Task 1 done
This commit is contained in:
Jonas Weinz
parent
2c625f7f92
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6c4358ced0
@ -225,19 +225,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 7,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"start training…\n",
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"training done\n",
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"Accuracy: 0.768551324916413\n"
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]
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"source": [
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"source": [
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"def model_01(X,y,tX,ty, max_size=1000):\n",
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"def model_01(X,y,tX,ty, max_size=1000):\n",
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" #classifier = DecisionTreeClassifier(criterion='entropy')\n",
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" #classifier = DecisionTreeClassifier(criterion='entropy')\n",
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@ -255,17 +245,9 @@
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 30,
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"execution_count": 8,
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"metadata": {},
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"metadata": {},
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"outputs": [
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"outputs": [],
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.8936074654423873\n"
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]
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}
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"source": [
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"source": [
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"def model_02(tX,ty):\n",
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"def model_02(tX,ty):\n",
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" m2_y = nltk.pos_tag([w['word'] for w in tX])\n",
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" m2_y = nltk.pos_tag([w['word'] for w in tX])\n",
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@ -283,7 +265,7 @@
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},
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 38,
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"execution_count": 9,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [],
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"source": [
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"source": [
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@ -292,9 +274,9 @@
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" patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n",
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" patterns = [(r'.*ing$', 'VBG'), (r'.*ed$', 'VBD'), (r'.*es$', 'VBZ'), (r'.*ould$', 'MD'), (r'.*\\'s$', 'NN$'), \n",
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" (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n",
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" (r'.*s$', 'NNS'), (r'^-?[0-9]+(.[0-9]+)?$', 'CD'), (r'.*', 'NN')]\n",
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" \n",
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" \n",
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" s = int(len(corpus_sents) * cut)\n",
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" s = int(len(corpus_tagged) * cut)\n",
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" train_sents = corpus_sents[:size]\n",
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" train_sents = corpus_tagged[:s]\n",
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" test_sents = corpus_sents[size:]\n",
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" test_sents = corpus_tagged[s:]\n",
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" \n",
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" \n",
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" models = {\n",
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" models = {\n",
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" 'def_model': nltk.DefaultTagger('NN'),\n",
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" 'def_model': nltk.DefaultTagger('NN'),\n",
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@ -320,24 +302,139 @@
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},
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"cell_type": "code",
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"cell_type": "code",
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"execution_count": 41,
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"execution_count": 10,
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"metadata": {},
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"metadata": {},
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"outputs": [],
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"P1.1\n",
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"start training…\n",
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"training done\n",
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"Accuracy: 0.7712959728529368\n",
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"P1.2\n",
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"P1.3\n",
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"P1.4\n",
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"start training…\n",
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"training done\n",
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"Accuracy: 0.6410882090489463\n",
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"P1.5\n",
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"P1.6\n",
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"{'P1.1': 0.7712959728529368,\n",
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" 'P1.2': 0.8936074654423873,\n",
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" 'P1.3 -- bi_model': 0.1132791057437996,\n",
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" 'P1.3 -- def_model': 0.1447677029791906,\n",
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" 'P1.3 -- regexp_model': 0.24232746145017217,\n",
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" 'P1.3 -- tri_model': 0.06736863116922003,\n",
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" 'P1.3 -- uni_model': 0.8608213982733669,\n",
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" 'P1.4': 0.6410882090489463,\n",
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" 'P1.5': 0.6044583741861567,\n",
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" 'P1.6 -- bi_model': 0.1132791057437996,\n",
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" 'P1.6 -- def_model': 0.1447677029791906,\n",
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" 'P1.6 -- regexp_model': 0.24232746145017217,\n",
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" 'P1.6 -- tri_model': 0.06736863116922003,\n",
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" 'P1.6 -- uni_model': 0.8608213982733669}\n"
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]
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}
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],
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"source": [
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"source": [
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"accs_p1 = [0] * 3\n",
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"performances = {}\n",
