reduce_dimensions_and_cluster_nonasthma_patients.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Dimensionality Reduction and Clustering of UK Biobank Asthma Patients\n",
    "\n",
    "This notebook does the following:\n",
    "\n",
    "* Reads in a sample of UKBB Asthma patients\n",
    "* Performs dimensionality reduction on UKBB Asthma Patients with PCA and finds the optimal number of principal components\n",
    "* Performs dimensionality reduction on UKBB Asthma patients wit tSNE, using the otpimal number of principal components\n",
    "* Performs kMeans clustering to find any clusters of UKBB asthma patients\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import rc\n",
    "rc('font', **{'family':'sans-serif','sans-serif':['Helvetica']})\n",
    "#rc('text', usetex=False)\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['figure.figsize']=(12,6)\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "from sklearn import decomposition\n",
    "from sklearn import manifold\n",
    "from sklearn.cluster import KMeans "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Read in UKBB asthma patients and subselect data\n",
    "\n",
    "Fields in UKBB phenotype data come in three types:\n",
    "\n",
    "1) 'QUANTITY' fields - these fields are continuous variables. Integers and Decimal numbers\n",
    "2) 'CATEGORY' fields - these fields are not continuous. There are a limited number of integers which label specfic category values\n",
    "3) 'BIN' fields - these are binary fields. They only have two possible values. They only label a field as being 'TRUE' or 'FALSE' \n",
    "\n",
    "For summary statistics and finding the most correlated variables, we only selected 'QUANTITY' fields.  For dimensionality reduction for further clustering, it's not so clear whether we have to select only 'QUANTITY' fields."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/wakari/anaconda/envs/default/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2714: DtypeWarning: Columns (3,2931,2943) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "#Read in 5K sample\n",
    "asthma_df=pd.read_csv(\"ukbb_asthma_sample_5k.csv\",sep='\\t')\n",
    "#asthma_df=pd.read_csv(\"ukbb_asthma_sample_5k_new.csv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Select columns to filter on\n",
    "cols = [col for col in asthma_df.columns if 'HES_p' not in col and 'PC' not in col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df=asthma_df[cols].drop('Unnamed: 0',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#select only 'QUANTITY' fields\n",
    "asthma_df_quant=asthma_df.loc[:, asthma_df.columns.str.contains('QUANT|age|BMI')].fillna(0.0)\n",
    "#select only fields with 10 or more unique values\n",
    "for col in asthma_df_quant.columns:\n",
    "    if len(asthma_df_quant[col].unique()) < 20 :\n",
    "        asthma_df_quant.drop(col,inplace=True,axis=1)\n",
    "\n",
    "        \n",
    "#select only 'QUANTITY' and \"CATEGORY\" fields\n",
    "asthma_df_quant_cat=asthma_df.loc[:, asthma_df.columns.str.contains('QUANT|age|BMI|CAT|sex')].fillna(0.0)\n",
    "asthma_df_quant_cat=asthma_df_quant_cat.drop(columns=['f_22182_0_0_f_CAT_HLA_imputation_values_and_quality'])\n",
    "#selects all features. \n",
    "\n",
    "asthma_df_all=asthma_df.loc[:,~asthma_df.columns.str.contains('ID|random|chip|asthma')].fillna(0.0)\n",
    "asthma_df_all=asthma_df_all.drop(columns=['f_22182_0_0_f_CAT_HLA_imputation_values_and_quality'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 638)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_quant.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: Reduce dimensions with PCA\n",
    "\n",
    "The next step is to perform PCA to reduce the dimensions in the data set. PCA is straightforward with Python. It's not straightforward to identify the ideal number of Principal Components to use and which data to include. We have three data frames in this notebook:\n",
    "\n",
    "* asthma_df_quant - this data frame only has 'QUANTITY' fields. This is exaclty the same as the exploratory analysis phase finding summary statistics and correlated/uncorrelated variables.\n",
    "* asthma_df_quant_cat - this data frame has both 'QUANTITY' fields and 'CATEGORY' fields. This totals about 1500 features\n",
    "* asthma_df_all - this data frame has everything - 'QUANTITY', 'CATEGORY', and 'BINARY' fields.\n",
    "\n",
    "\n",
    "In this step we have to:\n",
    "\n",
    "* Perform PCA in two configurations: Two principal components and the optimal number of principal components\n",
    "* We need to perform this PCA on all three data frames listed above. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Scaling\n",
    "Before dimensionality reduction and further clustering with kMeans, data has to be scaled. The standard scaler scales the data to an average value of 0 and a unit variance of 1. This is appropriate for the 'QUANTITY' features. We can't use the standard scaler for the other features, so we do 'min/max' scaling, which scales the data in the range from 0 to 1.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Scale the quantity features only\n",
    "data_quant=StandardScaler().fit_transform(asthma_df_quant.values)\n",
    "#Scale the quantity AND CATEGORY features\n",
    "data_quant_cat=StandardScaler().fit_transform(asthma_df_quant_cat.applymap(lambda x: float(x)).values)\n",
    "#Scale ALL THE features\n",
    "data_all=StandardScaler().fit_transform(asthma_df_all.applymap(lambda x:float(x)).values)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PCA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/wakari/anaconda/envs/default/lib/python2.7/site-packages/matplotlib/font_manager.py:1328: UserWarning: findfont: Font family [u'sans-serif'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_2d = decomposition.PCA(n_components=2)\n",
