Merge branch 'tmp/recheck_func' of https://github.com/ZKStats/zk-stat…

…s-lib into tmp/recheck_func

zkstats/core.py

@@ -2,13 +2,14 @@
 from pathlib import Path
 from typing import Type, Sequence, Mapping, Union, Literal, Callable
 from enum import Enum
-import torch
-import ezkl
 import os
 import numpy as np
 import json
 import time
 
+import torch
+import ezkl
+
 from zkstats.computation import IModel
 
 
@@ -277,11 +278,7 @@ def generate_data_commitment(data_path: str, scales: Sequence[int], data_commitm
   Generate and store data commitment maps for different scales so that verifiers can verify
   proofs with different scales.
 
-  :param data_path: path to the data file. The data file should be a JSON file with the following format:
-    {
-      "column_0": [number_0, number_1, ...],
-      "column_1": [number_0, number_1, ...],
-    }
+  :param data_path: data file path. The format must be anything defined in `DataExtension`
   :param scales: a list of scales to use for the commitments
   :param data_commitment_path: path to store the generated data commitment maps
   """
@@ -421,18 +418,59 @@ def _preprocess_data_file_to_json(data_path: Union[Path, str], out_data_json_pat
     preprocess_function(data_path, out_data_json_path)
 
 
+def _csv_file_to_json(old_file_path: Union[Path, str], out_data_json_path: Union[Path, str],  *, delimiter: str = ",") -> None:
+    data_csv_path = Path(old_file_path)
+    with open(data_csv_path, 'r') as f_csv:
+        reader = csv.reader(f_csv, delimiter=delimiter, strict=True)
+        # Read all data from the reader to `rows`
+        rows_with_column_name = tuple(reader)
+    if len(rows_with_column_name) < 1:
+        raise ValueError("No column names in the CSV file")
+    if len(rows_with_column_name) < 2:
+        raise ValueError("No data in the CSV file")
+    column_names = rows_with_column_name[0]
+    rows = rows_with_column_name[1:]
+
+    columns = [
+        [
+            float(rows[j][i])
+            for j in range(len(rows))
+        ]
+        for i in range(len(rows[0]))
+    ]
+    data = {
+        column_name: column_data
+        for column_name, column_data in zip(column_names, columns)
+    }
+    with open(out_data_json_path, "w") as f_json:
+        json.dump(data, f_json)
+
+
+class DataExtension(Enum):
+    CSV = ".csv"
+    JSON = ".json"
+
+
+DATA_FORMAT_PREPROCESSING_FUNCTION: dict[DataExtension, Callable[[Union[Path, str], Path], None]] = {
+    DataExtension.CSV: _csv_file_to_json,
+    DataExtension.JSON: lambda old_file_path, out_data_json_path: Path(out_data_json_path).write_text(Path(old_file_path).read_text())
+}
+
+def _preprocess_data_file_to_json(data_path: Union[Path, str], out_data_json_path: Path):
+    data_file_extension = DataExtension(data_path.suffix)
+    preprocess_function = DATA_FORMAT_PREPROCESSING_FUNCTION[data_file_extension]
+    preprocess_function(data_path, out_data_json_path)
+
+
 def _process_data(
     data_path: Union[str| Path],
     col_array: list[str],
     sel_data_path: list[str],
   ) -> list[torch.Tensor]:
     data_tensor_array=[]
     sel_data = []
-    data_path: Path = Path(data_path)
-    # Convert data file to json under the same directory but with suffix .json
-    data_json_path = Path(data_path).with_suffix(DataExtension.JSON.value)
-    _preprocess_data_file_to_json(data_path, data_json_path)
-    data_onefile = json.loads(open(data_json_path, "r").read())
+    data_onefile = json.loads(open(data_path, "r").read())
+
     for col in col_array:
       data = data_onefile[col]
       data_tensor = torch.tensor(data, dtype = torch.float32)
@@ -451,4 +489,4 @@ def _get_commitment_for_column(column: list[float], scale: int) -> str:
   res_poseidon_hash = ezkl.poseidon_hash(serialized_data)[0]
   # res_hex = ezkl.vecu64_to_felt(res_poseidon_hash[0])
 
-  return res_poseidon_hash
+  return res_poseidon_hash