#!/usr/bin/env python """ test_python_binding.py Test that passing types to Python bindings works successfully. mlpack is free software; you may redistribute it and/or modify it under the terms of the 3-clause BSD license. You should have received a copy of the 3-clause BSD license along with mlpack. If not, see http://www.opensource.org/licenses/BSD-3-Clause for more information. """ import unittest import pandas as pd import numpy as np import copy from mlpack.test_python_binding import test_python_binding class TestPythonBinding(unittest.TestCase): """ This class tests the basic functionality of the Python bindings. """ def testRunBindingCorrectly(self): """ Test that when we run the binding correctly (with correct input parameters), we get the expected output. """ output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True) self.assertEqual(output['string_out'], 'hello2') self.assertEqual(output['int_out'], 13) self.assertEqual(output['double_out'], 5.0) def testRunBindingNoFlag(self): """ If we forget the mandatory flag, we should get wrong results. """ output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0]) self.assertNotEqual(output['string_out'], 'hello2') self.assertNotEqual(output['int_out'], 13) self.assertNotEqual(output['double_out'], 5.0) def testRunBindingWrongString(self): """ If we give the wrong string, we should get wrong results. """ output = test_python_binding(string_in='goodbye', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True) self.assertNotEqual(output['string_out'], 'hello2') def testRunBindingWrongInt(self): """ If we give the wrong int, we should get wrong results. """ output = test_python_binding(string_in='hello', int_in=15, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True) self.assertNotEqual(output['int_out'], 13) def testRunBindingWrongDouble(self): """ If we give the wrong double, we should get wrong results. """ output = test_python_binding(string_in='hello', int_in=12, double_in=2.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True) self.assertNotEqual(output['double_out'], 5.0) def testRunBadFlag(self): """ If we give the second flag, this should fail. """ output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, flag2=True) self.assertNotEqual(output['string_out'], 'hello2') self.assertNotEqual(output['int_out'], 13) self.assertNotEqual(output['double_out'], 5.0) def testNumpyMatrix(self): """ The matrix we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = np.random.rand(100, 5); z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=z) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['matrix_out'][j, 2]) def testNumpyMatrixForceCopy(self): """ The matrix we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = np.random.rand(100, 5); output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['matrix_out'][j, 2]) def testNumpyFContiguousMatrix(self): """ The matrix with F_CONTIGUOUS set we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = np.array(np.random.rand(100, 5), order='F'); z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=z) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['matrix_out'][j, 2]) def testNumpyFContiguousMatrixForceCopy(self): """ The matrix with F_CONTIGUOUS set we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = np.array(np.random.rand(100, 5), order='F'); output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['matrix_out'][j, 2]) def testPandasSeriesMatrix(self): """ Test that we can pass pandas.Series as input parameter. """ x = pd.Series(np.random.rand(100)) z = x.copy(deep=True) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], smatrix_in=z) self.assertEqual(output['smatrix_out'].shape[0], 100) self.assertEqual(output['smatrix_out'].dtype, np.double) for i in range(100): self.assertEqual(output['smatrix_out'][i,0], x.iloc[i] * 2) def testPandasSeriesMatrixForceCopy(self): """ Test that we can pass pandas.Series as input parameter. """ x = pd.Series(np.random.rand(100)) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], smatrix_in=x, copy_all_inputs=True) self.assertEqual(output['smatrix_out'].shape[0], 100) self.assertEqual(output['smatrix_out'].dtype, np.double) for i in range(100): self.assertEqual(output['smatrix_out'][i,0], x.iloc[i] * 2) def testPandasSeriesUMatrix(self): """ Test that we can pass pandas.Series as input parameter. """ x = pd.Series(np.random.randint(0, high=500, size=100)) z = x.copy(deep=True) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], s_umatrix_in=z) self.assertEqual(output['s_umatrix_out'].shape[0], 100) self.assertEqual(output['s_umatrix_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['s_umatrix_out'][i, 0], x.iloc[i] * 2) def testPandasSeriesUMatrixForceCopy(self): """ Test that we can pass pandas.Series as input parameter. """ x = pd.Series(np.random.randint(0, high=500, size=100)) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], s_umatrix_in=x, copy_all_inputs=True) self.assertEqual(output['s_umatrix_out'].shape[0], 