Create CLI command morphoclass predict

docs/source/api/morphoclass.console.cmd_extract_features_and_predict.rst

@@ -0,0 +1,7 @@
+morphoclass.console.cmd\_extract\_features\_and\_predict module
+===============================================================
+
+.. automodule:: morphoclass.console.cmd_extract_features_and_predict
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/api/morphoclass.console.cmd_predict.rst

@@ -0,0 +1,7 @@
+morphoclass.console.cmd\_predict module
+=======================================
+
+.. automodule:: morphoclass.console.cmd_predict
+   :members:
+   :undoc-members:
+   :show-inheritance:

docs/source/api/morphoclass.console.rst

@@ -9,10 +9,12 @@ Submodules
 
    morphoclass.console.cmd_evaluate
    morphoclass.console.cmd_extract_features
+   morphoclass.console.cmd_extract_features_and_predict
    morphoclass.console.cmd_morphometrics
    morphoclass.console.cmd_organise_dataset
    morphoclass.console.cmd_performance_table
    morphoclass.console.cmd_plot_dataset_stats
+   morphoclass.console.cmd_predict
    morphoclass.console.cmd_preprocess_dataset
    morphoclass.console.cmd_train
    morphoclass.console.cmd_xai

requirements.txt

@@ -26,7 +26,7 @@ scipy==1.7.3
 seaborn==0.11.0
 shap[plots]==0.39.0
 tmd==2.1.0
-torch==1.7.1
+torch==1.9.0
 tqdm==4.53.0
 umap-learn==0.5.1
 xgboost==1.4.2

setup.cfg

@@ -36,8 +36,8 @@ install_requires =
     imbalanced-learn
     jinja2
     matplotlib
-    morphio
-    morphology-workflows>=0.3.0
+    #morphio
+    #morphology-workflows>=0.3.0
     networkx
     neurom>=3
     NeuroR>=1.6.1

src/morphoclass/console/cmd_extract_features.py

@@ -113,6 +113,29 @@ def cli(
     no_simplify_graph: bool,
     keep_diagram: bool,
     force: bool,
+) -> None:
+    """Extract morphology features."""
+    return extract_features(
+        csv_path,
+        neurite_type,
+        feature,
+        output_dir,
+        orient,
+        no_simplify_graph,
+        keep_diagram,
+        force,
+    )
+
+
+def extract_features(
+    csv_path: StrPath,
+    neurite_type: str,
+    feature: str,
+    output_dir: StrPath,
+    orient: bool,
+    no_simplify_graph: bool,
+    keep_diagram: bool,
+    force: bool,
 ) -> None:
     """Extract morphology features."""
     output_dir = pathlib.Path(output_dir)

src/morphoclass/console/cmd_extract_features_and_predict.py

@@ -0,0 +1,147 @@
+# Copyright © 2022-2022 Blue Brain Project/EPFL
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Implementation of the `morphoclass predict` CLI command."""
+from __future__ import annotations
+
+import logging
+import textwrap
+
+import click
+
+logger = logging.getLogger(__name__)
+
+
+@click.command(
+    name="predict",
+    help="Run inference.",
+)
+@click.help_option("-h", "--help")
+@click.option(
+    "-i",
+    "--input_csv",
+    required=True,
+    type=click.Path(exists=True, dir_okay=True),
+    help=textwrap.dedent(
+        """
+        The CSV path with the path to all the morphologies to classify.
+        """
+    ).strip(),
+)
+@click.option(
+    "-c",
+    "--checkpoint",
+    "checkpoint_file",
+    required=True,
+    type=click.Path(exists=True, file_okay=True, dir_okay=False),
+    help=textwrap.dedent(
+        """
+        The path to the pre-trained model checkpoint.
+        """
+    ).strip(),
+)
+@click.option(
+    "-o",
+    "--output-dir",
+    required=True,
+    type=click.Path(exists=False, file_okay=False, writable=True),
+    help="Output directory for the results.",
+)
+@click.option(
+    "-n",
+    "--results-name",
+    required=False,
+    type=click.STRING,
+    help="The filename of the results file",
+)
+def cli(input_csv, checkpoint_file, output_dir, results_name):
+    """Run the `morphoclass predict` CLI command.
+
+    Parameters
+    ----------
+    input_csv
+        The CSV with all the morphologies path.
+    checkpoint_file
+        The path to the checkpoint file.
+    output_dir
+        The path to the output directory.
+    results_name
+        File prefix for results output files.
+    """
+    import pathlib
+    from datetime import datetime
+
+    input_csv = pathlib.Path(input_csv).resolve()
+    output_dir = pathlib.Path(output_dir).resolve()
+    checkpoint_file = pathlib.Path(checkpoint_file).resolve()
+    if results_name is None:
+        timestamp = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+        results_name = f"results_{timestamp}"
+    results_path = output_dir / (results_name + ".json")
+    click.secho(f"Input CSV   : {input_csv}", fg="yellow")
+    click.secho(f"Output file : {results_path}", fg="yellow")
+    click.secho(f"Checkpoint  : {checkpoint_file}", fg="yellow")
+    if results_path.exists():
+        msg = f'Results file "{results_path}" exists, overwrite? (y/[n]) '
+        click.secho(msg, fg="red", bold=True, nl=False)
+        response = input()
+        if response.strip().lower() != "y":
+            click.secho("Stopping.", fg="red")
+            return
+        else:
+            click.secho("You chose to overwrite, proceeding...", fg="red")
+
+    click.secho("✔ Loading checkpoint...", fg="green", bold=True)
+    import torch
+
+    from morphoclass.console.cmd_extract_features import extract_features
+    from morphoclass.console.cmd_predict import predict
+
+    checkpoint = torch.load(checkpoint_file, map_location=torch.device("cpu"))
+    neurites = ["apical", "axon", "basal", "all"]
+    neurite_type = [
+        neurite for neurite in neurites if neurite in str(checkpoint["features_dir"])
+    ]
+    features_type = [
+        "graph-rd",
+        "graph-proj",
+        "diagram-tmd-rd",
+        "diagram-tmd-proj",
+        "diagram-deepwalk",
+        "image-tmd-rd",
+        "image-tmd-proj",
+        "image-deepwalk",
+    ]
+    feature = [
+        feature
+        for feature in features_type
+        if feature in str(checkpoint["features_dir"])
+    ]
+
+    extract_features(
+        input_csv,
+        neurite_type[0],
+        feature[0],
+        output_dir / "features",
+        False,
+        False,
+        False,
+        False,
+    )
+
+    predict(
+        features_dir=output_dir / "features",
+        checkpoint_file=checkpoint_file,
+        output_dir=output_dir,
+        results_name=results_name,
+    )