From 93d9f5d77a3a1e472caaa64a670ac32024bfdb26 Mon Sep 17 00:00:00 2001
From: kljk345 <lewis.mervin1@astrazeneca.com>
Date: Tue, 9 Jul 2024 18:31:52 +0100
Subject: [PATCH] Scaled descriptor fix for precomputed descriptor inference

---
 .../doctrees/environment.pickle               | Bin 1096462 -> 1096807 bytes
 .../notebooks/QSARtuna_Tutorial.doctree       | Bin 10429336 -> 10429336 bytes
 docs/sphinx-builddir/doctrees/optunaz.doctree | Bin 704028 -> 705547 bytes
 .../html/_modules/optunaz/predict.html        |  66 ++++++-----
 docs/sphinx-builddir/html/genindex.html       |   2 +
 docs/sphinx-builddir/html/objects.inv         | Bin 35645 -> 35664 bytes
 docs/sphinx-builddir/html/optunaz.html        |   5 +
 docs/sphinx-builddir/html/searchindex.js      |   2 +-
 ...drd2_50_precomputed_descriptor_scaled.json | 109 ++++++++++++++++++
 optunaz/predict.py                            |  66 ++++++-----
 tests/test_precomputed_descriptor.py          |  59 +++++++++-
 11 files changed, 241 insertions(+), 68 deletions(-)
 create mode 100644 examples/optimization/regression_drd2_50_precomputed_descriptor_scaled.json

diff --git a/docs/sphinx-builddir/doctrees/environment.pickle b/docs/sphinx-builddir/doctrees/environment.pickle
index 6866fcda5d5a1391993e50eb0900ab6fb5830f12..faadf3262d91705ba3bf30e4655af4a475563596 100644
GIT binary patch
delta 119117
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diff --git a/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree b/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree
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diff --git a/docs/sphinx-builddir/doctrees/optunaz.doctree b/docs/sphinx-builddir/doctrees/optunaz.doctree
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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 @@ <h1>Source code for optunaz.predict</h1><div class="highlight"><pre>
             <span class="k">raise</span> <span class="n">UncertaintyError</span><span class="p">(</span><span class="s2">&quot;Uncertainty not availble for this model&quot;</span><span class="p">)</span></div>
 
 
+<div class="viewcode-block" id="set_inference_params"><a class="viewcode-back" href="../../optunaz.html#optunaz.predict.set_inference_params">[docs]</a><span class="k">def</span> <span class="nf">set_inference_params</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">desc</span><span class="p">):</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">desc</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="s2">&quot;descriptor&quot;</span><span class="p">)</span> <span class="ow">and</span> <span class="nb">hasattr</span><span class="p">(</span>
+        <span class="n">desc</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">descriptor</span><span class="p">,</span> <span class="s2">&quot;inference_parameters&quot;</span>
+    <span class="p">):</span>  <span class="c1"># Scaled precomputed descriptors handled here</span>
+        <span class="n">desc</span> <span class="o">=</span> <span class="n">desc</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">descriptor</span>
+    <span class="k">if</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">desc</span><span class="p">,</span> <span class="s2">&quot;inference_parameters&quot;</span><span class="p">):</span>
+        <span class="n">check_precomp_args</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+        <span class="n">desc</span><span class="o">.</span><span class="n">inference_parameters</span><span class="p">(</span>
+            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_file</span><span class="p">,</span>
+            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_input_column</span><span class="p">,</span>
+            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_response_column</span><span class="p">,</span>
+        <span class="p">)</span>
+        <span class="n">logging</span><span class="o">.</span><span class="n">info</span><span class="p">(</span><span class="s2">&quot;Precomputed descriptor inference params set&quot;</span><span class="p">)</span>
+        <span class="k">return</span> <span class="kc">True</span>
+    <span class="k">return</span> <span class="kc">False</span></div>
+
+
 <div class="viewcode-block" id="check_precomp_args"><a class="viewcode-back" href="../../optunaz.html#optunaz.predict.check_precomp_args">[docs]</a><span class="k">def</span> <span class="nf">check_precomp_args</span><span class="p">(</span><span class="n">args</span><span class="p">):</span>
     <span class="k">try</span><span class="p">:</span>
         <span class="k">assert</span> <span class="p">(</span>
@@ -142,42 +159,29 @@ <h1>Source code for optunaz.predict</h1><div class="highlight"><pre>
 
