Sl dart ssda

clinicadl/generate/generate.py

@@ -165,6 +165,8 @@ def create_random_image(subject_id):
     write_missing_mods(output_dir, output_df)
     logger.info(f"Random dataset was generated at {output_dir}")
 
+    logger.info(f"Random dataset was generated at {output_dir}")
+
 
 def generate_trivial_dataset(
     caps_directory: Path,
@@ -355,6 +357,8 @@ def create_trivial_image(subject_id, output_df):
     write_missing_mods(output_dir, output_df)
     logger.info(f"Trivial dataset was generated at {output_dir}")
 
+    logger.info(f"Trivial dataset was generated at {output_dir}")
+
 
 def generate_shepplogan_dataset(
     output_dir: Path,

clinicadl/mlflow_test.py

@@ -0,0 +1,83 @@
+import logging
+import os
+import sys
+import warnings
+from urllib.parse import urlparse
+
+import mlflow
+import mlflow.sklearn
+import numpy as np
+import pandas as pd
+from sklearn.linear_model import ElasticNet
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+from sklearn.model_selection import train_test_split
+
+logging.basicConfig(level=logging.WARN)
+logger = logging.getLogger(__name__)
+
+
+def eval_metrics(actual, pred):
+    rmse = np.sqrt(mean_squared_error(actual, pred))
+    mae = mean_absolute_error(actual, pred)
+    r2 = r2_score(actual, pred)
+    return rmse, mae, r2
+
+
+if __name__ == "__main__":
+    warnings.filterwarnings("ignore")
+    np.random.seed(40)
+
+    # Read the wine-quality csv file from the URL
+    csv_url = "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/data/winequality-red.csv"
+    try:
+        data = pd.read_csv(csv_url, sep=";")
+    except Exception as e:
+        logger.exception(
+            "Unable to download training & test CSV, check your internet connection. Error: %s",
+            e,
+        )
+
+    # Split the data into training and test sets. (0.75, 0.25) split.
+    train, test = train_test_split(data)
+
+    # The predicted column is "quality" which is a scalar from [3, 9]
+    train_x = train.drop(["quality"], axis=1)
+    test_x = test.drop(["quality"], axis=1)
+    train_y = train[["quality"]]
+    test_y = test[["quality"]]
+
+    alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
+    l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
+
+    with mlflow.start_run():
+        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
+        lr.fit(train_x, train_y)
+
+        predicted_qualities = lr.predict(test_x)
+
+        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
+
+        print("Elasticnet model (alpha={:f}, l1_ratio={:f}):".format(alpha, l1_ratio))
+        print("  RMSE: %s" % rmse)
+        print("  MAE: %s" % mae)
+        print("  R2: %s" % r2)
+
+        mlflow.log_param("alpha", alpha)
+        mlflow.log_param("l1_ratio", l1_ratio)
+        mlflow.log_metric("rmse", rmse)
+        mlflow.log_metric("r2", r2)
+        mlflow.log_metric("mae", mae)
+
+        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+
+        # Model registry does not work with file store
+        if tracking_url_type_store != "file":
+            # Register the model
+            # There are other ways to use the Model Registry, which depends on the use case,
+            # please refer to the doc for more information:
+            # https://mlflow.org/docs/latest/model-registry.html#api-workflow
+            mlflow.sklearn.log_model(
+                lr, "model", registered_model_name="ElasticnetWineModel"
+            )
+        else:
+            mlflow.sklearn.log_model(lr, "model")

clinicadl/random_search/random_search_utils.py

@@ -128,6 +128,7 @@ def random_sampling(rs_options: Dict[str, Any]) -> Dict[str, Any]:
         "mode": "fixed",
         "multi_cohort": "fixed",
         "multi_network": "choice",
+        "ssda_netork": "fixed",
         "n_fcblocks": "randint",
         "n_splits": "fixed",
         "n_proc": "fixed",

clinicadl/resources/config/train_config.toml

@@ -4,6 +4,7 @@
 [Model]
 architecture = "default" # ex : Conv5_FC3
 multi_network = false
+ssda_network = false
 
 [Architecture]
 # CNN
@@ -66,6 +67,10 @@ data_augmentation = false
 sampler = "random"
 size_reduction=false
 size_reduction_factor=2
+caps_target = ""
+tsv_target_lab = ""
+tsv_target_unlab = ""
+preprocessing_dict_target = ""
 
