844110474,0.9393448025838771,0.9355744512556852,0.938751565486784,0.9369718541955045,0.9361017731197679 -ArrowHead,0.6714285714285714,0.6841269841269841,0.6841269841269841,0.6841269841269841,0.6841269841269841,0.6841269841269841,0.6714285714285714,0.6841269841269841,0.6825396825396826,0.6825396825396826,0.6841269841269841,0.6714285714285714,0.6825396825396826,0.6825396825396826,0.6841269841269841,0.6841269841269841,0.6825396825396826,0.6825396825396826,0.6841269841269841,0.6841269841269841,0.6825396825396826,0.6841269841269841,0.6825396825396826,0.6825396825396826,0.6825396825396826,0.6841269841269841,0.6825396825396826,0.6841269841269841,0.6825396825396826,0.6825396825396826 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv deleted file mode 100644 index c14fb2bc..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.14 -Adiac,0.010230179028132993 -ArrowHead,0.1657142857142857 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv deleted file mode 100644 index ce427b93..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.23644406714706817 -Adiac,0.4275679313392851 -ArrowHead,0.2711345305675745 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv deleted file mode 100644 index c264470f..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.09881175734834272 -Adiac,0.24480617918523637 -ArrowHead,0.17862432434213923 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv deleted file mode 100644 index 12bb49d9..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.6730758648607849 -Adiac,2.0688498754461895 -ArrowHead,0.24656422435203565 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv deleted file mode 100644 index d7bf70a3..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.3589554592992615 -Adiac,0.6115795184527972 -ArrowHead,0.28212306565267664 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv deleted file mode 100644 index 9b9b9034..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.6824242424242424 -Adiac,0.9476424683585809 -ArrowHead,0.5661740558292282 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv deleted file mode 100644 index 39576cee..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.15 -Adiac,0.010256410256410256 -ArrowHead,0.05555555555555555 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv deleted file mode 100644 index 96d6c944..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.28235751864245273 -Adiac,0.443757960468462 -ArrowHead,0.4254058481502334 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv deleted file mode 100644 index 40c1d186..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.14761082325849165 -Adiac,0.26730437319224465 -ArrowHead,0.38933041015803416 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv deleted file mode 100644 index 4baba27f..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.8309679086716271 -Adiac,2.1444328661256455 -ArrowHead,0.4322169479598048 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv deleted file mode 100644 index 6eae1f4c..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.42167060193431893 -Adiac,0.6284012770795142 -ArrowHead,0.46302448423404174 diff --git a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv b/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv deleted file mode 100644 index 7d2719db..00000000 --- a/examples/example_data/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.7280808080808081 -Adiac,0.9510645310131172 -ArrowHead,0.6968253968253968 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ACSF1/testResample0.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ACSF1/testResample0.csv deleted file mode 100644 index feb09fa4..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ACSF1/testResample0.csv +++ /dev/null @@ -1,103 +0,0 @@ -ACSF1,kmeans-dtw,test,0,MILLISECONDS,Generated by clustering_experiments on 2022-05-01 16:21:10.583389 -{'average_params': {'averaging_distance_metric': 'dtw'}, 'averaging_method': 'mean', 'distance_params': {'window': 0.2, 'epsilon': 0.05, 'g': 0.05, 'c': 1}, 'init_algorithm': 'random', 'max_iter': 300, 'metric': 'dtw', 'n_clusters': 10, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0,12589,12711,-1,-1,10,10 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -9,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -3,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -3,1,,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -3,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 -3,5,,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0 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a/examples/example_data/clustering_results/kmeans-dtw/Predictions/Adiac/trainResample1.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/Adiac/trainResample1.csv deleted file mode 100644 index 0142534f..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/Adiac/trainResample1.csv +++ /dev/null @@ -1,393 +0,0 @@ -Adiac,kmeans-dtw,train,1,MILLISECONDS,Generated by clustering_experiments on 2022-06-07 00:09:39.015814 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':37,'n_init':10,'random_state':2,'tol':1e-06,'verbose':False} -0,2044,2037,-1,-1,37,37 -14,35,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':11,'tol':1e-06,'verbose':False} -0,97,338,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':12,'tol':1e-06,'verbose':False} -0,90,435,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':13,'tol':1e-06,'verbose':False} -0,937,863,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':15,'tol':1e-06,'verbose':False} -0,103,335,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':17,'tol':1e-06,'verbose':False} -0,53944,401,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':18,'tol':1e-06,'verbose':False} -0,322,531,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':19,'tol':1e-06,'verbose':False} -0,245,267,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':20,'tol':1e-06,'verbose':False} -0,258,232,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':3,'tol':1e-06,'verbose':False} -0,499,683,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':21,'tol':1e-06,'verbose':False} -0,87,270,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':22,'tol':1e-06,'verbose':False} -0,85,239,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':23,'tol':1e-06,'verbose':False} -0,1483,3081,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':24,'tol':1e-06,'verbose':False} -0,124,456,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':25,'tol':1e-06,'verbose':False} -0,128174,284,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':26,'tol':1e-06,'verbose':False} -0,135,238,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':28,'tol':1e-06,'verbose':False} -0,554,1116,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':30,'tol':1e-06,'verbose':False} -0,84,216,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':5,'tol':1e-06,'verbose':False} -0,75,216,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':7,'tol':1e-06,'verbose':False} -0,243,245,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':9,'tol':1e-06,'verbose':False} -0,149,230,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 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-{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':10,'tol':1e-06,'verbose':False} -0,125,311,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 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-2,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample0.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample0.csv deleted file mode 100644 index 9b752b23..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample0.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,0,MILLISECONDS,Generated by clustering_experiments on 2022-05-01 12:53:25.991734 -{'average_params': {'averaging_distance_metric': 'dtw'}, 'averaging_method': 'mean', 'distance_params': {'window': 0.2, 'epsilon': 0.05, 'g': 0.05, 'c': 1}, 'init_algorithm': 'random', 'max_iter': 300, 'metric': 'dtw', 'n_clusters': 3, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0,51,195,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample1.