 <div class="viewcode-block" id="validate_set_precomputed"><a class="viewcode-back" href="../../optunaz.html#optunaz.predict.validate_set_precomputed">[docs]</a><span class="k">def</span> <span class="nf">validate_set_precomputed</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="p">):</span>
     <span class="n">descriptor_str</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="o">.</span><span class="n">name</span>
-    <span class="k">if</span> <span class="n">descriptor_str</span> <span class="o">==</span> <span class="s2">&quot;CompositeDescriptor&quot;</span><span class="p">:</span>
-        <span class="n">precomp_idx</span> <span class="o">=</span> <span class="p">[</span>
-            <span class="n">idx</span>
-            <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">d</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">descriptors</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">d</span><span class="o">.</span><span class="n">name</span> <span class="o">==</span> <span class="s2">&quot;PrecomputedDescriptorFromFile&quot;</span>
-        <span class="p">]</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">precomp_idx</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+    <span class="k">if</span> <span class="n">set_inference_params</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="p">):</span>
+        <span class="k">return</span> <span class="n">model</span>
+    <span class="k">elif</span> <span class="nb">hasattr</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="o">.</span><span class="n">parameters</span><span class="p">,</span> <span class="s2">&quot;descriptors&quot;</span><span class="p">):</span>
+        <span class="n">n_precomp</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">descriptors</span><span class="p">:</span>
+            <span class="n">n_precomp</span> <span class="o">+=</span> <span class="n">set_inference_params</span><span class="p">(</span><span class="n">args</span><span class="p">,</span> <span class="n">d</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">n_precomp</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
             <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
                 <span class="sa">f</span><span class="s2">&quot;</span><span class="si">{</span><span class="n">descriptor_str</span><span class="si">}</span><span class="s2"> has no Precomputed descriptors... ignoring precomputed descriptor parameters&quot;</span>
             <span class="p">)</span>
-            <span class="k">return</span> <span class="n">model</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">precomp_idx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
-                <span class="k">raise</span> <span class="n">PrecomputedError</span><span class="p">(</span>
-                    <span class="s2">&quot;Inference for &gt; precomputed descriptor not currently available&quot;</span>
-                <span class="p">)</span>
-            <span class="n">check_precomp_args</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
-            <span class="n">precomp_desc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">descriptor</span><span class="o">.</span><span class="n">parameters</span><span class="o">.</span><span class="n">descriptors</span><span class="p">[</span><span class="n">precomp_idx</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-            <span class="n">precomp_desc</span><span class="o">.</span><span class="n">inference_parameters</span><span class="p">(</span>
-                <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_file</span><span class="p">,</span>
-                <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_input_column</span><span class="p">,</span>
-                <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_response_column</span><span class="p">,</span>
+        <span class="k">elif</span> <span class="n">n_precomp</span> <span class="o">&gt;</span> <span class="mi">1</span><span class="p">:</span>
+            <span class="k">raise</span> <span class="n">PrecomputedError</span><span class="p">(</span>
+                <span class="s2">&quot;Inference for &gt; precomputed descriptor not currently available&quot;</span>
             <span class="p">)</span>
-    <span class="k">elif</span> <span class="n">descriptor_str</span> <span class="o">!=</span> <span class="s2">&quot;PrecomputedDescriptorFromFile&quot;</span><span class="p">:</span>
-        <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
-            <span class="sa">f</span><span class="s2">&quot;Model was trained using </span><span class="si">{</span><span class="n">descriptor_str</span><span class="si">}</span><span class="s2">... ignoring precomputed descriptor parameters&quot;</span>
-        <span class="p">)</span>
         <span class="k">return</span> <span class="n">model</span>
-    <span class="k">else</span><span class="p">:</span>  <span class="c1"># must be precomputed</span>
-        <span class="n">check_precomp_args</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
-        <span class="n">precomp_desc</span> <span class="o">=</span> <span class="n">model</span><span class="o">.</span><span class="n">descriptor</span>
-        <span class="n">precomp_desc</span><span class="o">.</span><span class="n">inference_parameters</span><span class="p">(</span>
-            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_file</span><span class="p">,</span>
-            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_input_column</span><span class="p">,</span>
-            <span class="n">args</span><span class="o">.</span><span class="n">input_precomputed_response_column</span><span class="p">,</span>
-        <span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">check_precomp_args</span><span class="p">(</span><span class="n">args</span><span class="p">)</span>
+            <span class="n">logging</span><span class="o">.</span><span class="n">warning</span><span class="p">(</span>
+                <span class="sa">f</span><span class="s2">&quot;Model was trained using </span><span class="si">{</span><span class="n">descriptor_str</span><span class="si">}</span><span class="s2">... ignoring precomputed descriptor parameters&quot;</span>
+            <span class="p">)</span>
+        <span class="k">except</span> <span class="n">PrecomputedError</span><span class="p">:</span>
+            <span class="k">pass</span>
     <span class="k">return</span> <span class="n">model</span></div>
 
 
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 @@ <h2 id="S">S</h2>
       <li><a href="optunaz.config.html#optunaz.config.build_from_opt.set_build_cache">set_build_cache() (in module optunaz.config.build_from_opt)</a>
 </li>
       <li><a href="optunaz.config.html#optunaz.config.optconfig.OptimizationConfig.set_cache">set_cache() (optunaz.config.optconfig.OptimizationConfig method)</a>
+</li>
+      <li><a href="optunaz.html#optunaz.predict.set_inference_params">set_inference_params() (in module optunaz.predict)</a>
 </li>
       <li><a href="optunaz.html#optunaz.descriptors.ScaledDescriptor.set_unfitted_scaler_data">set_unfitted_scaler_data() (optunaz.descriptors.ScaledDescriptor method)</a>
 </li>
diff --git a/docs/sphinx-builddir/html/objects.inv b/docs/sphinx-builddir/html/objects.inv
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+++ b/docs/sphinx-builddir/html/optunaz.html
@@ -1838,6 +1838,11 @@ <h2>Submodules<a class="headerlink" href="#submodules" title="Permalink to this
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 <dd></dd></dl>
 
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 <dt class="sig sig-object py" id="optunaz.predict.check_precomp_args">
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diff --git a/docs/sphinx-builddir/html/searchindex.js b/docs/sphinx-builddir/html/searchindex.js
index 9f2ae0e..3a903b4 100644
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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,
+        )