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Merge pull request #459 from datashinobi/yassine/datadrift2

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fix link to config nb & settingwithcopywarning
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Roope Astala
2019-07-03 12:41:31 -04:00
committed by GitHub
parent 14ecfb0bf3 e97e4742ba
commit 9e0fc4f0e7
1 changed files with 707 additions and 707 deletions
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20
how-to-use-azureml/data-drift/azure-ml-datadrift.ipynb
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@@ -82,7 +82,7 @@
"source": [
"## Set up Configuraton and Create Azure ML Workspace\n",
"\n",
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
"If you are using an Azure Machine Learning Notebook VM, you are all set. Otherwise, go through the [configuration notebook](../../configuration.ipynb) first if you haven't already to establish your connection to the AzureML Workspace."
]
},
{
@@ -140,26 +140,26 @@
"\n",
"columns = ['usaf', 'wban', 'datetime', 'latitude', 'longitude', 'elevation', 'windAngle', 'windSpeed', 'temperature', 'stationName', 'p_k']\n",
"\n",
"\n",
"def enrich_weather_noaa_data(noaa_df):\n",
" hours_in_day = 23\n",
" week_in_year = 52\n",
" \n",
" noaa_df[\"hour\"] = noaa_df[\"datetime\"].dt.hour\n",
" noaa_df[\"weekofyear\"] = noaa_df[\"datetime\"].dt.week\n",
" \n",
" noaa_df[\"sine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year))\n",
" noaa_df[\"cosine_weekofyear\"] = noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year))\n",
"\n",
" noaa_df[\"sine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day))\n",
" noaa_df[\"cosine_hourofday\"] = noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
" noaa_df = noaa_df.assign(hour=noaa_df[\"datetime\"].dt.hour,\n",
" weekofyear=noaa_df[\"datetime\"].dt.week,\n",
" sine_weekofyear=noaa_df['datetime'].transform(lambda x: np.sin((2*np.pi*x.dt.week-1)/week_in_year)),\n",
" cosine_weekofyear=noaa_df['datetime'].transform(lambda x: np.cos((2*np.pi*x.dt.week-1)/week_in_year)),\n",
" sine_hourofday=noaa_df['datetime'].transform(lambda x: np.sin(2*np.pi*x.dt.hour/hours_in_day)),\n",
" cosine_hourofday=noaa_df['datetime'].transform(lambda x: np.cos(2*np.pi*x.dt.hour/hours_in_day))\n",
" )\n",
" \n",
" return noaa_df\n",
"\n",
"\n",
"def add_window_col(input_df):\n",
" shift_interval = pd.Timedelta('-7 days') # your X days interval\n",
" df_shifted = input_df.copy()\n",
" df_shifted['datetime'] = df_shifted['datetime'] - shift_interval\n",
" df_shifted.loc[:,'datetime'] = df_shifted['datetime'] - shift_interval\n",
" df_shifted.drop(list(input_df.columns.difference(['datetime', 'usaf', 'wban', 'sine_hourofday', 'temperature'])), axis=1, inplace=True)\n",
"\n",
" # merge, keeping only observations where -1 lag is present\n",