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test: [RLOS_2023][WIP] updated test for regression weight #4600
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ataymano
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VowpalWabbit:rlos2023/test
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May 31, 2023
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import numpy as np | ||
from numpy.testing import assert_allclose, assert_array_almost_equal | ||
from vw_executor.vw import ExecutionStatus | ||
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def get_from_kwargs(kwargs, key, default=None): | ||
if key in kwargs: | ||
return kwargs[key] | ||
else: | ||
return default | ||
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def majority_close(arr1, arr2, rtol, atol, threshold): | ||
# Check if the majority of elements are close | ||
close_count = np.count_nonzero(np.isclose(arr1, arr2, rtol=rtol, atol=atol)) | ||
return close_count > len(arr1) * threshold | ||
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def assert_weight(job, **kwargs): | ||
atol = get_from_kwargs(kwargs, "atol", 10e-8) | ||
rtol = get_from_kwargs(kwargs, "rtol", 10e-5) | ||
expected_weights = kwargs["expected_weights"] | ||
assert job.status == ExecutionStatus.Success, f"{job.opts} job should be successful" | ||
data = job.outputs["--readable_model"] | ||
with open(data[0], "r") as f: | ||
data = f.readlines() | ||
data = [i.strip() for i in data] | ||
weights = job[0].model9('--readable_model').weights | ||
weights = weights["weight"].to_list() | ||
assert_allclose(weights, expected_weights, atol=atol, rtol=rtol), f"weights should be {expected_weights}" | ||
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def assert_prediction(job, **kwargs): | ||
assert job.status == ExecutionStatus.Success, "job should be successful" | ||
atol = kwargs.get("atol", 10e-8) | ||
rtol = kwargs.get("rtol", 10e-5) | ||
threshold = kwargs.get("threshold", 0.9) | ||
constant = kwargs["expected_value"] | ||
predictions = job.outputs['-p'] | ||
with open(predictions[0], "r") as f: | ||
predictions = f.readlines() | ||
predictions = [float(i) for i in predictions[1:]] | ||
assert majority_close(predictions, [constant]*len(predictions), rtol=rtol, atol=atol, threshold=threshold), f"predicted value should be {constant}" | ||
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def assert_functions(): | ||
return |
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import random | ||
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def constant_function(no_sample, constant, lower_bound, upper_bound): | ||
dataFile = f"constant_func_{no_sample}_{constant}_{upper_bound}_{lower_bound}.txt" | ||
with open(dataFile, "w") as f: | ||
random.seed(10) | ||
for _ in range(no_sample): | ||
x = random.uniform(lower_bound, upper_bound) | ||
f.write(f"{constant} |f x:{x}\n") | ||
return dataFile |
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[ | ||
{ | ||
"data_func": "constant_function", | ||
"data_func_args": [2000,5,1,100], | ||
"assert_func": "assert_prediction", | ||
"assert_func_args": { | ||
"expected_value": 5, | ||
"threshold":0.5 | ||
}, | ||
"grid": { | ||
"#base": ["-P 50000 --preserve_performance_counters --save_resume "], | ||
"#reg": ["", "--coin"] | ||
}, | ||
"output": ["--readable_model", "-p"] | ||
}, | ||
{ | ||
"data_func": "constant_function", | ||
"data_func_args": [2000,5,1,100], | ||
"assert_func": "assert_weight", | ||
"assert_func_args": { | ||
"expected_weights":[5, 0], | ||
"atol": 1, | ||
"rtol": 0.01 | ||
}, | ||
"grid": { | ||
"#base": ["-P 50000 --preserve_performance_counters --save_resume "], | ||
"#reg": ["", "--coin"] | ||
}, | ||
"output": ["--readable_model", "-p"] | ||
}, | ||
{ | ||
"data_func": "constant_function", | ||
"data_func_args": [2000,5,1,100], | ||
"assert_func": "assert_weight", | ||
"assert_func_args": { | ||
"expected_weights":[5, 0], | ||
"atol": 100, | ||
"rtol": 10 | ||
}, | ||
"grid": { | ||
"#base": ["-P 1 --preserve_performance_counters --save_resume"], | ||
"#reg": ["", "--coin", "--ftrl", "--pistol"] | ||
}, | ||
"output": ["--readable_model", "-p"], | ||
"*" : { | ||
"--learning_rate": [0.01, 0.001, 0.1], | ||
"--loss_function": ["absolute", "quantile"], | ||
"--power_t": [0.2, 0.5, ""] | ||
}, | ||
"+" :{ | ||
"--learning_rate": [0.01, 0.001, 0.1] | ||
} | ||
} | ||
] |
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import json | ||
import importlib | ||
import pytest | ||
import os | ||
import itertools | ||
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# Get the current directory | ||
current_dir = os.path.dirname(os.path.abspath(__file__)) | ||
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def json_to_dict_list(file): | ||
with open(current_dir + "/" + file, 'r') as file: | ||
# Load the JSON data | ||
return json.load(file) | ||
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def dynamic_function_call(module_name, function_name, *args, **kwargs): | ||
try: | ||
module = importlib.import_module(module_name) | ||
function = getattr(module, function_name) | ||
result = function(*args, **kwargs) | ||
return result | ||
except ImportError: | ||
print(f"Module '{module_name}' not found.") | ||
except AttributeError: | ||
print(f"Function '{function_name}' not found in module '{module_name}'.") | ||
