classification model for sentence function

README.md

@@ -10,7 +10,7 @@ source ./.venv/Scripts/activate
 ```
 
 ```
-pip install tensorflow keras mypy numpy types-tensorflow
+pip install tensorflow keras mypy pandas numpy types-tensorflow
 ```
 
 In settings, make sure to check `Run Using Active Interpreter` in mypy settings

current_goal.txt

@@ -7,14 +7,7 @@ sitting in a tavern by a fireplace
 
 
 
-Parse "Hello"
+Respond to "What color is your hat?"
-Nodes needed:
- - Conversation
- - Greeting
- - Participant
- - Instances of participants
- - Instances of conversations
- - Instances of greetings (created as the conversation starts)
 
 
 

data/model/sent_func/fingerprint.pb

@@ -0,0 +1 @@
+▄╘з▓┌кЪДЖ
Ь▄йЩ▄сЩлЖ
╔в╔▓ИКЙG ▄шЁЗаБ÷Т╒
(▀лЙ▀Рэ║Р
2

data/semantic/sent_func/test.csv

@@ -0,0 +1,4 @@
+"utility","transfer","query","imperative","sentence"
+1,0,0,0,"Hello"
+1,0,0,0,"Hi"
+1,0,0,0,"Howdy"

data/semantic/sent_func/train.csv

@@ -0,0 +1,4 @@
+"utility","transfer","query","imperative","sentence"
+1,0,0,0,"Hello"
+1,0,0,0,"Hi"
+1,0,0,0,"Howdy"

src/main/java/org/studiorailgun/Globals.java

@@ -2,6 +2,7 @@ package org.studiorailgun;
 
 import java.io.File;
 
+import org.studiorailgun.conversation.categorization.SentenceFunctionCategorizor;
 import org.studiorailgun.conversation.evaluators.greet.GreetingEval;
 import org.studiorailgun.conversation.tracking.Conversation;
 import org.studiorailgun.knowledge.KnowledgeWeb;
@@ -27,6 +28,7 @@ public class Globals {
     public static void init(String webPath){
         //initialize evaluators
         GreetingEval.init();
+        SentenceFunctionCategorizor.init();
 
         //init web
         Globals.web = FileUtils.loadObjectFromFile(new File(webPath), KnowledgeWeb.class);

src/main/java/org/studiorailgun/conversation/categorization/SentenceFunctionCategorizor.java

@@ -1,6 +1,18 @@
 package org.studiorailgun.conversation.categorization;
 
+import java.util.HashMap;
+import java.util.Map;
+
 import org.studiorailgun.conversation.tracking.Quote;
+import org.tensorflow.Result;
+import org.tensorflow.SavedModelBundle;
+import org.tensorflow.Tensor;
+import org.tensorflow.ndarray.FloatNdArray;
+import org.tensorflow.ndarray.NdArray;
+import org.tensorflow.ndarray.NdArrays;
+import org.tensorflow.ndarray.Shape;
+import org.tensorflow.types.TFloat32;
+import org.tensorflow.types.TString;
 
 /**
  * Categorizes sentences based on function
@@ -34,13 +46,78 @@ public class SentenceFunctionCategorizor {
         IMPERATIVE,
     }
 
+    /**
+     * The model
+     */
+    static SavedModelBundle model;
+
+    /**
+     * Initializes the categorization model
+     */
+    public static void init(){
+        model = SavedModelBundle.load("./data/model/sent_func");
+
+        //describe the model
+        // System.out.println(model.functions());
+        // System.out.println(model.functions().get(0).signature());
+        // System.out.println(model.functions().get(1).signature());
+        // System.out.println(model.functions().get(2).signature());
+        // System.out.println(model.functions().get(0).signature().getInputs());
+        // System.out.println(model.functions().get(0).signature().getInputs().values().iterator().next().name);
+        // System.out.println(model.functions().get(0).signature().getInputs().values().iterator().next().shape);
+    }
+
     /**
      * Categorizes the sentence by function
      * @param input The input quote
      * @return The function of the sentence
      */
     public static void categorize(Quote input){
-        input.setFunction(SentenceFunction.UTILITY);
+        //construct input
+        TString inputTensor = TString.scalarOf(input.getRaw());
+        inputTensor.shape().append(1);
+        NdArray<String> stringArr = NdArrays.ofObjects(String.class, Shape.of(1, 1));
+        stringArr.setObject(input.getRaw());
+        inputTensor = TString.tensorOf(stringArr);
+
+        Map<String,Tensor> inputMap = new HashMap<String,Tensor>();
+        inputMap.put("keras_tensor",inputTensor);
+
+        //call
+        Result result = model.function("serve").call(inputMap);
+
+        //parse results
+        TFloat32 resultFloats = (TFloat32)result.get(0);
+        FloatNdArray floatArr = resultFloats.get(0);
+        float[] classification = new float[]{
+            floatArr.getFloat(0),
+            floatArr.getFloat(1),
+            floatArr.getFloat(2),
+            floatArr.getFloat(3),
+        };
+
+        //figure out highest value category
+        int maxAt = 0;
+        for(int i = 0; i < classification.length; i++){
+            if(classification[i] > classification[maxAt]){
+                maxAt = i;
+            }
+        }
+
+        switch(maxAt){
+            case 0: {
+                input.setFunction(SentenceFunction.UTILITY);
+            } break;
+            case 1: {
