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rjurney /
Last active Feb 13, 2020
Utilities for making Snorkel display all LabelFunction errors
from snorkel.analysis import get_label_buckets
API = 1
rjurney /
Last active Feb 13, 2020
Pot is physically addictive and kills young adults by causing arrhythmia and strokes

Pot is not harmless. It is physically addictive and causes strokes in young people more than any other risk factor including smoking cigarettes.

  • Pot is physically addictive. This isn’t reefer madness and has been known for 20 years. Cannabinoid withdrawal work just like any other drug withdrawal: cannabinoid receptors down regulate when heavily and repeatedly activated and then you feel like shit when they are under stimulated. Every adult reading this has seen someone in withdrawal freaking out back when pot was illegal. This is why.
  • Pot is a depressant until you exceed a certain dose, then it causes severe anxiety, racing heart, etc. Every adult reading this has watched this happen to someone.

For 18-49 year olds, according to the American Heart Association:

  • Smoking cigarettes and pot raises your stroke risk 300%
  • Smoking pot 10 or more times a month without smoking cigarettes raises your stroke risk 250%

Keybase proof

I hereby claim:

To claim this, I am signing this object:

rjurney /
Created Jan 7, 2020
A script that removes all non-default security group rules and groups in a single REGION using boto3
import boto3
from botocore.exceptions import ClientError
REGION = 'us-east-1'
ec2 = boto3.client('ec2', region_name=REGION)
# Keep removing until all are gone
while True:
groups = ec2.describe_security_groups()['SecurityGroups']
rjurney /
Created Dec 19, 2019
How to fetch the README of any Github repository in one line of bash
curl "<user>/<repo>/readme" | jq -r .content | base64 -D
rjurney /
Last active Dec 4, 2019
How do you chain a preprocessor for an LF to occur AFTER SpacyPreprocessor?
spacy = SpacyPreprocessor(
@preprocessor(memoize=True, pre=[spacy])
def restore_entity(x):
rjurney /
Created Dec 2, 2019
Example of spaCy object Labeling Function
from spacy.matcher import Matcher
matcher = Matcher(nlp.vocab)
pattern = [{'POS': 'VERB'}, {'POS': 'ADP'}, {'POS': 'PROPN'}]
matcher.add("VERB_ADP_PROPN", None, pattern)
def lf_verb_in_noun(x):
"""Return positive if the pattern"""
sp = x['spacy']
matches = matcher(sp)
window = 5
candidates = []
for index, row in df.iterrows():
doc = nlp(row['_Body'])
for ent in doc.ents:
rec = {}
rec['body'] = doc.text
rec['entity'] = ent
rec['entity_text'] = ent.text
rec['entity_start'] = ent.start
rjurney / tty.txt
Created Nov 11, 2019
What /dev/ttyS* port does this correspond to?
View tty.txt
T: Bus=01 Lev=01 Prnt=01 Port=08 Cnt=04 Dev#= 5 Spd=12 MxCh= 0
D: Ver= 2.00 Cls=00(>ifc ) Sub=00 Prot=00 MxPS=64 #Cfgs= 1
P: Vendor=051d ProdID=0002 Rev=00.90
S: Manufacturer=American Power Conversion
S: Product=Back-UPS ES 850M2 FW:931.a7 .D USB FW:a7
S: SerialNumber=4B1716P37698
C: #Ifs= 1 Cfg#= 1 Atr=e0 MxPwr=2mA
I: If#= 0 Alt= 0 #EPs= 1 Cls=03(HID ) Sub=00 Prot=00 Driver=usbhid
rjurney /
Created Nov 4, 2019
Writing Predictions to MongoDB using Kafka and Structured Streaming
# Make the prediction
predictions = rfc.transform(final_vectorized_features)
# Drop the features vector and prediction metadata to give the original fields
predictions = predictions.drop("Features_vec")
final_predictions = predictions.drop("indices").drop("values").drop("rawPrediction").drop("probability")
# Store the results to MongoDB
class MongoWriter:
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