Feature names unseen at fit time

[yiannis-gkoufas]

Hi!

I want to use mljar for binary classification (category1+category2). The parameters I am passing to AutoML are the following:

automl = AutoML(results_path=str(model_directory),
                            mode="Compete",
                            total_time_limit=600 * 600,
                            golden_features=True,
                            features_selection=True,
                            ml_task="binary_classification")

In the params.json I see "best_model": "Ensemble_Stacked" When I try to run a prediction I get:

Feature names unseen at fit time:
- 100_LightGBM_GoldenFeatures_prediction
- 101_LightGBM_GoldenFeatures_prediction
- 103_LightGBM_GoldenFeatures_prediction
- 105_Xgboost_prediction
- 108_CatBoost_prediction
- ...
Feature names seen at fit time, yet now missing:
- 100_LightGBM_GoldenFeatures_prediction_0_for_category1_1_for_category2
- 101_LightGBM_GoldenFeatures_prediction_0_for_category1_1_for_category2
- 103_LightGBM_GoldenFeatures_prediction_0_for_category1_1_for_category2
- 105_Xgboost_prediction_0_for_category1_1_for_category2
- 108_CatBoost_prediction_0_for_category1_1_for_category2

Any help would be appreciated!

[pplonski]

Hi @yiannis-gkoufas,

I understand that you were able to train ML models with AutoML but there is problem with predictions only. Could you please provide code that you are using for computing predictions?

[yiannis-gkoufas]

Hi @pplonski!

I use the same constructor for AutoML and pass a dataframe.

automl = AutoML(results_path=str(model_directory),
                            mode="Compete",
                            total_time_limit=600 * 600,
                            golden_features=True,
                            features_selection=True,
                            ml_task="binary_classification")

Could it be an issue with the ensemble model?

[pplonski]

Thank you @yiannis-gkoufas for response. It looks like some bug with computing predictions for Stacked Ensemble. Is it possible to share full code and data to reproduce the issue?

[yiannis-gkoufas]

This code:

from sklearn.model_selection import train_test_split
from supervised import AutoML
import pandas as pd

if __name__ == '__main__':
    df = pd.read_csv(
        "https://raw.githubusercontent.com/pplonski/datasets-for-start/master/adult/data.csv",
        skipinitialspace=True,
    )
    X_train, X_test, y_train, y_test = train_test_split(
        df[df.columns[:-1]], df["income"], test_size=0.25
    )

    automl = AutoML(results_path="./model",
                    mode="Compete",
                    total_time_limit=600 * 600,
                    golden_features=True,
                    features_selection=True,
                    ml_task="binary_classification")
    automl.fit(X_train, y_train)

    predictions = automl.predict(X_test)
    print(predictions)

reproduced the issue for me, because the ensemble stacked is identified as the best model. It takes a while to run ofcourse. The message I got:

Traceback (most recent call last):
  File "/Users/prezi/Code/mljar_issue/mljar_issue/main.py", line 23, in <module>
    predictions = automl.predict(X_test)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/automl.py", line 451, in predict
    return self._predict(X)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/base_automl.py", line 1503, in _predict
    predictions = self._base_predict(X)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/base_automl.py", line 1465, in _base_predict
    predictions = model.predict(X, X_stacked)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/ensemble.py", line 434, in predict
    y_predicted_from_model = model.predict(X_stacked)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/model_framework.py", line 448, in predict
    y_p = learner.predict(X_data)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/supervised/algorithms/sklearn.py", line 66, in predict
    return self.model.predict_proba(X)[:, 1]
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/sklearn/ensemble/_forest.py", line 947, in predict_proba
    X = self._validate_X_predict(X)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/sklearn/ensemble/_forest.py", line 641, in _validate_X_predict
    X = self._validate_data(
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/sklearn/base.py", line 608, in _validate_data
    self._check_feature_names(X, reset=reset)
  File "/Users/prezi/Library/Caches/pypoetry/virtualenvs/mljar-issue-kQcsGfQC-py3.10/lib/python3.10/site-packages/sklearn/base.py", line 535, in _check_feature_names
    raise ValueError(message)
ValueError: The feature names should match those that were passed during fit.
Feature names unseen at fit time:
- 100_NearestNeighbors_prediction
- 101_NearestNeighbors_prediction
- 102_Xgboost_BoostOnErrors_prediction
- 102_Xgboost_prediction
- 103_Xgboost_prediction
- ...
Feature names seen at fit time, yet now missing:
- 100_NearestNeighbors_prediction_0_for_<=50K_1_for_>50K
- 101_NearestNeighbors_prediction_0_for_<=50K_1_for_>50K
- 102_Xgboost_BoostOnErrors_prediction_0_for_<=50K_1_for_>50K
- 102_Xgboost_prediction_0_for_<=50K_1_for_>50K
- 103_Xgboost_prediction_0_for_<=50K_1_for_>50K
- ...

[tuomassiren]

I have the same issue.