easypred 0.0.7

Easily store, assess and compare predictions obtained through Machine Learning models.

EasyPred: track your predictions with ease

What is it?

EasyPred is a Python package that allows to easily store, investigate, assess and compare the predictions obtained through your Machine Learning models.

The package allows to create different types of model-agnostic prediction objects simply by passing the real and fitted data. These objects have properties and methods that return various accuracy and error metrics.

Why EasyPred can be useful:

• All-in-one bundle: having data and accuracy metrics in a single object means less stuff you need to keep an eye on
• Minimize code redundancy: pass the data once and get all the information and metrics you want
• Easy and flexible comparison: create the predictions first and then decide what to compare. Changed your mind? The object is there, simply access another method

Quick Start

Installation

You can install EasyPred via pip

pip install easypred

Alternatively, you can install EasyPred by cloning the project to your local directory

git clone https://github.com/FilippoPisello/EasyPred

And then run setup.py

python setup.py install

Usage

At the moment, three types of predictions are implemented:

• Prediction -> any prediction
• BinaryPrediction -> fitted and real data attain only two values
• NumericPrediction -> fitted and real data are numeric

Prediction

Consider the example of a generic prediction over text categories:

>>> real_data = ["Foo", "Foo", "Bar", "Bar", "Baz"]
>>> fitted_data = ["Baz", "Bar", "Foo", "Bar", "Bar"]

>>> from easypred import Prediction
>>> pred = Prediction(real_data, fitted_data)

Let's check the rate of correctly classified observations:

>>> pred.accuracy_score
0.2

More detail is needed, let's investigate where predictions and real match:

>>> pred.matches()
array([False, False, False,  True, False])

Still not clear enough, display everything in a data frame:

>>> pred.as_dataframe()
  Real Values Fitted Values  Prediction Matches
0         Foo           Baz               False
1         Foo           Bar               False
2         Bar           Foo               False
3         Bar           Bar                True
4         Baz           Bar               False

BinaryPrediction

Consider the case of a classic binary context (note: the two values can be any value, no need to be 0 and 1):

>>> real_data = [1, 1, 0, 0]
>>> fitted_data = [0, 1, 0, 0]
>>> from easypred import BinaryPrediction
>>> bin_pred = BinaryPrediction(real_data, fitted_data, value_positive=1)

What are the false positive and false negative rates? What about sensitivity and specificity?

>>> bin_pred.false_positive_rate
0.0
>>> bin_pred.false_negative_rate
0.5
>>> bin_pred.recall_score
0.5
>>> bin_pred.specificity_score
1.0

Let's look now at the confusion matrix as a pandas data frame:

>>> bin_pred.confusion_matrix(as_dataframe=True)
        Pred 0  Pred 1
Real 0       2       0
Real 1       1       1

NumericPrediction

Let's look at the numeric use case:

>>> real_data = [1, 2, 3, 4, 5, 6, 7]
>>> fitted_data = [1, 2, 4, 3, 7, 2, 5]
>>> from easypred import NumericPrediction
>>> num_pred = NumericPrediction(real_data, fitted_data)

We can access the residuals with various flavours, let's go for the basic values:

>>> num_pred.residuals(squared=False, absolute=False, relative=False)
array([ 0,  0, -1,  1, -2,  4,  2])

The data frame representation has now more information:

>>> num_pred.as_dataframe()
   Fitted Values  Real Values  Prediction Matches  Absolute Difference  Relative Difference
0              1            1                True                    0             0.000000
1              2            2                True                    0             0.000000
2              4            3               False                   -1            -0.333333
3              3            4               False                    1             0.250000
4              7            5               False                   -2            -0.400000
5              2            6               False                    4             0.666667
6              5            7               False                    2             0.285714

There are then a number of dedicated error and accuracy metrics:

>>> num_pred.mae
1.4285714285714286
>>> num_pred.mse
3.7142857142857144
>>> num_pred.rmse
1.927248223318863
>>> num_pred.mape
0.27653061224489794
>>> num_pred.r_squared
0.31250000000000017

Use the help() function to get more information over the prediction objects and their functionalities.

Dependencies

EasyPred depends on the following libraries:

• NumPy
• pandas
• matplotlib

Documentation

Find the complete documentation on read the docs.

License

MIT