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03 california housing tabpfn

import numpy as np
import keras
import tensorflow as tf
import effector
from sklearn.datasets import fetch_california_housing
import tabpfn
import time

california_housing = fetch_california_housing(as_frame=True)

/Users/dimitriskyriakopoulos/Documents/ath/Effector/Code/eff-env/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

np.random.seed(21)

print(california_housing.DESCR)

.. _california_housing_dataset:

California Housing dataset
--------------------------

**Data Set Characteristics:**

:Number of Instances: 20640

:Number of Attributes: 8 numeric, predictive attributes and the target

:Attribute Information:
    - MedInc        median income in block group
    - HouseAge      median house age in block group
    - AveRooms      average number of rooms per household
    - AveBedrms     average number of bedrooms per household
    - Population    block group population
    - AveOccup      average number of household members
    - Latitude      block group latitude
    - Longitude     block group longitude

:Missing Attribute Values: None

This dataset was obtained from the StatLib repository.
https://www.dcc.fc.up.pt/~ltorgo/Regression/cal_housing.html

The target variable is the median house value for California districts,
expressed in hundreds of thousands of dollars ($100,000).

This dataset was derived from the 1990 U.S. census, using one row per census
block group. A block group is the smallest geographical unit for which the U.S.
Census Bureau publishes sample data (a block group typically has a population
of 600 to 3,000 people).

A household is a group of people residing within a home. Since the average
number of rooms and bedrooms in this dataset are provided per household, these
columns may take surprisingly large values for block groups with few households
and many empty houses, such as vacation resorts.

It can be downloaded/loaded using the
:func:`sklearn.datasets.fetch_california_housing` function.

.. rubric:: References

- Pace, R. Kelley and Ronald Barry, Sparse Spatial Autoregressions,
  Statistics and Probability Letters, 33 (1997) 291-297

feature_names = california_housing.feature_names
target_name= california_housing.target_names[0]
df = type(california_housing.frame)

X = california_housing.data
y = california_housing.target

print("Design matrix shape: {}".format(X.shape))
print("---------------------------------")
for col_name in X.columns:
    print("Feature: {:15}, unique: {:4d}, Mean: {:6.2f}, Std: {:6.2f}, Min: {:6.2f}, Max: {:6.2f}".format(col_name, len(X[col_name].unique()), X[col_name].mean(), X[col_name].std(), X[col_name].min(), X[col_name].max()))

print("\nTarget shape: {}".format(y.shape))
print("---------------------------------")
print("Target: {:15}, unique: {:4d}, Mean: {:6.2f}, Std: {:6.2f}, Min: {:6.2f}, Max: {:6.2f}".format(y.name, len(y.unique()), y.mean(), y.std(), y.min(), y.max()))

Design matrix shape: (20640, 8)
---------------------------------
Feature: MedInc         , unique: 12928, Mean:   3.87, Std:   1.90, Min:   0.50, Max:  15.00
Feature: HouseAge       , unique:   52, Mean:  28.64, Std:  12.59, Min:   1.00, Max:  52.00
Feature: AveRooms       , unique: 19392, Mean:   5.43, Std:   2.47, Min:   0.85, Max: 141.91
Feature: AveBedrms      , unique: 14233, Mean:   1.10, Std:   0.47, Min:   0.33, Max:  34.07
Feature: Population     , unique: 3888, Mean: 1425.48, Std: 1132.46, Min:   3.00, Max: 35682.00
Feature: AveOccup       , unique: 18841, Mean:   3.07, Std:  10.39, Min:   0.69, Max: 1243.33
Feature: Latitude       , unique:  862, Mean:  35.63, Std:   2.14, Min:  32.54, Max:  41.95
Feature: Longitude      , unique:  844, Mean: -119.57, Std:   2.00, Min: -124.35, Max: -114.31

Target shape: (20640,)
---------------------------------
Target: MedHouseVal    , unique: 3842, Mean:   2.07, Std:   1.15, Min:   0.15, Max:   5.00

def preprocess(X, y):
    # Compute mean and std for outlier detection
    X_mean = X.mean()
    X_std = X.std()

    # Exclude instances with any feature 2 std away from the mean
    mask = (X - X_mean).abs() <= 2 * X_std
    mask = mask.all(axis=1)

    X_filtered = X[mask]
    y_filtered = y[mask]

    # Standardize X
    X_mean = X_filtered.mean()
    X_std = X_filtered.std()
    X_standardized = (X_filtered - X_mean) / X_std

    # Standardize y
    y_mean = y_filtered.mean()
    y_std = y_filtered.std()
    y_standardized = (y_filtered - y_mean) / y_std

    return X_standardized, y_standardized, X_mean, X_std, y_mean, y_std



# shuffle and standarize all features
X_df, Y_df, x_mean, x_std, y_mean, y_std = preprocess(X, y)

def split(X_df, Y_df):
    # shuffle indices
    indices = np.arange(len(X_df))
    np.random.shuffle(indices)

    # data split
    train_size = int(0.8 * len(X_df))

    X_train = X_df.iloc[indices[:train_size]]
    Y_train = Y_df.iloc[indices[:train_size]]
    X_test = X_df.iloc[indices[train_size:]]
    Y_test = Y_df.iloc[indices[train_size:]]

    return X_train, Y_train, X_test, Y_test

# train/test split
X_train, Y_train, X_test, Y_test = split(X_df, Y_df)

X_train = X_train[:500].to_numpy()
Y_train = Y_train[:500].to_numpy()
X_test = X_test[:500].to_numpy()
Y_test = Y_test[:500].to_numpy()

model = tabpfn.TabPFNRegressor(n_jobs=7, device="cpu")
model.fit(X_train, Y_train)

/Users/dimitriskyriakopoulos/Documents/ath/Effector/Code/eff-env/lib/python3.10/site-packages/tabpfn/base.py:101: UserWarning: Downloading model to /Users/dimitriskyriakopoulos/Library/Caches/tabpfn/tabpfn-v2-regressor.ckpt.
  model, bardist, config_ = load_model_criterion_config(
/Users/dimitriskyriakopoulos/Documents/ath/Effector/Code/eff-env/lib/python3.10/site-packages/tabpfn/regressor.py:460: UserWarning: Running on CPU with more than 200 samples may be slow.
Consider using a GPU or the tabpfn-client API: https://github.com/PriorLabs/tabpfn-client
  check_cpu_warning(

TabPFNRegressor(device='cpu', n_jobs=7)
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
def model_forward(x):
    return model.predict(x)

scale_y = {"mean": y_mean, "std": y_std}
scale_x_list =[{"mean": x_mean.iloc[i], "std": x_std.iloc[i]} for i in range(len(x_mean))]

y_limits = [0, 4]
dy_limits = [-3, 3]

Global effects

ale = effector.ALE(data=X_test, model=model_forward, feature_names=feature_names, target_name=target_name, nof_instances="all")

tic = time.time()
ale.fit("all", centering=True)
toc = time.time()
print(toc - tic)

374.93519401550293

for i in range(8):
    ale.plot(feature=i, centering=True, scale_x=scale_x_list[i], scale_y=scale_y, y_limits=y_limits, dy_limits=dy_limits)

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Regional Effects

r_ale = effector.RegionalALE(data=X_train, model=model_forward, feature_names=feature_names, target_name=target_name)

Latitude (south to north)

r_ale.summary(features=6, scale_x_list=scale_x_list)

100%|██████████| 1/1 [00:43<00:00, 43.15s/it]



Feature 6 - Full partition tree:
🌳 Full Tree Structure:
───────────────────────
Latitude 🔹 [id: 0 | heter: 0.58 | inst: 500 | w: 1.00]
    Longitude ≤ -120.76 🔹 [id: 1 | heter: 0.09 | inst: 180 | w: 0.36]
        Longitude ≤ -122.31 🔹 [id: 2 | heter: 0.03 | inst: 41 | w: 0.08]
        Longitude > -122.31 🔹 [id: 3 | heter: 0.08 | inst: 139 | w: 0.28]
    Longitude > -120.76 🔹 [id: 4 | heter: 0.49 | inst: 320 | w: 0.64]
        MedInc ≤ 4.55 🔹 [id: 5 | heter: 0.30 | inst: 228 | w: 0.46]
        MedInc > 4.55 🔹 [id: 6 | heter: 0.44 | inst: 92 | w: 0.18]
--------------------------------------------------
Feature 6 - Statistics per tree level:
🌳 Tree Summary:
─────────────────
Level 0🔹heter: 0.58
    Level 1🔹heter: 0.35 | 🔻0.23 (40.17%)
        Level 2🔹heter: 0.24 | 🔻0.10 (29.77%)

r_ale.plot(feature=6, node_idx=0, scale_x_list=scale_x_list, scale_y=scale_y, y_limits=y_limits)

png

Global Trend: House prices decrease as we move north. 

for node_idx in [1, 4]:
    r_ale.plot(feature=6, node_idx=node_idx, centering=True, scale_x_list=scale_x_list, scale_y=scale_y, y_limits=y_limits)

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for node_idx in [2,3,5,6]:
    r_ale.plot(feature=6, node_idx=node_idx, centering=True, scale_x_list=scale_x_list, scale_y=scale_y, y_limits=y_limits)

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png

Global Trend: House prices decrease as we move north.

Regional Trends: Moreorless the same, with minor different curves.

Longitude (west to east)

r_ale.summary(features=7, scale_x_list=scale_x_list)

Feature 7 - Full partition tree:
🌳 Full Tree Structure:
───────────────────────
Longitude 🔹 [id: 0 | heter: 0.77 | inst: 500 | w: 1.00]
    Latitude ≤ 35.46 🔹 [id: 1 | heter: 0.58 | inst: 283 | w: 0.57]
        Latitude ≤ 33.65 🔹 [id: 2 | heter: 0.44 | inst: 55 | w: 0.11]
        Latitude > 33.65 🔹 [id: 3 | heter: 0.38 | inst: 228 | w: 0.46]
    Latitude > 35.46 🔹 [id: 4 | heter: 0.18 | inst: 217 | w: 0.43]
        AveRooms ≤ 6.19 🔹 [id: 5 | heter: 0.13 | inst: 170 | w: 0.34]
        AveRooms > 6.19 🔹 [id: 6 | heter: 0.12 | inst: 47 | w: 0.09]
--------------------------------------------------
Feature 7 - Statistics per tree level:
🌳 Tree Summary:
─────────────────
Level 0🔹heter: 0.77
    Level 1🔹heter: 0.40 | 🔻0.36 (47.30%)
        Level 2🔹heter: 0.28 | 🔻0.13 (31.51%)

r_ale.plot(feature=7, node_idx=0, centering=True, scale_x_list=scale_x_list, scale_y=scale_y)

100%|██████████| 1/1 [00:50<00:00, 50.61s/it]

png

Global Trend: House prices decrease as we move east. 

for node_idx in [1, 4]:
    r_ale.plot(feature=7, node_idx=node_idx, centering=True, scale_x_list=scale_x_list, scale_y=scale_y, y_limits=y_limits)

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for node_idx in [2, 3, 5, 6]:
    r_ale.plot(feature=7, node_idx=node_idx, centering=True, scale_x_list=scale_x_list, scale_y=scale_y, y_limits=y_limits)

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Global Trend: House prices decrease as we move eastward.

Regional Trends:
- South (latitude <= 35.85): Prices drop more sharply in the second half from west to east. - Latitude <= 33.65: A different pattern emerges: prices drop sharply in the first half, then increase in the second half, suggesting a U-shaped trend. - Latitude > 33.65: The decrease in the price is almost linear as we move to east. - North (latitude > 35.85): Prices drop more sharply in the first half from west to east.
- AveRooms <= 6.19: The pattern follows the overall northern trend, with a steep early drop. - AveRooms > 6.19: The decline becomes smoother and more linear