Add scaled_factor solver

geneve/solver/type_scaled_float.py

@@ -0,0 +1,94 @@
+# Licensed to Elasticsearch B.V. under one or more contributor
+# license agreements. See the NOTICE file distributed with
+# this work for additional information regarding copyright
+# ownership. Elasticsearch B.V. licenses this file to you under
+# the Apache License, Version 2.0 (the "License"); you may
+# not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# 	http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+
+"""Constraints solver for scaled_float fields."""
+
+from collections import namedtuple
+import math
+
+from ..constraints import ConflictError
+from ..utils import random
+from . import Field, solver
+
+NumberLimits = namedtuple("NumberLimits", ["MIN", "MAX"])
+
+# https://www.elastic.co/guide/en/elasticsearch/reference/current/number.html
+ScaledFloatLimits = NumberLimits(2**-1074, (2 - 2**-52) * 2**1023)
+
+
+@solver("&scaled_float")
+class ScaledFloatField(Field):
+    valid_constraints = ["==", "!=", ">=", "<=", ">", "<"]
+    ecs_constraints = {
+        "host.cpu.usage": [(">=", 0), ("<=", 1)],
+    }
+
+    def __init__(self, field, constraints, schema, group):
+        super().__init__(field, constraints, schema, group)
+
+        self.min_value = ScaledFloatLimits.MIN
+        self.max_value = ScaledFloatLimits.MAX
+        self.exclude_values = set()
+
+        for k, v, *_ in constraints:
+            if k == ">=":
+                v = float(v)
+                if self.min_value < v:
+                    self.min_value = v
+            elif k == "<=":
+                v = float(v)
+                if self.max_value > v:
+                    self.max_value = v
+            elif k == ">":
+                v = float(v)
+                if self.min_value < math.nextafter(v, math.inf):
+                    self.min_value = math.nextafter(v, math.inf)
+            elif k == "<":
+                v = float(v)
+                if self.max_value > math.nextafter(v, -math.ing):
+                    self.max_value = math.nextafter(v, -math.ing)
+        for k, v, *_ in constraints:
+            if k == "==":
+                v = int(v)
+                if self.value is None or self.value == v:
+                    self.value = v
+                else:
+                    raise ConflictError(f"is already {self.value}, cannot set to {v}", field, k)
+            elif k == "!=":
+                self.exclude_values.add(int(v))
+
+        while self.min_value in self.exclude_values:
+            self.min_value += 1
+        while self.max_value in self.exclude_values:
+            self.max_value -= 1
+        if self.min_value > self.max_value:
+            raise ConflictError(f"empty solution space, {self.min_value} <= x <= {self.max_value}", field)
+        self.exclude_values = {v for v in self.exclude_values if v >= self.min_value and v <= self.max_value}
+        if self.value is not None and self.value in self.exclude_values:
+            if len(self.exclude_values) == 1:
+                raise ConflictError(f"cannot be {self.exclude_values.pop()}", field)
+            else:
+                raise ConflictError(f"cannot be any of ({', '.join(str(v) for v in sorted(self.exclude_values))})", field)
+        if self.value is not None and (self.value < self.min_value or self.value > self.max_value):
+            raise ConflictError(f"out of boundary, {self.min_value} <= {self.value} <= {self.max_value}", field)
+
+    def solve(self, left_attempts, environment):
+        value = self.value
+        while left_attempts and (value is None or value in self.exclude_values):
+            value = random.uniform(self.min_value, self.max_value)
+            left_attempts -= 1
+        return {"value": value, "min": self.min_value, "max": self.max_value, "left_attempts": left_attempts}