    "pca_quant_2d=pca_2d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(pca_quant_2d[:,0],pca_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('QUANTITY FEATURES ONLY two principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finding the optimal number of principal components for each three data frames\n",
    "\n",
    "It's easier to talk about in person, but the best way to determine the optimal number of principal components is to plot the 'cummulative eigenvalue fraction' vs the number of eigenvalues. The code to do this is below. The example code is for 500 principal components - this is way too many. I'll explain why below:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA for 500 components\n",
    "pca_500d = decomposition.PCA(n_components=500)\n",
    "pca_quant_500d=pca_500d.fit_transform(data_quant)\n",
    "\n",
    "# Calculate the cummalative eigenvalue fraction\n",
    "eigen_fraction_500d=np.cumsum(pca_500d.explained_variance_)/(np.sum(pca_500d.explained_variance_))\n",
    "#Plot the Results\n",
    "plt.scatter(range(1,len(pca_500d.explained_variance_)+1),eigen_fraction_500d)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cummatlive Eigenvalue Fraction')\n",
    "plt.title('QUANTITY FEATURES ONLY 500 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Do you see how the plot is flat for most of it? It means 500 is way too many principal components. The optimal number of Principal components is when the above plot only starts to get flat. Copy and paste the code below to find the optimal number of Principal Components for all three data frames:\n",
    "\n",
    "* Quantitative Features Only\n",
    "* Quantitative + Categorical Features\n",
    "* All Features\n",
    "\n",
    "#### Hint\n",
    "\n",
    "This is not as hard as it looks. You can reduce the number of principal components in the above plot (to say 150 or so) and pick out the optimal number of components by eye. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# PCA for 300 components\n",
    "\n",
    "pca_300d = decomposition.PCA(n_components=300)\n",
    "pca_quant_300d=pca_300d.fit_transform(data_quant)\n",
    "\n",
    "# Calculate the cummalative eigenvalue fraction\n",
    "eigen_fraction_300d=np.cumsum(pca_300d.explained_variance_)/(np.sum(pca_300d.explained_variance_))\n",
    "#Plot the Results\n",
    "plt.scatter(range(1,len(pca_300d.explained_variance_)+1),eigen_fraction_300d)\n",
    "plt.xlabel('Number of Principal Components')\n",
    "plt.ylabel('Cummatlive Eigenvalue Fraction')\n",
    "plt.xlim(5,60)\n",
    "plt.ylim(0.1,0.8)\n",
    "plt.title('QUANTITY FEATURES ONLY 300 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca_55d = decomposition.PCA(n_components=55)\n",
    "pca_quant_55d=pca_55d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(pca_quant_55d[:,0],pca_quant_55d[:,1])\n",
    "\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.title('QUANTITY FEATURES ONLY 55 principal components')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3: Dimensionality Reduction with tSNE\n",
    "\n",
    "This part is straightforward. You will have to run the same code below and replace the number of dimensions with the optimal number of principal components you found above. You will have to do so again for all three data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\\n                     learning_rate=300, n_iter=400)\\n    #tsne = manifold.TSNE(n_components=2, init='pca')\\ntsne_quant_2d=tsne_2d.fit_transform(data_quant)\\n\\nplt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\\n\\nplt.xlabel('Dimension 1')\\nplt.ylabel('Dimension 2')\\nplt.title('QUANTITY FEATURES ONLY tSNE with two components')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                     learning_rate=300, n_iter=400)\n",
    "    #tsne = manifold.TSNE(n_components=2, init='pca')\n",
    "tsne_quant_2d=tsne_2d.fit_transform(data_quant)\n",
    "\n",
    "plt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Dimension 1')\n",
    "plt.ylabel('Dimension 2')\n",
    "plt.title('QUANTITY FEATURES ONLY tSNE with two components')\n",
    "plt.show()\n",
    "'''\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\\n                     learning_rate=300, n_iter=400)\\n    #tsne = manifold.TSNE(n_components=2, init='pca')\\ntsne_quant_2d=tsne_2d.fit_transform(pca_quant_55d)\\n\\nplt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\\n\\nplt.xlabel('Dimension 1')\\nplt.ylabel('Dimension 2')\\nplt.title('QUANTITY FEATURES ONLY tSNE with two components')\\nplt.show()\\n\""
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"tsne_2d = manifold.TSNE(n_components=2, init='pca', random_state=0, perplexity=20,\n",
    "                     learning_rate=300, n_iter=400)\n",
    "    #tsne = manifold.TSNE(n_components=2, init='pca')\n",
    "tsne_quant_2d=tsne_2d.fit_transform(pca_quant_55d)\n",
    "\n",
    "plt.scatter(tsne_quant_2d[:,0],tsne_quant_2d[:,1])\n",
    "\n",
    "plt.xlabel('Dimension 1')\n",
    "plt.ylabel('Dimension 2')\n",
    "plt.title('QUANTITY FEATURES ONLY tSNE with two components')\n",
    "plt.show()\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - kMeans Clustering\n",
    "\n",
    "We will have to do kMeans clustering on the data frame with quantitative features only, on the PCA transformed data. We will do kMeans on two different inputs:\n",
    "\n",
    "* Two principal components \n",
    "* The optimal number of principal components (55)\n",
    "\n",
    "\n",
    "## 2 principal component kMeans\n",
    "\n",
    "From the PCA plots above, it looks like there are three clusters, possibly 4. A tSNE visualization on the PCA output would give us a better idea how many clusters there are likely to be. To perform kMeans clustering, you must perform the following steps:\n",
    "\n",
