Sourcery refactored main branch #1

sourcery-ai · 2022-04-29T08:57:03Z

Branch main refactored by Sourcery.

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sourcery-ai · 2022-04-29T08:57:04Z

packages/features_importance.py

-def plot_explanations(W, S_list, nb_subgroups, c, att_names, patt_descriptions) : 
+def plot_explanations(W, S_list, nb_subgroups, c, att_names, patt_descriptions): 

-    for j in range(0, nb_subgroups) :
+    for j in range(nb_subgroups):

        print(j,'------------------------------------------------')
-        print(patt_descriptions[S_list[j]]) 
+        print(patt_descriptions[S_list[j]])
        coefficients = W[S_list[j]].coef_
        logic = coefficients > 0
        coefficients_abs = np.abs(coefficients)
        contributions = coefficients_abs / np.sum(coefficients_abs, axis = 1).reshape(-1,1)
        features_importance = contributions[c] * 100
        limit = 0.75
-        
+
        f_importance = features_importance[features_importance > limit]
        f_importance = f_importance / np.sum(f_importance) * 100
        f_importance = f_importance.round(2)
        att_names_ = list(pd.Series(att_names[:362])[features_importance > limit])

-        
+
        f_importance_1 = f_importance[logic[c][features_importance > limit]]
        att_names_1 = [x for i,x in enumerate (att_names_) if logic[c][features_importance > limit][i]]
-        
+
        f_importance_2 = f_importance[~logic[c][features_importance > limit]]
        att_names_2 = [x for i,x in enumerate (att_names_) if not logic[c][features_importance > limit][i]]
-        
+
        plt.style.use('fivethirtyeight')
        plt.figure(figsize =(3, 4))
        plt.barh(att_names_2, f_importance_2,color='#e74c3c',height=0.65)
-        plt.barh(att_names_1, f_importance_1,color='#1abc9c',height=0.65)        
+        plt.barh(att_names_1, f_importance_1,color='#1abc9c',height=0.65)
        all_f_importance = np.concatenate((f_importance_2,f_importance_1))
-        for i, v in enumerate(all_f_importance) :
-            plt.text(v + 0.4, i, str(v)+'%', fontsize = 9)
-        
+        for i, v in enumerate(all_f_importance):
+            plt.text(v + 0.4, i, f'{str(v)}%', fontsize = 9)
+
        plt.xlabel("Features Importance",fontsize=10)
        plt.xticks(fontsize=10)
        plt.yticks(fontsize=10)
        plt.grid(True, linestyle='--', which='major',color='grey', alpha=0.75)
-        plt.savefig('FIGURES/f_'+str(j))
+        plt.savefig(f'FIGURES/f_{str(j)}')


Function plot_explanations refactored with the following changes:

Replace range(0, x) with range(x) (remove-zero-from-range)

Use f-string instead of string concatenation (use-fstring-for-concatenation)

sourcery-ai · 2022-04-29T08:57:05Z

packages/features_importance.py

-def sort_subgroups_support(S,K) :
-	S_copy = S.copy()
-	l_best_s = []
-	for i in range(0,K) :
-		inter = 0
-		s_best = None 
-
-		for s in S_copy :
-			if len(s) > inter :
-				inter = len(s)
-				s_best = s
-		l_best_s.append(s_best)
-		S_copy.remove(s_best)
-	
-	return l_best_s
+def sort_subgroups_support(S,K):
+    S_copy = S.copy()
+    l_best_s = []
+    for _ in range(K):
+        inter = 0
+        s_best = None 
+
+        for s in S_copy :
+        	if len(s) > inter :
+        		inter = len(s)
+        		s_best = s
+        l_best_s.append(s_best)
+        S_copy.remove(s_best)
+
+    return l_best_s


Function sort_subgroups_support refactored with the following changes:

Replace unused for index with underscore (for-index-underscore)

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:05Z

packages/neighbors_generation.py

-def generate_all_neighbors (data, data_compressed, n_neigh, numerical_cols, numerical_cols_compressed, categ_unique, categ_unique_compressed,n_var, model) :
+def generate_all_neighbors(data, data_compressed, n_neigh, numerical_cols, numerical_cols_compressed, categ_unique, categ_unique_compressed,n_var, model):

    list_neighs = []
    num_size = numerical_cols.size
    num_size_compressed = numerical_cols_compressed.size
-    
-    n = np.size(data, 0) 
+
+    n = np.size(data, 0)
    covn = cal_covn(data, num_size, n_var)
    covn_compressed = cal_covn(data_compressed, num_size_compressed, n_var)
-    
+
    base = np.zeros(data.shape[1])
    neighbors_base = np.random.multivariate_normal(base, covn, n_neigh)
-    
+
    base_compressed = np.zeros(data_compressed.shape[1])
    neighbors_base_compressed = np.random.multivariate_normal(base_compressed, covn_compressed, n_neigh)
-    
-    for i in range(0,n) :
+
+    for i in range(n):
        neighbors = neighbors_base + data[i]
        neighbors_compressed = neighbors_base_compressed + data_compressed[i]
-        
+


Function generate_all_neighbors refactored with the following changes:

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:05Z

packages/patterns_extraction.py

-def patterns (P, split_point1, split_point2, data, att_names_) :
+def patterns(P, split_point1, split_point2, data, att_names_):


-    patt_dict = dict()
-    rank = 0
-    for s,p in P.items() :
+    patt_dict = {}
+    for rank, (s, p) in enumerate(P.items()):

-        description = ''
-        it = 0
-        d = dict ()
-        while (it < len(p)) :
+        d = {}
+        for it in range(0, len(p), 3):


Function patterns refactored with the following changes:

Replace dict() with {} (dict-literal)

Replace manual loop counter with call to enumerate (convert-to-enumerate)

Move assignment closer to its usage within a block (move-assign-in-block)

Replace while with for (while-to-for)

Merge duplicate blocks in conditional (merge-duplicate-blocks)

Use f-string instead of string concatenation (use-fstring-for-concatenation)

Remove redundant conditional (remove-redundant-if)

sourcery-ai · 2022-04-29T08:57:05Z

packages/performances.py

-def loss_global_wb (data_test,list_neigh,model, limit) :
+def loss_global_wb(data_test,list_neigh,model, limit):

    n = np.size(data_test,0)
    data_neigh_O, target_neigh_O_proba = sampling_sb(data_test,np.arange(n),list_neigh,model)
-    global_loss = calc_loss(data_neigh_O, target_neigh_O_proba, limit)
-    return global_loss
+    return calc_loss(data_neigh_O, target_neigh_O_proba, limit)


Function loss_global_wb refactored with the following changes:

Inline variable that is immediately returned (inline-immediately-returned-variable)

sourcery-ai · 2022-04-29T08:57:05Z

packages/performances.py

-def loss_local_models (n,list_neigh,model, limit) :
+def loss_local_models(n,list_neigh,model, limit):

    loss = 0
-    for i in range(0,n) :
+    for i in range(n):
        data_neigh_i= list_neigh[i][0]
        target_neigh_i_proba = list_neigh[i][1]
-        loss += calc_loss(data_neigh_i, target_neigh_i_proba, limit)    
+        loss += calc_loss(data_neigh_i, target_neigh_i_proba, limit)


Function loss_local_models refactored with the following changes:

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:05Z

packages/performances.py

-def fscore_sd (S,data_test,list_neigh,model,nb_classes) :
+def fscore_sd(S,data_test,list_neigh,model,nb_classes):

-    iteration = 0 
-    for s in S :
+    for iteration, s in enumerate(S):


Function fscore_sd refactored with the following changes:

