Predicting house prices with pycaret

Objectives

• Example end-to-end supervised learning workflow with Ames Housing dataset
• Focus on conceptual understanding of machine learning
• Demonstrate use of Predictive Power Score (PPS)
• Demonstrate capabilities of low-code tools

Attribution

Dataset

• Ames Housing dataset paper (original paper)
• Kaggle competition advanced regression techniques (link)

Python libraries

• Altair (docs)
• ydata-profiling (docs)
• Predictive Power Score (PPS, GitHub, blog)
• PyCaret: open-source, low-code machine learning library in Python that automates machine learning workflows (link)

import altair as alt
import pandas as pd
import ppscore as pps
from pycaret.regression import *
from ydata_profiling import ProfileReport


# customize Altair
def y_axis():
    return {
        "config": {
            "axisX": {"grid": False},
            "axisY": {
                "domain": False,
                "gridDash": [2, 4],
                "tickSize": 0,
                "titleAlign": "right",
                "titleAngle": 0,
                "titleX": -5,
                "titleY": -10,
            },
            "view": {
                "stroke": "transparent",
                # To keep the same height and width as the default theme:
                "continuousHeight": 300,
                "continuousWidth": 400,
            },
        }
    }


alt.themes.register("y_axis", y_axis)
alt.themes.enable("y_axis")


def get_descriptions():
    "Parse descriptions of columns of Ames Housing dataset"
    with open("data_description.txt") as reader:
        descriptions = {}
        for line in reader.readlines():
            if ":" in line and "2nd level" not in line:
                descriptions[line.split(": ")[0].strip()] = line.split(": ")[1].strip()
    return pd.Series(descriptions).rename("descriptions")


descriptions = get_descriptions()

Read and explore the data

%%time
train = pd.read_csv("train.csv")
test = pd.read_csv("test.csv")
profile = ProfileReport(train, minimal=True, title="Ames Housing Profiling Report")
profile.to_file("ames-housing-profiling-report-minimal.html")

CPU times: user 45 s, sys: 1.78 s, total: 46.7 s
Wall time: 16.4 s

profile.to_notebook_iframe()

Investigate features with largest predictive power

We use the Predictive Power Score to evaluate which features have the highest predictive power with respect to SalePrice.

predictors = (
    pps.predictors(train, "SalePrice")
    .round(3)
    .iloc[:, :-1]
    .merge(descriptions, how="left", left_on="x", right_index=True)
)
base = (
    alt.Chart(predictors)
    .encode(
        x=alt.Y("x:N").sort("-y"),
        y="ppscore",
        tooltip=["x", "ppscore", "descriptions"],
    )
    .transform_filter("datum.ppscore > 0")
)
base.mark_bar() + base.mark_text(align="center", dy=-5)

Investigate colinearity

pps_matrix = (
    pps.matrix(
        train.loc[:, predictors.query("ppscore > 0")["x"].tolist()],
    )
    .loc[:, ["x", "y", "ppscore"]]
    .round(3)
)
(
    alt.Chart(pps_matrix)
    .mark_rect()
    .encode(
        x="x:O",
        y="y:O",
        color="ppscore:Q",
        tooltip=["x", "y", "ppscore"])
)

Build models

We select the 30 features that have the highest predictive power score

selected_predictors = (
    predictors.sort_values("ppscore", ascending=False).head(30)["x"].to_list()
)
reg = setup(data = train.loc[:, selected_predictors + ["SalePrice"]], 
             target = 'SalePrice',
             numeric_imputation = 'mean',
             categorical_features =  list(train.loc[:, selected_predictors].select_dtypes("object").columns), 
             feature_selection = False,
             pca=False,
             remove_multicollinearity=True,
             remove_outliers = False,
             normalize = True,
             )

	Description	Value
0	Session id	8378
1	Target	SalePrice
2	Target type	Regression
3	Original data shape	(1460, 31)
4	Transformed data shape	(1460, 116)
5	Transformed train set shape	(1021, 116)
6	Transformed test set shape	(439, 116)
7	Ordinal features	1
8	Numeric features	16
9	Categorical features	14
10	Rows with missing values	94.7%
11	Preprocess	True
12	Imputation type	simple
13	Numeric imputation	mean
14	Categorical imputation	mode
15	Maximum one-hot encoding	25
16	Encoding method	None
17	Remove multicollinearity	True
18	Multicollinearity threshold	0.900000
19	Normalize	True
20	Normalize method	zscore
21	Fold Generator	KFold
22	Fold Number	10
23	CPU Jobs	-1
24	Use GPU	False
25	Log Experiment	False
26	Experiment Name	reg-default-name
27	USI	81f6

%%time
selected_models = [model for model in models().index if model not in ["lar", "lr", "ransac"]]
best_model = compare_models(sort='RMSLE', include=selected_models)

	Model	MAE	MSE	RMSE	R2	RMSLE	MAPE	TT (Sec)
lightgbm	Light Gradient Boosting Machine	18267.8967	969345616.0929	30245.0381	0.8400	0.1474	0.1051	0.3780
gbr	Gradient Boosting Regressor	18349.4461	1064907228.1139	31464.4286	0.8221	0.1497	0.1059	0.0810
rf	Random Forest Regressor	18834.6022	1052157810.7295	31669.8884	0.8263	0.1530	0.1091	0.1370
par	Passive Aggressive Regressor	18695.3332	1145943934.1128	32527.7429	0.8093	0.1535	0.1061	0.0560
en	Elastic Net	19941.1185	1212771199.2709	33679.9238	0.8018	0.1536	0.1131	0.0370
et	Extra Trees Regressor	19749.8604	1158574471.9795	33510.6172	0.8073	0.1591	0.1138	0.1370
huber	Huber Regressor	18580.7407	1172797296.1965	32571.8573	0.8024	0.1602	0.1069	0.0420
br	Bayesian Ridge	20557.3468	1251454965.3245	34036.3809	0.7934	0.1715	0.1191	0.0380
ard	Automatic Relevance Determination	20446.5401	1229331466.4696	33711.3986	0.7969	0.1747	0.1193	0.2740
omp	Orthogonal Matching Pursuit	21882.7966	1294135379.9217	34955.8947	0.7847	0.1849	0.1296	0.0340
ada	AdaBoost Regressor	24866.3282	1379609584.9159	36498.7175	0.7707	0.2036	0.1621	0.0580
knn	K Neighbors Regressor	26571.2016	1730405638.7521	40931.3774	0.7200	0.2050	0.1518	0.0360
dt	Decision Tree Regressor	27747.5148	2157234242.4490	45330.0191	0.6512	0.2169	0.1564	0.0350
llar	Lasso Least Angle Regression	21458.2025	1320695830.3446	35006.4301	0.7809	0.2187	0.1268	0.0380
lasso	Lasso Regression	21455.6793	1320742178.3808	35006.1951	0.7809	0.2189	0.1268	0.2100
ridge	Ridge Regression	21439.2241	1318937040.3720	34981.0548	0.7812	0.2196	0.1266	0.0360
svm	Support Vector Regression	55543.3805	6417749387.4994	79739.3850	-0.0524	0.3979	0.3195	0.0450
dummy	Dummy Regressor	57352.4774	6133919031.8184	78021.7431	-0.0086	0.4061	0.3635	0.0340
tr	TheilSen Regressor	29178.3219	2564758742.0908	49572.3895	0.5667	0.4258	0.1978	4.0290
kr	Kernel Ridge	182040.0692	34133507087.4154	184731.2672	-4.7500	1.7994	1.1623	0.0380
mlp	MLP Regressor	166456.5847	32851392125.7179	181040.0031	-4.4796	2.7703	0.9182	0.2890

CPU times: user 4.09 s, sys: 446 ms, total: 4.53 s
Wall time: 1min 3s

Evaluation

• With a standard, AutoML-like workflow, we achive RMSLE of 0.13 - 0.14 (over different runs), which is already in the top 25% of the 4,200 submissions on the leaderboard
• We can now make predictions on the test set

predictions = (
    predict_model(best_model, data=test)
    .rename(columns={"prediction_label": "SalePrice"})
    .loc[:, ["Id", "SalePrice"]]
)
predictions.head()

	Id	SalePrice
0	1461	126951.931078
1	1462	142402.002648
2	1463	185086.014955
3	1464	191718.590497
4	1465	186412.972060

Pipeline

plot_model(best_model, 'pipeline')

plot_model(best_model, 'feature')

plot_model(best_model, 'residuals')