look on train.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c84fa89",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0677627e6fba45088f8fe9f7669416a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# pip install -r requirements.txt\n",
    "\n",
    "# pip install --upgrade numpy\n",
    "\n",
    "# conda install -c conda-forge nglview \n",
    "import nglview as nv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b9a800a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import yaml\n",
    "import json\n",
    "\n",
    "import tensorflow as tf\n",
    "\n",
    "from pathlib import Path\n",
    "from pymatgen.core import Structure\n",
    "from sklearn.model_selection import train_test_split\n",
    "from megnet.models import MEGNetModel\n",
    "from megnet.data.crystal import CrystalGraph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc0d15e6",
   "metadata": {},
   "source": [
    "# Посмотрим на распределение таргета"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8127f570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_id</th>\n",
       "      <th>band_gap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6141cf0f51c1cbd9654b8870</td>\n",
       "      <td>1.0843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6141cf1051c1cbd9654b8872</td>\n",
       "      <td>1.1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6141cf11cc0e69a0cf28ab35</td>\n",
       "      <td>1.1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6141cf11b842c2e72e2f2d48</td>\n",
       "      <td>1.8068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6141cf11ae4fb853db2e3f14</td>\n",
       "      <td>0.3600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        _id  band_gap\n",
       "0  6141cf0f51c1cbd9654b8870    1.0843\n",
       "1  6141cf1051c1cbd9654b8872    1.1102\n",
       "2  6141cf11cc0e69a0cf28ab35    1.1484\n",
       "3  6141cf11b842c2e72e2f2d48    1.8068\n",
       "4  6141cf11ae4fb853db2e3f14    0.3600"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets = pd.read_csv('data/targets.csv')\n",
    "\n",
    "targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4d97a4d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9611b96a90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(targets.band_gap)), targets.band_gap, s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "98b1a59a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f960fb5a9d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(targets.band_gap)), sorted(targets.band_gap), s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdd77a68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f960fa17730>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(targets[(targets.band_gap < 1.25) & (targets.band_gap > 0.75)].band_gap)), \n",
    "            sorted(targets[(targets.band_gap < 1.25) & (targets.band_gap > 0.75)].band_gap), s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "7cd53e6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9157e58370>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(targets[targets.band_gap < 0.5].band_gap)), sorted(targets[targets.band_gap < 0.5].band_gap), s=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "4ab42d85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f62841bec10>"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(np.arange(len(targets[targets.band_gap > 1.5].band_gap)), sorted(targets[targets.band_gap > 1.5].band_gap), s=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0190b75e",
   "metadata": {},
   "source": [
    "# Посмотрим на данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "cbb53f7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_pymatgen_dict(file):\n",
    "    with open(file, \"r\") as f:\n",
    "        d = json.load(f)\n",
    "    return Structure.from_dict(d)\n",
    "\n",
    "\n",
    "def prepare_dataset(dataset_path):\n",
    "    dataset_path = Path(dataset_path)\n",
    "    targets = pd.read_csv(dataset_path / \"targets.csv\", index_col=0)\n",
    "    struct = {\n",
    "        item.name.strip(\".json\"): read_pymatgen_dict(item)\n",
    "        for item in (dataset_path / \"structures\").iterdir()\n",
    "    }\n",
    "\n",
    "    data = pd.DataFrame(columns=[\"structures\"], index=struct.keys())\n",
    "    data = data.assign(structures=struct.values(), targets=targets)\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b4dac3ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structures</th>\n",
       "      <th>targets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6142031bee0a3fd43fb47e23</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141d46031cf3ef3d4a9eee8</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614211354e27a1844a5f05b4</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614346254e27a1844a5f0a14</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141e2eb9cbada84a8676ab7</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.8092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614253223ac25c70a5c6cb7d</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.4256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614235e04e27a1844a5f06c2</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6143bc5931cf3ef3d4a9f792</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.3925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61429809ee0a3fd43fb47f6f</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.3511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141e79b4e27a1844a5f03a4</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1463</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2966 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 structures  \\\n",
