impact-prediction
Science Score: 18.0%
This score indicates how likely this project is to be science-related based on various indicators:
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✓CITATION.cff file
Found CITATION.cff file -
○codemeta.json file
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○.zenodo.json file
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○DOI references
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○Academic publication links
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○Academic email domains
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○Institutional organization owner
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○JOSS paper metadata
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○Scientific vocabulary similarity
Low similarity (3.1%) to scientific vocabulary
Last synced: 9 months ago
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Repository
Basic Info
- Host: gitlab.com
- Owner: nisha.antony
- Default Branch: master
Statistics
- Stars: 0
- Forks: 0
- Open Issues: 0
- Releases: 0
Created about 5 years ago
Metadata Files
Readme
Citation
README.md
Impact Prediction
Design and implement a big data system for predicting the long-term impact (number of citations or similar metrics) of a recently published scientific work.
Building model with historic data
We used AMiner Academic Citation Dataset from Kaggle to train our model.
We saved the model for citation count prediction of latest works.
Citation (Citation_Count_training_notebook.ipynb)
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
},
"colab": {
"name": "Citation_Count_training_notebook.ipynb",
"provenance": [],
"collapsed_sections": []
}
},
"cells": [
{
"cell_type": "code",
"metadata": {
"id": "Avu5Z9ZDYfyj"
},
"source": [
"from google.colab import drive\n",
"drive.mount('/content/gdrive')"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "zR-JKDjqYpFc"
},
"source": [
"!pip install -q kaggle\n",
"!mkdir -p ~/.kaggle\n",
"!cp \"gdrive/My Drive/citation-count-2021/kaggle.json\" ~/.kaggle\n",
"!ls ~/.kaggle\n",
"!chmod 600 /root/.kaggle/kaggle.json"
],
"execution_count": null,
<<<<<<< Updated upstream
"outputs": []
=======
"outputs": [
{
"output_type": "stream",
"text": [
"kaggle.json\n"
],
"name": "stdout"
}
]
>>>>>>> Stashed changes
},
{
"cell_type": "code",
"metadata": {
"id": "uLTHhNaOYs8N"
},
"source": [
"!kaggle datasets download 'kmader/aminer-academic-citation-dataset' -p /content/data --unzip"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Downloading aminer-academic-citation-dataset.zip to /content/data\n",
" 30% 712M/2.29G [00:04<00:10, 160MB/s]\n",
"User cancelled operation\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "uLGLH7XOYzT0"
},
"source": [
"\n",
"import numpy as np \n",
"import pandas as pd \n",
"\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/content/data'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"/content/data/citation-network2.txt\n",
"/content/data/dblp-ref-3.json\n",
"/content/data/AMiner-Author.txt\n",
"/content/data/dblp-ref-2.json\n",
"/content/data/dblp-ref-0.json\n",
"/content/data/AMiner-Coauthor.txt\n",
"/content/data/aminer-academic-citation-dataset.zip\n",
"/content/data/dblp-ref-1.json\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:49:02.804262Z",
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},
"id": "wvj-3F1lErUp"
},
"source": [
"!pip install jsonlines\n",
"!pip install pyLDAvis"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: jsonlines in /usr/local/lib/python3.7/dist-packages (2.0.0)\n",
"Requirement already satisfied: pyLDAvis in /usr/local/lib/python3.7/dist-packages (3.3.1)\n",
"Requirement already satisfied: scikit-learn in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.22.2.post1)\n",
"Requirement already satisfied: numpy>=1.20.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.21.1)\n",
"Requirement already satisfied: pandas>=1.2.0 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.3.1)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.4.1)\n",
"Requirement already satisfied: jinja2 in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.11.3)\n",
"Requirement already satisfied: funcy in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.16)\n",
"Requirement already satisfied: sklearn in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.0)\n",
"Requirement already satisfied: future in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (0.16.0)\n",
"Requirement already satisfied: joblib in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (1.0.1)\n",
"Requirement already satisfied: numexpr in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (2.7.3)\n",
"Requirement already satisfied: gensim in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (3.6.0)\n",
"Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from pyLDAvis) (57.2.0)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->pyLDAvis) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=1.2.0->pyLDAvis) (2.8.1)\n",
"Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas>=1.2.0->pyLDAvis) (1.15.0)\n",
"Requirement already satisfied: smart-open>=1.2.1 in /usr/local/lib/python3.7/dist-packages (from gensim->pyLDAvis) (5.1.0)\n",
"Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.7/dist-packages (from jinja2->pyLDAvis) (2.0.1)\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:32:24.790023Z",
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"shell.execute_reply.started": "2021-07-27T10:32:24.789978Z"
},
"id": "nwJ1sltqErUu"
},
"source": [
"import jsonlines\n",
"from tqdm import tqdm"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "eeQuxjCmErUw"
},
"source": [
""
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:32:27.289719Z",
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},
"id": "96OZ0Ti8ErUx"
},
"source": [
"journalmap = {\"abstract\":[],\n",
" \"authors\":[],\n",
" \"title\":[],\n",
" \"venue\":[],\n",
" \"year\":[],\n",
" \"n_citation\":[]\n",
" }"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:32:27.899900Z",
"iopub.status.busy": "2021-07-27T10:32:27.899510Z",
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},
"id": "io1LwqWNErUy"
},
"source": [
"def convertToRow(x):\n",
" global journalmap\n",
" try:\n",
" x[\"abstract\"],x[\"authors\"],x[\"title\"],x[\"venue\"],x[\"year\"],x[\"n_citation\"];\n",
" journalmap[\"abstract\"].append(x[\"abstract\"])\n",
" journalmap[\"authors\"].append(str(x[\"authors\"]))\n",
" journalmap[\"title\"].append(x[\"title\"])\n",
" journalmap[\"venue\"].append(x[\"venue\"])\n",
" journalmap[\"year\"].append(x[\"year\"])\n",
" journalmap[\"n_citation\"].append(x[\"n_citation\"])\n",
" except:\n",
" pass"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:32:28.844743Z",
"iopub.status.busy": "2021-07-27T10:32:28.844102Z"
},
"id": "dY0ICsdtErU0",
"outputId": "db8355f6-edad-46ae-88d9-34520cb196a3"
},
"source": [
"with jsonlines.open(\"/content/data/dblp-ref-1.json\") as reader:\n",
" for idx,x in tqdm(enumerate(reader)):\n",
" convertToRow(x)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"886670it [00:35, 30836.43it/s]"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:28:03.781101Z",
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},
"id": "TOwciwWAErU3"
},
"source": [
"data = pd.DataFrame(journalmap)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:28:05.579235Z",
"iopub.status.busy": "2021-07-27T10:28:05.578926Z",
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},
"id": "IJITdJHwErU5"
},
"source": [
"data = data.sample(frac=0.1)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:29:41.219853Z",
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},
"id": "-TGZWYfAErU8"
},
"source": [
"from sklearn.feature_extraction.text import TfidfVectorizer,CountVectorizer"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:29:43.099186Z",
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},
"id": "wSB0iHPoErVA"
},
"source": [
"data[\"abstract\"] = data[\"abstract\"].str.lower()\n",
"data[\"title\"] = data[\"title\"].str.lower()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:30:20.701370Z",
"iopub.status.busy": "2021-07-27T10:30:20.700696Z",
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"shell.execute_reply": "2021-07-27T10:30:20.706404Z",
"shell.execute_reply.started": "2021-07-27T10:30:20.701316Z"
},
"id": "Sf3P8A4DErVC"
},
"source": [
"countvectorizer = CountVectorizer(analyzer='word', \n",
" min_df=50,# minimum reqd occurences of a word \n",
" max_df=0.7,\n",
" stop_words='english', # remove stop words\n",
" max_features=3000,\n",
" token_pattern='[a-zA-Z0-9]{3,}'\n",
" )"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:30:22.156607Z",
"iopub.status.busy": "2021-07-27T10:30:22.156202Z",
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"shell.execute_reply.started": "2021-07-27T10:30:22.156572Z"
},
"id": "2J5E-wxPErVD"
},
"source": [
"tfidfvectorizer = TfidfVectorizer(analyzer='word',\n",
" use_idf=True, \n",
" min_df = 50,\n",
" max_df = 0.70, \n",
" max_features=3000,\n",
" stop_words= 'english')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:30:31.045718Z",
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},
"id": "EWVVJQDnErVG"
},
"source": [
"count_wm = countvectorizer.fit_transform(data[\"abstract\"])\n",
"tfidf_wm = tfidfvectorizer.fit_transform(data[\"abstract\"])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:31:07.001872Z",
"iopub.status.busy": "2021-07-27T10:31:07.001138Z",
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},
"id": "SNeobljNErVK",
"outputId": "afef3e09-0fec-4821-fa2a-dd575023bba3"
},
"source": [
"count_tokens = countvectorizer.get_feature_names()\n",
"tfidf_tokens = tfidfvectorizer.get_feature_names()\n",
"df_countvect = pd.DataFrame(data = count_wm.toarray()[:2],index = ['J1','J2'],columns = count_tokens)\n",
"df_tfidfvect = pd.DataFrame(data = tfidf_wm.toarray()[:2],index = ['J1','J2'],columns = tfidf_tokens)\n",
"print(\"Count Vectorizer\\n\")\n",
"print(df_countvect)\n",
"print(\"\\nTD-IDF Vectorizer\\n\")\n",
"print(df_tfidfvect)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Count Vectorizer\n",
"\n",
" 000 100 2000 2001 2002 2003 2004 2005 2006 2007 ... written \\\n",
"J1 0 0 0 0 0 0 0 0 0 0 ... 0 \n",
"J2 0 0 0 0 0 0 0 0 0 0 ... 0 \n",
"\n",
" wsn wsns www xml year years yield yields zero \n",
"J1 0 0 0 0 0 0 0 0 0 \n",
"J2 0 0 0 0 0 0 0 0 0 \n",
"\n",
"[2 rows x 3000 columns]\n",
"\n",
"TD-IDF Vectorizer\n",
"\n",
" 000 10 100 11 12 13 14 15 16 17 ... written wsn wsns \\\n",
"J1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"J2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 \n",
"\n",
" www xml year years yield yields zero \n",
"J1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"J2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 \n",
"\n",
"[2 rows x 3000 columns]\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:32:11.989787Z",
"iopub.status.busy": "2021-07-27T10:32:11.989191Z",
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},
"id": "gECcF4dtErVL"
},
"source": [
"# Materialize the sparse data\n",
"data_dense = count_wm.todense()\n",
"\n",
"# Compute Sparsicity = Percentage of Non-Zero cells\n",
"print(\"Sparsicity: \", ((data_dense > 0).sum()/data_dense.size)*100, \"%\") \n",
"\n",
"\n",
"data_vectorized = pd.DataFrame(count_wm.toarray(), columns = count_tokens)\n",
"data_vectorized[data_vectorized > 1] = 1"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Sparsicity: 0.26800316959121673 %\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:31:56.247365Z",
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},
"id": "pUaD1d9iErVM"
},
"source": [
"# Plotting tools\n",
"import pyLDAvis\n",
"import pyLDAvis.sklearn\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import warnings\n",
"from sklearn.decomposition import LatentDirichletAllocation\n",
"from sklearn.model_selection import GridSearchCV\n",
"warnings.filterwarnings(\"ignore\")"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:31:58.260013Z",
"iopub.status.busy": "2021-07-27T10:31:58.259430Z",
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"shell.execute_reply.started": "2021-07-27T10:31:58.259975Z"
},
"id": "dmVuTLd3ErVM"
},
"source": [
"# Init the Model\n",
"lda = LatentDirichletAllocation(n_components=100,random_state = 100,batch_size=64)\n",
"\n",
"# Do the Grid Search\n",
"lda.fit(data_vectorized)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LatentDirichletAllocation(batch_size=64, doc_topic_prior=None,\n",
" evaluate_every=-1, learning_decay=0.7,\n",
" learning_method='batch', learning_offset=10.0,\n",
" max_doc_update_iter=100, max_iter=10,\n",
" mean_change_tol=0.001, n_components=40, n_jobs=None,\n",
" perp_tol=0.1, random_state=100, topic_word_prior=None,\n",
" total_samples=1000000.0, verbose=0)"
]
},
"metadata": {
"tags": []
},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "FtD5LLB3ErVN"
},
"source": [
"import pickle"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "4pBOIXCnErVN"
},
"source": [
"with open(\"lda.sav\", \"wb\") as output_file:\n",
" pickle.dump(lda, output_file)\n",
"with open(\"countvectorizer.sav\", \"wb\") as output_file:\n",
" pickle.dump(countvectorizer, output_file)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:55:13.435196Z",
"iopub.status.busy": "2021-07-27T08:55:13.434790Z",
"iopub.status.idle": "2021-07-27T08:55:28.826408Z",
"shell.execute_reply": "2021-07-27T08:55:28.825221Z",
"shell.execute_reply.started": "2021-07-27T08:55:13.435164Z"
},
"id": "waqF4E9dErVO",
"outputId": "61d18bec-4912-419d-a98e-dd2a371069f1"
},
"source": [
"# Create Document - Topic Matrix\n",
"lda_output = lda.transform(data_vectorized)\n",
"\n",
"# column names\"\n",
"topicnames = [\"Topic\" + str(i) for i in range(lda.n_components)]\n",
"\n",
"# index names\n",
"docnames = [\"Review\" + str(i) for i in range(len(data))]\n",
"\n",
"# Make the pandas dataframe\n",
"df_document_topic = pd.DataFrame(np.round(lda_output, 2), columns=topicnames, index=docnames)\n",
"\n",
"# Get dominant topic for each document\n",
"dominant_topic = np.argmax(df_document_topic.values, axis=1)\n",
"df_document_topic['dominant_topic'] = dominant_topic\n",
"\n",
"# Styling\n",
"def color_green(val):\n",
" color = 'green' if val > .1 else 'black'\n",
" return 'color: {col}'.format(col=color)\n",
"\n",
"def make_bold(val):\n",
" weight = 700 if val > .1 else 400\n",
" return 'font-weight: {weight}'.format(weight=weight)\n",
"\n",
"# Apply Style\n",
"df_document_topics = df_document_topic.head(20).style.applymap(color_green).applymap(make_bold)\n",
"df_document_topics"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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col17\" >Topic17</th> <th class=\"col_heading level0 col18\" >Topic18</th> <th class=\"col_heading level0 col19\" >Topic19</th> <th class=\"col_heading level0 col20\" >Topic20</th> <th class=\"col_heading level0 col21\" >Topic21</th> <th class=\"col_heading level0 col22\" >Topic22</th> <th class=\"col_heading level0 col23\" >Topic23</th> <th class=\"col_heading level0 col24\" >Topic24</th> <th class=\"col_heading level0 col25\" >Topic25</th> <th class=\"col_heading level0 col26\" >Topic26</th> <th class=\"col_heading level0 col27\" >Topic27</th> <th class=\"col_heading level0 col28\" >Topic28</th> <th class=\"col_heading level0 col29\" >Topic29</th> <th class=\"col_heading level0 col30\" >Topic30</th> <th class=\"col_heading level0 col31\" >Topic31</th> <th class=\"col_heading level0 col32\" >Topic32</th> <th class=\"col_heading level0 col33\" >Topic33</th> <th class=\"col_heading level0 col34\" >Topic34</th> <th class=\"col_heading level0 col35\" >Topic35</th> <th class=\"col_heading level0 col36\" >Topic36</th> <th class=\"col_heading level0 col37\" >Topic37</th> <th class=\"col_heading level0 col38\" >Topic38</th> <th class=\"col_heading level0 col39\" >Topic39</th> <th class=\"col_heading level0 col40\" >Topic40</th> <th class=\"col_heading level0 col41\" >Topic41</th> <th class=\"col_heading level0 col42\" >Topic42</th> <th class=\"col_heading level0 col43\" >Topic43</th> <th class=\"col_heading level0 col44\" >Topic44</th> <th class=\"col_heading level0 col45\" >Topic45</th> <th class=\"col_heading level0 col46\" >Topic46</th> <th class=\"col_heading level0 col47\" >Topic47</th> <th class=\"col_heading level0 col48\" >Topic48</th> <th class=\"col_heading level0 col49\" >Topic49</th> <th class=\"col_heading level0 col50\" >Topic50</th> <th class=\"col_heading level0 col51\" >Topic51</th> <th class=\"col_heading level0 col52\" >Topic52</th> <th class=\"col_heading level0 col53\" >Topic53</th> <th class=\"col_heading level0 col54\" >Topic54</th> <th class=\"col_heading level0 col55\" >Topic55</th> <th class=\"col_heading level0 col56\" >Topic56</th> <th class=\"col_heading level0 col57\" >Topic57</th> <th class=\"col_heading level0 col58\" >Topic58</th> <th class=\"col_heading level0 col59\" >Topic59</th> <th class=\"col_heading level0 col60\" >Topic60</th> <th class=\"col_heading level0 col61\" >Topic61</th> <th class=\"col_heading level0 col62\" >Topic62</th> <th class=\"col_heading level0 col63\" >Topic63</th> <th class=\"col_heading level0 col64\" >Topic64</th> <th class=\"col_heading level0 col65\" >Topic65</th> <th class=\"col_heading level0 col66\" >Topic66</th> <th class=\"col_heading level0 col67\" >Topic67</th> <th class=\"col_heading level0 col68\" >Topic68</th> <th class=\"col_heading level0 col69\" >Topic69</th> <th class=\"col_heading level0 col70\" >Topic70</th> <th class=\"col_heading level0 col71\" >Topic71</th> <th class=\"col_heading level0 col72\" >Topic72</th> <th class=\"col_heading level0 col73\" >Topic73</th> <th class=\"col_heading level0 col74\" >Topic74</th> <th class=\"col_heading level0 col75\" >Topic75</th> <th class=\"col_heading level0 col76\" >Topic76</th> <th class=\"col_heading level0 col77\" >Topic77</th> <th class=\"col_heading level0 col78\" >Topic78</th> <th class=\"col_heading level0 col79\" >Topic79</th> <th class=\"col_heading level0 col80\" >Topic80</th> <th class=\"col_heading level0 col81\" >Topic81</th> <th class=\"col_heading level0 col82\" >Topic82</th> <th class=\"col_heading level0 col83\" >Topic83</th> <th class=\"col_heading level0 col84\" >Topic84</th> <th class=\"col_heading level0 col85\" >Topic85</th> <th class=\"col_heading level0 col86\" >Topic86</th> <th class=\"col_heading level0 col87\" >Topic87</th> <th class=\"col_heading level0 col88\" >Topic88</th> <th class=\"col_heading level0 col89\" >Topic89</th> <th class=\"col_heading level0 col90\" >Topic90</th> <th class=\"col_heading level0 col91\" >Topic91</th> <th class=\"col_heading level0 col92\" >Topic92</th> <th class=\"col_heading level0 col93\" >Topic93</th> <th class=\"col_heading level0 col94\" >Topic94</th> <th class=\"col_heading level0 col95\" >Topic95</th> <th class=\"col_heading level0 col96\" >Topic96</th> <th class=\"col_heading level0 col97\" >Topic97</th> <th class=\"col_heading level0 col98\" >Topic98</th> <th class=\"col_heading level0 col99\" >Topic99</th> <th class=\"col_heading level0 col100\" >dominant_topic</th> </tr></thead><tbody>\n",
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" <td id=\"T_407e7_row1_col36\" class=\"data row1 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col37\" class=\"data row1 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col38\" class=\"data row1 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col39\" class=\"data row1 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col40\" class=\"data row1 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col41\" class=\"data row1 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col42\" class=\"data row1 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col43\" class=\"data row1 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col44\" class=\"data row1 col44\" >0.180000</td>\n",
" <td id=\"T_407e7_row1_col45\" class=\"data row1 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col46\" class=\"data row1 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col47\" class=\"data row1 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col48\" class=\"data row1 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col49\" class=\"data row1 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col50\" class=\"data row1 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col51\" class=\"data row1 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col52\" class=\"data row1 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col53\" class=\"data row1 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col54\" class=\"data row1 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col55\" class=\"data row1 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col56\" class=\"data row1 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col57\" class=\"data row1 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col58\" class=\"data row1 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col59\" class=\"data row1 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col60\" class=\"data row1 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col61\" class=\"data row1 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col62\" class=\"data row1 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col63\" class=\"data row1 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col64\" class=\"data row1 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col65\" class=\"data row1 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col66\" class=\"data row1 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col67\" class=\"data row1 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col68\" class=\"data row1 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col69\" class=\"data row1 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col70\" class=\"data row1 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col71\" class=\"data row1 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col72\" class=\"data row1 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col73\" class=\"data row1 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col74\" class=\"data row1 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col75\" class=\"data row1 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col76\" class=\"data row1 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col77\" class=\"data row1 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col78\" class=\"data row1 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col79\" class=\"data row1 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col80\" class=\"data row1 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col81\" class=\"data row1 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col82\" class=\"data row1 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col83\" class=\"data row1 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col84\" class=\"data row1 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col85\" class=\"data row1 col85\" >0.160000</td>\n",
" <td id=\"T_407e7_row1_col86\" class=\"data row1 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col87\" class=\"data row1 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col88\" class=\"data row1 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col89\" class=\"data row1 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col90\" class=\"data row1 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col91\" class=\"data row1 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col92\" class=\"data row1 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col93\" class=\"data row1 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col94\" class=\"data row1 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col95\" class=\"data row1 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col96\" class=\"data row1 col96\" >0.130000</td>\n",