100) self.assertEqual(output['s_umatrix_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['s_umatrix_out'][i, 0], x.iloc[i] * 2) def testPandasSeries(self): """ Test a Pandas Series input paramter """ x = pd.Series(np.random.rand(100)) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=z) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], z[i] * 2) def testPandasSeriesForceCopy(self): """ Test a Pandas Series input paramter """ x = pd.Series(np.random.rand(100)) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=x, copy_all_inputs=True) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], x[i] * 2) def testPandasDataFrameMatrix(self): """ The matrix we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = pd.DataFrame(np.random.rand(100, 5)) z = x.copy(deep=True) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=z) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x.iloc[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x.iloc[j, 2], output['matrix_out'][j, 2]) def testPandasDataFrameMatrixForceCopy(self): """ The matrix we pass in, we should get back with the third dimension doubled and the fifth forgotten. """ x = pd.DataFrame(np.random.rand(100, 5)) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_out'].shape[0], 100) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x.iloc[j, i], output['matrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x.iloc[j, 2], output['matrix_out'][j, 2]) def testArraylikeMatrix(self): """ Test that we can pass an arraylike matrix. """ x = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=x) self.assertEqual(output['matrix_out'].shape[0], 3) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(output['matrix_out'].dtype, np.double) self.assertEqual(output['matrix_out'][0, 0], 1) self.assertEqual(output['matrix_out'][0, 1], 2) self.assertEqual(output['matrix_out'][0, 2], 6) self.assertEqual(output['matrix_out'][0, 3], 4) self.assertEqual(output['matrix_out'][1, 0], 6) self.assertEqual(output['matrix_out'][1, 1], 7) self.assertEqual(output['matrix_out'][1, 2], 16) self.assertEqual(output['matrix_out'][1, 3], 9) self.assertEqual(output['matrix_out'][2, 0], 11) self.assertEqual(output['matrix_out'][2, 1], 12) self.assertEqual(output['matrix_out'][2, 2], 26) self.assertEqual(output['matrix_out'][2, 3], 14) def testArraylikeMatrixForceCopy(self): """ Test that we can pass an arraylike matrix. """ x = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_out'].shape[0], 3) self.assertEqual(output['matrix_out'].shape[1], 4) self.assertEqual(len(x), 3) self.assertEqual(len(x[0]), 5) self.assertEqual(output['matrix_out'].dtype, np.double) self.assertEqual(output['matrix_out'][0, 0], 1) self.assertEqual(output['matrix_out'][0, 1], 2) self.assertEqual(output['matrix_out'][0, 2], 6) self.assertEqual(output['matrix_out'][0, 3], 4) self.assertEqual(output['matrix_out'][1, 0], 6) self.assertEqual(output['matrix_out'][1, 1], 7) self.assertEqual(output['matrix_out'][1, 2], 16) self.assertEqual(output['matrix_out'][1, 3], 9) self.assertEqual(output['matrix_out'][2, 0], 11) self.assertEqual(output['matrix_out'][2, 1], 12) self.assertEqual(output['matrix_out'][2, 2], 26) self.assertEqual(output['matrix_out'][2, 3], 14) def testNumpyUmatrix(self): """ Same as testNumpyMatrix() but with an unsigned matrix. """ x = np.random.randint(0, high=500, size=[100, 5]) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], umatrix_in=z) self.assertEqual(output['umatrix_out'].shape[0], 100) self.assertEqual(output['umatrix_out'].shape[1], 4) self.assertEqual(output['umatrix_out'].dtype, np.dtype('intp')) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['umatrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['umatrix_out'][j, 2]) def testNumpyUmatrixForceCopy(self): """ Same as testNumpyMatrix() but with an unsigned matrix. """ x = np.random.randint(0, high=500, size=[100, 5]) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], umatrix_in=x, copy_all_inputs=True) self.assertEqual(output['umatrix_out'].shape[0], 100) self.assertEqual(output['umatrix_out'].shape[1], 4) self.assertEqual(output['umatrix_out'].dtype, np.dtype('intp')) for i in [0, 1, 3]: for j in range(100): self.assertEqual(x[j, i], output['umatrix_out'][j, i]) for j in range(100): self.assertEqual(2 * x[j, 2], output['umatrix_out'][j, 2]) def testArraylikeUmatrix(self): """ Test that we can pass an arraylike unsigned matrix. """ x = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], umatrix_in=x) self.assertEqual(output['umatrix_out'].shape[0], 3) self.assertEqual(output['umatrix_out'].shape[1], 4) self.assertEqual(output['umatrix_out'].dtype, np.dtype('intp')) self.assertEqual(output['umatrix_out'][0, 0], 1) self.assertEqual(output['umatrix_out'][0, 1], 2) self.assertEqual(output['umatrix_out'][0, 2], 6) self.assertEqual(output['umatrix_out'][0, 3], 