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import pickle |
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import numpy as np |
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import pandas as pd |
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def test_cosine_similarity(target): |
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successful_cases = 0 |
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failed_cases = [] |
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word_embeddings = pickle.load(open("./data/word_embeddings_subset.p", "rb")) |
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test_cases = [ |
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{ |
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"name": "cosine_score_1", |
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"input": {"A": word_embeddings["king"], "B": word_embeddings["queen"]}, |
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"expected": [0.650, 0.6512, 0.6510957], |
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}, |
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{ |
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"name": "cosine_score_2", |
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"input": {"A": word_embeddings["Japan"], "B": word_embeddings["Tokyo"]}, |
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"expected": [0.699, 0.701, 0.70022535], |
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}, |
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{ |
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"name": "cosine_score_3", |
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"input": {"A": word_embeddings["Germany"], "B": word_embeddings["Beirut"]}, |
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"expected": [0.172, 0.174, 0.17339969], |
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}, |
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{ |
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"name": "cosine_score_4_to_catch_alternate_solution", |
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"input": {"A": word_embeddings["China"], "B": word_embeddings["Chile"]}, |
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"expected": [0.32, 0.381, 0.3801232], |
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}, |
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] |
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for test_case in test_cases: |
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result = target(**test_case["input"]) |
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try: |
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assert np.isclose(result, test_case["expected"][2]) or ( |
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test_case["expected"][0] <= result <= test_case["expected"][1] |
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) |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": test_case["expected"][2], |
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"got": result, |
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} |
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) |
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print( |
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f"Wrong output in cosine similarity function. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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if len(failed_cases) == 0: |
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print("\033[92m All tests passed") |
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else: |
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print("\033[92m", successful_cases, " Tests passed") |
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print("\033[91m", len(failed_cases), " Tests failed") |
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def test_euclidean(target): |
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successful_cases = 0 |
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failed_cases = [] |
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word_embeddings = pickle.load(open("./data/word_embeddings_subset.p", "rb")) |
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test_cases = [ |
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{ |
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"name": "euclidean_score_1", |
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"input": {"A": word_embeddings["king"], "B": word_embeddings["queen"]}, |
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"expected": [2.47, 2.48, 2.4796925], |
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}, |
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{ |
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"name": "euclidean_score_2", |
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"input": {"A": word_embeddings["Japan"], "B": word_embeddings["Tokyo"]}, |
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"expected": [2.43, 2.44, 2.4345345], |
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}, |
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{ |
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"name": "euclidean_score_3", |
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"input": {"A": word_embeddings["Germany"], "B": word_embeddings["Beirut"]}, |
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"expected": [4.0, 4.1, 4.0416517], |
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}, |
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{ |
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"name": "euclidean_score_4", |
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"input": {"A": word_embeddings["China"], "B": word_embeddings["Chile"]}, |
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"expected": [3.2, 3.3, 3.2326782], |
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}, |
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] |
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for test_case in test_cases: |
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result = target(**test_case["input"]) |
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try: |
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assert np.isclose( |
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result, test_case["expected"][2], rtol=1e-3, atol=1e-05 |
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) or (test_case["expected"][0] <= result <= test_case["expected"][1]) |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": test_case["expected"][2], |
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"got": result, |
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} |
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) |
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print( |
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f"Wrong output in the euclidean distance function. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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if len(failed_cases) == 0: |
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print("\033[92m All tests passed") |
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else: |
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print("\033[92m", successful_cases, " Tests passed") |
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print("\033[91m", len(failed_cases), " Tests failed") |
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def test_get_country(target): |
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successful_cases = 0 |
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failed_cases = [] |
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word_embeddings = pickle.load(open("./data/word_embeddings_subset.p", "rb")) |
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test_cases = [ |
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{ |
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"name": "get_country_score_1", |
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"input": { |
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"city1": "Athens", |
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"country1": "Greece", |
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"city2": "Cairo", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("Egypt", 0.7626821), |
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}, |
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{ |
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"name": "get_country_score_2_for_wrong_cosine_similarity", |
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"input": { |
