I want to make some experiments with Stanford dcoref package on our conversational data. Our data contains usernames (speakers) and the utterances. Is it possible to give a structured data as input (instead of the raw text) to Stanford dcoref annotator? If yes, what should be the format of conversational input data?
Thank you,
-berfin
I was able to get this basic example to work:
<doc id="speaker-example-1">
<post author="Joe Smith" datetime="2018-02-28T20:10:00" id="p1">
I am hungry!
</post>
<post author="Jane Smith" datetime="2018-02-28T20:10:05" id="p2">
Joe Smith is hungry.
</post>
</doc>
I used these properties:
annotators = tokenize,cleanxml,ssplit,pos,lemma,ner,parse,coref
coref.conll = true
coref.algorithm = clustering
# Clean XML tags for SGM (move to sgm specific conf file?)
clean.xmltags = headline|dateline|text|post
clean.singlesentencetags = HEADLINE|DATELINE|SPEAKER|POSTER|POSTDATE
clean.sentenceendingtags = P|POST|QUOTE
clean.turntags = TURN|POST|QUOTE
clean.speakertags = SPEAKER|POSTER
clean.docIdtags = DOCID
clean.datetags = DATETIME|DATE|DATELINE
clean.doctypetags = DOCTYPE
clean.docAnnotations = docID=doc[id],doctype=doc[type],docsourcetype=doctype[source]
clean.sectiontags = HEADLINE|DATELINE|POST
clean.sectionAnnotations = sectionID=post[id],sectionDate=post[date|datetime],sectionDate=postdate,author=post[author],author=poster
clean.quotetags = quote
clean.quoteauthorattributes = orig_author
clean.tokenAnnotations = link=a[href],speaker=post[author],speaker=quote[orig_author]
clean.ssplitDiscardTokens = \\n|\\*NL\\*
Also this document has great info on the coref system:
https://stanfordnlp.github.io/CoreNLP/coref.html
I am looking into using the neural option on my example .xml document, but you might have to put your data into the conll format to run our neural coref with the conll settings. The conll data has conversational data with speaker info among other document formats.
This document contains info on the CoNLL format you'd have to use for the neural algorithm to work.
CoNLL 2012 format: http://conll.cemantix.org/2012/data.html
You need to create a folder with a similar directory structure (but you can put your files in instead)
example:
/Path/to/conll_2012_dir/v9/data/test/data/english/annotations/wb/eng/00/eng_0009.v9_auto_conll
If you run this command:
java -Xmx20g edu.stanford.nlp.coref.CorefSystem -props speaker.properties
with these properties:
coref.algorithm = clustering
coref.conll = true
coref.conllOutputPath = /Path/to/output_dir
coref.data = /Path/to/conll_2012_dir
it will write conll output files to /Path/to/output_dir
That command should read in all files ending with _auto_conll
Related
I have written a pipeline in Snakemake. It's an ATAC-seq pipeline (bioinformatics pipeline to analyze genomics data from a specific experiment). Basically, until merging alignment step I use {sample_id} wildcard, to later switch to {sample} wildcard (merging two or more sample_ids into one sample).
working DAG here (for simplicity only one sample shown; orange and blue {sample_id}s are merged into one green {sample}
Tha all rule looks as follows:
configfile: "config.yaml"
SAMPLES_DICT = dict()
with open(config['SAMPLE_SHEET'], "r+") as fil:
next(fil)
for lin in fil.readlines():
row = lin.strip("\n").split("\t")
sample_id = row[0]
sample_name = row[1]
if sample_name in SAMPLES_DICT.keys():
SAMPLES_DICT[sample_name].append(sample_id)
else:
SAMPLES_DICT[sample_name] = [sample_id]
SAMPLES = list(SAMPLES_DICT.keys())
SAMPLE_IDS = [sample_id for sample in SAMPLES_DICT.values() for sample_id in sample]
rule all:
input:
# FASTQC output for RAW reads
expand(os.path.join(config['FASTQC'], '{sample_id}_R{read}_fastqc.zip'),
sample_id = SAMPLE_IDS,
read = ['1', '2']),
# Trimming
expand(os.path.join(config['TRIMMED'],
'{sample_id}_R{read}_val_{read}.fq.gz'),
sample_id = SAMPLE_IDS,
read = ['1', '2']),
# Alignment
expand(os.path.join(config['ALIGNMENT'], '{sample_id}_sorted.bam'),
sample_id = SAMPLE_IDS),
# Merging
expand(os.path.join(config['ALIGNMENT'], '{sample}_sorted_merged.bam'),
sample = SAMPLES),
# Marking Duplicates
expand(os.path.join(config['ALIGNMENT'], '{sample}_sorted_md.bam'),
sample = SAMPLES),
# Filtering
expand(os.path.join(config['FILTERED'],
'{sample}.bam'),
sample = SAMPLES),
expand(os.path.join(config['FILTERED'],
'{sample}.bam.bai'),
sample = SAMPLES),
# multiqc report
"multiqc_report.html"
message:
'\n#################### ATAC-seq pipeline #####################\n'
'Running all necessary rules to produce complete output.\n'
'############################################################'
I know it's too messy, I should only leave the necessary bits, but here my understanding of snakemake fails cause I don't know what I have to keep and what I should delete.
