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log_lin_model.py
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log_lin_model.py
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from sklearn.linear_model import LinearRegression
import pandas as pd
import numpy as np
import scipy
import matplotlib.pyplot as plt
import base64
from io import BytesIO
from sourceCode.func import create_t_figure
from sourceCode.func import create_p_figure
from sourceCode.func import create_b_figure
import platform
def getAns(dependent: str, independent: list, session: dict) -> dict:
"""get p-value, f-value, R-square etc."""
reg = LinearRegression()
filename = session["filename"]
data = pd.read_csv(filename)
y = data[dependent].values
xs = data[independent].values
ans = {}
try:
for i in y:
if i <= 0:
ans["flag"] = 0
session[
"error"] = "dependent variable has 0. exp_reg_model not suit"
return ans
ys = np.log(y)
reg.fit(xs, ys)
ans["var"] = [dependent, "Constant"] + independent
n = len(ys)
k = len(independent) + 1
tmp = np.append(reg.intercept_, reg.coef_).tolist()
ans["coefficient"] = [np.round(i, 4) for i in tmp]
ans["R-squared"] = np.round(reg.score(xs, ys), 4)
ans["Adjusted R-squared"] = np.round(
1 - (1 - ans["R-squared"]) * (n - 1) / (n - k), 4)
try:
ans["F-value"] = np.round(
ans["R-squared"] / (1 - ans["R-squared"]) * (n - k) / (k - 1),
4)
except ZeroDivisionError:
ans["F-value"] = 999999
ans["SS"] = {}
tmp = sum((ys - ys.mean())**2)
ans["observation"] = n
ans["df"] = k
ans["SS"]["ESS"] = np.round(tmp * ans["R-squared"], 4)
ans["SS"]["RSS"] = np.round(tmp - ans["SS"]["ESS"], 4)
ans["Prob>F"] = np.round(
scipy.stats.f.sf(ans["F-value"], k - 1, n - k), 4)
ans["Root MSE"] = np.round((ans["SS"]["RSS"] / (n - k))**0.5, 4)
sigma_square = ans["SS"]["RSS"] / (n - k)
one = np.ones(n)
xs = np.insert(xs, 0, values=one, axis=1)
tmp = np.dot(xs.T, xs)
var_cov_beta = sigma_square * np.linalg.inv(tmp)
tmp = np.sqrt(var_cov_beta.diagonal()).tolist()
ans["stderr"] = [np.round(i, 4) for i in tmp]
try:
ans["t"] = [
np.round(ans["coefficient"][i] / ans["stderr"][i], 4)
for i in range(k)
]
except ZeroDivisionError:
ans["t"] = 9999999
ans["P>|t|"] = [
np.round(scipy.stats.t.sf(i, n - k), 4) for i in ans["t"]
]
ans["flag"] = 1
return ans
except:
ans["flag"] = 0
session["error"] = "please check your data"
return ans
def format_(x):
return str(x).rjust(10, ' ')
def showAns(dependent: str, ans: dict, session: dict) -> str:
"""turn to html pages"""
if ans["flag"] == 1:
username = session["username"]
if platform.system() == "windows":
csvfile = open(".\\static\\{}\\downloads\\ans.csv".format(username),
"w",
encoding="utf-8")
else:
csvfile = open("./static/{}/downloads/ans.csv".format(username),
"w",
encoding="utf-8")
print("dependent variable: ", dependent, file=csvfile)
print("variables,Coefficients,Standard Errors,t values,Probabilities",
file=csvfile)
for i in range(ans["df"]):
print(ans["var"][i + 1],
ans["coefficient"][i],
ans["stderr"][i],
ans["t"][i],
ans["P>|t|"][i],
sep=',',
file=csvfile)
csvfile.close()
html = """
<table border="1" style="width: 600px;">
<tr align="middle">
<td>
Source
</td>
<td>SS</td>
<td>df</td>
<td>MS</td>
</tr>
<tr align="right">
<td>Model</td>
<td>{}</td>
<td>{}</td>
<td>{}</td>
</tr>
<tr align="right">
<td>Residual</td>
<td>{}</td>
<td>{}</td>
<td>{}</td>
</tr>
<tr align="right">
<td>Total</td>
<td>{}</td>
<td>{}</td>
<td>{}</td>
</tr>
</table>
""".format(
format_(ans["SS"]["ESS"]), format_(ans["df"] - 1),
format_(np.round(ans["SS"]["ESS"] / (ans["df"] - 1), 4)),
format_(ans["SS"]["RSS"]), format_(ans["observation"] - ans["df"]),
format_(
np.round(ans["SS"]["RSS"] / (ans["observation"] - ans["df"]),
4)), format_(ans["SS"]["ESS"] + ans["SS"]["RSS"]),
format_(ans["observation"] - 1),
format_(
np.round((ans["SS"]["ESS"] + ans["SS"]["RSS"]) /
(ans["observation"] - 1), 4)))
html += """
<table style="width: 600px;">
<tr>
<td align="left">
Number of obs
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
<tr>
<td align="left">
F({},{})
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
<tr>
<td align="left">
Prob>F
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
<tr>
<td align="left">
R-square
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
<tr>
<td align="left">
Adj R-square
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
<tr>
<td align="left">
Root MSE
</td>
<td align="middle">=</td>
<td align="right">{}</td>
</tr>
</table><table style="width:600px;" border="1">
""".format(str(ans["observation"]), str(ans["df"] - 1),
str(ans["observation"] - ans["df"]), str(ans["F-value"]),
str(ans["Prob>F"]), str(ans["R-squared"]),
str(ans["Adjusted R-squared"]), str(ans["Root MSE"]))
for i in range(len(ans["var"])):
if i == 0:
html += """<tr><td>{}</td><td>Coef.</td><td>Std. Err.</td><td>t</td><td> P>|t| </td></tr>""".format(
ans["var"][0])
else:
html += """<tr><td>{}</td><td>{}</td><td>{}</td><td>{}</td><td>{}</td></tr>""".format(
ans["var"][i], ans["coefficient"][i - 1],
ans["stderr"][i - 1], ans["t"][i - 1], ans["P>|t|"][i - 1])
return html + "</table>"
return """<h1>log_lin_model not suit</h1>"""
def showFigure(ans: dict) -> dict:
if ans["flag"] == 1:
tmp = {}
tmp["tvalue"] = create_t_figure(ans)
tmp["bvalue"] = create_b_figure(ans)
tmp["pvalue"] = create_p_figure(ans)
return tmp
return {}
if __name__ == "__main__":
print(getAns("年龄", ["是否使用互联网", "log年收入"], "dcy"))