def test_empty_cols(tmpdir, df, dataset, engine, cat_names, cont_names, label_name):
# test out https://github.com/NVIDIA/NVTabular/issues/149 making sure we can iterate over
# empty cats/conts
# first with no continuous columns
processor = nvt.Workflow(cat_names=cat_names, cont_names=cont_names, label_name=label_name)
processor.add_feature([ops.FillMedian()])
processor.add_feature(ops.Normalize())
processor.add_feature(ops.Categorify())
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.apply(
dataset,
apply_offline=True,
record_stats=True,
shuffle=nvt.io.Shuffle.PER_PARTITION,
output_format=None,
)
df_out = processor.get_ddf().compute(scheduler="synchronous")
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df_out), cats=cat_names, conts=cont_names, labels=label_name, batch_size=1
)
for nvt_batch in data_itr:
cats, conts, labels = nvt_batch
if cat_names:
assert cats.shape[-1] == len(cat_names)
if cont_names:
assert conts.shape[-1] == len(cont_names)
if label_name:
assert labels.shape[-1] == len(label_name)
def test_empty_cols(tmpdir, df, dataset, engine, cat_names, cont_names, label_name):
features = []
if cont_names:
features.append(cont_names >> ops.FillMedian() >> ops.Normalize())
if cat_names:
features.append(cat_names >> ops.Categorify())
# test out https://github.com/NVIDIA/NVTabular/issues/149 making sure we can iterate over
# empty cats/conts
graph = sum(features, nvt.ColumnGroup(label_name))
if not graph.columns:
# if we don't have conts/cats/labels we're done
return
processor = nvt.Workflow(sum(features, nvt.ColumnGroup(label_name)))
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
df_out = processor.fit_transform(dataset).to_ddf().compute(scheduler="synchronous")
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df_out), cats=cat_names, conts=cont_names, labels=label_name, batch_size=1
)
for nvt_batch in data_itr:
cats, conts, labels = nvt_batch
if cat_names:
assert cats.shape[-1] == len(cat_names)
if cont_names:
assert conts.shape[-1] == len(cont_names)
if label_name:
assert labels.shape[-1] == len(label_name)
def test_gpu_dl_break(tmpdir, df, dataset, batch_size, part_mem_fraction,
engine, device):
cat_names = ["name-cat", "name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
conts = cont_names >> ops.FillMedian() >> ops.Normalize()
cats = cat_names >> ops.Categorify()
processor = nvt.Workflow(conts + cats + label_name)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.fit_transform(dataset).to_parquet(
shuffle=nvt.io.Shuffle.PER_PARTITION,
output_path=output_train,
out_files_per_proc=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train)
if x.endswith("parquet")
]
nvt_data = nvt.Dataset(tar_paths[0],
engine="parquet",
part_mem_fraction=part_mem_fraction)
data_itr = torch_dataloader.TorchAsyncItr(
nvt_data,
batch_size=batch_size,
cats=cat_names,
conts=cont_names,
labels=["label"],
device=device,
)
len_dl = len(data_itr) - 1
first_chunk = 0
idx = 0
for idx, chunk in enumerate(data_itr):
if idx == 0:
first_chunk = len(chunk[0])
last_chk = len(chunk[0])
print(last_chk)
if idx == 1:
break
del chunk
assert idx < len_dl
first_chunk_2 = 0
for idx, chunk in enumerate(data_itr):
if idx == 0:
first_chunk_2 = len(chunk[0])
del chunk
assert idx == len_dl
assert first_chunk == first_chunk_2
def test_kill_dl(tmpdir, df, dataset, part_mem_fraction, engine):
cat_names = ["name-cat", "name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
processor = nvt.Workflow(cat_names=cat_names, cont_names=cont_names, label_name=label_name)
