Reformat test with new black version

update installation instructions

.github/workflows/codeql-analysis.yml

@@ -2,12 +2,12 @@ name: "CodeQL"
 
 on:
   push:
-    branches: [ master ]
+    branches: [master]
   pull_request:
     # The branches below must be a subset of the branches above
-    branches: [ master ]
+    branches: [master]
   schedule:
-    - cron: '16 17 * * 3'
+    - cron: "16 17 * * 3"
 
 jobs:
   analyze:
@@ -17,40 +17,40 @@ jobs:
     strategy:
       fail-fast: false
       matrix:
-        language: [ 'python' ]
+        language: ["python"]
         # CodeQL supports [ 'cpp', 'csharp', 'go', 'java', 'javascript', 'python' ]
         # Learn more:
         # https://docs.github.com/en/free-pro-team@latest/github/finding-security-vulnerabilities-and-errors-in-your-code/configuring-code-scanning#changing-the-languages-that-are-analyzed
 
     steps:
-    - name: Checkout repository
+      - name: Checkout repository
-      uses: actions/checkout@v2
+        uses: actions/checkout@v2
 
-    # Initializes the CodeQL tools for scanning.
+      # Initializes the CodeQL tools for scanning.
-    - name: Initialize CodeQL
+      - name: Initialize CodeQL
-      uses: github/codeql-action/init@v1
+        uses: github/codeql-action/init@v2
-      with:
+        with:
-        languages: ${{ matrix.language }}
+          languages: ${{ matrix.language }}
-        # If you wish to specify custom queries, you can do so here or in a config file.
+          # If you wish to specify custom queries, you can do so here or in a config file.
-        # By default, queries listed here will override any specified in a config file.
+          # By default, queries listed here will override any specified in a config file.
-        # Prefix the list here with "+" to use these queries and those in the config file.
+          # Prefix the list here with "+" to use these queries and those in the config file.
-        # queries: ./path/to/local/query, your-org/your-repo/queries@main
+          # queries: ./path/to/local/query, your-org/your-repo/queries@main
 
-    # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
+      # Autobuild attempts to build any compiled languages  (C/C++, C#, or Java).
-    # If this step fails, then you should remove it and run the build manually (see below)
+      # If this step fails, then you should remove it and run the build manually (see below)
-    - name: Autobuild
+      - name: Autobuild
-      uses: github/codeql-action/autobuild@v1
+        uses: github/codeql-action/autobuild@v2
 
-    # ℹ️ Command-line programs to run using the OS shell.
+      # ℹ️ Command-line programs to run using the OS shell.
-    # 📚 https://git.io/JvXDl
+      # 📚 https://git.io/JvXDl
 
-    # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
+      # ✏️ If the Autobuild fails above, remove it and uncomment the following three lines
-    #    and modify them (or add more) to build your code if your project
+      #    and modify them (or add more) to build your code if your project
-    #    uses a compiled language
+      #    uses a compiled language
 
-    #- run: |
+      #- run: |
-    #   make bootstrap
+      #   make bootstrap
-    #   make release
+      #   make release
 
-    - name: Perform CodeQL Analysis
+      - name: Perform CodeQL Analysis
-      uses: github/codeql-action/analyze@v1
+        uses: github/codeql-action/analyze@v2

.github/workflows/main.yml

@@ -16,9 +16,9 @@ jobs:
         python: [3.8, "3.10"]
 
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
       - name: Set up Python ${{ matrix.python }}
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python }}
       - name: Install dependencies
@@ -35,7 +35,7 @@ jobs:
           coverage run -m unittest -v stree.tests
           coverage xml
       - name: Upload coverage to Codecov
-        uses: codecov/codecov-action@v1
+        uses: codecov/codecov-action@v3
         with:
           token: ${{ secrets.CODECOV_TOKEN }}
           files: ./coverage.xml

README.md

@@ -1,7 +1,7 @@
 ![CI](https://github.com/Doctorado-ML/STree/workflows/CI/badge.svg)
+[![CodeQL](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml/badge.svg)](https://github.com/Doctorado-ML/STree/actions/workflows/codeql-analysis.yml)
 [![codecov](https://codecov.io/gh/doctorado-ml/stree/branch/master/graph/badge.svg)](https://codecov.io/gh/doctorado-ml/stree)
 [![Codacy Badge](https://app.codacy.com/project/badge/Grade/35fa3dfd53a24a339344b33d9f9f2f3d)](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)
-[![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/Doctorado-ML/STree.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/Doctorado-ML/STree/context:python)
 [![PyPI version](https://badge.fury.io/py/STree.svg)](https://badge.fury.io/py/STree)
 ![https://img.shields.io/badge/python-3.8%2B-blue](https://img.shields.io/badge/python-3.8%2B-brightgreen)
 [![DOI](https://zenodo.org/badge/262658230.svg)](https://zenodo.org/badge/latestdoi/262658230)
@@ -15,7 +15,7 @@ Oblique Tree classifier based on SVM nodes. The nodes are built and splitted wit
 ## Installation
 
 ```bash
-pip install git+https://github.com/doctorado-ml/stree
+pip install Stree
 ```
 
