From 93fcfdfe20cd1e054a0cf394dfb1433fc01c1b83 Mon Sep 17 00:00:00 2001
From: ptresson <paul.tresson@ird.fr>
Date: Thu, 24 Oct 2024 10:50:04 +0200
Subject: [PATCH] handle gt column as string
---
ml.py | 54 ++++++++++++++++++++++++++++++++----------------------
1 file changed, 32 insertions(+), 22 deletions(-)
diff --git a/ml.py b/ml.py
index 490bd4d..b0e0d52 100644
--- a/ml.py
+++ b/ml.py
@@ -309,36 +309,26 @@ class MLAlgorithm(SHPAlgorithm):
self.do_kfold = self.parameterAsBoolean(
parameters, self.DO_KFOLDS, context)
- self.gt_col = self.parameterAsString(
+ gt_col = self.parameterAsString(
parameters, self.GT_COL, context)
fold_col = self.parameterAsString(
parameters, self.FOLD_COL, context)
nfolds = self.parameterAsInt(
parameters, self.NFOLDS, context)
-
str_kwargs = self.parameterAsString(
parameters, self.SK_PARAM, context)
- if str_kwargs != '':
- self.passed_kwargs = ast.literal_eval(str_kwargs)
- else:
- self.passed_kwargs = {}
-
- ## If no test set is provided and the option to perform kfolds is true, we perform kfolds
## If a fold column is provided, this defines the folds. Otherwise, random split
-
## check that no column with name 'fold' exists, otherwise we use 'fold1' etc..
+ ## we also make a new column containing gt values
self.fold_col = get_unique_col_name(self.gdf, 'fold')
+ self.gt_col = get_unique_col_name(self.gdf, 'gt')
- if self.test_gdf == None and self.do_kfold:
- if fold_col.strip() != '' :
- self.gdf[self.fold_col] = self.gdf[fold_col]
- else:
- self.gdf[self.fold_col] = np.random.randint(1, nfolds + 1, size=len(self.gdf))
-
- ## Else, self.gdf is the train set
+ ## Instantiate model
+ if str_kwargs != '':
+ self.passed_kwargs = ast.literal_eval(str_kwargs)
else:
- self.train_gdf = self.gdf
+ self.passed_kwargs = {}
method_idx = self.parameterAsEnum(
parameters, self.METHOD, context)
@@ -357,6 +347,29 @@ class MLAlgorithm(SHPAlgorithm):
self.model = instantiate_sklearn_algorithm(neighbors, self.method_name, **kwargs)
+ ## different behaviours if we are doing classification or regression
+ ## If classification, we create a new col with unique integers for each classes
+ ## to ease inference
+ self.task_type = check_model_type(self.model)
+
+ if self.task_type == 'classification':
+ self.out_dtype = 'int8'
+ self.gdf[self.gt_col] = pd.factorize(self.gdf[gt_col])[0] # unique int for each class
+ else:
+ self.gt_col = gt_col
+
+
+ ## If no test set is provided and the option to perform kfolds is true, we perform kfolds
+ if self.test_gdf == None and self.do_kfold:
+ if fold_col.strip() != '' :
+ self.gdf[self.fold_col] = self.gdf[fold_col]
+ else:
+ self.gdf[self.fold_col] = np.random.randint(1, nfolds + 1, size=len(self.gdf))
+ ## Else, self.gdf is the train set
+ else:
+ self.train_gdf = self.gdf
+
+
def get_raster(self, mode='train'):
if mode == 'train':
@@ -407,10 +420,8 @@ class MLAlgorithm(SHPAlgorithm):
def get_metrics(self, test_gts, predictions, feedback):
- task_type = check_model_type(self.model)
metrics_dict = {}
-
- if task_type == 'classification':
+ if self.task_type == 'classification':
# Evaluate the model
metrics_dict['accuracy'] = accuracy_score(test_gts, predictions)
metrics_dict['precision'] = precision_score(test_gts, predictions, average='weighted') # Modify `average` for multiclass if necessary
@@ -418,10 +429,9 @@ class MLAlgorithm(SHPAlgorithm):
metrics_dict['f1'] = f1_score(test_gts, predictions, average='weighted')
metrics_dict['conf_matrix'] = confusion_matrix(test_gts, predictions)
metrics_dict['class_report'] = classification_report(test_gts, predictions)
- self.out_dtype = 'int8'
- elif task_type == 'regression':
+ elif self.task_type == 'regression':
metrics_dict['mae'] = mean_absolute_error(test_gts, predictions)
metrics_dict['mse'] = mean_squared_error(test_gts, predictions)
--
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