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample1.csv deleted file mode 100644 index 3462fd4f..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample1.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,1,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:15:46.533357 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':2,'tol':1e-06,'verbose':False} -0,700,1020,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample10.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample10.csv deleted file mode 100644 index d42efe78..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample10.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,10,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:04:13.602655 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':11,'tol':1e-06,'verbose':False} -0,97,338,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample11.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample11.csv deleted file mode 100644 index f630f7a6..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample11.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,11,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:34:32.422309 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':12,'tol':1e-06,'verbose':False} -0,90,435,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample12.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample12.csv deleted file mode 100644 index 1d1f4b9b..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample12.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,12,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:10:51.271157 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':13,'tol':1e-06,'verbose':False} -0,937,863,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample13.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample13.csv deleted file mode 100644 index 8144efdd..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample13.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,13,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:41:29.796638 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':14,'tol':1e-06,'verbose':False} -0,99,269,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample14.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample14.csv deleted file mode 100644 index 2405f6bb..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample14.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,14,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:29:14.789326 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':15,'tol':1e-06,'verbose':False} -0,103,335,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample15.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample15.csv deleted file mode 100644 index 57f36192..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample15.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,15,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:14:27.897817 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':16,'tol':1e-06,'verbose':False} -0,1555,1853,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample16.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample16.csv deleted file mode 100644 index 22890c79..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample16.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,16,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:33:08.193018 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':17,'tol':1e-06,'verbose':False} -0,53944,401,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample17.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample17.csv deleted file mode 100644 index 692eb2c2..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample17.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,17,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:46:56.805645 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':18,'tol':1e-06,'verbose':False} -0,322,531,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample18.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample18.csv deleted file mode 100644 index 84c87a3c..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample18.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,18,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:24:55.134668 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':19,'tol':1e-06,'verbose':False} -0,245,267,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample19.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample19.csv deleted file mode 100644 index c9c2b69d..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample19.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,19,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:27:21.689287 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':20,'tol':1e-06,'verbose':False} -0,258,232,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample2.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample2.csv deleted file mode 100644 index f193ed2f..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample2.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,2,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:18:42.237569 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':3,'tol':1e-06,'verbose':False} -0,499,683,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample20.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample20.csv deleted file mode 100644 index ecd91998..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample20.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,20,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:24:52.861145 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':21,'tol':1e-06,'verbose':False} -0,87,270,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample21.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample21.csv deleted file mode 100644 index b51923d9..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample21.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,21,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:24:55.047954 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':22,'tol':1e-06,'verbose':False} -0,85,239,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample22.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample22.csv deleted file mode 100644 index d537b87a..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample22.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,22,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:18:08.527281 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':23,'tol':1e-06,'verbose':False} -0,1483,3081,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample23.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample23.csv deleted file mode 100644 index 5c2f372f..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample23.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,23,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:27:22.479756 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':24,'tol':1e-06,'verbose':False} -0,124,456,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample24.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample24.csv deleted file mode 100644 index 318b6452..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample24.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,24,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:04:43.151088 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':25,'tol':1e-06,'verbose':False} -0,128174,284,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample25.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample25.csv deleted file mode 100644 index 805027bc..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample25.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,25,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 16:59:51.634042 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':26,'tol':1e-06,'verbose':False} -0,135,238,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample26.