ch08.ipynb Cell #4
TypeError: _generate_unsampled_indices() missing 1 required positional argument: 'n_samples_bootstrap'
1 import rfpimp
2 rfpimp.plot_dependence_heatmap(
----> 3 rfpimp.feature_dependence_matrix(X_train),
4 value_fontsize=12,
5 label_fontsize=14,
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in feature_dependence_matrix(X_train, rfmodel, zero, sort_by_dependence, n_samples)
712 rf = clone(rfmodel)
713 rf.fit(X,y)
--> 714 imp = permutation_importances_raw(rf, X, y, oob_regression_r2_score, n_samples)
715 """
716 Some importances could come back > 1.0 because removing that feature sends R^2
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in permutation_importances_raw(rf, X_train, y_train, metric, n_samples)
398 rf.fit(X_sample, y_sample)
399
--> 400 baseline = metric(rf, X_sample, y_sample)
401 X_train = X_sample.copy(deep=False) # shallow copy
402 y_train = y_sample
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in oob_regression_r2_score(rf, X_train, y_train)
453 n_predictions = np.zeros(n_samples)
454 for tree in rf.estimators_:
--> 455 unsampled_indices = _generate_unsampled_indices(tree.random_state, n_samples)
456 tree_preds = tree.predict(X[unsampled_indices, :])
457 predictions[unsampled_indices] += tree_preds
ch08.ipynb Cell #4
TypeError: _generate_unsampled_indices() missing 1 required positional argument: 'n_samples_bootstrap'
----> 3 rfpimp.feature_dependence_matrix(X_train),
4 value_fontsize=12,
5 label_fontsize=14,
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in feature_dependence_matrix(X_train, rfmodel, zero, sort_by_dependence, n_samples)
712 rf = clone(rfmodel)
713 rf.fit(X,y)
--> 714 imp = permutation_importances_raw(rf, X, y, oob_regression_r2_score, n_samples)
715 """
716 Some importances could come back > 1.0 because removing that feature sends R^2
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in permutation_importances_raw(rf, X_train, y_train, metric, n_samples)
398 rf.fit(X_sample, y_sample)
399
--> 400 baseline = metric(rf, X_sample, y_sample)
401 X_train = X_sample.copy(deep=False) # shallow copy
402 y_train = y_sample
C:\ProgramData\Anaconda3\envs\tf_SSJ_gpu\lib\site-packages\rfpimp.py in oob_regression_r2_score(rf, X_train, y_train)
453 n_predictions = np.zeros(n_samples)
454 for tree in rf.estimators_:
--> 455 unsampled_indices = _generate_unsampled_indices(tree.random_state, n_samples)
456 tree_preds = tree.predict(X[unsampled_indices, :])
457 predictions[unsampled_indices] += tree_preds