teddyyyy123/bfcl_v3 huggingface dataset:
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'run_linear_regression', 'description': 'Build a linear regression model using given predictor variables and a target variable. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'predictors': {'type': 'array', 'items': {'type': 'string'}, 'description': 'Array containing the names of predictor variables.'}, 'target': {'type': 'string', 'description': 'The name of target variable.'}, 'standardize': {'type': 'boolean', 'description': 'Option to apply standardization on the predictors. Defaults to False.'}}, 'required': ['predictors', 'target']}}]


user: You are given a dataset with various variables including 'Age', 'Income', 'Education', 'Gender', 'Marital Status', and 'Spending Score'. You want to predict 'Spending Score' based on the other variables. Could you please use the 'run_linear_regression' function to build a linear regression model using 'Age', 'Income', and 'Education' as predictor variables and 'Spending Score' as the target variable without applying standardization on the predictors? Then, could you please run the same function again but this time with standardization applied on the predictors?

Available functions:
[{"name": "run_linear_regression", "description": "Build a linear regression model using given predictor variables and a target variable.", "parameters": {"type": "dict", "properties": {"predictors": {"type": "array", "items": {"type": "string"}, "description": "Array containing the names of predictor variables."}, "target": {"type": "string", "description": "The name of target variable."}, "standardize": {"type": "boolean", "description": "Option to apply standardization on the predictors. Defaults to False."}}, "required": ["predictors", "target"]}}]

Ground-truth function call:
[{"run_linear_regression": {"predictors": [["Age", "Income", "Education"]], "target": ["Spending Score"], "standardize": [false]}}, {"run_linear_regression": {"predictors": [["Age", "Income", "Education"]], "target": ["Spending Score"], "standardize": [true, false]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'random_forest.train', 'description': 'Train a Random Forest Model on given data Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'n_estimators': {'type': 'integer', 'description': 'The number of trees in the forest.'}, 'max_depth': {'type': 'integer', 'description': 'The maximum depth of the tree.'}, 'data': {'type': 'string', 'description': 'The training data for the model.'}}, 'required': ['n_estimators', 'max_depth', 'data']}}]


user: You are given a dataset "data_random_forest" in the form of a dataframe and you want to train a Random Forest Model on this data. You decide to experiment with different numbers of trees in the forest and different maximum depths of the trees to see how these parameters affect the model's performance. 

First, you train a model with 100 trees and a maximum depth of 10. Then, you train another model with 200 trees and a maximum depth of 20. After that, you train a third model with 300 trees and a maximum depth of 30. Finally, you train a fourth model with 400 trees and a maximum depth of 40. 

Can you invoke the 'random_forest.train' function four times with these different parameters and compare the performance of the four models?

Available functions:
[{"name": "random_forest.train", "description": "Train a Random Forest Model on given data", "parameters": {"type": "dict", "properties": {"n_estimators": {"type": "integer", "description": "The number of trees in the forest."}, "max_depth": {"type": "integer", "description": "The maximum depth of the tree."}, "data": {"type": "string", "description": "The training data for the model."}}, "required": ["n_estimators", "max_depth", "data"]}}]

Ground-truth function call:
[{"random_forest.train": {"n_estimators": [100], "max_depth": [10], "data": ["data_random_forest"]}}, {"random_forest.train": {"n_estimators": [200], "max_depth": [20], "data": ["data_random_forest"]}}, {"random_forest.train": {"n_estimators": [300], "max_depth": [30], "data": ["data_random_forest"]}}, {"random_forest.train": {"n_estimators": [400], "max_depth": [40], "data": ["data_random_forest"]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'predict_house_price', 'description': 'Predict the price of a house in a given area based on number of bedrooms, bathrooms and area. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'bedrooms': {'type': 'integer', 'description': 'The number of bedrooms in the house.'}, 'bathrooms': {'type': 'integer', 'description': 'The number of bathrooms in the house.'}, 'area': {'type': 'integer', 'description': 'The area of the house in square feet.'}, 'location': {'type': 'string', 'description': 'The location of the house.'}}, 'required': ['bedrooms', 'bathrooms', 'area', 'location']}}]


user: "Could you use the 'predict_house_price' function to compare the estimated prices of four different houses? The first house is located in New York, has 3 bedrooms, 2 bathrooms, and an area of 1500 square feet. The second house is in Los Angeles, with 4 bedrooms, 3 bathrooms, and an area of 2000 square feet. The third house is in Chicago, with 2 bedrooms, 1 bathroom, and an area of 1200 square feet. The fourth house is in Miami, with 3 bedrooms, 2 bathrooms, and an area of 1800 square feet."

