From 5e2af0697e8366c2993a820c5861af06e71f6ba3 Mon Sep 17 00:00:00 2001
From: Jonas Weinz <s6jowein@uni-bonn.de>
Date: Sun, 10 Jun 2018 15:06:45 +0200
Subject: [PATCH] minor adjustments

---
 .../simple_twitter_learning.ipynb             | 47 +++++++++----------
 1 file changed, 21 insertions(+), 26 deletions(-)

diff --git a/Project/simple_approach/simple_twitter_learning.ipynb b/Project/simple_approach/simple_twitter_learning.ipynb
index a69701f..eccd552 100644
--- a/Project/simple_approach/simple_twitter_learning.ipynb
+++ b/Project/simple_approach/simple_twitter_learning.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -36,7 +36,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -60,7 +60,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -76,7 +76,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1191,7 +1191,7 @@
        "[68733 rows x 9 columns]"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1210,7 +1210,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1229,7 +1229,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1247,7 +1247,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1264,7 +1264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1277,18 +1277,11 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "mlb_multi = MultiLabelBinarizer()\n",
-    "mlb_single = MultiLabelBinarizer()\n",
-    "\n",
-    "multi_labels = mlb_multi.fit_transform(emojis)\n",
-    "single_labels = mlb_single.fit_transform(np.array([latest(x) for x in emojis]))\n",
-    "\n",
-    "mlb = mlb_single if SINGLE_LABEL else mlb_multi\n",
-    "labels = single_labels if SINGLE_LABEL else multi_labels"
+    "labels = [emoji2sent(latest(x)) for x in emojis]"
    ]
   },
   {
@@ -1300,7 +1293,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1341,7 +1334,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1350,16 +1343,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y1_weights = np.array([(sum([emoji_weights[e] for e in e_list]) / len(e_list)) if len(e_list) > 0 else 0 for e_list in mlb.inverse_transform(y1)])"
+    "y1_weights = np.array([(sum([emoji_weights[e] for e in e_list]) / len(e_list)) if len(e_list) > 0 else 0 for e_list in sent2emoji(y1)])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1619,7 +1612,9 @@
    "outputs": [],
    "source": [
     "# build a dataframe to visualize test results:\n",
-    "testlist = pd.DataFrame({'text': Xt1, 'teacher': (mlb_single if SINGLE_LABEL else mlb).inverse_transform(yt1), 'predict': (mlb_single if SINGLE_LABEL else mlb).inverse_transform(binary_pred)})"
+    "testlist = pd.DataFrame({'text': Xt1, \n",
+    "                         'teacher': mlb_single.inverse_transform(yt1) if SINGLE_LABEL else sent2emoji(yt1), \n",
+    "                         'predict': mlb_single.inverse_transform(binary_pred) if SINGLE_LABEL else sent2emoji(binary_pred)})"
    ]
   },
   {
@@ -2214,7 +2209,7 @@
     "        \n",
     "        binary_pred = np.array(binary_pred)\n",
     "        \n",
-    "        display(Markdown(\"# \" + str((mlb_single if SINGLE_LABEL else mlb).inverse_transform(binary_pred))))\n",
+    "        display(Markdown(\"# \" + str(mlb_single.inverse_transform(binary_pred) if SINGLE_LABEL else sent2emoji(binary_pred))))\n",
     "\n",
     "b.on_click(handle_submit)\n",
     "    \n",