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import numpy as np | |
import pandas as pd | |
TEST_EQUATIONS = ["sin(2*x)/x + 0.1*x"] | |
def generate_data(s: str, num_points: int, noise_level: float, data_seed: int): | |
rstate = np.random.RandomState(data_seed) | |
x = rstate.uniform(-10, 10, num_points) | |
for k, v in { | |
"sin": "np.sin", | |
"cos": "np.cos", | |
"exp": "np.exp", | |
"log": "np.log", | |
"tan": "np.tan", | |
"^": "**", | |
}.items(): | |
s = s.replace(k, v) | |
y = eval(s) | |
noise = rstate.normal(0, noise_level, y.shape) | |
y_noisy = y + noise | |
return pd.DataFrame({"x": x}), y_noisy | |
def read_csv(file_input: str, force_run: bool): | |
# Look at some statistics of the file: | |
df = pd.read_csv(file_input) | |
if len(df) == 0: | |
raise ValueError("The file is empty!") | |
if len(df.columns) == 1: | |
raise ValueError("The file has only one column!") | |
if len(df) > 10_000 and not force_run: | |
raise ValueError( | |
"You have uploaded a file with more than 10,000 rows. " | |
"This will take very long to run. " | |
"Please upload a subsample of the data, " | |
"or check the box 'Ignore Warnings'.", | |
) | |
col_to_fit = df.columns[-1] | |
y = np.array(df[col_to_fit]) | |
X = df.drop([col_to_fit], axis=1) | |
return X, y | |