def plot_shap(model, test, instance=None, feature=None, dataset=False):
"""
Displays shap plots to explain a black box model.
:param model: the model considered. The shap plots are calculated only after the model has been fit.
:param test: test dataset.
:param instance: instance of the test dataset to explain. default_value=None
:param feature: feature of the test dataset to explain. default_value=None
:param dataset: if True the entire dataset is taken into account. default_value=False
:return:
"""
# Make an explainer on the model given. Not all the models are supported
explainer = TreeExplainer(model)
# Compute SHAP values
shap_values = explainer.shap_values(test)
initjs()
# If not None explain single prediction
if instance is not None:
force_plot(explainer.expected_value,
shap_values[instance, :],
test.iloc[instance, :],
matplotlib=True)
# If not None explain single feature
if feature is not None:
fig, ax = plt.subplots(figsize=(13, 10))
dependence_plot(feature, shap_values, test, ax=ax)
# If True explain the entire dataset
if dataset:
summary_plot(shap_values, test, plot_size=(8, 8))
summary_plot(shap_values, test, plot_type="bar", plot_size=(8, 8))
def shap(self, X, plot=False, plot_type='bar'):
"""Method for shap values calculation and corresponding plot of feature importances.
Args:
X (:obj:`pd.DataFrame`, :obj:`pd.Series`): Data for shap values calculation.
plot (:obj:`boolean`, optional): Whether to plot a graph.
plot_type (:obj:`str`, optional): Type of feature importance graph, takes value in ['dot', 'bar'].
Returns:
JSON containing shap values.
"""
explainer = TreeExplainer(self.model)
X = DataFrame(X).T if isinstance(X, Series) else X
shap_values = explainer.shap_values(X)
shap_values = shap_values[0] if isinstance(
shap_values, list) and (len(shap_values) == 2) else shap_values
expected_value = (explainer.expected_value[0].tolist()
if isinstance(shap_values, list) and
(len(shap_values) == 2) else
[explainer.expected_value])
variables = ['Intercept'] + list(X.columns)
mean_shap = expected_value + shap_values.mean(axis=0).tolist()
if plot:
summary_plot(shap_values, X, plot_type=plot_type)
return {variables[i]: mean_shap[i] for i in range(len(variables))}
def test_shap_sklearn_classifier(iris_X, iris_y):
from shap import TreeExplainer
forest = RandomForestClassifier()
forest.fit(iris_X, iris_y)
explainer = TreeExplainer(model=forest, data=iris_X)
shap_values = explainer.shap_values(iris_X, check_additivity=False)
print(shap_values)
def test_shap_sklearn_regressor(boston_X, boston_y):
from shap import TreeExplainer
forest = RandomForestRegressor()
forest.fit(boston_X, boston_y)
explainer = TreeExplainer(model=forest, data=boston_X)
shap_values = explainer.shap_values(boston_X, check_additivity=False)
print(shap_values)
def test_shap_sklearn_classifier(iris_X, iris_y):
from shap import TreeExplainer
forest = RandomForestClassifier()
forest.fit(iris_X, iris_y)
explainer = TreeExplainer(model=forest)
shap_values = explainer.shap_values(iris_X)
print(shap_values)
def test_shap_sklearn_regressor(boston_X, boston_y):
from shap import TreeExplainer
forest = RandomForestRegressor()
forest.fit(boston_X, boston_y)
explainer = TreeExplainer(model=forest)
shap_values = explainer.shap_values(boston_X)
print(shap_values)
def test_shap_classifier(iris_X, iris_y):
from shap import TreeExplainer
forest = GRFForestClassifier(enable_tree_details=True)
forest.fit(iris_X, iris_y)
with shap_patch():
explainer = TreeExplainer(model=forest, data=iris_X)
shap_values = explainer.shap_values(iris_X, check_additivity=False)
print(shap_values)
def test_shap_classifier(iris_X, iris_y):
from shap import TreeExplainer
forest = RangerForestClassifier(enable_tree_details=True)
forest.fit(iris_X, iris_y)
with shap_patch():
explainer = TreeExplainer(model=forest)
shap_values = explainer.shap_values(iris_X)
