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Issue #8 #19

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merged 4 commits into from
Oct 2, 2024
Merged

Issue #8 #19

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f902b99
Update core.py
Prathmesh-rajurkar Oct 1, 2024
327caef
Update core.py
Prathmesh-rajurkar Oct 1, 2024
7e33ddb
Update core.py
Prathmesh-rajurkar Oct 2, 2024
f766bb3
Merge branch 'main' into patch-1
ombhojane Oct 2, 2024
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136 changes: 107 additions & 29 deletions explainableai/core.py
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Original file line number Diff line number Diff line change
@@ -1,3 +1,5 @@
# explainableai/core.py
from typing import List
import colorama
from colorama import Fore, Style

Expand Down Expand Up @@ -144,54 +146,130 @@ def analyze(self):
self.results = results
return results

def generate_report(self, filename='xai_report.pdf'):
def generate_report(self,filename='xai_report.pdf' ): #section=[] , includes _all , model_comparison , model_performance , etc
if self.results is None:
raise ValueError("No analysis results available. Please run analyze() first.")

report = ReportGenerator(filename)
report.add_heading("Explainable AI Report")

report.add_heading("Model Comparison", level=2)
model_comparison_data = [["Model", "CV Score", "Test Score"]]
for model, scores in self.results['model_comparison'].items():
model_comparison_data.append([model, f"{scores['cv_score']:.4f}", f"{scores['test_score']:.4f}"])
report.add_table(model_comparison_data)
# Model Comparison
def model_comparison():
report.add_heading("Model Comparison", level=2)
model_comparison_data = [["Model", "CV Score", "Test Score"]]
for model, scores in self.results['model_comparison'].items():
model_comparison_data.append([model, f"{scores['cv_score']:.4f}", f"{scores['test_score']:.4f}"])
report.add_table(model_comparison_data)


# Model Performance
report.add_heading("Model Performance", level=2)
for metric, value in self.results['model_performance'].items():
if isinstance(value, (int, float, np.float64)):
report.add_paragraph(f"**{metric}:** {value:.4f}")
else:
report.add_paragraph(f"**{metric}:**\n{value}")
def model_performance():
report.add_heading("Model Performance", level=2)
for metric, value in self.results['model_performance'].items():
if isinstance(value, (int, float, np.float64)):
report.add_paragraph(f"**{metric}:** {value:.4f}")
else:
report.add_paragraph(f"**{metric}:**\n{value}")

# Feature Importance
report.add_heading("Feature Importance", level=2)
feature_importance_data = [["Feature", "Importance"]] + [[feature, f"{importance:.4f}"] for feature, importance in self.feature_importance.items()]
report.add_table(feature_importance_data)
def feature_importance():
report.add_heading("Feature Importance", level=2)
feature_importance_data = [["Feature", "Importance"]] + [[feature, f"{importance:.4f}"] for feature, importance in self.feature_importance.items()]
report.add_table(feature_importance_data)

# Visualizations
report.add_heading("Visualizations", level=2)
report.add_image('feature_importance.png')
report.content.append(PageBreak())
report.add_image('partial_dependence.png')
report.content.append(PageBreak())
report.add_image('learning_curve.png')
report.content.append(PageBreak())
report.add_image('correlation_heatmap.png')
if self.is_classifier:
def visualization():
report.add_heading("Visualizations", level=2)
report.add_image('feature_importance.png')
report.content.append(PageBreak())
report.add_image('roc_curve.png')
report.add_image('partial_dependence.png')
report.content.append(PageBreak())
report.add_image('precision_recall_curve.png')
report.add_image('learning_curve.png')
report.content.append(PageBreak())
report.add_image('correlation_heatmap.png')
if self.is_classifier:
report.content.append(PageBreak())
report.add_image('roc_curve.png')
report.content.append(PageBreak())
report.add_image('precision_recall_curve.png')

# LLM Explanation
report.add_heading("LLM Explanation", level=2)
report.add_llm_explanation(self.results['llm_explanation'])
def llm_explanation():
report.add_heading("LLM Explanation", level=2)
report.add_llm_explanation(self.results['llm_explanation'])

report.generate()

while True:
all_section_perm = input("Do you want all sections in the xia_report? (y/n) ").lower()

if all_section_perm in ["yes", "y"]:
model_comparison()
model_performance()
feature_importance()
visualization()
llm_explanation()
break

elif all_section_perm in ["no", "n"]:
while True:
model_comp_perm = input("Do you want model_comparison in xia_report? (y/n) ").lower()
if model_comp_perm in ["yes", "y"]:
model_comparison()
break
elif model_comp_perm in ["no", "n"]:
break
else:
print("Invalid input. Please enter 'y' or 'n'.")

while True:
model_perf_perm = input("Do you want model_performance in xia_report? (y/n) ").lower()
if model_perf_perm in ["yes", "y"]:
model_performance()
break
elif model_perf_perm in ["no", "n"]:
break
else:
print("Invalid input. Please enter 'y' or 'n'.")

while True:
feature_imp_perm = input("Do you want feature_importance in xia_report? (y/n) ").lower()
if feature_imp_perm in ["yes", "y"]:
feature_importance()
break
elif feature_imp_perm in ["no", "n"]:
break
else:
print("Invalid input. Please enter 'y' or 'n'.")

while True:
visualization_perm = input("Do you want visualization in xia_report? (y/n) ").lower()
if visualization_perm in ["yes", "y"]:
visualization()
break
elif visualization_perm in ["no", "n"]:
break
else:
print("Invalid input. Please enter 'y' or 'n'.")

while True:
llm_expl_perm = input("Do you want llm_explanation in xia_report? (y/n) ").lower()
if llm_expl_perm in ["yes", "y"]:
llm_explanation()
break
elif llm_expl_perm in ["no", "n"]:
break
else:
print("Invalid input. Please enter 'y' or 'n'.")
break

else:
print("Invalid input. Please enter 'y' or 'n' ")


report.generate()



def predict(self, X):
if self.model is None:
raise ValueError("Model has not been fitted. Please run fit() first.")
Expand Down
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