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

This commit is contained in:
Carsten 2018-05-03 14:01:22 +02:00
commit 138d5e6f3d
7 changed files with 2240759 additions and 95 deletions

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@ -205,24 +205,14 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Exercises:" "## Task 01:"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Exercise 01\n" "### Performance 1\n"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"accs = [0] * 5\n",
"names = [\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"]\n"
] ]
}, },
{ {
@ -235,28 +225,15 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"name": "stdout",
"output_type": "stream",
"text": [
"start training…\n",
"training done\n"
]
}
],
"source": [ "source": [
"annotated_sent = nltk.corpus.treebank.tagged_sents()\n", "def model_01(X,y,tX,ty, max_size=1000):\n",
"\n",
"X,y,tX,ty = create_training_and_test_set(annotated_sentences=annotated_sent, \n",
" relative_cutoff=0.8)\n",
"\n",
" #classifier = DecisionTreeClassifier(criterion='entropy')\n", " #classifier = DecisionTreeClassifier(criterion='entropy')\n",
" from sklearn.neural_network import MLPClassifier\n", " from sklearn.neural_network import MLPClassifier\n",
"model01_clf = train_classifier(X,y,MLPClassifier(),max_size=10000)\n", " model01_clf = train_classifier(X,y,MLPClassifier(),max_size=max_size)\n",
"accs[0] = test_classifier(clf=clf, tX=tX, ty=ty)" " return test_classifier(clf=model01_clf, tX=tX, ty=ty)"
] ]
}, },
{ {
@ -266,79 +243,291 @@
"#### Model 02" "#### Model 02"
] ]
}, },
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"accs[1]"
]
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "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": [ "source": [
"'''\n", "def model_02(tX,ty):\n",
"import matplotlib.pyplot as plt\n", " m2_y = nltk.pos_tag([w['word'] for w in tX])\n",
"import numpy as np\n", " # compare results\n",
"\n", " n_correct = sum((1 if m2_y[i][1] == ty[i] else 0) for i in range(len(ty)))\n",
"weights = clf.named_steps['classifier'].feature_importances_\n", " return n_correct / len(ty)"
"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", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"```\n", "#### Model 03"
"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", "cell_type": "code",
" filled=True, rounded=True, \n", "execution_count": 9,
" special_characters=True)\n", "metadata": {},
"#graph = graphviz.Source(dot_data)\n", "outputs": [],
"#graph\n", "source": [
"```" "def model_03(corpus_tagged, corpus_sents, cut=0.8):\n",
" \n",
" patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n",
" (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n",
" \n",
" s = int(len(corpus_tagged) * cut)\n",
" train_sents = corpus_tagged[:s]\n",
" test_sents = corpus_tagged[s:]\n",
" \n",
" models = {\n",
" 'def_model': nltk.DefaultTagger('NN'),\n",
" 'regexp_model': nltk.RegexpTagger(patterns),\n",
" 'uni_model': nltk.UnigramTagger(train_sents),\n",
" 'bi_model': nltk.BigramTagger(train_sents),\n",
" 'tri_model': nltk.TrigramTagger(train_sents)\n",
" }\n",
" \n",
" performance = {}\n",
" for name,model in models.items():\n",
" performance[name] = model.evaluate(test_sents)\n",
" \n",
" return performance\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Applying models on Datasets"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[('Pierre', 'NNP'),\n",
" ('Vinken', 'NNP'),\n",
" (',', ','),\n",
" ('61', 'CD'),\n",
" ('years', 'NNS'),\n",
" ('old', 'JJ'),\n",
" (',', ','),\n",
" ('will', 'MD'),\n",
" ('join', 'VB'),\n",
" ('the', 'DT'),\n",
" ('board', 'NN'),\n",
" ('as', 'IN'),\n",
" ('a', 'DT'),\n",
" ('nonexecutive', 'JJ'),\n",
