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How to use _load_config method in selenium-respectful

Best Python code snippet using selenium-respectful_python

app.py

Source:app.py

...18import metrics19app = typer.Typer()20@app.command()21def train(config_file: str):22    estimator_config = _load_config(config_file, "estimator")23    split = "train"24    X, y = _get_dataset(_load_config(config_file, "data"), splits=[split])[split]25    estimator = model.build_estimator(estimator_config)26    estimator.fit(X, y)27    output_dir = _load_config(config_file, "export")["output_dir"]28    _save_versioned_estimator(estimator, estimator_config, output_dir)29def _get_dataset(data_config, splits):30    filepath = data_config["filepath"]31    reader = partial(pd.read_csv, filepath_or_buffer=filepath)32    return data.get_dataset(reader=reader, splits=splits)33def _save_versioned_estimator(34    estimator: BaseEstimator, config: model.EstimatorConfig, output_dir: str35):36    version = datetime.now(timezone.utc).strftime("%Y-%m-%d %H-%M")37    model_dir = os.path.join(output_dir, version)38    os.makedirs(model_dir, exist_ok=True)39    try:40        joblib.dump(estimator, os.path.join(model_dir, "model.joblib"))41        _save_yaml(config, os.path.join(model_dir, "params.yml"))42    except Exception as e:43        typer.echo(f"Coudln't serialize model due to error {e}")44        shutil.rmtree(model_dir)45@app.command()46def find_hyperparams(47    config_file: str,48):49    search_config = _load_config(config_file, "search")50    param_grid = search_config["grid"]51    n_jobs = search_config["jobs"]52    metric = _load_config(config_file, "metrics")[0]53    estimator_config = _load_config(config_file, "estimator")54    estimator = model.build_estimator(estimator_config)55    scoring = metrics.get_scoring_function(metric["name"], **metric["params"])56    gs = GridSearchCV(57        estimator,58        _param_grid_to_sklearn_format(param_grid),59        n_jobs=n_jobs,60        scoring=scoring,61        verbose=3,62    )63    split = "train"64    X, y = _get_dataset(_load_config(config_file, "data"), splits=[split])[split]65    gs.fit(X, y)66    estimator_config = _param_grid_to_custom_format(gs.best_params_)67    estimator = gs.best_estimator_68    output_dir = _load_config(config_file, "export")["output_dir"]69    _save_versioned_estimator(estimator, estimator_config, output_dir)70def _param_grid_to_sklearn_format(param_grid):71    result = {72        f"{spec['name']}__{pname}": pvalues73        for spec in param_grid74        for pname, pvalues in spec["params"].items()75    }76    return result77def _param_grid_to_custom_format(param_grid):78    grid = {}79    for name, values in param_grid.items():80        estimator_name, param_name = name.split("__", maxsplit=1)81        if estimator_name not in grid:82            grid[estimator_name] = {}83        grid[estimator_name][param_name] = values84    result = grid85    result = [{"name": name, "hparams": params} for name, params in grid.items()]86    return result87@app.command()88def eval(89    config_file: str,90    model_version: str,91    splits: t.List[str] = ["test"],92):93    output_dir = _load_config(config_file, "export")["output_dir"]94    saved_model = os.path.join(output_dir, model_version, "model.joblib")95    estimator = joblib.load(saved_model)96    dataset = _get_dataset(_load_config(config_file, "data"), splits=splits)97    report = defaultdict(list)98    all_metrics = _load_config(config_file, "metrics")99    for name, (X, y) in dataset.items():100        y_pred = estimator.predict(X)101        for m in all_metrics:102            metric_name, params = m["name"], m["params"]103            fn = metrics.get_metric_function(metric_name, **params)104            value = float(fn(y, y_pred))105            report[metric_name].append({"split": name, "value": value})106    reports_dir = _load_config(config_file, "reports")["dir"]107    os.makedirs(reports_dir, exist_ok=True)108    _save_yaml(109        dict(report),110        os.path.join(reports_dir, f"{model_version}.yml"),111    )112def _load_config(filepath: str, key: str):113    content = _load_yaml(filepath)114    config = content[key]115    return config116@lru_cache(None)117def _load_yaml(filepath: str) -> t.Dict[str, t.Any]:118    with open(filepath, "r") as f:119        content = yaml.load(f)120    return content121def _save_yaml(content: t.Dict[str, t.Any], filepath: str):122    with open(filepath, "w") as f:123        yaml.dump(content, f)124if __name__ == "__main__":...

config.py

Source:config.py

...9        Config.__CONFIG_FILE = path + '\\' + filename10    @staticmethod11    def set_general_option(option, value):12        """ Set an option in the general section """13        Config._load_config()14        Config._set('general', option, value)15    @staticmethod16    def set_requests_option(option, value):17        """ Set an option in the torrent section """18        Config._load_config()19        Config._set('requests', option, value)20    # ADD MORE SECTIONS HERE21    @staticmethod22    def option_exists(section, option):23        """ Returns True if the option exists in the specified section """24        Config._load_config()25        return Config.__config_parser.has_option(section, option)26    @staticmethod27    def getboolean(section, option):28        """ Get a boolean from the specified section """29        Config._load_config()30        return Config.__config_parser.getboolean(section, option)31    @staticmethod32    def getint(section, option):33        """ Get an int from the specified section """34        Config._load_config()35        return Config.__config_parser.getint(section, option)36    @staticmethod37    def getfloat(section, option):38        """ Get a float from the specified section """39        Config._load_config()40        return Config.__config_parser.getfloat(section, option)41    @staticmethod42    def getstring(section, option):43        """ Get a string from the specified section """44        Config._load_config()45        return Config.__config_parser.get(section, option)46    # Internal functions47    @staticmethod48    def _load_config():49        """ Loads the config file from disk into the config_parses object """50        if not Config.__config_loaded:51            Config.__config_loaded = True52            # Load the config file into the config_parses object53            try:54                with Config._open_config_file(read=True) as config_file:55                    Config.__config_parser.readfp(fp=config_file)56            except IOError:57                pass    # File does not exist, do not load it but don't create it either (what use is an empty file?)58    @staticmethod59    def _open_config_file(read=False, write=False):60        """ Open the config file for reading and/or writing """61        mode = ''62        if read: mode += 'r'...

test_config.py

Source:test_config.py

...7def _config_path():8    return os.path.abspath("account_foundations/config/all.yaml")910@pytest.fixture11def _load_config():12    return Config.load_config(os.path.abspath("account_foundations/config/all.yaml"))1314def test_config_loader(_load_config):15    yaml_config = _load_config16    assert yaml_config is not None17    assert isinstance(yaml_config, dict)1819def test_config_general_attributes(_load_config):20    yaml_config = _load_config21    assert "general" in yaml_config22    assert isinstance(yaml_config["general"], dict)23    assert "region" in yaml_config["general"]2425def test_config_vpc_attributes(_load_config):
...

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