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phdkiran/stackexchange-kaggle-submission.ipynb

Last active November 17, 2016 03:47
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MLtext3/submissions/stackexchange-kaggle-Copy3.ipynb
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stackexchange-kaggle-submission.ipynb
{
"cells": [
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#import all the packages\nfrom datetime import datetime\nimport pandas as pd\nimport numpy as np\nimport re\n%matplotlib inline\nfrom sklearn import naive_bayes, cross_validation, feature_extraction, metrics",
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": "/work/conda3/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n \"This module will be removed in 0.20.\", DeprecationWarning)\n",
"name": "stderr"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Data Ingestion and exploration"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Training data "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# read some sample rows to find what the rows look like\ndf = pd.read_csv('../data/train.csv', nrows=100)\n# df.columns\n# print(df.memory_usage(deep=True).sum(), 'mb')\n\n# Find all the columns that start with Date text\ndate_cols = [x for x in df.columns if 'Date' in x]\n# df = pd.read_csv('../data/train.csv', nrows=10000, index_col=0, parse_dates=date_cols)\n# Read the full set of data and parse the date columns \ndf = pd.read_csv('../data/train.csv', index_col=0, parse_dates=date_cols)\nprint(df.dtypes)",
"execution_count": 2,
"outputs": [
{
"output_type": "stream",
"text": "PostId int64\nPostCreationDate datetime64[ns]\nOwnerUserId int64\nOwnerCreationDate datetime64[ns]\nReputationAtPostCreation int64\nOwnerUndeletedAnswerCountAtPostTime int64\nTitle object\nBodyMarkdown object\nTag1 object\nTag2 object\nTag3 object\nTag4 object\nTag5 object\nPostClosedDate datetime64[ns]\nOpenStatus int64\ndtype: object\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Test data "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "test_df = pd.read_csv('../data/test.csv', nrows=100)\ndate_cols = [x for x in test_df.columns if 'Date' in x]\ntest_df = pd.read_csv('../data/test.csv', index_col=0, parse_dates=date_cols)\nprint(test_df.dtypes)\n(test_df.head(2))",
"execution_count": 3,
"outputs": [
{
"output_type": "stream",
"text": "PostCreationDate datetime64[ns]\nOwnerUserId int64\nOwnerCreationDate datetime64[ns]\nReputationAtPostCreation int64\nOwnerUndeletedAnswerCountAtPostTime int64\nTitle object\nBodyMarkdown object\nTag1 object\nTag2 object\nTag3 object\nTag4 object\nTag5 object\ndtype: object\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PostCreationDate</th>\n <th>OwnerUserId</th>\n <th>OwnerCreationDate</th>\n <th>ReputationAtPostCreation</th>\n <th>OwnerUndeletedAnswerCountAtPostTime</th>\n <th>Title</th>\n <th>BodyMarkdown</th>\n <th>Tag1</th>\n <th>Tag2</th>\n <th>Tag3</th>\n <th>Tag4</th>\n <th>Tag5</th>\n </tr>\n <tr>\n <th>PostId</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>11768878</th>\n <td>2012-08-01 23:10:12</td>\n <td>756422</td>\n <td>2011-05-16 21:49:59</td>\n <td>155</td>\n <td>11</td>\n <td>Maven &amp; yui-compressor Plugin issues</td>\n <td>I'm using the yui-compressor plugin for maven ...</td>\n <td>maven</td>\n <td>maven-3</td>\n <td>yui-compressor</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>11768880</th>\n <td>2012-08-01 23:10:21</td>\n <td>1569892</td>\n <td>2012-08-01 22:24:37</td>\n <td>1</td>\n <td>0</td>\n <td>Inconsistent behaviour of html select dropdowns</td>\n <td>I have written a javascript-generated web page...</td>\n <td>html</td>\n <td>select</td>\n <td>drop-down-menu</td>\n <td>scrollbar</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " PostCreationDate OwnerUserId OwnerCreationDate \\\nPostId \n11768878 2012-08-01 23:10:12 756422 2011-05-16 21:49:59 \n11768880 2012-08-01 23:10:21 1569892 2012-08-01 22:24:37 \n\n ReputationAtPostCreation OwnerUndeletedAnswerCountAtPostTime \\\nPostId \n11768878 155 11 \n11768880 1 0 \n\n Title \\\nPostId \n11768878 Maven & yui-compressor Plugin issues \n11768880 Inconsistent behaviour of html select dropdowns \n\n BodyMarkdown Tag1 Tag2 \\\nPostId \n11768878 I'm using the yui-compressor plugin for maven ... maven maven-3 \n11768880 I have written a javascript-generated web page... html select \n\n Tag3 Tag4 Tag5 \nPostId \n11768878 yui-compressor NaN NaN \n11768880 drop-down-menu scrollbar NaN "
},
"metadata": {},
"execution_count": 3
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Data exploration "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#explore date fields and find patterns based on weekday, night time\n# df.groupby(df.PostCreationDate.dt.weekday_name).OpenStatus.mean()\ndf['day'] = (df.PostCreationDate.dt.weekday)\ndf['hour'] = (df.PostCreationDate.dt.hour)\n# see if the weekend posts are closed more\ndf['weekend'] = (df.day > 5).astype(int) \nprint(df.groupby('OpenStatus').weekend.mean())\nprint(df.groupby('OpenStatus').hour.mean())\n# df.groupby('hour').OpenStatus.mean()\n# df.groupby('hour').OpenStatus.mean().plot()\ndf['posted_in_the_night'] = ((df.hour >0) & (df.hour < 8)).astype(int)\ndf.groupby('posted_in_the_night').OpenStatus.mean()",
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"text": "OpenStatus\n0 0.097197\n1 0.082197\nName: weekend, dtype: float64\nOpenStatus\n0 12.272428\n1 12.698999\nName: hour, dtype: float64\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "posted_in_the_night\n0 0.508320\n1 0.468776\nName: OpenStatus, dtype: float64"
},
"metadata": {},
"execution_count": 4
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Get the mean for all the columns we have added so far"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "df.groupby('OpenStatus').mean()",
