<s> """<STR_LIT>""" <EOL> from windml . util . distance import haversine <EOL> from windml . preprocessing . missing_data_finder import MissingDataFinder <EOL> from windml . preprocessing . override_missing import OverrideMissing <EOL> from sklearn . neighbors import KNeighborsRegressor <EOL> from sklearn . grid_search import GridSearchCV <EOL> from sklearn . cross_validation import KFold , cross_val_score <EOL> from sklearn . svm import SVR <EOL> from sklearn import linear_model <EOL> from numpy import zeros , int32 , float32 , nan , array <EOL> class MRegInterpolation ( object ) : <EOL> def multi_interpolate ( self , timeseries , args ) : <EOL> timestep = args [ '<STR_LIT>' ] <EOL> neighbor_series = args [ '<STR_LIT>' ] <EOL> reg = args [ '<STR_LIT>' ] <EOL> regargs = args [ '<STR_LIT>' ] <EOL> mdf = MissingDataFinder ( ) <EOL> order = [ ] <EOL> for i in xrange ( len ( neighbor_series ) ) : <EOL> misses = mdf . find ( neighbor_series [ i ] , timestep ) <EOL> missing = sum ( map ( lambda m : m [ <NUM_LIT:2> ] , misses ) ) <EOL> order . append ( ( i , missing - i ) ) <EOL> sorted ( order , key = lambda o : o [ <NUM_LIT:1> ] ) <EOL> merge_order = map ( lambda o : o [ <NUM_LIT:0> ] , order ) <EOL> data = neighbor_series <EOL> for i in xrange ( len ( data ) ) : <EOL> data [ i ] = OverrideMissing ( ) . override ( data [ i ] , timestep , - <NUM_LIT:1> ) <EOL> field = '<STR_LIT>' <EOL> merged = [ ] <EOL> for m in merge_order : <EOL> mseries = data [ m ] <EOL> regressors = { } <EOL> pattern_size = { } <EOL> useful = { } <EOL> repairable = { } <EOL> misses = [ ] <EOL> available_in_c = { } <EOL> cnt_patterns = { } <EOL> for i in xrange ( len ( mseries ) ) : <EOL> if ( mseries [ i ] [ field ] == - <NUM_LIT:1> ) : <EOL> if ( i not in useful . keys ( ) ) : <EOL> useful [ i ] = [ ] <EOL> for c in merge_order : <EOL> if ( c == m or c in merged ) : <EOL> continue <EOL> cseries = data [ c ] <EOL> cnt_patterns [ c ] = <NUM_LIT:0> <EOL> available_in_c [ c ] = [ ] <EOL> for i in xrange ( len ( mseries ) ) : <EOL> if ( mseries [ i ] [ field ] == - <NUM_LIT:1> and cseries [ i ] [ field ] != - <NUM_LIT:1> ) : <EOL> if ( i not in useful . keys ( ) ) : <EOL> useful [ i ] = [ ] <EOL> useful [ i ] . append ( c ) <EOL> continue <EOL> if ( mseries [ i ] [ field ] == - <NUM_LIT:1> or cseries [ i ] [ field ] == - <NUM_LIT:1> ) : <EOL> continue <EOL> available_in_c [ c ] . append ( i ) <EOL> cnt_patterns [ c ] += <NUM_LIT:1> <EOL> for missing , candidates in useful . iteritems ( ) : <EOL> if ( len ( candidates ) > <NUM_LIT:0> ) : <EOL> highest_ps = <NUM_LIT:0> <EOL> highest_candidate = None <EOL> for candidate in candidates : <EOL> if ( cnt_patterns [ candidate ] > highest_ps ) : <EOL> highest_ps = cnt_patterns [ candidate ] <EOL> highest_candidate = candidate <EOL> labels , patterns = [ ] , [ ] <EOL> for i in available_in_c [ highest_candidate ] : <EOL> labels . append ( mseries [ i ] [ field ] ) <EOL> pattern = [ ] <EOL> pattern . append ( data [ highest_candidate ] [ i ] [ field ] ) <EOL> for am in merged : <EOL> pattern . append ( data [ am ] [ i ] [ field ] ) <EOL> patterns . append ( pattern ) <EOL> if ( reg == '<STR_LIT>' ) : <EOL> regargs = args [ '<STR_LIT>' ] <EOL> neighbors = regargs [ '<STR_LIT:n>' ] <EOL> variant = regargs [ '<STR_LIT>' ] <EOL> regressor = KNeighborsRegressor ( neighbors , variant ) <EOL> reg = regressor . fit ( patterns , labels ) <EOL> pattern = [ ] <EOL> pattern . append ( data [ highest_candidate ] [ missing ] [ field ] ) <EOL> for am in merged : <EOL> pattern . append ( data [ am ] [ missing ] [ field ] ) <EOL> data [ m ] [ missing ] [ field ] = reg . predict ( pattern ) <EOL> else : <EOL> labels , patterns = [ ] , [ ] <EOL> for i in range ( len ( mseries ) ) : <EOL> if ( mseries [ i ] [ field ] == - <NUM_LIT:1> ) : <EOL> continue <EOL> labels . append ( mseries [ i ] [ field ] ) <EOL> pattern = [ ] <EOL> for am in merged : <EOL> pattern . append ( data [ am ] [ i ] [ field ] ) <EOL> patterns . append ( pattern ) <EOL> if ( reg == '<STR_LIT>' ) : <EOL> regargs = args [ '<STR_LIT>' ] <EOL> neighbors = regargs [ '<STR_LIT:n>' ] <EOL> variant = regargs [ '<STR_LIT>' ] <EOL> regressor = KNeighborsRegressor ( neighbors , variant ) <EOL> reg = regressor . fit ( patterns , labels ) <EOL> pattern = [ ] <EOL> for am in merged : <EOL> pattern . append ( data [ am ] [ missing ] [ field ] ) <EOL> data [ m ] [ missing ] [ field ] = reg . predict ( pattern ) <EOL> merged . append ( m ) <EOL> ovtimeseries = OverrideMissing ( ) . override ( timeseries , timestep , - <NUM_LIT:1> ) <EOL> labels , patterns = [ ] , [ ] <EOL> for i in xrange ( len ( timeseries ) ) : <EOL> if ( timeseries [ i ] [ field ] != - <NUM_LIT:1> ) : <EOL> labels . append ( ovtimeseries [ i ] [ field ] ) <EOL> pattern = [ ] <EOL> for series in data : <EOL> pattern . append ( series [ i ] [ field ] ) <EOL> patterns . append ( pattern ) <EOL> if ( reg == '<STR_LIT>' ) : <EOL> regargs = args [ '<STR_LIT>' ] <EOL> neighbors = regargs [ '<STR_LIT:n>' ] <EOL> variant = regargs [ '<STR_LIT>' ] <EOL> regressor = KNeighborsRegressor ( neighbors , variant ) <EOL> regressor . fit ( patterns , labels ) <EOL> for i in xrange ( len ( ovtimeseries ) ) : <EOL> if ( ovtimeseries [ i ] [ field ] == - <NUM_LIT:1> ) : <EOL> pattern = [ ] <EOL> for series in data : <EOL> pattern . append ( series [ i ] [ field ] ) <EOL> ovtimeseries [ i ] [ field ] = regressor . predict ( pattern ) <EOL> return ovtimeseries <EOL> def interpolate ( self , timeseries , ** args ) : <EOL> cs = '<STR_LIT>' <EOL> sp = '<STR_LIT>' <EOL> date = '<STR_LIT:date>' <EOL> fields = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> timestep = args [ '<STR_LIT>' ] <EOL> neighbor_series = args [ '<STR_LIT>' ] <EOL> reg = args [ '<STR_LIT>' ] <EOL> lnseries = len ( neighbor_series ) <EOL> for nseries in neighbor_series : <EOL> misses = MissingDataFinder ( ) . find ( nseries , timestep ) <EOL> if ( len ( misses ) > <NUM_LIT:0> ) : <EOL> return self . multi_interpolate ( timeseries , args ) <EOL> ovtimeseries = OverrideMissing ( ) . override ( timeseries , timestep , - <NUM_LIT:1> ) <EOL> for field in fields : <EOL> X , Y = [ ] , [ ] <EOL> for t in xrange ( len ( neighbor_series [ <NUM_LIT:0> ] ) ) : <EOL> if ( ovtimeseries [ t ] [ field ] != - <NUM_LIT:1> ) : <EOL> Y . append ( ovtimeseries [ t ] [ field ] ) <EOL> pattern = [ ] <EOL> for nseries in neighbor_series : <EOL> pattern . append ( nseries [ t ] [ field ] ) <EOL> X . append ( pattern ) <EOL> Xa , Ya = array ( X ) , array ( Y ) <EOL> if ( reg == '<STR_LIT>' ) : <EOL> regargs = args [ '<STR_LIT>' ] <EOL> variant = regargs [ '<STR_LIT>' ] <EOL> if ( '<STR_LIT>' in regargs . keys ( ) ) : <EOL> kfold = regargs [ '<STR_LIT>' ] <EOL> ncandidates = regargs [ '<STR_LIT:n>' ] <EOL> regressors = { } <EOL> best_n = ncandidates [ <NUM_LIT:0> ] <EOL> regressor = KNeighborsRegressor ( best_n , variant ) <EOL> regressors [ best_n ] = regressor <EOL> best_score = cross_val_score ( regressor , Xa , Ya , cv = kfold ) . mean ( ) <EOL> for n in ncandidates [ <NUM_LIT:1> : ] : <EOL> regressor = KNeighborsRegressor ( n , variant ) <EOL> regressors [ n ] = regressor <EOL> score = cross_val_score ( regressor , Xa , Ya , cv = kfold ) . mean ( ) <EOL> if ( score > best_score ) : <EOL> best_n = n <EOL> best_score = score <EOL> regressor = regressors [ best_n ] <EOL> else : <EOL> neighbors = regargs [ '<STR_LIT:n>' ] <EOL> regressor = KNeighborsRegressor ( neighbors , variant ) <EOL> elif ( reg == '<STR_LIT>' ) : <EOL> regressor = linear_model . LinearRegression ( ) <EOL> elif ( reg == '<STR_LIT>' ) : <EOL> regargs = args [ '<STR_LIT>' ] <EOL> if ( regargs [ '<STR_LIT>' ] == '<STR_LIT>' ) : <EOL> fold = regargs [ '<STR_LIT>' ] [ '<STR_LIT>' ] <EOL> pattern_count = Xa . shape [ <NUM_LIT:0> ] <EOL> cv_method = KFold ( pattern_count , fold ) <EOL> else : <EOL> raise Exception ( "<STR_LIT>" ) <EOL> kernel , epsilon , tuned_parameters = regargs [ '<STR_LIT>' ] , regargs [ '<STR_LIT>' ] , regargs [ '<STR_LIT>' ] <EOL> grid = GridSearchCV ( SVR ( kernel = kernel , epsilon = epsilon ) , param_grid = tuned_parameters , cv = cv_method , verbose = <NUM_LIT:0> ) <EOL> grid . fit ( Xa , Ya ) <EOL> regressor = SVR ( kernel = kernel , epsilon = <NUM_LIT:0.1> , C = grid . best_params_ [ '<STR_LIT:C>' ] , gamma = grid . best_params_ [ '<STR_LIT>' ] ) <EOL> if ( '<STR_LIT>' in args . keys ( ) ) : <EOL> args [ '<STR_LIT>' ] ( regressor ) <EOL> else : <EOL> raise Exception ( "<STR_LIT>" ) <EOL> regressor . fit ( Xa , Ya ) <EOL> for t in xrange ( len ( ovtimeseries ) ) : <EOL> if ( ovtimeseries [ t ] [ field ] == - <NUM_LIT:1> ) : <EOL> pattern = [ ] <EOL> for nseries in neighbor_series : <EOL> pattern . append ( nseries [ t ] [ field ] ) <EOL> y_hat = regressor . predict ( array ( pattern ) ) <EOL> if ( len ( y_hat . shape ) > <NUM_LIT:0> ) : <EOL> ovtimeseries [ t ] [ field ] = y_hat [ <NUM_LIT:0> ] <EOL> else : <EOL> ovtimeseries [ t ] [ field ] = y_hat <EOL> return ovtimeseries </s>
