diff --git a/.coveragerc b/.coveragerc
deleted file mode 100644
index 3f16940..0000000
--- a/.coveragerc
+++ /dev/null
@@ -1,15 +0,0 @@
-[run]
-branch = true
-parallel = true
-source = miceforest
-
-[report]
-exclude_lines =
- def __repr__
- raise
- if __name__ == '__main__':
- print(.*)
-
-omit =
- */setup.py
- tests/*
\ No newline at end of file
diff --git a/.github/workflows/run_tests.yml b/.github/workflows/run_tests.yml
index e9ed80c..d882dc1 100644
--- a/.github/workflows/run_tests.yml
+++ b/.github/workflows/run_tests.yml
@@ -2,7 +2,7 @@ name: tests + mypy
on:
push:
- branches: [ "master" ]
+ branches: [ "major_update_6", "master" ]
pull_request:
branches: [ "master" ]
@@ -16,28 +16,36 @@ jobs:
matrix:
python-version: ["3.9", "3.10", "3.11"]
steps:
- - uses: actions/checkout@v3
- - uses: actions/setup-python@v3
+ - uses: actions/checkout@v4
+ - uses: actions/setup-python@v5
with:
python-version: ${{ matrix.python-version }}
+
- name: Install dependencies
run: |
python -m pip install --upgrade pip
- pip install pytest
- pip install mypy
- pip install codecov
- pip install pytest-cov
- pip install blosc2
- pip install dill
- pip install pandas
- pip install seaborn
- pip install matplotlib
- pip install scipy
- pip install scikit-learn
- pip install lightgbm
- pip install pyarrow
- - name: Test with pytest
+ pip install pytest mypy codecov pytest-cov pandas
+ pip install plotnine matplotlib scipy scikit-learn
+ pip install lightgbm pyarrow black isort dill
+
+ - name: MyPy Checks
+ run: mypy miceforest --ignore-missing-imports
+
+ - name: Black Formatting - Package
+ run: black miceforest --check
+
+ - name: Black Formatting - Tests
+ run: black tests --check
+
+ - name: Isort Checks
+ run: isort miceforest --diff
+
+ - name: Pytest
+ run: pytest --cov=miceforest --cov-report html
+
+ - name: Upload coverage reports to Codecov
run: |
- mypy miceforest
- pytest --cov-config .coveragerc --cov-report html --cov=miceforest
- codecov
+ curl -Os https://cli.codecov.io/latest/linux/codecov
+ chmod +x codecov
+ ./codecov --verbose upload-process --fail-on-error -t ${{ secrets.CODECOV_TOKEN }} -n 'service'-${{ github.run_id }} -F service -f coverage-service.xml
+
diff --git a/.gitignore b/.gitignore
index 78b6de7..58f1cff 100644
--- a/.gitignore
+++ b/.gitignore
@@ -12,15 +12,17 @@ coverage.*
.codecovtoken
examples/icon_small.png
support/
-BuildAndInstall.bat
setupdev.py
*/_build/
*.bat
-release_commands.txt
dev/
-.Rproj.user
-miceforest.Rproj
.Rhistory
benchmarks/*
requirements.txt
.venv
+poetry.lock
+pyproject.toml
+*.DS_Store*
+.devcontainer
+Dockerfile
+dev_guide.md
diff --git a/README.Rmd b/README.Rmd
deleted file mode 100644
index 11ab09e..0000000
--- a/README.Rmd
+++ /dev/null
@@ -1,797 +0,0 @@
----
-output: github_document
-always_allow_html: true
----
-```{r EDITPATH,include=FALSE}
-library(knitr)
-opts_chunk$set(engine.path = "C:/Users/swilson/virtual_environments/3.9.6/Scripts/python.exe")
-initlines = readLines(file("miceforest/__init__.py"))
-initlines = initlines[grep("__version__", initlines)]
-vrzn = gsub("\"","",gsub("__version__ = ","",initlines))
-```
-
-[](https://zenodo.org/badge/latestdoi/289387436)
-[](https://pepy.tech/project/miceforest)
-[](https://pypi.python.org/pypi/miceforest)
-[](https://anaconda.org/conda-forge/miceforest)
-[](https://pypi.org/project/miceforest/)
-[](https://github.com/AnotherSamWilson/miceforest/actions/workflows/run_tests.yml)
-[](https://miceforest.readthedocs.io/en/latest/?badge=latest)
-[](https://codecov.io/gh/AnotherSamWilson/miceforest)
-
-
-
-
-
-
-## miceforest: Fast, Memory Efficient Imputation with LightGBM
-
-
-
-Fast, memory efficient Multiple Imputation by Chained Equations (MICE) with lightgbm. The R version of this package may be found [here](https://github.com/FarrellDay/miceRanger).
-
-`miceforest` was designed to be:
-
-* **Fast**
- * Uses lightgbm as a backend
- * Has efficient mean matching solutions.
- * Can utilize GPU training
-* **Flexible**
- * Can impute pandas dataframes and numpy arrays
- * Handles categorical data automatically
- * Fits into a sklearn pipeline
- * User can customize every aspect of the imputation process
-* **Production Ready**
- * Can impute new, unseen datasets quickly
- * Kernels are efficiently compressed during saving and loading
- * Data can be imputed in place to save memory
- * Can build models on non-missing data
-
-
-
-This document contains a thorough walkthrough of the package, benchmarks, and an introduction to multiple imputation. More information on MICE can be found in Stef van Buuren's excellent online book, which you can find [here](https://stefvanbuuren.name/fimd/ch-introduction.html).
-
-
-
-
-#### Table of Contents:
-* [Package Meta](https://github.com/AnotherSamWilson/miceforest#Package-Meta)
-* [The Basics](https://github.com/AnotherSamWilson/miceforest#The-Basics)
- + [Basic Examples](https://github.com/AnotherSamWilson/miceforest#Basic-Examples)
- + [Customizing LightGBM Parameters](https://github.com/AnotherSamWilson/miceforest#Customizing-LightGBM-Parameters)
- + [Available Mean Match Schemes](https://github.com/AnotherSamWilson/miceforest#Controlling-Tree-Growth)
- + [Imputing New Data with Existing Models](https://github.com/AnotherSamWilson/miceforest#Imputing-New-Data-with-Existing-Models)
- + [Saving and Loading Kernels](https://github.com/AnotherSamWilson/miceforest#Saving-and-Loading-Kernels)
- + [Implementing sklearn Pipelines](https://github.com/AnotherSamWilson/miceforest#Implementing-sklearn-Pipelines)
-* [Advanced Features](https://github.com/AnotherSamWilson/miceforest#Advanced-Features)
- + [Customizing the Imputation Process](https://github.com/AnotherSamWilson/miceforest#Customizing-the-Imputation-Process)
- + [Building Models on Nonmissing Data](https://github.com/AnotherSamWilson/miceforest#Building-Models-on-Nonmissing-Data)
- + [Tuning Parameters](https://github.com/AnotherSamWilson/miceforest#Tuning-Parameters)
- + [On Reproducibility](https://github.com/AnotherSamWilson/miceforest#On-Reproducibility)
- + [How to Make the Process Faster](https://github.com/AnotherSamWilson/miceforest#How-to-Make-the-Process-Faster)
- + [Imputing Data In Place](https://github.com/AnotherSamWilson/miceforest#Imputing-Data-In-Place)
-* [Diagnostic Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting)
- + [Imputed Distributions](https://github.com/AnotherSamWilson/miceforest#Distribution-of-Imputed-Values)
- + [Correlation Convergence](https://github.com/AnotherSamWilson/miceforest#Convergence-of-Correlation)
- + [Variable Importance](https://github.com/AnotherSamWilson/miceforest#Variable-Importance)
- + [Mean Convergence](https://github.com/AnotherSamWilson/miceforest#Variable-Importance)
-* [Benchmarks](https://github.com/AnotherSamWilson/miceforest#Benchmarks)
-* [Using the Imputed Data](https://github.com/AnotherSamWilson/miceforest#Using-the-Imputed-Data)
-* [The MICE Algorithm](https://github.com/AnotherSamWilson/miceforest#The-MICE-Algorithm)
- + [Introduction](https://github.com/AnotherSamWilson/miceforest#The-MICE-Algorithm)
- + [Common Use Cases](https://github.com/AnotherSamWilson/miceforest#Common-Use-Cases)
- + [Predictive Mean Matching](https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching)
- + [Effects of Mean Matching](https://github.com/AnotherSamWilson/miceforest#Effects-of-Mean-Matching)
-
-
-## Package Meta
-
-
-### Installation
-This package can be installed using either pip or conda, through conda-forge:
-
-``` {bash INSTALL1,eval=FALSE}
-# Using pip
-$ pip install miceforest --no-cache-dir
-
-# Using conda
-$ conda install -c conda-forge miceforest
-```
-
-You can also download the latest development version from this
-repository. If you want to install from github with conda, you
-must first run ```conda install pip git```.
-
-``` {bash INSTALL2,eval=FALSE}
-$ pip install git+https://github.com/AnotherSamWilson/miceforest.git
-```
-
-### Classes
-miceforest has 3 main classes which the user will interact with:
-
-* [`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel) - This class contains the raw data off of which the `mice` algorithm is performed. During this process, models will be trained, and the imputed (predicted) values will be stored. These values can be used to fill in the missing values of the raw data. The raw data can be copied, or referenced directly. Models can be saved, and used to impute new datasets.
-* [`ImputedData`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputedData.html#miceforest.ImputedData) - The result of `ImputationKernel.impute_new_data(new_data)`. This contains the raw data in `new_data` as well as the imputed values.
-* [`MeanMatchScheme`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.MeanMatchScheme.html#miceforest.MeanMatchScheme) - Determines how mean matching should be carried out. There are 3 built-in mean match schemes available in miceforest, discussed below.
-
-## The Basics
-
-We will be looking at a few simple examples of
-imputation. We need to load the packages, and define the data:
-
-``` {python SETUP}
-import miceforest as mf
-from sklearn.datasets import load_iris
-import pandas as pd
-import numpy as np
-
-# Load data and introduce missing values
-iris = pd.concat(load_iris(as_frame=True,return_X_y=True),axis=1)
-iris.rename({"target": "species"}, inplace=True, axis=1)
-iris['species'] = iris['species'].astype('category')
-iris_amp = mf.ampute_data(iris,perc=0.25,random_state=1991)
-```
-
-
-### Basic Examples
-If you only want to create a single imputed dataset, you can use [`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel) with some default settings:
-``` {python SIMPLESINGLE}
-# Create kernel.
-kds = mf.ImputationKernel(
- iris_amp,
- save_all_iterations=True,
- random_state=1991
-)
-
-# Run the MICE algorithm for 2 iterations
-kds.mice(2)
-
-# Return the completed dataset.
-iris_complete = kds.complete_data()
-```
-There are also an array of plotting functions available, these are discussed below in the section [Diagnostic Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting).
-
-
-We usually don't want to impute just a single dataset. In statistics, multiple imputation is a process by which the uncertainty/other effects caused by missing values can be examined by creating multiple different imputed datasets. [`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel) can contain an arbitrary number of different datasets, all of which have gone through mutually exclusive imputation processes:
-``` {python SIMPLEMULTI}
-# Create kernel.
-kernel = mf.ImputationKernel(
- iris_amp,
- datasets=4,
- save_all_iterations=True,
- random_state=1
-)
-
-# Run the MICE algorithm for 2 iterations on each of the datasets
-kernel.mice(2)
-
-# Printing the kernel will show you some high level information.
-print(kernel)
-```
-
-After we have run mice, we can obtain our completed dataset directly from the kernel:
-``` {python COMPLETE_NOCOPY}
-completed_dataset = kernel.complete_data(dataset=2)
-print(completed_dataset.isnull().sum(0))
-```
-
-### Customizing LightGBM Parameters
-Parameters can be passed directly to lightgbm in several different ways. Parameters you wish to apply globally to every model can simply be passed as kwargs to `mice`:
-``` {python TREEGROWTH}
-# Run the MICE algorithm for 1 more iteration on the kernel with new parameters
-kernel.mice(iterations=1,n_estimators=50)
-```
-
-You can also pass pass variable-specific arguments to `variable_parameters` in mice. For instance, let's say you noticed the imputation of the `[species]` column was taking a little longer, because it is multiclass. You could decrease the n_estimators specifically for that column with:
-``` {python TREEGROWTH2}
-# Run the MICE algorithm for 2 more iterations on the kernel
-kernel.mice(
- iterations=1,
- variable_parameters={'species': {'n_estimators': 25}},
- n_estimators=50
-)
-
-# Let's get the actual models for these variables:
-species_model = kernel.get_model(dataset=0,variable="species")
-sepalwidth_model = kernel.get_model(dataset=0,variable="sepal width (cm)")
-
-print(
-f"""Species used {str(species_model.params["num_iterations"])} iterations
-Sepal Width used {str(sepalwidth_model.params["num_iterations"])} iterations
-"""
-)
-```
-
-In this scenario, any parameters specified in `variable_parameters` takes presidence over the kwargs.
-
-Since we can pass any parameters we want to LightGBM, we can completely customize how our models are built. That includes how the data should be modeled. If your data contains count data, or any other data which can be parameterized by lightgbm, you can simply specify that variable to be modeled with the corresponding objective function.
-
-For example, let's pretend `sepal width (cm)` is a count field which can be parameterized by a Poisson distribution. Let's also change our boosting method to gradient boosted trees:
-``` {python DIFFOBJECTIVE}
-# Create kernel.
-cust_kernel = mf.ImputationKernel(
- iris_amp,
- datasets=1,
- random_state=1
-)
-
-cust_kernel.mice(
- iterations=1,
- variable_parameters={'sepal width (cm)': {'objective': 'poisson'}},
- boosting = 'gbdt',
- min_sum_hessian_in_leaf=0.01
-)
-```
-
-Other nice parameters like `monotone_constraints` can also be passed. Setting the parameter `device: 'gpu'` will utilize GPU learning, if LightGBM is set up to do this on your machine.
-
-
-### Available Mean Match Schemes
-
-Note: It is probably a good idea to read [this section](https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching) first, to get some context on how mean matching works.
-
-The class `miceforest.MeanMatchScheme` contains information about how mean matching should be performed, such as:
-
-1) Mean matching functions
-2) Mean matching candidates
-3) How to get predictions from a lightgbm model
-4) The datatypes predictions are stored as
-
-There are three pre-built mean matching schemes that come with `miceforest`:
-``` {python MMS}
-from miceforest import (
- mean_match_default,
- mean_match_fast_cat,
- mean_match_shap
-)
-
-# To get information for each, use help()
-# help(mean_match_default)
-```
-
-These schemes mostly differ in their strategy for performing mean matching
-
-* **mean_match_default** - medium speed, medium imputation quality
- * Categorical: perform a K Nearest Neighbors search on the candidate class probabilities, where K = mmc. Select 1 at random, and choose the associated candidate value as the imputation value.
- * Numeric: Perform a K Nearest Neighbors search on the candidate predictions, where K = mmc. Select 1 at random, and choose the associated candidate value as the imputation value.
-* **mean_match_fast_cat** - fastest speed, lowest imputation quality
- * Categorical: return class based on random draw weighted by class probability for each sample.
- * Numeric: perform a K Nearest Neighbors search on the candidate class probabilities, where K = mmc. Select 1 at random, and choose the associated candidate value as the imputation value.
-* **mean_match_shap** - slowest speed, highest imputation quality for large datasets
- * Categorical: perform a K Nearest Neighbors search on the candidate prediction shap values, where K = mmc. Select 1 at random, and choose the associated candidate value as the imputation value.
- * Numeric: perform a K Nearest Neighbors search on the candidate prediction shap values, where K = mmc. Select 1 at random, and choose the associated candidate value as the imputation value.
-
-As a special case, if the mean_match_candidates is set to 0, the following behavior is observed for all schemes:
-
-* Categorical: the class with the highest probability is chosen.
-* Numeric: the predicted value is used
-
-These mean matching schemes can be updated and customized, we show an example below in the advanced section.
-
-### Imputing New Data with Existing Models
-
-Multiple Imputation can take a long time. If you wish to impute a
-dataset using the MICE algorithm, but don’t have time to train new
-models, it is possible to impute new datasets using a `ImputationKernel` object.
-The `impute_new_data()` function uses the models collected by `ImputationKernel`
-to perform multiple imputation without updating the models at
-each iteration:
-
-``` {python IMPUTENEWDATA}
-# Our 'new data' is just the first 15 rows of iris_amp
-from datetime import datetime
-
-# Define our new data as the first 15 rows
-new_data = iris_amp.iloc[range(15)]
-
-# Imputing new data can often be made faster by
-# first compiling candidate predictions
-kernel.compile_candidate_preds()
-
-start_t = datetime.now()
-new_data_imputed = kernel.impute_new_data(new_data=new_data)
-print(f"New Data imputed in {(datetime.now() - start_t).total_seconds()} seconds")
-```
-
-All of the imputation parameters (variable_schema, mean_match_candidates, etc) will be
-carried over from the original `ImputationKernel` object. When mean matching,
-the candidate values are pulled from the original kernel dataset. To impute new data,
-the ```save_models``` parameter in ```ImputationKernel``` must be > 0. If
-```save_models == 1```, the model from the latest iteration is saved for each variable.
-If ```save_models > 1```, the model from each iteration is saved. This allows for new
-data to be imputed in a more similar fashion to the original mice procedure.
-
-
-### Saving and Loading Kernels
-
-Kernels can be saved using the `.save_kernel()` method, and then loaded again using the `utils.load_kernel()` function. Internally, this procedure uses `blosc2` and `dill` packages to do the following:
-
-1. Convert working data to parquet bytes (if it is a pandas dataframe)
-2. Serialize the kernel
-3. Compress this serialization
-4. Save to a file
-
-### Implementing sklearn Pipelines
-
-kernels can be fit into sklearn pipelines to impute training and scoring datasets:
-
-``` {python PIPELINE}
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
-from sklearn.pipeline import Pipeline
-import miceforest as mf
-
-# Define our data
-X, y = make_classification(random_state=0)
-
-# Ampute and split the training data
-X = mf.utils.ampute_data(X)
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-
-# Initialize our miceforest kernel. datasets parameter should be 1,
-# we don't want to return multiple datasets.
-pipe_kernel = mf.ImputationKernel(X_train, datasets=1)
-
-# Define our pipeline
-pipe = Pipeline([
- ('impute', pipe_kernel),
- ('scaler', StandardScaler()),
-])
-
-# Fit on and transform our training data.
-# Only use 2 iterations of mice.
-X_train_t = pipe.fit_transform(
- X_train,
- y_train,
- impute__iterations=2
-)
-
-# Transform the test data as well
-X_test_t = pipe.transform(X_test)
-
-# Show that neither now have missing values.
-assert not np.any(np.isnan(X_train_t))
-assert not np.any(np.isnan(X_test_t))
-```
-
-
-## Advanced Features
-Multiple imputation is a complex process. However, `miceforest` allows all of the major components to be switched out and customized by the user.
-
-### Customizing the Imputation Process
-
-It is possible to heavily customize our imputation procedure by variable. By
-passing a named list to `variable_schema`, you can specify the predictor
-variables for each imputed variable. You can also specify `mean_match_candidates`
-and `data_subset` by variable by passing a dict of valid values, with variable
-names as keys. You can even replace the entire default mean matching function
-for certain objectives if desired. Below is an _extremely_ convoluted setup,
-which you would probably never want to use. It simply shows what is possible:
-
-``` {python CUSTOMSCHEMA}
-# Use the default mean match schema as our base
-from miceforest import mean_match_default
-mean_match_custom = mean_match_default.copy()
-
-# Define a mean matching function that
-# just randomly shuffles the predictions
-def custom_mmf(bachelor_preds):
- np.random.shuffle(bachelor_preds)
- return bachelor_preds
-
-# Specify that our custom function should be
-# used to perform mean matching on any variable
-# that was modeled with a poisson objective:
-mean_match_custom.set_mean_match_function(
- {"poisson": custom_mmf}
-)
-
-# Set the mean match candidates by variable
-mean_match_custom.set_mean_match_candidates(
- {
- 'sepal width (cm)': 3,
- 'petal width (cm)': 0
- }
-)
-
-# Define which variables should be used to model others
-variable_schema = {
- 'sepal width (cm)': ['species','petal width (cm)'],
- 'petal width (cm)': ['species','sepal length (cm)']
-}
-
-# Subset the candidate data to 50 rows for sepal width (cm).
-variable_subset = {
- 'sepal width (cm)': 50
-}
-
-# Specify that petal width (cm) should be modeled by the
-# poisson objective. Our custom mean matching function
-# above will be used for this variable.
-variable_parameters = {
- 'petal width (cm)': {"objective": "poisson"}
-}
-
-cust_kernel = mf.ImputationKernel(
- iris_amp,
- datasets=3,
- mean_match_scheme=mean_match_custom,
- variable_schema=variable_schema,
- data_subset=variable_subset
-)
-cust_kernel.mice(iterations=1, variable_parameters=variable_parameters)
-```
-
-The mean matching function can take any number of the following arguments. If a function does not take one of these arguments, then the process will not prepare that data for mean matching.
-```{python MEANMATCHARGS}
-from miceforest.MeanMatchScheme import AVAILABLE_MEAN_MATCH_ARGS
-print("\n".join(AVAILABLE_MEAN_MATCH_ARGS))
-```
-
-### Building Models on Nonmissing Data
-The MICE process itself is used to impute missing data in a dataset. However, sometimes a variable can be fully recognized in the training data, but needs to be imputed later on in a different dataset. It is possible to train models to impute variables even if they have no missing values by setting `train_nonmissing=True`. In this case, `variable_schema` is treated as the list of variables to train models on. `imputation_order` only affects which variables actually have their values imputed, it does not affect which variables have models trained:
-``` {python TRAIN_NONMISSING}
-orig_missing_cols = ["sepal length (cm)", "sepal width (cm)"]
-new_missing_cols = ["sepal length (cm)", "sepal width (cm)", "species"]
-
-# Training data only contains 2 columns with missing data
-iris_amp2 = iris.copy()
-iris_amp2[orig_missing_cols] = mf.ampute_data(
- iris_amp2[orig_missing_cols],
- perc=0.25,
- random_state=1991
-)
-
-# Specify that models should also be trained for species column
-var_sch = new_missing_cols
-
-cust_kernel = mf.ImputationKernel(
- iris_amp2,
- datasets=1,
- variable_schema=var_sch,
- train_nonmissing=True
-)
-cust_kernel.mice(1)
-
-# New data has missing values in species column
-iris_amp2_new = iris.iloc[range(10),:].copy()
-iris_amp2_new[new_missing_cols] = mf.ampute_data(
- iris_amp2_new[new_missing_cols],
- perc=0.25,
- random_state=1991
-)
-
-# Species column can still be imputed
-iris_amp2_new_imp = cust_kernel.impute_new_data(iris_amp2_new)
-iris_amp2_new_imp.complete_data(0).isnull().sum()
-```
-
-Here, we knew that the species column in our new data would need to be imputed. Therefore, we specified that a model should be built for all 3 variables in the `variable_schema` (passing a dict of target - feature pairs would also have worked).
-
-
-### Tuning Parameters
-`miceforest` allows you to tune the parameters on a kernel dataset. These parameters can then be used to build the models in future iterations of mice. In its most simple invocation, you can just call the function with the desired optimization steps:
-``` {python TUNEPARAMETERS}
-# Using the first ImputationKernel in kernel to tune parameters
-# with the default settings.
-optimal_parameters, losses = kernel.tune_parameters(
- dataset=0,
- optimization_steps=5
-)
-
-# Run mice with our newly tuned parameters.
-kernel.mice(1, variable_parameters=optimal_parameters)
-
-# The optimal parameters are kept in ImputationKernel.optimal_parameters:
-print(optimal_parameters)
-```
-This will perform 10 fold cross validation on random samples of parameters. By default, all variables models are tuned. If you are curious about the default parameter space that is searched within, check out the `miceforest.default_lightgbm_parameters` module.
-
-The parameter tuning is pretty flexible. If you wish to set some model parameters static, or to change the bounds that are searched in, you can simply pass this information to either the `variable_parameters` parameter, `**kwbounds`, or both:
-``` {python TUNEPARAMETERS2}
-# Using a complicated setup:
-optimal_parameters, losses = kernel.tune_parameters(
- dataset=0,
- variables = ['sepal width (cm)','species','petal width (cm)'],
- variable_parameters = {
- 'sepal width (cm)': {'bagging_fraction': 0.5},
- 'species': {'bagging_freq': (5,10)}
- },
- optimization_steps=5,
- extra_trees = [True, False]
-)
-
-kernel.mice(1, variable_parameters=optimal_parameters)
-
-```
-In this example, we did a few things - we specified that only `sepal width (cm)`, `species`, and `petal width (cm)` should be tuned. We also specified some specific parameters in `variable_parameters.` Notice that `bagging_fraction` was passed as a scalar, `0.5`. This means that, for the variable `sepal width (cm)`, the parameter `bagging_fraction` will be set as that number and not be tuned. We did the opposite for `bagging_freq`. We specified bounds that the process should search in. We also passed the argument `extra_trees` as a list. Since it was passed to **kwbounds, this parameter will apply to all variables that are being tuned. Passing values as a list tells the process that it should randomly sample values from the list, instead of treating them as set of counts to search within.
-
-The tuning process follows these rules for different parameter values it finds:
-
-* Scalar: That value is used, and not tuned.
-* Tuple: Should be length 2. Treated as the lower and upper bound to search in.
-* List: Treated as a distinct list of values to try randomly.
-
-### On Reproducibility
-`miceforest` allows for different "levels" of reproducibility, global and record-level.
-
-##### **Global Reproducibility**
-Global reproducibility ensures that the same values will be imputed if the same code is run multiple times. To ensure global reproducibility, all the user needs to do is set a `random_state` when the kernel is initialized.
-
-##### **Record-Level Reproducibility**
-Sometimes we want to obtain reproducible imputations at the record level, without having to pass the same dataset. This is possible by passing a list of record-specific seeds to the `random_seed_array` parameter. This is useful if imputing new data multiple times, and you would like imputations for each row to match each time it is imputed.
-``` {python REPRODUCE_SEEDS}
-# Define seeds for the data, and impute iris
-random_seed_array = np.random.randint(9999, size=150)
-iris_imputed = kernel.impute_new_data(
- iris_amp,
- random_state=4,
- random_seed_array=random_seed_array
-)
-
-# Select a random sample
-new_inds = np.random.choice(150, size=15)
-new_data = iris_amp.loc[new_inds]
-new_seeds = random_seed_array[new_inds]
-new_imputed = kernel.impute_new_data(
- new_data,
- random_state=4,
- random_seed_array=new_seeds
-)
-
-# We imputed the same values for the 15 values each time,
-# because each record was associated with the same seed.
-assert new_imputed.complete_data(0).equals(iris_imputed.complete_data(0).loc[new_inds])
-
-```
-
-Note that record-level reproducibility is only possible in the `impute_new_data` function, there are no guarantees of record-level reproducibility in imputations between the kernel and new data.
-
-### How to Make the Process Faster
-Multiple Imputation is one of the most robust ways to handle missing data - but it can take a long time. There are several strategies you can use to decrease the time a process takes to run:
-
-* Decrease `data_subset`. By default all non-missing datapoints for each variable are used to train the model and perform mean matching. This can cause the model training nearest-neighbors search to take a long time for large data. A subset of these points can be searched instead by using `data_subset`.
-* If categorical columns are taking a long time, you can use the `mean_match_fast_cat` scheme. You can also set different parameters specifically for categorical columns, like smaller `bagging_fraction` or `num_iterations`.
-* If you need to impute new data faster, compile the predictions with the `compile_candidate_preds` method. This stores the predictions for each model, so it does not need to be re-calculated at each iteration.
-* Convert your data to a numpy array. Numpy arrays are much faster to index. While indexing overhead is avoided as much as possible, there is no getting around it. Consider comverting to `float32` datatype as well, as it will cause the resulting object to take up much less memory.
-* Decrease `mean_match_candidates`. The maximum number of neighbors that are considered with the default parameters is 10. However, for large datasets, this can still be an expensive operation. Consider explicitly setting `mean_match_candidates` lower.
-* Use different lightgbm parameters. lightgbm is usually not the problem, however if a certain variable has a large number of classes, then the max number of trees actually grown is (# classes) * (n_estimators). You can specifically decrease the bagging fraction or n_estimators for large multi-class variables, or grow less trees in general.
-* Use a faster mean matching function. The default mean matching function uses the scipy.Spatial.KDtree algorithm. There are faster alternatives out there, if you think mean matching is the holdup.
-
-
-### Imputing Data In Place
-It is possible to run the entire process without copying the dataset. If `copy_data=False`, then the data is referenced directly:
-```{python IMPUTE_NOCOPY}
-kernel_inplace = mf.ImputationKernel(
- iris_amp,
- datasets=1,
- copy_data=False
-)
-kernel_inplace.mice(2)
-```
-Note, that this probably won't (but could) change the original dataset in undesirable ways. Throughout the `mice` procedure, imputed values are stored directly in the original data. At the end, the missing values are put back as `np.NaN`.
-
-We can also complete our original data in place:
-```{python COMPLETE_REFERENCE}
-kernel_inplace.complete_data(dataset=0, inplace=True)
-print(iris_amp.isnull().sum(0))
-```
-This is useful if the dataset is large, and copies can't be made in memory.
-
-## Diagnostic Plotting
-
-As of now, miceforest has four diagnostic plots available.
-
-### Distribution of Imputed-Values
-We probably want to know how the imputed values are distributed. We can
-plot the original distribution beside the imputed distributions in each
-dataset by using the `plot_imputed_distributions` method of an
-`ImputationKernel` object:
-``` {python PLOT_DIST,eval=FALSE}
-kernel.plot_imputed_distributions(wspace=0.3,hspace=0.3)
-```
-```{r,eval=TRUE,echo=FALSE,out.width="600px"}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/distributions.png")
-```
-
-The red line is the original data, and each black line are the imputed
-values of each dataset.
-
-### Convergence of Correlation
-
-We are probably interested in knowing how our values between datasets
-converged over the iterations. The `plot_correlations` method shows you
-a boxplot of the correlations between imputed values in every
-combination of datasets, at each iteration. This allows you to see how
-correlated the imputations are between datasets, as well as the convergence
-over iterations:
-
-``` {python PLOT_CORRCONVERGENCE,eval=FALSE}
-kernel.plot_correlations()
-```
-```{r,eval=TRUE,echo=FALSE,out.width="600px"}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/plot_corr.png")
-```
-
-### Variable Importance
-We also may be interested in which variables were used to impute each variable. We can
-plot this information by using the `plot_feature_importance` method.
-``` {python PLOT_FEATIMP,eval=FALSE}
-kernel.plot_feature_importance(dataset=0, annot=True,cmap="YlGnBu",vmin=0, vmax=1)
-```
-```{r,eval=TRUE,echo=FALSE,out.width="600px"}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/var_imp.png")
-```
-
-The numbers shown are returned from the `lightgbm.Booster.feature_importance()` function. Each square represents the importance of the column variable in imputing the row variable.
-
-### Mean Convergence
-If our data is not missing completely at random, we may see that it takes a few iterations for our models to get the distribution of imputations right.
-We can plot the average value of our imputations to see if this is occurring:
-``` {python PLOT_MEANCON,eval=FALSE}
-kernel.plot_mean_convergence(wspace=0.3, hspace=0.4)
-```
-```{r,eval=TRUE,echo=FALSE,out.width="600px"}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/mean_convergence.png")
-```
-
-Our data was missing completely at random, so we don't see any convergence occurring here.
-
-## Using the Imputed Data
-
-To return the imputed data simply use the `complete_data` method:
-```{python completeData}
-dataset_1 = kernel.complete_data(0)
-```
-This will return a single specified dataset. Multiple datasets are typically created so that
-some measure of confidence around each prediction can be created.
-
-Since we know what the original data looked like, we can cheat and see how well the imputations
-compare to the original data:
-```{python IMP_PERFORMANCE}
-acclist = []
-for iteration in range(kernel.iteration_count()+1):
- species_na_count = kernel.na_counts[4]
- compdat = kernel.complete_data(dataset=0,iteration=iteration)
-
- # Record the accuract of the imputations of species.
- acclist.append(
- round(1-sum(compdat['species'] != iris['species'])/species_na_count,2)
- )
-
-# acclist shows the accuracy of the imputations
-# over the iterations.
-print(acclist)
-```
-In this instance, we went from a low accuracy (what is expected with random sampling) to a much higher accuracy.
-
-
-## The MICE Algorithm
-Multiple Imputation by Chained Equations 'fills in' (imputes) missing data in a dataset through an iterative series of predictive models. In each iteration, each specified variable in the dataset is imputed using the other variables in the dataset. These iterations should be run until it appears that convergence has been met.
-
-
-```{r eval=TRUE,echo=FALSE,fig.align='center'}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/MICEalgorithm.png")
-```
-
-This process is continued until all specified variables have been imputed. Additional iterations can be run if it appears that the average imputed values have not converged, although no more than 5 iterations are usually necessary.
-
-
-### Common Use Cases
-##### **Data Leakage:**
-MICE is particularly useful if missing values are associated with the target variable in a way that introduces leakage. For instance, let's say you wanted to model customer retention at the time of sign up. A certain variable is collected at sign up or 1 month after sign up. The absence of that variable is a data leak, since it tells you that the customer did not retain for 1 month.
-
-##### **Funnel Analysis:**
-Information is often collected at different stages of a 'funnel'. MICE can be used to make educated guesses about the characteristics of entities at different points in a funnel.
-
-##### **Confidence Intervals:**
-MICE can be used to impute missing values, however it is important to keep in mind that these imputed values are a prediction. Creating multiple datasets with different imputed values allows you to do two types of inference:
-
-* Imputed Value Distribution: A profile can be built for each imputed value, allowing you to make statements about the likely distribution of that value.
-* Model Prediction Distribution: With multiple datasets, you can build multiple models and create a distribution of predictions for each sample. Those samples with imputed values which were not able to be imputed with much confidence would have a larger variance in their predictions.
-
-
-### Predictive Mean Matching
-```miceforest``` can make use of a procedure called predictive mean matching (PMM) to select which values are imputed. PMM involves selecting a datapoint from the original, nonmissing data (candidates) which has a predicted value close to the predicted value of the missing sample (bachelors). The closest N (```mean_match_candidates``` parameter) values are selected, from which a value is chosen at random. This can be specified on a column-by-column basis. Going into more detail from our example above, we see how this works in practice:
-
-
-```{r eval=TRUE,echo=FALSE,fig.align='center'}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/PMM.png")
-```
-
-
-This method is very useful if you have a variable which needs imputing which has any of the following characteristics:
-
-* Multimodal
-* Integer
-* Skewed
-
-### Effects of Mean Matching
-As an example, let's construct a dataset with some of the above characteristics:
-```{python FAKEDATA, fig.height = 8, fig.width = 8,eval=FALSE}
-randst = np.random.RandomState(1991)
-# random uniform variable
-nrws = 1000
-uniform_vec = randst.uniform(size=nrws)
-
-def make_bimodal(mean1,mean2,size):
- bimodal_1 = randst.normal(size=nrws, loc=mean1)
- bimodal_2 = randst.normal(size=nrws, loc=mean2)
- bimdvec = []
- for i in range(size):
- bimdvec.append(randst.choice([bimodal_1[i], bimodal_2[i]]))
- return np.array(bimdvec)
-
-# Make 2 Bimodal Variables
-close_bimodal_vec = make_bimodal(2,-2,nrws)
-far_bimodal_vec = make_bimodal(3,-3,nrws)
-
-
-# Highly skewed variable correlated with Uniform_Variable
-skewed_vec = np.exp(uniform_vec*randst.uniform(size=nrws)*3) + randst.uniform(size=nrws)*3
-
-# Integer variable correlated with Close_Bimodal_Variable and Uniform_Variable
-integer_vec = np.round(uniform_vec + close_bimodal_vec/3 + randst.uniform(size=nrws)*2)
-
-# Make a DataFrame
-dat = pd.DataFrame(
- {
- 'uniform_var':uniform_vec,
- 'close_bimodal_var':close_bimodal_vec,
- 'far_bimodal_var':far_bimodal_vec,
- 'skewed_var':skewed_vec,
- 'integer_var':integer_vec
- }
-)
-
-# Ampute the data.
-ampdat = mf.ampute_data(dat,perc=0.25,random_state=randst)
-
-# Plot the original data
-import seaborn as sns
-import matplotlib.pyplot as plt
-g = sns.PairGrid(dat)
-g.map(plt.scatter,s=5)
-```
-```{r eval=TRUE,echo=FALSE,fig.align='center',out.width='600px'}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/dataset.png")
-```
-We can see how our variables are distributed and correlated in the graph above. Now let's run our imputation process twice, once using mean matching, and once using the model prediction.
-```{python, eval=FALSE}
-from miceforest import mean_match_default
-scheme_mmc_0 = mean_match_default.copy()
-scheme_mmc_5 = mean_match_default.copy()
-
-scheme_mmc_0.set_mean_match_candidates(0)
-scheme_mmc_5.set_mean_match_candidates(5)
-
-kernelmeanmatch = mf.ImputationKernel(ampdat, mean_match_scheme=scheme_mmc_5, datasets=1)
-kernelmodeloutput = mf.ImputationKernel(ampdat, mean_match_scheme=scheme_mmc_0, datasets=1)
-
-kernelmeanmatch.mice(2)
-kernelmodeloutput.mice(2)
-```
-
-Let's look at the effect on the different variables.
-
-##### With Mean Matching
-```{python,eval=FALSE}
-kernelmeanmatch.plot_imputed_distributions(wspace=0.2,hspace=0.4)
-```
-```{r eval=TRUE,echo=FALSE,fig.align='center',out.width='600px'}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/meanmatcheffects.png")
-```
-
-##### Without Mean Matching
-``` {python,eval=FALSE}
-kernelmodeloutput.plot_imputed_distributions(wspace=0.2,hspace=0.4)
-```
-```{r eval=TRUE,echo=FALSE,fig.align='center',out.width='600px'}
-knitr::include_graphics("https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/nomeanmatching.png")
-```
-
-You can see the effects that mean matching has, depending on the distribution of the data.
-Simply returning the value from the model prediction, while it may provide a better 'fit',
-will not provide imputations with a similair distribution to the original. This may be
-beneficial, depending on your goal.
\ No newline at end of file
diff --git a/README.md b/README.md
index b3f4914..41b5320 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,3 @@
-
[](https://zenodo.org/badge/latestdoi/289387436)
[](https://pepy.tech/project/miceforest)
[](https://pypi.python.org/pypi/miceforest)
@@ -14,7 +13,8 @@ Status](https://readthedocs.org/projects/miceforest/badge/?version=latest)](http
-## miceforest: Fast, Memory Efficient Imputation with LightGBM
+
+# miceforest: Fast, Memory Efficient Imputation with LightGBM
@@ -39,6 +39,7 @@ with lightgbm. The R version of this package may be found
- Data can be imputed in place to save memory
- Can build models on non-missing data
+
This document contains a thorough walkthrough of the package,
benchmarks, and an introduction to multiple imputation. More information
on MICE can be found in Stef van Buuren’s excellent online book, which
@@ -100,9 +101,7 @@ you can find
- [Effects of Mean
Matching](https://github.com/AnotherSamWilson/miceforest#Effects-of-Mean-Matching)
-## Package Meta
-
-### Installation
+## Installation
This package can be installed using either pip or conda, through
conda-forge:
@@ -123,7 +122,7 @@ first run `conda install pip git`.
$ pip install git+https://github.com/AnotherSamWilson/miceforest.git
```
-### Classes
+## Classes
miceforest has 3 main classes which the user will interact with:
@@ -142,12 +141,14 @@ miceforest has 3 main classes which the user will interact with:
built-in mean match schemes available in miceforest, discussed
below.
-## The Basics
+
+## Basic Usage
We will be looking at a few simple examples of imputation. We need to
load the packages, and define the data:
-``` python
+
+```python
import miceforest as mf
from sklearn.datasets import load_iris
import pandas as pd
@@ -160,17 +161,15 @@ iris['species'] = iris['species'].astype('category')
iris_amp = mf.ampute_data(iris,perc=0.25,random_state=1991)
```
-### Basic Examples
-
If you only want to create a single imputed dataset, you can use
[`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel)
with some default settings:
-``` python
+
+```python
# Create kernel.
kds = mf.ImputationKernel(
iris_amp,
- save_all_iterations=True,
random_state=1991
)
@@ -183,7 +182,7 @@ iris_complete = kds.complete_data()
There are also an array of plotting functions available, these are
discussed below in the section [Diagnostic
-Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting).
+Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting).
We usually don’t want to impute just a single dataset. In statistics,
multiple imputation is a process by which the uncertainty/other effects
@@ -193,12 +192,12 @@ imputed datasets.
can contain an arbitrary number of different datasets, all of which have
gone through mutually exclusive imputation processes:
-``` python
+
+```python
# Create kernel.
kernel = mf.ImputationKernel(
iris_amp,
- datasets=4,
- save_all_iterations=True,
+ num_datasets=4,
random_state=1
)
@@ -209,39 +208,45 @@ kernel.mice(2)
print(kernel)
```
- ##
- ## Class: ImputationKernel
- ## Datasets: 4
- ## Iterations: 2
- ## Data Samples: 150
- ## Data Columns: 5
- ## Imputed Variables: 5
- ## save_all_iterations: True
+
+ Class: ImputationKernel
+ Datasets: 4
+ Iterations: 2
+ Data Samples: 150
+ Data Columns: 5
+ Imputed Variables: 5
+ Modeled Variables: 5
+ All Iterations Saved: True
+
+
After we have run mice, we can obtain our completed dataset directly
from the kernel:
-``` python
+
+```python
completed_dataset = kernel.complete_data(dataset=2)
print(completed_dataset.isnull().sum(0))
```
- ## sepal length (cm) 0
- ## sepal width (cm) 0
- ## petal length (cm) 0
- ## petal width (cm) 0
- ## species 0
- ## dtype: int64
+ sepal length (cm) 0
+ sepal width (cm) 0
+ petal length (cm) 0
+ petal width (cm) 0
+ species 0
+ dtype: int64
-### Customizing LightGBM Parameters
+
+## Customizing LightGBM Parameters
Parameters can be passed directly to lightgbm in several different ways.
Parameters you wish to apply globally to every model can simply be
passed as kwargs to `mice`:
-``` python
+
+```python
# Run the MICE algorithm for 1 more iteration on the kernel with new parameters
-kernel.mice(iterations=1,n_estimators=50)
+kernel.mice(iterations=1, n_estimators=50)
```
You can also pass pass variable-specific arguments to
@@ -250,7 +255,8 @@ imputation of the `[species]` column was taking a little longer, because
it is multiclass. You could decrease the n\_estimators specifically for
that column with:
-``` python
+
+```python
# Run the MICE algorithm for 2 more iterations on the kernel
kernel.mice(
iterations=1,
@@ -269,8 +275,10 @@ Sepal Width used {str(sepalwidth_model.params["num_iterations"])} iterations
)
```
- ## Species used 25 iterations
- ## Sepal Width used 50 iterations
+ Species used 25 iterations
+ Sepal Width used 50 iterations
+
+
In this scenario, any parameters specified in `variable_parameters`
takes presidence over the kwargs.
@@ -285,11 +293,12 @@ For example, let’s pretend `sepal width (cm)` is a count field which can
be parameterized by a Poisson distribution. Let’s also change our
boosting method to gradient boosted trees:
-``` python
+
+```python
# Create kernel.
cust_kernel = mf.ImputationKernel(
iris_amp,
- datasets=1,
+ num_datasets=1,
random_state=1
)
@@ -305,71 +314,42 @@ Other nice parameters like `monotone_constraints` can also be passed.
Setting the parameter `device: 'gpu'` will utilize GPU learning, if
LightGBM is set up to do this on your machine.
-### Available Mean Match Schemes
+## Adjusting The Mean Matching Scheme
Note: It is probably a good idea to read [this
section](https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching)
first, to get some context on how mean matching works.
-The class `miceforest.MeanMatchScheme` contains information about how
-mean matching should be performed, such as:
+There are 4 imputation strategies employed by `miceforest`:
+- **Fast** Mean Matching: Available only on binary and categorical variables. Chooses a class randomly based on the predicted probabilities output by lightgbm.
+- **Normal** Mean Matching: Employs mean matching as described in the section below.
+- **Shap** Mean Matching: Runs a nearest neighbor search on the shap values of the bachelor predictions in the shap values of the candidate predictions. Finds the `mean_match_candidates` nearest neighbors, and chooses one randomly as the imputation value.
+- Value Imputation: Uses the value output by lightgbm as the imputation value. Skips mean matching entirely. To use, set `mean_match_candidates = 0`.
-1) Mean matching functions
-2) Mean matching candidates
-3) How to get predictions from a lightgbm model
-4) The datatypes predictions are stored as
+Here is the code required to use each method:
-There are three pre-built mean matching schemes that come with
-`miceforest`:
-``` python
-from miceforest import (
- mean_match_default,
- mean_match_fast_cat,
- mean_match_shap
+```python
+# Create kernel.
+cust_kernel = mf.ImputationKernel(
+ iris_amp,
+ num_datasets=1,
+ random_state=1,
+ mean_match_strategy={
+ 'sepal length (cm)': 'normal',
+ 'sepal width (cm)': 'shap',
+ 'species': 'fast',
+ },
+ mean_match_candidates={
+ 'petal length (cm)': 0,
+ }
)
-# To get information for each, use help()
-# help(mean_match_default)
+cust_kernel.mice(
+ iterations=1,
+)
```
-These schemes mostly differ in their strategy for performing mean
-matching
-
- - **mean\_match\_default** - medium speed, medium imputation quality
- - Categorical: perform a K Nearest Neighbors search on the
- candidate class probabilities, where K = mmc. Select 1 at
- random, and choose the associated candidate value as the
- imputation value.
- - Numeric: Perform a K Nearest Neighbors search on the candidate
- predictions, where K = mmc. Select 1 at random, and choose the
- associated candidate value as the imputation value.
- - **mean\_match\_fast\_cat** - fastest speed, lowest imputation
- quality
- - Categorical: return class based on random draw weighted by class
- probability for each sample.
- - Numeric: perform a K Nearest Neighbors search on the candidate
- class probabilities, where K = mmc. Select 1 at random, and
- choose the associated candidate value as the imputation value.
- - **mean\_match\_shap** - slowest speed, highest imputation quality
- for large datasets
- - Categorical: perform a K Nearest Neighbors search on the
- candidate prediction shap values, where K = mmc. Select 1 at
- random, and choose the associated candidate value as the
- imputation value.
- - Numeric: perform a K Nearest Neighbors search on the candidate
- prediction shap values, where K = mmc. Select 1 at random, and
- choose the associated candidate value as the imputation value.
-
-As a special case, if the mean\_match\_candidates is set to 0, the
-following behavior is observed for all schemes:
-
- - Categorical: the class with the highest probability is chosen.
- - Numeric: the predicted value is used
-
-These mean matching schemes can be updated and customized, we show an
-example below in the advanced section.
-
### Imputing New Data with Existing Models
Multiple Imputation can take a long time. If you wish to impute a
@@ -379,51 +359,53 @@ object. The `impute_new_data()` function uses the models collected by
`ImputationKernel` to perform multiple imputation without updating the
models at each iteration:
-``` python
+
+```python
# Our 'new data' is just the first 15 rows of iris_amp
from datetime import datetime
# Define our new data as the first 15 rows
-new_data = iris_amp.iloc[range(15)]
-
-# Imputing new data can often be made faster by
-# first compiling candidate predictions
-kernel.compile_candidate_preds()
+new_data = iris_amp.iloc[range(15)].reset_index(drop=True)
start_t = datetime.now()
-new_data_imputed = kernel.impute_new_data(new_data=new_data)
+new_data_imputed = cust_kernel.impute_new_data(new_data=new_data)
print(f"New Data imputed in {(datetime.now() - start_t).total_seconds()} seconds")
```
- ## New Data imputed in 0.507115 seconds
+ New Data imputed in 0.040396 seconds
+
+
+## Saving and Loading Kernels
+
+Saving `miceforest` kernels is efficient. During the pickling process, the following steps are taken:
+
+1. Convert working data to parquet bytes.
+2. Serialize the kernel.
+4. Save to a file.
-All of the imputation parameters (variable\_schema,
-mean\_match\_candidates, etc) will be carried over from the original
-`ImputationKernel` object. When mean matching, the candidate values are
-pulled from the original kernel dataset. To impute new data, the
-`save_models` parameter in `ImputationKernel` must be \> 0. If
-`save_models == 1`, the model from the latest iteration is saved for
-each variable. If `save_models > 1`, the model from each iteration is
-saved. This allows for new data to be imputed in a more similar fashion
-to the original mice procedure.
+You can save and load the kernel like any other object using `pickle` or `dill`:
-### Saving and Loading Kernels
-Kernels can be saved using the `.save_kernel()` method, and then loaded
-again using the `utils.load_kernel()` function. Internally, this
-procedure uses `blosc2` and `dill` packages to do the following:
-1. Convert working data to parquet bytes (if it is a pandas dataframe)
-2. Serialize the kernel
-3. Compress this serialization
-4. Save to a file
+```python
+from tempfile import mkstemp
+import dill
+new_file, filename = mkstemp()
-### Implementing sklearn Pipelines
+with open(filename, "wb") as f:
+ dill.dump(kernel, f)
-kernels can be fit into sklearn pipelines to impute training and scoring
+with open(filename, "rb") as f:
+ kernel_from_pickle = dill.load(f)
+```
+
+## Implementing sklearn Pipelines
+
+`miceforest` kernels can be fit into sklearn pipelines to impute training and scoring
datasets:
-``` python
+
+```python
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.datasets import make_classification
@@ -431,241 +413,294 @@ from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
import miceforest as mf
-# Define our data
-X, y = make_classification(random_state=0)
+kernel = mf.ImputationKernel(iris_amp, num_datasets=1, random_state=1)
-# Ampute and split the training data
-X = mf.utils.ampute_data(X)
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
-
-# Initialize our miceforest kernel. datasets parameter should be 1,
-# we don't want to return multiple datasets.
-pipe_kernel = mf.ImputationKernel(X_train, datasets=1)
-
-# Define our pipeline
pipe = Pipeline([
- ('impute', pipe_kernel),
+ ('impute', kernel),
('scaler', StandardScaler()),
])
-# Fit on and transform our training data.
-# Only use 2 iterations of mice.
+# The pipeline can be used as any other estimator
+# and avoids leaking the test set into the train set
X_train_t = pipe.fit_transform(
- X_train,
- y_train,
+ X=iris_amp,
+ y=None,
impute__iterations=2
)
-
-# Transform the test data as well
-X_test_t = pipe.transform(X_test)
+X_test_t = pipe.transform(new_data)
# Show that neither now have missing values.
assert not np.any(np.isnan(X_train_t))
assert not np.any(np.isnan(X_test_t))
```
-## Advanced Features
-
-Multiple imputation is a complex process. However, `miceforest` allows
-all of the major components to be switched out and customized by the
-user.
-
-### Customizing the Imputation Process
-
-It is possible to heavily customize our imputation procedure by
-variable. By passing a named list to `variable_schema`, you can specify
-the predictor variables for each imputed variable. You can also specify
-`mean_match_candidates` and `data_subset` by variable by passing a dict
-of valid values, with variable names as keys. You can even replace the
-entire default mean matching function for certain objectives if desired.
-Below is an *extremely* convoluted setup, which you would probably never
-want to use. It simply shows what is possible:
-
-``` python
-# Use the default mean match schema as our base
-from miceforest import mean_match_default
-mean_match_custom = mean_match_default.copy()
-
-# Define a mean matching function that
-# just randomly shuffles the predictions
-def custom_mmf(bachelor_preds):
- np.random.shuffle(bachelor_preds)
- return bachelor_preds
-
-# Specify that our custom function should be
-# used to perform mean matching on any variable
-# that was modeled with a poisson objective:
-mean_match_custom.set_mean_match_function(
- {"poisson": custom_mmf}
-)
+## Building Models on Nonmissing Data
-# Set the mean match candidates by variable
-mean_match_custom.set_mean_match_candidates(
- {
- 'sepal width (cm)': 3,
- 'petal width (cm)': 0
- }
-)
+The MICE process itself is used to impute missing data in a dataset.
+However, sometimes a variable can be fully recognized in the training
+data, but needs to be imputed later on in a different dataset. It is
+possible to train models to impute variables even if they have no
+missing values by specifying them in the `variable_schema` parameter.
+In this case, `variable_schema` is treated as the list of variables
+to train models on.
-# Define which variables should be used to model others
-variable_schema = {
- 'sepal width (cm)': ['species','petal width (cm)'],
- 'petal width (cm)': ['species','sepal length (cm)']
-}
-# Subset the candidate data to 50 rows for sepal width (cm).
-variable_subset = {
- 'sepal width (cm)': 50
-}
+```python
+# Set petal length (cm) in our amputed data
+# to original values with no missing data.
+iris_amp['sepal width (cm)'] = iris['sepal width (cm)'].copy()
+iris_amp.isnull().sum()
+```
-# Specify that petal width (cm) should be modeled by the
-# poisson objective. Our custom mean matching function
-# above will be used for this variable.
-variable_parameters = {
- 'petal width (cm)': {"objective": "poisson"}
-}
-cust_kernel = mf.ImputationKernel(
- iris_amp,
- datasets=3,
- mean_match_scheme=mean_match_custom,
- variable_schema=variable_schema,
- data_subset=variable_subset
+
+
+ sepal length (cm) 37
+ sepal width (cm) 0
+ petal length (cm) 37
+ petal width (cm) 37
+ species 37
+ dtype: int64
+
+
+
+
+```python
+kernel = mf.ImputationKernel(
+ data=iris_amp,
+ variable_schema=iris_amp.columns.to_list(),
+ num_datasets=1,
+ random_state=1,
)
-cust_kernel.mice(iterations=1, variable_parameters=variable_parameters)
+kernel.mice(1)
```
-The mean matching function can take any number of the following
-arguments. If a function does not take one of these arguments, then the
-process will not prepare that data for mean matching.
-``` python
-from miceforest.MeanMatchScheme import AVAILABLE_MEAN_MATCH_ARGS
-print("\n".join(AVAILABLE_MEAN_MATCH_ARGS))
+```python
+# Remember, the dataset we are imputing does have
+# missing values in the sepal width (cm) column
+new_data.isnull().sum()
```
- ## mean_match_candidates
- ## lgb_booster
- ## bachelor_preds
- ## bachelor_features
- ## candidate_values
- ## candidate_features
- ## candidate_preds
- ## random_state
- ## hashed_seeds
-### Building Models on Nonmissing Data
-The MICE process itself is used to impute missing data in a dataset.
-However, sometimes a variable can be fully recognized in the training
-data, but needs to be imputed later on in a different dataset. It is
-possible to train models to impute variables even if they have no
-missing values by setting `train_nonmissing=True`. In this case,
-`variable_schema` is treated as the list of variables to train models
-on. `imputation_order` only affects which variables actually have their
-values imputed, it does not affect which variables have models trained:
-
-``` python
-orig_missing_cols = ["sepal length (cm)", "sepal width (cm)"]
-new_missing_cols = ["sepal length (cm)", "sepal width (cm)", "species"]
-
-# Training data only contains 2 columns with missing data
-iris_amp2 = iris.copy()
-iris_amp2[orig_missing_cols] = mf.ampute_data(
- iris_amp2[orig_missing_cols],
- perc=0.25,
- random_state=1991
-)
-# Specify that models should also be trained for species column
-var_sch = new_missing_cols
+ sepal length (cm) 4
+ sepal width (cm) 3
+ petal length (cm) 1
+ petal width (cm) 3
+ species 3
+ dtype: int64
+
-cust_kernel = mf.ImputationKernel(
- iris_amp2,
- datasets=1,
- variable_schema=var_sch,
- train_nonmissing=True
-)
-cust_kernel.mice(1)
-# New data has missing values in species column
-iris_amp2_new = iris.iloc[range(10),:].copy()
-iris_amp2_new[new_missing_cols] = mf.ampute_data(
- iris_amp2_new[new_missing_cols],
- perc=0.25,
- random_state=1991
-)
-# Species column can still be imputed
-iris_amp2_new_imp = cust_kernel.impute_new_data(iris_amp2_new)
-iris_amp2_new_imp.complete_data(0).isnull().sum()
+```python
+new_data_imp = kernel.impute_new_data(new_data)
+new_data_imp = new_data_imp.complete_data()
+
+# All columns have been imputed.
+new_data_imp.isnull().sum()
```
- ## sepal length (cm) 0
- ## sepal width (cm) 0
- ## petal length (cm) 0
- ## petal width (cm) 0
- ## species 0
- ## dtype: int64
-Here, we knew that the species column in our new data would need to be
-imputed. Therefore, we specified that a model should be built for all 3
-variables in the `variable_schema` (passing a dict of target - feature
-pairs would also have worked).
-### Tuning Parameters
+
+ sepal length (cm) 0
+ sepal width (cm) 0
+ petal length (cm) 0
+ petal width (cm) 0
+ species 0
+ dtype: int64
+
+
+
+## Tuning Parameters
`miceforest` allows you to tune the parameters on a kernel dataset.
These parameters can then be used to build the models in future
iterations of mice. In its most simple invocation, you can just call the
function with the desired optimization steps:
-``` python
-# Using the first ImputationKernel in kernel to tune parameters
-# with the default settings.
-optimal_parameters, losses = kernel.tune_parameters(
- dataset=0,
- optimization_steps=5
+
+```python
+optimal_params = kernel.tune_parameters(
+ dataset=0,
+ use_gbdt=True,
+ num_iterations=500,
+ random_state=1,
)
+kernel.mice(1, variable_parameters=optimal_params)
+pd.DataFrame(optimal_params)
+```
-# Run mice with our newly tuned parameters.
-kernel.mice(1, variable_parameters=optimal_parameters)
-# The optimal parameters are kept in ImputationKernel.optimal_parameters:
-print(optimal_parameters)
-```
- ## {0: {'boosting': 'gbdt', 'num_iterations': 165, 'max_depth': 8, 'num_leaves': 20, 'min_data_in_leaf': 1, 'min_sum_hessian_in_leaf': 0.1, 'min_gain_to_split': 0.0, 'bagging_fraction': 0.2498838792503861, 'feature_fraction': 1.0, 'feature_fraction_bynode': 0.6020460898858531, 'bagging_freq': 1, 'verbosity': -1, 'objective': 'regression', 'learning_rate': 0.02, 'cat_smooth': 17.807024990062555}, 1: {'boosting': 'gbdt', 'num_iterations': 94, 'max_depth': 8, 'num_leaves': 14, 'min_data_in_leaf': 4, 'min_sum_hessian_in_leaf': 0.1, 'min_gain_to_split': 0.0, 'bagging_fraction': 0.7802435334180599, 'feature_fraction': 1.0, 'feature_fraction_bynode': 0.6856668707631843, 'bagging_freq': 1, 'verbosity': -1, 'objective': 'regression', 'learning_rate': 0.02, 'cat_smooth': 4.802568893662679}, 2: {'boosting': 'gbdt', 'num_iterations': 229, 'max_depth': 8, 'num_leaves': 4, 'min_data_in_leaf': 8, 'min_sum_hessian_in_leaf': 0.1, 'min_gain_to_split': 0.0, 'bagging_fraction': 0.9565982004313843, 'feature_fraction': 1.0, 'feature_fraction_bynode': 0.6065024947204825, 'bagging_freq': 1, 'verbosity': -1, 'objective': 'regression', 'learning_rate': 0.02, 'cat_smooth': 17.2138799939537}, 3: {'boosting': 'gbdt', 'num_iterations': 182, 'max_depth': 8, 'num_leaves': 20, 'min_data_in_leaf': 4, 'min_sum_hessian_in_leaf': 0.1, 'min_gain_to_split': 0.0, 'bagging_fraction': 0.7251674145835884, 'feature_fraction': 1.0, 'feature_fraction_bynode': 0.9262368919526676, 'bagging_freq': 1, 'verbosity': -1, 'objective': 'regression', 'learning_rate': 0.02, 'cat_smooth': 5.780326477879999}, 4: {'boosting': 'gbdt', 'num_iterations': 208, 'max_depth': 8, 'num_leaves': 4, 'min_data_in_leaf': 7, 'min_sum_hessian_in_leaf': 0.1, 'min_gain_to_split': 0.0, 'bagging_fraction': 0.6746301598613926, 'feature_fraction': 1.0, 'feature_fraction_bynode': 0.20999114041328495, 'bagging_freq': 1, 'verbosity': -1, 'objective': 'multiclass', 'num_class': 3, 'learning_rate': 0.02, 'cat_smooth': 8.604908973256704}}
+
+
+
+
+
+
+ |
+ sepal length (cm) |
+ petal length (cm) |
+ petal width (cm) |
+ species |
+
+
+
+
+ | boosting |
+ gbdt |
+ gbdt |
+ gbdt |
+ gbdt |
+
+
+ | data_sample_strategy |
+ bagging |
+ bagging |
+ bagging |
+ bagging |
+
+
+ | num_iterations |
+ 142 |
+ 248 |
+ 262 |
+ 172 |
+
+
+ | max_depth |
+ 4 |
+ 4 |
+ 5 |
+ 5 |
+
+
+ | num_leaves |
+ 12 |
+ 17 |
+ 2 |
+ 19 |
+
+
+ | min_data_in_leaf |
+ 2 |
+ 2 |
+ 15 |
+ 5 |
+
+
+ | min_sum_hessian_in_leaf |
+ 0.1 |
+ 0.1 |
+ 0.1 |
+ 0.1 |
+
+
+ | min_gain_to_split |
+ 0.0 |
+ 0.0 |
+ 0.0 |
+ 0.0 |
+
+
+ | bagging_fraction |
+ 0.580973 |
+ 0.501521 |
+ 0.586709 |
+ 0.795465 |
+
+
+ | feature_fraction_bynode |
+ 0.922566 |
+ 0.299912 |
+ 0.503182 |
+ 0.237637 |
+
+
+ | bagging_freq |
+ 1 |
+ 1 |
+ 1 |
+ 1 |
+
+
+ | verbosity |
+ -1 |
+ -1 |
+ -1 |
+ -1 |
+
+
+ | learning_rate |
+ 0.02 |
+ 0.02 |
+ 0.02 |
+ 0.02 |
+
+
+ | objective |
+ regression |
+ regression |
+ regression |
+ multiclass |
+
+
+ | num_class |
+ NaN |
+ NaN |
+ NaN |
+ 3 |
+
+
+
+
-
-The red line is the original data, and each black line are the imputed
-values of each dataset.
-### Convergence of Correlation
-
-We are probably interested in knowing how our values between datasets
-converged over the iterations. The `plot_correlations` method shows you
-a boxplot of the correlations between imputed values in every
-combination of datasets, at each iteration. This allows you to see how
-correlated the imputations are between datasets, as well as the
-convergence over iterations:
-
-``` python
-kernel.plot_correlations()
-```
+
+
+
-
-### Variable Importance
+### Plot Imputed Distributions
-We also may be interested in which variables were used to impute each
-variable. We can plot this information by using the
-`plot_feature_importance` method.
-``` python
-kernel.plot_feature_importance(dataset=0, annot=True,cmap="YlGnBu",vmin=0, vmax=1)
+```python
+kernel.plot_imputed_distributions()
```
-
-
-The numbers shown are returned from the
-`lightgbm.Booster.feature_importance()` function. Each square represents
-the importance of the column variable in imputing the row variable.
-
-### Mean Convergence
-If our data is not missing completely at random, we may see that it
-takes a few iterations for our models to get the distribution of
-imputations right. We can plot the average value of our imputations to
-see if this is occurring:
-
-``` python
-kernel.plot_mean_convergence(wspace=0.3, hspace=0.4)
-```
-
-
+
+
+
-Our data was missing completely at random, so we don’t see any
-convergence occurring here.
## Using the Imputed Data
To return the imputed data simply use the `complete_data` method:
-``` python
+
+```python
dataset_1 = kernel.complete_data(0)
```
@@ -888,10 +889,12 @@ prediction can be created.
Since we know what the original data looked like, we can cheat and see
how well the imputations compare to the original data:
-``` python
+
+```python
acclist = []
-for iteration in range(kernel.iteration_count()+1):
- species_na_count = kernel.na_counts[4]
+iterations = kernel.iteration_count()+1
+for iteration in range(iterations):
+ species_na_count = kernel.na_counts['species']
compdat = kernel.complete_data(dataset=0,iteration=iteration)
# Record the accuract of the imputations of species.
@@ -899,12 +902,24 @@ for iteration in range(kernel.iteration_count()+1):
round(1-sum(compdat['species'] != iris['species'])/species_na_count,2)
)
-# acclist shows the accuracy of the imputations
-# over the iterations.
-print(acclist)
+# acclist shows the accuracy of the imputations over the iterations.
+acclist = pd.Series(acclist).rename("Species Imputation Accuracy")
+acclist.index = range(iterations)
+acclist.index.name = "Iteration"
+acclist
```
- ## [0.35, 0.81, 0.84, 0.84, 0.89, 0.92, 0.89]
+
+
+
+ Iteration
+ 0 0.35
+ 1 0.81
+ 2 0.81
+ 3 0.78
+ Name: Species Imputation Accuracy, dtype: float64
+
+
In this instance, we went from a low accuracy (what is expected with
random sampling) to a much higher accuracy.
@@ -957,7 +972,8 @@ types of inference:
imputed with much confidence would have a larger variance in their
predictions.
-### Predictive Mean Matching
+
+## Predictive Mean Matching
`miceforest` can make use of a procedure called predictive mean matching
(PMM) to select which values are imputed. PMM involves selecting a
@@ -977,12 +993,14 @@ which has any of the following characteristics:
- Integer
- Skewed
+
### Effects of Mean Matching
As an example, let’s construct a dataset with some of the above
characteristics:
-``` python
+
+```python
randst = np.random.RandomState(1991)
# random uniform variable
nrws = 1000
@@ -1020,54 +1038,89 @@ dat = pd.DataFrame(
# Ampute the data.
ampdat = mf.ampute_data(dat,perc=0.25,random_state=randst)
+```
+
+
+```python
+import plotnine as p9
+import itertools
-# Plot the original data
-import seaborn as sns
-import matplotlib.pyplot as plt
-g = sns.PairGrid(dat)
-g.map(plt.scatter,s=5)
+def plot_matrix(df, columns):
+ pdf = []
+ for a1, b1 in itertools.combinations(columns, 2):
+ for (a,b) in ((a1, b1), (b1, a1)):
+ sub = df[[a, b]].rename(columns={a: "x", b: "y"}).assign(a=a, b=b)
+ pdf.append(sub)
+
+ g = (
+ p9.ggplot(pd.concat(pdf))
+ + p9.geom_point(p9.aes('x','y'))
+ + p9.facet_grid('b~a', scales='free')
+ + p9.theme(figure_size=(7, 7))
+ + p9.xlab("") + p9.ylab("")
+ )
+ return g
+
+plot_matrix(dat, dat.columns)
```
-
+
+
+
+
+
+
We can see how our variables are distributed and correlated in the graph
above. Now let’s run our imputation process twice, once using mean
matching, and once using the model prediction.
-``` python
-from miceforest import mean_match_default
-scheme_mmc_0 = mean_match_default.copy()
-scheme_mmc_5 = mean_match_default.copy()
-scheme_mmc_0.set_mean_match_candidates(0)
-scheme_mmc_5.set_mean_match_candidates(5)
+```python
+kernel_mean_match = mf.ImputationKernel(
+ data=ampdat,
+ num_datasets=3,
+ mean_match_candidates=5,
+ random_state=1
+)
+kernel_mean_match.mice(2)
+kernel_no_mean_match = mf.ImputationKernel(
+ data=ampdat,
+ num_datasets=3,
+ mean_match_candidates=0,
+ random_state=1
+)
+kernel_no_mean_match.mice(2)
+```
-kernelmeanmatch = mf.ImputationKernel(ampdat, mean_match_scheme=scheme_mmc_5, datasets=1)
-kernelmodeloutput = mf.ImputationKernel(ampdat, mean_match_scheme=scheme_mmc_0, datasets=1)
-kernelmeanmatch.mice(2)
-kernelmodeloutput.mice(2)
+```python
+kernel_mean_match.plot_imputed_distributions()
```
-Let’s look at the effect on the different variables.
-##### With Mean Matching
-
-``` python
-kernelmeanmatch.plot_imputed_distributions(wspace=0.2,hspace=0.4)
-```
+
+
+
-
-##### Without Mean Matching
-``` python
-kernelmodeloutput.plot_imputed_distributions(wspace=0.2,hspace=0.4)
+```python
+kernel_no_mean_match.plot_imputed_distributions()
```
-
+
+
+
+
+
You can see the effects that mean matching has, depending on the
distribution of the data. Simply returning the value from the model
prediction, while it may provide a better ‘fit’, will not provide
imputations with a similair distribution to the original. This may be
beneficial, depending on your goal.
+
+
+```python
+
+```
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@@ -0,0 +1,1561 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "[](https://zenodo.org/badge/latestdoi/289387436)\n",
+ "[](https://pepy.tech/project/miceforest)\n",
+ "[](https://pypi.python.org/pypi/miceforest)\n",
+ "[](https://anaconda.org/conda-forge/miceforest)\n",
+ "[](https://pypi.org/project/miceforest/) \n",
+ "[](https://github.com/AnotherSamWilson/miceforest/actions/workflows/run_tests.yml)\n",
+ "[](https://miceforest.readthedocs.io/en/latest/?badge=latest)\n",
+ "[](https://codecov.io/gh/AnotherSamWilson/miceforest)\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# miceforest: Fast, Memory Efficient Imputation with LightGBM"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "\n",
+ "Fast, memory efficient Multiple Imputation by Chained Equations (MICE)\n",
+ "with lightgbm. The R version of this package may be found\n",
+ "[here](https://github.com/FarrellDay/miceRanger).\n",
+ "\n",
+ "`miceforest` was designed to be:\n",
+ "\n",
+ " - **Fast**\n",
+ " - Uses lightgbm as a backend\n",
+ " - Has efficient mean matching solutions.\n",
+ " - Can utilize GPU training\n",
+ " - **Flexible**\n",
+ " - Can impute pandas dataframes and numpy arrays\n",
+ " - Handles categorical data automatically\n",
+ " - Fits into a sklearn pipeline\n",
+ " - User can customize every aspect of the imputation process\n",
+ " - **Production Ready**\n",
+ " - Can impute new, unseen datasets quickly\n",
+ " - Kernels are efficiently compressed during saving and loading\n",
+ " - Data can be imputed in place to save memory\n",
+ " - Can build models on non-missing data\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "This document contains a thorough walkthrough of the package,\n",
+ "benchmarks, and an introduction to multiple imputation. More information\n",
+ "on MICE can be found in Stef van Buuren’s excellent online book, which\n",
+ "you can find\n",
+ "[here](https://stefvanbuuren.name/fimd/ch-introduction.html).\n",
+ "\n",
+ "#### Table of Contents:\n",
+ "\n",
+ " - [Package\n",
+ " Meta](https://github.com/AnotherSamWilson/miceforest#Package-Meta)\n",
+ " - [The\n",
+ " Basics](https://github.com/AnotherSamWilson/miceforest#The-Basics)\n",
+ " - [Basic\n",
+ " Examples](https://github.com/AnotherSamWilson/miceforest#Basic-Examples)\n",
+ " - [Customizing LightGBM\n",
+ " Parameters](https://github.com/AnotherSamWilson/miceforest#Customizing-LightGBM-Parameters)\n",
+ " - [Available Mean Match\n",
+ " Schemes](https://github.com/AnotherSamWilson/miceforest#Controlling-Tree-Growth)\n",
+ " - [Imputing New Data with Existing\n",
+ " Models](https://github.com/AnotherSamWilson/miceforest#Imputing-New-Data-with-Existing-Models)\n",
+ " - [Saving and Loading\n",
+ " Kernels](https://github.com/AnotherSamWilson/miceforest#Saving-and-Loading-Kernels)\n",
+ " - [Implementing sklearn\n",
+ " Pipelines](https://github.com/AnotherSamWilson/miceforest#Implementing-sklearn-Pipelines)\n",
+ " - [Advanced\n",
+ " Features](https://github.com/AnotherSamWilson/miceforest#Advanced-Features)\n",
+ " - [Customizing the Imputation\n",
+ " Process](https://github.com/AnotherSamWilson/miceforest#Customizing-the-Imputation-Process)\n",
+ " - [Building Models on Nonmissing\n",
+ " Data](https://github.com/AnotherSamWilson/miceforest#Building-Models-on-Nonmissing-Data)\n",
+ " - [Tuning\n",
+ " Parameters](https://github.com/AnotherSamWilson/miceforest#Tuning-Parameters)\n",
+ " - [On\n",
+ " Reproducibility](https://github.com/AnotherSamWilson/miceforest#On-Reproducibility)\n",
+ " - [How to Make the Process\n",
+ " Faster](https://github.com/AnotherSamWilson/miceforest#How-to-Make-the-Process-Faster)\n",
+ " - [Imputing Data In\n",
+ " Place](https://github.com/AnotherSamWilson/miceforest#Imputing-Data-In-Place)\n",
+ " - [Diagnostic\n",
+ " Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting)\n",
+ " - [Imputed\n",
+ " Distributions](https://github.com/AnotherSamWilson/miceforest#Distribution-of-Imputed-Values)\n",
+ " - [Correlation\n",
+ " Convergence](https://github.com/AnotherSamWilson/miceforest#Convergence-of-Correlation)\n",
+ " - [Variable\n",
+ " Importance](https://github.com/AnotherSamWilson/miceforest#Variable-Importance)\n",
+ " - [Mean\n",
+ " Convergence](https://github.com/AnotherSamWilson/miceforest#Variable-Importance)\n",
+ " - [Benchmarks](https://github.com/AnotherSamWilson/miceforest#Benchmarks)\n",
+ " - [Using the Imputed\n",
+ " Data](https://github.com/AnotherSamWilson/miceforest#Using-the-Imputed-Data)\n",
+ " - [The MICE\n",
+ " Algorithm](https://github.com/AnotherSamWilson/miceforest#The-MICE-Algorithm)\n",
+ " - [Introduction](https://github.com/AnotherSamWilson/miceforest#The-MICE-Algorithm)\n",
+ " - [Common Use\n",
+ " Cases](https://github.com/AnotherSamWilson/miceforest#Common-Use-Cases)\n",
+ " - [Predictive Mean\n",
+ " Matching](https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching)\n",
+ " - [Effects of Mean\n",
+ " Matching](https://github.com/AnotherSamWilson/miceforest#Effects-of-Mean-Matching)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Installation\n",
+ "\n",
+ "This package can be installed using either pip or conda, through\n",
+ "conda-forge:\n",
+ "\n",
+ "``` bash\n",
+ "# Using pip\n",
+ "$ pip install miceforest --no-cache-dir\n",
+ "\n",
+ "# Using conda\n",
+ "$ conda install -c conda-forge miceforest\n",
+ "```\n",
+ "\n",
+ "You can also download the latest development version from this\n",
+ "repository. If you want to install from github with conda, you must\n",
+ "first run `conda install pip git`.\n",
+ "\n",
+ "``` bash\n",
+ "$ pip install git+https://github.com/AnotherSamWilson/miceforest.git\n",
+ "```"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Classes\n",
+ "\n",
+ "miceforest has 3 main classes which the user will interact with:\n",
+ "\n",
+ " - [`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel)\n",
+ " - This class contains the raw data off of which the `mice` algorithm\n",
+ " is performed. During this process, models will be trained, and the\n",
+ " imputed (predicted) values will be stored. These values can be used\n",
+ " to fill in the missing values of the raw data. The raw data can be\n",
+ " copied, or referenced directly. Models can be saved, and used to\n",
+ " impute new datasets.\n",
+ " - [`ImputedData`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputedData.html#miceforest.ImputedData)\n",
+ " - The result of `ImputationKernel.impute_new_data(new_data)`. This\n",
+ " contains the raw data in `new_data` as well as the imputed values. \n",
+ " - [`MeanMatchScheme`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.MeanMatchScheme.html#miceforest.MeanMatchScheme)\n",
+ " - Determines how mean matching should be carried out. There are 3\n",
+ " built-in mean match schemes available in miceforest, discussed\n",
+ " below.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Basic Usage\n",
+ "\n",
+ "We will be looking at a few simple examples of imputation. We need to\n",
+ "load the packages, and define the data:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import miceforest as mf\n",
+ "from sklearn.datasets import load_iris\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "\n",
+ "# Load data and introduce missing values\n",
+ "iris = pd.concat(load_iris(as_frame=True,return_X_y=True),axis=1)\n",
+ "iris.rename({\"target\": \"species\"}, inplace=True, axis=1)\n",
+ "iris['species'] = iris['species'].astype('category')\n",
+ "iris_amp = mf.ampute_data(iris,perc=0.25,random_state=1991)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "If you only want to create a single imputed dataset, you can use\n",
+ "[`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel)\n",
+ "with some default settings:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create kernel. \n",
+ "kds = mf.ImputationKernel(\n",
+ " iris_amp,\n",
+ " random_state=1991\n",
+ ")\n",
+ "\n",
+ "# Run the MICE algorithm for 2 iterations\n",
+ "kds.mice(2)\n",
+ "\n",
+ "# Return the completed dataset.\n",
+ "iris_complete = kds.complete_data()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "There are also an array of plotting functions available, these are\n",
+ "discussed below in the section [Diagnostic\n",
+ "Plotting](https://github.com/AnotherSamWilson/miceforest#Diagnostic-Plotting). \n",
+ "\n",
+ "We usually don’t want to impute just a single dataset. In statistics,\n",
+ "multiple imputation is a process by which the uncertainty/other effects\n",
+ "caused by missing values can be examined by creating multiple different\n",
+ "imputed datasets.\n",
+ "[`ImputationKernel`](https://miceforest.readthedocs.io/en/latest/ik/miceforest.ImputationKernel.html#miceforest.ImputationKernel)\n",
+ "can contain an arbitrary number of different datasets, all of which have\n",
+ "gone through mutually exclusive imputation processes:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\n",
+ " Class: ImputationKernel\n",
+ " Datasets: 4\n",
+ " Iterations: 2\n",
+ " Data Samples: 150\n",
+ " Data Columns: 5\n",
+ " Imputed Variables: 5\n",
+ " Modeled Variables: 5\n",
+ "All Iterations Saved: True\n",
+ " \n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create kernel. \n",
+ "kernel = mf.ImputationKernel(\n",
+ " iris_amp,\n",
+ " num_datasets=4,\n",
+ " random_state=1\n",
+ ")\n",
+ "\n",
+ "# Run the MICE algorithm for 2 iterations on each of the datasets\n",
+ "kernel.mice(2)\n",
+ "\n",
+ "# Printing the kernel will show you some high level information.\n",
+ "print(kernel)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After we have run mice, we can obtain our completed dataset directly\n",
+ "from the kernel:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "sepal length (cm) 0\n",
+ "sepal width (cm) 0\n",
+ "petal length (cm) 0\n",
+ "petal width (cm) 0\n",
+ "species 0\n",
+ "dtype: int64\n"
+ ]
+ }
+ ],
+ "source": [
+ "completed_dataset = kernel.complete_data(dataset=2)\n",
+ "print(completed_dataset.isnull().sum(0))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Customizing LightGBM Parameters\n",
+ "\n",
+ "Parameters can be passed directly to lightgbm in several different ways.\n",
+ "Parameters you wish to apply globally to every model can simply be\n",
+ "passed as kwargs to `mice`:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Run the MICE algorithm for 1 more iteration on the kernel with new parameters\n",
+ "kernel.mice(iterations=1, n_estimators=50)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can also pass pass variable-specific arguments to\n",
+ "`variable_parameters` in mice. For instance, let’s say you noticed the\n",
+ "imputation of the `[species]` column was taking a little longer, because\n",
+ "it is multiclass. You could decrease the n\\_estimators specifically for\n",
+ "that column with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Species used 25 iterations\n",
+ "Sepal Width used 50 iterations\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Run the MICE algorithm for 2 more iterations on the kernel \n",
+ "kernel.mice(\n",
+ " iterations=1,\n",
+ " variable_parameters={'species': {'n_estimators': 25}},\n",
+ " n_estimators=50\n",
+ ")\n",
+ "\n",
+ "# Let's get the actual models for these variables:\n",
+ "species_model = kernel.get_model(dataset=0,variable=\"species\")\n",
+ "sepalwidth_model = kernel.get_model(dataset=0,variable=\"sepal width (cm)\")\n",
+ "\n",
+ "print(\n",
+ "f\"\"\"Species used {str(species_model.params[\"num_iterations\"])} iterations\n",
+ "Sepal Width used {str(sepalwidth_model.params[\"num_iterations\"])} iterations\n",
+ "\"\"\"\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this scenario, any parameters specified in `variable_parameters`\n",
+ "takes presidence over the kwargs.\n",
+ "\n",
+ "Since we can pass any parameters we want to LightGBM, we can completely\n",
+ "customize how our models are built. That includes how the data should be\n",
+ "modeled. If your data contains count data, or any other data which can\n",
+ "be parameterized by lightgbm, you can simply specify that variable to be\n",
+ "modeled with the corresponding objective function.\n",
+ "\n",
+ "For example, let’s pretend `sepal width (cm)` is a count field which can\n",
+ "be parameterized by a Poisson distribution. Let’s also change our\n",
+ "boosting method to gradient boosted trees:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create kernel. \n",
+ "cust_kernel = mf.ImputationKernel(\n",
+ " iris_amp,\n",
+ " num_datasets=1,\n",
+ " random_state=1\n",
+ ")\n",
+ "\n",
+ "cust_kernel.mice(\n",
+ " iterations=1, \n",
+ " variable_parameters={'sepal width (cm)': {'objective': 'poisson'}},\n",
+ " boosting = 'gbdt',\n",
+ " min_sum_hessian_in_leaf=0.01\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Other nice parameters like `monotone_constraints` can also be passed.\n",
+ "Setting the parameter `device: 'gpu'` will utilize GPU learning, if\n",
+ "LightGBM is set up to do this on your machine."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Adjusting The Mean Matching Scheme\n",
+ "\n",
+ "Note: It is probably a good idea to read [this\n",
+ "section](https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching)\n",
+ "first, to get some context on how mean matching works.\n",
+ "\n",
+ "There are 4 imputation strategies employed by `miceforest`:\n",
+ "- **Fast** Mean Matching: Available only on binary and categorical variables. Chooses a class randomly based on the predicted probabilities output by lightgbm.\n",
+ "- **Normal** Mean Matching: Employs mean matching as described in the section below.\n",
+ "- **Shap** Mean Matching: Runs a nearest neighbor search on the shap values of the bachelor predictions in the shap values of the candidate predictions. Finds the `mean_match_candidates` nearest neighbors, and chooses one randomly as the imputation value.\n",
+ "- Value Imputation: Uses the value output by lightgbm as the imputation value. Skips mean matching entirely. To use, set `mean_match_candidates = 0`.\n",
+ "\n",
+ "Here is the code required to use each method:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Create kernel. \n",
+ "cust_kernel = mf.ImputationKernel(\n",
+ " iris_amp,\n",
+ " num_datasets=1,\n",
+ " random_state=1,\n",
+ " mean_match_strategy={\n",
+ " 'sepal length (cm)': 'normal',\n",
+ " 'sepal width (cm)': 'shap',\n",
+ " 'species': 'fast',\n",
+ " },\n",
+ " mean_match_candidates={\n",
+ " 'petal length (cm)': 0,\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "cust_kernel.mice(\n",
+ " iterations=1, \n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Imputing New Data with Existing Models\n",
+ "\n",
+ "Multiple Imputation can take a long time. If you wish to impute a\n",
+ "dataset using the MICE algorithm, but don’t have time to train new\n",
+ "models, it is possible to impute new datasets using a `ImputationKernel`\n",
+ "object. The `impute_new_data()` function uses the models collected by\n",
+ "`ImputationKernel` to perform multiple imputation without updating the\n",
+ "models at each iteration:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "New Data imputed in 0.040396 seconds\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Our 'new data' is just the first 15 rows of iris_amp\n",
+ "from datetime import datetime\n",
+ "\n",
+ "# Define our new data as the first 15 rows\n",
+ "new_data = iris_amp.iloc[range(15)].reset_index(drop=True)\n",
+ "\n",
+ "start_t = datetime.now()\n",
+ "new_data_imputed = cust_kernel.impute_new_data(new_data=new_data)\n",
+ "print(f\"New Data imputed in {(datetime.now() - start_t).total_seconds()} seconds\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Saving and Loading Kernels\n",
+ "\n",
+ "Saving `miceforest` kernels is efficient. During the pickling process, the following steps are taken:\n",
+ "\n",
+ "1. Convert working data to parquet bytes.\n",
+ "2. Serialize the kernel.\n",
+ "4. Save to a file.\n",
+ "\n",
+ "You can save and load the kernel like any other object using `pickle` or `dill`:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from tempfile import mkstemp\n",
+ "import dill\n",
+ "new_file, filename = mkstemp()\n",
+ "\n",
+ "with open(filename, \"wb\") as f:\n",
+ " dill.dump(kernel, f)\n",
+ "\n",
+ "with open(filename, \"rb\") as f:\n",
+ " kernel_from_pickle = dill.load(f)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Implementing sklearn Pipelines\n",
+ "\n",
+ "`miceforest` kernels can be fit into sklearn pipelines to impute training and scoring\n",
+ "datasets:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "from sklearn.preprocessing import StandardScaler\n",
+ "from sklearn.datasets import make_classification\n",
+ "from sklearn.model_selection import train_test_split\n",
+ "from sklearn.pipeline import Pipeline\n",
+ "import miceforest as mf\n",
+ "\n",
+ "kernel = mf.ImputationKernel(iris_amp, num_datasets=1, random_state=1)\n",
+ "\n",
+ "pipe = Pipeline([\n",
+ " ('impute', kernel),\n",
+ " ('scaler', StandardScaler()),\n",
+ "])\n",
+ "\n",
+ "# The pipeline can be used as any other estimator\n",
+ "# and avoids leaking the test set into the train set\n",
+ "X_train_t = pipe.fit_transform(\n",
+ " X=iris_amp,\n",
+ " y=None,\n",
+ " impute__iterations=2\n",
+ ")\n",
+ "X_test_t = pipe.transform(new_data)\n",
+ "\n",
+ "# Show that neither now have missing values.\n",
+ "assert not np.any(np.isnan(X_train_t))\n",
+ "assert not np.any(np.isnan(X_test_t))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Building Models on Nonmissing Data\n",
+ "\n",
+ "The MICE process itself is used to impute missing data in a dataset.\n",
+ "However, sometimes a variable can be fully recognized in the training\n",
+ "data, but needs to be imputed later on in a different dataset. It is\n",
+ "possible to train models to impute variables even if they have no\n",
+ "missing values by specifying them in the `variable_schema` parameter. \n",
+ "In this case, `variable_schema` is treated as the list of variables \n",
+ "to train models on."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sepal length (cm) 37\n",
+ "sepal width (cm) 0\n",
+ "petal length (cm) 37\n",
+ "petal width (cm) 37\n",
+ "species 37\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Set petal length (cm) in our amputed data \n",
+ "# to original values with no missing data.\n",
+ "iris_amp['sepal width (cm)'] = iris['sepal width (cm)'].copy()\n",
+ "iris_amp.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kernel = mf.ImputationKernel(\n",
+ " data=iris_amp, \n",
+ " variable_schema=iris_amp.columns.to_list(), \n",
+ " num_datasets=1,\n",
+ " random_state=1,\n",
+ ")\n",
+ "kernel.mice(1)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sepal length (cm) 4\n",
+ "sepal width (cm) 3\n",
+ "petal length (cm) 1\n",
+ "petal width (cm) 3\n",
+ "species 3\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Remember, the dataset we are imputing does have \n",
+ "# missing values in the sepal width (cm) column\n",
+ "new_data.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "sepal length (cm) 0\n",
+ "sepal width (cm) 0\n",
+ "petal length (cm) 0\n",
+ "petal width (cm) 0\n",
+ "species 0\n",
+ "dtype: int64"
+ ]
+ },
+ "execution_count": 15,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "new_data_imp = kernel.impute_new_data(new_data)\n",
+ "new_data_imp = new_data_imp.complete_data()\n",
+ "\n",
+ "# All columns have been imputed.\n",
+ "new_data_imp.isnull().sum()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Tuning Parameters\n",
+ "\n",
+ "`miceforest` allows you to tune the parameters on a kernel dataset.\n",
+ "These parameters can then be used to build the models in future\n",
+ "iterations of mice. In its most simple invocation, you can just call the\n",
+ "function with the desired optimization steps:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " sepal length (cm) | \n",
+ " petal length (cm) | \n",
+ " petal width (cm) | \n",
+ " species | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | boosting | \n",
+ " gbdt | \n",
+ " gbdt | \n",
+ " gbdt | \n",
+ " gbdt | \n",
+ "
\n",
+ " \n",
+ " | data_sample_strategy | \n",
+ " bagging | \n",
+ " bagging | \n",
+ " bagging | \n",
+ " bagging | \n",
+ "
\n",
+ " \n",
+ " | num_iterations | \n",
+ " 142 | \n",
+ " 248 | \n",
+ " 262 | \n",
+ " 172 | \n",
+ "
\n",
+ " \n",
+ " | max_depth | \n",
+ " 4 | \n",
+ " 4 | \n",
+ " 5 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " | num_leaves | \n",
+ " 12 | \n",
+ " 17 | \n",
+ " 2 | \n",
+ " 19 | \n",
+ "
\n",
+ " \n",
+ " | min_data_in_leaf | \n",
+ " 2 | \n",
+ " 2 | \n",
+ " 15 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " | min_sum_hessian_in_leaf | \n",
+ " 0.1 | \n",
+ " 0.1 | \n",
+ " 0.1 | \n",
+ " 0.1 | \n",
+ "
\n",
+ " \n",
+ " | min_gain_to_split | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
\n",
+ " \n",
+ " | bagging_fraction | \n",
+ " 0.580973 | \n",
+ " 0.501521 | \n",
+ " 0.586709 | \n",
+ " 0.795465 | \n",
+ "
\n",
+ " \n",
+ " | feature_fraction_bynode | \n",
+ " 0.922566 | \n",
+ " 0.299912 | \n",
+ " 0.503182 | \n",
+ " 0.237637 | \n",
+ "
\n",
+ " \n",
+ " | bagging_freq | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | verbosity | \n",
+ " -1 | \n",
+ " -1 | \n",
+ " -1 | \n",
+ " -1 | \n",
+ "
\n",
+ " \n",
+ " | learning_rate | \n",
+ " 0.02 | \n",
+ " 0.02 | \n",
+ " 0.02 | \n",
+ " 0.02 | \n",
+ "
\n",
+ " \n",
+ " | objective | \n",
+ " regression | \n",
+ " regression | \n",
+ " regression | \n",
+ " multiclass | \n",
+ "
\n",
+ " \n",
+ " | num_class | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
![](\"https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/MICEalgorithm.png\")
\n",
+ "\n",
+ "This process is continued until all specified variables have been\n",
+ "imputed. Additional iterations can be run if it appears that the average\n",
+ "imputed values have not converged, although no more than 5 iterations\n",
+ "are usually necessary.\n",
+ "\n",
+ "### Common Use Cases\n",
+ "\n",
+ "##### **Data Leakage:**\n",
+ "\n",
+ "MICE is particularly useful if missing values are associated with the\n",
+ "target variable in a way that introduces leakage. For instance, let’s\n",
+ "say you wanted to model customer retention at the time of sign up. A\n",
+ "certain variable is collected at sign up or 1 month after sign up. The\n",
+ "absence of that variable is a data leak, since it tells you that the\n",
+ "customer did not retain for 1 month.\n",
+ "\n",
+ "##### **Funnel Analysis:**\n",
+ "\n",
+ "Information is often collected at different stages of a ‘funnel’. MICE\n",
+ "can be used to make educated guesses about the characteristics of\n",
+ "entities at different points in a funnel.\n",
+ "\n",
+ "##### **Confidence Intervals:**\n",
+ "\n",
+ "MICE can be used to impute missing values, however it is important to\n",
+ "keep in mind that these imputed values are a prediction. Creating\n",
+ "multiple datasets with different imputed values allows you to do two\n",
+ "types of inference:\n",
+ "\n",
+ " - Imputed Value Distribution: A profile can be built for each imputed\n",
+ " value, allowing you to make statements about the likely distribution\n",
+ " of that value. \n",
+ " - Model Prediction Distribution: With multiple datasets, you can build\n",
+ " multiple models and create a distribution of predictions for each\n",
+ " sample. Those samples with imputed values which were not able to be\n",
+ " imputed with much confidence would have a larger variance in their\n",
+ " predictions.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Predictive Mean Matching\n",
+ "\n",
+ "`miceforest` can make use of a procedure called predictive mean matching\n",
+ "(PMM) to select which values are imputed. PMM involves selecting a\n",
+ "datapoint from the original, nonmissing data (candidates) which has a\n",
+ "predicted value close to the predicted value of the missing sample\n",
+ "(bachelors). The closest N (`mean_match_candidates` parameter) values\n",
+ "are selected, from which a value is chosen at random. This can be\n",
+ "specified on a column-by-column basis. Going into more detail from our\n",
+ "example above, we see how this works in practice:\n",
+ "\n",
+ "![](\"https://raw.githubusercontent.com/AnotherSamWilson/miceforest/master/examples/PMM.png\")
\n",
+ "\n",
+ "This method is very useful if you have a variable which needs imputing\n",
+ "which has any of the following characteristics:\n",
+ "\n",
+ " - Multimodal \n",
+ " - Integer \n",
+ " - Skewed\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Effects of Mean Matching\n",
+ "\n",
+ "As an example, let’s construct a dataset with some of the above\n",
+ "characteristics:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "randst = np.random.RandomState(1991)\n",
+ "# random uniform variable\n",
+ "nrws = 1000\n",
+ "uniform_vec = randst.uniform(size=nrws)\n",
+ "\n",
+ "def make_bimodal(mean1,mean2,size):\n",
+ " bimodal_1 = randst.normal(size=nrws, loc=mean1)\n",
+ " bimodal_2 = randst.normal(size=nrws, loc=mean2)\n",
+ " bimdvec = []\n",
+ " for i in range(size):\n",
+ " bimdvec.append(randst.choice([bimodal_1[i], bimodal_2[i]]))\n",
+ " return np.array(bimdvec)\n",
+ "\n",
+ "# Make 2 Bimodal Variables\n",
+ "close_bimodal_vec = make_bimodal(2,-2,nrws)\n",
+ "far_bimodal_vec = make_bimodal(3,-3,nrws)\n",
+ "\n",
+ "\n",
+ "# Highly skewed variable correlated with Uniform_Variable\n",
+ "skewed_vec = np.exp(uniform_vec*randst.uniform(size=nrws)*3) + randst.uniform(size=nrws)*3\n",
+ "\n",
+ "# Integer variable correlated with Close_Bimodal_Variable and Uniform_Variable\n",
+ "integer_vec = np.round(uniform_vec + close_bimodal_vec/3 + randst.uniform(size=nrws)*2)\n",
+ "\n",
+ "# Make a DataFrame\n",
+ "dat = pd.DataFrame(\n",
+ " {\n",
+ " 'uniform_var':uniform_vec,\n",
+ " 'close_bimodal_var':close_bimodal_vec,\n",
+ " 'far_bimodal_var':far_bimodal_vec,\n",
+ " 'skewed_var':skewed_vec,\n",
+ " 'integer_var':integer_vec\n",
+ " }\n",
+ ")\n",
+ "\n",
+ "# Ampute the data.\n",
+ "ampdat = mf.ampute_data(dat,perc=0.25,random_state=randst)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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"
+ },
+ "metadata": {
+ "image/png": {
+ "height": 700,
+ "width": 700
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotnine as p9\n",
+ "import itertools\n",
+ "\n",
+ "def plot_matrix(df, columns):\n",
+ " pdf = []\n",
+ " for a1, b1 in itertools.combinations(columns, 2):\n",
+ " for (a,b) in ((a1, b1), (b1, a1)):\n",
+ " sub = df[[a, b]].rename(columns={a: \"x\", b: \"y\"}).assign(a=a, b=b)\n",
+ " pdf.append(sub)\n",
+ "\n",
+ " g = (\n",
+ " p9.ggplot(pd.concat(pdf))\n",
+ " + p9.geom_point(p9.aes('x','y'))\n",
+ " + p9.facet_grid('b~a', scales='free')\n",
+ " + p9.theme(figure_size=(7, 7))\n",
+ " + p9.xlab(\"\") + p9.ylab(\"\")\n",
+ " )\n",
+ " return g\n",
+ "\n",
+ "plot_matrix(dat, dat.columns)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can see how our variables are distributed and correlated in the graph\n",
+ "above. Now let’s run our imputation process twice, once using mean\n",
+ "matching, and once using the model prediction."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "kernel_mean_match = mf.ImputationKernel(\n",
+ " data=ampdat,\n",
+ " num_datasets=3,\n",
+ " mean_match_candidates=5,\n",
+ " random_state=1\n",
+ ")\n",
+ "kernel_mean_match.mice(2)\n",
+ "kernel_no_mean_match = mf.ImputationKernel(\n",
+ " data=ampdat,\n",
+ " num_datasets=3,\n",
+ " mean_match_candidates=0,\n",
+ " random_state=1\n",
+ ")\n",
+ "kernel_no_mean_match.mice(2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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"
+ },
+ "metadata": {
+ "image/png": {
+ "height": 480,
+ "width": 640
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "kernel_mean_match.plot_imputed_distributions()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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"
+ },
+ "metadata": {
+ "image/png": {
+ "height": 480,
+ "width": 640
+ }
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "kernel_no_mean_match.plot_imputed_distributions()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "You can see the effects that mean matching has, depending on the\n",
+ "distribution of the data. Simply returning the value from the model\n",
+ "prediction, while it may provide a better ‘fit’, will not provide\n",
+ "imputations with a similair distribution to the original. This may be\n",
+ "beneficial, depending on your goal."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.10.14"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/conf.py b/docs/conf.py
index 4865062..baecc96 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -14,22 +14,23 @@
import sys
import sphinx
from sphinx.errors import VersionRequirementError
-sys.path.insert(0, os.path.abspath('..'))