"execution_count": 5,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>PostId</th>\n <th>OwnerUserId</th>\n <th>ReputationAtPostCreation</th>\n <th>OwnerUndeletedAnswerCountAtPostTime</th>\n <th>day</th>\n <th>hour</th>\n <th>weekend</th>\n <th>posted_in_the_night</th>\n </tr>\n <tr>\n <th>OpenStatus</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>7.589407e+06</td>\n <td>756253.370965</td>\n <td>355.813063</td>\n <td>13.529956</td>\n <td>2.641596</td>\n <td>12.272428</td>\n <td>0.097197</td>\n <td>0.223537</td>\n </tr>\n <tr>\n <th>1</th>\n <td>6.429625e+06</td>\n <td>548413.808002</td>\n <td>657.454745</td>\n <td>25.405826</td>\n <td>2.573785</td>\n <td>12.698999</td>\n <td>0.082197</td>\n <td>0.197260</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " PostId OwnerUserId ReputationAtPostCreation \\\nOpenStatus \n0 7.589407e+06 756253.370965 355.813063 \n1 6.429625e+06 548413.808002 657.454745 \n\n OwnerUndeletedAnswerCountAtPostTime day hour \\\nOpenStatus \n0 13.529956 2.641596 12.272428 \n1 25.405826 2.573785 12.698999 \n\n weekend posted_in_the_night \nOpenStatus \n0 0.097197 0.223537 \n1 0.082197 0.197260 "
},
"metadata": {},
"execution_count": 5
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Explore the date columns\n- author age on the site 'owner_age'\n- Group the data by owner_age and draw scatter plot for mean and count"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "df['days_created'] = (df['PostCreationDate'] - df['OwnerCreationDate']).dt.days \ndf['owner_age'] = (datetime.now() - df['OwnerCreationDate']).dt.days\nage = df.groupby('owner_age').OpenStatus.agg(['mean', 'count']) \nage.shape\nage.reset_index().plot(kind='scatter', x='owner_age', y='mean')",
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb13987128>"
},
"metadata": {},
"execution_count": 6
},
{
"output_type": "display_data",
"data": {
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JdaPom8lcRFNmbMJgSW8nLiN/kLlcme9617ti80aENOPVZpKfMhuCTKbIiYmJ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\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fdb3ef9e4a8>"
},
"metadata": {}
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "df[(df.owner_age >= 2000) & (df.owner_age <=3200)].OpenStatus.mean()",
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "0.5815277374382806"
},
"metadata": {},
"execution_count": 7
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Owner age looks like a good feature but length of the title post is useless.\nLets continue looking at Authors\n - plot mean and count on a scatter plot"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "authors = df.groupby('OwnerUserId').OpenStatus.agg(['mean', 'count']) \nauthors[authors['count'] > 0].plot(kind='scatter', x='count', y='mean')",
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x7fdb18307320>"
},
"metadata": {},
"execution_count": 8
},
{
"output_type": "display_data",
"data": {
"image/png": 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P4TdvIyjqx3s87gPJlc8jTAirwlxFMUIOfRxPA5NxOcSB56JeLNdbsQuHtTbm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\nISIh4RBCCBEJCYcQQohISDiEEEJE4v8DXimsoHSmIv4AAAAASUVORK5CYII=\n",
"text/plain": "<matplotlib.figure.Figure at 0x7fdb19dbaa58>"
},
"metadata": {}
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### see if the number of questions asked before "
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "#check if the topic has a question mark \n#hypothesis: real questions will have a question mark \n# df['has_qn'] = df['BodyMarkdown'].apply(lambda x: 1 if re.match(r'(how|why|what|when|\\?)', x, re.M) else 0) \n# find the words that lead to a qn and count the occurances \n\ndf['qn_count'] = df['BodyMarkdown'].str.count(r'(how|why|what|when|\\?)', re.M) \ndf['qn_count_title'] = df['Title'].str.count(r'(how|why|what|when|\\?)', re.M)",
"execution_count": 11,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "df[df.qn_count > 100].OpenStatus.mean()",
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "0.1875"
},
"metadata": {},
"execution_count": 12
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# df.plot(kind='scatter', x='qn_count', y='OpenStatus')\ndf.groupby('OpenStatus').qn_count.describe()",
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "OpenStatus \n0 count 70136.000000\n mean 2.174604\n std 5.591464\n min 0.000000\n 25% 1.000000\n 50% 2.000000\n 75% 3.000000\n max 1133.000000\n1 count 70136.000000\n mean 2.485414\n std 2.629112\n min 0.000000\n 25% 1.000000\n 50% 2.000000\n 75% 3.000000\n max 139.000000\nName: qn_count, dtype: float64"
},
"metadata": {},
"execution_count": 13
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Create a list of smart authors those who have high chances of keeping the qn open "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "smart_authors = authors[(authors['mean'] >= 0.7) & (authors['count'] >= 5)]\nsmart_authors.shape\nsmart_authors.mean()",
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "mean 0.848510\ncount 7.294529\ndtype: float64"
},
"metadata": {},
"execution_count": 14
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Create a list of fequent authors those who have high chances of keeping the qn closed "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "freq_authors = authors[(authors['mean'] <= 0.3) & (authors['count'] >= 5)]\nfreq_authors.shape\nfreq_authors.mean()",
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "mean 0.158347\ncount 7.589595\ndtype: float64"
},
"metadata": {},
"execution_count": 15
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Get all the good information from describe function "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "df.groupby('OpenStatus').describe()",
