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[dask] Support more meta data on functional interface. (#6132)

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* Add base_margin, label_(lower|upper)_bound.
* Test survival training with dask.
This commit is contained in:
Jiaming Yuan 2020-09-21 16:56:37 +08:00 committed by GitHub
parent 7065779afa
commit 33d80ffad0
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4 changed files with 154 additions and 48 deletions
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6
python-package/xgboost/core.py
View File

@ -320,7 +320,8 @@ class DataIter:
def data_handle(data, label=None, weight=None, base_margin=None,
group=None,
label_lower_bound=None, label_upper_bound=None,
feature_names=None, feature_types=None):
feature_names=None, feature_types=None,
feature_weights=None):
from .data import dispatch_device_quantile_dmatrix_set_data
from .data import _device_quantile_transform
data, feature_names, feature_types = _device_quantile_transform(
@ -333,7 +334,8 @@ class DataIter:
label_lower_bound=label_lower_bound,
label_upper_bound=label_upper_bound,
feature_names=feature_names,
feature_types=feature_types)
feature_types=feature_types,
feature_weights=feature_weights)
try:
# Differ the exception in order to return 0 and stop the iteration.
# Exception inside a ctype callback function has no effect except

136
python-package/xgboost/dask.py
View File

@ -178,6 +178,12 @@ class DaskDMatrix:
to be present as a missing value. If None, defaults to np.nan.
weight : dask.array.Array/dask.dataframe.DataFrame
Weight for each instance.
base_margin : dask.array.Array/dask.dataframe.DataFrame
Global bias for each instance.
label_lower_bound : dask.array.Array/dask.dataframe.DataFrame
Upper bound for survival training.
label_upper_bound : dask.array.Array/dask.dataframe.DataFrame
Lower bound for survival training.
feature_names : list, optional
Set names for features.
feature_types : list, optional
@ -191,6 +197,9 @@ class DaskDMatrix:
label=None,
missing=None,
weight=None,
base_margin=None,
label_lower_bound=None,
label_upper_bound=None,
feature_names=None,
feature_types=None):
_assert_dask_support()
@ -216,12 +225,17 @@ class DaskDMatrix:
self.is_quantile = False
self._init = client.sync(self.map_local_data,
client, data, label, weight)
client, data, label=label, weights=weight,
base_margin=base_margin,
label_lower_bound=label_lower_bound,
label_upper_bound=label_upper_bound)
def __await__(self):
return self._init.__await__()
async def map_local_data(self, client, data, label=None, weights=None):
async def map_local_data(self, client, data, label=None, weights=None,
base_margin=None,
label_lower_bound=None, label_upper_bound=None):
'''Obtain references to local data.'''
def inconsistent(left, left_name, right, right_name):
@ -241,10 +255,10 @@ class DaskDMatrix:
' chunks=(partition_size, X.shape[1])'
data = data.persist()
if label is not None:
label = label.persist()
if weights is not None:
weights = weights.persist()
for meta in [label, weights, base_margin, label_lower_bound,
label_upper_bound]:
if meta is not None:
meta = meta.persist()
# Breaking data into partitions, a trick borrowed from dask_xgboost.
# `to_delayed` downgrades high-level objects into numpy or pandas
@ -254,29 +268,37 @@ class DaskDMatrix:
check_columns(X_parts)
X_parts = X_parts.flatten().tolist()
if label is not None:
y_parts = label.to_delayed()
if isinstance(y_parts, numpy.ndarray):
check_columns(y_parts)
y_parts = y_parts.flatten().tolist()
if weights is not None:
w_parts = weights.to_delayed()
if isinstance(w_parts, numpy.ndarray):
check_columns(w_parts)
w_parts = w_parts.flatten().tolist()
def flatten_meta(meta):
if meta is not None:
meta_parts = meta.to_delayed()
if isinstance(meta_parts, numpy.ndarray):
check_columns(meta_parts)
meta_parts = meta_parts.flatten().tolist()
return meta_parts
return None
y_parts = flatten_meta(label)
w_parts = flatten_meta(weights)
margin_parts = flatten_meta(base_margin)
ll_parts = flatten_meta(label_lower_bound)
lu_parts = flatten_meta(label_upper_bound)
parts = [X_parts]
meta_names = []
