Add local config a finish report_score

experimentation/Database.py

@@ -45,12 +45,15 @@ class MySQL:
         if self._config_db["sqlite"] == "None":
             del self._config_db["sqlite"]
             self._config_db["buffered"] = True
+            # self._config_db["auth_plugin"] = "mysql_native_password"
             self._database = mysql.connector.connect(**self._config_db)
+            # kk = mysql.connector.connect(**self._config_db)
+            # self._database = sqlite3.connect("./data/stree.sqlite")
+            # self._database.row_factory = sqlite3.Row
         else:
             self._database = sqlite3.connect(self._config_db["sqlite"])
             # return dict as a result of select
             self._database.row_factory = sqlite3.Row
-
         return self._database
 
     def find_best(

experimentation/local/.myconfig

@@ -0,0 +1,6 @@
+host=127.0.0.1
+port=3306
+user=stree
+password=xtree
+database=stree
+sqlite=None

experimentation/local/.tunnel

@@ -0,0 +1,5 @@
+ssh_address_or_host=("<host>","<port>")
+ssh_username=<user>
+ssh_private_key=<path_to>/id_rsa
+remote_bind_address=('127.0.0.1', 3306)
+enabled=0

report_score.py

@@ -1,6 +1,7 @@
 import argparse
 import random
 import time
+import warnings
 from datetime import datetime
 import json
 import numpy as np
@@ -132,8 +133,9 @@ def process_dataset(dataset, verbose, model, params):
         np.random.seed(random_state)
         kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
         clf = get_classifier(model, random_state, hyperparameters)
-        print(hyperparameters)
+        with warnings.catch_warnings():
-        res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
+            warnings.filterwarnings("ignore")
+            res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
         scores.append(res["test_score"])
         times.append(res["fit_time"])
         for result_item in res["estimator"]:

