TFTS: Switch to using core feature columns
This fixes some shape issues that came up when using the tf.contrib.layers parsing functions. Adds a string -> embedding column API example to the LSTM example. PiperOrigin-RevId: 187076400
This commit is contained in:
Allen Lavoie
TensorFlower Gardener
parent
ba2cc572f9
commit
7765066e6a
tensorflow/contrib/timeseries
examples
python/timeseries
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47,0.889627293379,1.46207968841,1.78299357672,4.64466731095,4.56317887554,1.,0.,strkey
|
||||
48,0.520140662861,1.8996333843,1.41377633823,4.48899091177,4.78805049769,1.,0.,strkey
|
||||
49,-1.03816935616,2.08997002059,1.51218375351,4.84167764204,4.93026048606,1.,0.,strkey
|
||||
50,-0.40772951362,2.30878972136,1.44144415128,4.76854460997,5.01538444629,1.,0.,strkey
|
||||
51,0.792730684781,1.91367048509,1.58887384677,4.71739397335,5.25690012199,1.,0.,strkey
|
||||
52,0.371311881576,1.67565079528,1.81688563053,4.60353107555,5.44265822961,1.,0.,strkey
|
||||
53,-0.814398070371,1.13374634126,1.80328814859,4.72264252878,5.52674761122,1.,0.,strkey
|
||||
54,-0.469017949323,0.601244136627,2.29690896736,4.49859178859,5.54126153454,1.,0.,strkey
|
||||
55,0.871044371426,0.407597593794,2.7499112487,4.19060637761,5.57693767301,1.,0.,strkey
|
||||
56,0.523764933017,0.247705192709,3.09002071379,4.02095509006,5.80510362182,1.,0.,strkey
|
||||
57,-0.881326403531,0.31513103164,3.11358205718,3.96079100808,5.81000652365,1.,0.,strkey
|
||||
58,-0.357928025339,0.486163915865,3.17884556771,3.72634990659,5.85693642011,1.,0.,strkey
|
||||
59,0.853038779822,1.04218094475,3.45835384454,3.36703969978,5.9585988449,1.,0.,strkey
|
||||
60,0.435311516013,1.59715085283,3.63313338588,3.11276729421,5.93643818229,1.,0.,strkey
|
||||
61,-1.02703719138,1.92205832542,3.47606111735,3.06247155999,6.02106646259,1.,0.,strkey
|
||||
62,-0.246661325557,2.14653802542,3.29446326567,2.89936259181,5.67531541272,1.,0.,strkey
|
||||
63,1.02554736569,2.25943737733,3.07031591528,2.78176218013,5.78206328989,1.,0.,strkey
|
||||
64,0.337814475969,2.07589147224,2.80356226089,2.55888206331,5.7094075496,1.,0.,strkey
|
||||
65,-1.12023369929,1.25333011618,2.56497288445,2.77361359194,5.50799418376,1.,0.,strkey
|
||||
66,-0.178980246554,1.11937139901,2.51598681313,2.91438309151,5.47469577206,1.,0.,strkey
|
||||
67,0.97550951531,0.60553823137,2.11657741073,2.88081098981,5.37034999502,1.,0.,strkey
|
||||
68,0.136653357206,0.365828836075,1.97386033165,3.13217903204,5.07254490219,1.,0.,strkey
|
||||
69,-1.05607596951,0.153152115069,1.52110743825,3.01308794192,5.08902539125,1.,0.,strkey
|
||||
70,-0.13095280331,0.337113974483,1.52703079853,3.16687131599,4.86649398514,1.,0.,strkey
|
||||
71,1.07081057754,0.714247566736,1.53761382634,3.45151989484,4.75892309166,1.,0.,strkey
