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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/08/03 22:42:20 UTC
[GitHub] szha closed pull request #12023: [MXNET-770] Fix flaky test:
test_factorization_machine_module
szha closed pull request #12023: [MXNET-770] Fix flaky test: test_factorization_machine_module
URL: https://github.com/apache/incubator-mxnet/pull/12023
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diff --git a/tests/python/train/test_sparse_fm.py b/tests/python/train/test_sparse_fm.py
new file mode 100644
index 00000000000..99a22f54cbb
--- /dev/null
+++ b/tests/python/train/test_sparse_fm.py
@@ -0,0 +1,138 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you 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.
+
+import mxnet as mx
+import mxnet.ndarray as nd
+from mxnet.test_utils import *
+import numpy as np
+
+def test_factorization_machine_module(verbose=False):
+ """ Test factorization machine model with sparse operators """
+ def check_factorization_machine_module(optimizer=None, num_epochs=None):
+ print("check_factorization_machine_module( {} )".format(optimizer))
+
+ def fm(factor_size, feature_dim, init):
+ x = mx.symbol.Variable("data", stype='csr')
+ v = mx.symbol.Variable("v", shape=(feature_dim, factor_size),
+ init=init, stype='row_sparse')
+
+ w1_weight = mx.symbol.var('w1_weight', shape=(feature_dim, 1),
+ init=init, stype='row_sparse')
+ w1_bias = mx.symbol.var('w1_bias', shape=(1))
+ w1 = mx.symbol.broadcast_add(mx.symbol.dot(x, w1_weight), w1_bias)
+
+ v_s = mx.symbol._internal._square_sum(data=v, axis=1, keepdims=True)
+ x_s = mx.symbol.square(data=x)
+ bd_sum = mx.sym.dot(x_s, v_s)
+
+ w2 = mx.symbol.dot(x, v)
+ w2_squared = 0.5 * mx.symbol.square(data=w2)
+
+ w_all = mx.symbol.Concat(w1, w2_squared, dim=1)
+ sum1 = mx.symbol.sum(data=w_all, axis=1, keepdims=True)
+ sum2 = 0.5 * mx.symbol.negative(bd_sum)
+ model = mx.sym.elemwise_add(sum1, sum2)
+
+ y = mx.symbol.Variable("label")
+ model = mx.symbol.LinearRegressionOutput(data=model, label=y)
+ return model
+
+ # model
+ init = mx.initializer.Normal(sigma=0.01)
+ factor_size = 4
+ feature_dim = 10000
+ model = fm(factor_size, feature_dim, init)
+
+ # data iter
+ num_batches = 5
+ batch_size = 64
+ num_samples = batch_size * num_batches
+ # generate some random csr data
+ csr_nd = rand_ndarray((num_samples, feature_dim), 'csr', 0.1)
+ label = mx.nd.ones((num_samples,1))
+ # the alternative is to use LibSVMIter
+ train_iter = mx.io.NDArrayIter(data=csr_nd,
+ label={'label':label},
+ batch_size=batch_size,
+ last_batch_handle='discard')
+ # create module
+ mod = mx.mod.Module(symbol=model, data_names=['data'], label_names=['label'])
+ # allocate memory by given the input data and lable shapes
+ mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
+ # initialize parameters by uniform random numbers
+ mod.init_params(initializer=init)
+ if optimizer == 'sgd':
+ # use Sparse SGD with learning rate 0.1 to train
+ sgd = mx.optimizer.SGD(momentum=0.1, clip_gradient=5.0, learning_rate=0.01,
+ rescale_grad=1.0/batch_size)
+ mod.init_optimizer(optimizer=sgd)
+ if num_epochs is None:
+ num_epochs = 10
+ expected_accuracy = 0.02
+ elif optimizer == 'adam':
+ # use Sparse Adam to train
+ adam = mx.optimizer.Adam(clip_gradient=5.0, learning_rate=0.0005,
+ rescale_grad=1.0/batch_size)
+ mod.init_optimizer(optimizer=adam)
+ if num_epochs is None:
+ num_epochs = 10
+ expected_accuracy = 0.05
+ elif optimizer == 'adagrad':
+ # use Sparse AdaGrad with learning rate 0.1 to train
+ adagrad = mx.optimizer.AdaGrad(clip_gradient=5.0, learning_rate=0.01,
+ rescale_grad=1.0/batch_size)
+ mod.init_optimizer(optimizer=adagrad)
+ if num_epochs is None:
+ num_epochs = 20
+ expected_accuracy = 0.09
+ else:
+ raise AssertionError("Unsupported optimizer type '" + optimizer + "' specified")
+ # use accuracy as the metric
+ metric = mx.metric.create('MSE')
+ # train 'num_epochs' epoch
+ for epoch in range(num_epochs):
+ train_iter.reset()
+ metric.reset()
+ for batch in train_iter:
+ mod.forward(batch, is_train=True) # compute predictions
+ mod.update_metric(metric, batch.label) # accumulate prediction accuracy
+ mod.backward() # compute gradients
+ mod.update() # update parameters
+ print('Epoch %d, Training %s' % (epoch, metric.get()))
+ if num_epochs > 1:
+ assert(metric.get()[1] < expected_accuracy)
+
+ if verbose is True:
+ print("============ SGD ==========================")
+ start = time.clock()
+ check_factorization_machine_module('sgd')
+ if verbose is True:
+ print("Duration: {}".format(time.clock() - start))
+ print("============ ADAM ==========================")
+ start = time.clock()
+ check_factorization_machine_module('adam')
+ if verbose is True:
+ print("Duration: {}".format(time.clock() - start))
+ print("============ ADAGRAD ==========================")
+ start = time.clock()
+ check_factorization_machine_module('adagrad')
+ if verbose is True:
+ print("Duration: {}".format(time.clock() - start))
+
+# run as a script
+if __name__ == "__main__":
+ test_factorization_machine_module()
diff --git a/tests/python/unittest/test_module.py b/tests/python/unittest/test_module.py
index 41ea5828ddc..04302f11c4d 100644
--- a/tests/python/unittest/test_module.py
+++ b/tests/python/unittest/test_module.py
@@ -557,11 +557,12 @@ def check_shared_exec_group(sparse_embedding):
for opt in sparse_embedding_opt:
check_shared_exec_group(opt)
-@with_seed(11)
-def test_factorization_machine_module(verbose=False):
+@with_seed()
+def test_factorization_machine_module():
""" Test factorization machine model with sparse operators """
- def check_factorization_machine_module(optimizer=None, num_epochs=None):
- print("check_factorization_machine_module( {} )".format(optimizer))
+ # this unit test is to test the flow, training accuracy is tested in another test
+ def check_factorization_machine_module(num_epochs=None):
+ print("check_factorization_machine_module")
def fm(factor_size, feature_dim, init):
x = mx.symbol.Variable("data", stype='csr')
@@ -613,33 +614,16 @@ def fm(factor_size, feature_dim, init):
mod.bind(data_shapes=train_iter.provide_data, label_shapes=train_iter.provide_label)
# initialize parameters by uniform random numbers
mod.init_params(initializer=init)
- if optimizer == 'sgd':
- # use Sparse SGD with learning rate 0.1 to train
- sgd = mx.optimizer.SGD(momentum=0.1, clip_gradient=5.0, learning_rate=0.01,
- rescale_grad=1.0/batch_size)
- mod.init_optimizer(optimizer=sgd)
- if num_epochs is None:
- num_epochs = 10
- expected_accuracy = 0.02
- elif optimizer == 'adam':
- # use Sparse Adam to train
- adam = mx.optimizer.Adam(clip_gradient=5.0, learning_rate=0.0005,
- rescale_grad=1.0/batch_size)
- mod.init_optimizer(optimizer=adam)
- if num_epochs is None:
- num_epochs = 10
- expected_accuracy = 0.05
- elif optimizer == 'adagrad':
- # use Sparse AdaGrad with learning rate 0.1 to train
- adagrad = mx.optimizer.AdaGrad(clip_gradient=5.0, learning_rate=0.01,
- rescale_grad=1.0/batch_size)
- mod.init_optimizer(optimizer=adagrad)
- if num_epochs is None:
- num_epochs = 20
- expected_accuracy = 0.09
- else:
- raise AssertionError("Unsupported optimizer type '" + optimizer + "' specified")
- # use accuracy as the metric
+
+ # use Sparse SGD with learning rate 0.1 to train
+ sgd = mx.optimizer.SGD(momentum=0.1, clip_gradient=5.0, learning_rate=0.01,
+ rescale_grad=1.0/batch_size)
+ mod.init_optimizer(optimizer=sgd)
+ if num_epochs is None:
+ num_epochs = 50
+ expected_accuracy = 0.02
+
+ # use accuracy as the metric
metric = mx.metric.create('MSE')
# train 'num_epochs' epoch
for epoch in range(num_epochs):
@@ -654,23 +638,7 @@ def fm(factor_size, feature_dim, init):
if num_epochs > 1:
assert(metric.get()[1] < expected_accuracy)
- if verbose is True:
- print("============ SGD ==========================")
- start = time.clock()
- check_factorization_machine_module('sgd')
- if verbose is True:
- print("Duration: {}".format(time.clock() - start))
- print("============ ADAM ==========================")
- start = time.clock()
- check_factorization_machine_module('adam')
- if verbose is True:
- print("Duration: {}".format(time.clock() - start))
- print("============ ADAGRAD ==========================")
- start = time.clock()
- check_factorization_machine_module('adagrad')
- if verbose is True:
- print("Duration: {}".format(time.clock() - start))
-
+ check_factorization_machine_module()
@with_seed()
def test_module_initializer():
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