Skip to content

Conversation

@codeflash-ai
Copy link

@codeflash-ai codeflash-ai bot commented Nov 1, 2025

📄 10% (0.10x) speedup for Figure.add_violin in plotly/graph_objs/_figure.py

⏱️ Runtime : 22.8 milliseconds 20.8 milliseconds (best of 43 runs)

📝 Explanation and details

The optimized code achieves a 9% speedup by implementing import caching for the Violin class in the add_violin method.

Key optimization applied:

  • Cached import pattern: Instead of executing from plotly.graph_objs import Violin on every call, the code now caches the imported Violin class as a class attribute Figure._violin_class using a try/except pattern
  • Import overhead elimination: The original code spent 5.7% of execution time (7.3ms out of 127ms) on the import statement alone. The optimized version reduces this to just 0.4% (0.44ms) on first call, then eliminates it entirely on subsequent calls

Why this optimization works:

  • Python's import system has overhead even for already-imported modules due to namespace lookups and module resolution
  • By caching the class reference directly on the Figure class, we bypass the import machinery entirely after the first call
  • The try/except pattern adds minimal overhead (~0.09ms) while providing the caching benefit

Performance characteristics from test results:

  • High-frequency scenarios: The optimization shines when add_violin is called repeatedly, showing 19.3% speedup for adding 100 violin traces
  • Single calls: Still beneficial with 11-15% improvements for individual calls
  • Large data scenarios: Minimal impact (~0.4-0.9% improvement) since the import cost becomes negligible relative to data processing

This optimization is particularly effective for interactive plotting scenarios, dashboards, or any workflow that creates multiple violin plots programmatically.

Correctness verification report:

Test Status
⚙️ Existing Unit Tests 🔘 None Found
🌀 Generated Regression Tests 147 Passed
⏪ Replay Tests 🔘 None Found
🔎 Concolic Coverage Tests 🔘 None Found
📊 Tests Coverage 100.0%
🌀 Generated Regression Tests and Runtime
import random
import string

# imports
import pytest
from plotly.graph_objs._figure import Figure


# Minimal Violin trace mock for testing (since we don't import plotly)
class Violin:
    def __init__(self, **kwargs):
        self.props = kwargs
        self.type = 'violin'
    def __getitem__(self, item):
        return self.props[item]
    def __eq__(self, other):
        return isinstance(other, Violin) and self.props == other.props

# Minimal BaseFigure and Figure for testing
class BaseFigure:
    def __init__(self, data=None, layout=None, frames=None, skip_invalid=False, **kwargs):
        self.data = []
        if data is not None:
            if isinstance(data, list):
                self.data = data
            else:
                self.data = [data]
    def add_trace(self, trace, row=None, col=None, secondary_y=None, exclude_empty_subplots=False):
        # For our mock, just append the trace
        self.data.append(trace)
        return self
from plotly.graph_objs._figure import Figure

# --------------------
# Unit Tests for add_violin
# --------------------

# ---------
# 1. Basic Test Cases
# ---------

def test_add_simple_violin_y_only():
    # Basic: add a violin with y data only
    fig = Figure()
    y = [1, 2, 3, 4, 5]
    fig.add_violin(y=y) # 130μs -> 113μs (14.9% faster)
    trace = fig.data[-1]

def test_add_violin_x_and_y():
    # Basic: add violin with both x and y
    fig = Figure()
    x = ['A', 'A', 'B', 'B', 'C']
    y = [1, 2, 3, 4, 5]
    fig.add_violin(x=x, y=y) # 143μs -> 126μs (12.9% faster)
    trace = fig.data[-1]


def test_add_violin_with_text():
    # Basic: add violin with text annotation
    fig = Figure()
    fig.add_violin(y=[1,2,3], text=["a", "b", "c"]) # 142μs -> 125μs (13.4% faster)
    trace = fig.data[-1]


def test_add_violin_single_value():
    # Edge: single value in y
    fig = Figure()
    fig.add_violin(y=[42]) # 116μs -> 102μs (14.5% faster)
    trace = fig.data[-1]

def test_add_violin_large_numbers():
    # Edge: very large numbers in y
    fig = Figure()
    y = [1e100, 2e100, 3e100]
    fig.add_violin(y=y) # 121μs -> 107μs (13.0% faster)
    trace = fig.data[-1]

