def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[['please add your text here.']],
columns=['text'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pandas.DataFrame(data=[[
'The authors report on six cases of famotidine-associated delirium in hospitalized patients who cleared completely upon removal of famotidine.'
]],
columns=['text'])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Generate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='service-schema.json')
print("Schema generated")
def main():
# This is used to define the schema and example
df = pandas.DataFrame(data=[["That was a good meal"],
["What poor service"]],
columns=["Review"])
print("Initializing")
init()
print("After Init")
print(len(model))
results = run(df)
print(results)
print(df)
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
# The prepare statement writes the scoring file (main.py) and
# the scchema file (service_schema.json) the the output folder.
#prepare(run_func=run, init_func=init, input_types=inputs, )
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(
data=[[3.0, 3.6, 1.3, 0.25]],
columns=['sepal length', 'sepal width', 'petal length', 'petal width'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pandas.DataFrame([[3.0, 3.6, 1.3, 0.25]])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Genereate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas as pd
import json
df = pd.DataFrame(data=[[31.6, 4.7, 20.8, 77.7]], columns=['drone_batteryVoltage', \
'drone_responseTime', 'ambient_temperature', 'ambient_humidity'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = '{ "drone": { "batteryVoltage": 31.6, "responseTime": 4.7 }, \
"ambient": { "temperature": 20.8, "humidity": 77.7 },\
"timeCreated": "2017-12-27T16:02:09.1933728Z" }'
print("Result: " + run(input1))
inputs = {"input_str": SampleDefinition(DataTypes.PANDAS, df)}
# Genereate the schema
generate_schema(run_func=run, inputs=inputs, filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[['text-value']], columns=['text'])
text_data = {}
text_data['text'] = []
text_data['text'].append(
str('Famotidine is a histamine H2-receptor antagonist used in inpatient settings for prevention of stress ulcers.'
))
text_data['text'].append(str('Insulin is used to treat diabetes.'))
input1 = pandas.DataFrame.from_dict(text_data)
# Test the functions' output
init()
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
# The prepare statement writes the scoring file (main.py) and
# the schema file (service_schema.json) the the output folder.
generate_schema(run_func=run,
inputs=inputs,
filepath='service_schema.json')
print("Schema generated")
def main():
"""WebService unit test and swagger schema generation
"""
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
from PIL import Image
import json, os
# Save labels
generate_labels("data/train", "labels.json")
print("Labels generated")
# Create random image
pilImg = Image.fromarray(
(numpy.random.rand(224, 224, 3) * 255).astype('uint8'))
base64ImgString = pilImgToBase64(pilImg)
df = {'base64image': base64ImgString}
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the classification
init()
run(df)
inputs = {"input_df": SampleDefinition(DataTypes.STANDARD, df)}
#Genereate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
# Anomaly
df = pandas.DataFrame(data=[[33.66995566, 2.44341267, 21.39450979, 26]], columns=['machine_temperature', \
'machine_pressure','ambient_temperature','ambient_humidity'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
# Anomaly
#input1 = '{ "machine": { "temperature": 33.66995566, "pressure": 2.44341267 }, \
# "ambient": { "temperature": 21.39450979, "humidity": 26 },\
# "timeCreated": "2017-10-27T18:14:02.4911177Z" }'
# Normal
input1 = '{ "machine": { "temperature": 31.16469009, "pressure": 2.158002669 }, \
"ambient": { "temperature": 21.17794693, "humidity": 25 },\
"timeCreated": "2017-10-27T18:14:02.4911177Z" }'
print("Result-ilyas: " + str(run(input1)))
inputs = {"input_json": SampleDefinition(DataTypes.STANDARD, input1)}
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
# temp=45.9842594460449, volt=150.513223075022, rotate=277.294013981084, state=0.0, time=1.0, id=1.0
df = pandas.DataFrame(data=[[45.9842594460449, 150.513223075022, 277.294013981084, 1.0, 1.0], [46.9842594460449, 152.513223075022, 277.294013981084, 2.0, 1.0]], columns=['temp', 'volt','rotate','time', 'id'])
print(df.dtypes)
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
print("past init building inputs?")
