openeurollm/mock · Datasets at Hugging Face

question_id stringclasses 10 values	category stringclasses 1 value	domain stringclasses 10 values	prompt stringclasses 10 values	lang stringclasses 1 value
47e1fd0c1cd043fbb7223435d51b3fe6	arena-hard-v0.1	Network Configuration & Security	My situation is this: I’m setting up a server running at home Ubuntu to run an email server and a few other online services. As we all know, for my email to work reliably and not get blocked I need to have an unchanging public IP address. Due to my circumstances I am not able to get a static IP address through my ISP or change ISPs at the moment. The solution I have found is to buy a 4G SIM card with a static IP (from an ISP that offers that), which I can then use with a USB dongle. However this 4G connection costs me substantially per MB to use. But. Mail is the only server that needs a static IP address. For everything else using my home network connection and updating my DNS records with DDNS would be fine. I have tested this setup previously for other services and it has worked. So. I was wondering. Would it in theory be possible to: connect the server to two network interfaces at the same time and route traffic depending on destination port. I.e. all outgoing connections to ports 25, 465, 587, and possibly 993 should be sent through the 4G dongle interface (enx344b50000000) and all other connections sent over eth0. Similarly, the server should listen for incoming connections on the same ports on enx344b50000000 and listen on all other ports (if allowed by ufw) on eth0. I would then need DNS records from mail.mydomain.tld —> <4g static public IP> and mydomain.tld —> <home public IP> (updated with DDNS, and NAT configured on my home router). Computers on the internet would then be able to seamlessly connect to these two IP addresses, not “realising” that they are in fact the same machine, as long as requests to mail.mydomain.tld are always on the above mentioned ports. Question: Is this possible? Could it be a robust solution that works the way I hope? Would someone be able to help me set it up? I have come across a few different guides in my DuckDuckGo-ing, I understand it has to do with setting a mark in iptables and assigning them to a table using ip route. However I haven't managed to get it to work yet, and many of these guides are for VPNs and they all seem to be slightly different to each other. So I thought I would ask about my own specific use case	eng
7ef923a6af7e4b8480fde56cae992497	arena-hard-v0.1	Investment Growth Calculations	A 20-year annuity of forty $7,000 semiannual payments will begin 12 years from now, with the first payment coming 12.5 years from now. a. If the discount rate is 13 percent compounded monthly, what is the value of this annuity 6 years from now? b. What is the current value of the annuity?	eng
addaa796ee094f029f8014ea1468df8a	arena-hard-v0.1	PHP, CORS, and Server Requests	Assume the role of an API that provides a chart wizard feature. Given a dataset with the following dimensions: - Key: country, Label: Country, Units: null, DataType: text, PlotType: categorical - Key: region, Label: Region, Units: null, DataType: text, PlotType: categorical - Key: year, Label: Year, Units: null, DataType: date, PlotType: timeSeries - Key: income, Label: Income per capita, Units: Inflation adjusted dollars, DataType: numeric, PlotType: continuous - Key: population, Label: Population, Units: People, DataType: numeric, PlotType: discrete - Key: lifeExpectancy, Label: Life Expectancy, Units: Years, DataType: numeric, PlotType: continuous A user wants to create a chart with the following description (delimited by double tildes): ~~Life Expectency by region over time~~ Do not include any explanations, only provide a RFC8259 compliant JSON response containing a valid Vega Lite chart definition object. Please give the chart a suitable title and description. Do not include any data in this definition. The JSON response:	eng
53f4338f1a4b403c84ccf864c079a8c1	arena-hard-v0.1	E-Commerce Web Development	i want to create an online social marketplace with wordpress, please create a list of top 3 best themes, then create a list of plugins that essential, and finaly create a list of market entering strategye which can be use for Iran domestic market	eng
f51541e1565145bbbe143e14ea829031	arena-hard-v0.1	React Component Development	import FieldDropDown from "lib/hookForm/fieldDropDown" import { ICompanyLogo } from "services/api/company/companyTypes" import apiLoanQuery from "services/api/loan/apiLoanQuery" interface IProps { forcePlaceLoanGuid?: string companyGuid?: string } export default function LoanLogoDropdown(props: IProps) { const { data: companyLogos } = apiLoanQuery.useGetCompanyLogosInfoByLoanGuidQuery(props.forcePlaceLoanGuid) if (!!!companyLogos) return null const logoKeyValues = companyLogos.map((logo: ICompanyLogo) => ({ key: logo.portfolioIdentifier, value: logo.logoDescription, })) return ( <FieldDropDown label="Company Logo" name="portfolioIdentifier" data={logoKeyValues} placeholder="Select Logo" labelColSize={3} inputColSize={9} /> ) }	eng
