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[pre-commit.ci] pre-commit autoupdate #201

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68103a1
[pre-commit.ci] pre-commit autoupdate
pre-commit-ci[bot] May 25, 2026
4803b17
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] May 25, 2026
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6 changes: 3 additions & 3 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
@@ -1,16 +1,16 @@
repos:
# Ruff - Fast Python linter and formatter (replaces autoflake, isort, and many other linters)
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: v0.1.13
rev: v0.15.14
hooks:
# Run the linter with autofix
- id: ruff
args: ['--fix', '--exit-non-zero-on-fix']
# Ruff will use the configuration from pyproject.toml

# Black - Still keep for consistent formatting
- repo: https://github.com/psf/black
rev: 23.11.0
- repo: https://github.com/psf/black-pre-commit-mirror
rev: 26.5.1
hooks:
- id: black

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1 change: 1 addition & 0 deletions docs/examples/auto_model_usage.py
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Expand Up @@ -11,6 +11,7 @@

import matplotlib.pyplot as plt
import numpy as np

from tsbootstrap.utils.auto_order_selector import AutoOrderSelector


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128 changes: 32 additions & 96 deletions examples/performance_comparison_notebook.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,9 +35,7 @@ def create_performance_notebook():
)

# Setup cell
cells.append(
nbf.v4.new_code_cell(
"""# Import required libraries
cells.append(nbf.v4.new_code_cell("""# Import required libraries
import os
import time
import numpy as np
Expand All @@ -59,14 +57,10 @@ def create_performance_notebook():
# Set random seed for reproducibility
np.random.seed(42)

print("Setup complete!")"""
)
)
print("Setup complete!")"""))

# Performance measurement utilities
cells.append(
nbf.v4.new_code_cell(
"""# Utility functions for performance measurement
cells.append(nbf.v4.new_code_cell("""# Utility functions for performance measurement

def measure_performance(func, *args, n_runs=5, **kwargs):
\"\"\"Measure average performance over multiple runs.\"\"\"
Expand Down Expand Up @@ -122,22 +116,14 @@ def plot_performance_comparison(results_dict, title="Performance Comparison"):
plt.tight_layout()
plt.show()

print("Utility functions loaded!")"""
)
)
print("Utility functions loaded!")"""))

# Example 1: Single Model Fitting
cells.append(
nbf.v4.new_markdown_cell(
"""## Example 1: Single Model Fitting
cells.append(nbf.v4.new_markdown_cell("""## Example 1: Single Model Fitting

First, let's compare the performance of fitting a single ARIMA model using both backends."""
)
)
First, let's compare the performance of fitting a single ARIMA model using both backends."""))

cells.append(
nbf.v4.new_code_cell(
"""# Generate sample time series data
cells.append(nbf.v4.new_code_cell("""# Generate sample time series data
data = np.cumsum(np.random.randn(1000)) # Random walk with 1000 points

print(f"Data shape: {data.shape}")
Expand All @@ -149,13 +135,9 @@ def plot_performance_comparison(results_dict, title="Performance Comparison"):
plt.title("Sample Time Series Data")
plt.xlabel("Time")
plt.ylabel("Value")
plt.show()"""
)
)
plt.show()"""))

cells.append(
nbf.v4.new_code_cell(
"""# Compare single ARIMA model fitting
cells.append(nbf.v4.new_code_cell("""# Compare single ARIMA model fitting

def fit_arima_statsmodels(data):
\"\"\"Fit ARIMA model using statsmodels backend.\"\"\"
Expand Down Expand Up @@ -186,9 +168,7 @@ def fit_arima_statsforecast(data):

print(f"\\nStatsModels: {sm_results['mean']:.3f} ± {sm_results['std']:.3f} seconds")
print(f"StatsForecast: {sf_results['mean']:.3f} ± {sf_results['std']:.3f} seconds")
print(f"Speedup: {sm_results['mean'] / sf_results['mean']:.1f}x faster!")"""
)
)
print(f"Speedup: {sm_results['mean'] / sf_results['mean']:.1f}x faster!")"""))

# Example 2: Batch Processing
cells.append(
Expand All @@ -199,9 +179,7 @@ def fit_arima_statsforecast(data):
)
)

cells.append(
nbf.v4.new_code_cell(
"""# Generate multiple time series
cells.append(nbf.v4.new_code_cell("""# Generate multiple time series
n_series = 100
series_length = 500

Expand All @@ -226,13 +204,9 @@ def fit_arima_statsforecast(data):
ax.set_xlabel("Time")
ax.set_ylabel("Value")
plt.tight_layout()
plt.show()"""
)
)
plt.show()"""))

cells.append(
nbf.v4.new_code_cell(
"""# Compare batch processing performance
cells.append(nbf.v4.new_code_cell("""# Compare batch processing performance

def batch_fit_statsmodels(series_list):
\"\"\"Sequential fitting with statsmodels.\"\"\"
Expand Down Expand Up @@ -275,9 +249,7 @@ def batch_fit_statsforecast(series_list):
print(f"Speedup: {sm_batch_results['mean'] / sf_batch_results['mean']:.1f}x faster!")
print(f"\\nTime per model:")
print(f" StatsModels: {sm_batch_results['mean']/n_series*1000:.1f}ms")
print(f" StatsForecast: {sf_batch_results['mean']/n_series*1000:.1f}ms")"""
)
)
print(f" StatsForecast: {sf_batch_results['mean']/n_series*1000:.1f}ms")"""))

