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View Code? Open in Web Editor NEWFast Differentiable Forest lib with the advantages of both decision trees and neural networks
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niuwan1 frankfan007 timedcy jingmouren binbinmeng carinalyn123 atangfan abdalimran eladmw khuongnd brunocavagnaro xrosliang rfarrey yinjianxiang fusion-research imdmaheshwari 5l1v3r1 rhecoparadhika superpowerantquantumforest's Issues
Won't work on predict statement
AttributeError Traceback (most recent call last)
in
----> 1 learner.predict(ze.values)
/kaggle/input/quantumforest1/python-package/quantum_forest/QForest_Net.py in predict(self, X_, pred_leaf, pred_contrib, raw_score, num_iteration, flag)
426 The predicted values.
427 """
--> 428 self.profile.Snapshot("PRED_0")
429 # print("====== LiteMORT_predict X_={} ......".format(X_.shape))
430 Y_ = self.predict_raw(X_,pred_leaf, pred_contrib,raw_score,flag=flag)
AttributeError: 'QuantumForest' object has no attribute 'profile'
Create pip package
Looks like an interesting project, how about packaging this up for easy install via pip?
or failing that some installation instructions on the README
ValueError: INVALID self.config.attention_alg = eca_reponse
I was trying to run QuantumForest over my own dataset. I defined my data using the following code:
data = quantum_forest.TabularDataset(dataset = "MYDATA",
X_train = X_train,
y_train = y_train,
X_valid = X_val,
y_valid = y_val,
X_test = X_test,
y_test = y_test,
random_state=1337,
quantile_transform=True,
quantile_noise=1e-3)
Everything went fine without any error. But when I tried to run the following code it generates the given error.
learner = quantum_forest.QuantumForest(config,
data,
feat_info=None,
visual=visual)
Here is the error log:
ValueError Traceback (most recent call last)
<ipython-input-22-2ac6f6f82a68> in <module>()
1 learner = quantum_forest.QuantumForest(config,
----> 2 data)
3 frames
/content/QuantumForest/python-package/quantum_forest/DifferentiableTree.py in __init__(self, in_features, num_trees, config, flatten_output, feat_info, initialize_response_, initialize_selection_logits_, threshold_init_beta, threshold_init_cutoff)
234 print("!!! Empty attention_alg.Please try \"--attention=eca_response\" !!!\n")
235 else:
--> 236 raise ValueError( f'INVALID self.config.attention_alg = {self.config.attention_alg}' )
237
238
ValueError: INVALID self.config.attention_alg = eca_reponse
benchmark is accuracy or loss?
It's strange that the best accuracy is smallest number.
something wrong?
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