mirror of
https://github.com/bspeice/kiva-dig
synced 2024-12-04 20:58:09 -05:00
Add model definitions
This commit is contained in:
Bradlee Speice
parent
abaefe7e3c
commit
f70a10e6bd
@ -21,130 +21,30 @@
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"loans_lenders.registerTempTable('loans_lenders')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Custom Functions\n",
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"\n",
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"## Gender Ratio\n",
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"\n",
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"0 = All female\n",
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"\n",
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"1 = All male"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"root\n",
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" |-- activity: string (nullable = true)\n",
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" |-- basket_amount: long (nullable = true)\n",
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" |-- bonus_credit_eligibility: boolean (nullable = true)\n",
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" |-- borrowers: array (nullable = true)\n",
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" | |-- element: struct (containsNull = true)\n",
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" | | |-- first_name: string (nullable = true)\n",
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" | | |-- gender: string (nullable = true)\n",
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" | | |-- last_name: string (nullable = true)\n",
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" | | |-- pictured: boolean (nullable = true)\n",
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" |-- currency_exchange_loss_amount: double (nullable = true)\n",
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" |-- delinquent: boolean (nullable = true)\n",
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" |-- description: struct (nullable = true)\n",
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" | |-- languages: array (nullable = true)\n",
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" | | |-- element: string (containsNull = true)\n",
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" | |-- texts: struct (nullable = true)\n",
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" | | |-- ar: string (nullable = true)\n",
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" | | |-- en: string (nullable = true)\n",
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" | | |-- es: string (nullable = true)\n",
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" | | |-- fr: string (nullable = true)\n",
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" | | |-- id: string (nullable = true)\n",
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" | | |-- mn: string (nullable = true)\n",
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" | | |-- pt: string (nullable = true)\n",
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" | | |-- ru: string (nullable = true)\n",
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" | | |-- vi: string (nullable = true)\n",
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" |-- funded_amount: long (nullable = true)\n",
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" |-- funded_date: string (nullable = true)\n",
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" |-- id: long (nullable = true)\n",
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" |-- image: struct (nullable = true)\n",
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" | |-- id: long (nullable = true)\n",
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" | |-- template_id: long (nullable = true)\n",
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" |-- journal_totals: struct (nullable = true)\n",
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" | |-- bulkEntries: long (nullable = true)\n",
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" | |-- entries: long (nullable = true)\n",
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" |-- lender_count: long (nullable = true)\n",
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" |-- loan_amount: long (nullable = true)\n",
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" |-- location: struct (nullable = true)\n",
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" | |-- country: string (nullable = true)\n",
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" | |-- country_code: string (nullable = true)\n",
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" | |-- geo: struct (nullable = true)\n",
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" | | |-- level: string (nullable = true)\n",
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" | | |-- pairs: string (nullable = true)\n",
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" | | |-- type: string (nullable = true)\n",
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" | |-- town: string (nullable = true)\n",
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" |-- name: string (nullable = true)\n",
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" |-- paid_amount: double (nullable = true)\n",
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" |-- paid_date: string (nullable = true)\n",
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" |-- partner_id: long (nullable = true)\n",
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" |-- payments: array (nullable = true)\n",
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" | |-- element: struct (containsNull = true)\n",
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" | | |-- amount: double (nullable = true)\n",
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" | | |-- currency_exchange_loss_amount: double (nullable = true)\n",
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" | | |-- local_amount: double (nullable = true)\n",
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" | | |-- payment_id: long (nullable = true)\n",
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" | | |-- processed_date: string (nullable = true)\n",
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" | | |-- rounded_local_amount: double (nullable = true)\n",
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" | | |-- settlement_date: string (nullable = true)\n",
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" |-- planned_expiration_date: string (nullable = true)\n",
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" |-- posted_date: string (nullable = true)\n",
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" |-- sector: string (nullable = true)\n",
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" |-- status: string (nullable = true)\n",
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" |-- tags: array (nullable = true)\n",
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" | |-- element: struct (containsNull = true)\n",
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" | | |-- name: string (nullable = true)\n",
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" |-- terms: struct (nullable = true)\n",
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" | |-- disbursal_amount: double (nullable = true)\n",
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" | |-- disbursal_currency: string (nullable = true)\n",
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" | |-- disbursal_date: string (nullable = true)\n",
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" | |-- loan_amount: long (nullable = true)\n",
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" | |-- local_payments: array (nullable = true)\n",
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" | | |-- element: struct (containsNull = true)\n",
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" | | | |-- amount: double (nullable = true)\n",
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" | | | |-- due_date: string (nullable = true)\n",
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" | |-- loss_liability: struct (nullable = true)\n",
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" | | |-- currency_exchange: string (nullable = true)\n",
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" | | |-- currency_exchange_coverage_rate: double (nullable = true)\n",
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" | | |-- nonpayment: string (nullable = true)\n",
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" | |-- repayment_interval: string (nullable = true)\n",