       "6142031bee0a3fd43fb47e23  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141d46031cf3ef3d4a9eee8  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614211354e27a1844a5f05b4  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614346254e27a1844a5f0a14  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141e2eb9cbada84a8676ab7  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "...                                                                     ...   \n",
       "614253223ac25c70a5c6cb7d  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614235e04e27a1844a5f06c2  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6143bc5931cf3ef3d4a9f792  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "61429809ee0a3fd43fb47f6f  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141e79b4e27a1844a5f03a4  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "\n",
       "                          targets  \n",
       "6142031bee0a3fd43fb47e23   0.2754  \n",
       "6141d46031cf3ef3d4a9eee8   0.2839  \n",
       "614211354e27a1844a5f05b4   1.1450  \n",
       "614346254e27a1844a5f0a14   1.1405  \n",
       "6141e2eb9cbada84a8676ab7   1.8092  \n",
       "...                           ...  \n",
       "614253223ac25c70a5c6cb7d   0.4256  \n",
       "614235e04e27a1844a5f06c2   1.1441  \n",
       "6143bc5931cf3ef3d4a9f792   0.3925  \n",
       "61429809ee0a3fd43fb47f6f   0.3511  \n",
       "6141e79b4e27a1844a5f03a4   1.1463  \n",
       "\n",
       "[2966 rows x 2 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = prepare_dataset('data/dichalcogenides_public/')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3dee4985",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structures</th>\n",
       "      <th>targets</th>\n",
       "      <th>class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6142031bee0a3fd43fb47e23</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2754</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141d46031cf3ef3d4a9eee8</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2839</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614211354e27a1844a5f05b4</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1450</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614346254e27a1844a5f0a14</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1405</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141e2eb9cbada84a8676ab7</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.8092</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614253223ac25c70a5c6cb7d</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.4256</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614235e04e27a1844a5f06c2</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1441</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6143bc5931cf3ef3d4a9f792</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.3925</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61429809ee0a3fd43fb47f6f</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.3511</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141e79b4e27a1844a5f03a4</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1463</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2966 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 structures  \\\n",
       "6142031bee0a3fd43fb47e23  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141d46031cf3ef3d4a9eee8  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614211354e27a1844a5f05b4  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614346254e27a1844a5f0a14  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141e2eb9cbada84a8676ab7  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "...                                                                     ...   \n",
       "614253223ac25c70a5c6cb7d  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614235e04e27a1844a5f06c2  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6143bc5931cf3ef3d4a9f792  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "61429809ee0a3fd43fb47f6f  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141e79b4e27a1844a5f03a4  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "\n",
       "                          targets  class  \n",
       "6142031bee0a3fd43fb47e23   0.2754      0  \n",
       "6141d46031cf3ef3d4a9eee8   0.2839      0  \n",
       "614211354e27a1844a5f05b4   1.1450      1  \n",
       "614346254e27a1844a5f0a14   1.1405      1  \n",
       "6141e2eb9cbada84a8676ab7   1.8092      2  \n",
       "...                           ...    ...  \n",
       "614253223ac25c70a5c6cb7d   0.4256      0  \n",
       "614235e04e27a1844a5f06c2   1.1441      1  \n",
       "6143bc5931cf3ef3d4a9f792   0.3925      0  \n",
       "61429809ee0a3fd43fb47f6f   0.3511      0  \n",
       "6141e79b4e27a1844a5f03a4   1.1463      1  \n",
       "\n",
       "[2966 rows x 3 columns]"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'] = -1\n",
    "data.loc[(data.targets > 0.25) & (data.targets < 0.5), 'class'] = 0\n",
    "data.loc[(data.targets > 0.75) & (data.targets < 1.25), 'class'] = 1\n",
    "data.loc[(data.targets > 1.5), 'class'] = 2\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "ecab3c8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       " 0    1451\n",
       " 1    1114\n",
       " 2     374\n",
       "-1      27\n",
