Update README for new API and change factors_list to all_factors

Updated README.md and minor edits to file directory , imports, and parameter lists

Author: amit-sharma

README.md

@@ -25,7 +25,10 @@ pip install pywhyllm
 PyWhy-LLM seamlessly integrates into your existing causal inference process. Import the necessary classes and start exploring the power of LLM-augmented causal analysis.
 
 ```python
-from pywhyllm import ModelSuggester, IdentificationSuggester, ValidationSuggester
+from pywhyllm.suggesters.model_suggester import ModelSuggester 
+from pywhyllm.suggesters.identification_suggester import IdentificationSuggester
+from pywhyllm.suggesters.validation_suggester import ValidationSuggester
+from pywhyllm import RelationshipStrategy
 
 ```
 
@@ -34,17 +37,20 @@ from pywhyllm import ModelSuggester, IdentificationSuggester, ValidationSuggeste
 
 ```python
 # Create instance of Modeler
-modeler = Modeler()
+modeler = ModelSuggester('gpt-4')
+
+all_factors = ["smoking", "lung cancer", "exercise habits", "air pollution exposure"]
+treatment = "smoking"
+outcome = "lung cancer"
+
+# Suggest a list of domain expertises
+domain_expertises = modeler.suggest_domain_expertises(all_factors)
 
 # Suggest a set of potential confounders
-suggested_confounders = modeler.suggest_confounders(variables=_variables, treatment=treatment, outcome=outcome, llm=gpt4)
+suggested_confounders = modeler.suggest_confounders(treatment, outcome, all_factors, domain_expertises)
 
 # Suggest pair-wise relationship between variables
-suggested_dag = modeler.suggest_relationships(variables=selected_variables, llm=gpt4)
-
-plt.figure(figsize=(10, 5))
-nx.draw(suggested_dag, with_labels=True, node_color='lightblue')
-plt.show()
+suggested_dag = modeler.suggest_relationships(treatment, outcome, all_factors, domain_expertises, RelationshipStrategy.Pairwise)
 ```
 
 
@@ -54,15 +60,13 @@ plt.show()
 
 ```python
 # Create instance of Identifier
-identifier = Identifier()
+identifier = IdentificationSuggester('gpt-4')
 
-# Suggest a backdoor set, front door set, and iv set
-suggested_backdoor = identifier.suggest_backdoor(llm=gpt4, treatment=treatment, outcome=outcome, confounders=suggested_confounders)
-suggested_frontdoor = identifier.suggest_frontdoor(llm=gpt4, treatment=treatment, outcome=outcome,  confounders=suggested_confounders)
-suggested_iv = identifier.suggest_iv(llm=gpt4, treatment=treatment, outcome=outcome,  confounders=suggested_confounders)
+# Suggest a backdoor set, mediator set, and iv set
+suggested_backdoor = identifier.suggest_backdoor(treatment, outcome, all_factors, domain_expertises)
+suggested_mediators = identifier.suggest_mediators(treatment, outcome, all_factors, domain_expertises)
+suggested_iv = identifier.suggest_ivs(treatment, outcome, all_factors, domain_expertises)
 
-# Suggest an estimand based on the suggester backdoor set, front door set, and iv set
-estimand = identifier.suggest_estimand(confounders=suggested_confounders, treatment=treatment, outcome=outcome, backdoor=suggested_backdoor, frontdoor=suggested_frontdoor, iv=suggested_iv, llm=gpt4)  
 ```
 
 
@@ -72,20 +76,16 @@ estimand = identifier.suggest_estimand(confounders=suggested_confounders, treatm
 
 ```python
 # Create instance of Validator
-validator = Validator()
+validator = ValidationSuggester('gpt-4')
 
-# Suggest a critique of the provided DAG
-suggested_critiques_dag = validator.critique_graph(graph=suggested_dag, llm=gpt4)
+# Suggest a critique of the edges in provided DAG
+suggested_critiques_dag = validator.critique_graph(all_factors, suggested_dag, domain_expertises, RelationshipStrategy.Pairwise)
 
 # Suggest latent confounders
-suggested_latent_confounders = validator.suggest_latent_confounders(treatment=treatment, outcome=outcome, llm=gpt4)
+suggested_latent_confounders = validator.suggest_latent_confounders(treatment, outcome, all_factors, domain_expertises)
 
 # Suggest negative controls
-suggested_negative_controls = validator.suggest_negative_controls(variables=selected_variables, treatment=treatment, outcome=outcome, llm=gpt4)
-
-plt.figure(figsize=(10, 5))
-nx.draw(suggested_critiques_dag, with_labels=True, node_color='lightblue')
-plt.show()
+suggested_negative_controls = validator.suggest_negative_controls(treatment, outcome, all_factors, domain_expertises)
 
