hi Shojiro,
Thanks for your response but print
(np.argsort(rfregress.feature_importances_)[::-1]) returns the row indices
but what I want is the column names so it can give me information which
features are important.
On Mon, Aug 20, 2018 at 9:31 PM Shojiro Shibayama <notify.p...@gmail.com>
wrote:
> Dear Ali,
>
> Please run first the following code, which may help you:
>
> ```python
> import numpy as np
> np.argsort(rfregress.feature_importances_)[::-1]
> ```
>
> The `argsort` will return the indexes of the important features in
> ascending order and [::-1] reverses the order.
> The indexes for feature importance must correspond to the order of
> variables (or the order in 'allDescp' of your code), so use these
> variables, you'll get the information that you want.
>
> Sincerely yours,
> Shojiro
>
>
> On Tue, 21 Aug 2018 at 10:34, Ali Eftekhari <a.b.eftekh...@gmail.com>
> wrote:
>
>> Hello rdkit,
>>
>> This might be trivial but I am beginner and don't know how to do it.
>>
>> I am building a simple model to predict target property. I have pandas
>> dataframe (df) whose columns are 'SMILES' and 'Target'.
>>
>> #calculating the descriptors as below:
>> llDescp=[name[0] for name in Descriptors._descList]
>> calc=MoleculeDescriptors.MolecularDescriptorCalculator(allDescp)
>> df ['fp']=df['SMILES'].apply(lambda x:
>> calc.CalcDescriptors(Chem.MolFromSmiles(x)))
>>
>> #converting the fingerprint to numpy array
>> y=df['Target'].values
>> X=np.array(list(df['fp']))
>>
>> #preprocessing
>> X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.25,
>> random_state=42)
>> st=StandardScaler()
>> X=st.fit_transform(X)
>>
>> #random forest model
>> model=RandomForestRegressor(n_estimators=10)
>> model.fit(X_train, y_train)
>>
>> My problem is that I don't know how to get the meaningful
>> feature_importance. The following will return the values of descriptors
>> but there is no labels and so I don't know how to figure out which features
>> are important.
>>
>> print (sorted (rfregress.feature_importances_))
>>
>> Thanks for your help!
>>
>>
>>
>>
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>
>
> --
> ----
> The University of Tokyo
> 2nd year Ph.D. candidate
> Shojiro Shibayama
> ----
>
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