Quick Start

RegressionMetrics

>>> import numpy as np
>>> from SeqMetrics import RegressionMetrics

>>> true = np.random.random((20, 1))
>>> pred = np.random.random((20, 1))

>>> er = RegressionMetrics(true, pred)

>>> for m in er.all_methods: print("{:20}".format(m)) # get names of all availabe methods

>>> er.nse()   # calculate Nash Sutcliff efficiency

>>> er.calculate_all(verbose=True)  # or calculate errors using all available methods

ClassificationMetrics

>>> import numpy as np
>>> from SeqMetrics import ClassificationMetrics

using boolean array

>>> t = np.array([True, False, False, False])
>>> p = np.array([True, True, True, True])
>>> metrics = ClassificationMetrics(t, p)
>>> accuracy = metrics.accuracy()

binary classification with numerical labels

>>> true = np.array([1, 0, 0, 0])
>>> pred = np.array([1, 1, 1, 1])
>>> metrics = ClassificationMetrics(true, pred)
>>> accuracy = metrics.accuracy()

multiclass classification with numerical labels

>>> true = np.random.randint(1, 4, 100)
>>> pred = np.random.randint(1, 4, 100)
>>> metrics = ClassificationMetrics(true, pred)
>>> accuracy = metrics.accuracy()

You can also provide logits instead of labels.

>>> predictions = np.array([[0.25, 0.25, 0.25, 0.25],
>>>                        [0.01, 0.01, 0.01, 0.96]])
>>> targets = np.array([[0, 0, 0, 1],
>>>                     [0, 0, 0, 1]])
>>> metrics = ClassificationMetrics(targets, predictions, multiclass=True)
>>> metrics.cross_entropy()
...  0.71355817782

Streamlit App

The SeqMetrics library is available from the webapp which is deployed used streamlit app .

You can also launch the app locally if you do not wish to use the web-based app. Make sure you follow the below steps

git clone https://github.com/AtrCheema/SeqMetrics.git
cd SeqMetrics
pip install requirements.txt
pip install streamlit
streamlit run app.py

The following figure shows steps for using the streamlit-app

The following figure illustrates the method to provide true and predicted data from a csv/excel file.