def get_y_for_subject(pred_fn, test_set_0, test_set_1, iterator, final_layer):
"""Assumes there was no resampling!!"""
batch_gen_0 = iterator.get_batches(test_set_0, shuffle=False)
all_preds_0 = [pred_fn(batch[0]) for batch in batch_gen_0]
batch_gen_1 = iterator.get_batches(test_set_1, shuffle=False)
all_preds_1 = [pred_fn(batch[0]) for batch in batch_gen_1]
n_sample_preds = get_n_sample_preds(final_layer)
input_time_length = lasagne.layers.get_all_layers(final_layer)[0].shape[2]
n_samples_0 = test_set_0.get_topological_view().shape[0]
preds_arr_0 = get_reshaped_cnt_preds(all_preds_0, n_samples_0,
input_time_length, n_sample_preds)
n_samples_1 = test_set_1.get_topological_view().shape[0]
preds_arr_1 = get_reshaped_cnt_preds(all_preds_1, n_samples_1,
input_time_length, n_sample_preds)
series_preds = [preds_arr_0, preds_arr_1]
return series_preds
def get_y_for_subject(pred_fn, test_set_0, test_set_1, iterator, final_layer):
"""Assumes there was no resampling!!"""
batch_gen_0 = iterator.get_batches(test_set_0, shuffle=False)
all_preds_0 = [pred_fn(batch[0]) for batch in batch_gen_0]
batch_gen_1 = iterator.get_batches(test_set_1, shuffle=False)
all_preds_1 = [pred_fn(batch[0]) for batch in batch_gen_1]
n_sample_preds = get_n_sample_preds(final_layer)
input_time_length = lasagne.layers.get_all_layers(final_layer)[0].shape[2]
n_samples_0 = test_set_0.get_topological_view().shape[0]
preds_arr_0 = get_reshaped_cnt_preds(all_preds_0, n_samples_0,
input_time_length, n_sample_preds)
n_samples_1 = test_set_1.get_topological_view().shape[0]
preds_arr_1 = get_reshaped_cnt_preds(all_preds_1, n_samples_1,
input_time_length, n_sample_preds)
series_preds = [preds_arr_0, preds_arr_1]
return series_preds
def create_submission_csv_for_one_subject(folder_name, kaggle_set, iterator, preprocessor,
final_layer, submission_id):
### Load and preprocess data
kaggle_set.load()
# remember test series lengths before and after resampling to more accurately pad predictions
# later (padding due to the lost samples)
kaggle_set.load_test_data()
test_series_lengths = [len(series) for series in kaggle_set.test_X_series]
kaggle_set.resample_test_data()
test_series_lengths_resampled = [len(series) for series in kaggle_set.test_X_series]
X_train = deepcopy(np.concatenate(kaggle_set.train_X_series)[:,:,np.newaxis,np.newaxis])
X_test_0 = deepcopy(kaggle_set.test_X_series[0][:,:,np.newaxis,np.newaxis])
X_test_1 = deepcopy(kaggle_set.test_X_series[1][:,:,np.newaxis,np.newaxis])
# create dense design matrix sets
train_set = DenseDesignMatrixWrapper(
topo_view=X_train,
y=None, axes=('b','c',0,1))
fake_test_y = np.ones((len(X_test_0), 6))
test_set_0 = DenseDesignMatrixWrapper(
topo_view=X_test_0,
y=fake_test_y)
fake_test_y = np.ones((len(X_test_1), 6))
test_set_1 = DenseDesignMatrixWrapper(
topo_view=X_test_1,
y=fake_test_y)
log.info("Preprocessing data...")
preprocessor.apply(train_set, can_fit=True)
preprocessor.apply(test_set_0, can_fit=False)
preprocessor.apply(test_set_1, can_fit=False)
### Create prediction function and create predictions
log.info("Create prediction functions...")
input_var = lasagne.layers.get_all_layers(final_layer)[0].input_var
predictions = lasagne.layers.get_output(final_layer, deterministic=True)
pred_fn = theano.function([input_var], predictions)
log.info("Make predictions...")
