Add scripts for patient classification and visualization
- Created `classification.py` for comparing image-level and patient-level classification results using various CNN models. - Implemented `create_patient_groups.py` to extract features, generate PCA/t-SNE plots, and identify patient groups via K-means clustering. - Added `figure6.py` to generate boxplots for test accuracy across multiple seeds. - Developed `simple_patient_tsne.py` to perform t-SNE visualization of patient groups and save results in a manifest file. - Introduced `simple_patient_manifest.csv` to store patient IDs, classes, image counts, and associated images.
This commit is contained in:
rpotter6298
@@ -0,0 +1,170 @@
|
||||
"""
|
||||
PatientLeakageClassifier — runs the RF→GridSearchCV→SVM pipeline
|
||||
with patient-aware or image-level train/test splitting.
|
||||
"""
|
||||
|
||||
import os, csv, re
|
||||
import numpy as np
|
||||
from sklearn.model_selection import (
|
||||
train_test_split, GridSearchCV, StratifiedGroupKFold
|
||||
)
|
||||
from sklearn.ensemble import RandomForestClassifier
|
||||
from sklearn.svm import SVC
|
||||
from sklearn.pipeline import Pipeline
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
from sklearn.metrics import accuracy_score
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# Helpers
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
def _f2n(fname):
|
||||
m = re.search(r'\((\d+)\)', fname)
|
||||
num = int(m.group(1)) if m else None
|
||||
for cls_key, prefix in [("Bengin cases","B"),("Malignant cases","M"),("Normal cases","N")]:
|
||||
if fname.startswith(cls_key.rstrip("s")):
|
||||
return f"{prefix}_{num:03d}" if num else fname
|
||||
return fname
|
||||
|
||||
DEFAULT_NFEATURES = [50, 100, 200, 300, 400, 500, 750, 1000,
|
||||
1500, 2000, 3000, 5000]
|
||||
DEFAULT_GAMMA_RANGE = (-1.5, 1, 7) # logspace args
|
||||
|
||||
|
||||
class PatientLeakageClassifier:
|
||||
"""Run the classification pipeline for one model, seed, and split type.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
manifest_path : str Path to patient manifest CSV.
|
||||
features_dir : str Directory with {Model}_features.npz files.
|
||||
n_jobs : int Parallelism for RF and GridSearchCV.
|
||||
"""
|
||||
|
||||
def __init__(self, manifest_path, features_dir, n_jobs=8):
|
||||
self.features_dir = features_dir
|
||||
self.n_jobs = n_jobs
|
||||
self._image_to_patient = {}
|
||||
with open(manifest_path, newline="") as f:
|
||||
reader = csv.DictReader(f)
|
||||
img_col = ("confirmed_images" if "confirmed_images" in reader.fieldnames
|
||||
else "images")
|
||||
for row in reader:
|
||||
pid = row["patient_id"]
|
||||
for img in row[img_col].split(";"):
|
||||
if img:
|
||||
self._image_to_patient[img] = pid
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Public API
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def run(self, model_name, seed, split_type="patient",
|
||||
nfeatures_list=None, gamma_logspace=None):
|
||||
"""Run full pipeline: train/test split → RF importance →
|
||||
GridSearchCV over nfeatures×gamma → retrain → test.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
model_name : str e.g. "VGG16"
|
||||
seed : int Random seed for the train/test split.
|
||||
split_type : str "image" or "patient".
|
||||
nfeatures_list : list Feature counts to search over.
|
||||
gamma_logspace : tuple Args for np.logspace (start, stop, n).
