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Phylliida/ngram Feedback Modeling.ipynb

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NGram Feedback Modeling Notebook
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ngram Feedback Modeling.ipynb
{
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"id": "sound-stewart",
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"source": [
"from IPython.display import display\n",
"import graphviz\n",
"from matplotlib import pyplot as plt\n",
"from collections import defaultdict \n",
"import random\n",
"\n",
"BEGIN_TOKEN = \"___BEGIN__\"\n",
"END_TOKEN = \"___END__\"\n",
"\n",
"class NGramModel(object):\n",
" def __init__(self, inputSequences, contextSize):\n",
" '''\n",
" Creates an n-gram model of the given inputSequences\n",
" The input sequences should be an array of arrays.\n",
" Internally, each array has contextSize BEGIN_TOKEN appended to the start,\n",
" and a single END_TOKEN appended to the end,\n",
" then n-gram analysis is done.\n",
" contextSize=1 is bigram\n",
" contextSize=2 is trigram\n",
" etc.\n",
" '''\n",
" self.contextSize = contextSize\n",
" self.model = buildNGramModel(inputSequences=inputSequences, contextSize=contextSize)\n",
" \n",
" def gen(self):\n",
" '''\n",
" This function is a generator that can be used to generate samples from the trained nGram model\n",
" It will stop once it hits an end token, meaning that it could go on for a very long time\n",
" '''\n",
" context = [BEGIN_TOKEN]*self.contextSize\n",
" curToken = context[-1]\n",
" while True:\n",
" nextWordOptions = list(self.model[tuple(context)].items())\n",
" words = [nextWord for (nextWord, count) in nextWordOptions]\n",
" counts = [count for (nextWord, count) in nextWordOptions]\n",
" curToken = random.choices(words, weights=counts, k=1)[0] # for lower then python 3.6 you'll need to implement custom weighted choice here\n",
" context = context[1:] + [curToken]\n",
" if curToken == END_TOKEN: break\n",
" # we haven't reached end token, return token and continue generating\n",
" yield curToken\n",
" def __len__(self):\n",
" '''\n",
" Number of different types of context tuples\n",
" '''\n",
" return len(self.model)\n",
" \n",
"\n",
"def buildNGramModel(inputSequences, contextSize):\n",
" '''\n",
" Creates a dictionary of [contextTuple][nextWord] = numOfTimesSeen\n",
" Where contextTuple is of length contextSize, so \n",
" contextSize=1 is bigram\n",
" contextSize=2 is trigram\n",
" etc.\n",
" Every sequence is automatically given contextSize*[BEGIN_TOKEN] at the start, and an [END_TOKEN] at the end\n",
" '''\n",
" counts = defaultdict(lambda : defaultdict(int)) # default dict of default dict\n",
" for sequence in inputSequences:\n",
" context = [BEGIN_TOKEN]*contextSize\n",
" actualSequence = sequence + [END_TOKEN]\n",
" for word in actualSequence:\n",
" counts[tuple(context)][word] += 1 # because it's a default dict, it'll create entries if they don't exist\n",
" context = context[1:] + [word]\n",
" # convert default dicts to regular dict\n",
" return dict([(contextTuple, dict(nextWordDict)) for (contextTuple, nextWordDict) in counts.items()])\n",
"\n",
"\n",
"def generateTextWithMaxLen(model, maxSampleLen):\n",
" '''\n",
" Generates text that isn't allowed to be longer than the given length\n",
" If the model doesn't stop generating before that length, we throw out the sequence and make a new one\n",
" Warning: if maxSampleLen is too small, this could loop forever\n",
" '''\n",
" resultSent = list(model.gen())\n",
" while len(resultSent) > maxSampleLen:\n",
" resultSent = list(model.gen())\n",
" return resultSent\n",
"\n",
"\n",
"def generateDataset(model, maxSampleLen, minTotalGeneratedTokens):\n",
" '''\n",
" Generates samples of text from the given markov chain until the total\n",
" number of tokens generated is more than minTotalGeneratedTokens.\n",
" (If a sample generated is longer than maxSampleLen, it is ignored)\n",