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"city1": "oil", |
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"country1": "gas", |
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"city2": "town", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("village", 0.5611889), |
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}, |
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{ |
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"name": "get_country_score_3", |
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"input": { |
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"city1": "Doha", |
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"country1": "Qatar", |
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"city2": "Jakarta", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("Indonesia", 0.6782036), |
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}, |
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{ |
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"name": "get_country_score_4", |
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"input": { |
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"city1": "Tokyo", |
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"country1": "Japan", |
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"city2": "Canberra", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("Australia", 0.7139509), |
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}, |
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{ |
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"name": "get_country_score_5_for_wrong_cosine_similarity", |
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"input": { |
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"city1": "joyful", |
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"country1": "happy", |
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"city2": "sad", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("king", 0.09570546), |
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}, |
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{ |
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"name": "get_country_score_6_for_wrong_cosine_similarity", |
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"input": { |
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"city1": "happy", |
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"country1": "joyful", |
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"city2": "sad", |
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"embeddings": word_embeddings, |
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}, |
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"expected": ("Lebanon", 0.14527377), |
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}, |
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] |
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for test_case in test_cases: |
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result = target(**test_case["input"]) |
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try: |
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assert isinstance(result, tuple) |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": type(test_case["expected"]), |
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"got": type(result), |
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} |
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) |
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print( |
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f"Wrong output type. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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try: |
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assert result[0] == test_case["expected"][0] |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": test_case["expected"][0], |
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"got": result[0], |
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} |
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) |
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print( |
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f"Wrong output word. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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try: |
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assert np.isclose(result[1], test_case["expected"][1]) |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": test_case["expected"][1], |
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"got": result[1], |
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} |
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) |
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print( |
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f"Wrong output similarity. Maybe you should check your cosine_similarity implementation. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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if len(failed_cases) == 0: |
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print("\033[92m All tests passed") |
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else: |
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print("\033[92m", successful_cases, " Tests passed") |
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print("\033[91m", len(failed_cases), " Tests failed") |
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def test_get_accuracy(target, data): |
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successful_cases = 0 |
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failed_cases = [] |
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word_embeddings = pickle.load(open("./data/word_embeddings_subset.p", "rb")) |
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test_cases = [ |
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{ |
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"name": "default_check", |
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"input": {"word_embeddings": word_embeddings, "data": data}, |
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"expected": 0.9192082407594425, |
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}, |
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{ |
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"name": "smaller_check", |
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"input": { |
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"word_embeddings": word_embeddings, |
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"data": data.sample(frac=0.15, random_state=3), |
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}, |
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"expected": 0.9125168236877523, |
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}, |
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] |
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for test_case in test_cases: |
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result = target(**test_case["input"]) |
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try: |
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assert np.isclose(result, test_case["expected"]) |
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successful_cases += 1 |
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except: |
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failed_cases.append( |
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{ |
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"name": test_case["name"], |
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"expected": test_case["expected"], |
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"got": result, |
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} |
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) |
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print( |
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f"Wrong accuracy output. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
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) |
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if len(failed_cases) == 0: |
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print("\033[92m All tests passed") |
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else: |
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print("\033[92m", successful_cases, " Tests passed") |
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print("\033[91m", len(failed_cases), " Tests failed") |
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def test_compute_pca(target): |
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successful_cases = 0 |
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failed_cases = [] |
