add a small c_api test

src/c_api.cpp

@@ -675,7 +675,7 @@ RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
         std::vector<std::pair<int, double>> ret;
         int64_t start = ptr_indptr[idx];
         int64_t end = ptr_indptr[idx + 1];
-        CHECK(start >= 0 && end < nelem);
+        CHECK(start >= 0 && end <= nelem);
         for (int64_t i = start; i <= end; ++i) {
           ret.emplace_back(indices[i], data_ptr[i]);
         }
@@ -688,7 +688,7 @@ RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
         std::vector<std::pair<int, double>> ret;
         int64_t start = ptr_indptr[idx];
         int64_t end = ptr_indptr[idx + 1];
-        CHECK(start >= 0 && end < nelem);
+        CHECK(start >= 0 && end <= nelem);
         for (int64_t i = start; i <= end; ++i) {
           ret.emplace_back(indices[i], data_ptr[i]);
         }
@@ -706,7 +706,7 @@ RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
         std::vector<std::pair<int, double>> ret;
         int64_t start = ptr_indptr[idx];
         int64_t end = ptr_indptr[idx + 1];
-        CHECK(start >= 0 && end < nelem);
+        CHECK(start >= 0 && end <= nelem);
         for (int64_t i = start; i <= end; ++i) {
           ret.emplace_back(indices[i], data_ptr[i]);
         }
@@ -719,7 +719,7 @@ RowFunctionFromCSR(const void* indptr, int indptr_type, const int32_t* indices,
         std::vector<std::pair<int, double>> ret;
         int64_t start = ptr_indptr[idx];
         int64_t end = ptr_indptr[idx + 1];
-        CHECK(start >= 0 && end < nelem);
+        CHECK(start >= 0 && end <= nelem);
         for (int64_t i = start; i <= end; ++i) {
           ret.emplace_back(indices[i], data_ptr[i]);
         }

test_dll.py → tests/c_api_test/test.py

-from __future__ import absolute_import
-
 import sys
 import os
 import ctypes
 import collections
-import re
 
 import numpy as np
 from scipy import sparse
 
-def _load_lib():
-    """Load xgboost Library."""
-    lib_path = './windows/x64/DLL/lib_lightgbm.dll'
+def LoadDll():
+    lib_path = '../../windows/x64/DLL/lib_lightgbm.dll'
     if len(lib_path) == 0:
         return None
     lib = ctypes.cdll.LoadLibrary(lib_path)
     return lib
 
-
-LIB = _load_lib()
-LIB.LGBM_GetLastError.restype = ctypes.c_char_p
-def test_load_from_file():
-    handle = ctypes.c_void_p()
-    LIB.LGBM_CreateDatasetFromFile(ctypes.c_char_p('./examples/binary_classification/binary.train'),
-    ctypes.c_char_p('max_bin=15'), ctypes.c_void_p(None), ctypes.byref(handle))
-    num_data = ctypes.c_ulong()
-    LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
-    print num_data
-    num_feature = ctypes.c_ulong()
-    LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
-    return handle
+LIB = LoadDll()
 
 def c_array(ctype, values):
-    """Convert a python string to c array."""
     return (ctype * len(values))(*values)
 
 def c_str(string):
-    """Convert a python string to cstring."""
     return ctypes.c_char_p(string.encode('utf-8'))
 
-def test_load_from_matric():
-    data = []
-
-    inp = open('./examples/binary_classification/binary.train', 'r')
-    for line in inp.readlines():
-        data.append( [float(x) for x in line.split('\t')[1:]] )
-    inp.close()
-    mat = np.array(data)
-    print mat.shape
-    data = np.array(mat.reshape(mat.size), copy=False)
-    handle = ctypes.c_void_p()
-    LIB.LGBM_CreateDatasetFromMat(data.ctypes.data_as(ctypes.POINTER(ctypes.c_double)), 1, 
-        mat.shape[0], 
-        mat.shape[1], 1, 
-    ctypes.c_char_p('max_bin=15 is_sparse=false'), None, ctypes.byref(handle) )
-    LIB.LGBM_DatasetFree(ctypes.byref(handle))
-    # num_data = ctypes.c_ulong()
-    # LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
-    # print num_data
-    # num_feature = ctypes.c_ulong()
-    # LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
-    # print num_feature
-    return handle
-
 def test_load_from_csr(filename, reference):
     data = []
     label = []
@@ -73,41 +31,41 @@ def test_load_from_csr(filename, reference):
     inp.close()
     mat = np.array(data)
     label = np.array(label, dtype=np.float32)
-    print mat.shape
     csr = sparse.csr_matrix(mat)
     handle = ctypes.c_void_p()
     ref = None
     if reference != None:
         ref = ctypes.byref(reference)
-    LIB.LGBM_CreateDatasetFromCSR(c_array(ctypes.c_int, csr.indptr), 
+
+    LIB.LGBM_CreateDatasetFromCSR(c_array(ctypes.c_int, csr.indptr), 2, 
         c_array(ctypes.c_int, csr.indices), 
         csr.data.ctypes.data_as(ctypes.POINTER(ctypes.c_void_p)),
         1, len(csr.indptr), len(csr.data),
         csr.shape[1], ctypes.c_char_p('max_bin=15'), ref, ctypes.byref(handle) )
+
     num_data = ctypes.c_ulong()
     LIB.LGBM_DatasetGetNumData(handle, ctypes.byref(num_data) )
-    print num_data
     num_feature = ctypes.c_ulong()
     LIB.LGBM_DatasetGetNumFeature(handle, ctypes.byref(num_feature) )
+
     LIB.LGBM_DatasetSetField(handle, c_str('label'), c_array(ctypes.c_float, label), len(label), 0)
     return handle
 
 
-train = test_load_from_csr('./examples/binary_classification/binary.train', None)
-test = [test_load_from_csr('./examples/binary_classification/binary.test', train)]
+train = test_load_from_csr('../../examples/binary_classification/binary.train', None)
+test = [test_load_from_csr('../../examples/binary_classification/binary.test', train)]
 name = [c_str('test')]
 booster = ctypes.c_void_p()
-LIB.LGBM_BoosterCreate(train, c_array(ctypes.c_void_p, test), c_array(ctypes.c_char_p, name), 1, "app=binary metric=auc num_leaves=31", ctypes.byref(booster))
+LIB.LGBM_BoosterCreate(train, c_array(ctypes.c_void_p, test), c_array(ctypes.c_char_p, name), 
+    len(test), "app=binary metric=auc num_leaves=31 verbose=0", ctypes.byref(booster))
 is_finished = ctypes.c_int(0)
 for i in xrange(100):
     LIB.LGBM_BoosterUpdateOneIter(booster,ctypes.byref(is_finished))
     result = np.array([0.0], dtype=np.float32)
     out_len = ctypes.c_ulong(0)
-    LIB.LGBM_BoosterEval(booster, 0, ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_float)))
-    print result
+    LIB.LGBM_BoosterEval(booster, 1, ctypes.byref(out_len), result.ctypes.data_as(ctypes.POINTER(ctypes.c_float)))
+    print '%d Iteration test AUC %f' %(i, result[0])
 LIB.LGBM_BoosterSaveModel(booster, -1, c_str('model.txt'))
-booster2 = ctypes.c_void_p()
 
+booster2 = ctypes.c_void_p()
 LIB.LGBM_BoosterLoadFromModelfile(c_str('model.txt'), ctypes.byref(booster2))
-
-print type(len([0,0]))
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