predict_model with only integers, slices (:) #3568
Description
pycaret version checks
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I have checked that this issue has not already been reported here.
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I have confirmed this bug exists on the latest version of pycaret.
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I have confirmed this bug exists on the master branch of pycaret (pip install -U git+https://github.com/pycaret/pycaret.git@master).
Issue Description
Hi,
I have this issue with predict_model(): only integers, slices (:), ellipsis (...), numpy.newaxis (None) and integer or boolean arrays are valid indices
I tried to fix with the issue Error in prediction#1047, but it doesn’t worked.
Python 3.10.11
Pycaret 3.0.2
I appreciate any help. Thank you.
Luiz
Reproducible Example
!pip install pycaret
import pycaret
from pycaret.classification import *
s = setup(data = train, test_data = test, target = 'col68', session_id = 123,
numeric_imputation = 'knn', remove_outliers = True, index=False)
best = compare_models()
model_future = create_model(best)
model_future_tune = tune_model(model_future)
predicted_future_Test = predict_model(model_future_tune)Expected Behavior
The prediction of the value in the column col68 with 0 or 1.
Actual Results
Description Value
0 Session id 123
1 Target col68
2 Target type Binary
3 Original data shape (3095, 68)
4 Transformed data shape (2971, 70)
5 Transformed train set shape (2352, 70)
6 Transformed test set shape (619, 70)
7 Numeric features 66
8 Date features 1
9 Rows with missing values 1.0%
10 Preprocess True
11 Imputation type simple
12 Numeric imputation knn
13 Categorical imputation mode
14 Remove outliers True
15 Outliers threshold 0.050000
16 Fold Generator StratifiedKFold
17 Fold Number 10
18 CPU Jobs -1
19 Use GPU False
20 Log Experiment False
21 Experiment Name clf-default-name
22 USI 36af
Model Accuracy AUC Recall Prec. F1 Kappa MCC TT (Sec)
dummy Dummy Classifier 0.5998 0.5000 1.0000 0.5998 0.7498 0.0000 0.0000 0.2320
svm SVM - Linear Kernel 0.5796 0.0000 0.6201 0.4966 0.5295 0.1579 0.1521 0.5220
lr Logistic Regression 0.5792 0.5311 0.6295 0.6114 0.5430 0.1518 0.1441 1.2290
nb Naive Bayes 0.5443 0.8071 0.4128 0.3901 0.3559 0.1587 0.1787 0.2320
ridge Ridge Classifier 0.5115 0.0000 0.4443 0.5130 0.4719 0.0573 0.0488 0.3040
lda Linear Discriminant Analysis 0.5084 0.5659 0.5281 0.6203 0.5344 0.0129 0.0252 0.3550
gbc Gradient Boosting Classifier 0.4220 0.3957 0.4109 0.5258 0.4245 -0.1456 -0.1463 4.7640
lightgbm Light Gradient Boosting Machine 0.3957 0.3504 0.4178 0.5148 0.4305 -0.2146 -0.2073 1.3270
xgboost Extreme Gradient Boosting 0.3756 0.2996 0.4198 0.4872 0.4275 -0.2646 -0.2665 3.0240
ada Ada Boost Classifier 0.3659 0.3235 0.3907 0.4882 0.3964 -0.2707 -0.2706 1.8010
rf Random Forest Classifier 0.3359 0.2510 0.3436 0.4657 0.3718 -0.3240 -0.3201 1.9560
et Extra Trees Classifier 0.3179 0.2721 0.3424 0.4048 0.3442 -0.3600 -0.3735 0.8920
dt Decision Tree Classifier 0.3060 0.3183 0.2571 0.3943 0.3015 -0.3416 -0.3643 0.4310
qda Quadratic Discriminant Analysis 0.3006 0.2307 0.3868 0.3543 0.3556 -0.4171 -0.4520 0.2590
knn K Neighbors Classifier 0.2589 0.2036 0.2273 0.2810 0.2251 -0.4348 -0.4830 0.4010
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
0 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
1 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
2 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
3 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
4 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
5 0.5968 0.5000 1.0000 0.5968 0.7475 0.0000 0.0000
6 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
7 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
8 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
9 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
Mean 0.5998 0.5000 1.0000 0.5998 0.7498 0.0000 0.0000
Std 0.0013 0.0000 0.0000 0.0013 0.0010 0.0000 0.0000
Accuracy AUC Recall Prec. F1 Kappa MCC
Fold
0 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
1 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
2 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
3 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
4 0.6008 0.5000 1.0000 0.6008 0.7506 0.0000 0.0000
5 0.5968 0.5000 1.0000 0.5968 0.7475 0.0000 0.0000
6 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
7 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
8 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
9 0.5992 0.5000 1.0000 0.5992 0.7494 0.0000 0.0000
Mean 0.5998 0.5000 1.0000 0.5998 0.7498 0.0000 0.0000
Std 0.0013 0.0000 0.0000 0.0013 0.0010 0.0000 0.0000
Fitting 10 folds for each of 4 candidates, totalling 40 fits
Original model was better than the tuned model, hence it will be returned. NOTE: The display metrics are for the tuned model (not the original one).
Model Accuracy AUC Recall Prec. F1 Kappa MCC
0 Dummy Classifier 0.5506 0.5000 1.0000 0.5506 0.7101 0.0000 0.0000
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-116-7530072cf3b8> in <cell line: 9>()
7 model_future_tune = tune_model(model_future)
8
----> 9 predicted_future_all = predict_model(model_future_tune, data = price_target_df.drop(price_target_df.tail(n).index))
10 predicted_future_Test = predict_model(model_future_tune, data = test)
3 frames
/usr/local/lib/python3.10/dist-packages/pycaret/internal/pycaret_experiment/supervised_experiment.py in <listcomp>(.0)
5019
5020 score = pd.DataFrame(
-> 5021 data=[s[pred[i]] for i, s in enumerate(score)],
5022 index=X_test_.index,
5023 columns=[SCORE_COLUMN],
IndexError: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indicesInstalled Versions
PyCaret required dependencies:
pip: 23.1.2
setuptools: 67.7.2
pycaret: 3.0.2
IPython: 7.34.0
ipywidgets: 7.7.1
tqdm: 4.65.0
numpy: 1.22.4
pandas: 1.3.5
jinja2: 3.1.2
scipy: 1.10.1
joblib: 1.2.0
sklearn: 1.2.2
pyod: 1.0.9
imblearn: 0.10.1
category_encoders: 2.6.1
lightgbm: 3.3.5
numba: 0.56.4
requests: 2.27.1
matplotlib: 3.7.1
scikitplot: 0.3.7
yellowbrick: 1.5
plotly: 5.13.1
kaleido: 0.2.1
statsmodels: 0.13.5
sktime: 0.17.0
tbats: 1.1.3
pmdarima: 2.0.3
psutil: 5.9.5
PyCaret optional dependencies:
shap: Not installed
interpret: Not installed
umap: Not installed
pandas_profiling: Not installed
explainerdashboard: Not installed
autoviz: Not installed
fairlearn: Not installed
xgboost: 1.7.5
catboost: Not installed
kmodes: Not installed
mlxtend: 0.14.0
statsforecast: Not installed
tune_sklearn: Not installed
ray: Not installed
hyperopt: 0.2.7
optuna: Not installed
skopt: Not installed
mlflow: Not installed
gradio: Not installed
fastapi: Not installed
uvicorn: Not installed
m2cgen: Not installed
evidently: Not installed
fugue: Not installed
streamlit: Not installed
prophet: 1.1.2