Added table prototype views + model learner

AngularApp/prototype/src/app/api.service.ts

@@ -6,9 +6,11 @@ export interface RawData {
 }
 
 export interface LshData {
-  candidates: {[bucket: string]: number[]}[];
+  candidates: number[][][];
+  tables: {[bucket: string]: number[]}[];
   average_candidates: number[];
   average_distances: number[];
+  samples: number[];
   distances: number[][][];
   hash_functions: number[][][][];
   parameters?: number[];
@@ -62,6 +64,18 @@ export class ApiService {
     return await response.json();
   }
 
+  async getWeights(query: number[][], labels: {[index: number]: boolean}, weights: number[]): Promise<number[]> {
+    const response = await fetch('http://127.0.0.1:5000/weights', {
+      method: 'POST',
+      headers: {
+        'Accept': 'application/json',
+        'Content-Type': 'application/json'
+      },
+      body: new Blob( [ JSON.stringify({query, labels, weights}) ], { type: 'text/plain' } )
+    });
+    return await response.json();
+  }
+
   // Find candidates using LSH with weights
   async lshUpdate(query, weights, parameters): Promise<LshData> {
     const response = await fetch('http://127.0.0.1:5000/update', {

AngularApp/prototype/src/app/labeling-window/labeling-window.component.ts

@@ -10,7 +10,7 @@ export class LabelingWindowComponent implements OnInit {
   public topk: number[];
   public subplots = [];
   public labels: boolean[] = [];
-  private k = 12;
+  private k = 5;
 
   constructor(private state: StateService) { }
 
@@ -21,7 +21,7 @@ export class LabelingWindowComponent implements OnInit {
   async train() {
     this.state.labels = Object.assign({}, this.state.labels, this.labels);
     await this.state.getQueryWindow(this.state.labels);
-    await this.state.update();
+    await this.state.update(Object.assign({}, this.labels));
   }
 
   async updateQuery() {
@@ -44,9 +44,14 @@ export class LabelingWindowComponent implements OnInit {
   }
 
   async showSamples() {
-    this.topk = this.state.lshData.average_candidates
-      .filter((candidate) => this.state.labels[candidate] !== true)
-      .slice(0, this.k);
+    // const allowedCandidates = this.state.lshData.average_candidates.filter((candidate) => this.state.labels[candidate] !== true);
+    // this.topk = allowedCandidates.slice(0, this.k);
+    // for (let i = 0; i < this.k; i++) {
+    //   this.topk.push(allowedCandidates[Math.floor(Math.random() * allowedCandidates.length)]);
+    // }
+    this.labels = [];
+    this.topk = this.state.lshData.samples;
+
     this.subplots = [];
     const values: number[][][] = await this.state.getWindow(this.topk);
     for (const idx in this.topk) {

AngularApp/prototype/src/app/overview-window/overview-window.component.ts

@@ -148,7 +148,7 @@ export class OverviewWindowComponent implements OnInit {
     };
     console.log(this.config);
     console.log("showing plot");
-    // await this.debugClicked();
+    await this.debugClicked();
   }
 
   async updatePlot() {
@@ -256,7 +256,7 @@ export class OverviewWindowComponent implements OnInit {
   }
 
   async debugClicked() {
-    const index = 1234;
+    const index = 6713;
     await this.state.getQueryWindow(index);
     await this.state.lshInitial();
   }

AngularApp/prototype/src/app/state.service.ts

@@ -13,6 +13,7 @@ export class StateService {
   private _queryWindow: number[][];
   private _table: {[bucket: string]: number[]}[];
   public _averageTable: {[bucket: string]: number[]};
+  private _weights: number[];
 
   private _currentTab: number;
   private _labels = {};
@@ -69,18 +70,20 @@ export class StateService {
     this.lshData = await this.api.lshInitial(this._queryWindow);
     console.log('data loaded');
     this._lshParameters = this.lshData.parameters;
+    this._weights = [1, 1, 1];
     this.createTable();
   }
 
