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How to use iModel method in fMBT
Best Python code snippet using fMBT_python
mtransaction.py
Source:mtransaction.py 
1from __future__ import absolute_import2############################################################################3# Copyright (C) 2005 by Reithinger GmbH4# mreithinger@web.de5#6# This file is part of metapie.7# 8# metapie is free software; you can redistribute it and/or modify9# it under the terms of the GNU General Public License as published by10# the Free Software Foundation; either version 2 of the License, or11# (at your option) any later version.12#13# pyplan is distributed in the hope that it will be useful,14# but WITHOUT ANY WARRANTY; without even the implied warranty of15# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the16# GNU General Public License for more details.17#18# You should have received a copy of the GNU General Public License19# along with this program; if not, write to the20# Free Software Foundation, Inc.,21# 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.22############################################################################23from builtins import filter24from builtins import str25from builtins import map26from builtins import object27from . import events28import weakref29import metapie30class ConstraintError(ValueError):31 def __init__(self, **kwargs):32 ValueError.__init__(self, "Model constraints are not satisfied")33 self.message = kwargs34class _SequenceProxy(object):35 def __init__(self, proxy, sequence):36 self.proxy = proxy37 self.sequence = sequence38 for im in proxy._removed_imodels.values():39 try:40 del sequence[im]41 except KeyError:42 pass43 44 def __len__(self):45 return len(self.sequence) + len(self.proxy._new_imodels)46 def __delitem__(self, index):47 imodel = self[index]48 del self.proxy[imodel]49 try:50 del self.sequence[imodel]51 except KeyError:52 pass53 def is_dead(self):54 return self.proxy._dead55 def insert(self, imodel):56 self.proxy.insert(imodel)57 def __getitem__(self, index):58 if index >= len(self.sequence):59 return self.proxy._new_imodels[index - len(self.sequence)]60 imodel = self.sequence[index]61 self.proxy.mark(imodel)62 return imodel63class Transaction(events.Subject):64 active_transactions = { }65 class ContainerProxy(object):66 def __init__(self, src):67 self._src = src68 self._dead = False69 self._removed_imodels = {}70 self._new_imodels = []71 self._transaction = Transaction.get_transaction(src._imodel)72 try:73 keys = src._keys74 except AttributeError:75 return76 for k in list(keys.keys()):77 setattr(self, k, getattr(src, k))78 def mark(self, imodel):79 Transaction.mark_imodel(imodel, self._transaction)80 def commit(self):81 self._dead = True82 if not self._removed_imodels and not self._new_imodels: return83 for v in self._removed_imodels.values():84 self._src.delete(v, False)85 for v in self._new_imodels:86 self._src.insert(v, False)87 def rollback(self):88 self._dead = True89 def insert(self, imodel):90 self._src.check_peer(imodel)91 92 if imodel.id() in self._removed_imodels:93 del self._removed_imodels[imodel.id()]94 self._src._add_to_peer(imodel, self._src._imodel)95 self._src.fire()96 elif (imodel not in self._src \97 and id(imodel) not in list(map(id, self._new_imodels))):98 99 self.mark(imodel)100 self._new_imodels.append(imodel)101 self._src._add_to_peer(imodel, self._src._imodel)102 self._src.fire()103 def delete(self, imodel):104 def exclude_model():105 try:106 proxy = self._transaction.active_imodels[imodel]107 except KeyError:108 pass109 else:110 proxy.rollback(imodel)111 del self._transaction.active_imodels[imodel]112 if imodel.id() in self._removed_imodels:113 raise KeyError("model does not exist in container", imodel)114 if imodel in self._src:115 exclude_model()116 self._removed_imodels[imodel.id()] = imodel117 self._src._remove_from_peer(imodel, self._src._imodel)118 self._src.fire()119 else:120 try:121 index = list(map(id, self._new_imodels)).index(id(imodel))122 except ValueError:123 raise KeyError("model does not exist in container", imodel)124 else:125 exclude_model()126 del self._new_imodels[index]127 self._src.fire()128 def recatalog(self, obj):129 return self._src.recatalog(obj)130 def __delitem__(self, imodel):131 self.delete(imodel)132 133 def __len__(self):134 return len(self._src) \135 - len(self._removed_imodels) \136 + len(self._new_imodels)137 138 def __iter__(self):139 return iter(_SequenceProxy(self, self._src.sequence()))140 def subset(self, idset):141 return _SequenceProxy(self, self._src.subset(idset))142 def sequence(self):143 return _SequenceProxy(self, self._src.sequence())144 145 def __contains__(self, imodel):146 id_ = imodel.id()147 if id_ in self._removed_imodels: return False148 return imodel in self._src or imodel in self._new_imodels149 def __getitem__(self, key):150 if key in self._removed_imodels:151 raise KeyError("key does not exist", key)152 try:153 return self._src[key]154 except KeyError:155 for m in self._new_imodels:156 if m.id() == key: return m157 raise KeyError158 class ModelProxy(object):159 def __init__(self, transaction):160 self._transaction = weakref.ref(transaction)161 self.error = None162 def remove_error(self, attrib):163 try:164 del self.error.message[attrib]165 if not self.error.message: self.error = None166 except AttributeError:167 pass168 except KeyError:169 pass170 171 def rollback(self, imodel):172 for t in imodel.__attributes_map__.values():173 if hasattr(self, t.private_name):174 t._rollback_value(imodel, self)175 delattr(self, t.private_name)176 imodel.fire(t.name, t.name)177 self.error = None178 def commit(self, imodel):179 self.error = None180 181 for t in imodel.__attributes_map__.values():182 try:183 pname = t.private_name184 except AttributeError:185 #readonly attributes don't have a private_name186 return187 188 if hasattr(self, pname):189 t._commit_value(imodel, self)190 delattr(self, pname)191 imodel.reindex()192 193 def remove_proxy(cls, imodel):194 try:195 transaction = cls.get_transaction(imodel)196 except RuntimeError:197 return198 del transaction.active_transactions[imodel]199 try:200 del transaction.active_imodels[imodel]201 except KeyError: pass202 remove_proxy = classmethod(remove_proxy)203 def make_proxy(cls, imodel):204 #proxy or transaction205 206 p_or_t = cls.active_transactions.get(imodel)207 try:208 if not p_or_t: return imodel209 except ReferenceError:210 del cls.active_transactions[imodel]211 return imodel212 213 if isinstance(p_or_t, cls.ModelProxy):214 #it is a proxy215 return p_or_t216 #it is a transaction (weakproxy)217 return p_or_t.include(imodel)218 219 220 make_proxy = classmethod(make_proxy)221 def get_proxy(cls, imodel):222 try:223 p_or_t = cls.active_transactions.get(imodel)224 p_or_t.__class__225 226 if isinstance(p_or_t, cls.ModelProxy):227 return p_or_t228 except ReferenceError:229 del cls.active_transactions[imodel]230 return imodel231 232 get_proxy = classmethod(get_proxy)233 def get_transaction(cls, imodel):234 try:235 p_or_t = cls.active_transactions.get(imodel)236 return weakref.proxy(p_or_t._transaction())237 except ReferenceError:238 raise RuntimeError("no active transaction")239 except AttributeError:240 raise RuntimeError("no active transaction")241 242 243 get_transaction = classmethod(get_transaction)244 def mark_imodel(cls, imodel, transaction):245 assert(type(transaction) is weakref.ProxyType)246 try:247 proxy = cls.active_transactions.setdefault(imodel, transaction)248 proxy.__class__ #causes a ReferenceError if it is a broken reference249 except ReferenceError:250 cls.active_transactions[imodel] = transaction251 mark_imodel = classmethod(mark_imodel)252 253 def __init__(self):254 self.active_imodels = { }255 def __del__(self):256 self.destroy()257 258 def include(self, imodel):259 proxy = self.active_transactions.get(imodel, self)260 if proxy._transaction() is not self:261 raise RuntimeError("the object '%s' belongs "\262 "already to a transaction" % str(imodel))263 if isinstance(proxy, self.ModelProxy):264 #allready included265 return266 267 proxy = self.ModelProxy(self)268 self.active_transactions[imodel] = proxy269 self.active_imodels[imodel] = proxy270 return proxy271 272 def _transaction(self):273 return self274 275 def commit(self):276 #phase one try commit277 commit_ok = True278 for imodel, proxy in self.active_imodels.items():279 try:280 imodel.check_constraints()281 except ConstraintError as e:282 proxy.error = e283 commit_ok = False284 else:285 proxy.error = None286 287 if commit_ok:288 for imodel, proxy in self.active_imodels.items():289 proxy.commit(imodel)290 self.fire("commit", True)291 metapie._dbcommit()292 else:293 self.fire("commit", False)294 return commit_ok295 def get_errors(self):296 result = {}297 for imodel, proxy in self.active_imodels.items():298 try:299 result[imodel] = proxy.error300 except AttributeError:301 pass302 return result303 304 def rollback(self):305 for imodel, proxy in self.active_imodels.items():306 proxy.rollback(imodel)307 self.fire("rollback")308 def destroy(self):309 def is_my_item(item):310 imodel, p_or_t = item311 try:312 t = p_or_t._transaction()313 return t is self or not t314 except ReferenceError:315 return True316 my_models = list(filter(is_my_item, iter(self.active_transactions.items())))317 for k, v in my_models:318 del self.active_transactions[k]319 ...
dbtransient.py
Source:dbtransient.py
1############################################################################2# Copyright (C) 2005 by Reithinger GmbH3# mreithinger@web.de4#5# This file is part of metapie.6# 7# metapie is free software; you can redistribute it and/or modify8# it under the terms of the GNU General Public License as published by9# the Free Software Foundation; either version 2 of the License, or10# (at your option) any later version.11#12# pyplan is distributed in the hope that it will be useful,13# but WITHOUT ANY WARRANTY; without even the implied warranty of14# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the15# GNU General Public License for more details.16#17# You should have received a copy of the GNU General Public License18# along with this program; if not, write to the19# Free Software Foundation, Inc.,20# 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.21############################################################################22"""23Objects that will not be persistent24"""25from __future__ import absolute_import26from builtins import str27from builtins import object28import metapie29from . import peer30import bisect31class _ResultSet(object):32 """Lazily accessed set of objects."""33 def __init__(self, uids, uidutil):34 self.uids = uids35 self.uidutil = uidutil36 def __len__(self):37 return len(self.uids)38 def __delitem__(self, imodel):39 try:40 index = self.uids.index(imodel.id())41 except ValueError:42 raise KeyError("model does not exist in container", imodel)43 else:44 del self.uids[index]45 46 def __getitem__(self, index):47 return self.uidutil[self.uids[index]]48 49class FieldIndex(object):50 def __init__(self):51 self.index = []52 def index_id(self, docid, value):53 bisect.insort_right(self.index, (value, docid))54 def unindex_id(self, docid):55 pos = 056 while pos < len(self.index):57 if self.index[pos][1] == docid:58 del self.index[pos]59 else:60 pos += 161 def apply(self, min, max=None, excludemin=False, excludemax=False):62 pos = bisect.bisect_right(self.index, (min, 0))63 if excludemin:64 while pos < len(self.index):65 value, docid = self.index[pos]66 if value != min: break67 pos += 168 def all_keys():69 while pos < len(self.index):70 value, docid = self.index[pos]71 if max:72 if excludemax and value == max: break73 if value > max: break74 yield docid75 pos += 176 return list(all_keys())77class Container(peer.Peer):78 def __init__(self, imodel, peer_class, name_to_me, name_to_peer, index):79 self._container = {}80 self._imodel = imodel81 self._peer_class = peer_class82 self._name_to_me = name_to_me83 self._name_to_peer = name_to_peer84 85 def check_peer(self, imodel):86 if not isinstance(imodel, self._peer_class):87 raise ValueError("'%s' is not of type '%s'"%88 (str(imodel), self.contained_class.__name__))89 def fire(self):90 self._imodel.fire(self._name_to_peer, self._name_to_peer)91 self._imodel.fire("default", self._name_to_peer)92 93 def insert(self, imodel, fire=True):94 self.check_peer(imodel)95 if self._insert_item(imodel):96 self._add_to_peer(imodel, self._imodel)97 if fire: self.fire()98 def delete(self, imodel, fire=True):99 if self._del_item(imodel):100 self._remove_from_peer(imodel, self._imodel)101 if fire: self.fire()102 else:103 raise KeyError("model does not exist in container", imodel)104 def recatalog(self, obj):105 return False106 def sequence(self):107 keys = list(self._container.keys())108 keys.sort()109 return _ResultSet(keys, self._container)110 def __bool__(self): return len(self._container) > 0111 def __len__(self): return len(self._container)112 def __iter__(self): return iter(self._container.values())113 def __contains__(self, imodel): return imodel.id() in self._container114 def __getitem__(self, key): return self._container[key]115 def __delitem__(self, imodel): self.delete(imodel)116 def _insert_item(self, imodel):117 if imodel.id() not in self._container:118 self._container[imodel.id()] = imodel119 return True120 121 return False122 def _del_item(self, imodel):123 try:124 del self._container[imodel.id()]125 except KeyError:126 return False127 return True128class IdGenerator(object):129 instance = None130 def get_id(cls, type="default"):131 if not cls.instance: cls.instance = cls()132 counter = getattr(cls.instance, type, 0)133 setattr(cls.instance, type, counter + 1)134 return counter135 get_id = classmethod(get_id)136 137class _PersistentBase(object):138 __id_type__ = None139 140 def __init__(self):141 #generate a unique long lasting id142 self.__id = IdGenerator.get_id(self.__id_type__ \143 or self.__class__.__name__)144 #self.__id = int(time.time()) % 100000 + (id(self) % 10000) * 100000145 146 def id(self): return self.__id147 148def _commitdumy(): pass149metapie._init_db_module("transient", _PersistentBase, Container, _commitdumy)...
