Add model definitions

master
Bradlee Speice 2016-11-30 17:42:06 -05:00
parent abaefe7e3c
commit f70a10e6bd
1 changed files with 284 additions and 245 deletions

View File

@ -22,121 +22,21 @@
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"root\n",
" |-- activity: string (nullable = true)\n",
" |-- basket_amount: long (nullable = true)\n",
" |-- bonus_credit_eligibility: boolean (nullable = true)\n",
" |-- borrowers: array (nullable = true)\n",
" | |-- element: struct (containsNull = true)\n",
" | | |-- first_name: string (nullable = true)\n",
" | | |-- gender: string (nullable = true)\n",
" | | |-- last_name: string (nullable = true)\n",
" | | |-- pictured: boolean (nullable = true)\n",
" |-- currency_exchange_loss_amount: double (nullable = true)\n",
" |-- delinquent: boolean (nullable = true)\n",
" |-- description: struct (nullable = true)\n",
" | |-- languages: array (nullable = true)\n",
" | | |-- element: string (containsNull = true)\n",
" | |-- texts: struct (nullable = true)\n",
" | | |-- ar: string (nullable = true)\n",
" | | |-- en: string (nullable = true)\n",
" | | |-- es: string (nullable = true)\n",
" | | |-- fr: string (nullable = true)\n",
" | | |-- id: string (nullable = true)\n",
" | | |-- mn: string (nullable = true)\n",
" | | |-- pt: string (nullable = true)\n",
" | | |-- ru: string (nullable = true)\n",
" | | |-- vi: string (nullable = true)\n",
" |-- funded_amount: long (nullable = true)\n",
" |-- funded_date: string (nullable = true)\n",
" |-- id: long (nullable = true)\n",
" |-- image: struct (nullable = true)\n",
" | |-- id: long (nullable = true)\n",
" | |-- template_id: long (nullable = true)\n",
" |-- journal_totals: struct (nullable = true)\n",
" | |-- bulkEntries: long (nullable = true)\n",
" | |-- entries: long (nullable = true)\n",
" |-- lender_count: long (nullable = true)\n",
" |-- loan_amount: long (nullable = true)\n",
" |-- location: struct (nullable = true)\n",
" | |-- country: string (nullable = true)\n",
" | |-- country_code: string (nullable = true)\n",
" | |-- geo: struct (nullable = true)\n",
" | | |-- level: string (nullable = true)\n",
" | | |-- pairs: string (nullable = true)\n",
" | | |-- type: string (nullable = true)\n",
" | |-- town: string (nullable = true)\n",
" |-- name: string (nullable = true)\n",
" |-- paid_amount: double (nullable = true)\n",
" |-- paid_date: string (nullable = true)\n",
" |-- partner_id: long (nullable = true)\n",
" |-- payments: array (nullable = true)\n",
" | |-- element: struct (containsNull = true)\n",
" | | |-- amount: double (nullable = true)\n",
" | | |-- currency_exchange_loss_amount: double (nullable = true)\n",
" | | |-- local_amount: double (nullable = true)\n",
" | | |-- payment_id: long (nullable = true)\n",
" | | |-- processed_date: string (nullable = true)\n",
" | | |-- rounded_local_amount: double (nullable = true)\n",
" | | |-- settlement_date: string (nullable = true)\n",
" |-- planned_expiration_date: string (nullable = true)\n",
" |-- posted_date: string (nullable = true)\n",
" |-- sector: string (nullable = true)\n",
" |-- status: string (nullable = true)\n",
" |-- tags: array (nullable = true)\n",
" | |-- element: struct (containsNull = true)\n",
" | | |-- name: string (nullable = true)\n",
" |-- terms: struct (nullable = true)\n",
" | |-- disbursal_amount: double (nullable = true)\n",
" | |-- disbursal_currency: string (nullable = true)\n",
" | |-- disbursal_date: string (nullable = true)\n",
" | |-- loan_amount: long (nullable = true)\n",
" | |-- local_payments: array (nullable = true)\n",
" | | |-- element: struct (containsNull = true)\n",
" | | | |-- amount: double (nullable = true)\n",
" | | | |-- due_date: string (nullable = true)\n",
" | |-- loss_liability: struct (nullable = true)\n",
" | | |-- currency_exchange: string (nullable = true)\n",
" | | |-- currency_exchange_coverage_rate: double (nullable = true)\n",
" | | |-- nonpayment: string (nullable = true)\n",
