def testCrossedFeatures(self):
"""Tests LinearClassifier with LinearSDCA and crossed features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'language':
sparse_tensor.SparseTensor(
values=['english', 'italian', 'spanish'],
indices=[[0, 0], [1, 0], [2, 0]],
dense_shape=[3, 1]),
'country':
sparse_tensor.SparseTensor(values=['US', 'IT', 'MX'],
indices=[[0, 0], [1, 0], [2, 0]],
dense_shape=[3, 1])
}, constant_op.constant([[0], [0], [1]])
country_language = feature_column_lib.crossed_column(
['language', 'country'], hash_bucket_size=100)
optimizer = linear.LinearSDCA(example_id_column='example_id',
symmetric_l2_regularization=0.01)
classifier = linear.LinearClassifierV2(
feature_columns=[country_language], optimizer=optimizer)
classifier.train(input_fn=input_fn, steps=100)
loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
self.assertLess(loss, 0.2)
def testPartitionedVariables(self):
"""Tests LinearClassifier with LinearSDCA with partitioned variables."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column_lib.numeric_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.numeric_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.categorical_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, 'country'], hash_bucket_size=10)
optimizer = linear.LinearSDCA(example_id_column='example_id',
symmetric_l2_regularization=0.01)
classifier = linear.LinearClassifierV2(
feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
weight_column='weights',
partitioner=partitioned_variables.fixed_size_partitioner(
num_shards=2, axis=0),
optimizer=optimizer)
classifier.train(input_fn=input_fn, steps=100)
loss = classifier.evaluate(input_fn=input_fn, steps=1)['loss']
self.assertLess(loss, 0.2)
def test_sequential_model_with_crossed_column(self):
feature_columns = []
age_buckets = fc.bucketized_column(
fc.numeric_column('age'),
boundaries=[18, 25, 30, 35, 40, 45, 50, 55, 60, 65])
feature_columns.append(age_buckets)
# indicator cols
thal = fc.categorical_column_with_vocabulary_list(
'thal', ['fixed', 'normal', 'reversible'])
crossed_feature = fc.crossed_column([age_buckets, thal],
hash_bucket_size=1000)
crossed_feature = fc.indicator_column(crossed_feature)
feature_columns.append(crossed_feature)
feature_layer = df.DenseFeatures(feature_columns)
model = keras.models.Sequential([
feature_layer,
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(1, activation='sigmoid')
])
age_data = np.random.randint(10, 100, size=100)
thal_data = np.random.choice(['fixed', 'normal', 'reversible'],
size=100)
inp_x = {'age': age_data, 'thal': thal_data}
inp_y = np.random.randint(0, 1, size=100)
ds = dataset_ops.Dataset.from_tensor_slices((inp_x, inp_y)).batch(5)
model.compile(
optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'],
)
model.fit(ds, epochs=1)
model.fit(ds, epochs=1)
model.evaluate(ds)
model.predict(ds)
def testMixedFeaturesArbitraryWeights(self):
"""Tests LinearRegressor with LinearSDCA and a mix of features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([0.6, 0.8, 0.3]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [5.0], [7.0]])
}, constant_op.constant([[1.55], [-1.25], [-3.0]])
price = feature_column_lib.numeric_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.numeric_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.categorical_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, 'country'], hash_bucket_size=10)
optimizer = linear.LinearSDCA(example_id_column='example_id',
symmetric_l2_regularization=0.1)
regressor = linear.LinearRegressorV2(feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
weight_column='weights',
optimizer=optimizer)
regressor.train(input_fn=input_fn, steps=20)
loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
self.assertLess(loss, 0.05)