Merge d3e0562 into 3594e05

DeepRank · Feb 3, 2023 · e33f41d · e33f41d
2 parents 3594e05 + d3e0562
commit e33f41d
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Showing 4 changed files with 14 additions and 14 deletions.
diff --git a/deeprank/features/FullPSSM.py b/deeprank/features/FullPSSM.py
@@ -120,7 +120,7 @@ def read_PSSM_data(self):
             self.pssm_res_id = np.array(raw_data)[:, :3]
             self.pssm_res_id = [(r[0], int(r[1]), r[2])
                                 for r in self.pssm_res_id]
-            self.pssm_data = np.array(raw_data)[:, 3:].astype(np.float)
+            self.pssm_data = np.array(raw_data)[:, 3:].astype(np.float32)
 
         # new format with ≥2 files (each chain has one file)
         # and aligned mapping and IC (i.e. the iScore format)
@@ -148,9 +148,9 @@ def read_PSSM_data(self):
                 rd = np.array(raw_data)[1:, :2]
                 rd = [(chainID, int(r[0]), resmap[r[1]]) for r in rd]
                 if self.out_type == 'pssmvalue':
-                    pd = np.array(raw_data)[1:, 4:-1].astype(np.float)
+                    pd = np.array(raw_data)[1:, 4:-1].astype(np.float32)
                 else:
-                    pd = np.array(raw_data)[1:, -1].astype(np.float)
+                    pd = np.array(raw_data)[1:, -1].astype(np.float32)
                     pd = pd.reshape(pd.shape[0], -1)
 
                 if iiter == 0:

diff --git a/deeprank/learn/rankingMetrics.py b/deeprank/learn/rankingMetrics.py
@@ -47,7 +47,7 @@ def success(rs):
     """
     success = np.cumsum(rs) > 0
 
-    return success.astype(np.int)
+    return success.astype(np.int32)
 
 
 def avprec(rs):

diff --git a/deeprank/tools/sasa.py b/deeprank/tools/sasa.py
@@ -58,8 +58,8 @@ def get_residue_center(self, chain1='A', chain2='B'):
         resA = np.array(sql.get('resSeq,resName', chainID=chain1))
         resB = np.array(sql.get('resSeq,resName', chainID=chain2))
 
-        resSeqA = np.unique(resA[:, 0].astype(np.int))
-        resSeqB = np.unique(resB[:, 0].astype(np.int))
+        resSeqA = np.unique(resA[:, 0].astype(np.int32))
+        resSeqB = np.unique(resB[:, 0].astype(np.int32))
 
         self.xyz = {}
 
@@ -106,14 +106,14 @@ def get_residue_carbon_beta(self, chain1='A', chain2='B'):
                 chainID=chain2))
         sql._close()
 
-        assert len(resA[:, 0].astype(np.int).tolist()) == len(
-            np.unique(resA[:, 0].astype(np.int)).tolist())
-        assert len(resB[:, 0].astype(np.int).tolist()) == len(
-            np.unique(resB[:, 0].astype(np.int)).tolist())
+        assert len(resA[:, 0].astype(np.int32).tolist()) == len(
+            np.unique(resA[:, 0].astype(np.int32)).tolist())
+        assert len(resB[:, 0].astype(np.int32).tolist()) == len(
+            np.unique(resB[:, 0].astype(np.int32)).tolist())
 
         self.xyz = {}
-        self.xyz[chain1] = resA[:, 2:].astype(np.float)
-        self.xyz[chain2] = resB[:, 2:].astype(np.float)
+        self.xyz[chain1] = resA[:, 2:].astype(np.float32)
+        self.xyz[chain2] = resB[:, 2:].astype(np.float32)
 
         self.resinfo = {}
         self.resinfo[chain1] = resA[:, :2]

diff --git a/deeprank/utils/cal_hitrate_successrate.py b/deeprank/utils/cal_hitrate_successrate.py
@@ -103,9 +103,9 @@ def evaluate(data):
             for caseID, df_one_case in df_grped:
                 df_sorted = df_one_case.sort_values(by=M, ascending=True)
                 hitrate.extend(rankingMetrics.hitrate(
-                    df_sorted['target'].astype(np.int)))
+                    df_sorted['target'].astype(np.int32)))
                 success.extend(rankingMetrics.success(
-                    df_sorted['target'].astype(np.int)))
+                    df_sorted['target'].astype(np.int32)))
                 caseIDs.extend([caseID] * len(df_one_case))
 
             # success =[0, 0, 1, 1, 1,...]: starting from rank 3 this case is a success