This is working, to my knowledge exactly as I want.
However, I added a rule:
rule hmmratac:
input:
bam = os.path.join(config['FILTERED'], '{sample}.bam'),
index = os.path.join(config['FILTERED'], '{sample}.bam.bai')
output:
model = os.path.join(config['HMMRATAC'], '{sample}.model'),
gappedPeak = os.path.join(config['HMMRATAC'], '{sample}_peaks.gappedPeak'),
summits = os.path.join(config['HMMRATAC'], '{sample}_summits.bed'),
states = os.path.join(config['HMMRATAC'], '{sample}.bedgraph'),
logs = os.path.join(config['HMMRATAC'], '{sample}.log'),
sample_name = '{sample}'
log:
os.path.join(config['LOGS'], 'hmmratac', '{sample}.log')
params:
genomes = config['GENOMES'],
blacklisted = config['BLACKLIST']
resources:
mem_mb = 32000
message:
'\n######################### Peak calling ########################\n'
'Peak calling for {output.sample_name}\n.'
'############################################################'
shell:
'HMMRATAC -Xms2g -Xmx{resources.mem_mb}m '
'--bam {input.bam} --index {input.index} '
'--genome {params.genome} --blacklist {params.blacklisted} '
'--output {output.sample_name} --bedgraph true &> {log}'
And into the rule all, after filtering, before multiqc, I added:
# Peak calling
expand(os.path.join(config['HMMRATAC'], '{sample}.model'),
sample = SAMPLES),
Relevant config.yaml fragments:
# Path to blacklisted regions
BLACKLIST: "/mnt/data/.../hg38.blacklist.bed"
# Path to chromosome sizes
GENOMES: "/mnt/data/.../hg38_sizes.genome"
# Path to filtered alignment
FILTERED: "alignment/filtered"
# Path to peaks
HMMRATAC: "peaks/hmmratac"
This is the error* I get (It goes on for every input and output of the rule). *Technically it's a warning but it halts execution of snakemake so I am calling it an error.
File path alignment/filtered//mnt/data/.../hg38.blacklist.bed.bam contains double '/'. This is likely unintended. It can also lead to inconsistent results of the file-matching approach used by Snakemake.
WARNING:snakemake.logging:File path alignment/filtered//mnt/data/.../hg38.blacklist.bed.bam contains double '/'. This is likely unintended. It can also lead to inconsistent results of the file-matching approach used by Snakemake.
It isn't actually ... - I just didn't feel safe providing an absolute path here.
For a couple of days, I have struggled with this error. Looked through the documentation, listened to the introduction. I understand that the above description is far from perfect (it is huge bc I don't even know how to work it down to provide minimal reproducible example...) but I am desperate and hope you can be patient with me.
Any suggestions as to how to google it, where to look for an error would be much appreciated.
Technically it's a warning but it halts execution of snakemake so I am calling it an error.
It would be useful to post the logs from snakemake to see if snakemake terminated with an error and if so what error.
However, in addition to Eric C.'s suggestion to use wildcards.sample instead of {sample} as file name, I think that this is quite suspicious:
alignment/filtered//mnt/data/.../hg38.blacklist.bed.bam
/mnt/ is usually at the root of the file system and you are prepending to it a relative path (alignment/filtered). Are you sure it is correct?
