diff --git a/.coverage b/.coverage index dcecd69..4ad17e2 100644 Binary files a/.coverage and b/.coverage differ diff --git a/.github/workflows/pytest_loading.yml b/.github/workflows/pytest_loading.yml index ecebb01..a629ad3 100644 --- a/.github/workflows/pytest_loading.yml +++ b/.github/workflows/pytest_loading.yml @@ -24,6 +24,7 @@ jobs: sudo apt-get install libmlpack-dev sudo apt-get install libopenblas-dev sudo apt-get install python3-dev build-essential + pip install --upgrade google protobuf pip install -r requirements.txt pip install mypy pip install pytest @@ -32,4 +33,5 @@ jobs: - name: Run pytest run: | python -m pytest ./tests/test_loading.py - python -m pytest ./tests/test_exception.py \ No newline at end of file + python -m pytest ./tests/test_exception.py + python -m pytest ./tests/test_benchmarking.py \ No newline at end of file diff --git a/.idea/workspace.xml b/.idea/workspace.xml index 072923d..376a368 100644 --- a/.idea/workspace.xml +++ b/.idea/workspace.xml @@ -3,15 +3,7 @@ - - - - - - - - - + - - - - - + + + + + @@ -80,8 +72,8 @@ - - + + - + - + - + - + - - - - + + + + + + + + - - - - @@ -285,6 +260,14 @@ + + + + + + + + @@ -314,24 +297,26 @@ + - - + + - + - + + \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..46d4ca8 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,9 @@ +MIT License + +Copyright (c) 2019-2024 UNIFR + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. \ No newline at end of file diff --git a/README.md b/README.md index 8f4acbe..38dfc26 100644 --- a/README.md +++ b/README.md @@ -3,12 +3,12 @@ # Welcome to ImputeGAP -ImputeGAP is a comprehensive framework designed for time series imputation algorithms. It offers a streamlined interface that bridges algorithm evaluation and parameter tuning, utilizing datasets from diverse fields such as neuroscience, medicine, and energy. The framework includes advanced imputation algorithms from five different families, supports various patterns of missing data, and provides multiple evaluation metrics. Additionally, ImputeGAP enables AutoML optimization, feature extraction, and feature analysis with SHAP. The framework is built for easy integration of new algorithms, datasets, and evaluation metrics, enhancing its flexibility and adaptability. +ImputeGAP is a comprehensive framework designed for time series imputation algorithms. It offers a streamlined interface that bridges algorithm evaluation and parameter tuning, utilizing datasets from diverse fields such as neuroscience, medicine, and energy. The framework includes advanced imputation algorithms from five different families, supports various patterns of missing data, and provides multiple evaluation metrics. Additionally, ImputeGAP enables AutoML optimization, feature extraction, and feature analysis. The framework enables easy integration of new algorithms, datasets, and evaluation metrics. ![Python](https://img.shields.io/badge/Python-v3.12-blue) ![Release](https://img.shields.io/badge/Release-v0.2.2-brightgreen) ![License](https://img.shields.io/badge/License-GPLv3-blue?style=flat&logo=gnu) -![Coverage](https://img.shields.io/badge/Coverage-93%25-brightgreen) +![Coverage](https://img.shields.io/badge/Coverage-91%25-brightgreen) ![PyPI](https://img.shields.io/pypi/v/imputegap?label=PyPI&color=blue) ![Language](https://img.shields.io/badge/Language-English-blue) ![Platform](https://img.shields.io/badge/platform-Windows%20%7C%20Linux%20%7C%20MacOS-informational) @@ -251,9 +251,9 @@ To add your own imputation algorithm in Python or C++, please refer to the detai ## References -Mourad Khayati, Quentin Nater, and Jacques Pasquier. “ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data.” Proceedings of the VLDB Endowment (PVLDB). Demo Track 17, no. 1 (2024): 4329–32. +Mourad Khayati, Quentin Nater, and Jacques Pasquier. “ImputeVIS: An Interactive Evaluator to Benchmark Imputation Techniques for Time Series Data.” Proceedings of the VLDB Endowment (PVLDB). Demo Track 17, no. 1 (2024): 4329–32. -Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, and Philippe Cudre-Mauroux. “Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series.” In Proceedings of the VLDB Endowment (PVLDB), Vol. 13, 2020. +Mourad Khayati, Alberto Lerner, Zakhar Tymchenko, and Philippe Cudre-Mauroux. “Mind the Gap: An Experimental Evaluation of Imputation of Missing Values Techniques in Time Series.” In Proceedings of the VLDB Endowment (PVLDB), Vol. 13, 2020. --- diff --git a/assets/TimeSeriesData_plot.jpg b/assets/TimeSeriesData_plot.jpg new file mode 100644 index 0000000..01a7f08 Binary files /dev/null and b/assets/TimeSeriesData_plot.jpg differ diff --git a/assets/test_plot.jpg b/assets/test_plot.jpg new file mode 100644 index 0000000..0ddaf73 Binary files /dev/null and b/assets/test_plot.jpg differ diff --git a/build/lib/imputegap/__init__.py b/build/lib/imputegap/__init__.py index 984fc57..0058b93 100644 --- a/build/lib/imputegap/__init__.py +++ b/build/lib/imputegap/__init__.py @@ -1 +1 @@ -__version__ = "0.2.2" \ No newline at end of file +__version__ = "1.0.1" \ No newline at end of file diff --git a/docs/generation/source/conf.py b/docs/generation/source/conf.py index ec4da00..e52614f 100644 --- a/docs/generation/source/conf.py +++ b/docs/generation/source/conf.py @@ -33,8 +33,8 @@ html_css_files = ['custom.css'] # Set the version and release info -version = '0.2.2' -release = '0.2.2' +version = '1.0.1' +release = '1.0.1' # You can also add links to edit the documentation on GitHub diff --git a/imputegap.egg-info/PKG-INFO b/imputegap.egg-info/PKG-INFO index 683d5ae..bea69e4 100644 --- a/imputegap.egg-info/PKG-INFO +++ b/imputegap.egg-info/PKG-INFO @@ -1,6 +1,6 @@ Metadata-Version: 2.1 Name: imputegap -Version: 0.2.2 +Version: 1.0.1 Summary: A Library of Imputation Techniques for Time Series Data Home-page: https://github.com/eXascaleInfolab/ImputeGAP Author: Quentin Nater diff --git a/imputegap/__init__.py b/imputegap/__init__.py index 984fc57..0058b93 100644 --- a/imputegap/__init__.py +++ b/imputegap/__init__.py @@ -1 +1 @@ -__version__ = "0.2.2" \ No newline at end of file +__version__ = "1.0.1" \ No newline at end of file diff --git a/imputegap/__pycache__/__init__.cpython-312.pyc b/imputegap/__pycache__/__init__.cpython-312.pyc index 49e564d..2141673 100644 Binary files a/imputegap/__pycache__/__init__.cpython-312.pyc and b/imputegap/__pycache__/__init__.cpython-312.pyc differ diff --git a/imputegap/assets/shap/chlorine_cdrec_DTL_Beeswarm.png b/imputegap/assets/shap/chlorine_cdrec_DTL_Beeswarm.png index 1c38766..1b94847 100644 Binary files a/imputegap/assets/shap/chlorine_cdrec_DTL_Beeswarm.png and b/imputegap/assets/shap/chlorine_cdrec_DTL_Beeswarm.png differ diff --git a/imputegap/assets/shap/chlorine_cdrec_DTL_Waterfall.png b/imputegap/assets/shap/chlorine_cdrec_DTL_Waterfall.png index 898c65e..826dda3 100644 Binary files a/imputegap/assets/shap/chlorine_cdrec_DTL_Waterfall.png and b/imputegap/assets/shap/chlorine_cdrec_DTL_Waterfall.png differ diff --git a/imputegap/assets/shap/chlorine_cdrec_results.txt b/imputegap/assets/shap/chlorine_cdrec_results.txt index 3aab644..2265b48 100644 --- a/imputegap/assets/shap/chlorine_cdrec_results.txt +++ b/imputegap/assets/shap/chlorine_cdrec_results.txt @@ -1,22 +1,22 @@ -Feature : 6 cdrec with a score of 58.98 Geometry Proportion of high incremental changes in the series MD_hrv_classic_pnn40 -Feature : 5 cdrec with a score of 9.1 Correlation Time reversibility CO_trev_1_num -Feature : 2 cdrec with a score of 6.02 Correlation First 1/e crossing of the ACF CO_f1ecac -Feature : 1 cdrec with a score of 4.52 Geometry 10-bin histogram mode DN_HistogramMode_10 -Feature : 10 cdrec with a score of 4.42 Geometry Goodness of exponential fit to embedding distance distribution CO_Embed2_Dist_tau_d_expfit_meandiff -Feature : 15 cdrec with a score of 4.1 Transformation Power in the lowest 20% of frequencies SP_Summaries_welch_rect_area_5_1 -Feature : 21 cdrec with a score of 3.56 Trend Error of 3-point rolling mean forecast FC_LocalSimple_mean3_stderr -Feature : 12 cdrec with a score of 3.44 Correlation Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat -Feature : 0 cdrec with a score of 2.64 Geometry 5-bin histogram mode DN_HistogramMode_5 -Feature : 17 cdrec with a score of 1.41 Trend Entropy of successive pairs in symbolized series SB_MotifThree_quantile_hh -Feature : 4 cdrec with a score of 0.86 Correlation Histogram-based automutual information (lag 2, 5 bins) CO_HistogramAMI_even_2_5 -Feature : 8 cdrec with a score of 0.48 Geometry Transition matrix column variance SB_TransitionMatrix_3ac_sumdiagcov -Feature : 13 cdrec with a score of 0.46 Geometry Positive outlier timing DN_OutlierInclude_p_001_mdrmd -Feature : 14 cdrec with a score of 0.01 Geometry Negative outlier timing DN_OutlierInclude_n_001_mdrmd -Feature : 3 cdrec with a score of 0.0 Correlation First minimum of the ACF CO_FirstMin_ac +Feature : 1 cdrec with a score of 90.54 Geometry 10-bin histogram mode DN_HistogramMode_10 +Feature : 12 cdrec with a score of 3.99 Correlation Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat +Feature : 5 cdrec with a score of 3.83 Correlation Time reversibility CO_trev_1_num +Feature : 18 cdrec with a score of 0.57 Geometry Rescaled range fluctuation analysis (low-scale scaling) SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 +Feature : 13 cdrec with a score of 0.37 Geometry Positive outlier timing DN_OutlierInclude_p_001_mdrmd +Feature : 3 cdrec with a score of 0.33 Correlation First minimum of the ACF CO_FirstMin_ac +Feature : 14 cdrec with a score of 0.29 Geometry Negative outlier timing DN_OutlierInclude_n_001_mdrmd +Feature : 6 cdrec with a score of 0.09 Geometry Proportion of high incremental changes in the series MD_hrv_classic_pnn40 +Feature : 0 cdrec with a score of 0.0 Geometry 5-bin histogram mode DN_HistogramMode_5 +Feature : 2 cdrec with a score of 0.0 Correlation First 1/e crossing of the ACF CO_f1ecac +Feature : 4 cdrec with a score of 0.0 Correlation Histogram-based automutual information (lag 2, 5 bins) CO_HistogramAMI_even_2_5 Feature : 7 cdrec with a score of 0.0 Geometry Longest stretch of above-mean values SB_BinaryStats_mean_longstretch1 +Feature : 8 cdrec with a score of 0.0 Geometry Transition matrix column variance SB_TransitionMatrix_3ac_sumdiagcov Feature : 9 cdrec with a score of 0.0 Trend Wangs periodicity metric PD_PeriodicityWang_th0_01 +Feature : 10 cdrec with a score of 0.0 Geometry Goodness of exponential fit to embedding distance distribution CO_Embed2_Dist_tau_d_expfit_meandiff Feature : 11 cdrec with a score of 0.0 Correlation First minimum of the AMI function IN_AutoMutualInfoStats_40_gaussian_fmmi +Feature : 15 cdrec with a score of 0.0 Transformation Power in the lowest 20% of frequencies SP_Summaries_welch_rect_area_5_1 Feature : 16 cdrec with a score of 0.0 Geometry Longest stretch of decreasing values SB_BinaryStats_diff_longstretch0 -Feature : 18 cdrec with a score of 0.0 Geometry Rescaled range fluctuation analysis (low-scale scaling) SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 +Feature : 17 cdrec with a score of 0.0 Trend Entropy of successive pairs in symbolized series SB_MotifThree_quantile_hh Feature : 19 cdrec with a score of 0.0 Geometry Detrended fluctuation analysis (low-scale scaling) SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 Feature : 20 cdrec with a score of 0.0 Transformation Centroid frequency SP_Summaries_welch_rect_centroid +Feature : 21 cdrec with a score of 0.0 Trend Error of 3-point rolling mean forecast FC_LocalSimple_mean3_stderr diff --git a/imputegap/assets/shap/chlorine_cdrec_shap_aggregate_plot.png b/imputegap/assets/shap/chlorine_cdrec_shap_aggregate_plot.png index 8db13e2..136ae00 100644 Binary files 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a/imputegap/assets/shap/fmri-objectviewing_cdrec_results.txt b/imputegap/assets/shap/fmri-objectviewing_cdrec_results.txt new file mode 100644 index 0000000..06dee02 --- /dev/null +++ b/imputegap/assets/shap/fmri-objectviewing_cdrec_results.txt @@ -0,0 +1,22 @@ +Feature : 1 cdrec with a score of 33.18 Geometry 10-bin histogram mode DN_HistogramMode_10 +Feature : 0 cdrec with a score of 31.45 Geometry 5-bin histogram mode DN_HistogramMode_5 +Feature : 6 cdrec with a score of 8.37 Geometry Proportion of high incremental changes in the series MD_hrv_classic_pnn40 +Feature : 5 cdrec with a score of 7.89 Correlation Time reversibility CO_trev_1_num +Feature : 2 cdrec with a score of 4.04 Correlation First 1/e crossing of the ACF CO_f1ecac +Feature : 21 cdrec with a score of 3.72 Trend Error of 3-point rolling mean forecast FC_LocalSimple_mean3_stderr +Feature : 13 cdrec with a score of 2.65 Geometry Positive outlier timing DN_OutlierInclude_p_001_mdrmd +Feature : 17 cdrec with a score of 2.16 Trend Entropy of successive pairs in symbolized series SB_MotifThree_quantile_hh +Feature : 15 cdrec with a score of 2.02 Transformation Power in the lowest 20% of frequencies SP_Summaries_welch_rect_area_5_1 +Feature : 4 cdrec with a score of 1.76 Correlation Histogram-based automutual information (lag 2, 5 bins) CO_HistogramAMI_even_2_5 +Feature : 10 cdrec with a score of 1.32 Geometry Goodness of exponential fit to embedding distance distribution CO_Embed2_Dist_tau_d_expfit_meandiff +Feature : 12 cdrec with a score of 0.76 Correlation Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat +Feature : 14 cdrec with a score of 0.36 Geometry Negative outlier timing DN_OutlierInclude_n_001_mdrmd +Feature : 8 cdrec with a score of 0.33 Geometry Transition matrix column variance SB_TransitionMatrix_3ac_sumdiagcov +Feature : 3 cdrec with a score of 0.0 Correlation First minimum of the ACF CO_FirstMin_ac +Feature : 7 cdrec with a score of 0.0 Geometry Longest stretch of above-mean values SB_BinaryStats_mean_longstretch1 +Feature : 9 cdrec with a score of 0.0 Trend Wangs periodicity metric PD_PeriodicityWang_th0_01 +Feature : 11 cdrec with a score of 0.0 Correlation First minimum of the AMI function IN_AutoMutualInfoStats_40_gaussian_fmmi +Feature : 16 cdrec with a score of 0.0 Geometry Longest stretch of decreasing values SB_BinaryStats_diff_longstretch0 +Feature : 18 cdrec with a score of 0.0 Geometry Rescaled range fluctuation analysis (low-scale scaling) SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 +Feature : 19 cdrec with a score of 0.0 Geometry Detrended fluctuation analysis (low-scale scaling) SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 +Feature : 20 cdrec with a score of 0.0 Transformation Centroid frequency SP_Summaries_welch_rect_centroid diff --git a/imputegap/assets/shap/fmri-objectviewing_cdrec_shap_aggregate_plot.png b/imputegap/assets/shap/fmri-objectviewing_cdrec_shap_aggregate_plot.png new file mode 100644 index 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index 0000000..ec6acb8 --- /dev/null +++ b/imputegap/assets/shap/fmri-objectviewing_iim_results.txt @@ -0,0 +1,22 @@ +Feature : 0 iim with a score of 34.56 Geometry 5-bin histogram mode DN_HistogramMode_5 +Feature : 1 iim with a score of 25.08 Geometry 10-bin histogram mode DN_HistogramMode_10 +Feature : 4 iim with a score of 14.99 Correlation Histogram-based automutual information (lag 2, 5 bins) CO_HistogramAMI_even_2_5 +Feature : 15 iim with a score of 7.09 Transformation Power in the lowest 20% of frequencies SP_Summaries_welch_rect_area_5_1 +Feature : 21 iim with a score of 6.78 Trend Error of 3-point rolling mean forecast FC_LocalSimple_mean3_stderr +Feature : 5 iim with a score of 6.36 Correlation Time reversibility CO_trev_1_num +Feature : 6 iim with a score of 1.45 Geometry Proportion of high incremental changes in the series MD_hrv_classic_pnn40 +Feature : 14 iim with a score of 0.94 Geometry Negative outlier timing DN_OutlierInclude_n_001_mdrmd +Feature : 10 iim with a score of 0.9 Geometry Goodness of exponential fit to embedding distance distribution CO_Embed2_Dist_tau_d_expfit_meandiff +Feature : 17 iim with a score of 0.73 Trend Entropy of successive pairs in symbolized series SB_MotifThree_quantile_hh +Feature : 2 iim with a score of 0.66 Correlation First 1/e crossing of the ACF CO_f1ecac +Feature : 8 iim with a score of 0.23 Geometry Transition matrix column variance SB_TransitionMatrix_3ac_sumdiagcov +Feature : 12 iim with a score of 0.2 Correlation Change in autocorrelation timescale after incremental differencing FC_LocalSimple_mean1_tauresrat +Feature : 13 iim with a score of 0.03 Geometry Positive outlier timing DN_OutlierInclude_p_001_mdrmd +Feature : 3 iim with a score of 0.0 Correlation First minimum of the ACF CO_FirstMin_ac +Feature : 7 iim with a score of 0.0 Geometry Longest stretch of above-mean values SB_BinaryStats_mean_longstretch1 +Feature : 9 iim with a score of 0.0 Trend Wangs periodicity metric PD_PeriodicityWang_th0_01 +Feature : 11 iim with a score of 0.0 Correlation First minimum of the AMI function IN_AutoMutualInfoStats_40_gaussian_fmmi +Feature : 16 iim with a score of 0.0 Geometry Longest stretch of decreasing values SB_BinaryStats_diff_longstretch0 +Feature : 18 iim with a score of 0.0 Geometry Rescaled range fluctuation analysis (low-scale scaling) SC_FluctAnal_2_rsrangefit_50_1_logi_prop_r1 +Feature : 19 iim with a score of 0.0 Geometry Detrended fluctuation analysis (low-scale scaling) SC_FluctAnal_2_dfa_50_1_2_logi_prop_r1 +Feature : 20 iim with a score of 0.0 Transformation Centroid frequency SP_Summaries_welch_rect_centroid diff --git a/imputegap/assets/shap/fmri-objectviewing_iim_shap_aggregate_plot.png b/imputegap/assets/shap/fmri-objectviewing_iim_shap_aggregate_plot.png new file mode 100644 index 0000000..26f6368 Binary files /dev/null and b/imputegap/assets/shap/fmri-objectviewing_iim_shap_aggregate_plot.png differ diff --git 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a/imputegap/recovery/__pycache__/manager.cpython-312.pyc b/imputegap/recovery/__pycache__/manager.cpython-312.pyc index 7d9b909..01fe1b7 100644 Binary files a/imputegap/recovery/__pycache__/manager.cpython-312.pyc and b/imputegap/recovery/__pycache__/manager.cpython-312.pyc differ diff --git a/imputegap/recovery/benchmarking.py b/imputegap/recovery/benchmarking.py index 64e53b3..bd90b8f 100644 --- a/imputegap/recovery/benchmarking.py +++ b/imputegap/recovery/benchmarking.py @@ -1,13 +1,36 @@ +import os import time +import numpy as np +import matplotlib.pyplot as plt + from imputegap.tools import utils from imputegap.recovery.imputation import Imputation from imputegap.recovery.manager import TimeSeries -import os -import matplotlib.pyplot as plt class Benchmarking: - + """ + A class to evaluate the performance of imputation algorithms through benchmarking across datasets and scenarios. + + Methods + ------- + _config_optimization(): + Configure and execute optimization for a selected imputation algorithm and contamination scenario. + avg_results(): + Calculate average metrics (e.g., RMSE) across multiple datasets and algorithm runs. + generate_matrix(): + Generate and save a heatmap visualization of RMSE scores for datasets and algorithms. + generate_reports(): + Create detailed text-based reports summarizing metrics and timing results for all evaluations. + generate_plots(): + Visualize metrics (e.g., RMSE, MAE) and timing (e.g., imputation, optimization) across scenarios and datasets. + comprehensive_evaluation(): + Perform a complete benchmarking pipeline, including contamination, imputation, evaluation, and reporting. + + Example + ------- + output : {'drift': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9234927128429051, 'MAE': 0.7219362152785619, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0010309219360351562, 'optimization': 0, 'imputation': 0.0005755424499511719}}, '0.1': {'scores': {'RMSE': 0.9699990038879407, 'MAE': 0.7774057495176013, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0020699501037597656, 'optimization': 0, 'imputation': 0.00048422813415527344}}, '0.2': {'scores': {'RMSE': 0.9914069853975623, 'MAE': 0.8134840739732964, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.007096290588378906, 'optimization': 0, 'imputation': 0.000461578369140625}}, '0.4': {'scores': {'RMSE': 1.0552448338389784, 'MAE': 0.7426695186604741, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.043192148208618164, 'optimization': 0, 'imputation': 0.0005095005035400391}}, '0.6': {'scores': {'RMSE': 1.0143105930114702, 'MAE': 0.7610548321723654, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.17184901237487793, 'optimization': 0, 'imputation': 0.0005536079406738281}}, '0.8': {'scores': {'RMSE': 1.010712060535523, 'MAE': 0.7641520748788702, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6064670085906982, 'optimization': 0, 'imputation': 0.0005743503570556641}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.23303624184873978, 'MAE': 0.13619797235197734, 'MI': 1.2739817718416822, 'CORRELATION': 0.968435455112644}, 'times': {'contamination': 0.0009615421295166016, 'optimization': 0, 'imputation': 0.09218788146972656}}, '0.1': {'scores': {'RMSE': 0.18152059329152104, 'MAE': 0.09925566629402761, 'MI': 1.1516089897042538, 'CORRELATION': 0.9829398352220718}, 'times': {'contamination': 0.00482487678527832, 'optimization': 0, 'imputation': 0.09549617767333984}}, '0.2': {'scores': {'RMSE': 0.13894771223733138, 'MAE': 0.08459032692102293, 'MI': 1.186191167936035, 'CORRELATION': 0.9901338133811375}, 'times': {'contamination': 0.01713728904724121, 'optimization': 0, 'imputation': 0.1129295825958252}}, '0.4': {'scores': {'RMSE': 0.7544523683503829, 'MAE': 0.11218049973594252, 'MI': 0.021165172206064526, 'CORRELATION': 0.814120507570725}, 'times': {'contamination': 0.10881781578063965, 'optimization': 0, 'imputation': 1.9378046989440918}}, '0.6': {'scores': {'RMSE': 0.4355197572001326, 'MAE': 0.1380846624733049, 'MI': 0.10781252370591506, 'CORRELATION': 0.9166777087122915}, 'times': {'contamination': 0.2380077838897705, 'optimization': 0, 'imputation': 1.8785057067871094}}, '0.8': {'scores': {'RMSE': 0.7672558930795506, 'MAE': 0.32988968428439397, 'MI': 0.013509125598802707, 'CORRELATION': 0.7312998041323675}, 'times': {'contamination': 0.6805167198181152, 'optimization': 0, 'imputation': 1.9562773704528809}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5434405584289141, 'MAE': 0.346560495723809, 'MI': 0.7328867182584357, 'CORRELATION': 0.8519431955571422}, 'times': {'contamination': 0.0022056102752685547, 'optimization': 0, 'imputation': 52.07010293006897}}, '0.1': {'scores': {'RMSE': 0.39007056542870916, 'MAE': 0.2753022759369617, 'MI': 0.8280959876205578, 'CORRELATION': 0.9180937736429735}, 'times': {'contamination': 0.002231597900390625, 'optimization': 0, 'imputation': 52.543020248413086}}, '0.2': {'scores': {'RMSE': 0.37254427425455994, 'MAE': 0.2730547993858495, 'MI': 0.7425412593844177, 'CORRELATION': 0.9293322959355041}, 'times': {'contamination': 0.0072672367095947266, 'optimization': 0, 'imputation': 52.88247036933899}}, '0.4': {'scores': {'RMSE': 0.6027573766269363, 'MAE': 0.34494332493982044, 'MI': 0.11876685901414151, 'CORRELATION': 0.8390532279447225}, 'times': {'contamination': 0.04321551322937012, 'optimization': 0, 'imputation': 54.10793352127075}}, '0.6': {'scores': {'RMSE': 0.9004526656857551, 'MAE': 0.4924048353228427, 'MI': 0.011590260996247858, 'CORRELATION': 0.5650541301828254}, 'times': {'contamination': 0.1728806495666504, 'optimization': 0, 'imputation': 40.53373336791992}}, '0.8': {'scores': {'RMSE': 1.0112488396023014, 'MAE': 0.7646823531588104, 'MI': 0.00040669209664367576, 'CORRELATION': 0.0183962968474991}, 'times': {'contamination': 0.6077785491943359, 'optimization': 0, 'imputation': 35.151907444000244}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.4445625930776235, 'MAE': 0.2696133927362288, 'MI': 1.1167751522591498, 'CORRELATION': 0.8944975075266335}, 'times': {'contamination': 0.0010058879852294922, 'optimization': 0, 'imputation': 0.7380530834197998}}, '0.1': {'scores': {'RMSE': 0.2939506418814281, 'MAE': 0.16953644212278182, 'MI': 1.0160968166750064, 'CORRELATION': 0.9531900627237018}, 'times': {'contamination': 0.0019745826721191406, 'optimization': 0, 'imputation': 4.7826457023620605}}, '0.2': {'scores': {'RMSE': 0.2366529609250008, 'MAE': 0.14709529129218185, 'MI': 1.064299483512458, 'CORRELATION': 0.9711348247027318}, 'times': {'contamination': 0.00801849365234375, 'optimization': 0, 'imputation': 33.94813060760498}}, '0.4': {'scores': {'RMSE': 0.4155649406397416, 'MAE': 0.22056702659999994, 'MI': 0.06616526470761779, 'CORRELATION': 0.919934494058292}, 'times': {'contamination': 0.04391813278198242, 'optimization': 0, 'imputation': 255.31524085998535}}, '0.6': {'scores': {'RMSE': 0.38695094864012947, 'MAE': 0.24340565131372927, 'MI': 0.06361822797740405, 'CORRELATION': 0.9249744935121553}, 'times': {'contamination': 0.17044353485107422, 'optimization': 0, 'imputation': 840.7470128536224}}, '0.8': {'scores': {'RMSE': 0.5862696375344495, 'MAE': 0.3968159514130716, 'MI': 0.13422239939628303, 'CORRELATION': 0.8178796825899766}, 'times': {'contamination': 0.5999574661254883, 'optimization': 0, 'imputation': 1974.6101157665253}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9458508648057621, 'MAE': 0.7019459696903068, 'MI': 0.11924522547609226, 'CORRELATION': 0.02915935932568557}, 'times': {'contamination': 0.001056671142578125, 'optimization': 0, 'imputation': 49.42237901687622}}, '0.1': {'scores': {'RMSE': 1.0125309431502871, 'MAE': 0.761136543268339, 'MI': 0.12567590499764303, 'CORRELATION': -0.037161060882302754}, 'times': {'contamination': 0.003415822982788086, 'optimization': 0, 'imputation': 49.04829454421997}}, '0.2': {'scores': {'RMSE': 1.0317754516097355, 'MAE': 0.7952869439926, 'MI': 0.10908095436833125, 'CORRELATION': -0.04155403791391449}, 'times': {'contamination': 0.007429599761962891, 'optimization': 0, 'imputation': 49.42568325996399}}, '0.4': {'scores': {'RMSE': 1.0807965786089415, 'MAE': 0.7326965517264863, 'MI': 0.006171770470542263, 'CORRELATION': -0.020630168509677818}, 'times': {'contamination': 0.042899370193481445, 'optimization': 0, 'imputation': 49.479795694351196}}, '0.6': {'scores': {'RMSE': 1.0441472017887297, 'MAE': 0.7599852461729673, 'MI': 0.01121013333181846, 'CORRELATION': -0.007513931343350665}, 'times': {'contamination': 0.17329692840576172, 'optimization': 0, 'imputation': 50.439927101135254}}, '0.8': {'scores': {'RMSE': 1.0379347892718205, 'MAE': 0.757440007226372, 'MI': 0.0035880775657246428, 'CORRELATION': -0.0014975078469404196}, 'times': {'contamination': 0.6166613101959229, 'optimization': 0, 'imputation': 50.66455388069153}}}}}}} + """ def _config_optimization(self, opti_mean, ts_test, scenario, algorithm, block_size_mcar): """ @@ -33,7 +56,9 @@ def _config_optimization(self, opti_mean, ts_test, scenario, algorithm, block_si """ if scenario == "mcar": - infected_matrix_opti = ts_test.Contaminate.mcar(ts=ts_test.data, series_impacted=opti_mean, missing_rate=opti_mean, block_size=block_size_mcar, use_seed=True, seed=42) + infected_matrix_opti = ts_test.Contaminate.mcar(ts=ts_test.data, series_impacted=opti_mean, + missing_rate=opti_mean, block_size=block_size_mcar, + use_seed=True, seed=42) elif scenario == "mp": infected_matrix_opti = ts_test.Contaminate.missing_percentage(ts=ts_test.data, series_impacted=opti_mean, missing_rate=opti_mean) @@ -53,8 +78,131 @@ def _config_optimization(self, opti_mean, ts_test, scenario, algorithm, block_si return i_opti + def avg_results(self, *datasets): + """ + Calculate the average of all metrics and times across multiple datasets. + + Parameters + ---------- + datasets : dict + Multiple dataset dictionaries to be averaged. + + Returns + ------- + dict + Dictionary with averaged scores and times for all levels. + """ + + # Step 1: Compute average RMSE across runs for each dataset and algorithm + aggregated_data = {} + + for runs in datasets: + for dataset, dataset_items in runs.items(): + if dataset not in aggregated_data: + aggregated_data[dataset] = {} + + for scenario, scenario_items in dataset_items.items(): + for algo, algo_data in scenario_items.items(): + if algo not in aggregated_data[dataset]: + aggregated_data[dataset][algo] = [] + + for missing_values, missing_values_item in algo_data.items(): + for param, param_data in missing_values_item.items(): + rmse = param_data["scores"]["RMSE"] + aggregated_data[dataset][algo].append(rmse) + + # Step 2: Compute averages using NumPy + average_rmse_matrix = {} + for dataset, algos in aggregated_data.items(): + average_rmse_matrix[dataset] = {} + for algo, rmse_values in algos.items(): + rmse_array = np.array(rmse_values) + avg_rmse = np.mean(rmse_array) + average_rmse_matrix[dataset][algo] = avg_rmse + + # Step 3: Create a matrix representation of datasets and algorithms + datasets_list = list(average_rmse_matrix.keys()) + algorithms = {algo for algos in average_rmse_matrix.values() for algo in algos} + algorithms_list = sorted(algorithms) + # Prepare a NumPy matrix + comprehensive_matrix = np.zeros((len(datasets_list), len(algorithms_list))) + for i, dataset in enumerate(datasets_list): + for j, algo in enumerate(algorithms_list): + comprehensive_matrix[i, j] = average_rmse_matrix[dataset].get(algo, np.nan) + + print("Visualization of datasets:", datasets_list) + print("Visualization of algorithms:", algorithms_list) + print("Visualization of matrix:\n", comprehensive_matrix) + + return comprehensive_matrix, algorithms_list, datasets_list + + def generate_matrix(self, scores_list, algos, sets, save_dir="./reports", display=True): + """ + Generate and save RMSE matrix in HD quality. + + Parameters + ---------- + scores_list : np.ndarray + 2D numpy array containing RMSE values. + algos : list of str + List of algorithm names (columns of the heatmap). + sets : list of str + List of dataset names (rows of the heatmap). + save_dir : str, optional + Directory to save the generated plot (default is "./reports"). + display : bool, optional + Display or not the plot + + Returns + ------- + Bool + True if the matrix has been generated + """ + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + fig, ax = plt.subplots(figsize=(10, 6)) + cmap = plt.cm.Greys + norm = plt.Normalize(vmin=0, vmax=2) # Normalizing values between 0 and 2 (RMSE) + + # Create the heatmap + heatmap = ax.imshow(scores_list, cmap=cmap, norm=norm, aspect='auto') + + # Add color bar for reference + cbar = plt.colorbar(heatmap, ax=ax, orientation='vertical') + cbar.set_label('RMSE', rotation=270, labelpad=15) + + # Set the tick labels + ax.set_xticks(np.arange(len(algos))) + ax.set_xticklabels(algos) + ax.set_yticks(np.arange(len(sets))) + ax.set_yticklabels(sets) + + # Add titles and labels + ax.set_title('ImputeGAP Algorithms Comparison') + ax.set_xlabel('Algorithms') + ax.set_ylabel('Datasets') + + # Show values on the heatmap + for i in range(len(sets)): + for j in range(len(algos)): + ax.text(j, i, f"{scores_list[i, j]:.2f}", + ha='center', va='center', + color="black" if scores_list[i, j] < 1 else "white") # for visibility + + filename = f"benchmarking_rmse.jpg" + filepath = os.path.join(save_dir, filename) + plt.savefig(filepath, dpi=300, bbox_inches='tight') # Save in HD with tight layout + + # Show the plot + if display : + plt.tight_layout() + plt.show() + plt.close() + + return True def generate_reports(self, runs_plots_scores, save_dir="./reports", dataset=""): """ @@ -79,7 +227,7 @@ def generate_reports(self, runs_plots_scores, save_dir="./reports", dataset=""): """ os.makedirs(save_dir, exist_ok=True) - save_path = os.path.join(save_dir, "report_"+str(dataset)+".txt") + save_path = os.path.join(save_dir, "report_" + str(dataset) + ".txt") with open(save_path, "w") as file: file.write("dictionary of results : " + str(runs_plots_scores) + "\n\n") @@ -87,11 +235,11 @@ def generate_reports(self, runs_plots_scores, save_dir="./reports", dataset=""): header = "| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation |\n" file.write(header) - for dataset, algo_data in runs_plots_scores.items(): - for algorithm, opt_data in algo_data.items(): - for optimizer, scenario_data in opt_data.items(): - for scenario, x_data in scenario_data.items(): - for x, values in x_data.items(): + for dataset, algo_items in runs_plots_scores.items(): + for algorithm, optimizer_items in algo_items.items(): + for optimizer, scenario_data in optimizer_items.items(): + for scenario, x_data_items in scenario_data.items(): + for x, values in x_data_items.items(): metrics = values["scores"] times = values["times"] @@ -136,8 +284,8 @@ def generate_plots(self, runs_plots_scores, s="M", v="N", save_dir="./reports"): """ os.makedirs(save_dir, exist_ok=True) - for dataset, scenario_data in runs_plots_scores.items(): - for scenario, algo_data in scenario_data.items(): + for dataset, scenario_items in runs_plots_scores.items(): + for scenario, algo_items in scenario_items.items(): # Iterate over each metric, generating separate plots, including new timing metrics for metric in ["RMSE", "MAE", "MI", "CORRELATION", "imputation_time", "optimization_time", "contamination_time"]: @@ -145,10 +293,10 @@ def generate_plots(self, runs_plots_scores, s="M", v="N", save_dir="./reports"): has_data = False # Flag to check if any data is added to the plot # Iterate over each algorithm and plot them in the same figure - for algorithm, optimizer_data in algo_data.items(): + for algorithm, optimizer_items in algo_items.items(): x_vals = [] y_vals = [] - for optimizer, x_data in optimizer_data.items(): + for optimizer, x_data in optimizer_items.items(): for x, values in x_data.items(): # Differentiate between score metrics and timing metrics if metric == "imputation_time" and "imputation" in values["times"]: @@ -224,8 +372,9 @@ def generate_plots(self, runs_plots_scores, s="M", v="N", save_dir="./reports"): print("\nAll plots recorded in", save_dir) - - def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], scenarios=[], x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], save_dir="./reports", already_optimized=False, reports=1): + def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], scenarios=[], + x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8], save_dir="./reports", already_optimized=False, + reports=1): """ Execute a comprehensive evaluation of imputation algorithms over multiple datasets and scenarios. @@ -259,16 +408,14 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc print("initialization of the comprehensive evaluation. It can take time...