量化分析/分析方法/Factor Analysis/introduction-factor-analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Factor Analysis\n",
    "**文章说明：**\n",
    "- 这文章为Avinash Navlani在datacamp写的关于[FA的入门介绍](https://www.datacamp.com/tutorial/introduction-factor-analysis)\n",
    "- 这里使用了[factor_analyzer库用于FA分析](https://github.com/EducationalTestingService/factor_analyzer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from factor_analyzer import FactorAnalyzer\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入数据\n",
    "- 这是关于人格的数据集\n",
    "- 数据集介绍: https://vincentarelbundock.github.io/Rdatasets/doc/psych/bfi.html\n",
    "- 数据集下载: https://vincentarelbundock.github.io/Rdatasets/datasets.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/psych/bfi.csv\")\n",
    "# df= pd.read_csv(\"bfi.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据说明：\n",
    "- 这是从国际人格项目库（ipip.ori.org）中提取的25个人格项目报告\n",
    "- 其中包含来自 2800 个受试者的数据，用于规模构建、因子分析和项目响应理论分析的演示集\n",
    "- 其中还包括三个额外的人口统计变量（性别，教育和年龄）\n",
    "\n",
    "数据所包含的 28 个问题：\n",
    "- A1 我对别人的感受漠不关心。（q_146）\n",
    "- A2 询问他人的幸福感。（q_1162）\n",
    "- A3 知道如何安慰别人。（q_1206）\n",
    "- A4 爱孩子。（q_1364）\n",
    "- A5 让人们感到安心。（q_1419）\n",
    "- C1 我的工作非常严格。（q_124）\n",
    "- ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```{tip}\n",
    "剔除人口统计变量（性别，教育和年龄）这个3个变量特征，前25个变量特征实际上是由五个隐性变量（latent variables）组成：**亲和力，尽责性，外向性，神经质和开放性**。\n",
    "\n",
    "- c(“-A1”,“A2”,“A3”,“A4”,“A5”) => 亲和力\n",
    "- c(“C1”,“C2”,“C3”,“-C4”,“-C5”) => 尽责性\n",
    "- c(“-E1”,“-E2”,“E3”,“E4”,“E5”) => 外向性\n",
    "- c(“N1”,“N2”,“N3”,“N4”,“N5”) => 神经质\n",
    "- c(“O1”,“-O2”,“O3”,“O4”,“-O5”) => 开放性\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 剔除不必要的人口统计变量特征（性别，教育和年龄）\n",
    "df.drop(['Unnamed: 0', 'gender', 'education', 'age'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 去除缺失数据\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
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       "    A1   A2   A3   A4   A5   C1   C2   C3   C4   C5  ...   N1   N2   N3   N4  \\\n",
       "0  2.0  4.0  3.0  4.0  4.0  2.0  3.0  3.0  4.0  4.0  ...  3.0  4.0  2.0  2.0   \n",
       "1  2.0  4.0  5.0  2.0  5.0  5.0  4.0  4.0  3.0  4.0  ...  3.0  3.0  3.0  5.0   \n",
       "2  5.0  4.0  5.0  4.0  4.0  4.0  5.0  4.0  2.0  5.0  ...  4.0  5.0  4.0  2.0   \n",
       "3  4.0  4.0  6.0  5.0  5.0  4.0  4.0  3.0  5.0  5.0  ...  2.0  5.0  2.0  4.0   \n",
       "4  2.0  3.0  3.0  4.0  5.0  4.0  4.0  5.0  3.0  2.0  ...  2.0  3.0  4.0  4.0   \n",
       "\n",
       "    N5   O1  O2   O3   O4   O5  \n",
       "0  3.0  3.0   6  3.0  4.0  3.0  \n",
       "1  5.0  4.0   2  4.0  3.0  3.0  \n",
       "2  3.0  4.0   2  5.0  5.0  2.0  \n",
       "3  1.0  3.0   3  4.0  3.0  5.0  \n",
       "4  3.0  3.0   3  4.0  3.0  3.0  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2436, 25)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 充分性测试(Adequacy Test)\n",
    "\n",
    "在做因子分析之前, 我们需要先做充分性检测, 判断数据集中是否存在因子（factor）：\n",
    "- `Bartlett's Test`: 是用来检测已知（可观察）变量之间是否关联, 如果检测结果在统计学上不显著, 就不能采用因子分析\n",
    "- `Kaiser-Meyer-Olkin(KMO) Test`: 检测数据对因子分析的适用性。KMO 值的范围介于 0 和 1 之间。 KMO 值需要大于 0.6 才视为适用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18170.966350868897, 0.0)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bartlett's Test\n",
    "from factor_analyzer.factor_analyzer import calculate_bartlett_sphericity\n",
    "chi_square_value,p_value=calculate_bartlett_sphericity(df)\n",
    "chi_square_value, p_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**说明**：p_value=0, 表明数据变量间具有显著的关联性. `p_value需要少于0.5`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8485397221949232\n"
     ]
    }
   ],
   "source": [
    "# Kaiser-Meyer-Olkin(KMO) Test\n",
    "from factor_analyzer.factor_analyzer import calculate_kmo\n",
    "kmo_all,kmo_model=calculate_kmo(df)\n",
    "print(kmo_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**说明**： KMO 值大于 0.6 ，符合因子分析的适用性"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 选择因子个数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 数据集中共有25个变量，我们需要考虑究竟需要归集为多少个因子是最合适的\n",
    "fa = FactorAnalyzer(25, rotation=None)\n",
    "fa.fit(df)\n",
    "# 查看特征值\n",
