I have two set of data let's say A and B. I want to apply PCA and T-sne to A and fine tune the algo. Once, I am satisfied with my tuning I want to save the learnt things to some pickle file. Now I want to apply the same learnt PCA and t-sne to set B. I want t-sne to produce the same results every time on B. I am hoping this because I am assuming, we can save the state of learnt t-sne parametes also. If the parametes are same, and when I load the same file everytime, the result of applying t-sne on the set B should be same every time. How can I do this in Sklearn and python? I am sorry, I am new to ML and python, this may be a very basic question.
Fine-tuning T-SNE equals tuning some heuristic-algorithm (it's ill-conditioned after all; higher-dimension -> lower-dimension mapping is lossy) for your data.
Applying this tuned & learned mapping to other data is done by sklearn's transform.
But: you will see that there is no transform-method for T-SNE and the reason is given here (including further discussion):
It is a transductive learner, like many clustering algorithms: the model is not really applicable beyond the data points it is fed as training.
So whatever you tuned for dataset A, does not really apply to dataset B (including parameters)!
For PCA this is trivial. Use the methods described in docs: model_persistence and use PCA's transform-method (assuming compatible datasets; dimensions!).