4) self.assertEqual(output['umatrix_out'][1, 0], 6) self.assertEqual(output['umatrix_out'][1, 1], 7) self.assertEqual(output['umatrix_out'][1, 2], 16) self.assertEqual(output['umatrix_out'][1, 3], 9) self.assertEqual(output['umatrix_out'][2, 0], 11) self.assertEqual(output['umatrix_out'][2, 1], 12) self.assertEqual(output['umatrix_out'][2, 2], 26) self.assertEqual(output['umatrix_out'][2, 3], 14) def testArraylikeUmatrixForceCopy(self): """ Test that we can pass an arraylike unsigned matrix. """ x = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10], [11, 12, 13, 14, 15]] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], umatrix_in=x, copy_all_inputs=True) self.assertEqual(output['umatrix_out'].shape[0], 3) self.assertEqual(output['umatrix_out'].shape[1], 4) self.assertEqual(len(x), 3) self.assertEqual(len(x[0]), 5) self.assertEqual(output['umatrix_out'].dtype, np.dtype('intp')) self.assertEqual(output['umatrix_out'][0, 0], 1) self.assertEqual(output['umatrix_out'][0, 1], 2) self.assertEqual(output['umatrix_out'][0, 2], 6) self.assertEqual(output['umatrix_out'][0, 3], 4) self.assertEqual(output['umatrix_out'][1, 0], 6) self.assertEqual(output['umatrix_out'][1, 1], 7) self.assertEqual(output['umatrix_out'][1, 2], 16) self.assertEqual(output['umatrix_out'][1, 3], 9) self.assertEqual(output['umatrix_out'][2, 0], 11) self.assertEqual(output['umatrix_out'][2, 1], 12) self.assertEqual(output['umatrix_out'][2, 2], 26) self.assertEqual(output['umatrix_out'][2, 3], 14) def testCol(self): """ Test a column vector input parameter. """ x = np.random.rand(100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=z) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], x[i] * 2) def testColForceCopy(self): """ Test a column vector input parameter. """ x = np.random.rand(100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=x, copy_all_inputs=True) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], x[i] * 2) def testUcol(self): """ Test an unsigned column vector input parameter. """ x = np.random.randint(0, high=500, size=100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], ucol_in=z) self.assertEqual(output['ucol_out'].shape[0], 100) self.assertEqual(output['ucol_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['ucol_out'][i], x[i] * 2) def testUcolForceCopy(self): """ Test an unsigned column vector input parameter. """ x = np.random.randint(0, high=500, size=100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], ucol_in=x, copy_all_inputs=True) self.assertEqual(output['ucol_out'].shape[0], 100) self.assertEqual(output['ucol_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['ucol_out'][i], x[i] * 2) def testRow(self): """ Test a row vector input parameter. """ x = np.random.rand(100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], row_in=z) self.assertEqual(output['row_out'].shape[0], 100) self.assertEqual(output['row_out'].dtype, np.double) for i in range(100): self.assertEqual(output['row_out'][i], x[i] * 2) def testRowForceCopy(self): """ Test a row vector input parameter. """ x = np.random.rand(100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], row_in=x, copy_all_inputs=True) self.assertEqual(output['row_out'].shape[0], 100) self.assertEqual(output['row_out'].dtype, np.double) for i in range(100): self.assertEqual(output['row_out'][i], x[i] * 2) def testUrow(self): """ Test an unsigned row vector input parameter. """ x = np.random.randint(0, high=500, size=100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], urow_in=z) self.assertEqual(output['urow_out'].shape[0], 100) self.assertEqual(output['urow_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['urow_out'][i], x[i] * 2) def testUrowForceCopy(self): """ Test an unsigned row vector input parameter. """ x = np.random.randint(0, high=500, size=100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], urow_in=x, copy_all_inputs=True) self.assertEqual(output['urow_out'].shape[0], 100) self.assertEqual(output['urow_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['urow_out'][i], x[i] * 2) def testMatrixAndInfoNumpy(self): """ Test that we can pass a matrix with all numeric features. """ x = np.random.rand(100, 10) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=z) self.assertEqual(output['matrix_and_info_out'].shape[0], 100) self.assertEqual(output['matrix_and_info_out'].shape[1], 10) for i in range(10): for j in range(100): self.assertEqual(output['matrix_and_info_out'][j, i], x[j, i] * 2.0) def testMatrixAndInfoNumpyForceCopy(self): """ Test that we can pass a matrix with all numeric features. """ x = np.random.rand(100, 10) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_and_info_out'].shape[0], 100) self.assertEqual(output['matrix_and_info_out'].shape[1], 10) for i in range(10): for j in range(100): self.assertEqual(output['matrix_and_info_out'][j, i], x[j, i] * 2.0) def testMatrixAndInfoPandas(self): """ Test that we