def __init__(self, Config, DefConfig, kwargs):
print ("=== Word Embedding ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig;
if not os.path.isdir(os.path.dirname(fullPath(Config, "w2vmodelpath"))):
print ("Wrong path to W2V model. Word Embedding can't be done.")
Config["error"] = True
return
if Config["w2vcreate"] != "yes":
return
if len(Config["w2vcorpuspath"]) == 0 or not os.path.isfile(fullPath(Config, "w2vcorpuspath")):
print ("Wrong corpus path. W2V model can't be created.")
Config["error"] = True
return
try:
self.epochs = int(self.Config["w2vepochs"])
except ValueError:
print ("Wrong quantity of epochs for training. W2V model can't be created.")
Config["error"] = True
return
try:
self.ndim = int(self.Config["w2vdim"])
except ValueError:
print ("Wrong size of resulting vectors. W2V model can't be created.")
Config.error = True
return
self.createW2VModel()
def __init__(self, Config, DefConfig, kwargs):
self.Config = Config
self.DefConfig = DefConfig
self.Config["modelid"] += 1
print("=== Model " + str(self.Config["modelid"]) + " ===")
updateParams(self.Config, DefConfig, kwargs)
self.Config["type"] = self.Config["type"].lower()
self.Config["type_of_execution"] = self.Config[
"type_of_execution"].lower()
if self.Config["type_of_execution"] != "none" and self.Config[
"type"] not in modelTypes:
raise ValueError(
"Request contains definition of model with wrong type. Stop.")
if self.Config["type_of_execution"] not in modelGoals:
raise ValueError(
"Request doesn't define the goal of the model process. "
"It should be one of 'trainAndTest', 'train', 'test', 'crossValidation' or 'none'. Stop."
)
if self.Config["type_of_execution"] != "none":
print("Model type: " + self.Config["type"].upper() + ", " +
userInfo[self.Config["type_of_execution"]])
else:
print("Model : " + userInfo[self.Config["type_of_execution"]])
if self.Config["type_of_execution"] == "none":
return
if "predefined_categories" not in self.Config or "train_docs" not in self.Config or "test_docs" not in self.Config:
raise ValueError("Input data isn't loaded. Stop.")
def __init__(self, Config, DefConfig, kwargs):
print ("=== Loading data ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
self.exclude_categories = Config["exclude_categories"].split(",")
self.sz = 0
self.splitTrain = False
self.topBound = 0.9
self.charsTopBound = 0.6
self.run()
def __init__(self, Config, DefConfig, kwargs):
print("=== Preprocessing ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
if len(Config["sourcepath"]
) == 0 or Config["sourcepath"] == Config["targetpath"]:
print("Wrong source/target path(s). Tokenization can't be done.")
Config["error"] = True
return
self.process(Config)
def __init__(self, Config, DefConfig, kwargs):
print("=== Tokenization ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
#if Config["language_tokenization"] != "True":
# return
if not Config["source_path"] or Config["source_path"] == Config[
"target_path"]:
print("Wrong source/target path(s). Tokenization can't be done.")
Config["error"] = True
return
if Config["typetoks"] == "server":
tokens_from_server(Config)
elif Config["typetoks"] == "tagger":
tokens_from_tagger(Config)
else:
print("Wrong tokenization type. Tokenization can't be done.")
def __init__(self, Config, DefConfig, kwargs):
print("=== Tokenization ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
#if Config["actualtoks"] != "yes":
# return
if len(Config["sourcepath"]
) == 0 or Config["sourcepath"] == Config["targetpath"]:
print("Wrong source/target path(s). Tokenization can't be done.")
Config["error"] = True
return
if Config["typetoks"] == "server":
TokensFromServer(Config)
elif Config["typetoks"] == "tagger":
TokensFromTagger(Config)
else:
print("Wrong tokenization type. Tokenization can't be done.")
def __init__(self, Config, DefConfig, kwargs):
print("=== Word Embedding ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
test_path(Config, "model_path",
"Wrong path to W2V model. Word Embedding can't be done.")
if Config["need_create_model"] != "True":
return
test_path(Config, "data_corpus_path",
"Wrong corpus path. W2V model can't be created.")
try:
self.epochs = int(self.Config["epochs_total"])
except ValueError:
raise ValueError(
"Wrong quantity of epochs for training. W2V model can't be created."
)
try:
self.ndim = int(self.Config["vectors_dimension"])
except ValueError:
raise ValueError(
"Wrong size of resulting vectors. W2V model can't be created.")
def __init__(self, Config, DefConfig, kwargs):
Config["modelid"] += 1
print("=== Model " + str(Config["modelid"]) + " ===")
updateParams(Config, DefConfig, kwargs)
Config["type"] = Config["type"].lower()
Config["runfor"] = Config["runfor"].lower()
if Config["runfor"] != "none" and Config["type"] not in modelTypes:
print(
"Request contains definition of model with wrong type. Stop.")
Config["error"] = True
return
if Config["runfor"] not in modelGoals:
print("Request doesn't define the goal of the model process")
print(
"It should be one of 'trainAndTest', 'train', 'test', 'crossValidation' or 'none'. Stop."
)
Config["error"] = True
return
if Config["runfor"] != "none":
print("Model type: " + Config["type"].upper() + ", " +
userInfo[Config["runfor"]])
else:
print("Model : " + userInfo[Config["runfor"]])
if Config["runfor"] == "none":
return
self.Config = Config
self.DefConfig = DefConfig
if "cats" not in Config or "traindocs" not in Config or "testdocs" not in Config:
print("Input data isn't loaded. Stop.")
