def get_RMSE_pc(y=[],y_hat=[]): """ Get the mean absolute percentage error $RMSE_pc\frac{100}{\bar{y}}\sqrt{\frac{1}{n}\sum_{i=0}^{n-1} ( y_i-\hat{y_i} )^2}$ """ rmse=numpy.sqrt(skmetrics.mean_squared_error(y,y_hat)) rmse_pc=100*rmse/numpy.mean(y) return rmse_pc
def confidence_interval(self): #get sample std N=len(self.X) S=statistics.stdev(self.X) logging.debug(S) se=S/numpy.sqrt(self.X) #get t statistic pctile=stats.t.ppf(0.975,df=N-1) x_bar=numpy.mean(self.X) return (x_bar-pctile*se,x_bar+pctile*se)
def fit(self, X, y, n_jobs=1): """ y can be series or array X can be dataframe or ndarry (N datapoints x M features) """ self = super(LinModel, self).fit(X, y, n_jobs) self.nobs=X.shape[0] self.nparams=X.shape[1] #remove an extra 1 for the alpha (k-1) self.df_model=X.shape[1]-1 #(n-k-1) - we always assume an alpha is present self.df_resid=self.nobs-X.shape[1]-1 #standard error of the regression y_bar=y.mean() y_hat=self.predict(X) self.raw_data=X self.training=y # logging.debug(X) self.fittedvalues=y_hat #explained sum of squares SSE=numpy.sum([numpy.power(val-y_bar,2) for val in y_hat]) e=numpy.matrix(y-y_hat).T self.resid=numpy.ravel(e) # logging.debug(y_bar) # logging.debug(y) SST=numpy.sum([numpy.power(val-y_bar,2) for val in y]) SSR=numpy.sum([numpy.power(x,2) for x in e]) self.ssr=SSR #print(SSR) #mean squared error of the residuals (unbiased) #square root of this is the standard error of the regression s_2 = SSR / (self.df_resid+1) self.s_y=numpy.sqrt(s_2) self.RMSE_pc=metrics.get_RMSE_pc(y,y_hat) # logging.debug("s_y = {}".format(self.s_y)) #Also get the means of the independent variables if isinstance(X,pd.core.frame.DataFrame): #assume its' called alpha self.X_bar=X[X.columns[X.columns!='alpha']].mean() Z=numpy.matrix(X[X.columns[X.columns!='alpha']]) else: #assume its the first column self.X_bar=numpy.mean(X.values,axis=0)[1:] Z=numpy.matrix(X[:,1:]) i_n=numpy.matrix(numpy.ones(self.nobs)) M_0=numpy.matrix(numpy.eye(self.nobs))-numpy.power(self.nobs,-1)*i_n*i_n.T self.Z_M_Z=Z.T*M_0*Z # #print(numpy.sqrt(numpy.diagonal(sse * numpy.linalg.inv(numpy.dot(X.T, X))))) # #standard error of estimator bk X_mat=numpy.matrix(X.values) #print(X_mat) self.X_dash_X=X_mat.T*X_mat # we get nans using this approach so calculate each one separately # se=numpy.zeros(self.nparams) # for ii in range(self.nparams): # se[ii]=numpy.sqrt(X_dash_X[ii,ii]*s_2) # logging.debug(s_2) # logging.debug(numpy.linalg.inv(X_dash_X)) # #se = numpy.sqrt(numpy.diagonal(s_2 * numpy.linalg.inv(numpy.matrix(X.T, X)))) se=numpy.sqrt(numpy.diagonal(s_2 * numpy.linalg.inv(self.X_dash_X))) self.se= se self.t = self.coef_ / se self.p = 2 * (1 - stats.t.cdf(numpy.abs(self.t), y.shape[0] - X.shape[1])) self.independent_ = [] if isinstance(X,pd.DataFrame): self.independent_=X.columns.values #t_val=stats.t.ppf(1-0.05/2,y.shape[0] - X.shape[1]) #R2 - 1-SSR/SST self.rsquared=1-SSR/SST #adjusted r2 #1-[(1-R2)(n-1)/(n-k-1)] self.rsquared_adj=1-(((1-self.rsquared)*(self.nobs-1))/self.df_resid) #f-value f_value=(self.rsquared/(self.df_model))/\ ((1-self.rsquared)/(self.df_resid+1)) self.f_stat=f_value self.f_pvalue=stats.f.pdf(f_value,self.df_model,self.df_resid+1)