{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Understanding Variation in Negative Control Population\n",
    "\n",
    "1. Run through the 10x Cellranger pipeline and velocyto for single cell RNAseq quatification and using (2) guides quantifiction. all found in the cellranger files folder **bash**\n",
    "2.  Guide Calling for dual guide. Use repogle method to take molecule.h5 generated by cellranger and py to run through repogle version of guide calling or use cellranger_guidecalling.ipynb for Direct Capture Perturb-Seq dual guide. Formed guide-specific lists of cells.\n",
    "3. Pseudobulk analysis.\n",
    "    A. Seperation of guide-specific fastq files. **bash**\n",
    "    B. Whippet pseudobulk for transcript specific analysis, post UMI deduplication.  **bash**\n",
    "    C. Transcript quality control. **R**\n",
    "    D. Whippet result visualisation.\n",
    "4. Normalisation of adata object and E-distance of KD\n",
    "5. Check gene and neighboring gene expression\n",
    "6. Create individual umaps per gene of interest\n",
    "    A. UMAPs \n",
    "    B. Rand Index score\n",
    "7. Cell phase assignment model from FUCCI-matched single cell paper (GSE146773)\n",
    "8. Differential Expression analysis.\n",
    "    A. Find the shared P1 and P2 genes. \n",
    "    B. Check the shared P1 and P2 across different protospacers with the same A/B and C/D.\n",
    "9.  CNV Score & Numbat to quantify and Velocity quantification with loom file\n",
    "10. ESR1-specific analysis from proliferation analysis to rt-qpcr\n",
    "11.  Spectra analysis and visualisation for pathway enrichment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This script is dedicated to establishing the empirical null distribution for the study. By analyzing cells treated with non-targeting control (NTC) sgRNAs, the authors can distinguish true biological signals from the inherent noise and stochastic variability of single-cell sequencing.\n",
    "\n",
    "1. Defining Baseline Transcriptional Noise\n",
    "Single-cell RNA-seq is inherently sparse and variable. To ensure that the \"divergence\" measured in perturbed cells is meaningful, the authors must first know how much variation occurs naturally.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "What is happening: The code subsets the 10% of the library assigned to non-targeting control sgRNAs.\n",
    "\n",
    "\n",
    "\n",
    "The Logic: These cells represent the \"ground state\" where no specific promoter is being repressed. Any variation in expression here is considered the baseline noise level.\n",
    "\n",
    "\n",
    "2. Validating the E-statistic and Rand Scores\n",
    "The notebook uses these control cells to perform \"permutation tests\" or \"shuffled\" analyses.\n",
    "\n",
    "\n",
    "What is happening: The code calculates the e-distance and Rand Scores between different groups of NTC cells.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Expected Result: Since all these cells are controls, the e-distance should be near zero, and the Rand Score should show no significant clustering.\n",
    "\n",
    "Significance: This provides the \"p-value\" cutoff used in earlier notebooks. If the P1 vs. P2 divergence is significantly higher than this NTC-vs-NTC noise, it is confirmed as a real biological effect\n",
    "\n",
    "\n",
    "This notebook, 15_negativecontrol.ipynb, is dedicated to establishing the empirical null distribution for the study. By analyzing cells treated with non-targeting control (NTC) sgRNAs, the authors can distinguish true biological signals from the inherent noise and stochastic variability of single-cell sequencing.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Here is a breakdown of the computational workflow and its role in the study:\n",
    "\n",
    "1. Defining Baseline Transcriptional Noise\n",
    "\n",
    "- Single-cell RNA-seq is inherently sparse and variable. To ensure that the \"divergence\" measured in perturbed cells is meaningful, the authors must first know how much variation occurs naturally.\n",
    "\n",
    "- What is happening: The code subsets the 10% of the library assigned to non-targeting control sgRNAs.\n",
    "\n",
    "-The Logic: These cells represent the \"ground state\" where no specific promoter is being repressed. Any variation in expression here is considered the baseline noise level.\n",