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def get_function_object(module_name, function_name): | ||
try: | ||
module = importlib.import_module(module_name) | ||
function = getattr(module, function_name) | ||
return function | ||
except ImportError: | ||
print(f"Module '{module_name}' not found.") | ||
except AttributeError: | ||
print(f"Function '{function_name}' not found in module '{module_name}'.") | ||
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def generate_test_function(test_data): | ||
@pytest.dynamic | ||
def test_dynamic(): | ||
pass | ||
# Perform the test using the test_data | ||
# ... | ||
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# Set a custom name for the test function | ||
test_dynamic.__name__ = test_data["name"] | ||
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return test_dynamic | ||
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def generate_pytest_from_json(filepath): | ||
# Load the JSON data from a file | ||
with open(filepath, "r") as file: | ||
json_data = json.load(file) | ||
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# Iterate over the JSON data and dynamically generate the test functions | ||
for test_case in json_data: | ||
test_function = generate_test_function(test_case) | ||
globals()[test_function.__name__] = test_function | ||
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def generate_string_combinations(*lists): | ||
combinations = list(itertools.product(*lists)) | ||
combinations = [''.join(combination) for combination in combinations] | ||
return combinations |
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from vw_executor.vw import Vw | ||
from vw_executor.vw_opts import Grid | ||
from numpy.testing import assert_allclose | ||
import pandas as pd | ||
import numpy as np | ||
import pytest | ||
import os | ||
from test_helper import json_to_dict_list, dynamic_function_call, get_function_object, generate_string_combinations | ||
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CURR_DICT = os.path.dirname(os.path.abspath(__file__)) | ||
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def combine_list_cmds_grids(cmds, base_grid): | ||
list_of_key_val = [] | ||
grids = [] | ||
for key, value in cmds.items(): | ||
value = [i for i in value if i != ""] | ||
if str(value).isdigit(): | ||
list_of_key_val.append([f" {key} {format(li, '.5f').rstrip('0').rstrip('.') }" for li in value]) | ||
else: | ||
list_of_key_val.append([f" {key} {li}" for li in value]) | ||
for new_cmd in generate_string_combinations([base_grid["#base"][0]], *list_of_key_val): | ||
tmp_grid = base_grid.copy() | ||
tmp_grid["#base"][0] = new_cmd | ||
grids.append(tmp_grid) | ||
return grids | ||
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def cleanup_data_file(): | ||
script_directory = os.path.dirname(os.path.realpath(__file__)) | ||
# List all files in the directory | ||
files = os.listdir(script_directory) | ||
# Iterate over the files and remove the ones with .txt extension | ||
for file in files: | ||
if file.endswith(".txt"): | ||
file_path = os.path.join(script_directory, file) | ||
os.remove(file_path) | ||
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@pytest.fixture | ||
def test_description(request): | ||
resource = request.param | ||
yield resource # | ||
cleanup_data_file() | ||
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def core_test(files, grid, outputs, job_assert, job_assert_args): | ||
vw = Vw(CURR_DICT + "/.vw_cache", reset=True, handler=None) | ||
result = vw.train(files, grid, outputs) | ||
for j in result: | ||
job_assert(j, **job_assert_args) | ||
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@pytest.mark.parametrize('test_description', json_to_dict_list("pytest.json"), indirect=True) | ||
def test_all(test_description): | ||
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mutiply = test_description.get("*", None) | ||
plus = test_description.get("+", None) | ||
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base_grid = test_description['grid'] | ||
grids = [] | ||
if mutiply: | ||
grids = combine_list_cmds_grids(mutiply, base_grid) | ||
else: | ||
grids.append(base_grid) | ||
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for grid in grids: | ||
options = Grid( | ||
grid | ||
) | ||
data = dynamic_function_call("data_generation", test_description['data_func'], *test_description["data_func_args"]) | ||
assert_job = get_function_object("assert_job", test_description['assert_func']) | ||
core_test(data, options, test_description['output'], assert_job, test_description['assert_func_args']) |
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not sure if I follow the syntax here: seems like this is outside of "grid" object?
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so bascially * allow users to have multiple tests so for example,
if "--learning_rate": [0.01, 0.001, 0.1] is in *, the it will created a grid object with each of these values in --learning_rate.
if there are multiple parameters in *, then it will produce all combination for example:
{parameters in grid} --learning_rate 0.01 --loss_function absolute ..
{parameters in grid} --learning_rate 0.001 --loss_function absolute ..
{parameters in grid} --learning_rate 0.01 --loss_function quantile ..
{parameters in grid} --learning_rate 0.001 --loss_function quantile ..