def test_nnEstimator_fit_with_train_val_summary(self):
model = Sequential().add(Linear(2, 2))
criterion = MSECriterion()
df, val_df = self.get_estimator_df()
from bigdl.orca.learn.metrics import MAE
est = Estimator.from_bigdl(model=model,
loss=criterion,
optimizer=Adam(),
metrics=[MAE()],
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
tmp_dir = tempfile.mkdtemp()
est.set_tensorboard(log_dir=tmp_dir, app_name="estTest")
est.fit(df,
epochs=5,
batch_size=4,
validation_data=val_df,
validation_trigger=EveryEpoch(),
checkpoint_trigger=SeveralIteration(1))
res = est.predict(df)
loss_result = est.get_train_summary("Loss")
mae_result = est.get_validation_summary("MAE")
assert type(res).__name__ == 'DataFrame'
assert len(loss_result) == 5
assert len(mae_result) == 4
def test_nnEstimator_multiOutput_cols(self):
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.getOrCreate()
df = spark.createDataFrame([(1.0, 2.0, 1.0, 2.0), (2.0, 2.0, 2.0, 1.0),
(3.0, 2.0, 1.0, 2.0),
(4.0, 1.0, 1.0, 2.0)],
["user", "age", "label1", "label2"])
linear_model = Sequential().add(Linear(2, 2))
mse_criterion = MSECriterion()
est = Estimator.from_bigdl(model=linear_model,
loss=mse_criterion,
optimizer=Adam(),
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
est.fit(df,
1,
batch_size=4,
feature_cols=["user", "age"],
label_cols=["label1", "label2"])
result = est.predict(df, feature_cols=["user", "age"])
result_c = result.collect()
assert type(result).__name__ == 'DataFrame'
def test_nnEstimator_multiInput(self):
zx1 = ZLayer.Input(shape=(1, ))
zx2 = ZLayer.Input(shape=(1, ))
zz = ZLayer.merge([zx1, zx2], mode="concat")
zy = ZLayer.Dense(2)(zz)
zmodel = ZModel([zx1, zx2], zy)
criterion = MSECriterion()
df, _ = self.get_estimator_df()
estimator = Estimator.from_bigdl(model=zmodel,
loss=criterion,
feature_preprocessing=[[1], [1]])
estimator.fit(df, epochs=5, batch_size=4)
pred = estimator.predict(df)
pred_data = pred.collect()
assert type(pred).__name__ == 'DataFrame'
def test_nnEstimator_multiInput_cols(self):
from pyspark.ml.linalg import Vectors
from pyspark.sql import SparkSession
spark = SparkSession \
.builder \
.getOrCreate()
df = spark.createDataFrame(
[(1, 35, 109.0, Vectors.dense([2.0, 5.0, 0.5, 0.5]), 1.0),
(2, 58, 2998.0, Vectors.dense([4.0, 10.0, 0.5, 0.5]), 2.0),
(3, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 1.0),
(4, 18, 123.0, Vectors.dense([3.0, 15.0, 0.5, 0.5]), 1.0)],
["user", "age", "income", "history", "label"])
x1 = ZLayer.Input(shape=(1, ))
x2 = ZLayer.Input(shape=(2, ))
x3 = ZLayer.Input(shape=(
2,
2,
))
user_embedding = ZLayer.Embedding(5, 10)(x1)
flatten = ZLayer.Flatten()(user_embedding)
dense1 = ZLayer.Dense(2)(x2)
gru = ZLayer.LSTM(4, input_shape=(2, 2))(x3)
merged = ZLayer.merge([flatten, dense1, gru], mode="concat")
zy = ZLayer.Dense(2)(merged)
zmodel = ZModel([x1, x2, x3], zy)
criterion = ClassNLLCriterion()
est = Estimator.from_bigdl(model=zmodel,
loss=criterion,
optimizer=Adam(learningrate=0.1),
feature_preprocessing=[[1], [2], [2, 2]])
est.fit(df,
epochs=1,
batch_size=4,
feature_cols=["user", "age", "income", "history"])
res = est.predict(df,
feature_cols=["user", "age", "income", "history"])
res_c = res.collect()
assert type(res).__name__ == 'DataFrame'
def test_xshards_spark_estimator_multi_inputs(self):
resource_path = os.path.join(
os.path.split(__file__)[0], "../../resources")
def transform(df):
result = {
"x": [
np.expand_dims(df['user'].to_numpy(), axis=1),
np.expand_dims(df['item'].to_numpy(), axis=1)
],
"y":
df['label'].to_numpy()
}
return result
file_path = os.path.join(resource_path, "orca/learn/ncf2.csv")
data_shard = read_csv(file_path)
