Create deep_dive_agentic.py

analytics/deep_dive_agentic.py

@@ -0,0 +1,377 @@
+# deep_dive_agentic.py
+
+"""
+Agentic analytical code generation + execution engine using Hugging Face
+
+FLOW:
+User Question
+    ↓
+LLM generates pandas code
+    ↓
+Python executes code safely
+    ↓
+LLM interprets results
+    ↓
+Return code + interpretation
+
+Environment:
+export HUGGINGFACE_API_TOKEN=...
+"""
+
+# ---------------------------------------------------
+# IMPORTS
+# ---------------------------------------------------
+
+import pandas as pd
+import json
+import os
+import re
+
+try:
+    from huggingface_hub import InferenceClient
+except ImportError as exc:
+    raise ImportError(
+        "huggingface_hub is required. Install with `pip install huggingface-hub`."
+    ) from exc
+
+from analytics.performance_analysis import generate_metric_view
+
+# ---------------------------------------------------
+# HF CONFIG
+# ---------------------------------------------------
+
+HF_MODEL_ID = os.environ.get("HF_MODEL_ID", "Qwen/Qwen2.5-7B-Instruct")
+HF_TOKEN = os.environ.get("HUGGINGFACE_API_TOKEN")
+
+
+# ---------------------------------------------------
+# HELPER: GET INFERENCE CLIENT
+# ---------------------------------------------------
+
+def _get_hf_client():
+    if not HF_TOKEN:
+        raise RuntimeError(
+            "HUGGINGFACE_API_TOKEN is required. Set it in your environment."
+        )
+    return InferenceClient(token=HF_TOKEN)
+
+
+# ---------------------------------------------------
+# HELPER: EXTRACT JSON FROM LLM RESPONSE
+# ---------------------------------------------------
+
+def _extract_json(text: str):
+    match = re.search(r"\{.*\}", text, re.S)
+    if not match:
+        return None
+    payload = match.group(0)
+    try:
+        return json.loads(payload)
+    except json.JSONDecodeError:
+        try:
+            cleaned = re.sub(r"[\n\r]+", " ", payload)
+            cleaned = re.sub(r"(['\"])?([a-zA-Z0-9_]+)(['\"])?\s*:\s*", r'"\2": ', cleaned)
+            return json.loads(cleaned)
+        except Exception:
+            return None
+
+
+# ---------------------------------------------------
+# STEP 1: CODE GENERATION
+# ---------------------------------------------------
+
+def generate_analysis_requirements(question: str, acq: pd.DataFrame, perf: pd.DataFrame, master_df: pd.DataFrame):
+    """
+    LLM breaks down question into 1-3 structured analytics requirements.
+    Each requirement includes a description and executable pandas code.
+    """
+    
+    client = _get_hf_client()
+    
+    # Build detailed column descriptions
+    acq_cols = {
+        "account_id": "unique account identifier",
+        "booking_date": "when account was originated",
+        "booking_vintage": "year-month of origination (YYYY-MM)",
+        "fico_band": "FICO score bracket (e.g., 700-750, 750-800)",
+        "sourcing_channel": "acquisition channel (e.g., Online, Branch, Broker)",
+        "city_tier": "city classification (Tier-1, Tier-2, Tier-3)",
+        "occupation_type": "borrower occupation category",
+        "credit_limit": "approved credit line amount"
+    }
+    
+    perf_cols = {
+        "account_id": "unique account identifier",
+        "reporting_month": "month of performance observation (YYYY-MM)",
+        "mob": "months on books (age of account in months)",
+        "dpd": "days past due (0, 30, 60, 90+)",
+        "balance": "current outstanding balance",
+        "ncl_amount": "net charge-off amount (dollars)",
+        "payment": "payment amount in period"
+    }
+    
+    master_df_desc = "acq merged with perf on account_id; contains all acquisition + performance columns"
+    
+    prompt = (
+        "You are a senior retail credit risk analyst with 15+ years of portfolio management experience.\n\n"
+        "Your task:\n"
+        "1. Analyze the user's analytical question deeply\n"
+        "2. Determine 1-3 specific analytics requirements needed to fully answer the question\n"
+        "3. For EACH requirement, generate executable pandas code\n"
+        "4. Return ONLY valid JSON, no other text\n\n"
+        
