In [1]:
# adding this to suppress sklearn DeprecationWarnings...
import os
#os.environ["WORD_SENTIMENT_PATH"] = '.\\subjclueslen1-HLTEMNLP05.tff'
#os.environ["FEATURE_CACHE_DIR"] = '..\\cache\\'
from mpl_toolkits.mplot3d import Axes3D
from model.linear_regression_model import LinearRegressionModel
from model.MLPRegressionModel import MLPRegressionModel
from model.lstm_regression_model import LSTMRegressionModel
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
import time
from sklearn.model_selection import train_test_split
from util.config import Config, RegressionModels
import matplotlib.pyplot as plt
from matplotlib import cm
from model.overall_runner import corpora_to_day_features, \
init_corpora, combine_day_ranges, match_features_to_labels
import math
from matplotlib import pyplot as plt
import numpy as np
import os
import pandas as pd
import time
import cntk as C
import cntk.axis
from cntk.layers import Dense, Dropout, Recurrence
from IPython import display
%matplotlib inline
print('done importing')
In [2]:
PREDICT_DELAY_RANGE = range(1, 2)
DAY_RANGE_RANGE = range(1, 2)
delay = 1
day_range = 1
plot_x = [[]]
plot_y = [[]]
plot_z = [[]]
approval_ratings, political_article_corpora = init_corpora();
plot_x[delay - 1].append(delay)
plot_y[delay - 1].append(day_range)
Config.POLL_DELAY = delay
Config.DAY_RANGE = day_range
print("getting feature vectors...")
features_by_day = corpora_to_day_features(political_article_corpora)
#features_by_range = combine_day_ranges(features_by_day)
X, Y = match_features_to_labels(features_by_day, approval_ratings);
print("done.")
print("Number of days = {}".format(len(X)))
print("splitting test set...")
X_train_and_val, X_test, Y_train_and_val, Y_test = \
train_test_split(X, Y, test_size=Config.TRAINING_PARTITION, random_state=2)
print("done.")
print("splitting validation set...")
X_train, X_val, Y_train, Y_val = \
train_test_split(X_train_and_val, Y_train_and_val, test_size=0.125, random_state=2)
print("done.")
print("Number of days in train = {}".format(len(X_train)))
print("Number of days in test = {}".format(len(X_test)))
print("Number of days in validation = {}".format(len(X_val)))
if Config.REGRESSION_MODEL == RegressionModels.LINEAR_REGRESSION:
model = LSTMRegressionModel([X_train, Y_train], val_data=[X_val, Y_val], test_data=[X_test, Y_test])
elif Config.REGRESSION_MODEL == RegressionModels.MLP:
model = MLPRegressionModel([X_train, Y_train])
print("DONE WITH MY THINGS")
In [3]:
print(model)
model.train(save_intermediates=True, save_prefix="badass_lstm", num_epochs=10000)
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