\n") - for runs in range(0, reports): + for runs in range(0, abs(reports)): for dataset in datasets: - runs_plots_scores = {} - - limitation_series = 100 - limitation_values = 1000 + limitation_series, limitation_values = 100, 1000 block_size_mcar = 10 - print("1. evaluation launch for", dataset, "========================================================\n\n\n") + print("1. evaluation launch for", dataset, + "========================================================\n\n\n") ts_test = TimeSeries() header = False @@ -281,11 +428,17 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc elif dataset == "fmri-stoptask": limitation_series = 360 - ts_test.load_timeseries(data=utils.search_path(dataset), max_series=limitation_series, max_values=limitation_values, header=header) - start_time_opti = 0 - end_time_opti = 0 + if reports == -1: + limitation_series = 10 + limitation_values = 110 + print("TEST LOADED...") + + ts_test.load_timeseries(data=utils.search_path(dataset), max_series=limitation_series, + max_values=limitation_values, header=header) + start_time_opti, end_time_opti = 0, 0 M, N = ts_test.data.shape + if N < 250: block_size_mcar = 2 @@ -304,16 +457,19 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc start_time_contamination = time.time() # Record start time if scenario == "mcar": - infected_matrix = ts_test.Contaminate.mcar(ts=ts_test.data, series_impacted=x, missing_rate=x, block_size=block_size_mcar, use_seed=True, seed=42) + infected_matrix = ts_test.Contaminate.mcar(ts=ts_test.data, series_impacted=x, + missing_rate=x, block_size=block_size_mcar, + use_seed=True, seed=42) elif scenario == "mp": - infected_matrix = ts_test.Contaminate.missing_percentage(ts=ts_test.data, series_impacted=x, missing_rate=x) + infected_matrix = ts_test.Contaminate.missing_percentage(ts=ts_test.data, + series_impacted=x, + missing_rate=x) else: infected_matrix = ts_test.Contaminate.blackout(ts=ts_test.data, missing_rate=x) end_time_contamination = time.time() for optimizer in optimizers: optimizer_gt = {"ground_truth": ts_test.data, **optimizer} - if algorithm == "cdrec": algo = Imputation.MatrixCompletion.CDRec(infected_matrix) elif algorithm == "stmvl": @@ -328,20 +484,23 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc if not has_been_optimized and not already_optimized and algorithm != "mean": print("\t\t5. AutoML to set the parameters", optimizer, "\n") start_time_opti = time.time() # Record start time - i_opti = self._config_optimization(0.25, ts_test, scenario, algorithm, block_size_mcar) + i_opti = self._config_optimization(0.25, ts_test, scenario, algorithm, + block_size_mcar) i_opti.impute(user_defined=False, params=optimizer_gt) - utils.save_optimization(optimal_params=i_opti.parameters, algorithm=algorithm, dataset=dataset, optimizer="e") + utils.save_optimization(optimal_params=i_opti.parameters, algorithm=algorithm, + dataset=dataset, optimizer="e") has_been_optimized = True end_time_opti = time.time() if algorithm != "mean": - opti_params = utils.load_parameters(query="optimal", algorithm=algorithm, dataset=dataset, optimizer="e") + opti_params = utils.load_parameters(query="optimal", algorithm=algorithm, + dataset=dataset, optimizer="e") print("\t\t6. imputation", algorithm, "with optimal parameters", *opti_params) else: opti_params = None - start_time_imputation = time.time() # Record start time + start_time_imputation = time.time() algo.impute(params=opti_params) end_time_imputation = time.time() @@ -360,7 +519,8 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc optimizer_value = optimizer.get('optimizer') # or optimizer['optimizer'] - runs_plots_scores.setdefault(str(dataset_s), {}).setdefault(str(scenario), {}).setdefault( + runs_plots_scores.setdefault(str(dataset_s), {}).setdefault(str(scenario), + {}).setdefault( str(algorithm), {}).setdefault(str(optimizer_value), {})[str(x)] = { "scores": algo.metrics, "times": dic_timing @@ -374,6 +534,7 @@ def comprehensive_evaluation(self, datasets=[], optimizers=[], algorithms=[], sc self.generate_plots(runs_plots_scores=runs_plots_scores, s=str(M), v=str(N), save_dir=save_dir_runs) self.generate_reports(runs_plots_scores, save_dir_runs, dataset) - print("======================================================================================\n\n\n\n\n\n") + print( + "======================================================================================\n\n\n\n\n\n") return runs_plots_scores diff --git a/imputegap/recovery/explainer.py b/imputegap/recovery/explainer.py index 643ad03..e39ec29 100644 --- a/imputegap/recovery/explainer.py +++ b/imputegap/recovery/explainer.py @@ -294,7 +294,7 @@ def launch_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spli exp = shap.KernelExplainer(model.predict, x_test) shval = exp.shap_values(x_test) - shap_values = exp(x_train) + shval_x = exp(x_train) optimal_display = [] for desc, group in zip(x_descriptions[0], x_categories[0]): @@ -319,14 +319,14 @@ def launch_shap_model(x_dataset, x_information, y_dataset, file, algorithm, spli plt.close() print("\t\t\tGRAPH has benn computed : ", alpha) - shap.plots.waterfall(shap_values[0], show=display) + shap.plots.waterfall(shval_x[0], show=display) alpha = os.path.join(path_file + file + "_" + algorithm + "_DTL_Waterfall.png") plt.title("SHAP Waterfall Results") plt.savefig(alpha) plt.close() print("\t\t\tGRAPH has benn computed : ", alpha) - shap.plots.beeswarm(shap_values, show=display) + shap.plots.beeswarm(shval_x, show=display) alpha = os.path.join(path_file + file + "_" + algorithm + "_DTL_Beeswarm.png") plt.title("SHAP Beeswarm Results") plt.savefig(alpha) diff --git a/imputegap/reports/benchmarking_rmse.jpg b/imputegap/reports/benchmarking_rmse.jpg new file mode 100644 index 0000000..0a14d94 Binary files /dev/null and b/imputegap/reports/benchmarking_rmse.jpg differ diff --git a/imputegap/reports/benchmarking_time.jpg b/imputegap/reports/benchmarking_time.jpg new file mode 100644 index 0000000..fc82ce3 Binary files /dev/null and b/imputegap/reports/benchmarking_time.jpg differ diff --git a/imputegap/reports/report_01/fmriobjectviewing_mcar_CORRELATION.jpg b/imputegap/reports/report_01/fmriobjectviewing_mcar_CORRELATION.jpg new file mode 100644 index 0000000..9af01be Binary files /dev/null and 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+++ b/imputegap/reports/report_01/report_fmri-objectviewing.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmriobjectviewing': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0389734217605486, 'MAE': 0.8058577685345816, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0014805793762207031, 'optimization': 0, 'imputation': 0.0006771087646484375}}, '0.1': {'scores': {'RMSE': 1.039599691211445, 'MAE': 0.8190561891835487, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0026483535766601562, 'optimization': 0, 'imputation': 0.00042366981506347656}}, '0.2': {'scores': {'RMSE': 1.0062387656710172, 'MAE': 0.7979296742837627, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.006651163101196289, 'optimization': 0, 'imputation': 0.000431060791015625}}, '0.4': {'scores': {'RMSE': 0.9883754343533185, 'MAE': 0.7862876896101476, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.021806955337524414, 'optimization': 0, 'imputation': 0.0005443096160888672}}, '0.6': {'scores': {'RMSE': 0.987097660571777, 'MAE': 0.7834652940902236, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.053050994873046875, 'optimization': 0, 'imputation': 0.0006668567657470703}}, '0.8': {'scores': {'RMSE': 0.9871215644673538, 'MAE': 0.783016411575714, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.12003469467163086, 'optimization': 0, 'imputation': 0.0006778240203857422}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7921010903466756, 'MAE': 0.544583599318027, 'MI': 0.6452684348756488, 'CORRELATION': 0.6564536961355489}, 'times': {'contamination': 0.001192331314086914, 'optimization': 160.06343746185303, 'imputation': 0.01718902587890625}}, '0.1': {'scores': {'RMSE': 0.8734423329225157, 'MAE': 0.6770893621008395, 'MI': 0.17404003258531509, 'CORRELATION': 0.5463883586396225}, 'times': {'contamination': 0.00665283203125, 'optimization': 160.06343746185303, 'imputation': 0.023374319076538086}}, '0.2': {'scores': {'RMSE': 0.8860045404559919, 'MAE': 0.6822309993559906, 'MI': 0.13114386403484066, 'CORRELATION': 0.4879034991275287}, 'times': {'contamination': 0.015312671661376953, 'optimization': 160.06343746185303, 'imputation': 0.028914928436279297}}, '0.4': {'scores': {'RMSE': 0.85668086245811, 'MAE': 0.6554946643451944, 'MI': 0.13136521095105114, 'CORRELATION': 0.512333042486155}, 'times': {'contamination': 0.04993629455566406, 'optimization': 160.06343746185303, 'imputation': 0.025546550750732422}}, '0.6': {'scores': {'RMSE': 0.8734554290811476, 'MAE': 0.668388555663456, 'MI': 0.12207632358330225, 'CORRELATION': 0.4901910978698973}, 'times': {'contamination': 0.11890959739685059, 'optimization': 160.06343746185303, 'imputation': 0.029758214950561523}}, '0.8': {'scores': {'RMSE': 0.9101165604682941, 'MAE': 0.7007441393931623, 'MI': 0.08721517971477473, 'CORRELATION': 0.424583398592384}, 'times': {'contamination': 0.18577051162719727, 'optimization': 160.06343746185303, 'imputation': 0.03443717956542969}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.8717933443365717, 'MAE': 0.6555873875520205, 'MI': 0.5468713896761781, 'CORRELATION': 0.5360081770612317}, 'times': {'contamination': 0.002370595932006836, 'optimization': 47.9361207485199, 'imputation': 1.688127040863037}}, '0.1': {'scores': {'RMSE': 0.8625085002829386, 'MAE': 0.6689945733093743, 'MI': 0.2158458111360233, 'CORRELATION': 0.5598406577746278}, 'times': {'contamination': 0.0027587413787841797, 'optimization': 47.9361207485199, 'imputation': 1.7008082866668701}}, '0.2': {'scores': {'RMSE': 0.8974016981576581, 'MAE': 0.693506918834922, 'MI': 0.12539695399359563, 'CORRELATION': 0.4696294419377184}, 'times': {'contamination': 0.006667137145996094, 'optimization': 47.9361207485199, 'imputation': 1.7494020462036133}}, '0.4': {'scores': {'RMSE': 0.9058118302006622, 'MAE': 0.7072376811266821, 'MI': 0.09089971471437183, 'CORRELATION': 0.4307173907497016}, 'times': {'contamination': 0.021937847137451172, 'optimization': 47.9361207485199, 'imputation': 1.9955649375915527}}, '0.6': {'scores': {'RMSE': 0.9926298063877358, 'MAE': 0.7768854416569236, 'MI': 0.040884571524434955, 'CORRELATION': 0.2900818700028841}, 'times': {'contamination': 0.05295515060424805, 'optimization': 47.9361207485199, 'imputation': 1.580155849456787}}, '0.8': {'scores': {'RMSE': 1.1125302701236894, 'MAE': 0.8727960243823621, 'MI': 0.013958439365190834, 'CORRELATION': 0.16455618813674522}, 'times': {'contamination': 0.12042689323425293, 'optimization': 47.9361207485199, 'imputation': 2.0708627700805664}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.6263705795260325, 'MAE': 0.4548865753229437, 'MI': 0.781959674837021, 'CORRELATION': 0.7986062368219096}, 'times': {'contamination': 0.0012576580047607422, 'optimization': 2116.446483373642, 'imputation': 0.22168827056884766}}, '0.1': {'scores': {'RMSE': 0.6899987721177722, 'MAE': 0.5259878926891887, 'MI': 0.395810445074613, 'CORRELATION': 0.7477771679714831}, 'times': {'contamination': 0.002750396728515625, 'optimization': 2116.446483373642, 'imputation': 1.365168809890747}}, '0.2': {'scores': {'RMSE': 0.7621016037924634, 'MAE': 0.5758589580651329, 'MI': 0.24919261959916233, 'CORRELATION': 0.658146326506337}, 'times': {'contamination': 0.006661415100097656, 'optimization': 2116.446483373642, 'imputation': 7.104876279830933}}, '0.4': {'scores': {'RMSE': 0.7902203838415963, 'MAE': 0.5922773198020501, 'MI': 0.19381374823819753, 'CORRELATION': 0.6157623089917651}, 'times': {'contamination': 0.0220491886138916, 'optimization': 2116.446483373642, 'imputation': 45.60467576980591}}, '0.6': {'scores': {'RMSE': 0.8606721167494161, 'MAE': 0.6509795391102093, 'MI': 0.14703461141268756, 'CORRELATION': 0.5349197031621258}, 'times': {'contamination': 0.0532374382019043, 'optimization': 2116.446483373642, 'imputation': 138.31860542297363}}, '0.8': {'scores': {'RMSE': 0.9473077321399332, 'MAE': 0.721873093140729, 'MI': 0.09210269321275755, 'CORRELATION': 0.41686255415646745}, 'times': {'contamination': 0.12064862251281738, 'optimization': 2116.446483373642, 'imputation': 310.311674118042}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.564373760409394, 'MAE': 1.221169321990917, 'MI': 0.5391032425183402, 'CORRELATION': 0.14029162735376388}, 'times': {'contamination': 0.001262664794921875, 'optimization': 4433.628644704819, 'imputation': 363.13744926452637}}, '0.1': {'scores': {'RMSE': 1.5003009239093386, 'MAE': 1.178061468837976, 'MI': 0.09732624088216657, 'CORRELATION': -0.03457815118265904}, 'times': {'contamination': 0.0030307769775390625, 'optimization': 4433.628644704819, 'imputation': 348.3805763721466}}, '0.2': {'scores': {'RMSE': 1.48756511282537, 'MAE': 1.211117150696572, 'MI': 0.03465816567888362, 'CORRELATION': -0.04992241665116051}, 'times': {'contamination': 0.00698542594909668, 'optimization': 4433.628644704819, 'imputation': 349.00921535491943}}, '0.4': {'scores': {'RMSE': 1.3053260371206012, 'MAE': 1.0395260022271195, 'MI': 0.006322235025890169, 'CORRELATION': -0.007140449894312156}, 'times': {'contamination': 0.022205352783203125, 'optimization': 4433.628644704819, 'imputation': 359.71013283729553}}, '0.6': {'scores': {'RMSE': 1.3648044261884822, 'MAE': 1.0965613308947504, 'MI': 0.0030644481561666144, 'CORRELATION': -0.022828258162777018}, 'times': {'contamination': 0.054125308990478516, 'optimization': 4433.628644704819, 'imputation': 363.40745854377747}}, '0.8': {'scores': {'RMSE': 1.397320341356025, 'MAE': 1.109946360446546, 'MI': 0.0028064424558294984, 'CORRELATION': -0.03067013067996843}, 'times': {'contamination': 0.12269306182861328, 'optimization': 4433.628644704819, 'imputation': 359.04918146133423}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmriobjectviewing | mcar | mean | bayesian | 0.05 | 1.0389734217605486 | 0.8058577685345816 | 0.0 | 0 | 0.0014805793762207031 sec | 0 sec| 0.0006771087646484375 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.1 | 1.039599691211445 | 0.8190561891835487 | 0.0 | 0 | 0.0026483535766601562 sec | 0 sec| 0.00042366981506347656 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.2 | 1.0062387656710172 | 0.7979296742837627 | 0.0 | 0 | 0.006651163101196289 sec | 0 sec| 0.000431060791015625 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.4 | 0.9883754343533185 | 0.7862876896101476 | 0.0 | 0 | 0.021806955337524414 sec | 0 sec| 0.0005443096160888672 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.6 | 0.987097660571777 | 0.7834652940902236 | 0.0 | 0 | 0.053050994873046875 sec | 0 sec| 0.0006668567657470703 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.8 | 0.9871215644673538 | 0.783016411575714 | 0.0 | 0 | 0.12003469467163086 sec | 0 sec| 0.0006778240203857422 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.05 | 0.7921010903466756 | 0.544583599318027 | 0.6452684348756488 | 0.6564536961355489 | 0.001192331314086914 sec | 160.06343746185303 sec| 0.01718902587890625 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.1 | 0.8734423329225157 | 0.6770893621008395 | 0.17404003258531509 | 0.5463883586396225 | 0.00665283203125 sec | 160.06343746185303 sec| 0.023374319076538086 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.2 | 0.8860045404559919 | 0.6822309993559906 | 0.13114386403484066 | 0.4879034991275287 | 0.015312671661376953 sec | 160.06343746185303 sec| 0.028914928436279297 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.4 | 0.85668086245811 | 0.6554946643451944 | 0.13136521095105114 | 0.512333042486155 | 0.04993629455566406 sec | 160.06343746185303 sec| 0.025546550750732422 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.6 | 0.8734554290811476 | 0.668388555663456 | 0.12207632358330225 | 0.4901910978698973 | 0.11890959739685059 sec | 160.06343746185303 sec| 0.029758214950561523 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.8 | 0.9101165604682941 | 0.7007441393931623 | 0.08721517971477473 | 0.424583398592384 | 0.18577051162719727 sec | 160.06343746185303 sec| 0.03443717956542969 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.05 | 0.8717933443365717 | 0.6555873875520205 | 0.5468713896761781 | 0.5360081770612317 | 0.002370595932006836 sec | 47.9361207485199 sec| 1.688127040863037 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.1 | 0.8625085002829386 | 0.6689945733093743 | 0.2158458111360233 | 0.5598406577746278 | 0.0027587413787841797 sec | 47.9361207485199 sec| 1.7008082866668701 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.2 | 0.8974016981576581 | 0.693506918834922 | 0.12539695399359563 | 0.4696294419377184 | 0.006667137145996094 sec | 47.9361207485199 sec| 1.7494020462036133 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.4 | 0.9058118302006622 | 0.7072376811266821 | 0.09089971471437183 | 0.4307173907497016 | 0.021937847137451172 sec | 47.9361207485199 sec| 1.9955649375915527 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.6 | 0.9926298063877358 | 0.7768854416569236 | 0.040884571524434955 | 0.2900818700028841 | 0.05295515060424805 sec | 47.9361207485199 sec| 1.580155849456787 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.8 | 1.1125302701236894 | 0.8727960243823621 | 0.013958439365190834 | 0.16455618813674522 | 0.12042689323425293 sec | 47.9361207485199 sec| 2.0708627700805664 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.05 | 0.6263705795260325 | 0.4548865753229437 | 0.781959674837021 | 0.7986062368219096 | 0.0012576580047607422 sec | 2116.446483373642 sec| 0.22168827056884766 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.1 | 0.6899987721177722 | 0.5259878926891887 | 0.395810445074613 | 0.7477771679714831 | 0.002750396728515625 sec | 2116.446483373642 sec| 1.365168809890747 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.2 | 0.7621016037924634 | 0.5758589580651329 | 0.24919261959916233 | 0.658146326506337 | 0.006661415100097656 sec | 2116.446483373642 sec| 7.104876279830933 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.4 | 0.7902203838415963 | 0.5922773198020501 | 0.19381374823819753 | 0.6157623089917651 | 0.0220491886138916 sec | 2116.446483373642 sec| 45.60467576980591 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.6 | 0.8606721167494161 | 0.6509795391102093 | 0.14703461141268756 | 0.5349197031621258 | 0.0532374382019043 sec | 2116.446483373642 sec| 138.31860542297363 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.8 | 0.9473077321399332 | 0.721873093140729 | 0.09210269321275755 | 0.41686255415646745 | 0.12064862251281738 sec | 2116.446483373642 sec| 310.311674118042 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.05 | 1.564373760409394 | 1.221169321990917 | 0.5391032425183402 | 0.14029162735376388 | 0.001262664794921875 sec | 4433.628644704819 sec| 363.13744926452637 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.1 | 1.5003009239093386 | 1.178061468837976 | 0.09732624088216657 | -0.03457815118265904 | 0.0030307769775390625 sec | 4433.628644704819 sec| 348.3805763721466 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.2 | 1.48756511282537 | 1.211117150696572 | 0.03465816567888362 | -0.04992241665116051 | 0.00698542594909668 sec | 4433.628644704819 sec| 349.00921535491943 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.4 | 1.3053260371206012 | 1.0395260022271195 | 0.006322235025890169 | -0.007140449894312156 | 0.022205352783203125 sec | 4433.628644704819 sec| 359.71013283729553 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.6 | 1.3648044261884822 | 1.0965613308947504 | 0.0030644481561666144 | -0.022828258162777018 | 0.054125308990478516 sec | 4433.628644704819 sec| 363.40745854377747 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.8 | 1.397320341356025 | 1.109946360446546 | 0.0028064424558294984 | -0.03067013067996843 | 0.12269306182861328 sec | 4433.628644704819 sec| 359.04918146133423 sec | diff --git a/imputegap/reports/report_01/report_fmri-stoptask.txt b/imputegap/reports/report_01/report_fmri-stoptask.txt new file mode 100644 index 0000000..0713f04 --- /dev/null +++ b/imputegap/reports/report_01/report_fmri-stoptask.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmristoptask': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0591754233439183, 'MAE': 0.8811507908679529, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0015919208526611328, 'optimization': 0, 'imputation': 0.0009393692016601562}}, '0.1': {'scores': {'RMSE': 0.9651108444122715, 'MAE': 0.784231196318496, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0035066604614257812, 'optimization': 0, 'imputation': 0.000621795654296875}}, '0.2': {'scores': {'RMSE': 0.9932773680676918, 'MAE': 0.8034395750738844, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009276866912841797, 'optimization': 0, 'imputation': 0.0006399154663085938}}, '0.4': {'scores': {'RMSE': 1.0058748440484344, 'MAE': 0.8113341021149199, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.03150796890258789, 'optimization': 0, 'imputation': 0.0008380413055419922}}, '0.6': {'scores': {'RMSE': 0.9944066185522102, 'MAE': 0.8023296982336051, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.07896685600280762, 'optimization': 0, 'imputation': 0.0009694099426269531}}, '0.8': {'scores': {'RMSE': 0.9979990505486313, 'MAE': 0.8062359186814159, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.18951916694641113, 'optimization': 0, 'imputation': 0.0010123252868652344}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0815739858856455, 'MAE': 0.8947163048898044, 'MI': 0.23576973507164212, 'CORRELATION': -0.12274682282048005}, 'times': {'contamination': 0.0014772415161132812, 'optimization': 218.59592175483704, 'imputation': 0.0071277618408203125}}, '0.1': {'scores': {'RMSE': 0.9695699729418912, 'MAE': 0.7898385707592198, 'MI': 0.06571976951128125, 'CORRELATION': 0.016476991654415008}, 'times': {'contamination': 0.008227348327636719, 'optimization': 218.59592175483704, 'imputation': 0.0062563419342041016}}, '0.2': {'scores': {'RMSE': 1.0023712131611957, 'MAE': 0.8108602788128816, 'MI': 0.02538765630290373, 'CORRELATION': -0.016656543511887868}, 'times': {'contamination': 0.0209505558013916, 'optimization': 218.59592175483704, 'imputation': 0.006833791732788086}}, '0.4': {'scores': {'RMSE': 1.0138537110215022, 'MAE': 0.8167419153197173, 'MI': 0.0038274804707874484, 'CORRELATION': 0.002717578068034049}, 'times': {'contamination': 0.07195234298706055, 'optimization': 218.59592175483704, 'imputation': 0.006715297698974609}}, '0.6': {'scores': {'RMSE': 1.0022937958385385, 'MAE': 0.807293318305244, 'MI': 0.0018376453669024168, 'CORRELATION': 0.004596695453371254}, 'times': {'contamination': 0.14271855354309082, 'optimization': 218.59592175483704, 'imputation': 0.0066835880279541016}}, '0.8': {'scores': {'RMSE': 1.0104537937047533, 'MAE': 0.8149091851781165, 'MI': 0.0008945376054130945, 'CORRELATION': -0.0013082054469119196}, 'times': {'contamination': 0.2467949390411377, 'optimization': 218.59592175483704, 'imputation': 0.005556821823120117}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1715750158207363, 'MAE': 0.9389573934580852, 'MI': 0.30612963701823526, 'CORRELATION': -0.22056411372111834}, 'times': {'contamination': 0.0029337406158447266, 'optimization': 109.35183715820312, 'imputation': 10.097216844558716}}, '0.1': {'scores': {'RMSE': 1.0588476372168147, 'MAE': 0.8437403156914149, 'MI': 0.08955991417984446, 'CORRELATION': -0.1963089605999627}, 'times': {'contamination': 0.003466367721557617, 'optimization': 109.35183715820312, 'imputation': 10.141520977020264}}, '0.2': {'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009216070175170898, 'optimization': 109.35183715820312, 'imputation': 10.349437952041626}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.031234025955200195, 'optimization': 109.35183715820312, 'imputation': 11.021637439727783}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07903313636779785, 'optimization': 109.35183715820312, 'imputation': 8.597065448760986}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18648958206176758, 'optimization': 109.35183715820312, 'imputation': 8.484493017196655}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0692148314478316, 'MAE': 0.873400733402723, 'MI': 0.2787388945371119, 'CORRELATION': -0.02021145481191946}, 'times': {'contamination': 0.0014984607696533203, 'optimization': 5072.065117120743, 'imputation': 10.695194959640503}}, '0.1': {'scores': {'RMSE': 0.9719895445677292, 'MAE': 0.7851843420896756, 'MI': 0.0830808565046283, 'CORRELATION': 0.003268635254181307}, 'times': {'contamination': 0.0036919116973876953, 'optimization': 5072.065117120743, 'imputation': 49.85465955734253}}, '0.2': {'scores': {'RMSE': 0.99753636840165, 'MAE': 0.8012616128674659, 'MI': 0.019093143495502334, 'CORRELATION': 0.02540361203010324}, 'times': {'contamination': 0.009428024291992188, 'optimization': 5072.065117120743, 'imputation': 256.5751883983612}}, '0.4': {'scores': {'RMSE': 1.0155975152475738, 'MAE': 0.8140496119700683, 'MI': 0.004260439955627443, 'CORRELATION': 0.0006423716677864647}, 'times': {'contamination': 0.03140830993652344, 'optimization': 5072.065117120743, 'imputation': 1478.6712100505829}}, '0.6': {'scores': {'RMSE': 1.0040752264526889, 'MAE': 0.8052914143043017, 'MI': 0.0018099723977603893, 'CORRELATION': -0.006621752869444718}, 'times': {'contamination': 0.078643798828125, 'optimization': 5072.065117120743, 'imputation': 4524.759085655212}}, '0.8': {'scores': {'RMSE': 1.0078811833781343, 'MAE': 0.8090736592195691, 'MI': 0.001033941419470956, 'CORRELATION': -0.003099173821807945}, 'times': {'contamination': 0.18721938133239746, 'optimization': 5072.065117120743, 'imputation': 9412.6311917305}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1338764305073745, 'MAE': 0.9621062739244053, 'MI': 0.3598215610903952, 'CORRELATION': 0.025601496823399808}, 'times': {'contamination': 0.0015861988067626953, 'optimization': 4126.59906744957, 'imputation': 342.70579957962036}}, '0.1': {'scores': {'RMSE': 1.0482569597259581, 'MAE': 0.8581623399073744, 'MI': 0.06844413129644446, 'CORRELATION': -0.0014806171782817523}, 'times': {'contamination': 0.0036308765411376953, 'optimization': 4126.59906744957, 'imputation': 343.8899919986725}}, '0.2': {'scores': {'RMSE': 1.108104493013085, 'MAE': 0.8889218664347283, 'MI': 0.01428662029369019, 'CORRELATION': -0.01599849799033619}, 'times': {'contamination': 0.009608030319213867, 'optimization': 4126.59906744957, 'imputation': 337.21221137046814}}, '0.4': {'scores': {'RMSE': 1.0874206799897894, 'MAE': 0.8779506482944299, 'MI': 0.0033213857091975076, 'CORRELATION': -0.020652285279186847}, 'times': {'contamination': 0.031928300857543945, 'optimization': 4126.59906744957, 'imputation': 342.1561577320099}}, '0.6': {'scores': {'RMSE': 1.0761284785747784, 'MAE': 0.8685233944278966, 'MI': 0.0023801932977624415, 'CORRELATION': -0.018156792999867576}, 'times': {'contamination': 0.07982683181762695, 'optimization': 4126.59906744957, 'imputation': 337.27074241638184}}, '0.8': {'scores': {'RMSE': 1.0801585962955265, 'MAE': 0.8717671912922593, 'MI': 0.0011299889668764137, 'CORRELATION': -0.019436732822524068}, 'times': {'contamination': 0.1887671947479248, 'optimization': 4126.59906744957, 'imputation': 350.4762156009674}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmristoptask | mcar | mean | bayesian | 0.05 | 1.0591754233439183 | 0.8811507908679529 | 0.0 | 0 | 0.0015919208526611328 sec | 0 sec| 0.0009393692016601562 sec | +| fmristoptask | mcar | mean | bayesian | 0.1 | 0.9651108444122715 | 0.784231196318496 | 0.0 | 0 | 0.0035066604614257812 sec | 0 sec| 0.000621795654296875 sec | +| fmristoptask | mcar | mean | bayesian | 0.2 | 0.9932773680676918 | 0.8034395750738844 | 0.0 | 0 | 0.009276866912841797 sec | 0 sec| 0.0006399154663085938 sec | +| fmristoptask | mcar | mean | bayesian | 0.4 | 1.0058748440484344 | 0.8113341021149199 | 0.0 | 0 | 0.03150796890258789 sec | 0 sec| 0.0008380413055419922 sec | +| fmristoptask | mcar | mean | bayesian | 0.6 | 0.9944066185522102 | 0.8023296982336051 | 0.0 | 0 | 0.07896685600280762 sec | 0 sec| 0.0009694099426269531 sec | +| fmristoptask | mcar | mean | bayesian | 0.8 | 0.9979990505486313 | 0.8062359186814159 | 0.0 | 0 | 0.18951916694641113 sec | 0 sec| 0.0010123252868652344 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.05 | 1.0815739858856455 | 0.8947163048898044 | 0.23576973507164212 | -0.12274682282048005 | 0.0014772415161132812 sec | 218.59592175483704 sec| 0.0071277618408203125 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.1 | 0.9695699729418912 | 0.7898385707592198 | 0.06571976951128125 | 0.016476991654415008 | 0.008227348327636719 sec | 218.59592175483704 sec| 0.0062563419342041016 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.2 | 1.0023712131611957 | 0.8108602788128816 | 0.02538765630290373 | -0.016656543511887868 | 0.0209505558013916 sec | 218.59592175483704 sec| 0.006833791732788086 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.4 | 1.0138537110215022 | 0.8167419153197173 | 0.0038274804707874484 | 0.002717578068034049 | 0.07195234298706055 sec | 218.59592175483704 sec| 0.006715297698974609 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.6 | 1.0022937958385385 | 0.807293318305244 | 0.0018376453669024168 | 0.004596695453371254 | 0.14271855354309082 sec | 218.59592175483704 sec| 0.0066835880279541016 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.8 | 1.0104537937047533 | 0.8149091851781165 | 0.0008945376054130945 | -0.0013082054469119196 | 0.2467949390411377 sec | 218.59592175483704 sec| 0.005556821823120117 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.05 | 1.1715750158207363 | 0.9389573934580852 | 0.30612963701823526 | -0.22056411372111834 | 0.0029337406158447266 sec | 109.35183715820312 sec| 10.097216844558716 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.1 | 1.0588476372168147 | 0.8437403156914149 | 0.08955991417984446 | -0.1963089605999627 | 0.003466367721557617 sec | 109.35183715820312 sec| 10.141520977020264 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.2 | 1.0391969620815695 | 0.8364861943065512 | 0.02582105408815175 | -0.09232453336176588 | 0.009216070175170898 sec | 109.35183715820312 sec| 10.349437952041626 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.4 | 1.0340455393837413 | 0.832400199311948 | 0.00520789381175344 | -0.04499260926820861 | 0.031234025955200195 sec | 109.35183715820312 sec| 11.021637439727783 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.6 | 4.011139383889788 | 3.152797499531786 | 0.003672509477371519 | -0.05413975121078511 | 0.07903313636779785 sec | 109.35183715820312 sec| 8.597065448760986 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.8 | 2.97893158705676 | 1.0602936132635719 | 0.00079094933311715 | 0.006947773983399647 | 0.18648958206176758 sec | 109.35183715820312 sec| 8.484493017196655 sec | +| fmristoptask | mcar | iim | bayesian | 0.05 | 1.0692148314478316 | 0.873400733402723 | 0.2787388945371119 | -0.02021145481191946 | 0.0014984607696533203 sec | 5072.065117120743 sec| 10.695194959640503 sec | +| fmristoptask | mcar | iim | bayesian | 0.1 | 0.9719895445677292 | 0.7851843420896756 | 0.0830808565046283 | 0.003268635254181307 | 0.0036919116973876953 sec | 5072.065117120743 sec| 49.85465955734253 sec | +| fmristoptask | mcar | iim | bayesian | 0.2 | 0.99753636840165 | 0.8012616128674659 | 0.019093143495502334 | 0.02540361203010324 | 0.009428024291992188 sec | 5072.065117120743 sec| 256.5751883983612 sec | +| fmristoptask | mcar | iim | bayesian | 0.4 | 1.0155975152475738 | 0.8140496119700683 | 0.004260439955627443 | 0.0006423716677864647 | 0.03140830993652344 sec | 5072.065117120743 sec| 1478.6712100505829 sec | +| fmristoptask | mcar | iim | bayesian | 0.6 | 1.0040752264526889 | 0.8052914143043017 | 0.0018099723977603893 | -0.006621752869444718 | 0.078643798828125 sec | 5072.065117120743 sec| 4524.759085655212 sec | +| fmristoptask | mcar | iim | bayesian | 0.8 | 1.0078811833781343 | 0.8090736592195691 | 0.001033941419470956 | -0.003099173821807945 | 0.18721938133239746 sec | 5072.065117120743 sec| 9412.6311917305 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.05 | 1.1338764305073745 | 0.9621062739244053 | 0.3598215610903952 | 0.025601496823399808 | 0.0015861988067626953 sec | 4126.59906744957 sec| 342.70579957962036 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.1 | 1.0482569597259581 | 0.8581623399073744 | 0.06844413129644446 | -0.0014806171782817523 | 0.0036308765411376953 sec | 4126.59906744957 sec| 343.8899919986725 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.2 | 1.108104493013085 | 0.8889218664347283 | 0.01428662029369019 | -0.01599849799033619 | 0.009608030319213867 sec | 4126.59906744957 sec| 337.21221137046814 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.4 | 1.0874206799897894 | 0.8779506482944299 | 0.0033213857091975076 | -0.020652285279186847 | 0.031928300857543945 sec | 4126.59906744957 sec| 342.1561577320099 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.6 | 1.0761284785747784 | 0.8685233944278966 | 0.0023801932977624415 | -0.018156792999867576 | 0.07982683181762695 sec | 4126.59906744957 sec| 337.27074241638184 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.8 | 1.0801585962955265 | 0.8717671912922593 | 0.0011299889668764137 | -0.019436732822524068 | 0.1887671947479248 sec | 4126.59906744957 sec| 350.4762156009674 sec | diff --git a/imputegap/reports/report_02/fmriobjectviewing_mcar_CORRELATION.jpg b/imputegap/reports/report_02/fmriobjectviewing_mcar_CORRELATION.jpg new file mode 100644 index 0000000..f1ad484 Binary files /dev/null and b/imputegap/reports/report_02/fmriobjectviewing_mcar_CORRELATION.jpg differ diff --git a/imputegap/reports/report_02/fmriobjectviewing_mcar_MAE.jpg b/imputegap/reports/report_02/fmriobjectviewing_mcar_MAE.jpg new file mode 100644 index 0000000..124ef44 Binary files /dev/null and b/imputegap/reports/report_02/fmriobjectviewing_mcar_MAE.jpg differ diff --git a/imputegap/reports/report_02/fmriobjectviewing_mcar_MI.jpg 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Binary files /dev/null and b/imputegap/reports/report_02/fmristoptask_mcar_imputation_time.jpg differ diff --git a/imputegap/reports/report_02/fmristoptask_mcar_optimization_time.jpg b/imputegap/reports/report_02/fmristoptask_mcar_optimization_time.jpg new file mode 100644 index 0000000..aa8a991 Binary files /dev/null and b/imputegap/reports/report_02/fmristoptask_mcar_optimization_time.jpg differ diff --git a/imputegap/reports/report_02/report_fmri-objectviewing.txt b/imputegap/reports/report_02/report_fmri-objectviewing.txt new file mode 100644 index 0000000..f5a66c4 --- /dev/null +++ b/imputegap/reports/report_02/report_fmri-objectviewing.