Add build exreport.csv

src/Experiments.py

@@ -8,7 +8,7 @@ from tqdm import tqdm
 import numpy as np
 import pandas as pd
 from sklearn.model_selection import StratifiedKFold, cross_validate
-from sklearn.tree import DecisionTreeClassifier
+from sklearn.tree import DecisionTreeClassifier, ExtraTreeClassifier
 from stree import Stree
 from Utils import Folders, Files
 
@@ -24,6 +24,8 @@ class Models:
             return Stree
         elif name == "Cart":
             return DecisionTreeClassifier
+        elif name == "ExtraTree":
+            return ExtraTreeClassifier
         else:
             msg = f"No model recognized {name}"
             if name == "Stree" or name == "stree":
@@ -176,6 +178,20 @@ class Experiment:
         self.leaves = []
         self.depths = []
 
+    def _get_complexity(self, result):
+        if self.model_name == "Cart":
+            nodes = result.tree_.node_count
+            depth = result.tree_.max_depth
+            leaves = result.get_n_leaves()
+        if self.model_name == "ExtraTree":
+            nodes = 0
+            leaves = result.get_n_leaves()
+            depth = 0
+        else:
+            nodes, leaves = result.nodes_leaves()
+            depth = result.depth_ if hasattr(result, "depth_") else 0
+        return nodes, leaves, depth
+
     def _n_fold_crossval(self, X, y, hyperparameters):
         if self.scores != []:
             raise ValueError("Must init experiment before!")
@@ -201,17 +217,9 @@ class Experiment:
             self.scores.append(res["test_score"])
             self.times.append(res["fit_time"])
             for result_item in res["estimator"]:
-                if self.model_name == "Cart":
+                nodes_item, leaves_item, depth_item = self._get_complexity(
-                    nodes_item = result_item.tree_.node_count
+                    result_item
-                    depth_item = result_item.tree_.max_depth
+                )
-                    leaves_item = result_item.get_n_leaves()
-                else:
-                    nodes_item, leaves_item = result_item.nodes_leaves()
-                    depth_item = (
-                        result_item.depth_
-                        if hasattr(result_item, "depth_")
-                        else 0
-                    )
                 self.nodes.append(nodes_item)
                 self.leaves.append(leaves_item)
                 self.depths.append(depth_item)

src/Results.py

@@ -1,7 +1,10 @@
 import os
 import json
 import abc
+import shutil
+import subprocess
 import xlsxwriter
+from tqdm import tqdm
 from Experiments import Datasets, BestResults
 from Utils import Folders, Files, Symbols
 
@@ -201,7 +204,7 @@ class ReportBest(BaseReport):
     def header(self):
         self.header_line("*")
         self.header_line(
-            f" Report Best Accuracies with {self.model}" f" in any platform"
+            f" Report Best Accuracies with {self.model} in any platform"
         )
         self.header_line("*")
         print("")
@@ -394,3 +397,77 @@ class SQL(BaseReport):
 
