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w1_unittest.py
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# import nltk
import numpy as np
def test_sigmoid(target):
successful_cases = 0
failed_cases = []
test_cases = [
{"name": "default_check", "input": {"z": 0}, "expected": 0.5},
{
"name": "positive_check",
"input": {"z": 4.92},
"expected": 0.9927537604041685,
},
{"name": "negative_check", "input": {"z": -1}, "expected": 0.2689414213699951},
{
"name": "larger_neg_check",
"input": {"z": -20},
"expected": 2.0611536181902037e-09,
},
]
for test_case in test_cases:
result = target(**test_case["input"])
try:
assert np.isclose(result, test_case["expected"])
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"],
"got": result,
}
)
print(
f"Wrong output from sigmoid function. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
if len(failed_cases) == 0:
print("\033[92m All tests passed")
else:
print("\033[92m", successful_cases, " Tests passed")
print("\033[91m", len(failed_cases), " Tests failed")
# return failed_cases, len(failed_cases) + successful_cases
def test_gradientDescent(target):
successful_cases = 0
failed_cases = []
test_cases = [
{
"name": "default_check",
"input": {
"random_seed": 1,
"input_dict": {
"x": np.array(
[
[1.00000000e00, 8.34044009e02, 1.44064899e03],
[1.00000000e00, 2.28749635e-01, 6.04665145e02],
[1.00000000e00, 2.93511782e02, 1.84677190e02],
[1.00000000e00, 3.72520423e02, 6.91121454e02],
[1.00000000e00, 7.93534948e02, 1.07763347e03],
[1.00000000e00, 8.38389029e02, 1.37043900e03],
[1.00000000e00, 4.08904499e02, 1.75623487e03],
[1.00000000e00, 5.47751864e01, 1.34093502e03],
[1.00000000e00, 8.34609605e02, 1.11737966e03],
[1.00000000e00, 2.80773877e02, 3.96202978e02],
]
),
"y": np.array(
[
[1.0],
[1.0],
[0.0],
[1.0],
[1.0],
[1.0],
[0.0],
[0.0],
[0.0],
[1.0],
]
),
"theta": np.zeros((3, 1)),
"alpha": 1e-8,
"num_iters": 700,
},
},
"expected": {
"J": 0.6709497038162118,
"theta": np.array(
[[4.10713435e-07], [3.56584699e-04], [7.30888526e-05]]
),
},
},
{
"name": "larger_check",
"input": {
"random_seed": 2,
"input_dict": {
"x": np.array(
[
[1.0, 435.99490214, 25.92623183, 549.66247788],
[1.0, 435.32239262, 420.36780209, 330.334821],
[1.0, 204.64863404, 619.27096635, 299.65467367],
[1.0, 266.8272751, 621.13383277, 529.14209428],
[1.0, 134.57994534, 513.57812127, 184.43986565],
[1.0, 785.33514782, 853.97529264, 494.23683738],
[1.0, 846.56148536, 79.64547701, 505.24609012],
[1.0, 65.28650439, 428.1223276, 96.53091566],
[1.0, 127.1599717, 596.74530898, 226.0120006],
[1.0, 106.94568431, 220.30620707, 349.826285],
[1.0, 467.78748458, 201.74322626, 640.40672521],
[1.0, 483.06983555, 505.23672002, 386.89265112],
[1.0, 793.63745444, 580.00417888, 162.2985985],
[1.0, 700.75234661, 964.55108009, 500.00836117],
[1.0, 889.52006395, 341.61365267, 567.14412763],
[1.0, 427.5459633, 436.74726303, 776.559185],
[1.0, 535.6041735, 953.74222694, 544.20816015],
[1.0, 82.09492228, 366.34240168, 850.850504],
[1.0, 406.27504305, 27.20236589, 247.177239],
[1.0, 67.14437074, 993.85201142, 970.58031338],
]
),
"y": np.array(
[
[1.0],
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[0.0],
[0.0],
[1.0],
[0.0],
[1.0],
[0.0],
[0.0],
[0.0],
[1.0],
[1.0],
[0.0],
[0.0],
[1.0],
[0.0],
]
),
"theta": np.zeros((4, 1)),
"alpha": 1e-4,
"num_iters": 30,
},
},
"expected": {
"J": 6.5044107216556135,
"theta": np.array(
[
[9.45211976e-05],
[2.40577958e-02],
[-1.77876847e-02],
[1.35674845e-02],
]
),
},
},
]
for test_case in test_cases:
# Setting the random seed for reproducibility
result_J, result_theta = target(**test_case["input"]["input_dict"])
try:
assert isinstance(result_J, float)
successful_cases += 1
except:
failed_cases.append(
{"name": test_case["name"], "expected": float, "got": type(result_J),}
)
print(