    "ev, v = fa.get_eigenvalues()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.13431118, 2.75188667, 2.14270195, 1.85232761, 1.54816285,\n",
       "       1.07358247, 0.83953893, 0.79920618, 0.71898919, 0.68808879,\n",
       "       0.67637336, 0.65179984, 0.62325295, 0.59656284, 0.56309083,\n",
       "       0.54330533, 0.51451752, 0.49450315, 0.48263952, 0.448921  ,\n",
       "       0.42336611, 0.40067145, 0.38780448, 0.38185679, 0.26253902])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ev"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# 可视化\n",
    "plt.scatter(range(1,df.shape[1]+1),ev)\n",
    "plt.plot(range(1,df.shape[1]+1),ev)\n",
    "plt.title('Scree Plot')\n",
    "plt.xlabel('Factors')\n",
    "plt.ylabel('Eigenvalue')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "如图，这里显示有 6 个因子的特征值是大于 1。这意味着我们只需要选择6个因子（或未观察到的变量）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 因子分析（Factor Analysis）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "FactorAnalyzer(n_factors=6, rotation='varimax', rotation_kwargs={})"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fa = FactorAnalyzer(6, rotation=\"varimax\")\n",
    "fa.fit(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9.52197423e-02,  4.07831574e-02,  4.87338846e-02,\n",
       "        -5.30987347e-01, -1.13057329e-01,  1.61216352e-01],\n",
       "       [ 3.31312760e-02,  2.35538039e-01,  1.33714395e-01,\n",
       "         6.61140975e-01,  6.37337873e-02, -6.24353669e-03],\n",
       "       [-9.62088402e-03,  3.43008172e-01,  1.21353367e-01,\n",
       "         6.05932695e-01,  3.39902654e-02,  1.60106427e-01],\n",
       "       [-8.15175585e-02,  2.19716720e-01,  2.35139532e-01,\n",
       "         4.04594040e-01, -1.25338019e-01,  8.63557029e-02],\n",
       "       [-1.49615885e-01,  4.14457673e-01,  1.06382165e-01,\n",
       "         4.69698292e-01,  3.09765725e-02,  2.36519343e-01],\n",
       "       [-4.35840210e-03,  7.72477520e-02,  5.54582255e-01,\n",
       "         7.51069609e-03,  1.90123729e-01,  9.50350471e-02],\n",
       "       [ 6.83300836e-02,  3.83703837e-02,  6.74545448e-01,\n",
       "         5.70549881e-02,  8.75925913e-02,  1.52775079e-01],\n",
       "       [-3.99936734e-02,  3.18673004e-02,  5.51164438e-01,\n",
       "         1.01282241e-01, -1.13380869e-02,  8.99628374e-03],\n",
       "       [ 2.16283366e-01, -6.62407741e-02, -6.38475488e-01,\n",
       "        -1.02616940e-01, -1.43846475e-01,  3.18358894e-01],\n",
       "       [ 2.84187245e-01, -1.80811696e-01, -5.44837677e-01,\n",
       "        -5.99548223e-02,  2.58370943e-02,  1.32423444e-01],\n",
       "       [ 2.22797943e-02, -5.90450890e-01,  5.39149056e-02,\n",
       "        -1.30850532e-01, -7.12045796e-02,  1.56582655e-01],\n",
       "       [ 2.33623567e-01, -6.84577633e-01, -8.84970715e-02,\n",
       "        -1.16715665e-01, -4.55610414e-02,  1.15065401e-01],\n",
       "       [-8.95006251e-04,  5.56774178e-01,  1.03390347e-01,\n",
       "         1.79396481e-01,  2.41179903e-01,  2.67291315e-01],\n",
       "       [-1.36788076e-01,  6.58394906e-01,  1.13798005e-01,\n",
       "         2.41142962e-01, -1.07808203e-01,  1.58512853e-01],\n",
       "       [ 3.44895886e-02,  5.07535082e-01,  3.09812529e-01,\n",
       "         7.88042866e-02,  2.00821351e-01,  8.74730348e-03],\n",
       "       [ 8.05805939e-01,  6.80113035e-02, -5.12637874e-02,\n",
       "        -1.74849358e-01, -7.49771190e-02, -9.62661717e-02],\n",
       "       [ 7.89831685e-01,  2.29582937e-02, -3.74768699e-02,\n",
       "        -1.41134482e-01,  6.72646108e-03, -1.39822611e-01],\n",
       "       [ 7.25081218e-01, -6.56869337e-02, -5.90394378e-02,\n",
       "        -1.91838175e-02, -1.06635548e-02,  6.24953362e-02],\n",
       "       [ 5.78318850e-01, -3.45072325e-01, -1.62173862e-01,\n",
       "         4.03124773e-04,  6.29164834e-02,  1.47551242e-01],\n",
       "       [ 5.23097071e-01, -1.61675117e-01, -2.53049784e-02,\n",
       "         9.01247898e-02, -1.61891978e-01,  1.20049476e-01],\n",
       "       [-2.00040179e-02,  2.25338562e-01,  1.33200799e-01,\n",
       "         5.17793783e-03,  4.79477217e-01,  2.18689836e-01],\n",
