def test_compare_napkinxc_with_xclib():
k = 5
# Train model and predict
X_train, Y_train = load_dataset(TEST_DATASET, "train", root=TEST_DATA_PATH)
X_test, Y_test = load_dataset(TEST_DATASET, "test", root=TEST_DATA_PATH)
plt = PLT(MODEL_PATH)
plt.fit(X_train, Y_train)
Y_pred = plt.predict_proba(X_test, top_k=k)
shutil.rmtree(MODEL_PATH, ignore_errors=True)
# Prepare dataset
csr_Y_train = to_csr_matrix(Y_train)
csr_Y_test = to_csr_matrix(Y_test)
csr_Y_pred = to_csr_matrix(Y_pred, shape=csr_Y_test.shape)
# Calculate propensities
nxc_inv_ps = inverse_propensity(Y_train, A=0.55, B=1.5)
csr_nxc_inv_ps = inverse_propensity(csr_Y_train, A=0.55, B=1.5)
xcl_inv_ps = compute_inv_propesity(csr_Y_train, A=0.55, B=1.5)
assert np.allclose(nxc_inv_ps, csr_nxc_inv_ps)
assert np.allclose(nxc_inv_ps, xcl_inv_ps)
# Compare results
measures = {
"P@k": {
"nxc": precision_at_k,
"xclib": precision,
"inv_ps": False
},
"R@k": {
"nxc": recall_at_k,
"xclib": recall,
"inv_ps": False
},
"nDCG@k": {
"nxc": ndcg_at_k,
"xclib": ndcg,
"inv_ps": False
},
"PSP@k": {
"nxc": psprecision_at_k,
"xclib": psprecision,
"inv_ps": True
},
"PSR@k": {
"nxc": psrecall_at_k,
"xclib": psrecall,
"inv_ps": True
},
"PSnDCG@k": {
"nxc": psndcg_at_k,
"xclib": psndcg,
"inv_ps": True
}
}
print("\n")
for m, v in measures.items():
print("\n{} time comparison:".format(m))
t_start = time()
xclib_r = v["xclib"](csr_Y_pred, csr_Y_test, xcl_inv_ps,
k=k) if v["inv_ps"] else v["xclib"](
csr_Y_pred, csr_Y_test, k=k)
print("\txclib.evaluation.xc_metrics.{} with csr_matrices: {}s".format(
v["xclib"].__name__,
time() - t_start))
t_start = time()
nxc_r = v["nxc"](Y_test, Y_pred, xcl_inv_ps,
k=k) if v["inv_ps"] else v["nxc"](Y_test, Y_pred, k=k)
print("\tnapkinXC.measures.{} with lists: {}s".format(
v["nxc"].__name__,
time() - t_start))
t_start = time()
csr_nxc_r = v["nxc"](csr_Y_test, csr_Y_pred, csr_nxc_inv_ps,
k=k) if v["inv_ps"] else v["nxc"](
csr_Y_test, csr_Y_pred, k=k)
print("\tnapkinXC.measures.{} with csr_matrices: {}s".format(
v["nxc"].__name__,
time() - t_start))
assert np.allclose(nxc_r, csr_nxc_r)
assert np.allclose(nxc_r, xclib_r)
def test_compare_napkinxc_with_xclib():
# Train model and predict
model_path = os.path.join(os.path.dirname(os.path.abspath(__file__)),
"eurlex-model")
X_train, Y_train = load_dataset("eurlex-4k", "train")
X_test, Y_test = load_dataset("eurlex-4k", "test")
plt = PLT(model_path)
if not os.path.exists(model_path):
plt.fit(X_train, Y_train)
Y_pred = plt.predict_proba(X_test, top_k=5)
# Prepare dataset
csr_Y_train = to_csr_matrix(Y_train)
csr_Y_test = to_csr_matrix(Y_test)
csr_Y_pred = to_csr_matrix(Y_pred, shape=csr_Y_test.shape)
# Calculate propensities
nxc_inv_ps = inverse_propensity(Y_train, A=0.55, B=1.5)
csr_nxc_inv_ps = inverse_propensity(csr_Y_train, A=0.55, B=1.5)
xcl_inv_ps = compute_inv_propesity(csr_Y_train, A=0.55, B=1.5)
assert np.allclose(nxc_inv_ps, csr_nxc_inv_ps)
assert np.allclose(nxc_inv_ps, xcl_inv_ps)
# Compare results
measures = {
"P@k": {
"nxc": precision_at_k,
"xclib": precision,
"inv_ps": False
},
"R@k": {
"nxc": recall_at_k,
"xclib": recall,
"inv_ps": False
},
"nDCG@k": {
"nxc": ndcg_at_k,
"xclib": ndcg,
"inv_ps": False
},
"PSP@k": {
"nxc": psprecision_at_k,
"xclib": psprecision,
"inv_ps": True
},
"PSR@k": {
"nxc": psrecall_at_k,
"xclib": psrecall,
"inv_ps": True
},
"PSnDCG@k": {
"nxc": psndcg_at_k,
"xclib": psndcg,
"inv_ps": True
}
}
for m, v in measures.items():
print("\n{} time comparison:".format(m))
t_start = time()
xclib_r = v["xclib"](csr_Y_pred, csr_Y_test, xcl_inv_ps,
k=5) if v["inv_ps"] else v["xclib"](
csr_Y_pred, csr_Y_test, k=5)
print("\txclib.evaluation.xc_metrics.{} with csr_matrices: {}s".format(
v["xclib"].__name__,
time() - t_start))
t_start = time()
nxc_r = v["nxc"](Y_test, Y_pred, xcl_inv_ps,
k=5) if v["inv_ps"] else v["nxc"](Y_test, Y_pred, k=5)
print("\tnapkinXC.measures.{} with lists: {}s".format(
v["nxc"].__name__,
time() - t_start))
t_start = time()
csr_nxc_r = v["nxc"](csr_Y_test, csr_Y_pred, csr_nxc_inv_ps,
k=5) if v["inv_ps"] else v["nxc"](
csr_Y_test, csr_Y_pred, k=5)
print("\tnapkinXC.measures.{} with csr_matrices: {}s".format(
v["nxc"].__name__,
time() - t_start))
assert np.allclose(nxc_r, csr_nxc_r)
assert np.allclose(nxc_r, xclib_r)
def test_multilabel_csr_float64_input():
X_train, Y_train = load_dataset(TEST_DATASET, "train", root=TEST_DATA_PATH)
X_train = to_csr_matrix(X_train, dtype=np.float64)
Y_train = to_csr_matrix(Y_train)
eval_data_for_dataset(X_train, Y_train, PLT)