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rokroskar/local_bloomberg_sparkgrams.ipynb

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example sparkgrams package usage
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local_bloomberg_sparkgrams.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:af6593799dadc0dd1fe438bdf0178cce6a1a929730acd012ecf3deeca5c0310b"
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"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"import matplotlib.pylab as plt\n",
"plt.rcParams['figure.figsize'] = (10,6)\n",
"plt.rcParams['font.size'] = 18"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before doing anything else, make sure you can ssh into your own machine without a password. Executing this cell should print 'woohoo!!':"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"ssh localhost echo \"woohoo!\""
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"woohoo!\n"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"X11 forwarding request failed on channel 0\r\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If it doesn't, [set up ssh keys](https://www.google.com/webhp?sourceid=chrome-instant&ion=1&espv=2&ie=UTF-8#safe=off&q=set%20up%20ssh%20keys) first and then make sure the contents of your own public key (e.g. ``~/.ssh/id_rsa.pub``) are in ``~/.ssh/authorized_keys``. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Example using Apache Spark to process Bloomberg case data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Apache Spark](http://spark.apache.org) is a distributed computing runtime system that facilitates the processing of large amounts of data across many machines. See the [documentation](http://spark.apache.org/docs/latest/) for some examples and info. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook will show some example usage of the ``bloomberg_ngrams`` code that can run locally for testing and development before migrating to a larger cluster environment. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, we initialize some environment variables -- modify these to fit your needs:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"homedir = os.environ['HOME']\n",
"os.environ['SPARK_HOME'] = '%s/spark'%homedir\n",
"spark_home = os.environ['SPARK_HOME']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First make sure that if there are processes running from before, we can shut them down:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"~/spark/sbin/stop-all.sh"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"localhost: X11 forwarding request failed on channel 0\r\n",
"localhost: stopping org.apache.spark.deploy.worker.Worker\n",
"stopping org.apache.spark.deploy.master.Master\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"~/spark/sbin/start-all.sh "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"starting org.apache.spark.deploy.master.Master, logging to /Users/rokstar/src/spark-1.0.1-bin-hadoop1/sbin/../logs/spark-rokstar-org.apache.spark.deploy.master.Master-1-public-docking-als-0453.ethz.ch.out\n",
"localhost: X11 forwarding request failed on channel 0\r\n",
"localhost: starting org.apache.spark.deploy.worker.Worker, logging to /Users/rokstar/src/spark-1.0.1-bin-hadoop1/sbin/../logs/spark-rokstar-org.apache.spark.deploy.worker.Worker-1-public-docking-als-0453.ethz.ch.out\n"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This will start the spark runtime on the local machine using all available cores. \n",
"\n",
"Once the spark runtime is going, we can start the analysis"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Initialize the Spark Context"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import os\n",
"master_url = 'spark://%s:7077'%os.environ['HOST']\n",
"print master_url"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"spark://public-docking-als-0453.ethz.ch:7077\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"add the spark python library to python path"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import sys\n",
"sys.path.insert(0,os.environ['SPARK_HOME']+'/python')\n",
"sys.path.insert(0,os.environ['SPARK_HOME']+'/python/lib/py4j-0.8.1-src.zip')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pyspark\n",
"from pyspark import SparkContext"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We need to set the executor memory, i.e. how much memory each worker is allowed to use. By default this is something small like 0.5 Gb. Here it is adjusted to 4Gb, but set this to whatever is appropriate for your machine."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"os.environ['SPARK_EXECUTOR_MEMORY'] = '4g'\n",
"try : \n",
" sc.stop()\n",
"except : \n",
" pass\n",
"\n",
