def main():
#model = load_model('test_models/stock_multb_nosmooth_step4_1525242340/seq_len_50/model-1.h5')
model = load_model(
'D:\Source\Repos\StockPrediction\stock_single_nosmooth_1525405252\seq_len_50/model-1.h5'
)
seq_len = 50
predict_len = 7
#date_ranges = [(datetime.date(2016,11,1),datetime.date(2017,4,3)),(datetime.date(2002,3,18),datetime.date(2002,8,14)),(datetime.date(2015,3,8),datetime.date(2015,8,5))]
date_ranges = [(datetime.date(2015, 1, 1), datetime.date(2015, 6, 1)),
(datetime.date(2016, 1, 1), datetime.date(2016, 6, 1)),
(datetime.date(2017, 1, 1), datetime.date(2017, 6, 1))]
test_data = [
dataload.load_data('daily_spx.csv',
seq_len,
normalise_window=True,
smoothing=False,
date_range=date_range,
train=False) for date_range in date_ranges
]
predictions = [
dataload.predict_sequences_multiple(model, test[0], seq_len,
predict_len) for test in test_data
]
scores = [
model.evaluate(test[0], test[1], verbose=0) for test in test_data
]
for prediction_index in range(len(predictions)):
for sequence_index in range(len(predictions[prediction_index])):
predictions[prediction_index][
sequence_index] = dataload.denormalize_sequence(
test_data[prediction_index][2][sequence_index * 7],
predictions[prediction_index][sequence_index])
for test_data_index in range(len(test_data)):
for y_index in range(len(test_data[test_data_index][1])):
test_data[test_data_index][1][
y_index] = dataload.denormalize_point(
test_data[test_data_index][2][y_index],
test_data[test_data_index][1][y_index])
model_plot = [(predictions[0], '2015'), (predictions[1], '2016'),
(predictions[2], '2017')]
#model_plot = [(predictions[0], 'Bullish'), (predictions[1], 'Bearish'), (predictions[2], 'Neutral')]
results_fname = 'test_singleb_nosmooth_byyear_{}'.format(
int(datetime.datetime.now().timestamp()))
os.makedirs(results_fname)
plot_results_multiple(model_plot, [t[1] for t in test_data],
predict_len,
fig_path=results_fname + '/plots.pdf')
with open(results_fname + "/score.txt", "w") as fout:
for score in scores:
pprint.pprint(score, fout)
def main():
svm_model_path = 'svm_smoothed_1530436270/model.sav'
results_fname = 'test_svm_smooth_byyear_{}'.format(
int(datetime.datetime.now().timestamp()))
seq_len = 50
predict_len = 7
os.makedirs(results_fname)
# Define date ranges
date_ranges = [(datetime.date(2015, 1, 1), datetime.date(2015, 6, 1)),
(datetime.date(2016, 1, 1), datetime.date(2016, 6, 1)),
(datetime.date(2017, 1, 1), datetime.date(2017, 6, 1))]
# Load data
model = pickle.load(open(svm_model_path, 'rb'))
test_data = [
dataload.load_data('daily_spx.csv',
seq_len,
normalise_window=True,
smoothing=False,
date_range=date_range,
train=False) for date_range in date_ranges
]
# Generate predictions
#[[print(seq.shape) for seq in test_date_range[0]] for test_date_range in test_data]
#predictions = [[np.asscalar(model.predict(seq.transpose())) for seq in test_date_range[0]] for test_date_range in test_data]
predictions = [
dataload.predict_sequences_multiple(model, test[0], seq_len,
predict_len) for test in test_data
]
for prediction_index in range(len(predictions)):
for sequence_index in range(len(predictions[prediction_index])):
predictions[prediction_index][
sequence_index] = dataload.denormalize_sequence(
test_data[prediction_index][2][sequence_index * 7],
predictions[prediction_index][sequence_index])
for test_data_index in range(len(test_data)):
for y_index in range(len(test_data[test_data_index][1])):
test_data[test_data_index][1][
y_index] = dataload.denormalize_point(
test_data[test_data_index][2][y_index],
test_data[test_data_index][1][y_index])
# Save plot
model_plot = [(predictions[0], '2015'), (predictions[1], '2016'),
(predictions[2], '2017')]
plot_results_multiple(model_plot, [t[1] for t in test_data],
predict_len,
fig_path=results_fname + '/plots.pdf')
model = top_models[i]
callbacks = []
if (i == 0):
callbacks = [early_stopping]
else:
callbacks = [early_stopping]
model.fit(X_train,
y_train,
batch_size=model.batch_size,
