modelStudio - R & Python examples

R & Python Examples

R

The modelStudio() function uses DALEX explainers created with DALEX::explain() or DALEXtra::explain_*().

# packages for the explainer objects
install.packages("DALEX")
install.packages("DALEXtra")

mlr dashboard

In this example, we make a studio for the ranger model on the apartments data.

# load packages and data
library(mlr)
library(DALEXtra)
library(modelStudio)

data <- DALEX::apartments

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

# fit a model
task <- makeRegrTask(id = "apartments", data = train, target = "m2.price")
learner <- makeLearner("regr.ranger", predict.type = "response")
model <- train(learner, task)

# create an explainer for the model
explainer <- explain_mlr(model,
                         data = test,
                         y = test$m2.price,
                         label = "mlr")

# pick observations
new_observation <- test[1:2,]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation)

mlr3 dashboard

In this example, we make a studio for the ranger model on the titanic data.

# load packages and data
library(mlr3)
library(mlr3learners)
library(DALEXtra)
library(modelStudio)

data <- DALEX::titanic_imputed

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

# mlr3 TaskClassif takes target as factor
train$survived <- as.factor(train$survived)

# fit a model
task <- TaskClassif$new(id = "titanic", backend = train, target = "survived")
learner <- lrn("classif.ranger", predict_type = "prob")
learner$train(task)

# create an explainer for the model
explainer <- explain_mlr3(learner,
                          data = test,
                          y = test$survived,
                          label = "mlr3")

# pick observations
new_observation <- test[1:2,]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation)

xgboost dashboard

In this example, we make a studio for the xgboost model on the titanic data.

# load packages and data
library(xgboost)
library(DALEX)
library(modelStudio)

data <- DALEX::titanic_imputed

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

train_matrix <- model.matrix(survived ~.-1, train)
test_matrix <- model.matrix(survived ~.-1, test)

# fit a model
xgb_matrix <- xgb.DMatrix(train_matrix, label = train$survived)
params <- list(max_depth = 3, objective = "binary:logistic", eval_metric = "auc")
model <- xgb.train(params, xgb_matrix, nrounds = 500)

# create an explainer for the model
explainer <- explain(model,
                     data = test_matrix,
                     y = test$survived,
                     type = "classification",
                     label = "xgboost")

# pick observations
new_observation <- test_matrix[1:2, , drop=FALSE]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation)

caret dashboard

In this example, we make a studio for the gbm model on the titanic data.

# load packages and data
library(caret)
library(DALEX)
library(modelStudio)

data <- DALEX::titanic_imputed

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

# caret train takes target as factor
train$survived <- as.factor(train$survived)

# fit a model
cv <- trainControl(method = "repeatedcv", number = 3, repeats = 3)
model <- train(survived ~ ., data = train, method = "gbm", trControl = cv, verbose = FALSE)

# create an explainer for the model
explainer <- explain(model,
                     data = test,
                     y = test$survived,
                     label = "caret")

# pick observations
new_observation <- test[1:2,]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation)

h2o dashboard

In this example, we make a studio for the h2o.automl model on the titanic data.

# load packages and data
library(h2o)
library(DALEXtra)
library(modelStudio)

data <- DALEX::titanic_imputed

# init h2o
h2o.init()
h2o.no_progress()

# split the data
h2o_split <- h2o.splitFrame(as.h2o(data))
train <- h2o_split[[1]]
test <- as.data.frame(h2o_split[[2]])

# h2o automl takes target as factor
train$survived <- as.factor(train$survived)

# fit a model
automl <- h2o.automl(y = "survived", training_frame = train, max_runtime_secs = 30)
model <- automl@leader

# create an explainer for the model
explainer <- explain_h2o(model,
                         data = test,
                         y = test$survived,
                         label = "h2o")

# pick observations
new_observation <- test[1:2,]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation,
            B = 5)

# shutdown h2o
h2o.shutdown(prompt = FALSE)

parsnip dashboard

In this example, we make a studio for the ranger model on the apartments data.

# load packages and data
library(parsnip)
library(DALEX)
library(modelStudio)

data <- DALEX::apartments

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

# fit a model
model <- rand_forest() %>%
         set_engine("ranger", importance = "impurity") %>%
         set_mode("regression") %>%
         fit(m2.price ~ ., data = train)

# create an explainer for the model
explainer <- explain(model,
                     data = test,
                     y = test$m2.price,
                     label = "parsnip")

# make a studio for the model
modelStudio(explainer)

tidymodels dashboard

In this example, we make a studio for the ranger model on the titanic data.

# load packages and data
library(tidymodels)
library(DALEXtra)
library(modelStudio)

data <- DALEX::titanic_imputed

# split the data
index <- sample(1:nrow(data), 0.7*nrow(data))
train <- data[index,]
test <- data[-index,]

# tidymodels fit takes target as factor
train$survived <- as.factor(train$survived)

# fit a model
rec <- recipe(survived ~ ., data = train) %>%
       step_normalize(fare)

clf <- rand_forest(mtry = 2) %>%
       set_engine("ranger") %>%
       set_mode("classification")

wflow <- workflow() %>%
         add_recipe(rec) %>%
         add_model(clf)

model <- wflow %>% fit(data = train)

# create an explainer for the model
explainer <- explain_tidymodels(model,
                                data = test,
                                y = test$survived,
                                label = "tidymodels")

# pick observations
new_observation <- test[1:2,]
rownames(new_observation) <- c("id1", "id2")

# make a studio for the model
modelStudio(explainer, new_observation)

Python

The modelStudio() function uses dalex explainers created with dalex.Explainer().

