lin classification on ALFI

applications/contrastive_phenotyping/evaluation/alfi_linear_prob_early_late.py

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+# %% Imports
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import classification_report
+from typing import Mapping
+from numpy.typing import NDArray
+from xarray import DataArray
+from viscy.representation.embedding_writer import read_embedding_dataset
+from viscy.representation.evaluation.lca import fit_logistic_regression
+
+# %% Constants
+TRAIN_FOVS = ["/0/0/0", "/0/1/0", "/0/2/0"]
+
+# Function to load annotations
+def load_annotation(da, path, name, categories=None):
+    annotation = pd.read_csv(path)
+    annotation["fov_name"] = "/" + annotation["fov ID"]
+    annotation = annotation.set_index(["fov_name", "id"])
+    mi = pd.MultiIndex.from_arrays(
+        [da["fov_name"].values, da["id"].values], names=["fov_name", "id"]
+    )
+    selected = annotation.loc[mi][name]
+    if categories:
+        selected = selected.astype("category").cat.rename_categories(categories)
+    return selected
+
+# Model embeddings and annotation paths
+model_embeddings = {
+    "track": Path(
+        "/hpc/projects/organelle_phenotyping/ALFI_benchmarking/predictions_final/ALFI_opp_cellaware.zarr"
+    )
+}
+
+annotation_path = Path("/hpc/reference/imaging/ALFI_dataset/Analysis/train_annotations.csv")
+
+# %% Processing each model embedding
+for model_name, path_embedding in model_embeddings.items():
+    print(f"Model: {model_name}")
+    dataset = read_embedding_dataset(path_embedding)
+    features = dataset["features"]
+
+    # Load the division annotations
+    fineclass_mapping = {
+        0: "EarlyMitosis",
+        1: "LateMitosis",
+        -1: "NoAnnotation"
+    }
+
+    fineclass = load_annotation(
+        dataset,
+        annotation_path,
+        "fineclass",
+        fineclass_mapping
+    )
+
+    # print("Class distribution for fineclass before splitting:")
+    # print(fineclass.value_counts())
+
+    # Fit logistic regression model
+    log_reg = fit_logistic_regression(
+        features,
+        fineclass,
+        train_fovs=TRAIN_FOVS,
+        remove_background_class=True,
+        scale_features=False,
+        class_weight="balanced",
+        solver="liblinear",
+        random_state=42,
+    )
+
+    # # Load the division annotations
+    # division = load_annotation(
+    #     dataset,
+    #     annotation_path,
+    #     "division",
+    #     {0: "interphase", 1: "mitosis"}
+    # )
+
+    # # Fit logistic regression model
+    # log_reg = fit_logistic_regression(
+    #     features,
+    #     division,
+    #     train_fovs=TRAIN_FOVS,
+    #     remove_background_class=False,
+    #     scale_features=False,
+    #     class_weight="balanced",
+    #     solver="liblinear",
+    #     random_state=42,
+    # )
+# %%

applications/contrastive_phenotyping/evaluation/alfi_linear_prob_mitvsinter.py

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+# %% Imports
+from pathlib import Path
+import pandas as pd
+import numpy as np
+from sklearn.linear_model import LogisticRegression
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import classification_report
+from typing import Mapping
+from numpy.typing import NDArray
+from xarray import DataArray
+from viscy.representation.embedding_writer import read_embedding_dataset
+from viscy.representation.evaluation.lca import fit_logistic_regression
+
+# %% Constants
+TRAIN_FOVS = ["/0/0/0", "/0/1/0", "/0/2/0"]
+
+# Function to load annotations
+def load_annotation(da, path, name, categories=None):
+    annotation = pd.read_csv(path)
+    annotation["fov_name"] = "/" + annotation["fov ID"]
+    annotation = annotation.set_index(["fov_name", "id"])
+    mi = pd.MultiIndex.from_arrays(
+        [da["fov_name"].values, da["id"].values], names=["fov_name", "id"]
+    )
+    selected = annotation.loc[mi][name]
+    if categories:
+        selected = selected.astype("category").cat.rename_categories(categories)
+    return selected
+
+# Model embeddings and annotation paths
+model_embeddings = {
+    "track": Path(
+        "/hpc/projects/organelle_phenotyping/ALFI_benchmarking/predictions_final/ALFI_opp_cellaware.zarr"
+    )
+}
+
+annotation_path = Path("/hpc/reference/imaging/ALFI_dataset/Analysis/train_annotations.csv")
+
+# %% Processing each model embedding
+for model_name, path_embedding in model_embeddings.items():
+    print(f"Model: {model_name}")
+    dataset = read_embedding_dataset(path_embedding)
+    features = dataset["features"]
+
+    # Load the division annotations
+    division = load_annotation(
+        dataset,
+        annotation_path,
+        "division",
+        {0: "interphase", 1: "mitosis", -1: "NoAnnotation"}
+    )
+
+    # Fit logistic regression model
+    log_reg = fit_logistic_regression(
+        features,
+        division,
+        train_fovs=TRAIN_FOVS,
+        remove_background_class=True,
+        scale_features=False,
+        class_weight="balanced",
+        solver="liblinear",
+        random_state=42,
+    )
+
+    # # Load the division annotations
+    # fineclass_mapping = {
+    #     0: "EarlyMitosis",
+    #     1: "LateMitosis",
+    #     -1: "NoAnnotation"
+    # }
+
+    # fineclass = load_annotation(
+    #     dataset,
+    #     annotation_path,
+    #     "fineclass",
+    #     fineclass_mapping
+    # )
+
+    # # print("Class distribution for fineclass before splitting:")
+    # # print(fineclass.value_counts())
+
+    # # Fit logistic regression model
+    # log_reg = fit_logistic_regression(
+    #     features,
+    #     fineclass,
+    #     train_fovs=TRAIN_FOVS,
+    #     remove_background_class=False,
+    #     scale_features=False,
+    #     class_weight="balanced",
+    #     solver="liblinear",
+    #     random_state=42,
+    # )
+
+
+# %%