What are the limits of capping ratios to [0-1]?
In essence, you risk biasing the model by making the values 0 and 1 more likely than they actually are. This may or may not be a significant problem; really only cross-validation will tell you. 

I would go ahead and try it, but compare your results to a different method of filling invalid values, like using the mean() of the valid values plus an extra feature flagging the samples that were invalid. Make sure you cross-validate using a hold out dataset that's been transformed in the same way; you can then select the method that results in the best results of the cross-validation process.