Kenneth Enevoldsen KennethEnevoldsen

## wandb_log_metrics_after_finished.py
"""
Load in a wandb run and updates its run history with a new metric.

DOCS: https://docs.wandb.ai/guides/track/public-api-guide#update-config-for-an-existing-run
"""
import wandb

api = wandb.Api()

entity, project = "kenevoldsen", "mnist-test"  # set to your entity and project

## confection_example.py
import catalogue
import torch
import torch.nn as nn
from confection import Config, registry
from Pathlib import Path
from torchvision import datasets, transforms

CONFIG = """
[model]
@models = "my_mnist_model.v1"

## badge-snip-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"74%","color":"yellow","namedLogo":"python"}

## badge-conspiracies-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"74%","color":"yellow","namedLogo":"python"}

## badge-spacy-wrap-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"85%","color":"green","namedLogo":"python"}

## badge-dfm-coverage-comment.json
{"schemaVersion":1,"label":"Coverage","message":"81%","color":"green","namedLogo":"python"}

## badge-asent-pytest-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"95%","color":"brightgreen","namedLogo":"python"}

## ktom_fit_pymc3.py
"""
This script fits the k-ToM model to data using pymc3
"""

import pymc3 as pm
import tomsup as ts


# generating some sample data

## badge-augmenty-pytest-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"97%","color":"brightgreen","namedLogo":"python"}

## badge-tomsup-pytest-coverage.json
{"schemaVersion":1,"label":"Coverage","message":"86%","color":"green","namedLogo":"python"}
	"""
	Load in a wandb run and updates its run history with a new metric.

	DOCS: https://docs.wandb.ai/guides/track/public-api-guide#update-config-for-an-existing-run
	"""
	import wandb

	api = wandb.Api()

	entity, project = "kenevoldsen", "mnist-test" # set to your entity and project
	import catalogue
	import torch
	import torch.nn as nn
	from confection import Config, registry
	from Pathlib import Path
	from torchvision import datasets, transforms

	CONFIG = """
	[model]
	@models = "my_mnist_model.v1"
	"""
	This script fits the k-ToM model to data using pymc3
	"""

	import pymc3 as pm
	import tomsup as ts



	# generating some sample data