# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed 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.
# ==============================================================================
"""Tests for Ftrl optimizer."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import numpy as np

from tensorflow.compiler.tests.xla_test import XLATestCase
from tensorflow.python.framework import constant_op
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
from tensorflow.python.training import adagrad
from tensorflow.python.training import ftrl
from tensorflow.python.training import gradient_descent


class FtrlOptimizerTest(XLATestCase):

  def initVariableAndGradient(self, dtype):
    var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
    var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
    grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
    grads1 = constant_op.constant([0.02, 0.04], dtype=dtype)

    return var0, var1, grads0, grads1

  def equivAdagradTest_FtrlPart(self, steps, dtype):
    var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
    opt = ftrl.FtrlOptimizer(
        3.0,
        learning_rate_power=-0.5,  # using Adagrad learning rate
        initial_accumulator_value=0.1,
        l1_regularization_strength=0.0,
        l2_regularization_strength=0.0)
    ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    # Fetch params to validate initial values
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())

    # Run Ftrl for a few steps
    for _ in range(steps):
      ftrl_update.run()

    return var0.eval(), var1.eval()

  def equivAdagradTest_AdagradPart(self, steps, dtype):
    var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
    opt = adagrad.AdagradOptimizer(3.0, initial_accumulator_value=0.1)
    adagrad_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    # Fetch params to validate initial values
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())

    # Run Adagrad for a few steps
    for _ in range(steps):
      adagrad_update.run()

    return var0.eval(), var1.eval()

  def equivGradientDescentTest_FtrlPart(self, steps, dtype):
    var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
    opt = ftrl.FtrlOptimizer(
        3.0,
        learning_rate_power=-0.0,  # using Fixed learning rate
        initial_accumulator_value=0.1,
        l1_regularization_strength=0.0,
        l2_regularization_strength=0.0)
    ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    # Fetch params to validate initial values
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())

    # Run Ftrl for a few steps
    for _ in range(steps):
      ftrl_update.run()

    return var0.eval(), var1.eval()

  def equivGradientDescentTest_GradientDescentPart(self, steps, dtype):
    var0, var1, grads0, grads1 = self.initVariableAndGradient(dtype)
    opt = gradient_descent.GradientDescentOptimizer(3.0, name="sgd")
    sgd_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
    variables.global_variables_initializer().run()
    # Fetch params to validate initial values
    self.assertAllClose([0.0, 0.0], var0.eval())
    self.assertAllClose([0.0, 0.0], var1.eval())

    # Run GradientDescent for a few steps
    for _ in range(steps):
      sgd_update.run()

    return var0.eval(), var1.eval()

  def testFtrlwithoutRegularization(self):
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        var0 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
        var1 = resource_variable_ops.ResourceVariable([0.0, 0.0], dtype=dtype)
        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
        opt = ftrl.FtrlOptimizer(
            3.0,
            initial_accumulator_value=0.1,
            l1_regularization_strength=0.0,
            l2_regularization_strength=0.0)
        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([0.0, 0.0], var0.eval())
        self.assertAllClose([0.0, 0.0], var1.eval())

        # Run 3 steps FTRL
        for _ in range(3):
          ftrl_update.run()

        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-2.60260963, -4.29698515]), var0.eval(), float_rtol=1e-5)
        self.assertAllCloseAccordingToType(
            np.array([-0.28432083, -0.56694895]), var1.eval(), float_rtol=1e-5)

  def testFtrlwithoutRegularization2(self):
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
        var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
        opt = ftrl.FtrlOptimizer(
            3.0,
            initial_accumulator_value=0.1,
            l1_regularization_strength=0.0,
            l2_regularization_strength=0.0)
        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([4.0, 3.0], var1.eval())

        # Run 3 steps FTRL
        for _ in range(3):
          ftrl_update.run()

        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-2.55607247, -3.98729396]), var0.eval(), 1e-5, 1e-5)
        self.assertAllCloseAccordingToType(
            np.array([-0.28232238, -0.56096673]), var1.eval(), 1e-5, 1e-5)

  def testFtrlWithL1(self):
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
        var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
        opt = ftrl.FtrlOptimizer(
            3.0,
            initial_accumulator_value=0.1,
            l1_regularization_strength=0.001,
            l2_regularization_strength=0.0)
        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([4.0, 3.0], var1.eval())