     def footer(self, accuracy):
         self.file.close()
+
+
+class Benchmark:
+    @staticmethod
+    def _process_dataset(results, data):
+        model = data["model"]
+        for record in data["results"]:
+            dataset = record["dataset"]
+            if (model, dataset) in results:
+                if record["accuracy"] > results[model, dataset]:
+                    results[model, dataset] = record["accuracy"]
+            else:
+                results[model, dataset] = record["accuracy"]
+
+    @staticmethod
+    def compile_results():
+        # build Files.exreport
+        result_file_name = os.path.join(Folders.results, Files.exreport)
+        results = {}
+        init_suffix, end_suffix = Files.results_suffixes("")
+        all_files = list(os.walk(Folders.results))
+        for root, _, files in tqdm(all_files, desc="files"):
+            for name in files:
+                if name.startswith(init_suffix) and name.endswith(end_suffix):
+                    file_name = os.path.join(root, name)
+                    with open(file_name) as fp:
+                        data = json.load(fp)
+                        Benchmark._process_dataset(results, data)
+
+        with open(result_file_name, "w") as f:
+            f.write("classifier, dataset, accuracy\n")
+            for (model, dataset), accuracy in results.items():
+                f.write(f"{model}, {dataset}, {accuracy}\n")
+
+    @staticmethod
+    def report():
+        def end_message(message, file):
+            length = 100
+            print("*" * length)
+            print(message)
+            print("*" * length)
+            with open(os.path.join(Folders.results, file)) as f:
+                data = f.read().splitlines()
+                for line in data:
+                    print(line)
+
+        def is_exe(fpath):
+            return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
+
+        # Remove previous results
+        try:
+            shutil.rmtree(Folders.report)
+            os.remove(Files.exreport_pdf)
+        except FileNotFoundError:
+            pass
+        except OSError as e:
+            print("Error: %s : %s" % (Folders.report, e.strerror))
+        # Compute Friedman & Holm Tests
+        fout = open(os.path.join(Folders.results, Files.exreport_output), "w")
+        ferr = open(os.path.join(Folders.results, Files.exreport_err), "w")
+        result = subprocess.run(
+            ["Rscript", os.path.join(Folders.src, "benchmark.r")],
+            stdout=fout,
+            stderr=ferr,
+        )
+        fout.close()
+        ferr.close()
+        if result.returncode != 0:
+            end_message("Error computing benchmark", Files.exreport_err)
+        else:
+            end_message("Benchmark Ok", Files.exreport_output)
+
+        if is_exe(Files.cmd_open):
+            subprocess.run([Files.cmd_open, Files.exreport_pdf])

src/Utils.py

@@ -10,6 +10,7 @@ class Folders:
 
 class Files:
     index = "all.txt"
+    exreport = "exreport.csv"
     exreport_output = "exreport.txt"
     exreport_err = "exreport_err.txt"
     cmd_open = "/usr/bin/open"
@@ -25,7 +26,10 @@ class Files:
 
     @staticmethod
     def results_suffixes(model):
-        return f"results_{model}_", ".json"
+        if model == "":
+            return "results_", ".json"
+        else:
+            return f"results_{model}_", ".json"
 
     @staticmethod
     def dataset(name):

src/benchmark.py

@@ -1,46 +1,5 @@
-import os
+from Results import Benchmark
-import shutil
-import subprocess
-from Utils import Files, Folders
 
-
+benchmark = Benchmark()
-def end_message(message, file):
+benchmark.compile_results()
-    length = 100
+benchmark.report()
-    print("*" * length)
-    print(message)
-    print("*" * length)
-    with open(os.path.join(Folders.results, file)) as f:
-        data = f.read().splitlines()
-        for line in data:
-            print(line)
-
-
-def is_exe(fpath):
-    return os.path.isfile(fpath) and os.access(fpath, os.X_OK)
-
-
-# Remove previous results
-try:
-    shutil.rmtree(Folders.report)
-    os.remove(Files.exreport_pdf)
-except FileNotFoundError:
-    pass
-except OSError as e:
-    print("Error: %s : %s" % (Folders.report, e.strerror))
-# Compute Friedman & Holm Tests
-fout = open(os.path.join(Folders.results, Files.exreport_output), "w")
-ferr = open(os.path.join(Folders.results, Files.exreport_err), "w")
-result = subprocess.run(
-    ["Rscript", os.path.join(Folders.src, "benchmark.r")],
-    stdout=fout,
-    stderr=ferr,
-)
-fout.close()
-ferr.close()
-if result.returncode != 0:
-    end_message("Error computing benchmark", Files.exreport_err)
-else:
-    end_message("Benchmark Ok", Files.exreport_output)
-
-if is_exe(Files.cmd_open):
-    subprocess.run([Files.cmd_open, Files.exreport_pdf])