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Tim
2025-08-22 13:11:37 +02:00
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notebook.ipynb
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},
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"\n",
"#### Removing unpredicatble Datapoints\n",
"\n",
"Some genres have too little datapoints to be predictable. The 10k Dataset has 12 Classes that have less than 5 Datapoints, usually only 1 oder 2. These have too big of a probability that they will fall into only the train or test data and therefore will be removed. "
"Some genres have too little datapoints to be predictable. The 10k Dataset has 14 Classes that have less than 10 Datapoints, usually only 1 to 4. These have too big of a probability that they will fall into only the train or test data and therefore will be removed."
]
},
{
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"outputs": [
@@ -368,7 +368,7 @@
],
"source": [
"# remove genres that have less than min_entries entries -> probability of broken split to big\n",
"mask = (y == 1).sum() >= 5\n",
"mask = (y == 1).sum() >= 10\n",
"print(\"Before\" + str(y.shape))\n",
"y_prep = y.loc[:, mask]\n",
"print(\"After\" + str(y_prep.shape))"
@@ -385,7 +385,7 @@
},
{
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"id": "4919bf1b37d171a7",
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@@ -417,7 +417,7 @@
},
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@@ -441,17 +441,17 @@
},
{
"cell_type": "code",
"execution_count": 30,
"execution_count": null,
"id": "0b0a46a4",
"metadata": {},
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{
"data": {
"text/plain": [
"1905"
"99"
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@@ -502,49 +502,47 @@
"source": [
"# Excursion: Choosing a classification Model\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 small dataset and chose the best performing ones for the big dataset.\n",
"\n",
"### Initial Comparison\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",
"- All Hyperparameters are set to default\n",
"- All iteration limits are set to 3000 (exception: MLPClassifier with 300, where i-limit are epochs instead of iterations )\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",
"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",
"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 Dataset does not contain enough datapoints for every class (test data for 2 classes is 0 in the 2k dataset), so we should proceed to the 10k Dataset.\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",
"1. PassiveAggressiveClassifier \n",
"2. Perceptron\n",
"3. PassiveAggressiveClassifier\n",
"4. LinearSVC\n",
"5. SDGClassifer\n",
"6. HistGradientBoostingClassifier\n",
"3. LinearSVC\n",
"4. SDGClassifer\n",
"5. HistGradientBoostingClassifier\n",
"6. NearestCentroid\n",
"7. MLPClassifier\n",
"8. RidgeClassifier\n",
"9. GradientBoostingClassifier\n",
"10. LinearDiscriminationAnalysis\n",
"8. GradientBoostingClassifier \n",
"9. RidgeClassifier\n",
"10. AdaBoostClassifier (because of an evaluation mistake, we used LinearDiscriminantAnalysis instead)\n",
"\n",
"That gave us the following results:\n",
"\n",
"![Comparison Image 10k](./compare_models_10k.png)\n",
"![Comparison Image 10k](./compare_models_10k_3.png)\n",
"\n",
"We can also compare these models between datasets, to see if a bigger dataset always improves the performance.\n",
"The top 5 are the same, with the only exception of Perceptron falling behind against the RidgeClassifier.\n",
"When comparing these models between datasets, it is evident that a bigger dataset yields better performance (for exponentially higher compute and time cost). Only NearestCentroid lost performance when comparing the Datasets.\n",
"\n",
"![Comparison Image between 2k and 10k](./compare_models_2k_10k.png)\n",
"![Comparison Image between 2k and 10k](./compare_datasets_2k.png)\n",
"![Comparison Image between 2k and 10k, only 10k Models](./compare_datasets_10k.png)\n",
"\n",
"The final contenders are:\n",
"1.\n",
"2.\n",
"3.\n",
"4.\n",
"5.\n",
"\n",
"..."
"The final contenders are LinearSVC and PassiveAggressiveClassifier, which we would compare against each other using k-fold cross validation with different hyperparameters, but since training the model on the dataset takes a lot of time and a big strain on our computers, we will stop here and use the LinearSVC Classifier."
]
},
{
@@ -553,23 +551,27 @@
"metadata": {},
"source": [
"## Model Selection\n",
"**TODO Deciding which model to use for this task**\n",
"\n",
"As a game can have multiple genres, our Model(s) has to be capable of multi-label-classification. sklearn's ``MultiOutputClassifier`` can do this. As a backend for ``MultiOutputClassifier`` we use ``LogisticRegression``"
"As a game can have multiple genres, our Model(s) has to be capable of multi-label-classification. sklearn's ``MultiOutputClassifier`` can do this. As a backend for ``MultiOutputClassifier`` we use ``LinearSVC``"
]
},
{
"cell_type": "code",
"execution_count": 31,
"execution_count": null,
"id": "8c1d72c4532bd509",
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": []
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.svm import LinearSVC\n",
"from sklearn.multioutput import MultiOutputClassifier\n",
"\n",
"# n_jobs=1 since there seems to be some multithreading join issue in sklearn (or my pc is to bad)\n",
"multi_target_clf = MultiOutputClassifier(LogisticRegression(max_iter=1337, random_state=0), n_jobs=1)\n",
"multi_target_clf = MultiOutputClassifier(LinearSVC(max_iter=1337, random_state=0), n_jobs=1)\n",
"\n",
"multi_target_clf.fit(X_train, y_train)\n",
"\n",
@@ -587,7 +589,7 @@
},
{
"cell_type": "code",
"execution_count": 32,
"execution_count": null,
"id": "e2ebea6945193e07",
"metadata": {},
"outputs": [
@@ -597,23 +599,23 @@
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.78 0.91 0.84 300\n",
" 1 0.78 0.62 0.69 216\n",
" 2 1.00 0.03 0.07 86\n",
" 3 0.00 0.00 0.00 46\n",
" 4 1.00 0.04 0.07 83\n",
" 5 0.79 0.81 0.80 245\n",
" 6 0.00 0.00 0.00 42\n",
" 7 0.90 0.34 0.49 127\n",
" 8 0.00 0.00 0.00 12\n",
" 9 0.89 0.25 0.39 127\n",
" 10 0.00 0.00 0.00 14\n",
" 11 0.88 0.14 0.24 106\n",
" 0 0.84 0.86 0.85 300\n",
" 1 0.74 0.63 0.68 216\n",
" 2 0.77 0.31 0.45 86\n",
" 3 0.50 0.04 0.08 46\n",
" 4 0.69 0.33 0.44 83\n",
" 5 0.79 0.80 0.79 245\n",
" 6 0.69 0.26 0.38 42\n",
" 7 0.74 0.62 0.68 127\n",
" 8 1.00 0.67 0.80 12\n",
" 9 0.80 0.57 0.67 127\n",
" 10 1.00 0.50 0.67 14\n",
" 11 0.79 0.46 0.58 106\n",
"\n",
" micro avg 0.79 0.50 0.61 1404\n",
" macro avg 0.58 0.26 0.30 1404\n",
"weighted avg 0.77 0.50 0.53 1404\n",
" samples avg 0.77 0.56 0.60 1404\n",
" micro avg 0.79 0.62 0.69 1404\n",
" macro avg 0.78 0.51 0.59 1404\n",
"weighted avg 0.77 0.62 0.67 1404\n",
" samples avg 0.80 0.68 0.70 1404\n",
"\n"
]
}
@@ -633,15 +635,6 @@
"**TODO optimize the model based on the test results**"
]
},
{
"cell_type": "markdown",
"id": "79b20645",
"metadata": {},
"source": [
"# Validation\n",
"**TODO Predict actual values**"
]
},
{
"cell_type": "markdown",
"id": "3b709fb7",