├── LICENSE ├── README.md ├── missingpy ├── __init__.py ├── knnimpute.py ├── missforest.py ├── pairwise_external.py ├── tests │ ├── __init__.py │ ├── test_knnimpute.py │ └── test_missforest.py └── utils.py ├── requirements.txt └── setup.py /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC LICENSE 2 | Version 3, 29 June 2007 3 | 4 | Copyright (C) 2007 Free Software Foundation, Inc. 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | ## missingpy 2 | 3 | `missingpy` is a library for missing data imputation in Python. It has an 4 | API consistent with [scikit-learn](http://scikit-learn.org/stable/), so users 5 | already comfortable with that interface will find themselves in familiar 6 | terrain. Currently, the library supports the following algorithms: 7 | 1. k-Nearest Neighbors imputation 8 | 2. Random Forest imputation (MissForest) 9 | 10 | We plan to add other imputation tools in the future so please stay tuned! 11 | 12 | ## Installation 13 | 14 | `pip install missingpy` 15 | 16 | ## 1. k-Nearest Neighbors (kNN) Imputation 17 | 18 | ### Example 19 | ```python 20 | # Let X be an array containing missing values 21 | from missingpy import KNNImputer 22 | imputer = KNNImputer() 23 | X_imputed = imputer.fit_transform(X) 24 | ``` 25 | 26 | ### Description 27 | The `KNNImputer` class provides imputation for completing missing 28 | values using the k-Nearest Neighbors approach. Each sample's missing values 29 | are imputed using values from `n_neighbors` nearest neighbors found in the 30 | training set. Note that if a sample has more than one feature missing, then 31 | the sample can potentially have multiple sets of `n_neighbors` 32 | donors depending on the particular feature being imputed. 33 | 34 | Each missing feature is then imputed as the average, either weighted or 35 | unweighted, of these neighbors. Where the number of donor neighbors is less 36 | than `n_neighbors`, the training set average for that feature is used 37 | for imputation. The total number of samples in the training set is, of course, 38 | always greater than or equal to the number of nearest neighbors available for 39 | imputation, depending on both the overall sample size as well as the number of 40 | samples excluded from nearest neighbor calculation because of too many missing 41 | features (as controlled by `row_max_missing`). 42 | For more information on the methodology, see [1]. 43 | 44 | The following snippet demonstrates how to replace missing values, 45 | encoded as `np.nan`, using the mean feature value of the two nearest 46 | neighbors of the rows that contain the missing values:: 47 | 48 | >>> import numpy as np 49 | >>> from missingpy import KNNImputer 50 | >>> nan = np.nan 51 | >>> X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]] 52 | >>> imputer = KNNImputer(n_neighbors=2, weights="uniform") 53 | >>> imputer.fit_transform(X) 54 | array([[1. , 2. , 4. ], 55 | [3. , 4. , 3. ], 56 | [5.5, 6. , 5. ], 57 | [8. , 8. , 7. ]]) 58 | 59 | ### API 60 | KNNImputer(missing_values="NaN", n_neighbors=5, weights="uniform", 61 | metric="masked_euclidean", row_max_missing=0.5, 62 | col_max_missing=0.8, copy=True) 63 | 64 | Parameters 65 | ---------- 66 | missing_values : integer or "NaN", optional (default = "NaN") 67 | The placeholder for the missing values. All occurrences of 68 | `missing_values` will be imputed. For missing values encoded as 69 | ``np.nan``, use the string value "NaN". 70 | 71 | n_neighbors : int, optional (default = 5) 72 | Number of neighboring samples to use for imputation. 73 | 74 | weights : str or callable, optional (default = "uniform") 75 | Weight function used in prediction. Possible values: 76 | 77 | - 'uniform' : uniform weights. All points in each neighborhood 78 | are weighted equally. 79 | - 'distance' : weight points by the inverse of their distance. 80 | in this case, closer neighbors of a query point will have a 81 | greater influence than neighbors which are further away. 82 | - [callable] : a user-defined function which accepts an 83 | array of distances, and returns an array of the same shape 84 | containing the weights. 85 | 86 | metric : str or callable, optional (default = "masked_euclidean") 87 | Distance metric for searching neighbors. Possible values: 88 | - 'masked_euclidean' 89 | - [callable] : a user-defined function which conforms to the 90 | definition of _pairwise_callable(X, Y, metric, **kwds). In other 91 | words, the function accepts two arrays, X and Y, and a 92 | ``missing_values`` keyword in **kwds and returns a scalar distance 93 | value. 94 | 95 | row_max_missing : float, optional (default = 0.5) 96 | The maximum fraction of columns (i.e. features) that can be missing 97 | before the sample is excluded from nearest neighbor imputation. It 98 | means that such rows will not be considered a potential donor in 99 | ``fit()``, and in ``transform()`` their missing feature values will be 100 | imputed to be the column mean for the entire dataset. 101 | 102 | col_max_missing : float, optional (default = 0.8) 103 | The maximum fraction of rows (or samples) that can be missing 104 | for any feature beyond which an error is raised. 105 | 106 | copy : boolean, optional (default = True) 107 | If True, a copy of X will be created. If False, imputation will 108 | be done in-place whenever possible. Note that, if metric is 109 | "masked_euclidean" and copy=False then missing_values in the 110 | input matrix X will be overwritten with zeros. 111 | 112 | Attributes 113 | ---------- 114 | statistics_ : 1-D array of length {n_features} 115 | The 1-D array contains the mean of each feature calculated using 116 | observed (i.e. non-missing) values. This is used for imputing 117 | missing values in samples that are either excluded from nearest 118 | neighbors search because they have too many ( > row_max_missing) 119 | missing features or because all of the sample's k-nearest neighbors 120 | (i.e., the potential donors) also have the relevant feature value 121 | missing. 122 | 123 | Methods 124 | ------- 125 | fit(X, y=None): 126 | Fit the imputer on X. 127 | 128 | Parameters 129 | ---------- 130 | X : {array-like}, shape (n_samples, n_features) 131 | Input data, where ``n_samples`` is the number of samples and 132 | ``n_features`` is the number of features. 133 | 134 | Returns 135 | ------- 136 | self : object 137 | Returns self. 138 | 139 | 140 | transform(X): 141 | Impute all missing values in X. 142 | 143 | Parameters 144 | ---------- 145 | X : {array-like}, shape = [n_samples, n_features] 146 | The input data to complete. 147 | 148 | Returns 149 | ------- 150 | X : {array-like}, shape = [n_samples, n_features] 151 | The imputed dataset. 152 | 153 | 154 | fit_transform(X, y=None, **fit_params): 155 | Fit KNNImputer and impute all missing values in X. 156 | 157 | Parameters 158 | ---------- 159 | X : {array-like}, shape (n_samples, n_features) 160 | Input data, where ``n_samples`` is the number of samples and 161 | ``n_features`` is the number of features. 162 | 163 | Returns 164 | ------- 165 | X : {array-like}, shape (n_samples, n_features) 166 | Returns imputed dataset. 167 | 168 | ### References 169 | 1. Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor 170 | Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing value 171 | estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17 no. 6, 2001 172 | Pages 520-525. 173 | 174 | ## 2. Random Forest Imputation (MissForest) 175 | 176 | ### Example 177 | ```python 178 | # Let X be an array containing missing values 179 | from missingpy import MissForest 180 | imputer = MissForest() 181 | X_imputed = imputer.fit_transform(X) 182 | ``` 183 | 184 | ### Description 185 | MissForest imputes missing values using Random Forests in an iterative 186 | fashion [1]. By default, the imputer begins imputing missing values of the 187 | column (which is expected to be a variable) with the smallest number of 188 | missing values -- let's call this the candidate column. 189 | The first step involves filling any missing values of the remaining, 190 | non-candidate, columns with an initial guess, which is the column mean for 191 | columns representing numerical variables and the column mode for columns 192 | representing categorical variables. Note that the categorical variables 193 | need to be explicitly identified during the imputer's `fit()` method call 194 | (see API for more information). After that, the imputer fits a random 195 | forest model with the candidate column as the outcome variable and the 196 | remaining columns as the predictors over all rows where the candidate 197 | column values are not missing. 198 | After the fit, the missing rows of the candidate column are 199 | imputed using the prediction from the fitted Random Forest. The 200 | rows of the non-candidate columns act as the input data for the fitted 201 | model. 202 | Following this, the imputer moves on to the next candidate column with the 203 | second smallest number of missing values from among the non-candidate 204 | columns in the first round. The process repeats itself for each column 205 | with a missing value, possibly over multiple iterations or epochs for 206 | each column, until the stopping criterion is met. 207 | The stopping criterion is governed by the "difference" between the imputed 208 | arrays over successive iterations. For numerical variables (`num_vars_`), 209 | the difference is defined as follows: 210 | 211 | sum((X_new[:, num_vars_] - X_old[:, num_vars_]) ** 2) / 212 | sum((X_new[:, num_vars_]) ** 2) 213 | 214 | For categorical variables(`cat_vars_`), the difference is defined as follows: 215 | 216 | sum(X_new[:, cat_vars_] != X_old[:, cat_vars_])) / n_cat_missing 217 | 218 | where `X_new` is the newly imputed array, `X_old` is the array imputed in the 219 | previous round, `n_cat_missing` is the total number of categorical 220 | values that are missing, and the `sum()` is performed both across rows 221 | and columns. Following [1], the stopping criterion is considered to have 222 | been met when difference between `X_new` and `X_old` increases for the first 223 | time for both types of variables (if available). 224 | 225 | **Note: The categorical variables need to be one-hot-encoded (also known as 226 | dummy encoded) and they need to be explicitly identified during the 227 | imputer's fit() method call. See the API section for more information.** 228 | 229 | >>> from missingpy import MissForest 230 | >>> nan = float("NaN") 231 | >>> X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]] 232 | >>> imputer = MissForest(random_state=1337) 233 | >>> imputer.fit_transform(X) 234 | Iteration: 0 235 | Iteration: 1 236 | Iteration: 2 237 | array([[1. , 2. , 3.92 ], 238 | [3. , 4. , 3. ], 239 | [2.71, 6. , 5. ], 240 | [8. , 8. , 7. ]]) 241 | 242 | ### API 243 | MissForest(max_iter=10, decreasing=False, missing_values=np.nan, 244 | copy=True, n_estimators=100, criterion=('mse', 'gini'), 245 | max_depth=None, min_samples_split=2, min_samples_leaf=1, 246 | min_weight_fraction_leaf=0.0, max_features='auto', 247 | max_leaf_nodes=None, min_impurity_decrease=0.0, 248 | bootstrap=True, oob_score=False, n_jobs=-1, random_state=None, 249 | verbose=0, warm_start=False, class_weight=None) 250 | 251 | Parameters 252 | ---------- 253 | NOTE: Most parameter definitions below are taken verbatim from the 254 | Scikit-Learn documentation at [2] and [3]. 255 | 256 | max_iter : int, optional (default = 10) 257 | The maximum iterations of the imputation process. Each column with a 258 | missing value is imputed exactly once in a given iteration. 259 | 260 | decreasing : boolean, optional (default = False) 261 | If set to True, columns are sorted according to decreasing number of 262 | missing values. In other words, imputation will move from imputing 263 | columns with the largest number of missing values to columns with 264 | fewest number of missing values. 265 | 266 | missing_values : np.nan, integer, optional (default = np.nan) 267 | The placeholder for the missing values. All occurrences of 268 | `missing_values` will be imputed. 269 | 270 | copy : boolean, optional (default = True) 271 | If True, a copy of X will be created. If False, imputation will 272 | be done in-place whenever possible. 273 | 274 | criterion : tuple, optional (default = ('mse', 'gini')) 275 | The function to measure the quality of a split.The first element of 276 | the tuple is for the Random Forest Regressor (for imputing numerical 277 | variables) while the second element is for the Random Forest 278 | Classifier (for imputing categorical variables). 279 | 280 | n_estimators : integer, optional (default=100) 281 | The number of trees in the forest. 282 | 283 | max_depth : integer or None, optional (default=None) 284 | The maximum depth of the tree. If None, then nodes are expanded until 285 | all leaves are pure or until all leaves contain less than 286 | min_samples_split samples. 287 | 288 | min_samples_split : int, float, optional (default=2) 289 | The minimum number of samples required to split an internal node: 290 | - If int, then consider `min_samples_split` as the minimum number. 