def testMultiDimensionalRealValuedFeaturesWithL2Regularization(self):
"""Tests SVM with multi-dimensional real features and L2 regularization."""
# This is identical to the one in testRealValuedFeaturesWithL2Regularization
# where 2 tensors (dense features) of shape [3, 1] have been replaced by a
# single tensor (dense feature) of shape [3, 2].
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'multi_dim_feature':
constant_op.constant([[0.5, 1.0], [1.0, -1.0], [1.0, 0.5]]),
}, constant_op.constant([[1], [0], [1]])
multi_dim_feature = feature_column.real_valued_column(
'multi_dim_feature', dimension=2)
svm_classifier = svm.SVM(feature_columns=[multi_dim_feature],
example_id_column='example_id',
l1_regularization=0.0,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
loss = metrics['loss']
accuracy = metrics['accuracy']
self.assertLess(loss, 0.1)
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testBucketizedFeatures(self):
"""Tests SVM classifier with bucketized features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'price': constant_op.constant([[600.0], [800.0], [400.0]]),
'sq_footage': constant_op.constant([[1000.0], [800.0],
[500.0]]),
'weights': constant_op.constant([[1.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('price'),
boundaries=[500.0, 700.0])
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'),
boundaries=[650.0])
svm_classifier = svm.SVM(
feature_columns=[price_bucket, sq_footage_bucket],
example_id_column='example_id',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn,
steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testRealValuedFeaturesWithL2Regularization(self):
"""Tests SVM classifier with real valued features and L2 regularization."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'feature1': constant_op.constant([[0.5], [1.0], [1.0]]),
'feature2': constant_op.constant([[1.0], [-1.0], [0.5]]),
}, constant_op.constant([[1], [0], [1]])
feature1 = feature_column.real_valued_column('feature1')
feature2 = feature_column.real_valued_column('feature2')
svm_classifier = svm.SVM(feature_columns=[feature1, feature2],
example_id_column='example_id',
l1_regularization=0.0,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
loss = metrics['loss']
accuracy = metrics['accuracy']
# The points are in general separable. Also, if there was no regularization,
# the margin inequalities would be satisfied too (for instance by w1=1.0,
# w2=5.0). Due to regularization, smaller weights are chosen. This results
# to a small but non-zero uneregularized loss. Still, all the predictions
# will be correct resulting to perfect accuracy.
self.assertLess(loss, 0.1)
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testSparseFeatures(self):
"""Tests SVM classifier with (hashed) sparse features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.8], [0.6], [0.3]]),
'country':
sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 0], [2, 0]],
dense_shape=[3, 1]),
}, constant_op.constant([[0], [1], [1]])
price = feature_column.real_valued_column('price')
country = feature_column.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
svm_classifier = svm.SVM(feature_columns=[price, country],
example_id_column='example_id',
l1_regularization=0.0,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn,
steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testRealValuedFeaturesWithBigL1Regularization(self):
"""Tests SVM classifier with real valued features and L2 regularization."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'feature1': constant_op.constant([[0.5], [1.0], [1.0]]),
'feature2': constant_op.constant([[1.0], [-1.0], [0.5]]),
}, constant_op.constant([[1], [0], [1]])
feature1 = feature_column.real_valued_column('feature1')
feature2 = feature_column.real_valued_column('feature2')
svm_classifier = svm.SVM(feature_columns=[feature1, feature2],
example_id_column='example_id',
l1_regularization=3.0,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
loss = metrics['loss']
accuracy = metrics['accuracy']
