first version of the plot
This commit is contained in:
Maximilian Kany
140
comparison.py
140
comparison.py
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import os
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import numpy as np
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import pandas as pd
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from sklearn import set_config
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from sklearn.compose import ColumnTransformer
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from sklearn.preprocessing import FunctionTransformer
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from sklearn.preprocessing import MultiLabelBinarizer
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import ast
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LogisticRegression
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from sklearn.multioutput import MultiOutputClassifier
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from sklearn.metrics import classification_report
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from sklearn.model_selection import train_test_split
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from sklearn.datasets import load_iris
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from sklearn.metrics import accuracy_score, classification_report
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from sklearn.svm import SVC, LinearSVC
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from sklearn.tree import DecisionTreeClassifier
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from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
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from sklearn.naive_bayes import GaussianNB, MultinomialNB, BernoulliNB
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from sklearn.neighbors import KNeighborsClassifier
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from sklearn.neural_network import MLPClassifier
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set_config(transform_output="pandas") # dataframe supremacy
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def prepDataset(dataset): #returns X_train, X_test, y_train, y_test
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dataset = pd.read_csv("./games_march2025_cleaned_2k.csv",sep=",")
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# desc, genres, tags
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column_transformer = ColumnTransformer([
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# merge all descriptions
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('desc', FunctionTransformer(lambda X: X.fillna('').agg(' '.join, axis=1).to_frame(name="desc")),
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['detailed_description', 'about_the_game', 'short_description']),
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('pass', 'passthrough', ['genres']),#, 'tags'
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],
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verbose_feature_names_out=False
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)
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dataset = column_transformer.fit_transform(dataset)
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#### SET MISSING VALUES
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print("SETMISS")
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# Setting missing numeric values to the mean
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dataset.fillna(dataset.mean(numeric_only=True), inplace=True)
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# Setting missing text values to 'Unknown'
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dataset.fillna('', inplace=True)
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# Setting missing values in other columns to NaN
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dataset.dropna(inplace=True)
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##### STRUCTURIZE GENRES to onehot
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#serialize array
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dataset['genres'] = dataset['genres'].map(lambda s: ast.literal_eval(s))
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#print(dataset['genres']) # in py but not yet onehotenc
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# MultiLabelBinarizer does onehotenc for arrays
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mlb_genres = MultiLabelBinarizer()
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genres_encoded = mlb_genres.fit_transform(dataset.pop('genres'))
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#genres_count = len(mlb_genres.classes_) # for multi-label classifiction later
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genres_df = pd.DataFrame(genres_encoded, columns=mlb_genres.classes_)
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#print(genres_df)
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#dataset = pd.concat([dataset, genres_df], axis=1)
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#print(dataset)
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#### convert text to bag of words
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## Count vs Tfidf vectorizer
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vectorizer = TfidfVectorizer()
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tfidf_matrix = vectorizer.fit_transform(dataset['desc']) # matrix
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tfidf_df = pd.DataFrame(tfidf_matrix.toarray(), columns=vectorizer.get_feature_names_out())
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#print(tfidf_df)
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##### MODEL
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print("MODEL")
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X = tfidf_df
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y = genres_df
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# cleanup datapoints that dont have a target value (all target columns are 0)
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mask = y.sum(axis=1).map(lambda x: x > 0)
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#print((mask == False).sum()) #31 cases with all target columns 0
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X_clean = X[mask]
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y_clean = y[mask]
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# Split dataset
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return train_test_split(X_clean, y_clean, random_state=0)
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def comparison(X_train, X_test, y_train, y_test, estimator, jobs: int = 1): #returns class_report
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multi_target_clf = MultiOutputClassifier(estimator, n_jobs=jobs) # LogisticRegression(max_iter=1337, random_state=0)
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# model training
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multi_target_clf.fit(X_train, y_train)
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# predict against test data
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y_pred = multi_target_clf.predict(X_test)
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return classification_report(y_test, y_pred, zero_division=0.0)
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datasets = [
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'games_march2025_cleaned_2k.csv',
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'games_march2025_cleaned_10k.csv',
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'games_march2025_cleaned.csv'
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]
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estimators = {
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"LogisticRegression-i1000": LogisticRegression(max_iter=1000, random_state=0),
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"LogisticRegression-i10000": LogisticRegression(max_iter=10000, random_state=0),
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"LinearSVC-i5000": LinearSVC(max_iter=5000),
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"SVC-RBF-i10000": SVC(kernel="rbf", max_iter=10000),
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"DecisionTreeClassifier": DecisionTreeClassifier(random_state=0),
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"RandomForestClassifier": RandomForestClassifier(random_state=0),
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"GradientBoostingClassifier": GradientBoostingClassifier(random_state=0),
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"GaussianNB": GaussianNB(),
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"MultinomialNB": MultinomialNB(),
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"BernoulliNB": BernoulliNB(),
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"MLPClassifier-i10000": MLPClassifier(max_iter=10000, random_state=0),
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}
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for dataset in datasets:
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print("-" * 60)
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print("dataset -> " + dataset)
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print("-" * 60)
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print("mkdir")
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folder = dataset.split(".csv")[0]
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if not os.path.isdir(folder):
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os.mkdir(folder)
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X_train, X_test, y_train, y_test = prepDataset(dataset)
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for esti in estimators:
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compari = comparison(X_train, X_test, y_train, y_test, estimators[esti], 1) #TODO: change the job count if you can
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print("open")
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f = open(folder + "/" + esti +".txt", mode="w+", encoding="utf-8")
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f.write(compari)
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print("write")
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f.close()
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print("close")
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