CoCalc -- w4_unittest.py

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GitHub Repository: y33-j3T/Coursera-Deep-Learning
Path: blob/master/Natural Language Processing with Attention Models/Week 4 - Chatbot/w4_unittest.py
Views: ¹³³⁷³
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import numpy as np
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import trax
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#from trax import layers as tl
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#from trax.fastmath import numpy as fastnp
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#from trax.supervised import training
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# UNIT TEST for UNQ_C1
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def test_get_conversation(target):
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    data = {'file1.json': {'log':[{'text': 'hi'},
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                                  {'text': 'hello'},
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                                  {'text': 'nice'}]},
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            'file2.json':{'log':[{'text': 'a b'}, 
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                                 {'text': ''}, 
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                                 {'text': 'good '}, 
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                                 {'text': 'no?'}]}}
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    res1 = target('file1.json', data)
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    res2 = target('file2.json', data)
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    expected1 = ' Person 1: hi Person 2: hello Person 1: nice'
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    expected2 = ' Person 1: a b Person 2:  Person 1: good  Person 2: no?'
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    success = 0
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    fails = 0
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    try:
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        assert res1 == expected1
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        success += 1
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    except ValueError:
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        print('Error in test 1 \nResult  : ', res1, 'x \nExpected: ', expected1)
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        fails += 1
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    try:
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        assert res2 == expected2
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        success += 1
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    except:
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        print('Error in test 2 \nResult  : ', res2, ' \nExpected: ', expected2)
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        fails += 1
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    if fails == 0:
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        print("\033[92m All tests passed")
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    else:
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        print('\033[92m', success," Tests passed")
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        print('\033[91m', fails, " Tests failed")
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# UNIT TEST for UNQ_C2
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def test_reversible_layer_forward(target):
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    f1 = lambda x: x + 2
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    g1 = lambda x: x * 3
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    f2 = lambda x: x + 1
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    g2 = lambda x: x * 2
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    input_vector1 = np.array([1, 2, 3, 4, 5, 6, 7, 8])
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    expected1 = np.array([8, 10, 12, 14, 29, 36, 43, 50])
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    input_vector2 = np.array([1] * 128)
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    expected2 = np.array([3] * 64 + [7] * 64)
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    success = 0
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    fails = 0
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    try:
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        res = target(input_vector1, f1, g1)
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        assert isinstance(res, np.ndarray)
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        success += 1
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    except:
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        print('Wrong type! Output is not of type np.ndarray')
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        fails += 1
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    try:
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        res = target(input_vector1, f1, g1)
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        assert np.allclose(res, expected1)
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        success += 1
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    except ValueError:
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        print('Error in test 1 \nResult  : ', res, 'x \nExpected: ', expected1)
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        fails += 1
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    try:
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        res = target(input_vector2, f2, g2)
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        assert np.allclose(res, expected2)
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        success += 1
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    except:
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        print('Error in test 2 \nResult  : ', res, ' \nExpected: ', expected2)
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        fails += 1
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    if fails == 0:
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        print("\033[92m All tests passed")
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    else:
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        print('\033[92m', success," Tests passed")
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        print('\033[91m', fails, " Tests failed")
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# UNIT TEST for UNQ_C3
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def test_reversible_layer_reverse(target):
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    f1 = lambda x: x + 2
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    g1 = lambda x: x * 3
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    f2 = lambda x: x + 1
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    g2 = lambda x: x * 2
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    input_vector1 = np.array([1, 2, 3, 4, 5, 6, 7, 8])
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    expected1 = np.array([-3,  0,  3,  6,  2,  0, -2, -4])
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    input_vector2 = np.array([1] * 128)
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    expected2 = np.array([1] * 64 + [-1] * 64)
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    success = 0
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    fails = 0
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    try:
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        res = target(input_vector1, f1, g1)
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        assert isinstance(res, np.ndarray)
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        success += 1
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    except:
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        print('Wrong type! Output is not of type np.ndarray')
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        fails += 1
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    try:
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        res = target(input_vector1, f1, g1)
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        assert np.allclose(res, expected1)
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        success += 1
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    except ValueError:
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        print('Error in test 1 \nResult  : ', res, 'x \nExpected: ', expected1)
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        fails += 1
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    try:
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        res = target(input_vector2, f2, g2)
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        assert np.allclose(res, expected2)
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        success += 1
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    except:
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        print('Error in test 2 \nResult  : ', res, ' \nExpected: ', expected2)
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        fails += 1
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    if fails == 0:
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        print("\033[92m All tests passed")