|
||||
72,0.0153410376082,1.24631231847,1.61690939161,3.85481994498,4.35683752832,1.,0.,strkey
|
||||
73,-0.912801257303,1.60791309476,1.8729264524,4.03037260012,4.36072588913,1.,0.,strkey
|
||||
74,-0.0894895640338,2.02535207407,1.93484909619,4.09557485132,4.35327025188,1.,0.,strkey
|
||||
75,0.978646999652,2.20085086625,2.09003440427,4.27542353033,4.1805058388,1.,0.,strkey
|
||||
76,-0.113312642876,2.2444100761,2.50789248839,4.4151861502,4.03267168136,1.,0.,strkey
|
||||
77,-1.00215099149,1.84305628445,2.61691237246,4.45425147595,3.81203553766,1.,0.,strkey
|
||||
78,-0.0183234614205,1.49573923116,2.99308471214,4.71134960112,4.0273804959,1.,0.,strkey
|
||||
79,1.0823738177,1.12211589848,3.27079386925,4.94288270502,4.01851068083,1.,0.,strkey
|
||||
80,0.124370187893,0.616474412808,3.4284236674,4.76942168327,3.9749536483,1.,0.,strkey
|
||||
81,-0.929423379352,0.290977090976,3.34131726136,4.78590392707,4.10190661656,1.,0.,strkey
|
||||
82,0.23766302648,0.155302052254,3.49779513794,4.64605656795,4.15571321107,1.,0.,strkey
|
||||
83,1.03531486192,0.359702776204,3.4880725919,4.48167586667,4.21134561991,1.,0.,strkey
|
||||
84,-0.261234571382,0.713877760378,3.42756426614,4.426443869,4.25208300527,1.,0.,strkey
|
||||
85,-1.03572442277,1.25001113691,2.96908341113,4.25500915322,4.25723010649,1.,0.,strkey
|
||||
86,0.380034261243,1.70543355622,2.73605932518,4.16703432307,4.63700400788,1.,0.,strkey
|
||||
87,1.03734873488,1.97544410562,2.55586572141,3.84976673263,4.55282864289,1.,0.,strkey
|
||||
88,-0.177344253372,2.22614526325,2.09565864891,3.77378097953,4.82577400298,1.,0.,strkey
|
||||
89,-0.976821526892,2.18385079177,1.78522284118,3.67768223554,5.06302440873,1.,0.,strkey
|
||||
90,0.264820472091,1.86981946157,1.50048403865,3.43619796921,5.05651761669,1.,0.,strkey
|
||||
91,1.05642344868,1.47568646076,1.51347671977,3.20898518885,5.50149047462,1.,0.,strkey
|
||||
92,-0.311607433358,1.04226467636,1.52089650905,3.02291865417,5.4889046232,1.,0.,strkey
|
||||
93,-0.724285777937,0.553052311957,1.48573560173,2.7365973598,5.72549174225,1.,0.,strkey
|
||||
94,0.519859192905,0.226520626591,1.61543723167,2.84102086852,5.69330622288,1.,0.,strkey
|
||||
95,1.0323195039,0.260873217055,1.81913034804,2.83951143848,5.90325028086,1.,0.,strkey
|
||||
96,-0.53285682538,0.387695521405,1.70935609313,2.57977050631,5.79579213161,1.,0.,strkey
|
||||
97,-0.975127997215,0.920948771589,2.51292643636,2.71004616612,5.87016469227,1.,0.,strkey
|
||||
98,0.540246804099,1.36445470181,2.61949412896,2.98482553485,6.02447664937,1.,0.,strkey
|
||||
99,0.987764008058,1.85581989607,2.84685706149,2.94760204892,6.0212151724,1.,0.,strkey
|
||||
|
||||
|
@ -46,12 +46,12 @@ def train_and_evaluate_exogenous(csv_file_name=_DATA_FILE, train_steps=300):
