"""
Hypothesis Generator Agent

Generates testable research hypotheses based on literature, theories,
and research questions using systematic and creative approaches.
"""

from typing import Any, Dict, List
from agents.base import BaseAgent


class HypothesisGeneratorAgent(BaseAgent):
    """
    Research hypothesis generation and refinement agent.

    Capabilities:
    - Hypothesis formulation from research questions
    - Null and alternative hypothesis generation
    - Operationalization of constructs
    - Testability assessment
    - Theory-driven hypothesis development
    - Competing hypothesis identification
    - Prediction specificity enhancement
    """

    def __init__(self):
        super().__init__(
            name='hypothesis-generator',
            description='Generate testable research hypotheses',
            category='research',
            version='1.0.0',
            tags=['hypothesis', 'research', 'theory', 'prediction', 'scientific-method']
        )

    async def execute(self, params: Dict[str, Any]) -> Dict[str, Any]:
        """
        Generate research hypotheses.

        Args:
            params: {
                'research_question': str,
                'domain': str,
                'theoretical_framework': str,
                'variables': {
                    'independent': List[str],
                    'dependent': List[str],
                    'mediating': List[str],
                    'moderating': List[str]
                },
                'literature_context': List[Dict],
                'hypothesis_type': 'directional|non-directional|null|causal|correlational',
                'options': {
                    'generate_alternatives': bool,
                    'include_mechanisms': bool,
                    'assess_testability': bool,
                    'operationalize': bool
                }
            }

        Returns:
            {
                'status': 'success|failed',
                'hypothesis_id': str,
                'primary_hypothesis': Dict,
                'alternative_hypotheses': List[Dict],
                'null_hypothesis': str,
                'testability_assessment': Dict,
                'recommendations': List[str]
            }
        """
        research_question = params.get('research_question')
        domain = params.get('domain', 'General')
        theoretical_framework = params.get('theoretical_framework')
        variables = params.get('variables', {})
        options = params.get('options', {})

        self.logger.info(
            f"Generating hypotheses for: {research_question}"
        )

        # Mock hypothesis generation
        primary_hypothesis = {
            'hypothesis': 'Students who use AI-assisted learning tools will achieve significantly higher academic performance compared to students using traditional learning methods',
            'type': 'directional',
            'format': 'alternative',
            'components': {
                'independent_variable': 'Learning method (AI-assisted vs. traditional)',
                'dependent_variable': 'Academic performance',
                'predicted_relationship': 'positive effect',
                'direction': 'AI-assisted > traditional',
                'population': 'University students'
            },
            'operationalization': {
                'independent_variable': {
                    'name': 'Learning method',
                    'operational_definition': 'Type of learning tool used during 6-month study period',
                    'levels': ['AI-assisted learning platform', 'Traditional textbook-based learning'],
                    'manipulation': 'Random assignment to condition'
                },
                'dependent_variable': {
                    'name': 'Academic performance',
                    'operational_definition': 'Composite score on standardized achievement tests',
                    'measurement': 'Standardized test battery (0-100 scale)',
                    'timepoint': 'End of semester assessment'
                }
            },
            'theoretical_basis': {
                'framework': 'Cognitive Load Theory',
                'key_principles': [
                    'AI tools reduce extraneous cognitive load',
                    'Adaptive learning optimizes germane cognitive load',
                    'Personalization enhances knowledge construction'
                ],
                'supporting_literature': [
                    'Sweller (2011) - Cognitive load theory',
                    'Clark & Mayer (2016) - Multimedia learning',
                    'VanLehn (2011) - Intelligent tutoring systems'
                ]
            },
            'assumptions': [
                'Students have equal baseline knowledge',
                'AI tools are used as intended',
                'Traditional methods represent current practice',
                'Testing conditions are standardized',
                'Motivation levels are comparable across groups'
            ],
            'boundary_conditions': [
                'Limited to undergraduate students',
                'STEM subject domains',
                'Western educational contexts',
                '6-month intervention period',
                'Digital literacy sufficient for AI tool use'
            ],
            'predicted_effect_size': 'Medium (d = 0.5)',
            'confidence_level': 'Moderate - based on preliminary evidence'
        }

