This guide shows how to use the report_generation module to automatically generate HTML reports with statistics, model interpretation, and analysis suggestions.
from ai_aquatica.report_generation import (
generate_statistical_report,
generate_interpretation_report,
suggest_further_analysis
)
import pandas as pd
# Sample data
data = pd.DataFrame({
'NO3': [1.5, 1.7, 1.6, 1.8, 1.4],
'pH': [7.0, 6.9, 7.1, 7.2, 6.8]
})
generate_statistical_report(data, report_path='statistical_report.html')
🖼️ Note: This creates a heatmap image and uses a Jinja2 template in the templates/ directory.
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score
X = data[['NO3']]
y = [0, 1, 0, 1, 0]
X_train, X_test, y_train, y_test = train_test_split(X, y)
model = DecisionTreeClassifier().fit(X_train, y_train)
generate_interpretation_report(data, model, X_test, y_test, report_path='interpretation_report.html')
suggest_further_analysis(data, report_path='further_analysis_report.html')
.html) must exist in a folder named templates/.heatmap.png in the working directory.