" ('director', 'NN'),\n",
" ('Nov.', 'NNP'),\n",
" ('29', 'CD'),\n",
" ('.', '.')]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"P1.1\n",
"start training…\n",
"training done\n",
"Accuracy: 0.7671041469135187\n",
"P1.2\n",
"P1.3\n",
"P1.4\n",
"start training…\n",
"training done\n",
"Accuracy: 0.6359655404139998\n",
"P1.5\n",
"P1.6\n",
"{'P1.1': 0.7671041469135187,\n",
" 'P1.2': 0.8936074654423873,\n",
" 'P1.3 -- bi_model': 0.1132791057437996,\n",
" 'P1.3 -- def_model': 0.1447677029791906,\n",
" 'P1.3 -- regexp_model': 0.24232746145017217,\n",
" 'P1.3 -- tri_model': 0.06736863116922003,\n",
" 'P1.3 -- uni_model': 0.8608213982733669,\n",
" 'P1.4': 0.6359655404139998,\n",
" 'P1.5': 0.6044583741861567,\n",
" 'P1.6 -- bi_model': 0.1132791057437996,\n",
" 'P1.6 -- def_model': 0.1447677029791906,\n",
" 'P1.6 -- regexp_model': 0.24232746145017217,\n",
" 'P1.6 -- tri_model': 0.06736863116922003,\n",
" 'P1.6 -- uni_model': 0.8608213982733669}\n"
]
}
],
"source": [
"performances = {}\n",
"\n",
"treebank_tagged = nltk.corpus.treebank.tagged_sents()\n",
"treebank_sents = nltk.corpus.treebank.sents()\n",
"\n",
"brown_tagged = nltk.corpus.brown.tagged_sents()#(categories='news')\n",
"brown_sents = nltk.corpus.brown.sents()#(categories='news')\n",
"\n",
"display(treebank_tagged[0])\n",
"\n",
"X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n",
" relative_cutoff=0.8)\n",
"\n",
"X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n",
" relative_cutoff=0.8)\n",
"\n",
"\n",
"print(\"P1.1\")\n",
"performances['P1.1'] = model_01(X1,y1,tX1,ty1)\n",
"\n",
"print(\"P1.2\")\n",
"performances['P1.2'] = model_02(tX1,ty1)\n",
"\n",
"print(\"P1.3\")\n",
"p3 = model_03(treebank_tagged, treebank_sents)\n",
"for k,v in p3.items():\n",
" performances[\"P1.3 -- \" + k] = v\n",
"\n",
"print(\"P1.4\")\n",
"performances['P1.4'] = model_01(X2,y2,tX2,ty2)\n",
"\n",
"print(\"P1.5\")\n",
"performances['P1.5'] = model_02(tX2,ty2)\n",
"\n",
"print(\"P1.6\")\n",
"p6 = model_03(brown_tagged, brown_sents)\n",
"for k,v in p3.items():\n",
" performances[\"P1.6 -- \" + k] = v\n",
"\n",
"pprint.pprint(performances)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotting Data"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
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"text/plain": [
"FigureCanvasNbAgg()"
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],
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\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(performances)), performances.values())\n",
"plt.xticks(np.arange(len(performances)), performances.keys(), rotation=30, ha='right')\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"----\n",
"## Task 2"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"WORDS = 'words' #: column type for words\n",
"POS = 'pos' #: column type for part-of-speech tags\n",
"TREE = 'tree' #: column type for parse trees\n",
"CHUNK = 'chunk' #: column type for chunk structures\n",
"NE = 'ne' #: column type for named entities\n",
"SRL = 'srl' #: column type for semantic role labels\n",
"IGNORE = 'ignore' #: column type for column that should be ignored\n",
"train_path = \"./ru_syntagrus-ud-train.conllu\"\n",
"ru_corp = nltk.corpus.ConllCorpusReader(root=\"./\", \n",
" fileids=[\"ru_syntagrus-ud-train-uncommented.conllu\"],\n",
" columntypes=[IGNORE, WORDS, IGNORE, POS],\n",
" encoding='utf-8')\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"ru_tagged = ru_corp.tagged_sents()\n",
"\n",
"ru_tagged[0]\n",
"\n",
"X3,y3,tX3,ty3 = create_training_and_test_set(annotated_sentences=ru_tagged, \n",
" relative_cutoff=0.8)\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"P2.1\n",
"start training…\n",
"training done\n",
"Accuracy: 0.7043834741655548\n",
