def save_config(file_name: str, globals_d: dict):
"""
This function is used to save all enviroment variables to file,
allowing to later resume modeling without rerunning setup().
Example
-------
>>> save_config('myvars.pkl')
This will save all enviroment variables to 'myvars.pkl'.
"""
function_params_str = ", ".join(
[f"{k}={v}" for k, v in locals().items() if not k == "globals_d"])
logger = get_logger()
logger.info("Initializing save_config()")
logger.info(f"save_config({function_params_str})")
globals_to_dump = {
k: v
for k, v in globals_d.items() if k in globals_d["pycaret_globals"]
}
import joblib
joblib.dump(globals_to_dump, file_name)
logger.info(f"Global variables dumped to {file_name}")
logger.info(
"save_config() succesfully completed......................................"
)
def __init__(
self,
estimator,
X_train: pd.DataFrame,
y_train: pd.DataFrame,
groups=None,
**fit_kwargs,
):
logger = get_logger()
self.estimator = deepcopy(estimator)
if not is_fitted(self.estimator):
try:
self.estimator._carry_over_final_estimator_fit_vars()
except:
pass
if not is_fitted(self.estimator):
logger.info(
f"fit_if_not_fitted: {estimator} is not fitted, fitting")
try:
self.estimator.fit(X_train,
y_train,
groups=groups,
**fit_kwargs)
except:
self.estimator.fit(X_train, y_train, **fit_kwargs)
def can_early_stop(
estimator,
consider_partial_fit,
consider_warm_start,
consider_xgboost,
params,
):
"""
From https://github.com/ray-project/tune-sklearn/blob/master/tune_sklearn/tune_basesearch.py.
Helper method to determine if it is possible to do early stopping.
Only sklearn estimators with ``partial_fit`` or ``warm_start`` can be early
stopped. warm_start works by picking up training from the previous
call to ``fit``.
Returns
-------
bool
if the estimator can early stop
"""
logger = get_logger()
from sklearn.tree import BaseDecisionTree
from sklearn.ensemble import BaseEnsemble
try:
base_estimator = estimator.steps[-1][1]
except:
base_estimator = estimator
if consider_partial_fit:
can_partial_fit = supports_partial_fit(base_estimator, params=params)
else:
can_partial_fit = False
if consider_warm_start:
is_not_tree_subclass = not issubclass(type(base_estimator),
BaseDecisionTree)
is_ensemble_subclass = issubclass(type(base_estimator), BaseEnsemble)
can_warm_start = hasattr(base_estimator, "warm_start") and (
(hasattr(base_estimator, "max_iter") and is_not_tree_subclass
and not is_ensemble_subclass) or
(is_ensemble_subclass and hasattr(base_estimator, "n_estimators")))
else:
can_warm_start = False
if consider_xgboost:
from xgboost.sklearn import XGBModel
is_xgboost = isinstance(base_estimator, XGBModel)
else:
is_xgboost = False
logger.info(
f"can_partial_fit: {can_partial_fit}, can_warm_start: {can_warm_start}, is_xgboost: {is_xgboost}"
)
return can_partial_fit or can_warm_start or is_xgboost
def load_config(file_name: str, globals_d: dict):
"""
This function is used to load enviroment variables from file created with save_config(),
allowing to later resume modeling without rerunning setup().
Example
-------
>>> load_config('myvars.pkl')
This will load all enviroment variables from 'myvars.pkl'.
"""
function_params_str = ", ".join(
[f"{k}={v}" for k, v in locals().items() if not k == "globals_d"]
)
logger = get_logger()
logger.info("Initializing load_config()")
logger.info(f"load_config({function_params_str})")
import joblib
loaded_globals = joblib.load(file_name)
logger.info(f"Global variables loaded from {file_name}")
for k, v in loaded_globals.items():
globals_d[k] = v
globals_d["logger"] = get_logger()
logger.info(f"Global variables set to match those in {file_name}")
logger.info(
"load_config() succesfully completed......................................"
)
def get_groups(groups: Union[str, pd.DataFrame], X_train: pd.DataFrame,
default: pd.DataFrame):
logger = get_logger()
if groups is None:
return default
if isinstance(groups, str):
if groups not in X_train.columns:
raise ValueError(
f"Column {groups} used for groups is not present in the dataset."
)
groups = X_train[groups]
else:
if groups.shape[0] != X_train.shape[0]:
raise ValueError(
f"groups has lenght {groups.shape[0]} which doesn't match X_train length of {len(X_train)}."
)
return groups
def set_config(variable: str, value, globals_d: dict):
"""
This function is used to reset global environment variables.
Example
-------
>>> set_config('seed', 123)
This will set the global seed to '123'.
"""
function_params_str = ", ".join(
[f"{k}={v}" for k, v in locals().items() if not k == "globals_d"]
)
logger = get_logger()
logger.info("Initializing set_config()")
logger.info(f"set_config({function_params_str})")
if variable.startswith("_"):
raise ValueError(f"Variable {variable} is read only ('_' prefix).")
if not variable in globals_d["pycaret_globals"] or variable == "pycaret_globals":
raise ValueError(
f"Variable {variable} not found. Possible variables are: {globals_d['pycaret_globals']}"
)
globals_d[variable] = value
# special case
if not globals_d["gpu_param"] and variable == "n_jobs_param":
globals_d["_gpu_n_jobs_param"] = value
logger.info(f"Global variable: {variable} updated to {value}")
logger.info(
"set_config() succesfully completed......................................"
)
def get_config(variable: str, globals_d: dict):
"""
This function is used to access global environment variables.
Example
-------
>>> X_train = get_config('X_train')
This will return X_train transformed dataset.
Returns
-------
variable
"""
function_params_str = ", ".join(
[f"{k}={v}" for k, v in locals().items() if not k == "globals_d"]
)
logger = get_logger()
logger.info("Initializing get_config()")
logger.info(f"get_config({function_params_str})")
if not variable in globals_d["pycaret_globals"]:
raise ValueError(
f"Variable {variable} not found. Possible variables are: {globals_d['pycaret_globals']}"
)
global_var = globals_d[variable]
logger.info(f"Global variable: {variable} returned as {global_var}")
logger.info(
"get_config() succesfully completed......................................"
)
return global_var
def __init__(
self,
verbose: bool = True,
html_param: bool = True,
progress_args: Optional[Dict[str, Any]] = None,
master_display_columns: Optional[List[str]] = None,
monitor_rows: Optional[List[List[str]]] = None,
round: int = 4,
):
self.logger = get_logger()
self.verbose = verbose
self.html_param = html_param
self.round = round
try:
self.enviroment = str(get_ipython())
self.enviroment = "google.colab" if is_in_colab(
) else self.enviroment
except:
self.enviroment = ""
if not self.verbose:
return
self.logger.info("Preparing display monitor")
# progress bar
if progress_args and self.verbose and self.html_param:
progress_args = {**self.default_progress_args, **progress_args}
self.progress = ipw.IntProgress(**progress_args)
if master_display_columns:
self.master_display = pd.DataFrame(columns=master_display_columns)
if monitor_rows and self.html_param:
self.monitor = pd.DataFrame(
monitor_rows,
columns=[" " * i for i in range(len(monitor_rows[0]))],
).set_index("")
def show_yellowbrick_plot(
visualizer,
X_train,
y_train,
X_test,
y_test,
name: str,
handle_train: str = "fit",
handle_test: str = "score",
scale: float = 1,
save: bool = False,
fit_kwargs: Optional[dict] = None,
groups: Optional[Any] = None,
display: Optional[Display] = None,
display_format: Optional[str] = None,
**kwargs,
):
"""
Generic method to handle yellowbrick plots.
