Sklearn Model Summary

sklearn_model_summary.py

def model_summary(model, X, y, columns=[]):
	"""
	Takes a sklearn model and outputs basic stats,
	based on input features (X) and target (y)
	"""
	import pandas as pd
	from scipy import stats
	import numpy as np
	lm = model
	params = np.append(lm.intercept_,lm.coef_)
	predictions = lm.predict(X)
	
	def r_adjusted(r_squared, X, y):
		""" 
		Outputs the R^2 adjusted value for a sklearn linear regression model
		based on r_squared and the X and y of the data sets being used
		"""
		return 1 - (1-r_squared)*(len(y)-1)/(len(y)-X.shape[1]-1)
		
	r_squared = model.score(X, y)
	
	#from sklearn.feature_selection import f_regression
	#f_values, p_values = f_regression(X, y) # for each coefficient
	
	print("R^2: %s" % r_squared)
	print("R^2 Adjusted: %s" % r_adjusted(r_squared, X, y))
	if hasattr(model, "aic"): print("AIC: %s" % model.aic)
	if hasattr(model, "bic"): print("BIC: %s" % model.bic)
	
	newX = pd.DataFrame({"Constant":np.ones(len(X))}).join(pd.DataFrame(X))
	MSE = (sum((y-predictions)**2))/(len(newX)-len(newX.columns))

	# Note if you don't want to use a DataFrame replace the two lines above with
	# newX = np.append(np.ones((len(X),1)), X, axis=1)
	# MSE = (sum((y-predictions)**2))/(len(newX)-len(newX[0]))

	var_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())
	sd_b = np.sqrt(var_b)
	ts_b = params/ sd_b

	p_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-1))) for i in ts_b]

	sd_b = np.round(sd_b,3)
	ts_b = np.round(ts_b,3)
	p_values = np.round(p_values,3)
	params = np.round(params,4)

	myDF3 = pd.DataFrame()
	myDF3["Coefficients"],myDF3["Standard Errors"],myDF3["t values"],myDF3["Probabilites"] = [params,sd_b,ts_b,p_values]
	if any(columns): myDF3.index = ["Intercept"] + list(columns)
	print(myDF3)