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Tanushree Tunstall 2022-03-07 18:27:07 +00:00
parent 564e72fc2d
commit b637ebc6d2
2 changed files with 11 additions and 6 deletions
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3
imports.py
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@ -74,6 +74,9 @@ geneL_ppi2 = ['alr', 'embb', 'katg']
#%% get cols #%% get cols
mycols = my_df.columns mycols = my_df.columns
my_df['active_aa_pos'].dtype
my_df['active_aa_pos'] = my_df['active_aa_pos'].astype(object)
#%%============================================================================ #%%============================================================================
# GET Y # GET Y

14
my_data9.py
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@ -59,9 +59,9 @@ f2 = preprocessor.transform(numerical_features_df)
f3 = preprocessor.fit_transform(numerical_features_df) f3 = preprocessor.fit_transform(numerical_features_df)
(f3==f2).all() (f3==f2).all()
f4 = preprocessor.fit_transform(all_features_df) preprocessor.fit_transform(numerical_features_df)
f4
reprocessor.fit_transform(numerical_features_df) #preprocessor.fit_transform(all_features_df)
#%% #%%
model_log = Pipeline(steps = [ model_log = Pipeline(steps = [
@ -108,8 +108,6 @@ pd.DataFrame(output).mean()
t1_res = MultClassPipeline2(X_trainN, X_testN, y_trainN, y_testN, input_df = all_features_df) t1_res = MultClassPipeline2(X_trainN, X_testN, y_trainN, y_testN, input_df = all_features_df)
t1_res t1_res
t2_res = MultClassPipeline2(X_train, X_test, y_train, y_test, input_df = all_features_df)
t2_res
#%% #%%
# https://machinelearningmastery.com/columntransformer-for-numerical-and-categorical-data/ # https://machinelearningmastery.com/columntransformer-for-numerical-and-categorical-data/
#Each transformer is a three-element tuple that defines the name of the transformer, the transform to apply, and the column indices to apply it to. For example: #Each transformer is a three-element tuple that defines the name of the transformer, the transform to apply, and the column indices to apply it to. For example:
@ -129,4 +127,8 @@ col_transform = ColumnTransformer(transformers=t
# create pipeline (unlike example above where the col transfer was a preprocess step and it was fit_transformed) # create pipeline (unlike example above where the col transfer was a preprocess step and it was fit_transformed)
pipeline = Pipeline(steps=[('prep', col_transform) pipeline = Pipeline(steps=[('prep', col_transform)
, ('classifier', clf)]) , ('classifier', LogisticRegression())])
#%% Added this to the MultClassPipeline
t2_res = MultClassPipeline2(X_train, X_test, y_train, y_test, input_df = all_features_df)
t2_res