       "Name: class, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['class'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "64e30b10",
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 10\n",
    "sample = data[data['class'] == 2].sample(n)\n",
    "\n",
    "other = data[data['targets'] != 2].sample(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "d7c432ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Structure Summary\n",
       "Lattice\n",
       "    abc : 25.5225256 25.5225256 14.879004\n",
       " angles : 90.0 90.0 119.99999999999999\n",
       " volume : 8393.668021812642\n",
       "      A : 25.5225256 0.0 1.5628039641098191e-15\n",
       "      B : -12.761262799999994 22.10315553833868 1.5628039641098191e-15\n",
       "      C : 0.0 0.0 14.879004\n",
       "PeriodicSite: Mo (0.0000, 1.8419, 3.7198) [0.0417, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (-1.5952, 4.6048, 3.7198) [0.0417, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (-3.1903, 7.3677, 3.7198) [0.0417, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (-4.7855, 10.1306, 3.7198) [0.0417, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (-6.3806, 12.8935, 3.7198) [0.0417, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (-7.9758, 15.6564, 3.7198) [0.0417, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (-11.1661, 21.1822, 3.7198) [0.0417, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (3.1903, 1.8419, 3.7198) [0.1667, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (1.5952, 4.6048, 3.7198) [0.1667, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (0.0000, 7.3677, 3.7198) [0.1667, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (-1.5952, 10.1306, 3.7198) [0.1667, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (-3.1903, 12.8935, 3.7198) [0.1667, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (-4.7855, 15.6564, 3.7198) [0.1667, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (-6.3806, 18.4193, 3.7198) [0.1667, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (-7.9758, 21.1822, 3.7198) [0.1667, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (6.3806, 1.8419, 3.7198) [0.2917, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (4.7855, 4.6048, 3.7198) [0.2917, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (3.1903, 7.3677, 3.7198) [0.2917, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (1.5952, 10.1306, 3.7198) [0.2917, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (0.0000, 12.8935, 3.7198) [0.2917, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (-1.5952, 15.6564, 3.7198) [0.2917, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (-3.1903, 18.4193, 3.7198) [0.2917, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (-4.7855, 21.1822, 3.7198) [0.2917, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (9.5709, 1.8419, 3.7198) [0.4167, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (7.9758, 4.6048, 3.7198) [0.4167, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (6.3806, 7.3677, 3.7198) [0.4167, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (4.7855, 10.1306, 3.7198) [0.4167, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (3.1903, 12.8935, 3.7198) [0.4167, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (1.5952, 15.6564, 3.7198) [0.4167, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (0.0000, 18.4193, 3.7198) [0.4167, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (-1.5952, 21.1822, 3.7198) [0.4167, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (12.7613, 1.8419, 3.7198) [0.5417, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (11.1661, 4.6048, 3.7198) [0.5417, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (9.5709, 7.3677, 3.7198) [0.5417, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (7.9758, 10.1306, 3.7198) [0.5417, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (6.3806, 12.8935, 3.7198) [0.5417, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (4.7855, 15.6564, 3.7198) [0.5417, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (3.1903, 18.4193, 3.7198) [0.5417, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (1.5952, 21.1822, 3.7198) [0.5417, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (15.9516, 1.8419, 3.7198) [0.6667, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (14.3564, 4.6048, 3.7198) [0.6667, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (12.7613, 7.3677, 3.7198) [0.6667, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (11.1661, 10.1306, 3.7198) [0.6667, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (9.5709, 12.8935, 3.7198) [0.6667, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (7.9758, 15.6564, 3.7198) [0.6667, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (6.3806, 18.4193, 3.7198) [0.6667, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (4.7855, 21.1822, 3.7198) [0.6667, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (19.1419, 1.8419, 3.7198) [0.7917, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (17.5467, 4.6048, 3.7198) [0.7917, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (15.9516, 7.3677, 3.7198) [0.7917, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (14.3564, 10.1306, 3.7198) [0.7917, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (12.7613, 12.8935, 3.7198) [0.7917, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (11.1661, 15.6564, 3.7198) [0.7917, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (9.5709, 18.4193, 3.7198) [0.7917, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (7.9758, 21.1822, 3.7198) [0.7917, 0.9583, 0.2500]\n",
       "PeriodicSite: Mo (22.3322, 1.8419, 3.7198) [0.9167, 0.0833, 0.2500]\n",