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
print("calling run?")
res = run(df)
print(res)
#Genereate the schema
generate_schema(run_func=run, inputs=inputs, filepath='./outputs/service_schema.json')
print("Schema generated")
upload_schema('./outputs/service_schema.json')
print("Schema uploaded")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
# Create random 5x5 pixels image to use as sample input
#base64ImgString = "iVBORw0KGgoAAAANSUhEUgAAAAUAAAAFCAIAAAACDbGyAAAAFElEQVR4nGP8//8/AxJgYkAFpPIB6vYDBxf2tWQAAAAASUVORK5CYII="
#pilImg = pilImread("C:/Users/pabuehle/Desktop/vienna/iris4/tiny.jpg")
pilImg = Image.fromarray(
(np.random.rand(5, 5, 3) * 255).astype('uint8')) #.convert('RGB')
base64ImgString = pilImgToBase64(pilImg) #random 5x5 pixels image
# Call init() and run() function
init()
df = pandas.DataFrame(data=[[base64ImgString]],
columns=['image base64 string'])
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
resultString = run(df)
print("resultString = " + str(resultString))
# Genereate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='service_schema.json')
print("Schema generated.")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas as pd
# create the outputs folder
os.makedirs('./outputs', exist_ok=True)
df1 = pd.DataFrame(data=[[
"I absolutely love my bank. There's a reason this bank's customer base is so strong--their customer service actually acts like people and not robots. I love that anytime my card is swiped, I'm instantly notified. And the built in budgeting app is something that really makes life easier. The biggest setback is not being able to deposit cash (you have to get a money order), and if you have another, non-simple bank account, transferring money between accounts can take a few days, which frankly isn't acceptable with most ACH taking a business day or less. Overall, it's a great bank, and I would recommend it to anyone."
]],
columns=['review'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pd.DataFrame(data=[[
"I absolutely love my bank. There's a reason this bank's customer base is so strong--their customer service actually acts like people and not robots. I love that anytime my card is swiped, I'm instantly notified. And the built in budgeting app is something that really makes life easier. The biggest setback is not being able to deposit cash (you have to get a money order), and if you have another, non-simple bank account, transferring money between accounts can take a few days, which frankly isn't acceptable with most ACH taking a business day or less. Overall, it's a great bank, and I would recommend it to anyone."
]],
columns=['review'])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df1)}
#Genereate the schema
#generate_schema(run_func=run, inputs=inputs, filepath='./outputs/senti_schema2.json')
generate_schema(run_func=run,
inputs=inputs,
filepath='senti_service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
# create the outputs folder
os.makedirs('./outputs', exist_ok=True)
df = pandas.DataFrame(data=[['i loved the new movie and enjoyed the great acting']], columns=['reviewText'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pandas.DataFrame(data=[['i loved the new movie and enjoyed the great acting']], columns=['reviewText'])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Genereate the schema
generate_schema(run_func=run, inputs=inputs, filepath='myschema.json')
print("Schema generated")
def main(protocol):
try:
print("\nPython %s\n" % sys.version)
print("IoT Hub Client for Python")
hub_manager = HubManager(protocol)
print("Starting the IoT Hub Python sample using protocol %s..." %
hub_manager.client_protocol)
print(
"The sample is now waiting for messages and will indefinitely. Press Ctrl-C to exit. "
)
while True:
time.sleep(1)
except IoTHubError as iothub_error:
print("Unexpected error %s from IoTHub" % iothub_error)
return
except KeyboardInterrupt:
print("IoTHubModuleClient sample stopped")
# Anomaly
df = pandas.DataFrame(data=[[33.66995566, 2.44341267, 21.39450979, 26]],
columns=[
'machine_temperature', 'machine_pressure',
'ambient_temperature', 'ambient_humidity'
])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'false'
# Test the output of the functions
init()
# Anomaly
input1 = '{ "machine": { "temperature": 33.66995566, "pressure": 2.44341267 }, \
"ambient": { "temperature": 21.39450979, "humidity": 26 },\
"timeCreated": "2017-10-27T18:14:02.4911177Z" }'
# Normal
# input1 = '{ "machine": { "temperature": 31.16469009, "pressure": 2.158002669 }, \
# "ambient": { "temperature": 21.17794693, "humidity": 25 },\
# "timeCreated": "2017-10-27T18:14:02.4911177Z" }'
#print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
