fix: change khiops output classes attr to snake case

Author: ElouenGinat

src/khisto/core/backend.py

@@ -8,6 +8,7 @@
 
 import json
 import os
+import re
 import subprocess
 import tempfile
 from dataclasses import dataclass, field
@@ -22,42 +23,56 @@
     from numpy.typing import NDArray
 
 
+def camel_to_snake(name: str) -> str:
+    return re.sub(r"(?<!^)(?=[A-Z])", "_", name).lower()
+
+
 @dataclass
 class _HistogramPayload:
     """Histogram bin data from khisto JSON."""
 
-    lowerBounds: list[float] = field(default_factory=list)
-    upperBounds: list[float] = field(default_factory=list)
+    lower_bounds: list[float] = field(default_factory=list)
+    upper_bounds: list[float] = field(default_factory=list)
     lengths: list[float] = field(default_factory=list)
     frequencies: list[int] = field(default_factory=list)
     probabilities: list[float] = field(default_factory=list)
     densities: list[float] = field(default_factory=list)
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> _HistogramPayload:
-        return cls(**{k: v for k, v in data.items() if k in cls.__dataclass_fields__})
+        return cls(
+            **{
+                ck: v
+                for k, v in data.items()
+                if (ck := camel_to_snake(k)) in cls.__dataclass_fields__
+            }
+        )
 
 
 @dataclass
 class _SeriesPayload:
     """Histogram series from khisto JSON."""
 
-    histogramNumber: int = 0
-    interpretableHistogramNumber: int = 0
-    truncationEpsilon: float = 0.0
-    removedSingularIntervalNumber: int = 0
+    histogram_number: int = 0
+    interpretable_histogram_number: int = 0
+    truncation_epsilon: float = 0.0
+    removed_singular_interval_number: int = 0
     granularities: list[int] = field(default_factory=list)
-    intervalNumbers: list[int] = field(default_factory=list)
-    peakIntervalNumbers: list[int] = field(default_factory=list)
-    spikeIntervalNumbers: list[int] = field(default_factory=list)
-    emptyIntervalNumbers: list[int] = field(default_factory=list)
+    interval_numbers: list[int] = field(default_factory=list)
+    peak_interval_numbers: list[int] = field(default_factory=list)
+    spike_interval_numbers: list[int] = field(default_factory=list)
+    empty_interval_numbers: list[int] = field(default_factory=list)
     levels: list[float] = field(default_factory=list)
-    informationRates: list[float] = field(default_factory=list)
+    information_rates: list[float] = field(default_factory=list)
     histograms: list[_HistogramPayload] = field(default_factory=list)
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> _SeriesPayload:
-        kwargs = {k: v for k, v in data.items() if k in cls.__dataclass_fields__}
+        kwargs = {
+            ck: v
+            for k, v in data.items()
+            if (ck := camel_to_snake(k)) in cls.__dataclass_fields__
+        }
         if "histograms" in kwargs:
             kwargs["histograms"] = [
                 _HistogramPayload.from_dict(h) for h in kwargs["histograms"]
@@ -71,8 +86,8 @@ class _KhistoOutput:
 
     tool: str = ""
     version: str = ""
-    bestHistogram: _HistogramPayload = field(default_factory=_HistogramPayload)
-    histogramSeries: _SeriesPayload = field(default_factory=_SeriesPayload)
+    best_histogram: _HistogramPayload = field(default_factory=_HistogramPayload)
+    histogram_series: _SeriesPayload = field(default_factory=_SeriesPayload)
 
     @classmethod
     def from_dict(cls, data: dict[str, Any]) -> _KhistoOutput:
@@ -81,8 +96,8 @@ def from_dict(cls, data: dict[str, Any]) -> _KhistoOutput:
         return cls(
             tool=data.get("tool", ""),
             version=data.get("version", ""),
-            bestHistogram=_HistogramPayload.from_dict(data["bestHistogram"]),
-            histogramSeries=_SeriesPayload.from_dict(data["histogramSeries"]),
+            best_histogram=_HistogramPayload.from_dict(data["bestHistogram"]),
+            histogram_series=_SeriesPayload.from_dict(data["histogramSeries"]),
         )
 
 
@@ -157,7 +172,6 @@ def __post_init__(self) -> None:
             raise ValueError("densities must be non-negative")
 
         # Verify lower_bounds are equal to upper_bounds for adjacent bins
-        print(self.lower_bounds, self.upper_bounds)
         if not np.all(self.lower_bounds[1:] == self.upper_bounds[:-1]):
             raise ValueError(
                 "lower_bounds must be equal to upper_bounds for adjacent bins"
@@ -196,26 +210,26 @@ def _format_runtime_error(
 
 def _process_histogram_file(file_path: Path) -> list[HistogramResult]:
     """Process exploratory JSON generated by khisto CLI."""
-    with open(file_path, "r") as file:
+    with open(file_path, "r", encoding="utf-8") as file:
         khisto_output: _KhistoOutput = _KhistoOutput.from_dict(json.load(file))
 
-    histogram_series = khisto_output.histogramSeries
-    best_idx = histogram_series.interpretableHistogramNumber - 1
+    histogram_series = khisto_output.histogram_series
+    best_idx = histogram_series.interpretable_histogram_number - 1
 
     return [
         HistogramResult(
-            lower_bounds=np.asarray(h.lowerBounds, dtype=np.float64),
-            upper_bounds=np.asarray(h.upperBounds, dtype=np.float64),
+            lower_bounds=np.asarray(h.lower_bounds, dtype=np.float64),
+            upper_bounds=np.asarray(h.upper_bounds, dtype=np.float64),
             frequencies=np.asarray(h.frequencies, dtype=np.int64),
             probabilities=np.asarray(h.probabilities, dtype=np.float64),
             densities=np.asarray(h.densities, dtype=np.float64),
             is_best=(i == best_idx),
             granularity=histogram_series.granularities[i],
             level=histogram_series.levels[i],
-            information_rate=histogram_series.informationRates[i],
-            peak_interval_number=histogram_series.peakIntervalNumbers[i],
-            spike_interval_number=histogram_series.spikeIntervalNumbers[i],
-            empty_interval_number=histogram_series.emptyIntervalNumbers[i],
+            information_rate=histogram_series.information_rates[i],
+            peak_interval_number=histogram_series.peak_interval_numbers[i],
+            spike_interval_number=histogram_series.spike_interval_numbers[i],
+            empty_interval_number=histogram_series.empty_interval_numbers[i],
         )
         for i, h in enumerate(histogram_series.histograms)
     ]