fix issues

kiliautoml/utils/mapper/create.py

@@ -96,7 +96,7 @@ def __init__(
         job: JobT,
         job_name: str,
         assets_repository,  # check type
-        predictions: List[Any],
+        predictions: Any,
         focus_class: Optional[List[str]],
     ):
         self.job = job
@@ -226,7 +226,7 @@ def _get_assignments_and_lens_with_labels(self):
 
         label_id_array = [self.cat2id[label] for label in self.labels]
         prediction_true_class = [
-            self.predictions[enum][item] for enum, item in enumerate(label_id_array)
+            self.predictions[enum, item] for enum, item in enumerate(label_id_array)
         ]
         predicted_order = np.argsort(self.predictions, axis=1)
         predicted_class = predicted_order[:, -1]
@@ -274,14 +274,14 @@ def _get_custom_tooltip(self):
             # with labels available
             if len(self.lens_names) == 5:
                 return custom_tooltip_picture(
-                    np.column_stack((self.lens[:, 1], self.lens[:, 3])),
+                    np.column_stack((self.lens[:, 0], self.lens[:, 2])),
                     pict_data_type="img_list",
                     image_list=self.data,
                 )
             # without labels available
             else:
                 return custom_tooltip_picture(
-                    self.lens[:, 1],
+                    self.lens[:, 0],
                     pict_data_type="img_list",
                     image_list=self.data,
                 )
@@ -290,13 +290,13 @@ def _get_custom_tooltip(self):
             # with labels available
             if len(self.lens_names) == 5:
                 return custom_tooltip_text(
-                    np.column_stack((self.lens[:, 1], self.lens[:, 3])),
+                    np.column_stack((self.lens[:, 0], self.lens[:, 2])),
                     data=self.data,
                 )
             # without labels available
             else:
                 return custom_tooltip_text(
-                    self.lens[:, 1],
+                    self.lens[:, 0],
                     data=self.data,
                 )
         else:

mapper.py

@@ -2,6 +2,7 @@
 from typing import List, Optional
 
 import click
+import numpy as np
 import pandas as pd
 from kili.client import Kili
 from tabulate import tabulate
@@ -162,34 +163,36 @@ def main(
                     ncol = first_line.count(",") + 1
                     nrows = len(next_lines) + 1
 
-                if ncol == len(job["content"]["categories"]):
-                    index_col = None
+                if ncol == len(job["content"]["categories"]) + 1:
+                    index_col = 0
+                    header = None
                 elif ncol == len(job["content"]["categories"]):
                     index_col = None
+                    if nrows == len(assets):
+                        header = None
+                    elif ncol == len(assets) + 1:
+                        header = 0
+                    else:
+                        raise ValueError(
+                            "When there is no index column in csv file with predictions"
+                            "the number of row has to be equal to the number of assets"
+                            "or the number of assets + 1 if there is a header"
+                        )
                 else:
                     raise ValueError(
                         "Number of column in predictions should be either "
                         "the number of category of the number of category + 1 for the external id"
                     )
 
-                if nrows == len(assets):
-                    header = None
-                elif ncol == len(assets) + 1:
-                    header = 0
-                else:
-                    raise ValueError(
-                        "Number of rows in predictions should be either "
-                        "the number of assets of the number of assets + 1 for the header"
-                    )
-
                 predictions_df = pd.read_csv(predictions_path, index_col=index_col, header=header)
 
                 if index_col is None:
-                    predictions = list(predictions_df.to_numpy())
+                    predictions = predictions_df.to_numpy()
                 else:
                     predictions = []
                     for asset in assets:
                         predictions.append(predictions_df.loc[asset["externalId"]].to_numpy())
+                    predictions = np.array(predictions)
 
             mapper_image_classification = MapperClassification(
                 api_key=api_key,