optimization and fehler behebung

Project/Tools/User_Interface.ipynb

@@ -139,12 +139,22 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
-    "#TODO"
+    "#sys.path.append(\"..\")\n",
+    "#print(sys.path)\n",
+    "\n",
+    "import naive_approach.naive_approach as clf_naive"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp_dict = clf_naive.prepareData()"
    ]
   },
   {
@@ -157,25 +167,31 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def merged_prediction(msg , split = 1 , number = 8, target_emojis = top_emojis):\n",
+    "def merged_prediction(msg , split = 0.5 , number = 8, target_emojis = top_emojis):\n",
     "    \n",
     "    #calc ratio of prediction splitted between advanced aprroach and naive approach\n",
     "    number_advanced = round(split*number)\n",
-    "    number_naive = round(1-split)*number\n",
+    "    number_naive = round((1-split)*number)\n",
+    "    \n",
+    "    #predict emojis with the naive approach\n",
+    "    prediction_naive = clf_naive.predict(sentence = msg, lookup= tmp_dict, n = number_naive)\n",
+    "    \n",
+    "    if(len(prediction_naive) < number_naive):\n",
+    "        print(\"only few matches\")\n",
+    "        number_advanced = number - len(prediction_naive)\n",
+    "        \n",
+    "    #print(number, number_advanced, number_naive)\n",
     "    \n",
     "    #predict the advanced approach\n",
     "    sentiment = clf_advanced.predict([msg])\n",
     "    prediction_advanced = ed.sentiment_vector_to_emoji(sentiment,n_results = number_advanced, custom_target_emojis=target_emojis)\n",
     "        \n",
-    "    #predict emojis with the naive approach\n",
-    "    #prediction_naive = clf_naive.predict(msg, target_emojis)\n",
-    "    \n",
     "    #concat both predictions\n",
-    "    prediction = prediction_advanced#.append(prediction_naive)\n",
+    "    prediction = list(prediction_advanced)+list(prediction_naive)\n",
     "    \n",
     "    return prediction[:number]"
    ]
@@ -189,7 +205,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {
     "collapsed": true
    },
@@ -222,7 +238,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {
     "collapsed": true
    },
@@ -251,7 +267,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {
     "collapsed": true
    },
@@ -262,7 +278,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 11,
    "metadata": {
     "collapsed": true
    },
@@ -287,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "metadata": {
     "collapsed": true
    },
@@ -295,7 +311,7 @@
    "source": [
     "text_input.on_submit(submit_new_message)\n",
     "\n",
-    "text_input.observe(lambda b: trigger_new_prediction(None, text_input.value))"
+    "#text_input.observe(lambda b: trigger_new_prediction(None, text_input.value))"
    ]
   },
   {
@@ -308,7 +324,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "metadata": {
     "collapsed": true
    },
@@ -329,7 +345,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "metadata": {
     "collapsed": true
    },
@@ -347,7 +363,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {
     "collapsed": true
    },
@@ -365,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {
     "collapsed": true
    },
@@ -383,7 +399,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 17,
    "metadata": {
     "collapsed": true
    },
@@ -401,7 +417,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {
     "collapsed": true
    },
@@ -419,7 +435,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {
     "collapsed": true
    },
@@ -437,7 +453,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {
     "collapsed": true
    },
@@ -455,7 +471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {
     "collapsed": true
    },
@@ -473,7 +489,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 22,
    "metadata": {
     "collapsed": true
    },
@@ -511,13 +527,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "dc2c580ed3224ae7b2582c7c039813dd",
+       "model_id": "9b6fd63901c84db9a5a2d10399053cb3",
        "version_major": 2,
        "version_minor": 0
       },
@@ -527,6 +543,16 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8 4 4\n",
+      "8 4 4\n",
+      "8 4 4\n",
+      "8 4 4\n"
+     ]
     }
    ],
    "source": [
@@ -537,6 +563,15 @@
     "display(total_layout)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,