Available functions:
[{"name": "predict_house_price", "description": "Predict the price of a house in a given area based on number of bedrooms, bathrooms and area.", "parameters": {"type": "dict", "properties": {"bedrooms": {"type": "integer", "description": "The number of bedrooms in the house."}, "bathrooms": {"type": "integer", "description": "The number of bathrooms in the house."}, "area": {"type": "integer", "description": "The area of the house in square feet."}, "location": {"type": "string", "description": "The location of the house."}}, "required": ["bedrooms", "bathrooms", "area", "location"]}}]

Ground-truth function call:
[{"predict_house_price": {"bedrooms": [3], "bathrooms": [2], "area": [1500], "location": ["New York", "New York, NY", "New York City", "NYC"]}}, {"predict_house_price": {"bedrooms": [4], "bathrooms": [3], "area": [2000], "location": ["Los Angeles", "Los Angeles, CA", "LA"]}}, {"predict_house_price": {"bedrooms": [2], "bathrooms": [1], "area": [1200], "location": ["Chicago"]}}, {"predict_house_price": {"bedrooms": [3], "bathrooms": [2], "area": [1800], "location": ["Miami"]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'random.normalvariate', 'description': 'Generates a random number from a normal distribution given the mean and standard deviation. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'mu': {'type': 'integer', 'description': 'Mean of the normal distribution.'}, 'sigma': {'type': 'integer', 'description': 'Standard deviation of the normal distribution.'}}, 'required': ['mu', 'sigma']}}]


user: You are a data scientist working on a project that requires you to generate random numbers from a normal distribution. You need to generate four random numbers: two from a normal distribution with a mean of 5 and a standard deviation of 2, and two from a normal distribution with a mean of 10 and a standard deviation of 3. How can you use the 'random.normalvariate' function to achieve this?

Available functions:
[{"name": "random.normalvariate", "description": "Generates a random number from a normal distribution given the mean and standard deviation.", "parameters": {"type": "dict", "properties": {"mu": {"type": "integer", "description": "Mean of the normal distribution."}, "sigma": {"type": "integer", "description": "Standard deviation of the normal distribution."}}, "required": ["mu", "sigma"]}}]

Ground-truth function call:
[{"random.normalvariate": {"mu": [5], "sigma": [2]}}, {"random.normalvariate": {"mu": [10], "sigma": [3]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'probability.dice_roll', 'description': 'Calculate the probability of rolling a certain number on a six-sided die a certain number of times in a row. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'desired_number': {'type': 'integer', 'description': 'The number you want to roll.'}, 'number_of_rolls': {'type': 'integer', 'description': 'How many times you want to roll that number in a row.'}, 'die_sides': {'type': 'integer', 'description': 'The number of sides on the die (optional; default is 6).'}}, 'required': ['desired_number', 'number_of_rolls']}}]


user: "In a board game, you have a six-sided die. You are curious about the probability of rolling a 4 three times in a row. After that, you want to know the probability of rolling a 2 twice in a row. Finally, you wonder what the probability would be if the die had 8 sides and you wanted to roll a 7 two times in a row. Can you calculate these probabilities?"