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample26.csv deleted file mode 100644 index 949afa85..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample26.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,26,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:08:37.665414 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':27,'tol':1e-06,'verbose':False} -0,190,798,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample27.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample27.csv deleted file mode 100644 index d9e31211..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample27.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,27,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:11:21.581243 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':28,'tol':1e-06,'verbose':False} -0,554,1116,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample28.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample28.csv deleted file mode 100644 index 75caa919..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample28.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,28,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:12:12.886894 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':29,'tol':1e-06,'verbose':False} -0,726,732,-1,-1,3,3 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample29.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample29.csv deleted file mode 100644 index 1cdd56eb..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample29.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,29,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:34:20.842016 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':30,'tol':1e-06,'verbose':False} -0,84,216,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample3.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample3.csv deleted file mode 100644 index e2e5eed1..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample3.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,3,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:24:11.332326 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':4,'tol':1e-06,'verbose':False} -0,96,288,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample4.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample4.csv deleted file mode 100644 index 1bdcda66..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample4.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,4,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:35:09.218980 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':5,'tol':1e-06,'verbose':False} -0,75,216,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,1,,0.0,1.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample5.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample5.csv deleted file mode 100644 index 8222d649..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample5.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,5,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:26:14.959819 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':6,'tol':1e-06,'verbose':False} -0,84,351,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample6.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample6.csv deleted file mode 100644 index 6f392faa..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample6.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,6,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:38:04.331902 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':7,'tol':1e-06,'verbose':False} -0,243,245,-1,-1,3,3 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample7.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample7.csv deleted file mode 100644 index e7636f21..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample7.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,7,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:34:39.157718 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':8,'tol':1e-06,'verbose':False} -0,98,258,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,1,,0.0,1.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample8.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample8.csv deleted file mode 100644 index 8ba8b01b..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample8.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,8,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:38:01.612836 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':9,'tol':1e-06,'verbose':False} -0,149,230,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,0,,1.0,0.0,0.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,1,,0.0,1.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 -0,0,,1.0,0.0,0.0 -1,2,,0.0,0.0,1.0 -2,2,,0.0,0.0,1.0 diff --git a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample9.csv b/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample9.csv deleted file mode 100644 index 30155782..00000000 --- a/examples/example_data/clustering_results/kmeans-dtw/Predictions/ArrowHead/trainResample9.csv +++ /dev/null @@ -1,39 +0,0 @@ -ArrowHead,kmeans-dtw,train,9,MILLISECONDS,Generated by clustering_experiments on 2022-06-06 17:03:28.942927 -{'average_params':{'averaging_distance_metric':'dtw'},'averaging_method':'mean','distance_params':{'window':0.2,'epsilon':0.05,'g':0.05,'c':1,'nu':0.05,'lmbda':1.0},'init_algorithm':'random','max_iter':300,'metric':'dtw','n_clusters':3,'n_init':10,'random_state':10,'tol':1e-06,'verbose':False} -0,125,311,-1,-1,3,3 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,2,,0.0,0.0,1.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,1,,0.0,1.0,0.0 -0,0,,1.0,0.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,1,,0.0,1.0,0.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 -0,2,,0.0,0.0,1.0 -1,0,,1.0,0.0,0.0 -2,0,,1.0,0.0,0.0 diff --git a/examples/example_data/clustering_results/kmeans-euclidean/Predictions/ACSF1/testResample0.csv b/examples/example_data/clustering_results/kmeans-euclidean/Predictions/ACSF1/testResample0.csv deleted file mode 100644 index c3fa1b5f..00000000 --- a/examples/example_data/clustering_results/kmeans-euclidean/Predictions/ACSF1/testResample0.csv +++ /dev/null @@ -1,103 +0,0 @@ -ACSF1,kmeans-euclidean,test,0,MILLISECONDS,Generated by clustering_experiments on 2022-05-01 12:52:31.665417 -{'average_params': {'averaging_distance_metric': 'euclidean'}, 'averaging_method': 'mean', 'distance_params': {'window': 1.0, 'epsilon': 0.05, 'g': 0.05, 'c': 1}, 'init_algorithm': 'random', 'max_iter': 300, 'metric': 'euclidean', 'n_clusters': 10, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0,3,4,-1,-1,10,10 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,3,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 -3,3,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 -3,6,,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 -3,6,,0.0,0.0,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0 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a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/ACC.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/ACC.csv deleted file mode 100644 index d6fd99ec..