def run_fasttext_setting(setting_file, home_dir,
train_data_file, test_data_file,
overwrite_params=None):
properties = exp_util.load_properties(setting_file)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties, overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting("embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
if dnn_embedding_file.endswith('none'):
dnn_embedding_file = None
######## dnn #######
print("loading dataset...")
df, train_size, test_size = exp_util.\
load_and_merge_train_test_csvRakuten(train_data_file, test_data_file, delimiter="\t")
class_col = int(exp_util.load_setting("class_column", properties, overwrite_params))
y = df[:, class_col]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' + exp_util.load_setting("label", properties, overwrite_params))
print("fitting model...")
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns", properties, overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
dnn_classifier.fit_fasttext_holdout(df=df,
split_at_row=train_size,
class_col=class_col,
outfolder=outfolder,
task=exp_util.describe_task(properties, overwrite_params, setting_file),
text_norm_option=1,
text_input_info=input_text_info,
embedding_file=dnn_embedding_file)
print("Completed running on this setting file")
print(datetime.datetime.now())
def run_fasttext_model(setting_file: str, properties: dict, df: numpy.ndarray,
y, train_size: int, class_col: int, outfolder: str,
dnn_embedding_file, text_field_mapping: dict):
# this is the folder to save output to
print("\n" + str(datetime.datetime.now()))
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
print("fitting model...")
input_text_info = {}
count = 0
for x in exp_util.load_setting("text_fieldnames", properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = text_field_mapping[config[0]]
map["text_length"] = int(config[1])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
count += 1
dnn_classifier.fit_fasttext_holdout(df=df,
split_at_row=train_size,
class_col=class_col,
outfolder=outfolder,
task=exp_util.describe_task(
properties, overwrite_params,
setting_file),
text_norm_option=1,
text_input_info=input_text_info,
embedding_file=dnn_embedding_file)
print("Completed running on this setting file")
print(datetime.datetime.now())
def run_dnn_setting(setting_file, home_dir,
train_data_file, test_data_file,
overwrite_params=None,
embedding_format=None):
properties = exp_util.load_properties(setting_file)
word_weights_file = exp_util.load_setting('word_weights_file', properties, overwrite_params)
if word_weights_file == None:
word_weights = None
else:
word_weights = load_word_weights(word_weights_file)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties, overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting("embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
emb_model = embedding_util.load_emb_model(embedding_format, dnn_embedding_file)
# in order to test different DNN architectures, I implemented a parser that analyses a string following
# specific syntax, creates different architectures. This one here takes word embedding, pass it to 3
# cnn layer then concatenate the output by max pooling finally into a softmax
#
# So you can add mulitple descriptors in to a list, and the program will go through each model structure, apply them
# to the same dataset for experiments
#
# the descriptor is passed as a param to 'Classifer', which parses the string to create a model
# see 'classifier_learn.py - learn_dnn method for details
model_descriptors = [
"input=2d bilstm=100-False|dense=?-softmax|emb",
#"input=2d cnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=?-softmax|emb",
"input=2d han_2dinput"]
# model_descriptors = [
# "input=2d han_2dinput"]
# input=3d han_full|glv,
# input=2d lstm=100-False|dense=?-softmax|glv
# "scnn[2,3,4](conv1d=100,maxpooling1d=4)|maxpooling1d=4|flatten|dense=6-softmax|glv",
# "scnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=6-softmax|glv"]
######## dnn #######
print("loading dataset...")
