Delete self_construction.py

self_construction.py

@@ -1,171 +0,0 @@
-import random
-from sentence_transformers import SentenceTransformer
-from sklearn.cluster import KMeans
-from sklearn.decomposition import PCA
-import numpy as np
-import json
-import matplotlib.pyplot as plt
-import argparse
-from utils import fix_seed
-
-
-def parse_arguments():
-    parser = argparse.ArgumentParser(description="Zero-shot-CoT")
-    parser.add_argument(
-        "--task", type=str, default="strategyqa",
-        choices=["aqua", "gsm8k", "commonsensqa", "addsub", "multiarith", "strategyqa", "svamp", "singleeq", "coin_flip", "last_letters"], help="dataset used for experiment"
-    )
-    parser.add_argument(
-        "--max_ra_len", type=int, default=5, help="maximum number of reasoning chains"
-    )
-    parser.add_argument(
-        "--pred_file", type=str, default="log/multiarith_zero_shot_cot.log",
-        help="use the reasoning chains generated by zero-shot-cot."
-    )
-    parser.add_argument(
-        "--demo_save_dir", type=str, default="demos/multiarith", help="where to save the contructed demonstrations"
-    )
-    parser.add_argument("--random_seed", type=int, default=192, help="random seed")
-    parser.add_argument(
-        "--encoder", type=str, default="all-MiniLM-L6-v2", help="which sentence-transformer encoder for clustering"
-    )
-    parser.add_argument(
-        "--sampling", type=str, default="center", help="whether to sample the cluster center first"
-    )
-    parser.add_argument(
-        "--debug", type=bool, default=True, help="debug mode"
-    )
-    args = parser.parse_args()
-    return args
-
-def main():
-    args = parse_arguments()
-    fix_seed(args.random_seed)
-    encoder = SentenceTransformer(args.encoder)
-
-    task = args.task
-    pred_file = args.pred_file
-    save_file = args.demo_save_dir
-    max_ra_len = args.max_ra_len
-    if task == "last_letters":
-        max_ra_len = 7
-    if task == "aqua" or task == "last_letters":
-        num_clusters = 4
-    elif task == "commonsensqa":
-        num_clusters = 7
-    elif task == "strategyqa":
-        num_clusters = 6
-    else:
-        num_clusters = 8
-
-    corpus = []
-    question = []
-    rationale = []
-    gold_ans = []
-    pred_ans = []
-
-    with open(pred_file, "r", encoding="utf-8") as fp:
-        answer_seg = ""
-        for line in fp:
-            if "Q: " in line:
-                c_question = line.strip()
-            if "A: " in line:
-                answer_seg = line
-            elif "Therefore" in line and "the answer" in line:
-                c_rationale = answer_seg
-
-            elif answer_seg != "":
-                answer_seg += line
-            if "pred_mode" in line:
-                c_pred_ans = line.split(":")[1].strip()
-            if "GT :" in line:
-                c_gold_ans = line.split(":")[1].strip()
-
-                c_rationale = c_rationale.replace("A: Let's think step by step.", "Let's think step by step.")
-                c_question = c_question + "\nA:"
-
-                corpus.append(c_question)
-                question.append(c_question)
-                rationale.append(c_rationale)
-                pred_ans.append(c_pred_ans)
-                if args.debug:
-                    gold_ans.append(c_gold_ans)
-                answer_seg = ""
-
-    corpus_embeddings = encoder.encode(corpus)
-
-    # Perform kmean clustering
-    clustering_model = KMeans(n_clusters=num_clusters, random_state=args.random_seed)
-    clustering_model.fit(corpus_embeddings)
-    cluster_assignment = clustering_model.labels_
-
-    clustered_sentences = [[] for i in range(num_clusters)]
-
-    dist = clustering_model.transform(corpus_embeddings)
-    clustered_dists = [[] for i in range(num_clusters)]
-    clustered_idx = [[] for i in range(num_clusters)]
-    for sentence_id, cluster_id in enumerate(cluster_assignment):
-        clustered_sentences[cluster_id].append(corpus[sentence_id])
-        clustered_dists[cluster_id].append(dist[sentence_id][cluster_id])
-        clustered_idx[cluster_id].append(sentence_id)
-
-    demos = []
-
-    for i in range(len(clustered_dists)):
-        print("Cluster ", i+1)
-        tmp = list(map(list, zip(range(len(clustered_dists[i])), clustered_dists[i])))
-        top_min_dist = sorted(tmp, key=lambda x: x[1], reverse=False)
-        if not args.sampling == "center":
-            random.shuffle(top_min_dist)
-        for element in top_min_dist:
-            min_idx = element[0]
-            c_rationale = rationale[clustered_idx[i][min_idx]].strip()
-            c_pred_ans = pred_ans[clustered_idx[i][min_idx]].strip()
-
-            if len(question[clustered_idx[i][min_idx]].strip().split()) <= 60 \
-                and len(c_rationale.replace("\n\n", "\n").split("\n")) <= max_ra_len and c_rationale[-1] == "." and c_pred_ans != "":
-                if args.task in ["gsm8k", "multiarith", "singleeq", "addsub", "svamp"]:
-                    if not (c_pred_ans.strip() in c_rationale.split(".")[-2] or c_pred_ans.strip() in c_rationale.split()[-10:]):
-                        continue
-                c_question = question[clustered_idx[i][min_idx]]
-                c_rationale = c_rationale.replace("\n\n", "\n").replace("\n", " ").strip()
-                c_rationale = " ".join(c_rationale.split())
-                if args.debug:
-                    c_gold_ans = gold_ans[clustered_idx[i][min_idx]]
-                else:
-                    c_gold_ans = None
-                demo_element = {
-                    "question": c_question,
-                    "rationale": c_rationale,
-                    "pred_ans": c_pred_ans,
-                    "gold_ans": c_gold_ans,
-                }
-                demos.append(demo_element)
-                print(c_question)
-                print(c_rationale)
-                print(c_pred_ans)
-                print(c_gold_ans)
-                print("")
-                break
-
-    demos = {"demo": demos}
-
-    with open(args.demo_save_dir, 'w', encoding="utf-8") as write_f:
-        json.dump(demos, write_f, indent=4, ensure_ascii=False)
-
-    y_km = clustering_model.fit_predict(corpus_embeddings)
-    pca_model = PCA(n_components=2, random_state=args.random_seed)
-    transformed = pca_model.fit_transform(corpus_embeddings)
-    centers = pca_model.transform(clustering_model.cluster_centers_)
-
-    plt.scatter(x=transformed[:, 0], y=transformed[:, 1], c=y_km, s=50, cmap=plt.cm.Paired, alpha=0.4)
-    plt.scatter(centers[:, 0],centers[:, 1],
-            s=250, marker='*', label='centroids',
-            edgecolor='black',
-           c=np.arange(0,num_clusters),cmap=plt.cm.Paired,)
-    plt.xticks([])
-    plt.yticks([])
-    plt.savefig(save_file+".png", dpi=600)
-
-if __name__ == "__main__":
-    main()