291 | - If float, then `min_samples_split` is a fraction and 292 | `ceil(min_samples_split * n_samples)` are the minimum 293 | number of samples for each split. 294 | 295 | min_samples_leaf : int, float, optional (default=1) 296 | The minimum number of samples required to be at a leaf node. 297 | A split point at any depth will only be considered if it leaves at 298 | least ``min_samples_leaf`` training samples in each of the left and 299 | right branches. This may have the effect of smoothing the model, 300 | especially in regression. 301 | - If int, then consider `min_samples_leaf` as the minimum number. 302 | - If float, then `min_samples_leaf` is a fraction and 303 | `ceil(min_samples_leaf * n_samples)` are the minimum 304 | number of samples for each node. 305 | 306 | min_weight_fraction_leaf : float, optional (default=0.) 307 | The minimum weighted fraction of the sum total of weights (of all 308 | the input samples) required to be at a leaf node. Samples have 309 | equal weight when sample_weight is not provided. 310 | 311 | max_features : int, float, string or None, optional (default="auto") 312 | The number of features to consider when looking for the best split: 313 | - If int, then consider `max_features` features at each split. 314 | - If float, then `max_features` is a fraction and 315 | `int(max_features * n_features)` features are considered at each 316 | split. 317 | - If "auto", then `max_features=sqrt(n_features)`. 318 | - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto"). 319 | - If "log2", then `max_features=log2(n_features)`. 320 | - If None, then `max_features=n_features`. 321 | Note: the search for a split does not stop until at least one 322 | valid partition of the node samples is found, even if it requires to 323 | effectively inspect more than ``max_features`` features. 324 | 325 | max_leaf_nodes : int or None, optional (default=None) 326 | Grow trees with ``max_leaf_nodes`` in best-first fashion. 327 | Best nodes are defined as relative reduction in impurity. 328 | If None then unlimited number of leaf nodes. 329 | 330 | min_impurity_decrease : float, optional (default=0.) 331 | A node will be split if this split induces a decrease of the impurity 332 | greater than or equal to this value. 333 | The weighted impurity decrease equation is the following:: 334 | N_t / N * (impurity - N_t_R / N_t * right_impurity 335 | - N_t_L / N_t * left_impurity) 336 | where ``N`` is the total number of samples, ``N_t`` is the number of 337 | samples at the current node, ``N_t_L`` is the number of samples in the 338 | left child, and ``N_t_R`` is the number of samples in the right child. 339 | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, 340 | if ``sample_weight`` is passed. 341 | 342 | bootstrap : boolean, optional (default=True) 343 | Whether bootstrap samples are used when building trees. 344 | 345 | oob_score : bool (default=False) 346 | Whether to use out-of-bag samples to estimate 347 | the generalization accuracy. 348 | 349 | n_jobs : int or None, optional (default=-1) 350 | The number of jobs to run in parallel for both `fit` and `predict`. 351 | ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. 352 | ``-1`` means using all processors. 353 | 354 | random_state : int, RandomState instance or None, optional (default=None) 355 | If int, random_state is the seed used by the random number generator; 356 | If RandomState instance, random_state is the random number generator; 357 | If None, the random number generator is the RandomState instance used 358 | by `np.random`. 359 | 360 | verbose : int, optional (default=0) 361 | Controls the verbosity when fitting and predicting. 362 | 363 | warm_start : bool, optional (default=False) 364 | When set to ``True``, reuse the solution of the previous call to fit 365 | and add more estimators to the ensemble, otherwise, just fit a whole 366 | new forest. See :term:`the Glossary `. 367 | 368 | class_weight : dict, list of dicts, "balanced", "balanced_subsample" or \ 369 | None, optional (default=None) 370 | Weights associated with classes in the form ``{class_label: weight}``. 371 | If not given, all classes are supposed to have weight one. For 372 | multi-output problems, a list of dicts can be provided in the same 373 | order as the columns of y. 374 | Note that for multioutput (including multilabel) weights should be 375 | defined for each class of every column in its own dict. For example, 376 | for four-class multilabel classification weights should be 377 | [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of 378 | [{1:1}, {2:5}, {3:1}, {4:1}]. 379 | The "balanced" mode uses the values of y to automatically adjust 380 | weights inversely proportional to class frequencies in the input data 381 | as ``n_samples / (n_classes * np.bincount(y))`` 382 | The "balanced_subsample" mode is the same as "balanced" except that 383 | weights are computed based on the bootstrap sample for every tree 384 | grown. 385 | For multi-output, the weights of each column of y will be multiplied. 386 | Note that these weights will be multiplied with sample_weight (passed 387 | through the fit method) if sample_weight is specified. 388 | NOTE: This parameter is only applicable for Random Forest Classifier 389 | objects (i.e., for categorical variables). 390 | 391 | Attributes 392 | ---------- 393 | statistics_ : Dictionary of length two 394 | The first element is an array with the mean of each numerical feature 395 | being imputed while the second element is an array of modes of 396 | categorical features being imputed (if available, otherwise it 397 | will be None). 398 | 399 | Methods 400 | ------- 401 | fit(self, X, y=None, cat_vars=None): 402 | Fit the imputer on X. 403 | 404 | Parameters 405 | ---------- 406 | X : {array-like}, shape (n_samples, n_features) 407 | Input data, where ``n_samples`` is the number of samples and 408 | ``n_features`` is the number of features. 409 | 410 | cat_vars : int or array of ints, optional (default = None) 411 | An int or an array containing column indices of categorical 412 | variable(s)/feature(s) present in the dataset X. 413 | ``None`` if there are no categorical variables in the dataset. 414 | 415 | Returns 416 | ------- 417 | self : object 418 | Returns self. 419 | 420 | 421 | transform(X): 422 | Impute all missing values in X. 423 | 424 | Parameters 425 | ---------- 426 | X : {array-like}, shape = [n_samples, n_features] 427 | The input data to complete. 428 | 429 | Returns 430 | ------- 431 | X : {array-like}, shape = [n_samples, n_features] 432 | The imputed dataset. 433 | 434 | 435 | fit_transform(X, y=None, **fit_params): 436 | Fit MissForest and impute all missing values in X. 437 | 438 | Parameters 439 | ---------- 440 | X : {array-like}, shape (n_samples, n_features) 441 | Input data, where ``n_samples`` is the number of samples and 442 | ``n_features`` is the number of features. 443 | 444 | Returns 445 | ------- 446 | X : {array-like}, shape (n_samples, n_features) 447 | Returns imputed dataset. 448 | 449 | ### References 450 | 451 | * [1] Stekhoven, Daniel J., and Peter Bühlmann. "MissForest—non-parametric 452 | missing value imputation for mixed-type data." Bioinformatics 28.1 453 | (2011): 112-118. 454 | * [2] https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor 455 | * [3] https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier -------------------------------------------------------------------------------- /missingpy/__init__.py: -------------------------------------------------------------------------------- 1 | from .knnimpute import KNNImputer 2 | from .missforest import MissForest 3 | 4 | __all__ = ['KNNImputer', 'MissForest'] 5 | -------------------------------------------------------------------------------- /missingpy/knnimpute.py: -------------------------------------------------------------------------------- 1 | """KNN Imputer for Missing Data""" 2 | # Author: Ashim Bhattarai 3 | # License: GNU General Public License v3 (GPLv3) 4 | 5 | import warnings 6 | 7 | import numpy as np 8 | 9 | from sklearn.base import BaseEstimator, TransformerMixin 10 | from sklearn.utils import check_array 11 | from sklearn.utils.validation import check_is_fitted 12 | from sklearn.utils.validation import FLOAT_DTYPES 13 | from sklearn.neighbors.base import _check_weights 14 | from sklearn.neighbors.base import _get_weights 15 | 16 | from .pairwise_external import pairwise_distances 17 | from .pairwise_external import _get_mask 18 | from .pairwise_external import _MASKED_METRICS 19 | 20 | __all__ = [ 21 | 'KNNImputer', 22 | ] 23 | 24 | 25 | class KNNImputer(BaseEstimator, TransformerMixin): 26 | """Imputation for completing missing values using k-Nearest Neighbors. 27 | 28 | Each sample's missing values are imputed using values from ``n_neighbors`` 29 | nearest neighbors found in the training set. Each missing feature is then 30 | imputed as the average, either weighted or unweighted, of these neighbors. 31 | Note that if a sample has more than one feature missing, then the 32 | neighbors for that sample can be different depending on the particular 33 | feature being imputed. Finally, where the number of donor neighbors is 34 | less than ``n_neighbors``, the training set average for that feature is 35 | used during imputation. 36 | 37 | Parameters 38 | ---------- 39 | missing_values : integer or "NaN", optional (default = "NaN") 40 | The placeholder for the missing values. All occurrences of 41 | `missing_values` will be imputed. For missing values encoded as 42 | ``np.nan``, use the string value "NaN". 43 | 44 | n_neighbors : int, optional (default = 5) 45 | Number of neighboring samples to use for imputation. 46 | 47 | weights : str or callable, optional (default = "uniform") 48 | Weight function used in prediction. Possible values: 49 | 50 | - 'uniform' : uniform weights. All points in each neighborhood 51 | are weighted equally. 52 | - 'distance' : weight points by the inverse of their distance. 53 | in this case, closer neighbors of a query point will have a 54 | greater influence than neighbors which are further away. 55 | - [callable] : a user-defined function which accepts an 56 | array of distances, and returns an array of the same shape 57 | containing the weights. 58 | 59 | metric : str or callable, optional (default = "masked_euclidean") 60 | Distance metric for searching neighbors. Possible values: 61 | - 'masked_euclidean' 62 | - [callable] : a user-defined function which conforms to the 63 | definition of _pairwise_callable(X, Y, metric, **kwds). In other 64 | words, the function accepts two arrays, X and Y, and a 65 | ``missing_values`` keyword in **kwds and returns a scalar distance 66 | value. 67 | 68 | row_max_missing : float, optional (default = 0.5) 69 | The maximum fraction of columns (i.e. features) that can be missing 70 | before the sample is excluded from nearest neighbor imputation. It 71 | means that such rows will not be considered a potential donor in 72 | ``fit()``, and in ``transform()`` their missing feature values will be 73 | imputed to be the column mean for the entire dataset. 74 | 75 | col_max_missing : float, optional (default = 0.8) 76 | The maximum fraction of rows (or samples) that can be missing 77 | for any feature beyond which an error is raised. 78 | 79 | copy : boolean, optional (default = True) 80 | If True, a copy of X will be created. If False, imputation will 81 | be done in-place whenever possible. Note that, if metric is 82 | "masked_euclidean" and copy=False then missing_values in the 83 | input matrix X will be overwritten with zeros. 84 | 85 | Attributes 86 | ---------- 87 | statistics_ : 1-D array of length {n_features} 88 | The 1-D array contains the mean of each feature calculated using 89 | observed (i.e. non-missing) values. This is used for imputing 90 | missing values in samples that are either excluded from nearest 91 | neighbors search because they have too many ( > row_max_missing) 92 | missing features or because all of the sample's k-nearest neighbors 93 | (i.e., the potential donors) also have the relevant feature value 94 | missing. 95 | 96 | References 97 | ---------- 98 | * Olga Troyanskaya, Michael Cantor, Gavin Sherlock, Pat Brown, Trevor 99 | Hastie, Robert Tibshirani, David Botstein and Russ B. Altman, Missing 100 | value estimation methods for DNA microarrays, BIOINFORMATICS Vol. 17 101 | no. 6, 2001 Pages 520-525. 