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" | |-- repayment_term: long (nullable = true)\n",
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" | |-- scheduled_payments: array (nullable = true)\n",
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" | | |-- element: struct (containsNull = true)\n",
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" | | | |-- amount: double (nullable = true)\n",
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" | | | |-- due_date: string (nullable = true)\n",
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" |-- themes: array (nullable = true)\n",
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" | |-- element: string (containsNull = true)\n",
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" |-- translator: struct (nullable = true)\n",
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" | |-- byline: string (nullable = true)\n",
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" | |-- image: long (nullable = true)\n",
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" |-- use: string (nullable = true)\n",
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" |-- video: struct (nullable = true)\n",
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" | |-- id: long (nullable = true)\n",
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" | |-- thumbnailImageId: long (nullable = true)\n",
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" | |-- title: string (nullable = true)\n",
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" | |-- youtubeId: string (nullable = true)\n",
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"\n"
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]
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}
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],
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"source": [
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"loans.printSchema()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"import pyspark\n",
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"\n",
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"def male_proportion(array):\n",
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"def gender_ratio(array):\n",
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" num_males = 0\n",
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" for item in array:\n",
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" if item.gender == 'M':\n",
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@ -152,55 +52,9 @@
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" \n",
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" return float(num_males) / len(array)\n",
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"\n",
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"sparkSql.udf.register('male_proportion',\n",
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" male_proportion,\n",
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" pyspark.sql.types.FloatType())\n",
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"\n",
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"train, validation, test = loans.randomSplit([.6, .2, .2], 101)\n",
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"\n",
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"query = '''\n",
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"SELECT\n",
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" id,\n",
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" activity,\n",
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" size(borrowers) as num_borrowers,\n",
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" male_proportion(borrowers) as male_proportion,\n",
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" lender_count,\n",
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" location.country,\n",
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" location.country_code,\n",
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" partner_id,\n",
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" sector,\n",
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" tags,\n",
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" DATEDIFF(terms.disbursal_date, planned_expiration_date) as loan_length,\n",
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" terms.disbursal_amount,\n",
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" terms.disbursal_currency,\n",
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" terms.disbursal_date,\n",
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" size(terms.scheduled_payments) as num_repayments,\n",
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" terms.repayment_interval,\n",
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" CASE WHEN\n",
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" (status = 'refunded') OR\n",
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" (status = 'defaulted') OR\n",
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" (status = 'deleted') OR\n",
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" (status = 'issue') OR\n",
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" (status = 'inactive_expired') OR\n",
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" (status = 'expired') OR\n",
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" (status = 'inactive') OR\n",
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" (delinquent = True) THEN 1 ELSE 0 END AS bad_loan,\n",
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" gdp(location.country_code, terms.disbursal_date) as gdp,\n",
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" xchange_rate(location.country_code, terms.disbursal_date) as xchange_rate,\n",
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" status,\n",
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" delinquent\n",
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" \n",
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"FROM {}\n",
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"WHERE\n",
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" status != 'fundraising' AND\n",
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" status != 'funded'\n",
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"'''\n",
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"\n",
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"train.registerTempTable('loans_train')\n",
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"validation.registerTempTable('loans_validation')\n",
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"test.registerTempTable('loans_test')\n",
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"\n",
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"sparkSql.sql(query.format('loans_validation')).write.json('validation_data-filtered.json')"
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"sparkSql.udf.register('gender_ratio',\n",
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" gender_ratio,\n",
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" pyspark.sql.types.FloatType())"
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]
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},
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{
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@ -214,7 +68,7 @@
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": true
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"collapsed": false
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},
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"outputs": [],
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"source": [
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@ -225,17 +79,8 @@
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"\n",
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"# Load country info data\n",
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"country_codes_raw = pd.read_csv('economic-data/country-codes.csv')\n",
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"country_gdp_raw = pd.read_csv('economic-data/country-gdp.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"country_gdp_raw = pd.read_csv('economic-data/country-gdp.csv')\n",
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"\n",
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"# Clean country codes data\n",
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"country_codes = country_codes_raw[['official_name_en', 'ISO3166-1-Alpha-2', \n",
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" 'ISO3166-1-Alpha-3', 'ISO4217-currency_alphabetic_code']]\n",