report_test.py

@@ -0,0 +1,313 @@
+import argparse
+import random
+import time
+import json
+import numpy as np
+import xlsxwriter
+from stree import Stree
+from sklearn.model_selection import KFold, cross_validate
+from experimentation.Sets import Datasets
+from experimentation.Database import MySQL
+from experimentation.Utils import TextColor
+
+
+CHECK_MARK = "\N{heavy check mark}"
+EXCLAMATION_MARK = "\N{heavy exclamation mark symbol}"
+BLACK_STAR = "\N{black star}"
+
+
+def parse_arguments():
+    ap = argparse.ArgumentParser()
+    ap.add_argument(
+        "-x",
+        "--excel",
+        type=str,
+        default="",
+        required=False,
+        help="generate excel file",
+    )
+    ap.add_argument(
+        "-p", "--parameters", type=str, required=False, default="{}"
+    )
+    args = ap.parse_args()
+    return (
+        args.parameters,
+        args.excel,
+    )
+
+
+def get_classifier(model, random_state, hyperparameters):
+    clf = Stree(random_state=random_state)
+    clf.set_params(**hyperparameters)
+    return clf
+
+
+def process_dataset(dataset, verbose, model, params):
+    X, y = dt.load(dataset)
+    scores = []
+    times = []
+    nodes = []
+    leaves = []
+    depths = []
+    hyperparameters = json.loads(params)
+    for random_state in random_seeds:
+        random.seed(random_state)
+        np.random.seed(random_state)
+        kfold = KFold(shuffle=True, random_state=random_state, n_splits=5)
+        clf = get_classifier(model, random_state, hyperparameters)
+        res = cross_validate(clf, X, y, cv=kfold, return_estimator=True)
+        scores.append(res["test_score"])
+        times.append(res["fit_time"])
+        for result_item in res["estimator"]:
+            nodes_item, leaves_item = result_item.nodes_leaves()
+            depth_item = result_item.depth_
+            nodes.append(nodes_item)
+            leaves.append(leaves_item)
+            depths.append(depth_item)
+    return scores, times, json.dumps(hyperparameters), nodes, leaves, depths
+
+
+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, parameters):
+    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 STree: Parameters: {parameters} ",
+        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, accuracy_total):
+    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
+    message = (
+        f"** Accuracy compared to stree_default (liblinear-ovr) .: "
+        f"{accuracy_total/40.282203:7.4f}"
+    )
+    sheet.write(excel_write_line.row + i + 1, 0, message, bold)
+    time_spent = get_time(start, time.time())
+    sheet.write(excel_write_line.row + i + 3, 0, time_spent, bold)
+
+
+def compute_status(dbh, name, model, accuracy):
+    n_dig = 6
+    ac_round = round(accuracy, n_dig)
+    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 ac_round > round(best_default, n_dig) else " "
+    status = better_stree if ac_round > round(best_stree, n_dig) else status
+    status = best if ac_round > round(best_all, n_dig) else status
+    return status
+
+
+def get_best_score(dbh, name, model):
+    record = dbh.find_best(name, model, "crossval")
+    accuracy = record[5] if record is not None else 0.0
+    acc_std = record[11] if record is not None else 0.0
+    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",
+    "stree_default",
+    "wodt",
+    "j48svm",
+    "oc1",
+    "cart",
+    "baseRaF",
+]
+(
+    parameters,
+    excel,
+) = parse_arguments()
+dbh = MySQL()
+if excel != "":
+    file_name = f"{excel}.xlsx"
+    excel_wb = xlsxwriter.Workbook(file_name)
+    excel_ws = excel_wb.add_worksheet("STree")
+    excel_write_header(excel_wb, excel_ws, parameters)
+database = dbh.get_connection()
+dt = Datasets(normalize=False, standardize=False, set_of_files="tanveer")
+start = time.time()
+print(f"* Process all datasets set with STree: {parameters}")
+print(f"5 Fold Cross Validation with 10 random seeds {random_seeds}\n")
+header_cols = [
+    "Dataset",
+    "Samp",
+    "Var",
+    "Cls",
+    "Nodes",
+    "Leaves",
+    "Depth",
+    "Accuracy",
+    "Time",
+    "Parameters",
+]
+header_lengths = [30, 5, 3, 3, 7, 7, 7, 15, 15, 10]
+model = "stree_default"
+parameters = json.dumps(json.loads(parameters))
+if parameters != "{}" and len(parameters) > 10:
+    header_lengths.pop()
+    header_lengths.append(len(parameters))
+line_col = ""
+for field, underscore in zip(header_cols, header_lengths):
+    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
+    samples, features = X.shape
+    classes = len(np.unique(y))
+    print(
+        f"{name:30s} {samples:5d} {features:3d} {classes:3d} ",
+        end="",
+    )
+    scores, times, hyperparameters, nodes, leaves, depth = process_dataset(
+        dataset[0], verbose=False, model=model, params=parameters
+    )
+    complexity = dict(
+        nodes=float(np.mean(nodes)),
+        leaves=float(np.mean(leaves)),
+        depth=float(np.mean(depth)),
+    )
+    nodes_item, leaves_item, depth_item = complexity.values()
+    print(
+        f"{nodes_item:7.2f} {leaves_item:7.2f} {depth_item:7.2f} ",
+        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 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, accuracy_total)
+    excel_wb.close()
+stop = time.time()
+time_spent = get_time(start, time.time())
+print(f"{time_spent}")
+dbh.close()

score_all_cfs.py

@@ -4,7 +4,6 @@ import warnings
 
 from experimentation.Sets import Datasets
 from stree import Stree
-from mdlp import MDLP
 from mfs import MFS
 
 
@@ -33,11 +32,9 @@ datasets = Datasets(False, False, "tanveer")
 header(filter_name)
 better = worse = equal = 0
 for dataset in datasets:
-    # mdlp = MDLP(random_state=1)
     X, y = datasets.load(dataset[0])
     mfs = MFS(discrete=False)
     now_disc = time.time()
-    # X_disc = mdlp.fit_transform(X, y)
     X_disc = X
     time_disc = time.time() - now_disc
     now_selec = time.time()