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.20924291226746378,0.282923843426327,0.23274035654430322,0.2686061534857426,0.2755238173056425,0.2581847193312409,0.2955295578862532,0.22009146945965108,0.23770352808390155,0.2533284903302215,0.3081286574318272,0.2500239596478146,0.2538717089729458,0.3039245542470511,0.2752483276159773,0.26298760157938933,0.23274035654430317,0.21353449168403818,0.24119513369307152,0.22359899609190603,0.2705301956793383,0.26811509410699264,0.32170306225812484,0.253399243949853,0.23770352808390163,0.24567428227124904,0.24318651399681265,0.20344354259425912,0.2457456591380617,0.22146855936280369 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.07488708220415538,0.11623931623931624,0.08088104155519886,0.10545770819743422,0.09782253928288338,0.09036915204678363,0.13812492360347145,0.07254026255244282,0.08622185687537105,0.09246240968560827,0.1417052057393573,0.0905593093093093,0.09634408602150538,0.13546843364623595,0.12072594868293793,0.09773662551440329,0.08088104155519886,0.07808059631959004,0.09064963962656605,0.0823641947339274,0.08521367521367522,0.09245236327641673,0.174640522875817,0.09186840471756673,0.08622185687537105,0.09264094381587855,0.09060519882253155,0.07435012652403956,0.09373402841664111,0.08064831087678188 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-folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5874605201297491,0.7637610965741864,0.6026397915080688,0.7834508090216955,0.6852886842580331,0.6710270046635979,0.840894414754887,0.6412687938983437,0.6265352190388491,0.7056729160425392,0.8215004761046026,0.6672832601702757,0.6509643849758759,0.8717374122172064,0.7567007889926244,0.6734317119932957,0.6026397915080687,0.5859297686945218,0.6445028097053997,0.7334159254651921,0.6689700981782188,0.739097942408756,0.8478242080020043,0.6390156808706441,0.6265352190388492,0.7015100843231677,0.6997938622568186,0.5673198326387562,0.6689930117935741,0.6566872776390517 -Adiac,2.0738921474249783,2.055900224376182,1.9975210127954677,2.0494627510242425,2.0531318081220773,2.037776012353036,2.0743037863214657,2.0283234119481,2.067043305604328,1.9746969623861141,2.093783396505414,1.9950461537286504,2.0656530144127294,1.9505793529556434,2.073990645621701,2.030453083962504,2.043276767619979,2.0632334103125505,2.070611581174321,2.0953906842840544,2.0767757695369524,2.064572403546641,2.0333919148699464,2.0454091963467476,2.1156961418440847,1.9996991843108667,2.0623314320076025,2.0966325861648247,2.009036755328086,2.0104126245476412 -ArrowHead,0.2747624113440088,0.2631068438471188,0.2631068438471188,0.2631068438471188,0.2631068438471188,0.2631068438471189,0.2747624113440088,0.2631068438471188,0.2678758949245009,0.2678758949245009,0.2631068438471188,0.2747624113440088,0.267875894924501,0.267875894924501,0.2631068438471188,0.2631068438471188,0.267875894924501,0.267875894924501,0.2631068438471188,0.2631068438471188,0.267875894924501,0.2631068438471188,0.2678758949245009,0.267875894924501,0.2678758949245009,0.2631068438471188,0.267875894924501,0.2631068438471188,0.2678758949245009,0.267875894924501 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/NMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/NMI.csv deleted file mode 100644 index e5a311a8..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.33034923583536335,0.4012727434450675,0.34192886313609033,0.40508326517316673,0.3822168842373123,0.3727139872826651,0.42563782637641534,0.35307953294160416,0.3511844463757961,0.3806826988324769,0.4239431889753496,0.36862027248433366,0.36110476652477286,0.4374050650445742,0.3950219835383221,0.37409541854348033,0.3419288631360903,0.32760103408987684,0.3553679782885174,0.3815461950812601,0.3803315835422774,0.3951515972426224,0.43075551632484566,0.3568779330448218,0.3511844463757962,0.3789339641159387,0.3745707539858252,0.31901089602877897,0.361686260458979,0.35425687943721 -Adiac,0.6290746215363539,0.6196194937026408,0.6176006561510289,0.6300967518894279,0.628130970098436,0.6271779887387113,0.6367939088070222,0.6292488000080717,0.6312506814616371,0.6135341011722992,0.6361308818912998,0.6149270752244801,0.6324784712902737,0.6006685344213581,0.6343813906716046,0.6270093788726788,0.6336368633955416,0.6352600186013934,0.6279251915667969,0.63842804686898,0.634725142248856,0.6327042949909852,0.6291700324710613,0.623197445357611,0.6414473655019405,0.6122553702644901,0.6330015664404389,0.6347391575136858,0.6173970742169814,0.6217619773415387 -ArrowHead,0.2605591730451322,0.242953623169967,0.242953623169967,0.242953623169967,0.242953623169967,0.2429536231699671,0.2605591730451322,0.242953623169967,0.26009877975399853,0.26009877975399853,0.242953623169967,0.26055917304513226,0.2600987797539986,0.2600987797539986,0.242953623169967,0.242953623169967,0.2600987797539986,0.2600987797539986,0.242953623169967,0.242953623169967,0.2600987797539986,0.242953623169967,0.26009877975399853,0.2600987797539986,0.26009877975399853,0.242953623169967,0.2600987797539986,0.242953623169967,0.26009877975399853,0.2600987797539986 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/RI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/RI.csv deleted file mode 100644 index 6c3343ce..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/test/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.6161616161616161,0.6996495567924139,0.6136878994021852,0.691609977324263,0.637394351680066,0.6268810554524841,0.73572459286745,0.6147186147186147,0.6250257678829108,0.6516182230467945,0.7287157287157288,0.6367759224902082,0.6456400742115028,0.7359307359307359,0.7103689960832817,0.6384250669964956,0.6136878994021852,0.6291486291486291,0.647701504844362,0.6584209441352299,0.5988455988455988,0.6563595135023707,0.7633477633477633,0.6435786435786436,0.6250257678829108,0.6483199340342197,0.6584209441352299,0.6229643372500515,0.6676973819830962,0.6470830756545042 -Adiac,0.9393711686770813,0.9414013578538,0.932885109748863,0.9338079230110078,0.9362336035857887,0.931395425482829,0.933254235053721,0.9318436490672994,0.9369586711489025,0.9289433788148441,0.9388965789994068,0.9324368861643926,0.9367345593566673,0.9339133873838245,0.9364313492848197,0.9327796453760464,0.9296025311449476,0.9320282117197284,0.9385142706479468,0.9389097620460088,0.9354558038362666,0.9353107903236438,0.9311185815041856,0.9375914573858019,0.939318436490673,0.9365895458440445,0.9347571023663569,0.9395689143761123,0.93458572276053,0.9324632522575967 -ArrowHead,0.640289681748721,0.6437446016875955,0.6437446016875955,0.6437446016875955,0.6437446016875955,0.6437446016875955,0.640289681748721,0.6437446016875955,0.6206232144043585,0.6206232144043585,0.6437446016875955,0.640289681748721,0.6206232144043585,0.6206232144043585,0.6437446016875955,0.6437446016875955,0.6206232144043585,0.6206232144043585,0.6437446016875955,0.6437446016875955,0.6206232144043585,0.6437446016875955,0.6206232144043585,0.6206232144043585,0.6206232144043585,0.6437446016875955,0.6206232144043585,0.6437446016875955,0.6206232144043585,0.6206232144043585 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv deleted file mode 100644 index 12844dad..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.12121212121212122,0.1111111111111111,0.030303030303030304,0.010101010101010102,0.15151515151515152,0.050505050505050504,0.13131313131313133,0.09090909090909091,0.010101010101010102,0.21212121212121213,0.13131313131313133,0.1111111111111111,0.16161616161616163,0.04040404040404041,0.0707070707070707,0.1414141414141414,0.050505050505050504,0.1111111111111111,0.12121212121212122,0.030303030303030304,0.10101010101010101,0.020202020202020204,0.1414141414141414,0.1919191919191919,0.10101010101010101,0.0,0.030303030303030304,0.04040404040404041,0.08080808080808081,0.1111111111111111 -Adiac,0.02313624678663239,0.05141388174807198,0.04113110539845758,0.06426735218508997,0.04884318766066838,0.06940874035989718,0.02313624678663239,0.007712082262210797,0.038560411311053984,0.043701799485861184,0.030848329048843187,0.04627249357326478,0.02313624678663239,0.030848329048843187,0.012853470437017995,0.012853470437017995,0.005141388174807198,0.043701799485861184,0.06426735218508997,0.02570694087403599,0.033419023136246784,0.04884318766066838,0.03598971722365039,0.010282776349614395,0.012853470437017995,0.012853470437017995,0.07712082262210797,0.04113110539845758,0.002570694087403599,0.012853470437017995 -ArrowHead,0.11428571428571428,0.3142857142857143,0.2,0.6,0.3142857142857143,0.11428571428571428,0.6285714285714286,0.2,0.4857142857142857,0.42857142857142855,0.3142857142857143,0.4,0.08571428571428572,0.6571428571428571,0.3142857142857143,0.3142857142857143,0.2857142857142857,0.05714285714285714,0.6,0.2,0.05714285714285714,0.6,0.4857142857142857,0.2857142857142857,0.42857142857142855,0.2,0.2857142857142857,0.2,0.4857142857142857,0.05714285714285714 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv deleted file mode 100644 index 578f1643..