processor.add_feature([ops.FillMedian()])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.apply(
dataset,
apply_offline=True,
record_stats=True,
shuffle=nvt.io.Shuffle.PER_PARTITION,
output_path=output_train,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train) if x.endswith("parquet")
]
nvt_data = nvt.Dataset(tar_paths[0], engine="parquet", part_mem_fraction=part_mem_fraction)
data_itr = torch_dataloader.TorchAsyncItr(
nvt_data, cats=cat_names, conts=cont_names, labels=["label"]
)
results = {}
for batch_size in [2 ** i for i in range(9, 25, 1)]:
print("Checking batch size: ", batch_size)
num_iter = max(10 * 1000 * 1000 // batch_size, 100) # load 10e7 samples
# import pdb; pdb.set_trace()
data_itr.batch_size = batch_size
start = time.time()
for i, data in enumerate(data_itr):
if i >= num_iter:
break
del data
stop = time.time()
throughput = i * batch_size / (stop - start)
results[batch_size] = throughput
print(
"batch size: ",
batch_size,
", throughput: ",
throughput,
"items",
i * batch_size,
"time",
stop - start,
)
def test_gpu_dl(tmpdir, df, dataset, batch_size, gpu_memory_frac, engine):
cat_names = ["name-cat", "name-string"] if engine == "parquet" else ["name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
processor = nvt.Workflow(cat_names=cat_names, cont_names=cont_names, label_name=label_name,)
processor.add_feature([ops.FillMedian()])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.apply(
dataset,
apply_offline=True,
record_stats=True,
shuffle=True,
output_path=output_train,
num_out_files=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train) if x.endswith("parquet")
]
data_itr = nvt.torch_dataloader.TorchTensorBatchDatasetItr(
tar_paths[0],
engine="parquet",
sub_batch_size=batch_size,
gpu_memory_frac=gpu_memory_frac,
cats=cat_names,
conts=cont_names,
labels=["label"],
names=mycols_csv,
sep="\t",
)
num_rows, num_row_groups, col_names = cudf.io.read_parquet_metadata(tar_paths[0])
rows = 0
for idx, chunk in enumerate(data_itr):
rows += len(chunk)
del chunk
# accounts for incomplete batches at the end of chunks
# that dont necesssarily have the full batch_size
assert (idx + 1) * batch_size >= rows
assert rows == num_rows
if os.path.exists(output_train):
shutil.rmtree(output_train)
def test_fit_simple():
data = cudf.DataFrame({
"x": [0, 1, 2, None, 0, 1, 2],
"y": [None, 3, 4, 5, 3, 4, 5]
})
dataset = Dataset(data)
workflow = Workflow(["x", "y"] >> ops.FillMedian() >> (lambda x: x * x))
workflow.fit(dataset)
transformed = workflow.transform(dataset).to_ddf().compute()
expected = cudf.DataFrame({
"x": [0, 1, 4, 1, 0, 1, 4],
"y": [16, 9, 16, 25, 9, 16, 25]
})
assert_eq(expected, transformed)
def test_fit_simple():
data = nvt.dispatch._make_df({
"x": [0, 1, 2, None, 0, 1, 2],
"y": [None, 3, 4, 5, 3, 4, 5]
})
dataset = Dataset(data)
workflow = Workflow(["x", "y"] >> ops.FillMedian() >> (lambda x: x * x))
workflow.fit(dataset)
transformed = workflow.transform(dataset).to_ddf().compute()
expected = nvt.dispatch._make_df({
"x": [0, 1, 4, 1, 0, 1, 4],
"y": [16, 9, 16, 25, 9, 16, 25]
})
if not HAS_GPU:
transformed["x"] = transformed["x"].astype(expected["x"].dtype)
transformed["y"] = transformed["y"].astype(expected["y"].dtype)
assert_eq(expected, transformed)
def test_dataloader_schema(tmpdir, df, dataset, batch_size, engine, device):
cat_names = ["name-cat", "name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
conts = cont_names >> ops.FillMedian() >> ops.Normalize()
cats = cat_names >> ops.Categorify()
