fix for py27

rasbt · Dec 20, 2017 · 1e9bab8 · 1e9bab8
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diff --git a/docs/sources/user_guide/evaluate/mcnemar_tables.ipynb b/docs/sources/user_guide/evaluate/mcnemar_tables.ipynb
@@ -95,7 +95,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -135,9 +135,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAREAAADrCAYAAABO6qJFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvAOZPmwAADc1JREFUeJzt3X1sXfV9x/H3hzgyMEK3MuqsoBKkddDxoKhNmQh0ydZuApWpBY1KUwOKRLZpUlVaCTRYNi1dmYI2tYtUbaP0CRhdi9gkMkZAkKzJlvIQHpqKICADtUFkJCn/jLU0iRO++8MnrUttJ/hn514775d05eNzzj3n6yP7fY9voiRVhSRN1nG9HkDSzGZEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjMgslWZ9kwQTbVyVZ9qZ1pyfZmOS/knw7yaLpnnMmOlrXNsmaJKdOsH15kj8fY/1Hk7zrcMefSgNH82Tqa/8HfKyq9iT5deCLwAcmc6Akc6rq4JRON7O9pWvbXb9PTfJcHwVeBV6a5PPfMu9E+kiSBUm2JPlakm1JPp7k9iRPJbmx22coyf1JNiVZd+jVKsm1SZ5I8nXgbaOOubrb95Ekl4137qr636ra0326HzgwxnxPJjkuye8leaVbd2WSP+tmfzzJPwFfmmDOF5J8plt/V7duTpJ/7tbdnOSFqbmiPzN7v1/bVUluS/JvwMe6O5fTD3Ntzkvyr93X84EuUJcAX0hyd/NFO1JV5aNPHsACYCdwPDAf2Nt9HABe7PZZA1zdLV8NfB54B/AdYC5wMiOvRAu6b6hbun1PBL4LBFgFLBtnhjnAA8CHxtj2FeB9wN8Ba4FzgH8ELuzO9wPg5PHm7Ja/Dyzslh8EzgWuGDXnRcD3j8Fruwr44qjPNwKnj3dtgOXAPd3yYuBfuuXbgIuP5vetdyL957mq2ltVu4CdVbWrqg4AP04yBzgLeLjb92HgbOBMYFtVDVfVa8Bz3fbzgCVJNgLrgEHglMOc/4vAfVW1foxtG4APAr8G/H23vAh4vNu+rTs/48wJcKCqtnbLL3XzvHvUMR4Dpuv/du3na3vonG820bV5svt46Dr2hBHpPzXOMoy80j3PyCsP3cfnge8B5yQZSDKPn/7APgM8WFVLq2opcH5VvTreiZP8LfBKVX1hnF3+A/h9Rl6NNwNXAnu6H0SA0e+DjDXnmKcFXmAkRgDv79ZNh36+tvCz1++Qia7N6K/h0Pr9HOX3On1jdea5Gbg9yQrgdUZuv/ckuZORV6rtjHzjU1XrklzYvVoW8DJw1VgH7f7E4FPAt7v9f1BVV47ep6p2JfkFYGNVvZ7kDeBbRzrnBF/TPcCVSTYx8qq773AXYZr07NpO4K1em38H/irJs1X1x0d4jibpfo+SeirJ3KoaTnIRcGNVjftG5bGm36+NdyLqF99M8suMvLdwVF5BZ5C+vjbeiUhq4hurkpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGriP0p0FJ1wwgm79u7dO9TrOWaRN/CFcKrsrqr5k3mi/yjRUZSkvN5TJ5muf8/52FRVk7qgVlxSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqlJX0QkyfokCybYvirJsjHWfzXJK0m+PJ3zHUu2b9/O3Llz2bx5c69HmfEeeOAB9uzZw8qVK3s9yrTqi4g0+AvgD1oPkmTOFMwyK3z2s59lyZIlvR5jVrjmmmu4/vrrez3GtGuKSJIFSbYk+VqSbUk+nuT2JE8lubHbZyjJ/Uk2JVmX5NRu/bVJnkjydeBto465utv3kSSXTXT+qtp5mPk2JHl7kvOS7E8yL8n7k9zabd+R5B+AtUlOTHJ3d+5vJfnVbp+NSW5O8mB3vMFu/ee6GW9JsqPlOvaLLVu2MH/+fE4//fRejzIr7Nw54bfnrDEVdyKnAX8CfAj4CvCnwAXAim77jcA3qmoJ8E3gxiTvAJYDF3bPPRMgySXAL3X7fhD46yRpmG0j8FvAbwP3A7/ZLX+r2/4rwM1VdRnwR8DT3bn/EvibUcd5tKp+F3gR+J0k7wXOqaoLgdXAOxtm7Bs33XQTN9xwQ6/H0AwzFRF5rqr2VtUuYGdV7aqqA8CPu18TzgIe7vZ9GDibkWhsq6rhqnoNeK7bfh6wJMlGYB0wCJzSMNsGRmJ0EfCZbnkpP43Izqp6qVsea85Dnuw+vtTN827gcYCq2gHsbpixL9x3330sWrSIU05pudw6Fk1FRGqcZYAAzwOLu88Xd59/DzgnyUCSefz0B/YZ4MGqWlpVS4Hzq+rVhtm2AL8BDFbVU8A5wCld8AAOjtp3rDnH+roCvAC8DyDJu4Chhhn7wtatW9m4cSOXXHIJDz30ENdddx07dsyK39I0zQaOwjluBm5PsgJ4Hbi6qvYkuRN4DNjOSFSoqnVJLuzuRAp4GbhqvAMnuQm4FJifZD3wkar60aHtVXUgyS5ga7dqF/Df4xzuS8AdSf6zO/cfjnfeqnoyyfYkjwDbgBn/y+/KlSt/8qcIy5cvZ8WKFZxxxhk9nmpmu/XWW1m8eDGDg4MsWrSIyy+/vNcjTYtUvfnmQUciydyqGk5yBrC2qhYewXPK6z112t4u05tV1aQu6NG4E5mt1iQ5FzgJuK7Xw0i94p3IUeSdyNTyTmRqTfZOZKb/ZTNJPWZEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmgz0eoBjzBtJDPcUGRwcZN++fb0eY1YYHBx8Y7LPTVVN5SyaQBIv9hTz+3dqJKGqMpnn+qooqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIatLXEUmyPsmCCbavSrJsjPVfTfJKki8f4XluSHLeBNuXjnWsbv35R3KOmWDhwoVs3ryZTZs2sWHDBs4888xejzRjvfbaayxevJilS5dywQUXsGHDhl6PNH2qqm8fwHpgwQTbVwHLxlh/GrAU+PIRnGPOEewz5rHGO/8Ex6l+fgwNDdVJJ51UQF166aV1xx139Hymwz361cGDB2t4eLiqql588cVatGhRjyeaWHctJ/VzOi13IkkWJNmS5GtJtiX5eJLbkzyV5MZun6Ek9yfZlGRdklO79dcmeSLJ14G3jTrm6m7fR5JcNtH5q2rnYeZbnuTuJPcA1ya5LcnF3bbPdee4JcmOUU87Lck3kjyd5MokbweWAyuTbEwyZzLXqp/s3r2bH/7whwDs37+fAwcO9Hiimeu4445jYGAAGLkrOf/8WXPD+vMmW5+JHsACYCdwPDAf2Nt9HABe7PZZA1zdLV8NfB54B/AdYC5wMvBqd6xLgFu6fU8EvguECe4EmOBOhJEf/geAdJ/fBlwMvBd4oFt3BjA86lhPAnOAdwJP1Cy8Ezn0OPHEE+vRRx+t97znPT2f5XCPfvbyyy/XRRddVKeeemrde++9vR5nQvTbnUjnuaraW1W7gJ1VtauqDgA/7l61zwIe7vZ9GDgbOBPYVlXDVfUa8Fy3/TxgSZKNwDpgEDilcb5Huos32ruBxwGqagewe9S2rVV1sKr+B/jFxnP3rYGBAe666y5Wr17Ns88+2+txZrTTTjuNzZs3s2XLFj7xiU/0epxpM50RqXGWYeQu4nlgcff54u7z7wHnJBlIMo+RsAA8AzxYVUurailwflW92jjfwTHWvQC8DyDJu4ChCb4GgP2M3F3NCkm48847ueeee1i7dm2vx5nR9u3b95Plk08+mXnz5vVwmunVyx+Am4Hbk6wAXmfkV5s9Se4EHgO2MxIVqmpdkgu7O5ECXgauGu/ASW4CLgXmJ1kPfKSqfnS4garqySTbkzwCbGPkV7KJPASs6d6j+VhVvXG4c/SzK664gg9/+MMMDQ2xbNkynn76aT75yU/2eqwZadu2bXz6059mzpw5DA8Ps2bNml6PNG3y83f0x7Ykc6tqOMkZwNqqWjiFx/ZiTzG/f6dGEqoqk3nurLkVn0JrkpwLnARc1+thpH7nnchR5J3I1PP7d2q03In09d9YldT/jIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDUxIpKaGBFJTYyIpCZGRFKTgV4PcIzZDQz1eojZYnBw8I0kvhBOgeOPP373ZJ+bqprKWSQdY6y4pCZGRFITIyKpiRGR1MSISGpiRCQ1MSKSmhgRSU2MiKQmRkRSEyMiqYkRkdTEiEhqYkQkNTEikpoYEUlNjIikJkZEUhMjIqmJEZHUxIhIamJEJDX5f1Nk5fWRxpQGAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<matplotlib.figure.Figure at 0x10c56acc0>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from mlxtend.plotting import checkerboard_plot\n",
     "import matplotlib.pyplot as plt\n",
@@ -166,9 +177,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "model_0 vs model_1 \n",
+      " [[ 4.  1.]\n",
+      " [ 2.  3.]] \n",
+      "\n",
+      "model_0 vs model_2 \n",
+      " [[ 4.  2.]\n",
+      " [ 2.  2.]] \n",
+      "\n",
+      "model_1 vs model_2 \n",
+      " [[ 5.  1.]\n",
+      " [ 0.  4.]] \n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "import numpy as np\n",
     "from mlxtend.evaluate import mcnemar_tables\n",
@@ -197,9 +227,71 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "## mcnemar_tables\n",
+      "\n",
+      "*mcnemar_tables(y_target, *y_model_predictions)*\n",
+      "\n",