-  async update(): Promise<void> {
-    this.lshData = await this.api.lshUpdate(this._queryWindow, [], this._lshParameters);
+  async update(labels): Promise<void> {
+    this._weights = await this.api.getWeights(this._queryWindow, labels, this._weights);
+    console.log(this._weights);
+    this.lshData = await this.api.lshUpdate(this._queryWindow, this._weights, this._lshParameters);
     this.createTable();
   }
 
-  async getTableInfo(): Promise<TableInfoData> {
-    this.tableInfo = await this.api.getTableInfo(Object.values(this._averageTable));
-    console.log(this.tableInfo);
-    return this.tableInfo;
+  async getTableInfo(table: number[][]): Promise<TableInfoData> {
+    // console.log(this.tableInfo);
+    return await this.api.getTableInfo(table);
   }
 
   async getQueryWindow(windowIndex: number | {[index: number]: boolean}): Promise<number[][]> {
@@ -93,9 +96,9 @@ export class StateService {
     return await this.api.getWindowByIndices(indices);
   }
 
-  public createTable() {
+  async createTable() {
     console.log('setting table param');
-    this.table = this.lshData.candidates;
+    this.table = this.lshData.tables;
     console.log('table param set');
     const averageTable = {};
     const length = this.lshData.average_distances.length;
@@ -111,7 +114,7 @@ export class StateService {
     });
     this._averageTable = averageTable;
     console.log('table created');
-    this.getTableInfo();
+    this.tableInfo = await this.getTableInfo(Object.values(this._averageTable));
   }
 
   public set rawData(v: RawData[]) {

AngularApp/prototype/src/app/table-overview/table-overview.component.html

@@ -6,7 +6,7 @@
   </div>
   <div class="plots">
     <div class="subplot" *ngFor="let subplot of averages">
-      <plotly-plot class="plotly-plot" [data]="subplot.data" [layout]="subplot.layout"></plotly-plot>
+      <plotly-plot class="plotly-plot" [data]="subplot" [layout]="layout"></plotly-plot>
 <!--      <div class="button-holder">-->
 <!--        <button class="query-button" (click)="setQuery(subplot.data)">&#x21c4;</button>-->
 <!--      </div>-->

AngularApp/prototype/src/app/table-overview/table-overview.component.ts

@@ -9,65 +9,106 @@ import {StateService} from '../state.service';
 export class TableOverviewComponent implements OnInit {
   public subplots;
   public averages;
+  public layout;
   constructor(private state: StateService) { }
 
   ngOnInit(): void {
-    this.state.onNewTable.subscribe(() => this.createHistograms());
-    // this.state.onNewTableInfo.subscribe(() => this.createPrototypes());
+    this.state.onNewTable.subscribe(() => {
+      this.createHistograms();
+      this.createPrototypes();
+    });
   }
 
