Delete evaluate.py

evaluate.py

@@ -1,45 +0,0 @@
-from names_dataset import NameDataset
-
-
-# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_recall_fscore_support.html
-# Precision: The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.
-# Recall: The recall is intuitively the ability of the classifier to find all the positive samples.
-
-def read_dict_file(filename):
-    with open(filename, 'r', encoding='utf8') as r:
-        lines = r.read().strip().split('\n')
-    return lines
-
-
-def main():
-    m = NameDataset()
-
-    names = read_dict_file('eng_dictionary/names-from-forbes-wp_users.txt')
-    not_names = read_dict_file('eng_dictionary/google-10000-english-no-names.txt')
-    not_names.extend(read_dict_file('eng_dictionary/1000-no-names.txt'))
-
-    names = sorted(set(names))
-    not_names = sorted(set(not_names))
-
-    # 0 => not a name
-    # 1 => name
-
-    targets = []
-    predictions = []
-    for q in names:
-        predictions.append(m.search_first_name(q))
-        targets.append(True)
-
-    for q in not_names:
-        predictions.append(m.search_first_name(q))
-        targets.append(False)
-
-    from sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score
-    print('P', precision_score(y_true=targets, y_pred=predictions))
-    print('R', recall_score(y_true=targets, y_pred=predictions))
-    print('F', f1_score(y_true=targets, y_pred=predictions))
-    print('A', accuracy_score(y_true=targets, y_pred=predictions))
-
-
-if __name__ == '__main__':
-    main()