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.20921229828174803,0.2345924552291875,0.20594292031049705,0.23002820631496732,0.19592004803657043,0.2529229073515982,0.2796705969547298,0.19610154137053815,0.19476191738700532,0.2863002758040716,0.2575417895017769,0.2391648323627525,0.2879015262037476,0.2362607203972327,0.258935732186402,0.22404850572385873,0.21655320294561894,0.2692964193454451,0.2089336516662403,0.2614316967275377,0.20891689937510574,0.266845361476417,0.28466071904022244,0.16785700481184013,0.18982092344799165,0.2641543224561365,0.2670364513138819,0.18881793607047712,0.23775217143948166,0.26338642400291284 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-ArrowHead,0.35821988620752054,0.3112150978789794,0.3112150978789794,0.3112150978789794,0.3112150978789794,0.3112150978789794,0.35821988620752054,0.3112150978789794,0.3823628393978979,0.38236283939789784,0.3112150978789794,0.35821988620752054,0.38236283939789795,0.38236283939789784,0.3112150978789794,0.3112150978789794,0.38236283939789784,0.38236283939789784,0.3112150978789794,0.3112150978789794,0.38236283939789784,0.3112150978789794,0.3823628393978979,0.38236283939789784,0.38236283939789784,0.3112150978789794,0.38236283939789784,0.3112150978789794,0.3823628393978979,0.38236283939789784 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ARI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ARI.csv deleted file mode 100644 index 0e80ab5e..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/ARI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.0680786686838124,0.095616211745244,0.06635802469135803,0.08632199968948921,0.0667409263900492,0.08333333333333333,0.1167608286252354,0.06742289645118674,0.06474258970358815,0.10164088138771683,0.10902296955709419,0.0907088122605364,0.10602571483400498,0.09645341150773122,0.11020222676664394,0.0763435003631082,0.06558223599277047,0.11106239953236885,0.07450224791265253,0.08742854397847913,0.06305837453378436,0.09675844561609061,0.141436738752175,0.05861664712778429,0.06189958310676092,0.09752402594328458,0.10509031198686371,0.06348020911127707,0.09302325581395349,0.09997118985883031 -Adiac,0.25827355958595893,0.24258813087646852,0.22914683928337526,0.23003315567257535,0.23382855914715275,0.2517658374078536,0.23511164301965154,0.2255395415088289,0.2485338125188213,0.2271482836052374,0.2519143018807807,0.2408287767942315,0.24932712397877696,0.24745710789733172,0.24949571690185618,0.23909758618255472,0.23559691534866525,0.2603486179307835,0.25161661747959707,0.2331020762732109,0.2464925719638938,0.2596899182795019,0.22369299123253333,0.23584011538829788,0.24219667448067714,0.2481345564883484,0.24136983338221607,0.23672407797855818,0.2374794863491657,0.24282196935813216 -ArrowHead,0.3037712391214256,0.2747326203208556,0.2747326203208556,0.2747326203208556,0.2747326203208556,0.2747326203208556,0.3037712391214256,0.2747326203208556,0.35198229019923527,0.35198229019923527,0.2747326203208556,0.3037712391214256,0.35198229019923527,0.35198229019923527,0.2747326203208556,0.2747326203208556,0.35198229019923527,0.35198229019923527,0.2747326203208556,0.2747326203208556,0.35198229019923527,0.2747326203208556,0.35198229019923527,0.35198229019923527,0.35198229019923527,0.2747326203208556,0.35198229019923527,0.2747326203208556,0.35198229019923527,0.35198229019923527 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/MI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/MI.csv deleted file mode 100644 index b46e4f46..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5638278679346143,0.7290249133471942,0.6052338988661773,0.7593222676949273,0.6383036073065597,0.7168135846692135,0.8094131092064748,0.6488845424495203,0.5712016853582479,0.77008888881168,0.73453632916314,0.6932383676356518,0.7320874806002979,0.7782870332001585,0.7656440571649611,0.6365818989349502,0.6286729293459511,0.7321118867373979,0.6219490910113482,0.7735523811863717,0.6370146560027069,0.751379207381792,0.8169267634326187,0.5445941521849562,0.6166275583588042,0.7334737798407212,0.7771795746698008,0.5069888638091347,0.665835310048734,0.7225415754946216 -Adiac,2.1469935361666543,2.095552552367647,2.0163391683250778,2.068470817044254,2.0646629231350158,2.0945855843317935,2.0778738828354966,2.0281375872466496,2.0930928467433727,2.0375945571329086,2.1208171563165585,2.054098218497224,2.1006905941570295,2.062155011842154,2.1085259732923287,2.0594940174345786,2.073093379026154,2.1208223172665135,2.1130412183769627,2.074403877319456,2.085475697351538,2.1156119499622483,2.0226975492044703,2.0958132556622346,2.104694828176555,2.0690829822188004,2.060735222719453,2.0955572320431872,2.0690805106074674,2.0585651675158845 -ArrowHead,0.41401748880565126,0.3847809778608373,0.3847809778608373,0.3847809778608373,0.3847809778608373,0.3847809778608373,0.41401748880565126,0.3847809778608373,0.4262456296852554,0.42624562968525537,0.3847809778608373,0.41401748880565126,0.4262456296852555,0.42624562968525537,0.3847809778608373,0.3847809778608373,0.42624562968525537,0.42624562968525537,0.3847809778608373,0.3847809778608373,0.42624562968525537,0.3847809778608373,0.4262456296852554,0.42624562968525537,0.42624562968525537,0.3847809778608373,0.42624562968525537,0.3847809778608373,0.4262456296852554,0.42624562968525537 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv deleted file mode 100644 index 2286e8b0..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.32489406370737034,0.37648243701140455,0.3398019254646089,0.390428761745883,0.34483485842268247,0.3907067347910477,0.4148385995635434,0.35064406705657963,0.3225184549232228,0.41674943011728377,0.384990821149698,0.3743041153134131,0.4014282919509164,0.39177966501059147,0.39427497362956326,0.3525053699772272,0.35137776301697965,0.39680703153255237,0.34337556130805996,0.408804729319405,0.3522159539783132,0.4018852578212764,0.4137001036274807,0.30576961027125393,0.33652166065872013,0.3974422506768976,0.4107322628493655,0.296707280489806,0.36552647880279937,0.39213560213367626 -Adiac,0.6418113566276641,0.6256607378261938,0.6156948069147251,0.6273687723599601,0.6248392947692515,0.6387327806271229,0.6326105546012731,0.6193352758926971,0.6329256723576286,0.6255219950144891,0.6360014905202406,0.6272985110188457,0.6341809607960963,0.6260231464542031,0.6368787243899807,0.6287952432441525,0.6359264587991033,0.6449345019533756,0.6348740290604656,0.6256939522843739,0.6303301364937789,0.6382546887629899,0.6199151114588816,0.6306002096834967,0.6339258811286597,0.6262997307403826,0.6278914193038347,0.6284320660394707,0.6271606739663087,0.6266874622392683 -ArrowHead,0.39870599467731566,0.3505057737697503,0.3505057737697503,0.3505057737697503,0.3505057737697503,0.3505057737697503,0.39870599467731566,0.3505057737697503,0.424102354871614,0.42410235487161396,0.3505057737697503,0.39870599467731566,0.4241023548716141,0.42410235487161396,0.3505057737697503,0.3505057737697503,0.42410235487161396,0.42410235487161396,0.3505057737697503,0.3505057737697503,0.42410235487161396,0.3505057737697503,0.424102354871614,0.42410235487161396,0.42410235487161396,0.3505057737697503,0.42410235487161396,0.3505057737697503,0.424102354871614,0.42410235487161396 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/RI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/RI.csv deleted file mode 100644 index 15ccbf6d..