 [Cross_validation]
 n_splits = 0
@@ -82,4 +87,4 @@ accumulation_steps = 1
 profiler = false
 
 [Informations]
-emissions_calculator = false
+emissions_calculator = false

clinicadl/train/tasks/classification_cli.py

@@ -27,13 +27,18 @@
 # Model
 @train_option.architecture
 @train_option.multi_network
+@train_option.ssda_network
 # Data
 @train_option.multi_cohort
 @train_option.diagnoses
 @train_option.baseline
 @train_option.normalize
 @train_option.data_augmentation
 @train_option.sampler
+@train_option.caps_target
+@train_option.tsv_target_lab
+@train_option.tsv_target_unlab
+@train_option.preprocessing_dict_target
 # Cross validation
 @train_option.n_splits
 @train_option.split

clinicadl/train/tasks/reconstruction_cli.py

@@ -27,13 +27,18 @@
 # Model
 @train_option.architecture
 @train_option.multi_network
+@train_option.ssda_network
 # Data
 @train_option.multi_cohort
 @train_option.diagnoses
 @train_option.baseline
 @train_option.normalize
 @train_option.data_augmentation
 @train_option.sampler
+@train_option.caps_target
+@train_option.tsv_target_lab
+@train_option.tsv_target_unlab
+@train_option.preprocessing_dict_target
 # Cross validation
 @train_option.n_splits
 @train_option.split

clinicadl/train/tasks/regression_cli.py

@@ -27,13 +27,18 @@
 # Model
 @train_option.architecture
 @train_option.multi_network
+@train_option.ssda_network
 # Data
 @train_option.multi_cohort
 @train_option.diagnoses
 @train_option.baseline
 @train_option.normalize
 @train_option.data_augmentation
 @train_option.sampler
+@train_option.caps_target
+@train_option.tsv_target_lab
+@train_option.tsv_target_unlab
+@train_option.preprocessing_dict_target
 # Cross validation
 @train_option.n_splits
 @train_option.split

clinicadl/train/tasks/task_utils.py

@@ -50,6 +50,7 @@ def task_launcher(network_task: str, task_options_list: List[str], **kwargs):
         "learning_rate",
         "multi_cohort",
         "multi_network",
+        "ssda_network",
         "n_proc",
         "n_splits",
         "nb_unfrozen_layer",
@@ -65,6 +66,10 @@ def task_launcher(network_task: str, task_options_list: List[str], **kwargs):
         "sampler",
         "seed",
         "split",
+        "caps_target",
+        "tsv_target_lab",
+        "tsv_target_unlab",
+        "preprocessing_dict_target",
     ]
     all_options_list = standard_options_list + task_options_list
 
@@ -79,6 +84,13 @@ def task_launcher(network_task: str, task_options_list: List[str], **kwargs):
             / "tensor_extraction"
             / kwargs["preprocessing_json"]
         )
+
+        if train_dict["ssda_network"]:
+            preprocessing_json_target = (
+                Path(kwargs["caps_target"])
+                / "tensor_extraction"
+                / kwargs["preprocessing_dict_target"]
+            )
     else:
         caps_dict = CapsDataset.create_caps_dict(
             train_dict["caps_directory"], train_dict["multi_cohort"]
@@ -98,12 +110,33 @@ def task_launcher(network_task: str, task_options_list: List[str], **kwargs):
                 f"Preprocessing JSON {kwargs['preprocessing_json']} was not found for any CAPS "
                 f"in {caps_dict}."
             )
+        # To CHECK AND CHANGE
+        if train_dict["ssda_network"]:
+            caps_target = Path(kwargs["caps_target"])
+            preprocessing_json_target = (
+                caps_target / "tensor_extraction" / kwargs["preprocessing_dict_target"]
+            )
+
+            if preprocessing_json_target.is_file():
+                logger.info(
+                    f"Preprocessing JSON {preprocessing_json_target} found in CAPS {caps_target}."
+                )
+                json_found = True
+            if not json_found:
+                raise ValueError(
+                    f"Preprocessing JSON {kwargs['preprocessing_json_target']} was not found for any CAPS "
+                    f"in {caps_target}."
+                )
 
     # Mode and preprocessing
     preprocessing_dict = read_preprocessing(preprocessing_json)
     train_dict["preprocessing_dict"] = preprocessing_dict
     train_dict["mode"] = preprocessing_dict["mode"]
 
