[FAKE] GMM IC PR for comment #43
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| Different combinations of initialization, GMM, | ||
| and cluster numbers are used and the clustering | ||
| with the best selection criterion (BIC or AIC) is chosen. |
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suggest making this match LassoLarsIC a bit closer, eg "Such criteria are useful to select the value of the regularization parameter by making a trade-off between the goodness of fit and the complexity of the model." could basically replace "regularization parameter" with "gaussian mixture parameters"
| n_init : int, optional (default = 1) | ||
| If ``n_init`` is larger than 1, additional | ||
| ``n_init``-1 runs of :class:`sklearn.mixture.GaussianMixture` | ||
| initialized with k-means will be performed |
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not necessarily initialized with k-means, right?
| initialized with k-means will be performed | ||
| for all covariance parameters in ``covariance_type``. | ||
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| init_params : {‘kmeans’ (default), ‘k-means++’, ‘random’, ‘random_from_data’} |
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perhaps worth explaining the options, mainly i dont know what random_from_data is from this description
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also, is kmeans ++ not the default? if not, why not? i think it is in sklearn if i remember correctly
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yeah, not sure, apparently kmeans is the default in GaussianMixture
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| Attributes | ||
| ---------- | ||
| best_criterion_ : float |
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lasso lars IC calls this "criterion_"
| covariance_type_ : str | ||
| Covariance type for the model with the best bic/aic. | ||
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| best_model_ : :class:`sklearn.mixture.GaussianMixture` |
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in lassolarsIC, there is no "sub-object" with the best model; rather the whole class just operates as if it is that model. does that make sense? while i cant speak for them, my guess is this is closer to what they'd be expecting
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I add the attributes like weights_, means_ from GaussianMixture into GaussianMixtureIC, but I found that I still need to save the best model (I call best_estimator_ in the newest version) in order to all predict. Did I understand you correctly?
| best_model_ : :class:`sklearn.mixture.GaussianMixture` | ||
| Object with the best bic/aic. | ||
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| labels_ : array-like, shape (n_samples,) |
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not a property of GaussianMixture, recommend not storing
| self.criterion = criterion | ||
| self.n_jobs = n_jobs | ||
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| def _check_multi_comp_inputs(self, input, name, default): |
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i usually make any methods that dont access self into functions
| name="min_components", | ||
| target_type=int, | ||
| ) | ||
| check_scalar( |
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min value could be "min_components"?
| else: | ||
| criterion_value = model.aic(X) | ||
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| # change the precision of "criterion_value" based on sample size |
| ) | ||
| best_criter = [result.criterion for result in results] | ||
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| if sum(best_criter == np.min(best_criter)) == 1: |
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this all seems fine but just a suggestion - https://numpy.org/doc/stable/reference/generated/numpy.argmin.html
docs imply that for ties, argmin gives the first. so in other words if results are sorted in order of complexity, just using argmin would do what you want. (can even leave a comment to this effect, if you go this route).
note that i think having the results sorted by complexity anyway is probably desireable?
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| class _CollectResults: |
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this is effectively a dictionary - recommend just using one, or a named tuple? i am just anti classes that only store data and dont have any methods, but that is just my style :)
| param_grid = dict( | ||
| covariance_type=covariance_type, | ||
| n_components=range(self.min_components, self.max_components + 1), | ||
| ) | ||
| param_grid = list(ParameterGrid(param_grid)) | ||
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| seeds = random_state.randint(np.iinfo(np.int32).max, size=len(param_grid)) | ||
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| if parse_version(joblib.__version__) < parse_version("0.12"): | ||
| parallel_kwargs = {"backend": "threading"} | ||
| else: | ||
| parallel_kwargs = {"prefer": "threads"} | ||
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| results = Parallel(n_jobs=self.n_jobs, verbose=self.verbose, **parallel_kwargs)( | ||
| delayed(self._fit_cluster)(X, gm_params, seed) | ||
| for gm_params, seed in zip(param_grid, seeds) | ||
| ) | ||
| best_criter = [result.criterion for result in results] |
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why not just use GridSearchCV as in their example? https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py
it would abstract away some of the stuff you have to do to make parallel work, for instance
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Reference Issues/PRs
What does this implement/fix? Explain your changes.
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