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-dtw/train/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29 -ACSF1,0.5844155844155844,0.6926406926406926,0.5918367346938775,0.6691403834260977,0.6231704803133374,0.6202844774273346,0.70995670995671,0.6163677592249021,0.5955473098330241,0.6408987837559266,0.6961451247165533,0.6441970727685014,0.6507936507936508,0.7054215625644197,0.7064522778808493,0.6186353329210472,0.5930735930735931,0.658008658008658,0.6219336219336219,0.6439909297052154,0.5951350237064523,0.6532673675530818,0.741084312512884,0.5936920222634509,0.6120387548958978,0.6427540713254999,0.6629560915275201,0.5769944341372912,0.6382189239332097,0.6487322201607916 -Adiac,0.9425701640473856,0.9426496700500888,0.9340497707576922,0.9344738027721093,0.9367794768505022,0.9344340497707577,0.933599236742374,0.9332547107306601,0.9387008719158296,0.9304322476346965,0.9415233350117934,0.9345533087748125,0.9388598839212361,0.9379985688919513,0.9385153579095221,0.934566559775263,0.9309622876527178,0.9355206318077015,0.9408607849892667,0.9375480348766332,0.9372565128667214,0.9407547769856625,0.9301407256247847,0.9377998038851934,0.9378793098878966,0.9393766729388069,0.935533882808152,0.9387538759176318,0.9368987358545571,0.9359711658230196 -ArrowHead,0.6773109243697479,0.6873949579831933,0.6873949579831933,0.6873949579831933,0.6873949579831933,0.6873949579831933,0.6773109243697479,0.6873949579831933,0.6907563025210084,0.6907563025210084,0.6873949579831933,0.6773109243697479,0.6907563025210084,0.6907563025210084,0.6873949579831933,0.6873949579831933,0.6907563025210084,0.6907563025210084,0.6873949579831933,0.6873949579831933,0.6907563025210084,0.6873949579831933,0.6907563025210084,0.6907563025210084,0.6907563025210084,0.6873949579831933,0.6907563025210084,0.6873949579831933,0.6907563025210084,0.6907563025210084 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv deleted file mode 100644 index 3fd6404f..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.1414141414141414 -Adiac,0.010256410256410256 -ArrowHead,0.16666666666666666 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv deleted file mode 100644 index d75e1267..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.2318222450758055 -Adiac,0.42702860004396015 -ArrowHead,0.27071436875436816 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv deleted file mode 100644 index 28b7aafb..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/ARI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.09555555555555556 -Adiac,0.24514316183636367 -ArrowHead,0.17816815219948154 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv deleted file mode 100644 index f44e5bb6..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.6682364781634383 -Adiac,2.069077443930822 -ArrowHead,0.2464250654566637 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv deleted file mode 100644 index bbf2b3c2..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.3565006839593191 -Adiac,0.6115726018875358 -ArrowHead,0.2817626410208259 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv deleted file mode 100644 index a2a9bf6c..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/test/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.6796536796536796 -Adiac,0.9477424032693955 -ArrowHead,0.5664075476712511 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv deleted file mode 100644 index 6312508e..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ACC.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.15151515151515152 -Adiac,0.010282776349614395 -ArrowHead,0.05714285714285714 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv deleted file mode 100644 index 4178a340..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/AMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.2774403546866374 -Adiac,0.4428825072795552 -ArrowHead,0.4302064264352531 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv deleted file mode 100644 index a846c166..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/ARI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.14354291066619834 -Adiac,0.26685546610859334 -ArrowHead,0.40470405287199923 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv deleted file mode 100644 index 96594900..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/MI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.8262754409821411 -Adiac,2.144181400003993 -ArrowHead,0.43888046314400536 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv deleted file mode 100644 index fffde003..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/NMI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.4192251837308139 -Adiac,0.6282119421422171 -ArrowHead,0.46845721997968687 diff --git a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv b/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv deleted file mode 100644 index b48e0d87..00000000 --- a/examples/example_data/output/clustering_results/clustering_results/kmeans-euclidean/train/RI.csv +++ /dev/null @@ -1,4 +0,0 @@ -folds,0 -ACSF1,0.7258297258297258 -Adiac,0.9510905573370789 -ArrowHead,0.7058823529411765 diff --git a/examples/example_data/testResample0.csv b/examples/example_data/testResample0.csv deleted file mode 100644 index 56d73fcf..00000000 --- a/examples/example_data/testResample0.csv +++ /dev/null @@ -1,103 +0,0 @@ -ACSF1,kmeans-ddtw,test,0,MILLISECONDS,Generated by clustering_experiments on 2022-05-01 16:30:44.347516 -{'average_params': {'averaging_distance_metric': 'ddtw'}, 'averaging_method': 'mean', 'distance_params': {'window': 1.0, 'epsilon': 0.05, 'g': 0.05, 'c': 1}, 'init_algorithm': 'random', 'max_iter': 300, 'metric': 'ddtw', 'n_clusters': 10, 'n_init': 10, 'random_state': 1, 'tol': 1e-06, 'verbose': False} -0,19307,19283,-1,-1,10,10 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -9,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -3,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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-0,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -6,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,2,,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -5,3,,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0,0.0 -5,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 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-7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -7,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -1,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -1,9,,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,1.0 -1,4,,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 -1,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -1,4,,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 -1,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -1,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -1,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 -1,4,,0.0,0.0,0.0,0.0,1.0,0.0,0.0,0.0,0.0,0.0 -1,0,,1.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0 diff --git a/examples/forecasting_experiments.ipynb b/examples/forecasting_experiments.ipynb new file mode 100644 index 00000000..71d8a188 --- /dev/null +++ b/examples/forecasting_experiments.ipynb @@ -0,0 +1,49 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Time Series Forecasting Experiments\n", + "\n", + "Placeholder.\n", + "\n", + "\"time [​](./images/forecasting.png)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "print(\"Placeholder\")" + ], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/images/cd_diagram.png b/examples/images/cd_diagram.png index bea652f7..adf8a5a3 100644 Binary files a/examples/images/cd_diagram.png and b/examples/images/cd_diagram.png differ diff --git a/examples/images/data.png b/examples/images/data.png new file mode 100644 index 00000000..615db620 Binary files /dev/null and b/examples/images/data.png differ diff --git a/examples/images/forecasting.png b/examples/images/forecasting.png new file mode 100644 index 00000000..c9316dbe Binary files /dev/null and b/examples/images/forecasting.png differ diff --git a/examples/images/results.png b/examples/images/results.png new file mode 100644 index 00000000..3029115e Binary files /dev/null and b/examples/images/results.png differ diff --git a/examples/results_format.ipynb b/examples/results_format.ipynb index 1f4782a8..d121a621 100644 --- a/examples/results_format.ipynb +++ b/examples/results_format.ipynb @@ -5,9 +5,40 @@ "source": [ "# tsml-eval Results Format\n", "\n", - "Placeholder\n", + "`tsml-eval` experiment functions will output a .csv result file in the `tsml` format. This file can contain infromation about the experiment such as the predictions made by the algorithm and the time taken to run the experiment. These result files can be used in the evaluation module to compare the performance of different algorithms.