processor = nvt.Workflow(conts + cats + label_name)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.fit_transform(dataset).to_parquet(
shuffle=nvt.io.Shuffle.PER_PARTITION,
output_path=output_train,
out_files_per_proc=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train)
if x.endswith("parquet")
]
nvt_data = nvt.Dataset(tar_paths, engine="parquet")
data_loader = torch_dataloader.TorchAsyncItr(
nvt_data,
batch_size=batch_size,
shuffle=False,
labels=label_name,
)
batch = next(iter(data_loader))
assert all(name in batch[0] for name in cat_names)
assert all(name in batch[0] for name in cont_names)
num_label_cols = batch[1].shape[1] if len(batch[1].shape) > 1 else 1
assert num_label_cols == len(label_name)
def test_tf_gpu_dl(tmpdir, paths, use_paths, dataset, batch_size,
gpu_memory_frac, engine):
cont_names = ["x", "y", "id"]
cat_names = ["name-string"]
label_name = ["label"]
if engine == "parquet":
cat_names.append("name-cat")
columns = cont_names + cat_names
conts = cont_names >> ops.FillMedian() >> ops.Normalize()
cats = cat_names >> ops.Categorify()
workflow = nvt.Workflow(conts + cats + label_name)
workflow.fit(dataset)
workflow.transform(dataset).to_parquet(tmpdir + "/processed")
data_itr = tf_dataloader.KerasSequenceLoader(
str(tmpdir + "/processed"), # workflow.transform(dataset),
cat_names=cat_names,
cont_names=cont_names,
batch_size=batch_size,
buffer_size=gpu_memory_frac,
label_names=label_name,
engine=engine,
shuffle=False,
)
_ = tf.random.uniform((1, ))
rows = 0
for idx in range(len(data_itr)):
X, y = next(data_itr)
# first elements to check epoch-to-epoch consistency
if idx == 0:
X0, y0 = X, y
# check that we have at most batch_size elements
num_samples = y.shape[0]
if num_samples != batch_size:
try:
next(data_itr)
except StopIteration:
rows += num_samples
continue
else:
raise ValueError("Batch size too small at idx {}".format(idx))
# check that all the features in X have the
# appropriate length and that the set of
# their names is exactly the set of names in
# `columns`
these_cols = columns.copy()
for column, x in X.items():
try:
these_cols.remove(column)
except ValueError:
raise AssertionError
assert x.shape[0] == num_samples
assert len(these_cols) == 0
rows += num_samples
assert (idx + 1) * batch_size >= rows
assert rows == (60 * 24 * 3 + 1)
# if num_samples is equal to batch size,
# we didn't exhaust the iterator and do
# cleanup. Try that now
if num_samples == batch_size:
try:
next(data_itr)
except StopIteration:
pass
else:
raise ValueError
assert not data_itr._working
assert data_itr._batch_itr is None
# check start of next epoch to ensure consistency
X, y = next(data_itr)
assert (y.numpy() == y0.numpy()).all()
for column, x in X.items():
x0 = X0.pop(column)
assert (x.numpy() == x0.numpy()).all()
assert len(X0) == 0
data_itr.stop()
assert not data_itr._working
assert data_itr._batch_itr is None
def test_gpu_dl(tmpdir, df, dataset, batch_size, part_mem_fraction, engine,
devices):
cat_names = ["name-cat", "name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
conts = cont_names >> ops.FillMedian() >> ops.Normalize()
cats = cat_names >> ops.Categorify()
processor = nvt.Workflow(conts + cats + label_name)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.fit_transform(dataset).to_parquet(
shuffle=nvt.io.Shuffle.PER_PARTITION,
output_path=output_train,
out_files_per_proc=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train)
if x.endswith("parquet")
]
nvt_data = nvt.Dataset(tar_paths[0],
engine="parquet",
part_mem_fraction=part_mem_fraction)