data_shard = data_shard.transform_shard(transform)
zx1 = ZLayer.Input(shape=(1, ))
zx2 = ZLayer.Input(shape=(1, ))
zz = ZLayer.merge([zx1, zx2], mode="concat")
zy = ZLayer.Dense(2)(zz)
model = ZModel([zx1, zx2], zy)
optim_method = SGD(learningrate=0.01)
with tempfile.TemporaryDirectory() as temp_dir_name:
estimator = Estimator.from_bigdl(model=model,
optimizer=optim_method,
loss=ClassNLLCriterion(),
metrics=[Accuracy()],
model_dir=temp_dir_name)
estimator.set_constant_gradient_clipping(0.1, 1.2)
r1 = estimator.predict(data=data_shard)
r_c = r1.collect()
estimator.set_tensorboard(log_dir=temp_dir_name, app_name="test")
estimator.fit(data=data_shard,
epochs=5,
batch_size=8,
validation_data=data_shard,
checkpoint_trigger=EveryEpoch())
summary = estimator.get_train_summary(tag="Loss")
temp_path = os.path.join(temp_dir_name, "save_model")
estimator.save(temp_path)
eval_result = estimator.evaluate(data=data_shard, batch_size=8)
def test_nnEstimator_evaluation(self):
df = self.get_estimator_df2()
linear_model = Sequential().add(Linear(2, 2)).add(LogSoftMax())
est = Estimator.from_bigdl(model=linear_model,
loss=ClassNLLCriterion(),
optimizer=Adam(),
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([1]),
metrics=Accuracy())
est.fit(data=df, epochs=10, batch_size=8)
result = est.evaluate(df, batch_size=8)
shift = udf(lambda p: float(p.index(max(p))), DoubleType())
pred = est.predict(df).withColumn("prediction",
shift(col('prediction'))).cache()
correct = pred.filter("label=prediction").count()
overall = pred.count()
accuracy = correct * 1.0 / overall
assert accuracy == round(result['Top1Accuracy'], 2)
def test_nnEstimator(self):
from bigdl.dllib.nnframes import NNModel
linear_model = Sequential().add(Linear(2, 2))
mse_criterion = MSECriterion()
df, _ = self.get_estimator_df()
est = Estimator.from_bigdl(model=linear_model,
loss=mse_criterion,
optimizer=Adam(),
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
res0 = est.predict(df)
res0_c = res0.collect()
est.fit(df, 2, batch_size=4)
nn_model = NNModel(est.get_model(),
feature_preprocessing=SeqToTensor([2]))
res1 = nn_model.transform(df)
res2 = est.predict(df)
res1_c = res1.collect()
res2_c = res2.collect()
assert type(res1).__name__ == 'DataFrame'
assert type(res2).__name__ == 'DataFrame'
assert len(res1_c) == len(res2_c)
for idx in range(len(res1_c)):
assert res1_c[idx]["prediction"] == res2_c[idx]["prediction"]
with tempfile.TemporaryDirectory() as tempdirname:
temp_path = os.path.join(tempdirname, "model")
est.save(temp_path)
est2 = Estimator.from_bigdl(model=linear_model, loss=mse_criterion)
est2.load(temp_path,
optimizer=Adam(),
loss=mse_criterion,
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([2]))
est2.set_constant_gradient_clipping(0.1, 1.2)
est2.clear_gradient_clipping()
res3 = est2.predict(df)
res3_c = res3.collect()
assert type(res3).__name__ == 'DataFrame'
assert len(res1_c) == len(res3_c)
for idx in range(len(res1_c)):
assert res1_c[idx]["prediction"] == res3_c[idx]["prediction"]
est2.fit(df, 4, batch_size=4)
data = self.sc.parallelize([((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0)),
((2.0, 1.0), (1.0, 2.0)),
((1.0, 2.0), (2.0, 1.0))])
data_shard = SparkXShards(data)
data_shard = data_shard.transform_shard(
lambda feature_label_tuple: {
"x":
np.stack([
np.expand_dims(np.array(feature_label_tuple[0][0]), axis=0
),
np.expand_dims(np.array(feature_label_tuple[0][1]), axis=0)
],
axis=1),
"y":
np.stack([
np.expand_dims(np.array(feature_label_tuple[1][0]), axis=0
),
np.expand_dims(np.array(feature_label_tuple[1][1]), axis=0)
],
axis=1)
})
res4 = est.predict(data_shard)