# When L1 regularization parameter is large, the loss due to regularization
# outweights the unregularized loss. In this case, the classifier will favor
# very small weights (in current case 0) resulting both big unregularized
# loss and bad accuracy.
self.assertAlmostEqual(loss, 1.0, places=3)
self.assertAlmostEqual(accuracy, 1 / 3, places=3)
def testRealValuedFeaturesWithMildL1Regularization(self):
"""Tests SVM classifier with real valued features and L2 regularization."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'feature1': constant_op.constant([[0.5], [1.0], [1.0]]),
'feature2': constant_op.constant([[1.0], [-1.0], [0.5]]),
}, constant_op.constant([[1], [0], [1]])
feature1 = feature_column.real_valued_column('feature1')
feature2 = feature_column.real_valued_column('feature2')
svm_classifier = svm.SVM(feature_columns=[feature1, feature2],
example_id_column='example_id',
l1_regularization=0.5,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
loss = metrics['loss']
accuracy = metrics['accuracy']
# Adding small L1 regularization favors even smaller weights. This results
# to somewhat moderate unregularized loss (bigger than the one when there is
# no L1 regularization. Still, since L1 is small, all the predictions will
# be correct resulting to perfect accuracy.
self.assertGreater(loss, 0.1)
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testMixedFeatures(self):
"""Tests SVM classifier with 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], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column.real_valued_column('price')
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
svm_classifier = svm.SVM(feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
example_id_column='example_id',
weight_column_name='weights',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn,
steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testRealValuedFeaturesPerfectlySeparable(self):
"""Tests SVM classifier with real valued features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'feature1': constant_op.constant([[0.0], [1.0], [3.0]]),
'feature2': constant_op.constant([[1.0], [-1.2], [1.0]]),
}, constant_op.constant([[1], [0], [1]])
feature1 = feature_column.real_valued_column('feature1')
feature2 = feature_column.real_valued_column('feature2')
svm_classifier = svm.SVM(feature_columns=[feature1, feature2],
example_id_column='example_id',
l1_regularization=0.0,
l2_regularization=0.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
metrics = svm_classifier.evaluate(input_fn=input_fn, steps=1)
loss = metrics['loss']
accuracy = metrics['accuracy']
# The points are not only separable but there exist weights (for instance
# w1=0.0, w2=1.0) that satisfy the margin inequalities (y_i* w^T*x_i >=1).
# The unregularized loss should therefore be 0.0.
self.assertAlmostEqual(loss, 0.0, places=3)
self.assertAlmostEqual(accuracy, 1.0, places=3)
feature_cols = continuous_cols.copy()
feature_cols.update(id_col)
# Converts the label column into a constant Tensor.
# Returns the feature columns and the label.
return feature_cols
try:
shutil.rmtree('svm/')
except OSError:
pass
svm_model = svm.SVM(example_id_column="PassengerId",
feature_columns=[
tf.contrib.layers.real_valued_column(k)
for k in list(train)
if k not in ['Survived', 'PassengerId']
],
model_dir="svm/")
svm_model.fit(input_fn=train_input_fn, steps=100)
svm_pred = list(svm_model.predict_classes(input_fn=predict_input_fn))
if TEST:
target_names = ['Not Survived', 'Survived']
print("SVM Report")
print(
classification_report(test['Survived'],
svm_pred,
target_names=target_names))
print("SVM Confusion Matrix")
for k in CATEGORICAL_COLUMNS
}
# Merges the two dictionaries into one.
feature_cols = dict(continuous_cols.items() + categorical_cols.items())
# Add example id list
feature_cols['example_id'] = tf.constant(
[str(i + 1) for i in range(df['age'].size)])
# Converts the label column into a constant Tensor.
label = tf.constant(df[LABEL_COLUMN].values)
# Returns the feature columns and the label.
return feature_cols, label
def train_input_fn():
return input_fn(df_train)
def eval_input_fn():
return input_fn(df_test)
model_dir = '../svm_model_dir'
model = svm.SVM(example_id_column='example_id',
feature_columns=FEATURE_COLUMNS,
model_dir=model_dir)
model.fit(input_fn=train_input_fn, steps=100)
results = model.evaluate(input_fn=eval_input_fn, steps=1)
for key in sorted(results):
print("%s: %s" % (key, results[key]))