"sc = SparkContext(master = master_url, appName = 'Case Notebook', batchSize=10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we've got a ``SparkContext`` initialized, allowing us to distribute the computation across the set of compute nodes. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"##Start analyzing case data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up some constants: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import bloomberg_ngrams, sparkgram\n",
"data_path = '%s/project_data/decade_data'%homedir\n",
"JUDGE_BIO_PATH = data_path+'/inputs/bio_database_app_and_dist.csv'\n",
"CASE_DB_PATH = data_path+'/inputs/BloombergCASELEVEL_Touse.csv'\n",
"from sklearn import feature_extraction\n",
"sw = feature_extraction.text.ENGLISH_STOP_WORDS"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We create a ``SparkBloombergCaseVectorizer`` object that will process the cases from one year of cases. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"year_range = ['1900']"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can initialize the ``SparkBloombergCaseVectorizer``.\n",
"\n",
"It is highly recommended to specify the ``numPartitions`` to be something like 4-5 times the number of cores. This splits up the shuffling tasks that can otherwise lead to memory problems. If the spark jobs start dying off with obscure memory or IO errors, try increasing the number of partitions. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"reload(sparkgram)\n",
"reload(sparkgram.document_vectorizer)\n",
"reload(bloomberg_ngrams)\n",
"\n",
"cv = bloomberg_ngrams.SparkBloombergCaseVectorizer(sc, year_range, JUDGE_BIO_PATH, CASE_DB_PATH, datadir = data_path,\n",
" ngram_range=[1,3], stop_words = sw, num_partitions=sc.defaultParallelism*4)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 151
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets do the processing step by step to see how long each stage takes. First, construct the ``doc_rdd`` which reads all the case files and extracts the text: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time cv.load_text() "
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 17.3 ms, sys: 5.24 ms, total: 22.6 ms\n",
"Wall time: 29.5 s\n"
]
}
],
"prompt_number": 152
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can check on the [job stage timing](http://localhost:4040/stages/) and [cached storage](http://localhost:4040/storage/) at any time to see the jobs' progress."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ``doc_rdd`` is now a collection of ``(context,text)`` pairs:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.doc_rdd.first()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 154,
"text": [
"('XFKBB2||05feb1900||Insurance Law||1900||9||GILBERT, WILLIAM B.||MORROW, WILLIAM W.||ROSS, ERSKINE M.||MORROW, WILLIAM W.||MORROW||0||0||1',\n",
" \". this is an action upon a fire insurance policy, brought by the plaintiff, james f. mcelroy, in the superior court of the state of washington, to recover the sum of $2,530.85, with interest, alleged to be due upon a policy of fire insurance issued to mrs. j. c. powers, mcelroy's assignor, by the palatine insurance company, insuring the steamer cricket, her hull, cabins, tackle, furniture, etc., against loss or damage by fire to the extent of $3,500. upon the petition of the defendant insurance company the case was removed to the circuit court of the united states for the district of washington, where it was tried before a jury. a verdict was returned in favor of mcelroy for the full amount of his demand. motion for a new trial was denied, and judgment entered in favor of the plaintiff, to reverse which the defendant sued out this writ of error. the case of mcelroy v. british america assur. co. , reported in 36 c. c. a. 615 , 94 fed. 990 , was a companion case to this, the plaintiff being the same, and the action brought to recover on a policy issued by the defendant company upon the same risk, at the same time, and under substantially the same circumstances, except that the insurance against loss or damage by fire in that case was to the extent of $3,000. the negotiations leading to the issuance of the policies were had between the same parties in each case, and a detailed statement of the material facts will be found in the report of the british america assurance company case , supra . the two cases come before this court upon practically the same record, the only distinction to be noticed in the legal aspect of the two cases arising upon the assignments of error. in the case against the british america assurance company it was contended on the part of the defendant as a matter of law that the policy was void for the reason that it in express terms provided that, if the property be or become incumbered by a chattel mortgage, or if insurance should be obtained in excess of $6,500 in all concurrent with the amount covered by the policy, it should be void; and both of such forbidden acts on the part of the plaintiff had been established by the evidence. there