diff --git a/docs/sphinx-builddir/doctrees/environment.pickle b/docs/sphinx-builddir/doctrees/environment.pickle index 6866fcd..faadf32 100644 Binary files a/docs/sphinx-builddir/doctrees/environment.pickle and b/docs/sphinx-builddir/doctrees/environment.pickle differ diff --git a/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree b/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree index e54f82a..3d97099 100644 Binary files a/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree and b/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree differ diff --git a/docs/sphinx-builddir/doctrees/optunaz.doctree b/docs/sphinx-builddir/doctrees/optunaz.doctree index a6e56d1..98ffe45 100644 Binary files a/docs/sphinx-builddir/doctrees/optunaz.doctree and b/docs/sphinx-builddir/doctrees/optunaz.doctree differ diff --git a/docs/sphinx-builddir/html/_modules/optunaz/predict.html b/docs/sphinx-builddir/html/_modules/optunaz/predict.html index 746e2b0..d548a7c 100644 --- a/docs/sphinx-builddir/html/_modules/optunaz/predict.html +++ b/docs/sphinx-builddir/html/_modules/optunaz/predict.html @@ -125,6 +125,23 @@

Source code for optunaz.predict

             raise UncertaintyError("Uncertainty not availble for this model")
+
[docs]def set_inference_params(args, desc): + if hasattr(desc.parameters, "descriptor") and hasattr( + desc.parameters.descriptor, "inference_parameters" + ): # Scaled precomputed descriptors handled here + desc = desc.parameters.descriptor + if hasattr(desc, "inference_parameters"): + check_precomp_args(args) + desc.inference_parameters( + args.input_precomputed_file, + args.input_precomputed_input_column, + args.input_precomputed_response_column, + ) + logging.info("Precomputed descriptor inference params set") + return True + return False
+ +
[docs]def check_precomp_args(args): try: assert ( @@ -142,42 +159,29 @@

Source code for optunaz.predict

 
 
[docs]def validate_set_precomputed(args, model):
     descriptor_str = model.descriptor.name
-    if descriptor_str == "CompositeDescriptor":
-        precomp_idx = [
-            idx
-            for idx, d in enumerate(model.descriptor.parameters.descriptors)
-            if d.name == "PrecomputedDescriptorFromFile"
-        ]
-        if len(precomp_idx) == 0:
+    if set_inference_params(args, model.descriptor):
+        return model
+    elif hasattr(model.descriptor.parameters, "descriptors"):
+        n_precomp = 0
+        for d in model.descriptor.parameters.descriptors:
+            n_precomp += set_inference_params(args, d)
+        if n_precomp == 0:
             logging.warning(
                 f"{descriptor_str} has no Precomputed descriptors... ignoring precomputed descriptor parameters"
             )
-            return model
-        else:
-            if len(precomp_idx) > 1:
-                raise PrecomputedError(
-                    "Inference for > precomputed descriptor not currently available"
-                )
-            check_precomp_args(args)
-            precomp_desc = model.descriptor.parameters.descriptors[precomp_idx[0]]
-            precomp_desc.inference_parameters(
-                args.input_precomputed_file,
-                args.input_precomputed_input_column,
-                args.input_precomputed_response_column,
+        elif n_precomp > 1:
+            raise PrecomputedError(
+                "Inference for > precomputed descriptor not currently available"
             )
-    elif descriptor_str != "PrecomputedDescriptorFromFile":
-        logging.warning(
-            f"Model was trained using {descriptor_str}... ignoring precomputed descriptor parameters"
-        )
         return model
-    else:  # must be precomputed
-        check_precomp_args(args)
-        precomp_desc = model.descriptor
-        precomp_desc.inference_parameters(
-            args.input_precomputed_file,
-            args.input_precomputed_input_column,
-            args.input_precomputed_response_column,
-        )
+    else:
+        try:
+            check_precomp_args(args)
+            logging.warning(
+                f"Model was trained using {descriptor_str}... ignoring precomputed descriptor parameters"
+            )
+        except PrecomputedError:
+            pass
     return model

