Default parameters

This documents details the default parameters used in the ROBERT program.

AQME

Parameters

csv_namestr, default=''

Name of the CSV file containing the database with SMILES and code_name columns. A path can be provided (i.e. 'C:/Users/FOLDER/FILE.csv').

destinationstr, default=None,

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

ystr, default=''

Name of the column containing the response variable in the input CSV file (i.e. 'solubility').

qdescp_keywordsstr, default=''

Add extra keywords to the AQME-QDESCP run (i.e. qdescp_keywords="--qdescp_atoms ['Ir']")

descp_lvlstr, default='interpret'

Type of descriptor to be used in the AQME-ROBERT workflow. Options are 'interpret', 'denovo' or 'full'.

CURATE

Parameters

csv_namestr, default=''

Name of the CSV file containing the database. A path can be provided (i.e. 'C:/Users/FOLDER/FILE.csv').

ystr, default=''

Name of the column containing the response variable in the input CSV file (i.e. 'solubility').

discardlist, default=[]

List containing the columns of the input CSV file that will not be included as descriptors in the curated CSV file (i.e. "['name','SMILES']").

ignorelist, default=[]

List containing the columns of the input CSV file that will be ignored during the curation process (i.e. "['name','SMILES']"). The descriptors will be included in the curated CSV file. The y value is automatically ignored.

namesstr, default=''

Column of the names for each datapoint. Names are used to print outliers.

destinationstr, default=None,

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

categoricalstr, default='onehot'

Mode to convert data from columns with categorical variables. As an example, a variable containing 4 types of C atoms (i.e. primary, secondary, tertiary, quaternary) will be converted into categorical variables. Options: 1. 'onehot' (for one-hot encoding, ROBERT will create a descriptor for each type of C atom using 0s and 1s to indicate whether the C type is present) 2. 'numbers' (to describe the C atoms with numbers: 1, 2, 3, 4).

corr_filter_xbool, default=True

Activate the correlation filters of descriptors, based on the correlation of the descriptors with other descriptors (x filter).

corr_filter_ybool, default=False

Activate the correlation filters of descriptors, based on the correlation of the descriptors with the y values (y filter, for noise). This filter is only suggested for MVL.

desc_thresfloat, default=25

Threshold for the descriptor-to-datapoints ratio to loose the correlation filter. By default, the correlation filter is loosen if there are 25 times more datapoints than descriptors.

thres_xfloat, default=0.7

Thresolhold to discard descriptors based on high R**2 correlation with other descriptors (i.e. if thres_x=0.7, variables that show R**2 > 0.7 will be discarded).

thres_yfloat, default=0.001

Thresolhold to discard descriptors with poor correlation with the y values based on R**2 (i.e. if thres_y=0.001, variables that show R**2 < 0.001 will be discarded).

seedint, default=0

Random seed used in RFECV feature selector and other protocols.

kfoldint, default=5

Number of random data splits for the cross-validation of the RFECV feature selector.

repeat_kfoldsint, default=10

Number of repetitions for the k-fold cross-validation of the RFECV feature selector.

auto_typebool, default=True

If there are only two y values, the program automatically changes the type of problem to classification.

auto_fillbool, default = True

Complete missing values in columns with descriptors of "float" type using a KNN imputer

GENERATE

Parameters

csv_namestr, default=''

Name of the CSV file containing the database. A path can be provided (i.e. 'C:/Users/FOLDER/FILE.csv').

ystr, default=''

Name of the column containing the response variable in the input CSV file (i.e. 'solubility').

discardlist, default=[]

List containing the columns of the input CSV file that will not be included as descriptors in the curated CSV file (i.e. ['name','SMILES']).

ignorelist, default=[]

List containing the columns of the input CSV file that will be ignored during the curation process (i.e. ['name','SMILES']). The descriptors will be included in the curated CSV file. The y value is automatically ignored.

destinationstr, default=None

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

auto_typebool, default=True

If there are only two y values, the program automatically changes the type of problem to classification.

modellist, default=['RF','GB','NN','MVL'] (regression) and default=['RF','GB','NN','AdaB'] (classification)

ML models available: 1. 'RF' (Random forest) 2. 'MVL' (Multivariate lineal models) 3. 'GB' (Gradient boosting) 4. 'NN' (MLP neural network) 5. 'GP' (Gaussian Process) 6. 'AdaB' (AdaBoost)

custom_paramsstr, default=None

Define new parameters for the ML models used in the hyperoptimization workflow. The path to the folder containing all the yaml files should be specified (i.e. custom_params='YAML_FOLDER')

typestr, default='reg'

