def test_decision_tree():
runner = Runner(
'model/experiment/output/decision_tree_basic_full',
load_sample_data_frame(),
'arrest',
decision_tree_basic,
hyper_parameters=hyper_parameters
)
runner.run_classification_search_experiment(
'roc_auc',
sample=sample,
n_iter=iterations,
record_predict_proba=True
)
runner = Runner(
'model/experiment/output/decision_tree_under_sampled_full',
load_sample_data_frame(),
'arrest',
decision_tree_basic,
hyper_parameters=hyper_parameters
)
runner.run_classification_search_experiment(
'roc_auc',
sample=sample,
n_iter=iterations,
record_predict_proba=True,
sampling=RandomUnderSampler()
)
runner = Runner(
'model/experiment/output/decision_tree_over_sampled_full',
load_sample_data_frame(),
'arrest',
decision_tree_basic,
hyper_parameters=hyper_parameters
)
runner.run_classification_search_experiment(
'roc_auc',
sample=sample,
n_iter=iterations,
record_predict_proba=True,
sampling=SMOTE()
)
runner = Runner(
'model/experiment/output/decision_tree_combine_sampled_full',
load_sample_data_frame(),
'arrest',
decision_tree_basic,
hyper_parameters=hyper_parameters
)
runner.run_classification_search_experiment(
'roc_auc',
sample=sample,
n_iter=iterations,
record_predict_proba=True,
sampling=SMOTEENN()
)
def test_neural_network_basic():
runner = Runner('model/experiment/output/neural_network_basic',
load_sample_data_frame(), 'violation',
neural_network_basic, None)
runner.run_classification_experiment(sample=sample,
multiclass=True,
record_predict_proba=True)
def test_gaussian_naive_bayes_basic():
runner = Runner('model/experiment/output/complement_naive_bayes_basic',
load_sample_data_frame(), 'violation',
gaussian_naive_bayes_basic, None)
runner.run_classification_experiment(sample=sample,
multiclass=True,
record_predict_proba=True)
def test_lightgbm_basic():
runner = Runner('model/experiment/output/lightgbm_basic',
load_sample_data_frame(), 'violation', lightgbm_basic,
hyper_parameters)
runner.run_classification_search_experiment('neg_log_loss',
sample=sample,
n_iter=iterations,
multiclass=True,
record_predict_proba=True)
# -*- coding: utf-8 -*-
"""
Test the ticket model being serviced on localhost:8080
"""
__author__ = "John Hoff"
__email__ = "*****@*****.**"
__copyright__ = "Copyright 2019, John Hoff"
__license__ = "Creative Commons Attribution-ShareAlike 4.0 International License"
__version__ = "1.0.0"
import json
import requests
from utility import use_project_path
from model import load_sample_data_frame
if __name__ == '__main__':
use_project_path()
for index, row in load_sample_data_frame().iterrows():
print(row.to_json())
headers = {'Content-type': 'application/json', 'Accept': 'text/plain'}
response = requests.post('http://127.0.0.1:8080/ticketPrediction',
data=row.to_json(),
headers=headers)
print(json.loads(response.text))
if index > 100:
break