[SYSTEMML-234] [SYSTEMML-208] Updating the documentation

docs/algorithms-classification.md

@@ -127,6 +127,15 @@ Eqs. (1) and (2).
 ### Usage
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import SystemML as sml
+# C = 1/reg
+logistic = sml.mllearn.LogisticRegression(sqlCtx, fit_intercept=True, max_iter=100, max_inner_iter=0, tol=0.000001, C=1.0)
+# X_train, y_train and X_test can be NumPy matrices or Pandas DataFrame
+y_test = logistic.fit(X_train, y_train).predict(X_test)
+# df_train is DataFrame that contains two columns: "features" (of type Vector) and "label". df_test is a DataFrame that contains the column "features"
+y_test = logistic.fit(df_train).transform(df_test)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f MultiLogReg.dml
                             -nvargs X=<file>
@@ -214,6 +223,56 @@ SystemML Language Reference for details.
 ### Examples
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+# Scikit-learn way
+from sklearn import datasets, neighbors
+import SystemML as sml
+from pyspark.sql import SQLContext
+sqlCtx = SQLContext(sc)
+digits = datasets.load_digits()
+X_digits = digits.data
+y_digits = digits.target + 1
+n_samples = len(X_digits)
+X_train = X_digits[:.9 * n_samples]
+y_train = y_digits[:.9 * n_samples]
+X_test = X_digits[.9 * n_samples:]
+y_test = y_digits[.9 * n_samples:]
+logistic = sml.mllearn.LogisticRegression(sqlCtx)
+print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))
+
+# MLPipeline way
+from pyspark.ml import Pipeline
+import SystemML as sml
+from pyspark.ml.feature import HashingTF, Tokenizer
+from pyspark.sql import SQLContext
+sqlCtx = SQLContext(sc)
+training = sqlCtx.createDataFrame([
+    (0L, "a b c d e spark", 1.0),
+    (1L, "b d", 2.0),
+    (2L, "spark f g h", 1.0),
+    (3L, "hadoop mapreduce", 2.0),
+    (4L, "b spark who", 1.0),
+    (5L, "g d a y", 2.0),
+    (6L, "spark fly", 1.0),
+    (7L, "was mapreduce", 2.0),
+    (8L, "e spark program", 1.0),
+    (9L, "a e c l", 2.0),
+    (10L, "spark compile", 1.0),
+    (11L, "hadoop software", 2.0)
+], ["id", "text", "label"])
+tokenizer = Tokenizer(inputCol="text", outputCol="words")
+hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20)
+lr = sml.mllearn.LogisticRegression(sqlCtx)
+pipeline = Pipeline(stages=[tokenizer, hashingTF, lr])
+model = pipeline.fit(training)
+test = sqlCtx.createDataFrame([
+    (12L, "spark i j k"),
+    (13L, "l m n"),
+    (14L, "mapreduce spark"),
+    (15L, "apache hadoop")], ["id", "text"])
+prediction = model.transform(test)
+prediction.show()
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f MultiLogReg.dml
                             -nvargs X=/user/ml/X.mtx
@@ -393,6 +452,15 @@ support vector machine (`y` with domain size `2`).
 **Binary-Class Support Vector Machines**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import SystemML as sml
+# C = 1/reg
+svm = sml.mllearn.SVM(sqlCtx, fit_intercept=True, max_iter=100, tol=0.000001, C=1.0, is_multi_class=False)
+# X_train, y_train and X_test can be NumPy matrices or Pandas DataFrame
+y_test = svm.fit(X_train, y_train)
+# df_train is DataFrame that contains two columns: "features" (of type Vector) and "label". df_test is a DataFrame that contains the column "features"
+y_test = svm.fit(df_train)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f l2-svm.dml
                             -nvargs X=<file>
@@ -428,6 +496,12 @@ support vector machine (`y` with domain size `2`).
 **Binary-Class Support Vector Machines Prediction**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+# X_test can be NumPy matrices or Pandas DataFrame
+y_test = svm.predict(X_test)
+# df_test is a DataFrame that contains the column "features" of type Vector
+y_test = svm.transform(df_test)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f l2-svm-predict.dml
                             -nvargs X=<file>
@@ -630,6 +704,15 @@ class labels.
 **Multi-Class Support Vector Machines**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import SystemML as sml
+# C = 1/reg
+svm = sml.mllearn.SVM(sqlCtx, fit_intercept=True, max_iter=100, tol=0.000001, C=1.0, is_multi_class=True)
+# X_train, y_train and X_test can be NumPy matrices or Pandas DataFrame
+y_test = svm.fit(X_train, y_train)
+# df_train is DataFrame that contains two columns: "features" (of type Vector) and "label". df_test is a DataFrame that contains the column "features"
+y_test = svm.fit(df_train)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f m-svm.dml
                             -nvargs X=<file>
@@ -665,6 +748,12 @@ class labels.
 **Multi-Class Support Vector Machines Prediction**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+# X_test can be NumPy matrices or Pandas DataFrame
+y_test = svm.predict(X_test)
+# df_test is a DataFrame that contains the column "features" of type Vector
+y_test = svm.transform(df_test)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f m-svm-predict.dml
                             -nvargs X=<file>
@@ -747,6 +836,56 @@ SystemML Language Reference for details.
 **Multi-Class Support Vector Machines**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+# Scikit-learn way
+from sklearn import datasets, neighbors
+import SystemML as sml
+from pyspark.sql import SQLContext
+sqlCtx = SQLContext(sc)
+digits = datasets.load_digits()
+X_digits = digits.data
+y_digits = digits.target 
+n_samples = len(X_digits)
+X_train = X_digits[:.9 * n_samples]
+y_train = y_digits[:.9 * n_samples]
+X_test = X_digits[.9 * n_samples:]
+y_test = y_digits[.9 * n_samples:]
+svm = sml.mllearn.SVM(sqlCtx, is_multi_class=True)
+print('LogisticRegression score: %f' % svm.fit(X_train, y_train).score(X_test, y_test))
+
+# MLPipeline way
+from pyspark.ml import Pipeline
+import SystemML as sml
+from pyspark.ml.feature import HashingTF, Tokenizer
+from pyspark.sql import SQLContext
+sqlCtx = SQLContext(sc)
+training = sqlCtx.createDataFrame([
+    (0L, "a b c d e spark", 1.0),
+    (1L, "b d", 2.0),
+    (2L, "spark f g h", 1.0),
+    (3L, "hadoop mapreduce", 2.0),
+    (4L, "b spark who", 1.0),
+    (5L, "g d a y", 2.0),
+    (6L, "spark fly", 1.0),
+    (7L, "was mapreduce", 2.0),
+    (8L, "e spark program", 1.0),
+    (9L, "a e c l", 2.0),
+    (10L, "spark compile", 1.0),
+    (11L, "hadoop software", 2.0)
+], ["id", "text", "label"])
+tokenizer = Tokenizer(inputCol="text", outputCol="words")
+hashingTF = HashingTF(inputCol="words", outputCol="features", numFeatures=20)
+svm = sml.mllearn.SVM(sqlCtx, is_multi_class=True)
+pipeline = Pipeline(stages=[tokenizer, hashingTF, svm])
+model = pipeline.fit(training)
+test = sqlCtx.createDataFrame([
+    (12L, "spark i j k"),
+    (13L, "l m n"),
+    (14L, "mapreduce spark"),
+    (15L, "apache hadoop")], ["id", "text"])
+prediction = model.transform(test)
+prediction.show()
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f m-svm.dml
                             -nvargs X=/user/ml/X.mtx

docs/algorithms-regression.md

@@ -80,6 +80,15 @@ efficient when the number of features $m$ is relatively small
 **Linear Regression - Direct Solve**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import SystemML as sml
+# C = 1/reg
+lr = sml.mllearn.LinearRegression(sqlCtx, fit_intercept=True, max_iter=100, tol=0.000001, C=1.0, solver='direct-solve')
+# X_train, y_train and X_test can be NumPy matrices or Pandas DataFrame
+y_test = lr.fit(X_train, y_train)
+# df_train is DataFrame that contains two columns: "features" (of type Vector) and "label". df_test is a DataFrame that contains the column "features"
+y_test = lr.fit(df_train)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f LinearRegDS.dml
                             -nvargs X=<file>
@@ -111,6 +120,15 @@ efficient when the number of features $m$ is relatively small
 **Linear Regression - Conjugate Gradient**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import SystemML as sml
+# C = 1/reg
+lr = sml.mllearn.LinearRegression(sqlCtx, fit_intercept=True, max_iter=100, tol=0.000001, C=1.0, solver='newton-cg')
+# X_train, y_train and X_test can be NumPy matrices or Pandas DataFrame
+y_test = lr.fit(X_train, y_train)
+# df_train is DataFrame that contains two columns: "features" (of type Vector) and "label". df_test is a DataFrame that contains the column "features"
+y_test = lr.fit(df_train)
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f LinearRegCG.dml
                             -nvargs X=<file>
@@ -196,6 +214,28 @@ SystemML Language Reference for details.
 **Linear Regression - Direct Solve**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import numpy as np
+from sklearn import datasets
+import SystemML as sml
+from pyspark.sql import SQLContext
+# Load the diabetes dataset
+diabetes = datasets.load_diabetes()
+# Use only one feature
+diabetes_X = diabetes.data[:, np.newaxis, 2]
+# Split the data into training/testing sets
+diabetes_X_train = diabetes_X[:-20]
+diabetes_X_test = diabetes_X[-20:]
+# Split the targets into training/testing sets
+diabetes_y_train = diabetes.target[:-20]
+diabetes_y_test = diabetes.target[-20:]
+# Create linear regression object
+regr = sml.mllearn.LinearRegression(sqlCtx, solver='direct-solve')
+# Train the model using the training sets
+regr.fit(diabetes_X_train, diabetes_y_train)
+# The mean square error
+print("Residual sum of squares: %.2f" % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f LinearRegDS.dml
                             -nvargs X=/user/ml/X.mtx
@@ -227,6 +267,28 @@ SystemML Language Reference for details.
 **Linear Regression - Conjugate Gradient**:
 
 <div class="codetabs">
+<div data-lang="Python" markdown="1">
+import numpy as np
+from sklearn import datasets
+import SystemML as sml
+from pyspark.sql import SQLContext
+# Load the diabetes dataset
+diabetes = datasets.load_diabetes()
+# Use only one feature
+diabetes_X = diabetes.data[:, np.newaxis, 2]
+# Split the data into training/testing sets
+diabetes_X_train = diabetes_X[:-20]
+diabetes_X_test = diabetes_X[-20:]
+# Split the targets into training/testing sets
+diabetes_y_train = diabetes.target[:-20]
+diabetes_y_test = diabetes.target[-20:]
+# Create linear regression object
+regr = sml.mllearn.LinearRegression(sqlCtx, solver='newton-cg')
+# Train the model using the training sets
+regr.fit(diabetes_X_train, diabetes_y_train)
+# The mean square error
+print("Residual sum of squares: %.2f" % np.mean((regr.predict(diabetes_X_test) - diabetes_y_test) ** 2))
+</div>
 <div data-lang="Hadoop" markdown="1">
     hadoop jar SystemML.jar -f LinearRegCG.dml
                             -nvargs X=/user/ml/X.mtx