" | |-- repayment_interval: string (nullable = true)\n",
" | |-- repayment_term: long (nullable = true)\n",
" | |-- scheduled_payments: array (nullable = true)\n",
" | | |-- element: struct (containsNull = true)\n",
" | | | |-- amount: double (nullable = true)\n",
" | | | |-- due_date: string (nullable = true)\n",
" |-- themes: array (nullable = true)\n",
" | |-- element: string (containsNull = true)\n",
" |-- translator: struct (nullable = true)\n",
" | |-- byline: string (nullable = true)\n",
" | |-- image: long (nullable = true)\n",
" |-- use: string (nullable = true)\n",
" |-- video: struct (nullable = true)\n",
" | |-- id: long (nullable = true)\n",
" | |-- thumbnailImageId: long (nullable = true)\n",
" | |-- title: string (nullable = true)\n",
" | |-- youtubeId: string (nullable = true)\n",
"\n"
]
}
],
"cell_type": "markdown",
"metadata": {},
"source": [
"loans.printSchema()"
"# Custom Functions\n",
"\n",
"## Gender Ratio\n",
"\n",
"0 = All female\n",
"\n",
"1 = All male"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 2,
"metadata": {
"collapsed": false
},
@ -144,7 +44,7 @@
"source": [
"import pyspark\n",
"\n",
"def male_proportion(array):\n",
"def gender_ratio(array):\n",
" num_males = 0\n",
" for item in array:\n",
" if item.gender == 'M':\n",
@ -152,55 +52,9 @@
" \n",
" return float(num_males) / len(array)\n",
"\n",
"sparkSql.udf.register('male_proportion',\n",
" male_proportion,\n",
" pyspark.sql.types.FloatType())\n",
"\n",
"train, validation, test = loans.randomSplit([.6, .2, .2], 101)\n",
"\n",
"query = '''\n",
"SELECT\n",
" id,\n",
" activity,\n",
" size(borrowers) as num_borrowers,\n",
" male_proportion(borrowers) as male_proportion,\n",
" lender_count,\n",
" location.country,\n",
" location.country_code,\n",
" partner_id,\n",
" sector,\n",
" tags,\n",
" DATEDIFF(terms.disbursal_date, planned_expiration_date) as loan_length,\n",
" terms.disbursal_amount,\n",
" terms.disbursal_currency,\n",
" terms.disbursal_date,\n",
" size(terms.scheduled_payments) as num_repayments,\n",
" terms.repayment_interval,\n",
" CASE WHEN\n",
" (status = 'refunded') OR\n",
" (status = 'defaulted') OR\n",
" (status = 'deleted') OR\n",
" (status = 'issue') OR\n",
" (status = 'inactive_expired') OR\n",
" (status = 'expired') OR\n",
" (status = 'inactive') OR\n",
" (delinquent = True) THEN 1 ELSE 0 END AS bad_loan,\n",
" gdp(location.country_code, terms.disbursal_date) as gdp,\n",
" xchange_rate(location.country_code, terms.disbursal_date) as xchange_rate,\n",
" status,\n",
" delinquent\n",
" \n",
"FROM {}\n",
"WHERE\n",
" status != 'fundraising' AND\n",
" status != 'funded'\n",
"'''\n",
"\n",
"train.registerTempTable('loans_train')\n",
"validation.registerTempTable('loans_validation')\n",
"test.registerTempTable('loans_test')\n",
"\n",
"sparkSql.sql(query.format('loans_validation')).write.json('validation_data-filtered.json')"
"sparkSql.udf.register('gender_ratio',\n",
" gender_ratio,\n",
" pyspark.sql.types.FloatType())"
]
},
{
@ -214,7 +68,7 @@
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
"collapsed": false
},
"outputs": [],
"source": [
@ -225,17 +79,8 @@
"\n",
"# Load country info data\n",
"country_codes_raw = pd.read_csv('economic-data/country-codes.csv')\n",
"country_gdp_raw = pd.read_csv('economic-data/country-gdp.csv')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"country_gdp_raw = pd.read_csv('economic-data/country-gdp.csv')\n",
"\n",
"# Clean country codes data\n",
"country_codes = country_codes_raw[['official_name_en', 'ISO3166-1-Alpha-2', \n",
" 'ISO3166-1-Alpha-3', 'ISO4217-currency_alphabetic_code']]\n",
@ -243,18 +88,9 @@
"# Clean gdp data\n",
"country_gdp = country_gdp_raw.drop(country_gdp_raw.columns[[0, 1]], axis=1)\n",
"country_gdp.columns = ['name', 'country_code_3', '2002', '2003', '2004', '2005', '2006',\n",
" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Merde gdp and code\n",
" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']\n",