if label is not None:
assert len(X_parts) == len(
y_parts), inconsistent(X_parts, 'X', y_parts, 'labels')
parts.append(y_parts)
meta_names.append('labels')
if weights is not None:
assert len(X_parts) == len(
w_parts), inconsistent(X_parts, 'X', w_parts, 'weights')
parts.append(w_parts)
meta_names.append('weights')
def append_meta(m_parts, name: str):
if m_parts is not None:
assert len(X_parts) == len(
m_parts), inconsistent(X_parts, 'X', m_parts, name)
parts.append(m_parts)
meta_names.append(name)
append_meta(y_parts, 'labels')
append_meta(w_parts, 'weights')
append_meta(margin_parts, 'base_margin')
append_meta(ll_parts, 'label_lower_bound')
append_meta(lu_parts, 'label_upper_bound')
parts = list(map(delayed, zip(*parts)))
parts = client.compute(parts)
@ -339,6 +361,9 @@ def _get_worker_parts(worker_map, meta_names, worker):
data = None
labels = None
weights = None
base_margin = None
label_lower_bound = None
label_upper_bound = None
local_data = list(zip(*list_of_parts))
data = local_data[0]
@ -348,8 +373,15 @@ def _get_worker_parts(worker_map, meta_names, worker):
labels = part
if meta_names[i] == 'weights':
weights = part
if meta_names[i] == 'base_margin':
base_margin = part
if meta_names[i] == 'label_lower_bound':
label_lower_bound = part
if meta_names[i] == 'label_upper_bound':
label_upper_bound = part
return data, labels, weights
return (data, labels, weights, base_margin, label_lower_bound,
label_upper_bound)
class DaskPartitionIter(DataIter): # pylint: disable=R0902
@ -456,13 +488,22 @@ class DaskDeviceQuantileDMatrix(DaskDMatrix):
'''
def __init__(self, client, data, label=None, weight=None,
def __init__(self, client,
data,
label=None,
missing=None,
weight=None,
base_margin=None,
label_lower_bound=None,
label_upper_bound=None,
feature_names=None,
feature_types=None,
max_bin=256):
super().__init__(client=client, data=data, label=label, weight=weight,
super().__init__(client=client, data=data, label=label,
missing=missing,
weight=weight, base_margin=base_margin,
label_lower_bound=label_lower_bound,
label_upper_bound=label_upper_bound,
feature_names=feature_names,
feature_types=feature_types)
self.max_bin = max_bin
@ -491,8 +532,13 @@ def _create_device_quantile_dmatrix(feature_names, feature_types,
max_bin=max_bin)
return d
data, labels, weights = _get_worker_parts(worker_map, meta_names, worker)
it = DaskPartitionIter(data=data, label=labels, weight=weights)
(data, labels, weights, base_margin,
label_lower_bound, label_upper_bound) = _get_worker_parts(
worker_map, meta_names, worker)
it = DaskPartitionIter(data=data, label=labels, weight=weights,
base_margin=base_margin,
label_lower_bound=label_lower_bound,
label_upper_bound=label_upper_bound)
dmatrix = DeviceQuantileDMatrix(it,
missing=missing,
@ -524,20 +570,31 @@ def _create_dmatrix(feature_names, feature_types, meta_names, missing,
feature_types=feature_types)
return d
data, labels, weights = _get_worker_parts(worker_map, meta_names, worker)
data = concat(data)
def concat_or_none(data):
if data is not None:
return concat(data)
return data
if labels:
labels = concat(labels)
if weights:
weights = concat(weights)
(data, labels, weights, base_margin,
label_lower_bound, label_upper_bound) = _get_worker_parts(
worker_map, meta_names, worker)
labels = concat_or_none(labels)
weights = concat_or_none(weights)
base_margin = concat_or_none(base_margin)
label_lower_bound = concat_or_none(label_lower_bound)
label_upper_bound = concat_or_none(label_upper_bound)
data = concat(data)
dmatrix = DMatrix(data,
labels,
weight=weights,
missing=missing,
feature_names=feature_names,
feature_types=feature_types,
nthread=worker.nthreads)
dmatrix.set_info(base_margin=base_margin, weight=weights,
label_lower_bound=label_lower_bound,
label_upper_bound=label_upper_bound)
return dmatrix
@ -683,7 +740,8 @@ async def _direct_predict_impl(client, data, predict_fn):
# pylint: disable=too-many-statements
async def _predict_async(client: Client, model, data, missing=numpy.nan, **kwargs):
async def _predict_async(client: Client, model, data, missing=numpy.nan,
**kwargs):
if isinstance(model, Booster):
booster = model

