[demo] Add an option for word hashing vocab

demo/.soliumignore

@@ -1,3 +1,5 @@
+# This file is in the root folder for VS Code.
+
 # Abstract contracts do not work yet: https://github.com/duaraghav8/Ethlint/issues/281
 # grep -R --files-with-matches '^abstract contract' client/src/contracts/
 client/src/contracts/classification/Classifier.sol

demo/client/package.json

@@ -12,6 +12,7 @@
     "@tensorflow-models/universal-sentence-encoder": "^1.2.0",
     "@tensorflow/tfjs": "^1.3.2",
     "@types/jest": "^24.0.23",
+    "@types/murmurhash-js": "^1.0.3",
     "@types/node": "^12.12.14",
     "@types/react": "^16.9.14",
     "@types/react-dom": "^16.9.4",
@@ -21,6 +22,7 @@
     "dotenv": "^8.2.0",
     "immutability-helper": "^3.0.1",
     "moment": "^2.24.0",
+    "murmurhash-js": "^1.0.0",
     "notistack": "^0.9.7",
     "react": "^16.12.0",
     "react-addons-update": "^15.6.2",

demo/client/src/components/addModel.js

@@ -274,6 +274,42 @@ class AddModel extends React.Component {
                 onChange={this.handleInputChange}
               />
 
+              {/* Encoder */}
+              <Typography variant="h6" component="h6">
+                Encoder
+              </Typography>
+              <Typography component="p">
+                An encoder is the method that is used to convert the input (text, image, etc.) into a machine readable format.
+              </Typography>
+              <Select className={this.classes.selector}
+                onChange={this.handleInputChange}
+                value={this.state.encoder}
+                inputProps={{
+                  name: 'encoder',
+                }}
+              >
+                <Tooltip value="none" placement="top-start"
+                  title="No transformation will be applied">
+                  <MenuItem>None</MenuItem>
+                </Tooltip>
+                <Tooltip value="MurmurHash3" placement="top-start"
+                  title="Convert each word to a 32-bit number using MurmurHash3">
+                  <MenuItem>MurmurHash3</MenuItem>
+                </Tooltip>
+                <Tooltip value="IMDB vocab" placement="top-start"
+                  title="Convert each word in English text to a number using the 1000 most frequent words in the IMDB review dataset">
+                  <MenuItem>IMDB vocab</MenuItem>
+                </Tooltip>
+                <Tooltip value="universal sentence encoder" placement="top-start"
+                  title="Use Universal Sentence Encoder to convert English text to a vector of numbers">
+                  <MenuItem>Universal Sentence Encoder (for English text)</MenuItem>
+                </Tooltip>
+                <Tooltip value="MobileNetv2" placement="top-start"
+                  title="Use MobileNetv2 to convert images to a vector of numbers">
+                  <MenuItem>MobileNetv2 (for images)</MenuItem>
+                </Tooltip>
+              </Select>
+
               {/* Model */}
               {/* Don't need to ask for the model type since there is only one option and in the future, it should be inferred from the provided file.
               <InputLabel className={this.classes.selectorLabel} htmlFor="model-type">Model type</InputLabel>
@@ -307,36 +343,6 @@ class AddModel extends React.Component {
                 )}
               </Dropzone>
 
-              {/* Encoder */}
-              <Tooltip placement="top-start"
-                title="The method that will be used to convert the input (text, image, etc.) into a machine readable format">
-                <InputLabel className={this.classes.selectorLabel} htmlFor="encoder">Encoder</InputLabel>
-              </Tooltip>
-              <Select className={this.classes.selector}
-                onChange={this.handleInputChange}
-                value={this.state.encoder}
-                inputProps={{
-                  name: 'encoder',
-                }}
-              >
-                <Tooltip value="none" placement="top-start"
-                  title="No transformation will be applied">
-                  <MenuItem>None</MenuItem>
-                </Tooltip>
-                <Tooltip value="IMDB vocab" placement="top-start"
-                  title="Convert each word in English text to a number using the 1000 most frequent words in the IMDB review dataset">
-                  <MenuItem>IMDB vocab</MenuItem>
-                </Tooltip>
-                <Tooltip value="universal sentence encoder" placement="top-start"
-                  title="Use Universal Sentence Encoder to convert English text to a vector of numbers">
-                  <MenuItem>Universal Sentence Encoder (for English text)</MenuItem>
-                </Tooltip>
-                <Tooltip value="MobileNetv2" placement="top-start"
-                  title="Use MobileNetv2 to convert images to a vector of numbers">
-                  <MenuItem>MobileNetv2 (for images)</MenuItem>
-                </Tooltip>
-              </Select>
-
               {/* Incentive Mechanism */}
               <Tooltip placement="top-start"
                 title={"The system that will be used to determine rewards for data that is determined to be \"good\"."}>
@@ -387,7 +393,7 @@ class AddModel extends React.Component {
         </Paper>
         <Paper className={this.classes.root} elevation={1}>
           <Typography component="h3">
-              Deployment Status
+            Deployment Status
           </Typography>
           <Table className={this.classes.table} aria-label="Deployment Information Table">
             <TableHead>

demo/client/src/components/model.js

@@ -23,6 +23,7 @@ import * as tf from '@tensorflow/tfjs';
 import loadImage from 'blueimp-load-image';
 import update from 'immutability-helper';
 import moment from 'moment';
+import { murmur3 } from 'murmurhash-js';
 import { withSnackbar } from 'notistack';
 import PropTypes from 'prop-types';
 import React from 'react';
@@ -316,7 +317,11 @@ class Model extends React.Component {
   }
 
   async setTransformInputMethod() {
-    if (this.state.contractInfo.encoder === 'universal sentence encoder') {
+    let { encoder } = this.state.contractInfo
+    if (encoder) {
+      encoder = encoder.toLocaleLowerCase('en')
+    }
+    if (encoder === 'universal sentence encoder') {
       this.setState({ inputType: INPUT_TYPE_TEXT });
       UniversalSentenceEncoder.load().then(use => {
         this.transformInput = async (query) => {
@@ -335,7 +340,7 @@ class Model extends React.Component {
         };
         this.transformInput = this.transformInput.bind(this);
       });
-    } else if (this.state.contractInfo.encoder === 'MobileNetv2') {
+    } else if (encoder === 'MobileNetv2'.toLocaleLowerCase('en')) {
       this.setState({ inputType: INPUT_TYPE_IMAGE });
       // https://github.com/tensorflow/tfjs-models/tree/master/mobilenet
       mobilenet.load({
@@ -362,14 +367,14 @@ class Model extends React.Component {
         }
         this.transformInput = this.transformInput.bind(this);
       });
-    } else if (this.state.contractInfo.encoder === 'IMDB vocab') {
+    } else if (encoder === 'IMDB vocab'.toLocaleLowerCase('en')) {
       this.setState({ inputType: INPUT_TYPE_TEXT });
       this.vocab = [];
       Object.entries(ImdbVocab).forEach(([key, value]) => {
         this.vocab[value] = key;
       });
       this.transformInput = async (query) => {
-        const tokens = query.toLowerCase().split(" ");
+        const tokens = query.toLocaleLowerCase('en').split(/\s+/)
         return tokens.map(t => {
           let idx = ImdbVocab[t];
           if (idx === undefined) {
@@ -380,6 +385,13 @@ class Model extends React.Component {
         }).map(v => this.web3.utils.toHex(v));
       };
       this.transformInput = this.transformInput.bind(this);
+    } else if (encoder === 'MurmurHash3'.toLocaleLowerCase('en')) {
+      this.setState({ inputType: INPUT_TYPE_TEXT })
+      this.transformInput = async (query) => {
+        const tokens = query.toLocaleLowerCase('en').split(/\s+/)
+        return tokens.map(murmur3).map(v => this.web3.utils.toHex(v));
+      };
+      this.transformInput = this.transformInput.bind(this);
     } else {
       throw new Error(`Couldn't find encoder for ${this.state.contractInfo.encoder}`);
     }

