Starting point code for MDR implementation #1
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Assumption(s): Labels are only binary Implemented: fit(self, features, classes): simply build a dictionary that maps each instance of the feature vector to a tuple. The tuple keeps count of how many times a particular label value appears with that instance of feature vector. Key: tuple of feature values - Value: tuple of label frequency/label counts transform(self, features): After the dictionary is completed, combine each instance of feature vector above into one corresponding label that has the frequency ratio greater than its standard default ratio. score(self, features, classes): Compare the new combined feature vector with its corresponding class labels, and count the times the two match. Output the average accuracy by averaging the match count over the length of the new feature vector / classes vector. Implementation is tested in main() by training MDR on the training set and getting accuracy_score on the test set.
| description | ||
| tie_break: type int (default: 0) | ||
| description: specify the default label in case there's a tie in a given set of feature values | ||
| default_label: type int (default: 0) |
Changed fdict to feature_map Removed ‘type’ in line 34 & 36
Fixed all bugs according to Randal Olson’s comments.
| @@ -18,29 +18,33 @@ | |||
| """ | |||
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| import pandas as pd | |||
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| import numpy as np | |||
| from collections import defaultdict | |||
| from __future__ import print_function | |||
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Assumption(s): Labels are only binary
Implemented:
fit(self, features, classes): simply build a dictionary that maps each
instance of the feature vector to a tuple. The tuple keeps count of how
many times a particular label value appears with that instance of
feature vector. Key: tuple of feature values - Value: tuple of label
frequency/label counts
transform(self, features): After the dictionary is completed, combine
each instance of feature vector above into one corresponding label that
has the frequency ratio greater than its standard default ratio.
score(self, features, classes): Compare the new combined feature vector
with its corresponding class labels, and count the times the two match.
Output the average accuracy by averaging the match count over the
length of the new feature vector / classes vector.
Implementation is tested in main() by training MDR on the training set
and getting accuracy_score on the test set.