\n", "\n", - "\"wip\" [​](./images/wip.png)" + "While the result files have some common characteristics, each learning task has its own unique format. This notebook will outline the standard parts of the result files, them go over the individual format of each learning task.\n", + "\n", + "\"tsml-eval [​](./images/results.png)\n", + "\n", + "## Overview\n", + "\n", + "All experiments files will follow the same general format:\n", + "\n", + "* Line 1 contains information about the experiment being run; the items are the same for all results files:\n", + " * `dataset` - the name of the dataset used in the experiment.\n", + " * `algorithm` - the name of the algorithm used in the experiment.\n", + " * `split` - the split of the dataset used in the experiment (i.e. train or test).\n", + " * `resample id` - the dataset resample id or random seed used in the experiment.\n", + " * `time unit` - the time unit used to measure the experiment (i.e. milliseconds or nanoseconds).\n", + " * `description` - a description of the experiment, this is optional and can contain commas.\n", + "* Line 2 contains parameter information from the estimator used in the experiment. This is specific to the estimator used and is generally free form, containing both input parameters and relevant information from fit if required. This line can contain any number of commas. \n", + "* Line 3 contains results from the experiment. This is specific to the learning task and will be covered in the following sections, but will contain the following as the first 5 items:\n", + " * `performance` - a commonly used performance metric for the task (i.e. accuracy or MSE).\n", + " * `fit time` - the time taken to fit the model.\n", + " * `predict time` - the time taken to make predictions for all cases in the file.\n", + " * `benchmark time` - the time taken to run a simple function (i.e. sorting _n_ arrays), used as a hardware benchmark.\n", + " * `memory usage` - the memory usage of fit, in `tsml-eval` experiments this is the max memory usage recorded during the fit process.\n", + "* Remaining lines record the results for each case. There should be as many lines as there are cases in the dataset, the ordering for each case should match that of the dataset used. The format of these lines is again specific to the learning task and will be covered in the following sections. The lines will follow a similar format to the following, however, with some tasks not recording probabilities:\n", + " * `target` - the target variable for the case.\n", + " * `prediction` - the estimator prediction for the case.\n", + " * `probabilities` - the estimator probabilities for the case, this is available for all tasks. Preceded by an extra comma.\n", + " * `prediction time` - the time to make the prediction. Optional and preceded by an extra comma.\n", + " * `description` - additional details for the case. Optional and preceded by an extra comma but requires prediction time.\n", + "\n", + "Some results can hold default/missing values such as -1 if they are not recorded. This is sometimes the case for metrics such as memory usage and benchmark time.\n", + "\n", + "In the following, we provide an example of generating a classification results file." ], "metadata": { "collapsed": false @@ -15,19 +46,119 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Chinatown,DummyClassifier,TEST,0,MILLISECONDS,Generated by run_classification_experiment on 11/22/2023, 19:30:49. Encoder dictionary: {1.0: 0, 2.0: 1}\n", + "{'constant': None, 'random_state': 0, 'strategy': 'stratified', 'validate': False}\n", + "0.5,0,0,755,45056,2,,-1,-1\n", + "0,0,,1.0,0.0\n", + "0,0,,1.0,0.0\n", + "0,0,,1.0,0.0\n", + "0,0,,1.0,0.0\n" + ] + } + ], + "source": [ + "from tsml.datasets import load_minimal_chinatown\n", + "from tsml.dummy import DummyClassifier\n", + "\n", + "import tsml_eval.experiments.experiments as experiments\n", + "from tsml_eval.experiments import run_classification_experiment\n", + "\n", + "experiments.MEMRECORD_INTERVAL = 0.1\n", + "\n", + "X_train, y_train = load_minimal_chinatown(split=\"train\")\n", + "X_test, y_test = load_minimal_chinatown(split=\"test\")\n", + "classifier = DummyClassifier(strategy=\"stratified\", random_state=0)\n", + "\n", + "run_classification_experiment(\n", + " X_train,\n", + " y_train,\n", + " X_test,\n", + " y_test,\n", + " classifier,\n", + " \"./generated_results/\",\n", + " dataset_name=\"Chinatown\",\n", + " resample_id=0,\n", + ")\n", + "\n", + "with open(\n", + " \"./generated_results/DummyClassifier/Predictions/Chinatown/testResample0.csv\", \"r\"\n", + ") as f:\n", + " for _ in range(7):\n", + " print(f.readline().strip())" + ], "metadata": { - "collapsed": true - }, - "outputs": [], + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T19:30:49.273733200Z", + "start_time": "2023-11-22T19:30:46.333965800Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Classification\n", + "\n", + "This section covers results files for the classification learning task. The result files for classification contain the following task-specific information:\n", + "\n", + "The third line for classification results files contains the following values in the order: `accuracy`, `fit time`, `predict time`, `benchmark time`, `memory usage`, `number of classes`, `train error estimate method`, `train error estimate time`, `fit plus train error estimate time`. The final three items are only relevant if generating an estimate of error on the train set.\n", + "\n", + "The case lines for classification result files contain the following values in the order: `true class value`, `predicted class value`, (space), _n_classes_ * `class probabilities`, (space), `case prediction time`, (space), `case description`.\n", + "\n", + "Classifier results files can be loaded into a `ClassifierResults` object for use in the evaluation module:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 2, + "outputs": [ + { + "data": { + "text/plain": "0.9698736637512148" + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print(\"temp\")" - ] + "from tsml_eval.evaluation.storage import load_classifier_results\n", + "\n", + "cr = load_classifier_results(\n", + " \"../tsml_eval/testing/_test_result_files/classification/\"\n", + " \"ROCKET/Predictions/ItalyPowerDemand/testResample0.csv\"\n", + ")\n", + "cr.accuracy" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T19:30:49.286410800Z", + "start_time": "2023-11-22T19:30:49.274417Z" + } + } }, { "cell_type": "markdown", "source": [ - "Placeholder" + "## Clustering\n", + "\n", + "This section covers results files for the clustering learning task. The result files for clustering contain the following task-specific information:\n", + "\n", + "The third line for clustering results files contains the following values in the order: `clustering accuracy`, `fit time`, `predict time`, `benchmark time`, `memory usage`, `number of classes`, `number of clusters`.\n", + "\n", + "The case lines for clustering result files contain the following values in the order: `true class value`, `cluster label`, (space), _n_clusters_ * `cluster probabilities`, (space), `case prediction time`, (space), `case description`.