diff --git a/searchindex.js b/searchindex.js index 706f720b..e11ef721 100644 --- a/searchindex.js +++ b/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["faq", "feature_specifications", "index", "installation", "timeseriesflattener", "tutorials", "tutorials/01_basic", "tutorials/02_advanced", "tutorials/03_text"], "filenames": ["faq.rst", "feature_specifications.rst", "index.rst", "installation.rst", "timeseriesflattener.rst", "tutorials.rst", "tutorials/01_basic.ipynb", "tutorials/02_advanced.ipynb", "tutorials/03_text.ipynb"], "titles": ["Frequently Asked Questions", "Feature specifications", "timeseriesflattener", "Installation", "Timeseriesflattener", "Tutorials", "Introductory Tutorial", "Advanced Tutorial", "Adding text features"], "terms": {"If": [0, 6, 7, 8], "you": [0, 1, 4, 5, 6, 7, 8], "wish": 0, "us": [0, 1, 2, 3, 4, 5, 6, 7, 8], "librari": 0, "your": [0, 3, 6, 7, 8], "research": 0, "pleas": [0, 2], "joss": 0, "paper": 0, "articl": 0, "bernstorff2023timeseriesflatten": 0, "titl": 0, "timeseriesflatten": [0, 3, 6, 7, 8], "A": [0, 2, 4, 6, 7], "python": [0, 2], "summar": 0, "featur": [0, 2, 5, 6], "from": [0, 1, 2, 4, 5, 6, 7], "medic": [0, 2, 6, 8], "time": [0, 1, 2, 4, 5, 7, 8], "seri": [0, 1, 2, 4, 6], "author": 0, "bernstorff": 0, "martin": 0, "enevoldsen": 0, "kenneth": 0, "damgaard": 0, "jakob": 0, "danielsen": 0, "andrea": 0, "hansen": 0, "lass": 0, "journal": 0, "open": 0, "sourc": [0, 1, 4], "softwar": 0, "volum": 0, "8": [0, 6, 7], "number": [0, 2, 6, 7, 8], "83": 0, "page": [0, 2], "5197": 0, "year": [0, 6, 7], "2023": 0, "Or": [0, 7], "prefer": 0, "apa": 0, "m": 0, "k": 0, "j": 0, "l": 0, "come": [0, 7], "an": [0, 1, 2, 6, 7, 8], "extens": 0, "In": [0, 1, 6, 7], "order": [0, 5], "ll": [0, 6, 7], "usual": 0, "want": [0, 6, 7, 8], "clone": 0, "repositori": 0, "build": 0, "also": [0, 5, 6], "instal": [0, 6, 7], "requir": [0, 1, 2, 4, 6, 7], "develop": 0, "depend": 0, "util": 0, "defin": [0, 1, 4, 8], "pyproject": 0, "toml": 0, "pip": [0, 3], "e": [0, 1, 2, 6, 7, 8], "dev": 0, "pytest": 0, "which": [0, 1, 2, 4, 5, 6, 7], "all": [0, 6, 7, 8], "folder": 0, "specif": [0, 4, 5, 7, 8], "can": [0, 1, 2, 5, 6, 7, 8], "desired_test": 0, "py": [0, 6, 7], "sphinx": 0, "It": [0, 6], "furo": 0, "theme": 0, "custom": 0, "style": [0, 6, 7], "To": [0, 2, 3, 5, 6, 7, 8], "make": [0, 2, 6, 7, 8], "doc": [0, 6], "text": [0, 5], "html": 0, "c": [0, 2, 7], "class": [1, 4, 6, 7], "coercedfloat": 1, "lookperiod": [1, 7], "fallback": [1, 6, 7, 8], "union": [1, 4], "float": 1, "int": [1, 4], "base": [1, 4], "object": [1, 4, 6, 7], "min_dai": [1, 6, 7], "max_dai": [1, 6, 7], "outcomespec": [1, 4, 6], "timeseries_df": [1, 6], "datafram": [1, 2, 4, 6, 7, 8], "feature_base_nam": [1, 6, 7], "str": [1, 4, 8], "lookahead_dai": [1, 6], "tupl": [1, 6], "aggregation_fn": [1, 6, 7, 8], "callabl": 1, "dataframegroupbi": 1, "incid": [1, 6], "bool": [1, 4], "prefix": [1, 4, 6], "outc": [1, 4, 6], "basemodel": [1, 4], "outcom": [1, 2, 5, 7], "paramet": [1, 4, 6], "valu": [1, 2, 4, 6, 7, 8], "should": [1, 4, 6, 8], "contain": [1, 4, 6, 8], "column": [1, 2, 4, 6, 7, 8], "entity_id": [1, 4, 6, 7, 8], "id": [1, 2, 6, 8], "entiti": [1, 6], "each": [1, 2, 5, 6, 8], "belong": 1, "The": [1, 2, 5, 6, 7], "timeseri": [1, 4, 6], "timestamp": [1, 4, 6, 7, 8], "datetim": [1, 6, 7], "note": [1, 6, 7, 8], "name": [1, 4, 6, 7, 8], "overridden": 1, "when": [1, 2, 6, 7], "initialis": 1, "gener": [1, 2, 5, 6, 7], "g": [1, 2, 6, 7, 8], "_": [1, 6, 7], "feature_baase_nam": 1, "metadata": [1, 4, 6, 8], "interv": [1, 6], "predict": [1, 2, 4, 5, 7, 8], "look": [1, 2, 6, 7], "two": [1, 4, 6, 7], "specifi": [1, 2, 5, 7], "resolv": 1, "0": [1, 6, 7, 8], "how": [1, 2, 5, 7, 8], "aggreg": [1, 2, 6], "multipl": [1, 2, 6, 7, 8], "within": [1, 2, 6, 8], "lookahead": [1, 2, 6], "dai": [1, 6, 8], "take": [1, 4, 6, 7, 8], "group": [1, 7, 8], "input": 1, "return": [1, 4, 6, 7, 8], "singl": [1, 2, 6], "i": [1, 2, 3, 6, 7, 8], "found": [1, 6], "window": [1, 2, 6, 7], "whether": [1, 6], "type": [1, 2, 4, 6, 7], "2": [1, 6, 7, 8], "diabet": [1, 6], "becaus": [1, 4, 6, 7], "onli": [1, 2, 6, 7, 8], "experi": [1, 6], "onc": [1, 6], "handl": [1, 6], "vectoris": 1, "wai": 1, "dure": 1, "resolut": 1, "faster": [1, 6, 7, 8], "than": [1, 2, 6], "non": 1, "occur": [1, 2, 6], "feature_nam": [1, 7], "default": [1, 4, 6], "pred": [1, 4, 6], "get_output_col_nam": 1, "get": [1, 3, 4], "output": [1, 4, 7, 8], "is_dichotom": 1, "check": [1, 6, 7, 8], "dichotom": 1, "properti": [1, 6, 7], "lookahead_period": 1, "model_config": [1, 4], "classvar": [1, 4], "configdict": [1, 4], "arbitrary_types_allow": [1, 4], "true": [1, 2, 4, 6, 7], "extra": [1, 6, 7], "forbid": 1, "frozen": 1, "configur": [1, 4], "model": [1, 2, 4, 6, 8], "dictionari": [1, 4], "conform": [1, 4], "pydant": [1, 4], "config": [1, 4], "model_field": [1, 4], "dict": [1, 4], "fieldinfo": [1, 4], "annot": [1, 4], "list": [1, 4, 5, 6, 7, 8], "fals": [1, 4, 6, 8], "about": [1, 4, 6], "field": [1, 4], "map": [1, 4], "thi": [1, 2, 4, 6, 7, 8], "replac": [1, 4], "__fields__": [1, 4], "v1": [1, 4], "predictorspec": [1, 4, 6], "lookbehind_dai": [1, 6, 7, 8], "predictor": [1, 2, 4, 5, 7], "lookbehind": [1, 2, 6, 7, 8], "lookbehind_period": [1, 7], "staticspec": [1, 4, 6], "static": [1, 5, 7], "can_be_coerced_losslessly_to_int": 1, "coerce_float": 1, "get_temporal_col_nam": 1, "tempor": [1, 5, 7, 8], "packag": [2, 5, 6, 7], "data": [2, 4, 5, 7, 8], "machin": 2, "learn": 2, "implement": [2, 7], "method": [2, 4, 7], "includ": 2, "convert": [2, 8], "ani": [2, 4, 6, 7, 8], "irregular": [2, 6], "row": [2, 6, 7, 8], "desir": 2, "construct": 2, "raw": 2, "ar": [2, 4, 6, 7, 8], "allow": [2, 4, 7], "patient": [2, 6, 8], "independ": 2, "set": [2, 4, 6], "particular": 2, "sever": [2, 8], "choic": 2, "one": [2, 4, 6, 7, 8], "need": [2, 6, 7, 8], "issu": [2, 6], "everi": [2, 6, 7], "physic": 2, "visit": 2, "morn": 2, "anoth": [2, 6], "clinic": [2, 8], "meaning": 2, "far": [2, 6, 8], "back": [2, 6], "ahead": [2, 6], "exist": 2, "point": [2, 6], "abov": [2, 6, 7, 8], "figur": 2, "graphic": 2, "repres": [2, 6], "terminologi": [2, 6], "determin": [2, 6], "wherea": 2, "futur": [2, 6], "refer": [2, 6], "b": 2, "label": [2, 6], "neg": 2, "never": [2, 6], "happen": [2, 6], "outsid": [2, 6], "posit": [2, 6], "insid": [2, 6], "exampl": [2, 6, 7, 8], "mean": [2, 6, 7, 8], "shown": [2, 6], "max": [2, 6], "min": [2, 6], "etc": [2, 6], "d": 2, "drop": [2, 6, 7, 8], "extend": [2, 6], "further": [2, 4, 6], "start": [2, 3, 6, 7, 8], "dataset": [2, 4, 5, 6, 7], "end": [2, 6, 7], "behaviour": 2, "option": [2, 4], "obtain": 2, "rich": 2, "represent": 2, "see": [2, 4, 6], "tutori": [2, 4, 8], "placehold": 2, "case": [2, 6], "report": 2, "request": 2, "github": [2, 3], "tracker": 2, "otherwis": 2, "discuss": [2, 6], "forum": 2, "bug": 2, "idea": 2, "usag": 2, "index": 2, "run": [3, 5], "follow": [3, 6], "line": [3, 6, 7], "termin": 3, "There": [3, 6, 7, 8], "discrep": 3, "between": 3, "latest": 3, "version": [3, 6, 7], "flatten": [4, 5, 8], "describ": [4, 6, 8], "speccollect": 4, "outcome_spec": [4, 6], "predictor_spec": 4, "static_spec": 4, "collect": 4, "spec": [4, 6, 7, 8], "prediction_times_df": [4, 6, 7, 8], "drop_pred_times_with_insufficient_look_dist": [4, 6, 7, 8], "cach": [4, 5], "featurecach": [4, 7], "none": [4, 6, 7], "entity_id_col_nam": [4, 6, 7, 8], "timestamp_col_nam": [4, 6, 7, 8], "predictor_col_name_prefix": 4, "outcome_col_name_prefix": 4, "n_worker": [4, 6, 7, 8], "60": [4, 7], "log_to_stdout": 4, "turn": [4, 8], "tabular": [4, 8], "add_ag": 4, "date_of_birth_df": 4, "date_of_birth_col_nam": 4, "date_of_birth": 4, "output_prefix": 4, "add": [4, 6, 7], "ag": 4, "ha": [4, 6, 7, 8], "its": [4, 6], "own": [4, 7], "function": [4, 6, 8], "veri": 4, "frequent": [4, 6], "match": 4, "self": [4, 6, 7], "add_spec": [4, 6, 7, 8], "sequenc": [4, 7], "queue": 4, "unprocess": [4, 6, 7, 8], "process": [4, 6, 7, 8], "until": 4, "call": [4, 6, 7], "comput": [4, 6, 7, 8], "get_df": [4, 6, 7, 8], "u": 4, "more": [4, 6, 7], "effecti": 4, "parallelis": 4, "most": [4, 6, 7], "complex": 4, "li": 4, "For": [4, 6, 7, 8], "document": 4, "those": 4, "present": [4, 6], "we": [5, 6, 7, 8], "recommend": 5, "go": [5, 6], "through": 5, "below": 5, "jupyt": 5, "notebook": 5, "download": 5, "local": [5, 6, 7], "introductori": 5, "load": [5, 7, 8], "advanc": [5, 6], "creat": [5, 6, 8], "combin": 5, "ad": [5, 6], "embed": 5, "especi": 6, "help": 6, "have": [6, 7, 8], "complic": 6, "train": 6, "simpl": 6, "explain": 6, "appli": 6, "consist": 6, "3": [6, 7, 8], "step": 6, "": [6, 7, 8], "simplest": 6, "first": [6, 7, 8], "predictin": 6, "element": 6, "context": 6, "skimpi": [6, 7], "import": [6, 7, 8], "skim": [6, 7], "test": [6, 7, 8], "load_synth_data": [6, 7, 8], "load_synth_prediction_tim": [6, 7, 8], "df_prediction_tim": 6, "sort_valu": 6, "summari": [6, 7], "count": [6, 7], "10000": [6, 7], "int64": [6, 7], "1": [6, 7, 8], "datetime64": [6, 7], "column_nam": [6, 7], "na": [6, 7, 8], "sd": [6, 7], "p0": [6, 7], "p25": [6, 7], "p50": [6, 7], "p75": [6, 7], "p100": [6, 7], "hist": [6, 7], "5000": [6, 7], "2900": [6, 7], "2500": 6, "4900": [6, 7], "7400": [6, 7], "last": [6, 7, 8], "frequenc": [6, 7], "1965": [6, 8], "01": [6, 7, 8], "02": [6, 7, 8], "09": [6, 8], "35": 6, "00": [6, 7, 8], "1969": [6, 7, 8], "12": [6, 7, 8], "31": [6, 7, 8], "21": [6, 7, 8], "42": [6, 7], "628": 6, "11": [6, 7, 8], "55": 6, "2005": 6, "03": [6, 8], "15": [6, 8], "07": [6, 8], "16": [6, 8], "4370": 6, "13": [6, 7, 8], "23": [6, 7, 8], "18": [6, 7, 8], "6152": 6, "1968": [6, 7, 8], "04": [6, 8], "6873": 6, "4": [6, 7, 8], "28": [6, 8], "33": 6, "9688": 6, "9996": 6, "17": [6, 7, 8], "1463": 6, "30": [6, 7, 8], "19": [6, 8], "3952": 6, "9997": 6, "1967": [6, 8], "06": [6, 8], "08": [6, 8], "52": [6, 8], "7926": 6, "9999": 6, "22": [6, 7, 8], "24": 6, "5720": 6, "14": [6, 8], "59": [6, 7], "here": 6, "Then": [6, 7], "our": [6, 7, 8], "differ": [6, 7], "timepoint": 6, "load_synth_predictor_float": [6, 7], "df_synth_predictor": 6, "100000": 6, "float64": [6, 7], "7500": 6, "5": [6, 7, 8], "9": [6, 7], "00015": 6, "7": [6, 7, 8], "10": [6, 7, 8], "37": 6, "95792": 6, "29": [6, 7], "799246": 6, "82592": 6, "05": [6, 7, 8], "6": [6, 7], "630007": 6, "1377": 6, "174793": 6, "28579": 6, "26": [6, 8], "981185": 6, "81247": 6, "44": [6, 7], "970382": 6, "10277": 6, "20": [6, 