Profiling skforecast¶

This document shows the profiling of the main classes, methods and functions available in skforecast. Understanding the bottlenecks will help to:

• Use it more efficiently
• Improve the code for future releases

Libraries and data¶

In [1]:

# Libraries
# ==============================================================================
import time
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import platform
import psutil

import sklearn
from sklearn.linear_model import LinearRegression, Ridge
from sklearn.ensemble import HistGradientBoostingRegressor
from lightgbm import LGBMRegressor

import skforecast
from skforecast.recursive import ForecasterRecursive
from skforecast.direct import ForecasterDirect
from skforecast.model_selection import grid_search_forecaster, backtesting_forecaster

%load_ext pyinstrument

# Libraries # ============================================================================== import time import numpy as np import pandas as pd import matplotlib.pyplot as plt import platform import psutil import sklearn from sklearn.linear_model import LinearRegression, Ridge from sklearn.ensemble import HistGradientBoostingRegressor from lightgbm import LGBMRegressor import skforecast from skforecast.recursive import ForecasterRecursive from skforecast.direct import ForecasterDirect from skforecast.model_selection import grid_search_forecaster, backtesting_forecaster %load_ext pyinstrument

In [2]:

# Versions
# ==============================================================================
print(f"Python version      : {platform.python_version()}")
print(f"scikit-learn version: {sklearn.__version__}")
print(f"skforecast version  : {skforecast.__version__}")
print(f"pandas version      : {pd.__version__}")
print(f"numpy version       : {np.__version__}")
print(f"psutil version      : {psutil.__version__}")
print("")

# System information
# ==============================================================================
print(f"Machine type: {platform.machine()}")
print(f"Processor type: {platform.processor()}")
print(f"Platform type: {platform.platform()}")
print(f"Operating system: {platform.system()}")
print(f"Operating system release: {platform.release()}")
print(f"Operating system version: {platform.version()}")
print(f"Number of physical cores: {psutil.cpu_count(logical=False)}")
print(f"Number of logical cores: {psutil.cpu_count(logical=True)}")

# Versions # ============================================================================== print(f"Python version : {platform.python_version()}") print(f"scikit-learn version: {sklearn.__version__}") print(f"skforecast version : {skforecast.__version__}") print(f"pandas version : {pd.__version__}") print(f"numpy version : {np.__version__}") print(f"psutil version : {psutil.__version__}") print("") # System information # ============================================================================== print(f"Machine type: {platform.machine()}") print(f"Processor type: {platform.processor()}") print(f"Platform type: {platform.platform()}") print(f"Operating system: {platform.system()}") print(f"Operating system release: {platform.release()}") print(f"Operating system version: {platform.version()}") print(f"Number of physical cores: {psutil.cpu_count(logical=False)}") print(f"Number of logical cores: {psutil.cpu_count(logical=True)}")

Python version      : 3.12.9
scikit-learn version: 1.5.2
skforecast version  : 0.15.0
pandas version      : 2.2.3
numpy version       : 1.26.4
psutil version      : 7.0.0

Machine type: x86_64
Processor type: x86_64
Platform type: Linux-5.15.0-1077-aws-x86_64-with-glibc2.31
Operating system: Linux
Operating system release: 5.15.0-1077-aws
Operating system version: #84~20.04.1-Ubuntu SMP Mon Jan 20 22:14:54 UTC 2025
Number of physical cores: 4
Number of logical cores: 8

A time series of length 1000 with random values is created.

In [3]:

# Data
# ==============================================================================
np.random.seed(123)
n = 1_000
data = pd.Series(data = np.random.normal(size=n))

# Data # ============================================================================== np.random.seed(123) n = 1_000 data = pd.Series(data = np.random.normal(size=n))

Dummy regressor¶

To isolate the training process of the regressor from the other parts of the code, a dummy regressor class is created. This dummy regressor has a fit method that does nothing, and a predict method that returns a constant value.

In [4]:

class DummyRegressor(LinearRegression):
    """
    Dummy regressor with dummy fit and predict methods.
    """
    
    def fit(self, X, y):
        pass

    def predict(self, y):
        predictions = np.ones(shape = len(y))
        return predictions

class DummyRegressor(LinearRegression): """ Dummy regressor with dummy fit and predict methods. """ def fit(self, X, y): pass def predict(self, y): predictions = np.ones(shape = len(y)) return predictions

Profiling fit¶

In [5]:

%%pyinstrument

forecaster = ForecasterRecursive(
                 regressor = DummyRegressor(),
                 lags      = 24
             )

forecaster.fit(y=data)

%%pyinstrument forecaster = ForecasterRecursive( regressor = DummyRegressor(), lags = 24 ) forecaster.fit(y=data)

Almost all of the time spent by fit is required by the create_train_X_y method.

