Package: evalAIRR
evalAIRRA tool that allows comparison of real and simulated AIRR datasets by providing different statistical indicators and dataset visualizations in one report.InstallationIt is recommended to use a virtual python environment to run evalAIRR if another python environment is used. Here is a quick guide on how you can set up a virtual environment:https://docs.python.org/3/tutorial/venv.html#creating-virtual-environmentsInstall using pipRun this command to install the evalAIRR package:pip install evalairrQuickstartevalAIRR uses a YAML file for configuration. If you are unfamiliar with how YAML files are structured, read this guide to the syntax:https://docs.fileformat.com/programming/yaml/#syntaxThis is the stucture of a sample report configuration file you can use to start off with (it is included in the repository location ./yaml_files/quickstart.yaml):datasets:
  real:
    path: ./data/real_data.csv
  sim:
    path: ./data/sim_data.csv
reports:
  feature_based:
    report1:
      features:
        - CAS
        - SAS
      report_types:
        - ks
        - distr_densityplot
        - distance
        - statistics
  observation_based:
    report1:
      observations:
        - all
      report_types:
        - distr_densityplot
  general:
    feat_mean_vs_variance:
    obs_mean_vs_variance:
    pca_2d_feat:
    pca_2d_obs:
    corr_feat_hist:
    corr_obs_hist:
output:
  path: ./output/report.htmlThis report will process the two provided datasets (real and simulated) with encoded kmer data, and create an HTML report with multiple report types. These include feature-based report types - Kolmogorov–Smirnov test (indicated by report typeks), a feature distribution density plot (indicated by report typedistr_densityplot), Euclidean distance measures (indicated by report typedistance) and descriptive statistics (mean, median, variance and standard deviation)(indicated by report typestatistics) for the featuresCASandSAS. It will then export the report to the path./output/report.html. It will also create an observation-based report with the feature distribution density plot (indicated by report typedistr_densityplot) for all observations (indicated by keywordallin the observation list). Finally, these general reports will be generated: feature mean compared with feature variance (indicated by report typefeat_mean_vs_variance), observation mean compared with observation variance (indicated by report typeobs_mean_vs_variance), two dimensional representation of all features using PCA (indicated by report typepca_2d_feat), two dimensional representation of all observations using PCA (indicated by report typepca_2d_obs), feature correlation coefficient distribution histogram (indicated by report typecorr_feat_hist) and an observation correlation coefficient distribution histogram (indicated by report typecorr_obs_hist). More details on what reports can be created can be found in theYAML Configuration Guidelinessection.The repository contains sample datafiles and a quickstart YAML configuration files. You can clone the repository and run evalAIRR within it to use sample data.Within the cloned repository run the command:evalairr -i ./yaml_files/quickstart.yamlThe report will be generated in the specified output path in the configuration file or, if a specific path is not provided, in<CURRENT_DIRECTORY>/output/report.html. The report is exported in the HTML format.YAML Configuration GuidelinesThe configuration YAML file consists of 3 main sections:datasets,reportsandoutput.DatasetsIn thedatasetssection, you have to provide paths to a real and a simulated datasets that you are comparing. CSV files with encoded kmer data are supported. This can be done by specifying the file path of each file in thepathvariable under the sectionsrealandsimrespectively. Here is an example of how a configureddatasetssection looks like:datasets:
  real:
    path: ./data/real_data.csv
  sim:
    path: ./data/sim_data.csvReportsIn thereportssection, you can provide the list of report types you want to create and their parameters. There are three types of report groups depending on the different parameters:feature_based,observation_basedandgeneric. Here is the list of reports you can create that compare the features of the real dataset with the simulated dataset:Feature-based reportsks- Kolmogorov–Smirnov statistic. Parameters: list of features you are creating the report for.distr_histogram- feature distribution histogram. Parameters: list of features you are creating the report for.distr_boxplot- feature distribution boxplot. Parameters: list of features you are creating the report for.distr_violinplot- feature distribution violin plot. Parameters: list of features you are creating the report for.distr_densityplot- feature distribution density plot. Parameters: list of features you are creating the report for.distance- Euclidean distance between the real and simulated feature. Parameters: list of features you are creating the report for.statistics- statistical indicators (mean, median, standard deviation and variance) of a feature in both real and simulated datasets. Parameters: list of features you are creating the report for.Observation-based reportsks- Kolmogorov–Smirnov statistic. Parameters: list of observations you are creating the report for.distr_histogram- observation distribution histogram. Parameters: list of observations you are creating the report for.distr_boxplot- observation distribution boxplot. Parameters: list of observations you are creating the report for.distr_violinplot- observation distribution violin plot. Parameters: list of observations you are creating the report for.distr_densityplot- observation distribution density plot. Parameters: list of observations you are creating the report for. The observation indexallcan be used to report on all observations in one plot.with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset (only applies in reports withallobservations).ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.distance- Euclidean distance between the real and simulated observation. Parameters: list of observations you are creating the report for.statistics- statistical indicators (mean, median, standard deviation and variance) of an observation in both real and simulated datasets. Parameters: list of observations you are creating the report for.General reportsks_feat- Kolmogorov–Smirnov statistic for all features. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/ks_feat.csv). The csv file contains two rows, with the first row containing the ks-statistic and the second one - the p-values.ks_obs- Kolmogorov–Smirnov statistic for all observations. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/ks_obs.csv). The csv file contains two rows, with the first row containing the ks-statistic and the second one - the p-values.copula_2d- a 2D scatter plot that displays two features in a Gausian Multivariate copula space. Parameters: a report section of any name, under which the compared features are specified.copula_3d- a 3D scatter plot that displays three features in a Gausian Multivariate copula space. Parameters: a report section of any name, under which the compared features are specified.feat_mean_vs_variance- a scatter plot that displays the mean value of every feature on one axis and the variance of every feature on the other axis. Parameters:with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.obs_mean_vs_variance- a scatter plot that displays the mean value of every observation on one axis and the variance of every observation on the other axis. Parameters:with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.corr- correlation matrix heatmaps of the real and simulated datasets. Parameters:reduce_to_n_features- an optional parameter for dimensionality reduction using PCA. The number of features to reduce the dataset to (must be reduce_to_n_features < min(n_observations, n_features)).with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.corr_feat_hist- feature correlation matrix distribution histogram for the real and simulated datasets. Parameters:n_bins- an optional parameter that sets the number of bins in the histogram (default value is 30).reduce_to_n_features- an optional parameter for dimensionality reduction using PCA. The number of features to reduce the dataset to (must be reduce_to_n_features < min(n_observations, n_features)).with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.corr_obs_hist- observation correlation matrix distribution histogram for the real and simulated datasets. Parameters:n_bins- an optional parameter that sets the number of bins in the histogram (default value is 30).reduce_to_n_obs- an optional parameter for dimensionality reduction using PCA. The number of observations to reduce the dataset to (must be reduce_to_n_obs < min(n_observations, n_features)).with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.corr_csv- CSV file exporting of the difference between correlation matrices of the real and simulated datasets. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/corr.csv).pca_2d_feat- two feature-level scatter plots with both datasets reduced to two dimensions using PCA. Parameters:with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.pca_2d_obs- two observation-level scatter plots with both datasets reduced to two dimensions using PCA. Parameters:with_ml_sim- optional parameter, which if True, instructs the report to include a comparison with a generated dataset using a GaussianProcessRegressor machine learning model trained on the real dataset.ml_random_state- optional integer parameter, relevant only ifwith_ml_simis set to True, which sets a seed in the machine learning model random number generation.distance_feat- Euclidean distance between the real and simulated features. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/dist.csv).statistics_feat- statistical indicators (mean, median, standard deviation and variance) of all features in both real and simulated datasets. Parameters:output_dir- optional parameter, that specifies the directory for the csv files in which the csv result filesreal_stat.csvandsim_stat.csvwill be exported to (default value is set to./output/). Each csv file contain four rows, each with a different statistic: 1 - mean, 2 - median, 3 - standard deviation, 4 - variance.distance_obs- Euclidean distance between the real and simulated observations. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/obs_dist.csv).statistics_obs- statistical indicators (mean, median, standard deviation and variance) of all observation in both real and simulated datasets. Parameters:output_dir- optional parameter, that specifies the directory for the csv files in which the csv result filesreal_obs_stat.csvandsim_obs_stat.csvwill be exported to (default value is set to./output/). Each csv file contain four rows, each with a different statistic: 1 - mean, 2 - median, 3 - standard deviation, 4 - variance.jensen_shannon_feat- Jensen-Shannon divergence metric between the real and simulated features. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/jenshan.csv).jensen_shannon_obs- Jensen-Shannon divergence metric between the real and simulated observations. Parameters:output- optional parameter, that specifies the path of text/csv file the results will be exported to (default value is set to./output/obs_jenshan.csv).Here is a samplereportssection of a configuration file containing all of the reports:reports:
  feature_based:
    report1:
      features:
        - CAS
        - SAS
      report_types:
        - ks
        - distr_histogram
        - distr_boxplot
        - distr_violinplot
        - distr_densityplot
        - distance
        - statistics
  observation_based:
    report1:
      observations:
        - 20
      report_types:
        - ks
        - distr_histogram
        - distr_boxplot
        - distr_violinplot
        - distr_densityplot
        - distance
        - statistics
    report2:
      observations:
        - all
      report_types:
        - distr_densityplot
      with_ml_sim: True
      ml_random_state: 0
  general:
    copula_2d:
      report1:
        - CAS
        - SAS
    copula_3d:
      report1:
        - CAS
        - SAS
        - TGT
    feat_mean_vs_variance:
      with_ml_sim: True
      ml_random_state: 0
    obs_mean_vs_variance:
      with_ml_sim: True
      ml_random_state: 0
    corr_hist:
      n_bins: 30
      with_ml_sim: True
      ml_random_state: 0
      reduce_to_n_features: 200
    corr:
      reduce_to_n_features: 200
      with_ml_sim: True
      ml_random_state: 0
    pca_2d_feat:
      with_ml_sim: True
      ml_random_state: 0
    pca_2d_obs:
      with_ml_sim: True
      ml_random_state: 0
    corr_csv:
      output: ./output/corr.csv
    ks_feat:
      output: ./output/ks_feat.csv
    ks_obs:
      output: ./output/ks_obs.csv
    statistics_feat:
      output_dir: ./output/
    statistics_obs:
      output_dir: ./output/
    distance_feat:
      output: ./output/dist.csv
    distance_obs:
      output: ./output/obs_dist.csv
    jensen_shannon_feat:
      output: ./output/jenshan.csv
    jensen_shannon_obs:
      output: ./output/obs_jenshan.csvOutputAn optional section where you can specify the file path of the generated report. The default path of the generated report is<CURRENT_DIRECTORY>/output/report.html. The report is exported in the HTML format. If you declare the path as 'NONE', the report will not be created.An example output section:output:
  path: ./output/report.htmlFor example, this output section would result in a report file not being created:output:
  path: NONE