8], "304568": 6, "74701": 6, "671907": 6, "69566": 6, "41": [6, 8], "250538": 6, "40901": 6, "1966": [6, 8], "924175": 6, "96881": 6, "501553": 6, "again": 6, "could": 6, "sex": 6, "doesn": 6, "t": [6, 7], "chang": 6, "over": 6, "let": [6, 7, 8], "load_synth_sex": 6, "df_synth_sex": 6, "femal": 6, "9994": 6, "9995": 6, "9998": 6, "As": [6, 8], "And": 6, "lastli": 6, "ve": 6, "chosen": 6, "binari": 6, "store": 6, "infer": 6, "do": 6, "sinc": 6, "thei": [6, 7, 8], "section": 6, "load_synth_outcom": 6, "df_synth_outcom": 6, "3103": 6, "5100": 6, "7600": 6, "50": 6, "46": [6, 7], "6253": 6, "9964": 6, "6255": 6, "9966": 6, "6256": 6, "9968": 6, "6257": 6, "9970": 6, "6269": 6, "9992": 6, "53": [6, 7], "per": [6, 7], "now": [6, 7, 8], "recip": 6, "finish": 6, "firstli": 6, "main": 6, "decis": 6, "size": [6, 7], "given": 6, "indic": 6, "code": [6, 7], "feature_spec": [6, 7, 8], "single_spec": 6, "maximum": [6, 7], "panda": [6, 7, 8], "pd": [6, 8], "test_df": 6, "365": [6, 7, 8], "outcome_nam": 6, "argument": 6, "values_df": 6, "decid": 6, "least": 6, "correspond": [6, 8], "both": 6, "accomplish": 6, "dw_ek_borg": 6, "wa": [6, 8], "mark": 6, "after": 6, "where": 6, "event": 6, "perman": 6, "specifii": 6, "forward": 6, "search": 6, "certain": 6, "period": [6, 8], "befor": [6, 8], "instead": 6, "almost": 6, "entir": 6, "ident": 6, "except": 6, "past": 6, "numpi": [6, 7, 8], "np": [6, 7, 8], "temporal_predictor_spec": 6, "730": [6, 7, 8], "nan": [6, 7, 8], "predictor_nam": 6, "rang": 6, "similar": 6, "instanc": [6, 7], "might": [6, 7, 8], "182": 6, "easili": 6, "pass": [6, 8], "temporal_interval_predictor_spec": 6, "90": 6, "predictor_interval_nam": 6, "slightli": 6, "previou": 6, "provid": 6, "howev": [6, 7, 8], "By": 6, "filter": 6, "easi": 6, "manual": [6, 7], "sex_predictor_spec": 6, "input_col_name_overrid": 6, "df": [6, 7, 8], "tsflatten": 6, "re": [6, 8], "readi": 6, "instanti": 6, "along": 6, "add_": 6, "parallel": [6, 7, 8], "oper": 6, "across": 6, "core": [6, 7], "ts_flatten": [6, 7, 8], "applic": 6, "sai": [6, 7], "month": [6, 7, 8], "would": [6, 8], "compromis": 6, "generalis": 6, "some": [6, 7, 8], "edg": 6, "brief": 6, "2024": [6, 7, 8], "25": [6, 7, 8], "32": [6, 7, 8], "info": [6, 7, 8], "were": [6, 7, 8], "_drop_pred_time_if_insufficient_look_dist": [6, 7], "5999": 6, "99": 6, "worker": [6, 7, 8], "chunksiz": [6, 7, 8], "mai": [6, 7, 8], "progress": [6, 7, 8], "bar": [6, 7, 8], "move": [6, 7, 8], "batch": [6, 7, 8], "much": [6, 7, 8], "total": [6, 7, 8], "perform": [6, 7, 8], "100": [6, 7, 8], "39": [6, 7], "05it": 6, "align": [6, 7, 8], "littl": [6, 7, 8], "while": [6, 7, 8], "minut": [6, 7, 8], "000": [6, 7, 8], "concaten": [6, 7, 8], "Will": [6, 7, 8], "system": [6, 7, 8], "2_000_000": [6, 7, 8], "normal": [6, 7, 8], "took": [6, 7, 8], "004": 6, "second": [6, 7, 8], "merg": [6, 7, 8], "origin": [6, 7, 8], "4001": 6, "string": [6, 7], "2600": [6, 7], "outc_outcome_name_withi": 6, "064": 6, "n_365_days_maximum_fal": 6, "back_0_dichotom": 6, "pred_predictor_interv": 6, "2877": 6, "71": 6, "91": 6, "_name_within_30_to_90_d": 6, "ays_mean_fallback_nan": [6, 7], "pred_predictor_name_wit": 6, "72": 6, "097": 6, "hin_730_days_mean_fallb": 6, "ack_nan": 6, "pred_femal": 6, "49": 6, "word": [6, 7, 8], "prediction_time_uuid": [6, 7, 8], "outc_outcome_name_within_365_days_maximum_fallback_0_dichotom": 6, "pred_predictor_interval_name_within_30_to_90_days_mean_fallback_nan": 6, "pred_predictor_name_within_730_days_mean_fallback_nan": 6, "display": [6, 7], "shorten": [6, 7], "col": [6, 7], "shortened_pr": 6, "pred_x": 6, "shortened_pred_interv": 6, "pred_x_30_to_90": 6, "shortened_outcom": 6, "outc_i": 6, "renam": [6, 7], "pred_predictor_name_within_0_to_730_days_mean_fallback_nan": 6, "outc_outcome_name_within_0_to_365_days_maximum_fallback_0_dichotom": 6, "axi": [6, 7, 8], "set_table_attribut": [6, 7], "font": [6, 7], "14px": [6, 7], "importerror": [6, 7], "traceback": [6, 7], "recent": [6, 7], "cell": [6, 7], "file": [6, 7], "lib": [6, 7], "python3": [6, 7], "site": [6, 7], "frame": [6, 7], "1338": [6, 7], "1318": [6, 7], "1319": [6, 7, 8], "def": [6, 7, 8], "styler": [6, 7], "1320": [6, 7], "1321": [6, 7], "1322": [6, 7], "1336": [6, 7], "tabl": [6, 7], "visual": [6, 7], "user_guid": [6, 7], "ipynb": [6, 7], "1337": [6, 7], "io": [6, 7], "format": [6, 7, 8], "1340": [6, 7], "40": [6, 7], "shared_doc": [6, 7], "_shared_doc": [6, 7], "save_to_buff": [6, 7], "jinja2": [6, 7], "import_optional_depend": [6, 7], "style_rend": [6, 7], "47": [6, 7], "cssproperti": [6, 7], "48": [6, 7], "cssstyle": [6, 7], "56": [6, 7], "refactor_level": [6, 7], "57": [6, 7], "type_check": [6, 7], "compat": [6, 7], "_option": [6, 7], "161": [6, 7], "error": [6, 7], "min_vers": [6, 7], "159": [6, 7], "160": [6, 7], "elif": [6, 7], "rais": [6, 7], "msg": [6, 7], "163": [6, 7], "modul": [6, 7], "newer": [6, 7], "current": [6, 7], "classif": 6, "citizen": 6, "uniqu": 6, "identifi": 6, "prediciton": 6, "pred_": [6, 7], "outc_": 6, "basic": 7, "cover": [7, 8], "expand": 7, "effect": 7, "mani": 7, "so": [7, 8], "iter": 7, "without": 7, "complet": 7, "full": 7, "hand": 7, "rather": 7, "straightforward": 7, "what": 7, "hundr": 7, "amount": 7, "write": 7, "grow": 7, "quit": 7, "substanti": 7, "becom": 7, "consum": 7, "hard": 7, "navig": 7, "solv": 7, "problem": 7, "combinatori": 7, "group_spec": [7, 8], "predictorgroupspec": [7, 8], "nameddatafram": 7, "pprint": 7, "pred_spec_batch": 7, "named_datafram": [7, 8], "synth_predictor_float": 7, "1095": 7, "create_combin": [7, 8], "attribut": 7, "easier": 7, "namedatafram": 7, "exactli": 7, "load_synth_predictor_flaot": 7, "pred_synth_predictor_float_": 7, "result": [7, 8], "good": 7, "small": [7, 8], "highlight": 7, "pred_spec_batch_summari": 7, "pred_spec": 7, "__name__": 7, "print": [7, 8], "f": 7, "len": [7, 8], "know": 7, "bunch": 7, "quickli": 7, "But": 7, "next": 7, "ship": 7, "disk": 7, "feature_cach": 7, "cache_to_disk": 7, "diskcach": 7, "flattened_dataset": 7, "pathlib": 7, "path": 7, "feature_cache_dir": 7, "tmp": 7, "directori": 7, "save": 7, "just": 7, "them": 7, "won": 7, "alreadi": [7, 8], "new": 7, "abstract": 7, "redi": 7, "sql": 7, "everyth": 7, "work": 7, "6053": 7, "73it": 7, "92it": 7, "007": 7, "3947": 7, "pred_synth_predictor_fl": 7, "506": 7, "82": 7, "024": 7, "oat_within_365_to_730_d": 7, "ays_maximum_fallback_na": 7, "n": 7, "oat_within_1095_days_ma": 7, "ximum_fallback_nan": 7, "533": 7, "0084": 7, "oat_within_365_days_max": 7, "imum_fallback_nan": 7, "oat_within_365_days_mea": 7, "n_fallback_nan": 7, "oat_within_1095_days_m": 7, "an_fallback_nan": 7, "pred_synth_predictor_float_within_365_to_730_days_maximum_fallback_nan": 7, "pred_synth_predictor_float_within_1095_days_maximum_fallback_nan": 7, "pred_synth_predictor_float_within_365_days_maximum_fallback_nan": 7, "pred_synth_predictor_float_within_365_to_730_days_mean_fallback_nan": 7, "pred_synth_predictor_float_within_365_days_mean_fallback_nan": 7, "pred_synth_predictor_float_within_1095_days_mean_fallback_nan": 7, "pred_col": 7, "startswith": 7, "rename_dict": 7, "enumer": 7, "df_renam": 7, "base_col": 7, "renamed_col": 7, "dealt": 8, "show": 8, "out": 8, "synthet": 8, "other": 8, "load_synth_text": 8, "synth_text": 8, "head": 8, "4647": 8, "went": 8, "induc": 8, "coma": 8, "2007": 8, "taken": 8, "emerg": 8, "departm": 8, "5799": 8, "old": 8, "son": 8, "wh": 8, "had": 8, "been": 8, "left": 8, "bed": 8, "minu": 8, "4234": 8, "allergi": 8, "often": 8, "advantag": 8, "emb": 8, "speed": 8, "up": 8, "block": 8, "tf": 8, "idf": 8, "form": 8, "constraint": 8, "entitiy_id_col": 8, "timestamp_col": 8, "value_col": 8, "purpos": 8, "demonstr": 8, "fit": 8, "captur": 8, "sklearn": 8, "feature_extract": 8, "tfidfvector": 8, "embed_text_to_df": 8, "tfidf_model": 8, "max_featur": 8, "fit_transform": 8, "toarrai": 8, "get_feature_names_out": 8, "embedded_text": 8, "tolist": 8, "metadata_onli": 8, "embedded_text_with_metadata": 8, "concat": 8, "175872": 8, "182066": 8, "249848": 8, "158430": 8, "000000": 8, "023042": 8, "311389": 8, "529966": 8, "490203": 8, "479312": 8, "244870": 8, "135282": 8, "064337": 8, "465084": 8, "336859": 8, "151743": 8, "729861": 8, "179161": 8, "192367": 8, "232332": 8, "283402": 8, "336952": 8, "176422": 8, "238416": 8, "646879": 8, "250217": 8, "382277": 8, "165635": 8, "200046": 8, "183015": 8, "261115": 8, "125837": 8, "151906": 8, "205285": 8, "759528": 8, "403961": 8, "098747": 8, "493461": 8, "119196": 8, "272619": 8, "207444": 8, "045256": 8, "183475": 8, "588324": 8, "433253": 8, "235349": 8, "df_with_multiple_values_to_named_datafram": 8, "readili": 8, "suppli": 8, "df_transform": 8, "split": 8, "embedded_df": 8, "name_prefix": 8, "tfidf_": 8, "accord": 8, "inform": 8, "bow": 8, "kept": 8, "tfidf_and": 8, "emb_spec_batch": 8, "64it": 8, "68it": 8, "029": 8, "sake": 8, "dropna": 8, "pred_tfidf_was_within_365_days_mean_fallback_nan": 8, "pred_tfidf_and_within_365_days_mean_fallback_nan": 8, "pred_tfidf_or_within_365_days_mean_fallback_nan": 8, "pred_tfidf_in_within_730_days_mean_fallback_nan": 8, "pred_tfidf_of_within_730_days_mean_fallback_nan": 8, "pred_tfidf_the_within_730_days_mean_fallback_nan": 8, "pred_tfidf_to_within_730_days_mean_fallback_nan": 8, "pred_tfidf_was_within_730_days_mean_fallback_nan": 8, "pred_tfidf_for_within_365_days_mean_fallback_nan": 8, "pred_tfidf_and_within_730_days_mean_fallback_nan": 8, "pred_tfidf_of_within_365_days_mean_fallback_nan": 8, "pred_tfidf_that_within_365_days_mean_fallback_nan": 8, "pred_tfidf_the_within_365_days_mean_fallback_nan": 8, "pred_tfidf_patient_within_730_days_mean_fallback_nan": 8, "pred_tfidf_that_within_730_days_mean_fallback_nan": 8, "pred_tfidf_in_within_365_days_mean_fallback_nan": 8, "pred_tfidf_for_within_730_days_mean_fallback_nan": 8, "1917": 8, "4977": 8, "086927": 8, "145809": 8, "221549": 8, "483324": 8, "536339": 8, "534890": 8, "284485": 8, "088050": 8, "090356": 8, "133722": 8, "2463": 8, "6840": 8, "092896": 8, "155821": 8, "355142": 8, "258256": 8, "573168": 8, "285810": 8, "456030": 8, "376386": 8, "096561": 8, "071452": 8, "2580": 8, "260680": 8, "601521": 8, "639848": 8, "401014": 8, "2741": 8, "9832": 8, "36": 8, "335410": 8, "225044": 8, "128228": 8, "186493": 8, "236513": 8, "825558": 8, "164655": 8, "101924": 8, "103195": 8, "2931": 8, "7281": 8, "388547": 8, "289663": 8, "385111": 8, "280049": 8, "304425": 8, "464891": 8, "211934": 8, "043730": 8, "269251": 8, "332065": 8}, "objects": {"timeseriesflattener.feature_specs": [[1, 0, 0, "-", "single_specs"]], "timeseriesflattener.feature_specs.single_specs": [[1, 1, 1, "", "CoercedFloats"], [1, 1, 1, "", "LookPeriod"], [1, 1, 1, "", "OutcomeSpec"], [1, 1, 1, "", "PredictorSpec"], [1, 1, 1, "", "StaticSpec"], [1, 5, 1, "", "can_be_coerced_losslessly_to_int"], [1, 5, 1, "", "coerce_floats"], [1, 5, 1, "", "get_temporal_col_name"]], "timeseriesflattener.feature_specs.single_specs.CoercedFloats": [[1, 2, 1, "", "fallback"], [1, 2, 1, "", "lookperiod"]], "timeseriesflattener.feature_specs.single_specs.LookPeriod": [[1, 2, 1, "", "max_days"], [1, 2, 1, "", "min_days"]], "timeseriesflattener.feature_specs.single_specs.OutcomeSpec": [[1, 2, 1, "", "aggregation_fn"], [1, 2, 1, "", "fallback"], [1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "incident"], [1, 3, 1, "", "is_dichotomous"], [1, 2, 1, "", "lookahead_days"], [1, 4, 1, "", "lookahead_period"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener.feature_specs.single_specs.PredictorSpec": [[1, 2, 1, "", "aggregation_fn"], [1, 2, 1, "", "fallback"], [1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "lookbehind_days"], [1, 4, 1, "", "lookbehind_period"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener.feature_specs.single_specs.StaticSpec": [[1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener": [[4, 0, 0, "-", "flattened_dataset"]], "timeseriesflattener.flattened_dataset": [[4, 1, 1, "", "SpecCollection"], [4, 1, 1, "", "TimeseriesFlattener"]], "timeseriesflattener.flattened_dataset.SpecCollection": [[4, 2, 1, "", "model_config"], [4, 2, 1, "", "model_fields"], [4, 2, 1, "", "outcome_specs"], [4, 2, 1, "", "predictor_specs"], [4, 2, 1, "", "static_specs"]], "timeseriesflattener.flattened_dataset.TimeseriesFlattener": [[4, 3, 1, "", "add_age"], [4, 3, 1, "", "add_spec"], [4, 3, 1, "", "compute"], [4, 3, 1, "", "get_df"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:attribute", "3": "py:method", "4": "py:property", "5": "py:function"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "attribute", "Python attribute"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "function", "Python function"]}, "titleterms": {"frequent": 0, "ask": [0, 2], "question": [0, 2], "cite": 0, "thi": 0, "packag": 0, "how": [0, 6], "do": 0, "i": 0, "test": 0, "code": 0, "run": 0, "suit": 0, "document": 0, "gener": [0, 8], "featur": [1, 7, 8], "specif": [1, 6], "timeseriesflatten": [1, 2, 4], "feature_spec": 1, "single_spec": 1, "function": 2, "where": 2, "indic": 2, "search": 2, "instal": 3, "flattened_dataset": 4, "tutori": [5, 6, 7], "get": 5, "start": 5, "introductori": 6, "load": 6, "data": 6, "predict": 6, "time": 6, "tempor": 6, "predictor": [6, 8], "static": 6, "outcom": 6, "specifi": 6, "flatten": 6, "advanc": 7, "creat": 7, "combin": 7, "cach": 7, "ad": 8, "text": 8, "The": 8, "dataset": 8, "from": 8, "embed": 8}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx": 57}, "alltitles": {"Frequently Asked Questions": [[0, "frequently-asked-questions"]], "Citing this package": [[0, "citing-this-package"]], "How do I test the code and run the test suite?": [[0, "how-do-i-test-the-code-and-run-the-test-suite"]], "How is the documentation generated?": [[0, "how-is-the-documentation-generated"]], "Feature specifications": [[1, "feature-specifications"]], "timeseriesflattener.feature_specs.single_specs": [[1, "module-timeseriesflattener.feature_specs.single_specs"]], "timeseriesflattener": [[2, "timeseriesflattener"]], "Functionality": [[2, "functionality"]], "Where to ask questions?": [[2, "where-to-ask-questions"]], "Indices and search": [[2, "indices-and-search"]], "Installation": [[3, "installation"]], "Timeseriesflattener": [[4, "timeseriesflattener"]], "timeseriesflattener.flattened_dataset": [[4, "module-timeseriesflattener.flattened_dataset"]], "Tutorials": [[5, "tutorials"]], "Getting started": [[5, null]], "Introductory Tutorial": [[6, "introductory-tutorial"]], "Loading data": [[6, "loading-data"]], "Loading prediction times": [[6, "loading-prediction-times"]], "Loading a temporal predictor": [[6, "loading-a-temporal-predictor"]], "Loading a static predictor": [[6, "loading-a-static-predictor"]], "Loading a temporal outcome": [[6, "loading-a-temporal-outcome"]], "Specifying how to flatten the data": [[6, "specifying-how-to-flatten-the-data"]], "Temporal outcome specification": [[6, "temporal-outcome-specification"]], "Temporal predictor specification": [[6, "temporal-predictor-specification"]], "Static predictor specification": [[6, "static-predictor-specification"]], "Flattening": [[6, "flattening"]], "Advanced Tutorial": [[7, "advanced-tutorial"]], "Creating feature combinations": [[7, "creating-feature-combinations"]], "Caching": [[7, "caching"]], "Adding text features": [[8, "adding-text-features"]], "The dataset": [[8, "the-dataset"]], "Generating predictors from embedded text": [[8, "generating-predictors-from-embedded-text"]]}, "indexentries": {"coercedfloats (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats"]], "lookperiod (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod"]], "outcomespec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec"]], "predictorspec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec"]], "staticspec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec"]], "aggregation_fn (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.aggregation_fn"]], "aggregation_fn (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.aggregation_fn"]], "can_be_coerced_losslessly_to_int() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.can_be_coerced_losslessly_to_int"]], "coerce_floats() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.coerce_floats"]], "fallback (coercedfloats attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats.fallback"]], "fallback (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.fallback"]], "fallback (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.fallback"]], "feature_base_name (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.feature_base_name"]], "feature_base_name (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.feature_base_name"]], "feature_base_name (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.feature_base_name"]], "get_output_col_name() (outcomespec method)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.get_output_col_name"]], "get_output_col_name() (predictorspec method)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.get_output_col_name"]], "get_output_col_name() (staticspec method)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.get_output_col_name"]], "get_temporal_col_name() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.get_temporal_col_name"]], "incident (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.incident"]], "is_dichotomous() (outcomespec method)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.is_dichotomous"]], "lookahead_days (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.lookahead_days"]], "lookahead_period (outcomespec property)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.lookahead_period"]], "lookbehind_days (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.lookbehind_days"]], "lookbehind_period (predictorspec property)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.lookbehind_period"]], "lookperiod (coercedfloats attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats.lookperiod"]], "max_days (lookperiod attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod.max_days"]], "min_days (lookperiod attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod.min_days"]], "model_config (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.model_config"]], "model_config (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.model_config"]], "model_config (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.model_config"]], "model_fields (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.model_fields"]], "model_fields (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.model_fields"]], "model_fields (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.model_fields"]], "module": [[1, "module-timeseriesflattener.feature_specs.single_specs"], [4, "module-timeseriesflattener.flattened_dataset"]], "prefix (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.prefix"]], "prefix (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.prefix"]], "prefix (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.prefix"]], "timeseries_df (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.timeseries_df"]], "timeseries_df (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.timeseries_df"]], "timeseries_df (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.timeseries_df"]], "timeseriesflattener.feature_specs.single_specs": [[1, "module-timeseriesflattener.feature_specs.single_specs"]], "speccollection (class in timeseriesflattener.flattened_dataset)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection"]], "timeseriesflattener (class in timeseriesflattener.flattened_dataset)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener"]], "add_age() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.add_age"]], "add_spec() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.add_spec"]], "compute() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.compute"]], "get_df() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.get_df"]], "model_config (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.model_config"]], "model_fields (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.model_fields"]], "outcome_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.outcome_specs"]], "predictor_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.predictor_specs"]], "static_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.static_specs"]], "timeseriesflattener.flattened_dataset": [[4, "module-timeseriesflattener.flattened_dataset"]]}}) \ No newline at end of file +Search.setIndex({"docnames": ["faq", "feature_specifications", "index", "installation", "timeseriesflattener", "tutorials", "tutorials/01_basic", "tutorials/02_advanced", "tutorials/03_text"], "filenames": ["faq.rst", "feature_specifications.rst", "index.rst", "installation.rst", "timeseriesflattener.rst", "tutorials.rst", "tutorials/01_basic.ipynb", "tutorials/02_advanced.ipynb", "tutorials/03_text.ipynb"], "titles": ["Frequently