feat: add CSVSummary callback

docs/usage/3_perform_fit.ipynb

@@ -226,6 +226,7 @@
     "    ProgressBar,\n",
     "    TFSummary,\n",
     "    YAMLSummary,\n",
+    "    CSVSummary,\n",
     ")\n",
     "from tensorwaves.optimizer.minuit import Minuit2\n",
     "\n",
@@ -235,6 +236,7 @@
     "            ProgressBar(),\n",
     "            TFSummary(),\n",
     "            YAMLSummary(\"current_fit_result.yaml\", estimator),\n",
+    "            CSVSummary(\"current_fit_result.csv\", estimator),\n",
     "        ]\n",
     "    )\n",
     ")\n",
@@ -254,6 +256,17 @@
     "```"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "df=pd.read_csv(\"current_fit_result.csv\")\n",
+    "df.plot(\"Iteration\", \"Estimator_Value\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -398,9 +411,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.8.6-final"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 4
-}
+}

src/tensorwaves/optimizer/callbacks.py

@@ -6,6 +6,7 @@
 
 import tensorflow as tf
 import yaml
+from pandas import DataFrame
 from tqdm import tqdm
 
 from tensorwaves.interfaces import Estimator
@@ -84,6 +85,48 @@ def finalize(self) -> None:
         self.__stream.close()
 
 
+class CSVSummary(Callback):
+    def __init__(
+        self,
+        filename: str,
+        estimator: Estimator,
+        step_size: int = 10,
+    ) -> None:
+        """Log fit parameters and the estimator value to a CSV file."""
+        self.__function_call = 0
+        self.__step_size = step_size
+        self.__stream = open(filename, "w")
+        _empty_file(self.__stream)
+        if not isinstance(estimator, Estimator):
+            raise TypeError(f"Requires an in {Estimator.__name__} instance")
+        self.__estimator_type: str = estimator.__class__.__name__
+
+    def __call__(self, parameters: dict, estimator_value: float) -> None:
+        self.__function_call += 1
+        if self.__function_call % self.__step_size != 0:
+            return
+        output_dict = {
+            "Time": datetime.now(),
+            "Iteration": self.__function_call,
+            "Estimator_Type": self.__estimator_type,
+            "Estimator_Value": float(estimator_value),
+            # name: float(value) for name, value in parameters.items()
+        }
+        for name, value in parameters.items():
+            output_dict[name] = float(value)
+
+        df = DataFrame(output_dict, index=[self.__function_call])
+        df.to_csv(
+            self.__stream,
+            mode="a",
+            header=self.__function_call == 10,
+            index=False,
+        )
+
+    def finalize(self) -> None:
+        self.__stream.close()
+
+
 class TFSummary(Callback):
     def __init__(
         self,