batch_gen_0 = iterator.get_batches(test_set_0, shuffle=False)
all_preds_0 = [pred_fn(batch[0]) for batch in batch_gen_0]
batch_gen_1 = iterator.get_batches(test_set_1, shuffle=False)
all_preds_1 = [pred_fn(batch[0]) for batch in batch_gen_1]
### Pad and reshape predictions
n_sample_preds = get_n_sample_preds(final_layer)
input_time_length = lasagne.layers.get_all_layers(final_layer)[0].shape[2]
n_samples_0 = test_set_0.get_topological_view().shape[0]
preds_arr_0 = get_reshaped_cnt_preds(all_preds_0, n_samples_0,
input_time_length, n_sample_preds)
n_samples_1 = test_set_1.get_topological_view().shape[0]
preds_arr_1 = get_reshaped_cnt_preds(all_preds_1, n_samples_1,
input_time_length, n_sample_preds)
series_preds = [preds_arr_0, preds_arr_1]
assert len(series_preds[0]) == test_series_lengths_resampled[0]
assert len(series_preds[1]) == test_series_lengths_resampled[1]
assert False, ("TODO: here only duplicate if resample half is true for the dataset.. "
"also take care how to create submission cv if trained on all subjects")
series_preds_duplicated = [np.repeat(preds, 2,axis=0) for preds in series_preds]
n_classes = preds_arr_0.shape[1]
# pad missing ones with zeros
missing_0 = test_series_lengths[0] - len(series_preds_duplicated[0])
full_preds_0 = np.append(np.zeros((missing_0, n_classes), dtype=np.float32),
series_preds_duplicated[0], axis=0)
missing_1 = test_series_lengths[1] - len(series_preds_duplicated[1])
full_preds_1 = np.append(np.zeros((missing_1, n_classes), dtype=np.float32),
series_preds_duplicated[1], axis=0)
assert len(full_preds_0) == test_series_lengths[0]
assert len(full_preds_1) == test_series_lengths[1]
full_series_preds = [full_preds_0, full_preds_1]
assert sum([len(a) for a in full_series_preds]) == np.sum(test_series_lengths)
### Create csv
log.info("Create csv...")
csv_filename = "{:02d}".format(submission_id) + '.csv'
csv_filename = os.path.join(folder_name, csv_filename)
cols = ['HandStart','FirstDigitTouch',
'BothStartLoadPhase','LiftOff',
'Replace','BothReleased']
# collect ids
all_ids = []
all_preds = []
for i_series in (9,10):
id_prefix = "subj{:d}_series{:d}_".format(kaggle_set.i_subject, i_series)
this_preds = full_series_preds[i_series-9] # respect offsets
all_preds.extend(this_preds)
this_ids = [id_prefix + str(i_sample) for i_sample in range(this_preds.shape[0])]
all_ids.extend(this_ids)
all_ids = np.array(all_ids)
all_preds = np.array(all_preds)
submission = pd.DataFrame(index=all_ids,
columns=cols,
data=all_preds)
submission.to_csv(csv_filename, index_label='id',float_format='%.3f')
log.info("Done")
def create_submission_csv_for_one_subject(folder_name, kaggle_set, iterator,
preprocessor, final_layer,
submission_id):
### Load and preprocess data
kaggle_set.load()
# remember test series lengths before and after resampling to more accurately pad predictions
# later (padding due to the lost samples)
kaggle_set.load_test_data()
test_series_lengths = [len(series) for series in kaggle_set.test_X_series]
kaggle_set.resample_test_data()
test_series_lengths_resampled = [
len(series) for series in kaggle_set.test_X_series
]
X_train = deepcopy(
np.concatenate(kaggle_set.train_X_series)[:, :, np.newaxis,
np.newaxis])
X_test_0 = deepcopy(kaggle_set.test_X_series[0][:, :, np.newaxis,
np.newaxis])
X_test_1 = deepcopy(kaggle_set.test_X_series[1][:, :, np.newaxis,
np.newaxis])
# create dense design matrix sets
train_set = DenseDesignMatrixWrapper(topo_view=X_train,
y=None,
axes=('b', 'c', 0, 1))
fake_test_y = np.ones((len(X_test_0), 6))
test_set_0 = DenseDesignMatrixWrapper(topo_view=X_test_0, y=fake_test_y)
fake_test_y = np.ones((len(X_test_1), 6))
test_set_1 = DenseDesignMatrixWrapper(topo_view=X_test_1, y=fake_test_y)
log.info("Preprocessing data...")
