RandomForest.py

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import confusion_matrix

commitsMetrics = []



data = pd.read_csv('allData-Multi.csv', low_memory=False)

X=data[['addedLOC', 'deletedLOC', 'churnLOC', 'entropy','changesAge','changeFiles','devCont','devExp']]
y=data['target']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

clf=RandomForestClassifier(n_estimators=1000)
clf.fit(X_train,y_train)
y_pred=clf.predict(X_test)

feature_names = ['addedLOC', 'deletedLOC', 'churnLOC', 'entropy','changesAge','changeFiles','devCont','devExp']

print("Accuracy:",metrics.accuracy_score(y_test, y_pred))

clf=RandomForestClassifier(n_estimators=100)
clf.fit(X_train,y_train)


RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=1,
            oob_score=False, random_state=None, verbose=0,
            warm_start=False)

feature_imp = pd.Series(clf.feature_importances_,index=feature_names).sort_values(ascending=False)
print(feature_imp)


# CM = confusion_matrix(y, y_pred)
# sns.heatmap(CM)
# plt.show()


# sns.barplot(x=feature_imp, y=feature_imp.index)
# plt.xlabel('Feature Importance Score')
# plt.ylabel('Features')
# plt.title("Visualizing Important Features")
# plt.legend()
# plt.show()