Two Months ago, I wasrunning my following codes very well, but now,after I pip install google.cloud again, it is said : 'TableReference' object has no attribute "exists" , and either I can NOT use Dataset.name, it gone as well . so is there any big change about API sounds like I need to restructure my codes...
def createTable(client, ds, tb):
dataset = client.dataset(ds)
#assert not dataset.exists()
table = dataset.table(tb)
#assert not table.exists()
if not table.exists():
assert not table.exists()
table.schema = (bigquery.SchemaField('Name', 'STRING'),
bigquery.SchemaField('Age', 'INTEGER'),
bigquery.SchemaField('Weight', 'FLOAT'),)
table.create()
else:
print 'this table already existed in this dataset'
assert table.exists()
You probably haven't updated in a while, and you've brought in the breaking changes from 0.28.0, in particular:
Remove table.exists() (#4145)
and
Functions to create, get, update, delete datasets and tables moved to
the client class.
See:
https://github.com/GoogleCloudPlatform/google-cloud-python/releases/tag/bigquery-0.28.0
https://cloud.google.com/bigquery/docs/python-client-migration
In summary, you'll either need to migrate/port your code, or remain on an older version.
Related
my team is interested in a feature store solution that enables rapid experimentation of features, probably using feature versioning. In the Feast slack history, I found
#Benjamin Tan’s post that explains their feast workflow, and they explain FeatureView versioning:
insights_v1 = FeatureView(
features=[
Feature(name="insight_type", dtype=ValueType.STRING)
]
)
insights_v2 = FeatureView(
features=[
Feature(name="customer_id", dtype=ValueType.STRING)
Feature(name="insight_type", dtype=ValueType.STRING)
]
)
Is this the recommended best practice for FeatureView versioning? It looks like Features do not have a version field. Is there a recommended strategy for Feature versioning?
Creating a new column for each Feature version is one approach:
driver_rating_v1
driver_rating_v2
But that could get unwieldy if we want to experiment with dozens of permutations of the same Feature.
Featureform appears to have support for feature versions through the "variant" field, but their documentation is a bit unclear.
Adding additional clarity on Featureform: Variant is analogous to version. You'd supply a string which then becomes an immutable identifier for the version of the transformation, source, etc. Variant is one of the common metadata fields provided in the Featureform API.
Using the example of an ecommerce dataset & spark, here's an example of using the variant field to version a source (a parquet file in this case):
orders = spark.register_parquet_file(
name="orders",
variant="default",
description="This is the core dataset. From each order you might find all other information.",
file_path="path_to_file",
)
You can set the variant variable ahead of time:
VERSION="v1" # You can change this to rerun the definitions with with new variants
orders = spark.register_parquet_file(
name="orders",
variant=f"{VERSION}",
description="This is the core dataset. From each order you might find all other information.",
file_path="path_to_file",
)
And you can create versions or variants of the transformations -- here I'm taking a dataframe called total_paid_per_customer_per_day and aggregating it.
# Get average order value per day
#spark.df_transformation(inputs=[("total_paid_per_customer_per_day", "default")], variant="skeller88_20220110")
def average_daily_transaction(df):
from pyspark.sql.functions import mean
return df.groupBy("day_date").agg(mean("total_customer_order_paid").alias("average_order_value"))
There are some more details on the Featureform CLI here: https://docs.featureform.com/getting-started/interact-with-the-cli
I am trying to use SpaCy for entity context recognition in the world of ontologies. I'm a novice at using SpaCy and just playing around for starters.
I am using the ENVO Ontology as my 'patterns' list for creating a dictionary for entity recognition. In simple terms the data is an ID (CURIE) and the name of the entity it corresponds to along with its category.
Screenshot of my sample data:
The following is the workflow of my initial code:
Creating patterns and terms
# Set terms and patterns
terms = {}
patterns = []
for curie, name, category in envoTerms.to_records(index=False):
if name is not None:
terms[name.lower()] = {'id': curie, 'category': category}
patterns.append(nlp(name))
Setup a custom pipeline
#Language.component('envo_extractor')
def envo_extractor(doc):
matches = matcher(doc)
spans = [Span(doc, start, end, label = 'ENVO') for matchId, start, end in matches]
doc.ents = spans
for i, span in enumerate(spans):
span._.set("has_envo_ids", True)
for token in span:
token._.set("is_envo_term", True)
token._.set("envo_id", terms[span.text.lower()]["id"])
token._.set("category", terms[span.text.lower()]["category"])
return doc
# Setter function for doc level
def has_envo_ids(self, tokens):
return any([t._.get("is_envo_term") for t in tokens])
##EDIT: #################################################################
def resolve_substrings(matcher, doc, i, matches):