"""
logger = get_logger()
visualizer.fig.set_dpi(visualizer.fig.dpi * scale)
if not fit_kwargs:
fit_kwargs = {}
fit_kwargs_and_kwargs = {**fit_kwargs, **kwargs}
if handle_train == "draw":
logger.info("Drawing Model")
visualizer.draw(X_train, y_train, **kwargs)
elif handle_train == "fit":
logger.info("Fitting Model")
visualizer.fit(X_train, y_train, **fit_kwargs_and_kwargs)
elif handle_train == "fit_transform":
logger.info("Fitting & Transforming Model")
visualizer.fit_transform(X_train, y_train, **fit_kwargs_and_kwargs)
elif handle_train == "score":
logger.info("Scoring train set")
visualizer.score(X_train, y_train, **kwargs)
display.move_progress()
if handle_test == "draw":
visualizer.draw(X_test, y_test)
elif handle_test == "fit":
visualizer.fit(X_test, y_test, **fit_kwargs)
elif handle_test == "fit_transform":
visualizer.fit_transform(X_test, y_test, **fit_kwargs)
elif handle_test == "score":
logger.info("Scoring test/hold-out set")
visualizer.score(X_test, y_test)
display.move_progress()
display.clear_output()
if save:
logger.info(f"Saving '{name}.png' in current active directory")
visualizer.show(outpath=f"{name}.png", clear_figure=True)
else:
if display_format == "streamlit":
show_yellowbrick_in_streamlit(visualizer, clear_figure=True)
else:
visualizer.show(clear_figure=True)
logger.info("Visual Rendered Successfully")
def __create_resplots(
self,
model,
x: np.ndarray,
y: np.ndarray,
x_test: np.ndarray = None,
y_test: np.ndarray = None,
) -> widgets.VBox:
logger = get_logger()
with fit_if_not_fitted(model, x, y) as fitted_model:
fitted = fitted_model.predict(x)
fitted_residuals = fitted - y
if x_test is not None and y_test is not None:
pred = fitted_model.predict(x_test)
prediction_residuals = pred - y_test
predictions = np.concatenate((fitted, pred))
residuals = np.concatenate((fitted_residuals, prediction_residuals))
split_origin = np.concatenate(
(np.repeat("train", fitted.shape[0]), np.repeat("test", pred.shape[0]))
)
x = np.concatenate((x, x_test))
y = np.concatenate((y, y_test))
else:
predictions = fitted
residuals = fitted_residuals
split_origin = None
logger.info("Calculated model residuals")
self.display.move_progress()
tukey_anscombe_widget = TukeyAnscombeWidget(
predictions, residuals, split_origin=split_origin
)
logger.info("Calculated Tunkey-Anscombe Plot")
self.figures.append(tukey_anscombe_widget)
self.display.move_progress()
qq_plot_widget = QQPlotWidget(
predictions, y, split_origin=split_origin, featuresize=x.shape[1]
)
logger.info("Calculated Normal QQ Plot")
self.figures.append(qq_plot_widget)
self.display.move_progress()
standardized_residuals = helper.calculate_standardized_residual(
predictions, y, None
)
model_norm_residuals_abs_sqrt = np.sqrt(np.abs(standardized_residuals))
scale_location_widget = ScaleLocationWidget(
predictions, model_norm_residuals_abs_sqrt, split_origin=split_origin
)
logger.info("Calculated Scale-Location Plot")
self.figures.append(scale_location_widget)
self.display.move_progress()
leverage = helper.leverage_statistic(np.array(x))
n_model_params = len(model.get_params())
distance = helper.cooks_distance(
standardized_residuals, leverage, n_model_params=n_model_params
)
cooks_distance_widget = CooksDistanceWidget(
leverage,
distance,
standardized_residuals,
n_model_params,
split_origin=split_origin,
)
logger.info("Calculated Residual vs Leverage Plot inc. Cook's distance")
self.figures.append(cooks_distance_widget)
self.display.move_progress()
items_layout = Layout(width="1000px")
h0 = widgets.HBox(self.figures[:2], layout=items_layout)
h1 = widgets.HBox(self.figures[2:], layout=items_layout)
return widgets.VBox([h0, h1])