data_itr = torch_dataloader.TorchAsyncItr(
nvt_data,
batch_size=batch_size,
cats=cat_names,
conts=cont_names,
labels=["label"],
devices=devices,
)
columns = mycols_pq
df_test = cudf.read_parquet(tar_paths[0])[columns]
df_test.columns = [x for x in range(0, len(columns))]
num_rows, num_row_groups, col_names = cudf.io.read_parquet_metadata(
tar_paths[0])
rows = 0
# works with iterator alone, needs to test inside torch dataloader
for idx, chunk in enumerate(data_itr):
if devices is None:
assert float(df_test.iloc[rows][0]) == float(chunk[0][0][0])
rows += len(chunk[0])
del chunk
# accounts for incomplete batches at the end of chunks
# that dont necesssarily have the full batch_size
assert rows == num_rows
def gen_col(batch):
batch = batch[0]
return batch[0], batch[1], batch[2]
t_dl = torch_dataloader.DLDataLoader(data_itr,
collate_fn=gen_col,
pin_memory=False,
num_workers=0)
rows = 0
for idx, chunk in enumerate(t_dl):
if devices is None:
assert float(df_test.iloc[rows][0]) == float(chunk[0][0][0])
rows += len(chunk[0])
if os.path.exists(output_train):
shutil.rmtree(output_train)
def test_tf_gpu_dl(tmpdir, datasets, batch_size, gpu_memory_frac, engine):
paths = glob.glob(str(datasets[engine]) + "/*." + engine.split("-")[0])
cont_names = ["x", "y", "id"]
cat_names = ["name-string"]
label_name = ["label"]
if engine == "parquet":
cat_names.append("name-cat")
columns = cont_names + cat_names
processor = nvt.Workflow(
cat_names=cat_names, cont_names=cont_names, label_name=label_name, to_cpu=True,
)
processor.add_feature([ops.FillMedian()])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
processor.finalize()
data_itr = tf_dataloader.KerasSequenceDataset(
paths,
columns=columns,
batch_size=batch_size,
buffer_size=gpu_memory_frac,
label_name=label_name[0],
engine=engine,
shuffle=False,
)
processor.update_stats(data_itr.nvt_dataset, record_stats=True)
data_itr.map(processor)
rows = 0
for idx in range(len(data_itr)):
X, y = next(data_itr)
# first elements to check epoch-to-epoch consistency
if idx == 0:
X0, y0 = X, y
# check that we have at most batch_size elements
num_samples = y.shape[0]
assert num_samples <= batch_size
# check that all the features in X have the
# appropriate length and that the set of
# their names is exactly the set of names in
# `columns`
these_cols = columns.copy()
for column, x in X.items():
try:
these_cols.remove(column)
except ValueError:
raise AssertionError
assert x.shape[0] == num_samples
assert len(these_cols) == 0
rows += num_samples
# check start of next epoch to ensure consistency
X, y = next(data_itr)
assert (y.numpy() == y0.numpy()).all()
for column, x in X.items():
x0 = X0.pop(column)
assert (x.numpy() == x0.numpy()).all()
assert len(X0) == 0
# accounts for incomplete batches at the end of chunks
# that dont necesssarily have the full batch_size
assert (idx + 1) * batch_size >= rows
assert rows == (60 * 24 * 3 + 1)
def test_empty_cols(tmpdir, engine, cat_names, mh_names, cont_names,
label_name, num_rows):
json_sample["num_rows"] = num_rows
cols = datagen._get_cols_from_schema(json_sample)
df_gen = datagen.DatasetGen(datagen.PowerLawDistro(0.1))
dataset = df_gen.create_df(num_rows, cols)
dataset = nvt.Dataset(dataset)
features = []
if cont_names:
features.append(cont_names >> ops.FillMedian() >> ops.Normalize())
if cat_names or mh_names:
features.append(cat_names + mh_names >> ops.Categorify())
# test out https://github.com/NVIDIA/NVTabular/issues/149 making sure we can iterate over
# empty cats/conts
graph = sum(features, nvt.WorkflowNode(label_name))
