use newer model

modules/ml-pipeline/src/pipeline/dvc.lock

@@ -29,36 +29,36 @@ stages:
     outs:
     - path: data/prepared_data/
       hash: md5
-      md5: 26f4de635a4c10cd4a07df1ffb4bbb2c.dir
-      size: 33889000
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
   build_model:
     cmd: python 2_build_model.py
     deps:
     - path: 2_build_model.py
       hash: md5
-      md5: 039578b629d7cd204016e92cd079ea90
-      size: 5181
+      md5: 84699d208874c52accaff61c6af9bb0a
+      size: 5359
     - path: data/prepared_data
       hash: md5
-      md5: 951ad046d4fca2b977a314f9520e8235.dir
-      size: 28249626
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/build_model.yaml:
         default:
           build_model:
             model_type: AutogluonAutoML
-            model_save_filepath: ./data/model/autogluonmodel/
+            model_save_filepath: ./data/model/optimised/
             fit_metrics_filepath: ./metrics/fit_metrics.json
             SKLearnLinearRegression:
             SKLearnSVMRegression:
               kernel: linear
             AutogluonAutoML:
-              output_filepath: ./data/model/autogluonmodel/
+              output_filepath: ./data/model/allmodels/
               problem_type: regression
               eval_metric: mean_squared_error
-              time_limit: 1000
+              time_limit: 4000
               presets: medium_quality
               excluded_model_types:
               - KNN
@@ -66,29 +66,29 @@ stages:
     outs:
     - path: data/model/
       hash: md5
-      md5: 6cedf54b03ce278149c67ed118ed0c59.dir
-      size: 408028954
-      nfiles: 20
+      md5: 7bb5156243b4db39349e80a01ffecde4.dir
+      size: 473398662
+      nfiles: 27
     - path: metrics/fit_metrics.json
       hash: md5
-      md5: 6352c43a896bedc387ac6fb0e1c6e09f
+      md5: 2bb16ac67de8778fbc08171d562b34d5
       size: 184
   generate_predictions:
     cmd: python 3_generate_predictions.py
     deps:
     - path: 3_generate_predictions.py
       hash: md5
-      md5: 238b3fa9f3c6f3720e77c116857070ae
-      size: 4720
+      md5: 5ef2856a5a977304f1ec01f9b4205262
+      size: 3028
     - path: data/model
       hash: md5
-      md5: 6cedf54b03ce278149c67ed118ed0c59.dir
-      size: 408028954
-      nfiles: 20
+      md5: 7bb5156243b4db39349e80a01ffecde4.dir
+      size: 473398662
+      nfiles: 27
     - path: data/prepared_data
       hash: md5
-      md5: 951ad046d4fca2b977a314f9520e8235.dir
-      size: 28249626
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -100,8 +100,8 @@ stages:
     outs:
     - path: data/predictions/
       hash: md5
-      md5: 5f5b251bb260c366b73b9481b8ffec4c.dir
-      size: 374624
+      md5: 0bb3cf991906953def81c8204cdcfaf0.dir
+      size: 374532
       nfiles: 1
   generate_metrics:
     cmd: python 4_generate_metrics.py
@@ -112,13 +112,13 @@ stages:
       size: 4487
     - path: data/predictions
       hash: md5
-      md5: 5f5b251bb260c366b73b9481b8ffec4c.dir
-      size: 374624
+      md5: 0bb3cf991906953def81c8204cdcfaf0.dir
+      size: 374532
       nfiles: 1
     - path: data/prepared_data
       hash: md5
-      md5: 951ad046d4fca2b977a314f9520e8235.dir
-      size: 28249626
+      md5: 9ce5c45722da7fc40491b5a4d00daf9e.dir
+      size: 33881619
       nfiles: 2
     params:
       configs/settings.yaml:
@@ -128,8 +128,8 @@ stages:
     outs:
     - path: metrics/metrics.json
       hash: md5
-      md5: 1268ce542c45da1f38286aa941f21c1e
-      size: 183
+      md5: 2e13ae67759a64261d03224f1c0d4bf4
+      size: 185
   startup_cleanup:
     cmd: python 0_startup_cleanup.py
     deps: