--- a/doc/v2_planning/plugin_JB.py	Wed Sep 22 00:23:07 2010 -0400
+++ b/doc/v2_planning/plugin_JB.py	Wed Sep 22 01:37:55 2010 -0400
@@ -1,492 +1,1 @@
-"""plugin_JB - draft of potential library architecture using iterators
-
-This strategy makes use of a simple imperative language whose statements are python function
-calls to create learning algorithms that can be manipulated and executed in several desirable
-ways.  
-
-The training procedure for a PCA module is easy to express:
-
-    # allocate the relevant modules
-    dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
-    pca = PCA_Analysis()
-    pca_batchsize=1000
-
-    # define the control-flow of the algorithm
-    train_pca = SEQ([
-        BUFFER_REPEAT(pca_batchsize, CALL(dataset.next)), 
-        FILT(pca.analyze)])
-
-    # run the program
-    train_pca.run()
-
-The CALL, SEQ, FILT, and BUFFER_REPEAT are control-flow elements. The control-flow elements I
-defined so far are:
-
-- CALL - a basic statement, just calls a python function
-- FILT - like call, but passes the return value of the last CALL or FILT to the python function
-- SEQ - a sequence of elements to run in order
-- REPEAT - do something N times (and return None or maybe the last CALL?)
-- BUFFER_REPEAT - do something N times and accumulate the return value from each iter
-- LOOP - do something an infinite number of times
-- CHOOSE - like a switch statement (should rename to SWITCH)
-- WEAVE - interleave execution of multiple control-flow elements
-- POPEN - launch a process and return its status when it's complete
-- PRINT - a shortcut for CALL(print_obj)
-
-
-We don't have many requirements per-se for the architecture, but I think this design respects
-and realizes all of them.
-The advantages of this approach are:
-
-    - algorithms (including partially run ones) are COPYABLE, and SERIALIZABLE
-
-    - algorithms can be executed without seizing control of the python process (the run()
-      method does this, but if you look inside it you'll see it's a simple for loop)
-
-      - it is easy to execute an algorithm step by step in a main loop that also checks for
-        network or filesystem events related to e.g. job management.
-
-    - the library can provide learning algorithms via control-flow templates, and the user can
-      edit them (with search/replace calls) to include HOOKS, and DIAGNOSTIC plug-in
-      functionality
-
-      e.g. prog.find(CALL(cd1_update, layer=layer1)).replace_with(
-          SEQ([CALL(cd1_update, layer=layer1), CALL(my_debugfn)]))
-
-    - user can print the 'program code' of an algorithm built from library pieces
-
-    - program can be optimized automatically.
-      
-      - e.g. BUFFER(N, CALL(dataset.next))  could be replaced if dataset.next implements the
-        right attribute/protocol for 'bufferable' or something.
-
-      - e.g. SEQ([a,b,c,d])  could be compiled to a single CALL to a Theano-compiled function
-        if a, b, c, and d are calls to callable objects that export something like a
-        'theano_SEQ' interface
-
-
-"""
-
-__license__ = 'TODO'
-__copyright__ = 'TODO'
-
-import cPickle, copy, subprocess, sys, time
-import numpy
-
-####################################################
-# CONTROL-FLOW CONSTRUCTS
-
-class INCOMPLETE: 
-    """Return value for Element.step"""
-
-class ELEMENT(object):
-    """
-    Base class for control flow elements (e.g. CALL, REPEAT, etc.)
-
-    The design is that every element has a driver, that is another element, or the iterator
-    implementation in the ELEMENT class.
-
-    the driver calls start when entering a new control element
-       - this would be called once per e.g. outer loop iteration
-
-    the driver calls step to advance the control element
-       - which returns INCOMPLETE
-       - which returns any other object to indicate completion
-    """
-
-    # subclasses should override these methods:
-    def start(self, arg):
-        pass
-    def step(self):
-        pass
-
-    # subclasses should typically not override these:
-    def run(self, arg=None, n_steps=float('inf')):
-        self.start(arg)
-        i = 0
-        r = self.step()
-        while r is INCOMPLETE:
-            i += 1
-            #TODO make sure there is not an off-by-one error
-            if i > n_steps:
-                break
-            r = self.step()
-        return r
-
-class BUFFER_REPEAT(ELEMENT):
-    """
-    Accumulate a number of return values into one list / array.