 
 
diff --git a/docs/sphinx-builddir/html/genindex.html b/docs/sphinx-builddir/html/genindex.html
index 3f7a19b..c0ff654 100644
--- a/docs/sphinx-builddir/html/genindex.html
+++ b/docs/sphinx-builddir/html/genindex.html
@@ -3129,6 +3129,8 @@ 
S

       
  • set_build_cache() (in module optunaz.config.build_from_opt)
 
    • 
       
  • set_cache() (optunaz.config.optconfig.OptimizationConfig method)
+
    • 
+      
  • set_inference_params() (in module optunaz.predict)
 
    • 
       
  • set_unfitted_scaler_data() (optunaz.descriptors.ScaledDescriptor method)
 
    • 
diff --git a/docs/sphinx-builddir/html/objects.inv b/docs/sphinx-builddir/html/objects.inv
index 8568bdd..ed6e78e 100644
Binary files a/docs/sphinx-builddir/html/objects.inv and b/docs/sphinx-builddir/html/objects.inv differ
diff --git a/docs/sphinx-builddir/html/optunaz.html b/docs/sphinx-builddir/html/optunaz.html
index 0a44f60..1052b5f 100644
--- a/docs/sphinx-builddir/html/optunaz.html
+++ b/docs/sphinx-builddir/html/optunaz.html
@@ -1838,6 +1838,11 @@ 
    Submodulesoptunaz.predict.validate_uncertainty(args, model)[source]
 
    
 
+
    
+
    
+optunaz.predict.set_inference_params(args, desc)[source]
    
+
    
+
 
    
 
    
 optunaz.predict.check_precomp_args(args)[source]
    