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"names_p1 = [\"P1.1\", \"P1.2\", \"P1.3\"]\n",
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"\n",
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"\n",
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"treebank_tagged = nltk.corpus.treebank.tagged_sents()\n",
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"treebank_tagged = nltk.corpus.treebank.tagged_sents()\n",
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"treebank_sents = nltk.corpus.treebank.sents()\n",
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"treebank_sents = nltk.corpus.treebank.sents()\n",
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"\n",
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"\n",
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"brown_tagged = nltk.corpus.brown.tagged_sents()\n",
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"brown_tagged = nltk.corpus.brown.tagged_sents()#(categories='news')\n",
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"brown_sents = nltk.corpus.brown.sents()\n",
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"brown_sents = nltk.corpus.brown.sents()#(categories='news')\n",
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"\n",
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"\n",
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"X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n",
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"X1,y1,tX1,ty1 = create_training_and_test_set(annotated_sentences=treebank_tagged, \n",
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" relative_cutoff=0.8)\n",
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" relative_cutoff=0.8)\n",
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"\n",
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"\n",
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"X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n",
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"X2,y2,tX2,ty2 = create_training_and_test_set(annotated_sentences=brown_tagged, \n",
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" relative_cutoff=0.8)\n"
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" relative_cutoff=0.8)\n",
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"\n",
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"\n",
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"print(\"P1.1\")\n",
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"performances['P1.1'] = model_01(X1,y1,tX1,ty1)\n",
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"\n",
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"print(\"P1.2\")\n",
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"performances['P1.2'] = model_02(tX1,ty1)\n",
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"\n",
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"print(\"P1.3\")\n",
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"p3 = model_03(treebank_tagged, treebank_sents)\n",
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"for k,v in p3.items():\n",
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" performances[\"P1.3 -- \" + k] = v\n",
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"\n",
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"print(\"P1.4\")\n",
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"performances['P1.4'] = model_01(X2,y2,tX2,ty2)\n",
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"\n",
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"print(\"P1.5\")\n",
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"performances['P1.5'] = model_02(tX2,ty2)\n",
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"\n",
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"print(\"P1.6\")\n",
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"p6 = model_03(brown_tagged, brown_sents)\n",
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"for k,v in p3.items():\n",
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" performances[\"P1.6 -- \" + k] = v\n",
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"\n",
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"pprint.pprint(performances)\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Plotting Data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "80f8a41746b340f09c5d16a3071c7384",
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"version_major": 2,
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"version_minor": 0
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},
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"text/html": [
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"<p>Failed to display Jupyter Widget of type <code>FigureCanvasNbAgg</code>.</p>\n",
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"<p>\n",
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" If you're reading this message in the Jupyter Notebook or JupyterLab Notebook, it may mean\n",
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" that the widgets JavaScript is still loading. If this message persists, it\n",
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" likely means that the widgets JavaScript library is either not installed or\n",
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" not enabled. See the <a href=\"https://ipywidgets.readthedocs.io/en/stable/user_install.html\">Jupyter\n",
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" Widgets Documentation</a> for setup instructions.\n",
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"</p>\n",
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"<p>\n",
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" If you're reading this message in another frontend (for example, a static\n",
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" rendering on GitHub or <a href=\"https://nbviewer.jupyter.org/\">NBViewer</a>),\n",
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" it may mean that your frontend doesn't currently support widgets.\n",
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"</p>\n"
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],
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"text/plain": [
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"FigureCanvasNbAgg()"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"#weights = clf.named_steps['classifier'].feature_importances_\n",
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"#labels = clf.named_steps['vectorizer'].get_feature_names()\n",
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"\n",
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"#sort\n",
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"#weights, labels = (list(t) for t in zip(*sorted(zip(weights, labels))))\n",
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"\n",
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"fig_1, ax_1 = plt.subplots()\n",
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"plt.bar(np.arange(len(performances)), performances.values())\n",
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"plt.xticks(np.arange(len(performances)), performances.keys(), rotation=30, ha='right')\n",
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"plt.tight_layout()\n",
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"plt.show()\n"
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]
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]
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},
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},
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{
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{
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