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Posted to commits@systemml.apache.org by de...@apache.org on 2017/04/07 18:58:06 UTC
[02/50] [abbrv] incubator-systemml git commit: [SYSTEMML-1139]
Updated the beginner's guide
[SYSTEMML-1139] Updated the beginner's guide
The updated documentation reflect the installation steps as per commit
https://github.com/apache/incubator-systemml/commit/d225cbdc90e4d5f8e464182c237f5e4900467a38
Project: http://git-wip-us.apache.org/repos/asf/incubator-systemml/repo
Commit: http://git-wip-us.apache.org/repos/asf/incubator-systemml/commit/fa88464b
Tree: http://git-wip-us.apache.org/repos/asf/incubator-systemml/tree/fa88464b
Diff: http://git-wip-us.apache.org/repos/asf/incubator-systemml/diff/fa88464b
Branch: refs/heads/gh-pages
Commit: fa88464bab650ea0df736a2887391ce2847115c6
Parents: 313b1db
Author: Niketan Pansare <np...@us.ibm.com>
Authored: Wed Dec 7 14:49:02 2016 -0800
Committer: Niketan Pansare <np...@us.ibm.com>
Committed: Wed Dec 7 14:49:02 2016 -0800
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beginners-guide-python.md | 22 +++++++++++-----------
1 file changed, 11 insertions(+), 11 deletions(-)
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http://git-wip-us.apache.org/repos/asf/incubator-systemml/blob/fa88464b/beginners-guide-python.md
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diff --git a/beginners-guide-python.md b/beginners-guide-python.md
index d0598aa..c919f3f 100644
--- a/beginners-guide-python.md
+++ b/beginners-guide-python.md
@@ -75,8 +75,8 @@ We are working towards uploading the python package on pypi. Until then, please
```bash
git checkout https://github.com/apache/incubator-systemml.git
cd incubator-systemml
-mvn post-integration-test -P distribution -DskipTests
-pip install src/main/python/dist/systemml-incubating-0.12.0.dev1.tar.gz
+mvn clean package -P distribution
+pip install target/systemml-0.12.0-incubating-SNAPSHOT-python.tgz
```
The above commands will install Python package and place the corresponding Java binaries (along with algorithms) into the installed location.
@@ -214,10 +214,10 @@ 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:]
+X_train = X_digits[:int(.9 * n_samples)]
+y_train = y_digits[:int(.9 * n_samples)]
+X_test = X_digits[int(.9 * n_samples):]
+y_test = y_digits[int(.9 * n_samples):]
logistic = LogisticRegression(sqlCtx)
print('LogisticRegression score: %f' % logistic.fit(X_train, y_train).score(X_test, y_test))
```
@@ -245,13 +245,13 @@ X_digits = digits.data
y_digits = digits.target
n_samples = len(X_digits)
# Split the data into training/testing sets and convert to PySpark DataFrame
-df_train = sml.convertToLabeledDF(sqlContext, X_digits[:.9 * n_samples], y_digits[:.9 * n_samples])
-X_test = sqlCtx.createDataFrame(pd.DataFrame(X_digits[.9 * n_samples:]))
+df_train = sml.convertToLabeledDF(sqlContext, X_digits[:int(.9 * n_samples)], y_digits[:int(.9 * n_samples)])
+X_test = sqlCtx.createDataFrame(pd.DataFrame(X_digits[int(.9 * n_samples):]))
logistic = LogisticRegression(sqlCtx)
logistic.fit(df_train)
y_predicted = logistic.predict(X_test)
y_predicted = y_predicted.select('prediction').toPandas().as_matrix().flatten()
-y_test = y_digits[.9 * n_samples:]
+y_test = y_digits[int(.9 * n_samples):]
print('LogisticRegression score: %f' % accuracy_score(y_test, y_predicted))
```
@@ -331,8 +331,8 @@ X_digits = digits.data
y_digits = digits.target + 1
n_samples = len(X_digits)
# Split the data into training/testing sets and convert to PySpark DataFrame
-X_df = sqlCtx.createDataFrame(pd.DataFrame(X_digits[:.9 * n_samples]))
-y_df = sqlCtx.createDataFrame(pd.DataFrame(y_digits[:.9 * n_samples]))
+X_df = sqlCtx.createDataFrame(pd.DataFrame(X_digits[:int(.9 * n_samples)]))
+y_df = sqlCtx.createDataFrame(pd.DataFrame(y_digits[:int(.9 * n_samples)]))
ml = sml.MLContext(sc)
# Get the path of MultiLogReg.dml
scriptPath = os.path.join(imp.find_module("systemml")[1], 'systemml-java', 'scripts', 'algorithms', 'MultiLogReg.dml')