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmriobjectviewing': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0389734217605486, 'MAE': 0.8058577685345816, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001322031021118164, 'optimization': 0, 'imputation': 0.0010623931884765625}}, '0.1': {'scores': {'RMSE': 1.039599691211445, 'MAE': 0.8190561891835487, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0029027462005615234, 'optimization': 0, 'imputation': 0.0006194114685058594}}, '0.2': {'scores': {'RMSE': 1.0062387656710172, 'MAE': 0.7979296742837627, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0067632198333740234, 'optimization': 0, 'imputation': 0.0006532669067382812}}, '0.4': {'scores': {'RMSE': 0.9883754343533185, 'MAE': 0.7862876896101476, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0221099853515625, 'optimization': 0, 'imputation': 0.0007634162902832031}}, '0.6': {'scores': {'RMSE': 0.987097660571777, 'MAE': 0.7834652940902236, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0535585880279541, 'optimization': 0, 'imputation': 0.0008707046508789062}}, '0.8': {'scores': {'RMSE': 0.9871215644673538, 'MAE': 0.783016411575714, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1221921443939209, 'optimization': 0, 'imputation': 0.0009012222290039062}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7921010903466756, 'MAE': 0.544583599318027, 'MI': 0.6452684348756488, 'CORRELATION': 0.6564536961355489}, 'times': {'contamination': 0.0013003349304199219, 'optimization': 159.3788959980011, 'imputation': 0.0172426700592041}}, '0.1': {'scores': {'RMSE': 0.8734423329225157, 'MAE': 0.6770893621008395, 'MI': 0.17404003258531509, 'CORRELATION': 0.5463883586396225}, 'times': {'contamination': 0.006696939468383789, 'optimization': 159.3788959980011, 'imputation': 0.023551225662231445}}, '0.2': {'scores': {'RMSE': 0.8860045404559919, 'MAE': 0.6822309993559906, 'MI': 0.13114386403484066, 'CORRELATION': 0.4879034991275287}, 'times': {'contamination': 0.01536703109741211, 'optimization': 159.3788959980011, 'imputation': 0.029254913330078125}}, '0.4': {'scores': {'RMSE': 0.85668086245811, 'MAE': 0.6554946643451944, 'MI': 0.13136521095105114, 'CORRELATION': 0.512333042486155}, 'times': {'contamination': 0.049208879470825195, 'optimization': 159.3788959980011, 'imputation': 0.02578568458557129}}, '0.6': {'scores': {'RMSE': 0.8734554290811476, 'MAE': 0.668388555663456, 'MI': 0.12207632358330225, 'CORRELATION': 0.4901910978698973}, 'times': {'contamination': 0.11803746223449707, 'optimization': 159.3788959980011, 'imputation': 0.01870417594909668}}, '0.8': {'scores': {'RMSE': 0.9101165604682941, 'MAE': 0.7007441393931623, 'MI': 0.08721517971477473, 'CORRELATION': 0.424583398592384}, 'times': {'contamination': 0.1880052089691162, 'optimization': 159.3788959980011, 'imputation': 0.03435778617858887}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.8717933443365717, 'MAE': 0.6555873875520205, 'MI': 0.5468713896761781, 'CORRELATION': 0.5360081770612317}, 'times': {'contamination': 0.002277374267578125, 'optimization': 48.99785590171814, 'imputation': 1.7287921905517578}}, '0.1': {'scores': {'RMSE': 0.8625085002829386, 'MAE': 0.6689945733093743, 'MI': 0.2158458111360233, 'CORRELATION': 0.5598406577746278}, 'times': {'contamination': 0.0032448768615722656, 'optimization': 48.99785590171814, 'imputation': 1.7275073528289795}}, '0.2': {'scores': {'RMSE': 0.8974016981576581, 'MAE': 0.693506918834922, 'MI': 0.12539695399359563, 'CORRELATION': 0.4696294419377184}, 'times': {'contamination': 0.007249355316162109, 'optimization': 48.99785590171814, 'imputation': 1.807462215423584}}, '0.4': {'scores': {'RMSE': 0.9058118302006622, 'MAE': 0.7072376811266821, 'MI': 0.09089971471437183, 'CORRELATION': 0.4307173907497016}, 'times': {'contamination': 0.022536754608154297, 'optimization': 48.99785590171814, 'imputation': 2.028677225112915}}, '0.6': {'scores': {'RMSE': 0.9926298063877358, 'MAE': 0.7768854416569236, 'MI': 0.040884571524434955, 'CORRELATION': 0.2900818700028841}, 'times': {'contamination': 0.054079294204711914, 'optimization': 48.99785590171814, 'imputation': 1.5881314277648926}}, '0.8': {'scores': {'RMSE': 1.1125302701236894, 'MAE': 0.8727960243823621, 'MI': 0.013958439365190834, 'CORRELATION': 0.16455618813674522}, 'times': {'contamination': 0.12213516235351562, 'optimization': 48.99785590171814, 'imputation': 2.0539379119873047}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.6263705795260325, 'MAE': 0.4548865753229437, 'MI': 0.781959674837021, 'CORRELATION': 0.7986062368219096}, 'times': {'contamination': 0.0018770694732666016, 'optimization': 2117.437706708908, 'imputation': 0.22168946266174316}}, '0.1': {'scores': {'RMSE': 0.6899987721177722, 'MAE': 0.5259878926891887, 'MI': 0.395810445074613, 'CORRELATION': 0.7477771679714831}, 'times': {'contamination': 0.0028429031372070312, 'optimization': 2117.437706708908, 'imputation': 1.3405146598815918}}, '0.2': {'scores': {'RMSE': 0.7621016037924634, 'MAE': 0.5758589580651329, 'MI': 0.24919261959916233, 'CORRELATION': 0.658146326506337}, 'times': {'contamination': 0.0066187381744384766, 'optimization': 2117.437706708908, 'imputation': 7.121232271194458}}, '0.4': {'scores': {'RMSE': 0.7902203838415963, 'MAE': 0.5922773198020501, 'MI': 0.19381374823819753, 'CORRELATION': 0.6157623089917651}, 'times': {'contamination': 0.023590564727783203, 'optimization': 2117.437706708908, 'imputation': 45.16994309425354}}, '0.6': {'scores': {'RMSE': 0.8606721167494161, 'MAE': 0.6509795391102093, 'MI': 0.14703461141268756, 'CORRELATION': 0.5349197031621258}, 'times': {'contamination': 0.05335497856140137, 'optimization': 2117.437706708908, 'imputation': 138.5317099094391}}, '0.8': {'scores': {'RMSE': 0.9473077321399332, 'MAE': 0.721873093140729, 'MI': 0.09210269321275755, 'CORRELATION': 0.41686255415646745}, 'times': {'contamination': 0.12050509452819824, 'optimization': 2117.437706708908, 'imputation': 309.835489988327}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.5720458604683403, 'MAE': 1.2772582907839167, 'MI': 0.42568306113018717, 'CORRELATION': -0.14831612460275406}, 'times': {'contamination': 0.0014629364013671875, 'optimization': 4468.6066954135895, 'imputation': 352.0414505004883}}, '0.1': {'scores': {'RMSE': 1.4907337776914031, 'MAE': 1.1877772537536995, 'MI': 0.14090901585882215, 'CORRELATION': 0.10008244436430952}, 'times': {'contamination': 0.002844095230102539, 'optimization': 4468.6066954135895, 'imputation': 361.1672565937042}}, '0.2': {'scores': {'RMSE': 1.402763495604196, 'MAE': 1.1258676418974762, 'MI': 0.025496459386318313, 'CORRELATION': -0.02806308194006537}, 'times': {'contamination': 0.006960391998291016, 'optimization': 4468.6066954135895, 'imputation': 359.7817280292511}}, '0.4': {'scores': {'RMSE': 1.3340271011920504, 'MAE': 1.0653643637835586, 'MI': 0.006026542304077175, 'CORRELATION': -0.018817794328124735}, 'times': {'contamination': 0.022516489028930664, 'optimization': 4468.6066954135895, 'imputation': 363.93244767189026}}, '0.6': {'scores': {'RMSE': 1.3622649743673116, 'MAE': 1.0917232146475109, 'MI': 0.003570086902367028, 'CORRELATION': -0.026435073387852663}, 'times': {'contamination': 0.053468942642211914, 'optimization': 4468.6066954135895, 'imputation': 359.8436996936798}}, '0.8': {'scores': {'RMSE': 1.3476859394569145, 'MAE': 1.077975225740552, 'MI': 0.002668480785992482, 'CORRELATION': -0.02083562688022463}, 'times': {'contamination': 0.12250685691833496, 'optimization': 4468.6066954135895, 'imputation': 360.8773157596588}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmriobjectviewing | mcar | mean | bayesian | 0.05 | 1.0389734217605486 | 0.8058577685345816 | 0.0 | 0 | 0.001322031021118164 sec | 0 sec| 0.0010623931884765625 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.1 | 1.039599691211445 | 0.8190561891835487 | 0.0 | 0 | 0.0029027462005615234 sec | 0 sec| 0.0006194114685058594 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.2 | 1.0062387656710172 | 0.7979296742837627 | 0.0 | 0 | 0.0067632198333740234 sec | 0 sec| 0.0006532669067382812 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.4 | 0.9883754343533185 | 0.7862876896101476 | 0.0 | 0 | 0.0221099853515625 sec | 0 sec| 0.0007634162902832031 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.6 | 0.987097660571777 | 0.7834652940902236 | 0.0 | 0 | 0.0535585880279541 sec | 0 sec| 0.0008707046508789062 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.8 | 0.9871215644673538 | 0.783016411575714 | 0.0 | 0 | 0.1221921443939209 sec | 0 sec| 0.0009012222290039062 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.05 | 0.7921010903466756 | 0.544583599318027 | 0.6452684348756488 | 0.6564536961355489 | 0.0013003349304199219 sec | 159.3788959980011 sec| 0.0172426700592041 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.1 | 0.8734423329225157 | 0.6770893621008395 | 0.17404003258531509 | 0.5463883586396225 | 0.006696939468383789 sec | 159.3788959980011 sec| 0.023551225662231445 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.2 | 0.8860045404559919 | 0.6822309993559906 | 0.13114386403484066 | 0.4879034991275287 | 0.01536703109741211 sec | 159.3788959980011 sec| 0.029254913330078125 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.4 | 0.85668086245811 | 0.6554946643451944 | 0.13136521095105114 | 0.512333042486155 | 0.049208879470825195 sec | 159.3788959980011 sec| 0.02578568458557129 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.6 | 0.8734554290811476 | 0.668388555663456 | 0.12207632358330225 | 0.4901910978698973 | 0.11803746223449707 sec | 159.3788959980011 sec| 0.01870417594909668 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.8 | 0.9101165604682941 | 0.7007441393931623 | 0.08721517971477473 | 0.424583398592384 | 0.1880052089691162 sec | 159.3788959980011 sec| 0.03435778617858887 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.05 | 0.8717933443365717 | 0.6555873875520205 | 0.5468713896761781 | 0.5360081770612317 | 0.002277374267578125 sec | 48.99785590171814 sec| 1.7287921905517578 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.1 | 0.8625085002829386 | 0.6689945733093743 | 0.2158458111360233 | 0.5598406577746278 | 0.0032448768615722656 sec | 48.99785590171814 sec| 1.7275073528289795 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.2 | 0.8974016981576581 | 0.693506918834922 | 0.12539695399359563 | 0.4696294419377184 | 0.007249355316162109 sec | 48.99785590171814 sec| 1.807462215423584 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.4 | 0.9058118302006622 | 0.7072376811266821 | 0.09089971471437183 | 0.4307173907497016 | 0.022536754608154297 sec | 48.99785590171814 sec| 2.028677225112915 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.6 | 0.9926298063877358 | 0.7768854416569236 | 0.040884571524434955 | 0.2900818700028841 | 0.054079294204711914 sec | 48.99785590171814 sec| 1.5881314277648926 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.8 | 1.1125302701236894 | 0.8727960243823621 | 0.013958439365190834 | 0.16455618813674522 | 0.12213516235351562 sec | 48.99785590171814 sec| 2.0539379119873047 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.05 | 0.6263705795260325 | 0.4548865753229437 | 0.781959674837021 | 0.7986062368219096 | 0.0018770694732666016 sec | 2117.437706708908 sec| 0.22168946266174316 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.1 | 0.6899987721177722 | 0.5259878926891887 | 0.395810445074613 | 0.7477771679714831 | 0.0028429031372070312 sec | 2117.437706708908 sec| 1.3405146598815918 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.2 | 0.7621016037924634 | 0.5758589580651329 | 0.24919261959916233 | 0.658146326506337 | 0.0066187381744384766 sec | 2117.437706708908 sec| 7.121232271194458 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.4 | 0.7902203838415963 | 0.5922773198020501 | 0.19381374823819753 | 0.6157623089917651 | 0.023590564727783203 sec | 2117.437706708908 sec| 45.16994309425354 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.6 | 0.8606721167494161 | 0.6509795391102093 | 0.14703461141268756 | 0.5349197031621258 | 0.05335497856140137 sec | 2117.437706708908 sec| 138.5317099094391 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.8 | 0.9473077321399332 | 0.721873093140729 | 0.09210269321275755 | 0.41686255415646745 | 0.12050509452819824 sec | 2117.437706708908 sec| 309.835489988327 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.05 | 1.5720458604683403 | 1.2772582907839167 | 0.42568306113018717 | -0.14831612460275406 | 0.0014629364013671875 sec | 4468.6066954135895 sec| 352.0414505004883 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.1 | 1.4907337776914031 | 1.1877772537536995 | 0.14090901585882215 | 0.10008244436430952 | 0.002844095230102539 sec | 4468.6066954135895 sec| 361.1672565937042 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.2 | 1.402763495604196 | 1.1258676418974762 | 0.025496459386318313 | -0.02806308194006537 | 0.006960391998291016 sec | 4468.6066954135895 sec| 359.7817280292511 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.4 | 1.3340271011920504 | 1.0653643637835586 | 0.006026542304077175 | -0.018817794328124735 | 0.022516489028930664 sec | 4468.6066954135895 sec| 363.93244767189026 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.6 | 1.3622649743673116 | 1.0917232146475109 | 0.003570086902367028 | -0.026435073387852663 | 0.053468942642211914 sec | 4468.6066954135895 sec| 359.8436996936798 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.8 | 1.3476859394569145 | 1.077975225740552 | 0.002668480785992482 | -0.02083562688022463 | 0.12250685691833496 sec | 4468.6066954135895 sec| 360.8773157596588 sec | diff --git a/imputegap/reports/report_02/report_fmri-stoptask.txt b/imputegap/reports/report_02/report_fmri-stoptask.txt new file mode 100644 index 0000000..1f51c00 --- /dev/null +++ b/imputegap/reports/report_02/report_fmri-stoptask.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmristoptask': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0591754233439183, 'MAE': 0.8811507908679529, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0017461776733398438, 'optimization': 0, 'imputation': 0.001100778579711914}}, '0.1': {'scores': {'RMSE': 0.9651108444122715, 'MAE': 0.784231196318496, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0038170814514160156, 'optimization': 0, 'imputation': 0.0006277561187744141}}, '0.2': {'scores': {'RMSE': 0.9932773680676918, 'MAE': 0.8034395750738844, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009348630905151367, 'optimization': 0, 'imputation': 0.0006661415100097656}}, '0.4': {'scores': {'RMSE': 1.0058748440484344, 'MAE': 0.8113341021149199, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.03160667419433594, 'optimization': 0, 'imputation': 0.0009412765502929688}}, '0.6': {'scores': {'RMSE': 0.9944066185522102, 'MAE': 0.8023296982336051, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.07952380180358887, 'optimization': 0, 'imputation': 0.001110076904296875}}, '0.8': {'scores': {'RMSE': 0.9979990505486313, 'MAE': 0.8062359186814159, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.18988037109375, 'optimization': 0, 'imputation': 0.0012199878692626953}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0815739858856455, 'MAE': 0.8947163048898044, 'MI': 0.23576973507164212, 'CORRELATION': -0.12274682282048005}, 'times': {'contamination': 0.00153350830078125, 'optimization': 222.17338752746582, 'imputation': 0.007252931594848633}}, '0.1': {'scores': {'RMSE': 0.9695699729418912, 'MAE': 0.7898385707592198, 'MI': 0.06571976951128125, 'CORRELATION': 0.016476991654415008}, 'times': {'contamination': 0.00762939453125, 'optimization': 222.17338752746582, 'imputation': 0.006178379058837891}}, '0.2': {'scores': {'RMSE': 1.0023712131611957, 'MAE': 0.8108602788128816, 'MI': 0.02538765630290373, 'CORRELATION': -0.016656543511887868}, 'times': {'contamination': 0.020302534103393555, 'optimization': 222.17338752746582, 'imputation': 0.006856203079223633}}, '0.4': {'scores': {'RMSE': 1.0138537110215022, 'MAE': 0.8167419153197173, 'MI': 0.0038274804707874484, 'CORRELATION': 0.002717578068034049}, 'times': {'contamination': 0.07085132598876953, 'optimization': 222.17338752746582, 'imputation': 0.006796836853027344}}, '0.6': {'scores': {'RMSE': 1.0022937958385385, 'MAE': 0.807293318305244, 'MI': 0.0018376453669024168, 'CORRELATION': 0.004596695453371254}, 'times': {'contamination': 0.14228367805480957, 'optimization': 222.17338752746582, 'imputation': 0.006799221038818359}}, '0.8': {'scores': {'RMSE': 1.0104537937047533, 'MAE': 0.8149091851781165, 'MI': 0.0008945376054130945, 'CORRELATION': -0.0013082054469119196}, 'times': {'contamination': 0.24705862998962402, 'optimization': 222.17338752746582, 'imputation': 0.005573272705078125}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1715750158207363, 'MAE': 0.9389573934580852, 'MI': 0.30612963701823526, 'CORRELATION': -0.22056411372111834}, 'times': {'contamination': 0.002927064895629883, 'optimization': 109.24887442588806, 'imputation': 10.111968994140625}}, '0.1': {'scores': {'RMSE': 1.0588476372168147, 'MAE': 0.8437403156914149, 'MI': 0.08955991417984446, 'CORRELATION': -0.1963089605999627}, 'times': {'contamination': 0.0034782886505126953, 'optimization': 109.24887442588806, 'imputation': 10.12447738647461}}, '0.2': {'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009154081344604492, 'optimization': 109.24887442588806, 'imputation': 10.325854778289795}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.031117677688598633, 'optimization': 109.24887442588806, 'imputation': 11.087183237075806}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07905244827270508, 'optimization': 109.24887442588806, 'imputation': 8.649941444396973}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18860864639282227, 'optimization': 109.24887442588806, 'imputation': 8.43183708190918}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0692148314478316, 'MAE': 0.873400733402723, 'MI': 0.2787388945371119, 'CORRELATION': -0.02021145481191946}, 'times': {'contamination': 0.0014863014221191406, 'optimization': 5088.581882238388, 'imputation': 10.688252687454224}}, '0.1': {'scores': {'RMSE': 0.9719895445677292, 'MAE': 0.7851843420896756, 'MI': 0.0830808565046283, 'CORRELATION': 0.003268635254181307}, 'times': {'contamination': 0.0037031173706054688, 'optimization': 5088.581882238388, 'imputation': 50.06313109397888}}, '0.2': {'scores': {'RMSE': 0.99753636840165, 'MAE': 0.8012616128674659, 'MI': 0.019093143495502334, 'CORRELATION': 0.02540361203010324}, 'times': {'contamination': 0.00922083854675293, 'optimization': 5088.581882238388, 'imputation': 257.213321685791}}, '0.4': {'scores': {'RMSE': 1.0155975152475738, 'MAE': 0.8140496119700683, 'MI': 0.004260439955627443, 'CORRELATION': 0.0006423716677864647}, 'times': {'contamination': 0.03141498565673828, 'optimization': 5088.581882238388, 'imputation': 1488.7819337844849}}, '0.6': {'scores': {'RMSE': 1.0040752264526889, 'MAE': 0.8052914143043017, 'MI': 0.0018099723977603893, 'CORRELATION': -0.006621752869444718}, 'times': {'contamination': 0.07847213745117188, 'optimization': 5088.581882238388, 'imputation': 4525.959330558777}}, '0.8': {'scores': {'RMSE': 1.0078811833781343, 'MAE': 0.8090736592195691, 'MI': 0.001033941419470956, 'CORRELATION': -0.003099173821807945}, 'times': {'contamination': 0.18671298027038574, 'optimization': 5088.581882238388, 'imputation': 9460.7878510952}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.122220535003296, 'MAE': 0.9644508995813553, 'MI': 0.2759436355942961, 'CORRELATION': 0.09245761750327637}, 'times': {'contamination': 0.0015985965728759766, 'optimization': 4112.733412027359, 'imputation': 338.0099182128906}}, '0.1': {'scores': {'RMSE': 1.0832970108643896, 'MAE': 0.8823888940960694, 'MI': 0.0722893609050923, 'CORRELATION': -0.019930274489311815}, 'times': {'contamination': 0.0035643577575683594, 'optimization': 4112.733412027359, 'imputation': 337.6157658100128}}, '0.2': {'scores': {'RMSE': 1.0767155565632924, 'MAE': 0.8684991669552922, 'MI': 0.009245255133377466, 'CORRELATION': 0.0027516812337193518}, 'times': {'contamination': 0.009638309478759766, 'optimization': 4112.733412027359, 'imputation': 328.19135212898254}}, '0.4': {'scores': {'RMSE': 1.0934522863869605, 'MAE': 0.8840570779852788, 'MI': 0.003369568798431563, 'CORRELATION': -0.021061682051014274}, 'times': {'contamination': 0.03281116485595703, 'optimization': 4112.733412027359, 'imputation': 346.8224673271179}}, '0.6': {'scores': {'RMSE': 1.0783671319985777, 'MAE': 0.8704278560665365, 'MI': 0.00169355769499049, 'CORRELATION': -0.019325646685601}, 'times': {'contamination': 0.08042550086975098, 'optimization': 4112.733412027359, 'imputation': 340.2620213031769}}, '0.8': {'scores': {'RMSE': 1.081513280302422, 'MAE': 0.8746519908670293, 'MI': 0.0011728245783709944, 'CORRELATION': -0.016826349565356294}, 'times': {'contamination': 0.18836283683776855, 'optimization': 4112.733412027359, 'imputation': 341.4021186828613}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmristoptask | mcar | mean | bayesian | 0.05 | 1.0591754233439183 | 0.8811507908679529 | 0.0 | 0 | 0.0017461776733398438 sec | 0 sec| 0.001100778579711914 sec | +| fmristoptask | mcar | mean | bayesian | 0.1 | 0.9651108444122715 | 0.784231196318496 | 0.0 | 0 | 0.0038170814514160156 sec | 0 sec| 0.0006277561187744141 sec | +| fmristoptask | mcar | mean | bayesian | 0.2 | 0.9932773680676918 | 0.8034395750738844 | 0.0 | 0 | 0.009348630905151367 sec | 0 sec| 0.0006661415100097656 sec | +| fmristoptask | mcar | mean | bayesian | 0.4 | 1.0058748440484344 | 0.8113341021149199 | 0.0 | 0 | 0.03160667419433594 sec | 0 sec| 0.0009412765502929688 sec | +| fmristoptask | mcar | mean | bayesian | 0.6 | 0.9944066185522102 | 0.8023296982336051 | 0.0 | 0 | 0.07952380180358887 sec | 0 sec| 0.001110076904296875 sec | +| fmristoptask | mcar | mean | bayesian | 0.8 | 0.9979990505486313 | 0.8062359186814159 | 0.0 | 0 | 0.18988037109375 sec | 0 sec| 0.0012199878692626953 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.05 | 1.0815739858856455 | 0.8947163048898044 | 0.23576973507164212 | -0.12274682282048005 | 0.00153350830078125 sec | 222.17338752746582 sec| 0.007252931594848633 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.1 | 0.9695699729418912 | 0.7898385707592198 | 0.06571976951128125 | 0.016476991654415008 | 0.00762939453125 sec | 222.17338752746582 sec| 0.006178379058837891 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.2 | 1.0023712131611957 | 0.8108602788128816 | 0.02538765630290373 | -0.016656543511887868 | 0.020302534103393555 sec | 222.17338752746582 sec| 0.006856203079223633 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.4 | 1.0138537110215022 | 0.8167419153197173 | 0.0038274804707874484 | 0.002717578068034049 | 0.07085132598876953 sec | 222.17338752746582 sec| 0.006796836853027344 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.6 | 1.0022937958385385 | 0.807293318305244 | 0.0018376453669024168 | 0.004596695453371254 | 0.14228367805480957 sec | 222.17338752746582 sec| 0.006799221038818359 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.8 | 1.0104537937047533 | 0.8149091851781165 | 0.0008945376054130945 | -0.0013082054469119196 | 0.24705862998962402 sec | 222.17338752746582 sec| 0.005573272705078125 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.05 | 1.1715750158207363 | 0.9389573934580852 | 0.30612963701823526 | -0.22056411372111834 | 0.002927064895629883 sec | 109.24887442588806 sec| 10.111968994140625 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.1 | 1.0588476372168147 | 0.8437403156914149 | 0.08955991417984446 | -0.1963089605999627 | 0.0034782886505126953 sec | 109.24887442588806 sec| 10.12447738647461 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.2 | 1.0391969620815695 | 0.8364861943065512 | 0.02582105408815175 | -0.09232453336176588 | 0.009154081344604492 sec | 109.24887442588806 sec| 10.325854778289795 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.4 | 1.0340455393837413 | 0.832400199311948 | 0.00520789381175344 | -0.04499260926820861 | 0.031117677688598633 sec | 109.24887442588806 sec| 11.087183237075806 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.6 | 4.011139383889788 | 3.152797499531786 | 0.003672509477371519 | -0.05413975121078511 | 0.07905244827270508 sec | 109.24887442588806 sec| 8.649941444396973 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.8 | 2.97893158705676 | 1.0602936132635719 | 0.00079094933311715 | 0.006947773983399647 | 0.18860864639282227 sec | 109.24887442588806 sec| 8.43183708190918 sec | +| fmristoptask | mcar | iim | bayesian | 0.05 | 1.0692148314478316 | 0.873400733402723 | 0.2787388945371119 | -0.02021145481191946 | 0.0014863014221191406 sec | 5088.581882238388 sec| 10.688252687454224 sec | +| fmristoptask | mcar | iim | bayesian | 0.1 | 0.9719895445677292 | 0.7851843420896756 | 0.0830808565046283 | 0.003268635254181307 | 0.0037031173706054688 sec | 5088.581882238388 sec| 50.06313109397888 sec | +| fmristoptask | mcar | iim | bayesian | 0.2 | 0.99753636840165 | 0.8012616128674659 | 0.019093143495502334 | 0.02540361203010324 | 0.00922083854675293 sec | 5088.581882238388 sec| 257.213321685791 sec | +| fmristoptask | mcar | iim | bayesian | 0.4 | 1.0155975152475738 | 0.8140496119700683 | 0.004260439955627443 | 0.0006423716677864647 | 0.03141498565673828 sec | 5088.581882238388 sec| 1488.7819337844849 sec | +| fmristoptask | mcar | iim | bayesian | 0.6 | 1.0040752264526889 | 0.8052914143043017 | 0.0018099723977603893 | -0.006621752869444718 | 0.07847213745117188 sec | 5088.581882238388 sec| 4525.959330558777 sec | +| fmristoptask | mcar | iim | bayesian | 0.8 | 1.0078811833781343 | 0.8090736592195691 | 0.001033941419470956 | -0.003099173821807945 | 0.18671298027038574 sec | 5088.581882238388 sec| 9460.7878510952 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.05 | 1.122220535003296 | 0.9644508995813553 | 0.2759436355942961 | 0.09245761750327637 | 0.0015985965728759766 sec | 4112.733412027359 sec| 338.0099182128906 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.1 | 1.0832970108643896 | 0.8823888940960694 | 0.0722893609050923 | -0.019930274489311815 | 0.0035643577575683594 sec | 4112.733412027359 sec| 337.6157658100128 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.2 | 1.0767155565632924 | 0.8684991669552922 | 0.009245255133377466 | 0.0027516812337193518 | 0.009638309478759766 sec | 4112.733412027359 sec| 328.19135212898254 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.4 | 1.0934522863869605 | 0.8840570779852788 | 0.003369568798431563 | -0.021061682051014274 | 0.03281116485595703 sec | 4112.733412027359 sec| 346.8224673271179 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.6 | 1.0783671319985777 | 0.8704278560665365 | 0.00169355769499049 | -0.019325646685601 | 0.08042550086975098 sec | 4112.733412027359 sec| 340.2620213031769 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.8 | 1.081513280302422 | 0.8746519908670293 | 0.0011728245783709944 | -0.016826349565356294 | 0.18836283683776855 sec | 4112.733412027359 sec| 341.4021186828613 sec | diff --git a/imputegap/reports/report_03/fmriobjectviewing_mcar_CORRELATION.jpg 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a/imputegap/reports/report_03/report_fmri-objectviewing.txt b/imputegap/reports/report_03/report_fmri-objectviewing.txt new file mode 100644 index 0000000..b7de1b5 --- /dev/null +++ b/imputegap/reports/report_03/report_fmri-objectviewing.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmriobjectviewing': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0389734217605486, 'MAE': 0.8058577685345816, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0013332366943359375, 'optimization': 0, 'imputation': 0.0010461807250976562}}, '0.1': {'scores': {'RMSE': 1.039599691211445, 'MAE': 0.8190561891835487, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0028600692749023438, 'optimization': 0, 'imputation': 0.0005738735198974609}}, '0.2': {'scores': {'RMSE': 1.0062387656710172, 'MAE': 0.7979296742837627, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.006735324859619141, 'optimization': 0, 'imputation': 0.0006284713745117188}}, '0.4': {'scores': {'RMSE': 0.9883754343533185, 'MAE': 0.7862876896101476, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.025367259979248047, 'optimization': 0, 'imputation': 0.0009474754333496094}}, '0.6': {'scores': {'RMSE': 0.987097660571777, 'MAE': 0.7834652940902236, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.054114341735839844, 'optimization': 0, 'imputation': 0.0008347034454345703}}, '0.8': {'scores': {'RMSE': 0.9871215644673538, 'MAE': 0.783016411575714, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1226034164428711, 'optimization': 0, 'imputation': 0.0008599758148193359}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7921010903466756, 'MAE': 0.544583599318027, 'MI': 0.6452684348756488, 'CORRELATION': 0.6564536961355489}, 'times': {'contamination': 0.0011966228485107422, 'optimization': 157.82238936424255, 'imputation': 0.017590761184692383}}, '0.1': {'scores': {'RMSE': 0.8734423329225157, 'MAE': 0.6770893621008395, 'MI': 0.17404003258531509, 'CORRELATION': 0.5463883586396225}, 'times': {'contamination': 0.006558895111083984, 'optimization': 157.82238936424255, 'imputation': 0.023885011672973633}}, '0.2': {'scores': {'RMSE': 0.8860045404559919, 'MAE': 0.6822309993559906, 'MI': 0.13114386403484066, 'CORRELATION': 0.4879034991275287}, 'times': {'contamination': 0.015262842178344727, 'optimization': 157.82238936424255, 'imputation': 0.029367446899414062}}, '0.4': {'scores': {'RMSE': 0.85668086245811, 'MAE': 0.6554946643451944, 'MI': 0.13136521095105114, 'CORRELATION': 0.512333042486155}, 'times': {'contamination': 0.04946470260620117, 'optimization': 157.82238936424255, 'imputation': 0.025765419006347656}}, '0.6': {'scores': {'RMSE': 0.8734554290811476, 'MAE': 0.668388555663456, 'MI': 0.12207632358330225, 'CORRELATION': 0.4901910978698973}, 'times': {'contamination': 0.11716961860656738, 'optimization': 157.82238936424255, 'imputation': 0.029912710189819336}}, '0.8': {'scores': {'RMSE': 0.9101165604682941, 'MAE': 0.7007441393931623, 'MI': 0.08721517971477473, 'CORRELATION': 0.424583398592384}, 'times': {'contamination': 0.18310046195983887, 'optimization': 157.82238936424255, 'imputation': 0.034597158432006836}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.8717933443365717, 'MAE': 0.6555873875520205, 'MI': 0.5468713896761781, 'CORRELATION': 0.5360081770612317}, 'times': {'contamination': 0.0025048255920410156, 'optimization': 49.13337993621826, 'imputation': 1.7170403003692627}}, '0.1': {'scores': {'RMSE': 0.8625085002829386, 'MAE': 0.6689945733093743, 'MI': 0.2158458111360233, 'CORRELATION': 0.5598406577746278}, 'times': {'contamination': 0.002758026123046875, 'optimization': 49.13337993621826, 'imputation': 1.782259225845337}}, '0.2': {'scores': {'RMSE': 0.8974016981576581, 'MAE': 0.693506918834922, 'MI': 0.12539695399359563, 'CORRELATION': 0.4696294419377184}, 'times': {'contamination': 0.006672382354736328, 'optimization': 49.13337993621826, 'imputation': 1.8251664638519287}}, '0.4': {'scores': {'RMSE': 0.9058118302006622, 'MAE': 0.7072376811266821, 'MI': 0.09089971471437183, 'CORRELATION': 0.4307173907497016}, 'times': {'contamination': 0.02189946174621582, 'optimization': 49.13337993621826, 'imputation': 2.061220169067383}}, '0.6': {'scores': {'RMSE': 0.9926298063877358, 'MAE': 0.7768854416569236, 'MI': 0.040884571524434955, 'CORRELATION': 0.2900818700028841}, 'times': {'contamination': 0.05326199531555176, 'optimization': 49.13337993621826, 'imputation': 1.6660246849060059}}, '0.8': {'scores': {'RMSE': 1.1125302701236894, 'MAE': 0.8727960243823621, 'MI': 0.013958439365190834, 'CORRELATION': 0.16455618813674522}, 'times': {'contamination': 0.1214754581451416, 'optimization': 49.13337993621826, 'imputation': 2.0595123767852783}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.6263705795260325, 'MAE': 0.4548865753229437, 'MI': 0.781959674837021, 'CORRELATION': 0.7986062368219096}, 'times': {'contamination': 0.0012001991271972656, 'optimization': 2115.569543361664, 'imputation': 0.22057271003723145}}, '0.1': {'scores': {'RMSE': 0.6899987721177722, 'MAE': 0.5259878926891887, 'MI': 0.395810445074613, 'CORRELATION': 0.7477771679714831}, 'times': {'contamination': 0.0028693675994873047, 'optimization': 2115.569543361664, 'imputation': 1.332841396331787}}, '0.2': {'scores': {'RMSE': 0.7621016037924634, 'MAE': 0.5758589580651329, 'MI': 0.24919261959916233, 'CORRELATION': 0.658146326506337}, 'times': {'contamination': 0.0066070556640625, 'optimization': 2115.569543361664, 'imputation': 6.977942943572998}}, '0.4': {'scores': {'RMSE': 0.7902203838415963, 'MAE': 0.5922773198020501, 'MI': 0.19381374823819753, 'CORRELATION': 0.6157623089917651}, 'times': {'contamination': 0.023341894149780273, 'optimization': 2115.569543361664, 'imputation': 45.052905321121216}}, '0.6': {'scores': {'RMSE': 0.8606721167494161, 'MAE': 0.6509795391102093, 'MI': 0.14703461141268756, 'CORRELATION': 0.5349197031621258}, 'times': {'contamination': 0.053314924240112305, 'optimization': 2115.569543361664, 'imputation': 137.877295255661}}, '0.8': {'scores': {'RMSE': 0.9473077321399332, 'MAE': 0.721873093140729, 'MI': 0.09210269321275755, 'CORRELATION': 0.41686255415646745}, 'times': {'contamination': 0.12127208709716797, 'optimization': 2115.569543361664, 'imputation': 309.8284556865692}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.6414396019640038, 'MAE': 1.3240559958757634, 'MI': 0.5559452374102188, 'CORRELATION': -0.019190710334023774}, 'times': {'contamination': 0.001463174819946289, 'optimization': 4286.787290811539, 'imputation': 146.20701241493225}}, '0.1': {'scores': {'RMSE': 1.4931325738251233, 'MAE': 1.2291481963023954, 'MI': 0.10612382874060908, 'CORRELATION': 0.08822883294793381}, 'times': {'contamination': 0.003063201904296875, 'optimization': 4286.787290811539, 'imputation': 145.1298749446869}}, '0.2': {'scores': {'RMSE': 1.3592271642125449, 'MAE': 1.1023068858542104, 'MI': 0.031374496439453406, 'CORRELATION': 0.04531586048012379}, 'times': {'contamination': 0.00700068473815918, 'optimization': 4286.787290811539, 'imputation': 145.86979150772095}}, '0.4': {'scores': {'RMSE': 1.5155884162145739, 'MAE': 1.2095557823362952, 'MI': 0.007762134072031226, 'CORRELATION': -0.01994479803059748}, 'times': {'contamination': 0.022418737411499023, 'optimization': 4286.787290811539, 'imputation': 142.07973980903625}}, '0.6': {'scores': {'RMSE': 1.4205010123384363, 'MAE': 1.140500261582132, 'MI': 0.004244506579222641, 'CORRELATION': -0.017115141060066015}, 'times': {'contamination': 0.05402565002441406, 'optimization': 4286.787290811539, 'imputation': 139.75832986831665}}, '0.8': {'scores': {'RMSE': 1.4393703997870884, 'MAE': 1.1419154482992642, 'MI': 0.0026830949612693445, 'CORRELATION': -0.012083949814718867}, 'times': {'contamination': 0.12264132499694824, 'optimization': 4286.787290811539, 'imputation': 144.70407223701477}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmriobjectviewing | mcar | mean | bayesian | 0.05 | 1.0389734217605486 | 0.8058577685345816 | 0.0 | 0 | 0.0013332366943359375 sec | 0 sec| 0.0010461807250976562 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.1 | 1.039599691211445 | 0.8190561891835487 | 0.0 | 0 | 0.0028600692749023438 sec | 0 sec| 0.0005738735198974609 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.2 | 1.0062387656710172 | 0.7979296742837627 | 0.0 | 0 | 0.006735324859619141 sec | 0 sec| 0.0006284713745117188 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.4 | 0.9883754343533185 | 0.7862876896101476 | 0.0 | 0 | 0.025367259979248047 sec | 0 sec| 0.0009474754333496094 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.6 | 0.987097660571777 | 0.7834652940902236 | 0.0 | 0 | 0.054114341735839844 sec | 0 sec| 0.0008347034454345703 sec | +| fmriobjectviewing | mcar | mean | bayesian | 0.8 | 0.9871215644673538 | 0.783016411575714 | 0.0 | 0 | 0.1226034164428711 sec | 0 sec| 0.0008599758148193359 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.05 | 0.7921010903466756 | 0.544583599318027 | 0.6452684348756488 | 0.6564536961355489 | 0.0011966228485107422 sec | 157.82238936424255 sec| 0.017590761184692383 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.1 | 0.8734423329225157 | 0.6770893621008395 | 0.17404003258531509 | 0.5463883586396225 | 0.006558895111083984 sec | 157.82238936424255 sec| 0.023885011672973633 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.2 | 0.8860045404559919 | 0.6822309993559906 | 0.13114386403484066 | 0.4879034991275287 | 0.015262842178344727 sec | 157.82238936424255 sec| 0.029367446899414062 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.4 | 0.85668086245811 | 0.6554946643451944 | 0.13136521095105114 | 0.512333042486155 | 0.04946470260620117 sec | 157.82238936424255 sec| 0.025765419006347656 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.6 | 0.8734554290811476 | 0.668388555663456 | 0.12207632358330225 | 0.4901910978698973 | 0.11716961860656738 sec | 157.82238936424255 sec| 0.029912710189819336 sec | +| fmriobjectviewing | mcar | cdrec | bayesian | 0.8 | 0.9101165604682941 | 0.7007441393931623 | 0.08721517971477473 | 0.424583398592384 | 0.18310046195983887 sec | 157.82238936424255 sec| 0.034597158432006836 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.05 | 0.8717933443365717 | 0.6555873875520205 | 0.5468713896761781 | 0.5360081770612317 | 0.0025048255920410156 sec | 49.13337993621826 sec| 1.7170403003692627 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.1 | 0.8625085002829386 | 0.6689945733093743 | 0.2158458111360233 | 0.5598406577746278 | 0.002758026123046875 sec | 49.13337993621826 sec| 1.782259225845337 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.2 | 0.8974016981576581 | 0.693506918834922 | 0.12539695399359563 | 0.4696294419377184 | 0.006672382354736328 sec | 49.13337993621826 sec| 1.8251664638519287 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.4 | 0.9058118302006622 | 0.7072376811266821 | 0.09089971471437183 | 0.4307173907497016 | 0.02189946174621582 sec | 49.13337993621826 sec| 2.061220169067383 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.6 | 0.9926298063877358 | 0.7768854416569236 | 0.040884571524434955 | 0.2900818700028841 | 0.05326199531555176 sec | 49.13337993621826 sec| 1.6660246849060059 sec | +| fmriobjectviewing | mcar | stmvl | bayesian | 0.8 | 1.1125302701236894 | 0.8727960243823621 | 0.013958439365190834 | 0.16455618813674522 | 0.1214754581451416 sec | 49.13337993621826 sec| 2.0595123767852783 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.05 | 0.6263705795260325 | 0.4548865753229437 | 0.781959674837021 | 0.7986062368219096 | 0.0012001991271972656 sec | 2115.569543361664 sec| 0.22057271003723145 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.1 | 0.6899987721177722 | 0.5259878926891887 | 0.395810445074613 | 0.7477771679714831 | 0.0028693675994873047 sec | 2115.569543361664 sec| 1.332841396331787 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.2 | 0.7621016037924634 | 0.5758589580651329 | 0.24919261959916233 | 0.658146326506337 | 0.0066070556640625 sec | 2115.569543361664 sec| 6.977942943572998 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.4 | 0.7902203838415963 | 0.5922773198020501 | 0.19381374823819753 | 0.6157623089917651 | 0.023341894149780273 sec | 2115.569543361664 sec| 45.052905321121216 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.6 | 0.8606721167494161 | 0.6509795391102093 | 0.14703461141268756 | 0.5349197031621258 | 0.053314924240112305 sec | 2115.569543361664 sec| 137.877295255661 sec | +| fmriobjectviewing | mcar | iim | bayesian | 0.8 | 0.9473077321399332 | 0.721873093140729 | 0.09210269321275755 | 0.41686255415646745 | 0.12127208709716797 sec | 2115.569543361664 sec| 309.8284556865692 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.05 | 1.6414396019640038 | 1.3240559958757634 | 0.5559452374102188 | -0.019190710334023774 | 0.001463174819946289 sec | 4286.787290811539 sec| 146.20701241493225 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.1 | 1.4931325738251233 | 1.2291481963023954 | 0.10612382874060908 | 0.08822883294793381 | 0.003063201904296875 sec | 4286.787290811539 sec| 145.1298749446869 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.2 | 1.3592271642125449 | 1.1023068858542104 | 0.031374496439453406 | 0.04531586048012379 | 0.00700068473815918 sec | 4286.787290811539 sec| 145.86979150772095 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.4 | 1.5155884162145739 | 1.2095557823362952 | 0.007762134072031226 | -0.01994479803059748 | 0.022418737411499023 sec | 4286.787290811539 sec| 142.07973980903625 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.6 | 1.4205010123384363 | 1.140500261582132 | 0.004244506579222641 | -0.017115141060066015 | 0.05402565002441406 sec | 4286.787290811539 sec| 139.75832986831665 sec | +| fmriobjectviewing | mcar | mrnn | bayesian | 0.8 | 1.4393703997870884 | 1.1419154482992642 | 0.0026830949612693445 | -0.012083949814718867 | 0.12264132499694824 sec | 4286.787290811539 sec| 144.70407223701477 sec | diff --git a/imputegap/reports/report_03/report_fmri-stoptask.txt b/imputegap/reports/report_03/report_fmri-stoptask.txt new file mode 100644 index 0000000..44e33ac --- /dev/null +++ b/imputegap/reports/report_03/report_fmri-stoptask.txt @@ -0,0 +1,33 @@ +dictionary of results : {'fmristoptask': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0591754233439183, 'MAE': 0.8811507908679529, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001561880111694336, 'optimization': 0, 'imputation': 0.0010650157928466797}}, '0.1': {'scores': {'RMSE': 0.9651108444122715, 'MAE': 0.784231196318496, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0035762786865234375, 'optimization': 0, 'imputation': 0.0006108283996582031}}, '0.2': {'scores': {'RMSE': 0.9932773680676918, 'MAE': 0.8034395750738844, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009912252426147461, 'optimization': 0, 'imputation': 0.000682830810546875}}, '0.4': {'scores': {'RMSE': 1.0058748440484344, 'MAE': 0.8113341021149199, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.033663034439086914, 'optimization': 0, 'imputation': 0.0008401870727539062}}, '0.6': {'scores': {'RMSE': 0.9944066185522102, 'MAE': 0.8023296982336051, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.08425664901733398, 'optimization': 0, 'imputation': 0.0010020732879638672}}, '0.8': {'scores': {'RMSE': 0.9979990505486313, 'MAE': 0.8062359186814159, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1884922981262207, 'optimization': 0, 'imputation': 0.0009903907775878906}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0815739858856455, 'MAE': 0.8947163048898044, 'MI': 0.23576973507164212, 'CORRELATION': -0.12274682282048005}, 'times': {'contamination': 0.0014843940734863281, 'optimization': 216.33094692230225, 'imputation': 0.006989240646362305}}, '0.1': {'scores': {'RMSE': 0.9695699729418912, 'MAE': 0.7898385707592198, 'MI': 0.06571976951128125, 'CORRELATION': 0.016476991654415008}, 'times': {'contamination': 0.008179664611816406, 'optimization': 216.33094692230225, 'imputation': 0.0062677860260009766}}, '0.2': {'scores': {'RMSE': 1.0023712131611957, 'MAE': 0.8108602788128816, 'MI': 0.02538765630290373, 'CORRELATION': -0.016656543511887868}, 'times': {'contamination': 0.02096843719482422, 'optimization': 216.33094692230225, 'imputation': 0.006853580474853516}}, '0.4': {'scores': {'RMSE': 1.0138537110215022, 'MAE': 0.8167419153197173, 'MI': 0.0038274804707874484, 'CORRELATION': 0.002717578068034049}, 'times': {'contamination': 0.07195258140563965, 'optimization': 216.33094692230225, 'imputation': 0.00666499137878418}}, '0.6': {'scores': {'RMSE': 1.0022937958385385, 'MAE': 0.807293318305244, 'MI': 0.0018376453669024168, 'CORRELATION': 0.004596695453371254}, 'times': {'contamination': 0.14317655563354492, 'optimization': 216.33094692230225, 'imputation': 0.006315708160400391}}, '0.8': {'scores': {'RMSE': 1.0104537937047533, 'MAE': 0.8149091851781165, 'MI': 0.0008945376054130945, 'CORRELATION': -0.0013082054469119196}, 'times': {'contamination': 0.2480306625366211, 'optimization': 216.33094692230225, 'imputation': 0.005487203598022461}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1715750158207363, 'MAE': 0.9389573934580852, 'MI': 0.30612963701823526, 'CORRELATION': -0.22056411372111834}, 'times': {'contamination': 0.0031473636627197266, 'optimization': 110.04800581932068, 'imputation': 10.122947692871094}}, '0.1': {'scores': {'RMSE': 1.0588476372168147, 'MAE': 0.8437403156914149, 'MI': 0.08955991417984446, 'CORRELATION': -0.1963089605999627}, 'times': {'contamination': 0.003419160842895508, 'optimization': 110.04800581932068, 'imputation': 10.181205034255981}}, '0.2': {'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009185314178466797, 'optimization': 110.04800581932068, 'imputation': 10.448293685913086}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.030958890914916992, 'optimization': 110.04800581932068, 'imputation': 11.198593139648438}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07897067070007324, 'optimization': 110.04800581932068, 'imputation': 8.581665992736816}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18915915489196777, 'optimization': 110.04800581932068, 'imputation': 8.440712690353394}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0692148314478316, 'MAE': 0.873400733402723, 'MI': 0.2787388945371119, 'CORRELATION': -0.02021145481191946}, 'times': {'contamination': 0.0015034675598144531, 'optimization': 5124.588714838028, 'imputation': 10.759928226470947}}, '0.1': {'scores': {'RMSE': 0.9719895445677292, 'MAE': 0.7851843420896756, 'MI': 0.0830808565046283, 'CORRELATION': 0.003268635254181307}, 'times': {'contamination': 0.003936767578125, 'optimization': 5124.588714838028, 'imputation': 50.354418992996216}}, '0.2': {'scores': {'RMSE': 0.99753636840165, 'MAE': 0.8012616128674659, 'MI': 0.019093143495502334, 'CORRELATION': 0.02540361203010324}, 'times': {'contamination': 0.009255409240722656, 'optimization': 5124.588714838028, 'imputation': 259.3400568962097}}, '0.4': {'scores': {'RMSE': 1.0155975152475738, 'MAE': 0.8140496119700683, 'MI': 0.004260439955627443, 'CORRELATION': 0.0006423716677864647}, 'times': {'contamination': 0.0312647819519043, 'optimization': 5124.588714838028, 'imputation': 1500.3178548812866}}, '0.6': {'scores': {'RMSE': 1.0040752264526889, 'MAE': 0.8052914143043017, 'MI': 0.0018099723977603893, 'CORRELATION': -0.006621752869444718}, 'times': {'contamination': 0.07852554321289062, 'optimization': 5124.588714838028, 'imputation': 4581.28284406662}}, '0.8': {'scores': {'RMSE': 1.0078811833781343, 'MAE': 0.8090736592195691, 'MI': 0.001033941419470956, 'CORRELATION': -0.003099173821807945}, 'times': {'contamination': 0.18776154518127441, 'optimization': 5124.588714838028, 'imputation': 9590.927385091782}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.146433389804167, 'MAE': 0.9770400477715633, 'MI': 0.3372765709259859, 'CORRELATION': 0.0330859633180261}, 'times': {'contamination': 0.001608133316040039, 'optimization': 4109.78501701355, 'imputation': 347.9514887332916}}, '0.1': {'scores': {'RMSE': 1.0805589422598818, 'MAE': 0.8789487774083494, 'MI': 0.06450452519706741, 'CORRELATION': 0.0050948685955938995}, 'times': {'contamination': 0.0037963390350341797, 'optimization': 4109.78501701355, 'imputation': 342.1326117515564}}, '0.2': {'scores': {'RMSE': 1.113302451577659, 'MAE': 0.8972310309254206, 'MI': 0.013539230335286593, 'CORRELATION': -0.010746184336502297}, 'times': {'contamination': 0.010583162307739258, 'optimization': 4109.78501701355, 'imputation': 347.8061354160309}}, '0.4': {'scores': {'RMSE': 1.1059062825212693, 'MAE': 0.8920096539260874, 'MI': 0.0039427922204060845, 'CORRELATION': -0.021280076256874978}, 'times': {'contamination': 0.03199410438537598, 'optimization': 4109.78501701355, 'imputation': 351.9458327293396}}, '0.6': {'scores': {'RMSE': 1.0740866766668984, 'MAE': 0.8664850080628724, 'MI': 0.0015316126887234942, 'CORRELATION': -0.021487493774034198}, 'times': {'contamination': 0.08084416389465332, 'optimization': 4109.78501701355, 'imputation': 349.9893400669098}}, '0.8': {'scores': {'RMSE': 1.075891210325233, 'MAE': 0.8695393935351904, 'MI': 0.0011319165672490211, 'CORRELATION': -0.017885852991857847}, 'times': {'contamination': 0.19720029830932617, 'optimization': 4109.78501701355, 'imputation': 349.96222448349}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| fmristoptask | mcar | mean | bayesian | 0.05 | 1.0591754233439183 | 0.8811507908679529 | 0.0 | 0 | 0.001561880111694336 sec | 0 sec| 0.0010650157928466797 sec | +| fmristoptask | mcar | mean | bayesian | 0.1 | 0.9651108444122715 | 0.784231196318496 | 0.0 | 0 | 0.0035762786865234375 sec | 0 sec| 0.0006108283996582031 sec | +| fmristoptask | mcar | mean | bayesian | 0.2 | 0.9932773680676918 | 0.8034395750738844 | 0.0 | 0 | 0.009912252426147461 sec | 0 sec| 0.000682830810546875 sec | +| fmristoptask | mcar | mean | bayesian | 0.4 | 1.0058748440484344 | 0.8113341021149199 | 0.0 | 0 | 0.033663034439086914 sec | 0 sec| 0.0008401870727539062 sec | +| fmristoptask | mcar | mean | bayesian | 0.6 | 0.9944066185522102 | 0.8023296982336051 | 0.0 | 0 | 0.08425664901733398 sec | 0 sec| 0.0010020732879638672 sec | +| fmristoptask | mcar | mean | bayesian | 0.8 | 0.9979990505486313 | 0.8062359186814159 | 0.0 | 0 | 0.1884922981262207 sec | 0 sec| 0.0009903907775878906 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.05 | 1.0815739858856455 | 0.8947163048898044 | 0.23576973507164212 | -0.12274682282048005 | 0.0014843940734863281 sec | 216.33094692230225 sec| 0.006989240646362305 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.1 | 0.9695699729418912 | 0.7898385707592198 | 0.06571976951128125 | 0.016476991654415008 | 0.008179664611816406 sec | 216.33094692230225 sec| 0.0062677860260009766 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.2 | 1.0023712131611957 | 0.8108602788128816 | 0.02538765630290373 | -0.016656543511887868 | 0.02096843719482422 sec | 216.33094692230225 sec| 0.006853580474853516 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.4 | 1.0138537110215022 | 0.8167419153197173 | 0.0038274804707874484 | 0.002717578068034049 | 0.07195258140563965 sec | 216.33094692230225 sec| 0.00666499137878418 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.6 | 1.0022937958385385 | 0.807293318305244 | 0.0018376453669024168 | 0.004596695453371254 | 0.14317655563354492 sec | 216.33094692230225 sec| 0.006315708160400391 sec | +| fmristoptask | mcar | cdrec | bayesian | 0.8 | 1.0104537937047533 | 0.8149091851781165 | 0.0008945376054130945 | -0.0013082054469119196 | 0.2480306625366211 sec | 216.33094692230225 sec| 0.005487203598022461 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.05 | 1.1715750158207363 | 0.9389573934580852 | 0.30612963701823526 | -0.22056411372111834 | 0.0031473636627197266 sec | 110.04800581932068 sec| 10.122947692871094 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.1 | 1.0588476372168147 | 0.8437403156914149 | 0.08955991417984446 | -0.1963089605999627 | 0.003419160842895508 sec | 110.04800581932068 sec| 10.181205034255981 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.2 | 1.0391969620815695 | 0.8364861943065512 | 0.02582105408815175 | -0.09232453336176588 | 0.009185314178466797 sec | 110.04800581932068 sec| 10.448293685913086 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.4 | 1.0340455393837413 | 0.832400199311948 | 0.00520789381175344 | -0.04499260926820861 | 0.030958890914916992 sec | 110.04800581932068 sec| 11.198593139648438 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.6 | 4.011139383889788 | 3.152797499531786 | 0.003672509477371519 | -0.05413975121078511 | 0.07897067070007324 sec | 110.04800581932068 sec| 8.581665992736816 sec | +| fmristoptask | mcar | stmvl | bayesian | 0.8 | 2.97893158705676 | 1.0602936132635719 | 0.00079094933311715 | 0.006947773983399647 | 0.18915915489196777 sec | 110.04800581932068 sec| 8.440712690353394 sec | +| fmristoptask | mcar | iim | bayesian | 0.05 | 1.0692148314478316 | 0.873400733402723 | 0.2787388945371119 | -0.02021145481191946 | 0.0015034675598144531 sec | 5124.588714838028 sec| 10.759928226470947 sec | +| fmristoptask | mcar | iim | bayesian | 0.1 | 0.9719895445677292 | 0.7851843420896756 | 0.0830808565046283 | 0.003268635254181307 | 0.003936767578125 sec | 5124.588714838028 sec| 50.354418992996216 sec | +| fmristoptask | mcar | iim | bayesian | 0.2 | 0.99753636840165 | 0.8012616128674659 | 0.019093143495502334 | 0.02540361203010324 | 0.009255409240722656 sec | 5124.588714838028 sec| 259.3400568962097 sec | +| fmristoptask | mcar | iim | bayesian | 0.4 | 1.0155975152475738 | 0.8140496119700683 | 0.004260439955627443 | 0.0006423716677864647 | 0.0312647819519043 sec | 5124.588714838028 sec| 1500.3178548812866 sec | +| fmristoptask | mcar | iim | bayesian | 0.6 | 1.0040752264526889 | 0.8052914143043017 | 0.0018099723977603893 | -0.006621752869444718 | 0.07852554321289062 sec | 5124.588714838028 sec| 4581.28284406662 sec | +| fmristoptask | mcar | iim | bayesian | 0.8 | 1.0078811833781343 | 0.8090736592195691 | 0.001033941419470956 | -0.003099173821807945 | 0.18776154518127441 sec | 5124.588714838028 sec| 9590.927385091782 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.05 | 1.146433389804167 | 0.9770400477715633 | 0.3372765709259859 | 0.0330859633180261 | 0.001608133316040039 sec | 4109.78501701355 sec| 347.9514887332916 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.1 | 1.0805589422598818 | 0.8789487774083494 | 0.06450452519706741 | 0.0050948685955938995 | 0.0037963390350341797 sec | 4109.78501701355 sec| 342.1326117515564 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.2 | 1.113302451577659 | 0.8972310309254206 | 0.013539230335286593 | -0.010746184336502297 | 0.010583162307739258 sec | 4109.78501701355 sec| 347.8061354160309 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.4 | 1.1059062825212693 | 0.8920096539260874 | 0.0039427922204060845 | -0.021280076256874978 | 0.03199410438537598 sec | 4109.78501701355 sec| 351.9458327293396 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.6 | 1.0740866766668984 | 0.8664850080628724 | 0.0015316126887234942 | -0.021487493774034198 | 0.08084416389465332 sec | 4109.78501701355 sec| 349.9893400669098 sec | +| fmristoptask | mcar | mrnn | bayesian | 0.8 | 1.075891210325233 | 0.8695393935351904 | 0.0011319165672490211 | -0.017885852991857847 | 0.19720029830932617 sec | 4109.78501701355 sec| 349.96222448349 sec | diff --git a/imputegap/runner_benchmarking.py b/imputegap/runner_benchmarking.py index 067b9b8..53aaaf1 100644 --- a/imputegap/runner_benchmarking.py +++ b/imputegap/runner_benchmarking.py @@ -1,8 +1,9 @@ from imputegap.recovery.benchmarking import Benchmarking -reconstruction = False +reconstruction = True +matrix = True + datasets_full = ["eeg-alcohol", "eeg-reading", "fmri-objectviewing", "fmri-stoptask", "chlorine", "drift"] -datasets_fmri = ["fmri-objectviewing", "fmri-stoptask"] opti_bayesian = {"optimizer": "bayesian", "options": {"n_calls": 15, "n_random_starts": 50, "acq_func": "gp_hedge", "selected_metrics": "RMSE"}} opti_greedy = {"optimizer": "greedy", "options": {"n_calls": 250, "selected_metrics": "RMSE"}} @@ -15,12 +16,12 @@ scenarios_small = ["mcar"] scenarios_full = ["mcar", "missing_percentage"] -x_axis=[0.05, 0.1, 0.2, 0.4, 0.6, 0.8] +x_axis = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] if not reconstruction: - results = Benchmarking().comprehensive_evaluation(datasets=datasets_fmri, optimizers=optimizers, algorithms=algorithms_full, scenarios=scenarios_small, x_axis=x_axis, already_optimized=False, reports=3) + results = Benchmarking().comprehensive_evaluation(datasets=datasets_full, optimizers=optimizers, algorithms=algorithms_full, scenarios=scenarios_small, x_axis=x_axis, already_optimized=False, reports=3) print("\n\n\nresults:", results) -else: +elif reconstruction and not matrix: test_plots = {'chlorine': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9256738243031312, 'MAE': 0.8788758766429177, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001201629638671875, 'optimization': 0, 'imputation': 0.0005724430084228516}}, '0.1': {'scores': {'RMSE': 0.8239629739455251, 'MAE': 0.7297827051195541, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001814126968383789, 'optimization': 0, 'imputation': 0.0004563331604003906}}, '0.2': {'scores': {'RMSE': 0.8317409760747367, 'MAE': 0.7138664942301458, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.005623817443847656, 'optimization': 0, 'imputation': 0.0004363059997558594}}, '0.4': {'scores': {'RMSE': 0.866178542847881, 'MAE': 0.744937943856253, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.03413963317871094, 'optimization': 0, 'imputation': 0.0005552768707275391}}, '0.6': {'scores': {'RMSE': 0.8906205973878023, 'MAE': 0.7677632103385671, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.13074183464050293, 'optimization': 0, 'imputation': 0.0005936622619628906}}, '0.8': {'scores': {'RMSE': 0.9231926867636093, 'MAE': 0.7897697041316387, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.4494190216064453, 'optimization': 0, 'imputation': 0.0005834102630615234}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.19555801767314038, 'MAE': 0.14379634965165344, 'MI': 1.3195962394272744, 'CORRELATION': 0.9770377315860114}, 'times': {'contamination': 0.0010943412780761719, 'optimization': 1.6249148845672607, 'imputation': 0.09233546257019043}}, '0.1': {'scores': {'RMSE': 0.22211329096601584, 'MAE': 0.13682609208383795, 'MI': 1.225240202380491, 'CORRELATION': 0.9627754587047338}, 'times': {'contamination': 0.005602359771728516, 'optimization': 1.6249148845672607, 'imputation': 0.1246938705444336}}, '0.2': {'scores': {'RMSE': 0.26890140517000855, 'MAE': 0.16983555417798818, 'MI': 1.0650037012869458, 'CORRELATION': 0.945331872005451}, 'times': {'contamination': 0.017725229263305664, 'optimization': 1.6249148845672607, 'imputation': 0.1363234519958496}}, '0.4': {'scores': {'RMSE': 0.3143181342292365, 'MAE': 0.2041263696093189, 'MI': 0.9133456774887369, 'CORRELATION': 0.9309636417166443}, 'times': {'contamination': 0.1031486988067627, 'optimization': 1.6249148845672607, 'imputation': 0.2686195373535156}}, '0.6': {'scores': {'RMSE': 0.37514780116434926, 'MAE': 0.22156474038385332, 'MI': 0.7775541845220788, 'CORRELATION': 0.9078517283026865}, 'times': {'contamination': 0.20231366157531738, 'optimization': 1.6249148845672607, 'imputation': 0.8690693378448486}}, '0.8': {'scores': {'RMSE': 0.9117409046445515, 'MAE': 0.4801132374733116, 'MI': 0.2576488533530952, 'CORRELATION': 0.6589813814462316}, 'times': {'contamination': 0.5354366302490234, 'optimization': 1.6249148845672607, 'imputation': 2.865450143814087}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.3033328648259709, 'MAE': 0.2644983508914945, 'MI': 1.2263963519649825, 'CORRELATION': 0.9611641055318173}, 'times': {'contamination': 0.0029397010803222656, 'optimization': 500.0222601890564, 'imputation': 23.88236165046692}}, '0.1': {'scores': {'RMSE': 0.27434099749552526, 'MAE': 0.22744969879475732, 'MI': 1.0873378350271077, 'CORRELATION': 0.9481608575454046}, 'times': {'contamination': 0.001943349838256836, 'optimization': 500.0222601890564, 'imputation': 24.082878351211548}}, '0.2': {'scores': {'RMSE': 0.3354154243946063, 'MAE': 0.2667902544729111, 'MI': 0.9040935528948765, 'CORRELATION': 0.9224394175345223}, 'times': {'contamination': 0.007236480712890625, 'optimization': 500.0222601890564, 'imputation': 27.05676031112671}}, '0.4': {'scores': {'RMSE': 0.3663147584695216, 'MAE': 0.2683992893683706, 'MI': 0.7945562213511235, 'CORRELATION': 0.9086873163095024}, 'times': {'contamination': 0.03319692611694336, 'optimization': 500.0222601890564, 'imputation': 24.969536066055298}}, '0.6': {'scores': {'RMSE': 0.49178356901493514, 'MAE': 0.3590429489696727, 'MI': 0.568068131156551, 'CORRELATION': 0.8240735290572155}, 'times': {'contamination': 0.13401484489440918, 'optimization': 500.0222601890564, 'imputation': 17.722254991531372}}, '0.8': {'scores': {'RMSE': 5.286373452119497, 'MAE': 3.0120315981628085, 'MI': 0.0877803352414065, 'CORRELATION': 0.4417418016734377}, 'times': {'contamination': 0.46097803115844727, 'optimization': 500.0222601890564, 'imputation': 17.994383335113525}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.2246776140243064, 'MAE': 0.16265112492381306, 'MI': 1.0875116207955637, 'CORRELATION': 0.9694504836799154}, 'times': {'contamination': 0.0009558200836181641, 'optimization': 4871.80725812912, 'imputation': 1.680412769317627}}, '0.1': {'scores': {'RMSE': 0.3034580006710775, 'MAE': 0.20388299260278156, 'MI': 1.0526306210784155, 'CORRELATION': 0.9337303655141744}, 'times': {'contamination': 0.0018503665924072266, 'optimization': 4871.80725812912, 'imputation': 10.345388412475586}}, '0.2': {'scores': {'RMSE': 0.4104578379330223, 'MAE': 0.2785159738696005, 'MI': 0.7986686024303655, 'CORRELATION': 0.8658822456465257}, 'times': {'contamination': 0.0055084228515625, 'optimization': 4871.80725812912, 'imputation': 65.17643117904663}}, '0.4': {'scores': {'RMSE': 0.4911437971846393, 'MAE': 0.32455728476996504, 'MI': 0.6429014104572732, 'CORRELATION': 0.8180219110130202}, 'times': {'contamination': 0.032411813735961914, 'optimization': 4871.80725812912, 'imputation': 474.7696805000305}}, '0.6': {'scores': {'RMSE': 0.579715388344659, 'MAE': 0.4144431747763777, 'MI': 0.45413696197432313, 'CORRELATION': 0.7431519134806602}, 'times': {'contamination': 0.1278684139251709, 'optimization': 4871.80725812912, 'imputation': 1531.380850315094}}, '0.8': {'scores': {'RMSE': 0.8100585330320411, 'MAE': 0.6124983237048439, 'MI': 0.1600984202902365, 'CORRELATION': 0.48808679305097513}, 'times': {'contamination': 0.4592604637145996, 'optimization': 4871.80725812912, 'imputation': 3588.4590351581573}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0889986961845628, 'MAE': 0.8825193440526788, 'MI': 0.569311657025473, 'CORRELATION': 0.006110871130276294}, 'times': {'contamination': 0.0009238719940185547, 'optimization': 474.33066391944885, 'imputation': 37.89777088165283}}, '0.1': {'scores': {'RMSE': 0.8750845974360951, 'MAE': 0.7897191908914645, 'MI': 0.36542131337202255, 'CORRELATION': 0.1776164808833599}, 'times': {'contamination': 0.0020151138305664062, 'optimization': 474.33066391944885, 'imputation': 36.68788194656372}}, '0.2': {'scores': {'RMSE': 1.3935692458593014, 'MAE': 1.1278169009994172, 'MI': 0.23278876704617288, 'CORRELATION': -0.0043224216288866475}, 'times': {'contamination': 0.006083011627197266, 'optimization': 474.33066391944885, 'imputation': 34.238656997680664}}, '0.4': {'scores': {'RMSE': 1.2198343626008104, 'MAE': 1.004323747843723, 'MI': 0.11694146418635429, 'CORRELATION': -2.8855554502904036e-05}, 'times': {'contamination': 0.03404045104980469, 'optimization': 474.33066391944885, 'imputation': 37.132654428482056}}, '0.6': {'scores': {'RMSE': 1.1924360263528335, 'MAE': 0.9838535398356899, 'MI': 0.0794767096848362, 'CORRELATION': -0.06570944989748748}, 'times': {'contamination': 0.1405935287475586, 'optimization': 474.33066391944885, 'imputation': 37.741902351379395}}, '0.8': {'scores': {'RMSE': 1.3728850685938416, 'MAE': 1.1227443270722774, 'MI': 0.08611037233596197, 'CORRELATION': -0.012424819834313067}, 'times': {'contamination': 0.47881627082824707, 'optimization': 474.33066391944885, 'imputation': 37.675835847854614}}}}}}} Benchmarking().generate_plots(runs_plots_scores=test_plots, s="50", v="1000") @@ -32,3 +33,35 @@ test_plots = {'drift': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9234927128429051, 'MAE': 0.7219362152785619, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.008000850677490234, 'optimization': 0, 'imputation': 0.0005795955657958984}}, '0.1': {'scores': {'RMSE': 0.9699990038879407, 'MAE': 0.7774057495176013, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0019245147705078125, 'optimization': 0, 'imputation': 0.0005664825439453125}}, '0.2': {'scores': {'RMSE': 0.9914069853975623, 'MAE': 0.8134840739732964, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009830474853515625, 'optimization': 0, 'imputation': 0.0005776882171630859}}, '0.4': {'scores': {'RMSE': 1.0552448338389784, 'MAE': 0.7426695186604741, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.04627418518066406, 'optimization': 0, 'imputation': 0.0005333423614501953}}, '0.6': {'scores': {'RMSE': 1.0143105930114702, 'MAE': 0.7610548321723654, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.16058969497680664, 'optimization': 0, 'imputation': 0.0005693435668945312}}, '0.8': {'scores': {'RMSE': 1.010712060535523, 'MAE': 0.7641520748788702, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.49263739585876465, 'optimization': 0, 'imputation': 0.0005679130554199219}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.23303624184873972, 'MAE': 0.1361979723519773, 'MI': 1.2739817718416822, 'CORRELATION': 0.968435455112644}, 'times': {'contamination': 0.0011115074157714844, 'optimization': 2.84889817237854, 'imputation': 0.1434638500213623}}, '0.1': {'scores': {'RMSE': 0.18152059329152107, 'MAE': 0.09925566629402763, 'MI': 1.1516089897042538, 'CORRELATION': 0.982939835222072}, 'times': {'contamination': 0.004365444183349609, 'optimization': 2.84889817237854, 'imputation': 0.14118409156799316}}, '0.2': {'scores': {'RMSE': 0.13894771223733138, 'MAE': 0.0845903269210229, 'MI': 1.186191167936035, 'CORRELATION': 0.9901338133811375}, 'times': {'contamination': 0.01644587516784668, 'optimization': 2.84889817237854, 'imputation': 0.16940855979919434}}, '0.4': {'scores': {'RMSE': 0.7544523683503815, 'MAE': 0.1121804997359425, 'MI': 0.021165172206064526, 'CORRELATION': 0.8141205075707254}, 'times': {'contamination': 0.10604023933410645, 'optimization': 2.84889817237854, 'imputation': 2.0186331272125244}}, '0.6': {'scores': {'RMSE': 0.4355197572001314, 'MAE': 0.13808466247330484, 'MI': 0.10781252370591506, 'CORRELATION': 0.9166777087122918}, 'times': {'contamination': 0.2030637264251709, 'optimization': 2.84889817237854, 'imputation': 2.0608761310577393}}, '0.8': {'scores': {'RMSE': 0.7672558930795491, 'MAE': 0.3298896842843935, 'MI': 0.013509125598802707, 'CORRELATION': 0.7312998041323682}, 'times': {'contamination': 0.5499897003173828, 'optimization': 2.84889817237854, 'imputation': -0.47277092933654785}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5434405584289141, 'MAE': 0.346560495723809, 'MI': 0.7328867182584357, 'CORRELATION': 0.8519431955571422}, 'times': {'contamination': 0.0021185874938964844, 'optimization': 514.5863847732544, 'imputation': 34.6202232837677}}, '0.1': {'scores': {'RMSE': 0.39007056542870916, 'MAE': 0.2753022759369617, 'MI': 0.8280959876205578, 'CORRELATION': 0.9180937736429735}, 'times': {'contamination': 0.0018591880798339844, 'optimization': 514.5863847732544, 'imputation': 35.190133810043335}}, '0.2': {'scores': {'RMSE': 0.37254427425455994, 'MAE': 0.2730547993858495, 'MI': 0.7425412593844177, 'CORRELATION': 0.9293322959355041}, 'times': {'contamination': 0.005822181701660156, 'optimization': 514.5863847732544, 'imputation': 35.46649789810181}}, '0.4': {'scores': {'RMSE': 0.6027573766269363, 'MAE': 0.34494332493982044, 'MI': 0.11876685901414151, 'CORRELATION': 0.8390532279447225}, 'times': {'contamination': 0.03864097595214844, 'optimization': 514.5863847732544, 'imputation': 34.30042386054993}}, '0.6': {'scores': {'RMSE': 0.9004526656857551, 'MAE': 0.4924048353228427, 'MI': 0.011590260996247858, 'CORRELATION': 0.5650541301828254}, 'times': {'contamination': 0.14191699028015137, 'optimization': 514.5863847732544, 'imputation': 29.5026593208313}}, '0.8': {'scores': {'RMSE': 1.0112488396023014, 'MAE': 0.7646823531588104, 'MI': 0.00040669209664367576, 'CORRELATION': 0.0183962968474991}, 'times': {'contamination': 0.46815061569213867, 'optimization': 514.5863847732544, 'imputation': 22.864952564239502}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.4445625930776235, 'MAE': 0.2696133927362288, 'MI': 1.1167751522591498, 'CORRELATION': 0.8944975075266335}, 'times': {'contamination': 0.0008444786071777344, 'optimization': 5050.300735235214, 'imputation': 0.6499700546264648}}, '0.1': {'scores': {'RMSE': 0.2939506418814281, 'MAE': 0.16953644212278182, 'MI': 1.0160968166750064, 'CORRELATION': 0.9531900627237018}, 'times': {'contamination': 0.0019328594207763672, 'optimization': 5050.300735235214, 'imputation': 4.424615383148193}}, '0.2': {'scores': {'RMSE': 0.2366529609250008, 'MAE': 0.14709529129218185, 'MI': 1.064299483512458, 'CORRELATION': 0.9711348247027318}, 'times': {'contamination': 0.005669116973876953, 'optimization': 5050.300735235214, 'imputation': 28.64192819595337}}, '0.4': {'scores': {'RMSE': 0.4155649406397416, 'MAE': 0.22056702659999994, 'MI': 0.06616526470761779, 'CORRELATION': 0.919934494058292}, 'times': {'contamination': 0.03133583068847656, 'optimization': 5050.300735235214, 'imputation': 215.96445870399475}}, '0.6': {'scores': {'RMSE': 0.38695094864012947, 'MAE': 0.24340565131372927, 'MI': 0.06361822797740405, 'CORRELATION': 0.9249744935121553}, 'times': {'contamination': 0.1293776035308838, 'optimization': 5050.300735235214, 'imputation': 711.7917039394379}}, '0.8': {'scores': {'RMSE': 0.5862696375344495, 'MAE': 0.3968159514130716, 'MI': 0.13422239939628303, 'CORRELATION': 0.8178796825899766}, 'times': {'contamination': 0.45540356636047363, 'optimization': 5050.300735235214, 'imputation': 1666.3830137252808}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9458168886934889, 'MAE': 0.7087024488997395, 'MI': 0.11924522547609226, 'CORRELATION': -0.04225238590482719}, 'times': {'contamination': 0.0010085105895996094, 'optimization': 478.6599726676941, 'imputation': 41.931705474853516}}, '0.1': {'scores': {'RMSE': 1.012708832814332, 'MAE': 0.7612398956786116, 'MI': 0.125135259797581, 'CORRELATION': -0.037524204443007164}, 'times': {'contamination': 0.0019328594207763672, 'optimization': 478.6599726676941, 'imputation': 37.289856910705566}}, '0.2': {'scores': {'RMSE': 1.0293662762879399, 'MAE': 0.79543999581101, 'MI': 0.10908095436833125, 'CORRELATION': -0.03892162998680425}, 'times': {'contamination': 0.005481719970703125, 'optimization': 478.6599726676941, 'imputation': 39.732287645339966}}, '0.4': {'scores': {'RMSE': 1.08276653737942, 'MAE': 0.7324224949731254, 'MI': 0.008689250019683584, 'CORRELATION': -0.020719639766949276}, 'times': {'contamination': 0.032985687255859375, 'optimization': 478.6599726676941, 'imputation': 40.06472086906433}}, '0.6': {'scores': {'RMSE': 1.0436806660629465, 'MAE': 0.7612577768282424, 'MI': 0.011650658060022669, 'CORRELATION': -0.0069952780339244845}, 'times': {'contamination': 0.13504815101623535, 'optimization': 478.6599726676941, 'imputation': 41.86172533035278}}, '0.8': {'scores': {'RMSE': 1.0386764847922278, 'MAE': 0.7580243538074385, 'MI': 0.0035404637707733143, 'CORRELATION': -0.0010165957084160128}, 'times': {'contamination': 0.4962472915649414, 'optimization': 478.6599726676941, 'imputation': 44.58724093437195}}}}}}} Benchmarking().generate_plots(runs_plots_scores=test_plots, s="50", v="1000") + +if matrix : + run_1_chlorine = {'chlorine': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9256738243031312, 'MAE': 0.8788758766429177, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0009789466857910156, 'optimization': 0, 'imputation': 0.000560760498046875}}, '0.1': {'scores': {'RMSE': 0.8239629739455251, 'MAE': 0.7297827051195541, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.002305746078491211, 'optimization': 0, 'imputation': 0.0004634857177734375}}, '0.2': {'scores': {'RMSE': 0.8317409760747367, 'MAE': 0.7138664942301458, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.007703065872192383, 'optimization': 0, 'imputation': 0.0004649162292480469}}, '0.4': {'scores': {'RMSE': 0.866178542847881, 'MAE': 0.744937943856253, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.047789812088012695, 'optimization': 0, 'imputation': 0.0005023479461669922}}, '0.6': {'scores': {'RMSE': 0.8906205973878023, 'MAE': 0.7677632103385671, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.19488763809204102, 'optimization': 0, 'imputation': 0.0005488395690917969}}, '0.8': {'scores': {'RMSE': 0.9231926867636093, 'MAE': 0.7897697041316387, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6890411376953125, 'optimization': 0, 'imputation': 0.0005776882171630859}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.19554703625817557, 'MAE': 0.1437913973228053, 'MI': 1.3195962394272744, 'CORRELATION': 0.9770406565915004}, 'times': {'contamination': 0.0009171962738037109, 'optimization': 0, 'imputation': 0.05464982986450195}}, '0.1': {'scores': {'RMSE': 0.22212985201492597, 'MAE': 0.1368378161074427, 'MI': 1.225240202380491, 'CORRELATION': 0.9627706895400587}, 'times': {'contamination': 0.004944562911987305, 'optimization': 0, 'imputation': 0.070037841796875}}, '0.2': {'scores': {'RMSE': 0.268910630576598, 'MAE': 0.16983805083071585, 'MI': 1.0636573662919013, 'CORRELATION': 0.9453283753208437}, 'times': {'contamination': 0.01749396324157715, 'optimization': 0, 'imputation': 0.07790756225585938}}, '0.4': {'scores': {'RMSE': 0.31430310541683426, 'MAE': 0.2041005558473225, 'MI': 0.9124259582934485, 'CORRELATION': 0.9309696942537548}, 'times': {'contamination': 0.11426258087158203, 'optimization': 0, 'imputation': 0.1478443145751953}}, '0.6': {'scores': {'RMSE': 0.3737964229023613, 'MAE': 0.22131322530176772, 'MI': 0.7775995167572279, 'CORRELATION': 0.9083977308218121}, 'times': {'contamination': 0.2614400386810303, 'optimization': 0, 'imputation': 0.4230384826660156}}, '0.8': {'scores': {'RMSE': 0.9290440261799385, 'MAE': 0.4933255678502781, 'MI': 0.2021428083194056, 'CORRELATION': 0.6461059842947307}, 'times': {'contamination': 0.7493531703948975, 'optimization': 0, 'imputation': 4.412551164627075}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.16435641817881824, 'MAE': 0.13990340223545955, 'MI': 1.3785977665357232, 'CORRELATION': 0.9868224741901116}, 'times': {'contamination': 0.0036211013793945312, 'optimization': 0, 'imputation': 39.150184869766235}}, '0.1': {'scores': {'RMSE': 0.2228247553722344, 'MAE': 0.16815959364081734, 'MI': 1.2340069760129087, 'CORRELATION': 0.9623151173186535}, 'times': {'contamination': 0.002553224563598633, 'optimization': 0, 'imputation': 39.25465536117554}}, '0.2': {'scores': {'RMSE': 0.27923604567760596, 'MAE': 0.19211165697030474, 'MI': 1.0043820035861775, 'CORRELATION': 0.9430094313080399}, 'times': {'contamination': 0.008016109466552734, 'optimization': 0, 'imputation': 39.86703276634216}}, '0.4': {'scores': {'RMSE': 0.3255775619246775, 'MAE': 0.2194073917812186, 'MI': 0.8847163339667148, 'CORRELATION': 0.9259001258177321}, 'times': {'contamination': 0.04792189598083496, 'optimization': 0, 'imputation': 41.36716914176941}}, '0.6': {'scores': {'RMSE': 0.44447910257331374, 'MAE': 0.30600741310945195, 'MI': 0.6723738452451481, 'CORRELATION': 0.857466472714002}, 'times': {'contamination': 0.19208693504333496, 'optimization': 0, 'imputation': 30.92500948905945}}, '0.8': {'scores': {'RMSE': 2.9806206255800913, 'MAE': 1.530963982498524, 'MI': 0.05121884841141813, 'CORRELATION': 0.2903624430928721}, 'times': {'contamination': 0.6799006462097168, 'optimization': 0, 'imputation': 28.389225006103516}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.1560886685592231, 'MAE': 0.10320394166419149, 'MI': 1.2780123906233032, 'CORRELATION': 0.9851724611327715}, 'times': {'contamination': 0.0010797977447509766, 'optimization': 0, 'imputation': 0.8159213066101074}}, '0.1': {'scores': {'RMSE': 0.3006324748663841, 'MAE': 0.17773178955210425, 'MI': 1.2119149147233643, 'CORRELATION': 0.9321993026569703}, 'times': {'contamination': 0.0021529197692871094, 'optimization': 0, 'imputation': 5.404278039932251}}, '0.2': {'scores': {'RMSE': 0.30708253455892426, 'MAE': 0.18786443029344255, 'MI': 1.0350247745925767, 'CORRELATION': 0.9270935540980816}, 'times': {'contamination': 0.007862567901611328, 'optimization': 0, 'imputation': 39.23897194862366}}, '0.4': {'scores': {'RMSE': 0.36627844349732885, 'MAE': 0.23513471435395922, 'MI': 0.8536501396545491, 'CORRELATION': 0.9028949327632931}, 'times': {'contamination': 0.04749464988708496, 'optimization': 0, 'imputation': 291.0960524082184}}, '0.6': {'scores': {'RMSE': 0.44187263450733627, 'MAE': 0.3005295255111392, 'MI': 0.7070128664004881, 'CORRELATION': 0.8600506431175654}, 'times': {'contamination': 0.19056296348571777, 'optimization': 0, 'imputation': 961.3684046268463}}, '0.8': {'scores': {'RMSE': 0.6162987723847368, 'MAE': 0.4408568111584791, 'MI': 0.38562262881823584, 'CORRELATION': 0.7078269987710476}, 'times': {'contamination': 0.6741812229156494, 'optimization': 0, 'imputation': 2265.02947473526}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.2157597331971723, 'MAE': 1.0542417765804475, 'MI': 0.569311657025473, 'CORRELATION': -0.41037521809198385}, 'times': {'contamination': 0.0011243820190429688, 'optimization': 0, 'imputation': 18.271201610565186}}, '0.1': {'scores': {'RMSE': 1.1799455746309517, 'MAE': 1.0537900828112892, 'MI': 0.3698854611544671, 'CORRELATION': -0.30580392001607287}, 'times': {'contamination': 0.0025169849395751953, 'optimization': 0, 'imputation': 18.178789377212524}}, '0.2': {'scores': {'RMSE': 1.341883829249102, 'MAE': 1.1116623537227253, 'MI': 0.22703785144726024, 'CORRELATION': -0.13139818884461385}, 'times': {'contamination': 0.008020877838134766, 'optimization': 0, 'imputation': 18.227224111557007}}, '0.4': {'scores': {'RMSE': 1.4574773306729822, 'MAE': 1.221059892905018, 'MI': 0.1526121106442972, 'CORRELATION': -0.06171770589679702}, 'times': {'contamination': 0.04882335662841797, 'optimization': 0, 'imputation': 18.527106523513794}}, '0.6': {'scores': {'RMSE': 1.4501476980845394, 'MAE': 1.1589217747122664, 'MI': 0.08174182790842249, 'CORRELATION': -0.028201438478978574}, 'times': {'contamination': 0.19412755966186523, 'optimization': 0, 'imputation': 19.096518754959106}}, '0.8': {'scores': {'RMSE': 1.204799199247893, 'MAE': 1.002446633752256, 'MI': 0.08875526330977121, 'CORRELATION': -0.02097728376019728}, 'times': {'contamination': 0.6939215660095215, 'optimization': 0, 'imputation': 19.685445308685303}}}}}}} + run_2_chlorine = {'chlorine': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9256738243031312, 'MAE': 0.8788758766429177, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001043081283569336, 'optimization': 0, 'imputation': 0.0008816719055175781}}, '0.1': {'scores': {'RMSE': 0.8239629739455251, 'MAE': 0.7297827051195541, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.002270221710205078, 'optimization': 0, 'imputation': 0.00047469139099121094}}, '0.2': {'scores': {'RMSE': 0.8317409760747367, 'MAE': 0.7138664942301458, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.007225513458251953, 'optimization': 0, 'imputation': 0.0004715919494628906}}, '0.4': {'scores': {'RMSE': 0.866178542847881, 'MAE': 0.744937943856253, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.04382967948913574, 'optimization': 0, 'imputation': 0.0005059242248535156}}, '0.6': {'scores': {'RMSE': 0.8906205973878023, 'MAE': 0.7677632103385671, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.17531085014343262, 'optimization': 0, 'imputation': 0.0005536079406738281}}, '0.8': {'scores': {'RMSE': 0.9231926867636093, 'MAE': 0.7897697041316387, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6192398071289062, 'optimization': 0, 'imputation': 0.0005943775177001953}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.19554703625817557, 'MAE': 0.1437913973228053, 'MI': 1.3195962394272744, 'CORRELATION': 0.9770406565915004}, 'times': {'contamination': 0.0009462833404541016, 'optimization': 0, 'imputation': 0.060041189193725586}}, '0.1': {'scores': {'RMSE': 0.22212985201492597, 'MAE': 0.1368378161074427, 'MI': 1.225240202380491, 'CORRELATION': 0.9627706895400587}, 'times': {'contamination': 0.004572868347167969, 'optimization': 0, 'imputation': 0.0699300765991211}}, '0.2': {'scores': {'RMSE': 0.268910630576598, 'MAE': 0.16983805083071585, 'MI': 1.0636573662919013, 'CORRELATION': 0.9453283753208437}, 'times': {'contamination': 0.016742944717407227, 'optimization': 0, 'imputation': 0.07609176635742188}}, '0.4': {'scores': {'RMSE': 0.31430310541683426, 'MAE': 0.2041005558473225, 'MI': 0.9124259582934485, 'CORRELATION': 0.9309696942537548}, 'times': {'contamination': 0.10860323905944824, 'optimization': 0, 'imputation': 0.15946102142333984}}, '0.6': {'scores': {'RMSE': 0.3737964229023613, 'MAE': 0.22131322530176772, 'MI': 0.7775995167572279, 'CORRELATION': 0.9083977308218121}, 'times': {'contamination': 0.2411816120147705, 'optimization': 0, 'imputation': 0.43070363998413086}}, '0.8': {'scores': {'RMSE': 0.9290440261799385, 'MAE': 0.4933255678502781, 'MI': 0.2021428083194056, 'CORRELATION': 0.6461059842947307}, 'times': {'contamination': 0.6789627075195312, 'optimization': 0, 'imputation': 4.46994161605835}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.16435641817881824, 'MAE': 0.13990340223545955, 'MI': 1.3785977665357232, 'CORRELATION': 0.9868224741901116}, 'times': {'contamination': 0.002485513687133789, 'optimization': 0, 'imputation': 39.508928298950195}}, '0.1': {'scores': {'RMSE': 0.2228247553722344, 'MAE': 0.16815959364081734, 'MI': 1.2340069760129087, 'CORRELATION': 0.9623151173186535}, 'times': {'contamination': 0.0023517608642578125, 'optimization': 0, 'imputation': 39.52970552444458}}, '0.2': {'scores': {'RMSE': 0.27923604567760596, 'MAE': 0.19211165697030474, 'MI': 1.0043820035861775, 'CORRELATION': 0.9430094313080399}, 'times': {'contamination': 0.007275581359863281, 'optimization': 0, 'imputation': 39.95721387863159}}, '0.4': {'scores': {'RMSE': 0.3255775619246775, 'MAE': 0.2194073917812186, 'MI': 0.8847163339667148, 'CORRELATION': 0.9259001258177321}, 'times': {'contamination': 0.042914390563964844, 'optimization': 0, 'imputation': 41.303142786026}}, '0.6': {'scores': {'RMSE': 0.44447910257331374, 'MAE': 0.30600741310945195, 'MI': 0.6723738452451481, 'CORRELATION': 0.857466472714002}, 'times': {'contamination': 0.17032194137573242, 'optimization': 0, 'imputation': 30.968651294708252}}, '0.8': {'scores': {'RMSE': 2.9806206255800913, 'MAE': 1.530963982498524, 'MI': 0.05121884841141813, 'CORRELATION': 0.2903624430928721}, 'times': {'contamination': 0.6045393943786621, 'optimization': 0, 'imputation': 28.36435556411743}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.1560886685592231, 'MAE': 0.10320394166419149, 'MI': 1.2780123906233032, 'CORRELATION': 0.9851724611327715}, 'times': {'contamination': 0.000980377197265625, 'optimization': 0, 'imputation': 0.7417066097259521}}, '0.1': {'scores': {'RMSE': 0.3006324748663841, 'MAE': 0.17773178955210425, 'MI': 1.2119149147233643, 'CORRELATION': 0.9321993026569703}, 'times': {'contamination': 0.0019462108612060547, 'optimization': 0, 'imputation': 4.773505687713623}}, '0.2': {'scores': {'RMSE': 0.30708253455892426, 'MAE': 0.18786443029344255, 'MI': 1.0350247745925767, 'CORRELATION': 0.9270935540980816}, 'times': {'contamination': 0.008093833923339844, 'optimization': 0, 'imputation': 34.58026099205017}}, '0.4': {'scores': {'RMSE': 0.36627844349732885, 'MAE': 0.23513471435395922, 'MI': 0.8536501396545491, 'CORRELATION': 0.9028949327632931}, 'times': {'contamination': 0.04369974136352539, 'optimization': 0, 'imputation': 253.98769640922546}}, '0.6': {'scores': {'RMSE': 0.44187263450733627, 'MAE': 0.3005295255111392, 'MI': 0.7070128664004881, 'CORRELATION': 0.8600506431175654}, 'times': {'contamination': 0.16975879669189453, 'optimization': 0, 'imputation': 835.3046026229858}}, '0.8': {'scores': {'RMSE': 0.6162987723847368, 'MAE': 0.4408568111584791, 'MI': 0.38562262881823584, 'CORRELATION': 0.7078269987710476}, 'times': {'contamination': 0.5958583354949951, 'optimization': 0, 'imputation': 1967.7639136314392}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0136434251178998, 'MAE': 0.8848324237744947, 'MI': 0.569311657025473, 'CORRELATION': 0.29914348963401916}, 'times': {'contamination': 0.0010654926300048828, 'optimization': 0, 'imputation': 18.329164743423462}}, '0.1': {'scores': {'RMSE': 1.2969727084789213, 'MAE': 1.096550700485976, 'MI': 0.4844113002067355, 'CORRELATION': -0.14524582877234712}, 'times': {'contamination': 0.0023641586303710938, 'optimization': 0, 'imputation': 18.21089506149292}}, '0.2': {'scores': {'RMSE': 1.0905397356299984, 'MAE': 0.8836097265712998, 'MI': 0.173773514607323, 'CORRELATION': -0.11890703333812934}, 'times': {'contamination': 0.007399082183837891, 'optimization': 0, 'imputation': 18.337430715560913}}, '0.4': {'scores': {'RMSE': 1.4069154761905174, 'MAE': 1.1643367090708647, 'MI': 0.09571825537518668, 'CORRELATION': -0.022364037624607463}, 'times': {'contamination': 0.043769121170043945, 'optimization': 0, 'imputation': 18.840161323547363}}, '0.6': {'scores': {'RMSE': 1.382829866742193, 'MAE': 1.1269958882289104, 'MI': 0.09215558384208698, 'CORRELATION': -0.032372544249182615}, 'times': {'contamination': 0.1728811264038086, 'optimization': 0, 'imputation': 19.076626300811768}}, '0.8': {'scores': {'RMSE': 1.5039750591991847, 'MAE': 1.2211771463532568, 'MI': 0.08522464337328965, 'CORRELATION': 0.002752327584939554}, 'times': {'contamination': 0.6081700325012207, 'optimization': 0, 'imputation': 19.578737258911133}}}}}}} + run_3_chlorine = {'chlorine': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9256738243031312, 'MAE': 0.8788758766429177, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001058340072631836, 'optimization': 0, 'imputation': 0.0009882450103759766}}, '0.1': {'scores': {'RMSE': 0.8239629739455251, 'MAE': 0.7297827051195541, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0022249221801757812, 'optimization': 0, 'imputation': 0.0004658699035644531}}, '0.2': {'scores': {'RMSE': 0.8317409760747367, 'MAE': 0.7138664942301458, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.00716400146484375, 'optimization': 0, 'imputation': 0.0004811286926269531}}, '0.4': {'scores': {'RMSE': 0.866178542847881, 'MAE': 0.744937943856253, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.04335308074951172, 'optimization': 0, 'imputation': 0.0005075931549072266}}, '0.6': {'scores': {'RMSE': 0.8906205973878023, 'MAE': 0.7677632103385671, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1734917163848877, 'optimization': 0, 'imputation': 0.0005586147308349609}}, '0.8': {'scores': {'RMSE': 0.9231926867636093, 'MAE': 0.7897697041316387, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6150054931640625, 'optimization': 0, 'imputation': 0.0005857944488525391}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.19554703625817557, 'MAE': 0.1437913973228053, 'MI': 1.3195962394272744, 'CORRELATION': 0.9770406565915004}, 'times': {'contamination': 0.0009434223175048828, 'optimization': 0, 'imputation': 0.05546450614929199}}, '0.1': {'scores': {'RMSE': 0.22212985201492597, 'MAE': 0.1368378161074427, 'MI': 1.225240202380491, 'CORRELATION': 0.9627706895400587}, 'times': {'contamination': 0.004517078399658203, 'optimization': 0, 'imputation': 0.06634163856506348}}, '0.2': {'scores': {'RMSE': 0.268910630576598, 'MAE': 0.16983805083071585, 'MI': 1.0636573662919013, 'CORRELATION': 0.9453283753208437}, 'times': {'contamination': 0.017187833786010742, 'optimization': 0, 'imputation': 0.07271552085876465}}, '0.4': {'scores': {'RMSE': 0.31430310541683426, 'MAE': 0.2041005558473225, 'MI': 0.9124259582934485, 'CORRELATION': 0.9309696942537548}, 'times': {'contamination': 0.10760045051574707, 'optimization': 0, 'imputation': 0.15883731842041016}}, '0.6': {'scores': {'RMSE': 0.3737964229023613, 'MAE': 0.22131322530176772, 'MI': 0.7775995167572279, 'CORRELATION': 0.9083977308218121}, 'times': {'contamination': 0.2422795295715332, 'optimization': 0, 'imputation': 0.42018914222717285}}, '0.8': {'scores': {'RMSE': 0.9290440261799385, 'MAE': 0.4933255678502781, 'MI': 0.2021428083194056, 'CORRELATION': 0.6461059842947307}, 'times': {'contamination': 0.680551290512085, 'optimization': 0, 'imputation': 4.376981019973755}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.16435641817881824, 'MAE': 0.13990340223545955, 'MI': 1.3785977665357232, 'CORRELATION': 0.9868224741901116}, 'times': {'contamination': 0.003632068634033203, 'optimization': 0, 'imputation': 39.26680397987366}}, '0.1': {'scores': {'RMSE': 0.2228247553722344, 'MAE': 0.16815959364081734, 'MI': 1.2340069760129087, 'CORRELATION': 0.9623151173186535}, 'times': {'contamination': 0.002412080764770508, 'optimization': 0, 'imputation': 39.86172819137573}}, '0.2': {'scores': {'RMSE': 0.27923604567760596, 'MAE': 0.19211165697030474, 'MI': 1.0043820035861775, 'CORRELATION': 0.9430094313080399}, 'times': {'contamination': 0.007197856903076172, 'optimization': 0, 'imputation': 40.38218545913696}}, '0.4': {'scores': {'RMSE': 0.3255775619246775, 'MAE': 0.2194073917812186, 'MI': 0.8847163339667148, 'CORRELATION': 0.9259001258177321}, 'times': {'contamination': 0.04293513298034668, 'optimization': 0, 'imputation': 41.78527879714966}}, '0.6': {'scores': {'RMSE': 0.44447910257331374, 'MAE': 0.30600741310945195, 'MI': 0.6723738452451481, 'CORRELATION': 0.857466472714002}, 'times': {'contamination': 0.17196941375732422, 'optimization': 0, 'imputation': 31.38751482963562}}, '0.8': {'scores': {'RMSE': 2.9806206255800913, 'MAE': 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0.23513471435395922, 'MI': 0.8536501396545491, 'CORRELATION': 0.9028949327632931}, 'times': {'contamination': 0.0439915657043457, 'optimization': 0, 'imputation': 255.53748202323914}}, '0.6': {'scores': {'RMSE': 0.44187263450733627, 'MAE': 0.3005295255111392, 'MI': 0.7070128664004881, 'CORRELATION': 0.8600506431175654}, 'times': {'contamination': 0.16890335083007812, 'optimization': 0, 'imputation': 840.3006525039673}}, '0.8': {'scores': {'RMSE': 0.6162987723847368, 'MAE': 0.4408568111584791, 'MI': 0.38562262881823584, 'CORRELATION': 0.7078269987710476}, 'times': {'contamination': 0.6028788089752197, 'optimization': 0, 'imputation': 1966.1153359413147}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.029108480614302, 'MAE': 0.849997592084472, 'MI': 0.569311657025473, 'CORRELATION': -0.34135585229137194}, 'times': {'contamination': 0.0010657310485839844, 'optimization': 0, 'imputation': 17.878644943237305}}, '0.1': {'scores': {'RMSE': 1.8645374616997576, 'MAE': 1.6163656821639383, 'MI': 0.4813306460709136, 'CORRELATION': -0.39944268926751514}, 'times': {'contamination': 0.0021545886993408203, 'optimization': 0, 'imputation': 18.227890968322754}}, '0.2': {'scores': {'RMSE': 1.1049704980896, 'MAE': 0.9144454227691684, 'MI': 0.17197985846530675, 'CORRELATION': -0.04348618452798679}, 'times': {'contamination': 0.0074846744537353516, 'optimization': 0, 'imputation': 18.08056640625}}, '0.4': {'scores': {'RMSE': 1.3718667824151887, 'MAE': 1.1222752970024972, 'MI': 0.07628371768993472, 'CORRELATION': 0.010136633181027283}, 'times': {'contamination': 0.04388308525085449, 'optimization': 0, 'imputation': 18.583510637283325}}, '0.6': {'scores': {'RMSE': 1.3178885712841644, 'MAE': 1.0812817111954678, 'MI': 0.0898108080896041, 'CORRELATION': -0.020124428247071557}, 'times': {'contamination': 0.17338800430297852, 'optimization': 0, 'imputation': 18.885586261749268}}, '0.8': {'scores': {'RMSE': 1.229438316008386, 'MAE': 0.9979298148457775, 'MI': 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{'bayesian': {'0.05': {'scores': {'RMSE': 0.5434405584289141, 'MAE': 0.346560495723809, 'MI': 0.7328867182584357, 'CORRELATION': 0.8519431955571422}, 'times': {'contamination': 0.0034203529357910156, 'optimization': 0, 'imputation': 52.270103931427}}, '0.1': {'scores': {'RMSE': 0.39007056542870916, 'MAE': 0.2753022759369617, 'MI': 0.8280959876205578, 'CORRELATION': 0.9180937736429735}, 'times': {'contamination': 0.002481222152709961, 'optimization': 0, 'imputation': 52.581149101257324}}, '0.2': {'scores': {'RMSE': 0.37254427425455994, 'MAE': 0.2730547993858495, 'MI': 0.7425412593844177, 'CORRELATION': 0.9293322959355041}, 'times': {'contamination': 0.008099079132080078, 'optimization': 0, 'imputation': 52.98776412010193}}, '0.4': {'scores': {'RMSE': 0.6027573766269363, 'MAE': 0.34494332493982044, 'MI': 0.11876685901414151, 'CORRELATION': 0.8390532279447225}, 'times': {'contamination': 0.048757076263427734, 'optimization': 0, 'imputation': 54.611621379852295}}, '0.6': {'scores': {'RMSE': 0.9004526656857551, 'MAE': 0.4924048353228427, 'MI': 0.011590260996247858, 'CORRELATION': 0.5650541301828254}, 'times': {'contamination': 0.1966409683227539, 'optimization': 0, 'imputation': 40.79859209060669}}, '0.8': {'scores': {'RMSE': 1.0112488396023014, 'MAE': 0.7646823531588104, 'MI': 0.00040669209664367576, 'CORRELATION': 0.0183962968474991}, 'times': {'contamination': 0.6912062168121338, 'optimization': 0, 'imputation': 35.179917097091675}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.4445625930776235, 'MAE': 0.2696133927362288, 'MI': 1.1167751522591498, 'CORRELATION': 0.8944975075266335}, 'times': {'contamination': 0.0011439323425292969, 'optimization': 0, 'imputation': 0.8133630752563477}}, '0.1': {'scores': {'RMSE': 0.2939506418814281, 'MAE': 0.16953644212278182, 'MI': 1.0160968166750064, 'CORRELATION': 0.9531900627237018}, 'times': {'contamination': 0.0021233558654785156, 'optimization': 0, 'imputation': 5.425678014755249}}, '0.2': {'scores': {'RMSE': 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0.9446007612538053, 'MAE': 0.7071227725048369, 'MI': 0.11924522547609226, 'CORRELATION': -0.01944229566442761}, 'times': {'contamination': 0.0011224746704101562, 'optimization': 0, 'imputation': 49.38418102264404}}, '0.1': {'scores': {'RMSE': 1.018077695981055, 'MAE': 0.7618772118737108, 'MI': 0.12567590499764303, 'CORRELATION': -0.044145607442978164}, 'times': {'contamination': 0.0033884048461914062, 'optimization': 0, 'imputation': 49.42437410354614}}, '0.2': {'scores': {'RMSE': 1.0328353866969924, 'MAE': 0.7947328876841107, 'MI': 0.10908095436833125, 'CORRELATION': -0.039066506384315955}, 'times': {'contamination': 0.009317874908447266, 'optimization': 0, 'imputation': 49.775628089904785}}, '0.4': {'scores': {'RMSE': 1.084645568714648, 'MAE': 0.7406884025277696, 'MI': 0.00864358706683903, 'CORRELATION': -0.01785022064101569}, 'times': {'contamination': 0.04832720756530762, 'optimization': 0, 'imputation': 50.35092234611511}}, '0.6': {'scores': {'RMSE': 1.0417931248055596, 'MAE': 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0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0069735050201416016, 'optimization': 0, 'imputation': 0.0004620552062988281}}, '0.4': {'scores': {'RMSE': 1.0552448338389784, 'MAE': 0.7426695186604741, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.04297828674316406, 'optimization': 0, 'imputation': 0.0005059242248535156}}, '0.6': {'scores': {'RMSE': 1.0143105930114702, 'MAE': 0.7610548321723654, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1704411506652832, 'optimization': 0, 'imputation': 0.0005540847778320312}}, '0.8': {'scores': {'RMSE': 1.010712060535523, 'MAE': 0.7641520748788702, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6023464202880859, 'optimization': 0, 'imputation': 0.0005741119384765625}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.23303624184873978, 'MAE': 0.13619797235197734, 'MI': 1.2739817718416822, 'CORRELATION': 0.968435455112644}, 'times': {'contamination': 0.0009391307830810547, 'optimization': 0, 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{'RMSE': 0.5862696375344495, 'MAE': 0.3968159514130716, 'MI': 0.13422239939628303, 'CORRELATION': 0.8178796825899766}, 'times': {'contamination': 0.5977535247802734, 'optimization': 0, 'imputation': 1972.2190878391266}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9438252697840334, 'MAE': 0.7074066748495141, 'MI': 0.11924522547609226, 'CORRELATION': -0.021372042533312763}, 'times': {'contamination': 0.0010783672332763672, 'optimization': 0, 'imputation': 48.64605975151062}}, '0.1': {'scores': {'RMSE': 1.0158428722855914, 'MAE': 0.7616120997979859, 'MI': 0.12567590499764303, 'CORRELATION': -0.04445192812896842}, 'times': {'contamination': 0.0023419857025146484, 'optimization': 0, 'imputation': 49.69926905632019}}, '0.2': {'scores': {'RMSE': 1.0306600495361184, 'MAE': 0.7944217777859633, 'MI': 0.10908095436833125, 'CORRELATION': -0.038559181077793894}, 'times': {'contamination': 0.007108926773071289, 'optimization': 0, 'imputation': 49.629971504211426}}, '0.4': {'scores': {'RMSE': 1.084538358231139, 'MAE': 0.7413855539533567, 'MI': 0.00811911881953157, 'CORRELATION': -0.019716554461979955}, 'times': {'contamination': 0.04305720329284668, 'optimization': 0, 'imputation': 49.46027088165283}}, '0.6': {'scores': {'RMSE': 1.0438210426766994, 'MAE': 0.7593955061969807, 'MI': 0.011650658060022669, 'CORRELATION': -0.007809791713096682}, 'times': {'contamination': 0.1705613136291504, 'optimization': 0, 'imputation': 50.77842974662781}}, '0.8': {'scores': {'RMSE': 1.0386852722851065, 'MAE': 0.7587186222490558, 'MI': 0.0035129076969891296, 'CORRELATION': -0.0009994717223948948}, 'times': {'contamination': 0.6103465557098389, 'optimization': 0, 'imputation': 50.92327070236206}}}}}}} + run_3_drift = {'drift': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9234927128429051, 'MAE': 0.7219362152785619, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0010309219360351562, 'optimization': 0, 'imputation': 0.0005755424499511719}}, '0.1': {'scores': {'RMSE': 0.9699990038879407, 'MAE': 0.7774057495176013, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0020699501037597656, 'optimization': 0, 'imputation': 0.00048422813415527344}}, '0.2': {'scores': {'RMSE': 0.9914069853975623, 'MAE': 0.8134840739732964, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.007096290588378906, 'optimization': 0, 'imputation': 0.000461578369140625}}, '0.4': {'scores': {'RMSE': 1.0552448338389784, 'MAE': 0.7426695186604741, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.043192148208618164, 'optimization': 0, 'imputation': 0.0005095005035400391}}, '0.6': {'scores': {'RMSE': 1.0143105930114702, 'MAE': 0.7610548321723654, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.17184901237487793, 'optimization': 0, 'imputation': 0.0005536079406738281}}, '0.8': {'scores': {'RMSE': 1.010712060535523, 'MAE': 0.7641520748788702, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.6064670085906982, 'optimization': 0, 'imputation': 0.0005743503570556641}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.23303624184873978, 'MAE': 0.13619797235197734, 'MI': 1.2739817718416822, 'CORRELATION': 0.968435455112644}, 'times': {'contamination': 0.0009615421295166016, 'optimization': 0, 'imputation': 0.09218788146972656}}, '0.1': {'scores': {'RMSE': 0.18152059329152104, 'MAE': 0.09925566629402761, 'MI': 1.1516089897042538, 'CORRELATION': 0.9829398352220718}, 'times': {'contamination': 0.00482487678527832, 'optimization': 0, 'imputation': 0.09549617767333984}}, '0.2': {'scores': {'RMSE': 0.13894771223733138, 'MAE': 0.08459032692102293, 'MI': 1.186191167936035, 'CORRELATION': 0.9901338133811375}, 'times': {'contamination': 0.01713728904724121, 'optimization': 0, 'imputation': 0.1129295825958252}}, '0.4': {'scores': {'RMSE': 0.7544523683503829, 'MAE': 0.11218049973594252, 'MI': 0.021165172206064526, 'CORRELATION': 0.814120507570725}, 'times': {'contamination': 0.10881781578063965, 'optimization': 0, 'imputation': 1.9378046989440918}}, '0.6': {'scores': {'RMSE': 0.4355197572001326, 'MAE': 0.1380846624733049, 'MI': 0.10781252370591506, 'CORRELATION': 0.9166777087122915}, 'times': {'contamination': 0.2380077838897705, 'optimization': 0, 'imputation': 1.8785057067871094}}, '0.8': {'scores': {'RMSE': 0.7672558930795506, 'MAE': 0.32988968428439397, 'MI': 0.013509125598802707, 'CORRELATION': 0.7312998041323675}, 'times': {'contamination': 0.6805167198181152, 'optimization': 0, 'imputation': 1.9562773704528809}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5434405584289141, 'MAE': 0.346560495723809, 'MI': 0.7328867182584357, 'CORRELATION': 0.8519431955571422}, 'times': {'contamination': 0.0022056102752685547, 'optimization': 0, 'imputation': 52.07010293006897}}, '0.1': {'scores': {'RMSE': 0.39007056542870916, 'MAE': 0.2753022759369617, 'MI': 0.8280959876205578, 'CORRELATION': 0.9180937736429735}, 'times': {'contamination': 0.002231597900390625, 'optimization': 0, 'imputation': 52.543020248413086}}, '0.2': {'scores': {'RMSE': 0.37254427425455994, 'MAE': 0.2730547993858495, 'MI': 0.7425412593844177, 'CORRELATION': 0.9293322959355041}, 'times': {'contamination': 0.0072672367095947266, 'optimization': 0, 'imputation': 52.88247036933899}}, '0.4': {'scores': {'RMSE': 0.6027573766269363, 'MAE': 0.34494332493982044, 'MI': 0.11876685901414151, 'CORRELATION': 0.8390532279447225}, 'times': {'contamination': 0.04321551322937012, 'optimization': 0, 'imputation': 54.10793352127075}}, '0.6': {'scores': {'RMSE': 0.9004526656857551, 'MAE': 0.4924048353228427, 'MI': 0.011590260996247858, 'CORRELATION': 0.5650541301828254}, 'times': {'contamination': 0.1728806495666504, 'optimization': 0, 'imputation': 40.53373336791992}}, '0.8': {'scores': {'RMSE': 1.0112488396023014, 'MAE': 0.7646823531588104, 'MI': 0.00040669209664367576, 'CORRELATION': 0.0183962968474991}, 'times': {'contamination': 0.6077785491943359, 'optimization': 0, 'imputation': 35.151907444000244}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.4445625930776235, 'MAE': 0.2696133927362288, 'MI': 1.1167751522591498, 'CORRELATION': 0.8944975075266335}, 'times': {'contamination': 0.0010058879852294922, 'optimization': 0, 'imputation': 0.7380530834197998}}, '0.1': {'scores': {'RMSE': 0.2939506418814281, 'MAE': 0.16953644212278182, 'MI': 1.0160968166750064, 'CORRELATION': 0.9531900627237018}, 'times': {'contamination': 0.0019745826721191406, 'optimization': 0, 'imputation': 4.7826457023620605}}, '0.2': {'scores': {'RMSE': 0.2366529609250008, 'MAE': 0.14709529129218185, 'MI': 1.064299483512458, 'CORRELATION': 0.9711348247027318}, 'times': {'contamination': 0.00801849365234375, 'optimization': 0, 'imputation': 33.94813060760498}}, '0.4': {'scores': {'RMSE': 0.4155649406397416, 'MAE': 0.22056702659999994, 'MI': 0.06616526470761779, 'CORRELATION': 0.919934494058292}, 'times': {'contamination': 0.04391813278198242, 'optimization': 0, 'imputation': 255.31524085998535}}, '0.6': {'scores': {'RMSE': 0.38695094864012947, 'MAE': 0.24340565131372927, 'MI': 0.06361822797740405, 'CORRELATION': 0.9249744935121553}, 'times': {'contamination': 0.17044353485107422, 'optimization': 0, 'imputation': 840.7470128536224}}, '0.8': {'scores': {'RMSE': 0.5862696375344495, 'MAE': 0.3968159514130716, 'MI': 0.13422239939628303, 'CORRELATION': 0.8178796825899766}, 'times': {'contamination': 0.5999574661254883, 'optimization': 0, 'imputation': 1974.6101157665253}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 0.9458508648057621, 'MAE': 0.7019459696903068, 'MI': 0.11924522547609226, 'CORRELATION': 0.02915935932568557}, 'times': {'contamination': 0.001056671142578125, 'optimization': 0, 'imputation': 49.42237901687622}}, '0.1': {'scores': {'RMSE': 1.0125309431502871, 'MAE': 0.761136543268339, 'MI': 0.12567590499764303, 'CORRELATION': -0.037161060882302754}, 'times': {'contamination': 0.003415822982788086, 'optimization': 0, 'imputation': 49.04829454421997}}, '0.2': {'scores': {'RMSE': 1.0317754516097355, 'MAE': 0.7952869439926, 'MI': 0.10908095436833125, 'CORRELATION': -0.04155403791391449}, 'times': {'contamination': 0.007429599761962891, 'optimization': 0, 'imputation': 49.42568325996399}}, '0.4': {'scores': {'RMSE': 1.0807965786089415, 'MAE': 0.7326965517264863, 'MI': 0.006171770470542263, 'CORRELATION': -0.020630168509677818}, 'times': {'contamination': 0.042899370193481445, 'optimization': 0, 'imputation': 49.479795694351196}}, '0.6': {'scores': {'RMSE': 1.0441472017887297, 'MAE': 0.7599852461729673, 'MI': 0.01121013333181846, 'CORRELATION': -0.007513931343350665}, 'times': {'contamination': 0.17329692840576172, 'optimization': 0, 'imputation': 50.439927101135254}}, '0.8': {'scores': {'RMSE': 1.0379347892718205, 'MAE': 0.757440007226372, 'MI': 0.0035880775657246428, 'CORRELATION': -0.0014975078469404196}, 'times': {'contamination': 0.6166613101959229, 'optimization': 0, 'imputation': 50.66455388069153}}}}}}} + + run_1_eeg_a = {'eegalcohol': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.107394798606378, 'MAE': 0.9036474830477748, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0005795955657958984, 'optimization': 0, 'imputation': 0.0002789497375488281}}, '0.1': {'scores': {'RMSE': 0.8569349076796438, 'MAE': 0.6416542359734557, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0010943412780761719, 'optimization': 0, 'imputation': 0.00022482872009277344}}, '0.2': {'scores': {'RMSE': 0.9609255264919324, 'MAE': 0.756013835497571, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0021677017211914062, 'optimization': 0, 'imputation': 0.00021696090698242188}}, '0.4': {'scores': {'RMSE': 1.0184989120725458, 'MAE': 0.8150966718352457, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.012993097305297852, 'optimization': 0, 'imputation': 0.00023245811462402344}}, '0.6': {'scores': {'RMSE': 0.9997401940199045, 'MAE': 0.7985721718600829, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.04167890548706055, 'optimization': 0, 'imputation': 0.00025534629821777344}}, '0.8': {'scores': {'RMSE': 0.9895691678332014, 'MAE': 0.7901674118013952, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.15235233306884766, 'optimization': 0, 'imputation': 0.0004570484161376953}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.27658600512073456, 'MAE': 0.20204444801773774, 'MI': 1.6287285825717355, 'CORRELATION': 0.9837210171556283}, 'times': {'contamination': 0.0005352497100830078, 'optimization': 0, 'imputation': 0.02960658073425293}}, '0.1': {'scores': {'RMSE': 0.2322153312143858, 'MAE': 0.1729082341483471, 'MI': 1.1990748751673153, 'CORRELATION': 0.9640732993793864}, 'times': {'contamination': 0.0018682479858398438, 'optimization': 0, 'imputation': 0.03319096565246582}}, '0.2': {'scores': {'RMSE': 0.21796283300762773, 'MAE': 0.16255811567403466, 'MI': 1.184724280002774, 'CORRELATION': 0.9737521039022545}, 'times': {'contamination': 0.004289150238037109, 'optimization': 0, 'imputation': 0.03893113136291504}}, '0.4': {'scores': {'RMSE': 0.2852656711446442, 'MAE': 0.19577380664036, 'MI': 1.014828207927502, 'CORRELATION': 0.959485242427464}, 'times': {'contamination': 0.028098106384277344, 'optimization': 0, 'imputation': 0.10860562324523926}}, '0.6': {'scores': {'RMSE': 0.3360171448119046, 'MAE': 0.23184686418998596, 'MI': 0.8789374924043876, 'CORRELATION': 0.9418882413737133}, 'times': {'contamination': 0.13066554069519043, 'optimization': 0, 'imputation': 0.23463678359985352}}, '0.8': {'scores': {'RMSE': 0.5558362531202891, 'MAE': 0.37446346030237454, 'MI': 0.5772409317426037, 'CORRELATION': 0.8478935496183876}, 'times': {'contamination': 0.20974469184875488, 'optimization': 0, 'imputation': 0.45677614212036133}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7434750032306926, 'MAE': 0.5711687107703531, 'MI': 1.0614546580642759, 'CORRELATION': 0.7570103181096193}, 'times': {'contamination': 0.0016989707946777344, 'optimization': 0, 'imputation': 2.45868182182312}}, '0.1': {'scores': {'RMSE': 0.6079049353979786, 'MAE': 0.4565071330548986, 'MI': 0.5897845472515851, 'CORRELATION': 0.7033347467102922}, 'times': {'contamination': 0.0010311603546142578, 'optimization': 0, 'imputation': 2.412322521209717}}, '0.2': {'scores': {'RMSE': 0.5938200686690087, 'MAE': 0.4583475323523134, 'MI': 0.5238356117195857, 'CORRELATION': 0.789556744168648}, 'times': {'contamination': 0.0022623538970947266, 'optimization': 0, 'imputation': 2.4960315227508545}}, '0.4': {'scores': {'RMSE': 0.6922622994445695, 'MAE': 0.5327565871766037, 'MI': 0.3842117779328253, 'CORRELATION': 0.738304743934084}, 'times': {'contamination': 0.01298069953918457, 'optimization': 0, 'imputation': 2.7305381298065186}}, '0.6': {'scores': {'RMSE': 0.7719376402414535, 'MAE': 0.5756544384278333, 'MI': 0.268745121385816, 'CORRELATION': 0.6398387148302656}, 'times': {'contamination': 0.04132866859436035, 'optimization': 0, 'imputation': 2.097337245941162}}, '0.8': {'scores': {'RMSE': 1.0218833589128922, 'MAE': 0.8012134667654269, 'MI': 0.0051679642909252645, 'CORRELATION': 0.06083718960882358}, 'times': {'contamination': 0.14826583862304688, 'optimization': 0, 'imputation': 2.4140400886535645}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.26665906759668434, 'MAE': 0.21589657916392105, 'MI': 1.4930024107375521, 'CORRELATION': 0.9704001503125854}, 'times': {'contamination': 0.0005478858947753906, 'optimization': 0, 'imputation': 0.11310672760009766}}, '0.1': {'scores': {'RMSE': 0.28425094570125403, 'MAE': 0.22787684897303442, 'MI': 1.0594854362146846, 'CORRELATION': 0.9444192673990515}, 'times': {'contamination': 0.0010786056518554688, 'optimization': 0, 'imputation': 0.3150827884674072}}, '0.2': {'scores': {'RMSE': 0.334887339804727, 'MAE': 0.25851830743811066, 'MI': 0.9711245925356778, 'CORRELATION': 0.9390073163681255}, 'times': {'contamination': 0.0022890567779541016, 'optimization': 0, 'imputation': 2.11177921295166}}, '0.4': {'scores': {'RMSE': 0.4719169787140248, 'MAE': 0.35026878431372477, 'MI': 0.7196112128770917, 'CORRELATION': 0.8858920655062363}, 'times': {'contamination': 0.013253211975097656, 'optimization': 0, 'imputation': 16.908517837524414}}, '0.6': {'scores': {'RMSE': 0.47736733503847095, 'MAE': 0.35628454418236766, 'MI': 0.6157654491357567, 'CORRELATION': 0.8790867703136753}, 'times': {'contamination': 0.041519880294799805, 'optimization': 0, 'imputation': 50.78557777404785}}, '0.8': {'scores': {'RMSE': 0.5747595088880484, 'MAE': 0.4242587159311907, 'MI': 0.4843046739917606, 'CORRELATION': 0.8188927905931169}, 'times': {'contamination': 0.14832043647766113, 'optimization': 0, 'imputation': 126.20078611373901}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.458357678276516, 'MAE': 1.1907412656856677, 'MI': 0.8219987547394441, 'CORRELATION': 0.32490952909349474}, 'times': {'contamination': 0.0005905628204345703, 'optimization': 0, 'imputation': 39.80203366279602}}, '0.1': {'scores': {'RMSE': 1.2850820431076562, 'MAE': 1.060164753499244, 'MI': 0.2778923026896115, 'CORRELATION': 0.20633535214093737}, 'times': {'contamination': 0.0011925697326660156, 'optimization': 0, 'imputation': 39.42339515686035}}, '0.2': {'scores': {'RMSE': 1.251878172036014, 