In [6]:

%%pyinstrument

forecaster = ForecasterRecursive(
                 regressor = HistGradientBoostingRegressor(max_iter=10, random_state=123),
                 lags      = 24
             )

forecaster.fit(y=data)

%%pyinstrument forecaster = ForecasterRecursive( regressor = HistGradientBoostingRegressor(max_iter=10, random_state=123), lags = 24 ) forecaster.fit(y=data)

When training a forecaster with a real machine learning regressor, the time spent by create_train_X_y is negligible compared to the time needed by the fit method of the regressor. Therefore, improving the speed of create_train_X_y will not have much impact.

Profiling create_train_X_y¶

Understand how the create_train_X_y method is influenced by the length of the series and the number of lags.

In [7]:

# Profiling `create_train_X_y` for different length of series and number of lags
# ======================================================================================
series_length = np.linspace(1000, 1000000, num=5, dtype=int)
n_lags = [5, 10, 50, 100, 200]
results = {}

for lags in n_lags:
    execution_time = []
    forecaster = ForecasterRecursive(
                     regressor = DummyRegressor(),
                     lags      = lags
                 )

    for n in series_length:
        y = pd.Series(data = np.random.normal(size=n))
        tic = time.perf_counter()
        _ = forecaster.create_train_X_y(y=y)
        toc = time.perf_counter()
        execution_time.append(toc - tic)

    results[lags] = execution_time

results = pd.DataFrame(
              data = results,
              index = series_length
          )

results

# Profiling `create_train_X_y` for different length of series and number of lags # ====================================================================================== series_length = np.linspace(1000, 1000000, num=5, dtype=int) n_lags = [5, 10, 50, 100, 200] results = {} for lags in n_lags: execution_time = [] forecaster = ForecasterRecursive( regressor = DummyRegressor(), lags = lags ) for n in series_length: y = pd.Series(data = np.random.normal(size=n)) tic = time.perf_counter() _ = forecaster.create_train_X_y(y=y) toc = time.perf_counter() execution_time.append(toc - tic) results[lags] = execution_time results = pd.DataFrame( data = results, index = series_length ) results

Out[7]:

	5	10	50	100	200
1000	0.000890	0.000957	0.001248	0.002070	0.003482
250750	0.006234	0.006186	0.033500	0.063247	0.123518
500500	0.011425	0.018634	0.064680	0.126329	0.250267
750250	0.016728	0.023954	0.102774	0.200989	0.402883
1000000	0.016318	0.027664	0.107433	0.211251	0.425320

In [8]:

fig, ax = plt.subplots(figsize=(7, 4))
results.plot(ax=ax, marker='.')
ax.set_xlabel('length of series')
ax.set_ylabel('time (seconds)')
ax.set_title('Profiling create_train_X_y()')
ax.legend(title='number of lags');

fig, ax = plt.subplots(figsize=(7, 4)) results.plot(ax=ax, marker='.') ax.set_xlabel('length of series') ax.set_ylabel('time (seconds)') ax.set_title('Profiling create_train_X_y()') ax.legend(title='number of lags');

Profiling predict¶

In [9]:

forecaster = ForecasterRecursive(
                 regressor = DummyRegressor(),
                 lags      = 24
             )

forecaster.fit(y=data)

forecaster = ForecasterRecursive( regressor = DummyRegressor(), lags = 24 ) forecaster.fit(y=data)

In [10]:

%%pyinstrument

_ = forecaster.predict(steps=1000)

%%pyinstrument _ = forecaster.predict(steps=1000)

In [11]:

forecaster = ForecasterRecursive(
                 regressor = HistGradientBoostingRegressor(max_iter=10, random_state=123),
                 lags      = 24
             )

forecaster.fit(y=data)

forecaster = ForecasterRecursive( regressor = HistGradientBoostingRegressor(max_iter=10, random_state=123), lags = 24 ) forecaster.fit(y=data)

In [12]:

%%pyinstrument

_ = forecaster.predict(steps=1000)

%%pyinstrument _ = forecaster.predict(steps=1000)

Inside the predict method, the append action is the most expensive but, similar to what happen with fit, it is negligible compared to the time need by the predict method of the regressor.