MattLowrieDS · January 6, 2021 03:07
diff --git a/compute_class_weight b/compute_class_weight
 from sklearn.utils.class_weight import compute_class_weight

 pass_results = plays_df.loc[plays_df['passResult'].isin(category_lookup.keys()), 'passResult']
 all_labels = pass_results.apply(lambda lbl: category_lookup[lbl])
 # Create class weights to counter-balance classification during training
 y = np.stack(all_labels).argmax(axis=1)
 classes = np.unique(y)
 weights = compute_class_weight('balanced', classes=classes, y=y)
 class_weights = {k: v for k, v in zip(classes, weights)}
 print('Class weights:', class_weights)
	from sklearn.utils.class_weight import compute_class_weight

	pass_results = plays_df.loc[plays_df['passResult'].isin(category_lookup.keys()), 'passResult']
	all_labels = pass_results.apply(lambda lbl: category_lookup[lbl])
	# Create class weights to counter-balance classification during training
	y = np.stack(all_labels).argmax(axis=1)
	classes = np.unique(y)
	weights = compute_class_weight('balanced', classes=classes, y=y)
	class_weights = {k: v for k, v in zip(classes, weights)}
	print('Class weights:', class_weights)