|
||||
|
||||
# Indicate the format of our exogenous feature, in this case a string
|
||||
# representing a boolean value.
|
||||
string_feature = tf.contrib.layers.sparse_column_with_keys(
|
||||
column_name="is_changepoint", keys=["no", "yes"])
|
||||
string_feature = tf.feature_column.categorical_column_with_vocabulary_list(
|
||||
key="is_changepoint", vocabulary_list=["no", "yes"])
|
||||
# Specify the way this feature is presented to the model, here using a one-hot
|
||||
# encoding.
|
||||
one_hot_feature = tf.contrib.layers.one_hot_column(
|
||||
sparse_id_column=string_feature)
|
||||
one_hot_feature = tf.feature_column.indicator_column(
|
||||
categorical_column=string_feature)
|
||||
|
||||
estimator = tf.contrib.timeseries.StructuralEnsembleRegressor(
|
||||
periodicities=12,
|
||||
|
||||
@ -59,10 +59,10 @@ class _LSTMModel(ts_model.SequentialTimeSeriesModel):
|
||||
num_units: The number of units in the model's LSTMCell.
|
||||
num_features: The dimensionality of the time series (features per
|
||||
timestep).
|
||||
exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
|
||||
objects representing features which are inputs to the model but are
|
||||
not predicted by it. These must then be present for training,
|
||||
evaluation, and prediction.
|
||||
exogenous_feature_columns: A list of `tf.feature_column`s representing
|
||||
features which are inputs to the model but are not predicted by
|
||||
it. These must then be present for training, evaluation, and
|
||||
prediction.
|
||||
dtype: The floating point data type to use.
|
||||
"""
|
||||
super(_LSTMModel, self).__init__(
|
||||
@ -189,12 +189,16 @@ def train_and_predict(
|
||||
export_directory=None):
|
||||
"""Train and predict using a custom time series model."""
|
||||
# Construct an Estimator from our LSTM model.
|
||||
categorical_column = tf.feature_column.categorical_column_with_hash_bucket(
|
||||
key="categorical_exogenous_feature", hash_bucket_size=16)
|
||||
exogenous_feature_columns = [
|
||||
# Exogenous features are not part of the loss, but can inform
|
||||
# predictions. In this example the features have no extra information, but
|
||||
# are included as an API example.
|
||||
tf.contrib.layers.real_valued_column(
|
||||
"2d_exogenous_feature", dimension=2)]
|
||||
tf.feature_column.numeric_column(
|
||||
"2d_exogenous_feature", shape=(2,)),
|
||||
tf.feature_column.embedding_column(
|
||||
categorical_column=categorical_column, dimension=10)]
|
||||
estimator = ts_estimators.TimeSeriesRegressor(
|
||||
model=_LSTMModel(num_features=5, num_units=128,
|
||||
exogenous_feature_columns=exogenous_feature_columns),
|
||||
@ -205,7 +209,11 @@ def train_and_predict(
|
||||
csv_file_name,
|
||||
column_names=((tf.contrib.timeseries.TrainEvalFeatures.TIMES,)
|
||||
+ (tf.contrib.timeseries.TrainEvalFeatures.VALUES,) * 5
|
||||
+ ("2d_exogenous_feature",) * 2))
|
||||
+ ("2d_exogenous_feature",) * 2
|
||||
+ ("categorical_exogenous_feature",)),
|
||||
# Data types other than for `times` need to be specified if they aren't
|
||||
# float32. In this case one of our exogenous features has string dtype.
|
||||
column_dtypes=((tf.int64,) + (tf.float32,) * 7 + (tf.string,)))
|
||||
train_input_fn = tf.contrib.timeseries.RandomWindowInputFn(
|
||||
reader, batch_size=4, window_size=32)
|
||||
estimator.train(input_fn=train_input_fn, steps=training_steps)
|
||||
@ -215,7 +223,9 @@ def train_and_predict(
|
||||
predict_exogenous_features = {
|
||||
"2d_exogenous_feature": numpy.concatenate(
|
||||
[numpy.ones([1, 100, 1]), numpy.zeros([1, 100, 1])],
|
||||
axis=-1)}
|
||||
axis=-1),
|
||||
"categorical_exogenous_feature": numpy.array(
|
||||
["strkey"] * 100)[None, :, None]}
|
||||
(predictions,) = tuple(estimator.predict(
|
||||
input_fn=tf.contrib.timeseries.predict_continuation_input_fn(
|
||||
evaluation, steps=100,
|
||||
|
||||
@ -18,8 +18,6 @@ from __future__ import absolute_import
|
||||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from tensorflow.contrib.layers.python.layers import feature_column
|
||||
|
||||
from tensorflow.contrib.timeseries.python.timeseries import ar_model
|
||||
from tensorflow.contrib.timeseries.python.timeseries import feature_keys
|
||||
from tensorflow.contrib.timeseries.python.timeseries import head as ts_head_lib
|
||||
@ -31,10 +29,12 @@ from tensorflow.contrib.timeseries.python.timeseries.state_space_models.filterin
|
||||
|
||||
from tensorflow.python.estimator import estimator_lib
|
||||
from tensorflow.python.estimator.export import export_lib
|
||||
from tensorflow.python.feature_column import feature_column
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.framework import tensor_shape
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import parsing_ops
|
||||
from tensorflow.python.training import training as train
|
||||
|
||||
|
||||
@ -117,22 +117,29 @@ class TimeSeriesRegressor(estimator_lib.Estimator):
|
||||
dtype=self._model.dtype),
|
||||
shape=(default_batch_size, default_series_length,
|
||||
self._model.num_features)))
|
||||
with ops.Graph().as_default():
|
||||
# Default placeholders have only an unknown batch dimension. Make them
|
||||
# in a separate graph, then splice in the series length to the shapes
|
||||
# and re-create them in the outer graph.
|
||||
exogenous_feature_shapes = {
|
||||
key: (value.get_shape(), value.dtype) for key, value
|
||||
in feature_column.make_place_holder_tensors_for_base_features(
|
||||
self._model.exogenous_feature_columns).items()}
|
||||
for feature_key, (batch_only_feature_shape, value_dtype) in (
|
||||
exogenous_feature_shapes.items()):
|
||||
batch_only_feature_shape = batch_only_feature_shape.with_rank_at_least(
|
||||
1).as_list()
|
||||
feature_shape = ([default_batch_size, default_series_length]
|
||||
+ batch_only_feature_shape[1:])
|
||||
placeholders[feature_key] = array_ops.placeholder(
|
||||
dtype=value_dtype, name=feature_key, shape=feature_shape)
|
||||
if self._model.exogenous_feature_columns:
|
||||
with ops.Graph().as_default():
|
||||
# Default placeholders have only an unknown batch dimension. Make them
|
||||
# in a separate graph, then splice in the series length to the shapes
|
||||
# and re-create them in the outer graph.
|
||||
parsed_features = (
|
||||
feature_column.make_parse_example_spec(
|
||||
self._model.exogenous_feature_columns))
|
||||
placeholder_features = parsing_ops.parse_example(
|
||||
serialized=array_ops.placeholder(
|
||||
shape=[None], dtype=dtypes.string),
|
||||
features=parsed_features)
|
||||
exogenous_feature_shapes = {
|
||||
key: (value.get_shape(), value.dtype) for key, value
|
||||
in placeholder_features.items()}
|
||||
for feature_key, (batch_only_feature_shape, value_dtype) in (
|
||||
exogenous_feature_shapes.items()):
|
||||
batch_only_feature_shape = (
|
||||
batch_only_feature_shape.with_rank_at_least(1).as_list())
|
||||
feature_shape = ([default_batch_size, default_series_length]
|
||||
+ batch_only_feature_shape[1:])
|
||||
placeholders[feature_key] = array_ops.placeholder(
|
||||
dtype=value_dtype, name=feature_key, shape=feature_shape)
|
||||
# Models may not know the shape of their state without creating some
|
||||
# variables/ops. Avoid polluting the default graph by making a new one. We
|
||||
# use only static metadata from the returned Tensors.
|
||||
@ -333,11 +340,11 @@ class StructuralEnsembleRegressor(StateSpaceRegressor):
|
||||
determine the model size. Learning autoregressive coefficients
|
||||
typically requires more steps and a smaller step size than other
|
||||
components.
|
||||
exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
|
||||
objects (for example tf.contrib.layers.embedding_column) corresponding
|
||||
to exogenous features which provide extra information to the model but
|
||||
are not part of the series to be predicted. Passed to
|
||||
tf.contrib.layers.input_from_feature_columns.
|
||||
exogenous_feature_columns: A list of `tf.feature_column`s (for example
|
||||
`tf.feature_column.embedding_column`) corresponding to exogenous
|
||||
features which provide extra information to the model but are not part
|
||||
of the series to be predicted. Passed to
|
||||
`tf.feature_column.input_layer`.
|
||||
exogenous_update_condition: A function taking two Tensor arguments,
|
||||
`times` (shape [batch size]) and `features` (a dictionary mapping
|
||||
exogenous feature keys to Tensors with shapes [batch size, ...]), and
|
||||
|
||||
@ -21,18 +21,17 @@ from __future__ import print_function
|
||||
import abc
|
||||
import collections
|
||||
|
||||
from tensorflow.contrib import layers
|
||||
from tensorflow.contrib.layers import feature_column
|
||||
|
||||
from tensorflow.contrib.timeseries.python.timeseries import math_utils
|
||||
from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
|
||||
from tensorflow.contrib.timeseries.python.timeseries.feature_keys import TrainEvalFeatures
|
||||
|
||||
from tensorflow.python.feature_column import feature_column
|
||||
from tensorflow.python.framework import dtypes
|
||||
from tensorflow.python.framework import ops
|
||||
from tensorflow.python.ops import array_ops
|
||||
from tensorflow.python.ops import control_flow_ops
|
||||
from tensorflow.python.ops import math_ops
|
||||
from tensorflow.python.ops import parsing_ops
|
||||
from tensorflow.python.ops import tensor_array_ops
|
||||
from tensorflow.python.ops import variable_scope
|
||||
|
||||
@ -66,11 +65,11 @@ class TimeSeriesModel(object):
|
||||
|
||||
Args:
|
||||
num_features: Number of features for the time series
|
||||
exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
|
||||
objects (for example tf.contrib.layers.embedding_column) corresponding
|
||||
to exogenous features which provide extra information to the model but
|
||||
are not part of the series to be predicted. Passed to
|
||||
tf.contrib.layers.input_from_feature_columns.
|
||||
exogenous_feature_columns: A list of `tf.feature_column`s (for example
|
||||
`tf.feature_column.embedding_column`) corresponding to exogenous
|
||||
features which provide extra information to the model but are not
|
||||
part of the series to be predicted. Passed to
|
||||
`tf.feature_column.input_layer`.
|
||||
dtype: The floating point datatype to use.
|
||||
"""
|
||||
if exogenous_feature_columns:
|
||||
@ -86,7 +85,7 @@ class TimeSeriesModel(object):
|
||||
|
||||
@property
|
||||
def exogenous_feature_columns(self):
|
||||
"""`FeatureColumn` objects for features which are not predicted."""
|
||||
"""`tf.feature_colum`s for features which are not predicted."""
|
||||
return self._exogenous_feature_columns
|
||||
|
||||
# TODO(allenl): Move more of the generic machinery for generating and
|
||||
@ -265,11 +264,14 @@ class TimeSeriesModel(object):
|
||||
if not self._exogenous_feature_columns:
|
||||
return (0,)
|
||||
with ops.Graph().as_default():
|
||||
placeholder_features = (
|
||||
feature_column.make_place_holder_tensors_for_base_features(
|
||||
parsed_features = (
|
||||
feature_column.make_parse_example_spec(
|
||||
self._exogenous_feature_columns))
|
||||
embedded = layers.input_from_feature_columns(
|
||||
columns_to_tensors=placeholder_features,
|
||||
placeholder_features = parsing_ops.parse_example(
|
||||
serialized=array_ops.placeholder(shape=[None], dtype=dtypes.string),
|
||||
features=parsed_features)
|
||||
embedded = feature_column.input_layer(
|
||||
features=placeholder_features,
|
||||
feature_columns=self._exogenous_feature_columns)
|
||||
return embedded.get_shape().as_list()[1:]
|
||||
|
||||
@ -308,13 +310,13 @@ class TimeSeriesModel(object):
|
||||
# Avoid shape warnings when embedding "scalar" exogenous features (those
|
||||
# with only batch and window dimensions); input_from_feature_columns
|
||||
# expects input ranks to match the embedded rank.
|
||||
if tensor.get_shape().ndims == 1:
|
||||
if tensor.get_shape().ndims == 1 and tensor.dtype != dtypes.string:
|
||||
exogenous_features_single_batch_dimension[name] = tensor[:, None]
|
||||
else:
|
||||
exogenous_features_single_batch_dimension[name] = tensor
|
||||
embedded_exogenous_features_single_batch_dimension = (
|
||||
layers.input_from_feature_columns(
|
||||
columns_to_tensors=exogenous_features_single_batch_dimension,
|
||||
feature_column.input_layer(
|
||||
features=exogenous_features_single_batch_dimension,
|
||||
feature_columns=self._exogenous_feature_columns,
|
||||
trainable=True))
|
||||
exogenous_regressors = array_ops.reshape(
|
||||
@ -381,8 +383,8 @@ class SequentialTimeSeriesModel(TimeSeriesModel):
|
||||
may use _scale_back_data or _scale_back_variance to return predictions
|
||||
to the input scale.
|
||||
dtype: The floating point datatype to use.
|
||||
exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
|
||||
objects. See `TimeSeriesModel`.
|
||||
exogenous_feature_columns: A list of `tf.feature_column`s objects. See
|
||||
`TimeSeriesModel`.
|
||||
exogenous_update_condition: A function taking two Tensor arguments `times`
|
||||
(shape [batch size]) and `features` (a dictionary mapping exogenous
|
||||
feature keys to Tensors with shapes [batch size, ...]) and returning a
|
||||
|
||||
@ -112,11 +112,11 @@ class StateSpaceModelConfiguration(
|
||||
exogenous_noise_decreases: If True, exogenous regressors can "set" model
|
||||
state, decreasing uncertainty. If both this parameter and
|
||||
exogenous_noise_increases are False, exogenous regressors are ignored.
|
||||
exogenous_feature_columns: A list of tf.contrib.layers.FeatureColumn
|
||||
objects (for example tf.contrib.layers.embedding_column) corresponding
|
||||
to exogenous features which provide extra information to the model but
|
||||
are not part of the series to be predicted. Passed to
|
||||
tf.contrib.layers.input_from_feature_columns.
|
||||
exogenous_feature_columns: A list of `tf.feature_column`s (for example
|
||||
`tf.feature_column.embedding_column`) corresponding to exogenous
|
||||
features which provide extra information to the model but are not part
|
||||
of the series to be predicted. Passed to
|
||||
`tf.feature_column.input_layer`.
|
||||
exogenous_update_condition: A function taking two Tensor arguments `times`
|
||||
(shape [batch size]) and `features` (a dictionary mapping exogenous
|
||||
feature keys to Tensors with shapes [batch size, ...]) and returning a
|
||||
|
||||
Loading…
Reference in New Issue
Block a user