Asked Questions", "Feature specifications", "timeseriesflattener", "Installation", "Timeseriesflattener", "Tutorials", "Introductory Tutorial", "Advanced Tutorial", "Adding text features"], "terms": {"If": [0, 6, 7, 8], "you": [0, 1, 4, 5, 6, 7, 8], "wish": 0, "us": [0, 1, 2, 3, 4, 5, 6, 7, 8], "librari": 0, "your": [0, 3, 6, 7, 8], "research": 0, "pleas": [0, 2], "joss": 0, "paper": 0, "articl": 0, "bernstorff2023timeseriesflatten": 0, "titl": 0, "timeseriesflatten": [0, 3, 6, 7, 8], "A": [0, 2, 4, 6, 7], "python": [0, 2], "summar": 0, "featur": [0, 2, 5, 6], "from": [0, 1, 2, 4, 5, 6, 7], "medic": [0, 2, 6, 8], "time": [0, 1, 2, 4, 5, 7, 8], "seri": [0, 1, 2, 4, 6], "author": 0, "bernstorff": 0, "martin": 0, "enevoldsen": 0, "kenneth": 0, "damgaard": 0, "jakob": 0, "danielsen": 0, "andrea": 0, "hansen": 0, "lass": 0, "journal": 0, "open": 0, "sourc": [0, 1, 4], "softwar": 0, "volum": 0, "8": [0, 6, 7], "number": [0, 2, 6, 7, 8], "83": 0, "page": [0, 2], "5197": 0, "year": [0, 6, 7], "2023": 0, "Or": [0, 7], "prefer": 0, "apa": 0, "m": 0, "k": 0, "j": 0, "l": 0, "come": [0, 7], "an": [0, 1, 2, 6, 7, 8], "extens": 0, "In": [0, 1, 6, 7], "order": [0, 5], "ll": [0, 6, 7], "usual": 0, "want": [0, 6, 7, 8], "clone": 0, "repositori": 0, "build": 0, "also": [0, 5, 6], "instal": [0, 6, 7], "requir": [0, 1, 2, 4, 6, 7], "develop": 0, "depend": 0, "util": 0, "defin": [0, 1, 4, 8], "pyproject": 0, "toml": 0, "pip": [0, 3], "e": [0, 1, 2, 6, 7, 8], "dev": 0, "pytest": 0, "which": [0, 1, 2, 4, 5, 6, 7], "all": [0, 6, 7, 8], "folder": 0, "specif": [0, 4, 5, 7, 8], "can": [0, 1, 2, 5, 6, 7, 8], "desired_test": 0, "py": [0, 6, 7], "sphinx": 0, "It": [0, 6], "furo": 0, "theme": 0, "custom": 0, "style": [0, 6, 7], "To": [0, 2, 3, 5, 6, 7, 8], "make": [0, 2, 6, 7, 8], "doc": [0, 6], "text": [0, 5], "html": 0, "c": [0, 2, 7], "class": [1, 4, 6, 7], "coercedfloat": 1, "lookperiod": [1, 7], "fallback": [1, 6, 7, 8], "union": [1, 4], "float": 1, "int": [1, 4], "base": [1, 4], "object": [1, 4, 6, 7], "min_dai": [1, 6, 7], "max_dai": [1, 6, 7], "outcomespec": [1, 4, 6], "timeseries_df": [1, 6], "datafram": [1, 2, 4, 6, 7, 8], "feature_base_nam": [1, 6, 7], "str": [1, 4, 8], "lookahead_dai": [1, 6], "tupl": [1, 6], "aggregation_fn": [1, 6, 7, 8], "callabl": 1, "dataframegroupbi": 1, "incid": [1, 6], "bool": [1, 4], "prefix": [1, 4, 6], "outc": [1, 4, 6], "basemodel": [1, 4], "outcom": [1, 2, 5, 7], "paramet": [1, 4, 6], "valu": [1, 2, 4, 6, 7, 8], "should": [1, 4, 6, 8], "contain": [1, 4, 6, 8], "column": [1, 2, 4, 6, 7, 8], "entity_id": [1, 4, 6, 7, 8], "id": [1, 2, 6, 8], "entiti": [1, 6], "each": [1, 2, 5, 6, 8], "belong": 1, "The": [1, 2, 5, 6, 7], "timeseri": [1, 4, 6], "timestamp": [1, 4, 6, 7, 8], "datetim": [1, 6, 7], "note": [1, 6, 7, 8], "name": [1, 4, 6, 7, 8], "overridden": 1, "when": [1, 2, 6, 7], "initialis": 1, "gener": [1, 2, 5, 6, 7], "g": [1, 2, 6, 7, 8], "_": [1, 6, 7], "feature_baase_nam": 1, "metadata": [1, 4, 6, 8], "interv": [1, 6], "predict": [1, 2, 4, 5, 7, 8], "look": [1, 2, 6, 7], "two": [1, 4, 6, 7], "specifi": [1, 2, 5, 7], "resolv": 1, "0": [1, 6, 7, 8], "how": [1, 2, 5, 7, 8], "aggreg": [1, 2, 6], "multipl": [1, 2, 6, 7, 8], "within": [1, 2, 6, 8], "lookahead": [1, 2, 6], "dai": [1, 6, 8], "take": [1, 4, 6, 7, 8], "group": [1, 7, 8], "input": 1, "return": [1, 4, 6, 7, 8], "singl": [1, 2, 6], "i": [1, 2, 3, 6, 7, 8], "found": [1, 6], "window": [1, 2, 6, 7], "whether": [1, 6], "type": [1, 2, 4, 6, 7], "2": [1, 6, 7, 8], "diabet": [1, 6], "becaus": [1, 4, 6, 7], "onli": [1, 2, 6, 7, 8], "experi": [1, 6], "onc": [1, 6], "handl": [1, 6], "vectoris": 1, "wai": 1, "dure": 1, "resolut": 1, "faster": [1, 6, 7, 8], "than": [1, 2, 6], "non": 1, "occur": [1, 2, 6], "feature_nam": [1, 7], "default": [1, 4, 6], "pred": [1, 4, 6], "get_output_col_nam": 1, "get": [1, 3, 4], "output": [1, 4, 7, 8], "is_dichotom": 1, "check": [1, 6, 7, 8], "dichotom": 1, "properti": [1, 6, 7], "lookahead_period": 1, "model_config": [1, 4], "classvar": [1, 4], "configdict": [1, 4], "arbitrary_types_allow": [1, 4], "true": [1, 2, 4, 6, 7], "extra": [1, 6, 7], "forbid": 1, "frozen": 1, "configur": [1, 4], "model": [1, 2, 4, 6, 8], "dictionari": [1, 4], "conform": [1, 4], "pydant": [1, 4], "config": [1, 4], "model_field": [1, 4], "dict": [1, 4], "fieldinfo": [1, 4], "annot": [1, 4], "list": [1, 4, 5, 6, 7, 8], "fals": [1, 4, 6, 8], "about": [1, 4, 6], "field": [1, 4], "map": [1, 4], "thi": [1, 2, 4, 6, 7, 8], "replac": [1, 4], "__fields__": [1, 4], "v1": [1, 4], "predictorspec": [1, 4, 6], "lookbehind_dai": [1, 6, 7, 8], "predictor": [1, 2, 4, 5, 7], "lookbehind": [1, 2, 6, 7, 8], "lookbehind_period": [1, 7], "staticspec": [1, 4, 6], "static": [1, 5, 7], "can_be_coerced_losslessly_to_int": 1, "coerce_float": 1, "get_temporal_col_nam": 1, "tempor": [1, 5, 7, 8], "packag": [2, 5, 6, 7], "data": [2, 4, 5, 7, 8], "machin": 2, "learn": 2, "implement": [2, 7], "method": [2, 4, 7], "includ": 2, "convert": [2, 8], "ani": [2, 4, 6, 7, 8], "irregular": [2, 6], "row": [2, 6, 7, 8], "desir": 2, "construct": 2, "raw": 2, "ar": [2, 4, 6, 7, 8], "allow": [2, 4, 7], "patient": [2, 6, 8], "independ": 2, "set": [2, 4, 6], "particular": 2, "sever": [2, 8], "choic": 2, "one": [2, 4, 6, 7, 8], "need": [2, 6, 7, 8], "issu": [2, 6], "everi": [2, 6, 7], "physic": 2, "visit": 2, "morn": 2, "anoth": [2, 6], "clinic": [2, 8], "meaning": 2, "far": [2, 6, 8], "back": [2, 6], "ahead": [2, 6], "exist": 2, "point": [2, 6], "abov": [2, 6, 7, 8], "figur": 2, "graphic": 2, "repres": [2, 6], "terminologi": [2, 6], "determin": [2, 6], "wherea": 2, "futur": [2, 6], "refer": [2, 6], "b": 2, "label": [2, 6], "neg": 2, "never": [2, 6], "happen": [2, 6], "outsid": [2, 6], "posit": [2, 6], "insid": [2, 6], "exampl": [2, 6, 7, 8], "mean": [2, 6, 7, 8], "shown": [2, 6], "max": [2, 6], "min": [2, 6], "etc": [2, 6], "d": 2, "drop": [2, 6, 7, 8], "extend": [2, 6], "further": [2, 4, 6], "start": [2, 3, 6, 7, 8], "dataset": [2, 4, 5, 6, 7], "end": [2, 6, 7], "behaviour": 2, "option": [2, 4], "obtain": 2, "rich": 2, "represent": 2, "see": [2, 4, 6], "tutori": [2, 4, 8], "placehold": 2, "case": [2, 6], "report": 2, "request": 2, "github": [2, 3], "tracker": 2, "otherwis": 2, "discuss": [2, 6], "forum": 2, "bug": 2, "idea": 2, "usag": 2, "index": 2, "run": [3, 5], "follow": [3, 6], "line": [3, 6, 7], "termin": 3, "There": [3, 6, 7, 8], "discrep": 3, "between": 3, "latest": 3, "version": [3, 6, 7], "flatten": [4, 5, 8], "describ": [4, 6, 8], "speccollect": 4, "outcome_spec": [4, 6], "predictor_spec": 4, "static_spec": 4, "collect": 4, "spec": [4, 6, 7, 8], "prediction_times_df": [4, 6, 7, 8], "drop_pred_times_with_insufficient_look_dist": [4, 6, 7, 8], "cach": [4, 5], "featurecach": [4, 7], "none": [4, 6, 7], "entity_id_col_nam": [4, 6, 7, 8], "timestamp_col_nam": [4, 6, 7, 8], "predictor_col_name_prefix": 4, "outcome_col_name_prefix": 4, "n_worker": [4, 6, 7, 8], "60": [4, 7], "log_to_stdout": 4, "turn": [4, 8], "tabular": [4, 8], "add_ag": 4, "date_of_birth_df": 4, "date_of_birth_col_nam": 4, "date_of_birth": 4, "output_prefix": 4, "add": [4, 6, 7], "ag": 4, "ha": [4, 6, 7, 8], "its": [4, 6], "own": [4, 7], "function": [4, 6, 8], "veri": 4, "frequent": [4, 6], "match": 4, "self": [4, 6, 7], "add_spec": [4, 6, 7, 8], "sequenc": [4, 7], "queue": 4, "unprocess": [4, 6, 7, 8], "process": [4, 6, 7, 8], "until": 4, "call": [4, 6, 7], "comput": [4, 6, 7, 8], "get_df": [4, 6, 7, 8], "u": 4, "more": [4, 6, 7], "effecti": 4, "parallelis": 4, "most": [4, 6, 7], "complex": 4, "li": 4, "For": [4, 6, 7, 8], "document": 4, "those": 4, "present": [4, 6], "we": [5, 6, 7, 8], "recommend": 5, "go": [5, 6], "through": 5, "below": 5, "jupyt": 5, "notebook": 5, "download": 5, "local": [5, 6, 7], "introductori": 5, "load": [5, 7, 8], "advanc": [5, 6], "creat": [5, 6, 8], "combin": 5, "ad": [5, 6], "embed": 5, "especi": 6, "help": 6, "have": [6, 7, 8], "complic": 6, "train": 6, "simpl": 6, "explain": 6, "appli": 6, "consist": 6, "3": [6, 7, 8], "step": 6, "": [6, 7, 8], "simplest": 6, "first": [6, 7, 8], "predictin": 6, "element": 6, "context": 6, "skimpi": [6, 7], "import": [6, 7, 8], "skim": [6, 7], "test": [6, 7, 8], "load_synth_data": [6, 7, 8], "load_synth_prediction_tim": [6, 7, 8], "df_prediction_tim": 6, "sort_valu": 6, "summari": [6, 7], "count": [6, 7], "10000": [6, 7], "int64": [6, 7], "1": [6, 7, 8], "datetime64": [6, 7], "column_nam": [6, 7], "na": [6, 7, 8], "sd": [6, 7], "p0": [6, 7], "p25": [6, 7], "p50": [6, 7], "p75": [6, 7], "p100": [6, 7], "hist": [6, 7], "5000": [6, 7], "2900": [6, 7], "2500": 6, "4900": [6, 7], "7400": [6, 7], "last": [6, 7, 8], "frequenc": [6, 7], "1965": [6, 8], "01": [6, 7, 8], "02": [6, 7, 8], "09": [6, 8], "35": 6, "00": [6, 7, 8], "1969": [6, 7, 8], "12": [6, 7, 8], "31": [6, 7, 8], "21": [6, 7, 8], "42": [6, 7], "628": 6, "11": [6, 7, 8], "55": 6, "2005": 6, "03": [6, 8], "15": [6, 8], "07": [6, 8], "16": [6, 8], "4370": 6, "13": [6, 7, 8], "23": [6, 8], "18": [6, 7, 8], "6152": 6, "1968": [6, 7, 8], "04": [6, 8], "6873": 6, "4": [6, 7, 8], "28": [6, 8], "33": 6, "9688": 6, "9996": 6, "17": [6, 7, 8], "1463": 6, "30": [6, 7, 8], "19": [6, 8], "3952": 6, "9997": 6, "1967": [6, 8], "06": [6, 8], "08": [6, 8], "52": 6, "7926": 6, "9999": 6, "22": [6, 8], "24": 6, "5720": 6, "14": [6, 8], "59": [6, 7], "here": 6, "Then": [6, 7], "our": [6, 7, 8], "differ": [6, 7], "timepoint": 6, "load_synth_predictor_float": [6, 7], "df_synth_predictor": 6, "100000": 6, "float64": [6, 7], "7500": 6, "5": [6, 7, 8], "9": [6, 7], "00015": 6, "7": [6, 7, 8], "10": [6, 7, 8], "37": 6, "95792": 6, "29": [6, 7], "799246": 6, "82592": 6, "05": [6, 7, 8], "6": [6, 7], "630007": 6, "1377": 6, "174793": 6, "28579": 6, "26": [6, 8], "981185": 6, "81247": 6, "44": [6, 7], "970382": 6, "10277": 6, "20": [6, 8], "304568": 6, "74701": 6, "671907": 6, "69566": 6, "41": [6, 8], "250538": 6, "40901": 6, "1966": [6, 8], "924175": 6, "96881": 6, "501553": 6, "again": 6, "could": 6, "sex": 6, "doesn": 6, "t": [6, 7], "chang": 6, "over": 6, "let": [6, 7, 8], "load_synth_sex": 6, "df_synth_sex": 6, "femal": 6, "9994": 6, "9995": 6, "9998": 6, "As": [6, 8], "And": 6, "lastli": 6, "ve": 6, "chosen": 6, "binari": 6, "store": 6, "infer": 6, "do": 6, "sinc": 6, "thei": [6, 7, 8], "section": 6, "load_synth_outcom": 6, "df_synth_outcom": 6, "3103": 6, "5100": 6, "7600": 6, "50": [6, 7, 8], "46": [6, 7], "6253": 6, "9964": 6, "6255": 6, "9966": 6, "6256": 6, "9968": 6, "6257": 6, "9970": 6, "6269": 6, "9992": 6, "53": [6, 7], "per": [6, 7], "now": [6, 7, 8], "recip": 6, "finish": 6, "firstli": 6, "main": 6, "decis": 6, "size": [6, 7], "given": 6, "indic": 6, "code": [6, 7], "feature_spec": [6, 7, 8], "single_spec": 6, "maximum": [6, 7], "panda": [6, 7, 8], "pd": [6, 8], "test_df": 6, "365": [6, 7, 8], "outcome_nam": 6, "argument": 6, "values_df": 6, "decid": 6, "least": 6, "correspond": [6, 8], "both": 6, "accomplish": 6, "dw_ek_borg": 6, "wa": [6, 8], "mark": 6, "after": 6, "where": 6, "event": 6, "perman": 6, "specifii": 6, "forward": 6, "search": 6, "certain": 6, "period": [6, 8], "befor": [6, 8], "instead": 6, "almost": 6, "entir": 6, "ident": 6, "except": 6, "past": 6, "numpi": [6, 7, 8], "np": [6, 7, 8], "temporal_predictor_spec": 6, "730": [6, 7, 8], "nan": [6, 7, 8], "predictor_nam": 6, "rang": 6, "similar": 6, "instanc": [6, 7], "might": [6, 7, 8], "182": 6, "easili": 6, "pass": [6, 8], "temporal_interval_predictor_spec": 6, "90": 6, "predictor_interval_nam": 6, "slightli": 6, "previou": 6, "provid": 6, "howev": [6, 7, 8], "By": 6, "filter": 6, "easi": 6, "manual": [6, 7], "sex_predictor_spec": 6, "input_col_name_overrid": 6, "df": [6, 7, 8], "tsflatten": 6, "re": [6, 8], "readi": 6, "instanti": 6, "along": 6, "add_": 6, "parallel": [6, 7, 8], "oper": 6, "across": 6, "core": [6, 7], "ts_flatten": [6, 7, 8], "applic": 6, "sai": [6, 7], "month": [6, 7, 8], "would": [6, 8], "compromis": 6, "generalis": 6, "some": [6, 7, 8], "edg": 6, "brief": 6, "2024": [6, 7, 8], "25": [6, 7, 8], "info": [6, 7, 8], "were": [6, 7, 8], "_drop_pred_time_if_insufficient_look_dist": [6, 7], "5999": 6, "99": 6, "worker": [6, 7, 8], "chunksiz": [6, 7, 8], "mai": [6, 7, 8], "progress": [6, 7, 8], "bar": [6, 7, 8], "move": [6, 7, 8], "batch": [6, 7, 8], "much": [6, 7, 8], "total": [6, 7, 8], "perform": [6, 7, 8], "100": [6, 7, 8], "40": [6, 7], "31it": 6, "align": [6, 7, 8], "littl": [6, 7, 8], "while": [6, 7, 8], "minut": [6, 7, 8], "000": [6, 7, 8], "concaten": [6, 7, 8], "Will": [6, 7, 8], "system": [6, 7, 8], "2_000_000": [6, 7, 8], "normal": [6, 7, 8], "took": [6, 7, 8], "004": 6, "second": [6, 7, 8], "merg": [6, 7, 8], "origin": [6, 7, 8], "4001": 6, "string": [6, 7], "2600": [6, 7], "outc_outcome_name_withi": 6, "064": 6, "n_365_days_maximum_fal": 6, "back_0_dichotom": 6, "pred_predictor_name_wit": 6, "72": 6, "097": 6, "hin_730_days_mean_fallb": 6, "ack_nan": 6, "pred_predictor_interv": 6, "2877": 6, "71": 6, "91": 6, "_name_within_30_to_90_d": 6, "ays_mean_fallback_nan": [6, 7], "pred_femal": 6, "49": 6, "39": 6, "word": [6, 7, 8], "prediction_time_uuid": [6, 7, 8], "outc_outcome_name_within_365_days_maximum_fallback_0_dichotom": 6, "pred_predictor_name_within_730_days_mean_fallback_nan": 6, "pred_predictor_interval_name_within_30_to_90_days_mean_fallback_nan": 6, "display": [6, 7], "shorten": [6, 7], "col": [6, 7], "shortened_pr": 6, "pred_x": 6, "shortened_pred_interv": 6, "pred_x_30_to_90": 6, "shortened_outcom": 6, "outc_i": 6, "renam": [6, 7], "pred_predictor_name_within_0_to_730_days_mean_fallback_nan": 6, "outc_outcome_name_within_0_to_365_days_maximum_fallback_0_dichotom": 6, "axi": [6, 7, 8], "set_table_attribut": [6, 7], "font": [6, 7], "14px": [6, 7], "importerror": [6, 7], "traceback": [6, 7], "recent": [6, 7], "cell": [6, 7], "file": [6, 7], "lib": [6, 7], "python3": [6, 7], "site": [6, 7], "frame": [6, 7], "1338": [6, 7], "1318": [6, 7], "1319": [6, 7, 8], "def": [6, 7, 8], "styler": [6, 7], "1320": [6, 7], "1321": [6, 7], "1322": [6, 7], "1336": [6, 7], "tabl": [6, 7], "visual": [6, 7], "user_guid": [6, 7], "ipynb": [6, 7], "1337": [6, 7], "io": [6, 7], "format": [6, 7, 8], "1340": [6, 7], "shared_doc": [6, 7], "_shared_doc": [6, 7], "save_to_buff": [6, 7], "jinja2": [6, 7], "import_optional_depend": [6, 7], "style_rend": [6, 7], "47": [6, 7, 8], "cssproperti": [6, 7], "48": [6, 7], "cssstyle": [6, 7], "56": [6, 7], "refactor_level": [6, 7], "57": [6, 7], "type_check": [6, 7], "compat": [6, 7], "_option": [6, 7], "161": [6, 7], "error": [6, 7], "min_vers": [6, 7], "159": [6, 7], "160": [6, 7], "elif": [6, 7], "rais": [6, 7], "msg": [6, 7], "163": [6, 7], "modul": [6, 7], "newer": [6, 7], "current": [6, 7], "classif": 6, "citizen": 6, "uniqu": 6, "identifi": 6, "prediciton": 6, "pred_": [6, 7], "outc_": 6, "basic": 7, "cover": [7, 8], "expand": 7, "effect": 7, "mani": 7, "so": [7, 8], "iter": 7, "without": 7, "complet": 7, "full": 7, "hand": 7, "rather": 7, "straightforward": 7, "what": 7, "hundr": 7, "amount": 7, "write": 7, "grow": 7, "quit": 7, "substanti": 7, "becom": 7, "consum": 7, "hard": 7, "navig": 7, "solv": 7, "problem": 7, "combinatori": 7, "group_spec": [7, 8], "predictorgroupspec": [7, 8], "nameddatafram": 7, "pprint": 7, "pred_spec_batch": 7, "named_datafram": [7, 8], "synth_predictor_float": 7, "1095": 7, "create_combin": [7, 8], "attribut": 7, "easier": 7, "namedatafram": 7, "exactli": 7, "load_synth_predictor_flaot": 7, "pred_synth_predictor_float_": 7, "result": [7, 8], "good": 7, "small": [7, 8], "highlight": 7, "pred_spec_batch_summari": 7, "pred_spec": 7, "__name__": 7, "print": [7, 8], "f": 7, "len": [7, 8], "know": 7, "bunch": 7, "quickli": 7, "But": 7, "next": 7, "ship": 7, "disk": 7, "feature_cach": 7, "cache_to_disk": 7, "diskcach": 7, "flattened_dataset": 7, "pathlib": 7, "path": 7, "feature_cache_dir": 7, "tmp": 7, "directori": 7, "save": 7, "just": 7, "them": 7, "won": 7, "alreadi": [7, 8], "new": 7, "abstract": 7, "redi": 7, "sql": 7, "everyth": 7, "work": 7, "6053": 7, "63it": 7, "45": 7, "85it": 7, "006": 7, "3947": 7, "pred_synth_predictor_fl": 7, "oat_within_1095_days_m": 7, "an_fallback_nan": 7, "506": 7, "82": 7, "024": 7, "oat_within_365_to_730_d": 7, "oat_within_1095_days_ma": 7, "ximum_fallback_nan": 7, "533": 7, "0084": 7, "oat_within_365_days_max": 7, "imum_fallback_nan": 7, "oat_within_365_days_mea": 7, "n_fallback_nan": 7, "ays_maximum_fallback_na": 7, "n": 7, "pred_synth_predictor_float_within_1095_days_mean_fallback_nan": 7, "pred_synth_predictor_float_within_365_to_730_days_mean_fallback_nan": 7, "pred_synth_predictor_float_within_1095_days_maximum_fallback_nan": 7, "pred_synth_predictor_float_within_365_days_maximum_fallback_nan": 7, "pred_synth_predictor_float_within_365_days_mean_fallback_nan": 7, "pred_synth_predictor_float_within_365_to_730_days_maximum_fallback_nan": 7, "pred_col": 7, "startswith": 7, "rename_dict": 7, "enumer": 7, "df_renam": 7, "base_col": 7, "renamed_col": 7, "dealt": 8, "show": 8, "out": 8, "synthet": 8, "other": 8, "load_synth_text": 8, "synth_text": 8, "head": 8, "4647": 8, "went": 8, "induc": 8, "coma": 8, "2007": 8, "taken": 8, "emerg": 8, "departm": 8, "5799": 8, "old": 8, "son": 8, "wh": 8, "had": 8, "been": 8, "left": 8, "bed": 8, "minu": 8, "4234": 8, "allergi": 8, "often": 8, "advantag": 8, "emb": 8, "speed": 8, "up": 8, "block": 8, "tf": 8, "idf": 8, "form": 8, "constraint": 8, "entitiy_id_col": 8, "timestamp_col": 8, "value_col": 8, "purpos": 8, "demonstr": 8, "fit": 8, "captur": 8, "sklearn": 8, "feature_extract": 8, "tfidfvector": 8, "embed_text_to_df": 8, "tfidf_model": 8, "max_featur": 8, "fit_transform": 8, "toarrai": 8, "get_feature_names_out": 8, "embedded_text": 8, "tolist": 8, "metadata_onli": 8, "embedded_text_with_metadata": 8, "concat": 8, "175872": 8, "182066": 8, "249848": 8, "158430": 8, "000000": 8, "023042": 8, "311389": 8, "529966": 8, "490203": 8, "479312": 8, "244870": 8, "135282": 8, "064337": 8, "465084": 8, "336859": 8, "151743": 8, "729861": 8, "179161": 8, "192367": 8, "232332": 8, "283402": 8, "336952": 8, "176422": 8, "238416": 8, "646879": 8, "250217": 8, "382277": 8, "165635": 8, "200046": 8, "183015": 8, "261115": 8, "125837": 8, "151906": 8, "205285": 8, "759528": 8, "403961": 8, "098747": 8, "493461": 8, "119196": 8, "272619": 8, "207444": 8, "045256": 8, "183475": 8, "588324": 8, "433253": 8, "235349": 8, "df_with_multiple_values_to_named_datafram": 8, "readili": 8, "suppli": 8, "df_transform": 8, "split": 8, "embedded_df": 8, "name_prefix": 8, "tfidf_": 8, "accord": 8, "inform": 8, "bow": 8, "kept": 8, "tfidf_and": 8, "emb_spec_batch": 8, "36it": 8, "10it": 8, "028": 8, "sake": 8, "dropna": 8, "pred_tfidf_to_within_730_days_mean_fallback_nan": 8, "pred_tfidf_or_within_730_days_mean_fallback_nan": 8, "pred_tfidf_that_within_365_days_mean_fallback_nan": 8, "pred_tfidf_and_within_730_days_mean_fallback_nan": 8, "pred_tfidf_was_within_730_days_mean_fallback_nan": 8, "pred_tfidf_in_within_730_days_mean_fallback_nan": 8, "pred_tfidf_in_within_365_days_mean_fallback_nan": 8, "pred_tfidf_and_within_365_days_mean_fallback_nan": 8, "pred_tfidf_the_within_730_days_mean_fallback_nan": 8, "pred_tfidf_for_within_730_days_mean_fallback_nan": 8, "pred_tfidf_to_within_365_days_mean_fallback_nan": 8, "pred_tfidf_was_within_365_days_mean_fallback_nan": 8, "pred_tfidf_of_within_365_days_mean_fallback_nan": 8, "pred_tfidf_patient_within_730_days_mean_fallback_nan": 8, "pred_tfidf_the_within_365_days_mean_fallback_nan": 8, "pred_tfidf_for_within_365_days_mean_fallback_nan": 8, "pred_tfidf_or_within_365_days_mean_fallback_nan": 8, "1917": 8, "4977": 8, "284485": 8, "221549": 8, "090356": 8, "145809": 8, "086927": 8, "483324": 8, "534890": 8, "088050": 8, "536339": 8, "133722": 8, "2463": 8, "6840": 8, "456030": 8, "355142": 8, "096561": 8, "155821": 8, "092896": 8, "258256": 8, "285810": 8, "376386": 8, "573168": 8, "071452": 8, "2580": 8, "639848": 8, "260680": 8, "601521": 8, "401014": 8, "2741": 8, "9832": 8, "36": 8, "164655": 8, "128228": 8, "225044": 8, "335410": 8, "186493": 8, "825558": 8, "101924": 8, "236513": 8, "103195": 8, "2931": 8, "7281": 8, "211934": 8, "385111": 8, "269251": 8, "289663": 8, "388547": 8, "280049": 8, "464891": 8, "043730": 8, "304425": 8, "332065": 8}, "objects": {"timeseriesflattener.feature_specs": [[1, 0, 0, "-", "single_specs"]], "timeseriesflattener.feature_specs.single_specs": [[1, 1, 1, "", "CoercedFloats"], [1, 1, 1, "", "LookPeriod"], [1, 1, 1, "", "OutcomeSpec"], [1, 1, 1, "", "PredictorSpec"], [1, 1, 1, "", "StaticSpec"], [1, 5, 1, "", "can_be_coerced_losslessly_to_int"], [1, 