was, however, on the other hand, evidence tending to show that calhoun & co., a firm of insurance agents and brokers in seattle, who negotiated the insurance in the sum of $6,500, had notice and knowledge of the existence of a chattel mortgage, and of the intention of the insured to secure further insurance in the amount of $3,500 to cover the interest of the holder of that mortgage; that calhoun & co. endeavored to secure this insurance also, but it was placed elsewhere. there was also evidence that calhoun & co., having secured the application for the contract of insurance in the sum of $6,500, placed $3,000 with an agent of the british america assurance company, the defendant in that case, and $3,500 with the agents of the palatine insurance company, the defendant in this case; that calhoun & co. received the written policies in the amounts stated from the agents of the companies and delivered these policies to the agent of the insured, and at the same time collected a portion of the premium. upon the evidence of these facts it was contended on the part of the plaintiff that calhoun & co. were the agents of the defendant in the transaction, and had so dealt with the agent of the insured, in securing the contract of insurance and in the delivery of the policy in that case to the agent of the insured, as to bind themselves and the insurance company by way of estoppel not to dispute the validity of the policy on account of the conditions. to this claim the defendant replied that calhoun & co. were not its agents, but the agents of the insured, and therefore any notice or knowledge which they may have had concerning the mortgage and the excess of concurrent insurance was not a notice to or the knowledge of the defendant. the trial court, at the close of the testimony, instructed the jury to return a verdict for the insurance company, which was accordingly done, and judgment entered for the defendant. the case was brought before this court upon the single assignment of error that the court erred in giving such peremptory instruction, and the only question there presented was whether, upon the testimony introduced, plaintiff had the right to have the case submitted to the jury. it was held by this court that the evidence was sufficient to go to the jury, and the case was remanded, with instructions to grant a new trial. the case at bar was the first of the two cases to be heard in the trial court. the issues were the same, except that in the present case the fact that the property was incumbered with a chattel mortgage, without being provided for by an agreement indorsed on the policy, was not made a ground of defense. the evidence upon the remaining issues was submitted to the jury, and a verdict rendered for the plaintiff (defendant in error).\\n the first error assigned relates to the refusal of the court to instruct the jury to find a verdict for the defendant (plaintiff in error). as the evidence in this case is substantially the same as the evidence in the british america assurance case , that question has been considered and determined by this court, and requires no further discussion.\\n the remaining assignments of error relate to instructions given and instructions refused by the court concerning the question of agency. they involve the question whether, upon the evidence in the case, the knowledge of calhoun & co. as to the excessive insurance could be imputed to the company, and notice to them be considered notice to the company; and whether the acts of calhoun & co. in dealing with the agent of the insured in securing the contract of insurance and in delivering the policy to the agent of the insured bound themselves and the insurance company by way of estoppel not to deny the validity of the policy by reason of the alleged breach of condition. these questions were fully discussed and passed upon in the other case, and in accordance with the views there expressed the instructions complained of cannot be held to have been error. the judgment of the circuit court is affirmed. \\n\")"
]
}
],
"prompt_number": 154
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract the ngrams from each case text: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.ngram_rdd.cache()\n",
"%time cv.ngram_rdd.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 7.12 ms, sys: 1.75 ms, total: 8.87 ms\n",
"Wall time: 4.42 s\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 155,
"text": [
"695"
]
}
],
"prompt_number": 155
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Each document is now represented as a tuple of ``(context, ngram)`` where ``ngram`` = ``(ngram_string, count)``. Therefore, if we want to construct a vocabulary or extract the feature vectors we just need to slice this data in whatever way is needed. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.ngram_rdd.first()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 156,