102 | 103 | Examples 104 | -------- 105 | >>> from missingpy import KNNImputer 106 | >>> nan = float("NaN") 107 | >>> X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]] 108 | >>> imputer = KNNImputer(n_neighbors=2, weights="uniform") 109 | >>> imputer.fit_transform(X) 110 | array([[1. , 2. , 4. ], 111 | [3. , 4. , 3. ], 112 | [5.5, 6. , 5. ], 113 | [8. , 8. , 7. ]]) 114 | """ 115 | 116 | def __init__(self, missing_values="NaN", n_neighbors=5, 117 | weights="uniform", metric="masked_euclidean", 118 | row_max_missing=0.5, col_max_missing=0.8, copy=True): 119 | 120 | self.missing_values = missing_values 121 | self.n_neighbors = n_neighbors 122 | self.weights = weights 123 | self.metric = metric 124 | self.row_max_missing = row_max_missing 125 | self.col_max_missing = col_max_missing 126 | self.copy = copy 127 | 128 | def _impute(self, dist, X, fitted_X, mask, mask_fx): 129 | """Helper function to find and impute missing values""" 130 | 131 | # For each column, find and impute 132 | n_rows_X, n_cols_X = X.shape 133 | for c in range(n_cols_X): 134 | if not np.any(mask[:, c], axis=0): 135 | continue 136 | 137 | # Row index for receivers and potential donors (pdonors) 138 | receivers_row_idx = np.where(mask[:, c])[0] 139 | pdonors_row_idx = np.where(~mask_fx[:, c])[0] 140 | 141 | # Impute using column mean if n_neighbors are not available 142 | if len(pdonors_row_idx) < self.n_neighbors: 143 | warnings.warn("Insufficient number of neighbors! " 144 | "Filling in column mean.") 145 | X[receivers_row_idx, c] = self.statistics_[c] 146 | continue 147 | 148 | # Get distance from potential donors 149 | dist_pdonors = dist[receivers_row_idx][:, pdonors_row_idx] 150 | dist_pdonors = dist_pdonors.reshape(-1, 151 | len(pdonors_row_idx)) 152 | 153 | # Argpartition to separate actual donors from the rest 154 | pdonors_idx = np.argpartition( 155 | dist_pdonors, self.n_neighbors - 1, axis=1) 156 | 157 | # Get final donors row index from pdonors 158 | donors_idx = pdonors_idx[:, :self.n_neighbors] 159 | 160 | # Get weights or None 161 | dist_pdonors_rows = np.arange(len(donors_idx))[:, None] 162 | weight_matrix = _get_weights( 163 | dist_pdonors[ 164 | dist_pdonors_rows, donors_idx], self.weights) 165 | donor_row_idx_ravel = donors_idx.ravel() 166 | 167 | # Retrieve donor values and calculate kNN score 168 | fitted_X_temp = fitted_X[pdonors_row_idx] 169 | donors = fitted_X_temp[donor_row_idx_ravel, c].reshape( 170 | (-1, self.n_neighbors)) 171 | donors_mask = _get_mask(donors, self.missing_values) 172 | donors = np.ma.array(donors, mask=donors_mask) 173 | 174 | # Final imputation 175 | imputed = np.ma.average(donors, axis=1, 176 | weights=weight_matrix) 177 | X[receivers_row_idx, c] = imputed.data 178 | return X 179 | 180 | def fit(self, X, y=None): 181 | """Fit the imputer on X. 182 | 183 | Parameters 184 | ---------- 185 | X : {array-like}, shape (n_samples, n_features) 186 | Input data, where ``n_samples`` is the number of samples and 187 | ``n_features`` is the number of features. 188 | 189 | Returns 190 | ------- 191 | self : object 192 | Returns self. 193 | """ 194 | 195 | # Check data integrity and calling arguments 196 | force_all_finite = False if self.missing_values in ["NaN", 197 | np.nan] else True 198 | if not force_all_finite: 199 | if self.metric not in _MASKED_METRICS and not callable( 200 | self.metric): 201 | raise ValueError( 202 | "The selected metric does not support NaN values.") 203 | X = check_array(X, accept_sparse=False, dtype=np.float64, 204 | force_all_finite=force_all_finite, copy=self.copy) 205 | self.weights = _check_weights(self.weights) 206 | 207 | # Check for +/- inf 208 | if np.any(np.isinf(X)): 209 | raise ValueError("+/- inf values are not allowed.") 210 | 211 | # Check if % missing in any column > col_max_missing 212 | mask = _get_mask(X, self.missing_values) 213 | if np.any(mask.sum(axis=0) > (X.shape[0] * self.col_max_missing)): 214 | raise ValueError("Some column(s) have more than {}% missing values" 215 | .format(self.col_max_missing * 100)) 216 | X_col_means = np.ma.array(X, mask=mask).mean(axis=0).data 217 | 218 | # Check if % missing in any row > row_max_missing 219 | bad_rows = mask.sum(axis=1) > (mask.shape[1] * self.row_max_missing) 220 | if np.any(bad_rows): 221 | warnings.warn( 222 | "There are rows with more than {0}% missing values. These " 223 | "rows are not included as donor neighbors." 224 | .format(self.row_max_missing * 100)) 225 | 226 | # Remove rows that have more than row_max_missing % missing 227 | X = X[~bad_rows, :] 228 | 229 | # Check if sufficient neighboring samples available 230 | if X.shape[0] < self.n_neighbors: 231 | raise ValueError("There are only %d samples, but n_neighbors=%d." 232 | % (X.shape[0], self.n_neighbors)) 233 | self.fitted_X_ = X 234 | self.statistics_ = X_col_means 235 | 236 | return self 237 | 238 | def transform(self, X): 239 | """Impute all missing values in X. 240 | 241 | Parameters 242 | ---------- 243 | X : {array-like}, shape = [n_samples, n_features] 244 | The input data to complete. 245 | 246 | Returns 247 | ------- 248 | X : {array-like}, shape = [n_samples, n_features] 249 | The imputed dataset. 250 | """ 251 | 252 | check_is_fitted(self, ["fitted_X_", "statistics_"]) 253 | force_all_finite = False if self.missing_values in ["NaN", 254 | np.nan] else True 255 | X = check_array(X, accept_sparse=False, dtype=FLOAT_DTYPES, 256 | force_all_finite=force_all_finite, copy=self.copy) 257 | 258 | # Check for +/- inf 259 | if np.any(np.isinf(X)): 260 | raise ValueError("+/- inf values are not allowed in data to be " 261 | "transformed.") 262 | 263 | # Get fitted data and ensure correct dimension 264 | n_rows_fit_X, n_cols_fit_X = self.fitted_X_.shape 265 | n_rows_X, n_cols_X = X.shape 266 | 267 | if n_cols_X != n_cols_fit_X: 268 | raise ValueError("Incompatible dimension between the fitted " 269 | "dataset and the one to be transformed.") 270 | mask = _get_mask(X, self.missing_values) 271 | 272 | row_total_missing = mask.sum(axis=1) 273 | if not np.any(row_total_missing): 274 | return X 275 | 276 | # Check for excessive missingness in rows 277 | bad_rows = row_total_missing > (mask.shape[1] * self.row_max_missing) 278 | if np.any(bad_rows): 279 | warnings.warn( 280 | "There are rows with more than {0}% missing values. The " 281 | "missing features in these rows are imputed with column means." 282 | .format(self.row_max_missing * 100)) 283 | X_bad = X[bad_rows, :] 284 | X = X[~bad_rows, :] 285 | mask = mask[~bad_rows] 286 | row_total_missing = mask.sum(axis=1) 287 | row_has_missing = row_total_missing.astype(np.bool) 288 | 289 | if np.any(row_has_missing): 290 | 291 | # Mask for fitted_X 292 | mask_fx = _get_mask(self.fitted_X_, self.missing_values) 293 | 294 | # Pairwise distances between receivers and fitted samples 295 | dist = np.empty((len(X), len(self.fitted_X_))) 296 | dist[row_has_missing] = pairwise_distances( 297 | X[row_has_missing], self.fitted_X_, metric=self.metric, 298 | squared=False, missing_values=self.missing_values) 299 | 300 | # Find and impute missing 301 | X = self._impute(dist, X, self.fitted_X_, mask, mask_fx) 302 | 303 | # Merge bad rows to X and mean impute their missing values 304 | if np.any(bad_rows): 305 | bad_missing_index = np.where(_get_mask(X_bad, self.missing_values)) 306 | X_bad[bad_missing_index] = np.take(self.statistics_, 307 | bad_missing_index[1]) 308 | X_merged = np.empty((n_rows_X, n_cols_X)) 309 | X_merged[bad_rows, :] = X_bad 310 | X_merged[~bad_rows, :] = X 311 | X = X_merged 312 | return X 313 | 314 | def fit_transform(self, X, y=None, **fit_params): 315 | """Fit KNNImputer and impute all missing values in X. 316 | 317 | Parameters 318 | ---------- 319 | X : {array-like}, shape (n_samples, n_features) 320 | Input data, where ``n_samples`` is the number of samples and 321 | ``n_features`` is the number of features. 322 | 323 | Returns 324 | ------- 325 | X : {array-like}, shape (n_samples, n_features) 326 | Returns imputed dataset. 327 | """ 328 | return self.fit(X).transform(X) 329 | -------------------------------------------------------------------------------- /missingpy/missforest.py: -------------------------------------------------------------------------------- 1 | """MissForest Imputer for Missing Data""" 2 | # Author: Ashim Bhattarai 3 | # License: GNU General Public License v3 (GPLv3) 4 | 5 | import warnings 6 | 7 | import numpy as np 8 | from scipy.stats import mode 9 | 10 | from sklearn.base import BaseEstimator, TransformerMixin 11 | from sklearn.utils.validation import check_is_fitted, check_array 12 | from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor 13 | 14 | from .pairwise_external import _get_mask 15 | 16 | __all__ = [ 17 | 'MissForest', 18 | ] 19 | 20 | 21 | class MissForest(BaseEstimator, TransformerMixin): 22 | """Missing value imputation using Random Forests. 23 | 24 | MissForest imputes missing values using Random Forests in an iterative 25 | fashion. By default, the imputer begins imputing missing values of the 26 | column (which is expected to be a variable) with the smallest number of 27 | missing values -- let's call this the candidate column. 28 | The first step involves filling any missing values of the remaining, 29 | non-candidate, columns with an initial guess, which is the column mean for 30 | columns representing numerical variables and the column mode for columns 31 | representing categorical variables. After that, the imputer fits a random 32 | forest model with the candidate column as the outcome variable and the 33 | remaining columns as the predictors over all rows where the candidate 34 | column values are not missing. 35 | After the fit, the missing rows of the candidate column are 36 | imputed using the prediction from the fitted Random Forest. The 37 | rows of the non-candidate columns act as the input data for the fitted 38 | model. 39 | Following this, the imputer moves on to the next candidate column with the 40 | second smallest number of missing values from among the non-candidate 41 | columns in the first round. The process repeats itself for each column 42 | with a missing value, possibly over multiple iterations or epochs for 43 | each column, until the stopping criterion is met. 44 | The stopping criterion is governed by the "difference" between the imputed 45 | arrays over successive iterations. For numerical variables (num_vars_), 46 | the difference is defined as follows: 47 | 48 | sum((X_new[:, num_vars_] - X_old[:, num_vars_]) ** 2) / 49 | sum((X_new[:, num_vars_]) ** 2) 50 | 51 | For categorical variables(cat_vars_), the difference is defined as follows: 52 | 53 | sum(X_new[:, cat_vars_] != X_old[:, cat_vars_])) / n_cat_missing 54 | 55 | where X_new is the newly imputed array, X_old is the array imputed in the 56 | previous round, n_cat_missing is the total number of categorical 57 | values that are missing, and the sum() is performed both across rows 58 | and columns. Following [1], the stopping criterion is considered to have 59 | been met when difference between X_new and X_old increases for the first 60 | time for both types of variables (if available). 61 | 62 | Parameters 63 | ---------- 64 | NOTE: Most parameter definitions below are taken verbatim from the 65 | Scikit-Learn documentation at [2] and [3]. 66 | 67 | max_iter : int, optional (default = 10) 68 | The maximum iterations of the imputation process. Each column with a 69 | missing value is imputed exactly once in a given iteration. 70 | 71 | decreasing : boolean, optional (default = False) 72 | If set to True, columns are sorted according to decreasing number of 73 | missing values. In other words, imputation will move from imputing 74 | columns with the largest number of missing values to columns with 75 | fewest number of missing values. 76 | 77 | missing_values : np.nan, integer, optional (default = np.nan) 78 | The placeholder for the missing values. All occurrences of 79 | `missing_values` will be imputed. 80 | 81 | copy : boolean, optional (default = True) 82 | If True, a copy of X will be created. If False, imputation will 83 | be done in-place whenever possible. 84 | 85 | criterion : tuple, optional (default = ('mse', 'gini')) 86 | The function to measure the quality of a split.The first element of 87 | the tuple is for the Random Forest Regressor (for imputing numerical 88 | variables) while the second element is for the Random Forest 89 | Classifier (for imputing categorical variables). 90 | 91 | n_estimators : integer, optional (default=100) 92 | The number of trees in the forest. 93 | 94 | max_depth : integer or None, optional (default=None) 95 | The maximum depth of the tree. If None, then nodes are expanded until 96 | all leaves are pure or until all leaves contain less than 97 | min_samples_split samples. 98 | 99 | min_samples_split : int, float, optional (default=2) 100 | The minimum number of samples required to split an internal node: 101 | - If int, then consider `min_samples_split` as the minimum number. 102 | - If float, then `min_samples_split` is a fraction and 103 | `ceil(min_samples_split * n_samples)` are the minimum 104 | number of samples for each split. 105 | 106 | min_samples_leaf : int, float, optional (default=1) 107 | The minimum number of samples required to be at a leaf node. 108 | A split point at any depth will only be considered if it leaves at 109 | least ``min_samples_leaf`` training samples in each of the left and 110 | right branches. This may have the effect of smoothing the model, 111 | especially in regression. 112 | - If int, then consider `min_samples_leaf` as the minimum number. 113 | - If float, then `min_samples_leaf` is a fraction and 114 | `ceil(min_samples_leaf * n_samples)` are the minimum 115 | number of samples for each node. 116 | 117 | min_weight_fraction_leaf : float, optional (default=0.) 