Python run Examples

Example #1

def run():
    g = etl.run()
    etl.show_save(g, True)
    m = store.run(g)  # Not working, don't ask me why
    j, p, r = score.run()

    print(r)

    return

Example #2

File: mnistws.py Project: thinkgradient/operationalizing-cnn-with-onnx

def classify():
    input_img = np.fromstring(request.data, np.uint8)
    img = cv2.imdecode(input_img, cv2.IMREAD_GRAYSCALE)
    classify_response = "".join(map(str, score.run(img)))
    json_prediction = json.loads(classify_response)
    predicted_number = 0
    for i in range(0, 9):
        if float(json_prediction["prediction"][0][0][i]) > 0.5:
            predicted_number = i
            break

    return (str(predicted_number))

Example #3

import score
score.init()
#score.run("Anyone know who Brit's support act is for tonight, Ticketmaster say Ciara is going home and not performing Feeling deflated. Hubby saw swing set & thinks is a piece of junk. I thought I had found something good 4 the kids @MelissaPeterman wondering what happend to you?  I had to go #to a funeral today.  Very sad @piginthepoke well that is good news - long may it continue!  I bet you are going to be really busy today!")

#score.run("Anyone know who Brit's support act is for tonight, Ticketmaster say Ciara is going home and not performing")
#score.run("I like the weather in seattle")
score.run("ugly view in my window, angry man doing bad coding")

Example #4

File: main.py Project: mauricionoris/Recommender

import manageFiles

import processor
import score
import recommender

processor.parameters['index'] = "codigo"
processor.parameters['properties'] = ["desc","titulo","area_atuacao"]

processor.parameters['new_path'] = reachrAPI.getJobToRecommend()
processor.parameters['current_path'] = reachrAPI.getAllJobs()

print(processor.parameters['new_path'])
print(processor.parameters['current_path'])

score.parameters['properties'] =  ["desc","titulo","area_atuacao"]
score.parameters['weights'] = {"desc":0.3, "titulo":0.6, "area_atuacao":0.1}
score.parameters['input'] = processor.run()

recommender.parameters['threshold'] = 0.2
recommender.parameters['selection-step'] = 2
recommender.parameters['key'] = processor.parameters['index']
recommender.parameters['inner-list'] = 'processo_seletivo'
recommender.parameters['candidates-list'] = 'candidatos'

recommender.parameters['input-jobs'] = manageFiles.read(processor.parameters['current_path'])
recommender.parameters['input-similars'] = score.run()

ret = reachrAPI.postJobRecommendation(recommender.run())
print(ret)

Example #5

File: test.py Project: danielsc/azureml-and-r

import pandas as pd
import os
os.environ["AZUREML_MODEL_DIR"] = os.getcwd()

data_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), '..',
                         'data', 'attrition.csv')

data = pd.read_csv(data_path)
data.head()

from score import init, run

init()
ret = run(data.head())

print(ret.__class__)
print(ret)

Example #6

File: consume_score.py Project: santimacnet/MLOps-AzureDevops

import json
import argparse
import score

PARSER = argparse.ArgumentParser()
PARSER.add_argument('--DATA_FILE')
ARGS = PARSER.parse_args()

with open(ARGS.DATA_FILE) as json_file:
    MYDATA = json.load(json_file)

score.init()

RESULT = score.run(MYDATA)

print(RESULT)

Example #7

File: test_score.py Project: Munyasya/MlOpsRecipies

import json
import numpy as np
import os
import base64
from models.img-model.score import score
from PIL import Image

DATASET_PATH = "models/img-model/dataset/data/"

#load data into numpy array
img_path = os.path.join(DATASET_PATH, "0.png")
with open(img_path, 'rb') as img_file:
    img_b64 = base64.b64encode(img_file.read()).decode("UTF-8")
input_dict = {"image":img_b64}

#init & score
score.init()

score.run(json.dumps(input_dict))

Example #8

import score
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas as pd

df = pd.DataFrame(data=[['I like beautiful seattle']],
                  columns=['input_text_string'])
df.dtypes

score.init()
input1 = pd.DataFrame(data=[['I like beautiful seattle']],
                      columns=['input_text_string'])
result = score.run(input1)
#print(result)

inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
generate_schema(run_func=score.run,
                inputs=inputs,
                filepath='service_schema.json')

Example #9

File: test_score.py Project: MercyPrasanna/MLOps

def test_score():
    os.environ["AZUREML_MODEL_DIR"] = "./outputs"
    init()
    result = run(input_sample)
    print(result)
    assert 'predict_proba' in result

Example #10

File: scoretest.py Project: Ajay-MS/az-ml-movie-recommendation

import score

score.init()
data = "{\"uid\": \"20\"}"
print(score.run(data))

Example #11

File: test.score.py Project: mauricionoris/Recommender

import setup
setup.load()

import manageFiles
import score

root = '/home/mnf/reachr/projects/RecommenderSystem/test'
manageFiles.parameters['path'] = root + '/support/similiars.json'

score.parameters['properties'] = ["desc", "titulo", "area_atuacao"]
score.parameters['weights'] = {"desc": 0.5, "titulo": 0.4, "area_atuacao": 0.1}
score.parameters['input'] = manageFiles.read()

manageFiles.write(score.run())

Example #12

os.stat(file_path)

# Testing score.py

# Load test data:
from data_management import load_dataset
import configuracion
import score

data = load_dataset(file_name=configuracion.TRAINING_DATA_FILE)
data = data.iloc[:8, :]
data = data.to_json()

score.init()
pred = score.run(data)
print(pred)

# Create environment file
'''
- Add package requeriments in this file -> myenv.yml
'''

from azureml.core.conda_dependencies import CondaDependencies

myenv = CondaDependencies()
myenv.add_conda_package("scikit-learn")

with open("myenv.yml", "w") as f:
    f.write(myenv.serialize_to_string())