from __future__ import with_statement import mdp import sys as _sys import os as _os import inspect as _inspect import warnings as _warnings import traceback as _traceback import cPickle as _cPickle import tempfile as _tempfile import copy as _copy from mdp import numx class CrashRecoveryException(mdp.MDPException): """Class to handle crash recovery """ def __init__(self, *args): """Allow crash recovery. Arguments: (error_string, crashing_obj, parent_exception) The crashing object is kept in self.crashing_obj The triggering parent exception is kept in self.parent_exception. """ errstr = args[0] self.crashing_obj = args[1] self.parent_exception = args[2] # ?? python 2.5: super(CrashRecoveryException, self).__init__(errstr) mdp.MDPException.__init__(self, errstr) def dump(self, filename=None): """ Save a pickle dump of the crashing object on filename. If filename is None, the crash dump is saved on a file created by the tempfile module. Return the filename. """ if filename is None: # This 'temporary file' should actually stay 'forever', i.e. until # deleted by the user. (fd, filename)=_tempfile.mkstemp(suffix=".pic", prefix="MDPcrash_") fl = _os.fdopen(fd, 'w+b', -1) else: fl = open(filename, 'w+b', -1) _cPickle.dump(self.crashing_obj, fl) fl.close() return filename class FlowException(mdp.MDPException): """Base class for exceptions in Flow subclasses.""" pass class FlowExceptionCR(CrashRecoveryException, FlowException): """Class to handle flow-crash recovery """ def __init__(self, *args): """Allow crash recovery. Arguments: (error_string, flow_instance, parent_exception) The triggering parent exception is kept in self.parent_exception. If flow_instance._crash_recovery is set, save a crash dump of flow_instance on the file self.filename""" CrashRecoveryException.__init__(self, *args) rec = self.crashing_obj._crash_recovery errstr = args[0] if rec: if isinstance(rec, str): name = rec else: name = None name = CrashRecoveryException.dump(self, name) dumpinfo = '\nA crash dump is available on: "%s"' % name self.filename = name errstr = errstr+dumpinfo Exception.__init__(self, errstr) class Flow(object): """A 'Flow' is a sequence of nodes that are trained and executed together to form a more complex algorithm. Input data is sent to the first node and is successively processed by the subsequent nodes along the sequence. Using a flow as opposed to handling manually a set of nodes has a clear advantage: The general flow implementation automatizes the training (including supervised training and multiple training phases), execution, and inverse execution (if defined) of the whole sequence. Crash recovery is optionally available: in case of failure the current state of the flow is saved for later inspection. A subclass of the basic flow class ('CheckpointFlow') allows user-supplied checkpoint functions to be executed at the end of each phase, for example to save the internal structures of a node for later analysis. Flow objects are Python containers. Most of the builtin 'list' methods are available. A 'Flow' can be saved or copied using the corresponding 'save' and 'copy' methods. """ def __init__(self, flow, crash_recovery=False, verbose=False): """ Keyword arguments: flow -- a list of Nodes crash_recovery -- set (or not) Crash Recovery Mode (save node in case a failure) verbose -- if True, print some basic progress information """ self._check_nodes_consistency(flow) self.flow = flow self.verbose = verbose self.set_crash_recovery(crash_recovery) def _propagate_exception(self, except_, nodenr): # capture exception. the traceback of the error is printed and a # new exception, containing the identity of the node in the flow # is raised. Allow crash recovery. (etype, val, tb) = _sys.exc_info() prev = ''.join(_traceback.format_exception(except_.