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Merge branch 'fix_yref' of github.com:project-codeflare/codeflare into fix_yref
2 parents 164a242 + ff45027 commit 5e73919

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+56
-86
lines changed

5 files changed

+56
-86
lines changed

‎codeflare/pipelines/Runtime.py‎

Lines changed: 5 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -107,8 +107,8 @@ def execute_or_node_remote(node: dm.EstimatorNode, mode: ExecutionType, xy_ref:
107107
elif mode == ExecutionType.SCORE:
108108
if base.is_classifier(estimator) or base.is_regressor(estimator):
109109
estimator = node.get_estimator()
110-
res_Xref = ray.put(estimator.score(X, y))
111-
result = dm.XYRef(res_Xref, xy_ref.get_yref(), prev_node_ptr, prev_node_ptr, [xy_ref])
110+
score_ref = ray.put(estimator.score(X, y))
111+
result = dm.XYRef(score_ref, score_ref, prev_node_ptr, prev_node_ptr, [xy_ref])
112112
return result
113113
else:
114114
res_Xref = ray.put(estimator.transform(X))
@@ -118,8 +118,8 @@ def execute_or_node_remote(node: dm.EstimatorNode, mode: ExecutionType, xy_ref:
118118
elif mode == ExecutionType.PREDICT:
119119
# Test mode does not clone as it is a simple predict or transform
120120
if base.is_classifier(estimator) or base.is_regressor(estimator):
121-
res_Xref = ray.put(estimator.predict(X))
122-
result = dm.XYRef(res_Xref, xy_ref.get_yref(), prev_node_ptr, prev_node_ptr, [xy_ref])
121+
predict_ref = ray.put(estimator.predict(X))
122+
result = dm.XYRef(predict_ref, predict_ref, prev_node_ptr, prev_node_ptr, [xy_ref])
123123
return result
124124
else:
125125
res_Xref = ray.put(estimator.transform(X))
@@ -662,4 +662,4 @@ def save(pipeline_output: dm.PipelineOutput, xy_ref: dm.XYRef, filehandle):
662662
:return: None
663663
"""
664664
pipeline = select_pipeline(pipeline_output, xy_ref)
665-
pipeline.save(filehandle)
665+
pipeline.save(filehandle)

‎codeflare/pipelines/tests/test_pipeline_predict.py‎

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -77,8 +77,8 @@ def test_pipeline_predict():
7777

7878
predict_clf_output = predict_output.get_xyrefs(node_clf)
7979

80-
#y_pred = ray.get(predict_clf_output[0].get_yref())
81-
y_pred = ray.get(predict_clf_output[0].get_Xref())
80+
y_pred = ray.get(predict_clf_output[0].get_yref())
81+
#y_pred = ray.get(predict_clf_output[0].get_Xref())
8282

8383

8484
report_codeflare = classification_report(y_test, y_pred)

