![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {
"id": "Gjf94IPRhQmo"
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from sklearn.decomposition import PCA\n",
"from scipy import spatial"
]
},
{
"cell_type": "code",
"source": [
"# input params\n",
"n_cols = 10\n",
"n_data = 2000\n",
"n_highlight = 500\n",
"baseline_start = 0\n",
"baseline_end = n_data - n_highlight\n",
"highlight_start = baseline_end + 1\n",
"highlight_end = n_data\n",
"\n",
"# algo params\n",
"n_lag = 5\n",
"n_components = 1\n",
"\n",
"# make data\n",
"df = pd.DataFrame(np.random.rand(n_data,n_cols), columns=[f'col_{n}' for n in range(1,n_cols+1)])\n",
"\n",
"# smooth all data\n",
"df = df.rolling(15).mean().bfill()\n",
"\n",
"# randomly sample for col_1 in highlight section\n",
"df[highlight_start:highlight_end]['col_1'] = np.random.rand((highlight_end-highlight_start))\n",
"\n",
"# for col_2 lets just a max aggregation\n",
"df[highlight_start:highlight_end]['col_2'] = df[highlight_start:highlight_end]['col_2'].rolling(30).max().bfill()\n",
"\n",
"# for col_3 lets smooth even more\n",
"df[highlight_start:highlight_end]['col_3'] = df[highlight_start:highlight_end]['col_3'].rolling(30).mean().bfill()\n",
"\n",
"df_baseline = df[baseline_start:baseline_end]\n",
"df_highlight = df[highlight_start:]\n",
"print(df.shape)\n",
"print(df_baseline.shape)\n",
"print(df_highlight.shape)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "wOaom9L8hWXF",
"outputId": "b25ea44f-c6e1-453f-f5f5-a7dddd3280fe"
},
"execution_count": 124,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(2000, 10)\n",
"(1500, 10)\n",
"(499, 10)\n"
]
},
{
"output_type": "stream",
"name": "stderr",
"text": [
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:21: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:24: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
"/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame.\n",
"Try using .loc[row_indexer,col_indexer] = value instead\n",
"\n",
"See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"for col in df.columns:\n",
" fig = df[col].plot(title=col)\n",
" plt.show()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"id": "1BoEp5hkiW1k",
"outputId": "ffd7bf18-2458-4549-a508-1ab55b941050"
},
"execution_count": 125,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"| \n", " | cosine_similarity | \n", "
|---|---|
| dim | \n", "\n", " |
| col_3 | \n", "0.138601 | \n", "
| col_1 | \n", "0.264772 | \n", "
| col_2 | \n", "0.316359 | \n", "
| col_10 | \n", "0.381890 | \n", "
| col_9 | \n", "0.399939 | \n", "
| col_7 | \n", "0.400711 | \n", "
| col_4 | \n", "0.402699 | \n", "
| col_5 | \n", "0.412232 | \n", "
| col_8 | \n", "0.415525 | \n", "
| col_6 | \n", "0.444001 | \n", "