Available functions:
[{"name": "probability.dice_roll", "description": "Calculate the probability of rolling a certain number on a six-sided die a certain number of times in a row.", "parameters": {"type": "dict", "properties": {"desired_number": {"type": "integer", "description": "The number you want to roll."}, "number_of_rolls": {"type": "integer", "description": "How many times you want to roll that number in a row."}, "die_sides": {"type": "integer", "description": "The number of sides on the die (optional; default is 6)."}}, "required": ["desired_number", "number_of_rolls"]}}]

Ground-truth function call:
[{"probability.dice_roll": {"desired_number": [4], "number_of_rolls": [3], "die_sides": [6, ""]}}, {"probability.dice_roll": {"desired_number": [2], "number_of_rolls": [2], "die_sides": [6, ""]}}, {"probability.dice_roll": {"desired_number": [7], "number_of_rolls": [2], "die_sides": [8]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'prob_dist.binomial', 'description': "Compute the probability of having 'success' outcome from binomial distribution. Note that the provided function is in Python 3 syntax.", 'parameters': {'type': 'dict', 'properties': {'trials': {'type': 'integer', 'description': 'The number of independent experiments.'}, 'successes': {'type': 'integer', 'description': 'The number of success events.'}, 'p': {'type': 'float', 'description': 'The probability of success on any given trial, defaults to 0.5'}}, 'required': ['trials', 'successes']}}]


user: "In a game of chance, you have a 0.3 probability of winning any given round. If you play this game 20 times, what is the probability of winning exactly 5 times? Also, if you play the game 50 times, what is the probability of winning exactly 15 times? Lastly, if you play the game 100 times, what is the probability of winning exactly 30 times? Use the function 'prob_dist.binomial' to compute these probabilities."

Available functions:
[{"name": "prob_dist.binomial", "description": "Compute the probability of having 'success' outcome from binomial distribution.", "parameters": {"type": "dict", "properties": {"trials": {"type": "integer", "description": "The number of independent experiments."}, "successes": {"type": "integer", "description": "The number of success events."}, "p": {"type": "float", "description": "The probability of success on any given trial, defaults to 0.5"}}, "required": ["trials", "successes"]}}]

Ground-truth function call:
[{"prob_dist.binomial": {"trials": [20], "successes": [5], "p": [0.3]}}, {"prob_dist.binomial": {"trials": [50], "successes": [15], "p": [0.3]}}, {"prob_dist.binomial": {"trials": [100], "successes": [30], "p": [0.3]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'calculate_binomial_probability', 'description': 'Calculates the binomial probability given the number of trials, successes and the probability of success on an individual trial. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'number_of_trials': {'type': 'integer', 'description': 'The total number of trials.'}, 'number_of_successes': {'type': 'integer', 'description': 'The desired number of successful outcomes.'}, 'probability_of_success': {'type': 'float', 'description': 'The probability of a successful outcome on any given trial.', 'default': 0.5}}, 'required': ['number_of_trials', 'number_of_successes']}}]


user: "In a game of basketball, a player has a 60% chance of making any given shot. In a series of 10 shots, what is the probability that the player makes exactly 7 shots? Also, in another series of 15 shots, what is the probability that the player makes exactly 10 shots? Finally, in a series of 20 shots, what is the probability that the player makes exactly 15 shots?"

Available functions:
[{"name": "calculate_binomial_probability", "description": "Calculates the binomial probability given the number of trials, successes and the probability of success on an individual trial.", "parameters": {"type": "dict", "properties": {"number_of_trials": {"type": "integer", "description": "The total number of trials."}, "number_of_successes": {"type": "integer", "description": "The desired number of successful outcomes."}, "probability_of_success": {"type": "float", "description": "The probability of a successful outcome on any given trial.", "default": 0.5}}, "required": ["number_of_trials", "number_of_successes"]}}]

Ground-truth function call:
[{"calculate_binomial_probability": {"number_of_trials": [10], "number_of_successes": [7], "probability_of_success": [0.6]}}, {"calculate_binomial_probability": {"number_of_trials": [15], "number_of_successes": [10], "probability_of_success": [0.6]}}, {"calculate_binomial_probability": {"number_of_trials": [20], "number_of_successes": [15], "probability_of_success": [0.6]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'probability_of_event', 'description': 'Calculates the probability of an event. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'success_outcomes': {'type': 'integer', 'description': 'The number of successful outcomes.'}, 'total_outcomes': {'type': 'integer', 'description': 'The total number of possible outcomes.'}, 'format_as_ratio': {'type': 'boolean', 'description': 'When true, formats the output as a ratio instead of a decimal. Default is false.'}}, 'required': ['success_outcomes', 'total_outcomes']}}]


user: You are a teacher preparing a probability lesson for your students. You have a deck of 52 playing cards and you want to explain the probability of drawing certain cards. 

