cluster_2ways_ols = sm.ols(formula='y ~ x', data=df).fit(cov_type='cluster', cov_kwds={'groups': np.array(df[['firmid', 'year']])}, use_t=True)
I try to use 'groups': np.array(df[['firmid', 'year']]), the error said : Cannot change data-type for object array.
I don't know what happen. But if I only use one series such as df['year'], it works.
Related
I am a beginner of metaplex.
What I want to achieve is to provide the feature of presale, which means white list!.
I followed the instruction in metaplex to set config.json.
}
"whitelistMintSettings": {
"mode" : { "neverBurn": true },
"mint" : "xxxxKnH5",
"presale" : true,
"discountPrice" : 0.5
},
Here, I set xxxxKnH5 as a member of whitelist and let him mint before public mint.
Then I update_candy_machine (this works fine).
But in the UI interface, I always got error message:
There was a problem fetching whitelist token balance
Home.tsx:184 Error: failed to get token account balance: Invalid param: could not find account
Any idea of why I received this message and how can I fix it?
Make sure that the "mint": "XXX...X", line should contain a 0 decimals SPL-TOKEN.
Moreover, for airdropping it to users, you join the Metaplex Discord server and check the #airdrop-bash thread. It contains a JS script that'll make airdropping the token to wallets alot easier:)
I have a translation model (TM), which synthesizes its hypotheses using beam-search. For analysis purposes, I would like to study all hypotheses in each beam emitted by the TM’s ChoiceLayer. I’m able to fetch the hypotheses for each input sequence from the TM’s ChoiceLayer and write it to my file system, using the HDFDumpLayer:
'__SEARCH_dump_beam__': {
'class': 'hdf_dump',
'from': ['output'],
'filename': "<my-path>/beams.hdf",
'is_output_layer': True
}
But beside the hypotheses, I would also like to store the score of each hypothesis. I’m able to fetch the beam scores from the ChoiceLayer using a ChoiceGetBeamScoresLayer, but I was not able to dump the scores using an HDFDumpLayer:
'get_scores': {'class': 'choice_get_beam_scores', 'from': ['output']},
'__SEARCH_dump_scores__': {
'class': 'hdf_dump',
'from': ['get_scores'],
'filename': "<my-path>/beam_scores.hdf",
'is_output_layer': True
}
Running the config like likes this, makes RETURNN complain about the ChoiceGetBeamScoresLayer output not having a time axis:
Exception creating layer root/'__SEARCH_dump_scores__' of class HDFDumpLayer with opts:
{'filename': '<my-path>/beam_scores.hdf',
'is_output_layer': True,
'name': '__SEARCH_dump_scores__',
'network': <TFNetwork 'root' train=False search>,
'output': Data(name='__SEARCH_dump_scores___output', shape=(), time_dim_axis=None, beam=SearchBeam(name='output/output', beam_size=12, dependency=SearchBeam(name='output/prev:output', beam_size=12)), batch_shape_meta=[B]),
'sources': [<ChoiceGetBeamScoresLayer 'get_scores' out_type=Data(shape=(), time_dim_axis=None, beam=SearchBeam(name='output/output', beam_size=12, dependency=SearchBeam(name='output/prev:output', beam_size=12)), batch_shape_meta=[B])>]}
Unhandled exception <class 'AssertionError'> in thread <_MainThread(MainThread, started 139964674299648)>, proc 31228.
...
File "<...>/returnn/repository/returnn/tf/layers/basic.py", line 6226, in __init__
line: assert self.sources[0].output.have_time_axis()
locals:
self = <local> <HDFDumpLayer '__SEARCH_dump_scores__' out_type=Data(shape=(), time_dim_axis=None, beam=SearchBeam(name='output/output', beam_size=12, dependency=SearchBeam(name='output/prev:output', beam_size=12)), batch_shape_meta=[B])>
self.sources = <local> [<ChoiceGetBeamScoresLayer 'get_scores' out_type=Data(shape=(), time_dim_axis=None, beam=SearchBeam(name='output/output', beam_size=12, dependency=SearchBeam(name='output/prev:output', beam_size=12)), batch_shape_meta=[B])>]
output = <not found>
output.have_time_axis = <not found>
AssertionError
I tried to alter the shape of the score data using ExpandDimsLayer and EvalLayer, with several different configurations, but those all lead to different errors.
I’m sure I am not the first person trying to dump beam scores. Can anybody tell me, how to do that properly?