res4_c = res4.collect()
assert type(res4).__name__ == 'SparkXShards'
for idx in range(len(res4_c)):
assert abs(res4_c[idx]["prediction"][0][0] -
res3_c[idx]["prediction"][0]) == 0
assert abs(res4_c[idx]["prediction"][0][1] -
res3_c[idx]["prediction"][1]) == 0
est.fit(data_shard, 1, batch_size=4)
res5 = est.predict(data_shard)
res5_c = res5.collect()
res6 = est.predict(df)
res6_c = res6.collect()
for idx in range(len(res5_c)):
assert abs(res5_c[idx]["prediction"][0][0] -
res6_c[idx]["prediction"][0]) == 0
assert abs(res5_c[idx]["prediction"][0][1] -
res6_c[idx]["prediction"][1]) == 0
def test_xshards_spark_estimator(self):
resource_path = os.path.join(
os.path.split(__file__)[0], "../../resources")
def transform(df):
result = {
"x":
np.stack([df['user'].to_numpy(), df['item'].to_numpy()],
axis=1),
"y":
df['label'].to_numpy()
}
return result
file_path = os.path.join(resource_path, "orca/learn/ncf2.csv")
data_shard = read_csv(file_path)
data_shard = data_shard.transform_shard(transform)
model = Sequential()
model.add(Linear(2, 2))
model.add(LogSoftMax())
optim_method = SGD(learningrate=0.01)
with tempfile.TemporaryDirectory() as temp_dir_name:
estimator = Estimator.from_bigdl(
model=model,
optimizer=optim_method,
loss=ClassNLLCriterion(),
metrics=Accuracy(),
model_dir=temp_dir_name,
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([1]))
estimator.set_constant_gradient_clipping(0.1, 1.2)
r1 = estimator.predict(data=data_shard)
r_c = r1.collect()
estimator.set_tensorboard(log_dir=temp_dir_name, app_name="test")
estimator.fit(data=data_shard,
epochs=5,
batch_size=8,
validation_data=data_shard,
checkpoint_trigger=EveryEpoch())
summary = estimator.get_train_summary(tag="Loss")
temp_path = os.path.join(temp_dir_name, "save_model")
estimator.save(temp_path)
with self.assertRaises(Exception) as context:
Estimator.from_bigdl(model=model,
optimizer=optim_method,
loss=ClassNLLCriterion(),
metrics=['accuracy'],
model_dir=temp_dir_name,
feature_preprocessing=SeqToTensor([2]),
label_preprocessing=SeqToTensor([1]))
self.assertTrue('Only orca metrics are supported, but get str' in
str(context.exception))
eval_result = estimator.evaluate(data=data_shard, batch_size=8)
assert isinstance(eval_result, dict)
result = estimator.predict(data=data_shard)
assert type(result).__name__ == 'SparkXShards'
result_c = result.collect()
df = self.get_estimator_df2()
r0 = estimator.predict(df)
r0_c = r0.collect()
assert type(r0).__name__ == 'DataFrame'
for idx in range(len(r0_c)):
assert abs(r0_c[idx]["prediction"][0] -
result_c[0]["prediction"][idx][0]) <= 1e-06
assert abs(r0_c[idx]["prediction"][1] -
result_c[0]["prediction"][idx][1]) <= 1e-06
estimator.fit(data=df,
epochs=6,
batch_size=8,
validation_data=df,
validation_trigger=EveryEpoch())
summary = estimator.get_train_summary("Loss")
# test load from checkpoint
est2 = Estimator.from_bigdl(model=Sequential(),
optimizer=None,
loss=None,
metrics=[Accuracy()],
model_dir=None)
est2.load(temp_dir_name,
loss=ClassNLLCriterion(),
is_checkpoint=True)
r2 = est2.predict(data=data_shard)
r2_c = r2.collect()
assert (result_c[0]["prediction"] == r2_c[0]["prediction"]).all()
# resume training
est2.fit(data=data_shard,
epochs=10,
batch_size=8,
validation_data=data_shard,
checkpoint_trigger=EveryEpoch())
est2.evaluate(data=data_shard, batch_size=8)
# test load from saved model
est3 = Estimator.from_bigdl(model=Sequential(),
optimizer=None,
loss=None,
model_dir=None)
est3.load(temp_path,
optimizer=optim_method,
loss=ClassNLLCriterion())
r3 = est3.predict(data=data_shard)
r3_c = r3.collect()
assert (r3_c[0]["prediction"] == r2_c[0]["prediction"]).all()