def train_model(): training_data = pd.read_pickle("..//data//lemmatized_train_dataframe.pkl") train_features = [] for feature in training_data[0]: feature = feature.lower() train_features.append(feature) targets = [] targets.append(training_data[1]) targets.append(training_data[2]) targets.append(training_data[3]) targets.append(training_data[4]) targets.append(training_data[5]) targets.append(training_data[6]) logis = LogisticRegressor() logis = logis.train_model(train_features, targets) pickle.dump(logis, open("../data/logistic_model.pkl", "wb"))
import numpy as np import random from logistic_regression import LogisticRegressor X = np.random.rand(380, 30) y = [0] * 300 + [1] * 80 random.shuffle(y) y = np.array(y) # y = np.reshape(y,(380,1)) # print(y) print("X:\n", X) print("y:\n", y) lr = LogisticRegressor(X, y) lr.train_model(verbose=True)