can pass a matrix with some categorical features. """ x = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd')) x['e'] = pd.Series(['a', 'b', 'c', 'd', 'a', 'b', 'e', 'c', 'a', 'b'], dtype='category') z = x.copy(deep=True) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=z) self.assertEqual(output['matrix_and_info_out'].shape[0], 10) self.assertEqual(output['matrix_and_info_out'].shape[1], 5) cols = list('abcde') for i in range(4): for j in range(10): self.assertEqual(output['matrix_and_info_out'][j, i], z[cols[i]][j] * 2) for j in range(10): self.assertEqual(output['matrix_and_info_out'][j, 4], z[cols[4]][j]) def testMatrixAndInfoPandasForceCopy(self): """ Test that we can pass a matrix with some categorical features. """ x = pd.DataFrame(np.random.rand(10, 4), columns=list('abcd')) x['e'] = pd.Series(['a', 'b', 'c', 'd', 'a', 'b', 'e', 'c', 'a', 'b'], dtype='category') output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=x, copy_all_inputs=True) self.assertEqual(output['matrix_and_info_out'].shape[0], 10) self.assertEqual(output['matrix_and_info_out'].shape[1], 5) cols = list('abcde') for i in range(4): for j in range(10): self.assertEqual(output['matrix_and_info_out'][j, i], x[cols[i]][j] * 2) for j in range(10): self.assertEqual(output['matrix_and_info_out'][j, 4], x[cols[4]][j]) def testIntVector(self): """ Test that we can pass a vector of ints and get back that same vector but with the last element removed. """ x = [1, 2, 3, 4, 5] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], vector_in=x) self.assertEqual(output['vector_out'], [1, 2, 3, 4]) def testStringVector(self): """ Test that we can pass a vector of strings and get back that same vector but with the last element removed. """ x = ['one', 'two', 'three', 'four', 'five'] output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], str_vector_in=x) self.assertEqual(output['str_vector_out'], ['one', 'two', 'three', 'four']) def testModel(self): """ First create a GaussianKernel object, then send it back and make sure we get the right double value. """ output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], build_model=True) output2 = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], model_in=output['model_out']) self.assertEqual(output2['model_bw_out'], 20.0) def testOneDimensionNumpyMatrix(self): """ Test that we can pass one dimension matrix from matrix_in """ x = np.random.rand(100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], smatrix_in=z) self.assertEqual(output['smatrix_out'].shape[0], 100) self.assertEqual(output['smatrix_out'].dtype, np.double) for i in range(100): self.assertEqual(output['smatrix_out'][i, 0], x[i] * 2) def testOneDimensionNumpymatrixForceCopy(self): """ Test that we can pass one dimension matrix from matrix_in """ x = np.random.rand(100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], smatrix_in=x, copy_all_inputs=True) self.assertEqual(output['smatrix_out'].shape[0], 100) self.assertEqual(output['smatrix_out'].dtype, np.double) for i in range(100): self.assertEqual(output['smatrix_out'][i, 0], x[i] * 2) def testOneDimensionNumpyUmatrix(self): """ Same as testNumpyMatrix() but with an unsigned matrix and One Dimension Matrix. """ x = np.random.randint(0, high=500, size=100) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], s_umatrix_in=z) self.assertEqual(output['s_umatrix_out'].shape[0], 100) self.assertEqual(output['s_umatrix_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['s_umatrix_out'][i, 0], x[i] * 2) def testOneDimensionNumpyUmatrixForceCopy(self): """ Same as testNumpyMatrix() but with an unsigned matrix and One Dimension Matrix. """ x = np.random.randint(0, high=500, size=100) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], s_umatrix_in=x, copy_all_inputs=True) self.assertEqual(output['s_umatrix_out'].shape[0], 100) self.assertEqual(output['s_umatrix_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['s_umatrix_out'][i, 0], x[i] * 2) def testTwoDimensionCol(self): """ Test that we pass Two Dimension column vetor as input paramter """ x = np.random.rand(100,1) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=z) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], x[i] * 2) def testTwoDimensionColForceCopy(self): """ Test that we pass Two Dimension column vetor as input paramter """ x = np.random.rand(100,1) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], col_in=x, copy_all_inputs=True) self.assertEqual(output['col_out'].shape[0], 100) self.assertEqual(output['col_out'].dtype, np.double) for i in range(100): self.assertEqual(output['col_out'][i], x[i] * 2) def testTwoDimensionUcol(self): """ Test that we pass Two Dimension unsigned column vector input parameter. """ x = np.random.randint(0, high=500, size=[100, 1]) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], ucol_in=z) self.assertEqual(output['ucol_out'].shape[0], 