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Tim
2025-08-18 20:22:59 +02:00
parent 28df88c0bf
commit 530d312dfd
31 changed files with 725 additions and 20 deletions
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17
compare_dataset_sizes.py → compare_graph_maker.py
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@@ -22,17 +22,20 @@ for dataset_name, folder in datasets.items():
results[dataset_name][model_name] = f1_score results[dataset_name][model_name] = f1_score
# Plot # Plot
models = sorted(results["cleaned"].keys()) # alphabetisch sortieren für gleiche Reihenfolge #models = sorted(results["cleaned_2k"].keys()) # alphabetisch sortieren für gleiche Reihenfolge
models = dict(sorted(results["cleaned_2k"].items(), key=lambda i: i[1], reverse=True)) # nach values sortieren
x = range(len(models)) x = range(len(models))
plt.figure(figsize=(12,6)) plt.figure(figsize=(12,6))
plt.bar([i - 0.25 for i in x], [results["cleaned"][m] for m in models], width=0.25, label="cleaned") #plt.bar([i - 0.25 for i in x], [results["cleaned"][m] for m in models], width=0.25, label="cleaned")
plt.bar(x, [results["cleaned_2k"][m] for m in models], width=0.25, label="cleaned_2k") plt.bar(x, [results["cleaned_2k"][m] for m in models], width=0.5)#, label="cleaned_2k")
plt.bar([i + 0.25 for i in x], [results["cleaned_10k"][m] for m in models], width=0.25, label="cleaned_10k") #plt.bar([i + 0.25 for i in x], [results["cleaned_10k"][m] for m in models], width=0.25, label="cleaned_10k")
plt.xticks(x, models, rotation=45) plt.xticks(x, models, rotation=90)
plt.ylabel("F1-Score") plt.ylim(0, 1) # min max
plt.ylabel("Weighted F1-Score")
plt.title("Model Performance across Datasets") plt.title("Model Performance across Datasets")
plt.legend() #plt.legend()
plt.tight_layout() plt.tight_layout()
plt.savefig('compare_graph_latest.png')
plt.show() plt.show()

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compare_graph_maker_3.py Normal file
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@@ -0,0 +1,59 @@
import os
import matplotlib.pyplot as plt
import numpy as np
datasets = {
#"cleaned": "games_march2025_cleaned",
#"cleaned_2k": "games_march2025_cleaned_2k",
#"cleaned_10k": "games_march2025_cleaned_10k"
"cleaned_2k": "games_march2025_cleaned_2k_i3k",
}
# def results
results = {}
for dataset_name, folder in datasets.items():
results[dataset_name] = {}
for filename in os.listdir(folder):
if filename.endswith(".txt"):
model_name = filename.replace(".txt", "")
print("model " + model_name)
results[dataset_name][model_name] = {}
with open(os.path.join(folder, filename), "r") as f:
for line in f:
if line.strip().startswith("micro avg"):
print("micro")
results[dataset_name][model_name][0] = float(line.split()[4]) # micro f1
if line.strip().startswith("macro avg"):
print("macro")
results[dataset_name][model_name][1] = float(line.split()[4]) # macro f1
if line.strip().startswith("weighted avg"):
print("weight")
results[dataset_name][model_name][2] = float(line.split()[4]) # weighted avg f1
# Plot
#models = sorted(results["cleaned_2k"].keys()) # alphabetisch sortieren für gleiche Reihenfolge
models = dict(sorted(results["cleaned_2k"].items(), key=lambda i: i[1][2], reverse=True)) # nach values sortieren
print(models)
x = range(len(models))
fig = plt.figure()
#ax = fig.add_subplot(projection='3d')
plt.bar([i - 0.25 for i in x], [results["cleaned_2k"][m][0] for m in models], width=0.25, label="Micro")
plt.bar(x, [results["cleaned_2k"][m][1] for m in models], width=0.25, label="Macro")
plt.bar([i + 0.25 for i in x], [results["cleaned_2k"][m][2] for m in models], width=0.25, label="Weighted")
plt.xticks(x, models, rotation=90)
plt.ylabel("F1 Score")
#ax.set_zlabel("F1 Value")
plt.ylim(0,1)
plt.title("Model Performance - 2k Dataset")
plt.legend()
plt.tight_layout()
plt.savefig('compare_graph_latest_3.png')
plt.show()
# On the y-axis let's only label the discrete values that we have data for.
#ax.set_yticks(yticks)
plt.show()