    "* Run kMeans with the parameters n_init=15 and max_iter=400 on the 2-dimensional PCA output to find 3 clusters. Look at the documentation in http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html and https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html\n",
    "* Visualize the resulsting clusters on the 2-dimensional PCA scatter plot above. We want to just reproduce that plot and color the points with which cluster they belong to. Cell #4 in https://jakevdp.github.io/PythonDataScienceHandbook/05.11-k-means.html demonstrates how to do this\n",
    "* We next have to optimize the number of clusters. There are many ways to do this, but using the Silhouette Score to select the best number of clusters is probably the most direct and the fastest: http://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html and http://scikit-learn.org/stable/modules/generated/sklearn.metrics.silhouette_score.html#sklearn.metrics.silhouette_score Basically, we want the number of clusters such that the silhouette score (a measure of cluster separation) is as close to 1.0 as possible. Start with 2 clusters, Don't go over 8 clusters. \n",
    "\n",
    "## Optimal number (55) principal components\n",
    "\n",
    "Repeat the above steps, but with 55 principal components. You can't visualize 55 dimensions of course, so just plot the first two principal components of the 55. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f156da424d0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pca_quant_55d\n",
    "kmeans = KMeans(n_clusters=2, n_init=15, max_iter=400).fit(X)\n",
    "y_pred = kmeans.predict(X)\n",
    "\n",
    "plt.scatter(X[:,0], X[:,1], c=y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5000"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6863662741264069\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "X = pca_quant_55d\n",
    "kmeans = KMeans(n_clusters=2, n_init=15, max_iter=400)\n",
    "from sklearn.metrics import silhouette_samples, silhouette_score\n",
    "cluster_labels = kmeans.fit_predict(X)\n",
    "\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(silhouette_avg)\n",
    "plt.scatter(X[:,0], X[:,1], c=cluster_labels, cmap=\"coolwarm\")\n",
    "plt.title(\"Kmeans Clustering for 55 Principal Components\")\n",
    "plt.xlabel('Principal Component 1')\n",
    "plt.ylabel('Principal Component 2')\n",
    "plt.show()\n",
    "#plt.savefig('kmeans_55d_scatterplot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "\n",
    "X = pca_quant_55d\n",
    "n=2\n",
    "#clusterer = KMeans(n_clusters=n, random_state=1\n",
    "clusterer = AgglomerativeClustering(n_clusters=n,linkage='average',affinity='l1')\n",
    "\n",
    "cluster_labels = clusterer.fit_predict(X)\n",
    "\n",
    "silhouette_avg = silhouette_score(X, cluster_labels)\n",
    "print(silhouette_avg)\n",
    "plt.scatter(X[:,0], X[:,1], c=cluster_labels)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Step 5 - find which features contribute the most to PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## which variables change the data the most/ have the highest variance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/apps/wakari/anaconda/envs/default/lib/python2.7/site-packages/ipykernel/__main__.py:4: RuntimeWarning: invalid value encountered in log\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "plt.rcParams['figure.figsize']=(12,6)\n",
    "\n",
    "sns.heatmap(np.log(pca_55d.inverse_transform(np.eye(pca_quant_55d.shape[1])))[:,0:100], cmap=\"hot\", cbar=True)\n",
    "plt.ylabel('Principal Component')\n",
    "plt.xlabel('Quantitative Feature index')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## pale colors are most important "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above plot shows a heat map of feature importances. Because we have over 600 quantitative features, I only show 100. There are 55 principal components. The more yellow the heatmap is, the more important the feature is. Let's average over the principal components to get the most important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local/apps/wakari/anaconda/envs/default/lib/python2.7/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in log\n",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "log_importances=np.log(pca_55d.inverse_transform(np.eye(pca_quant_55d.shape[1])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "638\n"
     ]
    }
   ],
   "source": [
    "mean_log_importances=np.apply_along_axis(np.mean,0,np.nan_to_num(log_importances))\n",
    "print(len(mean_log_importances))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mean_log_importances_df=pd.DataFrame(data=mean_log_importances,columns=['importance'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    index  importance\n",
      "0       0   -2.982593\n",
      "1       1   -2.162948\n",
      "2       2   -1.907605\n",
      "3       3   -1.637405\n",
      "4       4   -2.612217\n",
      "5       5   -2.105688\n",
      "6       6   -2.298617\n",
      "7       7   -2.348412\n",
      "8       8   -1.534937\n",
      "9       9   -1.982937\n",
      "10     10   -2.479340\n",
      "11     11   -2.144028\n",
      "12     12   -2.364243\n",
      "13     13   -2.806970\n",
      "14     14   -2.202900\n"
     ]
    }
   ],
   "source": [
    "mean_log_importances_df=mean_log_importances_df.reset_index()\n",
    "print(mean_log_importances_df.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The numpy array above is an array of importances, of length 638. They map back to the original data frame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('age', 'specialRequest_QUANT_OA_HES_based_age_of_onset')\n"
     ]
    }
   ],
   "source": [
    "print(asthma_df_quant.columns[0],asthma_df_quant.columns[637])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## every entry in array, can get variabe name "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find the ten most important features to start, including their names, to focus on. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## labels 0-638: have value associated. \n",
