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metrics_utils.py
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metrics_utils.py
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import numpy as np
from sklearn import metrics, utils
import pandas as pd
COLS_FOR_RESULTS_W_BOOTSTRAP = [
"ROCAUC",
"ROCAUC-boot",
"vendOP",
"vendOP-boot",
"vendSens/Spec",
"GlobalOP",
"globalSens/Spec",
"globalSens/Spec - boot",
"Global Spec/Sens Diff",
]
COLS_FOR_RESULTS_SIMPLE = [
"ROCAUC",
"vendOP",
"vendSens/Spec",
"GlobalOP",
"globalSens/Spec",
"Global Spec/Sens Diff",
"Global Youden",
]
def get_op_threshold(true, pred, operating_point="diag"):
fpr, tpr, threshold = metrics.roc_curve(true, pred)
if operating_point == "diag":
op = np.argmin(np.abs(tpr - (1 - fpr)))
elif operating_point == "spec90":
op = np.argmin(np.abs(fpr - 0.10))
else:
raise ValueError("Operating point has to be diag, spec90")
return threshold[op], 1 - fpr[op], tpr[op]
def all_metrics(true, pred, global_op, return_bootstrap=True, operating_point="diag"):
roc = metrics.roc_auc_score(true, pred)
thres, spec, sens = get_op_threshold(true, pred, operating_point)
global_sens, global_spec = get_sens_spec_at_threshold(true, pred, global_op)
global_youden = global_sens + global_spec - 1
if return_bootstrap:
roc_b, sens_b, spec_b, thres_b, spec_sens_abs_diff_b = (
np.zeros(500),
np.zeros(500),
np.zeros(500),
np.zeros(500),
np.zeros(500),
)
for b in range(500):
true_b, pred_b = utils.resample(true, pred, stratify=true)
roc_b[b] = metrics.roc_auc_score(true_b, pred_b)
sens_b[b], spec_b[b] = get_sens_spec_at_threshold(true_b, pred_b, global_op)
spec_sens_abs_diff_b[b] = spec_b[b] - sens_b[b]
thres_b[b] = get_op_threshold(
true_b, pred_b, operating_point=operating_point
)[0]
roc_i, sens_i, spec_i, thres_i, spec_sens_diff_i = (
np.percentile(roc_b, [2.5, 97.5]),
np.percentile(sens_b, [2.5, 97.5]),
np.percentile(spec_b, [2.5, 97.5]),
np.percentile(thres_b, [2.5, 97.5]),
np.percentile(spec_sens_abs_diff_b, [2.5, 97.5]),
)
return {
"roc_ic": roc_i,
"roc": roc,
"vendor_threshold": thres,
"vendor_threshold_ic": thres_i,
"vendor_sens": sens,
"vendor_spec": spec,
"global_op": global_op,
"global_sens": global_sens,
"global_spec": global_spec,
"sens_ic": sens_i,
"spec_ic": spec_i,
"spec_sens_diff_ic": spec_sens_diff_i,
}
return {
"roc": roc,
"vendor_threshold": thres,
"vendor_sens": sens,
"vendor_spec": spec,
"global_op": global_op,
"global_sens": global_sens,
"global_spec": global_spec,
"spec_sens_diff": global_spec - global_sens,
"global_youden": global_youden,
}
def all_metrics_as_str(true, pred, global_op, bootstrap=True, operating_point="diag"):
metrics_dict = all_metrics(true, pred, global_op, bootstrap, operating_point)
if bootstrap:
return [
metrics_dict["roc"],
f"[{metrics_dict['roc_ic'][0]:.3f};{metrics_dict['roc_ic'][1]:.3f}]",
metrics_dict["vendor_threshold"],
f"[{metrics_dict['vendor_threshold_ic'][0]:.3f};{metrics_dict['vendor_threshold_ic'][1]:.3f}]",
f"{metrics_dict['vendor_sens']:.3f}/{metrics_dict['vendor_spec']:.3f}",
metrics_dict["global_op"],
f"{metrics_dict['global_sens']:.3f}/{metrics_dict['global_spec']:.3f}",
f"[{metrics_dict['sens_ic'][0]:.3f};{metrics_dict['sens_ic'][1]:.3f}] / [{metrics_dict['spec_ic'][0]:.3f};{metrics_dict['spec_ic'][1]:.3f}]",
f"[{metrics_dict['spec_sens_diff_ic'][0]:.3f};{metrics_dict['spec_sens_diff_ic'][1]:.3f}]",
]
return [
metrics_dict["roc"],
metrics_dict["vendor_threshold"],
f"{metrics_dict['vendor_sens']:.3f}/{metrics_dict['vendor_spec']:.3f}",
metrics_dict["global_op"],
f"{metrics_dict['global_sens']:.3f}/{metrics_dict['global_spec']:.3f}",
f"{metrics_dict['spec_sens_diff']:.3f}",
f"{metrics_dict['global_youden']:.3f}",
]
def get_summary_df_from_preds_df(
list_dfs,
global_threshold,
bootstrap=True,
label_column="malignant",
operating_point="diag",
):
results = {}
for df in list_dfs:
results[str(df.vendor.unique())] = all_metrics_as_str(
df[label_column].values,
df.pred1.values,
global_threshold,
bootstrap,
operating_point,
)
if bootstrap:
return pd.DataFrame.from_dict(
results, orient="index", columns=COLS_FOR_RESULTS_W_BOOTSTRAP
)
return pd.DataFrame.from_dict(
results, orient="index", columns=COLS_FOR_RESULTS_SIMPLE
)
def get_all_dfs_results(model_dir, datasets_to_fetch):
all_dfs = {}
for ds in datasets_to_fetch:
df = pd.read_csv(model_dir / f"{ds}.csv")
df["vendor"] = ds
all_dfs[ds] = df
print(len(df))
return all_dfs
def get_all_camelyon_results(model_dir):
all_dfs = {}
for split in ["id_val", "val", "test"]:
df = pd.read_csv(model_dir / f"{split}_outputs.csv")
df["vendor"] = split
all_dfs[split] = df
return all_dfs
def get_sens_spec_at_threshold(true, pred, op):
sens = metrics.recall_score(true, pred >= op, pos_label=1, zero_division=1)
spec = metrics.recall_score(true, pred >= op, pos_label=0, zero_division=1)
return sens, spec
def get_mcc_at_threshold(true, pred, op):
pred_pos = (pred >= op).astype(bool)
pos = true.astype(bool)
TP = (pred_pos & pos).sum()
TN = (~pred_pos & ~pos).sum()
FP = (pred_pos & ~pos).sum()
FN = (~pred_pos & pos).sum()
assert (TP + TN + FP + FN) == true.shape[0]
return (TN * TP - FN * FP) / np.sqrt((TP + FP) * (TP + FN) * (TN + FP) * (TN + FN))