From 6ccff33dc2ee0825c4ce423f75351e3774d8844d Mon Sep 17 00:00:00 2001
From: ptresson <paul.tresson@ird.fr>
Date: Mon, 2 Dec 2024 15:07:10 +0100
Subject: [PATCH] add manifold algorithms support, advances #34
---
utils/algo.py | 50 +++++++++++++++++++++++++++-----------------------
1 file changed, 27 insertions(+), 23 deletions(-)
diff --git a/utils/algo.py b/utils/algo.py
index 01fd39b..c00bf0b 100644
--- a/utils/algo.py
+++ b/utils/algo.py
@@ -37,6 +37,7 @@ import torch.nn as nn
import sklearn.decomposition as decomposition
import sklearn.cluster as cluster
+import sklearn.manifold as manifold
from sklearn.base import BaseEstimator
from sklearn.preprocessing import StandardScaler
# from sklearn.metrics import silhouette_score, silhouette_samples
@@ -439,12 +440,15 @@ class SKAlgorithm(IAMAPAlgorithm):
proj_methods = ["fit", "transform"]
clust_methods = ["fit", "fit_predict"]
+ mani_methods = ["fit", "fit_transform"]
+
if self.TYPE == "proj":
method_opt1 = get_sklearn_algorithms_with_methods(
decomposition, proj_methods
)
method_opt2 = get_sklearn_algorithms_with_methods(cluster, proj_methods)
- self.method_opt = method_opt1 + method_opt2
+ method_opt3 = get_sklearn_algorithms_with_methods(manifold, mani_methods)
+ self.method_opt = method_opt1 + method_opt2 + method_opt3
self.addParameter(
QgsProcessingParameterNumber(
@@ -543,33 +547,33 @@ class SKAlgorithm(IAMAPAlgorithm):
parameters["OUTPUT_RASTER"] = self.dst_path
- try:
- default_args = get_arguments(decomposition, self.method_name)
- except AttributeError:
- default_args = get_arguments(cluster, self.method_name)
+ for sk_module in [decomposition, cluster, manifold]:
+ try:
+ default_args = get_arguments(sk_module, self.method_name)
+ kwargs = self.update_kwargs(default_args)
+ model = instantiate_sklearn_algorithm(
+ sk_module, self.method_name, **kwargs
+ )
+ break
+ except AttributeError:
+ continue
+ except Exception:
+ feedback.pushWarning(
+ f"{self.method_name} not properly initialized ! Try passing custom parameters"
+ )
+ return {
+ "OUTPUT_RASTER": self.dst_path,
+ "OUTPUT_LAYER_NAME": self.layer_name,
+ }
+ else:
+ raise AttributeError("All attempts to call function failed.")
- kwargs = self.update_kwargs(default_args)
## some clustering algorithms need the entire dataset.
do_fit_predict = False
- try:
- model = instantiate_sklearn_algorithm(
- decomposition, self.method_name, **kwargs
- )
- except AttributeError:
- model = instantiate_sklearn_algorithm(cluster, self.method_name, **kwargs)
- ## if model does not have a 'predict()' method, then we do a fit_predict in one go
- if not hasattr(model, "predict"):
- do_fit_predict = True
- except Exception:
- feedback.pushWarning(
- f"{self.method_name} not properly initialized ! Try passing custom parameters"
- )
- return {
- "OUTPUT_RASTER": self.dst_path,
- "OUTPUT_LAYER_NAME": self.layer_name,
- }
+ if not hasattr(model, "predict") and sk_module==cluster:
+ do_fit_predict = True
if do_fit_predict:
proj_img, model = self.fit_predict(model, feedback)
--
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