def test_wide_model(pd_df, tmp):
data, users, items = pd_df
# Test wide model
# Test if wide column has two original features and one crossed feature
wide_columns, _ = build_feature_columns(users,
items,
model_type="wide",
crossed_feat_dim=10)
assert len(wide_columns) == 3
# Check crossed feature dimension
assert wide_columns[2].hash_bucket_size == 10
# Check model type
model = build_model(os.path.join(tmp, "wide_" + MODEL_DIR),
wide_columns=wide_columns)
assert isinstance(model, tf.estimator.LinearRegressor)
# Test if model train works
model.train(
input_fn=pandas_input_fn(
df=data,
y_col=DEFAULT_RATING_COL,
batch_size=1,
num_epochs=None,
shuffle=True,
),
steps=1,
)
# Close the event file so that the model folder can be cleaned up.
summary_writer = tf.summary.FileWriterCache.get(model.model_dir)
summary_writer.close()
def test_wide_deep_model(pd_df, tmp):
data, users, items = pd_df
# Test if wide and deep columns have correct features
wide_columns, deep_columns = build_feature_columns(users,
items,
model_type="wide_deep")
assert len(wide_columns) == 3
assert len(deep_columns) == 2
# Check model type
model = build_model(
os.path.join(tmp, "wide_deep_" + MODEL_DIR),
wide_columns=wide_columns,
deep_columns=deep_columns,
)
assert isinstance(model, tf.estimator.DNNLinearCombinedRegressor)
# Test if model train works
model.train(
input_fn=pandas_input_fn(
df=data,
y_col=DEFAULT_RATING_COL,
batch_size=1,
num_epochs=None,
shuffle=True,
),
steps=1,
)
# Close the event file so that the model folder can be cleaned up.
summary_writer = tf.summary.FileWriterCache.get(model.model_dir)
summary_writer.close()
def test_pandas_input_fn_for_saved_model(pd_df, tmp):
"""Test `export_model` and `pandas_input_fn_for_saved_model`"""
data, users, items = pd_df
model_dir = os.path.join(tmp, "model")
export_dir = os.path.join(tmp, "export")
_, deep_columns = build_feature_columns(users, items, model_type="deep")
# Train a model
model = build_model(
model_dir,
deep_columns=deep_columns,
)
train_fn = pandas_input_fn(
df=data, y_col=DEFAULT_RATING_COL, batch_size=1, num_epochs=None, shuffle=True
)
model.train(input_fn=train_fn, steps=1)
# Test export_model function
exported_path = export_model(
model=model,
train_input_fn=train_fn,
eval_input_fn=pandas_input_fn(df=data, y_col=DEFAULT_RATING_COL),
tf_feat_cols=deep_columns,
base_dir=export_dir,
)
saved_model = tf.saved_model.load(exported_path, tags="serve")
# Test pandas_input_fn_for_saved_model with the saved model
test = data.drop(DEFAULT_RATING_COL, axis=1)
test.reset_index(drop=True, inplace=True)
list(
itertools.islice(
saved_model.signatures["predict"](
examples=pandas_input_fn_for_saved_model(
df=test,
feat_name_type={
DEFAULT_USER_COL: int,
DEFAULT_ITEM_COL: int,
ITEM_FEAT_COL: list,
},
)()["inputs"]
),
len(test),
)
)
# Close the event file so that the model folder can be cleaned up.
summary_writer = tf.compat.v1.summary.FileWriterCache.get(model.model_dir)
summary_writer.close()
def test_evaluation_log_hook(pd_df, tmp):
data, users, items = pd_df
# Run hook 10 times
hook_frequency = 10
train_steps = 10
_, deep_columns = build_feature_columns(users, items, model_type="deep")
model = build_model(
tmp,
deep_columns=deep_columns,
save_checkpoints_steps=train_steps // hook_frequency,
)
evaluation_logger = MetricsLogger()
# Train a model w/ the hook
hooks = [
evaluation_log_hook(
model,
logger=evaluation_logger,
true_df=data,
y_col=DEFAULT_RATING_COL,
eval_df=data.drop(DEFAULT_RATING_COL, axis=1),
every_n_iter=train_steps // hook_frequency,
model_dir=tmp,
eval_fns=[rmse],
)
]
model.train(
input_fn=pandas_input_fn(
df=data,
y_col=DEFAULT_RATING_COL,
batch_size=1,
num_epochs=None,
shuffle=True,
),
hooks=hooks,
steps=train_steps,
)
# Check if hook logged the given metric
assert rmse.__name__ in evaluation_logger.get_log()
assert len(evaluation_logger.get_log()[rmse.__name__]) == hook_frequency
# Close the event file so that the model folder can be cleaned up.
summary_writer = tf.compat.v1.summary.FileWriterCache.get(model.model_dir)
summary_writer.close()
def test_deep_model(pd_df, tmp):
data, users, items = pd_df
# Test if deep columns have user and item features
_, deep_columns = build_feature_columns(users, items, model_type="deep")
assert len(deep_columns) == 2
# Check model type
model = build_model(os.path.join(tmp, "deep_" + MODEL_DIR),
deep_columns=deep_columns)
assert isinstance(model, tf.estimator.DNNRegressor)
# Test if model train works
model.train(input_fn=pandas_input_fn(df=data,
y_col=DEFAULT_RATING_COL,
batch_size=1,
num_epochs=1,
shuffle=False))
# Close the event file so that the model folder can be cleaned up.
summary_writer = tf.summary.FileWriterCache.get(model.model_dir)
summary_writer.close()