Config["error"] = True
return
stop = False
try:
self.testSize = float(Config["testsize"])
except ValueError:
self.testSize = -1
if len(Config["trainpath"]) == 0 or not os.path.isdir(
fullPath(Config, "trainpath")):
if Config["runfor"] != "test" or len(Config["testpath"]) == 0:
print(
"Wrong path to the training set: folder %s doesn't exist."
% (fullPath(Config, "trainpath")))
stop = True
if len(Config["testpath"]) == 0 or not os.path.isdir(
fullPath(Config, "testpath")):
if not (len(Config["testpath"]) == 0 and self.testSize > 0
and self.testSize < 1):
print(
"Wrong path to the testing set: folder %d doesn't exist." %
(fullPath(Config, "testpath")))
stop = True
if len(Config["modelpath"]) == 0 or not os.path.isdir(
fullPath(Config, "modelpath")):
print("Wrong path to the models' folder.")
stop = True
if len(Config["name"]) == 0:
Config["name"] = Config["type"] + str(Config["modelid"])
mPath = fullPath(Config, "modelpath", opt="name")
if Config["runfor"] == "test" and not os.path.isfile(mPath):
print("Wrong path to the tested model.")
stop = True
if Config["runfor"] != "test":
try:
self.epochs = int(Config["epochs"])
except ValueError:
print("Wrong quantity of epochs for training.")
stop = True
try:
self.trainBatch = int(Config["trainbatch"])
except ValueError:
print("Wrong batch size for training.")
stop = True
try:
self.verbose = int(Config["verbose"])
except ValueError:
print("Wrong value of 'verbose' flag for training.")
stop = True
if Config["tempsave"] == "yes":
if len(Config["temppath"]) == 0 or not os.path.isdir(
fullPath(Config, "temppath")):
print("Wrong path to folder with intermediate results.")
stop = True
if Config["runfor"] != "train" and Config["customrank"] == "yes":
try:
self.rankThreshold = float(Config["rankthreshold"])
except ValueError:
print("Wrong custom rank threshold.")
stop = True
if Config["runfor"] == "crossvalidation":
if Config["cvsave"] == "yes":
if len(Config["cvpath"]) == 0 or not os.path.isdir(
fullPath(Config, "cvpath")):
print(
"Wrong path to the cross-validation's resulting folder."
)
stop = True
try:
kfold = int(Config["kfold"])
except ValueError:
print("Wrong k-fold value.")
stop = True
if stop:
print("Stop.")
Config["error"] = True
return
if Config["type"].lower() == "snn":
SnnModel(Config)
elif Config["type"].lower() == "ltsm":
LTSMModel(Config)
elif Config["type"].lower() == "cnn":
CNNModel(Config)
elif Config["type"].lower() == "pac":
PacModel(Config)
elif Config["type"].lower() == "ridge":
RidgeModel(Config)
elif Config["type"].lower() == "svc":
SVCModel(Config)
elif Config["type"] == "perceptron":
PerceptronModel(Config)
elif Config["type"] == "sgd":
SGDModel(Config)
elif Config["type"] == "bert":
BertModel(Config)
def __init__(self, Config, DefConfig, kwargs):
print("=== Loading data ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
self.exCats = Config["excats"].split(",")
self.sz = 0
self.splitTrain = False
self.topBound = 0.9
self.charsTopBound = 0.6
if len(Config["trainpath"]) == 0 or not os.path.isdir(
fullPath(Config, "trainpath")):
print("Wrong path to training set. Data can't be loaded.")
Config["error"] = True
return
if len(Config["testpath"]) > 0 and not os.path.isdir(
fullPath(Config, "testpath")):
print("Wrong path to testing set. Data can't be loaded.")
Config["error"] = True
return
elif len(Config["testpath"]) == 0:
self.splitTrain = True
try:
self.sz = float(Config["testsize"])
except ValueError:
self.sz = 0
if len(Config["testpath"]) == 0 and (self.sz <= 0 or self.sz >= 1):
print("Wrong size of testing set. Data can't be loaded.")
Config["error"] = True
return
if Config["datatoks"] == "yes":
if Config["actualtoks"] == "yes":
taggerPath = fullPath(Config, 'rttaggerpath')
if (self.Config["rttaggerpath"] == 0
or not os.path.exists(taggerPath)):
print(
"Wrong path to the tagger's jar. Preprocessing can't be done"
)
Config["error"] = True
return
self.jar = subprocess.Popen('java -Xmx2g -jar ' + taggerPath +
' "' + self.Config["expos"] + '"',
stdin=subprocess.PIPE,
stdout=subprocess.PIPE,
stderr=subprocess.PIPE,
shell=True,
encoding="utf-8")
if self.Config["stopwords"] == "yes":
self.stopWords = set(nltk.corpus.stopwords.words('arabic'))
else:
self.stopWords = set()
if self.Config["normalization"] == "yes":
self.normalizer = ArabicNormalizer()
if Config["w2vload"] == "yes":
if len(Config["w2vmodelpath"]) == 0 or not os.path.isfile(
fullPath(Config, "w2vmodelpath")):
print("Wrong path to W2V model. Stop.")
Config["error"] = True
return
try:
self.ndim = int(self.Config["w2vdim"])
except ValueError:
print("Wrong size of vectors' dimentions. Stop.")
Config["error"] = True
return
self.Config["resources"]["w2v"]["modelPath"] = fullPath(
Config, "w2vmodelpath")
self.Config["resources"]["w2v"]["ndim"] = self.ndim
self.loadW2VModel()
else:
self.Config["w2vmodel"] = None
self.loadData()
if Config["analysis"] == "yes":
self.analysis()
def __init__(self, Config, DefConfig, kwargs):
print("=== Preprocessing ===")
updateParams(Config, DefConfig, kwargs)
self.Config = Config
self.DefConfig = DefConfig