118 | The minimum weighted fraction of the sum total of weights (of all 119 | the input samples) required to be at a leaf node. Samples have 120 | equal weight when sample_weight is not provided. 121 | 122 | max_features : int, float, string or None, optional (default="auto") 123 | The number of features to consider when looking for the best split: 124 | - If int, then consider `max_features` features at each split. 125 | - If float, then `max_features` is a fraction and 126 | `int(max_features * n_features)` features are considered at each 127 | split. 128 | - If "auto", then `max_features=sqrt(n_features)`. 129 | - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto"). 130 | - If "log2", then `max_features=log2(n_features)`. 131 | - If None, then `max_features=n_features`. 132 | Note: the search for a split does not stop until at least one 133 | valid partition of the node samples is found, even if it requires to 134 | effectively inspect more than ``max_features`` features. 135 | 136 | max_leaf_nodes : int or None, optional (default=None) 137 | Grow trees with ``max_leaf_nodes`` in best-first fashion. 138 | Best nodes are defined as relative reduction in impurity. 139 | If None then unlimited number of leaf nodes. 140 | 141 | min_impurity_decrease : float, optional (default=0.) 142 | A node will be split if this split induces a decrease of the impurity 143 | greater than or equal to this value. 144 | The weighted impurity decrease equation is the following:: 145 | N_t / N * (impurity - N_t_R / N_t * right_impurity 146 | - N_t_L / N_t * left_impurity) 147 | where ``N`` is the total number of samples, ``N_t`` is the number of 148 | samples at the current node, ``N_t_L`` is the number of samples in the 149 | left child, and ``N_t_R`` is the number of samples in the right child. 150 | ``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum, 151 | if ``sample_weight`` is passed. 152 | 153 | bootstrap : boolean, optional (default=True) 154 | Whether bootstrap samples are used when building trees. 155 | 156 | oob_score : bool (default=False) 157 | Whether to use out-of-bag samples to estimate 158 | the generalization accuracy. 159 | 160 | n_jobs : int or None, optional (default=None) 161 | The number of jobs to run in parallel for both `fit` and `predict`. 162 | ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. 163 | ``-1`` means using all processors. See :term:`Glossary ` 164 | for more details. 165 | 166 | random_state : int, RandomState instance or None, optional (default=None) 167 | If int, random_state is the seed used by the random number generator; 168 | If RandomState instance, random_state is the random number generator; 169 | If None, the random number generator is the RandomState instance used 170 | by `np.random`. 171 | 172 | verbose : int, optional (default=0) 173 | Controls the verbosity when fitting and predicting. 174 | 175 | warm_start : bool, optional (default=False) 176 | When set to ``True``, reuse the solution of the previous call to fit 177 | and add more estimators to the ensemble, otherwise, just fit a whole 178 | new forest. See :term:`the Glossary `. 179 | 180 | class_weight : dict, list of dicts, "balanced", "balanced_subsample" or \ 181 | None, optional (default=None) 182 | Weights associated with classes in the form ``{class_label: weight}``. 183 | If not given, all classes are supposed to have weight one. For 184 | multi-output problems, a list of dicts can be provided in the same 185 | order as the columns of y. 186 | Note that for multioutput (including multilabel) weights should be 187 | defined for each class of every column in its own dict. For example, 188 | for four-class multilabel classification weights should be 189 | [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of 190 | [{1:1}, {2:5}, {3:1}, {4:1}]. 191 | The "balanced" mode uses the values of y to automatically adjust 192 | weights inversely proportional to class frequencies in the input data 193 | as ``n_samples / (n_classes * np.bincount(y))`` 194 | The "balanced_subsample" mode is the same as "balanced" except that 195 | weights are computed based on the bootstrap sample for every tree 196 | grown. 197 | For multi-output, the weights of each column of y will be multiplied. 198 | Note that these weights will be multiplied with sample_weight (passed 199 | through the fit method) if sample_weight is specified. 200 | NOTE: This parameter is only applicable for Random Forest Classifier 201 | objects (i.e., for categorical variables). 202 | 203 | Attributes 204 | ---------- 205 | statistics_ : Dictionary of length two 206 | The first element is an array with the mean of each numerical feature 207 | being imputed while the second element is an array of modes of 208 | categorical features being imputed (if available, otherwise it 209 | will be None). 210 | 211 | References 212 | ---------- 213 | * [1] Stekhoven, Daniel J., and Peter Bühlmann. "MissForest—non-parametric 214 | missing value imputation for mixed-type data." Bioinformatics 28.1 215 | (2011): 112-118. 216 | * [2] https://scikit-learn.org/stable/modules/generated/sklearn.ensemble. 217 | RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor 218 | * [3] https://scikit-learn.org/stable/modules/generated/sklearn.ensemble. 219 | RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier 220 | 221 | Examples 222 | -------- 223 | >>> from missingpy import MissForest 224 | >>> nan = float("NaN") 225 | >>> X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]] 226 | >>> imputer = MissForest(random_state=1337) 227 | >>> imputer.fit_transform(X) 228 | Iteration: 0 229 | Iteration: 1 230 | Iteration: 2 231 | array([[1. , 2. , 3.92 ], 232 | [3. , 4. , 3. ], 233 | [2.71, 6. , 5. ], 234 | [8. , 8. , 7. ]]) 235 | """ 236 | 237 | def __init__(self, max_iter=10, decreasing=False, missing_values=np.nan, 238 | copy=True, n_estimators=100, criterion=('mse', 'gini'), 239 | max_depth=None, min_samples_split=2, min_samples_leaf=1, 240 | min_weight_fraction_leaf=0.0, max_features='auto', 241 | max_leaf_nodes=None, min_impurity_decrease=0.0, 242 | bootstrap=True, oob_score=False, n_jobs=-1, random_state=None, 243 | verbose=0, warm_start=False, class_weight=None): 244 | 245 | self.max_iter = max_iter 246 | self.decreasing = decreasing 247 | self.missing_values = missing_values 248 | self.copy = copy 249 | self.n_estimators = n_estimators 250 | self.criterion = criterion 251 | self.max_depth = max_depth 252 | self.min_samples_split = min_samples_split 253 | self.min_samples_leaf = min_samples_leaf 254 | self.min_weight_fraction_leaf = min_weight_fraction_leaf 255 | self.max_features = max_features 256 | self.max_leaf_nodes = max_leaf_nodes 257 | self.min_impurity_decrease = min_impurity_decrease 258 | self.bootstrap = bootstrap 259 | self.oob_score = oob_score 260 | self.n_jobs = n_jobs 261 | self.random_state = random_state 262 | self.verbose = verbose 263 | self.warm_start = warm_start 264 | self.class_weight = class_weight 265 | 266 | def _miss_forest(self, Ximp, mask): 267 | """The missForest algorithm""" 268 | 269 | # Count missing per column 270 | col_missing_count = mask.sum(axis=0) 271 | 272 | # Get col and row indices for missing 273 | missing_rows, missing_cols = np.where(mask) 274 | 275 | if self.num_vars_ is not None: 276 | # Only keep indices for numerical vars 277 | keep_idx_num = np.in1d(missing_cols, self.num_vars_) 278 | missing_num_rows = missing_rows[keep_idx_num] 279 | missing_num_cols = missing_cols[keep_idx_num] 280 | 281 | # Make initial guess for missing values 282 | col_means = np.full(Ximp.shape[1], fill_value=np.nan) 283 | col_means[self.num_vars_] = self.statistics_.get('col_means') 284 | Ximp[missing_num_rows, missing_num_cols] = np.take( 285 | col_means, missing_num_cols) 286 | 287 | # Reg criterion 288 | reg_criterion = self.criterion if type(self.criterion) == str \ 289 | else self.criterion[0] 290 | 291 | # Instantiate regression model 292 | rf_regressor = RandomForestRegressor( 293 | n_estimators=self.n_estimators, 294 | criterion=reg_criterion, 295 | max_depth=self.max_depth, 296 | min_samples_split=self.min_samples_split, 297 | min_samples_leaf=self.min_samples_leaf, 298 | min_weight_fraction_leaf=self.min_weight_fraction_leaf, 299 | max_features=self.max_features, 300 | max_leaf_nodes=self.max_leaf_nodes, 301 | min_impurity_decrease=self.min_impurity_decrease, 302 | bootstrap=self.bootstrap, 303 | oob_score=self.oob_score, 304 | n_jobs=self.n_jobs, 305 | random_state=self.random_state, 306 | verbose=self.verbose, 307 | warm_start=self.warm_start) 308 | 309 | # If needed, repeat for categorical variables 310 | if self.cat_vars_ is not None: 311 | # Calculate total number of missing categorical values (used later) 312 | n_catmissing = np.sum(mask[:, self.cat_vars_]) 313 | 314 | # Only keep indices for categorical vars 315 | keep_idx_cat = np.in1d(missing_cols, self.cat_vars_) 316 | missing_cat_rows = missing_rows[keep_idx_cat] 317 | missing_cat_cols = missing_cols[keep_idx_cat] 318 | 319 | # Make initial guess for missing values 320 | col_modes = np.full(Ximp.shape[1], fill_value=np.nan) 321 | col_modes[self.cat_vars_] = self.statistics_.get('col_modes') 322 | Ximp[missing_cat_rows, missing_cat_cols] = np.take(col_modes, missing_cat_cols) 323 | 324 | # Classfication criterion 325 | clf_criterion = self.criterion if type(self.criterion) == str \ 326 | else self.criterion[1] 327 | 328 | # Instantiate classification model 329 | rf_classifier = RandomForestClassifier( 330 | n_estimators=self.n_estimators, 331 | criterion=clf_criterion, 332 | max_depth=self.max_depth, 333 | min_samples_split=self.min_samples_split, 334 | min_samples_leaf=self.min_samples_leaf, 335 | min_weight_fraction_leaf=self.min_weight_fraction_leaf, 336 | max_features=self.max_features, 337 | max_leaf_nodes=self.max_leaf_nodes, 338 | min_impurity_decrease=self.min_impurity_decrease, 339 | bootstrap=self.bootstrap, 340 | oob_score=self.oob_score, 341 | n_jobs=self.n_jobs, 342 | random_state=self.random_state, 343 | verbose=self.verbose, 344 | warm_start=self.warm_start, 345 | class_weight=self.class_weight) 346 | 347 | # 2. misscount_idx: sorted indices of cols in X based on missing count 348 | misscount_idx = np.argsort(col_missing_count) 349 | # Reverse order if decreasing is set to True 350 | if self.decreasing is True: 351 | misscount_idx = misscount_idx[::-1] 352 | 353 | # 3. While new_gammas < old_gammas & self.iter_count_ < max_iter loop: 354 | self.iter_count_ = 0 355 | gamma_new = 0 356 | gamma_old = np.inf 357 | gamma_newcat = 0 358 | gamma_oldcat = np.inf 359 | col_index = np.arange(Ximp.shape[1]) 360 | 361 | while ( 362 | gamma_new < gamma_old or gamma_newcat < gamma_oldcat) and \ 363 | self.iter_count_ < self.max_iter: 364 | 365 | # 4. store previously imputed matrix 366 | Ximp_old = np.copy(Ximp) 367 | if self.iter_count_ != 0: 368 | gamma_old = gamma_new 369 | gamma_oldcat = gamma_newcat 370 | # 5. loop 371 | for s in misscount_idx: 372 | # Column indices other than the one being imputed 373 | s_prime = np.delete(col_index, s) 374 | 375 | # Get indices of rows where 's' is observed and missing 376 | obs_rows = np.where(~mask[:, s])[0] 377 | mis_rows = np.where(mask[:, s])[0] 378 | 379 | # If no missing, then skip 380 | if len(mis_rows) == 0: 381 | continue 382 | 383 | # Get observed values of 's' 384 | yobs = Ximp[obs_rows, s] 385 | 386 | # Get 'X' for both observed and missing 's' column 387 | xobs = Ximp[np.ix_(obs_rows, s_prime)] 388 | xmis = Ximp[np.ix_(mis_rows, s_prime)] 389 | 390 | # 6. Fit a random forest over observed and predict the missing 391 | if self.cat_vars_ is not None and s in self.cat_vars_: 392 | rf_classifier.fit(X=xobs, y=yobs) 393 | # 7. predict ymis(s) using xmis(x) 394 | ymis = rf_classifier.predict(xmis) 395 | # 8. update imputed matrix using predicted matrix ymis(s) 396 | Ximp[mis_rows, s] = ymis 397 | else: 398 | rf_regressor.fit(X=xobs, y=yobs) 399 | # 7. predict ymis(s) using xmis(x) 400 | ymis = rf_regressor.predict(xmis) 401 | # 8. update imputed matrix using predicted matrix ymis(s) 402 | Ximp[mis_rows, s] = ymis 403 | 404 | # 9. Update gamma (stopping criterion) 405 | if self.cat_vars_ is not None: 406 | gamma_newcat = np.sum( 407 | (Ximp[:, self.cat_vars_] != Ximp_old[:, self.cat_vars_])) / n_catmissing 408 | if self.num_vars_ is not None: 409 | gamma_new = np.sum((Ximp[:, self.num_vars_] - Ximp_old[:, self.num_vars_]) ** 2) / np.sum((Ximp[:, self.num_vars_]) ** 2) 410 | 411 | print("Iteration:", self.iter_count_) 412 | self.iter_count_ += 1 413 | 414 | return Ximp_old 415 | 416 | def fit(self, X, y=None, cat_vars=None): 417 | """Fit the imputer on X. 418 | 419 | Parameters 420 | ---------- 421 | X : {array-like}, shape (n_samples, n_features) 422 | Input data, where ``n_samples`` is the number of samples and 423 | ``n_features`` is the number of features. 424 | 425 | cat_vars : int or array of ints, optional (default = None) 426 | An int or an array containing column indices of categorical 427 | variable(s)/feature(s) present in the dataset X. 428 | ``None`` if there are no categorical variables in the dataset. 429 | 430 | Returns 431 | ------- 432 | self : object 433 | Returns self. 434 | """ 435 | 436 | # Check data integrity and calling arguments 437 | force_all_finite = False if self.missing_values in ["NaN", 438 | np.nan] else True 439 | 440 | X = check_array(X, accept_sparse=False, dtype=np.float64, 441 | force_all_finite=force_all_finite, copy=self.copy) 442 | 443 | # Check for +/- inf 444 | if np.any(np.isinf(X)): 445 | raise ValueError("+/- inf values are not supported.") 446 | 447 | # Check if any column has all missing 448 | mask = _get_mask(X, self.missing_values) 449 | if np.any(mask.sum(axis=0) >= (X.shape[0])): 450 | raise ValueError("One or more columns have all rows missing.") 451 | 452 | # Check cat_vars type and convert if necessary 453 | if cat_vars is not None: 454 | if type(cat_vars) == int: 455 | cat_vars = [cat_vars] 456 | elif type(cat_vars) == list or type(cat_vars) == np.ndarray: 457 | if np.array(cat_vars).dtype != int: 458 | raise ValueError( 459 | "cat_vars needs to be either an int or an array " 460 | "of ints.") 461 | else: 462 | raise ValueError("cat_vars needs to be either an int or an array " 463 | "of ints.") 464 | 465 | # Identify numerical variables 466 | num_vars = np.setdiff1d(np.arange(X.shape[1]), cat_vars) 467 | num_vars = num_vars if len(num_vars) > 0 else None 468 | 469 | # First replace missing values with NaN if it is something else 470 | if self.missing_values not in ['NaN', np.nan]: 471 | X[np.where(X == self.missing_values)] = np.nan 472 | 473 | # Now, make initial guess for missing values 474 | col_means = np.nanmean(X[:, num_vars], axis=0) if num_vars is not None else None 475 | col_modes = mode( 476 | X[:, cat_vars], axis=0, nan_policy='omit')[0] if cat_vars is not \ 477 | None else None 478 | 479 | self.cat_vars_ = cat_vars 480 | self.num_vars_ = num_vars 481 | self.statistics_ = {"col_means": col_means, "col_modes": col_modes} 482 | 483 | return self 484 | 485 | def transform(self, X): 486 | """Impute all missing values in X. 487 | 488 | Parameters 489 | ---------- 490 | X : {array-like}, shape = [n_samples, n_features] 491 | The input data to complete. 492 | 493 | Returns 494 | ------- 495 | X : {array-like}, shape = [n_samples, n_features] 496 | The imputed dataset. 497 | """ 498 | # Confirm whether fit() has been called 499 | check_is_fitted(self, ["cat_vars_", "num_vars_", "statistics_"]) 500 | 501 | # Check data integrity 502 | force_all_finite = False if self.missing_values in ["NaN", 503 | np.nan] else True 504 | X = check_array(X, accept_sparse=False, dtype=np.float64, 505 | force_all_finite=force_all_finite, copy=self.copy) 506 | 507 | # Check for +/- inf 508 | if np.any(np.isinf(X)): 509 | raise ValueError("+/- inf values are not supported.") 510 | 511 | # Check if any column has all missing 512 | mask = _get_mask(X, self.missing_values) 513 | if np.any(mask.sum(axis=0) >= (X.shape[0])): 514 | raise ValueError("One or more columns have all rows missing.") 515 | 516 | # Get fitted X col count and ensure correct dimension 517 | n_cols_fit_X = (0 if self.num_vars_ is None else len(self.num_vars_)) \ 518 | + (0 if self.cat_vars_ is None else len(self.cat_vars_)) 519 | _, n_cols_X = X.shape 520 | 521 | if n_cols_X != n_cols_fit_X: 522 | raise ValueError("Incompatible dimension between the fitted " 523 | "dataset and the one to be transformed.") 524 | 525 | # Check if anything is actually missing and if not return original X 526 | mask = _get_mask(X, self.missing_values) 527 | if not mask.sum() > 0: 528 | warnings.warn("No missing value located; returning original " 529 | "dataset.") 530 | return X 531 | 532 | # row_total_missing = mask.sum(axis=1) 533 | # if not np.any(row_total_missing): 534 | # return X 535 | 536 | # Call missForest function to impute missing 537 | X = self._miss_forest(X, mask) 538 | 539 | # Return imputed dataset 540 | return X 541 | 542 | def fit_transform(self, X, y=None, **fit_params): 543 | """Fit MissForest and impute all missing values in X. 544 | 545 | Parameters 546 | ---------- 547 | X : {array-like}, shape (n_samples, n_features) 548 | Input data, where ``n_samples`` is the number of samples and 549 | ``n_features`` is the number of features. 550 | 551 | Returns 552 | ------- 553 | X : {array-like}, shape (n_samples, n_features) 554 | Returns imputed dataset. 555 | """ 556 | return self.fit(X, **fit_params).transform(X) 557 | -------------------------------------------------------------------------------- /missingpy/pairwise_external.py: -------------------------------------------------------------------------------- 1 | # This file is a modification of sklearn.metrics.pairwise 2 | # Modifications by Ashim Bhattarai 3 | """ 4 | New BSD License 5 | 6 | Copyright (c) 2007–2018 The scikit-learn developers. 7 | All rights reserved. 8 | 9 | 10 | Redistribution and use in source and binary forms, with or without 11 | modification, are permitted provided that the following conditions are met: 12 | 13 | a. Redistributions of source code must retain the above copyright notice, 14 | this list of conditions and the following disclaimer. 15 | b. Redistributions in binary form must reproduce the above copyright 16 | notice, this list of conditions and the following disclaimer in the 17 | documentation and/or other materials provided with the distribution. 18 | c. Neither the name of the Scikit-learn Developers nor the names of 19 | its contributors may be used to endorse or promote products 20 | derived from this software without specific prior written 21 | permission. 22 | 23 | 24 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 25 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 26 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE 27 | ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR 28 | ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 29 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 30 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 31 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT 32 | LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY 33 | OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH 34 | DAMAGE. 35 | """ 36 | 37 | from __future__ import division 38 | from functools import partial 39 | import itertools 40 | 41 | import numpy as np 42 | from scipy.spatial import distance 43 | from scipy.sparse import issparse 44 | 45 | from sklearn.metrics.pairwise import _VALID_METRICS, _return_float_dtype 46 | from sklearn.metrics.pairwise import PAIRWISE_BOOLEAN_FUNCTIONS 47 | from sklearn.metrics.pairwise import PAIRWISE_DISTANCE_FUNCTIONS 48 | from sklearn.metrics.pairwise import _parallel_pairwise 49 | from sklearn.utils import check_array 50 | 51 | from .utils import masked_euclidean_distances 52 | 53 | _MASKED_METRICS = ['masked_euclidean'] 54 | _VALID_METRICS += ['masked_euclidean'] 55 | 56 | 57 | def _get_mask(X, value_to_mask): 58 | """Compute the boolean mask X == missing_values.""" 59 | if value_to_mask == "NaN" or np.isnan(value_to_mask): 60 | return np.isnan(X) 61 | else: 62 | return X == value_to_mask 63 | 64 | 65 | def check_pairwise_arrays(X, Y, precomputed=False, dtype=None, 66 | accept_sparse='csr', force_all_finite=True, 67 | copy=False): 68 | """ Set X and Y appropriately and checks inputs 69 | 70 | If Y is None, it is set as a pointer to X (i.e. not a copy). 71 | If Y is given, this does not happen. 72 | All distance metrics should use this function first to assert that the 73 | given parameters are correct and safe to use. 74 | 75 | Specifically, this function first ensures that both X and Y are arrays, 76 | then checks that they are at least two dimensional while ensuring that 77 | their elements are floats (or dtype if provided). Finally, the function 78 | checks that the size of the second dimension of the two arrays is equal, or 79 | the equivalent check for a precomputed distance matrix. 80 | 81 | Parameters 82 | ---------- 83 | X : {array-like, sparse matrix}, shape (n_samples_a, n_features) 84 | 85 | Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) 86 | 87 | precomputed : bool 88 | True if X is to be treated as precomputed distances to the samples in 89 | Y. 90 | 91 | dtype : string, type, list of types or None (default=None) 92 | Data type required for X and Y. If None, the dtype will be an 93 | appropriate float type selected by _return_float_dtype. 94 | 95 | .. versionadded:: 0.18 96 | 97 | accept_sparse : string, boolean or list/tuple of strings 98 | String[s] representing allowed sparse matrix formats, such as 'csc', 99 | 'csr', etc. If the input is sparse but not in the allowed format, 100 | it will be converted to the first listed format. True allows the input 101 | to be any format. False means that a sparse matrix input will 102 | raise an error. 103 | 104 | force_all_finite : bool 105 | Whether to raise an error on np.inf and np.nan in X (or Y if it exists) 106 | 107 | copy : bool 108 | Whether a forced copy will be triggered. If copy=False, a copy might 109 | be triggered by a conversion. 110 | 111 | Returns 112 | ------- 113 | safe_X : {array-like, sparse matrix}, shape (n_samples_a, n_features) 114 | An array equal to X, guaranteed to be a numpy array. 115 | 116 | safe_Y : {array-like, sparse matrix}, shape (n_samples_b, n_features) 117 | An array equal to Y if Y was not None, guaranteed to be a numpy array. 118 | If Y was None, safe_Y will be a pointer to X. 119 | 120 | """ 121 | X, Y, dtype_float = _return_float_dtype(X, Y) 122 | 123 | warn_on_dtype = dtype is not None 124 | estimator = 'check_pairwise_arrays' 125 | if dtype is None: 126 | dtype = dtype_float 127 | 128 | if Y is X or Y is None: 129 | X = Y = check_array(X, accept_sparse=accept_sparse, dtype=dtype, 130 | copy=copy, force_all_finite=force_all_finite, 131 | warn_on_dtype=warn_on_dtype, estimator=estimator) 132 | else: 133 | X = check_array(X, accept_sparse=accept_sparse, dtype=dtype, 134 | copy=copy, force_all_finite=force_all_finite, 135 | warn_on_dtype=warn_on_dtype, estimator=estimator) 136 | Y = check_array(Y, accept_sparse=accept_sparse, dtype=dtype, 137 | copy=copy, force_all_finite=force_all_finite, 138 | warn_on_dtype=warn_on_dtype, estimator=estimator) 139 | 140 | if precomputed: 141 | if X.shape[1] != Y.shape[0]: 142 | raise ValueError("Precomputed metric requires shape " 143 | "(n_queries, n_indexed). Got (%d, %d) " 144 | "for %d indexed." % 145 | (X.shape[0], X.shape[1], Y.shape[0])) 146 | elif X.shape[1] != Y.shape[1]: 147 | raise ValueError("Incompatible dimension for X and Y matrices: " 148 | "X.shape[1] == %d while Y.shape[1] == %d" % ( 149 | X.shape[1], Y.shape[1])) 150 | 151 | return X, Y 152 | 153 | 154 | def _pairwise_callable(X, Y, metric, **kwds): 155 | """Handle the callable case for pairwise_{distances,kernels} 156 | """ 157 | force_all_finite = False if callable(metric) else True 158 | X, Y = check_pairwise_arrays(X, Y, force_all_finite=force_all_finite) 159 | 160 | if X is Y: 161 | # Only calculate metric for upper triangle 162 | out = np.zeros((X.shape[0], Y.shape[0]), dtype='float') 163 | iterator = itertools.combinations(range(X.shape[0]), 2) 164 | for i, j in iterator: 165 | out[i, j] = metric(X[i], Y[j], **kwds) 166 | 167 | # Make symmetric 168 | # NB: out += out.T will produce incorrect results 169 | out = out + out.T 170 | 171 | # Calculate diagonal 172 | # NB: nonzero diagonals are allowed for both metrics and kernels 173 | for i in range(X.shape[0]): 174 | x = X[i] 175 | out[i, i] = metric(x, x, **kwds) 176 | 177 | else: 178 | # Calculate all cells 179 | out = np.empty((X.shape[0], Y.shape[0]), dtype='float') 180 | iterator = itertools.product(range(X.shape[0]), range(Y.shape[0])) 181 | for i, j in iterator: 182 | out[i, j] = metric(X[i], Y[j], **kwds) 183 | 184 | return out 185 | 186 | 187 | # Helper functions - distance 188 | PAIRWISE_DISTANCE_FUNCTIONS['masked_euclidean'] = masked_euclidean_distances 189 | 190 | 191 | def pairwise_distances(X, Y=None, metric="euclidean", n_jobs=1, **kwds): 192 | """ Compute the distance matrix from a vector array X and optional Y. 193 | 194 | This method takes either a vector array or a distance matrix, and returns 195 | a distance matrix. If the input is a vector array, the distances are 196 | computed. If the input is a distances matrix, it is returned instead. 197 | 198 | This method provides a safe way to take a distance matrix as input, while 199 | preserving compatibility with many other algorithms that take a vector 200 | array. 201 | 202 | If Y is given (default is None), then the returned matrix is the pairwise 203 | distance between the arrays from both X and Y. 204 | 205 | Valid values for metric are: 206 | 207 | - From scikit-learn: ['cityblock', 'cosine', 'euclidean', 'l1', 'l2', 208 | 'manhattan']. These metrics support sparse matrix 209 | inputs. 210 | Also, ['masked_euclidean'] but it does not yet support sparse matrices. 211 | 212 | - From scipy.spatial.distance: ['braycurtis', 'canberra', 'chebyshev', 213 | 'correlation', 'dice', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 214 | 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', 215 | 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'yule'] 216 | See the documentation for scipy.spatial.distance for details on these 217 | metrics. These metrics do not support sparse matrix inputs. 218 | 219 | Note that in the case of 'cityblock', 'cosine' and 'euclidean' (which are 220 | valid scipy.spatial.distance metrics), the scikit-learn implementation 221 | will be used, which is faster and has support for sparse matrices (except 222 | for 'cityblock'). For a verbose description of the metrics from 223 | scikit-learn, see the __doc__ of the sklearn.pairwise.distance_metrics 224 | function. 225 | 226 | Read more in the :ref:`User Guide `. 227 | 228 | Parameters 229 | ---------- 230 | X : array [n_samples_a, n_samples_a] if metric == "precomputed", or, \ 231 | [n_samples_a, n_features] otherwise 232 | Array of pairwise distances between samples, or a feature array. 233 | 234 | Y : array [n_samples_b, n_features], optional 235 | An optional second feature array. Only allowed if 236 | metric != "precomputed". 237 | 238 | metric : string, or callable 239 | The metric to use when calculating distance between instances in a 240 | feature array. If metric is a string, it must be one of the options 241 | allowed by scipy.spatial.distance.pdist for its metric parameter, or 242 | a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. 243 | If metric is "precomputed", X is assumed to be a distance matrix. 244 | Alternatively, if metric is a callable function, it is called on each 245 | pair of instances (rows) and the resulting value recorded. The callable 246 | should take two arrays from X as input and return a value indicating 247 | the distance between them. 248 | 249 | n_jobs : int 250 | The number of jobs to use for the computation. This works by breaking 251 | down the pairwise matrix into n_jobs even slices and computing them in 252 | parallel. 253 | 254 | If -1 all CPUs are used. If 1 is given, no parallel computing code is 255 | used at all, which is useful for debugging. For n_jobs below -1, 256 | (n_cpus + 1 + n_jobs) are used. Thus for n_jobs = -2, all CPUs but one 257 | are used. 258 | 259 | **kwds : optional keyword parameters 260 | Any further parameters are passed directly to the distance function. 261 | If using a scipy.spatial.distance metric, the parameters are still 262 | metric dependent. See the scipy docs for usage examples. 263 | 264 | Returns 265 | ------- 266 | D : array [n_samples_a, n_samples_a] or [n_samples_a, n_samples_b] 267 | A distance matrix D such that D_{i, j} is the distance between the 268 | ith and jth vectors of the given matrix X, if Y is None. 269 | If Y is not None, then D_{i, j} is the distance between the ith array 270 | from X and the jth array from Y. 271 | 272 | See also 273 | -------- 274 | pairwise_distances_chunked : performs the same calculation as this funtion, 275 | but returns a generator of chunks of the distance matrix, in order to 276 | limit memory usage. 277 | paired_distances : Computes the distances between corresponding 278 | elements of two arrays 279 | """ 280 | if (metric not in _VALID_METRICS and 281 | not callable(metric) and metric != "precomputed"): 282 | raise ValueError("Unknown metric %s. " 283 | "Valid metrics are %s, or 'precomputed', or a " 284 | "callable" % (metric, _VALID_METRICS)) 285 | 286 | if metric in _MASKED_METRICS or callable(metric): 287 | missing_values = kwds.get("missing_values") if kwds.get( 288 | "missing_values") is not None else np.nan 289 | 290 | if np.all(_get_mask(X.data if issparse(X) else X, missing_values)): 291 | raise ValueError( 292 | "One or more samples(s) only have missing values.") 293 | 294 | if metric == "precomputed": 295 | X, _ = check_pairwise_arrays(X, Y, precomputed=True) 296 | return X 297 | elif metric in PAIRWISE_DISTANCE_FUNCTIONS: 298 | func = PAIRWISE_DISTANCE_FUNCTIONS[metric] 299 | elif callable(metric): 300 | func = partial(_pairwise_callable, metric=metric, **kwds) 301 | else: 302 | if issparse(X) or issparse(Y): 303 | raise TypeError("scipy distance metrics do not" 304 | " support sparse matrices.") 305 | 306 | dtype = bool if metric in PAIRWISE_BOOLEAN_FUNCTIONS else None 307 | 308 | X, Y = check_pairwise_arrays(X, Y, dtype=dtype) 309 | 310 | if n_jobs == 1 and X is Y: 311 | return distance.squareform(distance.pdist(X, metric=metric, 312 | **kwds)) 313 | func = partial(distance.cdist, metric=metric, **kwds) 314 | 315 | return _parallel_pairwise(X, Y, func, n_jobs, **kwds) 316 | -------------------------------------------------------------------------------- /missingpy/tests/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/epsilon-machine/missingpy/49fb1f61647e5399d1164a63b44e2fbfbc4ed8ad/missingpy/tests/__init__.py -------------------------------------------------------------------------------- /missingpy/tests/test_knnimpute.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | 3 | from sklearn.utils.testing import assert_array_equal 4 | from sklearn.utils.testing import assert_array_almost_equal 5 | from sklearn.utils.testing import assert_raise_message 6 | from sklearn.utils.testing import assert_equal 7 | 8 | from missingpy import KNNImputer 9 | from missingpy.pairwise_external import masked_euclidean_distances 10 | from missingpy.pairwise_external import pairwise_distances 11 | 12 | 13 | def test_knn_imputation_shape(): 14 | # Verify the shapes of the imputed matrix for different weights and 15 | # number of neighbors. 16 | n_rows = 10 17 | n_cols = 2 18 | X = np.random.rand(n_rows, n_cols) 19 | X[0, 0] = np.nan 20 | 21 | for weights in ['uniform', 'distance']: 22 | for n_neighbors in range(1, 6): 23 | imputer = KNNImputer(n_neighbors=n_neighbors, weights=weights) 24 | X_imputed = imputer.fit_transform(X) 25 | assert_equal(X_imputed.shape, (n_rows, n_cols)) 26 | 27 | 28 | def test_knn_imputation_zero(): 29 | # Test imputation when missing_values == 0 30 | missing_values = 0 31 | n_neighbors = 2 32 | imputer = KNNImputer(missing_values=missing_values, 33 | n_neighbors=n_neighbors, 34 | weights="uniform") 35 | 36 | # Test with missing_values=0 when NaN present 37 | X = np.array([ 38 | [np.nan, 0, 0, 0, 5], 39 | [np.nan, 1, 0, np.nan, 3], 40 | [np.nan, 2, 0, 0, 0], 41 | [np.nan, 6, 0, 5, 13], 42 | ]) 43 | msg = "Input contains NaN, infinity or a value too large for %r." % X.dtype 44 | assert_raise_message(ValueError, msg, imputer.fit, X) 45 | 46 | # Test with % zeros in column > col_max_missing 47 | X = np.array([ 48 | [1, 0, 0, 0, 5], 49 | [2, 1, 0, 2, 3], 50 | [3, 2, 0, 0, 0], 51 | [4, 6, 0, 5, 13], 52 | ]) 53 | msg = "Some column(s) have more than {}% missing values".format( 54 | imputer.col_max_missing * 100) 55 | assert_raise_message(ValueError, msg, imputer.fit, X) 56 | 57 | 58 | def test_knn_imputation_zero_p2(): 59 | # Test with an imputable matrix and also compare with missing_values="NaN" 60 | X_zero = np.array([ 61 | [1, 0, 1, 1, 1.], 62 | [2, 2, 2, 2, 2], 63 | [3, 3, 3, 3, 0], 64 | [6, 6, 0, 6, 6], 65 | ]) 66 | 67 | X_nan = np.array([ 68 | [1, np.nan, 1, 1, 1.], 69 | [2, 2, 2, 2, 2], 70 | [3, 3, 3, 3, np.nan], 71 | [6, 6, np.nan, 6, 6], 72 | ]) 73 | statistics_mean = np.nanmean(X_nan, axis=0) 74 | 75 | X_imputed = np.array([ 76 | [1, 2.5, 1, 1, 1.], 77 | [2, 2, 2, 2, 2], 78 | [3, 3, 3, 3, 1.5], 79 | [6, 6, 2.5, 6, 6], 80 | ]) 81 | 82 | imputer_zero = KNNImputer(missing_values=0, n_neighbors=2, 83 | weights="uniform") 84 | 85 | imputer_nan = KNNImputer(missing_values="NaN", 86 | n_neighbors=2, 87 | weights="uniform") 88 | 89 | assert_array_equal(imputer_zero.fit_transform(X_zero), X_imputed) 90 | assert_array_equal(imputer_zero.statistics_, statistics_mean) 91 | assert_array_equal(imputer_zero.fit_transform(X_zero), 92 | imputer_nan.fit_transform(X_nan)) 93 | 94 | 95 | def test_knn_imputation_default(): 96 | # Test imputation with default parameter values 97 | 98 | # Test with an imputable matrix 99 | X = np.array([ 100 | [1, 0, 0, 1], 101 | [2, 1, 2, np.nan], 102 | [3, 2, 3, np.nan], 103 | [np.nan, 4, 5, 5], 104 | [6, np.nan, 6, 7], 105 | [8, 8, 8, 8], 106 | [16, 15, 18, 19], 107 | ]) 108 | statistics_mean = np.nanmean(X, axis=0) 109 | 110 | X_imputed = np.array([ 111 | [1, 0, 0, 1], 112 | [2, 1, 2, 8], 113 | [3, 2, 3, 8], 114 | [4, 4, 5, 5], 115 | [6, 3, 6, 7], 116 | [8, 8, 8, 8], 117 | [16, 15, 18, 19], 118 | ]) 119 | 120 | imputer = KNNImputer() 121 | assert_array_equal(imputer.fit_transform(X), X_imputed) 122 | assert_array_equal(imputer.statistics_, statistics_mean) 123 | 124 | # Test with % missing in row > row_max_missing 125 | X = np.array([ 126 | [1, 0, 0, 1], 127 | [2, 1, 2, np.nan], 128 | [3, 2, 3, np.nan], 129 | [np.nan, 4, 5, 5], 130 | [6, np.nan, 6, 7], 131 | [8, 8, 8, 8], 132 | [19, 19, 19, 19], 133 | [np.nan, np.nan, np.nan, 19], 134 | ]) 135 | statistics_mean = np.nanmean(X, axis=0) 136 | r7c0, r7c1, r7c2, _ = statistics_mean 137 | 138 | X_imputed = np.array([ 139 | [1, 0, 0, 1], 140 | [2, 1, 2, 8], 141 | [3, 2, 3, 8], 142 | [4, 4, 5, 5], 143 | [6, 3, 6, 7], 144 | [8, 8, 8, 8], 145 | [19, 19, 19, 19], 146 | [r7c0, r7c1, r7c2, 19], 147 | ]) 148 | 149 | imputer = KNNImputer() 150 | assert_array_almost_equal(imputer.fit_transform(X), X_imputed, decimal=6) 151 | assert_array_almost_equal(imputer.statistics_, statistics_mean, decimal=6) 152 | 153 | # Test with all neighboring donors also having missing feature values 154 | X = np.array([ 155 | [1, 0, 0, np.nan], 156 | [2, 1, 2, np.nan], 157 | [3, 2, 3, np.nan], 158 | [4, 4, 5, np.nan], 159 | [6, 7, 6, np.nan], 160 | [8, 8, 8, np.nan], 161 | [20, 20, 20, 20], 162 | [22, 22, 22, 22] 163 | ]) 164 | statistics_mean = np.nanmean(X, axis=0) 165 | 166 | X_imputed = np.array([ 167 | [1, 0, 0, 21], 168 | [2, 1, 2, 21], 169 | [3, 2, 3, 21], 170 | [4, 4, 5, 21], 171 | [6, 7, 6, 21], 172 | [8, 8, 8, 21], 173 | [20, 20, 20, 20], 174 | [22, 22, 22, 22] 175 | ]) 176 | 177 | imputer = KNNImputer() 178 | assert_array_equal(imputer.fit_transform(X), X_imputed) 179 | assert_array_equal(imputer.statistics_, statistics_mean) 180 | 181 | # Test when data in fit() and transform() are different 182 | X = np.array([ 183 | [0, 0], 184 | [np.nan, 2], 185 | [4, 3], 186 | [5, 6], 187 | [7, 7], 188 | [9, 8], 189 | [11, 16] 190 | ]) 191 | statistics_mean = np.nanmean(X, axis=0) 192 | 193 | Y = np.array([ 194 | [1, 0], 195 | [3, 2], 196 | [4, np.nan] 197 | ]) 198 | 199 | Y_imputed = np.array([ 200 | [1, 0], 201 | [3, 2], 202 | [4, 4.8] 203 | ]) 204 | 205 | imputer = KNNImputer() 206 | assert_array_equal(imputer.fit(X).transform(Y), Y_imputed) 207 | assert_array_equal(imputer.statistics_, statistics_mean) 208 | 209 | 210 | def test_default_with_invalid_input(): 211 | # Test imputation with default values and invalid input 212 | 213 | # Test with % missing in a column > col_max_missing 214 | X = np.array([ 215 | [np.nan, 0, 0, 0, 5], 216 | [np.nan, 1, 0, np.nan, 3], 217 | [np.nan, 2, 0, 0, 0], 218 | [np.nan, 6, 0, 5, 13], 219 | [np.nan, 7, 0, 7, 8], 220 | [np.nan, 8, 0, 8, 9], 221 | ]) 222 | imputer = KNNImputer() 223 | msg = "Some column(s) have more than {}% missing values".format( 224 | imputer.col_max_missing * 100) 225 | assert_raise_message(ValueError, msg, imputer.fit, X) 226 | 227 | # Test with insufficient number of neighbors 228 | X = np.array([ 229 | [1, 1, 1, 2, np.nan], 230 | [2, 1, 2, 2, 3], 231 | [3, 2, 3, 3, 8], 232 | [6, 6, 2, 5, 13], 233 | ]) 234 | msg = "There are only %d samples, but n_neighbors=%d." % \ 235 | (X.shape[0], imputer.n_neighbors) 236 | assert_raise_message(ValueError, msg, imputer.fit, X) 237 | 238 | # Test with inf present 239 | X = np.array([ 240 | [np.inf, 1, 1, 2, np.nan], 241 | [2, 1, 2, 2, 3], 242 | [3, 2, 3, 3, 8], 243 | [np.nan, 6, 0, 5, 13], 244 | [np.nan, 7, 0, 7, 8], 245 | [6, 6, 2, 5, 7], 246 | ]) 247 | msg = "+/- inf values are not allowed." 248 | assert_raise_message(ValueError, msg, KNNImputer().fit, X) 249 | 250 | # Test with inf present in matrix passed in transform() 251 | X = np.array([ 252 | [np.inf, 1, 1, 2, np.nan], 253 | [2, 1, 2, 2, 3], 254 | [3, 2, 3, 3, 8], 255 | [np.nan, 6, 0, 5, 13], 256 | [np.nan, 7, 0, 7, 8], 257 | [6, 6, 2, 5, 7], 258 | ]) 259 | 260 | X_fit = np.array([ 261 | [0, 1, 1, 2, np.nan], 262 | [2, 1, 2, 2, 3], 263 | [3, 2, 3, 3, 8], 264 | [np.nan, 6, 0, 5, 13], 265 | [np.nan, 7, 0, 7, 8], 266 | [6, 6, 2, 5, 7], 267 | ]) 268 | msg = "+/- inf values are not allowed in data to be transformed." 