update to python imports to reflect revoscalepy updates

Author: negust-microsoft

samples/features/machine-learning-services/python/getting-started/rental-prediction/rental_prediction.py

@@ -1,64 +1,73 @@
-import pandas
-import sklearn.linear_model
-import sklearn.metrics
-
-from revoscalepy.computecontext.RxInSqlServer import RxInSqlServer
-from revoscalepy.computecontext.RxInSqlServer import RxSqlServerData
-from revoscalepy.etl.RxImport import rx_import_datasource
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.metrics import mean_squared_error
 
+#If you are running SQL Server 2017 RC1 and above:
+from revoscalepy import RxComputeContext, RxInSqlServer, RxSqlServerData
+from revoscalepy import rx_import
 
 def get_rental_predictions():
-    conn_str = 'Driver=SQL Server;Server=MYSQLSERVER;Database=TutorialDB;Trusted_Connection=True;'
-    column_info = {
-        "Year": {"type": "integer"},
-        "Month": {"type": "integer"},
-        "Day": {"type": "integer"},
-        "RentalCount": {"type": "integer"},
-        "WeekDay": {
-            "type": "factor",
-            "levels": ["1", "2", "3", "4", "5", "6", "7"],
-        },
-        "Holiday": {
-            "type": "factor",
-            "levels": ["1", "0"],
-        },
-        "Snow": {
-            "type": "factor",
-            "levels": ["1", "0"],
-        }
-    }
+#Connection string to connect to SQL Server named instance
+ conn_str = 'Driver=SQL Server;Server=MYSQLSERVER;Database=TutorialDB;Trusted_Connection=True;'
+
+#Define the columns we wish to import
+ column_info = {
+         "Year" : { "type" : "integer" },
+         "Month" : { "type" : "integer" },
+         "Day" : { "type" : "integer" },
+         "RentalCount" : { "type" : "integer" },
+         "WeekDay" : {
+             "type" : "factor",
+             "levels" : ["1", "2", "3", "4", "5", "6", "7"]
+         },
+         "Holiday" : {
+             "type" : "factor",
+             "levels" : ["1", "0"]
+         },
+         "Snow" : {
+             "type" : "factor",
+             "levels" : ["1", "0"]
+         }
+     }
+
+ #Get the data from SQL Server Table
+ data_source = RxSqlServerData(table="dbo.rental_data",
+                              connection_string=conn_str, column_info=column_info)
+ computeContext = RxInSqlServer(
+     connection_string = conn_str,
+     num_tasks = 1,
+     auto_cleanup = False
+)
+
 
-    data_source = RxSqlServerData(table="dbo.rental_data",
-                                  connectionString=conn_str,
-                                  colInfo=column_info)
-    RxInSqlServer(connectionString=conn_str, numTasks=1, autoCleanup=False)
+ RxInSqlServer(connection_string=conn_str, num_tasks=1, auto_cleanup=False)
 
-    # import data source and convert to pandas dataframe
-    df = pandas.DataFrame(rx_import_datasource(data_source))
-    print("Data frame:", df)
-    # Get all the columns from the dataframe and filter out the ones we don't
-    # want.
-    columns = [x for x in df.columns if x == "Year"]
-    # Store the variable we'll be predicting on.
-    target = "RentalCount"
-    # Generate the training set.  Set random_state to be able to replicate results.
-    train = df.sample(frac=0.8, random_state=1)
-    # Select anything not in the training set and put it in the testing set.
-    test = df.loc[~df.index.isin(train.index)]
-    # Print the shapes of both sets.
-    print("Training set shape:", train.shape)
-    print("Testing set shape:", test.shape)
-    # Initialize the model class.
-    lin_model = sklearn.linear_model.LinearRegression()
-    # Fit the model to the training data.
-    lin_model.fit(train[columns], train[target])
-    # Generate our predictions for the test set.
-    lin_predictions = lin_model.predict(test[columns])
-    print("Predictions:", lin_predictions)
-    # Compute error between our test predictions and the actual values.
-    lin_mse = sklearn.metrics.mean_squared_error(lin_predictions, test[target])
-    print("Computed error:", lin_mse)
+ # import data source and convert to pandas dataframe
+ df = pd.DataFrame(rx_import(input_data = data_source))
+ print("Data frame:", df)
+ # Get all the columns from the dataframe.
+ columns = df.columns.tolist()
+ # Filter the columns to remove ones we don't want to use in the training
+ columns = [c for c in columns if c not in ["Year"]]
+  # Store the variable we'll be predicting on.
+ target = "RentalCount"
+ # Generate the training set.  Set random_state to be able to replicate results.
+ train = df.sample(frac=0.8, random_state=1)
+ # Select anything not in the training set and put it in the testing set.
+ test = df.loc[~df.index.isin(train.index)]
+ # Print the shapes of both sets.
+ print("Training set shape:", train.shape)
+ print("Testing set shape:", test.shape)
+ # Initialize the model class.
+ lin_model = LinearRegression()
+ # Fit the model to the training data.
+ lin_model.fit(train[columns], train[target])
 
+ # Generate our predictions for the test set.
+ lin_predictions = lin_model.predict(test[columns])
+ print("Predictions:", lin_predictions)
+ # Compute error between our test predictions and the actual values.
+ lin_mse = mean_squared_error(lin_predictions, test[target])
+ print("Computed error:", lin_mse)
 
-if __name__ == "__main__":
-    get_rental_predictions()
+get_rental_predictions()