def test_add_violin_negative_and_zero():
    # Edge: negative and zero values in y
    fig = Figure()
    y = [-10, 0, 10]
    fig.add_violin(y=y) # 119μs -> 103μs (15.2% faster)
    trace = fig.data[-1]

def test_add_violin_with_all_options():
    # Edge: all possible options set
    fig = Figure()
    options = dict(
        alignmentgroup="grp",
        bandwidth=0.5,
        box={"visible": True},
        customdata=[1,2,3],
        fillcolor="red",
        hoverinfo="y",
        jitter=0.2,
        legend="legend2",
        legendgroup="groupA",
        legendrank=10,
        legendwidth=200,
        line={"color": "black"},
        marker={"size": 10},
        meanline={"visible": True},
        meta="meta",
        name="full",
        offsetgroup="og",
        opacity=0.8,
        orientation="h",
        pointpos=0.3,
        points="all",
        quartilemethod="linear",
        scalegroup="sg",
        scalemode="count",
        selected={"marker": {"color": "blue"}},
        selectedpoints=[0,1],
        showlegend=True,
        side="positive",
        span=[0,10],
        spanmode="manual",
        stream={"token": "abc"},
        text=["a", "b", "c"],
        uid="123",
        uirevision=1,
        unselected={"marker": {"color": "gray"}},
        visible=True,
        width=0.9,
        x=[1,2,3],
        x0=1,
        xaxis="x2",
        xhoverformat=".2f",
        y=[4,5,6],
        y0=2,
        yaxis="y2",
        yhoverformat=".1f",
        zorder=5
    )
    fig.add_violin(**options) # 1.13ms -> 1.10ms (2.65% faster)
    trace = fig.data[-1]
    for k, v in options.items():
        pass

def test_add_violin_with_ids():
    # Edge: ids as strings
    fig = Figure()
    ids = ["id1", "id2", "id3"]
    fig.add_violin(y=[1,2,3], ids=ids) # 143μs -> 125μs (14.3% faster)
    trace = fig.data[-1]


def test_add_violin_with_boolean_false_points():
    # Edge: points set to False disables points
    fig = Figure()
    fig.add_violin(y=[1,2,3], points=False) # 133μs -> 119μs (11.7% faster)
    trace = fig.data[-1]

def test_add_violin_with_orientation_v():
    # Edge: orientation vertical
    fig = Figure()
    fig.add_violin(y=[1,2,3], orientation="v") # 134μs -> 117μs (14.4% faster)
    trace = fig.data[-1]

def test_add_violin_with_orientation_h():
    # Edge: orientation horizontal
    fig = Figure()
    fig.add_violin(x=[1,2,3], orientation="h") # 132μs -> 116μs (14.4% faster)
    trace = fig.data[-1]



def test_add_violin_large_y():
    # Large: y of length 1000
    fig = Figure()
    y = list(range(1000))
    fig.add_violin(y=y) # 1.14ms -> 1.13ms (0.880% faster)
    trace = fig.data[-1]

def test_add_violin_large_x_and_y():
    # Large: x and y of length 1000
    fig = Figure()
    x = [random.choice(string.ascii_letters) for _ in range(1000)]
    y = [random.uniform(-1000, 1000) for _ in range(1000)]
    fig.add_violin(x=x, y=y) # 2.27ms -> 2.26ms (0.390% faster)
    trace = fig.data[-1]

def test_add_many_violins():
    # Large: add 100 violin traces
    fig = Figure()
    for i in range(100):
        fig.add_violin(y=[i, i+1, i+2], name=f"v{i}") # 10.4ms -> 8.73ms (19.3% faster)
    for i in range(100):
        trace = fig.data[i]

def test_add_violin_with_large_customdata():
    # Large: customdata of length 1000
    fig = Figure()
    customdata = [{"a": i} for i in range(1000)]
    y = list(range(1000))
    fig.add_violin(y=y, customdata=customdata) # 3.52ms -> 3.54ms (0.419% slower)
    trace = fig.data[-1]

def test_add_violin_with_large_text():
    # Large: text of length 1000
    fig = Figure()
    y = list(range(1000))
    text = [str(i) for i in range(1000)]
    fig.add_violin(y=y, text=text) # 2.38ms -> 2.36ms (0.862% faster)
    trace = fig.data[-1]

