Catboost results don't make any sense

I'm running CatboostClassifier on an imbalanced dataset, binary classification, optimizing logloss and metric F1 Score. The resultant plot shows different results on F1:use_weights = True, F1:use_weights = False and gives different results from training predictions and validation predictions.

    params = {
    'iterations':500,
    'learning_rate':0.2,
    'eval_metric': 'F1', 
    'loss_function': 'Logloss',
    'custom_metric': ['F1', 'Precision', 'Recall'],
    'scale_pos_weight':19,
    'use_best_model':True,
    'max_depth':8
    }

    modelcat = CatBoostClassifier(**params)
    modelcat.fit(
    train_pool,
    eval_set=validation_pool,
    verbose=False,
    plot=True
    )

When I predict for validation and training set and check f1 score using sklearn's f1_score I get this score

    ypredcat0 = modelcat.predict(valX_cat0)  #validation predictions
    print(f"F1: {f1_score(y_val,ypredcat0)}")

F1: 0.4163473818646233

    ytrainpredcat0 = modelcat.predict(trainX_cat0) #training predictions
    print(f"F1: {f1_score(y_train,ytrainpredcat0)}")

F1: 0.42536905412793874

But when I look at the plot created by plot=True, I find different convergence scores

when use_weights = False

when use_weights = True

In the plots, clearly training F1 has reached the score of 1, but when making predictions it's only 0.42. Why is this different? And how is use_weights working here?

Okay I figured out an answer. The difference lies in how F1 score is calculated taking into account various averages. By default for binary classification scikit-learn uses average = 'binary', so binary F1 score is 0.42. When I changed the average = 'macro' it gave F1 score as 0.67 which is what the Catboost shows with use_weights = False. When I calculated with average = 'micro' it gave F1 score as 0.88, even higher than what the plot shows, but anyway, that solves both the questions I had.