"\n",
"# Merge gdp and code\n",
"country_gdp = pd.merge(country_gdp, country_codes, left_on='country_code_3', right_on='ISO3166-1-Alpha-3', how='left')\n",
"country_gdp.drop(['official_name_en', 'ISO3166-1-Alpha-3', 'country_code_3'], axis=1, inplace=True)\n",
"country_gdp = country_gdp.rename(columns = {'ISO3166-1-Alpha-2':'country_code',\n",
@ -265,17 +101,8 @@
"cols = list(country_gdp.columns)\n",
"cols.insert(1, cols.pop(cols.index('country_code')))\n",
"cols.insert(2, cols.pop(cols.index('currency_code')))\n",
"country_gdp = country_gdp.reindex(columns= cols)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"country_gdp = country_gdp.reindex(columns= cols)\n",
"\n",
"def gdp(country_code, disbursal_date):\n",
" def historical_gdp(array):\n",
" array = np.array(map(float, array))\n",
@ -316,37 +143,18 @@
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"currencies_raw = pd.read_csv('economic-data/currencies.csv')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"currencies_raw = pd.read_csv('economic-data/currencies.csv')\n",
"# Cleanup\n",
"currencies = currencies_raw.drop(country_gdp_raw.columns[[0, 1]], axis=1)\n",
"currencies.columns = ['country_name', 'country_code_3', '2002', '2003', '2004', '2005', '2006',\n",
" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']\n",
"\n",
"# Get ISO 2 code\n",
"currencies = pd.merge(currencies, country_codes, left_on='country_code_3', right_on='ISO3166-1-Alpha-3', how='left')\n",
"currencies.drop(['official_name_en', 'ISO3166-1-Alpha-3', 'country_code_3'], axis=1, inplace=True)\n",
@ -362,17 +170,8 @@
"cols = list(currencies.columns)\n",
"cols.insert(1, cols.pop(cols.index('country_code')))\n",
"cols.insert(2, cols.pop(cols.index('currency_code')))\n",
"currencies = currencies.reindex(columns=cols)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"currencies = currencies.reindex(columns=cols)\n",
"\n",
"def xchange_rate(country_code, disbursal_date):\n",
" def historical_rates(array):\n",
" array = np.array(map(float, array))\n",
@ -420,9 +219,161 @@
"sparkSql.udf.register('xchange_rate', xchange_rate, pyspark.sql.types.FloatType())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fetch actual data\n",
"\n",
"Get all data that we are going to use, get dummies, then split into train/validation/test."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Query our datasets to train on."
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"query = '''\n",
"SELECT\n",
" id,\n",
" activity,\n",
" size(borrowers) as num_borrowers,\n",
" gender_ratio(borrowers) as gender_ratio,\n",
" lender_count,\n",
" location.country,\n",
" location.country_code,\n",
" partner_id,\n",
" sector,\n",
" tags,\n",
" DATEDIFF(terms.disbursal_date, planned_expiration_date) as loan_length,\n",
" terms.disbursal_amount,\n",
" terms.disbursal_currency,\n",
" terms.disbursal_date,\n",
" size(terms.scheduled_payments) as num_repayments,\n",
" terms.repayment_interval,\n",
" CASE WHEN\n",
" (status = 'refunded') OR\n",
" (status = 'defaulted') OR\n",
" (status = 'deleted') OR\n",
" (status = 'issue') OR\n",
" (status = 'inactive_expired') OR\n",
" (status = 'expired') OR\n",
" (status = 'inactive') OR\n",
" (delinquent = True) THEN 1 ELSE 0 END AS bad_loan,\n",
" gdp(location.country_code, terms.disbursal_date) as gdp,\n",
" xchange_rate(location.country_code, terms.disbursal_date) as xchange_rate,\n",
" status,\n",
" delinquent\n",
" \n",
"FROM loans\n",
"WHERE\n",
" status != 'fundraising' AND\n",
" status != 'funded'\n",
"'''\n",
"\n",
"dataset = sparkSql.sql(query).toPandas()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Splits"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"X_columns = [\n",
" 'activity', 'num_borrowers', 'gender_ratio',\n",
" 'lender_count', 'country', 'partner_id', 'sector',\n",
" 'loan_length', 'disbursal_amount', 'disbursal_currency',\n",
" 'num_repayments', 'repayment_interval', 'gdp', 'xchange_rate'\n",
