Imported existing methods from decision_tree

Compiler/decision_tree_optimized.py

@@ -1,7 +1,7 @@
 from Compiler.types import *
 from Compiler.sorting import *
 from Compiler.library import *
-from Compiler.decision_tree import PrefixSum, PrefixSumR, PrefixSum_inv, PrefixSumR_inv, SortPerm, GroupSum, GroupPrefixSum, GroupFirstOne
+from Compiler.decision_tree import get_type, PrefixSum, PrefixSumR, PrefixSum_inv, PrefixSumR_inv, SortPerm, GroupSum, GroupPrefixSum, GroupFirstOne, output_decision_tree, pick, run_decision_tree, test_decision_tree
 from Compiler import util, oram
 
 from itertools import accumulate
@@ -11,18 +11,6 @@
 debug_split = False
 max_leaves = None
 
-def get_type(x):
-    if isinstance(x, (Array, SubMultiArray)):
-        return x.value_type
-    elif isinstance(x, (tuple, list)):
-        x = x[0] + x[-1]
-        if util.is_constant(x):
-            return cint
-        else:
-            return type(x)
-    else:
-        return type(x)
-
 def GetSortPerm(keys, *to_sort, n_bits=None, time=False):
     """
         Compute and return secret shared permutation that stably sorts :param keys.
@@ -36,7 +24,7 @@ def GetSortPerm(keys, *to_sort, n_bits=None, time=False):
     res = Matrix.create_from(get_vec(x).v if isinstance(get_vec(x), sfix) else x
                              for x in to_sort)
     res = res.transpose()
-    return radix_sort_permutation_from_matrix(bs, res)
+    return radix_sort_from_matrix(bs, res)
 
 def ApplyPermutation(perm, x):
     res = Array.create_from(x)
@@ -374,81 +362,6 @@ def get_tree(self, h, Label):
 def DecisionTreeTraining(x, y, h, binary=False):
     return TreeTrainer(x, y, h, binary=binary).train()
 
-def output_decision_tree(layers):
-    """ Print decision tree output by :py:class:`TreeTrainer`. """
-
-    print_ln('full model %s', util.reveal(layers))
-    for i, layer in enumerate(layers[:-1]):
-        print_ln('level %s:', i)
-        for j, x in enumerate(('NID', 'AID', 'Thr')):
-            print_ln(' %s: %s', x, util.reveal(layer[j]))
-    print_ln('leaves:')
-    for j, x in enumerate(('NID', 'result')):
-        print_ln(' %s: %s', x, util.reveal(layers[-1][j]))
-
-def pick(bits, x):
-    if len(bits) == 1:
-        return bits[0] * x[0]
-    else:
-        try:
-            return x[0].dot_product(bits, x)
-        except:
-            return sum(aa * bb for aa, bb in zip(bits, x))
-
-def run_decision_tree(layers, data):
-    """ Run decision tree against sample data.
-
-    :param layers: tree output by :py:class:`TreeTrainer`
-    :param data: sample data (:py:class:`~Compiler.types.Array`)
-    :returns: binary label
-
-    """
-    h = len(layers) - 1
-    index = 1
-    for k, layer in enumerate(layers[:-1]):
-        assert len(layer) == 3
-        for x in layer:
-            assert len(x) <= 2 ** k
-        bits = layer[0].equal(index, k)
-        threshold = pick(bits, layer[2])
-        key_index = pick(bits, layer[1])
-        if key_index.is_clear:
-            key = data[key_index]
-        else:
-            key = pick(
-                oram.demux(key_index.bit_decompose(util.log2(len(data)))), data)
-        child = 2 * key < threshold
-        index += child * 2 ** k
-    bits = layers[h][0].equal(index, h)
-    return pick(bits, layers[h][1])
-
-def test_decision_tree(name, layers, y, x, n_threads=None, time=False):
-    if time:
-        start_timer(100)
-    n = len(y)
-    x = x.transpose().reveal()
-    y = y.reveal()
-    guess = regint.Array(n)
-    truth = regint.Array(n)
-    correct = regint.Array(2)
-    parts = regint.Array(2)
-    layers = [[Array.create_from(util.reveal(x)) for x in layer]
-              for layer in layers]
-    @for_range_multithread(n_threads, 1, n)
-    def _(i):
-        guess[i] = run_decision_tree([[part[:] for part in layer]
-                                      for layer in layers], x[i]).reveal()
-        truth[i] = y[i].reveal()
-    @for_range(n)
-    def _(i):
-        parts[truth[i]] += 1
-        c = (guess[i].bit_xor(truth[i]).bit_not())
-        correct[truth[i]] += c
-    print_ln('%s for height %s: %s/%s (%s/%s, %s/%s)', name, len(layers) - 1,
-             sum(correct), n, correct[0], parts[0], correct[1], parts[1])
-    if time:
-        stop_timer(100)
-
 class TreeClassifier:
     """ Tree classification that uses
     :py:class:`TreeTrainer` internally.

Compiler/sorting.py

@@ -73,24 +73,4 @@ def _():
         @library.else_
         def _():
             reveal_sort(h, D, reverse=True)
-
-def radix_sort_permutation_from_matrix(bs, D):
-    n = len(D)
-    for b in bs:
-        assert(len(b) == n)
-    B = types.sint.Matrix(n, 2)
-    h = types.Array.create_from(types.sint(types.regint.inc(n)))
-    @library.for_range(len(bs))
-    def _(i):
-        b = bs[i]
-        B.set_column(0, 1 - b.get_vector())
-        B.set_column(1, b.get_vector())
-        c = types.Array.create_from(dest_comp(B))
-        reveal_sort(c, h, reverse=False)
-        @library.if_e(i < len(bs) - 1)
-        def _():
-            reveal_sort(h, bs[i + 1], reverse=True)
-        @library.else_
-        def _():
-            reveal_sort(h, D, reverse=True)
     return h