The "ImbalanceMetrics" Python package provides an extensive set of evaluation metrics designed for assessing the performance of machine learning models on imbalanced datasets, where traditional accuracy and error rate measurements may be inadequate. The package incorporates several evaluation metrics that tackle the challenges specific to imbalanced domains, offering a more accurate evaluation of model performance.
- Python 3
- scikit-learn
- NumPy
- Pandas
- Smogn
## install pypi release
pip install imbalance-metrics
## install developer version
pip install git+https://github.com/paobranco/ImbalanceMetrics.gitimport pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from imbalance_metrics import classification_metrics as cm
df = pd.read_csv('glass0.csv', header=None)
X,y=df.drop(columns=[9]),df[9]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)
clf = RandomForestClassifier(random_state=42)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)
y_proba=clf.predict_proba(X_test)
y_phi = cm.calculate_classification_phi(y_test) # Relevance by class frequency
gmean = cm.gmean_score(y_test, y_pred) # Weighted gmean
p_d0,r_d0,pra_d0=cm.pr_davis(y_test,y_proba,True) # Default minority as positive
p_d1,r_d1,pra_d1=cm.pr_davis(y_test,y_proba,True,pos_label=1) # 1 as positive
p_m0,r_m0,pra_m0=cm.pr_manning(y_test,y_proba,True) # Default minority as positive
p_m1,r_m1,pra_m1=cm.pr_manning(y_test,y_proba,True,pos_label=1) # 1 as positive
cv_davis=cm.cross_validate_auc(clf,X,y,cm.pr_davis,5)
cv_manning=cm.cross_validate_auc(clf,X,y,cm.pr_manning,5)import pandas as pd
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from imbalance_metrics import regression_metrics as rm
df = pd.read_csv('housing(processed).csv')
X,y=df.drop(columns="SalePrice"),df["SalePrice"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)
sc = StandardScaler()
y_train = sc.fit_transform(y_train.values.reshape(-1, 1))
y_test = sc.transform (y_test.values.reshape(-1, 1))
reg = SVR().fit(X_train, y_train)
y_pred = reg.predict(X_test)
y_test=y_test.reshape(-1)
y_pred=y_pred.reshape(-1)
wmse = rm.phi_weighted_mse (y_test , y_pred)
wmae = rm.phi_weighted_mae (y_test , y_pred)
wr2 = rm.phi_weighted_r2 (y_test , y_pred)
wrmse = rm.phi_weighted_root_mse (y_test , y_pred)
ser_t = rm.ser_t(y_test,y_pred,t=.7)
sera= rm.sera(y_test,y_pred,return_err = True)ImablanceMetrics is open for improvements and maintenance. Your help is valued to make the package better for everyone.
Licensed under the General Public License v3.0 (GPLv3).
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