From cbedefac0ad93ca841e2b7691d9db84c8c7a24c4 Mon Sep 17 00:00:00 2001
From: Marko Toplak <marko.toplak@gmail.com>
Date: Tue, 18 Jul 2023 13:04:02 +0200
Subject: [PATCH] SubarrayNorms and SubarrayImpute get __eq__ and __hash__

---
 Orange/preprocess/normalize.py  | 23 +++++++++++++++++++++++
 Orange/preprocess/preprocess.py | 22 ++++++++++++++++++++++
 2 files changed, 45 insertions(+)

diff --git a/Orange/preprocess/normalize.py b/Orange/preprocess/normalize.py
index 5847c45354e..1296a93cd24 100644
--- a/Orange/preprocess/normalize.py
+++ b/Orange/preprocess/normalize.py
@@ -16,6 +16,7 @@ def __init__(self, source_vars, offsets, factors):
         self.source_vars = tuple(source_vars)
         self.offsets = np.array(offsets)
         self.factors = np.array(factors)
+        self._hash = None
 
     def __call__(self, data, cols):
         X = data.transform(Domain(self.source_vars[cols])).X
@@ -29,6 +30,28 @@ def __call__(self, data, cols):
         else:
             return (X-offsets.reshape(1, -1)) * (factors.reshape(1, -1))
 
+    def __eq__(self, other):
+        if self is other:
+            return True
+        return type(self) is type(other) \
+            and self.source_vars == other.source_vars \
+            and np.all(self.offsets == other.offsets) \
+            and np.all(self.factors == other.factors)
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self._hash = None
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        del state["_hash"]
+        return state
+
+    def __hash__(self):
+        if self._hash is None:
+            self._hash = hash((self.source_vars, tuple(self.offsets), tuple(self.factors)))
+        return self._hash
+
 
 def compress_norm_to_subarray(domain):
     source_vars = []
diff --git a/Orange/preprocess/preprocess.py b/Orange/preprocess/preprocess.py
index a6195ee2d44..44f7cc6f6f2 100644
--- a/Orange/preprocess/preprocess.py
+++ b/Orange/preprocess/preprocess.py
@@ -155,6 +155,7 @@ class SubarrayImpute:
     def __init__(self, source_vars, vals):
         self.source_vars = tuple(source_vars)
         self.vals = np.array(vals)
+        self._hash = None
 
     def __call__(self, data, cols):
         X = data.transform(Orange.data.Domain(self.source_vars[cols])).X
@@ -172,6 +173,27 @@ def __call__(self, data, cols):
         else:
             return np.where(np.isnan(X), vals.reshape(1, -1), X)
 
+    def __eq__(self, other):
+        if self is other:
+            return True
+        return type(self) is type(other) \
+            and self.source_vars == other.source_vars \
+            and np.all(self.vals == other.vals)
+
+    def __setstate__(self, state):
+        self.__dict__.update(state)
+        self._hash = None
+
+    def __getstate__(self):
+        state = self.__dict__.copy()
+        del state["_hash"]
+        return state
+
+    def __hash__(self):
+        if self._hash is None:
+            self._hash = hash((self.source_vars, tuple(self.vals)))
+        return self._hash
+
 
 def compress_replace_unknowns_to_subarray(domain):
     source_vars = []