print(shap_values)
def test_shap_regressor(boston_X, boston_y):
from shap import TreeExplainer
forest = RangerForestRegressor(enable_tree_details=True)
forest.fit(boston_X, boston_y)
with shap_patch():
explainer = TreeExplainer(model=forest)
shap_values = explainer.shap_values(boston_X)
print(shap_values)
def test_shap_regressor(boston_X, boston_y):
from shap import TreeExplainer
forest = GRFForestRegressor(enable_tree_details=True)
forest.fit(boston_X, boston_y)
with shap_patch():
explainer = TreeExplainer(model=forest, data=boston_X)
shap_values = explainer.shap_values(boston_X, check_additivity=False)
print(shap_values)
def _explain_trees(
model: Model,
transformed_data: Table,
transformed_reference_data: Table,
progress_callback: Callable,
) -> Tuple[
Optional[List[np.ndarray]], Optional[np.ndarray], Optional[np.ndarray]
]:
"""
Computes and returns SHAP values for learners that are explained by
TreeExplainer: all sci-kit models based on trees. In case that explanation
with TreeExplainer is not possible it returns None
"""
if sparse.issparse(transformed_data.X):
# sparse not supported by TreeExplainer, KernelExplainer can handle it
return None, None, None
try:
explainer = TreeExplainer(
model.skl_model, data=sample(transformed_reference_data.X, 100),
)
# I know it is too broad but this is what TreeExplainer trows
except Exception:
return None, None, None
# TreeExplaner cannot explain in normal time more cases than 1000
data_sample, sample_mask = _subsample_data(transformed_data, 1000)
num_classes = (
len(model.domain.class_var.values)
if model.domain.class_var.is_discrete
else None
)
# this method will work in batches since explaining only one attribute
# at time the processing timed doubles comparing to batch size 10
shap_values = []
batch_size = 1 # currently set to 1 to minimize widget blocking
for i in range(0, len(data_sample), batch_size):
progress_callback(i / len(data_sample))
batch = data_sample.X[i : i + batch_size]
shap_values.append(
explainer.shap_values(batch, check_additivity=False)
)
shap_values = _join_shap_values(shap_values)
base_value = explainer.expected_value
# when in training phase one class value was missing skl_model do not
# output probability for it. For other models it is handled by Orange
if num_classes is not None:
missing_d = num_classes - len(shap_values)
shap_values += [
np.zeros(shap_values[0].shape) for _ in range(missing_d)
]
base_value = np.hstack((base_value, np.zeros(missing_d)))
return shap_values, sample_mask, base_value
def evaluate(
self,
study: Study,
params: Optional[List[str]] = None,
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
) -> Dict[str, float]:
if target is None and study._is_multi_objective():
raise ValueError(
"If the `study` is being used for multi-objective optimization, "
"please specify the `target`. For example, use "
"`target=lambda t: t.values[0]` for the first objective value."
)
distributions = _get_distributions(study, params=params)
if params is None:
params = list(distributions.keys())
assert params is not None
if len(params) == 0:
return OrderedDict()
trials: List[FrozenTrial] = _get_filtered_trials(study, params=params, target=target)
trans = _SearchSpaceTransform(distributions, transform_log=False, transform_step=False)
trans_params: np.ndarray = _get_trans_params(trials, trans)
target_values: np.ndarray = _get_target_values(trials, target)
forest = self._forest
forest.fit(X=trans_params, y=target_values)
# Create Tree Explainer object that can calculate shap values.
explainer = TreeExplainer(forest)
# Generate SHAP values for the parameters during the trials.
feature_shap_values: np.ndarray = explainer.shap_values(trans_params)
param_shap_values = np.zeros((len(trials), len(params)))
np.add.at(param_shap_values.T, trans.encoded_column_to_column, feature_shap_values.T)