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compare_models_10k.py Normal file
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import os
import numpy as np
import pandas as pd
from sklearn import set_config
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import FunctionTransformer, MultiLabelBinarizer
import ast
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.multioutput import MultiOutputClassifier
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.metrics import accuracy_score, classification_report
from sklearn.svm import SVC, LinearSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression, RidgeClassifier, PassiveAggressiveClassifier, Perceptron, SGDClassifier
from sklearn.neighbors import KNeighborsClassifier, NearestCentroid, RadiusNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, BaggingClassifier, AdaBoostClassifier, GradientBoostingClassifier, HistGradientBoostingClassifier, VotingClassifier, StackingClassifier
from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB, ComplementNB
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis
from sklearn.dummy import DummyClassifier
from sklearn.neural_network import MLPClassifier
set_config(transform_output="pandas") # dataframe supremacy
jobs = 12
max_iter = 3000
def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
dataset = pd.read_csv(dataset,sep=",")
# desc, genres, tags
column_transformer = ColumnTransformer([
# merge all descriptions
('desc', FunctionTransformer(lambda X: X.fillna('').agg(' '.join, axis=1).to_frame(name="desc")),
['detailed_description', 'about_the_game', 'short_description']),
('pass', 'passthrough', ['genres']),#, 'tags'
],
verbose_feature_names_out=False
)
dataset = column_transformer.fit_transform(dataset)
#### SET MISSING VALUES
print("SETMISS")
# Setting missing numeric values to the mean
dataset.fillna(dataset.mean(numeric_only=True), inplace=True)
# Setting missing text values to 'Unknown'
dataset.fillna('', inplace=True)
# Setting missing values in other columns to NaN
dataset.dropna(inplace=True)
##### STRUCTURIZE GENRES to onehot
#serialize array
dataset['genres'] = dataset['genres'].map(lambda s: ast.literal_eval(s))
#print(dataset['genres']) # in py but not yet onehotenc
# MultiLabelBinarizer does onehotenc for arrays
mlb_genres = MultiLabelBinarizer()
genres_encoded = mlb_genres.fit_transform(dataset.pop('genres'))
#genres_count = len(mlb_genres.classes_) # for multi-label classifiction later
genres_df = pd.DataFrame(genres_encoded, columns=mlb_genres.classes_)
#print(genres_df)
#dataset = pd.concat([dataset, genres_df], axis=1)
#print(dataset)
#### convert text to bag of words
## Count vs Tfidf vectorizer
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(dataset['desc']) # matrix
tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
#print(tfidf_df)
##### MODEL
print("MODEL")
X = tfidf_df
y = genres_df
# cleanup datapoints that dont have a target value (all target columns are 0)
mask = y.sum(axis=1).map(lambda x: x > 0)
#print((mask == False).sum()) #31 cases with all target columns 0
X_clean = X[mask]
y_clean = y[mask]
# Split dataset
return train_test_split(X_clean, y_clean, random_state=0)
def comparison(X_train, X_test, y_train, y_test, estimator,): #returns class_report
multi_target_clf = MultiOutputClassifier(estimator, n_jobs=jobs) # LogisticRegression(max_iter=1337, random_state=0)
# model training
multi_target_clf.fit(X_train, y_train)
# predict against test data
y_pred = multi_target_clf.predict(X_test)
return classification_report(y_test, y_pred, zero_division=0.0)
datasets = [
#'games_march2025_cleaned_2k.csv',
'games_march2025_cleaned_10k.csv',
#'games_march2025_cleaned.csv'
]
estimators = {
"RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter),
"PassiveAggressiveClassifier": PassiveAggressiveClassifier(random_state=0, max_iter=max_iter),
"Perceptron": Perceptron(random_state=0, max_iter=max_iter),
"SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter),
"NearestCentroid": NearestCentroid(),
"LinearSVC": LinearSVC(random_state=0, max_iter=max_iter),
"GradientBoostingClassifier": GradientBoostingClassifier(random_state=0),
"HistGradientBoostingClassifier": HistGradientBoostingClassifier(random_state=0, max_iter=max_iter),
"LinearDiscriminantAnalysis": LinearDiscriminantAnalysis(),
"MLPClassifier": MLPClassifier(random_state=0, max_iter=int(max_iter/20), early_stopping=True),
}
#"VotingClassifier": VotingClassifier(estimators=[('lr', LogisticRegression()), ('rf', RandomForestClassifier())]),
#"StackingClassifier": StackingClassifier(estimators=[('lr', LogisticRegression()), ('rf', RandomForestClassifier())]),
for dataset in datasets:
print("-" * 60)
print("dataset -> " + dataset)
print("mkdir")
folder = dataset.split(".csv")[0]
if not os.path.isdir(folder):
os.mkdir(folder)
X_train, X_test, y_train, y_test = prepDataset(dataset)
for esti in estimators:
print("model: " + esti)
compari = comparison(X_train, X_test, y_train, y_test, estimators[esti])
print("open")
f = open(folder + "/" + esti +".txt", mode="w+", encoding="utf-8")
f.write(compari)
print("write")
f.close()
print("close")