" '''\n",
" totalChars = 0\n",
" allSamples = []\n",
" while totalChars < minTotalGeneratedTokens:\n",
" nextData = generateTextWithMaxLen(model, maxSampleLen)\n",
" allSamples.append(nextData)\n",
" totalChars += len(nextData)\n",
" return allSamples\n",
"\n",
"def runExperiment(initialDataset, numSteps, contextSize, maxSampleLen, minTotalGeneratedTokens, debug=False):\n",
" '''\n",
" Runs an experiment where we generate numSamples of text, each of length sampleLen\n",
" Then we use that to train a new markovify model (bigram=stateSize:1, trigram=stateSize:2, etc.)\n",
" We repeat this process numSteps times, and return an array with the model after each step\n",
" '''\n",
" model = NGramModel(initialDataset, contextSize=contextSize)\n",
" models = []\n",
" for t in range(numSteps):\n",
" try:\n",
" if debug: print(t, numSteps, len(model))\n",
" models.append(model)\n",
" dataset = generateDataset(model, maxSampleLen=maxSampleLen, minTotalGeneratedTokens=minTotalGeneratedTokens)\n",
" model = NGramModel(dataset, contextSize=contextSize)\n",
" except:\n",
" # debugging info if you are running into errors\n",
" # display(modelToDot(models[-1]))\n",
" # display(modelToDot(model))\n",
" # print(t)\n",
" raise\n",
" return models\n",
"\n",
"def loopThroughEdgeCounts(model):\n",
" '''\n",
" # model chain stores counts as chain.model[(tuple, of, context)][nextWord]\n",
" # so we need to connect edges between (tuple, of, context) and (of, context, nextWord)\n",
" # so this loops through and returns each\n",
" (tuple, of, context), (of, context, nextWord), transitionCount\n",
" '''\n",
" for keyTuple, outputDict in model.model.items():\n",
" keyTupleList = list(keyTuple)\n",
" for outputNode, transitionPr in outputDict.items():\n",
" connectToNodeList = keyTupleList[1:] + [outputNode]\n",
" yield (keyTuple, tuple(connectToNodeList), transitionPr)\n",
"\n",
"def modelToDot(model):\n",
" '''\n",
" Converts a n-gram model into a dot graph that displays the transition counts\n",
" '''\n",
" dot = graphviz.Digraph()\n",
" # it stores probabilities as chain.model[(tuple, of, context)][nextWord]\n",
" # so we need to connect edges between (tuple, of, context) and (of, context, nextWord)\n",
" # made utility function loopThroughEdgeCounts that loops through these for us\n",
" for keyTuple, outputDict in model.model.items():\n",
" dot.node(\" \".join(list(keyTuple)))\n",
" for tupleIn, tupleOut, transitionCount in loopThroughEdgeCounts(model):\n",
" dot.edge(\" \".join(tupleIn), \" \".join(tupleOut), label=str(transitionCount))\n",
" return dot\n",
"\n",
"def modelHistoryToGraphs(modelHistory):\n",
" '''\n",
" Converts a n-gram model history into a dict with [(tupleIn, tupleOut)] = arrOfTransitionCounts\n",
" If a graph doesn't contain a given (tupleIn, tupleOut), a zero is placed in that position\n",
" '''\n",
" edges = dict()\n",
" # loop through all models and get all edges\n",
" for model in modelHistory:\n",
" for tupleIn, tupleOut, transitionCount in loopThroughEdgeCounts(model):\n",
" edge = (tupleIn, tupleOut)\n",
" if not edge in edges:\n",
" edges[edge] = []\n",
" \n",
" # for each edge, construct a history graph of transition counts\n",
" for model in modelHistory:\n",
" # append a zero to the end of every pair, this ensures if our model doesn't have it it still gets noted\n",
" for edge, arr in edges.items():\n",
" arr.append(0)\n",
" # for every pair our model has, overwrite those zeros at the end with the actual transition count\n",
" for tupleIn, tupleOut, transitionCount in loopThroughEdgeCounts(model):\n",
" edge = (tupleIn, tupleOut)\n",
" # overwrite last value to our transition count\n",
" edges[edge][-1] = transitionCount\n",
" return edges\n",
"\n",
"def printModelHistoryAsGraphs(modelHistory):\n",
" '''\n",
" Converts a markovify model history into a dict with [(tupleIn, tupleOut)] = arrOfTransitionCounts\n",
