149 lines
4.4 KiB
Python
149 lines
4.4 KiB
Python
import argparse
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import os.path
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from typing import Iterable, Optional
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import numpy as np
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import pandas as pd
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import seaborn as sns
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import tqdm
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from matplotlib import pyplot as plt
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from sklearn.manifold import TSNE
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search_data = __import__('search-data')
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TENSORFLOW_PATH_PREFIX: str = "./"
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OUT_DIR: str = os.path.join(os.path.dirname(__file__), "out")
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def read_ground_truth(file_path: str, df: pd.DataFrame) -> Iterable[tuple[str, int]]:
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records: list[list[str]] = []
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with open(file_path) as f:
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record_tmp = []
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for line in f:
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line = line.strip()
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if line == '':
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assert len(record_tmp) == 3
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records.append(record_tmp)
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record_tmp = []
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else:
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record_tmp.append(line)
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if len(record_tmp) == 3:
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records.append(record_tmp)
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for query, name, file_name in records:
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assert file_name.startswith(TENSORFLOW_PATH_PREFIX)
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file_name = file_name[len(TENSORFLOW_PATH_PREFIX):]
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row = df[(df.name == name) & (df.file == file_name)]
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assert len(row) == 1
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yield query, row.index[0]
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def better_index(li: list[tuple[int, float]], e: int) -> Optional[int]:
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for i, le in enumerate(li):
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if le[0] == e:
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return i
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return None
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def plot_df(results, query: str) -> Optional[pd.DataFrame]:
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if results.vectors is not None and results.query_vector is not None:
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tsne_vectors = np.array(results.vectors + [results.query_vector])
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tsne = TSNE(n_components=2, perplexity=2, n_iter=3000)
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tsne_results = tsne.fit_transform(tsne_vectors)
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df = pd.DataFrame(columns=['tsne-2d-one', 'tsne-2d-two', 'Query', 'Vector kind'])
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df['tsne-2d-one'] = tsne_results[:, 0]
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df['tsne-2d-two'] = tsne_results[:, 1]
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df['Query'] = [query] * (len(results.vectors) + 1)
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df['Vector kind'] = (['Result'] * len(results.vectors)) + ['Input query']
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return df
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else:
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return None
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def evaluate(method_name: str, file_path: str) -> tuple[float, float]:
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df = search_data.load_data()
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test_set = list(read_ground_truth(file_path, df))
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precision_sum = 0
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recall_sum = 0
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dfs = []
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for query, expected in tqdm.tqdm(test_set):
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search_results = search_data.search(query, method_name, df)
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df_q = plot_df(search_results, query)
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if df_q is not None:
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dfs.append(df_q)
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idx = better_index(search_results.indexes_scores, expected)
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if idx is None:
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precision = 0
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recall = 0
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else:
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precision = 1 / (idx + 1)
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recall = 1
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precision_sum += precision
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recall_sum += recall
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if not os.path.isdir(OUT_DIR):
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os.makedirs(OUT_DIR)
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precision = precision_sum * 100 / len(test_set)
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recall = recall_sum * 100 / len(test_set)
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output = "Precision: {0:.2f}%\nRecall: {1:.2f}%\n".format(precision, recall)
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print(output)
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with open(os.path.join(OUT_DIR, "{0}_prec_recall.txt".format(method_name)), "w") as f:
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f.write(output)
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if len(dfs) > 0:
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df = pd.concat(dfs)
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plt.figure(figsize=(12, 10))
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sns.scatterplot(
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x="tsne-2d-one", y="tsne-2d-two",
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hue="Query",
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style="Vector kind",
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palette=sns.color_palette("husl", n_colors=10),
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data=df,
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legend="full",
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alpha=1.0
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)
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plt.savefig(os.path.join(OUT_DIR, "{0}_plot.png".format(method_name)))
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return precision, recall
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def main():
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methods = ["tfidf", "freq", "lsi", "doc2vec"]
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parser = argparse.ArgumentParser()
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parser.add_argument("method", help="the method to compare similarities with", type=str, choices=methods + ["all"])
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parser.add_argument("ground_truth_file", help="file where ground truth comes from", type=str)
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args = parser.parse_args()
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if args.method == "all":
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df = pd.DataFrame(columns=["Engine", "Average Precision", "Average Recall"])
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for i, method in enumerate(methods):
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print(f"Applying method {method}:")
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precision, recall = evaluate(method, args.ground_truth_file)
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df.loc[i, "Engine"] = method
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df.loc[i, "Average Precision"] = f"{precision:.2f}%"
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df.loc[i, "Average Recall"] = f"{recall:.2f}%"
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print(df.to_markdown(index=False))
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else:
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evaluate(args.method, args.ground_truth_file)
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if __name__ == '__main__':
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main()
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