       "PeriodicSite: Mo (20.7371, 4.6048, 3.7198) [0.9167, 0.2083, 0.2500]\n",
       "PeriodicSite: Mo (19.1419, 7.3677, 3.7198) [0.9167, 0.3333, 0.2500]\n",
       "PeriodicSite: Mo (17.5467, 10.1306, 3.7198) [0.9167, 0.4583, 0.2500]\n",
       "PeriodicSite: Mo (15.9516, 12.8935, 3.7198) [0.9167, 0.5833, 0.2500]\n",
       "PeriodicSite: Mo (14.3564, 15.6564, 3.7198) [0.9167, 0.7083, 0.2500]\n",
       "PeriodicSite: Mo (12.7613, 18.4193, 3.7198) [0.9167, 0.8333, 0.2500]\n",
       "PeriodicSite: Mo (11.1661, 21.1822, 3.7198) [0.9167, 0.9583, 0.2500]\n",
       "PeriodicSite: W (-9.5709, 18.4193, 3.7198) [0.0417, 0.8333, 0.2500]\n",
       "PeriodicSite: Se (14.3564, 6.4468, 5.2846) [0.7083, 0.2917, 0.3552]\n",
       "PeriodicSite: Se (7.9758, 17.4983, 2.1549) [0.7083, 0.7917, 0.1448]\n",
       "PeriodicSite: S (1.5952, 0.9210, 2.1549) [0.0833, 0.0417, 0.1448]\n",
       "PeriodicSite: S (-0.0000, 3.6839, 2.1549) [0.0833, 0.1667, 0.1448]\n",
       "PeriodicSite: S (-1.5952, 6.4468, 2.1549) [0.0833, 0.2917, 0.1448]\n",
       "PeriodicSite: S (-3.1903, 9.2096, 2.1549) [0.0833, 0.4167, 0.1448]\n",
       "PeriodicSite: S (-4.7855, 11.9725, 2.1549) [0.0833, 0.5417, 0.1448]\n",
       "PeriodicSite: S (-6.3806, 14.7354, 2.1549) [0.0833, 0.6667, 0.1448]\n",
       "PeriodicSite: S (-7.9758, 17.4983, 2.1549) [0.0833, 0.7917, 0.1448]\n",
       "PeriodicSite: S (-9.5709, 20.2612, 2.1549) [0.0833, 0.9167, 0.1448]\n",
       "PeriodicSite: S (4.7855, 0.9210, 2.1549) [0.2083, 0.0417, 0.1448]\n",
       "PeriodicSite: S (3.1903, 3.6839, 2.1549) [0.2083, 0.1667, 0.1448]\n",
       "PeriodicSite: S (1.5952, 6.4468, 2.1549) [0.2083, 0.2917, 0.1448]\n",
       "PeriodicSite: S (-0.0000, 9.2096, 2.1549) [0.2083, 0.4167, 0.1448]\n",
       "PeriodicSite: S (-1.5952, 11.9725, 2.1549) [0.2083, 0.5417, 0.1448]\n",
       "PeriodicSite: S (-3.1903, 14.7354, 2.1549) [0.2083, 0.6667, 0.1448]\n",
       "PeriodicSite: S (-4.7855, 17.4983, 2.1549) [0.2083, 0.7917, 0.1448]\n",
       "PeriodicSite: S (-6.3806, 20.2612, 2.1549) [0.2083, 0.9167, 0.1448]\n",
       "PeriodicSite: S (7.9758, 0.9210, 2.1549) [0.3333, 0.0417, 0.1448]\n",
       "PeriodicSite: S (6.3806, 3.6839, 2.1549) [0.3333, 0.1667, 0.1448]\n",
       "PeriodicSite: S (4.7855, 6.4468, 2.1549) [0.3333, 0.2917, 0.1448]\n",
       "PeriodicSite: S (3.1903, 9.2096, 2.1549) [0.3333, 0.4167, 0.1448]\n",
       "PeriodicSite: S (1.5952, 11.9725, 2.1549) [0.3333, 0.5417, 0.1448]\n",
       "PeriodicSite: S (0.0000, 14.7354, 2.1549) [0.3333, 0.6667, 0.1448]\n",
       "PeriodicSite: S (-1.5952, 17.4983, 2.1549) [0.3333, 0.7917, 0.1448]\n",
       "PeriodicSite: S (-3.1903, 20.2612, 2.1549) [0.3333, 0.9167, 0.1448]\n",
       "PeriodicSite: S (11.1661, 0.9210, 2.1549) [0.4583, 0.0417, 0.1448]\n",
       "PeriodicSite: S (9.5709, 3.6839, 2.1549) [0.4583, 0.1667, 0.1448]\n",
       "PeriodicSite: S (7.9758, 6.4468, 2.1549) [0.4583, 0.2917, 0.1448]\n",
       "PeriodicSite: S (6.3806, 9.2096, 2.1549) [0.4583, 0.4167, 0.1448]\n",
       "PeriodicSite: S (4.7855, 11.9725, 2.1549) [0.4583, 0.5417, 0.1448]\n",
       "PeriodicSite: S (3.1903, 14.7354, 2.1549) [0.4583, 0.6667, 0.1448]\n",
       "PeriodicSite: S (1.5952, 17.4983, 2.1549) [0.4583, 0.7917, 0.1448]\n",
       "PeriodicSite: S (-0.0000, 20.2612, 2.1549) [0.4583, 0.9167, 0.1448]\n",
       "PeriodicSite: S (14.3564, 0.9210, 2.1549) [0.5833, 0.0417, 0.1448]\n",
       "PeriodicSite: S (12.7613, 3.6839, 2.1549) [0.5833, 0.1667, 0.1448]\n",
       "PeriodicSite: S (11.1661, 6.4468, 2.1549) [0.5833, 0.2917, 0.1448]\n",
       "PeriodicSite: S (9.5709, 9.2096, 2.1549) [0.5833, 0.4167, 0.1448]\n",
       "PeriodicSite: S (7.9758, 11.9725, 2.1549) [0.5833, 0.5417, 0.1448]\n",
       "PeriodicSite: S (6.3806, 14.7354, 2.1549) [0.5833, 0.6667, 0.1448]\n",
       "PeriodicSite: S (4.7855, 17.4983, 2.1549) [0.5833, 0.7917, 0.1448]\n",
       "PeriodicSite: S (3.1903, 20.2612, 2.1549) [0.5833, 0.9167, 0.1448]\n",
       "PeriodicSite: S (17.5467, 0.9210, 2.1549) [0.7083, 0.0417, 0.1448]\n",
       "PeriodicSite: S (15.9516, 3.6839, 2.1549) [0.7083, 0.1667, 0.1448]\n",
       "PeriodicSite: S (14.3564, 6.4468, 2.1549) [0.7083, 0.2917, 0.1448]\n",
       "PeriodicSite: S (12.7613, 9.2096, 2.1549) [0.7083, 0.4167, 0.1448]\n",
       "PeriodicSite: S (11.1661, 11.9725, 2.1549) [0.7083, 0.5417, 0.1448]\n",
       "PeriodicSite: S (9.5709, 14.7354, 2.1549) [0.7083, 0.6667, 0.1448]\n",
       "PeriodicSite: S (6.3806, 20.2612, 2.1549) [0.7083, 0.9167, 0.1448]\n",
       "PeriodicSite: S (20.7371, 0.9210, 2.1549) [0.8333, 0.0417, 0.1448]\n",
       "PeriodicSite: S (19.1419, 3.6839, 2.1549) [0.8333, 0.1667, 0.1448]\n",
       "PeriodicSite: S (17.5467, 6.4468, 2.1549) [0.8333, 0.2917, 0.1448]\n",
       "PeriodicSite: S (15.9516, 9.2096, 2.1549) [0.8333, 0.4167, 0.1448]\n",
       "PeriodicSite: S (14.3564, 11.9725, 2.1549) [0.8333, 0.5417, 0.1448]\n",
       "PeriodicSite: S (12.7613, 14.7354, 2.1549) [0.8333, 0.6667, 0.1448]\n",
       "PeriodicSite: S (11.1661, 17.4983, 2.1549) [0.8333, 0.7917, 0.1448]\n",
       "PeriodicSite: S (9.5709, 20.2612, 2.1549) [0.8333, 0.9167, 0.1448]\n",
       "PeriodicSite: S (23.9274, 0.9210, 2.1549) [0.9583, 0.0417, 0.1448]\n",
       "PeriodicSite: S (22.3322, 3.6839, 2.1549) [0.9583, 0.1667, 0.1448]\n",
       "PeriodicSite: S (20.7371, 6.4468, 2.1549) [0.9583, 0.2917, 0.1448]\n",
       "PeriodicSite: S (19.1419, 9.2096, 2.1549) [0.9583, 0.4167, 0.1448]\n",
       "PeriodicSite: S (17.5467, 11.9725, 2.1549) [0.9583, 0.5417, 0.1448]\n",
       "PeriodicSite: S (15.9516, 14.7354, 2.1549) [0.9583, 0.6667, 0.1448]\n",
       "PeriodicSite: S (14.3564, 17.4983, 2.1549) [0.9583, 0.7917, 0.1448]\n",
       "PeriodicSite: S (12.7613, 20.2612, 2.1549) [0.9583, 0.9167, 0.1448]\n",
       "PeriodicSite: S (1.5952, 0.9210, 5.2846) [0.0833, 0.0417, 0.3552]\n",
       "PeriodicSite: S (-0.0000, 3.6839, 5.2846) [0.0833, 0.1667, 0.3552]\n",
       "PeriodicSite: S (-1.5952, 6.4468, 5.2846) [0.0833, 0.2917, 0.3552]\n",
       "PeriodicSite: S (-3.1903, 9.2096, 5.2846) [0.0833, 0.4167, 0.3552]\n",
       "PeriodicSite: S (-4.7855, 11.9725, 5.2846) [0.0833, 0.5417, 0.3552]\n",
       "PeriodicSite: S (-6.3806, 14.7354, 5.2846) [0.0833, 0.6667, 0.3552]\n",
       "PeriodicSite: S (-7.9758, 17.4983, 5.2846) [0.0833, 0.7917, 0.3552]\n",
       "PeriodicSite: S (-9.5709, 20.2612, 5.2846) [0.0833, 0.9167, 0.3552]\n",
       "PeriodicSite: S (4.7855, 0.9210, 5.2846) [0.2083, 0.0417, 0.3552]\n",
       "PeriodicSite: S (3.1903, 3.6839, 5.2846) [0.2083, 0.1667, 0.3552]\n",
       "PeriodicSite: S (1.5952, 6.4468, 5.2846) [0.2083, 0.2917, 0.3552]\n",
       "PeriodicSite: S (-0.0000, 9.2096, 5.2846) [0.2083, 0.4167, 0.3552]\n",
       "PeriodicSite: S (-1.5952, 11.9725, 5.2846) [0.2083, 0.5417, 0.3552]\n",
       "PeriodicSite: S (-3.1903, 14.7354, 5.2846) [0.2083, 0.6667, 0.3552]\n",
       "PeriodicSite: S (-4.7855, 17.4983, 5.2846) [0.2083, 0.7917, 0.3552]\n",
       "PeriodicSite: S (-6.3806, 20.2612, 5.2846) [0.2083, 0.9167, 0.3552]\n",
       "PeriodicSite: S (7.9758, 0.9210, 5.2846) [0.3333, 0.0417, 0.3552]\n",
       "PeriodicSite: S (6.3806, 3.6839, 5.2846) [0.3333, 0.1667, 0.3552]\n",
       "PeriodicSite: S (4.7855, 6.4468, 5.2846) [0.3333, 0.2917, 0.3552]\n",
       "PeriodicSite: S (3.1903, 9.2096, 5.2846) [0.3333, 0.4167, 0.3552]\n",
       "PeriodicSite: S (1.5952, 11.9725, 5.2846) [0.3333, 0.5417, 0.3552]\n",
       "PeriodicSite: S (0.0000, 14.7354, 5.2846) [0.3333, 0.6667, 0.3552]\n",
       "PeriodicSite: S (-1.5952, 17.4983, 5.2846) [0.3333, 0.7917, 0.3552]\n",
       "PeriodicSite: S (-3.1903, 20.2612, 5.2846) [0.3333, 0.9167, 0.3552]\n",
       "PeriodicSite: S (11.1661, 0.9210, 5.2846) [0.4583, 0.0417, 0.3552]\n",
       "PeriodicSite: S (9.5709, 3.6839, 5.2846) [0.4583, 0.1667, 0.3552]\n",
       "PeriodicSite: S (7.9758, 6.4468, 5.2846) [0.4583, 0.2917, 0.3552]\n",
       "PeriodicSite: S (6.3806, 9.2096, 5.2846) [0.4583, 0.4167, 0.3552]\n",
       "PeriodicSite: S (4.7855, 11.9725, 5.2846) [0.4583, 0.5417, 0.3552]\n",
       "PeriodicSite: S (3.1903, 14.7354, 5.2846) [0.4583, 0.6667, 0.3552]\n",
       "PeriodicSite: S (1.5952, 17.4983, 5.2846) [0.4583, 0.7917, 0.3552]\n",
       "PeriodicSite: S (-0.0000, 20.2612, 5.2846) [0.4583, 0.9167, 0.3552]\n",
       "PeriodicSite: S (14.3564, 0.9210, 5.2846) [0.5833, 0.0417, 0.3552]\n",
       "PeriodicSite: S (12.7613, 3.6839, 5.2846) [0.5833, 0.1667, 0.3552]\n",
       "PeriodicSite: S (11.1661, 6.4468, 5.2846) [0.5833, 0.2917, 0.3552]\n",
       "PeriodicSite: S (9.5709, 9.2096, 5.2846) [0.5833, 0.4167, 0.3552]\n",
       "PeriodicSite: S (7.9758, 11.9725, 5.2846) [0.5833, 0.5417, 0.3552]\n",
       "PeriodicSite: S (6.3806, 14.7354, 5.2846) [0.5833, 0.6667, 0.3552]\n",
       "PeriodicSite: S (4.7855, 17.4983, 5.2846) [0.5833, 0.7917, 0.3552]\n",
       "PeriodicSite: S (3.1903, 20.2612, 5.2846) [0.5833, 0.9167, 0.3552]\n",
       "PeriodicSite: S (17.5467, 0.9210, 5.2846) [0.7083, 0.0417, 0.3552]\n",
       "PeriodicSite: S (15.9516, 3.6839, 5.2846) [0.7083, 0.1667, 0.3552]\n",
       "PeriodicSite: S (12.7613, 9.2096, 5.2846) [0.7083, 0.4167, 0.3552]\n",
       "PeriodicSite: S (11.1661, 11.9725, 5.2846) [0.7083, 0.5417, 0.3552]\n",
       "PeriodicSite: S (9.5709, 14.7354, 5.2846) [0.7083, 0.6667, 0.3552]\n",
       "PeriodicSite: S (7.9758, 17.4983, 5.2846) [0.7083, 0.7917, 0.3552]\n",
       "PeriodicSite: S (6.3806, 20.2612, 5.2846) [0.7083, 0.9167, 0.3552]\n",
       "PeriodicSite: S (20.7371, 0.9210, 5.2846) [0.8333, 0.0417, 0.3552]\n",
       "PeriodicSite: S (19.1419, 3.6839, 5.2846) [0.8333, 0.1667, 0.3552]\n",
       "PeriodicSite: S (17.5467, 6.4468, 5.2846) [0.8333, 0.2917, 0.3552]\n",
       "PeriodicSite: S (15.9516, 9.2096, 5.2846) [0.8333, 0.4167, 0.3552]\n",
       "PeriodicSite: S (14.3564, 11.9725, 5.2846) [0.8333, 0.5417, 0.3552]\n",
       "PeriodicSite: S (12.7613, 14.7354, 5.2846) [0.8333, 0.6667, 0.3552]\n",
       "PeriodicSite: S (11.1661, 17.4983, 5.2846) [0.8333, 0.7917, 0.3552]\n",
       "PeriodicSite: S (9.5709, 20.2612, 5.2846) [0.8333, 0.9167, 0.3552]\n",
       "PeriodicSite: S (23.9274, 0.9210, 5.2846) [0.9583, 0.0417, 0.3552]\n",
       "PeriodicSite: S (22.3322, 3.6839, 5.2846) [0.9583, 0.1667, 0.3552]\n",
       "PeriodicSite: S (20.7371, 6.4468, 5.2846) [0.9583, 0.2917, 0.3552]\n",
       "PeriodicSite: S (19.1419, 9.2096, 5.2846) [0.9583, 0.4167, 0.3552]\n",
       "PeriodicSite: S (17.5467, 11.9725, 5.2846) [0.9583, 0.5417, 0.3552]\n",
       "PeriodicSite: S (15.9516, 14.7354, 5.2846) [0.9583, 0.6667, 0.3552]\n",
       "PeriodicSite: S (14.3564, 17.4983, 5.2846) [0.9583, 0.7917, 0.3552]\n",
       "PeriodicSite: S (12.7613, 20.2612, 5.2846) [0.9583, 0.9167, 0.3552]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.structures[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "0c837ff8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pymatgen.core.sites.PeriodicSite"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(sample.structures[0].sites[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c9e5d2b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodicSite: Mo (0.0000, 1.8419, 3.7198) [0.0417, 0.0833, 0.2500]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample.structures[0].sites[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dc2413fe",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sample' 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-1-7d83879247b8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msample\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstructures\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msites\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\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 'sample' is not defined"
     ]
    }
   ],
   "source": [
    "vars(sample.structures[0].sites[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd52ea3",
   "metadata": {},
   "source": [
    "# что за магические 3 числа  в квадратных скобках????"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f5c663d",
   "metadata": {},
   "source": [
    "# Визуализация структур"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1315936a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f87c0503bdeb47b0aef129c68be40325",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nv.show_pymatgen(sample.structures[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bdba0f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a549c8e671904517b96c70f4b35eed0c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.show_pymatgen(other.structures[9])\n",
    "view.add_unitcell()\n",
    "view"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f03bbc2",
   "metadata": {},
   "source": [
    "# Получение хим состава"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "5eed68fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "def decompose(structure):\n",
    "    result = defaultdict(int)\n",
    "    for site in structure.sites:\n",
    "        result[site.species.formula] += 1\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "ae48e7d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample = data[data['class'] == 2]\n",
    "\n",
    "# composition_2 = []\n",
    "# for i in range(len(sample)):\n",
    "#     current_composition = decompose(sample.structures[i])\n",
    "#     if current_composition not in composition_2:\n",
    "#         composition_2.append(current_composition)\n",
    "        \n",
    "# cnt = [0, 0, 0, 0]\n",
    "# for i in range(len(sample)):\n",
    "#     current_composition = decompose(sample.structures[i])\n",
    "#     if current_composition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126}):\n",
    "#         cnt[0] += 1\n",
    "#     elif current_composition == defaultdict(int, {'Mo1': 64, 'Se1': 2, 'S1': 126}):\n",
    "#         cnt[1] += 1   \n",
    "#     elif current_composition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'S1': 128}):\n",
    "#         cnt[2] += 1\n",
    "#     elif current_composition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 127}):\n",
    "#         cnt[3] += 1\n",
    "    \n",
    "# for cmp, cnt_i in zip(composition_2, cnt):\n",
    "#     print(cnt_i, cmp.items())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "d87ae5f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structures</th>\n",
       "      <th>targets</th>\n",
       "      <th>class</th>\n",
       "      <th>decomposition</th>\n",
       "      <th>len_of_decomposition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6142031bee0a3fd43fb47e23</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2754</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Mo1': 63, 'Se1': 2, 'S1': 126}</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141d46031cf3ef3d4a9eee8</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>0.2839</td>\n",
       "      <td>0</td>\n",
       "      <td>{'Mo1': 63, 'Se1': 1, 'S1': 126}</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614211354e27a1844a5f05b4</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1450</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>614346254e27a1844a5f0a14</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.1405</td>\n",
       "      <td>1</td>\n",
       "      <td>{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6141e2eb9cbada84a8676ab7</th>\n",
       "      <td>[[1.27612629e-07 1.84192955e+00 3.71975100e+00...</td>\n",
       "      <td>1.8092</td>\n",
       "      <td>2</td>\n",
       "      <td>{'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126}</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                 structures  \\\n",
       "6142031bee0a3fd43fb47e23  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141d46031cf3ef3d4a9eee8  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614211354e27a1844a5f05b4  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "614346254e27a1844a5f0a14  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "6141e2eb9cbada84a8676ab7  [[1.27612629e-07 1.84192955e+00 3.71975100e+00...   \n",
       "\n",
       "                          targets  class  \\\n",
       "6142031bee0a3fd43fb47e23   0.2754      0   \n",
       "6141d46031cf3ef3d4a9eee8   0.2839      0   \n",
       "614211354e27a1844a5f05b4   1.1450      1   \n",
       "614346254e27a1844a5f0a14   1.1405      1   \n",
       "6141e2eb9cbada84a8676ab7   1.8092      2   \n",
       "\n",
       "                                                      decomposition  \\\n",
       "6142031bee0a3fd43fb47e23           {'Mo1': 63, 'Se1': 2, 'S1': 126}   \n",
       "6141d46031cf3ef3d4a9eee8           {'Mo1': 63, 'Se1': 1, 'S1': 126}   \n",
       "614211354e27a1844a5f05b4  {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}   \n",
