--- a/deep/stacked_dae/nist_sda.py	Tue Mar 16 12:01:31 2010 -0400
+++ b/deep/stacked_dae/nist_sda.py	Fri Mar 19 10:54:39 2010 -0400
@@ -25,69 +25,23 @@
 
 from sgd_optimization import SdaSgdOptimizer
 
-from ift6266.utils.scalar_series import *
-
-##############################################################################
-# GLOBALS
-
-TEST_CONFIG = False
-
-NIST_ALL_LOCATION = '/data/lisa/data/nist/by_class/all'
-JOBDB = 'postgres://ift6266h10@gershwin/ift6266h10_db/fsavard_sda4'
-EXPERIMENT_PATH = "ift6266.deep.stacked_dae.nist_sda.jobman_entrypoint"
-
-REDUCE_TRAIN_TO = None
-MAX_FINETUNING_EPOCHS = 1000
-# number of minibatches before taking means for valid error etc.
-REDUCE_EVERY = 1000
-
-if TEST_CONFIG:
-    REDUCE_TRAIN_TO = 1000
-    MAX_FINETUNING_EPOCHS = 2
-    REDUCE_EVERY = 10
+#from ift6266.utils.scalar_series import *
+from ift6266.utils.seriestables import *
+import tables
 
-# Possible values the hyperparameters can take. These are then
-# combined with produit_cartesien_jobs so we get a list of all
-# possible combinations, each one resulting in a job inserted
-# in the jobman DB.
-JOB_VALS = {'pretraining_lr': [0.1, 0.01],#, 0.001],#, 0.0001],
-        'pretraining_epochs_per_layer': [10,20],
-        'hidden_layers_sizes': [300,800],
-        'corruption_levels': [0.1,0.2,0.3],
-        'minibatch_size': [20],
-        'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
-        'finetuning_lr':[0.1, 0.01], #0.001 was very bad, so we leave it out
-        'num_hidden_layers':[2,3]}
-
-# Just useful for tests... minimal number of epochs
-DEFAULT_HP_NIST = DD({'finetuning_lr':0.1,
-                       'pretraining_lr':0.1,
-                       'pretraining_epochs_per_layer':20,
-                       'max_finetuning_epochs':2,
-                       'hidden_layers_sizes':800,
-                       'corruption_levels':0.2,
-                       'minibatch_size':20,
-                       #'reduce_train_to':300,
-                       'num_hidden_layers':2})
+from ift6266 import datasets
+from config import *
 
 '''
 Function called by jobman upon launching each job
-Its path is the one given when inserting jobs:
-ift6266.deep.stacked_dae.nist_sda.jobman_entrypoint
+Its path is the one given when inserting jobs: see EXPERIMENT_PATH
 '''
 def jobman_entrypoint(state, channel):
     # record mercurial versions of each package
     pylearn.version.record_versions(state,[theano,ift6266,pylearn])
+    # TODO: remove this, bad for number of simultaneous requests on DB
     channel.save()
 
-    workingdir = os.getcwd()
-
-    print "Will load NIST"
-
-    nist = NIST(minibatch_size=20)
-
-    print "NIST loaded"
-
     # For test runs, we don't want to use the whole dataset so
     # reduce it to fewer elements if asked to.
     rtt = None
@@ -95,59 +49,93 @@
         rtt = state['reduce_train_to']
     elif REDUCE_TRAIN_TO:
         rtt = REDUCE_TRAIN_TO
-
-    if rtt:
-        print "Reducing training set to "+str(rtt)+ " examples"
-        nist.reduce_train_set(rtt)
-
-    train,valid,test = nist.get_tvt()
-    dataset = (train,valid,test)
-
+ 
     n_ins = 32*32
     n_outs = 62 # 10 digits, 26*2 (lower, capitals)
+     
+    examples_per_epoch = NIST_ALL_TRAIN_SIZE
 
-    # b,b',W for each hidden layer 
-    # + b,W of last layer (logreg)
-    numparams = state.num_hidden_layers * 3 + 2
-    series_mux = None
-    series_mux = create_series(workingdir, numparams)
+    series = create_series(state.num_hidden_layers)
 
     print "Creating optimizer with state, ", state
 
-    optimizer = SdaSgdOptimizer(dataset=dataset, hyperparameters=state, \
+    optimizer = SdaSgdOptimizer(dataset=datasets.nist_all, 
+                                    hyperparameters=state, \
                                     n_ins=n_ins, n_outs=n_outs,\
-                                    input_divider=255.0, series_mux=series_mux)
+                                    examples_per_epoch=examples_per_epoch, \
+                                    series=series,
+                                    max_minibatches=rtt)
 
-    optimizer.pretrain()
+    optimizer.pretrain(datasets.nist_all)
     channel.save()
 
