From f6cac43edce6c035e0d26b83e2a18bbd36240d21 Mon Sep 17 00:00:00 2001
From: Keith Battocchi <kebatt@microsoft.com>
Date: Wed, 27 Jan 2021 21:24:01 -0500
Subject: [PATCH] Relax tensorflow version limit
---
econml/iv/nnet/_deepiv.py | 12 ++++++++----
econml/tests/test_deepiv.py | 26 +++++++++++++++++++++++---
setup.cfg | 8 ++++----
3 files changed, 35 insertions(+), 11 deletions(-)
diff --git a/econml/iv/nnet/_deepiv.py b/econml/iv/nnet/_deepiv.py
index 18123b0d7..040614b40 100644
--- a/econml/iv/nnet/_deepiv.py
+++ b/econml/iv/nnet/_deepiv.py
@@ -93,9 +93,13 @@ def mog_loss_model(n_components, d_t):
# LL = C - log(sum(pi_i/sig^d * exp(-d2/(2*sig^2))))
# Use logsumexp for numeric stability:
# LL = C - log(sum(exp(-d2/(2*sig^2) + log(pi_i/sig^d))))
- # TODO: does the numeric stability actually make any difference?
def make_logloss(d2, sig, pi):
- return -K.logsumexp(-d2 / (2 * K.square(sig)) + K.log(pi / K.pow(sig, d_t)), axis=-1)
+ # logsumexp doesn't exist in keras 2.4; simulate it
+ values = - d2 / (2 * K.square(sig)) + K.log(pi / K.pow(sig, d_t))
+ # logsumexp(a,b,c) = log(exp(a)+exp(b)+exp(c)) = log((exp(a-k)+exp(b-k)+exp(c-k))*exp(k))
+ # = log((exp(a-k)+exp(b-k)+exp(c-k))) + k
+ mx = K.max(values, axis=-1)
+ return -K.log(K.sum(K.exp(values - L.Reshape((-1, 1))(mx)), axis=-1)) - mx
ll = L.Lambda(lambda dsp: make_logloss(*dsp), output_shape=(1,))([d2, sig, pi])
@@ -350,7 +354,7 @@ def fit(self, Y, T, X, Z, *, inference=None):
model.add_loss(L.Lambda(K.mean)(ll))
model.compile(self._optimizer)
# TODO: do we need to give the user more control over other arguments to fit?
- model.fit([Z, X, T], [], **self._first_stage_options)
+ model.fit([Z, X, T], **self._first_stage_options)
lm = response_loss_model(lambda t, x: self._h(t, x),
lambda z, x: Model([z_in, x_in],
@@ -365,7 +369,7 @@ def fit(self, Y, T, X, Z, *, inference=None):
response_model.add_loss(L.Lambda(K.mean)(rl))
response_model.compile(self._optimizer)
# TODO: do we need to give the user more control over other arguments to fit?
- response_model.fit([Z, X, Y], [], **self._second_stage_options)
+ response_model.fit([Z, X, Y], **self._second_stage_options)
self._effect_model = Model([t_in, x_in], [self._h(t_in, x_in)])
diff --git a/econml/tests/test_deepiv.py b/econml/tests/test_deepiv.py
index 4bc55e277..27c51b7ae 100644
--- a/econml/tests/test_deepiv.py
+++ b/econml/tests/test_deepiv.py
@@ -32,7 +32,27 @@ def test_stop_grad(self):
model = keras.Model([x_input, y_input, z_input], [loss])
model.add_loss(K.mean(loss))
model.compile('nadam')
- model.fit([np.array([[1]]), np.array([[2]]), np.array([[0]])], [])
+ model.fit([np.array([[1]]), np.array([[2]]), np.array([[0]])])
+
+ def test_mog_loss(self):
+ inputs = [keras.layers.Input(shape=s) for s in [(3,), (3, 2), (3,), (2,)]]
+ ll_model = keras.engine.Model(inputs, mog_loss_model(3, 2)(inputs))
+
+ for n in range(10):
+ ps = -np.log(np.random.uniform(size=(3,)))
+ pi = ps / np.sum(ps)
+ mu = np.random.normal(size=(3, 2))
+ sig = np.exp(np.random.normal(size=3,))
+ t = np.random.normal(size=(2,))
+
+ pred = ll_model.predict([pi.reshape(1, 3), mu.reshape(1, 3, 2), sig.reshape(1, 3), t.reshape(1, 2)])
+
+ # LL = C - log(sum(pi_i/sig^d * exp(-d2/(2*sig^2))))
+ d = mu - t.reshape(-1, 2)
+ d2 = np.sum(d * d, axis=-1)
+ ll = -np.log(np.sum(pi / (sig * sig) * np.exp(-d2 / (2 * sig * sig)), axis=0))
+
+ assert np.allclose(ll, pred[0])
@pytest.mark.slow
def test_deepiv_shape(self):
@@ -500,7 +520,7 @@ def norm(lr):
model = keras.engine.Model([x_input, t_input], [ll])
model.add_loss(K.mean(ll))
model.compile('nadam')
- model.fit([x, t], [], epochs=5)
+ model.fit([x, t], epochs=5)
# For some reason this doesn't work at all when run against the CNTK backend...
# model.compile('nadam', loss=lambda _,l:l)
@@ -559,7 +579,7 @@ def sample(n):
model = keras.engine.Model([x_input, t_input], [ll])
model.add_loss(K.mean(ll))
model.compile('nadam')
- model.fit([x, t], [], epochs=100)
+ model.fit([x, t], epochs=100)
model2 = keras.engine.Model([x_input], [pi, mu, sig])
import matplotlib
diff --git a/setup.cfg b/setup.cfg
index 1a628616e..57818bb49 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -60,14 +60,14 @@ automl =
; azureml-sdk[explain,automl] == 1.0.83
azure-cli
tf =
- keras < 2.4
- tensorflow > 1.10, < 2.3
+ keras
+ tensorflow > 1.10, < 2.4
plt =
matplotlib
all =
azure-cli
- keras < 2.4
- tensorflow > 1.10, < 2.3
+ keras
+ tensorflow > 1.10, < 2.4
matplotlib
[options.packages.find]