To answer the question:
The HDFDumpLayer assert you are hitting is simply due to the fact that this is not implemented yet (support of dumping data without time axis).
You can create a pull request and add this support for HDFDumpLayer.
If you just want to dump this information in any way, not necessarily via HDFDumpLayer, there are a couple of other options:
With task="search", as the search_output_layer, just select the layer which still includes the beam information, i.e. not after a DecideLayer.
That will simply dump all hypotheses including their beam scores.
Use a custom EvalLayer and dump it in whatever way you want.
I'm trying to use the UpdateByQuery to update a property of a large amount of documents. But as each document will have a different value, I need to execute ir one by one. I'm traversing a big amount of documents, and for each document I call this funcion:
def update_references(self, query, script_source):
try:
ubq = UpdateByQuery(using=self.client, index=self.index).update_from_dict(query).script(source=script_source)
ubq.execute()
except Exception as err:
return False
return True
Some example values are:
query = {'query': {'match': {'_id': 'VpKI1msBNuDimFsyxxm4'}}}
script_source = 'ctx._source.refs = [\'python\', \'java\']'
The problem is that when I do that, I got an error: "Too many dynamic script compilations within, max: [75/5m]; please use indexed, or scripts with parameters instead; this limit can be changed by the [script.max_compilations_rate] setting".
If I change the max_compilations_rate using Kibana, it has no effect:
PUT _cluster/settings
{
"transient": {
"script.max_compilations_rate": "1500/1m"
}
}
Anyway, it would be better to use a parametrized script. I tried:
def update_references(self, query, script_source, script_params):
try:
ubq = UpdateByQuery(using=self.client, index=self.index).update_from_dict(query).script(source=script_source, params=script_params)
ubq.execute()
except Exception as err:
return False
return True
So, this time:
script_source = 'ctx._source.refs = params.value'
script_params = {'value': [\'python\', \'java\']}
But as I have to update the query and the parameters each time, I need to create a new instance of the UpdateByQuery for each document in the large collection, and the result is the same error.
I also tried to traverse and update the large collection with:
es.update(
index=kwargs["index"],
doc_type="paper",
id=paper["_id"],
body={"doc": {
"refs": paper["refs"] # e.g. [\\'python\\', \\'java\\']
}}
)
But I'm getting the following error: "Failed to establish a new connection: [Errno 99] Cannot assign requested address juil. 10 18:07:14 bib gunicorn[20891]: POST http://localhost:9200/papers/paper/OZKI1msBNuDimFsy0SM9/_update [status:N/A request:0.005s"
So, please, if you have any idea on how to solve this it will be really appreciated.
Best,
You can try it like this.
PUT _cluster/settings
{
"persistent" : {
"script.max_compilations_rate" : "1500/1m"
}
}
The version update is causing these errors.
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
I have jason files saved in S3 bucket. I am trying to load them as dataframe in spark R and I am getting error logs. Following is my code. Where am I going wrong?
devtools::install_github('apache/spark#v2.2.0',subdir='R/pkg',force=TRUE)
library(SparkR)
sc=sparkR.session(master='local')
Sys.setenv("AWS_ACCESS_KEY_ID"="xxxx",
"AWS_SECRET_ACCESS_KEY"= "yyyy",
"AWS_DEFAULT_REGION"="us-west-2")
movie_reviews <-SparkR::read.df(path="s3a://bucketname/reviews_Movies_and_TV_5.json",sep = "",source="json")
I have tried all combinations of s3a , s3n, s3 and none seems to work.
I get following error log in my sparkR console
17/12/09 06:56:06 WARN FileStreamSink: Error while looking for metadata directory.
17/12/09 06:56:06 ERROR RBackendHandler: loadDF on org.apache.spark.sql.api.r.SQLUtils failed
java.lang.reflect.InvocationTargetException
For me it works
read.df("s3://bucket/file.json", "json", header = "true", inferSchema = "true", na.strings = "NA")
What #Ankit said should work, but if you are trying to get something that looks more like a dataframe, you need to use a select statement. i.e.
rdd<- read.df("s3://bucket/file.json", "json", header = "true", inferSchema = "true", na.strings = "NA")
Then do a printSchema(rdd) to see the structure of the data.
If you see something that has root followed by no indentations to your data, you can probably go ahead and select using the names of the "columns" you want. If you see branching down your schema tree, you may have to put a headers.blah or a payload.blah in you select statement. Like this:
sdf<- SparkR::select(rdd, "headers.something", "headers.somethingElse", "payload.somethingInPayload", "payload.somethingElse")