diff --git a/examples/lightgbm/.gitattributes b/examples/lightgbm/.gitattributes new file mode 100644 index 000000000..d5799bd69 --- /dev/null +++ b/examples/lightgbm/.gitattributes @@ -0,0 +1,2 @@ +# GitHub syntax highlighting +pixi.lock linguist-language=YAML diff --git a/examples/lightgbm/.gitignore b/examples/lightgbm/.gitignore new file mode 100644 index 000000000..c9314b7c2 --- /dev/null +++ b/examples/lightgbm/.gitignore @@ -0,0 +1,2 @@ +# pixi environments +.pixi diff --git a/examples/lightgbm/Breast_cancer_data.csv b/examples/lightgbm/Breast_cancer_data.csv new file mode 100644 index 000000000..8671a46c1 --- /dev/null +++ b/examples/lightgbm/Breast_cancer_data.csv @@ -0,0 +1,570 @@ +mean_radius,mean_texture,mean_perimeter,mean_area,mean_smoothness,diagnosis +17.99,10.38,122.8,1001.0,0.1184,0 +20.57,17.77,132.9,1326.0,0.08474,0 +19.69,21.25,130.0,1203.0,0.1096,0 +11.42,20.38,77.58,386.1,0.1425,0 +20.29,14.34,135.1,1297.0,0.1003,0 +12.45,15.7,82.57,477.1,0.1278,0 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Check dataset: `breast_cancer_data.csv` +2. Run the code: + +```bash +pixi run start +``` + +## Results + +After running the code, the model's training and testing accuracy will be displayed. + +```bash +Model accuracy: 0.930 +True Positive(TP) = 55 +True Negatice(TN) = 104 +False Positive(FP) = 8 +False Negative(FN) = 4 +``` diff --git a/examples/lightgbm/main.py b/examples/lightgbm/main.py new file mode 100644 index 000000000..c33464729 --- /dev/null +++ b/examples/lightgbm/main.py @@ -0,0 +1,35 @@ +import pandas as pd +from sklearn.model_selection import train_test_split +import lightgbm as lgb +from sklearn.metrics import accuracy_score, confusion_matrix + +# load data +df = pd.read_csv("Breast_cancer_data.csv") + +# Declare feature vector and target variable +X = df[[ + 'mean_radius','mean_texture','mean_perimeter', + 'mean_area','mean_smoothness']] +y = df['diagnosis'] + +# split the dataset into the training set and test set +X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.3, random_state=42) + +# build the lightgbm model +clf = lgb.LGBMClassifier(verbose=-1) +clf.fit(X_train, y_train) + +# predict the results +y_pred = clf.predict(X_test) + +# view accuracy +accuracy = accuracy_score(y_pred, y_test) +print(f"Model accuracy: {accuracy:0.3f}") + +# view confusion-matrix +cm = confusion_matrix(y_test, y_pred) +print("True Positives(TP) = ", cm[0,0]) +print("True Negatives(TN) = ", cm[1,1]) +print("False Positives(FP) = ", cm[0,1]) +print("False Negatives(FN) = ", cm[1,0]) diff --git a/examples/lightgbm/pixi.lock b/examples/lightgbm/pixi.lock new file mode 100644 index 000000000..404de6cd9 --- /dev/null +++ b/examples/lightgbm/pixi.lock @@ -0,0 +1,3146 @@ +version: 2 +metadata: + content_hash: + linux-64: e90c2ee71ad70fc0a1c8302029533a7d1498f2bffcd0eaa8d2934700e775dc1d + osx-64: e90c2ee71ad70fc0a1c8302029533a7d1498f2bffcd0eaa8d2934700e775dc1d + osx-arm64: e90c2ee71ad70fc0a1c8302029533a7d1498f2bffcd0eaa8d2934700e775dc1d + win-64: e90c2ee71ad70fc0a1c8302029533a7d1498f2bffcd0eaa8d2934700e775dc1d + channels: + - 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