Python语言实现机器学习

一、验证在SQL Server中已开启执行外部脚本（Python语言）的功能，检查Python版本并查看Python包列表

EXECUTE sp_configure 'external scripts enabled' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import sys print(sys.version) ' GO

二、如果 Launchpad 正在运行，则可以运行简单的 Python 脚本，以验证外部脚本运行时是否可以与 SQL Server 通信

EXEC sp_execute_external_script @language = N'Python', @script = N' OutputDataSet=InputDataSet; ', @input_data_1=N'SELECT 1 AS Col1'; EXEC sp_execute_external_script @language =N'Python', @script=N' OutputDataSet = InputDataSet; ', @input_data_1 =N'SELECT 1 AS hello' WITH RESULT SETS (([hello] int not null)); GO EXEC sp_execute_external_script @language = N'Python', @script = N' MyOutput=MyInput; ', @input_data_1_name=N'MyInput', @input_data_1=N'SELECT 1 AS foo', @output_data_1_name=N'MyOutput'; EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd c=1/2 d=1*2 s= pd.Series([c,d]) df=pd.DataFrame(s) OutputDataSet=df ' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd s={"col1":[1,2], "col2":[3,4]} df=pd.DataFrame(s) OutputDataSet=df '

三、添加简单的Python语言脚本，并执行

EXEC sp_execute_external_script @language = N'Python', @script = N'print(3+4)' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' a = 1 b = 2 c = a/b d = a*b print(c, d) ' EXECUTE sp_execute_external_script @language = N'Python', @script = N' import sys import os print("*********************************") print(sys.version) print("!!Hello World!!") print(os.getcwd()) print("********************************") ' EXECUTE sp_execute_external_script @language = N'Python' , @script = N'OutputDataSet = InputDataSet' , @input_data_1 = N'SELECT 1 AS hello' WITH RESULT SETS(([Hello World] INT)); GO

四、实现机器学习服务

1.通过 SQL Server 机器学习服务运行简单 Python 脚本 CREATE TABLE PythonTestData (col1 INT NOT NULL) INSERT INTO PythonTestData VALUES (1); INSERT INTO PythonTestData VALUES (10); INSERT INTO PythonTestData VALUES (100); GO EXECUTE sp_execute_external_script @language = N'Python' , @script = N'OutputDataSet = InputDataSet;' , @input_data_1 = N'SELECT * FROM PythonTestData;' WITH RESULT SETS(([NewColName] INT NOT NULL)); EXECUTE sp_execute_external_script @language = N'Python' , @script = N'SQL_out = SQL_in;' , @input_data_1 = N'SELECT 12 as Col;' , @input_data_1_name = N'SQL_in' , @output_data_1_name = N'SQL_out' WITH RESULT SETS(([NewColName] INT NOT NULL)); EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd mytextvariable = pandas.Series(["hello", " ", "world"]); OutputDataSet = pd.DataFrame(mytextvariable); ' , @input_data_1 = N'' WITH RESULT SETS(([Col1] CHAR(20) NOT NULL));

2.在 SQL Server 机器学习服务中使用 Python 处理数据结构和对象

EXECUTE sp_execute_external_script @language = N'Python' , @script = N' a = 1 b = 2 c = a/b print(c) s = pandas.Series(c, index =["simple math example 1"]) print(s) ' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' a = 1 b = 2 c = a/b d = a*b s = pandas.Series([c,d]) print(s) ' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' a = 1 b = 2 c = a/b s = pandas.Series(c, index =["simple math example 1", "simple math example 2"]) print(s) ' EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd a = 1 b = 2 c = a/b d = a*b s = pandas.Series([c,d]) print(s) df = pd.DataFrame(s) OutputDataSet = df ' WITH RESULT SETS((ResultValue FLOAT)) EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd a = 1 b = 2 c = a/b d = a*b s = pandas.Series([c,d]) print(s) df = pd.DataFrame(s, index=[1]) OutputDataSet = df ' WITH RESULT SETS((ResultValue FLOAT)) EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pandas as pd a = 1 b = 2 c = a/b s = pandas.Series(c, index =["simple math example 1", "simple math example 2"]) print(s) df = pd.DataFrame(s, index=["simple math example 1"]) OutputDataSet = df ' WITH RESULT SETS((ResultValue FLOAT))