    "## output array as csv file -- find 10 highest values in the array and associated array index number \n",
    "\n",
    "#np.savetxt(\"Asthma Important Features.csv\", mean_log_importances_df, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     index  importance\n",
      "368    368   -1.169289\n",
      "481    481   -1.207705\n",
      "509    509   -1.333142\n",
      "45      45   -1.360170\n",
      "284    284   -1.372095\n",
      "508    508   -1.380811\n",
      "350    350   -1.384336\n",
      "477    477   -1.415512\n",
      "357    357   -1.419659\n",
      "405    405   -1.436403\n",
      "628    628   -1.437946\n",
      "247    247   -1.440160\n",
      "285    285   -1.450664\n",
      "222    222   -1.454610\n",
      "246    246   -1.454698\n"
     ]
    }
   ],
   "source": [
    "asthma_df_important_features = mean_log_importances_df.sort_values(by=\"importance\", ascending=False)\n",
    "print(asthma_df_important_features.head(15))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 6: ANOVA of the ten most important features between the two clusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Add the group label to the data frame:\n",
    "asthma_df_quant['Cluster Label']=y_pred\n",
    "asthma_df_quant['Cluster Label']=asthma_df_quant['Cluster Label'].apply(str).apply(lambda x: x.replace('0','A')).apply(lambda x: x.replace('1','B'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5000, 639)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_quant.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### adding the cluster labels allows you to visualize box plots broken down by cluster easily. For example, for age:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## include box plots for top 10 variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156a68b690>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G1d27b1C1LcnfJXlgd19SVS9J8uUkL0vyuSSd5AVJjurup+7uWNu2betLL710SOGwFraefkF2nnXyepcBsCn5jGWjqarLunvbntot50uOVyW5qrsvmZbPS3K/7r62u2/q7n9N8sokJ+59uQAAsDnsMWB392eSXFlV955WPTjJh6vqqAXNHpXkg6tQHwAAbCjL/RWRZyV5w/QLIlckeUqS36+qEzKbIrIzyc+uSoUAALCBLCtgd/flSRbPN3ni+HIAAGBjcyVHAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGOjg9S4A9sbxz397vnTDN9akr62nX7Dqfdz+NrfM+3/jx1a9HwBg9QnYbEhfuuEb2XnWyavez9zcXLZv377q/axFiAcA1oYpIgAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAA7nQDABwM66WC/tGwAYAbsbVcmHfmCICAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAy0rYFfVEVV1XlV9tKo+UlUPqKo7VtU7qupj0/0dVrtYAADY3y13BPslSS7s7vskOT7JR5KcnuSi7r5XkoumZQAAOKDtMWBX1eFJfijJHyZJd3+9u7+Y5JFJzpmanZPklNUqEgAANorlXGjm7kk+m+TVVXV8ksuSnJZkS3dfkyTdfU1V3WWpnatqR5IdSbJly5bMzc2NqBvW5L20a9euNXvP+rcB7E98xsLeW07APjjJ/ZI8q7svqaqXZAXTQbr77CRnJ8m2bdt6La7YxAHgwgvW5Opfa3WVsbV6PgDL4jMW9sly5mBfleSq7r5kWj4vs8B9bVUdlSTT/XWrUyIAAGwcewzY3f2ZJFdW1b2nVQ9O8uEkb01y6rTu1CRvWZUKAQBgA1nOFJEkeVaSN1TVIUmuSPKUzML5m6rqaUn+KcljVqdEAADYOJYVsLv78iTbltj04LHlAADAxuZKjgAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMNDB610A7I3D7nt6vvuc09ems3NWv4vD7pskJ69+RwDAqhOw2ZC+8pGzsvOs1Q+kc3Nz2b59+6r3s/X0C1a9DwBgbZgiAgAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkAvNAAA342q5sG8EbADgZlwtF/aNKSIAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMNCyAnZV7ayqf6iqy6vq0mnd86rq09O6y6vqYatbKgAA7P8OXkHbk7r7c4vWvai7f3dkQQAAsJGZIgIAAAMtdwS7k7y9qjrJK7r77Gn9M6vqSUkuTfKc7r5+8Y5VtSPJjiTZsmVL5ubm9r1qSNbkvbRr1641e8/6twHsT3zGwt5bbsB+YHdfXVV3SfKOqvpokj9I8oLMwvcLkrwwyVMX7ziF8bOTZNu2bb19+/YRdXOgu/CCrMV7aW5ubk36WavnA7AsPmNhnyxrikh3Xz3dX5fk/CQndve13X1Td/9rklcmOXH1ygQAgI1hjwG7qg6tqsPmHyf5sSQfrKqjFjR7VJIPrk6JAACwcSxnisiWJOdX1Xz7P+7uC6vqdVV1QmZTRHYm+dlVqxIAADaIPQbs7r4iyfFLrH/iqlQEAAAbmJ/pAwCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABhIwAYAgIEOXu8CAID9z9bTL1ibji5c/X5uf5tbrnofsJCADQDczM6zTl6TfraefsGa9QVryRQRAAAYSMAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBXGiGDctVxgCA/ZGAzYbkKmMAwP7KFBEAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYaFm/IlJVO5N8JclNSW7s7m1Vdcck5ybZmmRnksd29/WrUyYAAGwMKxnBPqm7T+jubdPy6Uku6u57JbloWgYAgAPavkwReWSSc6bH5yQ5Zd/LAQCAjW25F5rpJG+vqk7yiu4+O8mW7r4mSbr7mqq6y1I7VtWOJDuSZMuWLZmbm9v3qmENec8CrB6fsWxGyw3YD+zuq6cQ/Y6q+uhyO5jC+NlJsm3btt6+ffvKq4T1cuEF8Z4FWCU+Y9mkljVFpLuvnu6vS3J+khOTXFtVRyXJdH/dahUJAAAbxR4DdlUdWlWHzT9O8mNJPpjkrUlOnZqdmuQtq1UkAABsFMuZIrIlyflVNd/+j7v7wqp6T5I3VdXTkvxTksesXpkAALAx7DFgd/cVSY5fYv3nkzx4NYoCAICNypUcAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAYSsAEAYCABGwAABhKwAQBgIAEbAAAGErABAGAgARsAAAZadsCuqoOq6n1V9bZp+TVV9cmquny6nbB6ZQIAwMZw8AranpbkI0kOX7Dul7r7vLElAQDAxrWsEeyqOjbJyUletbrlAADAxrbcEewXJ/nlJIctWn9mVf16kouSnN7dX1u8Y1XtSLIjSbZs2ZK5ubm9rxbWgfcswOrxGctmtMeAXVUPT3Jdd19WVdsXbDojyWeSHJLk7CTPTfLfFu/f3WdP27Nt27bevn374iaw/7rwgnjPAqwSn7FsUsuZIvLAJI+oqp1J3pjkQVX1+u6+pme+luTVSU5cxToBAGBD2GPA7u4zuvvY7t6a5HFJ3tndP11VRyVJVVWSU5J8cFUrBQCADWAlvyKy2Buq6s5JKsnlSZ4xpiQAANi4VhSwu3suydz0+EGrUA8AAGxoruQIAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADCQgA0AAAMdvN4FAAAbX1Xt3X6/s/J9unuv+oK1YgQbANhn3b3i28UXX7xX+8H+TsAGAICBBGwAABhIwAYAgIEEbAAAGEjABgCAgZYdsKvqoKp6X1W9bVr+tqq6pKo+VlXnVtUhq1cmAABsDCsZwT4tyUcWLP9Okhd1972SXJ/kaSMLAwCAjWhZAbuqjk1ycpJXTcuV5EFJzpuanJPklNUoEAAANpLlXsnxxUl+Oclh0/Kdknyxu2+clq9KcsxSO1bVjiQ7kmTLli2Zm5vb62JhX5x00kl7td/eXGXs4osv3qu+AA4ku3btkgvYlPYYsKvq4Umu6+7Lqmr7/Oolmi55aaXuPjvJ2Umybdu23r59+1LNYNXtzdW/5ubm4j0LsDp8xrJZLWcE+4FJHlFVD0ty6ySHZzaifURVHTyNYh+b5OrVKxMAADaGPc7B7u4zuvvY7t6a5HFJ3tndT0hycZJHT81OTfKWVasSAAA2iH35HeznJnl2VX08sznZfzimJAAA2LiW+yXHJEl3zyWZmx5fkeTE8SUBAMDG5UqOAAAwkIANAAADCdgAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANAAADCdgAADBQdffadVb12SSfWrMOYd8dmeRz610EwCblM5aN5rjuvvOeGq1pwIaNpqou7e5t610HwGbkM5bNyhQRAAAYSMAGAICBBGzYvbPXuwCATcxnLJuSOdgAADCQEWwAABhIwAYAgIEEbFhCVT2qqrqq7rPetQBsNlV1U1VdXlXvr6r3VtV/WO+aYCQBG5b2+CR/neRx610IwCZ0Q3ef0N3HJzkjyW+vd0EwkoANi1TV7ZI8MMnTImADrLbDk1y/3kXASAevdwGwHzolyYXd/X+r6gtVdb/ufu96FwWwidymqi5PcuskRyV50DrXA0MZwYb/3+OTvHF6/MZpGYBx5qeI3CfJf0zy2qqq9S4KRvE72LBAVd0pyVVJrkvSSQ6a7o9r/1gAhqiqXd19uwXL1yb57u6+bh3LgmGMYMPNPTrJa7v7uO7e2t13S/LJJD+wznUBbErTrzUdlOTz610LjGIONtzc45OctWjdm5P8VJL/s/blAGxK83Owk6SSnNrdN61nQTCSKSIAADCQKSIAADCQgA0AAAMJ2AAAMJCADQAAAwnYAAAwkIANMFBV3bWq3lhVn6iqD1fVX1TVt1fV1qr64F4e88lVdfQ+1vXkqnrZCtrvrKojV+v4AJuZgA0wyHSp5/OTzHX3Pbr7O5L8SpIt+3joJydZUcCuKtc5AFgnAjbAOCcl+UZ3v3x+RXdf3t03u0jR4tHeqnpbVW2vqoOq6jVV9cGq+oeq+i9V9egk25K8oaour6rbVNX3VtW7quqyqvrLqjpqOs5cVf1WVb0ryWnLKbiq/qCqLq2qD1XV8xdt/qWq+vvpds+p/Z2r6s1V9Z7p9sC9OlMAm5gRDoBxvivJZfuw/wlJjunu70qSqjqiu79YVc9M8ovdfWlV3TLJS5M8srs/W1U/meTMJE+djnFEd//wCvr8r939hao6KMlFVfU93f2BaduXu/vEqnpSkhcneXiSlyR5UXf/dVX9uyR/meS++/CcATYdARtg/3FFkrtX1UuTXJDk7Uu0uXdmQf4dsxkpOSjJNQu2n7vCPh9bVTsy++/BUUm+I8l8wP6TBfcvmh7/SJLvmPpOksOr6rAV9gmwqQnYAON8KMmjl9Huxtx8it6tk6S7r6+q45M8JMnPJXlsvjkyPa+SfKi7H/Atjv3V5RZbVd+W5BeT3H/q+zXztUx6ice3SPKA7r5h0bGW2y3ApmcONsA470xyq6p6+vyKqrp/VS2esrEzyQlVdYuquluSE6e2Rya5RXe/OcmvJbnf1P4rSeZHif8xyZ2r6gHTPresqu/cy3oPzyyQf6mqtiR56KLtP7ng/t3T47cneeaC53fCXvYNsGkZwQYYpLu7qh6V5MVVdXqSf8ksTP/CoqZ/k+STSf4hyQeTvHdaf0ySV1fV/ODHGdP9a5K8vKpuSPKAzEbJf7+qbp/Z5/iLMxs935MnV9UpC5a/P8n7pn2vmOpa6FZVdUlmgzGPn9b9fJL/UVUfmPr+qyTPWEbfAAeM6u49twIAAJbFFBEAABhIwAYAgIEEbAAAGEjABgCAgQRsAAAYSMAGAICBBGwAABjo/wE6+EwKKN1d5gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[0],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "v = asthma_df_important_features['index'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d4bd310>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[v],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156a4dce90>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[237],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156dba4690>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[632],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d4c5050>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[332],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d7b6750>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[413],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156a26ac90>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[549],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d994c90>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[524],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156dba4990>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[621],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156a7d4e10>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[380],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156a269210>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[590],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156c95b7d0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[238],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156c97d910>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[554],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156dbe44d0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[544],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d955a50>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[214],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f156d9ddc90>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot(asthma_df_quant.columns[24],by='Cluster Label')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### which means the Data is not clustering by age. \n",
    "\n",
    "1) Visualize the data in box plots for all ten most important features\n",
    "2) Run ANOVA on the ten most important features above to derive p-values. Good resources for ANOVA in Python include: https://www.marsja.se/four-ways-to-conduct-one-way-anovas-using-python/ (first section with SciPy only) \n",
    "\n",
    "and\n",
    "\n",
    "https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.stats.f_oneway.html\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# ANOVA - analyzes groups of data (group A vs B) to quantify how different they are \n",
    "## do each variable separately - 15 different ANOVA's\n",
    "## lower the better p-value. we define the cutoff ( < 0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "asthma_df_quant_group1 = asthma_df_quant[asthma_df_quant['Cluster Label'].str.contains('A')]\n",
    "\n",
    "asthma_df_quant_group2 = asthma_df_quant[asthma_df_quant['Cluster Label'].str.contains('B')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_20016_0_0_f_QUANT_Fluid_intelligence_score_residualizedRelated']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[381]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.31831500983974526 and p-value 0.5726473422364362\n"
     ]
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "\n",
    "feature = asthma_df_quant.columns[381]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))\n",
    "## if pvalue < 0.05:\n",
    "\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 12.04689871911725 and p-value 0.0005231738357669493\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[237]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 1.1834504637868137 and p-value 0.2767071237150174\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[632]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.07210132887798207 and p-value 0.7883125454589924\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[332]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_23100_0_0_f_QUANT_Whole_body_fat_mass_residualizedRelated']"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[413]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 1.6128812174203702 and p-value 0.2041463861892404\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[413]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 2.960903996480184 and p-value 0.08536213034277715\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[549]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 2.311392394867185 and p-value 