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Multi-class classification

Classification is about predicting an outcome from a fixed list of classes. The prediction is a probability distribution that assigns a probability to each possible outcome.

A labeled classification sample is made up of a bunch of features and a class. The class is usually a string or a number in the case of multiclass classification. We'll use the image segments dataset as an example.

from river import datasets

dataset = datasets.ImageSegments()
dataset




Image segments classification.

This dataset contains features that describe image segments into 7 classes: brickface, sky,
foliage, cement, window, path, and grass.

References
----------
[^1]: [UCI page](https://archive.ics.uci.edu/ml/datasets/Statlog+(Image+Segmentation))

    Name  ImageSegments                                               
    Task  Multi-class classification                                  
 Samples  2,310                                                       
Features  18                                                          
 Classes  7                                                           
  Sparse  False                                                       
    Path  /home/runner/work/river/river/river/datasets/segment.csv.zip



This dataset is a streaming dataset which can be looped over.


for x, y in dataset:
    pass



Let's take a look at the first sample.


x, y = next(iter(dataset))
x






{
    'region-centroid-col': 218,
    'region-centroid-row': 178,
    'short-line-density-5': 0.11111111,
    'short-line-density-2': 0.0,
    'vedge-mean': 0.8333326999999999,
    'vegde-sd': 0.54772234,
    'hedge-mean': 1.1111094,
    'hedge-sd': 0.5443307,
    'intensity-mean': 59.629630000000006,
    'rawred-mean': 52.44444300000001,
    'rawblue-mean': 75.22222,
    'rawgreen-mean': 51.22222,
    'exred-mean': -21.555555,
    'exblue-mean': 46.77778,
    'exgreen-mean': -25.222220999999998,
    'value-mean': 75.22222,
    'saturation-mean': 0.31899637,
    'hue-mean': -2.0405545
}



y






'path'



A multiclass classifier's goal is to learn how to predict a class y from a bunch of features x. We'll attempt to do this with a decision tree.


from river import tree

model = tree.HoeffdingTreeClassifier()
model.predict_proba_one(x)






{}



The reason why the output dictionary is empty is because the model hasn't seen any data yet. It isn't aware of the dataset whatsoever. If this were a binary classifier, then it would output a probability of 50% for True and False because the classes are implicit. But in this case we're doing multiclass classification.


Likewise, the predict_one method initially returns None because the model hasn't seen any labeled data yet.


print(model.predict_one(x))



None



If we update the model and try again, then we see that a probability of 100% is assigned to the 'path' class because that's the only one the model is aware of.


model.learn_one(x, y)
model.predict_proba_one(x)






{'path': 1.0}



This is a strength of online classifiers: they're able to deal with new classes appearing in the data stream.


Typically, an online model makes a prediction, and then learns once the ground truth reveals itself. The prediction and the ground truth can be compared to measure the model's correctness. If you have a dataset available, you can loop over it, make a prediction, update the model, and compare the model's output with the ground truth. This is called progressive validation.


from river import metrics

model = tree.HoeffdingTreeClassifier()

metric = metrics.ClassificationReport()

for x, y in dataset:
    y_pred = model.predict_one(x)
    model.learn_one(x, y)
    if y_pred is not None:
        metric.update(y, y_pred)

metric






            Precision   Recall   F1       Support

brickface      77.13%   84.85%   80.81%       330  
   cement      79.60%   83.94%   81.71%       330  
  foliage      66.36%   21.52%   32.49%       330  
    grass     100.00%   97.27%   98.62%       330  
     path      90.91%   91.19%   91.05%       329  
      sky     100.00%   98.18%   99.08%       330  
   window      43.41%   67.88%   52.96%       330

    Macro      79.63%   77.83%   76.67%            
    Micro      77.83%   77.83%   77.83%            
 Weighted      79.62%   77.83%   76.67%

                  77.83% accuracy



This is a common way to evaluate an online model. In fact, there is a dedicated evaluate.progressive_val_score function that does this for you.


from river import evaluate

model = tree.HoeffdingTreeClassifier()
metric = metrics.ClassificationReport()

evaluate.progressive_val_score(dataset, model, metric)






            Precision   Recall   F1       Support

brickface      77.13%   84.85%   80.81%       330  
   cement      79.60%   83.94%   81.71%       330  
  foliage      66.36%   21.52%   32.49%       330  
    grass     100.00%   97.27%   98.62%       330  
     path      90.91%   91.19%   91.05%       329  
      sky     100.00%   98.18%   99.08%       330  
   window      43.41%   67.88%   52.96%       330

    Macro      79.63%   77.83%   76.67%            
    Micro      77.83%   77.83%   77.83%            
 Weighted      79.62%   77.83%   76.67%

                  77.83% accuracy