+        "AVAILABLE DATA:\n"
+        "- acq: acquisition data with columns: " + ", ".join(acq_cols.keys()) + "\n"
+        "- perf: performance data with columns: " + ", ".join(perf_cols.keys()) + "\n"
+        "- master_df: merged acq+perf, includes all above columns\n\n"
+        
+        "COLUMN DESCRIPTIONS:\n"
+        "Acquisition (acq):\n"
+        + "\n".join([f"  - {k}: {v}" for k, v in acq_cols.items()]) + "\n\n"
+        "Performance (perf):\n"
+        + "\n".join([f"  - {k}: {v}" for k, v in perf_cols.items()]) + "\n\n"
+        
+        "Available Risk Metrics via generate_metric_view(df, metric_name, group_col):\n"
+        "  - 30+@3 (30+ dpd at 3 months)\n"
+        "  - 30+@6 (30+ dpd at 6 months)\n"
+        "  - 60+@6 (60+ dpd at 6 months)\n"
+        "  - Yr1 NCL (Year 1 net charge-off rate)\n\n"
+        
+        "CODE GENERATION RULES:\n"
+        "- Generate pandas code ONLY\n"
+        "- Use meaningful variable names (e.g., vintage_analysis, segment_summary)\n"
+        "- Store analysis in a variable (e.g., result_1, result_2, result_3)\n"
+        "- Focus on GROUP BY aggregations for insights\n"
+        "- Calculate rates as dollars/total (percentage)\n"
+        "- Sort by risk metrics (descending) to identify worst segments\n"
+        "- Add brief comments for clarity\n"
+        "- NO markdown, NO explanations outside JSON\n\n"
+        
+        "JSON STRUCTURE:\n"
+        "{\n"
+        '  "requirements": [\n'
+        '    {\n'
+        '      "sequence": 1,\n'
+        '      "title": "Analysis title",\n'
+        '      "description": "What this code does and why it matters",\n'
+        '      "code": "pandas code here"\n'
+        "    }\n"
+        "  ]\n"
+        "}\n\n"
+        
+        "User Question:\n" + question
+    )
+    
+    messages = [
+        {"role": "system", "content": "You are a senior credit risk analyst who generates pandas code for portfolio analytics. Return ONLY valid JSON."},
+        {"role": "user", "content": prompt}
+    ]
+    
+    response = client.chat.completions.create(
+        model=HF_MODEL_ID,
+        messages=messages,
+        max_tokens=2048,
+        temperature=0.1,
+        top_p=0.95
+    )
+    
+    response_text = response.choices[0].message.content if hasattr(response, 'choices') else str(response)
+    
+    # Extract JSON
+    spec = _extract_json(response_text)
+    
+    if not spec or "requirements" not in spec:
+        return {
+            "success": False,
+            "requirements": [],
+            "error": "Failed to parse requirements from LLM response",
+            "raw_response": response_text
+        }
+    
+    return {
+        "success": True,
+        "requirements": spec.get("requirements", []),
+        "error": None
+    }
+
+
+# ---------------------------------------------------
+# STEP 2: CODE EXECUTION (LOOPED)
+# ---------------------------------------------------
+
+def execute_requirement_code(code: str, acq: pd.DataFrame, perf: pd.DataFrame, master_df: pd.DataFrame, requirement_num: int):
+    """
+    Safely execute generated pandas code for a single requirement.
+    """
+    
+    local_scope = {
+        "pd": pd,
+        "acq": acq,
+        "perf": perf,
+        "master_df": master_df,
+        "generate_metric_view": generate_metric_view
+    }
+    
+    try:
+        exec(code, {}, local_scope)
+        # Look for result variables (result_1, result_2, result_3, or final_result)
+        result_key = f"result_{requirement_num}" if f"result_{requirement_num}" in local_scope else "final_result"
+        result = local_scope.get(result_key, local_scope.get("result", "No result generated"))
+        
+        return {
+            "success": True,
+            "result": result,
+            "error": None
+        }
+    except Exception as e:
+        return {
+            "success": False,
+            "result": None,
+            "error": str(e)
+        }
+
+
+def execute_all_requirements(requirements: list, acq: pd.DataFrame, perf: pd.DataFrame, master_df: pd.DataFrame):
+    """
+    Execute all requirements sequentially, building context.
