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fix link to config nb & settingwithcopywarning

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Yassine Khelifi
2019-07-02 16:56:21 +00:00
parent 14ecfb0bf3
commit e97e4742ba
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",