"]\n",
"\n",
"y_column = ['bad_loan']\n",
"\n",
"dummy_set = pd.get_dummies(dataset[X_columns + y_column])\n",
"dummy_set.to_csv('processed_dummy.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can restart the kernel to clear memory, and start processing."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"processed_dummy = pd.read_csv('processed_dummy.csv', index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"train, validate, test = np.split(processed_dummy.sample(frac=1, random_state=0),\n",
" [int(.6*len(processed_dummy)),\n",
" int(.8*len(processed_dummy))])\n",
"\n",
"train.to_csv('processed_train.csv')\n",
"validate.to_csv('processed_validate.csv')\n",
"test.to_csv('processed_test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testing all the models"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pandas as pd\n",
"train = pd.read_csv('processed_train.csv', index_col=0).dropna(axis=1)\n",
"valid = pd.read_csv('processed_validate.csv', index_col=0).dropna(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
@ -430,26 +381,82 @@
{
"data": {
"text/plain": [
"[Row(id=507280, activity=u'Agriculture', num_borrowers=10, male_proportion=0.10000000149011612, lender_count=91, country=u'Rwanda', country_code=u'RW', partner_id=170, sector=u'Agriculture', tags=[], loan_length=-59, disbursal_amount=1500000.0, disbursal_currency=u'RWF', disbursal_date=u'2012-11-15T08:00:00Z', num_repayments=1, repayment_interval=u'At end of term', bad_loan=0, gdp=667.4146118164062, xchange_rate=614.295166015625, status=u'paid', delinquent=None),\n",
" Row(id=508860, activity=u'Agriculture', num_borrowers=1, male_proportion=1.0, lender_count=28, country=u'Rwanda', country_code=u'RW', partner_id=170, sector=u'Agriculture', tags=[], loan_length=-52, disbursal_amount=500000.0, disbursal_currency=u'RWF', disbursal_date=u'2012-11-26T08:00:00Z', num_repayments=1, repayment_interval=u'At end of term', bad_loan=0, gdp=667.4146118164062, xchange_rate=614.295166015625, status=u'paid', delinquent=None),\n",
" Row(id=498729, activity=u'Agriculture', num_borrowers=1, male_proportion=0.0, lender_count=6, country=u'Kenya', country_code=u'KE', partner_id=133, sector=u'Agriculture', tags=[], loan_length=-38, disbursal_amount=20000.0, disbursal_currency=u'KES', disbursal_date=u'2012-11-13T08:00:00Z', num_repayments=12, repayment_interval=u'Monthly', bad_loan=0, gdp=1184.9232177734375, xchange_rate=84.52960205078125, status=u'paid', delinquent=None),\n",
" Row(id=501877, activity=u'Agriculture', num_borrowers=1, male_proportion=1.0, lender_count=14, country=u'Peru', country_code=u'PE', partner_id=71, sector=u'Agriculture', tags=[], loan_length=-39, disbursal_amount=1000.0, disbursal_currency=u'PEN', disbursal_date=u'2012-11-20T08:00:00Z', num_repayments=8, repayment_interval=u'Monthly', bad_loan=0, gdp=6389.63037109375, xchange_rate=2.6375863552093506, status=u'paid', delinquent=None),\n",
" Row(id=504386, activity=u'Agriculture', num_borrowers=1, male_proportion=1.0, lender_count=16, country=u'Benin', country_code=u'BJ', partner_id=104, sector=u'Agriculture', tags=[], loan_length=-58, disbursal_amount=190000.0, disbursal_currency=u'XOF', disbursal_date=u'2012-11-08T08:00:00Z', num_repayments=4, repayment_interval=u'Irregularly', bad_loan=0, gdp=807.6884765625, xchange_rate=510.5271301269531, status=u'paid', delinquent=None),\n",
" Row(id=510144, activity=u'Agriculture', num_borrowers=1, male_proportion=1.0, lender_count=7, country=u'Senegal', country_code=u'SN', partner_id=108, sector=u'Agriculture', tags=[], loan_length=-53, disbursal_amount=150000.0, disbursal_currency=u'XOF', disbursal_date=u'2012-11-27T08:00:00Z', num_repayments=12, repayment_interval=u'Monthly', bad_loan=0, gdp=1019.272216796875, xchange_rate=510.5271301269531, status=u'paid', delinquent=None),\n",