__class__, except_,tb)) act = "\n! Exception in node #%d (%s):\n" % (nodenr, str(self.flow[nodenr])) errstr = ''.join(('\n', 40*'-', act, 'Node Traceback:\n', prev, 40*'-')) raise FlowExceptionCR(errstr, self, except_) def _train_node(self, data_iterable, nodenr): """Train a single node in the flow. nodenr -- index of the node in the flow """ node = self.flow[nodenr] if (data_iterable is not None) and (not node.is_trainable()): # attempted to train a node although it is not trainable. # raise a warning and continue with the next node. # wrnstr = "\n! Node %d is not trainable" % nodenr + \ # "\nYou probably need a 'None' iterable for"+\ # " this node. Continuing anyway." #_warnings.warn(wrnstr, mdp.MDPWarning) return elif (data_iterable is None) and node.is_training(): # None instead of iterable is passed to a training node err_str = ("\n! Node %d is training" " but instead of iterable received 'None'." % nodenr) raise FlowException(err_str) elif (data_iterable is None) and (not node.is_trainable()): # skip training if node is not trainable return try: train_arg_keys = self._get_required_train_args(node) train_args_needed = bool(len(train_arg_keys)) ## We leave the last training phase open for the ## CheckpointFlow class. ## Checkpoint functions must close it explicitly if needed! ## Note that the last training_phase is closed ## automatically when the node is executed. while True: empty_iterator = True for x in data_iterable: empty_iterator = False # the arguments following the first are passed only to the # currently trained node, allowing the implementation of # supervised nodes if (type(x) is tuple) or (type(x) is list): arg = x[1:] x = x[0] else: arg = () # check if the required number of arguments was given if train_args_needed: if len(train_arg_keys) != len(arg): err = ("Wrong number of arguments provided by " + "the iterable for node #%d " % nodenr + "(%d needed, %d given).\n" % (len(train_arg_keys), len(arg)) + "List of required argument keys: " + str(train_arg_keys)) raise FlowException(err) # filter x through the previous nodes if nodenr > 0: x = self._execute_seq(x, nodenr-1) # train current node node.train(x, *arg) if empty_iterator: if node.get_current_train_phase() == 1: err_str = ("The training data iteration for node " "no. %d could not be repeated for the " "second training phase, you probably " "provided an iterator instead of an " "iterable." % (nodenr+1)) raise FlowException(err_str) else: err_str = ("The training data iterator for node " "no. %d is empty." % (nodenr+1)) raise FlowException(err_str) self._stop_training_hook() if node.get_remaining_train_phase() > 1: # close the previous training phase node.stop_training() else: break except mdp.TrainingFinishedException, e: # attempted to train a node although its training phase is already # finished. raise a warning and continue with the next node. wrnstr = ("\n! Node %d training phase already finished" " Continuing anyway." % nodenr) _warnings.warn(wrnstr, mdp.MDPWarning) except FlowExceptionCR, e: # this exception was already propagated, # probably during the execution of a node upstream in the flow (exc_type, val) = _sys.exc_info()[:2] prev = ''.join(_traceback.format_exception_only(e.__class__, e)) prev = prev[prev.find('\n')+1:] act = "\nWhile training node #%d (%s):\n" % (nodenr, str(self.flow[nodenr])) err_str = ''.join(('\n', 40*'=', act, prev, 40*'=')) raise FlowException(err_str) except Exception, e: # capture any other exception occured during training. self._propagate_exception(e, nodenr) def _stop_training_hook(self): """Hook method that is called before stop_training is called.""" pass @staticmethod def _get_required_train_args(node): """Return arguments in addition to self and x for node.train. Argumentes that have a default value are ignored. """ train_arg_spec = _inspect.getargspec(node._train) train_arg_keys = train_arg_spec[0][2:] # ignore self, x if train_arg_spec[3]: # subtract arguments with a default value train_arg_keys = train_arg_keys[:-len(train_arg_spec[3])] return train_arg_keys def _train_check_iterables(self, data_iterables): """Return the data iterables after some checks and sanitizing. Note that this method does not distinguish between iterables and iterators, so this must be taken care of later. """ # verifies that the number of iterables matches that of # the signal nodes and multiplies them if needed. flow = self.flow # if a single array is given wrap it in a list of lists, # note that a list of 2d arrays is not valid if isinstance(data_iterables, numx.ndarray): data_iterables = [[data_iterables]] * len(flow) if not isinstance(data_iterables, list): err_str = ("'data_iterables' must be either a list of " "iterables or an array, and not %s" % type(data_iterables)) raise FlowException(err_str) # check that all elements are iterable for i, iterable in enumerate(data_iterables): if (iterable is not None) and (not hasattr(iterable, '__iter__')): err = ("Element number %d in the data_iterables" " list is not an iterable." % i) raise FlowException(err) # check that the number of data_iterables is correct if len(data_iterables) != len(flow): err_str = ("%d data iterables specified," " %d needed" % (len(data_iterables), len(flow))) raise FlowException(err_str) return data_iterables def _close_last_node(self): if self.verbose: print "Close the training phase of the last node" try: self.flow[-1].stop_training() except mdp.TrainingFinishedException: pass except Exception, e: self._propagate_exception(e, len(self.flow)-1) def set_crash_recovery(self, state = True): """Set crash recovery capabilities. When a node raises an Exception during training, execution, or inverse execution that the flow is unable to handle, a FlowExceptionCR is raised. If crash recovery is set, a crash dump of the flow instance is saved for later inspection. The original exception can be found as the 'parent_exception' attribute of the FlowExceptionCR instance. - If 'state' = False, disable crash recovery. - If 'state' is a string, the crash dump is saved on a file with that name. - If 'state' = True, the crash dump is saved on a file created by the tempfile module. """ self._crash_recovery = state def train(self, data_iterables): """Train all trainable nodes in the flow. 'data_iterables' is a list of iterables, one for each node in the flow. The iterators returned by the iterables must return data arrays that are then used for the node training (so the data arrays are the 'x' for the nodes). Note that the data arrays are processed by the nodes which are in front of the node that gets trained, so the data dimension must match the input dimension of the first node. If a node has only a single training phase then instead of an iterable you can alternatively provide an iterator (including generator-type iterators). For nodes with multiple training phases this is not possible, since the iterator cannot be restarted after the first iteration. For more information on iterators and iterables see http://docs.python.org/library/stdtypes.html#iterator-types . In the special case that 'data_iterables' is one single array, it is used as the data array 'x' for all nodes and training phases. Instead of a data array 'x' the iterators can also return a list or tuple, where the first entry is 'x' and the following are args for the training of the node (e.g. for supervised training). """ data_iterables = self._train_check_iterables(data_iterables) # train each Node successively for i in range(len(self.flow)): if self.verbose: print "Training node #%d (%s)" % (i, str(self.flow[i])) self._train_node(data_iterables[i], i) if self.verbose: print "Training finished" self._close_last_node() def _execute_seq(self, x, nodenr = None): # Filters input data 'x' through the nodes 0..'node_nr' included flow = self.flow if nodenr is None: nodenr = len(flow)-1 for i in range(nodenr+1): try: x = flow[i].execute(x) except Exception, e: self._propagate_exception(e, i) return x def execute(self, iterable, nodenr = None): """Process the data through all nodes in the flow. 'iterable' is an iterable or iterator (note that a list is also an iterable), which returns data arrays that are used as input to the flow. Alternatively, one can specify one data array as input. If 'nodenr' is specified, the flow is executed only up to node nr. 'nodenr'. This is equivalent to 'flow[:nodenr+1](iterable)'. """ if isinstance(iterable, numx.ndarray): return self._execute_seq(iterable, nodenr) res = [] empty_iterator = True for x in iterable: empty_iterator = False res.append(self._execute_seq(x, nodenr)) if empty_iterator: errstr = ("The execute data iterator is empty.") raise FlowException(errstr) return numx.concatenate(res) def _inverse_seq(self, x): #Successively invert input data 'x' through all nodes backwards flow = self.flow for i in range(len(flow)-1, -1, -1): try: x = flow[i].inverse(x) except Exception, e: self._propagate_exception(e, i) return x def inverse(self, iterable): """Process the data through all nodes in the flow backwards (starting from the last node up to the first node) by calling the inverse function of each node. Of course, all nodes in the flow must be invertible. 