--- a/deep/stacked_dae/v_sylvain/nist_sda.py	Fri Mar 19 10:54:39 2010 -0400
+++ b/deep/stacked_dae/v_sylvain/nist_sda.py	Fri Mar 19 10:56:16 2010 -0400
@@ -21,9 +21,8 @@
 import jobman, jobman.sql
 from pylearn.io import filetensor
 
-from ift6266 import datasets
-
 from utils import produit_cartesien_jobs
+from copy import copy
 
 from sgd_optimization import SdaSgdOptimizer
 
@@ -31,49 +30,8 @@
 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_sandbox_db/sylvainpl_sda_vsylvain'
-EXPERIMENT_PATH = "ift6266.deep.stacked_dae.v_sylvain.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
-    MINIBATCH_SIZE=20
-
-# 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],
-        'hidden_layers_sizes': [500],
-        'corruption_levels': [0.1],
-        'minibatch_size': [20],
-        'max_finetuning_epochs':[MAX_FINETUNING_EPOCHS],
-        'finetuning_lr':[0.1], #0.001 was very bad, so we leave it out
-        'num_hidden_layers':[1,1]}
-
-# 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':500,
-                       'corruption_levels':0.2,
-                       'minibatch_size':20,
-                       'reduce_train_to':10000,
-                       'num_hidden_layers':1})
+from ift6266 import datasets
+from config import *
 
 '''
 Function called by jobman upon launching each job
@@ -85,48 +43,82 @@
     # TODO: remove this, bad for number of simultaneous requests on DB
     channel.save()
 
-    workingdir = os.getcwd()
-
-      ###########   Il faudrait arranger ici pour train plus petit 
-
-##    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 = int(state['reduce_train_to']/state['minibatch_size'])
+        rtt = state['reduce_train_to']
     elif REDUCE_TRAIN_TO:
-        rtt = int(REDUCE_TRAIN_TO/MINIBATCH_SIZE)
-
-    if rtt:
-        print "Reducing training set to "+str(rtt*state['minibatch_size'])+ " examples"
-    else:
-        rtt=float('inf')    #No reduction
-##        nist.reduce_train_set(rtt)
-##
-##    train,valid,test = nist.get_tvt()
-##    dataset = (train,valid,test)
-
+        rtt = REDUCE_TRAIN_TO
+ 
     n_ins = 32*32
     n_outs = 62 # 10 digits, 26*2 (lower, capitals)
-    
+     
+    examples_per_epoch = NIST_ALL_TRAIN_SIZE
+
     series = create_series(state.num_hidden_layers)
 
     print "Creating optimizer with state, ", state
 
-    optimizer = SdaSgdOptimizer(dataset=datasets.nist_all, hyperparameters=state, \
+    optimizer = SdaSgdOptimizer(dataset=datasets.nist_all(), 
+                                    hyperparameters=state, \
                                     n_ins=n_ins, n_outs=n_outs,\
-                                    series=series)
+                                    examples_per_epoch=examples_per_epoch, \
+                                    series=series,
+                                    max_minibatches=rtt)
 
-    optimizer.pretrain(datasets.nist_all,rtt)
+    parameters=[]
+    optimizer.pretrain(datasets.nist_P07())
     channel.save()
+    
+    #Set some of the parameters used for the finetuning
+    if state.has_key('finetune_set'):
+        finetune_choice=state['finetune_set']
+    else:
+        finetune_choice=FINETUNE_SET
+    
+    if state.has_key('max_finetuning_epochs'):
+        max_finetune_epoch_NIST=state['max_finetuning_epochs']
+    else:
+        max_finetune_epoch_NIST=MAX_FINETUNING_EPOCHS
+    
+    if state.has_key('max_finetuning_epochs_P07'):
+        max_finetune_epoch_P07=state['max_finetuning_epochs_P07']
+    else:
+        max_finetune_epoch_P07=max_finetune_epoch_NIST
+    
+    #Decide how the finetune is done
+    
+    if finetune_choice==0:
+        print('\n\n\tfinetune avec nist\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
+    if finetune_choice==1:
+        print('\n\n\tfinetune avec P07\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
+    if finetune_choice==2:
+        print('\n\n\tfinetune avec nist suivi de P07\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
 
-    optimizer.finetune(datasets.nist_all,rtt)
+    if finetune_choice==-1:
+        print('\nSerie de 3 essais de fine-tuning')
+        print('\n\n\tfinetune avec nist\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
+        channel.save()
+        print('\n\n\tfinetune avec P07\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
+        channel.save()
+        print('\n\n\tfinetune avec nist suivi de P07\n\n')
+        optimizer.reload_parameters()
+        optimizer.finetune(datasets.nist_all(),datasets.nist_P07(),max_finetune_epoch_NIST,ind_test=1)
+        optimizer.finetune(datasets.nist_P07(),datasets.nist_all(),max_finetune_epoch_P07,ind_test=0)
+        channel.save()
+    
     channel.save()
 
     return channel.COMPLETE
@@ -207,98 +199,19 @@
 
     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':
         chanmock = DD({'COMPLETE':0,'save':(lambda:None)})
-        jobman_entrypoint(DEFAULT_HP_NIST, chanmock)
+        jobman_entrypoint(DD(DEFAULT_HP_NIST), chanmock)
 
     else:
         print "Bad arguments"