ml_test.py

from xml.etree.ElementInclude import include
from matplotlib.pyplot import cla
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn import metrics
from sklearn.preprocessing import OneHotEncoder
from sklearn import preprocessing
from sklearn.metrics import ConfusionMatrixDisplay, confusion_matrix
import numpy as np
import matplotlib.pyplot as plt

# Load data
df_1 = pd.read_csv('KN_features.csv')
df_2 = pd.read_csv('MDF_features.csv')

# Make the number of detections for the two classes equal
df_2 = df_2[:len(df_1)]

# Merge the dataframs
df = pd.concat([df_1,df_2])

# Make the matrices
x = df[['BAND','PRE-BAND','POST-BAND']]
y = df[['CLASS']]

# One hot encoding for passband features
enc = OneHotEncoder(sparse=False)
x = enc.fit_transform(x)

# Adding the time to prev and next det as features
x_new = np.zeros((len(df), 23))
x_new[:, :20] = x
x_new[:, 20] = df['TIME-TO-PREV']
x_new[:, 21] = df['TIME-TO-NEXT']
x_new[:, 22] = df['MDF_DENSITY']
# x_new[:, 23] = df['NEXT-PHOT-FLAG']
# x_new[:, 24] = df['NUM_DETECTIONS']


features = list(enc.get_feature_names_out())
features.append('TIME-TO-PREV')
features.append('TIME-TO-NEXT')
features.append('MDF_DENSITY')
# features.append('NEXT-PHOT-FLAG')
# features.append('NUM_DETECTIONS')

x_new = pd.DataFrame(x_new, columns=features)

x = x_new

print(x_new)

# Creating binary class column 
y = []
for c in df['CLASS']:
    if c == 'KN':
        y.append(0)
    else:
        y.append(1)

# Splitting data
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.3, random_state=42)

# Classifier
clf=RandomForestClassifier(n_estimators=1000, random_state=42)

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



print("Accuracy:",metrics.accuracy_score(y_test, y_pred))
cm = confusion_matrix(y_test, y_pred, labels=clf.classes_)

importance = clf.feature_importances_

disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=['Kilo Nova', 'M Dwarf flare'])
disp.plot()
plt.show()

for i in range(len(importance)):
    print(f'{features[i]}: {importance[i]}')