Replace manual loop counter with call to enumerate (convert-to-enumerate)

sourcery-ai · 2022-04-29T08:57:05Z

packages/performances.py

-def fscore_local_models (data_test,n,list_neigh,model,nb_classes) :
+def fscore_local_models(data_test,n,list_neigh,model,nb_classes):


-    iteration = 0 
-    for i in range(0,n) :
+    for iteration, i in enumerate(range(n)):


Function fscore_local_models refactored with the following changes:

Replace manual loop counter with call to enumerate (convert-to-enumerate)

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:06Z

packages/performances.py

-def similarity (W,nb_classes) :
+def similarity(W,nb_classes):


Function similarity refactored with the following changes:

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:06Z

packages/performances.py

-def avg_non_similar (dist,treshold) :
+def avg_non_similar(dist,treshold):

-    nb_non_sim = 0 
+    nb_non_sim = 0
    nb_sbgrps = np.size(dist,0)
-    for i in range (0, nb_sbgrps) :
+    for i in range(nb_sbgrps):


Function avg_non_similar refactored with the following changes:

Replace range(0, x) with range(x) (remove-zero-from-range)

sourcery-ai · 2022-04-29T08:57:10Z

Sourcery Code Quality Report

✅ Merging this PR will increase code quality in the affected files by 1.89%.

Quality metrics	Before	After	Change
Complexity	8.63 🙂	7.37 ⭐	-1.26 👍
Method Length	98.90 🙂	97.05 🙂	-1.85 👍
Working memory	11.03 😞	10.73 😞	-0.30 👍
Quality	53.83% 🙂	55.72% 🙂	1.89% 👍

Other metrics	Before	After	Change
Lines	336	331	-5

Changed files	Quality Before	Quality After	Quality Change
packages/features_importance.py	55.51% 🙂	55.53% 🙂	0.02% 👍
packages/neighbors_generation.py	38.31% 😞	38.33% 😞	0.02% 👍
packages/patterns_extraction.py	22.03% ⛔	27.19% 😞	5.16% 👍
packages/performances.py	68.80% 🙂	69.22% 🙂	0.42% 👍

Here are some functions in these files that still need a tune-up:

File	Function	Complexity	Length	Working Memory	Quality	Recommendation
packages/patterns_extraction.py	patterns	22 😞	270 ⛔	15 😞	27.19% 😞	Refactor to reduce nesting. Try splitting into smaller methods. Extract out complex expressions
packages/neighbors_generation.py	generate_all_neighbors	13 🙂	350 ⛔	16 ⛔	30.51% 😞	Try splitting into smaller methods. Extract out complex expressions
packages/features_importance.py	plot_explanations	3 ⭐	283 ⛔	13 😞	45.62% 😞	Try splitting into smaller methods. Extract out complex expressions
packages/performances.py	similarity	12 🙂	150 😞	10 😞	51.41% 🙂	Try splitting into smaller methods. Extract out complex expressions
packages/performances.py	fscore_local_models	4 ⭐	160 😞	10 😞	57.91% 🙂	Try splitting into smaller methods. Extract out complex expressions

Legend and Explanation

The emojis denote the absolute quality of the code:

⭐ excellent
🙂 good
😞 poor
⛔ very poor

The 👍 and 👎 indicate whether the quality has improved or gotten worse with this pull request.

Please see our documentation here for details on how these metrics are calculated.

We are actively working on this report - lots more documentation and extra metrics to come!

Help us improve this quality report!

'Refactored by Sourcery'

c74f9a2

sourcery-ai bot requested a review from RemilYoucef April 29, 2022 08:57

sourcery-ai bot commented Apr 29, 2022

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Sourcery refactored main branch #1

Sourcery refactored main branch #1

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sourcery-ai bot commented Apr 29, 2022

		def similarity (W,nb_classes) :
		def similarity(W,nb_classes):

Sourcery refactored main branch #1

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Sourcery refactored main branch #1

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sourcery-ai bot commented Apr 29, 2022

Sourcery Code Quality Report

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