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)