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    else:
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        print('\033[92m', success," Tests passed")
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        print('\033[91m', fails, " Tests failed")
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# UNIT TEST for UNQ_C4
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def test_ReformerLM(target):
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    test_cases = [
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                {
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                    "name":"layer_len_check",
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                    "expected":11,
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                    "error":"We found {} layers in your model. It should be 11.\nCheck the LSTM stack before the dense layer"
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                },
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                {
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                    "name":"simple_test_check",
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      "expected":"Serial[ShiftRight(1)Embedding_train_512DropoutPositionalEncodingDup_out2ReversibleSerial_in2_out2[ReversibleHalfResidualV2_in2_out2[Serial[LayerNorm]SelfAttention]ReversibleSwap_in2_out2ReversibleHalfResidualV2_in2_out2[Serial[LayerNormDense_2048DropoutFastGeluDense_512Dropout]]ReversibleSwap_in2_out2ReversibleHalfResidualV2_in2_out2[Serial[LayerNorm]SelfAttention]ReversibleSwap_in2_out2ReversibleHalfResidualV2_in2_out2[Serial[LayerNormDense_2048DropoutFastGeluDense_512Dropout]]ReversibleSwap_in2_out2]Concatenate_in2LayerNormDropoutDense_trainLogSoftmax]",
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                    "error":"The ReformerLM is not defined properly."
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                }
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            ]
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    temp_model = target('train')
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    success = 0
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    fails = 0
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    for test_case in test_cases:
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        try:
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            if test_case['name'] == "simple_test_check":
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                assert test_case["expected"] == str(temp_model).replace(' ', '').replace('\n','')
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                success += 1
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            if test_case['name'] == "layer_len_check":
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                if test_case["expected"] == len(temp_model.sublayers):
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                    success += 1
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                else:
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                    print(test_case["error"].format(len(temp_model.sublayers))) 
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                    fails += 1
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        except:
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            print(test_case['error'])
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            fails += 1
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    if fails == 0:
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        print("\033[92m All tests passed")
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    else:
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        print('\033[92m', success," Tests passed")
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        print('\033[91m', fails, " Tests failed")
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# UNIT TEST for UNQ_C5
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def test_tasks(train_task, eval_task):
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    target = train_task
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    success = 0
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    fails = 0
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    # Test the labeled data parameter for train_task
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    try:
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        strlabel = str(target._labeled_data)
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        assert ("generator" in strlabel) and ("add_loss_weights" in  strlabel)
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        success += 1
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    except:
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        fails += 1
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        print("Wrong labeled data parameter in train_task")
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    # Test the cross entropy loss data parameter
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    try:
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        strlabel = str(target._loss_layer)
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        assert(strlabel == "CrossEntropyLoss_in3")
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        success += 1
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    except:
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        fails += 1
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        print("Wrong loss functions. CrossEntropyLoss_in3 was expected")
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     # Test the optimizer parameter
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    try:
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        assert(isinstance(target.optimizer, trax.optimizers.adam.Adam))
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        success += 1
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    except:
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        fails += 1
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        print("Wrong optimizer")
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    # Test the schedule parameter
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    try:
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        assert(isinstance(target._lr_schedule,trax.supervised.lr_schedules._BodyAndTail))
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        success += 1
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    except:
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        fails += 1
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        print("Wrong learning rate schedule type")
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    # Test the _n_steps_per_checkpoint parameter
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    try:
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        assert(target._n_steps_per_checkpoint==10)
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        success += 1
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    except:
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        fails += 1
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        print("Wrong checkpoint step frequency")
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    target = eval_task
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    # Test the labeled data parameter for eval_task
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    try:
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        strlabel = str(target._labeled_data)
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        assert ("generator" in strlabel) and ("add_loss_weights" in  strlabel)
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        success += 1
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    except:
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        fails += 1
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        print("Wrong labeled data parameter in eval_task")
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    # Test the metrics in eval_task 
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    try:
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        strlabel = str(target._metrics).replace(' ', '')
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        assert(strlabel == "[CrossEntropyLoss_in3,Accuracy_in3]")
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        success += 1
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    except:
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        fails += 1
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        print(f"Wrong metrics. found {strlabel} but expected [CrossEntropyLoss_in3,Accuracy_in3]")
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    if fails == 0:
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        print("\033[92m All tests passed")
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    else:
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        print('\033[92m', success," Tests passed")
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        print('\033[91m', fails, " Tests failed")
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all in one place. Commercial Alternative to JupyterHub.