 ## Documentation

notebooks/benchmark.ipynb

@@ -178,7 +178,7 @@
    "outputs": [],
    "source": [
     "# Stree\n",
-    "stree = Stree(random_state=random_state, C=.01, max_iter=1e3, kernel=\"liblinear\", multiclass_strategy=\"ovr\")"
+    "stree = Stree(random_state=random_state, C=.01, max_iter=1000, kernel=\"liblinear\", multiclass_strategy=\"ovr\")"
    ]
   },
   {
@@ -198,7 +198,7 @@
    "outputs": [],
    "source": [
     "# SVC (linear)\n",
-    "svc = LinearSVC(random_state=random_state, C=.01, max_iter=1e3)"
+    "svc = LinearSVC(random_state=random_state, C=.01, max_iter=1000)"
    ]
   },
   {

notebooks/gridsearch.ipynb

@@ -1,253 +1,253 @@
 {
- "cells": [
+  "cells": [
-  {
+    {
-   "cell_type": "markdown",
+      "cell_type": "markdown",
-   "metadata": {},
+      "metadata": {},
-   "source": [
+      "source": [
-    "# Test Gridsearch\n",
+        "# Test Gridsearch\n",
-    "with different kernels and different configurations"
+        "with different kernels and different configurations"
-   ]
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Setup\n",
+        "Uncomment the next cell if STree is not already installed"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "#\n",
+        "# Google Colab setup\n",
+        "#\n",
+        "#!pip install git+https://github.com/doctorado-ml/stree\n",
+        "!pip install pandas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "zIHKVxthDZEa"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import os\n",
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from sklearn.ensemble import AdaBoostClassifier\n",
+        "from sklearn.svm import LinearSVC\n",
+        "from sklearn.model_selection import GridSearchCV, train_test_split\n",
+        "from stree import Stree"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "IEmq50QgDZEi"
+      },
+      "outputs": [],
+      "source": [
+        "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": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "z9Q-YUfBDZEq",
+        "outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "random_state=1\n",
+        "\n",
+        "def load_creditcard(n_examples=0):\n",
+        "    df = pd.read_csv('data/creditcard.csv')\n",
+        "    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()))\n",
+        "    y = df.Class\n",
+        "    X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
+        "    if n_examples > 0:\n",
+        "        # Take first n_examples samples\n",
+        "        X = X[:n_examples, :]\n",
+        "        y = y[:n_examples, :]\n",
+        "    else:\n",
+        "        # Take all the positive samples with a number of random negatives\n",
+        "        if n_examples < 0:\n",
+        "            Xt = X[(y == 1).ravel()]\n",
+        "            yt = y[(y == 1).ravel()]\n",
+        "            indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
+        "            X = np.append(Xt, X[indices], axis=0)\n",
+        "            y = np.append(yt, y[indices], axis=0)\n",
+        "    print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
+        "    print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
+        "    print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
+        "    Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
+        "    return Xtrain, Xtest, ytrain, ytest\n",
+        "\n",
+        "data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
+        "# data = load_creditcard(5000)  # Take the first 5000 samples\n",
+        "# data = load_creditcard(0) # Take all the samples\n",
+        "\n",
+        "Xtrain = data[0]\n",
+        "Xtest = data[1]\n",
+        "ytrain = data[2]\n",
+        "ytest = data[3]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "# Tests"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "HmX3kR4PDZEw"
+      },
+      "outputs": [],
+      "source": [
+        "parameters = [{\n",
+        "    'base_estimator': [Stree(random_state=random_state)],\n",
+        "    'n_estimators': [10, 25],\n",
+        "    'learning_rate': [.5, 1],\n",
+        "    'estimator__split_criteria': ['max_samples', 'impurity'],\n",
+        "    'estimator__tol': [.1,  1e-02],\n",
+        "    'estimator__max_depth': [3, 5, 7],\n",
+        "    'estimator__C': [1, 7, 55],\n",
+        "    'estimator__kernel': ['linear']\n",
