# -*- coding: utf-8 -*-
"""
Created on Mon Jan 29 14:15:21 2024
@author: Asus
"""
import pandas as pd
import numpy as np
import seaborn as sns
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
import time
import matplotlib.pyplot as plt
from collections import Counter
#%% heatmap_oneFeatureSelectionCV (featSel_folds, save_name); savename should be experim + iteration_max aka CV + foldName
## savename should be experim + iteration_max aka CV + foldName
def heatmap_oneFeatureSelectionCV (featSel_folds, save_name):
# Flatten the array of arrays and get unique elements
flat_data = [item for sublist in featSel_folds for item in sublist]
unique_elements = np.unique(flat_data)
print("Entered heatmap_oneFeatureSelectionCV and this is the save name: ", save_name)
# Create a dictionary to store counts for each element
counts_dict = {element: [sublist.count(element) for sublist in featSel_folds] + [flat_data.count(element)] for element in unique_elements}
# Create a DataFrame for seaborn
df = pd.DataFrame(counts_dict, index=[f"Array {i}" for i in range(1, len(featSel_folds) + 1)] + ['Total'])
name_to_save = ' '.join(["Heatmap_", save_name, ".png"])
# Create a heatmap using seaborn
plt.figure(figsize=(10, 7))
sns.heatmap(df, cmap='viridis', annot=True, fmt="d", cbar=True)
plt.xlabel('Element Name')
plt.ylabel('Array Index')
plt.title('Heatmap of Element Counts in Arrays')
# plt.savefig("Heatmap_" + save_name + ".png")
plt.savefig(name_to_save)
plt.show()
## we don't include lasso here beauase it never changes
def original_heatmap(*args):
save_name, shap_folds, rf_folds, xgb_folds, ridge_folds, logistic_folds = args
print("Entered Original Heatmap and this si the save name: ", save_name)
# Flatten the arrays of arrays and get unique elements
flat_data_rf = [item for sublist in rf_folds for item in sublist]
flat_data_xgb = [item for sublist in xgb_folds for item in sublist]
flat_data_ridge = [item for sublist in ridge_folds for item in sublist]
flat_data_logistic = [item for sublist in logistic_folds for item in sublist]
flat_data_shap = [item for sublist in shap_folds for item in sublist]
unique_elements_rf = np.unique(flat_data_rf)
unique_elements_xgb = np.unique(flat_data_xgb)
unique_elements_ridge = np.unique(flat_data_ridge)
unique_elements_logistic = np.unique(flat_data_logistic)
unique_elements_shap = np.unique(flat_data_shap)
# Create a dictionary to store counts for each element
counts_dict_rf = {element: flat_data_rf.count(element) for element in unique_elements_rf}
counts_dict_xgb= {element: flat_data_xgb.count(element) for element in unique_elements_xgb}
counts_dict_ridge = {element: flat_data_ridge.count(element) for element in unique_elements_ridge}
counts_dict_logistic = {element: flat_data_logistic.count(element) for element in unique_elements_logistic}
counts_dict_shap = {element: flat_data_shap.count(element) for element in unique_elements_shap}
# Create a DataFrame for seaborn
df_rf = pd.DataFrame(list(counts_dict_rf.items()), columns=['Element', 'Total RF'])
df_xgb = pd.DataFrame(list(counts_dict_xgb.items()), columns=['Element', 'Total XGB'])
df_ridge = pd.DataFrame(list(counts_dict_ridge.items()), columns=['Element', 'Total Ridge'])
df_logistic = pd.DataFrame(list(counts_dict_logistic.items()), columns=['Element', 'Total Logistic'])
df_shap = pd.DataFrame(list(counts_dict_shap.items()), columns=['Element', 'Total Shap'])
# Set index for both DataFrames
df_rf.set_index('Element', inplace=True)
