task 1 erweitert
This commit is contained in:
Carsten
parent
f8563aed44
commit
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 44,
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"metadata": {
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"collapsed": true
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"source": [
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"### Task 1\n",
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"* get results for english (plot a graph with all classifiers x results)\n",
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" * performance 1.1 = model1 in X1"
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" * performance 1.1 = model1 in X1\n",
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" * performance 1.2 = model2 in X1\n",
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" * performance 1.3.x = model3.x in X1\n",
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" * performance 1.4 = model1 in X2\n",
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" * performance 1.5 = model2 in X2\n",
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" * performance 1.6.x = model3.x in X2"
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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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"##### Generate Training and Testdata\n",
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"##### Generate Training and Testdata for X1\n",
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"1. split annotaed sentences into training and testdata\n",
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"2. split trainingdata into input data and teacherdata\n",
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" *input is the feature vector of each word\n",
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@ -241,7 +246,20 @@
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"execution_count": 45,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#to generate trainingsdata, delete the assigned tags as a function\n",
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"def untag(tagged_sentence):\n",
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" return [w for w, t in tagged_sentence]"
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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": 46,
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"metadata": {},
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"outputs": [
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{
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}
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],
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"source": [
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"#to generate trainingsdata, delete the assigned tags as a function\n",
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"def untag(tagged_sentence):\n",
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" return [w for w, t in tagged_sentence]\n",
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"\n",
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"#object including the annotated sentences\n",
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"annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
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"\n",
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"#to split the data, calculate the borders for ratio\n",
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"cutoff = int(.8 * len(annotated_sent))\n",
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"training_sentences = annotated_sent[:cutoff]\n",
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"test_sentences = annotated_sent[cutoff:]\n",
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"training_sentences_X1 = annotated_sent[:cutoff]\n",
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"test_sentences_X1 = annotated_sent[cutoff:]\n",
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"\n",
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"#show the amount of sentences\n",
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"print(\"got \",len(training_sentences),\" training sentences and \", len(test_sentences), \" test sentences\")\n",
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"\n",
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"print(\"got \",len(training_sentences_X1),\" training sentences and \", len(test_sentences_X1), \" test sentences\")"
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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": 47,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#for training split sentences with its tags into y (for a sentences its resulting tags for each word) and transform sentences and x as a list of the features extracet for echt word in the sentences\n",
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"def transform_to_dataset(tagged_sentences):\n",
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" X, y = [], []\n",
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" for index in range(len(tagged_sentence)):\n",
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" X.append(features(untag(tagged_sentence), index))\n",
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" y.append(tagged_sentence[index][1]) \n",
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" return X, y\n",
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"\n",
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" return X, y"
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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": 48,
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"metadata": {},
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"outputs": [],
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"source": [
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"#trainings inputset X and training teacher set y\n",
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"X, y = transform_to_dataset(training_sentences)"
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"X1, y1 = transform_to_dataset(training_sentences_X1)"
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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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"##### Generate Training and Testdata for X2\n",
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"1. split annotaed sentences into training and testdata\n",
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"2. split trainingdata into input data and teacherdata\n",
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" *input is the feature vector of each word\n",
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" *output is a list of POS tags for each word and sentences"
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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": 49,
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"metadata": {},
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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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"got 45872 training sentences and 11468 test sentences\n"
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]
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}
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],
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"source": [
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"#object including the annotated sentences\n",
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"annotated_sent = nltk.corpus.brown.tagged_sents()\n",
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"\n",
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"#to split the data, calculate the borders for ratio\n",
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"cutoff = int(.8 * len(annotated_sent))\n",
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"training_sentences_X2 = annotated_sent[:cutoff]\n",
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"test_sentences_X2 = annotated_sent[cutoff:]\n",
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"\n",
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"#show the amount of sentences\n",
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"print(\"got \",len(training_sentences_X2),\" training sentences and \", len(test_sentences_X2), \" test sentences\")"
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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": 50,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"#trainings inputset X and training teacher set y\n",
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"X2, y2 = transform_to_dataset(training_sentences_X2)"
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]
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},
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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": 13,
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"execution_count": 51,
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"metadata": {
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"collapsed": true
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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": 14,
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"execution_count": 52,
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"metadata": {
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"collapsed": true
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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": 15,