" <td id=\"T_407e7_row1_col97\" class=\"data row1 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col98\" class=\"data row1 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col99\" class=\"data row1 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row1_col100\" class=\"data row1 col100\" >30</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row2\" class=\"row_heading level0 row2\" >Review2</th>\n",
" <td id=\"T_407e7_row2_col0\" class=\"data row2 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col1\" class=\"data row2 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col3\" class=\"data row2 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col4\" class=\"data row2 col4\" >0.150000</td>\n",
" <td id=\"T_407e7_row2_col5\" class=\"data row2 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col6\" class=\"data row2 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col7\" class=\"data row2 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col8\" class=\"data row2 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col9\" class=\"data row2 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col10\" class=\"data row2 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col11\" class=\"data row2 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col12\" class=\"data row2 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col13\" class=\"data row2 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col14\" class=\"data row2 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col15\" class=\"data row2 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col16\" class=\"data row2 col16\" >0.150000</td>\n",
" <td id=\"T_407e7_row2_col17\" class=\"data row2 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col18\" class=\"data row2 col18\" >0.210000</td>\n",
" <td id=\"T_407e7_row2_col19\" class=\"data row2 col19\" >0.200000</td>\n",
" <td id=\"T_407e7_row2_col20\" class=\"data row2 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col21\" class=\"data row2 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col22\" class=\"data row2 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col23\" class=\"data row2 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col24\" class=\"data row2 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col25\" class=\"data row2 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col26\" class=\"data row2 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col27\" class=\"data row2 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col28\" class=\"data row2 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col29\" class=\"data row2 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col30\" class=\"data row2 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col31\" class=\"data row2 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col32\" class=\"data row2 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col33\" class=\"data row2 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col34\" class=\"data row2 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col35\" class=\"data row2 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col36\" class=\"data row2 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col37\" class=\"data row2 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col38\" class=\"data row2 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col39\" class=\"data row2 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col40\" class=\"data row2 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col41\" class=\"data row2 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col42\" class=\"data row2 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col43\" class=\"data row2 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col44\" class=\"data row2 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col45\" class=\"data row2 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col46\" class=\"data row2 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col47\" class=\"data row2 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col48\" class=\"data row2 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col49\" class=\"data row2 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col50\" class=\"data row2 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col51\" class=\"data row2 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col52\" class=\"data row2 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col53\" class=\"data row2 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col54\" class=\"data row2 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col55\" class=\"data row2 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col56\" class=\"data row2 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col57\" class=\"data row2 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col58\" class=\"data row2 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col59\" class=\"data row2 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col60\" class=\"data row2 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col61\" class=\"data row2 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col62\" class=\"data row2 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col63\" class=\"data row2 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col64\" class=\"data row2 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col65\" class=\"data row2 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col66\" class=\"data row2 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col67\" class=\"data row2 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col68\" class=\"data row2 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col69\" class=\"data row2 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col70\" class=\"data row2 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col71\" class=\"data row2 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col72\" class=\"data row2 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col73\" class=\"data row2 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col74\" class=\"data row2 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col75\" class=\"data row2 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col76\" class=\"data row2 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col77\" class=\"data row2 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col78\" class=\"data row2 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col79\" class=\"data row2 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col80\" class=\"data row2 col80\" >0.150000</td>\n",
" <td id=\"T_407e7_row2_col81\" class=\"data row2 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col82\" class=\"data row2 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col83\" class=\"data row2 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col84\" class=\"data row2 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col85\" class=\"data row2 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col86\" class=\"data row2 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col87\" class=\"data row2 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col88\" class=\"data row2 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col89\" class=\"data row2 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col90\" class=\"data row2 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col91\" class=\"data row2 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col92\" class=\"data row2 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col93\" class=\"data row2 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col94\" class=\"data row2 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col95\" class=\"data row2 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col96\" class=\"data row2 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col97\" class=\"data row2 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col98\" class=\"data row2 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col99\" class=\"data row2 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row2_col100\" class=\"data row2 col100\" >18</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row3\" class=\"row_heading level0 row3\" >Review3</th>\n",
" <td id=\"T_407e7_row3_col0\" class=\"data row3 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col1\" class=\"data row3 col1\" >0.860000</td>\n",
" <td id=\"T_407e7_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col3\" class=\"data row3 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col4\" class=\"data row3 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col5\" class=\"data row3 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col6\" class=\"data row3 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col7\" class=\"data row3 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col8\" class=\"data row3 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col9\" class=\"data row3 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col10\" class=\"data row3 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col11\" class=\"data row3 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col12\" class=\"data row3 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col13\" class=\"data row3 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col14\" class=\"data row3 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col15\" class=\"data row3 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col16\" class=\"data row3 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col17\" class=\"data row3 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col18\" class=\"data row3 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col19\" class=\"data row3 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col20\" class=\"data row3 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col21\" class=\"data row3 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col22\" class=\"data row3 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col23\" class=\"data row3 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col24\" class=\"data row3 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col25\" class=\"data row3 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col26\" class=\"data row3 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col27\" class=\"data row3 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col28\" class=\"data row3 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col29\" class=\"data row3 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col30\" class=\"data row3 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col31\" class=\"data row3 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col32\" class=\"data row3 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col33\" class=\"data row3 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col34\" class=\"data row3 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col35\" class=\"data row3 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col36\" class=\"data row3 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col37\" class=\"data row3 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col38\" class=\"data row3 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col39\" class=\"data row3 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col40\" class=\"data row3 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col41\" class=\"data row3 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col42\" class=\"data row3 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col43\" class=\"data row3 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col44\" class=\"data row3 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col45\" class=\"data row3 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col46\" class=\"data row3 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col47\" class=\"data row3 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col48\" class=\"data row3 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col49\" class=\"data row3 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col50\" class=\"data row3 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col51\" class=\"data row3 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col52\" class=\"data row3 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col53\" class=\"data row3 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col54\" class=\"data row3 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col55\" class=\"data row3 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col56\" class=\"data row3 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col57\" class=\"data row3 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col58\" class=\"data row3 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col59\" class=\"data row3 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col60\" class=\"data row3 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col61\" class=\"data row3 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col62\" class=\"data row3 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col63\" class=\"data row3 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col64\" class=\"data row3 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col65\" class=\"data row3 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col66\" class=\"data row3 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col67\" class=\"data row3 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col68\" class=\"data row3 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col69\" class=\"data row3 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col70\" class=\"data row3 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col71\" class=\"data row3 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col72\" class=\"data row3 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col73\" class=\"data row3 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col74\" class=\"data row3 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col75\" class=\"data row3 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col76\" class=\"data row3 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col77\" class=\"data row3 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col78\" class=\"data row3 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col79\" class=\"data row3 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col80\" class=\"data row3 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col81\" class=\"data row3 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col82\" class=\"data row3 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col83\" class=\"data row3 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col84\" class=\"data row3 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col85\" class=\"data row3 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col86\" class=\"data row3 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col87\" class=\"data row3 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col88\" class=\"data row3 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col89\" class=\"data row3 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col90\" class=\"data row3 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col91\" class=\"data row3 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col92\" class=\"data row3 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col93\" class=\"data row3 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col94\" class=\"data row3 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col95\" class=\"data row3 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col96\" class=\"data row3 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col97\" class=\"data row3 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col98\" class=\"data row3 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col99\" class=\"data row3 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row3_col100\" class=\"data row3 col100\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row4\" class=\"row_heading level0 row4\" >Review4</th>\n",
" <td id=\"T_407e7_row4_col0\" class=\"data row4 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col1\" class=\"data row4 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col3\" class=\"data row4 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col4\" class=\"data row4 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col5\" class=\"data row4 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col6\" class=\"data row4 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col7\" class=\"data row4 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col8\" class=\"data row4 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col9\" class=\"data row4 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col10\" class=\"data row4 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col11\" class=\"data row4 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col12\" class=\"data row4 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col13\" class=\"data row4 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col14\" class=\"data row4 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col15\" class=\"data row4 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col16\" class=\"data row4 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col17\" class=\"data row4 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col18\" class=\"data row4 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col19\" class=\"data row4 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col20\" class=\"data row4 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col21\" class=\"data row4 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col22\" class=\"data row4 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col23\" class=\"data row4 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col24\" class=\"data row4 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col25\" class=\"data row4 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col26\" class=\"data row4 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col27\" class=\"data row4 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col28\" class=\"data row4 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col29\" class=\"data row4 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col30\" class=\"data row4 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col31\" class=\"data row4 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col32\" class=\"data row4 col32\" >0.210000</td>\n",
" <td id=\"T_407e7_row4_col33\" class=\"data row4 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col34\" class=\"data row4 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col35\" class=\"data row4 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col36\" class=\"data row4 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col37\" class=\"data row4 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col38\" class=\"data row4 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col39\" class=\"data row4 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col40\" class=\"data row4 col40\" >0.150000</td>\n",
" <td id=\"T_407e7_row4_col41\" class=\"data row4 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col42\" class=\"data row4 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col43\" class=\"data row4 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col44\" class=\"data row4 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col45\" class=\"data row4 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col46\" class=\"data row4 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col47\" class=\"data row4 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col48\" class=\"data row4 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col49\" class=\"data row4 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col50\" class=\"data row4 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col51\" class=\"data row4 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col52\" class=\"data row4 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col53\" class=\"data row4 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col54\" class=\"data row4 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col55\" class=\"data row4 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col56\" class=\"data row4 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col57\" class=\"data row4 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col58\" class=\"data row4 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col59\" class=\"data row4 col59\" >0.190000</td>\n",
" <td id=\"T_407e7_row4_col60\" class=\"data row4 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col61\" class=\"data row4 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col62\" class=\"data row4 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col63\" class=\"data row4 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col64\" class=\"data row4 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col65\" class=\"data row4 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col66\" class=\"data row4 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col67\" class=\"data row4 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col68\" class=\"data row4 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col69\" class=\"data row4 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col70\" class=\"data row4 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col71\" class=\"data row4 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col72\" class=\"data row4 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col73\" class=\"data row4 col73\" >0.160000</td>\n",
" <td id=\"T_407e7_row4_col74\" class=\"data row4 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col75\" class=\"data row4 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col76\" class=\"data row4 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col77\" class=\"data row4 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col78\" class=\"data row4 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col79\" class=\"data row4 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col80\" class=\"data row4 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col81\" class=\"data row4 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col82\" class=\"data row4 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col83\" class=\"data row4 col83\" >0.150000</td>\n",
" <td id=\"T_407e7_row4_col84\" class=\"data row4 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col85\" class=\"data row4 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col86\" class=\"data row4 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col87\" class=\"data row4 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col88\" class=\"data row4 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col89\" class=\"data row4 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col90\" class=\"data row4 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col91\" class=\"data row4 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col92\" class=\"data row4 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col93\" class=\"data row4 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col94\" class=\"data row4 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col95\" class=\"data row4 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col96\" class=\"data row4 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col97\" class=\"data row4 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col98\" class=\"data row4 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col99\" class=\"data row4 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row4_col100\" class=\"data row4 col100\" >32</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row5\" class=\"row_heading level0 row5\" >Review5</th>\n",
" <td id=\"T_407e7_row5_col0\" class=\"data row5 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col1\" class=\"data row5 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col3\" class=\"data row5 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col4\" class=\"data row5 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col5\" class=\"data row5 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col6\" class=\"data row5 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col7\" class=\"data row5 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col8\" class=\"data row5 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col9\" class=\"data row5 col9\" >0.170000</td>\n",
" <td id=\"T_407e7_row5_col10\" class=\"data row5 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col11\" class=\"data row5 col11\" >0.170000</td>\n",
" <td id=\"T_407e7_row5_col12\" class=\"data row5 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col13\" class=\"data row5 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col14\" class=\"data row5 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col15\" class=\"data row5 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col16\" class=\"data row5 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col17\" class=\"data row5 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col18\" class=\"data row5 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col19\" class=\"data row5 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col20\" class=\"data row5 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col21\" class=\"data row5 col21\" >0.170000</td>\n",
" <td id=\"T_407e7_row5_col22\" class=\"data row5 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col23\" class=\"data row5 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col24\" class=\"data row5 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col25\" class=\"data row5 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col26\" class=\"data row5 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col27\" class=\"data row5 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col28\" class=\"data row5 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col29\" class=\"data row5 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col30\" class=\"data row5 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col31\" class=\"data row5 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col32\" class=\"data row5 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col33\" class=\"data row5 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col34\" class=\"data row5 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col35\" class=\"data row5 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col36\" class=\"data row5 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col37\" class=\"data row5 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col38\" class=\"data row5 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col39\" class=\"data row5 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col40\" class=\"data row5 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col41\" class=\"data row5 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col42\" class=\"data row5 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col43\" class=\"data row5 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col44\" class=\"data row5 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col45\" class=\"data row5 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col46\" class=\"data row5 col46\" >0.170000</td>\n",
" <td id=\"T_407e7_row5_col47\" class=\"data row5 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col48\" class=\"data row5 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col49\" class=\"data row5 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col50\" class=\"data row5 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col51\" class=\"data row5 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col52\" class=\"data row5 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col53\" class=\"data row5 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col54\" class=\"data row5 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col55\" class=\"data row5 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col56\" class=\"data row5 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col57\" class=\"data row5 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col58\" class=\"data row5 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col59\" class=\"data row5 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col60\" class=\"data row5 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col61\" class=\"data row5 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col62\" class=\"data row5 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col63\" class=\"data row5 col63\" >0.000000</td>\n",
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" <td id=\"T_407e7_row5_col65\" class=\"data row5 col65\" >0.000000</td>\n",
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" <td id=\"T_407e7_row5_col68\" class=\"data row5 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col69\" class=\"data row5 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col70\" class=\"data row5 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col71\" class=\"data row5 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col72\" class=\"data row5 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col73\" class=\"data row5 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col74\" class=\"data row5 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col75\" class=\"data row5 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col76\" class=\"data row5 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col77\" class=\"data row5 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col78\" class=\"data row5 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col79\" class=\"data row5 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col80\" class=\"data row5 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col81\" class=\"data row5 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col82\" class=\"data row5 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col83\" class=\"data row5 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col84\" class=\"data row5 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col85\" class=\"data row5 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col86\" class=\"data row5 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col87\" class=\"data row5 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col88\" class=\"data row5 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col89\" class=\"data row5 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col90\" class=\"data row5 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col91\" class=\"data row5 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col92\" class=\"data row5 col92\" >0.170000</td>\n",
" <td id=\"T_407e7_row5_col93\" class=\"data row5 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col94\" class=\"data row5 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col95\" class=\"data row5 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col96\" class=\"data row5 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col97\" class=\"data row5 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col98\" class=\"data row5 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col99\" class=\"data row5 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row5_col100\" class=\"data row5 col100\" >9</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row6\" class=\"row_heading level0 row6\" >Review6</th>\n",
" <td id=\"T_407e7_row6_col0\" class=\"data row6 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col1\" class=\"data row6 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col3\" class=\"data row6 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col4\" class=\"data row6 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col5\" class=\"data row6 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col6\" class=\"data row6 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col7\" class=\"data row6 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col8\" class=\"data row6 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col9\" class=\"data row6 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col10\" class=\"data row6 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col11\" class=\"data row6 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col12\" class=\"data row6 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col13\" class=\"data row6 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col14\" class=\"data row6 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col15\" class=\"data row6 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col16\" class=\"data row6 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col17\" class=\"data row6 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col18\" class=\"data row6 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col19\" class=\"data row6 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col20\" class=\"data row6 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col21\" class=\"data row6 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col22\" class=\"data row6 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col23\" class=\"data row6 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col24\" class=\"data row6 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col25\" class=\"data row6 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col26\" class=\"data row6 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col27\" class=\"data row6 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col28\" class=\"data row6 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col29\" class=\"data row6 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col30\" class=\"data row6 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col31\" class=\"data row6 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col32\" class=\"data row6 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col33\" class=\"data row6 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col34\" class=\"data row6 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col35\" class=\"data row6 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col36\" class=\"data row6 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col37\" class=\"data row6 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col38\" class=\"data row6 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col39\" class=\"data row6 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col40\" class=\"data row6 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col41\" class=\"data row6 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col42\" class=\"data row6 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col43\" class=\"data row6 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col44\" class=\"data row6 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col45\" class=\"data row6 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col46\" class=\"data row6 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col47\" class=\"data row6 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col48\" class=\"data row6 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col49\" class=\"data row6 col49\" >0.190000</td>\n",
" <td id=\"T_407e7_row6_col50\" class=\"data row6 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col51\" class=\"data row6 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col52\" class=\"data row6 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col53\" class=\"data row6 col53\" >0.160000</td>\n",
" <td id=\"T_407e7_row6_col54\" class=\"data row6 col54\" >0.120000</td>\n",
" <td id=\"T_407e7_row6_col55\" class=\"data row6 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col56\" class=\"data row6 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col57\" class=\"data row6 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col58\" class=\"data row6 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col59\" class=\"data row6 col59\" >0.170000</td>\n",
" <td id=\"T_407e7_row6_col60\" class=\"data row6 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col61\" class=\"data row6 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col62\" class=\"data row6 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col63\" class=\"data row6 col63\" >0.140000</td>\n",
" <td id=\"T_407e7_row6_col64\" class=\"data row6 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col65\" class=\"data row6 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col66\" class=\"data row6 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col67\" class=\"data row6 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col68\" class=\"data row6 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col69\" class=\"data row6 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col70\" class=\"data row6 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col71\" class=\"data row6 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col72\" class=\"data row6 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col73\" class=\"data row6 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col74\" class=\"data row6 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col75\" class=\"data row6 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col76\" class=\"data row6 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col77\" class=\"data row6 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col78\" class=\"data row6 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col79\" class=\"data row6 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col80\" class=\"data row6 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col81\" class=\"data row6 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col82\" class=\"data row6 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col83\" class=\"data row6 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col84\" class=\"data row6 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col85\" class=\"data row6 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col86\" class=\"data row6 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col87\" class=\"data row6 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col88\" class=\"data row6 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col89\" class=\"data row6 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col90\" class=\"data row6 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col91\" class=\"data row6 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col92\" class=\"data row6 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col93\" class=\"data row6 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col94\" class=\"data row6 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col95\" class=\"data row6 col95\" >0.110000</td>\n",
" <td id=\"T_407e7_row6_col96\" class=\"data row6 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col97\" class=\"data row6 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col98\" class=\"data row6 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col99\" class=\"data row6 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row6_col100\" class=\"data row6 col100\" >49</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row7\" class=\"row_heading level0 row7\" >Review7</th>\n",
" <td id=\"T_407e7_row7_col0\" class=\"data row7 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col1\" class=\"data row7 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col3\" class=\"data row7 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col4\" class=\"data row7 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col5\" class=\"data row7 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col6\" class=\"data row7 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col7\" class=\"data row7 col7\" >0.200000</td>\n",
" <td id=\"T_407e7_row7_col8\" class=\"data row7 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col9\" class=\"data row7 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col10\" class=\"data row7 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col11\" class=\"data row7 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col12\" class=\"data row7 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col13\" class=\"data row7 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col14\" class=\"data row7 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col15\" class=\"data row7 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col16\" class=\"data row7 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col17\" class=\"data row7 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col18\" class=\"data row7 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col19\" class=\"data row7 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col20\" class=\"data row7 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col21\" class=\"data row7 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col22\" class=\"data row7 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col23\" class=\"data row7 col23\" >0.140000</td>\n",
" <td id=\"T_407e7_row7_col24\" class=\"data row7 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col25\" class=\"data row7 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col26\" class=\"data row7 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col27\" class=\"data row7 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col28\" class=\"data row7 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col29\" class=\"data row7 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col30\" class=\"data row7 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col31\" class=\"data row7 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col32\" class=\"data row7 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col33\" class=\"data row7 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col34\" class=\"data row7 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col35\" class=\"data row7 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col36\" class=\"data row7 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col37\" class=\"data row7 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col38\" class=\"data row7 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col39\" class=\"data row7 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col40\" class=\"data row7 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col41\" class=\"data row7 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col42\" class=\"data row7 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col43\" class=\"data row7 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col44\" class=\"data row7 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col45\" class=\"data row7 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col46\" class=\"data row7 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col47\" class=\"data row7 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col48\" class=\"data row7 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col49\" class=\"data row7 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col50\" class=\"data row7 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col51\" class=\"data row7 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col52\" class=\"data row7 col52\" >0.110000</td>\n",
" <td id=\"T_407e7_row7_col53\" class=\"data row7 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col54\" class=\"data row7 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col55\" class=\"data row7 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col56\" class=\"data row7 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col57\" class=\"data row7 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col58\" class=\"data row7 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col59\" class=\"data row7 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col60\" class=\"data row7 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col61\" class=\"data row7 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col62\" class=\"data row7 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col63\" class=\"data row7 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col64\" class=\"data row7 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col65\" class=\"data row7 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col66\" class=\"data row7 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col67\" class=\"data row7 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col68\" class=\"data row7 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col69\" class=\"data row7 col69\" >0.150000</td>\n",
" <td id=\"T_407e7_row7_col70\" class=\"data row7 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col71\" class=\"data row7 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col72\" class=\"data row7 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col73\" class=\"data row7 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col74\" class=\"data row7 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col75\" class=\"data row7 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col76\" class=\"data row7 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col77\" class=\"data row7 col77\" >0.150000</td>\n",
" <td id=\"T_407e7_row7_col78\" class=\"data row7 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col79\" class=\"data row7 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col80\" class=\"data row7 col80\" >0.140000</td>\n",
" <td id=\"T_407e7_row7_col81\" class=\"data row7 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col82\" class=\"data row7 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col83\" class=\"data row7 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col84\" class=\"data row7 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col85\" class=\"data row7 col85\" >0.000000</td>\n",
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" <td id=\"T_407e7_row7_col88\" class=\"data row7 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col89\" class=\"data row7 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col90\" class=\"data row7 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col91\" class=\"data row7 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col92\" class=\"data row7 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col93\" class=\"data row7 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col94\" class=\"data row7 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col95\" class=\"data row7 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col96\" class=\"data row7 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col97\" class=\"data row7 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col98\" class=\"data row7 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col99\" class=\"data row7 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row7_col100\" class=\"data row7 col100\" >7</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row8\" class=\"row_heading level0 row8\" >Review8</th>\n",
" <td id=\"T_407e7_row8_col0\" class=\"data row8 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col1\" class=\"data row8 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col3\" class=\"data row8 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col4\" class=\"data row8 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col5\" class=\"data row8 col5\" >0.200000</td>\n",
" <td id=\"T_407e7_row8_col6\" class=\"data row8 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col7\" class=\"data row8 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col8\" class=\"data row8 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col9\" class=\"data row8 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col10\" class=\"data row8 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col11\" class=\"data row8 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col12\" class=\"data row8 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col13\" class=\"data row8 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col14\" class=\"data row8 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col15\" class=\"data row8 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col16\" class=\"data row8 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col17\" class=\"data row8 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col18\" class=\"data row8 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col19\" class=\"data row8 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col20\" class=\"data row8 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col21\" class=\"data row8 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col22\" class=\"data row8 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col23\" class=\"data row8 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col24\" class=\"data row8 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col25\" class=\"data row8 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col26\" class=\"data row8 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col27\" class=\"data row8 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col28\" class=\"data row8 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col29\" class=\"data row8 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col30\" class=\"data row8 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col31\" class=\"data row8 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col32\" class=\"data row8 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col33\" class=\"data row8 col33\" >0.200000</td>\n",
" <td id=\"T_407e7_row8_col34\" class=\"data row8 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col35\" class=\"data row8 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col36\" class=\"data row8 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col37\" class=\"data row8 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col38\" class=\"data row8 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col39\" class=\"data row8 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col40\" class=\"data row8 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col41\" class=\"data row8 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col42\" class=\"data row8 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col43\" class=\"data row8 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col44\" class=\"data row8 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col45\" class=\"data row8 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col46\" class=\"data row8 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col47\" class=\"data row8 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col48\" class=\"data row8 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col49\" class=\"data row8 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col50\" class=\"data row8 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col51\" class=\"data row8 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col52\" class=\"data row8 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col53\" class=\"data row8 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col54\" class=\"data row8 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col55\" class=\"data row8 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col56\" class=\"data row8 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col57\" class=\"data row8 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col58\" class=\"data row8 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col59\" class=\"data row8 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col60\" class=\"data row8 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col61\" class=\"data row8 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col62\" class=\"data row8 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col63\" class=\"data row8 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col64\" class=\"data row8 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col65\" class=\"data row8 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col66\" class=\"data row8 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col67\" class=\"data row8 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col68\" class=\"data row8 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col69\" class=\"data row8 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col70\" class=\"data row8 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col71\" class=\"data row8 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col72\" class=\"data row8 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col73\" class=\"data row8 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col74\" class=\"data row8 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col75\" class=\"data row8 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col76\" class=\"data row8 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col77\" class=\"data row8 col77\" >0.200000</td>\n",
" <td id=\"T_407e7_row8_col78\" class=\"data row8 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col79\" class=\"data row8 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col80\" class=\"data row8 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col81\" class=\"data row8 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col82\" class=\"data row8 col82\" >0.200000</td>\n",
" <td id=\"T_407e7_row8_col83\" class=\"data row8 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col84\" class=\"data row8 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col85\" class=\"data row8 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col86\" class=\"data row8 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col87\" class=\"data row8 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col88\" class=\"data row8 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col89\" class=\"data row8 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col90\" class=\"data row8 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col91\" class=\"data row8 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col92\" class=\"data row8 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col93\" class=\"data row8 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col94\" class=\"data row8 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col95\" class=\"data row8 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col96\" class=\"data row8 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col97\" class=\"data row8 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col98\" class=\"data row8 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col99\" class=\"data row8 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row8_col100\" class=\"data row8 col100\" >5</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row9\" class=\"row_heading level0 row9\" >Review9</th>\n",
" <td id=\"T_407e7_row9_col0\" class=\"data row9 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col1\" class=\"data row9 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col3\" class=\"data row9 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col4\" class=\"data row9 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col5\" class=\"data row9 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col6\" class=\"data row9 col6\" >0.090000</td>\n",
" <td id=\"T_407e7_row9_col7\" class=\"data row9 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col8\" class=\"data row9 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col9\" class=\"data row9 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col10\" class=\"data row9 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col11\" class=\"data row9 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col12\" class=\"data row9 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col13\" class=\"data row9 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col14\" class=\"data row9 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col15\" class=\"data row9 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col16\" class=\"data row9 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col17\" class=\"data row9 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col18\" class=\"data row9 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col19\" class=\"data row9 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col20\" class=\"data row9 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col21\" class=\"data row9 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col22\" class=\"data row9 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col23\" class=\"data row9 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col24\" class=\"data row9 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col25\" class=\"data row9 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col26\" class=\"data row9 col26\" >0.160000</td>\n",
" <td id=\"T_407e7_row9_col27\" class=\"data row9 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col28\" class=\"data row9 col28\" >0.110000</td>\n",
" <td id=\"T_407e7_row9_col29\" class=\"data row9 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col30\" class=\"data row9 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col31\" class=\"data row9 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col32\" class=\"data row9 col32\" >0.420000</td>\n",
" <td id=\"T_407e7_row9_col33\" class=\"data row9 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col34\" class=\"data row9 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col35\" class=\"data row9 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col36\" class=\"data row9 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col37\" class=\"data row9 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col38\" class=\"data row9 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col39\" class=\"data row9 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col40\" class=\"data row9 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col41\" class=\"data row9 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col42\" class=\"data row9 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col43\" class=\"data row9 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col44\" class=\"data row9 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col45\" class=\"data row9 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col46\" class=\"data row9 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col47\" class=\"data row9 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col48\" class=\"data row9 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col49\" class=\"data row9 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col50\" class=\"data row9 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col51\" class=\"data row9 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col52\" class=\"data row9 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col53\" class=\"data row9 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col54\" class=\"data row9 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col55\" class=\"data row9 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col56\" class=\"data row9 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col57\" class=\"data row9 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col58\" class=\"data row9 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col59\" class=\"data row9 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col60\" class=\"data row9 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col61\" class=\"data row9 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col62\" class=\"data row9 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col63\" class=\"data row9 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col64\" class=\"data row9 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col65\" class=\"data row9 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col66\" class=\"data row9 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col67\" class=\"data row9 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col68\" class=\"data row9 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col69\" class=\"data row9 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col70\" class=\"data row9 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col71\" class=\"data row9 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col72\" class=\"data row9 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col73\" class=\"data row9 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col74\" class=\"data row9 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col75\" class=\"data row9 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col76\" class=\"data row9 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col77\" class=\"data row9 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col78\" class=\"data row9 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col79\" class=\"data row9 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col80\" class=\"data row9 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col81\" class=\"data row9 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col82\" class=\"data row9 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col83\" class=\"data row9 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col84\" class=\"data row9 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col85\" class=\"data row9 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col86\" class=\"data row9 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col87\" class=\"data row9 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col88\" class=\"data row9 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col89\" class=\"data row9 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col90\" class=\"data row9 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col91\" class=\"data row9 col91\" >0.110000</td>\n",
" <td id=\"T_407e7_row9_col92\" class=\"data row9 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col93\" class=\"data row9 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col94\" class=\"data row9 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col95\" class=\"data row9 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col96\" class=\"data row9 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col97\" class=\"data row9 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col98\" class=\"data row9 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col99\" class=\"data row9 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row9_col100\" class=\"data row9 col100\" >32</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row10\" class=\"row_heading level0 row10\" >Review10</th>\n",
" <td id=\"T_407e7_row10_col0\" class=\"data row10 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col1\" class=\"data row10 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col3\" class=\"data row10 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col4\" class=\"data row10 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col5\" class=\"data row10 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col6\" class=\"data row10 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col7\" class=\"data row10 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col8\" class=\"data row10 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col9\" class=\"data row10 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col10\" class=\"data row10 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col11\" class=\"data row10 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col12\" class=\"data row10 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col13\" class=\"data row10 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col14\" class=\"data row10 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col15\" class=\"data row10 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col16\" class=\"data row10 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col17\" class=\"data row10 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col18\" class=\"data row10 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col19\" class=\"data row10 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col20\" class=\"data row10 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col21\" class=\"data row10 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col22\" class=\"data row10 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col23\" class=\"data row10 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col24\" class=\"data row10 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col25\" class=\"data row10 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col26\" class=\"data row10 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col27\" class=\"data row10 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col28\" class=\"data row10 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col29\" class=\"data row10 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col30\" class=\"data row10 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col31\" class=\"data row10 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col32\" class=\"data row10 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col33\" class=\"data row10 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col34\" class=\"data row10 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col35\" class=\"data row10 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col36\" class=\"data row10 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col37\" class=\"data row10 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col38\" class=\"data row10 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col39\" class=\"data row10 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col40\" class=\"data row10 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col41\" class=\"data row10 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col42\" class=\"data row10 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col43\" class=\"data row10 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col44\" class=\"data row10 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col45\" class=\"data row10 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col46\" class=\"data row10 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col47\" class=\"data row10 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col48\" class=\"data row10 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col49\" class=\"data row10 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col50\" class=\"data row10 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col51\" class=\"data row10 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col52\" class=\"data row10 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col53\" class=\"data row10 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col54\" class=\"data row10 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col55\" class=\"data row10 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col56\" class=\"data row10 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col57\" class=\"data row10 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col58\" class=\"data row10 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col59\" class=\"data row10 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col60\" class=\"data row10 col60\" >0.340000</td>\n",
" <td id=\"T_407e7_row10_col61\" class=\"data row10 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col62\" class=\"data row10 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col63\" class=\"data row10 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col64\" class=\"data row10 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col65\" class=\"data row10 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col66\" class=\"data row10 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col67\" class=\"data row10 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col68\" class=\"data row10 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col69\" class=\"data row10 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col70\" class=\"data row10 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col71\" class=\"data row10 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col72\" class=\"data row10 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col73\" class=\"data row10 col73\" >0.340000</td>\n",