9
tests/python/test_survival.py
View File

@ -3,9 +3,10 @@ import pytest
import numpy as np
import xgboost as xgb
import json
from pathlib import Path
import os
dpath = os.path.join(tm.PROJECT_ROOT, 'demo', 'data')
dpath = Path('demo/data')
def test_aft_survival_toy_data():
# See demo/aft_survival/aft_survival_viz_demo.py
@ -51,10 +52,10 @@ def test_aft_survival_toy_data():
for tree in model_json:
assert gather_split_thresholds(tree).issubset({2.5, 3.5, 4.5})
@pytest.mark.skipif(**tm.no_pandas())
@pytest.mark.skipif(**tm.no_pandas())
def test_aft_survival_demo_data():
import pandas as pd
df = pd.read_csv(dpath / 'veterans_lung_cancer.csv')
df = pd.read_csv(os.path.join(dpath, 'veterans_lung_cancer.csv'))
y_lower_bound = df['Survival_label_lower_bound']
y_upper_bound = df['Survival_label_upper_bound']

51
tests/python/test_with_dask.py
View File

@ -1,6 +1,5 @@
import testing as tm
import pytest
import unittest
import xgboost as xgb
import sys
import numpy as np
@ -482,16 +481,62 @@ def test_predict():
assert pred.ndim == 1
assert pred.shape[0] == kRows
margin = xgb.dask.predict(client, model=booster, data=dtrain, output_margin=True)
margin = xgb.dask.predict(client, model=booster, data=dtrain,
output_margin=True)
assert margin.ndim == 1
assert margin.shape[0] == kRows
shap = xgb.dask.predict(client, model=booster, data=dtrain, pred_contribs=True)
shap = xgb.dask.predict(client, model=booster, data=dtrain,
pred_contribs=True)
assert shap.ndim == 2
assert shap.shape[0] == kRows
assert shap.shape[1] == kCols + 1
def run_aft_survival(client, dmatrix_t):
# survival doesn't handle empty dataset well.
df = dd.read_csv(os.path.join(tm.PROJECT_ROOT, 'demo', 'data',
'veterans_lung_cancer.csv'))
y_lower_bound = df['Survival_label_lower_bound']
y_upper_bound = df['Survival_label_upper_bound']
X = df.drop(['Survival_label_lower_bound',
'Survival_label_upper_bound'], axis=1)
m = dmatrix_t(client, X, label_lower_bound=y_lower_bound,
label_upper_bound=y_upper_bound)
base_params = {'verbosity': 0,
'objective': 'survival:aft',
'eval_metric': 'aft-nloglik',
'learning_rate': 0.05,
'aft_loss_distribution_scale': 1.20,
'max_depth': 6,
'lambda': 0.01,
'alpha': 0.02}
nloglik_rec = {}
dists = ['normal', 'logistic', 'extreme']
for dist in dists:
params = base_params
params.update({'aft_loss_distribution': dist})
evals_result = {}
out = xgb.dask.train(client, params, m, num_boost_round=100,
evals=[(m, 'train')])
evals_result = out['history']
nloglik_rec[dist] = evals_result['train']['aft-nloglik']
# AFT metric (negative log likelihood) improve monotonically
assert all(p >= q for p, q in zip(nloglik_rec[dist],
nloglik_rec[dist][:1]))
# For this data, normal distribution works the best
assert nloglik_rec['normal'][-1] < 4.9
assert nloglik_rec['logistic'][-1] > 4.9
assert nloglik_rec['extreme'][-1] > 4.9
def test_aft_survival():
with LocalCluster(n_workers=1) as cluster:
with Client(cluster) as client:
run_aft_survival(client, DaskDMatrix)
class TestWithDask:
def run_updater_test(self, client, params, num_rounds, dataset,
tree_method):