‎notebooks/plot_nca_classification.ipynb‎

Lines changed: 8 additions & 15 deletions
Original file line numberDiff line numberDiff line change
@@ -10,7 +10,7 @@
1010
},
1111
{
1212
"cell_type": "code",
13-
"execution_count": 4,
13+
"execution_count": 1,
1414
"metadata": {},
1515
"outputs": [],
1616
"source": [
@@ -37,7 +37,7 @@
3737
},
3838
{
3939
"cell_type": "code",
40-
"execution_count": 36,
40+
"execution_count": 2,
4141
"metadata": {},
4242
"outputs": [
4343
{
@@ -150,14 +150,14 @@
150150
},
151151
{
152152
"cell_type": "code",
153-
"execution_count": 37,
153+
"execution_count": 4,
154154
"metadata": {},
155155
"outputs": [
156156
{
157157
"name": "stderr",
158158
"output_type": "stream",
159159
"text": [
160-
"2021-06-08 16:33:25,975\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
160+
"2021-07-19 10:50:40,647\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
161161
]
162162
},
163163
{
@@ -243,7 +243,7 @@
243243
"\n",
244244
"knn_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_knn)[0])\n",
245245
"knn_score = ray.get(rt.execute_pipeline(knn_pipeline, ExecutionType.SCORE, test_input)\n",
246-
" .get_xyrefs(node_knn)[0].get_Xref())\n",
246+
" .get_xyrefs(node_knn)[0].get_yref())\n",
247247
"\n",
248248
"# Plot the decision boundary. For that, we will assign a color to each\n",
249249
"# point in the mesh [x_min, x_max]x[y_min, y_max].\n",
@@ -254,7 +254,7 @@
254254
"predict_input.add_xy_arg(node_scalar, dm.Xy(meshinput, meshlabel))\n",
255255
"\n",
256256
"Z = ray.get(rt.execute_pipeline(knn_pipeline, ExecutionType.PREDICT, predict_input)\n",
257-
" .get_xyrefs(node_knn)[0].get_Xref())\n",
257+
" .get_xyrefs(node_knn)[0].get_yref())\n",
258258
"\n",
259259
"# Put the result into a color plot\n",
260260
"Z = Z.reshape(xx.shape)\n",
@@ -273,10 +273,10 @@
273273
"name = names[1]\n",
274274
"nca_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_knn_post_nca)[0])\n",
275275
"nca_score = ray.get(rt.execute_pipeline(nca_pipeline, ExecutionType.SCORE, test_input)\n",
276-
" .get_xyrefs(node_knn_post_nca)[0].get_Xref())\n",
276+
" .get_xyrefs(node_knn_post_nca)[0].get_yref())\n",
277277
"\n",
278278
"Z = ray.get(rt.execute_pipeline(nca_pipeline, ExecutionType.PREDICT, predict_input)\n",
279-
" .get_xyrefs(node_knn_post_nca)[0].get_Xref())\n",
279+
" .get_xyrefs(node_knn_post_nca)[0].get_yref())\n",
280280
"\n",
281281
"# Put the result into a color plot\n",
282282
"Z = Z.reshape(xx.shape)\n",
@@ -295,13 +295,6 @@
295295
"\n",
296296
"ray.shutdown()"
297297
]
298-
},
299-
{
300-
"cell_type": "code",
301-
"execution_count": null,
302-
"metadata": {},
303-
"outputs": [],
304-
"source": []
305298
}
306299
],
307300
"metadata": {

‎notebooks/plot_rbm_logistic_classification.ipynb‎

Lines changed: 30 additions & 53 deletions
Original file line numberDiff line numberDiff line change
@@ -27,16 +27,16 @@
2727
"output_type": "stream",
2828
"text": [
2929
"Automatically created module for IPython interactive environment\n",
30-
"[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
31-
"[BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.18s\n",
32-
"[BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.18s\n",
33-
"[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.16s\n",
34-
"[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.16s\n",
35-
"[BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.15s\n",
36-
"[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s\n",
30+
"[BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.10s\n",
31+
"[BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.14s\n",
32+
"[BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
33+
"[BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.14s\n",
34+
"[BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
35+
"[BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.10s\n",
36+
"[BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.13s\n",
3737
"[BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.13s\n",
38-
"[BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.14s\n",
39-
"[BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.14s\n",
38+
"[BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.13s\n",
39+
"[BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.13s\n",
4040
"Logistic regression using RBM features:\n",
4141
" precision recall f1-score support\n",
4242
"\n",
@@ -207,60 +207,30 @@
207207
},
208208
{
209209
"cell_type": "code",
210-
"execution_count": 16,
210+
"execution_count": 3,
211211
"metadata": {},
212212
"outputs": [
213213
{
214214
"name": "stderr",
215215
"output_type": "stream",
216216
"text": [
217-
"2021-06-09 10:48:44,778\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
217+
"2021-07-19 11:01:26,551\tINFO services.py:1267 -- View the Ray dashboard at \u001b[1m\u001b[32mhttp://127.0.0.1:8266\u001b[39m\u001b[22m\n"
218218
]
219219
},
220220
{
221221
"name": "stdout",
222222
"output_type": "stream",
223223
"text": [
224-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
225-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
226-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
227-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
228-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.15s\n",
229-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.15s\n",
230-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.15s\n",
231-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.15s\n",
232-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
233-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
234-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
235-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
236-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.14s\n",
237-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.14s\n",
238-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.14s\n",
239-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.14s\n",
240-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
241-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
242-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
243-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
244-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.14s\n",
245-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.14s\n",
246-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.14s\n",
247-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.14s\n",
248-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s\n",
249-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s\n",
250-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s\n",
251-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.15s\n",
252-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.15s\n",
253-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.15s\n",
254-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.15s\n",
255-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.15s\n",
256-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.13s\n",
257-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.13s\n",
258-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.13s\n",
259-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.13s\n",
260-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.15s\n",
261-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.15s\n",
262-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.15s\n",
263-
"\u001b[2m\u001b[36m(pid=4523)\u001b[0m [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.15s\n",
224+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 1, pseudo-likelihood = -25.57, time = 0.11s\n",
225+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 2, pseudo-likelihood = -23.68, time = 0.15s\n",
226+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 3, pseudo-likelihood = -22.74, time = 0.15s\n",
227+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 4, pseudo-likelihood = -21.83, time = 0.16s\n",
228+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 5, pseudo-likelihood = -21.62, time = 0.15s\n",
229+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 6, pseudo-likelihood = -21.11, time = 0.14s\n",
230+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 7, pseudo-likelihood = -20.88, time = 0.13s\n",
231+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 8, pseudo-likelihood = -20.58, time = 0.15s\n",
232+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 9, pseudo-likelihood = -20.32, time = 0.15s\n",
233+
"\u001b[2m\u001b[36m(pid=8995)\u001b[0m [BernoulliRBM] Iteration 10, pseudo-likelihood = -20.13, time = 0.15s\n",
264234
"Logistic regression using RBM features:\n",
265235
" precision recall f1-score support\n",
266236
"\n",
@@ -411,14 +381,14 @@
411381
"\n",
412382
"logistic_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_logistic)[0])\n",
413383
"Y_pred = ray.get(rt.execute_pipeline(logistic_pipeline, ExecutionType.PREDICT, predict_input)\n",
414-
" .get_xyrefs(node_logistic)[0].get_Xref())\n",
384+
" .get_xyrefs(node_logistic)[0].get_yref())\n",
415385
"\n",
416386
"print(\"Logistic regression using RBM features:\\n%s\\n\" % (\n",
417387
" metrics.classification_report(Y_test, Y_pred)))\n",
418388
"\n",
419389
"raw_pixel_pipeline = rt.select_pipeline(pipeline_fitted, pipeline_fitted.get_xyrefs(node_raw_pixel)[0])\n",
420390
"Y_pred = ray.get(rt.execute_pipeline(raw_pixel_pipeline, ExecutionType.PREDICT, predict_input)\n",
421-
" .get_xyrefs(node_raw_pixel)[0].get_Xref())\n",
391+
" .get_xyrefs(node_raw_pixel)[0].get_yref())\n",
422392
"\n",
423393
"print(\"Logistic regression using raw pixel features:\\n%s\\n\" % (\n",
424394
" metrics.classification_report(Y_test, Y_pred)))\n",
@@ -438,6 +408,13 @@
438408
"\n",
439409
"ray.shutdown()"
440410
]
411+
},
412+
{
413+
"cell_type": "code",
414+
"execution_count": null,
415+
"metadata": {},
416+
"outputs": [],
417+
"source": []
441418
}
442419
],
443420
"metadata": {

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