    "\n",
    "2. Validating the E-statistic and Rand Scores. The notebook uses these control cells to perform \"permutation tests\" or \"shuffled\" analyses.\n",
    "\n",
    "- What is happening: The code calculates the e-distance and Rand Scores between different groups of NTC cells.\n",
    "\n",
    "- Expected Result: Since all these cells are controls, the e-distance should be near zero, and the Rand Score should show no significant clustering.\n",
    "\n",
    "- Significance: This provides the \"p-value\" cutoff used in earlier notebooks. If the P1 vs. P2 divergence is significantly higher than this NTC-vs-NTC noise, it is confirmed as a real biological effect.\n",
    "\n",
    "\n",
    "\n",
    "3. Promoter Expression in the \"Wild-Type\" State. The notebook provides the baseline expression data for the P1 and P2 promoters before any CRISPRi intervention.\n",
    "- What you see: Violin plots and dot plots showing the relative expression of P1 and P2 promoters in the control population.\n",
    "\n",
    "- Discovery: This data confirmed that for most targeted genes, both promoters are active in the MCF-7 cell line, providing the necessary \"room\" for a knockdown to be measured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/helenking/anaconda3/envs/apu/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scanpy 1.10.3\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%matplotlib inline\n",
    "%autoreload 2\n",
    "#general\n",
    "import scanpy as sc\n",
    "import matplotlib.pyplot as pl\n",
    "import anndata as ad\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import hdf5plugin\n",
    "\n",
    "#form a location\n",
    "loc=\"../../../alt-prom-crispr-fiveprime/\"\n",
    "\n",
    "import seaborn as sns\n",
    "from tqdm.notebook import tqdm\n",
    "import scperturb\n",
    "import sys\n",
    "sys.path.append(loc+'scripts/')\n",
    "from apu_analysis import *\n",
    "import infercnvpy as cnv\n",
    "from apu_analysis.cell_import import CellPopulation\n",
    "from IPython.display import clear_output\n",
    "pd.options.display.float_format = '{:.4f}'.format\n",
    "import matplotlib.pyplot as plt\n",
    "import scvelo as scv\n",
    "from matplotlib_venn import venn3\n",
    "\n",
    "#for this python\n",
    "from scipy.special import rel_entr\n",
    "import sklearn.cluster as cluster\n",
    "import umap\n",
    "from scipy import stats\n",
    "from scipy.stats import bootstrap\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.stats.multitest import multipletests\n",
    "from numpy import reshape\n",
    "from numpy import array\n",
    "from sklearn.decomposition import PCA\n",
    "import os \n",
    "\n",
    "# Taken from: \n",
    "# Adamson, B.A., Norman, T.M., *et al.* \"A multiplexed CRISPR screening platform enables systematic dissection of the unfolded protein response\", *Cell*, 2016.\n",
    "# My experiment deals with two KDs- one of the MP, one of the AP using two guides. Positve controls include GINS1 ect. This is a combnatorial KD double for the same gene. No treatments were used\n",
    "\n",
    "# colours using garvan \n",
    "color1 ='#4d00c7'\n",
    "palecolor1=\"#b366ff\"\n",
    "color2= '#da3c07'\n",
    "palecolor2=\"#ff8954\"\n",
    "color3='#05d3d3'\n",
    "color4='#c6c7c5'\n",
    "color4=\"#434541\"\n",
    "color5=\"#eb31e1\"\n",
    "color6=\"#3175eb\"\n",
    "color7=\"#a7eb31\"\n",
    "color8=\"#b366ff\"\n",
    "color9=\"#ff8954\"\n",
    "color10=\"#35c9d4\"\n",
    "\n",
    "#use viridis\n",
    "color1=\"#fde725\"\n",
    "color2=\"#7ad151\"\n",
    "color3=\"#22a884\"\n",
    "color4=\"#2a788e\"\n",
    "color5=\"#2a788e\"\n",
    "color6='#440154'\n",
    "\n",
    "\n",
    "# Create the color palette\n",
    "palette = sns.color_palette([palecolor1,palecolor2])\n",
    "palette2 = sns.color_palette([color1, color2, color3, color4,color5,color6 ,color7])\n",
    "\n",
    "# Create the color palette\n",
    "palette = sns.color_palette([color1, color2,color3])\n",
    "new_palette = sns.color_palette([color1, color2,color1, color2,color1, color2,color1, color2,color1, color2,color1, color2, color3, color4])\n",
    "\n",
    "\n",
    "\n",
    "print(\"Scanpy\", sc.__version__)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata = ad.read_h5ad(loc+\"files/adata_normalised_cellcycle.h5ad\")\n",