'MAE': 0.99269752960842, 'MI': 0.13973052187935872, 'CORRELATION': 0.10410956282246875}, 'times': {'contamination': 0.002384662628173828, 'optimization': 0, 'imputation': 39.37762236595154}}, '0.4': {'scores': {'RMSE': 1.3824391140783348, 'MAE': 1.1213491541016083, 'MI': 0.041359464126164654, 'CORRELATION': 0.04142746993459159}, 'times': {'contamination': 0.01451253890991211, 'optimization': 0, 'imputation': 39.95635533332825}}, '0.6': {'scores': {'RMSE': 1.4105891423231767, 'MAE': 1.126363637928893, 'MI': 0.016249691557241253, 'CORRELATION': -0.06179933990411743}, 'times': {'contamination': 0.043706655502319336, 'optimization': 0, 'imputation': 40.308385610580444}}, '0.8': {'scores': {'RMSE': 1.2717422827656417, 'MAE': 1.018360187093311, 'MI': 0.006362338437000872, 'CORRELATION': -0.03134655880761642}, 'times': {'contamination': 0.15089750289916992, 'optimization': 0, 'imputation': 40.61707377433777}}}}}}} + run_2_eeg_a = {'eegalcohol': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.107394798606378, 'MAE': 0.9036474830477748, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0005807876586914062, 'optimization': 0, 'imputation': 0.00028061866760253906}}, '0.1': {'scores': {'RMSE': 0.8569349076796438, 'MAE': 0.6416542359734557, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0010428428649902344, 'optimization': 0, 'imputation': 0.00022554397583007812}}, '0.2': {'scores': {'RMSE': 0.9609255264919324, 'MAE': 0.756013835497571, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0020012855529785156, 'optimization': 0, 'imputation': 0.00021696090698242188}}, '0.4': {'scores': {'RMSE': 1.0184989120725458, 'MAE': 0.8150966718352457, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.011640310287475586, 'optimization': 0, 'imputation': 0.00023436546325683594}}, '0.6': {'scores': {'RMSE': 0.9997401940199045, 'MAE': 0.7985721718600829, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0368037223815918, 'optimization': 0, 'imputation': 0.00026416778564453125}}, '0.8': {'scores': {'RMSE': 0.9895691678332014, 'MAE': 0.7901674118013952, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.13379240036010742, 'optimization': 0, 'imputation': 0.0002701282501220703}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.27658600512073456, 'MAE': 0.20204444801773774, 'MI': 1.6287285825717355, 'CORRELATION': 0.9837210171556283}, 'times': {'contamination': 0.00048232078552246094, 'optimization': 0, 'imputation': 0.02683854103088379}}, '0.1': {'scores': {'RMSE': 0.2322153312143858, 'MAE': 0.1729082341483471, 'MI': 1.1990748751673153, 'CORRELATION': 0.9640732993793864}, 'times': {'contamination': 0.0033652782440185547, 'optimization': 0, 'imputation': 0.018512725830078125}}, '0.2': {'scores': {'RMSE': 0.21796283300762773, 'MAE': 0.16255811567403466, 'MI': 1.184724280002774, 'CORRELATION': 0.9737521039022545}, 'times': {'contamination': 0.0062105655670166016, 'optimization': 0, 'imputation': 0.021807193756103516}}, '0.4': {'scores': {'RMSE': 0.2852656711446442, 'MAE': 0.19577380664036, 'MI': 1.014828207927502, 'CORRELATION': 0.959485242427464}, 'times': {'contamination': 0.029900074005126953, 'optimization': 0, 'imputation': 0.04688239097595215}}, '0.6': {'scores': {'RMSE': 0.3360171448119046, 'MAE': 0.23184686418998596, 'MI': 0.8789374924043876, 'CORRELATION': 0.9418882413737133}, 'times': {'contamination': 0.09384703636169434, 'optimization': 0, 'imputation': 0.12701702117919922}}, '0.8': {'scores': {'RMSE': 0.5558362531202891, 'MAE': 0.37446346030237454, 'MI': 0.5772409317426037, 'CORRELATION': 0.8478935496183876}, 'times': {'contamination': 0.20301151275634766, 'optimization': 0, 'imputation': 0.45037055015563965}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7434750032306926, 'MAE': 0.5711687107703531, 'MI': 1.0614546580642759, 'CORRELATION': 0.7570103181096193}, 'times': {'contamination': 0.002616405487060547, 'optimization': 0, 'imputation': 2.4667086601257324}}, '0.1': {'scores': {'RMSE': 0.6079049353979786, 'MAE': 0.4565071330548986, 'MI': 0.5897845472515851, 'CORRELATION': 0.7033347467102922}, 'times': {'contamination': 0.0010187625885009766, 'optimization': 0, 'imputation': 2.4532482624053955}}, '0.2': {'scores': {'RMSE': 0.5938200686690087, 'MAE': 0.4583475323523134, 'MI': 0.5238356117195857, 'CORRELATION': 0.789556744168648}, 'times': {'contamination': 0.002056121826171875, 'optimization': 0, 'imputation': 2.4876415729522705}}, '0.4': {'scores': {'RMSE': 0.6922622994445695, 'MAE': 0.5327565871766037, 'MI': 0.3842117779328253, 'CORRELATION': 0.738304743934084}, 'times': {'contamination': 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'imputation': 112.25521159172058}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 2.1730235998246674, 'MAE': 1.8953551708094873, 'MI': 0.6072901854577394, 'CORRELATION': -0.7841845292123013}, 'times': {'contamination': 0.0005171298980712891, 'optimization': 0, 'imputation': 39.46493577957153}}, '0.1': {'scores': {'RMSE': 1.2781883021632698, 'MAE': 1.0810892114204538, 'MI': 0.3833337309697582, 'CORRELATION': -0.0447827943706207}, 'times': {'contamination': 0.0010881423950195312, 'optimization': 0, 'imputation': 39.73745322227478}}, '0.2': {'scores': {'RMSE': 1.8180439658597276, 'MAE': 1.546079091559085, 'MI': 0.15454756214708848, 'CORRELATION': -0.08987518519265314}, 'times': {'contamination': 0.0021600723266601562, 'optimization': 0, 'imputation': 40.31570887565613}}, '0.4': {'scores': {'RMSE': 1.3179315405249528, 'MAE': 1.0613913921061846, 'MI': 0.03968232666893745, 'CORRELATION': -0.028655714356183734}, 'times': {'contamination': 0.011851787567138672, 'optimization': 0, 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0.000225067138671875}}, '0.2': {'scores': {'RMSE': 0.9609255264919324, 'MAE': 0.756013835497571, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0020322799682617188, 'optimization': 0, 'imputation': 0.0002205371856689453}}, '0.4': {'scores': {'RMSE': 1.0184989120725458, 'MAE': 0.8150966718352457, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.011725425720214844, 'optimization': 0, 'imputation': 0.0002334117889404297}}, '0.6': {'scores': {'RMSE': 0.9997401940199045, 'MAE': 0.7985721718600829, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.037230491638183594, 'optimization': 0, 'imputation': 0.00025773048400878906}}, '0.8': {'scores': {'RMSE': 0.9895691678332014, 'MAE': 0.7901674118013952, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1363978385925293, 'optimization': 0, 'imputation': 0.000461578369140625}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.27658600512073456, 'MAE': 0.20204444801773774, 'MI': 1.6287285825717355, 'CORRELATION': 0.9837210171556283}, 'times': {'contamination': 0.0005204677581787109, 'optimization': 0, 'imputation': 0.027890920639038086}}, '0.1': {'scores': {'RMSE': 0.2322153312143858, 'MAE': 0.1729082341483471, 'MI': 1.1990748751673153, 'CORRELATION': 0.9640732993793864}, 'times': {'contamination': 0.007706642150878906, 'optimization': 0, 'imputation': 0.02409815788269043}}, '0.2': {'scores': {'RMSE': 0.21796283300762773, 'MAE': 0.16255811567403466, 'MI': 1.184724280002774, 'CORRELATION': 0.9737521039022545}, 'times': {'contamination': 0.004461050033569336, 'optimization': 0, 'imputation': 0.01602649688720703}}, '0.4': {'scores': {'RMSE': 0.2852656711446442, 'MAE': 0.19577380664036, 'MI': 1.014828207927502, 'CORRELATION': 0.959485242427464}, 'times': {'contamination': 0.025922298431396484, 'optimization': 0, 'imputation': 0.03365063667297363}}, '0.6': {'scores': {'RMSE': 0.3360171448119046, 'MAE': 0.23184686418998596, 'MI': 0.8789374924043876, 'CORRELATION': 0.9418882413737133}, 'times': {'contamination': 0.08993721008300781, 'optimization': 0, 'imputation': 0.22972464561462402}}, '0.8': {'scores': {'RMSE': 0.5558362531202891, 'MAE': 0.37446346030237454, 'MI': 0.5772409317426037, 'CORRELATION': 0.8478935496183876}, 'times': {'contamination': 0.19976544380187988, 'optimization': 0, 'imputation': 0.5038683414459229}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.7434750032306926, 'MAE': 0.5711687107703531, 'MI': 1.0614546580642759, 'CORRELATION': 0.7570103181096193}, 'times': {'contamination': 0.0024564266204833984, 'optimization': 0, 'imputation': 2.451982259750366}}, '0.1': {'scores': {'RMSE': 0.6079049353979786, 'MAE': 0.4565071330548986, 'MI': 0.5897845472515851, 'CORRELATION': 0.7033347467102922}, 'times': {'contamination': 0.0009958744049072266, 'optimization': 0, 'imputation': 2.4210727214813232}}, '0.2': {'scores': {'RMSE': 0.5938200686690087, 'MAE': 0.4583475323523134, 'MI': 0.5238356117195857, 'CORRELATION': 0.789556744168648}, 'times': {'contamination': 0.0020804405212402344, 'optimization': 0, 'imputation': 2.4876914024353027}}, '0.4': {'scores': {'RMSE': 0.6922622994445695, 'MAE': 0.5327565871766037, 'MI': 0.3842117779328253, 'CORRELATION': 0.738304743934084}, 'times': {'contamination': 0.011591196060180664, 'optimization': 0, 'imputation': 2.704968214035034}}, '0.6': {'scores': {'RMSE': 0.7719376402414535, 'MAE': 0.5756544384278333, 'MI': 0.268745121385816, 'CORRELATION': 0.6398387148302656}, 'times': {'contamination': 0.037017822265625, 'optimization': 0, 'imputation': 2.085197925567627}}, '0.8': {'scores': {'RMSE': 1.0218833589128922, 'MAE': 0.8012134667654269, 'MI': 0.0051679642909252645, 'CORRELATION': 0.06083718960882358}, 'times': {'contamination': 0.13096380233764648, 'optimization': 0, 'imputation': 2.230935573577881}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.26665906759668434, 'MAE': 0.21589657916392105, 'MI': 1.4930024107375521, 'CORRELATION': 0.9704001503125854}, 'times': {'contamination': 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'times': {'contamination': 0.0066070556640625, 'optimization': 2115.569543361664, 'imputation': 6.977942943572998}}, '0.4': {'scores': {'RMSE': 0.7902203838415963, 'MAE': 0.5922773198020501, 'MI': 0.19381374823819753, 'CORRELATION': 0.6157623089917651}, 'times': {'contamination': 0.023341894149780273, 'optimization': 2115.569543361664, 'imputation': 45.052905321121216}}, '0.6': {'scores': {'RMSE': 0.8606721167494161, 'MAE': 0.6509795391102093, 'MI': 0.14703461141268756, 'CORRELATION': 0.5349197031621258}, 'times': {'contamination': 0.053314924240112305, 'optimization': 2115.569543361664, 'imputation': 137.877295255661}}, '0.8': {'scores': {'RMSE': 0.9473077321399332, 'MAE': 0.721873093140729, 'MI': 0.09210269321275755, 'CORRELATION': 0.41686255415646745}, 'times': {'contamination': 0.12127208709716797, 'optimization': 2115.569543361664, 'imputation': 309.8284556865692}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.6414396019640038, 'MAE': 1.3240559958757634, 'MI': 0.5559452374102188, 'CORRELATION': -0.019190710334023774}, 'times': {'contamination': 0.001463174819946289, 'optimization': 4286.787290811539, 'imputation': 146.20701241493225}}, '0.1': {'scores': {'RMSE': 1.4931325738251233, 'MAE': 1.2291481963023954, 'MI': 0.10612382874060908, 'CORRELATION': 0.08822883294793381}, 'times': {'contamination': 0.003063201904296875, 'optimization': 4286.787290811539, 'imputation': 145.1298749446869}}, '0.2': {'scores': {'RMSE': 1.3592271642125449, 'MAE': 1.1023068858542104, 'MI': 0.031374496439453406, 'CORRELATION': 0.04531586048012379}, 'times': {'contamination': 0.00700068473815918, 'optimization': 4286.787290811539, 'imputation': 145.86979150772095}}, '0.4': {'scores': {'RMSE': 1.5155884162145739, 'MAE': 1.2095557823362952, 'MI': 0.007762134072031226, 'CORRELATION': -0.01994479803059748}, 'times': {'contamination': 0.022418737411499023, 'optimization': 4286.787290811539, 'imputation': 142.07973980903625}}, '0.6': {'scores': {'RMSE': 1.4205010123384363, 'MAE': 1.140500261582132, 'MI': 0.004244506579222641, 'CORRELATION': -0.017115141060066015}, 'times': {'contamination': 0.05402565002441406, 'optimization': 4286.787290811539, 'imputation': 139.75832986831665}}, '0.8': {'scores': {'RMSE': 1.4393703997870884, 'MAE': 1.1419154482992642, 'MI': 0.0026830949612693445, 'CORRELATION': -0.012083949814718867}, 'times': {'contamination': 0.12264132499694824, 'optimization': 4286.787290811539, 'imputation': 144.70407223701477}}}}}}} + + run_1_fmri_s = {'fmristoptask': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0591754233439183, 'MAE': 0.8811507908679529, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0015919208526611328, 'optimization': 0, 'imputation': 0.0009393692016601562}}, '0.1': {'scores': {'RMSE': 0.9651108444122715, 'MAE': 0.784231196318496, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0035066604614257812, 'optimization': 0, 'imputation': 0.000621795654296875}}, '0.2': {'scores': {'RMSE': 0.9932773680676918, 'MAE': 0.8034395750738844, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009276866912841797, 'optimization': 0, 'imputation': 0.0006399154663085938}}, '0.4': {'scores': {'RMSE': 1.0058748440484344, 'MAE': 0.8113341021149199, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.03150796890258789, 'optimization': 0, 'imputation': 0.0008380413055419922}}, '0.6': {'scores': {'RMSE': 0.9944066185522102, 'MAE': 0.8023296982336051, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.07896685600280762, 'optimization': 0, 'imputation': 0.0009694099426269531}}, '0.8': {'scores': {'RMSE': 0.9979990505486313, 'MAE': 0.8062359186814159, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.18951916694641113, 'optimization': 0, 'imputation': 0.0010123252868652344}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0815739858856455, 'MAE': 0.8947163048898044, 'MI': 0.23576973507164212, 'CORRELATION': -0.12274682282048005}, 'times': {'contamination': 0.0014772415161132812, 'optimization': 218.59592175483704, 'imputation': 0.0071277618408203125}}, '0.1': {'scores': {'RMSE': 0.9695699729418912, 'MAE': 0.7898385707592198, 'MI': 0.06571976951128125, 'CORRELATION': 0.016476991654415008}, 'times': {'contamination': 0.008227348327636719, 'optimization': 218.59592175483704, 'imputation': 0.0062563419342041016}}, '0.2': {'scores': {'RMSE': 1.0023712131611957, 'MAE': 0.8108602788128816, 'MI': 0.02538765630290373, 'CORRELATION': -0.016656543511887868}, 'times': {'contamination': 0.0209505558013916, 'optimization': 218.59592175483704, 'imputation': 0.006833791732788086}}, '0.4': {'scores': {'RMSE': 1.0138537110215022, 'MAE': 0.8167419153197173, 'MI': 0.0038274804707874484, 'CORRELATION': 0.002717578068034049}, 'times': {'contamination': 0.07195234298706055, 'optimization': 218.59592175483704, 'imputation': 0.006715297698974609}}, '0.6': {'scores': {'RMSE': 1.0022937958385385, 'MAE': 0.807293318305244, 'MI': 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{'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009216070175170898, 'optimization': 109.35183715820312, 'imputation': 10.349437952041626}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.031234025955200195, 'optimization': 109.35183715820312, 'imputation': 11.021637439727783}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07903313636779785, 'optimization': 109.35183715820312, 'imputation': 8.597065448760986}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18648958206176758, 'optimization': 109.35183715820312, 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-0.0013082054469119196}, 'times': {'contamination': 0.24705862998962402, 'optimization': 222.17338752746582, 'imputation': 0.005573272705078125}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1715750158207363, 'MAE': 0.9389573934580852, 'MI': 0.30612963701823526, 'CORRELATION': -0.22056411372111834}, 'times': {'contamination': 0.002927064895629883, 'optimization': 109.24887442588806, 'imputation': 10.111968994140625}}, '0.1': {'scores': {'RMSE': 1.0588476372168147, 'MAE': 0.8437403156914149, 'MI': 0.08955991417984446, 'CORRELATION': -0.1963089605999627}, 'times': {'contamination': 0.0034782886505126953, 'optimization': 109.24887442588806, 'imputation': 10.12447738647461}}, '0.2': {'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009154081344604492, 'optimization': 109.24887442588806, 'imputation': 10.325854778289795}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.031117677688598633, 'optimization': 109.24887442588806, 'imputation': 11.087183237075806}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07905244827270508, 'optimization': 109.24887442588806, 'imputation': 8.649941444396973}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18860864639282227, 'optimization': 109.24887442588806, 'imputation': 8.43183708190918}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0692148314478316, 'MAE': 0.873400733402723, 'MI': 0.2787388945371119, 'CORRELATION': -0.02021145481191946}, 'times': {'contamination': 0.0014863014221191406, 'optimization': 5088.581882238388, 'imputation': 10.688252687454224}}, '0.1': {'scores': {'RMSE': 0.9719895445677292, 'MAE': 0.7851843420896756, 'MI': 0.0830808565046283, 'CORRELATION': 0.003268635254181307}, 'times': {'contamination': 0.0037031173706054688, 'optimization': 5088.581882238388, 'imputation': 50.06313109397888}}, '0.2': {'scores': {'RMSE': 0.99753636840165, 'MAE': 0.8012616128674659, 'MI': 0.019093143495502334, 'CORRELATION': 0.02540361203010324}, 'times': {'contamination': 0.00922083854675293, 'optimization': 5088.581882238388, 'imputation': 257.213321685791}}, '0.4': {'scores': {'RMSE': 1.0155975152475738, 'MAE': 0.8140496119700683, 'MI': 0.004260439955627443, 'CORRELATION': 0.0006423716677864647}, 'times': {'contamination': 0.03141498565673828, 'optimization': 5088.581882238388, 'imputation': 1488.7819337844849}}, '0.6': {'scores': {'RMSE': 1.0040752264526889, 'MAE': 0.8052914143043017, 'MI': 0.0018099723977603893, 'CORRELATION': -0.006621752869444718}, 'times': {'contamination': 0.07847213745117188, 'optimization': 5088.581882238388, 'imputation': 4525.959330558777}}, '0.8': {'scores': {'RMSE': 1.0078811833781343, 'MAE': 0.8090736592195691, 'MI': 0.001033941419470956, 'CORRELATION': -0.003099173821807945}, 'times': {'contamination': 0.18671298027038574, 'optimization': 5088.581882238388, 'imputation': 9460.7878510952}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.122220535003296, 'MAE': 0.9644508995813553, 'MI': 0.2759436355942961, 'CORRELATION': 0.09245761750327637}, 'times': {'contamination': 0.0015985965728759766, 'optimization': 4112.733412027359, 'imputation': 338.0099182128906}}, '0.1': {'scores': {'RMSE': 1.0832970108643896, 'MAE': 0.8823888940960694, 'MI': 0.0722893609050923, 'CORRELATION': -0.019930274489311815}, 'times': {'contamination': 0.0035643577575683594, 'optimization': 4112.733412027359, 'imputation': 337.6157658100128}}, '0.2': {'scores': {'RMSE': 1.0767155565632924, 'MAE': 0.8684991669552922, 'MI': 0.009245255133377466, 'CORRELATION': 0.0027516812337193518}, 'times': {'contamination': 0.009638309478759766, 'optimization': 4112.733412027359, 'imputation': 328.19135212898254}}, '0.4': {'scores': {'RMSE': 1.0934522863869605, 'MAE': 0.8840570779852788, 'MI': 0.003369568798431563, 'CORRELATION': -0.021061682051014274}, 'times': {'contamination': 0.03281116485595703, 'optimization': 4112.733412027359, 'imputation': 346.8224673271179}}, '0.6': {'scores': {'RMSE': 1.0783671319985777, 'MAE': 0.8704278560665365, 'MI': 0.00169355769499049, 'CORRELATION': -0.019325646685601}, 'times': {'contamination': 0.08042550086975098, 'optimization': 4112.733412027359, 'imputation': 340.2620213031769}}, '0.8': {'scores': {'RMSE': 1.081513280302422, 'MAE': 0.8746519908670293, 'MI': 0.0011728245783709944, 'CORRELATION': -0.016826349565356294}, 'times': {'contamination': 0.18836283683776855, 'optimization': 4112.733412027359, 'imputation': 341.4021186828613}}}}}}} + run_3_fmri_s = {'fmristoptask': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0591754233439183, 'MAE': 0.8811507908679529, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.001561880111694336, 'optimization': 0, 'imputation': 0.0010650157928466797}}, '0.1': {'scores': {'RMSE': 0.9651108444122715, 'MAE': 0.784231196318496, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0035762786865234375, 'optimization': 0, 'imputation': 0.0006108283996582031}}, '0.2': {'scores': {'RMSE': 0.9932773680676918, 'MAE': 0.8034395750738844, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.009912252426147461, 'optimization': 0, 'imputation': 0.000682830810546875}}, '0.4': {'scores': {'RMSE': 1.0058748440484344, 'MAE': 0.8113341021149199, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.033663034439086914, 'optimization': 0, 'imputation': 0.0008401870727539062}}, '0.6': {'scores': {'RMSE': 0.9944066185522102, 'MAE': 0.8023296982336051, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.08425664901733398, 'optimization': 0, 'imputation': 0.0010020732879638672}}, '0.8': {'scores': {'RMSE': 0.9979990505486313, 'MAE': 0.8062359186814159, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.1884922981262207, 'optimization': 0, 'imputation': 0.0009903907775878906}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0815739858856455, 'MAE': 0.8947163048898044, 'MI': 0.23576973507164212, 'CORRELATION': -0.12274682282048005}, 'times': {'contamination': 0.0014843940734863281, 'optimization': 216.33094692230225, 'imputation': 0.006989240646362305}}, '0.1': {'scores': {'RMSE': 0.9695699729418912, 'MAE': 0.7898385707592198, 'MI': 0.06571976951128125, 'CORRELATION': 0.016476991654415008}, 'times': {'contamination': 0.008179664611816406, 'optimization': 216.33094692230225, 'imputation': 0.0062677860260009766}}, '0.2': {'scores': {'RMSE': 1.0023712131611957, 'MAE': 0.8108602788128816, 'MI': 0.02538765630290373, 'CORRELATION': -0.016656543511887868}, 'times': {'contamination': 0.02096843719482422, 'optimization': 216.33094692230225, 'imputation': 0.006853580474853516}}, '0.4': {'scores': {'RMSE': 1.0138537110215022, 'MAE': 0.8167419153197173, 'MI': 0.0038274804707874484, 'CORRELATION': 0.002717578068034049}, 'times': {'contamination': 0.07195258140563965, 'optimization': 216.33094692230225, 'imputation': 0.00666499137878418}}, '0.6': {'scores': {'RMSE': 1.0022937958385385, 'MAE': 0.807293318305244, 'MI': 0.0018376453669024168, 'CORRELATION': 0.004596695453371254}, 'times': {'contamination': 0.14317655563354492, 'optimization': 216.33094692230225, 'imputation': 0.006315708160400391}}, '0.8': {'scores': {'RMSE': 1.0104537937047533, 'MAE': 0.8149091851781165, 'MI': 0.0008945376054130945, 'CORRELATION': -0.0013082054469119196}, 'times': {'contamination': 0.2480306625366211, 'optimization': 216.33094692230225, 'imputation': 0.005487203598022461}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 1.1715750158207363, 'MAE': 0.9389573934580852, 'MI': 0.30612963701823526, 'CORRELATION': -0.22056411372111834}, 'times': {'contamination': 0.0031473636627197266, 'optimization': 110.04800581932068, 'imputation': 10.122947692871094}}, '0.1': {'scores': {'RMSE': 1.0588476372168147, 'MAE': 0.8437403156914149, 'MI': 0.08955991417984446, 'CORRELATION': -0.1963089605999627}, 'times': {'contamination': 0.003419160842895508, 'optimization': 110.04800581932068, 'imputation': 10.181205034255981}}, '0.2': {'scores': {'RMSE': 1.0391969620815695, 'MAE': 0.8364861943065512, 'MI': 0.02582105408815175, 'CORRELATION': -0.09232453336176588}, 'times': {'contamination': 0.009185314178466797, 'optimization': 110.04800581932068, 'imputation': 10.448293685913086}}, '0.4': {'scores': {'RMSE': 1.0340455393837413, 'MAE': 0.832400199311948, 'MI': 0.00520789381175344, 'CORRELATION': -0.04499260926820861}, 'times': {'contamination': 0.030958890914916992, 'optimization': 110.04800581932068, 'imputation': 11.198593139648438}}, '0.6': {'scores': {'RMSE': 4.011139383889788, 'MAE': 3.152797499531786, 'MI': 0.003672509477371519, 'CORRELATION': -0.05413975121078511}, 'times': {'contamination': 0.07897067070007324, 'optimization': 110.04800581932068, 'imputation': 8.581665992736816}}, '0.8': {'scores': {'RMSE': 2.97893158705676, 'MAE': 1.0602936132635719, 'MI': 0.00079094933311715, 'CORRELATION': 0.006947773983399647}, 'times': {'contamination': 0.18915915489196777, 'optimization': 110.04800581932068, 'imputation': 8.440712690353394}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 1.0692148314478316, 'MAE': 0.873400733402723, 'MI': 0.2787388945371119, 'CORRELATION': -0.02021145481191946}, 'times': {'contamination': 0.0015034675598144531, 'optimization': 5124.588714838028, 'imputation': 10.759928226470947}}, '0.1': {'scores': {'RMSE': 0.9719895445677292, 'MAE': 0.7851843420896756, 'MI': 0.0830808565046283, 'CORRELATION': 0.003268635254181307}, 'times': {'contamination': 0.003936767578125, 'optimization': 5124.588714838028, 'imputation': 50.354418992996216}}, '0.2': {'scores': {'RMSE': 0.99753636840165, 'MAE': 0.8012616128674659, 'MI': 0.019093143495502334, 'CORRELATION': 0.02540361203010324}, 'times': {'contamination': 0.009255409240722656, 'optimization': 5124.588714838028, 'imputation': 259.3400568962097}}, '0.4': {'scores': {'RMSE': 1.0155975152475738, 'MAE': 0.8140496119700683, 'MI': 0.004260439955627443, 'CORRELATION': 0.0006423716677864647}, 'times': {'contamination': 0.0312647819519043, 'optimization': 5124.588714838028, 'imputation': 1500.3178548812866}}, '0.6': {'scores': {'RMSE': 1.0040752264526889, 'MAE': 0.8052914143043017, 'MI': 0.0018099723977603893, 'CORRELATION': -0.006621752869444718}, 'times': {'contamination': 0.07852554321289062, 'optimization': 5124.588714838028, 'imputation': 4581.28284406662}}, '0.8': {'scores': {'RMSE': 1.0078811833781343, 'MAE': 0.8090736592195691, 'MI': 0.001033941419470956, 'CORRELATION': -0.003099173821807945}, 'times': {'contamination': 0.18776154518127441, 'optimization': 5124.588714838028, 'imputation': 9590.927385091782}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.146433389804167, 'MAE': 0.9770400477715633, 'MI': 0.3372765709259859, 'CORRELATION': 0.0330859633180261}, 'times': {'contamination': 0.001608133316040039, 'optimization': 4109.78501701355, 'imputation': 347.9514887332916}}, '0.1': {'scores': {'RMSE': 1.0805589422598818, 'MAE': 0.8789487774083494, 'MI': 0.06450452519706741, 'CORRELATION': 0.0050948685955938995}, 'times': {'contamination': 0.0037963390350341797, 'optimization': 4109.78501701355, 'imputation': 342.1326117515564}}, '0.2': {'scores': {'RMSE': 1.113302451577659, 'MAE': 0.8972310309254206, 'MI': 0.013539230335286593, 'CORRELATION': -0.010746184336502297}, 'times': {'contamination': 0.010583162307739258, 'optimization': 4109.78501701355, 'imputation': 347.8061354160309}}, '0.4': {'scores': {'RMSE': 1.1059062825212693, 'MAE': 0.8920096539260874, 'MI': 0.0039427922204060845, 'CORRELATION': -0.021280076256874978}, 'times': {'contamination': 0.03199410438537598, 'optimization': 4109.78501701355, 'imputation': 351.9458327293396}}, '0.6': {'scores': {'RMSE': 1.0740866766668984, 'MAE': 0.8664850080628724, 'MI': 0.0015316126887234942, 'CORRELATION': -0.021487493774034198}, 'times': {'contamination': 0.08084416389465332, 'optimization': 4109.78501701355, 'imputation': 349.9893400669098}}, '0.8': {'scores': {'RMSE': 1.075891210325233, 'MAE': 0.8695393935351904, 'MI': 0.0011319165672490211, 'CORRELATION': -0.017885852991857847}, 'times': {'contamination': 0.19720029830932617, 'optimization': 4109.78501701355, 'imputation': 349.96222448349}}}}}}} + + scores_list, algos, sets = Benchmarking().avg_results(run_1_chlorine, run_2_chlorine, run_3_chlorine, run_1_drift, run_2_drift, run_3_drift, run_1_eeg_a, run_2_eeg_a, run_3_eeg_a, run_1_eeg_r, run_2_eeg_r, run_3_eeg_r, run_1_fmri_o, run_2_fmri_o, run_3_fmri_o, run_1_fmri_s, run_2_fmri_s, run_3_fmri_s) + + result = Benchmarking().generate_matrix(scores_list, algos, sets) + + + diff --git a/imputegap/tools/__pycache__/utils.cpython-312.pyc b/imputegap/tools/__pycache__/utils.cpython-312.pyc index 0ad6ada..354cf48 100644 Binary files a/imputegap/tools/__pycache__/utils.cpython-312.pyc and b/imputegap/tools/__pycache__/utils.cpython-312.pyc differ diff --git a/params/optimal_parameters_b_eeg_cdrec.toml b/params/optimal_parameters_b_eeg_cdrec.toml new file mode 100644 index 0000000..22c898f --- /dev/null +++ b/params/optimal_parameters_b_eeg_cdrec.toml @@ -0,0 +1,4 @@ +[cdrec] +rank = 1 +epsilon = 0.1 +iteration = 10 diff --git a/params/optimal_parameters_b_eeg_iim.toml b/params/optimal_parameters_b_eeg_iim.toml new file mode 100644 index 0000000..3f9a8c9 --- /dev/null +++ b/params/optimal_parameters_b_eeg_iim.toml @@ -0,0 +1,2 @@ +[iim] +learning_neighbors = 1 diff --git a/params/optimal_parameters_b_eeg_mrnn.toml b/params/optimal_parameters_b_eeg_mrnn.toml new file mode 100644 index 0000000..b34002d --- /dev/null +++ b/params/optimal_parameters_b_eeg_mrnn.toml @@ -0,0 +1,4 @@ +[mrnn] +hidden_dim = 1 +learning_rate = 0.1 +iterations = 10 diff --git a/params/optimal_parameters_b_eeg_stmvl.toml b/params/optimal_parameters_b_eeg_stmvl.toml new file mode 100644 index 0000000..c01aa67 --- /dev/null +++ b/params/optimal_parameters_b_eeg_stmvl.toml @@ -0,0 +1,4 @@ +[stmvl] +window_size = 1 +gamma = 0.1 +alpha = 10 diff --git a/params/optimal_parameters_e_eeg-alcohol_cdrec.toml b/params/optimal_parameters_e_eeg-alcohol_cdrec.toml new file mode 100644 index 0000000..b5c579c --- /dev/null +++ b/params/optimal_parameters_e_eeg-alcohol_cdrec.toml @@ -0,0 +1,4 @@ +[cdrec] +rank = 2 +epsilon = 2.9103006766169248e-5 +iteration = 976 diff --git a/params/optimal_parameters_e_eeg-alcohol_iim.toml b/params/optimal_parameters_e_eeg-alcohol_iim.toml new file mode 100644 index 0000000..1250272 --- /dev/null +++ b/params/optimal_parameters_e_eeg-alcohol_iim.toml @@ -0,0 +1,2 @@ +[iim] +learning_neighbors = 27 diff --git a/params/optimal_parameters_e_eeg-alcohol_mrnn.toml b/params/optimal_parameters_e_eeg-alcohol_mrnn.toml new file mode 100644 index 0000000..5cf354f --- /dev/null +++ b/params/optimal_parameters_e_eeg-alcohol_mrnn.toml @@ -0,0 +1,4 @@ +[mrnn] +hidden_dim = 11 +learning_rate = 1.6593890383815785e-5 +iterations = 93 diff --git a/params/optimal_parameters_e_eeg-alcohol_stmvl.toml b/params/optimal_parameters_e_eeg-alcohol_stmvl.toml new file mode 100644 index 0000000..baa27de --- /dev/null +++ b/params/optimal_parameters_e_eeg-alcohol_stmvl.toml @@ -0,0 +1,4 @@ +[stmvl] +window_size = 40 +gamma = 0.22504996184885026 +alpha = 6 diff --git a/report.log b/report.log new file mode 100644 index 0000000..24556c3 --- /dev/null +++ b/report.log @@ -0,0 +1,340 @@ +2024-11-19 14:07:17,061 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:17,147 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:17,149 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:17,213 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:17,390 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 14:07:17,444 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 14:07:17,698 - shap - INFO - phi = array([ 0.00000000e+00, -1.50023544e-02, 0.00000000e+00, -2.02580848e-05, + 0.00000000e+00, -5.09701368e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 3.61519831e-04, -5.85833154e-05, -1.95096124e-05, + 0.00000000e+00, 0.00000000e+00, 4.17635045e-05, 0.00000000e+00]) +2024-11-19 14:07:17,765 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:17,770 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:17,774 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:17,820 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:17,892 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 14:07:17,994 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:18,181 - shap - INFO - phi = array([ 0.00000000e+00, 6.98755365e-03, 0.00000000e+00, 5.50026649e-05, + 0.00000000e+00, 3.23477891e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79513972e-04, 2.95546563e-05, -1.96320751e-05, + 0.00000000e+00, 0.00000000e+00, 3.64300667e-05, 0.00000000e+00]) +2024-11-19 14:07:18,244 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:18,247 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:18,250 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:18,285 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:18,362 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 14:07:18,374 - shap - INFO - np.sum(self.kernelWeights) = 1.0 +2024-11-19 14:07:18,544 - shap - INFO - phi = array([ 0.00000000e+00, 7.83514370e-03, 0.00000000e+00, 5.74602732e-05, + 0.00000000e+00, 3.20042444e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79854610e-04, 3.35067254e-05, -2.12039317e-05, + 0.00000000e+00, 0.00000000e+00, 3.39326044e-05, 0.00000000e+00]) +2024-11-19 14:07:18,644 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:18,647 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:18,649 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:18,684 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:18,753 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 14:07:18,756 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:18,850 - shap - INFO - phi = array([ 0.00000000e+00, 7.83300758e-03, 0.00000000e+00, -3.77635568e-05, + 0.00000000e+00, 3.22798631e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.06331673e-04, 3.42744327e-05, -2.10990653e-05, + 0.00000000e+00, 0.00000000e+00, 3.66266024e-05, 0.00000000e+00]) +2024-11-19 14:07:18,937 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:18,940 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:18,941 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:18,962 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:19,000 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 14:07:19,002 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 14:07:19,138 - shap - INFO - phi = array([ 0.00000000e+00, 7.85856112e-03, 0.00000000e+00, -3.68080669e-05, + 0.00000000e+00, 3.21980994e-04, -3.16954647e-05, 0.00000000e+00, + 0.00000000e+00, 2.10353156e-04, 3.22765781e-05, -1.67189690e-05, + 0.00000000e+00, 0.00000000e+00, 3.62269527e-05, 0.00000000e+00]) +2024-11-19 14:07:19,172 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:19,179 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:19,180 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:19,239 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:19,278 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 14:07:19,279 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:19,326 - shap - INFO - phi = array([ 0.00000000e+00, -1.54999197e-02, 0.00000000e+00, -1.76832343e-05, + 0.00000000e+00, -7.80192307e-04, 3.02281224e-05, 0.00000000e+00, + 0.00000000e+00, -1.35021310e-03, -5.77968265e-05, 9.95378354e-05, + 0.00000000e+00, 0.00000000e+00, -1.95827178e-04, 0.00000000e+00]) +2024-11-19 14:07:19,358 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:19,359 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-19 14:07:19,360 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 14:07:19,408 - shap - INFO - weight_left = 0.528623703595323 +2024-11-19 14:07:19,449 - shap - INFO - np.sum(w_aug) = 17.999999999999996 +2024-11-19 14:07:19,451 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999999 +2024-11-19 14:07:19,496 - shap - INFO - phi = array([ 0.00000000e+00, 8.73719721e-03, 8.70989873e-05, 0.00000000e+00, + 3.82326113e-03, 1.31848484e-02, 5.20288498e-03, 9.31428142e-03, + 4.69081262e-03, 2.61527800e-03, 1.35926816e-02, 6.77844836e-04, + 1.74856500e-02, -1.47938398e-03, 6.19260167e-03, 2.13404411e-05, + -8.58253570e-05, 2.94089462e-03]) +2024-11-19 14:07:19,528 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:19,529 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-19 14:07:19,531 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-19 14:07:19,585 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-19 14:07:19,627 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-19 14:07:19,629 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-19 14:07:19,699 - shap - INFO - phi = array([-4.27027606e-04, 6.05087701e-03, 1.50737404e-03, -6.98188770e-05, + 4.15252868e-03, -1.19120284e-04, 4.61029650e-03, 2.89296701e-03, + 3.16197712e-03, 1.08629331e-02, 4.47721753e-04, 1.13389074e-02, + -3.31697616e-04, 3.70992275e-03, -3.04629404e-03, -1.59238173e-03, + -1.83253053e-03, 3.71123323e-04, 1.31799752e-03]) +2024-11-19 14:07:19,752 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:19,759 