-  createPrototypes(): void {
-    this.averages = this.state.tableInfo.prototypes.map(prototype => {
-      return {
-        data: [
-          {
-            x: [...Array(prototype.average.length).keys()],
-            y: prototype.average,
-            type: 'line',
-          },
-          {
-            x: [...Array(prototype.average.length).keys()],
-            y: prototype.max,
-            type: 'scatter',
-            fill: null,
-            mode: 'lines',
-            line: {
-              color: 'rgb(55, 128, 191)',
-              width: 3
-            }
-          },
-          {
-            x: [...Array(prototype.average.length).keys()],
-            y: prototype.min,
-            type: 'scatter',
-            fill: 'tonexty',
-            mode: 'lines',
-            line: {
-              color: 'rgb(55, 128, 191)',
-              width: 3
-            }
+  async createPrototypes(): Promise<void> {
+    const representatives: number[][] = [];
+    this.state.lshData.candidates.forEach((grouphash) => {
+      grouphash.forEach((candidates) => {
+        representatives.push(candidates.slice(0, 20));
+      });
+    });
+    const prototypes = await this.state.getTableInfo(representatives);
+    const subplots = [];
+    this.averages = prototypes.prototypes.map((prototype) => {
+      const channelData = [];
+      prototype.max.forEach((channel, index) => {
+        channelData.push({
+          x: [...Array(channel.length).keys()],
+          y: channel,
+          xaxis: 'x',
+          yaxis: `y${index + 2}`,
+          type: 'scatter',
+          fill: null,
+          mode: 'lines',
+          line: {
+            color: 'rgb(55, 128, 191)',
+            width: 3
           }
-        ],
-        layout: {
-          showlegend: false,
-          hovermode: 'closest',
-          autosize: true,
-          margin: {
-            l: 10,
-            r: 10,
-            t: 10,
-            b: 10,
-            pad: 4
-          },
-          xaxis: {
-            showticklabels: false
-          },
-          yaxis: {
-            showticklabels: false
-          },
-          height: 100,
-          width: screen.width * 0.1,
-        }
+        });
+      });
+      prototype.min.forEach((channel, index) => {
+        channelData.push({
+          x: [...Array(channel.length).keys()],
+          y: channel,
+          xaxis: 'x',
+          yaxis: `y${index + 2}`,
+          type: 'scatter',
+          fill: 'tonexty',
+          mode: 'lines',
+          line: {
+            color: 'rgb(55, 128, 191)',
+            width: 3
+          }
+        });
+      });
+      prototype.average.forEach((channel, index) => {
+        channelData.push({
+          x: [...Array(channel.length).keys()],
+          y: channel,
+          xaxis: 'x',
+          yaxis: `y${index + 2}`,
+          type: 'line',
+          line: {
+            color: 'red',
+            width: 3
+          }
+        });
+      });
+      return channelData;
+    });
+    for (let index = 0; index < this.state.queryWindow.length; index++) {
+      subplots.push([`xy${index + 2}`]);
+    }
+    this.layout = {
+      grid: {
+        rows: this.state.queryWindow.length,
+        columns: 1,
+        subplots: subplots,
+      },
+      showlegend: false,
+      hovermode: 'closest',
+      autosize: true,
+      margin: {
+        l: 10,
+        r: 10,
+        t: 30,
+        pad: 4
+      },
+      xaxis: {
+        showgrid: false,
+        zeroline: false,
+        showticklabels: false,
+      },
+      yaxis: {
+        zeroline: false,
+        showticklabels: false,
+      },
+      height: 300,
+      width: screen.width * 0.1,
+    };
+    this.state.queryWindow.forEach((channel: number[], index: number) => {
+      this.layout[`yaxis${index + 2}`] = {
+        zeroline: false,
+        showticklabels: false,
       };
     });
   }