diff --git a/docs/sphinx-builddir/html/searchindex.js b/docs/sphinx-builddir/html/searchindex.js
index 9f2ae0e..3a903b4 100644
--- a/docs/sphinx-builddir/html/searchindex.js
+++ b/docs/sphinx-builddir/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["README", "algorithms", "deduplicator", "descriptors", "index", "modules", "notebooks/QSARtuna_Tutorial", "notebooks/preprocess_data", "optunaz", "optunaz.config", "optunaz.utils", "optunaz.utils.enums", "optunaz.utils.preprocessing", "splitters", "transform"], "filenames": ["README.md", "algorithms.rst", "deduplicator.rst", "descriptors.rst", "index.rst", "modules.rst", "notebooks/QSARtuna_Tutorial.ipynb", "notebooks/preprocess_data.ipynb", "optunaz.rst", "optunaz.config.rst", "optunaz.utils.rst", "optunaz.utils.enums.rst", "optunaz.utils.preprocessing.rst", "splitters.rst", "transform.rst"], "titles": ["QSARtuna \ud80c\udd9b: QSAR using Optimization for Hyperparameter Tuning (formerly Optuna AZ and QPTUNA)", "Available algorithms", "Available deduplicators", "Available descriptors", "Welcome to QSARtuna Documentation!", "optunaz", "QSARtuna CLI Tutorial", "Preprocessing data for QSARtuna", "optunaz package", "optunaz.config package", "optunaz.utils 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\ No newline at end of file
diff --git a/examples/optimization/regression_drd2_50_precomputed_descriptor_scaled.json b/examples/optimization/regression_drd2_50_precomputed_descriptor_scaled.json
new file mode 100644
index 0000000..e431584
--- /dev/null
+++ b/examples/optimization/regression_drd2_50_precomputed_descriptor_scaled.json
@@ -0,0 +1,109 @@
+{
+  "task": "optimization",
+  "data": {
+    "input_column": "canonical",
+    "response_column": "molwt",
+    "training_dataset_file": "tests/data/precomputed_descriptor/train_with_fp.csv"
+  },
+  "descriptors": [
+    {
+      "name": "ScaledDescriptor",
+      "parameters": {
+        "scaler": {
+          "name": "UnfittedSklearnScaler"
+        },
+        "descriptor": {
+          "name": "PrecomputedDescriptorFromFile",
+          "parameters": {
+            "file": "tests/data/precomputed_descriptor/train_with_fp.csv",
+            "input_column": "canonical",
+            "response_column": "fp"
+          }
+        }
+      }
+    }
+  ],
+  "settings": {
+    "mode": "regression",
+    "cross_validation": 3,
+    "direction": "maximize",
+    "n_trials": 15,
+    "n_startup_trials": 10
+  },
+  "visualization": null,
+  "algorithms": [
+    {
+      "name": "SVR",
+      "parameters": {
+        "C": {
+          "low": 1E-10,
+          "high": 100.0
+        },
+        "gamma": {
+          "low": 0.0001,
+          "high": 100.0
+        }
+      }
+    },
+    {
+      "name": "RandomForestRegressor",
+      "parameters": {
+        "max_depth": {
+          "low": 2,
+          "high": 32
+        },
+        "n_estimators": {
+          "low": 10,
+          "high": 250
+        },
+        "max_features": [
+          "auto"
+        ]
+      }
+    },
+    {
+      "name": "Ridge",
+      "parameters": {
+        "alpha": {
+          "low": 0,
+          "high": 2
+        }
+      }
+    },
+    {
+      "name": "Lasso",
+      "parameters": {
+        "alpha": {
+          "low": 0,
+          "high": 2
+        }
+      }
+    },
+    {
+      "name": "PLSRegression",
+      "parameters": {
+        "n_components": {
+          "low": 2,
+          "high": 3
+        }
+      }
+    },
+    {
+      "name": "XGBRegressor",
+      "parameters": {
+        "max_depth": {
+          "low": 2,
+          "high": 32
+        },
+        "n_estimators": {
+          "low": 3,
+          "high": 100
+        },
+        "learning_rate": {
+          "low": 0.1,
+          "high": 0.1
+        }
+      }
+    }
+  ]
+}
diff --git a/optunaz/predict.py b/optunaz/predict.py
index 03357e2..8d05a49 100644
--- a/optunaz/predict.py
+++ b/optunaz/predict.py
@@ -45,6 +45,23 @@ def validate_uncertainty(args, model):
             raise UncertaintyError("Uncertainty not availble for this model")
 
 
+def set_inference_params(args, desc):
+    if hasattr(desc.parameters, "descriptor") and hasattr(
+        desc.parameters.descriptor, "inference_parameters"
+    ):  # Scaled precomputed descriptors handled here
+        desc = desc.parameters.descriptor
+    if hasattr(desc, "inference_parameters"):
+        check_precomp_args(args)
+        desc.inference_parameters(
+            args.input_precomputed_file,
+            args.input_precomputed_input_column,
+            args.input_precomputed_response_column,
+        )
+        logging.info("Precomputed descriptor inference params set")
+        return True
+    return False
+
+
 def check_precomp_args(args):
     try:
         assert (
@@ -62,42 +79,29 @@ def check_precomp_args(args):
 