nb_epoch=epochs,
validation_split=0.10,
callbacks=[early_stopping, tensorboard])
fit_models.append(model)
predictions = [
dataload.predict_sequences_multiple(model, X_test, seq_len,
predict_len)
for model in top_models
]
scores = [
model.evaluate(X_test, y_test, verbose=0) for model in top_models
]
# Save results
os.makedirs(results_fname)
folder_name = 'seq_len_{}'.format(seq_len)
os.makedirs('{}/{}'.format(results_fname, folder_name))
results.to_csv('{0}/{1}/results.csv'.format(results_fname,
folder_name))
top_model_plots = [(predictions[i], 'Model {}'.format(i + 1))
for i in range(len(predictions))]
plot_results_multiple(top_model_plots,
def main():
# Original 4 pipelines
model = load_model('single_cnn_gru_1542783564/seq_len_50/model-3.h5')
# 2 New pipelines for Bidir GRU
#model = load_model('../final_sem_code/single_cnn_bidir_gru_1548982892/seq_len_50/model-3.h5')
seq_len = 50
predict_len = 7
date_ranges = [(datetime.date(2016, 1, 1), datetime.date(2016, 6, 1)),
(datetime.date(2017, 1, 1), datetime.date(2017, 6, 1)),
(datetime.date(2018, 1, 1), datetime.date(2018, 6, 1))]
'''
date_ranges = [(datetime.date(2015,1,1),datetime.date(2015,12,31)),
(datetime.date(2016,1,1),datetime.date(2016,12,31)),
(datetime.date(2017,1,1),datetime.date(2017,12,31))]
'''
#test_data = [dataload.load_data('daily_spx.csv', seq_len, normalise_window=True, smoothing=False, date_range=date_range, train=False) for date_range in date_ranges]
test_data = [
dataload.load_data('../2018_data/Yahoo_2000_to_2018.csv',
seq_len,
normalise_window=True,
smoothing=False,
date_range=date_range,
train=False) for date_range in date_ranges
]
predictions = [
dataload.predict_sequences_multiple(model, test[0], seq_len,
predict_len) for test in test_data
]
scores = [
model.evaluate(test[0], test[1], verbose=0) for test in test_data
]
for prediction_index in range(len(predictions)):
for sequence_index in range(len(predictions[prediction_index])):
predictions[prediction_index][
sequence_index] = dataload.denormalize_sequence(
test_data[prediction_index][2][sequence_index * 7],
predictions[prediction_index][sequence_index])
for test_data_index in range(len(test_data)):
for y_index in range(len(test_data[test_data_index][1])):
test_data[test_data_index][1][
y_index] = dataload.denormalize_point(
test_data[test_data_index][2][y_index],
test_data[test_data_index][1][y_index])
model_plot = [(predictions[0], '2016'), (predictions[1], '2017'),
(predictions[2], '2018')]
#model_plot = [(predictions[0], 'Mar 2002 to Aug 2002'), (predictions[1], 'Mar 2015 to Aug 2015'),(predictions[2], 'Jan 2016 to Apr 2017')]
#model_plot = [(predictions[0], 'Bullish'), (predictions[1], 'Bearish'), (predictions[2], 'Neutral')]
results_fname = 'test_single_cnn_bidir_gru_{}'.format(
int(datetime.datetime.now().timestamp()))
#os.makedirs(results_fname)
plot_results_multiple(model_plot, [t[1] for t in test_data],
predict_len,
fig_path='plots_test/plots.pdf')
with open('plots_test' + "/score.txt", "w") as fout:
for score in scores:
pprint.pprint(score, fout)
#lstm_model = lstm.build_model([1, seq_len, 100, 1])
model_layers = [1, 2, 3, 4]
cnn_models = [
cnn_batchnorm_lstm.build_model([1, seq_len, 100, 1], x) for x in model_layers
]
[model.fit(
X_train,
y_train,
batch_size=512,
nb_epoch=epochs,
validation_split=0.05)
for model in cnn_models
]
#lstm_predictions = dataload.predict_sequences_multiple(lstm_model, X_test, seq_len, seq_len)
cnn_predictions = [dataload.predict_sequences_multiple(model, X_test, seq_len, seq_len)
for model in cnn_models]
#predictions = dataload.predict_sequence_full(model, X_test, seq_len)
#predictions = dataload.predict_point_by_point(model, X_test)
#lstm_score = lstm_model.evaluate(X_test, y_test, verbose=0)
cnn_scores = [model.evaluate(X_test, y_test, verbose=0)
for model in cnn_models]
#print('LSTM')
#print(lstm_score)
#print('Test loss: ', lstm_score[0])
#print('Test accuracy: ', lstm_score[1])
for l in model_layers:
print('\nCNN ', l)
print(cnn_scores[l-1])
