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
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))
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")
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)
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)
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)
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))
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')
def test_score(): os.environ["AZUREML_MODEL_DIR"] = "./outputs" init() result = run(input_sample) print(result) assert 'predict_proba' in result
import score score.init() data = "{\"uid\": \"20\"}" print(score.run(data))
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())
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())