+        "},\n",
+        "{\n",
+        "    'base_estimator': [Stree(random_state=random_state)],\n",
+        "    'n_estimators': [10, 25],\n",
+        "    'learning_rate': [.5, 1],\n",
+        "    'estimator__split_criteria': ['max_samples', 'impurity'],\n",
+        "    'estimator__tol': [.1,  1e-02],\n",
+        "    'estimator__max_depth': [3, 5, 7],\n",
+        "    'estimator__C': [1, 7, 55],\n",
+        "    'estimator__degree': [3, 5, 7],\n",
+        "    'estimator__kernel': ['poly']\n",
+        "},\n",
+        "{\n",
+        "    'base_estimator': [Stree(random_state=random_state)],\n",
+        "    'n_estimators': [10, 25],\n",
+        "    'learning_rate': [.5, 1],\n",
+        "    'estimator__split_criteria': ['max_samples', 'impurity'],\n",
+        "    'estimator__tol': [.1,  1e-02],\n",
+        "    'estimator__max_depth': [3, 5, 7],\n",
+        "    'estimator__C': [1, 7, 55],\n",
+        "    'estimator__gamma': [.1, 1, 10],\n",
+        "    'estimator__kernel': ['rbf']\n",
+        "}]"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "Stree().get_params()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "CrcB8o6EDZE5",
+        "outputId": "7703413a-d563-4289-a13b-532f38f82762",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
+        "grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
+        "grid.fit(Xtrain, ytrain)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {},
+        "colab_type": "code",
+        "id": "ZjX88NoYDZE8",
+        "outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
+        "tags": []
+      },
+      "outputs": [],
+      "source": [
+        "print(\"Best estimator: \", grid.best_estimator_)\n",
+        "print(\"Best hyperparameters: \", grid.best_params_)\n",
+        "print(\"Best accuracy: \", grid.best_score_)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Best estimator:  AdaBoostClassifier(algorithm='SAMME',\n",
+        "                   base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
+        "                                        split_criteria='max_samples', tol=0.1),\n",
+        "                   learning_rate=0.5, n_estimators=25, random_state=1)\n",
+        "Best hyperparameters:  {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'estimator__C': 55, 'estimator__kernel': 'linear', 'estimator__max_depth': 7, 'estimator__split_criteria': 'max_samples', 'estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "Best accuracy:  0.9511777695988222"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "gridsearch.ipynb",
+      "provenance": []
+    },
+    "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": "3.8.2-final"
+    }
   },
-  {
+  "nbformat": 4,
-   "cell_type": "markdown",
+  "nbformat_minor": 4
-   "metadata": {},
+}
-   "source": [
-    "# Setup\n",
-    "Uncomment the next cell if STree is not already installed"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#\n",
-    "# Google Colab setup\n",
-    "#\n",
-    "#!pip install git+https://github.com/doctorado-ml/stree\n",
-    "!pip install pandas"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "zIHKVxthDZEa"
-   },
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "import os\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "from sklearn.ensemble import AdaBoostClassifier\n",
-    "from sklearn.svm import LinearSVC\n",
-    "from sklearn.model_selection import GridSearchCV, train_test_split\n",
-    "from stree import Stree"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "IEmq50QgDZEi"
-   },
-   "outputs": [],
-   "source": [
-    "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": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "z9Q-YUfBDZEq",
-    "outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "random_state=1\n",
-    "\n",
-    "def load_creditcard(n_examples=0):\n",
-    "    df = pd.read_csv('data/creditcard.csv')\n",
-    "    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()))\n",
-    "    y = df.Class\n",
-    "    X = df.drop(['Class', 'Time', 'Amount'], axis=1).values\n",
-    "    if n_examples > 0:\n",
-    "        # Take first n_examples samples\n",
-    "        X = X[:n_examples, :]\n",
-    "        y = y[:n_examples, :]\n",
-    "    else:\n",
-    "        # Take all the positive samples with a number of random negatives\n",
-    "        if n_examples < 0:\n",
-    "            Xt = X[(y == 1).ravel()]\n",
-    "            yt = y[(y == 1).ravel()]\n",
-    "            indices = random.sample(range(X.shape[0]), -1 * n_examples)\n",
-    "            X = np.append(Xt, X[indices], axis=0)\n",
-    "            y = np.append(yt, y[indices], axis=0)\n",
-    "    print(\"X.shape\", X.shape, \" y.shape\", y.shape)\n",
-    "    print(\"Fraud: {0:.3f}% {1}\".format(len(y[y == 1])*100/X.shape[0], len(y[y == 1])))\n",
-    "    print(\"Valid: {0:.3f}% {1}\".format(len(y[y == 0]) * 100 / X.shape[0], len(y[y == 0])))\n",
-    "    Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, train_size=0.7, shuffle=True, random_state=random_state, stratify=y)\n",
-    "    return Xtrain, Xtest, ytrain, ytest\n",
-    "\n",
-    "data = load_creditcard(-1000) # Take all true samples + 1000 of the others\n",
-    "# data = load_creditcard(5000)  # Take the first 5000 samples\n",
-    "# data = load_creditcard(0) # Take all the samples\n",
-    "\n",
-    "Xtrain = data[0]\n",
-    "Xtest = data[1]\n",
-    "ytrain = data[2]\n",
-    "ytest = data[3]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "HmX3kR4PDZEw"
-   },
-   "outputs": [],
-   "source": [
-    "parameters = [{\n",
-    "    'base_estimator': [Stree(random_state=random_state)],\n",
-    "    'n_estimators': [10, 25],\n",
-    "    'learning_rate': [.5, 1],\n",
-    "    'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
-    "    'base_estimator__tol': [.1,  1e-02],\n",
-    "    'base_estimator__max_depth': [3, 5, 7],\n",
-    "    'base_estimator__C': [1, 7, 55],\n",
-    "    'base_estimator__kernel': ['linear']\n",
-    "},\n",
-    "{\n",
-    "    'base_estimator': [Stree(random_state=random_state)],\n",
-    "    'n_estimators': [10, 25],\n",
-    "    'learning_rate': [.5, 1],\n",
-    "    'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
-    "    'base_estimator__tol': [.1,  1e-02],\n",
-    "    'base_estimator__max_depth': [3, 5, 7],\n",
-    "    'base_estimator__C': [1, 7, 55],\n",
-    "    'base_estimator__degree': [3, 5, 7],\n",
-    "    'base_estimator__kernel': ['poly']\n",
-    "},\n",
-    "{\n",
-    "    'base_estimator': [Stree(random_state=random_state)],\n",
-    "    'n_estimators': [10, 25],\n",
-    "    'learning_rate': [.5, 1],\n",
-    "    'base_estimator__split_criteria': ['max_samples', 'impurity'],\n",
-    "    'base_estimator__tol': [.1,  1e-02],\n",
-    "    'base_estimator__max_depth': [3, 5, 7],\n",
-    "    'base_estimator__C': [1, 7, 55],\n",
-    "    'base_estimator__gamma': [.1, 1, 10],\n",
-    "    'base_estimator__kernel': ['rbf']\n",
-    "}]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "Stree().get_params()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "CrcB8o6EDZE5",
-    "outputId": "7703413a-d563-4289-a13b-532f38f82762",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
-    "grid = GridSearchCV(clf, parameters, verbose=5, n_jobs=-1, return_train_score=True)\n",
-    "grid.fit(Xtrain, ytrain)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "colab": {},
-    "colab_type": "code",
-    "id": "ZjX88NoYDZE8",
-    "outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "print(\"Best estimator: \", grid.best_estimator_)\n",
-    "print(\"Best hyperparameters: \", grid.best_params_)\n",
-    "print(\"Best accuracy: \", grid.best_score_)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Best estimator:  AdaBoostClassifier(algorithm='SAMME',\n",
-    "                   base_estimator=Stree(C=55, max_depth=7, random_state=1,\n",
-    "                                        split_criteria='max_samples', tol=0.1),\n",
-    "                   learning_rate=0.5, n_estimators=25, random_state=1)\n",
-    "Best hyperparameters:  {'base_estimator': Stree(C=55, max_depth=7, random_state=1, split_criteria='max_samples', tol=0.1), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 7, 'base_estimator__split_criteria': 'max_samples', 'base_estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 25}"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Best accuracy:  0.9511777695988222"
-   ]
-  }
- ],
- "metadata": {
-  "colab": {
-   "name": "gridsearch.ipynb",
-   "provenance": []
-  },
-  "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": "3.8.2-final"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}

stree/Splitter.py

@@ -267,7 +267,6 @@ class Splitter:
         random_state=None,
         normalize=False,
     ):
-
         self._clf = clf
         self._random_state = random_state
         if random_state is not None:

stree/Strees.py

@@ -139,7 +139,7 @@ class Stree(BaseEstimator, ClassifierMixin):
         self,
         C: float = 1.0,
         kernel: str = "linear",
-        max_iter: int = 1e5,
+        max_iter: int = int(1e5),
         random_state: int = None,
         max_depth: int = None,
         tol: float = 1e-4,
@@ -153,7 +153,6 @@ class Stree(BaseEstimator, ClassifierMixin):
         multiclass_strategy: str = "ovo",
         normalize: bool = False,
     ):
-
         self.max_iter = max_iter
         self.C = C
         self.kernel = kernel
@@ -485,6 +484,43 @@ class Stree(BaseEstimator, ClassifierMixin):
         X = self.check_predict(X)
         return self.classes_[np.argmax(self.__predict_class(X), axis=1)]
 
+    def get_nodes(self) -> int:
+        """Return the number of nodes in the tree
+
+        Returns
+        -------
+        int
+            number of nodes
+        """
+        nodes = 0
+        for _ in self:
+            nodes += 1
+        return nodes
+
+    def get_leaves(self) -> int:
+        """Return the number of leaves in the tree
+
+        Returns
+        -------
+        int
+            number of leaves
+        """
+        leaves = 0
+        for node in self:
+            if node.is_leaf():
+                leaves += 1
+        return leaves
+
+    def get_depth(self) -> int:
+        """Return the depth of the tree
+
+        Returns
+        -------
+        int
+            depth of the tree
+        """
+        return self.depth_
+
     def nodes_leaves(self) -> tuple:
         """Compute the number of nodes and leaves in the built tree
 

stree/_version.py

@@ -1 +1 @@
-__version__ = "1.3.0"
+__version__ = "1.3.2"

stree/tests/Stree_test.py

@@ -239,6 +239,7 @@ class Stree_test(unittest.TestCase):
             )
             tcl.fit(*load_dataset(self._random_state))
             self.assertEqual(depth, tcl.depth_)
+            self.assertEqual(depth, tcl.get_depth())
 
     def test_unfitted_tree_is_iterable(self):
         tcl = Stree()
@@ -306,10 +307,10 @@ class Stree_test(unittest.TestCase):
             for criteria in ["max_samples", "impurity"]:
                 for kernel in self._kernels:
                     clf = Stree(
-                        max_iter=1e4,
+                        max_iter=int(1e4),
-                        multiclass_strategy="ovr"
+                        multiclass_strategy=(
-                        if kernel == "liblinear"
+                            "ovr" if kernel == "liblinear" else "ovo"
-                        else "ovo",
+                        ),
                         kernel=kernel,
                         random_state=self._random_state,
                     )
@@ -640,10 +641,12 @@ class Stree_test(unittest.TestCase):
         clf = Stree(random_state=self._random_state)
         clf.fit(X, y)
         self.assertEqual(6, clf.depth_)
+        self.assertEqual(6, clf.get_depth())
         X, y = load_wine(return_X_y=True)
         clf = Stree(random_state=self._random_state)
         clf.fit(X, y)
         self.assertEqual(4, clf.depth_)
+        self.assertEqual(4, clf.get_depth())
 
     def test_nodes_leaves(self):
         """Check number of nodes and leaves."""
@@ -657,13 +660,17 @@ class Stree_test(unittest.TestCase):
         clf.fit(X, y)
         nodes, leaves = clf.nodes_leaves()
         self.assertEqual(31, nodes)
+        self.assertEqual(31, clf.get_nodes())
         self.assertEqual(16, leaves)
+        self.assertEqual(16, clf.get_leaves())
         X, y = load_wine(return_X_y=True)
         clf = Stree(random_state=self._random_state)
         clf.fit(X, y)
         nodes, leaves = clf.nodes_leaves()
         self.assertEqual(11, nodes)
+        self.assertEqual(11, clf.get_nodes())
         self.assertEqual(6, leaves)
+        self.assertEqual(6, clf.get_leaves())
 
     def test_nodes_leaves_artificial(self):
         """Check leaves of artificial dataset."""
@@ -682,7 +689,9 @@ class Stree_test(unittest.TestCase):
         clf.tree_ = n1
         nodes, leaves = clf.nodes_leaves()
         self.assertEqual(6, nodes)
+        self.assertEqual(6, clf.get_nodes())
         self.assertEqual(2, leaves)
+        self.assertEqual(2, clf.get_leaves())
 
     def test_bogus_multiclass_strategy(self):
         """Check invalid multiclass strategy."""