--- a
+++ b/scripts/plcom2012/plcom2012.py
@@ -0,0 +1,123 @@
+import math
+from tqdm import tqdm
+import pickle
+
+
+class RiskModel(object):
+    def __init__(self, args):
+        self.args = args
+
+    def forward(self, batch):
+        x_transformed = {
+            key: func(batch[key]) for key, func in self.input_transformers.items()
+        }
+        x_scaled = self.scale_inputs(x_transformed)
+        risk = self.model(x_scaled)
+        return risk
+
+    def test(self, data):
+        results = []
+        for sample in tqdm(data.dataset):
+            sample["golds"] = sample["y"]
+            sample["probs"] = self.forward(sample)
+
+        if self.args.save_predictions:
+            self.save_predictions(data.dataset)
+
+    def save_predictions(self, data):
+        predictions_dict = [
+            {k: v for k, v in d.items() if k in self.save_keys} for d in data
+        ]
+        predictions_filename = "{}.{}.predictions".format(
+            self.args.results_path, self.save_prefix
+        )
+        pickle.dump(predictions_dict, open(predictions_filename, "wb"))
+
+    @property
+    def input_coef(self):
+        pass
+
+    @property
+    def input_transformers(self):
+        pass
+
+class PLCOm2012(RiskModel):
+    def __init__(self, args):
+        super(PLCOm2012, self).__init__(args)
+
+    def model(self, x):
+        return 1 / (1 + math.exp(-x))
+
+    def scale_inputs(self, x):
+        running_sum = -4.532506
+        for key, beta in self.input_coef.items():
+            if key == "race":
+                running_sum += beta[x["race"]]
+            else:
+                running_sum += x[key] * beta
+        return running_sum
+
+    @property
+    def input_coef(self):
+        coefs = {
+            "age": 0.0778868,
+            "race": {
+                "white": 0,
+                "black": 0.3944778,
+                "hispanic": -0.7434744,
+                "asian": -0.466585,
+                "native_hawaiian_pacific": 0,
+                "american_indian_alaskan": 1.027152,
+            },
+            "education": -0.0812744,
+            "bmi": -0.0274194,
+            "cancer_hx": 0.4589971,
+            "family_lc_hx": 0.587185,
+            "copd": 0.3553063,
+            "is_smoker": 0.2597431,
+            "smoking_intensity": -1.822606,
+            "smoking_duration": 0.0317321,
+            "years_since_quit_smoking": -0.0308572,
+        }
+        return coefs
+
+    @property
+    def input_transformers(self):
+        funcs = {
+            "age": lambda x: x - 62,
+            "race": lambda x: x,
+            "education": lambda x: x - 4,
+            "bmi": lambda x: x - 27,
+            "cancer_hx": lambda x: x,
+            "family_lc_hx": lambda x: x,
+            "copd": lambda x: x,
+            "is_smoker": lambda x: x,
+            "smoking_intensity": lambda x: 10 / x - 0.4021541613,
+            "smoking_duration": lambda x: x - 27,
+            "years_since_quit_smoking": lambda x: x - 10,
+        }
+        return funcs
+
+    @property
+    def save_keys(self):
+        return [
+            "pid",
+            "age",
+            "race",
+            "education",
+            "bmi",
+            "cancer_hx",
+            "family_lc_hx",
+            "copd",
+            "is_smoker",
+            "smoking_intensity",
+            "smoking_duration",
+            "years_since_quit_smoking",
+            "exam",
+            "golds",
+            "probs",
+            "time_at_event",
+            "y_seq",
+            "y_mask",
+            "screen_timepoint",
+        ]