0.12849221555623164\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[524]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.1631532023013307 and p-value 0.686287158808281\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[621]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_20015_0_0_f_QUANT_Sitting_height_residualizedRelated']"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[380]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.20939328522296596 and p-value 0.6472632476350051\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[380]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['f_20150_0_0_f_QUANT_Forced_expiratory_volume_in_1_second_FEV1_Best_measure_strict_residualizedRelated']"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[asthma_df_quant.columns[590]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 1.6592719049809435 and p-value 0.1977612900041745\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[590]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 11.591183543017596 and p-value 0.0006678675073058919\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[238]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.019477672016201947 and p-value 0.8890113494033246\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[554]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.19546659988975806 and p-value 0.6584243614559202\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[544]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.18236443638243874 and p-value 0.66936750100414\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[214]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ANOVA Statistic 0.12264393575366357 and p-value 0.7261991900201507\n"
     ]
    }
   ],
   "source": [
    "feature = asthma_df_quant.columns[24]\n",
    "(statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    \n",
    "print(\"ANOVA Statistic \" + str(statistic) + \" and p-value \" + str(pvalue))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'FID', u'IID', u'RGC_ID', u'random_normal', u'random_binary', u'sex',\n",
       "       u'chip', u'age', u'BMI', u'asthma',\n",
       "       ...\n",
       "       u'f_40019_0_p_D40_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_male_genital_organs',\n",
       "       u'f_40019_0_p_D41_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_urinary_organs',\n",
       "       u'f_40019_0_p_D42_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_meninges',\n",
       "       u'f_40019_0_p_D43_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_brain_and_CNS',\n",
       "       u'f_40019_0_p_D44_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_endocrine_glands',\n",
       "       u'f_40019_0_p_D45_CAT_Polycythaemia_vera',\n",
       "       u'f_40019_0_p_D46_CAT_Myelodysplastic_syndromes',\n",
       "       u'f_40019_0_p_D47_CAT_Other_neoplasms_of_uncertain_behaviour_of_lymphoid_haematopoietic',\n",
       "       u'f_40019_0_p_D48_CAT_Neoplasm_of_uncertain_or_unknown_behaviour_of_other_sites',\n",
       "       u'f_40019_0_p_O01_CAT_Hydatidiform_mole'],\n",
       "      dtype='object', length=5172)"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "asthma_df_2=pd.read_csv(\"ukbb_asthma_sample_5k_new.csv\",sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "asthma\n",
      "f_3786_0_0_f_QUANT_Age_asthma_diagnosed\n",
      "f_22147_0_0_f_QUANT_Age_asthma_diagnosed_by_doctor\n",
      "f_20009_0_dxAgeCode1111_QUANT_asthma\n",
      "f_3786_0_0_f_QUANT_Age_asthma_diagnosed_residualizedRelated\n",
      "f_22147_0_0_f_QUANT_Age_asthma_diagnosed_by_doctor_residualizedRelated\n",
      "f_20009_0_dxAgeCode1111_QUANT_asthma_residualizedRelated\n",
      "specialRequest_QUANT_type_of_asthma_exacerbation\n",
      "specialRequest_QUANT_asthma_hospital_exacerbations_raw\n",
      "specialRequest_QUANT_asthma_hospital_exacerbations_annual_rate\n",
      "specialRequest_QUANT_asthma_HES_based_age_of_onset\n",
      "specialRequest_QUANT_asthma_HES_or_NI_based_age_of_onset_youngest\n",
      "f_22127_0_0_f_BIN_Doctor_diagnosed_asthma\n",
      "f_22167_0_0_f_BIN_Recent_medication_for_asthma\n",
      "f_20002_0_dxCode1111_BIN_asthma\n",
      "HES_p_J46_BIN_Status_asthmaticus\n",
      "HES_primary_p_J46_BIN_Status_asthmaticus\n",
      "map2way_NI_code1111_BIN_asthma\n",
      "specialRequest_BIN_NI_pediatric_asthma_under16yo\n",
      "specialRequest_BIN_NI_adult_only_asthma_over16yo\n"
     ]
    }
   ],
   "source": [
    "for x in asthma_df_2.columns :\n",
    "    if 'asthma' in x:\n",
    "        print x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    1194\n",
       "0.0     158\n",
       "Name: f_22127_0_0_f_BIN_Doctor_diagnosed_asthma, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asthma_df_2['f_22127_0_0_f_BIN_Doctor_diagnosed_asthma'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello\n"
     ]
    }
   ],
   "source": [
    "print('hello')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p_values=[]\n",
    "features=list(asthma_df_quant_group1.columns.values[:-1])\n",
    "anova_dict=dict()\n",
    "\n",
    "for feature in asthma_df_quant_group1.columns.values[:-1]:\n",
    "    (statistic, pvalue) = stats.f_oneway(asthma_df_quant_group1[feature].values, asthma_df_quant_group2[feature].values)\n",
    "    p_values.append(pvalue)\n",
    "anova_dict['feature']=features\n",
    "anova_dict['pvalue']=p_values\n",
    "anova_df=pd.DataFrame.from_dict(anova_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "anova_df=anova_df.sort_values(by='pvalue')\n",
    "threshold=0.05\n",
    "anova_df_significant=anova_df.loc[(anova_df.pvalue < threshold) & (anova_df.pvalue > 0)].sort_values(by='pvalue')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "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>feature</th>\n",
       "      <th>pvalue</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>405</th>\n",