       "       [ 1.56230108e-01, -1.98152005e-03, -8.60468500e-02,\n",
       "         4.39891066e-02, -4.96639673e-01,  1.34692972e-01],\n",
       "       [ 1.18510152e-02,  3.25954482e-01,  9.38796109e-02,\n",
       "         7.66416474e-02,  5.66128047e-01,  2.10777220e-01],\n",
       "       [ 2.07280572e-01, -1.77745714e-01, -5.67146647e-03,\n",
       "         1.33655572e-01,  3.49227137e-01,  1.78068367e-01],\n",
       "       [ 6.32343666e-02, -1.42210628e-02, -4.70592292e-02,\n",
       "        -5.75607775e-02, -5.76742633e-01,  1.35935872e-01]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 25*6(变量个数*因子个数)\n",
    "fa.loadings_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Factor Analysis')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化\n",
    "import seaborn as sns\n",
    "\n",
    "df_cm = pd.DataFrame(fa.loadings_, index=df.columns)\n",
    "\n",
    "plt.figure(figsize = (14,14))\n",
    "sns.heatmap(df_cm, annot=True, cmap=\"BuPu\")\n",
    "ax.tick_params(axis='x',labelsize=15)\n",
    "ax.yaxis.set_tick_params(labelsize=15) \n",
    "plt.title('Factor Analysis', fontsize='xx-large')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**说明：**\n",
    "- 每一个变量都可以由6个隐变量所表示\n",
    "- **A1 = 0.0095*F1 + 0.041*F2 + 0.049*F3 + -0.53*F4 + -0.11*F5 + 0.16*F6**\n",
    "- **A2 = 0.033*F1 + 0.24*F2 + 0.13*F3 + 0.66*F4 + 0.064*F5 + -0.0062*F6**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><div id=f562014a-261f-4ae6-964e-23587575f203 style=\"display:none; background-color:#9D6CFF; color:white; width:200px; height:30px; padding-left:5px; border-radius:4px; flex-direction:row; justify-content:space-around; align-items:center;\" onmouseover=\"this.style.backgroundColor='#BA9BF8'\" onmouseout=\"this.style.backgroundColor='#9D6CFF'\" onclick=\"window.commands?.execute('create-mitosheet-from-dataframe-output');\">See Full Dataframe in Mito</div> <script> if (window.commands.hasCommand('create-mitosheet-from-dataframe-output')) document.getElementById('f562014a-261f-4ae6-964e-23587575f203').style.display = 'flex' </script> <table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>factor1</th>\n",
       "      <th>factor2</th>\n",
       "      <th>factor3</th>\n",
       "      <th>factor4</th>\n",
       "      <th>factor5</th>\n",
       "      <th>factor6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>variance</th>\n",
       "      <td>2.726989</td>\n",
       "      <td>2.602239</td>\n",
       "      <td>2.073471</td>\n",
       "      <td>1.713499</td>\n",
       "      <td>1.504831</td>\n",
       "      <td>0.630297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>proportional_variance</th>\n",
       "      <td>0.109080</td>\n",
       "      <td>0.104090</td>\n",
       "      <td>0.082939</td>\n",
       "      <td>0.068540</td>\n",
       "      <td>0.060193</td>\n",
       "      <td>0.025212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cumulative_variances</th>\n",
       "      <td>0.109080</td>\n",
       "      <td>0.213169</td>\n",
       "      <td>0.296108</td>\n",
       "      <td>0.364648</td>\n",
       "      <td>0.424841</td>\n",
       "      <td>0.450053</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table></div>"
      ],
      "text/plain": [
       "                        factor1   factor2   factor3   factor4   factor5  \\\n",
       "variance               2.726989  2.602239  2.073471  1.713499  1.504831   \n",
       "proportional_variance  0.109080  0.104090  0.082939  0.068540  0.060193   \n",
       "cumulative_variances   0.109080  0.213169  0.296108  0.364648  0.424841   \n",
       "\n",
       "                        factor6  \n",
       "variance               0.630297  \n",
       "proportional_variance  0.025212  \n",
       "cumulative_variances   0.450053  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 累计贡献率\n",
    "pd.DataFrame(fa.get_factor_variance(),index=['variance','proportional_variance','cumulative_variances'], columns=[f\"factor{x}\" for x in range(1,7)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**说明：** `6 个因子可以解释累积 45% 方差`"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}