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
appid10470 1
appid10480 1
appid105200 4
appid10560 2
appid10590 3
appid10630 1
appid10650 17
appid10660 7
appid10690 18
appid107000 7
appid11060 9
appid111000 24
appid111010 22
appid11120 7
appid111300 10
appid11520 7
appid11550 16
appid11900 24
appid11920 16
appid12300 6
appid12400 5
appid12550 2
appid12630 10
appid12660 1
appid12860 4
appid1320 5
appid13650 1
appid13660 1
appid13700 21
appid15060 14
appid15140 4
appid15150 13
appid15160 7
appid15260 5
appid15280 1
appid15290 8
appid15330 9
appid15350 1
appid15800 1
appid15910 21
appid15940 8
appid15970 23
appid15980 6
appid15990 11
appid16090 2
appid16100 11
appid16110 12
appid16120 13
appid16130 16
appid16180 11
appid16200 3
appid16480 6
appid16500 12
appid16620 20
appid17400 3
appid17420 5
appid17600 4
appid17800 6
appid17810 6
appid18100 2
appid18430 11
appid19030 18
appid19080 2
appid19090 1
appid1920 6
appid19810 3
appid19910 1
appid19990 4
appid200150 11
appid200530 2
appid201330 7
appid20200 3
appid202570 19
appid202670 12
appid203850 24
appid204510 15
appid20590 21
appid206230 11
appid206740 21
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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 [ ]:
Content source: Ankhee/steam-games-graph
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