       "614346254e27a1844a5f0a14  {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}   \n",
       "6141e2eb9cbada84a8676ab7  {'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126}   \n",
       "\n",
       "                          len_of_decomposition  \n",
       "6142031bee0a3fd43fb47e23                     3  \n",
       "6141d46031cf3ef3d4a9eee8                     3  \n",
       "614211354e27a1844a5f05b4                     4  \n",
       "614346254e27a1844a5f0a14                     4  \n",
       "6141e2eb9cbada84a8676ab7                     4  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['decomposition'] = data.structures.apply(decompose)\n",
    "data['len_of_decomposition'] = data['decomposition'].apply(len)\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "cfe8a464",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}    716\n",
       "{'Mo1': 63, 'Se1': 1, 'S1': 126}             702\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 126}              380\n",
       "{'Mo1': 63, 'Se1': 2, 'S1': 126}             377\n",
       "{'Mo1': 63, 'S1': 126}                       363\n",
       "{'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126}    360\n",
       "{'Mo1': 64, 'Se1': 1, 'S1': 126}              18\n",
       "{'Mo1': 64, 'S1': 126}                        12\n",
       "{'Mo1': 64, 'Se1': 2, 'S1': 126}               8\n",
       "{'Mo1': 63, 'S1': 127}                         8\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 127}                8\n",
       "{'Mo1': 63, 'Se1': 1, 'S1': 127}               7\n",
       "{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 127}      5\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 128}                1\n",
       "{'Mo1': 63, 'S1': 128}                         1\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "d463a405",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    365\n",
       "3      9\n",
       "Name: len_of_decomposition, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 2]['len_of_decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "e25a8870",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4    716\n",
       "3    387\n",
       "2     11\n",
       "Name: len_of_decomposition, dtype: int64"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 1]['len_of_decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "d7553066",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3    1083\n",
       "2     368\n",
       "Name: len_of_decomposition, dtype: int64"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 0]['len_of_decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "c363fa00",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126}    360\n",
       "{'Mo1': 64, 'Se1': 2, 'S1': 126}               8\n",
       "{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 127}      5\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 128}                1\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 2]['decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "ce8aae48",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}    716\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 126}              361\n",
       "{'Mo1': 64, 'Se1': 1, 'S1': 126}              18\n",
       "{'Mo1': 64, 'S1': 126}                        11\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 127}                8\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 1]['decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "5c3d8477",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'Se1': 1, 'S1': 126}    699\n",
       "{'Mo1': 63, 'Se1': 2, 'S1': 126}    377\n",
       "{'Mo1': 63, 'S1': 126}              359\n",
       "{'Mo1': 63, 'S1': 127}                8\n",
       "{'Mo1': 63, 'Se1': 1, 'S1': 127}      7\n",
       "{'Mo1': 63, 'S1': 128}                1\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data['class'] == 0]['decomposition'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9b72f07",
   "metadata": {},
   "source": [
    "# НАБЛЮДЕНИЯ\n",
    "\n",
    "\n",
    "class2:\n",
    "- почти все со структурой {'Mo1': 63, 'W1': 1, 'Se1': 2, 'S1': 126} (360)\n",
    "- уровень энергии 1.806 - 1.811, так как допустимый разброс ошибки 0.02 то спокойно всем задаем среднее значение 1.808\n",
    "    \n",
    "class1:\n",
    "- почти все со структурой {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126} (716)\n",
    "- почти все со структурой {'Mo1': 63, 'W1': 1, 'S1': 126}  \n",
    "\n",
    "class0:\n",
    "- почти все из 2 молекул тут, самая большая подгруппа из 2х со структурой {'Mo1': 63, 'S1': 126}  (368, и 11 попало в класс 1)\n",
    "- почти все со структурой {'Mo1': 63, 'Se1': 2, 'S1': 126} (377, 8 попало в класс 2)\n",
    "    \n",
    "у классов 0 и 1 все сложно с уровнями энергии\n",
    "\n",
    "в классе 1 есть 2 явных подкласса с непересекающимися  диапазонами"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "173266ef",
   "metadata": {},
   "source": [
    "# Посмотреть на энергии структур"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5601426",
   "metadata": {},
   "source": [
    "## Class 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "99e3a3b7",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}    716\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 126}              361\n",
       "{'Mo1': 64, 'Se1': 1, 'S1': 126}              18\n",
       "{'Mo1': 64, 'S1': 126}                        11\n",
