Return simple result json

optimizerapi/optimizer.py

@@ -1,7 +1,11 @@
+from json_tricks import dumps
 from ProcessOptimizer import Optimizer, expected_minimum
 from ProcessOptimizer.plots import plot_objective, plot_convergence
 from ProcessOptimizer.utils import cook_estimator
 from ProcessOptimizer.learning.gaussian_process.kernels import Matern
+import matplotlib.pyplot as plt
+import base64
+import io 
 from numbers import Number
 import numpy
 numpy.random.seed(42)
@@ -20,9 +24,59 @@ def run(params: str = None, Xi: [float] = [0.01], yi: [Number] = [1], kappa: flo
     }
     optimizer = Optimizer(space, **hyperparams)
     # TODO call optimizer with proper Xi and Yi values
+    response = {
+        "plots": []
+    }
+    prettyResult = {}
+    response["result"] = prettyResult
+
     result = optimizer.tell([Xi], yi)
-    if params:
-        return 'Run with {params} = {result}'.format(params=params, result=result)
-    else:
-        return 'Run with no parameters = {result}'.format(result=result)
-
+
+    ##################### Copied and modified from views.py::view_report #####################
+
+    next_exps = optimizer.ask(n_points=1)
+    prettyResult["next"] = next_exps
+
+    header_list = []
+    result_list = []
+    if "expected_minimum" in result:
+        temp_exp_min =[]
+        for entry,value in zip(header_list[:-1], result.expected_minimum[0]):
+            temp_exp_min.append([entry, value])
+        exp_min_out = {'value':temp_exp_min, 'result':result.expected_minimum[1]}
+        prettyResult['expected_minimum'] = exp_min_out
+
+    prettyResult['input_header'] = header_list   
+    prettyResult['input_values'] = result_list
+
+    ##################### END #####################
+
+    plot_convergence(result)
+    addPlot(response["plots"], "convergence")
+
+    dimensions = []
+    # names = result['config'].gen_opt_vars(result, request)
+    # for var in names:
+    #     dimensions.append(var.name[:20])           
+    #plot_objective(result, dimensions=dimensions, usepartialdependence=False)
+    #addPlot(result, "objective", debug=True)
+
+    return dumps(response)
+
+def addPlot(result, id="generic", close=True, debug=False):
+    pic_IObytes = io.BytesIO()
+    plt.savefig(pic_IObytes,  format='png')
+    pic_IObytes.seek(0)
+    pic_hash = base64.b64encode(pic_IObytes.read())
+    result.append({
+        "id": id,
+        "plot": str(pic_hash, "utf-8")
+    })
+
+    if debug:
+        with open('tmp/process_optimizer_' + id + '.png', 'wb') as imgfile:
+            plt.savefig(imgfile,  bbox_inches='tight', pad_inches=0)
+
+    # print("IMAGE: " + str(pic_hash, "utf-8"))
+    if close:
+        plt.clf()

requirements.txt

@@ -1,4 +1,5 @@
 connexion==2.7.0
 connexion[swagger-ui]
 Flask==1.1.2
-ProcessOptimizer==0.5.1
+ProcessOptimizer==0.5.1
+json-tricks==3.15.5

tests/test_optimizer.py

@@ -5,4 +5,4 @@ def test_first_sample():
     yi = 1
     result = optimizer.run(params="{}", Xi=Xi, yi=yi)
     print(result)
-    assert "Run with" in result
+    assert "plots" in result