+
+sys.path.insert(0, os.path.abspath(".."))
# -- Project information -----------------------------------------------------
-project = 'miceforest'
-copyright = '2021, Samuel Von Wilson'
-author = 'Samuel Von Wilson'
+project = "miceforest"
+copyright = "2021, Samuel Von Wilson"
+author = "Samuel Von Wilson"
# The full version, including alpha/beta/rc tags
-release = '2021-08-21'
+release = "2021-08-21"
# If your documentation needs a minimal Sphinx version, state it here.
-needs_sphinx = '4.2.0' # Due to sphinx.ext.napoleon, autodoc_typehints
+needs_sphinx = "4.2.0" # Due to sphinx.ext.napoleon, autodoc_typehints
if needs_sphinx > sphinx.__version__:
- message = f'This project needs at least Sphinx v{needs_sphinx}'
+ message = f"This project needs at least Sphinx v{needs_sphinx}"
raise VersionRequirementError(message)
# -- General configuration ---------------------------------------------------
@@ -38,14 +39,14 @@
# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
# ones.
extensions = [
- 'sphinx.ext.autodoc',
- 'sphinx.ext.autosummary',
- 'sphinx.ext.todo',
- 'sphinx.ext.viewcode',
- 'sphinx.ext.napoleon'
+ "sphinx.ext.autodoc",
+ "sphinx.ext.autosummary",
+ "sphinx.ext.todo",
+ "sphinx.ext.viewcode",
+ "sphinx.ext.napoleon",
]
-autodoc_default_flags = ['members', 'inherited-members', 'show-inheritance']
+autodoc_default_flags = ["members", "inherited-members", "show-inheritance"]
autodoc_default_options = {
"members": True,
"inherited-members": True,
@@ -54,54 +55,59 @@
# mock out modules
autodoc_mock_imports = [
- 'matplotlib',
- 'seaborn',
- 'numpy',
- 'pandas',
- 'scipy',
- 'scikit-learn',
- 'lightgbm'
+ "matplotlib",
+ "seaborn",
+ "numpy",
+ "pandas",
+ "scipy",
+ "scikit-learn",
+ "lightgbm",
]
-master_doc = 'index'
+master_doc = "index"
# hide type hints in API docs
autodoc_typehints = "none"
# Only the class' docstring is inserted.
-autoclass_content = 'class'
+autoclass_content = "class"
# Generate autosummary pages.
-autosummary_generate = ['ImputationKernel.rst', 'ImputedData.rst', "utils.rst", "MeanMatchScheme.rst"]
+autosummary_generate = [
+ "ImputationKernel.rst",
+ "ImputedData.rst",
+ "utils.rst",
+ "MeanMatchScheme.rst",
+]
# Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
# List of patterns, relative to source directory, that match files and
# directories to ignore when looking for source files.
# This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
# -- Options for HTML output -------------------------------------------------
# The theme to use for HTML and HTML Help pages. See the documentation for
# a list of builtin themes.
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
# Theme options are theme-specific and customize the look and feel of a theme
# further. For a list of options available for each theme, see the
# documentation.
html_theme_options = {
- 'includehidden': False,
- 'logo_only': True,
+ "includehidden": False,
+ "logo_only": True,
}
# Add any paths that contain custom static files (such as style sheets) here,
# relative to this directory. They are copied after the builtin static files,
# so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
+html_static_path = ["_static"]
def setup(app):
- app.add_css_file('themes.css')
+ app.add_css_file("themes.css")
diff --git a/miceforest/ImputationKernel.py b/miceforest/ImputationKernel.py
deleted file mode 100644
index fec80a4..0000000
--- a/miceforest/ImputationKernel.py
+++ /dev/null
@@ -1,1990 +0,0 @@
-from .ImputedData import ImputedData
-from .MeanMatchScheme import MeanMatchScheme
-from .utils import (
- _t_dat,
- _t_var_list,
- _t_var_dict,
- _t_var_sub,
- _t_random_state,
- _assert_dataset_equivalent,
- _draw_random_int32,
- _interpret_ds,
- _subset_data,
- ensure_rng,
- hash_int32,
- stratified_categorical_folds,
- stratified_continuous_folds,
- stratified_subset,
-)
-from .compat import pd_DataFrame, pd_Series
-from .default_lightgbm_parameters import default_parameters, make_default_tuning_space
-from .logger import Logger
-import numpy as np
-from numpy.random import RandomState
-from warnings import warn
-from lightgbm import train, Dataset, cv, log_evaluation, early_stopping, Booster
-from lightgbm.basic import _ConfigAliases
-from io import BytesIO
-import blosc2
-import dill
-from copy import copy
-from typing import Union, List, Dict, Any, Optional
-
-_DEFAULT_DATA_SUBSET = 1.0
-
-
-class ImputationKernel(ImputedData):
- """
- Creates a kernel dataset. This dataset can perform MICE on itself,
- and impute new data from models obtained during MICE.
-
- Parameters
- ----------
- data : np.ndarray or pandas DataFrame.
-
- .. code-block:: text
-
- The data to be imputed.
-
- variable_schema : None or list or dict, default=None
-
- .. code-block:: text
-
- Specifies the feature - target relationships used to train models.
- This parameter also controls which models are built. Models can be built
- even if a variable contains no missing values, or is not being imputed
- (train_nonmissing must be set to True).
-
- - If None, all columns will be used as features in the training of each model.
- - If list, all columns in data are used to impute the variables in the list
- - If dict the values will be used to impute the keys. Can be either column
- indices or names (if data is a pd.DataFrame).
-
- No models will be trained for variables not specified by variable_schema
- (either by None, a list, or in dict keys).
-
- imputation_order: str, list[str], list[int], default="ascending"
-
- .. code-block:: text
-
- The order the imputations should occur in. If a string from the
- items below, all variables specified by variable_schema with
- missing data are imputed:
- ascending: variables are imputed from least to most missing
- descending: most to least missing
- roman: from left to right in the dataset
- arabic: from right to left in the dataset.
- If a list is provided:
- - the variables will be imputed in that order.
- - only variables with missing values should be included in the list.
- - must be a subset of variables specified by variable_schema.
- If a variable with missing values is in variable_schema, but not in
- imputation_order, then models to impute that variable will be trained,
- but the actual values will not be imputed. See examples for details.
-
- train_nonmissing: boolean
-
- .. code-block:: text
-
- Should models be trained for variables with no missing values? Useful if you
- expect you will need to impute new data which will have missing values, but
- the training data is fully recognized.
-
- If True, parameters are interpreted like so:
- - models are run for all variables specified by variable_schema
- - if variable_schema is None, models are run for all variables
- - each iteration, models build for fully recognized variables are
- always trained after the models trained during mice.
- - imputation_order does not have any affect on fully recognized
- variable model training.
-
- WARNING: Setting this to True without specifying a variable schema will build
- models for all variables in the dataset, whether they have missing values or
- not. This may or may not be what you want.
-
- data_subset: None or int or float or dict.
-
- .. code-block:: text
-
- Subsets the data used in each iteration, which can save a significant amount of time.
- This can also help with memory consumption, as the candidate data must be copied to
- make a feature dataset for lightgbm.
-
- The number of rows used for each variable is (# rows in raw data) - (# missing variable values)
- for each variable. data_subset takes a random sample of this.
-
- If float, must be 0.0 < data_subset <= 1.0. Interpreted as a percentage of available candidates
- If int must be data_subset >= 0. Interpreted as the number of candidates.
- If 0, no subsetting is done.
- If dict, keys must be variable names, and values must follow two above rules.
-
- It is recommended to carefully select this value for each variable if dealing
- with very large data that barely fits into memory.
-
- mean_match_scheme: Dict, default = None
-
- .. code-block:: text
-
- An instance of the miceforest.MeanMatchScheme class.
-
- If None is passed, a sensible default scheme is used. There are multiple helpful
- schemes that can be accessed from miceforest.builtin_mean_match_schemes, or
- you can build your own.
-
- A description of the defaults:
- - mean_match_default (default, if mean_match_scheme is None))
- This scheme is has medium speed and accuracy for most data.
-
- Categorical:
- If mmc = 0, the class with the highest probability is chosen.
- If mmc > 0, get N nearest neighbors from class probabilities.
- Select 1 at random.
- Numeric:
- If mmc = 0, the predicted value is used
- If mmc > 0, obtain the mmc closest candidate
- predictions and collect the associated
- real candidate values. Choose 1 randomly.
-
- - mean_match_shap
- This scheme is the most accurate, but takes the longest.
- It works the same as mean_match_default, except all nearest
- neighbor searches are performed on the shap values of the
- predictions, instead of the predictions themselves.
-
- - mean_match_scheme_fast_cat:
- This scheme is faster for categorical variables,
- but may be less accurate as well..
-
- Categorical:
- If mmc = 0, the class with the highest probability is chosen.
- If mmc > 0, return class based on random draw weighted by
- class probability for each sample.
- Numeric or binary:
- If mmc = 0, the predicted value is used
- If mmc > 0, obtain the mmc closest candidate
- predictions and collect the associated
- real candidate values. Choose 1 randomly.
-
- categorical_feature: str or list, default="auto"
-
- .. code-block:: text
-
- The categorical features in the dataset. This handling depends on class of impute_data:
-
- pandas DataFrame:
- - "auto": categorical information is inferred from any columns with
- datatype category or object.
- - list of column names (or indices): Useful if all categorical columns
- have already been cast to numeric encodings of some type, otherwise you
- should just use "auto". Will throw an error if a list is provided AND
- categorical dtypes exist in data. If a list is provided, values in the
- columns must be consecutive integers starting at 0, as required by lightgbm.
-
- numpy ndarray:
- - "auto": no categorical information is stored.
- - list of column indices: Specified columns are treated as categorical. Column
- values must be consecutive integers starting at 0, as required by lightgbm.
-
- initialization: str
-
- .. code-block:: text
-
- "random" - missing values will be filled in randomly from existing values.
- "empty" - lightgbm will start MICE without initial imputation
-
- save_all_iterations: boolean, optional(default=True)
-
- .. code-block:: text
-
- Save all the imputation values from all iterations, or just
- the latest. Saving all iterations allows for additional
- plotting, but may take more memory
-
- save_models: int
-
- .. code-block:: text
-
- Which models should be saved:
- = 0: no models are saved. Cannot get feature importance or
- impute new data.
- = 1: only the last model iteration is saved. Can only get
- feature importance of last iteration. New data is
- imputed using the last model for all specified iterations.
- This is only an issue if data is heavily Missing At Random.
- = 2: all model iterations are saved. Can get feature importance
- for any iteration. When imputing new data, each iteration is
- imputed using the model obtained at that iteration in mice.
- This allows for imputations that most closely resemble those
- that would have been obtained in mice.
-
- copy_data: boolean (default = False)
-
- .. code-block:: text
-
- Should the dataset be referenced directly? If False, this will cause
- the dataset to be altered in place. If a copy is created, it is saved
- in self.working_data. There are different ways in which the dataset
- can be altered:
-
- 1) complete_data() will fill in missing values
- 2) To save space, mice() references and manipulates self.working_data directly.
- If self.working_data is a reference to the original dataset, the original
- dataset will undergo these manipulations during the mice process.
- At the end of the mice process, missing values will be set back to np.NaN
- where they were originally missing.
-
- save_loggers: boolean (default = False)
-
- .. code-block:: text
-
- A logger is created each time mice() or impute_new_data() is called.
- If True, the loggers are stored in a list ImputationKernel.loggers.
- If you wish to start saving logs, call ImputationKernel.start_logging().
- If you wish to stop saving logs, call ImputationKernel.stop_logging().
-
- random_state: None,int, or numpy.random.RandomState
-
- .. code-block:: text
-
- The random_state ensures script reproducibility. It only ensures reproducible
- results if the same script is called multiple times. It does not guarantee
- reproducible results at the record level, if a record is imputed multiple
- different times. If reproducible record-results are desired, a seed must be
- passed for each record in the random_seed_array parameter.
-
- """
-
- def __init__(
- self,
- data: _t_dat,
- datasets: int = 1,
- variable_schema: Union[_t_var_list, _t_var_dict, None] = None,
- imputation_order: Union[str, _t_var_list] = "ascending",
- train_nonmissing: bool = False,
- mean_match_scheme: Optional[MeanMatchScheme] = None,
- data_subset: Union[int, float, _t_var_sub, None] = None,
- categorical_feature: Union[str, _t_var_list] = "auto",
- initialization: str = "random",
- save_all_iterations: bool = True,
- save_models: int = 1,
- copy_data: bool = True,
- save_loggers: bool = False,
- random_state: _t_random_state = None,
- ):
- super().__init__(
- impute_data=data,
- datasets=datasets,
- variable_schema=variable_schema,
- imputation_order=imputation_order,
- train_nonmissing=train_nonmissing,
- categorical_feature=categorical_feature,
- save_all_iterations=save_all_iterations,
- copy_data=copy_data,
- )
-
- self.initialization = initialization
- self.train_nonmissing = train_nonmissing
- self.save_models = save_models
- self.save_loggers = save_loggers
- self.loggers: List[Logger] = []
- self.models: Dict[Any, Booster] = {}
- self.candidate_preds: Dict[Any, np.ndarray] = {}
- self.optimal_parameters: Dict[Any, Any] = {
- ds: {var: {} for var in self.variable_training_order}
- for ds in range(datasets)
- }
- self.optimal_parameter_losses: Dict[Any, Any] = {
- ds: {var: np.inf for var in self.variable_training_order}
- for ds in range(datasets)
- }
-
- # Format data_subset and available_candidates
- available_candidates = {
- v: (self.data_shape[0] - self.na_counts[v])
- for v in self.variable_training_order
- }
- data_subset = _DEFAULT_DATA_SUBSET if data_subset is None else data_subset
- if not isinstance(data_subset, dict):
- data_subset = {v: data_subset for v in self.variable_training_order}
- if set(data_subset) != set(self.variable_training_order):
- # Change variable names to indices
- for v in list(data_subset):
- data_subset[self._get_var_ind_from_scalar(v)] = data_subset.pop(v)
- ds_supplement = {
- v: _DEFAULT_DATA_SUBSET
- for v in self.variable_training_order
- if v not in data_subset.keys()
- }
- data_subset.update(ds_supplement)
- for v, ds in data_subset.items():
- assert v in self.variable_training_order, (
- f"Variable {self._get_var_name_from_scalar(v)} will not have a model trained "
- + "but it is in data_subset."
- )
- data_subset[v] = _interpret_ds(data_subset[v], available_candidates[v])
-
- self.available_candidates = available_candidates
- self.data_subset = data_subset
-
- # Get mean matching function
- if mean_match_scheme is None:
- from .builtin_mean_match_schemes import mean_match_default
-
- self.mean_match_scheme = mean_match_default.copy()
-
- else:
- assert isinstance(mean_match_scheme, MeanMatchScheme)
- self.mean_match_scheme = mean_match_scheme.copy()
-
- # Format and run through mean match candidate checks.
- self.mean_match_scheme._format_mean_match_candidates(data, available_candidates)
-
- # Ensure mmc and mms make sense:
- # mmc <= mms <= available candidates for each var
- for v in self.imputation_order:
- mmc = self.mean_match_scheme.mean_match_candidates[v]
- assert mmc <= data_subset[v], f"{v} mean_match_candidates > data_subset"
- assert (
- data_subset[v] <= available_candidates[v]
- ), f"{v} data_subset > available candidates"
-
- # Make sure all pandas categorical levels are used.
- rare_levels = []
- for cat in self.categorical_variables:
- cat_name = self._get_var_name_from_scalar(cat)
- cat_dat = self._get_nonmissing_values(cat)
- cat_levels, cat_count = np.unique(cat_dat, return_counts=True)
- cat_dtype = cat_dat.dtype
- if cat_dtype.name == "category":
- levels_in_data = set(cat_levels)
- levels_in_catdt = set(cat_dtype.categories)
- levels_not_in_data = levels_in_catdt - levels_in_data
- assert (
- len(levels_not_in_data) == 0
- ), f"{cat_name} has unused categories: {','.join(levels_not_in_data)}"
-
- if any(cat_count / cat_count.sum() < 0.002):
- rare_levels.append(cat_name)
-
- if len(rare_levels) > 0:
- warn(
- f"[{','.join(rare_levels)}] have very rare categories, it is a good "
- "idea to group these, or set the min_data_in_leaf parameter to prevent "
- "lightgbm from outputting 0.0 probabilities."
- )
-
- # Manage randomness
- self._completely_random_kernel = random_state is None
- self._random_state = ensure_rng(random_state)
-
- # Set initial imputations (iteration 0).
- self._initialize_dataset(
- self, random_state=self._random_state, random_seed_array=None
- )
-
- def __repr__(self):
- summary_string = f'\n{" " * 14}Class: ImputationKernel\n{self._ids_info()}'
- return summary_string
-
- # def _mm_type_handling(self, mm, available_candidates) -> int:
- # if isinstance(mm, float):
- #
- # assert (mm > 0.0) and (
- # mm <= 1.0
- # ), "mean_matching must be < 0.0 and >= 1.0 if a float"
- #
- # ret = int(mm * available_candidates)
- #
- # elif isinstance(mm, int):
- #
- # assert mm >= 0, "mean_matching must be above 0 if an int is passed."
- # ret = mm
- #
- # else:
- #
- # raise ValueError(
- # "mean_match_candidates type not recognized. "
- # + "Any supplied values must be a 0.0 < float <= 1.0 or int >= 1"
- # )
- #
- # return ret
-
- def _initialize_random_seed_array(self, random_seed_array, expected_shape):
- """
- Formats and takes the first hash of the random_seed_array.
- """
-
- # Format random_seed_array if it was passed.
- if random_seed_array is not None:
- if self._completely_random_kernel:
- warn(
- """
- This kernel is completely random (no random_state was provided on initialization).
- Values imputed using ThisKernel.impute_new_data() will be deterministic, however
- the kernel itself is non-reproducible.
- """
- )
- assert isinstance(random_seed_array, np.ndarray)
- assert (
- random_seed_array.dtype == "int32"
- ), "random_seed_array must be a np.ndarray of type int32"
- assert (
- random_seed_array.shape[0] == expected_shape
- ), "random_seed_array must be the same length as data."
- random_seed_array = hash_int32(random_seed_array)
- else:
- random_seed_array = None
-
- return random_seed_array
-
- def _iter_pairs(self, new_iterations):
- """
- Returns the absolute and relative iterations that are going to be
- run for a given function call.
- """
- current_iters = self.iteration_count()
- iter_pairs = [(current_iters + i + 1, i + 1) for i in range(new_iterations)]
- return iter_pairs
-
- def _initialize_dataset(self, imputed_data, random_state, random_seed_array):
- """
- Sets initial imputation values for iteration 0.
- If "random", draw values from the kernel at random.
- If "empty", keep the values missing, since missing values
- can be handled natively by lightgbm.
- """
-
- assert not imputed_data.initialized, "dataset has already been initialized"
-
- if self.initialization == "random":
- for var in imputed_data.imputation_order:
- kernel_nonmissing_ind = self._get_nonmissing_indx(var)
- candidate_values = _subset_data(
- self.working_data, kernel_nonmissing_ind, var, return_1d=True
- )
- n_candidates = kernel_nonmissing_ind.shape[0]
- missing_ind = imputed_data.na_where[var]
-
- for ds in range(imputed_data.dataset_count()):
- # Initialize using the random_state if no record seeds were passed.
- if random_seed_array is None:
- imputed_data[ds, var, 0] = random_state.choice(
- candidate_values,
- size=imputed_data.na_counts[var],
- replace=True,
- )
- else:
- assert (
- len(random_seed_array) == imputed_data.data_shape[0]
- ), "The random_seed_array did not match the number of rows being imputed."
- selection_ind = random_seed_array[missing_ind] % n_candidates
- init_imps = candidate_values[selection_ind]
- imputed_data[ds, var, 0] = np.array(init_imps)
- random_seed_array[missing_ind] = hash_int32(
- random_seed_array[missing_ind]
- )
-
- elif self.initialization == "empty":
- for var in imputed_data.imputation_order:
- for ds in range(imputed_data.dataset_count()):
- # Saves space, since np.nan will be broadcast.
- imputed_data[ds, var, 0] = np.array([np.nan])
-
- else:
- raise ValueError("initialization parameter not recognized.")
-
- imputed_data.initialized = True
-
- def _reconcile_parameters(self, defaults, user_supplied):
- """
- Checks in user_supplied for aliases of each parameter in defaults.
- Combines the dicts once the aliases have been reconciled.
- """
- params = defaults.copy()
- for par, val in defaults.items():
- alias_names = _ConfigAliases.get(par)
- user_supplied_aliases = [
- i for i in alias_names if i in list(user_supplied) and i != par
- ]
- if len(user_supplied_aliases) == 0:
- continue
- elif len(user_supplied_aliases) == 1:
- params[par] = user_supplied.pop(user_supplied_aliases[0])
- else:
- raise ValueError(
- f"Supplied 2 aliases for the same parameter: {user_supplied_aliases}"
- )
-
- params.update(user_supplied)
-
- return params
-
- def _format_variable_parameters(
- self, variable_parameters: Optional[Dict]
- ) -> Dict[int, Any]:
- """
- Unpacking will expect an empty dict at a minimum.
- This function collects parameters if they were
- provided, and returns empty dicts if they weren't.
- """
- if variable_parameters is None:
- vsp: Dict[int, Any] = {var: {} for var in self.variable_training_order}
-
- else:
- for variable in list(variable_parameters):
- variable_parameters[
- self._get_var_ind_from_scalar(variable)
- ] = variable_parameters.pop(variable)
- vsp_vars = set(variable_parameters)
-
- assert vsp_vars.issubset(
- self.variable_training_order
- ), "Some variable_parameters are not associated with models being trained."
- vsp = {
- var: variable_parameters[var] if var in vsp_vars else {}
- for var in self.variable_training_order
- }
-
- return vsp
-
- def _get_lgb_params(self, var, vsp, random_state, **kwlgb):
- """
- Builds the parameters for a lightgbm model. Infers objective based on
- datatype of the response variable, assigns a random seed, finds
- aliases in the user supplied parameters, and returns a final dict.
-
- Parameters
- ----------
- var: int
- The variable to be modeled
-
- vsp: dict
- Variable specific parameters. These are supplied by the user.
-
- random_state: np.random.RandomState
- The random state to use (used to set the seed).
-
- kwlgb: dict
- Any additional parameters that should take presidence
- over the defaults or user supplied.
- """
-
- seed = _draw_random_int32(random_state, size=1)[0]
-
- if var in self.categorical_variables:
- n_c = self.category_counts[var]
- if n_c > 2:
- obj = {"objective": "multiclass", "num_class": n_c}
- else:
- obj = {"objective": "binary"}
- else:
- obj = {"objective": "regression"}
-
- default_lgb_params = {**default_parameters, **obj, "seed": seed}
-
- # Priority is [variable specific] > [global in kwargs] > [defaults]
- params = self._reconcile_parameters(default_lgb_params, kwlgb)
- params = self._reconcile_parameters(params, vsp)
-
- return params
-
- def _get_random_sample(self, parameters, random_state):
- """
- Searches through a parameter set and selects a random
- number between the values in any provided tuple of length 2.
- """
-
- parameters = parameters.copy()
- for p, v in parameters.items():
- if hasattr(v, "__iter__"):
- if isinstance(v, list):
- parameters[p] = random_state.choice(v)
- elif isinstance(v, tuple):
- parameters[p] = random_state.uniform(v[0], v[1], size=1)[0]
- else:
- pass
- parameters = self._make_params_digestible(parameters)
- return parameters
-
- def _make_params_digestible(self, params):
- """
- Cursory checks to force parameters to be digestible
- """
-
- int_params = [
- "num_leaves",
- "min_data_in_leaf",
- "num_threads",
- "max_depth",
- "num_iterations",
- "bagging_freq",
- "max_drop",
- "min_data_per_group",
- "max_cat_to_onehot",
- ]
- params = {
- key: int(val) if key in int_params else val for key, val in params.items()
- }
- return params
-
- def _get_oof_performance(
- self, parameters, folds, train_pointer, categorical_feature
- ):
- """
- Performance is gathered from built-in lightgbm.cv out of fold metric.
- Optimal number of iterations is also obtained.
- """
-
- num_iterations = parameters.pop("num_iterations")
- lgbcv = cv(
- params=parameters,
- train_set=train_pointer,
- folds=folds,
- num_boost_round=num_iterations,
- categorical_feature=categorical_feature,
- return_cvbooster=True,
- callbacks=[
- early_stopping(stopping_rounds=10, verbose=False),
- log_evaluation(period=0),
- ],
- )
- best_iteration = lgbcv["cvbooster"].best_iteration
- loss_metric_key = list(lgbcv)[0]
- loss = np.min(lgbcv[loss_metric_key])
-
- return loss, best_iteration
-
- def _get_nonmissing_values(self, variable):
- """
- Returns the non-missing values of a column.
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- nonmissing_index = self._get_nonmissing_indx(variable)
- candidate_values = _subset_data(
- self.working_data, row_ind=nonmissing_index, col_ind=var_indx
- )
- return candidate_values
-
- def _get_candidate_subset(self, variable, subset_count, random_seed):
- """
- Returns a reproducible subset index of the
- non-missing values for a given variable.
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- nonmissing_index = self._get_nonmissing_indx(var_indx)
-
- # Get the subset indices
- if subset_count < len(nonmissing_index):
- candidate_values = _subset_data(
- self.working_data, row_ind=nonmissing_index, col_ind=var_indx
- )
- candidates = candidate_values.shape[0]
- groups = max(10, int(candidates / 1000))
- ss = stratified_subset(
- y=candidate_values,
- size=subset_count,
- groups=groups,
- cat=var_indx in self.categorical_variables,
- seed=random_seed,
- )
- candidate_subset = nonmissing_index[ss]
-
- else:
- candidate_subset = nonmissing_index
-
- return candidate_subset
-
- def _make_label(self, variable, subset_count, random_seed):
- """
- Returns a reproducible subset of the non-missing values of a variable.
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- candidate_subset = self._get_candidate_subset(
- var_indx, subset_count, random_seed
- )
- label = _subset_data(
- self.working_data, row_ind=candidate_subset, col_ind=var_indx
- )
-
- return label
-
- def _make_features_label(self, variable, subset_count, random_seed):
- """
- Makes a reproducible set of features and
- target needed to train a lightgbm model.
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- candidate_subset = self._get_candidate_subset(
- var_indx, subset_count, random_seed
- )
- xvars = self.variable_schema[var_indx]
- ret_cols = sorted(xvars + [var_indx])
- features = _subset_data(
- self.working_data, row_ind=candidate_subset, col_ind=ret_cols
- )
-
- if self.original_data_class == "pd_DataFrame":
- y_name = self._get_var_name_from_scalar(var_indx)
- label = features.pop(y_name)
-
- elif self.original_data_class == "np_ndarray":
- y_col = ret_cols.index(var_indx)
- label = features[:, y_col].copy()
- features = np.delete(features, y_col, axis=1)
-
- x_cat = [xvars.index(var) for var in self.categorical_variables if var in xvars]
-
- return features, label, x_cat
-
- def append(self, imputation_kernel):
- """
- Combine two imputation kernels together.
- For compatibility, the following attributes of each must be equal:
-
- - working_data
- - iteration_count
- - categorical_feature
- - mean_match_scheme
- - variable_schema
- - imputation_order
- - save_models
- - save_all_iterations
-
- Only cursory checks are done to ensure working_data is equal.
- Appending a kernel with different working_data could ruin this kernel.
-
- Parameters
- ----------
- imputation_kernel: ImputationKernel
- The kernel to merge.
-
- """
-
- _assert_dataset_equivalent(self.working_data, imputation_kernel.working_data)
- assert self.iteration_count() == imputation_kernel.iteration_count()
- assert self.variable_schema == imputation_kernel.variable_schema
- assert self.imputation_order == imputation_kernel.imputation_order
- assert self.variable_training_order == imputation_kernel.variable_training_order
- assert self.categorical_feature == imputation_kernel.categorical_feature
- assert self.save_models == imputation_kernel.save_models
- assert self.save_all_iterations == imputation_kernel.save_all_iterations
- assert (
- self.mean_match_scheme.objective_pred_dtypes
- == imputation_kernel.mean_match_scheme.objective_pred_dtypes
- )
- assert (
- self.mean_match_scheme.objective_pred_funcs
- == imputation_kernel.mean_match_scheme.objective_pred_funcs
- )
- assert (
- self.mean_match_scheme.objective_args
- == imputation_kernel.mean_match_scheme.objective_args
- )
- assert (
- self.mean_match_scheme.mean_match_candidates
- == imputation_kernel.mean_match_scheme.mean_match_candidates
- )
-
- current_datasets = self.dataset_count()
- new_datasets = imputation_kernel.dataset_count()
-
- for key, model in imputation_kernel.models.items():
- new_ds_indx = key[0] + current_datasets
- insert_key = new_ds_indx, key[1], key[2]
- self.models[insert_key] = model
-
- for key, cp in imputation_kernel.candidate_preds.items():
- new_ds_indx = key[0] + current_datasets
- insert_key = new_ds_indx, key[1], key[2]
- self.candidate_preds[insert_key] = cp
-
- for key, iv in imputation_kernel.imputation_values.items():
- new_ds_indx = key[0] + current_datasets
- self[new_ds_indx, key[1], key[2]] = iv
-
- # Combine dicts
- for ds in range(new_datasets):
- insert_index = current_datasets + ds
- self.optimal_parameters[
- insert_index
- ] = imputation_kernel.optimal_parameters[ds]
- self.optimal_parameter_losses[
- insert_index
- ] = imputation_kernel.optimal_parameter_losses[ds]
-
- # Append iterations
- self.iterations = np.append(
- self.iterations, imputation_kernel.iterations, axis=0
- )
-
- def compile_candidate_preds(self):
- """
- Candidate predictions can be pre-generated before imputing new data.
- This can save a substantial amount of time, especially if save_models == 1.
- """
-
- compile_objectives = (
- self.mean_match_scheme.get_objectives_requiring_candidate_preds()
- )
-
- for key, model in self.models.items():
- already_compiled = key in self.candidate_preds.keys()
- objective = model.params["objective"]
- if objective in compile_objectives and not already_compiled:
- var = key[1]
- candidate_features, _, _ = self._make_features_label(
- variable=var,
- subset_count=self.data_subset[var],
- random_seed=model.params["seed"],
- )
- self.candidate_preds[key] = self.mean_match_scheme.model_predict(
- model, candidate_features
- )
-
- else:
- continue
-
- def delete_candidate_preds(self):
- """
- Deletes the pre-computed candidate predictions.
- """
-
- self.candidate_preds = {}
-
- def fit(self, X, y, **fit_params):
- """
- Method for fitting a kernel when used in a sklearn pipeline.
- Should not be called by the user directly.
- """
-
- assert self.dataset_count() == 1, (
- "miceforest kernel should be initialized with datasets=1 if "
- + "being used in a sklearn pipeline."
- )
- _assert_dataset_equivalent(self.working_data, X), (
- "The dataset passed to fit() was not the same as the "
- "dataset passed to ImputationKernel()"
- )
- self.mice(**fit_params)
- return self
-
- def get_model(
- self, dataset: int, variable: Union[str, int], iteration: Optional[int] = None
- ):
- """
- Return the model for a specific dataset, variable, iteration.
-
- Parameters
- ----------
- dataset: int
- The dataset to return the model for.
-
- var: str
- The variable that was imputed
-
- iteration: int
- The model iteration to return. Keep in mind if save_models ==1,
- the model was not saved. If none is provided, the latest model
- is returned.
-
- Returns: lightgbm.Booster
- The model used to impute this specific variable, iteration.
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- itrn = (
- self.iteration_count(datasets=dataset, variables=var_indx)
- if iteration is None
- else iteration
- )
- try:
- return self.models[dataset, var_indx, itrn]
- except Exception:
- raise ValueError("Could not find model.")
-
- def get_raw_prediction(
- self,
- variable: Union[int, str],
- imp_dataset: int = 0,
- imp_iteration: Optional[int] = None,
- model_dataset: Optional[int] = None,
- model_iteration: Optional[int] = None,
- dtype: Union[str, np.dtype, None] = None,
- ):
- """
- Get the raw model output for a specific variable.
-
- The data is pulled from the imp_dataset dataset, at the imp_iteration iteration.
- The model is pulled from model_dataset dataset, at the model_iteration iteration.
-
- So, for example, it is possible to get predictions using the imputed values for
- dataset 3, at iteration 2, using the model obtained from dataset 10, at iteration
- 6. This is assuming desired iterations and models have been saved.
-
- Parameters
- ----------
- variable: int or str
- The variable to get the raw predictions for.
- Can be an index or variable name.
-
- imp_dataset: int
- The imputation dataset to use when creating the feature dataset.
-
- imp_iteration: int
- The iteration from which to draw the imputation values when
- creating the feature dataset. If None, the latest iteration
- is used.
-
- model_dataset: int
- The dataset from which to pull the trained model for this variable.
- If None, it is selected to be the same as imp_dataset.
-
- model_iteration: int
- The iteration from which to pull the trained model for this variable
- If None, it is selected to be the same as imp_iteration.
-
- dtype: str, np.dtype
- The datatype to cast the raw prediction as.
- Passed to MeanMatchScheme.model_predict().
-
- Returns
- -------
- np.ndarray of raw predictions.
-
- """
-
- var_indx = self._get_var_ind_from_scalar(variable)
- predictor_variables = self.variable_schema[var_indx]
-
- # Get the latest imputation iteration if imp_iteration was not specified
- if imp_iteration is None:
- imp_iteration = self.iteration_count(
- datasets=imp_dataset, variables=var_indx
- )
-
- # If model dataset / iteration wasn't specified, assume it is from the same
- # dataset / iteration we are pulling the imputation values from
- model_iteration = imp_iteration if model_iteration is None else model_iteration
- model_dataset = imp_dataset if model_dataset is None else model_dataset
-
- # Get our internal dataset ready
- self.complete_data(dataset=imp_dataset, iteration=imp_iteration, inplace=True)
-
- features = _subset_data(self.working_data, col_ind=predictor_variables)
- model = self.get_model(model_dataset, var_indx, iteration=model_iteration)
- preds = self.mean_match_scheme.model_predict(model, features, dtype=dtype)
-
- return preds
-
- def mice(
- self,
- iterations=2,
- verbose=False,
- variable_parameters=None,
- compile_candidates=False,
- **kwlgb,
- ):
- """
- Perform mice on a given dataset.
-
- Multiple Imputation by Chained Equations (MICE) is an
- iterative method which fills in (imputes) missing data
- points in a dataset by modeling each column using the
- other columns, and then inferring the missing data.
-
- For more information on MICE, and missing data in
- general, see Stef van Buuren's excellent online book:
- https://stefvanbuuren.name/fimd/ch-introduction.html
-
- For detailed usage information, see this project's
- README on the github repository:
- https://github.com/AnotherSamWilson/miceforest
-
- Parameters
- ----------
- iterations: int
- The number of iterations to run.
-
- verbose: bool
- Should information about the process be printed?
-
- variable_parameters: None or dict
- Model parameters can be specified by variable here. Keys should
- be variable names or indices, and values should be a dict of
- parameter which should apply to that variable only.
-
- compile_candidates: bool
- Candidate predictions can be stored as they are created while
- performing mice. This prevents kernel.compile_candidate_preds()
- from having to be called separately, and can save a significant
- amount of time if compiled candidate predictions are desired.
-
- kwlgb:
- Additional arguments to pass to lightgbm. Applied to all models.
-
- """
-
- __MICE_TIMED_EVENTS = ["prepare_xy", "training", "predict", "mean_matching"]
- iter_pairs = self._iter_pairs(iterations)
-
- # Delete models and candidate_preds if we shouldn't be saving every iteration
- if self.save_models < 2:
- self.models = {}
- self.candidate_preds = {}
-
- logger = Logger(
- name=f"mice {str(iter_pairs[0][0])}-{str(iter_pairs[-1][0])}",
- verbose=verbose,
- )
-
- vsp = self._format_variable_parameters(variable_parameters)
-
- for ds in range(self.dataset_count()):
- logger.log("Dataset " + str(ds))
-
- # set self.working_data to the most current iteration.
- self.complete_data(dataset=ds, inplace=True)
- last_iteration = False
-
- for iter_abs, iter_rel in iter_pairs:
- logger.log(str(iter_abs) + " ", end="")
- if iter_rel == iterations:
- last_iteration = True
- save_model = self.save_models == 2 or (
- last_iteration and self.save_models == 1
- )
-
- for variable in self.variable_training_order:
- var_name = self._get_var_name_from_scalar(variable)
- logger.log(" | " + var_name, end="")
- predictor_variables = self.variable_schema[variable]
- data_subset = self.data_subset[variable]
- nawhere = self.na_where[variable]
- log_context = {
- "dataset": ds,
- "variable_name": var_name,
- "iteration": iter_abs,
- }
-
- # Define the lightgbm parameters
- lgbpars = self._get_lgb_params(
- variable, vsp[variable], self._random_state, **kwlgb
- )
- objective = lgbpars["objective"]
-
- # These are necessary for building model in mice.
- logger.set_start_time()
- (
- candidate_features,
- candidate_values,
- feature_cat_index,
- ) = self._make_features_label(
- variable=variable,
- subset_count=data_subset,
- random_seed=lgbpars["seed"],
- )
- if (
- self.original_data_class == "pd_DataFrame"
- or len(feature_cat_index) == 0
- ):
- feature_cat_index = "auto"
-
- # lightgbm requires integers for label. Categories won't work.
- if candidate_values.dtype.name == "category":
- candidate_values = candidate_values.cat.codes
-
- num_iterations = lgbpars.pop("num_iterations")
- train_pointer = Dataset(
- data=candidate_features,
- label=candidate_values,
- categorical_feature=feature_cat_index,
- )
- logger.record_time(timed_event="prepare_xy", **log_context)
- logger.set_start_time()
- current_model = train(
- params=lgbpars,
- train_set=train_pointer,
- num_boost_round=num_iterations,
- categorical_feature=feature_cat_index,
- )
- logger.record_time(timed_event="training", **log_context)
-
- if save_model:
- self.models[ds, variable, iter_abs] = current_model
-
- # Only perform mean matching and insertion
- # if variable is being imputed.
- if variable in self.imputation_order:
- mean_match_args = self.mean_match_scheme.get_mean_match_args(
- objective
- )
-
- # Start creating kwargs for mean matching function
- mm_kwargs = {}
-
- if "lgb_booster" in mean_match_args:
- mm_kwargs["lgb_booster"] = current_model
-
- if {"bachelor_preds", "bachelor_features"}.intersection(
- mean_match_args
- ):
- logger.set_start_time()
- bachelor_features = _subset_data(
- self.working_data,
- row_ind=nawhere,
- col_ind=predictor_variables,
- )
- logger.record_time(timed_event="prepare_xy", **log_context)
- if "bachelor_features" in mean_match_args:
- mm_kwargs["bachelor_features"] = bachelor_features
-
- if "bachelor_preds" in mean_match_args:
- logger.set_start_time()
- bachelor_preds = self.mean_match_scheme.model_predict(
- current_model, bachelor_features
- )
- logger.record_time(timed_event="predict", **log_context)
- mm_kwargs["bachelor_preds"] = bachelor_preds
-
- if "candidate_values" in mean_match_args:
- mm_kwargs["candidate_values"] = candidate_values
-
- if "candidate_features" in mean_match_args:
- mm_kwargs["candidate_features"] = candidate_features
-
- # Calculate the candidate predictions if
- # the mean matching function calls for it
- if "candidate_preds" in mean_match_args:
- logger.set_start_time()
- candidate_preds = self.mean_match_scheme.model_predict(
- current_model, candidate_features
- )
- logger.record_time(timed_event="predict", **log_context)
- mm_kwargs["candidate_preds"] = candidate_preds
-
- if compile_candidates and save_model:
- self.candidate_preds[
- ds, variable, iter_abs
- ] = candidate_preds
-
- if "random_state" in mean_match_args:
- mm_kwargs["random_state"] = self._random_state
-
- # Hashed seeds are only to ensure record reproducibility
- # for impute_new_data().
- if "hashed_seeds" in mean_match_args:
- mm_kwargs["hashed_seeds"] = None
-
- logger.set_start_time()
- imp_values = self.mean_match_scheme._mean_match(
- variable, objective, **mm_kwargs
- )
- logger.record_time(timed_event="mean_matching", **log_context)
-
- assert imp_values.shape == (
- self.na_counts[variable],
- ), f"{variable} mean matching returned malformed array"
-
- # Updates our working data and saves the imputations.
- self._insert_new_data(
- dataset=ds, variable_index=variable, new_data=imp_values
- )
-
- self.iterations[
- ds, self.variable_training_order.index(variable)
- ] += 1
-
- logger.log("\n", end="")
-
- self._ampute_original_data()
- if self.save_loggers:
- self.loggers.append(logger)
-
- def transform(self, X, y=None):
- """
- Method for calling a kernel when used in a sklearn pipeline.
- Should not be called by the user directly.
- """
-
- new_dat = self.impute_new_data(X, datasets=[0])
- return new_dat.complete_data(dataset=0, inplace=False)
-
- def tune_parameters(
- self,
- dataset: int,
- variables: Union[List[int], List[str], None] = None,
- variable_parameters: Optional[Dict[Any, Any]] = None,
- parameter_sampling_method: str = "random",
- nfold: int = 10,
- optimization_steps: int = 5,
- random_state: _t_random_state = None,
- verbose: bool = False,
- **kwbounds,
- ):
- """
- Perform hyperparameter tuning on models at the current iteration.
-
- .. code-block:: text
-
- A few notes:
- - Underlying models will now be gradient boosted trees by default (or any
- other boosting type compatible with lightgbm.cv).
- - The parameters are tuned on the data that would currently be returned by
- complete_data(dataset). It is usually a good idea to run at least 1 iteration
- of mice with the default parameters to get a more accurate idea of the
- real optimal parameters, since Missing At Random (MAR) data imputations
- tend to converge over time.
- - num_iterations is treated as the maximum number of boosting rounds to run
- in lightgbm.cv. It is NEVER optimized. The num_iterations that is returned
- is the best_iteration returned by lightgbm.cv. num_iterations can be passed to
- limit the boosting rounds, but the returned value will always be obtained
- from best_iteration.
- - lightgbm parameters are chosen in the following order of priority:
- 1) Anything specified in variable_parameters
- 2) Parameters specified globally in **kwbounds
- 3) Default tuning space (miceforest.default_lightgbm_parameters.make_default_tuning_space)
- 4) Default parameters (miceforest.default_lightgbm_parameters.default_parameters)
- - See examples for a detailed run-through. See
- https://github.com/AnotherSamWilson/miceforest#Tuning-Parameters
- for even more detailed examples.
-
-
- Parameters
- ----------
-
- dataset: int (required)
-
- .. code-block:: text
-
- The dataset to run parameter tuning on. Tuning parameters on 1 dataset usually results
- in acceptable parameters for all datasets. However, tuning results are still stored
- seperately for each dataset.
-
- variables: None or list
-
- .. code-block:: text
-
- - If None, default hyper-parameter spaces are selected based on kernel data, and
- all variables with missing values are tuned.
- - If list, must either be indexes or variable names corresponding to the variables
- that are to be tuned.
-
- variable_parameters: None or dict
-
- .. code-block:: text
-
- Defines the tuning space. Dict keys must be variable names or indices, and a subset
- of the variables parameter. Values must be a dict with lightgbm parameter names as
- keys, and values that abide by the following rules:
- scalar: If a single value is passed, that parameter will be used to build the
- model, and will not be tuned.
- tuple: If a tuple is passed, it must have length = 2 and will be interpreted as
- the bounds to search within for that parameter.
- list: If a list is passed, values will be randomly selected from the list.
- NOTE: This is only possible with method = 'random'.
-
- example: If you wish to tune the imputation model for the 4th variable with specific
- bounds and parameters, you could pass:
- variable_parameters = {
- 4: {
- 'learning_rate: 0.01',
- 'min_sum_hessian_in_leaf: (0.1, 10),
- 'extra_trees': [True, False]
- }
- }
- All models for variable 4 will have a learning_rate = 0.01. The process will randomly
- search within the bounds (0.1, 10) for min_sum_hessian_in_leaf, and extra_trees will
- be randomly selected from the list. Also note, the variable name for the 4th column
- could also be passed instead of the integer 4. All other variables will be tuned with
- the default search space, unless **kwbounds are passed.
-
- parameter_sampling_method: str
-
- .. code-block:: text
-
- If 'random', parameters are randomly selected.
- Other methods will be added in future releases.
-
- nfold: int
-
- .. code-block:: text
-
- The number of folds to perform cross validation with. More folds takes longer, but
- Gives a more accurate distribution of the error metric.
-
- optimization_steps:
-
- .. code-block:: text
-
- How many steps to run the process for.
-
- random_state: int or np.random.RandomState or None (default=None)
-
- .. code-block:: text
-
- The random state of the process. Ensures reproduceability. If None, the random state
- of the kernel is used. Beware, this permanently alters the random state of the kernel
- and ensures non-reproduceable results, unless the entire process up to this point
- is re-run.
-
- kwbounds:
-
- .. code-block:: text
-
- Any additional arguments that you want to apply globally to every variable.
- For example, if you want to limit the number of iterations, you could pass
- num_iterations = x to this functions, and it would apply globally. Custom
- bounds can also be passed.
-
-
- Returns
- -------
- 2 dicts: optimal_parameters, optimal_parameter_losses
-
- - optimal_parameters: dict
- A dict of the optimal parameters found for each variable.
- This can be passed directly to the variable_parameters parameter in mice()
-
- .. code-block:: text
-
- {variable: {parameter_name: parameter_value}}
-
- - optimal_parameter_losses: dict
- The average out of fold cv loss obtained directly from
- lightgbm.cv() associated with the optimal parameter set.
-
- .. code-block:: text
-
- {variable: loss}
-
- """
-
- if random_state is None:
- random_state = self._random_state
- else:
- random_state = ensure_rng(random_state)
-
- if variables is None:
- variables = self.imputation_order
- else:
- variables = self._get_var_ind_from_list(variables)
-
- self.complete_data(dataset, inplace=True)
-
- logger = Logger(
- name=f"tune: {optimization_steps}",
- verbose=verbose,
- )
-
- vsp = self._format_variable_parameters(variable_parameters)
- variable_parameter_space = {}
-
- for var in variables:
- default_tuning_space = make_default_tuning_space(
- self.category_counts[var] if var in self.categorical_variables else 1,
- int((self.data_shape[0] - len(self.na_where[var])) / 10),
- )
-
- variable_parameter_space[var] = self._get_lgb_params(
- var=var,
- vsp={**kwbounds, **vsp[var]},
- random_state=random_state,
- **default_tuning_space,
- )
-
- if parameter_sampling_method == "random":
- for var, parameter_space in variable_parameter_space.items():
- logger.log(self._get_var_name_from_scalar(var) + " | ", end="")
-
- (
- candidate_features,
- candidate_values,
- feature_cat_index,
- ) = self._make_features_label(
- variable=var,
- subset_count=self.data_subset[var],
- random_seed=_draw_random_int32(
- random_state=self._random_state, size=1
- )[0],
- )
-
- # lightgbm requires integers for label. Categories won't work.
- if candidate_values.dtype.name == "category":
- candidate_values = candidate_values.cat.codes
-
- is_categorical = var in self.categorical_variables
-
- for step in range(optimization_steps):
- logger.log(str(step), end="")
-
- # Make multiple attempts to learn something.
- non_learners = 0
- learning_attempts = 10
- while non_learners < learning_attempts:
- # Pointer and folds need to be re-initialized after every run.
- train_pointer = Dataset(
- data=candidate_features,
- label=candidate_values,
- categorical_feature=feature_cat_index,
- free_raw_data=False,
- )
- if is_categorical:
- folds = stratified_categorical_folds(
- candidate_values, nfold
- )
- else:
- folds = stratified_continuous_folds(candidate_values, nfold)
- sampling_point = self._get_random_sample(
- parameters=parameter_space, random_state=random_state
- )
- try:
- loss, best_iteration = self._get_oof_performance(
- parameters=sampling_point.copy(),
- folds=folds,
- train_pointer=train_pointer,
- categorical_feature=feature_cat_index,
- )
- except:
- loss, best_iteration = np.inf, 0
-
- if best_iteration > 1:
- break
- else:
- non_learners += 1
-
- if loss < self.optimal_parameter_losses[dataset][var]:
- del sampling_point["seed"]
- sampling_point["num_iterations"] = best_iteration
- self.optimal_parameters[dataset][var] = sampling_point
- self.optimal_parameter_losses[dataset][var] = loss
-
- logger.log(" - ", end="")
-
- logger.log("\n", end="")
-
- self._ampute_original_data()
- return (
- self.optimal_parameters[dataset],
- self.optimal_parameter_losses[dataset],
- )
-
- def impute_new_data(
- self,
- new_data: _t_dat,
- datasets: Optional[List[int]] = None,
- iterations: Optional[int] = None,
- save_all_iterations: bool = True,
- copy_data: bool = True,
- random_state: _t_random_state = None,
- random_seed_array: Optional[np.ndarray] = None,
- verbose: bool = False,
- ) -> ImputedData:
- """
- Impute a new dataset
-
- Uses the models obtained while running MICE to impute new data,
- without fitting new models. Pulls mean matching candidates from
- the original data.
-
- save_models must be > 0. If save_models == 1, the last model
- obtained in mice is used for every iteration. If save_models > 1,
- the model obtained at each iteration is used to impute the new
- data for that iteration. If specified iterations is greater than
- the number of iterations run so far using mice, the last model
- is used for each additional iteration.
-
- Type checking is not done. It is up to the user to ensure that the
- kernel data matches the new data being imputed.
-
- Parameters
- ----------
- new_data: pandas DataFrame or numpy ndarray
- The new data to impute
-
- datasets: int or List[int] (default = None)
- The datasets from the kernel to use to impute the new data.
- If None, all datasets from the kernel are used.
-
- iterations: int
- The number of iterations to run.
- If None, the same number of iterations run so far in mice is used.
-
- save_all_iterations: bool
- Should the imputation values of all iterations be archived?
- If False, only the latest imputation values are saved.
-
- copy_data: boolean
- Should the dataset be referenced directly? This will cause the dataset to be altered
- in place. If a copy is created, it is saved in self.working_data. There are different
- ways in which the dataset can be altered:
-
- 1) complete_data() will fill in missing values
- 2) mice() references and manipulates self.working_data directly.
-
- random_state: int or np.random.RandomState or None (default=None)
- The random state of the process. Ensures reproducibility. If None, the random state
- of the kernel is used. Beware, this permanently alters the random state of the kernel
- and ensures non-reproduceable results, unless the entire process up to this point
- is re-run.
-
- random_seed_array: None or np.ndarray (int32)
-
- .. code-block:: text
-
- Record-level seeds.
-
- Ensures deterministic imputations at the record level. random_seed_array causes
- deterministic imputations for each record no matter what dataset each record is
- imputed with, assuming the same number of iterations and datasets are used.
- If random_seed_array os passed, random_state must also be passed.
-
- Record-level imputations are deterministic if the following conditions are met:
- 1) The associated seed is the same.
- 2) The same kernel is used.
- 3) The same number of iterations are run.
- 4) The same number of datasets are run.
-
- Notes:
- a) This will slightly slow down the imputation process, because random
- number generation in numpy can no longer be vectorized. If you don't have a
- specific need for deterministic imputations at the record level, it is better to
- keep this parameter as None.
-
- b) Using this parameter may change the global numpy seed by calling np.random.seed().
-
- c) Internally, these seeds are hashed each time they are used, in order
- to obtain different results for each dataset / iteration.
-
-
- verbose: boolean
- Should information about the process be printed?
-
- Returns
- -------
- miceforest.ImputedData
-
- """
-
- datasets = list(range(self.dataset_count())) if datasets is None else datasets
- kernel_iterations = self.iteration_count()
- iterations = kernel_iterations if iterations is None else iterations
- iter_pairs = self._iter_pairs(iterations)
- __IND_TIMED_EVENTS = ["prepare_xy", "predict", "mean_matching"]
- logger = Logger(
- name=f"ind {str(iter_pairs[0][1])}-{str(iter_pairs[-1][1])}",
- verbose=verbose,
- )
-
- if isinstance(self.working_data, pd_DataFrame):
- assert isinstance(new_data, pd_DataFrame)
- assert set(self.working_data.columns) == set(
- new_data.columns
- ), "Different columns from original dataset."
- assert all(
- [
- self.working_data[col].dtype == new_data[col].dtype
- for col in self.working_data.columns
- ]
- ), "Column types are not the same as the original data. Check categorical columns."
-
- if self.save_models < 1:
- raise ValueError("No models were saved.")
-
- imputed_data = ImputedData(
- impute_data=new_data,
- datasets=len(datasets),
- variable_schema=self.variable_schema.copy(),
- imputation_order=self.variable_training_order.copy(),
- train_nonmissing=False,
- categorical_feature=self.categorical_feature,
- save_all_iterations=save_all_iterations,
- copy_data=copy_data,
- )
-
- ### Manage Randomness.
- if random_state is None:
- assert (
- random_seed_array is None
- ), "random_state is also required when using random_seed_array"
- random_state = self._random_state
- else:
- random_state = ensure_rng(random_state)
- # use_seed_array = random_seed_array is not None
- random_seed_array = self._initialize_random_seed_array(
- random_seed_array=random_seed_array,
- expected_shape=imputed_data.data_shape[0],
- )
- self._initialize_dataset(
- imputed_data, random_state=random_state, random_seed_array=random_seed_array
- )
-
- for ds_kern in datasets:
- logger.log("Dataset " + str(ds_kern))
- self.complete_data(dataset=ds_kern, inplace=True)
- ds_new = datasets.index(ds_kern)
- imputed_data.complete_data(dataset=ds_new, inplace=True)
-
- for iter_abs, iter_rel in iter_pairs:
- logger.log(str(iter_rel) + " ", end="")
-
- # Determine which model iteration to grab
- if self.save_models == 1 or iter_abs > kernel_iterations:
- iter_model = kernel_iterations
- else:
- iter_model = iter_abs
-
- for var in imputed_data.imputation_order:
- var_name = self._get_var_name_from_scalar(var)
- logger.log(" | " + var_name, end="")
- log_context = {
- "dataset": ds_kern,
- "variable_name": var_name,
- "iteration": iter_rel,
- }
- nawhere = imputed_data.na_where[var]
- predictor_variables = self.variable_schema[var]
-
- # Select our model.
- current_model = self.get_model(
- variable=var, dataset=ds_kern, iteration=iter_model
- )
- objective = current_model.params["objective"]
- model_seed = current_model.params["seed"]
-
- # Start building mean matching kwargs
- mean_match_args = self.mean_match_scheme.get_mean_match_args(
- objective
- )
- mm_kwargs = {}
-
- if "lgb_booster" in mean_match_args:
- mm_kwargs["lgb_booster"] = current_model
-
- # Procure bachelor information
- if {"bachelor_preds", "bachelor_features"}.intersection(
- mean_match_args
- ):
- logger.set_start_time()
- bachelor_features = _subset_data(
- imputed_data.working_data,
- row_ind=imputed_data.na_where[var],
- col_ind=predictor_variables,
- )
- logger.record_time(timed_event="prepare_xy", **log_context)
- if "bachelor_features" in mean_match_args:
- mm_kwargs["bachelor_features"] = bachelor_features
-
- if "bachelor_preds" in mean_match_args:
- logger.set_start_time()
- bachelor_preds = self.mean_match_scheme.model_predict(
- current_model, bachelor_features
- )
- logger.record_time(timed_event="predict", **log_context)
- mm_kwargs["bachelor_preds"] = bachelor_preds
-
- # Procure candidate information
- if {
- "candidate_values",
- "candidate_features",
- "candidate_preds",
- }.intersection(mean_match_args):
- # Need to return candidate features if we need to calculate
- # candidate_preds or candidate_features is needed by mean matching function
- calculate_candidate_preds = (
- ds_kern,
- var,
- iter_model,
- ) not in self.candidate_preds.keys() and "candidate_preds" in mean_match_args
- return_features = (
- "candidate_features" in mean_match_args
- ) or calculate_candidate_preds
- # Set up like this so we only have to subset once
- logger.set_start_time()
- if return_features:
- (
- candidate_features,
- candidate_values,
- _,
- ) = self._make_features_label(
- variable=var,
- subset_count=self.data_subset[var],
- random_seed=model_seed,
- )
- else:
- candidate_values = self._make_label(
- variable=var,
- subset_count=self.data_subset[var],
- random_seed=model_seed,
- )
- logger.record_time(timed_event="prepare_xy", **log_context)
-
- if "candidate_values" in mean_match_args:
- # lightgbm requires integers for label. Categories won't work.
- if candidate_values.dtype.name == "category":
- candidate_values = candidate_values.cat.codes
- mm_kwargs["candidate_values"] = candidate_values
-
- if "candidate_features" in mean_match_args:
- mm_kwargs["candidate_features"] = candidate_features
-
- # Calculate the candidate predictions if
- # the mean matching function calls for it
- if "candidate_preds" in mean_match_args:
- if calculate_candidate_preds:
- logger.set_start_time()
- candidate_preds = self.mean_match_scheme.model_predict(
- current_model, candidate_features
- )
- logger.record_time(timed_event="predict", **log_context)
- else:
- candidate_preds = self.candidate_preds[
- ds_kern, var, iter_model
- ]
- mm_kwargs["candidate_preds"] = candidate_preds
-
- if "random_state" in mean_match_args:
- mm_kwargs["random_state"] = random_state
-
- if "hashed_seeds" in mean_match_args:
- if isinstance(random_seed_array, np.ndarray):
- seeds = random_seed_array[nawhere]
- rehash_seeds = True
-
- else:
- seeds = None
- rehash_seeds = False
-
- mm_kwargs["hashed_seeds"] = seeds
-
- else:
- rehash_seeds = False
-
- logger.set_start_time()
- imp_values = self.mean_match_scheme._mean_match(
- var, objective, **mm_kwargs
- )
- logger.record_time(timed_event="mean_matching", **log_context)
- imputed_data._insert_new_data(
- dataset=ds_new, variable_index=var, new_data=imp_values
- )
- # Refresh our seeds.
- if rehash_seeds:
- assert isinstance(random_seed_array, np.ndarray)
- random_seed_array[nawhere] = hash_int32(seeds)
-
- imputed_data.iterations[
- ds_new, imputed_data.imputation_order.index(var)
- ] += 1
-
- logger.log("\n", end="")
-
- imputed_data._ampute_original_data()
- if self.save_loggers:
- self.loggers.append(logger)
-
- return imputed_data
-
- def start_logging(self):
- """
- Start saving loggers to self.loggers
- """
-
- self.save_loggers = True
-
- def stop_logging(self):
- """
- Stop saving loggers to self.loggers
- """
-
- self.save_loggers = False
-
- def save_kernel(
- self, filepath, clevel=None, cname=None, n_threads=None, copy_while_saving=True
- ):
- """
- Compresses and saves the kernel to a file.
-
- Parameters
- ----------
- filepath: str
- The file to save to.
-
- clevel: int
- The compression level, sent to clevel argument in blosc2.compress()
-
- cname: str
- The compression algorithm used.
- Sent to cname argument in blosc2.compress.
- If None is specified, the default is lz4hc.
-
- n_threads: int
- The number of threads to use for compression.
- By default, all threads are used.
-
- copy_while_saving: boolean
- Should the kernel be copied while saving? Copying is safer, but
- may take more memory.
-
- """
-
- clevel = 9 if clevel is None else clevel
- cname = "lz4hc" if cname is None else cname
-
- # make interface compatible
- codec_mapping = {
- "blosclz": blosc2.Codec.BLOSCLZ,
- "lz4":blosc2.Codec.LZ4,
- "lz4hc":blosc2.Codec.LZ4HC,
- "zlib":blosc2.Codec.ZLIB,
- "zstd":blosc2.Codec.ZSTD,
- "ndlz":blosc2.Codec.NDLZ,
- "zfp_acc":blosc2.Codec.ZFP_ACC,
- "zfp_prec":blosc2.Codec.ZFP_PREC,
- "zfp_rate":blosc2.Codec.ZFP_RATE,
- "openhtj2k":blosc2.Codec.OPENHTJ2K,
- "grok":blosc2.Codec.GROK
- }
- if cname in codec_mapping.keys():
- codec=codec_mapping[cname]
- else:
- codec=blosc2.Codec.LZ4HC
-
- n_threads = blosc2.detect_number_of_cores() if n_threads is None else n_threads
-
- if copy_while_saving:
- kernel = copy(self)
- else:
- kernel = self
-
- # convert working data to parquet bytes object
- if kernel.original_data_class == "pd_DataFrame":
- working_data_bytes = BytesIO()
- kernel.working_data.to_parquet(working_data_bytes)
- kernel.working_data = working_data_bytes
-
- blosc2.set_nthreads(n_threads)
-
- with open(filepath, "wb") as f:
- dill.dump(
- blosc2.compress(
- dill.dumps(kernel),
- clevel=clevel,
- filter=blosc2.Filter.NOFILTER,
- codec=codec,
- ),
- f,
- )
-
- def get_feature_importance(self, dataset, iteration=None) -> np.ndarray:
- """
- Return a matrix of feature importance. The cells
- represent the normalized feature importance of the
- columns to impute the rows. This is calculated
- internally by lightgbm.Booster.feature_importance().
-
- Parameters
- ----------
- dataset: int
- The dataset to get the feature importance for.
-
- iteration: int
- The iteration to return the feature importance for.
- Right now, the model must be saved to return importance
-
- Returns
- -------
- np.ndarray of importance values. Rows are imputed variables, and
- columns are predictor variables.
-
- """
-
- if iteration is None:
- iteration = self.iteration_count(datasets=dataset)
-
- importance_matrix = np.full(
- shape=(len(self.imputation_order), len(self.predictor_vars)),
- fill_value=np.NaN,
- )
-
- for ivar in self.imputation_order:
- importance_dict = dict(
- zip(
- self.variable_schema[ivar],
- self.get_model(dataset, ivar, iteration).feature_importance(),
- )
- )
- for pvar in importance_dict:
- importance_matrix[
- np.sort(self.imputation_order).tolist().index(ivar),
- np.sort(self.predictor_vars).tolist().index(pvar),
- ] = importance_dict[pvar]
-
- return importance_matrix
-
- def plot_feature_importance(
- self,
- dataset,
- normalize: bool = True,
- iteration: Optional[int] = None,
- **kw_plot,
- ):
- """
- Plot the feature importance. See get_feature_importance()
- for more details.
-
- Parameters
- ----------
- dataset: int
- The dataset to plot the feature importance for.
-
- iteration: int
- The iteration to plot the feature importance of.
-
- normalize: book
- Should the values be normalize from 0-1?
- If False, values are raw from Booster.feature_importance()
-
- kw_plot
- Additional arguments sent to sns.heatmap()
-
- """
-
- # Move this to .compat at some point.
- try:
- from seaborn import heatmap
- except ImportError:
- raise ImportError("seaborn must be installed to plot importance")
-
- importance_matrix = self.get_feature_importance(
- dataset=dataset, iteration=iteration
- )
- if normalize:
- importance_matrix = (
- importance_matrix / np.nansum(importance_matrix, 1).reshape(-1, 1)
- ).round(2)
-
- imputed_var_names = [
- self._get_var_name_from_scalar(int(i))
- for i in np.sort(self.imputation_order)
- ]
- predictor_var_names = [
- self._get_var_name_from_scalar(int(i)) for i in np.sort(self.predictor_vars)
- ]
-
- params = {
- **{
- "cmap": "coolwarm",
- "annot": True,
- "fmt": ".2f",
- "xticklabels": predictor_var_names,
- "yticklabels": imputed_var_names,
- "annot_kws": {"size": 16},
- },
- **kw_plot,
- }
-
- print(heatmap(importance_matrix, **params))
diff --git a/miceforest/ImputedData.py b/miceforest/ImputedData.py
deleted file mode 100644
index 8fd1dbb..0000000
--- a/miceforest/ImputedData.py
+++ /dev/null
@@ -1,718 +0,0 @@
-import numpy as np
-from .compat import pd_DataFrame
-from .utils import (
- _t_dat,
- _t_var_list,
- _t_var_dict,
- _ensure_iterable,
- _dict_set_diff,
- _assign_col_values_without_copy,
- _slice,
- _subset_data,
-)
-from itertools import combinations
-from typing import Dict, List, Union, Any, Optional
-from warnings import warn
-
-
-class ImputedData:
- """
- Imputed Data
-
- This class should not be instantiated directly.
- Instead, it is returned when ImputationKernel.impute_new_data() is called.
- For parameter arguments, see ImputationKernel documentation.
- """
-
- def __init__(
- self,
- impute_data: _t_dat,
- datasets: int = 5,
- variable_schema: Union[_t_var_list, _t_var_dict, None] = None,
- imputation_order: Union[str, _t_var_list] = "ascending",
- train_nonmissing: bool = False,
- categorical_feature: Union[str, _t_var_list] = "auto",
- save_all_iterations: bool = True,
- copy_data: bool = True,
- ):
- # All references to the data should be through self.
- self.working_data = impute_data.copy() if copy_data else impute_data
- data_shape = self.working_data.shape
- int_storage_types = ["uint64", "uint32", "uint16", "uint8"]
- na_where_type = "uint64"
- for st in int_storage_types:
- if data_shape[0] <= np.iinfo(st).max:
- na_where_type = st
-
- # Collect metadata and format data
- if isinstance(self.working_data, pd_DataFrame):
- if len(self.working_data.shape) != 2 or self.working_data.shape[0] < 1:
- raise ValueError("Input data must be 2 dimensional and non empty.")
-
- original_data_class = "pd_DataFrame"
- column_names: List[str] = [str(x) for x in self.working_data.columns]
- self.column_names = column_names
- pd_dtypes_orig = self.working_data.dtypes
-
- if any([x.name == "object" for x in pd_dtypes_orig]):
- raise ValueError(
- "Please convert object columns to categorical or some numeric type."
- )
-
- # Assume categories are set dtypes.
- if categorical_feature == "auto":
- categorical_variables = [
- column_names.index(var)
- for var in pd_dtypes_orig.index
- if pd_dtypes_orig[var].name in ["category"]
- ]
-
- elif isinstance(categorical_feature, list):
- if any([x.name == "category" for x in pd_dtypes_orig]):
- raise ValueError(
- "If categories are already encoded as such, set categorical_feature = auto"
- )
- categorical_variables = self._get_var_ind_from_list(categorical_feature)
-
- else:
- raise ValueError("Unknown categorical_feature")
-
- # Collect category counts.
- category_counts = {}
- for cat in categorical_variables:
- cat_name = self._get_var_name_from_scalar(cat)
- cat_dat = self.working_data.iloc[:, cat]
- uniq = set(cat_dat.dropna())
- category_counts[cat] = len(uniq)
-
- # Collect info about what data is missing.
- na_where: Dict[int, np.ndarray] = {
- col: np.where(self.working_data.iloc[:, col].isnull())[0].astype(
- na_where_type
- )
- for col in range(data_shape[1])
- }
- na_counts = {col: len(nw) for col, nw in na_where.items()}
- vars_with_any_missing = [
- col for col, ind in na_where.items() if len(ind > 0)
- ]
- # if len(vars_with_any_missing) == 0:
- # raise ValueError("No missing values to impute.")
-
- # Keep track of datatypes. Needed for loading kernels.
- self.working_dtypes = self.working_data.dtypes
-
- elif isinstance(self.working_data, np.ndarray):
- if len(self.working_data.shape) != 2 or self.working_data.shape[0] < 1:
- raise ValueError("Input data must be 2 dimensional and non empty.")
-
- original_data_class = "np_ndarray"
-
- # DATASET ALTERATION
- if (
- self.working_data.dtype != np.float32
- and self.working_data.dtype != np.float64
- ):
- self.working_data = self.working_data.astype(np.float32)
-
- # Collect information about dataset
- column_names = [str(x) for x in range(self.working_data.shape[1])]
- self.column_names = column_names
- na_where = {
- col: np.where(np.isnan(self.working_data[:, col]))[0].astype(
- na_where_type
- )
- for col in range(data_shape[1])
- }
- na_counts = {col: len(nw) for col, nw in na_where.items()}
- vars_with_any_missing = [
- int(col) for col, ind in na_where.items() if len(ind > 0)
- ]
- if categorical_feature == "auto":
- categorical_variables = []
- elif isinstance(categorical_feature, list):
- categorical_variables = self._get_var_ind_from_list(categorical_feature)
- assert (
- max(categorical_variables) < self.working_data.shape[1]
- ), "categorical_feature not in dataset"
- else:
- raise ValueError("categorical_feature not recognized")
-
- # Collect category counts.
- category_counts = {}
- for cat in categorical_variables:
- cat_dat = self.working_data[:, cat]
- cat_dat = cat_dat[~np.isnan(cat_dat)]
- uniq = set(cat_dat)
- category_counts[cat] = len(uniq)
-
- # Keep track of datatype.
- self.working_dtypes = self.working_data.dtype
-
- else:
- raise ValueError("impute_data not recognized.")
-
- # Formatting of variable_schema.
- if variable_schema is None:
- variable_schema = _dict_set_diff(range(data_shape[1]), range(data_shape[1]))
- else:
- if isinstance(variable_schema, list):
- var_schem = self._get_var_ind_from_list(variable_schema)
- variable_schema = _dict_set_diff(var_schem, range(data_shape[1]))
-
- elif isinstance(variable_schema, dict):
- variable_schema = self._get_var_ind_from_dict(variable_schema)
-
- # Check for any self-impute attempts
- self_impute_attempt = [
- var for var, prd in variable_schema.items() if var in prd
- ]
- if len(self_impute_attempt) > 0:
- raise ValueError(
- ",".join(self._get_var_name_from_list(self_impute_attempt))
- + " variables cannot be used to impute itself."
- )
-
- # Format imputation order
- if isinstance(imputation_order, list):
- imputation_order = self._get_var_ind_from_list(imputation_order)
- assert set(imputation_order).issubset(
- variable_schema
- ), "variable_schema does not include all variables to be imputed."
- imputation_order = [i for i in imputation_order if na_counts[i] > 0]
- elif isinstance(imputation_order, str):
- if imputation_order in ["ascending", "descending"]:
- imputation_order = self._get_var_ind_from_list(
- np.argsort(list(na_counts.values())).tolist()
- if imputation_order == "ascending"
- else np.argsort(list(na_counts.values()))[::-1].tolist()
- )
- imputation_order = [
- int(i)
- for i in imputation_order
- if na_counts[i] > 0 and i in list(variable_schema)
- ]
- elif imputation_order == "roman":
- imputation_order = list(variable_schema).copy()
- elif imputation_order == "arabic":
- imputation_order = list(variable_schema).copy()
- imputation_order.reverse()
- else:
- raise ValueError("imputation_order not recognized.")
-
- self.imputation_order = imputation_order
- self.variable_schema = variable_schema
- self.unimputed_variables = list(
- np.setdiff1d(np.arange(data_shape[1]), imputation_order)
- )
- if train_nonmissing:
- self.variable_training_order = [
- v
- for v in self.imputation_order + self.unimputed_variables
- if v in list(self.variable_schema)
- ]
- else:
- self.variable_training_order = self.imputation_order
- predictor_vars = [prd for prd in variable_schema.values()]
- self.predictor_vars = list(
- dict.fromkeys([item for sublist in predictor_vars for item in sublist])
- )
- self.categorical_feature = categorical_feature
- self.categorical_variables = categorical_variables
- self.category_counts = category_counts
- self.original_data_class = original_data_class
- self.save_all_iterations = save_all_iterations
- self.data_shape = data_shape
- self.na_counts = na_counts
- self.na_where = na_where
- self.vars_with_any_missing = vars_with_any_missing
- self.imputed_variable_count = len(imputation_order)
- self.modeled_variable_count = len(self.variable_training_order)
- self.iterations = np.zeros(
- shape=(datasets, self.modeled_variable_count)
- ).astype(int)
-
- # Create structure to store imputation values.
- # These will be initialized by an ImputationKernel.
- self.imputation_values: Dict[Any, np.ndarray] = {}
- self.initialized = False
-
- # Sanity checks
- # if self.imputed_variable_count == 0:
- # raise ValueError("Something went wrong. No variables to impute.")
-
- # Subsetting allows us to get to the imputation values:
- def __getitem__(self, tup):
- ds, var, iter = tup
- return self.imputation_values[ds, var, iter]
-
- def __setitem__(self, tup, newitem):
- ds, var, iter = tup
- self.imputation_values[ds, var, iter] = newitem
-
- def __delitem__(self, tup):
- ds, var, iter = tup
- del self.imputation_values[ds, var, iter]
-
- def __repr__(self):
- summary_string = f'\n{" " * 14}Class: ImputedData\n{self._ids_info()}'
- return summary_string
-
- def _ids_info(self):
- summary_string = f"""\
- Datasets: {self.dataset_count()}
- Iterations: {self.iteration_count()}
- Data Samples: {self.data_shape[0]}
- Data Columns: {self.data_shape[1]}
- Imputed Variables: {len(self.imputation_order)}
-save_all_iterations: {self.save_all_iterations}"""
- return summary_string
-
- def dataset_count(self):
- """
- Return the number of datasets.
- Datasets are defined by how many different sets of imputation
- values we have accumulated.
- """
- return self.iterations.shape[0]
-
- def _get_var_name_from_scalar(self, ind: Union[str, int]) -> str:
- """
- Gets the variable name from an index.
- Returns a list of names if a list of indexes was passed.
- Otherwise, returns the variable name directly from self.column_names.
- """
- if isinstance(ind, str):
- return ind
- else:
- return self.column_names[ind]
-
- def _get_var_name_from_list(self, variable_list: _t_var_list) -> List[str]:
- ret = [
- self.column_names[x] if isinstance(x, int) else str(x)
- for x in variable_list
- ]
- return ret
-
- def _get_var_ind_from_dict(self, variable_dict) -> Dict[int, List[int]]:
- indx: Dict[int, List[int]] = {}
- for variable, value in variable_dict.items():
- if isinstance(variable, str):
- variable = self.column_names.index(variable)
- variable = int(variable)
- val = [
- int(self.column_names.index(v)) if isinstance(v, str) else int(v)
- for v in value
- ]
- indx[variable] = sorted(val)
-
- return indx
-
- def _get_var_ind_from_list(self, variable_list) -> List[int]:
- ret = [
- int(self.column_names.index(x)) if isinstance(x, str) else int(x)
- for x in variable_list
- ]
-
- return ret
-
- def _get_var_ind_from_scalar(self, variable) -> int:
- if isinstance(variable, str):
- variable = self.column_names.index(variable)
- variable = int(variable)
- return variable
-
- def _get_nonmissing_indx(self, var):
- non_missing_ind = np.setdiff1d(
- np.arange(self.data_shape[0]), self.na_where[var]
- )
- return non_missing_ind
-
- def _insert_new_data(self, dataset, variable_index, new_data):
- current_iter = self.iteration_count(datasets=dataset, variables=variable_index)
-
- # We need to insert the categories if the raw data is stored as a category.
- # Otherwise, pandas won't let us insert.
- view = _slice(self.working_data, col_slice=variable_index)
- if view.dtype.name == "category":
- new_data = np.array(view.cat.categories)[new_data]
-
- _assign_col_values_without_copy(
- dat=self.working_data,
- row_ind=self.na_where[variable_index],
- col_ind=variable_index,
- val=new_data,
- )
- self[dataset, variable_index, current_iter + 1] = new_data
- if not self.save_all_iterations:
- del self[dataset, variable_index, current_iter]
-
- def _ampute_original_data(self):
- """Need to put self.working_data back in its original form"""
- for c in self.imputation_order:
- _assign_col_values_without_copy(
- dat=self.working_data,
- row_ind=self.na_where[c],
- col_ind=c,
- val=np.array([np.nan]),
- )
-
- def _get_num_vars(self, subset: Optional[List] = None):
- """Returns the non-categorical imputed variable indexes."""
-
- num_vars = [
- v for v in self.imputation_order if v not in self.categorical_variables
- ]
-
- if subset is not None:
- subset = self._get_var_ind_from_list(subset)
- num_vars = [v for v in num_vars if v in subset]
-
- return num_vars
-
- def _prep_multi_plot(
- self,
- variables,
- ):
- plots = len(variables)
- plotrows, plotcols = int(np.ceil(np.sqrt(plots))), int(
- np.ceil(plots / np.ceil(np.sqrt(plots)))
- )
- return plots, plotrows, plotcols
-
- def iteration_count(self, datasets=None, variables=None):
- """
- Grabs the iteration count for specified variables, datasets.
- If the iteration count is not consistent across the provided
- datasets/variables, an error will be thrown. Providing None
- will use all datasets/variables.
-
- This is to ensure the process is in a consistent state when
- the iteration count is needed.
-
- Parameters
- ----------
- datasets: int or list[int]
- The datasets to check the iteration count for.
- variables: int, str, list[int] or list[str]:
- The variables to check the iteration count for.
- Variables can be specified by their names or indexes.
-
- Returns
- -------
- An integer representing the iteration count.
- """
-
- ds = (
- list(range(self.dataset_count()))
- if datasets is None
- else _ensure_iterable(datasets)
- )
-
- if variables is None:
- var = self.variable_training_order
- else:
- variables = _ensure_iterable(variables)
- var = self._get_var_ind_from_list(variables)
-
- assert set(var).issubset(self.variable_training_order)
-
- iter_indx = [self.variable_training_order.index(v) for v in var]
- ds_uniq = np.unique(self.iterations[np.ix_(ds, iter_indx)])
- if len(ds_uniq) == 0:
- return -1
- if len(ds_uniq) > 1:
- raise ValueError(
- "iterations were not consistent across provided datasets, variables."
- )
-
- return ds_uniq[0]
-
- def complete_data(
- self,
- dataset: int = 0,
- iteration: Optional[int] = None,
- inplace: bool = False,
- variables: Optional[_t_var_list] = None,
- ):
- """
- Return dataset with missing values imputed.
-
- Parameters
- ----------
- dataset: int
- The dataset to complete.
- iteration: int
- Impute data with values obtained at this iteration.
- If None, returns the most up-to-date iterations,
- even if different between variables. If not none,
- iteration must have been saved in imputed values.
- inplace: bool
- Should the data be completed in place? If True,
- self.working_data is imputed,and nothing is returned.
- This is useful if the dataset is very large. If
- False, a copy of the data is returned, with missing
- values imputed.
-
- Returns
- -------
- The completed data, with values imputed for specified variables.
-
- """
-
- # Return a copy if not inplace.
- impute_data = self.working_data if inplace else self.working_data.copy()
-
- # Figure out which variables we need to impute.
- # Never impute variables that are not in imputation_order.
- imp_vars = self.imputation_order if variables is None else variables
- imp_vars = self._get_var_ind_from_list(imp_vars)
- imp_vars = [v for v in imp_vars if v in self.imputation_order]
-
- for var in imp_vars:
- if iteration is None:
- iteration = self.iteration_count(datasets=dataset, variables=var)
- _assign_col_values_without_copy(
- dat=impute_data,
- row_ind=self.na_where[var],
- col_ind=var,
- val=self[dataset, var, iteration],
- )
-
- if not inplace:
- return impute_data
-
- def get_means(self, datasets, variables=None):
- """
- Return a dict containing the average imputation value
- for specified variables at each iteration.
- """
- num_vars = self._get_num_vars(variables)
-
- # For every variable, get the correlations between every dataset combination
- # at each iteration
- curr_iteration = self.iteration_count(datasets=datasets)
- if self.save_all_iterations:
- iter_range = list(range(curr_iteration + 1))
- else:
- iter_range = [curr_iteration]
- mean_dict = {
- ds: {
- var: {itr: np.mean(self[ds, var, itr]) for itr in iter_range}
- for var in num_vars
- }
- for ds in datasets
- }
-
- return mean_dict
-
- def plot_mean_convergence(self, datasets=None, variables=None, **adj_args):
- """
- Plots the average value of imputations over each iteration.
-
- Parameters
- ----------
- variables: None or list
- The variables to plot. Must be numeric.
- adj_args
- Passed to matplotlib.pyplot.subplots_adjust()
-
- """
-
- # Move this to .compat at some point.
- try:
- import matplotlib.pyplot as plt
- from matplotlib import gridspec
- except ImportError:
- raise ImportError("matplotlib must be installed to plot mean convergence")
-
- if self.iteration_count() < 2 or not self.save_all_iterations:
- raise ValueError("There is only one iteration.")
-
- if datasets is None:
- datasets = list(range(self.dataset_count()))
- else:
- datasets = _ensure_iterable(datasets)
- num_vars = self._get_num_vars(variables)
- mean_dict = self.get_means(datasets=datasets, variables=variables)
- plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
- gs = gridspec.GridSpec(plotrows, plotcols)
- fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
-
- for v in range(plots):
- axr, axc = next(iter(gs[v].rowspan)), next(iter(gs[v].colspan))
- var = num_vars[v]
- for d in mean_dict.values():
- ax[axr, axc].plot(list(d[var].values()), color="black")
- ax[axr, axc].set_title(var)
- ax[axr, axc].set_xlabel("Iteration")
- ax[axr, axc].set_ylabel("mean")
- plt.subplots_adjust(**adj_args)
-
- def plot_imputed_distributions(
- self, datasets=None, variables=None, iteration=None, **adj_args
- ):
- """
- Plot the imputed value distributions.
- Red lines are the distribution of original data
- Black lines are the distribution of the imputed values.
-
- Parameters
- ----------
- datasets: None, int, list[int]
- variables: None, str, int, list[str], or list[int]
- The variables to plot. If None, all numeric variables
- are plotted.
- iteration: None, int
- The iteration to plot the distribution for.
- If None, the latest iteration is plotted.
- save_all_iterations must be True if specifying
- an iteration.
- adj_args
- Additional arguments passed to plt.subplots_adjust()
-
- """
- # Move this to .compat at some point.
- try:
- import seaborn as sns
- import matplotlib.pyplot as plt
- from matplotlib import gridspec
- except ImportError:
- raise ImportError(
- "matplotlib and seaborn must be installed to plot distributions."
- )
-
- if datasets is None:
- datasets = list(range(self.dataset_count()))
- else:
- datasets = _ensure_iterable(datasets)
- if iteration is None:
- iteration = self.iteration_count(datasets=datasets, variables=variables)
- num_vars = self._get_num_vars(variables)
- plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
- gs = gridspec.GridSpec(plotrows, plotcols)
- fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
-
- for v in range(plots):
- var = num_vars[v]
- axr, axc = next(iter(gs[v].rowspan)), next(iter(gs[v].colspan))
- iteration_level_imputations = {
- ds: self[ds, var, iteration] for ds in datasets
- }
- plt.sca(ax[axr, axc])
- non_missing_ind = self._get_nonmissing_indx(var)
- nonmissing_values = _subset_data(
- self.working_data, row_ind=non_missing_ind, col_ind=var, return_1d=True
- )
- ax[axr, axc] = sns.kdeplot(nonmissing_values, color="red", linewidth=2)
- for imparray in iteration_level_imputations.values():
- ax[axr, axc] = sns.kdeplot(
- imparray, color="black", linewidth=1, warn_singular=False
- )
- ax[axr, axc].set(xlabel=self._get_var_name_from_scalar(var))
-
- plt.subplots_adjust(**adj_args)
-
- def get_correlations(
- self, datasets: List[int], variables: Union[List[int], List[str]]
- ):
- """
- Return the correlations between datasets for
- the specified variables.
-
- Parameters
- ----------
- variables: list[str], list[int]
- The variables to return the correlations for.
-
- Returns
- -------
- dict
- The correlations at each iteration for the specified
- variables.
-
- """
-
- if self.dataset_count() < 3:
- raise ValueError(
- "Not enough datasets to calculate correlations between them"
- )
- curr_iteration = self.iteration_count()
- var_indx = self._get_var_ind_from_list(variables)
-
- # For every variable, get the correlations between every dataset combination
- # at each iteration
- correlation_dict = {}
- if self.save_all_iterations:
- iter_range = list(range(1, curr_iteration + 1))
- else:
- # Make this iterable for code tidyness
- iter_range = [curr_iteration]
-
- for var in var_indx:
- # Get a dict of variables and imputations for all datasets for this iteration
- iteration_level_imputations = {
- iteration: {ds: self[ds, var, iteration] for ds in datasets}
- for iteration in iter_range
- }
-
- combination_correlations = {
- iteration: [
- round(np.corrcoef(impcomb)[0, 1], 3)
- for impcomb in list(combinations(varimps.values(), 2))
- ]
- for iteration, varimps in iteration_level_imputations.items()
- }
-
- correlation_dict[var] = combination_correlations
-
- return correlation_dict
-
- def plot_correlations(self, datasets=None, variables=None, **adj_args):
- """
- Plot the correlations between datasets.
- See get_correlations() for more details.
-
- Parameters
- ----------
- datasets: None or list[int]
- The datasets to plot.
- variables: None,list
- The variables to plot.
- adj_args
- Additional arguments passed to plt.subplots_adjust()
-
- """
-
- # Move this to .compat at some point.
- try:
- import matplotlib.pyplot as plt
- from matplotlib import gridspec
- except ImportError:
- raise ImportError("matplotlib must be installed to plot importance")
-
- if self.dataset_count() < 4:
- raise ValueError("Not enough datasets to make box plot")
- if datasets is None:
- datasets = list(range(self.dataset_count()))
- else:
- datasets = _ensure_iterable(datasets)
- var_indx = self._get_var_ind_from_list(variables)
- num_vars = self._get_num_vars(var_indx)
- plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
- correlation_dict = self.get_correlations(datasets=datasets, variables=num_vars)
- gs = gridspec.GridSpec(plotrows, plotcols)
- fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
-
- for v in range(plots):
- axr, axc = next(iter(gs[v].rowspan)), next(iter(gs[v].colspan))
- var = list(correlation_dict)[v]
- ax[axr, axc].boxplot(
- list(correlation_dict[var].values()),
- labels=range(len(correlation_dict[var])),
- )
- ax[axr, axc].set_title(self._get_var_name_from_scalar(var))
- ax[axr, axc].set_xlabel("Iteration")
- ax[axr, axc].set_ylabel("Correlations")
- ax[axr, axc].set_ylim([-1, 1])
- plt.subplots_adjust(**adj_args)
diff --git a/miceforest/MeanMatchScheme.py b/miceforest/MeanMatchScheme.py
deleted file mode 100644
index a15fbcd..0000000
--- a/miceforest/MeanMatchScheme.py
+++ /dev/null
@@ -1,395 +0,0 @@
-from .compat import pd_DataFrame
-import inspect
-from copy import deepcopy
-from typing import Callable, Union, Dict, Set
-from numpy import dtype
-
-
-_REGRESSIVE_OBJECTIVES = [
- "regression",
- "regression_l1",
- "poisson",
- "huber",
- "fair",
- "mape",
- "cross_entropy",
- "cross_entropy_lambda" "quantile",
- "tweedie",
- "gamma",
-]
-
-_CATEGORICAL_OBJECTIVES = [
- "binary",
- "multiclass",
- "multiclassova",
-]
-
-AVAILABLE_MEAN_MATCH_ARGS = [
- "mean_match_candidates",
- "lgb_booster",
- "bachelor_preds",
- "bachelor_features",
- "candidate_values",
- "candidate_features",
- "candidate_preds",
- "random_state",
- "hashed_seeds",
-]
-
-_DEFAULT_MMC = 5
-
-_t_mmc = Union[int, Dict[str, int], Dict[int, int]]
-_t_obj_func = Dict[str, Callable]
-_t_np_dt = Dict[str, str]
-
-
-class MeanMatchScheme:
- def __init__(
- self,
- mean_match_candidates: _t_mmc,
- mean_match_functions: _t_obj_func,
- lgb_model_pred_functions: _t_obj_func,
- objective_pred_dtypes: Dict[str, str],
- ):
- """
- Stores information and methods surrounding how mean matching should
- behave for each variable. This class is responsible for determining:
-
- * The mean matching function
- * How predictions are obtained from a lightgbm model
- * The datatype of the predictions
- * The number of mean matching candidates
- * Which variables will have candidate predictions compiled
-
- During the imputation process, the following series of events occur
- for each variable:
-
- 1) ImputationKernel trains a lightgbm model
- 2) MeanMatchScheme gets predictions from lightgbm model based on the model objective.
- 3) MeanMatchScheme performs mean matching on the predictions.
-
- Parameters
- ----------
- mean_match_candidates: int or dict, default = 5
- The number of mean matching candidates to pull.
- One of these candidates will be chosen randomly
- as the imputation value. To skip mean matching,
- set to 0.
-
- If int, that value is used for all variables.
-
- If a dict is passed, it should be of the form:
- {variable: int} pairs. Any variables not specified
- will use the default. Variables can be passed as
- column names or indexes.
-
- For more information about mean matching, see:
- https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching
-
- mean_match_functions: dict
- A dict of {objective: callable} pairs. The objective should be
- a lightgbm objective. The provided function will be used to
- perform mean matching for all models with the given objective.
-
- RULES FOR FUNCTIONS:
- 1) The only arguments allowed are those listed in
- miceforest.mean_match_schemes.AVAILABLE_MEAN_MATCH_ARGS
- 2) Not all of those arguments are required, the process
- will only pass arguments that the function requires
-
- lgb_model_pred_functions: dict
- A dict of {objective: callable} pairs. The objective should be
- a lightgbm objective. The provided function will be used to
- obtain predictions from lightgbm models with the paired objective.
-
- For example, passing: {"binary": func, "regression": func2} will call
- func(model, data) to obtain the predictions from the model, if the
- model objective was binary.
-
- objective_pred_dtypes: dict
- A dict of {objective: np.datatype} pairs.
- Datatype must be the string datatype name, i.e. "float32"
- How prediction data types will be cast. Casting to a smaller bit
- value can be beneficial when compiling candidates. Depending on
- the data, smaller bit rates tend to result in imputations of
- sufficient quality, while taking up much less space.
-
- """
-
- self.mean_match_functions: Dict[str, Callable] = {}
- self.objective_args: Dict[str, Set[str]] = {}
- self.objective_pred_funcs: Dict[str, Callable] = {}
- self.objective_pred_dtypes = objective_pred_dtypes.copy()
-
- for objective, function in mean_match_functions.items():
- self._add_mmf(objective, function)
-
- for objective, function in lgb_model_pred_functions.items():
- self._add_lgbpred(objective, function)
-
- self.mean_match_candidates = mean_match_candidates
- self._mmc_formatted = False
-
- def _add_mmf(self, objective: str, func: Callable):
- obj_args = set(inspect.getfullargspec(func).args)
- assert obj_args.issubset(
- set(AVAILABLE_MEAN_MATCH_ARGS)
- ), f"arg not available to mean match function for objective {objective}, check arguments."
- self.mean_match_functions[objective] = func
- self.objective_args[objective] = obj_args
-
- def _add_lgbpred(self, objective: str, func: Callable):
- obj_args = set(inspect.getfullargspec(func).args)
- assert obj_args == {
- "data",
- "model",
- }, f"functions in lgb_model_pred_functions should only have 2: parameters, model and data."
- self.objective_pred_funcs[objective] = func
-
- def _format_mean_match_candidates(self, data, available_candidates):
- var_indx_list = list(available_candidates)
- assert not self._mmc_formatted, "mmc are already formatted"
-
- if isinstance(self.mean_match_candidates, int):
- mmc_formatted = {v: self.mean_match_candidates for v in var_indx_list}
-
- elif isinstance(self.mean_match_candidates, dict):
- mmc_formatted = {}
- for v, mmc in self.mean_match_candidates.items():
- if isinstance(v, str):
- assert isinstance(
- data, pd_DataFrame
- ), "columns cannot be names unless data is pandas DF"
- v_ind = data.columns.tolist().index(v)
- assert (
- v_ind in var_indx_list
- ), f"{v} is not having a model built, should not get mmc."
- assert (
- mmc <= available_candidates[v_ind]
- ), f"{v} doesn't have enough candidates for mmc {mmc}"
-
- else:
- v_ind = v
-
- if isinstance(mmc, float):
- assert 0.0 < mmc < 1.0, f"{v} mmc malformed"
- print("A")
- mmc_formatted[v_ind] = int(mmc * available_candidates[v_ind])
- else:
- mmc_formatted[v_ind] = mmc
- print("B")
-
- for v in var_indx_list:
- if v not in list(mmc_formatted):
- mmc_formatted[v] = _DEFAULT_MMC
-
- else:
- raise ValueError(
- "malformed mean_match_candidates was passed to MeanMatchScheme"
- )
-
- self.mean_match_candidates = mmc_formatted
- self._mmc_formatted = True
-
- def copy(self):
- """
- Return a copy of this schema.
-
- Returns
- -------
- A copy of this MeanMatchSchema
- """
- return deepcopy(self)
-
- def set_mean_match_candidates(self, mean_match_candidates: _t_mmc):
- """
- Set the mean match candidates
-
- Parameters
- ----------
- mean_match_candidates: int or dict
- The number of mean matching candidates to pull.
- One of these candidates will be chosen randomly
- as the imputation value. To skip mean matching,
- set to 0.
-
- If int, that value is used for all variables.
-
- If a dict is passed, it should be of the form:
- {variable: int} pairs. Any variables not specified
- will use the default. Variables can be passed as
- column names or indexes.
-
- For more information about mean matching, see:
- https://github.com/AnotherSamWilson/miceforest#Predictive-Mean-Matching
- """
- self.mean_match_candidates = mean_match_candidates
- self._mmc_formatted = False
-
- def set_mean_match_function(self, mean_match_functions: _t_obj_func):
- """
- Overwrite the current mean matching functions for certain
- objectives.
-
- Parameters
- ----------
- mean_match_functions: dict
- A dict of {objective: callable} pairs. The objective should be
- a lightgbm objective. The provided function will be used to
- perform mean matching for all models with the given objective.
-
- RULES FOR FUNCTIONS:
- 1) The only arguments allowed are those listed in
- miceforest.mean_match_schemes.AVAILABLE_MEAN_MATCH_ARGS
- 2) Not all of those arguments are required, the process
- will only pass arguments that the function requires
-
- """
- for objective, function in mean_match_functions.items():
- self._add_mmf(objective, function)
-
- def set_lgb_model_pred_functions(self, lgb_model_pred_functions: _t_obj_func):
- """
- Overwrite the current prediction functions for certain
- objectives.
-
- Parameters
- ----------
- lgb_model_pred_functions: dict
- A dict of {objective: callable} pairs. The objective should be
- a lightgbm objective. The provided function will be used to
- obtain predictions from lightgbm models with the paired objective.
-
- For example, passing: {"binary": func, "regression": func2} will call
- func(model, data) to obtain the predictions from the model, if the
- model objective was binary.
- """
- for objective, function in lgb_model_pred_functions.items():
- self._add_lgbpred(objective, function)
-
- def set_objective_pred_dtypes(self, objective_pred_dtypes: Dict[str, str]):
- """
- Overwrite the current datatypes for certain objectives.
- Predictions obtained from lightgbm are stored as these
- datatypes.
-
- Parameters
- ----------
- objective_pred_dtypes: dict
- A dict of {objective: np.datatype} pairs.
- Datatype must be the string datatype name, i.e. "float32"
- How prediction data types will be cast. Casting to a smaller bit
- value can be beneficial when compiling candidates. Depending on
- the data, smaller bit rates tend to result in imputations of
- sufficient quality, while taking up much less space.
-
- """
- self.objective_pred_dtypes.update(objective_pred_dtypes)
-
- def get_objectives_requiring_candidate_preds(self):
- """
- Easy way to determine which lightgbm objectives will
- require candidate predictions to run mean matching.
-
- Returns
- -------
- list of objectives.
- """
- objs = []
- for objective, obj_args in self.objective_args.items():
- if "candidate_preds" in obj_args:
- objs.append(objective)
- return objs
-
- def get_mean_match_args(self, objective):
- """
- Determine what arguments we need to procure for a mean matching function.
-
- Parameters
- ----------
- objective: str
- The objective of the model that will create the candidate
- and bachelor predictions.
-
- Returns
- -------
- list of arguments required by this objectives mean matching function.
- """
- try:
- obj_args = self.objective_args[objective]
-
- except:
- raise ValueError(
- f"Could not get mean matching args for objective {objective}"
- + "Add this objective to the MeanMatchSchema by using .set_mean_match_function()"
- )
-
- return obj_args
-
- def model_predict(self, model, data, dtype=None):
- """
- Get the predictions from a model on data using the
- internal prediction functions.
-
- Parameters
- ----------
- model: Booster
- The model
-
- data: pd.DataFrame or np.array
- The data to get predictions for
-
- dtype: string or np.datatype
- Returns prediction as this datatype.
- Datatypes are kept track of internally, however
- you can overwrite the data type with this parameter.
-
- Returns
- -------
- np.ndarray of predictions
-
- """
- objective = model.params["objective"]
- if dtype is None:
- dtype = self.objective_pred_dtypes[objective]
- pred_func = self.objective_pred_funcs[objective]
- preds = pred_func(model=model, data=data).astype(dtype)
- return preds
-
- def _mean_match(self, variable, objective, **kwargs):
- """
- Perform mean matching
-
- Parameters
- ----------
- variable: int
- The variable being imputed
-
- objective: str
- The lightgbm objective used to create predictions for
- the variable.
-
- kwargs
- miceforest will automatically determine which objects
- need to be generated based on the mean matching function
- arguments. These arguments are passed as kwargs.
-
- Returns
- -------
- np.ndarray of imputation values.
-
- """
- obj_args = self.objective_args[objective]
- mmf = self.mean_match_functions[objective]
- mmc = self.mean_match_candidates[variable]
- if "mean_match_candidates" in obj_args:
- kwargs["mean_match_candidates"] = mmc
-
- for oa in obj_args:
- assert oa in list(
- kwargs
- ), f"objective {objective} requires {oa}, but it wasn't passed."
-
- mmf_args = {arg: val for arg, val in kwargs.items() if arg in obj_args}
- imp_values = mmf(**mmf_args)
- return imp_values
diff --git a/miceforest/__init__.py b/miceforest/__init__.py
index 57beced..70c7b93 100644
--- a/miceforest/__init__.py
+++ b/miceforest/__init__.py
@@ -8,24 +8,12 @@
https://github.com/AnotherSamWilson/miceforest
"""
-
-from .utils import ampute_data, load_kernel
-from .ImputedData import ImputedData
-from .ImputationKernel import ImputationKernel
-from .builtin_mean_match_schemes import (
- mean_match_default,
- mean_match_fast_cat,
- mean_match_shap,
-)
-
-__version__ = "5.7.0"
+from .imputation_kernel import ImputationKernel
+from .imputed_data import ImputedData
+from .utils import ampute_data
__all__ = [
"ImputedData",
"ImputationKernel",
- "mean_match_default",
- "mean_match_fast_cat",
- "mean_match_shap",
"ampute_data",
- "load_kernel",
]
diff --git a/miceforest/builtin_mean_match_functions.py b/miceforest/builtin_mean_match_functions.py
deleted file mode 100644
index 851fa93..0000000
--- a/miceforest/builtin_mean_match_functions.py
+++ /dev/null
@@ -1,219 +0,0 @@
-import numpy as np
-
-try:
- from scipy.spatial import KDTree
-except ImportError:
- raise ImportError(
- "scipy.spatial.KDTree is required for " + "the default mean matching function."
- )
-
-
-def _to_2d(x):
- if x.ndim == 1:
- x.shape = (-1, 1)
-
-
-def _mean_match_reg(
- mean_match_candidates,
- bachelor_preds,
- candidate_preds,
- candidate_values,
- random_state,
- hashed_seeds,
-):
- """
- Determines the values of candidates which will be used to impute the bachelors
- """
-
- if mean_match_candidates == 0:
- imp_values = bachelor_preds
-
- else:
- _to_2d(bachelor_preds)
- _to_2d(candidate_preds)
-
- num_bachelors = bachelor_preds.shape[0]
-
- # balanced_tree = False fixes a recursion issue for some reason.
- # https://github.com/scipy/scipy/issues/14799
- kd_tree = KDTree(candidate_preds, leafsize=16, balanced_tree=False)
- _, knn_indices = kd_tree.query(
- bachelor_preds, k=mean_match_candidates, workers=-1
- )
-
- # We can skip the random selection process if mean_match_candidates == 1
- if mean_match_candidates == 1:
- index_choice = knn_indices
-
- else:
- # Use the random_state if seed_array was not passed. Faster
- if hashed_seeds is None:
- ind = random_state.randint(mean_match_candidates, size=(num_bachelors))
- # Use the random_seed_array if it was passed. Deterministic.
- else:
- ind = hashed_seeds % mean_match_candidates
-
- index_choice = knn_indices[np.arange(num_bachelors), ind]
-
- imp_values = np.array(candidate_values)[index_choice]
-
- return imp_values
-
-
-def _mean_match_binary_accurate(
- mean_match_candidates,
- bachelor_preds,
- candidate_preds,
- candidate_values,
- random_state,
- hashed_seeds,
-):
- """
- Determines the values of candidates which will be used to impute the bachelors
- """
-
- if mean_match_candidates == 0:
- imp_values = np.floor(bachelor_preds + 0.5)
-
- else:
- _to_2d(bachelor_preds)
- _to_2d(candidate_preds)
-
- num_bachelors = bachelor_preds.shape[0]
-
- # balanced_tree = False fixes a recursion issue for some reason.
- # https://github.com/scipy/scipy/issues/14799
- kd_tree = KDTree(candidate_preds, leafsize=16, balanced_tree=False)
- _, knn_indices = kd_tree.query(
- bachelor_preds, k=mean_match_candidates, workers=-1
- )
-
- # We can skip the random selection process if mean_match_candidates == 1
- if mean_match_candidates == 1:
- index_choice = knn_indices
-
- else:
- # Use the random_state if seed_array was not passed. Faster
- if hashed_seeds is None:
- ind = random_state.randint(mean_match_candidates, size=(num_bachelors))
- # Use the random_seed_array if it was passed. Deterministic.
- else:
- ind = hashed_seeds % mean_match_candidates
-
- index_choice = knn_indices[np.arange(num_bachelors), ind]
-
- imp_values = np.array(candidate_values)[index_choice]
-
- return imp_values
-
-
-def _mean_match_binary_fast(
- mean_match_candidates, bachelor_preds, random_state, hashed_seeds
-):
- """
- Chooses 0/1 randomly based on probability obtained from prediction.
- If mean_match_candidates is 0, choose class with highest probability.
- """
- if mean_match_candidates == 0:
- imp_values = np.floor(bachelor_preds + 0.5)
-
- else:
- num_bachelors = bachelor_preds.shape[0]
- if hashed_seeds is None:
- imp_values = random_state.binomial(n=1, p=bachelor_preds)
- else:
- imp_values = []
- for i in range(num_bachelors):
- np.random.seed(seed=hashed_seeds[i])
- imp_values.append(np.random.binomial(n=1, p=bachelor_preds[i]))
-
- imp_values = np.array(imp_values)
-
- return imp_values
-
-
-def _mean_match_multiclass_fast(
- mean_match_candidates, bachelor_preds, random_state, hashed_seeds
-):
- """
- If mean_match_candidates is 0, choose class with highest probability.
- Otherwise, randomly choose class weighted by class probabilities.
- """
- if mean_match_candidates == 0:
- imp_values = np.argmax(bachelor_preds, axis=1)
-
- else:
- num_bachelors = bachelor_preds.shape[0]
-
- # Turn bachelor_preds into discrete cdf:
- bachelor_preds = bachelor_preds.cumsum(axis=1)
-
- # Randomly choose uniform numbers 0-1
- if hashed_seeds is None:
- # This is the fastest way to adjust for numeric
- # imprecision of float16 dtype. Actually ends up
- # barely taking any time at all.
- bp_dtype = bachelor_preds.dtype
- unif = np.minimum(
- random_state.uniform(0, 1, size=num_bachelors).astype(bp_dtype),
- bachelor_preds[:, -1],
- )
- else:
- unif = []
- for i in range(num_bachelors):
- np.random.seed(seed=hashed_seeds[i])
- unif.append(np.random.uniform(0, 1, size=1)[0])
- unif = np.array(unif)
-
- # Choose classes according to their cdf.
- # Distribution will match probabilities
- imp_values = np.array(
- [
- np.searchsorted(bachelor_preds[i, :], unif[i])
- for i in range(num_bachelors)
- ]
- )
-
- return imp_values
-
-
-def _mean_match_multiclass_accurate(
- mean_match_candidates,
- bachelor_preds,
- candidate_preds,
- candidate_values,
- random_state,
- hashed_seeds,
-):
- """
- Performs nearest neighbors search on class probabilities.
- """
- if mean_match_candidates == 0:
- imp_values = np.argmax(bachelor_preds, axis=1)
-
- else:
- _to_2d(bachelor_preds)
- _to_2d(candidate_preds)
-
- num_bachelors = bachelor_preds.shape[0]
- kd_tree = KDTree(candidate_preds, leafsize=16, balanced_tree=False)
- _, knn_indices = kd_tree.query(
- bachelor_preds, k=mean_match_candidates, workers=-1
- )
-
- # We can skip the random selection process if mean_match_candidates == 1
- if mean_match_candidates == 1:
- index_choice = knn_indices
-
- else:
- # Come up with random numbers 0-mean_match_candidates, with priority given to hashed_seeds
- if hashed_seeds is None:
- ind = random_state.randint(mean_match_candidates, size=(num_bachelors))
- else:
- ind = hashed_seeds % mean_match_candidates
-
- index_choice = knn_indices[np.arange(knn_indices.shape[0]), ind]
-
- imp_values = np.array(candidate_values)[index_choice]
-
- return imp_values
diff --git a/miceforest/builtin_mean_match_schemes.py b/miceforest/builtin_mean_match_schemes.py
deleted file mode 100644
index c95e893..0000000
--- a/miceforest/builtin_mean_match_schemes.py
+++ /dev/null
@@ -1,151 +0,0 @@
-"""
-Built-in mean matching schemes.
-These schemes vary in their speed and accuracy.
-"""
-
-from typing import Dict, Callable
-from .MeanMatchScheme import (
- MeanMatchScheme,
- _REGRESSIVE_OBJECTIVES,
- _CATEGORICAL_OBJECTIVES,
- _DEFAULT_MMC,
-)
-from .builtin_pred_funcs import (
- predict_binary_logodds,
- predict_multiclass_logodds,
- predict_multiclass_shap,
- predict_normal,
- predict_normal_shap,
-)
-from .builtin_mean_match_functions import (
- _mean_match_reg,
- _mean_match_binary_accurate,
- _mean_match_binary_fast,
- _mean_match_multiclass_accurate,
- _mean_match_multiclass_fast,
-)
-
-_DEFAULT_OBJECTIVE_DTYPES = {
- **{o: "float16" for o in _CATEGORICAL_OBJECTIVES},
- **{o: "float32" for o in _REGRESSIVE_OBJECTIVES},
-}
-
-
-##################################
-##### DEFAULT MEAN MATCHING SCHEME
-
-_MEAN_MATCH_FUNCTIONS_DEFAULT: Dict[str, Callable] = {
- "binary": _mean_match_binary_accurate,
- "multiclass": _mean_match_multiclass_accurate,
- "multiclassova": _mean_match_multiclass_accurate,
- **{o: _mean_match_reg for o in _REGRESSIVE_OBJECTIVES},
-}
-_LGB_PRED_FUNCTIONS_DEFAULT: Dict[str, Callable] = {
- "binary": predict_binary_logodds,
- "multiclass": predict_multiclass_logodds,
- "multiclassova": predict_multiclass_logodds,
- **{o: predict_normal for o in _REGRESSIVE_OBJECTIVES},
-}
-mean_match_default = MeanMatchScheme(
- mean_match_candidates=_DEFAULT_MMC,
- mean_match_functions=_MEAN_MATCH_FUNCTIONS_DEFAULT,
- lgb_model_pred_functions=_LGB_PRED_FUNCTIONS_DEFAULT,
- objective_pred_dtypes=_DEFAULT_OBJECTIVE_DTYPES,
-)
-mean_match_default.__doc__ = """
-Built-in instance of miceforest.MeanMatchScheme.
-
-This scheme is of medium speed and accuracy.
-
-The rules are:
- Categorical:
- If mmc = 0, the class with the highest probability is chosen.
- If mmc > 0, run K-Nearest-Neighbors search on candidate class
- probabilities, and choose 1 neighbor randomly for each bachelor.
- Use the candidate value of the associated selection to impute.
- Numeric:
- If mmc = 0, the predicted value is used
- If mmc > 0, run K-Nearest-Neighbors search on candidate
- predictions, and choose 1 neighbor randomly for each bachelor.
- Use the candidate value of the associated selection to impute.
-"""
-
-
-###################################
-##### FAST CAT MEAN MATCHING SCHEME
-
-_MEAN_MATCH_FUNCTIONS_FAST_CAT: Dict[str, Callable] = {
- "binary": _mean_match_binary_fast,
- "multiclass": _mean_match_multiclass_fast,
- "multiclassova": _mean_match_multiclass_fast,
- **{o: _mean_match_reg for o in _REGRESSIVE_OBJECTIVES},
-}
-_LGB_PRED_FUNCTIONS_FAST_CAT: Dict[str, Callable] = {
- "binary": predict_normal,
- "multiclass": predict_normal,
- "multiclassova": predict_normal,
- **{o: predict_normal for o in _REGRESSIVE_OBJECTIVES},
-}
-mean_match_fast_cat = MeanMatchScheme(
- mean_match_candidates=_DEFAULT_MMC,
- mean_match_functions=_MEAN_MATCH_FUNCTIONS_FAST_CAT,
- lgb_model_pred_functions=_LGB_PRED_FUNCTIONS_FAST_CAT,
- objective_pred_dtypes=_DEFAULT_OBJECTIVE_DTYPES,
-)
-mean_match_fast_cat.__doc__ = """
-Built-in instance of miceforest.MeanMatchScheme.
-
-This scheme is faster for categorical variables
-specifically, but may not be as accurate.
-
-The rules are:
- Categorical:
- If mmc = 0, the class with the highest probability is chosen.
- If mmc > 0, return class based on random draw weighted by
- class probability for each sample.
- Numeric:
- If mmc = 0, the predicted value is used
- If mmc > 0, obtain the mmc closest candidate predictions and
- collect the associated real candidate values. Choose 1 randomly.
-"""
-
-
-###############################
-##### SHAP MEAN MATCHING SCHEME
-
-_MEAN_MATCH_FUNCTIONS_SHAP: Dict[str, Callable] = {
- "binary": _mean_match_binary_accurate,
- "multiclass": _mean_match_multiclass_accurate,
- "multiclassova": _mean_match_multiclass_accurate,
- **{o: _mean_match_reg for o in _REGRESSIVE_OBJECTIVES},
-}
-_LGB_PRED_FUNCTIONS_SHAP: Dict[str, Callable] = {
- "binary": predict_normal_shap,
- "multiclass": predict_multiclass_shap,
- "multiclassova": predict_multiclass_shap,
- **{o: predict_normal_shap for o in _REGRESSIVE_OBJECTIVES},
-}
-mean_match_shap = MeanMatchScheme(
- mean_match_candidates=_DEFAULT_MMC,
- mean_match_functions=_MEAN_MATCH_FUNCTIONS_SHAP,
- lgb_model_pred_functions=_LGB_PRED_FUNCTIONS_SHAP,
- objective_pred_dtypes=_DEFAULT_OBJECTIVE_DTYPES,
-)
-mean_match_shap.__doc__ = """
-Built-in instance of miceforest.MeanMatchScheme.
-
-This scheme has the lowest speed and
-highest accuracy on high dimension data.
-
-The rules are:
- Categorical:
- If mmc = 0, the class with the highest probability is chosen.
- If mmc > 0, run K-Nearest-Neighbors search on candidate shap
- values, and choose 1 neighbor randomly for each bachelor.
- Use the candidate value of the associated selection to impute.
- Numeric:
- If mmc = 0, the predicted value is used
- If mmc > 0, run K-Nearest-Neighbors search on candidate
- shap values, and choose 1 neighbor randomly for each bachelor.
- Use the candidate value of the associated selection to impute.
-"""
diff --git a/miceforest/builtin_pred_funcs.py b/miceforest/builtin_pred_funcs.py
deleted file mode 100644
index d1c627e..0000000
--- a/miceforest/builtin_pred_funcs.py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""
-Default prediction functions that come with miceforest.
-"""
-from .utils import logodds
-from lightgbm import Booster
-import numpy as np
-
-
-# Lightgbm can output 0.0 probabilities for extremely
-# rare categories. This causes logodds to return inf.
-_LIGHTGBM_PROB_THRESHOLD = 0.00000001
-
-
-def _adjust_shap_for_rf(model, sv):
- if model.params["boosting"] in ["random_forest", "rf"]:
- sv /= model.current_iteration()
-
-
-def predict_normal(model: Booster, data):
- preds = model.predict(data)
- return preds
-
-
-def predict_normal_shap(model: Booster, data):
- preds = model.predict(data, pred_contrib=True)[:, :-1] # type: ignore
- _adjust_shap_for_rf(model, preds)
- return preds
-
-
-def predict_binary_logodds(model: Booster, data):
- preds = logodds(
- model.predict(data).clip( # type: ignore
- _LIGHTGBM_PROB_THRESHOLD, 1.0 - _LIGHTGBM_PROB_THRESHOLD
- )
- )
- return preds
-
-
-def predict_multiclass_logodds(model: Booster, data):
- preds = model.predict(data).clip( # type: ignore
- _LIGHTGBM_PROB_THRESHOLD, 1.0 - _LIGHTGBM_PROB_THRESHOLD
- )
- preds = logodds(preds)
- return preds
-
-
-def predict_multiclass_shap(model: Booster, data):
- """
- Returns a 3d array of shape (samples, columns, classes)
- It is faster to copy into a new array than delete from
- the old one.
- """
- preds = model.predict(data, pred_contrib=True)
- samples, cols = data.shape
- classes = model._Booster__num_class # type: ignore
- p = np.empty(shape=(samples, cols * classes), dtype=preds.dtype) # type: ignore
- for c in range(classes):
- s1 = slice(c * cols, (c + 1) * cols)
- s2 = slice(c * (cols + 1), (c + 1) * (cols + 1) - 1)
- p[:, s1] = preds[:, s2] # type: ignore
-
- # If objective is random forest, the shap values are summed
- # without ever taking an average, so we divide by the iters
- _adjust_shap_for_rf(model, p)
-
- return p
diff --git a/miceforest/compat.py b/miceforest/compat.py
deleted file mode 100644
index bf5d13e..0000000
--- a/miceforest/compat.py
+++ /dev/null
@@ -1,27 +0,0 @@
-"""Compatibility library."""
-"""Stolen from lightgbm"""
-
-"""pandas"""
-try:
- from pandas import DataFrame as pd_DataFrame
- from pandas import Series as pd_Series
- from pandas import read_parquet as pd_read_parquet
-
- PANDAS_INSTALLED = True
-except ImportError:
- PANDAS_INSTALLED = False
-
- class pd_Series: # type: ignore
- """Dummy class for pandas.Series."""
-
- pass
-
- class pd_DataFrame: # type: ignore
- """Dummy class for pandas.DataFrame."""
-
- pass
-
- def pd_read_parquet(filepath):
- """Dummy function for pandas.read_parquet."""
-
- pass
diff --git a/miceforest/default_lightgbm_parameters.py b/miceforest/default_lightgbm_parameters.py
index 0c0556c..f654e85 100644
--- a/miceforest/default_lightgbm_parameters.py
+++ b/miceforest/default_lightgbm_parameters.py
@@ -1,34 +1,77 @@
+import numpy as np
+
+from .utils import _draw_random_int32
+
+# A few parameters that generally benefit
+# from searching in the log space.
+_LOG_SPACE_SEARCH = [
+ "min_data_in_leaf",
+ "min_sum_hessian_in_leaf",
+ "lambda_l1",
+ "lambda_l2",
+ "cat_l2",
+ "cat_smooth",
+ "path_smooth",
+ "min_gain_to_split",
+]
+
+
# THESE VALUES WILL ALWAYS BE USED WHEN VALUES ARE NOT PASSED BY USER.
# seed is always set by the calling processes _random_state.
# These need to be main parameter names, not aliases
-default_parameters = {
+_DEFAULT_LGB_PARAMS = {
"boosting": "random_forest",
+ "data_sample_strategy": "bagging",
"num_iterations": 48,
"max_depth": 8,
"num_leaves": 128,
"min_data_in_leaf": 1,
- "min_sum_hessian_in_leaf": 0.00001,
+ "min_sum_hessian_in_leaf": 0.1,
"min_gain_to_split": 0.0,
"bagging_fraction": 0.632,
- "feature_fraction": 1.0,
"feature_fraction_bynode": 0.632,
"bagging_freq": 1,
"verbosity": -1,
}
-# WHEN TUNING, THESE PARAMETERS OVERWRITE THE DEFAULTS ABOVE
-# These need to be main parameter names, not aliases
-def make_default_tuning_space(min_samples, max_samples):
- space = {
- "boosting": "gbdt",
- "learning_rate": 0.02,
- "num_iterations": 250,
- "min_data_in_leaf": (min_samples, max_samples),
- "min_sum_hessian_in_leaf": 0.1,
- "num_leaves": (2, 25),
- "bagging_fraction": (0.1, 1.0),
- "feature_fraction_bynode": (0.1, 1.0),
- "cat_smooth": (0, 25),
- }
- return space
+def _sample_parameters(parameters: dict, random_state, parameter_sampling_method: str):
+ """
+ Searches through a parameter set and selects a random
+ number between the values in any provided tuple of length 2.
+ """
+ assert (
+ parameter_sampling_method == "random"
+ ), "Only random parameter sampling is supported right now."
+ parameters = parameters.copy()
+ for p, v in parameters.items():
+ if isinstance(v, list):
+ choice = random_state.choice(v)
+ elif isinstance(v, tuple):
+ assert (
+ len(v) == 2
+ ), "Tuples passed must be length 2, representing the bounds."
+ assert v[0] < v[1], f"{p} lower bound > upper bound"
+ if p in _LOG_SPACE_SEARCH:
+ choice = np.exp(
+ random_state.uniform(
+ np.log(v[0]),
+ np.log(v[1]),
+ size=1,
+ )[0]
+ )
+ else:
+ choice = random_state.uniform(
+ v[0],
+ v[1],
+ size=1,
+ )[0]
+ if isinstance(v[0], int):
+ choice = int(choice)
+ else:
+ choice = parameters[p]
+ parameters[p] = choice
+
+ parameters["seed"] = _draw_random_int32(random_state, size=1)[0]
+
+ return parameters
diff --git a/miceforest/imputation_kernel.py b/miceforest/imputation_kernel.py
new file mode 100644
index 0000000..ea46126
--- /dev/null
+++ b/miceforest/imputation_kernel.py
@@ -0,0 +1,1869 @@
+from copy import copy
+from io import BytesIO
+from typing import Any, Dict, Generator, List, Optional, Tuple, Union
+from warnings import warn
+
+import numpy as np
+from lightgbm import Booster, Dataset, cv, early_stopping, log_evaluation, train
+from lightgbm.basic import _ConfigAliases
+from pandas import Categorical, DataFrame, MultiIndex, Series, read_parquet
+from pandas.api.types import is_integer_dtype
+from scipy.spatial import KDTree
+
+from miceforest.default_lightgbm_parameters import (
+ _DEFAULT_LGB_PARAMS,
+ _sample_parameters,
+)
+from miceforest.imputed_data import ImputedData
+from miceforest.logger import Logger
+from miceforest.utils import (
+ _draw_random_int32,
+ _expand_value_to_dict,
+ _list_union,
+ ensure_rng,
+ logodds,
+ stratified_categorical_folds,
+ stratified_continuous_folds,
+)
+
+_DEFAULT_DATA_SUBSET = 0
+_DEFAULT_MEANMATCH_CANDIDATES = 5
+_DEFAULT_MEANMATCH_STRATEGY = "normal"
+_MICE_TIMED_LEVELS = ["Dataset", "Iteration", "Variable", "Event"]
+_IMPUTE_NEW_DATA_TIMED_LEVELS = ["Dataset", "Iteration", "Variable", "Event"]
+_TUNING_TIMED_LEVELS = ["Variable", "Iteration"]
+_PRE_LINK_DATATYPE = "float16"
+
+
+class ImputationKernel(ImputedData):
+ """
+ Creates a kernel dataset. This dataset can perform MICE on itself,
+ and impute new data from models obtained during MICE.
+
+ Parameters
+ ----------
+ data : pandas DataFrame.
+
+ .. code-block:: text
+
+ The data to be imputed.
+
+ variable_schema : None or list or dict, default=None
+
+ .. code-block:: text
+
+ Specifies the feature - target relationships used to train models.
+ This parameter also controls which models are built. Models can be built
+ even if a variable contains no missing values, or is not being imputed.
+
+ - If None, all columns with missing values will have models trained, and all
+ columns will be used as features in these models.
+ - If list, all columns in data are used to impute the variables in the list
+ - If dict the values will be used to impute the keys.
+
+ No models will be trained for variables not specified by variable_schema
+ (either by None, a list, or in dict keys).
+
+ imputation_order: str, list[str], list[int], default="ascending"
+
+ .. code-block:: text
+
+ The order the imputations should occur in.
+ - ascending: variables are imputed from least to most missing
+ - descending: most to least missing
+ - roman: from left to right in the dataset
+ - arabic: from right to left in the dataset.
+
+ data_subset: None or int or dict.
+
+ .. code-block:: text
+
+ Subsets the data used in each iteration, which can save a significant amount of time.
+ This can also help with memory consumption, as the candidate data must be copied to
+ make a feature dataset for lightgbm.
+
+ The number of rows used for each variable is (# rows in raw data) - (# missing variable values)
+ for each variable. data_subset takes a random sample of this.
+
+ If int must be data_subset >= 0. Interpreted as the number of candidates.
+ If 0, no subsetting is done.
+ If dict, keys must be variable names, and values must follow two above rules.
+
+ It is recommended to carefully select this value for each variable if dealing
+ with very large data that barely fits into memory.
+
+ mean_match_strategy: str or Dict[str, str]
+
+ .. code-block:: text
+
+ There are 3 mean matching strategies included in miceforest:
+ - "normal" - this is the default. For all predictions, K-nearest-neighbors
+ is performed on the candidate predictions and bachelor predictions.
+ The top MMC closest candidate values are chosen at random.
+ - "fast" - Only available for categorical and binary columns. A value
+ is selected at random weighted by the class probabilities.
+ - "shap" - Similar to "normal" but more robust. A K-nearest-neighbors
+ search is performed on the shap values of the candidate predictions
+ and the bachelor predictions. A value from the top MMC closest candidate
+ values is chosen at random.
+
+ A dict of strategies by variable can be passed as well. Any unmentioned variables
+ will be set to the default, "normal".
+
+ Special rules are enacted when mean_match_candidates == 0 for a variable. See the
+ mean_match_candidates parameter for more information.
+
+ mean_match_candidates: int or Dict[str, int]
+
+ .. code-block:: text
+
+ When mean matching relies on selecting one of the top N closest candidate predictions,
+ this number is used for N.
+
+ Special rules apply when this value is set to 0. This will skip mean matching entirely.
+ The algorithm that applies depends on the objective type:
+ - Regression: The bachelor predictions are used as the imputation values.
+ - Binary: The class with the higher probability is chosen.
+ - Multiclass: The class with the highest probability is chosen.
+
+ Setting mmc to 0 will result in much faster process times, but at the cost of random
+ variability that is desired when performing Multiple Imputation by Chained Equations.
+
+ initialize_empty: bool, default = False
+
+ .. code-block:: text
+
+ If True, missing data is not filled in randomly before model training starts.
+
+ save_all_iterations_data: boolean, optional(default=True)
+
+ .. code-block:: text
+
+ Setting to False will cause the process to not store the models and
+ candidate values obtained at each iteration. This can save significant
+ amounts of memory, but it means `impute_new_data()` will not be callable.
+
+ copy_data: boolean (default = False)
+
+ .. code-block:: text
+
+ Should the dataset be referenced directly? If False, this will cause
+ the dataset to be altered in place. If a copy is created, it is saved
+ in self.working_data. There are different ways in which the dataset
+ can be altered.
+
+ random_state: None,int, or numpy.random.RandomState
+
+ .. code-block:: text
+
+ The random_state ensures script reproducibility. It only ensures reproducible
+ results if the same script is called multiple times. It does not guarantee
+ reproducible results at the record level, if a record is imputed multiple
+ different times. If reproducible record-results are desired, a seed must be
+ passed for each record in the random_seed_array parameter.
+
+ """
+
+ def __init__(
+ self,
+ data: DataFrame,
+ num_datasets: int = 1,
+ variable_schema: Optional[Union[List[str], Dict[str, List[str]]]] = None,
+ imputation_order: str = "ascending",
+ mean_match_candidates: Union[
+ int, Dict[str, int]
+ ] = _DEFAULT_MEANMATCH_CANDIDATES,
+ mean_match_strategy: Optional[
+ Union[str, Dict[str, str]]
+ ] = _DEFAULT_MEANMATCH_STRATEGY,
+ data_subset: Union[int, Dict[str, int]] = _DEFAULT_DATA_SUBSET,
+ initialize_empty: bool = False,
+ save_all_iterations_data: bool = True,
+ copy_data: bool = True,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ ):
+
+ datasets = list(range(num_datasets))
+
+ super().__init__(
+ impute_data=data,
+ datasets=datasets,
+ variable_schema=variable_schema,
+ save_all_iterations_data=save_all_iterations_data,
+ copy_data=copy_data,
+ random_seed_array=None,
+ )
+
+ # Model Training / Imputation Order:
+ # Variables with missing data are always trained
+ # first, according to imputation_order. Afterwards,
+ # variables with no missing values have models trained.
+ if imputation_order in ["ascending", "descending"]:
+ _na_counts = {
+ key: value
+ for key, value in self.na_counts.items()
+ if key in self.imputed_variables
+ }
+ self.imputation_order = list(
+ Series(_na_counts).sort_values(ascending=False).index
+ )
+ if imputation_order == "decending":
+ self.imputation_order.reverse()
+ elif imputation_order == "roman":
+ self.imputation_order = self.imputed_variables.copy()
+ elif imputation_order == "arabic":
+ self.imputation_order = self.imputed_variables.copy()
+ self.imputation_order.reverse()
+ else:
+ raise ValueError("imputation_order not recognized.")
+
+ modeled_but_not_imputed_variables = [
+ col for col in self.modeled_variables if col not in self.imputed_variables
+ ]
+ model_training_order = self.imputation_order + modeled_but_not_imputed_variables
+ self.model_training_order = model_training_order
+
+ self.initialize_empty = initialize_empty
+ self.save_all_iterations_data = save_all_iterations_data
+
+ # Models are stored in a dict, keys are (variable, iteration, dataset)
+ self.models: Dict[Tuple[str, int, int], Booster] = {}
+
+ # Candidate preds are stored the same as models.
+ self.candidate_preds: Dict[str, DataFrame] = {}
+
+ # Optimal parameters can only be found on 1 dataset at the current iteration.
+ self.optimal_parameters: Dict[str, Dict[str, Any]] = {}
+
+ # Determine available candidates and interpret data subset.
+ available_candidates = {
+ v: (self.shape[0] - self.na_counts[v]) for v in self.model_training_order
+ }
+ data_subset = _expand_value_to_dict(
+ _DEFAULT_DATA_SUBSET, data_subset, keys=self.model_training_order
+ )
+ for col in self.model_training_order:
+ assert (
+ data_subset[col] <= available_candidates[col]
+ ), f"data_subset is more than available candidates for {col}"
+ self.available_candidates = available_candidates
+ self.data_subset = data_subset
+
+ # Collect category information.
+ categorical_columns: List[str] = [
+ var
+ for var, dtype in self.working_data.dtypes.items()
+ if dtype.name == "category"
+ ]
+ category_counts = {
+ col: len(self.working_data[col].cat.categories)
+ for col in categorical_columns
+ }
+ numeric_columns = [
+ col for col in self.working_data.columns if col not in categorical_columns
+ ]
+ binary_columns = []
+ for col, count in category_counts.items():
+ if count == 2:
+ binary_columns.append(col)
+ categorical_columns.remove(col)
+
+ # Probably a better way of doing this
+ assert set(categorical_columns).isdisjoint(set(numeric_columns))
+ assert set(categorical_columns).isdisjoint(set(binary_columns))
+ assert set(binary_columns).isdisjoint(set(numeric_columns))
+
+ self.category_counts = category_counts
+ self.modeled_categorical_columns = _list_union(
+ categorical_columns, self.model_training_order
+ )
+ self.modeled_numeric_columns = _list_union(
+ numeric_columns, self.model_training_order
+ )
+ self.modeled_binary_columns = _list_union(
+ binary_columns, self.model_training_order
+ )
+ predictor_columns = sum(self.variable_schema.values(), [])
+ self.predictor_columns = [
+ col for col in data.columns if col in predictor_columns
+ ]
+
+ # Make sure all pandas categorical levels are used.
+ rare_level_cols = []
+ for col in self.modeled_categorical_columns:
+ value_counts = data[col].value_counts(normalize=True)
+ if np.any(value_counts < 0.002):
+ rare_level_cols.append(col)
+ if rare_level_cols:
+ warn(
+ f"{','.join(rare_level_cols)} have very rare categories, it is a good "
+ "idea to group these, or set the min_data_in_leaf parameter to prevent "
+ "lightgbm from outputting 0.0 probabilities."
+ )
+
+ self.mean_match_candidates = _expand_value_to_dict(
+ _DEFAULT_MEANMATCH_CANDIDATES,
+ mean_match_candidates,
+ self.model_training_order,
+ )
+ self.mean_match_strategy = _expand_value_to_dict(
+ _DEFAULT_MEANMATCH_STRATEGY, mean_match_strategy, self.model_training_order
+ )
+
+ for col in self.model_training_order:
+ mmc = self.mean_match_candidates[col]
+ mms = self.mean_match_strategy[col]
+ assert not ((mmc == 0) and (mms == "shap")), (
+ f"Failing because {col} mean_match_candidates == 0 and "
+ "mean_match_strategy == shap. This implies an unintentional setup."
+ )
+
+ # Determine if the mean matching scheme will
+ # require candidate information for each variable
+ self.mean_matching_requires_candidates = []
+ for variable in self.model_training_order:
+ mean_match_strategy = self.mean_match_strategy[variable]
+ if (mean_match_strategy in ["normal", "shap"]) or (
+ variable in self.modeled_numeric_columns
+ ):
+ self.mean_matching_requires_candidates.append(variable)
+
+ self.loggers: List[Logger] = []
+
+ # Manage randomness
+ self._completely_random_kernel = random_state is None
+ self._random_state = ensure_rng(random_state)
+
+ # Set initial imputations (iteration 0).
+ self._initialize_dataset(self, random_state=self._random_state)
+
+ # Save for use later
+ self.optimal_parameter_losses: Dict[str, float] = dict()
+ self.optimal_parameters = dict()
+
+ def __getstate__(self):
+ """
+ For pickling
+ """
+ # Copy the entire object, minus the big stuff
+
+ special_handling = ["imputation_values"]
+ if self.save_all_iterations_data:
+ special_handling.append("candidate_preds")
+
+ state = {
+ key: value
+ for key, value in self.__dict__.items()
+ if key not in special_handling
+ }.copy()
+
+ state["imputation_values"] = {}
+ state["candidate_preds"] = {}
+
+ for col, df in self.imputation_values.items():
+ byte_stream = BytesIO()
+ df.to_parquet(byte_stream)
+ state["imputation_values"][col] = byte_stream
+ for col, df in self.candidate_preds.items():
+ byte_stream = BytesIO()
+ df.to_parquet(byte_stream)
+ state["candidate_preds"][col] = byte_stream
+
+ return state
+
+ def __setstate__(self, state):
+ """
+ For unpickling
+ """
+ self.__dict__ = state
+
+ for col, bytes in self.imputation_values.items():
+ self.imputation_values[col] = read_parquet(bytes)
+
+ if self.save_all_iterations_data:
+ for col, bytes in self.candidate_preds.items():
+ self.candidate_preds[col] = read_parquet(bytes)
+
+ def __repr__(self):
+ summary_string = f'\n{" " * 14}Class: ImputationKernel\n{self._ids_info()}'
+ return summary_string
+
+ def _initialize_dataset(self, imputed_data, random_state):
+ """
+ Sets initial imputation values for iteration 0.
+ If "random", draw values from the working data at random.
+ If "empty", keep the values missing, since missing values
+ can be handled natively by lightgbm.
+ """
+
+ assert not imputed_data.initialized, "dataset has already been initialized"
+
+ if self.initialize_empty:
+ # The default value when initialized is np.nan, nothing to do here
+ pass
+ else:
+ for variable in imputed_data.imputed_variables:
+ # Pulls from the kernel working data
+ candidate_values = self._get_nonmissing_values(variable)
+ candidate_num = candidate_values.shape[0]
+
+ # Pulls from the ImputedData
+ missing_ind = imputed_data.na_where[variable]
+ missing_num = imputed_data.na_counts[variable]
+
+ for dataset in imputed_data.datasets:
+ # Initialize using the random_state if no record seeds were passed.
+ if imputed_data.random_seed_array is None:
+ imputation_values = candidate_values.sample(
+ n=missing_num, replace=True, random_state=random_state
+ )
+ imputation_values.index = missing_ind
+ imputed_data[variable, 0, dataset] = imputation_values
+ else:
+ assert (
+ len(imputed_data.random_seed_array) == imputed_data.shape[0]
+ ), "The random_seed_array did not match the number of rows being imputed."
+ hashed_seeds = imputed_data._get_hashed_seeds(variable=variable)
+ selection_ind = hashed_seeds % candidate_num
+ imputation_values = candidate_values.iloc[selection_ind]
+ imputation_values.index = missing_ind
+ imputed_data[variable, 0, dataset] = imputation_values
+
+ imputed_data.initialized = True
+
+ @staticmethod
+ def _uncover_aliases(params):
+ """
+ Switches all aliases in the parameter dict to their
+ True name, easiest way to avoid duplicate parameters.
+ """
+ alias_dict = _ConfigAliases._get_all_param_aliases()
+ for param in list(params):
+ for true_name, aliases in alias_dict.items():
+ if param in aliases:
+ params[true_name] = params.pop(param)
+
+ def _make_lgb_params(
+ self,
+ variable: str,
+ default_parameters: dict,
+ variable_parameters: dict,
+ **kwlgb,
+ ):
+ """
+ Builds the parameters for a lightgbm model. Infers objective based on
+ datatype of the response variable, assigns a random seed, finds
+ aliases in the user supplied parameters, and returns a final dict.
+
+ Parameters
+ ----------
+ variable: int
+ The variable to be modeled
+
+ default_parameters: dict
+ The base set of parameters that should be used.
+
+ variable_parameters: dict
+ Variable specific parameters. These are supplied by the user.
+
+ random_state: np.random.RandomState
+ The random state to use (used to set the seed).
+
+ kwlgb: dict
+ Any additional parameters that should take presidence
+ over the defaults.
+ """
+
+ seed = _draw_random_int32(self._random_state, size=1)[0]
+
+ if variable in self.modeled_categorical_columns:
+ n_c = self.category_counts[variable]
+ obj = {"objective": "multiclass", "num_class": n_c}
+ elif variable in self.modeled_binary_columns:
+ obj = {"objective": "binary"}
+ else:
+ obj = {"objective": "regression"}
+
+ lgb_params = default_parameters.copy()
+ lgb_params.update(obj)
+ lgb_params["seed"] = seed
+
+ self._uncover_aliases(lgb_params)
+ self._uncover_aliases(kwlgb)
+ self._uncover_aliases(variable_parameters)
+
+ # Priority is [variable specific] > [global in kwargs] > [defaults]
+ lgb_params.update(kwlgb)
+ lgb_params.update(variable_parameters)
+
+ return lgb_params
+
+ # WHEN TUNING, THESE PARAMETERS OVERWRITE THE DEFAULTS ABOVE
+ # These need to be main parameter names, not aliases
+ def _make_tuning_space(
+ self,
+ variable: str,
+ variable_parameters: dict,
+ use_gbdt: bool,
+ min_samples: int,
+ max_samples: int,
+ **kwargs,
+ ):
+
+ # Start with the default parameters, update with the search space
+ params = _DEFAULT_LGB_PARAMS.copy()
+ search_space = {
+ "min_data_in_leaf": (min_samples, max_samples),
+ "max_depth": (2, 6),
+ "num_leaves": (2, 25),
+ "bagging_fraction": (0.1, 1.0),
+ "feature_fraction_bynode": (0.1, 1.0),
+ }
+ params.update(search_space)
+
+ # Set our defaults if using gbdt
+ if use_gbdt:
+ params["boosting"] = "gbdt"
+ params["learning_rate"] = 0.02
+ params["num_iterations"] = 250
+
+ params = self._make_lgb_params(
+ variable=variable,
+ default_parameters=params,
+ variable_parameters=variable_parameters,
+ **kwargs,
+ )
+
+ return params
+
+ @staticmethod
+ def _get_oof_performance(
+ parameters: dict,
+ folds: Generator,
+ train_set: Dataset,
+ ):
+ """
+ Performance is gathered from built-in lightgbm.cv out of fold metric.
+ Optimal number of iterations is also obtained.
+ """
+
+ num_iterations = parameters.pop("num_iterations")
+ lgbcv = cv(
+ params=parameters,
+ train_set=train_set,
+ folds=folds,
+ num_boost_round=num_iterations,
+ return_cvbooster=True,
+ callbacks=[
+ early_stopping(stopping_rounds=10, verbose=False),
+ log_evaluation(period=0),
+ ],
+ )
+ best_iteration = lgbcv["cvbooster"].best_iteration # type: ignore
+ loss_metric_key = list(lgbcv)[0]
+ loss: float = np.min(lgbcv[loss_metric_key]) # type: ignore
+
+ return loss, best_iteration
+
+ def _get_nonmissing_subset_index(self, variable: str, seed: int):
+ """
+ Get random indices for a subset of the data in which variable is not missing.
+ Used to create feature / label for training.
+
+ replace = False because it would NOT mimic bagging for random forests.
+ """
+
+ data_subset = self.data_subset[variable]
+ available_candidates = self.available_candidates[variable]
+ nonmissing_ind = self._get_nonmissing_index(variable=variable)
+ if (data_subset == 0) or (data_subset >= available_candidates):
+ subset_index = nonmissing_ind
+ else:
+ rs = np.random.RandomState(seed)
+ subset_index = rs.choice(nonmissing_ind, size=data_subset, replace=False)
+ return subset_index
+
+ def _make_label(self, variable: str, seed: int):
+ """
+ Returns a reproducible subset of the non-missing values of a variable.
+ """
+ # Don't subset at all if data_subset == 0 or we want more than there are candidates
+
+ subset_index = self._get_nonmissing_subset_index(variable=variable, seed=seed)
+ label = self.working_data.loc[subset_index, variable].copy()
+ return label
+
+ def _make_features_label(self, variable: str, seed: int):
+ """
+ Makes a reproducible set of features and
+ target needed to train a lightgbm model.
+ """
+ subset_index = self._get_nonmissing_subset_index(variable=variable, seed=seed)
+ predictor_columns = self.variable_schema[variable]
+ features = self.working_data.loc[
+ subset_index, predictor_columns + [variable]
+ ].copy()
+ label = features.pop(variable)
+ return features, label
+
+ @staticmethod
+ def _mean_match_nearest_neighbors(
+ mean_match_candidates: int,
+ bachelor_preds: DataFrame,
+ candidate_preds: DataFrame,
+ candidate_values: Series,
+ random_state: np.random.RandomState,
+ hashed_seeds: Optional[np.ndarray] = None,
+ ) -> Series:
+ """
+ Determines the values of candidates which will be used to impute the bachelors
+ """
+
+ assert mean_match_candidates > 0, "Do not use nearest_neighbors with 0 mmc."
+ num_bachelors = bachelor_preds.shape[0]
+
+ # balanced_tree = False fixes a recursion issue for some reason.
+ # https://github.com/scipy/scipy/issues/14799
+ kd_tree = KDTree(candidate_preds, leafsize=16, balanced_tree=False)
+ _, knn_indices = kd_tree.query(
+ bachelor_preds, k=mean_match_candidates, workers=-1
+ )
+
+ # We can skip the random selection process if mean_match_candidates == 1
+ if mean_match_candidates == 1:
+ index_choice = knn_indices
+
+ else:
+ # Use the random_state if seed_array was not passed. Faster
+ if hashed_seeds is None:
+ ind = random_state.randint(mean_match_candidates, size=(num_bachelors))
+ # Use the random_seed_array if it was passed. Deterministic.
+ else:
+ ind = hashed_seeds % mean_match_candidates
+
+ index_choice = knn_indices[np.arange(num_bachelors), ind]
+
+ imp_values = candidate_values.iloc[index_choice]
+
+ return imp_values
+
+ @staticmethod
+ def _mean_match_binary_fast(
+ mean_match_candidates: int,
+ bachelor_preds: DataFrame,
+ random_state: np.random.RandomState,
+ hashed_seeds: Optional[np.ndarray],
+ ) -> np.ndarray:
+ """
+ Chooses 0/1 randomly weighted by probability obtained from prediction.
+ If mean_match_candidates is 0, choose class with highest probability.
+
+ Returns a np.ndarray, because these get set to categorical later on.
+ """
+ if mean_match_candidates == 0:
+ imp_values = np.floor(bachelor_preds + 0.5)
+
+ else:
+ num_bachelors = bachelor_preds.shape[0]
+ if hashed_seeds is None:
+ imp_values = random_state.binomial(n=1, p=bachelor_preds)
+ else:
+ imp_values = []
+ for i in range(num_bachelors):
+ np.random.seed(seed=hashed_seeds[i])
+ imp_values.append(np.random.binomial(n=1, p=bachelor_preds.iloc[i]))
+
+ imp_values = np.array(imp_values)
+
+ imp_values.shape = (-1,)
+
+ return imp_values
+
+ @staticmethod
+ def _mean_match_multiclass_fast(
+ mean_match_candidates: int,
+ bachelor_preds: DataFrame,
+ random_state: np.random.RandomState,
+ hashed_seeds: Optional[np.ndarray],
+ ):
+ """
+ If mean_match_candidates is 0, choose class with highest probability.
+ Otherwise, randomly choose class weighted by class probabilities.
+
+ Returns a np.ndarray, because these get set to categorical later on.
+ """
+ if mean_match_candidates == 0:
+ imp_values = np.argmax(bachelor_preds, axis=1)
+
+ else:
+ num_bachelors = bachelor_preds.shape[0]
+ bachelor_preds = bachelor_preds.cumsum(axis=1).to_numpy()
+
+ if hashed_seeds is None:
+ compare = random_state.uniform(0, 1, size=(num_bachelors, 1))
+ imp_values = (bachelor_preds < compare).sum(1)
+
+ else:
+ dtype = hashed_seeds.dtype
+ dtype_max = np.iinfo(dtype).max
+ compare = np.abs(hashed_seeds / dtype_max)
+ compare.shape = (-1, 1)
+ imp_values = (bachelor_preds < compare).sum(1)
+
+ imp_values.shape = (-1,)
+
+ return imp_values
+
+ def _mean_match_fast(
+ self,
+ variable: str,
+ mean_match_candidates: int,
+ bachelor_preds: np.ndarray,
+ random_state: np.random.RandomState,
+ hashed_seeds: Optional[np.ndarray],
+ ):
+ """
+ Dispatcher and formatter for the fast mean matching functions
+ """
+ if variable in self.modeled_categorical_columns:
+ imputation_values = self._mean_match_multiclass_fast(
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ random_state=random_state,
+ hashed_seeds=hashed_seeds,
+ )
+ elif variable in self.modeled_binary_columns:
+ imputation_values = self._mean_match_binary_fast(
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ random_state=random_state,
+ hashed_seeds=hashed_seeds,
+ )
+ else:
+ raise ValueError("Shouldnt be able to get here")
+
+ dtype = self.working_data[variable].dtype
+ imputation_values = Categorical.from_codes(codes=imputation_values, dtype=dtype)
+
+ return imputation_values
+
+ def _impute_with_predictions(
+ self,
+ variable: str,
+ lgbmodel: Booster,
+ bachelor_features: DataFrame,
+ ):
+ bachelor_preds = lgbmodel.predict(
+ bachelor_features,
+ pred_contrib=False,
+ raw_score=False,
+ )
+ assert isinstance(bachelor_preds, np.ndarray)
+ dtype = self.working_data[variable].dtype
+ if variable in self.modeled_numeric_columns:
+ if is_integer_dtype(dtype):
+ bachelor_preds = bachelor_preds.round(0)
+ return Series(bachelor_preds, dtype=dtype)
+ else:
+ if variable in self.modeled_binary_columns:
+ selection_ind = (bachelor_preds > 0.5).astype("uint8")
+ else:
+ assert (
+ variable in self.modeled_categorical_columns
+ ), f"{variable} is not in numeric, binary or categorical columns"
+ selection_ind = np.argmax(bachelor_preds, axis=1)
+ values = dtype.categories[selection_ind]
+ return Series(values, dtype=dtype)
+
+ def _get_candidate_preds_mice(
+ self,
+ variable: str,
+ lgbmodel: Booster,
+ candidate_features: DataFrame,
+ dataset: int,
+ iteration: int,
+ ) -> DataFrame:
+ """
+ This function also records the candidate predictions
+ """
+ shap = self.mean_match_strategy[variable] == "shap"
+ fast = self.mean_match_strategy[variable] == "fast"
+ logistic = variable not in self.modeled_numeric_columns
+
+ assert hasattr(
+ lgbmodel, "train_set"
+ ), "Model was passed that does not have training data."
+ if shap:
+ candidate_preds = lgbmodel.predict(
+ candidate_features,
+ pred_contrib=True,
+ )
+ candidate_preds = candidate_preds.astype(_PRE_LINK_DATATYPE) # type: ignore
+ else:
+ candidate_preds = lgbmodel._Booster__inner_predict(0) # type: ignore
+ if logistic and not (shap or fast):
+ candidate_preds = logodds(candidate_preds).astype(_PRE_LINK_DATATYPE)
+
+ candidate_preds = self._prepare_prediction_multiindex(
+ variable=variable,
+ preds=candidate_preds,
+ shap=shap,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ if self.save_all_iterations_data:
+ self._record_candidate_preds(
+ variable=variable,
+ candidate_preds=candidate_preds,
+ )
+
+ return candidate_preds
+
+ def _get_candidate_preds_from_store(
+ self,
+ variable: str,
+ dataset: int,
+ iteration: int,
+ ) -> DataFrame:
+ """
+ Mean matching requires 2D array, so always return a dataframe
+ """
+ ret = self.candidate_preds[variable][iteration][[dataset]]
+ assert isinstance(ret, DataFrame)
+ return ret
+
+ def _get_bachelor_preds(
+ self,
+ variable: str,
+ lgbmodel: Booster,
+ bachelor_features: DataFrame,
+ dataset: int,
+ iteration: int,
+ ) -> DataFrame:
+
+ shap = self.mean_match_strategy[variable] == "shap"
+ fast = self.mean_match_strategy[variable] == "fast"
+ logistic = variable not in self.modeled_numeric_columns
+
+ bachelor_preds = lgbmodel.predict(
+ bachelor_features,
+ pred_contrib=shap,
+ )
+ assert isinstance(bachelor_preds, np.ndarray)
+
+ if shap:
+ bachelor_preds = bachelor_preds.astype(_PRE_LINK_DATATYPE)
+
+ # We want the logods if running k-nearest
+ # neighbors on logistic-link predictions
+ if logistic and not (shap or fast):
+ bachelor_preds = logodds(bachelor_preds).astype(_PRE_LINK_DATATYPE)
+
+ bachelor_preds = self._prepare_prediction_multiindex(
+ variable=variable,
+ preds=bachelor_preds,
+ shap=shap,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ return bachelor_preds
+
+ def _record_candidate_preds(
+ self,
+ variable: str,
+ candidate_preds: DataFrame,
+ ):
+
+ assign_col_index = candidate_preds.columns
+
+ if variable not in self.candidate_preds.keys():
+ inferred_iteration = assign_col_index.get_level_values("iteration").unique()
+ assert (
+ len(inferred_iteration) == 1
+ ), f"Malformed iteration multiindex for {variable}: {assign_col_index}"
+ inferred_iteration = inferred_iteration[0]
+ assert (
+ inferred_iteration == 1
+ ), "Adding initial candidate preds after iteration 1."
+ self.candidate_preds[variable] = candidate_preds
+ else:
+ self.candidate_preds[variable][assign_col_index] = candidate_preds
+
+ def _prepare_prediction_multiindex(
+ self,
+ variable: str,
+ preds: np.ndarray,
+ shap: bool,
+ dataset: int,
+ iteration: int,
+ ) -> DataFrame:
+
+ multiclass = variable in self.modeled_categorical_columns
+ cols = self.variable_schema[variable] + ["Intercept"]
+
+ if shap:
+
+ if multiclass:
+
+ categories = self.working_data[variable].dtype.categories
+ cat_count = self.category_counts[variable]
+ preds_df = DataFrame(preds, columns=cols * cat_count)
+ del preds_df["Intercept"]
+ cols.remove("Intercept")
+ assign_col_index = MultiIndex.from_product(
+ [[iteration], [dataset], categories, cols],
+ names=("iteration", "dataset", "categories", "predictor"),
+ )
+ preds_df.columns = assign_col_index
+
+ else:
+ preds_df = DataFrame(preds, columns=cols)
+ del preds_df["Intercept"]
+ cols.remove("Intercept")
+ assign_col_index = MultiIndex.from_product(
+ [[iteration], [dataset], cols],
+ names=("iteration", "dataset", "predictor"),
+ )
+ preds_df.columns = assign_col_index
+
+ else:
+
+ if multiclass:
+
+ categories = self.working_data[variable].dtype.categories
+ preds_df = DataFrame(preds, columns=categories)
+ assign_col_index = MultiIndex.from_product(
+ [[iteration], [dataset], categories],
+ names=("iteration", "dataset", "categories"),
+ )
+ preds_df.columns = assign_col_index
+
+ else:
+
+ preds_df = DataFrame(preds, columns=[variable])
+ assign_col_index = MultiIndex.from_product(
+ [[iteration], [dataset]], names=("iteration", "dataset")
+ )
+ preds_df.columns = assign_col_index
+
+ return preds_df
+
+ def mean_match_mice(
+ self,
+ variable: str,
+ lgbmodel: Booster,
+ bachelor_features: DataFrame,
+ candidate_features: DataFrame,
+ candidate_values: Series,
+ dataset: int,
+ iteration: int,
+ ):
+ mean_match_candidates = self.mean_match_candidates[variable]
+ using_candidate_data = variable in self.mean_matching_requires_candidates
+
+ use_mean_matching = mean_match_candidates > 0
+ if not use_mean_matching:
+ imputation_values = self._impute_with_predictions(
+ variable=variable,
+ lgbmodel=lgbmodel,
+ bachelor_features=bachelor_features,
+ )
+ return imputation_values
+
+ # Get bachelor predictions
+ bachelor_preds = self._get_bachelor_preds(
+ variable=variable,
+ lgbmodel=lgbmodel,
+ bachelor_features=bachelor_features,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ if using_candidate_data:
+
+ candidate_preds = self._get_candidate_preds_mice(
+ variable=variable,
+ lgbmodel=lgbmodel,
+ candidate_features=candidate_features,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ # By now, a numeric variable will be post-link, and
+ # categorical / binary variables will be pre-link.
+ imputation_values = self._mean_match_nearest_neighbors(
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ candidate_preds=candidate_preds,
+ candidate_values=candidate_values,
+ random_state=self._random_state,
+ hashed_seeds=None,
+ )
+
+ else:
+
+ imputation_values = self._mean_match_fast(
+ variable=variable,
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ random_state=self._random_state,
+ hashed_seeds=None,
+ )
+
+ return imputation_values
+
+ def mean_match_ind(
+ self,
+ variable: str,
+ lgbmodel: Booster,
+ bachelor_features: DataFrame,
+ dataset: int,
+ iteration: int,
+ hashed_seeds: Optional[np.ndarray] = None,
+ ):
+ mean_match_candidates = self.mean_match_candidates[variable]
+ using_candidate_data = variable in self.mean_matching_requires_candidates
+ use_mean_matching = mean_match_candidates > 0
+
+ if not use_mean_matching:
+ imputation_values = self._impute_with_predictions(
+ variable=variable,
+ lgbmodel=lgbmodel,
+ bachelor_features=bachelor_features,
+ )
+ return imputation_values
+
+ # Get bachelor predictions
+ bachelor_preds = self._get_bachelor_preds(
+ variable=variable,
+ lgbmodel=lgbmodel,
+ bachelor_features=bachelor_features,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ if using_candidate_data:
+
+ candidate_preds = self._get_candidate_preds_from_store(
+ variable=variable,
+ dataset=dataset,
+ iteration=iteration,
+ )
+
+ candidate_values = self._make_label(
+ variable=variable, seed=lgbmodel.params["seed"]
+ )
+
+ # By now, a numeric variable will be post-link, and
+ # categorical / binary variables will be pre-link.
+ imputation_values = self._mean_match_nearest_neighbors(
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ candidate_preds=candidate_preds,
+ candidate_values=candidate_values,
+ random_state=self._random_state,
+ hashed_seeds=hashed_seeds,
+ )
+
+ else:
+
+ imputation_values = self._mean_match_fast(
+ variable=variable,
+ mean_match_candidates=mean_match_candidates,
+ bachelor_preds=bachelor_preds,
+ random_state=self._random_state,
+ hashed_seeds=hashed_seeds,
+ )
+
+ return imputation_values
+
+ def mice(
+ self,
+ iterations: int,
+ verbose: bool = False,
+ variable_parameters: Dict[str, Any] = {},
+ **kwlgb,
+ ):
+ """
+ Perform mice on a given dataset.
+
+ Multiple Imputation by Chained Equations (MICE) is an
+ iterative method which fills in (imputes) missing data
+ points in a dataset by modeling each column using the
+ other columns, and then inferring the missing data.
+
+ For more information on MICE, and missing data in
+ general, see Stef van Buuren's excellent online book:
+ https://stefvanbuuren.name/fimd/ch-introduction.html
+
+ For detailed usage information, see this project's
+ README on the github repository:
+ https://github.com/AnotherSamWilson/miceforest
+
+ Parameters
+ ----------
+ iterations: int
+ The number of iterations to run.
+
+ verbose: bool
+ Should information about the process be printed?
+
+ variable_parameters: None or dict
+ Model parameters can be specified by variable here. Keys should
+ be variable names or indices, and values should be a dict of
+ parameter which should apply to that variable only.
+
+ compile_candidates: bool
+ Candidate predictions can be stored as they are created while
+ performing mice. This prevents kernel.compile_candidate_preds()
+ from having to be called separately, and can save a significant
+ amount of time if compiled candidate predictions are desired.
+
+ kwlgb:
+ Additional arguments to pass to lightgbm. Applied to all models.
+
+ """
+
+ current_iterations = self.iteration_count()
+ start_iter = current_iterations + 1
+ end_iter = current_iterations + iterations + 1
+ logger = Logger(
+ name=f"MICE Iterations {current_iterations + 1} - {current_iterations + iterations}",
+ timed_levels=_MICE_TIMED_LEVELS,
+ verbose=verbose,
+ )
+
+ if len(variable_parameters) > 0:
+ assert isinstance(
+ variable_parameters, dict
+ ), "variable_parameters should be a dict."
+ assert set(variable_parameters).issubset(self.model_training_order), (
+ "Variables in variable_parameters will not have models trained. "
+ "Check kernel.model_training_order"
+ )
+
+ for iteration in range(start_iter, end_iter, 1):
+ # absolute_iteration = self.iteration_count(datasets=dataset)
+ logger.log(str(iteration) + " ", end="")
+
+ for dataset in self.datasets:
+ logger.log("Dataset " + str(dataset))
+
+ # Set self.working_data to the most current iteration.
+ self.complete_data(dataset=dataset, inplace=True)
+
+ for variable in self.model_training_order:
+ logger.log(" | " + variable, end="")
+
+ # Define the lightgbm parameters
+ lgbpars = self._make_lgb_params(
+ variable=variable,
+ default_parameters=_DEFAULT_LGB_PARAMS.copy(),
+ variable_parameters=variable_parameters.get(variable, dict()),
+ **kwlgb,
+ )
+
+ time_key = dataset, iteration, variable, "Prepare XY"
+ logger.set_start_time(time_key)
+ (
+ candidate_features,
+ candidate_values,
+ ) = self._make_features_label(
+ variable=variable, seed=lgbpars["seed"]
+ )
+
+ # lightgbm requires integers for label. Categories won't work.
+ if candidate_values.dtype.name == "category":
+ label = candidate_values.cat.codes
+ else:
+ label = candidate_values
+
+ num_iterations = lgbpars.pop("num_iterations")
+ train_pointer = Dataset(
+ data=candidate_features,
+ label=label,
+ )
+ logger.record_time(time_key)
+
+ time_key = dataset, iteration, variable, "Training"
+ logger.set_start_time(time_key)
+ current_model = train(
+ params=lgbpars,
+ train_set=train_pointer,
+ num_boost_round=num_iterations,
+ keep_training_booster=True,
+ )
+ logger.record_time(time_key)
+
+ # Only perform mean matching and insertion
+ # if variable is being imputed.
+ if variable in self.imputation_order:
+ time_key = dataset, iteration, variable, "Mean Matching"
+ logger.set_start_time(time_key)
+ bachelor_features = self.get_bachelor_features(
+ variable=variable
+ )
+ imputation_values = self.mean_match_mice(
+ variable=variable,
+ lgbmodel=current_model,
+ bachelor_features=bachelor_features,
+ candidate_features=candidate_features,
+ candidate_values=candidate_values,
+ dataset=dataset,
+ iteration=iteration,
+ )
+ imputation_values.index = self.na_where[variable]
+ logger.record_time(time_key)
+
+ assert imputation_values.shape == (
+ self.na_counts[variable],
+ ), f"{variable} mean matching returned malformed array"
+
+ # Insert the imputation_values we obtained
+ self[variable, iteration, dataset] = imputation_values
+
+ if not self.save_all_iterations_data:
+ del self[variable, iteration - 1, dataset]
+
+ else:
+
+ # This is called to save the candidate predictions
+ _ = self._get_candidate_preds_mice(
+ variable=variable,
+ lgbmodel=current_model,
+ candidate_features=candidate_features,
+ dataset=dataset,
+ iteration=iteration,
+ )
+ del _
+
+ # Save the model, if we should be
+ if self.save_all_iterations_data:
+ self.models[variable, iteration, dataset] = (
+ current_model.free_dataset()
+ )
+
+ self.iteration_tab[variable, dataset] += 1
+
+ logger.log("\n", end="")
+
+ self._ampute_original_data()
+ self.loggers.append(logger)
+
+ def get_model(
+ self,
+ variable: str,
+ dataset: int,
+ iteration: int = -1,
+ ):
+ # Allow passing -1 to get the latest iteration's model
+ if iteration == -1:
+ iteration = self.iteration_count(dataset=dataset, variable=variable)
+ try:
+ model = self.models[variable, iteration, dataset]
+ except KeyError:
+ raise ValueError("Model was not saved.")
+ return model
+
+ def fit(self, X, y, **fit_params):
+ """
+ Method for fitting a kernel when used in a sklearn pipeline.
+ Should not be called by the user directly.
+ """
+ assert self.num_datasets == 1, (
+ "miceforest kernel should be initialized with datasets=1 if "
+ "being used in a sklearn pipeline."
+ )
+ assert X.equals(self.working_data), (
+ "It looks like this kernel is being used in a sklearn pipeline. "
+ "The data passed in fit() should be the same as the data that "
+ "was originally passed to the kernel. If this kernel is not being "
+ "used in an sklearn pipeline, please just use the mice() method."
+ )
+ self.mice(**fit_params)
+ return self
+
+ def transform(self, X, y=None):
+ """
+ Method for calling a kernel when used in a sklearn pipeline.
+ Should not be called by the user directly.
+ """
+
+ new_dat = self.impute_new_data(X, datasets=[0])
+ return new_dat.complete_data(dataset=0, inplace=False)
+
+ def tune_parameters(
+ self,
+ dataset: int = 0,
+ variables: Optional[List[str]] = None,
+ variable_parameters: Dict[str, Any] = dict(),
+ parameter_sampling_method: str = "random",
+ max_reattempts: int = 5,
+ use_gbdt: bool = True,
+ nfold: int = 10,
+ optimization_steps: int = 5,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ verbose: bool = False,
+ **kwargs,
+ ):
+ """
+ Perform hyperparameter tuning on models at the current iteration.
+ This method is not meant to be robust, but to get a decent set of
+ parameters to help with imputation.
+
+ .. code-block:: text
+
+ A few notes:
+ - The parameters are tuned on the data that would currently be returned by
+ complete_data(dataset). It is usually a good idea to run at least 1 iteration
+ of mice with the default parameters to get a more accurate idea of the
+ real optimal parameters, since Missing At Random (MAR) data imputations
+ tend to converge over time.
+ - num_iterations is treated as the maximum number of boosting rounds to run
+ in lightgbm.cv. It is NEVER optimized. The num_iterations that is returned
+ is the best_iteration returned by lightgbm.cv. num_iterations can be passed to
+ limit the boosting rounds, but the returned value will always be obtained
+ from best_iteration.
+ - lightgbm parameters are chosen in the following order of priority:
+ 1) Anything specified in variable_parameters
+ 2) Parameters specified globally in **kwbounds
+ 3) Default tuning space (miceforest.default_lightgbm_parameters)
+ 4) Default parameters (miceforest.default_lightgbm_parameters.default_parameters)
+ - See examples for a detailed run-through. See
+ https://github.com/AnotherSamWilson/miceforest#Tuning-Parameters
+ for even more detailed examples.
+
+
+ Parameters
+ ----------
+
+ dataset: int (required)
+ The dataset to run parameter tuning on. Tuning parameters on 1 dataset usually results
+ in acceptable parameters for all datasets. However, tuning results are still stored
+ seperately for each dataset.
+
+ variables: None or list
+ - If None, default hyper-parameter spaces are selected based on kernel data, and
+ all variables with missing values are tuned.
+ - If list, must either be indexes or variable names corresponding to the variables
+ that are to be tuned.
+
+ variable_parameters: None or dict
+ Defines the tuning space. Dict keys must be variable names or indices, and a subset
+ of the variables parameter. Values must be a dict with lightgbm parameter names as
+ keys, and values that abide by the following rules:
+ scalar: If a single value is passed, that parameter will be used to build the
+ model, and will not be tuned.
+ tuple: If a tuple is passed, it must have length = 2 and will be interpreted as
+ the bounds to search within for that parameter.
+ list: If a list is passed, values will be randomly selected from the list.
+ NOTE: This is only possible with method = 'random'.
+
+ example: If you wish to tune the imputation model for the 4th variable with specific
+ bounds and parameters, you could pass:
+ variable_parameters = {
+ 'column': {
+ 'learning_rate: 0.01',
+ 'min_sum_hessian_in_leaf: (0.1, 10),
+ 'extra_trees': [True, False]
+ }
+ }
+ All models for variable 'column' will have a learning_rate = 0.01. The process will randomly
+ search within the bounds (0.1, 10) for min_sum_hessian_in_leaf, and extra_trees will
+ be randomly selected from the list. Also note, the variable name for the 4th column
+ could also be passed instead of the integer 4. All other variables will be tuned with
+ the default search space, unless **kwbounds are passed.
+
+ parameter_sampling_method: str
+ If 'random', parameters are randomly selected.
+ Other methods will be added in future releases.
+
+ max_reattempts: int
+ The maximum number of failures (or non-learners) before the process stops, and moves to the
+ next variable. Failures can be caused by bad parameters passed to lightgbm. Non-learners
+ occur when trees cannot possibly be built (i.e. min_samples_in_leaf > dataset.shape[0]).
+
+ use_gbdt: bool
+ Whether the models should use gradient boosting instead of random forests.
+ If True, the optimal number of iterations will be found in lgb.cv, along
+ with the other parameters.
+
+ nfold: int
+ The number of folds to perform cross validation with. More folds takes longer, but
+ Gives a more accurate distribution of the error metric.
+
+ optimization_steps:
+ How many steps to run the process for.
+
+ random_state: int or np.random.RandomState or None (default=None)
+ The random state of the process. Ensures reproduceability. If None, the random state
+ of the kernel is used. Beware, this permanently alters the random state of the kernel
+ and ensures non-reproduceable results, unless the entire process up to this point
+ is re-run.
+
+ kwbounds:
+ Any additional arguments that you want to apply globally to every variable.
+ For example, if you want to limit the number of iterations, you could pass
+ num_iterations = x to this functions, and it would apply globally. Custom
+ bounds can also be passed.
+
+
+ Returns
+ -------
+ dict: optimal_parameters
+ A dict of the optimal parameters found for each variable.
+ This can be passed directly to the variable_parameters parameter in mice()
+ {variable: {parameter_name: parameter_value}}
+
+ """
+
+ random_state = ensure_rng(random_state)
+
+ if variables is None:
+ variables = self.imputation_order
+
+ self.complete_data(dataset, inplace=True)
+
+ logger = Logger(
+ name=f"tune: {optimization_steps}",
+ timed_levels=_TUNING_TIMED_LEVELS,
+ verbose=verbose,
+ )
+
+ for variable in variables:
+
+ logger.log(f"Optimizing {variable}")
+
+ seed = _draw_random_int32(random_state=random_state, size=1)
+
+ (
+ candidate_features,
+ candidate_values,
+ ) = self._make_features_label(variable=variable, seed=seed)
+
+ min_samples = (
+ self.category_counts[variable]
+ if variable in self.modeled_categorical_columns
+ else 1
+ )
+ max_samples = int(candidate_features.shape[0] / 5)
+
+ assert isinstance(
+ variable_parameters, dict
+ ), "variable_parameters should be a dict"
+ vp = variable_parameters.get(variable, dict()).copy()
+
+ tuning_space = self._make_tuning_space(
+ variable=variable,
+ variable_parameters=vp,
+ use_gbdt=use_gbdt,
+ min_samples=min_samples,
+ max_samples=max_samples,
+ **kwargs,
+ )
+
+ # lightgbm requires integers for label. Categories won't work.
+ if candidate_values.dtype.name == "category":
+ cat_cols = (
+ self.modeled_categorical_columns + self.modeled_binary_columns
+ )
+ assert variable in cat_cols, (
+ "Something went wrong in definining categorical "
+ f"status of variable {variable}. Please open an issue."
+ )
+ candidate_values = candidate_values.cat.codes
+ is_cat = True
+ else:
+ is_cat = False
+
+ for step in range(optimization_steps):
+
+ # Make multiple attempts to learn something.
+ non_learners = 0
+ while non_learners < max_reattempts:
+
+ # Sample parameters
+ sampled_parameters = _sample_parameters(
+ parameters=tuning_space,
+ random_state=random_state,
+ parameter_sampling_method=parameter_sampling_method,
+ )
+ logger.log(
+ f" Step {step} - Parameters: {sampled_parameters}", end=""
+ )
+
+ # Pointer and folds need to be re-initialized after every run.
+ train_set = Dataset(
+ data=candidate_features,
+ label=candidate_values,
+ )
+ if is_cat:
+ folds = stratified_categorical_folds(candidate_values, nfold)
+ else:
+ folds = stratified_continuous_folds(candidate_values, nfold)
+
+ try:
+ loss, best_iteration = self._get_oof_performance(
+ parameters=sampled_parameters.copy(),
+ folds=folds,
+ train_set=train_set,
+ )
+ except Exception as err:
+ non_learners += 1
+ logger.log(f" - Lightgbm Error {err=}, {type(err)=}")
+ continue
+
+ if best_iteration > 1:
+ logger.log(f" - Success - Loss: {loss}")
+ break
+ else:
+ logger.log(" - Non-Learner")
+ non_learners += 1
+
+ best_loss = self.optimal_parameter_losses.get(variable, np.inf)
+ if loss < best_loss:
+ del sampled_parameters["seed"]
+ sampled_parameters["num_iterations"] = best_iteration
+ self.optimal_parameters[variable] = sampled_parameters
+ self.optimal_parameter_losses[variable] = loss
+
+ self._ampute_original_data()
+ return self.optimal_parameters
+
+ def impute_new_data(
+ self,
+ new_data: DataFrame,
+ datasets: Optional[List[int]] = None,
+ iterations: Optional[int] = None,
+ save_all_iterations_data: bool = True,
+ copy_data: bool = True,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
+ random_seed_array: Optional[np.ndarray] = None,
+ verbose: bool = False,
+ ) -> ImputedData:
+ """
+ Impute a new dataset
+
+ Uses the models obtained while running MICE to impute new data,
+ without fitting new models. Pulls mean matching candidates from
+ the original data.
+
+ save_models must be > 0. If save_models == 1, the last model
+ obtained in mice is used for every iteration. If save_models > 1,
+ the model obtained at each iteration is used to impute the new
+ data for that iteration. If specified iterations is greater than
+ the number of iterations run so far using mice, the last model
+ is used for each additional iteration.
+
+ Type checking is not done. It is up to the user to ensure that the
+ kernel data matches the new data being imputed.
+
+ Parameters
+ ----------
+ new_data: pandas DataFrame or numpy ndarray
+ The new data to impute
+
+ datasets: int or List[int] (default = None)
+ The datasets from the kernel to use to impute the new data.
+ If None, all datasets from the kernel are used.
+
+ iterations: int
+ The number of iterations to run.
+ If None, the same number of iterations run so far in mice is used.
+
+ save_all_iterations: bool
+ Should the imputation values of all iterations be archived?
+ If False, only the latest imputation values are saved.
+
+ copy_data: boolean
+ Should the dataset be referenced directly? This will cause the dataset to be altered
+ in place. If a copy is created, it is saved in self.working_data. There are different
+ ways in which the dataset can be altered:
+
+ 1) complete_data() will fill in missing values
+ 2) mice() references and manipulates self.working_data directly.
+
+ random_state: int or np.random.RandomState or None (default=None)
+ The random state of the process. Ensures reproducibility. If None, the random state
+ of the kernel is used. Beware, this permanently alters the random state of the kernel
+ and ensures non-reproduceable results, unless the entire process up to this point
+ is re-run.
+
+ random_seed_array: None or np.ndarray (int32)
+
+ .. code-block:: text
+
+ Record-level seeds.
+
+ Ensures deterministic imputations at the record level. random_seed_array causes
+ deterministic imputations for each record no matter what dataset each record is
+ imputed with, assuming the same number of iterations and datasets are used.
+ If random_seed_array os passed, random_state must also be passed.
+
+ Record-level imputations are deterministic if the following conditions are met:
+ 1) The associated seed is the same.
+ 2) The same kernel is used.
+ 3) The same number of iterations are run.
+ 4) The same number of datasets are run.
+
+ Notes:
+ a) This will slightly slow down the imputation process, because random
+ number generation in numpy can no longer be vectorized. If you don't have a
+ specific need for deterministic imputations at the record level, it is better to
+ keep this parameter as None.
+
+ b) Using this parameter may change the global numpy seed by calling np.random.seed().
+
+ c) Internally, these seeds are hashed each time they are used, in order
+ to obtain different results for each dataset / iteration.
+
+
+ verbose: boolean
+ Should information about the process be printed?
+
+ Returns
+ -------
+ miceforest.ImputedData
+
+ """
+
+ assert self.save_all_iterations_data, (
+ "Cannot recreate imputation procedure, data was not saved during MICE. "
+ "To save this data, set save_all_iterations_data to True when making kernel."
+ )
+
+ # datasets = list(range(self.num_datasets)) if datasets is None else datasets
+ datasets = self.datasets if datasets is None else datasets
+ kernel_iterations = self.iteration_count()
+ iterations = kernel_iterations if iterations is None else iterations
+ logger = Logger(
+ name=f"Impute New Data {0}-{iterations}",
+ timed_levels=_IMPUTE_NEW_DATA_TIMED_LEVELS,
+ verbose=verbose,
+ )
+
+ assert isinstance(new_data, DataFrame)
+ assert self.working_data.columns.equals(
+ new_data.columns
+ ), "Different columns from original dataset."
+ assert np.all(
+ [
+ self.working_data[col].dtype == new_data[col].dtype
+ for col in self.column_names
+ ]
+ ), "Column types are not the same as the original data. Check categorical columns."
+
+ imputed_data = ImputedData(
+ impute_data=new_data,
+ # num_datasets=len(datasets),
+ datasets=datasets,
+ variable_schema=self.variable_schema.copy(),
+ save_all_iterations_data=save_all_iterations_data,
+ copy_data=copy_data,
+ random_seed_array=random_seed_array,
+ )
+ new_imputation_order = [
+ col
+ for col in self.model_training_order
+ if col in imputed_data.vars_with_any_missing
+ ]
+
+ ### Manage Randomness.
+ if random_state is None:
+ assert (
+ random_seed_array is None
+ ), "random_state is also required when using random_seed_array"
+ random_state = self._random_state
+ else:
+ random_state = ensure_rng(random_state)
+
+ self._initialize_dataset(
+ imputed_data,
+ random_state=random_state,
+ )
+
+ for iteration in range(1, iterations + 1):
+ logger.log(str(iteration) + " ", end="")
+
+ for dataset in datasets:
+ logger.log("Dataset " + str(dataset))
+ self.complete_data(dataset=dataset, inplace=True)
+ imputed_data.complete_data(dataset=dataset, inplace=True)
+
+ for variable in new_imputation_order:
+ logger.log(" | " + variable, end="")
+
+ # Select our model.
+ current_model = self.get_model(
+ variable=variable, dataset=dataset, iteration=iteration
+ )
+
+ time_key = dataset, iteration, variable, "Getting Bachelor Features"
+ logger.set_start_time(time_key)
+ bachelor_features = imputed_data.get_bachelor_features(variable)
+ hashed_seeds = imputed_data._get_hashed_seeds(variable)
+ logger.record_time(time_key)
+
+ time_key = dataset, iteration, variable, "Mean Matching"
+ logger.set_start_time(time_key)
+ na_where = imputed_data.na_where[variable]
+ imputation_values = self.mean_match_ind(
+ variable=variable,
+ lgbmodel=current_model,
+ bachelor_features=bachelor_features,
+ dataset=dataset,
+ iteration=iteration,
+ hashed_seeds=hashed_seeds,
+ )
+ # self.cycle_random_seed_array(variable)
+ imputation_values.index = na_where
+ logger.record_time(time_key)
+
+ assert imputation_values.shape == (
+ imputed_data.na_counts[variable],
+ ), f"{variable} mean matching returned malformed array"
+
+ # Insert the imputation_values we obtained
+ imputed_data[variable, iteration, dataset] = imputation_values
+
+ if not imputed_data.save_all_iterations_data:
+ del imputed_data[variable, iteration - 1, dataset]
+
+ logger.log("\n", end="")
+
+ imputed_data._ampute_original_data()
+ self.loggers.append(logger)
+
+ return imputed_data
+
+ def get_feature_importance(
+ self,
+ dataset: int = 0,
+ iteration: int = -1,
+ importance_type: str = "split",
+ normalize: bool = True,
+ ) -> DataFrame:
+ """
+ Return a matrix of feature importance. The cells
+ represent the normalized feature importance of the
+ columns to impute the rows. This is calculated
+ internally by lightgbm.Booster.feature_importance().
+
+ Parameters
+ ----------
+ dataset: int
+ The dataset to get the feature importance for.
+
+ iteration: int
+ The iteration to return the feature importance for.
+ The model must be saved to return importance.
+ Use -1 to specify the latest iteration.
+
+ Returns
+ -------
+ np.ndarray of importance values. Rows are imputed variables, and
+ columns are predictor variables.
+
+ """
+
+ if iteration == -1:
+ iteration = self.iteration_count(dataset=dataset)
+
+ modeled_vars = [
+ col for col in self.working_data.columns if col in self.model_training_order
+ ]
+
+ importance_matrix = DataFrame(
+ index=modeled_vars, columns=self.predictor_columns
+ )
+ for modeled_variable in modeled_vars:
+ predictor_vars = self.variable_schema[modeled_variable]
+ importances = self.get_model(
+ variable=modeled_variable, dataset=dataset, iteration=iteration
+ ).feature_importance(importance_type=importance_type)
+ importances = Series(importances, index=predictor_vars)
+ importance_matrix.loc[modeled_variable, predictor_vars] = importances
+
+ importance_matrix = importance_matrix.astype("float64")
+
+ if normalize:
+ importance_matrix /= importance_matrix.sum(1).to_numpy().reshape(-1, 1)
+
+ return importance_matrix
+
+ def plot_feature_importance(
+ self,
+ dataset,
+ importance_type: str = "split",
+ normalize: bool = True,
+ iteration: int = -1,
+ ):
+ """
+ Plot the feature importance. See get_feature_importance()
+ for more details.
+
+ Parameters
+ ----------
+ dataset: int
+ The dataset to plot the feature importance for.
+
+ iteration: int
+ The iteration to plot the feature importance of.
+ The model must be saved to plot feature importance.
+ Use -1 to return the latest iteration.
+
+ normalize: book
+ Should the values be normalize from 0-1?
+ If False, values are raw from Booster.feature_importance()
+
+ kw_plot
+ Additional arguments sent to sns.heatmap()
+
+ """
+
+ try:
+ from plotnine import (
+ aes,
+ element_blank,
+ element_text,
+ geom_label,
+ geom_tile,
+ ggplot,
+ ggtitle,
+ scale_fill_distiller,
+ theme,
+ xlab,
+ ylab,
+ )
+ except ImportError:
+ raise ImportError("plotnine must be installed to plot importance")
+
+ importance_matrix = self.get_feature_importance(
+ dataset=dataset,
+ iteration=iteration,
+ normalize=normalize,
+ importance_type=importance_type,
+ )
+ importance_matrix = importance_matrix.reset_index().melt(id_vars="index")
+ importance_matrix["Importance"] = importance_matrix["value"].round(2)
+ importance_matrix = importance_matrix.dropna()
+
+ fig = (
+ ggplot(importance_matrix, aes(x="variable", y="index", fill="Importance"))
+ + geom_tile(show_legend=False)
+ + ylab("Modeled Variable")
+ + xlab("Predictor")
+ + ggtitle("Feature Importance")
+ + geom_label(aes(label="Importance"), fill="white", size=8)
+ + scale_fill_distiller(palette=1, direction=1)
+ + theme(
+ axis_text_x=element_text(rotation=30, hjust=1),
+ plot_title=element_text(ha="left", size=20),
+ panel_background=element_blank(),
+ figure_size=(6, 6),
+ )
+ )
+
+ return fig
diff --git a/miceforest/imputed_data.py b/miceforest/imputed_data.py
new file mode 100644
index 0000000..51de161
--- /dev/null
+++ b/miceforest/imputed_data.py
@@ -0,0 +1,571 @@
+from io import BytesIO
+from itertools import combinations
+from typing import Any, Dict, List, Optional, Union
+from warnings import warn
+
+import numpy as np
+from pandas import DataFrame, MultiIndex, RangeIndex, Series, concat, read_parquet
+
+from .utils import get_best_int_downcast, hash_numpy_int_array
+
+
+class ImputedData:
+ def __init__(
+ self,
+ impute_data: DataFrame,
+ # num_datasets: int = 5,
+ datasets: List[int],
+ variable_schema: Optional[Union[List[str], Dict[str, List[str]]]] = None,
+ save_all_iterations_data: bool = True,
+ copy_data: bool = True,
+ random_seed_array: Optional[np.ndarray] = None,
+ ):
+ # All references to the data should be through self.
+ self.working_data = impute_data.copy() if copy_data else impute_data
+ self.shape = self.working_data.shape
+ self.save_all_iterations_data = save_all_iterations_data
+ self.datasets = datasets
+
+ assert isinstance(
+ self.working_data.index, RangeIndex
+ ), "Please reset the index on the dataframe"
+
+ column_names = []
+ pd_dtypes_orig = {}
+ for col, series in self.working_data.items():
+ assert isinstance(col, str), "column names must be strings"
+ assert (
+ series.dtype.name != "object"
+ ), "convert object dtypes to something else"
+ column_names.append(col)
+ pd_dtypes_orig[col] = series.dtype.name
+
+ self.column_names = column_names
+ pd_dtypes_orig = self.working_data.dtypes
+
+ # Collect info about what data is missing.
+ na_where = {}
+ for col in column_names:
+ nas = np.where(self.working_data[col].isnull())[0]
+ if len(nas) == 0:
+ best_downcast = "uint8"
+ else:
+ best_downcast = get_best_int_downcast(int(nas.max()))
+ na_where[col] = nas.astype(best_downcast)
+ na_counts = {col: len(nw) for col, nw in na_where.items()}
+ self.vars_with_any_missing = [
+ col for col, count in na_counts.items() if count > 0
+ ]
+
+ # If variable_schema was passed, use that as the
+ # list of variables that should have models trained.
+ # Otherwise, only train models on variables that have
+ # missing values.
+ if variable_schema is None:
+ modeled_variables = self.vars_with_any_missing.copy()
+ variable_schema = {
+ target: [
+ regressor for regressor in self.column_names if regressor != target
+ ]
+ for target in modeled_variables
+ }
+ elif isinstance(variable_schema, list):
+ variable_schema = {
+ target: [
+ regressor for regressor in self.column_names if regressor != target
+ ]
+ for target in variable_schema
+ }
+ elif isinstance(variable_schema, dict):
+ # Don't alter the original dict out of scope
+ variable_schema = variable_schema.copy()
+ for target, regressors in variable_schema.items():
+ if target in regressors:
+ raise ValueError(f"{target} being used to impute itself")
+
+ self.variable_schema = variable_schema
+
+ self.modeled_variables = list(self.variable_schema)
+ self.imputed_variables = [
+ col for col in self.modeled_variables if col in self.vars_with_any_missing
+ ]
+
+ if random_seed_array is not None:
+ assert isinstance(random_seed_array, np.ndarray)
+ assert (
+ random_seed_array.shape[0] == self.shape[0]
+ ), "random_seed_array must be the same length as data."
+ # Our hashing scheme doesn't work for specifically the value 0.
+ # Set any values == 0 to the value 1.
+ random_seed_array = random_seed_array.copy()
+ zero_value_seeds = random_seed_array == 0
+ random_seed_array[zero_value_seeds] = 1
+ hash_numpy_int_array(random_seed_array)
+ self.random_seed_array: Optional[np.ndarray] = random_seed_array
+ else:
+ self.random_seed_array = None
+
+ self.na_counts = na_counts
+ self.na_where = na_where
+ self.num_datasets = len(datasets)
+ self.initialized = False
+ self.imputed_variable_count = len(self.imputed_variables)
+ self.modeled_variable_count = len(self.modeled_variables)
+ # self.iterations = np.zeros(
+ # shape=(self.num_datasets, self.modeled_variable_count)
+ # ).astype(int)
+
+ # Create a multiindexed dataframe to store our imputation values
+ iv_multiindex = MultiIndex.from_product(
+ [[0], datasets], names=("iteration", "dataset")
+ )
+ self.imputation_values = {
+ var: DataFrame(index=na_where[var], columns=iv_multiindex).astype(
+ pd_dtypes_orig[var]
+ )
+ for var in self.imputed_variables
+ }
+
+ # Create an iteration counter
+ self.iteration_tab = {}
+ for variable in self.modeled_variables:
+ for dataset in datasets:
+ self.iteration_tab[variable, dataset] = 0
+
+ # Subsetting allows us to get to the imputation values:
+ def __getitem__(self, tup):
+ variable, iteration, dataset = tup
+ return self.imputation_values[variable].loc[:, (iteration, dataset)]
+
+ def __setitem__(self, tup, newitem):
+ variable, iteration, dataset = tup
+ imputation_iteration = self.iteration_count(dataset=dataset, variable=variable)
+
+ # Don't throw this warning on initialization
+ if (iteration <= imputation_iteration) and (iteration > 0):
+ warn(
+ f"Overwriting Variable: {variable} Dataset: {dataset} Iteration: iteration"
+ )
+
+ self.imputation_values[variable].loc[:, (iteration, dataset)] = newitem
+
+ def __delitem__(self, tup):
+ variable, iteration, dataset = tup
+ self.imputation_values[variable].drop(
+ [(iteration, dataset)], axis=1, inplace=True
+ )
+
+ def __getstate__(self):
+ """
+ For pickling
+ """
+ # Copy the entire object, minus the big stuff
+ state = {
+ key: value
+ for key, value in self.__dict__.items()
+ if key not in ["imputation_values"]
+ }.copy()
+
+ state["imputation_values"] = {}
+
+ for col, df in self.imputation_values.items():
+ byte_stream = BytesIO()
+ df.to_parquet(byte_stream)
+ state["imputation_values"][col] = byte_stream
+
+ return state
+
+ def __setstate__(self, state):
+ """
+ For unpickling
+ """
+ self.__dict__ = state
+
+ for col, bytes in self.imputation_values.items():
+ self.imputation_values[col] = read_parquet(bytes)
+
+ def __repr__(self):
+ summary_string = f'\n{" " * 14}Class: ImputedData\n{self._ids_info()}'
+ return summary_string
+
+ def _ids_info(self):
+ summary_string = f"""\
+ Datasets: {self.num_datasets}
+ Iterations: {self.iteration_count()}
+ Data Samples: {self.shape[0]}
+ Data Columns: {self.shape[1]}
+ Imputed Variables: {self.imputed_variable_count}
+ Modeled Variables: {self.modeled_variable_count}
+All Iterations Saved: {self.save_all_iterations_data}
+ """
+ return summary_string
+
+ def _get_nonmissing_index(self, variable: str):
+ na_where = self.na_where[variable]
+ dtype = na_where.dtype
+ non_missing_ind = np.setdiff1d(
+ np.arange(self.shape[0], dtype=dtype), na_where, assume_unique=True
+ )
+ return non_missing_ind
+
+ def _get_nonmissing_values(self, variable: str):
+ ind = self._get_nonmissing_index(variable)
+ return self.working_data.loc[ind, variable]
+
+ def _ampute_original_data(self):
+ """Need to put self.working_data back in its original form"""
+ for variable in self.imputed_variables:
+ na_where = self.na_where[variable]
+ self.working_data.loc[na_where, variable] = np.nan
+
+ def _get_hashed_seeds(self, variable: str):
+ if self.random_seed_array is not None:
+ na_where = self.na_where[variable]
+ hashed_seeds = self.random_seed_array[na_where].copy()
+ hash_numpy_int_array(self.random_seed_array, ind=na_where)
+ return hashed_seeds
+ else:
+ return None
+
+ def get_bachelor_features(self, variable):
+ na_where = self.na_where[variable]
+ predictors = self.variable_schema[variable]
+ bachelor_features = self.working_data.loc[na_where, predictors]
+ return bachelor_features
+
+ def iteration_count(
+ self,
+ dataset: Union[slice, int] = slice(None),
+ variable: Union[slice, str] = slice(None),
+ ):
+ """
+ Grabs the iteration count for specified variables, datasets.
+ If the iteration count is not consistent across the provided
+ datasets/variables, an error will be thrown. Providing None
+ will use all datasets/variables.
+
+ This is to ensure the process is in a consistent state when
+ the iteration count is needed.
+
+ Parameters
+ ----------
+ datasets: int or None
+ The datasets to check the iteration count for.
+ If None, all datasets are assumed (and assured)
+ to have the same iteration count, otherwise error.
+ variables: str or None
+ The variable to check the iteration count for.
+ If None, all variables are assumed (and assured)
+ to have the same iteration count, otherwise error.
+
+ Returns
+ -------
+ An integer representing the iteration count.
+ """
+
+ iteration_tab = Series(self.iteration_tab)
+ iteration_tab.index.names = ["variable", "dataset"]
+
+ iterations = np.unique(iteration_tab.loc[variable, dataset])
+ if iterations.shape[0] > 1:
+ raise ValueError("Multiple iteration counts found")
+ else:
+ return iterations[0]
+
+ def complete_data(
+ self,
+ dataset: int = 0,
+ iteration: int = -1,
+ inplace: bool = False,
+ variables: Optional[List[str]] = None,
+ ):
+ """
+ Return dataset with missing values imputed.
+
+ Parameters
+ ----------
+ dataset: int
+ The dataset to complete.
+ iteration: int
+ Impute data with values obtained at this iteration.
+ If -1, returns the most up-to-date iterations,
+ even if different between variables. If not -1,
+ iteration must have been saved in imputed values.
+ inplace: bool
+ Should the data be completed in place? If True,
+ self.working_data is imputed,and nothing is returned.
+ This is useful if the dataset is very large. If
+ False, a copy of the data is returned, with missing
+ values imputed.
+
+ Returns
+ -------
+ The completed data, with values imputed for specified variables.
+
+ """
+
+ # Return a copy if not inplace.
+ impute_data = self.working_data if inplace else self.working_data.copy()
+
+ # Figure out which variables we need to impute.
+ # Never impute variables that are not in imputed_variables.
+ imp_vars = self.imputed_variables if variables is None else variables
+ assert set(imp_vars).issubset(
+ set(self.imputed_variables)
+ ), "Not all variables specified were imputed."
+
+ for variable in imp_vars:
+ if iteration == -1:
+ iteration = self.iteration_count(dataset=dataset, variable=variable)
+ na_where = self.na_where[variable]
+ impute_data.loc[na_where, variable] = self[variable, iteration, dataset]
+
+ if not inplace:
+ return impute_data
+
+ # def get_means(self, datasets, variables=None):
+ # """
+ # Return a dict containing the average imputation value
+ # for specified variables at each iteration.
+ # """
+ # num_vars = self._get_num_vars(variables)
+
+ # # For every variable, get the correlations between every dataset combination
+ # # at each iteration
+ # curr_iteration = self.iteration_count(datasets=datasets)
+ # if self.save_all_iterations:
+ # iter_range = list(range(curr_iteration + 1))
+ # else:
+ # iter_range = [curr_iteration]
+ # mean_dict = {
+ # ds: {
+ # var: {itr: np.mean(self[ds, var, itr]) for itr in iter_range}
+ # for var in num_vars
+ # }
+ # for ds in datasets
+ # }
+
+ # return mean_dict
+
+ # def plot_mean_convergence(self, datasets=None, variables=None, **adj_args):
+ # """
+ # Plots the average value of imputations over each iteration.
+
+ # Parameters
+ # ----------
+ # variables: None or list
+ # The variables to plot. Must be numeric.
+ # adj_args
+ # Passed to matplotlib.pyplot.subplots_adjust()
+
+ # """
+
+ # try:
+ # import matplotlib.pyplot as plt
+ # from matplotlib import gridspec
+ # except ImportError:
+ # raise ImportError("matplotlib must be installed to plot mean convergence")
+
+ # if self.iteration_count() < 2 or not self.save_all_iterations:
+ # raise ValueError("There is only one iteration.")
+
+ # if datasets is None:
+ # datasets = list(range(self.dataset_count()))
+ # else:
+ # datasets = _ensure_iterable(datasets)
+ # num_vars = self._get_num_vars(variables)
+ # mean_dict = self.get_means(datasets=datasets, variables=variables)
+ # plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
+ # gs = gridspec.GridSpec(plotrows, plotcols)
+ # fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
+
+ # for v in range(plots):
+ # axr, axc = next(iter(gs[v].rowspan)), next(iter(gs[v].colspan))
+ # var = num_vars[v]
+ # for d in mean_dict.values():
+ # ax[axr, axc].plot(list(d[var].values()), color="black")
+ # ax[axr, axc].set_title(var)
+ # ax[axr, axc].set_xlabel("Iteration")
+ # ax[axr, axc].set_ylabel("mean")
+ # plt.subplots_adjust(**adj_args)
+
+ def plot_imputed_distributions(
+ self, variables: Optional[List[str]] = None, iteration: int = -1
+ ):
+ """
+ Plot the imputed value distributions.
+ Red lines are the distribution of original data
+ Black lines are the distribution of the imputed values.
+
+ Parameters
+ ----------
+ datasets: None, int, list[int]
+ variables: None, list[str]
+ The variables to plot. If None, all numeric variables
+ are plotted.
+ iteration: int
+ The iteration to plot the distribution for.
+ If None, the latest iteration is plotted.
+ save_all_iterations must be True if specifying
+ an iteration.
+ adj_args
+ Additional arguments passed to plt.subplots_adjust()
+
+ """
+
+ try:
+ from plotnine import (
+ aes,
+ facet_wrap,
+ geom_density,
+ ggplot,
+ ggtitle,
+ scale_color_manual,
+ theme,
+ xlab,
+ )
+ except ImportError:
+ raise ImportError("plotnine must be installed to plot distributions.")
+
+ if iteration == -1:
+ iteration = self.iteration_count()
+
+ colors = {str(i): "black" for i in range(self.num_datasets)}
+ colors["-1"] = "red"
+
+ num_vars = self.working_data.select_dtypes("number").columns.to_list()
+
+ if variables is None:
+ variables = [var for var in self.imputed_variables if var in num_vars]
+ else:
+ variables = [var for var in variables if var in num_vars]
+
+ dat = DataFrame()
+ for variable in variables:
+
+ imps = self.imputation_values[variable].loc[:, iteration].melt()
+ imps["variable"] = variable
+ ind = self._get_nonmissing_index(variable)
+ orig = self.working_data.loc[ind, variable].rename("value").to_frame()
+ orig["dataset"] = -1
+ orig["variable"] = variable
+ dat = concat([dat, imps, orig], axis=0)
+
+ dat["dataset"] = dat["dataset"].astype("string")
+
+ fig = (
+ ggplot()
+ + geom_density(
+ data=dat, mapping=aes(x="value", group="dataset", color="dataset")
+ )
+ + facet_wrap("variable", scales="free")
+ + scale_color_manual(values=colors)
+ + ggtitle("Distribution Plots")
+ + xlab("")
+ + theme(legend_position="none")
+ )
+
+ return fig
+
+ # def get_correlations(
+ # self, datasets: List[int], variables: Union[List[int], List[str]]
+ # ):
+ # """
+ # Return the correlations between datasets for
+ # the specified variables.
+
+ # Parameters
+ # ----------
+ # variables: list[str], list[int]
+ # The variables to return the correlations for.
+
+ # Returns
+ # -------
+ # dict
+ # The correlations at each iteration for the specified
+ # variables.
+
+ # """
+
+ # if self.dataset_count() < 3:
+ # raise ValueError(
+ # "Not enough datasets to calculate correlations between them"
+ # )
+ # curr_iteration = self.iteration_count()
+ # var_indx = self._get_var_ind_from_list(variables)
+
+ # # For every variable, get the correlations between every dataset combination
+ # # at each iteration
+ # correlation_dict = {}
+ # if self.save_all_iterations:
+ # iter_range = list(range(1, curr_iteration + 1))
+ # else:
+ # # Make this iterable for code tidyness
+ # iter_range = [curr_iteration]
+
+ # for var in var_indx:
+ # # Get a dict of variables and imputations for all datasets for this iteration
+ # iteration_level_imputations = {
+ # iteration: {ds: self[ds, var, iteration] for ds in datasets}
+ # for iteration in iter_range
+ # }
+
+ # combination_correlations = {
+ # iteration: [
+ # round(np.corrcoef(impcomb)[0, 1], 3)
+ # for impcomb in list(combinations(varimps.values(), 2))
+ # ]
+ # for iteration, varimps in iteration_level_imputations.items()
+ # }
+
+ # correlation_dict[var] = combination_correlations
+
+ # return correlation_dict
+
+ # def plot_correlations(self, datasets=None, variables=None, **adj_args):
+ # """
+ # Plot the correlations between datasets.
+ # See get_correlations() for more details.
+
+ # Parameters
+ # ----------
+ # datasets: None or list[int]
+ # The datasets to plot.
+ # variables: None,list
+ # The variables to plot.
+ # adj_args
+ # Additional arguments passed to plt.subplots_adjust()
+
+ # """
+
+ # try:
+ # import matplotlib.pyplot as plt
+ # from matplotlib import gridspec
+ # except ImportError:
+ # raise ImportError("matplotlib must be installed to plot importance")
+
+ # if self.dataset_count() < 4:
+ # raise ValueError("Not enough datasets to make box plot")
+ # if datasets is None:
+ # datasets = list(range(self.dataset_count()))
+ # else:
+ # datasets = _ensure_iterable(datasets)
+ # var_indx = self._get_var_ind_from_list(variables)
+ # num_vars = self._get_num_vars(var_indx)
+ # plots, plotrows, plotcols = self._prep_multi_plot(num_vars)
+ # correlation_dict = self.get_correlations(datasets=datasets, variables=num_vars)
+ # gs = gridspec.GridSpec(plotrows, plotcols)
+ # fig, ax = plt.subplots(plotrows, plotcols, squeeze=False)
+
+ # for v in range(plots):
+ # axr, axc = next(iter(gs[v].rowspan)), next(iter(gs[v].colspan))
+ # var = list(correlation_dict)[v]
+ # ax[axr, axc].boxplot(
+ # list(correlation_dict[var].values()),
+ # labels=range(len(correlation_dict[var])),
+ # )
+ # ax[axr, axc].set_title(self._get_var_name_from_scalar(var))
+ # ax[axr, axc].set_xlabel("Iteration")
+ # ax[axr, axc].set_ylabel("Correlations")
+ # ax[axr, axc].set_ylim([-1, 1])
+ # plt.subplots_adjust(**adj_args)
diff --git a/miceforest/logger.py b/miceforest/logger.py
index e4d9d9f..5a3e159 100644
--- a/miceforest/logger.py
+++ b/miceforest/logger.py
@@ -1,10 +1,16 @@
-from .compat import pd_Series, pd_DataFrame, PANDAS_INSTALLED
-from datetime import datetime as dt
-from typing import Dict, Any
+from datetime import datetime, timedelta
+from typing import Any, Dict, List, Optional, Tuple, Union
+
+from pandas import Series
class Logger:
- def __init__(self, name: str, verbose: bool = False) -> None:
+ def __init__(
+ self,
+ name: str,
+ timed_levels: List[str],
+ verbose: bool = False,
+ ):
"""
miceforest logger.
@@ -25,10 +31,12 @@ def __init__(self, name: str, verbose: bool = False) -> None:
"""
self.name = name
self.verbose = verbose
- self.initialization_time = dt.now()
+ self.initialization_time = datetime.now()
+ self.timed_levels = timed_levels
+ self.started_timers: dict = {}
if self.verbose:
- print(f"Initialized logger with name {name}")
+ print(f"Initialized logger with name {name} and {len(timed_levels)} levels")
self.time_seconds: Dict[Any, float] = {}
@@ -40,39 +48,31 @@ def log(self, *args, **kwargs):
if self.verbose:
print(*args, **kwargs)
- def set_start_time(self):
- self._start_time = dt.now()
+ def set_start_time(self, time_key: Tuple):
+ assert len(time_key) == len(self.timed_levels)
+ assert time_key not in list(
+ self.started_timers
+ ), f"Timer {time_key} already started"
+ self.started_timers[time_key] = datetime.now()
- def record_time(
- self,
- dataset: int,
- variable_name: str,
- iteration: int,
- timed_event: str,
- ):
+ def record_time(self, time_key: Tuple):
"""
Compares the current time with the start time, and records the time difference
in our time log in the appropriate register. Times can stack for a context.
"""
- seconds = (dt.now() - self._start_time).total_seconds()
- time_key = (dataset, variable_name, iteration, timed_event)
+ assert time_key in list(self.started_timers), f"Timer {time_key} never started"
+ seconds = (datetime.now() - self.started_timers[time_key]).total_seconds()
+ del self.started_timers[time_key]
if time_key in self.time_seconds:
self.time_seconds[time_key] += seconds
else:
self.time_seconds[time_key] = seconds
- def get_time_df_summary(self):
+ def get_time_spend_summary(self):
"""
Returns a frame of the total time taken per variable, event.
Returns a pandas dataframe if pandas is installed. Otherwise, np.array.
"""
-
- if PANDAS_INSTALLED:
- dat = pd_Series(self.time_seconds.values(), index=self.time_seconds.keys())
- agg = dat.groupby(level=[1, 3]).sum()
- df = pd_DataFrame(agg).reset_index()
- df.columns = ["Variable", "Event", "Seconds"]
- piv = df.pivot_table(values="Seconds", index="Variable", columns="Event")
- return piv
- else:
- raise ValueError("Returning times as a frame requires pandas")
+ summary = Series(self.time_seconds)
+ summary.index.names = self.timed_levels
+ return summary
diff --git a/miceforest/utils.py b/miceforest/utils.py
index e83577c..4c8e34b 100644
--- a/miceforest/utils.py
+++ b/miceforest/utils.py
@@ -1,23 +1,27 @@
-from .compat import pd_DataFrame, pd_Series, pd_read_parquet
+from typing import Dict, List, Optional, Union
+
import numpy as np
from numpy.random import RandomState
-import blosc2
-import dill
-from typing import Union, List, Dict, Optional
+from pandas import DataFrame, Series
-_t_var_list = Union[List[str], List[int]]
-_t_var_dict = Union[Dict[str, List[str]], Dict[int, List[int]]]
-_t_var_sub = Union[Dict[Union[int, int], Union[int, float]]]
-_t_dat = Union[pd_DataFrame, np.ndarray]
-_t_random_state = Union[int, RandomState, None]
+def get_best_int_downcast(x: int):
+ assert isinstance(x, int)
+ int_dtypes = ["uint8", "uint16", "uint32", "uint64"]
+ np_iinfo_max = {dtype: np.iinfo(dtype).max for dtype in int_dtypes}
+ for dtype, max in np_iinfo_max.items():
+ if x <= max:
+ break
+ if dtype == "uint64":
+ raise ValueError("Number too large to downcast")
+ return dtype
def ampute_data(
- data: _t_dat,
- variables: Optional[_t_var_list] = None,
+ data: DataFrame,
+ variables: Optional[List[str]] = None,
perc: float = 0.1,
- random_state: _t_random_state = None,
+ random_state: Optional[Union[int, np.random.RandomState]] = None,
):
"""
Ampute Data
@@ -44,76 +48,24 @@ def ampute_data(
The amputed data
"""
amputed_data = data.copy()
- data_shape = amputed_data.shape
- amp_rows = int(perc * data_shape[0])
+ num_rows = amputed_data.shape[0]
+ amp_rows = int(perc * num_rows)
random_state = ensure_rng(random_state)
+ variables = list(data.columns) if variables is None else variables
- if len(data_shape) > 1:
- if variables is None:
- variables = [i for i in range(amputed_data.shape[1])]
- elif isinstance(variables, list):
- if isinstance(variables[0], str):
- assert isinstance(
- data, pd_DataFrame
- ), "np array was passed but variables are strings"
- variables = [data.columns.tolist().index(i) for i in variables]
-
- if isinstance(amputed_data, pd_DataFrame):
- for v in variables:
- na_ind = random_state.choice(
- np.arange(data_shape[0]), replace=False, size=amp_rows
- )
- amputed_data.iloc[na_ind, v] = np.NaN
-
- if isinstance(amputed_data, np.ndarray):
- amputed_data = amputed_data.astype("float64")
- for v in variables:
- na_ind = random_state.choice(
- np.arange(data_shape[0]), replace=False, size=amp_rows
- )
- amputed_data[na_ind, v] = np.NaN
-
- else:
- na_ind = random_state.choice(
- np.arange(data_shape[0]), replace=False, size=amp_rows
- )
- amputed_data[na_ind] = np.NaN
+ for col in variables:
+ ind = random_state.choice(amputed_data.index, size=amp_rows, replace=False)
+ amputed_data.loc[ind, col] = np.nan
return amputed_data
-def load_kernel(filepath: str, n_threads: Optional[int] = None):
- """
- Loads a kernel that was saved using save_kernel().
-
- Parameters
- ----------
- filepath: str
- The filepath of the saved kernel
-
- n_threads: int
- The threads to use for decompression. By default, all threads are used.
-
- Returns
- -------
- ImputationKernel
- """
- n_threads = blosc2.detect_number_of_cores() if n_threads is None else n_threads
- blosc2.set_nthreads(n_threads)
- with open(filepath, "rb") as f:
- kernel = dill.loads(blosc2.decompress(dill.load(f)))
-
- if kernel.original_data_class == "pd_DataFrame":
- kernel.working_data = pd_read_parquet(kernel.working_data)
- for col in kernel.working_data.columns:
- kernel.working_data[col] = kernel.working_data[col].astype(
- kernel.working_dtypes[col]
- )
-
- return kernel
-
-
-def stratified_subset(y, size, groups, cat, seed):
+def stratified_subset(
+ y: Series,
+ size: int,
+ groups: int,
+ random_state: Optional[Union[int, np.random.RandomState]],
+):
"""
Subsample y using stratification. y is divided into quantiles,
and then elements are randomly chosen from each quantile to
@@ -138,12 +90,14 @@ def stratified_subset(y, size, groups, cat, seed):
The indices of y that have been chosen.
"""
- rs = RandomState(seed)
- if isinstance(y, pd_Series):
- if y.dtype.name == "category":
- y = y.cat.codes
- y = y.values
+ random_state = ensure_rng(random_state=random_state)
+
+ cat = False
+ if y.dtype.name == "category":
+ cat = True
+ y = y.cat.codes
+ y = y.to_numpy()
if cat:
digits = y
@@ -158,7 +112,9 @@ def stratified_subset(y, size, groups, cat, seed):
digits_s = (digits_p * size).round(0).astype("int32")
diff = size - digits_s.sum()
if diff != 0:
- digits_fix = rs.choice(digits_i, size=abs(diff), p=digits_p, replace=False)
+ digits_fix = random_state.choice(
+ digits_i, size=abs(diff), p=digits_p, replace=False
+ )
if diff < 0:
for d in digits_fix:
digits_s[d] -= 1
@@ -172,7 +128,7 @@ def stratified_subset(y, size, groups, cat, seed):
d_v = digits_v[d_i]
n = digits_s[d_i]
ind = np.where(digits == d_v)[0]
- choice = rs.choice(ind, size=n, replace=False)
+ choice = random_state.choice(ind, size=n, replace=False)
sub[added : (added + n)] = choice
added += n
@@ -181,31 +137,28 @@ def stratified_subset(y, size, groups, cat, seed):
return sub
-def stratified_continuous_folds(y, nfold):
+def stratified_continuous_folds(y: Series, nfold: int):
"""
Create primitive stratified folds for continuous data.
Should be digestible by lightgbm.cv function.
"""
- if isinstance(y, pd_Series):
- y = y.values
- elements = len(y)
+ y = y.to_numpy()
+ elements = y.shape[0]
assert elements >= nfold, "more splits then elements."
sorted = np.argsort(y)
val = [sorted[range(i, len(y), nfold)] for i in range(nfold)]
for v in val:
- yield (np.setdiff1d(range(elements), v), v)
+ yield (np.setdiff1d(np.arange(elements), v), v)
-def stratified_categorical_folds(y, nfold):
+def stratified_categorical_folds(y: Series, nfold: int):
"""
Create primitive stratified folds for categorical data.
Should be digestible by lightgbm.cv function.
"""
- if isinstance(y, pd_Series):
- y = y.values
- y = y.reshape(
- y.shape[0],
- ).copy()
+ assert isinstance(y, Series), "y must be a pandas Series"
+ assert y.dtype.name[0:3].lower() == "int", "y should be the category codes"
+ y = y.to_numpy()
elements = len(y)
uniq, inv, counts = np.unique(y, return_counts=True, return_inverse=True)
assert elements >= nfold, "more splits then elements."
@@ -219,22 +172,46 @@ def stratified_categorical_folds(y, nfold):
# https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key
-# We don't really need to worry that much about diffusion
-# since we take % n at the end, and n (mmc) is usually
-# very small. This hash performs well enough in testing.
-def hash_int32(x):
+# This hash performs well enough in testing.
+def hash_int32(x: np.ndarray):
"""
A hash function which generates random uniform (enough)
int32 integers. Used in mean matching and initialization.
"""
assert isinstance(x, np.ndarray)
- assert x.dtype == "int32", "x must be int32"
+ assert x.dtype in ["uint32", "int32"], "x must be int32"
x = ((x >> 16) ^ x) * 0x45D9F3B
x = ((x >> 16) ^ x) * 0x45D9F3B
x = (x >> 16) ^ x
return x
+def hash_uint64(x: np.ndarray):
+ assert isinstance(x, np.ndarray)
+ assert x.dtype == "uint64", "x must be uint64"
+ x = (x ^ (x >> 30)) * 0xBF58476D1CE4E5B9
+ x = (x ^ (x >> 27)) * 0x94D049BB133111EB
+ x = x ^ (x >> 31)
+ return x
+
+
+def hash_numpy_int_array(x: np.ndarray, ind: Union[np.ndarray, slice] = slice(None)):
+ """
+ Deterministically set the values of the elements in x
+ at the locations ind to some uniformly distributed number
+ within the range of the datatype of x.
+
+ This function acts on x in place
+ """
+ assert isinstance(x, np.ndarray)
+ if x.dtype in ["uint32", "int32"]:
+ x[ind] = hash_int32(x[ind])
+ elif x.dtype == "uint64":
+ x[ind] = hash_uint64(x[ind])
+ else:
+ raise ValueError("random_seed_array must be uint32, int32, or uint64 datatype")
+
+
def _draw_random_int32(random_state, size):
nums = random_state.randint(
low=0, high=np.iinfo("int32").max, size=size, dtype="int32"
@@ -257,108 +234,44 @@ def ensure_rng(random_state) -> RandomState:
return random_state
-def _ensure_iterable(x):
- """
- If the object is iterable, return the object.
- Else, return the object in a length 1 list.
- """
- return x if hasattr(x, "__iter__") else [x]
+# def _ensure_iterable(x):
+# """
+# If the object is iterable, return the object.
+# Else, return the object in a length 1 list.
+# """
+# return x if hasattr(x, "__iter__") else [x]
-def _assert_dataset_equivalent(ds1: _t_dat, ds2: _t_dat):
- if isinstance(ds1, pd_DataFrame):
- assert isinstance(ds2, pd_DataFrame)
- assert ds1.equals(ds2)
- else:
- assert isinstance(ds2, np.ndarray)
- np.testing.assert_array_equal(ds1, ds2)
+# def _assert_dataset_equivalent(ds1: _t_dat, ds2: _t_dat):
+# if isinstance(ds1, DataFrame):
+# assert isinstance(ds2, DataFrame)
+# assert ds1.equals(ds2)
+# else:
+# assert isinstance(ds2, np.ndarray)
+# np.testing.assert_array_equal(ds1, ds2)
-def _ensure_np_array(x):
- if isinstance(x, np.ndarray):
- return x
- if isinstance(x, pd_DataFrame) | isinstance(x, pd_Series):
- return x.values
- else:
- raise ValueError("Can't cast to numpy array")
+# def _ensure_np_array(x):
+# if isinstance(x, np.ndarray):
+# return x
+# if isinstance(x, DataFrame) | isinstance(x, Series):
+# return x.values
+# else:
+# raise ValueError("Can't cast to numpy array")
-def _interpret_ds(val, avail_can):
- if isinstance(val, int):
- assert val <= avail_can, "data subset is more than available candidates"
- elif isinstance(val, float):
- assert (val <= 1.0) and (val > 0.0), "if float, 0.0 < data_subset <= 1.0"
- val = int(val * avail_can)
+def _expand_value_to_dict(default, value, keys) -> dict:
+ if isinstance(value, dict):
+ ret = {key: value.get(key, default) for key in keys}
else:
- raise ValueError("malformed data_subset passed")
- return val
-
+ assert default.__class__ == value.__class__
+ ret = {key: value for key in keys}
-def _dict_set_diff(iter1, iter2) -> Dict[int, List[int]]:
- """
- Returns a dict, where the elements in iter1 are
- the keys, and the values are the set differences
- between the key and the values of iter2.
- """
- ret = {int(y): [int(x) for x in iter2 if int(x) != int(y)] for y in iter1}
return ret
-def _slice(dat, row_slice=slice(None), col_slice=slice(None)):
- """
- Returns a view of the subset data if possible.
- """
-
- if isinstance(dat, pd_DataFrame):
- return dat.iloc[row_slice, col_slice]
- elif isinstance(dat, np.ndarray):
- return dat[row_slice, col_slice]
- else:
- raise ValueError("Unknown data class passed.")
-
-
-def _assign_col_values_without_copy(dat, row_ind, col_ind, val):
- """
- Insert values into different data frame objects.
- """
-
- row_ind = _ensure_iterable(row_ind)
-
- if isinstance(dat, pd_DataFrame):
- # Remove iterable attribute if
- # we are only assigning 1 value
- if len(val) == 1:
- val = val[0]
-
- dat.iloc[row_ind, col_ind] = val
-
- elif isinstance(dat, np.ndarray):
- val.shape = -1
- dat[row_ind, col_ind] = val
-
- else:
- raise ValueError("Unknown data class passed.")
-
-
-def _subset_data(dat, row_ind=None, col_ind=None, return_1d=False):
- """
- Can subset data along 2 axis.
- Explicitly returns a copy.
- """
-
- row_ind = range(dat.shape[0]) if row_ind is None else row_ind
- col_ind = range(dat.shape[1]) if col_ind is None else col_ind
-
- if isinstance(dat, pd_DataFrame):
- data_copy = dat.iloc[row_ind, col_ind]
- return data_copy.to_numpy().flatten() if return_1d else data_copy
- elif isinstance(dat, np.ndarray):
- row_ind = _ensure_iterable(row_ind)
- col_ind = _ensure_iterable(col_ind)
- data_copy = dat[np.ix_(row_ind, col_ind)]
- return data_copy.flatten() if return_1d else data_copy
- else:
- raise ValueError("Unknown data class passed.")
+def _list_union(x: List, y: List):
+ return [z for z in x if z in y]
def logodds(probability):
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..f98e475
--- /dev/null
+++ b/poetry.lock
@@ -0,0 +1,3482 @@
+# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand.
+
+[[package]]
+name = "aiofiles"
+version = "22.1.0"
+description = "File support for asyncio."
+optional = false
+python-versions = ">=3.7,<4.0"
+files = [
+ {file = "aiofiles-22.1.0-py3-none-any.whl", hash = "sha256:1142fa8e80dbae46bb6339573ad4c8c0841358f79c6eb50a493dceca14621bad"},
+ {file = "aiofiles-22.1.0.tar.gz", hash = "sha256:9107f1ca0b2a5553987a94a3c9959fe5b491fdf731389aa5b7b1bd0733e32de6"},
+]
+
+[[package]]
+name = "aiosqlite"
+version = "0.20.0"
+description = "asyncio bridge to the standard sqlite3 module"
+optional = false
+python-versions = ">=3.8"
+files = [
+ {file = "aiosqlite-0.20.0-py3-none-any.whl", hash = "sha256:36a1deaca0cac40ebe32aac9977a6e2bbc7f5189f23f4a54d5908986729e5bd6"},
+ {file = "aiosqlite-0.20.0.tar.gz", hash = "sha256:6d35c8c256637f4672f843c31021464090805bf925385ac39473fb16eaaca3d7"},
+]
+
+[package.dependencies]
+typing_extensions = ">=4.0"
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+]
+
+[project.optional-dependencies]
+plotting = [
+ "plotnine>=0.13.6",
+ "matplotlib>=3.3.0"
+]
+pipeline = [
+ "scikit-learn!=0.22.0"
+]
+
+[project.urls]
+Homepage = "https://github.com/AnotherSamWilson/miceforest"
+Issues = "https://github.com/AnotherSamWilson/miceforest/issues"
+changelog = "https://github.com/AnotherSamWilson/miceforest/releases"
+
+[tool.setuptools]
+packages = ['miceforest']
+
+[tool.poetry]
+name = "miceforest"
+version = "6.0.0"
+description = "Multiple Imputation by Chained Equations with LightGBM"
+authors = ["Sam Von Wilson"]
+package-mode = true
+
+[tool.poetry.dependencies]
+python = "^3.10"
+
+lightgbm = "^4.1.0"
+pandas = {extras = ["parquet"], version = "2.2.0"}
+numpy = "^1.26.0"
+dill = "^0.3.7"
+scipy = "^1.11.1"
+seaborn = "^0.13.0"
+matplotlib = "^3.3.0"
+scikit-learn = "^1.4.0"
+black = "^24.4.2"
+plotnine = "^0.13.6"
+
+[tool.poetry.group.dev.dependencies]
+ipython = "^8.17.2"
+pytest = "^8.0.0"
+jupyterlab = "^3.5.0"
+nbconvert = "^7.16.4"
+pandoc = "^2.3"
+isort = "^5.13.2"
+mypy = "^1.11.0"
+build = "^1.2.1"
+pytest-cov = "^5.0.0"
+
+[tool.mypy]
+ignore_missing_imports = true
+
+[tool.isort]
+profile = "black"
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index edaa2b6..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,7 +0,0 @@
-[metadata]
-description-file = README.md
-license_files = LICENSE.txt
-version = attr: miceforest.__version__
-
-[mypy]
-ignore_missing_imports = True
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 6d24b85..0000000
--- a/setup.py
+++ /dev/null
@@ -1,50 +0,0 @@
-from setuptools import setup, find_packages
-
-with open("README.md", "r", encoding='utf-8', errors='ignore') as fh:
- long_description = fh.read()
-
-
-setup(
- name="miceforest",
- author="Samuel Wilson",
- license="MIT",
- author_email="samwilson303@gmail.com",
- test_suite="tests",
- description="Missing Value Imputation using LightGBM",
- keywords=['MICE','Imputation','Missing Values','Missing','Random Forest'],
- long_description=long_description,
- long_description_content_type="text/markdown",
- install_requires=[
- 'lightgbm >= 3.3.1',
- 'numpy',
- "blosc2",
- "dill"
- ],
- extras_require={
- "Plotting": [
- 'seaborn >= 0.11.0',
- 'matplotlib >= 3.3.0'
- ],
- "Default_MM": [
- 'scipy >= 1.6.0'
- ],
- "Testing": [
- "pandas",
- "sklearn",
- "pyarrow"
- ],
- },
- url="https://github.com/AnotherSamWilson/miceforest",
- packages=find_packages(exclude=["tests.*", "tests"]),
- classifiers=[
- 'Natural Language :: English',
- 'Operating System :: MacOS',
- 'Operating System :: Microsoft :: Windows',
- 'Programming Language :: Python :: 3.9',
- 'Programming Language :: Python :: 3.10',
- 'Programming Language :: Python :: 3.11',
- "License :: OSI Approved :: MIT License",
- "Operating System :: OS Independent",
- ],
- python_requires='>=3.7',
-)
diff --git a/tests/test_ImputationKernel.py b/tests/test_ImputationKernel.py
index fe2f90d..ed24bb9 100644
--- a/tests/test_ImputationKernel.py
+++ b/tests/test_ImputationKernel.py
@@ -1,419 +1,369 @@
-
from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
import miceforest as mf
from datetime import datetime
-from miceforest import (
- mean_match_fast_cat,
- mean_match_shap
-)
from matplotlib.pyplot import close
from tempfile import mkstemp
+import dill
+
# Make random state and load data
# Define data
random_state = np.random.RandomState(1991)
iris = pd.concat(load_iris(as_frame=True, return_X_y=True), axis=1)
-iris['sp'] = iris['target'].astype('category')
-del iris['target']
-iris.rename({
- 'sepal length (cm)': 'sl',
- 'sepal width (cm)': 'ws',
- 'petal length (cm)': 'pl',
- 'petal width (cm)': 'pw',
-}, axis=1, inplace=True)
-iris['bc'] = pd.Series(np.random.binomial(n=1, p=0.5, size=150)).astype('category')
-iris_amp = mf.ampute_data(iris, perc=0.25, random_state=random_state)
-
+# iris = iris.sample(100000, replace=True)
+iris["sp"] = (
+ iris["target"]
+ .map({0: "Category1", 1: "Category2", 2: "Category3"})
+ .astype("category")
+)
+del iris["target"]
+iris.rename(
+ {
+ "sepal length (cm)": "sl",
+ "sepal width (cm)": "ws",
+ "petal length (cm)": "pl",
+ "petal width (cm)": "pw",
+ },
+ axis=1,
+ inplace=True,
+)
+iris["bi"] = (
+ pd.Series(np.random.binomial(n=1, p=0.5, size=iris.shape[0]))
+ .map({0: "FOO", 1: "BAR"})
+ .astype("category")
+)
+iris["ui8"] = iris["sl"].round(0).astype("UInt8")
+iris["ws"] = iris["ws"].astype("float32")
+iris.reset_index(drop=True, inplace=True)
+amputed_variables = ["sl", "ws", "pl", "sp", "bi", "ui8"]
+iris_amp = mf.ampute_data(
+ iris, variables=amputed_variables, perc=0.25, random_state=random_state
+)
+na_where = {var: np.where(iris_amp[var].isnull())[0] for var in iris_amp.columns}
+notnan_where = {
+ var: np.setdiff1d(np.arange(iris_amp.shape[0]), na_where[var], assume_unique=True)[
+ 0
+ ]
+ for var in iris_amp.columns
+}
+
+new_amputed_data = iris_amp.loc[range(20), :].reset_index(drop=True).copy()
+new_nonmissing_data = iris.loc[range(20), :].reset_index(drop=True).copy()
+
+# Make special datasets that have weird edge cases
+# Multiple columns with all missing values
+# sp is categorical, and pw had no missing
+# values in the original kernel data
+new_amputed_data_special_1 = iris_amp.loc[range(20), :].reset_index(drop=True).copy()
+for col in ["sp", "pw"]:
+ new_amputed_data_special_1[col] = np.nan
+ dtype = iris[col].dtype
+ new_amputed_data_special_1[col] = new_amputed_data_special_1[col].astype(dtype)
+
+# Some columns with no missing values
+new_amputed_data_special_2 = iris_amp.loc[range(20), :].reset_index(drop=True).copy()
+new_amputed_data_special_2[["sp", "ui8"]] = iris.loc[range(20), ["sp", "ui8"]]
+
+
+def make_and_test_kernel(**kwargs):
+
+ # kwargs = {
+ # "data": iris_amp,
+ # "num_datasets": 2,
+ # "mean_match_strategy": "normal",
+ # "save_all_iterations_data": True,
+ # }
+
+ # Build a normal kernel, run mice, save, load, and run mice again
+ kernel = mf.ImputationKernel(**kwargs)
+ assert kernel.iteration_count() == 0
+ kernel.mice(iterations=2, verbose=True)
+ assert kernel.iteration_count() == 2
+ new_file, filename = mkstemp()
+ with open(filename, "wb") as file:
+ dill.dump(kernel, file)
+ del kernel
+ with open(filename, "rb") as file:
+ kernel = dill.load(file)
+ kernel.mice(iterations=1, verbose=True)
+ assert kernel.iteration_count() == 3
-def test_defaults_pandas():
+ modeled_variables = kernel.model_training_order
+ imputed_variables = kernel.imputed_variables
- new_data = iris_amp.loc[range(10), :].copy()
+ # pw has no missing values.
+ assert "pw" not in imputed_variables
- kernel = mf.ImputationKernel(
- data=iris_amp,
- datasets=2,
- save_models=1
- )
- kernel.mice(iterations=2, compile_candidates=True, verbose=True)
+ # Make a completed dataset
+ completed_data = kernel.complete_data(dataset=0, inplace=False)
- kernel2 = mf.ImputationKernel(
- data=iris_amp,
- datasets=1,
- save_models=1
- )
- kernel2.mice(iterations=2)
+ # Make sure the data was imputed
+ assert all(completed_data[imputed_variables].isnull().sum() == 0)
- # Test appending and then test kernel.
- kernel.append(kernel2)
- kernel.compile_candidate_preds()
+ # Make sure the dtypes didn't change
+ for col, series in iris_amp.items():
+ dtype = series.dtype
+ assert completed_data[col].dtype == dtype
+ # Make sure the working data wasn't imputed
+ for var, naw in na_where.items():
+ if len(naw) > 0:
+ assert kernel.working_data.loc[naw, var].isnull().mean() == 1.0
- # Test mice after appendage
- kernel.mice(1, verbose=True)
+ # Make sure the original nonmissing data wasn't changed
+ for var, naw in notnan_where.items():
+ assert completed_data.loc[naw, var] == iris_amp.loc[naw, var]
+ # Impute the data in place now
kernel.complete_data(0, inplace=True)
- assert all(kernel.working_data.isnull().sum() == 0)
- assert kernel.get_model(0, 0, 3).params['objective'] == 'regression'
- assert kernel.get_model(0, 'bc', 3).params['objective'] == 'binary'
- assert kernel.get_model(0, 'sp', 3).params['objective'] == 'multiclass'
-
- # Make sure we didn't touch the original data
- assert all(iris_amp.isnull().sum() > 0)
-
- imp_ds = kernel.impute_new_data(new_data, verbose=True)
- imp_ds.complete_data(2,inplace=True)
- assert all(imp_ds.working_data.isnull().sum(0) == 0)
- assert new_data.isnull().sum().sum() > 0
-
- # Make sure fully-recognized data can be passed through with no changes
- imp_fr = kernel.impute_new_data(iris)
- comp_fr = imp_fr.complete_data(0)
- assert np.all(comp_fr == iris), "values of fully-recognized data were modified"
- assert imp_fr.iteration_count() == -1
- # Make sure single rows can be imputed
- single_row = new_data.iloc[[0], :]
- imp_sr = kernel.impute_new_data(single_row)
- assert np.all(imp_sr.complete_data(0).dtypes == single_row.dtypes)
-
-
-def test_complex_pandas():
-
- working_set = iris_amp.copy()
- new_data = working_set.loc[range(10), :].copy()
-
- # Customize everything.