I'm having trouble saving the output given by the Google Vision API. I'm using Python and testing with a demo image. I get the following error:
TypeError: [mid:...] + is not JSON serializable
Code that I executed:
import io
import os
import json
# Imports the Google Cloud client library
from google.cloud import vision
from google.cloud.vision import types
# Instantiates a client
vision_client = vision.ImageAnnotatorClient()
# The name of the image file to annotate
file_name = os.path.join(
os.path.dirname(__file__),
'demo-image.jpg') # Your image path from current directory
# Loads the image into memory
with io.open(file_name, 'rb') as image_file:
content = image_file.read()
image = types.Image(content=content)
# Performs label detection on the image file
response = vision_client.label_detection(image=image)
labels = response.label_annotations
print('Labels:')
for label in labels:
print(label.description, label.score, label.mid)
with open('labels.json', 'w') as fp:
json.dump(labels, fp)
the output appears on the screen, however I do not know exactly how I can save it. Anyone have any suggestions?
FYI to anyone seeing this in the future, google-cloud-vision 2.0.0 has switched to using proto-plus which uses different serialization/deserialization code. A possible error you can get if upgrading to 2.0.0 without changing the code is:
object has no attribute 'DESCRIPTOR'
Using google-cloud-vision 2.0.0, protobuf 3.13.0, here is an example of how to serialize and de-serialize (example includes json and protobuf)
import io, json
from google.cloud import vision_v1
from google.cloud.vision_v1 import AnnotateImageResponse
with io.open('000048.jpg', 'rb') as image_file:
content = image_file.read()
image = vision_v1.Image(content=content)
client = vision_v1.ImageAnnotatorClient()
response = client.document_text_detection(image=image)
# serialize / deserialize proto (binary)
serialized_proto_plus = AnnotateImageResponse.serialize(response)
response = AnnotateImageResponse.deserialize(serialized_proto_plus)
print(response.full_text_annotation.text)
# serialize / deserialize json
response_json = AnnotateImageResponse.to_json(response)
response = json.loads(response_json)
print(response['fullTextAnnotation']['text'])
Note 1: proto-plus doesn't support converting to snake_case names, which is supported in protobuf with preserving_proto_field_name=True. So currently there is no way around the field names being converted from response['full_text_annotation'] to response['fullTextAnnotation']
There is an open closed feature request for this: googleapis/proto-plus-python#109
Note 2: The google vision api doesn't return an x coordinate if x=0. If x doesn't exist, the protobuf will default x=0. In python vision 1.0.0 using MessageToJson(), these x values weren't included in the json, but now with python vision 2.0.0 and .To_Json() these values are included as x:0
Maybe you were already able to find a solution to your issue (if that is the case, I invite you to share it as an answer to your own post too), but in any case, let me share some notes that may be useful for other users with a similar issue:
As you can check using the the type() function in Python, response is an object of google.cloud.vision_v1.types.AnnotateImageResponse type, while labels[i] is an object of google.cloud.vision_v1.types.EntityAnnotation type. None of them seem to have any out-of-the-box implementation to transform them to JSON, as you are trying to do, so I believe the easiest way to transform each of the EntityAnnotation in labels would be to turn them into Python dictionaries, then group them all into an array, and transform this into a JSON.
To do so, I have added some simple lines of code to your snippet:
[...]
label_dicts = [] # Array that will contain all the EntityAnnotation dictionaries
print('Labels:')
for label in labels:
# Write each label (EntityAnnotation) into a dictionary
dict = {'description': label.description, 'score': label.score, 'mid': label.mid}
# Populate the array
label_dicts.append(dict)
with open('labels.json', 'w') as fp:
json.dump(label_dicts, fp)
There is a library released by Google
from google.protobuf.json_format import MessageToJson
webdetect = vision_client.web_detection(blob_source)
jsonObj = MessageToJson(webdetect)
I was able to save the output with the following function:
# Save output as JSON
def store_json(json_input):
with open(json_file_name, 'a') as f:
f.write(json_input + '\n')
And as #dsesto mentioned, I had to define a dictionary. In this dictionary I have defined what types of information I would like to save in my output.
with open(photo_file, 'rb') as image:
image_content = base64.b64encode(image.read())
service_request = service.images().annotate(
body={
'requests': [{
'image': {
'content': image_content
},
'features': [{
'type': 'LABEL_DETECTION',
'maxResults': 20,
},
{
'type': 'TEXT_DETECTION',
'maxResults': 20,
},
{
'type': 'WEB_DETECTION',
'maxResults': 20,
}]
}]
})
The objects in the current Vision library lack serialization functions (although this is a good idea).
