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# | ||
# Copyright (c) 2023, NVIDIA CORPORATION. | ||
# | ||
# Licensed under the Apache License, Version 2.0 (the "License"); | ||
# you may not use this file except in compliance with the License. | ||
# You may obtain a copy of the License at | ||
# | ||
# http://www.apache.org/licenses/LICENSE-2.0 | ||
# | ||
# Unless required by applicable law or agreed to in writing, software | ||
# distributed under the License is distributed on an "AS IS" BASIS, | ||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
# See the License for the specific language governing permissions and | ||
# limitations under the License. | ||
# | ||
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import glob | ||
import math | ||
import os | ||
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import numpy as np | ||
import pytest | ||
from pandas.api.types import is_integer_dtype | ||
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import nvtabular as nvt | ||
from merlin.core import dispatch | ||
from merlin.core.dispatch import HAS_GPU | ||
from merlin.core.utils import set_dask_client | ||
from merlin.dag.ops.subgraph import Subgraph | ||
from nvtabular import Dataset, Workflow, ops | ||
from tests.conftest import get_cats | ||
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@pytest.mark.parametrize("gpu_memory_frac", [0.01, 0.1]) | ||
@pytest.mark.parametrize("engine", ["parquet", "csv", "csv-no-header"]) | ||
@pytest.mark.parametrize("dump", [True, False]) | ||
@pytest.mark.parametrize("replace", [True, False]) | ||
def test_workflow_subgraphs(tmpdir, client, df, dataset, gpu_memory_frac, engine, dump, replace): | ||
cat_names = ["name-cat", "name-string"] if engine == "parquet" else ["name-string"] | ||
cont_names = ["x", "y", "id"] | ||
label_name = ["label"] | ||
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norms = ops.Normalize() | ||
cat_features = cat_names >> ops.Categorify() | ||
if replace: | ||
cont_features = cont_names >> ops.FillMissing() >> ops.LogOp >> norms | ||
else: | ||
fillmissing_logop = ( | ||
cont_names | ||
>> ops.FillMissing() | ||
>> ops.LogOp | ||
>> ops.Rename(postfix="_FillMissing_1_LogOp_1") | ||
) | ||
cont_features = cont_names + fillmissing_logop >> norms | ||
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set_dask_client(client=client) | ||
wkflow_ops = Subgraph("cat_graph", cat_features) + Subgraph("cont_graph", cont_features) | ||
workflow = Workflow(wkflow_ops + label_name) | ||
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workflow.fit(dataset) | ||
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if dump: | ||
workflow_dir = os.path.join(tmpdir, "workflow") | ||
workflow.save(workflow_dir) | ||
workflow = None | ||
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workflow = Workflow.load(workflow_dir) | ||
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def get_norms(tar): | ||
ser_median = tar.dropna().quantile(0.5, interpolation="linear") | ||
gdf = tar.fillna(ser_median) | ||
gdf = np.log(gdf + 1) | ||
return gdf | ||
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# Check mean and std - No good right now we have to add all other changes; Clip, Log | ||
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concat_ops = "_FillMissing_1_LogOp_1" | ||
if replace: | ||
concat_ops = "" | ||
assert math.isclose(get_norms(df.x).mean(), norms.means["x" + concat_ops], rel_tol=1e-1) | ||
assert math.isclose(get_norms(df.y).mean(), norms.means["y" + concat_ops], rel_tol=1e-1) | ||
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assert math.isclose(get_norms(df.x).std(), norms.stds["x" + concat_ops], rel_tol=1e-1) | ||
assert math.isclose(get_norms(df.y).std(), norms.stds["y" + concat_ops], rel_tol=1e-1) | ||
# Check that categories match | ||
if engine == "parquet": | ||
cats_expected0 = df["name-cat"].unique().values_host if HAS_GPU else df["name-cat"].unique() | ||
cats0 = get_cats(workflow, "name-cat") | ||
# adding the None entry as a string because of move from gpu | ||
assert all(cat in sorted(cats_expected0.tolist()) for cat in cats0.tolist()) | ||
assert len(cats0.tolist()) == len(cats_expected0.tolist()) | ||
cats_expected1 = ( | ||
df["name-string"].unique().values_host if HAS_GPU else df["name-string"].unique() | ||
) | ||
cats1 = get_cats(workflow, "name-string") | ||
# adding the None entry as a string because of move from gpu | ||
assert all(cat in sorted(cats_expected1.tolist()) for cat in cats1.tolist()) | ||
assert len(cats1.tolist()) == len(cats_expected1.tolist()) | ||
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# Write to new "shuffled" and "processed" dataset | ||
workflow.transform(dataset).to_parquet( | ||
tmpdir, | ||
out_files_per_proc=10, | ||
shuffle=nvt.io.Shuffle.PER_PARTITION, | ||
) | ||
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dataset_2 = Dataset(glob.glob(str(tmpdir) + "/*.parquet"), part_mem_fraction=gpu_memory_frac) | ||
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df_pp = dispatch.concat(list(dataset_2.to_iter()), axis=0) | ||
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if engine == "parquet": | ||
assert is_integer_dtype(df_pp["name-cat"].dtype) | ||
assert is_integer_dtype(df_pp["name-string"].dtype) | ||
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num_rows, num_row_groups, col_names = dispatch.read_parquet_metadata(str(tmpdir) + "/_metadata") | ||
assert num_rows == len(df_pp) | ||
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subgraph_cat = workflow.get_subgraph("cat_graph") | ||
subgraph_cont = workflow.get_subgraph("cont_graph") | ||
assert isinstance(subgraph_cat, Workflow) | ||
assert isinstance(subgraph_cont, Workflow) | ||
# will not be the same nodes of saved out and loaded back | ||
if not dump: | ||
assert subgraph_cat.output_node == cat_features | ||
assert subgraph_cont.output_node == cont_features | ||
# check failure path works as expected | ||
with pytest.raises(ValueError) as exc: | ||
workflow.get_subgraph("not_exist") | ||
assert "No subgraph named" in str(exc.value) |