refractoring

src/WPembeddings.jl

@@ -0,0 +1,200 @@
+"
+version 0.4
+Word and Positional embedding module
+"
+module WPembeddings
+
+using Embeddings
+using JSON3
+using Redis
+
+include("Utils.jl")
+
+export get_word_embedding, get_positional_embedding, wp_embedding
+
+
+#----------------------------------------------------------------------------------------------
+# user setting for word embedding
+GloVe_embedding_filepath = "C:\\myWork\\my_projects\\AI\\NLP\\my_NLP\\glove.840B.300d.txt"
+max_GloVe_vocab_size = 0  # size 10000+ or "all"
+#----------------------------------------------------------------------------------------------
+
+
+
+
+
+
+# load GloVe word embedding. URL of the embedding file: https://nlp.stanford.edu/projects/glove/
+if max_GloVe_vocab_size == 0
+    # don't load vocab
+elseif max_GloVe_vocab_size != "all"
+    @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath, 
+                                    max_vocab_size=max_GloVe_vocab_size) # size 10000 or something
+    const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
+else
+    @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath)
+    const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
+end
+
+
+# if max_GloVe_vocab_size != "all"
+#     @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath, 
+#                                     max_vocab_size=max_GloVe_vocab_size) # size 10000 or something
+#     const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
+# elseif max_GloVe_vocab_size == 0
+# else
+#     @time const embtable = Embeddings.load_embeddings(GloVe{:en}, GloVe_embedding_filepath)
+#     const get_word_index = Dict(word=>ii for (ii,word) in enumerate(embtable.vocab))
+# end
+
+
+"""
+    get_word_embedding(word::String)
+
+Get embedding vector of a word. Its dimention is depend on GloVe file used
+
+# Example
+
+    we_matrix = get_word_embedding("blue")
+"""
+function get_word_embedding(word::String)
+    index = get_word_index[word]
+    embedding = embtable.embeddings[:,index]
+    return embedding
+end
+
+
+"""
+    get_positional_embedding(total_word_position::Integer, word_embedding_dimension::Integer=300)
+
+return positional embedding matrix of size [word_embedding_dimension * total_word_position]
+
+# Example
+
+    pe_matrix = get_positional_embedding(length(content), 300)
+"""
+function get_positional_embedding(total_word_position::Integer, word_embedding_dimension::Integer=300)
+    d = word_embedding_dimension
+    p = total_word_position
+    pe = [x = i%2 == 0 ? cos(j/(10^(2i/d))) : sin(j/(10^(2i/d))) for i = 1:d, j = 1:p]
+    return pe
+
+end
+
+
+"""
+    wp_embedding(tokenized_word::Array{String}, positional_embedding::Bool=false)
+
+Word embedding with positional embedding.
+tokenized_word = sentense's tokenized word (not sentense in English definition but BERT definition. 
+                    1-BERT sentense can be 20+ English's sentense)
+
+# Example
+
+            
+"""
+function wp_embedding(tokenized_word::Array{String}, positional_embedding::Bool=false)
+    we_matrix = 0
+    for (i, v) in enumerate(tokenized_word)
+        if i == 1
+            we_matrix = get_word_embedding(v)
+        else
+            we_matrix = hcat(we_matrix, get_word_embedding(v))
+        end
+    end
+
+    if positional_embedding
+        pe_matrix = get_positional_embedding(length(tokenized_word), 300) # positional embedding
+        wp_matrix = we_matrix + pe_matrix
+
+        return wp_matrix
+    else
+        return we_matrix
+    end
+end
+
+
+"""
+    wp_query(tokenized_word::Array{String}, positional_embedding::Bool=false)
+
+convert tokenized_word into JSON3 String to be sent to GloVe docker server
+"""
+function wp_query_send(tokenized_word::Array{String}, positional_embedding::Bool=false)
+    d = Dict("tokenized_word"=> tokenized_word, "positional_embedding"=>positional_embedding)
+    json3_str = JSON3.write(d)
+    return json3_str
+end
+
+
+"""
+    wp_query(tokenized_word::Array{String}, positional_embedding::Bool=false)
+
+Using inside word_embedding_server to receive word embedding job
+convert JSON3 String into tokenized_word and positional_embedding
+"""
+function wp_query_receive(json3_str::String)
+    d = JSON3.read(json3_str)
+    tokenized_word = Array(d.tokenized_word)
+    positional_embedding = d.positional_embedding
+
+    return tokenized_word, positional_embedding
+end
+
+
+"""
+    Send tokenized_word to word_embedding_server and return word embedding
+
+# Example
+
+    WPembeddings.query_wp_server(tokenized_word)
+"""
+function query_wp_server(query;
+                        host="0.0.0.0",
+                        port=6379,
+                        publish_channel="word_embedding_server/input",
+                        positional_encoding=true)
+
+    # channel used to receive JSON String from word_embedding_server
+    wp_channel = Channel(10)
+    function wp_receive(x)
+        array = Utils.JSON3_str_to_Array(x)
+        put!(wp_channel, array)
+    end
+
+    # establish connection to word_embedding_server using default port
+    conn = Redis.RedisConnection(host=host, port=port)   
+    sub = Redis.open_subscription(conn)
+    Redis.subscribe(sub, "word_embedding_server/output", wp_receive)
+    # Redis.subscribe(sub, "word_embedding_server/output", WPembeddings.wp_receive)
+
+    # set positional_encoding = true to enable positional encoding
+    query = WPembeddings.wp_query_send(query, positional_encoding) 
+    # Ask word_embedding_server for word embedding
+    Redis.publish(conn, publish_channel, query);
+    wait(wp_channel)    # wait for word_embedding_server to response
+    embedded_word = take!(wp_channel)
+
+    disconnect(conn)
+    return embedded_word  
+end
+
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+end