df, train_size, test_size = exp_util.\
load_and_merge_train_test_csvRakuten(train_data_file, test_data_file, delimiter="\t")
class_col = int(exp_util.load_setting("class_column", properties, overwrite_params))
y = df[:, class_col]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' + exp_util.load_setting("label", properties, overwrite_params))
for model_descriptor in model_descriptors:
print("\tML model=" + model_descriptor)
model_descriptor = model_descriptor.split(" ")[1]
dnn_branches = []
dnn_branch_input_shapes = []
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns", properties, overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
dnn_branch = dnn_classifier.create_dnn_branch(map["text_length"],
util.DNN_EMBEDDING_DIM,
model_descriptor=model_descriptor
)
dnn_branches.append(dnn_branch[0])
dnn_branch_input_shapes.append(dnn_branch[1])
count += 1
# now create DNN branches based on the required input text column sources
print("creating merged model (if multiple input branches)")
final_model = \
dnn_classifier.merge_dnn_branch(dnn_branches, dnn_branch_input_shapes,
target_classes)
print("fitting model..."+str(datetime.datetime.now()))
setting=os.path.splitext(os.path.basename(setting_file))[0]
setting=setting[setting.index("_")+1]
desc = 'setting=' + setting
desc += '|embedding='
desc += os.path.splitext(os.path.basename(
exp_util.load_setting('embedding_file', properties, overwrite_params)))[0]
dnn_classifier.fit_dnn_holdout(df=df,
split_at_row=train_size,
class_col=class_col,
final_model=final_model,
outfolder=outfolder,
task=desc,
model_descriptor=model_descriptor, text_norm_option=1,
text_input_info=input_text_info,
embedding_model=emb_model,
embedding_model_format=embedding_format,
word_weights=word_weights)
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_cml_setting(setting_file, home_dir,
train_data_file, test_data_file,
overwrite_params=None,
embedding_format=None):
properties = exp_util.load_properties(setting_file)
csv_training_text_data = home_dir + exp_util.load_setting('training_text_data', properties, overwrite_params)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties, overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting("embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
# print("embedding file is========="+dnn_embedding_file)
emb_model = embedding_util.load_emb_model(embedding_format, dnn_embedding_file)
print("loading dataset...")
df, train_size, test_size = exp_util.\
load_and_merge_train_test_csvRakuten(train_data_file, test_data_file, delimiter="\t")
class_col = int(exp_util.load_setting("class_column", properties, overwrite_params))
y = df[:, class_col]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' + exp_util.load_setting("label", properties, overwrite_params))
print("fitting model...")
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns", properties, overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
count += 1
print("creating feature matrix")
X_all = []
for k, v in input_text_info.items():
X_sub = tfe.get_aggr_embedding_vectors(df=df,
text_col=v["text_col"],
text_norm_option=1,
aggr_option=1,
emb_format=embedding_format,
emb_model=emb_model,
emb_dim=int(v["text_dim"]))
X_all.append(X_sub)
X_all=numpy.concatenate(X_all, axis=1)
setting_file = setting_file[setting_file.rfind("/") + 1:]
models=["svm_l"]
for model_name in models:
print("\tML model=" + model_name)
print("fitting model...")
cls = cml.Classifer(setting_file, model_name, X_all[0:train_size,:], y[0:train_size], outfolder,
categorical_targets=target_classes,
nfold=None, algorithms=[model_name])
trained_model=cls.run()["svm_l"]
cls.eval_holdout(trained_model, model_name, X_all[train_size:,:],y[train_size:] )
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_cml_setting(setting_file,
home_dir,
overwrite_params=None,
embedding_format=None):
properties = exp_util.load_properties(setting_file)
word_weights_file = exp_util.load_setting('word_weights_file', properties,
overwrite_params)
if word_weights_file == None:
word_weights = None
else:
print("using word weights to revise embedding vectors")
word_weights = load_word_weights(word_weights_file)
csv_training_text_data = home_dir + exp_util.load_setting(
'training_text_data', properties, overwrite_params)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties,
overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting(
"embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
# print("embedding file is========="+dnn_embedding_file)
emb_model = embedding_util.load_emb_model(embedding_format,
dnn_embedding_file)
n_fold = int(exp_util.load_setting("n_fold", properties, overwrite_params))
######## dnn #######
print("loading dataset...")
