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task 1 erweitert

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Carsten 2018-04-29 20:22:39 +02:00
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Carsten_Solutions/Exercise 1.ipynb
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@ -9,7 +9,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 39,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -29,7 +29,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 40,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -49,7 +49,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 8, "execution_count": 41,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -156,7 +156,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 42,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -183,7 +183,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -209,7 +209,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 44,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -225,14 +225,19 @@
"source": [ "source": [
"### Task 1\n", "### Task 1\n",
"* get results for english (plot a graph with all classifiers x results)\n", "* get results for english (plot a graph with all classifiers x results)\n",
" * performance 1.1 = model1 in X1" " * performance 1.1 = model1 in X1\n",
" * performance 1.2 = model2 in X1\n",
" * performance 1.3.x = model3.x in X1\n",
" * performance 1.4 = model1 in X2\n",
" * performance 1.5 = model2 in X2\n",
" * performance 1.6.x = model3.x in X2"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"##### Generate Training and Testdata\n", "##### Generate Training and Testdata for X1\n",
"1. split annotaed sentences into training and testdata\n", "1. split annotaed sentences into training and testdata\n",
"2. split trainingdata into input data and teacherdata\n", "2. split trainingdata into input data and teacherdata\n",
" *input is the feature vector of each word\n", " *input is the feature vector of each word\n",
@ -241,7 +246,20 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 45,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#to generate trainingsdata, delete the assigned tags as a function\n",
"def untag(tagged_sentence):\n",
" return [w for w, t in tagged_sentence]"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -253,21 +271,26 @@
} }
], ],
"source": [ "source": [
"#to generate trainingsdata, delete the assigned tags as a function\n",
"def untag(tagged_sentence):\n",
" return [w for w, t in tagged_sentence]\n",
"\n",
"#object including the annotated sentences\n", "#object including the annotated sentences\n",
"annotated_sent = nltk.corpus.treebank.tagged_sents()\n", "annotated_sent = nltk.corpus.treebank.tagged_sents()\n",
"\n", "\n",
"#to split the data, calculate the borders for ratio\n", "#to split the data, calculate the borders for ratio\n",
"cutoff = int(.8 * len(annotated_sent))\n", "cutoff = int(.8 * len(annotated_sent))\n",
"training_sentences = annotated_sent[:cutoff]\n", "training_sentences_X1 = annotated_sent[:cutoff]\n",
"test_sentences = annotated_sent[cutoff:]\n", "test_sentences_X1 = annotated_sent[cutoff:]\n",
"\n", "\n",
"#show the amount of sentences\n", "#show the amount of sentences\n",
"print(\"got \",len(training_sentences),\" training sentences and \", len(test_sentences), \" test sentences\")\n", "print(\"got \",len(training_sentences_X1),\" training sentences and \", len(test_sentences_X1), \" test sentences\")"
"\n", ]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#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", "#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",
"def transform_to_dataset(tagged_sentences):\n", "def transform_to_dataset(tagged_sentences):\n",
" X, y = [], []\n", " X, y = [], []\n",
@ -275,10 +298,66 @@
" for index in range(len(tagged_sentence)):\n", " for index in range(len(tagged_sentence)):\n",
" X.append(features(untag(tagged_sentence), index))\n", " X.append(features(untag(tagged_sentence), index))\n",
" y.append(tagged_sentence[index][1]) \n", " y.append(tagged_sentence[index][1]) \n",
" return X, y\n", " return X, y"
"\n", ]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"#trainings inputset X and training teacher set y\n", "#trainings inputset X and training teacher set y\n",
"X, y = transform_to_dataset(training_sentences)" "X1, y1 = transform_to_dataset(training_sentences_X1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##### Generate Training and Testdata for X2\n",
"1. split annotaed sentences into training and testdata\n",
"2. split trainingdata into input data and teacherdata\n",
" *input is the feature vector of each word\n",
" *output is a list of POS tags for each word and sentences"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"got 45872 training sentences and 11468 test sentences\n"
]
}
],
"source": [
"#object including the annotated sentences\n",
"annotated_sent = nltk.corpus.brown.tagged_sents()\n",
"\n",
"#to split the data, calculate the borders for ratio\n",
"cutoff = int(.8 * len(annotated_sent))\n",