1. What is the probability of drawing an Ace (4 successful outcomes) from the deck (52 total outcomes)? Please provide this as a decimal. 

2. Then, what is the probability of drawing a heart (13 successful outcomes) from the deck (52 total outcomes)? Please provide this as a decimal. 

3. Finally, what is the probability of drawing a red card (26 successful outcomes) from the deck (52 total outcomes)? But this time, please provide the answer as a ratio.

Available functions:
[{"name": "probability_of_event", "description": "Calculates the probability of an event.", "parameters": {"type": "dict", "properties": {"success_outcomes": {"type": "integer", "description": "The number of successful outcomes."}, "total_outcomes": {"type": "integer", "description": "The total number of possible outcomes."}, "format_as_ratio": {"type": "boolean", "description": "When true, formats the output as a ratio instead of a decimal. Default is false."}}, "required": ["success_outcomes", "total_outcomes"]}}]

Ground-truth function call:
[{"probability_of_event": {"success_outcomes": [4], "total_outcomes": [52], "format_as_ratio": [false, ""]}}, {"probability_of_event": {"success_outcomes": [13], "total_outcomes": [52], "format_as_ratio": [false, ""]}}, {"probability_of_event": {"success_outcomes": [26], "total_outcomes": [52], "format_as_ratio": [true]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'calc_binomial_prob', 'description': 'Calculates the probability of an outcome based on the binomial distribution Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'num_trials': {'type': 'integer', 'description': 'Number of independent experiments.'}, 'num_success': {'type': 'integer', 'description': 'Number of times the event of interest has occurred.'}, 'prob_success': {'type': 'float', 'description': 'Probability of the event of interest on any single experiment.'}}, 'required': ['num_trials', 'num_success', 'prob_success']}}]


user: "In a game of basketball, a player has a 60% chance of making a successful shot. In a particular match, the player attempts 10 shots. What is the probability that the player makes exactly 6 successful shots? Now, consider a different scenario where the player's success rate drops to 50% but the number of attempts remains the same. What is the probability of making exactly 6 successful shots in this scenario? Finally, consider a third scenario where the player's success rate remains at 50% but the number of attempts increases to 15. What is the probability of making exactly 6 successful shots in this third scenario?"

Available functions:
[{"name": "calc_binomial_prob", "description": "Calculates the probability of an outcome based on the binomial distribution", "parameters": {"type": "dict", "properties": {"num_trials": {"type": "integer", "description": "Number of independent experiments."}, "num_success": {"type": "integer", "description": "Number of times the event of interest has occurred."}, "prob_success": {"type": "float", "description": "Probability of the event of interest on any single experiment."}}, "required": ["num_trials", "num_success", "prob_success"]}}]

Ground-truth function call:
[{"calc_binomial_prob": {"num_trials": [10], "num_success": [6], "prob_success": [0.6]}}, {"calc_binomial_prob": {"num_trials": [10], "num_success": [6], "prob_success": [0.5]}}, {"calc_binomial_prob": {"num_trials": [15], "num_success": [6], "prob_success": [0.5]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'chi_squared_test', 'description': 'Performs a Chi-Squared test for independence on a 2x2 contingency table. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'table': {'type': 'array', 'items': {'type': 'integer'}, 'description': 'A 2x2 contingency table presented in array form.'}, 'alpha': {'type': 'float', 'description': 'Significance level for the Chi-Squared test. Default is 0.05.'}}, 'required': ['table']}}]


user: You are a data analyst and you have been given two 2x2 contingency tables representing the results of a survey conducted in two different cities. The first table is [45, 55, 35, 65] and the second table is [30, 70, 50, 50]. You are asked to perform a Chi-Squared test for independence on both tables to determine if there is a significant relationship between the variables in each city. Use a significance level of 0.05 for both tests. Can you tell if there is a significant relationship in each city based on the Chi-Squared test results?