df = pd.read_csv(
csv_training_text_data,
header=0,
delimiter=";",
quoting=0,
encoding="utf-8",
)
df = df.fillna('')
df = df.as_matrix()
class_col = int(
exp_util.load_setting("class_column", properties, overwrite_params))
y = df[:, class_col]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns", properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
count += 1
print("creating feature matrix")
X_all = []
for k, v in input_text_info.items():
X_sub = tfe.get_aggr_embedding_vectors(df=df,
text_col=v["text_col"],
text_norm_option=1,
aggr_option=0,
emb_format=embedding_format,
emb_model=emb_model,
emb_dim=int(v["text_dim"]))
X_all.append(X_sub)
X_all = numpy.concatenate(X_all, axis=1)
setting_file = setting_file[setting_file.rfind("/") + 1:]
models = ["svm_l"]
for model_name in models:
print("\tML model=" + model_name)
print("fitting model...")
cls = cml.Classifer(setting_file,
model_name,
X_all,
y,
outfolder,
categorical_targets=target_classes,
nfold=n_fold,
algorithms=[model_name])
cls.run()
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_fasttext_setting(setting_file, home_dir, overwrite_params=None):
properties = exp_util.load_properties(setting_file)
csv_training_text_data = home_dir + exp_util.load_setting(
'training_text_data', properties, overwrite_params)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties,
overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting(
"embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
if dnn_embedding_file.endswith('none'):
dnn_embedding_file = None
n_fold = int(exp_util.load_setting("n_fold", properties, overwrite_params))
######## dnn #######
print("loading dataset...")
df = pd.read_csv(
csv_training_text_data,
header=0,
delimiter=";",
quoting=0,
encoding="utf-8",
)
df = df.fillna('')
df = df.as_matrix()
class_col = int(
exp_util.load_setting("class_column", properties, overwrite_params))
y = df[:, class_col]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
print("fitting model...")
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns", properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
dnn_classifier.fit_fasttext(df=df,
nfold=n_fold,
class_col=class_col,
outfolder=outfolder,
task=exp_util.describe_task(
properties, overwrite_params,
setting_file),
text_norm_option=1,
text_input_info=input_text_info,
embedding_file=dnn_embedding_file)
print("Completed running on this setting file")
print(datetime.datetime.now())
def run_mtl_setting(setting_file,
home_dir,
overwrite_params=None,
embedding_format=None):
properties = exp_util.load_properties(setting_file)
word_weights_file = exp_util.load_setting('word_weights_file', properties,
overwrite_params)
if word_weights_file == None:
word_weights = None
else:
print("using word weights to revise embedding vectors...")
word_weights = load_word_weights(word_weights_file)
csv_training_text_data = home_dir + exp_util.load_setting(
'training_text_data', properties, overwrite_params)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties,
overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting(
"embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
# print("embedding file is========="+dnn_embedding_file)
emb_model = embedding_util.load_emb_model(embedding_format,
dnn_embedding_file)
n_fold = int(exp_util.load_setting("n_fold", properties, overwrite_params))
# in order to test different DNN architectures, I implemented a parser that analyses a string following
# specific syntax, creates different architectures. This one here takes word embedding, pass it to 3
# cnn layer then concatenate the output by max pooling finally into a softmax
#
# So you can add mulitple descriptors in to a list, and the program will go through each model structure, apply them
# to the same dataset for experiments
#
# the descriptor is passed as a param to 'Classifer', which parses the string to create a model
# see 'classifier_learn.py - learn_dnn method for details
model_descriptors = [
"input=2d bilstm=100-False|dense=?-softmax|emb",
# "input=2d cnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=?-softmax|emb",
"input=2d han_2dinput"
]
# model_descriptors = [
# "input=2d han_2dinput"]
# input=3d han_full|glv,
# input=2d lstm=100-False|dense=?-softmax|glv
# "scnn[2,3,4](conv1d=100,maxpooling1d=4)|maxpooling1d=4|flatten|dense=6-softmax|glv",
# "scnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=6-softmax|glv"]
######## dnn #######
print("loading dataset...")
