t-sne final



In [1]:
import pandas as pd
import numpy as np
from sklearn.manifold import TSNE

df = pd.read_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\profiles0_scraped_csv.csv",
                 index_col=0)
df.head()


Out[1]:
appid10 appid100 appid10000 appid1002 appid100400 appid100410 appid10080 appid10090 appid100970 appid100980 ... appid99700 appid9980 appid99810 appid99830 appid99890 appid9990 appid99900 appid99910 appid99920 profile
76561198028573551 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 76561198028573551
76561197976244447 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 76561197976244447
76561198319406913 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 76561198319406913
76561198073334029 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 76561198073334029
76561198105675882 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN ... NaN NaN NaN NaN NaN NaN NaN NaN NaN 76561198105675882

5 rows × 14792 columns


In [2]:
df.drop("profile", axis=1, inplace=True)
# df.drop("time", axis=1, inplace=True)
for index, col in df.iteritems():
    if col.count() < 25:
        print(index, col.count())
        df.drop(index, axis=1, inplace=True)
df.fillna(0, inplace=True)
df.head()


appid10000 20
appid100400 20
appid10080 15
appid100970 9
appid100980 11
appid10120 7
appid10140 14
appid10230 24
appid10400 5
appid104100 24
appid10420 1
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appid602790 9
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appid602920 1
appid602940 21
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appid603120 8
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appid603220 1
appid603230 2
appid603260 14
appid603630 4
appid603740 5
appid603790 1
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appid603810 17
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appid604000 2
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appid604380 19
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appid604400 9
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appid604830 10
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appid606580 1
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appid606651 4
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appid606690 2
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appid606860 7
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appid607270 4
appid607380 11
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appid608270 4
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appid608281 2
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appid608283 3
appid608284 2
appid608285 2
appid608286 3
appid608287 3
appid608288 2
appid608289 2
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appid608300 3
appid608301 3
appid608302 3
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appid608830 7
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appid608900 7
appid608901 7
appid608980 7
appid609020 2
appid609021 1
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appid609023 2
appid609024 1
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appid609026 1
appid609027 1
appid609028 1
appid609029 1
appid609030 1
appid609031 1
appid609032 3
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appid609041 1
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appid609051 2
appid609052 1
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appid609060 1
appid609061 1
appid609062 1
appid609063 1
appid609064 1
appid609065 1
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appid609067 1
appid609068 1
appid609069 1
appid609070 1
appid610310 1
appid610780 1
appid611090 7
appid611140 10
appid611320 8
appid611350 18
appid611530 14
appid612110 4
appid612170 21
appid61220 13
appid61230 2
appid612310 2
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appid612670 4
appid612790 9
appid612810 1
appid613240 1
appid613420 1
appid613730 1
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appid614050 3
appid614460 4
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appid614900 12
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appid617240 2
appid617590 17
appid617600 7
appid617700 3
appid617710 2
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appid619020 11
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appid619400 3
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appid622310 3
appid6290 13
appid6330 10
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appid6350 2
appid65100 9
appid65200 14
appid6820 3
appid71310 2
appid71320 20
appid7250 3
appid72500 23
appid73000 5
appid7340 5
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appid7440 3
appid7450 4
appid7470 3
appid7810 1
appid7870 14
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appid7900 8
appid7910 7
appid7970 22
appid7980 3
appid7990 1
appid8010 1
appid8040 24
appid8840 7
appid90800 9
appid92400 15
appid99120 19
appid99200 12
appid9970 16
Out[2]:
appid10 appid100 appid1002 appid100410 appid10090 appid10100 appid10110 appid10130 appid10150 appid10170 ... appid9960 appid99700 appid9980 appid99810 appid99830 appid99890 appid9990 appid99900 appid99910 appid99920
76561198028573551 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76561197976244447 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76561198319406913 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76561198073334029 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
76561198105675882 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

5 rows × 9536 columns


In [11]:
df_matrix = df.transpose().as_matrix()
df_cleaned = pd.DataFrame(df_matrix, index = [index for index, _ in df.iteritems()])
df_cleaned.to_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\_dfcleaned.csv")

In [3]:
df_matrix = df.transpose().as_matrix()
dim2 = TSNE(n_components=2, init='pca', verbose=2)
dim2_matrix = dim2.fit_transform(df_matrix)


[t-SNE] Computing pairwise distances...
[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Computed conditional probabilities for sample 1000 / 9536
[t-SNE] Computed conditional probabilities for sample 2000 / 9536
[t-SNE] Computed conditional probabilities for sample 3000 / 9536
[t-SNE] Computed conditional probabilities for sample 4000 / 9536
[t-SNE] Computed conditional probabilities for sample 5000 / 9536
[t-SNE] Computed conditional probabilities for sample 6000 / 9536
[t-SNE] Computed conditional probabilities for sample 7000 / 9536
[t-SNE] Computed conditional probabilities for sample 8000 / 9536
[t-SNE] Computed conditional probabilities for sample 9000 / 9536
[t-SNE] Computed conditional probabilities for sample 9536 / 9536
[t-SNE] Mean sigma: 0.000000
[t-SNE] Iteration 25: error = 0.9766449, gradient norm = 0.0057259
[t-SNE] Iteration 50: error = 0.9699630, gradient norm = 0.0048932
[t-SNE] Iteration 75: error = 0.9349148, gradient norm = 0.0028863
[t-SNE] Iteration 100: error = 0.9242552, gradient norm = 0.0027387
[t-SNE] KL divergence after 100 iterations with early exaggeration: 0.924255
[t-SNE] Iteration 125: error = 0.9022275, gradient norm = 0.0021417
[t-SNE] Iteration 150: error = 0.8940516, gradient norm = 0.0024183
[t-SNE] Iteration 175: error = 0.8917906, gradient norm = 0.0021139
[t-SNE] Iteration 200: error = 0.8911987, gradient norm = 0.0021493
[t-SNE] Iteration 225: error = 0.8909519, gradient norm = 0.0023410
[t-SNE] Iteration 250: error = 0.8908670, gradient norm = 0.0023514
[t-SNE] Iteration 275: error = 0.8909009, gradient norm = 0.0023638
[t-SNE] Iteration 300: error = 0.8909017, gradient norm = 0.0023530
[t-SNE] Iteration 300: did not make any progress during the last 30 episodes. Finished.
[t-SNE] Error after 300 iterations: 0.924255

In [8]:
df_dim2 = pd.DataFrame(dim2_matrix, index = [index for index, _ in df.iteritems()])
df_dim2.rename(index=str, columns = {0: 'x', 1: 'y'}, inplace=True)
df_dim2.to_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\_tsne2dim.csv")

In [9]:
df_matrix = df.transpose().as_matrix()
dim3 = TSNE(n_components=3, init='pca', verbose=2)
dim3_matrix = dim3.fit_transform(df_matrix)


[t-SNE] Computing pairwise distances...
[t-SNE] Computing 91 nearest neighbors...
[t-SNE] Computed conditional probabilities for sample 1000 / 9536
[t-SNE] Computed conditional probabilities for sample 2000 / 9536
[t-SNE] Computed conditional probabilities for sample 3000 / 9536
[t-SNE] Computed conditional probabilities for sample 4000 / 9536
[t-SNE] Computed conditional probabilities for sample 5000 / 9536
[t-SNE] Computed conditional probabilities for sample 6000 / 9536
[t-SNE] Computed conditional probabilities for sample 7000 / 9536
[t-SNE] Computed conditional probabilities for sample 8000 / 9536
[t-SNE] Computed conditional probabilities for sample 9000 / 9536
[t-SNE] Computed conditional probabilities for sample 9536 / 9536
[t-SNE] Mean sigma: 0.000000
[t-SNE] Iteration 25: error = 0.9565571, gradient norm = 0.0050417
[t-SNE] Iteration 50: error = 0.9452615, gradient norm = 0.0046845
[t-SNE] Iteration 75: error = 0.8818662, gradient norm = 0.0025988
[t-SNE] Iteration 100: error = 0.8622393, gradient norm = 0.0028478
[t-SNE] KL divergence after 100 iterations with early exaggeration: 0.862239
[t-SNE] Iteration 125: error = 0.8251112, gradient norm = 0.0016679
[t-SNE] Iteration 150: error = 0.8117050, gradient norm = 0.0015312
[t-SNE] Iteration 175: error = 0.8085733, gradient norm = 0.0014823
[t-SNE] Iteration 200: error = 0.8075957, gradient norm = 0.0014755
[t-SNE] Iteration 225: error = 0.8073301, gradient norm = 0.0014754
[t-SNE] Iteration 250: error = 0.8072733, gradient norm = 0.0014756
[t-SNE] Iteration 275: error = 0.8072502, gradient norm = 0.0014756
[t-SNE] Iteration 300: error = 0.8072429, gradient norm = 0.0014756
[t-SNE] Iteration 325: error = 0.8072408, gradient norm = 0.0014756
[t-SNE] Iteration 350: error = 0.8071882, gradient norm = 0.0014750
[t-SNE] Iteration 375: error = 0.8072405, gradient norm = 0.0014755
[t-SNE] Iteration 400: error = 0.8071808, gradient norm = 0.0014758
[t-SNE] Iteration 400: error difference 0.000000. Finished.
[t-SNE] Error after 400 iterations: 0.862239

In [10]:
df_dim3 = pd.DataFrame(dim3_matrix, index = [index for index, _ in df.iteritems()])
df_dim3.rename(index=str, columns = {0: 'x', 1: 'y', 2: 'z'}, inplace=True)
df_dim3.to_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\_tsne3dim.csv")

In [12]:
# COLORS! see https://github.com/kylemcdonald/Coloring-t-SNE/

import numpy as np
import pandas as pd
from sklearn.decomposition import IncrementalPCA

df = pd.read_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\_dfcleaned.csv", 
                     index_col=0)

def projection_to_colors(projection, bits_per_channel=8):
    basis = 2**np.arange(bits_per_channel)[::-1] # [128  64  32  16   8   4   2   1]
    basis = np.hstack([basis, basis, basis]) # [128  64  32  16   8   4   2   1 128  64  32  16   8   4   2   1 128  64 32  16   8   4   2   1] 
    shuffled = np.hstack([projection[:,0::3], projection[:,1::3], projection[:,2::3]]) # shuffles rows and concats back into 24-col array
    bits = (shuffled > np.median(shuffled, axis=0)) * basis # 0 or 1 when value is larger than mean value of said column, multiplied by 128, 64..
    colors = np.vstack([bits[:,:(bits_per_channel)].sum(axis=1),
                       bits[:,(bits_per_channel):(2*bits_per_channel)].sum(axis=1),
                       bits[:,(2*bits_per_channel):(3*bits_per_channel)].sum(axis=1)]).astype(float).T
    return colors / (2**bits_per_channel - 1)

def pack_binary_pca(data, bits_per_channel=8):
    bits_per_color = 3 * bits_per_channel
    pca = IncrementalPCA(n_components=bits_per_color)
    pca_projection = pca.fit_transform(data)
    # print('pca_projection', pca_projection,pca_projection.shape)
    return projection_to_colors(pca_projection, bits_per_channel)

%time colors = pack_binary_pca(df.as_matrix(), 8)

colors_df = pd.DataFrame(colors, index = [index for index, _ in df.iterrows()])
colors_df.rename(index=str, columns = {0: 'r', 1: 'g', 2: 'b'}, inplace=True)
colors_df.to_csv("C:\\Users\\Admin\\Documents\\GitHub\\GamesGraph\\scripts\\wip_data\\_colors_from_pca.csv")


Wall time: 6min 42s

In [ ]: