| from sklearn.metrics import precision_score, accuracy_score, recall_score, f1_score, classification_report |
| from sklearn.preprocessing import LabelEncoder |
|
|
| class TextClassifier: |
|
|
| def __init__(self, train_features, train_targets, test_features, test_targets): |
| self.train_features = train_features |
| self.train_targets = train_targets |
| self.test_features = test_features |
| self.test_targets = test_targets |
|
|
| self.model = None |
| self.classification_report = None |
| self.accuracy = None |
| self.precision = None |
| self.recall = None |
| self.f1 = None |
|
|
| def train(self) -> None: |
| raise NotImplementedError |
|
|
| def predict(self, text_samples:list, inverse_transform:bool=True) -> list: |
| raise NotImplementedError |
|
|
| def evaluate(self) -> dict: |
|
|
| predictions = self.predict(self.test_features, inverse_transform=False) |
|
|
| self.accuracy = accuracy_score(self.test_targets, predictions) |
| self.precision = precision_score(self.test_targets, predictions, average='weighted') |
| self.recall = recall_score(self.test_targets, predictions, average='weighted') |
| self.f1 = f1_score(self.test_targets, predictions, average='weighted') |
| self.classification_report = classification_report(self.test_targets, predictions) |
|
|
| return {'accuracy' : self.accuracy, |
| 'precision' : self.precision, |
| 'recall' : self.recall} |
