feat: convert data file to json in core lib

tests/helpers.py

@@ -16,7 +16,7 @@ ERROR_CIRCUIT_STRICT = 0.0001
 ERROR_CIRCUIT_RELAXED = 0.1
 
 
-def data_to_file(data_path: Path, data: list[torch.Tensor]) -> dict[str, list]:
+def data_to_json_file(data_path: Path, data: list[torch.Tensor]) -> dict[str, list]:
     column_names = [f"columns_{i}" for i in range(len(data))]
     column_to_data = {
         column: d.tolist()
@@ -45,7 +45,7 @@ def compute(
     data_path = basepath / "data.json"
     data_commitment_path = basepath / "commitments.json"
 
-    column_to_data = data_to_file(data_path, data)
+    column_to_data = data_to_json_file(data_path, data)
     # If selected_columns_params is None, select all columns
     if selected_columns_params is None:
         selected_columns = list(column_to_data.keys())

tests/test_core.py

@@ -2,10 +2,10 @@ import json
 
 import torch
 
-from zkstats.core import generate_data_commitment
+from zkstats.core import generate_data_commitment, _preprocess_data_file_to_json
 from zkstats.computation import computation_to_model
 
-from .helpers import data_to_file, compute
+from .helpers import data_to_json_file, compute
 
 
 def test_get_data_commitment_maps(tmp_path, column_0, column_1, scales):
@@ -16,7 +16,7 @@ def test_get_data_commitment_maps(tmp_path, column_0, column_1, scales):
     #     "columns_0": [1, 2, 3, 4, 5],
     #     "columns_1": [6, 7, 8, 9, 10],
     # }
-    data_json = data_to_file(data_path, [column_0, column_1])
+    data_json = data_to_json_file(data_path, [column_0, column_1])
     # data_commitment is a mapping[scale -> mapping[column_name, commitment_hex]]
     # {
     #     scale_0: {
@@ -51,7 +51,7 @@ def test_get_data_commitment_maps_hardcoded(tmp_path):
     data_commitment_path = tmp_path / "commitments.json"
     column_0 = torch.tensor([3.0, 4.5, 1.0, 2.0, 7.5, 6.4, 5.5])
     column_1 = torch.tensor([2.7, 3.3, 1.1, 2.2, 3.8, 8.2, 4.4])
-    data_to_file(data_path, [column_0, column_1])
+    data_to_json_file(data_path, [column_0, column_1])
     scales = [2, 3]
     generate_data_commitment(data_path, scales, data_commitment_path)
     with open(data_commitment_path, "r") as f:
@@ -63,7 +63,7 @@ def test_get_data_commitment_maps_hardcoded(tmp_path):
 
 def test_integration_select_partial_columns(tmp_path, column_0, column_1, error, scales):
     data_path = tmp_path / "data.json"
-    data_json = data_to_file(data_path, [column_0, column_1])
+    data_json = data_to_json_file(data_path, [column_0, column_1])
     columns = list(data_json.keys())
     assert len(columns) == 2
     # Select only the first column from two columns
@@ -75,3 +75,45 @@ def test_integration_select_partial_columns(tmp_path, column_0, column_1, error,
     _, model = computation_to_model(simple_computation, error)
     # gen settings, setup, prove, verify
     compute(tmp_path, [column_0, column_1], model, scales, selected_columns)
+
+
+def json_file_to_csv(data_json_path, data_csv_path):
+    with open(data_json_path, "r") as f:
+        data_from_json = json.load(f)
+    # Generate csv file from json
+    column_names = list(data_from_json.keys())
+    len_columns = len(data_from_json[column_names[0]])
+    for column in column_names:
+        assert len(data_from_json[column]) == len_columns, "All columns should have the same length"
+    rows = [
+        [str(data_from_json[column][i]) for column in column_names]
+        for i in range(len_columns)
+    ]
+    with open(data_csv_path, "w") as f:
+        f.write(",".join(column_names) + "\n")
+        for row in rows:
+            f.write(",".join(row) + "\n")
+
+
+def test__preprocess_data_file_to_json(tmp_path, column_0, column_1):
+    data_json_path = tmp_path / "data.json"
+    data_from_json = data_to_json_file(data_json_path, [column_0, column_1])
+
+    # Test: csv can be converted to json
+    # 1. Generate a csv file from json
+    data_csv_path = tmp_path / "data.csv"
+    json_file_to_csv(data_json_path, data_csv_path)
+    # 2. Convert csv to json
+    data_from_csv_json_path = tmp_path / "data_from_csv.json"
+    _preprocess_data_file_to_json(data_csv_path, data_from_csv_json_path)
+    with open(data_from_csv_json_path, "r") as f:
+        data_from_csv = json.load(f)
+    # 3. Compare the two json files
+    assert data_from_csv == data_from_json
+
+    # Test: this function can also handle json format by just copying the file
+    new_data_json_path = tmp_path / "new_data.json"
+    _preprocess_data_file_to_json(data_json_path, new_data_json_path)
+    with open(new_data_json_path, "r") as f:
+        new_data_from_json = json.load(f)
+    assert new_data_from_json == data_from_json

zkstats/core.py

@@ -1,11 +1,15 @@
-from typing import Type, Sequence, Mapping, Union, Literal
-import torch
-import ezkl
+import csv
+from pathlib import Path
+from typing import Type, Sequence, Mapping, Union, Literal, Callable
+from enum import Enum
 import os
 import numpy as np
 import json
 import time
 
+import torch
+import ezkl
+
 from zkstats.computation import IModel
 
 
@@ -40,7 +44,11 @@ def create_dummy(data_path: str, dummy_data_path: str) -> None:
     """
     Create a dummy data file with randomized data based on the shape of the original data.
     """
-    data = json.loads(open(data_path, "r").read())
+    # Convert data file to json under the same directory but with suffix .json
+    data_path: Path = Path(data_path)
+    data_json_path = Path(data_path).with_suffix(DataExtension.JSON.value)
+
+    data = json.loads(open(data_json_path, "r").read())
     # assume all columns have same number of rows
     dummy_data ={}
     for col in data:
@@ -270,16 +278,17 @@ 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
   """
 
-  with open(data_path) as f:
+  # Convert `data_path` to json file `data_json_path`
+  data_path: Path = Path(data_path)
+  data_json_path = Path(data_path).with_suffix(DataExtension.JSON.value)
+  _preprocess_data_file_to_json(data_path, data_json_path)
+
+  with open(data_json_path) as f:
     data_json = json.load(f)
   data_commitments = {
     str(scale): {
@@ -367,14 +376,62 @@ def _gen_settings(
   print("setting: ", f_setting.read())
 
 
+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: str,
+    data_path: Union[str | Path],
     col_array: list[str],
     sel_data_path: list[str],
   ) -> list[torch.Tensor]:
     data_tensor_array=[]
     sel_data = []
-    data_onefile = json.loads(open(data_path, "r").read())
+    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())
 
     for col in col_array:
       data = data_onefile[col]
@@ -394,4 +451,4 @@ 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