"text": [
"('XFKBB2||05feb1900||Insurance Law||1900||9||GILBERT, WILLIAM B.||MORROW, WILLIAM W.||ROSS, ERSKINE M.||MORROW, WILLIAM W.||MORROW||0||0||1',\n",
" [('accordance', 1),\n",
" ('accordance views', 1),\n",
" ('accordance views expressed', 1),\n",
" ('accordingly', 1),\n",
" ('accordingly judgment', 1),\n",
" ('accordingly judgment entered', 1),\n",
" ('account', 1),\n",
" ('account conditions', 1),\n",
" ('account conditions claim', 1),\n",
" ('action', 2),\n",
" ('action brought', 1),\n",
" ('action brought recover', 1),\n",
" ('action insurance', 1),\n",
" ('action insurance policy', 1),\n",
" ('acts', 2),\n",
" ('acts calhoun', 1),\n",
" ('acts calhoun dealing', 1),\n",
" ('acts plaintiff', 1),\n",
" ('acts plaintiff established', 1),\n",
" ('affirmed', 1),\n",
" ('agency', 1),\n",
" ('agency involve', 1),\n",
" ('agency involve question', 1),\n",
" ('agent', 6),\n",
" ('agent british', 1),\n",
" ('agent british america', 1),\n",
" ('agent insured', 5),\n",
" ('agent insured bind', 1),\n",
" ('agent insured bound', 1),\n",
" ('agent insured securing', 2),\n",
" ('agent insured time', 1),\n",
" ('agents', 6),\n",
" ('agents agents', 1),\n",
" ('agents agents insured', 1),\n",
" ('agents brokers', 1),\n",
" ('agents brokers seattle', 1),\n",
" ('agents companies', 1),\n",
" ('agents companies delivered', 1),\n",
" ('agents defendant', 1),\n",
" ('agents defendant transaction', 1),\n",
" ('agents insured', 1),\n",
" ('agents insured notice', 1),\n",
" ('agents palatine', 1),\n",
" ('agents palatine insurance', 1),\n",
" ('agreement', 1),\n",
" ('agreement indorsed', 1),\n",
" ('agreement indorsed policy', 1),\n",
" ('alleged', 2),\n",
" ('alleged breach', 1),\n",
" ('alleged breach condition', 1),\n",
" ('alleged policy', 1),\n",
" ('alleged policy insurance', 1),\n",
" ('america', 5),\n",
" ('america assur', 1),\n",
" ('america assur reported', 1),\n",
" ('america assurance', 4),\n",
" ('america assurance case', 1),\n",
" ('america assurance company', 3),\n",
" ('amounts', 1),\n",
" ('amounts stated', 1),\n",
" ('amounts stated agents', 1),\n",
" ('application', 1),\n",
" ('application contract', 1),\n",
" ('application contract insurance', 1),\n",
" ('arising', 1),\n",
" ('arising assignments', 1),\n",
" ('arising assignments error', 1),\n",
" ('aspect', 1),\n",
" ('aspect cases', 1),\n",
" ('aspect cases arising', 1),\n",
" ('assigned', 1),\n",
" ('assigned relates', 1),\n",
" ('assigned relates refusal', 1),\n",
" ('assignment', 1),\n",
" ('assignment error', 1),\n",
" ('assignment error court', 1),\n",
" ('assignments', 2),\n",
" ('assignments error', 2),\n",
" ('assignments error case', 1),\n",
" ('assignments error relate', 1),\n",
" ('assignor', 1),\n",
" ('assignor palatine', 1),\n",
" ('assignor palatine insurance', 1),\n",
" ('assur', 1),\n",
" ('assur reported', 1),\n",
" ('assur reported c', 1),\n",
" ('assurance', 4),\n",
" ('assurance case', 1),\n",
" ('assurance case question', 1),\n",
" ('assurance company', 3),\n",
" ('assurance company case', 1),\n",
" ('assurance company contended', 1),\n",
" ('assurance company defendant', 1),\n",
" ('bar', 1),\n",
" ('bar cases', 1),\n",
" ('bar cases heard', 1),\n",
" ('bind', 1),\n",
" ('bind insurance', 1),\n",
" ('bind insurance company', 1),\n",
" ('bound', 1),\n",
" ('bound insurance', 1),\n",
" ('bound insurance company', 1),\n",
" ('breach', 1),\n",
" ('breach condition', 1),\n",
" ('breach condition questions', 1),\n",
" ('british', 5),\n",
" ('british america', 5),\n",
" ('british america assur', 1),\n",
" ('british america assurance', 4),\n",
" ('brokers', 1),\n",
" ('brokers seattle', 1),\n",
" ('brokers seattle negotiated', 1),\n",
" ('brought', 3),\n",
" ('brought court', 1),\n",
" ('brought court single', 1),\n",
" ('brought plaintiff', 1),\n",
" ('brought plaintiff james', 1),\n",
" ('brought recover', 1),\n",
" ('brought recover policy', 1),\n",
" ('c', 3),\n",
" ('c c', 1),\n",
" ('c c fed', 1),\n",
" ('c fed', 1),\n",
" ('c fed companion', 1),\n",
" ('c powers', 1),\n",
" (\"c powers mcelroy's\", 1),\n",
" ('cabins', 1),\n",
" ('cabins tackle', 1),\n",
" ('cabins tackle furniture', 1),\n",
" ('calhoun', 8),\n",
" ('calhoun agents', 2),\n",
" ('calhoun agents agents', 1),\n",
" ('calhoun agents defendant', 1),\n",
" ('calhoun dealing', 1),\n",
" ('calhoun dealing agent', 1),\n",
" ('calhoun endeavored', 1),\n",
" ('calhoun endeavored secure', 1),\n",
" ('calhoun excessive', 1),\n",
" ('calhoun excessive insurance', 1),\n",
" ('calhoun firm', 1),\n",
" ('calhoun firm insurance', 1),\n",
" ('calhoun having', 1),\n",
" ('calhoun having secured', 1),\n",
" ('calhoun received', 1),\n",
" ('calhoun received written', 1),\n",
" ('case', 19),\n",
" ('case accordance', 1),\n",
" ('case accordance views', 1),\n",
" ('case agent', 1),\n",
" ('case agent insured', 1),\n",
" ('case agents', 1),\n",
" ('case agents palatine', 1),\n",