       "      <td>f_22424_2_0_f_QUANT_Cardiac_output_residualize...</td>\n",
       "      <td>1.825525e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...</td>\n",
       "      <td>1.961590e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>f_12340_2_0_f_QUANT_QRS_duration_residualizedR...</td>\n",
       "      <td>7.207754e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>specialRequest_QUANT_LVESVi_residualizedRelated</td>\n",
       "      <td>5.638355e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>f_23279_2_0_f_QUANT_Total_fat_free_mass_residu...</td>\n",
       "      <td>4.449092e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>f_47_46_QUANT_grip_strength_maximumValue_resid...</td>\n",
       "      <td>5.065731e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>f_47_f_QUANT_grip_strength_maximumValue_residu...</td>\n",
       "      <td>6.664593e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>456</th>\n",
       "      <td>f_23247_2_0_f_QUANT_Android_tissue_fat_percent...</td>\n",
       "      <td>7.680038e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>f_23286_2_0_f_QUANT_Trunk_tissue_fat_percentag...</td>\n",
       "      <td>1.372156e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>472</th>\n",
       "      <td>f_23263_2_0_f_QUANT_Gynoid_lean_mass_residuali...</td>\n",
       "      <td>2.086299e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>f_46_f_QUANT_grip_strength_maximumValue_residu...</td>\n",
       "      <td>2.578400e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>404</th>\n",
       "      <td>f_22423_2_0_f_QUANT_LV_stroke_volume_residuali...</td>\n",
       "      <td>3.880855e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>237</th>\n",
       "      <td>f_47_f_QUANT_grip_strength_maximumValue</td>\n",
       "      <td>5.231738e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>238</th>\n",
       "      <td>f_47_46_QUANT_grip_strength_maximumValue</td>\n",
       "      <td>6.678675e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>f_23280_2_0_f_QUANT_Total_lean_mass_residualiz...</td>\n",
       "      <td>7.074520e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>f_23278_2_0_f_QUANT_Total_fat_mass_residualize...</td>\n",
       "      <td>7.112977e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>f_46_f_QUANT_grip_strength_maximumValue</td>\n",
       "      <td>9.003280e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>f_23269_2_0_f_QUANT_Leg_total_mass_left_residu...</td>\n",
       "      <td>1.073306e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>f_23273_2_0_f_QUANT_Leg_total_mass_right_resid...</td>\n",
       "      <td>1.247801e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>f_23318_2_0_f_QUANT_Legs_combined_bone_area_re...</td>\n",
       "      <td>1.686469e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>f_23271_2_0_f_QUANT_Leg_lean_mass_right_residu...</td>\n",
       "      <td>1.827128e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>f_22680_0_0_f_QUANT_Mean_carotid_IMT_intima_me...</td>\n",
       "      <td>1.967820e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>f_12336_2_0_f_QUANT_Ventricular_rate_residuali...</td>\n",
       "      <td>3.216578e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>f_4022_0_0_f_QUANT_Age_pulmonary_embolism_bloo...</td>\n",
       "      <td>3.358694e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>474</th>\n",
       "      <td>f_23265_2_0_f_QUANT_Gynoid_total_mass_residual...</td>\n",
       "      <td>3.577889e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>f_23288_2_0_f_QUANT_VAT_visceral_adipose_tissu...</td>\n",
       "      <td>5.455736e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>f_23289_2_0_f_QUANT_VAT_visceral_adipose_tissu...</td>\n",
       "      <td>5.491601e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>f_23267_2_0_f_QUANT_Leg_lean_mass_left_residua...</td>\n",
       "      <td>6.651748e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>f_23127_0_0_f_QUANT_Trunk_fat_percentage</td>\n",
       "      <td>8.539258e-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>466</th>\n",
       "      <td>f_23257_2_0_f_QUANT_Arms_fat_mass_residualized...</td>\n",
       "      <td>1.067122e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>f_40008_0_p_C44_QUANT_Other_malignant_neoplasm...</td>\n",
       "      <td>1.165644e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>372</th>\n",
       "      <td>f_5257_0_0_f_QUANT_Corneal_resistance_factor_r...</td>\n",
       "      <td>1.454197e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>f_23123_0_0_f_QUANT_Arm_fat_percentage_left</td>\n",
       "      <td>1.511328e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>f_23119_0_0_f_QUANT_Arm_fat_percentage_right</td>\n",
       "      <td>2.156369e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio_ever_smoke...</td>\n",
       "      <td>2.455003e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>f_3536_0_0_f_QUANT_Age_started_hormone_replace...</td>\n",
       "      <td>2.938291e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>f_46_0_0_f_QUANT_Hand_grip_strength_left</td>\n",
       "      <td>2.946257e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357</th>\n",
       "      <td>f_135_0_0_f_QUANT_Number_of_self_reported_non_...</td>\n",
       "      <td>3.049433e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>f_46_0_0_f_QUANT_Hand_grip_strength_left_resid...</td>\n",
       "      <td>3.121070e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>f_102_0_f_QUANT_pulse_minimumValue</td>\n",
       "      <td>3.185171e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>f_47_0_0_f_QUANT_Hand_grip_strength_right_resi...</td>\n",
       "      <td>3.263554e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>530</th>\n",
       "      <td>f_30160_0_0_f_QUANT_Basophill_count_residualiz...</td>\n",