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test_cases = [ |
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{ |
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"name": "default_check", |
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"input": { |
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"X": np.array( |
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[ |
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[ |
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4.17022005e-01, |
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7.20324493e-01, |
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1.14374817e-04, |
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3.02332573e-01, |
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1.46755891e-01, |
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9.23385948e-02, |
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1.86260211e-01, |
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3.45560727e-01, |
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3.96767474e-01, |
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5.38816734e-01, |
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], |
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[ |
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4.19194514e-01, |
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6.85219500e-01, |
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2.04452250e-01, |
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8.78117436e-01, |
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2.73875932e-02, |
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6.70467510e-01, |
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4.17304802e-01, |
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5.58689828e-01, |
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1.40386939e-01, |
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1.98101489e-01, |
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], |
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[ |
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8.00744569e-01, |
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9.68261576e-01, |
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3.13424178e-01, |
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6.92322616e-01, |
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8.76389152e-01, |
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8.94606664e-01, |
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8.50442114e-02, |
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3.90547832e-02, |
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1.69830420e-01, |
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8.78142503e-01, |
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], |
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] |
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), |
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"n_components": 2, |
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}, |
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"expected": np.array( |
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[ |
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[0.43437323, 0.49820384], |
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[0.42077249, -0.50351448], |
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[-0.85514571, 0.00531064], |
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] |
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), |
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}, |
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{ |
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"name": "larger_check", |
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"input": { |
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"X": np.array( |
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[ |
|
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[ |
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0.4359949, |
|
|
0.02592623, |
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|
0.54966248, |
|
|
0.43532239, |
|
|
0.4203678, |
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0.33033482, |
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0.20464863, |
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0.61927097, |
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0.29965467, |
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0.26682728, |
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0.62113383, |
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0.52914209, |
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0.13457995, |
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0.51357812, |
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0.18443987, |
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], |
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[ |
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0.78533515, |
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|
0.85397529, |
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|
0.49423684, |
|
|
0.84656149, |
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|
0.07964548, |
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|
0.50524609, |
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|
0.0652865, |
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|
0.42812233, |
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|
0.09653092, |
|
|
0.12715997, |
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|
0.59674531, |
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0.226012, |
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0.10694568, |
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0.22030621, |
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0.34982629, |
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], |
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[ |
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0.46778748, |
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|
0.20174323, |
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|
0.64040673, |
|
|
0.48306984, |
|
|
0.50523672, |
|
|
0.38689265, |
|
|
0.79363745, |
|
|
0.58000418, |
|
|
0.1622986, |
|
|
0.70075235, |
|
|
0.96455108, |
|
|
0.50000836, |
|
|
0.88952006, |
|
|
0.34161365, |
|
|
0.56714413, |
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|
], |
|
|
[ |
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|
0.42754596, |
|
|
0.43674726, |
|
|
0.77655918, |
|
|
0.53560417, |
|
|
0.95374223, |
|
|
0.54420816, |
|
|
0.08209492, |
|
|
0.3663424, |
|
|
0.8508505, |
|
|
0.40627504, |
|
|
0.02720237, |
|
|
0.24717724, |
|
|
0.06714437, |
|
|
0.99385201, |
|
|
0.97058031, |
|
|
], |
|
|
[ |
|
|
0.80025835, |
|
|
0.60181712, |
|
|
0.76495986, |
|
|
0.16922545, |
|
|
0.29302323, |
|
|
0.52406688, |
|
|
0.35662428, |
|
|
0.04567897, |
|
|
0.98315345, |
|
|
0.44135492, |
|
|
0.50400044, |
|
|
0.32354132, |
|
|
0.25974475, |
|
|
0.38688989, |
|
|
0.8320169, |
|
|
], |
|
|
] |
|
|
), |
|
|
"n_components": 3, |
|
|
}, |
|
|
"expected": np.array( |
|
|
[ |
|
|
[-0.32462796, 0.01881248, -0.51389463], |
|
|
[-0.36781354, 0.88364184, 0.05985815], |
|
|
[-0.75767901, -0.69452194, 0.12223214], |
|
|
[1.01698298, -0.17990871, -0.33555475], |
|
|
[0.43313753, -0.02802368, 0.66735909], |
|
|
] |
|
|
), |
|
|
}, |
|
|
] |
|
|
|
|
|
for test_case in test_cases: |
|
|
result = target(**test_case["input"]) |
|
|
|
|
|
try: |
|
|
assert isinstance(result, np.ndarray) |
|
|
successful_cases += 1 |
|
|
except: |
|
|
failed_cases.append( |
|
|
{ |
|
|
"name": test_case["name"], |
|
|
"expected": np.ndarray, |
|
|
"got": type(result), |
|
|
} |
|
|
) |
|
|
print( |
|
|
f"Wrong output type. \n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
|
|
) |
|
|
|
|
|
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. Check if you are taking the proper number of dimensions.\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 accuracy output.\n\tExpected: {failed_cases[-1].get('expected')}.\n\tGot: {failed_cases[-1].get('got')}." |
|
|
) |
|
|
|
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if len(failed_cases) == 0: |
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print("\033[92m All tests passed") |
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else: |
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print("\033[92m", successful_cases, " Tests passed") |
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print("\033[91m", len(failed_cases), " Tests failed") |
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