Azure Machine Learning の Jupyter Notebook 対応 ( Variable Selection 編 )

# initiate the linear modellm = LinearRegression()

# scale the featuresmin_max_scaler = preprocessing.MinMaxScaler()x_scaled_minmax = min_max_scaler.fit_transform(X)x_scaled_minmax_df = pd.DataFrame(x_scaled_minmax, columns = feature_names)

# recursive feature elimination with cross validation, using r-squared as metricrfecv = RFECV(estimator=lm, step=1, cv=5, scoring='r2')rfecv.fit(x_scaled_minmax_df, y)

# print the optimal number of feature　(最適な特徴の数)print("Optimal number of features : %d" % rfecv.n_features_)

# save the selected features (選択された特徴)feature_cols_selected = list(np.array(feature_names)[rfecv.support_])print("Features selected: " + str(feature_cols_selected))

# plot number of features VS. cross-validation scoresplt.figure()plt.xlabel("Number of features selected")plt.ylabel("R-squared")plt.plot(range(1, len(rfecv.grid_scores_) + 1), rfecv.grid_scores_)plt.show()

#%% fit model with selected featuresX_new = X[:,rfecv.support_]lm2 = LinearRegression()lm2.fit(X_new, y)

# print the R-squaredprint("The R-squared value is: {0:0.4f} \n".format(lm2.score(X_new, y)))# save intercept and coefficientsparam_df = pd.DataFrame({"Features": ['intercept'] + feature_cols_selected, "Coef": [lm2.intercept_] + list(lm2.coef_)})cols = param_df.columns.tolist()cols = cols[-1:]+cols[:-1]param_df = param_df[cols]print(param_df)

# assign test datanewX = X_newnewY = y

# make predictionspredicted = lm2.predict(newX)predictedDf = pd.DataFrame({"predicted": predicted})

# dataframe for XnewXDf = pd.DataFrame(newX)newXDf.columns = feature_cols_selected# dataframe for ynewYDf = pd.DataFrame(newY)newYDf.columns = ['MEDV']# join X, y and predictionmydata_with_pd = newXDf.join(newYDf).join(predictedDf)mydata_with_pd.head()

# check performance metricsimport numpy as npobs = mydata_with_pd.MEDVpred = mydata_with_pd.predicted

mae = np.mean(abs(pred-obs))rmse = np.sqrt(np.mean((pred-obs)**2))rae = np.mean(abs(pred-obs))/np.mean(abs(obs-np.mean(obs)))rse = np.mean((pred-obs)**2)/np.mean((obs-np.mean(obs))**2)

print("Mean Absolute Error: {0:0.6f}".format(mae))print("Root Mean Squared Error: {0:0.6f}".format(rmse))print("Relative Absolute Error: {0:0.6f}".format(rae))print("Relative Squared Error: {0:0.6f}".format(rse))

# extract workspace infofrom azureml import Workspacews = Workspace()workdspace_id = ws.workspace_idauthorization_token = ws.authorization_token

# set up web servicefrom azureml import services@services.publish(workdspace_id, authorization_token)@services.types(crim = float, nox=float, rm=float, dis=float, ptratio=float, lstat=float)@services.returns(float)def demoserviceVarSel(crim, nox, rm, dis, ptratio, lstat):     feature_vector = [crim, nox, rm, dis, ptratio,  lstat]     return lm2.predict(feature_vector)

import urllib2# If you are using Python 3+, import urllib instead of urllib2

import json

data =  {

        "Inputs": {

                "input1":                {                    "ColumnNames": ["crim", "lstat", "nox", "rm", "ptratio", "dis"],                    "Values": [ [ "0.00632", "4.98", "0.538", "6.575", "15.3", "4.0900" ],                                [ "0.02731", "9.14", "0.469", "6.421", "17.8", "4.9671" ], ]                },        },            "GlobalParameters": {}    }

body = str.encode(json.dumps(data))

url = service_urlapi_key = api_key # Replace this with the API key for the web service

headers = {'Content-Type':'application/json', 'Authorization':('Bearer '+ api_key)}

req = urllib2.Request(url, body, headers)

try:    response = urllib2.urlopen(req)

    # If you are using Python 3+, replace urllib2 with urllib.request in the above code:    # req = urllib.request.Request(url, body, headers)     # response = urllib.request.urlopen(req)

    result = response.read()    print(result) except urllib2.HTTPError, error:    print("The request failed with status code: " + str(error.code))

    # Print the headers - they include the requert ID and the timestamp, which are useful for debugging the failure    print(error.info())

    print(json.loads(error.read()))