-    optimizer.finetune()
+    optimizer.finetune(datasets.nist_all)
     channel.save()
 
     return channel.COMPLETE
 
 # These Series objects are used to save various statistics
 # during the training.
-def create_series(basedir, numparams):
-    mux = SeriesMultiplexer()
+def create_series(num_hidden_layers):
+
+    # Replace series we don't want to save with DummySeries, e.g.
+    # series['training_error'] = DummySeries()
+
+    series = {}
+
+    basedir = os.getcwd()
+
+    h5f = tables.openFile(os.path.join(basedir, "series.h5"), "w")
+
+    # reconstruction
+    reconstruction_base = \
+                ErrorSeries(error_name="reconstruction_error",
+                    table_name="reconstruction_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'),
+                    title="Reconstruction error (mean over "+str(REDUCE_EVERY)+" minibatches)")
+    series['reconstruction_error'] = \
+                AccumulatorSeriesWrapper(base_series=reconstruction_base,
+                    reduce_every=REDUCE_EVERY)
+
+    # train
+    training_base = \
+                ErrorSeries(error_name="training_error",
+                    table_name="training_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'),
+                    title="Training error (mean over "+str(REDUCE_EVERY)+" minibatches)")
+    series['training_error'] = \
+                AccumulatorSeriesWrapper(base_series=training_base,
+                    reduce_every=REDUCE_EVERY)
+
+    # valid and test are not accumulated/mean, saved directly
+    series['validation_error'] = \
+                ErrorSeries(error_name="validation_error",
+                    table_name="validation_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'))
+
+    series['test_error'] = \
+                ErrorSeries(error_name="test_error",
+                    table_name="test_error",
+                    hdf5_file=h5f,
+                    index_names=('epoch','minibatch'))
+
+    param_names = []
+    for i in range(num_hidden_layers):
+        param_names += ['layer%d_W'%i, 'layer%d_b'%i, 'layer%d_bprime'%i]
+    param_names += ['logreg_layer_W', 'logreg_layer_b']
 
     # comment out series we don't want to save
-    mux.add_series(AccumulatorSeries(name="reconstruction_error",
-                    reduce_every=REDUCE_EVERY, # every 1000 batches, we take the mean and save
-                    mean=True,
-                    directory=basedir, flush_every=1))
+    series['params'] = SharedParamsStatisticsWrapper(
+                        new_group_name="params",
+                        base_group="/",
+                        arrays_names=param_names,
+                        hdf5_file=h5f,
+                        index_names=('epoch',))
 
-    mux.add_series(AccumulatorSeries(name="training_error",
-                    reduce_every=REDUCE_EVERY, # every 1000 batches, we take the mean and save
-                    mean=True,
-                    directory=basedir, flush_every=1))
-
-    mux.add_series(BaseSeries(name="validation_error", directory=basedir, flush_every=1))
-    mux.add_series(BaseSeries(name="test_error", directory=basedir, flush_every=1))
-
-    mux.add_series(ParamsArrayStats(numparams,name="params",directory=basedir))
-
-    return mux
+    return series
 
 # Perform insertion into the Postgre DB based on combination
 # of hyperparameter values above
@@ -162,93 +150,14 @@
 
     print "inserted"
 
-class NIST:
-    def __init__(self, minibatch_size, basepath=None, reduce_train_to=None):
-        global NIST_ALL_LOCATION
-
-        self.minibatch_size = minibatch_size
-        self.basepath = basepath and basepath or NIST_ALL_LOCATION
-
-        self.set_filenames()
-
-        # arrays of 2 elements: .x, .y
-        self.train = [None, None]
-        self.test = [None, None]
-
-        self.load_train_test()
-
-        self.valid = [[], []]
-        self.split_train_valid()
-        if reduce_train_to:
-            self.reduce_train_set(reduce_train_to)
-
-    def get_tvt(self):
-        return self.train, self.valid, self.test
-
-    def set_filenames(self):
-        self.train_files = ['all_train_data.ft',
-                                'all_train_labels.ft']
-
-        self.test_files = ['all_test_data.ft',
-                            'all_test_labels.ft']
-
-    def load_train_test(self):
-        self.load_data_labels(self.train_files, self.train)
-        self.load_data_labels(self.test_files, self.test)
-
-    def load_data_labels(self, filenames, pair):
-        for i, fn in enumerate(filenames):
-            f = open(os.path.join(self.basepath, fn))
-            pair[i] = filetensor.read(f)
-            f.close()
-
-    def reduce_train_set(self, max):
-        self.train[0] = self.train[0][:max]
-        self.train[1] = self.train[1][:max]
-
-        if max < len(self.test[0]):
-            for ar in (self.test, self.valid):
-                ar[0] = ar[0][:max]
-                ar[1] = ar[1][:max]
-
-    def split_train_valid(self):
-        test_len = len(self.test[0])
-        
-        new_train_x = self.train[0][:-test_len]
-        new_train_y = self.train[1][:-test_len]
-
-        self.valid[0] = self.train[0][-test_len:]
-        self.valid[1] = self.train[1][-test_len:]
-
-        self.train[0] = new_train_x
-        self.train[1] = new_train_y
-
-def test_load_nist():
-    print "Will load NIST"
-
-    import time
-    t1 = time.time()
-    nist = NIST(20)
-    t2 = time.time()
-
-    print "NIST loaded. time delta = ", t2-t1
-
-    tr,v,te = nist.get_tvt()
-
-    print "Lenghts: ", len(tr[0]), len(v[0]), len(te[0])
-
-    raw_input("Press any key")
-
 if __name__ == '__main__':
 
-    import sys
-
     args = sys.argv[1:]
 
-    if len(args) > 0 and args[0] == 'load_nist':
-        test_load_nist()
+    #if len(args) > 0 and args[0] == 'load_nist':
+    #    test_load_nist()
 
-    elif len(args) > 0 and args[0] == 'jobman_insert':
+    if len(args) > 0 and args[0] == 'jobman_insert':
         jobman_insert_nist()
 
     elif len(args) > 0 and args[0] == 'test_jobman_entrypoint':