SOARegression 0.0.3

Sample Optimized Adaptive Regression.

SOARegression

Sample Optimized Adaptive Regression

IDK if this is a thing, but I wanted adjustments to coefficients for each sample but in a way that shrinks the difference between the original coefficients and the 'perfect' ones.

To acheive this there is a 2 step process:

1. Fit a basic linear regression and get the coefficients
2. Do an optimization problem to minimize the squared differences of the coefficients with the constraint that the fitted errors are 0.

[ \begin{aligned} & \text{Let } \mathbf{X} \in \mathbb{R}^{n \times p} \text{ be the design matrix, } \mathbf{y} \in \mathbb{R}^n \text{ the target vector, and } \mathbf{\beta}0 \text{ the coefficients from the initial fit.} \ & \text{We aim to adjust the coefficients } \mathbf{\beta} \text{ to minimize:} \ & \min{\mathbf{\beta}} \quad | \mathbf{\beta} - \mathbf{\beta}_0 |_2^2 \ & \text{Subject to: } \mathbf{y} - \mathbf{X} \mathbf{\beta} = \mathbf{0} \end{aligned} ]

Explanation

• Objective Function: The term (| \mathbf{\beta} - \mathbf{\beta}_0 |_2^2) represents the squared difference between the adjusted coefficients (\mathbf{\beta}) and the original coefficients (\mathbf{\beta}_0). This is what we aim to minimize.
• Constraint: The equality constraint (\mathbf{y} - \mathbf{X} \mathbf{\beta} = \mathbf{0}) ensures that the fitted errors are zero, i.e., the adjusted model perfectly predicts the target values (\mathbf{y}).

A regularization parameter is introduced to control how tight the fit is.

Possible use cases

Time series coefficient weight scheme like a moving average of the last n samples' coefficients

'Parameter based' outlier detection - you can look at the coefficient swings to find outliers

Further Enhancements

I want to add in the coefficient standard errors into the optimization so it is less about absolute magnitude changes and more about minimizing the 'energy'/'entropy' in the system while perfectly fitting.

import pandas as pd 
import numpy as np 
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')


X = pd.DataFrame([1,2,3,4])
y = pd.Series([3,5,7,10])

model = SOAR(scale=False)
model.fit(X, y)

# Optimize coefficients for each point individually
optimized_coefficients = model.optimize_coefficients(X, y)
#grab normal OLS predictions
predictions = model.insample_predict(X, use_optimized=False)
predictions = pd.Series(predictions, index=X.iloc[:, 0])
#plot each individual sample linear model
for i in range(1,5):
    lin = np.linspace(i-.25, i+.25, 3)
    sample_equation = np.ones(3).reshape(-1, 1) * optimized_coefficients[i-1, 0] + lin.reshape(-1, 1)*optimized_coefficients[i-1, 1]
    plt.plot(pd.Series(sample_equation.reshape(-1), index=lin))
plt.scatter(x=X, y=y, label='Actuals')
plt.plot(predictions, label='OLS')
plt.legend()
plt.show()

import pandas as pd
import numpy as np 

# Load the Air Passengers dataset
data_url = "https://raw.githubusercontent.com/jbrownlee/Datasets/master/airline-passengers.csv"
df = pd.read_csv(data_url, header=0, parse_dates=['Month'], index_col='Month')
df.columns = ['Passengers']

# Extract the time variable
df['Time'] = np.arange(len(df))  # Create a time index for trend

# Extract month from the index
df['Month_Index'] = df.index.month

# One-hot encode the months for seasonality
month_dummies = pd.get_dummies(df['Month_Index'], prefix='Month', drop_first=True)

# Combine time and seasonal features
X = pd.concat([df[['Time']], month_dummies], axis=1) * 1 
y = df['Passengers'].values 

# Instantiate and fit the model
model = SOAR(scale=False)
model.fit(X, y)

# Optimize coefficients for each point individually
optimized_coefficients = model.optimize_coefficients(X, y)
# Predict using the optimized coefficients
predictions = model.insample_predict(X, use_optimized=True)

# Plot coefficients for a specific sample
model.plot_coefficients(sample_index=10)

plt.plot(model.insample_predict(X, use_optimized=False), linestyle='dashed', alpha=.5, label='No optimization')
plt.plot(model.insample_predict(X, use_optimized=True), linestyle='dashed', alpha=.5, label='With optimization')
plt.plot(y, alpha=.5, label='Actual')
plt.legend()
plt.show()

model = SOAR(sample_weight=sample_weight)
model.fit(X, y)

