Merge branch 'release/1.3'

.docker-compose/Dockerfile

@@ -6,6 +6,10 @@ RUN python -m pip install pulp
 RUN python -m pip install pydantic
 RUN python -m pip install daemonize
 RUN python -m pip install sqspy
+RUN python -m pip install -U pytest
+RUN python -m pip install pytest-cov
+RUN python -m pip install pytest-stub
+RUN python -m pip install pytest-mock
 
 RUN mkdir /app
 WORKDIR /app

Dockerfile

@@ -6,6 +6,10 @@ RUN python -m pip install pulp
 RUN python -m pip install pydantic
 RUN python -m pip install daemonize
 RUN python -m pip install sqspy
+RUN python -m pip install -U pytest
+RUN python -m pip install pytest-cov
+RUN python -m pip install pytest-stub
+RUN python -m pip install pytest-mock
 
 RUN mkdir /app
 WORKDIR /app

app/models/objective_function.py

@@ -27,8 +27,6 @@ class ObjectiveFunction(BaseModel):
         else:
             for target in targets:
                 target.drift = round(target.drift + amount, 2)
-        print(self.tif_targets)
-        print(self.tcc_targets)
         return amount
 
     def update_targets_drift(self, amount: float = 0.0):

app/test/test_example.py

@@ -0,0 +1,60 @@
+# content of example.py
+from pulp import LpProblem, LpVariable, LpMinimize, LpStatus, lpSum
+
+
+def func(x):
+    return x + 1
+
+
+def test_pass():
+    assert func(4) == 5
+
+
+def test_failure():
+    assert func(3) == 5
+
+
+def yosh_loop():
+    Items = [1, 2, 3, 4, 5]
+    tif = {1: 0.2, 2: 0.5, 3: 0.3, 4: 0.8, 5: 0.1}
+    iif = {1: 0.09, 2: 0.2, 3: 0.113, 4: 0.3, 5: 0.1}
+    drift = 0.0
+    drift_limit = 0.2
+    iif_target = 0.5
+    tif_target = 0.9
+    item_vars = LpVariable.dicts("Item", Items, cat="Binary")
+    result = 'Infeasible'
+
+    while drift <= drift_limit:
+        prob = LpProblem("tif_tcc_test", LpMinimize)
+        prob += lpSum([(tif[i] + iif[i]) * item_vars[i]
+                       for i in Items]), "TifTccSum"
+        prob += lpSum([item_vars[i] for i in Items]) == 3, "TotalItems"
+        prob += lpSum([tif[i] * item_vars[i] for i in Items
+                       ]) >= tif_target - (tif_target * drift), 'TifMin'
+        prob += lpSum([tif[i] * item_vars[i] for i in Items
+                       ]) <= tif_target + (tif_target * drift), 'TifMax'
+        prob += lpSum([iif[i] * item_vars[i] for i in Items
+                       ]) >= iif_target - (iif_target * drift), 'TccMin'
+        prob += lpSum([iif[i] * item_vars[i] for i in Items
+                       ]) <= iif_target + (iif_target * drift), 'TccMax'
+
+        prob.solve()
+
+        if LpStatus[prob.status] == "Infeasible":
+            for v in prob.variables():
+                print(v.name, "=", v.varValue)
+
+            drift += 0.02
+        else:
+            for v in prob.variables():
+                print(v.name, "=", v.varValue)
+
+            result = LpStatus[prob.status]
+            break
+
+    return result
+
+
+def test_solver():
+    assert yosh_loop() == 'Optimal'