" ('case bar', 1),\n",
" ('case bar cases', 1),\n",
" ('case british', 1),\n",
" ('case british america', 1),\n",
" ('case brought', 1),\n",
" ('case brought court', 1),\n",
" ('case calhoun', 1),\n",
" ('case calhoun received', 1),\n",
" ('case detailed', 1),\n",
" ('case detailed statement', 1),\n",
" ('case extent', 1),\n",
" ('case extent negotiations', 1),\n",
" ('case fact', 1),\n",
" ('case fact property', 1),\n",
" ('case knowledge', 1),\n",
" ('case knowledge calhoun', 1),\n",
" ('case mcelroy', 1),\n",
" ('case mcelroy v', 1),\n",
" ('case plaintiff', 1),\n",
" ('case plaintiff action', 1),\n",
" ('case question', 1),\n",
" ('case question considered', 1),\n",
" ('case remanded', 1),\n",
" ('case remanded instructions', 1),\n",
" ('case removed', 1),\n",
" ('case removed circuit', 1),\n",
" ('case submitted', 1),\n",
" ('case submitted jury', 1),\n",
" ('case substantially', 1),\n",
" ('case substantially evidence', 1),\n",
" ('case supra', 1),\n",
" ('case supra cases', 1),\n",
" ('cases', 3),\n",
" ('cases arising', 1),\n",
" ('cases arising assignments', 1),\n",
" ('cases come', 1),\n",
" ('cases come court', 1),\n",
" ('cases heard', 1),\n",
" ('cases heard trial', 1),\n",
" ('chattel', 3),\n",
" ('chattel mortgage', 3),\n",
" ('chattel mortgage insurance', 1),\n",
" ('chattel mortgage intention', 1),\n",
" ('chattel mortgage provided', 1),\n",
" ('circuit', 2),\n",
" ('circuit court', 2),\n",
" ('circuit court affirmed', 1),\n",
" ('circuit court united', 1),\n",
" ('circumstances', 1),\n",
" ('circumstances insurance', 1),\n",
" ('circumstances insurance loss', 1),\n",
" ('claim', 1),\n",
" ('claim defendant', 1),\n",
" ('claim defendant replied', 1),\n",
" ('close', 1),\n",
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" ('secure insurance placed', 1),\n",
" ('secured', 1),\n",
" ('secured application', 1),\n",
" ('secured application contract', 1),\n",
" ('securing', 2),\n",
" ('securing contract', 2),\n",
" ('securing contract insurance', 2),\n",
" ('single', 1),\n",
" ('single assignment', 1),\n",
" ('single assignment error', 1),\n",
" ('state', 1),\n",
" ('state washington', 1),\n",
" ('state washington recover', 1),\n",
" ('stated', 1),\n",
" ('stated agents', 1),\n",
" ('stated agents companies', 1),\n",
" ('statement', 1),\n",
" ('statement material', 1),\n",
" ('statement material facts', 1),\n",
" ('states', 1),\n",
" ('states district', 1),\n",
" ('states district washington', 1),\n",
" ('steamer', 1),\n",
" ('steamer cricket', 1),\n",
" ('steamer cricket hull', 1),\n",
" ('submitted', 2),\n",
" ('submitted jury', 2),\n",
" ('submitted jury held', 1),\n",
" ('submitted jury verdict', 1),\n",
" ('substantially', 2),\n",
" ('substantially circumstances', 1),\n",
" ('substantially circumstances insurance', 1),\n",
" ('substantially evidence', 1),\n",
" ('substantially evidence british', 1),\n",
" ('sued', 1),\n",
" ('sued writ', 1),\n",
" ('sued writ error', 1),\n",
" ('sufficient', 1),\n",
" ('sufficient jury', 1),\n",
" ('sufficient jury case', 1),\n",
" ('sum', 3),\n",
" ('sum alleged', 1),\n",
" ('sum alleged policy', 1),\n",
" ('sum notice', 1),\n",
" ('sum notice knowledge', 1),\n",
" ('sum placed', 1),\n",
" ('sum placed agent', 1),\n",
" ('superior', 1),\n",
" ('superior court', 1),\n",
" ('superior court state', 1),\n",
" ('supra', 1),\n",
" ('supra cases', 1),\n",
" ('supra cases come', 1),\n",
" ('tackle', 1),\n",
" ('tackle furniture', 1),\n",
" ('tackle furniture loss', 1),\n",
" ('tending', 1),\n",
" ('tending calhoun', 1),\n",
" ('tending calhoun firm', 1),\n",
" ('terms', 1),\n",
" ('terms provided', 1),\n",
" ('terms provided property', 1),\n",
" ('testimony', 2),\n",
" ('testimony instructed', 1),\n",
" ('testimony instructed jury', 1),\n",
" ('testimony introduced', 1),\n",
" ('testimony introduced plaintiff', 1),\n",
" ('time', 2),\n",
" ('time collected', 1),\n",
" ('time collected portion', 1),\n",
" ('time substantially', 1),\n",
" ('time substantially circumstances', 1),\n",
" ('transaction', 1),\n",
" ('transaction dealt', 1),\n",
" ('transaction dealt agent', 1),\n",
" ('trial', 4),\n",
" ('trial case', 1),\n",
" ('trial case bar', 1),\n",
" ('trial court', 2),\n",
" ('trial court close', 1),\n",
" ('trial court issues', 1),\n",
" ('trial denied', 1),\n",
" ('trial denied judgment', 1),\n",
" ('tried', 1),\n",
" ('tried jury', 1),\n",
" ('tried jury verdict', 1),\n",
" ('united', 1),\n",
" ('united states', 1),\n",
" ('united states district', 1),\n",
" ('v', 1),\n",
" ('v british', 1),\n",
" ('v british america', 1),\n",
" ('validity', 2),\n",
" ('validity policy', 2),\n",
" ('validity policy account', 1),\n",
" ('validity policy reason', 1),\n",
" ('verdict', 4),\n",
" ('verdict defendant', 1),\n",
" ('verdict defendant plaintiff', 1),\n",
" ('verdict insurance', 1),\n",
" ('verdict insurance company', 1),\n",
" ('verdict rendered', 1),\n",
" ...])"
