CURATE

class curate(**kwargs)

Class containing all the functions from the CURATE module.

Parameters

kwargsargument class

Specify any arguments from the CURATE module (for a complete list of variables, visit the ROBERT documentation)

dup_filter(csv_df_dup)

Removes duplicated datapoints and descriptors

save_curate(csv_df)

Saves the curated database and options used in 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