preprocessor.apply(train_set, can_fit=True)
preprocessor.apply(test_set_0, can_fit=False)
preprocessor.apply(test_set_1, can_fit=False)
### Create prediction function and create predictions
log.info("Create prediction functions...")
input_var = lasagne.layers.get_all_layers(final_layer)[0].input_var
predictions = lasagne.layers.get_output(final_layer, deterministic=True)
pred_fn = theano.function([input_var], predictions)
log.info("Make predictions...")
batch_gen_0 = iterator.get_batches(test_set_0, shuffle=False)
all_preds_0 = [pred_fn(batch[0]) for batch in batch_gen_0]
batch_gen_1 = iterator.get_batches(test_set_1, shuffle=False)
all_preds_1 = [pred_fn(batch[0]) for batch in batch_gen_1]
### Pad and reshape predictions
n_sample_preds = get_n_sample_preds(final_layer)
input_time_length = lasagne.layers.get_all_layers(final_layer)[0].shape[2]
n_samples_0 = test_set_0.get_topological_view().shape[0]
preds_arr_0 = get_reshaped_cnt_preds(all_preds_0, n_samples_0,
input_time_length, n_sample_preds)
n_samples_1 = test_set_1.get_topological_view().shape[0]
preds_arr_1 = get_reshaped_cnt_preds(all_preds_1, n_samples_1,
input_time_length, n_sample_preds)
series_preds = [preds_arr_0, preds_arr_1]
assert len(series_preds[0]) == test_series_lengths_resampled[0]
assert len(series_preds[1]) == test_series_lengths_resampled[1]
assert False, (
"TODO: here only duplicate if resample half is true for the dataset.. "
"also take care how to create submission cv if trained on all subjects"
)
series_preds_duplicated = [
np.repeat(preds, 2, axis=0) for preds in series_preds
]
n_classes = preds_arr_0.shape[1]
# pad missing ones with zeros
missing_0 = test_series_lengths[0] - len(series_preds_duplicated[0])
full_preds_0 = np.append(np.zeros((missing_0, n_classes),
dtype=np.float32),
series_preds_duplicated[0],
axis=0)
missing_1 = test_series_lengths[1] - len(series_preds_duplicated[1])
full_preds_1 = np.append(np.zeros((missing_1, n_classes),
dtype=np.float32),
series_preds_duplicated[1],
axis=0)
assert len(full_preds_0) == test_series_lengths[0]
assert len(full_preds_1) == test_series_lengths[1]
full_series_preds = [full_preds_0, full_preds_1]
assert sum([len(a)
for a in full_series_preds]) == np.sum(test_series_lengths)
### Create csv
log.info("Create csv...")
csv_filename = "{:02d}".format(submission_id) + '.csv'
csv_filename = os.path.join(folder_name, csv_filename)
cols = [
'HandStart', 'FirstDigitTouch', 'BothStartLoadPhase', 'LiftOff',
'Replace', 'BothReleased'
]
# collect ids
all_ids = []
all_preds = []
for i_series in (9, 10):
id_prefix = "subj{:d}_series{:d}_".format(kaggle_set.i_subject,
i_series)
this_preds = full_series_preds[i_series - 9] # respect offsets
all_preds.extend(this_preds)
this_ids = [
id_prefix + str(i_sample)
for i_sample in range(this_preds.shape[0])
]
all_ids.extend(this_ids)
all_ids = np.array(all_ids)
all_preds = np.array(all_preds)
submission = pd.DataFrame(index=all_ids, columns=cols, data=all_preds)
submission.to_csv(csv_filename, index_label='id', float_format='%.3f')
log.info("Done")