+                input.setFunction(SentenceFunction.TRANSFER);
+            } break;
+            case 2: {
+                input.setFunction(SentenceFunction.QUERY);
+            } break;
+            case 3: {
+                input.setFunction(SentenceFunction.IMPERATIVE);
+            } break;
+        }
     }
     
 }

src/main/python/conversation/sentence/function.py

@@ -5,6 +5,8 @@ from keras.api.layers import TextVectorization, Embedding, LSTM, Dense, Input
 from keras.api.models import Sequential
 import numpy as np
 import numpy.typing as npt
+import pandas as pd
+from pandas import DataFrame
 
 
 
@@ -33,17 +35,48 @@ sequence_length: int = 500
 epochs: int = 50
 max_tokens: int = 5000
 output_sequence_length: int = 4
+num_classes: int = 4
 
 
-# read sentences
-data_path: str = './data/semantic/subject.txt'
-data_raw: str = open(data_path).read()
-vocab: list[str] = data_raw.split('\n')
 
-# read labels
+#
-label_data_path: str = './data/semantic/subject_label.txt'
+# LOAD DATA
-label_data_raw: str = open(label_data_path).read()
+#
-labels: list[int] = list(map(int,label_data_raw.split()))
+
+
+# read training sentences
+train_csv_path: str = './data/semantic/sent_func/train.csv'
+train_csv_raw: DataFrame = pd.read_csv(open(train_csv_path), dtype={
+    "utility": "int64",
+    "transfer": "int64",
+    "query": "int64",
+    "imperative": "int64",
+    "sentence": "string",
+})
+train_data_raw: DataFrame = train_csv_raw[["sentence"]]
+train_data_split: DataFrame = train_data_raw
+vocab: list[str] = train_data_raw.to_numpy()
+train_labels: DataFrame = train_csv_raw[["utility", "transfer", "query", "imperative"]]
+
+# read testing sentences
+test_csv_path: str = './data/semantic/sent_func/test.csv'
+test_csv_raw: DataFrame = pd.read_csv(open(test_csv_path), dtype={
+    "utility": "int64",
+    "transfer": "int64",
+    "query": "int64",
+    "imperative": "int64",
+    "sentence": "string",
+})
+test_data_raw: DataFrame = test_csv_raw[["sentence"]]
+test_data_split: DataFrame = test_data_raw
+test_labels: DataFrame = test_csv_raw[["utility", "transfer", "query", "imperative"]]
+
+
+
+#
+# CREATE VECTORIZER
+#
+
 
 # init vectorizer
 textVec: TextVectorization = TextVectorization(
@@ -54,15 +87,24 @@ textVec: TextVectorization = TextVectorization(
 
 # Add the vocab to the tokenizer
 textVec.adapt(vocab) 
-input_data: list[str] = vocab
+input_data: list[str] = train_data_split
-data: Tensor = textVec.call(input_data)
+train_data: Tensor = textVec.call(input_data)
+
+
+
+
+#
+# CREATE MODEL
+#
+
 
 # construct model
 model: Sequential = Sequential([
-    keras.Input(shape=(None,), dtype="int64"),
+    keras.Input(shape=(1,), dtype=tf.string),
+    textVec,
     Embedding(max_features + 1, embedding_dim),
     LSTM(64),
-    Dense(1, activation='sigmoid')
+    Dense(num_classes, activation='sigmoid')
 ])
 
 #compile the model
@@ -70,26 +112,53 @@ model: Sequential = Sequential([
 model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
 
 
-# fit the training data
+
-npData = np.array(data)
+#
-npLabel = np.array(labels)
+# TRAIN MODEL
-model.fit(npData,npLabel,epochs=epochs)
+#
+
+# Final formatting of data
+npTrainData = train_data_split.to_numpy(dtype=object).flatten()
+npTrainLabel: npt.NDArray[np.complex64] = train_labels.to_numpy()
+if(npTrainData.shape[0] != npTrainLabel.shape[0]):
+    print("Data label size mismatch!")
+    print(npTrainData.shape)
+    print(npTrainLabel.shape)
+
+# try fitting data
+print("Training..")
+model.fit(npTrainData,npTrainLabel,epochs=epochs)
+
+
+
+
+#
+# EVALUATE MODEL
+#
 
 
 # evaluate here
+npTestData: npt.NDArray = test_data_split.to_numpy(dtype=object).flatten()
+npTestLabel: npt.NDArray[np.complex64] = test_labels.to_numpy()
+if(npTestData.shape[0] != npTestLabel.shape[0]):
+    print("Data label size mismatch!")
+    print(npTestData.shape)
+    print(npTestLabel.shape)
+
+print("Evaluating..")
+model.evaluate(npTestData,npTestLabel)
 
 
 # predict
-predictTargetRaw: list[str] = ['saf']
+predictTargetRaw: Tensor = tf.constant(['Hello'])
-predictTargetToken: list[int] = textVec.call(predictTargetRaw)
+npPredict: npt.NDArray = np.array(predictTargetRaw, dtype=object)
-npPredict: npt.NDArray[np.complex64] = np.array(predictTargetToken)
+print("Prediction test..")
-# print(npPredict)
+result: list[int] = model.predict(predictTargetRaw)
-result: list[int] = model.predict(npPredict)
 print("predict result:")
-print(predictTargetToken)
+print(predictTargetRaw)
 print(result)
-print(data)
+print(train_data)
-print(labels)
+print(train_labels)
 
 
 
@@ -97,8 +166,14 @@ print(labels)
 # savePath: str = './data/semantic/model.keras'
 # model.save(savePath)
 
+
+#
+# SAVE MODEL
+#
+
 # export the model so java can leverage it
-exportPath: str = './data/semantic/model'
+print("Saving..")
+exportPath: str = './data/model/sent_func'
 model.export(exportPath)
 
 # tf.keras.utils.get_file('asdf')