-
-    The source of return values `src` is a control element that will be restarted repeatedly in
-    order to fulfil the requiement of gathering N samples.
-
-    TODO: support accumulating of tuples of arrays
-    """
-    def __init__(self, N, src, storage=None):
-        """
-        TODO: use preallocated `storage`
-        """
-        self.N = N
-        self.n = 0
-        self.src = src
-        self.storage = storage
-        self.src.start(None)
-        if self.storage != None:
-            raise NotImplementedError()
-    def start(self, arg):
-        self.buf = [None] * self.N
-        self.n = 0
-        self.finished = False
-    def step(self):
-        assert not self.finished
-        r = self.src.step()
-        if r is INCOMPLETE:
-            return r
-        self.src.start(None) # restart our stream
-        self.buf[self.n] = r
-        self.n += 1
-        if self.n == self.N:
-            self.finished = True
-            return self.buf
-        else:
-            return INCOMPLETE
-        assert 0
-
-class CALL(ELEMENT):
-    """
-    Control flow terminal - call a python function or method.
-
-    Returns the return value of the call.
-    """
-    def __init__(self, fn, *args, **kwargs):
-        self.fn = fn
-        self.args = args
-        self.kwargs=kwargs
-        self.use_start_arg = kwargs.pop('use_start_arg', False)
-    def start(self, arg):
-        self.start_arg = arg
-        self.finished = False
-        return self
-    def step(self):
-        assert not self.finished
-        self.finished = True
-        if self.use_start_arg:
-            if self.args:
-                raise TypeError('cant get positional args both ways')
-            return self.fn(self.start_arg, **self.kwargs)
-        else:
-            return self.fn(*self.args, **self.kwargs)
-    def __getstate__(self):
-        rval = dict(self.__dict__)
-        if type(self.fn) is type(self.step): #instancemethod
-            fn = rval.pop('fn')
-            rval['i fn'] = fn.im_func, fn.im_self, fn.im_class
-        return rval
-    def __setstate__(self, dct):
-        if 'i fn' in dct:
-            dct['fn'] = type(self.step)(*dct.pop('i fn'))
-        self.__dict__.update(dct)
-
-def FILT(fn, **kwargs):
-    """
-    Return a CALL object that uses the return value from the previous CALL as the first and
-    only positional argument.
-    """
-    return CALL(fn, use_start_arg=True, **kwargs)
-
-def CHOOSE(which, options):
-    """
-    Execute one out of a number of optional control flow paths
-    """
-    raise NotImplementedError()
-
-def LOOP(elements):
-    #TODO: implement a true infinite loop
-    try:
-        iter(elements)
-        return REPEAT(sys.maxint, elements)
-    except TypeError:
-        return REPEAT(sys.maxint, [elements])
-
-class REPEAT(ELEMENT):
-    def __init__(self, N, elements, pass_rvals=False):
-        self.N = N
-        self.elements = elements
-        self.pass_rvals = pass_rvals
-
-    #TODO: check for N being callable
-    def start(self, arg):
-        self.n = 0   #loop iteration
-        self.idx = 0 #element idx
-        self.finished = False
-        self.elements[0].start(arg)
-    def step(self):
-        assert not self.finished
-        r = self.elements[self.idx].step()
-        if r is INCOMPLETE:
-            return INCOMPLETE
-        self.idx += 1
-        if self.idx < len(self.elements):
-            self.elements[self.idx].start(r)
-            return INCOMPLETE
-        self.n += 1
-        if self.n < self.N:
-            self.idx = 0
-            self.elements[self.idx].start(r)
-            return INCOMPLETE
-        else:
-            self.finished = True
-            return r
-
-def SEQ(elements):
-    return REPEAT(1, elements)
-
-class WEAVE(ELEMENT):
-    """
-    Interleave execution of a number of elements.
