Global Variables

The sam_ml library has some global variables that one can set to modify output or change default values of functions.

Note

You have to set the global variables before the first import of the sam_ml library if you want to use them.

Logging

You can set the log level of the sam_ml library with the environment variable SAM_ML_LOG_LEVEL. (the default value is log level “info”)

Example:

import os
os.environ["SAM_ML_LOG_LEVEL"] = "debug"

from sam_ml.models.classifier import LR

n_jobs

Some functions in sklearn like crossvalidate and also some models like RandomForestClassifier have a parameter n_jobs for parallel running. In sam_ml, this is set to “-1” (use all capacity) as default. You can change it with the global variable SAM_ML_N_JOBS. Possible values are “-1”, “none” (no parallelisation), or “<positive integer>” (for number of workers).

Example:

import os
os.environ["SAM_ML_N_JOBS"] = "-1"

from sam_ml.models.classifier import LR

Note

This was added because of some issues with the n_jobs parameter on a windows virtual machine.

Sounds On

The functions in the class CTest make a microwave-finish-sound when they trained the model so that the Data Scientist knows that they are finished if he/she/they are doing something else in the train time (e.g. coffee-break). In sam_ml, this is set to “True” (make sound) as default. You can change it with the global variable SAM_ML_SOUND_ON. Possible values are “True” and “False”.

Example:

import os
os.environ["SAM_ML_SOUND_ON"] = "True"

from sam_ml.models.classifier import LR

Note

This was added because of some issues when the running machine has no audio output (e.g., GitHub actions).

Scoring Variables

The following global variables are for setting the classification scoring parameters at the start of your code so that you do not have to worry about setting the parameters in every function. The values can be the following:

SAM_ML_AVG has to be “none”, “micro”, “macro”, “binary”, or “weighted”
SAM_ML_POS_LABEL has to be “-1” or a string of an integer greater or equal 0
SAM_ML_SCORING has to be “precision”, “recall”, “accuracy”, “s_score”, or “l_score”
SAM_ML_SECONDARY_SCORING has to be “none”, “precision”, or “recall”
SAM_ML_STRENGTH has to be a string of an integer greater 0

Example:

import os
os.environ["SAM_ML_AVG"] = "macro"
os.environ["SAM_ML_POS_LABEL"] = "-1"
os.environ["SAM_ML_SCORING"] = "s_score"
os.environ["SAM_ML_SECONDARY_SCORING"] = "none"
os.environ["SAM_ML_STRENGTH"] = "3"

from sam_ml.models.classifier import LR