" If a graph doesn't contain a given (tupleIn, tupleOut), a zero is placed in that position\n",
" Then this history is plotted as graphs, for each tuple\n",
" Warning, if you have a lot of tuples this could get slow\n",
" '''\n",
" graphs = modelHistoryToGraphs(modelHistory)\n",
" for edgeTuple, historyOfCounts in graphs.items():\n",
" plt.plot(historyOfCounts)\n",
" plt.title(str(edgeTuple))\n",
" plt.show()\n",
" \n",
"\n",
"def printOutputGenerations(model, maxSampleLen, minTotalGeneratedTokens):\n",
" '''\n",
" Print the generated dataset of the model\n",
" '''\n",
" dataset = generateDataset(model, maxSampleLen=maxSampleLen, minTotalGeneratedTokens=minTotalGeneratedTokens)\n",
" return \"\\n\".join([\" \".join(sample) for sample in dataset])\n",
" \n",
"def runAndVisualizeExperiment(initialDataset, printRange, numSteps, contextSize, maxSampleLen, minTotalGeneratedTokens):\n",
" '''\n",
" Runs an experiment where we generate at least minTotalGeneratedTokens tokens per step worth of samples\n",
" (rejecting individual samples that are longer than maxSampleLen)\n",
" Then we use that to train a new n-gram model (bigram=contextSize:1, trigram=contextSize:2, etc.)\n",
" We repeat this process numSteps times, and return an array with the models after each step\n",
" printRange is which models we actually want to visualize as dot models (for example, [0,-1] will display the first and last)\n",
" This will also print a graph of the history of each transition pr over time\n",
" '''\n",
" modelHistory = runExperiment(initialDataset=initialDataset, numSteps=numSteps, contextSize=contextSize, maxSampleLen=maxSampleLen, minTotalGeneratedTokens=minTotalGeneratedTokens)\n",
" for i in printRange:\n",
" print(i, \"model:\")\n",
" display(modelToDot(modelHistory[i]))\n",
" print(printOutputGenerations(modelHistory[i], minTotalGeneratedTokens=30, maxSampleLen=maxSampleLen))\n",
" printModelHistoryAsGraphs(modelHistory)\n",
" return modelHistory\n",
" \n"
]
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},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"random.seed(5) # make deterministic\n",
"# shakespeare data from https://www.kaggle.com/kingburrito666/shakespeare-plays?select=alllines.txt\n",
"#with open(\"alllines.txt\", \"r\") as f:\n",
"# text = f.read()\n",
"text = ((\"a b c b c b a \"*2) + \"\\n\")*10\n",
"datas = [a.split() for a in text.split(\"\\n\")]\n",
"# USE runExperiment INSTEAD OF runAndVisualizeExperiment WHEN YOU DO shakespeare,\n",
"# OTHERWISE DOT WILL CRASH YOUR COMPUTER BECAUSE THE FIRST BIGRAM MODEL IS 80,000 NODES,\n",
"# ALSO THERE WILL BE TOO MANY EDGES AND NOTEBOOK WILL GET VERY LARGE\n",
"# models = runExperiment(initialDataset=datas, numSteps=10000, contextSize=1, maxSampleLen=100, minTotalGeneratedTokens=10000, debug=True)\n",
"# runAndVisualize is safe for toy stuff\n",
"models = runAndVisualizeExperiment(initialDataset=datas, printRange=[0, -1], numSteps=2000, contextSize=1, maxSampleLen=100, minTotalGeneratedTokens=1000)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "aboriginal-pressure",
"metadata": {},
"outputs": [
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],
"source": [
"# how to visualize any model in the history\n",
"# (warning: don't visualize early models when using large datasets,\n",
"# examine len of the model first and make sure it's not more than a few thousand)\n",
"display(modelToDot(models[600]))"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "black-lesson",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'\"By being miss\\'d, I will not wish thee apart Cousin of duty,\"'"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# how to generate text from any model in the history\n",
"\" \".join(list(models[-1].gen()))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "filled-western",
"metadata": {},
"outputs": [],
"source": []
}
],
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