temporal_classifier_integration_ex.py

Author: dyumanaditya

examples/temporal_classifier_integration_ex.py

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+import pyreason as pr
+import torch
+import torch.nn as nn
+import numpy as np
+import random
+
+# Set a seed for reproducibility.
+seed_value = 65     # Good Gap Gap
+# seed_value = 47     # Good Gap Good
+# seed_value = 43     # Good Good Good
+random.seed(seed_value)
+np.random.seed(seed_value)
+torch.manual_seed(seed_value)
+
+# --- Part 1: Weld Quality Model Integration ---
+
+# Create a dummy PyTorch model for detecting weld quality.
+# Each weld is represented by 3 features and is classified as "good" or "gap".
+weld_model = nn.Linear(3, 2)
+class_names = ["good", "gap"]
+
+# Define integration options:
+# Only consider probabilities above 0.5, adjust lower bound for high confidence, and use a snap value.
+interface_options = pr.ModelInterfaceOptions(
+    threshold=0.5,
+    set_lower_bound=True,
+    set_upper_bound=False,
+    snap_value=1.0
+)
+
+# Wrap the model using LogicIntegratedClassifier.
+weld_quality_checker = pr.LogicIntegratedClassifier(
+    weld_model,
+    class_names,
+    identifier="weld_object",
+    interface_options=interface_options
+)
+
+# --- Part 2: Simulate Weld Inspections Over Time ---
+pr.add_rule(pr.Rule("repair_attempted(weld_object) <-1 gap(weld_object)", "repair attempted rule"))
+pr.add_rule(pr.Rule("defective(weld_object) <-0 gap(weld_object), repair_attempted(weld_object)", "defective rule"))
+
+# Time step 1: Initial inspection shows the weld is good.
+features_t0 = torch.rand(1, 3)  # Values chosen to indicate a good weld.
+logits_t0, probs_t0, classifier_facts_t0 = weld_quality_checker(features_t0, t1=0, t2=0)
+print("=== Weld Inspection at Time 0 ===")
+print("Logits:", logits_t0)
+print("Probabilities:", probs_t0)
+for fact in classifier_facts_t0:
+    pr.add_fact(fact)
+
+# Time step 2: Second inspection detects a gap.
+features_t1 = torch.rand(1, 3)  # Values chosen to simulate a gap.
+logits_t1, probs_t1, classifier_facts_t1 = weld_quality_checker(features_t1, t1=1, t2=1)
+print("\n=== Weld Inspection at Time 1 ===")
+print("Logits:", logits_t1)
+print("Probabilities:", probs_t1)
+for fact in classifier_facts_t1:
+    pr.add_fact(fact)
+
+
+# Time step 3: Third inspection, the gap still persists.
+features_t2 = torch.rand(1, 3)  # Values chosen to simulate persistent gap.
+logits_t2, probs_t2, classifier_facts_t2 = weld_quality_checker(features_t2, t1=2, t2=2)
+print("\n=== Weld Inspection at Time 2 ===")
+print("Logits:", logits_t2)
+print("Probabilities:", probs_t2)
+for fact in classifier_facts_t2:
+    pr.add_fact(fact)
+
+
+# --- Part 3: Run the Reasoning Engine ---
+
+# Enable atom tracing for debugging the rule application process.
+pr.settings.atom_trace = True
+interpretation = pr.reason(timesteps=2)
+trace = pr.get_rule_trace(interpretation)
+
+print("\n=== Reasoning Rule Trace ===")
+print(trace[0])