df = pd.read_csv(
csv_training_text_data,
header=0,
delimiter=";",
quoting=0,
encoding="utf-8",
)
df = df.fillna('')
df = df.as_matrix()
#stats about main task
maintask_class_col = int(
exp_util.load_setting("class_column", properties, overwrite_params))
main_y = df[:, maintask_class_col]
target_classes = len(set(main_y))
print("\ttotal classes=" + str(target_classes))
#stats about auxiliary tasks
auxtask_class_col = exp_util.load_setting("class_auxiliary", properties,
overwrite_params)
if auxtask_class_col == None:
print("Not MTL, quit.")
exit(1)
auxtask_class_cols = []
aux_classes = []
for i in auxtask_class_col.split(","):
i = int(i)
aux_y = df[:, i]
aux_cls = len(set(aux_y))
print("\t\t auxiliary task with classes=" + str(aux_classes))
auxtask_class_cols.append(i)
aux_classes.append(aux_cls)
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
for model_descriptor in model_descriptors:
print("\tML shared model=" + model_descriptor)
model_descriptor = model_descriptor.split(" ")[1]
dnn_branches = []
dnn_branch_input_shapes = []
input_text_info = {}
count = 0
for x in exp_util.load_setting("training_text_data_columns",
properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = config[0]
map["text_length"] = int(config[2])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
dnn_branch = dnn_classifier.create_dnn_branch(
map["text_length"],
util.DNN_EMBEDDING_DIM,
model_descriptor=model_descriptor)
dnn_branches.append(dnn_branch[0])
dnn_branch_input_shapes.append(dnn_branch[1])
count += 1
# now create DNN branches based on the required input text column sources
print("creating MTL model (if multiple input branches)")
final_model = \
mtl_classifier.create_mtl_layers(dnn_branches, dnn_branch_input_shapes,
target_classes, aux_classes)
print("fitting model...")
mtl_classifier.fit_dnn_mtl(df=df,
nfold=n_fold,
main_class_col=maintask_class_col,
aux_class_cols=auxtask_class_cols,
final_model=final_model,
outfolder=outfolder,
task=exp_util.describe_task(
properties, overwrite_params,
setting_file),
model_descriptor=model_descriptor,
text_norm_option=1,
text_input_info=input_text_info,
embedding_model=emb_model,
embedding_model_format=embedding_format,
word_weights=word_weights)
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_single_setting(setting_file,
home_dir,
remove_rare_classes,
remove_no_desc_instances,
overwrite_params=None,
gensimFormat=None):
properties = exp_util.load_properties(setting_file)
csv_training_text_data = home_dir + exp_util.load_setting(
'training_text_data', properties, overwrite_params)
# this is the folder containing other numeric features that are already pre-extracted
csv_training_other_feaures = home_dir + exp_util.load_setting(
'training_other_features', properties, overwrite_params)
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties,
overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = home_dir + exp_util.load_setting(
"embedding_file", properties,
overwrite_params) # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
if gensimFormat is None:
gensimFormat = ".gensim" in dnn_embedding_file
if gensimFormat:
pretrained_embedding_models = gensim.models.KeyedVectors.load(
dnn_embedding_file, mmap='r')
else:
pretrained_embedding_models = gensim.models.KeyedVectors. \
load_word2vec_format(dnn_embedding_file, binary=True)
n_fold = int(exp_util.load_setting("n_fold", properties, overwrite_params))
# in order to test different DNN architectures, I implemented a parser that analyses a string following
# specific syntax, creates different architectures. This one here takes word embedding, pass it to 3
# cnn layer then concatenate the output by max pooling finally into a softmax
#
# So you can add mulitple descriptors in to a list, and the program will go through each model structure, apply them
# to the same dataset for experiments
#
# the descriptor is passed as a param to 'Classifer', which parses the string to create a model
# see 'classifier_learn.py - learn_dnn method for details
model_descriptors = [
#"input=2d bilstm=100-False|dense=?-softmax|emb",
"input=2d cnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=?-softmax|emb"
]
# "input=2d han_2dinput"]
# model_descriptors = [
# "input=2d han_2dinput"]
# input=3d han_full|glv,
# input=2d lstm=100-False|dense=?-softmax|glv
# "scnn[2,3,4](conv1d=100,maxpooling1d=4)|maxpooling1d=4|flatten|dense=6-softmax|glv",
# "scnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=6-softmax|glv"]
######## dnn #######
print("loading dataset...")
df = pd.read_csv(
csv_training_text_data,
header=0,
delimiter=";",
quoting=0,
encoding="utf-8",
).as_matrix()
df.astype(str)
if remove_no_desc_instances:
print(
"you have chosen to remove instances whose description are empty")
df = exp_util.remove_empty_desc_instances(df, 5)
y = df[:,
int(
exp_util.
load_setting("class_column", properties, overwrite_params))]
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
remove_instance_indexes = []
if remove_rare_classes:
print(
"you have chosen to remove classes whose instances are less than n_fold"
)
instance_labels = list(y)
class_dist = {x: instance_labels.count(x) for x in instance_labels}
remove_labels = []
for k, v in class_dist.items():
if v < n_fold:
remove_labels.append(k)
remove_instance_indexes = []
for i in range(len(y)):
label = y[i]
if label in remove_labels:
remove_instance_indexes.append(i)
y = numpy.delete(y, remove_instance_indexes)
target_classes = len(set(y))
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
for model_descriptor in model_descriptors:
print("\tML model=" + model_descriptor)
input_shape = model_descriptor.split(" ")[0]
model_descriptor = model_descriptor.split(" ")[1]
if input_shape.endswith("2d"):
input_as_2D = True
else:
input_as_2D = False
if "han" in model_descriptor or "lstm" in model_descriptor:
dnn_embedding_mask_zero = True
else:
dnn_embedding_mask_zero = False
input_column_sources = \
[x for x in exp_util.load_setting("training_text_data_columns", properties, overwrite_params).split("|")]
# now create DNN branches based on the required input text column sources
dnn_branches = []
dnn_branch_input_shapes = []
dnn_branch_input_features = []
for string in input_column_sources:
print("\tcreating model branch=" + string)
config = string.split(",")
col_index = config[0]
embedding_trainable = False
if col_index == '13':
embedding_trainable = True
text_data = cc.create_text_input_data(config[0], df)
col_text_length = int(config[2])
text_data = numpy.delete(text_data, remove_instance_indexes)
data = ["" if type(x) is float else str(x) for x in text_data]
dnn_branch = dnn_classifier.create_dnn_branch_textinput(
pretrained_embedding_models,
input_text_data=data,
input_text_sentence_length=col_text_length,
input_text_word_embedding_dim=util.DNN_EMBEDDING_DIM,
model_descriptor=model_descriptor,
embedding_trainable=embedding_trainable,
embedding_mask_zero=dnn_embedding_mask_zero)
dnn_branches.append(dnn_branch[0])
dnn_branch_input_shapes.append(dnn_branch[1])
dnn_branch_input_features.append(dnn_branch[2])
print("creating merged model (if multiple input branches)")
final_model = \
dnn_classifier.merge_dnn_branch(dnn_branches, dnn_branch_input_shapes,
target_classes)
print("fitting model...")
dnn_classifier.fit_dnn(inputs=dnn_branch_input_features,
nfold=n_fold,
y_train=y,
final_model=final_model,
outfolder=outfolder,
task=exp_util.describe_task(
properties, overwrite_params, setting_file),
model_descriptor=model_descriptor)
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_setting(
setting_file,
home_dir,
train_data_file,
test_data_file,
model_choice, #dnn - including cnn,bilstm,han; cml -svm ; fasttext-fasttext
dataset_type: str, #mwpd, wdc, rakuten, icecat
dataset_text_field_mapping: dict,
overwrite_params=None,
embedding_format=None):
properties = exp_util.load_properties(setting_file)
word_weights_file = exp_util.load_setting('word_weights_file', properties,
overwrite_params)
if word_weights_file == None:
word_weights = None
else:
print("using word weights to revise embedding vectors...")
