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@alongubkin
Created Jun 7, 2021
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LightGBM example
sepal.length sepal.width petal.length petal.width variety
5.1 3.5 1.4 .2 Setosa
4.9 3 1.4 .2 Setosa
4.7 3.2 1.3 .2 Setosa
4.6 3.1 1.5 .2 Setosa
5 3.6 1.4 .2 Setosa
5.4 3.9 1.7 .4 Setosa
4.6 3.4 1.4 .3 Setosa
5 3.4 1.5 .2 Setosa
4.4 2.9 1.4 .2 Setosa
4.9 3.1 1.5 .1 Setosa
5.4 3.7 1.5 .2 Setosa
4.8 3.4 1.6 .2 Setosa
4.8 3 1.4 .1 Setosa
4.3 3 1.1 .1 Setosa
5.8 4 1.2 .2 Setosa
5.7 4.4 1.5 .4 Setosa
5.4 3.9 1.3 .4 Setosa
5.1 3.5 1.4 .3 Setosa
5.7 3.8 1.7 .3 Setosa
5.1 3.8 1.5 .3 Setosa
5.4 3.4 1.7 .2 Setosa
5.1 3.7 1.5 .4 Setosa
4.6 3.6 1 .2 Setosa
5.1 3.3 1.7 .5 Setosa
4.8 3.4 1.9 .2 Setosa
5 3 1.6 .2 Setosa
5 3.4 1.6 .4 Setosa
5.2 3.5 1.5 .2 Setosa
5.2 3.4 1.4 .2 Setosa
4.7 3.2 1.6 .2 Setosa
4.8 3.1 1.6 .2 Setosa
5.4 3.4 1.5 .4 Setosa
5.2 4.1 1.5 .1 Setosa
5.5 4.2 1.4 .2 Setosa
4.9 3.1 1.5 .2 Setosa
5 3.2 1.2 .2 Setosa
5.5 3.5 1.3 .2 Setosa
4.9 3.6 1.4 .1 Setosa
4.4 3 1.3 .2 Setosa
5.1 3.4 1.5 .2 Setosa
5 3.5 1.3 .3 Setosa
4.5 2.3 1.3 .3 Setosa
4.4 3.2 1.3 .2 Setosa
5 3.5 1.6 .6 Setosa
5.1 3.8 1.9 .4 Setosa
4.8 3 1.4 .3 Setosa
5.1 3.8 1.6 .2 Setosa
4.6 3.2 1.4 .2 Setosa
5.3 3.7 1.5 .2 Setosa
5 3.3 1.4 .2 Setosa
7 3.2 4.7 1.4 Versicolor
6.4 3.2 4.5 1.5 Versicolor
6.9 3.1 4.9 1.5 Versicolor
5.5 2.3 4 1.3 Versicolor
6.5 2.8 4.6 1.5 Versicolor
5.7 2.8 4.5 1.3 Versicolor
6.3 3.3 4.7 1.6 Versicolor
4.9 2.4 3.3 1 Versicolor
6.6 2.9 4.6 1.3 Versicolor
5.2 2.7 3.9 1.4 Versicolor
5 2 3.5 1 Versicolor
5.9 3 4.2 1.5 Versicolor
6 2.2 4 1 Versicolor
6.1 2.9 4.7 1.4 Versicolor
5.6 2.9 3.6 1.3 Versicolor
6.7 3.1 4.4 1.4 Versicolor
5.6 3 4.5 1.5 Versicolor
5.8 2.7 4.1 1 Versicolor
6.2 2.2 4.5 1.5 Versicolor
5.6 2.5 3.9 1.1 Versicolor
5.9 3.2 4.8 1.8 Versicolor
6.1 2.8 4 1.3 Versicolor
6.3 2.5 4.9 1.5 Versicolor
6.1 2.8 4.7 1.2 Versicolor
6.4 2.9 4.3 1.3 Versicolor
6.6 3 4.4 1.4 Versicolor
6.8 2.8 4.8 1.4 Versicolor
6.7 3 5 1.7 Versicolor
6 2.9 4.5 1.5 Versicolor
5.7 2.6 3.5 1 Versicolor
5.5 2.4 3.8 1.1 Versicolor
5.5 2.4 3.7 1 Versicolor
5.8 2.7 3.9 1.2 Versicolor
6 2.7 5.1 1.6 Versicolor
5.4 3 4.5 1.5 Versicolor
6 3.4 4.5 1.6 Versicolor
6.7 3.1 4.7 1.5 Versicolor
6.3 2.3 4.4 1.3 Versicolor
5.6 3 4.1 1.3 Versicolor
5.5 2.5 4 1.3 Versicolor
5.5 2.6 4.4 1.2 Versicolor
6.1 3 4.6 1.4 Versicolor
5.8 2.6 4 1.2 Versicolor
5 2.3 3.3 1 Versicolor
5.6 2.7 4.2 1.3 Versicolor
5.7 3 4.2 1.2 Versicolor
5.7 2.9 4.2 1.3 Versicolor
6.2 2.9 4.3 1.3 Versicolor
5.1 2.5 3 1.1 Versicolor
5.7 2.8 4.1 1.3 Versicolor
6.3 3.3 6 2.5 Virginica
5.8 2.7 5.1 1.9 Virginica
7.1 3 5.9 2.1 Virginica
6.3 2.9 5.6 1.8 Virginica
6.5 3 5.8 2.2 Virginica
7.6 3 6.6 2.1 Virginica
4.9 2.5 4.5 1.7 Virginica
7.3 2.9 6.3 1.8 Virginica
6.7 2.5 5.8 1.8 Virginica
7.2 3.6 6.1 2.5 Virginica
6.5 3.2 5.1 2 Virginica
6.4 2.7 5.3 1.9 Virginica
6.8 3 5.5 2.1 Virginica
5.7 2.5 5 2 Virginica
5.8 2.8 5.1 2.4 Virginica
6.4 3.2 5.3 2.3 Virginica
6.5 3 5.5 1.8 Virginica
7.7 3.8 6.7 2.2 Virginica
7.7 2.6 6.9 2.3 Virginica
6 2.2 5 1.5 Virginica
6.9 3.2 5.7 2.3 Virginica
5.6 2.8 4.9 2 Virginica
7.7 2.8 6.7 2 Virginica
6.3 2.7 4.9 1.8 Virginica
6.7 3.3 5.7 2.1 Virginica
7.2 3.2 6 1.8 Virginica
6.2 2.8 4.8 1.8 Virginica
6.1 3 4.9 1.8 Virginica
6.4 2.8 5.6 2.1 Virginica
7.2 3 5.8 1.6 Virginica
7.4 2.8 6.1 1.9 Virginica
7.9 3.8 6.4 2 Virginica
6.4 2.8 5.6 2.2 Virginica
6.3 2.8 5.1 1.5 Virginica
6.1 2.6 5.6 1.4 Virginica
7.7 3 6.1 2.3 Virginica
6.3 3.4 5.6 2.4 Virginica
6.4 3.1 5.5 1.8 Virginica
6 3 4.8 1.8 Virginica
6.9 3.1 5.4 2.1 Virginica
6.7 3.1 5.6 2.4 Virginica
6.9 3.1 5.1 2.3 Virginica
5.8 2.7 5.1 1.9 Virginica
6.8 3.2 5.9 2.3 Virginica
6.7 3.3 5.7 2.5 Virginica
6.7 3 5.2 2.3 Virginica
6.3 2.5 5 1.9 Virginica
6.5 3 5.2 2 Virginica
6.2 3.4 5.4 2.3 Virginica
5.9 3 5.1 1.8 Virginica
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, log_loss
import pandas as pd
import lightgbm as lgb
# Prepare training data
df = pd.read_csv('data/iris.csv')
flower_names = {'Setosa': 0, 'Versicolor': 1, 'Virginica': 2}
X = df[['sepal.length', 'sepal.width', 'petal.length', 'petal.width']]
y = df['variety'].map(flower_names)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
train_data = lgb.Dataset(X_train, label=y_train)
def main():
# Train model
params = {
"objective": "multiclass",
"num_class": 3,
"learning_rate": 0.2,
"metric": "multi_logloss",
"feature_fraction": 0.8,
"bagging_fraction": 0.9,
"seed": 42,
}
model = lgb.train(params, train_data, valid_sets=[train_data])
# Evaluate model
y_proba = model.predict(X_test)
y_pred = y_proba.argmax(axis=1)
loss = log_loss(y_test, y_proba)
acc = accuracy_score(y_test, y_pred)
if __name__ == "__main__":
main()
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