[{"_1":2,"_168":-5,"_169":-5},"loaderData",{"_3":4,"_13":14,"_21":22,"_143":144},"root",{"_5":-5,"_6":-7,"_7":8,"_9":-7,"_10":11,"_12":-7},"currentUser","primaryEmail","siteVerification","6LeIxAcTAAAAAJcZVRqyHh71UMIEGNQ_MXjiZKhI","toast","enableDev",false,"incidentStatus","routes/_layout",{"_15":-5,"_16":17,"_18":17,"_19":11,"_20":17},"username","isAskGordonEnabled",true,"isUsingNewTemplate","showMinimalLanding","showHoneypotTrap","routes/_layout.r.$namespace.$reponame",{"_23":24,"_25":-5,"_26":11,"_27":11,"_28":29,"_30":31,"_32":-5,"_33":11,"_34":35,"_84":-5,"_85":86,"_134":-5,"_135":11,"_136":17,"_137":11,"_138":-5,"_139":11,"_140":-5,"_141":142},"canonicalUrl","https://hub.docker.com/r/aryandoustarsam/altility/tags","mcpServer","canSeeDockerfile","canSeeAutobuilds","namespace","aryandoustarsam","reponame","altility","cloudBuildSource","isAutobuildsAvailable","repo",{"_36":37,"_38":39,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":51,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":70,"_75":11,"_76":77,"_81":29,"_82":83},"categories",[],"content_types",[40],"image","date_registered","2021-11-23T07:37:56.209671Z","description","","is_private","last_updated","2022-01-08T22:16:28.723125Z","last_modified","2024-10-16T13:48:34.145251Z","media_types",[52],"application/vnd.docker.container.image.v1+json","name","pull_count",176,"repository_type","star_count",0,"status",1,"status_description","active","source","collaborator_count","full_description","Altility stands for 'actively learning utility'. It is a package that provides \nan active deep learning model for enhancing spatio-temporal predicitions. Originally,\nwe developed this to help electric utilities in the process of placing new smart \nmeters in space and collecting their data at different time. \n\n\n### Installation:\n```\npip install altility\n```\n\n\n### Docker:\nFor using altility within an Ubuntu docker container\n```\ndocker run -it aryandoustarsam/altility\n```\n\nFor using altility with Jupyter notebook inside a docker container\n```\ndocker run -it -p 3333:1111 -v ~/path_to_data/data:/data aryandoustarsam/altility:jupyter\n[inside running container]: jupyter notebook --ip 0.0.0.0 --port 1111 --no-browser --allow-root\n[in local machine browser]: localhost:3333 \n[in local machine browser, type token shown in terminal]\n```\n\n\n### Usage guide:\nAt the core of altility stands the class **altility.ADL_model**. It bundles properties\nand methods of the active deep learning (ADL) model that we want to train. Bellow\nis a list of all parameters, attributes, methods and generated results.\n\n\n\n \n \n \n \n \n \n \n \n \n

Parameters
\n name (='adl_model'): string\n	\n The name of active deep learning (ADL) model\n

\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Methods
\n initialize(y, x_t=None, x_s=None, x_st=None, kwargs):**\n	\n Initializes prediction model.\n
\n collect(x_t_cand=None, x_s_cand=None, x_st_cand=None, kwargs):**\n	\n Performs active learning.\n

\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Results
\n batch_index_list: list of integers\n	\n List of indices for most informative data points suggested to collect.\n
\n inf_score_list: list of floats\n	\n List of information scores for most informative data points suggested to \n collect.\n

\n\n\n### Datasets:\nThe package can be tested with a public dataset for making spatio-temporal predictions\nof electric load that we provide in our Github repository. To prepare the data\nfor usage with altility, use the **prep_load_forecasting_data()** function provided\nin load_forecasting.py with the following parameter and return values:\n\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n

Parameters
\n path_to_data (='data/public/electric load forecasting/'): string\n	\n The path to where data is stored. This is 'data/public/electric load forecasting/'\n in our original repository.\n
\n dataset_name (='profiles_100'): string\n	\n Choose between 'profiles_100' and 'profiles_400'. These are two distinct\n datasets containing load profiles from either 100 or 400 industrial, commercial,\n and residential buildings of different sizes, shapes, consumption and occupancy\n patterns in Switzerland.\n
\n label_type (='feature_scaled'): string\n	\n Decide which labels to consider. Choose from 'random_scaled' and 'feature_scaled'.\n
\n spatial_features (='histogram'): string\n	\n Decide how to treat aerial imagery. Choose one from 'average' and 'histogram'.\n
\n meteo_types : list\n	\n Decide which meteo data types to consider. Choose from 'air_density', \n 'cloud_cover', 'precipitation', 'radiation_surface', 'radiation_toa',\n 'snow_mass', 'snowfall', 'temperature' and 'wind_speed'. The default is a \n list of all meteorological conditions.\n
\n timestamp_data : list\n	\n Decide which time stamp information to consider. Choose from: '15min',\n 'hour', 'day', 'month' and 'year'.\n
\n time_encoding (='ORD'): string\n	\n Decide how to encode time stamp data. Choose one of 'ORD', 'ORD-1D' or 'OHE'\n
\n histo_bins (=100): int\n	\n Set the number of histogram bins that you want to use. Applied if parameter\n spatial_features = 'histogram'.\n
\n grey_scale (=False): bool\n	\n Decide whether you want to consider underlying RGB images in grey-scale.\n
\n profiles_per_year (=1): float\n	\n Decide how many building-year profiles you want to consider for each year. \n Choose a share between 0 and 1. A value of 1 corresponds to about 100 profiles \n for the profiles_100 and 400 profiles for the profiles_400 dataset.\n
\n points_per_profile (=0.003): float\n	\n Decide how many data points per building-year profile you want to consider. \n Choose a share between 0 and 1. A value of 0.01 corresponds to approximately \n 350 points per profile.\n
\n history_window_meteo (=24): int\n	\n Choose past time window for the meteo data. Resolution is hourly.\n
\n prediction_window (=96): int\n	\n Decide how many time steps to predict consumption into the future. Resolution \n is 15 min. A values of 96 corresponds to 24h.\n
\n train_split (=0.3): float\n	\n Decides on the splitting ratio between training and validation datasets.\n
\n test_split (=0.7): float\n	\n Decides how many buildings and how much of the time period to separate for\n testing.\n
\n normalization (=True): bool\n	\n Decide whether or not to normalize features.\n
\n standardization (=True): bool\n	\n Decide whether to standardize features to zero mean and unit variance.\n
\n silent (=True): bool\n	\n Decide whether or not to print out progress of data processing.\n
\n plot (=False): bool\n	\n Decide whether or not to visualize examples of processed data.\n