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generate_compare_dataset.py → compare_models_2k.py
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@@ -27,6 +27,10 @@ from sklearn.dummy import DummyClassifier
from sklearn.neural_network import MLPClassifier from sklearn.neural_network import MLPClassifier
set_config(transform_output="pandas") # dataframe supremacy set_config(transform_output="pandas") # dataframe supremacy
jobs = 12
max_iter = 3000
def prepDataset(dataset): #returns X_train, X_test, y_train, y_test def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
dataset = pd.read_csv(dataset,sep=",") dataset = pd.read_csv(dataset,sep=",")
# desc, genres, tags # desc, genres, tags
@@ -76,7 +80,7 @@ def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
y_clean = y[mask] y_clean = y[mask]
# Split dataset # Split dataset
return train_test_split(X_clean, y_clean, random_state=0) return train_test_split(X_clean, y_clean, random_state=0)
def comparison(X_train, X_test, y_train, y_test, estimator, jobs: int = 1): #returns class_report def comparison(X_train, X_test, y_train, y_test, estimator,): #returns class_report
multi_target_clf = MultiOutputClassifier(estimator, n_jobs=jobs) # LogisticRegression(max_iter=1337, random_state=0) multi_target_clf = MultiOutputClassifier(estimator, n_jobs=jobs) # LogisticRegression(max_iter=1337, random_state=0)
# model training # model training
multi_target_clf.fit(X_train, y_train) multi_target_clf.fit(X_train, y_train)
@@ -88,9 +92,6 @@ datasets = [
#'games_march2025_cleaned_10k.csv', #'games_march2025_cleaned_10k.csv',
#'games_march2025_cleaned.csv' #'games_march2025_cleaned.csv'
] ]
max_iter = 3000 # <-- set your desired value here
estimators = { estimators = {
"LogisticRegression": LogisticRegression(random_state=0, max_iter=max_iter), "LogisticRegression": LogisticRegression(random_state=0, max_iter=max_iter),
"RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter), "RidgeClassifier": RidgeClassifier(random_state=0, max_iter=max_iter),
@@ -99,8 +100,8 @@ estimators = {
"SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter), "SGDClassifier": SGDClassifier(random_state=0, max_iter=max_iter),
"KNeighborsClassifier": KNeighborsClassifier(), "KNeighborsClassifier": KNeighborsClassifier(),
"NearestCentroid": NearestCentroid(), "NearestCentroid": NearestCentroid(),
"RadiusNeighborsClassifier": RadiusNeighborsClassifier(), # "RadiusNeighborsClassifier": RadiusNeighborsClassifier(), # failed bcs no neighbours in range :sob:
"LinearSVC-i5000": LinearSVC(random_state=0, max_iter=max_iter), "LinearSVC": LinearSVC(random_state=0, max_iter=max_iter),
"SVC": SVC(random_state=0, max_iter=max_iter), "SVC": SVC(random_state=0, max_iter=max_iter),
"DecisionTreeClassifier": DecisionTreeClassifier(random_state=0), "DecisionTreeClassifier": DecisionTreeClassifier(random_state=0),
"RandomForestClassifier": RandomForestClassifier(random_state=0), "RandomForestClassifier": RandomForestClassifier(random_state=0),
@@ -114,8 +115,7 @@ estimators = {
"BernoulliNB": BernoulliNB(), "BernoulliNB": BernoulliNB(),
"ComplementNB": ComplementNB(), "ComplementNB": ComplementNB(),
"LinearDiscriminantAnalysis": LinearDiscriminantAnalysis(), "LinearDiscriminantAnalysis": LinearDiscriminantAnalysis(),
"QuadraticDiscriminantAnalysis": QuadraticDiscriminantAnalysis(), "MLPClassifier": MLPClassifier(random_state=0, max_iter=int(max_iter/5), verbose=True),
"MLPClassifier-i10000": MLPClassifier(max_iter=10000, random_state=0),
"DummyClassifier": DummyClassifier(random_state=0) "DummyClassifier": DummyClassifier(random_state=0)
} }
@@ -131,7 +131,7 @@ for dataset in datasets:
X_train, X_test, y_train, y_test = prepDataset(dataset) X_train, X_test, y_train, y_test = prepDataset(dataset)
for esti in estimators: for esti in estimators:
print("model: " + esti) print("model: " + esti)
compari = comparison(X_train, X_test, y_train, y_test, estimators[esti], 1) #TODO: change the job count if you can compari = comparison(X_train, X_test, y_train, y_test, estimators[esti])
print("open") print("open")
f = open(folder + "/" + esti +".txt", mode="w+", encoding="utf-8") f = open(folder + "/" + esti +".txt", mode="w+", encoding="utf-8")
f.write(compari) f.write(compari)