]
}
],
"prompt_number": 156
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Extract the unique vocabulary -- this uses the ``nmin`` and ``nmax`` settings if they are used in the initialization of the ``SparkBloombergCaseVectorizer`` instance:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.vocab_rdd.cache()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 157,
"text": [
"PythonRDD[367] at RDD at PythonRDD.scala:37"
]
}
],
"prompt_number": 157
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%time cv.vocab_rdd.count()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"CPU times: user 6.76 ms, sys: 2.36 ms, total: 9.13 ms\n",
"Wall time: 2.37 s\n"
]
},
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 158,
"text": [
"945483"
]
}
],
"prompt_number": 158
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The \"vocabulary\" is a collection of unique (filtered) ngrams:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.vocab_rdd.take(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 159,
"text": [
"['ab',\n",
" 'ab initio',\n",
" 'ab initio notwithstanding',\n",
" 'ab initio prevent',\n",
" 'ab initio voidable',\n",
" 'aback',\n",
" 'aback course',\n",
" 'aback course swung',\n",
" 'aback wind',\n",
" 'aback wind light']"
]
}
],
"prompt_number": 159
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can extract specific ones if we want : (note that something this could easily be added as a convenience function to ``SparkBloombergCaseVectorizer``)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.vocab_rdd.filter(lambda x: 'evidence' in x).takeSample(False, 10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 160,
"text": [
"['total lack evidence',\n",
" 'evidence shows use',\n",
" 'tendency evidence',\n",
" 'evidence general allegations',\n",
" 'certain oral evidence',\n",
" 'evidence objection',\n",
" 'evidence result thoughtless',\n",
" 'evidence master choctaw',\n",
" 'evidence fully establishes',\n",
" 'circumstantial evidence']"
]
}
],
"prompt_number": 160
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can get a mapping from words to indices that can be used later to lookup features: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.get_vocab_map().take(10)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 161,
"text": [
"[('ab', 1038175),\n",
" ('ab initio', 69239),\n",
" ('ab initio notwithstanding', 33049),\n",
" ('ab initio prevent', 362316),\n",
" ('ab initio voidable', 736135),\n",
" ('aback', 72242),\n",
" ('aback course', 764017),\n",
" ('aback course swung', 235052),\n",
" ('aback wind', 833153),\n",
" ('aback wind light', 843844)]"
]
}
],
"prompt_number": 161
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If we just want the feature vectors for passing data into another algorithm, we can get them with the ``docvec_rdd``."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we convert the feature counts into a [``SparseVector``](http://spark.apache.org/docs/latest/api/python/pyspark.mllib.linalg.SparseVector-class.html) (maybe not the best choice -- no ``add`` method -- could use ``scipy.sparse`` types instead)"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sparkgram.document_vectorizer.next_power_of_two(cv.vocab_rdd.count())"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 133,
"text": [
"131072"
]
}
],
"prompt_number": 133
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.docvec_rdd.first()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 134,
"text": [
"('XFLMS2||01oct1900||Criminal Law||1900||9||GILBERT, WILLIAM B.||MORROW, WILLIAM W.||ROSS, ERSKINE M.||GILBERT, WILLIAM B.||GILBERT||0||0||1',\n",
" SparseVector(131072, {179: 1.0, 261: 1.0, 262: 1.0, 336: 1.0, 569: 1.0, 603: 1.0, 749: 1.0, 921: 1.0, 943: 2.0, 1019: 1.0, 1061: 1.0, 1513: 1.0, 1516: 1.0, 1554: 1.0, 1563: 1.0, 1626: 1.0, 1639: 1.0, 1725: 1.0, 1874: 1.0, 2040: 1.0, 2045: 1.0, 2092: 1.0, 2111: 2.0, 2127: 1.0, 2195: 1.0, 2285: 1.0, 2518: 3.0, 2560: 1.0, 2613: 2.0, 2630: 1.0, 2875: 1.0, 2897: 1.0, 2939: 1.0, 3092: 1.0, 3128: 1.0, 3149: 1.0, 3156: 1.0, 3312: 1.0, 3330: 1.0, 3346: 1.0, 3394: 3.0, 3417: 1.0, 3421: 1.0, 3818: 1.0, 3935: 1.0, 3937: 1.0, 3998: 20.0, 4286: 1.0, 4320: 1.0, 4618: 1.0, 4668: 1.0, 4670: 1.0, 4696: 2.0, 4738: 1.0, 4822: 1.0, 4877: 1.0, 4889: 1.0, 4937: 1.0, 4960: 1.0, 5020: 1.0, 5028: 1.0, 5034: 1.0, 5054: 1.0, 5135: 1.0, 5225: 1.0, 5507: 2.0, 5579: 3.0, 5712: 1.0, 5826: 2.0, 5933: 1.0, 5984: 1.0, 5987: 1.0, 6151: 1.0, 6171: 1.0, 6195: 1.0, 6233: 2.0, 6417: 3.0, 6465: 1.0, 6468: 4.0, 6533: 1.0, 6674: 3.0, 6701: 1.0, 6756: 1.0, 7130: 1.0, 7392: 1.0, 7599: 1.0, 7639: 1.0, 7646: 1.0, 7699: 1.0, 7746: 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]
}
],
"prompt_number": 134
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The number of ngrams found in this particular document: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(cv.docvec_rdd.first()[1].values)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 135,
"text": [
"1353"
]
}
],
"prompt_number": 135
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's define a simple histogram of the number of ngrams occuring across the corpus: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def get_bins(x, binedges) :\n",
" import bisect\n",
