debug.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Dec  9 13:26:08 2018

@author: Darcane
"""

import numpy as np
from sklearn import ensemble

from loader import load_data, prepare_data


np.random.seed(42)
print("Loading data")
X_train, y_train, X_test, test_id = load_data('data/glove.twitter.27B/glove.twitter.27B.25d.txt',
                                         'data/train_pos.txt', 'data/train_neg.txt', 'data/test_data.txt')

print("Preparing data")
X_train, y_train, X_test, test_id = prepare_data(X_train, y_train, X_test, test_id)

# let's create a SVM with fixed hyperparameters (we must tune that later on)
# clf = svm.SVC(kernel='linear', C=10)
# -> SVM are a bad choice because we have too much data
clf = ensemble.RandomForestClassifier(n_estimators=100)
# clf = LogisticRegression(C=1)
# clf = RidgeClassifier()

print("Training")
clf.fit(X_train, y_train)
print("Predicting")
prediction = clf.predict(X_test)

# np.savetxt("prediction.csv.gz", np.c_[test_id, prediction], header="Id,Prediction", comments='', delimiter=",",
#           fmt="%d")

print(np.c_[test_id, prediction])