def modelBuilding(X, y, cat_features):
X_train, X_validation, y_train, y_validation = train_test_split(X,\
y, train_size=0.8, random_state=1234)
model = CatBoostClassifier(iterations=2000,
learning_rate=0.01,
task_type="GPU"
#loss_function='CrossEntropy'
)
model.fit(X_train,
y_train,
cat_features=cat_features,
eval_set=(X_validation, y_validation),
verbose=True)
print('Model is fitted: ' + str(model.is_fitted()))
print('Model params:')
print(model.get_params())
return model
validation_pool = Pool(data=X_validation,
label=y_validation,
cat_features=cat_features)
#######################
# BETTER/BEST MODEL
#######################
# Note: You can tinker with learning rates
model = CatBoostClassifier(
iterations=5,
learning_rate=0.1,
)
model.fit(train_pool, eval_set=validation_pool, verbose=False)
# Print model info
print('Model is fitted: {}'.format(model.is_fitted()))
print('Model params:\n{}'.format(model.get_params()))
# Choose the best iteration
# Note: There is a parameter: use_best_model ( = True or False)
model = CatBoostClassifier(iterations=100, )
model.fit(
train_pool,
eval_set=validation_pool,
verbose=False,
)
print('Tree count: ' + str(model.tree_count_))
from catboost import CatBoostClassifier
clf = CatBoostClassifier(
iterations=2000,
learning_rate=0.1,
#loss_function='CrossEntropy'
)
clf.fit(X_train,
y_train,
cat_features=categorical_columns,
eval_set=(X_val, y_val),
verbose=False)
print('CatBoost model is fitted: ' + str(clf.is_fitted()))
print('CatBoost model parameters:')
print(clf.get_params())
predictions = clf.predict(X_val)
print("accuracy_score", accuracy_score(y_val, predictions))
predictions_probas = clf.predict_proba(X_val)
print("roc-auc score for the class 1, from target 'HasDetections' ",
roc_auc_score(y_val, predictions_probas[:, 1]))
val_cnf_matrix = confusion_matrix(y_val, predictions)
sns.heatmap(val_cnf_matrix, annot=True, fmt='.2f',
cmap="BrBG").set_title("Validation")
plt.show()
# In[12]:
from catboost import CatBoostClassifier
model = CatBoostClassifier(
iterations=5,
learning_rate=0.1,
# loss_function='CrossEntropy'
)
model.fit(
X_train, y_train,
cat_features=cat_features,
eval_set=(X_validation, y_validation),
verbose=False
)
print('Model is fitted: ' + str(model.is_fitted()))
print('Model params:')
print(model.get_params())
# In[13]:
from catboost import CatBoostClassifier
model = CatBoostClassifier(
iterations=50,
random_seed=63,
learning_rate=0.5,
custom_loss=['AUC', 'Accuracy']
)
model.fit(
