Add excel gener. Fayyad discretiz. to report_score

Add stree_default comparison at the end of the report

report_score.py

@@ -4,6 +4,7 @@ import time
 from datetime import datetime
 import json
 import numpy as np
+import xlsxwriter
 from sklearn.tree import DecisionTreeClassifier
 from stree import Stree
 from sklearn.model_selection import KFold, cross_validate
@@ -11,6 +12,12 @@ from experimentation.Sets import Datasets
 from experimentation.Database import MySQL
 from wodt import TreeClassifier
 from experimentation.Utils import TextColor
+from mdlp import MDLP
+
+
+CHECK_MARK = "\N{heavy check mark}"
+EXCLAMATION_MARK = "\N{heavy exclamation mark symbol}"
+BLACK_STAR = "\N{black star}"
 
 
 def parse_arguments():
@@ -52,6 +59,22 @@ def parse_arguments():
         type=int,
         required=True,
     )
+    ap.add_argument(
+        "-x",
+        "--excel",
+        type=str,
+        default="",
+        required=False,
+        help="generate excel file",
+    )
+    ap.add_argument(
+        "-di",
+        "--discretize",
+        type=bool,
+        default=False,
+        required=False,
+        help="Discretize datasets",
+    )
     ap.add_argument(
         "-p", "--parameters", type=str, required=False, default="{}"
     )
@@ -63,6 +86,8 @@ def parse_arguments():
         args.sql,
         bool(args.normalize),
         args.parameters,
+        args.excel,
+        args.discretize,
     )
 
 
@@ -79,6 +104,9 @@ def get_classifier(model, random_state, hyperparameters):
 
 def process_dataset(dataset, verbose, model, params):
     X, y = dt.load(dataset)
+    if discretize:
+        mdlp = MDLP(random_state=1)
+        X = mdlp.fit_transform(X, y)
     scores = []
     times = []
     nodes = []
@@ -179,16 +207,118 @@ def store_string(
     return result
 
 
+def excel_write_line(
+    book,
+    sheet,
+    name,
+    samples,
+    features,
+    classes,
+    accuracy,
+    times,
+    hyperparameters,
+    complexity,
+    status,
+):
+    try:
+        excel_write_line.row += 1
+    except AttributeError:
+        excel_write_line.row = 4
+    size_n = 14
+    decimal = book.add_format({"num_format": "0.000000", "font_size": size_n})
+    integer = book.add_format({"num_format": "#,###", "font_size": size_n})
+    normal = book.add_format({"font_size": size_n})
+    col = 0
+    status, _ = excel_status(status)
+    sheet.write(excel_write_line.row, col, name, normal)
+    sheet.write(excel_write_line.row, col + 1, samples, integer)
+    sheet.write(excel_write_line.row, col + 2, features, normal)
+    sheet.write(excel_write_line.row, col + 3, classes, normal)
+    sheet.write(excel_write_line.row, col + 4, complexity["nodes"], normal)
+    sheet.write(excel_write_line.row, col + 5, complexity["leaves"], normal)
+    sheet.write(excel_write_line.row, col + 6, complexity["depth"], normal)
+    sheet.write(excel_write_line.row, col + 7, accuracy, decimal)
+    sheet.write(excel_write_line.row, col + 8, status, normal)
+    sheet.write(excel_write_line.row, col + 9, np.mean(times), decimal)
+    sheet.write(excel_write_line.row, col + 10, hyperparameters, normal)
+
+
+def excel_write_header(book, sheet):
+    header = book.add_format()
+    header.set_font_size(18)
+    subheader = book.add_format()
+    subheader.set_font_size(16)
+    sheet.write(
+        0,
+        0,
+        f"Process all datasets set with {model}: {set_of_files} "
+        f"norm: {normalize} std: {standardize} discretize: {discretize} "
+        f"store in: {model}",
+        header,
+    )
+    sheet.write(
+        1,
+        0,
+        "5 Fold Cross Validation with 10 random seeds",
+        subheader,
+    )
+    sheet.write(1, 5, f"{random_seeds}", subheader)
+    header_cols = [
+        ("Dataset", 30),
+        ("Samples", 10),
+        ("Variables", 7),
+        ("Classes", 7),
+        ("Nodes", 7),
+        ("Leaves", 7),
+        ("Depth", 7),
+        ("Accuracy", 10),
+        ("Stat", 3),
+        ("Time", 10),
+        ("Parameters", 50),
+    ]
+    bold = book.add_format({"bold": True, "font_size": 14})
+    i = 0
+    for item, length in header_cols:
+        sheet.write(3, i, item, bold)
+        sheet.set_column(i, i, length)
+        i += 1
+
+
+def excel_status(status):
+    if status == TextColor.GREEN + CHECK_MARK + TextColor.ENDC:
+        return EXCLAMATION_MARK, "Accuracy better than stree optimized"
+    elif status == TextColor.RED + BLACK_STAR + TextColor.ENDC:
+        return BLACK_STAR, "Best accuracy of al models"
+    elif status != " ":
+        return CHECK_MARK, "Accuracy better than original stree_default"
+    return " ", ""
+
+
+def excel_write_totals(book, sheet, totals, start):
+    i = 2
+    bold = book.add_format({"bold": True, "font_size": 16})
+    for key, total in totals.items():
+        status, text = excel_status(key)
+        sheet.write(excel_write_line.row + i, 1, status, bold)
+        sheet.write(excel_write_line.row + i, 2, total, bold)
+        sheet.write(excel_write_line.row + i, 3, text, bold)
+        i += 1
+    time_spent = get_time(start, time.time())
+    sheet.write(excel_write_line.row + i + 1, 0, time_spent, bold)
+
+
 def compute_status(dbh, name, model, accuracy):
-    better_default = "\N{heavy check mark}"
+    n_dig = 6
-    better_stree = TextColor.GREEN + "\N{heavy check mark}" + TextColor.ENDC
+    ac_round = round(accuracy, n_dig)
-    best = TextColor.RED + "\N{black star}" + TextColor.ENDC
+    better_default = CHECK_MARK
+    better_stree = TextColor.GREEN + CHECK_MARK + TextColor.ENDC
+    best = TextColor.RED + BLACK_STAR + TextColor.ENDC
     best_default, _ = get_best_score(dbh, name, model)
     best_stree, _ = get_best_score(dbh, name, "stree")
     best_all, _ = get_best_score(dbh, name, models_tree)
-    status = better_default if accuracy >= best_default else " "
+    status = better_default if ac_round > round(best_default, n_dig) else " "
-    status = better_stree if accuracy >= best_stree else status
+    status = better_stree if ac_round > round(best_stree, n_dig) else status
-    status = best if accuracy >= best_all else status
+    status = best if ac_round > round(best_all, n_dig) else status
     return status
 
 
@@ -199,6 +329,12 @@ def get_best_score(dbh, name, model):
     return accuracy, acc_std
 
 
+def get_time(start, stop):
+    hours, rem = divmod(stop - start, 3600)
+    minutes, seconds = divmod(rem, 60)
+    return f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s"
+
+
 random_seeds = [57, 31, 1714, 17, 23, 79, 83, 97, 7, 1]
 models_tree = [
     "stree",
@@ -210,17 +346,32 @@ models_tree = [
     "baseRaF",
 ]
 standardize = False
-(set_of_files, model, dataset, sql, normalize, parameters) = parse_arguments()
+(
+    set_of_files,
+    model,
+    dataset,
+    sql,
+    normalize,
+    parameters,
+    excel,
+    discretize,
+) = parse_arguments()
 dbh = MySQL()
 if sql:
     sql_output = open(f"{model}.sql", "w")
+if excel != "":
+    file_name = f"{excel}.xlsx"
+    excel_wb = xlsxwriter.Workbook(file_name)
+    excel_ws = excel_wb.add_worksheet(model)
+    excel_write_header(excel_wb, excel_ws)
 database = dbh.get_connection()
 dt = Datasets(normalize, standardize, set_of_files)
 start = time.time()
 if dataset == "all":
     print(
         f"* Process all datasets set with {model}: {set_of_files} "
-        f"norm: {normalize} std: {standardize} store in: {model}"
+        f"norm: {normalize} std: {standardize} discretize: {discretize}"
+        f" store in: {model}"
     )
     print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}\n")
     header_cols = [
@@ -245,6 +396,8 @@ if dataset == "all":
         print(f"{field:{underscore}s} ", end="")
         line_col += "=" * underscore + " "
     print(f"\n{line_col}")
+    totals = {}
+    accuracy_total = 0.0
     for dataset in dt:
         name = dataset[0]
         X, y = dt.load(name)  # type: ignore
@@ -268,11 +421,17 @@ if dataset == "all":
             end="",
         )
         accuracy = np.mean(scores)
+        accuracy_total += accuracy
         status = (
             compute_status(dbh, name, model, accuracy)
             if model == "stree_default"
             else " "
         )
+        if status != " ":
+            if status not in totals:
+                totals[status] = 1
+            else:
+                totals[status] += 1
         print(f"{accuracy:8.6f}±{np.std(scores):6.4f}{status}", end="")
         print(f"{np.mean(times):8.6f}±{np.std(times):6.4f} {hyperparameters}")
         if sql:
@@ -280,6 +439,29 @@ if dataset == "all":
                 name, model, scores, times, hyperparameters, complexity
             )
             print(command, file=sql_output)
+        if excel != "":
+            excel_write_line(
+                excel_wb,
+                excel_ws,
+                name,
+                samples,
+                features,
+                classes,
+                accuracy,
+                times,
+                hyperparameters,
+                complexity,
+                status,
+            )
+    for key, value in totals.items():
+        print(f"{key} .....: {value:2d}")
+    print(
+        f"** Accuracy compared to stree_default (liblinear-ovr) .: "
+        f"{accuracy_total/40.282203:7.4f}"
+    )
+    if excel != "":
+        excel_write_totals(excel_wb, excel_ws, totals, start)
+        excel_wb.close()
 else:
     scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
         dataset, verbose=True, model=model, params=parameters
@@ -302,9 +484,8 @@ else:
     )
     print(f"- Hyperparameters ...: {hyperparameters}")
 stop = time.time()
-hours, rem = divmod(stop - start, 3600)
+time_spent = get_time(start, time.time())
-minutes, seconds = divmod(rem, 60)
+print(f"{time_spent}")
-print(f"Time: {int(hours):2d}h {int(minutes):2d}m {int(seconds):2d}s")
 if sql:
     sql_output.close()
 dbh.close()