        alternative_hypotheses = [
            {
                'hypothesis': 'The effect of AI-assisted learning on academic performance is moderated by students\' prior achievement level',
                'type': 'interaction/moderation',
                'rationale': 'High-achievers may benefit more from adaptive features',
                'testability': 'High',
                'variables': {
                    'independent': 'Learning method',
                    'dependent': 'Academic performance',
                    'moderator': 'Prior achievement level'
                },
                'prediction': 'Stronger effect for high-achieving students'
            },
            {
                'hypothesis': 'Student engagement mediates the relationship between AI-assisted learning and academic performance',
                'type': 'mediation',
                'rationale': 'AI tools increase engagement, which improves performance',
                'testability': 'High',
                'variables': {
                    'independent': 'Learning method',
                    'dependent': 'Academic performance',
                    'mediator': 'Student engagement'
                },
                'prediction': 'Indirect effect through engagement pathway'
            },
            {
                'hypothesis': 'AI-assisted learning improves academic performance only when combined with instructor guidance',
                'type': 'conditional',
                'rationale': 'Technology effectiveness depends on pedagogical context',
                'testability': 'Moderate',
                'variables': {
                    'independent': 'Learning method',
                    'dependent': 'Academic performance',
                    'condition': 'Instructor guidance level'
                },
                'prediction': 'Effect only present with adequate guidance'
            },
            {
                'hypothesis': 'Self-regulation skills moderate the effectiveness of AI-assisted learning',
                'type': 'moderation',
                'rationale': 'Self-regulated learners better utilize adaptive features',
                'testability': 'High',
                'variables': {
                    'independent': 'Learning method',
                    'dependent': 'Academic performance',
                    'moderator': 'Self-regulation skills'
                },
                'prediction': 'Stronger benefit for self-regulated learners'
            }
        ]

        null_hypothesis = {
            'statement': 'There is no significant difference in academic performance between students who use AI-assisted learning tools and students who use traditional learning methods',
            'statistical_form': 'H₀: μ₁ = μ₂',
            'alternative_statistical_form': 'H₁: μ₁ ≠ μ₂',
            'rejection_criteria': 'p-value < 0.05 (two-tailed test)'
        }

        competing_hypotheses = [
            {
                'hypothesis': 'Traditional learning methods produce better academic performance due to deeper processing',
                'rationale': 'AI tools may promote surface learning',
                'plausibility': 'Low',
                'distinguishing_test': 'Include deep learning measures'
            },
            {
                'hypothesis': 'No difference in performance; any observed effect is due to novelty',
                'rationale': 'Hawthorne effect or novelty bias',
                'plausibility': 'Moderate',
                'distinguishing_test': 'Extended time period, habituation controls'
            },
            {
                'hypothesis': 'Performance differences are due to student selection bias',
                'rationale': 'Tech-savvy students self-select into AI condition',
                'plausibility': 'Low with randomization',
                'distinguishing_test': 'Random assignment, check baseline equivalence'
            }
        ]

        testability_assessment = {
            'overall_testability': 'High',
            'criteria': {
                'falsifiability': {
                    'score': 9,
                    'rationale': 'Clear predictions that can be proven false'
                },
                'operationalizability': {
                    'score': 9,
                    'rationale': 'Variables can be clearly measured'
                },
                'specificity': {
                    'score': 8,
                    'rationale': 'Specific predictions with defined parameters'
                },
                'parsimony': {
                    'score': 8,
                    'rationale': 'Simple, direct relationship proposed'
                },
                'scope': {
                    'score': 7,
                    'rationale': 'Defined scope, clear boundaries'
                }
            },
            'potential_challenges': [
                'Ensuring fidelity of AI tool implementation',
                'Controlling for instructor effects',
                'Measuring long-term retention',
                'Accounting for individual differences'
            ],
            'required_resources': {
                'sample_size': '240 participants (power = 0.80)',
                'duration': '6 months',
                'instruments': ['Standardized tests', 'Engagement scales', 'Demographics'],
                'budget_estimate': '$150,000 - $250,000'
            }
        }

