Detect similar application content

fplus-database/src/database/applications.rs

@@ -423,3 +423,16 @@ pub async fn get_applications_by_client_id(
         .await?;
     Ok(result)
 }
+
+pub async fn get_applications_by_clients_addresses(
+    clients_addresses: Vec<String>,
+) -> Result<Vec<ApplicationModel>, sea_orm::DbErr> {
+    let conn = get_database_connection().await?;
+
+    let result = Application::find()
+        .filter(Column::Id.is_in(clients_addresses))
+        .all(&conn)
+        .await?;
+
+    Ok(result)
+}

fplus-database/src/database/comparable_applications.rs

@@ -3,7 +3,8 @@ use crate::models::comparable_applications::{
     ActiveModel, ApplicationComparableData, Entity as ComparableApplication,
     Model as ComparableApplicationModel,
 };
-use sea_orm::{entity::*, DbErr};
+use sea_orm::prelude::Expr;
+use sea_orm::{entity::*, Condition, DbErr, QueryFilter};
 
 pub async fn create_comparable_application(
     client_address: &str,
@@ -20,6 +21,14 @@ pub async fn create_comparable_application(
 
 pub async fn get_comparable_applications() -> Result<Vec<ComparableApplicationModel>, DbErr> {
     let conn = get_database_connection().await?;
-    let response = ComparableApplication::find().all(&conn).await?;
+    let condition = Condition::any()
+        .add(Expr::cust("char_length(application->>'project_desc') > 40"))
+        .add(Expr::cust(
+            "char_length(application->>'stored_data_desc') > 40",
+        ));
+    let response = ComparableApplication::find()
+        .filter(condition)
+        .all(&conn)
+        .await?;
     Ok(response)
 }

fplus-lib/Cargo.toml

@@ -38,6 +38,9 @@ tempfile = "3.10.1"
 size = "0.5.0-preview2"
 alloy = { version = "0.3.2", features = ["full"] }
 fvm_shared = "4.4.0"
+tfidf-summarizer = "2.0.0"
+ndarray = "0.16.1"
+strsim = "0.10"
 
 [dev-dependencies]
 actix-rt = "2.9.0"

fplus-lib/src/config.rs

@@ -41,6 +41,8 @@ pub fn default_env_vars() -> &'static HashMap<&'static str, &'static str> {
             "0x640bD4be149f40714D95aBcD414338bc7CfF39a3",
         );
         m.insert("AUTOALLOCATION_AMOUNT", "1099511627776"); // 1099511627776 B == 1 TiB
+        m.insert("TFIDF_THRESHOLD", "0.4");
+        m.insert("LEVENSHTEIN_THRESHOLD", "8");
         m
     })
 }

fplus-lib/src/external_services/mod.rs

@@ -2,3 +2,4 @@ pub mod blockchain;
 pub mod dmob;
 pub mod filecoin;
 pub mod github;
+pub mod similarity_detection;