    "adata.X = adata.layers[\"counts\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.\n"
     ]
    }
   ],
   "source": [
    "#calculate leiden\n",
    "sc.tl.pca(adata, n_comps=50, svd_solver='arpack')\n",
    "sc.pp.neighbors(adata, n_neighbors=10, n_pcs=50)\n",
    "sc.tl.leiden(adata, resolution=0.5, key_added=\"leiden\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "def run_single_guide_regression(adataControl, controlGuides, guideIndex, expressionMatrix, method='NB'):\n",
    "    \"\"\"\n",
    "    Test whether a single control guide induces transcriptional changes across genes.\n",
    "    \n",
    "    Parameters:\n",
    "    - adataControl: AnnData object filtered to only include control guide cells.\n",
    "    - controlGuides: List of control guide names.\n",
    "    - guideIndex: Index of the control guide to test.\n",
    "    - expressionMatrix: DataFrame of gene expression (cells x genes).\n",
    "    - method: 'NB' or 'OLS'\n",
    "    \n",
    "    Returns:\n",
    "    - DataFrame with results per gene (Gene, Coef, StdErr, Pval)\n",
    "    \"\"\"\n",
    "    guide_name = controlGuides[guideIndex]\n",
    "    print(f\"Running regression for guide: {guide_name}\")\n",
    "    \n",
    "    # Create binary predictor: 1 = cell has this guide, 0 = other control guides\n",
    "    covariate_df = adataControl.obs[[\"guide_identity\", \"n_genes_by_counts\", \"pct_counts_mt\", \"leiden\"]].copy()\n",
    "    covariate_df[\"is_guide\"] = (covariate_df[\"guide_identity\"] == guide_name).astype(int)\n",
    "    \n",
    "    results = []\n",
    "    for gene in expressionMatrix.columns:\n",
    "        covariate_df[\"y\"] = expressionMatrix[gene].values\n",
    "        \n",
    "        formula = \"y ~ is_guide + n_genes_by_counts + pct_counts_mt + C(leiden)\"  # Use C() for categorical leiden\n",
    "        try:\n",
    "            if method == 'NB':\n",
    "                model = smf.glm(formula=formula, data=covariate_df,\n",
    "                                family=sm.families.NegativeBinomial()).fit()\n",
    "            elif method == 'OLS':\n",
    "                model = smf.ols(formula=formula, data=covariate_df).fit()\n",
    "            else:\n",
    "                raise ValueError(\"Method must be 'NB' or 'OLS'\")\n",
    "            \n",
    "            coef = model.params.get(\"is_guide\", np.nan)\n",
    "            pval = model.pvalues.get(\"is_guide\", np.nan)\n",
    "            stderr = model.bse.get(\"is_guide\", np.nan)\n",
    "            results.append((gene, coef, stderr, pval))\n",
    "        except Exception as e:\n",
    "            results.append((gene, np.nan, np.nan, np.nan))\n",
    "\n",
    "    res_df = pd.DataFrame(results, columns=[\"Gene\", \"Coef\", \"StdErr\", \"Pval\"])\n",
    "    res_df[\"Guide\"] = guide_name\n",
    "    return res_df\n",
    "\n",
    "\n",
    "\n",
    "    \"\"\"\n",
    "    Create a volcano plot for regression results.\n",
    "    \"\"\"\n",
    "    res_df = res_df.copy()\n",
    "    res_df[\"-log10Pval\"] = -np.log10(res_df[\"Pval\"])\n",
    "    \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    sns.scatterplot(data=res_df, x=\"Coef\", y=\"-log10Pval\", alpha=0.6, edgecolor=None)\n",
    "\n",
    "    # Highlight significant hits\n",
    "    sig_hits = res_df[(res_df[\"Pval\"] < pval_threshold) & (res_df[\"Coef\"].abs() > lfc_threshold)]\n",
    "    plt.scatter(sig_hits[\"Coef\"], sig_hits[\"-log10Pval\"], color=\"red\", alpha=0.8)\n",
    "\n",
    "    plt.axhline(-np.log10(pval_threshold), color='gray', linestyle='--', label=f'p={pval_threshold}')\n",
    "    plt.axvline(-lfc_threshold, color='gray', linestyle='--')\n",
    "    plt.axvline(lfc_threshold, color='gray', linestyle='--')\n",
    "\n",
    "    plt.title(f\"Volcano Plot for Guide: {guide_name}\")\n",
    "    plt.xlabel(\"Coefficient (effect size)\")\n",
    "    plt.ylabel(\"-log10(p-value)\")\n",
    "    plt.grid(True)\n",
    "    plt.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "# STEP 1: Define control guide cells (e.g. NTC or CTRL in identity)\n",
    "#get all guides names into a list for adata.obs['guide_target'][].unique(\n",