f"Wrong output type for loss function. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert np.isclose(result_J, test_case["expected"]["J"])
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"]["J"],
"got": result_J,
}
)
print(
f"Wrong output for the loss function. Check how you are implementing the matrix multiplications. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert result_theta.shape == test_case["input"]["input_dict"]["theta"].shape
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["input"]["input_dict"]["theta"].shape,
"got": result_theta.shape,
}
)
print(
f"Wrong shape for weights matrix theta. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert np.allclose(
np.squeeze(result_theta), np.squeeze(test_case["expected"]["theta"]),
)
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"]["theta"],
"got": result_theta,
}
)
print(
f"Wrong values for weight's matrix theta. Check how you are updating the matrix of weights. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
if len(failed_cases) == 0:
print("\033[92m All tests passed")
else:
print("\033[92m", successful_cases, " Tests passed")
print("\033[91m", len(failed_cases), " Tests failed")
# +
def test_extract_features(target, freqs):
successful_cases = 0
failed_cases = []
test_cases = [
{
"name": "default_check",
"input": {
"tweet": "#FollowFriday @France_Inte @PKuchly57 @Milipol_Paris for being top engaged members in my community this week :)",
"freqs": freqs,
},
"expected": np.array(
[[1.00e00, 3.133e03, 6.10e01]]
),
},
{
"name": "unk_words_check",
"input": {"tweet": "blorb bleeeeb bloooob", "freqs": freqs},
"expected": np.array([[1.0, 0.0, 0.0]]),
},
{
"name": "good_words_check",
"input": {"tweet": "Hello world! All's good!", "freqs": freqs},
"expected": np.array([[1.0, 263.0, 106.0]]),
},
{
"name": "bad_words_check",
"input": {"tweet": "It is so sad!", "freqs": freqs},
"expected": np.array([[1.0, 5.0, 100.0]]),
},
]
for test_case in test_cases:
result = target(**test_case["input"])
try:
assert result.shape == test_case["expected"].shape
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"].shape,
"got": result.shape,
}
)
print(
f"Wrong output shape. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert np.allclose(result, test_case["expected"])
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"],
"got": result,
}
)
print(
f"Wrong output values. Check how you are computing the positive or negative word count. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
if len(failed_cases) == 0:
print("\033[92m All tests passed")
else:
print("\033[92m", successful_cases, " Tests passed")
print("\033[91m", len(failed_cases), " Tests failed")
# -
def test_predict_tweet(target, freqs, theta):
successful_cases = 0
failed_cases = []
test_cases = [
{
"name": "default_check1",
"input": {"tweet": "I am happy", "freqs": freqs, "theta": theta},
"expected": np.array([[0.5192746]]),
},
{
"name": "default_check2",
"input": {"tweet": "I am bad", "freqs": freqs, "theta": theta},
"expected": np.array([[0.49434685]]),
},
{
"name": "default_check3",
"input": {
"tweet": "this movie should have been great",
"freqs": freqs,
"theta": theta,
},
"expected": np.array([[0.5159792]]),
},
{
"name": "default_check5",
"input": {"tweet": "It is a good day", "freqs": freqs, "theta": theta,},
"expected": np.array([[0.52320595]]),
},
{
"name": "default_check6",
"input": {"tweet": "It is a bad bad day", "freqs": freqs, "theta": theta,},
"expected": np.array([[0.49780224]]),
},
{
"name": "default_check7",
"input": {
"tweet": "It is a good day",
"freqs": freqs,
"theta": np.array([[5.0000e-04], [-3.4e-02], [3.2e-02]]),
},
"expected": np.array([[0.00147813]]),
},
{
"name": "default_check8",
"input": {
"tweet": "It is a bad bad day",
"freqs": freqs,