" return bisect.bisect_left(binedges,x)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 136
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def histogram_rdd(rdd, nbins, minval, maxval) : \n",
" import numpy as np\n",
" import bisect\n",
"\n",
" binedges = np.logspace(minval,maxval,nbins+1)\n",
" bins = 0.5*(binedges[1:]+binedges[:-1])\n",
" \n",
" # First figure out which bin each document falls into: \n",
" binned = rdd.map(lambda (_,x): get_bins(len(x),binedges))\n",
"\n",
" # Then add up all the bins: \n",
" res = binned.countByValue()\n",
"\n",
" # This is a sparse result -- turn into a dense vector for plotting: \n",
" res_full = np.zeros(nbins)\n",
" overflow = 0\n",
" for item in res.iteritems() : \n",
" if item[0] > len(res_full)-1 : overflow += item[1]\n",
" else: res_full[item[0]] = item[1]\n",
" res_full[-1] += overflow\n",
" \n",
" return bins, res_full"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 137
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Filtering on number of judges"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the histogram of the full ngram count:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bins, hist = histogram_rdd(cv.ngram_rdd, 100, 1, 5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 138
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'd like to filter on words used by 5 or more judges. First we can check what the phrases that fit the selection criteria are: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"judge_vocab_rdd = cv.get_judge_vocab_rdd(5,sys.maxint)\n",
"judge_vocab = judge_vocab_rdd.collect()\n",
"print len(judge_vocab)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"502\n"
]
}
],
"prompt_number": 139
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"judge_vocab.sort()\n",
"judge_vocab[0:10]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 140,
"text": [
"['according',\n",
" 'accordingly',\n",
" 'account',\n",
" 'act',\n",
" 'acting',\n",
" 'action',\n",
" 'acts',\n",
" 'actual',\n",
" 'actually',\n",
" 'addition']"
]
}
],
"prompt_number": 140
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Generate an RDD that contains the filtered ngrams for all the cases: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"filtered_ngram = cv.filter_by_rdd(judge_vocab_rdd)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 141
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can plot the distribution of ngram counts for the filtered vs. unfiltered data: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bins, hist_filtered = histogram_rdd(filtered_ngram, 100, 1, 5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 142
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"plt.plot(bins, hist, label = 'full')\n",
"plt.plot(bins, hist_filtered, label = 'filtered')\n",
"plt.xlabel('\\# of ngrams')\n",
"plt.semilogx()\n",
"plt.legend()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 143,
"text": [
"<matplotlib.legend.Legend at 0x113129650>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Ccv+Afp8qiipCykWUqNoM4I889z3X/b7V/nDIOGHT/kvT+FN/Xh72n3eSOsBK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\n2JY0PvgAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x113052e10>"
]
}
],
"prompt_number": 143
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you want to apply the filter to the RDDs that are generated automatically, you can use the ``apply_filter`` method. This uses the default ``nmin`` and ``nmax`` that you initialize the ``SparkBloombergCaseVectorizer`` with, or you can specify a different set at this point. By default, this filters on the number times an ngram occurs in unique documents. If we want to change this, we can pass in a different function, e.g. to do the filtering by judge usage: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.apply_filter(nmin = 5, nmax = 10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 144
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(cv.ngram_rdd.first()[1]) # number of ngrams in the first document"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 145,
"text": [
"127"
]
}
],
"prompt_number": 145
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"bins, hist_filtered2 = histogram_rdd(cv.ngram_rdd, 100, 1, 5)\n",
"plt.plot(bins,hist_filtered2)\n",
"plt.xlabel('\\# of ngrams')\n",
"plt.semilogx()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 146,
"text": [
"[]"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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+LhPMXpZ0Vmas2DOSXnXdKXui4Vt/8YqqjKXtppSSh7EklbHr101ItF1/LmHM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\nH855v1BNLn9blmTGmz0lM9/qSTlkmTL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"text": [
"<matplotlib.figure.Figure at 0x113284450>"
]
}
],
"prompt_number": 146
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nice, many fewer dimensions to deal with!"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.docvec_rdd.first()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 147,
"text": [
"('XFKJ1B||21nov1900||Mortgages & Liens||1900||8||CALDWELL, HENRY C.||THAYER, AMOS MADDEN||SANBORN, WALTER H.||CALDWELL, HENRY C.||CALDWELL||0||0||1',\n",