" <td id=\"T_407e7_row10_col74\" class=\"data row10 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col75\" class=\"data row10 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col76\" class=\"data row10 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col77\" class=\"data row10 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col78\" class=\"data row10 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col79\" class=\"data row10 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col80\" class=\"data row10 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col81\" class=\"data row10 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col82\" class=\"data row10 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col83\" class=\"data row10 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col84\" class=\"data row10 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col85\" class=\"data row10 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col86\" class=\"data row10 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col87\" class=\"data row10 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col88\" class=\"data row10 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col89\" class=\"data row10 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col90\" class=\"data row10 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col91\" class=\"data row10 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col92\" class=\"data row10 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col93\" class=\"data row10 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col94\" class=\"data row10 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col95\" class=\"data row10 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col96\" class=\"data row10 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col97\" class=\"data row10 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col98\" class=\"data row10 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col99\" class=\"data row10 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row10_col100\" class=\"data row10 col100\" >60</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row11\" class=\"row_heading level0 row11\" >Review11</th>\n",
" <td id=\"T_407e7_row11_col0\" class=\"data row11 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col1\" class=\"data row11 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col3\" class=\"data row11 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col4\" class=\"data row11 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col5\" class=\"data row11 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col6\" class=\"data row11 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col7\" class=\"data row11 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col8\" class=\"data row11 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col9\" class=\"data row11 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col10\" class=\"data row11 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col11\" class=\"data row11 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col12\" class=\"data row11 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col13\" class=\"data row11 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col14\" class=\"data row11 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col15\" class=\"data row11 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col16\" class=\"data row11 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col17\" class=\"data row11 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col18\" class=\"data row11 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col19\" class=\"data row11 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col20\" class=\"data row11 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col21\" class=\"data row11 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col22\" class=\"data row11 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col23\" class=\"data row11 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col24\" class=\"data row11 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col25\" class=\"data row11 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col26\" class=\"data row11 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col27\" class=\"data row11 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col28\" class=\"data row11 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col29\" class=\"data row11 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col30\" class=\"data row11 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col31\" class=\"data row11 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col32\" class=\"data row11 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col33\" class=\"data row11 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col34\" class=\"data row11 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col35\" class=\"data row11 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col36\" class=\"data row11 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col37\" class=\"data row11 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col38\" class=\"data row11 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col39\" class=\"data row11 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col40\" class=\"data row11 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col41\" class=\"data row11 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col42\" class=\"data row11 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col43\" class=\"data row11 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col44\" class=\"data row11 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col45\" class=\"data row11 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col46\" class=\"data row11 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col47\" class=\"data row11 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col48\" class=\"data row11 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col49\" class=\"data row11 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col50\" class=\"data row11 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col51\" class=\"data row11 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col52\" class=\"data row11 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col53\" class=\"data row11 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col54\" class=\"data row11 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col55\" class=\"data row11 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col56\" class=\"data row11 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col57\" class=\"data row11 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col58\" class=\"data row11 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col59\" class=\"data row11 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col60\" class=\"data row11 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col61\" class=\"data row11 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col62\" class=\"data row11 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col63\" class=\"data row11 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col64\" class=\"data row11 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col65\" class=\"data row11 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col66\" class=\"data row11 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col67\" class=\"data row11 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col68\" class=\"data row11 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col69\" class=\"data row11 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col70\" class=\"data row11 col70\" >0.380000</td>\n",
" <td id=\"T_407e7_row11_col71\" class=\"data row11 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col72\" class=\"data row11 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col73\" class=\"data row11 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col74\" class=\"data row11 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col75\" class=\"data row11 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col76\" class=\"data row11 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col77\" class=\"data row11 col77\" >0.180000</td>\n",
" <td id=\"T_407e7_row11_col78\" class=\"data row11 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col79\" class=\"data row11 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col80\" class=\"data row11 col80\" >0.280000</td>\n",
" <td id=\"T_407e7_row11_col81\" class=\"data row11 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col82\" class=\"data row11 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col83\" class=\"data row11 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col84\" class=\"data row11 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col85\" class=\"data row11 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col86\" class=\"data row11 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col87\" class=\"data row11 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col88\" class=\"data row11 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col89\" class=\"data row11 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col90\" class=\"data row11 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col91\" class=\"data row11 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col92\" class=\"data row11 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col93\" class=\"data row11 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col94\" class=\"data row11 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col95\" class=\"data row11 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col96\" class=\"data row11 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col97\" class=\"data row11 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col98\" class=\"data row11 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col99\" class=\"data row11 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row11_col100\" class=\"data row11 col100\" >70</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row12\" class=\"row_heading level0 row12\" >Review12</th>\n",
" <td id=\"T_407e7_row12_col0\" class=\"data row12 col0\" >0.400000</td>\n",
" <td id=\"T_407e7_row12_col1\" class=\"data row12 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col2\" class=\"data row12 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col3\" class=\"data row12 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col4\" class=\"data row12 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col5\" class=\"data row12 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col6\" class=\"data row12 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col7\" class=\"data row12 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col8\" class=\"data row12 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col9\" class=\"data row12 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col10\" class=\"data row12 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col11\" class=\"data row12 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col12\" class=\"data row12 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col13\" class=\"data row12 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col14\" class=\"data row12 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col15\" class=\"data row12 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col16\" class=\"data row12 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col17\" class=\"data row12 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col18\" class=\"data row12 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col19\" class=\"data row12 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col20\" class=\"data row12 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col21\" class=\"data row12 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col22\" class=\"data row12 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col23\" class=\"data row12 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col24\" class=\"data row12 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col25\" class=\"data row12 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col26\" class=\"data row12 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col27\" class=\"data row12 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col28\" class=\"data row12 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col29\" class=\"data row12 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col30\" class=\"data row12 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col31\" class=\"data row12 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col32\" class=\"data row12 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col33\" class=\"data row12 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col34\" class=\"data row12 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col35\" class=\"data row12 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col36\" class=\"data row12 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col37\" class=\"data row12 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col38\" class=\"data row12 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col39\" class=\"data row12 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col40\" class=\"data row12 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col41\" class=\"data row12 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col42\" class=\"data row12 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col43\" class=\"data row12 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col44\" class=\"data row12 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col45\" class=\"data row12 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col46\" class=\"data row12 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col47\" class=\"data row12 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col48\" class=\"data row12 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col49\" class=\"data row12 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col50\" class=\"data row12 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col51\" class=\"data row12 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col52\" class=\"data row12 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col53\" class=\"data row12 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col54\" class=\"data row12 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col55\" class=\"data row12 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col56\" class=\"data row12 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col57\" class=\"data row12 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col58\" class=\"data row12 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col59\" class=\"data row12 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col60\" class=\"data row12 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col61\" class=\"data row12 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col62\" class=\"data row12 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col63\" class=\"data row12 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col64\" class=\"data row12 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col65\" class=\"data row12 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col66\" class=\"data row12 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col67\" class=\"data row12 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col68\" class=\"data row12 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col69\" class=\"data row12 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col70\" class=\"data row12 col70\" >0.200000</td>\n",
" <td id=\"T_407e7_row12_col71\" class=\"data row12 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col72\" class=\"data row12 col72\" >0.200000</td>\n",
" <td id=\"T_407e7_row12_col73\" class=\"data row12 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col74\" class=\"data row12 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col75\" class=\"data row12 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col76\" class=\"data row12 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col77\" class=\"data row12 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col78\" class=\"data row12 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col79\" class=\"data row12 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col80\" class=\"data row12 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col81\" class=\"data row12 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col82\" class=\"data row12 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col83\" class=\"data row12 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col84\" class=\"data row12 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col85\" class=\"data row12 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col86\" class=\"data row12 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col87\" class=\"data row12 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col88\" class=\"data row12 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col89\" class=\"data row12 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col90\" class=\"data row12 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col91\" class=\"data row12 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col92\" class=\"data row12 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col93\" class=\"data row12 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col94\" class=\"data row12 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col95\" class=\"data row12 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col96\" class=\"data row12 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col97\" class=\"data row12 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col98\" class=\"data row12 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col99\" class=\"data row12 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row12_col100\" class=\"data row12 col100\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row13\" class=\"row_heading level0 row13\" >Review13</th>\n",
" <td id=\"T_407e7_row13_col0\" class=\"data row13 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col1\" class=\"data row13 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col2\" class=\"data row13 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col3\" class=\"data row13 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col4\" class=\"data row13 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col5\" class=\"data row13 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col6\" class=\"data row13 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col7\" class=\"data row13 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col8\" class=\"data row13 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col9\" class=\"data row13 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col10\" class=\"data row13 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col11\" class=\"data row13 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col12\" class=\"data row13 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col13\" class=\"data row13 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col14\" class=\"data row13 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col15\" class=\"data row13 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col16\" class=\"data row13 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col17\" class=\"data row13 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col18\" class=\"data row13 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col19\" class=\"data row13 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col20\" class=\"data row13 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col21\" class=\"data row13 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col22\" class=\"data row13 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col23\" class=\"data row13 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col24\" class=\"data row13 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col25\" class=\"data row13 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col26\" class=\"data row13 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col27\" class=\"data row13 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col28\" class=\"data row13 col28\" >0.170000</td>\n",
" <td id=\"T_407e7_row13_col29\" class=\"data row13 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col30\" class=\"data row13 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col31\" class=\"data row13 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col32\" class=\"data row13 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col33\" class=\"data row13 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col34\" class=\"data row13 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col35\" class=\"data row13 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col36\" class=\"data row13 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col37\" class=\"data row13 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col38\" class=\"data row13 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col39\" class=\"data row13 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col40\" class=\"data row13 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col41\" class=\"data row13 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col42\" class=\"data row13 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col43\" class=\"data row13 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col44\" class=\"data row13 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col45\" class=\"data row13 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col46\" class=\"data row13 col46\" >0.170000</td>\n",
" <td id=\"T_407e7_row13_col47\" class=\"data row13 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col48\" class=\"data row13 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col49\" class=\"data row13 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col50\" class=\"data row13 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col51\" class=\"data row13 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col52\" class=\"data row13 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col53\" class=\"data row13 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col54\" class=\"data row13 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col55\" class=\"data row13 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col56\" class=\"data row13 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col57\" class=\"data row13 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col58\" class=\"data row13 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col59\" class=\"data row13 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col60\" class=\"data row13 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col61\" class=\"data row13 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col62\" class=\"data row13 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col63\" class=\"data row13 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col64\" class=\"data row13 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col65\" class=\"data row13 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col66\" class=\"data row13 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col67\" class=\"data row13 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col68\" class=\"data row13 col68\" >0.210000</td>\n",
" <td id=\"T_407e7_row13_col69\" class=\"data row13 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col70\" class=\"data row13 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col71\" class=\"data row13 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col72\" class=\"data row13 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col73\" class=\"data row13 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col74\" class=\"data row13 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col75\" class=\"data row13 col75\" >0.300000</td>\n",
" <td id=\"T_407e7_row13_col76\" class=\"data row13 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col77\" class=\"data row13 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col78\" class=\"data row13 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col79\" class=\"data row13 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col80\" class=\"data row13 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col81\" class=\"data row13 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col82\" class=\"data row13 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col83\" class=\"data row13 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col84\" class=\"data row13 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col85\" class=\"data row13 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col86\" class=\"data row13 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col87\" class=\"data row13 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col88\" class=\"data row13 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col89\" class=\"data row13 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col90\" class=\"data row13 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col91\" class=\"data row13 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col92\" class=\"data row13 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col93\" class=\"data row13 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col94\" class=\"data row13 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col95\" class=\"data row13 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col96\" class=\"data row13 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col97\" class=\"data row13 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col98\" class=\"data row13 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col99\" class=\"data row13 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row13_col100\" class=\"data row13 col100\" >75</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row14\" class=\"row_heading level0 row14\" >Review14</th>\n",
" <td id=\"T_407e7_row14_col0\" class=\"data row14 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col1\" class=\"data row14 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col2\" class=\"data row14 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col3\" class=\"data row14 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col4\" class=\"data row14 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col5\" class=\"data row14 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col6\" class=\"data row14 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col7\" class=\"data row14 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col8\" class=\"data row14 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col9\" class=\"data row14 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col10\" class=\"data row14 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col11\" class=\"data row14 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col12\" class=\"data row14 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col13\" class=\"data row14 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col14\" class=\"data row14 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col15\" class=\"data row14 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col16\" class=\"data row14 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col17\" class=\"data row14 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col18\" class=\"data row14 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col19\" class=\"data row14 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col20\" class=\"data row14 col20\" >0.140000</td>\n",
" <td id=\"T_407e7_row14_col21\" class=\"data row14 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col22\" class=\"data row14 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col23\" class=\"data row14 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col24\" class=\"data row14 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col25\" class=\"data row14 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col26\" class=\"data row14 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col27\" class=\"data row14 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col28\" class=\"data row14 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col29\" class=\"data row14 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col30\" class=\"data row14 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col31\" class=\"data row14 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col32\" class=\"data row14 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col33\" class=\"data row14 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col34\" class=\"data row14 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col35\" class=\"data row14 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col36\" class=\"data row14 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col37\" class=\"data row14 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col38\" class=\"data row14 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col39\" class=\"data row14 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col40\" class=\"data row14 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col41\" class=\"data row14 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col42\" class=\"data row14 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col43\" class=\"data row14 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col44\" class=\"data row14 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col45\" class=\"data row14 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col46\" class=\"data row14 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col47\" class=\"data row14 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col48\" class=\"data row14 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col49\" class=\"data row14 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col50\" class=\"data row14 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col51\" class=\"data row14 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col52\" class=\"data row14 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col53\" class=\"data row14 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col54\" class=\"data row14 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col55\" class=\"data row14 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col56\" class=\"data row14 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col57\" class=\"data row14 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col58\" class=\"data row14 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col59\" class=\"data row14 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col60\" class=\"data row14 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col61\" class=\"data row14 col61\" >0.290000</td>\n",
" <td id=\"T_407e7_row14_col62\" class=\"data row14 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col63\" class=\"data row14 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col64\" class=\"data row14 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col65\" class=\"data row14 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col66\" class=\"data row14 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col67\" class=\"data row14 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col68\" class=\"data row14 col68\" >0.140000</td>\n",
" <td id=\"T_407e7_row14_col69\" class=\"data row14 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col70\" class=\"data row14 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col71\" class=\"data row14 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col72\" class=\"data row14 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col73\" class=\"data row14 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col74\" class=\"data row14 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col75\" class=\"data row14 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col76\" class=\"data row14 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col77\" class=\"data row14 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col78\" class=\"data row14 col78\" >0.290000</td>\n",
" <td id=\"T_407e7_row14_col79\" class=\"data row14 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col80\" class=\"data row14 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col81\" class=\"data row14 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col82\" class=\"data row14 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col83\" class=\"data row14 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col84\" class=\"data row14 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col85\" class=\"data row14 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col86\" class=\"data row14 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col87\" class=\"data row14 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col88\" class=\"data row14 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col89\" class=\"data row14 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col90\" class=\"data row14 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col91\" class=\"data row14 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col92\" class=\"data row14 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col93\" class=\"data row14 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col94\" class=\"data row14 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col95\" class=\"data row14 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col96\" class=\"data row14 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col97\" class=\"data row14 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col98\" class=\"data row14 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col99\" class=\"data row14 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row14_col100\" class=\"data row14 col100\" >61</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row15\" class=\"row_heading level0 row15\" >Review15</th>\n",
" <td id=\"T_407e7_row15_col0\" class=\"data row15 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col1\" class=\"data row15 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col2\" class=\"data row15 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col3\" class=\"data row15 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col4\" class=\"data row15 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col5\" class=\"data row15 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col6\" class=\"data row15 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col7\" class=\"data row15 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col8\" class=\"data row15 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col9\" class=\"data row15 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col10\" class=\"data row15 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col11\" class=\"data row15 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col12\" class=\"data row15 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col13\" class=\"data row15 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col14\" class=\"data row15 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col15\" class=\"data row15 col15\" >0.170000</td>\n",
" <td id=\"T_407e7_row15_col16\" class=\"data row15 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col17\" class=\"data row15 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col18\" class=\"data row15 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col19\" class=\"data row15 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col20\" class=\"data row15 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col21\" class=\"data row15 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col22\" class=\"data row15 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col23\" class=\"data row15 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col24\" class=\"data row15 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col25\" class=\"data row15 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col26\" class=\"data row15 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col27\" class=\"data row15 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col28\" class=\"data row15 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col29\" class=\"data row15 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col30\" class=\"data row15 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col31\" class=\"data row15 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col32\" class=\"data row15 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col33\" class=\"data row15 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col34\" class=\"data row15 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col35\" class=\"data row15 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col36\" class=\"data row15 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col37\" class=\"data row15 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col38\" class=\"data row15 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col39\" class=\"data row15 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col40\" class=\"data row15 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col41\" class=\"data row15 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col42\" class=\"data row15 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col43\" class=\"data row15 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col44\" class=\"data row15 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col45\" class=\"data row15 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col46\" class=\"data row15 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col47\" class=\"data row15 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col48\" class=\"data row15 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col49\" class=\"data row15 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col50\" class=\"data row15 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col51\" class=\"data row15 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col52\" class=\"data row15 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col53\" class=\"data row15 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col54\" class=\"data row15 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col55\" class=\"data row15 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col56\" class=\"data row15 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col57\" class=\"data row15 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col58\" class=\"data row15 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col59\" class=\"data row15 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col60\" class=\"data row15 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col61\" class=\"data row15 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col62\" class=\"data row15 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col63\" class=\"data row15 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col64\" class=\"data row15 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col65\" class=\"data row15 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col66\" class=\"data row15 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col67\" class=\"data row15 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col68\" class=\"data row15 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col69\" class=\"data row15 col69\" >0.220000</td>\n",
" <td id=\"T_407e7_row15_col70\" class=\"data row15 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col71\" class=\"data row15 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col72\" class=\"data row15 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col73\" class=\"data row15 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col74\" class=\"data row15 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col75\" class=\"data row15 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col76\" class=\"data row15 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col77\" class=\"data row15 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col78\" class=\"data row15 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col79\" class=\"data row15 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col80\" class=\"data row15 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col81\" class=\"data row15 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col82\" class=\"data row15 col82\" >0.260000</td>\n",
" <td id=\"T_407e7_row15_col83\" class=\"data row15 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col84\" class=\"data row15 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col85\" class=\"data row15 col85\" >0.220000</td>\n",
" <td id=\"T_407e7_row15_col86\" class=\"data row15 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col87\" class=\"data row15 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col88\" class=\"data row15 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col89\" class=\"data row15 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col90\" class=\"data row15 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col91\" class=\"data row15 col91\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col92\" class=\"data row15 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col93\" class=\"data row15 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col94\" class=\"data row15 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col95\" class=\"data row15 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col96\" class=\"data row15 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col97\" class=\"data row15 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col98\" class=\"data row15 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col99\" class=\"data row15 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row15_col100\" class=\"data row15 col100\" >82</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row16\" class=\"row_heading level0 row16\" >Review16</th>\n",
" <td id=\"T_407e7_row16_col0\" class=\"data row16 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col1\" class=\"data row16 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col2\" class=\"data row16 col2\" >0.190000</td>\n",
" <td id=\"T_407e7_row16_col3\" class=\"data row16 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col4\" class=\"data row16 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col5\" class=\"data row16 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col6\" class=\"data row16 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col7\" class=\"data row16 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col8\" class=\"data row16 col8\" >0.160000</td>\n",
" <td id=\"T_407e7_row16_col9\" class=\"data row16 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col10\" class=\"data row16 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col11\" class=\"data row16 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col12\" class=\"data row16 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col13\" class=\"data row16 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col14\" class=\"data row16 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col15\" class=\"data row16 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col16\" class=\"data row16 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col17\" class=\"data row16 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col18\" class=\"data row16 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col19\" class=\"data row16 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col20\" class=\"data row16 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col21\" class=\"data row16 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col22\" class=\"data row16 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col23\" class=\"data row16 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col24\" class=\"data row16 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col25\" class=\"data row16 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col26\" class=\"data row16 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col27\" class=\"data row16 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col28\" class=\"data row16 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col29\" class=\"data row16 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col30\" class=\"data row16 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col31\" class=\"data row16 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col32\" class=\"data row16 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col33\" class=\"data row16 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col34\" class=\"data row16 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col35\" class=\"data row16 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col36\" class=\"data row16 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col37\" class=\"data row16 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col38\" class=\"data row16 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col39\" class=\"data row16 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col40\" class=\"data row16 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col41\" class=\"data row16 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col42\" class=\"data row16 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col43\" class=\"data row16 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col44\" class=\"data row16 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col45\" class=\"data row16 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col46\" class=\"data row16 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col47\" class=\"data row16 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col48\" class=\"data row16 col48\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col49\" class=\"data row16 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col50\" class=\"data row16 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col51\" class=\"data row16 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col52\" class=\"data row16 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col53\" class=\"data row16 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col54\" class=\"data row16 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col55\" class=\"data row16 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col56\" class=\"data row16 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col57\" class=\"data row16 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col58\" class=\"data row16 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col59\" class=\"data row16 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col60\" class=\"data row16 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col61\" class=\"data row16 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col62\" class=\"data row16 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col63\" class=\"data row16 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col64\" class=\"data row16 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col65\" class=\"data row16 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col66\" class=\"data row16 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col67\" class=\"data row16 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col68\" class=\"data row16 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col69\" class=\"data row16 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col70\" class=\"data row16 col70\" >0.150000</td>\n",
