classifiers in scikit-learn that handle nan/null
Question:
I was wondering if there are classifiers that handle nan/null values in scikit-learn. I thought random forest regressor handles this but I got an error when I call predict.
X_train = np.array([[1, np.nan, 3],[np.nan, 5, 6]])
y_train = np.array([1, 2])
clf = RandomForestRegressor(X_train, y_train)
X_test = np.array([7, 8, np.nan])
y_pred = clf.predict(X_test) # Fails!
Can I not call predict with any scikit-learn algorithm with missing values?
Edit.
Now that I think about this, it makes sense. It’s not an issue during training but when you predict how do you branch when the variable is null? maybe you could just split both ways and average the result? It seems like k-NN should work fine as long as the distance function ignores nulls though.
Edit 2 (older and wiser me)
Some gbm libraries (such as xgboost) use a ternary tree instead of a binary tree precisely for this purpose: 2 children for the yes/no decision and 1 child for the missing decision. sklearn is using a binary tree
Answers:
I made an example that contains both missing values in training and the test sets
I just picked a strategy to replace missing data with the mean, using the SimpleImputer class. There are other strategies.
from __future__ import print_function
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
X_train = [[0, 0, np.nan], [np.nan, 1, 1]]
Y_train = [0, 1]
X_test_1 = [0, 0, np.nan]
X_test_2 = [0, np.nan, np.nan]
X_test_3 = [np.nan, 1, 1]
# Create our imputer to replace missing values with the mean e.g.
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp = imp.fit(X_train)
# Impute our data, then train
X_train_imp = imp.transform(X_train)
clf = RandomForestClassifier(n_estimators=10)
clf = clf.fit(X_train_imp, Y_train)
for X_test in [X_test_1, X_test_2, X_test_3]:
# Impute each test item, then predict
X_test_imp = imp.transform(X_test)
print(X_test, '->', clf.predict(X_test_imp))
# Results
[0, 0, nan] -> [0]
[0, nan, nan] -> [0]
[nan, 1, 1] -> [1]
If you are using DataFrame, you could use fillna. Here I replaced the missing data with the mean of that column.
df.fillna(df.mean(), inplace=True)
Short answer
Sometimes missing values are simply not applicable. Imputing them is meaningless. In these cases you should use a model that can handle missing values. Scitkit-learn’s models cannot handle missing values. XGBoost can.
More on scikit-learn and XGBoost
As mentioned in this article, scikit-learn’s decision trees and KNN algorithms are not (yet) robust enough to work with missing values. If imputation doesn’t make sense, don’t do it.
Consider situtations when imputation doesn’t make sense.
keep in mind this is a made-up example
Consider a dataset with rows of cars ("Danho Diesel", "Estal Electric", "Hesproc Hybrid") and columns with their properties (Weight, Top speed, Acceleration, Power output, Sulfur Dioxide Emission, Range).
Electric cars do not produce exhaust fumes – so the Sulfur dioxide emission of the Estal Electric should be a NaN-value (missing). You could argue that it should be set to 0 – but electric cars cannot produce sulfur dioxide. Imputing the value will ruin your predictions.
As mentioned in this article, scikit-learn’s decision trees and KNN algorithms are not (yet) robust enough to work with missing values. If imputation doesn’t make sense, don’t do it.
For NoData located at the edge of a GeoTIFF image (which can obviously not be interpolated using the average of the values of neighbouring pixels), I masked it in a few lines of code. Please note that this was performed on one band (VH band of a Sentinel 1 image, which was first converted into an array). After I performed a Random Forest classification on my initial image, I did the following:
image[image>0]=1.0
image[image==0]=-1.0
RF_prediction=np.multiply(RF_prediction,image)
RF_prediction[RF_prediction<0]=-9999.0 #assign a NoData value
When saving it, do not forget to assign a NoData value:
class_ds = gdal.GetDriverByName('GTiff').Create('RF_classified.tif',img_ds.RasterXSize,
img_ds.RasterYSize,1,gdal.GDT_Float32)
RF_ds.SetGeoTransform(img_ds.GetGeoTransform())
srs = osr.SpatialReference()
srs.ImportFromEPSG(32733)
RF_ds.SetProjection(srs.ExportToWkt()) # export coords to file
RF_ds.GetRasterBand(1).SetNoDataValue(-9999.0) #set NoData value
RF_ds.GetRasterBand(1).WriteArray(RF_prediction)
RF_ds.FlushCache() # write to disk
RF_ds = None
I was wondering if there are classifiers that handle nan/null values in scikit-learn. I thought random forest regressor handles this but I got an error when I call predict.
X_train = np.array([[1, np.nan, 3],[np.nan, 5, 6]])
y_train = np.array([1, 2])
clf = RandomForestRegressor(X_train, y_train)
X_test = np.array([7, 8, np.nan])
y_pred = clf.predict(X_test) # Fails!
Can I not call predict with any scikit-learn algorithm with missing values?
Edit.
Now that I think about this, it makes sense. It’s not an issue during training but when you predict how do you branch when the variable is null? maybe you could just split both ways and average the result? It seems like k-NN should work fine as long as the distance function ignores nulls though.
Edit 2 (older and wiser me)
Some gbm libraries (such as xgboost) use a ternary tree instead of a binary tree precisely for this purpose: 2 children for the yes/no decision and 1 child for the missing decision. sklearn is using a binary tree
I made an example that contains both missing values in training and the test sets
I just picked a strategy to replace missing data with the mean, using the SimpleImputer class. There are other strategies.
from __future__ import print_function
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.impute import SimpleImputer
X_train = [[0, 0, np.nan], [np.nan, 1, 1]]
Y_train = [0, 1]
X_test_1 = [0, 0, np.nan]
X_test_2 = [0, np.nan, np.nan]
X_test_3 = [np.nan, 1, 1]
# Create our imputer to replace missing values with the mean e.g.
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp = imp.fit(X_train)
# Impute our data, then train
X_train_imp = imp.transform(X_train)
clf = RandomForestClassifier(n_estimators=10)
clf = clf.fit(X_train_imp, Y_train)
for X_test in [X_test_1, X_test_2, X_test_3]:
# Impute each test item, then predict
X_test_imp = imp.transform(X_test)
print(X_test, '->', clf.predict(X_test_imp))
# Results
[0, 0, nan] -> [0]
[0, nan, nan] -> [0]
[nan, 1, 1] -> [1]
If you are using DataFrame, you could use fillna. Here I replaced the missing data with the mean of that column.
df.fillna(df.mean(), inplace=True)
Short answer
Sometimes missing values are simply not applicable. Imputing them is meaningless. In these cases you should use a model that can handle missing values. Scitkit-learn’s models cannot handle missing values. XGBoost can.
More on scikit-learn and XGBoost
As mentioned in this article, scikit-learn’s decision trees and KNN algorithms are not (yet) robust enough to work with missing values. If imputation doesn’t make sense, don’t do it.
Consider situtations when imputation doesn’t make sense.
keep in mind this is a made-up example
Consider a dataset with rows of cars ("Danho Diesel", "Estal Electric", "Hesproc Hybrid") and columns with their properties (Weight, Top speed, Acceleration, Power output, Sulfur Dioxide Emission, Range).
Electric cars do not produce exhaust fumes – so the Sulfur dioxide emission of the Estal Electric should be a NaN-value (missing). You could argue that it should be set to 0 – but electric cars cannot produce sulfur dioxide. Imputing the value will ruin your predictions.
As mentioned in this article, scikit-learn’s decision trees and KNN algorithms are not (yet) robust enough to work with missing values. If imputation doesn’t make sense, don’t do it.
For NoData located at the edge of a GeoTIFF image (which can obviously not be interpolated using the average of the values of neighbouring pixels), I masked it in a few lines of code. Please note that this was performed on one band (VH band of a Sentinel 1 image, which was first converted into an array). After I performed a Random Forest classification on my initial image, I did the following:
image[image>0]=1.0
image[image==0]=-1.0
RF_prediction=np.multiply(RF_prediction,image)
RF_prediction[RF_prediction<0]=-9999.0 #assign a NoData value
When saving it, do not forget to assign a NoData value:
class_ds = gdal.GetDriverByName('GTiff').Create('RF_classified.tif',img_ds.RasterXSize,
img_ds.RasterYSize,1,gdal.GDT_Float32)
RF_ds.SetGeoTransform(img_ds.GetGeoTransform())
srs = osr.SpatialReference()
srs.ImportFromEPSG(32733)
RF_ds.SetProjection(srs.ExportToWkt()) # export coords to file
RF_ds.GetRasterBand(1).SetNoDataValue(-9999.0) #set NoData value
RF_ds.GetRasterBand(1).WriteArray(RF_prediction)
RF_ds.FlushCache() # write to disk
RF_ds = None