" SparseVector(512, {8: 1.0, 8: 4.0, 10: 1.0, 10: 1.0, 10: 2.0, 11: 1.0, 15: 1.0, 20: 9.0, 24: 3.0, 29: 1.0, 37: 3.0, 41: 6.0, 52: 1.0, 55: 1.0, 61: 3.0, 70: 1.0, 71: 1.0, 78: 1.0, 79: 1.0, 91: 1.0, 92: 2.0, 98: 1.0, 99: 1.0, 99: 2.0, 111: 1.0, 114: 7.0, 115: 10.0, 119: 2.0, 120: 7.0, 127: 2.0, 127: 6.0, 135: 1.0, 144: 2.0, 149: 1.0, 155: 1.0, 157: 2.0, 158: 2.0, 159: 1.0, 162: 3.0, 182: 3.0, 185: 3.0, 186: 1.0, 187: 1.0, 190: 2.0, 196: 1.0, 197: 1.0, 197: 2.0, 201: 1.0, 201: 5.0, 204: 2.0, 206: 1.0, 211: 1.0, 223: 8.0, 225: 1.0, 238: 1.0, 242: 2.0, 243: 1.0, 243: 2.0, 248: 1.0, 250: 2.0, 251: 3.0, 258: 2.0, 271: 1.0, 278: 1.0, 280: 10.0, 286: 1.0, 289: 1.0, 291: 1.0, 303: 3.0, 305: 2.0, 305: 3.0, 306: 2.0, 314: 1.0, 314: 6.0, 315: 2.0, 321: 1.0, 325: 1.0, 327: 2.0, 333: 2.0, 335: 3.0, 336: 1.0, 348: 1.0, 349: 1.0, 349: 7.0, 351: 10.0, 352: 2.0, 358: 1.0, 359: 1.0, 359: 4.0, 360: 1.0, 364: 1.0, 368: 1.0, 371: 2.0, 372: 1.0, 373: 1.0, 374: 1.0, 375: 2.0, 380: 1.0, 380: 4.0, 382: 3.0, 385: 1.0, 388: 3.0, 394: 1.0, 402: 15.0, 409: 2.0, 415: 17.0, 419: 1.0, 422: 1.0, 424: 1.0, 427: 3.0, 428: 1.0, 434: 1.0, 436: 1.0, 457: 1.0, 470: 1.0, 471: 4.0, 475: 8.0, 480: 2.0, 488: 1.0, 494: 1.0, 495: 1.0, 495: 2.0, 499: 1.0, 499: 1.0, 499: 2.0, 504: 1.0, 504: 1.0}))"
]
}
],
"prompt_number": 147
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"You can see the mapping between these vector indices and the vocabulary: "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then if you want to go back to the unfiltered data set, you can call the ``reset()`` method"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.reset()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 162
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"len(cv.ngram_rdd.first()[1])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 163,
"text": [
"1026"
]
}
],
"prompt_number": 163
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Output matrix"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Quick and dirty data output:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%bash\n",
"mkdir temp"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 165
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cd temp"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"/Users/rokstar/Nbody/projects/bloomberg_ngrams/notebooks/temp\n"
]
}
],
"prompt_number": 166
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"cv.apply_filter(nmin = 5, nmax = 10)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 173
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pairs = cv.docvec_rdd.collect()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 195
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from scipy.sparse import csr_matrix"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 196
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the total length: "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"lengths = np.zeros(len(data),'int')\n",
"for i,pair in enumerate(pairs) : \n",
" lengths[i] = len((pair[1].values))\n",
"total = lengths.sum()\n",
"print total"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"43259\n"
]
}
],
"prompt_number": 198
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up the arrays:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data = np.empty(total,'int')\n",
"indices = np.empty(total,'int')\n",
"indptr = np.zeros(len(pairs)+1, 'int')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 243
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Get the data from the SparseVectors"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"n = 0\n",
"for context, vec in pairs : \n",
" l = len(vec.indices)\n",
" indices[n:n+l] = vec.indices\n",
" data[n:n+l] = vec.values\n",
" n += l"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 244
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"indptr[1:] = np.cumsum(lengths)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 245
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set up the sparse matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"mat = csr_matrix((data,indices,indptr), shape = (len(pairs),vec.size))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 247
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Save the data to a file:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"np.savez('docvec_data.npz',data=data,indices=indices,indptr=indptr)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 248
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Load the data and make a sparse matrix"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"loaded = np.load('docvec_data.npz')\n",
"mat2 = csr_matrix((loaded['data'],loaded['indices'],loaded['indptr']))"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 257
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Shutdown Spark"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Before you exit your session, it's important to close everything down and clean up after yourself to avoid any zombie jobs running on the cluster. The ``if False`` is there to prevent accidental shut-down... remove it when you're ready to quit"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"if False: \n",
" os.system('%s/sbin/stop-all.sh'%spark_home)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 164
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 164
}
],
"metadata": {}
}
]
}
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