"0.7043834741655548\n"
]
}
],
"source": [
"print(\"P2.1\")\n",
"performances['P2.1'] = model_01(X3,y3,tX3,ty3, max_size=1000)\n",
"print(performances['P2.1'])"
] ]
}, },
{ {
@ -365,7 +554,7 @@
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} }
}, },
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@ -0,0 +1,382 @@
True : object.conclusion = "NN"
object.tag == "ADV" : object.conclusion = "ADV"
object.suffixL3 == "ено" : object.conclusion = "VERB"
object.suffixL3 == "ано" : object.conclusion = "VERB"
object.nextWord1 == "-" : object.conclusion = "ADV"
object.nextTag1 == "PUNCT" and object.nextTag2 == "NOUN" : object.conclusion = "NOUN"
object.word == "так" and object.nextWord1 == "как" : object.conclusion = "SCONJ"
object.suffixL3 == "ино" : object.conclusion = "PROPN"
object.nextWord1 == "-" : object.conclusion = "ADV"
object.suffixL3 == "нко" : object.conclusion = "PROPN"
object.word == "всё" and object.nextTag1 == "NOUN" : object.conclusion = "DET"
object.nextTag1 == "SYM" : object.conclusion = "NUM"
object.suffixL3 == "бро" : object.conclusion = "NOUN"
object.suffixL3 == "ако" : object.conclusion = "PROPN"
object.suffixL3 == "йко" : object.conclusion = "PROPN"
object.word == "Сити" and object.nextWord1 == "&#39;&#39;" : object.conclusion = "PROPN"
object.suffixL4 == "бюро" : object.conclusion = "PROPN"
object.nextWord1 == "км" : object.conclusion = "NUM"
object.nextWord1 == "года" : object.conclusion = "ADJ"
object.tag == "NOUN" : object.conclusion = "NOUN"
object.suffixL4 == "ском" : object.conclusion = "ADJ"
object.word == "войском" : object.conclusion = "NOUN"
object.suffixL3 == "кие" : object.conclusion = "ADJ"
object.prevTag1 == "PUNCT" and object.word == "русские" and object.nextTag1 == "PUNCT" : object.conclusion = "NOUN"
object.suffixL3 == "ыми" : object.conclusion = "ADJ"
object.prevTag1 == "PUNCT" and object.nextTag1 == "ADP" : object.conclusion = "VERB"
object.prevTag1 == "PROPN" and object.nextTag1 == "PUNCT" : object.conclusion = "PROPN"
object.suffixL2 == "es" : object.conclusion = "NOUN"
object.suffixL3 == "" : object.conclusion = "ADV"
object.suffixL2 == "ми" : object.conclusion = "NOUN"
object.suffixL2 == "ия" : object.conclusion = "NOUN"
object.nextWord1 == "-" : object.conclusion = "ADV"
object.word == "Entertainment" : object.conclusion = "NOUN"
object.suffixL2 == "ns" : object.conclusion = "NOUN"
object.suffixL3 == "ing" : object.conclusion = "NOUN"
object.nextWord2 == ":" : object.conclusion = "NOUN"
object.nextWord1 == "-" : object.conclusion = "ADV"
object.nextTag1 == "PUNCT" and object.nextTag2 == "NUM" : object.conclusion = "NOUN"
object.nextTag1 == "PUNCT" and object.nextTag2 == "ADP" : object.conclusion = "PROPN"
object.prevTag2 == "VERB" and object.prevTag1 == "NOUN" : object.conclusion = "PROPN"
object.suffixL3 == "ена" : object.conclusion = "NOUN"
object.suffixL3 == "ими" : object.conclusion = "ADJ"
object.prevWord1 == "," : object.conclusion = "VERB"
object.word == "русскими" : object.conclusion = "NOUN"
object.suffixL4 == "ьном" : object.conclusion = "ADJ"
object.suffixL3 == "шие" : object.conclusion = "VERB"
object.prevWord1 == "" : object.conclusion = "ADJ"
object.nextTag1 == "NOUN" and object.nextTag2 == "NOUN" : object.conclusion = "AUX"
object.suffixL3 == "ово" : object.conclusion = "PROPN"
object.word == "слово" : object.conclusion = "NOUN"
object.suffixL3 == "ова" : object.conclusion = "PROPN"
object.word == "слова" : object.conclusion = "NOUN"
object.word == "острова" : object.conclusion = "NOUN"
object.prevTag1 == "ADJ" and object.nextTag1 == "PUNCT" : object.conclusion = "NOUN"
object.prevTag1 == "DET" and object.word == "вдова" : object.conclusion = "NOUN"