# Calculate the mean absolute SHAP value for each parameter.
# List of tuples ("feature_name": mean_abs_shap_value).
mean_abs_shap_values = np.abs(param_shap_values).mean(axis=0)
return _sort_dict_by_importance(_param_importances_to_dict(params, mean_abs_shap_values))
class HyperGBMExplainer:
def __init__(self, hypergbm_estimator, data=None):
if not has_shap:
raise RuntimeError(
'Please install `shap` package first. command: pip install shap'
)
self.hypergbm_estimator = hypergbm_estimator
if data is not None:
data = self.hypergbm_estimator.transform_data(data)
self.explainer = TreeExplainer(self.hypergbm_estimator.estimator, data)
@property
def expected_value(self):
return self.explainer.expected_value
def shap_values(self,
X,
y=None,
tree_limit=None,
approximate=False,
check_additivity=True,
from_call=False,
**kwargs):
X = self.hypergbm_estimator.transform_data(X, **kwargs)
return self.explainer.shap_values(X,
y,
tree_limit=tree_limit,
approximate=approximate,
check_additivity=check_additivity,
from_call=from_call)
def shap_interaction_values(self, X, y=None, tree_limit=None, **kwargs):
X = self.hypergbm_estimator.transform_data(X, **kwargs)
return self.explainer.shap_interaction_values(X, y, tree_limit)
def transform_data(self, X, **kwargs):
X = self.hypergbm_estimator.transform_data(X, **kwargs)
return X
def model_interpretation(self, patient_id, patient_preprocessed, pred,
prob, model):
'''
Fazer gráficos avaliativos do modelo.
Argumentos:
patient_id = string referente a identificação do paciente
patient_preprocessed = dicionario contendo dados do exame do paciente
pred = classe predita pelo modelo
prob = probabilidade referente a classe predita pelo modelo
model = objeto do modelo
'''
#### Pegar variaveis necessárias para o plot (import csv)
#### Nome dos plots
plot_1_name = 'app/ai_models/temp/probacurve-' + str(
patient_id) + '.png'
plot_2_name = 'app/ai_models/temp/shap-' + str(patient_id) + '.png'
plot_3_name = 'app/ai_models/temp/dist-' + str(patient_id) + '.png'
plot_4_name = 'app/ai_models/temp/mapa-' + str(patient_id) + '.png'
#URL API PLOTS
plot_1_api = "http://" + self.IP + ":" + self.API_PORT + "/api/media/probacurve-" + str(
patient_id) + ".png"
plot_2_api = "http://" + self.IP + ":" + self.API_PORT + "/api/media/shap-" + str(
patient_id) + ".png"
plot_3_api = "http://" + self.IP + ":" + self.API_PORT + "/api/media/dist-" + str(
patient_id) + ".png"
plot_4_api = "http://" + self.IP + ":" + self.API_PORT + "/api/media/mapa-" + str(
patient_id) + ".png"
#### Configurações gerais do plt
DPI_IMAGES = 100
FONT_SIZE = 8
FONT_NAME = 'sans-serif'
plt.rc('font', family=FONT_NAME, size=FONT_SIZE)
plt.rc('axes', titlesize=FONT_SIZE, labelsize=FONT_SIZE)
plt.rc('xtick', labelsize=FONT_SIZE)
plt.rc('ytick', labelsize=FONT_SIZE)
plt.rc('legend', fontsize=FONT_SIZE)
#### PLOT 1 - Distribuição da probabilidade dada pelo modelo para pacientes positivos
# Itens Necessário: self.probs_df(csv importado) e pred
exame_resp = pred
exame_prob = prob
# Plot
fig, axis = plt.subplots(nrows=1, ncols=1, figsize=(5, 5))
sns.kdeplot(self.probs_df['prob_neg'],
shade=True,
color='#386796',
ax=axis,
linestyle="--",
label='Casos Negativos')
sns.kdeplot(self.probs_df['prob_pos'],
shade=True,
color='#F06C61',
ax=axis,
label='Casos positivos')
# Pegar eixo XY do Plt object para fazer a interpolação
if exame_resp == 0:
xi = 1 - exame_prob
data_x, data_y = axis.lines[0].get_data()
elif exame_resp == 1:
xi = exame_prob
data_x, data_y = axis.lines[1].get_data()
# Fazer a interpolação e plot
yi = np.interp(xi, data_x, data_y)
axis.plot([xi], [yi],
linestyle='None',
marker="*",
color='black',
markersize=10,
label='Paciente')
# Outras configuracoes do plot
axis.legend(loc="upper right")
#axis.set_title('Probabilidade de ser COVID Positivo pelo modelo', fontweight='bold')
axis.set_xlim([0, 1])
axis.set_ylim([0, axis.get_ylim()[1]])
plt.tight_layout()
# Salvar plot 1
plt.savefig(plot_1_name,
dpi=DPI_IMAGES,
bbox_inches='tight',
pad_inches=0.1)
plt.close()
#### PLOT 2 - SHAP
# Necessário: patient_preprocessed, pred e model
features = np.array(list(patient_preprocessed.keys()))
sample_x = np.array(list(patient_preprocessed.values()))
# Calcular SHAP Value
explainer = TreeExplainer(model=model) # Faz o objeto SHAP
shap_values_sample = explainer.shap_values(sample_x) # Calculo do SHAP
expected_value = explainer.expected_value[
exame_resp] # Pega o baseline para a classe predita pelo modelo
shap_values_sample = explainer.shap_values(
sample_x) # Calcular os SHAP values
# Plot
#plt.title('Valores SHAP', fontweight='bold')
waterfall_plot(expected_value,
shap_values_sample[exame_resp],
sample_x,
feature_names=features,
max_display=20,
show=False)
# Salvar imagem
plt.tight_layout()
plt.savefig(plot_2_name,
dpi=DPI_IMAGES,
bbox_inches='tight',
pad_inches=0)
plt.close()
#### PLOT 3 - Distribuição das variáveis mais importantes para o modelo
# Necessário: self.train_df(csv importado), patient_preprocessed, pred
important_features = [
'Leucócitos', 'Plaquetas', 'Hemácias', 'Eosinófilos'
]
target_0 = self.train_df[self.train_df['target'] == 0][[
'Leucócitos', 'Plaquetas', 'Hemácias', 'Eosinófilos'
]]
target_1 = self.train_df[self.train_df['target'] == 1][[
'Leucócitos', 'Plaquetas', 'Hemácias', 'Eosinófilos'
]]
# Plot
fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(10, 5))
# Plot settings
#sns.set_color_codes()
#st = fig.suptitle("Distribuição das variáveis importantes para o modelo", fontweight='bold')
#st.set_y (1.05)
# Index col/row
r = 0
c = 0
# Loop to plot
for feat in important_features:
# Plot distribuição
sns.kdeplot(list(target_0[feat]),
shade=True,
color='#386796',
ax=axes[r][c],
label='Casos Negativos',
linestyle="--")
sns.kdeplot(list(target_1[feat]),
shade=True,
color='#F06C61',
ax=axes[r][c],
label='Casos positivos')
# Pegar a curva de densidade a partir do resultado do modelo
if pred == 0:
data_x, data_y = axes[r][c].lines[0].get_data()
elif pred == 1:
data_x, data_y = axes[r][c].lines[1].get_data()
# Pegar a informação (valor) daquela variável importante
xi = patient_preprocessed[feat]
yi = np.interp(xi, data_x, data_y)
## Plot ponto na curva
axes[r][c].plot([xi], [yi],
linestyle='None',
marker="*",
color='black',
markersize=10,
label='Paciente')
axes[r][c].set_title(feat)