fplus-lib/src/external_services/similarity_detection.rs

@@ -0,0 +1,174 @@
+use crate::{config::get_env_var_or_default, error::LDNError};
+use fplus_database::{
+    database::{
+        applications::get_applications_by_clients_addresses,
+        comparable_applications::get_comparable_applications,
+    },
+    models::comparable_applications::ApplicationComparableData,
+};
+use ndarray::Array1;
+use std::collections::{HashMap, HashSet};
+use strsim::levenshtein;
+
+#[derive(Debug, Clone)]
+pub struct Document {
+    pub client_address: String,
+    pub text: String,
+}
+
+pub async fn detect_similar_applications(
+    client_address: &str,
+    comparable_data: &ApplicationComparableData,
+) -> Result<(), LDNError> {
+    let comparable_applications = get_comparable_applications().await.map_err(|e| {
+        LDNError::New(format!(
+            "Failed to get comparable applications from database: {}",
+            e
+        ))
+    })?;
+
+    let mut projects_descriptions = Vec::new();
+    projects_descriptions.push(Document {
+        client_address: client_address.to_string(),
+        text: comparable_data.project_desc.clone(),
+    });
+
+    let mut stored_data_descriptions = Vec::new();
+    stored_data_descriptions.push(Document {
+        client_address: client_address.to_string(),
+        text: comparable_data.stored_data_desc.clone(),
+    });
+
+    let mut projects_and_stored_data_descriptions = Vec::new();
+    projects_and_stored_data_descriptions.push(Document {
+        client_address: client_address.to_string(),
+        text: comparable_data.project_desc.clone() + &comparable_data.stored_data_desc.clone(),
+    });
+
+    let mut data_set_samples = Vec::new();
+    data_set_samples.push(Document {
+        client_address: client_address.to_string(),
+        text: comparable_data.data_set_sample.clone(),
+    });
+
+    let mut existing_data_owner_name = Vec::new();
+    for app in comparable_applications.iter() {
+        projects_descriptions.push(Document {
+            client_address: app.client_address.clone(),
+            text: app.application.project_desc.clone(),
+        });
+        stored_data_descriptions.push(Document {
+            client_address: app.client_address.clone(),
+            text: app.application.stored_data_desc.clone(),
+        });
+        projects_and_stored_data_descriptions.push(Document {
+            client_address: app.client_address.clone(),
+            text: app.application.project_desc.clone() + &app.application.stored_data_desc.clone(),
+        });
+        data_set_samples.push(Document {
+            client_address: app.client_address.clone(),
+            text: app.application.data_set_sample.clone(),
+        });
+        if comparable_data.data_owner_name == app.application.data_owner_name {
+            existing_data_owner_name.push(app.client_address.clone());
+        }
+    }
+    let similar_project_desciptions = get_similar_texts_tfidf(&projects_descriptions)?;
+    let similar_stored_data_desciptions = get_similar_texts_tfidf(&stored_data_descriptions)?;
+    let similar_project_and_stored_data_desciptions =
+        get_similar_texts_tfidf(&projects_and_stored_data_descriptions)?;
+    let similar_data_set_sample = get_similar_texts_levenshtein(&data_set_samples)?;
+
+    let unique_addresses: HashSet<String> = similar_project_desciptions
+        .into_iter()
+        .chain(similar_stored_data_desciptions.into_iter())
+        .chain(similar_project_and_stored_data_desciptions.into_iter())
+        .chain(similar_data_set_sample.into_iter())
+        .collect();
+    let unique_addresses: Vec<String> = unique_addresses.into_iter().collect();
+
+    let _applications = get_applications_by_clients_addresses(unique_addresses)
+        .await
+        .map_err(|e| LDNError::New(format!("Failed to get applications from database: {}", e)))?;
+    Ok(())
+}
+
+fn get_similar_texts_tfidf(documents: &[Document]) -> Result<Vec<String>, LDNError> {
+    let tokenized_documents: Vec<Vec<String>> = documents
+        .iter()
+        .map(|doc| tfidf_summarizer::tokenize(&doc.text))
+        .collect();
+
+    let df = tfidf_summarizer::document_frequency(&tokenized_documents);
+    let documents_words: Vec<String> = df.keys().cloned().collect();
+    let idf = tfidf_summarizer::inverse_document_frequency(&df, tokenized_documents.len());
+    let tfidf_result: Vec<HashMap<String, f64>> = tokenized_documents
+        .iter()
+        .map(|tokens| tfidf_summarizer::tf_idf(tokens.clone(), &idf))
+        .collect();
+
+    let documents_converted_to_array = convert_to_ndarray(&tfidf_result, &documents_words);
+    let mut similar_applications: Vec<String> = Vec::new();
+    let tfidf_threshold = get_env_var_or_default("TFIDF_THRESHOLD")
+        .parse::<f64>()
+        .map_err(|e| LDNError::New(format!("Parse tfidf threshold score to f64 failed: {}", e)))?;
+    for i in 1..documents_converted_to_array.len() {
+        let similarity = cosine_similarity(
+            &documents_converted_to_array[0],
+            &documents_converted_to_array[i],
+        );
+        if similarity > tfidf_threshold {
+            similar_applications.push(documents[i].client_address.clone());
+        }
+    }
+
+    Ok(similar_applications)
+}
+
+fn get_similar_texts_levenshtein(documents: &[Document]) -> Result<Vec<String>, LDNError> {
+    let levenshtein_threshold = get_env_var_or_default("LEVENSHTEIN_THRESHOLD")
+        .parse::<usize>()
+        .map_err(|e| {
+            LDNError::New(format!(
+                "Parse tfidf threshold score to usize failed: {}",
+                e
+            ))
+        })?;
+
+    let similar_texts: Vec<String> = documents
+        .iter()
+        .skip(1)
+        .filter(|doc| levenshtein(&documents[0].text, &doc.text) < levenshtein_threshold)
+        .map(|doc| doc.client_address.clone())
+        .collect();
+
+    Ok(similar_texts)
+}
+
+fn convert_to_ndarray(
+    tfidf_vectors: &[HashMap<String, f64>],
+    words: &[String],
+) -> Vec<Array1<f64>> {
+    tfidf_vectors
+        .iter()
+        .map(|doc_vector| {
+            let vec: Vec<f64> = words
+                .iter()
+                .map(|word| *doc_vector.get(word).unwrap_or(&0.0))
+                .collect();
+            Array1::from(vec)
+        })
+        .collect()
+}
+
+fn cosine_similarity(v1: &Array1<f64>, v2: &Array1<f64>) -> f64 {
+    let dot_product = v1.dot(v2);
+    let norm_v1 = v1.dot(v1).sqrt();
+    let norm_v2 = v2.dot(v2).sqrt();
+
+    if norm_v1 == 0.0 || norm_v2 == 0.0 {
+        0.0
+    } else {
+        dot_product / (norm_v1 * norm_v2)
+    }
+}