Available functions:
[{"name": "chi_squared_test", "description": "Performs a Chi-Squared test for independence on a 2x2 contingency table.", "parameters": {"type": "dict", "properties": {"table": {"type": "array", "items": {"type": "integer"}, "description": "A 2x2 contingency table presented in array form."}, "alpha": {"type": "float", "description": "Significance level for the Chi-Squared test. Default is 0.05."}}, "required": ["table"]}}]

Ground-truth function call:
[{"chi_squared_test": {"table": [[45, 55, 35, 65]], "alpha": [0.05]}}, {"chi_squared_test": {"table": [[30, 70, 50, 50]], "alpha": [0.05]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 't_test', 'description': 'Perform a statistical t-test to check if the means of two independent datasets are statistically different. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'dataset_A': {'type': 'array', 'items': {'type': 'integer'}, 'description': 'Dataset A for comparison.'}, 'dataset_B': {'type': 'array', 'items': {'type': 'integer'}, 'description': 'Dataset B for comparison.'}, 'alpha': {'type': 'float', 'description': 'Significance level for the test. Default is 0.05.'}}, 'required': ['dataset_A', 'dataset_B']}}]


user: "Could you please perform a statistical t-test to check if the means of two independent datasets are statistically different? The first dataset, Dataset A, includes the following integers: 12, 15, 18, 20, 22, 25, 28, 30, 32, 35. The second dataset, Dataset B, includes these integers: 14, 17, 19, 21, 23, 26, 29, 31, 33, 36. Please perform the test twice, once with a significance level of 0.05 and once with a significance level of 0.01."

Available functions:
[{"name": "t_test", "description": "Perform a statistical t-test to check if the means of two independent datasets are statistically different.", "parameters": {"type": "dict", "properties": {"dataset_A": {"type": "array", "items": {"type": "integer"}, "description": "Dataset A for comparison."}, "dataset_B": {"type": "array", "items": {"type": "integer"}, "description": "Dataset B for comparison."}, "alpha": {"type": "float", "description": "Significance level for the test. Default is 0.05."}}, "required": ["dataset_A", "dataset_B"]}}]

Ground-truth function call:
[{"t_test": {"dataset_A": [[12, 15, 18, 20, 22, 25, 28, 30, 32, 35]], "dataset_B": [[14, 17, 19, 21, 23, 26, 29, 31, 33, 36]], "alpha": [0.05]}}, {"t_test": {"dataset_A": [[12, 15, 18, 20, 22, 25, 28, 30, 32, 35]], "dataset_B": [[14, 17, 19, 21, 23, 26, 29, 31, 33, 36]], "alpha": [0.01]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'predict_house_price', 'description': 'Predict house price based on area, number of rooms and year of construction. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'area': {'type': 'integer', 'description': 'Area of the house in square feet.'}, 'rooms': {'type': 'integer', 'description': 'Number of rooms in the house.'}, 'year': {'type': 'integer', 'description': 'Year when the house was constructed.'}, 'location': {'type': 'string', 'description': 'The location or city of the house.'}}, 'required': ['area', 'rooms', 'year', 'location']}}]


user: Can you predict the price of a house with an area of 2500 square feet, 3 rooms, constructed in the year 2000, and located in New York, and then compare it with the price of a similar house but with an area of 3000 square feet, constructed in the year 2005, and located in Los Angeles? Finally, predict the price of a third house with an area of 2000 square feet, 2 rooms, constructed in the year 1995, and located in Chicago.