e80139e32fb54b95a9a4effa86d8ddf5	arena-hard-v0.1	Expert Analysis on Preppers	# Role You are a world renown Certification Exam Psychometrician. Your job is to use the best practices in psychometrics and technical certification exams to generate 5 questions/distractors/correct_answers following the defined Answer_Format and Guidelines. The question must be based on the provided data. Only use the provided Dataset to generate the questions. # Answer_Format You provide only the mentioned Variables. No explanation, no salutes, nothing other than the variables response. { Number = "n", Question = "Technical Environment/Business Problem: part of the question that refers to Technical Environment/Business Problem. Goal Statement: Part of the question that refers to the Goal Statement. Question Sentence: Part of the question that refers to the Question Sentence", Distractors = ["First Distractor", "Second Distractor", ..., "Last Distractor"], Correct_Answers = ["First Correct Answer", "Second Correct Answer", ..., "Last Correct Answer"] Correct_Reasoning = ["Reasoning on the first correct Answer", "Reasoning on the second correct Answer", ... , "Reasoning on the last correct Answer"] } # Guidelines - You need to follow the Answer format to provide the answer. - Each distractor and Correct_Answer should be about the same size. ## Question Rules - Each question needs to have 3 parts. Each part have its own rules. Please follow the rules contained in each part. The parts are: Technical Environment/Business Problem, Goal Statement, and Question Sentence ### Technical Environment/Business Problem - Describe from general to specific - Include only necessary information; no extraneous text - Questions must not provide cues or clues that will give away the correct answer to an unqualified candidate. ### Goal Statement - Precise, clear, and logically connect to stem and answer choices - Typically begins with “You need to…” - Specify parameters for completing goal (e.g., lowest software cost, least amount of time, least amount of coding lines/effort, etc.) ### Question Sentence - Typically “What should you do?” or “What should you do next?” - May incorporate text from answer choices where appropriate - Example: If all answer choices are tools: “Which tool should you install?” - Should not be a negative question; i.e., “Which of the following is NOT…” ## Distractor Rules - Distractors are wrong answers to the provided questions. - You need to provide 3 distractors. - Distractors need to be somewhat believable answers. - The correct_answ	eng
ee9ae71956724d4591d4d9bc457d598d	arena-hard-v0.1	CSV Data Manipulation in Pandas	%%writefile app.py import streamlit as st import pandas as pd import io import joblib import base64 import matplotlib.pyplot as plt import seaborn as sns import datetime from sklearn import tree from sklearn.tree import _tree import numpy as np # Function to upload and generate predictions def upload_and_generate_predictions(): # File upload and prediction code def get_base64(bin_file): with open(bin_file, "rb") as f: data = f.read() return base64.b64encode(data).decode() def set_background(png_file): bin_str = get_base64(png_file) page_bg_img = ( """ <style> .stApp { background-image: url("data:image/png;base64,%s"); background-size: cover; } </style> """ % bin_str ) st.markdown(page_bg_img, unsafe_allow_html=True) set_background("Screenshot (29).png") red_title = '<h1 style="color: white;">Equipment Failure Prediction</h1>' # Display the red title using st.markdown st.markdown(red_title, unsafe_allow_html=True) # Display the custom CSS style uploaded_file = st.file_uploader( "Upload an Excel or CSV file", type=["xlsx", "csv"] ) if uploaded_file is not None: # Read the file into a DataFrame if ( uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ): # Excel file df = pd.read_excel(uploaded_file, engine="openpyxl") else: # CSV file df = pd.read_csv(uploaded_file) # st.session_state.predictions_df = df # st.session_state.uploaded_file=uploaded_file # Display the first screen if st.button("Generate predictions"): model = joblib.load("des_tree_clss.joblib") prediction = "" if "machine_status" in df.columns.to_list(): prediction = model.predict(df.drop(columns=["machine_status"])) else: prediction = model.predict(df) df["Predicted_Status"] = prediction st.success("Predictions made successfully!") st.session_state.predictions_df = df st.session_state.uploaded_file = uploaded_file # Display the modified DataFrame with predictions # Save the DataFrame with predictions to st.session_state # Move to the second screen (graph display) def display_graph(predictions_df, uploaded_file): def get_base64(bin_file): with open(bin_file, "rb") as f: data = f.read() return base64.b64encode(data).decode() def set_background(png_file): bin_str = get_base64(png_file) page_bg_img = ( """ <style> .stApp { background-image: url("data:image/png;base64,%s"); background-size: cover; } </style> """ % bin_str ) st.markdown(page_bg_img, unsafe_allow_html=True) set_background("Screenshot (32).png") st.markdown('<div style="margin-top: 50px;"></div>', unsafe_allow_html=True) st.subheader("Early warning Signal:") # Create a DataFrame with the first 10 records with prediction status 1 df_status_1 = predictions_df[predictions_df["Predicted_Status"] == 1].head(10) # Create a DataFrame with all records with prediction status 0 df_status_0 = predictions_df[predictions_df["Predicted_Status"] == 0].head(10) # Combine the DataFrames df_combined = pd.concat([df_status_0, df_status_1]) start_timestamp = datetime.datetime(2023, 1, 1) df_combined["Synthetic_Timestamp"] = pd.date_range( start=start_timestamp, periods=len(df_combined), freq="T" ) # df_combined['Synthetic_Timestamp'] = pd.date_range(start='2023-01-01', periods=len(df_combined), freq='T') plt.figure(figsize=(10, 3)) sns.scatterplot( x="Synthetic_Timestamp", y="Predicted_Status", hue="Predicted_Status", marker="o", s=200, data=df_combined, palette={1: "red", 0: "green"}, ) plt.xticks(rotation=45, ha="right") # plt.title("Machine Status Prediction - Combined") plt.xlabel("Timestamp") plt.ylabel("Value") st.pyplot() # Create a download link st.subheader("Download the File with Predictions:") st.write("Download the File with Predictions:") # st.markdown(title1, unsafe_allow_html=True) modified_file_name = ( f"file_with_predictions_{uploaded_file.name}" if uploaded_file.name else "file_with_predictions.xlsx" ) # Convert DataFrame to binary stream modified_file = io.BytesIO() if ( uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ): # Excel file predictions_df.to_excel(modified_file, index=False, engine="xlsxwriter") else: # CSV file predictions_df.to_csv(modified_file, index=False) modified_file.seek(0) # Create a download link st.download_button( label="Download File with Predictions", data=modified_file, file_name=modified_file_name, key="download_file_with_predictions", ) # Rules functions def get_rules(tree, feature_names, class_names): tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!" for i in tree_.feature ] paths = [] path = [] def recurse(node, path, paths): if tree_.feature[node] != _tree.TREE_UNDEFINED: name = feature_name[node] threshold = tree_.threshold[node] p1, p2 = list(path), list(path) p1 += [f"({name} <= {np.round(threshold, 3)})"] recurse(tree_.children_left[node], p1, paths) p2 += [f"({name} > {np.round(threshold, 3)})"] recurse(tree_.children_right[node], p2, paths) else: path += [(tree_.value[node], tree_.n_node_samples[node])] paths += [path] recurse(0, path, paths) # sort by samples count samples_count = [p[-1][1] for p in paths] ii = list(np.argsort(samples_count)) paths = [paths[i] for i in reversed(ii)] rules = [] for path in paths: rule = "if " for p in path[:-1]: if rule != "if ": rule += " and " rule += str(p) rule += " then " if class_names is None: rule += "response: " + str(np.round(path[-1][0][0][0], 3)) else: classes = path[-1][0][0] l = np.argmax(classes) rule += f"class: {class_names[l]} (proba: {np.round(100.0*classes[l]/np.sum(classes),2)}%)" rule += f" \| based on {path[-1][1]:,} samples" rules += [rule] return rules st.subheader("Model Explainability:") model = joblib.load("des_tree_clss.joblib") rules = get_rules(model, predictions_df.columns, range(2)) table_list = [] for r in rules: colon_split = r.split(":") col_1 = colon_split[0] pipe_split = str(colon_split[1] + colon_split[2]).split("\|") # print(colon_split) # print(pipe_split) col_2 = pipe_split[0] col_3 = pipe_split[1] table_list.append([col_1, col_2, col_3]) table_df = pd.DataFrame( table_list, columns=["rule_details", "class_probabilities", "samples_count"] ) rules_data_file = io.BytesIO() table_df.to_csv(rules_data_file, index=False) rules_data_file.seek(0) # Create a download link st.download_button( label="Model Explainability", data=rules_data_file, file_name="rules_data.csv", key="download_rules_data", ) # Run the app if __name__ == "__main__": st.set_option("deprecation.showPyplotGlobalUse", False) st.set_page_config(page_title="Equipment Failure Prediction", page_icon="📈") pages = ["Upload and Predict", "Graph and Download"] page = st.sidebar.selectbox("Select a page", pages) if page == "Upload and Predict": upload_and_generate_predictions() elif page == "Graph and Download": if hasattr(st.session_state, "predictions_df"): display_graph( st.session_state.predictions_df, st.session_state.uploaded_file ) else: st.warning("Please upload a file on the 'Upload and Predict' page first.") this is mu code inthis i have a scatterplot graph i want to modify the code in a way that draw ploltly graph usw click events of ploltly when i click the instance of the circle it should give descion rule for the instance using lime.after graph if i click one circle or instance lime table and rule list should print there it self you can add download predictions and model explananbility in new side bar	eng
15ad9bcf7527456098cbd1fa5734a312	arena-hard-v0.1	Innovative Online Market Models	Can you market size revenue that can earned by UK Management Consultancy by advising and implementing FinTech solutions to Capital Markets clients	eng
379a490a6eae40608abf3501807b2545	arena-hard-v0.1	Advanced Algebra and Number Theory	Consider the state: $$\ket{\psi} = \frac{\ket{00} + \ket{01} + \ket{10}}{\sqrt{3}}$$ (a). Calculate the reduced density matrix of the second qubit of $\ket{\psi}$.	eng
990d35320cca477fa60c3df15f629364	arena-hard-v0.1	Weather Forecast Accuracy	The prediction is in the IF stage while updating is in the ID stage. Think about two continuous branch instructions: the first one is in the ID stage, and the second is in the IF stage. What is the order of updating the first result and querying the second prediction? How to control the order? How do local-based and global-based prediction algorithms be affected by the order?	eng

47e1fd0c1cd043fbb7223435d51b3fe6

arena-hard-v0.1

Network Configuration & Security

My situation is this: I’m setting up a server running at home Ubuntu to run an email server and a few other online services. As we all know, for my email to work reliably and not get blocked I need to have an unchanging public IP address. Due to my circumstances I am not able to get a static IP address through my ISP or change ISPs at the moment. The solution I have found is to buy a 4G SIM card with a static IP (from an ISP that offers that), which I can then use with a USB dongle. However this 4G connection costs me substantially per MB to use. But. Mail is the only server that needs a static IP address. For everything else using my home network connection and updating my DNS records with DDNS would be fine. I have tested this setup previously for other services and it has worked. So. I was wondering. Would it in theory be possible to: connect the server to two network interfaces at the same time and route traffic depending on destination port. I.e. all outgoing connections to ports 25, 465, 587, and possibly 993 should be sent through the 4G dongle interface (enx344b50000000) and all other connections sent over eth0. Similarly, the server should listen for incoming connections on the same ports on enx344b50000000 and listen on all other ports (if allowed by ufw) on eth0. I would then need DNS records from mail.mydomain.tld —> <4g static public IP> and mydomain.tld —> <home public IP> (updated with DDNS, and NAT configured on my home router). Computers on the internet would then be able to seamlessly connect to these two IP addresses, not “realising” that they are in fact the same machine, as long as requests to mail.mydomain.tld are always on the above mentioned ports. Question: Is this possible? Could it be a robust solution that works the way I hope? Would someone be able to help me set it up? I have come across a few different guides in my DuckDuckGo-ing, I understand it has to do with setting a mark in iptables and assigning them to a table using ip route. However I haven't managed to get it to work yet, and many of these guides are for VPNs and they all seem to be slightly different to each other. So I thought I would ask about my own specific use case

eng

7ef923a6af7e4b8480fde56cae992497

arena-hard-v0.1

Investment Growth Calculations

A 20-year annuity of forty $7,000 semiannual payments will begin 12 years from now, with the first payment coming 12.5 years from now. a. If the discount rate is 13 percent compounded monthly, what is the value of this annuity 6 years from now? b. What is the current value of the annuity?

eng

addaa796ee094f029f8014ea1468df8a

arena-hard-v0.1

PHP, CORS, and Server Requests

Assume the role of an API that provides a chart wizard feature. Given a dataset with the following dimensions: - Key: country, Label: Country, Units: null, DataType: text, PlotType: categorical - Key: region, Label: Region, Units: null, DataType: text, PlotType: categorical - Key: year, Label: Year, Units: null, DataType: date, PlotType: timeSeries - Key: income, Label: Income per capita, Units: Inflation adjusted dollars, DataType: numeric, PlotType: continuous - Key: population, Label: Population, Units: People, DataType: numeric, PlotType: discrete - Key: lifeExpectancy, Label: Life Expectancy, Units: Years, DataType: numeric, PlotType: continuous A user wants to create a chart with the following description (delimited by double tildes): ~~Life Expectency by region over time~~ Do not include any explanations, only provide a RFC8259 compliant JSON response containing a valid Vega Lite chart definition object. Please give the chart a suitable title and description. Do not include any data in this definition. The JSON response:

eng

53f4338f1a4b403c84ccf864c079a8c1

arena-hard-v0.1

E-Commerce Web Development

i want to create an online social marketplace with wordpress, please create a list of top 3 best themes, then create a list of plugins that essential, and finaly create a list of market entering strategye which can be use for Iran domestic market

eng

f51541e1565145bbbe143e14ea829031

arena-hard-v0.1

React Component Development

import FieldDropDown from "lib/hookForm/fieldDropDown" import { ICompanyLogo } from "services/api/company/companyTypes" import apiLoanQuery from "services/api/loan/apiLoanQuery" interface IProps { forcePlaceLoanGuid?: string companyGuid?: string } export default function LoanLogoDropdown(props: IProps) { const { data: companyLogos } = apiLoanQuery.useGetCompanyLogosInfoByLoanGuidQuery(props.forcePlaceLoanGuid) if (!!!companyLogos) return null const logoKeyValues = companyLogos.map((logo: ICompanyLogo) => ({ key: logo.portfolioIdentifier, value: logo.logoDescription, })) return ( <FieldDropDown label="Company Logo" name="portfolioIdentifier" data={logoKeyValues} placeholder="Select Logo" labelColSize={3} inputColSize={9} /> ) }

eng

e80139e32fb54b95a9a4effa86d8ddf5

arena-hard-v0.1

Expert Analysis on Preppers

# Role You are a world renown Certification Exam Psychometrician. Your job is to use the best practices in psychometrics and technical certification exams to generate 5 questions/distractors/correct_answers following the defined **Answer_Format** and **Guidelines**. The question must be based on the provided data. Only use the provided **Dataset** to generate the questions. # Answer_Format You provide only the mentioned Variables. No explanation, no salutes, nothing other than the variables response. { Number = "n", Question = "Technical Environment/Business Problem: part of the question that refers to **Technical Environment/Business Problem**. Goal Statement: Part of the question that refers to the **Goal Statement**. Question Sentence: Part of the question that refers to the **Question Sentence**", Distractors = ["First Distractor", "Second Distractor", ..., "Last Distractor"], Correct_Answers = ["First Correct Answer", "Second Correct Answer", ..., "Last Correct Answer"] Correct_Reasoning = ["Reasoning on the first correct Answer", "Reasoning on the second correct Answer", ... , "Reasoning on the last correct Answer"] } # Guidelines - You need to follow the Answer format to provide the answer. - Each distractor and Correct_Answer should be about the same size. ## Question Rules - Each question needs to have 3 parts. Each part have its own rules. Please follow the rules contained in each part. The parts are: **Technical Environment/Business Problem**, **Goal Statement**, and **Question Sentence** ### Technical Environment/Business Problem - Describe from general to specific - Include only necessary information; no extraneous text - Questions must not provide cues or clues that will give away the correct answer to an unqualified candidate. ### Goal Statement - Precise, clear, and logically connect to stem and answer choices - Typically begins with “You need to…” - Specify parameters for completing goal (e.g., lowest software cost, least amount of time, least amount of coding lines/effort, etc.) ### Question Sentence - Typically “What should you do?” or “What should you do next?” - May incorporate text from answer choices where appropriate - Example: If all answer choices are tools: “Which tool should you install?” - Should not be a negative question; i.e., “Which of the following is NOT…” ## Distractor Rules - Distractors are wrong answers to the provided questions. - You need to provide 3 distractors. - Distractors need to be somewhat believable answers. - The correct_answ

eng

ee9ae71956724d4591d4d9bc457d598d

arena-hard-v0.1

CSV Data Manipulation in Pandas

%%writefile app.py import streamlit as st import pandas as pd import io import joblib import base64 import matplotlib.pyplot as plt import seaborn as sns import datetime from sklearn import tree from sklearn.tree import _tree import numpy as np # Function to upload and generate predictions def upload_and_generate_predictions(): # File upload and prediction code def get_base64(bin_file): with open(bin_file, "rb") as f: data = f.read() return base64.b64encode(data).decode() def set_background(png_file): bin_str = get_base64(png_file) page_bg_img = ( """ <style> .stApp { background-image: url("data:image/png;base64,%s"); background-size: cover; } </style> """ % bin_str ) st.markdown(page_bg_img, unsafe_allow_html=True) set_background("Screenshot (29).png") red_title = '<h1 style="color: white;">Equipment Failure Prediction</h1>' # Display the red title using st.markdown st.markdown(red_title, unsafe_allow_html=True) # Display the custom CSS style uploaded_file = st.file_uploader( "Upload an Excel or CSV file", type=["xlsx", "csv"] ) if uploaded_file is not None: # Read the file into a DataFrame if ( uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ): # Excel file df = pd.read_excel(uploaded_file, engine="openpyxl") else: # CSV file df = pd.read_csv(uploaded_file) # st.session_state.predictions_df = df # st.session_state.uploaded_file=uploaded_file # Display the first screen if st.button("Generate predictions"): model = joblib.load("des_tree_clss.joblib") prediction = "" if "machine_status" in df.columns.to_list(): prediction = model.predict(df.drop(columns=["machine_status"])) else: prediction = model.predict(df) df["Predicted_Status"] = prediction st.success("Predictions made successfully!") st.session_state.predictions_df = df st.session_state.uploaded_file = uploaded_file # Display the modified DataFrame with predictions # Save the DataFrame with predictions to st.session_state # Move to the second screen (graph display) def display_graph(predictions_df, uploaded_file): def get_base64(bin_file): with open(bin_file, "rb") as f: data = f.read() return base64.b64encode(data).decode() def set_background(png_file): bin_str = get_base64(png_file) page_bg_img = ( """ <style> .stApp { background-image: url("data:image/png;base64,%s"); background-size: cover; } </style> """ % bin_str ) st.markdown(page_bg_img, unsafe_allow_html=True) set_background("Screenshot (32).png") st.markdown('<div style="margin-top: 50px;"></div>', unsafe_allow_html=True) st.subheader("Early warning Signal:") # Create a DataFrame with the first 10 records with prediction status 1 df_status_1 = predictions_df[predictions_df["Predicted_Status"] == 1].head(10) # Create a DataFrame with all records with prediction status 0 df_status_0 = predictions_df[predictions_df["Predicted_Status"] == 0].head(10) # Combine the DataFrames df_combined = pd.concat([df_status_0, df_status_1]) start_timestamp = datetime.datetime(2023, 1, 1) df_combined["Synthetic_Timestamp"] = pd.date_range( start=start_timestamp, periods=len(df_combined), freq="T" ) # df_combined['Synthetic_Timestamp'] = pd.date_range(start='2023-01-01', periods=len(df_combined), freq='T') plt.figure(figsize=(10, 3)) sns.scatterplot( x="Synthetic_Timestamp", y="Predicted_Status", hue="Predicted_Status", marker="o", s=200, data=df_combined, palette={1: "red", 0: "green"}, ) plt.xticks(rotation=45, ha="right") # plt.title("Machine Status Prediction - Combined") plt.xlabel("Timestamp") plt.ylabel("Value") st.pyplot() # Create a download link st.subheader("Download the File with Predictions:") st.write("Download the File with Predictions:") # st.markdown(title1, unsafe_allow_html=True) modified_file_name = ( f"file_with_predictions_{uploaded_file.name}" if uploaded_file.name else "file_with_predictions.xlsx" ) # Convert DataFrame to binary stream modified_file = io.BytesIO() if ( uploaded_file.type == "application/vnd.openxmlformats-officedocument.spreadsheetml.sheet" ): # Excel file predictions_df.to_excel(modified_file, index=False, engine="xlsxwriter") else: # CSV file predictions_df.to_csv(modified_file, index=False) modified_file.seek(0) # Create a download link st.download_button( label="Download File with Predictions", data=modified_file, file_name=modified_file_name, key="download_file_with_predictions", ) # Rules functions def get_rules(tree, feature_names, class_names): tree_ = tree.tree_ feature_name = [ feature_names[i] if i != _tree.TREE_UNDEFINED else "undefined!" for i in tree_.feature ] paths = [] path = [] def recurse(node, path, paths): if tree_.feature[node] != _tree.TREE_UNDEFINED: name = feature_name[node] threshold = tree_.threshold[node] p1, p2 = list(path), list(path) p1 += [f"({name} <= {np.round(threshold, 3)})"] recurse(tree_.children_left[node], p1, paths) p2 += [f"({name} > {np.round(threshold, 3)})"] recurse(tree_.children_right[node], p2, paths) else: path += [(tree_.value[node], tree_.n_node_samples[node])] paths += [path] recurse(0, path, paths) # sort by samples count samples_count = [p[-1][1] for p in paths] ii = list(np.argsort(samples_count)) paths = [paths[i] for i in reversed(ii)] rules = [] for path in paths: rule = "if " for p in path[:-1]: if rule != "if ": rule += " and " rule += str(p) rule += " then " if class_names is None: rule += "response: " + str(np.round(path[-1][0][0][0], 3)) else: classes = path[-1][0][0] l = np.argmax(classes) rule += f"class: {class_names[l]} (proba: {np.round(100.0*classes[l]/np.sum(classes),2)}%)" rule += f" | based on {path[-1][1]:,} samples" rules += [rule] return rules st.subheader("Model Explainability:") model = joblib.load("des_tree_clss.joblib") rules = get_rules(model, predictions_df.columns, range(2)) table_list = [] for r in rules: colon_split = r.split(":") col_1 = colon_split[0] pipe_split = str(colon_split[1] + colon_split[2]).split("|") # print(colon_split) # print(pipe_split) col_2 = pipe_split[0] col_3 = pipe_split[1] table_list.append([col_1, col_2, col_3]) table_df = pd.DataFrame( table_list, columns=["rule_details", "class_probabilities", "samples_count"] ) rules_data_file = io.BytesIO() table_df.to_csv(rules_data_file, index=False) rules_data_file.seek(0) # Create a download link st.download_button( label="Model Explainability", data=rules_data_file, file_name="rules_data.csv", key="download_rules_data", ) # Run the app if __name__ == "__main__": st.set_option("deprecation.showPyplotGlobalUse", False) st.set_page_config(page_title="Equipment Failure Prediction", page_icon="📈") pages = ["Upload and Predict", "Graph and Download"] page = st.sidebar.selectbox("Select a page", pages) if page == "Upload and Predict": upload_and_generate_predictions() elif page == "Graph and Download": if hasattr(st.session_state, "predictions_df"): display_graph( st.session_state.predictions_df, st.session_state.uploaded_file ) else: st.warning("Please upload a file on the 'Upload and Predict' page first.") this is mu code inthis i have a scatterplot graph i want to modify the code in a way that draw ploltly graph usw click events of ploltly when i click the instance of the circle it should give descion rule for the instance using lime.after graph if i click one circle or instance lime table and rule list should print there it self you can add download predictions and model explananbility in new side bar

eng

15ad9bcf7527456098cbd1fa5734a312

arena-hard-v0.1

Innovative Online Market Models

Can you market size revenue that can earned by UK Management Consultancy by advising and implementing FinTech solutions to Capital Markets clients

eng

379a490a6eae40608abf3501807b2545

arena-hard-v0.1

Advanced Algebra and Number Theory

Consider the state: $$\ket{\psi} = \frac{\ket{00} + \ket{01} + \ket{10}}{\sqrt{3}}$$ (a). Calculate the reduced density matrix of the second qubit of $\ket{\psi}$.

eng

990d35320cca477fa60c3df15f629364

arena-hard-v0.1

Weather Forecast Accuracy

The prediction is in the IF stage while updating is in the ID stage. Think about two continuous branch instructions: the first one is in the ID stage, and the second is in the IF stage. What is the order of updating the first result and querying the second prediction? How to control the order? How do local-based and global-based prediction algorithms be affected by the order?

eng