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Updated content after dry run
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‎part0_intro.ipynb

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"metadata": {},
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"source": [
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"# Automating Machine Learning with Python and Azure\n",
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"By [Matt Eland](https://MattEland.dev) | [@IntegerMan](https://twitter.com/IntegerMan)"
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"By [Matt Eland](https://MattEland.dev) | [@IntegerMan](https://twitter.com/IntegerMan)\n",
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"\n",
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"**Note:** Full source code for this talk is available at [https://github.com/IntegerMan/AutoMLwithPythonAndAzure](https://github.com/IntegerMan/AutoMLwithPythonAndAzure)"
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]
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},
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{

‎part1_dataprep.ipynb

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},
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{
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"cell_type": "code",
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"execution_count": 20,
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{
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"2000020005 52.0"
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]
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},
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"execution_count": 1,
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"execution_count": 20,
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"output_type": "execute_result"
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}
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"4 2017020586 20172018 R 20 24"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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"# Historical information is helpful, but does become outdated. Let's look at 2015 onwards, then drop our identifiers that might throw off machine learning\n",
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"df_games = df_games[df_games['season'] >= 20152016]\n",
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"\n",
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"# A few unusual values occur. Make sure we're dealing with playoff or regular season games only\n",
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"df_games = df_games[(df_games['type'] == 'R') | (df_games['type'] == 'P')] \n",
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"\n",
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"# Remap the season to a string type\n",
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"df_games['season'] = df_games['season'].map(str)\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 45,
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"metadata": {},
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{
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>2000020001</td>\n",
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" <td>42.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <th>26292</th>\n",
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" <td>2019030414</td>\n",
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" <td>36.0</td>\n",
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" <td>20192020</td>\n",
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" <td>P</td>\n",
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" <td>14.0</td>\n",
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" <td>25.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>2000020002</td>\n",
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" <td>32.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <th>26293</th>\n",
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" <td>2019030415</td>\n",
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" <td>12.0</td>\n",
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" <td>20192020</td>\n",
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" <td>P</td>\n",
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" <td>25.0</td>\n",
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" <td>14.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2000020003</td>\n",
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" <td>45.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <th>26294</th>\n",
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" <td>2019030415</td>\n",
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" <td>12.0</td>\n",
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" <td>20192020</td>\n",
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" <td>P</td>\n",
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" <td>25.0</td>\n",
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" <td>14.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>2000020004</td>\n",
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" <th>26295</th>\n",
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" <td>2019030416</td>\n",
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" <td>24.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>20192020</td>\n",
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" <td>P</td>\n",
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" <td>14.0</td>\n",
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" <td>25.0</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>2000020005</td>\n",
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" <td>52.0</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <td>NaN</td>\n",
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" <th>26296</th>\n",
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" <td>2019030416</td>\n",
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" <td>24.0</td>\n",
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" <td>20192020</td>\n",
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" <td>P</td>\n",
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" <td>14.0</td>\n",
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" <td>25.0</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" game_id penaltyMinutes season type away_team_id home_team_id\n",
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"0 2000020001 42.0 NaN NaN NaN NaN\n",
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"1 2000020002 32.0 NaN NaN NaN NaN\n",
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"2 2000020003 45.0 NaN NaN NaN NaN\n",
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"3 2000020004 24.0 NaN NaN NaN NaN\n",
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"4 2000020005 52.0 NaN NaN NaN NaN"
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" game_id penaltyMinutes season type away_team_id home_team_id\n",
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"26292 2019030414 36.0 20192020 P 14.0 25.0\n",
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"26293 2019030415 12.0 20192020 P 25.0 14.0\n",
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"26294 2019030415 12.0 20192020 P 25.0 14.0\n",
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"26295 2019030416 24.0 20192020 P 14.0 25.0\n",
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"26296 2019030416 24.0 20192020 P 14.0 25.0"
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]
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},
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"execution_count": 3,
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"execution_count": 45,
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"output_type": "execute_result"
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}
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"# Aggregate everything together\n",
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"df_detailed_pens = pd.merge(left=df_team_stats, right=df_games, how='left', left_on='game_id', right_on='game_id')\n",
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"\n",
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"df_detailed_pens.head()"
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"# Drop NaN occurrences\n",
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"df_detailed_pens = df_detailed_pens.dropna()\n",
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"\n",
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"df_detailed_pens.tail()"
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]
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},
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"cell_type": "code",
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"execution_count": 41,
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{
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"4 6 Bruins"
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},
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"4 2017021101 10.0 20172018 R Maple Leafs Canadiens"
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]
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"execution_count": 5,
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"source": [
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"# Now get the home and away teams into the detailed penalties dataframe\n",
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"df_detailed_pens = pd.merge(left=df_detailed_pens, right=df_teams, how='inner', left_on='home_team_id', right_on='team_id')\n",
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"df_detailed_pens = df_detailed_pens.rename(columns={'teamName': 'homeTeam'})\n",
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"df_detailed_pens = df_detailed_pens.drop(['home_team_id','team_id'], axis=1)\n",
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"df_detailed_pens.rename(columns={'teamName': 'homeTeam'}, inplace=True)\n",
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"df_detailed_pens.drop(columns=['home_team_id','team_id'], axis=1, inplace=True)\n",
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"\n",
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"df_detailed_pens = pd.merge(left=df_detailed_pens, right=df_teams, how='inner', left_on='away_team_id', right_on='team_id')\n",
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"df_detailed_pens = df_detailed_pens.rename(columns={'teamName': 'awayTeam'})\n",
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"df_detailed_pens = df_detailed_pens.drop(['away_team_id','team_id'], axis=1)\n",
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"df_detailed_pens.rename(columns={'teamName': 'awayTeam'}, inplace=True)\n",
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"df_detailed_pens.drop(columns=['away_team_id','team_id'], axis=1, inplace=True)\n",
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"\n",
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"df_detailed_pens.head()"
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"Name: penaltyMinutes, dtype: float64"
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"4 10.0 R Maple Leafs Canadiens"
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"# Connect to the workspace by reading from the config.json file (downloaded from the Machine Learning page in the portal)\n",
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"from azureml.core import Workspace, Dataset\n",
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"\n",
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"\n",
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"ws = Workspace.from_config() # config.json is needed in this folder for this to work\n",
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"Validating arguments.\n",
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"Arguments validated.\n",
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"Successfully obtained datastore reference and path.\n",
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"Uploading file to managed-dataset/783d4c3f-e99e-47b9-ac1d-df55a2564c72/\n",
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"Uploading file to managed-dataset/edbbcf3b-8a61-49d3-b9a0-040a45e85c16/\n",
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"Successfully uploaded file to datastore.\n",
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"Creating and registering a new dataset.\n",
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"Successfully created and registered a new dataset.\n",
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"NHL-Penalties-2020 v6 (ID: 69058fd8-167e-4e66-9aa8-733590aa2e4c)\n"
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"NHL-Penalties-2020 v7 (ID: 8b25d268-7a7a-40d4-b496-4c016aeaaca0)\n"
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]
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}
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],

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