        # Run 10 steps FTRL
        for _ in range(10):
          ftrl_update.run()

        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-7.66718769, -10.91273689]), var0.eval(), rtol=1e-4)
        self.assertAllCloseAccordingToType(
            np.array([-0.93460727, -1.86147261]), var1.eval(), rtol=1e-4)

  def testFtrlWithL1_L2(self):
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
        var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
        opt = ftrl.FtrlOptimizer(
            3.0,
            initial_accumulator_value=0.1,
            l1_regularization_strength=0.001,
            l2_regularization_strength=2.0)
        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllClose([1.0, 2.0], var0.eval())
        self.assertAllClose([4.0, 3.0], var1.eval())

        # Run 10 steps FTRL
        for _ in range(10):
          ftrl_update.run()

        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-0.24059935, -0.46829352]), var0.eval(), rtol=1e-5)
        self.assertAllCloseAccordingToType(
            np.array([-0.02406147, -0.04830509]), var1.eval(), rtol=1e-5)

  def testFtrlWithL1_L2_L2Shrinkage(self):
    """Test the new FTRL op with support for l2 shrinkage.

    The addition of this parameter which places a constant pressure on weights
    towards the origin causes the gradient descent trajectory to differ. The
    weights will tend to have smaller magnitudes with this parameter set.
    """
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype)
        var1 = resource_variable_ops.ResourceVariable([4.0, 3.0], dtype=dtype)
        grads0 = constant_op.constant([0.1, 0.2], dtype=dtype)
        grads1 = constant_op.constant([0.01, 0.02], dtype=dtype)
        opt = ftrl.FtrlOptimizer(
            3.0,
            initial_accumulator_value=0.1,
            l1_regularization_strength=0.001,
            l2_regularization_strength=2.0,
            l2_shrinkage_regularization_strength=0.1)
        ftrl_update = opt.apply_gradients(zip([grads0, grads1], [var0, var1]))
        variables.global_variables_initializer().run()
        # Fetch params to validate initial values
        self.assertAllCloseAccordingToType([1.0, 2.0], var0.eval())
        self.assertAllCloseAccordingToType([4.0, 3.0], var1.eval())

        # Run 10 steps FTRL
        for _ in range(10):
          ftrl_update.run()

        # Validate updated params
        self.assertAllCloseAccordingToType(
            np.array([-0.21931979, -0.40642974]), var0.eval(), rtol=1e-4)
        self.assertAllCloseAccordingToType(
            np.array([-0.0282721, -0.07188385]), var1.eval(), rtol=1e-4)

  # When variables are initialized with Zero, FTRL-Proximal has two properties:
  # 1. Without L1&L2 but with fixed learning rate, FTRL-Proximal is identical
  # with GradientDescent.
  # 2. Without L1&L2 but with adaptive learning rate, FTRL-Proximal is idential
  # with Adagrad.
  # So, basing on these two properties, we test if our implementation of
  # FTRL-Proximal performs same updates as Adagrad or GradientDescent.
  def testEquivAdagradwithoutRegularization(self):
    steps = 5
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        val0, val1 = self.equivAdagradTest_FtrlPart(steps, dtype)
      with self.test_session(), self.test_scope():
        val2, val3 = self.equivAdagradTest_AdagradPart(steps, dtype)

    self.assertAllCloseAccordingToType(val0, val2, rtol=1e-4)
    self.assertAllCloseAccordingToType(val1, val3, rtol=1e-4)

  def testEquivGradientDescentwithoutRegularization(self):
    steps = 5
    for dtype in self.float_types:
      with self.test_session(), self.test_scope():
        val0, val1 = self.equivGradientDescentTest_FtrlPart(steps, dtype)
      with self.test_session(), self.test_scope():
        val2, val3 = self.equivGradientDescentTest_GradientDescentPart(
            steps, dtype)

    self.assertAllCloseAccordingToType(val0, val2, rtol=1e-5)
    self.assertAllCloseAccordingToType(val1, val3, rtol=1e-5)


if __name__ == "__main__":
  test.main()