+    if train_dict["ssda_network"]:
+        preprocessing_dict_target = read_preprocessing(preprocessing_json_target)
+        train_dict["preprocessing_dict_target"] = preprocessing_dict_target
+
     # Add default values if missing
     if (
         preprocessing_dict["mode"] == "roi"

clinicadl/tsvtools/split/split.py

@@ -281,6 +281,8 @@ def split_diagnoses(
 
     if categorical_split_variable is None:
         categorical_split_variable = "diagnosis"
+    else:
+        categorical_split_variable.append("diagnosis")
 
     # Read files
     diagnosis_df_path = data_tsv.name

clinicadl/utils/caps_dataset/data.py

@@ -71,8 +71,11 @@ def __init__(
             raise AttributeError("Child class of CapsDataset, must set mode attribute.")
 
         self.df = data_df
-
-        mandatory_col = {"participant_id", "session_id", "cohort"}
+        mandatory_col = {
+            "participant_id",
+            "session_id",
+            "cohort",
+        }
         if self.label_presence and self.label is not None:
             mandatory_col.add(self.label)
 
@@ -108,6 +111,18 @@ def label_fn(self, target: Union[str, float, int]) -> Union[float, int]:
         else:
             return self.label_code[str(target)]
 
+    def domain_fn(self, target: Union[str, float, int]) -> Union[float, int]:
+        """
+        Returns the label value usable in criterion.
+
+        Args:
+            target: value of the target.
+        Returns:
+            label: value of the label usable in criterion.
+        """
+        domain_code = {"t1": 0, "flair": 1}
+        return domain_code[str(target)]
+
     def __len__(self) -> int:
         return len(self.df) * self.elem_per_image
 
@@ -209,7 +224,12 @@ def _get_meta_data(self, idx: int) -> Tuple[str, str, str, int, int]:
         else:
             label = -1
 
-        return participant, session, cohort, elem_idx, label
+        if "domain" in self.df.columns:
+            domain = self.df.loc[image_idx, "domain"]
+            domain = self.domain_fn(domain)
+        else:
+            domain = ""  # TO MODIFY
+        return participant, session, cohort, elem_idx, label, domain
 
     def _get_full_image(self) -> torch.Tensor:
         """
@@ -323,7 +343,7 @@ def elem_index(self):
         return None
 
     def __getitem__(self, idx):
-        participant, session, cohort, _, label = self._get_meta_data(idx)
+        participant, session, cohort, _, label, domain = self._get_meta_data(idx)
 
         image_path = self._get_image_path(participant, session, cohort)
         image = torch.load(image_path)
@@ -341,6 +361,7 @@ def __getitem__(self, idx):
             "session_id": session,
             "image_id": 0,
             "image_path": image_path.as_posix(),
+            "domain": domain,
         }
 
         return sample
@@ -400,7 +421,9 @@ def elem_index(self):
         return self.patch_index
 
     def __getitem__(self, idx):
-        participant, session, cohort, patch_idx, label = self._get_meta_data(idx)
+        participant, session, cohort, patch_idx, label, domain = self._get_meta_data(
+            idx
+        )
         image_path = self._get_image_path(participant, session, cohort)
 
         if self.prepare_dl:
@@ -507,7 +530,7 @@ def elem_index(self):
         return self.roi_index
 
     def __getitem__(self, idx):
-        participant, session, cohort, roi_idx, label = self._get_meta_data(idx)
+        participant, session, cohort, roi_idx, label, domain = self._get_meta_data(idx)
         image_path = self._get_image_path(participant, session, cohort)
 
         if self.roi_list is None:
@@ -672,7 +695,9 @@ def elem_index(self):
         return self.slice_index
 
     def __getitem__(self, idx):
-        participant, session, cohort, slice_idx, label = self._get_meta_data(idx)
+        participant, session, cohort, slice_idx, label, domain = self._get_meta_data(
+            idx
+        )
         slice_idx = slice_idx + self.discarded_slices[0]
         image_path = self._get_image_path(participant, session, cohort)
 

clinicadl/utils/cli_param/option.py

@@ -57,6 +57,13 @@
     multiple=True,
     default=None,
 )
+ssda_network = click.option(
+    "--ssda_network",
+    type=bool,
+    default=False,
+    show_default=True,
+    help="ssda training.",
+)
 # GENERATE
 participant_list = click.option(
     "--participants_tsv",