    "controlGuides = adata.obs['guide_identity'][adata.obs['guide_identity'].str.startswith('non-tar')].unique().tolist()\n",
    "control_mask = adata.obs['guide_identity'].isin(controlGuides[5:25])  # Adjust this to select your control guides\n",
    "adataControl = adata[control_mask].copy()\n",
    "#filter for first twenty guides \n",
    "# STEP 2: Prepare expression matrix\n",
    "\n",
    "expressionMatrix = pd.DataFrame(adataControl.layers[\"log1p\"])\n",
    "expressionMatrix.columns = adata.var_names\n",
    "expressionMatrix.index = adataControl.obs_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=100)\n",
    "pcs = pca.fit_transform(expressionMatrix)  # shape = (n_cells, 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import OneHotEncoder\n",
    "encoder = OneHotEncoder(sparse_output=False)\n",
    "design_matrix = encoder.fit_transform(adataControl.obs[['guide_assignment']])\n",
    "guide_names = encoder.categories_[0]  # guide column names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "betas = []\n",
    "for i in range(pcs.shape[1]):  # for each PC\n",
    "    model = sm.OLS(pcs[:, i], sm.add_constant(design_matrix)).fit()\n",
    "    betas.append(model.params[1:])  # skip intercept\n",
    "\n",
    "coeff_matrix = np.vstack(betas).T  # shape = (n_guides x n_PCs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "# Optionally standardize for heatmap\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaled = StandardScaler().fit_transform(coeff_matrix)\n",
    "\n",
    "# Select top 20 PCs (columns)\n",
    "sns.clustermap(pd.DataFrame(scaled[:, :20], index=guide_names),\n",
    "               cmap=\"vlag\", metric=\"euclidean\", figsize=(12, 10),\n",
    "               xticklabels=[f\"PC{i+1}\" for i in range(20)],\n",
    "                yticklabels=[f\"non-targeting control {i+1}\" for i in range(20)])\n",
    "\n",
    "plt.title(\"Guide × PC Coefficient Heatmap (top 20 PCs)\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running model: IsolationForest\n",
      "Running model: EllipticEnvelope\n",
      "Running model: LocalOutlierFactor\n",
      "Running model: OneClassSVM\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import IsolationForest\n",
    "from sklearn.covariance import EllipticEnvelope\n",
    "from sklearn.neighbors import LocalOutlierFactor\n",
    "from sklearn.svm import OneClassSVM\n",
    "\n",
    "models = {\n",
    "    \"IsolationForest\": IsolationForest(random_state=0, contamination=0.1),\n",
    "    \"EllipticEnvelope\": EllipticEnvelope(contamination=0.1),\n",
    "    \"LocalOutlierFactor\": LocalOutlierFactor(),\n",
    "    \"OneClassSVM\": OneClassSVM( nu=0.01)\n",
    "}\n",
    "c=0\n",
    "outlier_scores = {}\n",
    "#subset coeff_matrix array to be only first 20 \n",
    "for name, model in models.items():\n",
    "    print(f\"Running model: {name}\")\n",
    "    \n",
    "    if name == \"LocalOutlierFactor\":\n",
    "        pred = model.fit_predict(coeff_matrix)\n",
    "    else:\n",
    "        model.fit(coeff_matrix)\n",
    "        pred = model.predict(coeff_matrix)\n",
    "    #add the votes with the name to the guide\n",
    "    #if reaching 20 then break\n",
    "    outlier_scores[name] = pd.Series(pred, index=guide_names, name=name)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "outlier_counts_side = pd.DataFrame(outlier_scores).apply(lambda x: (x == -1).sum(), axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IsolationForest        0\n",
       "EllipticEnvelope       2\n",
       "LocalOutlierFactor     0\n",
       "OneClassSVM           20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outlier_counts_side"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sum the number of -1 per row in pd.DataFrame(outlier_scores) and make into barplit\n",
    "#subset to only first 20 guides\n",
    "outlier_counts = pd.DataFrame(outlier_scores).apply(lambda x: (x == -1).sum(), axis=1)\n",
    "outlier_counts = outlier_counts.head(20)\n",
    "\n",
    "outlier_counts = outlier_counts.sort_values(ascending=False)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=outlier_counts.index, y=outlier_counts.values, palette=\"viridis\")\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Guide\")\n",
    "plt.ylabel(\"Number of Outliers\")\n",
    "plt.title(\"Outlier Counts by Guide\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  count\n",
