Create run_detect.py

models/Reporting the unreported: Event Extraction for Analyzing the Local Representation of Hate Crimes/run_detect.py

@@ -0,0 +1,92 @@
+import json
+import os
+from MICNN import *
+from Hi_Attn import *
+import argparse
+
+def _load_data(params, args):
+    if os.path.isfile("Data/" + args.dataset + "/data.pkl"):
+        print("Loading data.pkl for analyzing", args.dataset)
+        data = pickle.load(open("Data/" + args.dataset + "/data.pkl", "rb"))
+    else:
+        print("data.pkl not found. Generating bags ..")
+        data = GenerateTrain(params["batch_size"], args.dataset)
+
+    print("Loading train and test sets")
+    return data
+
+def _load_unlabeled(params, args, vocabs):
+    if args.goal != "train":
+        if os.path.isfile("Data/" + args.dataset + "/patch.pkl"):
+            print("Loading unlabeled patch batches")
+            unlabeled_batches = pickle.load(open("Data/" + args.dataset + "/patch.pkl", "rb"))
+        else:
+            print("Batching unlabeled patch data")
+            unlabeled_batches = GenerateUnlabeled(vocabs, params["batch_size"], args.dataset)
+    else:
+        unlabeled_batches = []
+    return unlabeled_batches
+
+def _detect(params, data):
+    train_batches, dev_batches, test_batches, vocabs, embedding = data
+    unlabeled_batches = _load_unlabeled(params, args, vocabs)
+
+    if args.model == "MICNN":
+        model = MICNN(params, vocabs, embedding)
+    elif args.model == "ATTN":
+        model = Hi_Attn(params, vocabs, embedding)
+
+    model.build()
+
+    if args.goal == "train":
+        train_sent, dev_sent, test_sent = model.run_model(
+            BatchIt(train_batches, params["batch_size"], vocabs),
+            BatchIt(dev_batches, params["batch_size"], vocabs),
+            BatchIt(test_batches, params["batch_size"], vocabs))
+        best_sent = [train_sent, test_sent, dev_sent]
+        if train_sent:
+            for j, articles in enumerate([train_batches, test_batches, dev_batches]):
+                for i in range(len(articles)):
+                    articles[i]["best_sent"] = best_sent[j][i]
+
+            pickle.dump((train_batches, dev_batches, test_batches, vocabs, embedding),
+                        open("Data/" + args.dataset + "/data.pkl", "wb"))
+
+    elif args.goal == "predict":
+        hate_pred, indice_pred = model.predict(unlabeled_batches)
+        pickle.dump(hate_pred, open("Data/" + args.dataset + "/labels.pkl", "wb"))
+        hate_batches = list()
+        for i in range(len(unlabeled_batches)):
+            if hate_pred[i] == 1:
+                if args.model == "MICNN":
+                    hate_batches.append(unlabeled_batches[i] + (hate_pred[i], indice_pred[i], i))
+                else:
+                    hate_batches.append(unlabeled_batches[i] + (hate_pred[i], i))
+        pickle.dump(hate_batches, open("Data/" + args.dataset + "/predict.pkl", "wb"))
+
+    elif args.goal == "active":
+        probabilities = model.predict(unlabeled_batches, active_learning=True)
+        pickle.dump(probabilities, open("probability.pkl", "wb"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument("--model", help = "Model is either MICNN or ATTN")
+    parser.add_argument("--goal", help = "Goal can be either train or predict")
+    parser.add_argument("--dataset", help="Dataset is either hate, homicide or kidnap")
+    parser.add_argument("--params", help = "Path to the params file, a json file "
+                                           "that contains model parameters")
+
+    args = parser.parse_args()
+    
+    try:
+        params = json.load(open(args.params, "r"))
+    except Exception:
+        print("Error in reading from the provided path, loading the default"
+              "parameters instead")
+        params = json.load(open("params.json", "r"))
+
+    params["dataset"] = args.dataset
+    data = _load_data(params, args)
+    _detect(params, data)