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games_march2025_cleaned_2k_i3k/AdaBoostClassifier.txt Normal file
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precision recall f1-score support
0 0.87 0.76 0.81 300
1 0.70 0.59 0.64 216
2 0.58 0.13 0.21 86
3 0.56 0.11 0.18 46
4 0.71 0.30 0.42 83
5 0.00 0.00 0.00 0
6 0.69 0.70 0.69 245
7 0.62 0.31 0.41 42
8 0.76 0.41 0.53 127
9 1.00 0.50 0.67 12
10 0.67 0.50 0.57 127
11 0.40 0.29 0.33 14
12 0.74 0.45 0.56 106
13 0.00 0.00 0.00 0
micro avg 0.74 0.54 0.62 1404
macro avg 0.59 0.36 0.43 1404
weighted avg 0.73 0.54 0.60 1404
samples avg 0.74 0.59 0.61 1404

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games_march2025_cleaned_2k_i3k/BaggingClassifier.txt Normal file
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precision recall f1-score support
0 0.86 0.70 0.77 300
1 0.72 0.50 0.59 216
2 0.47 0.09 0.16 86
3 0.50 0.04 0.08 46
4 0.58 0.23 0.33 83
5 0.00 0.00 0.00 0
6 0.71 0.64 0.67 245
7 0.80 0.29 0.42 42
8 0.79 0.46 0.58 127
9 1.00 0.25 0.40 12
10 0.71 0.43 0.53 127
11 0.40 0.29 0.33 14
12 0.68 0.42 0.52 106
13 0.00 0.00 0.00 0
micro avg 0.74 0.49 0.59 1404
macro avg 0.59 0.31 0.39 1404
weighted avg 0.72 0.49 0.56 1404
samples avg 0.70 0.54 0.57 1404

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games_march2025_cleaned_2k_i3k/BernoulliNB.txt Normal file
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precision recall f1-score support
0 0.75 0.90 0.82 300
1 0.72 0.68 0.70 216
2 0.50 0.08 0.14 86
3 0.27 0.07 0.11 46
4 0.40 0.07 0.12 83
5 0.00 0.00 0.00 0
6 0.77 0.82 0.79 245
7 0.33 0.10 0.15 42
8 0.67 0.40 0.50 127
9 0.00 0.00 0.00 12
10 0.71 0.37 0.49 127
11 0.00 0.00 0.00 14
12 0.49 0.31 0.38 106
13 0.00 0.00 0.00 0
micro avg 0.70 0.55 0.62 1404
macro avg 0.40 0.27 0.30 1404
weighted avg 0.64 0.55 0.56 1404
samples avg 0.73 0.59 0.61 1404

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games_march2025_cleaned_2k_i3k/ComplementNB.txt Normal file
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precision recall f1-score support
0 0.67 0.98 0.80 300
1 0.81 0.36 0.50 216
2 0.67 0.05 0.09 86
3 0.00 0.00 0.00 46
4 0.80 0.05 0.09 83
5 0.00 0.00 0.00 0
6 0.77 0.81 0.79 245
7 0.40 0.05 0.09 42
8 0.83 0.04 0.08 127
9 0.00 0.00 0.00 12
10 0.43 0.02 0.04 127
11 0.00 0.00 0.00 14
12 1.00 0.05 0.09 106
13 0.00 0.00 0.00 0
micro avg 0.70 0.42 0.53 1404
macro avg 0.46 0.17 0.18 1404
weighted avg 0.69 0.42 0.42 1404
samples avg 0.71 0.46 0.52 1404

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games_march2025_cleaned_2k_i3k/DecisionTreeClassifier.txt Normal file
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precision recall f1-score support
0 0.76 0.73 0.75 300
1 0.56 0.53 0.54 216
2 0.36 0.33 0.34 86
3 0.33 0.26 0.29 46
4 0.40 0.46 0.43 83
5 0.00 0.00 0.00 0
6 0.65 0.61 0.63 245
7 0.39 0.40 0.40 42
8 0.59 0.57 0.58 127
9 0.60 0.25 0.35 12
10 0.56 0.51 0.53 127
11 0.39 0.50 0.44 14
12 0.52 0.49 0.50 106
13 0.00 0.00 0.00 0
micro avg 0.58 0.55 0.57 1404
macro avg 0.44 0.40 0.41 1404
weighted avg 0.58 0.55 0.57 1404
samples avg 0.59 0.59 0.55 1404