5, 1, "", "coerce_floats"], [1, 5, 1, "", "get_temporal_col_name"]], "timeseriesflattener.feature_specs.single_specs.CoercedFloats": [[1, 2, 1, "", "fallback"], [1, 2, 1, "", "lookperiod"]], "timeseriesflattener.feature_specs.single_specs.LookPeriod": [[1, 2, 1, "", "max_days"], [1, 2, 1, "", "min_days"]], "timeseriesflattener.feature_specs.single_specs.OutcomeSpec": [[1, 2, 1, "", "aggregation_fn"], [1, 2, 1, "", "fallback"], [1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "incident"], [1, 3, 1, "", "is_dichotomous"], [1, 2, 1, "", "lookahead_days"], [1, 4, 1, "", "lookahead_period"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener.feature_specs.single_specs.PredictorSpec": [[1, 2, 1, "", "aggregation_fn"], [1, 2, 1, "", "fallback"], [1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "lookbehind_days"], [1, 4, 1, "", "lookbehind_period"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener.feature_specs.single_specs.StaticSpec": [[1, 2, 1, "", "feature_base_name"], [1, 3, 1, "", "get_output_col_name"], [1, 2, 1, "", "model_config"], [1, 2, 1, "", "model_fields"], [1, 2, 1, "", "prefix"], [1, 2, 1, "", "timeseries_df"]], "timeseriesflattener": [[4, 0, 0, "-", "flattened_dataset"]], "timeseriesflattener.flattened_dataset": [[4, 1, 1, "", "SpecCollection"], [4, 1, 1, "", "TimeseriesFlattener"]], "timeseriesflattener.flattened_dataset.SpecCollection": [[4, 2, 1, "", "model_config"], [4, 2, 1, "", "model_fields"], [4, 2, 1, "", "outcome_specs"], [4, 2, 1, "", "predictor_specs"], [4, 2, 1, "", "static_specs"]], "timeseriesflattener.flattened_dataset.TimeseriesFlattener": [[4, 3, 1, "", "add_age"], [4, 3, 1, "", "add_spec"], [4, 3, 1, "", "compute"], [4, 3, 1, "", "get_df"]]}, "objtypes": {"0": "py:module", "1": "py:class", "2": "py:attribute", "3": "py:method", "4": "py:property", "5": "py:function"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "class", "Python class"], "2": ["py", "attribute", "Python attribute"], "3": ["py", "method", "Python method"], "4": ["py", "property", "Python property"], "5": ["py", "function", "Python function"]}, "titleterms": {"frequent": 0, "ask": [0, 2], "question": [0, 2], "cite": 0, "thi": 0, "packag": 0, "how": [0, 6], "do": 0, "i": 0, "test": 0, "code": 0, "run": 0, "suit": 0, "document": 0, "gener": [0, 8], "featur": [1, 7, 8], "specif": [1, 6], "timeseriesflatten": [1, 2, 4], "feature_spec": 1, "single_spec": 1, "function": 2, "where": 2, "indic": 2, "search": 2, "instal": 3, "flattened_dataset": 4, "tutori": [5, 6, 7], "get": 5, "start": 5, "introductori": 6, "load": 6, "data": 6, "predict": 6, "time": 6, "tempor": 6, "predictor": [6, 8], "static": 6, "outcom": 6, "specifi": 6, "flatten": 6, "advanc": 7, "creat": 7, "combin": 7, "cach": 7, "ad": 8, "text": 8, "The": 8, "dataset": 8, "from": 8, "embed": 8}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1, "sphinx": 57}, "alltitles": {"Frequently Asked Questions": [[0, "frequently-asked-questions"]], "Citing this package": [[0, "citing-this-package"]], "How do I test the code and run the test suite?": [[0, "how-do-i-test-the-code-and-run-the-test-suite"]], "How is the documentation generated?": [[0, "how-is-the-documentation-generated"]], "Feature specifications": [[1, "feature-specifications"]], "timeseriesflattener.feature_specs.single_specs": [[1, "module-timeseriesflattener.feature_specs.single_specs"]], "timeseriesflattener": [[2, "timeseriesflattener"]], "Functionality": [[2, "functionality"]], "Where to ask questions?": [[2, "where-to-ask-questions"]], "Indices and search": [[2, "indices-and-search"]], "Installation": [[3, "installation"]], "Timeseriesflattener": [[4, "timeseriesflattener"]], "timeseriesflattener.flattened_dataset": [[4, "module-timeseriesflattener.flattened_dataset"]], "Tutorials": [[5, "tutorials"]], "Getting started": [[5, null]], "Introductory Tutorial": [[6, "introductory-tutorial"]], "Loading data": [[6, "loading-data"]], "Loading prediction times": [[6, "loading-prediction-times"]], "Loading a temporal predictor": [[6, "loading-a-temporal-predictor"]], "Loading a static predictor": [[6, "loading-a-static-predictor"]], "Loading a temporal outcome": [[6, "loading-a-temporal-outcome"]], "Specifying how to flatten the data": [[6, "specifying-how-to-flatten-the-data"]], "Temporal outcome specification": [[6, "temporal-outcome-specification"]], "Temporal predictor specification": [[6, "temporal-predictor-specification"]], "Static predictor specification": [[6, "static-predictor-specification"]], "Flattening": [[6, "flattening"]], "Advanced Tutorial": [[7, "advanced-tutorial"]], "Creating feature combinations": [[7, "creating-feature-combinations"]], "Caching": [[7, "caching"]], "Adding text features": [[8, "adding-text-features"]], "The dataset": [[8, "the-dataset"]], "Generating predictors from embedded text": [[8, "generating-predictors-from-embedded-text"]]}, "indexentries": {"coercedfloats (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats"]], "lookperiod (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod"]], "outcomespec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec"]], "predictorspec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec"]], "staticspec (class in timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec"]], "aggregation_fn (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.aggregation_fn"]], "aggregation_fn (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.aggregation_fn"]], "can_be_coerced_losslessly_to_int() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.can_be_coerced_losslessly_to_int"]], "coerce_floats() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.coerce_floats"]], "fallback (coercedfloats attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats.fallback"]], "fallback (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.fallback"]], "fallback (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.fallback"]], "feature_base_name (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.feature_base_name"]], "feature_base_name (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.feature_base_name"]], "feature_base_name (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.feature_base_name"]], "get_output_col_name() (outcomespec method)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.get_output_col_name"]], "get_output_col_name() (predictorspec method)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.get_output_col_name"]], "get_output_col_name() (staticspec method)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.get_output_col_name"]], "get_temporal_col_name() (in module timeseriesflattener.feature_specs.single_specs)": [[1, "timeseriesflattener.feature_specs.single_specs.get_temporal_col_name"]], "incident (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.incident"]], "is_dichotomous() (outcomespec method)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.is_dichotomous"]], "lookahead_days (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.lookahead_days"]], "lookahead_period (outcomespec property)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.lookahead_period"]], "lookbehind_days (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.lookbehind_days"]], "lookbehind_period (predictorspec property)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.lookbehind_period"]], "lookperiod (coercedfloats attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.CoercedFloats.lookperiod"]], "max_days (lookperiod attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod.max_days"]], "min_days (lookperiod attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.LookPeriod.min_days"]], "model_config (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.model_config"]], "model_config (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.model_config"]], "model_config (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.model_config"]], "model_fields (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.model_fields"]], "model_fields (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.model_fields"]], "model_fields (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.model_fields"]], "module": [[1, "module-timeseriesflattener.feature_specs.single_specs"], [4, "module-timeseriesflattener.flattened_dataset"]], "prefix (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.prefix"]], "prefix (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.prefix"]], "prefix (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.prefix"]], "timeseries_df (outcomespec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.OutcomeSpec.timeseries_df"]], "timeseries_df (predictorspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.PredictorSpec.timeseries_df"]], "timeseries_df (staticspec attribute)": [[1, "timeseriesflattener.feature_specs.single_specs.StaticSpec.timeseries_df"]], "timeseriesflattener.feature_specs.single_specs": [[1, "module-timeseriesflattener.feature_specs.single_specs"]], "speccollection (class in timeseriesflattener.flattened_dataset)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection"]], "timeseriesflattener (class in timeseriesflattener.flattened_dataset)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener"]], "add_age() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.add_age"]], "add_spec() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.add_spec"]], "compute() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.compute"]], "get_df() (timeseriesflattener method)": [[4, "timeseriesflattener.flattened_dataset.TimeseriesFlattener.get_df"]], "model_config (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.model_config"]], "model_fields (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.model_fields"]], "outcome_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.outcome_specs"]], "predictor_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.predictor_specs"]], "static_specs (speccollection attribute)": [[4, "timeseriesflattener.flattened_dataset.SpecCollection.static_specs"]], "timeseriesflattener.flattened_dataset": [[4, "module-timeseriesflattener.flattened_dataset"]]}}) \ No newline at end of file diff --git a/tutorials/01_basic.html b/tutorials/01_basic.html index a6be672a..d77a3e95 100644 --- a/tutorials/01_basic.html +++ b/tutorials/01_basic.html @@ -991,31 +991,31 @@

