![]() ![]() plot ( sigmas, feature_importance, 'o', color = color ) ax. log2 ( sigma_min ) 1 ), base = 2, endpoint = True ) for ch, color in zip ( range ( 3 ), ): ax. feature_importances_ ) feature_importance = ( clf. subplots ( 1, 2, figsize = ( 9, 4 )) l = len ( clf. However, this can lead to overfitting and a degraded result at the boundaryįig, ax = plt. It can be tempting to use this information to reduce the number ofįeatures given to the classifier, in order to reduce the computing time. Intensity features have a much higher importance than textureįeatures. We inspect below the importance of the different features, as computed by set_title ( 'Image, mask and segmentation boundaries' ) ax. mark_boundaries ( img, result, mode = 'thick' )) ax. subplots ( 1, 2, sharex = True, sharey = True, figsize = ( 9, 4 )) ax. predict_segmenter ( features, clf ) fig, ax = plt. fit_segmenter ( training_labels, features, clf ) result = future. multiscale_basic_features, intensity = True, edges = False, texture = True, sigma_min = sigma_min, sigma_max = sigma_max, channel_axis =- 1 ) features = features_func ( img ) clf = RandomForestClassifier ( n_estimators = 50, n_jobs =- 1, max_depth = 10, max_samples = 0.05 ) clf = future. uint8 ) training_labels = 1 training_labels = 1 training_labels = 2 training_labels = 3 training_labels = 4 training_labels = 4 sigma_min = 1 sigma_max = 16 features_func = partial ( feature. # Here we use rectangles but visualization libraries such as plotly # (and napari?) can be used to draw a mask on the image. skin () img = full_img # Build an array of labels for training the segmentation. RF_ds.GetRasterBand(1).Import numpy as np import matplotlib.pyplot as plt from skimage import data, segmentation, feature, future from sklearn.ensemble import RandomForestClassifier from functools import partial full_img = data. RF_ds.GetRasterBand(1).SetNoDataValue(-9999.0) #set NoData value RF_ds.SetProjection(srs.ExportToWkt()) # export coords to file RF_ds.SetGeoTransform(img_ds.GetGeoTransform()) When saving it, do not forget to assign a NoData value: class_ds = gdal.GetDriverByName('GTiff').Create('RF_classified.tif',img_ds.RasterXSize,\ RF_prediction=-9999.0 #assign a NoData value RF_prediction=np.multiply(RF_prediction,image) After I performed a Random Forest classification on my initial image, I did the following: image=1.0 Please note that this was performed on one band (VH band of a Sentinel 1 image, which was first converted into an array). Print(X_test, '->', clf.predict(X_test_imp))įor 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. Imp = SimpleImputer(missing_values=np.nan, strategy='mean')Ĭlf = RandomForestClassifier(n_estimators=10)įor X_test in : # Create our imputer to replace missing values with the mean e.g. from _future_ import print_functionįrom sklearn.ensemble import RandomForestClassifier I just picked a strategy to replace missing data with the mean, using the SimpleImputer class. I made an example that contains both missing values in training and the test sets If imputation doesn't make sense, don't do it. Imputing the value will ruin your predictions.Īs mentioned in this article, scikit-learn's decision trees and KNN algorithms are not ( yet) robust enough to work with missing values. You could argue that it should be set to 0 - but electric cars cannot produce sulfur dioxide. Consider situtations when imputation doesn't make sense.Ĭonsider 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).Įlectric cars do not produce exhaust fumes - so the Sulfur dioxide emission of the Estal Electric should be a NaN-value (missing). XGBoost can.Īs mentioned in this article, scikit-learn's decision trees and KNN algorithms are not ( yet) robust enough to work with missing values. ![]() Scitkit-learn's models cannot handle missing values. ![]() In these cases you should use a model that can handle missing values. Sometimes missing values are simply not applicable. ![]()
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