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games_march2025_cleaned_2k_i3k/DummyClassifier.txt Normal file
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precision recall f1-score support
0 0.60 1.00 0.75 300
1 0.00 0.00 0.00 216
2 0.00 0.00 0.00 86
3 0.00 0.00 0.00 46
4 0.00 0.00 0.00 83
5 0.00 0.00 0.00 0
6 0.00 0.00 0.00 245
7 0.00 0.00 0.00 42
8 0.00 0.00 0.00 127
9 0.00 0.00 0.00 12
10 0.00 0.00 0.00 127
11 0.00 0.00 0.00 14
12 0.00 0.00 0.00 106
13 0.00 0.00 0.00 0
micro avg 0.60 0.21 0.32 1404
macro avg 0.04 0.07 0.05 1404
weighted avg 0.13 0.21 0.16 1404
samples avg 0.60 0.26 0.34 1404

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games_march2025_cleaned_2k_i3k/ExtraTreesClassifier.txt Normal file
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precision recall f1-score support
0 0.81 0.91 0.86 300
1 0.78 0.62 0.69 216
2 1.00 0.03 0.07 86
3 0.00 0.00 0.00 46
4 1.00 0.04 0.07 83
5 0.00 0.00 0.00 0
6 0.78 0.73 0.75 245
7 0.00 0.00 0.00 42
8 0.84 0.24 0.38 127
9 1.00 0.17 0.29 12
10 0.90 0.21 0.34 127
11 1.00 0.14 0.25 14
12 0.83 0.18 0.29 106
13 0.00 0.00 0.00 0
micro avg 0.80 0.48 0.60 1404
macro avg 0.64 0.23 0.29 1404
weighted avg 0.79 0.48 0.52 1404
samples avg 0.78 0.54 0.60 1404

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games_march2025_cleaned_2k_i3k/GaussianNB.txt Normal file
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precision recall f1-score support
0 0.76 0.80 0.78 300
1 0.62 0.51 0.56 216
2 0.63 0.14 0.23 86
3 0.17 0.02 0.04 46
4 0.42 0.10 0.16 83
5 0.00 0.00 0.00 0
6 0.68 0.66 0.67 245
7 0.56 0.12 0.20 42
8 0.55 0.33 0.41 127
9 0.67 0.17 0.27 12
10 0.65 0.31 0.42 127
11 1.00 0.14 0.25 14
12 0.53 0.29 0.38 106
13 0.00 0.00 0.00 0
micro avg 0.66 0.47 0.55 1404
macro avg 0.52 0.26 0.31 1404
weighted avg 0.62 0.47 0.51 1404
samples avg 0.67 0.53 0.55 1404

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games_march2025_cleaned_2k_i3k/GradientBoostingClassifier.txt Normal file
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precision recall f1-score support
0 0.87 0.80 0.83 300
1 0.77 0.61 0.68 216
2 0.55 0.13 0.21 86
3 0.42 0.11 0.17 46
4 0.68 0.33 0.44 83
5 0.00 0.00 0.00 0
6 0.71 0.76 0.74 245
7 0.61 0.26 0.37 42
8 0.81 0.50 0.61 127
9 0.75 0.25 0.38 12
10 0.81 0.54 0.65 127
11 0.40 0.43 0.41 14
12 0.69 0.42 0.53 106
13 0.00 0.00 0.00 0
micro avg 0.76 0.57 0.65 1404
macro avg 0.58 0.37 0.43 1404
weighted avg 0.74 0.57 0.63 1404
samples avg 0.77 0.63 0.65 1404

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games_march2025_cleaned_2k_i3k/HistGradientBoostingClassifier.txt Normal file
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precision recall f1-score support
0 0.83 0.83 0.83 300
1 0.74 0.69 0.72 216
2 0.80 0.28 0.41 86
3 1.00 0.04 0.08 46
4 0.70 0.39 0.50 83
5 0.00 0.00 0.00 0
6 0.72 0.76 0.74 245
7 0.73 0.19 0.30 42
8 0.85 0.59 0.70 127
9 1.00 0.33 0.50 12
10 0.78 0.54 0.64 127
11 0.43 0.21 0.29 14
12 0.77 0.52 0.62 106
13 0.00 0.00 0.00 0
micro avg 0.78 0.61 0.68 1404
macro avg 0.67 0.38 0.45 1404
weighted avg 0.78 0.61 0.66 1404
samples avg 0.79 0.67 0.69 1404

