added runnable instance of README example

README.md

@@ -39,7 +39,7 @@ X_filled_softimpute_normalized = softImpute.complete(X_incomplete_normalized)
 X_filled_softimpute = biscaler.inverse_transform(X_filled_softimpute_normalized)
 
 # print mean squared error for the three imputation methods above
-nnm_mse = ((X_filled_knn[missing_mask] - X[missing_mask]) ** 2).mean()
+nnm_mse = ((X_filled_nnm[missing_mask] - X[missing_mask]) ** 2).mean()
 print("Nuclear norm minimization MSE: %f" % nnm_mse)
 
 softImpute_mse = ((X_filled_softimpute[missing_mask] - X[missing_mask]) ** 2).mean()

experiments/readme_example.py

@@ -0,0 +1,59 @@
+import numpy as np
+from fancyimpute import BiScaler, KNN, NuclearNormMinimization, SoftImpute, SimpleFill
+
+n = 200
+m = 20
+inner_rank = 4
+X = np.dot(np.random.randn(n, inner_rank), np.random.randn(inner_rank, m))
+print("Mean squared element: %0.4f" % (X ** 2).mean())
+
+# X is a data matrix which we're going to randomly drop entries from
+missing_mask = np.random.rand(*X.shape) < 0.1
+X_incomplete = X.copy()
+# missing entries indicated with NaN
+X_incomplete[missing_mask] = np.nan
+
+meanFill = SimpleFill("mean")
+X_filled_mean = meanFill.complete(X_incomplete)
+
+# Use 3 nearest rows which have a feature to fill in each row's missing features
+knnImpute = KNN(k=3)
+X_filled_knn = knnImpute.complete(X_incomplete)
+
+# matrix completion using convex optimization to find low-rank solution
+# that still matches observed values. Slow!
+X_filled_nnm = NuclearNormMinimization().complete(X_incomplete)
+
+# Instead of solving the nuclear norm objective directly, instead
+# induce sparsity using singular value thresholding
+softImpute = SoftImpute()
+
+# simultaneously normalizes the rows and columns of your observed data,
+# sometimes useful for low-rank imputation methods
+biscaler = BiScaler()
+
+# rescale both rows and columns to have zero mean and unit variance
+X_incomplete_normalized = biscaler.fit_transform(X_incomplete)
+
+X_filled_softimpute_normalized = softImpute.complete(X_incomplete_normalized)
+X_filled_softimpute = biscaler.inverse_transform(X_filled_softimpute_normalized)
+
+X_filled_softimpute_no_biscale = softImpute.complete(X_incomplete)
+
+meanfill_mse = ((X_filled_mean[missing_mask] - X[missing_mask]) ** 2).mean()
+print("meanFill MSE: %f" % meanfill_mse)
+
+# print mean squared error for the three imputation methods above
+nnm_mse = ((X_filled_nnm[missing_mask] - X[missing_mask]) ** 2).mean()
+print("Nuclear norm minimization MSE: %f" % nnm_mse)
+
+softImpute_mse = ((X_filled_softimpute[missing_mask] - X[missing_mask]) ** 2).mean()
+print("SoftImpute MSE: %f" % softImpute_mse)
+
+softImpute_no_biscale_mse = (
+    (X_filled_softimpute_no_biscale[missing_mask] - X[missing_mask]) ** 2).mean()
+print("SoftImpute without BiScale MSE: %f" % softImpute_no_biscale_mse)
+
+
+knn_mse = ((X_filled_knn[missing_mask] - X[missing_mask]) ** 2).mean()
+print("knnImpute MSE: %f" % knn_mse)