-
-    TODO: allow a schedule (at least relative frequency) of elements from each program
-    """
-    def __init__(self, n_required, elements):
-        self.elements = elements
-        if n_required == -1:
-            self.n_required = len(elements)
-        else:
-            self.n_required = n_required
-    def start(self, arg):
-        for el in self.elements:
-            el.start(arg)
-        self.elem_finished = [0] * len(self.elements)
-        self.idx = 0
-        self.finished= False 
-    def step(self):
-        assert not self.finished # if this is triggered, we have a broken driver
-
-        #start with this check in case there were no elements
-        # it's possible for the number of finished elements to exceed the threshold
-        if sum(self.elem_finished) >= self.n_required:
-            self.finished = True
-            return None
-
-        # step the active element
-        r = self.elements[self.idx].step()
-
-        if r is not INCOMPLETE:
-            self.elem_finished[self.idx] = True
-
-            # check for completion
-            if sum(self.elem_finished) >= self.n_required:
-                self.finished = True
-                return None
-
-        # advance to the next un-finished element
-        self.idx = (self.idx+1) % len(self.elements)
-        while self.elem_finished[self.idx]:
-            self.idx = (self.idx+1) % len(self.elements)
-
-        return INCOMPLETE
-
-class POPEN(ELEMENT):
-    def __init__(self, args):
-        self.args = args
-    def start(self, arg):
-        self.p = subprocess.Popen(self.args)
-    def step(self):
-        r = self.p.poll() 
-        if r is None:
-            return INCOMPLETE
-        return r
-
-def PRINT(obj):
-    return CALL(print_obj, obj)
-
-####################################################
-# [Dummy] Components involved in learning algorithms
-
-class Dataset(object):
-    def __init__(self, data):
-        self.pos = 0
-        self.data = data
-    def next(self):
-        rval = self.data[self.pos]
-        self.pos += 1
-        if self.pos == len(self.data):
-            self.pos = 0
-        return rval
-    def seek(self, pos):
-        self.pos = pos
-
-class KFold(object):
-    def __init__(self, data, K):
-        self.data = data
-        self.k = -1
-        self.scores = [None]*K
-        self.K = K
-    def next_fold(self):
-        self.k += 1
-        self.data.seek(0) # restart the stream
-    def next(self):
-        #TODO: skip the examples that are ommitted in this split
-        return self.data.next()
-    def init_test(self):
-        pass
-    def next_test(self):
-        return self.data.next()
-    def test_size(self):
-        return 5
-    def store_scores(self, scores):
-        self.scores[self.k] = scores
-
-    def prog(self, clear, train, test):
-        return REPEAT(self.K, [
-            CALL(self.next_fold),
-            clear,
-            train,
-            CALL(self.init_test),
-            BUFFER_REPEAT(self.test_size(),
-                SEQ([ CALL(self.next_test), test])),
-            FILT(self.store_scores) ])
-
-class PCA_Analysis(object):
-    def __init__(self):
-        self.clear()
-
-    def clear(self):
-        self.mean = 0
-        self.eigvecs=0
-        self.eigvals=0
-    def analyze(self, X):
-        self.mean = numpy.mean(X, axis=0)
-        self.eigvecs=1
-        self.eigvals=1
-    def filt(self, X):
-        return (X - self.mean) * self.eigvecs #TODO: divide by root eigvals?
-    def pseudo_inverse(self, Y):
-        return Y
-
-class Layer(object):
-    def __init__(self, w):
-        self.w = w
-    def filt(self, x):
-        return self.w*x
-    def clear(self):
-        self.w =0
-
-def print_obj(obj):
-    print obj
-def print_obj_attr(obj, attr):
-    print getattr(obj, attr)
-def no_op(*args, **kwargs):
-    pass
-
-def cd1_update(X, layer, lr):
-    # update self.layer from observation X
-    layer.w += X.mean() * lr #TODO: not exactly correct math!