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": "<div>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th></th>\n <th>OwnerUndeletedAnswerCountAtPostTime</th>\n <th>OwnerUserId</th>\n <th>PostId</th>\n <th>ReputationAtPostCreation</th>\n <th>day</th>\n <th>days_created</th>\n <th>hour</th>\n <th>owner_age</th>\n <th>posted_in_the_night</th>\n <th>qn_count</th>\n <th>qn_count_title</th>\n <th>weekend</th>\n </tr>\n <tr>\n <th>OpenStatus</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"8\" valign=\"top\">0</th>\n <th>count</th>\n <td>70136.000000</td>\n <td>7.013600e+04</td>\n <td>7.013600e+04</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>13.529956</td>\n <td>7.562534e+05</td>\n <td>7.589407e+06</td>\n <td>355.813063</td>\n <td>2.641596</td>\n <td>180.737681</td>\n <td>12.272428</td>\n <td>2098.656168</td>\n <td>0.223537</td>\n <td>2.174604</td>\n <td>0.457525</td>\n <td>0.097197</td>\n </tr>\n <tr>\n <th>std</th>\n <td>72.280095</td>\n <td>4.411708e+05</td>\n <td>3.106982e+06</td>\n <td>1917.610162</td>\n <td>1.898102</td>\n <td>252.647218</td>\n <td>6.163602</td>\n <td>385.402181</td>\n <td>0.416618</td>\n <td>5.591464</td>\n <td>0.655759</td>\n <td>0.296228</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000000</td>\n <td>4.000000e+00</td>\n <td>2.300000e+01</td>\n <td>-17.000000</td>\n <td>0.000000</td>\n <td>-1177.000000</td>\n <td>0.000000</td>\n <td>1568.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.000000</td>\n <td>3.938680e+05</td>\n <td>5.724392e+06</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>1.000000</td>\n <td>8.000000</td>\n <td>1799.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>0.000000</td>\n <td>7.670040e+05</td>\n <td>8.117328e+06</td>\n <td>18.000000</td>\n <td>3.000000</td>\n <td>62.000000</td>\n <td>13.000000</td>\n <td>2013.000000</td>\n <td>0.000000</td>\n <td>2.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>5.000000</td>\n <td>1.106342e+06</td>\n <td>1.017955e+07</td>\n <td>148.000000</td>\n <td>4.000000</td>\n <td>268.000000</td>\n <td>17.000000</td>\n <td>2331.000000</td>\n <td>0.000000</td>\n <td>3.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>5772.000000</td>\n <td>1.567070e+06</td>\n <td>1.175062e+07</td>\n <td>123242.000000</td>\n <td>6.000000</td>\n <td>1420.000000</td>\n <td>23.000000</td>\n <td>3030.000000</td>\n <td>1.000000</td>\n <td>1133.000000</td>\n <td>32.000000</td>\n <td>1.000000</td>\n </tr>\n <tr>\n <th rowspan=\"8\" valign=\"top\">1</th>\n <th>count</th>\n <td>70136.000000</td>\n <td>7.013600e+04</td>\n <td>7.013600e+04</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n <td>70136.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>25.405826</td>\n <td>5.484138e+05</td>\n <td>6.429625e+06</td>\n <td>657.454745</td>\n <td>2.573785</td>\n <td>250.666662</td>\n <td>12.698999</td>\n <td>2278.689104</td>\n <td>0.197260</td>\n <td>2.485414</td>\n <td>0.379377</td>\n <td>0.082197</td>\n </tr>\n <tr>\n <th>std</th>\n <td>104.635848</td>\n <td>4.119402e+05</td>\n <td>3.251247e+06</td>\n <td>2955.744169</td>\n <td>1.821750</td>\n <td>282.437137</td>\n <td>6.216131</td>\n <td>392.882446</td>\n <td>0.397933</td>\n <td>2.629112</td>\n <td>0.600391</td>\n <td>0.274667</td>\n </tr>\n <tr>\n <th>min</th>\n <td>0.000000</td>\n <td>3.000000e+00</td>\n <td>1.300000e+01</td>\n <td>-34.000000</td>\n <td>0.000000</td>\n <td>-1214.000000</td>\n <td>0.000000</td>\n <td>1568.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>0.000000</td>\n <td>1.960002e+05</td>\n <td>3.714598e+06</td>\n <td>6.000000</td>\n <td>1.000000</td>\n <td>21.000000</td>\n <td>8.000000</td>\n <td>1955.000000</td>\n <td>0.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>2.000000</td>\n <td>4.604070e+05</td>\n <td>6.607204e+06</td>\n <td>64.000000</td>\n <td>2.000000</td>\n <td>149.000000</td>\n <td>13.000000</td>\n <td>2253.000000</td>\n <td>0.000000</td>\n <td>2.000000</td>\n <td>0.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>13.000000</td>\n <td>8.510290e+05</td>\n <td>9.263690e+06</td>\n <td>394.000000</td>\n <td>4.000000</td>\n <td>391.000000</td>\n <td>18.000000</td>\n <td>2591.000000</td>\n <td>0.000000</td>\n <td>3.000000</td>\n <td>1.000000</td>\n <td>0.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>5117.000000</td>\n <td>1.567174e+06</td>\n <td>1.175065e+07</td>\n <td>209631.000000</td>\n <td>6.000000</td>\n <td>1437.000000</td>\n <td>23.000000</td>\n <td>3030.000000</td>\n <td>1.000000</td>\n <td>139.000000</td>\n <td>23.000000</td>\n <td>1.000000</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " OwnerUndeletedAnswerCountAtPostTime OwnerUserId \\\nOpenStatus \n0 count 70136.000000 7.013600e+04 \n mean 13.529956 7.562534e+05 \n std 72.280095 4.411708e+05 \n min 0.000000 4.000000e+00 \n 25% 0.000000 3.938680e+05 \n 50% 0.000000 7.670040e+05 \n 75% 5.000000 1.106342e+06 \n max 5772.000000 1.567070e+06 \n1 count 70136.000000 7.013600e+04 \n mean 25.405826 5.484138e+05 \n std 104.635848 4.119402e+05 \n min 0.000000 3.000000e+00 \n 25% 0.000000 1.960002e+05 \n 50% 2.000000 4.604070e+05 \n 75% 13.000000 8.510290e+05 \n max 5117.000000 1.567174e+06 \n\n PostId ReputationAtPostCreation day \\\nOpenStatus \n0 count 7.013600e+04 70136.000000 70136.000000 \n mean 7.589407e+06 355.813063 2.641596 \n std 3.106982e+06 1917.610162 1.898102 \n min 2.300000e+01 -17.000000 0.000000 \n 25% 5.724392e+06 1.000000 1.000000 \n 50% 8.117328e+06 18.000000 3.000000 \n 75% 1.017955e+07 148.000000 4.000000 \n max 1.175062e+07 123242.000000 6.000000 \n1 count 7.013600e+04 70136.000000 70136.000000 \n mean 6.429625e+06 657.454745 2.573785 \n std 3.251247e+06 2955.744169 1.821750 \n min 1.300000e+01 -34.000000 0.000000 \n 25% 3.714598e+06 6.000000 1.000000 \n 50% 6.607204e+06 64.000000 2.000000 \n 75% 9.263690e+06 394.000000 4.000000 \n max 1.175065e+07 209631.000000 6.000000 \n\n days_created hour owner_age \\\nOpenStatus \n0 count 70136.000000 70136.000000 70136.000000 \n mean 180.737681 12.272428 2098.656168 \n std 252.647218 6.163602 385.402181 \n min -1177.000000 0.000000 