" <td id=\"T_407e7_row16_col71\" class=\"data row16 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col72\" class=\"data row16 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col73\" class=\"data row16 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col74\" class=\"data row16 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col75\" class=\"data row16 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col76\" class=\"data row16 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col77\" class=\"data row16 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col78\" class=\"data row16 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col79\" class=\"data row16 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col80\" class=\"data row16 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col81\" class=\"data row16 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col82\" class=\"data row16 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col83\" class=\"data row16 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col84\" class=\"data row16 col84\" >0.180000</td>\n",
" <td id=\"T_407e7_row16_col85\" class=\"data row16 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col86\" class=\"data row16 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col87\" class=\"data row16 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col88\" class=\"data row16 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col89\" class=\"data row16 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col90\" class=\"data row16 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col91\" class=\"data row16 col91\" >0.220000</td>\n",
" <td id=\"T_407e7_row16_col92\" class=\"data row16 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col93\" class=\"data row16 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col94\" class=\"data row16 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col95\" class=\"data row16 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col96\" class=\"data row16 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col97\" class=\"data row16 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col98\" class=\"data row16 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col99\" class=\"data row16 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row16_col100\" class=\"data row16 col100\" >91</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row17\" class=\"row_heading level0 row17\" >Review17</th>\n",
" <td id=\"T_407e7_row17_col0\" class=\"data row17 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col1\" class=\"data row17 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col2\" class=\"data row17 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col3\" class=\"data row17 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col4\" class=\"data row17 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col5\" class=\"data row17 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col6\" class=\"data row17 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col7\" class=\"data row17 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col8\" class=\"data row17 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col9\" class=\"data row17 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col10\" class=\"data row17 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col11\" class=\"data row17 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col12\" class=\"data row17 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col13\" class=\"data row17 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col14\" class=\"data row17 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col15\" class=\"data row17 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col16\" class=\"data row17 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col17\" class=\"data row17 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col18\" class=\"data row17 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col19\" class=\"data row17 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col20\" class=\"data row17 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col21\" class=\"data row17 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col22\" class=\"data row17 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col23\" class=\"data row17 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col24\" class=\"data row17 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col25\" class=\"data row17 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col26\" class=\"data row17 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col27\" class=\"data row17 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col28\" class=\"data row17 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col29\" class=\"data row17 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col30\" class=\"data row17 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col31\" class=\"data row17 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col32\" class=\"data row17 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col33\" class=\"data row17 col33\" >0.200000</td>\n",
" <td id=\"T_407e7_row17_col34\" class=\"data row17 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col35\" class=\"data row17 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col36\" class=\"data row17 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col37\" class=\"data row17 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col38\" class=\"data row17 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col39\" class=\"data row17 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col40\" class=\"data row17 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col41\" class=\"data row17 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col42\" class=\"data row17 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col43\" class=\"data row17 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col44\" class=\"data row17 col44\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col45\" class=\"data row17 col45\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col46\" class=\"data row17 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col47\" class=\"data row17 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col48\" class=\"data row17 col48\" >0.170000</td>\n",
" <td id=\"T_407e7_row17_col49\" class=\"data row17 col49\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col50\" class=\"data row17 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col51\" class=\"data row17 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col52\" class=\"data row17 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col53\" class=\"data row17 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col54\" class=\"data row17 col54\" >0.250000</td>\n",
" <td id=\"T_407e7_row17_col55\" class=\"data row17 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col56\" class=\"data row17 col56\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col57\" class=\"data row17 col57\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col58\" class=\"data row17 col58\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col59\" class=\"data row17 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col60\" class=\"data row17 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col61\" class=\"data row17 col61\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col62\" class=\"data row17 col62\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col63\" class=\"data row17 col63\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col64\" class=\"data row17 col64\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col65\" class=\"data row17 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col66\" class=\"data row17 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col67\" class=\"data row17 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col68\" class=\"data row17 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col69\" class=\"data row17 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col70\" class=\"data row17 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col71\" class=\"data row17 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col72\" class=\"data row17 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col73\" class=\"data row17 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col74\" class=\"data row17 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col75\" class=\"data row17 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col76\" class=\"data row17 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col77\" class=\"data row17 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col78\" class=\"data row17 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col79\" class=\"data row17 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col80\" class=\"data row17 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col81\" class=\"data row17 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col82\" class=\"data row17 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col83\" class=\"data row17 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col84\" class=\"data row17 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col85\" class=\"data row17 col85\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col86\" class=\"data row17 col86\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col87\" class=\"data row17 col87\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col88\" class=\"data row17 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col89\" class=\"data row17 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col90\" class=\"data row17 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col91\" class=\"data row17 col91\" >0.220000</td>\n",
" <td id=\"T_407e7_row17_col92\" class=\"data row17 col92\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col93\" class=\"data row17 col93\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col94\" class=\"data row17 col94\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col95\" class=\"data row17 col95\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col96\" class=\"data row17 col96\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col97\" class=\"data row17 col97\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col98\" class=\"data row17 col98\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col99\" class=\"data row17 col99\" >0.000000</td>\n",
" <td id=\"T_407e7_row17_col100\" class=\"data row17 col100\" >54</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_407e7_level0_row18\" class=\"row_heading level0 row18\" >Review18</th>\n",
" <td id=\"T_407e7_row18_col0\" class=\"data row18 col0\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col1\" class=\"data row18 col1\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col2\" class=\"data row18 col2\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col3\" class=\"data row18 col3\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col4\" class=\"data row18 col4\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col5\" class=\"data row18 col5\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col6\" class=\"data row18 col6\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col7\" class=\"data row18 col7\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col8\" class=\"data row18 col8\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col9\" class=\"data row18 col9\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col10\" class=\"data row18 col10\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col11\" class=\"data row18 col11\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col12\" class=\"data row18 col12\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col13\" class=\"data row18 col13\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col14\" class=\"data row18 col14\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col15\" class=\"data row18 col15\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col16\" class=\"data row18 col16\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col17\" class=\"data row18 col17\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col18\" class=\"data row18 col18\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col19\" class=\"data row18 col19\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col20\" class=\"data row18 col20\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col21\" class=\"data row18 col21\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col22\" class=\"data row18 col22\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col23\" class=\"data row18 col23\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col24\" class=\"data row18 col24\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col25\" class=\"data row18 col25\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col26\" class=\"data row18 col26\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col27\" class=\"data row18 col27\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col28\" class=\"data row18 col28\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col29\" class=\"data row18 col29\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col30\" class=\"data row18 col30\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col31\" class=\"data row18 col31\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col32\" class=\"data row18 col32\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col33\" class=\"data row18 col33\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col34\" class=\"data row18 col34\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col35\" class=\"data row18 col35\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col36\" class=\"data row18 col36\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col37\" class=\"data row18 col37\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col38\" class=\"data row18 col38\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col39\" class=\"data row18 col39\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col40\" class=\"data row18 col40\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col41\" class=\"data row18 col41\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col42\" class=\"data row18 col42\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col43\" class=\"data row18 col43\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col44\" class=\"data row18 col44\" >0.130000</td>\n",
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" <td id=\"T_407e7_row18_col46\" class=\"data row18 col46\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col47\" class=\"data row18 col47\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col48\" class=\"data row18 col48\" >0.000000</td>\n",
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" <td id=\"T_407e7_row18_col50\" class=\"data row18 col50\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col51\" class=\"data row18 col51\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col52\" class=\"data row18 col52\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col53\" class=\"data row18 col53\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col54\" class=\"data row18 col54\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col55\" class=\"data row18 col55\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col56\" class=\"data row18 col56\" >0.750000</td>\n",
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" <td id=\"T_407e7_row18_col59\" class=\"data row18 col59\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col60\" class=\"data row18 col60\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col61\" class=\"data row18 col61\" >0.000000</td>\n",
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" <td id=\"T_407e7_row18_col63\" class=\"data row18 col63\" >0.000000</td>\n",
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" <td id=\"T_407e7_row18_col65\" class=\"data row18 col65\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col66\" class=\"data row18 col66\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col67\" class=\"data row18 col67\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col68\" class=\"data row18 col68\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col69\" class=\"data row18 col69\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col70\" class=\"data row18 col70\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col71\" class=\"data row18 col71\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col72\" class=\"data row18 col72\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col73\" class=\"data row18 col73\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col74\" class=\"data row18 col74\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col75\" class=\"data row18 col75\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col76\" class=\"data row18 col76\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col77\" class=\"data row18 col77\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col78\" class=\"data row18 col78\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col79\" class=\"data row18 col79\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col80\" class=\"data row18 col80\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col81\" class=\"data row18 col81\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col82\" class=\"data row18 col82\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col83\" class=\"data row18 col83\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col84\" class=\"data row18 col84\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col85\" class=\"data row18 col85\" >0.000000</td>\n",
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" <td id=\"T_407e7_row18_col88\" class=\"data row18 col88\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col89\" class=\"data row18 col89\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col90\" class=\"data row18 col90\" >0.000000</td>\n",
" <td id=\"T_407e7_row18_col91\" class=\"data row18 col91\" >0.000000</td>\n",
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],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f7a028e5dd0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:55:28.830391Z",
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},
"id": "l_y8fH9nErVS",
"outputId": "be617462-f8a7-4e38-a3d2-a6cc0b8e534a"
},
"source": [
"df_topic_distribution = df_document_topic['dominant_topic'].value_counts().reset_index(name=\"Num Reviews\")\n",
"df_topic_distribution.columns = ['Topic Num', 'Num Reviews']\n",
"df_topic_distribution"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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],
"text/plain": [
" Topic Num Num Reviews\n",
"0 2 3045\n",
"1 1 2631\n",
"2 48 2525\n",
"3 4 2286\n",
"4 0 2203\n",
".. ... ...\n",
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"97 97 365\n",
"98 72 337\n",
"99 94 313\n",
"\n",
"[100 rows x 2 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:55:28.854501Z",
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},
"id": "3C0xbTpQErVT",
"outputId": "b6cfdb1c-a78a-4745-f6d8-20c7167a8ef3"
},
"source": [
"# Topic-Keyword Matrix\n",
"df_topic_keywords = pd.DataFrame(lda.components_)\n",
"\n",
"# Assign Column and Index\n",
"df_topic_keywords.columns = countvectorizer.get_feature_names()\n",
"df_topic_keywords.index = topicnames\n",
"\n",
"# View\n",
"df_topic_keywords.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<div>\n",
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" <th>802</th>\n",
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" <th>academic</th>\n",
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" <th>acceptance</th>\n",
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" <th>...</th>\n",
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" <th>Topic4</th>\n",
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" </tbody>\n",
"</table>\n",
"<p>5 rows × 2141 columns</p>\n",
"</div>"
],
"text/plain": [
" 264 802 abstract abstraction academic accelerated \\\n",
"Topic0 0.01 0.01 0.01 0.01 0.01 0.01 \n",
"Topic1 0.01 0.01 0.01 0.01 0.01 0.01 \n",
"Topic2 0.01 0.01 0.01 87.01 0.01 0.01 \n",
"Topic3 0.01 0.01 0.01 0.01 0.01 0.01 \n",
"Topic4 0.01 0.01 0.01 0.01 0.01 0.01 \n",
"\n",
" accelerating acceleration acceptance access ... workflows \\\n",
"Topic0 0.01 0.01 0.01 0.01 ... 0.01 \n",
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"Topic3 0.01 83.01 0.01 0.01 ... 0.01 \n",
"Topic4 0.01 0.01 0.01 0.01 ... 0.01 \n",
"\n",
" working workload workshop world worst xml years zero zone \n",
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"Topic1 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 \n",
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"\n",
"[5 rows x 2141 columns]"
]
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"metadata": {
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},
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},
{
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"source": [
"# Show top n keywords for each topic\n",
"def show_topics(vectorizer=countvectorizer, lda_model=lda, n_words=20):\n",
" keywords = np.array(vectorizer.get_feature_names())\n",
" topic_keywords = []\n",
" for topic_weights in lda_model.components_:\n",
" top_keyword_locs = (-topic_weights).argsort()[:n_words]\n",
" topic_keywords.append(keywords.take(top_keyword_locs))\n",
" return topic_keywords\n",
"\n",
"topic_keywords = show_topics(vectorizer=countvectorizer, lda_model=lda, n_words=15) \n",
"\n",
"# Topic - Keywords Dataframe\n",
"df_topic_keywords = pd.DataFrame(topic_keywords)\n",
"df_topic_keywords.columns = ['Word '+str(i) for i in range(df_topic_keywords.shape[1])]\n",
"df_topic_keywords.index = ['Topic '+str(i) for i in range(df_topic_keywords.shape[0])]\n",
"df_topic_keywords"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
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" <th>Word 13</th>\n",
" <th>Word 14</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Topic 0</th>\n",
" <td>aware</td>\n",
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" <td>behavior</td>\n",
" <td>chip</td>\n",
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" <td>petri</td>\n",
" <td>post</td>\n",
" <td>using</td>\n",
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" <tr>\n",
" <th>Topic 1</th>\n",
" <td>recognition</td>\n",
" <td>classification</td>\n",
" <td>based</td>\n",
" <td>using</td>\n",
" <td>features</td>\n",
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" <td>learning</td>\n",
" <td>invariant</td>\n",
" <td>supervised</td>\n",
" <td>simultaneous</td>\n",
" <td>image</td>\n",
" <td>detection</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 2</th>\n",
" <td>network</td>\n",
" <td>performance</td>\n",
" <td>neural</td>\n",
" <td>networks</td>\n",
" <td>effect</td>\n",
" <td>analysis</td>\n",
" <td>artificial</td>\n",
" <td>based</td>\n",
" <td>evaluation</td>\n",
" <td>using</td>\n",
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" <td>incremental</td>\n",
" <td>refinement</td>\n",
" <td>design</td>\n",
" <td>recurrent</td>\n",
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" <td>particle</td>\n",
" <td>vision</td>\n",
" <td>circuit</td>\n",
" <td>swarm</td>\n",
" <td>optimization</td>\n",
" <td>using</td>\n",
" <td>based</td>\n",
" <td>average</td>\n",
" <td>ratio</td>\n",
" <td>pulse</td>\n",
" <td>uwb</td>\n",
" <td>laser</td>\n",
" <td>estimates</td>\n",
" <td>acceleration</td>\n",
" <td>deformation</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 4</th>\n",
" <td>time</td>\n",
" <td>real</td>\n",
" <td>embedded</td>\n",
" <td>systems</td>\n",
" <td>series</td>\n",
" <td>varying</td>\n",
" <td>processor</td>\n",
" <td>space</td>\n",
" <td>delays</td>\n",
" <td>gpu</td>\n",
" <td>delay</td>\n",
" <td>hard</td>\n",
" <td>simulator</td>\n",
" <td>applications</td>\n",
" <td>animation</td>\n",
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" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 95</th>\n",
" <td>scheme</td>\n",
" <td>transmission</td>\n",
" <td>based</td>\n",
" <td>aggregation</td>\n",
" <td>watermarking</td>\n",
" <td>cache</td>\n",
" <td>plane</td>\n",
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" <td>faults</td>\n",
" <td>using</td>\n",
" <td>preference</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 96</th>\n",
" <td>generalized</td>\n",
" <td>collaborative</td>\n",
" <td>variable</td>\n",
" <td>structured</td>\n",
" <td>inference</td>\n",
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" <td>based</td>\n",
" <td>pressure</td>\n",
" <td>recommender</td>\n",
" <td>logical</td>\n",
" <td>systems</td>\n",
" <td>kernels</td>\n",
" <td>using</td>\n",
" <td>entity</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 97</th>\n",
" <td>optical</td>\n",
" <td>array</td>\n",
" <td>exploring</td>\n",
" <td>protocols</td>\n",
" <td>threshold</td>\n",
" <td>expert</td>\n",
" <td>likelihood</td>\n",
" <td>healthcare</td>\n",
" <td>affine</td>\n",
" <td>enabled</td>\n",
" <td>dense</td>\n",
" <td>networks</td>\n",
" <td>elastic</td>\n",
" <td>table</td>\n",
" <td>ultrasound</td>\n",
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" <td>stochastic</td>\n",
" <td>solutions</td>\n",
" <td>games</td>\n",
" <td>weighted</td>\n",
" <td>extended</td>\n",
" <td>databases</td>\n",
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" <td>periodic</td>\n",
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" <td>message</td>\n",
" <td>exponential</td>\n",
" <td>existence</td>\n",
" <td>ring</td>\n",
" <td>principles</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Topic 99</th>\n",
" <td>business</td>\n",
" <td>students</td>\n",
" <td>model</td>\n",
" <td>advanced</td>\n",
" <td>intelligence</td>\n",
" <td>checking</td>\n",
" <td>perception</td>\n",
" <td>consistency</td>\n",
" <td>fully</td>\n",
" <td>aspects</td>\n",
" <td>effects</td>\n",
" <td>primary</td>\n",
" <td>relation</td>\n",
" <td>school</td>\n",
" <td>regional</td>\n",
" </tr>\n",
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"<p>100 rows × 15 columns</p>\n",
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],
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" Word 0 Word 1 Word 2 Word 3 Word 4 \\\n",
"Topic 0 aware context behavior chip event \n",
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"Topic 3 particle vision circuit swarm optimization \n",
"Topic 4 time real embedded systems series \n",
"... ... ... ... ... ... \n",
"Topic 95 scheme transmission based aggregation watermarking \n",
"Topic 96 generalized collaborative variable structured inference \n",
"Topic 97 optical array exploring protocols threshold \n",
"Topic 98 stochastic solutions games weighted extended \n",
"Topic 99 business students model advanced intelligence \n",
"\n",
" Word 5 Word 6 Word 7 Word 8 \\\n",
"Topic 0 methodology based ontology developing \n",
"Topic 1 pattern face localization semi \n",
"Topic 2 analysis artificial based evaluation \n",
"Topic 3 using based average ratio \n",
"Topic 4 varying processor space delays \n",
"... ... ... ... ... \n",
"Topic 95 cache plane priority shift \n",
"Topic 96 transformation registration based pressure \n",
"Topic 97 expert likelihood healthcare affine \n",
"Topic 98 databases automata periodic generic \n",
"Topic 99 checking perception consistency fully \n",
"\n",
" Word 9 Word 10 Word 11 Word 12 Word 13 \\\n",
"Topic 0 predicting centric nets petri post \n",
"Topic 1 learning invariant supervised simultaneous image \n",
"Topic 2 using high incremental refinement design \n",
"Topic 3 pulse uwb laser estimates acceleration \n",
"Topic 4 gpu delay hard simulator applications \n",
"... ... ... ... ... ... \n",
"Topic 95 architectural wind node faults using \n",
"Topic 96 recommender logical systems kernels using \n",
"Topic 97 enabled dense networks elastic table \n",
"Topic 98 errors message exponential existence ring \n",
"Topic 99 aspects effects primary relation school \n",
"\n",
" Word 14 \n",
"Topic 0 using \n",
"Topic 1 detection \n",
"Topic 2 recurrent \n",
"Topic 3 deformation \n",
"Topic 4 animation \n",
"... ... \n",
"Topic 95 preference \n",
"Topic 96 entity \n",
"Topic 97 ultrasound \n",
"Topic 98 principles \n",
"Topic 99 regional \n",
"\n",
"[100 rows x 15 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 21
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
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"id": "zCNh50tEErVY"
},
"source": [
"X = lda_output.copy()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:55:28.990706Z",
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"source": [
"X = np.hstack([X, data[[\"year\"]].values])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:55:29.031994Z",
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"outputId": "5d865f69-87e7-434c-b2e3-d8764d08d9ac"
},
"source": [
"total_journals = len(data)\n",
"\n",
"# for each categorical variable\n",
"for col in [\"venue\"]:\n",
"\n",
"\n",
" temp_df = pd.Series(data[col].value_counts() / total_journals)\n",
"\n",
" # make plot with the above percentages\n",
" fig = temp_df.sort_values(ascending=False).plot.bar()\n",
" fig.set_xlabel(col)\n",
"\n",
" # add a line at 5 % to flag the threshold for rare categories\n",
" fig.axhline(y=0.001, color='red')\n",
" fig.set_ylabel('Percentage of journals')\n",
" plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:26:08.076117Z",
"iopub.status.busy": "2021-07-27T10:26:08.075444Z",
"iopub.status.idle": "2021-07-27T10:26:08.082350Z",
"shell.execute_reply": "2021-07-27T10:26:08.081351Z",
"shell.execute_reply.started": "2021-07-27T10:26:08.076073Z"
},
"id": "z1HdzLQsErVa"
},
"source": [
"def group_rare_labels(df, var):\n",
"\n",
" total = len(df)\n",
"\n",
" # first I calculate the % of houses for each category\n",
" temp_df = pd.Series(df[var].value_counts() / total)\n",
"\n",
" # now I create a dictionary to replace the rare labels with the\n",
" # string 'rare' if they are present in less than 5% of houses\n",
"\n",
" grouping_dict = {\n",
" k: ('rare' if k not in temp_df[temp_df >= 0.01].index else k)\n",
" for k in temp_df.index\n",
" }\n",
"\n",
" # now I replace the rare categories\n",
" tmp = df[var].map(grouping_dict)\n",
"\n",
" return tmp"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:51.454749Z",
"iopub.status.busy": "2021-07-27T08:56:51.454427Z",
"iopub.status.idle": "2021-07-27T08:56:52.472706Z",
"shell.execute_reply": "2021-07-27T08:56:52.471478Z",
"shell.execute_reply.started": "2021-07-27T08:56:51.454720Z"
},
"id": "VWSDVZCzErVb"
},
"source": [
"data['venue'] = group_rare_labels(data, 'venue')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.474320Z",
"iopub.status.busy": "2021-07-27T08:56:52.474018Z",
"iopub.status.idle": "2021-07-27T08:56:52.479226Z",
"shell.execute_reply": "2021-07-27T08:56:52.477959Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.474291Z"
},
"id": "_3JzbAVmErVb"
},
"source": [
"from sklearn.preprocessing import OneHotEncoder"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.480820Z",
"iopub.status.busy": "2021-07-27T08:56:52.480489Z",
"iopub.status.idle": "2021-07-27T08:56:52.741578Z",
"shell.execute_reply": "2021-07-27T08:56:52.740077Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.480790Z"
},
"id": "iZMaPD2XErVb"
},
"source": [
"ohe = OneHotEncoder(sparse=False)\n",
"venuefeat = ohe.fit_transform(data[[\"venue\"]])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.743483Z",
"iopub.status.busy": "2021-07-27T08:56:52.743135Z",
"iopub.status.idle": "2021-07-27T08:56:52.851987Z",
"shell.execute_reply": "2021-07-27T08:56:52.850871Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.743452Z"
},
"id": "G6dh5PwfErVc"
},
"source": [
"X = np.hstack([X, venuefeat])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.853873Z",
"iopub.status.busy": "2021-07-27T08:56:52.853400Z",
"iopub.status.idle": "2021-07-27T08:56:52.898516Z",
"shell.execute_reply": "2021-07-27T08:56:52.897139Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.853823Z"
},
"id": "tXoiJlmWErVc"
},
"source": [
"data[\"n_citation\"] = data[\"n_citation\"].replace(0,np.exp(0))"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.900934Z",
"iopub.status.busy": "2021-07-27T08:56:52.900225Z",
"iopub.status.idle": "2021-07-27T08:56:52.909636Z",
"shell.execute_reply": "2021-07-27T08:56:52.908402Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.900876Z"
},
"id": "TYLZW-q3ErVd",
"outputId": "e26f3d05-b641-4ba7-b337-42ecda4addad"
},
"source": [
"np.exp(0)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"1.0"
]
},
"metadata": {
"tags": []
},
"execution_count": 31
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:52.911615Z",
"iopub.status.busy": "2021-07-27T08:56:52.911267Z",
"iopub.status.idle": "2021-07-27T08:56:54.085069Z",
"shell.execute_reply": "2021-07-27T08:56:54.084261Z",
"shell.execute_reply.started": "2021-07-27T08:56:52.911583Z"
},
"id": "oa3jRk7lErVd",
"outputId": "a0ebf362-6e4a-4e1b-fbc8-ea118b37f69b"
},
"source": [
"import seaborn as sns\n",
"sns.displot(np.log(data[\"n_citation\"]),kind = \"kde\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<seaborn.axisgrid.FacetGrid at 0x7f78781b7ed0>"
]
},
"metadata": {
"tags": []
},
"execution_count": 32
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 360x360 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:54.086831Z",
"iopub.status.busy": "2021-07-27T08:56:54.086346Z",
"iopub.status.idle": "2021-07-27T08:56:54.287161Z",
"shell.execute_reply": "2021-07-27T08:56:54.286019Z",
"shell.execute_reply.started": "2021-07-27T08:56:54.086798Z"
},
"id": "d5nJ0M6qErVe"
},
"source": [
"from catboost import CatBoostRegressor\n",
"from sklearn.ensemble import RandomForestRegressor\n",
"from sklearn.model_selection import train_test_split"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting catboost\n",
" Downloading catboost-0.26.1-cp37-none-manylinux1_x86_64.whl (67.4 MB)\n",
"\u001b[K |████████████████████████████████| 67.4 MB 103 kB/s \n",
"\u001b[?25hRequirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
"Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
"Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.21.1)\n",
"Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.3.1)\n",
"Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
"Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
"Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
"Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
"Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
"Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
"Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
"Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
"Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
"Installing collected packages: catboost\n",
"Successfully installed catboost-0.26.1\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:54.290730Z",
"iopub.status.busy": "2021-07-27T08:56:54.290138Z",
"iopub.status.idle": "2021-07-27T08:56:54.294525Z",
"shell.execute_reply": "2021-07-27T08:56:54.293589Z",
"shell.execute_reply.started": "2021-07-27T08:56:54.290690Z"
},
"id": "0x0g2LouErVe"
},
"source": [
"rf = RandomForestRegressor()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T08:56:54.296348Z",
"iopub.status.busy": "2021-07-27T08:56:54.296028Z",
"iopub.status.idle": "2021-07-27T08:56:54.412407Z",
"shell.execute_reply": "2021-07-27T08:56:54.411432Z",
"shell.execute_reply.started": "2021-07-27T08:56:54.296319Z"
},
"id": "h0du-hGhErVf"
},
"source": [
"X_train, X_test, y_train, y_test = train_test_split(X,np.log(data[\"n_citation\"]), random_state = 100, test_size = 0.3)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T09:18:54.133819Z",
"iopub.status.busy": "2021-07-27T09:18:54.133439Z",
"iopub.status.idle": "2021-07-27T09:18:54.140041Z",
"shell.execute_reply": "2021-07-27T09:18:54.138469Z",
"shell.execute_reply.started": "2021-07-27T09:18:54.133788Z"
},
"id": "_3MIG3FWErVg"
},
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"param_grid = {\"max_depth\":[10,15],\n",
" \"min_samples_split\":[10],\n",
" \"min_samples_leaf\":[10]}\n",
"\n",
"grid = GridSearchCV(rf, param_grid=param_grid,cv = 3, verbose = 3)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T09:20:19.138369Z",
"iopub.status.busy": "2021-07-27T09:20:19.137953Z",
"iopub.status.idle": "2021-07-27T09:20:20.793811Z",
"shell.execute_reply": "2021-07-27T09:20:20.792610Z",
"shell.execute_reply.started": "2021-07-27T09:20:19.138332Z"
},
"id": "Hrlxrrr5ErVh",
"outputId": "ae730e9c-e6d0-486c-e2d8-c9970350e755"
},
"source": [
"from sklearn.decomposition import PCA\n",
"\n",
"pca = PCA(n_components=0.992)\n",
"X_train_pca = pca.fit_transform(X_train)\n",
"X_train_pca.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(64566, 47)"
]
},
"metadata": {
"tags": []
},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T09:20:23.774358Z",
"iopub.status.busy": "2021-07-27T09:20:23.773948Z",
"iopub.status.idle": "2021-07-27T10:13:26.094940Z",
"shell.execute_reply": "2021-07-27T10:13:26.093609Z",
"shell.execute_reply.started": "2021-07-27T09:20:23.774324Z"
},
"id": "ybUe2hdMErVi",
"outputId": "a7a90a3a-078d-4937-e395-e2148961b038"
},
"source": [
"grid.fit(X_train_pca, y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Fitting 3 folds for each of 8 candidates, totalling 24 fits\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5 ............\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5, score=0.095, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5 ............\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 1.5min remaining: 0.0s\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5, score=0.094, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5 ............\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 3.0min remaining: 0.0s\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=5, score=0.091, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10 ...........\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10, score=0.095, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10 ...........\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10, score=0.093, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10 ...........\n",
"[CV] max_depth=5, min_samples_leaf=5, min_samples_split=10, score=0.091, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5 ...........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5, score=0.095, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5 ...........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5, score=0.093, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5 ...........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=5, score=0.091, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10 ..........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10, score=0.095, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10 ..........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10, score=0.094, total= 1.5min\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10 ..........\n",
"[CV] max_depth=5, min_samples_leaf=10, min_samples_split=10, score=0.091, total= 1.5min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5 ...........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5, score=0.105, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5 ...........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5, score=0.103, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5 ...........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=5, score=0.098, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10 ..........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10, score=0.105, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10 ..........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10, score=0.103, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10 ..........\n",
"[CV] max_depth=10, min_samples_leaf=5, min_samples_split=10, score=0.100, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5 ..........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5, score=0.107, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5 ..........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5, score=0.104, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5 ..........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=5, score=0.100, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10 .........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10, score=0.107, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10 .........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10, score=0.104, total= 2.6min\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10 .........\n",
"[CV] max_depth=10, min_samples_leaf=10, min_samples_split=10, score=0.099, total= 2.6min\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"[Parallel(n_jobs=1)]: Done 24 out of 24 | elapsed: 48.9min finished\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=3, estimator=RandomForestRegressor(),\n",
" param_grid={'max_depth': [5, 10], 'min_samples_leaf': [5, 10],\n",
" 'min_samples_split': [5, 10]},\n",
" verbose=3)"
]
},
"metadata": {
"tags": []
},
"execution_count": 61
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:26:52.779455Z",
"iopub.status.busy": "2021-07-27T10:26:52.779026Z",
"iopub.status.idle": "2021-07-27T10:26:52.786896Z",
"shell.execute_reply": "2021-07-27T10:26:52.785802Z",
"shell.execute_reply.started": "2021-07-27T10:26:52.779418Z"
},
"id": "XnoxqsVDErVj",
"outputId": "c5366a40-cb09-454f-b3d0-1f883ab5c48b"
},
"source": [
"grid.best_estimator_"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"RandomForestRegressor(max_depth=10, min_samples_leaf=10, min_samples_split=10)"
]
},
"metadata": {
"tags": []
},
"execution_count": 76
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:13:26.098427Z",
"iopub.status.busy": "2021-07-27T10:13:26.097933Z",
"iopub.status.idle": "2021-07-27T10:13:26.248469Z",
"shell.execute_reply": "2021-07-27T10:13:26.247127Z",
"shell.execute_reply.started": "2021-07-27T10:13:26.098373Z"
},
"id": "2qinZe8AErVj"
},
"source": [
"X_test_pca = pca.transform(X_test)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:13:26.251989Z",
"iopub.status.busy": "2021-07-27T10:13:26.251104Z",
"iopub.status.idle": "2021-07-27T10:13:27.463636Z",
"shell.execute_reply": "2021-07-27T10:13:27.462833Z",
"shell.execute_reply.started": "2021-07-27T10:13:26.251931Z"
},
"id": "3W4-gWKhErVk"
},
"source": [
"y_train_pred = grid.best_estimator_.predict(X_train_pca)\n",
"y_test_pred = grid.best_estimator_.predict(X_test_pca)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:23:09.540960Z",
"iopub.status.busy": "2021-07-27T10:23:09.540359Z",
"iopub.status.idle": "2021-07-27T10:23:09.546473Z",
"shell.execute_reply": "2021-07-27T10:23:09.545389Z",
"shell.execute_reply.started": "2021-07-27T10:23:09.540907Z"
},
"id": "hJr5qSw5ErVk"
},
"source": [
"from sklearn.metrics import r2_score, mean_absolute_error"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:23:09.779767Z",
"iopub.status.busy": "2021-07-27T10:23:09.779171Z",
"iopub.status.idle": "2021-07-27T10:23:11.084171Z",
"shell.execute_reply": "2021-07-27T10:23:11.083010Z",
"shell.execute_reply.started": "2021-07-27T10:23:09.779728Z"
},
"id": "wp8S60_KErVk",
"outputId": "720f0232-f89d-4972-eb55-e4a7021439e9"
},
"source": [
"import seaborn as sns\n",
"sns.scatterplot(y_train_pred, y_train)\n",
"sns.scatterplot(y_train_pred, y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<AxesSubplot:ylabel='n_citation'>"
]
},
"metadata": {
"tags": []
},
"execution_count": 65
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:23:19.970608Z",
"iopub.status.busy": "2021-07-27T10:23:19.970021Z",
"iopub.status.idle": "2021-07-27T10:23:19.976292Z",
"shell.execute_reply": "2021-07-27T10:23:19.975052Z",
"shell.execute_reply.started": "2021-07-27T10:23:19.970568Z"
},
"id": "I_qpv3_1ErVl"
},
"source": [
"def mape(y_true, y_pred):\n",
" return np.mean((y_pred - y_true)/y_true) * 100"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:25:15.109347Z",
"iopub.status.busy": "2021-07-27T10:25:15.108861Z",
"iopub.status.idle": "2021-07-27T10:25:15.117385Z",
"shell.execute_reply": "2021-07-27T10:25:15.116541Z",
"shell.execute_reply.started": "2021-07-27T10:25:15.109221Z"
},
"id": "_hy84-kyErVl",
"outputId": "0b554c0b-d2ee-416e-d335-f82ab5f0a144"
},
"source": [
"print(r2_score(y_train, y_train_pred))\n",
"print(r2_score(y_test, y_test_pred))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"0.17997797428750728\n",
"0.10710776849544634\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T10:24:54.830214Z",
"iopub.status.busy": "2021-07-27T10:24:54.829644Z",
"iopub.status.idle": "2021-07-27T10:24:54.841220Z",
"shell.execute_reply": "2021-07-27T10:24:54.840352Z",
"shell.execute_reply.started": "2021-07-27T10:24:54.830179Z"
},
"id": "S-extChgErVm",
"outputId": "3e4858c0-b820-4633-9692-4172426d746f"
},
"source": [
"print(mape(np.exp(y_train),np.exp(y_train_pred)))\n",
"print(mape(np.exp(y_test),np.exp(y_test_pred)))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"214.00390979821245\n",
"239.23319817441526\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T09:17:14.478336Z",
"iopub.status.busy": "2021-07-27T09:17:14.477919Z",
"iopub.status.idle": "2021-07-27T09:17:14.488805Z",
"shell.execute_reply": "2021-07-27T09:17:14.487684Z",
"shell.execute_reply.started": "2021-07-27T09:17:14.478299Z"
},
"id": "5U7e5hpvErVm",
"outputId": "90b3d39e-651b-4128-b976-786dba93122b"
},
"source": [
"np.exp(y_test)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(243673 8.0\n",
" 886821 26.0\n",
" 589020 50.0\n",
" 52623 1.0\n",
" 609517 1.0\n",
" ... \n",
" 78857 7.0\n",
" 457586 71.0\n",
" 324489 84.0\n",
" 688956 80.0\n",
" 232332 1.0\n",
" Name: n_citation, Length: 27672, dtype: float64,\n",
" array([23.23592927, 20.07572666, 7.354631 , ..., 19.40244497,\n",
" 28.13140204, 10.38365387]))"
]
},
"metadata": {
"tags": []
},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"metadata": {
"execution": {
"iopub.execute_input": "2021-07-27T09:17:36.589329Z",
"iopub.status.busy": "2021-07-27T09:17:36.588882Z",
"iopub.status.idle": "2021-07-27T09:17:36.596237Z",
"shell.execute_reply": "2021-07-27T09:17:36.595331Z",
"shell.execute_reply.started": "2021-07-27T09:17:36.589291Z"
},
"id": "zFQLFr7_ErVn",
"outputId": "8dc2b710-d3bc-4ff3-8f5c-b618ff046471"
},
"source": [
"np.exp(y_test_pred)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([23.23592927, 20.07572666, 7.354631 , ..., 19.40244497,\n",
" 28.13140204, 10.38365387])"
]
},
"metadata": {
"tags": []
},
"execution_count": 49
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "k73aeYHbh4vy"
},
"source": [
"with open(\"model.sav\", \"wb\") as output_file:\n",
" pickle.dump(rf, output_file)"
],
"execution_count": null,
"outputs": []
}
]
}
Dependencies
requirements.txt
pypi
- beautifulsoup4 *
- numpy *
- psycopg2 *
- scikit-learn *