generate_schema(run_func=run,
inputs=input1,
filepath='./outputs/service_schema.json')
def main():
# Load the Azure ML libraries to generate a schema
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
# Test the init and run functions using test data
test_doc_text = "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"
init()
category = run(test_doc_text)
print(category)
# Generate the schema file (schema.json)
inputs = {"doc_base64": SampleDefinition(DataTypes.STANDARD, test_doc_text)}
generate_schema(run_func=run, inputs=inputs, filepath='./outputs/schema.json')
def main():
# Load the Azure ML libraries to generate a schema
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
# Test the init and run functions using test data
test_doc_text = "OpenGL on the GPU is fast"
init()
category = run(test_doc_text)
print(category)
# Generate the schema file (schema.json)
inputs = {"doc_text": SampleDefinition(DataTypes.STANDARD, test_doc_text)}
generate_schema(run_func=run, inputs=inputs, filepath='schema.json')
def generate_api_schema():
import os
print("create schema")
df = pd.read_csv("sample.csv")
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
os.makedirs('outputs', exist_ok=True)
print(generate_schema(inputs=inputs, filepath="outputs/schema.json", run_func=run))
def generate_api_schema():
import os
print("create schema")
sample_input = "byestring_representing_image"
inputs = {"input_bytes": SampleDefinition(DataTypes.STANDARD, sample_input)}
os.makedirs('outputs', exist_ok=True)
print(generate_schema(inputs=inputs, filepath=os.path.join("outputs","schema.json"), run_func=run))
def generate_api_schema():
import os
print("create schema")
sample_input = "sample data text"
inputs = {"text": SampleDefinition(DataTypes.STANDARD, sample_input)}
os.makedirs('outputs', exist_ok=True)
print(generate_schema(inputs=inputs, filepath="outputs/schema.json", run_func=run))
def createwebserviceschema():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
inputs = {"inputData": SampleDefinition(DataTypes.NUMPY, X_test)}
print(generate_schema(run_func=run, inputs=inputs, filepath="./outputs/schema.json"))
def main():
from tatk.pipelines.text_classification.text_classifier import TextClassifier
from sklearn.linear_model import LogisticRegression
import pandas
init()
df = pandas.DataFrame(data=[['please add your good text here.']],
columns=['tweets'])
#input1 = pandas.DataFrame(data=[['please add your good text here.']], columns=['tweets'])
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Generate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='service-schema.json')
print("Schema generated")
def main():
# Generating random 28x28 pixels to use as sample input
sample_input = (np.random.rand(28, 28, 1) * 255) #.astype('uint8')
sample_input = sample_input.reshape(1, 28, 28,
1) # Reshaping to match training data
# Calling init() and run()
init()
inputs = {"input_array": SampleDefinition(DataTypes.NUMPY, sample_input)}
result_string = run(sample_input)
print("resultString = " + str(result_string))
# Generating the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='outputs/schema.json')
print('Schema generated')
def main():
# Test the init and run functions using test data
init()
test_doc_text = "🍪"
category = run(test_doc_text)
print(category)
test_doc_text = "tomato pizza"
# Generate the schema file (schema.json) needed for AML operationalization
inputs = {"doc_text": SampleDefinition(DataTypes.STANDARD, test_doc_text)}
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/schema.json')
block_blob_service.create_blob_from_path('embeddings', 'schema.json',
'./outputs/schema.json')
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[[380, 120, 76]], columns=['indicator1', 'NF1', 'cellprofiling'])
# Check the output of the function
init()
input1 = pandas.DataFrame([[380, 120, 76]])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
# Generate the service_schema.json
generate_schema(run_func=run, inputs=inputs, filepath='output/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[[190, 60, 38]], columns=['height', 'width', 'shoe_size'])
# Test the functions' output
init()
input1 = pandas.DataFrame([[190, 60, 38]])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
# Generate the service_schema.json
generate_schema(run_func=run, inputs=inputs, filepath='service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
sentences = ['This is a sentence.', 'This is another one.']
# Test the output of the functions
init()
result = run(sentences)
inputs = {"sentences": SampleDefinition(DataTypes.STANDARD, sentences)}
#Generate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='infersent_service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
inputs = {
"input_df": SampleDefinition(DataTypes.PANDAS, yourinputdataframe)
}
generate_schema(run_func=run,
inputs=inputs,
filepath='service_schema.json')
print("Schema generated")
if __name__ == "__main__":
main()
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import numpy as np
text_entry = 'This is a sentence. This is an awesome sentence!'
# Test the output of the functions
init()
print("Result: " + run(text_entry))
inputs = {"text_entry": SampleDefinition(DataTypes.STANDARD, text_entry)}
#Generate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='./outputs/cnn_service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=["What a waste of time and money! The story was not realistic at all! Actually it was completely far fetched!"], columns=['Text'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pandas.DataFrame(data=["What a waste of time and money! The story was not realistic at all! Actually it was completely far fetched!"], columns=['Text'])
print("The input {0} created the following output: {1}".format(input1['Text'], run(input1)))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Genereate the schema
generate_schema(run_func=run, inputs=inputs, filepath='./outputs/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[[1,1,1,1,1,1,1,1,1,1]], columns=['MAX_ACCX', 'MAX_ACCY', 'MAX_ACCZ', 'MAX_GYROX', 'MAX_GYROY', 'MAX_GYROZ', 'ACC_AVG', 'ACC_VAR', 'GYRO_AVG', 'GYRO_VAR'])
# Turn on data collection debug mode to view output in stdout
os.environ["AML_MODEL_DC_DEBUG"] = 'true'
# Test the output of the functions
init()
input1 = pandas.DataFrame([[1,1,1,1,1,1,1,1,1,1]])
print("The input {0} created the following output: {1}".format(input1.values, run(input1)))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Genereate the schema
generate_schema(run_func=run, inputs=inputs, filepath='./outputs/service_schema.json')
print("Schema generated")
def generate_api_schema():
inputs = {"input_array": SampleDefinition(DataTypes.STANDARD, "dGVzdA==")}
outputs = {"coordinates": SampleDefinition(DataTypes.STANDARD, [1, 2, 4, 5, 8, 9, 5, 6])}
print(generate_schema(inputs=inputs, filepath="schema.json", run_func=run, outputs=outputs))
# generate_api_schema()
# import base64
# init()
# with open("E:\\Repos\\esk-form-scanner-model\\src\\data\\validation\\i0.jpg", "rb") as file:
# print(base64.b64encode(file.read(-1)))
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(data=[[380, 120, 76]],
columns=['indicator1', 'NF1', 'cellprofiling'])
# Check the output of the function
init()
input1 = pandas.DataFrame([[380, 120, 76]])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
# Generate the service_schema.json
generate_schema(run_func=run,
inputs=inputs,
filepath='output/service_schema.json')
print("Schema generated")
def main():
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
import pandas
df = pandas.DataFrame(
data=[[3.0, 3.6, 1.3, 0.25]],
columns=['sepal length', 'sepal width', 'petal length', 'petal width'])
# Test the output of the functions
init()
input1 = pandas.DataFrame([[3.0, 3.6, 1.3, 0.25]])
print("Result: " + run(input1))
inputs = {"input_df": SampleDefinition(DataTypes.PANDAS, df)}
#Genereate the schema
generate_schema(run_func=run,
inputs=inputs,
filepath='service_schema.json')
print("Schema generated")
#========================= CREATE WEB SERVICE SCHEMA =========================
def run(inputData):
import json
prediction = classifier.predict(input_df)
prediction = "%s %d" % (str(input_df), classifier)
return json.dumps(str(prediction))
from azureml.api.schema.dataTypes import DataTypes
from azureml.api.schema.sampleDefinition import SampleDefinition
from azureml.api.realtime.services import generate_schema
inputs = {"inputData": SampleDefinition(DataTypes.NUMPY, X_test)}
print(generate_schema(run_func=run, inputs=inputs, filepath="./outputs/schema.json"))
'''
#========================= VISUALISING THE RESULTS =========================
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
# Visualising the Training set results
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
alpha = 0.75, cmap = ListedColormap(('red', 'green')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
