Spaces:
Running
Running
File size: 8,684 Bytes
0831f73 6c26cfe 0831f73 6c26cfe 0831f73 6c26cfe 0831f73 28ce91d 6c26cfe 0831f73 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 | # -*- coding: utf-8 -*-
"""Quantitative data preparation for SmartPaper's AMJ track."""
from __future__ import annotations
import re
import numpy as np
import pandas as pd
from i18n import tr
from utils.file_parser import extract_data_from_files
class QuantitativeEngine:
"""Keep quantitative preparation isolated from the Streamlit UI."""
@staticmethod
def generate_psd_corr_matrix(n_constructs, max_corr=0.5):
if n_constructs == 1:
return np.array([[1.0]])
base_corr = np.random.uniform(0.2, max_corr - 0.1)
matrix = np.random.uniform(
base_corr - 0.1,
base_corr + 0.1,
(n_constructs, n_constructs),
)
matrix = (matrix + matrix.T) / 2
np.fill_diagonal(matrix, 1.0)
min_eig = np.min(np.linalg.eigvals(matrix))
if min_eig < 0:
matrix -= (min_eig - 0.01) * np.eye(n_constructs)
diagonal = np.sqrt(np.diag(matrix))
return matrix / np.outer(diagonal, diagonal)
@staticmethod
def calculate_validity_metrics(df, constructs_info, true_loadings):
metrics = []
for construct in constructs_info:
construct_name = construct["name"]
items = construct["items"]
sub_df = df[items]
var_total = sub_df.sum(axis=1).var(ddof=1)
sum_var = sub_df.var(ddof=1).sum()
n_items = len(items)
alpha = (
0.0
if var_total <= 0
else (n_items / (n_items - 1))
* (1 - sum_var / var_total)
)
loadings = np.array(true_loadings[construct_name])
sum_loadings_sq = np.sum(loadings) ** 2
sum_sq_loadings = np.sum(loadings**2)
sum_error = np.sum(1 - loadings**2)
cr = sum_loadings_sq / (sum_loadings_sq + sum_error)
ave = sum_sq_loadings / len(loadings)
metrics.append(
{
"Construct": construct_name,
"Items": n_items,
"Cronbach's α": round(alpha, 3),
"CR": round(cr, 3),
"AVE": round(ave, 3),
}
)
return pd.DataFrame(metrics)
def generate_empirical_data_robust(
self,
construct_names=None,
n=300,
scale_points=7,
):
if not construct_names or len(construct_names) < 2:
construct_names = [
"Independent_Var",
"Mediator_Var",
"Dependent_Var",
]
n_constructs = len(construct_names)
corr = self.generate_psd_corr_matrix(n_constructs, max_corr=0.55)
latent_scores = np.random.multivariate_normal(
mean=[0] * n_constructs,
cov=corr,
size=n,
)
data = {}
true_loadings = {}
constructs_info = []
target_mean = scale_points / 2.0 + 0.5
target_std = scale_points / 5.0
for index, construct_name in enumerate(construct_names):
true_score = latent_scores[:, index]
n_items = np.random.randint(3, 6)
loadings = np.random.uniform(0.72, 0.88, n_items)
true_loadings[construct_name] = loadings
item_names = []
for item_index, loading in enumerate(loadings):
error = np.random.normal(
0,
np.sqrt(1 - loading**2),
n,
)
item_score = loading * true_score + error
raw_scaled = item_score * target_std + target_mean
item_int = np.clip(
np.round(raw_scaled),
1,
scale_points,
).astype(int)
column_name = f"{construct_name}_{item_index + 1}"
data[column_name] = item_int
item_names.append(column_name)
constructs_info.append(
{"name": construct_name, "items": item_names}
)
df = pd.DataFrame(data)
validity_df = self.calculate_validity_metrics(
df,
constructs_info,
true_loadings,
)
mean_scores = pd.DataFrame(
{
construct["name"]: df[construct["items"]].mean(axis=1)
for construct in constructs_info
}
)
fl_matrix = mean_scores.corr().astype(object)
for _, row in validity_df.iterrows():
construct_name = row["Construct"]
sqrt_ave = np.sqrt(row["AVE"])
fl_matrix.loc[construct_name, construct_name] = (
f"({sqrt_ave:.3f})"
)
for row_index in range(len(fl_matrix.columns)):
for column_index in range(row_index + 1, len(fl_matrix.columns)):
fl_matrix.iloc[row_index, column_index] = ""
desc_stats = mean_scores.mean().to_frame("Mean").join(
mean_scores.std().to_frame("SD")
)
return df, validity_df, desc_stats.join(fl_matrix)
def resolve_empirical_data(self, uploaded_files=None, language="zh"):
if uploaded_files:
user_md = extract_data_from_files(uploaded_files)
if user_md:
evidence_sections = [
heading.strip()
for heading in re.findall(
r"(?m)^#{1,3}\s+(.+?)\s*$",
user_md,
)
if heading.strip()
]
metadata = {
"mode": "quantitative",
"source_type": "user_provided",
"disclosure_required": False,
"evidence_sections": evidence_sections[:20]
or ["User-provided quantitative evidence"],
}
return None, user_md, tr(language, "actual_stats_parsed"), metadata
df, validity_df, fl_matrix_df = self.generate_empirical_data_robust()
generated_prompt = f"""### Table 1: Reliability and Convergent Validity
{validity_df.to_markdown(index=False)}
### Table 2: Descriptive Statistics and Discriminant Validity (Fornell-Larcker Criterion)
*Note: Diagonal elements in parentheses are the square roots of AVE.*
{fl_matrix_df.to_markdown()}
[STRICT EMPIRICAL INSTRUCTION]
1. ZERO HALLUCINATION: Report the exact metrics provided above.
2. VALIDITY DEFENSE: State convergent validity only when the displayed AVE and CR values support it.
3. DISCRIMINANT DEFENSE: State discriminant validity only when the displayed diagonal and correlation values support it.
4. CONSISTENCY: Use these tables as the single source of numerical truth throughout the paper.
"""
metadata = {
"mode": "quantitative",
"source_type": "system_generated",
"disclosure_required": True,
"sample_size": int(len(df)),
"scale_points": 7,
"method": (
"Positive-semidefinite latent correlation generation, "
"indicator loadings, discretization, and derived "
"reliability/validity summaries."
),
"generation_parameters": {
"constructs": [
"Independent_Var",
"Mediator_Var",
"Dependent_Var",
],
"sample_size": int(len(df)),
"likert_scale_points": 7,
"items_per_construct": "random integer from 3 to 5",
"indicator_loading_range": [0.72, 0.88],
"latent_correlation_control": "positive-semidefinite matrix with maximum target correlation 0.55",
"item_generation": "loading × latent score + normally distributed measurement error",
"discretization": "round to nearest integer and clamp to 1–7",
"reported_summaries": [
"Cronbach alpha",
"composite reliability",
"average variance extracted",
"descriptive statistics",
"Fornell-Larcker matrix",
],
},
"evidence_sections": [
"Reliability and convergent validity",
"Descriptive statistics and discriminant validity",
],
}
return (
df,
generated_prompt,
tr(language, "synthetic_stats_generated"),
metadata,
)
def get_quantitative_engine():
return QuantitativeEngine()
|