calculations.py

import numpy as np

#Function to compute F1-score from p_matrices and val_matrices
def get_metric_f1(ref_p_matrix, p_matrix, ref_val_matrix, val_matrix, alpha, 
            tau_min=0, tau_diff=1, same_sign=True):

    N, N, taumaxp1 = val_matrix.shape
    TP = 0
    FP = 0
    FN = 0
    auto = 0
    count = 0
    for i in range(N):
        for j in range(N):
            if i != j:
                for tau in range(tau_min, taumaxp1):
#                     print(np.sum(ref_p_matrix[i,j,tau] < alpha),np.sum(p_matrix[i,j,tau] < alpha))
                    if ref_p_matrix[i,j,tau] > alpha and p_matrix[i,j,tau] < alpha:
                        FP += 1
                    elif ref_p_matrix[i,j,tau] < alpha and np.any(p_matrix[i,j,max(0,tau-tau_diff):tau+tau_diff+1] < alpha):
                        count +=1
                        if same_sign==True and np.sign(ref_val_matrix[i,j,tau]) == np.sign(val_matrix[i,j,tau]):
                            TP += 1
                        elif same_sign==True and np.sign(ref_val_matrix[i,j,tau]) != np.sign(val_matrix[i,j,tau]):
                            FN += 1
                        elif same_sign==False:
                            TP += 1
                    elif ref_p_matrix[i,j,tau] < alpha and not(np.any(p_matrix[i,j,max(0,tau-tau_diff):tau+tau_diff+1] < alpha)):
                        FN += 1
            else:
                auto +=1
    precision =  float(TP+1e-10) / float(TP + FP +1e-10)
    recall = float(TP+1e-10) / float(TP + FN +1e-10)
    f1 = 2.0*precision*recall/float(precision + recall)
    return precision, recall, TP, FP, FN, f1, auto, count

def put_row_first(df,row_name):
    #put the row_name row at the first position of the df
    df_out=df
    df_out["new"] = range(1,len(df)+1)
    df_out.loc[row_name, 'new'] = 0
    df_out=df_out.sort_values("new").drop('new', axis=1)
    return df_out

def put_column_first(df,column_name):
    #put the column_name col at the first position of the df
    df_out=df
    col= df_out.pop(column_name)
    df_out.insert(0,column_name,col)
    return df_out