Add tunnel to mysql

add any kernel to script generator

.gitignore

@@ -132,3 +132,4 @@ dmypy.json
 .vscode
 .pre-commit-config.yaml
 experimentation/.myconfig
+experimentation/.tunnel

analysis_mysql.py

@@ -1,5 +1,6 @@
 from experimentation.Sets import Datasets
-from experimentation.Utils import TextColor, MySQL
+from experimentation.Utils import TextColor
+from experimentation.Database import MySQL
 
 models = ["stree", "odte", "adaBoost", "bagging"]
 title = "Best model results"
@@ -78,7 +79,8 @@ def report_footer(agg):
         )
 
 
-database = MySQL.get_connection()
+dbh = MySQL()
+database = dbh.get_connection()
 dt = Datasets(False, False, "tanveer")
 fields = ("Dataset", "Reference")
 for model in models:
@@ -131,3 +133,4 @@ for dataset in dt:
         )
         print(report_line(line))
 report_footer(agg)
+dbh.close()

cross_all.py

@@ -32,22 +32,18 @@ def parse_arguments() -> Tuple[str, str, str, bool, bool]:
         default="aaai",
     )
     args = ap.parse_args()
-    return (
+    return (args.host, args.model, args.set_of_files)
-        args.host,
-        args.model,
-        args.set_of_files,
-    )
 
 
-(
+(host, model, set_of_files) = parse_arguments()
-    host,
-    model,
-    set_of_files,
-) = parse_arguments()
 datasets = Datasets(False, False, set_of_files)
 clf = None
 experiment = Experiment(
-    random_state=1, model=model, host=host, set_of_files=set_of_files
+    random_state=1,
+    model=model,
+    host=host,
+    set_of_files=set_of_files,
+    kernel="any",
 )
 for dataset in datasets:
     print(f"-Cross validation on {dataset[0]}")

experimentation/.myconfig.dist

@@ -1,5 +1,4 @@
 host=<server>
-port=3306
 user=stree
 password=<password>
 database=stree_experiments

experimentation/.tunnel.dist

@@ -0,0 +1,4 @@
+ssh_address_or_host=(<host>, <port>)
+ssh_username=<user>
+ssh_private_key=<path_to>/id_rsa
+remote_bind_address=('127.0.0.1', 3306)

experimentation/Database.py

@@ -3,9 +3,43 @@ import sqlite3
 from datetime import datetime
 from abc import ABC
 from typing import List
-
+import mysql.connector
+from ast import literal_eval as make_tuple
+from sshtunnel import SSHTunnelForwarder
 from .Models import ModelBase
-from .Utils import TextColor, MySQL
+from .Utils import TextColor
+
+
+class MySQL:
+    def __init__(self):
+        self.server = None
+
+    def get_connection(self):
+        config_db = dict()
+        dir_path = os.path.dirname(os.path.realpath(__file__))
+        with open(os.path.join(dir_path, ".myconfig")) as f:
+            for line in f.read().splitlines():
+                key, value = line.split("=")
+                config_db[key] = value
+        config_tunnel = dict()
+        with open(os.path.join(dir_path, ".tunnel")) as f:
+            for line in f.read().splitlines():
+                key, value = line.split("=")
+                config_tunnel[key] = value
+        config_tunnel["remote_bind_address"] = make_tuple(
+            config_tunnel["remote_bind_address"]
+        )
+        config_tunnel["ssh_address_or_host"] = make_tuple(
+            config_tunnel["ssh_address_or_host"]
+        )
+        self.server = SSHTunnelForwarder(**config_tunnel)
+        self.server.daemon_forward_servers = True
+        self.server.start()
+        config_db["port"] = self.server.local_bind_port
+        return mysql.connector.connect(**config_db)
+
+    def close(self):
+        self.server.close()
 
 
 class BD(ABC):
@@ -108,7 +142,8 @@ class BD(ABC):
         :param record: data to insert in database
         :type record: dict
         """
-        database = MySQL.get_connection()
+        dbh = MySQL()
+        database = dbh.get_connection()
         command_insert = (
             "replace into results (date, time, type, accuracy, "
             "dataset, classifier, norm, stand, parameters) values (%s, %s, "
@@ -131,6 +166,7 @@ class BD(ABC):
         cursor = database.cursor()
         cursor.execute(command_insert, values)
         database.commit()
+        dbh.close()
 