df_xgb.set_index('Element', inplace=True)
df_ridge.set_index('Element', inplace=True)
df_logistic.set_index('Element', inplace=True)
df_shap.set_index('Element', inplace=True)
# Combine the DataFrames
df_combined = pd.concat([df_shap, df_rf, df_xgb, df_ridge, df_logistic], axis=1)
# Convert the DataFrame values to integers
df_combined = df_combined.fillna(0)
df_combined = df_combined.astype(int)
total_totals = df_combined.sum(axis=1)
total_totals = total_totals.sort_values(ascending=False)
print("Total of included features: ", total_totals.size)
top_10_absolute = total_totals.nlargest(10)
# Print the absolute top 10 most selected features
print("Absolute Top 10 most selected features:")
print(top_10_absolute)
top10_ever = top_10_absolute.index
top10_ever_list = top10_ever.tolist()
plt.figure(figsize=(10, 18))
ax = sns.heatmap(pd.DataFrame(total_totals, columns=['Total']), cmap=plt.cm.BuPu, annot=True, fmt="d", cbar=True, annot_kws={"size": 18})
plt.xticks(fontsize=18) # X-axis ticks
plt.yticks(fontsize=18) # Y-axis ticks
cbar = ax.collections[0].colorbar
cbar.ax.tick_params(labelsize=16)
ax.set_xticklabels([]) # so the 'Total' doesn't appear right under the plot and only then 'Prevalence'. this removes the 'Total'
plt.ylabel('Feature Name', fontsize=18)
plt.xlabel('Prevalence', fontsize=18)
plt.title('Heatmap of Feature Selection Prevalence', fontsize = 20)
plt.savefig("Heatmap of features" + save_name + ".png", bbox_inches='tight', dpi = 600)
plt.show()
return top10_ever_list
# #%%
# def heatmaps_allFeatureSelectionsCV (*args):
# x = 10
# arrays, arra2 = args
# nr_models = len(auc_score)
# count = 0
# while count < nr_models:
# auc = auc_score[count]
# plt.plot(fpr[count], tpr[count], linestyle = '-', label = model[count] + ' AUROC ' + str(auc))
# count = count + 1
# plt.title("ROC AUC plot")
# plt.xlabel("False Positive Rate (FPR)")
# plt.ylabel("True Positive Rate (TPR)")
# plt.legend()
# plt.savefig("AUC ROC" + experim + "baseline_allFeats_models.png")
# plt.show()
# def plot_auprc_models (*args):
# recall, precision, auprc_score, model, experim = args
# # print("fpr", fpr)
# # print("auc", auc_score)
# # print(model)
# nr_models = len(auprc_score)
# count = 0
# while count < nr_models:
# auprc = auprc_score[count]
# plt.plot(recall[count], precision[count], linestyle = '-', label = model[count] + ' AUPRC ' + str(auprc))
# count = count + 1
# plt.title("AUPRC plot")
# plt.xlabel("Recall (Sensitivity, TPR)")
# plt.ylabel("Precision (PPV))")
# plt.legend()
# plt.savefig("AUPRC" + experim + "baseline_allFeats_models.png")
# plt.show()
# def feat_imp_xgb(model_xgb, names):
# xgb_feat_imp = model_xgb.feature_importances_
# # print(xgb_feat_imp)
# res_xgb = {}
# for i in range(0,len(xgb_feat_imp)):
# res_xgb[names[i]] = xgb_feat_imp[i]
# print(" ----------------------------------------------------------------------- ")
# #print(" All features with their XGBoost Importance")
# sorted_res_xgb = dict(sorted(res_xgb.items(), key=lambda item: item[1], reverse = True))
# #print(sorted_res_xgb)
# # print(" ----------------------------------------------------------------------- ")
# # print(" Selected XGBoost features based on importance")
# selected_xgb = {}
# count_xgb = 1
# for key, value in sorted_res_xgb.items():
# if value >= 0.01 and count_xgb <= 10:
# selected_xgb[key] = value
# count_xgb = count_xgb + 1
# # print(selected_xgb)
# # print(selected_xgb.keys())
# keys = [k for k, v in selected_xgb.items()]
# # print(keys)
# # print(len(keys))
# return keys
# def feat_imp_ridge(model_ridge, names):
# coefficients = model_ridge.coef_[0]
# feature_importance_ridge = pd.DataFrame({'Feature': names, 'Importance': np.abs(coefficients)})
# feature_importance_ridge = feature_importance_ridge.sort_values('Importance', ascending=False)
# #feature_importance_ridge.plot(x='Feature', y='Importance', kind='barh', figsize=(10, 6))
# feature_importance_ridge_arr = feature_importance_ridge.query('Importance > 0.1')['Feature'].values
# #print(feature_importance_ridge_arr)
# #print(len(feature_importance_ridge_arr))
# print(feature_importance_ridge_arr[0:10])
# #print(len(feature_importance_ridge_arr[0:10]))
# keys = feature_importance_ridge_arr[0:10].tolist()
# return keys
# def feat_imp_logistic(model_logistic, names):
# coefficients = model_logistic.coef_[0]
# feature_importance_logistic = pd.DataFrame({'Feature': names, 'Importance': np.abs(coefficients)})
# feature_importance_logistic = feature_importance_logistic.sort_values('Importance', ascending=False)
# #feature_importance_logistic.plot(x='Feature', y='Importance', kind='barh', figsize=(10, 6))
# #print(feature_importance_logistic)
# feature_importance_logistic_arr = feature_importance_logistic.query('Importance > 0.1')['Feature'].values
# #print(feature_importance_logistic_arr)
# #print(len(feature_importance_logistic_arr))
# keys = feature_importance_logistic_arr[0:10].tolist()
# print(keys)
# #print(len(feature_importance_logistic_arr[0:10]))
# return keys
# def feat_imp_shap(model, names, kind, subset): # it is just model because it may change every time
# #make the explainer type based on a persed string. like TreeExplainer, LinearExplainer
# #subset means that we can use this to explain what was going on in the training or in the test
# # but according to practice, it is more useful to see what it does to test data.
# if kind == 'rf' or kind == 'random forest' or kind == 'svm':
# explainer = shap.KernelExplainer(model.predict, subset)
# shap_values = explainer.shap_values(subset, check_additivity=False)
# print(shap_values)
# # Get top 10 features based on SHAP values
# vals = np.abs(shap_values).mean(axis=0)
# top_10_features_indices = np.argsort(vals)[::-1][:10]
# top_10_features = names[top_10_features_indices]
# return top_10_features.tolist()
# elif kind == 'xgb':
# explainer = shap.TreeExplainer(model, subset)
# elif kind == 'linear':
# explainer = shap.LinearExplainer(model, subset)
# # Calculate SHAP values for the training set
# shap_values = explainer.shap_values(subset)
# # Get top 10 features based on SHAP values
# vals = np.abs(shap_values).mean(axis=0)
# top_10_features_indices = np.argsort(vals)[::-1][:10]
# top_10_features = names[top_10_features_indices]
# # Create a DataFrame with SHAP values and top 10 features
# shap_df = pd.DataFrame(shap_values, columns=names)
# #shap_df['target'] = y_train # Assuming 'target' is your target variable
# shap_df['abs_shap_values_mean'] = np.abs(shap_values).mean(axis=1)
# # Add top 10 features to the DataFrame
# #shap_df_top_10 = shap_df[['target', 'abs_shap_values_mean'] + top_10_features.tolist()]
# shap_df_top_10 = shap_df[['abs_shap_values_mean'] + top_10_features.tolist()]
# # Display the DataFrame with top 10 features
# print(shap_df_top_10.head())
# return top_10_features.tolist()
# #%% How to combine the feat imp into a pd df
Datasets
Models