1568.000000 \n 25% 1.000000 8.000000 1799.000000 \n 50% 62.000000 13.000000 2013.000000 \n 75% 268.000000 17.000000 2331.000000 \n max 1420.000000 23.000000 3030.000000 \n1 count 70136.000000 70136.000000 70136.000000 \n mean 250.666662 12.698999 2278.689104 \n std 282.437137 6.216131 392.882446 \n min -1214.000000 0.000000 1568.000000 \n 25% 21.000000 8.000000 1955.000000 \n 50% 149.000000 13.000000 2253.000000 \n 75% 391.000000 18.000000 2591.000000 \n max 1437.000000 23.000000 3030.000000 \n\n posted_in_the_night qn_count qn_count_title \\\nOpenStatus \n0 count 70136.000000 70136.000000 70136.000000 \n mean 0.223537 2.174604 0.457525 \n std 0.416618 5.591464 0.655759 \n min 0.000000 0.000000 0.000000 \n 25% 0.000000 1.000000 0.000000 \n 50% 0.000000 2.000000 0.000000 \n 75% 0.000000 3.000000 1.000000 \n max 1.000000 1133.000000 32.000000 \n1 count 70136.000000 70136.000000 70136.000000 \n mean 0.197260 2.485414 0.379377 \n std 0.397933 2.629112 0.600391 \n min 0.000000 0.000000 0.000000 \n 25% 0.000000 1.000000 0.000000 \n 50% 0.000000 2.000000 0.000000 \n 75% 0.000000 3.000000 1.000000 \n max 1.000000 139.000000 23.000000 \n\n weekend \nOpenStatus \n0 count 70136.000000 \n mean 0.097197 \n std 0.296228 \n min 0.000000 \n 25% 0.000000 \n 50% 0.000000 \n 75% 0.000000 \n max 1.000000 \n1 count 70136.000000 \n mean 0.082197 \n std 0.274667 \n min 0.000000 \n 25% 0.000000 \n 50% 0.000000 \n 75% 0.000000 \n max 1.000000 "
},
"metadata": {},
"execution_count": 16
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Make features from all the data analysis above"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Find all the columns that start with 'Tag' so that we can count, combine them in make features\ntag_cols = [x for x in df.columns if 'Tag' in x]\n\n# off topic, not constructive, not a real question, or too localized\ndef make_features(df):\n \n #get the length of the title \n #hypothesis: longer title means its a real question\n df['len_Title'] = df['Title'].apply(len)\n df['wc_Title'] = df['Title'].apply(lambda x: len(x.split()) )\n \n #get the length of the post \n #hypothesis: longer post means its a real question\n df['len_Post'] = df['BodyMarkdown'].apply(len)\n df['wc_Post'] = df['BodyMarkdown'].apply(lambda x: len(x.split()) )\n \n #clean negative reputation since some models dont work well\n df.loc[df['ReputationAtPostCreation'] < 0, 'ReputationAtPostCreation'] = 0\n \n #get the count of real valid tags excluding NaN \n #hypothesis: real questions will have atleast one tag \n df['tag_count'] = df[tag_cols].apply(lambda tag_list: tag_list.notnull().sum(), axis=1)\n # append word 'Tag ' to distinguish it in text vectorization\n df['tag_all'] = df[tag_cols].apply(lambda tag_list: tag_list.str.cat(sep= 'Tag , '), axis=1) \n # append word 'Tag ' to the last element in list\n df['tag_all'] = df['tag_all'].astype(str) + 'Tag '\n \n #check if the topic has a question mark\n #hypothesis: real questions will have a question mark\n# df['has_qn'] = df['BodyMarkdown'].apply(lambda x: 1 if re.match(r'(how|why|what|when|\\?)', x, re.M) else 0)\n # find the words that lead to a qn and count the occurances\n df['qn_count'] = df['BodyMarkdown'].str.count(r'(how|why|what|when|\\?)', re.M)\n df['qn_count_title'] = df['Title'].str.count(r'(how|why|what|when|\\?)', re.M)\n \n #check if the topic and op creation date are within the same day \n #hypothesis: same day postings might be more prone to closing \n df['days_created'] = (df['PostCreationDate'] - df['OwnerCreationDate']).dt.days\n df['owner_age'] = (datetime.now() - df['OwnerCreationDate']).dt.days\n# df['days_closed_owner'] = (df['PostClosedDate'] - df['OwnerCreationDate']).dt.days\n \n #get the mean on the reputation of the owner\n #hypothesis: same day postings might be more prone to closing \n df['more_history'] = (df['days_created'] > 7.).astype(int)\n df['more_tags'] = (df['tag_count'] > 2.).astype(int)\n df['more_posts'] = (df['OwnerUndeletedAnswerCountAtPostTime'] > 5.).astype(int)\n df['more_reps'] = (df['ReputationAtPostCreation'] > 18.).astype(int)\n \n df['smart_author'] = df.OwnerUserId.isin(smart_authors.index).astype(int)\n df['freq_author'] = df.OwnerUserId.isin(freq_authors.index).astype(int)\n \n df['day'] = (df.PostCreationDate.dt.weekday)\n df['hour'] = (df.PostCreationDate.dt.hour)\n df['weekend'] = (df.day > 5).astype(int) \n df['posted_in_the_night'] = ((df.hour >0) & (df.hour < 8)).astype(int)\n df['seasoned_authors'] = ((df.owner_age >= 2000) & (df.owner_age <=3200)).astype(int)\n \n return df\n\ndef train_features(df):\n #Also, check if the topic close and create dates delta\n #hypothesis: want to see how long it takes to close the topic might be more prone to closing \n df['days_closed'] = (df['PostClosedDate'] - df['PostCreationDate']).dt.days\n return df\n\ndf = make_features(df)\n# df = train_features(df)",
"execution_count": 17,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Make functions for the Pipelines later use"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "def ft_get_text_title(df):\n return df['Title'].str.cat(df['tag_all'], sep=' __ ')\n\ndef ft_get_text_topic(df):\n return df['BodyMarkdown'].str.cat(df['tag_all'], sep=' __ ')\n\ndef ft_get_text_all(df):\n return df['tag_all'].str.cat(df['Title'], sep=' __ ').str.cat(df['BodyMarkdown'], sep=' __ ')\n\nft_cols = ['ReputationAtPostCreation', 'OwnerUndeletedAnswerCountAtPostTime']\nft_cols_extra = ['owner_age', 'len_Title', 'len_Post', 'tag_count', 'more_history', 'more_reps', 'more_tags', \n 'wc_Post', 'wc_Title', 'more_posts', 'smart_author', 'freq_author', \n 'weekend', 'posted_in_the_night', 'seasoned_authors', 'qn_count', 'qn_count_title']\n# 'has_qn', 'has_qn_title', \n\n\ndef ft_get_features(df):\n return df[ft_cols]\n",
