def infer(raw, flags):
"""
:param raw:
:param flags:
:return:
"""
data_processor = DataProcessor.load(flags.checkpoint_path)
model = LSTM(data_processor.num_features,
flags.num_units,
output_dim=flags.output_dim,
num_layers=flags.num_layers,
batch_first=True,
dropout=flags.dropout)
model, _, _ = load_checkpoint(flags.checkpoint_path, model, None)
model.eval()
# predict
results = {}
loader, ts_types = data_processor.get_forecast_data(raw)
with torch.no_grad():
for type, data in zip(ts_types, loader):
scale = data[4]
_, outputs = _infer(data, model, flags.window,
flags.forecast_length)
results[type] = [(output * scale).detach().numpy()[0]
for output in outputs]
print(results)
return results
def run_k_folds(self, n_runs=5, n_folds=2):
dp = DataProcessor()
dp.load('data/SQuAD/squad-v7.file')
model = LogRegModel()
model.load_vectors(dp.articles, n_folds=n_folds)
baseline_results = []
sentiment_results = []
for run in range(n_runs):
print("k-fold run:", run)
baseline_results.append(model.run_k_fold(with_sentiment=False))
sentiment_results.append(model.run_k_fold())
model.create_new_folds(n_folds=n_folds)
self.save_results(baseline_results, "results/5x2_baseline")
self.save_results(sentiment_results, "results/5x2_sentiment")
result["conf_matrices"].append(
confusion_matrix(
self.model.predict(test_vectors),
test_targets
))
result["roc_curves"].append(
roc_curve(
test_targets,
self.model.predict_proba(test_vectors)[:,1])
)
result["roc_auc_scores"].append(
roc_auc_score(
test_targets,
self.model.predict(test_vectors))
)
result["coefficients"].append(
self.model.coef_[0]
)
return result
if __name__ == "__main__":
dp = DataProcessor()
dp.load('data/SQuAD/squad-v7.file')
model = LogRegModel()
model.load_vectors(dp.articles)
model.run_k_fold()