Instructions:
optional arguments:
-h, --help show this help message and exit
-nb Discrete Naive Bayes Classifier
-mnb Multinomial Naive Bayes Classifier
-lr Logistic Regression Classifier
-sgd Stochastic Gradient Descent Classifier
-train train_data_path
-test test_data_pathFor example, run NBClassifier and SGDClassifier for dataset1/train and dataset1/test
python ./__main__.py -train "dataset1/train" -test "dataset1/test" -nb -sgd +------------------------------------------------------------------------------------------------------------+
| dataset1 |
+------------+--------------+----------+-----------+--------+--------+---------------------------------------+
| classifier | data mode | accuracy | precision | recall | f1 | hyper-parameter |
+------------+--------------+----------+-----------+--------+--------+---------------------------------------+
| nb | bernoulli | 76.36% | 100.00% | 13.08% | 23.13% | |
| mnb | bag_of_words | 94.14% | 93.22% | 84.62% | 88.71% | |
| lr | bag_of_words | 93.93% | 87.97% | 90.00% | 88.97% | alpha:0.5 |
| lr | bernoulli | 96.23% | 93.08% | 93.08% | 93.08% | alpha:0.25 |
| sgd | bag_of_words | 91.63% | 86.89% | 81.54% | 84.13% | max_iter:100, alpha:0.1, penalty:l2 |
| sgd | bernoulli | 96.03% | 95.12% | 90.00% | 92.49% | max_iter:1000, alpha:0.01, penalty:l2 |
+------------+--------------+----------+-----------+--------+--------+---------------------------------------+
+-------------------------------------------------------------------------------------------------------------+
| dataset2 |
+------------+--------------+----------+-----------+---------+--------+---------------------------------------+
| classifier | data mode | accuracy | precision | recall | f1 | hyper-parameter |
+------------+--------------+----------+-----------+---------+--------+---------------------------------------+
| nb | bernoulli | 87.85% | 85.56% | 100.00% | 92.22% | |
| mnb | bag_of_words | 94.48% | 94.79% | 97.70% | 96.22% | |
| lr | bag_of_words | 97.05% | 96.30% | 99.74% | 97.99% | alpha:0.1 |
| lr | bernoulli | 96.87% | 95.83% | 100.00% | 97.87% | alpha:0.1 |
| sgd | bag_of_words | 95.03% | 95.73% | 97.44% | 96.58% | max_iter:100, alpha:0.01, penalty:l2 |
| sgd | bernoulli | 97.24% | 96.31% | 100.00% | 98.12% | max_iter:2500, alpha:0.01, penalty:l2 |
+------------+--------------+----------+-----------+---------+--------+---------------------------------------+
+-----------------------------------------------------------------------------------------------------------+
| dataset3 |
+------------+--------------+----------+-----------+--------+--------+--------------------------------------+
| classifier | data mode | accuracy | precision | recall | f1 | hyper-parameter |
+------------+--------------+----------+-----------+--------+--------+--------------------------------------+
| nb | bernoulli | 71.49% | 100.00% | 12.75% | 22.62% | |
| mnb | bag_of_words | 93.86% | 94.81% | 85.91% | 90.14% | |
| lr | bag_of_words | 93.86% | 89.54% | 91.95% | 90.73% | alpha:0.5 |
| lr | bernoulli | 95.39% | 91.56% | 94.63% | 93.07% | alpha:0.5 |
| sgd | bag_of_words | 94.08% | 90.13% | 91.95% | 91.03% | max_iter:2500, alpha:0.1, penalty:l2 |
| sgd | bernoulli | 95.39% | 91.03% | 95.30% | 93.11% | max_iter:100, alpha:0.01, penalty:l2 |
+------------+--------------+----------+-----------+--------+--------+--------------------------------------+