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added and ran hyperparam script for all different classifiers, but couldn't successfully run the feature selection and hyperparam together

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Tanushree Tunstall 2022-05-20 08:09:24 +01:00
parent 74af5ef890
commit 37bda41f44
18 changed files with 131 additions and 142 deletions
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11
uq_ml_models/UQ_DT.py
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@ -32,10 +32,9 @@ class ClfSwitcher(BaseEstimator):
parameters = [
{
'clf__estimator': [DecisionTreeClassifier(**rs
, **njobs)]
'clf__estimator': [DecisionTreeClassifier(**rs)]
, 'clf__estimator__max_depth': [None, 2, 4, 6, 8, 10, 12, 16, 20]
, 'clf__estimator__class_weight':['balanced','balanced_subsample']
, 'clf__estimator__class_weight':['balanced']
, 'clf__estimator__criterion': ['gini', 'entropy', 'log_loss']
, 'clf__estimator__max_features': [None, 'sqrt', 'log2']
, 'clf__estimator__min_samples_leaf': [1, 2, 3, 4, 5, 10]
@ -106,17 +105,15 @@ dt_bts_dict['bts_jaccard'] = round(jaccard_score(y_bts, test_predict),2)
dt_bts_dict
# Create a df from dict with all scores
pd.DataFrame.from_dict(dt_bts_dict, orient = 'index', columns = 'best_model')
dt_bts_df = pd.DataFrame.from_dict(dt_bts_dict,orient = 'index')
dt_bts_df.columns = ['Logistic_Regression']
dt_bts_df.columns = ['DT']
print(dt_bts_df)
# Create df with best model params
model_params = pd.Series(['best_model_params', list(gscv_dt_fit_be_mod.items() )])
model_params_df = model_params.to_frame()
model_params_df
model_params_df.columns = ['Logistic_Regression']
model_params_df.columns = ['DT']
model_params_df.columns
# Combine the df of scores and the best model params