processor = nvt.Workflow(graph)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
df_out = processor.fit_transform(dataset).to_ddf().compute(
scheduler="synchronous")
if processor.output_node.output_schema.apply_inverse(
ColumnSelector("lab_1")):
# if we don't have conts/cats/labels we're done
return
data_itr = None
with pytest.raises(ValueError) as exc_info:
data_itr = torch_dataloader.TorchAsyncItr(
nvt.Dataset(df_out),
cats=cat_names + mh_names,
conts=cont_names,
labels=label_name,
batch_size=2,
)
assert "Neither Categorical or Continuous columns were found by the dataloader. " in str(
exc_info.value)
if data_itr:
for nvt_batch in data_itr:
cats_conts, labels = nvt_batch
if cat_names:
assert set(cat_names).issubset(set(list(cats_conts.keys())))
if cont_names:
assert set(cont_names).issubset(set(list(cats_conts.keys())))
if cat_names or cont_names or mh_names:
emb_sizes = nvt.ops.get_embedding_sizes(processor)
EMBEDDING_DROPOUT_RATE = 0.04
DROPOUT_RATES = [0.001, 0.01]
HIDDEN_DIMS = [1000, 500]
LEARNING_RATE = 0.001
model = Model(
embedding_table_shapes=emb_sizes,
num_continuous=len(cont_names),
emb_dropout=EMBEDDING_DROPOUT_RATE,
layer_hidden_dims=HIDDEN_DIMS,
layer_dropout_rates=DROPOUT_RATES,
).cuda()
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)
def rmspe_func(y_pred, y):
"Return y_pred and y to non-log space and compute RMSPE"
y_pred, y = torch.exp(y_pred) - 1, torch.exp(y) - 1
pct_var = (y_pred - y) / y
return (pct_var**2).mean().pow(0.5)
train_loss, y_pred, y = process_epoch(
data_itr,
model,
train=True,
optimizer=optimizer,
amp=False,
)
train_rmspe = None
train_rmspe = rmspe_func(y_pred, y)
assert train_rmspe is not None
assert len(y_pred) > 0
assert len(y) > 0
def test_gpu_preproc(tmpdir, df, dataset, dump, gpu_memory_frac, engine,
preprocessing):
cat_names = ["name-cat", "name-string"
] if engine == "parquet" else ["name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
processor = nvt.Workflow(cat_names=cat_names,
cont_names=cont_names,
label_name=label_name)
processor.add_feature(
[ops.FillMedian(),
ops.LogOp(preprocessing=preprocessing)])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
processor.finalize()
processor.update_stats(dataset)
if dump:
config_file = tmpdir + "/temp.yaml"
processor.save_stats(config_file)
processor.clear_stats()
processor.load_stats(config_file)
def get_norms(tar: cudf.Series):
ser_median = tar.dropna().quantile(0.5, interpolation="linear")
gdf = tar.fillna(ser_median)
gdf = np.log(gdf + 1)
return gdf
# Check mean and std - No good right now we have to add all other changes; Zerofill, Log
x_col = "x" if preprocessing else "x_LogOp"
y_col = "y" if preprocessing else "y_LogOp"
assert math.isclose(get_norms(df.x).mean(),
processor.stats["means"][x_col],
rel_tol=1e-2)
assert math.isclose(get_norms(df.y).mean(),
processor.stats["means"][y_col],
rel_tol=1e-2)
assert math.isclose(get_norms(df.x).std(),
processor.stats["stds"][x_col],
rel_tol=1e-2)
assert math.isclose(get_norms(df.y).std(),
processor.stats["stds"][y_col],
rel_tol=1e-2)
# Check median (TODO: Improve the accuracy)
x_median = df.x.dropna().quantile(0.5, interpolation="linear")
y_median = df.y.dropna().quantile(0.5, interpolation="linear")
id_median = df.id.dropna().quantile(0.5, interpolation="linear")
assert math.isclose(x_median, processor.stats["medians"]["x"], rel_tol=1e1)
assert math.isclose(y_median, processor.stats["medians"]["y"], rel_tol=1e1)
assert math.isclose(id_median,