     def execute(self, command: str) -> None:
         c = self._con.cursor()

experimentation/Utils.py

@@ -1,7 +1,3 @@
-import os
-import mysql.connector
-
-
 class TextColor:
     BLUE = "\033[94m"
     CYAN = "\033[96m"
@@ -18,15 +14,3 @@ class TextColor:
     ENDC = "\033[0m"
     BOLD = "\033[1m"
     UNDERLINE = "\033[4m"
-
-
-class MySQL:
-    @staticmethod
-    def get_connection():
-        config = dict()
-        dir_path = os.path.dirname(os.path.realpath(__file__))
-        with open(os.path.join(dir_path, ".myconfig")) as f:
-            for line in f.read().splitlines():
-                key, value = line.split("=")
-                config[key] = value
-        return mysql.connector.connect(**config)

param_analysis.ipynb

@@ -30,10 +30,10 @@
     "import json\n",
     "import sqlite3\n",
     "import mysql.connector\n",
-    "from experimentation.Utils import MySQL\n",
+    "from experimentation.Database import MySQL\n",
     "from experimentation.Sets import Datasets\n",
-    "\n",
+    "dbh = MySQL()\n",
-    "database = MySQL.get_connection()"
+    "database = dbh.get_connection()"
    ]
   },
   {
@@ -375,6 +375,15 @@
     "find_values('max_features', 'linear')"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dbh.close()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

report_mysql.py

@@ -1,7 +1,8 @@
 import argparse
 from typing import Tuple
 from experimentation.Sets import Datasets
-from experimentation.Utils import TextColor, MySQL
+from experimentation.Utils import TextColor
+from experimentation.Database import MySQL
 
 models = ["stree", "adaBoost", "bagging", "odte"]
 
@@ -114,7 +115,8 @@ def report_footer(agg):
     classifier,
     exclude_parameters,
 ) = parse_arguments()
-database = MySQL.get_connection()
+dbh = MySQL()
+database = dbh.get_connection()
 dt = Datasets(False, False, "tanveer")
 title = "Best Hyperparameters found for datasets"
 lengths = (10, 8, 10, 10, 30, 3, 3, 9, 11)
@@ -151,3 +153,4 @@ for dataset in dt:
         )
         print(color + report_line(record, agg))
 report_footer(agg)
+dbh.close()

scripts/genall.sh

@@ -2,7 +2,7 @@
 for i in gridsearch gridbest cross; do
 	echo "*** Building $i experiments"
 	for j in stree odte bagging adaBoost; do
-        for k in linear poly rbf; do
+        for k in linear poly rbf any; do
 		    ./genjobs.sh $i $j $k
         done
 	done

setup.py

@@ -36,6 +36,8 @@ setuptools.setup(
         "ipympl",
         "stree",
         "odte",
+        "sshtunnel",
+        "mysql-connector-python",
     ],
     zip_safe=False,
 )

test_validation.ipynb

@@ -21,11 +21,12 @@
     "dataset_name = \"cylinder-bands\"\n",
     "dataset_name = \"pima\"\n",
     "dataset_name = \"conn-bench-sonar-mines-rocks\"\n",
+    "dataset_name = \"libras\"\n",
     "parameters = {\"C\": .15, \"degree\": 6, \"gamma\": .7, \"kernel\": \"poly\", \"max_features\": None, \"max_iter\": 100000.0, \"random_state\": 0}\n",
-    "parameters = {'C': 7, 'degree': 7, 'gamma': 0.1, 'kernel': 'poly', 'max_features': 'auto', 'max_iter': 10000.0, 'random_state': 1, 'split_criteria': 'impurity'}\n",
+    "#parameters = {'C': .17, 'degree': 5, 'gamma': 0.1, 'kernel': 'poly', 'max_features': 'auto', 'max_iter': 10000.0, 'random_state': 1, 'split_criteria': 'impurity'}\n",
-    "parameters = {\"C\": 0.2, \"max_iter\": 10000.0, \"random_state\": 1}\n",
+    "#parameters = {\"C\": 0.2, \"max_iter\": 10000.0, \"random_state\": 1}\n",
-    "parameters = {\"C\": 0.55, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0, \"random_state\": 1}\n",
+    "#parameters = {\"C\": 0.55, \"gamma\": 0.1, \"kernel\": \"rbf\", \"max_iter\": 10000.0, \"random_state\": 1}\n",
-    "parameters = {\"C\": 55, \"max_iter\": 10000.0, \"random_state\": 1}"
+    "#parameters = {\"C\": 55, \"max_iter\": 10000.0, \"random_state\": 1}"
    ]
   },
   {
@@ -40,61 +41,102 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf = Stree(**parameters)\n",
+    "results = cross_validate(clf, X, y, n_jobs=1, return_estimator=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "/Users/rmontanana/.virtualenvs/general/lib/python3.8/site-packages/sklearn/svm/_base.py:976: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
+      "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.9999 counts=(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]), array([19, 19, 19, 19, 20, 19, 19, 19, 19, 20, 19, 19, 19, 19, 20]))\n",
-      "  warnings.warn(\"Liblinear failed to converge, increase \"\n"
+      "root - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.9613 counts=(array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]), array([19, 19, 18, 11, 10, 18,  4, 19, 20,  8, 19,  3, 18, 11]))\n",
+      "root - Down - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.8726 counts=(array([ 5, 13, 14]), array([ 3,  4, 10]))\n",
+      "root - Down - Down - Down, <pure> - Leaf class=5 belief= 1.000000 impurity=0.0000 counts=(array([5]), array([2]))\n",
+      "root - Down - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.7312 counts=(array([ 5, 13, 14]), array([ 1,  4, 10]))\n",
+      "root - Down - Down - Up - Down, <pure> - Leaf class=5 belief= 1.000000 impurity=0.0000 counts=(array([5]), array([1]))\n",
+      "root - Down - Down - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.8631 counts=(array([13, 14]), array([ 4, 10]))\n",
+      "root - Down - Down - Up - Up - Down, <pure> - Leaf class=13 belief= 1.000000 impurity=0.0000 counts=(array([13]), array([4]))\n",
+      "root - Down - Down - Up - Up - Up, <pure> - Leaf class=14 belief= 1.000000 impurity=0.0000 counts=(array([14]), array([10]))\n",
+      "root - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.9359 counts=(array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]), array([19, 19, 18, 11,  7, 18,  4, 19, 20,  8, 19,  3, 14,  1]))\n",
+      "root - Down - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.9548 counts=(array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13]), array([19, 19, 18, 11,  7, 18,  4, 19, 20,  8, 19,  3, 14]))\n",
+      "root - Down - Up - Down - Down, <pure> - Leaf class=12 belief= 1.000000 impurity=0.0000 counts=(array([12]), array([3]))\n",
+      "root - Down - Up - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.9675 counts=(array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 13]), array([19, 19, 18, 11,  7, 18,  4, 19, 20,  8, 19, 14]))\n",
+      "root - Down - Up - Down - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.7248 counts=(array([5, 6, 7]), array([5, 1, 1]))\n",
+      "root - Down - Up - Down - Up - Down - Down, <pure> - Leaf class=6 belief= 1.000000 impurity=0.0000 counts=(array([6]), array([1]))\n",
+      "root - Down - Up - Down - Up - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89) impurity=0.6500 counts=(array([5, 7]), array([5, 1]))\n",
+      "root - Down - Up - Down - Up - Down - Up - Down, <pure> - Leaf class=5 belief= 1.000000 impurity=0.0000 counts=(array([5]), array([5]))\n",
+      "root - Down - Up - Down - Up - Down - Up - Up, <pure> - Leaf class=7 belief= 1.000000 impurity=0.0000 counts=(array([7]), array([1]))\n",
+      "root - Down - Up - Down - Up - Up, <cgaf> - Leaf class=9 belief= 0.118343 impurity=0.9488 counts=(array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 13]), array([19, 19, 18, 11,  2, 17,  3, 19, 20,  8, 19, 14]))\n",
+      "root - Down - Up - Up, <pure> - Leaf class=14 belief= 1.000000 impurity=0.0000 counts=(array([14]), array([1]))\n",
+      "root - Up, <cgaf> - Leaf class=3 belief= 0.208791 impurity=0.8775 counts=(array([ 2,  3,  4,  5,  6,  7, 10, 12, 13, 14]), array([ 1, 19,  9,  9,  1, 15, 11, 16,  1,  9]))\n",
+      "\n"
      ]
     }
    ],
    "source": [
-    "clf = Stree(**parameters)\n",
+    "print(results['estimator'][0])"
-    "results = cross_validate(clf, X, y, n_jobs=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "dt = DecisionTreeClassifier(random_state=1)\n",
+    "resdt = cross_validate(dt, X, y, n_jobs=1, return_estimator=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
-     "data": {
+     "name": "stdout",
-      "text/plain": [
+     "output_type": "stream",
-       "{'fit_time': array([0.0078361 , 0.03171897, 0.01422501, 0.06850815, 0.05387974]),\n",
+     "text": [
-       " 'score_time': array([0.0005939 , 0.00044203, 0.00043583, 0.00050902, 0.00044012]),\n",
+      "0.5416666666666667\n"
-       " 'test_score': array([0.4047619 , 0.61904762, 0.66666667, 0.92682927, 0.58536585])}"
+     ]
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
    "source": [
-    "results"
+    "print(resdt['test_score'].mean()) ki"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
-     "data": {
+     "name": "stdout",
-      "text/plain": [
+     "output_type": "stream",
-       "0.640534262485482"
+     "text": [
-      ]
+      "[0.09722222 0.25       0.54166667 0.31944444 0.13888889]\n"
-     },
+     ]
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "print(results['test_score'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "results['test_score'].mean()"
    ]
@@ -371,9 +413,9 @@
     }
    ],
    "source": [
-    "clf = Stree()\n",
+    "#clf = Stree()\n",
-    "model = GridSearchCV(clf, n_jobs=1, verbose=10, param_grid=param_grid, cv=2)\n",
+    "#model = GridSearchCV(clf, n_jobs=1, verbose=10, param_grid=param_grid, cv=2)\n",
-    "model.fit(X, y)"
+    "#model.fit(X, y)"
    ]
   },
   {
@@ -677,6 +719,68 @@
     "b._base_model.get_model_name()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def _get_subspaces_set(\n",
+    "        self, dataset: np.array, labels: np.array, max_features: int\n",
+    "    ) -> np.array:\n",
+    "        features = range(dataset.shape[1])\n",
+    "        features_sets = list(combinations(features, max_features))\n",
+    "        if len(features_sets) > 1:\n",
+    "            if self._splitter_type == \"random\":\n",
+    "                index = random.randint(0, len(features_sets) - 1)\n",
+    "                return features_sets[index]\n",
+    "            else:\n",
+    "                # get only 3 sets at most\n",
+    "                if len(features_sets) > 3:\n",
+    "                    features_sets = random.sample(features_sets, 3)\n",
+    "                return self._select_best_set(dataset, labels, features_sets)\n",
+    "        else:\n",
+    "            return features_sets[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "def generate_subspaces(features: int, max_features: int) -> list:\n",
+    "    combs = set()\n",
+    "    # take 3 combinations at most\n",
+    "    combinations = 1 if max_features == features else 3\n",
+    "    while len(combs) < combinations:\n",
+    "        combs.add(tuple(sorted(random.sample(range(features), max_features))))\n",
+    "    return list(combs)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(2, 17, 51, 66, 79, 88, 98, 105, 145, 150),\n",
+       " (1, 11, 27, 48, 86, 117, 124, 157, 180, 194),\n",
+       " (11, 31, 42, 43, 61, 79, 138, 149, 150, 189)]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "generate_subspaces(200, 10)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,