| 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} |