'iterable' is an iterable or iterator (note that a list is also an iterable), which returns data arrays that are used as input to the flow. Alternatively, one can specify one data array as input. Note that this is _not_ equivalent to 'flow[::-1](iterable)', which also executes the flow backwards but calls the 'execute' function of each node.""" if isinstance(iterable, numx.ndarray): return self._inverse_seq(iterable) res = [] empty_iterator = True for x in iterable: empty_iterator = False res.append(self._inverse_seq(x)) if empty_iterator: errstr = ("The inverse data iterator is empty.") raise FlowException(errstr) return numx.concatenate(res) def copy(self, protocol=None): """Return a deep copy of the flow. The protocol parameter should not be used. """ if protocol is not None: _warnings.warn("protocol parameter to copy() is ignored", mdp.MDPDeprecationWarning, stacklevel=2) return _copy.deepcopy(self) def save(self, filename, protocol=-1): """Save a pickled serialization of the flow to 'filename'. If 'filename' is None, return a string. Note: the pickled Flow is not guaranteed to be upward or backward compatible.""" if filename is None: return _cPickle.dumps(self, protocol) else: # if protocol != 0 open the file in binary mode mode = 'w' if protocol == 0 else 'wb' with open(filename, mode) as flh: _cPickle.dump(self, flh, protocol) def __call__(self, iterable, nodenr = None): """Calling an instance is equivalent to call its 'execute' method.""" return self.execute(iterable, nodenr=nodenr) ###### string representation def __str__(self): nodes = ', '.join([str(x) for x in self.flow]) return '['+nodes+']' def __repr__(self): # this should look like a valid Python expression that # could be used to recreate an object with the same value # eval(repr(object)) == object name = type(self).__name__ pad = len(name)+2 sep = ',\n'+' '*pad nodes = sep.join([repr(x) for x in self.flow]) return '%s([%s])' % (name, nodes) ###### private container methods def __len__(self): return len(self.flow) def _check_dimension_consistency(self, out, inp): """Raise ValueError when both dimensions are set and different.""" if ((out and inp) is not None) and out != inp: errstr = "dimensions mismatch: %d != %d" % (out, inp) raise ValueError(errstr) def _check_nodes_consistency(self, flow = None): """Check the dimension consistency of a list of nodes.""" if flow is None: flow = self.flow len_flow = len(flow) for i in range(1, len_flow): out = flow[i-1].output_dim inp = flow[i].input_dim self._check_dimension_consistency(out, inp) def _check_value_type_isnode(self, value): if not isinstance(value, mdp.Node): raise TypeError("flow item must be Node instance") def __getitem__(self, key): if isinstance(key, slice): flow_slice = self.flow[key] self._check_nodes_consistency(flow_slice) return self.__class__(flow_slice) else: return self.flow[key] def __setitem__(self, key, value): if isinstance(key, slice): [self._check_value_type_isnode(item) for item in value] else: self._check_value_type_isnode(value) # make a copy of list flow_copy = list(self.flow) flow_copy[key] = value # check dimension consistency self._check_nodes_consistency(flow_copy) # if no exception was raised, accept the new sequence self.flow = flow_copy def __delitem__(self, key): # make a copy of list flow_copy = list(self.flow) del flow_copy[key] # check dimension consistency self._check_nodes_consistency(flow_copy) # if no exception was raised, accept the new sequence self.flow = flow_copy def __contains__(self, item): return self.flow.__contains__(item) def __iter__(self): return self.flow.__iter__() def __add__(self, other): # append other to self if isinstance(other, Flow): flow_copy = list(self.flow).__add__(other.flow) # check dimension consistency self._check_nodes_consistency(flow_copy) # if no exception was raised, accept the new sequence return self.__class__(flow_copy) elif isinstance(other, mdp.Node): flow_copy = list(self.flow) flow_copy.append(other) # check dimension consistency self._check_nodes_consistency(flow_copy) # if no exception was raised, accept the new sequence return self.