        research_design_implications = {
            'recommended_design': 'Randomized Controlled Trial with pre-post measures',
            'essential_controls': [
                'Random assignment to conditions',
                'Baseline equivalence testing',
                'Standardized assessment procedures',
                'Intervention fidelity monitoring'
            ],
            'measurement_timepoints': [
                'Baseline (Week 0)',
                'Mid-intervention (Week 12)',
                'Post-intervention (Week 24)',
                'Follow-up (Week 36)'
            ],
            'statistical_approach': 'Mixed-effects ANOVA with repeated measures',
            'effect_size_benchmarks': {
                'small': 0.2,
                'medium': 0.5,
                'large': 0.8
            }
        }

        return {
            'status': 'success',
            'hypothesis_id': 'HYP-20251116-001',
            'research_question': research_question,
            'domain': domain,
            'theoretical_framework': theoretical_framework,
            'primary_hypothesis': primary_hypothesis,
            'null_hypothesis': null_hypothesis,
            'alternative_hypotheses': alternative_hypotheses,
            'competing_hypotheses': competing_hypotheses,
            'testability_assessment': testability_assessment,
            'research_design_implications': research_design_implications,
            'conceptual_model': {
                'nodes': [
                    {'id': 'learning_method', 'type': 'independent'},
                    {'id': 'engagement', 'type': 'mediator'},
                    {'id': 'self_regulation', 'type': 'moderator'},
                    {'id': 'academic_performance', 'type': 'dependent'}
                ],
                'edges': [
                    {'from': 'learning_method', 'to': 'engagement', 'type': 'direct'},
                    {'from': 'engagement', 'to': 'academic_performance', 'type': 'direct'},
                    {'from': 'learning_method', 'to': 'academic_performance', 'type': 'direct'},
                    {'from': 'self_regulation', 'to': 'academic_performance', 'type': 'moderating'}
                ]
            },
            'measurement_plan': {
                'primary_outcome': {
                    'construct': 'Academic performance',
                    'instrument': 'Standardized Achievement Test Battery',
                    'reliability': 'α = 0.92',
                    'validity': 'Criterion validity established'
                },
                'secondary_outcomes': [
                    {'construct': 'Engagement', 'instrument': 'Student Engagement Scale'},
                    {'construct': 'Self-efficacy', 'instrument': 'Academic Self-Efficacy Scale'},
                    {'construct': 'Motivation', 'instrument': 'Intrinsic Motivation Inventory'}
                ],
                'process_measures': [
                    'Time on task',
                    'Platform usage frequency',
                    'Help-seeking behavior'
                ]
            },
            'recommendations': [
                'Pre-register hypothesis before data collection',
                'Conduct pilot study to refine measures (n=30)',
                'Include manipulation checks for intervention fidelity',
                'Test competing hypotheses simultaneously',
                'Plan for multiple testing correction',
                'Consider longitudinal follow-up for sustained effects',
                'Include qualitative data for mechanism exploration',
                'Specify analysis plan a priori to prevent p-hacking'
            ],
            'next_steps': [
                'Develop detailed research protocol',
                'Obtain ethics approval',
                'Pre-register study and hypotheses',
                'Conduct power analysis for final sample size',
                'Design measurement instruments',
                'Create analysis syntax/code in advance'
            ],
            'literature_gaps_addressed': [
                'Limited experimental evidence for AI learning tools',
                'Unclear mechanisms of technology effectiveness',
                'Need for rigorous controlled comparisons',
                'Lack of moderator analysis in existing studies'
            ]
        }

    def validate_params(self, params: Dict[str, Any]) -> bool:
        """Validate hypothesis generation parameters."""
        if 'research_question' not in params:
            self.logger.error("Missing required field: research_question")
            return False

        valid_types = ['directional', 'non-directional', 'null', 'causal', 'correlational']
        hypothesis_type = params.get('hypothesis_type', 'directional')
        if hypothesis_type not in valid_types:
            self.logger.error(f"Invalid hypothesis_type: {hypothesis_type}")
            return False

        return True
