| from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer |
| from sklearn.naive_bayes import MultinomialNB |
| from sklearn.pipeline import Pipeline |
| from sklearn.metrics import classification_report |
| from sklearn.metrics import confusion_matrix |
| from sklearn.linear_model import LogisticRegression |
| import sys |
| import os |
| parent_root = os.path.abspath(os.path.join(os.path.dirname(os.path.abspath(__file__)), os.pardir, os.pardir)) |
| sys.path.append(parent_root) |
| from src.dataset import * |
|
|
| from src.config.configs import Params |
| param = Params() |
|
|
| |
|
|
| class BaseLine(object): |
| def __init__(self, vectorizer, model): |
| self.vectorizer = vectorizer |
| self.model = model |
|
|
|
|
| def train(self, X_train, y_train, X_val, y_val, X_test, y_test): |
| model= Pipeline([ |
| ("vectorizer", self.vectorizer), |
| ("model", self.model) |
| ]) |
| print("Vectorizer: ", self.vectorizer) |
| print("Model: ", self.model) |
| model.fit(X_train, y_train) |
| val_score = model.score(X_val, y_val) |
| test_score = model.score(X_test, y_test) |
| print("Model accuracy on val set", model.score(X_val, y_val)) |
| print("Model accuracy on test set", model.score(X_test, y_test)) |
| return val_score, test_score |
| |
| if __name__ == "__main__": |
| print("---------------Baseline ML model------------------------") |
| params = Params() |
| |
| dataset = Dataset(train_txt=params.TRAIN_DIR, val_txt=params.VAL_DIR, test_txt=params.TEST_DIR, num_inputs=1) |
|
|
| |
| train_sentences = dataset.train_sentences |
| val_sentences = dataset.val_sentences |
| test_sentences = dataset.test_sentences |
| y_train = dataset.y_train |
| y_val = dataset.y_val |
| y_test = dataset.y_test |
| val_score, test_score =BaseLine(vectorizer=TfidfVectorizer(), |
| model=LogisticRegression()).train( |
| X_train=train_sentences, y_train=y_train, |
| X_val=val_sentences, y_val=y_val, |
| X_test=test_sentences, y_test=y_test) |
| |
| print("Baseline Val score: ", val_score) |
| print("Baseline Test score: ", test_score) |