\n\n\n\n### Examples:\n\n```\nimport altility.adl_model as adl_model\nimport altility.datasets.load_forecasting as load_forecasting\n\n### Import and prepare load forecasting data\ndatasets = load_forecasting.prep_load_forecasting_data(\n silent=False,\n plot=True\n)\n\n### Get features and labels for available data\ny = datasets[0]['Y']\nx_t = datasets[0]['X_t']\nx_s = datasets[0]['X_s1']\nx_st = datasets[0]['X_st']\n\n### Create a class instance\nADL_model = adl_model.ADL_model('ADL model f_nn')\n\n### Initialize model by creating and training it\nADL_model.initialize(\n y,\n x_t,\n x_s,\n x_st,\n silent=False,\n plot=True\n)\n\n### Collect candidate data\nADL_model.collect(\n x_t_cand,\n x_s_cand,\n x_st_cand,\n silent=True,\n plot=False\n)\n```","has_starred","hub_user","immutable_tags_settings",{"_71":11,"_72":73},"enabled","rules",[74],".*","is_automated","permissions",{"_78":17,"_79":11,"_80":11},"read","write","admin","user","storage_size",391616613,"orgData","recentTags",[87,124],{"_88":40,"_89":90,"_91":92,"_93":94,"_95":96,"_46":113,"_114":90,"_115":29,"_116":52,"_53":117,"_118":119,"_120":104,"_121":113,"_122":62,"_123":17},"content_type","creator",13522077,"full_size",247929001,"id",178734571,"images",[97],{"_98":99,"_100":101,"_102":44,"_103":104,"_105":106,"_107":108,"_109":44,"_110":-5,"_111":92,"_59":62,"_112":-5},"architecture","amd64","digest","sha256:564bc12d7cd1c01dda0a8b140cdfd0e0cebce3449427ecd30f7b8da939d92e3d","features","last_pulled","2026-09-26T16:00:40.044542828Z","last_pushed","2021-11-23T07:37:57Z","os","linux","os_features","os_version","size","variant","2022-01-08T22:16:08.838522Z","last_updater","last_updater_username","media_type","latest","repository",15881085,"tag_last_pulled","tag_last_pushed","tag_status","v2",{"_88":40,"_89":90,"_91":125,"_93":126,"_95":127,"_46":132,"_114":90,"_115":29,"_116":52,"_53":133,"_118":119,"_120":130,"_121":132,"_122":62,"_123":17},291880614,178734716,[128],{"_98":99,"_100":129,"_102":44,"_103":130,"_105":131,"_107":108,"_109":44,"_110":-5,"_111":125,"_59":62,"_112":-5},"sha256:0722f677b8fd0c4bb92fdc99a8f130aeef1951831e0af51cf93f59430b6b0dd8","2026-09-26T16:00:36.237034238Z","2021-11-23T07:39:03Z","2022-01-08T22:16:28.417966Z","jupyter","sbxKitMetadata","isPotentialSpam","isHubSbxKitsEnabled","showSbxLogoOverride","sbxTrademarkText","displaySecurityMetadata","tagAttestations","tagsHaveAttestations",[],"routes/_layout.r.$namespace.$reponame.tags",{"_34":145,"_152":153,"_141":166,"_167":11},{"_36":146,"_38":147,"_41":42,"_43":44,"_45":11,"_46":47,"_48":49,"_50":148,"_53":31,"_28":29,"_54":55,"_56":40,"_57":58,"_59":60,"_61":62,"_63":-5,"_64":58,"_65":66,"_67":11,"_68":29,"_69":149,"_75":11,"_76":151,"_81":29,"_82":83},[],[40],[52],{"_71":11,"_72":150},[74],{"_78":17,"_79":11,"_80":11},"tags",{"_154":155,"_156":-5,"_157":-5,"_158":159},"count",2,"next","previous","results",[160,163],{"_88":40,"_89":90,"_91":125,"_93":126,"_95":161,"_46":132,"_114":90,"_115":29,"_116":52,"_53":133,"_118":119,"_120":130,"_121":132,"_122":62,"_123":17},[162],{"_98":99,"_100":129,"_102":44,"_103":130,"_105":131,"_107":108,"_109":44,"_110":-5,"_111":125,"_59":62,"_112":-5},{"_88":40,"_89":90,"_91":92,"_93":94,"_95":164,"_46":113,"_114":90,"_115":29,"_116":52,"_53":117,"_118":119,"_120":104,"_121":113,"_122":62,"_123":17},[165],{"_98":99,"_100":101,"_102":44,"_103":104,"_105":106,"_107":108,"_109":44,"_110":-5,"_111":92,"_59":62,"_112":-5},[],"canDeleteTags","actionData","errors"]

aryandoustarsam/altility

Tags for aryandoustarsam/altility