Remove tf.contrib.timeseries dependency on TF distributions.

PiperOrigin-RevId: 227582617

tensorflow/contrib/timeseries/python/timeseries/BUILD

@@ -361,9 +361,10 @@ py_library(
     srcs_version = "PY2AND3",
     deps = [
         ":feature_keys",
+        ":math_utils",
         ":model",
         ":model_utils",
-        "//tensorflow/contrib/distributions:distributions_py",
+        "//tensorflow/contrib/rnn:rnn_py",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",
         "//tensorflow/python:constant_op",

tensorflow/contrib/timeseries/python/timeseries/ar_model.py

@@ -18,9 +18,8 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib import distributions
-
 from tensorflow.contrib.rnn.python.ops import lstm_ops
+from tensorflow.contrib.timeseries.python.timeseries import math_utils
 from tensorflow.contrib.timeseries.python.timeseries import model
 from tensorflow.contrib.timeseries.python.timeseries import model_utils
 from tensorflow.contrib.timeseries.python.timeseries.feature_keys import PredictionFeatures
@@ -462,8 +461,8 @@ class ARModel(model.TimeSeriesModel):
     if self.loss == ARModel.NORMAL_LIKELIHOOD_LOSS:
       covariance = prediction_ops["covariance"]
       sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
-      normal = distributions.Normal(loc=targets, scale=sigma)
-      loss_op = -math_ops.reduce_sum(normal.log_prob(prediction))
+      loss_op = -math_ops.reduce_sum(
+          math_utils.normal_log_prob(targets, sigma, prediction))
     else:
       assert self.loss == ARModel.SQUARED_LOSS, self.loss
       loss_op = math_ops.reduce_sum(math_ops.square(prediction - targets))
@@ -965,16 +964,11 @@ class AnomalyMixtureARModel(ARModel):
       anomaly_variance = prediction_ops["anomaly_params"]
       anomaly_sigma = math_ops.sqrt(
           gen_math_ops.maximum(anomaly_variance, 1e-5))
-      normal = distributions.Normal(loc=targets, scale=anomaly_sigma)
-      log_prob = normal.log_prob(prediction)
+      log_prob = math_utils.normal_log_prob(targets, anomaly_sigma, prediction)
     else:
       assert self._anomaly_distribution == AnomalyMixtureARModel.CAUCHY_ANOMALY
       anomaly_scale = prediction_ops["anomaly_params"]
-      cauchy = distributions.StudentT(
-          df=array_ops.ones([], dtype=anomaly_scale.dtype),
-          loc=targets,
-          scale=anomaly_scale)
-      log_prob = cauchy.log_prob(prediction)
+      log_prob = math_utils.cauchy_log_prob(targets, anomaly_scale, prediction)
     return log_prob
 
   def loss_op(self, targets, prediction_ops):
@@ -983,8 +977,7 @@ class AnomalyMixtureARModel(ARModel):
     covariance = prediction_ops["covariance"]
     # Normal data log probability.
     sigma = math_ops.sqrt(gen_math_ops.maximum(covariance, 1e-5))
-    normal1 = distributions.Normal(loc=targets, scale=sigma)
-    log_prob1 = normal1.log_prob(prediction)
+    log_prob1 = math_utils.normal_log_prob(targets, sigma, prediction)
     log_prob1 += math_ops.log(1 - self._anomaly_prior_probability)
     # Anomaly log probability.
     log_prob2 = self._anomaly_log_prob(targets, prediction_ops)

tensorflow/contrib/timeseries/python/timeseries/math_utils.py

@@ -21,6 +21,8 @@ from __future__ import print_function
 import collections
 import math
 
+import numpy as np
+
 from tensorflow.contrib import lookup
 from tensorflow.contrib.layers.python.layers import layers
 
@@ -43,6 +45,32 @@ from tensorflow.python.ops import variable_scope
 from tensorflow.python.util import nest
 