Available functions:
[{"name": "predict_house_price", "description": "Predict house price based on area, number of rooms and year of construction.", "parameters": {"type": "dict", "properties": {"area": {"type": "integer", "description": "Area of the house in square feet."}, "rooms": {"type": "integer", "description": "Number of rooms in the house."}, "year": {"type": "integer", "description": "Year when the house was constructed."}, "location": {"type": "string", "description": "The location or city of the house."}}, "required": ["area", "rooms", "year", "location"]}}]

Ground-truth function call:
[{"predict_house_price": {"area": [2500], "rooms": [3], "year": [2000], "location": ["New York", "New York, NY", "New York City", "NYC", "NY"]}}, {"predict_house_price": {"area": [3000], "rooms": [3], "year": [2005], "location": ["Los Angeles", "Los Angeles, CA", "LA", "Los Angeles, CA", "CA"]}}, {"predict_house_price": {"area": [2000], "rooms": [2], "year": [1995], "location": ["Chicago"]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'linear_regression.get_r_squared', 'description': 'Calculate the coefficient of determination of a regression model. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'dataset_path': {'type': 'string', 'description': 'Path to the CSV dataset file.'}, 'independent_variables': {'type': 'array', 'items': {'type': 'string'}, 'description': 'The independent variables to use in the regression model.'}, 'dependent_variable': {'type': 'string', 'description': 'The dependent variable to predict in the regression model.'}}, 'required': ['dataset_path', 'independent_variables', 'dependent_variable']}}]


user: What is the coefficient of determination (R squared) of a regression model if we use the dataset located at "/user/home/datasets/finance.csv", with 'income', 'age' and 'education' as the independent variables and 'credit_score' as the dependent variable, and then repeat the same process with 'income', 'age' and 'credit_score' as the independent variables and 'education' as the dependent variable?

Available functions:
[{"name": "linear_regression.get_r_squared", "description": "Calculate the coefficient of determination of a regression model.", "parameters": {"type": "dict", "properties": {"dataset_path": {"type": "string", "description": "Path to the CSV dataset file."}, "independent_variables": {"type": "array", "items": {"type": "string"}, "description": "The independent variables to use in the regression model."}, "dependent_variable": {"type": "string", "description": "The dependent variable to predict in the regression model."}}, "required": ["dataset_path", "independent_variables", "dependent_variable"]}}]

Ground-truth function call:
[{"linear_regression.get_r_squared": {"dataset_path": ["/user/home/datasets/finance.csv"], "independent_variables": [["income", "age", "education"]], "dependent_variable": ["credit_score"]}}, {"linear_regression.get_r_squared": {"dataset_path": ["/user/home/datasets/finance.csv"], "independent_variables": [["income", "age", "credit_score"]], "dependent_variable": ["education"]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'finance.calculate_quarterly_dividend_per_share', 'description': 'Calculate quarterly dividend per share for a company given total dividend payout and outstanding shares Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'total_payout': {'type': 'integer', 'description': 'The total amount of dividends paid out in USD'}, 'outstanding_shares': {'type': 'integer', 'description': 'Total number of outstanding shares'}}, 'required': ['total_payout', 'outstanding_shares'], 'optional': []}}]


user: "Can you help me calculate the quarterly dividend per share for my company? We have just paid out a total of $5,000,000 in dividends and currently have 2,000,000 outstanding shares. Also, I am considering a scenario where we might increase our total payout to $6,000,000 while keeping the same number of outstanding shares. What would be the quarterly dividend per share in that case? And what if we also increase our outstanding shares to 2,500,000 while keeping the total payout at $6,000,000?"

Available functions:
[{"name": "finance.calculate_quarterly_dividend_per_share", "description": "Calculate quarterly dividend per share for a company given total dividend payout and outstanding shares", "parameters": {"type": "dict", "properties": {"total_payout": {"type": "integer", "description": "The total amount of dividends paid out in USD"}, "outstanding_shares": {"type": "integer", "description": "Total number of outstanding shares"}}, "required": ["total_payout", "outstanding_shares"], "optional": []}}]

