[tests][python] Handle data types more accurate in C API test (#4297)

tests/c_api_test/test_.py

@@ -52,10 +52,6 @@ dtype_int32 = 2
 dtype_int64 = 3
 
 
-def c_array(ctype, values):
-    return (ctype * len(values))(*values)
-
-
 def c_str(string):
     return ctypes.c_char_p(string.encode('utf-8'))
 
@@ -71,9 +67,9 @@ def load_from_file(filename, reference):
         ref,
         ctypes.byref(handle))
     print(LIB.LGBM_GetLastError())
-    num_data = ctypes.c_long()
+    num_data = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data))
-    num_feature = ctypes.c_long()
+    num_feature = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
     print(f'#data: {num_data.value} #feature: {num_feature.value}')
     return handle
@@ -91,7 +87,7 @@ def load_from_csr(filename, reference):
             values = line.split('\t')
             data.append([float(x) for x in values[1:]])
             label.append(float(values[0]))
-    mat = np.array(data)
+    mat = np.array(data, dtype=np.float64)
     label = np.array(label, dtype=np.float32)
     csr = sparse.csr_matrix(mat)
     handle = ctypes.c_void_p()
@@ -100,22 +96,27 @@ def load_from_csr(filename, reference):
         ref = reference
 
     LIB.LGBM_DatasetCreateFromCSR(
-        c_array(ctypes.c_int, csr.indptr),
-        dtype_int32,
-        c_array(ctypes.c_int, csr.indices),
-        csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
-        dtype_float64,
+        csr.indptr.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
+        ctypes.c_int(dtype_int32),
+        csr.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
+        csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
+        ctypes.c_int(dtype_float64),
         ctypes.c_int64(len(csr.indptr)),
         ctypes.c_int64(len(csr.data)),
         ctypes.c_int64(csr.shape[1]),
         c_str('max_bin=15'),
         ref,
         ctypes.byref(handle))
-    num_data = ctypes.c_long()
+    num_data = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data))
-    num_feature = ctypes.c_long()
+    num_feature = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
-    LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
+    LIB.LGBM_DatasetSetField(
+        handle,
+        c_str('label'),
+        label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
+        ctypes.c_int(len(label)),
+        ctypes.c_int(dtype_float32))
     print(f'#data: {num_data.value} #feature: {num_feature.value}')
     return handle
 
@@ -128,31 +129,36 @@ def load_from_csc(filename, reference):
             values = line.split('\t')
             data.append([float(x) for x in values[1:]])
             label.append(float(values[0]))
-    mat = np.array(data)
+    mat = np.array(data, dtype=np.float64)
     label = np.array(label, dtype=np.float32)
-    csr = sparse.csc_matrix(mat)
+    csc = sparse.csc_matrix(mat)
     handle = ctypes.c_void_p()
     ref = None
     if reference is not None:
         ref = reference
 
     LIB.LGBM_DatasetCreateFromCSC(
-        c_array(ctypes.c_int, csr.indptr),
-        dtype_int32,
-        c_array(ctypes.c_int, csr.indices),
-        csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
-        dtype_float64,
-        ctypes.c_int64(len(csr.indptr)),
-        ctypes.c_int64(len(csr.data)),
-        ctypes.c_int64(csr.shape[0]),
+        csc.indptr.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
+        ctypes.c_int(dtype_int32),
+        csc.indices.ctypes.data_as(ctypes.POINTER(ctypes.c_int32)),
+        csc.data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
+        ctypes.c_int(dtype_float64),
+        ctypes.c_int64(len(csc.indptr)),
+        ctypes.c_int64(len(csc.data)),
+        ctypes.c_int64(csc.shape[0]),
         c_str('max_bin=15'),
         ref,
         ctypes.byref(handle))
-    num_data = ctypes.c_long()
+    num_data = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data))
-    num_feature = ctypes.c_long()
+    num_feature = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
-    LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
+    LIB.LGBM_DatasetSetField(
+        handle,
+        c_str('label'),
+        label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
+        ctypes.c_int(len(label)),
+        ctypes.c_int(dtype_float32))
     print(f'#data: {num_data.value} #feature: {num_feature.value}')
     return handle
 
@@ -165,8 +171,8 @@ def load_from_mat(filename, reference):
             values = line.split('\t')
             data.append([float(x) for x in values[1:]])
             label.append(float(values[0]))
-    mat = np.array(data)
-    data = np.array(mat.reshape(mat.size), copy=False)
+    mat = np.array(data, dtype=np.float64)
+    data = np.array(mat.reshape(mat.size), dtype=np.float64, copy=False)
     label = np.array(label, dtype=np.float32)
     handle = ctypes.c_void_p()
     ref = None
@@ -174,19 +180,24 @@ def load_from_mat(filename, reference):
         ref = reference
 