|
||||
|
||||
Returns
|
||||
-------
|
||||
dict with keys: model, seed, split_type, cv, test, nfeat, gamma,
|
||||
n_train, n_test, n_train_patients, n_test_patients
|
||||
"""
|
||||
if nfeatures_list is None:
|
||||
nfeatures_list = DEFAULT_NFEATURES
|
||||
if gamma_logspace is None:
|
||||
gamma_logspace = DEFAULT_GAMMA_RANGE
|
||||
|
||||
X, Y, groups = self._load_data(model_name)
|
||||
|
||||
# --- Train/test split ---
|
||||
if split_type == "image":
|
||||
X_tr, X_te, y_tr, y_te = train_test_split(
|
||||
X, Y, test_size=0.2, random_state=seed, stratify=Y)
|
||||
cv = 5
|
||||
g_tr = None
|
||||
n_tr_patients = None
|
||||
n_te_patients = None
|
||||
else:
|
||||
unique_g = np.unique(groups)
|
||||
g_labels = np.array([Y[groups == g][0] for g in unique_g])
|
||||
tr_g, te_g = train_test_split(
|
||||
unique_g, test_size=0.2, random_state=seed, stratify=g_labels)
|
||||
tr_mask = np.isin(groups, tr_g)
|
||||
te_mask = np.isin(groups, te_g)
|
||||
X_tr, X_te = X[tr_mask], X[te_mask]
|
||||
y_tr, y_te = Y[tr_mask], Y[te_mask]
|
||||
g_tr = groups[tr_mask]
|
||||
n_tr_patients = len(np.unique(g_tr))
|
||||
n_te_patients = len(np.unique(groups[te_mask]))
|
||||
cv = StratifiedGroupKFold(n_splits=5, shuffle=True,
|
||||
random_state=seed)
|
||||
|
||||
# --- RF feature importance ---
|
||||
fs = RandomForestClassifier(n_estimators=500, random_state=15,
|
||||
class_weight="balanced", n_jobs=self.n_jobs)
|
||||
fs.fit(X_tr, y_tr)
|
||||
sorted_idx = np.argsort(fs.feature_importances_)[::-1]
|
||||
|
||||
# --- Grid search over nfeatures × gamma ---
|
||||
pipe = Pipeline([("scaler", StandardScaler()),
|
||||
("svm", SVC(kernel="rbf", C=10))])
|
||||
valid_nfeats = [n for n in nfeatures_list if n <= X_tr.shape[1]]
|
||||
best_cv, best_n, best_g = 0, None, None
|
||||
|
||||
for nfeat in valid_nfeats:
|
||||
sel = sorted_idx[:nfeat]
|
||||
pg = {"svm__gamma": (1.0 / nfeat) * np.logspace(*gamma_logspace)}
|
||||
grid = GridSearchCV(pipe, pg, cv=cv, scoring="accuracy",
|
||||
n_jobs=self.n_jobs, verbose=0)
|
||||
if g_tr is not None:
|
||||
grid.fit(X_tr[:, sel], y_tr, groups=g_tr)
|
||||
else:
|
||||
grid.fit(X_tr[:, sel], y_tr)
|
||||
if grid.best_score_ > best_cv:
|
||||
best_cv = grid.best_score_
|
||||
best_n = nfeat
|
||||
best_g = grid.best_params_["svm__gamma"]
|
||||
|
||||
# --- Retrain + test ---
|
||||
final = Pipeline([("scaler", StandardScaler()),
|
||||
("svm", SVC(kernel="rbf", C=10, gamma=best_g))])
|
||||
final.fit(X_tr[:, sorted_idx[:best_n]], y_tr)
|
||||
y_pred = final.predict(X_te[:, sorted_idx[:best_n]])
|
||||
|
||||
return {
|
||||
"model": model_name,
|
||||
"seed": seed,
|
||||
"split_type": split_type,
|
||||
"cv": float(best_cv),
|
||||
"test": float(accuracy_score(y_te, y_pred)),
|
||||
"nfeat": best_n,
|
||||
"gamma": float(best_g),
|
||||
"n_train": X_tr.shape[0],
|
||||
"n_test": X_te.shape[0],
|
||||
"n_train_patients": n_tr_patients,
|
||||
"n_test_patients": n_te_patients,
|
||||
}
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Internals
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _load_data(self, model_name):
|
||||
data = np.load(os.path.join(self.features_dir,
|
||||
f"{model_name}_features.npz"),
|
||||
allow_pickle=True)
|
||||
X = data["X"]
|
||||
Y = data["Y"]
|
||||
filenames = data["filenames"]
|
||||
groups = np.array([self._image_to_patient.get(_f2n(f), f"unk_{i}")
|
||||
for i, f in enumerate(filenames)])
|
||||
return X, Y, groups
|
||||
Reference in New Issue
Block a user