       "      <td>3.525243e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>f_47_0_0_f_QUANT_Hand_grip_strength_right</td>\n",
       "      <td>3.608462e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>f_23099_0_0_f_QUANT_Body_fat_percentage</td>\n",
       "      <td>3.769309e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>f_23128_0_0_f_QUANT_Trunk_fat_mass</td>\n",
       "      <td>3.991216e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>f_5263_0_0_f_QUANT_Intra_ocular_pressure_Goldm...</td>\n",
       "      <td>4.443762e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>specialRequest_QUANT_FEV1_never_smoked_only</td>\n",
       "      <td>4.492000e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>specialRequest_QUANT_FEV1_FVC_ratio_never_smok...</td>\n",
       "      <td>4.525378e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>370</th>\n",
       "      <td>f_5255_0_0_f_QUANT_Intra_ocular_pressure_Goldm...</td>\n",
       "      <td>4.541391e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>458</th>\n",
       "      <td>f_23249_2_0_f_QUANT_Arm_fat_mass_left_residual...</td>\n",
       "      <td>4.810045e-02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               feature        pvalue\n",
       "405  f_22424_2_0_f_QUANT_Cardiac_output_residualize...  1.825525e-07\n",
       "379  f_12702_2_0_f_QUANT_Cardiac_index_during_PWA_r...  1.961590e-07\n",
       "378  f_12340_2_0_f_QUANT_QRS_duration_residualizedR...  7.207754e-07\n",
       "605    specialRequest_QUANT_LVESVi_residualizedRelated  5.638355e-06\n",
       "488  f_23279_2_0_f_QUANT_Total_fat_free_mass_residu...  4.449092e-05\n",
       "567  f_47_46_QUANT_grip_strength_maximumValue_resid...  5.065731e-05\n",
       "566  f_47_f_QUANT_grip_strength_maximumValue_residu...  6.664593e-05\n",
       "456  f_23247_2_0_f_QUANT_Android_tissue_fat_percent...  7.680038e-05\n",
       "495  f_23286_2_0_f_QUANT_Trunk_tissue_fat_percentag...  1.372156e-04\n",
       "472  f_23263_2_0_f_QUANT_Gynoid_lean_mass_residuali...  2.086299e-04\n",
       "565  f_46_f_QUANT_grip_strength_maximumValue_residu...  2.578400e-04\n",
       "404  f_22423_2_0_f_QUANT_LV_stroke_volume_residuali...  3.880855e-04\n",
       "237            f_47_f_QUANT_grip_strength_maximumValue  5.231738e-04\n",
       "238           f_47_46_QUANT_grip_strength_maximumValue  6.678675e-04\n",
       "489  f_23280_2_0_f_QUANT_Total_lean_mass_residualiz...  7.074520e-04\n",
       "487  f_23278_2_0_f_QUANT_Total_fat_mass_residualize...  7.112977e-04\n",
       "236            f_46_f_QUANT_grip_strength_maximumValue  9.003280e-04\n",
       "478  f_23269_2_0_f_QUANT_Leg_total_mass_left_residu...  1.073306e-03\n",
       "482  f_23273_2_0_f_QUANT_Leg_total_mass_right_resid...  1.247801e-03\n",
       "509  f_23318_2_0_f_QUANT_Legs_combined_bone_area_re...  1.686469e-03\n",
       "480  f_23271_2_0_f_QUANT_Leg_lean_mass_right_residu...  1.827128e-03\n",
       "411  f_22680_0_0_f_QUANT_Mean_carotid_IMT_intima_me...  1.967820e-03\n",
       "510  f_12336_2_0_f_QUANT_Ventricular_rate_residuali...  3.216578e-03\n",
       "21   f_4022_0_0_f_QUANT_Age_pulmonary_embolism_bloo...  3.358694e-03\n",
       "474  f_23265_2_0_f_QUANT_Gynoid_total_mass_residual...  3.577889e-03\n",
       "497  f_23288_2_0_f_QUANT_VAT_visceral_adipose_tissu...  5.455736e-03\n",
       "498  f_23289_2_0_f_QUANT_VAT_visceral_adipose_tissu...  5.491601e-03\n",
       "476  f_23267_2_0_f_QUANT_Leg_lean_mass_left_residua...  6.651748e-03\n",
       "104           f_23127_0_0_f_QUANT_Trunk_fat_percentage  8.539258e-03\n",
       "466  f_23257_2_0_f_QUANT_Arms_fat_mass_residualized...  1.067122e-02\n",
       "584  f_40008_0_p_C44_QUANT_Other_malignant_neoplasm...  1.165644e-02\n",
       "372  f_5257_0_0_f_QUANT_Corneal_resistance_factor_r...  1.454197e-02\n",
       "100        f_23123_0_0_f_QUANT_Arm_fat_percentage_left  1.511328e-02\n",
       "96        f_23119_0_0_f_QUANT_Arm_fat_percentage_right  2.156369e-02\n",
       "277  specialRequest_QUANT_FEV1_FVC_ratio_ever_smoke...  2.455003e-02\n",
       "13   f_3536_0_0_f_QUANT_Age_started_hormone_replace...  2.938291e-02\n",
       "32            f_46_0_0_f_QUANT_Hand_grip_strength_left  2.946257e-02\n",
       "357  f_135_0_0_f_QUANT_Number_of_self_reported_non_...  3.049433e-02\n",
       "350  f_46_0_0_f_QUANT_Hand_grip_strength_left_resid...  3.121070e-02\n",
       "248                 f_102_0_f_QUANT_pulse_minimumValue  3.185171e-02\n",
       "351  f_47_0_0_f_QUANT_Hand_grip_strength_right_resi...  3.263554e-02\n",
       "530  f_30160_0_0_f_QUANT_Basophill_count_residualiz...  3.525243e-02\n",
       "33           f_47_0_0_f_QUANT_Hand_grip_strength_right  3.608462e-02\n",
       "84             f_23099_0_0_f_QUANT_Body_fat_percentage  3.769309e-02\n",
       "105                 f_23128_0_0_f_QUANT_Trunk_fat_mass  3.991216e-02\n",
       "374  f_5263_0_0_f_QUANT_Intra_ocular_pressure_Goldm...  4.443762e-02\n",
       "271        specialRequest_QUANT_FEV1_never_smoked_only  4.492000e-02\n",
       "275  specialRequest_QUANT_FEV1_FVC_ratio_never_smok...  4.525378e-02\n",
       "370  f_5255_0_0_f_QUANT_Intra_ocular_pressure_Goldm...  4.541391e-02\n",
       "458  f_23249_2_0_f_QUANT_Arm_fat_mass_left_residual...  4.810045e-02"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_significant.tail(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova_df_significant[anova_df_significant['feature'].str.contains('FEV|FVC')].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f1566df59d0>"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "asthma_df_quant.boxplot('f_22200_0_0_f_QUANT_Year_of_birth',by='Cluster Label')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:anaconda-default]",
   "language": "python",
   "name": "conda-env-anaconda-default-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}