3.结合使用 Python 函数和 SQL Server 机器学习服务

CREATE PROCEDURE MyPyNorm ( @param1 INT , @param2 INT , @param3 INT ) AS EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import numpy import pandas OutputDataSet = pandas.DataFrame(numpy.random.normal(size=mynumbers, loc=mymean, scale=mysd)); ' , @input_data_1 = N' ;' , @params = N' @mynumbers int, @mymean int, @mysd int' , @mynumbers = @param1 , @mymean = @param2 , @mysd = @param3 WITH RESULT SETS(([Density] FLOAT NOT NULL)); EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import time start_time = time.time() # Run Python processes elapsed_time = time.time() - start_time ' , @input_data_1 = N' ;';

4.数据分析和回归预测

execute sp_execute_external_script @language = N'Python', @script = N' import math a = 1 b = 2 c = a*b print(a,b,c) d = math.pi/6 print(math.sin(d)) ' execute sp_execute_external_script @language = N'Python', @script = N' from sklearn import linear_model import matplotlib.pyplot as plt import numpy as np #X表示匹萨尺寸 Y表示匹萨价格 X = [[6], [8], [10], [14], [18]] Y = [[7], [9], [13], [17.5], [18]] print(X) print(Y) #回归训练 clf = linear_model.LinearRegression() clf.fit(X, Y) res = clf.predict(np.array([12]).reshape(-1, 1))[0] print(u"预测一张12英寸匹萨价格：$%.2f" % res) #预测结果 X2 = [[0], [10], [14], [25]] Y2 = clf.predict(X2) ' CREATE PROCEDURE [dbo].[py_generate_customer_scores] AS BEGIN -- Input query to generate the customer data DECLARE @input_query NVARCHAR(MAX) = N'SELECT customer, orders, items, cost FROM dbo.Sales.Orders' EXEC sp_execute_external_script @language = N'Python', @script = N' import pandas as pd from sklearn.cluster import KMeans # Get data from input query customer_data = my_input_data # Define the model n_clusters = 4 est = KMeans(n_clusters=n_clusters, random_state=111).fit(customer_data[["orders","items","cost"]]) clusters = est.labels_ customer_data["cluster"] = clusters OutputDataSet = customer_data ' , @input_data_1 = @input_query , @input_data_1_name = N'my_input_data' WITH RESULT SETS (("CustomerID" int, "Orders" float,"Items" float,"Cost" float,"ClusterResult" float)); END; GO

5.使用 Python 包 scikit-learn 和 revoscalepy 来定型机器学习模型

DROP PROCEDURE IF EXISTS PyTrainTestSplit; GO CREATE PROCEDURE [dbo].[PyTrainTestSplit] (@pct int) AS DROP TABLE IF EXISTS dbo.nyctaxi_sample_training SELECT * into nyctaxi_sample_training FROM nyctaxi_sample WHERE (ABS(CAST(BINARY_CHECKSUM(medallion,hack_license) as int)) % 100) < @pct DROP TABLE IF EXISTS dbo.nyctaxi_sample_testing SELECT * into nyctaxi_sample_testing FROM nyctaxi_sample WHERE (ABS(CAST(BINARY_CHECKSUM(medallion,hack_license) as int)) % 100) > @pct GO DROP PROCEDURE IF EXISTS PyTrainScikit; GO CREATE PROCEDURE [dbo].[PyTrainScikit] (@trained_model varbinary(max) OUTPUT) AS BEGIN EXEC sp_execute_external_script @language = N'Python', @script = N' import numpy import pickle from sklearn.linear_model import LogisticRegression ##Create SciKit-Learn logistic regression model X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]] y = numpy.ravel(InputDataSet[["tipped"]]) SKLalgo = LogisticRegression() logitObj = SKLalgo.fit(X, y) ##Serialize model trained_model = pickle.dumps(logitObj) ', @input_data_1 = N' select tipped, fare_amount, passenger_count, trip_time_in_secs, trip_distance, dbo.fnCalculateDistance(pickup_latitude, pickup_longitude, dropoff_latitude, dropoff_longitude) as direct_distance from nyctaxi_sample_training ', @input_data_1_name = N'InputDataSet', @params = N'@trained_model varbinary(max) OUTPUT', @trained_model = @trained_model OUTPUT; ; END; GO DROP PROCEDURE IF EXISTS TrainTipPredictionModelRxPy; GO CREATE PROCEDURE [dbo].[TrainTipPredictionModelRxPy] (@trained_model varbinary(max) OUTPUT) AS BEGIN EXEC sp_execute_external_script @language = N'Python', @script = N' import numpy import pickle from revoscalepy.functions.RxLogit import rx_logit ## Create a logistic regression model using rx_logit function from revoscalepy package logitObj = rx_logit("tipped ~ passenger_count + trip_distance + trip_time_in_secs + direct_distance", data = InputDataSet); ## Serialize model trained_model = pickle.dumps(logitObj) ', @input_data_1 = N' select tipped, fare_amount, passenger_count, trip_time_in_secs, trip_distance, dbo.fnCalculateDistance(pickup_latitude, pickup_longitude, dropoff_latitude, dropoff_longitude) as direct_distance from nyctaxi_sample_training ', @input_data_1_name = N'InputDataSet', @params = N'@trained_model varbinary(max) OUTPUT', @trained_model = @trained_model OUTPUT; ; END; GO DROP PROCEDURE IF EXISTS PredictTipSciKitPy; GO CREATE PROCEDURE [dbo].[PredictTipSciKitPy] (@model varchar(50), @inquery nvarchar(max)) AS BEGIN DECLARE @lmodel2 varbinary(max) = (select model from nyc_taxi_models where name = @model); EXEC sp_execute_external_script @language = N'Python', @script = N' import pickle; import numpy; from sklearn import metrics mod = pickle.loads(lmodel2) X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]] y = numpy.ravel(InputDataSet[["tipped"]]) probArray = mod.predict_proba(X) probList = [] for i in range(len(probArray)): probList.append((probArray[i])[1]) probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 = @inquery, @input_data_1_name = N'InputDataSet', @params = N'@lmodel2 varbinary(max)', @lmodel2 = @lmodel2 WITH RESULT SETS ((Score float)); END GO DROP PROCEDURE IF EXISTS PredictTipRxPy; GO CREATE PROCEDURE [dbo].[PredictTipRxPy] (@model varchar(50), @inquery nvarchar(max)) AS BEGIN DECLARE @lmodel2 varbinary(max) = (select model from nyc_taxi_models where name = @model); EXEC sp_execute_external_script @language = N'Python', @script = N' import pickle; import numpy; from sklearn import metrics from revoscalepy.functions.RxPredict import rx_predict; mod = pickle.loads(lmodel2) X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]] y = numpy.ravel(InputDataSet[["tipped"]]) probArray = rx_predict(mod, X) probList = probArray["tipped_Pred"].values probArray = numpy.asarray(probList) fpr, tpr, thresholds = metrics.roc_curve(y, probArray) aucResult = metrics.auc(fpr, tpr) print ("AUC on testing data is: " + str(aucResult)) OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 = @inquery, @input_data_1_name = N'InputDataSet', @params = N'@lmodel2 varbinary(max)', @lmodel2 = @lmodel2 WITH RESULT SETS ((Score float)); END GO DROP PROCEDURE IF EXISTS PredictTipSingleModeSciKitPy; GO CREATE PROCEDURE [dbo].