     LIB.LGBM_DatasetCreateFromMat(
-        data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
-        dtype_float64,
-        mat.shape[0],
-        mat.shape[1],
-        1,
+        data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
+        ctypes.c_int(dtype_float64),
+        ctypes.c_int32(mat.shape[0]),
+        ctypes.c_int32(mat.shape[1]),
+        ctypes.c_int(1),
         c_str('max_bin=15'),
         ref,
         ctypes.byref(handle))
-    num_data = ctypes.c_long()
+    num_data = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data))
-    num_feature = ctypes.c_long()
+    num_feature = ctypes.c_int(0)
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature))
-    LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
+    LIB.LGBM_DatasetSetField(
+        handle,
+        c_str('label'),
+        label.ctypes.data_as(ctypes.POINTER(ctypes.c_float)),
+        ctypes.c_int(len(label)),
+        ctypes.c_int(dtype_float32))
     print(f'#data: {num_data.value} #feature: {num_feature.value}')
     return handle
 
@@ -228,20 +239,25 @@ def test_booster():
     for i in range(1, 51):
         LIB.LGBM_BoosterUpdateOneIter(booster, ctypes.byref(is_finished))
         result = np.array([0.0], dtype=np.float64)
-        out_len = ctypes.c_ulong(0)
+        out_len = ctypes.c_int(0)
         LIB.LGBM_BoosterGetEval(
             booster,
-            0,
+            ctypes.c_int(0),
             ctypes.byref(out_len),
             result.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
         if i % 10 == 0:
             print(f'{i} iteration test AUC {result[0]:.6f}')
-    LIB.LGBM_BoosterSaveModel(booster, 0, -1, 0, c_str('model.txt'))
+    LIB.LGBM_BoosterSaveModel(
+        booster,
+        ctypes.c_int(0),
+        ctypes.c_int(-1),
+        ctypes.c_int(0),
+        c_str('model.txt'))
     LIB.LGBM_BoosterFree(booster)
     free_dataset(train)
     free_dataset(test)
     booster2 = ctypes.c_void_p()
-    num_total_model = ctypes.c_long()
+    num_total_model = ctypes.c_int(0)
     LIB.LGBM_BoosterCreateFromModelfile(
         c_str('model.txt'),
         ctypes.byref(num_total_model),
@@ -251,20 +267,20 @@ def test_booster():
                            '../../examples/binary_classification/binary.test'), 'r') as inp:
         for line in inp.readlines():
             data.append([float(x) for x in line.split('\t')[1:]])
-    mat = np.array(data)
+    mat = np.array(data, dtype=np.float64)
     preb = np.zeros(mat.shape[0], dtype=np.float64)
-    num_preb = ctypes.c_long()
-    data = np.array(mat.reshape(mat.size), copy=False)
+    num_preb = ctypes.c_int64(0)
+    data = np.array(mat.reshape(mat.size), dtype=np.float64, copy=False)
     LIB.LGBM_BoosterPredictForMat(
         booster2,
-        data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
-        dtype_float64,
-        mat.shape[0],
-        mat.shape[1],
-        1,
-        1,
-        0,
-        25,
+        data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
+        ctypes.c_int(dtype_float64),
+        ctypes.c_int32(mat.shape[0]),
+        ctypes.c_int32(mat.shape[1]),
+        ctypes.c_int(1),
+        ctypes.c_int(1),
+        ctypes.c_int(0),
+        ctypes.c_int(25),
         c_str(''),
         ctypes.byref(num_preb),
         preb.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
@@ -272,20 +288,20 @@ def test_booster():
         booster2,
         c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                            '../../examples/binary_classification/binary.test')),
-        0,
-        0,
-        0,
-        25,
+        ctypes.c_int(0),
+        ctypes.c_int(0),
+        ctypes.c_int(0),
+        ctypes.c_int(25),
         c_str(''),
         c_str('preb.txt'))
     LIB.LGBM_BoosterPredictForFile(
         booster2,
         c_str(os.path.join(os.path.dirname(os.path.realpath(__file__)),
                            '../../examples/binary_classification/binary.test')),
-        0,
-        0,
-        10,
-        25,
+        ctypes.c_int(0),
+        ctypes.c_int(0),
+        ctypes.c_int(10),
+        ctypes.c_int(25),
         c_str(''),
         c_str('preb.txt'))
     LIB.LGBM_BoosterFree(booster2)