I am writing a custom loss function that requires calculating ratios of predicted values per group. As a **simplified** example, here is what my Data and model code looks like:

```
def main():df = pd.DataFrame(columns=["feature_1", "feature_2", "condition_1", "condition_2", "label"],data=[[5, 10, "a", "1", 0],[30, 20, "a", "1", 1],[50, 40, "a", "1", 0],[15, 20, "a", "2", 0],[25, 30, "b", "2", 1],[35, 40, "b", "1", 0],[10, 80, "b", "1", 1]])features = ["feature_1", "feature_2"]conds_and_label = ["condition_1", "condition_2", "label"]X = df[features]Y = df[conds_and_label]model = my_model(input_shape=len(features))model.fit(X, Y, epochs=10, batch_size=128)model.evaluate(X, Y)def custom_loss(conditions, y_pred): # this is what I need help withconds = ["condition_1", "condition_2"]conditions["label_pred"] = y_predg = conditions.groupby(by=conds,as_index=False).apply(lambda x: x["label_pred"].sum() /len(x)).reset_index(name="pred_ratio")# true_ratios will be a constant, external DataFrame. Simplified example here:true_ratios = pd.DataFrame(columns=["condition_1", "condition_2", "true_ratio"],data=[["a", "1", 0.1],["a", "2", 0.2],["b", "1", 0.8],["b", "2", 0.9]])merged = pd.merge(g, true_ratios, on=conds)merged["diff"] = merged["pred_ratio"] - merged["true_ratio"]return K.mean(K.abs(merged["diff"]))def joint_loss(conds_and_label, y_pred):y_true = conds_and_label[:, 2]conditions = tf.gather(conds_and_label, [0, 1], axis=1)loss_1 = standard_loss(y_true=y_true, y_pred=y_pred) # not shownloss_2 = custom_loss(conditions=conditions, y_pred=y_pred)return 0.5 * loss_1 + 0.5 * loss_2def my_model(input_shape=None):model = Sequential()model.add(Dense(units=2, activation="relu"), input_shape=(input_shape,))model.add(Dense(units=1, activation='sigmoid'))model.add(Flatten())model.compile(loss=joint_loss, optimizer="Adam",metrics=[joint_loss, custom_loss, "accuracy"])return model
```

What I need help with is the `custom_loss`

function. As you can see, it is currently written as if the inputs are Pandas DataFrames. However, the inputs will be Keras Tensors (with tensorflow backend), so I am trying to figure out **how to convert the current code in custom_loss to use Keras/TF backend functions**. For example, I searched online and couldn't find out a way to do a groupby in Keras/TF to get the ratios I need...

Some context/explanation that might be helpful to you:

- My main loss function is
`joint_loss`

, which consists of`standard_loss`

(not shown) and`custom_loss`

. But I only need help converting`custom_loss`

. - What
`custom_loss`

does is:- Groupby on two condition columns (these two columns represent the groups of the data).
- Get the ratio of predicted 1s to total number of batch samples per each group.
- Compare the "pred_ratio" to a set of "true_ratio" and get the difference.
- Calculate mean absolute error from the differences.