From f475d532c05b5f57f7929cef7a4dcbb86392ba08 Mon Sep 17 00:00:00 2001
From: ptresson <paul.tresson@ird.fr>
Date: Thu, 24 Oct 2024 15:10:25 +0200
Subject: [PATCH] save model to pkl and metrics to json
---
ml.py | 37 ++++++++++++++++++++++++++++++++-----
1 file changed, 32 insertions(+), 5 deletions(-)
diff --git a/ml.py b/ml.py
index d5c998e..824cf84 100644
--- a/ml.py
+++ b/ml.py
@@ -1,3 +1,4 @@
+import os
import ast
import numpy as np
from pathlib import Path
@@ -63,6 +64,7 @@ class MLAlgorithm(SHPAlgorithm):
DO_KFOLDS = 'DO_KFOLDS'
FOLD_COL = 'FOLD_COL'
NFOLDS = 'NFOLDS'
+ SAVE_MODEL = 'SAVE_MODEL'
SK_PARAM = 'SK_PARAM'
TEMPLATE_TEST = 'TEMPLATE_TEST'
METHOD = 'METHOD'
@@ -159,8 +161,15 @@ class MLAlgorithm(SHPAlgorithm):
maxValue=10
)
+ save_param = QgsProcessingParameterBoolean(
+ self.SAVE_MODEL,
+ self.tr("Save model after fit."),
+ defaultValue=True
+ )
+
for param in (
nfold_param,
+ save_param,
):
param.setFlags(
param.flags() | QgsProcessingParameterDefinition.FlagAdvanced)
@@ -182,6 +191,7 @@ class MLAlgorithm(SHPAlgorithm):
if self.do_kfold:
best_metric = 0
+ best_metrics_dict = {}
for fold in sorted(self.gdf[self.fold_col].unique()):
feedback.pushInfo(f'==== Fold {fold} ====')
self.test_gdf = self.gdf.loc[self.gdf[self.fold_col] == fold]
@@ -194,6 +204,7 @@ class MLAlgorithm(SHPAlgorithm):
used_metric = metrics_dict['accuracy']
if used_metric >= best_metric:
best_metric = used_metric
+ best_metrics_dict = metrics_dict
self.best_model = self.model
if (self.test_gdf is None) and not self.do_kfold:
@@ -202,6 +213,20 @@ class MLAlgorithm(SHPAlgorithm):
feedback.pushWarning(f'No test set was provided and no cross-validation is done, unable to assess model quality !')
self.best_model = self.model
+
+ feedback.pushInfo(f'Fitting done, saving model\n')
+ save_file = f'{self.method_name}.pkl'.lower()
+ metrics_save_file = f'{self.method_name}-metrics.json'.lower()
+ if self.save_model:
+ out_path = os.path.join(self.output_dir, save_file)
+ joblib.dump(self.best_model, out_path)
+ with open(os.path.join(self.output_dir, metrics_save_file), "w") as json_file:
+ ## confusion matrix is a np array that does not fit in a json
+ best_metrics_dict.pop('conf_matrix', None)
+ best_metrics_dict.pop('class_report', None)
+ print(best_metrics_dict)
+ json.dump(best_metrics_dict, json_file, indent=4)
+
self.infer_model(feedback)
return {'OUTPUT_RASTER':self.dst_path, 'OUTPUT_LAYER_NAME':self.layer_name, 'USED_SHP':self.used_shp_path}
@@ -257,17 +282,17 @@ class MLAlgorithm(SHPAlgorithm):
def process_ml_shp(self, parameters, context, feedback):
- template_test_layer = self.parameterAsVectorLayer(
+ template_test = self.parameterAsVectorLayer(
parameters, self.TEMPLATE_TEST, context)
- template_test = template_test_layer.dataProvider().dataSourceUri()
+ feedback.pushInfo(f'template_test: {template_test}')
self.test_gdf=None
- if template_test != '' :
+ if template_test is not None :
random_samples = self.parameterAsInt(
parameters, self.RANDOM_SAMPLES, context)
- gdf = gpd.read_file(template_test)
+ gdf = gpd.read_file(template_test.dataProvider().dataSourceUri())
gdf = gdf.to_crs(self.crs.toWkt())
feedback.pushInfo(f'before samples: {len(gdf)}')
@@ -291,6 +316,8 @@ class MLAlgorithm(SHPAlgorithm):
def process_ml_options(self, parameters, context, feedback):
+ self.save_model = self.parameterAsBoolean(
+ parameters, self.SAVE_MODEL, context)
self.do_kfold = self.parameterAsBoolean(
parameters, self.DO_KFOLDS, context)
gt_col = self.parameterAsString(
@@ -518,7 +545,7 @@ class MLAlgorithm(SHPAlgorithm):
should provide a basic description about what the algorithm does and the
parameters and outputs associated with it..
"""
- return self.tr(f"Fit a Machine Learning model using input template\n{self.get_help_sk_methods()}")
+ return self.tr(f"Fit a Machine Learning model using input template. Only RandomForestClassifier is throughfully tested. \n{self.get_help_sk_methods()}")
def icon(self):
return 'E'
--
GitLab