+      "Compute multiple 2x2 contigency tables for McNemar's\n",
+      "test or Cochran's Q test.\n",
+      "\n",
+      "**Parameters**\n",
+      "\n",
+      "- `y_target` : array-like, shape=[n_samples]\n",
+      "\n",
+      "    True class labels as 1D NumPy array.\n",
+      "\n",
+      "\n",
+      "- `y_model_predictions` : array-like, shape=[n_samples]\n",
+      "\n",
+      "    Predicted class labels for a model.\n",
+      "\n",
+      "**Returns**\n",
+      "\n",
+      "\n",
+      "- `tables` : dict\n",
+      "\n",
+      "    Dictionary of NumPy arrays with shape=[2, 2]. Each dictionary\n",
+      "    key names the two models to be compared based on the order the\n",
+      "    models were passed as `*y_model_predictions`. The number of\n",
+      "    dictionary entries is equal to the number of pairwise combinations\n",
+      "    between the m models, i.e., \"m choose 2.\"\n",
+      "\n",
+      "    For example the following target array (containing the true labels)\n",
+      "    and 3 models\n",
+      "\n",
+      "    - y_true = np.array([0, 0, 0, 0, 0, 1, 1, 1, 1, 1])\n",
+      "    - y_mod0 = np.array([0, 1, 0, 0, 0, 1, 1, 0, 0, 0])\n",
+      "    - y_mod0 = np.array([0, 0, 1, 1, 0, 1, 1, 0, 0, 0])\n",
+      "    - y_mod0 = np.array([0, 1, 1, 1, 0, 1, 0, 0, 0, 0])\n",
+      "\n",
+      "    would result in the following dictionary:\n",
+      "\n",
+      "\n",
+      "    {'model_0 vs model_1': array([[ 4.,  1.],\n",
+      "    [ 2.,  3.]]),\n",
+      "    'model_0 vs model_2': array([[ 3.,  0.],\n",
+      "    [ 3.,  4.]]),\n",
+      "    'model_1 vs model_2': array([[ 3.,  0.],\n",
+      "    [ 2.,  5.]])}\n",
+      "\n",
+      "    Each array is structured in the following way:\n",
+      "\n",
+      "    - tb[0, 0]: # of samples that both models predicted correctly\n",
+      "    - tb[0, 1]: # of samples that model a got right and model b got wrong\n",
+      "    - tb[1, 0]: # of samples that model b got right and model a got wrong\n",
+      "    - tb[1, 1]: # of samples that both models predicted incorrectly\n",
+      "\n",
+      "\n"
+     ]
+    }
+   ],
    "source": [
     "with open('../../api_modules/mlxtend.evaluate/mcnemar_tables.md', 'r') as f:\n",
     "    s = f.read() \n",

diff --git a/mlxtend/evaluate/mcnemar.py b/mlxtend/evaluate/mcnemar.py
@@ -110,7 +110,8 @@ def mcnemar_tables(y_target, *y_model_predictions):
 
     """
     model_lens = set()
-    for ary in (y_target, *y_model_predictions):
+    y_model_predictions = list(y_model_predictions)
+    for ary in ([y_target] + y_model_predictions):
         if len(ary.shape) != 1:
             raise ValueError('One or more input arrays are not 1-dimensional.')
         model_lens.add(ary.shape[0])