sumprices_tabular_iwerm_target_shift.py

import os
import sys

sys.path.append(os.path.join(os.path.dirname(__file__), ".."))

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import RANSACRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.linear_model import HuberRegressor
from sklearn.linear_model import TheilSenRegressor
from sklearn.metrics import mean_absolute_error as mae
from scipy.stats import norm
from scipy.stats import wasserstein_distance
import tqdm
from shift15m.datasets import SumPricesRegression


sns.set_style("whitegrid")


dataset = SumPricesRegression()

n_trials = 20
train_sample_size = 100000
test_sample_size = 100000
test_mu = 80
test_sigma = 10

n_trials = 20
train_sample_size = 100000
test_sample_size = 100000
test_mu = 180000
test_sigma = 10000

shifts = [
    {"train_mu": 180000, "train_sigma": 100000},
    {"train_mu": 160000, "train_sigma": 100000},
    {"train_mu": 140000, "train_sigma": 100000},
    {"train_mu": 120000, "train_sigma": 100000},
    {"train_mu": 100000, "train_sigma": 100000},
    {"train_mu": 80000, "train_sigma": 100000},
    {"train_mu": 60000, "train_sigma": 100000},
    {"train_mu": 40000, "train_sigma": 100000},
    {"train_mu": 20000, "train_sigma": 100000},
]

models = [
    {"model": LinearRegression, "sample_weights": "ERM"},
    {"model": LinearRegression, "sample_weights": "IWERM (optimal)"},
    {
        "model": LinearRegression,
        "sample_weights": r"RIWERM ($\alpha=0.25$)",
        "alpha": 0.25,
    },
    {
        "model": LinearRegression,
        "sample_weights": r"RIWERM ($\alpha=0.5$)",
        "alpha": 0.5,
    },
    {
        "model": LinearRegression,
        "sample_weights": r"RIWERM ($\alpha=0.75$)",
        "alpha": 0.75,
    },
]

models_errors_mean = []
models_errors_std = []
dists = []

for k in range(len(models)):
    model = models[k]["model"]
    weighting = models[k]["sample_weights"]

    model_errors_mean = []
    model_errors_std = []
    model_dists = []
    for shift in shifts:
        errors = []

        rv_train = np.random.normal(shift["train_mu"], shift["train_sigma"], 10000)
        rv_test = np.random.normal(test_mu, test_sigma, 10000)
        wd = wasserstein_distance(rv_train, rv_test)
        model_dists.append(wd)

        for i in tqdm.tqdm(range(n_trials)):
            (x_train, y_train), (x_test, y_test) = dataset.load_dataset(
                target_shift=True,
                train_size=train_sample_size,
                test_size=test_sample_size,
                test_mu=test_mu,
                test_sigma=test_sigma,
                train_mu=shift["train_mu"],
                train_sigma=shift["train_sigma"],
                random_seed=i,
            )

            x_train = x_train.reshape(-1, 1)
            x_test = x_test.reshape(-1, 1)

            p_tr = norm.pdf(y_train, loc=shift["train_mu"], scale=shift["train_sigma"])
            p_te = norm.pdf(y_train, loc=test_mu, scale=test_sigma)

            p_tr = p_tr.reshape(-1)
            p_te = p_te.reshape(-1)

            reg = model()
            if weighting == "ERM":
                reg.fit(x_train, y_train)
            elif weighting.split()[0] == "IWERM":
                reg.fit(x_train, y_train, sample_weight=p_te / (p_tr + 1e-9))
            elif weighting.split()[0] == "AIWERM":
                alpha = float(models[k]["alpha"])
                w = (p_te / (p_tr + 1e-9)) ** alpha
                reg.fit(x_train, y_train, sample_weight=w)
            elif weighting.split()[0] == "RIWERM":
                alpha = float(models[k]["alpha"])
                w = p_te / ((1 - alpha) * p_te + alpha * p_tr + 1e-9)
                reg.fit(x_train, y_train, sample_weight=w)

            errors.append(mae(reg.predict(x_test), y_test))

        model_errors_mean.append(np.mean(errors))
        model_errors_std.append(np.std(errors))
        print(model_errors_mean)
        print(model_errors_std)
    models_errors_mean.append(model_errors_mean)
    models_errors_std.append(model_errors_std)
    dists.append(model_dists)

models_errors_mean = np.array(models_errors_mean)
models_errors_std = np.array(models_errors_std)

colors = ["purple", "green", "blue", "darkcyan", "red"]

print(dists)
print(models_errors_mean)
print(models_errors_std)

fig = plt.figure(figsize=(12, 6))
for i in range(len(models)):
    plt.plot(
        dists[i],
        models_errors_mean[i],
        alpha=0.8,
        color=colors[i],
        label=models[i]["sample_weights"],
    )
    plt.fill_between(
        dists[i],
        models_errors_mean[i],
        models_errors_mean[i] + models_errors_std[i],
        alpha=0.2,
        color=colors[i],
    )
    plt.fill_between(
        dists[i],
        models_errors_mean[i],
        models_errors_mean[i] - models_errors_std[i],
        alpha=0.2,
        color=colors[i],
    )

plt.legend()
plt.xlabel(r"$W_1(P_{train}, P_{test})$")
plt.ylabel("MAE")
plt.savefig("sumprices_regression_iwerm_target_shift.png")
plt.show()