axes[r][c].legend(loc="upper right")
axes[r][c].set_ylim([0, axes[r][c].get_ylim()[1]])
# Mudar onde sera plotado
if c == 0:
c += 1
else:
r += 1
c = 0
# Ajeitar o plot
plt.tight_layout()
# Salvar imagem
plt.savefig(plot_3_name,
dpi=DPI_IMAGES,
bbox_inches='tight',
pad_inches=0.1)
plt.close()
#### PLOT 4 - Mapa com SVD para os pacientes
# Necessário: train_df(csv importado), patient_preprocessed
amostra = pd.DataFrame(patient_preprocessed, index=[
0,
]).drop(axis=1, columns=['Outra gripe'])
# Fazer PCA com SVD via prince package
y_train = self.train_df['target'] # Salvar coluna target
dados = self.train_df.drop(
axis=1, columns=['Outra gripe',
'target']).copy() # Dataset para criar o mapa
pca_obj = PCA(n_components=2, random_state=42) # Objeto do PCA
pca_obj.fit(dados) # Fit no conjunto de dados
componentes = pca_obj.transform(
dados) # Criar os componentes principais dos dados
transf = pca_obj.transform(amostra) # Transformar paciente para PCA
xi = transf.loc[0, 0] # Eixo X do paciente para plot
yi = transf.loc[0, 1] # Eixo Y do paciente para plot
comp = pd.DataFrame() # Dataframe para conter os componentes
comp['C1'] = componentes[0] # Componente Principal 1
comp['C2'] = componentes[1] # Componente Principal 2
comp['TG'] = y_train # Variável target para a mascara
comp_0 = comp[comp['TG'] == 0][['C1', 'C2'
]] # Dataframe de CP para negativos
comp_1 = comp[comp['TG'] == 1][['C1', 'C2'
]] # Dataframe de CP para positivos
# Plot
fig, ax = plt.subplots(figsize=(8, 8))
plt.margins(0, 0)
sns.scatterplot(ax=ax,
data=comp_0,
x='C1',
y='C2',
color='#386796',
label='Casos Negativos')
sns.scatterplot(ax=ax,
data=comp_1,
x='C1',
y='C2',
color='#F06C61',
label='Casos Positivos')
x_mean, y_mean, width, height, angle = self.build_ellipse(
comp_0['C1'], comp_0['C2'])
ax.add_patch(
Ellipse((x_mean, y_mean),
width,
height,
angle=angle,
linewidth=2,
color='#386796',
fill=True,
alpha=0.2))
x_mean, y_mean, width, height, angle = self.build_ellipse(
comp_1['C1'], comp_1['C2'])
ax.add_patch(
Ellipse((x_mean, y_mean),
width,
height,
angle=angle,
linewidth=2,
color='#F06C61',
fill=True,
alpha=0.2))
ax.plot([xi], [yi],
linestyle='None',
marker="*",
color='black',
markersize=10,
label='Paciente')
# Configurações do plot
#ax.set_title('Similaridade entre pacientes',fontweight='bold')
ax.set_xticks([])
ax.set_yticks([])
ax.set_ylabel('')
ax.set_xlabel('')
handles, labels = ax.get_legend_handles_labels()
labels, handles = zip(
*sorted(zip(labels, handles), key=lambda t: t[0]))
ax.legend(handles, labels, loc="upper right")
# Salvar imagem
plt.axis('off')
plt.savefig(plot_4_name,
dpi=DPI_IMAGES,
bbox_inches='tight',
pad_inches=0)
plt.close()
# Retornar
model_result = {
'prediction': pred,
'probability': str(round(prob * 100, 2)),
'probacurve': plot_1_api,
'shap_img': plot_2_api,
'dist_img': plot_3_api,
'mapa_img': plot_4_api
}
return model_result
"""
class ShapleyImportanceEvaluator(BaseImportanceEvaluator):
"""Shapley (SHAP) parameter importance evaluator.
This evaluator fits a random forest that predicts objective values given hyperparameter
configurations. Feature importances are then computed as the mean absolute SHAP values.
.. note::
This evaluator requires the `sklearn <https://scikit-learn.org/stable/>`_ Python package
and `SHAP <https://shap.readthedocs.io/en/stable/index.html>`_.
The model for the SHAP calculation is based on `sklearn.ensemble.RandomForestClassifier
<https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html>`_.
Args:
n_trees:
Number of trees in the random forest.
max_depth:
The maximum depth of each tree in the random forest.
seed:
Seed for the random forest.
"""
def __init__(
self, *, n_trees: int = 64, max_depth: int = 64, seed: Optional[int] = None
) -> None:
_imports.check()
# Use the RandomForest as the surrogate model to evaluate the feature importances.
self._backend_evaluator = MeanDecreaseImpurityImportanceEvaluator(
n_trees=n_trees, max_depth=max_depth, seed=seed
)
# Use the TreeExplainer from the SHAP module.
self._explainer: TreeExplainer = None
def evaluate(
self,
study: Study,
params: Optional[List[str]] = None,
*,
target: Optional[Callable[[FrozenTrial], float]] = None,
) -> Dict[str, float]:
# Train a RandomForest from the backend evaluator.
self._backend_evaluator.evaluate(study=study, params=params, target=target)
# Create Tree Explainer object that can calculate shap values.
self._explainer = TreeExplainer(self._backend_evaluator._forest)
# Generate SHAP values for the parameters during the trials.
shap_values = self._explainer.shap_values(self._backend_evaluator._trans_params)
# Calculate the mean absolute SHAP value for each parameter.
# List of tuples ("feature_name": mean_abs_shap_value).
mean_abs_shap_values = list(
zip(self._backend_evaluator._param_names, np.abs(shap_values).mean(axis=0))
)
# Use the mean absolute SHAP values as the feature importance.
mean_abs_shap_values.sort(key=lambda t: t[1], reverse=True)
feature_importances = OrderedDict(mean_abs_shap_values)
return feature_importances
def cross_val(self, X, y, scoring=None, cv=None, **kwargs):
"""Method for performing cross-validation given the hyperparameters of initialized or fitted model.
Args:
X (:obj:`pd.DataFrame`, :obj:`pd.Series`): Training data.
y (:obj:`pd.DataFrame`, :obj:`pd.Series`): Training target values.
scoring (:obj:`callable`): Metrics passed to sklearn.model_selection.cross_validate calculation.
cv (:obj:`int, cross-validation generator or an iterable`, optional): Cross-validation strategy from
sklearn. Performs 5-fold cv by default.
**kwargs: Other parameters passed to sklearn.model_selection.cross_validate.
Returns:
pd.DataFrame, pd.DataFrame: DataFrame with metrics on folds, DataFrame with shap values on folds.
"""
scoring = mean_squared_error if scoring is None else scoring
models, metrics = self._cross_val(X,
y,
scoring=scoring,
cv=cv,
**kwargs)
if callable(scoring):
scorers = {
scoring.__name__.replace('_', ' '):
array([scoring(y, self.model.predict(X))])
}
elif isinstance(scoring, (tuple, list)):
scorers = {
scorer.__name__.replace('_', ' '):
array([scorer(y, self.model.predict(X))])
for scorer in scoring
}
elif isinstance(scoring, str):
if scoring in SCORERS:
scorers = {
scoring.replace('_', ' '):
array([SCORERS[scoring](self.model, X=X, y=y)])
}
else:
raise ValueError(f'Scorer {scoring} is not supported.')
