def write_whole_set():
# p_df = loader.load_profile_info()
# print "profile information is loaded"
#
# user_feature_df = loader.read_csv_dataset(profile_ids=list(p_df.index))
user_feature_df = loader.read_csv_feature_set(param.FEATURE_SET)
print "user feature data set is loaded"
feat_num_org = len(user_feature_df)
user_feature_df = user_feature_df.dropna(how='all', axis=0)
user_feature_df = user_feature_df.dropna(how='all', axis=1)
print "some %s features are dropped." % (feat_num_org - len(user_feature_df))
# user_feature_df.reset_index(level=2, inplace=True)
# user_feature_df = user_feature_df[~user_feature_df['feature'].str.contains("DiscretVD")]
# user_feature_df = user_feature_df[~user_feature_df['feature'].str.contains("_h")]
# user_feature_df.set_index('feature', append=True, inplace=True)
# user_feature_df = user_feature_df.reorder_levels(['modality', 'field', 'feature', 'type'])
# user_feature_df.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.REDUCED_SUFFIX + ".csv")
# print "filling nan values.."
# dense_df = DataFrame(pc.fill_nan_features(user_feature_df),
# index=user_feature_df.index, columns=user_feature_df.columns)
# # dense_df = user_feature_df
# dense_df.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.REDUCED_SUFFIX + param.FILL_SUFFIX + ".csv")
# print "normalize.."
# norm_df = dense_df.T
# norm_df = (norm_df - norm_df.mean()) / norm_df.std()
# norm_df = norm_df.T
# norm_df = norm_df.replace(np.nan, 0)
# norm_df.to_csv(param.DATA_PATH + "/" + param.FEATURE_SET + param.FILL_SUFFIX + param.NORMALIZE_SUFFIX + ".csv")
print "zero-one scaling.."
scaled = user_feature_df.T
# scaled = dense_df.T
s_max, s_min = scaled.max(), scaled.min()
# id_to_set_v = (s_max != s_min).index
scaled = (scaled - s_min) / (s_max - s_min)
# scaled[id_to_set_v] -= s_min[id_to_set_v]
# scaled[id_to_set_v] /= (s_max - s_min)[id_to_set_v]
scaled = scaled.replace(np.nan, 0)
scaled = scaled.replace(np.inf, 0)
scaled = scaled.replace(-np.inf, 0)
scaled = scaled.T
# scaled.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.SCALING_SUFFIX + ".csv")
# scaled.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.REDUCED_SUFFIX + param.FILL_SUFFIX + param.SCALING_SUFFIX + ".csv")
# scaled.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.REDUCED_SUFFIX + param.SCALING_SUFFIX + ".csv")
scaled.to_csv(param.FEATURE_SET_PATH + "/" + param.FEATURE_SET + param.SCALING_SUFFIX + ".csv")
def test_feature_set_performance(target_label=info.LABEL_GEN, base_fset=param.FEATURE_SET_ORIGINAL,
reduced=False, fillna=True, scaling=True,
feat_sel=False, feat_num=None, with_pred=False, clf_name='LR', reg_param=1.0):
p_df = loader.load_profile_info()
print "profile information is loaded"
feature_set_name = base_fset
feature_set_name += param.REDUCED_SUFFIX if reduced else ""
feature_set_name += param.FILL_SUFFIX if fillna else ""
feature_set_name += param.SCALING_SUFFIX if scaling else ""
print feature_set_name
user_feature_df = loader.read_csv_feature_set(feature_set_name)
user_feature_df.columns = map(lambda x: int(x), user_feature_df.columns)
# user_feature_df = loader.read_csv_feature_set(param.FEATURE_SET_EXTENSION_APP,
# fill_na=True, normalize=False)
print "user feature data set is loaded"
if with_pred:
pred_df = pd.read_csv(param.DATA_PATH + "/predictions/item_preds.csv", index_col='profile_id')
# pred_df = pd.read_csv(param.DATA_PATH + "/predictions/content_preds.csv", index_col='profile_id')
for col in pred_df.columns:
uls = list(pred_df[col].unique())
uls.sort()
pred_df.loc[:, col] = pred_df[col].apply(lambda x: uls.index(x))
p_df = p_df.loc[pred_df.index]
user_feature_df = user_feature_df[pred_df.index]
pred_df.columns = [[info.APP] * len(pred_df.columns), ['itemBased_prediction'] * len(pred_df.columns),
list(pred_df.columns), [feat.NOMINAL_VAL] * len(pred_df.columns)]
pred_df.columns.names = ['modality', 'field', 'feature', 'type']
user_feature_df = pd.concat([user_feature_df.T, pred_df], axis=1).T
# method_types = ["LR", "MI", "MI-min10", "FS", "FS-min10", "RF-100"]
method_type = "MI" if feat_sel else None
cv = 10
repeat_num = 20
nf = feat_num if feat_sel else None
if not os.path.isdir(param.EXPERIMENT_PATH):
os.makedirs(param.EXPERIMENT_PATH)
print "\nlabel, fillna, scaling, feat_sel, clf_name, reg_param, k-CV, ith-fold, featNum, accuracy"
for repeat in range(repeat_num):
temp_score = clf.classify(user_feature_df, p_df, feature_set_name, features=None, label=target_label,
reg_param=reg_param, selection=feat_sel, num_feat=nf,
sel_method=method_type, cv=cv)
def load_feature_set(self):
feature_set_name = self.get_feature_set_name()
user_feature_df = loader.read_csv_feature_set(feature_set_name)
user_feature_df.columns = map(lambda x: int(x), user_feature_df.columns)
return user_feature_df