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"execution_count": 53,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"training OK\n",
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"Accuracy: 0.880832376865\n"
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"Accuracy: 0.87983432307\n"
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]
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}
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],
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" \n",
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"print('training OK')\n",
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" \n",
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"X_test, y_test = transform_to_dataset(test_sentences)\n",
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"X1_test, y1_test = transform_to_dataset(test_sentences_X1)\n",
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"\n",
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"performance1_1 = clf.score(X_test, y_test)\n",
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"performance1_1 = clf.score(X1_test, y1_test)\n",
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"\n",
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"print(\"Accuracy:\", performance1_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": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"execution_count": 58,
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"metadata": {},
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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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"calculated perfomance 1.4= 0.756485959481\n"
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]
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}
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],
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"source": [
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"performance1_2 = 0\n",
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"\n",
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"performance1_3 = 0\n",
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"performance1_4 = 0\n",
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"\n",
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"# performance1_4\n",
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"size=10000\n",
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"clf.fit(X2[:size], y2[:size])\n",
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"X2_test, y2_test = transform_to_dataset(test_sentences_X2)\n",
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"performance1_4 = clf.score(X2_test, y2_test)\n",
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"print(\"calculated perfomance 1.4= \",performance1_4)\n",
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"\n",
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"performance1_5 = 0\n",
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"\n",
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"performance1_6 = 0"
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]
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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": 17,
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"execution_count": 56,
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"metadata": {},
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"outputs": [
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{
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"text": [
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"3.6.3\n",
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"checking...\n",
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"[('Hello', 'NNP'), ('world', 'VBD'), (',', ','), ('lets', 'NNS'), ('do', 'VB'), ('something', 'VBG'), ('awesome', 'NN'), ('today', 'NN'), ('!', 'CD')]\n"
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"[('Hello', 'CS'), ('world', 'NN'), (',', ','), ('lets', 'NNS'), ('do', 'DO'), ('something', 'PN'), ('awesome', 'NN'), ('today', 'NR'), ('!', 'CD')]\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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"cell_type": "code",
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"execution_count": 22,
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"execution_count": 57,
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"metadata": {
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"scrolled": true
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},
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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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"High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~carsten95/0 or inside your plot.ly account where it is named 'basic-bar'\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~carsten95/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
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],
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"text/plain": [
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"<plotly.tools.PlotlyDisplay object>"
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]
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},
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"execution_count": 57,
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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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"import plotly\n",
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"plotly.tools.set_credentials_file(username='carsten95', api_key='vElf5IOxiFheQdjTxjXW')\n",
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"plotly.__version__\n",
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"import plotly.plotly as py\n",
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"import plotly.graph_objs as go\n",
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"\n",
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"data = [go.Bar(\n",
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" x=['performance 1.1', 'performance 1.2', 'performance 1.3', 'performance 1.4', 'performance 1.5' , 'performance 1.6'],\n",
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" y=[performance1_1, performance1_2, performance1_3, performance1_4, performance1_5, performance1_6]\n",
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" )]\n",
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"\n",
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"py.iplot(data, filename='basic-bar')"
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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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"collapsed": true
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},
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"source": [
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"### Results for Task 2\n",
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"* train your model with standard features (plot a graph with all classifiers x results)\n",
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" * performance 2.1 = model4 in X3\n",
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" * performance 2.2 = model5 in X3"
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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": 60,
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"metadata": {},
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"outputs": [
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{
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"<plotly.tools.PlotlyDisplay object>"
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]
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},
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"execution_count": 22,
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"execution_count": 60,
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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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"import plotly\n",
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"plotly.tools.set_credentials_file(username='carsten95', api_key='vElf5IOxiFheQdjTxjXW')\n",
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"plotly.__version__\n",
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"import plotly.plotly as py\n",
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"import plotly.graph_objs as go\n",
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"performance2_1 = 0\n",
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"performance2_2 = 0\n",
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"\n",
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"data = [go.Bar(\n",
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" x=['performance 1.1', 'performance 1.2', 'performance 1.3', 'performance 1.4', 'performance 1.5' , 'performance 1.6'],\n",
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" y=[performance1_1, performance1_2, performance1_3, performance1_4, performance1_5, performance1_6]\n",
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" x=['performance 2.1', 'performance 2.2'],\n",
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" y=[performance2_1, performance2_2]\n",
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" )]\n",
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"\n",
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"py.iplot(data, filename='basic-bar')"
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