evaluations(ty, pv) -> (ACC, MSE, SCC) Calculate accuracy, mean squared error and squared correlation coefficient using the true values (ty) and predicted values (pv).
(ty, pv)
| 48 | liblinear.save_model(model_file_name.encode(), model) |
| 49 | |
| 50 | def evaluations(ty, pv): |
| 51 | """ |
| 52 | evaluations(ty, pv) -> (ACC, MSE, SCC) |
| 53 | |
| 54 | Calculate accuracy, mean squared error and squared correlation coefficient |
| 55 | using the true values (ty) and predicted values (pv). |
| 56 | """ |
| 57 | if len(ty) != len(pv): |
| 58 | raise ValueError("len(ty) must equal to len(pv)") |
| 59 | total_correct = total_error = 0 |
| 60 | sumv = sumy = sumvv = sumyy = sumvy = 0 |
| 61 | for v, y in zip(pv, ty): |
| 62 | if y == v: |
| 63 | total_correct += 1 |
| 64 | total_error += (v-y)*(v-y) |
| 65 | sumv += v |
| 66 | sumy += y |
| 67 | sumvv += v*v |
| 68 | sumyy += y*y |
| 69 | sumvy += v*y |
| 70 | l = len(ty) |
| 71 | ACC = 100.0*total_correct/l |
| 72 | MSE = total_error/l |
| 73 | try: |
| 74 | SCC = ((l*sumvy-sumv*sumy)*(l*sumvy-sumv*sumy))/((l*sumvv-sumv*sumv)*(l*sumyy-sumy*sumy)) |
| 75 | except: |
| 76 | SCC = float('nan') |
| 77 | return (ACC, MSE, SCC) |
| 78 | |
| 79 | def train(arg1, arg2=None, arg3=None): |
| 80 | """ |