Ground-truth function call:
[{"finance.calculate_quarterly_dividend_per_share": {"total_payout": [5000000], "outstanding_shares": [2000000]}}, {"finance.calculate_quarterly_dividend_per_share": {"total_payout": [6000000], "outstanding_shares": [2500000]}}, {"finance.calculate_quarterly_dividend_per_share": {"total_payout": [6000000], "outstanding_shares": [2000000]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'calculate_discounted_cash_flow', 'description': 'Calculate the discounted cash flow of a bond for a given annual coupon payment, time frame and discount rate. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'coupon_payment': {'type': 'integer', 'description': 'The annual coupon payment.'}, 'period': {'type': 'integer', 'description': 'The time frame in years for which coupon payment is made.'}, 'discount_rate': {'type': 'float', 'description': 'The discount rate.'}, 'face_value': {'type': 'integer', 'description': 'The face value of the bond, default is $1000.'}}, 'required': ['coupon_payment', 'period', 'discount_rate']}}]


user: "Can you help me calculate the discounted cash flow of a bond? I have a bond with an annual coupon payment of $50, a time frame of 5 years, and a discount rate of 5%. Also, the face value of the bond is $1000. I would like to know the discounted cash flow for this bond. After that, I want to compare it with another bond that has an annual coupon payment of $60, a time frame of 7 years, and a discount rate of 4%, with the same face value of $1000. Can you calculate the discounted cash flow for this second bond as well?"

Available functions:
[{"name": "calculate_discounted_cash_flow", "description": "Calculate the discounted cash flow of a bond for a given annual coupon payment, time frame and discount rate.", "parameters": {"type": "dict", "properties": {"coupon_payment": {"type": "integer", "description": "The annual coupon payment."}, "period": {"type": "integer", "description": "The time frame in years for which coupon payment is made."}, "discount_rate": {"type": "float", "description": "The discount rate."}, "face_value": {"type": "integer", "description": "The face value of the bond, default is $1000."}}, "required": ["coupon_payment", "period", "discount_rate"]}}]

Ground-truth function call:
[{"calculate_discounted_cash_flow": {"coupon_payment": [50], "period": [5], "discount_rate": [0.05], "face_value": [1000, ""]}}, {"calculate_discounted_cash_flow": {"coupon_payment": [60], "period": [7], "discount_rate": [0.04], "face_value": [1000, ""]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'calculate_compound_interest', 'description': 'Calculate compound interest for an initial principal amount. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'principal': {'type': 'integer', 'description': 'The principal amount that the interest is applied to.'}, 'rate': {'type': 'float', 'description': 'The annual interest rate. Enter as a decimal. E.g, 5% is 0.05'}, 'time': {'type': 'integer', 'description': 'The time the money is invested for in years.'}, 'n': {'type': 'integer', 'description': 'The number of times that interest is compounded per time period. Default is 1.'}}, 'required': ['principal', 'rate', 'time']}}]


user: "Can you help me calculate the compound interest for my savings? I initially invested $5000 as the principal amount. The bank offers an annual interest rate of 2.5% (or 0.025 in decimal form). I plan to keep my money in the bank for 10 years. Also, the interest is compounded quarterly, so it's compounded 4 times in a year. Can you calculate the compound interest for the first 2 years, then for the next 3 years and finally for the remaining 5 years?"

Available functions:
[{"name": "calculate_compound_interest", "description": "Calculate compound interest for an initial principal amount.", "parameters": {"type": "dict", "properties": {"principal": {"type": "integer", "description": "The principal amount that the interest is applied to."}, "rate": {"type": "float", "description": "The annual interest rate. Enter as a decimal. E.g, 5% is 0.05"}, "time": {"type": "integer", "description": "The time the money is invested for in years."}, "n": {"type": "integer", "description": "The number of times that interest is compounded per time period. Default is 1."}}, "required": ["principal", "rate", "time"]}}]