-
-
-###############################################################
-# Example algorithms written in this control flow mini-language
-
-def main_weave():
-    # Uses weave to demonstrate the interleaving of two bufferings of a single stream
-
-    l = [0]
-    def f(a):
-        print l
-        l[0] += a
-        return l[0]
-
-    print WEAVE(1, [
-        BUFFER_REPEAT(3,CALL(f,1)),
-        BUFFER_REPEAT(5,CALL(f,1)),
-        ]).run()
-
-def main_weave_popen():
-    # Uses weave and Popen to demonstrate the control of a program with some asynchronous
-    # parallelism
-
-    p = WEAVE(2,[
-        SEQ([POPEN(['sleep', '5']), PRINT('done 1')]),
-        SEQ([POPEN(['sleep', '10']), PRINT('done 2')]),
-        LOOP([ 
-            CALL(print_obj, 'polling...'),
-            CALL(time.sleep, 1)])])
-    # The LOOP would forever if the WEAVE were not configured to stop after 2 of its elements
-    # complete.
-
-    p.run()
-    # Note that the program can be run multiple times...
-    p.run()
-
-main = main_weave_popen
-def main_kfold_dbn():
-    # Uses many of the control-flow elements to define the k-fold evaluation of a dbn
-    # The algorithm is not quite right, but the example shows off all of the required
-    # control-flow elements I think.
-
-    # create components
-    dataset = Dataset(numpy.random.RandomState(123).randn(13,1))
-    pca = PCA_Analysis()
-    layer1 = Layer(w=4)
-    layer2 = Layer(w=3)
-    kf = KFold(dataset, K=10)
-
-    pca_batchsize=1000
-    cd_batchsize = 5
-    n_cd_updates_layer1 = 10
-    n_cd_updates_layer2 = 10
-
-    # create algorithm
-
-    train_pca = SEQ([
-        BUFFER_REPEAT(pca_batchsize, CALL(kf.next)), 
-        FILT(pca.analyze)])
-
-    train_layer1 = REPEAT(n_cd_updates_layer1, [
-        BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
-        FILT(pca.filt), 
-        FILT(cd1_update, layer=layer1, lr=.01)])
-
-    train_layer2 = REPEAT(n_cd_updates_layer2, [
-        BUFFER_REPEAT(cd_batchsize, CALL(kf.next)),
-        FILT(pca.filt), 
-        FILT(layer1.filt),
-        FILT(cd1_update, layer=layer2, lr=.01)])
-
-    kfold_prog = kf.prog(
-            clear = SEQ([   # FRAGMENT 1: this bit is the reset/clear stage
-                CALL(pca.clear),
-                CALL(layer1.clear),
-                CALL(layer2.clear),
-                ]),
-            train = SEQ([
-                train_pca,
-                WEAVE(1, [    # Silly example of how to do debugging / loggin with WEAVE
-                    train_layer1, 
-                    LOOP(CALL(print_obj_attr, layer1, 'w'))]),
-                train_layer2,
-                ]),
-            test=SEQ([
-                FILT(pca.filt),       # may want to allow this SEQ to be 
-                FILT(layer1.filt),    # optimized into a shorter one that
-                FILT(layer2.filt),    # compiles these calls together with 
-                FILT(numpy.mean)]))   # Theano
-
-    pkg1 = dict(prog=kfold_prog, kf=kf)
-    pkg2 = copy.deepcopy(pkg1)       # programs can be copied
-
-    try:
-        pkg3 = cPickle.loads(cPickle.dumps(pkg1)) 
-    except:
-        print >> sys.stderr, "pickling doesnt work, but it can be fixed I think"
-
-    pkg = pkg2
-
-    # running a program updates the variables in its package, but not the other package
-    pkg['prog'].run()
-    print pkg['kf'].scores
-
-
-if __name__ == '__main__':
-    sys.exit(main())
-
+print "Moved to ./arch_src/plugin_JB.py"