# Optimize coefficients for each point individually
optimized_coefficients = model.optimize_coefficients(X, y)
# Predict using the optimized coefficients
actual_coefs = model.coefficients 
optimized_coefs = model.optimized_coefficients_per_sample
predictions = model.insample_predict(X, use_optimized=True)
mean_coefs = np.resize(optimized_coefs[-12:, :], (len(X), 13))
mean_coefs = mean_coefs
# model.optimized_coefficients_per_sample = mean_coefs
plt.plot(model.insample_predict(X, use_optimized=False), linestyle='dashed', alpha=.5, label='No optimization')
plt.plot(model.insample_predict(X, use_optimized=True), linestyle='dotted', alpha=.5, label='With optimization')
plt.plot(y, alpha=.5, label='Actual')
plt.legend()
plt.show()

sample_weight = (1+np.arange(len(X)))

model = SOAR(sample_weight=sample_weight)
model.fit(X, y)

# Optimize coefficients for each point individually
optimized_coefficients = model.optimize_coefficients(X, y, column_freeze=[0])
# Predict using the optimized coefficients
predictions = model.insample_predict(X, use_optimized=True)
mean_coefs = np.resize(np.average(model.optimized_coefficients_per_sample, 
                               axis=0,
                               weights=sample_weight), 
                        (len(X), 13))
# model.optimized_coefficients_per_sample = mean_coefs
plt.plot(model.insample_predict(X, use_optimized=False), linestyle='dashed', alpha=.5, label='No optimization')
plt.plot(model.insample_predict(X, use_optimized=True), linestyle='dotted', alpha=.5, label='With optimization')
plt.plot(y, alpha=.5, label='Actual')
plt.legend()
plt.show()

import seaborn as sns 
sns.set_style('darkgrid')
for i in [0,  .5,  1]:
    model = SOAR(regularization=i) 
    model.fit(X, y)

    # Optimize coefficients for each point individually
    optimized_coefficients = model.optimize_coefficients(X, y)

    # Predict using the optimized coefficients
    predictions = model.insample_predict(X, use_optimized=True)
    
    plt.plot(model.insample_predict(X, use_optimized=True), linestyle='dotted', alpha=.9, label=f'regularization={i}')
plt.plot(y, alpha=.5, label='Actual')
plt.legend()
plt.show()

A little more complicated of an example

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Set a random seed for reproducibility
np.random.seed(42)

# Generate a time index
time = np.arange(0, 200)

# Define trend segments
trend_1 = 0.5 * time[:50]
trend_2 = trend_1[-1] + 0.2 * (time[50:100] - time[50])
trend_3 = trend_2[-1] - 0.3 * (time[100:150] - time[100])
trend_4 = trend_3[-1] + 0.4 * (time[150:200] - time[150])

# Combine the trend segments
trend = np.concatenate([trend_1, trend_2, trend_3, trend_4])

# Add some random noise
noise = np.random.normal(0, 2, size=len(time))

# Create the time series
time_series = trend + noise

# Create a pandas DataFrame for convenience
ts_df = pd.DataFrame({'Time': time, 'Value': time_series})

# Plot the time series
plt.figure(figsize=(10, 6))
plt.plot(ts_df['Time'], ts_df['Value'], label='Time Series with Changepoints', color='blue')
plt.axvline(x=50, color='red', linestyle='--', label='Changepoint 1')
plt.axvline(x=100, color='green', linestyle='--', label='Changepoint 2')
plt.axvline(x=150, color='orange', linestyle='--', label='Changepoint 3')
plt.xlabel('Time')
plt.ylabel('Value')
plt.title('Synthetic Time Series with Trend Changepoints')
plt.legend()
plt.show()

time = pd.DataFrame(time)
time_series = time_series - time_series[0]
model = SOAR(scale=False)
model.fit(time, time_series)
predicted = model.insample_predict(time, use_optimized=False)
# Optimize coefficients for each point individually
optimized_coefficients = model.optimize_coefficients(time, time_series)
entropy = model.sample_entropy()
plt.plot(entropy)
plt.show()
plt.plot(predicted)
plt.plot(time_series)
plt.show() 
for i in range(1,200):
    lin = np.linspace(i-5, i+5, 3)
    sample_equation = np.ones(3).reshape(-1, 1) * optimized_coefficients[i-1, 0] + lin.reshape(-1, 1)*optimized_coefficients[i-1, 1]
    plt.plot(pd.Series(sample_equation.reshape(-1), index=lin))
plt.scatter(x=time, y=time_series, label='Actuals')
plt.plot(predicted, label='OLS', color='black')
plt.xlim(35, 200)
plt.ylim(0, 50)
plt.legend()
plt.show()