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games_march2025_cleaned_2k_i3k/KNeighborsClassifier.txt Normal file
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precision recall f1-score support
0 0.82 0.62 0.70 300
1 0.69 0.46 0.55 216
2 0.62 0.06 0.11 86
3 0.20 0.02 0.04 46
4 0.72 0.16 0.26 83
5 0.00 0.00 0.00 0
6 0.78 0.55 0.64 245
7 0.38 0.12 0.18 42
8 0.59 0.65 0.62 127
9 1.00 0.67 0.80 12
10 0.68 0.44 0.54 127
11 1.00 0.29 0.44 14
12 0.34 0.76 0.48 106
13 0.00 0.00 0.00 0
micro avg 0.64 0.48 0.55 1404
macro avg 0.56 0.34 0.38 1404
weighted avg 0.68 0.48 0.53 1404
samples avg 0.64 0.54 0.55 1404

21
games_march2025_cleaned_2k_i3k/LinearDiscriminantAnalysis.txt Normal file
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precision recall f1-score support
0 0.63 0.68 0.66 300
1 0.47 0.56 0.51 216
2 0.27 0.59 0.37 86
3 0.06 0.28 0.10 46
4 0.21 0.52 0.30 83
5 0.00 0.00 0.00 0
6 0.63 0.67 0.65 245
7 0.06 0.29 0.10 42
8 0.28 0.52 0.36 127
9 0.03 0.42 0.06 12
10 0.29 0.52 0.38 127
11 0.04 0.43 0.07 14
12 0.53 0.44 0.48 106
13 0.00 0.00 0.00 0
micro avg 0.30 0.57 0.39 1404
macro avg 0.25 0.42 0.29 1404
weighted avg 0.44 0.57 0.48 1404
samples avg 0.42 0.62 0.40 1404

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games_march2025_cleaned_2k_i3k/LinearSVC.txt Normal file
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precision recall f1-score support
0 0.85 0.87 0.86 300
1 0.76 0.66 0.70 216
2 0.77 0.20 0.31 86
3 0.00 0.00 0.00 46
4 0.76 0.27 0.39 83
5 0.00 0.00 0.00 0
6 0.78 0.81 0.79 245
7 0.89 0.19 0.31 42
8 0.77 0.60 0.67 127
9 1.00 0.58 0.74 12
10 0.85 0.54 0.66 127
11 1.00 0.29 0.44 14
12 0.82 0.42 0.56 106
13 0.00 0.00 0.00 0
micro avg 0.80 0.61 0.69 1404
macro avg 0.66 0.39 0.46 1404
weighted avg 0.78 0.61 0.66 1404
samples avg 0.81 0.67 0.69 1404

21
games_march2025_cleaned_2k_i3k/LogisticRegression.txt Normal file
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precision recall f1-score support
0 0.78 0.91 0.84 300
1 0.78 0.62 0.69 216
2 1.00 0.03 0.07 86
3 0.00 0.00 0.00 46
4 1.00 0.04 0.07 83
5 0.00 0.00 0.00 0
6 0.79 0.81 0.80 245
7 0.00 0.00 0.00 42
8 0.90 0.34 0.49 127
9 0.00 0.00 0.00 12
10 0.89 0.25 0.39 127
11 0.00 0.00 0.00 14
12 0.88 0.14 0.24 106
13 0.00 0.00 0.00 0
micro avg 0.79 0.50 0.61 1404
macro avg 0.50 0.22 0.26 1404
weighted avg 0.77 0.50 0.53 1404
samples avg 0.77 0.56 0.60 1404

21
games_march2025_cleaned_2k_i3k/MLPClassifier.txt Normal file
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precision recall f1-score support
0 0.84 0.85 0.84 300
1 0.73 0.67 0.70 216
2 0.74 0.30 0.43 86
3 0.50 0.02 0.04 46
4 0.69 0.24 0.36 83
5 0.00 0.00 0.00 0
6 0.79 0.79 0.79 245
7 0.86 0.14 0.24 42
8 0.76 0.63 0.69 127
9 1.00 0.33 0.50 12
10 0.81 0.52 0.63 127
11 1.00 0.14 0.25 14
12 0.75 0.41 0.53 106
13 0.00 0.00 0.00 0
micro avg 0.79 0.60 0.68 1404
macro avg 0.68 0.36 0.43 1404
weighted avg 0.78 0.60 0.65 1404
samples avg 0.80 0.66 0.68 1404

21
games_march2025_cleaned_2k_i3k/MultinomialNB.txt Normal file
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precision recall f1-score support
0 0.64 0.99 0.78 300
1 0.85 0.24 0.37 216
2 0.60 0.03 0.07 86
3 0.00 0.00 0.00 46
4 0.80 0.05 0.09 83
5 0.00 0.00 0.00 0
6 0.78 0.80 0.79 245
7 0.40 0.05 0.09 42
8 1.00 0.04 0.08 127
9 0.00 0.00 0.00 12
10 0.20 0.01 0.02 127
11 0.00 0.00 0.00 14
12 1.00 0.05 0.09 106
13 0.00 0.00 0.00 0
micro avg 0.69 0.40 0.51 1404
macro avg 0.45 0.16 0.17 1404
weighted avg 0.68 0.40 0.39 1404
samples avg 0.70 0.44 0.50 1404