269 | assert_raise_message(ValueError, msg, KNNImputer().fit(X_fit).transform, X) 270 | 271 | 272 | def test_knn_n_neighbors(): 273 | 274 | X = np.array([ 275 | [0, 0], 276 | [np.nan, 2], 277 | [4, 3], 278 | [5, np.nan], 279 | [7, 7], 280 | [np.nan, 8], 281 | [14, 13] 282 | ]) 283 | statistics_mean = np.nanmean(X, axis=0) 284 | 285 | # Test with 1 neighbor 286 | X_imputed_1NN = np.array([ 287 | [0, 0], 288 | [4, 2], 289 | [4, 3], 290 | [5, 3], 291 | [7, 7], 292 | [7, 8], 293 | [14, 13] 294 | ]) 295 | 296 | n_neighbors = 1 297 | imputer = KNNImputer(n_neighbors=n_neighbors) 298 | 299 | assert_array_equal(imputer.fit_transform(X), X_imputed_1NN) 300 | assert_array_equal(imputer.statistics_, statistics_mean) 301 | 302 | # Test with 6 neighbors 303 | X = np.array([ 304 | [0, 0], 305 | [np.nan, 2], 306 | [4, 3], 307 | [5, np.nan], 308 | [7, 7], 309 | [np.nan, 8], 310 | [14, 13] 311 | ]) 312 | 313 | X_imputed_6NN = np.array([ 314 | [0, 0], 315 | [6, 2], 316 | [4, 3], 317 | [5, 5.5], 318 | [7, 7], 319 | [6, 8], 320 | [14, 13] 321 | ]) 322 | 323 | n_neighbors = 6 324 | imputer = KNNImputer(n_neighbors=6) 325 | imputer_plus1 = KNNImputer(n_neighbors=n_neighbors + 1) 326 | 327 | assert_array_equal(imputer.fit_transform(X), X_imputed_6NN) 328 | assert_array_equal(imputer.statistics_, statistics_mean) 329 | assert_array_equal(imputer.fit_transform(X), imputer_plus1.fit( 330 | X).transform(X)) 331 | 332 | 333 | def test_weight_uniform(): 334 | X = np.array([ 335 | [0, 0], 336 | [np.nan, 2], 337 | [4, 3], 338 | [5, 6], 339 | [7, 7], 340 | [9, 8], 341 | [11, 10] 342 | ]) 343 | 344 | # Test with "uniform" weight (or unweighted) 345 | X_imputed_uniform = np.array([ 346 | [0, 0], 347 | [5, 2], 348 | [4, 3], 349 | [5, 6], 350 | [7, 7], 351 | [9, 8], 352 | [11, 10] 353 | ]) 354 | 355 | imputer = KNNImputer(weights="uniform") 356 | assert_array_equal(imputer.fit_transform(X), X_imputed_uniform) 357 | 358 | # Test with "callable" weight 359 | def no_weight(dist=None): 360 | return None 361 | 362 | imputer = KNNImputer(weights=no_weight) 363 | assert_array_equal(imputer.fit_transform(X), X_imputed_uniform) 364 | 365 | 366 | def test_weight_distance(): 367 | X = np.array([ 368 | [0, 0], 369 | [np.nan, 2], 370 | [4, 3], 371 | [5, 6], 372 | [7, 7], 373 | [9, 8], 374 | [11, 10] 375 | ]) 376 | 377 | # Test with "distance" weight 378 | 379 | # Get distance of "n_neighbors" neighbors of row 1 380 | dist_matrix = pairwise_distances(X, metric="masked_euclidean") 381 | 382 | index = np.argsort(dist_matrix)[1, 1:6] 383 | dist = dist_matrix[1, index] 384 | weights = 1 / dist 385 | values = X[index, 0] 386 | imputed = np.dot(values, weights) / np.sum(weights) 387 | 388 | # Manual calculation 389 | X_imputed_distance1 = np.array([ 390 | [0, 0], 391 | [3.850394, 2], 392 | [4, 3], 393 | [5, 6], 394 | [7, 7], 395 | [9, 8], 396 | [11, 10] 397 | ]) 398 | 399 | # NearestNeighbor calculation 400 | X_imputed_distance2 = np.array([ 401 | [0, 0], 402 | [imputed, 2], 403 | [4, 3], 404 | [5, 6], 405 | [7, 7], 406 | [9, 8], 407 | [11, 10] 408 | ]) 409 | 410 | imputer = KNNImputer(weights="distance") 411 | assert_array_almost_equal(imputer.fit_transform(X), X_imputed_distance1, 412 | decimal=6) 413 | assert_array_almost_equal(imputer.fit_transform(X), X_imputed_distance2, 414 | decimal=6) 415 | 416 | # Test with weights = "distance" and n_neighbors=2 417 | X = np.array([ 418 | [np.nan, 0, 0], 419 | [2, 1, 2], 420 | [3, 2, 3], 421 | [4, 5, 5], 422 | ]) 423 | statistics_mean = np.nanmean(X, axis=0) 424 | 425 | X_imputed = np.array([ 426 | [2.3828, 0, 0], 427 | [2, 1, 2], 428 | [3, 2, 3], 429 | [4, 5, 5], 430 | ]) 431 | 432 | imputer = KNNImputer(n_neighbors=2, weights="distance") 433 | assert_array_almost_equal(imputer.fit_transform(X), X_imputed, 434 | decimal=4) 435 | assert_array_equal(imputer.statistics_, statistics_mean) 436 | 437 | # Test with varying missingness patterns 438 | X = np.array([ 439 | [1, 0, 0, 1], 440 | [0, np.nan, 1, np.nan], 441 | [1, 1, 1, np.nan], 442 | [0, 1, 0, 0], 443 | [0, 0, 0, 0], 444 | [1, 0, 1, 1], 445 | [10, 10, 10, 10], 446 | ]) 447 | statistics_mean = np.nanmean(X, axis=0) 448 | 449 | # Get weights of donor neighbors 450 | dist = masked_euclidean_distances(X) 451 | r1c1_nbor_dists = dist[1, [0, 2, 3, 4, 5]] 452 | r1c3_nbor_dists = dist[1, [0, 3, 4, 5, 6]] 453 | r1c1_nbor_wt = (1/r1c1_nbor_dists) 454 | r1c3_nbor_wt = (1 / r1c3_nbor_dists) 455 | 456 | r2c3_nbor_dists = dist[2, [0, 3, 4, 5, 6]] 457 | r2c3_nbor_wt = 1/r2c3_nbor_dists 458 | 459 | # Collect donor values 460 | col1_donor_values = np.ma.masked_invalid(X[[0, 2, 3, 4, 5], 1]).copy() 461 | col3_donor_values = np.ma.masked_invalid(X[[0, 3, 4, 5, 6], 3]).copy() 462 | 463 | # Final imputed values 464 | r1c1_imp = np.ma.average(col1_donor_values, weights=r1c1_nbor_wt) 465 | r1c3_imp = np.ma.average(col3_donor_values, weights=r1c3_nbor_wt) 466 | r2c3_imp = np.ma.average(col3_donor_values, weights=r2c3_nbor_wt) 467 | 468 | print(r1c1_imp, r1c3_imp, r2c3_imp) 469 | X_imputed = np.array([ 470 | [1, 0, 0, 1], 471 | [0, r1c1_imp, 1, r1c3_imp], 472 | [1, 1, 1, r2c3_imp], 473 | [0, 1, 0, 0], 474 | [0, 0, 0, 0], 475 | [1, 0, 1, 1], 476 | [10, 10, 10, 10], 477 | ]) 478 | 479 | imputer = KNNImputer(weights="distance") 480 | assert_array_almost_equal(imputer.fit_transform(X), X_imputed, decimal=6) 481 | assert_array_equal(imputer.statistics_, statistics_mean) 482 | 483 | 484 | def test_metric_type(): 485 | X = np.array([ 486 | [0, 0], 487 | [np.nan, 2], 488 | [4, 3], 489 | [5, 6], 490 | [7, 7], 491 | [9, 8], 492 | [11, 10] 493 | ]) 494 | 495 | # Test with a metric type without NaN support 496 | imputer = KNNImputer(metric="euclidean") 497 | bad_metric_msg = "The selected metric does not support NaN values." 498 | assert_raise_message(ValueError, bad_metric_msg, imputer.fit, X) 499 | 500 | 501 | def test_callable_metric(): 502 | 503 | # Define callable metric that returns the l1 norm: 504 | def custom_callable(x, y, missing_values="NaN", squared=False): 505 | x = np.ma.array(x, mask=np.isnan(x)) 506 | y = np.ma.array(y, mask=np.isnan(y)) 507 | dist = np.nansum(np.abs(x-y)) 508 | return dist 509 | 510 | X = np.array([ 511 | [4, 3, 3, np.nan], 512 | [6, 9, 6, 9], 513 | [4, 8, 6, 9], 514 | [np.nan, 9, 11, 10.] 515 | ]) 516 | 517 | X_imputed = np.array([ 518 | [4, 3, 3, 9], 519 | [6, 9, 6, 9], 520 | [4, 8, 6, 9], 521 | [5, 9, 11, 10.] 522 | ]) 523 | 524 | imputer = KNNImputer(n_neighbors=2, metric=custom_callable) 525 | assert_array_equal(imputer.fit_transform(X), X_imputed) 526 | 527 | 528 | def test_complete_features(): 529 | 530 | # Test with use_complete=True 531 | X = np.array([ 532 | [0, np.nan, 0, np.nan], 533 | [1, 1, 1, np.nan], 534 | [2, 2, np.nan, 2], 535 | [3, 3, 3, 3], 536 | [4, 4, 4, 4], 537 | [5, 5, 5, 5], 538 | [6, 6, 6, 6], 539 | [np.nan, 7, 7, 7] 540 | ]) 541 | 542 | r0c1 = np.mean(X[1:6, 1]) 543 | r0c3 = np.mean(X[2:-1, -1]) 544 | r1c3 = np.mean(X[2:-1, -1]) 545 | r2c2 = np.nanmean(X[:6, 2]) 546 | r7c0 = np.mean(X[2:-1, 0]) 547 | 548 | X_imputed = np.array([ 549 | [0, r0c1, 0, r0c3], 550 | [1, 1, 1, r1c3], 551 | [2, 2, r2c2, 2], 552 | [3, 3, 3, 3], 553 | [4, 4, 4, 4], 554 | [5, 5, 5, 5], 555 | [6, 6, 6, 6], 556 | [r7c0, 7, 7, 7] 557 | ]) 558 | 559 | imputer_comp = KNNImputer() 560 | assert_array_almost_equal(imputer_comp.fit_transform(X), X_imputed) 561 | 562 | 563 | def test_complete_features_weighted(): 564 | 565 | # Test with use_complete=True 566 | X = np.array([ 567 | [0, 0, 0, np.nan], 568 | [1, 1, 1, np.nan], 569 | [2, 2, np.nan, 2], 570 | [3, 3, 3, 3], 571 | [4, 4, 4, 4], 572 | [5, 5, 5, 5], 573 | [6, 6, 6, 6], 574 | [np.nan, 7, 7, 7] 575 | ]) 576 | 577 | dist = pairwise_distances(X, 578 | metric="masked_euclidean", 579 | squared=False) 580 | 581 | # Calculate weights 582 | r0c3_w = 1.0 / dist[0, 2:-1] 583 | r1c3_w = 1.0 / dist[1, 2:-1] 584 | r2c2_w = 1.0 / dist[2, (0, 1, 3, 4, 5)] 585 | r7c0_w = 1.0 / dist[7, 2:7] 586 | 587 | # Calculate weighted averages 588 | r0c3 = np.average(X[2:-1, -1], weights=r0c3_w) 589 | r1c3 = np.average(X[2:-1, -1], weights=r1c3_w) 590 | r2c2 = np.average(X[(0, 1, 3, 4, 5), 2], weights=r2c2_w) 591 | r7c0 = np.average(X[2:7, 0], weights=r7c0_w) 592 | 593 | X_imputed = np.array([ 594 | [0, 0, 0, r0c3], 595 | [1, 1, 1, r1c3], 596 | [2, 2, r2c2, 2], 597 | [3, 3, 3, 3], 598 | [4, 4, 4, 4], 599 | [5, 5, 5, 5], 600 | [6, 6, 6, 6], 601 | [r7c0, 7, 7, 7] 602 | ]) 603 | 604 | imputer_comp_wt = KNNImputer(weights="distance") 605 | assert_array_almost_equal(imputer_comp_wt.fit_transform(X), X_imputed) 606 | -------------------------------------------------------------------------------- /missingpy/tests/test_missforest.py: -------------------------------------------------------------------------------- 1 | import numpy as np 2 | from scipy.stats import mode 3 | 4 | from sklearn.utils.testing import assert_array_equal 5 | from sklearn.utils.testing import assert_raise_message 6 | from sklearn.utils.testing import assert_equal 7 | from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor 8 | 9 | from missingpy import MissForest 10 | 11 | def gen_array(n_rows=20, n_cols=5, missingness=0.2, min_val=0, max_val=10, 12 | missing_values=np.nan, rand_seed=1337): 13 | """Generate an array with NaNs""" 14 | 15 | rand_gen = np.random.RandomState(seed=rand_seed) 16 | X = rand_gen.randint( 17 | min_val, max_val, n_rows * n_cols).reshape(n_rows, n_cols).astype( 18 | np.float) 19 | 20 | # Introduce NaNs if missingness > 0 21 | if missingness > 0: 22 | # If missingness >= 1 then use it as approximate (see below) count 23 | if missingness >= 1: 24 | n_missing = missingness 25 | else: 26 | # If missingness is between (0, 1] then use it as approximate % 27 | # of total cells that are NaNs 28 | n_missing = int(np.ceil(missingness * n_rows * n_cols)) 29 | 30 | # Generate row, col index pairs and introduce NaNs 31 | # NOTE: Below does not account for repeated index pairs so NaN 32 | # count/percentage might be less than specified in function call 33 | nan_row_idx = rand_gen.randint(0, n_rows, n_missing) 34 | nan_col_idx = rand_gen.randint(0, n_cols, n_missing) 35 | X[nan_row_idx, nan_col_idx] = missing_values 36 | 37 | return X 38 | 39 | 40 | def test_missforest_imputation_shape(): 41 | # Verify the shapes of the imputed matrix 42 | n_rows = 10 43 | n_cols = 2 44 | X = gen_array(n_rows, n_cols) 45 | imputer = MissForest() 46 | X_imputed = imputer.fit_transform(X) 47 | assert_equal(X_imputed.shape, (n_rows, n_cols)) 48 | 49 | 50 | def test_missforest_zero(): 51 | # Test imputation when missing_values == 0 52 | missing_values = 0 53 | imputer = MissForest(missing_values=missing_values, 54 | random_state=0) 55 | 56 | # Test with missing_values=0 when NaN present 57 | X = gen_array(min_val=0) 58 | msg = "Input contains NaN, infinity or a value too large for %r." % X.dtype 59 | assert_raise_message(ValueError, msg, imputer.fit, X) 60 | 61 | # Test with all zeroes in a column 62 | X = np.array([ 63 | [1, 0, 0, 0, 5], 64 | [2, 1, 0, 2, 3], 65 | [3, 2, 0, 0, 0], 66 | [4, 6, 0, 5, 13], 67 | ]) 68 | msg = "One or more columns have all rows missing." 69 | assert_raise_message(ValueError, msg, imputer.fit, X) 70 | 71 | 72 | def test_missforest_zero_part2(): 73 | # Test with an imputable matrix and compare with missing_values="NaN" 74 | X_zero = gen_array(min_val=1, missing_values=0) 75 | X_nan = gen_array(min_val=1, missing_values=np.nan) 76 | statistics_mean = np.nanmean(X_nan, axis=0) 77 | 78 | imputer_zero = MissForest(missing_values=0, random_state=1337) 79 | imputer_nan = MissForest(missing_values=np.nan, random_state=1337) 80 | 81 | assert_array_equal(imputer_zero.fit_transform(X_zero), 82 | imputer_nan.fit_transform(X_nan)) 83 | assert_array_equal(imputer_zero.statistics_.get("col_means"), 84 | statistics_mean) 85 | 86 | 87 | def test_missforest_numerical_single(): 88 | # Test imputation with default parameter values 89 | 90 | # Test with a single missing value 91 | df = np.array([ 92 | [1, 0, 0, 1], 93 | [2, 1, 2, 2], 94 | [3, 2, 3, 2], 95 | [np.nan, 4, 5, 5], 96 | [6, 7, 6, 7], 97 | [8, 8, 8, 8], 98 | [16, 15, 18, 19], 99 | ]) 100 | statistics_mean = np.nanmean(df, axis=0) 101 | 102 | y = df[:, 0] 103 | X = df[:, 1:] 104 | good_rows = np.where(~np.isnan(y))[0] 105 | bad_rows = np.where(np.isnan(y))[0] 106 | 107 | rf = RandomForestRegressor(n_estimators=10, random_state=1337) 108 | rf.fit(X=X[good_rows], y=y[good_rows]) 109 | pred_val = rf.predict(X[bad_rows]) 110 | 111 | df_imputed = np.array([ 112 | [1, 0, 0, 1], 113 | [2, 1, 2, 2], 114 | [3, 2, 3, 2], 115 | [pred_val, 4, 5, 5], 116 | [6, 7, 6, 7], 117 | [8, 8, 8, 8], 118 | [16, 15, 18, 19], 119 | ]) 120 | 121 | imputer = MissForest(n_estimators=10, random_state=1337) 122 | assert_array_equal(imputer.fit_transform(df), df_imputed) 123 | assert_array_equal(imputer.statistics_.get('col_means'), statistics_mean) 124 | 125 | 126 | def test_missforest_numerical_multiple(): 127 | # Test with two missing values for multiple iterations 128 | df = np.array([ 129 | [1, 0, np.nan, 1], 130 | [2, 1, 2, 2], 131 | [3, 2, 3, 2], 132 | [np.nan, 4, 5, 5], 133 | [6, 7, 6, 7], 134 | [8, 8, 8, 8], 135 | [16, 15, 18, 19], 136 | ]) 137 | statistics_mean = np.nanmean(df, axis=0) 138 | n_rows, n_cols = df.shape 139 | 140 | # Fit missforest and transform 141 | imputer = MissForest(random_state=1337) 142 | df_imp1 = imputer.fit_transform(df) 143 | 144 | # Get iterations used by missforest above 145 | max_iter = imputer.iter_count_ 146 | 147 | # Get NaN mask 148 | nan_mask = np.isnan(df) 149 | nan_rows, nan_cols = np.where(nan_mask) 150 | 151 | # Make initial guess for missing values 152 | df_imp2 = df.copy() 153 | df_imp2[nan_rows, nan_cols] = np.take(statistics_mean, nan_cols) 154 | 155 | # Loop for max_iter count over the columns with NaNs 156 | for _ in range(max_iter): 157 | for c in nan_cols: 158 | # Identify all other columns (i.e. predictors) 159 | not_c = np.setdiff1d(np.arange(n_cols), c) 160 | # Identify rows with NaN and those without in 'c' 161 | y = df_imp2[:, c] 162 | X = df_imp2[:, not_c] 163 | good_rows = np.where(~nan_mask[:, c])[0] 164 | bad_rows = np.where(nan_mask[:, c])[0] 165 | 166 | # Fit model and predict 167 | rf = RandomForestRegressor(n_estimators=100, random_state=1337) 168 | rf.fit(X=X[good_rows], y=y[good_rows]) 169 | pred_val = rf.predict(X[bad_rows]) 170 | 171 | # Fill in values 172 | df_imp2[bad_rows, c] = pred_val 173 | 174 | assert_array_equal(df_imp1, df_imp2) 175 | assert_array_equal(imputer.statistics_.get('col_means'), statistics_mean) 176 | 177 | 178 | def test_missforest_categorical_single(): 179 | # Test imputation with default parameter values 180 | 181 | # Test with a single missing value 182 | df = np.array([ 183 | [0, 0, 0, 1], 184 | [0, 1, 2, 2], 185 | [0, 2, 3, 2], 186 | [np.nan, 4, 5, 5], 187 | [1, 7, 6, 7], 188 | [1, 8, 8, 8], 189 | [1, 15, 18, 19], 190 | ]) 191 | 192 | y = df[:, 0] 193 | X = df[:, 1:] 194 | good_rows = np.where(~np.isnan(y))[0] 195 | bad_rows = np.where(np.isnan(y))[0] 196 | 197 | rf = RandomForestClassifier(n_estimators=10, random_state=1337) 198 | rf.fit(X=X[good_rows], y=y[good_rows]) 199 | pred_val = rf.predict(X[bad_rows]) 200 | 201 | df_imputed = np.array([ 202 | [0, 0, 0, 1], 203 | [0, 1, 2, 2], 204 | [0, 2, 3, 2], 205 | [pred_val, 4, 5, 5], 206 | [1, 7, 6, 7], 207 | [1, 8, 8, 8], 208 | [1, 15, 18, 19], 209 | ]) 210 | 211 | imputer = MissForest(n_estimators=10, random_state=1337) 212 | assert_array_equal(imputer.fit_transform(df, cat_vars=0), df_imputed) 213 | assert_array_equal(imputer.fit_transform(df, cat_vars=[0]), df_imputed) 214 | 215 | 216 | def test_missforest_categorical_multiple(): 217 | # Test with two missing values for multiple iterations 218 | df = np.array([ 219 | [0, 0, np.nan, 1], 220 | [0, 1, 1, 2], 221 | [0, 2, 1, 2], 222 | [np.nan, 4, 1, 5], 223 | [1, 7, 0, 7], 224 | [1, 8, 0, 8], 225 | [1, 15, 0, 19], 226 | [1, 18, 0, 17], 227 | ]) 228 | cat_vars = [0, 2] 229 | statistics_mode = mode(df, axis=0, nan_policy='omit').mode[0] 230 | n_rows, n_cols = df.shape 231 | 232 | # Fit missforest and transform 233 | imputer = MissForest(random_state=1337) 234 | df_imp1 = imputer.fit_transform(df, cat_vars=cat_vars) 235 | 236 | # Get iterations used by missforest above 237 | max_iter = imputer.iter_count_ 238 | 239 | # Get NaN mask 240 | nan_mask = np.isnan(df) 241 | nan_rows, nan_cols = np.where(nan_mask) 242 | 243 | # Make initial guess for missing values 244 | df_imp2 = df.copy() 245 | df_imp2[nan_rows, nan_cols] = np.take(statistics_mode, nan_cols) 246 | 247 | # Loop for max_iter count over the columns with NaNs 248 | for _ in range(max_iter): 249 | for c in nan_cols: 250 | # Identify all other columns (i.e. predictors) 251 | not_c = np.setdiff1d(np.arange(n_cols), c) 252 | # Identify rows with NaN and those without in 'c' 253 | y = df_imp2[:, c] 254 | X = df_imp2[:, not_c] 255 | good_rows = np.where(~nan_mask[:, c])[0] 256 | bad_rows = np.where(nan_mask[:, c])[0] 257 | 258 | # Fit model and predict 259 | rf = RandomForestClassifier(n_estimators=100, random_state=1337) 260 | rf.fit(X=X[good_rows], y=y[good_rows]) 261 | pred_val = rf.predict(X[bad_rows]) 262 | 263 | # Fill in values 264 | df_imp2[bad_rows, c] = pred_val 265 | 266 | assert_array_equal(df_imp1, df_imp2) 267 | assert_array_equal(imputer.statistics_.get('col_modes')[0], 268 | statistics_mode[cat_vars]) 269 | 270 | 271 | def test_missforest_mixed_multiple(): 272 | # Test with mixed data type 273 | df = np.array([ 274 | [np.nan, 0, 0, 1], 275 | [0, 1, 2, 2], 276 | [0, 2, 3, 2], 277 | [1, 4, 5, 5], 278 | [1, 7, 6, 7], 279 | [1, 8, 8, 8], 280 | [1, 15, 18, np.nan], 281 | ]) 282 | 283 | n_rows, n_cols = df.shape 284 | cat_vars = [0] 285 | num_vars = np.setdiff1d(range(n_cols), cat_vars) 286 | statistics_mode = mode(df, axis=0, nan_policy='omit').mode[0] 287 | statistics_mean = np.nanmean(df, axis=0) 288 | 289 | # Fit missforest and transform 290 | imputer = MissForest(random_state=1337) 291 | df_imp1 = imputer.fit_transform(df, cat_vars=cat_vars) 292 | 293 | # Get iterations used by missforest above 294 | max_iter = imputer.iter_count_ 295 | 296 | # Get NaN mask 297 | nan_mask = np.isnan(df) 298 | nan_rows, nan_cols = np.where(nan_mask) 299 | 300 | # Make initial guess for missing values 301 | df_imp2 = df.copy() 302 | df_imp2[0, 0] = statistics_mode[0] 303 | df_imp2[6, 3] = statistics_mean[3] 304 | 305 | # Loop for max_iter count over the columns with NaNs 306 | for _ in range(max_iter): 307 | for c in nan_cols: 308 | # Identify all other columns (i.e. predictors) 309 | not_c = np.setdiff1d(np.arange(n_cols), c) 310 | # Identify rows with NaN and those without in 'c' 311 | y = df_imp2[:, c] 312 | X = df_imp2[:, not_c] 313 | good_rows = np.where(~nan_mask[:, c])[0] 314 | bad_rows = np.where(nan_mask[:, c])[0] 315 | 316 | # Fit model and predict 317 | if c in cat_vars: 318 | rf = RandomForestClassifier(n_estimators=100, 319 | random_state=1337) 320 | else: 321 | rf = RandomForestRegressor(n_estimators=100, 322 | random_state=1337) 323 | rf.fit(X=X[good_rows], y=y[good_rows]) 324 | pred_val = rf.predict(X[bad_rows]) 325 | 326 | # Fill in values 327 | df_imp2[bad_rows, c] = pred_val 328 | 329 | assert_array_equal(df_imp1, df_imp2) 330 | assert_array_equal(imputer.statistics_.get('col_means'), 331 | statistics_mean[num_vars]) 332 | assert_array_equal(imputer.statistics_.get('col_modes')[0], 333 | statistics_mode[cat_vars]) 334 | 335 | 336 | def test_statstics_fit_transform(): 337 | # Test statistics_ when data in fit() and transform() are different 338 | X = np.array([ 339 | [1, 0, 0, 1], 340 | [2, 1, 2, 2], 341 | [3, 2, 3, 2], 342 | [np.nan, 4, 5, 5], 343 | [6, 7, 6, 7], 344 | [8, 8, 8, 8], 345 | [16, 15, 18, 19], 346 | ]) 347 | statistics_mean = np.nanmean(X, axis=0) 348 | 349 | Y = np.array([ 350 | [0, 0, 0, 0], 351 | [2, 2, 2, 1], 352 | [3, 2, 3, 2], 353 | [np.nan, 4, 5, 5], 354 | [6, 7, 6, 7], 355 | [9, 9, 8, 8], 356 | [16, 15, 18, 19], 357 | ]) 358 | 359 | imputer = MissForest() 360 | imputer.fit(X).transform(Y) 361 | assert_array_equal(imputer.statistics_.get('col_means'), statistics_mean) 362 | 363 | 364 | def test_default_with_invalid_input(): 365 | # Test imputation with default values and invalid input 366 | 367 | # Test with all rows missing in a column 368 | X = np.array([ 369 | [np.nan, 0, 0, 1], 370 | [np.nan, 1, 2, np.nan], 371 | [np.nan, 2, 3, np.nan], 372 | [np.nan, 4, 5, 5], 373 | ]) 374 | imputer = MissForest(random_state=1337) 375 | msg = "One or more columns have all rows missing." 376 | assert_raise_message(ValueError, msg, imputer.fit, X) 377 | 378 | # Test with inf present 379 | X = np.array([ 380 | [np.inf, 1, 1, 2, np.nan], 381 | [2, 1, 2, 2, 3], 382 | [3, 2, 3, 3, 8], 383 | [np.nan, 6, 0, 5, 13], 384 | [np.nan, 7, 0, 7, 8], 385 | [6, 6, 2, 5, 7], 386 | ]) 387 | msg = "+/- inf values are not supported." 388 | assert_raise_message(ValueError, msg, MissForest().fit, X) 389 | 390 | # Test with inf present in matrix passed in transform() 391 | X = np.array([ 392 | [np.inf, 1, 1, 2, np.nan], 393 | [2, 1, 2, 2, 3], 394 | [3, 2, 3, 3, 8], 395 | [np.nan, 6, 0, 5, 13], 396 | [np.nan, 7, 0, 7, 8], 397 | [6, 6, 2, 5, 7], 398 | ]) 399 | 400 | X_fit = np.array([ 401 | [0, 1, 1, 2, np.nan], 402 | [2, 1, 2, 2, 3], 403 | [3, 2, 3, 3, 8], 404 | [np.nan, 6, 0, 5, 13], 405 | [np.nan, 7, 0, 7, 8], 406 | [6, 6, 2, 5, 7], 407 | ]) 408 | msg = "+/- inf values are not supported." 409 | assert_raise_message(ValueError, msg, MissForest().fit(X_fit).transform, X) 410 | -------------------------------------------------------------------------------- /missingpy/utils.py: -------------------------------------------------------------------------------- 1 | """Utility Functions""" 2 | # Author: Ashim Bhattarai 3 | # License: BSD 3 clause 4 | 5 | import numpy as np 6 | 7 | 8 | def masked_euclidean_distances(X, Y=None, squared=False, 9 | missing_values="NaN", copy=True): 10 | """Calculates euclidean distances in the presence of missing values 11 | 12 | Computes the euclidean distance between each pair of samples (rows) in X 13 | and Y, where Y=X is assumed if Y=None. 14 | When calculating the distance between a pair of samples, this formulation 15 | essentially zero-weights feature coordinates with a missing value in either 16 | sample and scales up the weight of the remaining coordinates: 17 | 18 | dist(x,y) = sqrt(weight * sq. distance from non-missing coordinates) 19 | where, 20 | weight = Total # of coordinates / # of non-missing coordinates 21 | 22 | Note that if all the coordinates are missing or if there are no common 23 | non-missing coordinates then NaN is returned for that pair. 24 | 25 | Read more in the :ref:`User Guide `. 26 | 27 | Parameters 28 | ---------- 29 | X : {array-like, sparse matrix}, shape (n_samples_1, n_features) 30 | 31 | Y : {array-like, sparse matrix}, shape (n_samples_2, n_features) 32 | 33 | squared : boolean, optional 34 | Return squared Euclidean distances. 35 | 36 | missing_values : "NaN" or integer, optional 37 | Representation of missing value 38 | 39 | copy : boolean, optional 40 | Make and use a deep copy of X and Y (if Y exists) 41 | 42 | Returns 43 | ------- 44 | distances : {array}, shape (n_samples_1, n_samples_2) 45 | 46 | Examples 47 | -------- 48 | >>> from missingpy.utils import masked_euclidean_distances 49 | >>> nan = float("NaN") 50 | >>> X = [[0, 1], [1, nan]] 51 | >>> # distance between rows of X 52 | >>> masked_euclidean_distances(X, X) 53 | array([[0. , 1.41421356], 54 | [1.41421356, 0. ]]) 55 | 56 | >>> # get distance to origin 57 | >>> masked_euclidean_distances(X, [[0, 0]]) 58 | array([[1. ], 59 | [1.41421356]]) 60 | 61 | References 62 | ---------- 63 | * John K. Dixon, "Pattern Recognition with Partly Missing Data", 64 | IEEE Transactions on Systems, Man, and Cybernetics, Volume: 9, Issue: 65 | 10, pp. 617 - 621, Oct. 1979. 66 | http://ieeexplore.ieee.org/abstract/document/4310090/ 67 | 68 | See also 69 | -------- 70 | paired_distances : distances betweens pairs of elements of X and Y. 71 | """ 72 | # Import here to prevent circular import 73 | from .pairwise_external import _get_mask, check_pairwise_arrays 74 | 75 | # NOTE: force_all_finite=False allows not only NaN but also +/- inf 76 | X, Y = check_pairwise_arrays(X, Y, accept_sparse=False, 77 | force_all_finite=False, copy=copy) 78 | if (np.any(np.isinf(X)) or 79 | (Y is not X and np.any(np.isinf(Y)))): 80 | raise ValueError( 81 | "+/- Infinite values are not allowed.") 82 | 83 | # Get missing mask for X and Y.T 84 | mask_X = _get_mask(X, missing_values) 85 | 86 | YT = Y.T 87 | mask_YT = mask_X.T if Y is X else _get_mask(YT, missing_values) 88 | 89 | # Check if any rows have only missing value 90 | if np.any(mask_X.sum(axis=1) == X.shape[1])\ 91 | or (Y is not X and np.any(mask_YT.sum(axis=0) == Y.shape[1])): 92 | raise ValueError("One or more rows only contain missing values.") 93 | 94 | # else: 95 | if missing_values not in ["NaN", np.nan] and ( 96 | np.any(np.isnan(X)) or (Y is not X and np.any(np.isnan(Y)))): 97 | raise ValueError( 98 | "NaN values present but missing_value = {0}".format( 99 | missing_values)) 100 | 101 | # Get mask of non-missing values set Y.T's missing to zero. 102 | # Further, casting the mask to int to be used in formula later. 103 | not_YT = (~mask_YT).astype(np.int32) 104 | YT[mask_YT] = 0 105 | 106 | # Get X's mask of non-missing values and set X's missing to zero 107 | not_X = (~mask_X).astype(np.int32) 108 | X[mask_X] = 0 109 | 110 | # Calculate distances 111 | # The following formula derived by: 112 | # Shreya Bhattarai 113 | 114 | distances = ( 115 | (X.shape[1] / (np.dot(not_X, not_YT))) * 116 | (np.dot(X * X, not_YT) - 2 * (np.dot(X, YT)) + 117 | np.dot(not_X, YT * YT))) 118 | 119 | if X is Y: 120 | # Ensure that distances between vectors and themselves are set to 0.0. 121 | # This may not be the case due to floating point rounding errors. 122 | distances.flat[::distances.shape[0] + 1] = 0.0 123 | 124 | return distances if squared else np.sqrt(distances, out=distances) 125 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.15.4 2 | scipy==1.1.0 3 | scikit-learn==0.20.1 -------------------------------------------------------------------------------- /setup.py: -------------------------------------------------------------------------------- 1 | import setuptools 2 | 3 | with open("README.md", "r") as fh: 4 | long_description = fh.read() 5 | 6 | setuptools.setup( 7 | name="missingpy", 8 | version="0.2.0", 9 | author="Ashim Bhattarai", 10 | description="Missing Data Imputation for Python", 11 | long_description=long_description, 12 | long_description_content_type="text/markdown", 13 | url="https://github.com/epsilon-machine/missingpy", 14 | packages=setuptools.find_packages(), 15 | classifiers=( 16 | "Programming Language :: Python :: 3", 17 | "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 18 | "Operating System :: OS Independent", 19 | ), 20 | ) 21 | --------------------------------------------------------------------------------