# Example 3: Bootstrap Performance
cells.append(
Expand All @@ -288,9 +260,7 @@ def batch_fit_statsforecast(series_list):
)
)

cells.append(
nbf.v4.new_code_cell(
"""# Compare bootstrap performance
cells.append(nbf.v4.new_code_cell("""# Compare bootstrap performance
data = np.cumsum(np.random.randn(365)) # One year of daily data
n_bootstraps = 500

Expand Down Expand Up @@ -330,22 +300,14 @@ def bootstrap_statsforecast(data, n_bootstraps):

print(f"\\nStatsModels: {sm_bootstrap['mean']:.2f} seconds")
print(f"StatsForecast: {sf_bootstrap['mean']:.2f} seconds")
print(f"Speedup: {sm_bootstrap['mean'] / sf_bootstrap['mean']:.1f}x faster!")"""
)
)
print(f"Speedup: {sm_bootstrap['mean'] / sf_bootstrap['mean']:.1f}x faster!")"""))

# Example 4: Scaling Analysis
cells.append(
nbf.v4.new_markdown_cell(
"""## Example 4: Scaling Analysis
cells.append(nbf.v4.new_markdown_cell("""## Example 4: Scaling Analysis

Let's analyze how performance scales with the number of models."""
)
)
Let's analyze how performance scales with the number of models."""))

cells.append(
nbf.v4.new_code_cell(
"""# Scaling analysis
cells.append(nbf.v4.new_code_cell("""# Scaling analysis
n_series_list = [10, 25, 50, 100, 200]
sm_times = []
sf_times = []
Expand Down Expand Up @@ -407,22 +369,14 @@ def bootstrap_statsforecast(data, n_bootstraps):

print(f"\\nSpeedup increases with scale:")
for n, speedup in zip(n_series_list, speedups):
print(f" {n} models: {speedup:.1f}x faster")"""
)
)
print(f" {n} models: {speedup:.1f}x faster")"""))

# Example 5: Memory Usage
cells.append(
nbf.v4.new_markdown_cell(
"""## Example 5: Memory Usage Comparison
cells.append(nbf.v4.new_markdown_cell("""## Example 5: Memory Usage Comparison

Besides speed, statsforecast also uses memory more efficiently."""
)
)
Besides speed, statsforecast also uses memory more efficiently."""))

cells.append(
nbf.v4.new_code_cell(
"""import psutil
cells.append(nbf.v4.new_code_cell("""import psutil
import gc

def measure_memory_usage(backend_type, n_models=100):
Expand Down Expand Up @@ -495,9 +449,7 @@ def measure_memory_usage(backend_type, n_models=100):
plt.show()

print(f"\\nMemory reduction: {reduction:.1f}%")
print(f"StatsForecast uses {sm_memory/sf_memory:.1f}x less memory!")"""
)
)
print(f"StatsForecast uses {sm_memory/sf_memory:.1f}x less memory!")"""))

# Example 6: Real-world scenario
cells.append(
Expand All @@ -508,9 +460,7 @@ def measure_memory_usage(backend_type, n_models=100):
)
)

cells.append(
nbf.v4.new_code_cell(
"""# Simulate production forecasting pipeline
cells.append(nbf.v4.new_code_cell("""# Simulate production forecasting pipeline
def production_pipeline(use_backend=False):
\"\"\"Simulate a production forecasting pipeline.\"\"\"
results = {
Expand Down Expand Up @@ -624,14 +574,10 @@ def production_pipeline(use_backend=False):
ax2.set_title('Processing Throughput', fontsize=14, fontweight='bold')

plt.tight_layout()
plt.show()"""
)
)
plt.show()"""))

# Summary and conclusions
cells.append(
nbf.v4.new_markdown_cell(
"""## Summary and Conclusions
cells.append(nbf.v4.new_markdown_cell("""## Summary and Conclusions

### Performance Improvements Achieved:

Expand Down Expand Up @@ -674,22 +620,14 @@ def production_pipeline(use_backend=False):
model = TimeSeriesModel(X=data, model_type="arima", use_backend=True)
```

The migration is designed to be gradual and safe, with 100% backward compatibility!"""
)
)
The migration is designed to be gradual and safe, with 100% backward compatibility!"""))

# Add rollout monitoring example
cells.append(
nbf.v4.new_markdown_cell(
"""## Bonus: Monitor Your Rollout
cells.append(nbf.v4.new_markdown_cell("""## Bonus: Monitor Your Rollout

Track the success of your migration with built-in monitoring tools."""
)
)
Track the success of your migration with built-in monitoring tools."""))

cells.append(
nbf.v4.new_code_cell(
"""# Check current rollout status
cells.append(nbf.v4.new_code_cell("""# Check current rollout status
from tsbootstrap.backends.feature_flags import get_rollout_monitor

monitor = get_rollout_monitor()
Expand All @@ -712,9 +650,7 @@ def production_pipeline(use_backend=False):
# Calculate overall speedup from real usage
if report['statsmodels']['avg_duration'] > 0 and report['statsforecast']['avg_duration'] > 0:
real_speedup = report['statsmodels']['avg_duration'] / report['statsforecast']['avg_duration']
print(f"\\nReal-world speedup: {real_speedup:.1f}x")"""
)
)
print(f"\\nReal-world speedup: {real_speedup:.1f}x")"""))

nb.cells = cells
return nb
Expand Down
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