Upload 3 files

ap.py

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+import csv
+import time
+from tqdm import tqdm
+
+dataset = []
+
+min_value = -10000
+max_value = 10000
+
+# Calculate the total number of combinations
+total_combinations = (max_value - min_value + 1) ** 2
+
+# Save the dataset to a CSV file
+output_file = "output_dataset.csv"
+with open(output_file, mode="w", newline="", encoding="utf-8") as file:
+    writer = csv.writer(file)
+    writer.writerow(["instruction", "output"])  # Write header
+
+    with tqdm(total=total_combinations, desc="Generating Dataset") as pbar:
+        start_time = time.time()
+        for a in range(min_value, max_value + 1):
+            for b in range(min_value, max_value + 1):
+                # Generate the instruction
+                if b < 0:
+                    instruction = f"{a}-({abs(b)})"
+                else:
+                    instruction = f"{a}+{b}"
+
+                # Calculate the output
+                output = a + b
+
+                # Write the row to the CSV file in real-time
+                writer.writerow([instruction, str(output)])
+
+                pbar.update(1)
+
+        end_time = time.time()
+        elapsed_time = end_time - start_time
+        print(f"Total time taken: {elapsed_time:.2f} seconds")
+
+print(f"Dataset saved to {output_file}.")

paShow.py

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+import pandas as pd
+import pyarrow.parquet as pq
+
+parquet_file_path = './output_chunk_0.parquet'  # 読み込むParquetファイルのパスを指定
+
+# Parquetファイルを読み込む
+table = pq.read_table(parquet_file_path)
+
+# PyArrowのTableをPandasのDataFrameに変換
+df = table.to_pandas()
+
+# 読み込んだデータを表示（例: 最初の10行を表示）
+print(df.head(10))

parq.py

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+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+from tqdm import tqdm  # tqdmをインポート
+
+def process_chunk(chunk, idx):
+    # ここで各チャンクの処理を行う
+    # 例えば、欲しい項目を選択してParquetに変換する
+
+    selected_columns = ['instruction', 'output']
+    new_df = chunk[selected_columns].copy()
+
+    # Parquetファイルに変換
+    parquet_file_path = './output_chunk_{}.parquet'.format(idx)
+    table = pa.Table.from_pandas(new_df)
+    pq.write_table(table, parquet_file_path)
+
+csv_file_path = './input.csv'
+chunksize = 100000000  # 例: 100万行ごとに分割
+df_chunks = pd.read_csv(csv_file_path, chunksize=chunksize)
+
+for idx, chunk in tqdm(enumerate(df_chunks)):
+    process_chunk(chunk, idx)