diff options
Diffstat (limited to 'lib/parameters.py')
| -rw-r--r-- | lib/parameters.py | 241 |
1 files changed, 34 insertions, 207 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 06dc70a..83063c2 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -951,9 +951,6 @@ class ModelAttribute: :returns: SplitFunction or StaticFunction """ - categorical_to_scalar = bool( - int(os.getenv("DFATOOL_PARAM_CATEGORICAL_TO_SCALAR", "0")) - ) if with_function_leaves is None: with_function_leaves = bool( int(os.getenv("DFATOOL_DTREE_FUNCTION_LEAVES", "1")) @@ -984,235 +981,65 @@ class ModelAttribute: ) if with_sklearn_cart or with_sklearn_decart: - from sklearn.tree import DecisionTreeRegressor - - max_depth = int(os.getenv("DFATOOL_CART_MAX_DEPTH", "0")) - if max_depth == 0: - max_depth = None - cart = DecisionTreeRegressor(max_depth=max_depth) - if with_sklearn_cart: - fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, - with_nan=False, - categorical_to_scalar=categorical_to_scalar, - ) - elif with_sklearn_decart: - fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, - with_nan=False, - categorical_to_scalar=categorical_to_scalar, - ignore_indexes=self.scalar_param_indexes, - ) - if fit_parameters.shape[1] == 0: - logger.warning( - f"Cannot generate CART for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" - ) - self.model_function = df.StaticFunction( - np.mean(data), n_samples=len(data) - ) - return - logger.debug("Fitting sklearn CART ...") - cart.fit(fit_parameters, data) - self.model_function = df.CARTFunction( + mf = df.CARTFunction( np.mean(data), - cart, - category_to_index, - ignore_index, n_samples=len(data), param_names=self.param_names, arg_count=self.arg_count, + decart=with_sklearn_decart, ) - logger.debug("Fitted sklearn CART") - return - - if with_xgboost: - import xgboost - # <https://xgboost.readthedocs.io/en/stable/python/python_api.html#module-xgboost.sklearn> - # <https://xgboost.readthedocs.io/en/stable/parameter.html#parameters-for-tree-booster> - # n_estimators := number of trees in forest - # max_depth := maximum tree depth - # eta <=> learning_rate - - # n_estimators : Optional[int] - # Number of gradient boosted trees. Equivalent to number of boosting - # rounds. - # xgboost/sklearn.py: DEFAULT_N_ESTIMATORS = 100 - n_estimators = int(os.getenv("DFATOOL_XGB_N_ESTIMATORS", "100")) - - # max_depth : Optional[int] [default=6] - # Maximum tree depth for base learners. - # Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit. 0 indicates no limit on depth. Beware - # that XGBoost aggressively consumes memory when training a deep tree. exact tree method requires non-zero value. - # range: [0,∞] - max_depth = int(os.getenv("DFATOOL_XGB_MAX_DEPTH", "6")) - - # max_leaves : [default=0] - # Maximum number of leaves; 0 indicates no limit. - # Maximum number of nodes to be added. Not used by exact tree method. - max_leaves = int(os.getenv("DFATOOL_XGB_MAX_LEAVES", "0")) - - # learning_rate : Optional[float] [default=0.3] - # Boosting learning rate (xgb's "eta") - # Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features, and eta - # shrinks the feature weights to make the boosting process more conservative. - # range: [0,1] - learning_rate = float(os.getenv("DFATOOL_XGB_ETA", "0.3")) - - # gamma : Optional[float] [default=0] - # (min_split_loss) Minimum loss reduction required to make a further partition on a - # leaf node of the tree. - # Minimum loss reduction required to make a further partition on a leaf node of the tree. The larger gamma is, the more conservative the algorithm will be. - # range: [0,∞] - gamma = float(os.getenv("DFATOOL_XGB_GAMMA", "0")) - - # subsample : Optional[float] [default=1] - # Subsample ratio of the training instance. - # Subsample ratio of the training instances. Setting it to 0.5 means that XGBoost would randomly sample half of the training data prior to growing - # trees. and this will prevent overfitting. Subsampling will occur once in every boosting iteration. - # range: (0,1] - subsample = float(os.getenv("DFATOOL_XGB_SUBSAMPLE", "1")) - - # reg_alpha : Optional[float] [default=0] - # L1 regularization term on weights (xgb's alpha). - # L1 regularization term on weights. Increasing this value will make model more conservative. - # range: [0, ∞] - reg_alpha = float(os.getenv("DFATOOL_XGB_REG_ALPHA", "0")) - - # reg_lambda : Optional[float] [default=1] - # L2 regularization term on weights (xgb's lambda). - # L2 regularization term on weights. Increasing this value will make model more conservative. - # range: [0, ∞] - reg_lambda = float(os.getenv("DFATOOL_XGB_REG_LAMBDA", "1")) - - xgb = xgboost.XGBRegressor( - n_estimators=n_estimators, - max_depth=max_depth, - max_leaves=max_leaves, - subsample=subsample, - learning_rate=learning_rate, - gamma=gamma, - reg_alpha=reg_alpha, - reg_lambda=reg_lambda, - ) - fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, with_nan=False, categorical_to_scalar=categorical_to_scalar + mf.fit( + parameters, + data, + scalar_param_indexes=self.scalar_param_indexes, ) - if fit_parameters.shape[1] == 0: - logger.warning( - f"Cannot run XGBoost for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" - ) + + if mf.fit_success: + self.model_function = mf + else: + logger.warning(f"CART generation for {self.name} {self.attr} faled") self.model_function = df.StaticFunction( np.mean(data), n_samples=len(data) ) - return - xgb.fit(fit_parameters, np.reshape(data, (-1, 1))) - self.model_function = df.XGBoostFunction( + return + + if with_xgboost: + mf = df.XGBoostFunction( np.mean(data), - xgb, - category_to_index, - ignore_index, n_samples=len(data), param_names=self.param_names, arg_count=self.arg_count, ) - output_filename = os.getenv("DFATOOL_XGB_DUMP_MODEL", None) - if output_filename: - xgb.get_booster().dump_model( - output_filename, dump_format="json", with_stats=True - ) - return - if with_lmt: - from sklearn.linear_model import LinearRegression - from dfatool.lineartree import LinearTreeRegressor - - # max_depth : int, default=5 - # The maximum depth of the tree considering only the splitting nodes. - # A higher value implies a higher training time. - max_depth = int(os.getenv("DFATOOL_LMT_MAX_DEPTH", "5")) - - # min_samples_split : int or float, default=6 - # The minimum number of samples required to split an internal node. - # The minimum valid number of samples in each node is 6. - # A lower value implies a higher training time. - # - If int, then consider `min_samples_split` as the minimum number. - # - If float, then `min_samples_split` is a fraction and - # `ceil(min_samples_split * n_samples)` are the minimum - # number of samples for each split. - if "." in os.getenv("DFATOOL_LMT_MIN_SAMPLES_SPLIT", ""): - min_samples_split = float(os.getenv("DFATOOL_LMT_MIN_SAMPLES_SPLIT")) - else: - min_samples_split = int(os.getenv("DFATOOL_LMT_MIN_SAMPLES_SPLIT", "6")) - - # min_samples_leaf : int or float, default=0.1 - # The minimum number of samples required to be at a leaf node. - # A split point at any depth will only be considered if it leaves at - # least `min_samples_leaf` training samples in each of the left and - # right branches. - # The minimum valid number of samples in each leaf is 3. - # A lower value implies a higher training time. - # - If int, then consider `min_samples_leaf` as the minimum number. - # - If float, then `min_samples_leaf` is a fraction and - # `ceil(min_samples_leaf * n_samples)` are the minimum - # number of samples for each node. - if "." in os.getenv("DFATOOL_LMT_MIN_SAMPLES_LEAF", "0.1"): - min_samples_leaf = float( - os.getenv("DFATOOL_LMT_MIN_SAMPLES_LEAF", "0.1") - ) + mf.fit(parameters, data) + + if mf.fit_success: + self.model_function = mf else: - min_samples_leaf = int(os.getenv("DFATOOL_LMT_MIN_SAMPLES_LEAF")) - - # max_bins : int, default=25 - # The maximum number of bins to use to search the optimal split in each - # feature. Features with a small number of unique values may use less than - # ``max_bins`` bins. Must be lower than 120 and larger than 10. - # A higher value implies a higher training time. - max_bins = int(os.getenv("DFATOOL_LMT_MAX_BINS", "120")) - - # criterion : {"mse", "rmse", "mae", "poisson"}, default="mse" - # The function to measure the quality of a split. "poisson" - # requires ``y >= 0``. - criterion = os.getenv("DFATOOL_LMT_CRITERION", "mse") - - lmt = LinearTreeRegressor( - base_estimator=LinearRegression(), - max_depth=max_depth, - min_samples_split=min_samples_split, - min_samples_leaf=min_samples_leaf, - max_bins=max_bins, - criterion=criterion, - ) - fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, with_nan=False, categorical_to_scalar=categorical_to_scalar - ) - if fit_parameters.shape[1] == 0: - logger.warning( - f"Cannot generate LMT for {self.name} {self.attr} due to lack of parameters: parameter shape is {np.array(parameters).shape}, fit_parameter shape is {fit_parameters.shape}" - ) + logger.warning(f"XGB generation for {self.name} {self.attr} faled") self.model_function = df.StaticFunction( np.mean(data), n_samples=len(data) ) - return - logger.debug("Fitting LMT ...") - try: - lmt.fit(fit_parameters, data) - except np.linalg.LinAlgError as e: - logger.error(f"LMT generation for {self.name} {self.attr} failed: {e}") - self.model_function = df.StaticFunction( - np.mean(data), n_samples=len(data) - ) - return - logger.debug("Fitted LMT") - self.model_function = df.LMTFunction( + return + + if with_lmt: + mf = df.LMTFunction( np.mean(data), - lmt, - category_to_index, - ignore_index, n_samples=len(data), param_names=self.param_names, arg_count=self.arg_count, ) + + mf.fit(parameters, data) + + if mf.fit_success: + self.model_function = mf + else: + logger.warning(f"LMT generation for {self.name} {self.attr} faled") + self.model_function = df.StaticFunction( + np.mean(data), n_samples=len(data) + ) return if loss_ignore_scalar and not with_function_leaves: |