100) self.assertEqual(output['ucol_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['ucol_out'][i], x[i] * 2) def testTwoDimensionUcolForceCopy(self): """ Test that we pass Two Dimension unsigned column vector input parameter. """ x = np.random.randint(0, high=500, size=[100, 1]) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], ucol_in=x, copy_all_inputs=True) self.assertEqual(output['ucol_out'].shape[0], 100) self.assertEqual(output['ucol_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['ucol_out'][i], x[i] * 2) def testTwoDimensionRow(self): """ Test a two dimensional row vector input parameter. """ x = np.random.rand(100,1) z =copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], row_in=x) self.assertEqual(output['row_out'].shape[0], 100) self.assertEqual(output['row_out'].dtype, np.double) for i in range(100): self.assertEqual(output['row_out'][i], z[i] * 2) def testTwoDimensionRowForceCopy(self): """ Test a two dimensional row vector input parameter. """ x = np.random.rand(100,1) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], row_in=x, copy_all_inputs=True) self.assertEqual(output['row_out'].shape[0], 100) self.assertEqual(output['row_out'].dtype, np.double) for i in range(100): self.assertEqual(output['row_out'][i], x[i] * 2) def testTwoDimensionUrow(self): """ Test an unsigned two dimensional row vector input parameter. """ x = np.random.randint(0, high=500, size=[100, 1]) z = copy.deepcopy(x) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], urow_in=z) self.assertEqual(output['urow_out'].shape[0], 100) self.assertEqual(output['urow_out'].dtype, np.dtype('intp')) for i in range(100): self.assertEqual(output['urow_out'][i], x[i] * 2) def testTwoDimensionUrowForceCopy(self): """ Test an unsigned two dimensional row vector input parameter. """ x = np.random.randint(5, high=500, size=[1, 101]) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], urow_in=x, copy_all_inputs=True) self.assertEqual(output['urow_out'].shape[0], 101) self.assertEqual(output['urow_out'].dtype, np.dtype('intp')) for i in range(101): self.assertEqual(output['urow_out'][i], x[0][i] * 2) def testOneDimensionMatrixAndInfoPandas(self): """ Test that we can pass a one dimension matrix with some categorical features. """ x = pd.DataFrame(np.random.rand(10)) z = x.copy(deep=True) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=z[0]) self.assertEqual(output['matrix_and_info_out'].shape[0], 10) for i in range(10): self.assertEqual(output['matrix_and_info_out'][i, 0], x[0][i] * 2) def testOneDimensionMatrixAndInfoPandasForceCopy(self): """ Test that we can pass a one dimension matrix with some categorical features. """ x = pd.DataFrame(np.random.rand(10)) output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], matrix_and_info_in=x[0], copy_all_inputs=True) self.assertEqual(output['matrix_and_info_out'].shape[0], 10) for j in range(10): self.assertEqual(output['matrix_and_info_out'][j, 0], x[0][j]*2) def testThrownException(self): """ Test that we pass wrong type and get back TypeError """ self.assertRaises(TypeError, lambda : test_python_binding(string_in=10, int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=10.0, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in='bad', mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, flag2=10)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, matrix_in= 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, matrix_in= 1)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, matrix_and_info_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, copy_all_inputs = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, col_in = 10)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, row_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, str_vector_in = 'bad')) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, urow_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, ucol_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, umatrix_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, verbose = 10)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], flag1=True, vector_in = 10.0)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=False, col_req_in=[1.0], flag1=True)) self.assertRaises(TypeError, lambda : test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=False, flag1=True)) def testModelForceCopy(self): """ First create a GaussianKernel object, then send it back and make sure we get the right double value. """ output = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], build_model=True) output2 = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], model_in=output['model_out'], copy_all_inputs=True) output3 = test_python_binding(string_in='hello', int_in=12, double_in=4.0, mat_req_in=[[1.0]], col_req_in=[1.0], model_in=output['model_out']) self.assertEqual(output2['model_bw_out'], 20.0) self.assertEqual(output3['model_bw_out'], 20.0) if __name__ == '__main__': unittest.main()