__class__(flow_copy) else: err_str = ('can only concatenate flow or node' ' (not \'%s\') to flow' % (type(other).__name__)) raise TypeError(err_str) def __iadd__(self, other): # append other to self if isinstance(other, Flow): self.flow += other.flow elif isinstance(other, mdp.Node): self.flow.append(other) else: err_str = ('can only concatenate flow or node' ' (not \'%s\') to flow' % (type(other).__name__)) raise TypeError(err_str) self._check_nodes_consistency(self.flow) return self ###### public container methods def append(self, x): """flow.append(node) -- append node to flow end""" self[len(self):len(self)] = [x] def extend(self, x): """flow.extend(iterable) -- extend flow by appending elements from the iterable""" if not isinstance(x, Flow): err_str = ('can only concatenate flow' ' (not \'%s\') to flow' % (type(x).__name__)) raise TypeError(err_str) self[len(self):len(self)] = x def insert(self, i, x): """flow.insert(index, node) -- insert node before index""" self[i:i] = [x] def pop(self, i = -1): """flow.pop([index]) -> node -- remove and return node at index (default last)""" x = self[i] del self[i] return x class CheckpointFlow(Flow): """Subclass of Flow class that allows user-supplied checkpoint functions to be executed at the end of each phase, for example to save the internal structures of a node for later analysis.""" def _train_check_checkpoints(self, checkpoints): if not isinstance(checkpoints, list): checkpoints = [checkpoints]*len(self.flow) if len(checkpoints) != len(self.flow): error_str = ("%d checkpoints specified," " %d needed" % (len(checkpoints), len(self.flow))) raise FlowException(error_str) return checkpoints def train(self, data_iterables, checkpoints): """Train all trainable nodes in the flow. In addition to the basic behavior (see 'Node.train'), calls the checkpoint function 'checkpoint[i]' when the training phase of node #i is over. A checkpoint function takes as its only argument the trained node. If the checkpoint function returns a dictionary, its content is added to the instance dictionary. The class CheckpointFunction can be used to define user-supplied checkpoint functions. """ data_iterables = self._train_check_iterables(data_iterables) checkpoints = self._train_check_checkpoints(checkpoints) # train each Node successively for i in range(len(self.flow)): node = self.flow[i] if self.verbose: print "Training node #%d (%s)" % (i, type(node).__name__) self._train_node(data_iterables[i], i) if (i <= len(checkpoints)) and (checkpoints[i] is not None): dic = checkpoints[i](node) if dic: self.__dict__.update(dic) if self.verbose: print "Training finished" self._close_last_node() class CheckpointFunction(object): """Base class for checkpoint functions. This class can be subclassed to build objects to be used as a checkpoint function in a CheckpointFlow. Such objects would allow to define parameters for the function and save informations for later use.""" def __call__(self, node): """Execute the checkpoint function. This is the method that is going to be called at the checkpoint. Overwrite it to match your needs.""" pass class CheckpointSaveFunction(CheckpointFunction): """This checkpoint function saves the node in pickle format. The pickle dump can be done either before the training phase is finished or right after that. In this way, it is for example possible to reload it in successive sessions and continue the training. """ def __init__(self, filename, stop_training=0, binary=1, protocol=2): """CheckpointSaveFunction constructor. 'filename' -- the name of the pickle dump file. 'stop_training' -- if set to 0 the pickle dump is done before closing the training phase if set to 1 the training phase is closed and then the node is dumped 'binary' -- sets binary mode for opening the file. When using a protocol higher than 0, make sure the file is opened in binary mode. 'protocol' -- is the 'protocol' argument for the pickle dump (see Pickle documentation for details) """ self.filename = filename self.proto = protocol self.stop_training = stop_training if binary or protocol > 0: self.mode = 'wb' else: self.mode = 'w' def __call__(self, node): with open(self.filename, self.mode) as fid: if self.stop_training: node.stop_training() _cPickle.dump(node, fid, self.proto)