[PredictTipSingleModeSciKitPy] (@model varchar(50), @passenger_count int = 0, @trip_distance float = 0, @trip_time_in_secs int = 0, @pickup_latitude float = 0, @pickup_longitude float = 0, @dropoff_latitude float = 0, @dropoff_longitude float = 0) AS BEGIN DECLARE @inquery nvarchar(max) = N' SELECT * FROM [dbo].[fnEngineerFeatures]( @passenger_count, @trip_distance, @trip_time_in_secs, @pickup_latitude, @pickup_longitude, @dropoff_latitude, @dropoff_longitude) ' DECLARE @lmodel2 varbinary(max) = (select model from nyc_taxi_models where name = @model); EXEC sp_execute_external_script @language = N'Python', @script = N' import pickle; import numpy; # Load model and unserialize mod = pickle.loads(model) # Get features for scoring from input data X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]] # Score data to get tip prediction probability as a list (of float) probList = [] probList.append((mod.predict_proba(X)[0])[1]) # Create output data frame OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 = @inquery, @params = N'@model varbinary(max),@passenger_count int,@trip_distance float, @trip_time_in_secs int , @pickup_latitude float , @pickup_longitude float , @dropoff_latitude float , @dropoff_longitude float', @model = @lmodel2, @passenger_count =@passenger_count , @trip_distance=@trip_distance, @trip_time_in_secs=@trip_time_in_secs, @pickup_latitude=@pickup_latitude, @pickup_longitude=@pickup_longitude, @dropoff_latitude=@dropoff_latitude, @dropoff_longitude=@dropoff_longitude WITH RESULT SETS ((Score float)); END GO DROP PROCEDURE IF EXISTS PredictTipSingleModeRxPy; GO CREATE PROCEDURE [dbo].[PredictTipSingleModeRxPy] (@model varchar(50), @passenger_count int = 0, @trip_distance float = 0, @trip_time_in_secs int = 0, @pickup_latitude float = 0, @pickup_longitude float = 0, @dropoff_latitude float = 0, @dropoff_longitude float = 0) AS BEGIN DECLARE @inquery nvarchar(max) = N' SELECT * FROM [dbo].[fnEngineerFeatures]( @passenger_count, @trip_distance, @trip_time_in_secs, @pickup_latitude, @pickup_longitude, @dropoff_latitude, @dropoff_longitude) ' DECLARE @lmodel2 varbinary(max) = (select model from nyc_taxi_models where name = @model); EXEC sp_execute_external_script @language = N'Python', @script = N' import pickle; import numpy; from revoscalepy.functions.RxPredict import rx_predict; # Load model and unserialize mod = pickle.loads(model) # Get features for scoring from input data X = InputDataSet[["passenger_count", "trip_distance", "trip_time_in_secs", "direct_distance"]] # Score data to get tip prediction probability as a list (of float) probArray = rx_predict(mod, X) probList = [] probList = probArray["tipped_Pred"].values # Create output data frame OutputDataSet = pandas.DataFrame(data = probList, columns = ["predictions"]) ', @input_data_1 = @inquery, @params = N'@model varbinary(max),@passenger_count int,@trip_distance float, @trip_time_in_secs int , @pickup_latitude float , @pickup_longitude float , @dropoff_latitude float , @dropoff_longitude float', @model = @lmodel2, @passenger_count =@passenger_count , @trip_distance=@trip_distance, @trip_time_in_secs=@trip_time_in_secs, @pickup_latitude=@pickup_latitude, @pickup_longitude=@pickup_longitude, @dropoff_latitude=@dropoff_latitude, @dropoff_longitude=@dropoff_longitude WITH RESULT SETS ((Score float)); END GO

