vamseeachanta
vamseeachanta/workspace-hub
相关职业SOC
基于 SOC 职业分类
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npx skills add https://github.com/vamseeachanta/workspace-hub --skill autoviz-5-correlation-detection命令会保持在同一行。复制前请横向滚动并检查完整内容。
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基于 SOC 职业分类
| name | autoviz-5-correlation-detection |
| description | Sub-skill of autoviz: 5. Correlation Detection. |
| version | 1.0.0 |
| category | data-analysis |
| type | reference |
| scripts_exempt | true |
Automatic Correlation Analysis:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Create dataset with known correlations
np.random.seed(42)
n = 1000
# Base variables
x1 = np.random.randn(n)
x2 = np.random.randn(n)
df = pd.DataFrame({
"x1": x1,
"x2": x2,
# Strongly correlated with x1
"y1": x1 * 2 + np.random.randn(n) * 0.5,
# Moderately correlated with x2
"y2": x2 + np.random.randn(n) * 1.5,
# Negatively correlated
"y3": -x1 + np.random.randn(n) * 0.8,
# No correlation
"y4": np.random.randn(n),
# Non-linear relationship
"y5": x1 ** 2 + np.random.randn(n) * 0.5,
# Target
"target": (x1 + x2 > 0).astype(int)
})
AV = AutoViz_Class()
# AutoViz generates:
# 1. Correlation heatmap
# 2. Scatter plots for highly correlated pairs
# 3. Pair plots for feature relationships
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
verbose=2,
chart_format="svg"
)
Correlation with Lowess Smoothing:
from autoviz import AutoViz_Class
import pandas as pd
import numpy as np
# Dataset with non-linear relationships
np.random.seed(42)
x = np.linspace(0, 10, 500)
df = pd.DataFrame({
"x": x,
"linear": 2 * x + np.random.randn(500) * 2,
"quadratic": x ** 2 + np.random.randn(500) * 5,
"sinusoidal": 10 * np.sin(x) + np.random.randn(500) * 2,
"logarithmic": 5 * np.log(x + 1) + np.random.randn(500),
"target": x + np.random.randn(500)
})
AV = AutoViz_Class()
# Enable lowess smoothing to see trends
df_analyzed = AV.AutoViz(
filename="",
dfte=df,
depVar="target",
lowess=True, # Enable lowess smoothing
verbose=1,
chart_format="png"
)