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169 | class AntakIA:
"""AntakIA class.
Antakia instances provide data and methods to explain a ML model.
Instance attributes
-------------------
X : pd.DataFrame the training dataset
y : pd.Series the target value
model : Model
the model to explain
variables : a list of Variables, describing X_list[0]
X_test : pd.DataFrame the test dataset
y_test : pd.Series the test target value
score : reference scoring function
"""
def __init__(
self,
X: pd.DataFrame,
y: pd.Series,
model,
variables: DataVariables | List[Dict[str, Any]] | pd.DataFrame | None = None,
X_test: pd.DataFrame = None,
y_test: pd.Series = None,
X_exp: pd.DataFrame | None = None,
score: callable | str = 'auto',
problem_category: str = 'auto'
):
"""
AntakiIA constructor.
Parameters:
X : pd.DataFrame the training dataset
y : pd.Series the target value
model : Model
the model to explain
variables : a list of Variables, describing X_list[0]
X_test : pd.DataFrame the test dataset
y_test : pd.Series the test target value
score : reference scoring function
"""
load_dotenv()
if not is_valid_model(model):
raise ValueError(model, " should implement predict and score methods")
X, y, X_exp = self._preprocess_data(X, y, X_exp)
X_test, y_test, _ = self._preprocess_data(X_test, y_test, None)
self.X = X
if y.ndim > 1:
y = y.squeeze()
self.y = y.astype(float)
self.X_test = X_test
if y_test is not None and y_test.ndim > 1:
y_test = y_test.squeeze()
self.y_test = y_test
self.model = model
self.X_exp = X_exp
self.problem_category = self._preprocess_problem_category(problem_category, model, X)
self.score = self._preprocess_score(score, self.problem_category)
self.set_variables(X, variables)
self.gui = GUI(
self.X,
self.y,
self.model,
self.variables,
self.X_test,
self.y_test,
self.X_exp,
self.score,
self.problem_category
)
def set_variables(self, X, variables):
if variables is not None:
if isinstance(variables, list):
self.variables: DataVariables = Variable.import_variable_list(variables)
if len(self.variables) != len(X.columns):
raise ValueError("Provided variable list must be the same length of the dataframe")
elif isinstance(variables, pd.DataFrame):
self.variables = Variable.import_variable_df(variables)
else:
raise ValueError("Provided variable list must be a list or a pandas DataFrame")
else:
self.variables = Variable.guess_variables(X)
def start_gui(self) -> GUI:
return self.gui.show_splash_screen()
def export_regions(self):
return self.gui.region_set
def _preprocess_data(self, X: pd.DataFrame, y, X_exp: pd.DataFrame | None):
if isinstance(X, np.ndarray):
X = pd.DataFrame(X)
if isinstance(X_exp, np.ndarray):
X_exp = pd.DataFrame(X_exp)
if isinstance(y, np.ndarray):
y = pd.Series(y)
X.columns = [str(col) for col in X.columns]
if X_exp is not None:
X_exp.columns = X.columns
if X_exp is not None:
pd.testing.assert_index_equal(X.index, X_exp.index, check_names=False)
if X.reindex(X_exp.index).iloc[:, 0].isna().sum() != X.iloc[:, 0].isna().sum():
raise IndexError('X and X_exp must share the same index')
pd.testing.assert_index_equal(X.index, y.index, check_names=False)
return X, y, X_exp
def _preprocess_problem_category(self, problem_category: str, model, X: pd.DataFrame) -> ProblemCategory:
if problem_category not in [e.name for e in ProblemCategory]:
raise ValueError('Invalid problem category')
if problem_category == 'auto':
if hasattr(model, 'predict_proba'):
return ProblemCategory['classification_with_proba']
pred = self.model.predict(self.X.sample(min(100, len(self.X))))
if len(pred.shape) > 1 and pred.shape[1] > 1:
return ProblemCategory['classification_proba']
return ProblemCategory['regression']
if problem_category == 'classification':
if hasattr(model, 'prodict_proba'):
return ProblemCategory['classification_with_proba']
pred = model.predict(X.sample(min(100, len(X))))
if len(pred.shape) > 1 and pred.shape[1] > 1:
return ProblemCategory['classification_proba']
return ProblemCategory['classification_label_only']
return ProblemCategory[problem_category]
def _preprocess_score(self, score, problem_category):
if callable(score):
return score
if score != 'auto':
return score
if problem_category == ProblemCategory.regression:
return 'mse'
return 'accuracy'
|