Commit 572d923b authored by Aileen Reichelt's avatar Aileen Reichelt
Browse files

Sync changes from colab (cosine DIST to SIM)

parent cf4fae2b

weat_reproduction.ipynb

+47 −30
Original line number Diff line number Diff line 
%% Cell type:code id: tags:



``` python

from scipy.spatial.distance import cosine

%pip install fasttext

```



%% Cell type:code id: tags:



``` python

from google.colab import drive

drive.mount('/content/drive')

```



%% Output



    Mounted at /content/drive



%% Cell type:code id: tags:



``` python

from sklearn.metrics.pairwise import cosine_similarity as cosine

import numpy as np

from itertools import combinations, islice

import fasttext

```



%% Cell type:code id: tags:



``` python

np.random.seed(42)

```



%% Cell type:code id: tags:



``` python

# names and words as per Kurpicz-Briki 2020

# vectors copied and pasted from output file "vectors.txt" which got produced by weat_reproduction_vectors.py

A = {

    "Peter": [1.30510051e-03, -5.08842617e-03, 3.75517942e-02, 1.08466148e-02, 2.76715644e-02, 3.42287235e-02, -5.58771901e-02, 5.22472449e-02, 2.01407429e-02, -7.32279420e-02, 1.15079228e-02, 4.67902049e-03, -4.27846387e-02, -1.41070317e-03, 1.89671982e-02, 1.08954664e-02, 1.56221300e-01, -5.51430732e-02, -1.95362568e-02, 5.61355799e-02, 5.54958582e-02, -2.62831300e-02, -1.13851223e-02, 3.80259473e-03, 1.39492126e-02, 2.39284802e-02, -6.71362830e-03, -7.43165985e-03, -7.58671714e-03, 2.37719901e-02, -1.22724488e-01, -2.37718690e-02, -2.32288502e-02, -3.39118689e-02, -2.48577818e-02, 6.65033562e-03, -6.42682286e-03, -2.34186649e-04, -5.13004623e-02, 5.27133346e-02, 3.29300351e-02, -4.53031901e-03, 8.23754668e-02, -5.83701301e-04, -3.57623622e-02, -5.65942563e-03, 4.85159829e-02, 7.93073475e-02, 2.81627011e-03, 1.16489619e-01, -8.09962582e-03, 5.25344256e-03, -1.82690099e-04, -1.15161482e-02, 4.87077143e-03, 2.27340609e-02, -2.42659319e-02, 1.23414462e-02, 1.28962696e-02, 8.45453609e-03, -3.16633023e-02, -6.35160878e-03, -1.64190754e-02, -2.06807479e-02, -1.45387053e-02, -4.31240611e-02, 7.04212040e-02, -1.81752890e-02, 2.32929904e-02, 2.48948727e-02, 5.20726740e-02, -2.67387200e-02, 7.68177118e-03, -1.66655164e-02, 5.68796508e-02, -2.17142031e-02, -3.66420113e-02, -4.64846119e-02, -1.02316756e-02, 1.41008431e-02, -3.79884318e-02, -1.23578563e-01, -2.69708745e-02, -1.47822741e-02, -8.78412351e-02, 1.12614691e-01, -6.12347387e-02, -4.31382935e-03, -1.15508009e-02, 2.47146040e-02, 7.45444447e-02, 3.97270262e-01, -8.78935680e-02, -1.82931982e-02, -6.98829517e-02, -2.92605590e-02, -1.25390952e-02, -1.06970876e-01, -3.13056633e-02, 4.46038693e-03, 1.41855460e-02, -1.06732100e-02, 2.37745475e-02, -1.89669859e-02, -3.26256454e-02, -7.54329115e-02, 6.81317504e-03, 3.37612331e-02, 1.95681229e-02, 1.43691637e-02, 1.77318696e-02, 4.57271338e-02, -7.16301799e-03, -2.48377994e-02, 4.37149778e-03, 2.61858348e-02, 2.29622424e-03, -3.24722677e-02, 2.13739816e-02, -4.67488542e-03, 6.41578436e-02, 3.68293412e-02, -5.18823564e-02, -7.59700360e-03, 3.89139429e-02, 2.55057812e-02, -6.52322620e-02, -2.13685688e-02, 2.49576848e-02, -2.91764550e-02, -7.86766261e-02, 1.20354809e-01, 1.24494303e-02, 7.95155391e-02, 6.65257648e-02, 6.64366409e-02, 3.64274122e-02, 8.52738842e-02, 3.17443013e-02, 1.15469582e-02, 9.64375660e-02, -1.58811301e-01, -4.86994870e-02, 6.65284693e-03, -7.02694245e-03, -2.87044477e-02, 1.48679791e-02, 9.19473246e-02, -7.84127973e-04, -3.21534695e-03, 3.37267295e-02, 8.22991133e-04, 3.70076858e-02, 2.14180335e-01, 6.89787045e-02, -5.11198957e-03, -2.32495517e-02, -7.09315948e-03, 1.17758373e-02, -3.28491442e-02, -4.41560410e-02, 3.34748253e-02, 