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updated data loading functionalities
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mloning committed Mar 21, 2019
1 parent 12a7a8e commit 9d4746e
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2 changes: 1 addition & 1 deletion MANIFEST.in
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@@ -1,4 +1,4 @@
include *.rst
recursive-include examples *
recursive-include sktime *.c *.h *.pyx *.pxd *.pxi
recursive-include sktime/datasets *.csv *.csv.gz *.arff *.arff.gz *.txt
recursive-include sktime/datasets *.csv *.csv.gz *.arff *.arff.gz *.txt *.ts
455 changes: 401 additions & 54 deletions examples/Example - Shapelet Transform.ipynb
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106 changes: 64 additions & 42 deletions examples/highlevel-walkthrough.ipynb
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{
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>x_axis</th>\n",
" <th>label</th>\n",
" <th>dim_0</th>\n",
" <th>class_val</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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"</div>"
],
"text/plain": [
" x_axis label\n",
"0 [-0.6478854, -0.64199155, -0.63818632, -0.6382... 2\n",
"1 [-0.64442658, -0.64540093, -0.64705511, -0.647... 2\n",
"2 [-0.77835282, -0.77827907, -0.77715084, -0.777... 1\n",
"3 [-0.75006044, -0.7481029, -0.746164, -0.745925... 1\n",
"4 [-0.59953861, -0.59742191, -0.59926903, -0.598... 2"
" dim_0 class_val\n",
"0 0 -0.64789\n",
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"3... 1\n",
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"3... 1\n",
"4 0 -0.59954\n",
"1 -0.59742\n",
"2 -0.59927\n",
"3... 2"
]
},
"execution_count": 17,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 18,
"execution_count": 5,
"metadata": {},
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{
"data": {
"text/plain": [
"{'_case': 'TSC',\n",
" '_target': 'label',\n",
" '_features': Index(['x_axis'], dtype='object'),\n",
" '_target': 'class_val',\n",
" '_features': Index(['dim_0'], dtype='object'),\n",
" '_meta': {'nrow': 50,\n",
" 'ncol': 2,\n",
" 'target_type': {'label': int},\n",
" 'feature_type': {'x_axis': {numpy.ndarray}}}}"
" 'target_type': {'class_val': str},\n",
" 'feature_type': {'dim_0': {pandas.core.series.Series}}}}"
]
},
"execution_count": 18,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"task = Task(case='TSC', data=train, target='label')\n",
"task = Task(case='TSC', data=train, target='class_val')\n",
"task.__dict__"
]
},
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},
{
"cell_type": "code",
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"execution_count": 6,
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},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 1.59 s, sys: 8.38 ms, total: 1.6 s\n",
"Wall time: 1.6 s\n"
]
}
],
"outputs": [],
"source": [
"strategy.fit(task, train)"
]
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},
{
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"execution_count": 36,
"execution_count": 8,
"metadata": {},
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},
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{
"data": {
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"0.9733333333333334"
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"execution_count": 12,
"execution_count": 9,
"metadata": {},
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],
"source": [
"y_test = test['class_val']\n",
"accuracy_score(y_test, y_pred)"
]
}
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.2"
"version": "3.7.1"
}
},
"nbformat": 4,
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