word_weights = load_word_weights(word_weights_file)
print("loading dataset...")
if dataset_type == "mwpd":
df, train_size, test_size = exp_util. \
load_and_merge_train_test_data_jsonMPWD(train_data_file, test_data_file)
elif dataset_type == "rakuten":
df, train_size, test_size = exp_util. \
load_and_merge_train_test_csvRakuten(train_data_file, test_data_file, delimiter="\t")
elif dataset_type == "icecat":
df, train_size, test_size = exp_util. \
load_and_merge_train_test_data_jsonIceCAT(train_data_file, test_data_file)
elif dataset_type == "fakerev":
df, train_size, test_size = exp_util. \
load_and_merge_train_test_data_productfakerev(train_data_file, test_data_file)
else: #wdc
df, train_size, test_size = exp_util. \
load_and_merge_train_test_data_jsonWDC(train_data_file, test_data_file)
#numpy.nan_to_num(df)
class_fieldname = exp_util.load_setting("class_fieldname", properties,
overwrite_params)
class_col = dataset_text_field_mapping[class_fieldname]
y = df[:, class_col]
# this is the folder to save output to
outfolder = home_dir + exp_util.load_setting("output_folder", properties,
overwrite_params)
print("\n" + str(datetime.datetime.now()))
print("loading embedding models...")
# this the Gensim compatible embedding file
dnn_embedding_file = exp_util.load_setting("embedding_file", properties,
overwrite_params)
if dnn_embedding_file is not None and dnn_embedding_file.lower() != 'none':
dnn_embedding_file = home_dir + dnn_embedding_file # "H:/Python/glove.6B/glove.840B.300d.bin.gensim"
print("\t will use this embedding data model: " + dnn_embedding_file)
# print("embedding file is========="+dnn_embedding_file)
if embedding_format == 'none':
emb_model = None
else:
emb_model = embedding_util.load_emb_model(embedding_format,
dnn_embedding_file)
if model_choice == 'dnn':
run_dnn_models(properties, df, y, train_size, class_col, outfolder,
emb_model, embedding_format, word_weights,
dataset_text_field_mapping)
elif model_choice == 'cml':
run_cml_models(setting_file, properties, df, y, train_size, class_col,
outfolder, dnn_embedding_file, emb_model,
embedding_format, dataset_text_field_mapping)
else:
run_fasttext_model(setting_file, properties, df, y, train_size,
class_col, outfolder, dnn_embedding_file,
dataset_text_field_mapping)
def run_cml_models(setting_file: str, properties: dict, df: numpy.ndarray, y,
train_size: int, class_col: int, out_folder: str,
embeddingmodel_file: str, embeddingmodel, embeddingformat,
text_field_mapping: dict):
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
input_text_info = {}
count = 0
for x in exp_util.load_setting("text_fieldnames", properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = text_field_mapping[config[0]]
map["text_length"] = int(config[1])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
count += 1
print("creating feature matrix")
X_all = []
for k, v in input_text_info.items():
# print(v)
# if v["text_col"]==5:
# print("here")
X_sub = tfe.get_aggr_embedding_vectors(df=df,
text_col=v["text_col"],
text_norm_option=1,
aggr_option=0,
emb_format=embeddingformat,
emb_model=embeddingmodel,
emb_dim=int(v["text_dim"]))
X_all.append(X_sub)
X_all = numpy.concatenate(X_all, axis=1)
X_train = X_all[0:train_size]
X_test = X_all[train_size:]
y_train = y[0:train_size]
y_test = y[train_size:]
setting_file = setting_file[setting_file.rfind("/") + 1:]
models = ["svm_l"]
for model_name in models:
identifier = model_name + "|" + embeddingmodel_file[embeddingmodel_file
.rfind("/") + 1:]
print("\tML model and embedding=" + model_name)
print("fitting model...")