object.suffixL4 == "ющей" : object.conclusion = "VERB"
object.suffixL3 == "щие" : object.conclusion = "VERB"
object.nextWord1 == "," : object.conclusion = "NOUN"
object.prevTag1 == "PROPN" and object.nextTag1 == "VERB" : object.conclusion = "PROPN"
object.nextWord2 == "альбом" : object.conclusion = "NOUN"
object.prevWord2 == "-" : object.conclusion = "NOUN"
object.prevTag1 == "PUNCT" and object.word == "м" : object.conclusion = "ADJ"
object.suffixL4 == "нном" : object.conclusion = "ADJ"
object.prevTag1 == "PUNCT" : object.conclusion = "VERB"
object.suffixL4 == "рном" : object.conclusion = "ADJ"
object.suffixL4 == "тном" : object.conclusion = "ADJ"
object.prevWord1 == "городе" : object.conclusion = "PROPN"
object.suffixL3 == "ева" : object.conclusion = "PROPN"
object.word == "королева" and object.nextTag1 == "NOUN" and object.nextTag2 == "PUNCT" : object.conclusion = "NOUN"
object.suffixL3 == "ево" : object.conclusion = "PROPN"
object.suffixL4 == "ески" : object.conclusion = "ADV"
object.prevTag1 == "ADV" : object.conclusion = "PROPN"
object.prevWord1 == "город" : object.conclusion = "PROPN"
object.suffixL4 == "овом" : object.conclusion = "ADJ"
object.suffixL4 == "чном" : object.conclusion = "ADJ"
object.prevWord1 == "реки" : object.conclusion = "PROPN"
object.suffixL3 == "эля" : object.conclusion = "PROPN"
object.suffixL4 == "вшей" : object.conclusion = "VERB"
object.suffixL4 == "дние" : object.conclusion = "ADJ"
object.suffixL4 == "дном" : object.conclusion = "ADJ"
object.suffixL4 == "арда" : object.conclusion = "PROPN"
object.suffixL4 == "тоне" : object.conclusion = "PROPN"
object.suffixL3 == "рге" : object.conclusion = "PROPN"
object.prevWord1 == "города" : object.conclusion = "PROPN"
object.suffixL3 == "йти" : object.conclusion = "VERB"
object.prevWord1 == "*" : object.conclusion = "ADV"
object.suffixL3 == "кии" : object.conclusion = "PROPN"
object.suffixL4 == "поле" : object.conclusion = "PROPN"
object.suffixL4 == "овка" : object.conclusion = "PROPN"
object.nextTag1 == "NOUN" : object.conclusion = "NOUN"
object.prevWord1 == "провинцию" : object.conclusion = "PROPN"
object.suffixL4 == "ичем" : object.conclusion = "PROPN"
object.nextWord2 == "родился" : object.conclusion = "PROPN"
object.suffixL3 == "ёва" : object.conclusion = "PROPN"
object.suffixL4 == "поль" : object.conclusion = "PROPN"
object.prevWord1 == "деревня" : object.conclusion = "PROPN"
object.suffixL4 == "алия" : object.conclusion = "PROPN"
object.suffixL4 == "льда" : object.conclusion = "PROPN"
object.suffixL3 == "ите" : object.conclusion = "VERB"
object.prevTag1 == "ADP" : object.conclusion = "NOUN"
object.suffixL4 == "бном" : object.conclusion = "ADJ"
object.suffixL3 == "ида" : object.conclusion = "PROPN"
object.suffixL4 == "" : object.conclusion = "NOUN"
object.suffixL4 == "ндия" : object.conclusion = "PROPN"
object.suffixL4 == "тове" : object.conclusion = "PROPN"
object.suffixL4 == "асом" : object.conclusion = "PROPN"
object.suffixL3 == "нчи" : object.conclusion = "PROPN"
object.suffixL3 == "бии" : object.conclusion = "PROPN"
object.suffixL3 == "эль" : object.conclusion = "PROPN"
object.suffixL3 == "мми" : object.conclusion = "PROPN"
object.suffixL4 == "евка" : object.conclusion = "PROPN"
object.suffixL3 == "кию" : object.conclusion = "PROPN"
object.suffixL3 == "нне" : object.conclusion = "PROPN"
object.prevWord1 == "у" and object.nextWord1 == ";" : object.conclusion = "PROPN"
object.suffixL3 == "тти" : object.conclusion = "PROPN"
object.suffixL4 == "кера" : object.conclusion = "PROPN"
object.suffixL4 == "кове" : object.conclusion = "PROPN"
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object.suffixL4 == "рант" : object.conclusion = "PROPN"
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