Flattening -
2024-01-25 13:32:19 [INFO] There were unprocessed specs, computing...
+
2024-01-25 13:50:07 [INFO] There were unprocessed specs, computing...
 

 

-
2024-01-25 13:32:19 [INFO] _drop_pred_time_if_insufficient_look_distance: Dropped 5999 (59.99%) rows
+
2024-01-25 13:50:07 [INFO] _drop_pred_time_if_insufficient_look_distance: Dropped 5999 (59.99%) rows
 

 

-
2024-01-25 13:32:19 [INFO] Processing 3 temporal features in parallel with 1 workers. Chunksize is 3. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
+
2024-01-25 13:50:07 [INFO] Processing 3 temporal features in parallel with 1 workers. Chunksize is 3. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
 

 

 
  0%|          | 0/3 [00:00<?, ?it/s]
 

 

-
100%|██████████| 3/3 [00:00<00:00, 39.05it/s]
+
100%|██████████| 3/3 [00:00<00:00, 40.31it/s]
 

 

-
2024-01-25 13:32:19 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
+
2024-01-25 13:50:07 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
 

 

-
2024-01-25 13:32:19 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
+
2024-01-25 13:50:07 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
 

 

-
2024-01-25 13:32:19 [INFO] Concatenation took 0.004 seconds
+
2024-01-25 13:50:07 [INFO] Concatenation took 0.004 seconds
 