cls = cml.Classifer(setting_file,
identifier,
X_train,
y_train,
out_folder,
categorical_targets=y,
nfold=None,
algorithms=[model_name])
trained_model = cls.run()[model_name]
cls.eval_holdout(trained_model, model_name, X_test, y_test)
print("Completed running all models on this setting file")
print(datetime.datetime.now())
def run_dnn_models(properties: dict, df: numpy.ndarray, y, train_size: int,
class_col: int, out_folder: str, embeddingmodel,
embeddingformat, word_weights: list,
text_field_mapping: dict):
# in order to test different DNN architectures, I implemented a parser that analyses a string following
# specific syntax, creates different architectures. This one here takes word embedding, pass it to 3
# cnn layer then concatenate the output by max pooling finally into a softmax
#
# So you can add mulitple descriptors in to a list, and the program will go through each model structure, apply them
# to the same dataset for experiments
#
# the descriptor is passed as a param to 'Classifer', which parses the string to create a model
# see 'classifier_learn.py - learn_dnn method for details
model_descriptors = [
"input=2d bilstm=100-False|dense=?-softmax|emb",
#"input=2d cnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=?-softmax|emb",
"input=2d han_2dinput"
]
# model_descriptors = [
# "input=2d han_2dinput"]
# input=3d han_full|glv,
# input=2d lstm=100-False|dense=?-softmax|glv
# "scnn[2,3,4](conv1d=100,maxpooling1d=4)|maxpooling1d=4|flatten|dense=6-softmax|glv",
# "scnn[2,3,4](conv1d=100)|maxpooling1d=4|flatten|dense=6-softmax|glv"]
######## dnn #######
target_classes = len(set(y))
print("\ttotal classes=" + str(target_classes))
print('[STARTED] running settings with label=' +
exp_util.load_setting("label", properties, overwrite_params))
for model_descriptor in model_descriptors:
print("\tML model=" + model_descriptor)
model_descriptor = model_descriptor.split(" ")[1]
dnn_branches = []
dnn_branch_input_shapes = []
input_text_info = {}
count = 0
for x in exp_util.load_setting("text_fieldnames", properties,
overwrite_params).split("|"):
config = x.split(",")
map = {}
map["text_col"] = text_field_mapping[config[0]]
map["text_length"] = int(config[1])
map["text_dim"] = util.DNN_EMBEDDING_DIM
input_text_info[count] = map
# if config[1] == 'simple':
# dnn_branch = dnn_classifier.create_dnn_branch(map["text_length"],
# util.DNN_EMBEDDING_DIM,
# model_descriptor='simple'
# )
# else:
dnn_branch = dnn_classifier.create_dnn_branch(
map["text_length"],
util.DNN_EMBEDDING_DIM,
model_descriptor=model_descriptor)
dnn_branches.append(dnn_branch[0])
dnn_branch_input_shapes.append(dnn_branch[1])
count += 1
# now create DNN branches based on the required input text column sources
print("creating merged model (if multiple input branches)")
final_model = \
dnn_classifier.merge_dnn_branch(dnn_branches, dnn_branch_input_shapes,
target_classes)
print("fitting model...")
dnn_classifier.fit_dnn_holdout(df=df,
split_at_row=train_size,
class_col=class_col,
final_model=final_model,
outfolder=out_folder,
task=exp_util.describe_task(
properties, overwrite_params,
setting_file),
model_descriptor=model_descriptor,
text_norm_option=1,
text_input_info=input_text_info,
embedding_model=embeddingmodel,
embedding_model_format=embeddingformat,
word_weights=word_weights)
print("Completed running all models on this setting file")
print(datetime.datetime.now())