 def validate_set_precomputed(args, model):
     descriptor_str = model.descriptor.name
-    if descriptor_str == "CompositeDescriptor":
-        precomp_idx = [
-            idx
-            for idx, d in enumerate(model.descriptor.parameters.descriptors)
-            if d.name == "PrecomputedDescriptorFromFile"
-        ]
-        if len(precomp_idx) == 0:
+    if set_inference_params(args, model.descriptor):
+        return model
+    elif hasattr(model.descriptor.parameters, "descriptors"):
+        n_precomp = 0
+        for d in model.descriptor.parameters.descriptors:
+            n_precomp += set_inference_params(args, d)
+        if n_precomp == 0:
             logging.warning(
                 f"{descriptor_str} has no Precomputed descriptors... ignoring precomputed descriptor parameters"
             )
-            return model
-        else:
-            if len(precomp_idx) > 1:
-                raise PrecomputedError(
-                    "Inference for > precomputed descriptor not currently available"
-                )
-            check_precomp_args(args)
-            precomp_desc = model.descriptor.parameters.descriptors[precomp_idx[0]]
-            precomp_desc.inference_parameters(
-                args.input_precomputed_file,
-                args.input_precomputed_input_column,
-                args.input_precomputed_response_column,
+        elif n_precomp > 1:
+            raise PrecomputedError(
+                "Inference for > precomputed descriptor not currently available"
             )
-    elif descriptor_str != "PrecomputedDescriptorFromFile":
-        logging.warning(
-            f"Model was trained using {descriptor_str}... ignoring precomputed descriptor parameters"
-        )
         return model
-    else:  # must be precomputed
-        check_precomp_args(args)
-        precomp_desc = model.descriptor
-        precomp_desc.inference_parameters(
-            args.input_precomputed_file,
-            args.input_precomputed_input_column,
-            args.input_precomputed_response_column,
-        )
+    else:
+        try:
+            check_precomp_args(args)
+            logging.warning(
+                f"Model was trained using {descriptor_str}... ignoring precomputed descriptor parameters"
+            )
+        except PrecomputedError:
+            pass
     return model
 
 
diff --git a/tests/test_precomputed_descriptor.py b/tests/test_precomputed_descriptor.py
index bd7c113..d1ba466 100644
--- a/tests/test_precomputed_descriptor.py
+++ b/tests/test_precomputed_descriptor.py
@@ -1,8 +1,11 @@
 import csv
 import json
 import os
+import sys
 import tempfile
-
+from unittest.mock import patch
+import numpy as np
+import pandas as pd
 import numpy.testing as npt
 from apischema import deserialize
 
@@ -10,11 +13,9 @@
 from optunaz.config.optconfig import OptimizationConfig
 from optunaz.descriptors import PrecomputedDescriptorFromFile, ECFP
 from optunaz.utils.preprocessing.transform import VectorFromColumn
-
-import numpy as np
-import pandas as pd
-
+from optunaz import optbuild
 from optunaz.utils.files_paths import attach_root_path
+from optunaz import predict
 
 
 def test_1():
@@ -147,3 +148,51 @@ def test_5(shared_datadir):
     )
 
     os.unlink(f.name)
+
+
+def test_scaled_precomputed(shared_datadir):
+    testargs = [
+        "prog",
+        "--config",
+        str(
+            attach_root_path(
+                "examples/optimization/regression_drd2_50_precomputed_descriptor_scaled.json"
+            )
+        ),
+        "--best-buildconfig-outpath",
+        str(shared_datadir / "buildconfig.json"),
+        "--best-model-outpath",
+        str(shared_datadir / "best.pkl"),
+    ]
+    with patch.object(sys, "argv", testargs):
+        optbuild.main()
+
+    predict_args = [
+        "prog",
+        "--model-file",
+        str(shared_datadir / "best.pkl"),
+        "--input-smiles-csv-file",
+        str(shared_datadir / "precomputed_descriptor/train_with_fp.csv"),
+        "--input-smiles-csv-column",
+        "canonical",
+        "--input-precomputed-file",
+        str(shared_datadir / "precomputed_descriptor/train_with_fp.csv"),
+        "--input-precomputed-input-column",
+        "canonical",
+        "--input-precomputed-response-column",
+        "fp",
+        "--output-prediction-csv-file",
+        str(shared_datadir / "outprediction"),
+    ]
+    with patch.object(sys, "argv", predict_args):
+        predict.main()
+
+        predictions = pd.read_csv(
+            str(shared_datadir / "outprediction"), usecols=["Prediction"]
+        )
+        npt.assert_allclose(
+            predictions.loc[[0, 1, 2]].values.flatten(),
+            [385.872, 388.57, 379.173],
+            rtol=1e-05,
+            atol=1e-05,
+        )