# ---------
# Negative Test Cases (should raise)
# ---------

def test_add_violin_invalid_points_value():
    # Negative: points must be valid value or False
    fig = Figure()
    with pytest.raises(KeyError):
        # Our mock Violin will just store, but in real, invalid points should raise
        # Let's simulate by not allowing "invalid" value
        class StrictViolin(Violin):
            def __init__(self, **kwargs):
                if 'points' in kwargs and kwargs['points'] not in [None, "outliers", "suspectedoutliers", "all", False]:
                    raise KeyError("Invalid points value")
                super().__init__(**kwargs)
        # Patch Figure to use StrictViolin
        old_violin = Figure.add_violin
        def strict_add_violin(self, **kwargs):
            violin_args = {k: v for k, v in kwargs.items() if v is not None}
            new_trace = StrictViolin(**violin_args)
            return self.add_trace(new_trace)
        Figure.add_violin = strict_add_violin
        try:
            fig.add_violin(y=[1,2,3], points="invalid")
        finally:
            Figure.add_violin = old_violin

def test_add_violin_with_row_without_col():
    # Negative: row without col should raise
    fig = Figure()
    with pytest.raises(ValueError):
        fig.add_violin(y=[1,2,3], row=1) # 67.0μs -> 44.3μs (51.1% faster)

def test_add_violin_with_col_without_row():
    # Negative: col without row should raise
    fig = Figure()
    with pytest.raises(ValueError):
        fig.add_violin(y=[1,2,3], col=1) # 63.0μs -> 42.1μs (49.8% faster)
# codeflash_output is used to check that the output of the original code is the same as that of the optimized code.
#------------------------------------------------
import pytest
from plotly.graph_objs._figure import Figure


# Function to test
def add_violin(fig, **kwargs):
    """
    Adds a violin trace to a figure.
    - fig: a dict representing the figure, with a 'data' key (list of traces).
    - kwargs: properties for the violin trace.
    Returns the modified figure (in-place).
    """
    # Minimal validation for required structure
    if not isinstance(fig, dict) or 'data' not in fig or not isinstance(fig['data'], list):
        raise TypeError("fig must be a dict with a 'data' list")
    # Compose the trace
    trace = {'type': 'violin'}
    trace.update(kwargs)
    fig['data'].append(trace)
    return fig

# ----------- Unit Tests -----------

# ----------- Basic Test Cases -----------

To edit these changes git checkout codeflash/optimize-Figure.add_violin-mhfzmwet and push.

Codeflash Static Badge

The optimized code achieves a **9% speedup** by implementing **import caching** for the `Violin` class in the `add_violin` method.

**Key optimization applied:**
- **Cached import pattern**: Instead of executing `from plotly.graph_objs import Violin` on every call, the code now caches the imported `Violin` class as a class attribute `Figure._violin_class` using a try/except pattern
- **Import overhead elimination**: The original code spent 5.7% of execution time (7.3ms out of 127ms) on the import statement alone. The optimized version reduces this to just 0.4% (0.44ms) on first call, then eliminates it entirely on subsequent calls

**Why this optimization works:**
- Python's import system has overhead even for already-imported modules due to namespace lookups and module resolution
- By caching the class reference directly on the Figure class, we bypass the import machinery entirely after the first call
- The try/except pattern adds minimal overhead (~0.09ms) while providing the caching benefit

**Performance characteristics from test results:**
- **High-frequency scenarios**: The optimization shines when `add_violin` is called repeatedly, showing 19.3% speedup for adding 100 violin traces
- **Single calls**: Still beneficial with 11-15% improvements for individual calls
- **Large data scenarios**: Minimal impact (~0.4-0.9% improvement) since the import cost becomes negligible relative to data processing

This optimization is particularly effective for interactive plotting scenarios, dashboards, or any workflow that creates multiple violin plots programmatically.
@codeflash-ai codeflash-ai bot requested a review from mashraf-222 November 1, 2025 07:56
@codeflash-ai codeflash-ai bot added ⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash labels Nov 1, 2025
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

⚡️ codeflash Optimization PR opened by Codeflash AI 🎯 Quality: Medium Optimization Quality according to Codeflash

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant