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@ -2130,6 +2130,7 @@ def create_pipeline() -> Pipeline:
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</clipboard-copy>
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</div>
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<div class="highlight-ipynb hl-python"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
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<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
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<span class="n">optimisation_pipeline</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span>
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<span class="n">create_pipeline</span><span class="p">(),</span>
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@ -2150,6 +2151,7 @@ def create_pipeline() -> Pipeline:
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<span class="n">results</span><span class="o">.</span><span class="n">sort_values</span><span class="p">(</span><span class="s2">"rank_test_score"</span><span class="p">)</span>
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</pre></div>
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<div id="cell-4" class="clipboard-copy-txt">from sklearn.model_selection import GridSearchCV
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import pandas as pd
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optimisation_pipeline = GridSearchCV(
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create_pipeline(),
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@ -2197,25 +2199,481 @@ results.sort_values("rank_test_score")</div>
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<div class="jp-OutputArea-child">
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<div class="jp-OutputPrompt jp-OutputArea-prompt"></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[4]:</div>
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<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
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<pre>
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<span class="ansi-red-fg">---------------------------------------------------------------------------</span>
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<span class="ansi-red-fg">NameError</span> Traceback (most recent call last)
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<span class="ansi-green-intense-fg ansi-bold">/data/projects/great-ai/docs/examples/simple/train.ipynb Cell 6</span> in <span class="ansi-cyan-fg"><cell line: 18></span><span class="ansi-blue-fg">()</span>
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=2'>3</a></span> optimisation_pipeline = GridSearchCV(
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=3'>4</a></span> create_pipeline(),
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=4'>5</a></span> {
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<span class="ansi-green-fg"> (...)</span>
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=13'>14</a></span> verbose=1,
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=14'>15</a></span> )
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=15'>16</a></span> optimisation_pipeline.fit(X, y)
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<span class="ansi-green-fg">---> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=17'>18</a></span> results = pd.DataFrame(optimisation_pipeline.cv_results_)
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<span class="ansi-green-intense-fg ansi-bold"> <a href='vscode-notebook-cell:/data/projects/great-ai/docs/examples/simple/train.ipynb#ch0000005?line=18'>19</a></span> results.sort_values("rank_test_score")
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<span class="ansi-red-fg">NameError</span>: name 'pd' is not defined</pre>
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<div class="jp-RenderedHTMLCommon jp-RenderedHTML jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/html">
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<div>
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<style scoped>
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.dataframe tbody tr th:only-of-type {
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vertical-align: middle;
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}
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.dataframe tbody tr th {
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vertical-align: top;
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}
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.dataframe thead th {
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text-align: right;
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}
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</style>
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<table border="1" class="dataframe">
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<thead>
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<tr style="text-align: right;">
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<th></th>
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<th>mean_fit_time</th>
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<th>std_fit_time</th>
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<th>mean_score_time</th>
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<th>std_score_time</th>
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<th>param_classifier__alpha</th>
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<th>param_classifier__fit_prior</th>
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<th>param_vectorizer__max_df</th>
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<th>param_vectorizer__min_df</th>
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<th>params</th>
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<th>split0_test_score</th>
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<th>split1_test_score</th>
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<th>split2_test_score</th>
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<th>mean_test_score</th>
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<th>std_test_score</th>
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<th>rank_test_score</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<th>7</th>
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<td>14.549476</td>
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<td>0.361685</td>
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<td>8.476837</td>
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<td>0.222398</td>
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<td>0.5</td>
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<td>False</td>
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<td>0.05</td>
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<td>20</td>
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<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
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<td>0.518061</td>
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<td>0.514842</td>
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<td>0.511599</td>
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<td>0.514834</td>
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<td>0.002638</td>
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<td>1</td>
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</tr>
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<tr>
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<th>10</th>
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<td>11.235289</td>
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<td>0.130426</td>
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<td>4.092868</td>
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<td>0.082518</td>
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<td>0.5</td>
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<td>False</td>
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<td>0.1</td>
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<td>20</td>
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<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
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<td>0.513897</td>
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<td>0.515661</td>
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<td>0.507867</td>
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<td>0.512475</td>
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<td>0.003337</td>
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<td>2</td>
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</tr>
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<tr>
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<th>19</th>
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<td>7.383645</td>
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<td>0.138110</td>
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<td>4.130709</td>
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<td>0.250048</td>
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<td>1</td>
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<td>False</td>
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<td>0.05</td>
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<td>20</td>
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<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
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<td>0.496825</td>
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<td>0.501045</td>
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||||
<td>0.496854</td>
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||||
<td>0.498241</td>
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||||
<td>0.001983</td>
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<td>3</td>
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</tr>
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<tr>
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<th>11</th>
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<td>10.435154</td>
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<td>0.305144</td>
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<td>3.882101</td>
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||||
<td>0.128886</td>
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||||
<td>0.5</td>
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||||
<td>False</td>
|
||||
<td>0.1</td>
|
||||
<td>100</td>
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||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
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||||
<td>0.493247</td>
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||||
<td>0.497814</td>
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||||
<td>0.502245</td>
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<td>0.497769</td>
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<td>0.003674</td>
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<td>4</td>
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</tr>
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<tr>
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<th>8</th>
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<td>13.643193</td>
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<td>0.310696</td>
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<td>4.173707</td>
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||||
<td>0.142980</td>
|
||||
<td>0.5</td>
|
||||
<td>False</td>
|
||||
<td>0.05</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
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||||
<td>0.489609</td>
|
||||
<td>0.495207</td>
|
||||
<td>0.498154</td>
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||||
<td>0.494323</td>
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||||
<td>0.003544</td>
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<td>5</td>
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</tr>
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<tr>
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<th>22</th>
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<td>7.048340</td>
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||||
<td>0.050070</td>
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<td>3.172948</td>
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||||
<td>0.152418</td>
|
||||
<td>1</td>
|
||||
<td>False</td>
|
||||
<td>0.1</td>
|
||||
<td>20</td>
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||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
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<td>0.487456</td>
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||||
<td>0.493865</td>
|
||||
<td>0.491157</td>
|
||||
<td>0.490826</td>
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||||
<td>0.002627</td>
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<td>6</td>
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</tr>
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<tr>
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<th>23</th>
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<td>7.564685</td>
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||||
<td>0.146092</td>
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<td>2.374111</td>
|
||||
<td>0.285026</td>
|
||||
<td>1</td>
|
||||
<td>False</td>
|
||||
<td>0.1</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
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||||
<td>0.485160</td>
|
||||
<td>0.494039</td>
|
||||
<td>0.490127</td>
|
||||
<td>0.489776</td>
|
||||
<td>0.003633</td>
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<td>7</td>
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</tr>
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<tr>
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<th>20</th>
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<td>7.172353</td>
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<td>0.212599</td>
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<td>3.747219</td>
|
||||
<td>0.130217</td>
|
||||
<td>1</td>
|
||||
<td>False</td>
|
||||
<td>0.05</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
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||||
<td>0.481303</td>
|
||||
<td>0.490002</td>
|
||||
<td>0.488269</td>
|
||||
<td>0.486524</td>
|
||||
<td>0.003759</td>
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<td>8</td>
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</tr>
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<tr>
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<th>6</th>
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<td>14.276345</td>
|
||||
<td>0.456576</td>
|
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<td>8.318859</td>
|
||||
<td>0.268701</td>
|
||||
<td>0.5</td>
|
||||
<td>False</td>
|
||||
<td>0.05</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
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||||
<td>0.482429</td>
|
||||
<td>0.487735</td>
|
||||
<td>0.484888</td>
|
||||
<td>0.485017</td>
|
||||
<td>0.002168</td>
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<td>9</td>
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</tr>
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<tr>
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<th>2</th>
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<td>14.902358</td>
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||||
<td>0.737693</td>
|
||||
<td>5.975091</td>
|
||||
<td>0.171150</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.469598</td>
|
||||
<td>0.474490</td>
|
||||
<td>0.473637</td>
|
||||
<td>0.472575</td>
|
||||
<td>0.002134</td>
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<td>10</td>
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||||
</tr>
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<tr>
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<th>9</th>
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<td>12.677349</td>
|
||||
<td>0.145143</td>
|
||||
<td>4.374204</td>
|
||||
<td>0.175674</td>
|
||||
<td>0.5</td>
|
||||
<td>False</td>
|
||||
<td>0.1</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.468872</td>
|
||||
<td>0.476451</td>
|
||||
<td>0.470921</td>
|
||||
<td>0.472082</td>
|
||||
<td>0.003201</td>
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<td>11</td>
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</tr>
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<tr>
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<th>5</th>
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<td>13.423686</td>
|
||||
<td>0.482872</td>
|
||||
<td>8.008324</td>
|
||||
<td>0.442975</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.465726</td>
|
||||
<td>0.474548</td>
|
||||
<td>0.471879</td>
|
||||
<td>0.470718</td>
|
||||
<td>0.003694</td>
|
||||
<td>12</td>
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</tr>
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<tr>
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<th>1</th>
|
||||
<td>13.819117</td>
|
||||
<td>0.838347</td>
|
||||
<td>6.161175</td>
|
||||
<td>0.336590</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>20</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.463395</td>
|
||||
<td>0.473982</td>
|
||||
<td>0.471262</td>
|
||||
<td>0.469546</td>
|
||||
<td>0.004489</td>
|
||||
<td>13</td>
|
||||
</tr>
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||||
<tr>
|
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<th>4</th>
|
||||
<td>13.281476</td>
|
||||
<td>0.588822</td>
|
||||
<td>8.335852</td>
|
||||
<td>0.254627</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>20</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.458734</td>
|
||||
<td>0.468053</td>
|
||||
<td>0.464418</td>
|
||||
<td>0.463735</td>
|
||||
<td>0.003835</td>
|
||||
<td>14</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>14</th>
|
||||
<td>7.282247</td>
|
||||
<td>0.444940</td>
|
||||
<td>3.567094</td>
|
||||
<td>0.044519</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.438189</td>
|
||||
<td>0.450160</td>
|
||||
<td>0.446180</td>
|
||||
<td>0.444843</td>
|
||||
<td>0.004978</td>
|
||||
<td>15</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>17</th>
|
||||
<td>7.098797</td>
|
||||
<td>0.196241</td>
|
||||
<td>3.838628</td>
|
||||
<td>0.091128</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>100</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.436488</td>
|
||||
<td>0.444503</td>
|
||||
<td>0.445900</td>
|
||||
<td>0.442297</td>
|
||||
<td>0.004147</td>
|
||||
<td>16</td>
|
||||
</tr>
|
||||
<tr>
|
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<th>18</th>
|
||||
<td>7.492791</td>
|
||||
<td>0.288889</td>
|
||||
<td>3.843224</td>
|
||||
<td>0.073438</td>
|
||||
<td>1</td>
|
||||
<td>False</td>
|
||||
<td>0.05</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.428196</td>
|
||||
<td>0.431945</td>
|
||||
<td>0.430160</td>
|
||||
<td>0.430100</td>
|
||||
<td>0.001531</td>
|
||||
<td>17</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>21</th>
|
||||
<td>7.229826</td>
|
||||
<td>0.099823</td>
|
||||
<td>3.656332</td>
|
||||
<td>0.073780</td>
|
||||
<td>1</td>
|
||||
<td>False</td>
|
||||
<td>0.1</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.403130</td>
|
||||
<td>0.410170</td>
|
||||
<td>0.409801</td>
|
||||
<td>0.407700</td>
|
||||
<td>0.003235</td>
|
||||
<td>18</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>13</th>
|
||||
<td>7.158370</td>
|
||||
<td>0.169818</td>
|
||||
<td>3.765632</td>
|
||||
<td>0.082924</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>20</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.399237</td>
|
||||
<td>0.412872</td>
|
||||
<td>0.407982</td>
|
||||
<td>0.406697</td>
|
||||
<td>0.005640</td>
|
||||
<td>19</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>16</th>
|
||||
<td>7.064643</td>
|
||||
<td>0.119529</td>
|
||||
<td>3.810983</td>
|
||||
<td>0.125897</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>20</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.388060</td>
|
||||
<td>0.399247</td>
|
||||
<td>0.396325</td>
|
||||
<td>0.394544</td>
|
||||
<td>0.004737</td>
|
||||
<td>20</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>0</th>
|
||||
<td>13.749660</td>
|
||||
<td>0.465174</td>
|
||||
<td>6.407841</td>
|
||||
<td>0.549166</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.384852</td>
|
||||
<td>0.386487</td>
|
||||
<td>0.386796</td>
|
||||
<td>0.386045</td>
|
||||
<td>0.000853</td>
|
||||
<td>21</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>3</th>
|
||||
<td>15.954147</td>
|
||||
<td>0.318013</td>
|
||||
<td>6.337361</td>
|
||||
<td>0.261697</td>
|
||||
<td>0.5</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 0.5, 'classifier__fit_pr...</td>
|
||||
<td>0.369785</td>
|
||||
<td>0.375645</td>
|
||||
<td>0.375858</td>
|
||||
<td>0.373763</td>
|
||||
<td>0.002814</td>
|
||||
<td>22</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>12</th>
|
||||
<td>7.120198</td>
|
||||
<td>0.050452</td>
|
||||
<td>3.833905</td>
|
||||
<td>0.069540</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.05</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.277741</td>
|
||||
<td>0.280564</td>
|
||||
<td>0.285337</td>
|
||||
<td>0.281214</td>
|
||||
<td>0.003135</td>
|
||||
<td>23</td>
|
||||
</tr>
|
||||
<tr>
|
||||
<th>15</th>
|
||||
<td>7.497707</td>
|
||||
<td>0.183054</td>
|
||||
<td>3.870714</td>
|
||||
<td>0.062888</td>
|
||||
<td>1</td>
|
||||
<td>True</td>
|
||||
<td>0.1</td>
|
||||
<td>5</td>
|
||||
<td>{'classifier__alpha': 1, 'classifier__fit_prio...</td>
|
||||
<td>0.255578</td>
|
||||
<td>0.263381</td>
|
||||
<td>0.266184</td>
|
||||
<td>0.261714</td>
|
||||
<td>0.004487</td>
|
||||
<td>24</td>
|
||||
</tr>
|
||||
</tbody>
|
||||
</table>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
|
|
@ -2223,6 +2681,8 @@ results.sort_values("rank_test_score")</div>
|
|||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
||||
</div>
|
||||
</div><div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||||
<div class="jp-Cell jp-CodeCell jp-Notebook-cell ">
|
||||
|
|
@ -2230,7 +2690,7 @@ results.sort_values("rank_test_score")</div>
|
|||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||||
</div>
|
||||
<div class="jp-InputArea jp-Cell-inputArea">
|
||||
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||||
<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||||
|
||||
|
||||
<div class="CodeMirror cm-s-jupyter">
|
||||
|
|
@ -2277,7 +2737,7 @@ classifier.fit(X, y)</div>
|
|||
<div class="jp-OutputArea-child">
|
||||
|
||||
|
||||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[ ]:</div>
|
||||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[5]:</div>
|
||||
|
||||
|
||||
|
||||
|
|
@ -2313,7 +2773,7 @@ classifier.fit(X, y)</div>
|
|||
<div class="jp-Collapser jp-InputCollapser jp-Cell-inputCollapser">
|
||||
</div>
|
||||
<div class="jp-InputArea jp-Cell-inputArea">
|
||||
<div class="jp-InputPrompt jp-InputArea-prompt">In [ ]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||||
<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div><div class="jp-CodeMirrorEditor jp-Editor jp-InputArea-editor" data-type="inline">
|
||||
|
||||
|
||||
<div class="CodeMirror cm-s-jupyter">
|
||||
|
|
@ -2359,9 +2819,9 @@ save_model(classifier, key="small-domain-prediction", keep_last_n=5)</div>
|
|||
|
||||
<div class="jp-RenderedText jp-OutputArea-output" data-mime-type="application/vnd.jupyter.stderr">
|
||||
<pre><span style="color: rgb(0,175,255)">Fetching cached versions of small-domain-prediction</span>
|
||||
<span style="color: rgb(0,175,255)">Copying file for small-domain-prediction-1</span>
|
||||
<span style="color: rgb(0,175,255)">Compressing small-domain-prediction-1</span>
|
||||
<span style="color: rgb(0,175,255)">Model small-domain-prediction uploaded with version 1</span>
|
||||
<span style="color: rgb(0,175,255)">Copying file for small-domain-prediction-2</span>
|
||||
<span style="color: rgb(0,175,255)">Compressing small-domain-prediction-2</span>
|
||||
<span style="color: rgb(0,175,255)">Model small-domain-prediction uploaded with version 2</span>
|
||||
</pre>
|
||||
</div>
|
||||
</div>
|
||||
|
|
@ -2369,13 +2829,13 @@ save_model(classifier, key="small-domain-prediction", keep_last_n=5)</div>
|
|||
<div class="jp-OutputArea-child">
|
||||
|
||||
|
||||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[ ]:</div>
|
||||
<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[6]:</div>
|
||||
|
||||
|
||||
|
||||
|
||||
<div class="jp-RenderedText jp-OutputArea-output jp-OutputArea-executeResult" data-mime-type="text/plain">
|
||||
<pre>'small-domain-prediction:1'</pre>
|
||||
<pre>'small-domain-prediction:2'</pre>
|
||||
</div>
|
||||
|
||||
</div>
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue