diff --git a/code_mario/clef18_task1_data.py b/code_mario/clef18_task1_data.py
index 0ee0be9ccaafaf891dca6fd03a22bdb9ba551f2c..7ea5a8cc18f473cad1e42559661ce5fe79f51533 100644
--- a/code_mario/clef18_task1_data.py
+++ b/code_mario/clef18_task1_data.py
@@ -13,25 +13,29 @@ class Clef18Task1Data(LoggingMixin):
def __init__(self):
LoggingMixin.__init__(self, self.__class__.__name__)
- def read_train_certifcates_by_language(self, lang: str) -> DataFrame:
- if lang == "it":
+ def read_train_certifcates_by_id(self, id: str) -> DataFrame:
+ if id == "it":
return self.read_it_train_certificates()
- elif lang == "hu":
+ elif id == "hu":
return self.read_hu_train_certificates()
- elif lang == "fr":
+ elif id == "fr":
return self.read_fr_train_certificates()
+ elif id == "all-con":
+ return self.read_all_con_certificates()
else:
- raise AssertionError("Unsupported language: " + lang)
+ raise AssertionError("Unsupported language: " + id)
- def read_dictionary_by_language(self, lang: str) -> DataFrame:
- if lang == "it":
+ def read_dictionary_by_id(self, id: str) -> DataFrame:
+ if id == "it":
return self.read_it_dictionary()
- elif lang == "hu":
+ elif id == "hu":
return self.read_hu_dictionary()
- elif lang == "fr":
+ elif id == "fr":
return self.read_fr_dictionary()
+ elif id == "all-con":
+ return self.read_all_con_dictionary()
else:
- raise AssertionError("Unsupported language: " + lang)
+ raise AssertionError("Unsupported language: " + id)
# --------------------------------------------------------------------------------
@@ -83,6 +87,18 @@ class Clef18Task1Data(LoggingMixin):
# --------------------------------------------------------------------------------
+ def read_all_con_dictionary(self) -> DataFrame:
+ return pd.concat([self.read_fr_dictionary(),
+ self.read_it_dictionary(),
+ self.read_hu_dictionary()])
+
+ def read_all_con_certificates(self) -> DataFrame:
+ return pd.concat([self.read_fr_train_certificates(),
+ self.read_it_train_certificates(),
+ self.read_hu_train_certificates()])
+
+ # --------------------------------------------------------------------------------
+
def filter_single_code_lines(self, certificate_df: DataFrame) -> DataFrame:
multi_code_lines = [key for key, group in groupby(certificate_df.index.values) if len(list(group)) > 1]
self.logger.info("Start filtering %s lines with multiple codes", len(multi_code_lines))
diff --git a/code_mario/clef18_task1_emb1.py b/code_mario/clef18_task1_emb1.py
index cc9529225480f80f4edb5d6539a2eb276fac0aab..c5b380efebe6722e93c030a15ae323759e2d794e 100644
--- a/code_mario/clef18_task1_emb1.py
+++ b/code_mario/clef18_task1_emb1.py
@@ -33,7 +33,7 @@ import ft_embeddings
from app_context import AppContext
from clef18_task1_data import Clef18Task1Data
from dnn_classifiers import NeuralNetworkClassifiers as nnc
-from ft_embeddings import FastTextEmbeddings
+from ft_embeddings import FastTextEmbeddings, FastTextModel
from preprocessing import DataPreparationUtil as pdu
from keras_extension import KerasUtil as ku
from util import LoggingMixin
@@ -80,7 +80,7 @@ class Clef18Task1Emb1(LoggingMixin):
def __init__(self):
LoggingMixin.__init__(self, self.__class__.__name__, AppContext.default().default_log_file)
- def train_embedding_model(self, config: Configuration, ft_model: FastText, neg_sampling_strategy: Callable, epochs: int, batch_size: int) -> Model:
+ def train_embedding_model(self, config: Configuration, ft_model: FastTextModel, neg_sampling_strategy: Callable, epochs: int, batch_size: int) -> Model:
self.logger.info("Start building training pairs")
train_pair_data = self.build_pairs(config.train_cert_df, config.dict_df, neg_sampling_strategy)
self.logger.info("Label distribution:\n%s", train_pair_data["Label"].value_counts())
@@ -296,14 +296,14 @@ class Clef18Task1Emb1(LoggingMixin):
return training_data, test_data
- def build_embedding_model(self, word_index: Dict, ft_model: FastText, max_cert_length: int, max_dict_length: int):
+ def build_embedding_model(self, word_index: Dict, ft_model: FastTextModel, max_cert_length: int, max_dict_length: int):
# TODO: Make hyper-parameter configurable!
# TODO: Think about using CNNs instead of RNNs since we can have multiple ICD-10 per line!
embedding_matrix = np.zeros((len(word_index) + 1, ft_model.vector_size))
for word, i in word_index.items():
try:
- embedding_vector = ft_model[word]
+ embedding_vector = ft_model.lookup(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
@@ -333,7 +333,7 @@ class Clef18Task1Emb1(LoggingMixin):
return model
- def prepare_data_set(self, cert_df: DataFrame, dict_df: DataFrame, ft_model: FastText, train_ratio: float, val_ratio: float,
+ def prepare_data_set(self, cert_df: DataFrame, dict_df: DataFrame, ft_model: FastTextModel, train_ratio: float, val_ratio: float,
strat_column: str, samples: int=None, stratified_splits: bool=False) -> Configuration:
if samples:
@@ -635,7 +635,7 @@ if __name__ == "__main__":
subparsers = parser.add_subparsers(dest="mode")
train_emb_parser = subparsers.add_parser("train-emb")
- train_emb_parser.add_argument("lang", help="Language to train on", choices=["it", "fr", "hu"])
+ train_emb_parser.add_argument("lang", help="Language to train on", choices=["it", "fr", "hu", "all-con"])
train_emb_parser.add_argument("--epochs", help="Number of epochs to train", default=10, type=int)
train_emb_parser.add_argument("--batch_size", help="Batch size during training", default=10, type=int)
train_emb_parser.add_argument("--train_ratio", help="Ratio of samples (from the complete data set) to use for training", default=0.8, type=float)
@@ -669,10 +669,10 @@ if __name__ == "__main__":
AppContext.initialize_by_app_name(Clef18Task1Emb1.__name__ + "-" + args.mode)
clef_data = Clef18Task1Data()
- dictionary = clef_data.read_dictionary_by_language(args.lang)
+ dictionary = clef_data.read_dictionary_by_id(args.lang)
#dictionary = dictionary.sample(1200)
- certificates = clef_data.read_train_certifcates_by_language(args.lang)
+ certificates = clef_data.read_train_certifcates_by_id(args.lang)
certificates = clef_data.filter_single_code_lines(certificates)
certificates = clef_data.add_masked_icd10_column(certificates, 10)
@@ -680,7 +680,7 @@ if __name__ == "__main__":
#ft_model = FastText(sentences, min_count=1)
ft_embeddings = FastTextEmbeddings()
- ft_model = ft_embeddings.load_embeddings_by_language(args.lang)
+ ft_model = ft_embeddings.load_embeddings_by_id(args.lang)
clef18_task1 = Clef18Task1Emb1()
clef18_task1.save_arguments(args)
diff --git a/code_mario/clef18_task1_emb2.py b/code_mario/clef18_task1_emb2.py
index 1d6ddb1135593ff67190eb6fc23aa1f1d9282890..1eeb7d8e46ece374856cefebda986fa6987eaf0f 100644
--- a/code_mario/clef18_task1_emb2.py
+++ b/code_mario/clef18_task1_emb2.py
@@ -33,7 +33,7 @@ import ft_embeddings
from app_context import AppContext
from clef18_task1_data import Clef18Task1Data
from dnn_classifiers import NeuralNetworkClassifiers as nnc
-from ft_embeddings import FastTextEmbeddings
+from ft_embeddings import FastTextEmbeddings, FastTextModel
from preprocessing import DataPreparationUtil as pdu
from keras_extension import KerasUtil as ku
from util import LoggingMixin
@@ -77,7 +77,7 @@ class Clef18Task1Emb2(LoggingMixin):
def __init__(self):
LoggingMixin.__init__(self, self.__class__.__name__, AppContext.default().default_log_file)
- def train_embedding_model(self, config: Configuration, ft_model: FastText, neg_sampling_strategy: Callable, epochs: int, batch_size: int) -> Model:
+ def train_embedding_model(self, config: Configuration, ft_model: FastTextModel, neg_sampling_strategy: Callable, epochs: int, batch_size: int) -> Model:
self.logger.info("Start building training pairs")
train_pair_data = self.build_pairs(config.train_df, neg_sampling_strategy)
self.logger.info("Label distribution:\n%s", train_pair_data["Label"].value_counts())
@@ -282,14 +282,14 @@ class Clef18Task1Emb2(LoggingMixin):
return training_data, test_data
- def build_embedding_model(self, word_index: Dict, ft_model: FastText, conf: Configuration):
+ def build_embedding_model(self, word_index: Dict, ft_model: FastTextModel, conf: Configuration):
# TODO: Make hyper-parameter configurable!
# TODO: Think about using CNNs instead of RNNs since we can have multiple ICD-10 per line!
embedding_matrix = np.zeros((len(word_index) + 1, ft_model.vector_size))
for word, i in word_index.items():
try:
- embedding_vector = ft_model[word]
+ embedding_vector = ft_model.lookup(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
embedding_matrix[i] = embedding_vector
@@ -321,7 +321,7 @@ class Clef18Task1Emb2(LoggingMixin):
return model
- def prepare_data_set(self, cert_df: DataFrame, dict_df: DataFrame, ft_model: FastText, train_ratio: float, val_ratio: float,
+ def prepare_data_set(self, cert_df: DataFrame, dict_df: DataFrame, ft_model: FastTextModel, train_ratio: float, val_ratio: float,
strat_column: str, samples: int=None, stratified_splits: bool=False) -> Configuration:
cert_df = cert_df[["RawText", "ICD10"]]
@@ -584,7 +584,7 @@ if __name__ == "__main__":
subparsers = parser.add_subparsers(dest="mode")
train_emb_parser = subparsers.add_parser("train-emb")
- train_emb_parser.add_argument("lang", help="Language to train on", choices=["it", "fr", "hu"])
+ train_emb_parser.add_argument("lang", help="Language to train on", choices=["it", "fr", "hu", "all-con"])
train_emb_parser.add_argument("--epochs", help="Number of epochs to train", default=10, type=int)
train_emb_parser.add_argument("--batch_size", help="Batch size during training", default=10, type=int)
train_emb_parser.add_argument("--train_ratio", help="Ratio of samples (from the complete data set) to use for training", default=0.8, type=float)
@@ -618,10 +618,10 @@ if __name__ == "__main__":
AppContext.initialize_by_app_name(Clef18Task1Emb2.__name__ + "-" + args.mode)
clef_data = Clef18Task1Data()
- dictionary = clef_data.read_dictionary_by_language(args.lang)
+ dictionary = clef_data.read_dictionary_by_id(args.lang)
#dictionary = dictionary.sample(1200)
- certificates = clef_data.read_train_certifcates_by_language(args.lang)
+ certificates = clef_data.read_train_certifcates_by_id(args.lang)
#certificates = clef_data.filter_single_code_lines(certificates)
#certificates = clef_data.add_masked_icd10_column(certificates, 10)
@@ -629,7 +629,7 @@ if __name__ == "__main__":
#ft_model = FastText(sentences, min_count=1)
ft_embeddings = FastTextEmbeddings()
- ft_model = ft_embeddings.load_embeddings_by_language(args.lang)
+ ft_model = ft_embeddings.load_embeddings_by_id(args.lang)
clef18_task1 = Clef18Task1Emb2()
clef18_task1.save_arguments(args)
diff --git a/code_mario/ft_embeddings.py b/code_mario/ft_embeddings.py
index 644b5b51bd426ca3f839bff705bcd9eadf2963e9..59bc279584ac83ab4e2255261143b6d26c114760 100644
--- a/code_mario/ft_embeddings.py
+++ b/code_mario/ft_embeddings.py
@@ -1,22 +1,54 @@
+import numpy as np
+
+from typing import List
from gensim.models import FastText
+from app_context import AppContext
from util import LoggingMixin
+class FastTextModel(LoggingMixin):
+
+ def __init__(self, name: str, ft_models: List[FastText]):
+ LoggingMixin.__init__(self, self.__class__.__name__, AppContext.default().default_log_file)
+ self.name = name
+ self.ft_models = ft_models
+ self.vector_size = sum([ft_model.vector_size for ft_model in self.ft_models])
+
+ def lookup(self, word: str):
+ embeddings = []
+ for ft_model in self.ft_models:
+ try:
+ embeddings.append(ft_model[word])
+ except KeyError:
+ self.logger.warn("Can't create embedding for " + word)
+ embeddings.append(np.zeros(ft_model.vector_size))
+
+ if len(embeddings) == 1:
+ return embeddings[0]
+ else:
+ return np.concatenate(embeddings)
+
+
class FastTextEmbeddings(LoggingMixin):
def __init__(self):
- LoggingMixin.__init__(self, __class__.__name__)
-
- def load_embeddings_by_language(self, lang: str) -> FastText:
- if lang == "it":
- return self.load_it_embeddings()
- elif lang == "hu":
- return self.load_hu_embeddings()
- elif lang == "fr":
- return self.load_fr_embeddings()
+ LoggingMixin.__init__(self, self.__class__.__name__, AppContext.default().default_log_file)
+
+ def load_embeddings_by_id(self, id: str) -> FastTextModel:
+ if id == "it":
+ return FastTextModel("it", [self.load_it_embeddings()])
+ elif id == "hu":
+ return FastTextModel("hu", [self.load_hu_embeddings()])
+ elif id == "fr":
+ return FastTextModel("fr", [self.load_fr_embeddings()])
+ elif id == "all-con":
+ return FastTextModel("all-con", [self.load_fr_embeddings(),
+ self.load_it_embeddings(),
+ self.load_hu_embeddings()])
+
else:
- raise AssertionError("Unsupported language: " + lang)
+ raise AssertionError("Unsupported language: " + id)
# ------------------------------------------------------------------------------------
diff --git a/code_mario/preprocessing.py b/code_mario/preprocessing.py
index 47a2a6bb397910687cd39f80e44269cb5135bee4..9cba3822b3cf044a8db9bdc91a19fcbce2d2bff2 100644
--- a/code_mario/preprocessing.py
+++ b/code_mario/preprocessing.py
@@ -88,6 +88,7 @@ class DataPreparationUtil(object):
return MapFunction(icd10_column, _mask_icd10, target_column)
+
class FitMixin(object):
def fit(self, data, y=None):