+    """
+    
+    all_results = []
+    context_text = ""
+    
+    for i, req in enumerate(requirements, 1):
+        code = req.get("code", "")
+        description = req.get("description", "")
+        title = req.get("title", f"Analysis {i}")
+        
+        exec_result = execute_requirement_code(code, acq, perf, master_df, i)
+        
+        all_results.append({
+            "sequence": i,
+            "title": title,
+            "description": description,
+            "code": code,
+            "execution_success": exec_result["success"],
+            "result": exec_result["result"],
+            "error": exec_result.get("error")
+        })
+        
+        # Build context for interpretation
+        if exec_result["success"]:
+            context_text += f"\nAnalysis {i} ({title}):\n{str(exec_result['result'])}\n"
+        else:
+            context_text += f"\nAnalysis {i} ({title}) FAILED:\n{exec_result['error']}\n"
+    
+    return all_results, context_text
+
+
+# ---------------------------------------------------
+# STEP 3: RESULT INTERPRETATION
+# ---------------------------------------------------
+
+def interpret_all_results(question: str, all_results: list, context_text: str):
+    """
+    Senior risk analyst LLM interprets all results holistically.
+    """
+    
+    client = _get_hf_client()
+    
+    # Format all analyses
+    analyses_text = ""
+    for res in all_results:
+        analyses_text += f"\n{'='*60}\n"
+        analyses_text += f"Analysis {res['sequence']}: {res['title']}\n"
+        analyses_text += f"Description: {res['description']}\n"
+        analyses_text += f"{'='*60}\n"
+        if res['execution_success']:
+            analyses_text += f"Result:\n{str(res['result'])}\n"
+        else:
+            analyses_text += f"Execution Error: {res['error']}\n"
+    
+    prompt = (
+        "You are a senior retail credit risk analyst with 15+ years of portfolio management experience.\n\n"
+        "Your task:\n"
+        "Synthesize the analytical results and provide comprehensive risk insights.\n\n"
+        
+        "Focus on:\n"
+        "- Key findings and patterns across all analyses\n"
+        "- Risk deterioration or improvement trends\n"
+        "- Vintage/segment concentration issues and implications\n"
+        "- Root causes of observed patterns\n"
+        "- Unusual trends, anomalies, or red flags\n"
+        "- Actionable recommendations for portfolio management\n"
+        "- Comparative risk assessment (which segments/vintages are most/least risky)\n\n"
+        
+        "Guidelines:\n"
+        "- Be analytical and specific (not generic)\n"
+        "- Focus on business implications, not just statistics\n"
+        "- Avoid repeating raw tables; interpret the meaning\n"
+        "- Provide 3-5 key insights\n"
+        "- Suggest next investigative steps if needed\n\n"
+        
+        "User's Original Question:\n" + question + "\n\n"
+        
+        "Analyses Performed:\n" + analyses_text + "\n\n"
+        
+        "Provide your senior analyst interpretation:"
+    )
+    
+    messages = [
+        {"role": "system", "content": "You are a senior credit risk analyst providing executive insights from portfolio analytics."},
+        {"role": "user", "content": prompt}
+    ]
+    
+    response = client.chat.completions.create(
+        model=HF_MODEL_ID,
+        messages=messages,
+        max_tokens=1024,
+        temperature=0.3,
+        top_p=0.95
+    )
+    
+    interpretation = response.choices[0].message.content if hasattr(response, 'choices') else str(response)
+    return interpretation
+
+
+# ---------------------------------------------------
+# MASTER ORCHESTRATOR FUNCTION
+# ---------------------------------------------------
+
+def run_deep_dive_analysis(question: str, acq: pd.DataFrame, perf: pd.DataFrame, master_df: pd.DataFrame):
+    """
+    End-to-end deep dive analysis:
+    1. Break question into 1-3 structured requirements
+    2. Generate code for each requirement
+    3. Execute each requirement's code sequentially
+    4. Synthesize results and provide senior analyst interpretation
+    """
+    
+    # Step 1: Generate requirements
+    req_response = generate_analysis_requirements(question, acq, perf, master_df)
+    
+    if not req_response["success"]:
+        return {
+            "success": False,
+            "question": question,
+            "requirements": [],
+            "all_results": [],
+            "interpretation": f"Failed to generate requirements: {req_response['error']}",
+            "error": req_response["error"]
+        }
+    
+    requirements = req_response["requirements"][:3]  # Cap at 3
+    
+    # Step 2 & 3: Execute all requirements
+    all_results, context_text = execute_all_requirements(requirements, acq, perf, master_df)
+    
+    # Step 4: Interpret results
+    interpretation = interpret_all_results(question, all_results, context_text)
+    
+    return {
+        "success": True,
+        "question": question,
+        "requirements": requirements,
+        "all_results": all_results,
+        "interpretation": interpretation,
+        "error": None
+    }