Flaskserver/.idea/workspace.xml

@@ -23,12 +23,16 @@
       <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/api.service.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/api.service.ts" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/labeling-window/labeling-window.component.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/labeling-window/labeling-window.component.ts" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/overview-window/overview-window.component.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/overview-window/overview-window.component.ts" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/progress-view/progress-view.component.css" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/progress-view/progress-view.component.css" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/progress-view/progress-view.component.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/progress-view/progress-view.component.ts" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/state.service.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/state.service.ts" afterDir="false" />
-      <change beforePath="$PROJECT_DIR$/../AngularApp/prototype/src/app/table-overview/table-overview.component.ts" beforeDir="false" afterPath="$PROJECT_DIR$/../AngularApp/prototype/src/app/table-overview/table-overview.component.ts" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/_lsh.cpython-38-x86_64-linux-gnu.so" beforeDir="false" afterPath="$PROJECT_DIR$/_lsh.cpython-38-x86_64-linux-gnu.so" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/build/lib.linux-x86_64-3.8/_lsh.cpython-38-x86_64-linux-gnu.so" beforeDir="false" afterPath="$PROJECT_DIR$/build/lib.linux-x86_64-3.8/_lsh.cpython-38-x86_64-linux-gnu.so" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/build/temp.linux-x86_64-3.8/locality-sensitive-hashing-visual-analytics/lsh-fast/_lsh.o" beforeDir="false" afterPath="$PROJECT_DIR$/build/temp.linux-x86_64-3.8/locality-sensitive-hashing-visual-analytics/lsh-fast/_lsh.o" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/build/temp.linux-x86_64-3.8/locality-sensitive-hashing-visual-analytics/lsh-fast/lsh.o" beforeDir="false" afterPath="$PROJECT_DIR$/build/temp.linux-x86_64-3.8/locality-sensitive-hashing-visual-analytics/lsh-fast/lsh.o" afterDir="false" />
       <change beforePath="$PROJECT_DIR$/main.py" beforeDir="false" afterPath="$PROJECT_DIR$/main.py" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/processed-data.npy" beforeDir="false" />
+      <change beforePath="$PROJECT_DIR$/../experiments/.ipynb_checkpoints/EEG data test-checkpoint.ipynb" beforeDir="false" afterPath="$PROJECT_DIR$/../experiments/.ipynb_checkpoints/EEG data test-checkpoint.ipynb" afterDir="false" />
+      <change beforePath="$PROJECT_DIR$/../experiments/EEG data test.ipynb" beforeDir="false" afterPath="$PROJECT_DIR$/../experiments/EEG data test.ipynb" afterDir="false" />
     </list>
     <option name="SHOW_DIALOG" value="false" />
     <option name="HIGHLIGHT_CONFLICTS" value="true" />
@@ -48,7 +52,7 @@
     <property name="RunOnceActivity.OpenProjectViewOnStart" value="true" />
     <property name="SHARE_PROJECT_CONFIGURATION_FILES" value="true" />
     <property name="WebServerToolWindowFactoryState" value="false" />
-    <property name="last_opened_file_path" value="$PROJECT_DIR$/../../hashing-based-network-discovery-master" />
+    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
     <property name="nodejs_interpreter_path.stuck_in_default_project" value="undefined stuck path" />
     <property name="nodejs_npm_path_reset_for_default_project" value="true" />
   </component>
@@ -143,10 +147,10 @@
       <screen x="72" y="27" width="1848" height="1053" />
     </state>
     <state x="479" y="254" width="1200" height="800" key="DiffContextDialog/72.27.1848.1053@72.27.1848.1053" timestamp="1603129938934" />
-    <state x="779" y="311" width="424" height="491" key="FileChooserDialogImpl" timestamp="1605528689693">
+    <state x="779" y="311" width="424" height="491" key="FileChooserDialogImpl" timestamp="1606260652750">
       <screen x="72" y="27" width="1848" height="1053" />
     </state>
-    <state x="779" y="311" width="424" height="491" key="FileChooserDialogImpl/72.27.1848.1053@72.27.1848.1053" timestamp="1605528689693" />
+    <state x="779" y="311" width="424" height="491" key="FileChooserDialogImpl/72.27.1848.1053@72.27.1848.1053" timestamp="1606260652750" />
     <state x="659" y="259" width="672" height="678" key="search.everywhere.popup" timestamp="1604929652702">
       <screen x="72" y="27" width="1848" height="1053" />
     </state>

Flaskserver/main.py

@@ -175,15 +175,19 @@ def initialize():
     t0 = time()
     raw_data = orjson.loads(request.data)
     data = np.load('processed-data.npy')
-    data = np.reshape(data, (len(data), len(data[0][0]), len(data[0])))
+    data = np.swapaxes(data, 1, 2)
+    # data = np.reshape(data, (len(data), len(data[0][0]), len(data[0])))
     query = raw_data["query"]
-    query = np.reshape(query, (len(query[0]), len(query)))
-    parameters = np.load('parameters.npy')
+    query = np.swapaxes(query, 0, 1)
+    # query = np.reshape(query, (len(query[0]), len(query)))
+    parameters = preprocess(data)
+    # parameters = np.load('parameters.npy')
     r = parameters[0]
     a = parameters[1]
     sd = parameters[2]
 
