diff --git a/.gitignore b/.gitignore index 1085034..1ba06b9 100644 --- a/.gitignore +++ b/.gitignore @@ -131,4 +131,6 @@ dmypy.json # Misc. .DS_Store *.csv -*.parquet \ No newline at end of file +*.parquet +diabetes_demo.ipynb +ensemble_fpg_plots.png \ No newline at end of file diff --git a/plots.ipynb b/plots.ipynb index 3ee74d4..96e1cc6 100644 --- a/plots.ipynb +++ b/plots.ipynb @@ -170,7 +170,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -178,20 +178,20 @@ "output_type": "stream", "text": [ "current combo: \n", - " nid 398127\n", - "location_id 380\n", + " nid 238012\n", + "location_id 207\n", "year_id 1.0\n", - "sex Male\n", + "sex Female\n", "age_start3 15\n", - "Name: 240101, dtype: object\n", - "num. rows: 76\n", - "FPG bounds: 3.900000095 9.699999809\n", - "distributions and weights: {'gamma': 3.577373949725191e-10, 'invgamma': 1.0257228679559219e-10, 'lognormal': 4.53533582338816e-10, 'fisk': 0.9999999990861572}\n" + "Name: 164388, dtype: object\n", + "num. rows: 311\n", + "FPG bounds: 0.0 33.0\n", + "distributions and weights: {'gamma': 0.33723955987723486, 'invgamma': 0.6627604397743512, 'lognormal': 3.6576540266740374e-10, 'fisk': -1.7350594879708437e-11}\n" ] }, { "data": { - "image/png": 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", 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", 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" ] @@ -242,7 +242,7 @@ "ax[1].plot(support, res.ensemble_distribution.cdf(support))\n", "# shows individual cdf fits but makes graph too busy\n", "# for i in range(len(distributions)):\n", - "# ax[1].plot(support, res.ensemble_distribution.my_objs[i].cdf(support), color=\"orange\", alpha=0.25)\n", + "# ax[1].plot(support, res.ensemble_distribution.fitted_distributions[i].cdf(support), color=\"orange\", alpha=0.25)\n", "ax[1].set_xlabel(\"FPG (mmol/L)\")\n", "ax[1].set_ylabel(\"density\")\n", "ax[1].set_title(\"Empirical vs Ensemble CDF Comparison\")\n", diff --git a/src/ensemble/model.py b/src/ensemble/model.py index 8a1cae7..9fea758 100644 --- a/src/ensemble/model.py +++ b/src/ensemble/model.py @@ -1,3 +1,4 @@ +import json from typing import List, Tuple, Union import cvxpy as cp @@ -32,19 +33,24 @@ class EnsembleDistribution: def __init__( self, - distributions: List[str], - weights: List[float], + # distributions: List[str], + # weights: List[float], + named_weights: dict, mean: float, variance: float, ): - _check_valid_ensemble(distributions, weights) - self.support = _check_supports_match(distributions) + self._distributions = list(named_weights.keys()) + self._weights = list(named_weights.values()) - self.distributions = distributions - self.my_objs = [] - for distribution in distributions: - self.my_objs.append(distribution_dict[distribution](mean, variance)) - self.weights = weights + _check_valid_ensemble(self._distributions, self._weights) + self.support = _check_supports_match(self._distributions) + + self.named_weights = named_weights + self.fitted_distributions = [] + for distribution in self.named_weights.keys(): + self.fitted_distributions.append( + distribution_dict[distribution](mean, variance) + ) self.mean = mean self.variance = variance @@ -179,13 +185,13 @@ def rvs(self, size: int = 1) -> np.ndarray: # ensemble_cdf(x) - p, where p is aforementioned Unif(0, 1) sample # return quantiles which minimize the objective function (i.e. which # values of x minimize ensemble_cdf(x) - q) - dist_counts = np.random.multinomial(size, self.weights) + dist_counts = np.random.multinomial(size, self._weights) samples = np.hstack( [ distribution_dict[dist](self.mean, self.variance).rvs( size=counts ) - for dist, counts in zip(self.distributions, dist_counts) + for dist, counts in zip(self._distributions, dist_counts) ] ) np.random.shuffle(samples) @@ -214,7 +220,7 @@ def stats_temp( if "v" in moments: res_list.append(self.variance) - res_list = [res[()] for res in res_list] + # res_list = [res[()] for res in res_list] if len(res_list) == 1: return res_list[0] else: @@ -242,6 +248,62 @@ def plot(self): ax[1].set_ylabel("density") ax[1].set_title("ensemble CDF") + def to_json(self, file_path: str, appending: bool = False) -> None: + """serializes EnsembleDistribution object as a JSON file with the option + to append instead of writing a new file + + Parameters + ---------- + file_path : str + path to file to write in + appending : bool, optional + option to append to existing file instead of overwrite, + by default False + """ + distribution_summary = { + "named_weights": self.named_weights, + "mean": self.mean, + "variance": self.variance, + } + + if appending: + with open(file_path, "r") as outfile: + existing = json.load(outfile) + with open(file_path, "w") as outfile: + existing.append(distribution_summary) + json.dump(existing, outfile) + else: + with open(file_path, "w") as outfile: + json.dump([distribution_summary], outfile) + + +def from_json(file_path: str) -> List[EnsembleDistribution]: + """deserializes JSON object into list of Ensemble Distribution objects + + Parameters + ---------- + file_path : str + path to file that JSON object is stored in + + Returns + ------- + List[EnsembleDistribution] + list of EnsembleDistribution objects + """ + with open(file_path, "r") as infile: + distribution_summaries = json.load(infile) + + res = [None] * len(distribution_summaries) + for i in range(len(distribution_summaries)): + named_weights, mean, variance = ( + distribution_summaries[i]["named_weights"], + distribution_summaries[i]["mean"], + distribution_summaries[i]["variance"], + ) + res[i] = EnsembleDistribution(named_weights, mean, variance) + + return res + class EnsembleResult: """Result from ensemble distribution fitting @@ -405,7 +467,9 @@ def fit(self, data: npt.ArrayLike) -> EnsembleResult: res = EnsembleResult( weights=fitted_weights, ensemble_distribution=EnsembleDistribution( - self.distributions, fitted_weights, sample_mean, sample_variance + dict(zip(self.distributions, fitted_weights)), + sample_mean, + sample_variance, ), ) diff --git a/test_read.json b/test_read.json new file mode 100644 index 0000000..9e4fb7a --- /dev/null +++ b/test_read.json @@ -0,0 +1 @@ +[{"named_weights": {"normal": 0.5, "gumbel": 0.5}, "mean": 1, "variance": 1}, {"named_weights": {"gamma": 0.2, "invgamma": 0.8}, "mean": 1, "variance": 1}] \ No newline at end of file diff --git a/tests/test_model.py b/tests/test_model.py index c786044..6898413 100644 --- a/tests/test_model.py +++ b/tests/test_model.py @@ -2,46 +2,48 @@ import pytest import scipy.stats as stats -from ensemble.model import EnsembleDistribution, EnsembleFitter +from ensemble.model import EnsembleDistribution, EnsembleFitter, from_json STD_NORMAL_DRAWS = stats.norm(loc=0, scale=1).rvs(100) ENSEMBLE_RL_DRAWS = EnsembleDistribution( - distributions=["normal", "gumbel"], weights=[0.7, 0.3], mean=0, variance=1 + named_weights={"normal": 0.7, "gumbel": 0.3}, mean=0, variance=1 ).rvs(size=100) ENSEMBLE_POS_DRAWS = EnsembleDistribution( - distributions=["exponential", "lognormal"], - weights=[0.5, 0.5], + named_weights={"exponential": 0.5, "lognormal": 0.5}, mean=5, variance=1, ).rvs(size=100) ENSEMBLE_POS_DRAWS2 = EnsembleDistribution( - distributions=["exponential", "lognormal", "fisk"], - weights=[0.3, 0.5, 0.2], + named_weights={"exponential": 0.3, "lognormal": 0.5, "fisk": 0.2}, mean=40, variance=5, ) -DEFAULT_SETTINGS = ([0.5, 0.5], 1, 1) +DEFAULT_SETTINGS = (1, 1) def test_bad_weights(): with pytest.raises(ValueError): - EnsembleDistribution(["normal", "gumbel"], [1, 0.1], 1, 1) + EnsembleDistribution({"normal": 1, "gumbel": 0.1}, *DEFAULT_SETTINGS) with pytest.raises(ValueError): - EnsembleDistribution(["normal", "gumbel"], [0.3, 0.69], 1, 1) + EnsembleDistribution({"normal": 0.3, "gumbel": 0.69}, *DEFAULT_SETTINGS) def test_incompatible_dists(): with pytest.raises(ValueError): - EnsembleDistribution(["normal", "exponential"], *DEFAULT_SETTINGS) + EnsembleDistribution( + {"normal": 0.5, "exponential": 0.5}, *DEFAULT_SETTINGS + ) with pytest.raises(ValueError): - EnsembleDistribution(["beta", "normal"], *DEFAULT_SETTINGS) + EnsembleDistribution({"beta": 0.5, "normal": 0.5}, *DEFAULT_SETTINGS) with pytest.raises(ValueError): - EnsembleDistribution(["beta", "exponential"], *DEFAULT_SETTINGS) + EnsembleDistribution( + {"beta": 0.5, "exponential": 0.5}, *DEFAULT_SETTINGS + ) def test_incompatible_data(): @@ -64,3 +66,19 @@ def test_resulting_weights(): model2 = EnsembleFitter(["exponential", "lognormal", "fisk"], "KS") res2 = model2.fit(ENSEMBLE_POS_DRAWS) assert np.isclose(np.sum(res2.weights), 1) + + +def test_json(): + model0 = EnsembleDistribution( + {"normal": 0.5, "gumbel": 0.5}, *DEFAULT_SETTINGS + ) + model0.to_json("test_read.json") + model1 = EnsembleDistribution( + {"gamma": 0.2, "invgamma": 0.8}, *DEFAULT_SETTINGS + ) + model1.to_json("test_read.json", appending=True) + + m1 = from_json("test_read.json")[1] + assert m1.stats_temp("mv") == DEFAULT_SETTINGS + assert m1._distributions == ["gamma", "invgamma"] + assert m1._weights == [0.2, 0.8]