demo/client/src/containers/modelList.js

@@ -2,6 +2,7 @@ import Button from '@material-ui/core/Button';
 import CircularProgress from '@material-ui/core/CircularProgress';
 import Container from '@material-ui/core/Container';
 import IconButton from '@material-ui/core/IconButton';
+import Link from '@material-ui/core/Link';
 import List from '@material-ui/core/List';
 import ListItem from '@material-ui/core/ListItem';
 import ListItemSecondaryAction from '@material-ui/core/ListItemSecondaryAction';
@@ -10,7 +11,6 @@ import Modal from '@material-ui/core/Modal';
 import Paper from '@material-ui/core/Paper';
 import { withStyles } from '@material-ui/core/styles';
 import Typography from '@material-ui/core/Typography';
-import AddIcon from '@material-ui/icons/Add';
 import ClearIcon from '@material-ui/icons/Clear';
 import DeleteIcon from '@material-ui/icons/Delete';
 import { withSnackbar } from 'notistack';
@@ -233,16 +233,12 @@ class ModelList extends React.Component {
             </Typography>
             <Typography component="p">
               Here you will find models stored on a blockchain that you can interact with.
-              Models are added to this list if you have recorded them locally in this browser
+              Models are added to this list if you have recorded them on your device in this browser
               {serviceStorageEnabled ? " or if they are listed on a centralized database" : ""}.
             </Typography>
-            <div>
-              <Button className={this.props.classes.button} variant="outlined" color="primary"
-                href='/addDeployedModel'
-              >
-                <AddIcon />&nbsp;Use a deployed model not listed
-              </Button>
-            </div>
+            <Typography component="p">
+              You can deploy your own model <Link href='/addModel'>here</Link> or use an already deployed model by filling in the information <Link href='/addDeployedModel'>here</Link>.
+            </Typography>
           </div>
           {this.state.loadingModels ?
             <div className={this.props.classes.spinnerDiv}>
@@ -277,7 +273,7 @@ class ModelList extends React.Component {
               :
               <div className={this.props.classes.descriptionDiv}>
                 <Typography component="p">
-                  No models found.
+                  You do not have any models listed.
                 </Typography>
               </div>
           }

demo/client/src/contracts/classification/NaiveBayesClassifier.sol

@@ -62,7 +62,7 @@ contract NaiveBayesClassifier is Classifier64 {
      * @param _classifications The classifications supported by the model.
      * @param _classCounts The number of occurrences of each class in the training data.
      * @param _featureCounts For each class, the number of times each feature occurs within that class.
-     * Each innermost list is a tuple of the feature index and the number of times that feature occurs within the class.
+     * Each innermost array is a tuple of the feature index and the number of times that feature occurs within the class.
      * @param _totalNumFeatures The total number of features throughout all classes.
      * @param _smoothingFactor The smoothing factor (sometimes called alpha). Use toFloat (1 mapped) for Laplace smoothing.
      */
@@ -97,7 +97,8 @@ contract NaiveBayesClassifier is Classifier64 {
      * Set feature counts for an existing classification.
      * For efficiency, features are overriden them if they have already been set.
      * Made to be called just after the contract is created and never again.
-     * @param _featureCounts The index to start placing `_weights` into the model's weights.
+     * @param _featureCounts The number of times each feature occurs.
+     * Each innermost array is a tuple of the feature index and the number of times that feature occurs within the class.
      * @param classification The class to add counts to.
      */
     function initializeCounts(uint32[][] memory _featureCounts, uint64 classification) public onlyOwner {