"execution_count": 18,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### null accuracy"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "total_closed = (df.OpenStatus == 0)\nprint('null accuracy is: ', total_closed.sum()/df['OpenStatus'].count())",
"execution_count": 19,
"outputs": [
{
"output_type": "stream",
"text": "null accuracy is: 0.5\n",
"name": "stdout"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Model Evaluation\nTrying three models for the feature evaluation\n - knn\n - LDA\n - LogReg"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#Model building cell\n# Try out knn LDA and logreg\n\nfrom sklearn.neighbors import KNeighborsClassifier\nknn = KNeighborsClassifier(n_neighbors=100)\n\nfrom sklearn.lda import LDA\nlda = LDA()\n\nfrom sklearn.linear_model import LogisticRegression, LinearRegression\nlinreg = LinearRegression()\nlogreg = LogisticRegression()\n\n# model_list = [knn, lda, linreg, logreg]\nmodel_list = [knn, lda, logreg]",
"execution_count": 20,
"outputs": [
{
"output_type": "stream",
"text": "/work/conda3/lib/python3.5/site-packages/sklearn/lda.py:4: DeprecationWarning: lda.LDA has been moved to discriminant_analysis.LinearDiscriminantAnalysis in 0.17 and will be removed in 0.19\n \"in 0.17 and will be removed in 0.19\", DeprecationWarning)\n",
"name": "stderr"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Model Split and evaluation in a loop"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "X_train, X_test, y_train, y_test = cross_validation.train_test_split(df[ft_cols], df['OpenStatus'], random_state = 1)\n\ndef model_fit_predict(model):\n model.fit(X_train, y_train)\n pred_probs = model.predict_proba(X_test)\n # return the model, log loss score and roc_auc score (just for exploration)\n print('log loss value is: ', metrics.log_loss(y_test, pred_probs[:, 1]))\n print('roc auc value is: ', metrics.roc_auc_score(y_test, pred_probs[:, 1])) #just for comparison and not used \n return model, metrics.log_loss(y_test, pred_probs[:, 1]), metrics.roc_auc_score(y_test, pred_probs[:, 1])\n\n# Try out each model one by one\n[model_fit_predict(model) for model in model_list]",
"execution_count": 21,
"outputs": [
{
"output_type": "stream",
"text": "log loss value is: 0.676232990432\nroc auc value is: 0.605230954073\nlog loss value is: 0.690411966135\nroc auc value is: 0.608855364454\nlog loss value is: 0.689967792451\nroc auc value is: 0.607034694214\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "[(KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=100, p=2,\n weights='uniform'), 0.676232990431617, 0.60523095407296323),\n (LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None,\n solver='svd', store_covariance=False, tol=0.0001),\n 0.69041196613491651,\n 0.60885536445383315),\n (LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False),\n 0.68996779245069317,\n 0.60703469421430767)]"
},
"metadata": {},
"execution_count": 21
}
]
},
{
"metadata": {
"collapsed": false
},
"cell_type": "raw",
"source": "### commented out for now\n# feature engineering\n#knn is the winner here\n# take the knn predicted value and try to add features that would improve this score\nbase_pred = 0.676232990431617 \nbase_ft_cols = ft_cols.copy()\n\ndef add_feature(x):\n ft_cols.append(x)\n X_train, X_test, y_train, y_test = cross_validation.train_test_split(df[ft_cols], df['OpenStatus'], random_state = 1)\n score_ = model_fit_predict(knn)\n print('prediction = ', score_)\n if score_[1] > base_pred:\n print('feature did not improve score, removing ', x)\n ft_cols.remove(x)\n else:\n print('feature improved score, adding ', x, ft_cols)\n# base_pred = score_[1]\n print(ft_cols) \n \n \n[add_feature(x) for x in ft_cols_extra]\nft_cols"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### knn seems to be the winner here with the lowest prediction\n### Lets build Countvectorizer and analyze the text"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Lets mine the title and topic of the post for useful information\n# Use Count vectorizer \nfrom sklearn.pipeline import Pipeline, make_pipeline, make_union \nfrom sklearn.preprocessing import FunctionTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom sklearn.feature_extraction.text import TfidfVectorizer \nfrom sklearn.naive_bayes import MultinomialNB\n\ncv = CountVectorizer()\n# CountVectorizer(input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, \n# tokenizer=None, stop_words=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b', \n# ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype=)\n# cv = CountVectorizer(token_pattern=r'([\\w ]+)')\nnb = MultinomialNB()\ntf = TfidfVectorizer()",
"execution_count": 120,
"outputs": []
},
{
"metadata": {
"collapsed": true,
"trusted": false
},
"cell_type": "markdown",
"source": "### Let's analyze the text data now from the Body title and tags"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "text_ = ft_get_text_all(df)\nX_train, X_test, y_train, y_test = cross_validation.train_test_split(text_, df['OpenStatus'], random_state = 0)\nX_test.head()",
"execution_count": 121,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "135949 mysqlTag , linuxTag , ubuntuTag __ Adding use...\n79828 e-commerceTag __ What technology to use for m...\n131502 apacheTag , tomcat6Tag , struts2Tag , redirect...\n5510 objective-cTag , osxTag , cocoaTag __ iOS-lik...\n94976 consultingTag , contractingTag __ Where do yo...\nName: tag_all, dtype: object"
},
"metadata": {},
"execution_count": 121
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# helper function to dump useful info from CV\ndef analyze_cv(cv):\n cv.fit(X_train, y_train) \n print('features count is: ', len(cv.get_feature_names()))\n print('features are: ', cv.get_feature_names()[:50])\n print('stopwords are: ', len(cv.stop_words_))\n# cv.transform(X_test)\n# return metrics.log_loss(cv.predict_proba(X_test))",
"execution_count": 122,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Creating the regex pattern to include relevant data. \n# include words starting with alphabets only - [^\\W\\d]\nreg_pattern = r'([^\\W\\d][\\w.-]{2,})'\ncv = CountVectorizer(token_pattern= reg_pattern, min_df=1, max_features=21000, stop_words='english')\n# cv = CountVectorizer(token_pattern= r'([a-zA-Z.-]{3,})', min_df=20, max_features=11000, stop_words='english')\nanalyze_cv(cv)",
"execution_count": 123,
"outputs": [
{
"output_type": "stream",
"text": "features count is: 21000\nfeatures are: ['_22', '___', '___.', '___.__', '____', '_____', '______', '__adjust_heap', '__android_log_print', '__asm', '__attribute__', '__call', '__call__', '__cdecl', '__cfrunlooprun', '__construct', '__declspec', '__device__', '__dopostback', '__file__', '__getitem__', '__gnu_cxx', '__init__', '__init__.py', '__line__', '__main__', '__name__', '__new__', '__normal_iterator', '__nsautoreleasenopool', '__syncthreads', '__text', '__thiscall', '__unicode__', '_alloc', '_bitmapcache.get', '_blank', '_chart', '_context', '_cookie', '_data', '_debug', '_default', '_dword', '_elem', '_files', '_gaq', '_gaq.push', '_get', '_helper-']\nstopwords are: 457789\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "tf = TfidfVectorizer(token_pattern= reg_pattern, min_df=20, max_features=21000, stop_words='english')\n# cv = CountVectorizer(token_pattern= r'([a-zA-Z.-]{3,})', min_df=20, max_features=11000, stop_words='english')\nanalyze_cv(tf)",
"execution_count": 124,
"outputs": [
{
"output_type": "stream",
"text": "features count is: 14584\nfeatures are: ['___', '____', '__cdecl', '__construct', '__declspec', '__file__', '__init__', '__init__.py', '__main__', '__name__', '__unicode__', '_blank', '_cookie', '_default', '_files', '_get', '_id', '_name', '_post', '_request', '_server', '_session', '_tchar', '_tmain', 'a-z', 'a-z0-9', 'a-za-z', 'a-za-z0-9', 'a.id', 'a.length', 'aaa', 'aac', 'abandon', 'abandoned', 'abc', 'abc.com', 'abcd', 'abilities', 'ability', 'able', 'abort', 'aborted', 'aborting', 'about.', 'above.', 'abs', 'absence', 'absent', 'absolute', 'absolutely']\nstopwords are: 464205\n",
"name": "stdout"
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# helper function to calculate score\ndef calculate_score(cv, model):\n # fit the model\n model.fit(cv.transform(X_train), y_train)\n #predict the test set\n model_pred = model.predict_proba(cv.transform(X_test))\n # calculate the log loss\n return metrics.log_loss(y_test, model_pred[:, 1]), model_pred",
"execution_count": 132,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Evaluate Logreg and NB for this data and pick the winner\nml = [LogisticRegression(), MultinomialNB()]\n# ml = [MultinomialNB()]\nresults = [calculate_score(cv, x) for x in ml]\nresults",
"execution_count": 133,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "[(0.62316285511688785, array([[ 0.35072733, 0.64927267],\n [ 0.93336409, 0.06663591],\n [ 0.29216522, 0.70783478],\n ..., \n [ 0.99118097, 0.00881903],\n [ 0.03791873, 0.96208127],\n [ 0.6758225 , 0.3241775 ]])),\n (2.0321573062968925, array([[ 9.87772058e-01, 1.22279419e-02],\n [ 1.00000000e+00, 1.32165500e-11],\n [ 1.34182381e-01, 8.65817619e-01],\n ..., \n [ 1.00000000e+00, 3.35806988e-10],\n [ 9.54901831e-10, 9.99999999e-01],\n [ 9.83588649e-01, 1.64113510e-02]]))]"
},
"metadata": {},
"execution_count": 133
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Create pipelines\nI have created these different pipelines\n - (Get_text --> CV & Get_features) --> Logreg\n - (Get_text --> TF & Get_features) --> Logreg\n - (Get_text --> TF & Get_features) --> Knn \n - Get_features --> Logreg "
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#create pipelines\npipe_cv = make_pipeline(FunctionTransformer(ft_get_text_all, validate=False), cv)\npipe_tf = make_pipeline(FunctionTransformer(ft_get_text_all, validate=False), tf)\n# union_cv_ft = make_union(pipe_cv_2, FunctionTransformer(ft_get_features, validate=False))\nunion_cv_ft = make_union(pipe_cv, FunctionTransformer(ft_get_features, validate=False))\nunion_tf_ft = make_union(pipe_tf, FunctionTransformer(ft_get_features, validate=False))\nunion_cv_ft_nb = make_pipeline(union_cv_ft, nb)\nunion_tf_ft_nb = make_pipeline(union_tf_ft, nb)\nunion_cv_ft_knn = make_pipeline(union_cv_ft, knn)\nunion_cv_ft_logreg = make_pipeline(union_cv_ft, logreg)\nunion_tf_ft_logreg = make_pipeline(union_tf_ft, logreg)\nunion_cv_ft_lda = make_pipeline(union_cv_ft, lda)\n\nstandalone_ft_knn = make_pipeline(FunctionTransformer(ft_get_features, validate=False), knn) \nstandalone_ft_logreg = make_pipeline(FunctionTransformer(ft_get_features, validate=False), logreg) \nstandalone_cv_logreg = make_pipeline(pipe_cv, logreg) \nstandalone_tf_logreg = make_pipeline(pipe_tf, logreg) \nstandalone_cv_nb = make_pipeline(pipe_cv, nb) \nstandalone_tf_nb = make_pipeline(pipe_tf, nb) \nstandalone_cv_lda = make_pipeline(pipe_cv, lda) \n##Standalone knn \n#pipe line for knn\n# from sklearn.neighbors import KNeighborsClassifier \n# knn = KNeighborsClassifier(n_neighbors=100)",
"execution_count": 134,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "ft_cols = ['ReputationAtPostCreation', 'OwnerUndeletedAnswerCountAtPostTime', 'owner_age', 'len_Post', 'tag_count', 'wc_Post', \n 'wc_Title', 'smart_author', 'freq_author', 'weekend', 'posted_in_the_night', 'seasoned_authors', 'qn_count'] ",
"execution_count": 135,
"outputs": []
},
{
"metadata": {},
"cell_type": "raw",
"source": "## Try out feature selection later"
},
{
"metadata": {},
"cell_type": "raw",
"source": "from sklearn.feature_selection import SelectKBest, chi2\nX_new = SelectKBest(chi2, k=7).fit_transform(df[ft_cols], df['OpenStatus'])\nX_new.shape\nX_new.dtype.names"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Feature list"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "ft_cols = [ 'smart_author', 'freq_author', 'weekend', 'posted_in_the_night', 'seasoned_authors' ]\nextra_cols = ['ReputationAtPostCreation', 'OwnerUndeletedAnswerCountAtPostTime', 'owner_age', 'tag_count', 'wc_Post', 'wc_Title',\n 'qn_count', 'more_history', 'more_reps', 'more_tags', 'more_posts'] \nlen(ft_cols)",
"execution_count": 136,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "5"
},
"metadata": {},
"execution_count": 136
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "#### Feature engineering \nStart with basic features, add extra feature one at a time and keep looking at the score to make a decision to keep it or remove it"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "base_Score = cross_validation.cross_val_score(standalone_ft_logreg, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)[0]\nfor x in extra_cols:\n# print('testing feature: ', x)\n ft_cols.append(x)\n res = cross_validation.cross_val_score(standalone_ft_logreg, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)\n print('scores are: ', res)\n if res[0] > base_Score:\n print('feature improved score, adding: ', x)\n else:\n# print('feature did not improve score, removing it')\n ft_cols.remove(x)\nft_cols",
"execution_count": 137,
"outputs": [
{
"output_type": "stream",
"text": "scores are: [-0.65552302 -0.65418349]\nfeature improved score, adding: ReputationAtPostCreation\nscores are: [-0.65535369 -0.6540151 ]\nfeature improved score, adding: OwnerUndeletedAnswerCountAtPostTime\nscores are: [-0.65136671 -0.64981793]\nfeature improved score, adding: owner_age\nscores are: [-0.64631204 -0.64531099]\nfeature improved score, adding: tag_count\nscores are: [-0.64171825 -0.63983102]\nfeature improved score, adding: wc_Post\nscores are: [-0.64057469 -0.63956445]\nfeature improved score, adding: wc_Title\nscores are: [-0.64068241 -0.63993371]\nfeature improved score, adding: qn_count\nscores are: [-0.63898029 -0.6375153 ]\nfeature improved score, adding: more_history\nscores are: [-0.63849748 -0.63697206]\nfeature improved score, adding: more_reps\nscores are: [-0.63853454 -0.63935321]\nfeature improved score, adding: more_tags\nscores are: [-0.63842891 -0.6365 ]\nfeature improved score, adding: more_posts\n",
"name": "stdout"
},
{
"output_type": "execute_result",
"data": {
"text/plain": "['smart_author',\n 'freq_author',\n 'weekend',\n 'posted_in_the_night',\n 'seasoned_authors',\n 'ReputationAtPostCreation',\n 'OwnerUndeletedAnswerCountAtPostTime',\n 'owner_age',\n 'tag_count',\n 'wc_Post',\n 'wc_Title',\n 'qn_count',\n 'more_history',\n 'more_reps',\n 'more_tags',\n 'more_posts']"
},
"metadata": {},
"execution_count": 137
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": true
},
"cell_type": "code",
"source": "### Try out CV scores for different pipelines we created and pick the best by looking at the scores",
"execution_count": 138,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# get scores for cv + nb\ncross_validation.cross_val_score(union_cv_ft_nb, X=((df)), y=df['OpenStatus'], scoring='neg_log_loss', cv=5)",
"execution_count": 139,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "array([-15.44386128, -15.4065413 , -15.45383486, -15.40184179, -15.39544195])"
},
"metadata": {},
"execution_count": 139
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# get scores for cv + nb\ncross_validation.cross_val_score(union_cv_ft_nb, X=((df)), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)",
"execution_count": 140,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "array([-15.44005523, -15.40238521])"
},
"metadata": {},
"execution_count": 140
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Get scores for cv _ logreg\ncross_validation.cross_val_score(union_cv_ft_logreg, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)",
"execution_count": 141,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "array([-0.52021453, -0.52051107])"
},
"metadata": {},
"execution_count": 141
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# Get scores for tf _ logreg\ncross_validation.cross_val_score(union_tf_ft_logreg, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=5)\n",
"execution_count": 142,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "array([-0.47722675, -0.47911976, -0.47324098, -0.47876792, -0.49501057])"
},
"metadata": {},
"execution_count": 142
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "### Randomized grid to tune the parameters"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "from sklearn.grid_search import RandomizedSearchCV\nnew_param_grid = { \n 'featureunion__pipeline__tfidfvectorizer__token_pattern':[reg_pattern],\n 'featureunion__pipeline__tfidfvectorizer__min_df': list(range(0, 51, 10)),\n 'featureunion__pipeline__tfidfvectorizer__max_features': list(range(5000, 105001, 5000)),\n 'featureunion__pipeline__tfidfvectorizer__stop_words': ['english', None],\n 'featureunion__n_jobs': [-1],\n 'logisticregression__n_jobs': [-1],\n# 'multinomialnb__alpha': [0.1, 0.3, 0.6, 0.8, 1] #list(range(0, 1, 0.1))]\n }\nrand_grid = RandomizedSearchCV(union_tf_ft_logreg, new_param_grid, cv=5, n_iter=8, scoring='neg_log_loss')\nrand_grid.fit(df, df['OpenStatus'])\nrand_grid",
"execution_count": 143,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "RandomizedSearchCV(cv=5, error_score='raise',\n estimator=Pipeline(steps=[('featureunion', FeatureUnion(n_jobs=1,\n transformer_list=[('pipeline', Pipeline(steps=[('functiontransformer', FunctionTransformer(accept_sparse=False,\n func=<function ft_get_text_all at 0x7fdb198448c8>,\n inv_kw_args=None, inverse_func=None, kw_args=None, pass_y=False...ty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False))]),\n fit_params={}, iid=True, n_iter=8, n_jobs=1,\n param_distributions={'logisticregression__n_jobs': [-1], 'featureunion__pipeline__tfidfvectorizer__token_pattern': ['([^\\\\W\\\\d][\\\\w.-]{2,})'], 'featureunion__n_jobs': [-1], 'featureunion__pipeline__tfidfvectorizer__min_df': [0, 10, 20, 30, 40, 50], 'featureunion__pipeline__tfidfvectorizer__stop_word...40000, 45000, 50000, 55000, 60000, 65000, 70000, 75000, 80000, 85000, 90000, 95000, 100000, 105000]},\n pre_dispatch='2*n_jobs', random_state=None, refit=True,\n scoring='neg_log_loss', verbose=0)"