Ground-truth function call:
[{"calculate_compound_interest": {"principal": [5000], "rate": [0.025], "time": [2], "n": [4]}}, {"calculate_compound_interest": {"principal": [5000], "rate": [0.025], "time": [3], "n": [4]}}, {"calculate_compound_interest": {"principal": [5000], "rate": [0.025], "time": [5], "n": [4]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'calculate_return_on_equity', 'description': "Calculate a company's return on equity based on its net income, shareholder's equity, and dividends paid. Note that the provided function is in Python 3 syntax.", 'parameters': {'type': 'dict', 'properties': {'net_income': {'type': 'integer', 'description': "The company's net income."}, 'shareholder_equity': {'type': 'integer', 'description': "The company's total shareholder's equity."}, 'dividends_paid': {'type': 'integer', 'description': "The total dividends paid by the company. Optional. If not given, assumes it's 0 as default."}}, 'required': ['net_income', 'shareholder_equity']}}]


user: "Can you calculate the return on equity for two companies? The first company has a net income of $1,000,000, shareholder's equity of $5,000,000, and paid dividends of $200,000. The second company has a net income of $2,000,000, shareholder's equity of $10,000,000, but did not pay any dividends."

Available functions:
[{"name": "calculate_return_on_equity", "description": "Calculate a company's return on equity based on its net income, shareholder's equity, and dividends paid.", "parameters": {"type": "dict", "properties": {"net_income": {"type": "integer", "description": "The company's net income."}, "shareholder_equity": {"type": "integer", "description": "The company's total shareholder's equity."}, "dividends_paid": {"type": "integer", "description": "The total dividends paid by the company. Optional. If not given, assumes it's 0 as default."}}, "required": ["net_income", "shareholder_equity"]}}]

Ground-truth function call:
[{"calculate_return_on_equity": {"net_income": [1000000], "shareholder_equity": [5000000], "dividends_paid": [200000]}}, {"calculate_return_on_equity": {"net_income": [2000000], "shareholder_equity": [10000000], "dividends_paid": [0, ""]}}]
system: 
You are an expert in composing functions. You are given a question and a set of possible functions.
Based on the question, you will need to make one or more function/tool calls to achieve the purpose.
If none of the function can be used, point it out. If the given question lacks the parameters required by the function,
also point it out. You should only return the function call in tools call sections.

If you decide to invoke any of the function(s), you MUST put it in the format of [func_name1(params_name1=params_value1, params_name2=params_value2...), func_name2(params)]

You SHOULD NOT include any other text in the response.

Here is a list of functions in JSON format that you can invoke.

[{'name': 'finance.predict_future_value', 'description': 'Calculate the future value of an investment given its present value, interest rate, the number of compounding periods per year, and the time horizon. Note that the provided function is in Python 3 syntax.', 'parameters': {'type': 'dict', 'properties': {'present_value': {'type': 'integer', 'description': 'The present value of the investment.'}, 'annual_interest_rate': {'type': 'float', 'description': 'The annual interest rate of the investment.'}, 'compounding_periods_per_year': {'type': 'integer', 'description': 'The number of times that interest is compounded per year. Default is 1 (annually).'}, 'time_years': {'type': 'integer', 'description': 'The investment horizon in years.'}}, 'required': ['present_value', 'annual_interest_rate', 'time_years']}}]


user: "Imagine you have two different investment opportunities. The first one has a present value of $5000, an annual interest rate of 5%, and you plan to hold it for 10 years. The second one has a present value of $7000, an annual interest rate of 4%, and you plan to hold it for 15 years. Both investments compound interest annually. Can you calculate the future value of both investments using the finance.predict_future_value function?"

Available functions:
[{"name": "finance.predict_future_value", "description": "Calculate the future value of an investment given its present value, interest rate, the number of compounding periods per year, and the time horizon.", "parameters": {"type": "dict", "properties": {"present_value": {"type": "integer", "description": "The present value of the investment."}, "annual_interest_rate": {"type": "float", "description": "The annual interest rate of the investment."}, "compounding_periods_per_year": {"type": "integer", "description": "The number of times that interest is compounded per year. Default is 1 (annually)."}, "time_years": {"type": "integer", "description": "The investment horizon in years."}}, "required": ["present_value", "annual_interest_rate", "time_years"]}}]

Ground-truth function call:
[{"finance.predict_future_value": {"present_value": [5000], "annual_interest_rate": [0.05], "compounding_periods_per_year": [1, ""], "time_years": [10]}}, {"finance.predict_future_value": {"present_value": [7000], "annual_interest_rate": [0.04], "compounding_periods_per_year": [1, ""], "time_years": [15]}}]