       "{'Mo1': 63, 'W1': 1, 'S1': 127}                8\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data[data['class'] == 1].copy()\n",
    "sample.decomposition.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "d1497614",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_colour(decomposition):\n",
    "    if decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}):\n",
    "        return 'red'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'S1': 126}):\n",
    "        return 'green'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 64, 'Se1': 1, 'S1': 126} ):\n",
    "        return 'blue'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 64, 'S1': 126}):\n",
    "        return 'black'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'S1': 127}):\n",
    "        return 'orange'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "936da8b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f915a46d8e0>"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = sample['decomposition'].apply(\n",
    "    lambda x: encode_colour(x)\n",
    ")\n",
    "\n",
    "plt.scatter(np.arange(len(sample)), sample.targets, s=2, c=colours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "id": "5d65028d",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_colour(decomposition):\n",
    "    if decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'Se1': 1, 'S1': 126}):\n",
    "        return 0\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'S1': 126}):\n",
    "        return 1\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 64, 'Se1': 1, 'S1': 126} ):\n",
    "        return 2\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 64, 'S1': 126}):\n",
    "        return 3\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'W1': 1, 'S1': 127}):\n",
    "        return 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "2f50ac64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f915a178a30>"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = sample['decomposition'].apply(\n",
    "    lambda x: encode_colour(x)\n",
    ")\n",
    "\n",
    "plt.scatter(colours, sample.targets, s=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2998b04f",
   "metadata": {},
   "source": [
    "## Class 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "5e1aec08",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'collections.defaultdict'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.map_locations\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: unhashable type: 'collections.defaultdict'"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Exception ignored in: 'pandas._libs.index.IndexEngine._call_map_locations'\n",
      "Traceback (most recent call last):\n",
      "  File \"pandas/_libs/hashtable_class_helper.pxi\", line 5231, in pandas._libs.hashtable.PyObjectHashTable.map_locations\n",
      "TypeError: unhashable type: 'collections.defaultdict'\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Mo1': 63, 'Se1': 1, 'S1': 126}    699\n",
       "{'Mo1': 63, 'Se1': 2, 'S1': 126}    377\n",
       "{'Mo1': 63, 'S1': 126}              359\n",
       "{'Mo1': 63, 'S1': 127}                8\n",
       "{'Mo1': 63, 'Se1': 1, 'S1': 127}      7\n",
       "{'Mo1': 63, 'S1': 128}                1\n",
       "Name: decomposition, dtype: int64"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample = data[data['class'] == 0].copy()\n",
    "sample.decomposition.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "46959a7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_colour(decomposition):\n",
    "    if decomposition == defaultdict(int, {'Mo1': 63, 'Se1': 1, 'S1': 126}):\n",
    "        return 'red'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'Se1': 2, 'S1': 126}):\n",
    "        return 'green'\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'S1': 126}):\n",
    "        return 'blue'\n",
    "    else:\n",
    "        return 'black'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "a7b32268",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f91573e7d30>"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = sample['decomposition'].apply(\n",
    "    lambda x: encode_colour(x)\n",
    ")\n",
    "\n",
    "plt.scatter(np.arange(len(sample)), sample.targets, s=2, c=colours)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "1ad133d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def encode_colour(decomposition):\n",
    "    if decomposition == defaultdict(int, {'Mo1': 63, 'Se1': 1, 'S1': 126}):\n",
    "        return 0\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'Se1': 2, 'S1': 126}):\n",
    "        return 1\n",
    "    elif decomposition == defaultdict(int, {'Mo1': 63, 'S1': 126}):\n",
    "        return 2\n",
    "    else:\n",
    "        return 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "f0f9d571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f91573aa850>"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colours = sample['decomposition'].apply(\n",
    "    lambda x: encode_colour(x)\n",
    ")\n",
    "\n",
    "plt.scatter(colours, sample.targets, s=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "aaec79d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f9156f25100>"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# подкласс класса 0\n",
    "\n",
    "sample = data[data['len_of_decomposition'] == 2].copy()\n",
    "\n",
    "plt.scatter(np.arange(len(sample)), sample.targets, s=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08b96a2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}