Regression - Concrete Compressive Strength Prediction
Predicting concrete compressive strength from mixture components using materials science data.
Dataset Source: UCI ML Repository - Concrete Compressive Strength Problem Type: Regression Target Variable: Concrete compressive strength (MPa) Use Case: Construction engineering, quality control, materials optimization
Package Imports
!pip install xplainable
!pip install xplainable-client
import pandas as pd
import xplainable as xp
from xplainable.core.models import XRegressor
from xplainable.core.optimisation.genetic import XEvolutionaryNetwork
from xplainable.core.optimisation.layers import Evolve, Tighten
from xplainable.preprocessing.pipeline import XPipeline
from xplainable.preprocessing import transformers as xtf
from sklearn.model_selection import train_test_split
import requests
import json
# Additional imports specific to this example
import numpy as np
import matplotlib.pyplot as plt
from ucimlrepo import fetch_ucirepo
import xplainable_client
Xplainable Cloud Setup
# Initialize Xplainable Cloud client
client = xplainable_client.Client(
api_key="83b8d99c-ca2c-4132-b1e9-ed86db83f306",
hostname="https://xplainable-api-uat-itdcj.ondigitalocean.app/"
)
Data Loading and Exploration
Load the Concrete Compressive Strength dataset from UCI ML Repository.
# Load dataset using ucimlrepo
try:
# Fetch dataset
concrete = fetch_ucirepo(id=165)
# Data (as pandas dataframes)
X = concrete.data.features
y = concrete.data.targets
# Combine features and target
df = pd.concat([X, y], axis=1)
# Display basic information
print(f"Dataset shape: {df.shape}")
print(f"\nFeatures: {list(X.columns)}")
print(f"\nTarget variable statistics:")
print(y.describe())
df.head()
except Exception as e:
print(f"Error loading dataset: {e}")
print("Install ucimlrepo: pip install ucimlrepo")
1. Data Preprocessing
Preprocess the concrete mixture data for optimal model performance.
# # Create preprocessing pipeline
# pipeline = XPipeline()
# pipeline.add_stages([
# # Check for missing values (this dataset typically has none)
# # {"transformer": xtf.FillMissing(method="median")},
# # Optionally scale features if needed
# # {"transformer": xtf.StandardScaler()},
# ])
# # Apply preprocessing
# df_processed = pipeline.fit_transform(df)
# print(f"Processed dataset shape: {df_processed.shape}")
# print(f"Missing values: {df_processed.isnull().sum().sum()}")
# df_processed.head()
Create Train/Test Split
df.columns
# Assuming the target column is the last one
target_col = "Concrete compressive strength"
X, y = df.drop(columns=[target_col]), df[target_col]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42
)
print(f"Training set: {X_train.shape[0]} samples")
print(f"Test set: {X_test.shape[0]} samples")
model = XRegressor(ignore_nan=False)
model.fit(X_train, y_train)
2. Model Optimization
Optimize hyperparameters for concrete strength prediction.
network = XEvolutionaryNetwork(model)
# Add the layers
# Start with an initial Tighten layer
network.add_layer(
Tighten(
iterations=100,
learning_rate=0.1,
early_stopping=20
)
)
# Add an Evolve layer with a high severity
network.add_layer(
Evolve(
mutations=100,
generations=50,
max_severity=0.5,
max_leaves=20,
early_stopping=20
)
)
# Add another Evolve layer with a lower severity and reach
network.add_layer(
Evolve(
mutations=100,
generations=50,
max_severity=0.3,
max_leaves=15,
early_stopping=20
)
)
# Add a final Tighten layer with a low learning rate
network.add_layer(
Tighten(
iterations=100,
learning_rate=0.025,
early_stopping=20
)
)
# Fit the network (before or after adding layers)
network.fit(X_train, y_train)
# Run the network
network.optimise()
4. Model Interpretability and Explainability
Understand which concrete mixture components most influence compressive strength.
model.explain()
5. Model Persistence (Optional)
Save the model to Xplainable Cloud.
# Uncomment to save model to Xplainable Cloud
# model_id = client.create_model(
# model=model,
# model_name="Concrete Compressive Strength Model",
# model_description="Predicting concrete strength from mixture components",
# x=X_train,
# y=y_train
# )
6. Model Deployment (Optional)
Deploy the model for real-time strength predictions.
# Uncomment to deploy model
# deployment = client.deploy(
# model_version_id=model_id["version_id"]
# )
7. Model Testing
Evaluate model performance on concrete strength predictions.
# Evaluate on test set
test_predictions = model.predict(X_test)
test_performance = model.evaluate(X_test, y_test)
print("Test Set Performance:")
for metric, value in test_performance.items():
print(f"{metric}: {value:.4f}")
# Plot predictions vs actual
plt.figure(figsize=(10, 6))
plt.scatter(y_test, test_predictions, alpha=0.6)
plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)
plt.xlabel('Actual Compressive Strength (MPa)')
plt.ylabel('Predicted Compressive Strength (MPa)')
plt.title('Concrete Compressive Strength: Predictions vs Actual')
plt.grid(True, alpha=0.3)
plt.show()
# Feature importance analysis
print("\nTop features influencing concrete strength:")
print("- Cement content typically shows high importance")
print("- Age of concrete is usually a strong predictor")
print("- Water-to-cement ratio affects strength significantly")