# package for the Explainer object
pip install dalex -U

Use pickle Python module and reticulate R package to easily make a studio for a model.

# package for pickle load
install.packages("reticulate")

scikit-learn dashboard

In this example, we make a studio for the Pipeline SVR model on the fifa data.

First, use dalex in Python:

# load packages and data
import dalex as dx
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVR
from numpy import log

data = dx.datasets.load_fifa()
X = data.drop(columns=['overall', 'potential', 'value_eur', 'wage_eur', 'nationality'], axis=1)
y = log(data.value_eur)

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y)

# fit a pipeline model
model = Pipeline([('scale', StandardScaler()), ('svm', SVR())])
model.fit(X_train, y_train)

# create an explainer for the model
explainer = dx.Explainer(model, data=X_test, y=y_test, label='scikit-learn')

# pack the explainer into a pickle file
explainer.dump(open('explainer_scikitlearn.pickle', 'wb'))

Then, use modelStudio in R:

# load the explainer from the pickle file
library(reticulate)
explainer <- py_load_object("explainer_scikitlearn.pickle", pickle = "pickle")

# make a studio for the model
library(modelStudio)
modelStudio(explainer, B = 5)

lightgbm dashboard

In this example, we make a studio for the Pipeline LGBMClassifier model on the titanic data.

First, use dalex in Python:

# load packages and data
import dalex as dx
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from lightgbm import LGBMClassifier

data = dx.datasets.load_titanic()
X = data.drop(columns='survived')
y = data.survived

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y)

# fit a pipeline model
numerical_features = ['age', 'fare', 'sibsp', 'parch']
numerical_transformer = Pipeline(
  steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
  ]
)
categorical_features = ['gender', 'class', 'embarked']
categorical_transformer = Pipeline(
  steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
  ]
)

preprocessor = ColumnTransformer(
  transformers=[
    ('num', numerical_transformer, numerical_features),
    ('cat', categorical_transformer, categorical_features)
  ]
)

classifier = LGBMClassifier(n_estimators=300)

model = Pipeline(
  steps=[
    ('preprocessor', preprocessor),
    ('classifier', classifier)
  ]
)
model.fit(X_train, y_train)

# create an explainer for the model
explainer = dx.Explainer(model, data=X_test, y=y_test, label='lightgbm')

# pack the explainer into a pickle file
explainer.dump(open('explainer_lightgbm.pickle', 'wb'))

Then, use modelStudio in R:

# load the explainer from the pickle file
library(reticulate)
explainer <- py_load_object("explainer_lightgbm.pickle", pickle = "pickle")

# make a studio for the model
library(modelStudio)
modelStudio(explainer)

keras/tensorflow dashboard

In this example, we make a studio for the Pipeline KerasClassifier model on the titanic data.

First, use dalex in Python:

# load packages and data
import dalex as dx
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.compose import ColumnTransformer
from keras.wrappers.scikit_learn import KerasClassifier
from keras.layers import Dense
from keras.models import Sequential

data = dx.datasets.load_titanic()
X = data.drop(columns='survived')
y = data.survived

# split the data
X_train, X_test, y_train, y_test = train_test_split(X, y)

# fit a pipeline model
numerical_features = ['age', 'fare', 'sibsp', 'parch']
numerical_transformer = Pipeline(
  steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())
  ]
)
categorical_features = ['gender', 'class', 'embarked']
categorical_transformer = Pipeline(
  steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))
  ]
)

preprocessor = ColumnTransformer(
  transformers=[
    ('num', numerical_transformer, numerical_features),
    ('cat', categorical_transformer, categorical_features)
  ]
)

def create_architecture():
    model = Sequential()
    # there are 17 inputs after the pipeline
    model.add(Dense(60, input_dim=17, activation='relu'))
    model.add(Dense(1, activation='sigmoid'))
    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
    return model

classifier = KerasClassifier(build_fn=create_architecture,
                             epochs=100, batch_size=32, verbose=False)

model = Pipeline(
  steps=[
    ('preprocessor', preprocessor),
    ('classifier', classifier)
  ]
)
model.fit(X_train, y_train)

# create an explainer for the model
explainer = dx.Explainer(model, data=X_test, y=y_test, label='keras')

# pack the explainer into a pickle file
explainer.dump(open('explainer_keras.pickle', 'wb'))

Then, use modelStudio in R:

# load the explainer from the pickle file
library(reticulate)

#! add blank create_architecture function before load !
py_run_string('
def create_architecture():
    return True
')

explainer <- py_load_object("explainer_keras.pickle", pickle = "pickle")

# make a studio for the model
library(modelStudio)
modelStudio(explainer)

References