     candidates, distances, hf = _lsh.lsh(data, query, r, a, sd)
+    print(distances)
 
     dict = defaultdict(int)
     for l in range(len(candidates)):
@@ -195,9 +199,11 @@ def initialize():
     average_distances = list(sorted_dict.values())
 
     tables = []
+    samples_set = set()
     candidates = candidates.tolist()
     for l in range(len(candidates)):
         for k in range(len(candidates[0])):
+            samples_set.update(candidates[l][k][0:5])
             dict = defaultdict(list)
             length = len(distances[l][k])
             median = distances[l][k][math.ceil(length/2)]
@@ -207,13 +213,17 @@ def initialize():
                 dict[str(indices[i])].append(candidates[l][k][i])
             tables.append(dict)
 
+    samples = np.array(list(filter(lambda x: x in samples_set, average_candidates))).tolist()
+
 
     response = {
         "hash_functions": hf.tolist(),
-        "candidates": tables,
+        "candidates": candidates,
+        "tables": tables,
+        "distances": distances.tolist(),
+        "samples": list(samples),
         "average_candidates": np.array(average_candidates).tolist(),
         "average_distances": np.array(average_distances).tolist(),
-        "distances": distances.tolist(),
         "parameters": [float(r), float(a), float(sd)]
     }
     response = orjson.dumps(response)
@@ -222,12 +232,40 @@ def initialize():
 
 @app.route('/weights', methods=['POST'])
 def weights():
+    alpha = 0.2
     raw_data = orjson.loads(request.data)
-    parameters = raw_data["labels"]
+    labels = raw_data["labels"]
+    query = raw_data["query"]
+    old_weights = raw_data["weights"]
+    data = np.load('processed-data.npy')
+    all_good_windows = data[[[int(index) for index, value in labels.items() if value is True]]]
+    all_bad_windows = data[[[int(index) for index, value in labels.items() if value is False]]]
+
+    good_distances = np.zeros(len(query))
+    for window in all_good_windows:
+        for i in range(len(all_good_windows[0])):
+            good_distances[i] += _ucrdtw.ucrdtw(query[i], window[i], 0.05, False)[1]
+    if len(all_good_windows) != 0:
+        good_distances /= np.sum(good_distances)
+        good_distances = np.ones(len(query)) - good_distances
+        good_distances /= np.sum(good_distances)
+        good_distances *= len(all_good_windows[0])
+        good_distances = np.sqrt(good_distances)
+    good_distances = alpha * np.array(old_weights) + (1-alpha) * good_distances
+
+    # bad_distances = np.zeros(len(query))
+    # for window in all_bad_windows:
+    #     for i in range(len(all_bad_windows[0])):
+    #         bad_distances[i] += _ucrdtw.ucrdtw(query[i], window[i], 0.05, False)[1]
+    # if len(all_bad_windows) != 0:
+    #     bad_distances /= np.sum(bad_distances)
+    #     bad_distances = np.ones(len(query)) - bad_distances
+
+    print(good_distances)
 
     # Caculate weights
 
-    response = weights
+    response = orjson.dumps(good_distances.tolist())
     return response
 
 
@@ -236,13 +274,15 @@ def update():
     t0 = time()
     raw_data = orjson.loads(request.data)
     data = np.load('processed-data.npy')
-    data = np.reshape(data, (len(data), len(data[0][0]), len(data[0])))
+    data = np.swapaxes(data, 1, 2)
+    # data = np.reshape(data, (len(data), len(data[0][0]), len(data[0])))
     query = raw_data["query"]
-    query = np.reshape(query, (len(query[0]), len(query)))
+    query = np.swapaxes(query, 0, 1)
+    # query = np.reshape(query, (len(query[0]), len(query)))
     weights = raw_data["weights"]
     parameters = raw_data["parameters"]
 