- vs = {
- 'sl': ['ws', 'pl', 'pw', 'sp', 'bc'],
- 'ws': ['sl'],
- 'pl': ['sp', 'bc'],
- 'sp': ['sl', 'ws', 'pl', 'pw', 'bc'],
- 'pw': ['sl', 'ws', 'pl', 'sp', 'bc'],
- }
- mmc = {"sl": 4, 'ws': 0.01, "pl": 0}
- ds = {"sl": 100, "ws": 0.5}
- io = ['pw', 'pl', 'ws', 'sl']
+ # Assert we actually imputed the working data
+ assert all(kernel.working_data[imputed_variables].isnull().sum() == 0)
+
+ # Assert the original data was not touched
+ assert all(iris_amp[imputed_variables].isnull().sum() > 0)
+
+ # Make sure the models were trained the way we expect
+ for variable in modeled_variables:
+ if variable == "sp":
+ objective = "multiclass"
+ elif variable == "bi":
+ objective = "binary"
+ else:
+ objective = "regression"
+ assert (
+ kernel.get_model(variable=variable, dataset=0, iteration=1).params[
+ "objective"
+ ]
+ == objective
+ )
+ assert (
+ kernel.get_model(variable=variable, dataset=0, iteration=2).params[
+ "objective"
+ ]
+ == objective
+ )
+ assert (
+ kernel.get_model(variable=variable, dataset=1, iteration=1).params[
+ "objective"
+ ]
+ == objective
+ )
+ assert (
+ kernel.get_model(variable=variable, dataset=1, iteration=2).params[
+ "objective"
+ ]
+ == objective
+ )
+
+ # Impute a new dataset, and complete the data
+ imputed_new_data = kernel.impute_new_data(new_amputed_data, verbose=True)
+ imputed_dataset_0 = imputed_new_data.complete_data(
+ dataset=0, iteration=2, inplace=False
+ )
+ imputed_dataset_1 = imputed_new_data.complete_data(
+ dataset=1, iteration=2, inplace=False
+ )
- imputed_var_names = io
- non_imputed_var_names = [c for c in iris_amp if c not in imputed_var_names]
+ # Assert we didn't just impute the same thing for all values
+ assert not np.all(imputed_dataset_0 == imputed_dataset_1)
- from miceforest.builtin_mean_match_schemes import mean_match_shap
- mean_match_custom = mean_match_shap.copy()
- mean_match_custom.set_mean_match_candidates(mmc)
+ # Make sure we can impute the special cases
+ imputed_data_special_1 = kernel.impute_new_data(new_amputed_data_special_1)
- kernel = mf.ImputationKernel(
- data=working_set,
- datasets=2,
- variable_schema=vs,
- mean_match_scheme=mean_match_shap,
- imputation_order=io,
- train_nonmissing=True,
- data_subset=ds,
- categorical_feature='auto',
- copy_data=False
+ # Before we do anything else, make sure saving / loading works
+ new_file, filename = mkstemp()
+ with open(filename, "wb") as file:
+ dill.dump(imputed_data_special_1, file)
+ del imputed_data_special_1
+ with open(filename, "rb") as file:
+ imputed_data_special_1 = dill.load(file)
+
+ imputed_data_special_2 = kernel.impute_new_data(new_amputed_data_special_2)
+ imputed_dataset_special_1 = imputed_data_special_1.complete_data(0)
+ imputed_dataset_special_2 = imputed_data_special_2.complete_data(0)
+ assert not np.any(imputed_dataset_special_1[modeled_variables].isnull())
+ assert not np.any(imputed_dataset_special_2[modeled_variables].isnull())
+
+ # Reproducibility
+ random_seed_array = np.random.randint(
+ 9999, size=new_amputed_data_special_1.shape[0], dtype="uint32"
)
- kernel2 = mf.ImputationKernel(
- data=working_set,
- datasets=1,
- variable_schema=vs,
- mean_match_scheme=mean_match_shap,
- imputation_order=io,
- train_nonmissing=True,
- data_subset=ds,
- categorical_feature='auto',
- copy_data=False
+ imputed_data_special_3 = kernel.impute_new_data(
+ new_data=new_amputed_data_special_1,
+ random_seed_array=random_seed_array,
+ random_state=1,
)
- new_file, filename = mkstemp()
- kernel2.save_kernel(filename)
- kernel2 = mf.utils.load_kernel(filename)
-
- assert kernel.data_subset == {0: 100, 1: 56, 3: 113, 2: 113, 4: 113}, "mean_match_subset initialization failed"
- assert kernel.iteration_count() == 0, "iteration initialization failed"
- assert kernel.categorical_variables == [4, 5], "categorical recognition failed."
-
- # This section tests many things:
- # After saving / loading a kernel, and appending 2 kernels together:
- # mice can continue
- # Aliases are fixed, even when different aliases are passed
- # variable specific parameters supercede globally specified parameters
- # The parameters come through the actual model
- nround = 2
- kernel.mice(
- nround - 1,
- compile_candidates=True,
- variable_parameters={"sl": {"n_iter": 15}},
- num_trees=10,
- verbose=True
+ imputed_data_special_4 = kernel.impute_new_data(
+ new_data=new_amputed_data_special_1,
+ random_seed_array=random_seed_array,
+ random_state=1,
)
- kernel.compile_candidate_preds()
- kernel2.mice(nround - 1, variable_parameters={"sl": {"n_estimators": 15}}, n_estimators=10, verbose=True)
- kernel.append(kernel2)
- kernel.compile_candidate_preds()
- assert kernel.get_model(0, 0, nround - 1).num_trees() == 15
- assert kernel.get_model(0, 1, nround - 1).num_trees() == 10
- kernel.mice(1, variable_parameters={1: {"n_iter": 15}}, num_trees=10, verbose=True)
- assert kernel.iteration_count() == nround, "iteration counting is incorrect."
- assert kernel.get_model(0, 1, nround).num_trees() == 15
- assert kernel.get_model(0, 2, nround).num_trees() == 10
-
- # Make sure we only impute variables in variable_schema
- compdat = kernel.complete_data(0)
- assert all(compdat[imputed_var_names].isnull().sum() == 0)
- assert all(compdat[non_imputed_var_names].isnull().sum() > 0)
-
- # Test the ability to tune parameters with custom setup
- optimization_steps = 2
- op, ol = kernel.tune_parameters(
- dataset=0,
- optimization_steps=optimization_steps,
- variable_parameters={1: {"bagging_fraction": 0.9, "feature_fraction_bynode": (0.85, 0.9)}},
- bagging_fraction=0.8,
- feature_fraction_bynode=(0.70,0.75),
- verbose=True
+ assert imputed_data_special_3.complete_data(0).equals(
+ imputed_data_special_4.complete_data(0)
)
- assert op[1]["bagging_fraction"] == 0.9
- assert op[2]["bagging_fraction"] == 0.8
- assert (op[1]["feature_fraction_bynode"] >= 0.85) and (op[1]["feature_fraction_bynode"] <= 0.9)
- assert (op[2]["feature_fraction_bynode"] >= 0.70) and (op[2]["feature_fraction_bynode"] <= 0.75)
- kernel.mice(1, variable_parameters=op, verbose=True)
- model_2_params = kernel.get_model(0, 2, nround + 1).params
- model_1_params = kernel.get_model(0, 1, nround + 1).params
- assert model_2_params["bagging_fraction"] == 0.8
- assert model_1_params["bagging_fraction"] == 0.9
- assert (model_2_params["feature_fraction_bynode"] >= 0.70) and (model_2_params["feature_fraction_bynode"] <= 0.75)
- assert (model_1_params["feature_fraction_bynode"] >= 0.85) and (model_1_params["feature_fraction_bynode"] <= 0.9)
-
- new_imp_dat = kernel.impute_new_data(new_data=new_data, verbose=True)
- new_imp_complete = new_imp_dat.complete_data(0)
- assert all(new_imp_complete[imputed_var_names].isnull().sum() == 0)
-
- # Plotting on multiple imputed dataset
- new_imp_dat.plot_mean_convergence()
- close()
- new_imp_dat.plot_imputed_distributions()
- close()
-
- # Plotting on Multiple Imputed Kernel
- kernel.plot_feature_importance(0)
- close()
- kernel.plot_mean_convergence()
- close()
- kernel.plot_imputed_distributions()
- close()
-
-
-def test_defaults_numpy():
-
- iris_np = iris.copy()
- iris_np["sp"] = iris_np["sp"].cat.codes
- iris_np = iris_np.values
- iris_np_amp = mf.ampute_data(iris_np, perc=0.25)
- new_data = iris_np_amp[range(10), :].copy()
-
- s = datetime.now()
- kernel = mf.ImputationKernel(
- data=iris_np_amp,
- datasets=3,
- categorical_feature=[4],
- mean_match_scheme=mean_match_fast_cat
+ # Ensure kernel imputes new data on a subset of datasets deterministically
+ if kernel.num_datasets > 1:
+ datasets = list(range(kernel.num_datasets))
+ datasets.remove(0)
+ imputed_data_special_5 = kernel.impute_new_data(
+ new_data=new_amputed_data_special_1,
+ datasets=datasets,
+ random_seed_array=random_seed_array,
+ random_state=1,
+ verbose=True,
+ )
+ imputed_data_special_6 = kernel.impute_new_data(
+ new_data=new_amputed_data_special_1,
+ datasets=datasets,
+ random_seed_array=random_seed_array,
+ random_state=1,
+ )
+ assert imputed_data_special_5.complete_data(1).equals(
+ imputed_data_special_6.complete_data(1)
+ )
+
+ mv = kernel.modeled_variables
+
+ # Test tuning parameters
+ kernel.tune_parameters(
+ optimization_steps=2,
+ use_gbdt=True,
+ random_state=1,
+ variable_parameters={
+ mv[0]: {
+ "min_data_in_leaf": (1, 10),
+ "cat_l2": 0.5,
+ }
+ },
+ extra_trees=[True, False],
)
- kernel.mice(iterations=1, verbose=True)
- kernel.compile_candidate_preds()
-
- # Complete data with copy.
- comp_dat = kernel.complete_data(0, inplace=False)
-
- # We didn't complete data in place. Make sure we created
- # a copy, and did not affect internal data or original data.
- assert all(np.isnan(comp_dat).sum(0) == 0)
- assert all(np.isnan(kernel.working_data).sum(0) > 0)
- assert all(np.isnan(iris_np_amp).sum(0) > 0)
+ op = kernel.optimal_parameters[mv[0]]
+ assert "extra_trees" in list(op)
+ assert op["cat_l2"] == 0.5
+ assert 1 <= op["min_data_in_leaf"] <= 10
+
+ kernel.tune_parameters(
+ optimization_steps=2,
+ use_gbdt=False,
+ random_state=1,
+ variable_parameters={
+ mv[0]: {
+ "min_data_in_leaf": (1, 10),
+ "cat_l2": 0.5,
+ }
+ },
+ extra_trees=[True, False],
+ )
+ op = kernel.optimal_parameters[mv[0]]
+ assert "extra_trees" in list(op)
+ assert op["cat_l2"] == 0.5
+ assert 1 <= op["min_data_in_leaf"] <= 10
- # Complete data in place
- kernel.complete_data(0, inplace=True)
+ # Test plotting
+ kernel.plot_imputed_distributions()
+ kernel.plot_feature_importance(dataset=0)
- # We completed data in place. Make sure we only affected
- # the kernel.working_data and not the original data.
- assert all(np.isnan(kernel.working_data).sum(0) == 0)
- assert all(np.isnan(iris_np_amp).sum(0) > 0)
+ return kernel
- imp_ds = kernel.impute_new_data(new_data)
- imp_ds.complete_data(0,inplace=True)
- assert all(np.isnan(imp_ds.working_data).sum(0) == 0)
- assert np.isnan(new_data).sum() > 0
- # Make sure fully-recognized data can be passed through with no changes
- imp_fr = kernel.impute_new_data(iris_np)
- comp_fr = imp_fr.complete_data(0)
- assert np.all(comp_fr == iris_np), "values of fully-recognized data were modified"
- assert imp_fr.iteration_count() == -1
+def test_defaults():
+ kernel_normal = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ mean_match_strategy="normal",
+ save_all_iterations_data=True,
+ )
+ kernel_fast = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ mean_match_strategy="fast",
+ save_all_iterations_data=True,
+ )
+ kernel_shap = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ mean_match_strategy="shap",
+ save_all_iterations_data=True,
+ )
+ kernel_iwp = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ mean_match_candidates=0,
+ save_all_iterations_data=True,
+ )
-def test_complex_numpy():
- iris_np = iris.copy()
- iris_np["sp"] = iris_np["sp"].cat.codes
- iris_np = iris_np.values
- iris_np_amp = mf.ampute_data(iris_np, perc=0.25)
- new_data = iris_np_amp[range(25), :].copy()
+def test_complex():
- # Specify that models should be built for variables 1, 2, 3, 4
# Customize everything.
vs = {
- 0: [1, 2, 3, 4, 5],
- 1: [0],
- 2: [4, 5],
- 3: [0, 1, 2, 4, 5],
- 4: [0, 1, 2, 3, 5],
+ "sl": ["ws", "pl", "pw", "sp", "bi"],
+ "ws": ["sl"],
+ "pl": ["sp", "bi"],
+ # 'sp': ['sl', 'ws', 'pl', 'pw', 'bc'], # Purposely don't train a variable that does have missing values
+ "pw": ["sl", "ws", "pl", "sp", "bi"],
+ "bi": ["ws", "pl", "sp"],
+ "ui8": ["sp", "ws"],
}
- mmc = {0: 4, 1: 0.1, 2: 0}
- ds = {0: 100, 1: 0.5}
- io = [0, 1, 2, 3, 4]
- niv = [v for v in range(iris_amp.shape[1]) if v not in list(vs)]
-
- mmfc = mean_match_fast_cat.copy()
- mmfc.set_mean_match_candidates(mean_match_candidates=mmc)
- kernel = mf.ImputationKernel(
- data=iris_np_amp,
- datasets=2,
+ mmc = {"sl": 4, "ws": 0, "bi": 5}
+ ds = {"sl": int(iris_amp.shape[0] / 2), "ws": 50}
+
+ imputed_var_names = list(vs)
+ non_imputed_var_names = [c for c in iris_amp if c not in imputed_var_names]
+
+ # Build a normal kernel, run mice, save, load, and run mice again
+ kernel = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
variable_schema=vs,
- imputation_order=io,
- train_nonmissing=True,
+ mean_match_candidates=mmc,
data_subset=ds,
- mean_match_scheme=mmfc,
- categorical_feature=[4],
- copy_data=False,
- save_loggers=True
+ mean_match_strategy="normal",
+ save_all_iterations_data=True,
)
-
- kernel2 = mf.ImputationKernel(
- data=iris_np_amp,
- datasets=1,
+ assert kernel.data_subset == {
+ "sl": 75,
+ "ws": 50,
+ "pl": 0,
+ "bi": 0,
+ "ui8": 0,
+ "pw": 0,
+ }, "mean_match_subset initialization failed"
+
+ kernel_fast = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
variable_schema=vs,
- imputation_order=io,
- train_nonmissing=True,
+ mean_match_candidates=mmc,
data_subset=ds,
- mean_match_scheme=mmfc.copy(),
- categorical_feature=[4],
- copy_data=False,
- save_loggers=True
+ mean_match_strategy="fast",
+ save_all_iterations_data=True,
)
- new_file, filename = mkstemp()
- kernel2.save_kernel(filename)
- kernel2 = mf.utils.load_kernel(filename)
-
- assert kernel.data_subset == {0: 100, 1: 56, 2: 113, 3: 113, 4: 113}, "mean_match_subset initialization failed"
- assert kernel.iteration_count() == 0, "iteration initialization failed"
- assert kernel.categorical_variables == [4], "categorical recognition failed."
-
- nround = 2
- kernel.mice(nround - 1, variable_parameters={1: {"n_iter": 15}}, num_trees=10, verbose=True)
- kernel.compile_candidate_preds()
- kernel2.mice(nround - 1, variable_parameters={1: {"n_iter": 15}}, num_trees=10, verbose=True)
- kernel.append(kernel2)
- kernel.compile_candidate_preds()
- assert kernel.get_model(0, 1, nround - 1).num_trees() == 15
- assert kernel.get_model(0, 2, nround - 1).num_trees() == 10
- kernel.mice(1, variable_parameters={1: {"n_estimators": 15}}, n_estimators=10, verbose=True)
- assert kernel.iteration_count() == nround, "iteration counting is incorrect."
- assert kernel.get_model(0, 1, nround).num_trees() == 15
- assert kernel.get_model(0, 2, nround).num_trees() == 10
-
- # Complete data with copy. Make sure only correct datasets and variables were affected.
- compdat = kernel.complete_data(0, inplace=False)
- assert all(np.isnan(compdat[:,io]).sum(0) == 0)
- assert all(np.isnan(compdat[:,niv]).sum(0) > 0)
-
- # Should have no affect on working_data
- assert all(np.isnan(kernel.working_data).sum(0) > 0)
-
- # Should have no affect on working_set
- assert all(np.isnan(iris_np_amp).sum(0) > 0)
-
- # Now complete the data in place
- kernel.complete_data(0, inplace=True)
- # Should have affect on working_data and original data
- assert all(np.isnan(kernel.working_data[:, io]).sum(0) == 0)
- assert all(np.isnan(iris_np_amp[:, io]).sum(0) == 0)
- assert all(np.isnan(kernel.working_data[:, niv]).sum(0) > 0)
- assert all(np.isnan(iris_np_amp[:, niv]).sum(0) > 0)
-
- # Test the ability to tune parameters with custom setup
- optimization_steps = 2
- op, ol = kernel.tune_parameters(
- dataset=0,
- optimization_steps=optimization_steps,
- variable_parameters={1: {"bagging_fraction": 0.9, "feature_fraction_bynode": (0.85, 0.9)}},
- bagging_fraction=0.8,
- feature_fraction_bynode=(0.70,0.75),
- verbose=True
+ mmc_shap = mmc.copy()
+ mmc_shap["ws"] = 1
+ kernel_shap = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ variable_schema=vs,
+ mean_match_candidates=mmc_shap,
+ data_subset=ds,
+ mean_match_strategy="shap",
+ save_all_iterations_data=True,
)
- assert op[1]["bagging_fraction"] == 0.9
- assert op[2]["bagging_fraction"] == 0.8
- assert (op[1]["feature_fraction_bynode"] >= 0.85) and (op[1]["feature_fraction_bynode"] <= 0.9)
- assert (op[2]["feature_fraction_bynode"] >= 0.70) and (op[2]["feature_fraction_bynode"] <= 0.75)
- kernel.mice(1, variable_parameters=op, verbose=True)
- model_2_params = kernel.get_model(0, 2, nround + 1).params
- model_1_params = kernel.get_model(0, 1, nround + 1).params
- assert model_2_params["bagging_fraction"] == 0.8
- assert model_1_params["bagging_fraction"] == 0.9
- assert (model_2_params["feature_fraction_bynode"] >= 0.70) and (model_2_params["feature_fraction_bynode"] <= 0.75)
- assert (model_1_params["feature_fraction_bynode"] >= 0.85) and (model_1_params["feature_fraction_bynode"] <= 0.9)
-
- new_imp_dat = kernel.impute_new_data(new_data=new_data, copy_data=True, verbose=True)
-
- # Not in place
- new_imp_complete = new_imp_dat.complete_data(0, inplace=False)
- assert all(np.isnan(new_imp_complete[:, list(vs)]).sum(0) == 0)
- assert all(np.isnan(new_imp_complete[:, niv]).sum(0) > 0)
-
-
- # Should have no affect on working_data or original data
- assert all(np.isnan(new_imp_dat.working_data).sum(0) > 0)
- assert all(np.isnan(new_data[:, list(vs)]).sum(0) > 0)
-
- # complete data in place
- new_imp_dat.complete_data(0, inplace=True)
- assert all(np.isnan(new_imp_dat.working_data[:, list(vs)]).sum(0) == 0)
- assert all(np.isnan(new_data[:, niv]).sum(0) > 0)
-
- # Alter in place
- new_imp_dat = kernel.impute_new_data(new_data=new_data, copy_data=False, verbose=True)
-
- # Before completion, nan's should still exist in data:
- assert all(np.isnan(new_data).sum(0) > 0)
- assert all(np.isnan(new_imp_dat.working_data).sum(0) > 0)
-
- # Complete data not in place
- new_imp_complete = new_imp_dat.complete_data(0, inplace=False)
- assert all(np.isnan(new_imp_complete[:, niv]).sum(0) > 0)
- assert all(np.isnan(new_imp_complete[:, list(vs)]).sum(0) == 0)
- assert all(np.isnan(new_data).sum(0) > 0)
- assert all(np.isnan(new_imp_dat.working_data).sum(0) > 0)
-
- # Complete data in place
- new_imp_dat.complete_data(0, inplace=True)
- assert all(np.isnan(new_data[:, niv]).sum(0) > 0)
- assert all(np.isnan(new_data[:, list(vs)]).sum(0) == 0)
- assert all(np.isnan(new_imp_dat.working_data[:, niv]).sum(0) > 0)
- assert all(np.isnan(new_imp_dat.working_data[:, list(vs)]).sum(0) == 0)
-
-
- # Plotting on multiple imputed dataset
- new_imp_dat.plot_mean_convergence()
- close()
- new_imp_dat.plot_imputed_distributions()
- close()
-
- # Plotting on Multiple Imputed Kernel
- kernel.plot_feature_importance(0)
- close()
- kernel.plot_mean_convergence()
- close()
- kernel.plot_imputed_distributions()
- close()
+ mixed_mms = {"sl": "shap", "ws": "fast", "ui8": "fast", "bi": "normal"}
+ kernel_mixed = make_and_test_kernel(
+ data=iris_amp,
+ num_datasets=2,
+ variable_schema=vs,
+ mean_match_candidates=mmc,
+ data_subset=ds,
+ mean_match_strategy=mixed_mms,
+ save_all_iterations_data=True,
+ )
diff --git a/tests/test_ImputedData.py b/tests/test_ImputedData.py
deleted file mode 100644
index e69de29..0000000
diff --git a/tests/test_imputed_accuracy.py b/tests/test_imputed_accuracy.py
index b59fb23..929c0b3 100644
--- a/tests/test_imputed_accuracy.py
+++ b/tests/test_imputed_accuracy.py
@@ -1,311 +1,184 @@
-
-
from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
import miceforest as mf
-from miceforest.utils import logistic_function
from sklearn.metrics import roc_auc_score
-from miceforest import (
- mean_match_fast_cat,
- mean_match_default,
- mean_match_shap
-)
-
-random_state = np.random.RandomState(5)
-iris = pd.concat(load_iris(return_X_y=True, as_frame=True), axis=1)
-iris["binary"] = random_state.binomial(1,(iris["target"] + 0.2) / 2.5, size=150)
-iris["target"] = iris["target"].astype("category")
-iris["binary"] = iris["binary"].astype("category")
-iris.columns = [c.replace(" ", "") for c in iris.columns]
-iris = pd.concat([iris] * 2, axis=0, ignore_index=True)
-iris_amp = mf.utils.ampute_data(iris, perc=0.20)
-iris_new = iris.iloc[random_state.choice(iris.index, iris.shape[0], replace=False)].reset_index(drop=True)
-iris_new_amp = mf.utils.ampute_data(iris_new, perc=0.20)
-
-
-def mse(x, y):
- return np.mean((x-y) ** 2)
-
-iterations = 2
-
-kernel_sm2 = mf.ImputationKernel(
- iris_amp,
- datasets=1,
- data_subset=0.75,
- mean_match_scheme=mean_match_fast_cat,
- save_models=2,
- random_state=1
-)
-kernel_sm2.mice(
- iterations,
- boosting='random_forest',
- num_iterations=100,
- num_leaves=31
-)
-
-kernel_sm1 = mf.ImputationKernel(
- iris_amp,
- datasets=1,
- data_subset=0.75,
- mean_match_scheme=mean_match_default,
- save_models=1,
- random_state=1
-)
-kernel_sm1.mice(
- iterations,
- boosting='random_forest',
- num_iterations=100,
- num_leaves=31
-)
-
-kernel_shap = mf.ImputationKernel(
- iris_amp,
- datasets=1,
- data_subset=0.75,
- mean_match_scheme=mean_match_shap,
- save_models=1,
- random_state=1
-)
-kernel_shap.mice(
- iterations,
- boosting='random_forest',
- num_iterations=100,
- num_leaves=31,
-)
-
-
-def test_sm2_mice_cat():
-
- # Binary
- col = 5
- ind = kernel_sm2.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_sm2[0, col, iterations]
- preds = logistic_function(kernel_sm2.get_raw_prediction(col, dtype="float32"))
- roc = roc_auc_score(orig, preds[ind])
- acc = (imps == orig).mean()
- assert roc > 0.6
- assert acc > 0.6
-
- # Multiclass
- col = 4
- ind = kernel_sm2.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_sm2[0, col, iterations]
- preds = kernel_sm2.get_raw_prediction(col, dtype="float32")
- roc = roc_auc_score(orig, preds[ind,:], multi_class='ovr', average='macro')
- acc = (imps == orig).mean()
- assert roc > 0.7
- assert acc > 0.7
-
-def test_sm2_mice_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- modeled_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = kernel_sm2.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris.iloc[ind, col]
- preds = kernel_sm2.get_raw_prediction(col)
- imps = kernel_sm2[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- modeled_errors[col] = mse(orig, preds[ind])
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > modeled_errors[col]
- assert random_sample_error[col] > imputed_errors[col]
-
-
-def test_sm1_mice_cat():
-
- # Binary
- col = 5
- ind = kernel_sm1.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_sm1[0, col, iterations]
- preds = logistic_function(kernel_sm1.get_raw_prediction(col, dtype="float32"))
- roc = roc_auc_score(orig, preds[ind])
- acc = (imps == orig).mean()
- assert roc > 0.6
- assert acc > 0.6
-
- # Multiclass
- col = 4
- ind = kernel_sm1.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_sm1[0, col, iterations]
- preds = logistic_function(kernel_sm1.get_raw_prediction(col, dtype="float32"))
- roc = roc_auc_score(orig, preds[ind,:], multi_class='ovr', average='macro')
- acc = (imps == orig).mean()
- assert roc > 0.7
- assert acc > 0.7
-
-
-def test_sm1_mice_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- modeled_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = kernel_sm1.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris.iloc[ind, col]
- preds = kernel_sm1.get_raw_prediction(col)
- imps = kernel_sm1[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- modeled_errors[col] = mse(orig, preds[ind])
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > modeled_errors[col]
- assert random_sample_error[col] > imputed_errors[col]
-
-
-def test_shap_mice_cat():
-
- # Binary
- col = 5
- ind = kernel_shap.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_shap[0, col, iterations]
- preds = kernel_shap.get_raw_prediction(col, dtype="float32")
- roc = roc_auc_score(orig, logistic_function(preds[ind, :].sum(1)))
- acc = (imps == orig).mean()
- assert roc > 0.6
- assert acc > 0.6
-
- # Multiclass
- col = 4
- ind = kernel_shap.na_where[col]
- orig = iris.values[ind, col]
- imps = kernel_shap[0, col, iterations]
- # preds = logistic_function(kernel_shap.get_raw_prediction(col, dtype="float32"))
- # roc = roc_auc_score(orig, preds[ind,:], multi_class='ovr', average='macro')
- acc = (imps == orig).mean()
- # assert roc > 0.7
- assert acc > 0.7
-
-
-def test_shap_mice_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- modeled_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = kernel_shap.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris.iloc[ind, col]
- preds = kernel_shap.get_raw_prediction(col).sum(1) + orig.mean()
- imps = kernel_shap[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- modeled_errors[col] = mse(orig, preds[ind])
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > modeled_errors[col]
- assert random_sample_error[col] > imputed_errors[col]
-
-
-################################
-### IMPUTE NEW DATA TESTING
-
-new_imp_sm2 = kernel_sm2.impute_new_data(iris_new_amp)
-new_imp_sm1 = kernel_sm1.impute_new_data(iris_new_amp)
-new_imp_shap = kernel_shap.impute_new_data(iris_new_amp)
-
-
-def test_sm2_ind_cat():
-
- # Binary
- col = 5
- ind = new_imp_sm2.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_sm2[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.6
-
- # Multiclass
- col = 4
- ind = new_imp_sm2.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_sm2[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.7
-
-def test_sm2_ind_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = new_imp_sm2.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris_new.iloc[ind, col]
- imps = new_imp_sm2[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > imputed_errors[col]
-
-def test_sm1_ind_cat():
-
- # Binary
- col = 5
- ind = new_imp_sm1.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_sm1[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.6
-
- # Multiclass
- col = 4
- ind = new_imp_sm1.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_sm1[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.7
-
-def test_sm1_ind_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = new_imp_sm1.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris_new.iloc[ind, col]
- imps = new_imp_sm1[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > imputed_errors[col]
-
-def test_shap_ind_cat():
-
- # Binary
- col = 5
- ind = new_imp_shap.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_shap[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.6
- # Multiclass
- col = 4
- ind = new_imp_shap.na_where[col]
- orig = iris_new.values[ind, col]
- imps = new_imp_shap[0, col, iterations]
- acc = (imps == orig).mean()
- assert acc > 0.7
-def test_shap_ind_reg():
- # Square error of the model predictions should be less than
- # if we just predicted the mean every time.
- imputed_errors = {}
- random_sample_error = {}
- for col in [0,1,2,3]:
- ind = new_imp_shap.na_where[col]
- nonmissind = np.delete(range(iris.shape[0]), ind)
- orig = iris_new.iloc[ind, col]
- imps = new_imp_shap[0, col, iterations]
- random_sample_error[col] = mse(orig, np.mean(iris.iloc[nonmissind, col]))
- imputed_errors[col] = mse(orig, imps)
- assert random_sample_error[col] > imputed_errors[col]
+def make_dataset(seed):
+
+ random_state = np.random.RandomState(seed)
+ iris = pd.concat(load_iris(return_X_y=True, as_frame=True), axis=1)
+ iris["bi"] = random_state.binomial(
+ 1, (iris["target"] == 0).map({True: 0.85, False: 0.15}), size=150
+ )
+ iris["bi"] = iris["bi"].astype("category")
+ iris["sp"] = iris["target"].map({0: "A", 1: "B", 2: "C"}).astype("category")
+ del iris["target"]
+ iris.rename(
+ {
+ "sepal length (cm)": "sl",
+ "sepal width (cm)": "sw",
+ "petal length (cm)": "pl",
+ "petal width (cm)": "pw",
+ },
+ axis=1,
+ inplace=True,
+ )
+ iris_amp = mf.utils.ampute_data(iris, perc=0.20)
+
+ return iris, iris_amp
+
+
+def get_numeric_performance(kernel, variables, iris):
+ r_squares = {}
+ iterations = kernel.iteration_count()
+ for col in variables:
+ ind = kernel.na_where[col]
+ orig = iris.loc[ind, col]
+ imps = kernel[col, iterations, 0]
+ r_squares[col] = np.corrcoef(orig, imps)[0, 1] ** 2
+ r_squares = pd.Series(r_squares)
+ return r_squares
+
+
+def get_imp_mse(kernel, variables, iris):
+ mses = {}
+ iterations = kernel.iteration_count()
+ for col in variables:
+ ind = kernel.na_where[col]
+ orig = iris.loc[ind, col]
+ imps = kernel[col, iterations, 0]
+ mses[col] = ((orig - imps) ** 2).sum()
+ mses = pd.Series(mses)
+ return mses
+
+
+def get_mean_pred_mse(kernel: mf.ImputationKernel, variables, iris):
+ mses = {}
+ for col in variables:
+ ind = kernel.na_where[col]
+ orig = iris.loc[ind, col]
+ target = kernel._get_nonmissing_values(col)
+ pred = target.mean()
+ mses[col] = ((orig - pred) ** 2).sum()
+ mses = pd.Series(mses)
+ return mses
+
+
+def get_categorical_performance(kernel: mf.ImputationKernel, variables, iris):
+
+ rocs = {}
+ accs = {}
+ rand_accs = {}
+ iterations = kernel.iteration_count()
+ for col in variables:
+ ind = kernel.na_where[col]
+ model = kernel.get_model(col, 0, -1)
+ cand = kernel._make_label(col, seed=model.params["seed"])
+ orig = iris.loc[ind, col]
+ imps = kernel[col, iterations, 0]
+ bf = kernel.get_bachelor_features(col)
+ preds = model.predict(bf)
+ rocs[col] = roc_auc_score(orig, preds, multi_class="ovr", average="macro")
+ accs[col] = (imps == orig).mean()
+ rand_accs[col] = np.sum(
+ cand.value_counts(normalize=True) * imps.value_counts(normalize=True)
+ )
+ rocs = pd.Series(rocs)
+ accs = pd.Series(accs)
+ rand_accs = pd.Series(rand_accs)
+ return rocs, accs, rand_accs
+
+
+def test_defaults():
+
+ for i in range(10):
+ # i = 0
+ iris, iris_amp = make_dataset(i)
+ kernel_1 = mf.ImputationKernel(
+ iris_amp,
+ num_datasets=1,
+ data_subset=0,
+ mean_match_candidates=3,
+ initialize_empty=True,
+ random_state=i,
+ )
+ kernel_1.mice(4, verbose=False)
+ kernel_1.complete_data(0, inplace=True)
+
+ rocs, accs, rand_accs = get_categorical_performance(
+ kernel_1, ["bi", "sp"], iris
+ )
+ assert np.all(accs > rand_accs)
+ assert np.all(rocs > 0.6)
+
+ # sw Just doesn't have the information density to pass this test reliably.
+ # It's definitely the hardest variable to model.
+ mses = get_imp_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ mpses = get_mean_pred_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ assert np.all(mpses > mses)
+
+
+def test_no_mean_match():
+
+ for i in range(10):
+ # i = 0
+ iris, iris_amp = make_dataset(i)
+ kernel_1 = mf.ImputationKernel(
+ iris_amp,
+ num_datasets=1,
+ data_subset=0,
+ mean_match_candidates=0,
+ initialize_empty=True,
+ random_state=i,
+ )
+ kernel_1.mice(4, verbose=False)
+ kernel_1.complete_data(0, inplace=True)
+
+ rocs, accs, rand_accs = get_categorical_performance(
+ kernel=kernel_1, variables=["bi", "sp"], iris=iris
+ )
+ assert np.all(accs > rand_accs)
+ assert np.all(rocs > 0.5)
+
+ # sw Just doesn't have the information density to pass this test reliably.
+ # It's definitely the hardest variable to model.
+ mses = get_imp_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ mpses = get_mean_pred_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ assert np.all(mpses > mses)
+
+
+def test_custom_params():
+
+ for i in range(10):
+ # i = 0
+ iris, iris_amp = make_dataset(i)
+ kernel_1 = mf.ImputationKernel(
+ iris_amp,
+ num_datasets=1,
+ data_subset=0,
+ mean_match_candidates=1,
+ initialize_empty=True,
+ random_state=i,
+ )
+ kernel_1.mice(
+ iterations=4,
+ verbose=False,
+ boosting="random_forest",
+ num_iterations=500,
+ min_data_in_leaf=2,
+ )
+ kernel_1.complete_data(0, inplace=True)
+
+ rocs, accs, rand_accs = get_categorical_performance(
+ kernel=kernel_1, variables=["bi", "sp"], iris=iris
+ )
+ assert np.all(accs > rand_accs)
+ assert np.all(rocs > 0.5)
+
+ # sw Just doesn't have the information density to pass this test reliably.
+ # It's definitely the hardest variable to model.
+ mses = get_imp_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ mpses = get_mean_pred_mse(kernel_1, ["sl", "pl", "pw"], iris)
+ assert np.all(mpses > mses)
diff --git a/tests/test_reproducibility.py b/tests/test_reproducibility.py
index 14b526e..0ce2167 100644
--- a/tests/test_reproducibility.py
+++ b/tests/test_reproducibility.py
@@ -1,4 +1,3 @@
-
from sklearn.datasets import load_iris
import pandas as pd
import numpy as np
@@ -9,115 +8,119 @@
# Define data
random_state = np.random.RandomState(1991)
iris = pd.concat(load_iris(as_frame=True, return_X_y=True), axis=1)
-iris['sp'] = iris['target'].astype('category')
-del iris['target']
-iris.rename({
- 'sepal length (cm)': 'sl',
- 'sepal width (cm)': 'ws',
- 'petal length (cm)': 'pl',
- 'petal width (cm)': 'pw',
-}, axis=1, inplace=True)
-iris['bc'] = pd.Series(np.random.binomial(n=1, p=0.5, size=150)).astype('category')
+iris["sp"] = iris["target"].astype("category")
+del iris["target"]
+iris.rename(
+ {
+ "sepal length (cm)": "sl",
+ "sepal width (cm)": "ws",
+ "petal length (cm)": "pl",
+ "petal width (cm)": "pw",
+ },
+ axis=1,
+ inplace=True,
+)
+iris["bc"] = pd.Series(np.random.binomial(n=1, p=0.5, size=150)).astype("category")
iris_amp = mf.ampute_data(iris, perc=0.25, random_state=random_state)
rows = iris_amp.shape[0]
-random_seed_array = np.random.choice(
- range(1000),
- size=rows,
- replace=False
-).astype("int32")
+random_seed_array = np.random.choice(range(1000), size=rows, replace=False).astype(
+ "int32"
+)
def test_pandas_reproducibility():
datasets = 2
kernel = mf.ImputationKernel(
- data=iris_amp,
- datasets=datasets,
- initialization="random",
- save_models=2,
- random_state=2
+ data=iris_amp, num_datasets=datasets, initialize_empty=False, random_state=2
)
kernel2 = mf.ImputationKernel(
- data=iris_amp,
- datasets=datasets,
- initialization="random",
- save_models=2,
- random_state=2
+ data=iris_amp, num_datasets=datasets, initialize_empty=False, random_state=2
)
- assert kernel.complete_data(0).equals(kernel2.complete_data(0)), (
- "random_state initialization failed to be deterministic"
- )
+ assert kernel.complete_data(0).equals(
+ kernel2.complete_data(0)
+ ), "random_state initialization failed to be deterministic"
+ assert kernel.complete_data(1).equals(
+ kernel2.complete_data(1)
+ ), "random_state initialization failed to be deterministic"
# Run mice for 2 iterations
kernel.mice(2)
kernel2.mice(2)
- assert kernel.complete_data(0).equals(kernel2.complete_data(0)), (
- "random_state after mice() failed to be deterministic"
- )
+ assert kernel.complete_data(0).equals(
+ kernel2.complete_data(0)
+ ), "random_state after mice() failed to be deterministic"
+ assert kernel.complete_data(1).equals(
+ kernel2.complete_data(1)
+ ), "random_state after mice() failed to be deterministic"
kernel_imputed_as_new = kernel.impute_new_data(
- iris_amp,
- random_state=4,
- random_seed_array=random_seed_array
+ iris_amp, random_state=4, random_seed_array=random_seed_array
)
# Generate and impute new data as a reordering of original
new_order = np.arange(rows)
random_state.shuffle(new_order)
- new_data = iris_amp.loc[new_order]
+ new_data = iris_amp.loc[new_order].reset_index(drop=True)
new_seeds = random_seed_array[new_order]
new_imputed = kernel.impute_new_data(
- new_data,
- random_state=4,
- random_seed_array=new_seeds
+ new_data, random_state=4, random_seed_array=new_seeds
)
# Expect deterministic imputations at the record level, since seeds were passed.
for i in range(datasets):
- reordered_kernel_completed = kernel_imputed_as_new.complete_data(dataset=0).loc[new_order]
+ reordered_kernel_completed = (
+ kernel_imputed_as_new.complete_data(dataset=0)
+ .loc[new_order]
+ .reset_index(drop=True)
+ )
new_data_completed = new_imputed.complete_data(dataset=0)
- assert (reordered_kernel_completed == new_data_completed).all().all(), (
- "Seeds did not cause deterministic imputations when data was reordered."
- )
+ assert (
+ (reordered_kernel_completed == new_data_completed).all().all()
+ ), "Seeds did not cause deterministic imputations when data was reordered."
# Generate and impute new data as a subset of original
- new_ind = [0,1,4,7,8,10]
- new_data = iris_amp.loc[new_ind]
+ new_ind = [0, 1, 4, 7, 8, 10]
+ new_data = iris_amp.loc[new_ind].reset_index(drop=True)
new_seeds = random_seed_array[new_ind]
new_imputed = kernel.impute_new_data(
- new_data,
- random_state=4,
- random_seed_array=new_seeds
+ new_data, random_state=4, random_seed_array=new_seeds
)
# Expect deterministic imputations at the record level, since seeds were passed.
for i in range(datasets):
- reordered_kernel_completed = kernel_imputed_as_new.complete_data(dataset=0).loc[new_ind]
+ reordered_kernel_completed = (
+ kernel_imputed_as_new.complete_data(dataset=0)
+ .loc[new_ind]
+ .reset_index(drop=True)
+ )
new_data_completed = new_imputed.complete_data(dataset=0)
- assert (reordered_kernel_completed == new_data_completed).all().all(), (
- "Seeds did not cause deterministic imputations when data was reordered."
- )
+ assert (
+ (reordered_kernel_completed == new_data_completed).all().all()
+ ), "Seeds did not cause deterministic imputations when data was reordered."
# Generate and impute new data as a reordering of original
new_order = np.arange(rows)
random_state.shuffle(new_order)
- new_data = iris_amp.loc[new_order]
+ new_data = iris_amp.loc[new_order].reset_index(drop=True)
new_imputed = kernel.impute_new_data(
- new_data,
- random_state=4,
- random_seed_array=random_seed_array
+ new_data, random_state=4, random_seed_array=random_seed_array
)
# Expect deterministic imputations at the record level, since seeds were passed.
for i in range(datasets):
- reordered_kernel_completed = kernel_imputed_as_new.complete_data(dataset=0).loc[new_order]
+ reordered_kernel_completed = (
+ kernel_imputed_as_new.complete_data(dataset=0)
+ .loc[new_order]
+ .reset_index(drop=True)
+ )
new_data_completed = new_imputed.complete_data(dataset=0)
- assert not (reordered_kernel_completed == new_data_completed).all().all(), (
- "Different seeds caused deterministic imputations for all rows / columns."
- )
+ assert (
+ not (reordered_kernel_completed == new_data_completed).all().all()
+ ), "Different seeds caused deterministic imputations for all rows / columns."
diff --git a/tests/test_sklearn_pipeline.py b/tests/test_sklearn_pipeline.py
index 1bcdffd..2575b63 100644
--- a/tests/test_sklearn_pipeline.py
+++ b/tests/test_sklearn_pipeline.py
@@ -1,31 +1,48 @@
-
import numpy as np
from sklearn.preprocessing import StandardScaler
-from sklearn.datasets import make_classification
-from sklearn.model_selection import train_test_split
+from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
import miceforest as mf
-X, y = make_classification(random_state=0)
-X = mf.utils.ampute_data(X)
-X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+import pandas as pd
+
+
+def make_dataset(seed):
+
+ iris = pd.concat(load_iris(return_X_y=True, as_frame=True), axis=1)
+ del iris["target"]
+ iris.rename(
+ {
+ "sepal length (cm)": "sl",
+ "sepal width (cm)": "sw",
+ "petal length (cm)": "pl",
+ "petal width (cm)": "pw",
+ },
+ axis=1,
+ inplace=True,
+ )
+ iris_amp = mf.utils.ampute_data(iris, perc=0.20)
+
+ return iris_amp
def test_pipeline():
- kernel = mf.ImputationKernel(X_train, datasets=1)
- pipe = Pipeline([
- ('impute', kernel),
- ('scaler', StandardScaler()),
- ])
+ iris_amp_train = make_dataset(1)
+ iris_amp_test = make_dataset(2)
+
+ kernel = mf.ImputationKernel(iris_amp_train, num_datasets=1)
+
+ pipe = Pipeline(
+ [
+ ("impute", kernel),
+ ("scaler", StandardScaler()),
+ ]
+ )
# The pipeline can be used as any other estimator
# and avoids leaking the test set into the train set
- X_train_t = pipe.fit_transform(
- X_train,
- y_train,
- impute__iterations=2
- )
- X_test_t = pipe.transform(X_test)
+ X_train_t = pipe.fit_transform(X=iris_amp_train, y=None, impute__iterations=2)
+ X_test_t = pipe.transform(iris_amp_test)
assert not np.any(np.isnan(X_train_t))
- assert not np.any(np.isnan(X_test_t))
\ No newline at end of file
+ assert not np.any(np.isnan(X_test_t))
diff --git a/tests/test_utils.py b/tests/test_utils.py
index f1136d5..fecae0b 100644
--- a/tests/test_utils.py
+++ b/tests/test_utils.py
@@ -1,25 +1,17 @@
-
-from miceforest.utils import (
- stratified_subset
-)
+from miceforest.utils import stratified_subset
import numpy as np
+import pandas as pd
+
def test_subset():
strat_std_closer = []
strat_mean_closer = []
for i in range(1000):
- y = np.random.normal(size=1000)
+ y = pd.Series(np.random.normal(size=1000))
size = 100
- y_strat_sub = y[
- stratified_subset(
- y,
- size,
- groups=10,
- cat=False,
- seed=i
- )
- ]
+ ss_ind = stratified_subset(y, size, groups=10, random_state=i)
+ y_strat_sub = y[ss_ind]
y_rand_sub = np.random.choice(y, size, replace=False)
# See which random sample has a closer stdev
@@ -42,11 +34,11 @@ def test_subset():
def test_subset_continuous_reproduce():
# Tests for reproducibility in numeric stratified subsetting
for i in range(100):
- y = np.random.normal(size=1000)
+ y = pd.Series(np.random.normal(size=1000))
size = 100
- ss1 = stratified_subset(y, size, groups=10, cat=False, seed=i)
- ss2 = stratified_subset(y, size, groups=10, cat=False, seed=i)
+ ss1 = stratified_subset(y, size, groups=10, random_state=i)
+ ss2 = stratified_subset(y, size, groups=10, random_state=i)
assert np.all(ss1 == ss2)
@@ -54,10 +46,10 @@ def test_subset_continuous_reproduce():
def test_subset_categorical_reproduce():
# Tests for reproducibility in categorical stratified subsetting
for i in range(100):
- y = np.random.randint(low=1, high=10, size=1000)
+ y = pd.Series(np.random.randint(low=1, high=10, size=1000)).astype("category")
size = 100
- ss1 = stratified_subset(y, size, groups=10, cat=True, seed=i)
- ss2 = stratified_subset(y, size, groups=10, cat=True, seed=i)
+ ss1 = stratified_subset(y, size, groups=10, random_state=i)
+ ss2 = stratified_subset(y, size, groups=10, random_state=i)
- assert np.all(ss1 == ss2)
\ No newline at end of file
+ assert np.all(ss1 == ss2)