"training_sentences_X2 = annotated_sent[:cutoff]\n",
"test_sentences_X2 = annotated_sent[cutoff:]\n",
"\n",
"#show the amount of sentences\n",
"print(\"got \",len(training_sentences_X2),\" training sentences and \", len(test_sentences_X2), \" test sentences\")"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#trainings inputset X and training teacher set y\n",
"X2, y2 = transform_to_dataset(training_sentences_X2)"
] ]
}, },
{ {
@ -295,7 +374,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 13, "execution_count": 51,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -315,7 +394,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 14, "execution_count": 52,
"metadata": { "metadata": {
"collapsed": true "collapsed": true
}, },
@ -338,7 +417,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 53,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -346,7 +425,7 @@
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"training OK\n", "training OK\n",
"Accuracy: 0.880832376865\n" "Accuracy: 0.87983432307\n"
] ]
} }
], ],
@ -356,9 +435,9 @@
" \n", " \n",
"print('training OK')\n", "print('training OK')\n",
" \n", " \n",
"X_test, y_test = transform_to_dataset(test_sentences)\n", "X1_test, y1_test = transform_to_dataset(test_sentences_X1)\n",
"\n", "\n",
"performance1_1 = clf.score(X_test, y_test)\n", "performance1_1 = clf.score(X1_test, y1_test)\n",
"\n", "\n",
"print(\"Accuracy:\", performance1_1)" "print(\"Accuracy:\", performance1_1)"
] ]
@ -372,16 +451,31 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 58,
"metadata": { "metadata": {},
"collapsed": true "outputs": [
}, {
"outputs": [], "name": "stdout",
"output_type": "stream",
"text": [
"calculated perfomance 1.4= 0.756485959481\n"
]
}
],
"source": [ "source": [
"performance1_2 = 0\n", "performance1_2 = 0\n",
"\n",
"performance1_3 = 0\n", "performance1_3 = 0\n",
"performance1_4 = 0\n", "\n",
"# performance1_4\n",
"size=10000\n",
"clf.fit(X2[:size], y2[:size])\n",
"X2_test, y2_test = transform_to_dataset(test_sentences_X2)\n",
"performance1_4 = clf.score(X2_test, y2_test)\n",
"print(\"calculated perfomance 1.4= \",performance1_4)\n",
"\n",
"performance1_5 = 0\n", "performance1_5 = 0\n",
"\n",
"performance1_6 = 0" "performance1_6 = 0"
] ]
}, },
@ -396,7 +490,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 17, "execution_count": 56,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -405,7 +499,7 @@
"text": [ "text": [
"3.6.3\n", "3.6.3\n",
"checking...\n", "checking...\n",
"[('Hello', 'NNP'), ('world', 'VBD'), (',', ','), ('lets', 'NNS'), ('do', 'VB'), ('something', 'VBG'), ('awesome', 'NN'), ('today', 'NN'), ('!', 'CD')]\n" "[('Hello', 'CS'), ('world', 'NN'), (',', ','), ('lets', 'NNS'), ('do', 'DO'), ('something', 'PN'), ('awesome', 'NN'), ('today', 'NR'), ('!', 'CD')]\n"
] ]
} }
], ],
@ -438,7 +532,62 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 22, "execution_count": 57,
"metadata": {
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"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"
]
},
{
"data": {
"text/html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~carsten95/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
],
"text/plain": [
"<plotly.tools.PlotlyDisplay object>"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import plotly\n",
"plotly.tools.set_credentials_file(username='carsten95', api_key='vElf5IOxiFheQdjTxjXW')\n",
"plotly.__version__\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"\n",
"data = [go.Bar(\n",
" x=['performance 1.1', 'performance 1.2', 'performance 1.3', 'performance 1.4', 'performance 1.5' , 'performance 1.6'],\n",
" y=[performance1_1, performance1_2, performance1_3, performance1_4, performance1_5, performance1_6]\n",
" )]\n",
"\n",
"py.iplot(data, filename='basic-bar')"
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Results for Task 2\n",
"* train your model with standard features (plot a graph with all classifiers x results)\n",
" * performance 2.1 = model4 in X3\n",
" * performance 2.2 = model5 in X3"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
@ -457,21 +606,18 @@
"<plotly.tools.PlotlyDisplay object>" "<plotly.tools.PlotlyDisplay object>"
] ]
}, },
"execution_count": 22, "execution_count": 60,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"import plotly\n", "performance2_1 = 0\n",
"plotly.tools.set_credentials_file(username='carsten95', api_key='vElf5IOxiFheQdjTxjXW')\n", "performance2_2 = 0\n",
"plotly.__version__\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go\n",
"\n", "\n",
"data = [go.Bar(\n", "data = [go.Bar(\n",
" x=['performance 1.1', 'performance 1.2', 'performance 1.3', 'performance 1.4', 'performance 1.5' , 'performance 1.6'],\n", " x=['performance 2.1', 'performance 2.2'],\n",
" y=[performance1_1, performance1_2, performance1_3, performance1_4, performance1_5, performance1_6]\n", " y=[performance2_1, performance2_2]\n",
" )]\n", " )]\n",
"\n", "\n",
"py.iplot(data, filename='basic-bar')" "py.iplot(data, filename='basic-bar')"