sebwrede commented on a change in pull request #1061: URL: https://github.com/apache/systemds/pull/1061#discussion_r494152273
########## File path: src/main/python/docs/source/guide/algorithms.rst ########## @@ -26,18 +26,173 @@ Prerequisite: - :doc:`/getting_started/install` +This example goes through an algorithm from the list of builtin algorithms that can be applied to a dataset. +For simplicity the dataset used for this is `MNIST <http://yann.lecun.com/exdb/mnist/>`_, +since it is commonly known and explored. + +If one wants to skip the explanation then the full script is available at the bottom of this page. Step 1: Get Dataset ------------------- -TODO +SystemDS provides builtin for downloading and setup of the MNIST dataset. +To setup this simply use:: -Step 2: Train model -------------------- + from systemds.examples.tutorials.mnist import DataManager + d = DataManager() + X = d.get_train_data() + Y = d.get_train_labels() + +Here the DataManager contains the code for downloading and setting up numpy arrays containing the data. + +Step 2: Reshape & Format +------------------------ + +Usually data does not come in formats that perfectly fits the algorithms, to make this tutorial more +realistic some data preprocessing is required to change the input to fit. + +First the Training data, X, has multiple dimensions resulting in a shape (60000, 28, 28). +The dimensions correspond to first the number of images 60000, then the number of row pixels, 28, +and finally the column pixels, 28. + +To use this data for Logistic Regression we have to reduce the dimensions. +The input X is the training data. +It require the data to have two dimensions, the first resemble the +number of inputs, and the other the number of features. + +Therefore to make the data fit the algorithm we reshape the X dataset, like so:: + + X = X.reshape((60000, 28*28)) + +This takes each row of pixels and append to each other making a single feature vector per image. + +The Y dataset also does not perfectly fit the Logistic Regression algorithm, this is because the labels +for this dataset is values ranging from 0, to 9, each label correspond to the integer shown in the image. +unfortunately the algorithm require the labels to be distinct integers from 1 and upwards. + +Therefore we add 1 to each label such that the labels go from 1 to 10, like this:: + + Y = Y + 1 + +With these steps we are now ready to train a simple logistic model. + +Step 3: Training +---------------- + +To start with, we setup a SystemDS context:: + + from systemds.context import SystemDSContext + sds = SystemDSContext() + +Then setup the data:: + + from systemds.matrix import Matrix + X_ds = Matrix(sds, X) + Y_ds = Matrix(sds, Y) + +to reduce the training time and verify everything works, it is usually good to reduce the amount of data, +to train on a smaller sample to start with:: + + sample_size = 1000 + X_ds = Matrix(sds, X[:sample_size]) + Y_ds = Matrix(sds, Y[:sample_size]) + +And now everything is ready for our algorithm:: + + from systemds.operator.algorithm import multiLogReg + + bias = multiLogReg(X_ds, Y_ds) + +Note that nothing has been calculated yet, in SystemDS, since it only happens when you call compute:: + + bias_r = bias.compute() -TODO +bias is a matrix, that if matrix multiplied with an instance returns a value distribution where, the highest value is the predicted type. +This is the matrix that could be saved and used for predicting labels later. Step 3: Validate ---------------- -TODO +To see what accuracy the model achieves, we have to load in the test dataset as well. + +this can also be extracted from our builtin MNIST loader, to keep the tutorial short the operations are combined:: + + Xt = Matrix(sds, d.get_test_data().reshape((10000, 28*28))) + Yt = Matrix(sds, d.get_test_labels()) + 1 + +The above loads the test data, and reshapes the X data the same way the training data was reshaped. + +Finally we verify the accuracy by calling:: + + from systemds.operator.algorithm import multiLogRegPredict + [m, y_pred, acc] = multiLogRegPredict(Xt, bias, Yt).compute() + print(acc) + +There are three outputs from the multiLogRegPredict call. + +m, is the mean probability of correctly classifying each label. +y_pred, is the predictions made using the model, bias, trained. +acc, is the accuracy achieved by the model. + +If the subset of the training data is used then you could expect an accuracy of 85% in this example +using 1000 pictures of the training data. + +Step 4: Tuning +-------------- + +Now that we have a working baseline we can start tuning parameters. + +But first it is valuable to know how much of a difference in performance there is on the training data, vs the test data. +This gives an indication of if we have exhausted the learning potential of the training data. + +To see how our accuracy is on the training data we use the Predict function again, but with our training data:: + + [m, y_pred, acc] = multiLogRegPredict(X_ds, bias, Y_ds).compute() + print(acc) + +In this specific case we achieve 100% accuracy on the training data, indicating that we have fit the training data, +and have nothing more to learn from the data as it is now. + +To improve further we have to increase the training data, here for example we increase it +from our sample of 1k to the full training dataset of 60k, in this example the maxi is set to reduce the number of iterations the algorithm takes, +to again reduce training time:: + + X_ds = Matrix(sds, X) + Y_ds = Matrix(sds, Y) + + bias = multiLogReg(X_ds, Y_ds, maxi=30) + + [_, _, train_acc] = multiLogRegPredict(X_ds, bias, Y_ds).compute() + [_, _, test_acc] = multiLogRegPredict(Xt, bias, Yt).compute() + print(train_acc " " test_acc) + +With this change the accuracy achieved changes from the previous value to 92%. This is still low on this dataset as can be seen on `MNIST <http://yann.lecun.com/exdb/mnist/>`_. +But this is a basic implementation, that can be replaced by a variety of algorithm and techniques. Review comment: But this is a basic implementation that can be replaced by a variety of algorithms and techniques. ########## File path: src/main/python/docs/source/guide/algorithms.rst ########## @@ -26,18 +26,173 @@ Prerequisite: - :doc:`/getting_started/install` +This example goes through an algorithm from the list of builtin algorithms that can be applied to a dataset. +For simplicity the dataset used for this is `MNIST <http://yann.lecun.com/exdb/mnist/>`_, +since it is commonly known and explored. + +If one wants to skip the explanation then the full script is available at the bottom of this page. Step 1: Get Dataset ------------------- -TODO +SystemDS provides builtin for downloading and setup of the MNIST dataset. +To setup this simply use:: -Step 2: Train model -------------------- + from systemds.examples.tutorials.mnist import DataManager + d = DataManager() + X = d.get_train_data() + Y = d.get_train_labels() + +Here the DataManager contains the code for downloading and setting up numpy arrays containing the data. + +Step 2: Reshape & Format +------------------------ + +Usually data does not come in formats that perfectly fits the algorithms, to make this tutorial more +realistic some data preprocessing is required to change the input to fit. + +First the Training data, X, has multiple dimensions resulting in a shape (60000, 28, 28). +The dimensions correspond to first the number of images 60000, then the number of row pixels, 28, +and finally the column pixels, 28. + +To use this data for Logistic Regression we have to reduce the dimensions. +The input X is the training data. +It require the data to have two dimensions, the first resemble the +number of inputs, and the other the number of features. + +Therefore to make the data fit the algorithm we reshape the X dataset, like so:: + + X = X.reshape((60000, 28*28)) + +This takes each row of pixels and append to each other making a single feature vector per image. + +The Y dataset also does not perfectly fit the Logistic Regression algorithm, this is because the labels +for this dataset is values ranging from 0, to 9, each label correspond to the integer shown in the image. +unfortunately the algorithm require the labels to be distinct integers from 1 and upwards. + +Therefore we add 1 to each label such that the labels go from 1 to 10, like this:: + + Y = Y + 1 + +With these steps we are now ready to train a simple logistic model. + +Step 3: Training +---------------- + +To start with, we setup a SystemDS context:: + + from systemds.context import SystemDSContext + sds = SystemDSContext() + +Then setup the data:: + + from systemds.matrix import Matrix + X_ds = Matrix(sds, X) + Y_ds = Matrix(sds, Y) + +to reduce the training time and verify everything works, it is usually good to reduce the amount of data, +to train on a smaller sample to start with:: + + sample_size = 1000 + X_ds = Matrix(sds, X[:sample_size]) + Y_ds = Matrix(sds, Y[:sample_size]) + +And now everything is ready for our algorithm:: + + from systemds.operator.algorithm import multiLogReg + + bias = multiLogReg(X_ds, Y_ds) + +Note that nothing has been calculated yet, in SystemDS, since it only happens when you call compute:: + + bias_r = bias.compute() -TODO +bias is a matrix, that if matrix multiplied with an instance returns a value distribution where, the highest value is the predicted type. +This is the matrix that could be saved and used for predicting labels later. Step 3: Validate ---------------- -TODO +To see what accuracy the model achieves, we have to load in the test dataset as well. + +this can also be extracted from our builtin MNIST loader, to keep the tutorial short the operations are combined:: + + Xt = Matrix(sds, d.get_test_data().reshape((10000, 28*28))) + Yt = Matrix(sds, d.get_test_labels()) + 1 + +The above loads the test data, and reshapes the X data the same way the training data was reshaped. + +Finally we verify the accuracy by calling:: + + from systemds.operator.algorithm import multiLogRegPredict + [m, y_pred, acc] = multiLogRegPredict(Xt, bias, Yt).compute() + print(acc) + +There are three outputs from the multiLogRegPredict call. + +m, is the mean probability of correctly classifying each label. +y_pred, is the predictions made using the model, bias, trained. +acc, is the accuracy achieved by the model. + +If the subset of the training data is used then you could expect an accuracy of 85% in this example +using 1000 pictures of the training data. + +Step 4: Tuning +-------------- + +Now that we have a working baseline we can start tuning parameters. + +But first it is valuable to know how much of a difference in performance there is on the training data, vs the test data. +This gives an indication of if we have exhausted the learning potential of the training data. + +To see how our accuracy is on the training data we use the Predict function again, but with our training data:: + + [m, y_pred, acc] = multiLogRegPredict(X_ds, bias, Y_ds).compute() + print(acc) + +In this specific case we achieve 100% accuracy on the training data, indicating that we have fit the training data, +and have nothing more to learn from the data as it is now. + +To improve further we have to increase the training data, here for example we increase it +from our sample of 1k to the full training dataset of 60k, in this example the maxi is set to reduce the number of iterations the algorithm takes, +to again reduce training time:: + + X_ds = Matrix(sds, X) + Y_ds = Matrix(sds, Y) + + bias = multiLogReg(X_ds, Y_ds, maxi=30) + + [_, _, train_acc] = multiLogRegPredict(X_ds, bias, Y_ds).compute() + [_, _, test_acc] = multiLogRegPredict(Xt, bias, Yt).compute() + print(train_acc " " test_acc) Review comment: This is invalid syntax. I think it should be: `print(train_acc, " ", test_acc)` ########## File path: src/main/python/docs/source/guide/algorithms.rst ########## @@ -26,18 +26,173 @@ Prerequisite: - :doc:`/getting_started/install` +This example goes through an algorithm from the list of builtin algorithms that can be applied to a dataset. +For simplicity the dataset used for this is `MNIST <http://yann.lecun.com/exdb/mnist/>`_, +since it is commonly known and explored. + +If one wants to skip the explanation then the full script is available at the bottom of this page. Step 1: Get Dataset ------------------- -TODO +SystemDS provides builtin for downloading and setup of the MNIST dataset. +To setup this simply use:: -Step 2: Train model -------------------- + from systemds.examples.tutorials.mnist import DataManager + d = DataManager() + X = d.get_train_data() + Y = d.get_train_labels() + +Here the DataManager contains the code for downloading and setting up numpy arrays containing the data. + +Step 2: Reshape & Format +------------------------ + +Usually data does not come in formats that perfectly fits the algorithms, to make this tutorial more +realistic some data preprocessing is required to change the input to fit. + +First the Training data, X, has multiple dimensions resulting in a shape (60000, 28, 28). +The dimensions correspond to first the number of images 60000, then the number of row pixels, 28, +and finally the column pixels, 28. + +To use this data for Logistic Regression we have to reduce the dimensions. +The input X is the training data. +It require the data to have two dimensions, the first resemble the +number of inputs, and the other the number of features. + +Therefore to make the data fit the algorithm we reshape the X dataset, like so:: + + X = X.reshape((60000, 28*28)) + +This takes each row of pixels and append to each other making a single feature vector per image. + +The Y dataset also does not perfectly fit the Logistic Regression algorithm, this is because the labels +for this dataset is values ranging from 0, to 9, each label correspond to the integer shown in the image. +unfortunately the algorithm require the labels to be distinct integers from 1 and upwards. + +Therefore we add 1 to each label such that the labels go from 1 to 10, like this:: + + Y = Y + 1 + +With these steps we are now ready to train a simple logistic model. + +Step 3: Training +---------------- + +To start with, we setup a SystemDS context:: + + from systemds.context import SystemDSContext + sds = SystemDSContext() + +Then setup the data:: + + from systemds.matrix import Matrix + X_ds = Matrix(sds, X) + Y_ds = Matrix(sds, Y) + +to reduce the training time and verify everything works, it is usually good to reduce the amount of data, +to train on a smaller sample to start with:: + + sample_size = 1000 + X_ds = Matrix(sds, X[:sample_size]) + Y_ds = Matrix(sds, Y[:sample_size]) + +And now everything is ready for our algorithm:: + + from systemds.operator.algorithm import multiLogReg + + bias = multiLogReg(X_ds, Y_ds) + +Note that nothing has been calculated yet, in SystemDS, since it only happens when you call compute:: + + bias_r = bias.compute() -TODO +bias is a matrix, that if matrix multiplied with an instance returns a value distribution where, the highest value is the predicted type. +This is the matrix that could be saved and used for predicting labels later. Step 3: Validate ---------------- -TODO +To see what accuracy the model achieves, we have to load in the test dataset as well. + +this can also be extracted from our builtin MNIST loader, to keep the tutorial short the operations are combined:: + + Xt = Matrix(sds, d.get_test_data().reshape((10000, 28*28))) + Yt = Matrix(sds, d.get_test_labels()) + 1 + +The above loads the test data, and reshapes the X data the same way the training data was reshaped. + +Finally we verify the accuracy by calling:: + + from systemds.operator.algorithm import multiLogRegPredict + [m, y_pred, acc] = multiLogRegPredict(Xt, bias, Yt).compute() + print(acc) + +There are three outputs from the multiLogRegPredict call. + +m, is the mean probability of correctly classifying each label. +y_pred, is the predictions made using the model, bias, trained. +acc, is the accuracy achieved by the model. Review comment: This is displayed as one line when opening in IntelliJ. 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