 ```
 

pywhyllm/suggesters/identification_suggester.py

@@ -19,7 +19,7 @@ def __init__(self, llm=None):
     #     self,
     #     treatment: str,
     #     outcome: str,
-    #     factors_list: list(),
+    #     all_factors: list(),
     #     llm: guidance.models,
     #     backdoor: Set[str] = None,
     #     frontdoor: Set[str] = None,
@@ -41,7 +41,7 @@ def __init__(self, llm=None):
     #         backdoor_edges, backdoor_set = self.suggest_backdoor(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -66,7 +66,7 @@ def __init__(self, llm=None):
     #         frontdoor_edges, frontdoor_set = self.suggest_frontdoor(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -87,7 +87,7 @@ def __init__(self, llm=None):
     #         ivs_edges, ivs_set = self.suggest_ivs(
     #             treatment=treatment,
     #             outcome=outcome,
-    #             factors_list=factors_list,
+    #             all_factors=all_factors,
     #             llm=llm,
     #             experts=experts,
     #             analysis_context=analysis_context,
@@ -116,15 +116,15 @@ def suggest_backdoor(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         backdoor_set = self.model_suggester.suggest_confounders(
             treatment=treatment,
             outcome=outcome,
-            factors_list=factors_list,
+            all_factors=all_factors,
             expertise_list=expertise_list,
             analysis_context=analysis_context,
             stakeholders=stakeholders
@@ -136,9 +136,9 @@ def suggest_frontdoor(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         pass
@@ -147,9 +147,9 @@ def suggest_mediators(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -164,16 +164,16 @@ def suggest_mediators(
         mediators_edges[(treatment, outcome)] = 1
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expert_list:
             mediators_edges, mediators_list = self.request_mediators(
                 treatment=treatment,
                 outcome=outcome,
                 domain_expertise=expert,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 mediators_edges=mediators_edges,
                 analysis_context=analysis_context
             )
@@ -187,9 +187,9 @@ def request_mediators(
             treatment,
             outcome,
             domain_expertise,
-            factors_list,
+            all_factors,
             mediators_edges,
-            analysis_context=CONTEXT
+            analysis_context: str = CONTEXT
     ):
         mediators: List[str] = list()
 
@@ -218,7 +218,7 @@ def request_mediators(
                  on the causal chain that links the {treatment} to the {outcome}? From your perspective as an expert in 
                  {domain_expertise}, which factor(s) of the following factors, if any at all, mediates, is/are on the causal 
                  chain, that links the {treatment} to the {outcome}? Then provide your step by step chain of thoughts within 
-                 the tags <thinking></thinking>. factor_names : {factors_list} Wrap the name of the factor(s), if any at all, 
+                 the tags <thinking></thinking>. factor_names : {all_factors} Wrap the name of the factor(s), if any at all, 
                  that has/have a high likelihood of directly influencing and causing the assignment of the {outcome} and also 
                  has/have a high likelihood of being directly influenced and caused by the assignment of the {treatment} within
                   the tags <mediating_factor>factor_name</mediating_factor>. Where factor_name is one of the items within the 
@@ -237,7 +237,7 @@ def request_mediators(
                 if mediating_factor:
                     for factor in mediating_factor:
                         # to not add it twice into the list
-                        if factor in factors_list and factor not in mediators:
+                        if factor in all_factors and factor not in mediators:
                             mediators.append(factor)
                 success = True
 
@@ -262,9 +262,9 @@ def suggest_ivs(
             self,
             treatment: str,
             outcome: str,
-            factors_list: list(),
+            all_factors: list(),
             expertise_list: list(),
-            analysis_context=CONTEXT,
+            analysis_context: str = CONTEXT,
             stakeholders: list() = None
     ):
         expert_list: List[str] = list()
@@ -279,17 +279,17 @@ def suggest_ivs(
         iv_edges[(treatment, outcome)] = 1
 
         edited_factors_list: List[str] = []
-        for i in range(len(factors_list)):
-            if factors_list[i] != treatment and factors_list[i] != outcome:
-                edited_factors_list.append(factors_list[i])
+        for i in range(len(all_factors)):
+            if all_factors[i] != treatment and all_factors[i] != outcome:
+                edited_factors_list.append(all_factors[i])
 
         for expert in expert_list:
             iv_edges, iv_list = self.request_ivs(
                 treatment=treatment,
                 outcome=outcome,
                 analysis_context=analysis_context,
                 domain_expertise=expert,
-                factors_list=edited_factors_list,
+                all_factors=edited_factors_list,
                 iv_edges=iv_edges,
             )
 
@@ -305,7 +305,7 @@ def request_ivs(
             outcome,
             analysis_context,
             domain_expertise,
-            factors_list,
+            all_factors,
             iv_edges
     ):
         ivs: List[str] = list()
@@ -338,7 +338,7 @@ def request_ivs(
                        the {outcome}? Which factor(s) of the following factors, if any at all, are (an) instrumental variable(s) 
                        to the causal relationship of the {treatment} causing the {outcome}? Be concise and keep your thinking 
                        within two paragraphs. Then provide your step by step chain of thoughts within the tags 
-                       <thinking></thinking>. factor_names : {factors_list} Wrap the name of the factor(s), if there are any at 
+                       <thinking></thinking>. factor_names : {all_factors} Wrap the name of the factor(s), if there are any at 
                        all, that both has/have a high likelihood of influecing and causing the {treatment} and has/have a very low 
                        likelihood of influencing and causing the {outcome}, within the tags <iv_factor>factor_name</iv_factor>. 
                        Where factor_name is one of the items within the factor_names list. If a factor does not have a high 
@@ -353,7 +353,7 @@ def request_ivs(
 
                 if iv_factors:
                     for factor in iv_factors:
-                        if factor in factors_list and factor not in ivs:
+                        if factor in all_factors and factor not in ivs:
                             ivs.append(factor)
                 success = True