 
+def normal_log_prob(loc, scale, x):
+  """Computes the Normal log pdf."""
+  z = (x - loc) / scale
+  return -0.5 * (math_ops.square(z)
+                 + np.log(2. * np.pi) + math_ops.log(scale))
+
+
+def cauchy_log_prob(loc, scale, x):
+  """Computes the Cauchy log pdf."""
+  z = (x - loc) / scale
+  return (-np.log(np.pi) - math_ops.log(scale) -
+          math_ops.log1p(math_ops.square(z)))
+
+
+def mvn_tril_log_prob(loc, scale_tril, x):
+  """Computes the MVN log pdf under tril scale. Doesn't handle batches."""
+  x0 = x - loc
+  z = linalg_ops.matrix_triangular_solve(
+      scale_tril, x0[..., array_ops.newaxis])[..., 0]
+  log_det_cov = 2. * math_ops.reduce_sum(math_ops.log(
+      array_ops.matrix_diag_part(scale_tril)), axis=-1)
+  d = math_ops.cast(array_ops.shape(scale_tril)[-1], log_det_cov.dtype)
+  return -0.5 * (math_ops.reduce_sum(math_ops.square(z), axis=-1)
+                 + d * np.log(2. * np.pi) + log_det_cov)
+
+
 def clip_covariance(
     covariance_matrix, maximum_variance_ratio, minimum_variance):
   """Enforce constraints on a covariance matrix to improve numerical stability.

tensorflow/contrib/timeseries/python/timeseries/state_space_models/BUILD

@@ -78,7 +78,6 @@ py_library(
     srcs = ["kalman_filter.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:control_flow_ops",
@@ -235,7 +234,6 @@ py_library(
     srcs = ["filtering_postprocessor.py"],
     srcs_version = "PY2AND3",
     deps = [
-        "//tensorflow/contrib/distributions:distributions_py",
         "//tensorflow/contrib/timeseries/python/timeseries:math_utils",
         "//tensorflow/python:array_ops",
         "//tensorflow/python:check_ops",

tensorflow/contrib/timeseries/python/timeseries/state_space_models/filtering_postprocessor.py

@@ -22,8 +22,6 @@ import abc
 
 import six
 
-from tensorflow.contrib import distributions
-
 from tensorflow.contrib.timeseries.python.timeseries import math_utils
 
 from tensorflow.python.framework import dtypes
@@ -91,10 +89,10 @@ def cauchy_alternative_to_gaussian(current_times, current_values, outputs):
   """
   del current_times  # unused
   cauchy_scale = math_utils.entropy_matched_cauchy_scale(outputs["covariance"])
-  individual_log_pdfs = distributions.StudentT(
-      df=array_ops.ones([], dtype=current_values.dtype),
+  individual_log_pdfs = math_utils.cauchy_log_prob(
       loc=outputs["mean"],
-      scale=cauchy_scale).log_prob(current_values)
+      scale=cauchy_scale,
+      x=current_values)
   return math_ops.reduce_sum(individual_log_pdfs, axis=1)
 
 

tensorflow/contrib/timeseries/python/timeseries/state_space_models/kalman_filter.py

@@ -18,8 +18,6 @@ from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
 
-from tensorflow.contrib import distributions
-
 from tensorflow.contrib.timeseries.python.timeseries import math_utils
 
 from tensorflow.python.framework import dtypes
@@ -137,9 +135,10 @@ class KalmanFilter(object):
     with ops.control_dependencies([non_negative_assert]):
       observation_covariance_cholesky = linalg_ops.cholesky(
           symmetrized_observation_covariance)
-    log_prediction_prob = distributions.MultivariateNormalTriL(
-        predicted_observation, observation_covariance_cholesky).log_prob(
-            observation)
+    log_prediction_prob = math_utils.mvn_tril_log_prob(
+        loc=predicted_observation,
+        scale_tril=observation_covariance_cholesky,
+        x=observation)
     (posterior_state,
      posterior_state_var) = self.posterior_from_prior_state(
          prior_state=estimated_state,