Type of the pedictions. Options: 1. 'reg' (Regressor) 2. 'clas' (Classifier)

seedint, default=0

Random seed used in the ML predictor models and other protocols.

error_typestr, default: rmse (regression), mcc (classification)

Target value used during the hyperopt optimization. Options: Regression: 1. rmse (root-mean-square error) 2. mae (mean absolute error) 3. r2 (R-squared, not recommended since R2 might be good even with high errors in small datasets) Classification: 1. mcc (Matthew's correlation coefficient) 2. f1 (F1 score) 3. acc (accuracy, fraction of correct predictions)

init_pointsint, default=10

Number of initial points for Bayesian optimization (exploration)

n_iterint, default=10

Number of iterations for Bayesian optimization (exploitation)

expect_improvint, default=0.05

Expected improvement for Bayesian optimization

pfi_filterbool, default=True

Activate the PFI filter of descriptors.

pfi_epochsint, default=5

Sets the number of times a feature is randomly shuffled during the PFI analysis (standard from sklearn webpage: 5).

pfi_thresholdfloat, default=0.2

The PFI filter is X% of the model's score (% adjusted, 0.2 = 20% of the total score during PFI).

pfi_maxint, default=0

Number of features to keep after the PFI filter. If pfi_max is 0, all the features that pass the PFI filter are used.

auto_testbool, default=True

Raises % of test points to 20% if test_set is lower than that.

test_setfloat, default=0.2

Amount of datapoints to separate as external test set (0.2 = 20%). These points will not be used during the hyperoptimization, and PREDICT will use the points as test set during ROBERT workflows. Select --test_set 0 to use only training and validation.

kfoldint, default=5

Number of random data splits for the cross-validation of the models.

repeat_kfoldsint, default=10

Number of repetitions for the k-fold cross-validation of the models.

splitstr, default= 'even' (regression) or 'rnd' (classification)

Specifies how the data is split into training and test sets. Options: 1. 'even': splits the data evenly into training and test sets. 2. 'RND': randomly splits the data. 3. 'stratified': splits the data while preserving the distribution of the target variable. 4. 'KN': uses a k-means approach to select representative samples for training (good for intrapolation, bad for extrapolation). 5. 'extra_q1': selects the 20% lowest values. 6. 'extra_q5': selects the 20% highest values.

PREDICT

Parameters

destinationstr, default=None,

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

params_dirstr, default=''

Folder containing the database and parameters of the ML model.

csv_teststr, default=''

Name of the CSV file containing the test set (if any). A path can be provided (i.e. 'C:/Users/FOLDER/FILE.csv').

t_valuefloat, default=2

t-value that will be the threshold to identify outliers (check tables for t-values elsewhere). The higher the t-value the more restrictive the analysis will be (i.e. there will be more outliers with t-value=1 than with t-value = 4).

alphafloat, default=0.05

Significance level, or probability of making a wrong decision. This parameter is related to the confidence intervals (i.e. 1-alpha is the confidence interval). By default, an alpha value of 0.05 is used, which corresponds to a confidence interval of 95%.

shap_showint, default=10,

Number of descriptors shown in the plot of the SHAP analysis.

pfi_showint, default=10,

Number of descriptors shown in the plot of the PFI analysis.

pfi_epochsint, default=5,

Sets the number of times a feature is randomly shuffled during the PFI analysis (standard from sklearn webpage: 5).

namesstr, default=''

Column of the names for each datapoint. Names are used to print outliers.

VERIFY

Parameters

destinationstr, default=None,

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

params_dirstr, default=''

Folder containing the database and parameters of the ML model to analyze.

seedint, default=0

Random seed used in the ML predictor models and other protocols.

kfoldint, default=5

Number of random data splits for the cross-validation of the models.

repeat_kfoldsint, default=10

Number of repetitions for the k-fold cross-validation of the models.

REPORT

Parameters

destinationstr, default=None,

Directory to create the output file(s).

varfilestr, default=None

Option to parse the variables using a yaml file (specify the filename, i.e. varfile=FILE.yaml).

report_moduleslist of str, default=['CURATE','GENERATE','VERIFY','PREDICT']

List of the modules to include in the report.

debug_reportbool, default=False

Debug mode using during the pytests of report.py