21
games_march2025_cleaned_2k_i3k/NearestCentroid.txt Normal file
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precision recall f1-score support
0 0.83 0.75 0.79 300
1 0.65 0.75 0.70 216
2 0.43 0.72 0.54 86
3 0.18 0.33 0.23 46
4 0.46 0.61 0.53 83
5 0.00 0.00 0.00 0
6 0.74 0.76 0.75 245
7 0.31 0.62 0.41 42
8 0.47 0.69 0.55 127
9 1.00 0.67 0.80 12
10 0.59 0.69 0.64 127
11 0.60 0.64 0.62 14
12 0.42 0.66 0.52 106
13 0.00 0.00 0.00 0
micro avg 0.57 0.70 0.63 1404
macro avg 0.48 0.56 0.50 1404
weighted avg 0.62 0.70 0.65 1404
samples avg 0.63 0.74 0.64 1404

21
games_march2025_cleaned_2k_i3k/PassiveAggressiveClassifier.txt Normal file
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precision recall f1-score support
0 0.84 0.86 0.85 300
1 0.74 0.63 0.68 216
2 0.77 0.31 0.45 86
3 0.50 0.04 0.08 46
4 0.69 0.33 0.44 83
5 0.00 0.00 0.00 0
6 0.79 0.80 0.79 245
7 0.69 0.26 0.38 42
8 0.74 0.62 0.68 127
9 1.00 0.67 0.80 12
10 0.80 0.57 0.67 127
11 1.00 0.50 0.67 14
12 0.79 0.46 0.58 106
13 0.00 0.00 0.00 0
micro avg 0.79 0.62 0.69 1404
macro avg 0.67 0.43 0.50 1404
weighted avg 0.77 0.62 0.67 1404
samples avg 0.80 0.68 0.70 1404

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games_march2025_cleaned_2k_i3k/Perceptron.txt Normal file
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precision recall f1-score support
0 0.78 0.94 0.85 300
1 0.60 0.88 0.71 216
2 0.54 0.60 0.57 86
3 0.33 0.04 0.08 46
4 0.68 0.16 0.25 83
5 0.00 0.00 0.00 0
6 0.74 0.86 0.80 245
7 0.63 0.29 0.39 42
8 0.62 0.80 0.69 127
9 1.00 0.67 0.80 12
10 0.89 0.43 0.58 127
11 0.70 0.50 0.58 14
12 0.88 0.27 0.42 106
13 0.00 0.00 0.00 0
micro avg 0.70 0.68 0.69 1404
macro avg 0.60 0.46 0.48 1404
weighted avg 0.71 0.68 0.66 1404
samples avg 0.72 0.74 0.69 1404

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games_march2025_cleaned_2k_i3k/RandomForestClassifier.txt Normal file
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precision recall f1-score support
0 0.80 0.88 0.84 300
1 0.78 0.55 0.64 216
2 1.00 0.03 0.07 86
3 0.00 0.00 0.00 46
4 1.00 0.06 0.11 83
5 0.00 0.00 0.00 0
6 0.74 0.78 0.76 245
7 0.00 0.00 0.00 42
8 0.84 0.24 0.38 127
9 0.00 0.00 0.00 12
10 0.91 0.24 0.38 127
11 1.00 0.14 0.25 14
12 1.00 0.25 0.39 106
13 0.00 0.00 0.00 0
micro avg 0.79 0.48 0.59 1404
macro avg 0.58 0.23 0.27 1404
weighted avg 0.78 0.48 0.52 1404
samples avg 0.77 0.54 0.60 1404

21
games_march2025_cleaned_2k_i3k/RidgeClassifier.txt Normal file
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precision recall f1-score support
0 0.84 0.88 0.86 300
1 0.76 0.66 0.70 216
2 0.80 0.14 0.24 86
3 0.00 0.00 0.00 46
4 0.85 0.20 0.33 83
5 0.00 0.00 0.00 0
6 0.78 0.82 0.80 245
7 0.86 0.14 0.24 42
8 0.79 0.54 0.64 127
9 1.00 0.42 0.59 12
10 0.88 0.50 0.64 127
11 1.00 0.14 0.25 14
12 0.83 0.38 0.52 106
13 0.00 0.00 0.00 0
micro avg 0.81 0.59 0.68 1404
macro avg 0.67 0.34 0.42 1404
weighted avg 0.79 0.59 0.63 1404
samples avg 0.81 0.65 0.68 1404

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games_march2025_cleaned_2k_i3k/SGDClassifier.txt Normal file
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precision recall f1-score support
0 0.86 0.84 0.85 300
1 0.80 0.52 0.63 216
2 0.68 0.35 0.46 86
3 0.44 0.09 0.15 46
4 0.68 0.34 0.45 83
5 0.00 0.00 0.00 0
6 0.77 0.80 0.79 245
7 0.71 0.24 0.36 42
8 0.75 0.55 0.64 127
9 1.00 0.58 0.74 12
10 0.85 0.52 0.64 127
11 0.89 0.57 0.70 14
12 0.60 0.64 0.62 106
13 0.00 0.00 0.00 0
micro avg 0.77 0.61 0.68 1404
macro avg 0.65 0.43 0.50 1404
weighted avg 0.77 0.61 0.66 1404
samples avg 0.79 0.67 0.69 1404

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games_march2025_cleaned_2k_i3k/SVC.txt Normal file
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precision recall f1-score support
0 0.81 0.90 0.85 300
1 0.76 0.63 0.69 216
2 1.00 0.03 0.07 86
3 0.00 0.00 0.00 46
4 1.00 0.05 0.09 83
5 0.00 0.00 0.00 0
6 0.77 0.83 0.80 245
7 0.00 0.00 0.00 42
8 0.84 0.40 0.54 127
9 1.00 0.17 0.29 12
10 0.90 0.34 0.49 127
11 1.00 0.14 0.25 14
12 0.92 0.21 0.34 106
13 0.00 0.00 0.00 0
micro avg 0.80 0.53 0.63 1404
macro avg 0.64 0.26 0.32 1404
weighted avg 0.79 0.53 0.56 1404
samples avg 0.79 0.59 0.63 1404

42
notebook.ipynb
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@@ -304,13 +304,47 @@
"``sklearn`` has many different classification Models to choose from, but we only have limited time and computing power.\n", "``sklearn`` has many different classification Models to choose from, but we only have limited time and computing power.\n",
"As such, we tested many different models on the 2k Dataset and chose the 5 best performing ones for the big dataset.\n", "As such, we tested many different models on the 2k Dataset and chose the 5 best performing ones for the big dataset.\n",
"\n", "\n",
"### The comparison\n", "### Initial Comparison\n",
"We won't put the comparison script in this notebook, but you can find it in the ``compare_models.py`` file and try it out yourself.\n", "We won't put the comparison script in this notebook, but you can find it in the ``compare_models_2k.py`` file and try it out yourself.\n",
"There were some rules as a baseline for comparison:\n", "There were some rules as a baseline for comparison:\n",
"- All Hyperparameters are set to default\n", "- All Hyperparameters are set to default\n",
"- All iteration limits are set to 3000\n", "- All iteration limits are set to 3000 (exception: MLPClassifier with 300, where i-limit are epochs instead of iterations )\n",
"- All ``random_state``s are set to 0\n",
"\n", "\n",
"![Comparison Image](./compare_models_2k.png)" "Running all models with that configuration yields the following weighted F1-Scores (results as seen in the ``games_march2025_cleaned_2k_i3k`` folder): \n",
"\n",
"![Comparison Image 2k](./compare_models_2k.png)\n",
"\n",
"If we also compare Micro/Macro values, we see that all models have a much lower Macro-F1 than Micro/Weighted-F1. That is because the 2k Dataset does not contain enough datapoints for every class (test data for 2 classes is 0), so we should proceed to the 10k Dataset before making major choices.\n",
"\n",
"![Comparison Image 2k Micro/Macro/Weighted](./compare_models_2k_3.png)\n",
"\n",
"The 10 best performing models which will run on the 10k Dataset with the same rules as before:\n",
"1. NearestCentroid\n",
"2. Perceptron\n",
"3. PassiveAggressiveClassifier\n",
"4. LinearSVC\n",
"5. SDGClassifer\n",
"6. HistGradientBoostingClassifier\n",
"7. MLPClassifier\n",
"8. RidgeClassifier\n",
"9. GradientBoostingClassifier\n",
"10. LinearDiscriminationAnalysis\n",
"\n",
"![Comparison Image 10k](./compare_models_10k.png)\n",
"\n",
"We can also compare these models between datasets, to see if a bigger dataset always improves the performance.\n",
"\n",
"![Comparison Image between 2k and 10k](./compare_models_2k_10k.png)\n",
"\n",
"The final contenders are:\n",
"1.\n",
"2.\n",
"3.\n",
"4.\n",
"5.\n",
"\n",
"..."
] ]
}, },
{ {