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SivananthamandGitHub
a869657f25
* Fix python linter Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format e2e tests Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Format hack, docs Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Add py-fmt target Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Update python lint workflow Signed-off-by: Sivanantham Chinnaiyan <[email protected]> * Rebase master Signed-off-by: Sivanantham Chinnaiyan <[email protected]> --------- Signed-off-by: Sivanantham Chinnaiyan <[email protected]>
90 lines
2.4 KiB
Python
90 lines
2.4 KiB
Python
import sys
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import requests
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import json
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from matplotlib import pyplot as plt
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import numpy as np
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from sklearn.datasets import fetch_20newsgroups
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import time
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print("************************************************************")
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print("************************************************************")
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print("************************************************************")
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print("starting query")
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if len(sys.argv) < 3:
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raise Exception("No endpoint specified. ")
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endpoint = sys.argv[1]
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headers = {"Host": sys.argv[2]}
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parameters = {}
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test_num = 1002
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is_file = False
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if len(sys.argv) > 3:
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try:
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test_num = int(sys.argv[3])
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except Exception: # pylint: disable=broad-except
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is_file = True
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if len(sys.argv) > 4:
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try:
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parameters = json.loads(sys.argv[4])
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except Exception: # pylint: disable=broad-except
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raise Exception("Failed to convert to json format. ")
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if is_file:
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inputs = open(sys.argv[2])
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inputs = json.load(inputs)
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actual = "unk"
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else:
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data = fetch_20newsgroups() # input dataset
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inputs = data.data[test_num]
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labels = data.target[test_num]
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actual = data.target_names[labels]
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# to do list is below
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input_text = {"instances": [inputs]}
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input_text.update(parameters)
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print("Sending Explain Query")
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x = time.time()
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res = requests.post(endpoint, json=input_text, headers=headers)
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print("TIME TAKEN: ", time.time() - x)
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print(res)
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if not res.ok:
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res.raise_for_status()
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res_json = res.json()
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temp = res_json["explanations"]
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# get class name from dataset
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nameset = fetch_20newsgroups(subset="train")
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class_names = [
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x.split(".")[-1] if "misc" not in x else ".".join(x.split(".")[-2:])
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for x in nameset.target_names
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]
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# print detailed values
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for feature_label in temp.keys():
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print("\nExplanation of " + class_names[int(feature_label)] + ":")
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for composition in temp[feature_label]:
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print(composition)
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# draw bar chart
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features = []
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composition_value = []
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subplot_count = 1
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for feature_label in temp.keys():
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plt.subplot(3, 4, subplot_count)
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plt.title(class_names[int(feature_label)])
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for i in temp[feature_label]:
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features.append(i[0])
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composition_value.append(i[1])
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x_axis = np.arange(len(features))
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plt.barh(x_axis, composition_value)
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plt.yticks(x_axis, features)
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subplot_count += 1
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features = []
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composition_value = []
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plt.subplots_adjust(left=0.125, bottom=0.1, right=0.9, top=0.9, wspace=0.5, hspace=0.5)
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plt.show()
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