diff --git a/experiments/procesing/contaminator.py b/experiments/procesing/contaminator.py
new file mode 100644
index 0000000..0a3651d
--- /dev/null
+++ b/experiments/procesing/contaminator.py
@@ -0,0 +1,44 @@
+import pandas as pd
+import random
+from sim.rl.behavior_loader import AgentBehaviorModel
+
+base_dir = "/home/velocitatem/Documents/Projects/PHANTOM/experiments"
+human_dir, agent_dir = f"{base_dir}/collected_data/", f"{base_dir}/agents/collected_data/"
+
+
+
+def remap_schema(df : pd.DataFrame, mapping: dict, on: str = "event_type"):
+    df = df.copy()
+    df[on] = df[on].map(mapping).fillna(df[on])
+    return df
+
+
+def contaminate_dataset(df : pd.DataFrame, on : str = "event_type",
+                        contamination_rate: float = 0.1) -> pd.DataFrame:
+    model = AgentBehaviorModel(agent_dir)
+    target_df_schema = df[on].unique().tolist()
+    mapping = {
+        'view': 'view_page'
+        # TODO: define properly for the given dataset
+    }
+    OG_event_distribution = df[on].value_counts(normalize=True).to_dict()
+    # normalize to weights
+    OG_event_distribution = {k: v / sum(OG_event_distribution.values()) for k, v in OG_event_distribution.items()}
+    mapped_df = remap_schema(df, mapping, on=on)
+    N = len(df)
+    N_final = N / (1 - contamination_rate) # TODO: explain this in paper
+    N_contaminate = int(N_final - N)
+    start_event_types = random.choices(list(OG_event_distribution.keys()),
+                                    weights=list(OG_event_distribution.values()), k=N_contaminate)
+    # it makes sense
+    new_trajectories = []
+    for start_event in start_event_types:
+        # sample from og start
+        start = None # TODO: defin start accoding to dataset (randomly sample with weights of event distr)
+        trajectory = model.sample_trajectory(start) # TODO: explain this method in paper
+        new_trajectories.extend(trajectory)
+
+    # TODO: make sure the new trajctories schema conforms with dataset
+    contaminate_df = pd.DataFrame(new_trajectories)
+    df = pd.concat([df, contaminate_df], ignore_index=True)
+    return df