"theta": np.array([[5.0000e-04], [-3.4e-02], [3.2e-02]]),
},
"expected": np.array([[0.45673348]]),
},
{
"name": "default_check9",
"input": {
"tweet": "this movie should have been great",
"freqs": freqs,
"theta": np.array([[5.0000e-04], [-3.4e-02], [3.2e-02]]),
},
"expected": np.array([[0.01561938]]),
},
]
for test_case in test_cases:
result = target(**test_case["input"])
try:
assert result.shape == test_case["expected"].shape
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"].shape,
"got": result.shape,
}
)
print(
f"Wrong output shape. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert np.allclose(result, test_case["expected"])
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"],
"got": result,
}
)
print(
f"Wrong predicted values. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
if len(failed_cases) == 0:
print("\033[92m All tests passed")
else:
print("\033[92m", successful_cases, " Tests passed")
print("\033[91m", len(failed_cases), " Tests failed")
def unittest_test_logistic_regression(target, freqs, theta):
successful_cases = 0
failed_cases = []
test_cases = [
{
"name": "default_check1",
"input": {
"test_x": [
"Bro:U wan cut hair anot,ur hair long Liao bo\nMe:since ord liao,take it easy lor treat as save $ leave it longer :)\nBro:LOL Sibei xialan",
"@heyclaireee is back! thnx God!!! i'm so happy :)",
"@BBCRadio3 thought it was my ears which were malfunctioning, thank goodness you cleared that one up with an apology :-)",
"@HumayAG 'Stuck in the centre right with you. Clowns to the right, jokers to the left...' :) @orgasticpotency @ahmedshaheed @AhmedSaeedGahaa",
"Happy Friday :-) http://t.co/iymPIlWXFY",
"I wanna change my avi but uSanele :(",
"MY PUPPY BROKE HER FOOT :(",
"where's all the jaebum baby pictures :((",
"But but Mr Ahmad Maslan cooks too :( https://t.co/ArCiD31Zv6",
"@eawoman As a Hull supporter I am expecting a misserable few weeks :-(",
],
"test_y": np.array(
[
[1.0],
[1.0],
[1.0],
[1.0],
[1.0],
[0.0],
[0.0],
[0.0],
[0.0],
[0.0],
]
),
"freqs": freqs,
"theta": theta,
},
"expected": 1.0,
},
{
"name": "default_check1",
"input": {
"test_x": [
"Bro:U wan cut hair anot,ur hair long Liao bo\nMe:since ord liao,take it easy lor treat as save $ leave it longer :)\nBro:LOL Sibei xialan",
"@heyclaireee is back! thnx God!!! i'm so happy :)",
"@BBCRadio3 thought it was my ears which were malfunctioning, thank goodness you cleared that one up with an apology :-)",
"@HumayAG 'Stuck in the centre right with you. Clowns to the right, jokers to the left...' :) @orgasticpotency @ahmedshaheed @AhmedSaeedGahaa",
"Happy Friday :-) http://t.co/iymPIlWXFY",
"I wanna change my avi but uSanele :(",
"MY PUPPY BROKE HER FOOT :(",
"where's all the jaebum baby pictures :((",
"But but Mr Ahmad Maslan cooks too :( https://t.co/ArCiD31Zv6",
"@eawoman As a Hull supporter I am expecting a misserable few weeks :-(",
],
"test_y": np.array(
[
[1.0],
[1.0],
[1.0],
[1.0],
[1.0],
[0.0],
[0.0],
[0.0],
[0.0],
[0.0],
]
),
"freqs": freqs,
"theta": np.array([[5.0000e-04], [-3.4e-02], [3.2e-02]]),
},
"expected": 0.0,
},
]
for test_case in test_cases:
result = target(**test_case["input"])
try:
assert isinstance(result, np.float64)
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": np.float64,
"got": type(result),
}
)
print(
f"Wrong output type. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
try:
assert np.isclose(result, test_case["expected"])
successful_cases += 1
except:
failed_cases.append(
{
"name": test_case["name"],
"expected": test_case["expected"],
"got": result,
}
)
print(
f"Wrong accuracy value. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}."
)
if len(failed_cases) == 0:
print("\033[92m All tests passed")
else:
print("\033[92m", successful_cases, " Tests passed")
print("\033[91m", len(failed_cases), " Tests failed")