" Row(id=497262, activity=u'Agriculture', num_borrowers=1, male_proportion=0.0, lender_count=11, country=u'Nicaragua', country_code=u'NI', partner_id=74, sector=u'Agriculture', tags=[], loan_length=-35, disbursal_amount=7000.0, disbursal_currency=u'NIO', disbursal_date=u'2012-11-14T08:00:00Z', num_repayments=1, repayment_interval=u'At end of term', bad_loan=0, gdp=1776.209228515625, xchange_rate=23.546663284301758, status=u'paid', delinquent=None),\n",
" Row(id=503327, activity=u'Agriculture', num_borrowers=1, male_proportion=0.0, lender_count=7, country=u'Mexico', country_code=u'MX', partner_id=224, sector=u'Agriculture', tags=[], loan_length=-7, disbursal_amount=3000.0, disbursal_currency=u'MXN', disbursal_date=u'2012-12-28T08:00:00Z', num_repayments=1, repayment_interval=u'At end of term', bad_loan=0, gdp=9720.5615234375, xchange_rate=13.169458389282227, status=u'paid', delinquent=None),\n",
" Row(id=500119, activity=u'Agriculture', num_borrowers=1, male_proportion=0.0, lender_count=30, country=u'Mexico', country_code=u'MX', partner_id=224, sector=u'Agriculture', tags=[], loan_length=6, disbursal_amount=12000.0, disbursal_currency=u'MXN', disbursal_date=u'2012-12-28T08:00:00Z', num_repayments=1, repayment_interval=u'At end of term', bad_loan=0, gdp=9720.5615234375, xchange_rate=13.169458389282227, status=u'paid', delinquent=None),\n",
" Row(id=153403, activity=u'Agriculture', num_borrowers=1, male_proportion=0.0, lender_count=37, country=u'Togo', country_code=u'TG', partner_id=22, sector=u'Agriculture', tags=[], loan_length=None, disbursal_amount=450000.0, disbursal_currency=u'XOF', disbursal_date=u'2009-10-26T07:00:00Z', num_repayments=14, repayment_interval=u'Irregularly', bad_loan=1, gdp=508.54052734375, xchange_rate=472.186279296875, status=u'defaulted', delinquent=True)]"
"342"
]
},
"execution_count": 13,
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sparkSql.sql(query.format('loans_validation')).take(10)\n",
"sparkSql.sql(query.format('loans_validation')).write.json('validation_data-filtered.json')"
"len(train.columns)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Naive guess:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"0.89836166750827584"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_x = train.drop('bad_loan', axis=1)\n",
"train_y = train['bad_loan']\n",
"valid_x = valid.drop('bad_loan', axis=1)\n",
"valid_y = valid['bad_loan']\n",
"\n",
"1 - train_y.mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"SVM"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from itertools import product\n",
"import pickle\n",
"from sklearn.svm import SVC\n",
"\n",
"svc_params = product([1, .5, 1.5], [.001, .01, .1])\n",
"\n",
"for C, gamma in svc_params:\n",
" svc = SVC(C=C, gamma=gamma)\n",
"\n",
" svc.fit(train_x, train_y)\n",
" with open('svc_{}_{}.pickle'.format(C, gamma), 'w') as handle:\n",
" pickle.dump(svc, handle)\n",
" \n",
" print(\"C: {}; gamma: {}; score: {}\".format(\n",
" C, gamma, svc.score(train_x, train_y)))"
]
},
{
@ -459,7 +466,39 @@
"collapsed": true
},
"outputs": [],
"source": []
"source": [
"from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
"\n",
"# Number of columns is 342\n",
"for n_components in [342, 250, 150, 75]\n",
" lda = LinearDiscriminantAnalysis(n_components=n_components)\n",
" lda.fit(train_x, train_y)\n",
" with open('lda_{}.pickle'.format(n_components), 'w') as handle:\n",
" pickle.dump(lda, handle)\n",
" \n",
" print(\"N_components: {}; score: {}\".format(\n",
" n_components, lda.score(valid_x, valid_y)))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"\n",
"for n_estimators in [10, 50, 75, 100]:\n",
" rf = RandomForestClassifier(n_estimators=n_estimators)\n",
" rf.fit(train_x, train_y)\n",
" with open('rf_{}.pickle'.format(n_estimators), 'w') as handle:\n",
" pickle.dump(rf, handle)\n",
" \n",
" print(\"N_estimators: {}; score: {}\".format(\n",
" n_estimators, score(valid_x, valid_y)))"
]
}
],
"metadata": {
@ -478,7 +517,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.10"
"version": "2.7.12"
}
},
"nbformat": 4,