},
"metadata": {},
"execution_count": 143
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#print the best model\nrand_grid.best_score_, rand_grid.best_estimator_",
"execution_count": 144,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "(-0.47570733351921424, Pipeline(steps=[('featureunion', FeatureUnion(n_jobs=-1,\n transformer_list=[('pipeline', Pipeline(steps=[('functiontransformer', FunctionTransformer(accept_sparse=False,\n func=<function ft_get_text_all at 0x7fdb198448c8>,\n inv_kw_args=None, inverse_func=None, kw_args=None, pass_y=Fals...ty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False))]))"
},
"metadata": {},
"execution_count": 144
}
]
},
{
"metadata": {
"trusted": true,
"collapsed": false
},
"cell_type": "code",
"source": "rand_grid.best_params_",
"execution_count": 149,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "{'featureunion__n_jobs': -1,\n 'featureunion__pipeline__tfidfvectorizer__max_features': 45000,\n 'featureunion__pipeline__tfidfvectorizer__min_df': 0,\n 'featureunion__pipeline__tfidfvectorizer__stop_words': None,\n 'featureunion__pipeline__tfidfvectorizer__token_pattern': '([^\\\\W\\\\d][\\\\w.-]{2,})',\n 'logisticregression__n_jobs': -1}"
},
"metadata": {},
"execution_count": 149
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "# rand_grid_.best_score_\n# rand_grid.fit(df, df['OpenStatus'])\npred2 = rand_grid.predict_proba(make_features(test_df))",
"execution_count": 145,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "pd.DataFrame({'id': test_df.index, 'OpenStatus': pred2[:, 1]}).set_index('id').to_csv('../data/kaggle_stackoverflow_tf_logreg3.csv')",
"execution_count": 146,
"outputs": []
},
{
"metadata": {},
"cell_type": "raw",
"source": "------ End of Model -------\nBelow, code is for manual ensembling"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "## Manual Ensembling"
},
{
"metadata": {},
"cell_type": "raw",
"source": "# Get scores for knn alone \ncross_validation.cross_val_score(standalone_ft_knn, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)\n\n# Get scores for cv _ nb alone - no features added \ncross_validation.cross_val_score(standalone_cv_nb, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)\n\n# Get scores for tf _ nb \ncross_validation.cross_val_score(standalone_tf_nb, X=(df), y=df['OpenStatus'], scoring='neg_log_loss', cv=2)"
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "union_cv_ft_logreg.steps",
"execution_count": 147,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": "[('featureunion', FeatureUnion(n_jobs=1,\n transformer_list=[('pipeline', Pipeline(steps=[('functiontransformer', FunctionTransformer(accept_sparse=False,\n func=<function ft_get_text_all at 0x7fdb198448c8>,\n inv_kw_args=None, inverse_func=None, kw_args=None, pass_y=False,\n validate=False)), ('countvectorizer', Coun... inv_kw_args=None, inverse_func=None, kw_args=None, pass_y=False,\n validate=False))],\n transformer_weights=None)),\n ('logisticregression',\n LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False))]"
},
"metadata": {},
"execution_count": 147
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "#for manual ensemble model\ndef avg_predictions(m1, m2, test_df):\n df_ = make_features(test_df)\n pred1 = m1.predict_proba(df_)\n pred2 = rand_grid_.predict_proba(df_)\n return (pred1 + pred2)/2\n \n \navg_pred = avg_predictions(rand_grid, rand_grid_, test_df)\nrand_grid.best_score_, rand_grid_.best_score_\n# pred_rand = rand_grid_nb.predict_proba((make_features(test_df)))\n# test_df.columns",
"execution_count": 148,
"outputs": [
{
"output_type": "error",
"ename": "NameError",
"evalue": "name 'rand_grid_' is not defined",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[1;32m<ipython-input-148-83b91ab30592>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0mavg_pred\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mavg_predictions\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrand_grid\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrand_grid_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtest_df\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 10\u001b[0m \u001b[0mrand_grid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mrand_grid_\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest_score_\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 11\u001b[0m \u001b[1;31m# pred_rand = rand_grid_nb.predict_proba((make_features(test_df)))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
"\u001b[1;31mNameError\u001b[0m: name 'rand_grid_' is not defined"
]
}
]
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "avg_pred[:, 1].shape",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "code",
"source": "cv",
"execution_count": null,
"outputs": []
},
{
"metadata": {
"collapsed": false,
"trusted": true
},
"cell_type": "raw",
"source": "#check if we have the tags captured as features\nlen([x for x in cv.stop_words_ if x.endswith('tag')])\n\nft_list = set(cv.get_feature_names())\n\nft_set = set() \n# tag_set = df[tag_cols].apply(lambda x: ft_set.update(set(x)))\ntag_set = df[tag_cols].apply(lambda x: (set(x)))\n\n[ft_set.update(x) for x in tag_set.values]\n\nlen(ft_list.intersection(tags_set))"
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3",
"language": "python"
},
"language_info": {
"nbconvert_exporter": "python",
"version": "3.5.1",
"name": "python",
"file_extension": ".py",
"codemirror_mode": {
"version": 3,
"name": "ipython"
},
"mimetype": "text/x-python",
"pygments_lexer": "ipython3"
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