File size: 2,226 Bytes
c8e9e79 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 |
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.experimental import enable_halving_search_cv
from sklearn.model_selection import HalvingGridSearchCV, RandomizedSearchCV
from catboost import CatBoostClassifier, Pool
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from pandas.core.common import random_state
# load catboost_df
catboost_df = pd.read_csv('datasets/catboost_df.csv', index_col=0)
# drop label name_x and name_y
catboost_df = catboost_df.drop(['name_x', 'name_y'], axis=1)
# get the categorical and float features
cat_features = list(catboost_df.select_dtypes(include=['object']).columns)
float_features = list(catboost_df.select_dtypes(include=['float64']).columns)
for feature in float_features:
# Fill NaN values with the mean of non-missing values in the same column
mean_value = catboost_df[feature].mean()
catboost_df[feature].fillna(mean_value, inplace=True)
for feature in cat_features:
catboost_df[feature] = catboost_df[feature].astype(str)
# create test and train set
X, y = catboost_df.drop('interaction', axis=1), catboost_df['interaction']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
catb_model = CatBoostClassifier(random_state=42, task_type="GPU", max_ctr_complexity=1, boosting_type="Plain",
cat_features=cat_features, gpu_ram_part=0.4)
catb_param = {
'max_depth': [6],
'learning_rate': [0.01],
'reg_lambda': [2.5],
'n_estimators': [1000],
}
# pool_train = Pool(X_train, y_train, cat_features = cat_features)
# pool_test = Pool(X_test, cat_features = cat_features)
# grid search
grid_search = HalvingGridSearchCV(
catb_model, catb_param, cv=3, n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)
print("Done")
best_model = grid_search.best_estimator_
best_model.save_model('models/catboost_model2.cbm')
# print best parameters
print(grid_search.best_params_)
# print best score
print(grid_search.best_score_)
y_p = grid_search.predict_proba(X_test)
print(f"Test AUC_ROC score = {roc_auc_score(y_test, y_p[:, 1])}")
print("---------------------Done--------------------------------")
|