6.使用机器学习服务训练预测模型，调用该模型为新数据生成预测（回归或分类），并将结果返回到新的实体表中

DROP PROCEDURE IF EXISTS generate_model_rfc; go CREATE PROCEDURE generate_model_rfc (@trained_model varbinary(max) OUTPUT) AS BEGIN EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import numpy as np import pandas as pd import pickle from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn import metrics dt = train_data dt["Level"] = dt["Level"].astype(''category'') X = dt[["Fixed Acidity","Volatile Acidity","Citric acid","Residual Sugar","Chlorides","Free Sulfur Dioxide","Total Sulfur Dioxide","Density","PH","Sulphates","Alcohol"]] Y = dt[["Level"]] X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.3, random_state=42) RFC=RandomForestClassifier(n_estimators=100,criterion=''gini'',min_samples_split=2,max_depth=2) RFC.fit(X_Train,Y_Train.values.ravel()) trained_model = pickle.dumps(RFC) ' , @input_data_1 = N'select "Fixed Acidity", "Volatile Acidity", "Citric acid", "Residual Sugar", "Chlorides", "Free Sulfur Dioxide", "Total Sulfur Dioxide", "Density", "PH", "Sulphates", "Alcohol", "Level" from dbo.RedWineQuality' , @input_data_1_name = N'train_data' , @params = N'@trained_model varbinary(max) OUTPUT' , @trained_model = @trained_model OUTPUT; END; GO CREATE TABLE dbo.my_py_models ( model_name VARCHAR(50) NOT NULL DEFAULT('default model') PRIMARY KEY, model_object VARBINARY(MAX) NOT NULL ); GO DECLARE @model_object VARBINARY(MAX); EXEC generate_model_rfc @model_object OUTPUT; INSERT INTO my_py_models (model_name, model_object) VALUES('RandomForestClassification(RFC)', @model_object); DROP PROCEDURE IF EXISTS py_predict_rfc; GO CREATE PROCEDURE py_predict_rfc (@model varchar(100)) AS BEGIN DECLARE @py_model varbinary(max) = (select model_object from dbo.my_py_models where model_name = @model); EXEC sp_execute_external_script @language = N'Python', @script = N' import pickle import numpy as np import pandas as pd from sklearn.model_selection import train_test_split py_model_rfc = pickle.loads(py_model) dt = data dt["Level"] = dt["Level"].astype(''category'') X = dt[["Fixed Acidity","Volatile Acidity","Citric acid","Residual Sugar","Chlorides","Free Sulfur Dioxide","Total Sulfur Dioxide","Density","PH","Sulphates","Alcohol"]] Y = dt[["Level"]] X_Train, X_Test, Y_Train, Y_Test = train_test_split(X, Y, test_size=0.3, random_state=42) Predictions = py_model_rfc.predict(X_Test) # print("Accuracy:",metrics.accuracy_score(Y_Test, Predictions)) OutputDataSet=pd.DataFrame(data=Predictions,columns=[''Prediction'']) OutputDataSet[''Actual''] = pd.Series(Y_Test[''Level''].values,index=np.arange(0,len(Y_Test))) ' , @input_data_1 = N'select "Fixed Acidity", "Volatile Acidity", "Citric acid", "Residual Sugar", "Chlorides", "Free Sulfur Dioxide", "Total Sulfur Dioxide", "Density", "PH", "Sulphates", "Alcohol", "Level" from dbo.RedWineQuality' , @input_data_1_name = N'data' , @params = N'@py_model varbinary(max)' , @py_model = @py_model with result sets (("Prediction" int, "Actual" int)); END; GO CREATE TABLE [dbo].[py_rfc_predictions]( [Prediction] int, [Actual] int ) INSERT INTO py_rfc_predictions EXEC py_predict_rfc 'RandomForestClassification(RFC)'; -- Select contents of the table SELECT * FROM py_rfc_predictions;

