Predict comparison

import vowpalwabbit


def my_predict(vw, ex):
    pp = 0.0
    for f, v in ex.iter_features():
        pp += vw.get_weight(f) * v
    return pp


def ensure_close(a, b, eps=1e-6):
    if abs(a - b) > eps:
        raise Exception(
            "test failed: expected "
            + str(a)
            + " and "
            + str(b)
            + " to be "
            + str(eps)
            + "-close, but they differ by "
            + str(abs(a - b))
        )


###############################################################################
vw = vowpalwabbit.Workspace("--quiet")


###############################################################################
vw.learn("1 |x a b")


###############################################################################
print("# do some stuff with a read example:")
ex = vw.example("1 |x a b |y c")
ex.learn()
ex.learn()
ex.learn()
ex.learn()

updated_pred = ex.get_updated_prediction()
print("current partial prediction =", updated_pred)

# compute our own prediction
print(
    "        my view of example =",
    str([(f, v, vw.get_weight(f)) for f, v in ex.iter_features()]),
)
my_pred = my_predict(vw, ex)
print("     my partial prediction =", my_pred)
ensure_close(updated_pred, my_pred)
print("")
vw.finish_example(ex)

###############################################################################
print("# make our own example from scratch")
ex = vw.example()
ex.set_label_string("0")
ex.push_features("x", ["a", "b"])
ex.push_features("y", [("c", 1.0)])
ex.setup_example()

print(
    "        my view of example =",
    str([(f, v, vw.get_weight(f)) for f, v in ex.iter_features()]),
)
my_pred2 = my_predict(vw, ex)
print("     my partial prediction =", my_pred2)
ensure_close(my_pred, my_pred2)

ex.learn()
ex.learn()
ex.learn()
ex.learn()
print("  final partial prediction =", ex.get_updated_prediction())
ensure_close(ex.get_updated_prediction(), my_predict(vw, ex))
print("")
vw.finish_example(ex)


###############################################################################
exList = []
for i in range(120):
    ex = vw.example()
    exList.append(ex)

# this is the safe way to delete the examples for VW to reuse:
for ex in exList:
    vw.finish_example(ex)

exList = []  # this should __del__ the examples, we hope :)
for i in range(120):
    ex = vw.example()
    exList.append(ex)

for ex in exList:
    vw.finish_example(ex)


###############################################################################

for i in range(2):
    ex = vw.example("1 foo| a b")
    ex.learn()
    print("tag =", ex.get_tag())
    print("partial pred =", ex.get_partial_prediction())
    print("loss =", ex.get_loss())

    print("label =", ex.get_label())
    vw.finish_example(ex)


# to be safe, finish explicity (should happen by default anyway)
vw.finish()


###############################################################################
print("# test some save/load behavior")
vw = vowpalwabbit.Workspace("--quiet -f test.model")
ex = vw.example("1 |x a b |y c")
ex.learn()
ex.learn()
ex.learn()
ex.learn()
before_save = ex.get_updated_prediction()
print("before saving, prediction =", before_save)
vw.finish_example(ex)

vw.finish()  # this should create the file

# now re-start vw by loading that model
vw = vowpalwabbit.Workspace("--quiet -i test.model")
ex = vw.example("1 |x a b |y c")  # test example
ex.learn()
after_save = ex.get_partial_prediction()
print(" after saving, prediction =", after_save)
vw.finish_example(ex)

ensure_close(before_save, after_save)
vw.finish()  # this should create the file

print("done!")

# do some stuff with a read example:
current partial prediction = 0.9978890419006348
        my view of example = [(167017, 1.0, 0.26836955547332764), (162468, 1.0, 0.26836955547332764), (173048, 1.0, 0.19278039038181305), (11650396, 1.0, 0.26836955547332764)]
     my partial prediction = 0.997889056801796

# make our own example from scratch
        my view of example = [(167017, 1.0, 0.26836955547332764), (162468, 1.0, 0.26836955547332764), (173048, 1.0, 0.19278039038181305), (11650396, 1.0, 0.26836955547332764)]
     my partial prediction = 0.997889056801796
  final partial prediction = 0.057791002094745636

tag = foo
partial pred = 0.05145413801074028
loss = 0.8997392654418945
label = 1.0
tag = foo
partial pred = 0.5196833610534668
loss = 0.23070406913757324
label = 1.0
# test some save/load behavior
before saving, prediction = 0.977569580078125
 after saving, prediction = 0.977569580078125
done!