# Get the current match and create tuple of entity label, start and end.
# Append entity to the doc's entity. (Don't overwrite doc.ents!)
match_id, start, end = matches[i]
entity = Span(doc, start, end, label="ENVO")
doc.ents += (entity,)
print(entity.text)
#########################################################################
Implement the custom pipeline
nlp = spacy.load("en_core_web_sm")
matcher = PhraseMatcher(nlp.vocab)
#### EDIT: Added 'on_match' rule ################################
matcher.add("ENVO", None, *patterns, on_match=resolve_substrings)
nlp.add_pipe('envo_extractor', after='ner')
and the pipeline looks like this
[('tok2vec', <spacy.pipeline.tok2vec.Tok2Vec at 0x7fac00c03bd0>),
('tagger', <spacy.pipeline.tagger.Tagger at 0x7fac0303fcc0>),
('parser', <spacy.pipeline.dep_parser.DependencyParser at 0x7fac02fe7460>),
('ner', <spacy.pipeline.ner.EntityRecognizer at 0x7fac02f234c0>),
('envo_extractor', <function __main__.envo_extractor(doc)>),
('attribute_ruler',
<spacy.pipeline.attributeruler.AttributeRuler at 0x7fac0304a940>),
('lemmatizer',
<spacy.lang.en.lemmatizer.EnglishLemmatizer at 0x7fac03068c40>)]
Set extensions
# Set extensions to tokens, spans and docs
Token.set_extension('is_envo_term', default=False, force=True)
Token.set_extension("envo_id", default=False, force=True)
Token.set_extension("category", default=False, force=True)
Doc.set_extension("has_envo_ids", getter=has_envo_ids, force=True)
Doc.set_extension("envo_ids", default=[], force=True)
Span.set_extension("has_envo_ids", getter=has_envo_ids, force=True)
Now when I run the text 'tissue culture', it throws me an error:
nlp('tissue culture')
ValueError: [E1010] Unable to set entity information for token 0 which is included in more than one span in entities, blocked, missing or outside.
I know why the error occurred. It is because there are 2 entries for the 'tissue culture' phrase in the ENVO database as shown below:
Ideally I'd expect the appropriate CURIE to be tagged depending on the phrase that was present in the text. How do I address this error?
My SpaCy Info:
============================== Info about spaCy ==============================
spaCy version 3.0.5
Location *irrelevant*
Platform macOS-10.15.7-x86_64-i386-64bit
Python version 3.9.2
Pipelines en_core_web_sm (3.0.0)
It might be a little late nowadays but, complementing Sofie VL's answer a little bit, and to anyone who might be still interested in it, what I (another spaCy newbie, lol) have done to get rid of overlapping spans, goes as follows:
import spacy
from spacy.util import filter_spans
# [Code to obtain 'entity']...
# 'entity' should be a list, i.e.:
# entity = ["Carolina", "North Carolina"]
pat_orig = len(entity)
filtered = filter_spans(ents) # THIS DOES THE TRICK
pat_filt =len(filtered)
doc.ents = filtered
print("\nCONVERSION REPORT:")
print("Original number of patterns:", pat_orig)
print("Number of patterns after overlapping removal:", pat_filt)
Important to mention that I am using the most recent version of spaCy at this date, v3.1.1. Additionally, it will work only if you actually do not mind about overlapping spans being removed, but if you do, then you might want to give this thread a look. More info regarding 'filter_spans' here.
Best regards.
Since spacy v3, you can use doc.spans to store entities that may be overlapping. This functionality is not supported by doc.ents.
So you have two options:
Implement an on_match callback that will filter out the results of the matcher before you use the result to set doc.ents. From a quick glance at your code (and the later edits), I don't think resolve_substrings is actually resolving conflicts? Ideally, the on_match function should check whether there are conflicts with existing ents, and decide which of them to keep.
Use doc.spans instead of doc.ents if that works for your use-case.
I am writing a script with Jira python and I have encountered a big obstacle here.
I need to access to one of the issuelinks under "is duplicated by" but I don't have any idea about the attributes I can use.
I can get to the issuelinks field but I can't go further from here.
This is I've got so far:
issue = jira.issue(ISSUE_NUM) #this is the issue I am handling
link = issue.fields.issuelinks # I 've accessed to the issuelinks field
if hasattr(link, "inwardIssue"):
inwardIssue = link.inwardIssue
and I want to do this from here :
if(str(inwardIssue.type(?)) == "is duplicated by"):
inward Issues can be
is cloned by
is duplicated by
and so on.
how can I get the type of inward Issues??
There seem to be a few types of issue links. So far I've seen: Blocker, Cause, Duplicate and Reference.
In order to identify the type that the IssueLink is you can do the following:
issue = jira.issue(ISSUE_NUM)
all_issue_links = issue.fields.issuelinks
for link in all_issue_links:
if link.type.name == 'Duplicate':
inward_issue = link.inwardIssue
# Do something with link
This is a bit of a specific question, but somebody must have done this before. I would like to get the latest papers from pubmed. Not papers about a certain subjects, but all of them. I thought to query depending on modification date (mdat). I use biopython.py and my code looks like this
handle = Entrez.egquery(mindate='2015/01/10',maxdate='2017/02/19',datetype='mdat')
results = Entrez.read(handle)
for row in results["eGQueryResult"]:
if row["DbName"]=="nuccore":
print(row["Count"])
However, this results in zero papers. If I add term='cancer' I get heaps of papers. So the query seems to need the term keyword... but I want all papers, not papers on a certain subjects. Any ideas how to do this?
thanks
carl
term is a required parameter, so you can't omit it in your call to Entrez.egquery.
