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run_classifier_evaluation.py
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run_classifier_evaluation.py
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from sklearn import linear_model
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
from songpop.data import (Dataset, COL_YEAR, COLS_MUSICAL_DEGREES, COL_KEY, COL_MODE,
COL_TEMPO, COL_TIME_SIGNATURE, COL_LOUDNESS, COL_DURATION_MS)
def main():
# define & load dataset
dataset = Dataset(10000)
X, y = dataset.load_xy()
# project to columns used by models
cols_used_by_models = [COL_YEAR, *COLS_MUSICAL_DEGREES, COL_KEY, COL_MODE, COL_TEMPO, COL_TIME_SIGNATURE, COL_LOUDNESS, COL_DURATION_MS]
X = X[cols_used_by_models]
scaler = StandardScaler()
model_X = scaler.fit(X)
X_scaled = model_X.transform(X)
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, random_state=42, test_size=0.3, shuffle=True)
# define models to be evaluated
models = [
linear_model.LogisticRegression(solver='lbfgs', max_iter=1000),
KNeighborsClassifier(n_neighbors=1),
RandomForestClassifier(n_estimators=100),
DecisionTreeClassifier(random_state=42, max_depth=2)
]
# evaluate models
for model in models:
print(f"Evaluating model:\n{model}")
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(confusion_matrix(y_test, y_pred))
print(classification_report(y_test, y_pred))
if __name__ == '__main__':
main()