7.通过 SQL Server 机器学习服务在 Python 中创建预测模型并对其进行评分

CREATE DATABASE irissql GO USE irissql GO DROP TABLE IF EXISTS iris_data; GO CREATE TABLE iris_data ( id INT NOT NULL IDENTITY PRIMARY KEY , "Sepal.Length" FLOAT NOT NULL, "Sepal.Width" FLOAT NOT NULL , "Petal.Length" FLOAT NOT NULL, "Petal.Width" FLOAT NOT NULL , "Species" VARCHAR(100) NOT NULL, "SpeciesId" INT NOT NULL ); DROP TABLE IF EXISTS iris_models; GO CREATE TABLE iris_models ( model_name VARCHAR(50) NOT NULL DEFAULT('default model') PRIMARY KEY, model VARBINARY(MAX) NOT NULL ); GO CREATE PROCEDURE get_iris_dataset AS BEGIN EXEC sp_execute_external_script @language = N'Python', @script = N' from sklearn import datasets iris = datasets.load_iris() iris_data = pandas.DataFrame(iris.data) iris_data["Species"] = pandas.Categorical.from_codes(iris.target, iris.target_names) iris_data["SpeciesId"] = iris.target ', @input_data_1 = N'', @output_data_1_name = N'iris_data' WITH RESULT SETS (("Sepal.Length" float not null, "Sepal.Width" float not null, "Petal.Length" float not null, "Petal.Width" float not null, "Species" varchar(100) not null, "SpeciesId" int not null)); END; GO INSERT INTO iris_data ("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "Species", "SpeciesId") EXEC dbo.get_iris_dataset; SELECT TOP(10) * FROM iris_data; SELECT COUNT(*) FROM iris_data; USE irissql GO sp_configure 'external scripts enabled', 1; RECONFIGURE WITH override; CREATE PROCEDURE generate_iris_model (@trained_model VARBINARY(max) OUTPUT) AS BEGIN EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pickle from sklearn.naive_bayes import GaussianNB GNB = GaussianNB() trained_model = pickle.dumps(GNB.fit(iris_data[["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"]], iris_data[["SpeciesId"]].values.ravel())) ' , @input_data_1 = N'select "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "SpeciesId" from iris_data' , @input_data_1_name = N'iris_data' , @params = N'@trained_model varbinary(max) OUTPUT' , @trained_model = @trained_model OUTPUT; END; GO DECLARE @model varbinary(max); DECLARE @new_model_name varchar(50) SET @new_model_name = 'Naive Bayes' EXECUTE generate_iris_model @model OUTPUT; DELETE iris_models WHERE model_name = @new_model_name; INSERT INTO iris_models (model_name, model) values(@new_model_name, @model); GO SELECT * FROM dbo.iris_models CREATE PROCEDURE predict_species (@model VARCHAR(100)) AS BEGIN DECLARE @nb_model VARBINARY(max) = ( SELECT model FROM iris_models WHERE model_name = @model ); EXECUTE sp_execute_external_script @language = N'Python' , @script = N' import pickle irismodel = pickle.loads(nb_model) species_pred = irismodel.predict(iris_data[["Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"]]) iris_data["PredictedSpecies"] = species_pred OutputDataSet = iris_data[["id","SpeciesId","PredictedSpecies"]] print(OutputDataSet) ' , @input_data_1 = N'select id, "Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width", "SpeciesId" from iris_data' , @input_data_1_name = N'iris_data' , @params = N'@nb_model varbinary(max)' , @nb_model = @nb_model WITH RESULT SETS(( "id" INT , "SpeciesId" INT , "SpeciesId.Predicted" INT )); END; GO EXECUTE predict_species 'Naive Bayes'; GO

8.通过 SQL 机器学习对较大功能集做建模限制

from numpy.random import randn matrix = randn(2000, 2001) import pandas data = pandas.DataFrame(data=matrix, columns=["Label"] + ["f%s" % i for i in range(1, matrix.shape[1])]) data["Label"] = (data["Label"] > 0.5).apply(lambda x: 1.0 if x else 0.0) print("problem dimension:", data.shape) print(data[["Label", "f1", "f2", data.columns[-1]]].head()) ################################################### # Let's train a logistic regression. formula = "Label ~ {0}".format(" + ".join(data.columns[1:])) print(formula[:50] + " + ...") from numpy.random import randn matrix = randn(2000, 3) import random import pandas data = pandas.DataFrame(data=matrix, columns=["Label"] + ["f%s" % i for i in range(1, matrix.shape[1])]) data["Label"] = (data["Label"] > 0.5).apply(lambda x: 1.0 if x else 0.0) data["cat"] = [["0", "1"][random.randint(0,1)] for i in range(0, data.shape[0])] ################################ # We define this column as a category. data["cat"] = data["cat"].astype("category") print("problem dimension:", data.shape) print(data.head()) ################################################### # Let's train a logistic regression. formula = "Label ~ {0}".format(" + ".join(data.columns[1:])) print(formula)