0

im working on a data set and i do label encoding for cat features and i tried to do the inverse now but an error appears like this

----> 1 original_labels = labelencoder.inverse_transform(df['model'])
      2 
      3 # create a new DataFrame with the original labels
      4 original_labels_df = pd.DataFrame({'model': original_labels})

/opt/conda/lib/python3.7/site-packages/sklearn/preprocessing/_label.py in inverse_transform(self, y)
    159         diff = np.setdiff1d(y, np.arange(len(self.classes_)))
    160         if len(diff):
--> 161             raise ValueError("y contains previously unseen labels: %s" % str(diff))
    162         y = np.asarray(y)
    163         return self.classes_[y]

ValueError: y contains previously unseen labels: [  6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21  22  23
  24  25  26  27  28  29  30  31  32  33  34  35  36  37  38  39  40  41
  42  43  44  45  46  47  48  49  50  51  52  53  54  55  56  57  58  59
  60  61  62  63  64  65  66  67  68  69  70  71  72  73  74  75  76  77
  78  79  80  81  82  83  84  85  86  87  88  89  90  91  92  93  94  95
  96  97  98  99 100 101 102 103 104 105 106 107 108 109 110 111 112 113
 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203
 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221
 222 223 224 225 226 227 228 229]

here is the codes that i applied

df['model'] = labelencoder.fit_transform(df['model'])

original_labels = labelencoder.inverse_transform(df['model'])

# create a new DataFrame with the original labels
original_labels_df = pd.DataFrame({'model': original_labels})
kim seol
  • 15
  • 2

1 Answers1

0

You could just create a new variable to store your encoded labels, and the original labels would still be stored in df['model'].

encoded_labels = labelencoder.fit_transform(df['model'])

To avoid the unseen labels error, you should encode your labels before you split your data into training and testing sets. As when you fit your encoder to only your training data any labels that aren't seen in the training data will not be encoded.

If you really need to do it this way, instead of using labelencoder.inverse_transform(df['model']), you can use labelencoder.classes_ to get the original labels.

lukemac
  • 1
  • 2