Add initialization parameters

Add first notebook

notebooks/benchmark.ipynb

@@ -0,0 +1,414 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Compare STree with different estimators"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Setup\n",
+    "Uncomment the next cell if STree is not already installed"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#\n",
+    "# Google Colab setup\n",
+    "#\n",
+    "#!pip install git+https://github.com/doctorado-ml/stree"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datetime, time\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn import tree\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
+    "from stree import Stree\n",
+    "from odte import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "if not os.path.isfile('data/creditcard.csv'):\n",
+    "    !wget --no-check-certificate --content-disposition http://nube.jccm.es/index.php/s/Zs7SYtZQJ3RQ2H2/download\n",
+    "    !tar xzf creditcard.tgz"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Tests"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2020-06-15 00:46:56\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(datetime.date.today(), time.strftime(\"%H:%M:%S\"))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load dataset and normalize values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load Dataset\n",
+    "df = pd.read_csv('data/creditcard.csv')\n",
+    "df.shape\n",
+    "random_state = 2020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Fraud: 0.173% 492\n",
+      "Valid: 99.827% 284,315\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Fraud: {0:.3f}% {1}\".format(df.Class[df.Class == 1].count()*100/df.shape[0], df.Class[df.Class == 1].count()))\n",
+    "print(\"Valid: {0:.3f}% {1:,}\".format(df.Class[df.Class == 0].count()*100/df.shape[0], df.Class[df.Class == 0].count()))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Normalize Amount\n",
+    "from sklearn.preprocessing import RobustScaler\n",
+    "values = RobustScaler().fit_transform(df.Amount.values.reshape(-1, 1))\n",
+    "df['Amount_Scaled'] = values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X shape: (284807, 29)\n",
+      "y shape: (284807,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove unneeded features\n",
+    "y = df.Class.values\n",
+    "X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
+    "print(f\"X shape: {X.shape}\\ny shape: {y.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Build the models"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Divide dataset\n",
+    "train_size = .7\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=train_size, shuffle=True, random_state=random_state, stratify=y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Linear Tree\n",
+    "linear_tree = tree.DecisionTreeClassifier(random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Random Forest\n",
+    "random_forest = RandomForestClassifier(random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Stree\n",
+    "stree = Stree(random_state=random_state, C=.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# AdaBoost\n",
+    "adaboost = AdaBoostClassifier(random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Gradient Boosting\n",
+    "gradient = GradientBoostingClassifier(random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'Odte' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-15-98265fce1448>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0modte\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mOdte\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m: name 'Odte' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "odte = Odte(random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Do the test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def try_model(name, model):\n",
+    "    print(f\"************************** {name} **********************\")\n",
+    "    now = time.time()\n",
+    "    model.fit(Xtrain, ytrain)\n",
+    "    spent = time.time() - now\n",
+    "    print(f\"Train Model {name} took: {spent:.4} seconds\")\n",
+    "    predict = model.predict(Xtrain)\n",
+    "    predictt = model.predict(Xtest)\n",
+    "    print(f\"=========== {name} - Train {Xtrain.shape[0]:,} samples =============\",)\n",
+    "    print(classification_report(ytrain, predict, digits=6))\n",
+    "    print(f\"=========== {name} - Test {Xtest.shape[0]:,} samples =============\")\n",
+    "    print(classification_report(ytest, predictt, digits=6))\n",
+    "    print(\"Confusion Matrix in Train\")\n",
+    "    print(confusion_matrix(ytrain, predict))\n",
+    "    print(\"Confusion Matrix in Test\")\n",
+    "    print(confusion_matrix(ytest, predictt))\n",
+    "    return f1_score(ytest, predictt), spent"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train & Test models\n",
+    "models = {\n",
+    "    'Linear Tree':linear_tree, 'Random Forest': random_forest, 'Stree (SVM Tree)': stree,  \n",
+    "    'AdaBoost model': adaboost, 'Odte': odte #'Gradient Boost.': gradient\n",
+    "}\n",
+    "\n",
+    "best_f1 = 0\n",
+    "outcomes = []\n",
+    "for name, model in models.items():\n",
+    "    f1, time_spent = try_model(name, model)\n",
+    "    outcomes.append((name, f1, time_spent))\n",
+    "    if f1 > best_f1:\n",
+    "        best_model = name\n",
+    "        best_time = time_spent\n",
+    "        best_f1 = f1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(\"*\"*110)\n",
+    "print(f\"*The best f1 model is {best_model}, with a f1 score: {best_f1:.4} in {best_time:.6} seconds with {train_size:,} samples in train dataset\")\n",
+    "print(\"*\"*110)\n",
+    "for name, f1, time_spent in outcomes:\n",
+    "    print(f\"Model: {name}\\t Time: {time_spent:6.2f} seconds\\t f1: {f1:.4}\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "metadata": {},
+   "source": [
+    "**************************************************************************************************************\n",
+    "*The best f1 model is Random Forest, with a f1 score: 0.8815 in 152.54 seconds with 0.7 samples in train dataset\n",
+    "**************************************************************************************************************\n",
+    "Model: Linear Tree\t Time:  13.52 seconds\t f1: 0.7645\n",
+    "Model: Random Forest\t Time: 152.54 seconds\t f1: 0.8815\n",
+    "Model: Stree (SVM Tree)\t Time:  32.55 seconds\t f1: 0.8603\n",
+    "Model: AdaBoost model\t Time:  47.34 seconds\t f1: 0.7509\n",
+    "Model: Gradient Boost.\t Time: 244.12 seconds\t f1: 0.5259"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "```\n",
+    "******************************************************************************************************************\n",
+    "*The best f1 model is Random Forest, with a f1 score: 0.8815 in 218.966 seconds with 0.7 samples in train dataset\n",
+    "******************************************************************************************************************\n",
+    "Model: Linear Tree       Time:  23.05 seconds\t f1: 0.7645\n",
+    "Model: Random Forest\t Time: 218.97 seconds\t f1: 0.8815\n",
+    "Model: Stree (SVM Tree)\t Time:  49.45 seconds\t f1: 0.8467\n",
+    "Model: AdaBoost model\t Time:  73.83 seconds\t f1: 0.7509\n",
+    "Model: Gradient Boost.\t Time: 388.69 seconds\t f1: 0.5259\n",
+    "Model: Neural Network\t Time:  25.47 seconds\t f1: 0.8328\n",
+    "```"
+   ]
+  }
+ ],
+ "metadata": {
+  "hide_input": false,
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  },
+  "varInspector": {
+   "cols": {
+    "lenName": 16,
+    "lenType": 16,
+    "lenVar": 40
+   },
+   "kernels_config": {
+    "python": {
+     "delete_cmd_postfix": "",
+     "delete_cmd_prefix": "del ",
+     "library": "var_list.py",
+     "varRefreshCmd": "print(var_dic_list())"
+    },
+    "r": {
+     "delete_cmd_postfix": ") ",
+     "delete_cmd_prefix": "rm(",
+     "library": "var_list.r",
+     "varRefreshCmd": "cat(var_dic_list()) "
+    }
+   },
+   "position": {
+    "height": "392px",
+    "left": "1518px",
+    "right": "20px",
+    "top": "40px",
+    "width": "392px"
+   },
+   "types_to_exclude": [
+    "module",
+    "function",
+    "builtin_function_or_method",
+    "instance",
+    "_Feature"
+   ],
+   "window_display": true
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

odte/Odte.py

@@ -34,12 +34,14 @@ class Odte(BaseEstimator, ClassifierMixin):
         min_samples_split: int = 0,
         bootstrap: bool = True,
         split_criteria: str = "min_distance",
+        criterion: str = "gini",
         tol: float = 1e-4,
         gamma="scale",
         degree: int = 3,
         kernel: str = "linear",
         max_features="auto",
         max_samples=None,
+        splitter: str = "random",
     ):
         self.n_estimators = n_estimators
         self.bootstrap = bootstrap
@@ -52,11 +54,14 @@ class Odte(BaseEstimator, ClassifierMixin):
             min_samples_split=min_samples_split,
             max_depth=max_depth,
             split_criteria=split_criteria,
+            criterion=criterion,
             kernel=kernel,
             max_iter=max_iter,
             tol=tol,
             degree=degree,
             gamma=gamma,
+            splitter=splitter,
+            max_features=max_features,
         )
 
     def _initialize_random(self) -> np.random.mtrand.RandomState:

odte/tests/Odte_tests.py

@@ -14,7 +14,7 @@ class Odte_test(unittest.TestCase):
         super().__init__(*args, **kwargs)
 
     def test_max_samples_bogus(self):
-        values = [0, 3000, 1.1, 0.0, "hi!"]
+        values = [0, 3000, 1.1, 0.0, "duck"]
         for max_samples in values:
             with self.assertRaises(ValueError):
                 tclf = Odte(max_samples=max_samples)
@@ -75,7 +75,10 @@ class Odte_test(unittest.TestCase):
         X, y = load_dataset(self._random_state)
         expected = y
         tclf = Odte(
-            random_state=self._random_state, n_estimators=10, kernel="linear"
+            random_state=self._random_state,
+            max_features=None,
+            kernel="linear",
+            max_samples=0.1,
         )
         computed = tclf.fit(X, y).predict(X)
         self.assertListEqual(expected[:27].tolist(), computed[:27].tolist())
@@ -83,6 +86,23 @@ class Odte_test(unittest.TestCase):
     def test_score(self):
         X, y = load_dataset(self._random_state)
         expected = 0.9526666666666667
-        tclf = Odte(random_state=self._random_state, n_estimators=10)
+        tclf = Odte(
+            random_state=self._random_state, max_features=None, n_estimators=10
+        )
+        computed = tclf.fit(X, y).score(X, y)
+        self.assertAlmostEqual(expected, computed)
+
+    def test_score_splitter_max_features(self):
+        X, y = load_dataset(self._random_state, n_features=12, n_samples=150)
+        results = [1.0, 0.94, 0.9933333333333333, 0.9933333333333333]
+        for max_features in ["auto", None]:
+            for splitter in ["best", "random"]:
+                tclf = Odte(
+                    random_state=self._random_state,
+                    splitter=splitter,
+                    max_features=max_features,
+                    n_estimators=10,
+                )
+                expected = results.pop(0)
                 computed = tclf.fit(X, y).score(X, y)
                 self.assertAlmostEqual(expected, computed)

odte/tests/utils.py

@@ -1,10 +1,10 @@
 from sklearn.datasets import make_classification
 
 
-def load_dataset(random_state=0, n_classes=2):
+def load_dataset(random_state=0, n_classes=2, n_features=3, n_samples=1500):
     X, y = make_classification(
-        n_samples=1500,
+        n_samples=n_samples,
-        n_features=3,
+        n_features=n_features,
         n_informative=3,
         n_redundant=0,
         n_repeated=0,