3.16593610e-02, -5.25549278e-02, -1.16120372e-02, 2.40153093e-02, 4.29542325e-02, -2.25029159e-02, -3.04618925e-02, -1.54144969e-03, -1.07113440e-02, 7.17588235e-03, 1.93383861e-02, -1.01669598e-03, -2.34595742e-02, 2.98131518e-02, 4.92391884e-02, -1.24873668e-02, 5.76976389e-02, 4.92615625e-02, 5.83112277e-02, 2.20610462e-02, 9.28400159e-02, -1.42824963e-01, -1.54371299e-02, -3.71951088e-02, 1.04882285e-01, 7.00968206e-02, -1.93535909e-03, 1.48898354e-02, -2.63313521e-02, -1.47363236e-02, -3.60105373e-02, 1.43261459e-02, -1.10473465e-02, 1.28881708e-01, 8.41979906e-02, -1.76961608e-02, -5.71419019e-04, -5.88932112e-02, 1.43955171e-01, -5.62796108e-02, -2.03224681e-02, 2.31422335e-02, 8.34433176e-03, 3.55136096e-02, 2.83356272e-02, -2.35773735e-02, -2.27429084e-02, -3.84787805e-02, 2.02939622e-02, 7.34768212e-02, -3.66599709e-02, 4.11577187e-02, 4.90642861e-02, -2.58062202e-02, -1.39653802e-01, 4.08925628e-03, 1.91960819e-02, -2.71783266e-02, -8.53594579e-03, -1.64592154e-02, -2.67967209e-02, 2.93173231e-02, 2.26567611e-02, 4.41789255e-02, -2.97275744e-02, 4.41407412e-03, -2.09276173e-02, 4.09975275e-02, 9.73287672e-02, -2.09102873e-02, -2.22138520e-02, -7.44027831e-03, 1.81558728e-01, -4.86836284e-02, 1.57423094e-02, -1.27776135e-02, -5.24293259e-03, -2.16024928e-04, -2.81784032e-03, 4.03835252e-02, -6.22062385e-03, -8.18042830e-02, 2.14127786e-02, 1.30096590e-02, -5.98603562e-02, -1.65367853e-02, -6.95040682e-03, 3.62128243e-02, -4.78424281e-02, 5.03532998e-02, -9.14288089e-02, -2.09806822e-02, 4.82726246e-02, 8.13319720e-03, 4.28929459e-03, -4.20476645e-02, 6.49334043e-02, 3.01586948e-02, -3.87246814e-03, -1.45675465e-02, -1.31122656e-02, -2.06421688e-03, 1.09762084e-02, 9.91224684e-03, -5.02934912e-04, 1.32820504e-02, 2.62503233e-02, 1.77233696e-01, -1.65441562e-03, -6.13052696e-02, -1.66561276e-01, -9.34360828e-03, 6.09870255e-03, 1.02998503e-02, 1.26445919e-01, -1.79331657e-02, -9.05540511e-02, 1.01842461e-02, -2.33132705e-01, -4.35102582e-02, 1.39146551e-01, 4.79026772e-02, -2.83947755e-02, -2.10007560e-02, 1.89622212e-03, 8.66729766e-03, -1.24250129e-02, 4.35968712e-02, -2.13938281e-02, 1.43771702e-02, 1.34118153e-02, -2.10571289e-03, -4.41115126e-02, 6.17400371e-02, 3.31647694e-02, -3.20875421e-02, 9.76765901e-02, -4.82707657e-03],

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}



B = {

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    "Versagen": [2.16300879e-02, -3.67377065e-02, -6.70705736e-03, 4.50690426e-02, -1.65741798e-02, -6.52637258e-02, 1.14325760e-02, -1.95046386e-03, 5.55940391e-03, -8.95650014e-02, 3.84908989e-02, -1.94154195e-02, 8.30326136e-03, -4.43386696e-02, 8.59699212e-03, 3.13691376e-03, 9.05513540e-02, 4.05145846e-02, 3.84223945e-02, -3.34761739e-02, 4.00390811e-02, 9.36319493e-03, -4.13590185e-02, 3.87151130e-02, 1.02511309e-02, -5.23667000e-02, -1.49661927e-02, 3.20104416e-03, -1.58437286e-02, -2.28829514e-02, 2.10357942e-02, -2.55504660e-02, 1.23754106e-02, 1.67310459e-03, -4.48020548e-02, 2.43513566e-02, 5.68008749e-03, 2.60669179e-02, 6.01899736e-02, -5.22245914e-02, -3.34317191e-03, 5.94784366e-03, -2.58426387e-02, -6.20170049e-02, -5.54340258e-02, 3.35053168e-02, -2.41807811e-02, -5.11077121e-02, 6.15920350e-02, 2.19859406e-02, -1.43306181e-02, -1.89875066e-02, -5.05392067e-02, -4.96712560e-03, 2.28337478e-02, 6.58568460e-03, 4.33356268e-03, -1.26468800e-02, 3.00080124e-02, 7.77030457e-03, -2.12209858e-02, -1.07181314e-02, 3.91817419e-03, -3.34879272e-02, 3.00832447e-02, -1.01290001e-02, 1.26518095e-02, -8.52486268e-02, -6.23203069e-02, -1.97548373e-03, 3.80374044e-02, -1.27034951e-02, -3.72979641e-02, 2.37437394e-02, 8.25168099e-03, 1.03797782e-02, -1.98551323e-02, -3.72315459e-02, -2.23435853e-02, -9.37397312e-03, -1.56416488e-03, -2.62623969e-02, 1.55421032e-03, 1.63787138e-02, -3.57584842e-02, 3.22076157e-02, -1.81553438e-02, -5.48372790e-02, 4.61008586e-03, -4.80247997e-02, 1.84836742e-02, 3.76109667e-02, -2.18248670e-03, 5.87845147e-02, -3.48306373e-02, -6.48338627e-03, -3.82526107e-02, -3.85745615e-02, -2.32559759e-02, -1.04145601e-03, 1.47861857e-02, -5.79034761e-02, 1.44159161e-02, -4.25224677e-02, 4.13171425e-02, 8.21480155e-02, 3.95518020e-02, 5.26165543e-03, -2.03830991e-02, -4.79147956e-02, -8.89163241e-02, -1.50684342e-02, -1.39889549e-02, 4.65862602e-02, -5.05126528e-02, 5.50701283e-03, -8.31312966e-03, 6.14055607e-05, 2.01290082e-02, -3.04709165e-03, -1.08930469e-01, 2.52147131e-02, 4.91034165e-02, -3.63854654e-02, -9.43457894e-03, -2.26860158e-02, -3.15462351e-02, -1.36103500e-02, 7.24818034e-04, -5.36577515e-02, 6.79103583e-02, -1.41802505e-02, -5.17122820e-02, -2.68090982e-02, -4.72191088e-02, 7.16245617e-04, -2.66579110e-02, 3.99144180e-02, -1.86459217e-02, 3.42071764e-02, -2.68489048e-02, -4.47375998e-02, 7.61248823e-03, -1.55924689e-02, -3.31643894e-02, -4.94597480e-02, -1.77100822e-02, 3.42075005e-02, -3.26224323e-03, -9.53740161e-03, 8.33578315e-03, -4.23545309e-04, 4.69625108e-02, -4.71549220e-02, 3.84666398e-02, 1.63574740e-02, -1.46095436e-02, 1.70715880e-02, -4.81061731e-03, -8.53035375e-02, 2.98752058e-02, 4.73387074e-03, -2.71136798e-02, -1.22233923e-03, 1.34174079e-02, -8.50502867e-03, 5.96712669e-03, -2.54670694e-03, 3.18076387e-02, 1.10895447e-02, 2.97324434e-02, 7.85674080e-02, 2.75761783e-02, 6.01844303e-02, 9.25832242e-03, 9.09834355e-02, -4.82213385e-02, -1.21635757e-02, 4.03720979e-03, 1.01036197e-02, 7.88773596e-03, -1.49228321e-02, 4.93326858e-02, -3.59172300e-02, -5.36763109e-02, -4.47849147e-02, 2.92628594e-02, -2.41644364e-02, -2.97537036e-02, 1.16427075e-02, 2.91341096e-02, 1.29965432e-02, -9.49432421e-03, 1.45364963e-02, -5.92791587e-02, 7.65234791e-03, 6.88512251e-02, 3.03782318e-02, 3.73202318e-04, 9.79856588e-03, -7.13158445e-03, 3.23625430e-02, -7.67169660e-03, 7.83887692e-03, 5.82986325e-02, 2.63717268e-02, -5.11347167e-02, -5.31423464e-02, -3.49396914e-02, 1.65668093e-02, 5.56667186e-02, -2.27529462e-02, -4.17272262e-02, 2.84549184e-02, 4.50903662e-02, 3.22217122e-02, 9.42747854e-03, 2.26430222e-02, 3.15370336e-02, 4.92527038e-02, -2.18442408e-03, -1.85509101e-02, 7.69141875e-03, 2.46580895e-02, 6.41135219e-03, 2.01237332e-02, 9.98840667e-03, 1.91025287e-02, 2.36063544e-02, 1.47125386e-02, 4.33573313e-02, 3.10153309e-02, 1.33874323e-02, 1.94050260e-02, 4.51295525e-02, 4.00410369e-02, -1.23033803e-02, -3.48118017e-03, -1.82310503e-03, 3.79981380e-03, 1.63473636e-02, -2.62550283e-02, -6.83508348e-03, -5.55094332e-02, 2.17260439e-02, 4.67461534e-02, 1.85878221e-02, -2.75286473e-02, -5.82901686e-02, -2.51152236e-02, 1.67801362e-02, -1.13718258e-02, 3.29802036e-02, -8.69373512e-03, -3.40674967e-02, 2.66510025e-02, 5.25921164e-03, 2.87155733e-02, -2.46202778e-02, -2.86633223e-02, -7.19205430e-03, 1.13209216e-02, -1.23677872e-01, 4.18893769e-02, 2.34424081e-02, -1.73209477e-02, 2.38107163e-02, 5.54139800e-02, -9.87559371e-03, -2.00106502e-02, 2.20716111e-02, 2.97704488e-02, -8.14967901e-02, -2.48850882e-03, 8.81967135e-03, -3.32272537e-02, 5.89142069e-02, -1.28297438e-03, -2.34463643e-02, -2.66914424e-02, -4.92406776e-03, -3.64634395e-02, -7.65622035e-02, -1.39979608e-02, 1.78422835e-02, 2.49008965e-02, 3.78775671e-02, -2.09846366e-02, 4.57040742e-02, 2.61497088e-02, 2.69888248e-03, -6.63390709e-03, 2.29781936e-03, 1.03185121e-02, 1.02985306e-02, -4.24420042e-03, 7.17425570e-02, 2.40989719e-02, 2.71775723e-02, 4.63960087e-03]

}

```



%% Cell type:code id: tags:



``` python

# Load model into colab drive

!gunzip /content/drive/MyDrive/BA/cc.de.300.bin.gz -c > /content/cc.de.300.bin

```



%% Cell type:code id: tags:



``` python

# Check if all the words are in the model vocabulary

ft = fasttext.load_model("/content/cc.de.300.bin")

for words in [A, B, X, Y]:

    for word in words:

        if word not in ft:

            print(f"{word} not in vocabulary")

          print(f"{word} not in vocabulary")

```



%% Cell type:code id: tags:



``` python

def differential_association_s(target1, target2, attr1, attr2):

    sum1 = sum([attribute_association_s(vec, attr1, attr2) for vec in list(target1.values())])

    sum2 = sum([attribute_association_s(vec, attr1, attr2) for vec in list(target2.values())])

    return sum1 - sum2

def attribute_association_s(word_vector, attr_set1, attr_set2):

    reshaped_word_vector = np.array(word_vector).reshape(1, -1)

    sims1 = [cosine(reshaped_word_vector, np.array(vec).reshape(1, -1)) for vec in list(attr_set1.values())]

    sims2 = [cosine(reshaped_word_vector, np.array(vec).reshape(1, -1)) for vec in list(attr_set2.values())]

    return np.mean(sims1) - np.mean(sims2)

```



%% Cell type:code id: tags:



``` python

def attribute_association_s(word_vector, attr_set1, attr_set2):

    sims1 = [cosine(word_vector, vec) for vec in list(attr_set1.values())]

    sims2 = [cosine(word_vector, vec) for vec in list(attr_set2.values())]

    return np.mean(sims1) - np.mean(sims2)

def differential_association_s(target1, target2, attr1, attr2):

    sum1 = sum([attribute_association_s(vec, attr1, attr2) for vec in list(target1.values())])

    sum2 = sum([attribute_association_s(vec, attr1, attr2) for vec in list(target2.values())])

    return sum1 - sum2

```



%% Cell type:code id: tags:



``` python

original_diff_association = differential_association_s(X, Y, A, B)

original_diff_association

```



%% Output



    -0.2237976959104735



%% Cell type:code id: tags:



``` python

def cohens_d(target1, target2, attr1, attr2):

    mean1 = np.mean([attribute_association_s(x, attr1, attr2) for x in list(target1.values())])

    mean2 = np.mean([attribute_association_s(x, attr1, attr2) for x in list(target2.values())])

    join = list(target1.values()) + (list(target2.values()))

    joint_association = [attribute_association_s(x, attr1, attr2) for x in join]

    stddev = np.std(joint_association)

    return (mean1 - mean2) / stddev

```



%% Cell type:code id: tags:



``` python

cohens_d(X, Y, A, B)

```



%% Output



    -1.3178438472250877

    -1.3593411124410815



%% Cell type:code id: tags:



``` python

def permutations(attr1, attr2):

    """attributes or targets?"""

    join = list(attr1.keys()) + list(attr2.keys())

    combs = list(islice(combinations(join, int(len(join)/2)), 10000))

def permutations(target1, target2):

    join = list(target1.keys()) + list(target2.keys())

    combs = list(islice(combinations(join, int(len(join)/2)), 100000))

    first_groups = []

    second_groups = []

    for c in combs:

        rest = []

        for e in join:

            if e not in c:

                rest.append(e)

        first_groups.append(c)

        second_groups.append(rest)

    return first_groups, second_groups

```



%% Cell type:code id: tags:



``` python

A_i, B_i = permutations(A, B)

X_i, Y_i = permutations(X, Y)

```



%% Cell type:code id: tags:



``` python

def p_value(comparison, A_perms, B_perms, target1, target2, A, B):

def p_value(comparison, X_perms, Y_perms, target1, target2, attr1, attr2):

    counter = 0

    joint_dict = {**A, **B}  # to easily get access to all word-vector pairs

    for i, _ in enumerate(A_perms):

    joint_dict = {**target1, **target2}  # to easily get access to all word-vector pairs

    for i, _ in enumerate(X_perms):

        print(i, end="\r")  # progress indicator

        A_subset = dict()  # in A_perms there are only the keys, we need the values (vectors) back

        for key in A_perms[i]:

            A_subset[key] = joint_dict[key]

        B_subset = dict()

        for key in B_perms[i]:

            B_subset[key] = joint_dict[key]

        if differential_association_s(target1, target2, A_subset, B_subset) >= comparison:  # in paper: >

        X_subset = dict()  # in A_perms there are only the keys, we need the values (vectors) back

        for key in X_perms[i]:

            X_subset[key] = joint_dict[key]

        Y_subset = dict()

        for key in Y_perms[i]:

            Y_subset[key] = joint_dict[key]

        if differential_association_s(X_subset, Y_subset, A, B) > comparison:  # why not >=?

            counter += 1

    return 0/len(A_perms)

    return counter

```



%% Cell type:code id: tags:



``` python

p_value(original_diff_association, A_i, B_i, X, Y, A, B)

p_value_count = p_value(original_diff_association, X_i, Y_i, X, Y, A, B)

p_value_count/100000

```



%% Output



    9999



    0.0
 
    6412