philschmid

How to use Pytest

Structure

test/
-- test_add.py
src/
-- add.py

Execution

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import boto3
import os
### source aws config parameter
source_aws_region='eu-west-1'
source_aws_access_key_id='####'
source_aws_secret_acess_key='####'
source_dynamodb_table='table_name'
### target aws config parameter
target_aws_region='eu-west-1'
target_aws_access_key_id='####'

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Resources:
  LambdaExecutionAnomalyDetector:
    Type: AWS::CloudWatch::AnomalyDetector
    Properties:
      MetricName: Duration
      Namespace: AWS/Lambda
      Stat: Sum
      
  LambdaExecutionAlarm:
    Type: AWS::CloudWatch::Alarm

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var copy = require('copy-dynamodb-table').copy
 
var globalAWSConfig = { // AWS Configuration object http://docs.aws.amazon.com/AWSJavaScriptSDK/latest/AWS/Config.html#constructor-property
  accessKeyId: 'AKID',
  secretAccessKey: 'SECRET',
  region: 'eu-west-1'
}
 
var sourceAWSConfig = {
  accessKeyId: 'AKID',

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import numpy as np
from sklearn.model_selection import KFold
# data sample
data = np.array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6])
# prepare cross validation
kfold = KFold(n_splits=3, shuffle=True, random_state=1)
# enumerate splits
for train, test in kfold.split(data):
  print('train: %s, test: %s' % (data[train], data[test]))

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from simpletransformers.classification import ClassificationModel
from sklearn.model_selection import KFold
from sklearn.metrics import accuracy_score
import pandas as pd


# Dataset
dataset = [["Example sentence belonging to class 1", 1],
      ["Example sentence belonging to class 0", 0],
      ["Example eval sentence belonging to class 1", 1],

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from autogluon import TabularPrediction as task  

predictor = task.fit(train_data=task.Dataset(file_path="TRAIN_DATA.csv"), label="PREDICT_COLUMN") 
predictions = predictor.predict(task.Dataset(file_path="TEST_DATA.csv"))

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# Here we assume CUDA 10.0 is installed.  You should change the number
# according to your own CUDA version (e.g. mxnet-cu101 for CUDA 10.1).
!pip install --upgrade mxnet-cu100
!pip install autogluon

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!pip install -U ipykernel

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import autogluon as ag
from autogluon import ObjectDetection as task

print(ag.__version__)

# >>>> '0.0.6'