       "0      1     18\n",
       "1      2      2"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x600 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#create a count plot of the number of outlier \n",
    "plt.figure(figsize=(10, 6))\n",
    "county=pd.DataFrame(outlier_counts.value_counts()).reset_index()\n",
    "county.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n",
      "INFO:matplotlib.category:Using categorical units to plot a list of strings that are all parsable as floats or dates. If these strings should be plotted as numbers, cast to the appropriate data type before plotting.\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.barplot(county, x=\"index\",y=\"count\", palette=\"viridis\")\n",
    "#add line at y=3\n",
    "plt.xticks(rotation=45)\n",
    "plt.xlabel(\"Count of Outliers\")\n",
    "plt.ylabel(\"Number of Guides\")\n",
    "plt.title(\"Number of times guide assigned as an outlier\")\n",
    "#make y axis to lavel every two\n",
    "plt.yticks(np.arange(0, county['count'].max()+1, 2))\n",
    "#save as pdf\n",
    "plt.tight_layout()\n",
    "plt.savefig(loc+\"figures/number_of_outliers_per_guide.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'outlier_counts_df' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m outlier_counts_df \u001b[38;5;241m=\u001b[39m \u001b[43moutlier_counts_df\u001b[49m\u001b[38;5;241m.\u001b[39msort_values(by\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOutlier Count\u001b[39m\u001b[38;5;124m'\u001b[39m, ascending\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m      2\u001b[0m \u001b[38;5;66;03m#which guides is this make graph plit\u001b[39;00m\n\u001b[1;32m      3\u001b[0m outlier_counts_df\u001b[38;5;241m.\u001b[39mplot(kind\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mbar\u001b[39m\u001b[38;5;124m'\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m), color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mskyblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'outlier_counts_df' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "outlier_counts_df = outlier_counts_df.sort_values(by='Outlier Count', ascending=False)\n",
    "#which guides is this make graph plit\n",
    "outlier_counts_df.plot(kind='bar', figsize=(10, 6), color='skyblue')\n",
    "plt.title('Number of Outliers Detected by Each Model')\n",
    "plt.xlabel('Model')\n",
    "plt.ylabel('Outlier Count')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>IsolationForest</th>\n",
       "      <th>EllipticEnvelope</th>\n",
       "      <th>LocalOutlierFactor</th>\n",
       "      <th>OneClassSVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>non-targeting_00084</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00202</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00303</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00362</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00373</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00715</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00759</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00790</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00850</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00867</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_00949</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01357</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01500</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01651</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01660</th>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01777</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01815</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_01967</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02135</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02295</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02541</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02771</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02790</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02839</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02847</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_02969</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_03155</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_03351</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>non-targeting_03447</th>\n",
       "      <td>-1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     IsolationForest  EllipticEnvelope  LocalOutlierFactor  \\\n",
       "non-targeting_00084               -1                 1                  -1   \n",
       "non-targeting_00202               -1                 1                  -1   \n",
       "non-targeting_00303               -1                 1                  -1   \n",
       "non-targeting_00362               -1                 1                   1   \n",
       "non-targeting_00373               -1                 1                   1   \n",
       "non-targeting_00715               -1                -1                  -1   \n",
       "non-targeting_00759               -1                 1                  -1   \n",
       "non-targeting_00790               -1                 1                  -1   \n",
       "non-targeting_00850               -1                 1                  -1   \n",
       "non-targeting_00867               -1                 1                  -1   \n",
       "non-targeting_00949               -1                -1                   1   \n",
       "non-targeting_01357               -1                 1                   1   \n",
       "non-targeting_01500               -1                 1                  -1   \n",
       "non-targeting_01651               -1                -1                  -1   \n",
       "non-targeting_01660               -1                -1                  -1   \n",
       "non-targeting_01777               -1                 1                  -1   \n",
       "non-targeting_01815               -1                 1                   1   \n",
       "non-targeting_01967               -1                 1                   1   \n",
       "non-targeting_02135               -1                 1                   1   \n",
       "non-targeting_02295               -1                 1                  -1   \n",
       "non-targeting_02541               -1                 1                  -1   \n",
       "non-targeting_02771               -1                 1                   1   \n",
       "non-targeting_02790               -1                 1                  -1   \n",
       "non-targeting_02839               -1                 1                  -1   \n",
       "non-targeting_02847               -1                 1                  -1   \n",
       "non-targeting_02969               -1                 1                  -1   \n",
       "non-targeting_03155               -1                 1                  -1   \n",
       "non-targeting_03351               -1                 1                  -1   \n",
       "non-targeting_03447               -1                 1                   1   \n",
       "\n",
       "                     OneClassSVM  \n",
       "non-targeting_00084           -1  \n",
       "non-targeting_00202           -1  \n",
       "non-targeting_00303           -1  \n",
       "non-targeting_00362           -1  \n",
       "non-targeting_00373           -1  \n",
       "non-targeting_00715           -1  \n",
       "non-targeting_00759           -1  \n",
       "non-targeting_00790           -1  \n",
       "non-targeting_00850           -1  \n",
       "non-targeting_00867           -1  \n",
       "non-targeting_00949           -1  \n",
       "non-targeting_01357           -1  \n",
       "non-targeting_01500           -1  \n",
       "non-targeting_01651           -1  \n",
       "non-targeting_01660           -1  \n",
       "non-targeting_01777           -1  \n",
       "non-targeting_01815           -1  \n",
       "non-targeting_01967           -1  \n",
       "non-targeting_02135           -1  \n",
       "non-targeting_02295           -1  \n",
       "non-targeting_02541           -1  \n",
       "non-targeting_02771           -1  \n",
       "non-targeting_02790           -1  \n",
       "non-targeting_02839           -1  \n",
       "non-targeting_02847           -1  \n",
       "non-targeting_02969           -1  \n",
       "non-targeting_03155           -1  \n",
       "non-targeting_03351           -1  \n",
       "non-targeting_03447           -1  "
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  }
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