If you need all the papers within a specified timeframe, you will probably need a local copy of MEDLINE and PubMed Central:
For MEDLINE, this involves getting a license. For PubMed Central, you
can download the Open Access subset without a license by ftp.
EDIT for python3. The idea is that the latest pubmed id is the same thing as the latest paper (which I'm not sure is true). Basically does a binary search for the latest PMID, then gives a list of the n most recent. This does not look at dates, and only returns PMIDs.
There is an issue however where not all PMIDs exist, for example https://pubmed.ncbi.nlm.nih.gov/34078719/ exists, https://pubmed.ncbi.nlm.nih.gov/34078720/ does not (retraction?), and https://pubmed.ncbi.nlm.nih.gov/34078721/ exists. This ruins the binary search since it can't know if it's found a PMID that hasn't been used yet, or if it has found one that has previously existed.
CODE:
import urllib
def pmid_exists(pmid):
url_stem = 'https://www.ncbi.nlm.nih.gov/pubmed/'
query = url_stem+str(pmid)
try:
request = urllib.request.urlopen(query)
return True
except urllib.error.HTTPError:
return False
def get_latest_pmid(guess = 27239557, _min_guess=None, _max_guess=None):
#print(_min_guess,'<=',guess,'<=',_max_guess)
if _min_guess and _max_guess and _max_guess-_min_guess <= 1:
#recursive base case, this guess must be the largest PMID
return guess
elif pmid_exists(guess):
#guess PMID exists, search for larger ids
_min_guess = guess
next_guess = (_min_guess+_max_guess)//2 if _max_guess else guess*2
else:
#guess PMID does not exist, search for smaller ids
_max_guess = guess
next_guess = (_min_guess+_max_guess)//2 if _min_guess else guess//2
return get_latest_pmid(next_guess, _min_guess, _max_guess)
#Start of program
n = 5
latest_pmid = get_latest_pmid()
most_recent_n_pmids = range(latest_pmid-n, latest_pmid)
print(most_recent_n_pmids)
OUTPUT:
[28245638, 28245639, 28245640, 28245641, 28245642]
We have a weekly backup process which exports our production Google Appengine Datastore onto Google Cloud Storage, and then into Google BigQuery. Each week, we create a new dataset named like YYYY_MM_DD that contains a copy of the production tables on that day. Over time, we have collected many datasets, like 2014_05_10, 2014_05_17, etc. I want to create a data set Latest_Production_Data that contains a view for each of the tables in the most recent YYYY_MM_DD dataset. This will make it easier for downstream reports to write their query once and always retrieve the most recent data.
To do this, I have code that gets the most recent dataset and the names of all the tables that dataset contains from the BigQuery API. Then, for each of these tables, I fire a tables.insert call to create a view that is a SELECT * from the table I am looking to create a reference to.
This fails for tables that contain a RECORD field, from what looks to be a pretty benign column-naming rule.
For example, I have this table:
For which I issue this API call:
{
'tableReference': {
'projectId': 'redacted',
'tableId': u'AccountDeletionRequest',
'datasetId': 'Latest_Production_Data'
}
'view': {
'query': u'SELECT * FROM [2014_05_17.AccountDeletionRequest]'
},
}
This results in the following error:
HttpError: https://www.googleapis.com/bigquery/v2/projects//datasets/Latest_Production_Data/tables?alt=json returned "Invalid field name "__key__.namespace". Fields must contain only letters, numbers, and underscores, start with a letter or underscore, and be at most 128 characters long.">
When I execute this query in the BigQuery web console, the columns are renamed to translate the . to an _. I kind of expected the same thing to happen when I issued the create view API call.
Is there an easy way I can programmatically create a view for each of the tables in my dataset, regardless of their underlying schema? The problem I'm encountering now is for record columns, but another problem I anticipate is for tables that have repeated fields. Is there some magic alternative to SELECT * that will take care of all these intricacies for me?
Another idea I had was doing a table copy, but I would prefer not to duplicate the data if I can at all avoid it.
Here is the workaround code I wrote to dynamically generate a SELECT statement for each of the tables:
def get_leaf_column_selectors(dataset, table):
schema = table_service.get(
projectId=BQ_PROJECT_ID,
datasetId=dataset,
tableId=table
).execute()['schema']
return ",\n".join([
_get_leaf_selectors("", top_field)
for top_field in schema["fields"]
])
def _get_leaf_selectors(prefix, field):
if prefix:
format = prefix + ".%s"
else:
format = "%s"
if 'fields' not in field:
# Base case
actual_name = format % field["name"]
safe_name = actual_name.replace(".", "_")
return "%s as %s" % (actual_name, safe_name)
else:
# Recursive case
return ",\n".join([
_get_leaf_selectors(format % field["name"], sub_field)
for sub_field in field["fields"]
])
We had a bug where you needed to need to select out the individual fields in the view and use an 'as' to rename the fields to something legal (i.e they don't have '.' in the name).
The bug is now fixed, so you shouldn't see this issue any more. Please ping this thread or start a new question if you see it again.