-    candidates, distances, hf = _lsh.lsh(data, query, parameters[0], parameters[1], parameters[2])
+    candidates, distances, hf = _lsh.lsh(data, query, parameters[0], parameters[1], parameters[2], weights)
     dict = defaultdict(int)
     for l in range(len(candidates)):
         for k in range(len(candidates[0])):
@@ -253,9 +293,11 @@ def update():
     average_distances = list(sorted_dict.values())
 
     tables = []
+    samples_set = set()
     candidates = candidates.tolist()
     for l in range(len(candidates)):
         for k in range(len(candidates[0])):
+            samples_set.update(candidates[l][k][0:5])
             dict = defaultdict(list)
             length = len(distances[l][k])
             median = distances[l][k][math.ceil(length/2)]
@@ -265,9 +307,13 @@ def update():
                 dict[str(indices[i])].append(candidates[l][k][i])
             tables.append(dict)
 
+    samples = np.array(list(filter(lambda x: x in samples_set, average_candidates))).tolist()
+
     response = {
         "hash_functions": hf.tolist(),
-        "candidates": tables,
+        "candidates": candidates,
+        "tables": tables,
+        "samples": list(samples),
         "average_candidates": np.array(average_candidates).tolist(),
         "average_distances": np.array(average_distances).tolist(),
         "distances": distances.tolist(),
@@ -322,8 +368,8 @@ def table_info():
             'max': max_values.tolist(),
             'min': min_values.tolist()
         })
-    distances = [[dtw(np.array(v["average"]), np.array(w["average"]), global_constraint='sakoe_chiba', sakoe_chiba_radius=int(0.05 * 120)) for j, w in enumerate(prototypes)] for i, v in enumerate(prototypes)]
-    response = orjson.dumps({'prototypes': prototypes, 'distances': distances})
+    # distances = [[dtw(np.array(v["average"]), np.array(w["average"]), global_constraint='sakoe_chiba', sakoe_chiba_radius=int(0.05 * 120)) for j, w in enumerate(prototypes)] for i, v in enumerate(prototypes)]
+    response = orjson.dumps({'prototypes': prototypes, 'distances': []})
     print("Averages calculated: " + str(time() - t0))
     return response
 

experiments/.ipynb_checkpoints/EEG data test-checkpoint.ipynb

 %% Cell type:code id: tags:
 
 ``` python
 %load_ext autoreload
 %autoreload 2
 ```
 
+%%%% Output: stream
+
+    The autoreload extension is already loaded. To reload it, use:
+      %reload_ext autoreload
+
 %% Cell type:code id: tags:
 
 ``` python
 import pandas as pd
 import numpy as np
 
 datafile = 'data/21.csv'
 
 data = pd.read_csv(datafile, header=None)
 
 #and convert it to numpy array:
 npdata = np.array(data)
 
 print(npdata.shape)
 ```
 
 %%%% Output: stream
 
     (900096, 74)
 
 %% Cell type:code id: tags:
 
 ``` python
-window_data = [npdata[i:i+120] for i in range(0, npdata.shape[0]-120, int(120/8))]
+window_data = [npdata[i:i+120, 0:40] for i in range(0, npdata.shape[0]-120, int(120/8))]
+del npdata
 data = np.reshape(window_data, (len(window_data), len(window_data[0][0]), len(window_data[0])))
+del window_data
+data = np.reshape(data, (len(data), len(data[0][0]), len(data[0])))
+# data = np.concatenate((data, data))
 print(data.shape)
 ```
 
 %%%% Output: stream
 
-    (59999, 74, 120)
-
-%% Cell type:code id: tags:
-
-``` python
-data = data[:,0:20,:]
-# data = np.concatenate((data, data), axis=0)
-print(data.shape)
-```
-
-%%%% Output: stream
-
-    (59999, 20, 120)
+    (59999, 120, 40)
 
 %% Cell type:code id: tags: