Source code for

Simple ML models for classifcation and regression.

Designed for educational purposes only
from collections import defaultdict
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.preprocessing import StandardScaler
from sklearn.feature_selection import VarianceThreshold
from sklearn.pipeline import Pipeline
from sklearn.neural_network import MLPClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.gaussian_process import GaussianProcessClassifier
from sklearn.multiclass import OneVsRestClassifier
from sklearn.ensemble import (
from sklearn.svm import SVC
import pickle
import joblib
import matplotlib.pyplot as plt
from import binary_class_dat

simple_class_models = [

[docs]def classify_roc_ml( X=[], y=[], classes=[0, 1, 2], names=["High val", "Low val", ""], n_plot=1, method="", preprocess=True, plot=False, test_size=0.1, ): """ Classifcation module for ROC curve for upto three classes. It can be expanded in more classes as well. Args: X: input feature vectors y: target data obtained from binary_class_dat classes: dummy classes names: name holders for the target data method: ML method preprocess: whether to apply standard preprocessing techniques plot: whether to plot the ROC curve """ if plot: plt.close() plt.rcParams.update({"font.size": 22}) plt.figure(figsize=(12, 8)) y = label_binarize(y, classes=classes) n_classes = y.shape[1] pipe = Pipeline( [ ("stdscal", StandardScaler()), ("vart", VarianceThreshold(1e-4)), ("est", method), ] ) if preprocess: model = pipe else: model = method X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=test_size, random_state=0 ) classifier = OneVsRestClassifier(model) if hasattr(model, "decision_function"): y_score =, y_train).decision_function(X_test) else: y_score =, y_train).predict_proba(X_test) lw = 3 fpr = dict() tpr = dict() roc_auc = dict() for i in range(n_classes): fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_score[:, i]) roc_auc[i] = auc(fpr[i], tpr[i]) colors = ["blue", "red", "green"] count = 0 for i, color, name in zip(range(n_classes), colors, names): if name != "": if count < n_plot: count = count + 1 if plot: plt.plot( fpr[i], tpr[i], color=color, lw=lw, label="ROC {0} (area = {1:0.2f})" "".format(name, roc_auc[i]), ) if plot: plt.plot([0, 1], [0, 1], "k--", lw=lw) plt.xlim([-0.05, 1.0]) plt.ylim([0.0, 1.05]) plt.xlabel("False Positive Rate") plt.ylabel("True Positive Rate") plt.legend(loc="lower right") model =, y) return model, roc_auc
[docs]def classification( X=[], Y=[], tol=100, plot=False, preprocess=True, models=simple_class_models, model_name="my_model", save_model=False, ): """Quickly train some of the classifcation algorithms in scikit-learn.""" X_class, Y_class = binary_class_dat(X=X, Y=Y, tol=tol) info = defaultdict() for i in models: m, r = classify_roc_ml( X=X_class, y=Y_class, method=i, preprocess=preprocess, plot=plot ) print(type(i).__name__, r[0]) info[type(i).__name__] = {} info[type(i).__name__]["roc_auc"] = r if save_model: pk = ( str(model_name) + "_" + str(type(i).__name__) + "_" + str(".pk") ) jb = ( str(model_name) + "_" + str(type(i).__name__) + "_" + str(".jb") ) pickle.dump(m, open(pk, "wb")) joblib.dump(m, jb) return info
""" if __name__ == "__main__": from import get_ml_data X, Y, jid = get_ml_data(dataset = 'cfid_3d', ml_property='exfoliation_energy') X_class, Y_class = binary_class_dat(X=X, Y=Y, tol=100) info = classification(X=X,Y=Y,tol=100, save_model=True) #print (info) #print () #print (info['GradientBoostingClassifier']['roc_auc'][0]) """