catboost
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catboost
Slow single prediction in python
Problem: Prediction on a single row works slowly in python catboost version: 0.23.2 Operating System: Linux CPU: Intel
Catboost prediction works slower than XGBoost/lightGBM
Evironment : Colab
from catboost import CatBoostClassifier
from lightgbm import LGBMClassifier
from xgboost import XGBClassifier
import time
train_size = 1000
train_features = 30
X_train = np.random.randn(train_size, train_features)
y_train = np.random.randint(2, size=train_size)
nrow = np.random.randn(1, train_features)
n_esimators = 200
cb = CatBoostClassifier(verbose=0, iterations=n_esimators, thread_count=2)
cb.fit(X_train, y_train)
xgb = XGBClassifier(n_jobs=2, n_estimators=n_esimators)
xgb.fit(X_train, y_train)
lgb = LGBMClassifier(n_jobs=2, n_estimators=n_esimators)
lgb.fit(X_train, y_train)
c0 = time.time()
for _ in range(1000):
__ = cb.predict_proba(nrow, thread_count=1)
print(time.time() - c0) # 0.4030110836029053
c0 = time.time()
for _ in range(1000):
_ = xgb.predict_proba(nrow)
print(time.time() - c0) # 0.0883171558380127
c0 = time.time()
for _ in range(1000):
_ = lgb.predict_proba(nrow)
print(time.time() - c0) # 0.14197039604187012
Catboost prediction works slower with thread_count > 1
Operating System: Ubuntu 18.04 CPU Cores/Thread : 8/16
for thread_count in [1, 2, 8, 16]:
c0 = time.time()
for _ in range(1000):
__ = cb.predict_proba(nrow, thread_count=thread_count)
print(time.time() - c0)
0.30289459228515625
0.32805943489074707
0.5098705291748047
0.9639191627502441
Simple cpp model export to python works 30+ times faster
Environment: Colab
For this experiment i used cppyy module that can easily export functions from cpp
import cppyy
cb = CatBoost(params=dict(verbose=0, iterations=n_esimators, thread_count=2))
cb.fit(X_train, y_train)
cb.save_model("cpp_model.cpp", format="cpp")
with open('cpp_model.cpp', 'r') as f:
cf = f.read()
cppyy.cppdef(cf)
nrow_py = list(nrow[0])
rounds = 1000
c0 = time.time()
for _ in range(rounds):
__ = cppyy.gbl.ApplyCatboostModel(nrow_py)
cpptime = time.time() - c0
print(cpptime)
c0 = time.time()
for _ in range(rounds):
__ = cb.predict(nrow_py, prediction_type='RawFormulaVal')
pytime = time.time() - c0
print(pytime)
print(pytime/cpptime)
0.013201713562011719
0.46095943450927734
34.91663656721809
I'm sure you can increase speed for single prediction by many times
Bump. Having same issue. Python predictions are so much slower than just exporting to binary format and predicting through CLI.