processor.stats["medians"]["id"],
rel_tol=1e1)
# Check that categories match
if engine == "parquet":
cats_expected0 = df["name-cat"].unique().values_host
cats0 = get_cats(processor, "name-cat")
assert cats0.tolist() == [None] + cats_expected0.tolist()
cats_expected1 = df["name-string"].unique().values_host
cats1 = get_cats(processor, "name-string")
assert cats1.tolist() == [None] + cats_expected1.tolist()
# Write to new "shuffled" and "processed" dataset
processor.write_to_dataset(tmpdir,
dataset,
nfiles=10,
shuffle=True,
apply_ops=True)
processor.create_final_cols()
# if preprocessing
if not preprocessing:
for col in cont_names:
assert f"{col}_LogOp" in processor.columns_ctx["final"]["cols"][
"continuous"]
dlc = torch_dataloader.DLCollator(preproc=processor, apply_ops=False)
data_files = [
torch_dataloader.FileItrDataset(x,
use_row_groups=True,
gpu_memory_frac=gpu_memory_frac,
names=allcols_csv)
for x in glob.glob(str(tmpdir) + "/*.parquet")
]
data_itr = torch.utils.data.ChainDataset(data_files)
dl = torch_dataloader.DLDataLoader(data_itr,
collate_fn=dlc.gdf_col,
pin_memory=False,
num_workers=0)
len_df_pp = 0
for chunk in dl:
len_df_pp += len(chunk[0][0])
dataset = Dataset(glob.glob(str(tmpdir) + "/*.parquet"),
part_mem_fraction=gpu_memory_frac)
x = processor.ds_to_tensors(dataset.to_iter(), apply_ops=False)
num_rows, num_row_groups, col_names = cudf.io.read_parquet_metadata(
str(tmpdir) + "/_metadata")
assert len(x[0]) == len_df_pp
itr_ds = torch_dataloader.TensorItrDataset([x[0], x[1], x[2]],
batch_size=512000)
count_tens_itr = 0
for data_gd in itr_ds:
count_tens_itr += len(data_gd[1])
assert data_gd[0].shape[1] > 0
assert data_gd[1].shape[1] > 0
assert len_df_pp == count_tens_itr
def test_gpu_dl(tmpdir, df, dataset, batch_size, gpu_memory_frac, engine):
cat_names = ["name-cat", "name-string"
] if engine == "parquet" else ["name-string"]
cont_names = ["x", "y", "id"]
label_name = ["label"]
processor = nvt.Workflow(cat_names=cat_names,
cont_names=cont_names,
label_name=label_name)
processor.add_feature([ops.FillMedian()])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.apply(
dataset,
apply_offline=True,
record_stats=True,
shuffle=True,
output_path=output_train,
num_out_files=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train)
if x.endswith("parquet")
]
data_itr = nvt.torch_dataloader.TorchTensorBatchDatasetItr(
tar_paths[0],
engine="parquet",
sub_batch_size=batch_size,
gpu_memory_frac=gpu_memory_frac,
cats=cat_names,
conts=cont_names,
labels=["label"],
names=mycols_csv,
sep="\t",
)
columns = mycols_pq if engine == "parquet" else mycols_csv
df_test = cudf.read_parquet(tar_paths[0])[columns]
df_test.columns = [x for x in range(0, len(columns))]
num_rows, num_row_groups, col_names = cudf.io.read_parquet_metadata(
tar_paths[0])
rows = 0
# works with iterator alone, needs to test inside torch dataloader
for idx, chunk in enumerate(data_itr):
assert float(df_test.iloc[rows][0]) == float(chunk[0][0][0])
rows += len(chunk[0])
del chunk
# accounts for incomplete batches at the end of chunks
# that dont necesssarily have the full batch_size
assert rows == num_rows
def gen_col(batch):
batch = batch[0]
return batch[0], batch[1], batch[2]
t_dl = nvt.torch_dataloader.DLDataLoader(data_itr,
collate_fn=gen_col,
pin_memory=False,
num_workers=0)
rows = 0
for idx, chunk in enumerate(t_dl):
assert float(df_test.iloc[rows][0]) == float(chunk[0][0][0])
rows += len(chunk[0])
if os.path.exists(output_train):
shutil.rmtree(output_train)
def processing(
self,
X_pd,
y_names=[],
