Merge branch 'master' of ssh://the-cake-is-a-lie.net:20022/jonas/NLP-LAB

Jonas_Solutions/Exercise01.ipynb

@@ -14,6 +14,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "%matplotlib ipympl\n",
     "import nltk\n",
     "import pprint\n",
     "from sklearn.tree import DecisionTreeClassifier\n",
@@ -56,20 +57,29 @@
     "    word = sentence[index]\n",
     "    is_punctuation_mark = word == \"!\" or word == \".\" or word == \",\" or word == \"?\"\n",
     "    sentence_length = len(sentence)\n",
-    "    relative_third = (index * 2) // sentence_length \n",
+    "    relative_third = (index * 3) // sentence_length \n",
+    "    vowels = word.count('a') + word.count('e') + word.count('i') + word.count('o') + word.count('u')\n",
     "    return {\n",
-    "        'word': sentence[index],\n",
+    "        'word': word,\n",
     "        'is_capitalized': sentence[index][0].upper() == sentence[index][0],\n",
     "        'prefix-1': sentence[index][0],\n",
     "        'suffix-1': sentence[index][-1],\n",
+    "        'prefix-2': sentence[index][1] if len(word) > 1 else '',\n",
+    "        'suffix-2': sentence[index][-2] if len(word) > 1 else '',\n",
     "        'prev_word': '' if index == 0 else sentence[index - 1],\n",
     "        'next_word': '' if index == len(sentence) - 1 else sentence[index + 1],\n",
+    "        'length': len(word),\n",
+    "        'index' : index,\n",
+    "        'rev_index': len(sentence) - index,\n",
+    "        'sentence_length_': len(sentence),\n",
+    "        'relative_third': relative_third,\n",
     "        'numerical': word.isnumeric(),\n",
     "        'is_punctuation_mark': is_punctuation_mark,\n",
     "        ',': word == \",\",\n",
     "        '.': word == \".\",\n",
     "        '!': word == \"!\",\n",
-    "        '?': word == \"?\"\n",
+    "        '?': word == \"?\",\n",
+    "        'vowels' : vowels\n",
     "    }\n",
     "'''\n",
     "    return {\n",
@@ -187,7 +197,8 @@
     "def test_classifier(clf, tX, ty):\n",
     "    accuracy = clf.score(tX, ty)\n",
     "    print(\"Accuracy: \", accuracy)\n",
-    "    # TODO: more analytics"
+    "    # TODO: more analytics\n",
+    "    return accuracy"
    ]
   },
   {
@@ -201,13 +212,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Exercise 01\n",
+    "### Exercise 01\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "accs = [0] * 5\n",
+    "names = [\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Model 01\n",
     "* train and testing english custom POS tagger model:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -215,8 +243,7 @@
      "output_type": "stream",
      "text": [
       "start training…\n",
-      "training done\n",
+      "training done\n"
-      "Accuracy:  0.8842756624582065\n"
      ]
     }
    ],
@@ -226,46 +253,92 @@
     "X,y,tX,ty = create_training_and_test_set(annotated_sentences=annotated_sent, \n",
     "                                         relative_cutoff=0.8)\n",
     "\n",
-    "classifier = DecisionTreeClassifier(criterion='entropy', splitter='random')\n",
+    "#classifier = DecisionTreeClassifier(criterion='entropy')\n",
-    "clf = train_classifier(X,y,classifier)\n",
+    "from sklearn.neural_network import MLPClassifier\n",
-    "test_classifier(clf=clf, tX=tX, ty=ty)"
+    "model01_clf = train_classifier(X,y,MLPClassifier(),max_size=10000)\n",
+    "accs[0] = test_classifier(clf=clf, tX=tX, ty=ty)"
    ]
   },
   {
-   "cell_type": "code",
+   "cell_type": "markdown",
-   "execution_count": 24,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "8257\n"
-     ]
-    }
-   ],
    "source": [
-    "print(len(classifier.feature_importances_))"
+    "#### Model 02"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "80637"
+       "0"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "len(y)"
+    "accs[1]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\n\\nweights = clf.named_steps[\\'classifier\\'].feature_importances_\\nlabels = clf.named_steps[\\'vectorizer\\'].get_feature_names()\\n\\n#sort\\nweights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\\n\\n#fig_1, ax_1 = plt.subplots()\\n#plt.bar(np.arange(len(weights)), weights)\\n#plt.xticks(np.arange(len(weights)), labels, rotation=90)\\n#plt.show()\\n\\nprint(\"Most important features:\")\\npprint.pprint(list(reversed(labels[-20:])))\\nprint(\"with weights: \")\\npprint.pprint(list(reversed(weights[-20:])))\\n'"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "'''\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "weights = clf.named_steps['classifier'].feature_importances_\n",
+    "labels = clf.named_steps['vectorizer'].get_feature_names()\n",
+    "\n",
+    "#sort\n",
+    "weights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\n",
+    "\n",
+    "#fig_1, ax_1 = plt.subplots()\n",
+    "#plt.bar(np.arange(len(weights)), weights)\n",
+    "#plt.xticks(np.arange(len(weights)), labels, rotation=90)\n",
+    "#plt.show()\n",
+    "\n",
+    "print(\"Most important features:\")\n",
+    "pprint.pprint(list(reversed(labels[-20:])))\n",
+    "print(\"with weights: \")\n",
+    "pprint.pprint(list(reversed(weights[-20:])))\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "from sklearn import tree\n",
+    "import graphviz\n",
+    "dot_data = tree.export_graphviz(clf.named_steps['classifier'], out_file='test',\n",
+    "                         feature_names=labels,\n",
+    "                         filled=True, rounded=True,  \n",
+    "                         special_characters=True)\n",
+    "#graph = graphviz.Source(dot_data)\n",
+    "#graph\n",
+    "```"
    ]
   },
   {