--- a
+++ b/aggmap/aggmodel/explain_dev.py
@@ -0,0 +1,200 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Feb  2 14:54:38 2021
+
+@author: wanxiang.shen@u.nus.edu
+"""
+
+
+import numpy as np
+import pandas as pd
+
+from tqdm import tqdm
+from copy import copy
+
+from aggmap.utils.matrixopt import conv2
+from sklearn.metrics import mean_squared_error, log_loss
+from sklearn.preprocessing import StandardScaler
+
+
+
+def islice(lst, n):
+    return [lst[i:i + n] for i in range(0, len(lst), n)]
+
+
+def GlobalIMP(clf, mp, X, Y, task_type = 'classification', 
+              binary_task = False,
+              sigmoidy = False, 
+              apply_logrithm = False,
+              apply_smoothing = False, 
+              kernel_size = 5, 
+              sigma = 1.6):
+    '''
+    Forward prop. Feature importance
+    
+    apply_scale_smothing: alpplying a smothing on the map
+    
+    '''
+
+    if task_type == 'classification':
+        f = log_loss
+    else:
+        f = mean_squared_error
+        
+    def sigmoid(x):
+        return 1 / (1 + np.exp(-x))
+
+    scaler = StandardScaler()
+    df_grid = mp.df_grid_reshape
+    backgroud = mp.transform_mpX_to_df(clf.X_).min().values #min value in the training set
+
+    dfY = pd.DataFrame(Y)
+    Y_true = Y
+    Y_prob = clf._model.predict(X)
+    N, W, H, C = X.shape
+    T = len(df_grid)
+    nX = 20 # 10 arrX to predict
+
+    if (sigmoidy) & (task_type == 'classification'):
+        Y_prob = sigmoid(Y_prob)
+    
+
+    final_res = {}
+    for k, col in enumerate(dfY.columns):
+        if (task_type == 'classification') & (binary_task):
+            if k == 0:
+                continue
+        print('calculating feature importance for column %s ...' % col)
+        results = []
+        loss = f(Y_true[:, k].tolist(), Y_prob[:, k].tolist())
+        
+        tmp_X = []
+        flag = 0
+        for i in tqdm(range(T), ascii= True):
+            ts = df_grid.iloc[i]
+            y = ts.y
+            x = ts.x
+            
+            ## step 1: make permutaions
+            X1 = np.array(X)
+            #x_min = X[:, y, x,:].min()
+            vmin = backgroud[i]
+            X1[:, y, x,:] = np.full(X1[:, y, x,:].shape, fill_value = vmin)
+            tmp_X.append(X1)
+
+            if (flag == nX) | (i == T-1):
+                X2p = np.concatenate(tmp_X)
+                ## step 2: make predictions
+                Y_pred_prob = clf._model.predict(X2p) #predict ont by one is not efficiency
+                if (sigmoidy) & (task_type == 'classification'):
+                    Y_pred_prob = sigmoid(Y_pred_prob)    
+
+                ## step 3: calculate changes
+                for Y_pred in islice(Y_pred_prob, N):
+                    mut_loss = f(Y_true[:, k].tolist(), Y_pred[:, k].tolist()) 
+                    res =  mut_loss - loss # if res > 0, important, othervise, not important
+                    results.append(res)
+
+                flag = 0
+                tmp_X = []
+            flag += 1
+
+        ## step 4:apply scaling or smothing 
+        s = pd.DataFrame(results).values
+        if apply_logrithm:
+            s = np.log(s)
+        smin = np.nanmin(s[s != -np.inf])
+        smax = np.nanmax(s[s != np.inf])
+        s = np.nan_to_num(s, nan=smin, posinf=smax, neginf=smin) #fillna with smin
+        a = scaler.fit_transform(s)
+        a = a.reshape(*mp._S.fmap_shape)
+        if apply_smoothing:
+            covda = conv2(a, kernel_size=kernel_size, sigma=sigma)
+            results = covda.reshape(-1,).tolist()
+        else:
+            results = a.reshape(-1,).tolist()
+
+        final_res.update({col:results})
+        
+    df = pd.DataFrame(final_res)
+    df.columns = df.columns.astype(str)
+    df.columns = 'col_' + df.columns + '_importance'
+    df = df_grid.join(df)
+    return df
+
+
+
+def LocalIMP(clf, mp, X, Y, 
+             task_type = 'classification', 
+             binary_task = False,
+             sigmoidy = False,  
+             apply_logrithm = False, 
+             apply_smoothing = False,
+             kernel_size = 3, sigma = 1.2):
+    '''
+    Forward prop. Feature importance
+    '''
+    
+    assert len(X) == 1, 'each for only one image!'
+    
+    if task_type == 'classification':
+        f = log_loss
+    else:
+        f = mean_squared_error
+        
+    def sigmoid(x):
+        return 1 / (1 + np.exp(-x))
+    
+    scaler = StandardScaler()
+    df_grid = mp.df_grid_reshape
+    backgroud = mp.transform_mpX_to_df(clf.X_).min().values #min value in the training set
+    
+    Y_true = Y
+    Y_prob = clf._model.predict(X)
+    N, W, H, C = X.shape
+
+    if (sigmoidy) & (task_type == 'classification'):
+        Y_prob = sigmoid(Y_prob)
+
+    results = []
+    loss = f(Y_true.ravel().tolist(),  Y_prob.ravel().tolist())
+    
+    all_X1 = []
+    for i in tqdm(range(len(df_grid)), ascii= True):
+        ts = df_grid.iloc[i]
+        y = ts.y
+        x = ts.x
+        X1 = np.array(X)
+        vmin = backgroud[i]
+        X1[:, y, x,:] = np.full(X1[:, y, x,:].shape, fill_value = vmin)
+        all_X1.append(X1)
+        
+    all_X = np.concatenate(all_X1)
+    all_Y_pred_prob = clf._model.predict(all_X)
+
+    for Y_pred_prob in all_Y_pred_prob:
+        if (sigmoidy) & (task_type == 'classification'):
+            Y_pred_prob = sigmoid(Y_pred_prob)
+        mut_loss = f(Y_true.ravel().tolist(), Y_pred_prob.ravel().tolist()) 
+        res =  mut_loss - loss # if res > 0, important, othervise, not important
+        results.append(res)
+
+    ## apply smothing and scalings
+    s = pd.DataFrame(results).values
+    if apply_logrithm:
+        s = np.log(s)
+    smin = np.nanmin(s[s != -np.inf])
+    smax = np.nanmax(s[s != np.inf])
+    s = np.nan_to_num(s, nan=smin, posinf=smax, neginf=smin) #fillna with smin
+    a = scaler.fit_transform(s)
+    a = a.reshape(*mp._S.fmap_shape)
+    if apply_smoothing:
+        covda = conv2(a, kernel_size=kernel_size, sigma=sigma)
+        results = covda.reshape(-1,).tolist()
+    else:
+        results = a.reshape(-1,).tolist()
+
+    df = pd.DataFrame(results, columns = ['imp'])
+    #df.columns = df.columns + '_importance'
+    df = df_grid.join(df)
+    return df
\ No newline at end of file