--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/deep/stacked_dae/v2/nist_sda.py	Fri Mar 12 10:31:10 2010 -0500
@@ -0,0 +1,299 @@
+#!/usr/bin/python
+# coding: utf-8
+
+import ift6266
+import pylearn
+
+import numpy 
+import theano
+import time
+
+import pylearn.version
+import theano.tensor as T
+from theano.tensor.shared_randomstreams import RandomStreams
+
+import copy
+import sys
+import os
+import os.path
+
+from jobman import DD
+import jobman, jobman.sql
+from pylearn.io import filetensor
+
+from utils import produit_cartesien_jobs
+
+from sgd_optimization import SdaSgdOptimizer
+
+#from ift6266.utils.scalar_series import *
+from ift6266.utils.seriestables import *
+import tables
+
+##############################################################################
+# 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 = 100
+
+if TEST_CONFIG:
+    REDUCE_TRAIN_TO = 1000
+    MAX_FINETUNING_EPOCHS = 2
+    REDUCE_EVERY = 10
+
+# 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':2,
+                       'max_finetuning_epochs':2,
+                       'hidden_layers_sizes':800,
+                       'corruption_levels':0.2,
+                       'minibatch_size':20,
+                       'reduce_train_to':30000,
+                       'num_hidden_layers':1})
+
+'''
+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
+'''
+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
+    if state.has_key('reduce_train_to'):
+        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)
+
+    series = create_series(state.num_hidden_layers)
+
+    print "Creating optimizer with state, ", state
+
+    optimizer = SdaSgdOptimizer(dataset=dataset, hyperparameters=state, \
+                                    n_ins=n_ins, n_outs=n_outs,\
+                                    input_divider=255.0, series=series)
+
+    optimizer.pretrain()
+    channel.save()
+
+    optimizer.finetune()
+    channel.save()
+
+    return channel.COMPLETE
+
+# These Series objects are used to save various statistics
+# during the training.
+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
+    series['params'] = SharedParamsStatisticsWrapper(
+                        new_group_name="params",
+                        base_group="/",
+                        arrays_names=param_names,
+                        hdf5_file=h5f,
+                        index_names=('epoch',))
+
+    return series
+
+# Perform insertion into the Postgre DB based on combination
+# of hyperparameter values above
+# (see comment for produit_cartesien_jobs() to know how it works)
+def jobman_insert_nist():
+    jobs = produit_cartesien_jobs(JOB_VALS)
+
+    db = jobman.sql.db(JOBDB)
+    for job in jobs:
+        job.update({jobman.sql.EXPERIMENT: EXPERIMENT_PATH})
+        jobman.sql.insert_dict(job, db)
+
+    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()
+
+    elif len(args) > 0 and args[0] == 'jobman_insert':
+        jobman_insert_nist()
+
+    elif len(args) > 0 and args[0] == 'test_jobman_entrypoint':
+        chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
+        jobman_entrypoint(DEFAULT_HP_NIST, chanmock)
+
+    else:
+        print "Bad arguments"
+