encode_categor_type=None,
#'categorify', 'onehotencoding',
outliers_detection_technique=None,
#'iqr_proximity_rule', 'gaussian_approximation','quantiles'
fill_with_value=None,
#'extreme_values', 'zeros','mean-median'
targetencoding=False,
file_path=None,
):
X = dd.from_pandas(X_pd, npartitions=self.n_gpus)
X = X.replace(np.nan, None)
try:
self.time_columns
except AttributeError:
try:
self.initialize_types(
X,
n_unique_val_th=n_unique_val_th_,
categor_columns_keep=categor_columns_keep_,
numer_columns_keep=numer_columns_keep_)
except NameError:
self.initialize_types(X)
workflow = nvt.Workflow(cat_names=self.categor_columns,
cont_names=self.numer_columns,
label_name=y_names,
client=self.client)
# Operators: https://nvidia.github.io/NVTabular/main/api/ops/index.html
# Categorify https://nvidia.github.io/NVTabular/main/api/ops/categorify.html
if encode_categor_type == 'categorify':
if len(self.categor_columns) != 0:
workflow.add_preprocess(
ops.Categorify(columns=self.categor_columns,
out_path='./'))
if encode_categor_type == 'onehotencoding':
#OneHotEncoder().get_feature_names(input_features=<list of features encoded>) does not work
#lengths=True - chunk sizes can be computed
for column in self.categor_columns:
#X[column] = X[column].astype(str)
X_cat_encoded = OneHotEncoder().fit_transform(
X[column].to_dask_array(lengths=True).reshape(-1, 1))
uvs = X[column].unique().compute().values
X = X.drop([column], axis=1)
X_cat_encoded = dd.from_array(
X_cat_encoded.compute().todense())
X_cat_encoded.columns = [
column + '_{}'.format(uv) for uv in uvs
]
X = dd.concat([X, X_cat_encoded], axis=1)
X = X.repartition(npartitions=2)
for column in X.columns:
if any(str(column)[-4:] == t
for t in ['_nan', 'None']): # What else?
X = X.drop([column], axis=1)
self.initialize_types(X)
print('Retyping:', self.initialize_types(X))
# Reinitialize workflow
workflow = nvt.Workflow(cat_names=self.categor_columns,
cont_names=self.numer_columns,
label_name=y_names,
client=self.client)
# OutlDetect https://nvidia.github.io/NVTabular/main/api/ops/clip.html
if (len(self.numer_columns) != 0) and (outliers_detection_technique !=
None):
lower, upper = self.outldetect(outliers_detection_technique,
X[self.numer_columns])
for i in range(len(self.numer_columns)):
logging.info(
f'column: {self.numer_columns[i]}, lower: {lower[i]}, upper: {upper[i]}'
)
print(
f'column: {self.numer_columns[i]}, lower: {lower[i]}, upper: {upper[i]}'
)
workflow.add_preprocess(
ops.Clip(min_value=lower[i],
max_value=upper[i],
columns=[self.numer_columns[i]]))
# FillMissing https://nvidia.github.io/NVTabular/main/api/ops/fillmissing.html
if fill_with_value == 'zeros':
workflow.add_preprocess(
ops.FillMissing(fill_val=0,
columns=self.categor_columns +
self.numer_columns))
if fill_with_value == 'extreme_values':
extrim_values = {}
if len(self.numer_columns) != 0:
extrim_values.update(
self.extrvalsdetect(X[self.numer_columns],
'numer_columns'))
if len(self.categor_columns) != 0:
extrim_values.update(
self.extrvalsdetect(X[self.categor_columns],
'categor_columns'))
logging.info(f'extrim_values: {extrim_values}')
output = open('extrim_values', 'wb')
pickle.dump(extrim_values, output)
output.close()
for fill_val, column in zip(list(extrim_values.values()),
list(extrim_values.keys())):
workflow.add_preprocess(
ops.FillMissing(fill_val=fill_val, columns=[column]))
if fill_with_value == 'mean-median':
if len(self.categor_columns) != 0:
workflow.add_preprocess(
ops.FillMedian(columns=self.categor_columns,
preprocessing=True,
