Metrics

The class for testing models and reporting results

A collection of methods to simplify your code.

smltk.metrics.Metrics

Targets and Predictions management

smltk.metrics.Metrics.split_tuples	Splits tuples of sample and its target in the relative lists
smltk.metrics.Metrics.prediction	Predicts with your model

Metrics management

smltk.metrics.Metrics.create_confusion_matrix	Creates and prints confusion matrix
smltk.metrics.Metrics.get_classification_metrics	Gets classification metrics
smltk.metrics.Metrics.scoring	Gets classification metrics after prediction
smltk.metrics.Metrics.modeling	Gets classification metrics after training and prediction
smltk.metrics.Metrics.print_metrics	Prints metrics

Model management

smltk.metrics.Metrics.save_model	Saves model
smltk.metrics.Metrics.resume_model	Resumes model

Detailed list

class smltk.metrics.Metrics

clean_binary_classification(y_test, y_pred)

Transforms the target and prediction in integer 0 and 1

Arguments:

y_test (list[]):

list of targets

y_pred (list[]):

list of predictions

Returns:

y_test and y_pred with only 0 and 1 values

create_confusion_matrix(y_test, y_pred, is_test=False)

Creates and prints confusion matrix

Arguments:

y_test (list[]):

list of targets

y_pred (list[]):

list of predictions

is_test (bool):

default is False

Returns:

confusion matrix

fit_exists(model)

Get a boolean True if fit method exists

Arguments:

model (obj):

object of your model

Returns:

boolean

get_classification_metrics(params={})

Gets classification metrics

Arguments: params (dict) with the keys below

model (obj):

object of your model

X_train (list[]|list[tuple]):

list of samples or list of tuples with sample and its target

y_train (list[]):

list of targets

X_test (list[] | list[tuple]):

list of samples or list of tuples with sample and its target

y_test (list[]):

list of targets

y_pred (list[]):

list of predictions

loss (str):

parameter of bias_variance_decomp, default mse

num_rounds (int):

parameter of bias_variance_decomp, default 200

random_seed (int):

parameter of bias_variance_decomp, default 3

Returns:

dictionary with Loss, Bias, Variance, MCC, ROC_AUC, Accuracy, Precision, Recall, Fscore

is_binary_classification(y_test, y_pred)

Gets if the classification is binary or not

Arguments:

y_test (list[]):

list of targets

y_pred (list[]):

list of predictions

Returns:

boolean

modeling(model, X_train, y_train, X_test, y_test)

Gets classification metrics after training and prediction

Arguments:

model (obj):

object of your model

X_train (list[]|list[tuple]):

list of samples or list of tuples with sample and its target

y_train (list[]):

list of targets

X_test (list[] | list[tuple]):

list of samples or list of tuples with sample and its target

y_test (list[]):

list of targets

Returns:

dictionary with Loss, Bias, Variance, MCC, ROC_AUC, Accuracy, Precision, Recall, Fscore

predict_exists(model)

Get a boolean True if predict method exists

Arguments:

model (obj):

object of your model

Returns:

boolean

prediction(model, method, X_test, y_test=[])

Predicts with your model

Arguments:

model (obj):

object of your model

method (str):

name of method

X_test (list[]|list[tuple]):

list of samples or list of tuples with sample and its target

y_test (list[]):

list of targets

Returns:

tuple of list of targets and list of predictions

print_metrics(metrics)

Prints metrics

Arguments:

metrics (dict):

dictionary of metrics with their value

Returns:

only the print of metrics

resume_model(filename)

Resumes model

Arguments:

filename (str):

pathname and filename where you want to save your model

Returns:

object of your model

save_model(model, filename)

Saves model

Arguments:

model (obj):

object of your model

filename (str):

pathname and filename where you want to save your model

scoring(model, X_test, y_test)

Gets classification metrics after prediction

Arguments:

model (obj):

object of your model

X_test (list[] | list[tuple]):

list of samples or list of tuples with sample and its target

y_test (list[]):

list of targets

Returns:

dictionary with Loss, Bias, Variance, MCC, ROC_AUC, Accuracy, Precision, Recall, Fscore

split_tuples(tuples)

Splits tuples of sample and its target in the relative lists

Arguments:

tuples (list[tuple]):

list of tuples with sample and its target

Returns:

tuple of list of samples and list of targets