It is worth noting that they are about to release a substantially different library for Vision (it is on master of vision's repo now, although not released to PyPI yet) where this will be possible. Note that it is a backwards-incompatible upgrade, so there will be some (hopefully not too much) conversion effort.
That library returns plain protobuf objects, which can be serialized to JSON using:
from google.protobuf.json_format import MessageToJson
serialized = MessageToJson(original)
You can also use something like protobuf3-to-dict
Am working with file with following format:
<badges>
<row Id="1" UserId="1" Name="Teacher" Date="2009-09-30T15:17:50.66"/>
<row Id="2" UserId="3" Name="Teacher" Date="2009-09-30T15:17:50.69"/>
</badges>
I am using pig xmlloader to fetch this xml data into hdfs.
A = LOAD '/badges' using org.apache.pig.piggybank.storage.XMLLoader('row') as (x:chararray);
B = foreach A generate xpath(x, '/row#Id').
Dump B;
Output I get () - No values.
I want the file output as text i.e 1,1,Teacher,2009-09-30T15:17:50.66. How can I do this?
I'm not familiar with pig xmlloader, but /row#Id has two problems:
It's not valid XPath
If it were, it would be an absolute path
Try:
B = foreach A generate xpath(x, 'row/#Id').
It uses valid syntax and a relative path.
Use XPathAll for extracting attributes.Xpath has an issue when it comes to attributes.
REGISTER '/path/piggybank-0.15.0.jar'; -- Use the jar name you downloaded
DEFINE XPathAll org.apache.pig.piggybank.evaluation.xml.XPathAll();
B = foreach A generate XPathAll(x, 'row/#Id', true, false).$0 as (id:chararray);
I am able to read arvo file into avroRDD and am trying to convert into csvRDD which contain all the values in comma separated. With the following code I am able to read specific field into csvRDD.
val csvRDD = avroRDD .map({case (u, _) => u.datum.get("empname")})
How can I read all the values into csvRDD instead of specifying field names. My result csvRDD should contain records as follows
(100,John,25,IN)
(101,Ricky,38,AUS)
(102,Chris,68,US)
Using Spark 1.2+ with the Spark-Avro integration library by Databricks, one can convert an avro rdd to a csv rdd as follows:
val sqlContext = new SQLContext(sc)
val episodes = sqlContext.avroFile("episodes.avro")
val csv = episodes.map(_.mkString(","))
Running csv.collect().foreach(println) using this sample avro file prints
The Eleventh Hour,3 April 2010,11
The Doctor's Wife,14 May 2011,11
Horror of Fang Rock,3 September 1977,4
An Unearthly Child,23 November 1963,1
The Mysterious Planet,6 September 1986,6
Rose,26 March 2005,9
...
With $AdminApp view <applicationName> -MapResRefToEJB it is possible to list the resource references defined for a deployed EJB module. However, the result of that command is plain text (that in addition may be localized). To extract that information one would have to parse this text, which is not very convenient. Is there a way to get the same information (i.e. the resources references of an application) in a structured form using $AdminConfig?
The AppManagement MBean provides this data in a structured format (Vector of AppDeploymentTasks). To obtain this data using wsadmin scripting (jython):
import javax.management as mgmt
appName = sys.argv[0]
appMgmt = mgmt.ObjectName(AdminControl.completeObjectName("WebSphere:*,type=AppManagement"))
appInfo = AdminControl.invoke_jmx(appMgmt, "getApplicationInfo", [appName, java.util.Hashtable(), None], ["java.lang.String", "java.util.Hashtable", "java.lang.String"])
for task in appInfo :
if (task.getName() == "MapResRefToEJB") :
resRefs = task.getTaskData()
# skip the first row since it contains the headers
for i in range(1, len(resRefs)) :
resRef = resRefs[i]
print
print "URI:", resRef[4]
print "EJB:", resRef[3]
print "Name:", resRef[5]
print "Type:", resRef[6]
print "JNDI:", resRef[8]