\n", + "\n", + "Clusterer results files can be loaded into a `ClustererResults` object for use in the evaluation module:" ], "metadata": { "collapsed": false @@ -35,14 +166,127 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 3, + "outputs": [ + { + "data": { + "text/plain": "0.7" + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "print(\"temp2\")" + "from tsml_eval.evaluation.storage import load_clusterer_results\n", + "\n", + "cr = load_clusterer_results(\n", + " \"../tsml_eval/testing/_test_result_files/clustering/\"\n", + " \"KMeans-msm/Predictions/Chinatown/trainResample0.csv\"\n", + ")\n", + "cr.clustering_accuracy" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T19:30:49.308325900Z", + "start_time": "2023-11-22T19:30:49.287382300Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Regression\n", + "\n", + "This section covers results files for the regression learning task. The result files for regression contain the following task-specific information:\n", + "\n", + "The third line for regression results files contains the following values in the order: `mean squared error`, `fit time`, `predict time`, `benchmark time`, `memory usage`, `train error estimate method`, `train error estimate time`, `fit plus train error estimate time`. The final three items are only relevant if generating an estimate of error on the train set.\n", + "\n", + "The case lines for regression result files contain the following values in the order: `target value`, `predicted value`, (space), `case prediction time`, (space), `case description`.\n", + "\n", + "Regressor results files can be loaded into a `RegressorResults` object for use in the evaluation module:" ], "metadata": { "collapsed": false } + }, + { + "cell_type": "code", + "execution_count": 4, + "outputs": [ + { + "data": { + "text/plain": "0.0015126663111567206" + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tsml_eval.evaluation.storage import load_regressor_results\n", + "\n", + "cr = load_regressor_results(\n", + " \"../tsml_eval/testing/_test_result_files/regression/\"\n", + " \"ROCKET/Predictions/Covid3Month/testResample0.csv\"\n", + ")\n", + "cr.mean_squared_error" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T19:30:49.309323500Z", + "start_time": "2023-11-22T19:30:49.298352800Z" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "## Forecasting\n", + "\n", + "This section covers results files for the forecasting learning task. Each case line is a time series value to being forecasted, rather than an independent instance. Forecasting is relatively underdeveloped in `tsml-eval`, and it is likely the functionality will change over time. The result files for forecasting contain the following task-specific information:\n", + "\n", + "The third line for forecasting results files contains the following values in the order: `mean absolute percentage error`, `fit time`, `predict time`, `benchmark time`, `memory usage`.\n", + "\n", + "The case lines for forecasting result files contain the following values in the order: `target value`, `predicted value`, (space), `case prediction time`, (space), `case description`.\n", + "\n", + "Forecaster results files can be loaded into a `ForecasterResults` object for use in the evaluation module:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 5, + "outputs": [ + { + "data": { + "text/plain": "0.2603808539887312" + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from tsml_eval.evaluation.storage import load_forecaster_results\n", + "\n", + "cr = load_forecaster_results(\n", + " \"../tsml_eval/testing/_test_result_files/forecasting/\"\n", + " \"NaiveForecaster/Predictions/ShampooSales/testResample0.csv\"\n", + ")\n", + "cr.mean_absolute_percentage_error" + ], + "metadata": { + "collapsed": false, + "ExecuteTime": { + "end_time": "2023-11-22T19:30:49.317302200Z", + "start_time": "2023-11-22T19:30:49.306331700Z" + } + } } ], "metadata": { diff --git a/pyproject.toml b/pyproject.toml index fa029720..2df31115 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -83,13 +83,16 @@ binder = [ docs = [ "sphinx<8.0.0", "sphinx-design", + "sphinx-version-warning", + "sphinx_issues", + "sphinx-copybutton", + "sphinx-remove-toctrees", + "sphinxext-opengraph", "nbsphinx", "numpydoc", + "myst-parser", "jupyter", "furo", - "sphinx_issues", - "myst-parser", - "sphinx-copybutton", ] [project.urls] @@ -121,7 +124,6 @@ ignore = [ [tool.flake8] exclude = [ "tsml_eval/_wip/*", - "examples/_wip/*", "tsml_eval/estimators/*" ] per-file-ignores = [ @@ -136,11 +138,6 @@ per-file-ignores = [ "tsml_eval/publications/*:E402,T201", ] -[tool.nbqa.exclude] -black = "examples/_wip/" -flake8 = "examples/_wip/" -isort = "examples/_wip/" - [tool.pytest.ini_options] testpaths = "tsml_eval" addopts = ''' diff --git a/tsml_eval/datasets/regression_problems.py b/tsml_eval/datasets/regression_problems.py deleted file mode 100644 index b43a377d..00000000 --- a/tsml_eval/datasets/regression_problems.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -List of datasets available from the http://tseregression.org/ archive, described in [1]. - -[1] Tan, C.W., Bergmeir, C., Petitjean, F. et al. Time series extrinsic regression. -Data Min Knowl Disc 35, 1032–1060 (2021). https://doi.org/10.1007/s10618-021-00745-9 -""" - -__author__ = ["Tony Bagnall"] - -# 14 Regression equal length no missing problems [1] -monash_regression = [ - "AppliancesEnergy", - "AustraliaRainfall", - "BIDMC32HR", - "BIDMC32RR", - "BIDMC32SpO2", - "Covid3Month", - "FloodModeling1", - "FloodModeling2", - "FloodModeling3", - "IEEEPPG", - "LiveFuelMoistureContent", - "NewsHeadlineSentiment", - "NewsTitleSentiment", - "PPGDalia", -] - -# 19 Regression problems from [1] with interpolated missing and truncated unequal -monash_regression_nm_eq = [ - "AppliancesEnergy", - "AustraliaRainfall", - "BeijingPM10Quality-no-missing", - "BeijingPM25Quality-no-missing", - "BenzeneConcentration-no-missing", - "BIDMC32HR", - "BIDMC32RR", - "BIDMC32SpO2", - "Covid3Month", - "FloodModeling1", - "FloodModeling2", - "FloodModeling3", - "HouseholdPowerConsumption1-no-missing", - "HouseholdPowerConsumption2-no-missing", - "IEEEPPG", - "LiveFuelMoistureContent", - "NewsHeadlineSentiment", - "NewsTitleSentiment", - "PPGDalia-equal-length", -] diff --git a/tsml_eval/evaluation/__init__.py b/tsml_eval/evaluation/__init__.py index 46a35c37..99a43147 100644 --- a/tsml_eval/evaluation/__init__.py +++ b/tsml_eval/evaluation/__init__.py @@ -1 +1,31 @@ """Results evaluation tools.""" + +__all__ = [ + "evaluate_classifiers", + "evaluate_classifiers_from_file", + "evaluate_classifiers_by_problem", + "evaluate_clusterers", + "evaluate_clusterers_from_file", + "evaluate_clusterers_by_problem", + "evaluate_regressors", + "evaluate_regressors_from_file", + "evaluate_regressors_by_problem", + "evaluate_forecasters", + "evaluate_forecasters_from_file", + "evaluate_forecasters_by_problem", +] + +from tsml_eval.evaluation.multiple_estimator_evaluation import ( + evaluate_classifiers, + evaluate_classifiers_by_problem, + evaluate_classifiers_from_file, + evaluate_clusterers, + evaluate_clusterers_by_problem, + evaluate_clusterers_from_file, + evaluate_forecasters, + evaluate_forecasters_by_problem, + evaluate_forecasters_from_file, + evaluate_regressors, + evaluate_regressors_by_problem, + evaluate_regressors_from_file, +) diff --git a/tsml_eval/evaluation/multiple_estimator_evaluation.py b/tsml_eval/evaluation/multiple_estimator_evaluation.py index 50041fec..6346a11d 100644 --- a/tsml_eval/evaluation/multiple_estimator_evaluation.py +++ b/tsml_eval/evaluation/multiple_estimator_evaluation.py @@ -17,6 +17,21 @@ ) from tsml_eval.utils.functions import rank_array, time_to_milliseconds +__all__ = [ + "evaluate_classifiers", + "evaluate_classifiers_from_file", + "evaluate_classifiers_by_problem", + "evaluate_clusterers", + "evaluate_clusterers_from_file", + "evaluate_clusterers_by_problem", + "evaluate_regressors", + "evaluate_regressors_from_file", + "evaluate_regressors_by_problem", + "evaluate_forecasters", + "evaluate_forecasters_from_file", + "evaluate_forecasters_by_problem", +] + def evaluate_classifiers( classifier_results, diff --git a/tsml_eval/experiments/experiments.py b/tsml_eval/experiments/experiments.py index fe4524cf..899f5d8f 100644 --- a/tsml_eval/experiments/experiments.py +++ b/tsml_eval/experiments/experiments.py @@ -159,7 +159,7 @@ def run_classification_experiment( benchmark = timing_benchmark(random_state=resample_id) first_comment = ( - "PREDICTIONS,Generated by run_classification_experiment on " + "Generated by run_classification_experiment on " f"{datetime.now().strftime('%m/%d/%Y, %H:%M:%S')}. " f"Encoder dictionary: {str(encoder_dict)}" )