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@ -243,18 +88,9 @@
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"# Clean gdp data\n",
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"country_gdp = country_gdp_raw.drop(country_gdp_raw.columns[[0, 1]], axis=1)\n",
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"country_gdp.columns = ['name', 'country_code_3', '2002', '2003', '2004', '2005', '2006',\n",
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" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Merde gdp and code\n",
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" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']\n",
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"\n",
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"# Merge gdp and code\n",
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"country_gdp = pd.merge(country_gdp, country_codes, left_on='country_code_3', right_on='ISO3166-1-Alpha-3', how='left')\n",
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"country_gdp.drop(['official_name_en', 'ISO3166-1-Alpha-3', 'country_code_3'], axis=1, inplace=True)\n",
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"country_gdp = country_gdp.rename(columns = {'ISO3166-1-Alpha-2':'country_code',\n",
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@ -265,17 +101,8 @@
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"cols = list(country_gdp.columns)\n",
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"cols.insert(1, cols.pop(cols.index('country_code')))\n",
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"cols.insert(2, cols.pop(cols.index('currency_code')))\n",
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"country_gdp = country_gdp.reindex(columns= cols)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"country_gdp = country_gdp.reindex(columns= cols)\n",
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"\n",
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"def gdp(country_code, disbursal_date):\n",
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" def historical_gdp(array):\n",
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" array = np.array(map(float, array))\n",
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@ -316,37 +143,18 @@
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"currencies_raw = pd.read_csv('economic-data/currencies.csv')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"execution_count": 4,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"currencies_raw = pd.read_csv('economic-data/currencies.csv')\n",
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"# Cleanup\n",
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"currencies = currencies_raw.drop(country_gdp_raw.columns[[0, 1]], axis=1)\n",
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"currencies.columns = ['country_name', 'country_code_3', '2002', '2003', '2004', '2005', '2006',\n",
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" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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" '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016']\n",
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"\n",
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"# Get ISO 2 code\n",
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"currencies = pd.merge(currencies, country_codes, left_on='country_code_3', right_on='ISO3166-1-Alpha-3', how='left')\n",
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"currencies.drop(['official_name_en', 'ISO3166-1-Alpha-3', 'country_code_3'], axis=1, inplace=True)\n",
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@ -362,17 +170,8 @@
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"cols = list(currencies.columns)\n",
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"cols.insert(1, cols.pop(cols.index('country_code')))\n",
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"cols.insert(2, cols.pop(cols.index('currency_code')))\n",
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"currencies = currencies.reindex(columns=cols)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"currencies = currencies.reindex(columns=cols)\n",
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"\n",
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"def xchange_rate(country_code, disbursal_date):\n",
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" def historical_rates(array):\n",
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" array = np.array(map(float, array))\n",
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@ -420,9 +219,161 @@
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"sparkSql.udf.register('xchange_rate', xchange_rate, pyspark.sql.types.FloatType())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Fetch actual data\n",
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"\n",
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"Get all data that we are going to use, get dummies, then split into train/validation/test."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Query our datasets to train on."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 6,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"query = '''\n",
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"SELECT\n",
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" id,\n",
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" activity,\n",
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" size(borrowers) as num_borrowers,\n",
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" gender_ratio(borrowers) as gender_ratio,\n",
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" lender_count,\n",
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" location.country,\n",
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" location.country_code,\n",
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" partner_id,\n",
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" sector,\n",
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" tags,\n",
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" DATEDIFF(terms.disbursal_date, planned_expiration_date) as loan_length,\n",
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" terms.disbursal_amount,\n",
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" terms.disbursal_currency,\n",
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" terms.disbursal_date,\n",
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" size(terms.scheduled_payments) as num_repayments,\n",
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" terms.repayment_interval,\n",
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" CASE WHEN\n",
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" (status = 'refunded') OR\n",
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" (status = 'defaulted') OR\n",
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" (status = 'deleted') OR\n",
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" (status = 'issue') OR\n",
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" (status = 'inactive_expired') OR\n",
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" (status = 'expired') OR\n",
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" (status = 'inactive') OR\n",
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" (delinquent = True) THEN 1 ELSE 0 END AS bad_loan,\n",
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" gdp(location.country_code, terms.disbursal_date) as gdp,\n",
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" xchange_rate(location.country_code, terms.disbursal_date) as xchange_rate,\n",
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" status,\n",
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" delinquent\n",
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" \n",
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"FROM loans\n",
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"WHERE\n",
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" status != 'fundraising' AND\n",
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" status != 'funded'\n",
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"'''\n",
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"\n",
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"dataset = sparkSql.sql(query).toPandas()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Data Splits"
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]
|
||||
},
|
||||
{
|
||||
"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,
|
||||
|
||||
Loading…
Reference in New Issue
Block a user