replace=True))
if len(self.numer_columns) != 0:
means = list(
dd.from_pandas(
X[self.numer_columns],
npartitions=self.n_gpus).mean().compute().values)
for fill_val, column in zip(means, self.numer_columns):
workflow.add_preprocess(
ops.FillMissing(fill_val=fill_val, columns=[column]))
if targetencoding:
#https://nvidia.github.io/NVTabular/main/api/ops/targetencoding.html
if len(self.y_names) != 0:
if len(self.cat_groups) == 0:
print(
'\n Target encoding will be applied to all categorical columns'
)
workflow.add_preprocess(
ops.TargetEncoding(cat_groups=self.categor_columns,
cont_target=self.y_names))
else:
workflow.add_preprocess(
ops.TargetEncoding(cat_groups=self.cat_groups,
cont_target=self.y_names))
#-----------------------------------------------------------------------------------------
workflow.finalize()
dataset = nvt.Dataset(X)
tmp_output_path = "./parquet_data_tmp"
workflow.apply(
dataset,
output_format="parquet",
output_path=tmp_output_path,
shuffle=Shuffle.PER_WORKER, # Shuffle algorithm
out_files_per_proc=1, # Number of output files per worker
)
files = glob.glob(tmp_output_path + "/*.parquet")
X_final = cudf.read_parquet(files[0])
for i in range(1, len(files)):
X_final = X_final.append(cudf.read_parquet(files[i]))
# Delete temporary files
shutil.rmtree(tmp_output_path, ignore_errors=True)
# if len(self.rest_col_names) != 0:
# print(1)
# X_final = pd.concat([X_final.to_pandas(), X_pd[self.rest_col_names]], axis=1)
if file_path is not None:
X_final.to_csv(file_path, index=False)
return X_final
def test_gpu_dl(tmpdir, datasets, batch_size, gpu_memory_frac, engine):
paths = glob.glob(str(datasets[engine]) + "/*." + engine.split("-")[0])
if engine == "parquet":
df1 = cudf.read_parquet(paths[0])[mycols_pq]
df2 = cudf.read_parquet(paths[1])[mycols_pq]
else:
df1 = cudf.read_csv(paths[0], header=False, names=allcols_csv)[mycols_csv]
df2 = cudf.read_csv(paths[1], header=False, names=allcols_csv)[mycols_csv]
df = cudf.concat([df1, df2], axis=0)
df["id"] = df["id"].astype("int64")
if engine == "parquet":
cat_names = ["name-cat", "name-string"]
columns = mycols_pq
else:
cat_names = ["name-string"]
columns = mycols_csv
cont_names = ["x", "y", "id"]
label_name = ["label"]
processor = nvt.Workflow(
cat_names=cat_names, cont_names=cont_names, label_name=label_name, to_cpu=True,
)
processor.add_feature([ops.FillMedian()])
processor.add_preprocess(ops.Normalize())
processor.add_preprocess(ops.Categorify())
data_itr = nvtabular.io.GPUDatasetIterator(
paths,
columns=columns,
use_row_groups=True,
gpu_memory_frac=gpu_memory_frac,
names=allcols_csv,
)
output_train = os.path.join(tmpdir, "train/")
os.mkdir(output_train)
processor.apply(
data_itr,
apply_offline=True,
record_stats=True,
shuffle=True,
output_path=output_train,
num_out_files=2,
)
tar_paths = [
os.path.join(output_train, x) for x in os.listdir(output_train) if x.endswith("parquet")
]
data_itr = nvt.torch_dataloader.TorchTensorBatchDatasetItr(
tar_paths[0],
engine="parquet",
sub_batch_size=batch_size,
gpu_memory_frac=gpu_memory_frac,
cats=cat_names,
conts=cont_names,
labels=["label"],
names=mycols_csv,
sep="\t",
)
num_rows, num_row_groups, col_names = cudf.io.read_parquet_metadata(tar_paths[0])
rows = 0
for idx, chunk in enumerate(data_itr):
rows += len(chunk)
del chunk
# accounts for incomplete batches at the end of chunks
# that dont necesssarily have the full batch_size
assert (idx + 1) * batch_size >= rows
assert rows == num_rows
if os.path.exists(output_train):
shutil.rmtree(output_train)