train.py
Source:train.py
1import tensorflow as tf2import keras3from keras.models import Sequential4from keras.layers import Dense, MaxPool2D, AveragePooling2D5from keras.layers import Conv2D, Activation, Dropout, Flatten6from keras.preprocessing.image import ImageDataGenerator7import numpy as np8import matplotlib.pyplot as plt9# cpu configuration10config = tf.ConfigProto( device_count = {'GPU': 1 , 'CPU': 56} ) #max: 1 gpu, 56 cpu11sess = tf.Session(config=config)12keras.backend.set_session(sess)13#14num_classes = 7 # sad, happy, surprise, angry, fear, nutral, disgust15batch_size = 25616epoch = 4417with open("fer2013.csv") as f:18 content = f.readlines()19lines = np.array(content)20num_instance =lines.size21# train test22x_train, y_train, x_test, y_test = [], [], [], []23for i in range(1,num_instance):24 try:25 emotion, img, usage = lines[i].split(",")26 val = img.split(" ")27 pixels = np.array(val, 'float32')28 emotion = keras.utils.to_categorical(emotion, num_classes)29 if 'Training' in usage:30 y_train.append(emotion)31 x_train.append(pixels)32 elif 'PublicTest' in usage:33 y_test.append(emotion)34 x_test.append(pixels)35 except:36 print("", end="")37# data transformation for train and test sets38x_train = np.array(x_train, 'float32')39y_train = np.array(y_train, 'float32')40x_test = np.array(x_test, 'float32')41y_test = np.array(y_test, 'float32')42x_train /= 255 #normalize inputs between [0, 1]43x_test /= 25544x_train = x_train.reshape(x_train.shape[0], 48, 48, 1)45x_train = x_train.astype('float32')46x_test = x_test.reshape(x_test.shape[0], 48, 48, 1)47x_test = x_test.astype('float32')48print(x_train.shape[0], 'train samples')49print(x_test.shape[0], 'test samples')50# creating the sequential model51imodel_fe = Sequential()52#1st convolution layer53imodel_fe.add(Conv2D(64, (5, 5), activation='relu', input_shape=(48,48,1)))54imodel_fe.add(MaxPool2D(pool_size=(5, 5), strides=(2, 2)))55#2nd c layer56imodel_fe.add(Conv2D(64, (3, 3), activation='relu'))#12857imodel_fe.add(Conv2D(64, (3,3), activation='relu'))58imodel_fe.add(AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))59#3rd c layer60imodel_fe.add(Conv2D(64, (3, 3), activation='relu'))#12861imodel_fe.add(Conv2D(64, (3,3), activation='relu'))62imodel_fe.add(AveragePooling2D(pool_size=(3, 3),strides=(2, 2)))63imodel_fe.add(Flatten())64# fully connected neural network65imodel_fe.add(Dense(1024, activation='relu'))66imodel_fe.add(Dropout(0.2))67imodel_fe.add(Dense(1024, activation='relu'))68imodel_fe.add(Dropout(0.2))69imodel_fe.add(Dense(num_classes, activation='softmax'))70#--------------------------------------71#batch process72gen = ImageDataGenerator()# this is used to normalise images in format keras can understand73train_generator = gen.flow(x_train, y_train, batch_size=batch_size)74imodel_fe.compile(loss='categorical_crossentropy'75 , optimizer=keras.optimizers.Adam()76 , metrics=['accuracy']77)78#---------------------------------------------79fit = True80if fit == True:81 #model.fit_generator(x_train, y_train, epochs=epochs) #train for all trainset82 imodel_fe.fit_generator(train_generator, steps_per_epoch=batch_size, epochs=epoch) #train for randomly selected one83else:84 imodel_fe.load_weights('model33.h5') #load weights85# ------------------------------86# function for drawing bar chart for emotion preditions87def emotion_analysis(emotions):88 objects = ('angry', 'disgust', 'fear', 'happy', 'sad', 'surprise', 'neutral')89 y_pos = np.arange(len(objects))90 plt.bar(y_pos, emotions, align='center', alpha=0.5)91 plt.xticks(y_pos, objects)92 plt.ylabel('percentage')93 plt.title('emotion')94 plt.show()95# ------------------------------96imodel_fe.save('model2.h5')97#--------------------------------98monitor_testset_results = True99if monitor_testset_results == True:100 # make predictions for test set101 predictions = imodel_fe.predict(x_test)102 index = 0103 for i in predictions:104 if index < 30 and index >= 20:105 # print(i) #predicted scores106 # print(y_test[index]) #actual scores107 testing_img = np.array(x_test[index], 'float32')108 testing_img = testing_img.reshape([48, 48]);109 plt.gray()110 plt.imshow(testing_img)111 plt.show()112 print(i)113 emotion_analysis(i)114 print("----------------------------------------------")115 index = index + 1116#------------------------------------------------------------------------------117from keras.models import load_model...
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