<s> import sys <EOL> import os <EOL> from latent_factor import * <EOL> from arffio import * <EOL> from common import * <EOL> import copy <EOL> import logging , Logger <EOL> import pickle <EOL> import numpy as np <EOL> import scipy . sparse as sp <EOL> import sampler <EOL> import random <EOL> import time <EOL> from common import * <EOL> np . random . seed ( <NUM_LIT:0> ) <EOL> random . seed ( <NUM_LIT:0> ) <EOL> def checkParamValid ( param ) : <EOL> if param [ "<STR_LIT>" ] == op . alternative_least_square : <EOL> if act . linear != param [ "<STR_LIT>" ] or act . linear != param [ "<STR_LIT>" ] : <EOL> print "<STR_LIT>" "<STR_LIT>" <EOL> return False ; <EOL> if m . internal_memory != param [ "<STR_LIT:m>" ] : <EOL> print "<STR_LIT>" <EOL> return False ; <EOL> if lo . negative_log_likelihood == param [ "<STR_LIT:l>" ] and act . sgmoid != param [ "<STR_LIT>" ] : <EOL> print "<STR_LIT>" <EOL> return False ; <EOL> if lo . least_square == param [ "<STR_LIT:l>" ] and act . linear != param [ "<STR_LIT>" ] : <EOL> print "<STR_LIT>" <EOL> return False ; <EOL> return True ; <EOL> def train_internal ( model , train_file , parameters ) : <EOL> batch = parameters [ "<STR_LIT:b>" ] <EOL> niter = parameters [ "<STR_LIT:i>" ] <EOL> sample = sampler . get_sampler ( parameters ) <EOL> logger = logging . getLogger ( Logger . project_name ) <EOL> logger . info ( "<STR_LIT>" ) <EOL> train_reader = SvmReader ( train_file ) <EOL> x , y = train_reader . full_read ( ) <EOL> num , _ = y . shape <EOL> logger . info ( "<STR_LIT>" ) <EOL> sample . update ( y ) <EOL> logger . info ( "<STR_LIT>" ) <EOL> start_time = time . time ( ) <EOL> if op . gradient == parameters [ "<STR_LIT>" ] : <EOL> for iter1 in xrange ( niter ) : <EOL> start = <NUM_LIT:0> <EOL> end = batch <EOL> while start < num : <EOL> if end > num : end = num <EOL> batch_x = x [ start : end , : ] <EOL> batch_y = y [ start : end , : ] <EOL> batch_i = sample . sample ( batch_y ) <EOL> model . grad_update ( batch_x , batch_y , batch_i ) <EOL> start += batch ; <EOL> end += batch ; <EOL> logger . info ( "<STR_LIT>" % ( iter1 + <NUM_LIT:1> ) ) ; <EOL> elif op . alternative_least_square == parameters [ "<STR_LIT>" ] : <EOL> model . b [ <NUM_LIT:0> ] = np . zeros ( model . b [ <NUM_LIT:0> ] . shape ) <EOL> model . lb = np . zeros ( model . lb . shape ) <EOL> for iter1 in xrange ( niter ) : <EOL> model . al_update ( x , y , None ) <EOL> logger . info ( "<STR_LIT>" % ( iter1 + <NUM_LIT:1> ) ) ; <EOL> else : <EOL> logger . error ( "<STR_LIT>" % paramters [ "<STR_LIT>" ] ) <EOL> total = <NUM_LIT:0> <EOL> start = <NUM_LIT:0> <EOL> end = batch <EOL> while start < num and total < <NUM_LIT:1000> : <EOL> if end > num : end = num <EOL> batch_x = x [ start : end , : ] <EOL> batch_y = y [ start : end , : ] <EOL> batch_p = model . ff ( batch_x ) <EOL> model . thrsel . update ( batch_p , batch_y ) <EOL> start += batch <EOL> end += batch <EOL> total += <NUM_LIT:1> <EOL> logger . info ( "<STR_LIT>" ) <EOL> end_time = time . time ( ) <EOL> logger . info ( "<STR_LIT>" % ( end_time - start_time ) ) <EOL> return model <EOL> def train_external ( model , train_file , parameters ) : <EOL> batch = parameters [ "<STR_LIT:b>" ] <EOL> niter = parameters [ "<STR_LIT:i>" ] <EOL> sample = sampler . get_sampler ( parameters ) <EOL> logger = logging . getLogger ( Logger . project_name ) <EOL> train_reader = SvmReader ( train_file , batch ) <EOL> has_next = True <EOL> while has_next : <EOL> x , y , has_next = train_reader . read ( ) <EOL> sample . update ( y ) <EOL> for iter1 in xrange ( niter ) : <EOL> train_reader = SvmReader ( train_file , batch ) <EOL> has_next = True <EOL> while has_next : <EOL> x , y , has_next = train_reader . read ( ) <EOL> idx = sample . sample ( y ) <EOL> model . grad_update ( x , y , idx ) <EOL> logger . info ( "<STR_LIT>" % ( iter1 + <NUM_LIT:1> ) ) ; <EOL> train_reader . close ( ) <EOL> train_reader = SvmReader ( train_file , batch ) <EOL> x , y , has_next = train_reader . read ( ) <EOL> while has_next : <EOL> p = model . ff ( x ) <EOL> model . thrsel . update ( p , y ) <EOL> x , y , has_next = train_reader . read ( ) <EOL> return model </s>