 

-
2024-01-25 13:32:19 [INFO] Merging with original df
+
2024-01-25 13:50:07 [INFO] Merging with original df
 

 

 
╭──────────────────────────────────────────────── skimpy summary ─────────────────────────────────────────────────╮
@@ -1036,12 +1036,12 @@ 
Flatteningoutc_outcome_name_withi │     0 │      0 │  0.064 │  0.25 │      0 │     0 │     0 │    0 │     1 │ ▇    ▁ │  │
 │ │ n_365_days_maximum_fall │       │        │        │       │        │       │       │      │       │        │  │
 │ │ back_0_dichotomous      │       │        │        │       │        │       │       │      │       │        │  │
-│ │ pred_predictor_interval │  2877 │  71.91 │      5 │   2.8 │   0.02 │   2.6 │   5.1 │  7.4 │    10 │ ▇▇▇▇▇▇ │  │
-│ │ _name_within_30_to_90_d │       │        │        │       │        │       │       │      │       │        │  │
-│ │ ays_mean_fallback_nan   │       │        │        │       │        │       │       │      │       │        │  │
 │ │ pred_predictor_name_wit │    72 │    1.8 │      5 │   1.6 │  0.097 │   3.9 │     5 │    6 │   9.9 │ ▁▃▇▇▃▁ │  │
 │ │ hin_730_days_mean_fallb │       │        │        │       │        │       │       │      │       │        │  │
 │ │ ack_nan                 │       │        │        │       │        │       │       │      │       │        │  │
+│ │ pred_predictor_interval │  2877 │  71.91 │      5 │   2.8 │   0.02 │   2.6 │   5.1 │  7.4 │    10 │ ▇▇▇▇▇▇ │  │
+│ │ _name_within_30_to_90_d │       │        │        │       │        │       │       │      │       │        │  │
+│ │ ays_mean_fallback_nan   │       │        │        │       │        │       │       │      │       │        │  │
 │ │ pred_female             │     0 │      0 │   0.49 │   0.5 │      0 │     0 │     0 │    1 │     1 │ ▇    ▇ │  │
 │ └─────────────────────────┴───────┴────────┴────────┴───────┴────────┴───────┴───────┴──────┴───────┴────────┘  │
 │                                                    datetime                                                     │
@@ -1062,8 +1062,8 @@ 
Flattening
-
2024-01-25 13:32:22 [INFO] There were unprocessed specs, computing...
+
2024-01-25 13:50:10 [INFO] There were unprocessed specs, computing...
 

 

-
2024-01-25 13:32:22 [INFO] _drop_pred_time_if_insufficient_look_distance: Dropped 6053 (60.53%) rows
+
2024-01-25 13:50:10 [INFO] _drop_pred_time_if_insufficient_look_distance: Dropped 6053 (60.53%) rows
 

 

-
2024-01-25 13:32:22 [INFO] Processing 6 temporal features in parallel with 4 workers. Chunksize is 2. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
+
2024-01-25 13:50:10 [INFO] Processing 6 temporal features in parallel with 4 workers. Chunksize is 2. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
 

 

 
  0%|          | 0/6 [00:00<?, ?it/s]
 

 

-
 17%|█▋        | 1/6 [00:00<00:00,  9.73it/s]
+
 17%|█▋        | 1/6 [00:00<00:00,  8.63it/s]
 

 

-
100%|██████████| 6/6 [00:00<00:00, 39.92it/s]
+
100%|██████████| 6/6 [00:00<00:00, 45.85it/s]
 

 

-

+
2024-01-25 13:50:10 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
 

 

-
2024-01-25 13:32:23 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
+
2024-01-25 13:50:10 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
 

 

-
2024-01-25 13:32:23 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
+
2024-01-25 13:50:10 [INFO] Concatenation took 0.006 seconds
 

 

-
2024-01-25 13:32:23 [INFO] Concatenation took 0.007 seconds
-

-

-
2024-01-25 13:32:23 [INFO] Merging with original df
+
2024-01-25 13:50:10 [INFO] Merging with original df
 

 

 

@@ -438,25 +435,25 @@ 
Caching column_name             ┃ NA   ┃ NA %   ┃ mean  ┃ sd    ┃ p0      ┃ p25   ┃ p50   ┃ p75   ┃ p100  ┃ hist   ┃  │
 │ ┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━┩  │
 │ │ entity_id               │    0 │      0 │  5000 │  2900 │       0 │  2600 │  4900 │  7400 │ 10000 │ ▇▇▇▇▇▇ │  │
-│ │ pred_synth_predictor_fl │  506 │  12.82 │   6.6 │   2.6 │   0.024 │   4.8 │   7.3 │   8.8 │    10 │ ▂▂▃▃▆▇ │  │
+│ │ pred_synth_predictor_fl │    7 │   0.18 │     5 │   1.3 │    0.29 │   4.1 │     5 │   5.8 │   9.9 │  ▂▇▇▁  │  │
+│ │ oat_within_1095_days_me │      │        │       │       │         │       │       │       │       │        │  │
+│ │ an_fallback_nan         │      │        │       │       │         │       │       │       │       │        │  │
+│ │ pred_synth_predictor_fl │  506 │  12.82 │   5.1 │   2.2 │   0.024 │   3.6 │     5 │   6.5 │    10 │ ▂▅▇▇▅▂ │  │
 │ │ oat_within_365_to_730_d │      │        │       │       │         │       │       │       │       │        │  │
-│ │ ays_maximum_fallback_na │      │        │       │       │         │       │       │       │       │        │  │
-│ │ n                       │      │        │       │       │         │       │       │       │       │        │  │
+│ │ ays_mean_fallback_nan   │      │        │       │       │         │       │       │       │       │        │  │
 │ │ pred_synth_predictor_fl │    7 │   0.18 │   8.4 │   1.5 │    0.29 │   7.8 │   8.9 │   9.5 │    10 │    ▁▃▇ │  │
 │ │ oat_within_1095_days_ma │      │        │       │       │         │       │       │       │       │        │  │
 │ │ ximum_fallback_nan      │      │        │       │       │         │       │       │       │       │        │  │
 │ │ pred_synth_predictor_fl │  533 │   13.5 │   6.6 │   2.6 │  0.0084 │   4.8 │   7.3 │   8.8 │    10 │ ▁▂▃▃▆▇ │  │
 │ │ oat_within_365_days_max │      │        │       │       │         │       │       │       │       │        │  │
 │ │ imum_fallback_nan       │      │        │       │       │         │       │       │       │       │        │  │
-│ │ pred_synth_predictor_fl │  506 │  12.82 │   5.1 │   2.2 │   0.024 │   3.6 │     5 │   6.5 │    10 │ ▂▅▇▇▅▂ │  │
-│ │ oat_within_365_to_730_d │      │        │       │       │         │       │       │       │       │        │  │
-│ │ ays_mean_fallback_nan   │      │        │       │       │         │       │       │       │       │        │  │
 │ │ pred_synth_predictor_fl │  533 │   13.5 │     5 │   2.1 │  0.0084 │   3.6 │     5 │   6.4 │   9.9 │ ▂▅▇▇▅▂ │  │
 │ │ oat_within_365_days_mea │      │        │       │       │         │       │       │       │       │        │  │
 │ │ n_fallback_nan          │      │        │       │       │         │       │       │       │       │        │  │
-│ │ pred_synth_predictor_fl │    7 │   0.18 │     5 │   1.3 │    0.29 │   4.1 │     5 │   5.8 │   9.9 │  ▂▇▇▁  │  │
-│ │ oat_within_1095_days_me │      │        │       │       │         │       │       │       │       │        │  │
-│ │ an_fallback_nan         │      │        │       │       │         │       │       │       │       │        │  │
+│ │ pred_synth_predictor_fl │  506 │  12.82 │   6.6 │   2.6 │   0.024 │   4.8 │   7.3 │   8.8 │    10 │ ▂▂▃▃▆▇ │  │
+│ │ oat_within_365_to_730_d │      │        │       │       │         │       │       │       │       │        │  │
+│ │ ays_maximum_fallback_na │      │        │       │       │         │       │       │       │       │        │  │
+│ │ n                       │      │        │       │       │         │       │       │       │       │        │  │
 │ └─────────────────────────┴──────┴────────┴───────┴───────┴─────────┴───────┴───────┴───────┴───────┴────────┘  │
 │                                                    datetime                                                     │
 │ ┏━━━━━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓  │
@@ -475,12 +472,12 @@ 
Caching
['entity_id',
  'timestamp',
  'prediction_time_uuid',
- 'pred_synth_predictor_float_within_365_to_730_days_maximum_fallback_nan',
+ 'pred_synth_predictor_float_within_1095_days_mean_fallback_nan',
+ 'pred_synth_predictor_float_within_365_to_730_days_mean_fallback_nan',
  'pred_synth_predictor_float_within_1095_days_maximum_fallback_nan',
  'pred_synth_predictor_float_within_365_days_maximum_fallback_nan',
- 'pred_synth_predictor_float_within_365_to_730_days_mean_fallback_nan',
  'pred_synth_predictor_float_within_365_days_mean_fallback_nan',
- 'pred_synth_predictor_float_within_1095_days_mean_fallback_nan']
+ 'pred_synth_predictor_float_within_365_to_730_days_maximum_fallback_nan']
 

 

 

diff --git a/tutorials/03_text.html b/tutorials/03_text.html
index 896fdc3e..05dde920 100644
--- a/tutorials/03_text.html
+++ b/tutorials/03_text.html
@@ -623,31 +623,31 @@ 
Generating predictors from embedded text
-
2024-01-25 13:32:26 [INFO] There were unprocessed specs, computing...
+
2024-01-25 13:50:13 [INFO] There were unprocessed specs, computing...
 

 

-
2024-01-25 13:32:26 [INFO] Processing 20 temporal features in parallel with 1 workers. Chunksize is 20. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
+
2024-01-25 13:50:13 [INFO] Processing 20 temporal features in parallel with 1 workers. Chunksize is 20. If this is above 1, it may take some time for the progress bar to move, as processing is batched. However, this makes for much faster total performance.
 

 

 
  0%|          | 0/20 [00:00<?, ?it/s]
 

 

-
  5%|▌         | 1/20 [00:00<00:07,  2.64it/s]
+
  5%|▌         | 1/20 [00:00<00:08,  2.36it/s]
 

 

-
100%|██████████| 20/20 [00:00<00:00, 52.68it/s]
+
100%|██████████| 20/20 [00:00<00:00, 47.10it/s]
 

 

-
2024-01-25 13:32:26 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
+
2024-01-25 13:50:13 [INFO] Checking alignment of dataframes - this might take a little while (~2 minutes for 1.000 dataframes with 2.000.000 rows).
 

 

-
2024-01-25 13:32:26 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
+
2024-01-25 13:50:13 [INFO] Starting concatenation. Will take some time on performant systems, e.g. 30s for 100 features and 2_000_000 prediction times. This is normal.
 

 

-
2024-01-25 13:32:26 [INFO] Concatenation took 0.029 seconds
+
2024-01-25 13:50:13 [INFO] Concatenation took 0.028 seconds
 

 

-
2024-01-25 13:32:26 [INFO] Merging with original df
+
2024-01-25 13:50:13 [INFO] Merging with original df
 

 

 

@@ -682,24 +682,24 @@ 
Generating predictors from embedded text