def main():
np.random.seed(42)
data_dir_path = '../DS Bootcamp/dailycaller'
very_large_data_dir_path = './very_large_data'
model_dir_path = './models'
print('loading csv file ...')
df = pd.read_csv('dcr Man_Cleaned.csv')
#X = df['Article']
X = df['Title']
#X = df['Joined']
Y = df.Title
config = np.load(Seq2SeqGloVeSummarizerV2.get_config_file_path(model_dir_path=model_dir_path)).item()
summarizer = Seq2SeqGloVeSummarizerV2(config)
summarizer.load_glove(very_large_data_dir_path)
summarizer.load_weights(weight_file_path=Seq2SeqGloVeSummarizerV2.get_weight_file_path(model_dir_path=model_dir_path))
print('start predicting ...')
for i in np.random.permutation(np.arange(len(X)))[0:20]:
x = X[i]
actual_headline = Y[i]
headline = summarizer.summarize(x)
print('Generated Headline: ', headline)
print('Original Headline: ', actual_headline)
def main():
np.random.seed(42)
data_dir_path = './data'
very_large_data_dir_path = './very_large_data'
model_dir_path = './models'
print('loading csv file ...')
df = pd.read_csv(data_dir_path + "/Papers.csv")
Y = df.Abstract
X = df['PaperText']
config = np.load(
Seq2SeqGloVeSummarizerV2.get_config_file_path(
model_dir_path=model_dir_path)).item()
summarizer = Seq2SeqGloVeSummarizerV2(config)
summarizer.load_glove(very_large_data_dir_path)
summarizer.load_weights(
weight_file_path=Seq2SeqGloVeSummarizerV2.get_weight_file_path(
model_dir_path=model_dir_path))
print('start predicting ...')
for i in np.random.permutation(np.arange(len(X)))[0:20]:
x = X[i]
actual_headline = Y[i]
headline = summarizer.summarize(x)
print('Generated Headline: ', headline)
print('Original Headline: ', actual_headline)
def main():
np.random.seed(42)
data_dir_path = './data'
very_large_data_dir_path = './very_large_data'
report_dir_path = './reports'
model_dir_path = './models'
print('loading csv file ...')
df = pd.read_csv(data_dir_path + "/fake_or_real_news.csv")
print('extract configuration from input texts ...')
Y = df.title
X = df['text']
config = fit_text(X, Y)
print('configuration extracted from input texts ...')
summarizer = Seq2SeqGloVeSummarizerV2(config)
summarizer.load_glove(very_large_data_dir_path)
if LOAD_EXISTING_WEIGHTS:
summarizer.load_weights(weight_file_path=Seq2SeqGloVeSummarizerV2.get_weight_file_path(model_dir_path=model_dir_path))
Xtrain, Xtest, Ytrain, Ytest = train_test_split(X, Y, test_size=0.2, random_state=42)
print('demo size: ', len(Xtrain))
print('testing size: ', len(Xtest))
print('start fitting ...')
history = summarizer.fit(Xtrain, Ytrain, Xtest, Ytest, epochs=20, batch_size=16)
history_plot_file_path = report_dir_path + '/' + Seq2SeqGloVeSummarizerV2.model_name + '-history.png'
if LOAD_EXISTING_WEIGHTS:
history_plot_file_path = report_dir_path + '/' + Seq2SeqGloVeSummarizerV2.model_name + '-history-v' + str(summarizer.version) + '.png'
plot_and_save_history(history, summarizer.model_name, history_plot_file_path, metrics={'loss', 'acc'})