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kmeans

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LinlyBoi
2023-05-15 16:17:07 +03:00
parent 4dffa3dc88
commit d01fa8ee1d
1 changed files with 21 additions and 5 deletions
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26
dwarves/mining_hq.py
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@@ -6,6 +6,8 @@ import numpy as np
# containment breach # containment breach
import scipy as scp import scipy as scp
from sklearn.model_selection import train_test_split from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
from sklearn import metrics
import gunner, digger, gunner, scout import gunner, digger, gunner, scout
# Instantiating globals to be used in other files # Instantiating globals to be used in other files
@@ -114,11 +116,6 @@ print(gammas["Critic_Score_Norm"].head(10))
# Saving all into a file # Saving all into a file
gammas = gammas.dropna(how="any", axis=0) # nuke them empty poopers gammas = gammas.dropna(how="any", axis=0) # nuke them empty poopers
gammas.to_csv("datasets/videogames/games_cleanish.csv", index=False) gammas.to_csv("datasets/videogames/games_cleanish.csv", index=False)
# split the data set
gammas_train, gammas_test = train_test_split(gammas, test_size=0.20, random_state=69)
gammas_train.to_csv("datasets/videogames/games_train.csv", index=False)
gammas_test.to_csv("datasets/videogames/games_test.csv", index=False)
# Need similarity and dissimialrity, scipy time # Need similarity and dissimialrity, scipy time
# Selecting 5 random rows # Selecting 5 random rows
chosen_idx = np.random.choice(len(gammas), replace=False, size=5) chosen_idx = np.random.choice(len(gammas), replace=False, size=5)
@@ -127,3 +124,22 @@ print(sample_rows.head())
scout.dissimilarity(sample_rows) scout.dissimilarity(sample_rows)
scout.similarity(sample_rows) scout.similarity(sample_rows)
# split the data set
gammas_train, gammas_test = train_test_split(gammas, test_size=0.20, random_state=69)
gammas_train.to_csv("datasets/videogames/games_train.csv", index=False)
gammas_test.to_csv("datasets/videogames/games_test.csv", index=False)
# kmeans pls
gammas_train_kmeans = KMeans(n_clusters=10, random_state=420, n_init="auto").fit(
gammas_train[["Critic_Score", "User_Score", "Total_Shipped"]]
)
gammas_labels = gammas_train_kmeans.labels_
silh_score = metrics.silhouette_score(
gammas_train[["Critic_Score", "User_Score", "Total_Shipped"]],
gammas_labels,
metric="euclidean",
)
print(silh_score)
gammas_train["Kmean Labels"] = gammas_labels
print(gammas_train.head())