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[REVIEW] Fix Stochastic Gradient Descent Example #3136

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Nov 17, 2020
Merged

[REVIEW] Fix Stochastic Gradient Descent Example #3136

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9d2635b
Fix Stochastic Gradient Descent Example
tylerjthomas9 Nov 13, 2020
2536cd1
Added PR #3136 to 0.17 Bug Fixes
tylerjthomas9 Nov 15, 2020
742f071
Merge pull request #1 from tylerjthomas9/patch-2
tylerjthomas9 Nov 16, 2020
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1 change: 1 addition & 0 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -51,6 +51,7 @@
- PR #3117: Fix two crashes in experimental RF backend
- PR #3119: Fix memset args for benchmark
- PR #3130: Return Python string from `dump_as_json()` of RF
- PR #3136: Fix stochastic gradient descent example

# cuML 0.16.0 (Date TBD)

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22 changes: 11 additions & 11 deletions python/cuml/solvers/sgd.pyx
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Expand Up @@ -137,15 +137,15 @@ class SGD(Base):
import cudf
from cuml.solvers import SGD as cumlSGD
X = cudf.DataFrame()
X['col1'] = np.array([1,1,2,2], dtype = np.float32)
X['col2'] = np.array([1,2,2,3], dtype = np.float32)
X['col1'] = np.array([1,1,2,2], dtype=np.float32)
X['col2'] = np.array([1,2,2,3], dtype=np.float32)
y = cudf.Series(np.array([1, 1, 2, 2], dtype=np.float32))
pred_data = cudf.DataFrame()
pred_data['col1'] = np.asarray([3, 2], dtype=dtype)
pred_data['col2'] = np.asarray([5, 5], dtype=dtype)
cu_sgd = cumlSGD(learning_rate=lrate, eta0=0.005, epochs=2000,
pred_data['col1'] = np.asarray([3, 2], dtype=np.float32)
pred_data['col2'] = np.asarray([5, 5], dtype=np.float32)
cu_sgd = cumlSGD(learning_rate='constant', eta0=0.005, epochs=2000,
fit_intercept=True, batch_size=2,
tol=0.0, penalty=penalty, loss=loss)
tol=0.0, penalty='none', loss='squared_loss')
cu_sgd.fit(X, y)
cu_pred = cu_sgd.predict(pred_data).to_array()
print(" cuML intercept : ", cu_sgd.intercept_)
Expand All @@ -156,11 +156,11 @@ class SGD(Base):

.. code-block:: python

cuML intercept : 0.004561662673950195
cuML coef : 0 0.9834546
1 0.010128272
dtype: float32
cuML predictions : [3.0055666 2.0221121]
cuML intercept : 0.0041877031326293945
cuML coef : 0 0.984174
1 0.009776
dtype: float32
cuML predictions : [3.005588 2.0214138]


Parameters
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