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-19 14:07:19,761 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 14:07:19,826 - shap - INFO - weight_left = 0.528623703595323 +2024-11-19 14:07:19,904 - shap - INFO - np.sum(w_aug) = 18.0 +2024-11-19 14:07:19,907 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-19 14:07:20,096 - shap - INFO - phi = array([-0.00999016, -0.01238472, 0.00114272, 0.00017878, 0.00491261, + 0.00068625, 0.00612538, 0. , 0.00258732, 0.01295154, + 0.00075907, 0.01299131, -0.00110445, 0.00643902, -0.00222172, + 0. , 0.00056204, 0.00261269]) +2024-11-19 14:07:20,179 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:20,183 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-19 14:07:20,187 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-19 14:07:20,220 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-19 14:07:20,292 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-19 14:07:20,295 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999996 +2024-11-19 14:07:20,548 - shap - INFO - phi = array([-9.31286008e-03, -1.31748326e-02, 1.29378700e-03, 1.69462623e-04, + 4.55101767e-03, 3.09714247e-04, 6.49898379e-03, 0.00000000e+00, + 2.49537394e-03, 5.44153541e-03, 9.05871572e-04, 4.47591939e-05, + -1.78593088e-04, 6.48523956e-03, -1.85529336e-03, -4.81050964e-05, + -4.71326274e-04, 1.19859543e-03, 2.93209247e-03]) +2024-11-19 14:07:20,658 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:20,661 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, + 0.13480029]) +2024-11-19 14:07:20,665 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 14:07:20,705 - shap - INFO - weight_left = 0.5181019626448611 +2024-11-19 14:07:20,791 - shap - INFO - np.sum(w_aug) = 17.000000000000007 +2024-11-19 14:07:20,794 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:21,007 - shap - INFO - phi = array([-0.01516889, -0.02375469, 0.00212133, -0.00010974, 0.00498366, + 0.00019539, 0.00562799, -0.00049359, 0.00024028, 0.00213718, + -0.00126649, -0.00473163, -0.00189329, 0.00249173, -0.00193585, + -0.00016726, 0.00179716]) +2024-11-19 14:07:21,100 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:21,104 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:21,107 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:21,142 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:21,220 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 14:07:21,224 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 14:07:21,340 - shap - INFO - phi = array([-0.01549215, -0.0247453 , 0.00192913, -0.00018629, 0.00321626, + -0.00060339, 0.0020008 , -0.000509 , 0.00221582, -0.00186174, + -0.00520739, -0.00220376, 0.0011295 , -0.00219331, -0.00024098, + 0.00131353]) +2024-11-19 14:07:21,427 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:21,431 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:21,434 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:21,471 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:21,540 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 14:07:21,543 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:21,757 - shap - INFO - phi = array([-1.80105641e-02, -3.21919336e-02, 3.95965453e-04, -1.83903487e-05, + 1.29916230e-03, -7.92199895e-04, 4.29598682e-04, -4.15250765e-04, + 9.51280340e-04, -3.07515091e-03, -5.30119057e-03, -9.01749893e-03, + 8.06046464e-05, -2.29043339e-03, -9.87074497e-05, -1.69110457e-04]) +2024-11-19 14:07:21,852 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:21,856 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:21,860 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:21,895 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:21,966 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 14:07:21,968 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:22,229 - shap - INFO - phi = array([-1.72785041e-02, -3.45600067e-02, 0.00000000e+00, -9.32098977e-05, + 1.65728458e-04, -6.49205028e-04, -8.24981328e-05, 4.10920449e-03, + -1.10003219e-04, 0.00000000e+00, -5.15045042e-03, -7.98900699e-03, + 0.00000000e+00, 0.00000000e+00, -8.28004351e-05, -1.51021332e-04]) +2024-11-19 14:07:22,273 - shap - INFO - num_full_subsets = 2 +2024-11-19 14:07:22,283 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 14:07:22,286 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 14:07:22,328 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 14:07:22,409 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 14:07:22,475 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 14:07:22,676 - shap - INFO - phi = array([ 0.00000000e+00, -1.58921592e-02, 0.00000000e+00, -2.01331423e-05, + -5.04716525e-06, -5.07116946e-04, 2.85889491e-05, -5.10054262e-04, + 0.00000000e+00, 3.59597734e-04, -5.66061833e-05, -2.68997150e-05, + 0.00000000e+00, 0.00000000e+00, 5.30719090e-05, 0.00000000e+00]) +2024-11-19 14:12:36,807 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} +2024-11-19 14:12:38,714 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.919337618172575, best pos: [3.00104154e+00 2.62311705e-01 5.20573580e+02] +2024-11-22 14:58:29,913 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:29,958 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:30,532 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:30,905 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:31,284 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-22 14:58:31,353 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-22 14:58:31,686 - shap - INFO - phi = array([ 0.00000000e+00, -1.50023544e-02, 0.00000000e+00, -2.02580848e-05, + 0.00000000e+00, -5.09701368e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 3.61519831e-04, -5.85833154e-05, -1.95096124e-05, + 0.00000000e+00, 0.00000000e+00, 4.17635045e-05, 0.00000000e+00]) +2024-11-22 14:58:31,792 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:31,796 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:31,799 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:31,839 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:31,929 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-22 14:58:31,934 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:32,101 - shap - INFO - phi = array([ 0.00000000e+00, 6.98755365e-03, 0.00000000e+00, 5.50026649e-05, + 0.00000000e+00, 3.23477891e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79513972e-04, 2.95546563e-05, -1.96320751e-05, + 0.00000000e+00, 0.00000000e+00, 3.64300667e-05, 0.00000000e+00]) +2024-11-22 14:58:32,180 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:32,187 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:32,190 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:32,229 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:32,319 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-22 14:58:32,322 - shap - INFO - np.sum(self.kernelWeights) = 1.0 +2024-11-22 14:58:32,413 - shap - INFO - phi = array([ 0.00000000e+00, 7.83514370e-03, 0.00000000e+00, 5.74602732e-05, + 0.00000000e+00, 3.20042444e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79854610e-04, 3.35067254e-05, -2.12039317e-05, + 0.00000000e+00, 0.00000000e+00, 3.39326044e-05, 0.00000000e+00]) +2024-11-22 14:58:32,462 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:32,481 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:32,484 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:32,529 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:32,620 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-22 14:58:32,624 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:32,718 - shap - INFO - phi = array([ 0.00000000e+00, 7.83300758e-03, 0.00000000e+00, -3.77635568e-05, + 0.00000000e+00, 3.22798631e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.06331673e-04, 3.42744327e-05, -2.10990653e-05, + 0.00000000e+00, 0.00000000e+00, 3.66266024e-05, 0.00000000e+00]) +2024-11-22 14:58:32,753 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:32,759 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:32,767 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:32,835 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:32,963 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-22 14:58:32,965 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-22 14:58:33,121 - shap - INFO - phi = array([ 0.00000000e+00, 7.85856112e-03, 0.00000000e+00, -3.68080669e-05, + 0.00000000e+00, 3.21980994e-04, -3.16954647e-05, 0.00000000e+00, + 0.00000000e+00, 2.10353156e-04, 3.22765781e-05, -1.67189690e-05, + 0.00000000e+00, 0.00000000e+00, 3.62269527e-05, 0.00000000e+00]) +2024-11-22 14:58:33,154 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:33,159 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:33,161 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:33,206 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:33,267 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-22 14:58:33,270 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:33,385 - shap - INFO - phi = array([ 0.00000000e+00, -1.54999197e-02, 0.00000000e+00, -1.76832343e-05, + 0.00000000e+00, -7.80192307e-04, 3.02281224e-05, 0.00000000e+00, + 0.00000000e+00, -1.35021310e-03, -5.77968265e-05, 9.95378354e-05, + 0.00000000e+00, 0.00000000e+00, -1.95827178e-04, 0.00000000e+00]) +2024-11-22 14:58:33,420 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:33,428 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-22 14:58:33,432 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-22 14:58:33,482 - shap - INFO - weight_left = 0.528623703595323 +2024-11-22 14:58:33,543 - shap - INFO - np.sum(w_aug) = 17.999999999999996 +2024-11-22 14:58:33,546 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999999 +2024-11-22 14:58:33,630 - shap - INFO - phi = array([ 0.00000000e+00, 8.73719721e-03, 8.70989873e-05, 0.00000000e+00, + 3.82326113e-03, 1.31848484e-02, 5.20288498e-03, 9.31428142e-03, + 4.69081262e-03, 2.61527800e-03, 1.35926816e-02, 6.77844836e-04, + 1.74856500e-02, -1.47938398e-03, 6.19260167e-03, 2.13404411e-05, + -8.58253570e-05, 2.94089462e-03]) +2024-11-22 14:58:33,711 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:33,715 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-22 14:58:33,717 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-22 14:58:33,755 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-22 14:58:33,818 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-22 14:58:33,823 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-22 14:58:33,931 - shap - INFO - phi = array([-4.27027606e-04, 6.05087701e-03, 1.50737404e-03, -6.98188770e-05, + 4.15252868e-03, -1.19120284e-04, 4.61029650e-03, 2.89296701e-03, + 3.16197712e-03, 1.08629331e-02, 4.47721753e-04, 1.13389074e-02, + -3.31697616e-04, 3.70992275e-03, -3.04629404e-03, -1.59238173e-03, + -1.83253053e-03, 3.71123323e-04, 1.31799752e-03]) +2024-11-22 14:58:34,032 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:34,072 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-22 14:58:34,094 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-22 14:58:34,208 - shap - INFO - weight_left = 0.528623703595323 +2024-11-22 14:58:34,334 - shap - INFO - np.sum(w_aug) = 18.0 +2024-11-22 14:58:34,352 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-22 14:58:34,545 - shap - INFO - phi = array([-0.00999016, -0.01238472, 0.00114272, 0.00017878, 0.00491261, + 0.00068625, 0.00612538, 0. , 0.00258732, 0.01295154, + 0.00075907, 0.01299131, -0.00110445, 0.00643902, -0.00222172, + 0. , 0.00056204, 0.00261269]) +2024-11-22 14:58:34,646 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:34,653 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-22 14:58:34,657 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-22 14:58:34,698 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-22 14:58:34,829 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-22 14:58:34,844 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999996 +2024-11-22 14:58:35,102 - shap - INFO - phi = array([-9.31286008e-03, -1.31748326e-02, 1.29378700e-03, 1.69462623e-04, + 4.55101767e-03, 3.09714247e-04, 6.49898379e-03, 0.00000000e+00, + 2.49537394e-03, 5.44153541e-03, 9.05871572e-04, 4.47591939e-05, + -1.78593088e-04, 6.48523956e-03, -1.85529336e-03, -4.81050964e-05, + -4.71326274e-04, 1.19859543e-03, 2.93209247e-03]) +2024-11-22 14:58:35,225 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:35,283 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, + 0.13480029]) +2024-11-22 14:58:35,331 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-22 14:58:35,507 - shap - INFO - weight_left = 0.5181019626448611 +2024-11-22 14:58:35,622 - shap - INFO - np.sum(w_aug) = 17.000000000000007 +2024-11-22 14:58:35,626 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:35,728 - shap - INFO - phi = array([-0.01516889, -0.02375469, 0.00212133, -0.00010974, 0.00498366, + 0.00019539, 0.00562799, -0.00049359, 0.00024028, 0.00213718, + -0.00126649, -0.00473163, -0.00189329, 0.00249173, -0.00193585, + -0.00016726, 0.00179716]) +2024-11-22 14:58:35,765 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:35,785 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:35,817 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:35,861 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:35,938 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-22 14:58:35,945 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-22 14:58:36,240 - shap - INFO - phi = array([-0.01549215, -0.0247453 , 0.00192913, -0.00018629, 0.00321626, + -0.00060339, 0.0020008 , -0.000509 , 0.00221582, -0.00186174, + -0.00520739, -0.00220376, 0.0011295 , -0.00219331, -0.00024098, + 0.00131353]) +2024-11-22 14:58:36,320 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:36,325 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:36,333 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:36,385 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:36,498 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-22 14:58:36,504 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:36,690 - shap - INFO - phi = array([-1.80105641e-02, -3.21919336e-02, 3.95965453e-04, -1.83903487e-05, + 1.29916230e-03, -7.92199895e-04, 4.29598682e-04, -4.15250765e-04, + 9.51280340e-04, -3.07515091e-03, -5.30119057e-03, -9.01749893e-03, + 8.06046464e-05, -2.29043339e-03, -9.87074497e-05, -1.69110457e-04]) +2024-11-22 14:58:36,812 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:36,819 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:36,823 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:36,888 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:37,005 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-22 14:58:37,012 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:37,146 - shap - INFO - phi = array([-1.72785041e-02, -3.45600067e-02, 0.00000000e+00, -9.32098977e-05, + 1.65728458e-04, -6.49205028e-04, -8.24981328e-05, 4.10920449e-03, + -1.10003219e-04, 0.00000000e+00, -5.15045042e-03, -7.98900699e-03, + 0.00000000e+00, 0.00000000e+00, -8.28004351e-05, -1.51021332e-04]) +2024-11-22 14:58:37,217 - shap - INFO - num_full_subsets = 2 +2024-11-22 14:58:37,229 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-22 14:58:37,235 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-22 14:58:37,275 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-22 14:58:37,368 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-22 14:58:37,379 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-22 14:58:37,567 - shap - INFO - phi = array([ 0.00000000e+00, -1.58921592e-02, 0.00000000e+00, -2.01331423e-05, + -5.04716525e-06, -5.07116946e-04, 2.85889491e-05, -5.10054262e-04, + 0.00000000e+00, 3.59597734e-04, -5.66061833e-05, -2.68997150e-05, + 0.00000000e+00, 0.00000000e+00, 5.30719090e-05, 0.00000000e+00]) +2024-11-22 15:04:47,135 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} +2024-11-22 15:04:48,054 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.919337618172575, best pos: [3.00104154e+00 2.62311705e-01 5.20573580e+02] diff --git a/reports/benchmarking_rmse.jpg b/reports/benchmarking_rmse.jpg new file mode 100644 index 0000000..29d2ef7 Binary files /dev/null and b/reports/benchmarking_rmse.jpg differ diff --git a/reports/report_0/eegalcohol_mcar_CORRELATION.jpg b/reports/report_0/eegalcohol_mcar_CORRELATION.jpg new file mode 100644 index 0000000..fd1b41d Binary files 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a/reports/report_0/eegalcohol_mcar_imputation_time.jpg b/reports/report_0/eegalcohol_mcar_imputation_time.jpg new file mode 100644 index 0000000..8f7588e Binary files /dev/null and b/reports/report_0/eegalcohol_mcar_imputation_time.jpg differ diff --git a/reports/report_0/eegalcohol_mcar_optimization_time.jpg b/reports/report_0/eegalcohol_mcar_optimization_time.jpg new file mode 100644 index 0000000..fb7086d Binary files /dev/null and b/reports/report_0/eegalcohol_mcar_optimization_time.jpg differ diff --git a/reports/report_0/report_eeg-alcohol.txt b/reports/report_0/report_eeg-alcohol.txt new file mode 100644 index 0000000..47c4408 --- /dev/null +++ b/reports/report_0/report_eeg-alcohol.txt @@ -0,0 +1,33 @@ +dictionary of results : {'eegalcohol': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5197922283008971, 'MAE': 0.4543356516868202, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0004687309265136719, 'optimization': 0, 'imputation': 0.00037550926208496094}}, '0.1': {'scores': {'RMSE': 1.0659202645786816, 'MAE': 0.9085417731383956, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0006086826324462891, 'optimization': 0, 'imputation': 0.00027751922607421875}}, '0.2': {'scores': {'RMSE': 1.1400385999631493, 'MAE': 0.9394950730289477, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0007326602935791016, 'optimization': 0, 'imputation': 0.0003376007080078125}}, '0.4': {'scores': {'RMSE': 1.0333061850175014, 'MAE': 0.817160720129779, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0011751651763916016, 'optimization': 0, 'imputation': 0.00022101402282714844}}, '0.6': {'scores': {'RMSE': 1.0938413270459857, 'MAE': 0.8545290213993658, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0027205944061279297, 'optimization': 0, 'imputation': 0.0002579689025878906}}, '0.8': {'scores': {'RMSE': 1.07436956341757, 'MAE': 0.8291370178635111, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.005261421203613281, 'optimization': 0, 'imputation': 0.0002639293670654297}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.37483452324301586, 'MAE': 0.3375262694281006, 'MI': 1.0397207708399179, 'CORRELATION': 0.7365655689896633}, 'times': {'contamination': 0.00044655799865722656, 'optimization': 0.6753551959991455, 'imputation': 0.03617668151855469}}, '0.1': {'scores': {'RMSE': 1.3799678230195285, 'MAE': 1.1003322284844623, 'MI': 1.732867951399863, 'CORRELATION': -0.500100644242659}, 'times': {'contamination': 0.0006062984466552734, 'optimization': 0.6753551959991455, 'imputation': 0.029273509979248047}}, '0.2': {'scores': {'RMSE': 0.5279485898506157, 'MAE': 0.42431581904234256, 'MI': 1.342409426595628, 'CORRELATION': 0.9071070625126642}, 'times': {'contamination': 0.0012328624725341797, 'optimization': 0.6753551959991455, 'imputation': 0.06831860542297363}}, '0.4': {'scores': {'RMSE': 0.6529812630837011, 'MAE': 0.42858056477338186, 'MI': 0.8905639332827393, 'CORRELATION': 0.7722811146383882}, 'times': {'contamination': 0.002039670944213867, 'optimization': 0.6753551959991455, 'imputation': 0.018771648406982422}}, '0.6': {'scores': {'RMSE': 0.6798826891423311, 'MAE': 0.47135122101632737, 'MI': 0.6001676421795947, 'CORRELATION': 0.7742382236368857}, 'times': {'contamination': 0.004843473434448242, 'optimization': 0.6753551959991455, 'imputation': 0.008089065551757812}}, '0.8': {'scores': {'RMSE': 0.7608485588056992, 'MAE': 0.5479154581689161, 'MI': 0.42721564894947844, 'CORRELATION': 0.7017141157422242}, 'times': {'contamination': 0.009251832962036133, 'optimization': 0.6753551959991455, 'imputation': 0.05532050132751465}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.3251125774837754, 'MAE': 0.26797673641099284, 'MI': 1.0397207708399179, 'CORRELATION': 0.6142581896031455}, 'times': {'contamination': 0.0003840923309326172, 'optimization': 1.6969048976898193, 'imputation': 0.20527243614196777}}, '0.1': {'scores': {'RMSE': 0.299492451492057, 'MAE': 0.26432871720074347, 'MI': 1.9061547465398494, 'CORRELATION': 0.967896575643492}, 'times': {'contamination': 0.0003895759582519531, 'optimization': 1.6969048976898193, 'imputation': 0.07459807395935059}}, '0.2': {'scores': {'RMSE': 0.32852543256899075, 'MAE': 0.27202573018354975, 'MI': 1.5996631161656454, 'CORRELATION': 0.9558373872353643}, 'times': {'contamination': 0.0006082057952880859, 'optimization': 1.6969048976898193, 'imputation': 0.05372500419616699}}, '0.4': {'scores': {'RMSE': 0.4508488005700101, 'MAE': 0.34941433537269606, 'MI': 0.8543113555966528, 'CORRELATION': 0.8959297471926679}, 'times': {'contamination': 0.0011188983917236328, 'optimization': 1.6969048976898193, 'imputation': 0.05996990203857422}}, '0.6': {'scores': {'RMSE': 18.797539991079297, 'MAE': 7.812583796335101, 'MI': 0.36244773022350796, 'CORRELATION': 0.6210142190959098}, 'times': {'contamination': 0.003797769546508789, 'optimization': 1.6969048976898193, 'imputation': 0.08603262901306152}}, '0.8': {'scores': {'RMSE': 3.1451455567216193, 'MAE': 1.1637520656636082, 'MI': 0.0643204354315137, 'CORRELATION': 0.22737088719870605}, 'times': {'contamination': 0.004836320877075195, 'optimization': 1.6969048976898193, 'imputation': 0.06641578674316406}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.2311363556202525, 'MAE': 0.22809317150257158, 'MI': 0.6931471805599452, 'CORRELATION': 0.8754093900930757}, 'times': {'contamination': 0.00037360191345214844, 'optimization': 6.430053234100342, 'imputation': 0.02318882942199707}}, '0.1': {'scores': {'RMSE': 0.21734571962767568, 'MAE': 0.20142183555276616, 'MI': 1.4941751382893083, 'CORRELATION': 0.9836625389334559}, 'times': {'contamination': 0.0008437633514404297, 'optimization': 6.430053234100342, 'imputation': 0.03475832939147949}}, '0.2': {'scores': {'RMSE': 0.2763681623559098, 'MAE': 0.21205899863451294, 'MI': 1.692828654044598, 'CORRELATION': 0.9663556239228223}, 'times': {'contamination': 0.0005030632019042969, 'optimization': 6.430053234100342, 'imputation': 0.31818413734436035}}, '0.4': {'scores': {'RMSE': 0.32470532661816204, 'MAE': 0.24836184775095202, 'MI': 1.0631520030142667, 'CORRELATION': 0.9435024215665483}, 'times': {'contamination': 0.0017516613006591797, 'optimization': 6.430053234100342, 'imputation': 1.1410706043243408}}, '0.6': {'scores': {'RMSE': 0.45693859713260937, 'MAE': 0.3350566242376081, 'MI': 0.836724518636222, 'CORRELATION': 0.9015668975756113}, 'times': {'contamination': 0.004426717758178711, 'optimization': 6.430053234100342, 'imputation': 3.560849905014038}}, '0.8': {'scores': {'RMSE': 0.7301676007328138, 'MAE': 0.5391664379693699, 'MI': 0.43198783605819785, 'CORRELATION': 0.7329833767632488}, 'times': {'contamination': 0.007398843765258789, 'optimization': 6.430053234100342, 'imputation': 6.609754800796509}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5024988990302908, 'MAE': 0.3532664138792619, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0004961490631103516, 'optimization': 57.25147891044617, 'imputation': 11.884265422821045}}, '0.1': {'scores': {'RMSE': 1.0479653438678436, 'MAE': 0.88868359265646, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0003769397735595703, 'optimization': 57.25147891044617, 'imputation': 10.59131383895874}}, '0.2': {'scores': {'RMSE': 1.0101544508265186, 'MAE': 0.8577559000889281, 'MI': 0.39764078655391905, 'CORRELATION': 0.3382991313433674}, 'times': {'contamination': 0.0002589225769042969, 'optimization': 57.25147891044617, 'imputation': 9.025201797485352}}, '0.4': {'scores': {'RMSE': 1.1572234865914741, 'MAE': 0.9734353610729596, 'MI': 0.14748851101284274, 'CORRELATION': 0.18549616252501083}, 'times': {'contamination': 0.0016748905181884766, 'optimization': 57.25147891044617, 'imputation': 12.469851970672607}}, '0.6': {'scores': {'RMSE': 1.057870088456209, 'MAE': 0.8489916473101708, 'MI': 0.07704199607608173, 'CORRELATION': 0.12192044664601719}, 'times': {'contamination': 0.0020356178283691406, 'optimization': 57.25147891044617, 'imputation': 15.139116287231445}}, '0.8': {'scores': {'RMSE': 1.1476648377159235, 'MAE': 0.9395411477909109, 'MI': 0.09201849488022656, 'CORRELATION': 0.02754490882006385}, 'times': {'contamination': 0.004424333572387695, 'optimization': 57.25147891044617, 'imputation': 14.801337242126465}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| eegalcohol | mcar | mean | bayesian | 0.05 | 0.5197922283008971 | 0.4543356516868202 | 0.0 | 0 | 0.0004687309265136719 sec | 0 sec| 0.00037550926208496094 sec | +| eegalcohol | mcar | mean | bayesian | 0.1 | 1.0659202645786816 | 0.9085417731383956 | 0.0 | 0 | 0.0006086826324462891 sec | 0 sec| 0.00027751922607421875 sec | +| eegalcohol | mcar | mean | bayesian | 0.2 | 1.1400385999631493 | 0.9394950730289477 | 0.0 | 0 | 0.0007326602935791016 sec | 0 sec| 0.0003376007080078125 sec | +| eegalcohol | mcar | mean | bayesian | 0.4 | 1.0333061850175014 | 0.817160720129779 | 0.0 | 0 | 0.0011751651763916016 sec | 0 sec| 0.00022101402282714844 sec | +| eegalcohol | mcar | mean | bayesian | 0.6 | 1.0938413270459857 | 0.8545290213993658 | 0.0 | 0 | 0.0027205944061279297 sec | 0 sec| 0.0002579689025878906 sec | +| eegalcohol | mcar | mean | bayesian | 0.8 | 1.07436956341757 | 0.8291370178635111 | 0.0 | 0 | 0.005261421203613281 sec | 0 sec| 0.0002639293670654297 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.05 | 0.37483452324301586 | 0.3375262694281006 | 1.0397207708399179 | 0.7365655689896633 | 0.00044655799865722656 sec | 0.6753551959991455 sec| 0.03617668151855469 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.1 | 1.3799678230195285 | 1.1003322284844623 | 1.732867951399863 | -0.500100644242659 | 0.0006062984466552734 sec | 0.6753551959991455 sec| 0.029273509979248047 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.2 | 0.5279485898506157 | 0.42431581904234256 | 1.342409426595628 | 0.9071070625126642 | 0.0012328624725341797 sec | 0.6753551959991455 sec| 0.06831860542297363 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.4 | 0.6529812630837011 | 0.42858056477338186 | 0.8905639332827393 | 0.7722811146383882 | 0.002039670944213867 sec | 0.6753551959991455 sec| 0.018771648406982422 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.6 | 0.6798826891423311 | 0.47135122101632737 | 0.6001676421795947 | 0.7742382236368857 | 0.004843473434448242 sec | 0.6753551959991455 sec| 0.008089065551757812 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.8 | 0.7608485588056992 | 0.5479154581689161 | 0.42721564894947844 | 0.7017141157422242 | 0.009251832962036133 sec | 0.6753551959991455 sec| 0.05532050132751465 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.05 | 0.3251125774837754 | 0.26797673641099284 | 1.0397207708399179 | 0.6142581896031455 | 0.0003840923309326172 sec | 1.6969048976898193 sec| 0.20527243614196777 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.1 | 0.299492451492057 | 0.26432871720074347 | 1.9061547465398494 | 0.967896575643492 | 0.0003895759582519531 sec | 1.6969048976898193 sec| 0.07459807395935059 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.2 | 0.32852543256899075 | 0.27202573018354975 | 1.5996631161656454 | 0.9558373872353643 | 0.0006082057952880859 sec | 1.6969048976898193 sec| 0.05372500419616699 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.4 | 0.4508488005700101 | 0.34941433537269606 | 0.8543113555966528 | 0.8959297471926679 | 0.0011188983917236328 sec | 1.6969048976898193 sec| 0.05996990203857422 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.6 | 18.797539991079297 | 7.812583796335101 | 0.36244773022350796 | 0.6210142190959098 | 0.003797769546508789 sec | 1.6969048976898193 sec| 0.08603262901306152 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.8 | 3.1451455567216193 | 1.1637520656636082 | 0.0643204354315137 | 0.22737088719870605 | 0.004836320877075195 sec | 1.6969048976898193 sec| 0.06641578674316406 sec | +| eegalcohol | mcar | iim | bayesian | 0.05 | 0.2311363556202525 | 0.22809317150257158 | 0.6931471805599452 | 0.8754093900930757 | 0.00037360191345214844 sec | 6.430053234100342 sec| 0.02318882942199707 sec | +| eegalcohol | mcar | iim | bayesian | 0.1 | 0.21734571962767568 | 0.20142183555276616 | 1.4941751382893083 | 0.9836625389334559 | 0.0008437633514404297 sec | 6.430053234100342 sec| 0.03475832939147949 sec | +| eegalcohol | mcar | iim | bayesian | 0.2 | 0.2763681623559098 | 0.21205899863451294 | 1.692828654044598 | 0.9663556239228223 | 0.0005030632019042969 sec | 6.430053234100342 sec| 0.31818413734436035 sec | +| eegalcohol | mcar | iim | bayesian | 0.4 | 0.32470532661816204 | 0.24836184775095202 | 1.0631520030142667 | 0.9435024215665483 | 0.0017516613006591797 sec | 6.430053234100342 sec| 1.1410706043243408 sec | +| eegalcohol | mcar | iim | bayesian | 0.6 | 0.45693859713260937 | 0.3350566242376081 | 0.836724518636222 | 0.9015668975756113 | 0.004426717758178711 sec | 6.430053234100342 sec| 3.560849905014038 sec | +| eegalcohol | mcar | iim | bayesian | 0.8 | 0.7301676007328138 | 0.5391664379693699 | 0.43198783605819785 | 0.7329833767632488 | 0.007398843765258789 sec | 6.430053234100342 sec| 6.609754800796509 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.05 | 0.5024988990302908 | 0.3532664138792619 | 0.0 | 0 | 0.0004961490631103516 sec | 57.25147891044617 sec| 11.884265422821045 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.1 | 1.0479653438678436 | 0.88868359265646 | 0.0 | 0 | 0.0003769397735595703 sec | 57.25147891044617 sec| 10.59131383895874 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.2 | 1.0101544508265186 | 0.8577559000889281 | 0.39764078655391905 | 0.3382991313433674 | 0.0002589225769042969 sec | 57.25147891044617 sec| 9.025201797485352 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.4 | 1.1572234865914741 | 0.9734353610729596 | 0.14748851101284274 | 0.18549616252501083 | 0.0016748905181884766 sec | 57.25147891044617 sec| 12.469851970672607 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.6 | 1.057870088456209 | 0.8489916473101708 | 0.07704199607608173 | 0.12192044664601719 | 0.0020356178283691406 sec | 57.25147891044617 sec| 15.139116287231445 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.8 | 1.1476648377159235 | 0.9395411477909109 | 0.09201849488022656 | 0.02754490882006385 | 0.004424333572387695 sec | 57.25147891044617 sec| 14.801337242126465 sec | diff --git a/setup.py b/setup.py index 7f41470..fb66cce 100644 --- a/setup.py +++ b/setup.py @@ -4,24 +4,24 @@ setuptools.setup( name="imputegap", - version="0.2.2", + version="1.0.1", description="A Library of Imputation Techniques for Time Series Data", long_description=open('README.md').read(), long_description_content_type="text/markdown", url="https://github.com/eXascaleInfolab/ImputeGAP", author="Quentin Nater", author_email="quentin.nater@unifr.ch", - license="GNU General Public License v3.0", + license="MIT License", project_urls = { "Documentation": "https://exascaleinfolab.github.io/ImputeGAP/", "Source" : "https://github.com/eXascaleInfolab/ImputeGAP" }, classifiers=[ - "Development Status :: 2 - Pre-Alpha", + "Development Status :: 4 - Beta", "Intended Audience :: Developers", "Programming Language :: Python :: 3.12", "Topic :: Scientific/Engineering :: Information Analysis", - "License :: OSI Approved :: GNU General Public License v3 (GPLv3)" + "License :: OSI Approved :: MIT License", ], python_requires=">= 3.12.0,<3.12.8", install_requires=open('requirements.txt').read().splitlines(), diff --git a/tests/.coverage b/tests/.coverage new file mode 100644 index 0000000..8167e38 Binary files /dev/null and b/tests/.coverage differ diff --git a/tests/__pycache__/test_benchmarking.cpython-312-pytest-8.3.3.pyc b/tests/__pycache__/test_benchmarking.cpython-312-pytest-8.3.3.pyc new file mode 100644 index 0000000..010f51e Binary files /dev/null and b/tests/__pycache__/test_benchmarking.cpython-312-pytest-8.3.3.pyc differ diff --git a/tests/coverage.log b/tests/coverage.log index 6e89fee..e9bfc98 100644 --- a/tests/coverage.log +++ b/tests/coverage.log @@ -10,18 +10,18 @@ imputegap/algorithms/mrnn.py 9 0 100% imputegap/algorithms/stmvl.py 36 0 100% imputegap/algorithms/zero_impute.py 4 0 100% imputegap/recovery/__init__.py 0 0 100% -imputegap/recovery/evaluation.py 35 0 100% +imputegap/recovery/evaluation.py 39 1 97% imputegap/recovery/explainer.py 290 24 92% -imputegap/recovery/imputation.py 170 2 99% -imputegap/recovery/manager.py 186 10 95% -imputegap/recovery/optimization.py 145 23 84% +imputegap/recovery/imputation.py 172 2 99% +imputegap/recovery/manager.py 200 18 91% +imputegap/recovery/optimization.py 157 28 82% imputegap/tools/__init__.py 0 0 100% imputegap/tools/algorithm_parameters.py 22 0 100% -imputegap/tools/utils.py 123 14 89% +imputegap/tools/utils.py 134 23 83% imputegap/wrapper/AlgoPython/IIM/testerIIM.py 133 52 61% imputegap/wrapper/AlgoPython/MRNN/Data_Loader.py 50 0 100% imputegap/wrapper/AlgoPython/MRNN/M_RNN.py 184 2 99% -imputegap/wrapper/AlgoPython/MRNN/testerMRNN.py 40 4 90% +imputegap/wrapper/AlgoPython/MRNN/testerMRNN.py 36 4 89% imputegap/wrapper/__init__.py 0 0 100% tests/test_contamination_blackout.py 29 1 97% tests/test_contamination_mcar.py 95 4 96% @@ -37,10 +37,10 @@ tests/test_loading.py 55 0 100% tests/test_opti_bayesian_cdrec.py 27 0 100% tests/test_opti_bayesian_cdrec_eeg.py 21 0 100% tests/test_opti_bayesian_iim.py 27 0 100% -tests/test_opti_bayesian_mrnn.py 25 0 100% +tests/test_opti_bayesian_mrnn.py 24 0 100% tests/test_opti_bayesian_stmvl.py 27 0 100% tests/test_opti_greedy_cdrec.py 27 0 100% tests/test_opti_pso_cdrec.py 21 0 100% tests/test_opti_sh_cdrec.py 21 0 100% ---------------------------------------------------------------------- -TOTAL 2115 138 93% \ No newline at end of file +TOTAL 2153 161 93% \ No newline at end of file diff --git a/tests/params/optimal_parameters_e_eeg-alcohol_cdrec.toml b/tests/params/optimal_parameters_e_eeg-alcohol_cdrec.toml new file mode 100644 index 0000000..b5c579c --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-alcohol_cdrec.toml @@ -0,0 +1,4 @@ +[cdrec] +rank = 2 +epsilon = 2.9103006766169248e-5 +iteration = 976 diff --git a/tests/params/optimal_parameters_e_eeg-alcohol_iim.toml b/tests/params/optimal_parameters_e_eeg-alcohol_iim.toml new file mode 100644 index 0000000..1250272 --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-alcohol_iim.toml @@ -0,0 +1,2 @@ +[iim] +learning_neighbors = 27 diff --git a/tests/params/optimal_parameters_e_eeg-alcohol_mrnn.toml b/tests/params/optimal_parameters_e_eeg-alcohol_mrnn.toml new file mode 100644 index 0000000..5cf354f --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-alcohol_mrnn.toml @@ -0,0 +1,4 @@ +[mrnn] +hidden_dim = 11 +learning_rate = 1.6593890383815785e-5 +iterations = 93 diff --git a/tests/params/optimal_parameters_e_eeg-alcohol_stmvl.toml b/tests/params/optimal_parameters_e_eeg-alcohol_stmvl.toml new file mode 100644 index 0000000..baa27de --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-alcohol_stmvl.toml @@ -0,0 +1,4 @@ +[stmvl] +window_size = 40 +gamma = 0.22504996184885026 +alpha = 6 diff --git a/tests/params/optimal_parameters_e_eeg-reading_cdrec.toml b/tests/params/optimal_parameters_e_eeg-reading_cdrec.toml new file mode 100644 index 0000000..8645c2a --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-reading_cdrec.toml @@ -0,0 +1,4 @@ +[cdrec] +rank = 0 +epsilon = 0.005852837813969283 +iteration = 275 diff --git a/tests/params/optimal_parameters_e_eeg-reading_iim.toml b/tests/params/optimal_parameters_e_eeg-reading_iim.toml new file mode 100644 index 0000000..3c2989a --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-reading_iim.toml @@ -0,0 +1,2 @@ +[iim] +learning_neighbors = 2 diff --git a/tests/params/optimal_parameters_e_eeg-reading_mrnn.toml b/tests/params/optimal_parameters_e_eeg-reading_mrnn.toml new file mode 100644 index 0000000..ba9f166 --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-reading_mrnn.toml @@ -0,0 +1,4 @@ +[mrnn] +hidden_dim = 10 +learning_rate = 9.636495791434045e-5 +iterations = 14 diff --git a/tests/params/optimal_parameters_e_eeg-reading_stmvl.toml b/tests/params/optimal_parameters_e_eeg-reading_stmvl.toml new file mode 100644 index 0000000..4df0cda --- /dev/null +++ b/tests/params/optimal_parameters_e_eeg-reading_stmvl.toml @@ -0,0 +1,4 @@ +[stmvl] +window_size = 9 +gamma = 0.00024027179393825565 +alpha = 1 diff --git a/tests/report.log b/tests/report.log index d61c645..22a03f8 100644 --- a/tests/report.log +++ b/tests/report.log @@ -854,3 +854,173 @@ 0.00000000e+00, 0.00000000e+00, 5.30719090e-05, 0.00000000e+00]) 2024-11-14 17:09:00,051 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} 2024-11-14 17:09:00,885 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.919337618172575, best pos: [3.00104154e+00 2.62311705e-01 5.20573580e+02] +2024-11-19 13:56:01,209 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:01,214 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:01,219 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:01,281 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:01,394 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 13:56:01,448 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 13:56:01,650 - shap - INFO - phi = array([ 0.00000000e+00, -1.50023544e-02, 0.00000000e+00, -2.02580848e-05, + 0.00000000e+00, -5.09701368e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 3.61519831e-04, -5.85833154e-05, -1.95096124e-05, + 0.00000000e+00, 0.00000000e+00, 4.17635045e-05, 0.00000000e+00]) +2024-11-19 13:56:01,734 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:01,739 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:01,742 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:01,780 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:01,865 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 13:56:01,870 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:02,028 - shap - INFO - phi = array([ 0.00000000e+00, 6.98755365e-03, 0.00000000e+00, 5.50026649e-05, + 0.00000000e+00, 3.23477891e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79513972e-04, 2.95546563e-05, -1.96320751e-05, + 0.00000000e+00, 0.00000000e+00, 3.64300667e-05, 0.00000000e+00]) +2024-11-19 13:56:02,099 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:02,123 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:02,126 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:02,165 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:02,236 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 13:56:02,238 - shap - INFO - np.sum(self.kernelWeights) = 1.0 +2024-11-19 13:56:02,318 - shap - INFO - phi = array([ 0.00000000e+00, 7.83514370e-03, 0.00000000e+00, 5.74602732e-05, + 0.00000000e+00, 3.20042444e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.79854610e-04, 3.35067254e-05, -2.12039317e-05, + 0.00000000e+00, 0.00000000e+00, 3.39326044e-05, 0.00000000e+00]) +2024-11-19 13:56:02,375 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:02,380 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:02,381 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:02,428 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:02,485 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 13:56:02,488 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:02,648 - shap - INFO - phi = array([ 0.00000000e+00, 7.83300758e-03, 0.00000000e+00, -3.77635568e-05, + 0.00000000e+00, 3.22798631e-04, 0.00000000e+00, 0.00000000e+00, + 0.00000000e+00, 2.06331673e-04, 3.42744327e-05, -2.10990653e-05, + 0.00000000e+00, 0.00000000e+00, 3.66266024e-05, 0.00000000e+00]) +2024-11-19 13:56:02,708 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:02,741 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:02,745 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:02,778 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:02,906 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 13:56:02,911 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 13:56:03,057 - shap - INFO - phi = array([ 0.00000000e+00, 7.85856112e-03, 0.00000000e+00, -3.68080669e-05, + 0.00000000e+00, 3.21980994e-04, -3.16954647e-05, 0.00000000e+00, + 0.00000000e+00, 2.10353156e-04, 3.22765781e-05, -1.67189690e-05, + 0.00000000e+00, 0.00000000e+00, 3.62269527e-05, 0.00000000e+00]) +2024-11-19 13:56:03,140 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:03,162 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:03,168 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:03,216 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:03,304 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 13:56:03,309 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:03,374 - shap - INFO - phi = array([ 0.00000000e+00, -1.54999197e-02, 0.00000000e+00, -1.76832343e-05, + 0.00000000e+00, -7.80192307e-04, 3.02281224e-05, 0.00000000e+00, + 0.00000000e+00, -1.35021310e-03, -5.77968265e-05, 9.95378354e-05, + 0.00000000e+00, 0.00000000e+00, -1.95827178e-04, 0.00000000e+00]) +2024-11-19 13:56:03,475 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:03,480 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-19 13:56:03,485 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 13:56:03,523 - shap - INFO - weight_left = 0.528623703595323 +2024-11-19 13:56:03,624 - shap - INFO - np.sum(w_aug) = 17.999999999999996 +2024-11-19 13:56:03,628 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999999 +2024-11-19 13:56:03,759 - shap - INFO - phi = array([ 0.00000000e+00, 8.73719721e-03, 8.70989873e-05, 0.00000000e+00, + 3.82326113e-03, 1.31848484e-02, 5.20288498e-03, 9.31428142e-03, + 4.69081262e-03, 2.61527800e-03, 1.35926816e-02, 6.77844836e-04, + 1.74856500e-02, -1.47938398e-03, 6.19260167e-03, 2.13404411e-05, + -8.58253570e-05, 2.94089462e-03]) +2024-11-19 13:56:03,873 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:03,879 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-19 13:56:03,883 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-19 13:56:03,966 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-19 13:56:04,067 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-19 13:56:04,074 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-19 13:56:04,194 - shap - INFO - phi = array([-4.27027606e-04, 6.05087701e-03, 1.50737404e-03, -6.98188770e-05, + 4.15252868e-03, -1.19120284e-04, 4.61029650e-03, 2.89296701e-03, + 3.16197712e-03, 1.08629331e-02, 4.47721753e-04, 1.13389074e-02, + -3.31697616e-04, 3.70992275e-03, -3.04629404e-03, -1.59238173e-03, + -1.83253053e-03, 3.71123323e-04, 1.31799752e-03]) +2024-11-19 13:56:04,272 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:04,281 - shap - INFO - remaining_weight_vector = array([0.20732477, 0.16660026, 0.14353254, 0.12957798, 0.12116383, + 0.11662019, 0.11518043]) +2024-11-19 13:56:04,290 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 13:56:04,332 - shap - INFO - weight_left = 0.528623703595323 +2024-11-19 13:56:04,426 - shap - INFO - np.sum(w_aug) = 18.0 +2024-11-19 13:56:04,436 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999998 +2024-11-19 13:56:04,666 - shap - INFO - phi = array([-0.00999016, -0.01238472, 0.00114272, 0.00017878, 0.00491261, + 0.00068625, 0.00612538, 0. , 0.00258732, 0.01295154, + 0.00075907, 0.01299131, -0.00110445, 0.00643902, -0.00222172, + 0. , 0.00056204, 0.00261269]) +2024-11-19 13:56:04,736 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:04,749 - shap - INFO - remaining_weight_vector = array([0.21046803, 0.16837443, 0.14432094, 0.12951879, 0.12026745, + 0.11480075, 0.11224962]) +2024-11-19 13:56:04,754 - shap - INFO - num_paired_subset_sizes = 9 +2024-11-19 13:56:04,785 - shap - INFO - weight_left = 0.5381032434909889 +2024-11-19 13:56:04,873 - shap - INFO - np.sum(w_aug) = 19.0 +2024-11-19 13:56:04,878 - shap - INFO - np.sum(self.kernelWeights) = 0.9999999999999996 +2024-11-19 13:56:05,089 - shap - INFO - phi = array([-9.31286008e-03, -1.31748326e-02, 1.29378700e-03, 1.69462623e-04, + 4.55101767e-03, 3.09714247e-04, 6.49898379e-03, 0.00000000e+00, + 2.49537394e-03, 5.44153541e-03, 9.05871572e-04, 4.47591939e-05, + -1.78593088e-04, 6.48523956e-03, -1.85529336e-03, -4.81050964e-05, + -4.71326274e-04, 1.19859543e-03, 2.93209247e-03]) +2024-11-19 13:56:05,137 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:05,145 - shap - INFO - remaining_weight_vector = array([0.23108621, 0.18664656, 0.16176035, 0.14705486, 0.13865173, + 0.13480029]) +2024-11-19 13:56:05,161 - shap - INFO - num_paired_subset_sizes = 8 +2024-11-19 13:56:05,212 - shap - INFO - weight_left = 0.5181019626448611 +2024-11-19 13:56:05,304 - shap - INFO - np.sum(w_aug) = 17.000000000000007 +2024-11-19 13:56:05,309 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:05,419 - shap - INFO - phi = array([-0.01516889, -0.02375469, 0.00212133, -0.00010974, 0.00498366, + 0.00019539, 0.00562799, -0.00049359, 0.00024028, 0.00213718, + -0.00126649, -0.00473163, -0.00189329, 0.00249173, -0.00193585, + -0.00016726, 0.00179716]) +2024-11-19 13:56:05,506 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:05,510 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:05,515 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:05,551 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:05,652 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 13:56:05,659 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000004 +2024-11-19 13:56:05,866 - shap - INFO - phi = array([-0.01549215, -0.0247453 , 0.00192913, -0.00018629, 0.00321626, + -0.00060339, 0.0020008 , -0.000509 , 0.00221582, -0.00186174, + -0.00520739, -0.00220376, 0.0011295 , -0.00219331, -0.00024098, + 0.00131353]) +2024-11-19 13:56:05,960 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:05,975 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:05,979 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:06,022 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:06,160 - shap - INFO - np.sum(w_aug) = 15.999999999999996 +2024-11-19 13:56:06,166 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:06,369 - shap - INFO - phi = array([-1.80105641e-02, -3.21919336e-02, 3.95965453e-04, -1.83903487e-05, + 1.29916230e-03, -7.92199895e-04, 4.29598682e-04, -4.15250765e-04, + 9.51280340e-04, -3.07515091e-03, -5.30119057e-03, -9.01749893e-03, + 8.06046464e-05, -2.29043339e-03, -9.87074497e-05, -1.69110457e-04]) +2024-11-19 13:56:06,448 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:06,462 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:06,466 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:06,501 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:06,601 - shap - INFO - np.sum(w_aug) = 16.0 +2024-11-19 13:56:06,606 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:06,726 - shap - INFO - phi = array([-1.72785041e-02, -3.45600067e-02, 0.00000000e+00, -9.32098977e-05, + 1.65728458e-04, -6.49205028e-04, -8.24981328e-05, 4.10920449e-03, + -1.10003219e-04, 0.00000000e+00, -5.15045042e-03, -7.98900699e-03, + 0.00000000e+00, 0.00000000e+00, -8.28004351e-05, -1.51021332e-04]) +2024-11-19 13:56:06,819 - shap - INFO - num_full_subsets = 2 +2024-11-19 13:56:06,821 - shap - INFO - remaining_weight_vector = array([0.22727203, 0.18465852, 0.16115653, 0.14772682, 0.14069221, + 0.13849389]) +2024-11-19 13:56:06,824 - shap - INFO - num_paired_subset_sizes = 7 +2024-11-19 13:56:06,859 - shap - INFO - weight_left = 0.5063344810024111 +2024-11-19 13:56:06,938 - shap - INFO - np.sum(w_aug) = 15.999999999999998 +2024-11-19 13:56:06,941 - shap - INFO - np.sum(self.kernelWeights) = 1.0000000000000002 +2024-11-19 13:56:07,090 - shap - INFO - phi = array([ 0.00000000e+00, -1.58921592e-02, 0.00000000e+00, -2.01331423e-05, + -5.04716525e-06, -5.07116946e-04, 2.85889491e-05, -5.10054262e-04, + 0.00000000e+00, 3.59597734e-04, -5.66061833e-05, -2.68997150e-05, + 0.00000000e+00, 0.00000000e+00, 5.30719090e-05, 0.00000000e+00]) +2024-11-19 14:01:18,837 - pyswarms.single.global_best - INFO - Optimize for 10 iters with {'c1': 0.5, 'c2': 0.3, 'w': 0.9} +2024-11-19 14:01:19,620 - pyswarms.single.global_best - INFO - Optimization finished | best cost: 2.919337618172575, best pos: [3.00104154e+00 2.62311705e-01 5.20573580e+02] diff --git a/tests/reports/benchmarking_rmse.jpg b/tests/reports/benchmarking_rmse.jpg new file mode 100644 index 0000000..29d2ef7 Binary files /dev/null and b/tests/reports/benchmarking_rmse.jpg differ diff --git a/tests/reports/report_0/eegalcohol_mcar_CORRELATION.jpg b/tests/reports/report_0/eegalcohol_mcar_CORRELATION.jpg new file mode 100644 index 0000000..fee156e Binary files /dev/null and b/tests/reports/report_0/eegalcohol_mcar_CORRELATION.jpg differ diff --git a/tests/reports/report_0/eegalcohol_mcar_MAE.jpg b/tests/reports/report_0/eegalcohol_mcar_MAE.jpg new file mode 100644 index 0000000..667348a Binary files /dev/null and b/tests/reports/report_0/eegalcohol_mcar_MAE.jpg differ diff --git a/tests/reports/report_0/eegalcohol_mcar_MI.jpg b/tests/reports/report_0/eegalcohol_mcar_MI.jpg new file mode 100644 index 0000000..0e6c9c4 Binary files /dev/null and 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+++ b/tests/reports/report_0/report_eeg-alcohol.txt @@ -0,0 +1,33 @@ +dictionary of results : {'eegalcohol': {'mcar': {'mean': {'bayesian': {'0.05': {'scores': {'RMSE': 0.5197922283008971, 'MAE': 0.4543356516868202, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0005006790161132812, 'optimization': 0, 'imputation': 0.0003077983856201172}}, '0.1': {'scores': {'RMSE': 1.0659202645786816, 'MAE': 0.9085417731383956, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.00016045570373535156, 'optimization': 0, 'imputation': 8.916854858398438e-05}}, '0.2': {'scores': {'RMSE': 1.1400385999631493, 'MAE': 0.9394950730289477, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.00021266937255859375, 'optimization': 0, 'imputation': 8.797645568847656e-05}}, '0.4': {'scores': {'RMSE': 1.0333061850175014, 'MAE': 0.817160720129779, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0005984306335449219, 'optimization': 0, 'imputation': 8.702278137207031e-05}}, '0.6': {'scores': {'RMSE': 1.0938413270459857, 'MAE': 0.8545290213993658, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0017769336700439453, 'optimization': 0, 'imputation': 0.00021004676818847656}}, '0.8': {'scores': {'RMSE': 1.07436956341757, 'MAE': 0.8291370178635111, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0017156600952148438, 'optimization': 0, 'imputation': 0.0001709461212158203}}}}, 'cdrec': {'bayesian': {'0.05': {'scores': {'RMSE': 0.37483452324301586, 'MAE': 0.3375262694281006, 'MI': 1.0397207708399179, 'CORRELATION': 0.7365655689896633}, 'times': {'contamination': 0.0002193450927734375, 'optimization': 0.38846516609191895, 'imputation': 0.004354953765869141}}, '0.1': {'scores': {'RMSE': 1.3799678230195285, 'MAE': 1.1003322284844623, 'MI': 1.732867951399863, 'CORRELATION': -0.500100644242659}, 'times': {'contamination': 0.0001366138458251953, 'optimization': 0.38846516609191895, 'imputation': 0.0026137828826904297}}, '0.2': {'scores': {'RMSE': 0.5279485898506157, 'MAE': 0.42431581904234256, 'MI': 1.342409426595628, 'CORRELATION': 0.9071070625126642}, 'times': {'contamination': 0.00019049644470214844, 'optimization': 0.38846516609191895, 'imputation': 0.011534452438354492}}, '0.4': {'scores': {'RMSE': 0.6529812630837011, 'MAE': 0.42858056477338186, 'MI': 0.8905639332827393, 'CORRELATION': 0.7722811146383882}, 'times': {'contamination': 0.000324249267578125, 'optimization': 0.38846516609191895, 'imputation': 0.004487276077270508}}, '0.6': {'scores': {'RMSE': 0.6798826891423311, 'MAE': 0.47135122101632737, 'MI': 0.6001676421795947, 'CORRELATION': 0.7742382236368857}, 'times': {'contamination': 0.0006392002105712891, 'optimization': 0.38846516609191895, 'imputation': 0.007712364196777344}}, '0.8': {'scores': {'RMSE': 0.7608485588056992, 'MAE': 0.5479154581689161, 'MI': 0.42721564894947844, 'CORRELATION': 0.7017141157422242}, 'times': {'contamination': 0.001476287841796875, 'optimization': 0.38846516609191895, 'imputation': 0.007761716842651367}}}}, 'stmvl': {'bayesian': {'0.05': {'scores': {'RMSE': 0.3251125774837754, 'MAE': 0.26797673641099284, 'MI': 1.0397207708399179, 'CORRELATION': 0.6142581896031455}, 'times': {'contamination': 0.0003612041473388672, 'optimization': 0.20192217826843262, 'imputation': 0.038892269134521484}}, '0.1': {'scores': {'RMSE': 0.299492451492057, 'MAE': 0.26432871720074347, 'MI': 1.9061547465398494, 'CORRELATION': 0.967896575643492}, 'times': {'contamination': 0.0001418590545654297, 'optimization': 0.20192217826843262, 'imputation': 0.04026174545288086}}, '0.2': {'scores': {'RMSE': 0.32852543256899075, 'MAE': 0.27202573018354975, 'MI': 1.5996631161656454, 'CORRELATION': 0.9558373872353643}, 'times': {'contamination': 0.00017714500427246094, 'optimization': 0.20192217826843262, 'imputation': 0.036742210388183594}}, '0.4': {'scores': {'RMSE': 0.4508488005700101, 'MAE': 0.34941433537269606, 'MI': 0.8543113555966528, 'CORRELATION': 0.8959297471926679}, 'times': {'contamination': 0.0003261566162109375, 'optimization': 0.20192217826843262, 'imputation': 0.05383801460266113}}, '0.6': {'scores': {'RMSE': 18.797539991079297, 'MAE': 7.812583796335101, 'MI': 0.36244773022350796, 'CORRELATION': 0.6210142190959098}, 'times': {'contamination': 0.0006361007690429688, 'optimization': 0.20192217826843262, 'imputation': 0.03060746192932129}}, '0.8': {'scores': {'RMSE': 3.1451455567216193, 'MAE': 1.1637520656636082, 'MI': 0.0643204354315137, 'CORRELATION': 0.22737088719870605}, 'times': {'contamination': 0.001417398452758789, 'optimization': 0.20192217826843262, 'imputation': 0.03860116004943848}}}}, 'iim': {'bayesian': {'0.05': {'scores': {'RMSE': 0.2311363556202525, 'MAE': 0.22809317150257158, 'MI': 0.6931471805599452, 'CORRELATION': 0.8754093900930757}, 'times': {'contamination': 0.0001418590545654297, 'optimization': 0.8676083087921143, 'imputation': 0.008327007293701172}}, '0.1': {'scores': {'RMSE': 0.21734571962767568, 'MAE': 0.20142183555276616, 'MI': 1.4941751382893083, 'CORRELATION': 0.9836625389334559}, 'times': {'contamination': 0.0005736351013183594, 'optimization': 0.8676083087921143, 'imputation': 0.017409324645996094}}, '0.2': {'scores': {'RMSE': 0.2763681623559098, 'MAE': 0.21205899863451294, 'MI': 1.692828654044598, 'CORRELATION': 0.9663556239228223}, 'times': {'contamination': 0.0006301403045654297, 'optimization': 0.8676083087921143, 'imputation': 0.05528140068054199}}, '0.4': {'scores': {'RMSE': 0.32470532661816204, 'MAE': 0.24836184775095202, 'MI': 1.0631520030142667, 'CORRELATION': 0.9435024215665483}, 'times': {'contamination': 0.00033092498779296875, 'optimization': 0.8676083087921143, 'imputation': 0.4662141799926758}}, '0.6': {'scores': {'RMSE': 0.45693859713260937, 'MAE': 0.3350566242376081, 'MI': 0.836724518636222, 'CORRELATION': 0.9015668975756113}, 'times': {'contamination': 0.0018763542175292969, 'optimization': 0.8676083087921143, 'imputation': 1.346935749053955}}, '0.8': {'scores': {'RMSE': 0.7301676007328138, 'MAE': 0.5391664379693699, 'MI': 0.43198783605819785, 'CORRELATION': 0.7329833767632488}, 'times': {'contamination': 0.0029129981994628906, 'optimization': 0.8676083087921143, 'imputation': 1.884641408920288}}}}, 'mrnn': {'bayesian': {'0.05': {'scores': {'RMSE': 1.8888571092045499, 'MAE': 1.8500318680460206, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.00013375282287597656, 'optimization': 41.83157467842102, 'imputation': 11.03073787689209}}, '0.1': {'scores': {'RMSE': 1.5313532904397844, 'MAE': 1.3237393808067868, 'MI': 0.0, 'CORRELATION': 0}, 'times': {'contamination': 0.0007200241088867188, 'optimization': 41.83157467842102, 'imputation': 10.905119180679321}}, '0.2': {'scores': {'RMSE': 1.3755299921307305, 'MAE': 1.1483280216501393, 'MI': 0.397640786553919, 'CORRELATION': 0.02308952990216339}, 'times': {'contamination': 0.0004470348358154297, 'optimization': 41.83157467842102, 'imputation': 11.329344034194946}}, '0.4': {'scores': {'RMSE': 1.2745248166990786, 'MAE': 1.0870430599973084, 'MI': 0.1572442415087993, 'CORRELATION': -0.13151214992987914}, 'times': {'contamination': 0.0009887218475341797, 'optimization': 41.83157467842102, 'imputation': 14.02935528755188}}, '0.6': {'scores': {'RMSE': 1.0836713249663261, 'MAE': 0.8821860011769823, 'MI': 0.11389826782453707, 'CORRELATION': 0.14049086543856532}, 'times': {'contamination': 0.0020742416381835938, 'optimization': 41.83157467842102, 'imputation': 13.940861463546753}}, '0.8': {'scores': {'RMSE': 1.3072027837327744, 'MAE': 1.0359769879313836, 'MI': 0.07343223617106094, 'CORRELATION': 0.020957442976883907}, 'times': {'contamination': 0.0034284591674804688, 'optimization': 41.83157467842102, 'imputation': 13.018044233322144}}}}}}} + +| dataset_value | algorithm_value | optimizer_value | scenario_value | x_value | RMSE | MAE | MI | CORRELATION | time_contamination | time_optimization | time_imputation | +| eegalcohol | mcar | mean | bayesian | 0.05 | 0.5197922283008971 | 0.4543356516868202 | 0.0 | 0 | 0.0005006790161132812 sec | 0 sec| 0.0003077983856201172 sec | +| eegalcohol | mcar | mean | bayesian | 0.1 | 1.0659202645786816 | 0.9085417731383956 | 0.0 | 0 | 0.00016045570373535156 sec | 0 sec| 8.916854858398438e-05 sec | +| eegalcohol | mcar | mean | bayesian | 0.2 | 1.1400385999631493 | 0.9394950730289477 | 0.0 | 0 | 0.00021266937255859375 sec | 0 sec| 8.797645568847656e-05 sec | +| eegalcohol | mcar | mean | bayesian | 0.4 | 1.0333061850175014 | 0.817160720129779 | 0.0 | 0 | 0.0005984306335449219 sec | 0 sec| 8.702278137207031e-05 sec | +| eegalcohol | mcar | mean | bayesian | 0.6 | 1.0938413270459857 | 0.8545290213993658 | 0.0 | 0 | 0.0017769336700439453 sec | 0 sec| 0.00021004676818847656 sec | +| eegalcohol | mcar | mean | bayesian | 0.8 | 1.07436956341757 | 0.8291370178635111 | 0.0 | 0 | 0.0017156600952148438 sec | 0 sec| 0.0001709461212158203 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.05 | 0.37483452324301586 | 0.3375262694281006 | 1.0397207708399179 | 0.7365655689896633 | 0.0002193450927734375 sec | 0.38846516609191895 sec| 0.004354953765869141 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.1 | 1.3799678230195285 | 1.1003322284844623 | 1.732867951399863 | -0.500100644242659 | 0.0001366138458251953 sec | 0.38846516609191895 sec| 0.0026137828826904297 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.2 | 0.5279485898506157 | 0.42431581904234256 | 1.342409426595628 | 0.9071070625126642 | 0.00019049644470214844 sec | 0.38846516609191895 sec| 0.011534452438354492 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.4 | 0.6529812630837011 | 0.42858056477338186 | 0.8905639332827393 | 0.7722811146383882 | 0.000324249267578125 sec | 0.38846516609191895 sec| 0.004487276077270508 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.6 | 0.6798826891423311 | 0.47135122101632737 | 0.6001676421795947 | 0.7742382236368857 | 0.0006392002105712891 sec | 0.38846516609191895 sec| 0.007712364196777344 sec | +| eegalcohol | mcar | cdrec | bayesian | 0.8 | 0.7608485588056992 | 0.5479154581689161 | 0.42721564894947844 | 0.7017141157422242 | 0.001476287841796875 sec | 0.38846516609191895 sec| 0.007761716842651367 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.05 | 0.3251125774837754 | 0.26797673641099284 | 1.0397207708399179 | 0.6142581896031455 | 0.0003612041473388672 sec | 0.20192217826843262 sec| 0.038892269134521484 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.1 | 0.299492451492057 | 0.26432871720074347 | 1.9061547465398494 | 0.967896575643492 | 0.0001418590545654297 sec | 0.20192217826843262 sec| 0.04026174545288086 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.2 | 0.32852543256899075 | 0.27202573018354975 | 1.5996631161656454 | 0.9558373872353643 | 0.00017714500427246094 sec | 0.20192217826843262 sec| 0.036742210388183594 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.4 | 0.4508488005700101 | 0.34941433537269606 | 0.8543113555966528 | 0.8959297471926679 | 0.0003261566162109375 sec | 0.20192217826843262 sec| 0.05383801460266113 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.6 | 18.797539991079297 | 7.812583796335101 | 0.36244773022350796 | 0.6210142190959098 | 0.0006361007690429688 sec | 0.20192217826843262 sec| 0.03060746192932129 sec | +| eegalcohol | mcar | stmvl | bayesian | 0.8 | 3.1451455567216193 | 1.1637520656636082 | 0.0643204354315137 | 0.22737088719870605 | 0.001417398452758789 sec | 0.20192217826843262 sec| 0.03860116004943848 sec | +| eegalcohol | mcar | iim | bayesian | 0.05 | 0.2311363556202525 | 0.22809317150257158 | 0.6931471805599452 | 0.8754093900930757 | 0.0001418590545654297 sec | 0.8676083087921143 sec| 0.008327007293701172 sec | +| eegalcohol | mcar | iim | bayesian | 0.1 | 0.21734571962767568 | 0.20142183555276616 | 1.4941751382893083 | 0.9836625389334559 | 0.0005736351013183594 sec | 0.8676083087921143 sec| 0.017409324645996094 sec | +| eegalcohol | mcar | iim | bayesian | 0.2 | 0.2763681623559098 | 0.21205899863451294 | 1.692828654044598 | 0.9663556239228223 | 0.0006301403045654297 sec | 0.8676083087921143 sec| 0.05528140068054199 sec | +| eegalcohol | mcar | iim | bayesian | 0.4 | 0.32470532661816204 | 0.24836184775095202 | 1.0631520030142667 | 0.9435024215665483 | 0.00033092498779296875 sec | 0.8676083087921143 sec| 0.4662141799926758 sec | +| eegalcohol | mcar | iim | bayesian | 0.6 | 0.45693859713260937 | 0.3350566242376081 | 0.836724518636222 | 0.9015668975756113 | 0.0018763542175292969 sec | 0.8676083087921143 sec| 1.346935749053955 sec | +| eegalcohol | mcar | iim | bayesian | 0.8 | 0.7301676007328138 | 0.5391664379693699 | 0.43198783605819785 | 0.7329833767632488 | 0.0029129981994628906 sec | 0.8676083087921143 sec| 1.884641408920288 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.05 | 1.8888571092045499 | 1.8500318680460206 | 0.0 | 0 | 0.00013375282287597656 sec | 41.83157467842102 sec| 11.03073787689209 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.1 | 1.5313532904397844 | 1.3237393808067868 | 0.0 | 0 | 0.0007200241088867188 sec | 41.83157467842102 sec| 10.905119180679321 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.2 | 1.3755299921307305 | 1.1483280216501393 | 0.397640786553919 | 0.02308952990216339 | 0.0004470348358154297 sec | 41.83157467842102 sec| 11.329344034194946 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.4 | 1.2745248166990786 | 1.0870430599973084 | 0.1572442415087993 | -0.13151214992987914 | 0.0009887218475341797 sec | 41.83157467842102 sec| 14.02935528755188 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.6 | 1.0836713249663261 | 0.8821860011769823 | 0.11389826782453707 | 0.14049086543856532 | 0.0020742416381835938 sec | 41.83157467842102 sec| 13.940861463546753 sec | +| eegalcohol | mcar | mrnn | bayesian | 0.8 | 1.3072027837327744 | 1.0359769879313836 | 0.07343223617106094 | 0.020957442976883907 | 0.0034284591674804688 sec | 41.83157467842102 sec| 13.018044233322144 sec | diff --git a/tests/test_benchmarking.py b/tests/test_benchmarking.py new file mode 100644 index 0000000..565af3a --- /dev/null +++ b/tests/test_benchmarking.py @@ -0,0 +1,200 @@ +import unittest +from imputegap.recovery.benchmarking import Benchmarking + +class TestBenchmarking(unittest.TestCase): + + def test_benchmarking(self): + """ + the goal is to test if only the simple imputation with ST-MVL has the expected outcome + """ + expected_datasets = ["eeg-alcohol"] + + opti_bayesian = {"optimizer": "bayesian", "options": {"n_calls": 2, "n_random_starts": 50, "acq_func": "gp_hedge", "selected_metrics": "RMSE"}} + optimizers = [opti_bayesian] + + algorithms_full = ["mean", "cdrec", "stmvl", "iim", "mrnn"] + + scenarios_small = ["mcar"] + + x_axis = [0.05, 0.1, 0.2, 0.4, 0.6, 0.8] + + results_benchmarking = Benchmarking().comprehensive_evaluation(datasets=expected_datasets, optimizers=optimizers, algorithms=algorithms_full, scenarios=scenarios_small, x_axis=x_axis, already_optimized=False, reports=-1) + + expected_datasets = ["eegalcohol"] + + # Check that all datasets exist + actual_datasets = list(results_benchmarking.keys()) + self.assertCountEqual( + actual_datasets, expected_datasets, + f"Missing datasets. Expected: {expected_datasets}, Found: {actual_datasets}" + ) + + # For each dataset, validate structure and values + for dataset, dataset_data in results_benchmarking.items(): + if not dataset_data: # If dataset is empty, skip validation + continue + + # Check that scenarios exist (e.g., 'mcar') + self.assertIn( + "mcar", dataset_data, + f"Dataset '{dataset}' is missing 'mcar' scenario." + ) + scenario_data = dataset_data["mcar"] + + # Check that all algorithms exist + expected_algorithms = {"stmvl", "cdrec", "iim", "mean", "mrnn"} + actual_algorithms = set(scenario_data.keys()) + self.assertTrue( + expected_algorithms.issubset(actual_algorithms), + f"Missing algorithms in dataset '{dataset}'. Expected: {expected_algorithms}, Found: {actual_algorithms}" + ) + + # Check that each algorithm contains the expected keys + expected_keys = {"0.05", "0.1", "0.2", "0.4", "0.8"} + for algorithm, algorithm_data in scenario_data.items(): + for key in expected_keys: + self.assertIn( + key, algorithm_data.get("bayesian", {}), + f"Algorithm '{algorithm}' in dataset '{dataset}' is missing key '{key}'." + ) + + sub_data = algorithm_data["bayesian"].get(key, {}) + for score_key, score_value in sub_data.get("scores", {}).items(): + self.assertIsInstance( + score_value, + (float, int), # Correct usage + f"Score '{score_key}' in dataset '{dataset}', algorithm '{algorithm}', key '{key}' is not a float or int." + ) + for time_key, time_value in sub_data.get("times", {}).items(): + self.assertIsInstance( + time_value, + (float, int), # Correct usage + f"Time '{time_key}' in dataset '{dataset}', algorithm '{algorithm}', key '{key}' is not a float." + ) + + def test_benchmarking_matrix(self): + """ + the goal is to test if only the simple imputation with ST-MVL has the expected outcome + """ + alpha_1 = {"eeg-alcohol": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": 0, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": -10, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": -40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": -30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": 40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + alpha_2 = {"eeg-alcohol": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": -1, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": 50, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": 20, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": 30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": -100, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + beta_1 = {"eeg-reading": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": 0, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": -10, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": -40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": -30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": 40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + beta_2 = {"eeg-reading": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": -1, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": 50, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": 20, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": 30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": -100, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + delta_1 = {"fmri-objectviewing": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": 0, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": -10, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": -40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": -30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": 40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + delta_2 = {"fmri-objectviewing": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": -1, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": 50, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": 20, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": 30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": -100, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + epsilon_1 = {"chlorine": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": 0, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": -10, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": -40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": -30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": 40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + epsilon_2 = {"chlorine": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": -1, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": 50, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": 20, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": 30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": -100, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + gamma_1 = {"drift": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": 0, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": -10, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": -40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": -30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": 40, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + gamma_2 = {"drift": {"mcar": + {"mean": {"bayesian": { "0.05": {"scores": {"RMSE": -1, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "cdrec": {"bayesian": { "0.05": {"scores": {"RMSE": 50, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "stmvl": {"bayesian": { "0.05": {"scores": {"RMSE": 20, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "mrnn": {"bayesian": { "0.05": {"scores": {"RMSE": 30, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + "iim": {"bayesian": { "0.05": {"scores": {"RMSE": -100, "MAE": 1, "MI": 2, "CORRELATION": 3}, "times": {"contamination": 4, "optimization": 5, "imputation": 6}}, "0.2": {"scores": {"RMSE": 1, "MAE": 2, "MI": 3, "CORRELATION": 4}, "times": {"contamination": 5, "optimization": 6, "imputation": 7}}, "0.4": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.6": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}, "0.8": {"scores": {"RMSE": 0.5, "MAE": 1.5, "MI": 2.5, "CORRELATION": 3.5}, "times": {"contamination": 4.5, "optimization": 5.5, "imputation": 6.5}}}}, + }}} + + scores_list, algos, sets = Benchmarking().avg_results(alpha_1, alpha_2, beta_1, beta_2, delta_1, delta_2, epsilon_1, epsilon_2, gamma_1, gamma_2) + + print(scores_list) + + # Check that algos contains all expected algorithms + expected_algos = ["stmvl", "cdrec", "iim", "mean", "mrnn"] + self.assertCountEqual( + algos, expected_algos, + f"Missing algorithms in Benchmarking. Expected: {expected_algos}, Found: {algos}" + ) + + # Check that sets contains all expected datasets + expected_sets = ["eeg-alcohol", "eeg-reading", "fmri-objectviewing", "chlorine", "drift"] + self.assertCountEqual( + sets, expected_sets, + f"Missing datasets in Benchmarking. Expected: {expected_sets}, Found: {sets}" + ) + + # Check average values for each dataset and algorithm + algo_order = ['cdrec', 'iim', 'mean', 'mrnn', 'stmvl'] + expected_rmse = { + "cdrec": 4.5, + "iim": -5.5, + "mean": 0.4, + "mrnn": 0.5, + "stmvl": -1.5 + } + + # Iterate through the matrix + for i, dataset_scores in enumerate(scores_list): + for j, rmse_value in enumerate(dataset_scores): + algo = algo_order[j] # Match the algorithm based on column index + expected_value = expected_rmse[algo] + self.assertEqual( + rmse_value, + expected_value, + f"Unexpected RMSE for algorithm '{algo}' at dataset index {i}." + ) + + validation = Benchmarking().generate_matrix(scores_list, algos, sets, "./reports", False) + + self.assertTrue(validation)