<s> from sklearn . externals import joblib <EOL> from sklearn . grid_search import GridSearchCV <EOL> import os <EOL> import numpy as np <EOL> import pandas as pd <EOL> import xarray as xr <EOL> from . import build_model <EOL> from . import featurize <EOL> from . import time_series <EOL> from . import util <EOL> __all__ = [ '<STR_LIT>' , '<STR_LIT>' ] <EOL> def model_predictions ( featureset , model , return_probs = True ) : <EOL> """<STR_LIT>""" <EOL> feature_df = build_model . rectangularize_featureset ( featureset ) <EOL> if return_probs : <EOL> try : <EOL> preds = model . predict_proba ( feature_df ) <EOL> except AttributeError : <EOL> preds = model . predict ( feature_df ) <EOL> else : <EOL> preds = model . predict ( feature_df ) <EOL> if len ( preds . shape ) == <NUM_LIT:1> : <EOL> return pd . Series ( preds , index = feature_df . index , name = '<STR_LIT>' ) <EOL> else : <EOL> if isinstance ( model , GridSearchCV ) : <EOL> columns = model . best_estimator_ . classes_ <EOL> else : <EOL> columns = model . classes_ <EOL> return pd . DataFrame ( preds , index = feature_df . index , columns = columns ) <EOL> def predict_data_files ( ts_paths , features_to_use , model , <EOL> custom_features_script = None ) : <EOL> """<STR_LIT>""" <EOL> new_featureset = featurize . featurize_data_files ( ts_paths , <EOL> features_to_use = features_to_use , <EOL> custom_script_path = custom_features_script ) <EOL> preds_df = pd . DataFrame ( model_predictions ( new_featureset , model ) ) <EOL> results_dict = { } <EOL> feature_df = build_model . rectangularize_featureset ( new_featureset ) <EOL> all_time_series = { util . shorten_fname ( ts_path ) : <EOL> time_series . from_netcdf ( ts_path ) <EOL> for ts_path in ts_paths } <EOL> all_ts_data = { fname : np . vstack ( ( ts . time , ts . measurement , ts . error ) ) <EOL> for fname , ts in all_time_series . items ( ) } <EOL> results_dict = { fname : { "<STR_LIT>" : "<STR_LIT>" , <EOL> "<STR_LIT>" : all_ts_data [ fname ] , <EOL> "<STR_LIT>" : feature_df . loc [ fname ] . to_dict ( ) , <EOL> "<STR_LIT>" : ( list ( row . sort_values ( inplace = False , <EOL> ascending = False ) . iteritems ( ) ) <EOL> if len ( row ) > <NUM_LIT:1> else row ) } <EOL> for fname , row in preds_df . iterrows ( ) } <EOL> return results_dict </s>
