fix flake error msg

Fix wine_iris notebook

notebooks/wine_iris.ipynb

@@ -0,0 +1,177 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "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, cross_validate\n",
+    "from sklearn import tree\n",
+    "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
+    "from stree import Stree\n",
+    "from odte import Odte\n",
+    "\n",
+    "random_state = 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_wine\n",
+    "X, y = load_wine(return_X_y=True)\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_estimators = 20\n",
+    "clf = {}\n",
+    "clf[\"stree\"] = Stree(random_state=random_state, max_depth=5)\n",
+    "clf[\"stree\"].set_params(**dict(splitter=\"best\", kernel=\"linear\", max_features=\"auto\"))\n",
+    "clf[\"odte\"] = Odte(base_estimator=clf[\"stree\"], random_state=random_state, n_estimators=n_estimators, max_features=.8)\n",
+    "clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
+    "clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "****************************** Results for wine ******************************\nTraining stree...\nScore: 94.444 in 0.17 seconds\nTraining odte...\nScore: 97.222 in 2.70 seconds\nTraining adaboost...\nScore: 94.444 in 0.60 seconds\nTraining bagging...\nScore: 100.000 in 2.55 seconds\n"
+    }
+   ],
+   "source": [
+    "print(\"*\"*30,\"Results for wine\", \"*\"*30)\n",
+    "for clf_type, item in clf.items():\n",
+    "    print(f\"Training {clf_type}...\")\n",
+    "    now = time.time()\n",
+    "    item.fit(Xtrain, ytrain)\n",
+    "    print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.datasets import load_iris\n",
+    "X, y = load_iris(return_X_y=True)\n",
+    "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=.2, random_state=random_state)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n_estimators = 10\n",
+    "clf = {}\n",
+    "clf[\"stree\"] = Stree(random_state=random_state, max_depth=3)\n",
+    "clf[\"odte\"] = Odte(random_state=random_state, n_estimators=n_estimators, max_features=1.0)\n",
+    "clf[\"adaboost\"] = AdaBoostClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators, random_state=random_state, algorithm=\"SAMME\")\n",
+    "clf[\"bagging\"] = BaggingClassifier(base_estimator=clf[\"stree\"], n_estimators=n_estimators)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "****************************** Results for iris ******************************\nTraining stree...\nScore: 100.000 in 0.02 seconds\nTraining odte...\nScore: 93.333 in 0.12 seconds\nTraining adaboost...\nScore: 83.333 in 0.01 seconds\nTraining bagging...\nScore: 100.000 in 0.11 seconds\n"
+    }
+   ],
+   "source": [
+    "print(\"*\"*30,\"Results for iris\", \"*\"*30)\n",
+    "for clf_type, item in clf.items():\n",
+    "    print(f\"Training {clf_type}...\")\n",
+    "    now = time.time()\n",
+    "    item.fit(Xtrain, ytrain)\n",
+    "    print(f\"Score: {item.score(Xtest, ytest) * 100:.3f} in {time.time()-now:.2f} seconds\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "{'fit_time': array([0.15752316, 0.18354201, 0.14742589, 0.13827896, 0.14534211]), 'score_time': array([0.00940681, 0.01064587, 0.01085019, 0.00925183, 0.00878191]), 'test_score': array([0.8       , 0.93333333, 0.93333333, 0.93333333, 0.96666667]), 'train_score': array([0.875     , 0.95      , 0.98333333, 0.98333333, 0.95833333])}\n91.333 +- 0.058\n"
+    }
+   ],
+   "source": [
+    "cross = cross_validate(estimator=clf[\"odte\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
+    "print(cross)\n",
+    "print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "{'fit_time': array([0.01752877, 0.03304005, 0.03542018, 0.03398919, 0.03945518]), 'score_time': array([0.00135112, 0.00164104, 0.00159597, 0.0018959 , 0.00189495]), 'test_score': array([1.        , 0.93333333, 0.93333333, 0.93333333, 0.96666667]), 'train_score': array([0.93333333, 0.96666667, 0.96666667, 0.96666667, 0.95      ])}\n95.333 +- 0.027\n"
+    }
+   ],
+   "source": [
+    "cross = cross_validate(estimator=clf[\"adaboost\"], X=X, y=y, n_jobs=-1, return_train_score=True)\n",
+    "print(cross)\n",
+    "print(f\"{np.mean(cross['test_score'])*100:.3f} +- {np.std(cross['test_score']):.3f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "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-final"
+  },
+  "orig_nbformat": 2,
+  "kernelspec": {
+   "name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39",
+   "display_name": "Python 3.7.6 64-bit ('general': venv)"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

odte/Odte.py

@@ -192,10 +192,12 @@ class Odte(BaseEnsemble, ClassifierMixin):
         # Input validation
         X = check_array(X)
         if self.n_features_ != X.shape[1]:
-            raise ValueError("Number of features of the model must "
+            raise ValueError(
+                "Number of features of the model must "
                 "match the input. Model n_features is {0} and "
                 "input n_features is {1}."
-                             "".format(self.n_features_, X.shape[1]))
+                "".format(self.n_features_, X.shape[1])
+            )
         for tree, features in zip(self.estimators_, self.subspaces_):
             n_samples = X.shape[0]
             result = np.zeros((n_samples, self.n_classes_))