def predict(model, X_test, y_test):
X_train_encoded, y_train, X_test_encoded, y_test, encoder = load_encoded_data(
)
y_hat = model.predict(X_test_encoded)
#print("Accuracy Score: ", "\n")
print("Accuracy Score: ", accuracy_score(y_test, y_hat))
def get(self):
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data(
)
print("possible instances: ", X_test_encoded.shape[0])
possible_instance = []
for i in range(0, X_test_encoded.shape[0]):
possible_instance.append(i)
return possible_instance
def post(self):
reqparse_args = reqparse.RequestParser()
reqparse_args.add_argument("chosen_model", type=str)
args = reqparse_args.parse_args()
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data(
)
# Choose a model and train it
model, model_name = model_training.model_selection(
args["chosen_model"])
model = model_training.train_model(X_train_encoded, y_train, model)
model_training.save_trained_model(model, model_name)
return "model successfully trained"
def get(self):
reqparse_args = reqparse.RequestParser()
reqparse_args.add_argument("chosen_model", type=str)
args = reqparse_args.parse_args()
model = model_training.load_trained_model(args["chosen_model"])
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data(
)
y_hat = model.predict(X_test_encoded)
y_score = model.predict.proba(X_test_encoded[:, 1])
print("ChosenModel: Accuracy score", accuracy_score(y_test, y_hat))
print("ChosenModel: AUC", roc_auc_score(y_test,
y_score)) # need proba?
return "run successfully"
def main_global(model_name):
"""Main function for the global explanations."""
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data()
print(X_train_encoded)
print(type(X_train_encoded))
X_test, y_test = data_loading.load_data(type="testing")
model = model_training.load_trained_model(model_name)
# categorical_features_indices = dataset["german_credit_dataset"]["categorical_features_indices"]
categorical_features_names, categorical_features_indices, feature_names = data_loading.analyze_dataset()
df_credit = data_loading.load_data(type="raw")
df_credit = data_loading.data_preparation(df_credit)
df_credit, categorical_encoding, categorical_encoding_label = data_loading.encoding(df_credit)
explain_with_shap_global(X_test_encoded , model, model_name)
def main_init_xai(model_name, chosen_instance=4):
"""Prepare plots and data for the xai-dashboard"""
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data()
X_test, y_test = data_loading.load_data(type="testing")
model = model_training.load_trained_model(model_name)
# categorical_features_indices = dataset["german_credit_dataset"]["categorical_features_indices"]
categorical_features_names, categorical_features_indices, feature_names = data_loading.analyze_dataset()
df_credit = data_loading.load_data(type="raw")
df_credit = data_loading.data_preparation(df_credit)
df_credit, categorical_encoding, categorical_encoding_label = data_loading.encoding(df_credit)
#explain_with_lime(X_test, model, model_name, encoder, categorical_features_indices,
# categorical_encoding, categorical_encoding_label,
# feature_names, test_instance=chosen_instance)
explain_with_shap_global(X_train_encoded, model, model_name)
def main_local(model_name, chosen_instance=1):
"""Main function for local explanations"""
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data()
X_test, y_test = data_loading.load_data(type="testing")
model = model_training.load_trained_model(model_name)
categorical_features_names, categorical_features_indices, feature_names = data_loading.analyze_dataset()
df_credit = data_loading.load_data(type="raw")
df_credit = data_loading.data_preparation(df_credit)
df_credit, categorical_encoding, categorical_encoding_label = data_loading.encoding(df_credit)
explain_with_lime(X_test, model, model_name, encoder, categorical_features_indices, categorical_encoding,
categorical_encoding_label, feature_names, test_instance=chosen_instance)
explain_with_shap_local(X_test_encoded, model, model_name, feature_names=X_test_encoded.columns.tolist()
, test_instance=chosen_instance)
from xgboost import XGBClassifier
import matplotlib.pyplot as plt
from backend.xai_cockpit.model_explaining import *
from backend.inference.predict import *
import dataframe_image as dfi
if __name__ == '__main__':
s = (input("Type 'start' to initialize: "))
initialize()
df = load_data(type="raw")
s
# explore_data(df)
data_exploration(df)
X_train_encoded, y_train, X_test_encoded, y_test, encoder = data_loading.load_encoded_data(
)
# Choose a model and train it
model, model_name = model_training.model_selection(input("Select Model: "))
print("Trained model: ", model_name)
X_test = data_loading.train_test_split(df)
model = model_training.train_model(X_train_encoded, y_train, model)
model_training.save_trained_model(model, model_name)
print("Training score data: ")
print(model.score(X_train_encoded, y_train))
#good: 20, bad:35
i = int(input("Enter instance for prediction (0-100):"))
print("Chosen Instance: ", i)
print(X_test_encoded.iloc[i])
#print(y_test.iloc[i])
#print(X_test)