I'm Blue, dabedi dabedei...
This commit is contained in:
Jonas Weinz
parent
d5c17c09a8
commit
2c625f7f92
@ -205,24 +205,14 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Exercises:"
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"## Exercise 01:"
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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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"### Exercise 01\n"
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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": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"accs = [0] * 5\n",
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"names = [\"M1\", \"M2\", \"M3\", \"M4\", \"M5\"]\n"
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"### Performance 1\n"
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]
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},
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{
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@ -235,7 +225,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"execution_count": 17,
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"metadata": {},
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"outputs": [
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{
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@ -243,20 +233,17 @@
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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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"training done\n",
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"Accuracy: 0.768551324916413\n"
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]
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}
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],
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"source": [
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"annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
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"\n",
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"X,y,tX,ty = create_training_and_test_set(annotated_sentences=annotated_sent, \n",
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" relative_cutoff=0.8)\n",
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"\n",
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"#classifier = DecisionTreeClassifier(criterion='entropy')\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"model01_clf = train_classifier(X,y,MLPClassifier(),max_size=10000)\n",
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"accs[0] = test_classifier(clf=clf, tX=tX, ty=ty)"
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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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" from sklearn.neural_network import MLPClassifier\n",
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" model01_clf = train_classifier(X,y,MLPClassifier(),max_size=1000)\n",
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" return test_classifier(clf=model01_clf, tX=tX, ty=ty)"
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]
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},
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{
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@ -268,77 +255,89 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 30,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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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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],
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"source": [
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"accs[1]"
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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": 8,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'\\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'"
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]
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},
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"execution_count": 8,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"'''\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\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(weights)), weights)\n",
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"#plt.xticks(np.arange(len(weights)), labels, rotation=90)\n",
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"#plt.show()\n",
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"\n",
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"print(\"Most important features:\")\n",
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"pprint.pprint(list(reversed(labels[-20:])))\n",
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"print(\"with weights: \")\n",
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"pprint.pprint(list(reversed(weights[-20:])))\n",
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"'''"
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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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" # compare results\n",
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" n_correct = sum((1 if m2_y[i][1] == ty[i] else 0) for i in range(len(ty)))\n",
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" return n_correct / len(ty)"
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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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"```\n",
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"from sklearn import tree\n",
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"import graphviz\n",
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"dot_data = tree.export_graphviz(clf.named_steps['classifier'], out_file='test',\n",
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" feature_names=labels,\n",
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" filled=True, rounded=True, \n",
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" special_characters=True)\n",
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"#graph = graphviz.Source(dot_data)\n",
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"#graph\n",
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"```"
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"#### Model 03"
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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": 38,
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"metadata": {},
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"outputs": [],
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"source": [
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"def model_03(corpus_tagged, corpus_sents, cut=0.8):\n",
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" \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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" \n",
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" s = int(len(corpus_sents) * cut)\n",
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" train_sents = corpus_sents[:size]\n",
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" test_sents = corpus_sents[size:]\n",
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" \n",
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" models = {\n",
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" 'def_model': nltk.DefaultTagger('NN'),\n",
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" 'regexp_model': nltk.RegexpTagger(patterns),\n",
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" 'uni_model': nltk.UnigramTagger(train_sents),\n",
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" 'bi_model': nltk.BigramTagger(train_sents),\n",
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" 'tri_model': nltk.TrigramTagger(train_sents)\n",
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" }\n",
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" \n",
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" performance = {}\n",
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" for name,model in models.items():\n",
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" performance[name] = model.evaluate(test_sents)\n",
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" \n",
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" return performance\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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"### Applying models on Datasets"
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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": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"accs_p1 = [0] * 3\n",
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"names_p1 = [\"P1.1\", \"P1.2\", \"P1.3\"]\n",
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"\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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"\n",
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"brown_tagged = nltk.corpus.brown.tagged_sents()\n",
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"brown_sents = nltk.corpus.brown.sents()\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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" relative_cutoff=0.8)\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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" relative_cutoff=0.8)\n"
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]
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},
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{
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@ -365,7 +364,7 @@
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.6.3"
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"version": "3.6.5"
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}
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},
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"nbformat": 4,
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