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"))
def predict(): ids, comments = loadQuestionsFromTestDF() logis = LogisticRegressor() final_submission = open("final.csv", "w+") for i, text in enumerate(comments): print(ids[i]) text = str(text).lower() text = re.sub(r'[^\w\s]', '', text) text = remove_stop_words([text.lower()]) text = lemmatize(text[0]) results = logis.predict([text])[0] line = str(ids[i]) + "," for res in results: line += str(round(res, 2)) + "," line = line[0:len(line) - 1] print(line) final_submission.write(line + "\n")
def setUp(self): self.logreg = LogisticRegressor(self.train_dataset, self.train_labels)
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)
# Importing libraries and modules import pandas as pd from utils import * from logistic_regression import LogisticRegressor # Reading data into variables. X = pd.read_csv("breast_data.csv", header=None).to_numpy() y = pd.read_csv("breast_truth.csv", header=None).to_numpy() # Splitting data into train/test sets X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) # Creating model model = LogisticRegressor(learning_rate=0.0001, n_iterations=100) model.fit(X_train, y_train) # Predict test data for evaluate model predict_test = model.predict(X_test) print("Accuracy of model on test data: %2.2f" % accuracy(y_test, predict_test))