else:
raise NotImplementedError(
f'Scoring of type {scoring} is not supported')
metrics = DataFrame({
key: concatenate((scorers[key], metrics[key]))
for key in scorers.keys()
}).T
metrics.columns = [
f'Fold {i}' if i != 0 else 'Overall'
for i in range(metrics.shape[1])
]
shap_coefs = []
explainer = TreeExplainer(self.model)
shap_coefs.append(
([explainer.expected_value] if explainer.expected_value is None
else explainer.expected_value.tolist()) +
explainer.shap_values(X).mean(axis=0).tolist())
for model in models:
explainer = TreeExplainer(model)
shap_coefs.append(
([explainer.expected_value] if explainer.expected_value is None
else explainer.expected_value.tolist()) +
explainer.shap_values(X).mean(axis=0).tolist())
shapdf = DataFrame(array(shap_coefs).T,
columns=['Overall'] +
[f'Fold {x}' for x in range(1,
len(models) + 1)],
index=['Intercept'] + X.columns.tolist())
return metrics, shapdf
def shap_explain(self,
data,
index=None,
link=None,
show=True,
layout_dict=None):
"""Method for plotting a waterfall graph or return corresponding JSON if show=False.
Args:
data (:obj:`pd.DataFrame`, :obj:`pd.Series`): Data for shap values calculation.
index (:obj:`int`, optional): Index of the observation of interest, if data is pd.DataFrame.
link (:obj:`callable`, optional): A function for transforming shap values into predictions.
Unnecessary if self.objective is present and it takes values in ['binary', 'poisson', 'gamma'].
show (:obj:`boolean`, optional): Whether to plot a graph or return a json.
layout_dict (:obj:`boolean`, optional): Dictionary containing the parameters of plotly figure layout.
Returns:
None or dict: Waterfall graph or corresponding JSON.
"""
def logit(x):
return true_divide(1, add(1, exp(-x)))
explainer = TreeExplainer(self.model)
if isinstance(self.model, (XGBClassifier, XGBRegressor)):
feature_names = self.model.get_booster().feature_names
elif isinstance(self.model, (LGBMClassifier, LGBMRegressor)):
feature_names = self.model.feature_name_
elif isinstance(self.model, (CatBoostClassifier, CatBoostRegressor)):
feature_names = self.model.feature_names_
else:
raise NotImplementedError(
f'Error with the backend choice. Supported backends: {self._backends}'
)
index = index if (isinstance(
data, DataFrame)) and (index is not None) else None
data = DataFrame(data).T[feature_names] if isinstance(
data, Series) else data[feature_names]
data = data if index is None else data.loc[[index], :]
shap_values = explainer.shap_values(data)
cond_bool = isinstance(shap_values, list) and (len(shap_values) == 2)
shap_values = shap_values[0] if cond_bool else shap_values
expected_value = explainer.expected_value[
0] if cond_bool else explainer.expected_value
prediction = DataFrame([expected_value] +
shap_values.reshape(-1).tolist(),
index=['Intercept'] + feature_names,
columns=['SHAP Value'])
prediction['CumSum'] = cumsum(prediction['SHAP Value'])
prediction['Value'] = append(nan, data.values.reshape(-1))
if (self.objective is not None) and (link is None):
link = exp if self.objective in [
'poisson', 'gamma'
] else logit if self.objective == 'binary' else None
if link is not None:
prediction['Link'] = link(prediction['CumSum'])
prediction['Contribution'] = [link(expected_value)] + list(
diff(prediction['Link']))
else:
prediction['Contribution'] = [expected_value] + list(
diff(prediction['CumSum']))
fig = Figure(
Waterfall(
name=f'Prediction {index}',
orientation='h',
measure=['relative'] * len(prediction),
y=[
prediction.index[i] if i == 0 else
f'{prediction.index[i]}={data.values.reshape(-1)[i-1]}'
for i in range(len(prediction.index))
],
x=prediction['Contribution']))
fig.update_layout(**(layout_dict if layout_dict is not None else {}))
if show:
fig.show()
else:
json_ = prediction[['Value', 'SHAP Value',
'Contribution']].T.to_dict()
fig_base64 = b64encode(
to_image(fig, format='jpeg', engine='kaleido')).decode('ascii')
json_.update({
'id': int(data.index.values),
'predict': prediction['Link'][-1],
"ShapValuesPlot": fig_base64
})
return json_