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Tanushree Tunstall 2022-03-16 10:11:13 +00:00
parent a1631ea54b
commit e28a296d98
8 changed files with 153 additions and 212 deletions
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64
loopity_loop_CALL.py
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@ -7,55 +7,32 @@ Created on Fri Mar 11 11:15:50 2022
"""
#%%
del(t3_res)
t3_res = MultClassPipeSKF(input_df = numerical_features_df
, y_targetF = target1
# t3_res = MultClassPipeSKF(input_df = numerical_features_df
# , y_targetF = target1
# , var_type = 'numerical'
# , skf_splits = 10)
# pp.pprint(t3_res)
# #print(t3_res)
t3_res = MultClassPipeSKF(input_df = num_df_wtgt[numerical_FN]
, y_targetF = num_df_wtgt['mutation_class']
, var_type = 'numerical'
, skf_splits = 10)
pp.pprint(t3_res)
#print(t3_res)
#%% Manually: mean for each model, each metric
model_name = 'Logistic Regression'
model_name = 'Naive Bayes'
model_name = 'K-Nearest Neighbors'
model_name = 'SVM'
#%%
model_metric = 'F1_score'
log_reg_f1 = []
for key in t3_res[model_name]:
log_reg_f1.append(t3_res[model_name][key][model_metric])
log_reg_f1M = mean(log_reg_f1)
print('key:', key, model_metric, ':', log_reg_f1)
print(log_reg_f1M)
log_reg_f1df = pd.DataFrame({model_name: [log_reg_f1M]}, index = [model_metric])
log_reg_f1df
#%%
model_metric = 'MCC'
log_reg_mcc = []
for key in t3_res[model_name]:
log_reg_mcc.append(t3_res[model_name][key][model_metric])
log_reg_mccM = mean(log_reg_mcc)
print('key:', key, model_metric, ':', log_reg_mcc)
print(log_reg_mccM)
log_reg_mccdf = pd.DataFrame({model_name: [log_reg_mccM]}, index = [model_metric])
log_reg_mccdf
#%%
################################################################
# extract items from wwithin a nested dict
#%% Classification Metrics we need to mean()
classification_metrics = {
'F1_score': []
,'MCC': []
,'Precision': []
,'Recall': []
,'Accuracy': []
}
# classification_metrics = {
# 'F1_score': []
# ,'MCC': []
# ,'Precision': []
# ,'Recall': []
# ,'Accuracy': []
# ,'ROC_AUC':[]
# }
# "mean() of the current metric across all folds for this model"
# the output containing all the metrics across all folds for this model
out={}
# Just the mean() for each of the above metrics-per-model
@ -64,16 +41,16 @@ out_means={}
# Build up out{} from t3_res, which came from loopity_loop
for model in t3_res:
# NOTE: can't copy objects in Python!!!
out[model]={'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}
out[model]={'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}
out_means[model]={} # just to make life easier
print(model)
for fold in t3_res[model]:
for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}:
for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}:
metric_value = t3_res[model][fold][metric]
out[model][metric].append(metric_value)
# now that we've built out{}, let's mean() each metric
for model in out:
for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': []}:
for metric in {'F1_score': [], 'MCC': [], 'Precision': [], 'Recall': [], 'Accuracy': [], 'ROC_AUC':[]}:
metric_mean = mean(out[model][metric])
# just some debug output
# print('model:', model
@ -84,3 +61,4 @@ for model in out:
out_means[model].update({(metric+'_mean'): metric_mean })
out_scores = pd.DataFrame(out_means)
out_scores2 = round(out_scores, 2)